ALBERT

Description: The validity of a local positioning system (LPS) to measure inter-unit distance was investigated during a team sport movement circuit. Eight recreationally active, female indoor team-sport players completed a circuit, comprising seven types of movements (walk, jog, jump, sprint, 45° change of direction and shuffle), on an indoor court. Participants wore a receiver tag (ClearSky T6, Catapult Sports) and seven reflective markers, to allow for a comparison with the reference system (©Vicon Motion Systems, Oxford Metrics, UK). Inter-unit distance was collected for each combination of participants. Validity was assessed via root mean square error, mean bias and percentage of variance accounted for, both as an overall dataset and split into distance bands. The results presented a mean root mean square error of 0.20 ± 0.05 m, and mean bias detected an overestimation for all distance bands. The LPS shows acceptable accuracy for measuring inter-unit distance, opening up opportunities to utilise player tracking for tactical variables indoors.

Description: Vegetable growers require vigorous transplants in order to reduce the period of transplant shock during early stand establishment. Organic media containing solid humic substances (HS) are amendments that have not been comprehensively explored for applications in containerized vegetable transplant production systems. In this study, HS (1% v/v) were applied to a peat-based growth medium to evaluate pre- and post-transplant growth modulation of four economically important vegetable species. Those were: pepper, tomato, watermelon, and lettuce. Seeding for all species was performed in two periods in order to evaluate their post-transplant yield performance under drought (water deficit vs. well-watered) and heat (hot vs. cool season) stresses. Compared with control, HS-treated plants had: (1) increased leaf and root biomass after transplanting due to faster growth rates; (2) lower root/shoot ratio before transplanting, but higher after 10 days of field establishment; and (3) increased root length and surface area. The negative effects of heat and drought stresses on crop yield were more prominent in control plants, while HS-treated transplants were able to mitigate yield decreases. The results clearly demonstrated the benefits of using solid HS as a management input to improve transplant quality in these crop species.

Description: Economic and environmental concerns limit peat use for substrate production, promoting interest in alternative materials. Hence, in this study, 16 substrates were obtained by mixing, in a factorial combination, eight substrates with different ratios of peat, rice hulls (RH), and anaerobic digestion residues (ADR) and two types of RH: whole (WRH) or ground RH (GRH). Substrates were physically and chemically characterized and then tested as potting substrates for Pelargonium peltatum ‘Ville de Paris’ and Rosa × hybrida ‘La Sevillana’ production. Physical characteristics worsened the increasing RH content. This problem was partly solved using GRH and adding ADR to the substrates. As for chemical characteristics, RH increased P and K, reducing cation exchange capacity, NO3-N, and Ca, thus causing a possible nutritional imbalance. ADR addition increased all nutrients, restoring the nutritional balance. Geranium and rose plants were negatively affected by an increasing rate of RH. In both species, the use of GRH improved the considered parameters, whereas ADR improved some parameters but only in geranium. It was possible to partly substitute peat with 33% RH, but GRH plus ADR is necessary for geranium production, and facultative for rose. The multiple regression method and principal component analysis appear to be useful tools to understand which substrate parameters, and to what extent, influence the growth of ornamental plants.

Description: Traditional monitoring methods cannot obtain the overall thermal information for power transformers. To solve this problem, a distributed fiber optic sensor (DFOS) was creatively applied inside an operating 35 kV power transformer by highly integrating with the electromagnetic wires. Then, the transformer prototype with totally global sensing capability was successfully developed and it was qualified for power grid application through the strict ex-factory tests. The as designed optical fiber sensor works stably all the time with a temperature accuracy of ±0.2 °C and spatial positioning accuracy of 0.8 m. Based on the obtained internal temperature distribution, Gaussian convolution was further applied for the signal processing and hereby, the hotspots for all the windings and iron cores could be accurately traced. The hottest points were located at 89.1% (55 °C) of the high voltage winding height and 89.7% (77.5 °C) of the low voltage winding height. The actual precise hotspot location corrected the traditional cognition on the transformer windings and it would serve as an essential reference for the manufactures. This new nondestructive internal sensing and condition monitoring method also exhibits a promising future for the DFOS applying in the high-voltage electrical apparatus industry.

Description: The Position and Orientation System (POS) is the core device of high-resolution aerial remote sensing systems, which can obtain the real-time object position and collect target attitude information. The goal of exceeding 0.015°/0.003° of its real-time heading/attitude measurement accuracy is unlikely to be achieved without gravity disturbance compensation. In this paper, a high-precision gravity data architecture for gravity disturbance compensation technology is proposed, and a gravity database with accuracy better than 1 mGal is constructed in the test area. Based on the “Block-Time Variation” Markov Model (B-TV-MM), a gravity disturbance compensation device is developed. The gravity disturbance compensation technology is applied to POS products for the first time, and is applied in the field of aerial remote sensing. Flight test results show that the heading accuracy and attitude accuracy of POS products are improved by at least 6% and 16%, respectively. The device can be used for the gravity disturbance compensation of various inertial technology products.

Description: In this paper, an actuator fault estimation technique is proposed for quadcopters under uncertainties. In previous studies, matching conditions were required for the observer design, but they were found to be complex for solving linear matrix inequalities (LMIs). To overcome these limitations, in this study, an improved intermediate estimator algorithm was applied to the quadcopter model, which can be used to estimate actuator faults and system states. The system stability was validated using Lyapunov theory. It was shown that system errors are uniformly ultimately bounded. To increase the accuracy of the proposed fault estimation algorithm, a magnitude order balance method was applied. Experiments were verified with four scenarios to show the effectiveness of the proposed algorithm. Two first scenarios were compared to show the effectiveness of the magnitude order balance method. The remaining scenarios were described to test the reliability of the presented method in the presence of multiple actuator faults. Different from previous studies on observer-based fault estimation, this proposal not only can estimate the fault magnitude of the roll, pitch, yaw, and thrust channel, but also can estimate the loss of control effectiveness of each actuator under uncertainties.

Description: The benefits of automatic identification technologies in healthcare have been largely recognized. Nevertheless, unlocking their potential to support the most knowledge-intensive medical tasks requires to go beyond mere item identification. This paper presents an innovative Decision Support System (DSS), based on a semantic enhancement of Near Field Communication (NFC) standard. Annotated descriptions of medications and patient’s case history are stored in NFC transponders and used to help caregivers providing the right therapy. The proposed framework includes a lightweight reasoning engine to infer possible incompatibilities in treatment, suggesting substitute therapies. A working prototype is presented in a rheumatology case study and preliminary performance tests are reported. The approach is independent from back-end infrastructures. The proposed DSS framework is validated in a limited but realistic case study, and performance evaluation of the prototype supports its practical feasibility. Automated reasoning on knowledge fragments extracted via NFC enables effective decision support not only in hospital centers, but also in pervasive IoT-based healthcare contexts such as first aid, ambulance transport, rehabilitation facilities and home care.

Description: Stability is a prerequisite for legged robots to execute tasks and traverse rough terrains. To guarantee the stability of quadruped locomotion and improve the terrain adaptability of quadruped robots, a stability-guaranteed and high terrain adaptability static gait for quadruped robots is addressed. Firstly, three chosen stability-guaranteed static gaits: intermittent gait 1&2 and coordinated gait are investigated. In addition, then the static gait: intermittent gait 1, which is with the biggest stability margin, is chosen to do a further research about quadruped robots walking on rough terrains. Secondly, a position/force based impedance control is employed to achieve a compliant behavior of quadruped robots on rough terrains. Thirdly, an exploratory gait planning method on uneven terrains with touch sensing and an attitude-position adjustment strategy with terrain estimation are proposed to improve the terrain adaptability of quadruped robots. Finally, the proposed methods are validated by simulations.

Description: Many human activities and states are related to the facial muscles’ actions: from the expression of emotions, stress, and non-verbal communication through health-related actions, such as coughing and sneezing to nutrition and drinking. In this work, we describe, in detail, the design and evaluation of a wearable system for facial muscle activity monitoring based on a re-configurable differential array of stethoscope-microphones. In our system, six stethoscopes are placed at locations that could easily be integrated into the frame of smart glasses. The paper describes the detailed hardware design and selection and adaptation of appropriate signal processing and machine learning methods. For the evaluation, we asked eight participants to imitate a set of facial actions, such as expressions of happiness, anger, surprise, sadness, upset, and disgust, and gestures, like kissing, winkling, sticking the tongue out, and taking a pill. An evaluation of a complete data set of 2640 events with 66% training and a 33% testing rate has been performed. Although we encountered high variability of the volunteers’ expressions, our approach shows a recall = 55%, precision = 56%, and f1-score of 54% for the user-independent scenario(9% chance-level). On a user-dependent basis, our worst result has an f1-score = 60% and best result with f1-score = 89%. Having a recall ≥60% for expressions like happiness, anger, kissing, sticking the tongue out, and neutral(Null-class).

Description: This article focuses on the specificity of present enclave structures in rural areas that were created decades ago as a result of decisions to establish housing estates for employees, and which acquired negative features as a result of the liquidation of the monopolistic employer. An attempt has been made to answer if workers' housing complexes can turn into permanent enclaves of isolation and neglect after the liquidation of a monopolistic workplace, especially if they are located in rural areas? The aim of the paper is to picture the process of social exclusion emergence in the rural areas, which results from the still unresolved socio-economic problem of the existence of such enclaves of isolation and neglect in Polish rural areas in the 21st century. Despite almost 30 years passing, the areas with spatially and socially isolated settlements have very high unemployment rates, a low level of technical and social infrastructure and a widespread sense of injustice among the people who live there. The empirical basis for the analysis was four cases. The presented stories took place against the historical background of the Warmińsko-Mazurskie Voivodship and the socio-economic background. Studies were conducted in the selected villages with a free-form interview and photographic documentation was prepared. In the result, a close picture of the enclaves was drawn. The examples presented in this article proved that workers' settlements, located around large economic entities distant from existing settlement networks can develop into enclaves of isolation and neglect.

Description: This paper presents a two-layer controller for accurate and robust lateral path tracking control of highly automated vehicles. The upper-layer controller, which produces the front wheel steering angle, is implemented with a Linear Time-Varying MPC (LTV-MPC) whose prediction and control horizon are both optimized offline with particle swarm optimization (PSO) under varying working conditions. A constraint on the slip angle is imposed to prevent lateral forces from saturation to guarantee vehicle stability. The lower layer is a radial basis function neural network proportion-integral-derivative (RBFNN-PID) controller that generates electric current control signals executable by the steering motor to rapidly track the target steering angle. The nonlinear characteristics of the steering system are modeled and are identified on-line with the RBFNN so that the PID controller’s control parameters can be adjusted adaptively. The results of CarSim-Matlab/Simulink joint simulations show that the proposed hierarchical controller achieves a good level of path tracking accuracy while maintaining vehicle stability throughout the path tracking process, and is robust to dynamic changes in vehicle velocities and road adhesion coefficients.

Description: There is a strong policy move in Britain to improve forest resilience to climate change by increasing stand structural and species diversity. Although currently little used in Britain, the technique of underplanting allows regeneration and diversification of stands while avoiding some of the disadvantages of clearfelling. Two experiments were examined: (1) the growth and survival of five underplanted conifer species of differing shade tolerance in a shelterwood and (2) compared performance of underplanted and open-grown Douglas-fir seedlings on restocking sites. Underplanted Sitka spruce, Norway spruce, noble fir, European silver fir and Douglas-fir were all able to survive and grow. However increased exposure following overstorey removal resulted in some damage and ‘socketing’, especially to taller seedlings, particularly Douglas-fir. This may be linked to poor root development when growing under an overstorey. Microclimate conditions on some underplanted sites were more sheltered from extreme climatic conditions, and in some cases this improved survival of Douglas-fir seedlings. However, seedling growth rates were reduced compared with those on open sites probably due to lower light levels. Underplanting may help to improve establishment success of some species, particularly in exposed areas. However, the shelter benefits of underplanting must be carefully balanced against the trade-off with lower light, and underplanting is likely to be more successful where low canopy density is maintained.

Description: Iron deficiency (ID) is the most prevalent and severe nutritional disorder globally and is the leading cause of iron deficiency anemia (IDA). IDA often progresses subtly symptomatic in children, whereas prolonged deficiency may permanently impair development. Early detection and frequent screening are, therefore, essential to avoid the consequences of IDA. In order to reduce the production cost and complexities involved in building advanced ID sensors, the devices were fabricated using a home-built patterning procedure that was developed and used for this work instead of lithography, which allows for fast prototyping of dimensions. In this article, we report the development of graphene-based field-effect transistors (GFETs) functionalized with anti-ferritin antibodies through a linker molecule (1-pyrenebutanoic acid, succinimidyl ester), to facilitate specific conjugation with ferritin antigen. The resulting biosensors feature an unprecedented ferritin detection limit of 10 fM, indicating a tremendous potential for non-invasive (e.g., saliva) ferritin detection.

Description: The recent emergence of Olive Quick Decline Syndrome in Italy, caused by Xylella fastidiosa, has drawn attention to the risks posed by this vector-borne bacterium to important crops in Europe (especially fruit trees and grapevine). Comparatively very little is known on actual and potential impacts of this pathogen in forests, in the native (North American) and introduced (European) regions, respectively. The present review aims to address important questions related to the threat posed by X. fastidiosa to European forests, such as the following: What are the symptoms, hosts and impact of bacterial leaf scorch caused by X. fastidiosa on trees in North America? Which forest tree species have been found infected in the introduction area in Europe? How does X. fastidiosa cause disease in susceptible hosts? Are there any X. fastidiosa genotypes (subspecies and sequence types) specifically associated with forest trees? How is X. fastidiosa transmitted? What are the known and potential vectors for forest trees? How does vector ecology affect disease? Is the distribution of X. fastidiosa, especially the strains associated with trees, restricted by climatic factors? Is disease risk for trees different in forest ecosystems as compared with urban settings? We conclude by pointing to important knowledge gaps related to all these questions and strongly advocate for more research about the Xylella-forest pathosystems, in both North America and Europe.

Description: In the process of urbanization in China, the problem of atmospheric suspended particulate matter pollution is becoming increasingly serious. It has been impossible to completely rely on pollution source control measures to solve this problem for a long time due to the diversity and complexity of pollution sources. Therefore, there is an urgent need to explore alternate solutions to significantly improve air quality through increasing the capacity of green space in cities as these locations can provide multiple ecosystem services. In this study, a three-dimensional classification system was created by utilizing Beijing’s urban forest as a study area. Considering the meteorological factors, change of month and time and the characteristics of the forest itself, the significance and difference of reducing the concentration of atmospheric suspended particulate matter by the forest with different vegetation structures were tested. The results showed that meteorological factors such as wind velocity, temperature, and relative humidity all had a very significant effect on the concentration of atmospheric suspended particulate matter in the conditions of this study. The concentration was highest in winter, followed by spring and autumn, and lowest in summer. The concentration in the morning was the lowest of the day, increasing in the noon and afternoon with time. It was the lowest in the closed single-layered mixed forest, and the highest in the open lawn green space. By comparing the forest with gradient areas of 0.5 ha and 3.0 ha, it was found that with the increase in the area, the green space did not necessarily show a better dust retention effect, and the vegetation structure type of the green space often had more important influence. There was a very significant correlation between vegetation structure and plant diversity (Shannon–Wiener Diversity Index and Simpson Diversity Index). Biodiversity could significantly support and strengthen urban forest ecological service functions that improve air quality. Increasing the plant species diversity could lead to lower particulate matter concentration. The research conclusions could provide theoretical and practical bases for how to select the combination of vegetation structure in the planning and design of urban forest oriented to improve air quality.

Description: Drought is a major abiotic stress factor limiting cotton yield. It is important to identify the genotypes that can conserve water under drought stress conditions and improve yield. The objective of the current study was to evaluate cotton genotypes for water conservation traits, i.e., high FTSW (Fraction of Transpirable Soil Water) threshold for transpiration. Plants utilize water slowly by declining transpiration at high FTSW and conserving soil water, which can be used by the plant later in the growing season to improve yield. Fifteen cotton varieties were selected based on their differences in transpiration response to elevated vapor pressure deficit (VPD) to study drought responses. Two pot experiments were carried out in the greenhouse to determine the FTSW threshold for the transpiration rate as the soil dried. A significant variation (p 〈 0.01) in the FTSW threshold values for transpiration decline was observed, ranging from 0.35 to 0.60 among cotton cultivars. Genotypes with high FTSW thresholds also displayed low transpiration under well-watered conditions. Further studies with four selected genotype contrasts in FTSW threshold values for transpiration showed differences (p 〈 0.05 to 0.001) in gas exchange parameters and water potentials. This study demonstrated that there are alternate traits among the cotton genotypes for enhancing soil water conservation to improve yield under water-limited conditions.

Description: Biochar is a promising material for the improvement of soil quality. However, studies on biochar have mostly been carried out in laboratory conditions or have focused on agricultural aspects. The impacts of the application of biochar on soil characteristics and related ecological processes of the forest ecosystem have not been fully resolved. In this study, we investigated the effects of regular biochar and bacteria-loaded biochar on the microbial communities in the bulk soil and the rhizosphere soil of an annual Chinese fir plantation. In early spring (April), the two types of biochar were added to the soil at the rates of 2.22 t·ha−1, 4.44 t·ha−1, 6.67 t·ha−1, 8.89 t·ha−1, and 11.11 t·ha−1 by ring furrow application around the seedlings, and soil samples were collected at the end of autumn (November). The results showed that biochar addition increased the soil nutrient content and promoted the growth and diversity of soil microbial communities. The diversity of soil fungi was significantly increased, and the diversity of soil bacteria was significantly decreased. Principal component analysis under the different biochar types and application rates demonstrated that microbial communities differed significantly between the treatments and controls and that the effect of biochar on the microbial community of the bulk soil was more significant than that of the rhizosphere soil. Under the same dosage, the effect of bacteria-loaded biochar on soil was more significant than that of regular biochar.

Description: Noise appears in images captured by real cameras. This paper studies the influence of noise on monocular feature-based visual Simultaneous Localization and Mapping (SLAM). First, an open-source synthetic dataset with different noise levels is introduced in this paper. Then the images in the dataset are denoised using the Fast and Flexible Denoising convolutional neural Network (FFDNet); the matching performances of Scale Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF) and Oriented FAST and Rotated BRIEF (ORB) which are commonly used in feature-based SLAM are analyzed in comparison and the results show that ORB has a higher correct matching rate than that of SIFT and SURF, the denoised images have a higher correct matching rate than noisy images. Next, the Absolute Trajectory Error (ATE) of noisy and denoised sequences are evaluated on ORB-SLAM2 and the results show that the denoised sequences perform better than the noisy sequences at any noise level. Finally, the completely clean sequence in the dataset and the sequences in the KITTI dataset are denoised and compared with the original sequence through comprehensive experiments. For the clean sequence, the Root-Mean-Square Error (RMSE) of ATE after denoising has decreased by 16.75%; for KITTI sequences, 7 out of 10 sequences have lower RMSE than the original sequences. The results show that the denoised image can achieve higher accuracy in the monocular feature-based visual SLAM under certain conditions.

Description: Multi-enzyme immobilization onto a capacitive field-effect biosensor by nano-spotting technique is presented. The nano-spotting technique allows to immobilize different enzymes simultaneously on the sensor surface with high spatial resolution without additional photolithographical patterning. The amount of applied enzymatic cocktail on the sensor surface can be tailored. Capacitive electrolyte-insulator-semiconductor (EIS) field-effect sensors with Ta2O5 as pH-sensitive transducer layer have been chosen to immobilize the three different (pL droplets) enzymes penicillinase, urease, and glucose oxidase. Nano-spotting immobilization is compared to conventional drop-coating method by defining different geometrical layouts on the sensor surface (fully, half-, and quarter-spotted). The drop diameter is varying between 84 µm and 102 µm, depending on the number of applied drops (1 to 4) per spot. For multi-analyte detection, penicillinase and urease are simultaneously nano-spotted on the EIS sensor. Sensor characterization was performed by C/V (capacitance/voltage) and ConCap (constant capacitance) measurements. Average penicillin, glucose, and urea sensitivities for the spotted enzymes were 81.7 mV/dec, 40.5 mV/dec, and 68.9 mV/dec, respectively.

Description: The paper describes and presents a new calculation strategy for the determination of the aircraft’s resultant position using the GPS (Global Positioning System) SPP (Single Point Positioning) code method. The paper developed a concept of using the weighted average model with the use of measuring weights to improve the quality of determination of the coordinates and accuracy of GPS SPP positioning. In this research, measurement weights were used as a function of the number of GPS satellites being tracked, and geometric PDOP (Position Dilution of Precision) coefficient. The calculations were made using navigation data recorded by two independent GPS receivers: Thales Mobile Mapper and Topcon HiPerPro. On the basis of the obtained results, it was found that the RMS (Root Mean Square) accuracy of positioning for XYZ geocentric coordinates was better than 1.2% to 33.7% for the weighted average method compared to a single GPS SPP solution. The proposed approach is therefore of practical application in air navigation to improve the quality of aircraft positioning.

Description: Currently, many intelligent building energy management systems (BEMSs) are emerging for saving energy in new and existing buildings and realizing a sustainable society worldwide. However, installing an intelligent BEMS in existing buildings does not realize an innovative and advanced society because it only involves simple equipment replacement (i.e., replacement of old equipment or LED (Light Emitting Diode) lamps) and energy savings based on a stand-alone system. Therefore, artificial intelligence (AI) is applied to a BEMS to implement intelligent energy optimization based on the latest ICT (Information and Communications Technologies) technology. AI can analyze energy usage data, predict future energy requirements, and establish an appropriate energy saving policy. In this paper, we present a dynamic heating, ventilation, and air conditioning (HVAC) scheduling method that collects, analyzes, and infers energy usage data to intelligently save energy in buildings based on reinforcement learning (RL). In this regard, a hotel is used as the testbed in this study. The proposed method collects, analyzes, and infers IoT data from a building to provide an energy saving policy to realize a futuristic HVAC (heating system) system based on RL. Through this process, a purpose-oriented energy saving methodology to achieve energy saving goals is proposed.

Description: With the development of commodity economy, the emergence of fake and shoddy products has seriously harmed the interests of consumers and enterprises. To tackle this challenge, customized 2D barcode is proposed to satisfy the requirements of the enterprise anti-counterfeiting certification. Based on information hiding technology, the proposed approach can solve these challenging problems and provide a low-cost, difficult to forge, and easy to identify solution, while achieving the function of conventional 2D barcodes. By weighting between the perceptual quality and decoding robustness in sensing recognition, the customized 2D barcode can maintain a better aesthetic appearance for anti-counterfeiting and achieve fast encoding. A new picture-embedding scheme was designed to consider 2D barcode, within a unit image block as a basic encoding unit, where the 2D barcode finder patterns were embedded after encoding. Experimental results demonstrated that the proposed customized barcode could provide better encoding characteristics, while maintaining better decoding robustness than several state-of-the-art methods. Additionally, as a closed source 2D barcode that could be visually anti-counterfeit, the customized 2D barcode could effectively prevent counterfeiting that replicate physical labels. Benefitting from the high-security, high information capacity, and low-cost, the proposed customized 2D barcode with sensing recognition scheme provide a highly practical, valuable in terms of marketing, and anti-counterfeiting traceable solution for future smart IoT applications.

Description: In the last decades, the applications of structural monitoring are moving toward the field of civil engineering and infrastructures. Nevertheless, if the structures have damages, it does not mean that they have a complete loss of functionality, but rather that the system is no longer in an optimal condition so that, if the damage increases, the structure can collapse. Structural Health Monitoring (SHM), a process for the identification of damage, periodically collects data from suitable sensors that allow to characterize the damage and establishes the health status of the structure. Therefore, this monitoring will provide information on the structure condition, mostly about its integrity, in a short time, and, for infrastructures and civil structures, it is necessary to assess performance and health status. The aim of this work is to design an Internet of Things (IoT) system for Structural Health Monitoring to find possible damages and to see how the structure behaves over time. For this purpose, a customized datalogger and nodes have been designed. The datalogger is able to acquire the data coming from the nodes through RS485 communication and synchronize acquisitions. Furthermore, it has an internal memory to allow for the post-processing of the collected data. The nodes are composed of a digital triaxial accelerometer, a general-purpose microcontroller, and an external memory for storage measures. The microcontroller communicates with an accelerometer, acquires values, and then saves them in the memory. The system has been characterized and the damage indicator has been evaluated on a testing structure. Experimental results show that the estimated damage indicator increases when the structure is perturbed. In the present work, the damage indicator increased by a maximum value of 24.65 when the structure is perturbed by a 2.5 mm engraving.

Description: This paper describes the design and the performance of simultaneous, multifrequency impedance measurement system for four inductive-loop (IL) sensors which have been developed for vehicle parameters measurement based on vehicle magnetic profile (VMP) analysis. Simultaneous impedance measurement on several excitation frequencies increases the VMP measurement reliability because typical electromagnetic interferences (EMI) are narrowband, and should not simultaneously affect, in the same way, all measurement bands that are spread in the frequency, i.e., it is expected that at least one measurement band is disturbance-free. The system consists of two standard and two slim IL sensors, specially designed and installed, the analogue front-end, and an industrial computer with digital-to-analogue and analogue-to-digital converters accessed via field-programmable gate array (FPGA). The impedance of the IL sensors is obtained by vector measurement of voltages from auto-balancing bridge (ABB) front-end. Complex voltages are demodulated from excitation frequencies with FIR filters designed with the flat-top windows. The system is capable of delivering VMPs in real-time mode, and also storing voltages for off-line postprocessing and analysis. Field distributions and sensitivities of slim and standard IL sensors are also discussed. Field test confirmed assumed increased reliability of VMP measurement for proposed simultaneous multifrequency operational mode.

Description: This article shows the development of a computer-controlled lab-on-a-chip device with three magnetohydrodynamic (MHD) pumps and a pneumatic valve. The chip was made of a stack of layers of polymethylmethacrylate (PMMA), cut using a laser engraver and thermally bonded. The MHD pumps were built using permanent magnets (neodymium) and platinum electrodes, all of them controlled by an Arduino board and a set of relays. The implemented pumps were able to drive solutions in the open channels with a flow rate that increased proportionally with the channel width and applied voltage. To address the characteristic low pressures generated by this kind of pump, all channels were interconnected. Because the electrodes were immersed in the electrolyte, causing electrolysis and pH variations, the composition and ionic strength of the electrolyte solution were controlled. Additionally, side structures for releasing bubbles were integrated. With this multi-pump and valve solution, the device was used to demonstrate the possibility of performing an injection sequence in a system that resembles a traditional flow injection analysis system. Ultimately, the results demonstrate the possibility of performing injection sequences using an array of MHD pumps that can perform fluid handling in the 0–5 µL s−1 range.

Description: Distributed sensor networks are at the heart of smart buildings, providing greater detail and valuable insights into their energy consumption patterns. The problem is particularly complex for older buildings retrofitted with Building Energy Management Systems (BEMS) where extracting useful knowledge from large sensor data streams without full understanding of the underlying system variables is challenging. This paper presents an application of Q-Analysis, a computationally simple topological approach for summarizing large sensor data sets and revealing useful relationships between different variables. Q-Analysis can be used to extract novel structural features called Q-vectors. The Q-vector magnitude visualizations are shown to be very effective in providing insights on macro behaviors, i.e., building floor behaviors in the present case, which are not evident from the use of unsupervised learning algorithms applied on individual terminal units. It has been shown that the building floors exhibited distinct behaviors that are dependent on the set-point distribution, but independent of the time and season of the year.

Description: Optical fiber based immunosensors are very attractive for biomarker detection. In order to improve the sensor response, we propose a promising strategy which combines porous-layer modification of the fiber surface and streptavidin-biotin-peroxidase nano-complex signal amplification in chemiluminescent detection. Two hepatitis B antigens, hepatitis B surface antigen (HBsAg) and hepatitis B e antigen (HBeAg), are used as the targets for analysis using the proposed sensor. Comparing to immunoassays using normal optical fiber sensors, the response of the present sensor is enhanced by a factor of 4.8 and 6.7 for detection of HBsAg and HBeAg, respectively. The limit-of-quantitation of the proposed method is as low as 0.3 fg/mL (0.01 fg/mL) with a wide linear response range of 3 fg/mL–150 ng/mL (0.1 fg/mL–160 ng/mL) for sensing HBsAg (HBeAg). Quantitative determination of HBsAg and HBeAg in human serum samples is performed, showing the applicability of the proposed method for biomarker detection.

Description: Xylella fastidiosa (Xf) is a well-known bacterial plant pathogen mainly transmitted by vector insects and is associated with serious diseases affecting a wide variety of plants, both wild and cultivated; it is known that over 350 plant species are prone to Xf attack. In olive trees, it causes olive quick decline syndrome (OQDS), which is currently a serious threat to the survival of hundreds of thousands of olive trees in the south of Italy and in other countries in the European Union. Controls and countermeasures are in place to limit the further spreading of the bacterium, but it is a tough war to fight mainly due to the invasiveness of the actions that can be taken against it. The most effective weapons against the spread of Xf infection in olive trees are the detection of its presence as early as possible and attacks to the development of its vector insects. In this paper, image processing of high-resolution visible and multispectral images acquired by a purposely equipped multirotor unmanned aerial vehicle (UAV) is proposed for fast detection of Xf symptoms in olive trees. Acquired images were processed using a new segmentation algorithm to recognize trees which were subsequently classified using linear discriminant analysis. Preliminary experimental results obtained by flying over olive groves in selected sites in the south of Italy are presented, demonstrating a mean Sørensen–Dice similarity coefficient of about 70% for segmentation, and 98% sensitivity and 93% precision for the classification of affected trees. The high similarity coefficient indicated that the segmentation algorithm was successful at isolating the regions of interest containing trees, while the high sensitivity and precision showed that OQDS can be detected with a low relative number of both false positives and false negatives.

Description: Strawberry cropping system relies heavily on proper disease management to maintain high crop yield. Powdery mildew, caused by Sphaerotheca macularis (Wall. Ex Fries) is one of the major leaf diseases in strawberry which can cause significant yield losses up to 70%. Field scouts manually walk beside strawberry fields and visually observe the plants to monitor for powdery mildew disease infection each week during summer months which is a laborious and time-consuming endeavor. The objective of this research was to increase the efficiency of field scouting by automatically detecting powdery mildew disease in strawberry fields by using a real-time machine vision system. A global positioning system, two cameras, a custom image processing program, and a ruggedized laptop computer were utilized for development of the disease detection system. The custom image processing program was developed using color co-occurrence matrix-based texture analysis along with artificial neural network technique to process and classify continuously acquired image data simultaneously. Three commercial strawberry field sites in central Nova Scotia were used to evaluate the performance of the developed system. A total of 36 strawberry rows (~1.06 ha) were tested within three fields and powdery mildew detected points were measured manually followed by automatic detection system. The manually detected points were compared with automatically detected points to ensure the accuracy of the developed system. Results of regression and scatter plots revealed that the system was able to detect disease having mean absolute error values of 4.00, 3.42, and 2.83 per row and root mean square error values of 4.12, 3.71, and 3.00 per row in field site-I, field site-II, and field site-III, respectively. The slight deviation in performance was likely caused by high wind speeds (〉8 km h−1), leaf overlapping, leaf angle, and presence of spider mite disease during field testing.

Description: A 5-year study evaluated the change in the quantity of soil total C (STC), soil organic C (SOC), and soil inorganic C (SIC) stored in the surface 60 cm of the soil profile on two adjacent blocks of land with a long-term history of cropping (CH) or undisturbed grassland (NH) on similar soil types between 1999 and 2004. The NH area was tilled and a grass-legume species mix was seeded into plots on both the NH and the CH areas. Selected plots of restored grass were established so they could be grazed (GG) by livestock while other plots were left ungrazed (UG). Original undisturbed (and ungrazed) grassland plots within the NH area were used as a control treatment. Initially, STC and SOC in CH were lower than NH when compared under the semi-arid environmental conditions found in southwestern North Dakota. Over the study period, the undisturbed grass control plots had increases in STC and SOC levels in the soil profile of 3.90 kg·m−2 and 3.34 kg·m−2, respectively. Restored grass on the NH area with grazing showed increases in STC and SOC values of 2.11 and 1.26 kg·m−2, respectively, while without grazing, profile STC and SOC had values of 3.80 and 3.28 kg·m−2, respectively. Restored grass on the CH area showed increases in profile STC and SOC values of 0.55 and 1.96 kg·m−2, respectively, for the grazed plots and 0.78 and 2.11 kg·m−2, respectively, when left ungrazed. Soil inorganic C, though present in the soils, did not significantly change during the study. The lower C accumulation in the CH plots may be due to a lag time in the establishment of mycorrhizal associations with the seeded species, the inoculums of which were already present in the NH soils. Changes in STC were likely due to changes in water relationships in the soil profile where management changes affected water infiltration and its movement causing leaching of SIC below the 60 cm depth evaluated. Soils under undisturbed grassland continue to accumulate carbon while soils of the disturbed grassland or cropped prior to re-establishing grass showed losses that occurred due to either accumulating C at a lower rate or perhaps to C loss during the initial establishment period (1–2 years).

Description: Artisanal and small-scale mining is a significant economic sector in Rwanda. Mining activities often use a watercourse, in which secondary extraction takes place and minerals are washed. Mining thus greatly affects the geomorphological conditions in the area. The aim of this paper is a digest of environmental impacts of alluvial artisanal and small-scale mining with a focus on anthropogenic influences on topography with regard to the methods used in raw material mining. The author draws on a case study from the mining site of Rutsiro district in Rwanda. Main findings of alluvial artisanal mining in a riverscape are changes in landscape structure, deforestation, intensification of geomorphological processes, new relief shapes (suffosion depressions, check dams, gravel benches, anthropogenic channels) and hydrological river regime, chemical pollution of soil and watercourses. Artisanal and small-scale mining may lead to a significant change and acceleration of fluvial processes. This paper covers a broad understanding of environmental impacts of alluvial artisanal and small-scale mining with a focus on anthropogenic influencing.

Description: Windthrow and storm damage are crucial issues in practical forestry. We propose a method for rapid detection of windthrow hotspots in airborne digital orthophotos. Therefore, we apply Haralick’s texture features on 50 × 50 m cells of the orthophotos and classify the cells with a random forest algorithm. We apply the classification results from a training data set on a validation set. The overall classification accuracy of the proposed method varies between 76% for fine distinction of the cells and 96% for a distinction level that tried to detect only severe damaged cells. The proposed method enables the rapid detection of windthrow hotspots in forests immediately after their occurrence in single-date data. It is not adequate for the determination of areas with only single fallen trees. Future research will investigate the possibilities and limitations when applying the method on other data sources (e.g., optical satellite data).

Description: Subsurface drainage systems are commonly used to remove surplus water from the soil profile of a poorly drained farmland. Traditional methods for drainage mapping involve the use of tile probes and trenching equipment that are time-consuming, labor-intensive, and invasive, thereby entailing an inherent risk of damaging the drainpipes. Effective and efficient methods are needed in order to map the buried drain lines: (1) to comprehend the processes of leaching and offsite release of nutrients and pesticides and (2) for the installation of a new set of drain lines between the old ones to enhance the soil water removal. Non-invasive geophysical soil sensors provide a potential alternative solution. Previous research has mainly showcased the use of time-domain ground penetrating radar, with variable success, depending on local soil and hydrological conditions and the central frequency of the specific equipment used. The objectives of this study were: (1) to test the use of a stepped-frequency continuous wave three-dimensional ground penetrating radar (3D-GPR) with a wide antenna array for subsurface drainage mapping and (2) to evaluate its performance with the use of a single-frequency multi-receiver electromagnetic induction (EMI) sensor in-combination. This sensor combination was evaluated on twelve different study sites with various soil types with textures ranging from sand to clay till. While the 3D-GPR showed a high success rate in finding the drainpipes at five sites (sandy, sandy loam, loamy sand, and organic topsoils), the results at the other seven sites were less successful due to the limited penetration depth of the 3D-GPR signal. The results suggest that the electrical conductivity estimates produced by the inversion of apparent electrical conductivity data measured by the EMI sensor could be a useful proxy for explaining the success achieved by the 3D-GPR in finding the drain lines.

Description: A simple and low-cost electrochemical sensor based on multimodified screen-printed electrodes (SPEs) was successfully synthesized for the sensitive detection of β-lactoglobulin (β-Lg). The surface treatment of SPEs was accomplished by a simple drip coating method using polyethyleneimine (PEI), reduced graphene oxide (rGO), and gold nanoclusters (AuNCs), and the treated SPEs showed excellent electrical conductivity. The modified SPEs were then characterized with UV-Vis, SEM, TEM, and FTIR to analyze the morphology and composition of the AuNCs and the rGO. An anti-β-Lg antibody was then immobilized on the composite material obtained by modifying rGO with PEI and AuNCs (PEI-rGO-AuNCs), leading to the remarkable reduction in conductivity of the SPEs due to the reaction between antigen and antibody. The sensor obtained using this novel approach enabled a limit of detection (LOD) of 0.08 ng/mL and a detection range from 0.01 to 100 ng/mL for β-Lg. Furthermore, pure milk samples from four milk brands were measured using electrochemical sensors, and the results were in excellent agreement with those from commercial enzyme-linked immunosorbent assay (ELISA) methods.

Description: Body Sensor Networks (BSN) have emerged as a particularization of Wireless Sensor Networks (WSN) in the context of body monitoring environments, closely linked to healthcare applications. These networks are made up of smart biomedical sensors that allow the monitoring of physiological parameters and serve as the basis for e-Health applications. This Special Issue collects some of the latest developments in the field of BSN related to new developments in biomedical sensor technologies, the design and experimental characterization of on-body/in-body antennas and new communication protocols for BSN, including some review studies.

Description: This paper presents a vision-based navigation system designed for indoor localization. The suggested framework works as a standalone 3 D positioning system by fusing a sophisticated optical-flow pedometry with map constrains using an advanced particle filter. The presented method requires no personal calibration and works on standard smartphones with relatively low energy consumption. Field experiments on Android smartphones show that the expected 3 D error is about 1–2 m in most real-life scenarios.

Description: Recently, unmanned aerial vehicle (UAV)-based communications gained a lot of attention due to their numerous applications, especially in rescue services in post-disaster areas where the terrestrial network is wholly malfunctioned. Multiple access/gateway UAVs are distributed to fully cover the post-disaster area as flying base stations to provide communication coverage, collect valuable information, disseminate essential instructions, etc. The access UAVs after gathering/broadcasting the necessary information should select and fly towards one of the surrounding gateways for relaying their information. In this paper, the gateway UAV selection problem is addressed. The main aim is to maximize the long-term average data rates of the UAVs relays while minimizing the flights’ battery cost, where millimeter wave links, i.e., using 30~300 GHz band, employing antenna beamforming, are used for backhauling. A tool of machine learning (ML) is exploited to address the problem as a budget-constrained multi-player multi-armed bandit (MAB) problem. In this setup, access UAVs act as the players, and the arms are the gateway UAVs, while the rewards are the average data rates of the constructed relays constrained by the battery cost of the access UAV flights. In this decentralized setting, where information is neither prior available nor exchanged among UAVs, a selfish and concurrent multi-player MAB strategy is suggested. Towards this end, three battery-aware MAB (BA-MAB) algorithms, namely upper confidence bound (UCB), Thompson sampling (TS), and the exponential weight algorithm for exploration and exploitation (EXP3), are proposed to realize gateways selection efficiently. The proposed BA-MAB-based gateway UAV selection algorithms show superior performance over approaches based on near and random selections in terms of total system rate and energy efficiency.

Description: This work demonstrates an integrated multiple-input multiple-output (MIMO) antenna solution for Long Term Evolution (LTE) and Millimeter-Wave (mm-wave) 5G wireless communication services. The proposed structure is comprised of a two-element LTE MIMO antenna, and a four-element 5G MIMO configuration with rectangular and circular defects in the ground plane. For experimental validation, the proposed structure is fabricated on a Rogers RO4350B substrate with 0.76 mm thickness. The overall substrate dimensions are 75 mm × 110 mm. The proposed structure is capable of operating at 5.29–6.12 GHz (LTE 46 and 47 bands) and 26–29.5 GHz (5G mm-wave) frequency bands. Additionally, the measured peak gain of 5.13 and 9.53 dB is attained respectively for the microwave and mm-wave antennas. Furthermore, the analysis of the MIMO performance metrics demonstrates good characteristics, and excellent field correlation performance across the operating bands. Furthermore, the analysis of the Specific Absorption Rate (SAR) and Power Density (PD) at the lower frequency band (5.9 GHz) and PD only at mm-Wave frequency band (28 GHz) verifies that the proposed antenna system satisfies the international human safety standards. Therefore, the proposed integrated MIMO antenna configuration ascertains to be a potential contender for the forthcoming communication applications.

Description: In this article, a zigzag graphene nanoribbon (ZGNR)-based sensor was built utilizing the Atomistic ToolKit Virtual NanoLab (ATK-VNL), and used to detect nitric oxide (NO), nitrogen dioxide (NO2), and ammonia (NH3). The successful adsorption of these gases on the surface of the ZGNR was investigated using adsorption energy (Eads), adsorption distance (D), charge transfer (∆Q), density of states (DOS), and band structure. Among the three gases, the ZGNR showed the highest adsorption energy for NO with −0.273 eV, the smallest adsorption distance with 2.88 Å, and the highest charge transfer with −0.104 e. Moreover, the DOS results reflected a significant increase of the density at the Fermi level due to the improvement of ZGNR conductivity as a result of gas adsorption. The surface of ZGNR was then modified with an epoxy group (-O-) once, then with a hydroxyl group (-OH), and finally with both (-O-) and (-OH) groups in order to improve the adsorption capacity of ZGNR. The adsorption parameters of ZGNR were improved significantly after the modification. The highest adsorption energy was found for the case of ZGNR-O-OH-NO2 with −0.953 eV, while the highest charge transfer was found for the case of ZGNR-OH-NO with −0.146 e. Consequently, ZGNR-OH and ZGNR-O-OH can be considered as promising gas sensors for NO and NO2, respectively.

Description: Fault diagnosis in manufacturing systems represents one of the most critical challenges dealing with condition-based monitoring in the recent era of smart manufacturing. In the current Industry 4.0 framework, maintenance strategies based on traditional data-driven fault diagnosis schemes require enhanced capabilities to be applied over modern production systems. In fact, the integration of multiple mechanical components, the consideration of multiple operating conditions, and the appearance of combined fault patterns due to eventual multi-fault scenarios lead to complex electromechanical systems requiring advanced monitoring strategies. In this regard, data fusion schemes supported with advanced deep learning technology represent a promising approach towards a big data paradigm using cloud-based software services. However, the deep learning models’ structure and hyper-parameters selection represent the main limitation when applied. Thus, in this paper, a novel deep-learning-based methodology for fault diagnosis in electromechanical systems is presented. The main benefits of the proposed methodology are the easiness of application and high adaptability to available data. The methodology is supported by an unsupervised stacked auto-encoders and a supervised discriminant analysis.

Description: Aiming at the poor accuracy and difficult verification of maneuver modeling induced by the wind, waves and sea surface currents in the actual sea, a novel sea trials correction method for ship maneuvering is proposed. The wind and wave drift forces are calculated according to the measurement data. Based on the steady turning hypothesis and pattern search algorithm, the adjustment parameters of wind, wave and sea surface currents were solved, the drift distances and drift velocities of wind, waves and sea surface currents were calculated and the track and velocity data of the experiment were corrected. The hydrodynamic coefficients were identified by the test data and the ship maneuvering motion model was established. The results show that the corrected data were more accurate than log data, the hydrodynamic coefficients can be completely identified, the prediction accuracy of the advance and tactical diameters were 93% and 97% and the prediction of the maneuvering model was accurate. Numerical cases verify the correction method and full-scale maneuvering model. The turning circle advance and tactical diameter satisfy the standards of the ship maneuverability of International Maritime Organization (IMO).

Description: The aim of this study is to compare the accuracy of Airgo™, a non-invasive wearable device that records breath, with respect to a gold standard. In 21 healthy subjects (10 males, 11 females), four parameters were recorded for four min at rest and in different positions simultaneously by Airgo™ and SensorMedics 2900 metabolic cart. Then, a cardio-pulmonary exercise test was performed using the Erg 800S cycle ergometer in order to test Airgo™’s accuracy during physical effort. The results reveal that the relative error median percentage of respiratory rate was of 0% for all positions at rest and for different exercise intensities, with interquartile ranges between 3.5 (standing position) and 22.4 (low-intensity exercise) breaths per minute. During exercise, normalized amplitude and ventilation relative error medians highlighted the presence of an error proportional to the volume to be estimated. For increasing intensity levels of exercise, Airgo™’s estimate tended to underestimate the values of the gold standard instrument. In conclusion, the Airgo™ device provides good accuracy and precision in the estimate of respiratory rate (especially at rest), an acceptable estimate of tidal volume and minute ventilation at rest and an underestimation for increasing volumes.

Description: Unmanned aerial vehicles (UAVs) have become popular in surveillance, security, and remote monitoring. However, they also pose serious security threats to public privacy. The timely detection of a malicious drone is currently an open research issue for security provisioning companies. Recently, the problem has been addressed by a plethora of schemes. However, each plan has a limitation, such as extreme weather conditions and huge dataset requirements. In this paper, we propose a novel framework consisting of the hybrid handcrafted and deep feature to detect and localize malicious drones from their sound and image information. The respective datasets include sounds and occluded images of birds, airplanes, and thunderstorms, with variations in resolution and illumination. Various kernels of the support vector machine (SVM) are applied to classify the features. Experimental results validate the improved performance of the proposed scheme compared to other related methods.

Description: In an indoor environment, object identification and localization are paramount for human-object interaction. Visual or laser-based sensors can achieve the identification and localization of the object based on its appearance, but these approaches are computationally expensive and not robust against the environment with obstacles. Radio Frequency Identification (RFID) has a unique tag ID to identify the object, but it cannot accurately locate it. Therefore, in this paper, the data of RFID and laser range finder are fused for the better identification and localization of multiple dynamic objects in an indoor environment. The main method is to use the laser range finder to estimate the radial velocities of objects in a certain environment, and match them with the object’s radial velocities estimated by the RFID phase. The method also uses a fixed time series as “sliding time window” to find the cluster with the highest similarity of each RFID tag in each window. Moreover, the Pearson correlation coefficient (PCC) is used in the update stage of the particle filter (PF) to estimate the moving path of each cluster in order to improve the accuracy in a complex environment with obstacles. The experiments were verified by a SCITOS G5 robot. The results show that this method can achieve an matching rate of 90.18% and a localization accuracy of 0.33m in an environment with the presence of obstacles. This method effectively improves the matching rate and localization accuracy of multiple objects in indoor scenes when compared to the Bray-Curtis (BC) similarity matching-based approach as well as the particle filter-based approach.

Description: A higher number of people increasingly uses digital games. This growing interest in games, with different objectives, justifies the investigation of some aspects and concepts involved, such as product quality (game), usability, playability, and user or player experience, topics investigated by the multidisciplinary area called Human–Computer Interaction (HCI). Although the majority of users of these games are children and young people, an increasing number of older adults join technology and use different types of digital games. Several studies establish the increase in learning, socialization and exercise promotion, and cognitive and psychomotor skills improvement, all within the context of active and healthy aging. The objective of this work is to carry out a systematic literature review investigating the player experience of the elderly in digital games. The work allowed answering five research questions that were formulated. The evolution and maturity level of the research area are studied together with the research methods used. The factors that motivate adults to play were also analyzed; what are the recommended technical characteristics for games and some tools and metrics with which games are evaluated for older adults? Research gaps were detected in the area; there are not many specific studies on playability and player experience applied to the older adult, nor are there proven tools and metrics to evaluate them. Particular techniques for assessing and designing games focused on older adults are lacking, and quantitative studies that better identify the factors that affect the playability and experience of older adults in digital games.

Description: Increasing the area of planted forests is rather important for compensation the loss of natural forests and slowing down the global warming. Forest growing stem volume (GSV) is a key indicator for monitoring and evaluating the quality of planted forest. To improve the accuracy of planted forest GSV located in south China, four L-band ALOS PALSAR-2 quad-polarimetric synthetic aperture radar (SAR) images were acquired from June to September with short intervals. Polarimetric characteristics (un-fused and fused) derived by the Yamaguchi decomposition from time series SAR images with different intervals were considered as independent variables for the GSV estimation. Then, the general linear model (GLM) obeyed the exponential distribution were proposed to retrieve the stand-level GSV in plantation. The results show that the un-fused power of double bounce scatters and four fused variables derived from single SAR image is highly sensitive to the GSV, and these polarimeric characteristics derived from the time series images more significantly contribute to improved estimation of GSV. Moreover, compared with the estimated GSV using the semi-exponential model, the employed GLM model with less limitations and simple algorithm has a higher saturation level (nearly to 300 m3/ha) and higher sensitivity to high forest GSV values than the semi-exponential model. Furthermore, by reducing the external disturbance with the help of time average, the accuracy of estimated GSV is improved using fused polarimeric characteristics, and the estimation accuracy of forest GSV was improved as the images increase. Using the fused polarimetric characteristics (Dbl×Vol/Odd) and the GLM, the minimum RRMSE was reduced from 33.87% from single SAR image to 24.42% from the time series SAR images. It is implied that the GLM is more suitable for polarimetric characteristics derived from the time series SAR images and has more potential to improve the planted forest GSV.

Description: In this paper, an approach to measure both the refractive index (RI) and the pressure simultaneously using two Whispering-Gallery Modes (WGMs) in a microdisk resonator is theoretically proposed. Due to the difference in the energy distribution of the first and second order WGMs, the sensitivity of two modes toward the variation of RI and pressure applied to the device show differences. An RI sensitivity of 29.07 nm/RIU and pressure sensitivity of 0.576 pm/kPa for WGM (1,36), and an RI sensitivity of 38.68 nm/RIU and a pressure sensitivity of 0.589 pm/kPa for WGM (2,28) are obtained through the 3D finite-difference time-domain (3D-FDTD) simulation. Dual parametric sensing can be achieved by solving the second-order inverse sensitivity matrix. Therefore, strain–optical coupling behavior is effectively eliminated. The dual sensing scheme we proposed provides a novel approach to overcome the difficulty of multi-sensing applications based on the flexible photonic device.

Description: The Internet of Things (IoT) is a world of connected networks and modern technology devices, among them vehicular networks considered more challenging due to high speed and network dynamics. Future trends in IoT allow these inter networks to share information. Also, the previous security solutions to vehicular IoT (VIoT) much emphasize on privacy protection and security related issues using public keys infrastructure. However, the primary concern about efficient trust assessment, authorized users malfunctioning, and secure information dissemination in vehicular wireless networks have not been explored. To cope with these challenges, we propose a trust enhanced on-demand routing (TER) scheme, which adopts TrustWalker (TW) algorithm for efficient trust assessment and route search technique in VIoT. TER comprised of novel three-valued subjective logic (3VSL), TW algorithm, and ad hoc on-demand distance vector (AODV) routing protocol. The simulated results validate the accuracy of the proposed scheme in term of throughput, packet drop ratio (PDR), and end to end (E2E) delay. In the simulation, the execution time of the TW algorithm is analyzed and compared with another route search algorithm, i.e., Assess-Trust (AT), by considering real-world online datasets such as Pretty Good Privacy and Advogato. The accuracy and efficiency of the TW algorithm, even with a large number of vehicle users, are also demonstrated through simulations.

Description: The incorporation of a proper sensing material towards the construction of high selectivity optical sensing devices is vital. Polysaccharides, such as chitosan and carrageenan, are among the bio-based sensing materials that are extensively employed due to their remarkable physicochemical attributes. This paper highlights the critical aspects of the design of suitable polysaccharides for the recognition of specific analytes through physical and chemical modifications of polysaccharide structure. Such modifications lead to the enhancement of physicochemical properties of polysaccharides and optical sensor performance. Chitosan and carrageenan are two materials that possess excellent features which are capable of sensing target analytes via various interactions. The interaction between polysaccharides and analytes is dependent on the availability of functional groups in their structure. The integration of polysaccharides with various optical sensing techniques further improves optical sensor performance. The application of polysaccharides as sensing materials in various optical sensing techniques is also highlighted, particularly for metal ion sensing.

Description: Indoor localization based on unsynchronized, low-complexity, passive radio frequency identification (RFID) using the received signal strength indicator (RSSI) has a wide potential for a variety of internet of things (IoTs) applications due to their energy-harvesting capabilities and low complexity. However, conventional RSSI-based algorithms present inaccurate ranging, especially in indoor environments, mainly because of the multipath randomness effect. In this work, we propose RSSI-based localization with low-complexity, passive RFID infrastructure utilizing the potential benefits of large-scale MIMO technology operated in the millimeter-wave band, which offers channel hardening, in order to alleviate the effect of small-scale fading. Particularly, by investigating an indoor environment equipped with extremely simple dielectric resonator (DR) tags, we propose an efficient localization algorithm that enables a smart object equipped with large-scale MIMO exploiting the RSSI measurements obtained from the reference DR tags in order to improve the localization accuracy. In this context, we also derive Cramer–Rao lower bound of the proposed technique. Numerical results evidence the effectiveness of the proposed algorithms considering various arbitrary network topologies, and results are compared with an existing algorithm, where the proposed algorithms not only produce higher localization accuracy but also achieve a greater robustness against inaccuracies in channel modeling.

Description: Technologies and techniques of location and navigation are advancing, allowing greater precision in locating people in complex and challenging conditions. These advances have attracted growing interest from the scientific community in using indoor positioning systems (IPSs) with a higher degree of precision and fast delivery time, for groups of people such as the visually impaired, to some extent improving their quality of life. Much research brings together various works that deal with the physical and logical approaches of IPSs to give the reader a more general view of the models. These surveys, however, need to be continuously revisited to update the literature on the features described. This paper presents an expansion of the range of technologies and methodologies for assisting the visually impaired in previous works, providing readers and researchers with a more recent version of what was done and the advantages and disadvantages of each approach to guide reviews and discussions about these topics. Finally, we discuss a series of considerations and future trends for the construction of indoor navigation and location systems for the visually impaired.

Description: Coatings offer a means to control nanoparticle (NP) size, regulate dissolution, and mitigate runoff when added to crops through soil. Simultaneously, coatings can enhance particle binding to plants and provide an additional source of nutrients, making them a valuable component to existing nanoparticle delivery systems. Here, the surface functionalization of metal and metal-oxide nanoparticles to inhibit aggregation and preserve smaller agglomerate sizes for enhanced transport to the rooting zone and improved uptake in plants is reviewed. Coatings are classified by type and by their efficacy to mitigate agglomeration in soils with variable pH, ionic concentration, and natural organic matter profiles. Varying degrees of success have been reported using a range of different polymers, biomolecules, and inorganic surface coatings. Advances in zwitterionic coatings show the best results for maintaining nanoparticle stability in solutions even under high salinity and temperature conditions, whereas coating by the soil component humic acid may show additional benefits such as promoting dissolution and enhancing bioavailability in soils. Pre-tuning of NP surface properties through exposure to select natural organic matter, microbial products, and other biopolymers may yield more cost-effective nonagglomerating metal/metal-oxide NPs for soil applications in agriculture.

Description: According to the recommendations of the World Health Organization (WHO), due to the increased risk of cardiovascular disease, the daily consumption of table salt should be reduced. To avoid the health consequences of iodine deficiency, it is necessary to include alternative food sources of this trace element in the human diet. One of the most effective ways of improving nutrition is the biofortification of crops with minerals and vitamins. The purpose of this study was to determine the influence of iodine biofortification (potassium iodate/KIO3/, 5-iodosalicylic acid/5-ISA/and 3.5-diiodosalicylic acid/3.5-diISA/) on the chemical composition of lettuce (Lactuca sativa L. capitata) cv. ‘Melodion’. Plants were cultivated in a hydroponic system NFT (Nutrient Film Technique). We compared the effect of iodine fertilization on the basic chemical composition, fatty acid profile, macro- and micronutrients, content of sugars, nitrogenous compounds, chlorides, and iodine compounds. The results obtained in this research indicate that the application of iodine compounds has an influence on changes of concentration of iodine and other compounds in the treated samples. In lettuce, the main fatty acid was linolenic acid; however, fertilization with iodine did not affect the fatty acid profile in plants, except for concentrations of myristic and arachidic acids. We also found that iodine fortification has positive effects on concentrations of some micro- and micronutrients. Moreover, the application of 3.5-diISA decreased the concentration of nitrates as compared to control and other treatments. Therefore, it may be postulated that the production of lettuce fortified with iodosalicylates is worthy of consideration due to the fact that it may be a good source of iodine and other compounds in the human diet.

Description: Carbonization of cage layer chicken manure (CLCM) can improve its bio-recalcitrance which might improve nitrogen (N) bioavailability in soil. However, temperature(s) to exhibit appropriate variations in the chemical makeup of the manure during carbonization in order to achieve this objective is unknown. In this study, we investigated the alterations in chemical compositions, surface functionalities, and N speciation initiated by different carbonization temperatures (350, 500, and 650 °C) and the effects of these alterations on N bioavailability in soil. The objective was to identify suitable temperature condition(s) for the conversion of CLCM into a carbonized product of appropriate bio-recalcitrance that is capable of improving N bioavailability in soil more than the un-carbonized CLCM. The results showed an increased bio-recalcitrance of the manure with increasing carbonization temperatures due to drastic changes in the chemical makeup and accumulation of heterocyclic aromatic N structures. Subsequently, these alterations in the chemical makeup and state of the organic N species in the manure affected N bioavailability in soil. Notably, N bioavailability of CLCM and benefits on plants were improved when soil was supplied with CLCM that was altered at 350 °C. With these observations, we concluded that alterations in chemical and surface structural compositions and N speciation at 350 °C are optimum for instituting the required bio-recalcitrance to CLCM in order to improve N bioavailability in soil for plants.

Description: Medicago truncatula (barrel medic) and Pisum sativum subsp. elatius (wild pea) accessions originating from variable environmental conditions in the Mediterranean basin were used to study physical seed dormancy (PY) release. The effect of soil burial on PY release was tested on 112 accessions of medic and 46 accessions of pea over the period of 3 months in situ at three common gardens (Hungary, Spain and Greece) from 2017 through 2019. PY release after soil exhumation followed by experimental laboratory germination of remaining dormant seeds (wet, 25 °C, 21 days) were related to the environmental conditions of the common garden and macroclimatic variables of the site of origin of the accessions. Higher PY release was observed in buried seeds under humid rather than under dry and hot environments. Exposure of remaining dormant seeds to experimental laboratory conditions increased total PY release up to 70% and 80% in barrel medic and wild pea, respectively. Wild pea showed higher phenotypic plasticity on PY release than barrel medic, which had higher bet-hedging within-season. Wild pea showed lower bet-hedging among-season (PY 〈 10%) in relation to precipitation than barrel medic, which was more conservative (PY ≈ 20%). Observed variability suggests that these species have the capability to cope with ongoing climate change.

Description: The communication and connectivity functions of vehicles increase their vulnerability to hackers. The unintended failure and malfunction of in-vehicle systems caused by external factors threaten the security and safety of passengers. As the controller area network alone cannot protect vehicles from external attacks, techniques to analyze and detect external attacks are required. Therefore, we propose a multi-labeled hierarchical classification (MLHC) intrusion detection model that analyzes and detects external attacks caused by message injection. This model quickly determines the occurrence of attacks and classifies the attack using only existing classified attack data. We evaluated the performance of the model by analyzing its learning space. We further verified the model by comparing its accuracy, F1 score and data learning and evaluation times with the two layers multi-class detection (TLMD) and single-layer multi-class classification (SLMC) models. The simulation results show that the MLHC model has the highest F1 score of 0.9995 and is 87.30% and 99.92% faster than the SLMC and TLMD models in terms of detection time, respectively. Consequently, the proposed model can classify both the type and existence or absence of attacks with high accuracy and can be used in interior communication environments of high-speed vehicles with a high throughput.

Description: We studied the use of a rotating multi-layer 3D Light Detection And Ranging (LiDAR) sensor (specifically the Velodyne HDL-32E) mounted on a social robot for the estimation of features of people around the robot. While LiDARs are often used for robot self-localization and people tracking, we were interested in the possibility of using them to estimate the people’s features (states or attributes), which are important in human–robot interaction. In particular, we tested the estimation of the person’s body orientation and their gender. As collecting data in the real world and labeling them is laborious and time consuming, we also looked into other ways for obtaining data for training the estimators: using simulations, or using LiDAR data collected in the lab. We trained convolutional neural network-based estimators and tested their performance on actual LiDAR measurements of people in a public space. The results show that with a rotating 3D LiDAR a usable estimate of the body angle can indeed be achieved (mean absolute error 33.5 ° ), and that using simulated data for training the estimators is effective. For estimating gender, the results are satisfactory (accuracy above 80%) when the person is close enough; however, simulated data do not work well and training needs to be done on actual people measurements.

Description: The non-destructive evaluation (NDE) of civil infrastructure has been an active area of research in recent decades. The traditional inspection of civil infrastructure mostly relies on visual inspection using human inspectors. To facilitate this process, different sensors for data collection and techniques for data analyses have been used to effectively carry out this task in an automated fashion. This review-based study will examine some of the recent developments in the field of autonomous robotic platforms for NDE and the structural health monitoring (SHM) of bridges. Some of the salient features of this review-based study will be discussed in the light of the existing surveys and reviews that have been published in the recent past, which will enable the clarification regarding the novelty of the present review-based study. The review methodology will be discussed in sufficient depth, which will provide insights regarding some of the primary aspects of the review methodology followed by this review-based study. In order to provide an in-depth examination of the state-of-the-art, the current research will examine the three major research streams. The first stream relates to technological robotic platforms developed for NDE of bridges. The second stream of literature examines myriad sensors used for the development of robotic platforms for the NDE of bridges. The third stream of literature highlights different algorithms for the surface- and sub-surface-level analysis of bridges that have been developed by studies in the past. A number of challenges towards the development of robotic platforms have also been discussed.

Description: Implantable sensors capable of real-time measurements are powerful tools to diagnose disease and maintain health by providing continuous or regular biometric monitoring. In this paper, we present a dental implantable temperature sensor that can send early warning signals in real time before the implant fails. Using a microfabrication process on a flexible polyimide film, we successfully fabricated a multi-channel temperature sensor that can be wrapped around a dental implant abutment wing. In addition, the feasibility, durability, and implantability of the sensor were investigated. First, high linearity and repeatability between electrical resistance and temperature confirmed the feasibility of the sensor with a temperature coefficient of resistance (TCR) value of 3.33 × 10–3/°C between 20 and 100 °C. Second, constant TCR values and robust optical images without damage validated sufficient thermal, chemical, and mechanical durability in the sensor’s performance and structures. Lastly, the elastic response of the sensor’s flexible substrate film to thermal and humidity variations, simulating in the oral environment, suggested its successful long-term implantability. Based on these findings, we have successfully developed a polymer-based flexible temperature sensor for dental implant systems.

Description: This work investigates the responses of the fully-depleted silicon-on-insulator (FD-SOI) Hall sensors to the three main types of irradiation ionization effects, including the total ionizing dose (TID), transient dose rate (TDR), and single event transient (SET) effects. Via 3D technology computer aided design (TCAD) simulations with insulator fixed charge, radiation, heavy ion, and galvanomagnetic transport models, the performances of the transient current, Hall voltage, sensitivity, efficiency, and offset voltage have been evaluated. For the TID effect, the Hall voltage and sensitivity of the sensor increase after irradiation, while the efficiency and offset voltage decrease. As for TDR and SET effects, when the energy deposited on the sensor during a nuclear explosion or heavy ion injection is small, the transient Hall voltage of the off-state sensor first decreases and then returns to the initial value. However, if the energy deposition is large, the transient Hall voltage first decreases, then increases to a peak value and decreases to a fixed value. The physical mechanisms that produce different trends in the transient Hall voltage have been analyzed in detail.

Description: This paper presents the investigations on the improvement of search object detection during search and rescue (SAR) action at sea using thermal imaging and radar sensors. The introduction of new materials in the construction of lifesaving appliances increasing their detectability has been studied for the selected example of a pneumatic life raft. The research was based on laboratory tests and open sea trials. The presented experimental investigations on the new materials that can be used for pneumatic life raft construction showed the enhancement of its thermal and radar signatures, which directly affect life raft detectability and influence reliability of SAR action and probability of success (POS). The improved detectability of a life raft related to the time to survive of a person in the water (PIW) allowed to present the modified search pattern for both PIW and life raft, significantly increasing POS.

Description: In the critical setting of a trauma team activation, team composition is crucial information that should be accessible at a glance. This calls for a technological solution, which are widely available, that allows access to the whereabouts of personnel. This diversity presents decision makers and users with many choices and considerations. The aim of this review is to give a comprehensive overview of available real-time person identification techniques and their respective characteristics. A systematic literature review was performed to create an overview of identification techniques that have been tested in medical settings or already have been implemented in clinical practice. These techniques have been investigated on a total of seven characteristics: costs, usability, accuracy, response time, hygiene, privacy, and user safety. The search was performed on 11 May 2020 in PubMed and the Web of Science Core Collection. PubMed and Web of Science yielded a total n = 265 and n = 228 records, respectively. The review process resulted in n = 23 included records. A total of seven techniques were identified: (a) active and (b) passive Radio-Frequency Identification (RFID) based systems, (c) fingerprint, (d) iris, and (e) facial identification systems and infrared (IR) (f) and ultrasound (US) (g) based systems. Active RFID was largely documented in the included literature. Only a few could be found about the passive systems. Biometric (c, d, and e) technologies were described in a variety of applications. IR and US techniques appeared to be a niche, as they were only spoken of in few (n = 3) studies.

Description: A micro vibratory platform driven by converse piezoelectric effects is a promising in-situ recalibration platform to eliminate the influence of bias and scale factor drift caused by long-term storage of micro-electro–mechanical system (MEMS) inertial sensors. The calibration accuracy is critically determined by the stable and repeatable vibration of platform, and it is unavoidably impacted by the residual stress of micro structures and lead zirconate titanate (PZT) hysteresis. The abnormal phenomenon of the observed displacement response in experiments was investigated analytically using the stiffness model of beams and hysteresis model of piezoelectric material. Rather than the hysteresis, the initial deflection formed by the residual stress of the beam was identified as the main cause of the response error around the zero position. This conclusion provides guidelines to improve the performance and control of micro vibratory platforms.

Description: Two loofah (Luffa) species, the ridge gourd (Luffa acutangula (L.) Roxb.) and the sponge gourd (L. cylindrica (L.) M. Roem.; syn. L. aegyptiaca), are cultivated widely in Asia by smallholder famers. Both species have significant economic and nutritional importance. However, Tomato leaf curl New Delhi virus (ToLCNDV) and downy mildew (DM) caused by Pseudoperonospora cubensis are important biotic constraints to loofah production throughout Asia. Loofah landrace-derived breeding lines, developed at the World Vegetable Center (WorldVeg), were evaluated at the WorldVeg East and Southeast Asia Research and Training Station, Kasetsart University, Kamphaeng Saen, Thailand—where natural epidemics of ToLCNDV and DM regularly occur. The lines were also evaluated for other commercially important horticultural traits such as days to 50% staminate and pistillate flowering, fruit color, fruit bitterness, and market segment classification. Thirteen and 59 lines of ridge gourd and sponge gourd, respectively, were determined to be resistant to both ToLCNDV and DM. These lines covered all market segments of loofah and exhibited variability for all of the evaluated horticultural traits. The results of these evaluations and their implications on loofah breeding are discussed.

Description: Soybean (Glycine max L. Merrill) is one of the most important crops worldwide. In several European countries such as Germany, soybean cultivation is not traditional and as such remains to be optimized. One option to increase soybean production is to adapt and improve the seeding system. To investigate the effects of different seeding systems on seed yield, seed protein, and oil content, plant characteristics were assessed using a two-factorial (two seeding systems and four cultivars) field trial over two years (2017–2018) at two locations in southeast Germany. The seeding systems were drill (row spacing 14 cm) and precision seeding (row spacing 28 cm), and the cultivars were Viola 000, Lissabon 000, ES Mentor 00, and Orion 00. Depending on the seeding system, a seed yield of 3.8 t ha−1 dry matter (DM), 40.9% protein content (DM), and 18.8% oil content (DM) was achieved by drilling, and 3.6 t ha−1 yield (DM), 40.1% protein content (DM), and 19.1% oil content (DM) with precision seeding (average across four cultivars, two locations, and two years). No significant effects of the seeding system on all seed and plant characteristics were observed. As drilling and precision seeding did not affect the seed yield, seed protein, oil contents, and plant characteristics of soybean in this study, farmers are able to choose the seeding system which fits best into their farms and is economically most viable.

Description: A series of non-spatial and spatial hedonic models of feeding and replacement cattle prices at video auctions in Uruguay (2002 to 2009) were specified with predictors measuring marketing conditions (e.g., steer price), cattle characteristics (e.g., breed) and agro-ecological factors (e.g., soil productivity, water characteristics, pasture condition, season). Results indicated that cattle prices produced under extensive production systems were influenced by all of predictor categories, confirming that found previously. Although many of the agro-ecological predictors were inherently spatial in nature, the incorporation of spatial effects into the estimation of the hedonic model itself, through either a spatially-autocorrelated error term or allowing the regression coefficients to vary spatially and at different scales, was able to provide greater insight into the cattle price process. Through the latter extension, using a multiscale geographically weighted regression, which was the most informative and most accurate model, relationships between cattle price and predictors operated at a mixture of global, regional, local and highly local spatial scales. This result is considered a key advance, where uncovering, interpreting, and utilizing such rich spatial information can help improve the geographical provenance of Uruguayan beef and is critically important for maintaining Uruguay’s status as a key exporter of beef with respect to the health and safety benefits of natural, open-sky, grass-fed production systems.

Description: Tree peony is a deciduous shrub endemic to China, and peony seed oil (PSO) is an important plant oil resource. However, at present, pruning and fruits harvesting of oil tree peony are mainly completed by manual work, which has seriously affected production efficiency. By the need to develop efficient pruning and harvesting equipment for oil tree peony, this study investigated the effect of cutting tool geometric parameters and cutting speed on cutting force and energy. A cutting device was set up, and six cutting tools with different blade angle and sliding-cutting angle were prepared for this research. Stems in different growth stages (anthesis and fructescence) were collected for cutting experiments. In this paper, four blade angles (25°, 30°, 35°, and 40°), three sliding-cutting angles (0°, 10°, and 20°), and four cutting speeds (25 mm/min, 50 mm/min, 100 mm/min, and 200 mm/min) were considered in the experiments. The results showed that cutting force and energy are positively related to blade angle, and the minimum cutting force and energy are required in cutting with 25° blade angle. Compared to straight cutting (sliding-cutting angle is 0°), sliding cutting can obviously reduce the cutting force and energy. Furthermore, the best sliding-cutting angles of cutting tools have discrepancies dependent on the stems in different growth stages. In fructescence, 10° sliding cutting required the lowest cutting force and energy. In contrast, the best sliding-cutting angle of stems in anthesis was 20°. On the other hand, the cutting force and energy were obviously proportional to the cutting speed, which increased by the cutting speed increasing. Therefore, under the condition of ensuring the cutting efficiency, reducing the cutting speed can effectively reduce the power consumption. These results are an important basis for pruning and harvesting machine development for oil tree peony.

Description: A rock fracture test is a very important method in the study of rock mechanics. Based on the Mechanics Test System (MTS), the dynamic strain response of the failure process of cylindrical granite specimens under uniaxial compression was observed by using distributed optical fiber strain sensors. Two groups of tests were designed and studied for rock sample fracturing. The main comparison and analysis were made between the distributed optical fiber testing technology and the MTS testing system in terms of the circumferential strain response curve and the evolution characteristics of strain with time. The strain characterization of distributed optical fiber in the process of rock fracturing was obtained. The results show that the ring strains measured by the distributed optical fiber sensor and the circumferential strain gauge were consistent, with a minimum ring strain error of 1.27%. The relationship between the strain jump or gradient band of the distributed optical fiber and the crack space on the sample surface is clear, which can reasonably determine the time of crack initiation and propagation, point out the location of the rock failure area, and provide precursory information about rock fracture. The distributed optical fiber strain sensor can realize the linear and continuous measurement of rock mass deformation, which can provide some reference for the study of macro damage evolution and the fracture instability prediction of field engineering rock mass.

Description: The aim of this study was to evaluate the effect on intake, digestibility and fractionation of carbohydrates and proteins of cottonseed cake as an alternative protein source in rams diets. The diets were composed of corn silage, soybean meal, ground corn, urea, and cottonseed cake (0, 70, 140 and 210 g/kg dry matter (DM)) in a roughage/concentrate ratio of 50:50. Thirty-two Santa Ines rams, average age 12 months and weight 27.48 ± 4.96 kg were distributed in a completely randomised design. A reduction of 0.161 g/day in crude protein (CP) intake was observed for each gram of cottonseed cake inclusion. The intake of non-fiber carbohydrate (NFC) decreased by 0.511 g/day for each gram of cottonseed cake inclusion. Similarly, the ratio between intake of DM (p = 0.034), CP (p 〈 0.001), NFC (p = 0.004) and body weight decreased with cottonseed cake inclusion. Inclusion of cottonseed cake caused decreases in the digestibility coefficients of DM (p 〈 0.001), CP (p 〈 0.001) and NFC (p = 0.002). A reduction of 0.716 g was observed in carbohydrate fraction A + B1 (p = 0.006). The inclusion of cottonseed cake in the rams feed should be carried out with caution because this by-product promotes linear reduction in intake and digestibility nutrient, in addition to modifying the amounts of digestible and indigestible fractions in the diets.

Description: With the advent of the Internet of things (IoT), intelligent transportation has evolved over time to improve traffic safety and efficiency as well as to reduce congestion and environmental pollution. However, there are some challenging issues to be addressed so that it can be implemented to its full potential. The major challenge in intelligent transportation is that vehicles and pedestrians, as the main types of edge nodes in IoT infrastructure, are on the constant move. Hence, the topology of the large scale network is changing rapidly over time and the service chain may need reestablishment frequently. Existing Virtual Network Function (VNF) chain placement methods are mostly good at static network topology and any evolvement of the network requires global computation, which leads to the inefficiency in computing and the waste of resources. Mapping the network topology to a graph, we propose a novel VNF placement method called BVCP (Border VNF Chain Placement) to address this problem by elaborately dividing the graph into multiple subgraphs and fully exploiting border hypervisors. Experimental results show that BVCP outperforms the state-of-the-art method in VNF chain placement, which is highly efficient in large scale IoT of intelligent transportation.

Description: Recent advances in time series classification (TSC) have exploited deep neural networks (DNN) to improve the performance. One promising approach encodes time series as recurrence plot (RP) images for the sake of leveraging the state-of-the-art DNN to achieve accuracy. Such an approach has been shown to achieve impressive results, raising the interest of the community in it. However, it remains unsolved how to handle not only the variability in the distinctive region scale and the length of sequences but also the tendency confusion problem. In this paper, we tackle the problem using Multi-scale Signed Recurrence Plots (MS-RP), an improvement of RP, and propose a novel method based on MS-RP images and Fully Convolutional Networks (FCN) for TSC. This method first introduces phase space dimension and time delay embedding of RP to produce multi-scale RP images; then, with the use of asymmetrical structure, constructed RP images can represent very long sequences (〉700 points). Next, MS-RP images are obtained by multiplying designed sign masks in order to remove the tendency confusion. Finally, FCN is trained with MS-RP images to perform classification. Experimental results on 45 benchmark datasets demonstrate that our method improves the state-of-the-art in terms of classification accuracy and visualization evaluation.

Description: The external and internal factors in the tree development process affect wooden structures and properties. They depend on, for example, a plant species, its age, part and growth conditions. The present study investigated the chemical structure and physical properties of the common urban tree species: small-leaved lime (Tilia cordata Mill.). The properties of trees growing in an urban agglomeration, by a roadside and in a forest, i.e., areas varying in degrees of an environmental stress impact, were compared. Tree-ring width and density, selected hygroscopic properties (sorption hysteresis and isotherms), wood chemical content (percentage content of: holocellulose, cellulose, pentosanes, lignin and substances soluble in 1% NaOH and EtOH) as well as the content of selected chemical elements (Fe, Zn, Cu, Pb, Cd, K, Na, Mg and Ca) were studied. The analysis of the case shows no impact of the environmental stress on the growth patterns characterized by ring width index (RWI). Two-factor analysis of variance (ANOVA) revealed impact significance of growth environment and cambial age on the content of each of the investigated components and chemical elements. There was a relationship demonstrated that for a tree growing in an agglomeration with the highest environmental stress the content of cellulose and lignin was the lowest, whereas the content of substances soluble in NaOH and EtOH was the highest. For mature wood growing under the same conditions, the results corresponded to the highest Zn, Cu, K, Na, Mg and Ca content. It was also shown that the environmental stress affected hygroscopicity which was the consequence of chemical component percentage content. The research proved that Tilia cordata Mill. responded to environmental stress with alternations in its chemical or/and physical properties.

Description: Source positioning using hybrid angle-of-arrival (AOA) estimation and received signal strength indicator (RSSI) is attractive because no synchronization is required among unknown nodes and anchors. Conventionally, hybrid AOA/RSSI localization combines the same number of these measurements to estimate the agents’ locations. However, since AOA estimation requires anchors to be equipped with large antenna arrays and complicated signal processing, this conventional combination makes the wireless sensor network (WSN) complicated. This paper proposes an unbalanced integration of the two measurements, called 1AOA/nRSSI, to simplify the WSN. Instead of using many anchors with large antenna arrays, the proposed method only requires one master anchor to provide one AOA estimation, while other anchors are simple single-antenna transceivers. By simply transforming the 1AOA/1RSSI information into two corresponding virtual anchors, the problem of integrating one AOA and N RSSI measurements is solved using the least square and subspace methods. The solutions are then evaluated to characterize the impact of angular and distance measurement errors. Simulation results show that the proposed network achieves the same level of precision as in a fully hybrid nAOA/nRSSI network with a slightly higher number of simple anchors.

Description: Knowledge of the dynamics of dryland vegetation in recent years is essential for combating desertification. Here, we aimed to characterize nonlinear changes in dryland vegetation greenness over East Inner Mongolia, an ecotone of forest–grassland–cropland in northern China, with time series of Moderate-resolution Imaging Spectroradiometer (MODIS) enhanced vegetation index (EVI) and GEOV2 leaf area index (LAI) values during 2000 to 2016. Changes in the growing season EVI and LAI were detected with the polynomial change fitting method. This method characterizes nonlinear changes in time series by polynomial fitting with the highest polynomial order of three, and simultaneously provides an estimation of monotonic trends over the time series by linear fitting. The relative contribution of climatic factors (precipitation and temperature) to changes in the EVI and LAI were analyzed using linear regression. In general, we observed similar patterns of change in the EVI and LAI. Nonlinear changes in the EVI were detected for about 21% of the region, and for the LAI, the percentage of nonlinear changes was about 16%. The major types of nonlinear changes include decrease–increase, decrease–increase–decrease, and increase–decrease–increase changes. For the overall monotonic trends, very small percentages of decrease (less than 1%) and widespread increases in the EVI and LAI were detected. Furthermore, large areas where the effects of climate variation on vegetation changes were not significant were observed for all major types of change in the grasslands and rainfed croplands. Changes with an increase–decrease–increase process had large percentages of non-significant effects of climate. The further analysis of increase–decrease–increase changes in different regions suggest that the increasing phases were likely to be mainly driven by human activities, and droughts induced the decreasing phase. In particular, some increase–decrease changes were observed around the large patch of bare areas. This may be an early signal of degradation, to which more attention needs to be paid to combat desertification.

Description: Process-based crop models are advantageous for the identification of management strategies to cope with both temporal and spatial variability of sugarcane yield. However, global optimization of such models is often computationally expensive. Therefore, we performed global sensitivity analysis based on Gaussian process emulation to evaluate the sensitivity of cane dry weight to trait parameters implemented in the Agricultural Productions System Simulator (APSIM)-Sugar model under selected environmental and management conditions in Khon Kaen (KK), Thailand. Emulators modeled 30 years, three soil types and irrigated or rainfed conditions, and emulator performance was investigated. rue, green_leaf_no, transp_eff_cf, tt_emerg_to_begcane and cane_fraction were identified as the most influential parameters and together they explained more than 90% of total variance on the simulator output. Moreover, results indicate that the sensitivity of sugarcane yield to the most influential parameters is affected by water stress conditions and nitrogen stress. Our findings can be used to improve the efficiency and accuracy of modeling and to identify appropriate management strategies to address temporal and spatial variability of sugarcane yield in KK.

Description: This work explores energy harvesting from rotary motion using a Wiegand sensor, which is a magnetic sensor that induces a voltage pulse when the magnetization is reversed. The main feature of the Wiegand sensor is that a pulse is generated regardless of how slowly magnetism reversal occurs. Self-sustained sensors play major roles in advancing the Internet of Things (IoT) and wireless sensor networks (WSN). In this study, we identified a linear relationship between rotational motion, magnetic field reversal, and the rotational frequency generated by the Wiegand sensor. In addition, the maximum energy per pulse and its dependence were derived analytically. A maximum energy of 130 nJ per pulse was reported for the sensor used. We developed a single-bit, self-powered digital counter that was sufficiently driven with 38 nJ of energy. In this study, single rotations were measured without the need for external power.

Description: The increasing frequency and severity of natural disturbances (e.g., storms and insect outbreaks) due to climate change are expected to reduce the abundance of Norway spruce stands in the European forests. Under such conditions, the assessment of status quo on focusing on survival of Norway spruce stands are essential for the agility of forest management strategies. The dynamics (mortality rate) of Norway spruce stands in hemiboreal forests based on forest inventories for the period from 1975 to 2016 (inventories of 1975, 1985, 1999, 2011 and 2016) were analyzed in two forest landscapes in the western and eastern parts of Latvia (Vane and Dviete, respectively). The spatiotemporal changes in age-dependent mortality differing by abundance of Norway spruce and disturbance regime were assessed, focusing on the transitions of stands between age groups (inventories). The age-related changes in probability of stands transitioning into the next age group contrasted (p 〈 0.001) between sites. In Vane, the survival of stands between inventories was constant (ca. 90%), while in Dviete, it decreased sharply from 85.7% during 1985–1999 inventories to 49.3% in 2011–2016. Age-related decreases in stand survival showed local dependencies between both landscapes, namely, in Vane, notable decreases started from 61 years, while in Dviete, the downward trends started already from 31 years, probably due to different disturbance regimes. This suggests that, in forest management planning, the different outcomes for mortality patterns between both landscapes must be considered and should not be generalized for a whole country.

Description: Strain and crack are critical indicators of structural safety. As a novel sensing device, a patch antenna sensor can be utilized to wirelessly estimate structural strain and surface crack growth through resonance frequency shift. The main challenges for the sensor are other effects such as temperature fluctuation that can generate unwanted resonance frequency shift and result in large noise in the measurement. Another challenge for existing designs of patch antenna sensor is the limited interrogation distance. In this research, thermally stable patch antenna sensors are investigated for more reliable measurement. Fabricated on a substrate material with a steady dielectric constant, a new passive (battery-free) patch antenna sensor is designed to improve reliability under temperature fluctuations. In addition, another newly designed dual-mode patch antenna sensor is proposed to achieve a longer interrogation distance. Extensive experiments are conducted to characterize the patch antenna sensor performance, including thermal stability, tensile strain sensing, and emulated crack sensing. The two new patch antenna sensors are demonstrated to be effective in wireless strain and crack measurements and have potential applications in structural health monitoring (SHM).

Description: Target Tracking (TT) is a fundamental application of wireless sensor networks. TT based on received signal strength indication (RSSI) is by far the cheapest and simplest approach, but suffers from a low stability and precision owing to multiple paths, occlusions, and decalibration effects. To address this problem, we propose an innovative TT algorithm, known as the SVM+KF method, which combines the support vector machine (SVM) and an improved Kalman filter (KF). We first use the SVM to obtain an initial estimate of the target’s position based on the RSSI. This enhances the ability of our algorithm to process nonlinear data. We then apply an improved KF to modify this estimated position. Our improved KF adds the threshold value of the innovation update in the traditional KF. This value changes dynamically according to the target speed and network parameters to ensure the stability of the results. Simulations and real experiments in different scenarios demonstrate that our algorithm provides a superior tracking accuracy and stability compared to similar algorithms.

Description: Hydrogen sensor technologies have been rapidly developing. For effective and safe sensing, we proposed a hydrogen sensor composed of magnesium (Mg), silver (Ag), and palladium (Pd) nano-blocks that overcomes the spectral resolution limit. This sensor exploited the properties of Mg and Pd when absorbing hydrogen. Mg became a dielectric material, and the atomic lattice of Pd expanded. These properties led to changes in the plasmonic gap mode between the nano-blocks. Owing to the changing gap mode, the far-field scattering pattern significantly changed with the hydrogen concentration. Thus, sensing the hydrogen concentration was able to be achieved simply by detecting the far-field intensity at a certain angle for incident light with a specific wavelength.

Description: The Wireless Local Area Network (WLAN) has become a dominant piece of technology to carry wireless traffic for Internet of Things (IoT). The next-generation high-density WLAN scenario is very suitable for the development trend of the industrial wireless sensor network. However, in the high-density deployed WLAN scenarios, the access efficiency is low due to severe collisions, and the interference is diffused due to the scattered locations of the parallel access stations (STAs), which results in low area throughput, i.e., low spatial reuse gain. A spatial group-based multi-user full-duplex orthogonal frequency division multiple access (OFDMA) (GFDO) multiple access control (MAC) protocol is proposed. Firstly, the STAs in the network are divided into several spatial groups according to the neighbor channel sensing ability. Secondly, a two-level buffer state report (BSR) information collection mechanism based on P-probability is designed. Initially, intra-group STAs report their BSR information to the group header using low transmission power. After that, group headers report both their BSR information collected from their members and inter-group interference information to the access point (AP). Finally, AP schedules two spatial groups without mutual interference to carry on multi-user full duplex transmission on the subchannels in cascading mode. The closed-form formulas are theoretically derived, including the number of uplink STAs successfully collected by AP, the network throughput and area throughput under saturated traffic. The simulation results show that the theoretical analysis coincide with the simulation results. The system throughput of the GFDO protocol is 16.8% higher than that of EnFD-OMAX protocol.

Description: A new predictor–corrector filter for attitude and heading reference systems (AHRS) using data from an orthogonal sensor combination of three accelerometers, three magnetometers and three gyroscopes is proposed. The filter uses the predictor—corrector structure, with prediction based on gyroscopes and independent correction steps for acceleration and magnetic field sensors. We propose two variants of the filter: (i) one using mathematical operations of special orthogonal group SO(3), that are accurate for nonlinear operations, for highest possible accuracy, and (ii) one using linearization of nonlinear operations for fast evaluation. Both approaches are quaternion-based filter realizations without redundant steps. The filters are compared to state of the art methods in this field on data recorded using low-cost microelectromechanical systems (MEMS) sensors with ground truth measured by the VICON optical system. Both filters achieved better accuracy than conventional methods at lower computational cost. The recorded data with ground truth reference and the source codes of both filters are publicly available.

Description: Hypertension affects a huge number of people around the world. It also has a great contribution to cardiovascular- and renal-related diseases. This study investigates the ability of a deep convolutional autoencoder (DCAE) to generate continuous arterial blood pressure (ABP) by only utilizing photoplethysmography (PPG). A total of 18 patients are utilized. LeNet-5- and U-Net-based DCAEs, respectively abbreviated LDCAE and UDCAE, are compared to the MP60 IntelliVue Patient Monitor, as the gold standard. Moreover, in order to investigate the data generalization, the cross-validation (CV) method is conducted. The results show that the UDCAE provides superior results in producing the systolic blood pressure (SBP) estimation. Meanwhile, the LDCAE gives a slightly better result for the diastolic blood pressure (DBP) prediction. Finally, the genetic algorithm-based optimization deep convolutional autoencoder (GDCAE) is further administered to optimize the ensemble of the CV models. The results reveal that the GDCAE is superior to either the LDCAE or UDCAE. In conclusion, this study exhibits that systolic blood pressure (SBP) and diastolic blood pressure (DBP) can also be accurately achieved by only utilizing a single PPG signal.

Description: Target detection and tracking is important in military as well as in civilian applications. In order to detect and track high-speed incoming threats, modern surveillance systems are equipped with multiple sensors to overcome the limitations of single-sensor based tracking systems. This research proposes the use of information from RADAR and Infrared sensors (IR) for tracking and estimating target state dynamics. A new technique is developed for information fusion of the two sensors in a way that enhances performance of the data association algorithm. The measurement acquisition and processing time of these sensors is not the same; consequently the fusion center measurements arrive out of sequence. To ensure the practicality of system, proposed algorithm compensates the Out of Sequence Measurements (OOSMs) in cluttered environment. This is achieved by a novel algorithm which incorporates a retrodiction based approach to compensate the effects of OOSMs in a modified Bayesian technique. The proposed modification includes a new gating strategy to fuse and select measurements from two sensors which originate from the same target. The state estimation performance is evaluated in terms of Root Mean Squared Error (RMSE) for both position and velocity, whereas, track retention statistics are evaluated to gauge the performance of the proposed tracking algorithm. The results clearly show that the proposed technique improves track retention and and false track discrimination (FTD).

Description: Cognitive radio networks (CRNs), which allow secondary users (SUs) to dynamically access a network without affecting the primary users (PUs), have been widely regarded as an effective approach to mitigate the shortage of spectrum resources and the inefficiency of spectrum utilization. However, the SUs suffer from frequent spectrum handoffs and transmission limitations. In this paper, considering the quality of service (QoS) requirements of PUs and SUs, we propose a novel dynamic flow-adaptive spectrum leasing with channel aggregation. Specifically, we design an adaptive leasing algorithm, which adaptively adjusts the portion of leased channels based on the number of ongoing and buffered PU flows. Furthermore, in the leased spectrum band, the SU flows with access priority employ dynamic spectrum access of channel aggregation, which enables one flow to occupy multiple channels for transmission in a dynamically changing environment. For performance evaluation, the continuous time Markov chain (CTMC) is developed to model our proposed strategy and conduct theoretical analyses. Numerical results demonstrate that the proposed strategy effectively improves the spectrum utilization and network capacity, while significantly reducing the forced termination probability and blocking probability of SU flows.

Description: This paper presents the design of a high precision telescopic system consisting in three lines, with measuring principle based on simultaneous laser multilateration. The system offers the high precision of the interferometer systems and allows the autonomous tracking of a sphere joined to the spindle nose of the machine tool by simultaneous contact of all the lines. The main advantage of the system is that it allows data capture to be carried out in a single cycle thanks to simultaneous operation with at least three telescopic arms using a novel multipoint kinematic coupling. This results in a significant reduction of the time taken for data capture and improves measurement accuracy due to avoiding the effect of temperature variations between cycles and machine tool repeatability. The work explains the working principle of the system, its main components, and the design parameters considered for the development of the system. The system is simple to operate, compact, agile, and suitable for the verification of small- or medium-sized machine tools with linear and/or rotary axes.

Description: Criminals and related illegal activities represent problems that are neither trivial to predict nor easy to handle once they are identified. The Police Forces (PFs) typically base their strategies solely on their intra-communication, by neglecting the involvement of third parties, such as the citizens, in the investigation chain which results in a lack of timeliness among the occurrence of the criminal event, its identification, and intervention. In this regard, a system based on IoT social devices, for supporting the detection and tracking of criminals in the real world, is proposed. It aims to enable the communication and collaboration between citizens and PFs in the criminal investigation process by combining app-based technologies and embracing the advantages of an Edge-based architecture in terms of responsiveness, energy saving, local data computation, and distribution, along with information sharing. The proposed model as well as the algorithms, defined on the top of it, have been evaluated through a simulator for showing the logic of the system functioning, whereas the functionality of the app was assessed through a user study conducted upon a group of 30 users. Finally, the additional advantage in terms of intervention time was compared to statistical results.

Description: Integrated fiber-wireless (FiWi) should be regarded as a promising access network architecture in future 5G networks, and beyond; this due to its seamless combination of flexibility, ubiquity, mobility of the wireless mesh network (WMN) frontend with a large capacity, high bandwidth, strong robustness in time, and a wavelength-division multiplexed passive optical network (TWDM-PON) backhaul. However, the key issue in both traditional human-to-human (H2H) traffic and emerging Tactile Internet is the energy conservation network operation. Therefore, a power-saving method should be instrumental in the wireless retransmission-enabled architecture design. Toward this end, this paper firstly proposes a novel energy-supply paradigm of the FiWi converged network infrastructure, i.e., the emerging power over fiber (PoF) technology instead of an external power supply. Then, the existing time-division multiplexing access (TDMA) scheme and PoF technology are leveraged to carry out joint dynamic bandwidth allocation (DBA) and provide enough power for the sleep schedule in each integrated optical network unit mesh portal point (ONU-MPP) branch. Additionally, the correlation between the transmitted optical power of the optical line terminal (OLT) and the quality of experience (QoE) guarantee caused by multiple hops in the wireless frontend is taken into consideration in detail. The research results prove that the envisioned paradigm can significantly reduce the energy consumption of the whole FiWi system while satisfying the average delay constraints, thus providing enough survivability for multimode optical fiber.

Description: The core of agricultural development depends on agricultural production efficiency improvement, and total-factor productivity growth is its significant embodiment. Hence, it is essential to address the question of “how to improve China’s agricultural productivity and efficiency in order to achieve growth and sustainability of agriculture in the future”. This paper estimates indices of China’s agricultural technical efficiency (TE) scores, total-factor productivity (TFP), and its two components, technological change/progress (TC) and technical efficiency change (EC), using provincial-level panel data of 30 provinces from 2002 to 2017 by applying a stochastic frontier approach (SFA). The paper also identifies determinants of TE, TC, and TFP using selected indicators from four hierarchical levels of the economy, i.e., farm level, production environment level, provincial level, and the state level, by applying a system-GMM method. Results reveal that agricultural labor, machinery, agricultural plastic film, and pesticides are the significant drivers of agricultural productivity, with no significant role of land area under cultivation. Constant returns to scale exist in China’s agriculture. The agricultural technical efficiency level fluctuated between 80% and 91% with a stable trend and a slight decline in later years, while TFP improved consistently over time, mainly driven by technological progress. Among the determinants, government investment in agricultural development projects significantly drives TC and TE, while the experienced labor force significantly increases TE. The disaster rate significantly reduces TE but promotes TC and TFP. The literacy rate significantly improves TC and TFP. However, government expenditures in “agriculture, forestry, and water” significantly reduce TE, TC, and TFP. Policy recommendations include (1) increased levels of mechanization and agriculture film use while avoiding an increase in pesticide use, (2) a continued increase in government expenditure in agricultural development projects, R&D to improve technological progress, and diffusion of modern agricultural technologies, and (3) investment in education targeted at the farming population in order to continue the growth in the productivity and sustainability of China’s agriculture.

Description: To sustain agricultural development in Africa, innovative strategies for addressing a myriad of biotic and abiotic constraints facing the agricultural systems must be established. One current biotic stress is the mango infesting fruit flies. In the effort to contain the widely spreading and damaging invasive species of tephritid fruit fly (Bactrocera dorsalis) (Hendel), an area-wide integrated pest management (IPM) program is being promoted in the horticultural sub-sector in sub-Saharan Africa. Such a new technology in which farmers have limited information before commercialization may have diffusion paths that are different from the often-assumed sigmoid (or “s”) shaped curve. We apply the descriptive and econometric analysis of ex ante and ex post integrated fruit fly management used by mango farmers in Kenya and Ethiopia. The results reveal that this technology has a relatively high adoption rate and high prospects for adoption growth in Kenya compared to Ethiopia in the near future.

Description: Performance degradation prediction plays a key role in realizing aviation pump health management and condition-based maintenance. Thus, this paper proposes a new approach that combines a Gaussian mixture model (GMM) and optimized support vector regression (SVR) to predict aviation pumps’ degradation processes based on the pump outlet pressure signals. Different from other feature extraction methods in which the information of intrinsic mode functions (IMFs) is not fully utilized, some useful IMF components are firstly chosen, and the corresponding multi-domain features are extracted from each selected component. Considering that it is not the case that all features are equally sensitive to degradation assessment, PCA is used to select more sensitive degradation features. Since the distribution of these extracted features is a stochastic process in feature space, meanwhile, self-information quantity can describe the uncertainty of system by measuring the average information quantity contained in the probability distribution, self-information quantity based on GMM is defined as degradation index (DI) to describe the degradation degree of the pump quantitatively. Finally, an SVR model is constructed to predict the degradation status of the pump. To achieve higher prediction accuracy, phase space reconstruction theory is first employed to determine the number of the inputs of the SVR model, then a new method combining particle swarm optimization (PSO) with grid search (GS) is developed to optimize the parameters of the SVR model. Finally, both the online data and historical data are utilized for the construction of the SVR model, respectively. The effectiveness of the proposed approach is validated by full life cycle data collected from an aviation pump test rig. The results demonstrate that the DI extracted from pump outlet pressure signals can effectively identify and track the current deterioration stage, and the established SVR model has better prediction ability when compared with previously published methods.

Description: A soil tillage system adjusted to the soil type and agro-ecological conditions, together with the optimal nutrient supply, is a prerequisite for high maize (Zea mays L.) yield. However, there is little knowledge about the influence of soil tillage and nitrogen (N) rates on maize grain quality. A study was initiated in 1978 in Zemun Polje, Belgrade, Serbia, on the chernozem soil type. The effects of three N rates—0, 180 and 240 kg N ha−1—within three tillage practices—no-tillage, reduced and conventional tillage—and their influence on grain yield (GY) and the content of proteins, starch, oil, total tocopherols and carotenoids, phytic phosphorus (Phy), glutathione (GSH), phenols and inorganic P (Pi) in maize grain were analysed in the period 2016–2018. Differences in maize GY were 2.57 and 3.01 t ha−1 for reduced- or no-tillage, respectively, in comparison to conventional tillage. Conventional tillage and higher N rates contributed to the increase in maize yield and the grain nutritional quality with regard to the increased level of proteins, GSH and Phy. On the other hand reduced inputs were beneficial only for grain quality, in relation to the increase in tocopherols and phenols.

Description: ×Amarine hybrids are attractive ornamental geophytes grown for cut flower production. Their cultivation is limited due to lesser flowering percentages and lesser bulb weight gain. To optimize the growth and propagation of geophytes, plant growth regulators (PGRs) are used, but so far none have been tested in ×Amarine. We investigated the effect of gibberellic acid (GA3; 50, 100, and 200 mg dm−3) and methyl jasmonate (MeJA; 100, 500, and 1000 µmol dm−3) on growth, flowering, bulb yield, and select physiological parameters of ×A. tubergenii “Zwanenburg”. PGRs were applied as foliar sprays on the 70th and 77th day after planting. GA3 treatment at 200 mg dm−3 exhibited the greatest leaf number, leaf length, bulb weight, daughter bulb number, CO2 assimilation intensity, greenness index, total sugars, and total protein content in bulbs. GA3 application at 100 and 200 mg dm−3 accelerated flowering and at 50 and 100 mg dm−3 significantly increased the bulb flowering percentage. MeJA at all tested concentrations prolonged anthesis time and reduced the bulb flowering percentage. GA3 at all concentrations and MeJA at 500 and 1000 µmol dm−3 stimulated daughter bulbs formation. GA3, especially at 200 mg dm−3 can improve anthesis and increase ×A. tubergenii “Zwanenburg” bulb yield.

Description: Research Highlights: A low-cost experimental system was developed to enable the production monitoring of small-scale wood processing facilities by the means of sensor-collected data and the implementation of artificial intelligence (AI) techniques, which provided accurate results for the most important work operations. Background and Objectives: The manufacturing of wood-based products by small-scale family-held business is commonly affected by a lack of monitoring data that, on the one hand, may prevent the decision-making process and, on the other hand, may lead to less technical efficiency that could result in business failure. Long-term performance of such manufacturing facilities is limited because data collection and analysis require significant resources, thus preventing the approaches that could be pursued for competitivity improvement. Materials and Methods: An external sensor system composed of two dataloggers—a triaxial accelerometer and a sound pressure level meter—was used in combination with a video camera to provide the input signals and meta-documentation for the training and testing of an artificial neural network (ANN) to check the accuracy of automatic classification of the time spent in operations. The study was based on a sample of ca. 90 k observations collected at a frequency of 1 Hz. Results: The approach provided promising results in both the training (ca. 20 k) and testing (ca. 60 k) datasets, with global classification accuracies of ca. 85%. However, the events characterizing the effective sawing, which requires electrical power, were even better recognized, reaching a classification accuracy of 98%. Conclusions: The system requires low-cost devices and freely available software that could enable data feeding on local computers by their direct connection to the devices. As such, it could collect, analyze and plot production data that could be used for maintaining the competitiveness of traditional technologies.

Description: Plants have evolved photoprotective mechanisms in order to counteract the damaging effects of excess light in hyper-arid desert environments. We evaluated the impact of surface canopy positions on the photosynthetic adjustments and chlorophyll fluorescence attributes (photosystem II photochemistry, quantum yield, fluorescence quenching, and photon energy dissipation), leaf biomass and nutrient content of sun-exposed leaves at the south east (SE canopy position) and shaded-leaves at the north west (NW canopy position) in the invasive Prosopis juliflora and native Prosopis cineraria in the extreme environment (hyper-arid desert area, United Arab Emirates (UAE)). The main aim of this research was to study the photoprotection mechanism in invasive and native Prosopis congeners via the safe removal—as thermal energy—of excess solar energy absorbed by the light collecting system, which counteracts the formation of reactive oxygen species. Maximum photosynthetic efficiency (Fv/Fm) from dark-adapted leaves in P. juliflora and P. cineraria was higher on NW than SE canopy position while insignificant difference was observed within the two Prosopis congeners. Greater quantum yield was observed in P. juliflora than P. cineraria on the NW canopy position than SE. With the change of canopy positions from NW to SE, the reduction of the PSII reaction center activity in the leaves of both Prosopis congeners was accelerated. On the SE canopy position, a significant decline in the electron transport rate (ETR) of in the leaves of both Prosopis congeners occurred, which might be due to the blockage of electron transfer from QA to QB on the PSII acceptor side. On the SE canopy position; Prosopis leaves dissipated excess light energy by increasing non-photochemical quenching (NPQ). However, in P. cineraria, the protective ability of NPQ decreased, which led to the accumulation of excess excitation energy (1 − qP)/NPQ and the aggravation of photoinhibition. The results also explain the role of different physiological attributes contributing to invasiveness of P. juliflora and to evaluate its liaison between plasticity of these characters and invasiveness.

Description: Sensor-based human activity recognition has various applications in the arena of healthcare, elderly smart-home, sports, etc. There are numerous works in this field—to recognize various human activities from sensor data. However, those works are based on data patterns that are clean data and have almost no missing data, which is a genuine concern for real-life healthcare centers. Therefore, to address this problem, we explored the sensor-based activity recognition when some partial data were lost in a random pattern. In this paper, we propose a novel method to improve activity recognition while having missing data without any data recovery. For the missing data pattern, we considered data to be missing in a random pattern, which is a realistic missing pattern for sensor data collection. Initially, we created different percentages of random missing data only in the test data, while the training was performed on good quality data. In our proposed approach, we explicitly induce different percentages of missing data randomly in the raw sensor data to train the model with missing data. Learning with missing data reinforces the model to regulate missing data during the classification of various activities that have missing data in the test module. This approach demonstrates the plausibility of the machine learning model, as it can learn and predict from an identical domain. We exploited several time-series statistical features to extricate better features in order to comprehend various human activities. We explored both support vector machine and random forest as machine learning models for activity classification. We developed a synthetic dataset to empirically evaluate the performance and show that the method can effectively improve the recognition accuracy from 80.8% to 97.5%. Afterward, we tested our approach with activities from two challenging benchmark datasets: the human activity sensing consortium (HASC) dataset and single chest-mounted accelerometer dataset. We examined the method for different missing percentages, varied window sizes, and diverse window sliding widths. Our explorations demonstrated improved recognition performances even in the presence of missing data. The achieved results provide persuasive findings on sensor-based activity recognition in the presence of missing data.