bluuebunny/crossref_metadata_2025 huggingface dataset:
Ponte: Represent Totally Binary Neural Network Toward Efficiency
Authors: Xu Jia , Pu Han , Wang Dong
Published: October 2024

In the quest for computational efficiency, binary neural networks (BNNs) have emerged as a promising paradigm, offering significant reductions in memory footprint and computational latency. In traditional BNN implementation, the first and last layers are typically full-precision, which causes higher logic usage in field-programmable gate array (FPGA) implementation. To solve these issues, we introduce a novel approach named Ponte (Represent Totally Binary Neural Network Toward Efficiency) that extends the binarization process to the first and last layers of BNNs. We challenge the convention by proposing a fully binary layer replacement that mitigates the computational overhead without compromising accuracy. Our method leverages a unique encoding technique, Ponte::encoding, and a channel duplication strategy, Ponte::dispatch, and Ponte::sharing, to address the non-linearity and capacity constraints posed by binary layers. Surprisingly, all of them are back-propagation-supported, which allows our work to be implemented in the last layer through extensive experimentation on benchmark datasets, including CIFAR-10 and ImageNet. We demonstrate that Ponte not only preserves the integrity of input data but also enhances the representational capacity of BNNs. The proposed architecture achieves comparable, if not superior, performance metrics while significantly reducing the computational demands, thereby marking a step forward in the practical deployment of BNNs in resource-constrained environments.
Quantifying Hand Motion Complexity in Simulated Sailing Using Inertial Sensors
Authors: Sarai Gurdeep , Jayaraman Prem Prakash , Wickramasinghe Nilmini , Tirosh Oren
Published: October 2024

The control of hand movement during sailing is important for performance. To quantify the amount of regularity and the unpredictability of hand fluctuations during the task, the mathematical algorithm Approximate Entropy (ApEn) of the hand acceleration can be used. Approximate Entropy is a mathematical algorithm that depends on the combination of two input parameters including (1) the length of the sequences to be compared (m), and (2) the tolerance threshold for accepting similar patterns between two segments (r). The aim of this study is to identify the proper combinations of ‘m’ and ‘r’ parameter values for ApEn measurement in the hand movement acceleration data during sailing. Inertial Measurement Units (IMUs) recorded acceleration data for both the mainsail (non-dominant) and tiller (dominant) hands across the X-, Y-, and Z-axes, as well as vector magnitude. ApEn values were computed for 24 parameter combinations, with ‘m’ ranging from 2 to 5 and ‘r’ from 0.10 to 0.50. The analysis revealed significant differences in acceleration ApEn regularity between the two hands, particularly along the Z-axis, where the mainsail hand exhibited higher entropy values (p = 0.000673), indicating greater acceleration complexity and unpredictability. In contrast, the tiller hand displayed more stable and predictable acceleration patterns, with lower ApEn values. ANOVA results confirmed that parameter ‘m’ had a significant effect on acceleration complexity for both hands, highlighting differing motor control demands between the mainsail and tiller hands. These findings demonstrate the utility of IMU sensors and ApEn in detecting nuanced variations in acceleration dynamics during sailing tasks. This research contributes to the understanding of hand-specific acceleration patterns in sailing and provides a foundation for further studies on adaptive sailing techniques and motor control strategies for both novice and expert sailors.
BodyFlow: An Open-Source Library for Multimodal Human Activity Recognition
Authors: del-Hoyo-Alonso Rafael , Hernández-Ruiz Ana Caren , Marañes-Nueno Carlos , López-Bosque Irene , Aznar-Gimeno Rocío , Salvo-Ibañez Pilar , Pérez-Lázaro Pablo , Abadía-Gallego David , Rodrigálvarez-Chamarro María de la Vega
Published: October 2024

Human activity recognition is a critical task for various applications across healthcare, sports, security, gaming, and other fields. This paper presents BodyFlow, a comprehensive library that seamlessly integrates human pose estimation and multiple-person estimation and tracking, along with activity recognition modules. BodyFlow enables users to effortlessly identify common activities and 2D/3D body joints from input sources such as videos, image sets, or webcams. Additionally, the library can simultaneously process inertial sensor data, offering users the flexibility to choose their preferred input, thus facilitating multimodal human activity recognition. BodyFlow incorporates state-of-the-art algorithms for 2D and 3D pose estimation and three distinct models for human activity recognition.
WS2 Nanosheet-Based Ultrascaled Field-Effect Transistor for Hydrogen Gas Sensing: Addressing the Sensitivity-Downscaling Trade-Off
Authors: Tamersit Khalil
Published: October 2024

In this paper, we propose an ultrascaled WS2 field-effect transistor equipped with a Pd/Pt sensitive gate for high-performance and low-power hydrogen gas sensing applications. The proposed nanosensor is simulated by self-consistently solving a quantum transport equation with electrostatics at the ballistic limit. The gas sensing principle is based on the gas-induced change in the metal gate work function. The hydrogen gas nanosensor leverages the high sensitivity of two-dimensional WS2 to its sur-rounding electrostatic environment. The computational investigation encompasses the nanosensor’s behavior in terms of potential profile, charge density, current spectrum, local density of states (LDOS), transfer characteristics, and sensitivity. Additionally, the downscaling-sensitivity trade-off is analyzed by considering the impact of drain-to-source voltage and the electrostatics parameters on subthreshold performance. The simulation results indicate that the downscaling-sensitivity trade-off can be optimized through enhancements in electrostatics, such as utilizing high-k dielectrics and reducing oxide thickness, as well as applying a low drain-to-source voltage, which also contributes to improved energy efficiency. The proposed nanodevice meets the prerequisites for cutting-edge gas nanosensors, offering high sensing performance, improved scaling capability, low power consumption, and complementary metal–oxide–semiconductor compatibility, making it a compelling candidate for the next generation of ultrascaled FET-based gas nanosensors.
Inkjet-Printed Localized Surface Plasmon Resonance Subpixel Gas Sensor Array for Enhanced Identification and Visualization of Gas Spatial Distributions from Multiple Odor Sources
Authors: Jiang Tianshu , Guo Hao , Ge Lingpu , Sassa Fumihiro , Hayashi Kenshi
Published: October 2024

The visualization of the spatial distributions of gases from various sources is essential to understanding the composition, localization, and behavior of these gases. In this study, an inkjet-printed localized surface plasmon resonance (LSPR) subpixel gas sensor array was developed to visualize the spatial distributions of gases and to differentiate between acetic acid, geraniol, pentadecane, and cis-jasmone. The sensor array, which integrates gold nanoparticles (AuNPs), silver nanoparticles (AgNPs), and fluorescent pigments, was positioned 3 cm above the gas source. Hyperspectral imaging was used to capture the LSPR spectra across the sensor array, and these spectra were then used to construct gas information matrices. Principal component analysis (PCA) enabled effective classification of the gases and localization of their sources based on observed spectral differences. Heat maps that visualized the gas concentrations were generated using the mean squared error (MSE) between the sensor responses and reference spectra. The array identified and visualized the four gas sources successfully, thus demonstrating its potential for gas localization and detection applications. The study highlights a straightforward, cost-effective approach to gas sensing and visualization, and in future work, we intend to refine the sensor fabrication process and enhance the detection of complex gas mixtures.
Indoor Pedestrian Positioning Method Based on Ultra-Wideband with a Graph Convolutional Network and Visual Fusion
Authors: Mu Huizhen , Yu Chao , Jiang Shuna , Luo Yujing , Zhao Kun , Chen Wen
Published: October 2024

To address the challenges of low accuracy in indoor positioning caused by factors such as signal interference and visual distortions, this paper proposes a novel method that integrates ultra-wideband (UWB) technology with visual positioning. In the UWB positioning module, the powerful feature-extraction ability of the graph convolutional network (GCN) is used to integrate the features of adjacent positioning points and improve positioning accuracy. In the visual positioning module, the residual results learned from the bidirectional gate recurrent unit (Bi-GRU) network are compensated into the mathematical visual positioning model’s solution results to improve the positioning results’ continuity. Finally, the two positioning coordinates are fused based on particle filter (PF) to obtain the final positioning results and improve the accuracy. The experimental results show that the positioning accuracy of the proposed UWB positioning method based on a GCN is less than 0.72 m in a single UWB positioning, and the positioning accuracy is improved by 55% compared with the Chan–Taylor algorithm. The proposed visual positioning method based on Bi-GRU and residual fitting has a positioning accuracy of 0.42 m, 71% higher than the Zhang Zhengyou visual positioning algorithm. In the fusion experiment, 80% of the positioning accuracy is within 0.24 m, and the maximum error is 0.66 m. Compared with the single UWB and visual positioning, the positioning accuracy is improved by 56% and 52%, respectively, effectively enhancing indoor pedestrian positioning accuracy.
An Adaptive Parameter Optimization Deep Learning Model for Energetic Liquid Vision Recognition Based on Feedback Mechanism
Authors: Chen Lu , Yang Yuhao , Wu Tianci , Liu Chiang , Li Yang , Tan Jie , Qian Weizhong , Yang Liang , Xiu Yue , Li Gun
Published: October 2024

The precise detection of liquid flow and viscosity is a crucial challenge in industrial processes and environmental monitoring due to the variety of liquid samples and the complex reflective properties of energetic liquids. Traditional methods often struggle to maintain accuracy under such conditions. This study addresses the complexity arising from sample diversity and the reflective properties of energetic liquids by introducing a novel model based on computer vision and deep learning. We propose the DBN-AGS-FLSS, an integrated deep learning model for high-precision, real-time liquid surface pointer detection. The model combines Deep Belief Networks (DBN), Feedback Least-Squares SVM classifiers (FLSS), and Adaptive Genetic Selectors (AGS). Enhanced by bilateral filtering and adaptive contrast enhancement algorithms, the model significantly improves image clarity and detection accuracy. The use of a feedback mechanism for reverse judgment dynamically optimizes model parameters, enhancing system accuracy and robustness. The model achieved an accuracy, precision, F1 score, and recall of 99.37%, 99.36%, 99.16%, and 99.36%, respectively, with an inference speed of only 1.5 ms/frame. Experimental results demonstrate the model’s superior performance across various complex detection scenarios, validating its practicality and reliability. This study opens new avenues for industrial applications, especially in real-time monitoring and automated systems, and provides valuable reference for future advancements in computer vision-based detection technologies.
Dynamical Embedding of Single-Channel Electroencephalogram for Artifact Subspace Reconstruction
Authors: Hazarika Doli , Vishnu K. N. , Ransing Ramdas , Gupta Cota Navin
Published: October 2024

This study introduces a novel framework to apply the artifact subspace reconstruction (ASR) algorithm on single-channel electroencephalogram (EEG) data. ASR is known for its ability to remove artifacts like eye-blinks and movement but traditionally relies on multiple channels. Embedded ASR (E-ASR) addresses this by incorporating a dynamical embedding approach. In this method, an embedded matrix is created from single-channel EEG data using delay vectors, followed by ASR application and reconstruction of the cleaned signal. Data from four subjects with eyes open were collected using Fp1 and Fp2 electrodes via the CameraEEG android app. The E-ASR algorithm was evaluated using metrics like relative root mean square error (RRMSE), correlation coefficient (CC), and average power ratio. The number of eye-blinks with and without the E-ASR approach was also estimated. E-ASR achieved an RRMSE of 43.87% and had a CC of 0.91 on semi-simulated data and effectively reduced artifacts in real EEG data, with eye-blink counts validated against ground truth video data. This framework shows potential for smartphone-based EEG applications in natural environments with minimal electrodes.
MEGA: Maximum-Entropy Genetic Algorithm for Router Nodes Placement in Wireless Mesh Networks
Authors: Ussipov Nurzhan , Akhtanov Sayat , Turlykozhayeva Dana , Temesheva Symbat , Akhmetali Almat , Zaidyn Marat , Namazbayev Timur , Bolysbay Aslan , Akniyazova Aigerim , Tang Xiao
Published: October 2024

Over the past decade, wireless mesh networks (WMNs) have seen significant advancements due to their simple deployment, cost-effectiveness, ease of implementation, and reliable service coverage. However, despite these advantages, the placement of nodes in WMNs presents a critical challenge that significantly impacts their performance. This issue is recognized as an NP-hard problem, underscoring the necessity of development optimization algorithms, such as heuristic and metaheuristic approaches. This motivated us to develop the Maximum Entropy Genetic Algorithm (MEGA) to address the issue of mesh router node placement in WMNs. To assess the proposed method, we conducted experiments across various scenarios with different settings, focusing on key metrics such as network connectivity and user coverage. The simulation results showed the comparative performance of MEGA in relation to other prominent algorithms, such as the Coyote Optimization Algorithm (COA), Firefly Algorithm (FA), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), revealing MEGA’s effectiveness and usability in determining optimal locations for mesh routers.
Investigation of Unsafe Construction Site Conditions Using Deep Learning Algorithms Using Unmanned Aerial Vehicles
Authors: Kumar Sourav , Poyyamozhi Mukilan , Murugesan Balasubramanian , Rajamanickam Narayanamoorthi , Alroobaea Roobaea , Nureldeen Waleed
Published: October 2024

The rapid adoption of Unmanned Aerial Vehicles (UAVs) in the construction industry has revolutionized safety, surveying, quality monitoring, and maintenance assessment. UAVs are increasingly used to prevent accidents caused by falls from heights or being struck by falling objects by ensuring workers comply with safety protocols. This study focuses on leveraging UAV technology to enhance labor safety by monitoring the use of personal protective equipment, particularly helmets, among construction workers. The developed UAV system utilizes the tensorflow technique and an alert system to detect and identify workers not wearing helmets. Employing the high-precision, high-speed, and widely applicable Faster R-CNN method, the UAV can accurately detect construction workers with and without helmets in real-time across various site conditions. This proactive approach ensures immediate feedback and intervention, significantly reducing the risk of injuries and fatalities. Additionally, the implementation of UAVs minimizes the workload of site supervisors by automating safety inspections and monitoring, allowing for more efficient and continuous oversight. The experimental results indicate that the UAV system’s high precision, recall, and processing capabilities make it a reliable and cost-effective solution for improving construction site safety. The precision, mAP, and FPS of the developed system with the R-CNN are 93.1%, 58.45%, and 27 FPS. This study demonstrates the potential of UAV technology to enhance safety compliance, protect workers, and improve the overall quality of safety management in the construction industry.
Wearable Biosensor Smart Glasses Based on Augmented Reality and Eye Tracking
Authors: Gao Lina , Wang Changyuan , Wu Gongpu
Published: October 2024

With the rapid development of wearable biosensor technology, the combination of head-mounted displays and augmented reality (AR) technology has shown great potential for health monitoring and biomedical diagnosis applications. However, further optimizing its performance and improving data interaction accuracy remain crucial issues that must be addressed. In this study, we develop smart glasses based on augmented reality and eye tracking technology. Through real-time information interaction with the server, the smart glasses realize accurate scene perception and analysis of the user’s intention and combine with mixed-reality display technology to provide dynamic and real-time intelligent interaction services. A multi-level hardware architecture and optimized data processing process are adopted during the research process to enhance the system’s real-time accuracy. Meanwhile, combining the deep learning method with the geometric model significantly improves the system’s ability to perceive user behavior and environmental information in complex environments. The experimental results show that when the distance between the subject and the display is 1 m, the eye tracking accuracy of the smart glasses can reach 1.0° with an error of no more than ±0.1°. This study demonstrates that the effective integration of AR and eye tracking technology dramatically improves the functional performance of smart glasses in multiple scenarios. Future research will further optimize smart glasses’ algorithms and hardware performance, enhance their application potential in daily health monitoring and medical diagnosis, and provide more possibilities for the innovative development of wearable devices in medical and health management.
Design and On-Orbit Performance of Ku-Band Phased-Array Synthetic-Aperture Radar Payload System
Authors: Yan Wei , Tan Xiaomin , Wu Jiang , Yuan Mingze , Dang Hongxing , Chang Wujun
Published: October 2024

The current emphasis in the advancement of space-based synthetic-aperture radar (SAR) is on lightweight payloads under 100 kg with resolutions surpassing 1 m. This focus is directed toward meeting the launch criteria for multiple satellites on a single rocket and cutting costs. This article discusses the creation and progress of a Ku-band SAR payload for the Taijing-4(03) satellite, launched on 23 January 2024 and accompanied by four other satellites. The SAR payload design was customized to meet the demands of a micro-nano satellite platform, resulting in a lightweight, flat design weighing less than 80 kg, seamlessly integrated with the plate-shaped satellite platform. The article also introduces a beam optimization strategy for the phased array SAR antenna, significantly boosting the SAR system’s performance. The SAR payload provides various operating modes like slide-spot, strip, Scan 1, Scan 2, and others, with a maximum achievable resolution exceeding 1 m. Extensive in-orbit testing of the payload produced numerous high-quality SAR images with potential uses in emergency disaster mitigation, safeguarding ecosystems, monitoring forests, managing crops, tracking sea ice, and more.
MulCPred: Learning Multi-Modal Concepts for Explainable Pedestrian Action Prediction
Authors: Feng Yan , Carballo Alexander , Fujii Keisuke , Karlsson Robin , Ding Ming , Takeda Kazuya
Published: October 2024

Pedestrian action prediction is crucial for many applications such as autonomous driving. However, state-of-the-art methods lack the explainability needed for trustworthy predictions. In this paper, a novel framework called MulCPred is proposed that explains its predictions based on multi-modal concepts represented by training samples. Previous concept-based methods have limitations, including the following: (1) they cannot be directly applied to multi-modal cases; (2) they lack the locality needed to attend to details in the inputs; (3) they are susceptible to mode collapse. These limitations are tackled accordingly through the following approaches: (1) a linear aggregator to integrate the activation results of the concepts into predictions, which associates concepts of different modalities and provides ante hoc explanations of the relevance between the concepts and the predictions; (2) a channel-wise recalibration module that attends to local spatiotemporal regions, which enables the concepts with locality; (3) a feature regularization loss that encourages the concepts to learn diverse patterns. MulCPred is evaluated on multiple datasets and tasks. Both qualitative and quantitative results demonstrate that MulCPred is promising in improving the explainability of pedestrian action prediction without obvious performance degradation. Moreover, by removing unrecognizable concepts, MulCPred shows improved cross-dataset prediction performance, suggesting its potential for further generalization.
Passive Inclination Sensor Based on a Patch Antenna with a Reconfigurable Water Load
Authors: Yi Zhuoran , Xia Zihan , Li Xianzhi , Xu Kangqian , Xie Liyu , Xue Songtao , Wu Yiyu
Published: October 2024

In order to ensure the safety and preserve the value of historical buildings, inclination is an essential parameter during the continuous structural health monitoring process. However, the wire and price of a traditional sensor limit application. This paper proposes a low-cost inclination sensor based on a patch antenna with a reconfigurable water load. Only the water directly on the antenna is considered effective. The different volume of the effective water load, which is determined by the inclination of the attached surface, will affect the effective permittivity of the dielectric plate of the patch antenna, further causing a variation in the resonant frequency. Therefore, the proposed antenna sensor can monitor the inclination of the attached surface by interrogating the resonant frequency. The working mechanism is first clarified by theoretically investigating the relationship between the dielectric properties and the inclination of the covering medium. The antenna sensor is then simulated using High-Frequency Structure Simulator ver.15 (HFSS 15), which helps to determine geometric parameters and confirm accuracy and sensitivity. An experiment has been conducted based on the design verified in the simulation. The inclination detection shows a correlation coefficient of 0.9771 with a sensitivity of 7.92 MHz/°, indicating a potential for real application.
Vision-Based Real-Time Bolt Loosening Detection by Identifying Anti-Loosening Lines
Authors: Lei Wenyang , Yuan Fang , Guo Jiang , Wang Haoyang , Geng Zaiming , Wu Tao , Gong Haipeng
Published: October 2024

Bolt loosening detection is crucial for ensuring the safe operation of equipment. This paper presents a vision-based real-time detection method that identifies bolt loosening by recognizing anti-loosening line markers at bolt connections. The method employs the YOLOv10-S deep learning model for high-precision, real-time bolt detection, followed by a two-step Fast-SCNN image segmentation technique. This approach effectively isolates the bolt and nut regions, enabling accurate extraction of the anti-loosening line markers. Key intersection points are calculated using ellipse and line fitting techniques, and the loosening angle is determined through spatial projection transformation. The experimental results demonstrate that, for high-resolution images of 2048 × 1024 pixels, the proposed method achieves an average angle detection error of 1.145° with a detection speed of 32 FPS. Compared to traditional methods and other vision-based approaches, this method offers non-contact measurement, real-time detection capabilities, reduced detection error, and general adaptability to various bolt types and configurations, indicating significant application potential.
Lumbar Sitting Behavior of Individuals with Low Back Pain: A Preliminary Study Using Extended Real-World Data
Authors: McClintock Frederick A. , Callaway Andrew J. , Clark Carol J. , Alqhtani Raee S. , Williams Jonathan M.
Published: October 2024

Low back pain affects 619 million people worldwide and is commonly provoked by sitting. Current assessment methods constrain task variability, removing real-world, task-switching behaviors. This study utilized accelerometers to provide an original validated method of in vivo real-world assessment of lumbar sitting behavior throughout a full day. A three-stage study design was used, which involved (1) blinded verification of our sitting detection algorithm, (2) full-day data collection from participants with low back discomfort, quantifying lumbar angles, and end-user acceptability explored, (3) case study application to two clinical low back pain (LBP) patients, incorporating measurement of provocative sitting. Focus group discussions demonstrated that data collection methods were acceptable. Sitting ‘windows’ were created and analyzed using novel histograms, amplitude probability distribution functions, and variability, demonstrating that sitting behavior was unique and varied across individuals. One LBP patient demonstrated two frequent lumbar postures (<15% flexion and ~75% flexion), with pain provocation at 62% lumbar flexion. The second patient demonstrated a single dominant posture (~90% flexion), with pain provoked at 86% lumbar flexion. Our in vivo approach offers an acceptable method to gain new insights into provocative sitting behavior in individuals with LBP, allowing individualized unconstrained data for full-day postures and pain provocation behaviors to be quantified, which are otherwise unattainable.
Spatial Resolution Enhancement Framework Using Convolutional Attention-Based Token Mixer
Authors: Peng Mingyuan , Li Canhai , Li Guoyuan , Zhou Xiaoqing
Published: October 2024

Spatial resolution enhancement in remote sensing data aims to augment the level of detail and accuracy in images captured by satellite sensors. We proposed a novel spatial resolution enhancement framework using the convolutional attention-based token mixer method. This approach leveraged spatial context and semantic information to improve the spatial resolution of images. This method used the multi-head convolutional attention block and sub-pixel convolution to extract spatial and spectral information and fused them using the same technique. The multi-head convolutional attention block can effectively utilize the local information of spatial and spectral dimensions. The method was tested on two kinds of data types, which were the visual-thermal dataset and the visual-hyperspectral dataset. Our method was also compared with the state-of-the-art methods, including traditional methods and deep learning methods. The experiment results showed that the method was effective and outperformed state-of-the-art methods in overall, spatial, and spectral accuracies.
Comparison of Lower-Limb Muscle Synergies Between Young and Old People During Cycling Based on Electromyography Sensors—A Preliminary Cross-Sectional Study
Authors: Kong Li , Yang Kun , Li Haojie , Wu Xie , Zhang Qiang
Published: October 2024

The purpose of this study was to analyze the lower-limb muscle synergies of young and older adults during stationary cycling across various mechanical conditions to reveal adaptive strategies employed by the elderly to address various common pedaling tasks and function degradation. By comparing lower-limb muscle synergies during stationary cycling between young and old people, this study examined changes in muscle synergy patterns during exercise in older individuals. This is crucial for understanding neuromuscular degeneration and changes in movement patterns in older individuals. Sixteen young and sixteen older experienced cyclists were recruited to perform stationary cycling tasks at two levels of power (60 and 100 W) and three cadences (40, 60, and 90 rpm) in random order. The lower-limb muscle synergies and their inter- and intra-individual variability were analyzed. Three synergies were extracted in this study under all riding conditions in both groups while satisfying overall variance accounted for (VAF) > 85% and muscle VAF > 75%. The older adults exhibited lower variability in synergy vector two and a higher trend in the variability of activation coefficient three, as determined by calculating the variance ratio. Further analyses of muscle synergy structures revealed increased weighting in major contribution muscles, the forward-shifting peak activation in synergy one, and lower peak magnitude in synergy three among older adults. To produce the same cycling power and cadence as younger individuals, older adults make adaptive adjustments in muscle control—increased weighting in major contribution muscles, greater consistency in the use of primary force-producing synergies, and earlier peak activation of subsequent synergy.
Research on Road Internal Disease Identification Algorithm Based on Attention Fusion Mechanisms
Authors: Wang Yangyang , Yan Shoujing , Xi Chenchen , Yu Zhi , Zhou Chunpeng , Chi Fengxia , Wei Jintao
Published: October 2024

Internal disease in asphalt pavement is a crucial indicator of pavement health and serves as a vital basis for maintenance and rehabilitation decisions. It is closely related to the optimization and allocation of funds by highway maintenance management departments. Accurate and rapid identification of internal pavement diseases is essential for improving overall pavement quality. This study aimed to identify internal pavement diseases using deep learning algorithms, thereby improving the efficiency of determining internal pavement diseases. In this work, a multi-view recognition algorithm model based on deep learning is proposed, with attention fusion mechanisms embedded both between channels and between views. By comparing and analyzing the training and recognition results of different neural networks, it was found that the multi-view recognition algorithm model based on attention fusion demonstrates the best performance in identifying internal pavement diseases.
Thought-Controlled Computer Applications: A Brain–Computer Interface System for Severe Disability Support
Authors: Belwafi Kais , Ghaffari Fakhreddine
Published: October 2024

This study introduces an integrated computational environment that leverages Brain–Computer Interface (BCI) technology to enhance information access for individuals with severe disabilities. Traditional assistive technologies often rely on physical interactions, which can be challenging for this demographic. Our innovation focuses on creating new assistive technologies that use novel Human–Computer interfaces to provide a more intuitive and accessible experience. The proposed system offers four key applications to users controlled by four thoughts: an email client, a web browser, an e-learning tool, and both command-line and graphical user interfaces for managing computer resources. The BCI framework translates ElectroEncephaloGraphy (EEG) signals into commands or events using advanced signal processing and machine learning techniques. These identified commands are then processed by an integrative strategy that triggers the appropriate actions and provides real-time feedback on the screen. Our study shows that our framework achieved an 82% average classification accuracy using four distinct thoughts of 62 subjects and a 95% recognition rate for P300 signals from two users, highlighting its effectiveness in translating brain signals into actionable commands. Unlike most existing prototypes that rely on visual stimulation, our system is controlled by thought, inducing brain activity to manage the system’s Application Programming Interfaces (APIs). It switches to P300 mode for a virtual keyboard and text input. The proposed BCI system significantly improves the ability of individuals with severe disabilities to interact with various applications and manage computer resources. Our approach demonstrates superior performance in terms of classification accuracy and signal recognition compared to existing methods.
Structure-Based Training: A Training Method Aimed at Pixel Errors for a Correlation-Coefficient-Based Neural Network
Authors: Su Jun , He Wei , Wang Yingguan , Bu Zhiyong , Zhang Tiantian
Published: October 2024

In research on building a one-shot learning neural network without pre-training using mass data, the limitation on the information obtained from a single training sample downgrades the performance of the network. In order to improve performance and take full advantage of the support set, in this study, we design three kinds of shadow nodes and propose a structure-based training method for a correlation-coefficient-based neural network. This training strategy focuses on branches that are not activated or inactivated as expected. In contrast to existing networks that optimize the parameters using back-propagation, the training method proposed in this paper optimizes the structure of the correlation-coefficient-based network by correcting its pixel errors. For the shadow nodes and training process based on this strategy, the intersection over union (IOU) of a detected target increases by 4.83% in the experiments when using the Fashion-Mnist dataset, increases by 4.02% when using the Omniglot dataset, and increases by 3.89% when using the Cifar-10 dataset. The samples in category “7” wrongly classified as “1” decreased by 27.32% when using the Mnist dataset after training. This training strategy, along with shadow nodes, makes the correlation-coefficient-based network a more practical model and enables the network to develop during the accumulation of reliable samples, thus making it more suitable for simple target detection projects that collect samples over time. Moreover, the shadow nodes and training method proposed in this paper supplement the non-gradient-based parameter-gaining strategy. Additionally, it is a new attempt to explore the imitation of a human’s ability to learn a new pattern from a low number of references.
Enhanced Vital Parameter Estimation Using Short-Range Radars with Advanced Motion Compensation and Super-Resolution Techniques
Authors: Yoon Sewon , Baek Seungjae , Choi Inoh , Kim Soobum , Koo Bontae , Baek Youngseok , Jung Jooho , Park Sanghong , Kim Min
Published: October 2024

Various short-range radars, such as impulse-radio ultra-wideband (IR-UWB) and frequency-modulated continuous-wave (FMCW) radars, are currently employed to monitor vital signs, including respiratory and cardiac rates (RRs and CRs). However, these methods do not consider the motion of an individual, which can distort the phase of the reflected signal, leading to inaccurate estimation of RR and CR because of a smeared spectrum. Therefore, motion compensation (MOCOM) is crucial for accurately estimating these vital rates. This paper proposes an efficient method incorporating MOCOM to estimate RR and CR with super-resolution accuracy. The proposed method effectively models the radar signal phase and compensates for motion. Additionally, applying the super-resolution technique to RR and CR separately further increases the estimation accuracy. Experimental results from the IR-UWB and FMCW radars demonstrate that the proposed method successfully estimates RRs and CRs even in the presence of body movement.
Exploring the Potential of Microservices in Internet of Things: A Systematic Review of Security and Prospects
Authors: El Akhdar Abir , Baidada Chafik , Kartit Ali , Hanine Mohamed , García Carlos Osorio , Lara Roberto Garcia , Ashraf Imran
Published: October 2024

With the rapid growth of Internet of Things (IoT) systems, ensuring robust security measures has become paramount. Microservices Architecture (MSA) has emerged as a promising approach for enhancing IoT systems security, yet its adoption in this context lacks comprehensive analysis. This systematic review addresses this research gap by examining the incorporation of MSA in IoT systems from 2010 to 2024. From an initial pool of 4388 studies, selected articles underwent thorough quality assessment with weighted critical appraisal questions and a defined inclusion threshold. This study represents the first comprehensive systematic review to investigate the potential of microservices in IoT, with a particular focus on security aspects. The review explores the merits of MSA, highlighting twelve benefits, eight key challenges, and eight security risks. Additionally, the eight best practices for implementing MSA in IoT systems are extracted. The findings underscore MSA’s utility in fortifying IoT security while also acknowledging complexities and potential vulnerabilities. Moreover, the study calls attention to the importance of incorporating complementary technologies including blockchain and machine learning to address identified gaps effectively. Finally, we propose a taxonomic classification for Microservice-based IoT security patterns, facilitating the categorization and organization of security measures in this context. Such a review can help researchers and practitioners identify existing gaps, highlight potential research directions, and provide guidelines for designing secure and efficient microservice-based IoT systems.
A Hollow Hemispherical Mixed Matrix Lithium Adsorbent with High Interfacial Interaction for Lithium Recovery from Brine
Authors: Feng Yuyang , Zhang Yifei , Wang Lin , Wang Shiqiang , Xu Lina , Han Senjian , Deng Tianlong
Published: October 2024

Mixed matrix lithium adsorbents have attracted much interest for lithium recovery from brine. However, the absence of an interfacial interaction between the inorganic lithium-ion sieves (LISs) and the organic polymer matrix resulted in the poor structural stability and attenuated lithium adsorption efficiency. Here, a novel hollow hemispherical mixed matrix lithium adsorbent (H-LIS) with high interfacial compatibility was constructed based on mussel-bioinspired surface chemistry using a solvent evaporation induced phase transition method. The effects of types of functional modifiers, LIS loading amount, adsorption temperature and pH on their structural stability and lithium adsorption performance were systematically investigated. The optimized H-LIS adsorbent with the LIS loading amount of 50 wt.% possessed the structural merit that the LIS functionally modified by dopamine exposed on both the inner and outer surfaces of the hollow hemispheres. At the best adsorption pH of 12.0, it showed a comparable lithium adsorption capacity of 25.68 mg·g−1 to the powdery LIS within 4 h, favorable adsorption selectivity of Mg/Li and good reusability that could maintain over 90% of lithium adsorption capacity after the LiCl adsorption—0.25 M HCl pickling-DI water cleaning cycling processes for three times. The interfacial interaction mechanism of H-LIS for lithium adsorption was innovatively explored via advanced microcalorimetry technology. It suggested the nature of the Li+ adsorption process was exothermic and dopamine modification could reduce the activation energy for lithium adsorption from 15.68 kJ·mol−1 to 13.83 kJ·mol−1 and trigger a faster response to Li+ by strengthening the Li+-H+ exchange rate, which established the thermodynamic relationship between the structure and Li+ adsorption performance of H-LIS. This work will provide a technical support for the structural regulation of functional materials for lithium extraction from brine.
Regional Strategies for Implementing Methane Fermentation Technology in Waste Management: Environmental, Technological, and Social Perspectives
Authors: Kasiński Sławomir , Szuszkiewicz Jarosław , Rudnicki Marcin
Published: October 2024

This article discusses regional strategies for implementing methane fermentation technology in waste management, considering environmental, technological, and social perspectives. Methane fermentation plays a crucial role in modern waste management and renewable energy production. The article highlights the importance of local climatic, economic, and social conditions that affect the efficiency and profitability of biogas installations. Both barriers and opportunities for technology development in various regions are analyzed, particularly in terms of raw material availability, regulatory support, and social acceptance. Examples of biogas technology applications in different countries are provided, identifying key success factors. The article offers valuable insights for decision-makers responsible for designing and implementing methane fermentation systems, taking into account diverse regional conditions and local needs.
Sustainability Strategies in Municipal Wastewater Treatment
Authors: Derco Ján , Guľašová Patrícia , Legan Maša , Zakhar Ronald , Žgajnar Gotvajn Andreja
Published: October 2024

The European Parliament adopted a legislative resolution of 10 April 2024 on the proposal for a directive of the European Parliament and of the Council concerning urban wastewater treatment. The reduction in pollution in discharged treated wastewater in the parameters of BOD5, total nitrogen, and total phosphorus was emphasized. Based on these results, it stated that the impacts on the quality of lakes, rivers, and seas in the EU are visible and tangible. At the same time, it was emphasized that the sector of urban wastewater removal and treatment is responsible for 0.8% of total electricity consumption and about 0.86% of all greenhouse gas emissions in the entire EU. Almost a third of these emissions could be prevented by improving the treatment process, better use of sewage sludge, and increasing energy efficiency, as well as a higher rate of use of renewable resource technologies. It is also necessary to integrate treatment processes into the circular economy. Sludge management and water reuse are suboptimal as too many valuable resources are still being wasted. This article focuses on sustainable municipal wastewater treatment, innovative and new wastewater treatment processes and technologies (combined and hybrid processes, ANAMMOX, etc.) and their use in practice with the aim of increasing environmental and energy efficiency and reducing the carbon footprint. The research is focused on the possibilities of increasing the efficiency of energy processing of sludge, reuse of nitrogen and phosphorus, sludge, and reuse of treated wastewater.
Measurement and Analysis of Carbon Emission Efficiency in the Three Urban Agglomerations of China
Authors: Wu Dan , Mei Xuan , Zhou Haili
Published: October 2024

China aims to reduce its carbon emissions to achieve carbon peaking and neutrality. Measuring the carbon emission efficiency of three urban agglomerations in China, exploring their spatiotemporal characteristics, and investigating the main influencing factors are crucial for achieving regional sustainable development and dual carbon goals. Using the super-slack-based measurement (super-SBM) model, we calculated the carbon emission efficiency of the Beijing–Tianjin–Hebei (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD) urban agglomerations from 2011 to 2021 and explored the spatiotemporal non-equilibrium characteristics of carbon emission efficiency and its influencing factors. The results indicated that: (1) Overall, the carbon emission efficiency showed an N-type trend, with the PRD having the highest average efficiency. Regional differences between the YRD and BTH regions gradually increased. (2) The efficiency hotspots shifted from the PRD to the YRD, whereas the cold spots were mainly concentrated in the BTH region. The variation in the standard deviation ellipse radius of carbon emission efficiency in the urban agglomerations was clear, and the spatial disequilibrium was significant. (3) Economic level and opening up had positive impacts on carbon emission efficiency, whereas energy intensity and industrial structure had negative impacts. The effects of population size, government intervention, and technological level varied among the regions.
Synthesis of Novel Fe-CNs-P/S Carbon Materials for Sustainable Water Treatment: Activation of Persulfate for Efficient Tetracycline Degradation
Authors: Zhang Huali , Zhang Kanghui , Liu Qin , Shi Tongshan , Cui Jiaheng , Li Jinxiu
Published: October 2024

This study presents a novel Fe-CNs-P/S carbon composite material, synthesized by doping elements P and S into NH2-MIL-101 (Fe) using the carbonization method. The material’s application in sustainable water treatment was evaluated, focusing on its effectiveness in activating persulfate for pollutant degradation. The research thoroughly investigates the synthesis process, structural characteristics, and performance in degrading pollutants. The results indicate that Fe-CNs-P/S-5 with 50% P and S co-doping is higher than that of other samples, where the degradation rate of TC in 30 min is as high as 98.11% under the optimum conditions, that is temperature at 25 °C, 0.05 g/L of catalyst concentration, and 0.2 g/L of PMS concentration. The composite material demonstrates robust versatility and stability, maintaining high degradation efficiency across multiple organic pollutants, with no significant reduction in catalytic performance after four cycles. Furthermore, the free radical quenching experiments display that the singlet oxygen 1O2 is the main active species. It is demonstrated that the doping of P and S play a role in the enhancement of PMS activation over the Fe-CNs-P/S catalyst. This material demonstrates remarkable efficacy in treating a range of organic contaminants and exhibits excellent reusability, presenting a promising approach for enhancing sustainability in water treatment applications.
Examining the Influence of Recycled Aggregates on the Fresh and Mechanical Characteristics of High-Strength Concrete: A Comprehensive Review
Authors: Jagadesh P. , Karthik K. , Kalaivani P. , Karalar Memduh , Althaqafi Essam , Madenci Emrah , Özkılıç Yasin Onuralp
Published: October 2024

This review examines the impact of recycled aggregates (RAs) on the fresh and mechanical properties of high-strength concrete (HSC). The results revealed that incorporating RAs can reduce the compressive strength of HSC by up to 25%, with strength values ranging from 40 to 70 MPa depending on the RA content. The addition of supplementary materials like silica fume, fly ash, and polycarboxylate ether significantly mitigated these negative effects, enhancing the compressive strength by approximately 15–20% compared with the control mixes without additives. Furthermore, the tensile strength was observed to decrease by up to 18% with increasing RA content, but fiber reinforcement improved this by 10%, demonstrating the potential of additives to offset mechanical weaknesses. The modulus of elasticity also declined by up to 30% with higher RA dosages, highlighting the critical impact of the adhered mortar quality on the overall stiffness of the concrete. According to the literature, it was noticed that, when the dosage of RCAs is increased, there is a drop in the strength activity index (SAI). When the substitute dosage exceeded 50%, the SAI decreased. These findings underscore the importance of using optimized additive combinations to improve the mechanical performance of RA concrete, making it a viable option for sustainable construction. Overall, the findings suggest that, although RAs may negatively affect certain physical traits of HSC, the use of appropriate additives can optimize its performance, making it a viable option for sustainable construction practices.