bluuebunny/crossref_metadata_2025 huggingface dataset:
A Novel Continuous Roll-Forming Process of Elastomer Molds
Authors: Chen Yuan-Yang , Weng Yung-Jin
Published: March 2023

This study proposed a novel continuous roll-forming process of elastomer molds, which can control the deformation of the mold using the rolling belt stack combination method. This study analyzed various rolling belt combinations, assembled the system based on simulation and experimental data according to the deformation requirement design, and obtained a controllable microstructure mold rolling belt with tensile deformation. Mold thickness and microstructure size are key microstructure mold deformation parameters. This study designed and assembled a controllable microstructure mold rolling belt-type imprint molding system and conducted a series of experiments. The impact and application of different experimental system operation procedures and fabrication methods of the auxetic structure rolling belt on replication molding were analyzed. The innovative controllable microstructure mold rolling belt-type imprint replication molding technique proposed in this study had a stable and controllable mold deformation mechanism. It can control and replicate molding.
Green Separation and Extraction of Isofraxidin from Acanthopanax senticosus Using Deep Eutectic Solvent Synthesized from Choline Chloride and Citric Acid
Authors: Sun Chang-hai , Hou Jing-hua , Sun Shi-yuan , Zhang Yu , Zhang Xin-ran , Mu Li-ting
Published: March 2023

Acanthopanax senticosus (Rupr. Maxim.; AS) is a medicinal plant used in the clinical treatment of cerebrovascular diseases and central nervous system disorders, and it significantly improves blood lipid levels and endothelial cell function in patients with acute cerebral infarction. Isofraxidin, one of the active ingredients of AS, is the core of the plant’s medical effects, and its extraction depends on organic solvents. Deep eutectic solvents (DESs) are new green solvents synthesized by intermolecular hydrogen bonding between hydrogen bond donors (HBD) and hydrogen bond acceptors (HBA) which are non-toxic, have a high separation and extraction efficiency, and are environmentally friendly compared to traditional organic solvents. In this paper, DES was used for the extraction of isofraxidin from AS. The primary findings demonstrated that the DES had a viscosity higher than that of ethanol, and even adding a small amount of water (approximately 10%) would trigger solvent redistribution, leading to a considerable reduction in solvent viscosity. In comparison to ethanol, the extraction rate of isofraxidin by DES was 2–3 times higher. Thus, this work developed a new technique for using green extraction of isofraxidin that has some practical implications.
Multiple-Rack Strategies Using Optimization of Location Assignment Based on MRCGA in Miniload Automated Storage and Retrieval System
Authors: He Miao , Guan Zailin , Wang Chuangjian , Hou Guoxiang
Published: March 2023

This paper aimed to introduce multiple-rack strategies in miniload automated storage and retrieval systems (AS/RSs), which included first fit (FF) and best fit (BF) assignment methods based on a matrix real-coded genetic algorithm (MRCGA) in the storage and retrieval process. We validated the probability occurrence of item sizes as a contributory factor in multiple-rack strategies, and compared their capacities, utilization of units and space by equal probabilities or the 80/20 law. According to the analytical methods, BF showed a reduction of more than 11.2% than FF on travel distance, and Type B-FF, Type B-BF and Type C-BF were better able to meet high-density requirements. These strategies provide diversified storage and retrieval solutions for the manufacturing and express delivery industry.
Fuzzy Harmony Search Technique for Cyber Risks in Industry 4.0 Wireless Communication Networks
Authors: Diao Zhifeng , Sun Fanglei
Published: March 2023

Industry 4.0 houses diverse technologies including wireless communication and shared networks for internal and external operations. Due to the wireless nature and remote operability, the exposure to security threats is high. Cyber risk detection and mitigation are prominent for secure industrial operations and planned outcomes. In addition, the system faces the threat of intelligence attacks, security standards issues, privacy concerns and scalability problems. The cyber risk related research problems influence overall data transmission in industry wireless communication networks. For augmenting communication security through cyber risk detection, this article introduces an Explicit Risk Detection and Assessment Technique (ERDAT) for cyber threat mitigation in the industrial process. A fuzzy harmony search algorithm powers this technique for identifying the risk and preventing its impact. The harmony search algorithm mimics the adversary impact using production factors such as process interruption or halting and production outcome. The search performs a mimicking operation for a high objective function based on production output for the admitted plan. The fuzzy operation admits the above factors for identifying the cyber impacting risk, either for its impacts or profitable outcome. In this process, the fuzzy optimization identifies the maximum or minimum objective output targeted for either outcome or risk interrupts, respectively. The fuzzy threshold is identified using a mediated acceptable range, computed as the ratio between minimum and maximum, mimicking occurrences between the risk and scheduled production outcomes. Therefore, the mimicking crossing or falling behind the threshold for the interruption/halting or production, respectively, are identified as risks and their source is detected. The detection communication source is disconnected from the industrial process for preventing further adversary impacts. The introduced system achieves 8.52% high-risk detection, 12.5% fewer outcome interrupts, 8.3% fewer halted schedules, 8.08% less interrupt span, and 7.94% less detection time compared to traditional methods.
ESG Capitalism from a Law and Religion Perspective
Authors: Corsalini Matteo
Published: March 2023

In an age of fear, insecurity, and multiple and overlapping crises, the fortunes of ESG—the idea that companies should include environmental, social, and governance standards of good performance—are now steadily growing. This is all the truer in the West, where corporate financial misconduct and, more worryingly, corporate political activities impacting democratic processes, have become a matter of evident concern. Business scholars and policymakers are, however, still uncertain about which corporate activities merit an ESG label, with pressure groups pushing for their own ESG definitions and standards according to their agendas and beliefs. Against this background, this paper contributes to this Special Issues’ guiding question of how religions can act as agents of civic mobilisation by critically examining their role in addressing sustainability topics due to religious concerns from a legal perspective. This current paper hopes to create a preliminary intellectual bridge between two apparently unrelated fields of research (law and religion; corporate governance) that could help scholars in both areas to develop expertise and sophistication in applying their respective specialities to an otherwise unfamiliar area.
A New Way of Life: The Challenge of Cultural Witness in the Early Jesus Movement
Authors: Schliesser Benjamin
Published: March 2023

This article portrays innovative and distinct features of the Christ groups of the first decades with the underlying premise that the lived reality of the early Christian communities has the potential to inspire present-day churches in Europe when they face the challenge of cultural witness. People were drawn to Christ groups because they were different from the surrounding culture. Christianity would not have survived if it did not offer a counter-cultural ethical stance; a new social imaginary; alternative membership options; a flexible organizational structure; a holistic worldview; and a creative, innovative communication style—in one phrase: a new way of life.
Estimation of Knee Assistive Moment in a Gait Cycle Using Knee Angle and Knee Angular Velocity through Machine Learning and Artificial Stiffness Control Strategy (MLASCS)
Authors: Pornpipatsakul Khemwutta , Ajavakom Nopdanai
Published: March 2023

Nowadays, many people around the world cannot walk perfectly because of their knee problems. A knee-assistive device is one option to support walking for those with low or not enough knee muscle forces. Many research studies have created knee devices with control systems implementing different techniques and sensors. This study proposes an alternative version of the knee device control system without using too many actuators and sensors. It applies the machine learning and artificial stiffness control strategy (MLASCS) that uses one actuator combined with an encoder for estimating the amount of assistive support in a walking gait from the recorded gait data. The study recorded several gait data and analyzed knee moments, and then trained a k-nearest neighbor model using the knee angle and the angular velocity to classify a state in a gait cycle. This control strategy also implements instantaneous artificial stiffness (IAS), a control system that requires only knee angle in each state to determine the amount of supporting moment. After validating the model via simulation, the accuracy of the machine learning model is around 99.9% with the speed of 165 observers/s, and the walking effort is reduced by up to 60% in a single gait cycle.
A Pre-Pruning Quadtree Isolation Method with Changing Threshold for ICESat-2 Bathymetric Photon Extraction
Authors: Zhang Guoping , Xing Shuai , Xu Qing , Li Pengcheng , Wang Dandi
Published: March 2023

The new generation of spaceborne laser altimeter, the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), which can simultaneously generate laser reflections on the water surface and underwater, is a potential data source for exploring water depth in nearshore environments. To achieve this scientific goal, accurate bathymetric photon extraction is needed. This study proposed a pre-pruning quadtree isolation (PQI) method with changing threshold. Firstly, the pre-pruning step is introduced. Photons are transformed into different levels in the isolated quadtree structure according to spatial density. Then, the frequency histogram of photon elevation and isolated level (IL) is generated, the IL thresholds in different depth ranges are calculated by the Otsu method, and the bathymetric photons are extracted. The results in the Culebra archive show that this method achieved a 92.71% F1 score. Noise rate and water depth are the main factors affecting the extraction of sounding photons. When the photon density gradually increases from 2–4 pts/m to 6–8 pts/m, the F1 score of PQI decreases by no more than two percent. In different depth ranges, the extraction results of PQI are also better than those of comparison methods. Therefore, PQI can provide reliable theoretical support for nearshore areas lacking water depth data.
Forecasting Precipitation from Radar Wind Profiler Mesonet and Reanalysis Using the Random Forest Algorithm
Authors: Wu Yizhi , Guo Jianping , Chen Tianmeng , Chen Aijun
Published: March 2023

Data-driven machine learning technology can learn and extract features, a factor which is well recognized to be powerful in the warning and prediction of severe weather. With the large-scale deployment of the radar wind profile (RWP) observational network in China, dynamical variables with higher temporal and spatial resolution in the vertical become strong supports for machine-learning-based severe convection prediction. Based on the RWP mesonet that has been deployed in Beijing, this study uses the measurements from four triangles composed of six RWP stations to determine the profiles of divergence, vorticity, and vertical velocity before rainfall onsets. These dynamic feature variables, combined with cloud properties from Himawari-8 and ERA-5 reanalysis, serve as key input parameters for two rainfall forecast models based on the random forest (RF) classification algorithm. One is for the rainfall/non-rainfall forecast and another for the rainfall grade forecast. The roles of dynamic features such as divergence, vorticity, and vertical velocity are examined from ERA-5 reanalysis data and RWP measurements. The contribution of each feature variable to the performance of the RF model in independent tests is also discussed here. The results show that the usage of RWP observational data as the RF model input tends to result in better performance in rainfall/non-rainfall forecast 30 min in advance of rainfall onset than using the ERA-5 data as inputs. For the rainfall grade forecast, the divergence and vorticity that were estimated from the RWP measurements at 800 hPa show importance in improving the model performance in heavy and moderate rain forecasts. This indicates that the atmospheric dynamic variable measurements from RWP have great potential to improve the prediction skill of convection with the aid of a machine learning model.
Automatic Extraction of Bare Soil Land from High-Resolution Remote Sensing Images Based on Semantic Segmentation with Deep Learning
Authors: He Chen , Liu Yalan , Wang Dacheng , Liu Shufu , Yu Linjun , Ren Yuhuan
Published: March 2023

Accurate monitoring of bare soil land (BSL) is an urgent need for environmental governance and optimal utilization of land resources. High-resolution imagery contains rich semantic information, which is beneficial for the recognition of objects on the ground. Simultaneously, it is susceptible to the impact of its background. We propose a semantic segmentation model, Deeplabv3+-M-CBAM, for extracting BSL. First, we replaced the Xception of Deeplabv3+ with MobileNetV2 as the backbone network to reduce the number of parameters. Second, to distinguish BSL from the background, we employed the convolutional block attention module (CBAM) via a combination of channel attention and spatial attention. For model training, we built a BSL dataset based on BJ-2 satellite images. The test result for the F1 of the model was 88.42%. Compared with Deeplabv3+, the classification accuracy improved by 8.52%, and the segmentation speed was 2.34 times faster. In addition, compared with the visual interpretation, the extraction speed improved by 11.5 times. In order to verify the transferable performance of the model, Jilin-1GXA images were used for the transfer test, and the extraction accuracies for F1, IoU, recall and precision were 86.07%, 87.88%, 87.00% and 95.80%, respectively. All of these experiments show that Deeplabv3+-M-CBAM achieved efficient and accurate extraction results and a well transferable performance for BSL. The methodology proposed in this study exhibits its application value for the refinement of environmental governance and the surveillance of land use.
Temperature and Relative Humidity Profile Retrieval from Fengyun-3D/VASS in the Arctic Region Using Neural Networks
Authors: Hu Jingjing , Wu Jie , Petropoulos George P. , Bao Yansong , Liu Jian , Lu Qifeng , Wang Fu , Zhang Heng , Liu Hui
Published: March 2023

In this study, a new technique is proposed to retrieve temperature and relative humidity profiles under clear sky conditions in the Arctic region based on the artificial neural network (ANN) algorithm using Fengyun-3D (FY-3D) vertical atmospheric sounder suit (VASS: HIRAS, MWTS-II, and MWHS-II) observations. This technology combines infrared (IR) and microwave (MW) observations to improve retrieval accuracy in the middle and low troposphere by reducing the sensitivity of the neural networks (NNs) to cloud coverage. The approach was compared against other methods available in the literature on retrieving profiles only from FY-3D/HIRAS data. Furthermore, its retrieval performance was tested by comparing the NNs’ prediction accuracy versus the corresponding FY-3D/VASS and Aqua/AIRS L2 products. The results showed that: (1) NNs retrieval accuracy is higher during the warm season and over the ocean; (2) the retrieval accuracy of NNs has been significantly improved compared with satellite L2 products; (3) referring to radiosonde observations, the retrieval accuracy of NNs below 600 hPa is effectively improved by adding the information of the MW channel, especially on land where cloud clearing is more difficult. The root mean square error (RMSE) of temperature and relative humidity in the cold season were reduced by 0.3 K and 2%, respectively. The advanced NNs proposed herein offer a more stable retrieval performance compared with NNs built only by FY-3D/HIRAS data. The study results indicated the potential value in time and space domain of the NN algorithm in retrieving temperature and relative humidity profiles of the Arctic region from FY-3D/VASS observations under clear-sky conditions. All in all, this work enhances our knowledge towards improving operational use of FY-3D satellite data in the Arctic region.
Prediction of the Area of High-Turbidity Water in the Yatsushiro Sea, Japan, Using Machine Learning with Satellite, Meteorological, and Oceanographic Data
Authors: Nagayama Kazutaka , Tonooka Hideyuki
Published: March 2023

Turbid water is known to affect aquatic ecosystems. If the spread of turbid water can be predicted, it is expected to lead to the prediction of damage caused by turbid water in rich aquatic ecosystems and aquaculture farms, and to countermeasures against turbid water. In this study, we developed a method for predicting the area of high-turbidity water using machine learning with satellite-observed total suspended solids (TSS) product and relatively readily available meteorological and oceanographic data (rainfall, wind direction and speed, atmospheric pressure, and tide level) in the past and evaluated it for the Kuma River estuary of the Yatsushiro Sea in Japan. The results showed that the highest accuracy was obtained using random forest regression, with a coefficient of determination of 0.552, when the area of high-turbidity water based on the previous day’s TSS product and hourly meteorological and oceanographic data from the previous day were used as inputs. The most important factor for the prediction was the area of high-turbidity water, followed by wind, and tide level, but the effect of rainfall was small, which was probably due to the flood-control function of the river. Our future work will be to evaluate the applicability of the method to other areas, improve the accuracy, and predict the distribution area.
Cloud-Based Remote Sensing for Wetland Monitoring—A Review
Authors: Abdelmajeed Abdallah Yussuf Ali , Albert-Saiz Mar , Rastogi Anshu , Juszczak Radosław
Published: March 2023

The rapid expansion of remote sensing provides recent and developed advances in monitoring wetlands. Integrating cloud computing with these techniques has been identified as an effective tool, especially for dealing with heterogeneous datasets. In this study, we conducted a systematic literature review (SLR) to determine the current state-of-the-art knowledge for integrating remote sensing and cloud computing in the monitoring of wetlands. The results of this SLR revealed that platform-as-a-service was the only cloud computing service model implemented in practice for wetland monitoring. Remote sensing applications for wetland monitoring included prediction, time series analysis, mapping, classification, and change detection. Only 51% of the reviewed literature, focused on the regional scale, used satellite data. Additionally, the SLR found that current cloud computing and remote sensing technologies are not integrated enough to benefit from their potential in wetland monitoring. Despite these gaps, the analysis revealed that economic benefits could be achieved by implementing cloud computing and remote sensing for wetland monitoring. To address these gaps and pave the way for further research, we propose integrating cloud computing and remote sensing technologies with the Internet of Things (IoT) to monitor wetlands effectively.
An Aftershock Deletion Method Based on Fault Buffer Zone
Authors: Yang Guangliang , Zhao Bingjie , Liu Yijun
Published: March 2023

The existence of aftershocks in an earthquake sequence can impact the analysis of the mainshock. In this study, we present a method for deleting an aftershock sequence based on the spatial relationship between earthquakes and faults. This method improves the performance of space window selection in the classical K-K method by eliminating aftershocks with an ideal fault buffer zone. The determination of fault buffer zones is based on a trial-and-error analysis of 69,714 earthquake records from the China Seismic Network Center (CENC) collected between 1980 and 2020. We selected 20 typical big earthquakes (ML7.0–8.0 or ~Ms6.6–8.0; for earthquakes above magnitude Ms7 or ML7.2, ML is approximately equal to Ms) as the mainshocks to establish the fault buffer zones. We also propose an empirical formula to determine the distance of the fault buffer zone by counting the aftershock deletion effect at different buffer distances. Compared with the classical K-K method, our method considers the correlation between the spatial distribution of aftershocks and faults, eliminates earthquake groups that are not related to the mainshock, greatly reduces the spatial range of aftershocks, improves the performance of deleting aftershocks of different magnitudes, and provides a new rule and reference for aftershock deletion.
Accurate Discharge Estimation Based on River Widths of SWOT and Constrained At-Many-Stations Hydraulic Geometry
Authors: Du Bin , Jin Taoyong , Liu Dong , Wang Youkun , Wu Xuequn
Published: March 2023

River discharge monitoring is an important component of the hydrology objectives of Surface Water and Ocean Topography mission (SWOT). River discharge can be estimated Solely using river widths and At Many-stations Hydraulic Geometry (AMHG), but the accuracy is low due to the parameters of At a-station Hydraulic Geometry (AHG) given by AMHG deviate from the truth. In view of this, a Constrained At-Many-Stations Hydraulic Geometry (CAMHG) is proposed to optimize AHG parameters. The performance of CAMHG is verified in three reaches of the Yangtze River using river widths derived from SWOT. After using CAMHG, the relative root mean square error (RRMSE) of estimated discharge reduce 100.1% to 24.4%, 1137.1% to 49.9% and 48.6% to 45.5% for Hankou, Shashi and Luoshan respectively. In addition, CAMHG can also weaken the accuracy difference of estimated discharge in dry and wet seasons benefited from its more reliable AHG parameters. Thus, the proposed CAMHG can dramatically improves the accuracy of discharge estimations and it is meaningful for the discharge calculation after SWOT data release.
Development of Hybrid Models to Estimate Gross Primary Productivity at a Near-Natural Peatland Using Sentinel 2 Data and a Light Use Efficiency Model
Authors: Ingle Ruchita , Bhatnagar Saheba , Ghosh Bidisha , Gill Laurence , Regan Shane , Connolly John , Saunders Matthew
Published: March 2023

Peatlands store up to 2320 Mt of carbon (C) on only ~20% of the land area in Ireland; however, approximately 90% of this area has been drained and is emitting up to 10 Mt C per year. Gross primary productivity (GPP) is a one of the key components of the peatland carbon cycle, and detailed knowledge of the spatial and temporal extent of GPP under changing management practices is imperative to improve our predictions of peatland ecology and biogeochemistry. This research assesses the relationship between remote sensing and ground-based estimates of GPP for a near-natural peatland in Ireland using eddy covariance (EC) techniques and high-resolution Sen-tinel 2A satellite imagery. Hybrid models were developed using multiple linear regression along with six widely used conventional indices and a light use efficiency model. Estimates of GPP using NDVI, EVI, and NDWI2 hybrid models performed well using literature-based light use efficiency parameters and showed a significant correlation from 89 to 96% with EC-derived GPP. This study also reports additional site-specific light use efficiency parameters for dry and hydrologically normal years on the basis of light response curve methods (LRC). Overall, this research has demonstrated the potential of combining EC techniques with satellite-derived models to better understand and monitor key drivers and patterns of GPP for raised bog ecosystems under different climate scenarios and has also provided light use efficiency parameters values for dry and wetter conditions that can be used for the estimation of GPP using LUE models across various site and scales.
Behavior and Task Classification Using Wearable Sensor Data: A Study across Different Ages
Authors: Gasparini Francesca , Grossi Alessandra , Giltri Marta , Nishinari Katsuhiro , Bandini Stefania
Published: March 2023

In this paper, we face the problem of task classification starting from physiological signals acquired using wearable sensors with experiments in a controlled environment, designed to consider two different age populations: young adults and older adults. Two different scenarios are considered. In the first one, subjects are involved in different cognitive load tasks, while in the second one, space varying conditions are considered, and subjects interact with the environment, changing the walking conditions and avoiding collision with obstacles. Here, we demonstrate that it is possible not only to define classifiers that rely on physiological signals to predict tasks that imply different cognitive loads, but it is also possible to classify both the population group age and the performed task. The whole workflow of data collection and analysis, starting from the experimental protocol, data acquisition, signal denoising, normalization with respect to subject variability, feature extraction and classification is described here. The dataset collected with the experiments together with the codes to extract the features of the physiological signals are made available for the research community.
Modern Electrochemical Biosensing Based on Nucleic Acids and Carbon Nanomaterials
Authors: Szymczyk Anna , Ziółkowski Robert , Malinowska Elżbieta
Published: March 2023

To meet the requirements of novel therapies, effective treatments should be supported by diagnostic tools characterized by appropriate analytical and working parameters. These are, in particular, fast and reliable responses that are proportional to analyte concentration, with low detection limits, high selectivity, cost-efficient construction, and portability, allowing for the development of point-of-care devices. Biosensors using nucleic acids as receptors has turned out to be an effective approach for meeting the abovementioned requirements. Careful design of the receptor layers will allow them to obtain DNA biosensors that are dedicated to almost any analyte, including ions, low and high molecular weight compounds, nucleic acids, proteins, and even whole cells. The impulse for the application of carbon nanomaterials in electrochemical DNA biosensors is rooted in the possibility to further influence their analytical parameters and adjust them to the chosen analysis. Such nanomaterials enable the lowering of the detection limit, the extension of the biosensor linear response, or the increase in selectivity. This is possible thanks to their high conductivity, large surface-to-area ratio, ease of chemical modification, and introduction of other nanomaterials, such as nanoparticles, into the carbon structures. This review discusses the recent advances on the design and application of carbon nanomaterials in electrochemical DNA biosensors that are dedicated especially to modern medical diagnostics.
Movement Prototypes in a Complex Teamgym Gymnastics Technique on the Vaulting Table and Their Relationship with Judges’ Scores
Authors: Barreto Joana , Henriques Rui , Cabral Sílvia , Pedro Bruno , Peixoto César , Veloso António
Published: March 2023

A successful high-level gymnastics performance is the result of the coordination and inter-relation of body segments to produce movement prototypes. In this context, the exploration of different movement prototypes, as well as their relations with judges’ scores, can aid coaches to design better learning and practice methodologies. Therefore, we investigate if there are different movement prototypes of the technique of the handspring tucked somersault with a half twist (HTB) on a mini trampoline with a vaulting table and its relations with judges’ scores. We assessed flexion/extension angles of five joints during fifty trials, using an inertial measurement unit system. All trials were scored by international judges for execution. A multivariate time series cluster analysis was performed to identify movement prototypes and their differential association with judges’ scores was statistically assessed. Nine different movement prototypes were identified for the HTB technique, with two of them associated with higher scores. Statistically strong associations were found between scores and movement phases one (i.e., from the last step on the carpet to the initial contact of both feet with the mini trampoline), two (i.e., from the initial contact to the take-off on the mini trampoline) and four (i.e., from the initial contact of both hands with the vaulting table to take-off on the vaulting table) and moderate associations with movement phase six (i.e., from the tucked body position to landing with both feet on the landing mat). Our findings suggest (a) the presence of multiple movement prototypes yielding successful scoring and (b) the moderate-to-strong association of movement variations along phases one, two, four and six with judges’ scores. We suggest and provide guidelines for coaches to encourage movement variability that can lead their gymnasts to functionally adapt their performance and succeed when facing different constraints.
Research on Omnidirectional Stereo Measurement Using Convex Mirrors and Vertical Disparity
Authors: Ozawa Yuki , Kimura Shingo , Zhu Yiling , Kurihara Atsutoshi , Bao Yue
Published: March 2023

We propose an omnidirectional measurement method without blind spots by using a convex mirror, which in principle does not cause chromatic aberration, and by using vertical disparity by installing cameras at the top and bottom of the image. In recent years, there has been significant research in the fields of autonomous cars and robots. In these fields, three-dimensional measurements of the surrounding environment have become indispensable. Depth sensing with cameras is one of the most important sensors for recognizing the surrounding environment. Previous studies have attempted to measure a wide range of areas using fisheye and full spherical panoramic cameras. However, these approaches have limitations such as blind spots and the need for multiple cameras to measure all directions. Therefore, this paper describes a stereo camera system that uses a device capable of taking an omnidirectional image with a single shot, enabling omnidirectional measurement with only two cameras. This achievement was challenging to attain with conventional stereo cameras. The results of experiments confirmed an improvement in accuracy of up to 37.4% compared to previous studies. In addition, the system succeeded in generating depth image that can recognize distances in all directions in a single frame, demonstrating the possibility of omnidirectional measurement with two cameras.
A Comparative Study on the Effects of Spray Coating Methods and Substrates on Polyurethane/Carbon Nanofiber Sensors
Authors: Karlapudi Mounika Chowdary , Vahdani Mostafa , Bandari Sheyda Mirjalali , Peng Shuhua , Wu Shuying
Published: March 2023

Thermoplastic polyurethane (TPU) has been widely used as the elastic polymer substrate to be combined with conductive nanomaterials to develop stretchable strain sensors for a variety of applications such as health monitoring, smart robotics, and e-skins. However, little research has been reported on the effects of deposition methods and the form of TPU on their sensing performance. This study intends to design and fabricate a durable, stretchable sensor based on composites of thermoplastic polyurethane and carbon nanofibers (CNFs) by systematically investigating the influences of TPU substrates (i.e., either electrospun nanofibers or solid thin film) and spray coating methods (i.e., either air-spray or electro-spray). It is found that the sensors with electro-sprayed CNFs conductive sensing layers generally show a higher sensitivity, while the influence of the substrate is not significant and there is no clear and consistent trend. The sensor composed of a TPU solid thin film with electro-sprayed CNFs exhibits an optimal performance with a high sensitivity (gauge factor ~28.2) in a strain range of 0–80%, a high stretchability of up to 184%, and excellent durability. The potential application of these sensors in detecting body motions has been demonstrated, including finger and wrist-joint movements, by using a wooden hand.
Defect Detection in Printed Circuit Boards Using Semi-Supervised Learning
Authors: Pham Thi Tram Anh , Thoi Do Kieu Trang , Choi Hyohoon , Park Suhyun
Published: March 2023

Defect inspection is essential in the semiconductor industry to fabricate printed circuit boards (PCBs) with minimum defect rates. However, conventional inspection systems are labor-intensive and time-consuming. In this study, a semi-supervised learning (SSL)-based model called PCB_SS was developed. It was trained using labeled and unlabeled images under two different augmentations. Training and test PCB images were acquired using automatic final vision inspection systems. The PCB_SS model outperformed a completely supervised model trained using only labeled images (PCB_FS). The performance of the PCB_SS model was more robust than that of the PCB_FS model when the number of labeled data is limited or comprises incorrectly labeled data. In an error-resilience test, the proposed PCB_SS model maintained stable accuracy (error increment of less than 0.5%, compared with 4% for PCB_FS) for noisy training data (with as much as 9.0% of the data labeled incorrectly). The proposed model also showed superior performance when comparing machine-learning and deep-learning classifiers. The unlabeled data utilized in the PCB_SS model helped with the generalization of the deep-learning model and improved its performance for PCB defect detection. Thus, the proposed method alleviates the burden of the manual labeling process and provides a rapid and accurate automatic classifier for PCB inspections.
YOLO Series for Human Hand Action Detection and Classification from Egocentric Videos
Authors: Nguyen Hung-Cuong , Nguyen Thi-Hao , Scherer Rafał , Le Van-Hung
Published: March 2023

Hand detection and classification is a very important pre-processing step in building applications based on three-dimensional (3D) hand pose estimation and hand activity recognition. To automatically limit the hand data area on egocentric vision (EV) datasets, especially to see the development and performance of the “You Only Live Once” (YOLO) network over the past seven years, we propose a study comparing the efficiency of hand detection and classification based on the YOLO-family networks. This study is based on the following problems: (1) systematizing all architectures, advantages, and disadvantages of YOLO-family networks from version (v)1 to v7; (2) preparing ground-truth data for pre-trained models and evaluation models of hand detection and classification on EV datasets (FPHAB, HOI4D, RehabHand); (3) fine-tuning the hand detection and classification model based on the YOLO-family networks, hand detection, and classification evaluation on the EV datasets. Hand detection and classification results on the YOLOv7 network and its variations were the best across all three datasets. The results of the YOLOv7-w6 network are as follows: FPHAB is P = 97% with TheshIOU = 0.5; HOI4D is P = 95% with TheshIOU = 0.5; RehabHand is larger than 95% with TheshIOU = 0.5; the processing speed of YOLOv7-w6 is 60 fps with a resolution of 1280 × 1280 pixels and that of YOLOv7 is 133 fps with a resolution of 640 × 640 pixels.
Energy Harvesting and Task-Aware Multi-Robot Task Allocation in Robotic Wireless Sensor Networks
Authors: Gul Omer Melih
Published: March 2023

In this work, we investigate an energy-aware multi-robot task-allocation (MRTA) problem in a cluster of the robot network that consists of a base station and several clusters of energy-harvesting (EH) robots. It is assumed that there are M+1 robots in the cluster and M tasks exist in each round. In the cluster, a robot is elected as the cluster head, which assigns one task to each robot in that round. Its responsibility (or task) is to collect the resultant data from the remaining M robots to aggregate and transmit directly to the BS. This paper aims to allocate the M tasks to the remaining M robots optimally or near optimally by considering the distance to be traveled by each node, the energy required for executing each task, the battery level at each node, and the energy-harvesting capabilities of the nodes. Then, this work presents three algorithms: Classical MRTA Approach, Task-aware MRTA Approach, EH and Task-aware MRTA Approach. The performances of the proposed MRTA algorithms are evaluated under both independent and identically distributed (i.i.d.) and Markovian energy-harvesting processes for different scenarios with five robots and 10 robots (with the same number of tasks). EH and Task-aware MRTA Approach shows the best performance among all MRTA approaches by keeping up to 100% more energy in the battery than the Classical MRTA Approach and keeping up to 20% more energy in the battery than the Task-aware MRTA Approach.
Returns to Relationships: Social Capital and Household Welfare in India
Authors: Jha Jaya , Kelley Edward
Published: March 2023

Sociological scholarship, economic theory, and empirical studies all indicate that interpersonal relationships are valuable productive assets and deserve to be formally incorporated into the study of human development. This paper employs the India Human Development Survey to examine, using OLS and logistic regressions, the impact of different dimensions of social capital on multiple proxies for household welfare. Social capital in the form of memberships in local community organizations and social network connections has a statistically and economically significant association with household consumption expenditures, physical asset ownership, and the probability of a household living in poverty. Households that are members of any formal community organization are expected to have higher monthly per capita consumption expenditures than households without any memberships. Estimates of a similar magnitude are observed when modeling a household’s stock of physical assets, a longer-term indicator of economic welfare. These indicators of social capital are also significantly associated with lower odds of a household living below the poverty line. Organizational memberships and social networks are also associated with considerably higher odds of a household assessing its own economic situation positively. Overall, social capital is a catalyst for increasing household welfare along multiple dimensions, and, therefore, a critical area of focus for economists, sociologists, development practitioners, and policymakers.
Public Trust in the Time of Pandemic: An Analysis of Social Networks in the Discourse of Large-Scale Social Restrictions in Indonesia
Authors: Paskarina Caroline
Published: March 2023

This article discusses public trust in the Indonesian government’s response to the COVID-19 pandemic, explicitly focusing on the discourse surrounding large-scale social restrictions (LSSR). In a time of uncertainty, the public requires timely and actual information, most of which is gathered through online media, with Twitter being one such medium. This article applies social network analysis to examine how information about the restrictions is shared and discussed on social media platforms and how this discourse may impact public trust in government institutions in the first phase of pandemic handling. Although LSSR is the government’s policy, this study shows that the interpersonal network plays a more significant part in distributing information, indicating a legitimacy crisis of formal and authoritative sources of information. The negative sentiment voiced by critics did not show public rejection of the implementation of LSSR. On the contrary, what was implied by those critics was public doubt against the consistency and firmness of LSSR implementation—because of this, restoring public trust requires planned information management to communicate risks to those who are affected by LSSR implementation, as well as managing negative sentiments that arise as a response.
Merkel Government’s Refugee Policy: Under Bounded Rationality
Authors: Niu Zhongqi , Song Wenlong , Lu Yantong , Bao Xingyu
Published: March 2023

As the country hosting the most significant number of refugees in Europe, Germany’s Merkel government’s refugee policy has been repeatedly adjusted and plagued by inconsistencies and management failures. What factors have influenced the formation of the Merkel government’s refugee policy and its two shifts? The traditional rational decision-making model does not effectively explain government decisions’ motivation in complex challenges. This article develops a framework for analyzing “bounded rational decision-making”. It identifies the three factors that influenced the Merkel government’s refugee policy: (a) strict border controls, (b) welcome culture, and (c) welcome culture under restrained policies. It explains the process and logic of their development. Based on the theory of “bounded rational decision making”, the article examines the “key events and problem identification”, “goal selection”, “national interests”, and “political psychology”. We found that in the early stages of the refugee crisis, (a) public opinions and pressure, (b) the pursuit of a positive national image, and (c) a shift in the leader’s psychology led Merkel to shift away from a pragmatic and rational course. These factors led to the first shift in German refugee policy from “hesitation” to a “welcoming culture”. At the end of 2015, however, the shortcomings of the irrational policy were quickly revealed, and the frequency of refugee-related social problems reversed German public opinion, forcing Merkel’s government to seek a balance between humanitarianism and national interests. As a result, refugee policy was adjusted for the second time, returning to a strict refugee examination system. The development of the Merkel government’s refugee policy exposed the shortcomings of the traditional crisis decision-making model. Moreover, it provided a new perspective for rethinking the governance of the refugee crisis.
Hygienic Boundaries: Roma Communities and the Racialisation of Public Health Discourses during the COVID-19 Pandemic
Authors: Cârstocea Andreea
Published: March 2023

Public health measures instituted during the COVID-19 pandemic included both social distancing measures (including lockdowns), as well as personal hygiene measures (i.e., washing hands, wearing masks), with the purpose of preventing the spread of the virus. Using primary data obtained from stakeholder interviews, surveys, and desktop research from seven non-EU countries in Eastern Europe, this article shows how a new discursive fault line with hygiene as its core emerged across these countries in the wake of the COVID-19 pandemic, symbolically (and sometimes physically) separating the Roma from the non-Roma. Lockdowns reduced the ability of the Roma people to earn a living, due to the often-informal nature of their employment; as a result, many faced difficulties in covering basic living costs. These difficulties were compounded by poor living conditions, which limited the extent to which Roma people were able to follow social distancing rules and the measures regulating personal hygiene. All these factors were used to depict Roma communities as both lacking in personal hygiene and as wilfully non-compliant with public health rules. Public discourses emphasised the gap between the (self-perceived) clean and rule-observing non-Roma, and Roma communities, constructed as lacking in discipline and personal hygiene. These discourses, centred on hygiene, reinforced social boundaries and justified abuse and exclusion.
An Investigation of Saudi Arabia’s Ambitious Reform Programme with Vision 2030 to Incentivise Investment in the Country’s Non-Oil Industries
Authors: Alam Firoz , Alam Shahid , Asif Mohammad , Hani Umme , Khan Mohd Naved
Published: March 2023

Background/Objectives: Vision 2030 of Saudi Arabia is an important project that mainly targets the reduction in dependency on the oil economy through the growth of non-oil Industries. This study investigates Saudi Arabia’s effort for Vision 2030 to incentivise investment in the non-oil industry’s growth. Material and Methods:A sample of 225 employees of the non-oil industries of Saudi Arabia was selected as sample participants. A survey was conducted for data collection, and SPSS software was used to analyse the data. Findings:The findings indicate that sig values for all tests were 0.000 < 0.05 to support the selection of an alternative hypothesis. Therefore, it can easily be determined from the analysis that a more robust relationship is present between Vision 2030 with incentivised investment, economic stability, and financial accommodation.The advantages of reform programs have been effective in non-oil industries of Saudi Arabia to increase production standards and obtain more customer engagement.Conclusion: This research contributes to providing a straightforward concept in Vision 2030, which is Saudi Arabia’s reform program to develop the economic condition of Non-Oil Sectors. Hence, reform in the policy for economic stability can help to maintain the growth of different sectors and can further support financial assets.
How Has the COVID-19 Crisis Transformed Entrepreneurs into Sustainable Leaders?
Authors: Haider Murtaza , Shannon Randall , Moschis George P. , Autio Erkko
Published: March 2023

EntREsilience, a five-country longitudinal qualitative study, was launched in 2020 in China, Malaysia, the Philippines, Thailand and the UK to understand how entrepreneurs manifested resilience in response to the COVID-19 pandemic crisis events from March 2020 to February 2022. EntREsilience proposed a resilience-manifestation process model describing how entrepreneurs responded to the COVID-19 disruption, aided by external and internal enablers, adjusting their businesses to stabilise and even hunting for opportunities to grow their businesses. The present research adds to the findings of EntREsilience by analysing the strategies applied by entrepreneurs in their response to the crisis. This exploratory study focused on the entrepreneurs’ community interactions and studied the effects of these interactions on the response measures adopted by the entrepreneurs. The results describe how the awareness of their stakeholder challenges shaped the entrepreneurial response. Realising the importance of stakeholder well-being to the sustainability of their enterprise motivated the entrepreneurs to develop sustainability competencies towards their stakeholder challenges, innovating solutions for their mutual well-being. By extending the resilience-manifestation process model, this paper proposes a transformation model depicting the process of entrepreneurs transforming into sustainable leaders triggered by stakeholder challenge awareness and moderated by contextual factors.