bluuebunny/crossref_metadata_2025 huggingface dataset:
A Convolutional Neural Network-Based Defect Recognition Method for Power Insulator
Authors: Li Nan , Zeng Dejun , Zhao Yun , Wang Jiahao , Wang Bo
Published: September 2024

As the scale of the power grid rapidly expands, its operation becomes increasingly complex, with higher demands on personnel proficiency, grid stability, equipment safety, and operational efficiency. In this study, a novel power insulator defect detection method based on convolutional neural networks (CNNs) is proposed. This method innovatively combines the feature extraction advantages of deep learning to build an efficient binary classification model capable of accurately detecting defects in power insulators in complex backgrounds. To avoid the impact of a small dataset on model performance, transfer learning was employed during model training to enhance the model’s generalization ability. A combination of Grid Search and Random Search was used for hyperparameter tuning, and the Early Stopping strategy was introduced to effectively prevent the model from overfitting to the training set, ensuring generalization performance on the validation set. Experimental results show that the proposed method achieves an average accuracy of 98.6%, a recall of 96.8%, and an F1 score of 97.7% on the test set. Compared to traditional Faster RCNN and PCA-SVM methods, the proposed CNN model significantly improves detection accuracy and computational efficiency in complex backgrounds, exhibiting superior recognition precision and model generalization ability for efficiently and accurately identifying defective insulators.
Integrating Suspended Sludge and Fixed Film into a Biological Wastewater Treatment System to Enhance Nitrogen Removal
Authors: Bui Quang Chi , Phan Nguyen Nguyet Minh , Nguyen Trung Viet , Yang Chih-Chi , Chen Ku-Fan , Tsai Yung-Pin
Published: September 2024

Integrated fixed-film activated sludge (IFAS) technology greatly enhances nitrogen removal effectiveness and treatment capacity in municipal wastewater treatment plants, addressing the issue of limited land availability. Hence, this method is appropriate for treating household wastewater from office buildings. The research was conducted at the wastewater treatment plant in an office building in Ho Chi Minh City, Vietnam. Experiments were conducted to ascertain the most favorable working conditions, including hydraulic retention time (HRT), alkalinity dosage, and dissolved oxygen (DO). According to the study, the IFAS system had the highest nitrogen removal effectiveness when operated at a hydraulic retention time (HRT) of 7 h, an alkalinity dose of 7.14 mgCaCO3/mgN-NH4+, and a dissolved oxygen (DO) value of 6 mg/L. The nitrification efficiency ranges from 89.2% to 98.8%. The N-NO3− concentration post-treatment is within the range of 27–45 mgN-NO3−/L, which is lower than the allowable discharge limit of 60 mg/L as per Vietnam’s wastewater discharge requirements. The research findings have enhanced the efficiency of the office building management process, thereby promoting the sustainable growth of society.
Interdisciplinary Insights and Global Perspectives on ADHD in Children: A Comprehensive Bibliometric Analysis (2014–2024)
Authors: Elnageeb Mohamed E. , Ahmed Elsadig Mohamed , Adam Khalid M. , Edris Ali M. , Ali Elshazali Widaa , Eltieb Elmoiz Idris , Idress Eltayeb Abdelazeem , Swamy D. S. Veerabhadra , Moreljwab Mohammed Hassan , Eleragi Ali M. S.
Published: September 2024

This study used bibliometric analysis to investigate the research on Attention-Deficit/Hyperactivity Disorder (ADHD) in pediatric populations between January 2014 and January 2024. The Scopus database is utilized to gather a diverse array of scholarly research on this complex ailment. Our objective was to compile a comprehensive dataset on understanding and managing ADHD by selecting specific terms such as “ADHD in Children”, “ADHD Treatment and Management”, and “Attention-Deficit/Hyperactivity Disorder”. We utilized the advanced analytical capabilities of Biblioshiny (bibliometrix R-package) and VOSviewer (VOSviewer version 1.6.19), within our methodological framework, to do network analysis. By conducting this analysis, we were able to examine patterns in publications, author affiliations, the geographic spread of research, and identify influential texts and developing research topics. The findings underscore the collaborative endeavors of medicine, psychology, and neuroscience in tackling the physiological and psychological aspects of ADHD, with a focus on interdisciplinary contributions. The extensive global impact of ADHD research is highlighted by the significant contributions made by countries including the United States, China, the UK, the Netherlands, and Canada. Our data indicates a notable shift towards holistic strategies that encompass socioeconomic, environmental, and behavioral aspects, alongside emerging practices like the utilization of non-invasive brain stimulation techniques in research. This bibliometric study offers a comprehensive view of ADHD research by identifying significant patterns and clusters of themes. It illuminates the shifts in scientific conversation over time and identifies areas that show potential for additional research. The study advocates for ongoing collaboration across various disciplines and nations, emphasizing the significance of innovative strategies to enhance the well-being of those affected by ADHD.
Spatial Expansion, Planning, and Their Influences on the Urban Landscape of Christian Churches in Canton (1582–1732 and 1844–1911)
Authors: Li Yonggu
Published: September 2024

Canton (present-day Guangzhou, China) has a long history as a trading port and serves as a window for studying the history of Sino-Western cultural exchanges. Canton was a city built under Confucian orders, leading to significant differences (when compared to Christian cities) in urban functional zoning, layout, urban landscape, and methods for shaping spatial order. Therefore, the churches constructed by Christian missionary societies in Canton merit particular attention in missionary history research and urban planning history. Based on local gazetteers, historical maps, export paintings, Western travelogues, and archives at that time, from a cultural landscape perspective, this article compares and analyzes the spatial expansion of Christian churches and their influences on the urban landscape in Canton in two stages. In the late Ming and early Qing dynasties, the spatial layout of the churches indicated an active integration into Canton City. After the Opium War, churches were not only used for religious purposes but also served as symbols asserting the presence of Christians and Western powers (which made the situation more complicated). Missionary societies attracted believers through the construction of public facilities, building Christian communities centered around churches, thereby competing with authorities for spatial power and influencing the urban functional system and spatial layout controlled by the authorities. Comparatively, the Roman Catholic Cathedral has profoundly changed the traditional landscape order in Canton, while the Protestant Dongshan Church interacted more closely with the city.
A Deep–Learning Network for Wheat Yield Prediction Combining Weather Forecasts and Remote Sensing Data
Authors: Peng Dailiang , Cheng Enhui , Feng Xuxiang , Hu Jinkang , Lou Zihang , Zhang Hongchi , Zhao Bin , Lv Yulong , Peng Hao , Zhang Bing
Published: September 2024

Accurately predicting winter wheat yield before harvest could greatly benefit decision-makers when making management decisions. In this study, we utilized weather forecast (WF) data combined with Sentinel-2 data to establish the deep-learning network and achieved an in-season county-scale wheat yield prediction in China’s main wheat-producing areas. We tested a combination of short-term WF data from the China Meteorological Administration to predict in-season yield at different forecast lengths. The results showed that explicitly incorporating WF data can improve the accuracy in crop yield predictions [Root Mean Square Error (RMSE) = 0.517 t/ha] compared to using only remote sensing data (RMSE = 0.624 t/ha). After comparing a series of WF data with different time series lengths, we found that adding 25 days of WF data can achieve the highest yield prediction accuracy. Specifically, the highest accuracy (RMSE = 0.496 t/ha) is achieved when predictions are made on Day of The Year (DOY) 215 (40 days before harvest). Our study established a deep-learning model which can be used for early yield prediction at the county level, and we have proved that weather forecast data can also be applied in data-driven deep-learning yield prediction tasks.
Changes in Anthropogenic Aerosols during the First Wave of COVID-19 Lockdowns in the Context of Long-Term Historical Trends at 51 AERONET Stations
Authors: Blaga Robert , Calinoiu Delia , Trif-Tordai Gavrila
Published: September 2024

A quasi-consensus has steadily formed in the scientific literature on the fact that the prevention measures implemented by most countries to curb the 2020 COVID-19 pandemic have led to significant reductions in pollution levels around the world, especially in urban environments. Fewer studies have looked at how these reductions at the ground level translate into variations in the whole atmosphere. In this study, we examine the columnar values of aerosols at 51 mainland European stations of the Aerosol Robotic Network (AERONET). We show that when considered in the context of the long-term trend over the last decade, the columnar aerosol levels for 2020, at the regional level, do not appear exceptional. Both the yearly means and the number of episodes with extreme values for this period are within one standard deviation of the long-term trends. We conclude that the spatially and temporally localized reductions do not add up to statistically significant reductions at the global levels of aerosols. Furthermore, considering that pandemic lockdowns can be thought of as a simulation of a climate change mitigation scenario, we conclude that such lifestyle-based changes present a very low potential as a global climate change mitigation strategy.
Iterative Optimization-Enhanced Contrastive Learning for Multimodal Change Detection
Authors: Tang Yuqi , Yang Xin , Han Te , Sun Kai , Guo Yuqiang , Hu Jun
Published: September 2024

Multimodal change detection (MCD) harnesses multi-source remote sensing data to identify surface changes, thereby presenting prospects for applications within disaster management and environmental surveillance. Nonetheless, disparities in imaging mechanisms across various modalities impede the direct comparison of multimodal images. In response, numerous methodologies employing deep learning features have emerged to derive comparable features from such images. Nevertheless, several of these approaches depend on manually labeled samples, which are resource-intensive, and their accuracy in distinguishing changed and unchanged regions is not satisfactory. In addressing these challenges, a new MCD method based on iterative optimization-enhanced contrastive learning is proposed in this paper. With the participation of positive and negative samples in contrastive learning, the deep feature extraction network focuses on extracting the initial deep features of multimodal images. The common projection layer unifies the deep features of two images into the same feature space. Then, the iterative optimization module expands the differences between changed and unchanged areas, enhancing the quality of the deep features. The final change map is derived from the similarity measurements of these optimized features. Experiments conducted across four real-world multimodal datasets, benchmarked against eight well-established methodologies, incontrovertibly illustrate the superiority of our proposed approach.
Spatial Feature-Based ISAR Image Registration for Space Targets
Authors: Zhao Lizhi , Wang Junling , Su Jiaoyang , Luo Haoyue
Published: September 2024

Image registration is essential for applications requiring the joint processing of inverse synthetic aperture radar (ISAR) images, such as interferometric ISAR, image enhancement, and image fusion. Traditional image registration methods, developed for optical images, often perform poorly with ISAR images due to their differing imaging mechanisms. This paper introduces a novel spatial feature-based ISAR image registration method. The method encodes spatial information by utilizing the distances and angles between dominant scatterers to construct translation and rotation-invariant feature descriptors. These feature descriptors are then used for scatterer matching, while the coordinate transformation of matched scatterers is employed to estimate image registration parameters. To mitigate the glint effects of scatterers, the random sample consensus (RANSAC) algorithm is applied for parameter estimation. By extracting global spatial information, the constructed feature curves exhibit greater stability and reliability. Additionally, using multiple dominant scatterers ensures adaptability to low signal-to-noise (SNR) ratio conditions. The effectiveness of the method is validated through both simulated and natural ISAR image sequences. Comparative performance results with traditional image registration methods, such as the SIFT, SURF and SIFT+SURF algorithms, are also included.
Few-Shot Object Detection for Remote Sensing Imagery Using Segmentation Assistance and Triplet Head
Authors: Zhang Jing , Hong Zhaolong , Chen Xu , Li Yunsong
Published: September 2024

The emergence of few-shot object detection provides a new approach to address the challenge of poor generalization ability due to data scarcity. Currently, extensive research has been conducted on few-shot object detection in natural scene datasets, and notable progress has been made. However, in the realm of remote sensing, this technology is still lagging behind. Furthermore, many established methods rely on two-stage detectors, prioritizing accuracy over speed, which hinders real-time applications. Considering both detection accuracy and speed, in this paper, we propose a simple few-shot object detection method based on the one-stage detector YOLOv5 with transfer learning. First, we propose a Segmentation Assistance (SA) module to guide the network’s attention toward foreground targets. This module assists in training and enhances detection accuracy without increasing inference time. Second, we design a novel detection head called the Triplet Head (Tri-Head), which employs a dual distillation mechanism to mitigate the issue of forgetting base-class knowledge. Finally, we optimize the classification loss function to emphasize challenging samples. Evaluations on the NWPUv2 and DIOR datasets showcase the method’s superiority.
Satellite Remote Sensing Grayscale Image Colorization Based on Denoising Generative Adversarial Network
Authors: Fu Qing , Xia Siyuan , Kang Yifei , Sun Mingwei , Tan Kai
Published: September 2024

Aiming to solve the challenges of difficult training, mode collapse in current generative adversarial networks (GANs), and the efficiency issue of requiring multiple samples for Denoising Diffusion Probabilistic Models (DDPM), this paper proposes a satellite remote sensing grayscale image colorization method using a denoising GAN. Firstly, a denoising optimization method based on U-ViT for the generator network is introduced to further enhance the model’s generation capability, along with two optimization strategies to significantly reduce the computational burden. Secondly, the discriminator network is optimized by proposing a feature statistical discrimination network, which imposes fewer constraints on the generator network. Finally, grayscale image colorization comparative experiments are conducted on three real satellite remote sensing grayscale image datasets. The results compared with existing typical colorization methods demonstrate that the proposed method can generate color images of higher quality, achieving better performance in both subjective human visual perception and objective metric evaluation. Experiments in building object detection show that the generated color images can improve target detection performance compared to the original grayscale images, demonstrating significant practical value.
Extraction of Alteration Information from Hyperspectral Data Base on Kernel Extreme Learning Machine
Authors: Yang Shuhan , Tian Shufang
Published: September 2024

Machine learning, as an increasingly prominent method in recent years, has introduced new methodologies and perspectives for extracting geological alteration information. To enhance the accuracy of remote-sensing-alteration mineral information, this study focuses on the extraction of alteration information from hyperspectral remote sensing data using the Kernel-Based Extreme Learning Machine (KELM) optimized with the Sparrow Search Algorithm (SSA). The ideal parameters of the Kernel Extreme Learning Machine model were successfully acquired by utilizing the sparrow optimization method for continuous search and iteration, avoiding the blindness and arbitrariness associated with parameter selection by humans. Spectral Angle Mapper (SAM) technology was used to extract sample data from hyperspectral imagery, which were then used to train the machine learning model for alteration information extraction. The experimental results show that, when compared to the Random Forest and the Support Vector Machine algorithms, the Kernel-Based Extreme Learning Machine algorithm achieved the highest accuracy and the best effect in the extraction results. It closely matches the known mineral points and geochemical anomalies in the area, confirming that the method has a clear advantage in the extraction of hyperspectral alteration information.
Advances and Challenges Associated with Low-Cost Pulse Oximeters in Home Care Programs: A Review
Authors: Rojas Anisbed Naranjo , Mosquera Freiser Cruz
Published: September 2024

Oximeters have significantly evolved since their invention and are essential for monitoring chronic diseases in home care. However, commercial models can present an economic barrier. Therefore, we conducted a review of the use of low-cost pulse oximeters in the home care of patients with respiratory diseases. Our review included studies addressing oxygen saturation and heart rate monitoring in adults, focusing on the use of portable devices. Our search identified advances in vital signs monitoring that could provide accessible solutions for non-clinical settings. Although there are challenges related to clinical validation and accuracy, these oximeters may improve medical care, particularly in resource-limited areas. As a result, the accessibility of these devices opens up new possibilities for patients with chronic respiratory diseases in home care, enabling regular self-monitoring and increasing control over their health.
A Transformer-Based Approach to Leakage Detection in Water Distribution Networks
Authors: Luo Juan , Wang Chongxiao , Yang Jielong , Zhong Xionghu
Published: September 2024

The efficient detection of leakages in water distribution networks (WDNs) is crucial to ensuring municipal water supply safety and improving urban operations. Traditionally, machine learning methods such as Convolutional Neural Networks (CNNs) and Autoencoders (AEs) have been used for leakage detection. However, these methods heavily rely on local pressure information and often fail to capture long-term dependencies in pressure series. In this paper, we propose a transformer-based model for detecting leakages in WDNs. The transformer incorporates an attention mechanism to learn data distributions and account for correlations between historical pressure data and data from the same time on different days, thereby emphasizing long-term dependencies in pressure series. Additionally, we apply pressure data normalization across each leakage scenario and concatenate position embeddings with pressure data in the transformer model to avoid feature misleading. The performance of the proposed method is evaluated by using detection accuracy and F1-score. The experimental studies conducted on simulated pressure datasets from three different WDNs demonstrate that the transformer-based model significantly outperforms traditional CNN methods.
An Arrhythmia Classification Model Based on a CNN-LSTM-SE Algorithm
Authors: Sun Ao , Hong Wei , Li Juan , Mao Jiandong
Published: September 2024

Arrhythmia is the main cause of sudden cardiac death, and ECG signal analysis is a common method for the noninvasive diagnosis of arrhythmia. In this paper, we propose an arrhythmia classification model based on the combination of a channel attention mechanism (SE module), convolutional neural network (CNN), and long short-term memory neural network (LSTM). The data of this model use the MIT-BIH arrhythmia database, and after noise reduction of raw ECG data by the EEMD denoising algorithm, a CNN-LSTM is used to learn features from the data, and the fusion channel attention mechanism is used to adjust the weight of the feature map. The CNN-LSTM-SE model is compared with the LSTM, CNN-LSTM, and LSTM-attention models, and the models are evaluated using Precision, Recall, and F1-Score. The classification performance of the tested CNN-LSTM-SE classification prediction model is better, with a classification accuracy of 98.5%, a classification precision rate of more than 97% for each label, a recall rate of more than 98%, and an F1-score of more than 0.98. It meets the requirements of arrhythmia classification prediction and has a certain practical value.
Effects of Magnetostatic Interactions in FeNi-Based Multilayered Magnetoimpedance Elements
Authors: Melnikov Grigory Yu. , Komogortsev Sergey V. , Svalov Andrey V. , Gorchakovskiy Alexander A. , Vazhenina Irina G. , Kurlyandskaya Galina V.
Published: September 2024

Multilayered [Cu(3 nm)/FeNi(100 nm)]5/Cu(150 nm)/FeNi(10 nm)/Cu(150 nm)/FeNi(10 nm)/Cu(150 nm)/[Cu(3 nm)/FeNi(100 nm)]5 structures were obtained by using the magnetron sputtering technique in the external in-plane magnetic field. From these, multilayer magnetoimpedance elements were fabricated in the shape of elongated stripes using the lift-off lithographic process. In order to obtain maximum magnetoimpedance (MI) sensitivity with respect to the external magnetic field, the short side of the rectangular element was oriented along the direction of the technological magnetic field applied during the multilayered structure deposition. MI sensitivity was defined as the change of the total impedance or its real part per unit of the magnetic field. The design of the elements (multilayered structure, shape of the element, etc.) contributed to the dynamic and static magnetic properties. The magnetostatic properties of the MI elements, including analysis of the magnetic domain structure, indicated the crucial importance of magnetostatic interactions between FeNi magnetic layers in the analyzed [Cu(3 nm)/FeNi(100 nm)]5 multilayers. In addition, the uniformity of the magnetic parameters was defined by the advanced technique of the local measurements of the ferromagnetic resonance field. Dynamic methods allowed investigation of the elements at different thicknesses by varying the frequency of the electromagnetic excitation. The maximum sensitivity of 40%/Oe with respect to the applied field in the range of the fields of 3 Oe to 5 Oe is promising for different applications.
Recent Methods for Evaluating Crop Water Stress Using AI Techniques: A Review
Authors: Cho Soo Been , Soleh Hidayat Mohamad , Choi Ji Won , Hwang Woon-Ha , Lee Hoonsoo , Cho Young-Son , Cho Byoung-Kwan , Kim Moon S. , Baek Insuck , Kim Geonwoo
Published: September 2024

This study systematically reviews the integration of artificial intelligence (AI) and remote sensing technologies to address the issue of crop water stress caused by rising global temperatures and climate change; in particular, it evaluates the effectiveness of various non-destructive remote sensing platforms (RGB, thermal imaging, and hyperspectral imaging) and AI techniques (machine learning, deep learning, ensemble methods, GAN, and XAI) in monitoring and predicting crop water stress. The analysis focuses on variability in precipitation due to climate change and explores how these technologies can be strategically combined under data-limited conditions to enhance agricultural productivity. Furthermore, this study is expected to contribute to improving sustainable agricultural practices and mitigating the negative impacts of climate change on crop yield and quality.
Petri-Net-Based Charging Scheduling Optimization in Rechargeable Sensor Networks
Authors: Qin Huaiyu , Ding Wei , Xu Lei , Ruan Chenzhi
Published: September 2024

In order to express the energy flow, motion flow, and control flow in wireless rechargeable sensor networks accurately and intuitively, and to maximize the charging benefit of MVs (mobile vehicles), a type of MTS-HACO (Mobile Transition Sequence Hybrid Ant Colony Optimization) is proposed. Firstly, node places are grouped according to the firing time of node’s energy consumption transition to ensure that in each time slot, MV places only enable charging transitions for the node places with lower remaining lifetimes. Then, the FSOMCT (Firing Sequence Optimization of Mobile Charging Transition) problem is formulated under the constraints of MV places capacity, travelling arc weight, charging arc weight, and so on. The elite strategy and the Max–Min Ant Colony system are further introduced to improve the ant colony algorithm, while the improved FWA (fireworks algorithm) optimizes the path constructed by each ant. Finally, the optimal mobile charging transition firing sequence and charging times are obtained, ensuring that MVs have sufficient energy to return to the base station. Simulation results indicate that, compared with the periodic algorithm and the PE-FWA algorithm, the proposed method can improve charging benefit by approximately 48.7% and 26.3%, respectively.
Continuous Gait Phase Estimation for Multi-Locomotion Tasks Using Ground Reaction Force Data
Authors: Park Ji Su , Kim Choong Hyun
Published: September 2024

Existing studies on gait phase estimation generally involve walking experiments using inertial measurement units under limited walking conditions (WCs). In this study, a gait phase estimation algorithm is proposed that uses data from force sensing resistors (FSRs) and a Bi-LSTM model. The proposed algorithm estimates gait phases in real time under various WCs, e.g., walking on paved/unpaved roads, ascending and descending stairs, and ascending or descending on ramps. The performance of the proposed algorithm is evaluated by performing walking experiments on ten healthy adult participants. An average gait estimation accuracy exceeding 90% is observed with a small error (root mean square error = 0.794, R2 score = 0.906) across various WCs. These results demonstrate the wide applicability of the proposed gait phase estimation algorithm using various insole devices, e.g., in walking aid control, gait disturbance diagnosis in daily life, and motor ability analysis.
Experiment Study on Rock Mass Classification Based on RCM-Equipped Sensors and Diversified Ensemble-Learning Model
Authors: Li Feng , Zeng Huike , Xu Hongbin , Sun Haokai
Published: September 2024

The geological condition monitoring and identification based on TBM-equipped sensors is of great significance for efficient and safe tunnel construction. Full-scale rotary cutting experiments are carried out using tunnel-boring machine disc cutters. Thrust, torque and vibration sensors are equipped on the rotary cutting machine (RCM). A stacking ensemble-learning model for real-time prediction of rock mass classification using features of mathematical statistics is proposed. Three signals, thrust, torque and a novel vibration spectrogram-based local amplification feature, are fed into the model and trained separately. The results show that the stacked ensemble-learning model has better accuracy and stability than any single model, showing a good application prospect in the rock mass classification.
SWiLoc: Fusing Smartphone Sensors and WiFi CSI for Accurate Indoor Localization
Authors: Mottakin Khairul , Davuluri Kiran , Allison Mark , Song Zheng
Published: September 2024

Dead reckoning is a promising yet often overlooked smartphone-based indoor localization technology that relies on phone-mounted sensors for counting steps and estimating walking directions, without the need for extensive sensor or landmark deployment. However, misalignment between the phone’s direction and the user’s actual movement direction can lead to unreliable direction estimates and inaccurate location tracking. To address this issue, this paper introduces SWiLoc (Smartphone and WiFi-based Localization), an enhanced direction correction system that integrates passive WiFi sensing with smartphone-based sensing to form Correction Zones. Our two-phase approach accurately measures the user’s walking directions when passing through a Correction Zone and further refines successive direction estimates outside the zones, enabling continuous and reliable tracking. In addition to direction correction, SWiLoc extends its capabilities by incorporating a localization technique that leverages corrected directions to achieve precise user localization. This extension significantly enhances the system’s applicability for high-accuracy localization tasks. Additionally, our innovative Fresnel zone-based approach, which utilizes unique hardware configurations and a fundamental geometric model, ensures accurate and robust direction estimation, even in scenarios with unreliable walking directions. We evaluate SWiLoc across two real-world environments, assessing its performance under varying conditions such as environmental changes, phone orientations, walking directions, and distances. Our comprehensive experiments demonstrate that SWiLoc achieves an average 75th percentile error of 8.89 degrees in walking direction estimation and an 80th percentile error of 1.12 m in location estimation. These figures represent reductions of 64% and 49%, respectively for direction and location estimation error, over existing state-of-the-art approaches.
Robust Indoor Positioning with Smartphone by Utilizing Encoded Chirp Acoustic Signal
Authors: Cheng Bingbing , Huang Ying , Zou Chuanyi
Published: September 2024

Recently, indoor positioning has been one of the hot topics in the field of navigation and positioning. Among different solutions on indoor positioning, positioning with acoustic signals has its promise due to its relatively high accuracy in the line of sight scenarios, low cost, and ease of being implemented in smartphones. In this work, a novel acoustic positioning method, called RATBILS, is proposed, in which encoded chirp acoustic signals are modulated and transmitted by different acoustic base stations. The smartphones receive the signals and perform the following three steps: (1) preprocessing; (2) time of arrival (TOA) estimation; and (3) time difference of arrival (TDOA) calculation and location estimation. In the preprocessing stage, we use band pass filters to filter out low-frequency noise from the environment. At the same time, we perform a signal decoding function in order to lock onto the positioning source. In the TOA estimation stage, we conduct both coarse and fine detection to enhance the accuracy and robustness of TOA estimation. The primary goal of coarse detection is to establish a noise range for fine detection. The main objective of fine detection is to emphasize the intensity of the first arrival diameter and resistance with multipath and non-line-of-sight (NLOS) caused by human body obstruction. In the TDOA calculation and location estimation stage, we estimate the TDOA based on the TOA estimation and then use the TDOA results for position estimation. In order to evaluate the performance of the proposed RATBILS system, two indoor field tests are carried out. The test results show that the RATBILS system achieves a positioning error of 0.23 m at 92% in region 1 of scene 1 and is superior to the traditional threshold method. The RATBILS system achieves a positioning error of 0.56 m at 92% in region 2 of scene 1 and is superior to the traditional threshold method. In scene 2, the maximum average positioning error was 1.26 m, which is better than the 3.33 m and 3.87 m of the two traditional threshold methods.
Focal Causal Temporal Convolutional Neural Networks: Advancing IIoT Security with Efficient Detection of Rare Cyber-Attacks
Authors: Miryahyaei Meysam , Fartash Mehdi , Akbari Torkestani Javad
Published: September 2024

The Industrial Internet of Things (IIoT) deals with vast amounts of data that must be safeguarded against tampering or theft. Identifying rare attacks and addressing data imbalances pose significant challenges in the detection of IIoT cyberattacks. Innovative detection methods are important for effective cybersecurity threat mitigation. While many studies employ resampling methods to tackle these issues, they often face drawbacks such as the use of artificially generated data and increased data volume, which limit their effectiveness. In this paper, we introduce a cutting-edge deep binary neural network known as the focal causal temporal convolutional neural network to address imbalanced data when detecting rare attacks in IIoT. The model addresses imbalanced data challenges by transforming the attack detection into a binary classification task, giving priority to minority attacks through a descending order strategy in the tree-like structure. This approach substantially reduces computational complexity, surpassing existing methods in managing imbalanced data challenges in rare attack detection for IoT security. Evaluation of various datasets, including UNSW-NB15, CICIDS-2017, BoT-IoT, NBaIoT-2018, and TON-IIOT, reveals an accuracy of over 99%, demonstrating the effectiveness of FCTCNNs in detecting attacks and handling imbalanced IoT data with efficiency.
A High-Precision Inverse Finite Element Method for Shape Sensing and Structural Health Monitoring
Authors: Yan Hongsheng , Tang Jiangpin
Published: September 2024

In the contemporary era, the further exploitation of deep-sea resources has led to a significant expansion of the role of ships in numerous domains, such as in oil and gas extraction. However, the harsh marine environments to which ships are frequently subjected can result in structural failures. In order to ensure the safety of the crew and the ship, and to reduce the costs associated with such failures, it is imperative to utilise a structural health monitoring (SHM) system to monitor the ship in real time. Displacement reconstruction is one of the main objectives of SHM, and the inverse finite element method (iFEM) is a powerful SHM method for the full-field displacement reconstruction of plate and shell structures. However, existing inverse shell elements applied to curved shell structures with irregular geometry or large curvature may result in element distortion. This paper proposes a high-precision iFEM for curved shell structures that does not alter the displacement mode of the element or increase the mesh and node quantities. In reality, it just modifies the methods of calculation. This method is based on the establishment of a local coordinate system on the Gaussian integration point and the subsequent alteration of the stiffness integration. The results of numerical examples demonstrate that the high-precision iFEM is capable of effectively reducing the displacement difference resulting from inverse finite element method reconstruction. Furthermore, it performs well in practical engineering applications.
Using the Nintendo™ Wii to Improve Physical Function and Reduce the Risk of Falls in Older Adults: A Randomized Controlled Clinical Trial
Authors: Carcelén-Fraile María del Carmen , Aibar-Almazán Agustín , Hita-Contreras Fidel , Sánchez-Alcalá Marcelina , Parra-Díaz Ana Belén , Infante-Guedes Aday , Castellote-Caballero Yolanda
Published: September 2024

(1) Background: Numerous exercise programs that improve physical capacity and the risk of falls in older adults have been proposed with varying degrees of success. A novel approach may be to use a video game system that uses real-time force feedback to train older adults. The aim of this study was to evaluate the effects of a Nintendo™ Wii-based exercise program on physical function and risk of falls in older people. (2) Methods: This 12-week randomized controlled clinical trial involved 73 participants: 36 individuals participating in a control group (CG) and 37 in an experimental group (EG) participating in a combined program. Balance was measured using the Tinetti scale, flexibility was assessed with the back scratch test and the sit-and-reach test, and lower body strength was assessed with the 30 s chair stand-up test. (3) Results: The results of this study show significant improvements in balance, gait, flexibility, and strength of the lower limbs compared to a control group. (4) Conclusions: A Nintendo™ Wii-based exercise program for seniors produces improvements in the physical health of older adults. These improvements highlight the importance of integrating physical exercise through video games as an effective strategy to improve the general health and quality of life of older adults.
SemConvTree: Semantic Convolutional Quadtrees for Multi-Scale Event Detection in Smart City
Authors: Kovalchuk Mikhail Andeevich , Filatova Anastasiia , Korneev Aleksei , Koreneva Mariia , Nasonov Denis , Voskresenskii Aleksandr , Boukhanovsky Alexander
Published: September 2024

The digital world is increasingly permeating our reality, creating a significant reflection of the processes and activities occurring in smart cities. Such activities include well-known urban events, celebrations, and those with a very local character. These widespread events have a significant influence on shaping the spirit and atmosphere of urban environments. This work presents SemConvTree, an enhanced semantic version of the ConvTree algorithm. It incorporates the semantic component of data through semi-supervised learning of a topic modeling ensemble, which consists of improved models: BERTopic, TSB-ARTM, and SBert-Zero-Shot. We also present an improved event search algorithm based on both statistical evaluations and semantic analysis of posts. This algorithm allows for fine-tuning the mechanism of discovering the required entities with the specified particularity (such as a particular topic). Experimental studies were conducted within the area of New York City. They showed an improvement in the detection of posts devoted to events (about 40% higher f1-score) due to the accurate handling of events of different scales. These results suggest the long-term potential for creating a semantic platform for the analysis and monitoring of urban events in the future.
Barriers to Empowerment: Examining the Transition of Pakistani Women from Higher Education (HE) to Professional Life
Authors: Rana Muhammad Qasim , Lee Angela , Saher Noreen , Shabbir Zeba
Published: September 2024

Gender equality is essential for economic and societal advancement, as outlined in the United Nations’ Sustainable Development Goal 5. This research examines the obstacles that hinder women’s progression from higher education to empowerment in Pakistan. Using a comprehensive descriptive survey questionnaire, we collected quantitative data to gain insights from a diverse sample of various provinces. The survey consisted of two sections: background information and 25 constructs of barriers from the existing literature. Data analysis involving 399 respondents included descriptive and inferential statistics, cross-tabulation, and significance testing using non-parametric tests. The findings highlighted significant sociocultural, familial, and professional barriers, with regional disparities underscoring the need for targeted interventions. Women respondents consistently perceived barriers more keenly than men, underscoring the gender-specific nature of these challenges. This study emphasizes the need for focused policies and educational initiatives to address barriers and promote gender equality in Pakistan’s higher education and professional spheres.
Policing Is Reproductive Oppression: How Policing and Carceral Systems Criminalize Parenting and Maintain Reproductive Oppression
Authors: Pendleton Maya , Dettlaff Alan J.
Published: September 2024

Since the era of chattel slavery, the state has used institutionalized abuse and violence as a tool for reproductive control. Today, public institutions and social services have been established by the state to police and surveil the behavior of poor communities and parents to maintain the reproductive violence and oppression that began centuries ago. This paper uses a reproductive justice framework to explore how the history of criminalizing pregnancy, surveilling Black and Indigenous communities, and denying reproductive autonomy are connected to and maintained by the present-day family policing system. In doing so, this paper expands on existing literature to create a stronger link and build solidarity between the movements against family policing and reproductive oppression.
Societal Perceptions and Acceptance of Virtual Humans: Trust and Ethics across Different Contexts
Authors: Gerlich Michael
Published: September 2024

This article examines public perceptions of virtual humans across various contexts, including social media, business environments, and personal interactions. Using an experimental approach with 371 participants in the United Kingdom, this research explores how the disclosure of virtual human technology influences trust, performance perception, usage likelihood, and overall acceptance. Participants interacted with virtual humans in simulations, initially unaware of their virtual nature, and then completed surveys to capture their perceptions before and after disclosure. The results indicate that trust and acceptance are higher in social media contexts, whereas business and general settings reveal significant negative shifts post-disclosure. Trust emerged as a critical factor influencing overall acceptance, with social media interactions maintaining higher levels of trust and performance perceptions than business environments and general interactions. A qualitative analysis of open-ended responses and follow-up interviews highlights concerns about transparency, security, and the lack of human touch. Participants expressed fears about data exploitation and the ethical implications of virtual human technology, particularly in business and personal settings. This study underscores the importance of ethical guidelines and transparent protocols to enhance the adoption of virtual humans in diverse sectors. These findings offer valuable insights for developers, marketers, and policymakers to optimise virtual human integration while addressing societal apprehensions, ultimately contributing to more effective and ethical deployment of virtual human technologies.
The Externalisation Gamble: Italy and Spain at the Forefront of Maritime Irregular Migration Governance
Authors: Echeverría Gabriel , Abbondanza Gabriele , Finotelli Claudia
Published: September 2024

Irregular migration is rapidly becoming a permanent feature of the twenty-first century. Amid the European “refugee crisis”, Italy and Spain represent two major destination countries that are affected by substantial irregular flows. Despite this comparable condition, and notwithstanding the significance of their relevant policies, they have rarely been compared, a gap in the literature that this research addresses through a novel comparative analysis of their irregular migration governance. Following a broad contextualisation of destination countries’ strategies against irregular migration, this article delves into the two case studies’ external dimension of control policies from the 1990s to 2024 inclusive. In doing so, it assesses the nature of their foreign policy on irregular migration, their specific measures, and their geographical scope. It finds that there is a noticeable convergence towards readmission measures and externalisation, along with the growing use of informal deals. In terms of differences, the article highlights Italy’s much higher number of irregular arrivals compared to Spain, and the impact that this has had on domestic debates, priorities, and relevant policy formulation processes. As a novel investigation of two influential case studies, this article therefore contributes to the literature on both externalisation and Italy’s and Spain’s foreign policy.
White by Force and the Racialized State of Exception
Authors: Jungkunz Vincent
Published: September 2024

White identity is forged through violence. The moment that whites aspired to be white, they set themselves up for immediate, inevitable failure: they try to inhabit an identity of superiority that cannot exist, and this sets them into a perpetual identity crisis, an existential emergency that threatens who they are and who they think they want to be. The ensuing identity formation—white by force—comprises an entire set of strategies, tactics, institutions, and structures meant to prop up an inherently failure-based identity and to do so through brutality, resentment, anger, contrived fear, and murder. Such an identity impacts everyone, including white people themselves, in devastating ways. In what following article, I will put forward a theoretical model called “White by Force and the Racialized State of Exception”, conceptualizing key aspects of this model to provide an emerging vocabulary for studying, discussing, and dismantling white identity, violence, authoritarianism, racism, and the existential crises that we are all facing. Our discourse around race and racism, since the Civil Rights Era, needs new language from which to diagnose an inherently violent identity formation that ultimately benefits no one, especially not people of color or white people from lower socioeconomic strata.
Mentoring Women in Corrections: Encouraging the Next Corrections Executive
Authors: Collica-Cox Kimberly
Published: September 2024

Mentoring can positively influence the mentee, the mentor, and the organization in which both are employed. Mentoring can help employees to overcome workplace challenges, including barriers to advancement, while increasing job satisfaction, enhancing career development, and producing a greater sense of value within the organization, often leading to increased productivity. While mentoring clearly has a multitude of benefits, women have traditionally had difficulty establishing mentorships in the workplace, which appear especially problematic in traditionally male-dominated careers, such as corrections. Based on surveys and interviews with members of the Association of Women Executives in Corrections (AWEC), this study investigates the role of mentorship in women’s advancement within corrections departments. Data show that support, particularly from one’s supervisor, is critical as women consider advancement, regardless of the supervisor’s gender. These results imply that when there are few women in the administrative ranks, limiting the opportunity for female mentorship, women can find equally effective mentorship from male superiors. Moreover, mentorships do not necessarily have to be formalized to be effective and can develop from outside one’s department when none exist within. Corrections agencies interested in diversifying their administrative ranks should plan to establish more opportunities to foster mentoring relationships, which would benefit all employees.