bluuebunny/crossref_metadata_2025 huggingface dataset:
Hydroxyethyl Starch, a Synthetic Colloid Used to Restore Blood Volume, Attenuates Shear-Induced Distortion but Accelerates the Convection of Sodium Hyaluronic Acid
Authors: Tatara Tsuneo
Published: October 2024

Hyaluronic acid (HA) plays important roles in tissue hydration and the transport of fluid and solutes through the interstitium. Hydroxyethyl starch (HES) solution is a synthetic colloid solution used during surgery. As HES leaks into the interstitium under inflammatory conditions during surgery, the effects of HES on HA’s structure and distribution are of clinical relevance. To examine these under fluid shear stress, dynamic shear moduli of 0.1% sodium hyaluronic acid (NaHA) solution with or without HES during shear stress loading were measured using a rotational rheometer for 8 h. The loss shear modulus of NaHA in 0.15 M NaCl solution decreased over time by 30% relative to that before shear stress loading. The presence of 1% and 2% HES reduced the decrease in loss shear modulus of NaHA solution to 20% and 4%, respectively. To investigate the convective transport of 0.1% fluorescein-labeled hyaluronic acid (FHA) by infusion of 0.15 M NaCl or HES solution, the absorbance of FHA in a UV flow cell was continuously measured. HES solution of 0.5% increased transported FHA quantities by 120% compared to 0.15 M NaCl solution. HES-induced attenuation of shear-induced distortion of HA and acceleration of convective transport of HA should be considered during surgery.
The Transformation Path of Industrial Parks under the Goals of Carbon Peak and Neutrality in China
Authors: Li Meirong , Liu Huiyuan , Sun Yongyang , Xu Zhanxing , Tian Haoyong , Fu Haoyang
Published: October 2024

China’s coal-based energy structure and its large proportion of the manufacturing industry have resulted in China having the highest CO2 emissions in the world, accounting for about one-third of the world’s total emissions. Achieving the carbon peak by 2030 and carbon neutrality by 2060, while maintaining economic development, presents a significant challenge. Industrial parks, as core units of industrial clusters, are major sites for energy consumption and CO2 emissions. Thus, industry parks with great carbon reduction potential are crucial for China’s carbon reduction efforts. This review systematically examines the current state of the carbon emissions of industrial parks in China and analyzes the situation and challenges faced in the low-carbon transition of these parks. Then, the major pathways, technologies, and key policies for the low-carbon transition are summarized based on the current situation. This paper assists in the low-carbon development of industrial parks and promotes the proposal and implementation of carbon emission reduction strategies.
A Dynamic Simulation of a Piezoelectric Energy-Harvesting System Integrated with a Closed-Loop Voltage Source Converter for Sustainable Power Generation
Authors: Ali Ahmed K. , Abdulrazzaq Ali Abdulwahhab , Mohsin Ali H.
Published: October 2024

Numerous recent studies address the concept of energy harvesting from natural wind excitation vibration to piezoelectric surfaces, aerodynamic losses, and electromagnetic dampers. All these techniques require a connection to an energy-management circuit. However, the simulation model for energy conversion and management dedicated to this task has not yet been described. This paper presents a model-based simulation for an energy conversion system using piezoelectric energy-harvester system (PEHS) technology. A controlled pulse width modulation (PWM) rectifier, a closed-loop buck-boost converter, and a piezoelectric transducer comprise a dynamic mathematical model of a PEHS. The control blocks of the closed-loop buck-boost converter use the perturbation and observation (P&O) algorithm based on maximum power point tracking (MPPT), which adapts the operational voltage of the piezoelectric source to deliver the maximum power to load. A simulation program is employed to perform mathematical analysis on various wind vibration scenarios, piezoelectric sources without PWM converters, and piezoelectric vibration sources connected to a closed-loop P&O converter. The crucial results of this paper demonstrated that the proposed dynamic PEHS model effectively fed low-power electronic loads by directly adjusting the output voltage level to the set voltage, even under different vibration severity levels. As a result, the proposed PEHS dynamic model serves as a guideline for researchers in the development of self-powered sensors, which contributes to understanding sustainable energy alternatives.
Fabrication and Characterization of CeO2-Doped Yttria-Stabilized ZrO2 Composite Particles
Authors: Kim Young Seo , Oh Yoon-Suk , An Gye Seok
Published: October 2024

The present study focuses on the fabrication and characterization of cerium oxide (CeO2)-doped yttria-stabilized zirconia (YSZ) composite particles, aiming to enhance the durability of thermal barrier coatings (TBCs) in high-temperature applications such as gas turbines and aircraft engines. The incorporation of CeO2 into the YSZ matrix was motivated by the need to address the limitations of YSZ coatings, particularly their phase transformation and thermal degradation at temperatures exceeding 1300 °C. The synthesis of a composite with a core–shell structure, where CeO2 is doped into YSZ particles, was pursued to improve the thermal stability and reduce the thermal conductivity of the material. The fabrication process involved surface treatment of YSZ particles with HCl and NH4OH to enhance their dispersion characteristics, followed by the adsorption of CeO2 nanoparticles precipitated from Ce precursors. The study revealed a reduction in the average particle size and improved the dispersion stability of the surface-treated YSZ. Notably, base-treated YSZ exhibited increased CeO2 adsorption due to the strong interaction between Ce ions and surface hydroxyl groups. The successful formation of the YSZ@CeO2 core–shell structure was confirmed through XRD, HR-TEM, and SAED analyses. The study suggest that base-treated YSZ@CeO2 composites have the potential to extend the operating life and improve the performance of TBCs under extreme temperature conditions, which may contribute to the development of more resilient thermal barrier systems.
Optimisation of Ammonia Production and Supply Chain from Sugarcane Ethanol and Biomethane: A Robust Mixed-Integer Linear Programming Approach
Authors: Garcia Victor Fernandes , Palacios Reynaldo , Ensinas Adriano
Published: October 2024

Low-carbon ammonia production is crucial for sustainable development. Brazil, a top ethanol producer, can boost competitiveness and cut emissions by integrating ammonia and ethanol production. However, optimal location and production strategy identification is challenging due to existing possibilities and uncertainties. For that, a new MILP superstructure with robust optimisation was developed and used to analyse low-carbon ammonia production integration in the ethanol industry in São Paulo state by ethanol and biomethane routes, in two different scenarios. As for the results, in scenario 1, biomethane and ethanol investments were USD 3.846 M and USD 314 M. In scenario 2, the investments were USD 316 M for biomethane and USD 259 M for ethanol. Despite the higher investment, the biomethane route results in lower hydrogen production cost (USD 1880/tonne) due to raw material prices; however, ethanol displays a higher hydrogen potential, consuming just 8% of total production against 54% of vinasse availability, which is used for biodigestion and biomethane production. In conclusion, the results suggest that the northern region of São Paulo has greater potential for ammonia facilities due to resource availability. These findings can inform and support more comprehensive studies and public incentive policies.
Structural Control of Copper-Based MOF Catalysts for Electroreduction of CO2: A Review
Authors: Fu Hongxin , Ma Hailing , Zhao Shuaifei
Published: October 2024

With the excessive use of fossil fuels, atmospheric carbon dioxide (CO2) concentrations have risen dramatically in recent decades, leading to serious environmental and social issues linked to global climate change. The emergence of renewable energy sources, such as solar, tidal, and wind energy, has created favorable conditions for large-scale electricity production. Recently, significant attention has been drawn to utilizing renewable energy to catalyze the conversion of CO2 into fuels, producing substantial industrial feedstocks. In these CO2 conversion processes, the structure and performance of catalysts are critical. Metal-organic frameworks (MOFs) and their derivatives have emerged as promising electrocatalysts for CO2 reduction, offering advantages such as high surface area, porosity, exceptional functionality, and high conversion efficiency. This article provides a comprehensive review of structural regulation strategies for copper-based MOFs, highlighting innovative mechanisms like synergistic bimetallic catalysis, targeted doping strategies, and the construction of heterostructures. These novel approaches distinguish this review from previous studies, offering new insights into the electrocatalytic performance of copper-based MOFs and proposing future research directions for improved catalyst design.
A Review of New Materials and Methods Used in the Production of Protein-Based Fat Replacers for the Food Industry
Authors: Stožinić Milica , Lončarević Ivana , Pajin Biljana , Zarić Danica , Nikolić Ivana , Šojić Branislav , Petrović Jovana
Published: October 2024

In the last decade, with the rise in customer awareness about the quality of the food they consume and its health benefits, new methods for producing food fat replacers have been developed. Since then, significant progress has been made in enhancing these techniques. Methods such as emulsion template, foam template, and solvent exchange are frequently employed for creating fat replacers known as oleo- or emulsion gels, commonly used in food products. As the interest in developing fat replacers continues to grow, it has become essential to explore and pursue new materials suitable for producing protein-based fat replacers. Given the increasing food consumption, food waste is on the rise. The goal is to maximize food utilization and create high-protein, nutritionally rich foods with minimal waste. This involves using new materials, such as alternative proteins or food by-products, and finding effective methods for their utilization. This review aims to provide insights into the variety of materials and methods employed to prepare protein-based fat replacers as documented in the available literature.
The Effect of a ‘Humanistic’ Intervention on the Social Responsibility of University Students
Authors: García-Taibo Olalla , Baena-Morales Salvador , Cadenas Arturo , Vázquez Jaime , Ferriz-Valero Alberto
Published: October 2024

Today’s youth navigate a complex balance between traditional and modern values in a changing world. In this context, education plays a crucial role in transforming society and promoting values that align with the Sustainable Development Goals, such as justice and equality, with Ethics and Christianity supporting these principles. Moreover, Physical Education (PE) is highlighted as a tool for promoting moral development and social inclusion; however, gaps exist in studies focusing on the integration of Ethics and Christian values in PE and sports. The aim of this study was to evaluate the impact of an intervention in the subjects of Ethics and Christianity on social responsibility among university students in PE Sciences. The design of this study was quasi-experimental with quantitative pre- and post-test measures and three groups (one Control Group). The sample comprised 95 students who responded to the University Social Responsibility Scale. Experimental Group 1 participated in Christianity class, and Experimental Group 2 in Ethics class. A Mann–Whitney U, Wilcoxon test, and ANOVA were used. The effect associated with the group variable was confirmed. We have partially affirmed that ethical and religious intervention positively influences students’ perception and practice of social responsibility. Future studies should explore pedagogical models that effectively enhance social responsibility among university students.
“Jewish Meditation Reconsidered”: Hitbodedut as a Meditative Practice and Its Transmission from the Egyptian Pietists to the Hasidic Masters
Authors: Weil Matan
Published: October 2024

This research challenges the prevailing consensus in the field of Jewish meditation that there is no longstanding tradition of Jewish meditation, but rather a plethora of independent, unrelated techniques. By applying a context-sensitive research methodology, this study reconsiders the common understanding of Hitbodedut as ‘concentration’ and suggests instead a new view of Hitbodedut as a three-step solitary meditation technique, used as a means for Devekut (cleave to God). Drawing on the work of past scholars, this research demonstrates the potential transmission of Hitbodedut from the school of Jewish Egyptian Pietists to the 13th-century Kabbalists of Acre, then to the 16th-century Kabbalists of Safed, and eventually to 18th-century Hasidism.
Nested Cross-Validation for HBV Conceptual Rainfall–Runoff Model Spatial Stability Analysis in a Semi-Arid Context
Authors: El Garnaoui Mohamed , Boudhar Abdelghani , Nifa Karima , El Jabiri Yousra , Karaoui Ismail , El Aloui Abdenbi , Midaoui Abdelbasset , Karroum Morad , Mosaid Hassan , Chehbouni Abdelghani
Published: October 2024

Accurate and efficient streamflow simulations are necessary for sustainable water management and conservation in arid and semi-arid contexts. Conceptual hydrological models often underperform in these catchments due to the high climatic variability and data scarcity, leading to unstable parameters and biased results. This study evaluates the stability of the HBV model across seven sub-catchments of the Oum Er Rabia river basin (OERB), focusing on the HBV model regionalization process and the effectiveness of Earth Observation data in enhancing predictive capability. Therefore, we developed a nested cross-validation framework for spatiotemporal stability assessment, using optimal parameters from a donor-single-site calibration (DSSC) to inform target-multi-site calibration (TMSC). The results show that the HBV model remains spatially transferable from one basin to another with moderate to high performances (KGE (0.1~0.9 NSE (0.5~0.8)). Furthermore, calibration using KGE improves model stability over NSE. Some parameter sets exhibit spatial instability, but inter-annual parameter behavior remains stable, indicating potential climate change impacts. Model performance declines over time (18–124%) with increasing dryness. As a conclusion, this study presents a framework for analyzing parameter stability in hydrological models and highlights the need for more research on spatial and temporal factors affecting hydrological response variability.
Lightweight Design for Infrared Dim and Small Target Detection in Complex Environments
Authors: Chang Yan , Ma Decao , Ding Yao , Chen Kefu , Zhou Daming
Published: October 2024

In the intricate and dynamic infrared imaging environment, the detection of infrared dim and small targets becomes notably challenging due to their feeble radiation intensity, intricate background noise, and high interference characteristics. To tackle this issue, this paper introduces a lightweight detection and recognition algorithm, named YOLOv5-IR, and further presents an even more lightweight version, YOLOv5-IRL. Firstly, a lightweight network structure incorporating spatial and channel attention mechanisms is proposed. Secondly, a detection head equipped with an attention mechanism is designed to intensify focus on small target information. Lastly, an adaptive weighted loss function is devised to improve detection performance for low-quality samples. Building upon these advancements, the network size can be further compressed to create the more lightweight YOLOv5-IRL version, which is better suited for deployment on resource-constrained mobile platforms. Experimental results on infrared dim and small target detection datasets with complex backgrounds indicate that, compared to the baseline model YOLOv5, the proposed YOLOv5-IR and YOLOv5-IRL detection algorithms reduce model parameter counts by 42.9% and 45.6%, shorten detection time by 13.6% and 16.9%, and enhance mAP0.5 by 2.4% and 1.8%, respectively. These findings demonstrate that the proposed algorithms effectively elevate detection efficiency, meeting future demands for infrared dim and small target detection.
Advancing Arctic Sea Ice Remote Sensing with AI and Deep Learning: Opportunities and Challenges
Authors: Li Wenwen , Hsu Chia-Yu , Tedesco Marco
Published: October 2024

Revolutionary advances in artificial intelligence (AI) in the past decade have brought transformative innovation across science and engineering disciplines. In the field of Arctic science, we have witnessed an increasing trend in the adoption of AI, especially deep learning, to support the analysis of Arctic big data and facilitate new discoveries. In this paper, we provide a comprehensive review of the applications of deep learning in sea ice remote sensing domains, focusing on problems such as sea ice lead detection, thickness estimation, sea ice concentration and extent forecasting, motion detection, and sea ice type classification. In addition to discussing these applications, we also summarize technological advances that provide customized deep learning solutions, including new loss functions and learning strategies to better understand sea ice dynamics. To promote the growth of this exciting interdisciplinary field, we further explore several research areas where the Arctic sea ice community can benefit from cutting-edge AI technology. These areas include improving multimodal deep learning capabilities, enhancing model accuracy in measuring prediction uncertainty, better leveraging AI foundation models, and deepening integration with physics-based models. We hope that this paper can serve as a cornerstone in the progress of Arctic sea ice research using AI and inspire further advances in this field.
Integrated Precision High-Frequency Signal Conditioner for Variable Impedance Sensors
Authors: Brkić Miodrag , Radić Jelena , Babković Kalman , Damnjanović Mirjana
Published: October 2024

In this paper, a signal conditioner intended for use in variable impedance sensors is presented. First, an inductive linear displacement sensor design is described, and the signal conditioner discrete realization is presented. Second, based on this system’s requirements, the integrated conditioner is proposed. The conditioner comprises an amplifier, a tunable band-pass filter, and a precision high-frequency AC-DC converter. It is designed in a low-cost AMS 0.35 µm CMOS process. The presented conditioner measures the sensor’s impedance magnitude by using a simplified variation of the sensor voltage and current vector measurement. It can be used for the real-time measurement of fast sensors, having small output impedance. The post-layout simulation results show that the integrated conditioner has an inductance measurement range from 10 nH to 550 nH with a nonlinearity of 1.2%. The operating frequency in this case was 8 MHz, but the circuit can be easily adjusted to different operating frequencies (due to the tunable filter). The designed IC area is 500 × 330 μm2, and the total power consumption is 93.8 mW.
Self-Organizing and Routing Approach for Condition Monitoring of Railway Tunnels Based on Linear Wireless Sensor Network
Authors: Yang Haibo , Guo Huidong , Jia Junying , Jia Zhengfeng , Ren Aiyang
Published: October 2024

Real-time status monitoring is crucial in ensuring the safety of railway tunnel traffic. The primary monitoring method currently involves deploying sensors to form a Wireless Sensor Network (WSN). Due to the linear characteristics of railway tunnels, the resulting sensor networks usually have a linear topology known as a thick Linear Wireless Sensor Network (LWSN). In practice, sensors are deployed randomly within the area, and to balance the energy consumption among nodes and extend the network’s lifespan, this paper proposes a self-organizing network and routing method based on thick LWSNs. This method can discover the topology, form the network from randomly deployed sensor nodes, establish adjacency relationships, and automatically form clusters using a timing mechanism. In the routing, considering the cluster heads’ load, residual energy, and the distance to the sink node, the optimal next-hop cluster head is selected to minimize energy disparity among nodes. Simulation experiments demonstrate that this method has significant advantages in balancing network energy and extending network lifespan for LWSNs.
Evaluation of Two Particle Number (PN) Counters with Different Test Protocols for the Periodic Technical Inspection (PTI) of Gasoline Vehicles
Authors: Melas Anastasios , Franzetti Jacopo , Suarez-Bertoa Ricardo , Giechaskiel Barouch
Published: October 2024

Thousands of particle number (PN) counters have been introduced to the European market, following the implementation of PN tests during the periodic technical inspection (PTI) of diesel vehicles equipped with particulate filters. Expanding the PN-PTI test to gasoline vehicles may face several challenges due to the different exhaust aerosol characteristics. In this study, two PN-PTI instruments, type-examined for diesel vehicles, measured fifteen petrol passenger cars with different test protocols: low and high idling, with or without additional load, and sharp accelerations. The instruments, one based on diffusion charging and the other on condensation particle counting, demonstrated good linearity compared to the reference instrumentation with R-squared values of 0.93 and 0.92, respectively. However, in a considerable number of tests, they registered higher particle concentrations due to the presence of high concentrations below their theoretical 23 nm cut-off size. The evaluation of the different test protocols showed that gasoline direct injection engine vehicles without particulate filters (GPFs) generally emitted an order of magnitude or higher PN compared to those with GPFs. However, high variations in concentration levels were observed for each vehicle. Port-fuel injection vehicles without GPFs mostly emitted PN concentrations near the lower detection limit of the PN-PTI instruments.
Asymmetric-Based Residual Shrinkage Encoder Bearing Health Index Construction and Remaining Life Prediction
Authors: Zhang Baobao , Zhang Jianjie , Yu Peibo , Cao Jianhui , Peng Yihang
Published: October 2024

Predicting the remaining useful life (RUL) of bearings is crucial for maintaining the reliability and availability of mechanical systems. Constructing health indicators (HIs) is a fundamental step in the methodology for predicting the RUL of rolling bearings. Traditional HI construction often involves determining the degradation stage of the bearing by extracting time–frequency domain features from raw data using a priori knowledge and setting artificial thresholds; this approach does not fully utilize the vibration information in the bearing data. In order to address the above problems, this paper proposes an Asymmetric Residual Shrinkage Convolutional Autoencoder (ARSCAE) model. The asymmetric structure of the ARSCAE model is characterized by the soft thresholding of signal features in the encoder part to achieve noise reduction. The decoder part consists of convolutional and pooling layers for data reconstruction. This model can directly construct HIs from the original vibration signals collected, and comparisons with other models show that it constructs better HIs from the original vibration signals. Finally, experiments on the FEMTO dataset show that the results indicate that the HIS constructed by the ARSCAE model has better lifetime prediction capability compared to other methods.
High Sensitivity Bi2O3/Ti3C2Tx Ammonia Sensor Based on Improved Synthetic MXene Method at Room Temperature
Authors: Zhou Baocang , Zhao Zhihua , Lv Zhenli , Chen Zhuo , Kang Sibo
Published: October 2024

The MXene Ti3C2Tx was synthesized using hydrofluoric acid and an improved multilayer method in this study. Subsequently, a Bi2O3/Ti3C2Tx composite material was produced through hydrothermal synthesis. This composite boasts a unique layered structure, offering a large surface area that provides numerous contact and reaction sites, facilitating the adsorption of ammonia on its surface. The prepared Bi2O3/Ti3C2Tx-based sensor exhibits excellent sensing performance for ammonia gas, including high responsiveness, good repeatability, and rapid response–recovery time. The sensor’s response to 100 ppm ammonia gas is 61%, which is 11.3 times and 1.6 times the response values of the Ti3C2Tx gas sensor and Bi2O3 gas sensor, with response/recovery times of 61 s/164 s at room temperature, respectively. Additionally, the gas sensitivity mechanism of the Bi2O3/Ti3C2Tx-based sensor was analyzed, and the gas sensing response mechanism was proposed. This study shows that the sensor can effectively enhance the accuracy and precision of ammonia detection at room temperature and has a wide range of application scenarios.
Application of Single-Frequency Arbitrarily Directed Split Beam Metasurface Reflector in Refractive Index Measurements
Authors: Wells Brian M. , Tripp Joseph F. , Krupa Nicholas W. , Rittenberg Andrew J. , Williams Richard J.
Published: October 2024

We present a sensor that utilizes a modified single-frequency split beam metasurface reflector to measure the refractive index of materials ranging from one to three. Samples are placed into a cavity between a PCB-etched dielectric and a reflecting ground plane. It is illuminated using a 10.525 GHz free-space transmit horn with reflecting angles measured by sweeping a receiving horn around the setup. Predetermined changes in measured angles determined through simulations will coincide with the material’s index. The sensor is designed using the Fourier transform method of array synthesis and verified with FEM simulations. The device is fabricated using PCB milling and 3D printing. The quality of the sensor is verified by characterizing 3D printed dielectric samples of various infill percentages and thicknesses. Without changing the metasurface design, the sensing performance is extended to accommodate larger sample thicknesses by including a modified 3D printed fish-eye lens mounted in front of the beam splitter; this helps to exaggerate changes in reflected angles for those samples. All the methods presented are in agreement and verified with single-frequency index measurements using Snell’s law. This device may offer a viable alternative to traditional index characterization methods, which often require large sample sizes for single-frequency measurements or expensive equipment for multi-frequency parameter extraction.
Enhanced Timing Performance of Dual-Ended PET Detectors for Brain Imaging Using Dual-Finishing Crystal Approach
Authors: Ko Guen Bae , Kwak Dongjin , Lee Jae Sung
Published: October 2024

This study presents a novel approach to enhancing the timing performance of dual-ended positron emission tomography (PET) detectors for brain imaging by employing a dual-finishing crystal method. The proposed method integrates both polished and unpolished surfaces within the scintillation crystal block to optimize time-of-flight (TOF) and depth-of-interaction (DOI) resolutions. A dual-finishing detector was constructed using an 8 × 8 LGSO array with a 2 mm pitch, and its performance was compared against fully polished and unpolished crystal blocks. The results indicate that the dual-finishing method significantly improves the timing resolution while maintaining good energy and DOI resolutions. Specifically, the timing resolution achieved with the dual-finishing block was superior, measuring 192.0 ± 12.8 ps, compared to 206.3 ± 9.4 ps and 234.8 ± 17.9 ps for polished and unpolished blocks, respectively. This improvement in timing is crucial for high-performance PET systems, particularly in brain imaging applications where high sensitivity and spatial resolution are paramount.
Electromyography- and Bioimpedance-Based Detection of Swallow Onset for the Control of Dysphagia Treatment
Authors: Riebold Benjamin , Seidl Rainer O. , Schauer Thomas
Published: October 2024

Several studies support the benefits of biofeedback and Functional Electrical Stimulation (FES) in dysphagia therapy. Most commonly, adhesive electrodes are placed on the submental region of the neck to conduct Electromyography (EMG) measurements for controlling gamified biofeedback and functional electrical stimulation. Due to the diverse origin of EMG activity at the neck, it can be assumed that EMG measurements alone do not accurately reflect the onset of the pharyngeal swallowing phase (onset of swallowing). To date, no study has addressed the timing and detection performance of swallow onsets on a comprehensive database including dysphagia patients. This study includes EMG and BioImpedance (BI) measurements of 41 dysphagia patients to compare the timing and performance in the Detection of Swallow Onsets (DoSO) using EMG alone versus combined BI and EMG measurements. The latter approach employs a BI-based data segmentation of potential swallow onsets and a machine-learning-based classifier to distinguish swallow onsets from non-swallow events. Swallow onsets labeled by an expert serve as a reference. In addition to the F1 score, the mean and standard deviation of the detection delay regarding reference events have been determined. The EMG-based DoSO achieved an F1 score of 0.289 with a detection delay of 0.018 s ± 0.203 s. In comparison, the BI/EMG-based DoSO achieved an F1 score of 0.546 with a detection delay of 0.033 s ± 0.1 s. Therefore, the BI/EMG-based DoSO has better timing and detection performance compared to the EMG-based DoSO and potentially improves biofeedback and FES in dysphagia therapy.
Flexible Electromagnetic Sensor with Inkjet-Printed Silver Nanoparticles on PET Substrate for Chemical and Biomedical Applications
Authors: Ejaz Muhammad Usman , Irum Tayyaba , Qamar Muhammad , Alomainy Akram
Published: October 2024

For this article, a low-cost, compact, and flexible inkjet-printed electromagnetic sensor was investigated for its chemical and biomedical applications. The investigated sensor design was used to estimate variations in the concentration of chemicals (ethanol and methanol) and biochemicals (hydrocortisone—a chemical derivative of cortisol, a biomarker of stress and cardiovascular effects). The proposed design’s sensitivity was further improved by carefully choosing the frequency range (0.5–4 GHz), so that the analyzed samples showed approximately linear variations in their dielectric properties. The dielectric properties were measured using a vector network analyzer (VNA) and an Agilent 85070E Dielectric Probe Kit. The sensor design had a resonant frequency at 2.2 GHz when investigated without samples, and a consistent shift in resonant frequency was observed, with variation in the concentrations of the investigated chemicals. The sensitivity of the designed sensor is decent and is comparable to its non-flexible counterparts. Furthermore, the simulation and measured results were in agreement and were comparable to similar investigated sensor prototypes based on non-flexible Rogers substrates (Rogers RO4003C) and Rogers Droid/RT 5880), demonstrating true potential for chemical, biomedical applications, and healthcare.
Ensemble and Gossip Learning-Based Framework for Intrusion Detection System in Vehicle-to-Everything Communication Environment
Authors: Ali Muhammad Nadeem , Imran Muhammad , Ullah Ihsan , Raza Ghulam Musa , Kim Hye-Young , Kim Byung-Seo
Published: October 2024

Autonomous vehicles are revolutionizing the future of intelligent transportation systems by integrating smart and intelligent onboard units (OBUs) that minimize human intervention. These vehicles can communicate with their environment and one another, sharing critical information such as emergency alerts or media content. However, this communication infrastructure is susceptible to cyber-attacks, necessitating robust mechanisms for detection and defense. Among these, the most critical threat is the denial-of-service (DoS) attack, which can target any entity within the system that communicates with autonomous vehicles, including roadside units (RSUs), or other autonomous vehicles. Such attacks can lead to devastating consequences, including the disruption or complete cessation of service provision by the infrastructure or the autonomous vehicle itself. In this paper, we propose a system capable of detecting DoS attacks in autonomous vehicles across two scenarios: an infrastructure-based scenario and an infrastructureless scenario, corresponding to vehicle-to-everything communication (V2X) Mode 3 and Mode 4, respectively. For Mode 3, we propose an ensemble learning (EL) approach, while for the Mode 4 environment, we introduce a gossip learning (GL)-based approach. The gossip and ensemble learning approaches demonstrate remarkable achievements in detecting DoS attacks on the UNSW-NB15 dataset, with efficiencies of 98.82% and 99.16%, respectively. Moreover, these methods exhibit superior performance compared to existing schemes.
A Survey on Data-Driven Approaches for Reliability, Robustness, and Energy Efficiency in Wireless Body Area Networks
Authors: Majumdar Pulak , Roy Satyaki , Sikdar Sudipta , Ghosh Preetam , Ghosh Nirnay
Published: October 2024

Wireless Body Area Networks (WBANs) are pivotal in health care and wearable technologies, enabling seamless communication between miniature sensors and devices on or within the human body. These biosensors capture critical physiological parameters, ranging from body temperature and blood oxygen levels to real-time electrocardiogram readings. However, WBANs face significant challenges during and after deployment, including energy conservation, security, reliability, and failure vulnerability. Sensor nodes, which are often battery-operated, expend considerable energy during sensing and transmission due to inherent spatiotemporal patterns in biomedical data streams. This paper provides a comprehensive survey of data-driven approaches that address these challenges, focusing on device placement and routing, sampling rate calibration, and the application of machine learning (ML) and statistical learning techniques to enhance network performance. Additionally, we validate three existing models (statistical, ML, and coding-based models) using two real datasets, namely the MIMIC clinical database and biomarkers collected from six subjects with a prototype biosensing device developed by our team. Our findings offer insights into strategies for optimizing energy efficiency while ensuring security and reliability in WBANs. We conclude by outlining future directions to leverage approaches to meet the evolving demands of healthcare applications.
Design of Docking Interfaces for On-Orbit Assembly of Large Structures in Space
Authors: Liu Shuai , Zhang Enyang , Xu Zhenbang , Zhang Jingxu
Published: October 2024

Considering the complexity of on-orbit assembly during space missions and the super-large size of space structures, this paper presents the design for a new type of docking interface with an androgynous body that exhibits a number of advantages, including high connection strength and a compact structure. The androgynous body has a conical guided symmetric design with a symmetry of 90°. The geometric design of the docking surface is described in detail in order to prove its advantages. Structural design was carried out using UG modeling as well as dynamic simulation using Recur Dyn to obtain the displacement coordinate curves of the docking port. The geometry of the docking port’s high docking misalignment tolerance was verified, and misalignment tolerance and lens splicing experiments were also performed. The docking port’s ability to be quickly connected or disconnected within a translation tolerance of 23.5 mm and a tilt tolerance of 24° was verified. This article provides a useful reference for space missions in terms of module docking and on-orbit assembly.
Enhancing Syslog Message Security and Reliability over Unidirectional Fiber Optics
Authors: Anton Alin-Adrian , Csereoka Petra , Capota Eugenia Ana , Cioargă Răzvan-Dorel
Published: October 2024

Standard log transmission protocols do not offer a robust way of segregating the log network from potential threats. A secure log transmission system and the realization of a data diode using affordable components are proposed. Unidirectional data flow prevents unauthorized access and eavesdropping, ensuring the integrity and confidentiality of sensitive log data. The system uses an encryption protocol that requires that the upstream and the downstream of the data diode are perfectly synchronized, mitigating replay attacks. It has been shown that message amplification can mitigate UDP packet loss, but this is only required when the data diode traffic is congested. The implementation of the encryption algorithm is suitable for resource-constrained devices and it has been shown to produce random-looking output even on a reduced number of rounds when compared to the parent cipher. Several improvements have been made to the original encryption algorithm for which an actual implementation was missing. Free software and datasets have been made available to reproduce the results. The complete solution is easy to reproduce in order to secure the segregation of a log network inside any scenario where logging is required by the law and log tampering must be prevented.
Ultra-Short-Term Wind Farm Power Prediction Considering Correlation of Wind Power Fluctuation
Authors: Li Chuandong , Zhang Minghui , Zhang Yi , Yi Ziyuan , Niu Huaqing
Published: October 2024

Accurate ultra-short-term power prediction for wind farms is challenging under rapid wind speed fluctuations, complicating production planning and power balancing. This paper proposes a new method considering spatial and temporal correlations of wind fluctuations among adjacent wind farms. The method first calculates the time difference between power fluctuations based on wind speed, direction, and relative positions, determining the prior information period. The variational Bayesian model is then used to extract implicit relationships between power fluctuations of adjacent wind farms, enabling power prediction during the prior information period. Finally, the non-prior information period is predicted to complete the ultra-short-term power prediction. Using measured data from three wind farms in Fujian Province, compared to other models, the method demonstrates improved accuracy by effectively leveraging the power fluctuation characteristics of adjacent wind farms, and it has a certain amount of generalizability.
Exploiting Temporal Features in Calculating Automated Morphological Properties of Spiky Nanoparticles Using Deep Learning
Authors: Rafique Muhammad Aasim
Published: October 2024

Object segmentation in images is typically spatial and focuses on the spatial coherence of pixels. Nanoparticles in electron microscopy images are also segmented frame by frame, with subsequent morphological analysis. However, morphological analysis is inherently sequential, and a temporal regularity is evident in the process. In this study, we extend the spatially focused morphological analysis by incorporating a fusion of hard and soft inductive bias from sequential machine learning techniques to account for temporal relationships. Previously, spiky Au nanoparticles (Au-SNPs) in electron microscopy images were analyzed, and their morphological properties were automatically generated using a hourglass convolutional neural network architecture. In this study, recurrent layers are integrated to capture the natural, sequential growth of the particles. The network is trained with a spike-focused loss function. Continuous segmentation of the images explores the regressive relationships among natural growth features, generating morphological statistics of the nanoparticles. This study comprehensively evaluates the proposed approach by comparing the results of segmentation and morphological properties analysis, demonstrating its superiority over earlier methods.
Performance Analysis and Prediction of 5G Round-Trip Time Based on the VMD-LSTM Method
Authors: Zhu Sanying , Zhou Shutong , Wang Liuquan , Zang Chenxin , Liu Yanqiang , Liu Qiang
Published: October 2024

With the increasing level of industrial informatization, massive industrial data require real-time and high-fidelity wireless transmission. Although some industrial wireless network protocols have been designed over the last few decades, most of them have limited coverage and narrow bandwidth. They cannot always ensure the certainty of information transmission, making it especially difficult to meet the requirements of low latency in industrial manufacturing fields. The 5G technology is characterized by a high transmission rate and low latency; therefore, it has good prospects in industrial applications. To apply 5G technology to factory environments with low latency requirements for data transmission, in this study, we analyze the statistical performance of the round-trip time (RTT) in a 5G-R15 communication system. The results indicate that the average value of 5G RTT is about 11 ms, which is less than the 25 ms of WIA-FA. We then consider 5G RTT data as a group of time series, utilizing the augmented Dickey–Fuller (ADF) test method to analyze the stability of the RTT data. We conclude that the RTT data are non-stationary. Therefore, firstly, the original 5G RTT series are subjected to first-order differencing to obtain differential sequences with stronger stationarity. Then, a time series analysis-based variational mode decomposition–long short-term memory (VMD-LSTM) method is proposed to separately predict each differential sequence. Finally, the predicted results are subjected to inverse difference to obtain the predicted value of 5G RTT, and a predictive error of 4.481% indicates that the method performs better than LSTM and other methods. The prediction results could be used to evaluate network performance based on business requirements, reduce the impact of instruction packet loss, and improve the robustness of control algorithms. The proposed early warning accuracy metrics for control issues can also be used to indicate when to retrain the model and to indicate the setting of the control cycle. The field of industrial control, especially in the manufacturing industry, which requires low latency, will benefit from this analysis. It should be noted that the above analysis and prediction methods are also applicable to the R16 and R17 versions.
Multi-Feature-Filtering-Based Road Curb Extraction from Unordered Point Clouds
Authors: Lang Hong , Peng Yuan , Zou Zheng , Zhu Shengxue , Peng Yichuan , Du Hao
Published: October 2024

Road curb extraction is a critical component of road environment perception, being essential for calculating road geometry parameters and ensuring the safe navigation of autonomous vehicles. The existing research primarily focuses on extracting curbs from ordered point clouds, which are constrained by their structure of point cloud organization, making it difficult to apply them to unordered point cloud data and making them susceptible to interference from obstacles. To overcome these limitations, a multi-feature-filtering-based method for curb extraction from unordered point clouds is proposed. This method integrates several techniques, including the grid height difference, normal vectors, clustering, an alpha-shape algorithm based on point cloud density, and the MSAC (M-Estimate Sample Consensus) algorithm for multi-frame fitting. The multi-frame fitting approach addresses the limitations of traditional single-frame methods by fitting the curb contour every five frames, ensuring more accurate contour extraction while preserving local curb features. Based on our self-developed dataset and the Toronto dataset, these methods are integrated to create a robust filter capable of accurately identifying curbs in various complex scenarios. Optimal threshold values were determined through sensitivity analysis and applied to enhance curb extraction performance under diverse conditions. Experimental results demonstrate that the proposed method accurately and comprehensively extracts curb points in different road environments, proving its effectiveness and robustness. Specifically, the average curb segmentation precision, recall, and F1 score values across scenarios A, B (intersections), C (straight road), and scenarios D and E (curved roads and ghosting) are 0.9365, 0.782, and 0.8523, respectively.
Insights and Future Perspectives in Calcinosis Cutis Associated with Systemic Sclerosis
Authors: Lazar Luna , Mogensen Mette , Boesen Mikael Ploug , Olesen Anne Braae
Published: October 2024

Introduction: Calcinosis cutis (CC), the pathological deposition of calcium salts in the skin, is a frequent and challenging complication of systemic sclerosis (SSc). Despite its high prevalence, the underlying pathophysiology remains poorly understood, complicating treatment strategies. Material and Methods: This narrative review synthesizes the literature on CC in the context of SSc. The current understanding and treatment of CC in SSc is reviewed, focusing on the role of hypoxia in its pathogenesis and the therapeutic potential of sodium thiosulfate (STS). Results and Discussion: Research indicates a potential link between hypoxia and the development of CC in SSc, shedding light on novel pathogenic mechanisms. Additionally, promising results from treatments such as STS spurs interest in conducting larger, randomized controlled trials to validate these findings.
Return to Physical Activity in Individuals with Surgical Stomas: A Scoping Review
Authors: Mena-Jiménez Andrea-Victoria , Rodríguez-Suárez Claudio-Alberto , González-de la Torre Héctor
Published: October 2024

In surgically treated individuals with surgical stomas, the return to physical activity is an indicator of quality of life that reflects their well-being. With the aim of synthesizing the available evidence regarding the return to physical activity in individuals with surgical stomas, a scoping review was developed following the methodological approach of the Joanna Briggs Institute and the Preferred Reporting Items for Systematic reviews and Meta-Analyses for Scoping Reviews criteria. Searches were conducted in Medline (PubMed), Scopus, Web of Science, Cinahl, and Lilacs, as well as the meta-search engines TripDatabase and Epistemonikos, using MeSH terms. Included studies were written in Spanish, English, Portuguese, and German, without any limitation on the year of publication. A total of n = 15 studies was included (n = 2 qualitative; n = 2 case reports; n = 1 case series; n = 1 cohort; n = 8 cross-sectional; and n = 1 randomized clinical trial), which showed variability in the quality of the designs. The qualitative studies explored themes such as motivation, beliefs about physical activity, and other lifestyle factors. The case reports described physiological, psychological, and functional implications of returning to physical activity for specific individuals after ostomy surgery. Quantitative studies evaluated the effects of different types of physical activity on quality of life and tolerance to physical activity in these individuals, employing various measurement instruments. In conclusion, the evidence on returning to sports and physical activity after stoma surgery is limited and varied. While studies highlight the importance of social support and self-confidence, they generally lack rigor and primarily focus on adults and oncology patients. There is a need for more research to establish clear guidelines on physical activity type, frequency, and intensity to ensure safe and beneficial outcomes for individuals with stomas.