bluuebunny/crossref_metadata_2025 huggingface dataset:
Mapping Paddy Rice Fields by Combining Multi-Temporal Vegetation Index and Synthetic Aperture Radar Remote Sensing Data Using Google Earth Engine Machine Learning Platform
Authors: Chen Nengcheng , Yu Lixiaona , Zhang Xiang , Shen Yonglin , Zeng Linglin , Hu Qiong , Niyogi Dev
Published: September 2020

The knowledge of the area and spatial distribution of paddy rice fields is important for water resource management. However, accurate map of paddy rice is a long-term challenge because of its spatiotemporal discontinuity and short duration. To solve this problem, this study proposed a paddy rice area extraction approach by using the combination of optical vegetation indices and synthetic aperture radar (SAR) data. This method is designed to overcome the data-missing problem due to cloud contamination and spatiotemporal discontinuities of the traditional optical remote sensing method. More specifically, the Sentinel-1A SAR and the Sentinel-2 multispectral imager (MSI) Level-2A imagery are used to identify paddy rice with a high temporal and spatial resolution. Three vegetation indices, namely normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and land surface water index (LSWI), are estimated from optical bands. Two polarization bands (VH (vertical-horizontal) and VV (vertical-vertical)) are used to overcome the cloud contamination problem. This approach was applied with the random forest machine learning algorithm on the Google Earth Engine platform for the Jianghan Plain in China as an experimental area. The results of 39 experiments uncovered the effect of different factors. The results indicated that the combination of VV and VH band showed a better performance compared with other polarization bands; the average producer’s accuracy of paddy rice (PA) is 72.79%, 1.58% higher than the second one VH. Secondly, the combination of three indices also showed a better result than others, with average PA 73.82%, 1.42% higher than using NDVI alone. The classification result presented the best combination is EVI, VV, and VH polarization band. The producer’s accuracy of paddy rice was 76.67%, with the overall accuracy (OA) of 66.07%, and Kappa statistics of 0.45. However, NDVI, EVI, and VH showed better performance in mapping the morphology. The results demonstrated the method developed in this study can be successfully applied to the cloud-prone area for mapping paddy rice to overcome the data missing caused by cloud and rain during the paddy growing season.
Preliminary Utility of the Retrospective IMERG Precipitation Product for Large-Scale Drought Monitoring over Mainland China
Authors: Wei Linyong , Jiang Shanhu , Ren Liliang , Zhang Linqi , Wang Menghao , Duan Zheng
Published: September 2020

This study evaluated the suitability of the latest retrospective Integrated Multi-satellitE Retrievals for Global Precipitation Measurement V06 (IMERG) Final Run product with a relatively long period (beginning from June 2000) for drought monitoring over mainland China. First, the accuracy of IMERG was evaluated by using observed precipitation data from 807 meteorological stations at multiple temporal (daily, monthly, and yearly) and spatial (pointed and regional) scales. Second, the IMERG-based standardized precipitation index (SPI) was validated and analyzed through statistical indicators. Third, a light–extreme–light drought-event process was adopted as the case study to dissect the latent performance of IMERG-based SPI in capturing the spatiotemporal variation of drought events. Our results demonstrated a sufficient consistency and small error of the IMERG precipitation data against the gauge observations with the regional mean correlation coefficient (CC) at the daily (0.7), monthly (0.93), and annual (0.86) scales for mainland China. The IMERG possessed a strong capacity for estimating intra-annual precipitation changes; especially, it performed well at the monthly scale. There was a strong agreement between the IMERG-based SPI values and gauge-based SPI values for drought monitoring in most regions in China (with CCs above 0.8). In contrast, there was a comparatively poorer capability and notably higher heterogeneity in the Xinjiang and Qinghai-Tibet Plateau regions with more widely varying statistical metrics. The IMERG featured the advantage of satisfactory spatiotemporal accuracy in terms of depicting the onset and extinction of representative drought disasters for specific consecutive months. Furthermore, the IMERG has obvious drought monitoring abilities, which was also complemented when compared with the Precipitation Estimation from the Remotely Sensed Information using Artificial Neural Networks Climate Data Record (PERSIANN-CDR), Climate Hazards Group Infrared Precipitation with Stations (CHIRPS), and Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA) 3B42V7. The outcomes of this study demonstrate that the retrospective IMERG can provide a more competent data source and potential opportunity for better drought monitoring utility across mainland China, particularly for eastern China.
LS-SSDD-v1.0: A Deep Learning Dataset Dedicated to Small Ship Detection from Large-Scale Sentinel-1 SAR Images
Authors: Zhang Tianwen , Zhang Xiaoling , Ke Xiao , Zhan Xu , Shi Jun , Wei Shunjun , Pan Dece , Li Jianwei , Su Hao , Zhou Yue , Kumar Durga
Published: September 2020

Ship detection in synthetic aperture radar (SAR) images is becoming a research hotspot. In recent years, as the rise of artificial intelligence, deep learning has almost dominated SAR ship detection community for its higher accuracy, faster speed, less human intervention, etc. However, today, there is still a lack of a reliable deep learning SAR ship detection dataset that can meet the practical migration application of ship detection in large-scene space-borne SAR images. Thus, to solve this problem, this paper releases a Large-Scale SAR Ship Detection Dataset-v1.0 (LS-SSDD-v1.0) from Sentinel-1, for small ship detection under large-scale backgrounds. LS-SSDD-v1.0 contains 15 large-scale SAR images whose ground truths are correctly labeled by SAR experts by drawing support from the Automatic Identification System (AIS) and Google Earth. To facilitate network training, the large-scale images are directly cut into 9000 sub-images without bells and whistles, providing convenience for subsequent detection result presentation in large-scale SAR images. Notably, LS-SSDD-v1.0 has five advantages: (1) large-scale backgrounds, (2) small ship detection, (3) abundant pure backgrounds, (4) fully automatic detection flow, and (5) numerous and standardized research baselines. Last but not least, combined with the advantage of abundant pure backgrounds, we also propose a Pure Background Hybrid Training mechanism (PBHT-mechanism) to suppress false alarms of land in large-scale SAR images. Experimental results of ablation study can verify the effectiveness of the PBHT-mechanism. LS-SSDD-v1.0 can inspire related scholars to make extensive research into SAR ship detection methods with engineering application value, which is conducive to the progress of SAR intelligent interpretation technology.
Investigating Seasonal Effects of Dominant Driving Factors on Urban Land Surface Temperature in a Snow-Climate City in China
Authors: Yang Chaobin , Yan Fengqin , Lei Xuelei , Ding Xiuli , Zheng Yue , Liu Lifeng , Zhang Shuwen
Published: September 2020

Land surface temperature (LST) is a crucial parameter in surface urban heat island (SUHI) studies. A better understanding of the driving mechanisms, influencing variations in LST dynamics, is required for the sustainable development of a city. This study used Changchun, a city in northeast China, as an example, to investigate the seasonal effects of different dominant driving factors on the spatial patterns of LST. Twelve Landsat 8 images were used to retrieve monthly LST, to characterize the urban thermal environment, and spectral mixture analysis was employed to estimate the effect of the driving factors, and correlation and linear regression analyses were used to explore their relationships. Results indicate that, (1) the spatial pattern of LST has dramatic monthly and seasonal changes. August has the highest mean LST of 38.11 °C, whereas December has the lowest (−19.12 °C). The ranking of SUHI intensity is as follows: summer (4.89 °C) > winter with snow cover (1.94 °C) > spring (1.16 °C) > autumn (0.89 °C) > winter without snow cover (−1.24 °C). (2) The effects of driving factors also have seasonal variations. The proportion of impervious surface area (ISA) in summer (49.01%) is slightly lower than those in spring (56.64%) and autumn (50.85%). Almost half of the area is covered with snow (43.48%) in winter. (3) The dominant factors are quite different for different seasons. LST possesses a positive relationship with ISA for all seasons and has the highest Pearson coefficient for summer (r = 0.89). For winter, the effect of vegetation on LST is not obvious, and snow becomes the dominant driving factor. Despite its small area proportion, water has the strongest cooling effect from spring to autumn, and has a warming effect in winter. (4) Human activities, such as agricultural burning, harvest, and different choices of crop species, could also affect the spatial patterns of LST.
Structural Monitoring of Underground Structures in Multi-Layer Media by Dynamic Methods
Authors: Lyapin Alexandr , Beskopylny Alexey , Meskhi Besarion
Published: September 2020

The actual problem of structural monitoring and modeling of dynamic response from buried building is considered in the framework of arbitrary dynamic load. The results can be used for designing underground transport constructions, crossings, buried reservoirs and foundations. In existing methods, the system of sensors that register the response to a dynamic action does not allow for effective interpretation of the signal without understanding the dynamic features and resonance phenomena. The analytical and numerical solution of the problem of the dynamics of a buried object in a layered medium is considered. A multilayer half-space is a set of rigidly interconnected layers characterized by elastic properties. At a distance, an arbitrary dynamic load acts on the half-space, which causes oscillations in the embedded structure, and the sensor system registers the response. The problem of assessing the dynamic stress-strain state (DSSS) is solved using Fourier transforms with the principle of limiting absorption. As an example, the behavior of an embedded massive structure of an underground pedestrian crossing under the influence of a dynamic surface source on a multilayer medium is considered, as well as instrumental support of the sensor system. The solution in the form of stress, strain and displacement fields is obtained and compared with the experimental data. The frequency-dependent characteristics of the system are determined and the possibility of determining the DSSS by a shock pulse is shown.
Process-Driven and Flow-Based Processing of Industrial Sensor Data
Authors: Kammerer Klaus , Pryss Rüdiger , Hoppenstedt Burkhard , Sommer Kevin , Reichert Manfred
Published: September 2020

For machine manufacturing companies, besides the production of high quality and reliable machines, requirements have emerged to maintain machine-related aspects through digital services. The development of such services in the field of the Industrial Internet of Things (IIoT) is dealing with solutions such as effective condition monitoring and predictive maintenance. However, appropriate data sources are needed on which digital services can be technically based. As many powerful and cheap sensors have been introduced over the last years, their integration into complex machines is promising for developing digital services for various scenarios. It is apparent that for components handling recorded data of these sensors they must usually deal with large amounts of data. In particular, the labeling of raw sensor data must be furthered by a technical solution. To deal with these data handling challenges in a generic way, a sensor processing pipeline (SPP) was developed, which provides effective methods to capture, process, store, and visualize raw sensor data based on a processing chain. Based on the example of a machine manufacturing company, the SPP approach is presented in this work. For the company involved, the approach has revealed promising results.
A Paper-Based Near-Infrared Optical Biosensor for Quantitative Detection of Protease Activity Using Peptide-Encapsulated SWCNTs
Authors: Shumeiko Vlad , Paltiel Yossi , Bisker Gili , Hayouka Zvi , Shoseyov Oded
Published: September 2020

A protease is an enzyme that catalyzes proteolysis of proteins into smaller polypeptides or single amino acids. As crucial elements in many biological processes, proteases have been shown to be informative biomarkers for several pathological conditions in humans, animals, and plants. Therefore, fast, reliable, and cost-effective protease biosensors suitable for point-of-care (POC) sensing may aid in diagnostics, treatment, and drug discovery for various diseases. This work presents an affordable and simple paper-based dipstick biosensor that utilizes peptide-encapsulated single-wall carbon nanotubes (SWCNTs) for protease detection. Upon enzymatic digestion of the peptide, a significant drop in the photoluminescence (PL) of the SWCNTs was detected. As the emitted PL is in the near-infrared region, the developed biosensor has a good signal to noise ratio in biological fluids. One of the diseases associated with abnormal protease activity is pancreatitis. In acute pancreatitis, trypsin concentration could reach up to 84 µg/mL in the urine. For proof of concept, we demonstrate the feasibility of the proposed biosensor for the detection of the abnormal levels of trypsin activity in urine samples.
Robot-Aided Systems for Improving the Assessment of Upper Limb Spasticity: A Systematic Review
Authors: de-la-Torre Rubén , Oña Edwin Daniel , Balaguer Carlos , Jardón Alberto
Published: September 2020

Spasticity is a motor disorder that causes stiffness or tightness of the muscles and can interfere with normal movement, speech, and gait. Traditionally, the spasticity assessment is carried out by clinicians using standardized procedures for objective evaluation. However, these procedures are manually performed and, thereby, they could be influenced by the clinician’s subjectivity or expertise. The automation of such traditional methods for spasticity evaluation is an interesting and emerging field in neurorehabilitation. One of the most promising approaches is the use of robot-aided systems. In this paper, a systematic review of systems focused on the assessment of upper limb (UL) spasticity using robotic technology is presented. A systematic search and review of related articles in the literature were conducted. The chosen works were analyzed according to the morphology of devices, the data acquisition systems, the outcome generation method, and the focus of intervention (assessment and/or training). Finally, a series of guidelines and challenges that must be considered when designing and implementing fully-automated robot-aided systems for the assessment of UL spasticity are summarized.
A Holistic Systems Security Approach Featuring Thin Secure Elements for Resilient IoT Deployments
Authors: Ramalingam Soodamani , Gan Hock , Epiphaniou Gregory , Mistretta Emilio
Published: September 2020

IoT systems differ from traditional Internet systems in that they are different in scale, footprint, power requirements, cost and security concerns that are often overlooked. IoT systems inherently present different fail-safe capabilities than traditional computing environments while their threat landscapes constantly evolve. Further, IoT devices have limited collective security measures in place. Therefore, there is a need for different approaches in threat assessments to incorporate the interdependencies between different IoT devices. In this paper, we run through the design cycle to provide a security-focused approach to the design of IoT systems using a use case, namely, an intelligent solar-panel project called Daedalus. We utilise STRIDE/DREAD approaches to identify vulnerabilities using a thin secure element that is an embedded, tamper proof microprocessor chip that allows the storage and processing of sensitive data. It benefits from low power demand and small footprint as a crypto processor as well as is compatible with IoT requirements. Subsequently, a key agreement based on an asymmetric cryptographic scheme, namely B-SPEKE was used to validate and authenticate the source. We find that end-to-end and independent stand-alone procedures used for validation and encryption of the source data originating from the solar panel are cost-effective in that the validation is carried out once and not several times in the chain as is often the case. The threat model proved useful not so much as a panacea for all threats but provided the framework for the consideration of known threats, and therefore appropriate mitigation plans to be deployed.
Voltage-Frequency Domain Optimization for Energy-Neutral Wearable Health Devices
Authors: Tuncel Yigit , An Sizhe , Bhat Ganapati , Raja Naga , Lee Hyung Gyu , Ogras Umit
Published: September 2020

Wearable health and activity monitoring devices must minimize the battery charging and replacement requirements to be practical. Numerous design techniques, such as power gating and multiple voltage-frequency (VF) domains, can be used to optimize power consumption. However, circuit-level techniques alone cannot minimize energy consumption unless they exploit domain-specific knowledge. To this end, we propose a system-level framework that minimizes the energy consumption of wearable health and activity monitoring applications by combining domain-specific knowledge with low-power design techniques. The proposed technique finds the energy-optimal VF domain partitioning and the corresponding VF assignments to each partition. We evaluate this framework with experiments on two activity monitoring and one electrocardiogram applications. Our approach decreases the energy consumption by 33–58% when compared to baseline designs. It also achieves 20–46% more savings compared to a state-of-the-art approach.
Improving Radar Rainfall Estimations with Scaled Raindrop Size Spectra in Mei-Yu Frontal Rainstorms
Authors: Zheng Hepeng , Wu Zuhang , Zhang Lifeng , Xie Yanqiong , Lei Hengchi
Published: September 2020

Hydrological calibration of raw weather radar rainfall estimation relies on in situ rainfall measurements. Raindrop size distribution (DSD) was collected during three typical Mei-Yu rainstorms in July 2014 using three particle size velocity (Parsivel) DSD sensors along the Mei-Yu front in Nanjing, Chuzhou, and the western Pacific, respectively. To improve the radar precipitation estimation in different parts of the Mei-Yu front, a scaling method was adopted to formulate the DSD model and further derive the Z–R relations. The results suggest a distinct variation of DSDs in different parts of the Mei-Yu front. Compared with statistical radar Z–ARb relations obtained by mathematical fitting techniques, the use of a DSD model fitting based on a scaling law formulation theoretically shows a significant improvement in both stratiform (33.9%) and convective (2.8%) rainfall estimations of the Mei-Yu frontal system, which indicates that using a scaling law can better reflect the DSD variations in different parts of the Mei-Yu front. Polarimetric radar has indisputable advantages with multiparameter detection ability. Several dual-polarization radar estimators are also established by DSD sensor data, and the R(ZH, ZDR) estimator is proven to be more accurate than traditional Z–R relations in Mei-Yu frontal rainfall, with potential applications for operational C-band polarimetric radar.
VI-Net—View-Invariant Quality of Human Movement Assessment
Authors: Sardari Faegheh , Paiement Adeline , Hannuna Sion , Mirmehdi Majid
Published: September 2020

We propose a view-invariant method towards the assessment of the quality of human movements which does not rely on skeleton data. Our end-to-end convolutional neural network consists of two stages, where at first a view-invariant trajectory descriptor for each body joint is generated from RGB images, and then the collection of trajectories for all joints are processed by an adapted, pre-trained 2D convolutional neural network (CNN) (e.g., VGG-19 or ResNeXt-50) to learn the relationship amongst the different body parts and deliver a score for the movement quality. We release the only publicly-available, multi-view, non-skeleton, non-mocap, rehabilitation movement dataset (QMAR), and provide results for both cross-subject and cross-view scenarios on this dataset. We show that VI-Net achieves average rank correlation of 0.66 on cross-subject and 0.65 on unseen views when trained on only two views. We also evaluate the proposed method on the single-view rehabilitation dataset KIMORE and obtain 0.66 rank correlation against a baseline of 0.62.
Multimodal Non-Contact Luminescence Thermometry with Cr-Doped Oxides
Authors: Mykhaylyk Vitaliy , Kraus Hans , Zhydachevskyy Yaroslav , Tsiumra Volodymyr , Luchechko Andriy , Wagner Armin , Suchocki Andrzej
Published: September 2020

Luminescence methods for non-contact temperature monitoring have evolved through improvements of hardware and sensor materials. Future advances in this field rely on the development of multimodal sensing capabilities of temperature probes and extend the temperature range across which they operate. The family of Cr-doped oxides appears particularly promising and we review their luminescence characteristics in light of their application in non-contact measurements of temperature over the 5–300 K range. Multimodal sensing utilizes the intensity ratio of emission lines, their wavelength shift, and the scintillation decay time constant. We carried out systematic studies of the temperature-induced changes in the luminescence of the Cr3+-doped oxides Al2O3, Ga2O3, Y3Al5O12, and YAlO3. The mechanism responsible for the temperature-dependent luminescence characteristic is discussed in terms of relevant models. It is shown that the thermally-induced processes of particle exchange, governing the dynamics of Cr3+ ion excited state populations, require low activation energy. This then translates into tangible changes of a luminescence parameter with temperature. We compare different schemes of temperature sensing and demonstrate that Ga2O3-Cr is a promising material for non-contact measurements at cryogenic temperatures. A temperature resolution better than ±1 K can be achieved by monitoring the luminescence intensity ratio (40–140 K) and decay time constant (80–300 K range).
Enhanced Spatial and Extended Temporal Graph Convolutional Network for Skeleton-Based Action Recognition
Authors: Li Fanjia , Li Juanjuan , Zhu Aichun , Xu Yonggang , Yin Hongsheng , Hua Gang
Published: September 2020

In the skeleton-based human action recognition domain, the spatial-temporal graph convolution networks (ST-GCNs) have made great progress recently. However, they use only one fixed temporal convolution kernel, which is not enough to extract the temporal cues comprehensively. Moreover, simply connecting the spatial graph convolution layer (GCL) and the temporal GCL in series is not the optimal solution. To this end, we propose a novel enhanced spatial and extended temporal graph convolutional network (EE-GCN) in this paper. Three convolution kernels with different sizes are chosen to extract the discriminative temporal features from shorter to longer terms. The corresponding GCLs are then concatenated by a powerful yet efficient one-shot aggregation (OSA) + effective squeeze-excitation (eSE) structure. The OSA module aggregates the features from each layer once to the output, and the eSE module explores the interdependency between the channels of the output. Besides, we propose a new connection paradigm to enhance the spatial features, which expand the serial connection to a combination of serial and parallel connections by adding a spatial GCL in parallel with the temporal GCLs. The proposed method is evaluated on three large scale datasets, and the experimental results show that the performance of our method exceeds previous state-of-the-art methods.
Salinity Independent Flow Measurement of Vertical Upward Gas-Liquid Flows in a Small Pipe Using Conductance Method
Authors: Wang Dayang , Jin Ningde , Zhai Lusheng , Ren Yingyu
Published: September 2020

Flow measurement in gas-liquid two-phase flow is always a challenging work, because of the non-uniform phase distribution, severe slippage effect between phases, and different flow structures. Furthermore, the variation of salinity changes the water conductivity, which brings more difficulties to multiphase flow measurement. In this study, a methodology for flow measurement using the conductance method in gas-liquid two-phase flow with salinity change is proposed. The methodology includes the suitable conductivity detection method, the strategy of using combined sensors, and the measurement models of flow parameters. A suitable conductivity detection method that can guarantee that the sensor output is linearly proportional to the conductivity is proposed. This conductivity detection method can ensure that the sensors have a high and constant resolution in the conductivity variation caused by water holdup under the conditions of water conductivity change. Afterward, a combined sensor system consisting of a water holdup sensor, velocity sensor, and water conductivity sensor is designed and experimentally evaluated in gas-water two-phase flow in a 20 mm inner diameter pipe. Considering the non-uniform phase distribution, severe slippage effect between phases, different flow structures, and the variation of salinity, a new water holdup measurement model and flow velocity measurement models are established to achieve salinity independent water holdup measurement and flow velocity measurement for the first time.
Drop Drying on the Sensor: One More Way for Comparative Analysis of Liquid Media
Authors: Yakhno Tatiana , Pakhomov Alexander , Sanin Anatoly , Kazakov Vyacheslav , Ginoyan Ruben , Yakhno Vladimir
Published: September 2020

It is known that the processes of self-organization of the components of drying a liquid drop on a solid substrate are well reproduced under the same external conditions and are determined only by the composition and dispersion of the liquid. If the drop dries on the surface of the sensor device, these processes can be recorded and used as a passport characteristic of the liquid. The first half of the article is devoted to the description of the principles of the method and the proof of the validity of our assumptions. The second half of the article is devoted to the development of a user-friendly version of the device, where the change in the real and imaginary parts of the electrical impedance of the resonator was used as an informative parameter. The measure of the closeness of the relative positions of the hodographs of the compared samples on the complex plane is used as a criterion for the similarity-/-difference of various liquids. The design of a new sensor device and the results of its tests for distinguishing between different brands of alcoholic beverages and reconstituted milk of different concentrations are presented.
Evaluation of Swallowing Related Muscle Activity by Means of Concentric Ring Electrodes
Authors: Garcia-Casado Javier , Prats-Boluda Gema , Ye-Lin Yiyao , Restrepo-Agudelo Sebastián , Perez-Giraldo Estefanía , Orozco-Duque Andrés
Published: September 2020

Surface electromyography (sEMG) can be helpful for evaluating swallowing related muscle activity. Conventional recordings with disc electrodes suffer from significant crosstalk from adjacent muscles and electrode-to-muscle fiber orientation problems, while concentric ring electrodes (CREs) offer enhanced spatial selectivity and axial isotropy. The aim of this work was to evaluate CRE performance in sEMG recordings of the swallowing muscles. Bipolar recordings were taken from 21 healthy young volunteers when swallowing saliva, water and yogurt, first with a conventional disc and then with a CRE. The signals were characterized by the root-mean-square amplitude, signal-to-noise ratio, myopulse, zero-crossings, median frequency, bandwidth and bilateral muscle cross-correlations. The results showed that CREs have advantages in the sEMG analysis of swallowing muscles, including enhanced spatial selectivity and the associated reduction in crosstalk, the ability to pick up a wider range of EMG frequency components and easier electrode placement thanks to its radial symmetry. However, technical changes are recommended in the future to ensure that the lower CRE signal amplitude does not significantly affect its quality. CREs show great potential for improving the clinical monitoring and evaluation of swallowing muscle activity. Future work on pathological subjects will assess the possible advantages of CREs in dysphagia monitoring and diagnosis.
Fault Detection and Isolation Methods in Subsea Observation Networks
Authors: Xiao Sa , Yao Jiajie , Chen Yanhu , Li Dejun , Zhang Feng , Wu Yong
Published: September 2020

Subsea observation networks have gradually become the main means of deep-sea exploration. The reliability of the observation network is greatly affected by the severe undersea conditions. This study mainly focuses on theoretical research and the experimental platform verification of high-impedance and open-circuit fault detection for an underwater observation network. With the aid of deep learning, we perform the fault detection and prediction of the network operation. For the high-impedance and open-circuit fault detection of submarine cables, the entire system is modeled and simulated, and the voltage and current values of the operating nodes under different fault types are collected. Numerous calibrated data samples are supervised by a deep learning algorithm, and a fault location system model is built in the laboratory to verify the feasibility and superiority of the scheme. This paper also studies the fault isolation of the observation network, focusing on the communication protocol and the design of the fault isolation system. Experimental results verify the effectiveness of the proposed algorithm for the location and prediction of high-impedance and open-circuit faults, and the feasibility of the fault isolation system has also been verified. Moreover, the proposed methods greatly improve the reliability of undersea observation network systems.
Improving the Recovery of Patients with Subacromial Pain Syndrome with the DAid Smart Textile Shirt
Authors: Semjonova Guna , Vetra Janis , Cauce Vinita , Oks Alexander , Katashev Alexei , Eizentals Peteris
Published: September 2020

Wearable technologies provide many possibilities for applications in medicine, and especially in physiotherapy, where tracking and evaluation of body motion are of utmost importance. Despite the existence of multiple smart garments produced for applications in physiotherapy, there is limited information available on the actual impact of these technologies on the clinical outcomes. The objective of this paper is to evaluate the impact of the Double Aid (DAid) smart shirt, a purely textile-based system, on the training process of patients with subacromial pain syndrome. A randomized controlled trial was performed where patients with subacromial pain syndrome had to perform the assigned training exercises while employing the DAid smart shirt system. The core point of each exercise was to perform a movement while holding the shoulders stationary. The smart shirt was designed to sense even slight shoulder motion thus providing the patient with feedback on the accuracy of the motion, and allowing the patient to adjust the movement. The appropriate muscles should be strengthened through an increased effort to control the shoulder motion. The recovery of patients using the feedback system at the end of the treatment was compared to that of a reference group through standardized tests—the Disabilities of the Arm, Shoulder, and Hand score (DASH score), Closed Kinetic Chain Upper Extremity Stability test (CKCUES test), and internal/external rotation ratio. The test group that used the DAid system demonstrated significantly better results of the performed tests for all applied outcome measures compared to the reference group (p < 0.001). An overall positive impact on the patient recovery was observed from the DAid smart shirt system when applied for rehabilitation training of patients with subacromial pain syndrome.
Assisted Living System with Adaptive Sensor’s Contribution
Authors: Smoleń Magdalena , Augustyniak Piotr
Published: September 2020

Multimodal sensing and data processing have become a common approach in modern assisted living systems. This is widely justified by the complementary properties of sensors based on different sensing paradigms. However, all previous proposals assume data fusion to be made based on fixed criteria. We proved that particular sensors show different performance depending on the subject’s activity and consequently present the concept of an adaptive sensor’s contribution. In the proposed prototype architecture, the sensor information is first unified and then modulated to prefer the most reliable sensors. We also take into consideration the dynamics of the subject’s behavior and propose two algorithms for the adaptation of sensors’ contribution, and discuss their advantages and limitations based on case studies.
Evaluation of Thermal Comfort and Energy Consumption of Water Flow Glazing as a Radiant Heating and Cooling System: A Case Study of an Office Space
Authors: Moreno Santamaria Belen , Ama Gonzalo Fernando del , Aguirregabiria Benito Lauret , Hernandez Ramos Juan A.
Published: September 2020

Large glass areas, even high-performance glazing with Low-E coating, could lead to discomfort if exposed to solar radiation due to radiant asymmetry. In addition, air-to-air cooling systems affect the thermal environment indoors. Water-Flow Glazing (WFG) is a disruptive technology that enables architects and engineers to design transparent and translucent facades with new features, such as energy management. Water modifies the thermal behavior of glass envelopes, the spectral distribution of solar radiation, the non-uniform nature of radiation absorption, and the diffusion of heat by conduction across the glass pane. The main goal of this article was to assess energy consumption and comfort conditions in office spaces with a large glass area by using WFG as a radiant heating and cooling system. This article evaluates the design and operation of an energy management system coupled with WFG throughout a year in an actual office space. Temperature, relative humidity, and solar radiation sensors were connected to a control unit that actuated the different devices to keep comfortable conditions with minimum energy consumption. The results in summer conditions revealed that if the mean radiant temperature ranged from 19.3 to 23 °C, it helped reduce the operative temperature to comfortable levels when the indoor air temperature was between 25 and 27.5 °C. The Predicted Mean Vote in summer conditions was between 0 and −0.5 in working hours, within the recommended values of ASHRAE-55 standard.
Ionic Liquids Roles and Perspectives in Electrolyte for Dye-Sensitized Solar Cells
Authors: Abu Talip Ruwaida Asyikin , Yahya Wan Zaireen Nisa , Bustam Mohamad Azmi
Published: September 2020

Exploration of renewable energy, such as solar energy, is imminent not only to cater to the escalating energy demand but also to address the uprising environmental issues due to heavy usage of non-renewable fossil fuel. The dye-sensitized solar cells (DSSCs) which are considered as the third-generation solar cells, have a huge potential to be commercialized due to their low cost, simplicity in fabrication, and promising photon-to-electrical energy conversion efficiency. Nevertheless, a high cell efficiency can only be achieved when an organic solvent is incorporated into the formulation of the electrolyte, which is prone to evaporation and leakage. As a result, DSSCs become unsuitable for long-run usage due to thermal instability in the electrolyte. The early intention of incorporating ionic liquids (ILs) into the electrolyte was to curb the abovementioned problem and to enable the DSSCs to function as a sustainable energy device. As such, this article briefly reviews how ILs have been incorporated into the electrolyte formulation and the extent of how the ILs can affect the cell efficiency in various electrolyte states. The role of the ILs in a range of electrolytes is also highlighted. This sheds light on the true purpose of introducing ILs into DSSC electrolyte, which is to enhance the ionicity of the electrolyte.
The Demographic and Economic Determinants of Financial Sustainability: An Analysis of Italian Local Governments
Authors: Santis Serena
Published: September 2020

The aim of this study was to analyze the influence of demographic and economic variables on financial sustainability in Italian local governments (ILGs). Many international organizations have highlighted the importance of pursuing financial sustainability, and identifying what the factors impacting financial sustainability are, allowing to manage risk and opportunities as well as to support the decision-making process better. A total of 104 ILGs with more than 60,000 inhabitants were investigated for the year 2018. The adjusted income statements served as the dependent variable of a regression model for testing several demographic and economic variables. The results showed that demographic factors did not influence financial sustainability while, regarding the economic variables, there was a negative influence of financial autonomy and positive influence of the level of indebtedness. These values indicate a pathological scenario where local governments pursue short-term strategies that will impact future generations.
Eco-Physiological Responses of Black Chokeberries as Affected by Applications of Oil Cake
Authors: Choi Hyun-Sug
Published: September 2020

This study was carried out to examine the optimum amount of oil cake necessary for the desired nutritional status of “Nero” black chokeberry (Aronia melanocarpa (Michx.) Elliot) in an experimental field plot between the years 2018 and 2019. The treatments included 0% (0.0 kg/ha), 25% (4.4 kg/ha), 50% (8.8 kg/ha), 75% (13.1 kg/ha), and 100% (17.5 kg/ha) of a recommended amount of oil cake. The pH in the plots with 8.8, 13.1, and 17.5 kg per ha applied ranged between 7.0 and 7.3, and these values were lower than the values observed on the plots with 0.0 and 4.4 kg per ha applied at the end of July in the years 2018 and 2019, with the concentrations of soil NO3-N and NH4-N remaining low in the off-season. The foliar concentration of total-nitrogen (T-N) was higher for the plants treated with all the oil cake treatments in 2018 and with the oil cake of 17.5 kg/ha in 2019 compared to that of 0.0 kg/ha. The foliar soil plant analysis development values for June and August increased on the bushes treated with 13.1 and 17.5 kg per ha in both the years of 2018 and 2019. The cane diameter, canopy width, and total dry weight were significantly increased by bushes treated with 8.8, 13.1, and 17.5 kg per ha in both years. The fruit yield, harvest index, and percentage of T-N partitioning into fruit were maximized by the treatment with 13.1 kg per ha. An amount of 75% of the recommend application for young black chokeberry may be the prominent application rate in terms of maximized fruit productivity while balancing with the demands of vegetative growth in order to reset the recommended amount of fertilizer.
The Potential Implications of the Introduction of Bioelectricity in South Korea
Authors: Hochman Gal , Tabakis Chrysostomos
Published: September 2020

We investigate the bioelectricity potential of South Korea and the ramifications of the introduction of biomass use in electricity production for the Korean electricity market. The novelty of our study lies in that we consider a broad portfolio of biomass-energy technologies and carefully analyze their potential economic and environmental implications for South Korea given its biomass availability. To the best of our knowledge, this is the first study to attempt this in the context of South Korea. We first offer a preliminary assessment of South Korea’s theoretical biomass potential from forestry residues, livestock manure, and staple crops and of the amount of electricity that could be generated using these different biomass feedstocks. Our analysis suggests that biomass can be used to produce a substantial portion of the total electricity consumed annually in South Korea. In addition, out of all the feedstocks and technologies considered, pyrolysis of forestry residues could potentially impact the electricity market the most. Next, we simulate different bioelectricity supply shocks while randomly perturbing our model’s demand and supply elasticity parameters using the Monte Carlo methodology. Our results demonstrate that the introduction of bioelectricity could significantly affect South Korea’s electricity market as well as its CO2 emissions.
Influence of Inter-Firm Network Relationships on Circular Economy Eco-Innovation Adoption
Authors: Ramkumar Shyaam
Published: September 2020

Research has shown that inter-firm networks and relationships play a key role in innovation adoption; however, these concepts have not specifically been applied to study their role in the adoption of circular economy eco-innovations. This paper considers whether the embedded relationships within inter-firm networks also influence circular economy eco-innovation adoption. Using a historical case study of the REALCAR closed-loop recycling initiative, by Jaguar Land Rover, from 2013 to 2017, the paper conducted qualitative interviews to reconstruct the structure and nature of the relationships between Jaguar Land Rover and its suppliers. This was complemented with a network regression analysis to determine the influence of these relationships on the adoption and implementation decisions of the closed-loop recycling process by the suppliers of Jaguar Land Rover. The results show that Jaguar Land Rover’s relationship as a key customer, facilitation of knowledge sharing among peer suppliers, and resistance from suppliers impacted by changing supply chain relationships played a role in the adoption decisions and adoption timeframe of the REALCAR closed-loop recycling innovation. This has implications for companies and supply chains to consider leveraging the inter-firm relationships embedded in their supply chain networks to accelerate the adoption of circular economy eco-innovations.
Sexual Violence in the City: Space, Gender, and the Occurrence of Sexual Violence in Rotterdam
Authors: Miranda Julia Vansetti , van Nes Akkelies
Published: September 2020

There is a need for knowledge of how the spatial features of the urban environment can shape the potential for safe streets and a gender inclusive society. This research reveals the relationship between a built environment’s spatial features, the presence of various types of people, and gender-based sexual violence in the public space of four neighborhoods in Rotterdam. Detailed sexual violence data are obtained from the police on a street resolution level for correlation with the spatial data on a micro and macro scale level (the space syntax method) and registrations regarding human behavior on streets at different time periods. Pooled Poisson regression models were created to explain the number of sexual violence reports per street and per block. The result is that there are correlations between the occurrence of sexual crimes, the number of people and women on the streets, local spatial integration, the land use of streets, and temporal aspects. Non-residential streets are safe during the day but become dangerous at night, and mixed land use is safer than mono-functional areas. A high degree of inter-visibility for entrances generates high degree of natural surveillance, resulting in greater safety on streets. A residential street with higher flow of people has fewer incidents than mono-functional commercial blocks. Commercial blocks have higher numbers of incidents at night due to the lack of natural surveillance from windows on the ground floor after shops close.
Effect of the C/N Ratio on Biodegradation of Ciprofloxacin and Denitrification from Low C/N Wastewater as Assessed by a Novel 3D-BER System
Authors: Hassan Mahdi , Zhu Guangcan , Yang Zhonglian , Lu Yongze , Lang Yan , Gong Liying , Shan Huang
Published: September 2020

Emerging pollutants in the form of pharmaceuticals have drawn international attention during the past few decades. Ciprofloxacin (CIP) is a common drug widely found in effluents from hospitals, industrial and different wastewater treatment plants, as well as rivers. In this work, the lab-scale 3D-BER system was established, and more than 90% of the antibiotic CIP was removed from Low C/N wastewater. The best results were obtained with the current intensity being taken into account, and a different C/N ratio significantly improved the removal of CIP and nitrates when the ideal conditions were C/N = 1.5–3.5, pH = 7.0–7.5 and I = 60 mA. The highest removal efficiency occurred when CIP = 94.2%, NO3−-N = 95.5% and total nitrogen (TN) = 84.3%, respectively. In this novel system, the autotrophic-heterotrophic denitrifying bacteria played a vital role in the removal of CIP and an enhanced denitrification process. Thus, autotrophic denitrifying bacteria uses CO2 and H2 as carbon sources to reduce nitrates to N2. This system has the assortment and prosperous community revealed at the current intensity of 60 mA, and the analysis of bacterial community structure in effluent samples fluctuates under different conditions of C/N ratios. Based on the results of LC-MS/MS analysis, the intermediate products were proposed after efficient biodegradation of CIP. The microbial community on biodegrading was mostly found at phylum, and the class level was dominantly responsible for the NO3−-N and biodegradation of CIP. This work can provide some new insights towards the biodegradation of CIP and the efficient removal of nitrates from low C/N wastewater treatment through the novel 3D-BER system.
Levels of Alternaria Toxins in Selected Food Commodities Including Green Coffee
Authors: Mujahid Claudia , Savoy Marie-Claude , Baslé Quentin , Woo Pei Mun , Ee Edith Chin Yean , Mottier Pascal , Bessaire Thomas
Published: September 2020

Alternaria toxins are emerging mycotoxins, candidates for regulation by European Authorities. Therefore, highly sensitive, confirmatory, and reliable analytical methodologies are required for their monitoring in food. In that context, an isotope dilution LC-MS/MS method was developed for the analysis of five Alternaria toxins (Altenuene, Alternariol, Alternariol monomethylether, Tentoxin, and Tenuazonic Acid) in a broad range of commodities including cereals and cereal-based products, tomato-based products, tree nuts, vegetable oils, dried fruits, cocoa, green coffee, spices, herbs, and tea. Validation data collected in two different laboratories demonstrated the robustness of the method. Underestimation of Tenuazonic Acid level in dry samples such as cereals was reported when inappropriate extraction solvent mixtures were used as currently done in several published methodologies. An investigation survey performed on 216 food items evidenced large variations of Alternaria toxins levels, in line with data reported in the last EFSA safety assessment. The analysis of 78 green coffee samples collected from 21 producing countries demonstrated that coffee is a negligible source of exposure to Alternaria toxins. Its wide scope of application, adequate sample throughput, and high sensitivity make this method fit for purpose for the regular monitoring of Alternaria toxins in foods.