bluuebunny/crossref_metadata_2025 huggingface dataset:
Mechanical Design, Manufacturing, and Testing of a Soft Pneumatic Actuator with a Reconfigurable Modular Reinforcement
Authors: Antonelli Michele Gabrio , Beomonte Zobel Pierluigi , Mattei Enrico , Stampone Nicola
Published: November 2024

Soft actuators have enabled the growth of soft robotics, overcoming several drawbacks of rigid robotics by providing devices with many degrees of freedom and the ability to grasp, bend, move, jump, and more. The reconfiguration of the workspace is still a limitation of these actuators. Indeed, once the actuator is designed and developed, it is used for a specific task. This work presents a reconfigurable soft pneumatic actuator with a novel reconfigurable modular reinforcement. The latter is wrapped around an inner tube in silicone rubber and is made of components whose assembly can be configured based on the task. A formulation is identified by a hybrid approach based on finite element analysis and response surface methodology for predicting and designing the behavior of the actuator. The prototyping revealed the ease of fabrication and reconfigurability as the strength of this new actuator. The experimental tests demonstrated the feasibility of adopting the actuator as a finger in a gripper for handling and moving objects of different shapes, masses, and stiffness. Furthermore, the evaluated performance shows a good trade-off between mass, developed force, implementation time, easy reconfigurability, and cost-effectiveness.
Submarine Landslide Identification Based on Improved DeepLabv3 with Spatial and Channel Attention
Authors: Huang Jingwen , Song Weijing , Liu Tao , Cui Xiaoyu , Yan Jining , Wang Xiaoyu
Published: November 2024

As one of the most destructive, hazardous, and frequent marine geohazards, correctly recognizing submarine landslides holds substantial importance for regional risk assessment, disaster prevention, and marine resource development. Many conventional approaches to prediction and mapping necessitate the involvement of expert insights, oversight, and extensive field investigations, which can result in significant time and effort invested in the prediction process. This paper focuses on employing a deep neural network semantic segmentation technique to detect submarine landslides to replace previous methods, such as numerical analysis and physical modeling, to predict and identify the landslide areas quickly. The peripheral zone of the western Iberian Sea is selected as the study area. Since the neural network image recognition task usually requires RGB images as input data, factors such as slope, hillshade, and elevation extracted from digital elevation model (DEM) data are used to synthesize RGB images through band synthesis methods, and the number and diversity of data are increased utilizing data enhancement. Based on the classical semantic segmentation model DeepLabV3, this paper proposes an improved deep learning method, which strengthens the ability of model feature extraction for complex situations by adding an attention mechanism module, improving the spatial pyramid pooling module, and improving the landslide intersection over union metric from 0.4257 to 0.5219 and the F1-score metric from 0.609 to 0.6631 to achieve effective identification of submarine landslides.
Seamless Optimization of Wavelet Parameters for Denoising LFM Radar Signals: An AI-Based Approach
Authors: Abdelfattah Talaat , Maher Ali , Youssef Ahmed , Driessen Peter F.
Published: November 2024

Linear frequency modulation (LFM) signals are pivotal in radar systems, enabling high-resolution measurements and target detection. However, these signals are often degraded by noise, significantly impacting their processing and interpretation. Traditional denoising methods, including wavelet-based techniques, have been extensively used to address this issue, yet they often fall short in terms of optimizing performance due to fixed parameter settings. This paper introduces an innovative approach by combining wavelet denoising with long short-term memory (LSTM) networks specifically tailored for LFM signals in radar systems. By generating a dataset of LFM signals at various signal-to-noise Ratios (SNR) to ensure diversity, we systematically identified the optimal wavelet parameters for each noisy instance. These parameters served as training labels for the proposed LSTM-based architecture, which learned to predict the most effective denoising parameters for a given noisy LFM signal. Our findings reveal a significant enhancement in denoising performance, attributed to the optimized wavelet parameters derived from the LSTM predictions. This advancement not only demonstrates a superior denoising capability but also suggests a substantial improvement in radar signal processing, potentially leading to more accurate and reliable radar detections and measurements. The implications of this paper extend beyond modern radar applications, offering a framework for integrating deep learning techniques with traditional signal processing methods to optimize performance across various noise-dominated domains.
Behaviour of Cows with Johne’s Disease (Paratuberculosis)
Authors: Charlton Gemma L. , Churches Jeanette , Bleach Emma C. L. , Thorup Vivi M.
Published: November 2024

Johne’s disease (JD) significantly reduces the welfare of cattle worldwide. As changes in lying and feeding behaviours are considered important tools for assessing health and early detection of diseases, the aim of this study was to compare lying and feeding behaviours of JD-positive (JD5) and JD-negative (JD0) cows around peak lactation. The cows were fitted with an accelerometer-based sensor to record step counts and lying behaviour. They were also fitted with a pressure-based halter from approximately 56 d post-partum to collect feeding and rumination data. Every 3 months, the cows were milk sampled to test for naturally occurring JD using an ELISA. JD5 cows [n = 14 (two positive results in any four consecutive ELISAs)] were matched to JD0 cows [n = 14 (consecutive negative ELISAs)] based on lactation stage, parity, age, and milk yield. Of the 28 cows, 9 JD5 and 9 JD0 cows provided sensor data for analysis. JD5 cows spent 1.7 h/d less lying compared to JD0 cows. No differences in time spent eating were found; however, JD5 cows spent 1.1 h/d longer ruminating and produced 80 more feed boluses/d than JD0 cows around week 8 of lactation. The reason JD5 and JD0 cows behave differently around peak lactation is unclear and therefore warrants further investigation focusing on behaviour, milk yield, and feed intake among cows with JD.
Machine Health Monitoring and Fault Diagnosis Techniques (Volume II)
Authors: Sun Shilong , Shen Changqing , Wang Dong
Published: November 2024

This Special Issue highlights a diverse range of pioneering research dedicated to fault diagnosis, condition monitoring, and defect detection in various engineering systems [...]
An AI-Driven Model to Enhance Sustainability for the Detection of Cyber Threats in IoT Environments
Authors: Alsulami Majid H.
Published: November 2024

In the face of constantly changing cyber threats, a variety of actions, tools, and regulations must be considered to safeguard information assets and guarantee the confidentiality, reliability, and availability of digital resources. The purpose of this research is to create an artificial intelligence (AI)-driven system to enhance sustainability for cyber threat detection in Internet of Things (IoT) environments. This study proposes a modern technique named Artificial Fish Swarm-driven Weight-normalized Adaboost (AF-WAdaBoost) for optimizing accuracy and sustainability in identifying attacks, thus contributing to heightening security in IoT environments. CICIDS2017, NSL-KDD, and UNSW-NB15 were used in this study. Min-max normalization is employed to pre-process the obtained raw information. The proposed model AF-WAdaBoost dynamically adjusts classifiers, enhancing accuracy and resilience against evolving threats. Python is used for model implementation. The effectiveness of the suggested AF-WAdaBoost model in identifying different kinds of cyber-threats in IoT systems is examined through evaluation metrics like accuracy (98.69%), F-measure (94.86%), and precision (95.72%). The experimental results unequivocally demonstrate that the recommended model performed better than other traditional approaches, showing essential enhancements in accuracy and strength, particularly in a dynamic environment. Integrating AI-driven detection balances offers sustainability in cybersecurity, ensuring the confidentiality, reliability, and availability of information assets, and also helps in optimizing the accuracy of systems.
Evaluating the User Experience and Usability of the MINI Robot for Elderly Adults with Mild Dementia and Mild Cognitive Impairment: Insights and Recommendations
Authors: Mahmoudi Asl Aysan , Toribio-Guzmán Jose Miguel , Castro-González Álvaro , Malfaz María , Salichs Miguel A. , Franco Martín Manuel
Published: November 2024

Introduction: In recent years, the integration of robotic systems into various aspects of daily life has become increasingly common. As these technologies continue to advance, ensuring user-friendly interfaces and seamless interactions becomes more essential. For social robots to genuinely provide lasting value to humans, a favourable user experience (UX) emerges as an essential prerequisite. This article aimed to evaluate the usability of the MINI robot, highlighting its strengths and areas for improvement based on user feedback and performance. Materials and Methods: In a controlled lab setting, a mixed-method qualitative study was conducted with ten individuals aged 65 and above diagnosed with mild dementia (MD) and mild cognitive impairment (MCI). Participants engaged in individual MINI robot interaction sessions, completing cognitive tasks as per written instructions. Video and audio recordings documented interactions, while post-session System Usability Scale (SUS) questionnaires quantified usability perception. Ethical guidelines were followed, ensuring informed consent, and the data underwent qualitative and quantitative analyses, contributing insights into the MINI robot’s usability for this demographic. Results: The study addresses the ongoing challenges that tasks present, especially for MD individuals, emphasizing the importance of user support. Most tasks require both verbal and physical interactions, indicating that MD individuals face challenges when switching response methods within subtasks. These complexities originate from the selection and use of response methods, including difficulties with voice recognition, tablet touch, and tactile sensors. These challenges persist across tasks, with individuals with MD struggling to comprehend task instructions and provide correct answers and individuals with MCI struggling to use response devices, often due to the limitations of the robot’s speech recognition. Technical shortcomings have been identified. The results of the SUS indicate positive perceptions, although there are lower ratings for instructor assistance and pre-use learning. The average SUS score of 68.3 places device usability in the “good” category. Conclusions: Our study examines the usability of the MINI robot, revealing strengths in quick learning, simple system and operation, and integration of features, while also highlighting areas for improvement. Careful design and modifications are essential for meaningful engagement with people with dementia. The robot could better benefit people with MD and MCI if clear, detailed instructions and instructor assistance were available.
An IoT-Based Framework for Automated Assessing and Reporting of Light Sensitivities in Children with Autism Spectrum Disorder
Authors: Reddy Dundi Umamaheswara , Kumar Kanaparthi V. Phani , Ramakrishna Bandaru , Umaiorubagam Ganapathy Sankar
Published: November 2024

Identification of light sensitivities, manifesting either as hyper-sensitive (over-stimulating) or hypo-sensitive (under-stimulating) in children with autism spectrum disorder (ASD), is crucial for the development of personalized sensory environments and therapeutic strategies. Traditional methods for identifying light sensitivities often depend on subjective assessments and manual video coding methods, which are time-consuming, and very keen observations are required to capture the diverse sensory responses of children with ASD. This can lead to challenges for clinical practitioners in addressing individual sensory needs effectively. The primary objective of this work is to develop an automated system using Internet of Things (IoT), computer vision, and data mining techniques for assessing visual sensitivities specifically associated with light (color and illumination). For this purpose, an Internet of Things (IoT)-based light sensitivities assessing system (IoT-LSAS) was designed and developed using a visual stimulating device, a bubble tube (BT). The IoT-LSAS integrates various electronic modules for (i) generating colored visual stimuli with different illumination levels and (ii) capturing images to identify children’s emotional responses during sensory stimulation sessions. The system is designed to operate in two different modes: a child control mode (CCM) and a system control mode (SCM). Each mode uses a distinct approach for assessing light sensitivities, where CCM uses a preference-based approach, and SCM uses an emotional response tracking approach. The system was tested on a sample of 20 children with ASD, and the results showed that the IoT-LSAS effectively identified light sensitivities, with a 95% agreement rate in the CCM and a 90% agreement rate in the SCM when compared to the practitioner’s assessment report. These findings suggest that the IoT-LSAS can be used as an alternative to traditional assessment methods for diagnosing light sensitivities in children with ASD, significantly reducing the practitioner’s time required for diagnosis.
Immersive Virtual Reality: A Novel Approach to Second Language Vocabulary Acquisition in K-12 Education
Authors: Alfadil Mohammed
Published: November 2024

Today, immersive virtual reality (IVR) is increasing in popularity in a broad range of fields, including science, pedagogy, engineering and so forth. Therefore, this study discusses the Unified Theory of Acceptance and Use of Technology (UTAUT), which can be used to examine the factors that influence the adoption of immersive VR in the classroom, particularly in second language (L2) vocabulary acquisition. The sample for this study included 32 intermediate students and their teacher. For the purpose of evaluation, the researcher used partial least squares structural equation modelling (PLS-SEM) techniques to analyze the results. The findings of the students’ survey showed that performance expectancy, effort expectancy and social influence were seen to have had a positive impact on the intention to use immersive VR. Likewise, the findings indicated that facilitating conditions were seen to have had a positive impact on the use behavior of actually using immersive VR, whereas behavioral intentions did not. In addition, the teacher’s survey demonstrated a favorable view regarding the potential of immersive VR technology to support teaching L2 vocabulary acquisition. This particular study encouraged educators and educational technologists to utilize immersive VR games as a teaching–learning tool to reduce the challenge of L2 vocabulary acquisition.
Determining Validity and Reliability of an In-Field Performance Analysis System for Swimming
Authors: Born Dennis-Peter , Polach Marek , Staunton Craig
Published: November 2024

To permit the collection of quantitative data on start, turn and clean swimming performances in any swimming pool, the aims of the present study were to (1) validate a mobile in-field performance analysis system (PAS) against the Kistler starting block equipped with force plates and synchronized to a 2D camera system (KiSwim, Kistler, Winterthur, Switzerland), (2) assess the PAS’s interrater reliability and (3) provide percentiles as reference values for elite junior and adult swimmers. Members of the Swiss junior and adult national swimming teams including medalists at Olympic Games, World and European Championships volunteered for the present study (n = 47; age: 17 ± 4 [range: 13–29] years; World Aquatics Points: 747 ± 100 [range: 527–994]). All start and turn trials were video-recorded and analyzed using two methods: PAS and KiSwim. The PAS involves one fixed view camera recording overwater start footage and a sport action camera that is moved underwater along the side of the pool perpendicular to the swimming lane on a 1.55 m long monostand. From a total of 25 parameters determined with the PAS, 16 are also measurable with the KiSwim, of which 7 parameters showed satisfactory validity (r = 0.95–1.00, p < 0.001, %-difference < 1%). Interrater reliability was determined for all 25 parameters of the PAS and reliability was accepted for 21 of those start, turn and swimming parameters (ICC = 0.78–1.00). The percentiles for all valid and reliable parameters provide reference values for assessment of start, turn and swimming performance for junior and adult national team swimmers. The in-field PAS provides a mobile method to assess start, turn and clean swimming performance with high validity and reliability. The analysis template and manual included in the present article aid the practical application of the PAS in research and development projects as well as academic works.
Deep Learning-Based Classification of Macrofungi: Comparative Analysis of Advanced Models for Accurate Fungi Identification
Authors: Ozsari Sifa , Kumru Eda , Ekinci Fatih , Akata Ilgaz , Guzel Mehmet Serdar , Acici Koray , Ozcan Eray , Asuroglu Tunc
Published: November 2024

This study focuses on the classification of six different macrofungi species using advanced deep learning techniques. Fungi species, such as Amanita pantherina, Boletus edulis, Cantharellus cibarius, Lactarius deliciosus, Pleurotus ostreatus and Tricholoma terreum were chosen based on their ecological importance and distinct morphological characteristics. The research employed 5 different machine learning techniques and 12 deep learning models, including DenseNet121, MobileNetV2, ConvNeXt, EfficientNet, and swin transformers, to evaluate their performance in identifying fungi from images. The DenseNet121 model demonstrated the highest accuracy (92%) and AUC score (95%), making it the most effective in distinguishing between species. The study also revealed that transformer-based models, particularly the swin transformer, were less effective, suggesting room for improvement in their application to this task. Further advancements in macrofungi classification could be achieved by expanding datasets, incorporating additional data types such as biochemical, electron microscopy, and RNA/DNA sequences, and using ensemble methods to enhance model performance. The findings contribute valuable insights into both the use of deep learning for biodiversity research and the ecological conservation of macrofungi species.
Commodity Wi-Fi-Based Wireless Sensing Advancements over the Past Five Years
Authors: Zhu Hai , Dong Enlai , Xu Mengmeng , Lv Hongxiang , Wu Fei
Published: November 2024

With the compelling popularity of integrated sensing and communication (ISAC), Wi-Fi sensing has drawn increasing attention in recent years. Starting from 2010, Wi-Fi channel state information (CSI)-based wireless sensing has enabled various exciting applications such as indoor localization, target imaging, activity recognition, and vital sign monitoring. In this paper, we retrospect the latest achievements of Wi-Fi sensing using commodity-off-the-shelf (COTS) devices from the past 5 years in detail. Specifically, this paper first presents the background of the CSI signal and related sensing models. Then, recent studies are categorized from two perspectives, i.e., according to their application scenario diversity and the corresponding sensing methodology difference, respectively. Next, this paper points out the challenges faced by Wi-Fi sensing, including domain dependency and sensing range limitation. Finally, three imperative research directions are highlighted, which are critical for realizing more ubiquitous and practical Wi-Fi sensing in real-life applications.
LiDAR-360 RGB Camera-360 Thermal Camera Targetless Calibration for Dynamic Situations
Authors: Tran Khanh Bao , Carballo Alexander , Takeda Kazuya
Published: November 2024

Integrating multiple types of sensors into autonomous systems, such as cars and robots, has become a widely adopted approach in modern technology. Among these sensors, RGB cameras, thermal cameras, and LiDAR are particularly valued for their ability to provide comprehensive environmental data. However, despite their advantages, current research primarily focuses on the one or combination of two sensors at a time. The full potential of utilizing all three sensors is often neglected. One key challenge is the ego-motion compensation of data in dynamic situations, which results from the rotational nature of the LiDAR sensor, and the blind spots of standard cameras due to their limited field of view. To resolve this problem, this paper proposes a novel method for the simultaneous registration of LiDAR, panoramic RGB cameras, and panoramic thermal cameras in dynamic environments without the need for calibration targets. Initially, essential features from RGB images, thermal data, and LiDAR point clouds are extracted through a novel method, designed to capture significant raw data characteristics. These extracted features then serve as a foundation for ego-motion compensation, optimizing the initial dataset. Subsequently, the raw features can be further refined to enhance calibration accuracy, achieving more precise alignment results. The results of the paper demonstrate the effectiveness of this approach in enhancing multiple sensor calibration compared to other ways. In the case of a high speed of around 9 m/s, some situations can improve the accuracy about 30 percent higher for LiDAR and Camera calibration. The proposed method has the potential to significantly improve the reliability and accuracy of autonomous systems in real-world scenarios, particularly under challenging environmental conditions.
TouchView: Mid-Air Touch on Zoomable 2D View for Distant Freehand Selection on a Virtual Reality User Interface
Authors: Kim Woojoo , Xiong Shuping
Published: November 2024

Selection is a fundamental interaction element in virtual reality (VR) and 3D user interfaces (UIs). Raycasting, one of the most common object selection techniques, is known to have difficulties in selecting small or distant objects. Meanwhile, recent advancements in computer vision technology have enabled seamless vision-based hand tracking in consumer VR headsets, enhancing accessibility to freehand mid-air interaction and highlighting the need for further research in this area. This study proposes a new technique called TouchView, which utilizes a virtual panel with a modern adaptation of the Through-the-Lens metaphor to improve freehand selection for VR UIs. TouchView enables faster and less demanding target selection by allowing direct touch interaction with the magnified object proxies reflected on the panel view. A repeated-measures ANOVA on the results of a follow-up experiment on multitarget selection with 23 participants showed that TouchView outperformed the current market-dominating freehand raycasting technique, Hybrid Ray, in terms of task performance, perceived workload, and preference. User behavior was also analyzed to understand the underlying reasons for these improvements. The proposed technique can be used in VR UI applications to enhance the selection of distant objects, especially for cases with frequent view shifts.
Monitoring of Non-Lame Horses and Horses with Unilateral Hindlimb Lameness at Rest with the Aid of Accelerometers
Authors: Uellendahl Anja , Schramel Johannes P. , Tichy Alexander , Peham Christian
Published: November 2024

The aim of this study was to determine whether horses exhibiting unilateral hindlimb lameness unload (rest) the lame limb more than the contralateral limb. The resting/unloading of the hindlimbs and the time spent lying down were measured using accelerometers. Ten non-lame horses and 20 lame horses were recruited for participation and monitored for 11 h overnight with accelerometers (MSR145, sampling rate: 1 Hz, and measuring range: ±15 g) attached to the lateral metatarsal and metacarpal regions of each limb. Metatarsal and metacarpal orientation were used to determine whether the limb was unloaded (rested) or loaded, respectively, or whether the horses were lying down. The relation of resting time between non-lame and lame limbs (non-lame/lame: 0.85 ± 1.2) of the lame horses differed significantly (p = 0.035) from that of the non-lame horses (right/left: 1.08 ± 0.47). Non-lame horses rested their hindlimbs evenly (left: 15 ± 10%; right: 17 ± 16%). Horses with unilateral hindlimb lameness unloaded the lame limb longer (lame limb: 61.8 ± 25.3%, non-lame limb: 38.2 ± 25.3%) than their contralateral limb. The lame horses (13 ± 11%) lay down longer (p = 0.012) than the non-lame horses (3 ± 6%). The degree of lameness determined by the participating veterinarians (Vet Score) (r = −0.691, p < 0.01) and the asymmetry evaluated by the lameness locator (ALL) (r = −0.426, p = 0.019) correlated with the resting ratio (rest time ratio). Both factors were also correlated with the time spent lying down (Vet Score (r = 0.364, p = 0.048) and the ALL (r = 0.398, p = 0.03)). The ALL and VET Score were significantly correlated (r = 0.557, p = 0.01). The results of this study provide a good baseline for future research into how individual resting patterns may help to detect pain.
Research on a Method for Measuring the Pile Height of Materials in Agricultural Product Transport Vehicles Based on Binocular Vision
Authors: Qian Wang , Wang Pengyong , Wang Hongjie , Wu Shuqin , Hao Yang , Zhang Xiaoou , Wang Xinyu , Sun Wenyan , Guo Haijie , Guo Xin
Published: November 2024

The advancement of unloading technology in combine harvesting is crucial for the intelligent development of agricultural machinery. Accurately measuring material pile height in transport vehicles is essential, as uneven accumulation can lead to spillage and voids, reducing loading efficiency. Relying solely on manual observation for measuring stack height can decrease harvesting efficiency and pose safety risks due to driver distraction. This research applies binocular vision to agricultural harvesting, proposing a novel method that uses a stereo matching algorithm to measure material pile height during harvesting. By comparing distance measurements taken in both empty and loaded states, the method determines stack height. A linear regression model processes the stack height data, enhancing measurement accuracy. A binocular vision system was established, applying Zhang’s calibration method on the MATLAB (R2019a) platform to correct camera parameters, achieving a calibration error of 0.15 pixels. The study implemented block matching (BM) and semi-global block matching (SGBM) algorithms using the OpenCV (4.8.1) library on the PyCharm (2020.3.5) platform for stereo matching, generating disparity, and pseudo-color maps. Three-dimensional coordinates of key points on the piled material were calculated to measure distances from the vehicle container bottom and material surface to the binocular camera, allowing for the calculation of material pile height. Furthermore, a linear regression model was applied to correct the data, enhancing the accuracy of the measured pile height. The results indicate that by employing binocular stereo vision and stereo matching algorithms, followed by linear regression, this method can accurately calculate material pile height. The average relative error for the BM algorithm was 3.70%, and for the SGBM algorithm, it was 3.35%, both within the acceptable precision range. While the SGBM algorithm was, on average, 46 ms slower than the BM algorithm, both maintained errors under 7% and computation times under 100 ms, meeting the real-time measurement requirements for combine harvesting. In practical operations, this method can effectively measure material pile height in transport vehicles. The choice of matching algorithm should consider container size, material properties, and the balance between measurement time, accuracy, and disparity map completeness. This approach aids in manual adjustment of machinery posture and provides data support for future autonomous master-slave collaborative operations in combine harvesting.
A Novel Approach for Improving XML Querying over Wireless Broadcast Channels
Authors: Ahlawat Vinay Kumar , Agarwal Gaurav , Goel Vikas , Sanghi Akash , Choi Sun Young , Hui Kueh Lee , Sain Mangal
Published: November 2024

The querying of large XML data over wireless broadcast channels can reduce bandwidth utilization, cause significant latency, and produce inefficient energy usage. This paper proposes a scheme to improve XML querying over wireless broadcast channels in order to address the issues mentioned above. Various techniques, including partitioning, load balancing, and query routing, have been combined into one approach. The proposed scheme partitions the XML data stream into several partitions based on criteria like document size, type, or content. Each partition is routed to a separate channel to balance the load on each wireless broadcast channel. A query routing mechanism that directs queries to the right channel or combination of channels that hold the relevant XML data partition was implemented. This study simulates, evaluates, and compares the proposed scheme’s performance. The results from the comparison study with existing schemes demonstrate a considerable reduction in the access time for XML querying via wireless broadcast channels.
Targeted Biofeedback Training to Improve Gait Parameters in Subacute Stroke Patients: A Single-Blind Randomized Controlled Trial
Authors: Skvortsov Dmitry V. , Kaurkin Sergey N. , Ivanova Galina E.
Published: November 2024

Biofeedback (BFB) is a rehabilitation method, which, among other things, is used for the restitution of motor and gait function. As of now, it has become technically feasible to use BFB training based on target gait parameters to improve the gait function in stroke patients. The walking patterns of stroke patients are generally characterized by significant gait phase asymmetries, mostly of the stance phase and the single stance phase. The aim of the study was to investigate the restoration of gait function using BFB training with gait phases as feedback targets. The study included two patient groups, each of 20 hemiparetic patients in the subacute stage of stroke and a control group of 20 healthy subjects. Each patient group received BFB training with either stance phase or single stance phase as the feedback target, respectively. The patients received a total of 8 to 11 training sessions. Assessments based on clinical scales and gait analysis data (spatiotemporal, kinematic, and EMG parameters) were performed before and after the training course. The score-based clinical assessments showed a significant improvement in both patient groups. According to the assessments of gait biomechanics, the subjects in the Single Stance Phase group had significantly more severe dysfunctions. In both patient groups, the unaffected limb responded to the BFB training, while the stance phase significantly changed after training in the unaffected limb only. The other patient group, trained using the single stance phase as the feedback target, showed no changes in the target parameter either in the affected or in the contralateral limb. The clinical and instrumental assessments showed different, non-equivalent sensitivity. The results of the study demonstrated the possibility to use targeted BFB training to improve walking function. However, a significant effect of such training was only observed with stance phase as the target parameter. A response to training was observed predominantly in the unaffected limb and facilitated the desired increase in the functional ability of the paretic limb. Training based on stance phase as the target parameter is probably preferable for the patient population under study.
Improved Kalman Filtering Algorithm Based on Levenberg–Marquart Algorithm in Ultra-Wideband Indoor Positioning
Authors: Xie Changping , Fang Xinjian , Yang Xu
Published: November 2024

To improve the current indoor positioning algorithms, which have insufficient positioning accuracy, an ultra-wideband (UWB) positioning algorithm based on the Levenberg–Marquardt algorithm with improved Kalman filtering is proposed. An alternative double-sided two-way ranging (ADS-TWR) algorithm is used to obtain the distance from the UWB tag to each base station and calculate the initial position of the tag by the least squares method. The Levenberg–Marquardt algorithm is used to correct the covariance matrix of the Kalman filter, and the improved Kalman filtering algorithm is used to filter the initial position to obtain the final position of the tag. The feasibility and effectiveness of the algorithm are verified by MATLAB simulation. Finally, the UWB positioning system is constructed, and the improved Kalman filter algorithm is experimentally verified in LOS and NLOS environments. The average X-axis and the Y-axis positioning errors in the LOS environment are 6.9 mm and 5.4 mm, respectively, with a root mean square error of 10.8 mm. The average positioning errors for the X-axis and Y-axis in the NLOS environment are 20.8 mm and 18.0 mm, respectively, while the root mean square error is 28.9 mm. The experimental results show that the improved algorithm has high accuracy and good stability. At the same time, it can effectively improve the convergence speed of the Kalman filter.
Resonance-State Temperature Compensation Method for Ultrasonic Resonance Wind Speed and Direction Sensors
Authors: Zeng Xiangbao , Yuan Yupeng , Jiang Zhuoya , Wang Lu , Long Shuai , Yu Hua
Published: November 2024

To achieve high-precision wind speed and direction measurements in complex environments, a resonance-state temperature compensation method is proposed based on an ultrasonic resonance principle. This method effectively addresses the issue of sound velocity compensation errors caused by the temperature difference between the internal and external environments when using an internal temperature sensor for temperature compensation. By utilizing an adaptive resonance-state tracking model, the resonance frequency shift issues under varying conditions such as altitude, pressure, and temperature are mitigated. This approach ensures that the resonance frequency is strongly correlated with temperature, enabling temperature compensation through resonance frequency alone, without the need for a temperature sensor. The experimental results indicate that the resonance frequency variation rate with temperature for the resonance-state temperature-compensated ultrasonic resonance wind speed and direction sensor is approximately 0.08 kHz/°C. The wind speed measurement accuracy is ±0.3 m/s (≤15 m/s)/±2.3% (15 m/s~50 m/s), which is superior to the measurement accuracy of traditional ultrasonic wind speed and direction sensors (±0.5 m/s (≤15 m/s)/±4% (15 m/s~50 m/s)). The consistency of wind speed measurement is ≤±0.3%, representing an improvement of approximately 3% compared to ultrasonic resonance wind speed and direction sensors without resonance-state temperature compensation.
MSDG: Multi-Scale Dynamic Graph Neural Network for Industrial Time Series Anomaly Detection
Authors: Zhao Zhilei , Xiao Zhao , Tao Jie
Published: November 2024

A large number of sensors are typically installed in industrial plants to collect real-time operational data. These sensors monitor data with time series correlation and spatial correlation over time. In previous studies, GNN has built many successful models to deal with time series data, but most of these models have fixed perspectives and struggle to capture the dynamic correlations in time and space simultaneously. Therefore, this paper constructs a multi-scale dynamic graph neural network (MSDG) for anomaly detection in industrial sensor data. First, a multi-scale sliding window mechanism is proposed to input different scale sensor data into the corresponding network. Then, a dynamic graph neural network is constructed to capture the spatial–temporal dependencies of multivariate sensor data. Finally, the model comprehensively considers the extracted features for sequence reconstruction and utilizes the reconstruction errors for anomaly detection. Experiments have been conducted on three real public datasets, and the results show that the proposed method outperforms the mainstream methods.
Trading Community Analysis of Countries’ Roll-On/Roll-Off Shipping Networks Using Fine-Grained Vessel Trajectory Data
Authors: Huang Shichen , Sun Tengda , Shi Jing , Gong Piqiang , Yang Xue , Zheng Jun , Zhuang Huanshuai , Ouyang Qi
Published: November 2024

Roll-on/roll-off vessels (RO/RO vessels) are playing an increasingly critical role in international automobile transport, facilitating the efficient movement of vehicles and heavy machinery across continents. Despite this growing significance, there is still limited research specifically focused on the RO/RO shipping network and its impact on global trade. This paper studies the global RO/RO shipping network using AIS data on RO/RO vessels collected from 2020 to 2023. We construct a method based on the complex network theory and the graph feature extraction method to quantitatively assess the features of the RO/RO shipping network. This method assesses the complexity, sparsity, homogeneity, modularity, and hierarchy of the RO/RO shipping network across various ports and countries and employs the graph convolutional neural network (GCN) model to extract network features for community detection. This process enables the identification of port clusters that are frequently linked to RO/RO vessels, as well as regional transport modes. The paper’s findings support these conclusions: (1) From 2020 to 2023, the number of nodes in the RO/RO shipping network increased by 22%, primarily concentrated in African countries. The RO/RO shipping network underwent restructuring after the pandemic, with major complex network parameters showing an upward trend. (2) The RO/RO shipping network is complex, with a stable graph density of 0.106 from 2020 to 2023. The average degree increased by 7% to 4.224. Modularity decreased by 6.5% from 0.431 in 2022 to 0.403, while the hierarchy coefficient rose to 0.575, suggesting that post-pandemic, community routes have become more diverse, reflecting the reconstruction and maturation of the overall network. (3) The model yielded a silhouette coefficient of 0.548 and a Davies–Bouldin index of 0.559 using an improved automatic feature extraction method. In comparison between 2020 and 2023, the changes in the two indicators are small. This shows that GINs can effectively extract network features and give us results that we can understand for community detection. (4) In 2023, key communities divide the RO/RO shipping network, with one community handling 39% of global routes (primarily Europe–Asia), another community handling 23% (serving Asia–Pacific, Africa, and the Middle East), and a third community managing 38% (linking Asia, Europe, and South America).
An Indicator Based on Spatial Coordinate Information for Assessing the Capability for Dynamic Machining Performance of Five-Axis Flank Milling
Authors: Yao Chenglin , He Gaiyun , Sang Yicun , Yue Chen , Yan Yichen , Wang Sitong
Published: November 2024

As a spatial coordinate sensor, the touch-trigger on-machine probe is a key equipment in manufacturing that ensures machining quality, and it has played an important role in five-axis flank milling. However, in flank milling, the utilization of the deviation as a conventional indicator for quality assessment of the machining performance is incomprehensive without considering the characteristics of the machining method. In this paper, the error mutual moment is introduced as an indicator to assess the capability for dynamic machining performance of the machine tool in flank milling based on the spatial coordinate information of the touch-trigger on-machine probe considering the characteristic of the error distribution of the flank milling. Experiments are carried out to validate the advantages of the error mutual moment to assess the capability for dynamic machining performance compared with the deviation. Results show that the error mutual moment shows more significant discrepancies than the deviation in assessing the capability for dynamic machining performance of flank milling. The error mutual moment has the potential to be applied as a quality assessment sensor.
Fusion of Telecommunications and IT Services Boosted by Application Programming Interfaces
Authors: Tündik Máté Ákos , Szabó Zsolt , Hilt Attila , Járó Gábor
Published: November 2024

Our long journey on the road of telecommunications is continuously evolving. We have experienced several technological changes, modernizations, optimizations, and various mergers in the past decades. Virtualization and ‘cloudification’ of legacy telecommunication equipment has made communication networks not only more flexible, but also opened new doors. Brand new types of services have become available thanks to the ongoing fusion of the two domains of telecommunications and IT (Information Technology). This overview paper first discusses the evolution of services with an enhanced focus on mobile networks. Then, the possibilities offered by IT are shown. Finally, some examples are given of how Communication Service Providers and end users can benefit from these recent changes.
Empowerment and Sustainability: Investigating Barriers to Women’s Transition from Higher Education to Empowerment in Brazil
Authors: Rana Muhammad Qasim , Lee Angela , Rodrigues Bezerra José Fernando , Villas Boas Guilherme Hissa
Published: November 2024

Gender equality continues to be a global issue, with significant disparities in attention and action between developed and developing nations. Women, in particular, face unique challenges when transitioning from higher education (HE) to empowerment compared to their male counterparts. In many developing countries, barriers to accessing formal education are prevalent, while, in others, despite access to education, women struggle to leverage their learning for societal advancement. This paper adopts the development theory approach by exploring the role of gender equality in fostering sustainable development, emphasizing how women’s empowerment is integral to overall societal progress. This study focuses on Brazil, where, despite high levels of female educational attainment, women still encounter substantial obstacles in transforming their education into meaningful societal empowerment. These challenges include restricted freedom and mobility, deeply ingrained gender norms, violence and harassment, cultural and societal expectations, economic limitations, and family obligations. Survey data were collated from 192 students, alumni, and staff at three academic higher education (HE) institutions in Brazil, utilizing a range of descriptive and inferential statistical analyses to uncover the most significant barriers. This study concludes with policy recommendations aimed at various stakeholders, including government bodies, employers, and other organizations, to support a smoother transition for women from higher education to empowerment. These recommendations include improving workplace policies, enhancing legal protections, and promoting gender equality in leadership roles, all of which are essential to narrowing the gender gap in Brazil.
The Inherent Shield: Learning-Based Stress Symptoms Among People Diagnosed with Specific Learning Disability and Its Relationship with Dispositional Mindfulness
Authors: Polak Nimrod , Grossman Ephraim S.
Published: November 2024

Background: Past research suggests that individuals with learning disabilities experience higher levels of emotional difficulties. Objective: The aim of the current study was to investigate the relationships between specific learning disabilities (SLDs) and learning-based stress symptoms (LBSSs). Further, this study examines whether dispositional mindfulness (DM) can be used as a moderating factor for these symptoms resulting from the same stressful learning experience. Methods: The sample consisted of 133 participants (73 male) aged 23–44 years (M = 34.41; SD = 9.69). In this sample, 56 reported that they have been officially diagnosed with a SLD. Participants completed an online survey comprising a demographics questionnaire, self-report SLD questionnaire, school learning-based stress symptoms, and five-facet mindfulness questionnaire (FFMQ-24). Results: Results revealed that participants with an SLD had higher levels of LBSSs compared to the general population. Furthermore, findings revealed that DM significantly predicts LBSS levels. Conclusions: The present study provides further evidence on the impact of learning experiences on stress symptoms among individuals with SLDs, and this phenomenon we have termed as “learning-based stress symptoms”. Additionally, our findings introduce novel evidence that DM may influence the relationship between LBSSs and SLDs, suggesting that a high level of DM can serve as a protective factor against these learning-based stress symptoms.
The Impact of Divorce Cooling-Off Period on Registered Divorces: Evidence from China
Authors: Zheng Wenge , Niu Bowen , Chen Zhenguo
Published: November 2024

The divorce rate in China has continued to rise since the 21st century. In order to reduce impulsive divorces, the divorce cooling-off period (DCOP) system was established in the Civil Code of China. The aim of this research is to examine the impact of the DCOP on registered divorces. Data from the Ministry of Civil Affairs, National Bureau of Statistics, civil affairs departments, and statistical bureaus of 31 provinces, the 2018 to 2023 editions of the China Statistical Yearbook, and the China Civil Affairs Statistical Yearbook were utilized in this work. The event study analysis reveals that the number of registered divorces decreased significantly in China after the DCOP provision’s enforcement. The ordinary least squares model indicates an average annual decrease of about 2.6 million registered divorces when the DCOP provision takes effect, and the DCOP plays a decisive role in this decrease, which suggests that the DCOP system has achieved its original intention. This research not only highlights the important role of the DCOP in reducing registered divorces but also provides insights on how to improve the DCOP system and promote the stability of marriage.