ALBERT

Description: Gait is a characteristic that has been utilized for identifying individuals. As human gait information is now able to be captured by several types of devices, many studies have proposed biometric identification methods using gait information. As research continues, the performance of this technology in terms of identification accuracy has been improved by gathering information from multi-modal sensors. However, in past studies, gait information was collected using ancillary devices while the identification accuracy was not high enough for biometric identification. In this study, we propose a deep learning-based biometric model to identify people by their gait information collected through a wearable device, namely an insole. The identification accuracy of the proposed model when utilizing multi-modal sensing is over 99%.

Description: With the Internet of Things (IoT), the number of monitoring applications deployed is considerably increasing, whatever the field considered: smart city, smart agriculture, environment monitoring, air pollution monitoring, to name a few. The LoRaWAN (Long Range Wide Area Network)architecture with its long range communication, its robustness to interference and its reduced energy consumption is an excellent candidate to support such applications. However, if the number of end devices is high, the reliability of LoRaWAN, measured by the Packet Delivery Ratio (PDR), becomes unacceptable due to an excessive number of collisions. In this paper, we propose two different families of solutions ensuring collision-free transmissions. The first family is TDMA (Time-Division Multiple Access)-based. All clusters transmit in sequence and up to six end devices with different spreading factors belonging to the same cluster are allowed to transmit in parallel. The second family is FDMA (Frequency Divsion Multiple Access)-based. All clusters transmit in parallel, each cluster on its own frequency. Within each cluster, all end devices transmit in sequence. Their performance are compared in terms of PDR, energy consumption by end device and maximum number of end devices supported. Simulation results corroborate the theoretical results and show the high efficiency of the solutions proposed.

Description: Federated learning (FL) is a privacy-preserving technique for training a vast amount of decentralized data and making inferences on mobile devices. As a typical language modeling problem, mobile keyboard prediction aims at suggesting a probable next word or phrase and facilitating the human-machine interaction in a virtual keyboard of the smartphone or laptop. Mobile keyboard prediction with FL hopes to satisfy the growing demand that high-level data privacy be preserved in artificial intelligence applications even with the distributed models training. However, there are two major problems in the federated optimization for the prediction: (1) aggregating model parameters on the server-side and (2) reducing communication costs caused by model weights collection. To address the above issues, traditional FL methods simply use averaging aggregation or ignore communication costs. We propose a novel Federated Mediation (FedMed) framework with the adaptive aggregation, mediation incentive scheme, and topK strategy to address the model aggregation and communication costs. The performance is evaluated in terms of perplexity and communication rounds. Experiments are conducted on three datasets (i.e., Penn Treebank, WikiText-2, and Yelp) and the results demonstrate that our FedMed framework achieves robust performance and outperforms baseline approaches.

Description: High frequency and spatially explicit irrigated land maps are important for understanding the patterns and impacts of consumptive water use by agriculture. We built annual, 30 m resolution irrigation maps using Google Earth Engine for the years 1986–2018 for 11 western states within the conterminous U.S. Our map classifies lands into four classes: irrigated agriculture, dryland agriculture, uncultivated land, and wetlands. We built an extensive geospatial database of land cover from each class, including over 50,000 human-verified irrigated fields, 38,000 dryland fields, and over 500,000 km 2 of uncultivated lands. We used 60,000 point samples from 28 years to extract Landsat satellite imagery, as well as climate, meteorology, and terrain data to train a Random Forest classifier. Using a spatially independent validation dataset of 40,000 points, we found our classifier has an overall binary classification (irrigated vs. unirrigated) accuracy of 97.8%, and a four-class overall accuracy of 90.8%. We compared our results to Census of Agriculture irrigation estimates over the seven years of available data and found good overall agreement between the 2832 county-level estimates (r 2 = 0.90), and high agreement when estimates are aggregated to the state level (r 2 = 0.94). We analyzed trends over the 33-year study period, finding an increase of 15% (15,000 km 2 ) in irrigated area in our study region. We found notable decreases in irrigated area in developing urban areas and in the southern Central Valley of California and increases in the plains of eastern Colorado, the Columbia River Basin, the Snake River Plain, and northern California.

Description: Due to the influence of equipment instability and surveying environment, scattering echoes and other factors, it is sometimes difficult to obtain high-quality sub-bottom profile (SBP) images by traditional denoising methods. In this paper, a novel SBP image denoising method is developed for obtaining underlying clean images based on a non-local low-rank framework. Firstly, to take advantage of the inherent layering structures of the SBP image, a direction image is obtained and used as a guidance image. Secondly, the robust guidance weight for accurately selecting the similar patches is given. A novel denoising method combining the weight and a non-local low-rank filtering framework is proposed. Thirdly, after discussing the filtering parameter settings, the proposed method is tested in actual measurements of sub-bottom, both in deep water and shallow water. Experimental results validate the excellent performance of the proposed method. Finally, the proposed method is verified and compared with other methods quantificationally based on the synthetic images and has achieved the total average peak signal-to-noise ratio (PSNR) of 21.77 and structural similarity index (SSIM) of 0.573, which is far better than other methods.

Description: We present analyses of Global Navigation Satellite System (GNSS) carrier phase observations in multiple kinematic scenarios for different receiver types. Multi-GNSS observations are recorded on high sensitivity and geodetic-grade receivers operating on a moving zero-baseline by conducting terrestrial urban and aerial flight experiments. The captured data is post-processed; carrier phase residuals are computed using the double difference (DD) concept. The estimated noise levels of carrier phases are analysed with respect to different parameters. We find DD noise levels for L1 carrier phase observations in the range of 1.4–2 mm (GPS, Global Positioning System), 2.8–4.6 mm (GLONASS, Global Navigation Satellite System), and 1.5–1.7 mm (Galileo) for geodetic receiver pairs. The noise level for high sensitivity receivers is at least higher by a factor of 2. For satellites elevating above 30 ∘ , the dominant noise process is white phase noise. For the flight experiment, the elevation dependency of the noise is well described by the exponential model, while for the terrestrial urban experiment, multipath and diffraction effects overlay; hence no elevation dependency is found. For both experiments, a carrier-to-noise density ratio (C/N 0 ) dependency for carrier phase DDs of GPS and Galileo is clearly visible with geodetic-grade receivers. In addition, C/N 0 dependency is also visible for carrier phase DDs of GLONASS with geodetic-grade receivers for the terrestrial urban experiment.

Description: Hemoglobinopathies represent the most common single-gene defects in the world and pose a major public health problem, particularly in tropical countries, where they occur with high frequency. Diagnosing hemoglobinopathies can sometimes be difficult due to the coexistence of different causes of anemia, such as thalassemia and iron deficiency, and blood transfusions, among other factors, and requires expensive and complex molecular tests. This work explores the possibility of using spectral confocal microscopy as a diagnostic tool for thalassemia in pediatric patients, a disease caused by mutations in the globin genes that result in changes of the globin chains that form hemoglobin—in pediatric patients. Red blood cells (RBCs) from patients with different syndromes of alpha-thalassemia and iron deficiency (including anemia) as well as healthy (control) subjects were analyzed under a Leica TCS SP8 confocal microscope following different image acquisition protocols. We found that diseased RBCs exhibited autofluorescence when excited at 405 nm and their emission was collected in the spectral range from 425 nm to 790 nm. Three experimental descriptors calculated from the mean emission intensities at 502 nm, 579 nm, 628 nm, and 649 nm allowed us to discriminate between diseased and healthy cells. According to the results obtained, spectral confocal microscopy could serve as a tool in the diagnosis of thalassemia.

Description: River discharge is a fundamental hydrologic quantity that summarizes how a watershed transforms the input of precipitation into output as channelized streamflow [...]

Description: Violent tornadoes are rare in Europe but they can have devastating effects. Damage associated with individual tornadoes can reach several billion euros and they have caused hundreds of fatalities. The tornado risk varies considerably over Europe, but so far only a few national maps of tornado risk and one Europe-wide map exist. We show several different ways to create quantitative maps of tornado occurrence rates as follows: Kernel smoothing of observations, climatologies of convective parameters from reanalysis, output of a logistic regression model to link convective parameters with observed tornadoes, orography-dependent climatologies and finally the population-bias corrected tornado occurrence rates from the Risk Management Solutions (RMS) Europe Severe Convective Storm Model. We discuss advantages and disadvantages of each approach and compare the results. While the climatologies created from the individual methods show a lot of qualitative similarities, we advocate to combine the methods to achieve the most reliable quantitative climatology.

Description: Dryland ecosystems are frequently struck by droughts. Yet, woody vegetation is often able to recover from mortality events once precipitation returns to pre-drought conditions. Climate change, however, may impact woody vegetation resilience due to more extreme and frequent droughts. Thus, better understanding how woody vegetation responds to drought events is essential. We used a phenology-based remote sensing approach coupled with field data to estimate the severity and recovery rates of a large scale die-off event that occurred in 2014–2015 in Senegal. Novel low (L-band) and high-frequency (Ku-band) passive microwave vegetation optical depth (VOD), and optical MODIS data, were used to estimate woody vegetation dynamics. The relative importance of soil, human-pressure, and before-drought vegetation dynamics influencing the woody vegetation response to the drought were assessed. The die-off in 2014–2015 represented the highest dry season VOD drop for the studied period (1989–2017), even though the 2014 drought was not as severe as the droughts in the 1980s and 1990s. The spatially explicit Die-off Severity Index derived in this study, at 500 m resolution, highlights woody plants mortality in the study area. Soil physical characteristics highly affected die-off severity and post-disturbance recovery, but pre-drought biomass accumulation (i.e., in areas that benefited from above-normal rainfall conditions before the 2014 drought) was the most important variable in explaining die-off severity. This study provides new evidence supporting a better understanding of the “greening Sahel”, suggesting that a sudden increase in woody vegetation biomass does not necessarily imply a stable ecosystem recovery from the droughts in the 1980s. Instead, prolonged above-normal rainfall conditions prior to a drought may result in the accumulation of woody biomass, creating the basis for potentially large-scale woody vegetation die-off events due to even moderate dry spells.

Description: Collaborative representation (CR)-based classification has been successfully applied to plant disease recognition in cases with sufficient training samples of each disease. However, collecting enough training samples is usually time consuming and labor-intensive. Moreover, influenced by the non-ideal measurement environment, samples may be corrupted by variables introduced by bad illumination and occlusions of adjacent leaves. Consequently, an extended collaborative representation (ECR)-based classification model is presented in this paper. Then, it is applied to cucumber leaf disease recognition, which constructs a pure spectral library consisting of several representative samples for each disease and designs a universal variation spectral library that deals with linear variables superimposed on samples. Thus, each query sample is encoded as a linear combination of atoms from these two spectral libraries and disease identity is determined by the disease of minimal reconstruction residuals. Experiments are conducted on spectral curves extracted from normal leaves and the disease lesions of leaves infected with cucumber anthracnose and brown spot. The diagnostic accuracy is higher than 94.7% and the average online diagnosis time is short, about 1 to 1.3 ms. The results indicate that the ECR-based classification model is feasible in the fast and accurate diagnosis of cucumber leaf diseases.

Description: Photoacoustic imaging (PAI) combines optical contrast with ultrasound spatial resolution and can be obtained up to a depth of a few centimeters. Hand-held PAI systems using linear array usually operate in reflection mode using a dark-field illumination scheme, where the optical fiber output is attached to both sides of the elevation plane (short-axis) of the transducer. More recently, bright-field strategies where the optical illumination is coaxial with acoustic detection have been proposed to overcome some limitations of the standard dark-field approach. In this paper, a novel multiangle long-axis lateral illumination is proposed. Monte Carlo simulations were conducted to evaluate light delivery for three different illumination schemes: bright-field, standard dark-field, and long-axis lateral illumination. Long-axis lateral illumination showed remarkable improvement in light delivery for targets with a width smaller than the transducer lateral dimension. A prototype was developed to experimentally demonstrate the feasibility of the proposed approach. In this device, the fiber bundle terminal ends are attached to both sides of the transducer’s long-axis and the illumination angle of each fiber bundle can be independently controlled. The final PA image is obtained by the coherent sum of subframes acquired using different angles. The prototype was experimentally evaluated by taking images from a phantom, a mouse abdomen, forearm, and index finger of a volunteer. The system provided light delivery enhancement taking advantage of the geometry of the target, achieving sufficient signal-to-noise ratio at clinically relevant depths.

Description: Longitudinal and perpendicular changes in the vocal fold’s blood vessels are associated with the development of benign and malignant laryngeal lesions. The combination of Contact Endoscopy (CE) and Narrow Band Imaging (NBI) can provide intraoperative real-time visualization of the vascular changes in the laryngeal mucosa. However, the visual evaluation of vascular patterns in CE-NBI images is challenging and highly depends on the clinicians’ experience. The current study aims to evaluate and compare the performance of a manual and an automatic approach for laryngeal lesion’s classification based on vascular patterns in CE-NBI images. In the manual approach, six observers visually evaluated a series of CE+NBI images that belong to a patient and then classified the patient as benign or malignant. For the automatic classification, an algorithm based on characterizing the level of the vessel’s disorder in combination with four supervised classifiers was used to classify CE-NBI images. The results showed that the manual approach’s subjective evaluation could be reduced by using a computer-based approach. Moreover, the automatic approach showed the potential to work as an assistant system in case of disagreements among clinicians and to reduce the manual approach’s misclassification issue.

Description: Twenty-eight quartz crystal microbalance (QCM) sensors coated with different sensing films were tested and analyzed in this work; twenty-three sensors were coated in different room temperature ionic liquids (RTILs) and five additional QCM sensors were coated with conventional films commonly used as stationary phases in gas chromatography. Four volatile organic compounds (VOCs), in gaseous phase—hexanol, butyl acetate, 2-hexanone, and hexanoic acid—were measured. Two transducer mechanisms were used; resonant frequency shift and resistance shift of a QCM Mason equivalent circuit. The sensors were characterized by their sensitivity to the VOCs and their discrimination power of the four VOCs. The highest separation among VOCs was obtained when frequency and resistance information of both RTIL and conventional films was used, a sensor array composed by two RTILs (1-butyl-1-methylpyrrolidinium bis(trifluoromethanesulfonyl)imide and 1-hexyl-3-methylimidazolium hexafluorophosphate) and two conventional films (tricresyl phosphate and apiezon-L) was found to improve the Wilks lambda separation for the tested gases two orders of magnitude compared to the Wilks lambda using only a conventional films array.

Description: Augmented reality can improve construction and facility management by visualizing an as-planned model on its corresponding surface for fast, easy, and correct information retrieval. This requires the localization registration of an as-built model in an as-planned model. However, the localization and registration of indoor environments fail, owing to self-similarity in an indoor environment, relatively large as-planned models, and the presence of additional unplanned objects. Therefore, this paper proposes a computer vision-based method to (1) homogenize indoor as-planned and as-built models, (2) reduce the search space of model matching, and (3) localize the structure (e.g., room) for registration of the scanned area in its as-planned model. This method extracts a representative horizontal cross section from the as-built and as-planned point clouds to make these models similar, restricts unnecessary transformation to reduce the search space, and corresponds the line features for the estimation of the registration transformation matrix. The performance of this method, in terms of registration accuracy, is evaluated on as-built point clouds of rooms and a hallway on a building floor. A rotational error of 0.005 rad and a translational error of 0.088 m are observed in the experiments. Hence, the geometric feature described on a representative cross section with transformation restrictions can be a computationally cost-effective solution for indoor localization and registration.

Description: Emotion recognition has increased the potential of affective computing by getting an instant feedback from users and thereby, have a better understanding of their behavior. Physiological sensors have been used to recognize human emotions in response to audio and video content that engages single (auditory) and multiple (two: auditory and vision) human senses, respectively. In this study, human emotions were recognized using physiological signals observed in response to tactile enhanced multimedia content that engages three (tactile, vision, and auditory) human senses. The aim was to give users an enhanced real-world sensation while engaging with multimedia content. To this end, four videos were selected and synchronized with an electric fan and a heater, based on timestamps within the scenes, to generate tactile enhanced content with cold and hot air effect respectively. Physiological signals, i.e., electroencephalography (EEG), photoplethysmography (PPG), and galvanic skin response (GSR) were recorded using commercially available sensors, while experiencing these tactile enhanced videos. The precision of the acquired physiological signals (including EEG, PPG, and GSR) is enhanced using pre-processing with a Savitzky-Golay smoothing filter. Frequency domain features (rational asymmetry, differential asymmetry, and correlation) from EEG, time domain features (variance, entropy, kurtosis, and skewness) from GSR, heart rate and heart rate variability from PPG data are extracted. The K nearest neighbor classifier is applied to the extracted features to classify four (happy, relaxed, angry, and sad) emotions. Our experimental results show that among individual modalities, PPG-based features gives the highest accuracy of 78.57 % as compared to EEG- and GSR-based features. The fusion of EEG, GSR, and PPG features further improved the classification accuracy to 79.76 % (for four emotions) when interacting with tactile enhanced multimedia.

Description: Individual spacecraft manual navigation by human operators from ground station is expected to be an emerging problem as the number of spacecraft for space exploration increases. Hence, as an attempt to reduce the burden to control multiple spacecraft, future missions will employ smart spacecraft able to navigate and operate autonomously. Recently, image-based optical navigation systems have proved to be promising solutions for inexpensive autonomous navigation. In this paper, we propose a robust image processing pipeline for estimating the center and radius of planets and moons in an image taken by an on-board camera. Our custom image pre-processing pipeline is tailored for resource-constrained applications, as it features a computationally simple processing flow with a limited memory footprint. The core of the proposed pipeline is a best-fitting model based on the RANSAC algorithm that is able to handle images corrupted with Gaussian noise, image distortions, and frame drops. We report processing time, pixel-level error of estimated body center and radius and the effect of noise on estimated body parameters for a dataset of synthetic images.

Description: An alternative to the Carnot-Clausius approach for introducing entropy and the second law of thermodynamics is outlined that establishes entropy as a nonequilibrium property from the onset. Five simple observations lead to entropy for nonequilibrium and equilibrium states, and its balance. Thermodynamic temperature is identified, its positivity follows from the stability of the rest state. It is shown that the equations of engineering thermodynamics are valid for the case of local thermodynamic equilibrium, with inhomogeneous states. The main findings are accompanied by examples and additional discussion to firmly imbed classical and engineering thermodynamics into nonequilibrium thermodynamics.

Description: Region of interest (ROI) localization is one of the key preprocessing technologies for a finger-vein identification system, so an effective ROI definition can improve the matching accuracy. However, due to the impact of uneven illumination, equipment noise, as well as the distortion of finger position, etc., these make accurate ROI localization a very difficult task. To address these issues, in this paper, we propose a robust finger-vein ROI localization method, which is based on the 3 σ criterion dynamic threshold strategy. The proposed method includes three main steps: First, the Kirsch edge detector is introduced to detect the horizontal-like edges in the acquired finger-vein image. Then, the obtained edge gradient image is divided into four parts: upper-left, upper-right, lower-left, and lower-right. For each part of the image, the three-level dynamic threshold, which is based on the 3 σ criterion of the normal distribution, is imposed to obtain more distinct and complete edge information. Finally, through labeling the longest connected component at the same horizontal line, two reliable finger boundaries, which represent the upper and lower boundaries, respectively, are defined, and the ROI is localized in the region between these two boundaries. Extensive experiments are carried out on four different finger-vein image datasets, including three publicly available datasets and one of our newly developed finger-vein datasets with 37,080 finger-vein samples and 1030 individuals. The experimental results indicate that our proposed method has very competitive ROI localization performance, as well as satisfactory matching results on different datasets.

Description: Thermal inspection is a powerful tool that enables the diagnosis of several components at its early stages. One critical aspect that influences thermal inspection outputs is the infrared reflection from external sources. This situation may change the readings, demanding that an expert correctly define the camera position, which is a time consuming and expensive operation. To mitigate this problem, this work proposes an autonomous system capable of identifying infrared reflections by filtering and fusing data obtained from both stereo and thermal cameras. The process starts by acquiring readings from multiples Observation Points (OPs) where, at each OP, the system processes the 3D point cloud and thermal image by fusing them together. The result is a dense point cloud where each point has its spatial position and temperature. Considering that each point’s information is acquired from multiple poses, it is possible to generate a temperature profile of each spatial point and filter undesirable readings caused by interference and other phenomena. To deploy and test this approach, a Directional Robotic System (DRS) is mounted over a traditional human-operated service vehicle. In that way, the DRS autonomously tracks and inspects any desirable equipment as the service vehicle passes them by. To demonstrate the results, this work presents the algorithm workflow, a proof of concept, and a real application result, showing improved performance in real-life conditions.

Description: Surface electromyographic signal (sEMG) is a kind of bioelectrical signal, which records the data of muscle activity intensity. Most sEMG-based hand gesture recognition, which uses machine learning as the classifier, depends on feature extraction of sEMG data. Recently, a deep leaning-based approach such as recurrent neural network (RNN) has provided a choice to automatically learn features from raw data. This paper presents a novel hand gesture prediction method by using an RNN model to learn from raw sEMG data and predict gestures. The sEMG signals of 21 short-term hand gestures of 13 subjects were recorded with a Myo armband, which is a non-intrusive, low cost, commercial portable device. At the start of the gesture, the trained model outputs an instantaneous prediction for the sEMG data. Experimental results showed that the more time steps of data that were known, the higher instantaneous prediction accuracy the proposed model gave. The predicted accuracy reached about 89.6% when the data of 40-time steps (200 ms) were used to predict hand gesture. This means that the gesture could be predicted with a delay of 200 ms after the hand starts to perform the gesture, instead of waiting for the end of the gesture.

Description: Salt marshes provide important services to coastal ecosystems in the southeastern United States. In many locations, salt marsh habitats are threatened by coastal development and erosion, necessitating large-scale monitoring. Assessing vegetation height across the extent of a marsh can provide a comprehensive analysis of its health, as vegetation height is associated with Above Ground Biomass (AGB) and can be used to track degradation or growth over time. Traditional methods to do this, however, rely on manual measurements of stem heights that can cause harm to the marsh ecosystem. Moreover, manual measurements are limited in scale and are often time and labor intensive. Unoccupied Aircraft Systems (UAS) can provide an alternative to manual measurements and generate continuous results across a large spatial extent in a short period of time. In this study, a multirotor UAS equipped with optical Red Green Blue (RGB) and multispectral sensors was used to survey five salt marshes in Beaufort, North Carolina. Structure-from-Motion (SfM) photogrammetry of the resultant imagery allowed for continuous modeling of the entire marsh ecosystem in a three-dimensional space. From these models, vegetation height was extracted and compared to ground-based manual measurements. Vegetation heights generated from UAS data consistently under-predicted true vegetation height proportionally and a transformation was developed to predict true vegetation height. Vegetation height may be used as a proxy for Above Ground Biomass (AGB) and contribute to blue carbon estimates, which describe the carbon sequestered in marine ecosystems. Employing this transformation, our results indicate that UAS and SfM are capable of producing accurate assessments of salt marsh health via consistent and accurate vegetation height measurements.

Description: Accounting for endmember variability is a challenging issue when unmixing hyperspectral data. This paper models the variability that is associated with each endmember as a conical hull defined by extremal pixels from the data set. These extremal pixels are considered as so-called prototypal endmember spectra that have meaningful physical interpretation. Capitalizing on this data-driven modeling, the pixels of the hyperspectral image are then described as combinations of these prototypal endmember spectra weighted by bundling coefficients and spatial abundances. The proposed unmixing model not only extracts and clusters the prototypal endmember spectra, but also estimates the abundances of each endmember. The performance of the approach is illustrated thanks to experiments conducted on simulated and real hyperspectral data and it outperforms state-of-the-art methods.

Description: Assessments of long-term changes of air quality and global radiative forcing at a large scale heavily rely on satellite aerosol optical depth (AOD) datasets, particularly their temporal binning products. Although some attempts focusing on the validation of long-term satellite AOD have been conducted, there is still a lack of comprehensive quantification and understanding of the representativeness of satellite AOD at different temporal binning scales. Here, we evaluated the performances of the Moderate Resolution Imaging Spectroradiometer (MODIS) AOD products at various temporal scales by comparing the MODIS AOD datasets from both the Terra and Aqua satellites with the entire global AErosol RObotic NETwork (AERONET) observation archive between 2000 and 2017. The uncertainty levels of the MODIS hourly and daily AOD products were similarly high, indicating that MODIS AOD retrievals could be used to represent daily aerosol conditions. The MODIS data showed the reduced quality when integrated from the daily to monthly scale, where the relative mean bias (RMB) changed from 1.09 to 1.21 for MODIS Terra and from 1.04 to 1.17 for MODIS Aqua, respectively. The limitation of valid data availability within a month appeared to be the primary reason for the increased uncertainties in the monthly binning products, and the monthly data associated uncertainties could be reduced when the number of valid AOD retrievals reached 15 times in one month. At all three temporal scales, the uncertainty levels of satellite AOD products decreased with increasing AOD values. The results of this study could provide crucial information for satellite AOD users to better understand the reliability of different temporal AOD binning products and associated uncertainties in their derived long-term trends.

Description: Human activities are mainly responsible for the Aral Sea crisis, and excessive farmland expansion and unreasonable irrigation regimes are the main manifestations. The conflicting needs of agricultural water consumption and ecological water demand of the Aral Sea are increasingly prominent. However, the quantitative relationship among the water balance elements in the oasis located in the lower reaches of the Amu Darya River Basin and their impact on the retreat of the Aral Sea remain unclear. Therefore, this study focused on the water consumption of the Nukus irrigation area in the delta of the Amu Darya River and analyzed the water balance variations and their impacts on the Aral Sea. The surface energy balance algorithm for land (SEBAL) was employed to retrieve daily and seasonal evapotranspiration (ET) levels from 1992 to 2018, and a water balance equation was established based on the results of a remote sensing evapotranspiration inversion. The results indicated that the actual evapotranspiration (ETa) simulated by the SEBAL model matched the crop evapotranspiration (ETc) calculated by the Penman–Monteith method well, and the correlation coefficients between the two ETa sources were greater than 0.8. The total ETa levels in the growing seasons decreased from 1992 to 2005 and increased from 2005 to 2015, which is consistent with the changes in the cultivated land area and inflows from the Amu Darya River. In 2000, 2005 and 2010, the groundwater recharge volumes into the Aral Sea during the growing season were 6.74×109 m3, 1.56×109 m3 and 8.40×109 m3; respectively; in the dry year of 2012, regional ET exceeded the river inflow, and 2.36×109 m3 of groundwater was extracted to supplement the shortage of irrigation water. There is a significant two-year lag correlation between the groundwater level and the area of the southern Aral Sea. This study can provide useful information for water resources management in the Aral Sea region.

Description: The reporting of U wave abnormalities is clinically important, but the measurement of this small electrocardiographic (ECG) feature is extremely difficult, especially in challenging recording conditions, such as stress exercise, due to contaminating noise. Furthermore, it is widely stated that ECG U waves are rarely observable at heart rates greater than 90 bpm. The aims of the study were (i) to assess the ability of multi-beat averaging to reveal the presence of U waves in ECGs contaminated by noise following exercise and (ii) to quantify the effect of exercise on U wave amplitude. The multi-beat averaging algorithm was applied to recover U waves in 20 healthy subjects in pre- and post-exercise recordings. Average beats were generated from 30 beat epochs. The prevalence of U waves and their amplitudes were measured in pre- and post-exercise recordings and changes in amplitude due to exercise were quantified. U waves were present in all subjects in pre-exercise recordings. Following exercise, U waves could not be seen in standard ECG but were observable in all 20 subjects by multi-beat averaging and despite significantly increased mean (±SD) heart rate (63 ± 8 bpm vs. 100 ± 9 bpm, p 〈 0.0001). Furthermore, U waves were observable in all subjects with heart rates greater than 90 bpm. U waves significantly increased in amplitude following exercise (38 ± 15 μV vs. 80 ± 48 μV, p = 0.0005). Multi-beat averaging is effective at recovering U waves contaminated by noise due to exercise. U waves were measurable in all subjects, dispelling the myth that U waves are rarely seen at elevated heart rates. U waves exhibit increased amplitudes at elevated heart rates following exercise.

Description: In the literature of pan-sharpening based on neural networks, high resolution multispectral images as ground-truth labels generally are unavailable. To tackle the issue, a common method is to degrade original images into a lower resolution space for supervised training under the Wald’s protocol. In this paper, we propose an unsupervised pan-sharpening framework, referred to as “perceptual pan-sharpening”. This novel method is based on auto-encoder and perceptual loss, and it does not need the degradation step for training. For performance boosting, we also suggest a novel training paradigm, called “first supervised pre-training and then unsupervised fine-tuning”, to train the unsupervised framework. Experiments on the QuickBird dataset show that the framework with different generator architectures could get comparable results with the traditional supervised counterpart, and the novel training paradigm performs better than random initialization. When generalizing to the IKONOS dataset, the unsupervised framework could still get competitive results over the supervised ones.

Description: More people appreciate the importance of global sustainability [...]

Description: Real-time monitoring of fruit ripeness in storage and during logistics allows traders to minimize the chances of financial losses and maximize the quality of the fruit during storage through accurate prediction of the present condition of fruits. In Pakistan, banana production faces different difficulties from production, post-harvest management, and trade marketing due to atmosphere and mismanagement in storage containers. In recent research development, Wireless Sensor Networks (WSNs) are progressively under investigation in the field of fruit ripening due to their remote monitoring capability. Focused on fruit ripening monitoring, this paper demonstrates an Xbee-based wireless sensor nodes network. The role of the network architecture of the Xbee sensor node and sink end-node is discussed in detail regarding their ability to monitor the condition of all the required diagnosis parameters and stages of banana ripening. Furthermore, different features are extracted using the gas sensor, which is based on diverse values. These features are utilized for training in the Artificial Neural Network (ANN) through the Back Propagation (BP) algorithm for further data validation. The experimental results demonstrate that the projected WSN architecture can identify the banana condition in the storage area. The proposed Neural Network (NN) architectural design works well with selecting the feature data sets. It seems that the experimental and simulation outcomes and accuracy in banana ripening condition monitoring in the given feature vectors is attained and acceptable, through the classification performance, to make a better decision for effective monitoring of current fruit condition.

Description: Retrievals of ice cloud properties require accurate estimates of ice particle mass. Empirical mass–dimensional (m–D) relationships in the form m = a D b are widely used and usually universally applied across the complete range of particle sizes. For the first time, the dependence of a and b coefficients in m–D relationships on median mass diameter (Dmm) is studied. Using combined cloud microphysical data collected during the Olympic Mountains Experiment and coincident observations from Airborne Precipitation Radar Third Generation, Dmm-dependent (a, b) coefficients are derived and represented as surfaces of equally plausible solutions determined by some tolerance in the chi-squared difference χ 2 that minimizes the difference between observed and retrieved radar reflectivity. Robust dependences of a and b on Dmm are shown with both parameters significantly decreasing with Dmm, leading to smaller effective densities for larger Dmm ranges. A universally applied constant m–D relationship overestimates the mass of large aggregates when Dmm is between 3–6 mm and temperatures are between −15–0 °C. Multiple m–D relations should be applied for different Dmm ranges in retrievals and simulations to account for the variability of particle sizes that are responsible for the mass and thus for the variability of particle shapes and densities.

Description: The visual fidelity of a virtual environment lacks the exceedingly complex layers from the physical world, but the continuous improvements of image rendering technology and computation powers have led to greater demands for virtual simulations. Our study employs Crime Prevention through Environmental Design (CPTED) as a risk control measure and utilizes two principles: Access Control and Natural Surveillance. We conducted an experiment with (n-sample: 100) graduate students. For the experiment, we utilized the Factor Analysis of Information Risk (FAIR) to quantitatively analyze the risk. Furthermore, we adopted the lme4 package for R to estimate the mixed effect of the 6,242,880 observations retrieved from Kaggle. Based on the two experiments, we were able to critically evaluate the contributions of CPTED through a multi-component analysis. Our study investigates how spatial syntax and territorial demarcation may translate in the cyberspace realm. We found that the corollaries of the mophology in the virtual environment effects the distribution of crime. The results of our study discusses how to determine the criminogenic designs and capacity in the cyberspace realm.

Description: Roadside vegetation can affect the performance of installed road lighting. We demonstrate a workflow in which a car-mounted measurement system is used to assess the light-obstructing effect of roadside vegetation. The mobile mapping system (MMS) includes a panoramic camera system, laser scanner, inertial measurement unit, and satellite positioning system. The workflow and the measurement system were applied to a road section of Munkkiniemenranta, Helsinki, Finland, in 2015 and 2019. The relative luminance distribution on a road surface and the obstructing vegetation were measured before and after roadside vegetation pruning applying a luminance-calibrated mobile mapping system. The difference between the two measurements is presented, and the opportunities provided by the mobile 3D luminance measurement system are discussed.

Description: Density-dependent population growth regulates long-term urban expansion and shapes distinctive socioeconomic trends. Despite a marked heterogeneity in the spatial distribution of the resident population, Mediterranean European countries are considered more homogeneous than countries in other European regions as far as settlement structure and processes of metropolitan growth are concerned. However, rising socioeconomic inequalities among Southern European regions reflect latent demographic and territorial transformations that require further investigation. An integrated assessment of the spatio-temporal distribution of resident populations in more than 1000 municipalities (1961–2011) was carried out in this study to characterize density-dependent processes of metropolitan growth in Greece. Using geographically weighted regressions, the results of our study identified distinctive local relationships between population density and growth rates over time. Our results demonstrate that demographic growth rates were non-linearly correlated with other variables, such as population density, with positive and negative impacts during the first (1961–1971) and the last (2001–2011) observation decade, respectively. These findings outline a progressive shift over time from density-dependent processes of population growth, reflecting a rapid development of large metropolitan regions (Athens, Thessaloniki) in the 1960s, to density-dependent processes more evident in medium-sized cities and accessible rural regions in the 2000s. Density-independent processes of population growth have been detected in the intermediate study period (1971–2001). This work finally discusses how a long-term analysis of demographic growth, testing for density-dependent mechanisms, may clarify the intrinsic role of population concentration and dispersion in different phases of the metropolitan cycle in Mediterranean Europe.

Description: The complexity and high sensitivity of proteins to environmental factors give rise to a multitude of variables, which affect the stabilization mechanisms in protein foams. Interfacial and foaming properties of proteins have been widely studied, but the reported unique effect of pH, which can be of great interest to applications, has been investigated to a lesser extent. In this paper, we focus on the impact of pH on the stability of black foam films and corresponding foams obtained from solutions of a model globular protein—the whey β-lactoglobulin (BLG). Foam stability was analyzed utilizing three characteristic parameters (deviation time, transition time and half-lifetime) for monitoring the foam decay, while foam film stability was measured in terms of the critical disjoining pressure of film rupture. We attempt to explain correlations between the macroscopic properties of a foam system and those of its major building blocks (foam films and interfaces), and thus, to identify structure-property relationships in foam. Good correlations were found between the stabilities of black foam films and foams, while relations to the properties of adsorption layers appeared to be intricate. That is because pH-dependent interfacial properties of proteins usually exhibit an extremum around the isoelectric point (pI), but the stability of BLG foam films increases with increasing pH (3–7), which is well reflected in the foam stability. We discuss the possible reasons behind these intriguingly different behaviors on the basis of pH-induced changes in the molecular properties of BLG, which seem to be determining the mechanism of film rupture at the critical disjoining pressure.

Description: The oxidation state and local atomic environment of admixtures of In, Cu, and Ag in synthetic sphalerite crystals were determined by X-ray absorption spectroscopy (XAS). The sphalerite crystals doped with In, Cu, Ag, In–Cu, and In–Ag were synthesized utilizing gas transport, salt flux, and dry synthesis techniques. Oxidation states of dopants were determined using X-ray absorption near edge structure (XANES) technique. The local atomic structure was studied by X-ray absorption fine structure spectroscopy (EXAFS). The spectra were recorded at Zn, In, Ag, and Cu K-edges. In all studied samples, In was in the 3+ oxidation state and replaced Zn in the structure of sphalerite, which occurs with the expansion of the nearest coordination shells due to the large In ionic radius. In the presence of In, the oxidation state of Cu and Ag is 1+, and both metals can form an isomorphous solid solution where they substitute for Zn according to the coupled substitution scheme 2Zn2+ ↔ Me+ + In3+. Moreover, Ag K-edges EXAFS spectra fitting, combined with the results obtained for In- and Au-bearing sphalerite shows that the Me-S distances in the first coordination shell in the solid solution state are correlated with the ionic radii and increase in the order of Cu 〈 Ag 〈 Au. The distortion of the atomic structure increases in the same order. The distant (second and third) coordination shells of Cu and Ag in sphalerite are split into two subshells, and the splitting is more pronounced for Ag. Analysis of the EXAFS spectra, coupled with the results of DFT (Density Function Theory) simulations, showed that the In–In and Me+–In3+ clustering is absent when the metals are present in the sphalerite solid solution. Therefore, all studied admixtures (In, Cu, Ag), as well as Au, are randomly distributed in the matrix of sphalerite, where the concentration of the elements in the “invisible” form can reach a few tens wt.%.

Description: In urban planning and transportation management, the centrality characteristics of urban streets are vital measures to consider. Centrality can help in understanding the structural properties of dense traffic networks that affect both human life and activity in cities. Many cities classify urban streets to provide stakeholders with a group of street guidelines for possible new rehabilitation such as sidewalks, curbs, and setbacks. Transportation research always considers street networks as a connection between different urban areas. The street functionality classification defines the role of each element of the urban street network (USN). Some potential factors such as land use mix, accessible service, design goal, and administrators’ policies can affect the movement pattern of urban travelers. In this study, nine centrality measures are used to classify the urban roads in four cities evaluating the structural importance of street segments. In our work, a Stacked Denoising Autoencoder (SDAE) predicts a street’s functionality, then logistic regression is used as a classifier. Our proposed classifier can differentiate between four different classes adopted from the U.S. Department of Transportation (USDT): principal arterial road, minor arterial road, collector road, and local road. The SDAE-based model showed that regular grid configurations with repeated patterns are more influential in forming the functionality of road networks compared to those with less regularity in their spatial structure.

Description: This study investigates the robustness of the physically-based hydrological model WaSiM (water balance and flow simulation model) for simulating hydrological processes in two data sparse small-scale inland valley catchments (Bankandi-Loffing and Mebar) in Burkina Faso. An intensive instrumentation with two weather stations, three rain recorders, 43 piezometers, and one soil moisture station was part of the general effort to reduce the scarcity of hydrological data in West Africa. The data allowed us to successfully parameterize, calibrate (2014–2015), and validate (2016) WaSiM for the Bankandi-Loffing catchment. Good model performance concerning discharge in the calibration period (R2 = 0.91, NSE = 0.88, and KGE = 0.82) and validation period (R2 = 0.82, NSE = 0.77, and KGE = 0.57) was obtained. The soil moisture (R2 = 0.7, NSE = 0.7, and KGE = 0.8) and the groundwater table (R2 = 0.3, NSE = 0.2, and KGE = 0.5) were well simulated, although not explicitly calibrated. The spatial transposability of the model parameters from the Bankandi-Loffing model was investigated by applying the best parameter-set to the Mebar catchment without any recalibration. This resulted in good model performance in 2014–2015 (R2 = 0.93, NSE = 0.92, and KGE = 0.84) and in 2016 (R2 = 0.65, NSE = 0.64, and KGE = 0.59). This suggests that the parameter-set achieved in this study can be useful for modeling ungauged inland valley catchments in the region. The water balance shows that evaporation is more important than transpiration (76% and 24%, respectively, of evapotranspiration losses) and the surface flow is very sensitive to the observed high interannual variability of rainfall. Interflow dominates the uplands, but base flow is the major component of stream flow in inland valleys. This study provides useful information for the better management of soil and scarce water resources for smallholder farming in the area.

Description: The frequency and intensity of flood quantiles and its attendant damage in agricultural establishments have generated a lot of issues in Ethiopia. Moreover, precise estimates of flood quantiles are needed for efficient design of hydraulic structures; however, quantification of these quantiles in data-scarce regions has been a continuing challenge in hydrologic design. Flood frequency analysis is thus essential to reduce possible flood damage by investigating the most suitable flood prediction model. The annual maximum discharges from six representative stations in the Upper Blue Nile River Basin were fitted to the commonly used nine statistical distributions. This study also assessed the performance evolution of the probability distributions with varying spatial scales, such that three different spatial scales of small-, medium-, and large-scale basins in the Blue Nile River Basin were considered. The performances of the candidate probability distributions were assessed using three goodness-of-fit test statistics, root mean square error, and graphical interpretation approaches to investigate the robust probability distribution for flood frequency analysis over different basin spatial scales. Based on the overall analyses, the generalized extreme value distribution was proven to be a robust model for flood frequency analysis in the study region. The generalized extreme value distribution significantly improved the performance of the flood prediction over different spatial scales. The generalized extreme value flood prediction performance improvement measured in root mean square error varied between 5.84 and 67.91% over other commonly used probability distribution models. Thus, the flood frequency analysis using the generalized extreme value distribution could be essential for the efficient planning and design of hydraulic structures in the Blue Nile River Basin. Furthermore, this study suggests that, in the future, significant efforts should be put to conduct similar flood frequency analyses over the other major river basins of Ethiopia.

Description: The framework of information dynamics allows the dissection of the information processed in a network of multiple interacting dynamical systems into meaningful elements of computation that quantify the information generated in a target system, stored in it, transferred to it from one or more source systems, and modified in a synergistic or redundant way. The concepts of information transfer and modification have been recently formulated in the context of linear parametric modeling of vector stochastic processes, linking them to the notion of Granger causality and providing efficient tools for their computation based on the state–space (SS) representation of vector autoregressive (VAR) models. Despite their high computational reliability these tools still suffer from estimation problems which emerge, in the case of low ratio between data points available and the number of time series, when VAR identification is performed via the standard ordinary least squares (OLS). In this work we propose to replace the OLS with penalized regression performed through the Least Absolute Shrinkage and Selection Operator (LASSO), prior to computation of the measures of information transfer and information modification. First, simulating networks of several coupled Gaussian systems with complex interactions, we show that the LASSO regression allows, also in conditions of data paucity, to accurately reconstruct both the underlying network topology and the expected patterns of information transfer. Then we apply the proposed VAR-SS-LASSO approach to a challenging application context, i.e., the study of the physiological network of brain and peripheral interactions probed in humans under different conditions of rest and mental stress. Our results, which document the possibility to extract physiologically plausible patterns of interaction between the cardiovascular, respiratory and brain wave amplitudes, open the way to the use of our new analysis tools to explore the emerging field of Network Physiology in several practical applications.

Description: Many developing nations are facing severe food insecurity partly because of their dependence on rainfed agriculture. Climate variability threatens agriculture-based community livelihoods. With booming population growth, agricultural land expands, and natural resource extraction increases, leading to changes in land use and land cover characterized by persistent vegetation greening and browning. This can modify local climate variability due to changing land–atmosphere interactions. Yet, for landscapes with significant interannual variability, such as the Mount Elgon ecosystem in Kenya and Uganda, characterizing these changes is a difficult task and more robust methods have been recommended. The current study combined trend (Mann–Kendall and Sen’s slope) and breakpoint (bfast) analysis methods to comprehensively examine recent vegetation greening and browning in Mount Elgon at multiple time scales. The study used both Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) and Climate Hazards group Infrared Precipitation with Stations (CHIRPS) data and attempted to disentangle nature- versus human-driven vegetation greening and browning. Inferences from a 2019 field study were valuable in explaining some of the observed patterns. The results indicate that Mount Elgon vegetation is highly variable with both greening and browning observable at all time scales. Mann–Kendall and Sen’s slope revealed major changes (including deforestation and reforestation), while bfast detected most of the subtle vegetation changes (such as vegetation degradation), especially in the savanna and grasslands in the northeastern parts of Mount Elgon. Precipitation in the area had significantly changed (increased) in the post-2000 era than before, particularly in 2006–2010, thus influencing greening and browning during this period. The greenness–precipitation relationship was weak in other periods. The integration of Mann–Kendall and bfast proved useful in comprehensively characterizing vegetation greenness. Such a comprehensive description of Mount Elgon vegetation dynamics is an important first step to instigate policy changes for simultaneously conserving the environment and improving livelihoods that are dependent on it.

Description: This research aims to improve dietetic-nutritional treatment using state-of-the-art RGB-D sensors and virtual reality (VR) technology. Recent studies show that adherence to treatment can be improved using multimedia technologies. However, there are few studies using 3D data and VR technologies for this purpose. On the other hand, obtaining 3D measurements of the human body and analyzing them over time (4D) in patients undergoing dietary treatment is a challenging field. The main contribution of the work is to provide a framework to study the effect of 4D body model visualization on adherence to obesity treatment. The system can obtain a complete 3D model of a body using low-cost technology, allowing future straightforward transference with sufficient accuracy and realistic visualization, enabling the analysis of the evolution (4D) of the shape during the treatment of obesity. The 3D body models will be used for studying the effect of visualization on adherence to obesity treatment using 2D and VR devices. Moreover, we will use the acquired 3D models to obtain measurements of the body. An analysis of the accuracy of the proposed methods for obtaining measurements with both synthetic and real objects has been carried out.

Description: Airborne laser scanning (ALS) systems tuned to the near-infrared (NIR; 1064 nm) wavelength have become the best available data source for characterizing vegetation structure. Proliferation of multi-spectral ALS (M-ALS) data with lasers tuned at two additional wavelengths (commonly 532 nm; green, and 1550 nm; short-wave infrared (SWIR)) has promoted interest in the benefit of additional wavelengths for forest inventory modelling. In this study, structural and intensity based M-ALS metrics were derived from wavelengths independently and combined to assess their value for modelling forest inventory attributes (Lorey’s height (HL), gross volume (V), and basal area (BA)) and overstorey species diversity (Shannon index (H), Simpson index (D), and species richness (R)) in a diverse mixed-wood forest in Ontario, Canada. The area-based approach (ABA) to forest attribute modelling was used, where structural- and intensity-based metrics were calculated and used as inputs for random forest models. Structural metrics from the SWIR channel (SWIRstruc) were found to be the most accurate for H and R (%RMSE = 14.3 and 14.9), and NIRstruc were most accurate for V (%RMSE = 20.4). The addition of intensity metrics marginally increased the accuracy of HL models for SWIR and combined channels (%RMSE = 7.5). Additionally, a multi-resolution (0.5, 1, 2 m) voxel analysis was performed, where intensity data were used to calculate a suite of spectral indices. Plot-level summaries of spectral indices from each voxel resolution alone, as well as combined with structural metrics from the NIR wavelength, were used as random forest predictors. The addition of structural metrics from the NIR band reduced %RMSE for all models with HL, BA, and V realizing the largest improvements. Intensity metrics were found to be important variables in the 1 m and 2 m voxel models for D and H. Overall, results indicated that structural metrics were the most appropriate. However, the inclusion of intensity metrics, and continued testing of their potential for modelling diversity indices is warranted, given minor improvements when included. Continued analyses using M-ALS intensity metrics and voxel-based indices would help to better understand the value of these data, and their future role in forest management.

Description: Transformation to Continuous Cover Forestry (CCF) is a long and difficult process in which frequent management interventions rapidly alter forest structure and dynamics with long lasting impacts. Therefore, a critical component of transformation is the acquisition of up-to-date forest inventory data to direct future management decisions. Recently, the use of single tree detection methods derived from unmanned aerial vehicle (UAV) has been identified as being a cost effective method for inventorying forests. However, the rapidly changing structure of forest stands in transformation amplifies the difficultly in transferability of current individual tree detection (ITD) methods. This study presents a novel ITD Bayesian parameter optimisation approach that uses quantile regression and external biophysical tree data sets to provide a transferable and low cost ITD approach to monitoring stands in transformation. We applied this novel method to 5 stands in a variety of transformation stages in the UK and to a independent test study site in California, USA, to assess the accuracy and transferability of this method. Requiring small amounts of training data (15 reference trees) this approach had a mean test accuracy (F-score = 0.88) and provided mean tree diameter estimates (RMSE = 5.6 cm) with differences that were not significance to the ground data (p 〈 0.05). We conclude that this method can be used to monitor forests stands in transformation and thus can also be applied to a wide range of forest structures with limited manual parameterisation between sites.

Description: Glacier surface temperature (GST) is influenced by both the energy flux from the atmosphere above and the thermal dynamics at the ice–water–debris interfaces. However, previous studies on GST are inadequate in time series research and mountain glacier surface temperature retrieval. We evaluate the GST variability at Hailuogou glacier, a temperate glacier located in Southeastern Tibetan Plateau, from 1990 to 2018. We utilized a modified mono-window algorithm to calculate the GST using the Landsat 8 thermal infrared sensor (TIRS) band 10 data and Landsat 5 thematic mapper (TM) band 6 data. Three essential parameters, including the emissivity of ice and snow, atmospheric transmittance, and effective mean atmospheric temperature, were employed in the GST algorithm. The remotely-sensed temperatures were compared with two other single-channel algorithms to validate GST algorithm’s accuracy. Results from different algorithms showed a good agreement, with a mean difference of about 0.6 ℃. Our results showed that the GST of the Hailuogou glacier, both in the upper debris-free part and the lower debris-covered tongue, has experienced a slightly increasing trend at a rate of 0.054 ℃ a−1 during the past decades. Atmospheric warming, expanding debris cover in the lower part, and a darkening debris-free accumulation area are the main causes of the warming of the glacier surface.

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: Building Information Modeling (BIM) has a crucial role in smart road applications, not only limited to the design and construction stages, but also to traffic monitoring, autonomous vehicle navigation, road condition assessment, and real-time data delivery to drivers, among others. Point clouds collected through LiDAR are a powerful solution to capture as-built conditions, notwithstanding the lack of commercial tools able to automatically reconstruct road geometry in a BIM environment. This paper illustrates a two-step procedure in which roads are automatically detected and classified, providing GIS layers with basic road geometry that are turned into parametric BIM objects. The proposed system is an integrated BIM-GIS with a structure based on multiple proposals, in which a single project file can handle different versions of the model using a variable level of detail. The model is also refined by adding parametric elements for buildings and vegetation. Input data for the integrated BIM-GIS can also be existing cartographic layers or outputs generated with algorithms able to handle LiDAR data. This makes the generation of the BIM-GIS more flexible and not limited to the use of specific algorithms for point cloud processing.

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: Tourism is a primary socio-economic factor on many coastal islands. Tourism contributes to the livelihoods of the residents, but also influences natural resources and energy consumption and can become a significant driver of land conversion and environmental change. Understanding the influence of tourist-related activities is vital for sustainable tourism development. We chose Hainan Island in South China as a research area to study the influence of tourist-driven activities on environmental variables (as Land Surface Temperatures (LST) and related ecosystem variables) during the period of 2000 to 2019. In Hainan, the local economy relies heavily on tourism, with an ever-growing influx of tourists each year. We categorised location-based points of interest (POIs) into two classes, non-tourism sites and tourism-related sites, and utilised satellite data from the cloud-based platform Google Earth Engine (GEE) to extract LST and Normalized Difference Vegetation Index (NDVI) data. We analysed the LST variations, NDVI changes and the land use/land cover (LULC) changes and compared the relative difference in LST and NDVI between the tourism-related sites and non-tourism-related sites. The main findings of this study were: (1) The median LST in the tourism-related sites was relatively higher (1.3) than the LST in the non-tourism-related sites for the 20 years. Moreover, every annual mean LST of tourism-related sites was higher than the LST values in non-tourism-related sites, with an average difference of 1.2 °C for the 20 years and a maximum difference of 1.7 °C. We found higher annual LST anomalies for tourist-related sites compared to non-tourism sites after 2010, which indicated the likely positive differences in LST above the average LST during 20 years for tourism-related sites when compared against the non-tourism related sites, thus highlighting the potential influence of tourism activities on LST. (2) The annual mean NDVI value for tourism-related sites was significantly lower than for non-tourism places every year, with an average NDVI difference of 0.26 between the two sites. (3) The land cover changed significantly: croplands and forests reduced by 3.5% and 2.8% respectively, while the areas covered by orchards and urban areas increased by 2% and 72.3% respectively. These results indicate the influence of the tourism-driven activities includes the relatively high LST, vegetation degradation and land-use conversion particular to urban cover type. The outcome of this work provides a method that combines cloud-based satellite-derived data with location-based POIs data for quantifying the long-term influence of tourism-related activities on sensitive coastal ecosystems. It contributes to designing evidence-driven management plans and policies for the sustainable tourism development in coastal areas.

Description: To study the influence of complex terrain with different scales on the structure of near-surface turbulence, the turbulence observational data from Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL) were analyzed. SACOL is located in typical Loess Plateau topography. The terrain around the site varies greatly with the direction. Representative data from the Northeast, Southeast, Southwest, and Northwest were selected to study the structure characteristics of the near surface turbulence. The complex topography within the flux footprint enhances the vertical scale of turbulence σw and thereby increases the vertical–longitudinal ratio of turbulence Ar. While the turbulent horizontal scale σu is also significantly affected by the distant terrain beyond the flux footprint. The upwind terrain undulation increases σu and reduces Ar. Affected by the complex terrain, the ratio of the spectrum of the vertical velocity to that of the longitudinal velocity, Sw(n)/Su(n), is far less than 4/3 in the southwest direction, and the turbulence is significantly anisotropic.

Description: Providing recommendations in cold start situations is one of the most challenging problems for collaborative filtering based recommender systems (RSs). Although user social context information has largely contributed to the cold start problem, most of the RSs still suffer from the lack of initial social links for newcomers. For this study, we are going to address this issue using a proposed user similarity detection engine (USDE). Utilizing users’ personal smart devices enables the proposed USDE to automatically extract real-world social interactions between users. Moreover, the proposed USDE uses user clustering algorithm that includes contextual information for identifying similar users based on their profiles. The dynamically updated contextual information for the user profiles helps with user similarity clustering and provides more personalized recommendations. The proposed RS is evaluated using movie recommendations as a case study. The results show that the proposed RS can improve the accuracy and personalization level of recommendations as compared to two other widely applied collaborative filtering RSs. In addition, the performance of the USDE is evaluated in different scenarios. The conducted experimental results on USDE show that the proposed USDE outperforms widely applied similarity measures in cold start and data sparsity situations.

Description: Predictive models play a central role in decision making. Penalized regression approaches, such as least absolute shrinkage and selection operator (LASSO), have been widely used to construct predictive models and explain the impacts of the selected predictors, but the estimates are typically biased. Moreover, when data are ultrahigh-dimensional, penalized regression is usable only after applying variable screening methods to downsize variables. We propose a stepwise procedure for fitting generalized linear models with ultrahigh dimensional predictors. Our procedure can provide a final model; control both false negatives and false positives; and yield consistent estimates, which are useful to gauge the actual effect size of risk factors. Simulations and applications to two clinical studies verify the utility of the method.

Description: The properties of binary colloidal systems have gained the interest of researchers because they have much richer structures than their one-component counterpart. Continuing efforts are being made on the theoretical side on binary colloidal systems, while many issues remained unsolved for the lack of solid experimental supports, especially for study in the field of two-dimensional (2D) binary colloids system. Oil–water interfaces can serve as a good stringent 2D confinement for colloidal particles and can avoid anomalous problems caused by the quasi-two-dimensional environment in previous experimental reports. In this work, we conduct experimental research of binary colloids system in an oil–water interface to revisit theoretical predication. We measure an ultra-long-range attraction and discuss the possible mechanism of this attraction by comparing the experimental result with existing model and theory. This study could contribute more understanding of the binary colloidal system in both experimental aspects and theoretical aspects.

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: The variation of soil moisture (SM) is a complex and synthetic process, which is impacted by numerous factors. The effects of these factors on soil moisture are dynamic. As a result, the relationship between soil moisture and explanatory variables varies with time and season. This kind of change should be considered in obtaining fine spatial resolution soil moisture products. We chose a study area with four distinct seasons in the temperate monsoon region. In this research, we established seasonal downscaling models to avoid the influence of seasonal differences. Precipitation, land surface temperature, evapotranspiration, vegetation index, land cover, elevation, slope, aspect and soil texture were taken as explanatory variables to produce fine spatial resolution SM. SM products derived from Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E) and Advanced Microwave Scanning Radiometer 2 (AMSR2) were downscaled with the help of machine learning algorithms. We compared three machine learning algorithms of random forest (RF), support vector machine (SVM), and K-nearest neighbors (KNN) to determine the most suitable algorithm for this study. The results show that season-based downscaling is even better than continuous time series. In the analysis of seasonal differences, precipitation plays a dominant role, but its contribution rate is different in each season. Moreover, the influence of vegetation is more prominent in winter, while the influence of terrain is more important in the other three seasons. It could be noted that the accuracy of the RF model is the best among three machine learning algorithms, and the RF-downscaled products have superior matching performance to both AMSR (AMSR-E and AMSR2) SM products and in-situ measurements. The analysis indicates considering seasonal difference and the application of machine learning has high potential for spatial downscaling in remote sensing applications.

Description: Satellite remote sensing and model data play an important role in research and applications of tropical meteorology and climatology over vast, data-sparse oceans and remote continents. Since the first weather satellite was launched by NASA in 1960, a large collection of NASA’s Earth science data is freely available to the research and application communities around the world, significantly improving our overall understanding of the Earth system and environment. Established in the mid-1980s, the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), located in Maryland, USA, is a data archive center for multidisciplinary, satellite and model assimilation data products. As one of the 12 NASA data centers in Earth sciences, GES DISC hosts several important NASA satellite missions for tropical meteorology and climatology such as the Tropical Rainfall Measuring Mission (TRMM), the Global Precipitation Measurement (GPM) Mission and the Modern-Era Retrospective analysis for Research and Applications (MERRA). Over the years, GES DISC has developed data services to facilitate data discovery, access, distribution, analysis and visualization, including Giovanni, an online analysis and visualization tool without the need to download data and software. Despite many efforts for improving data access, a significant number of challenges remain, such as finding datasets and services for a specific research topic or project, especially for inexperienced users or users outside the remote sensing community. In this article, we list and describe major NASA satellite remote sensing and model datasets and services for tropical meteorology and climatology along with examples of using the data and services, so this may help users better utilize the information in their research and applications.

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: Data acquisition and an efficient processing method for hydrological model initialization, such as soil moisture and parameter value identification are critical for a physics-based distributed watershed modelling of flood and flood related disasters such as sediment and debris flow. Site measurements can provide accurate estimates of soil moisture, but such techniques are limited due to the number of physical sensors required to cover a large area effectively. Available satellite-based digital soil moisture data ranges from 9 km to 20 km in resolution which obscures the soil moisture details of a hill slope scale. This resolution limitation of available satellite-based distributed soil moisture data has impacted critical analysis of soil moisture resolution variance on physics-based distributed simulation results. Moreover, available satellite-based digital soil moisture data represents only a few centimeters of the top soil column and that would inform little about the effective root-zone wetness. A recently developed soil moisture estimation method called SERVES (Soil moisture Estimation of Root zone through Vegetation index-based Evapotranspiration fraction and Soil properties) overcomes this limitation of satellite-based soil moisture data by estimating distributed effective root zone soil moisture at 30 m resolution. In this study, a distributed watershed hydrological model of a sub-catchment of Reynolds Creek Experimental Watershed was developed with the GSSHA (Gridded Surface Sub-surface Hydrological Analysis) Model. SERVES soil moisture estimated at 30 m resolution was deployed in the watershed hydrological parameter value calibration and identification process. The 30 m resolution SERVES soil moisture data was resampled to 4500 m and 9000 m resolutions and was separately employed in the calibrated hydrological model to determine the soil moisture resolution effect on the model simulated outputs and the model parameter values. It was found that the simulated discharge is underestimated, infiltration rate/volume is overestimated and higher soil moisture state distribution is filtered out as the initial soil moisture resolution was coarsened. To compensate for this disparity in the simulated results, the soil saturated hydraulic conductivity value decreased with respect to the decreased resolutions.

Description: On 22 March 2020, Zagreb was struck by an M5.5 earthquake that had been expected for more than 100 years and revealed all the failures in the construction of residential buildings in the Croatian capital, especially those built in the first half of the 20th century. Because of that, extensive seismological, geological, geodetic and structural engineering surveys were conducted immediately after the main shock. This study provides descriptions of damage, specifying the building performances and their correlation with the local soil characteristics, i.e., seismic motion amplification. Co-seismic vertical ground displacement was estimated, and the most affected area is identified according to Sentinel-1 interferometric wide-swath data. Finally, preliminary 3D structural modeling of the earthquake sequence was performed, and two major faults were modeled using inverse distance weight (IDW) interpolation of the grouped hypocenters. The first-order assessment of seismic amplification (due to site conditions) in the Zagreb area for the M5.5 earthquake shows that ground motions of approximately 0.16–0.19 g were amplified at least twice. The observed co-seismic deformation (based on Sentinel-1A IW SLC images) implies an approximately 3 cm uplift of the epicentral area that covers approximately 20 km2. Based on the preliminary spatial and temporal analyses of the Zagreb 2020 earthquake sequence, the main shock and the first aftershocks evidently occurred in the subsurface of the Medvednica Mountains along a deep-seated southeast-dipping thrust fault, recognized as the primary (master) fault. The co-seismic rupture propagated along the thrust towards northwest during the first half-hour of the earthquake sequence, which can be clearly seen from the time-lapse visualization. The preliminary results strongly support one of the debated models of the active tectonic setting of the Medvednica Mountains and will contribute to a better assessment of the seismic hazard for the wider Zagreb area.

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: Micro, small, and medium-sized enterprises (MSMEs) are an essential part of economies at the national, regional, and local levels. Understanding the determinants of the development of this sector is interesting not only for researchers but also for local governments to support the development of this sector. This paper analyses micro, small, and medium enterprises at the gmina (local) level in one region, the Kujawsko-Pomorskie voivodship (NUTS2) in Poland. The authors use multivariate linear regression, spatial econometrics, and classification trees to model the influence of different factors on the number of enterprises relative to population size. The authors found that the most crucial factor in all cases, independently of the method used, is the local government’s revenue from personal income tax per capita. This finding, together with the lack of significance of variables related to the distance to technological parks or economic zones, indicates that the enterprises in the region produce mainly for local consumption and lack innovativeness. The authors also examined the influence of spatial context on the number of enterprises. The most important factor seems to be the percentage of built-up areas, but there are also others, depending on the model type; again, this confirms the local character of the activity of micro, small, and medium enterprises in the region. Variables representing the spatial context can explain the relative number of enterprises with coefficient of determination (R2) between 0.30 and 0.45, which shows that this context played a relatively significant role in the development of the MSME sector in the region. On the other hand, the econometric models (that include the neighborhood) are only significant (improving R2) for medium enterprises, which means that medium enterprises expand their activity beyond the local range.

Description: The impact of aerosol spatio-temporal variability on the Arctic radiative budget is not fully constrained. This case study focuses on the intra-Arctic modification of long-range transported aerosol and its direct aerosol radiative effect (ARE). Different types of air-borne and ground-based remote sensing observations (from Lidar and sun-photometer) revealed a high tropospheric aerosol transport episode over two parts of the European Arctic in April 2018. By incorporating the derived aerosol optical and microphysical properties into a radiative transfer model, we assessed the ARE over the two locations. Our study displayed that even in neighboring Arctic upper tropospheric levels, aged aerosol was transformed due to the interplay of removal processes (nucleation scavenging and dry deposition) and alteration of the aerosol source regions (northeast Asia and north Europe). Along the intra-Arctic transport, the coarse aerosol mode was depleted and the visible wavelength Lidar ratio (LR) increased significantly (from 15 to 64–82 sr). However, the aerosol modifications were not reflected on the ARE. More specifically, the short-wave (SW) atmospheric column ARE amounted to +4.4 - +4.9 W m−2 over the ice-covered Fram Strait and +4.5 W m−2 over the snow-covered Ny-Ålesund. Over both locations, top-of-atmosphere (TOA) warming was accompanied by surface cooling. These similarities can be attributed to the predominant accumulation mode, which drives the SW radiative budget, as well as to the similar layer altitude, solar geometry, and surface albedo conditions over both locations. However, in the context of retreating sea ice, the ARE may change even along individual transport episodes due to the ice albedo feedback.

Description: This study aimed to understand which vegetation indices (VIs) are an ideal proxy for describing phenology and interannual variability of Gross Primary Productivity (GPP) in short-rotation coppice (SRC) plantations. Canopy structure- and chlorophyll-sensitive VIs derived from Sentinel-2 images were used to estimate the start and end of the growing season (SOS and EOS, respectively) during the period 2016–2018, for an SRC poplar (Populus spp.) plantation in Lochristi (Belgium). Three different filtering methods (Savitzky–Golay (SavGol), polynomial (Polyfit) and Harmonic Analysis of Time Series (HANTS)) and five SOS- and EOS threshold methods (first derivative function, 10% and 20% percentages and 10% and 20% percentiles) were applied to identify the optimal methods for the determination of phenophases. Our results showed that the MEdium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) had the best fit with GPP phenology, as derived from eddy covariance measurements, in identifying SOS- and EOS-dates. For SOS, the performance was only slightly better than for several other indices, whereas for EOS, MTCI performed markedly better. The relationship between SOS/EOS derived from GPP and VIs varied interannually. MTCI described best the seasonal pattern of the SRC plantation’s GPP (R2 = 0.52 when combining all three years). However, during the extreme dry year 2018, the Chlorophyll Red Edge Index performed slightly better in reproducing growing season GPP variability than MTCI (R2 = 0.59; R2 = 0.49, respectively). Regarding smoothing functions, Polyfit and HANTS methods showed the best (and very similar) performances. We further found that defining SOS as the date at which the 10% or 20% percentile occurred, yielded the best agreement between the VIs and the GPP; while for EOS the dates of the 10% percentile threshold came out as the best.

Description: High-resolution real-time satellite-based precipitation estimation datasets can play a more essential role in flood forecasting and risk analysis of infrastructures. This is particularly true for extended deserts or mountainous areas with sparse rain gauges like Iran. However, there are discrepancies between these satellite-based estimations and ground measurements, and it is necessary to apply adjustment methods to reduce systematic bias in these products. In this study, we apply a quantile mapping method with gauge information to reduce the systematic error of the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS). Due to the availability and quality of the ground-based measurements, we divide Iran into seven climate regions to increase the sample size for generating cumulative probability distributions within each region. The cumulative distribution functions (CDFs) are then employed with a quantile mapping 0.6° × 0.6° filter to adjust the values of PERSIANN-CCS. We use eight years (2009–2016) of historical data to calibrate our method, generating nonparametric cumulative distribution functions of ground-based measurements and satellite estimations for each climate region, as well as two years (2017–2018) of additional data to validate our approach. The results show that the bias correction approach improves PERSIANN-CCS data at aggregated to monthly, seasonal and annual scales for both the calibration and validation periods. The areal average of the annual bias and annual root mean square errors are reduced by 98% and 56% during the calibration and validation periods, respectively. Furthermore, the averages of the bias and root mean square error of the monthly time series decrease by 96% and 26% during the calibration and validation periods, respectively. There are some limitations in bias correction in the Southern region of the Caspian Sea because of shortcomings of the satellite-based products in recognizing orographic clouds.

Description: Collecting in situ observations from remote, high mountain rivers presents major challenges, yet real-time, high temporal resolution (e.g., daily) discharge data are critical for flood hazard mitigation and river management. In this study, we propose a method for estimating daily river discharge (RD) based on free, operational remote sensing precipitation data (Tropical Rainfall Measuring Mission (TRMM), since 2001). In this method, an exponential filter was implemented to produce a new precipitation time series from daily basin-averaged precipitation data to model the time lag of precipitation in supplying RD, and a linear-regression relationship was constructed between the filtered precipitation time series and observed discharge records. Because of different time lags in the wet season (rainfall-dominant) and dry season (snowfall-dominant), the precipitation data were processed in a segmented way (from June to October and from November to May). The method was evaluated at two hydrological gauging stations in the Upper Brahmaputra (UB) river basin, where Nash–Sutcliffe Efficiency (NSE) coefficients for Nuxia (〉0.85) and Yangcun (〉0.80) indicate good performance. By using the degree-day method to estimate the snowmelt and acquire the time series of new active precipitation (rainfall plus snowmelt) in the target basins, the discharge estimations were improved (NSE 〉 0.9 for Nuxia) compared to the original data. This makes the method applicable for most rivers on the Tibetan Plateau, which are fed mainly by precipitation (including snowfall) and are subject to limited human interference. The method also performs well for reanalysis precipitation data (Chinese Meteorological Forcing Dataset (CMFD), 1980–2000). The real-time or historical discharges can be derived from satellite precipitation data (or reanalysis data for earlier historical years) by using our method.

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: Moss biomonitoring technique was used for a heavy-metal pollution study in Macedonia in the framework of the International Cooperative Program on Effects of Air Pollution on Natural Vegetation and Crops (UNECE IPC Vegetation). Moss samples (n = 72) were collected during the summers of 2002, 2005, and 2010. The contents of 41 elements were determined by neutron activation analysis, atomic absorption spectrometry, and inductively coupled plasma atomic emission spectrometry. Using factor and cluster analyses, three geogenic factors were determined (Factor 1, including Al, As, Co, Cs, Fe, Hf, Na, Rb, Sc, Ta, Th, Ti, U, V, Zr, and rare-earth elements–RE; Factor 4 with Ba, K, and Sr; and Factor 5 with Br and I), one anthropogenic factor (Factor 2, including Cd, Pb, Sb, and Zn), and one geogenic-anthropogenic factor (Factor 3, including Cr and Ni). The highest anthropogenic impact of heavy metal to the air pollution in the country was from the ferronickel smelter near Kavadraci (Ni and Cr), the lead and zinc mines in the vicinity of Makedonska Kamenica, Probištip, and Kriva Palanka in the eastern part of the country (Cd, Pb, and Zn), and the former lead and zinc smelter plant in Veles. Beside the anthropogenic influences, the lithology and the composition of the soil also play an important role in the distribution of the elements.

Description: Industrial nitrogen liquefaction cycles are based on the Collins topology but integrate variations. Several pressure levels with liquefaction to medium pressure and compressor–expander sets are common. The cycle must be designed aiming to minimise specific power consumption rather than to maximise liquid yield. For these reasons, conclusions of general studies cannot be extrapolated directly. This article calculates the optimal share of total compressed flow to be expanded in an industrial Collins-based cycle for nitrogen liquefaction. Simulations in Unisim Design R451 using Peng Robinson EOS for nitrogen resulted in 88% expanded flow, which is greater than the 75–80% for conventional Collins cycles with helium or other substances. Optimum specific compression work resulted 430.7 kWh/ton of liquid nitrogen. For some operating conditions, the relation between liquid yield and specific power consumption was counterintuitive: larger yield entailed larger consumption. Exergy analysis showed 40.3% exergy efficiency of the optimised process. The exergy destruction distribution and exergy flow across the cycle is provided. Approximately 40% of the 59.7% exergy destruction takes place in the cooling after compression. This exergy could be used for secondary applications such as industrial heating, energy storage or for lower temperature applications as heat conditioning.

Description: This paper develops a non-equilibrium thermodynamic approach to life, with particular regards to the membrane role. The Onsager phenomenological coefficients are introduced in order to point out the thermophysical properties of the cell systems. The fundamental role of the cell membrane electric potential is highlighted, in relation to ions and heat fluxes, pointing out the strictly relation between heat exchange and the membrane electric potential. A Seebeck-like and Peltier-like effects emerge in order to simplify the description of the heat and the ions fluxes. Life is described as a continuos transition between the Peltier-like effect to the Seebeck-like one, and viceversa.

Description: The Metropolitan Region of Sao Paulo (MRSP) is one of the main regions of Brazil that in recent years has shown an increase in the number of days with heat waves, mainly affecting the health of the most sensitive populations, such as the elderly. In this study, we identified the heat waves in the MRSP using three different definitions regarding the maximum daily temperature threshold. To analyze the impact of heat waves on elderly mortality, we used distributed lag nonlinear models (dlnm) and we quantified the heat wave-related excess mortality of elderly people from 1985 to 2005 and made projections for the near future (2030 to 2050) and the distant future (2079–2099) under the climate change scenarios RCP4.5 and RCP8.5 (RCP: Representative Concentration Paths). An important aspect of this research is that for the projections we take into account two assumptions: non-adaptation and adaptation to the future climate. Our projections show that the heat wave-related excess of elderly mortality will increase in the future, being highest when we consider no adaptation, mainly from cardiovascular diseases in women (up to 587 deaths per 100,000 inhabitants per year). This study can be used for public policies to implement preventive and adaptive measures in the MRSP.

Description: The paper is focused on the idea of multi-fuel combustion in a large-scale circulating fluidized bed (CFB) boiler. The article discusses the concept of simultaneous coal and syngas combustion. A comprehensive three-dimensional computational fluid dynamics (CFD) model is developed, which allows us to describe complex phenomena that occur in the combustion chamber of the CFB boiler burning coal and syngas produced from coal sludge.

Description: Emotional and physical stress can cause various health problems. In this paper, we used tissue blood oxygen saturation (StO2), a newly proposed physiological signal, to classify the human stress. We firstly constructed a public StO2 database including 42 volunteers subjected to two types of stress. During the physical stress experiment, we observed that the facial StO2 right after the stress can be either increased or decreased comparing to the baseline. We investigated the StO2 feature combinations for the classification and found that the average StO2 values from left cheek, chin, and the middle of the eyebrow can provide the highest classification rate of 95.56%. Comparison with other stress classification method shows that StO2 based method can provide best classification performance with lowest feature dimension. These results suggest that facial StO2 can be used as a promising features to identify stress states, including emotional and physical stress.

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: Clean hydrocarbon technologies have a key role to play in achieving the circular carbon economy while meeting climate targets in many countries around the world. The aim of this work is to assess which technology, or combination of technologies, is the most cost-effective in achieving climate targets by 2030 leading to a quick and smooth transition to a low carbon energy system in Saudi Arabia and similar oil-based economies. We find that low carbon policy support by banning crude oil in power generation, leads to accelerated underground oil gasification, in the absence of carbon prices. We also find that setting a policy for carbon reduction targets leads to a more flexible energy system transition enabling more technologies in the mix with an increasing transition period. Our results also show that clean hydrocarbon technologies may be sufficient to achieve new climate targets, as shown by the stabilised emissions in scenario 3 by 2025, without the implementation of renewable sources of energy which most studies do not include. We propose that by investing in clean hydrocarbon technologies over the short term, the transition towards a low carbon economy will be accelerated while developing renewable sources of energy over the long term.

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: The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) mission with the MIRAS (Microwave Imaging Radiometer using Aperture Synthesis) L-band radiometer provides global soil moisture (SM) data. SM data and products from remote sensing are relatively new, but they are providing significant observations for weather forecasting, water resources management, agriculture, land surface, and climate models assessment, etc. However, the accuracy of satellite measurements is still subject to error from the retrieval algorithms and vegetation cover. Therefore, the validation of satellite measurements is crucial to understand the quality of retrieval products. The objectives of this study, precisely framed within this mission, are (i) validation of the SMOS Level 1C Brightness Temperature (TBSMOS) products in comparison with simulated products from the L-MEB model (TBL-MEB) and (ii) validation of the SMOS Level 2 SM (SMSMOS) products against ground-based measurements at 10 significant Iranian agrometeorological stations. The validations were performed for the period of January 2012 to May 2015 over the Southwest and West of Iran. The results of the validation analysis showed an RMSE ranging between 9 to 13 K and a strong correlation (R = 0.61–0.84) between TBSMOS and TBL-MEB at all stations. The bias values (0.1 to 7.5 K) showed a slight overestimation for TBSMOS at most of the stations. The results of SMSMOS validation indicated a high agreement (RMSE = 0.046–0.079 m3 m−3 and R = 0.65–0.84) between the satellite SM and in situ measurements over all the stations. The findings of this research indicated that SMSMOS shows high accuracy and agreement with in situ measurements which validate its potential. Due to the limitation of SM measurements in Iran, the SMOS products can be used in different scientific and practical applications at different Iranian study areas.

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: European satellite missions Sentinel-1 (S1) and Sentinel-2 (S2) provide at high spatial resolution and high revisit time, respectively, radar and optical images that support a wide range of Earth surface monitoring tasks, such as Land Use/Land Cover mapping. A long-standing challenge in the remote sensing community is about how to efficiently exploit multiple sources of information and leverage their complementarity, in order to obtain the most out of radar and optical data. In this work, we propose to deal with land cover mapping in an object-based image analysis (OBIA) setting via a deep learning framework designed to leverage the multi-source complementarity provided by radar and optical satellite image time series (SITS). The proposed architecture is based on an extension of Recurrent Neural Network (RNN) enriched via a modified attention mechanism capable to fit the specificity of SITS data. Our framework also integrates a pretraining strategy that allows to exploit specific domain knowledge, shaped as hierarchy over the set of land cover classes, to guide the model training. Thorough experimental evaluations, involving several competitive approaches were conducted on two study sites, namely the Reunion island and a part of the Senegalese groundnut basin. Classification results, 79% of global accuracy on the Reunion island and 90% on the Senegalese site, respectively, have demonstrated the suitability of the proposal.

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: Spaceborne lidar (light detection and ranging) is a very promising tool for the optical properties of global atmosphere and ocean detection. Although some studies have shown spaceborne lidar’s potential in ocean application, there is no spaceborne lidar specifically designed for ocean studies at present. In order to investigate the detection mechanism of the spaceborne lidar and analyze its detection performance, a spaceborne oceanic lidar simulator is established based on the semianalytic Monte Carlo (MC) method. The basic principle, the main framework, and the preliminary results of the simulator are presented. The whole process of the laser emitting, transmitting, and receiving is executed by the simulator with specific atmosphere–ocean optical properties and lidar system parameters. It is the first spaceborne oceanic lidar simulator for both atmosphere and ocean. The abilities of this simulator to characterize the effect of multiple scattering on the lidar signals of different aerosols, clouds, and seawaters with different scattering phase functions are presented. Some of the results of this simulator are verified by the lidar equation. It is confirmed that the simulator is beneficial to study the principle of spaceborne oceanic lidar and it can help develop a high-precision retrieval algorithm for the inherent optical properties (IOPs) of seawater.

Description: Agricultural production in sub-Saharan Africa remains dependent on high inputs of human labor, a situation associated with direct exposure to daylight heat during critical periods of the agricultural calendar. We ask the question: how is the Wet-Bulb Globe Temperature (WBGT) going to be distributed in the future, and how will this affect the ability of smallholder farmers to perform agricultural activities? Data from general circulation models are used to estimate the distribution of WBGT in 2000, 2050 and 2100, and for high activity periods in the agricultural calendar. The distribution of WBGT is divided into recommended maximum WBGT exposure levels (°C) at different work intensities, and rest/work ratios for an average acclimatized worker wearing light clothing (ISO, 18). High WBGTs are observed during the two periods of the East African. In February to March, eastern and coastal regions of Kenya and Tanzania witness high WBGT values—some necessitating up to 75% rest/hour work intensities in 2050 and 2100. In August to September, eastern and northern Kenya and north and central Uganda are vulnerable to high WBGT values. Designing policies to address this key challenge is a critical element in adaptation methods to address the impact of climate change.

Description: Micrometeorites (MMs) are small particles that account for most of the extraterrestrial material deposited on Earth. Synchrotron X-ray fluorescence and diffraction allowed for chemical and mineral characterization to distinguish MM from atmospheric particulate. The relative components of iron, nickel, and other elements were considered in the identification of ferrous MM while high amounts of titanium were considered an indication that the particles were of atmospheric origin. Out of 100 samples collected by high school students and teachers, eight were taken to a synchrotron for analysis. Of those eight, three exhibited extraterrestrial compositions. X-ray absorption near-edge structure analysis revealed that the same three samples contained sulfide, the main sulfur form constituent in MM. X-ray microdiffraction analysis showed the presence of the minerals pentlandite and forsterite. Collectively, these results support the extraterrestrial nature of the three particles.

Description: The Yiliu tungsten polymetallic deposit, located in the south central portion of the Nanling nonferrous metal metallogenic province in South China, is an area with common Yanshanian tectonothermal events. Early Yanshanian magmatism leads to the emplacement of voluminous tungsten-bearing granite intrusions, such as the Baoshan, Benggangling and Junye plutons, which are considered temporally and spatially associated with W-polymetallic mineralization in the Yiliu region. Here, we investigate the basic geological and petrological characteristics of the Junye granites, and present major and trace element geochemical data and bulk-rock Sr-Nd-Pb-Hf isotopic data to gain insight into the petrogenesis and tectonic setting of granitic intrusions in the region. The Junye granites are high-K calc-alkaline and metaluminous to weakly peraluminous [A/CNK = molar ratios of Al2O3/(CaO + Na2O + K2O) = 0.97–1.02] with enrichment in SiO2 (75.68–76.44 wt.%), relatively high total alkalis (K2O + Na2O = 8.06–8.45 wt.%) with K2O/Na2O ratios ranging from 1.12 to 1.42, and moderate Al2O3 (12.62–13.00 wt.%), but low in P2O5 (

Description: We used observational data and the results from a high-resolution numerical simulation model to analyze the occurrence and development of an extreme precipitation event in the Ili Valley, Xinjiang, China on 26 June 2015. We analyzed the horizontal wavelength, period, speed, ducting, energy propagation and feedback mechanism of inertial gravity waves. A low-level convergence line was formed in the valley by the northerly and westerly winds as a result of Central Asian vortices and the trumpet-shaped topography of the Ili Valley. There was sufficient water vapor in the valley for the precipitation event to develop. A mesoscale vortex formed and developed on the low-level convergence line and the rainfall was distributed either near the convergence line or the mesoscale vortex. The low-level convergence line and the uplift caused by the terrain triggered convection, and then the convection triggered waves at lower levels. The combination of ascending motion induced by the lower level waves and the mesoscale vortex led to the development of convection, causing the precipitation to intensify. When the convection moved eastward to Gongliu County, it was coupled with the ascending phase of upper level waves, causing both the convection and precipitation to intensify again. We applied spectral analysis methods to verify that the waves were inertial gravity waves. The upper level inertial gravity waves propagated westward at a mean speed of −12 m s−1 with periods of 73–179 min and horizontal wavelengths of 50–55 km. The lower level inertial gravity waves propagated eastward at a mean speed of 8 m s−1 with periods of 73–200 min and a horizontal wavelength of 85 km. The more (less) favorable waveguide conditions determined whether the gravity waves persisted for a long (short) time and propagated for a longer (shorter) distance. Based on the mesoscale Eliassen–Palm flux theory, the wave energy of inertial gravity waves had an important effect on the maintenance and development of convection and precipitation by affecting wind strength and wind divergence. Feedback was mainly through the meridional and vertical transport of zonal momentum and the meridional transport of heat.

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: In convergent zones, several parts of the geodynamic system (e.g., continental margins, back-arc regions) can be deformed, uplifted, and eroded through time, each of them potentially delivering clastic sediments to neighboring basins. Tectonically driven events are mostly recorded in syntectonic clastic systems accumulated into different kinds of basins: trench, fore-arc, and back-arc basins in subduction zones and foredeep, thrust-top, and episutural basins in collisional settings. The most widely used tools for provenance analysis of synorogenic sediments and for unraveling the tectonic evolution of convergent zones are sandstone petrography and U–Pb dating of detrital zircon. In this paper, we present a comparison of previously published data discussing how these techniques are used to constrain provenance reconstructions and contribute to a better understanding of the tectonic evolution of (i) the Cretaceous transition from extensional to compressional regimes in the back-arc region of the southern Andean system; and (ii) the involvement of the passive European continental margin in the Western Alps subduction system during impending Alpine collision. In both cases, sediments delivered from the down-bending continental block are significantly involved. Our findings highlight its role as a detrital source, which is generally underestimated or even ignored in current tectonic models.

Description: Urban air pollution from gaseous pollutants is a growing public health problem in many countries including South Africa. Examining the levels, trends and health risk of exposure to ambient gaseous pollutants will assist in understanding the effectiveness of existing control measures and plan for suitable management strategies. This study determined the concentration levels and non-cancer risk of CO, SO2, NO2, and O3 at an industrial area in Pretoria West, South Africa. We utilised a set of secondary data for CO, NO2, SO2, and O3 that was obtained from a monitoring station. Analysis of the hourly monitored data was done. Their non-cancer risk (HQ) was determined using the human health risk assessment model for different age categories. The annual levels of NO2 (39.442 µg/m3), SO2 (22.464 µg/m3), CO (722.003 µg/m3) and the 8-hour concentration of CO (649.902 µg/m3) and O3 (33.556 µg/m3) did not exceed the South African National Ambient Air Quality Standards for each pollutant. The HQ for each pollutant across exposed groups (except children) was less than 1. This indicates that the recorded levels could not pose non-cancer risk to susceptible individuals.

Description: We report on the application of the U-Th-He method for the direct dating of pyrite and provide an original methodological approach for measurement of U, Th and He in single grains without loss of parent nuclides during thermal extraction of He. The U-Th-He age of ten samples of high-crystalline stoichiometric pyrite from unoxidized massive ores of the Uzelga volcanogenic massive sulfide (VMS) deposit, South Urals, is 382 ± 12 Ma (2σ) (U concentrations ~1–5 ppm; 4He ~10−4 cm3 STP g−1). This age is consistent with independent (biostratigraphic) estimations of the age of ore formation (ca, 389–380 Ma) and is remarkably older than the probable age of the regional prehnite-pumpellyite facies metamorphism (~340–345 Ma). Our results indicate that the U-Th-He dating of ~1 mg weight pyrite sample is possible and open new perspectives for the dating of ore deposits. The relative simplicity of U-Th-He dating in comparison with other geochronological methods makes this approach interesting for further application.