bluuebunny/crossref_metadata_2025 huggingface dataset:
Contribution of Reverse Dune Migration to Stabilization of a Transgressive Coastal Dune Field at Lagoa do Peixe National Park Dune Field (South of Brazil)
Authors: Manzolli Rogério Portantiolo , Portz Luana Carla , Fontán-Bouzas Angela , Bitencourt Volney Junior Borges , Alcántara-Carrió Javier
Published: July 2023

Coastal dunes that transgress typically move landward, while their reverse movement is not well understood. The article discusses the study of barchan and barchanoid dunes in the Lagoa do Peixe National Park in the coastal plain of Rio Grande do Sul, Brazil. The aim of the study is to analyze seasonal patterns and long-term trends in the direction and migration rates of these dunes, which can pose a threat to the lagoon if they invade its space. The crest migration of 12 dunes was monitored by satellite images between July 2003 and December 2018, and DGPS topographic surveys were performed on five dunes between 2010 and 2018. The migration rates obtained were combined with an analysis of the meteorological data and calculations of the drift potential for eolian sediment transport. The wind regime in the study area shows a multidirectional pattern, with the predominant wind direction being from the NE, followed by the ENE direction. The wind direction also exhibits a seasonal behavior, with the winds from the first quadrant being dominant during spring and summer months and a gradual increase in winds from the second and third quadrants from the end of summer to winter. The dune crest migration rates in the Lagoa do Peixe National Park show an average of 16.55 m·yr−1 towards WSW–W, mainly controlled by the direction of the effective winds. However, intense SSW–WSW winds caused by cold fronts in the past generate the reverse migration of dunes towards ENE–E. The reverse migration of dunes explains the steadiness of the dune fields at CPRGS and is a factor controlling dune stabilization and the geomorphological evolution of transgressive coastal dune fields. The article highlights the importance of monitoring dune movement to understand their responses to natural and anthropogenic stressors and to protect sensitive ecosystems.
Band-Optimized Bidirectional LSTM Deep Learning Model for Bathymetry Inversion
Authors: Xi Xiaotao , Chen Ming , Wang Yingxi , Yang Hua
Published: July 2023

Shallow water bathymetry is of great significance in understanding, managing, and protecting coastal ecological environments. Many studies have shown that both empirical models and deep learning models can achieve promising results from satellite imagery bathymetry inversion. However, the spectral information available today in multispectral or/and hyperspectral satellite images has not been explored thoroughly in many models. The Band-optimized Bidirectional Long Short-Term Memory (BoBiLSTM) model proposed in this paper feeds only the optimized bands and band ratios to the deep learning model, and a series of experiments were conducted in the shallow waters of Molokai Island, Hawaii, using hyperspectral satellite imagery (PRISMA) and multispectral satellite imagery (Sentinel-2) with ICESat-2 data and multibeam scan data as training data, respectively. The experimental results of the BoBiLSTM model demonstrate its robustness over other compared models. For example, using PRISMA data as the source image, the BoBiLSTM model achieves RMSE values of 0.82 m (using ICESat-2 as the training data) and 1.43 m (using multibeam as the training data), respectively, and because of using the bidirectional strategy, the inverted bathymetry reaches as far as a depth of 25 m. More importantly, the BoBiLSTM model does not overfit the data in general, which is one of its advantages over many other deep learning models. Unlike other deep learning models, which require a large amount of training data and all available bands as the inputs, the BoBiLSTM model can perform very well using equivalently less training data and a handful of bands and band ratios. With ICESat-2 data becoming commonly available and covering many shallow water regions around the world, the proposed BoBiLSTM model holds potential for bathymetry inversion for any region around the world where satellite images and ICESat-2 data are available.
Long-Range Perception System for Road Boundaries and Objects Detection in Trains
Authors: Pan Wenbo , Fan Xianghua , Li Hongbo , He Kai
Published: July 2023

This article introduces a long-range sensing system based on millimeter-wave radar, which is used to detect the roadside boundaries and track trains for trains. Due to the high speed and long braking distance of trains, existing commercial vehicle sensing solutions cannot meet their needs for long-range target detection. To address this challenge, this study proposes a long-range perception system for detecting road boundaries and trains based on millimeter-wave radar. The system uses high-resolution, long-range millimeter-wave radar customized for the strong scattering environment of rail transit. First, we established a multipath scattering theory in complex scenes such as track tunnels and fences and used the azimuth scattering characteristics to eliminate false detections. A set of accurate calculation methods of the train’s ego-velocity is proposed, which divides the radar detection point clouds into static target point clouds and dynamic target point clouds based on the ego-velocity of the train. We then used the road boundary curvature, global geometric parallel information, and multi-frame information fusion to extract and fit the boundary in the static target point stably. Finally, we performed clustering and shape estimation on the radar track information to identify the train and judge the collision risk based on the position and speed of the detected train and the extracted boundary information. The paper makes a significant contribution by establishing a multipath scattering theory for complex scenes of rail transit to eliminate radar false detection and proposing a train speed estimation strategy and a road boundary feature point extraction method that adapt to the rail environment. As well as building a perception system and installing it on the train for verification, the main line test results showed that the system can reliably detect the road boundary more than 400 m ahead of the train and can stably detect and track the train.
Characteristics of Internal Tides from ECCO Salinity Estimates and Observations in the Bay of Bengal
Authors: Subrahmanyam Bulusu , Murty V.S.N. , Hall Sarah B.
Published: July 2023

Internal waves (IWs) are generated in all the oceans, and their amplitudes are large, especially in regions that receive a large amount of freshwater from nearby rivers, which promote highly stratified waters. When barotropic tides encounter regions of shallow bottom-topography, internal tides (known as IWs of the tidal period) are generated and propagated along the pycnocline due to halocline or thermocline. In the North Indian Ocean, the Bay of Bengal (BoB) and the Andaman Sea receive a large volume of freshwater from major rivers and net precipitation during the summer monsoon. This study addresses the characteristics of internal tides in the BoB and Andaman Sea using NASA’s Estimating the Circulation and Climate of the Ocean (ECCO) project’s high-resolution (1/48° and hourly) salinity estimates at 1 m depth (hereafter written as ECCO salinity) during September 2011–October 2012, time series of temperature, and salinity profiles from moored buoys. A comparison is made between ECCO salinity and NASA’s Soil Moisture Active Passive (SMAP) salinity and Aquarius salinity. The time series of ECCO salinity and observed salinity are subjected to bandpass filtering with an 11–14 h period and 22–26 h period to detect and estimate the characteristics of semi-diurnal and diurnal period internal tides. Our analysis reveals that the ECCO salinity captured well the surface imprints of diurnal period internal tide propagating through shallow pycnocline (~50 m depth) due to halocline, and the latter suppresses the impact of semi-diurnal period internal tide propagating at thermocline (~100 m depth) reaching the sea surface. The semi-diurnal (diurnal) period internal tides have their wavelengths and phase speeds increased (decreased) from the central Andaman Sea to the Sri Lanka coast. Propagation of diurnal period internal tide is dominant in the northern BoB and northern Andaman Sea.
The Effects of Discharge Changes in Siberian Rivers on Arctic Sea-Ice Melting
Authors: Hu Didi , Xu Min , Kang Shichang , Chen Jinlei , Yang Chengde , Yang Qian
Published: July 2023

Arctic river discharge is one of the important factors affecting sea-ice melting of Arctic shelf seas. However, such effects have not been given much attention. In this study, the changes in discharge of the Ob, Yenisei, and Lena Rivers and the sea ice of the Kara and Laptev Seas during 1979–2019 were analyzed. Substantial increases in discharge and heat from the discharge and decreases in sea ice concentration (SIC) were detected. The effects of changes in discharge and riverine heat on sea ice changes were investigated. The results showed that the influence of the discharge, accumulated discharge, heat, and accumulated heat on SIC mainly occurred at the beginning and final stages of sea-ice melting. Discharge accelerated the melting of sea ice by increasing the absorption of solar radiation as the impurities contained in the discharge washed to the sea ice surface during the initial and late stages of sea-ice melting. Changes in cumulative riverine heat from May to September greatly contributed to the SIC changes in the Kara and Laptev Seas at the seasonal scale. The SIC reduced by 1% when the cumulative riverine heat increased by 213.2 × 106 MJ, 181.5 × 106 MJ, and 154.6 × 106 MJ in the Lena, Yenisei, and Ob Rivers, respectively, from May to September. However, even in the plume coverage areas in the Kara and Laptev Seas, discharge changes from the three rivers had a limited contribution to the reduction in SIC at annual scales. This work is helpful for understanding the changes in Arctic sea ice.
Dynamics of Freezing/Thawing Indices and Frozen Ground from 1961 to 2010 on the Qinghai-Tibet Plateau
Authors: Fang Xuewei , Wang Anqi , Lyu Shihua , Fraedrich Klaus
Published: July 2023

Freezing/thawing indices are important indicators of the dynamics of frozen ground on the Qinghai-Tibet Plateau (QTP), especially in areas with limited observations. Based on the numerical outputs of Community Land Surface Model version 4.5 (CLM4.5) from 1961 to 2010, this study compared the spatial and temporal variations between air freezing/thawing indices (2 m above the ground) and ground surface freezing/thawing indices in permafrost and seasonally frozen ground (SFG) across the QTP after presenting changes in frozen ground distribution in each decade in the context of warming and wetting. The results indicate that an area of 0.60 × 106 km2 of permafrost in the QTP degraded to SFG in the 1960s–2000s, and the primary shrinkage period occurred in the 2000s. The air freezing index (AFI) and ground freezing index (GFI) decreased dramatically at rates of 71.00 °C·d/decade and 34.33 °C·d/decade from 1961 to 2010, respectively. In contrast, the air thawing index (ATI) and ground thawing index (GTI) increased strikingly, with values of 48.13 °C·d/decade and 40.37 °C·d/decade in the past five decades, respectively. Permafrost showed more pronounced changes in freezing/thawing indices since the 1990s compared to SFG. The changes in thermal regimes in frozen ground showed close relations to air warming until the late 1990s, especially in 1998, when the QTP underwent the most progressive warming. However, a sharp increase in the annual precipitation from 1998 began to play a more controlling role in thermal degradation in frozen ground than the air warming in the 2000s. Meanwhile, the following vegetation expansion hiatus further promotes the thermal instability of frozen ground in this highly wet period.
Understanding Spatial-Temporal Interactions of Ecosystem Services and Their Drivers in a Multi-Scale Perspective of Miluo Using Multi-Source Remote Sensing Data
Authors: Cao Shiyi , Hu Xijun , Wang Yezi , Chen Cunyou , Xu Dong , Bai Tingting
Published: July 2023

In the face of rapid urbanization and global climate change, understanding the trade-offs and synergies of wetland city ecosystem services is vital for mitigating regional ecological and environmental risks, and enhancing human well-being. The Dongting Lake Basin is an ecologically fragile area of global significance. Uncontrolled resource utilization and intensive human activities have severely damaged the ecological environment, including in Miluo. Thus, it is of paramount research importance to uncover the trade-offs and synergies of ecosystem services and their driving mechanisms in Miluo. To achieve this, we classified Miluo’s land use data over the past two decades using a random forest model and Landsat imagery. We quantified the major ecosystem services in Miluo by employing ecological process models such as InVEST, RUSLE, and CASA. Additionally, we examined the trade-offs and synergies between ecosystem services at different scales and identified the driving mechanisms using multi-source remote sensing data. The results revealed that forests exhibited the highest level of ecosystem services, while urban ecosystem services experienced a significant decline. Over the past two decades, Miluo displayed notable trade-offs and synergies between ecosystem services, with synergies prevailing as the dominant pattern, particularly at the county scale. Furthermore, human activities emerged as the primary driver of changes in Miluo’s ecosystem services during the 20-year period. Therefore, it is imperative for scientists, policymakers, and civil society to develop effective and scientifically sound strategies to mitigate the ecological risks resulting from rapid urbanization and climate change in the future.
A Novel Framework for Stratified-Coupled BLS Tree Trunk Detection and DBH Estimation in Forests (BSTDF) Using Deep Learning and Optimization Adaptive Algorithm
Authors: Zhang Huacong , Zhang Huaiqing , Xu Keqin , Li Yueqiao , Wang Linlong , Liu Ren , Qiu Hanqing , Yu Longhua
Published: July 2023

Diameter at breast height (DBH) is a critical metric for quantifying forest resources, and obtaining accurate, efficient measurements of DBH is crucial for effective forest management and inventory. A backpack LiDAR system (BLS) can provide high-resolution representations of forest trunk structures, making it a promising tool for DBH measurement. However, in practical applications, deep learning-based tree trunk detection and DBH estimation using BLS still faces numerous challenges, such as complex forest BLS data, low proportions of target point clouds leading to imbalanced class segmentation accuracy in deep learning models, and low fitting accuracy and robustness of trunk point cloud DBH methods. To address these issues, this study proposed a novel framework for BLS stratified-coupled tree trunk detection and DBH estimation in forests (BSTDF). This framework employed a stratified coupling approach to create a tree trunk detection deep learning dataset, introduced a weighted cross-entropy focal-loss function module (WCF) and a cosine annealing cyclic learning strategy (CACL) to enhance the WCF-CACL-RandLA-Net model for extracting trunk point clouds, and applied a (least squares adaptive random sample consensus) LSA-RANSAC cylindrical fitting method for DBH estimation. The findings reveal that the dataset based on the stratified-coupled approach effectively reduces the amount of data for deep learning tree trunk detection. To compare the accuracy of BSTDF, synchronous control experiments were conducted using the RandLA-Net model and the RANSAC algorithm. To benchmark the accuracy of BSTDF, we conducted synchronized control experiments utilizing a variety of mainstream tree trunk detection models and DBH fitting methodologies. Especially when juxtaposed with the RandLA-Net model, the WCF-CACL-RandLA-Net model employed by BSTDF demonstrated a 6% increase in trunk segmentation accuracy and a 3% improvement in the F1 score with the same training sample volume. This effectively mitigated class imbalance issues encountered during the segmentation process. Simultaneously, when compared to RANSAC, the LSA-RANCAC method adopted by BSTDF reduced the RMSE by 1.08 cm and boosted R2 by 14%, effectively tackling the inadequacies of RANSAC’s filling. The optimal acquisition distance for BLS data is 20 m, at which BSTDF’s overall tree trunk detection rate (ER) reaches 90.03%, with DBH estimation precision indicating an RMSE of 4.41 cm and R2 of 0.87. This study demonstrated the effectiveness of BSTDF in forest DBH estimation, offering a more efficient solution for forest resource monitoring and quantification, and possessing immense potential to replace field forest measurements.
A Metasurface-Based LTC Polarization Converter with S-Shaped Split Ring Resonator Structure for Flexible Applications
Authors: Li Erfeng , Li Xue Jun , Seet Boon-Chong , Ghaffar Adnan , Aneja Aayush
Published: July 2023

This paper presents a metasurface-based linear-to-circular polarization converter with a flexible structure for conformal and wearable applications. The converter consists of nested S- and C-shaped split ring resonators in the unit cell and can convert linearly polarized incident waves into left-handed circularly polarized ones at 12.4 GHz. Simulation results show that the proposed design has a high polarization conversion rate and efficiency at the operating frequency. Conformal tests are also conducted to evaluate the performance under curvature circumstances. A minor shift in the operating frequency is observed when the converter is applied on a sinusoidal wavy surface.
Wireless Channel Prediction of GRU Based on Experience Replay and Snake Optimizer
Authors: Liu Qingli , Wang Peiling , Sun Jiaxu , Li Rui , Li Yangyang
Published: July 2023

Aiming at the problem of poor prediction accuracy of Channel State Information (CSI) caused by fast time-varying channels in wireless communication systems, this paper proposes a gated recurrent network based on experience replay and Snake Optimizer for real-time prediction in real-world non-stationary channels. Firstly, a two-channel prediction model is constructed by gated recurrent unit, which adapts to the real and imaginary parts of CSI. Secondly, we use the Snake Optimizer to find the optimal learning rate and the number of hidden layer elements to build the model. Finally, we utilize the experience pool to store recent historical CSI data for fast learning and complete learning. The simulation results show that, compared with LSTM, BiLSTM, and BiGRU, the gated recurrent network based on experience replay and Snake Optimizer has better performance in the optimization ability and convergence speed. The prediction accuracy of the model is also significantly improved under the dynamic non-stationary environment.
An IoT System Proposed for Higher Education: Approaches and Challenges in Economics, Computational Linguistics, and Engineering
Authors: Boca Liana Luminița , Ciortea Elisabeta Mihaela , Boghean Carmen , Begov-Ungur Andreea , Boghean Florin , Dădârlat Vasile Teodor
Published: July 2023

The technological revolution and the evolution of technology have significantly facilitated the applicability of the IoT in various domains, such as healthcare, transportation, agriculture, retail, education, and, especially, higher education, which encompasses countless areas. Petri nets can be a useful tool to model the behavior of an IoT system. The main objective of this paper was to propose, model, and analyze a complex IoT system for higher education. The system involves the integration of IoT devices for monitoring data. An educational cloud was used as a support tool through which tracking, and control actions were implemented both internally, between the cloud and entities, and externally, between the cloud and the IoT. The system was modeled using Petri nets, which are systems with discrete events, and for simulation, we used the Visual Object Net++ package. Using this application, information was obtained in real time, and it was possible to intervene with changes even in the design phase. The diagrams were easy to read and interpret, which is an advantage for the decision-making system. The general structure of the application was based on n entities, where each entity represented a higher education field. In this paper, we discuss at least three fields: economics, computational linguistics, and engineering.
Online Monitoring of Seawater Carbon Dioxide Based on an Infrared Rear Beam Splitter
Authors: Liu Luyin , Liu Ruzhang , Ma Guochao , Feng Shanshan , Mu Yuanhui , Meng Dexi , Wang Shuying , Cai Enlin
Published: July 2023

The ocean is one of the most extensive ecosystems on Earth and can absorb large amounts of carbon dioxide. Changes in seawater carbon dioxide concentrations are one of the most important factors affecting marine ecosystems. Excess carbon dioxide can lead to ocean acidification, threatening the stability of marine ecosystems and species diversity. Dissolved carbon dioxide detection in seawater has great scientific significance. Conducting online monitoring of seawater carbon dioxide can help to understand the health status of marine ecosystems and to protect marine ecosystems. Current seawater detection equipment is large and costly. This study designed a low-cost infrared carbon dioxide detection system based on molecular theory. Using the HITRAN database, the absorption spectra and coefficients of carbon dioxide molecules under different conditions were calculated and derived, and a wavelength of 2361 cm−1 was selected as the measurement channel for carbon dioxide. In addition, considering the interference effect of direct light, an infrared post-splitting method was proposed to eliminate the interference of light and improve the detection accuracy of the system. The system was designed for the online monitoring of carbon dioxide in seawater, including a peristaltic pump to accelerate gas–liquid separation, an optical path structure, and carbon dioxide concentration inversion. The experimental results showed that the standard deviation of the gas test is 3.05, the standard deviation of the seawater test is 6.04, and the error range is within 20 ppm. The system can be flexibly deployed and has good stability and portability, which can meet the needs of the online monitoring of seawater carbon dioxide concentration.
A Compact and Low-Power SoC Design for Spiking Neural Network Based on Current Multiplier Charge Injector Synapse
Authors: Asghar Malik Summair , Arslan Saad , Al-Hamid Ali A. , Kim HyungWon
Published: July 2023

This paper presents a compact analog system-on-chip (SoC) implementation of a spiking neural network (SNN) for low-power Internet of Things (IoT) applications. The low-power implementation of an SNN SoC requires the optimization of not only the SNN model but also the architecture and circuit designs. In this work, the SNN has been constituted from the analog neuron and synaptic circuits, which are designed to optimize both the chip area and power consumption. The proposed synapse circuit is based on a current multiplier charge injector (CMCI) circuit, which can significantly reduce power consumption and chip area compared with the previous work while allowing for design scalability for higher resolutions. The proposed neuron circuit employs an asynchronous structure, which makes it highly sensitive to input synaptic currents and enables it to achieve higher energy efficiency. To compare the performance of the proposed SoC in its area and power consumption, we implemented a digital SoC for the same SNN model in FPGA. The proposed SNN chip, when trained using the MNIST dataset, achieves a classification accuracy of 96.56%. The presented SNN chip has been implemented using a 65 nm CMOS process for fabrication. The entire chip occupies 0.96 mm2 and consumes an average power of 530 μW, which is 200 times lower than its digital counterpart.
Human Interaction Classification in Sliding Video Windows Using Skeleton Data Tracking and Feature Extraction
Authors: Puchała Sebastian , Kasprzak Włodzimierz , Piwowarski Paweł
Published: July 2023

A “long short-term memory” (LSTM)-based human activity classifier is presented for skeleton data estimated in video frames. A strong feature engineering step precedes the deep neural network processing. The video was analyzed in short-time chunks created by a sliding window. A fixed number of video frames was selected for every chunk and human skeletons were estimated using dedicated software, such as OpenPose or HRNet. The skeleton data for a given window were collected, analyzed, and eventually corrected. A knowledge-aware feature extraction from the corrected skeletons was performed. A deep network model was trained and applied for two-person interaction classification. Three network architectures were developed—single-, double- and triple-channel LSTM networks—and were experimentally evaluated on the interaction subset of the ”NTU RGB+D” data set. The most efficient model achieved an interaction classification accuracy of 96%. This performance was compared with the best reported solutions for this set, based on “adaptive graph convolutional networks” (AGCN) and “3D convolutional networks” (e.g., OpenConv3D). The sliding-window strategy was cross-validated on the ”UT-Interaction” data set, containing long video clips with many changing interactions. We concluded that a two-step approach to skeleton-based human activity classification (a skeleton feature engineering step followed by a deep neural network model) represents a practical tradeoff between accuracy and computational complexity, due to an early correction of imperfect skeleton data and a knowledge-aware extraction of relational features from the skeletons.
Indoor Human Detection from a Building’s Exterior Using 433 MHz Wireless Transceivers
Authors: Jo Sunghoon , Park Sehee , Kwon Gu-In
Published: July 2023

This study introduces a novel system for detecting humans inside a building by utilizing RF signals from the building’s exterior. Existing RF communication devices encounter signal attenuation issues when passing through walls, limiting their effectiveness. In contrast, our system employs a low-power, long-distance communication signal operating at 433 MHz to enhance signal permeability, enabling the accurate detection of individuals within the building. The system analyzes received signal strength indicator (RSSI) data using variance and mean analysis algorithms to determine the presence or absence of people. The evaluation results indicate promising average accuracies of 88% for the variance analysis algorithm and 97.7% for the mean analysis algorithm. The proposed system holds potential for real-world deployment, particularly in challenging scenarios such as fire incidents, where pre-installation is challenging. Continued research and development efforts aim to enhance the system’s performance and address any limitations, making it more effective and robust in various practical applications.
A Compact Snake Optimization Algorithm in the Application of WKNN Fingerprint Localization
Authors: Zheng Weimin , Pang Senyuan , Liu Ning , Chai Qingwei , Xu Lindong
Published: July 2023

Indoor localization has broad application prospects, but accurately obtaining the location of test points (TPs) in narrow indoor spaces is a challenge. The weighted K-nearest neighbor algorithm (WKNN) is a powerful localization algorithm that can improve the localization accuracy of TPs. In recent years, with the rapid development of metaheuristic algorithms, it has shown efficiency in solving complex optimization problems. The main research purpose of this article is to study how to use metaheuristic algorithms to improve indoor positioning accuracy and verify the effectiveness of heuristic algorithms in indoor positioning. This paper presents a new algorithm called compact snake optimization (cSO). The novel algorithm introduces a compact strategy to the snake optimization (SO) algorithm, which ensures optimal performance in situations with limited computing and memory resources. The performance of cSO is evaluated on 28 test functions of CEC2013 and compared with several intelligent computing algorithms. The results demonstrate that cSO outperforms these algorithms. Furthermore, we combine the cSO algorithm with WKNN fingerprint positioning and RSSI positioning. The simulation experiments demonstrate that the cSO algorithm can effectively reduce positioning errors.
Synergistic Integration of Skeletal Kinematic Features for Vision-Based Fall Detection
Authors: Inturi Anitha Rani , Manikandan Vazhora Malayil , Kumar Mahamkali Naveen , Wang Shuihua , Zhang Yudong
Published: July 2023

According to the World Health Organisation, falling is a major health problem with potentially fatal implications. Each year, thousands of people die as a result of falls, with seniors making up 80% of these fatalities. The automatic detection of falls may reduce the severity of the consequences. Our study focuses on developing a vision-based fall detection system. Our work proposes a new feature descriptor that results in a new fall detection framework. The body geometry of the subject is analyzed and patterns that help to distinguish falls from non-fall activities are identified in our proposed method. An AlphaPose network is employed to identify 17 keypoints on the human skeleton. Thirteen keypoints are used in our study, and we compute two additional keypoints. These 15 keypoints are divided into five segments, each of which consists of a group of three non-collinear points. These five segments represent the left hand, right hand, left leg, right leg and craniocaudal section. A novel feature descriptor is generated by extracting the distances from the segmented parts, angles within the segmented parts and the angle of inclination for every segmented part. As a result, we may extract three features from each segment, giving us 15 features per frame that preserve spatial information. To capture temporal dynamics, the extracted spatial features are arranged in the temporal sequence. As a result, the feature descriptor in the proposed approach preserves the spatio-temporal dynamics. Thus, a feature descriptor of size [m×15] is formed where m is the number of frames. To recognize fall patterns, machine learning approaches such as decision trees, random forests, and gradient boost are applied to the feature descriptor. Our system was evaluated on the UPfall dataset, which is a benchmark dataset. It has shown very good performance compared to the state-of-the-art approaches.
Beam-Switching Antennas for 5G Millimeter-Wave Wireless Terminals
Authors: Morshed Khaled M. , Karmokar Debabrata K. , Esselle Karu P. , Matekovits Ladislau
Published: July 2023

Beam-switching is one of the paramount focuses of 28 GHz millimeter-wave 5G devices. In this paper, a one-dimensional (1D) pattern reconfigurable leaky-wave antenna (LWA) was investigated and developed for wireless terminals. In order to provide a cost-effective solution, a uniform half-width LWA was used. The 1D beam-switching LWA was designed using three feed points at three different positions; by selecting the feeds, the direction of the beam can be switched. The antenna can switch the beam in three different directions along the antenna axis, such as backward, broadside, and forward. The 1D beam-switching antenna was fabricated, and because of the wide beamwidth, the measured radiation patterns can fill 128∘ of space (3 dB coverage), from θ = −64∘ to +64∘ at ϕ = 0∘. Following this, two of these antennas were placed at right angles to each other to achieve two-directional (2D) beam switching. The 2D beam-switching antenna pair was also prototyped and tested after integrating them into the ground plane of a wireless device. The antenna is able to point the beam in five different directions; moreover, its beam covers 167∘ (θ = −89∘ to +78∘) at ϕ = 0∘, and 154∘ (θ = −72∘ to +82∘) at ϕ = 90∘.
Overview of Social Policies for Town and Village Development in Response to Rural Shrinkage in East Asia: The Cases of Japan, South Korea and China
Authors: Li Wenqi , Zhang Li , Lee Inhee , Gkartzios Menelaos
Published: July 2023

Globally speaking, Asian countries, especially East Asian countries, are facing acute national depopulation situation and severe rural shrinkage development. Based on the continuous surveys of town and village development in Japan, South Korea, and China, this study aims to provide an overview of social policies that have been implemented in the past or more recently in these three countries in response to rural shrinkage, and to outline the core philosophy of these practices to cope with the repercussions. In this paper, we analyze the overall process of rural depopulation and the present features of town and village development in three countries. We subsequently present the social policies over the last few decades and summarize them into four major groups. Furthermore, we highlight that the focus of social policies is not to seek possible ways to reestablish growth but to provide positive support and effective reform to adjust and satisfy the changing needs of towns and villages under the circumstances of shrinking development, including the optimization of public resource allocation, exploring institutional innovation to valorize abandoned assets, and developing endogenous potentials for future sustainable development. Qualitative methods from a combination of literature review, policy review, and field surveys have mainly been adopted in this research. The study of East Asian practices may be instructive for other Asia-Pacific countries, as well as European countries that have been experiencing or will eventually face the challenges of rural shrinkage.
A Novel Robotic-Vision-Based Defect Inspection System for Bracket Weldments in a Cloud–Edge Coordination Environment
Authors: Li Hao , Wang Xiaocong , Liu Yan , Liu Gen , Zhai Zhongshang , Yan Xinyu , Wang Haoqi , Zhang Yuyan
Published: July 2023

Arc-welding robots are widely used in the production of automotive bracket parts. The large amounts of fumes and toxic gases generated during arc welding can affect the inspection results, as well as causing health problems, and the product needs to be sent to an additional checkpoint for manual inspection. In this work, the framework of a robotic-vision-based defect inspection system was proposed and developed in a cloud–edge computing environment, which can drastically reduce the manual labor required for visual inspection, minimizing the risks associated with human error and accidents. Firstly, a passive vision sensor was installed on the end joint of the arc-welding robot, the imaging module was designed to capture bracket weldments images after the arc-welding process, and datasets with qualified images were created in the production line for deep-learning-based research on steel surface defects. To enhance the detection precision, a redesigned lightweight inspection network was then employed, while a fast computation speed was ensured through the utilization of a cloud–edge-computing computational framework. Finally, virtual simulation and Internet of Things technologies were adopted to develop the inspection and control software in order to monitor the whole process remotely. The experimental results demonstrate that the proposed approach can realize the faster identification of quality issues, achieving higher steel production efficiency and economic profits.
Regional Economic Growth and Environmental Protection in China: The Yellow River Basin Economic Zone as an Example
Authors: Feng Dong , Han Jiayi , Jia Han , Chang Xinyuan , Guo Jiaqi , Huang Pinghua
Published: July 2023

This study used the coordinated development of the regional economy and ecological environment in the Yellow River basin as the research object and selected 13 indicators representing the regional economic subsystem that reflect economic scale, economic structure, and economic efficiency in order to explore a path of high-quality economic development suitable for China’s national conditions and realize the modernization of China with common prosperity for all people. The coupling weights of each indicator were then calculated separately, along with the degree of orderliness between the two subsystems of the regional economy and ecological environment. Finally, the degree of synergy between the two subsystems was calculated. The subjective weights and objective weights of each indicator were first estimated using hierarchical analysis (AHP) and conflicting correlation among criteria (CRITIC). The findings reveal a strong growth tendency in the regional economic development of the Yellow River basin and a wave-like growth pattern in ecological environment indicators. Since it overtook the ecological economy in 2017, the orderliness of the regional economic subsystem has continued this growth trend, demonstrating that the regional economy is in front of the ecological environment and is in a dominant position. The Yellow River basin’s composite system of regional economic and natural environment exhibits a low-level synergy stage and an M-shaped development pattern of alternating increases and decreases. As a result, many solutions are suggested, including promoting high-quality growth with greater levels of opening, modifying the regional economic structure, and stepping up ecological environmental protection.
Robust Adaptive Super Twisting Algorithm Sliding Mode Control of a Wind System Based on the PMSG Generator
Authors: Zine Laabidine Nada , Bossoufi Badre , El Kafazi Ismail , El Bekkali Chakib , El Ouanjli Najib
Published: July 2023

In the field of optimizing wind system control approaches and enhancing the quality of electricity generated on the grid, this research makes a fresh addition. The Sliding Mode Control (SMC) technique produces some fairly intriguing outcomes, but it has a severe flaw in the oscillations (phenomenon of reluctance: chattering) that diminish the system’s efficiency. In this paper, an AST (adaptive super twisting) approach is proposed to control the wind energy conversion system of the permanent magnet synchronous generator (PMSG), which is connected to the electrical system via two converters (grid-side and machine-side) and a capacitor serves as a DC link between them. This research seeks to regulate the generator and grid-side converter to monitor the wind rate reference given by the MPPT technique in order to eliminate the occurrence of the chattering phenomenon. With the help of this approach, precision and stability flaws will be resolved, and the wind system will perform significantly better in terms of productivity. To evaluate the performance of each control in terms of reference tracking, response time, stability, and the quality of the signal sent to the network under different wind conditions, a detailed description of the individual controls is given, preceded by a simulation in the Matlab/Simulink environment. The simulation study validates the control method and demonstrates that the AST control based on the Lyapunov stability theory provides excellent THD and power factor results. This work is completed by a comparative analysis of the other commands to identify the effect on the PMSG wind energy conversion system.
Surrogate Model Development for Slope Stability Analysis Using Machine Learning
Authors: Li Xianfeng , Nishio Mayuko , Sugawara Kentaro , Iwanaga Shoji , Chun Pang-jo
Published: July 2023

In many countries, slope failure is a complex natural issue that can result in serious natural hazards, such as landslide dams. It is associated with the challenge of slope stability evaluation, which involves the classification problem of slopes and the regression problem of predicting the factor of safety (FOS) value. This study explored the implementation of machine learning to analyze slope stability using a comprehensive database of 880 homogenous slopes (266 unstable and 614 stable) based on a simulation model developed as a surrogate model. A classification model was developed to categorize slopes into three classes, including S (stable, FOS > 1.2), M (marginally stable, 1.0 ≤ FOS ≤ 1.2), and U (unstable, FOS < 1.0), and a regression model was used to predict the target FOS value. The results confirmed the efficiency of the developed classification model via testing, achieving an accuracy of 0.9222, with 96.2% accuracy for the U class, 55% for the M class, and 95.2% for the S class. When U and M are in the same class (i.e., the U + M class), the test accuracy is 0.9315, with 93.3% accuracy for the S class and 92.9% accuracy for the U + M class. The low accuracy level for class M led to minor inaccuracies, which can be attributed to a data imbalance. Additionally, the regression model was found to have a high correlation coefficient R-square value of 0.9989 and a low test mean squared error value of 5.03 × 10−4, which indicates a strong relationship between the FOS values and the selected slope parameters. The significant difference in the elapsed time between the traditional method and the developed surrogate model for slope stability analysis highlights the potential benefits of machine learning.
Sustainable FDI in the Digital Economy
Authors: Bobenič Hintošová Aneta , Bódy Glória
Published: July 2023

The shift towards a digital economy should lead to changes in the allocation methods of foreign direct investment (FDI), especially given the reduced need to transfer physical assets. At the same time, the need to understand and examine the sustainability of FDI as a relevant attribute throughout the life cycle of a given investment should be emphasized. The paper seeks to answer the research question whether more sustainable foreign direct investment is attracted in the digital economy. Hence, the paper explores the interlinks between the sustainability attributes of FDI and the development of the digital economy. For this purpose, a cluster analysis under the conditions of the countries of the European Union is conducted. The results of the cluster analysis carried out for the two periods show certain similarities, especially within the Nordic, Visegrad, Balkan, and Baltic groups of countries. The first group mentioned can be characterized by advanced digital development as one of the possible driving forces to attract sustainable FDI. The remaining groups show certain differences in this regard. Based on the results, the paper brings some policy implications towards emphasizing the sustainability attributes of foreign direct investment in the digital economy, especially through the implementation of the concept of sustainable investment promotion policy.
Constructing and Validating Professional Competence Indicators for Underwater Welding Technicians for Offshore Wind Power Generation in Taiwan
Authors: Liao Chin-Wen , Yao Kai-Chao , Tsai Chin-Tang , Xu Jing-Ran , Huang Wei-Lun , Ho Wei-Sho , Wang Yu-Peng
Published: July 2023

This study aims to develop professional competence indicators for underwater welding technicians for offshore wind power generation in Taiwan. A literature analysis methodology was employed to gather and investigate research studies related to competence indicators in the underwater welding domain of offshore wind power generation. Subsequently, the Delphi method was utilized to conduct a three-round questionnaire survey, aiming to seek expert opinions regarding the appropriateness and differentiation of these competency indicators. To examine the consistency and significance of expert opinions, the data were subjected to K–S single-sample analysis and K–W one-way analysis of variance. The study identified three main dimensions of professional competency indicators for underwater welding technicians in offshore wind power generation: professional skills, professional knowledge, and workplace attitudes. These dimensions further led to the identification of 10 sub-dimensions, including equipment operation, welding practice, welding inspection, metal materials, welding graphics, occupational safety, quality standards, process improvement, self-management, and teamwork. These sub-dimensions further informed the identification of 75 specific behavioral components as criteria. This study provides findings to enhance future staff training and talent recruitment, benefiting relevant units and managers. These results contribute to enhancing the competence and performance of personnel in underwater welding for offshore wind power generation.
The Application of Graph-Structured Cox Model in Financial Risk Early Warning of Companies
Authors: Tao Xiangxing , Wang Mingxin , Ji Yanting
Published: July 2023

An effective financial risk forecast depends on the selection of important indicators from a broad set of financial indicators that are often correlated with one another. In this paper, we address this challenge by proposing a Cox model with a graph structure that allows us to identify and filter out the crucial indicators for financial risk forecasting. The Cox model can be converted to a weighted least squares form for the purpose of solution, where the regularization l0 compresses the signs of the variable coefficients and reduces the error caused by the compression of the coefficients. The graph structure reflects the correlations among different financial indicators and is incorporated into the model by introducing a Laplace penalty term to construct the Graph Regularization–Cox (GR-Cox) model. Monte Carlo simulation results show that the GR-Cox model outperforms the model without a graph structure with respect to the choice of parameters. Here, we apply the GR-Cox model to the forecast of the financial risk of listed companies and find that it shows good classification accuracy in practical applications. The GR-Cox model provides a new approach for improving the accuracy of financial risk early warning.
General Knowledge Representation and Sharing, with Illustrations in Risk/Emergency Management
Authors: Martin Philippe A. , Tanzi Tullio J.
Published: July 2023

Many decision-making tasks, including the sustainability-oriented ones and those related to the management of risks or emergencies, must gather, integrate, and analyze an important amount of information of various kinds and origins. Hence, how should information be best organized and shared by agents – people or software – for all and only the pieces of information looked for by these agents to be maximize their retrieval, reuse, organization and analysis by these agents? To that end, various logic-based knowledge representation (KR) and sharing (KS) techniques, and hence KR bases, have been used. However, most KS researchers focus on what this article defines as “restricted KR and KS”, where information providers and consumers can or have to discuss for solving information ambiguities and other problems. The first part of this article highlights the usefulness of “general KR and KS” and, for supporting them, provides a panorama of complementary techniques, and hence, indirectly, best practices or kinds of tools to use for general KS purposes. These techniques collectively answer research questions about how to support Web users in the collaborative building of KR bases. The second part uses the risk/emergency management domain to illustrate the ways different types of information can be represented to support general KS.