ALBERT

Description: Poor public health is always associated with the mismanagement of municipal solid waste (MSW). Many cities are besieged by MSW in the world. It is essential to do a good job in MSW management (MSWM). In order to improve the efficiency of MSWM, the Chinese government has intensively implemented relevant policies. There are still few studies on MSWM efficiency in China. The research aims to comprehensively analyze MSWM efficiency, find high-efficiency MSWM policy implementation routes and the breakthrough on improving MSWM efficiency. To measure Chinese MSWM efficiency accurately, this paper introduced the three-stage data envelopment analysis (DEA) model into the research. According to the results of DEA, Fuzzy c-Means algorithm was used to the cluster analysis of 33 typical cities. After eliminating the interference of the external environment and random disturbance, the mean value of MSWM efficiency declined from 0.575 to 0.544. The mean of pure technical efficiency (PTE) was declined from 0.966 to 0.611, while the mean of scale efficiency (SE) increased from 0.600 to 0.907. The PTE of central and northeastern cities was relatively low. The SE of western cities was comparatively high and the efficiency distribution of the eastern region was relatively scattered. In general, MSWM efficiency is low and expected to be improved. Regional differences in MSWM efficiency have been shown. The management effectiveness of eight pilot cities (MSW classification) is affirmative but not that significant. To improve MSWM efficiency, differential management for four types of cities should be carried out.

Description: A series of graphite oxide samples were prepared using the modified Hummers method. Flake graphite was used as the raw material and the reaction temperature of the aqueous solution was changed (0 °C, 30 °C, 50 °C, 60 °C, 70 °C, 80 °C, and 100 °C). X-ray diffraction, Fourier-transform infrared spectroscopy, Raman spectral analysis, X-ray photoelectron spectroscopy, and contact angle tests were performed to characterize the structure, chemical bonding, type, and content of oxygen-containing functional groups of the graphite oxide samples. The results showed that the type and content of each oxygen-containing functional group could be controlled by changing the reaction temperature with the addition of water. As the temperature of the system increased, the degree of oxidation of the graphite oxide samples first increased and then decreased. Too high a temperature (100 °C) of the system led to the formation of epoxy groups by the decomposition of some hydroxyl groups in the samples, causing the reduction of oxygen-containing functional groups between the graphite layers, poor hydrophilic properties, and low moisture content. When the system temperature was 50 °C, the interlayer spacing of the graphite oxide samples was at its highest, the graphite was completely oxidized (C/O = 1.85), and the oxygen-containing functional groups were mainly composed of hydroxyl groups (accounting for approximately 28.88% of the total oxygen-containing functional groups). The high content of hydroxyl and carboxyl groups had good hydrophilic ability and showed the highest moisture content. The sample at 50 °C had better sensitivity to ammonia because of its high hydroxyl group and carboxyl group content, with the sample showing an excellent profile when the ammonia concentration was 20–60 ppm.

Description: Time domain reflectometry (TDR) is one of the most widely used techniques for indirect determination of soil volumetric water content (θ). TDR measures the relative dielectric constant (εr) which, in a three-phase system like the soil, depends on water, air, and solid matrix dielectric constants. Since dielectric constant of water is much larger than the other two, εr of bulk soil mainly depends on water content. In many cases, the application of TDR requires a specific calibration of the relationship θ(εr) to get quantitatively accurate estimates of soil water content. In fact, the relationship θ(εr) is influenced by various soil properties, such as clay content, organic matter content, bulk density, and aggregation. Numerous studies have shown that pyroclastic soils often exhibit a peculiar dielectric behavior. In Campania (Southern Italy) wide mountainous areas are covered by layered pyroclastic deposits of ashes (loamy sands) and pumices (sandy gravels), often involved in the triggering of landslides induced by rainwater infiltration. Reliable field measurements of water content of such soils are therefore important for the assessment of landslide risk. Hence, in this paper, the θ(εr) relationship has been experimentally determined on samples of typical pyroclastic soil of Campania, collected around Sarno, reconstituted with different porosities. The aim of the study is to identify specific calibration relationships for such soils based not only on empirical approaches. In this respect, a three-phase dielectric mixing model with a variable exponent is introduced, and the variable value of the exponent is related to the different dielectric properties of bond and free water within the soil pores.

Description: Energy efficiency is crucial in the design of battery-powered end devices, such as smart sensors for the Internet of Things applications. Wireless communication between these distributed smart devices consumes significant energy, and even more when data need to reach several kilometers in distance. Low-power and long-range communication technologies such as LoRaWAN are becoming popular in IoT applications. However, LoRaWAN has drawbacks in terms of (i) data latency; (ii) limited control over the end devices by the gateway; and (iii) high rate of packet collisions in a dense network. To overcome these drawbacks, we present an energy-efficient network architecture and a high-efficiency on-demand time-division multiple access (TDMA) communication protocol for IoT improving both the energy efficiency and the latency of standard LoRa networks. We combine the capabilities of short-range wake-up radios to achieve ultra-low power states and asynchronous communication together with the long-range connectivity of LoRa. The proposed approach still works with the standard LoRa protocol, but improves performance with an on-demand TDMA. Thanks to the proposed network and protocol, we achieve a packet delivery ratio of 100% by eliminating the possibility of packet collisions. The network also achieves a round-trip latency on the order of milliseconds with sensing devices dissipating less than 46 mJ when active and 1.83 μ W during periods of inactivity and can last up to three years on a 1200-mAh lithium polymer battery.

Description: Purpose Biological age (BA) has recently emerged as a substitute for chronological age (CA), and many subjects seek to optimally control their BA. However, in South Korea, no study has adequately explored factors that affect BA, although individual health management is essential to preventing chronic diseases. In the present study, we focus on the use of health information, in particular nutrition facts, to control BA. Methods We used data from the Korea National Health and Nutrition Examination Surveys (2010–2015; 26,914 eligible participants) using BA and age differences as outcome variables. We used multiple linear regression to explore the relationship between the use of nutrition data and differences in BA after adjusting for covariates. In addition, we used multiple linear regression to examine subgroup differences in such relationships. Results 12.8% of males and 27.5% of females used nutrition facts when deciding which foods to purchase. The more attention paid to such facts, the lower the BA and BA differences in both males and females (males: β = −2.646, females: β = −2.787, p 〈 0.05, for BA; males: β = −1.181, females, β = −2.161, p 〈 0.05, for BA differences). However, BA differences were more significant in subjects with chronic disease, obesity, and/or a family history of chronic disease. Conclusion High-level awareness of and active use of nutrition facts permitted effective self-management in preventing chronic disease and improving BA, particularly in subjects at higher risk for chronic disease. Thus, considering nutrition facts when deciding what to purchase is important.

Description: Samples of wheat and soil were collected in the Lihe watershed in East China, the migration and accumulation processes of four common trace metals (Cu, Pb, Cd and Ni) in each part of the wheat plant (root, stem, leaf and grain) were analyzed, and a mechanistic model was proposed to simulate these processes based on wheat growth techniques. Model results show that Cu and Cd migrate more easily with wheat grains, while most Pb and Ni accumulate in roots. Modeling results were shown to be relatively good, with an error of 25.29% in value and 26.38% in fluctuation, and had smaller dispersion degree than actual measurement results. Monte Carlo simulation results also match quite well with actual measurement results, and modeling results are slightly smaller in the simulation of Leaf-Cu, Grain-Cu and Leaf-Ni. Trace metal pollution risk in wheat is evaluated based on this model; our results show that the northwest and northeast parts in the research area are not suitable for growing wheat. In general, this model is relatively accurate, and can evaluate the wheat pollution risk before seeding wheat, providing scientific references for the early selection of wheat safety sowing areas.

Description: African Americans have a disproportionately higher risk of chronic conditions such as cardiovascular disease (CVD), type 2 diabetes, and hypertension than other ethnic or racial groups. Data regarding CVD-related perceptions and beliefs among African Americans are limited, particularly in the Southwest US. Assessment of current views regarding health and health behaviors is needed to tailor interventions to meet the unique needs of specific populations. We sought to examine knowledge, attitudes, and perceptions of African Americans living in Arizona toward CVD and etiological factors associated with health behaviors and chronic disease development to inform state health agency program development. Transcripts from 14 focus groups (n = 103) were analyzed using Grounded Theory for perceived disease risk, knowledge of CVD risk factors, nutrition, preventative behaviors, and barriers and motivators to behavior change. Participants identified CVD, stroke, and diabetes as leading health concerns among African-Americans but were less certain about the physiological consequences of these diseases. Diet, stress, low physical activity, family history, hypertension, and stroke were described as key CVD risk factors, but overweight and obesity were mentioned rarely. Participants described low socio-economic status and limited access to healthy foods as contributors to disease risk. Focus group members were open to modifying health behaviors if changes incorporated their input and were culturally acceptable. Respondents were 41% male and 59% female with a mean age of 46 years. This study provides insight into CVD and associated disease-related perceptions, knowledge, and attitudes among African Americans in the Southwest and recommendations for interventions to reduce CVD risk.

Description: Background: Leptospirosis is a zoonotic disease with a worldwide distribution, especially in developing countries such as Malaysia. This study was designed to explore the knowledge, attitudes, beliefs and practices (KABP) toward leptospirosis among the communities in northeastern Malaysia and to determine the sociodemographic factors associated with the KABP toward leptospirosis. A cross-sectional study using a stratified sampling method was conducted among 214 individuals in four locales in northeastern Malaysia. Methods: A cross-sectional study was conducted among 214 respondents in northeastern Malaysia using a multi-stage stratified random sampling method. The study population was divided into two groups based on geographical locations: urban and rural. All data were entered and analyzed using the IBM Statistics for Social Sciences (SPSS) version 22.0 software for Windows (IBM, Armonk, NY, USA). The continuous variables were presented using mean and standard deviation (SD), whereas the categorical variables were described using frequency and percentage. Multiple logistic regression was performed to determine the associated factors for good KABP toward leptospirosis among the respondents. Results: It was found that 52.8% of respondents had good knowledge, 84.6% had positive attitudes, 59.8% had positive beliefs, and 53.7% had satisfactory practices. There were no significant sociodemographic factors associated with knowledge and practice, except for educational status, which was significant in the attitude and belief domains. Those with higher education exhibited better attitudes (Odds Ratio (OR) 3.329; 95% Coefficient Interval (CI): 1.140, 9.723; p = 0.028) and beliefs (OR 3.748; 95% CI: 1.485, 9.459; p = 0.005). The communities in northeastern Malaysia generally have good knowledge and a high level of positive attitude; however, this attitude cannot be transformed into practice as the number of people with satisfactory practice habits is much lower compared to those with positive attitudes. As for the belief domain, the communities must have positive beliefs to perceive the threat of the disease. Conclusions: Our current health program on preventing leptospirosis is good in creating awareness and a positive attitude among the communities, but is not sufficient in promoting satisfactory practice habits. In conclusion, more attention needs to be paid to promoting satisfactory practice habits among the communities, as they already possess good knowledge and positive attitudes and beliefs.

Description: Studies have demonstrated that, for urban children, dust represents the main exposure to sources of metals like lead (Pb) and manganese (Mn). We aimed to investigate the exposure to these metals and their association with intellectual deficit in children living in an industrial region. This cross-sectional study recruited volunteers from four elementary schools in the town of Simões Filho, Brazil. We evaluated 225 school-aged children (7–12 years) for blood lead (PbB) and manganese hair (MnH) and toenails (MnTn) by graphite furnace atomic absorption spectrometry. Child and maternal IQs were estimated using the Wechsler Abbreviated Scale for Intelligence (WASI). Median and range PbB were 1.2 (0.3–15.6) μg/dL. MnH and MnTn medians (ranges) were 0.74 (0.16–8.79) μg/g and 0.85 (0.15–13.30) μg/g, respectively. After adjusting for maternal IQ, age and Mn exposure, child IQ drops by 8.6 points for a 10-fold increase in PbB levels. Moreover, an effect modification of Mn co-exposure was observed. In children with low MnTn, association between Pb and child IQ was not significant (β = −6.780, p = 0.172). However, in those with high MnTn, the association was increased by 27.9% (β = −8.70, p = 0.036). Low Pb exposure is associated with intellectual deficit in children, especially in those with high MnTn.

Description: Accurately measuring the number of viable microorganisms plays an essential role in microbiological studies. Since the conventional agar method of enumerating visible colonies is time-consuming and not accurate, efforts have been made towards overcoming these limitations by counting the invisible micro-colonies. However, none of studies on micro-colony counting was able to save significant time or provide accurate results. Herein, we developed an on-glass-slide cell culture device that enables rapid formation of micro-colonies on a 0.38 mm-thick gel film without suffering from nutrient and oxygen deprivation during bacteria culturing. Employing a phase contrast imaging setup, we achieved rapid microscopic scanning of micro-colonies within a large sample area on the thin film without the need of fluorescent staining. Using Escherichia coli (E. coli) as a demonstration, our technique was able to shorten the culturing time to within 5 h and automatically enumerate the micro-colonies from the phase contrast images. Moreover, this method delivered more accurate counts than the conventional visible colony counting methods. Due to these advantages, this imaging-based micro-colony enumeration technique provides a new platform for the quantification of viable microorganisms.

Description: Global registration is an important step in the three-dimensional reconstruction of multi-view laser point clouds for moving objects, but the severe noise, density variation, and overlap ratio between multi-view laser point clouds present significant challenges to global registration. In this paper, a multi-view laser point cloud global registration method based on low-rank sparse decomposition is proposed. Firstly, the spatial distribution features of point clouds were extracted by spatial rasterization to realize loop-closure detection, and the corresponding weight matrix was established according to the similarities of spatial distribution features. The accuracy of adjacent registration transformation was evaluated, and the robustness of low-rank sparse matrix decomposition was enhanced. Then, the objective function that satisfies the global optimization condition was constructed, which prevented the solution space compression generated by the column-orthogonal hypothesis of the matrix. The objective function was solved by the Augmented Lagrange method, and the iterative termination condition was designed according to the prior conditions of single-object global registration. The simulation analysis shows that the proposed method was robust with a wide range of parameters, and the accuracy of loop-closure detection was over 90%. When the pairwise registration error was below 0.1 rad, the proposed method performed better than the three compared methods, and the global registration accuracy was better than 0.05 rad. Finally, the global registration results of real point cloud experiments further proved the validity and stability of the proposed method.

Description: Antibiotic residues in the aquatic environment have the potential to induce resistance in environmental bacteria, which ultimately might get transferred to pathogens making treatment of diseases difficult and poses a serious threat to public health. If antibiotic residues in the environment could be eliminated or reduced, it could contribute to minimizing antibiotic resistance. Towards this objective, water containing ciprofloxacin was treated by sunlight-assisted photocatalysis using Fe- doped ZnO nanoparticles for assessing the degradation potential of this system. Parameters like pH, temperature, catalytic dosage were assessed for the optimum performance of the system. To evaluate degradation of ciprofloxacin, both spectrophotometric as well as microbiological (loss of antibiotic activity) methods were employed. 100 mg/L Fe-doped ZnO nanoparticle catalyst and sunlight intensity of 120,000–135,000 lux system gave optimum performance at pH 9 at 30 °C and 40 °C. Under these conditions spectrophotometric analysis showed complete degradation of ciprofloxacin (10 mg/L) at 210 min. Microbiological studies showed loss of antibacterial activity of the photocatalytically treated ciprofloxacin-containing water against Staphylococcus aureus (108 CFU) in 60 min and for Escherichia coli (108 CFU) in 75 min. The developed system, thus possess a potential for treatment of antibiotic contaminated waters for eliminating/reducing antibiotic residues from environment.

Description: Recently, modern smartphones equipped with a variety of embedded-sensors, such as accelerometers and gyroscopes, have been used as an alternative platform for human activity recognition (HAR), since they are cost-effective, unobtrusive and they facilitate real-time applications. However, the majority of the related works have proposed a position-dependent HAR, i.e., the target subject has to fix the smartphone in a pre-defined position. Few studies have tackled the problem of position-independent HAR. They have tackled the problem either using handcrafted features that are less influenced by the position of the smartphone or by building a position-aware HAR. The performance of these studies still needs more improvement to produce a reliable smartphone-based HAR. Thus, in this paper, we propose a deep convolution neural network model that provides a robust position-independent HAR system. We build and evaluate the performance of the proposed model using the RealWorld HAR public dataset. We find that our deep learning proposed model increases the overall performance compared to the state-of-the-art traditional machine learning method from 84% to 88% for position-independent HAR. In addition, the position detection performance of our model improves superiorly from 89% to 98%. Finally, the recognition time of the proposed model is evaluated in order to validate the applicability of the model for real-time applications.

Description: Current bias compensation methods for distributed localization consider the time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements noise, but ignore the negative influence by the sensor location uncertainties on source localization accuracy. Therefore, a new bias compensation method for distributed localization is proposed to improve the localization accuracy in this paper. This paper derives the theoretical bias of maximum likelihood estimation when the sensor location errors and positioning measurements noise both exist. Using the rough estimate result by MLE to subtract the theoretical bias can obtain a more accurate source location estimation. Theoretical analysis and simulation results indicate that the theoretical bias derived in this paper matches well with the actual bias in moderate noise level so that it can prove the correctness of the theoretical derivation. Furthermore, after bias compensation, the estimate accuracy of the proposed method achieves a certain improvement compared with existing methods.

Description: A novel ultraviolet (UV) optical fiber sensor (UVOFS) based on the scintillating material La2O2S:Eu has been designed, tested, and its performance compared with other scintillating materials and other conventional UV detectors. The UVOFS is based on PMMA (polymethyl methacrylate) optical fiber which includes a scintillating material. Scintillating materials provide a unique opportunity to measure UV light intensity even in the presence of strong electromagnetic interference. Five scintillating materials were compared in order to select the most appropriate one for the UVOFS. The characteristics of the sensor are reported, including a highly linear response to radiation intensity, reproducibility, temperature response, and response time (to pulsed light) based on emission from a UV source (UV fluorescence tube) centered on a wavelength of 308 nm. A direct comparison with the commercially available semiconductor-based UV sensor proves the UVOFS of this investigation shows superior performance in terms of accuracy, long-term reliability, response time and linearity.

Description: This paper proposes a new interferometric near-field 3-D imaging approach based on multi-channel joint sparse reconstruction to solve the problems of conventional methods, i.e., the irrespective correlation of different channels in single-channel independent imaging which may lead to deviated positions of scattering points, and the low accuracy of imaging azimuth angle for real anisotropic targets. Firstly, two full-apertures are divided into several sub-apertures by the same standard; secondly, the joint sparse metric function is constructed based on scattering characteristics of the target in multi-channel status, and the improved Orthogonal Matching Pursuit (OMP) method is used for imaging solving, so as to obtain high-precision 3-D image of each sub-aperture; thirdly, comprehensive sub-aperture processing is performed using all sub-aperture 3-D images to obtain the final 3-D images; finally, validity of the proposed approach is verified by using simulation electromagnetic data and data measured in the anechoic chamber. Experimental results show that, compared with traditional interferometric ISAR imaging approaches, the algorithm proposed in this paper is able to provide a higher accuracy in scattering center reconstruction, and can effectively maintain relative phase information of channels.

Description: New amphiphilic star or multi-arm block copolymers with different structures were synthesized for enabling the use of hydrophobic oxygen probe of platinum (II)-tetrakis (pentafluorophenyl) porphyrin (PtTFPP) for bioanalysis. The amphiphilic star polymers were prepared through the Atom Transfer Radical Polymerization (ATRP) method by using hydrophilic 4-arm polyethylene glycol (4-arm-PEG) as an initiator. Among the five block copolymers, P1 series (P1a, P1b, and P1c) and P3 possess fluorine-containing moieties to improve the oxygen sensitivity with its excellent capacity to dissolve and carry oxygen. A polymer P2 without fluorine units was also synthesized for comparison. The structure-property relationship was investigated. Under nitrogen atmosphere, high quantum efficiency of PtTFPP in fluorine-containing micelles could reach to 22% and long lifetime could reach to 76 μs. One kind of representative PtTFPP-containing micelles was used to detect the respiration of Escherichia coli (E. coli) JM109 and macrophage cell J774A.1 by a high throughput plate reader. In vivo hypoxic imaging of tumor-bearing mice was also achieved successfully. This study demonstrated that using well-designed fluoropolymers to load PtTFPP could achieve high oxygen sensing properties, and long lifetime, showing the great capability for further in vivo sensing and imaging.

Description: The development of smart cities calls for improved accuracy in navigation and positioning services; due to the effects of satellite orbit error, ionospheric error, poor quality of navigation signals and so on, it is difficult for existing navigation technology to achieve further improvements in positioning accuracy. Distributed cooperative positioning technology can further improve the accuracy of navigation and positioning with existing GNSS (Global Navigation Satellite System) systems. However, the measured range error and the positioning error of the cooperative nodes exhibit larger reductions in positioning accuracy. In response to this question, this paper proposed a factor graph-aided distributed cooperative positioning algorithm. It establishes the confidence function of factor graphs theory with the ranging error and the positioning error of the coordinated nodes and then fuses the positioning information of the coordinated nodes by the confidence function. It can avoid the influence of positioning error and ranging error and improve the positioning accuracy of cooperative nodes. In the simulation part, the proposed algorithm is compared with a mainly coordinated positioning algorithm from four aspects: the measured range error, positioning error, convergence speed, and mutation error. The simulation results show that the proposed algorithm leads to a 30–60% improvement in positioning accuracy compared with other algorithms under the same measured range error and positioning error. The convergence rate and mutation error elimination times are only 1 / 5 to 1 / 3 of the other algorithms.

Description: The amperometric determination of cysteine, using an electrode based on ternary silver-copper sulfide, is presented. Electrochemical characterizations (using cyclic voltammetry) of three electrode materials revealed that the electrode based on the material that consists of jalpaite (Ag3CuS2), doped with a small amount of metallic silver, has the best electrocatalytical performance for cysteine oxidation. For the amperometric determination, 0.142 V at pH 5 and 0.04 V at pH 7 vs. Ag/AgCl, related to the electrocatalytical oxidation of thiol group, were chosen. Electrochemical impedance spectroscopy together with Fourier transform infrared spectroscopy (FTIR) revealed that oxidation takes place on the electrode surface with fouling effect, which does not affect a wide linear working range between 1 μM and 100 μM. Sensitivities, at pH 5 and pH 7, are calculated to be 0.11 μA μM−1 and 0.10 μA μM−1, respectively. The detection limits were determined to be 0.036 μM and 0.024 μM for pH 5 and pH 7, respectively. In the presence of uric acid, folic acid, ascorbic acid, and glucose no interference was noticed. This electrode showed remarkable stability and excellent reproducibility. The electrode was exploited for the determination of cysteine in a dietary supplement with the excellent recoveries.

Description: Drones have recently become extremely popular, especially in military and civilian applications. Examples of drone utilization include reconnaissance, surveillance, and packet delivery. As time has passed, drones’ tasks have become larger and more complex. As a result, swarms or clusters of drones are preferred, because they offer more coverage, flexibility, and reliability. However, drone systems have limited computing power and energy resources, which means that sometimes it is difficult for drones to finish their tasks on schedule. A solution to this is required so that drone clusters can complete their work faster. One possible solution is an offloading scheme between drone clusters. In this study, we propose an opportunistic computational offloading system, which allows for a drone cluster with a high intensity task to borrow computing resources opportunistically from other nearby drone clusters. We design an artificial neural network-based response time prediction module for deciding whether it is faster to finish tasks by offloading them to other drone clusters. The offloading scheme is conducted only if the predicted offloading response time is smaller than the local computing time. Through simulation results, we show that our proposed scheme can decrease the response time of drone clusters through an opportunistic offloading process.

Description: When the nodes in the network are deployed in the target area with an appropriate density, the effective aggregation and transmission of the data gathered in the monitoring area remain to be solved. The existing Compressed Sensing (CS) based on data aggregation schemes are accomplished in a centralized manner and the Sink node achieves the task of data aggregation. However, these existing schemes may suffer from load imbalance and coverage void issues. In order to address these problems, we propose a Compressed Sensing based on Fault-tolerant Correcting Data Aggregation (CS-FCDA) scheme to accurately reconstruct the compressed data. Therefore, the network communication overhead can be greatly reduced while maintaining the quality of the reconstructed data. Meanwhile, we adopt the node clustering mechanism to optimize and balance the network load. It is shown via simulation results, compared with other data aggregation schemes, that the proposed scheme shows obvious improvement in terms of the Fault-tolerant correcting capability and the network energy efficiency of the data reconstruction.

Description: As one of the key crop traits, plant height is traditionally evaluated manually, which can be slow, laborious and prone to error. Rapid development of remote and proximal sensing technologies in recent years allows plant height to be estimated in more objective and efficient fashions, while research regarding direct comparisons between different height measurement methods seems to be lagging. In this study, a ground-based multi-sensor phenotyping system equipped with ultrasonic sensors and light detection and ranging (LiDAR) was developed. Canopy heights of 100 wheat plots were estimated five times during a season by the ground phenotyping system and an unmanned aircraft system (UAS), and the results were compared to manual measurements. Overall, LiDAR provided the best results, with a root-mean-square error (RMSE) of 0.05 m and an R2 of 0.97. UAS obtained reasonable results with an RMSE of 0.09 m and an R2 of 0.91. Ultrasonic sensors did not perform well due to our static measurement style. In conclusion, we suggest LiDAR and UAS are reliable alternative methods for wheat height evaluation.

Description: Efficient matching of incoming events of data streams to persistent queries is fundamental to event stream processing systems in wireless sensor networks. These applications require dealing with high volume and continuous data streams with fast processing time on distributed complex event processing (CEP) systems. Therefore, a well-managed parallel processing technique is needed for improving the performance of the system. However, the specific properties of pattern operators in the CEP systems increase the difficulties of the parallel processing problem. To address these issues, a parallelization model and an adaptive parallel processing strategy are proposed for the complex event processing by introducing a histogram and utilizing the probability and queue theory. The proposed strategy can estimate the optimal event splitting policy, which can suit the most recent workload conditions such that the selected policy has the least expected waiting time for further processing of the arriving events. The proposed strategy can keep the CEP system running fast under the variation of the time window sizes of operators and the input rates of streams. Finally, the utility of our work is demonstrated through the experiments on the StreamBase system.

Description: 360-degree video streaming for high-quality virtual reality (VR) is challenging for current wireless systems because of the huge bandwidth it requires. However, millimeter wave (mmWave) communications in the 60 GHz band has gained considerable interest from the industry and academia because it promises gigabit wireless connectivity in the huge unlicensed bandwidth (i.e., up to 7 GHz). This massive unlicensed bandwidth offers great potential for addressing the demand for 360-degree video streaming. This paper investigates the problem of 360-degree video streaming for mobile VR using the SHVC, the scalable of High-Efficiency Video Coding (HEVC) standard and PC offloading over 60 GHz networks. We present a conceptual architecture based on advanced tiled-SHVC and mmWave communications. This architecture comprises two main parts. (1) Tile-based SHVC for 360-degree video streaming and optimizing parallel decoding. (2) Personal Computer (PC) offloading mechanism for transmitting uncompressed video (viewport only). The experimental results show that our tiled extractor method reduces the bandwidth required for 360-degree video streaming by more than 47% and the tile partitioning mechanism was improved by up to 25% in terms of the decoding time. The PC offloading mechanism was also successful in offloading 360-degree decoded (or viewport only) video to mobile devices using mmWave communication and the proposed transmission schemes.

Description: Suicide is the leading cause of death among young men aged 15–29 in Greenland, but few epidemiological studies have described this problem. We aimed to summarise descriptive epidemiological studies of suicide in young men in Greenland compared with other demographic groups in Denmark and Greenland to inform future suicide prevention strategy. We searched PubMed, PsycINFO, and Embase using an agreed search strategy to identify English-language papers describing suicide epidemiology in Greenlandic men aged 15–29. We followed PRISMA guidelines in screening and appraising eligible publications. Eight articles fulfilled inclusion criteria of 64 meeting search criteria. Findings covering 1970–2011 supported a dramatic rise in suicide rates in Greenlandic men aged 15–24 from 1976, who remained the highest-ranking demographic group over 1976–2011 compared with men and women of all age groups in Denmark and Greenland. Highest rates recorded were almost 600 per 100,000 per year in men aged approximately 20–23 over 1977–1986. No studies described suicide epidemiology after 2011, and no studies described risk factors for suicide in young men. Given the very high suicide rates recorded for young men over 1976–2011, such studies will be essential for informing the development and evaluation of appropriate preventive interventions.

Description: (1) Background: Several researches have shown the relationship between healthy habits and physical and mental health. Thus, it is essential to study how some psychosocial factors can promote positive behaviours in university students, specifically in those who will be teachers of Physical Education. (2) Methods: This descriptive and cross-sectional research was conducted on 775 university students of Physical Education from Spain. This study aims to develop an explanatory model for the relationships between motivational climate, healthy habits (Mediterranean Diet (MD), Physical Activity (PA) and substance consumption) and some indicators of physical health (oxygen consumption (VO2MAX)) and mental health (self-concept) according to gender, using structural equations analysis. (3) Results: The motivational climate was positively associated with PA, showing a stronger relationship for ego-oriented climate in women. The adherence to MD showed a positive association with PA and self-concept, while it is negative for the consumption of tobacco in men. Likewise, PA was directly related to VO2MAX with a higher regression weight for women. The ego-oriented motivational climate was negatively related to tobacco consumption in women. (4) Conclusions: Task-oriented goals are related to more positive and healthy behaviours such as PA, although it was no association was found with MD. Therefore, we can conclude the importance of promoting task-oriented goals in educational contexts linked to Physical Education in order to avoid negative behaviours.

Description: In the experiment of inertial confinement fusion, soft X-ray spectrum unfolding can provide important information to optimize the design of the laser and target. As the laser beams increase, there are limited locations for installing detection channels to obtain measurements, and the soft X-ray spectrum can be difficult to recover. In this paper, a novel recovery method of soft X-ray spectrum unfolding based on compressive sensing is proposed, in which (1) the spectrum recovery is formulated as a problem of accurate signal recovery from very few measurements (i.e., compressive sensing), and (2) the proper basis atoms are selected adaptively over a Legendre orthogonal basis dictionary with a large size and Lasso regression in the sense of ℓ1 norm, which enables the spectrum to be accurately recovered with little measured data from the limited detection channels. Finally, the presented approach is validated with experimental data. The results show that it can still achieve comparable accuracy from only 8 spectrometer detection channels as it has previously done from 14 detection channels. This means that the presented approach is capable of recovering spectrum from the data of limited detection channels, and it can be used to save more space for other detectors.

Description: The exploration of new food sources and natural products is the result of the increase in world population as well as the need for a healthier diet; in this context, microalgae are undoubtedly an interesting solution. With the intent to enhance their value in new commercial applications, this paper aims to characterize microalgae that have already been recognized as safe or authorized as additives for humans and animals (Chlorella vulgaris, Arthrospira platensis, Haematococcus pluvialis, Dunaliella salina) as well as those that have not yet been marketed (Scenedesmus almeriensis and Nannocholoropsis sp.). In this scope, the content of proteins, carbohydrates, lipids, total dietary fiber, humidity, ash, and carotenoids has been measured via standard methods. In addition, individual carotenoids (beta-carotene, astaxanthin, and lutein) as well as individual saturated, monounsaturated, and polyunsaturated fatty acids have been identified and quantified chromatographically. The results confirm the prerogative of some species to produce certain products such as carotenoids, polyunsaturated fatty acids, and proteins, but also show how their cellular content is rich and diverse. H. pluvialis green and red phases, and Nannochloropsis sp., in addition to producing astaxanthin and omega-3, contain about 25–33% w/w proteins on a dry basis. D. salina is rich in beta-carotene (3.45% w/w on a dry basis), S. Almeriensis is a source of lutein (0.30% w/w on a dry basis), and the C. vulgaris species is a protein-based microalgae (45% w/w on a dry basis). All, however, can also produce important fatty acids such as palmitic acid, γ-linolenic acid, and oleic acid. Considering their varied composition, these microalgae can find applications in multiple sectors. This is true for microalgae already on the market as well as for promising new sources of bioproducts such as S. almeriensis and Nannochloropsis sp.

Description: The study assessed changes in water quality between the water source and the tap of locally produced low cost ceramic water filters used by a community living in hygienically critical conditions in a remote mountainous area in Western Nepal. Data was collected from 42 rural households during two visits. The effectiveness of filter handling on its performance was assessed through microbiological analysis, structured household interviews and structured observations. Water quality decreased significantly when source water was filled into transport containers, while the use of the filters improved drinking water quality for about 40% of the households. Highly inadequate filter cleaning practices involving the use of contaminated raw water, hands (geo mean = 110 E. coli CFU/100 mL) and cleaning tools (geo mean = 80 E. coli CFU/100 mL) stained hygienic parts of the filter. The use of boiling water to disinfect the filters was significantly correlated with improved filter performance and should be further promoted. However, even disinfected filters achieved a very low average LRV for E. coli of 0.4 in the field and performed worse than during laboratory tests (LRV for E. coli of 1.5–2). Comprehensive training on adequate filter handling, as well as better filter products, are required to improve the impact of filter use.

Description: The relationship between energy drink (ED) use and risky behaviours has not been explored in the Caribbean, where youth risky behaviours are prevalent. This paper examines the relationship between ED use and risky behaviours and the moderating effect of gender among 1994 undergraduate students in Trinidad and Tobago. Analyses examined the association between ED use and risky behaviours, and the significant predictors of risky behaviours. Analysis of variance determined whether there were differences in risky behaviours between those who used only alcohol or EDs, both alcohol and EDs, alcohol mixed with EDs and neither alcohol nor EDs; and the difference between those with high and low intensity of ED use. In both males and females current use of energy drinks was positively associated with harmful substance use, risky sexual behaviours, and risky non-sexual behaviours, more strongly for males. The strongest predictor of risky behaviours was gender. Males consuming both alcohol and EDs, independently or mixed, were most likely to have risky behaviours. Consistent with previous reports, harmful alcohol use and other risk taking health behaviours appear to be predicted by a combination of high intensity use of EDs and alcohol. In countries like Trinidad and Tobago where violence, alcohol use disorders, STI/HIV infection and teenaged pregnancy are prevalent, the assessment of consumption of EDs and alcohol may be an important potential predictor of health compromising behaviours.

Description: Recently, the emergence of low-cost sensors have allowed electronic noses to be considered for densifying the actual air pollution monitoring networks in urban areas. Electronic noses are affected by changes in environmental conditions and sensor drifts over time. Therefore, they need to be calibrated periodically and also individually because the characteristics of identical sensors are slightly different. For these reasons, the calibration process has become very expensive and time consuming. To cope with these drawbacks, calibration transfer between systems constitutes a satisfactory alternative. Among them, direct standardization shows good efficiency for calibration transfer. In this paper, we propose to improve this method by using kernel SPXY (sample set partitioning based on joint x-y distances) for data selection and support vector machine regression to match between electronic noses. The calibration transfer approach introduced in this paper was tested using two identical electronic noses dedicated to monitoring nitrogen dioxide. Experimental results show that our method gave the highest efficiency compared to classical direct standardization.

Description: Background: People on long-term sick leave often have a long-lasting process back to work, where the individuals may be in multiple and recurrent states; i.e., receiving different social security benefits or working, and over time they may shift between these states. The purpose of this study was to evaluate the effects of two vocational rehabilitation programs, compared to a control, on return-to-work (RTW) or increased employability in patients on long-term sick leave due to mental illness and/or chronic pain. Methods: In this randomized controlled study, 427 women and men were allocated to either (1) multidisciplinary team management, i.e., multidisciplinary assessments and individual rehabilitation management, (2) acceptance and commitment therapy (ACT), or (3) control. A positive outcome was defined as RTW or increased employability. The outcome was considered negative if the (part-time) wage was reduced or ceased, or if there was an indication of decreased employability. The outcome was measured one year after entry in the project and analyzed using binary and multinomial logistic regressions. Results: Participants in the multidisciplinary team group reported having RTW odds ratio (OR) 3.31 (95% CI 1.39–7.87) compared to the control group in adjusted models. Participants in the ACT group reported having increased employability OR 3.22 (95% CI 1.13–9.15) compared to the control group in adjusted models. Conclusions: This study of vocational rehabilitation in mainly female patients on long-term sick leave due to mental illness and/or chronic pain suggests that multidisciplinary team assessments and individually adapted rehabilitation interventions increased RTW and employability. Solely receiving the ACT intervention also increased employability.

Description: By means of the time derivatives of Global Navigation Satellite System (GNSS) carrier-phase measurements, the instantaneous velocity of a stand-alone, single GNSS receiver can be estimated with a high precision of a few mm/s; it is feasible to even obtain the level of tenths of mm/s. Therefore, only data from the satellite navigation message are needed, thus discarding any data from a reference network. Combining this method with an efficient movement-detection algorithm opens some interesting applications for geohazard monitoring; an example is the detection of strong earthquakes. This capability is demonstrated for a case study of the 6.5 Mw earthquake of October 30, 2016, near the city of Norcia in Italy; in that region, there are densely deployed GNSS stations. It is shown that GNSS sensors can detect seismic compressional (P) waves, which are the first to arrive at a measurement station. These findings are substantiated by a comparison with data of strong-motion (SM) seismometers. Furthermore, it is shown that the GNSS-only hypocenter localization comes close (less than a kilometer) to the solutions provided by official seismic services. Finally, we conclude that this method can provide important contributions to a real-time geohazard early-warning system.

Description: Since the 1940s, infrared (IR) detection and imaging at wavelengths in the two atmospheric windows of 3 to 5 and 8 to 14 μm has been extensively researched. Through several generations, these detectors have undergone considerable developments and have found use in various applications in different fields including military, space science, medicine and engineering. For the most recently proposed generation, these detectors are required to achieve high-speed detection with spectral and polarization selectivity while operating at room temperature. Antenna coupled IR detectors appear to be the most promising candidate to achieve these requirements and has received substantial attention from research in recent years. This paper sets out to present a review of the antenna coupled IR detector family, to explore the main concepts behind the detectors as well as outline their critical and challenging design considerations. In this context, the design of both elements, the antenna and the sensor, will be presented individually followed by the challenging techniques in the impedance matching between both elements. Some hands-on fabrication techniques will then be explored. Finally, a discussion on the coupled IR detector is presented with the aim of providing some useful insights into promising future work.

Description: The publication presents a comparative study of two fibre-optic sensors in the application of heart rate (HR) and respiratory rate (RR) monitoring of the human body. After consultation with clinical practitioners, two types of non-invasive measuring and analysis systems based on fibre Bragg grating (FBG) and fibre-optic interferometer (FOI) have been designed and assembled. These systems use probes (both patent pending) that have been encapsulated in the bio-compatible polydimethylsiloxane (PMDS). The main advantage of PDMS is that it is electrically non-conductive and, as well as optical fibres, has low permeability. The initial verification measurement of the system designed was performed on four subjects in a harsh magnetic resonance (MR) environment under the supervision of a senior radiology assistant. A follow-up comparative study was conducted, upon a consent of twenty volunteers, in a laboratory environment with a minimum motion load and discussed with a head doctor of the Radiodiagnostic Institute. The goal of the laboratory study was to perform measurements that would simulate as closely as possible the environment of harsh MR or the environment of long-term health care facilities, hospitals and clinics. Conventional HR and RR measurement systems based on ECG measurements and changes in the thoracic circumference were used as references. The data acquired was compared by the objective Bland–Altman (B–A) method and discussed with practitioners. The results obtained confirmed the functionality of the designed probes, both in the case of RR and HR measurements (for both types of B–A, more than 95% of the values lie within the ±1.96 SD range), while demonstrating higher accuracy of the interferometric probe (in case of the RR determination, 95.66% for the FOI probe and 95.53% for the FBG probe, in case of the HR determination, 96.22% for the FOI probe and 95.23% for the FBG probe).

Description: Three-dimensional (3D) imaging has attracted more and more interest because of its widespread applications, especially in information and life science. These techniques can be broadly divided into two types: ray-based and wavefront-based 3D imaging. Issues such as imaging quality and system complexity of these techniques limit the applications significantly, and therefore many investigations have focused on 3D imaging from depth measurements. This paper presents an overview of 3D imaging from depth measurements, and provides a summary of the connection between the ray-based and wavefront-based 3D imaging techniques.

Description: Optical communication promises to be a high-rate supplement for acoustic communication in short-range underwater applications. In the photic zone of oceanic and coastal waters, underwater optical communication systems are exposed by remaining sunlight. This ambient light generates additional noise in photodetectors, thus degrading system performance. This effect can be diminished by the use of optical filters. This paper investigates light field characteristics of different water types and potential interactions with optical underwater communication. A colored glass and different thin film bandpass filters are examined as filter/detector combinations under varying light and water conditions, and their physical constraints are depicted. This is underlined by various spectral measurements as well as optical signal-to-noise ratio calculations. The importance of matching the characteristics of the light emitting diode (LED) light source, the photodetector, and the filter on the ambient conditions using wider angle of incidents is emphasized.

Description: Background: All of society is affected by mental health problems, not just a minor, isolated part. Mental health problems represent a major challenge to the global development of community health. This study examined the community health knowledge, perceived beliefs, and associated factors of mental distress (MD) in Mekelle city in Northern Ethiopia. Methods: The current study used a cross-sectional approach calculating a sample of 260 adults living in the two sub-cities of Mekelle city. To select the sample households in each sub-city, systematic random sampling was used. Self-reported questionnaire (SRQ-20 with a cutoff point of 7), and Mental Health Knowledge Schedule (MAKS) instruments were included within the structured questionnaire tool to clarify community occurrence and the level of health mental knowledge. Results: The likelihood of having MD was higher among the study participants who were female, employed, self-employed, and daily alcohol and khat users. The results also showed that the level of mental health knowledge among the participants was low. Conclusions: Factors such as being male, having a higher level of education, and having strong levels of social support were found to be the independent predictors of good mental health and community mental health knowledge.

Description: In this paper, the design of MEMS piezoresistive out-of-plane shear and normal stress sensor is described. To improve the sensor sensitivity, a methodology by the incorporation of stress concentration regions, namely surface trenches in the proximity of sensing elements was explored in detail. The finite element (FE) model, verified by a five-layer analytical model was developed as a tool to model the performance of the sensor and guide the geometric optimization of the surface trenches. Optimum location and dimensions of the surface trenches have been obtained through a comprehensive FE analysis. The microfabrication and packing scheme was introduced to prototype the sensor with optimum geometric characteristics of surface trenches. Signal output from the prototyped sensor was tested and compared with those from FE simulation. Good agreement has been achieved between the simulation and experimental results. Moreover, the results suggest the incorporation of surface trenches can help improve the sensor sensitivity. More specifically, the sum of signal output from the sensor chip with surface trenches are 4.52, 5.06 and 5.72 times higher compared to flat sensor chip for center sensing area, edge sensing areas 1 and 2, respectively.

Description: Clean-in-place (CIP) processes are extensively used to clean industrial equipment without the need for disassembly. In food manufacturing, cleaning can account for up to 70% of water use and is also a heavy user of energy and chemicals. Due to a current lack of real-time in-process monitoring, the non-optimal control of the cleaning process parameters and durations result in excessive resource consumption and periods of non-productivity. In this paper, an optical monitoring system is designed and realized to assess the amount of fouling material remaining in process tanks, and to predict the required cleaning time. An experimental campaign of CIP tests was carried out utilizing white chocolate as fouling medium. During the experiments, an image acquisition system endowed with a digital camera and ultraviolet light source was employed to collect digital images from the process tank. Diverse image segmentation techniques were considered to develop an image processing procedure with the aim of assessing the area of surface fouling and the fouling volume throughout the cleaning process. An intelligent decision-making support system utilizing nonlinear autoregressive models with exogenous inputs (NARX) Neural Network was configured, trained and tested to predict the cleaning time based on the image processing results. Results are discussed in terms of prediction accuracy and a comparative study on computation time against different image resolutions is reported. The potential benefits of the system for resource and time efficiency in food manufacturing are highlighted.

Description: Bluetooth Low-Energy (BLE) beacons-based indoor positioning is a promising method for indoor positioning, especially in applications of position-based services (PbS). It has low deployment cost and it is suitable for a wide range of mobile devices. Existing BLE beacon-based positioning methods can be categorized as range-based methods and fingerprinting-based methods. For range-based methods, the positions of the beacons should be known before positioning. For fingerprinting-based methods, a pre-requisite is the reference fingerprinting map (RFM). Many existing methods focus on how to perform the positioning assuming the beacon positions or RFM are known. However, in practical applications, determining the beacon positions or RFM in the indoor environment is normally a difficult task. This paper proposed an efficient and graph optimization-based way for estimating the beacon positions and the RFM, which combines the range-based method and the fingerprinting-based method. The method exists without need for any dedicated surveying instruments. A user equipped with a BLE-enabled mobile device walks in the region collecting inertial readings and BLE received signal strength indication (RSSI) readings. The inertial measurements are processed through the pedestrian dead reckoning (PDR) method to generate the constraints at adjacent poses. In addition, the BLE fingerprints are adopted to generate constraints between poses (with similar fingerprints) and the RSSIs are adopted to generate distance constraints between the poses and the beacon positions (according to a pre-defined path-loss model). The constraints are then adopted to form a cost function with a least square structure. By minimizing the cost function, the optimal user poses at different times and the beacon positions are estimated. In addition, the RFM can be generated through the pose estimations. Experiments are carried out, which validates that the proposed method for estimating the pre-requisites (including beacon positions and the RFM). These estimated pre-requisites are of sufficient quality for both range-based and fingerprinting-based positioning.

Description: Detecting the cognitive profiles of learners is an important step towards personalized and adaptive learning. Electroencephalograms (EEG) have been used to detect the subject’s emotional and cognitive states. In this paper, an approach for detecting two cognitive skills, focused attention and working memory, using EEG signals is proposed. The proposed approach consists of the following main steps: first, subjects undergo a scientifically-validated cognitive assessment test that stimulates and measures their full cognitive profile while putting on a 14-channel wearable EEG headset. Second, the scores of focused attention and working memory are extracted and encoded for a classification problem. Third, the collected EEG data are analyzed and a total of 280 time- and frequency-domain features are extracted. Fourth, several classifiers were trained to correctly classify and predict three levels (low, average, and high) of the two cognitive skills. The classification accuracies that were obtained on 86 subjects were 84% and 81% for the focused attention and working memory, respectively. In comparison with similar approaches, the obtained results indicate the generalizability and suitability of the proposed approach for the detection of these two skills. Thus, the presented approach can be used as a step towards adaptive learning where real-time adaptation is to be done according to the predicted levels of the measured cognitive skills.

Description: GPS trajectories generated by moving objects provide researchers with an excellent resource for revealing patterns of human activities. Relevant research based on GPS trajectories includes the fields of location-based services, transportation science, and urban studies among others. Research relating to how to obtain GPS data (e.g., GPS data acquisition, GPS data processing) is receiving significant attention because of the availability of GPS data collecting platforms. One such problem is the GPS data classification based on transportation mode. The challenge of classifying trajectories by transportation mode has approached detecting different modes of movement through the application of several strategies. From a GPS data acquisition point of view, this paper macroscopically classifies the transportation mode of GPS data into single-mode and mixed-mode. That means GPS trajectories collected based on one type of transportation mode are regarded as single-mode data; otherwise it is considered as mixed-mode data. The one big difference of classification strategy between single-mode and mixed-mode GPS data is whether we need to recognize the transition points or activity episodes first. Based on this, we systematically review existing classification methods for single-mode and mixed-mode GPS data and introduce the contributions of these methods as well as discuss their unresolved issues to provide directions for future studies in this field. Based on this review and the transportation application at hand, researchers can select the most appropriate method and endeavor to improve them.

Description: Personalized emotion recognition provides an individual training model for each target user in order to mitigate the accuracy problem when using general training models collected from multiple users. Existing personalized speech emotion recognition research has a cold-start problem that requires a large amount of emotionally-balanced data samples from the target user when creating the personalized training model. Such research is difficult to apply in real environments due to the difficulty of collecting numerous target user speech data with emotionally-balanced label samples. Therefore, we propose the Robust Personalized Emotion Recognition Framework with the Adaptive Data Boosting Algorithm to solve the cold-start problem. The proposed framework incrementally provides a customized training model for the target user by reinforcing the dataset by combining the acquired target user speech with speech from other users, followed by applying SMOTE (Synthetic Minority Over-sampling Technique)-based data augmentation. The proposed method proved to be adaptive across a small number of target user datasets and emotionally-imbalanced data environments through iterative experiments using the IEMOCAP (Interactive Emotional Dyadic Motion Capture) database.

Description: We demonstrate that the rectifying field effect transistor, biased to the subthreshold regime, in a large signal regime exhibits a super-linear response to the incident terahertz (THz) power. This phenomenon can be exploited in a variety of experiments which exploit a nonlinear response, such as nonlinear autocorrelation measurements, for direct assessment of intrinsic response time using a pump-probe configuration or for indirect calibration of the oscillating voltage amplitude, which is delivered to the device. For these purposes, we employ a broadband bow-tie antenna coupled Si CMOS field-effect-transistor-based THz detector (TeraFET) in a nonlinear autocorrelation experiment performed with picoseconds-scale pulsed THz radiation. We have found that, in a wide range of gate bias (above the threshold voltage V th = 445 mV), the detected signal follows linearly to the emitted THz power. For gate bias below the threshold voltage (at 350 mV and below), the detected signal increases in a super-linear manner. A combination of these response regimes allows for performing nonlinear autocorrelation measurements with a single device and avoiding cryogenic cooling.

Description: Current environmental impact analyses are mainly focused on land, soil, energy, and material consumption, while studies regarding blue water consumption are scarce. Based on the water footprint concept, this study evaluates the impacts of blue water consumption on human health, ecosystem quality, and water resources in China from the production and consumption perspective, respectively. The results indicate that environmental impacts due to blue water consumption in China were 15.82 × 106 DALY (disability-adjusted life years), 96.54 × 109 m2∙year, and 175.20 × 109 MJ, and provinces such as Xinjiang, Shandong, and Hebei could be targets for achieving smaller environmental impacts in the future. More than 80% of environmental impacts were related to the agricultural sector. In terms of agricultural production, about 70% of the environmental impacts were related to product export. Measures such as the shift of the agricultural production pattern from water-intensive crops and animal products toward less water-intensive ones, the increase of agricultural water use efficiency, and the adoption of water-saving technologies could contribute to smaller environmental impacts. In terms of agricultural consumption, more than 95% of the environmental impacts were related to agricultural products produced locally. The focus was on increasing awareness of the importance of saving water and whether products were imported from regions with relatively small environmental impacts.

Description: The prevalence of food allergy (FA) has not been estimated at a population level in Central American countries and, consequently, the magnitude and relevance of the problem in the Central American region remains unknown. Thus, our aim was to evaluate the parent-reported prevalence of FA in a population of schoolchildren from the Central American country El Salvador. A Spanish version of a structured questionnaire was utilized. Five hundred and eight (508) parents returned the questionnaire with valid responses (response rate, 32%). The estimated prevalence rates (95% CI) were: adverse food reactions 15.9 (13.0–19.3), “perceived FA, ever” 11.6 (9.1–14.6), “physician-diagnosed FA, ever” 5.7% (4.0–8.0), “immediate-type FA, ever” 8.8% (6.6–11.6), “immediate-type FA, current” 5.3% (3.6–7.6), and anaphylaxis 2.5% (1.5–4.3). The most common food allergens were milk (1.7%), shrimp (1.3), chili (0.7%), chocolate (0.7%), and nuts (0.3%). Most of the “food-dependent anaphylaxis” cases (60.5%) sought medical attention, but only one case reported the prescription of an epinephrine autoinjector. Mild and severe FA cases are not uncommon among Salvadoran schoolchildren and both the prescription of epinephrine autoinjectors by healthcare personnel and the use of the autoinjectors by anaphylactic individuals should be encouraged.

Description: Respiratory rate (RR) is a key parameter used in healthcare for monitoring and predicting patient deterioration. However, continuous and automatic estimation of this parameter from wearable sensors is still a challenging task. Various methods have been proposed to estimate RR from wearable sensors using windowed segments of the data; e.g., often using a minimum of 32 s. Little research has been reported in the literature concerning the instantaneous detection of respiratory rate from such sources. In this paper, we develop and evaluate a method to estimate instantaneous respiratory rate (IRR) from body-worn reflectance photoplethysmography (PPG) sensors. The proposed method relies on a nonlinear time-frequency representation, termed the wavelet synchrosqueezed transform (WSST). We apply the latter to derived modulations of the PPG that arise from the act of breathing.We validate the proposed algorithm using (i) a custom device with a PPG probe placed on various body positions and (ii) a commercial wrist-worn device (WaveletHealth Inc., Mountain View, CA, USA). Comparator reference data were obtained via a thermocouple placed under the nostrils, providing ground-truth information concerning respiration cycles. Tracking instantaneous frequencies was performed in the joint time-frequency spectrum of the (4 Hz re-sampled) respiratory-induced modulation using the WSST, from data obtained from 10 healthy subjects. The estimated instantaneous respiratory rates have shown to be highly correlated with breath-by-breath variations derived from the reference signals. The proposed method produced more accurate results compared to averaged RR obtained using 32 s windows investigated with overlap between successive windows of (i) zero and (ii) 28 s. For a set of five healthy subjects, the averaged similarity between reference RR and instantaneous RR, given by the longest common subsequence (LCSS) algorithm, was calculated as 0.69; this compares with averaged similarity of 0.49 using 32 s windows with 28 s overlap between successive windows. The results provide insight into estimation of IRR and show that upper body positions produced PPG signals from which a better respiration signal was extracted than for other body locations.

Description: Although radon is a well-established contributor to lung cancer mortality among uranium miners, the effects of radon decay products on different histopathologies of lung carcinoma are not well established. Using a retrospective cohort design, this study aims to examine the risks of lung cancer by histological subtypes associated with exposure to radon decay products among the Ontario Uranium Miners cohort. Cases were stratified by histological groups, and associated risks were estimated for cumulative radon exposure after adjustment for attained age and calendar period. Between 1969 and 2005, 1274 incident cases of primary lung cancer were identified. Of these, 1256 diagnoses (99%) contained information on histology. Squamous cell carcinoma was most common (31%), followed by adenocarcinoma (20%), large cells (18%), small cell lung carcinoma (14%), and other or unspecified cell types (17%). Of the histological sub-groups, small cell lung carcinoma had the strongest association with cumulative radon exposure; compared to the reference group (〈1 cumulative working level months (WLM)), the highest exposure category (〉60 cumulative WLM) had a relative risk (RR) of 2.76 (95% CI: 1.67–4.57). Adenocarcinoma had the lowest risk and was not significantly associated with exposure to radon decay products (RR = 1.49, 95% CI: 0.96–2.31). An increasing, linear trend in relative risk was noted with increasing cumulative WLM across small cell, squamous cell, and large cell lung carcinomas (Ptrend 〈 0.05). Similarly, the excess relative risk (ERR) per WLM was highest for small cell lung carcinoma (ERR/WLM = 0.15, p 〈 0.01), followed by squamous cell carcinoma (ERR/WLM = 0.12, p 〈 0.01). Non-statistically significant excess risk was observed for adenocarcinoma (ERR/WLM = 0.004, p = 0.07). Our analysis of the Ontario Uranium Miners cohort data shows differences in the magnitude of the risks across four histological subtypes of lung carcinoma; the strongest association was noted for small cell lung carcinoma, followed by squamous cell, large cell, and lastly adenocarcinoma, which showed no significant associations with exposure to radon decay products.

Description: Engagement is described as a state in which an individual involved in an activity can ignore other influences. The engagement level is important to obtaining good performance especially under study conditions. Numerous methods using electroencephalograph (EEG), electrocardiograph (ECG), and near-infrared spectroscopy (NIRS) for the recognition of engagement have been proposed. However, the results were either unsatisfactory or required many channels. In this study, we introduce the implementation of a low-density hybrid system for engagement recognition. We used a two-electrode wireless EEG, a wireless ECG, and two wireless channels NIRS to measure engagement recognition during cognitive tasks. We used electrooculograms (EOG) and eye tracking to record eye movements for data labeling. We calculated the recognition accuracy using the combination of correlation-based feature selection and k-nearest neighbor algorithm. Following that, we did a comparative study against a stand-alone system. The results show that the hybrid system had an acceptable accuracy for practical use (71.65 ± 0.16%). In comparison, the accuracy of a pure EEG system was (65.73 ± 0.17%), pure ECG (67.44 ± 0.19%), and pure NIRS (66.83 ± 0.17%). Overall, our results demonstrate that the proposed method can be used to improve performance in engagement recognition.

Description: Recently, a project was initiated in Japan to transport a large amount of liquid hydrogen (LH2) from Australia to Japan by sea. It is important to understand the sloshing and boil-off that are likely to occur inside an LH2 tank during marine transportation by ship, but such characteristics are yet to be experimentally clarified. To do so, we combined the liquid level detected by five 500 mm long external-heating-type magnesium diboride (MgB2) level sensors with synchronous measurements of temperature, pressure, ship motion, and acceleration during a zigzag maneuver. During this zigzag maneuver, the pressure of gaseous hydrogen (GH2) in the small LH2 tank increased to roughly 0.67 MPaG/h, and the temperature of the GH2 in the small LH2 tank increased at the position of gaseous hydrogen at roughly 1.0 K/min when the maximum rolling angle was 5°; the average rolling and liquid-oscillation periods were 114 and 118 s, respectively, as detected by the MgB2 level sensors, which therefore detected a long-period LH2 wave due to the ship’s motion.

Description: The implementation of damage-detection methods for continuously assessing structural integrity entails systems with attractive features such as storage capabilities, memory capacity, computational complexity and time-consuming processing. In this sense, embedded hardware platforms are a promising technology for developing integrated solutions in Structural Health Monitoring. In this paper, design, test, and specifications for a standalone inspection prototype are presented, which take advantage of piezo-diagnostics principle, statistical processing via Principal Component Analysis (PCA) and embedded systems. The equipment corresponds to a piezoelectric active system with the capability to detect defects in structures, by using a PCA-based algorithm embedded in the Odroid-U3 ARM Linux platform. The operation of the equipment consists of applying, at one side of the structure, wide guided waves by means of piezoelectric devices operated in actuation mode and to record the wave response in another side of the structure by using the same kind of piezoelectric devices operated in sensor mode. Based on the nominal response of the guide wave (no damages), represented by means of a PCA statistical model, the system can detect damages between the actuated/sensed points through squared prediction error (Q-statistical index). The system performance was evaluated in a pipe test bench where two kinds of damages were studied: first, a mass is added to the pipe surface, and then leaks are provoked to the pipe structure by means of a drill tool. The experiments were conducted on two lab structures: (i) a meter carbon-steel pipe section and (ii) a pipe loop structure. The wave response was recorded between the instrumented points for two conditions: (i) The pipe in nominal conditions, where several repetitions will be applied to build the nominal statistical model and (ii) when damage is caused to the pipe (mass adding or leak). Damage conditions were graphically recognized through the Q-statistic chart. Thus, the feasibility to implement an automated real-time diagnostic system is demonstrated with minimum processing resources and hardware flexibility.

Description: There is an increase in the awareness of the importance of spatial data in epidemiology and exposure assessment (EA) studies. Most studies use governmental and ordnance surveys, which are often expensive and sparsely updated, while in most developing countries, there are often no official geo-spatial data sources. OpenStreetMap (OSM) is an open source Volunteered Geographic Information (VGI) mapping project. Yet very few environmental epidemiological and EA studies have used OSM as a source for road data. Since VGI data is either noncommercial or governmental, the validity of OSM is often questioned. We investigate the robustness and validity of OSM data for use in epidemiological and EA studies. We compared OSM and Governmental Major Road Data (GRD) in three different regions: Massachusetts, USA; Bern, Switzerland; and Beer-Sheva, South Israel. The comparison was done by calculating data completeness, positional accuracy, and EA using traditional exposure methods. We found that OSM data is fairly complete and accurate in all regions. The results in all regions were robust, with Massachusetts showing the best fits (R2 0.93). Results in Bern (R2 0.78) and Beer-Sheva (R2 0.77) were only slightly lower. We conclude by suggesting that OSM data can be used reliably in environmental assessment studies.

Description: Environmental changes significantly impact health behavior. Active travel behavior is mostly affected by increasing motorization, urban sprawl, and traffic safety. Especially for children, active and independent travel can contribute to physical activity, social and motor development, and other health-related outcomes. A reduced number of children engaging in independent mobility over the last 20 years demanded researchers to further examine the construct of children’s independent mobility. By examining relevant literature, this narrative review aims to provide the current state of knowledge on children’s independent mobility, and identify future directions in research, as well as practical implications. From a public health perspective, considering children’s independent mobility in intervention programs is recommended, since it is associated with numerous health and environmental benefits. To develop interventions, multilevel socio-ecological influences on children’s independent mobility are widely examined; however, evidence is limited due to heterogeneous measurements and a lack of high-quality prospective studies. To oppose the decline in children’s independent mobility, further analysis using comparable measures is needed to understand the determinants of children’s independent mobility and to enable international comparison.

Description: Urban land cover and land use mapping plays an important role in urban planning and management. In this paper, novel multi-scale deep learning models, namely ASPP-Unet and ResASPP-Unet are proposed for urban land cover classification based on very high resolution (VHR) satellite imagery. The proposed ASPP-Unet model consists of a contracting path which extracts the high-level features, and an expansive path, which up-samples the features to create a high-resolution output. The atrous spatial pyramid pooling (ASPP) technique is utilized in the bottom layer in order to incorporate multi-scale deep features into a discriminative feature. The ResASPP-Unet model further improves the architecture by replacing each layer with residual unit. The models were trained and tested based on WorldView-2 (WV2) and WorldView-3 (WV3) imageries over the city of Beijing. Model parameters including layer depth and the number of initial feature maps (IFMs) as well as the input image bands were evaluated in terms of their impact on the model performances. It is shown that the ResASPP-Unet model with 11 layers and 64 IFMs based on 8-band WV2 imagery produced the highest classification accuracy (87.1% for WV2 imagery and 84.0% for WV3 imagery). The ASPP-Unet model with the same parameter setting produced slightly lower accuracy, with overall accuracy of 85.2% for WV2 imagery and 83.2% for WV3 imagery. Overall, the proposed models outperformed the state-of-the-art models, e.g., U-Net, convolutional neural network (CNN) and Support Vector Machine (SVM) model over both WV2 and WV3 images, and yielded robust and efficient urban land cover classification results.

Description: The use of electrochemical sensors for the analysis of biological samples is nowadays widespread and highly demanded from diagnostic and pharmaceutical research, but the reliability and repeatability still remain debated issues. In the expanding field of printed electronics, Aerosol Jet Printing (AJP) appears promising to bring an improvement in resolution, miniaturization, and flexibility. In this paper, the use of AJP is proposed to design and fabricate customized electrochemical sensors in term of geometry, materials and 3D liquid sample confinement, reducing variability in the functionalization process. After an analysis of geometrical, electrical and surface features, the optimal layout has been selected. An electrochemical test has been then performed quantifying Interleukin-8, selected as reference protein, by means of Anodic Stripping Voltammetry. AJP sensors have been compared with standard screen-printed electrodes in terms of current density and relative standard deviation. Results from AJP sensors with Ag-based Anodic Stripping Voltammetry confirmed nanostructures capability to reduce the limit of detection (from 2.1 to 0.3 ng/mL). Furthermore, AJP appeared to bring an improvement in term of relative standard deviation from 50 to 10%, if compared to screen-printed sensors. This is promising to improve reliability and repeatability of measurement techniques integrable in several biotechnological applications.

Description: In this study, we design and verify an intake system using the wake of a pocket-sized quadcopter for the chemical plume tracing (CPT) problem. Solving CPT represents an important technique in the field of engineering because it can be used to perform rescue operations at the time of a disaster and to identify sources of harmful substances. An appropriate intake of air when sensing odors plays an important role in performing CPT. Hence, we used the air flow generated by a quadcopter itself to intake chemical particles into two alcohol sensors. By experimental evaluation, we verified that the quadcopter wake intake method has good directivity and can be used to realize CPT. Concretely, even at various odor source heights, the quadcopter had a three-dimensional CPT success rate of at least 70%. These results imply that, although a further development of three-dimensional CPT is necessary in order to conduct it in unknown and cluttered environments, the intake method proposed in this paper enables a pocket-sized quadcopter to perform three-dimensional CPT.

Description: This paper presents and applies an inductive directional coupling technology based on spread spectrum time domain reflectometry (SSTDR) for non-intrusive power cable fault diagnosis. Different from existing capacitive coupling approaches with large signal attenuation, an inductive coupling approach with a capacitive trapper is proposed to restrict the detection signal from transmitting to power source and to eliminate the effect of the power source impedance mismatch. The development, analysis, and implementation of the proposed approach are discussed in detail. A series of simulations and experiments on cables with different fault modes are conducted, along with comparison of existing capacitive coupling, to verify and demonstrate the effectiveness of the proposed method.

Description: Texas Instruments ADS1299 is an attractive choice for low cost electroencephalography (EEG) devices owing to its low power consumption and low input referred noise. To date, there have been no rigorous evaluations of its performance. In this EEG experimental study we evaluated the performance of the ADS1299 against a high quality laboratory-based system. Two self-paced lower limb motor tasks were performed by 22 healthy participants. Recorded power across delta, theta, alpha, and beta EEG bands, the power ratio across the motor tasks, pre-movement noise, and signal-to-noise ratio were obtained for evaluation. The amplitude and time of the negative peak in the movement-related cortical potentials (MRCPs) extracted from the EEG data were also obtained. Using linear mixed models, no statistically significant differences (p 〉 0.05) were found in any of these measures across the two systems. These findings were further supported by evaluation of cosine similarity, waveform differences, and topographic maps. There were statistically significant differences in MRCPs across the motor tasks in both systems. We conclude that the performance of the ADS1299 in combination with wet Ag/AgCl electrodes is analogous to that of a laboratory-based system in a low frequency (〈40 Hz) EEG recording.

Description: Swimming pool water ingestion volumes are necessary for assessing infection risk from swimming. Pool water ingestion volumes can be estimated by questionnaire or measuring a chemical tracer in swimmer urine. Questionnaires are often preferred to the chemical tracer method because surveys are less time consuming, but no research exists validating questionnaires accurately quantify pool water ingestion volumes. The objective of this study was to explore if questionnaires are a reliable tool for collecting pool water ingestion volumes. A questionnaire was issued at four pool sites in Tucson, Arizona to 46 swimmers who also submitted a urine sample for analyzing cyanuric acid, a chemical tracer. Perceived ingestion volumes reported on the questionnaire were compared with pool water ingestion volumes, quantified by analyzing cyanuric acid in swimmer urine. Swimmers were asked if they swallowed (1) no water or only a few drops, (2) one to two mouthfuls, (3) three to five mouthfuls, or (4) six to eight mouthfuls. One mouthful is the equivalent of 27 mL of water. The majority (81%) of swimmers ingested 〈27 mL of pool water but reported ingesting 〉27 mL (“one mouthful”) on the questionnaire. More than half (52%) of swimmers overestimated their ingestion volume. These findings suggest swimmers are over-estimating pool water ingestion because they perceive one mouthful is 〈27 mL. The questionnaire did not reliably collect pool water ingestion volumes and should be improved for future exposure assessment studies. Images of the ingestion volume categories should be included on the questionnaire to help swimmers visualize the response options.

Description: Recent assessments have found that a warming climate, with associated increases in extreme heat events, could profoundly affect human health. This paper describes a new modeling and analysis framework, built around the Benefits Mapping and Analysis Program—Community Edition (BenMAP), for estimating heat-related mortality as a function of changes in key factors that determine the health impacts of extreme heat. This new framework has the flexibility to integrate these factors within health risk assessments, and to sample across the uncertainties in them, to provide a more comprehensive picture of total health risk from climate-driven increases in extreme heat. We illustrate the framework’s potential with an updated set of projected heat-related mortality estimates for the United States. These projections combine downscaled Coupled Modeling Intercomparison Project 5 (CMIP5) climate model simulations for Representative Concentration Pathway (RCP)4.5 and RCP8.5, using the new Locating and Selecting Scenarios Online (LASSO) tool to select the most relevant downscaled climate realizations for the study, with new population projections from EPA’s Integrated Climate and Land Use Scenarios (ICLUS) project. Results suggest that future changes in climate could cause approximately from 3000 to more than 16,000 heat-related deaths nationally on an annual basis. This work demonstrates that uncertainties associated with both future population and future climate strongly influence projected heat-related mortality. This framework can be used to systematically evaluate the sensitivity of projected future heat-related mortality to the key driving factors and major sources of methodological uncertainty inherent in such calculations, improving the scientific foundations of risk-based assessments of climate change and human health.

Description: The Job Demand-Control and Job Demand-Control-Support (JDCS) models constitute the theoretical approaches used to analyze the relationship between the characteristics of labor and occupational health. Few studies have investigated the main effects and multiplicative model in relation to the perceived occupational health of professional accountants. Accountants are subject to various types of pressure in performing their work; this pressure influences their health and, ultimately, their ability to perform a job well. The objective of this study is to investigate the effects of job demands on the occupational health of 739 accountants, as well as the role of the moderator that internal resources (locus of control) and external resources (social support) have in occupational health. The proposed hypotheses are tested by applying different models of neural networks using the algorithm of the Extreme Learning Machine. The results confirm the relationship between certain stress factors that affect the health of the accountants, as well as the direct effect that the recognition of superiors in occupational health has. Additionally, the results highlight the moderating effect of professional development and the support of superiors on the job’s demands.

Description: Background: Vaccination hesitancy and skepticism among parents hinders progress in achieving full vaccination coverage. Swedish measles, mumps and rubella (MMR) vaccine coverage is high however some areas with low vaccination coverage risk outbreaks. This study aimed to explore factors influencing the decision of Somali parents living in the Rinkeby and Tensta districts of Stockholm, Sweden, on whether or not to vaccinate their children with the measles, mumps and rubella (MMR) vaccine. Method: Participants were 13 mothers of at least one child aged 18 months to 5 years, who were recruited using snowball sampling. In-depth interviews were conducted in Somali and Swedish languages and the data generated was analysed using qualitative content analysis. Both written and verbal informed consent were obtained from participants. Results: Seven of the mothers had not vaccinated their youngest child at the time of the study and decided to postpone the vaccination until their child became older (delayers). The other six mothers had vaccinated their child for MMR at the appointed time (timely vaccinators). The analysis of the data revealed two main themes: (1) barriers to vaccinate on time, included issues surrounding fear of the child not speaking and unpleasant encounters with nurses and (2) facilitating factors to vaccinate on time, included heeding vaccinating parents’ advice, trust in nurses and trust in God. The mothers who had vaccinated their children had a positive impact in influencing other mothers to also vaccinate. Conclusions: Fear, based on the perceived risk that vaccination will lead to autism, among Somali mothers in Tensta and Rinkeby is evident and influenced by the opinions of friends and relatives. Child Healthcare Center nurses are important in the decision-making process regarding acceptance of MMR vaccination. There is a need to address mothers’ concerns regarding vaccine safety while improving the approach of nurses as they address these concerns.

Description: The presence of harmful algal blooms (HABs) can cause significant problems to the quality of the water, the marine ecosystems, and the human health, and economy worldwide. Biological remediation can inhibit harmful algal growth efficiently in an environmental-friendly manner. Therefore, the research conducted on biological remediation with regard to the inhibition of HABs is becoming a major focus in marine ecology. To date, no study has been reported with regard to the red tides occurring in mangrove wetlands. Therefore, the present study used two mangrove species, namely Bruguiera gymnorrhiza and Kandelia candel and one harmful algae species Phaeocystis globosa as experimental organisms. The present study determined the inhibitory effects and algae physiology of specific aqueous extracts from mangrove leaves on the viability of harmful algae, and analyzed the main chemical composition of the aqueous extracts by ultra-performance liquid chromatography coupled to high resolution mass spectrometry (UPLC-QTOF-MS). The results indicated that the aqueous extracts from different leaf ages of B. gymnorrhiza and K. candel leaves exhibited apparent inhibitory effects on the growth of P. globosa. The inhibitory effects of B. gymnorrhiza and K. candel leaves aqueous extracts on the growth of P. globosa were in the following order: senescent 〉 mature 〉 young leaves. The levels of the parameters superoxide dismutase (SOD) activity, glutathione (GSH), and malondialdehyde (MDA)content in P. globosa following treatment with B. gymnorrhiza and K. candel leaves aqueous extracts were increased as follows: senescent 〉 mature 〉 young leaves. Simultaneously, the intensity of the ion peaks of the specific secondary metabolites assigned 4 (No.: 4 Rt: 2.83 min), 7 (No.: 7 Rt: 3.14 min), 8 (No.: 8 Rt: 3.24 min), 9 (No.: 9 Rt: 3.82min) and 10 (No.: 10 Rt: 4.10 min) were increased. These metabolites were found in the aqueous extracts from B. gymnorrhiza leaves. The intensities of the ion peaks of the secondary metabolites 7, 8 in the aqueous extracts from the K. candel leaves were also increased. The majority of the substances that inhibited the algae found in the mangrove plants were secondary metabolites. Therefore, we considered that the norsesquiterpenes compounds 4, 8, 9, and 10 and a phenolic glycoside compound 7 were the active constituents in the aqueous extracts of the mangrove leaves responsible for the inhibition of algae growth. This evidence provided theoretical guidance for the development of biological methods to control red tides and for the further use of substances with antiproliferative activity against algae.

Description: Background: The incidences of typhoid and paratyphoid remain high and these diseases still pose a public health problem in China and in Zhejiang Province in particular. This study aimed to investigate the trend of typhoid and paratyphoid in Zhejiang Province from 1953 to 2014 and to provide a theoretical basis for the prevention and control of these diseases. Methods: Included in this study were compiled epidemiological data of typhoid and paratyphoid cases in Zhejiang from 1953 to 2003 and epidemiological data of those from 2004 to 2014 registered in the China Information System for Diseases Control and Prevention. Description methods were employed to explore the epidemiological characteristics, including long-term trend, gender distribution, age distribution, and occupation distribution. Incidence maps were made to represent the annual average incidences for each municipality. Spearman’s rank correlation was performed to detect the correlation between incidence and average elevation, and circular distribution was calculated to identify the seasonality and peak days of the diseases. A p-value of 〈0.05 was considered statistically significant. Results: A total of 182,602 typhoid and paratyphoid cases were reported in Zhejiang Province from 1953 to 2014, and the average annual incidence was 7.89 per 100,000 population. The incidence in 2014 decreased by 93.82% compared with that in 1953 and by 95.00% compared with the highest incidence rate. The average incidence before 2003 was negatively correlated with the average elevation of each region in Zhejiang province (r 〈 0, p 〈 0.05), but there was no statistically significant correlation from 2003. The peak period of diseases fell in the months from April to October every year. The incidence among the population group aged over 35 rose gradually but declined sharply among those between 20 and 34. Conclusions: The incidence of typhoid and paratyphoid decreased in Zhejiang Province from 1953 to 2014 but remained high in some regions. Proper measures for prevention and control are warranted in the southeast coast areas and for high-risk populations.

Description: This paper proposes a deep convolutional neural network (CNN) -based technique for the detection of micro defects on metal screw surfaces. The defects we consider include surface damage, surface dirt, and stripped screws. Images of metal screws with different types of defects are collected using industrial cameras, which are then employed to train the designed deep CNN. To enable efficient detection, we first locate screw surfaces in the pictures captured by the cameras, so that the images of screw surfaces can be extracted, which are then input to the CNN-based defect detector. Experiment results show that the proposed technique can achieve a detection accuracy of 98%; the average detection time per picture is 1.2 s. Comparisons with traditional machine vision techniques, e.g., template matching-based techniques, demonstrate the superiority of the proposed deep CNN-based one.

Description: In this paper, a novel parallel factor (PARAFAC) model for processing the nested vector-sensor array is proposed. It is first shown that a nested vector-sensor array can be divided into multiple nested scalar-sensor subarrays. By means of the autocorrelation matrices of the measurements of these subarrays and the cross-correlation matrices among them, it is then demonstrated that these subarrays can be transformed into virtual scalar-sensor uniform linear arrays (ULAs). When the measurement matrices of these scalar-sensor ULAs are combined to form a third-order tensor, a novel PARAFAC model is obtained, which corresponds to a longer vector-sensor ULA and includes all of the measurements of the difference co-array constructed from the original nested vector-sensor array. Analyses show that the proposed PARAFAC model can fully use all of the measurements of the difference co-array, instead of its partial measurements as the reported models do in literature. It implies that all of the measurements of the difference co-array can be fully exploited to do the 2-D direction of arrival (DOA) and polarization parameter estimation effectively by a PARAFAC decomposition method so that both the better estimation performance and slightly improved identifiability are achieved. Simulation results confirm the efficiency of the proposed model.

Description: Provision of smart city services often relies on users contribution, e.g., of data, which can be costly for the users in terms of privacy. Privacy risks, as well as unfair distribution of benefits to the users, should be minimized as they undermine user participation, which is crucial for the success of smart city applications. This paper investigates privacy, fairness, and social welfare in smart city applications by means of computer simulations grounded on real-world data, i.e., smart meter readings and participatory sensing. We generalize the use of public good theory as a model for resource management in smart city applications, by proposing a design principle that is applicable across application scenarios, where provision of a service depends on user contributions. We verify its applicability by showing its implementation in two scenarios: smart grid and traffic congestion information system. Following this design principle, we evaluate different classes of algorithms for resource management, with respect to human-centered measures, i.e., privacy, fairness and social welfare, and identify algorithm-specific trade-offs that are scenario independent. These results could be of interest to smart city application designers to choose a suitable algorithm given a scenario-specific set of requirements, and to users to choose a service based on an algorithm that matches their privacy preferences.

Description: The objective of the article is to present the slow sport movement as a phenomenon developing in the postmodern era in opposition to the idea of citius, altius, fortius (Eng. faster, higher, stronger). The theoretical part of the article describes the health repercussions of slow movement and its implications for the sports industry and sports tourism. It also points to new challenges in sports management and sports tourism implemented in the slow style. The empirical part of the article aims at determining what influence the achievement of a self-set sports goal has on the degree of satisfaction with participation in a running event among runners. Could runners who did not set themselves any sports goal and ran for pleasure (according to the idea of slow sport) achieve the same degree of satisfaction as runners who set themselves an ambitious sports goal and achieved it (according to the idea of citius, altius, fortius)? The case study is the 6th Poznan Half Marathon, a cyclical, popular running event taking place in Poland. A total of 560 runners (n = 560) took part in the diagnostic survey conducted using the interview technique. The ANOVA Rang Kruskal-Wallis test and Dunn’s test were used in the study. The results show that athletes who did not set a sporting goal (ran for pleasure, company, atmosphere, participation, etc.) experienced the same level of satisfaction as athletes who achieved their intended sporting goal. It turns out, therefore, that sport and physical activity done for pleasure in accordance with the slow sport idea can provide the same level of satisfaction as sport practiced in the spirit of citius, altius, fortius.

Description: Healthcare accessibility has become an issue of social equity. An accurate estimation of existing healthcare accessibility is vital to plan and allocate health resources. Healthcare capacity, population demand, and geographic impedance are three essential factors to measure spatial accessibility. Additionally, geographic impedance is usually represented with a function of travel time. In this paper, the three-step floating catchment area (3SFCA) method is improved from the perspectives of the temporal dimension and population demand. Specifically, the travel time from the population location to the service site is precisely calculated by introducing real-time traffic conditions instead of utilizing empirical speed in previous studies. Additionally, with the utilization of real-time traffic, a dynamic result of healthcare accessibility is derived during different time periods. In addition, since the medical needs of the elderly are higher than that of the young, a demand weight index of demand is introduced to adjust the population demand. A case study of healthcare accessibility in Wuhan shows that the proposed method is effective to measure healthcare accessibility during different time periods. The spatial accessibility disparities of communities and crowdedness of hospitals are identified as an important reference for the balance between the supply and demand of medical resources.

Description: Organized and ritual child sexual abuse (ORA) is often rooted in the child’s own family. Empirical evidence on possible associations between ORA and trauma-related symptoms in those who report this kind of extreme and prolonged violence is rare. The aim of our study was to explore socio-demographic and clinical characteristics of the individuals reporting ORA experiences, and to investigate protective as well as promotive factors in the link between ORA and trauma-related symptom severity. Within the framework of a project of the Independent Inquiry into Child Sexual Abuse in Germany, we recruited 165 adults who identified themselves as ORA victims via abuse- and trauma-specific networks and mailing lists, and they completed an anonymous online survey. We used variance analyses to examine correlations between several variables in the ORA context and PTSD symptoms (PCL-5) as well as somatoform dissociation (SDQ-5). Results revealed a high psychic strain combined with an adverse health care situation in individuals who report experiences with ORA. Ideological strategies used by perpetrators as well as Dissociative Identity Disorders experienced by those affected are associated with more severe symptoms (η2p = 0.11; η2p = 0.15), while an exit out of the ORA structures is associated with milder symptoms (η2p = 0.11). Efforts are needed to improve health care services for individuals who experience severe and complex psychiatric disorders due to ORA in their childhood.

Description: Social Network Analysis (SNA) is a set of techniques developed in the field of social and behavioral sciences research, in order to characterize and study the social relationships that are established among a set of individuals. When building a social network for performing an SNA analysis, an initial process of data gathering is achieved in order to extract the characteristics of the individuals and their relationships. This is usually done by completing a questionnaire containing different types of questions that will be later used to obtain the SNA measures needed to perform the study. There are, then, a great number of different possible network-generating questions and also many possibilities for mapping the responses to the corresponding characteristics and relationships. Many variations may be introduced into these questions (the way they are posed, the weights given to each of the responses, etc.) that may have an effect on the resulting networks. All these different variations are difficult to achieve manually, because the process is time-consuming and error-prone. The tool described in this paper uses semantic knowledge representation techniques in order to facilitate this kind of sensitivity studies. The base of the tool is a conceptual structure, called “ontology” that is able to represent the different concepts and their definitions. The tool is compared to other similar ones, and the advantages of the approach are highlighted, giving some particular examples from an ongoing SNA study about alcohol consumption habits in adolescents.

Description: Organisations may benefit from training champions to promote healthy workplace environments and initiatives. This study compared the perceived usefulness and relative effectiveness of an employee training course offered via online and face-to-face formats. Individuals who took part in the training course were assessed on their perceived competence and confidence to implement changes pre- and post-training. Repeated measures Analysis of Variance (ANOVA) and a t-test were conducted to test for significant differences between pre- and post-training scores and/or mode of training, respectively. Although the face-to-face training course was rated as slightly more useful, there were no significant differences between the two modes of training for the other dependent variables, and both modes led to significantly greater perceived competence and confidence post-training. These findings demonstrate the potential benefits of training employees to implement changes in their workplaces.

Description: Dust exposure is one of the major risk factors for respiratory health in many workplaces, including coffee factories. The aim of this study was to assess the prevalence of respiratory symptoms and lung function reduction among workers in Ethiopian primary coffee processing factories, compared to a control group of workers. A total of 115 coffee workers and 110 water bottling workers were involved in this study, from 12 coffee and 3 water bottling factories in Ethiopia, respectively. The chronic respiratory symptoms were assessed using a structured interview, using a standardized questionnaire adopted from the American Thoracic Society (ATS). The lung function tests were performed according to the ATS recommendation for spirometry. The coffee workers had a significantly higher prevalence of coughing, coughing with sputum, breathlessness, work-related shortness of breath, and wheezing compared with the controls. The prevalence ratio of work-related shortness of breath (PR = 3.7, 95% CI: 1.6–8.7) and wheezing (PR = 3.3, 95% CI: 1.3–8.4) was significantly higher for the coffee workers compared to the controls. The coffee workers in the age groups 28–39 years and ≥40 years, had a significantly lower forced vital capacity and forced expiratory volume in 1 s compared to the controls in the similar age groups. The findings indicated the need for longitudinal studies on the possible effect of coffee dust on respiratory health of coffee production workers.

Description: This paper presents a stereo camera-based head-eye calibration method that aims to find the globally optimal transformation between a robot’s head and its eye. This method is highly intuitive and simple, so it can be used in a vision system for humanoid robots without any complex procedures. To achieve this, we introduce an extended minimum variance approach for head-eye calibration using surface normal vectors instead of 3D point sets. The presented method considers both positional and orientational error variances between visual measurements and kinematic data in head-eye calibration. Experiments using both synthetic and real data show the accuracy and efficiency of the proposed method.

Description: The aim of this study is to examine the efficiency of biobased Spanish broom (SB) surface modified cellulose fibers to remove bisphenol A (BPA), a well-known endocrine disruptor, from water. Spanish brooms are flowering plants, which are native and abundant to Mediterranean regions. The functionalized fibers (FF) were found to have the best adsorption efficiency at pH 5, due to the optimal hydrophobic interaction between the FF fiber and BPA. Adsorption kinetics of BPA was found to fit well a pseudo-second order reaction. Equilibrium isotherm data were fitted by Langmuir and Freundlich models. A very fast and simple regeneration method was developed and it was observed that adsorption capacity of the fibers was kept almost unchanged after 3 consecutive uses. Bottled water and synthetic wastewater were also tested to assess the efficiency of the process under more realistic water and wastewater treatment conditions. It was found that BPA removal was slightly decreased from 77% in ultrapure water to 64% in synthetic wastewater matrix, indicating that FF has a high selectivity toward BPA, even in the presence of other organic compounds. Overall, it was observed that SB-modified fibers can be a new promising green biotechnology for water purification.

Description: Total Worker Health® (TWH) frameworks call for attention to organizational leadership in the implementation and effectiveness of TWH approaches. It is especially important to study this within in the small business environment where employees face significant health, safety, and well-being concerns and employers face barriers to addressing these concerns. The purpose of this study was to gain a better understanding of how small business leaders perceive employee health, safety, and well-being in the context of their own actions. We conducted semi-structured interviews with 18 small business senior leaders and used a qualitative coding approach to analyze the transcripts to determine the frequency with which leaders discussed each code. When we asked leaders about their leadership practices for health, safety, and well-being, leaders reflected upon their business (65%), themselves (28%), and their employees (7%). Leaders rarely discussed the ways in which they integrate health, safety, and well-being. The interviews demonstrate that small business leaders care about the health of their employees, but because of the perceived value to their business, not to employees or themselves. Thus, they may lack the knowledge and skills to be successful TWH leaders. The present study supports a need for continued small business TWH leadership research.

Description: In this study, we identified the sources of trace metals, investigated their spatial distribution in topsoil and assessed their potential ecological risk in the area surrounding a typical manganese mining area in Xiangtan, Hunan Province, China. The concentrations of Mn, Cu, Pb, Zn, Cd, Ni, Cr and Hg in the topsoil of the study area were measured. Except for Cr and Hg, all trace metals exceeded the corresponding soil background values for Hunan Province. The spatial variation in trace metals was visualized by GIS, and the results show that trace metals in topsoil are enriched mainly around mines and smelters. Two groups of trace metals were identified using the spatial distribution, trend analysis, Pearson’s correlation and principal component analysis: Mn, Cu, Pb, Zn, Cd and Ni can be attributed to industrial and mining activities, whereas Cr and Hg are of natural origin. The results also revealed the extent of the influence of secondary processes such as the prevailing wind direction, erosion of mine tailings and rainwater runoff play significant roles in the wider dispersal and transfer of trace metals. In addition, the environmental risk of metal pollution was evaluated by applying the geoaccumulation index and potential ecological risk index (PERI) to the study area. The accumulated PERI for metals of concern is at highest risk level in the main manganese mine area. This decreases to a moderate risk around the manganese mine area, highlighting locations for further risk management concern. Furthermore, nearly 80% of the potential ecological risk was from Cd across the study area.

Description: The motivation of this work is to help outpatients find their corresponding departments or clinics, thus, it needs to provide indoor positioning services with a room-level accuracy. Unlike wireless outdoor localization that is dominated by the global positioning system (GPS), wireless indoor localization is still an open issue. Many different schemes are being developed to meet the increasing demand for indoor localization services. In this paper, we investigated the AoA-based wireless indoor localization for outpatients’ wayfinding in a hospital, where Wi-Fi access points (APs) are deployed, in line, on the ceiling. The target position can be determined by a mobile device, like a smartphone, through an efficient geometric calculation with two known APs coordinates and the angles of the incident radios. All possible positions in which the target may appear have been comprehensively investigated, and the corresponding solutions were proven to be the same. Experimental results show that localization error was less than 2.5 m, about 80% of the time, which can satisfy the outpatients’ requirements for wayfinding.

Description: The working–sleeping cycle strategy used for sensor nodes with limited power supply in wireless sensor networks can effectively save their energy, but also causes opportunistic node connections due to the intermittent communication mode, which can affect the reliability of data transmission. To address this problem, a data collection scheme based on opportunistic node connections is proposed to achieve efficient data collection in a network with a mobile sink. In this scheme, the mobile sink first broadcasts a tag message to start a data collection period, and all nodes that receive this message will use the probe message to forward their own source information to the mobile sink. On receiving these probe messages, the mobile sink then constructs an opportunistic connection random graph by analyzing the source information included in them, and calculates the optimal path from itself to each node in this random graph, therefore a spanning tree could be generated with the mobile sink play as the root node, finally, it broadcasts this spanning tree so that each node could obtain an optimal path from itself to the mobile sink to forward the sensing data. In addition, a routing protocol that adapts to different nodes operating statuses is proposed to improve the reliability of data transmission. Simulation results show that the proposed scheme works better concerning the packet delivery rate, energy consumption and network lifetime.

Description: Depth discrimination is a key procedure in acoustic detection or target classification for low-frequency underwater sources. Conventional depth-discrimination methods use a vertical line array, which has disadvantage of poor mobility due to the size of the sensor array. In this paper, we propose a depth-discrimination method for low-frequency sources using a horizontal line array (HLA) of acoustic vector sensors based on mode extraction. First, we establish linear equations related to the modal amplitudes based on modal beamforming in the vector mode space. Second, we solve the linear equations by introducing the total least square algorithm and estimate modal amplitudes. Third, we select the power percentage of the low-order modes as the decision metric and construct testing hypotheses based on the modal amplitude estimation. Compared with a scalar sensor, a vector sensor improves the depth discrimination, because the mode weights are more appropriate for doing so. The presented linear equations and the solution algorithm allow the method to maintain good performance even using a relatively short HLA. The constructed testing hypotheses are highly robust against mismatched environments. Note that the method is not appropriate for the winter typical sound speed waveguide, because the characteristics of the modes differ from those in downward-refracting sound speed waveguide. Robustness analysis and simulation results validate the effectiveness of the proposed method.

Description: Mining activities make important contributions to economic growth, but they can also produce massive amounts of solid waste, such as tailings and metal accumulations. Taking the Urad Houqi mining area in Inner Mongolia as the study area, this study systematically assessed the contamination risk of arsenic and heavy metals in the soils of the study area and explored the contamination characteristics in a key polymetallic mining area. For the whole study area, based on the Nemerow comprehensive pollution method, almost half of the investigated sites were contaminated, and the most contaminated site was Urad Houqi Qianzhen Mineral Concentration Co., Ltd. (Bayannaoer, China), a cooperation between the lead and zinc mining industry. The assessment results indicated that Cd and As were the elements of greatest concern, followed by Pb, Cr and Hg. Particularly, for the typical Dongshengmiao mining area, when compared with the GB15618-1995 standard values, As, Zn and Cd posed the most serious contamination threat, while Cr and Ni exhibited clean conditions. In addition, the vertical distribution maps demonstrated that the contents of arsenic and metals in some soil profiles were correlated with sampling depth. Therefore, arsenic and heavy metals pose high threat to soil ecosystems in this area, there is encouragement for some control and remediation measures to be taken into effect.

Description: Young women in their late teens and early 20s are at the highest risk for depression onset. The present study aimed to assess depressive symptoms among female college students in Fukushima. More specifically, it aimed to clarify factors predicting possible symptom profiles, with an emphasis on determining how nuclear radiation risks affect the reporting of depression symptoms. A cross-sectional survey was conducted of 310 female students at a college in the Fukushima prefecture, Japan, in December 2015, and 288 participants submitted valid questionnaires. In total, 222 (77.1%) participants lived in Fukushima at the time of the Great East Japan Earthquake. The measures included the World Health Organization-Five Well-Being Index, the Fukushima Future Parents Attitude Measure, and risk perception of radiation health effects. A total of 46.5% of participants reported depressive symptoms. Path analysis revealed that higher radiation risk perceptions and reduced efficacy with reproduction related to a decline in self-esteem and self-efficacy, which was subsequently associated with increased depressive symptoms. These findings highlight the importance of radiation education among children and young adults, both after a nuclear accident and during disaster preparation, particularly in the context of reproductive and mental health.

Description: Job stress is considered one of the critical causes of construction workers’ unsafe behaviors. As a mainstay industry in many countries, the construction industry has a considerable number of employees and the research on how job stress affects workers’ unsafe behaviors has important theoretical and practical significance to improve construction safety performance through better job stress management. In this study, the authors thoroughly reviewed the literature and conducted semi-structured interviews to identify the dimensions of job stress, designed the job stress scale and cited the safety behavior measurement scale. After that, a questionnaire survey was developed using the proposed measurement scale and distributed to the construction employees from a project in Beijing. One hundred fifty responses were collected and analyzed using reliability analysis to validate the scale’s internal consistency. Results from factor analysis indicate that the scales of job stress measurement can be grouped into six dimensions. To demonstrate the applicability of the developed scale on construction safety management research, the collected data was used to test the hypothesis that job stress has a negative correlation with safety behavior. Results show that the hypothesis is valid, and there is a negative correlation between job stress and safety behavior. In addition, finer results of the relationship between the six dimensions of job stress and safety behavior can be obtained. In summary, this study developed an improved stress scale for construction workers in China, and the proposed scale was validated by analyzing the data from an empirical study in Beijing.

Description: Mobile Crowdsensing (MCS) is a paradigm for collecting large-scale sensor data by leveraging mobile devices equipped with small and low-powered sensors. MCS has recently received considerable attention from diverse fields, because it can reduce the cost incurred in the process of collecting a large amount of sensor data. However, in the task assignment process in MCS, to allocate the requested tasks efficiently, the workers need to send their specific location to the requester, which can raise serious location privacy issues. In this paper, we focus on the methods for publishing differentially a private spatial histogram to guarantee the location privacy of the workers. The private spatial histogram is a sanitized spatial index where each node represents the sub-regions and contains the noisy counts of the objects in each sub-region. With the sanitized spatial histograms, it is possible to estimate approximately the number of workers in the arbitrary area, while preserving their location privacy. However, the existing methods have given little concern to the domain size of the input dataset, leading to the low estimation accuracy. This paper proposes a partitioning technique SAGA (Skew-Aware Grid pArtitioning) based on the hotspots, which is more appropriate to adjust the domain size of the dataset. Further, to optimize the overall errors, we lay a uniform grid in each hotspot. Experimental results on four real-world datasets show that our method provides an enhanced query accuracy compared to the existing methods.

Description: A cross-capacitance liquid level sensor is based on the principle of cross capacitance. This study designed a new single-tube cross-capacitance fuel-level sensor. The fuel-level measurement model is established for a single-tube cross-capacitive sensor, and the relationship between the measured liquid level and sensor output capacitance is derived. The characteristics of the sensor were tested experimentally. The experimental results demonstrate that the linearity error of the liquid-level sensor of the single-tube calculation for the spacecraft is ±0.48%, the repeatability error is ±0.47%, and the hysteresis error is ±0.68%. The cross-capacitive fuel-level sensor developed in this study can be used in the fuel tank of spacecrafts owing to its low weight and high precision.

Description: The objective of this study was to gain a better understanding of the mechanisms underlying falls in the elderly. The results were based on a group of 28 women in a control group (CON) and 16 women in an experimental group (EXP), aged 60–70. Participants took part in the six-weeks Elderly Recreation Movement Program (ERMP) with the only difference that the EXP group practiced twice as often as the CON group. The measurement of variations in the index called limits of stability (LOS) was performed by application of Kistler force plate and the coactivation index (CI) was registered by means of sEMG. The results demonstrate the existence of statistically significant differences in terms of the principal outcome of the exercise time in the measurements of LOS (F(1.42) = 10.0, p = 0.003), and CI (F(1.42) = 10.5, p = 0.002). The effect of the program was associated with an increase the level of the maximum LOS, and a decrease of the CI level, especially in the experimental group. Hence, the implementation of an innovative ERMP exercise program results in the improvement of the physical capabilities of senior subjects.

Description: With the development in the exploitation of maritime resources, the structural health monitoring (SHM) of offshore structures becomes necessary. This study focuses on addressing the practical issues of application of fiber Bragg grating (FBG) sensors for the SHM of offshore structures, in particular an FPSO (floating, production, storage, and offloading unit) vessel. Due to the harsh marine environment and tough working conditions, the FBG sensors must have sufficient protection and good repeatability for long-term monitoring. Thorough research has been conducted to identify the most suitable, commercially available protection packaging for FBG sensors for offshore applications. Further, the performance of the selected FBG sensor packaging is tested under conditions of strong sunlight, heavy rain, and salty water in order to emulate the marine environment. Moreover, the installation method of the packaged FBG sensors is equally important, as it ensures the repeatability and durability of the sensors for their long-term performance. It is shown that the packaged FBG sensors can be installed using resin-based epoxy to maintain the repeatability of the sensor over the long-term. Further, the packaged FBG sensors are installed and tested on a simple FPSO model. The experimental results under full load and ballast draft conditions show that the proposed FBG sensors are competent for the SHM of offshore structures.

Description: Highly accurate and easy-to-operate calibration (to determine the interior and distortion parameters) and orientation (to determine the exterior parameters) methods for cameras in large volume is a very important topic for expanding the application scope of 3D vision and photogrammetry techniques. This paper proposes a method for simultaneously calibrating, orienting and assessing multi-camera 3D measurement systems in large measurement volume scenarios. The primary idea is building 3D point and length arrays by moving a scale bar in the measurement volume and then conducting a self-calibrating bundle adjustment that involves all the image points and lengths of both cameras. Relative exterior parameters between the camera pair are estimated by the five point relative orientation method. The interior, distortion parameters of each camera and the relative exterior parameters are optimized through bundle adjustment of the network geometry that is strengthened through applying the distance constraints. This method provides both internal precision and external accuracy assessment of the calibration performance. Simulations and real data experiments are designed and conducted to validate the effectivity of the method and analyze its performance under different network geometries. The RMSE of length measurement is less than 0.25 mm and the relative precision is higher than 1/25,000 for a two camera system calibrated by the proposed method in a volume of 12 m × 8 m × 4 m. Compared with the state-of-the-art point array self-calibrating bundle adjustment method, the proposed method is easier to operate and can significantly reduce systematic errors caused by wrong scaling.

Description: We evaluate the spectral resolution and the detection thresholds achievable for a photoacoustic spectroscopy (PAS) system in the broadband infrared wavelength region 3270 n m ≲ λ ≲ 3530 n m driven by a continuous wave optical parametric oscillator (OPO) with P ¯ ≈ 1.26 W . The absorption spectra, I PAS ( λ i ) , for diluted propane, ethane and methane test gases at low concentrations ( c ∼ 100 ppm ) were measured for ∼1350 discrete wavelengths λ i . The I PAS ( λ i ) spectra were then compared to the high resolution cross section data, σ FTIR , obtained by Fourier Transform Infrared Spectroscopy published in the HITRAN database. Deviations of 7.1(6)% for propane, 8.7(11)% for ethane and 15.0(14)% for methane with regard to the average uncertainty between I PAS ( λ i ) and the expected reference values based on σ FTIR were recorded. The characteristic absorption wavelengths λ res can be resolved with an average resolution of δ λ res ∼ 0.08 nm . Detection limits range between 7.1 ppb (ethane) to 13.6 ppb (methane). In an additional step, EUREQA, an artificial intelligence (AI) program, was successfully applied to deconvolute simulated PAS spectra of mixed gas samples at low limits of detection. The results justify a further development of PAS technology to support e.g., biomedical research.

Description: The massive dissemination of smart devices in current markets provides innovative technologies that can be used in energy management systems. Particularly, smart plugs enable efficient remote monitoring and control capabilities of electrical resources at a low cost. However, smart plugs, besides their enabling capabilities, are not able to acquire and communicate information regarding the resource’s context. This paper proposes the EnAPlug, a new environmental awareness smart plug with knowledge capabilities concerning the context of where and how users utilize a controllable resource. This paper will focus on the abilities to learn and to share knowledge between different EnAPlugs. The EnAPlug is tested in two different case studies where user habits and consumption profiles are learned. A case study for distributed resource optimization is also shown, where a central heater is optimized according to the shared knowledge of five EnAPlugs.

Description: Recognition of human actions form videos has been an active area of research because it has applications in various domains. The results of work in this field are used in video surveillance, automatic video labeling and human-computer interaction, among others. Any advancements in this field are tied to advances in the interrelated fields of object recognition, spatio- temporal video analysis and semantic segmentation. Activity recognition is a challenging task since it faces many problems such as occlusion, view point variation, background differences and clutter and illumination variations. Scientific achievements in the field have been numerous and rapid as the applications are far reaching. In this survey, we cover the growth of the field from the earliest solutions, where handcrafted features were used, to later deep learning approaches that use millions of images and videos to learn features automatically. By this discussion, we intend to highlight the major breakthroughs and the directions the future research might take while benefiting from the state-of-the-art methods.

Description: The performance of a quality of service (QoS) control scheme in a multi-hop wireless body area network (WBAN) based on the IEEE Std. 802.15.6 is evaluated. In medical Internet of Things systems, WBANs are an important technology. In a previous study, an optimal quality of service control scheme that employs a multiplexing layer for priority scheduling and a decomposable error control coding scheme for WBANs were proposed. However, the two-hop extension supported by IEEE Std.802.15.6 has not been considered. Here, the two-hop extension is applied. Then, the packet error ratio, number of transmissions, and energy efficiency of our previously proposed system are compared to a standard scheme under several conditions. Also, novel evaluations based on communication distance are conducted. Numerical results demonstrate that our proposed scheme, in which coding rates change relative to channel conditions, outperforms standard schemes in many aspects. In addition, those systems show the best performance when the communication distance of the first hop equals that of the second hop. In addition, the above result is theoretically clarified.

Description: As is well known, multi-hop range-free localization algorithms demonstrate pretty good performance in isotropic networks in which sensor nodes distribute evenly and densely. However, these algorithms are easily affected by network topology, causing a significant decrease in positioning accuracy. To improve the localization performance in anisotropic networks, this paper presents a multi-hop range-free localization algorithm based on Least Square Regularized Regression (LSRR). By building a mapping relationship between hop counts and real distances, we can regard the process of localization as a regularized regression. Firstly, the proximity information of the given network is measured. Then, a mapping model between the geographical distances and the hop distances is constructed by LSRR. Finally, each sensor node finds its own position via this mapping. The Average Localization Error (ALE) metric is used to evaluate the proposed method in our experiments, and results show that, compared with similar methods, our approach can effectively decrease the effect of anisotropy, thus considerably improving the positioning accuracy.

Description: Geographic information systems (GIS) provide accurate maps of terrain, roads, waterways, and building footprints and heights. Aircraft, particularly small unmanned aircraft systems (UAS), can exploit this and additional information such as building roof structure to improve navigation accuracy and safely perform contingency landings particularly in urban regions. However, building roof structure is not fully provided in maps. This paper proposes a method to automatically label building roof shape from publicly available GIS data. Satellite imagery and airborne LiDAR data are processed and manually labeled to create a diverse annotated roof image dataset for small to large urban cities. Multiple convolutional neural network (CNN) architectures are trained and tested, with the best performing networks providing a condensed feature set for support vector machine and decision tree classifiers. Satellite image and LiDAR data fusion is shown to provide greater classification accuracy than using either data type alone. Model confidence thresholds are adjusted leading to significant increases in models precision. Networks trained from roof data in Witten, Germany and Manhattan (New York City) are evaluated on independent data from these cities and Ann Arbor, Michigan.

Description: Inertial navigation systems (INSs) use the temperature control system to ensure the stability of the temperature of the inertial sensors for improving the navigation accuracy of the INSs. That is, the temperature control accuracy affects the performance of the INSs. Thus, the performance of temperature control systems must be evaluated before their application. However, nearly all high-precision INSs are large and heavy and require long-term testing under many different experimental conditions. As a result, conducting an outdoor navigation experiment, which involves high–low temperature and heading rotation tests, is time consuming, laborious, and costly for researchers. To address this issue, an economical high–low temperature and heading rotation test method for high-precision platform INSs is proposed, and an evaluation system based on this method is developed to evaluate the performance of the temperature control systems for high-precision platform INSs indoors. The evaluation system uses an acrylic chamber, exhaust fans, temperature sensors, and an air conditioner to simulate the environment temperature change. The outer gimbals of the platform INSs are utilized to simulate the heading rotation. The temperature control system of a high-precision platform INS is evaluated using the proposed evaluation method. The temperature difference of the gyros is obtained in the high–low temperature test, and the temperature fluctuation of the temperature control system is observed in the rotation test. These tests verify the effectiveness of the proposed evaluation method. Then, the corresponding optimization method for the temperature control system of this high-precision platform INS is put forward on the basis of the test results of the evaluation system. Experimental results show that the maximum temperature differences of the two gyros between high- and low-temperature tests are decreased from 1.51 °C to 0.50 °C, and the maximum temperature fluctuation value of the temperature control system is decreased from 0.81 °C to 0.27 °C after the proposed evaluation and optimization processes. Therefore, the proposed methods are cost effective and useful for evaluating and optimization of the temperature control system for INSs.

Description: With the new advancements in flight control and integrated circuit (IC) technology, unmanned aerial vehicles (UAVs) have been widely used in various applications. One of the typical application scenarios is data collection for large-scale and remote sensor devices in the Internet of things (IoT). However, due to the characteristics of massive connections, access collisions in the MAC layer lead to high power consumption for both sensor devices and UAVs, and low efficiency for the data collection. In this paper, a dynamic speed control algorithm for UAVs (DSC-UAV) is proposed to maximize the data collection efficiency, while alleviating the access congestion for the UAV-based base stations. With a cellular network considered for support of the communication between sensor devices and drones, the connection establishment process was analyzed and modeled in detail. In addition, the data collection efficiency is also defined and derived. Based on the analytical models, optimal speed under different sensor device densities is obtained and verified. UAVs can dynamically adjust the speed according to the sensor device density under their coverages to keep high data collection efficiency. Finally, simulation results are also conducted to verify the accuracy of the proposed analytical models and show that the DSC-UAV outperforms others with the highest data collection efficiency, while maintaining a high successful access probability, low average access delay, low block probability, and low collision probability.

Description: A major contributor to longitudinal vibration in marine propulsion systems is propeller induced excitation. This constitutes a key source of underwater acoustical radiation through excitation of the hull. Understanding this hydrodynamic force at the interface of the thrust bearing is important in order to develop an accurate vibrational model of the propulsion system and in determining potential control mechanisms. In order to investigate the thrust force during operation of a propulsion system, Polyvinylidene Fluoride (PVDF) was embedded into the stationery collar inside a custom thrust bearing in a scaled model of a typical propulsion system. The number of blades of the propeller and its rotational speed were altered to obtain an understanding of the characteristic vibrations of the shaft propulsion system. The rig comprised of the propeller, shaft, journal bearings and a thrust bearing. A two and three blade propeller and a four, five and six pad bearing were tested. A strain gauge and accelerometer were used to infer the propeller force and enable comparison with the PVDF signals. As a result of the asymmetrical flow around the propeller, the blade passing frequencies (BPF) are clearly observed. This frequency contribution was present at all speeds tested. The PVDF signal also showed significant pad passing frequency (PPF) and BPF and modulation of both.

Description: As modern workplace environments are becoming increasingly diverse, the experiences of disenfranchised employees have become a topic of great interest to scholars and business professionals alike. While the experiences of individuals with singular stigmatized identities have been well-established, a dearth of research has assessed how intersectionality, i.e., holding multiple stigmatized identities, combine and intertwine to shape workplace experiences. We contribute to a growing literature on intersectionality by assessing the extent to which employees identifying with multiple stigmatized identities may constitute a risk factor for the experience of job insecurity, a prevalent and potent economic stressor. Additionally, we propose that job insecurity will partially mediate the relationship between intersectionality and a variety of adverse workplace outcomes associated with increased job insecurity perceptions. In order to test these hypotheses, we collected survey data from 449 employed individuals within the United States over two timepoints. Results of the tests of our direct and indirect hypotheses revealed that individuals with more stigmatized identities reported greater perceptions of job insecurity, and intersectionality indirectly affected workplace outcomes via this heightened job insecurity. Our results highlight a new antecedent of job insecurity for consideration and is meant to motivate others to approach diversity-related research questions with multiple identities in mind, in an effort to encapsulate the full spectrum of one’s experience based on their identity makeup.

Description: The fraction of absorbed photosynthetically active radiation (FPAR) is a key variable in the model of vegetation productivity. Vegetation indices (VIs) that were derived from instantaneous remote-sensing data have been successfully used to estimate the FPAR of a day or a longer period. However, it has not yet been verified whether continuous VIs can be used to accurately estimate the diurnal dynamics of a vegetation canopy FPAR, which may fluctuate dramatically within a day. In this study, we measured the high temporal resolution spectral data (480 to 850 nm) and FPAR data of a maize canopy from the jointing stage to the tasseling stage under different irrigation and illumination conditions using two automatic observation systems. To estimate the FPAR, we developed regression models based on a quadratic function using 13 kinds of VIs. The results show the following: (1) Under nondrought conditions, although the illumination condition (sunny or cloudy) influenced the trend of the canopy diurnal FPAR, it had only a slight effect on the model accuracies of the FPAR-VIs. The maximum coefficients of determination (R2) of the FPAR-VIs models generated for the sunny nondrought data, the cloudy nondrought data, and all of the nondrought data were 0.895, 0.88, and 0.828, respectively. The VIs—including normalized difference vegetation index (NDVI), green NDVI (GNDVI), red-edge simple ratio (SR705), modified simple ratio 2 (mSR2), red-edge normalized difference vegetation index (NDVI705), and enhanced vegetation index (EVI)—that were related to the canopy structure had higher estimation accuracies (R2 〉 0.8) than the other VIs that were related to the soil adjustment, chlorophyll, and physiology. The estimation accuracies of the GNDVI and some red-edge VIs (including NDVI705, SR705, and mSR2) were higher than the estimation accuracy of the NDVI. (2) Under drought stress, the FPAR decreased significantly because of leaf wilting and the effective leaf area index decrease around noon. When we included drought data in the model, accuracies were reduced dramatically and the R2 value of the best model was only 0.59. When we built the regression models based only on drought data, the EVI, which can weaken the influence of soil, had the best estimate accuracy (R2 = 0.68).