bluuebunny/crossref_metadata_2025 huggingface dataset:
Humidity-Activated Ammonia Sensor Based on Carboxylic Functionalized Cross-Linked Hydrogel
Authors: Song Yaping , Xia Yihan , Zhang Wei , Yu Yunlong , Cui Yanyu , Liu Lichao , Zhang Tong , Liu Sen , Zhao Hongran , Fei Teng
Published: December 2024

Owing to its extensive use and intrinsic toxicity, NH3 detection is very crucial. Moisture can cause significant interference in the performance of sensors, and detecting NH3 in high humidity is still a challenge. In this work, a humidity-activated NH3 sensor was prepared by urocanic acid (URA) modifying poly (ethylene glycol) diacrylate (PEGDA) via a thiol-ene click cross-linking reaction. The optimized sensor achieved a response of 70% to 50 ppm NH3 at 80% RH, with a response time of 105.6 s and a recovery time of 346.8 s. The sensor was improved for response and recovery speed. In addition, the prepared sensor showed excellent selectivity to NH3 in high-humidity environments, making it suitable for use in some areas with high humidity all the year round or in high-humidity areas such as the detection of respiratory gas. A detailed investigation of the humidity-activated NH3-sensing mechanism was conducted using complex impedance plot (CIP) measurements.
Comprehensive Improvement of Binocular Structured Light Calibration Method Based on Radical-Conservative Cooperative Particle Swarm
Authors: Ma Jun , Meng Xing , Wang Haoseng , Jiang Fangdi , Wang Shifeng , Kodagoda Sarath
Published: December 2024

To achieve high-precision 3D reconstruction, a comprehensive improvement has been made to the binocular structured light calibration method. During the calibration process, the calibration object’s imaging quality and the camera parameters’ nonlinear optimization effect directly affect the caibration accuracy. Firstly, to address the issue of poor imaging quality of the calibration object under tilted conditions, a pixel-level adaptive fill light method was designed using the programmable light intensity feature of the structured light projector, allowing the calibration object to receive uniform lighting and thus improve the quality of the captured images. Then, collaborative Particle Swarm Optimization was studied to optimize the camera parameters. Compared with other optimization algorithms, this algorithm has higher global search capability and can obtain more accurate camera parameters. Under comprehensive improvement, the 3D reconstruction accuracy of binocular structured light is 0.053 mm, showing a 36.33% improvement in reconstruction accuracy compared to mainstream calibration methods.
MambaPose: A Human Pose Estimation Based on Gated Feedforward Network and Mamba
Authors: Zhang Jianqiang , Hou Jing , He Qiusheng , Yuan Zhengwei , Xue Hao
Published: December 2024

Human pose estimation is an important research direction in the field of computer vision, which aims to accurately identify the position and posture of keypoints of the human body through images or videos. However, multi-person pose estimation yields false detection or missed detection in dense crowds, and it is still difficult to detect small targets. In this paper, we propose a Mamba-based human pose estimation. First, we design a GMamba structure to be used as a backbone network to extract human keypoints. A gating mechanism is introduced into the linear layer of Mamba, which allows the model to dynamically adjust the weights according to the different input images to locate the human keypoints more precisely. Secondly, GMamba as the backbone network can effectively solve the long-sequence problem. The direct use of convolutional downsampling reduces selectivity for different stages of information flow. We used slice downsampling (SD) to reduce the resolution of the feature map to half the original size, and then fused local features from four different locations. The fusion of multi-channel information helped the model obtain rich pose information. Finally, we introduced an adaptive threshold focus loss (ATFL) to dynamically adjust the weights of different keypoints. We assigned higher weights to error-prone keypoints to strengthen the model’s attention to these points. Thus, we effectively improved the accuracy of keypoint identification in cases of occlusion, complex background, etc., and significantly improved the overall performance of attitude estimation and anti-interference ability. Experimental results showed that the AP and AP50 of the proposed algorithm on the COCO 2017 validation set were 72.2 and 92.6. Compared with the typical algorithm, it was improved by 1.1% on AP50. The proposed method can effectively detect the keypoints of the human body, and provides stronger robustness and accuracy for the estimation of human posture in complex scenes.
Wavelet-Driven Multi-Band Feature Fusion for RGB-T Salient Object Detection
Authors: Zhao Jianxun , Wen Xin , He Yu , Yang Xiaowei , Song Kechen
Published: December 2024

RGB-T salient object detection (SOD) has received considerable attention in the field of computer vision. Although existing methods have achieved notable detection performance in certain scenarios, challenges remain. Many methods fail to fully utilize high-frequency and low-frequency features during information interaction among different scale features, limiting detection performance. To address this issue, we propose a method for RGB-T salient object detection that enhances performance through wavelet transform and channel-wise attention fusion. Through feature differentiation, we effectively extract spatial characteristics of the target, enhancing the detection capability for global context and fine-grained details. First, input features are passed through the channel-wise criss-cross module (CCM) for cross-modal information fusion, adaptively adjusting the importance of features to generate rich fusion information. Subsequently, the multi-scale fusion information is input into the feature selection wavelet transforme module (FSW), which selects beneficial low-frequency and high-frequency features to improve feature aggregation performance and achieves higher segmentation accuracy through long-distance connections. Extensive experiments demonstrate that our method outperforms 22 state-of-the-art methods.
Emerging Trends in the Integration of Smart Sensor Technologies in Structural Health Monitoring: A Contemporary Perspective
Authors: Sivasuriyan Arvindan , Vijayan Dhanasingh Sivalinga , Devarajan Parthiban , Stefańska Anna , Dixit Saurav , Podlasek Anna , Sitek Wiktor , Koda Eugeniusz
Published: December 2024

In recent years, civil engineering has increasingly embraced communication tools for automation, with sensors playing a pivotal role, especially in structural health monitoring (SHM). These sensors enable precise data acquisition, measuring parameters like force, displacement, and temperature and transmit data for timely interventions to prevent failures. This approach reduces reliance on manual inspections, offering more accurate outcomes. This review explores various sensor technologies in SHM, such as piezoelectric, fibre optic, force, MEMS devices, GPS, LVDT, electromechanical impedance techniques, Doppler effect, and piezoceramic sensors, focusing on advancements from 2019 to 2024. A bibliometric analysis of 1468 research articles from WOS and Scopus databases shows a significant increase in publications, from 15 in 2019 to 359 in 2023 and 52 in 2024 (and still counting). This analysis identifies emerging trends and applications in smart sensor integration in civil and structural health monitoring, enhancing safety and efficiency in infrastructure management.
Free-Space to SMF Integration and Green to C-Band Conversion Based on PPLN
Authors: Kodama Takahiro , Kuwahara Kiichiro , Kariya Ayumu , Ikuta Rikizo
Published: December 2024

In this study, we experimentally demonstrate a PPLN-based free-space to SMF (single-mode fiber) conversion system capable of efficient long-wavelength down-conversion from 518 nm, optimized for minimal loss in highly turbid water, to 1540 nm, which is ideal for low-loss transmission in standard SMF. Leveraging the nonlinear optical properties of periodically poled lithium niobate (PPLN), we achieve a wavelength conversion efficiency of 1.6% through difference frequency generation while maintaining a received optical signal-to-noise ratio of 10.4 dB. Our findings underscore the potential of integrating PPLN-based wavelength conversion with fiber optic networks, offering a viable solution for next-generation optical sensor systems that demand real-time, low-latency, and reliable data transmission. This work represents a significant advancement in developing robust and efficient optical sensor technologies, addressing the challenges associated with long-distance transmission and broad-linewidth light sources in optical remote sensing applications.
A WSN and LoRa Hybrid Multimedia Transmission Protocol for Scalar Data and Image Transmission
Authors: Ta Quoc Hop , Ta Van Khoe , Nguyen Trang Tien , Oh Hoon
Published: December 2024

The proposed protocol features reliable and fast image transmission while periodically transmitting scalar data without interruption by allowing two networks, a LoRa network and a wireless sensor network, with different transmission characteristics to cooperate. It adopts the RT-LoRa protocol for periodic scalar data transmission and uses a WSN-based pipelined transmission method that leverages single-hop message transmission of a LoRa network for image transmission. Thus, it can not only eliminate the control message overhead for time synchronization, slot scheduling, and path establishment for pipelined image transmission in WSNs but also eliminate interferences within WSNs, such as data collisions and data and message collisions, during pipelined image transmission, thereby enabling high reliability and fast transmission. According to experimental results obtained inside a university building, the proposed protocol achieved an image transfer rate of approximately 96% without packet loss, transmitted one 24 KB image in approximately 0.3 s, and achieved an image transfer rate of 100% under the tolerance of one image packet loss. These results indicate a speedup of about 25% compared to a recent pipelined protocol while ensuring near-perfect image transmission quality.
Systematic Review of EEG-Based Imagined Speech Classification Methods
Authors: Alzahrani Salwa , Banjar Haneen , Mirza Rsha
Published: December 2024

This systematic review examines EEG-based imagined speech classification, emphasizing directional words essential for development in the brain–computer interface (BCI). This study employed a structured methodology to analyze approaches using public datasets, ensuring systematic evaluation and validation of results. This review highlights the feature extraction techniques that are pivotal to classification performance. These include deep learning, adaptive optimization, and frequency-specific decomposition, which enhance accuracy and robustness. Classification methods were explored by comparing traditional machine learning with deep learning and emphasizing the role of brain lateralization in imagined speech for effective recognition and classification. This study discusses the challenges of generalizability and scalability in imagined speech recognition, focusing on subject-independent approaches and multiclass scalability. Performance benchmarking across various datasets and methodologies revealed varied classification accuracies, reflecting the complexity and variability of EEG signals. This review concludes that challenges remain despite progress, particularly in classifying directional words. Future research directions include improved signal processing techniques, advanced neural network architectures, and more personalized, adaptive BCI systems. This review is critical for future efforts to develop practical communication tools for individuals with speech and motor impairments using EEG-based BCIs.
Applying Multi-Purpose Commercial Inertial Sensors for Monitoring Equine Locomotion in Equestrian Training
Authors: Fercher Christina , Bartsch Julia , Kluge Steffen , Schneider Franziska , Liedtke Anna M. , Schleichardt Axel , Ueberschär Olaf
Published: December 2024

Inappropriate, excessive, or overly strenuous training of sport horses can result in long-term injury, including the premature cessation of a horse’s sporting career. As a countermeasure, this study demonstrates the easy implementation of a biomechanical load monitoring system consisting of five commercial, multi-purpose inertial sensor units non-invasively attached to the horse’s distal limbs and trunk. From the data obtained, specific parameters for evaluating gait and limb loads are derived, providing the basis for objective exercise load management and successful injury prevention. Applied under routine in-the-field training conditions, our pilot study results show that tri-axial peak impact limb load increases progressively from walk to trot to canter, in analogy to stride frequency. While stance and swing phases shorten systematically with increasing riding speed across subjects, longitudinal and lateral load asymmetry are affected by gait at an individual level, revealing considerable variability between and within individual horses. This individualized, everyday approach facilitates gaining valuable insights into specific training effects and responses to changing environmental factors in competitive sport horses. It promises to be of great value in optimizing exercise management in equestrian sports to benefit animal welfare and long-term health in the future.
A Systematic Review on the Advancements in Remote Sensing and Proximity Tools for Grapevine Disease Detection
Authors: Portela Fernando , Sousa Joaquim J. , Araújo-Paredes Cláudio , Peres Emanuel , Morais Raul , Pádua Luís
Published: December 2024

Grapevines (Vitis vinifera L.) are one of the most economically relevant crops worldwide, yet they are highly vulnerable to various diseases, causing substantial economic losses for winegrowers. This systematic review evaluates the application of remote sensing and proximal tools for vineyard disease detection, addressing current capabilities, gaps, and future directions in sensor-based field monitoring of grapevine diseases. The review covers 104 studies published between 2008 and October 2024, identified through searches in Scopus and Web of Science, conducted on 25 January 2024, and updated on 10 October 2024. The included studies focused exclusively on the sensor-based detection of grapevine diseases, while excluded studies were not related to grapevine diseases, did not use remote or proximal sensing, or were not conducted in field conditions. The most studied diseases include downy mildew, powdery mildew, Flavescence dorée, esca complex, rots, and viral diseases. The main sensors identified for disease detection are RGB, multispectral, hyperspectral sensors, and field spectroscopy. A trend identified in recent published research is the integration of artificial intelligence techniques, such as machine learning and deep learning, to improve disease detection accuracy. The results demonstrate progress in sensor-based disease monitoring, with most studies concentrating on specific diseases, sensor platforms, or methodological improvements. Future research should focus on standardizing methodologies, integrating multi-sensor data, and validating approaches across diverse vineyard contexts to improve commercial applicability and sustainability, addressing both economic and environmental challenges.
A Robust Method for Validating Orientation Sensors Using a Robot Arm as a High-Precision Reference
Authors: Kuti József , Piricz Tamás , Galambos Péter
Published: December 2024

This paper presents a robust and efficient method for validating the accuracy of orientation sensors commonly used in practical applications, leveraging measurements from a commercial robotic manipulator as a high-precision reference. The key concept lies in determining the rotational transformations between the robot’s base frame and the sensor’s reference, as well as between the TCP (Tool Center Point) frame and the sensor frame, without requiring precise alignment. Key advantages of the proposed method include its independence from the exact measurement of rotations between the reference instrumentation and the sensor, systematic testing capabilities, and the ability to produce repeatable excitation patterns under controlled conditions. This approach enables automated, high-precision, and comparative evaluation of various orientation sensing devices in a reproducible manner. Moreover, it facilitates efficient calibration and analysis of sensor errors, such as drift, noise, and response delays under various motion conditions. The method’s effectiveness is demonstrated through experimental validation of an Inertial Navigation System module and the SLAM-IMU fusion capabilities of the HTC VIVE VR headset, highlighting its versatility and reliability in addressing the challenges associated with orientation sensor validation.
HKAN: A Hybrid Kolmogorov–Arnold Network for Robust Fabric Defect Segmentation
Authors: Li Min , Ye Pei , Cui Shuqin , Zhu Ping , Liu Junping
Published: December 2024

Currently, fabric defect detection methods predominantly rely on CNN models. However, due to the inherent limitations of CNNs, such models struggle to capture long-distance dependencies in images and fail to accurately detect complex defect features. While Transformers excel at modeling long-range dependencies, their quadratic computational complexity poses significant challenges. To address these issues, we propose combining CNNs with Transformers and introduce Kolmogorov–Arnold Networks (KANs) to enhance feature extraction capabilities. Specifically, we designed a novel network for fabric defect segmentation, named HKAN, consisting of three components: encoder, bottleneck, and decoder. First, we developed a simple yet effective KANConv Block using KAN convolutions. Next, we replaced the MLP in PoolFormer with KAN, creating a lightweight KANTransformer Block. Finally, we unified the KANConv Block and the KANTransformer Block into a Hybrid KAN Block, which serves as both the encoder and bottleneck of HKAN. Extensive experiments on three fabric datasets demonstrate that HKAN outperforms mainstream semantic segmentation models, achieving superior segmentation performance and delivering prominent results across diverse fabric images.
Dynamic Boundary Estimation of Suspended Sediment Plume Benefit by the Autonomous Underwater Vehicle Sensing
Authors: Zhang Yanxin , Li Shaoyuan
Published: December 2024

The suspended sediment plume generated in the deep-sea mining process significantly impacts the marine environment and seabed ecosystem. Accurate boundary estimation can effectively monitor the scope of environmental impact, guiding mining operations to prevent ecological damage. In this paper, we propose a dynamic boundary estimation approach for the suspended sediment plume, leveraging the sensing capability of the Autonomous Underwater Vehicles (AUVs). Based on the plume model and the point-by-point sensor measurements, a Luenberger-type observer is established for designing the AUV control algorithm. To address the challenge of unknown and time-varying environmental parameters, the estimation errors are reduced by using the projection modification unit. Rigorous convergence and stability analyses of the proposed control algorithm are provided by the Lyapunov method. Numerical simulations demonstrate that the improved algorithm enhances the estimation accuracy of unknown parameters and enables the AUV to patrol along the dynamic boundary in a shorter time, thereby verifying the effectiveness of the boundary estimation algorithm based on AUV sensing.
A Green Computing Business Aggregation Strategy for Low Earth Orbit Satellite Networks
Authors: Wang Bo , Lv Jiaqi , Huang Dongyan , Lu Zelin , Fang Yuhang
Published: December 2024

This paper proposes a green computing strategy for low Earth orbit (LEO) satellite networks (LSNs), addressing energy efficiency and delay optimization in dynamic and energy-constrained environments. By integrating a Markov Decision Process (MDP) with a Double Deep Q-Network (Double DQN) and introducing the Energy–Delay Ratio (EDR) metric, this study effectively quantifies and balances energy savings with delay costs. Simulations demonstrate significant energy savings, with reductions of up to 47.87% under low business volumes, accompanied by a minimal delay increase of only 0.0161 s. For medium business volumes, energy savings reach 26.75%, with a delay increase of 0.0189 s, while high business volumes achieve a 4.36% energy reduction and a delay increase of 0.0299 s. These results highlight the proposed strategy’s ability to effectively balance energy efficiency and delay, showcasing its adaptability and suitability for sustainable operations in LEO satellite networks under varying traffic loads.
Probabilistic Attention Map: A Probabilistic Attention Mechanism for Convolutional Neural Networks
Authors: Liu Yifeng , Tian Jing
Published: December 2024

The attention mechanism is essential to convolutional neural network (CNN) vision backbones used for sensing and imaging systems. Conventional attention modules are designed heuristically, relying heavily on empirical tuning. To tackle the challenge of designing attention mechanisms, this paper proposes a novel probabilistic attention mechanism. The key idea is to estimate the probabilistic distribution of activation maps within CNNs and construct probabilistic attention maps based on the correlation between attention weights and the estimated probabilistic distribution. The proposed approach consists of two main components: (i) the calculation of the probabilistic attention map and (ii) its integration into existing CNN architectures. In the first stage, the activation values generated at each CNN layer are modeled by using a Laplace distribution, which assigns probability values to each activation, representing its relative importance. Next, the probabilistic attention map is applied to the feature maps via element-wise multiplication and is seamlessly integrated as a plug-and-play module into existing CNN architectures. The experimental results show that the proposed probabilistic attention mechanism effectively boosts image classification accuracy performance across various CNN backbone models, outperforming both baseline and other attention mechanisms.
Analyzing and Modeling the Dynamic Electrical Characteristics of Nanocomposite Large-Range Strain Gauges
Authors: Wonnacott Alex M. , Bowden Anton E. , Mitchell Ulrike H. , Fullwood David T.
Published: December 2024

Flexible high-deflection strain gauges have been demonstrated to be cost-effective and accessible sensors for capturing human biomechanical deformations. However, the interpretation of these sensors is notably more complex compared to conventional strain gauges, particularly during dynamic motion. In addition to the non-linear viscoelastic behavior of the strain gauge material itself, the dynamic response of the sensors is even more difficult to capture due to spikes in the resistance during strain path changes. Hence, models for extracting strain from resistance measurements of the gauges most often only work well under quasi-static conditions. The present work develops a novel model that captures the complete dynamic strain–resistance relationship of the sensors, including resistance spikes, during cyclical movements. The forward model, which converts strain to resistance, comprises the following four parts to accurately capture the different aspects of the sensor response: a quasi-static linear model, a spike magnitude model, a long-term creep decay model, and a short-term decay model. The resulting sensor-specific model accurately predicted the resistance output, with an R-squared value of 0.90. Additionally, an inverse model which predicts the strain vs. time data that would result in the observed resistance data was created. The inverse model was calibrated for a particular sensor from a small amount of cyclic data during a single test. The inverse model accurately predicted key strain characteristics with a percent error as low as 0.5%. Together, the models provide new functionality for interpreting high-deflection strain sensors during dynamic strain measurement applications, including wearables sensors used for biomechanical modeling and analysis.
Design and Evaluation of a Cloud Computing System for Real-Time Measurements in Polarization-Independent Long-Range DAS Based on Coherent Detection
Authors: Nur Abdusomad , Demise Almaz , Muanenda Yonas
Published: December 2024

CloudSim is a versatile simulation framework for modeling cloud infrastructure components that supports customizable and extensible application provisioning strategies, allowing for the simulation of cloud services. On the other hand, Distributed Acoustic Sensing (DAS) is a ubiquitous technique used for measuring vibrations over an extended region. Data handling in DAS remains an open issue, as many applications need continuous monitoring of a volume of samples whose storage and processing in real time require high-capacity memory and computing resources. We employ the CloudSim tool to design and evaluate a cloud computing scheme for long-range, polarization-independent DAS using coherent detection of Rayleigh backscattering signals and uncover valuable insights on the evolution of the processing times for a diverse range of Virtual Machine (VM) capacities as well as sizes of blocks of processed data. Our analysis demonstrates that the choice of VM significantly impacts computational times in real-time measurements in long-range DAS and that achieving polarization independence introduces minimal processing overheads in the system. Additionally, the increase in the block size of processed samples per cycle results in diminishing increments in overall processing times per batch of new samples added, demonstrating the scalability of cloud computing schemes in long-range DAS and its capability to manage larger datasets efficiently.
Structural Design and Electromagnetic Performance Analysis of Octupole Active Radial Magnetic Bearing
Authors: Zhu Qixuan , Lu Yujun , Shao Zhongkui
Published: December 2024

This study addresses the challenges of magnetic circuit coupling and control complexity in active radial magnetic bearings (ARMBs) by systematically investigating the electromagnetic performance of four magnetic pole configurations (NNSS, NSNS, NNNN, and SSSS). Initially, equivalent magnetic circuit modeling and finite element analysis (FEA) were employed to analyze the magnetic circuit coupling phenomena and their effects on the magnetic flux density distribution for each configuration. Subsequently, the air gap flux density and electromagnetic force were quantified under rotor eccentricity caused by unbalanced disturbances, and the dynamic performances of the ARMBs were evaluated for eccentricity along the x-axis and at 45°. Finally, experiments measured the electromagnetic forces acting on the rotor under the NNSS and NSNS configurations during eccentric conditions. The results indicate that the NNSS configuration significantly reduces magnetic circuit coupling, improves the uniformity of electromagnetic force distribution, and offers superior stability and control efficiency under asymmetric conditions. Experimental results deviated by less than 10% from the simulations, confirming the reliability and practicality of the proposed design. These findings provide valuable insights for optimizing ARMB pole configurations and promote their application in high-speed, high-precision industrial fields such as aerospace and power engineering.
On Security Performance of SWIPT Multi-User Jamming Based on Mixed RF/FSO Systems with Untrusted Relay
Authors: Guo Xingyue , Tu Shan , Yan Dexian , Wang Yi
Published: December 2024

This paper presents research on the security performance of a multi-user interference-based mixed RF/FSO system based on SWIPT untrusted relay. In this work, the RF and FSO channels experience Nakagami-m fading distribution and Málaga (M) turbulence, respectively. Multiple users transmit messages to the destination with the help of multiple cooperating relays, one of which may become an untrusted relay as an insider attacker. In a multi-user network, SWIPT acts as a charging device for each user node. In order to prevent the untrusted relays from eavesdropping on the information, some users are randomly assigned to transmit artificial noise in order to interfere with untrusted relays, and the remaining users send information to relay nodes. Based on the above system model, the closed-form expressions of secrecy outage probability (SOP) and average secrecy capacity (ASC) for the mixed RF/FSO system are derived. The correctness of these expressions is verified by the Monte Carlo method. The influences of various key factors on the safety performance of the system are analyzed by simulations. The results show that the security performance of the system is considerably improved by increasing the signal–interference noise ratio, the number of interfering users, the time distribution factor and the energy conversion efficiency when the instantaneous signal-to-noise ratio (SNR) of the RF link instantaneous SNR is low.
Overview of Wearable Healthcare Devices for Clinical Decision Support in the Prehospital Setting
Authors: Gathright Rachel , Mejia Isiah , Gonzalez Jose M. , Hernandez Torres Sofia I. , Berard David , Snider Eric J.
Published: December 2024

Prehospital medical care is a major challenge for both civilian and military situations as resources are limited, yet critical triage and treatment decisions must be rapidly made. Prehospital medicine is further complicated during mass casualty situations or remote applications that require more extensive medical treatments to be monitored. It is anticipated on the future battlefield where air superiority will be contested that prolonged field care will extend to as much 72 h in a prehospital environment. Traditional medical monitoring is not practical in these situations and, as such, wearable sensor technology may help support prehospital medicine. However, sensors alone are not sufficient in the prehospital setting where limited personnel without specialized medical training must make critical decisions based on physiological signals. Machine learning-based clinical decision support systems can instead be utilized to interpret these signals for diagnosing injuries, making triage decisions, or driving treatments. Here, we summarize the challenges of the prehospital medical setting and review wearable sensor technology suitability for this environment, including their use with medical decision support triage or treatment guidance options. Further, we discuss recommendations for wearable healthcare device development and medical decision support technology to better support the prehospital medical setting. With further design improvement and integration with decision support tools, wearable healthcare devices have the potential to simplify and improve medical care in the challenging prehospital environment.
Real-Time Freezing of Gait Prediction and Detection in Parkinson’s Disease
Authors: Pardoel Scott , AlAkhras Ayham , Jafari Ensieh , Kofman Jonathan , Lemaire Edward D. , Nantel Julie
Published: December 2024

Freezing of gait (FOG) is a walking disturbance that can lead to postural instability, falling, and decreased mobility in people with Parkinson’s disease. This research used machine learning to predict and detect FOG episodes from plantar-pressure data and compared the performance of decision tree ensemble classifiers when trained on three different datasets. Dataset 1 (n = 11) was collected in a previous study. Dataset 2 (n = 10) included six new participants and four participants from Dataset 1 who were re-tested (approximately 2 years later), and Dataset 3 (n = 21) combined Datasets 1 and 2. The prediction model trained on Dataset 3 had a 2.28% higher sensitivity and 3.09% lower specificity compared to the models trained on Dataset 1. The model trained on Dataset 3 identified 86.84% of the total FOG episodes compared to 74.31% from the model trained on Dataset 1. Also, the model using Dataset 3 identified the FOG episodes 0.3 s earlier than the model developed with Dataset 1. The model trained using Dataset 3 showed improved performance in sensitivity, identification time, and FOG identification. The improvements using the expanded dataset (Dataset 3) in this study compared to the previous model reinforce the validity and generalizability of the original model. The model was able to predict and detect FOG well and is, therefore, ready to be implemented in a FOG prevention device.
Innovative Nafion- and Lignin-Based Cation Exchange Materials Against Standard Resins for the Removal of Heavy Metals During Water Treatment
Authors: Bergamasco Sara , Hein Luis Alexander , Silvestri Laura , Hartmann Robert , Menegatti Giampiero , Pozio Alfonso , Rinaldi Antonio
Published: December 2024

The contamination of water by heavy metals poses an escalating risk to human health and the environment, underscoring the critical need for efficient removal methods to secure safe water resources. This study evaluated the performance of four cationic exchange materials (labeled “PS—DVB”, “PA—DVB”, “TFSA”, and “OGL”) in removing or harvesting metals such as copper, silver, lead, cobalt, and nickel from aqueous solutions, several of which are precious and/or classified as Critical Raw Materials (CRMs) due to their economic importance and supply risk. The objective was to screen and benchmark the four ion exchange materials for water treatment applications by investigating their metal sequestration capacities. Experiments were conducted using synthetic solutions with controlled metal concentrations, analyzed through ICP-OES, and supported by kinetic modeling. The adsorption capacities (qe) obtained experimentally were compared with those predicted by pseudo-first-order and pseudo-second-order models. This methodology enables high precision and reproducibility, validating its applicability for assessing ion exchange performance. The results indicated that PS—DVB and PA—DVB resins proved to be of “wide range”, exhibiting high efficacy for most of the metals tested, including CRM-designated ones, and suggesting their suitability for water purification. Additionally, the second-life Nafion-based “TFSA” material demonstrated commendable performance, highlighting its potential as a viable and technologically advanced alternative in water treatment. Lastly, the lignin-based material, “OGL”, representing the most innovative and sustainability apt option, offered relevant performance only in selected cases. The significant differences in performance among the resins underscore the impact of structural and compositional factors on adsorption efficiency. This study offers valuable insights for investigating and selecting new sustainable materials for treating contaminated water, opening new pathways for targeted and optimized solutions in environmental remediation.
The Effect of Plasma Pretreatment on the Flotation of Lithium Aluminate and Gehlenite Using Light-Switchable Collectors
Authors: Zgheib Ali , Fischer Maximilian Hans , Tsanang Stéphanie Mireille , El Hraoui Iliass , Zhang Shukang , Wollmann Annett , Weber Alfred P. , Fittschen Ursula E. A. , Schirmer Thomas , Schmidt Andreas
Published: December 2024

The pyridinium phenolate punicine is a switchable molecule from Punica granatum. Depending on the pH, punicine exists as a cation, neutral molecule, anion, or dianion. In addition, punicine reacts to light, under the influence of which it forms radical species. We report on three punicine derivatives that possess an adamantyl, 2-methylnonyl, or heptadecyl substituent and on their performance in the flotation of lithium aluminate, an engineered artificial mineral (EnAM) for the recycling of lithium, e.g., from lithium-ion batteries. By optimizing the parameters: pH and light conditions (daylight, darkness), recovery rates of 92% of LiAlO2 are achieved. In all cases, the flotation of the gangue material gehlenite (Ca2Al[AlSiO7]) is suppressed. IR, the contact angle, zeta potential measurements, TG-MS, and PXRD confirm that the punicines interact with the surface of LiAlO2, which is covered by LiAl2(OH)7 after contact to water, resulting in a hydrophobization of the particle. The plasma pretreatment of the lithium aluminate has a significant influence on the flotation results and increases the recovery rates of lithium aluminate in blank tests by 58%. The oxidative plasma leads to a partial dehydratisation of the LiAl2(OH)7 and thus to a hydrophobization of the particles, while a reductive plasma causes a more hydrophilic particle surface.
Internet and Social Media Addictions in the Post-Pandemic Era: Consequences for Mental Well-Being and Self-Esteem
Authors: Mousoulidou Marilena , Christodoulou Andri , Averkiou Elena , Pavlou Irene
Published: December 2024

The increasing integration of digital technologies into daily life, particularly during the COVID-19 pandemic, has raised concerns about internet and social media addictions and their potential impact on mental health. This study aimed to examine the prevalence of internet and social media addictions among adults in Cyprus in the post-pandemic period and explore their relationship with demographic characteristics, mental well-being (i.e., depression, anxiety, and stress), and self-esteem. Participants included 502 adults from Cyprus recruited using the convenience and snowball sampling methods. The data were collected via an internet-based questionnaire that examined participants’ levels of internet addiction, social media addiction, mental well-being, and self-esteem. The results suggest that (a) while the prevalence of severe addiction was low for both internet and social media addictions, approximately one-third of participants exhibited mild to moderate levels of addiction; (b) younger adults, particularly those between 18 and 28 years of age, were more prone to problematic online behaviors; (c) internet and social media addictions were positively associated with depression, anxiety, and stress and negatively associated with self-esteem; and (d) stress was found to be a significant predictor of both internet and social media addictions, while self-esteem acted as a protective factor against problematic use. These findings highlight the enduring mental health implications of increased digital engagement and emphasize the need for targeted interventions to promote healthy online behaviors, manage stress, and enhance self-esteem.
Transforming Soil: Climate-Smart Amendments Boost Soil Physical and Hydrological Properties
Authors: Veettil Anoop Valiya , Rahman Atikur , Awal Ripendra , Fares Ali , Melaku Nigus Demelash , Thapa Binita , Elhassan Almoutaz , Woldesenbet Selamawit
Published: December 2024

A field study was conducted to investigate the effects of selected climate-smart agriculture practices on soil bulk density (ρ), porosity (β), hydraulic conductivity (Ksat), and nutrient dynamics in southeast Texas. Treatment combinations of two types of organic manure (chicken and dairy) with three rates (0, 224, and 448 kg N ha−1) and two levels of biochar (2500 and 5000 kg ha−1) were used in a factorial randomized block design. Bulk density and porosity measurements were conducted on undisturbed soil core samples collected from the topsoil (0–10 cm) of a field cultivated with sweet corn. Ksat was calculated from the steady-state infiltration measured using the Tension Infiltrometer (TI). The ANOVA results indicated that the manure application rates, and biochar levels significantly affected the soil properties. Compared to the control, β increased by 15% and 29% for the recommended and double recommended manure rates. Similarly, hydraulic conductivity increased by 25% in the double-recommended rate plots compared to the control. Also, we applied the concept of non-parametric elasticity to understand the sensitivity of soil physical and chemical properties to Ksat. ρ and β are critical physical properties that are highly sensitive to Ksat. Among soil nutrients, Boron showed the highest sensitivity to Ksat. Hydraulic conductivity can be enhanced by employing selected climate-smart practices and improving water management. Future directions for this study focus on scaling these findings to diverse cropping systems and soil types while integrating long-term assessments to evaluate the cumulative effects of climate-smart practices on soil health, crop productivity, and ecosystem sustainability.
Sustainability Education in Geomatics Students: Nature of STEM Through Meteorology and Ecology of Fire
Authors: Martínez-Martínez Víctor , Ortiz-Revilla Jairo , Brasca Merlin Almendra , Sammaritano Mariela , Molina Rodrigo , López Matías , Greca Ileana María
Published: December 2024

To address the urgent challenges of sustainability in our changing world, STEM education must evolve to integrate a stronger focus on socioenvironmental dimensions. This study examines how students in geomatics courses understand the nature of STEM (NoSTEM) in the context of meteorology and fire ecology—fields closely tied to sustainability. Using two validated mixed-method instruments comprising closed- and open-ended items, we assessed students’ comprehension across cognitive–epistemic and socio-institutional dimensions, framed within the family resemblance approach (FRA). Data collected from 44 students in meteorology and 57 in fire ecology were analyzed using descriptive statistics and phenomenographic methods. Our findings indicate that, while students demonstrate a stronger grasp of technical concepts, their understanding of socio-institutional implications is comparatively limited. These results highlight the need to align STEM education with sustainability education, emphasizing real-world applications and the integration of socio-institutional elements into the curriculum. Addressing these gaps is essential for preparing students to engage with complex sustainability challenges, such as those posed by climate change, resource management, and disaster mitigation. Future research should investigate long-term interdisciplinary educational strategies to foster a holistic understanding of NoSTEM and its role in promoting sustainable development.
Twenty Years of Resilient City Research: Reviews and Perspectives
Authors: Wang Zongrun , Tan Yiyun , Lu Xin
Published: December 2024

The resilient city plays an increasingly important role in coping with the challenges raised by economic, social, and environmental risks. In this review, we examine approximately 27,094 papers published in the Web of Science Core Collection (WOSCC) and perform extensive bibliometric and scientometric analyses to identify the research themes, evolutionary history, and potential research trends in the state of the art in resilient city studies. Seven main resilient city research themes are identified, with significant differences persisting across regions. Specifically, the research on resilient cities in Europe, Asia, Africa, and North America reveals clear regional characteristics in macro development planning and strategies, technological methods, urban economic growth, urban water resource protection, and so on. The analysis also reveals the collaborative networks among different countries and regions in the study of resilient cities. The evolutionary history of resilient city research shows that it has gradually evolved from a single research field into a multidisciplinary field and further formed a unique discipline. Moreover, the urban ecological environment, urban economic development, urban sprawl, and urban mobility have become key research hot spots and trends in resilient city research. This study provides a systematic and data-driven analytical demonstration of resilient city research, which provides empirical support for policy formulation and practice.
Biocides Based on Essential Oils for Sustainable Conservation and Restoration of Mural Paintings in Built Cultural Heritage
Authors: Mateus Dina , Costa Fernando , de Jesus Vasco , Malaquias Leonor
Published: December 2024

Microbial deterioration poses a significant threat to built heritage, particularly mural paintings, where traditional synthetic biocides can have adverse environmental and material impacts. This study evaluates the effectiveness of essential oils derived from four aromatic plants—thyme (Thymus mastichina L.), fennel (Foeniculum vulgare Mill.), pennyroyal (Mentha pulegium L.), and green lavender (Lavandula viridis L’Hér.)—as natural biocides against microorganisms isolated from mural paintings in the House of Moscadim, an 18th-century manor house in Portugal. The antimicrobial activity of the essential oils was assessed using both direct contact and micro-atmosphere methods. Four microorganisms were isolated, including two bacteria, Bacillus wiedmannii and Bacillus mobilis, and two fungi, Penicillium brevicompactum and Cladosporium cladosporioides. Fennel essential oil exhibited the strongest antifungal activity against both fungal species, surpassing the efficacy of the commercial biocide Biotin T® in some cases. Pennyroyal and lavender essential oils also showed significant inhibitory effects. The micro-atmosphere method demonstrated the potential for noninvasive application of essential oils while preserving the integrity of delicate mural surfaces. These findings suggest that essential oils, particularly fennel oil, constitute a promising natural alternative to synthetic biocides for the sustainable conservation of cultural heritage. Further research is recommended to explore their long-term effects and to optimize application techniques.
Characteristics and Needs of Vietnamese Technical Intern Training Candidates (Care Workers) in Japan: A Qualitative Study
Authors: Hara Koji , Yamamura Tomokazu , Li Ningyi , Huong Pham Thu
Published: December 2024

In Japan, where the shortage of care workers poses a challenge to the sustainability of the long-term care system, foreign care workers play a crucial role. This study aimed to identify the needs and challenges of Vietnamese Technical Intern Training candidates, the largest group of foreign care workers, to facilitate program reforms and a more efficient recruitment process. A semi-structured interview survey was conducted with 27 candidates in Vietnam. Interview items included reasons for choosing the training program in Japan, the desired length of stay, expectations, and career advancement after returning home. Descriptive statistics and K-means clustering were used to analyze the data. Survey results showed that all participants independently decided to pursue care worker training in Japan; 44% had considered other countries; most wanted to stay in Japan for as long as possible; and 37% wanted to live in Japan permanently. The K-means method revealed three clusters: a Japanophile cluster (preferred Japan for its landscape, culture, and national character); a word-of-mouth cluster (influenced by personal referrals); and an intellectual cluster (influenced by Japan’s economic development and care levels). Our findings indicate that support for obtaining qualifications, Japanese language skills, and caregiving skills are important to secure the stability of foreign care workers. It is necessary to tailor recruitment, training, and support for each cluster.