bluuebunny/crossref_metadata_2025 huggingface dataset:
Novel Automated Chemiluminescent Immunoassay for the Detection of Autoantibodies Against Aquaporin-4 in Neuromyelitis Optica Spectrum Disorders
Authors: Yamazaki Nozomi , Takahashi Toshiyuki , Misu Tatsuro , Nishikawa Yukihiro
Published: January 2025

Background/Objectives: Neuromyelitis optica spectrum disorder (NMOSD) is an autoimmune-related neurological disease that primarily affects the optic nerve and spinal cord. According to current international consensus guidelines for NMOSD, confirming the presence of aquaporin-4 immunoglobulin G antibody (AQP4-IgG) is one of the most important diagnostic criteria because AQP4-IgG is a significant diagnostic biomarker of NMOSD. Several assays are currently available for detecting AQP4-IgG, including cell-based assays (CBAs) and enzyme-linked immunosorbent assays (ELISAs). However, each assay has certain disadvantages, including insufficient sensitivity and specificity, the need for sophisticated techniques, and semi-quantitative results. Methods: We developed a fully automated chemiluminescent enzyme immunoassay (CLEIA) to detect AQP4-IgG (AQP4-CLEIA). This assay utilizes the recombinant antigen purified from the newly generated AQP4-M23 stably expressing Chinese hamster ovary cell line and an anti-AQP4 monoclonal antibody as a calibrator. Results: In analytical performance studies, the assay demonstrates good precision and linearity over the entire measurement range. Moreover, this assay showed no high-dose hook effect and interference from endogenous substances. In clinical validation studies, patients with AQP4-IgG positive NMOSD, multiple sclerosis, or myelin oligodendrocyte glycoprotein antibody-associated disorder and healthy individuals were tested. A cutoff value of 10.0 U/mL was determined by receiver operating characteristic curves based on the results of a microscopic live CBA. The sensitivity and specificity for AQP4-IgG-positive NMOSD were 97.0% and 100.0%, respectively, at the cutoff value. Conclusions: The results suggest that AQP4-CLEIA is a convenient automated method for measuring AQP4-IgG titers in hospitals and clinical laboratories, offering an effective alternative to the gold-standard CBA.
Novel Esthetic Technique for Restoring Dental Implant Access Holes: A Case Report
Authors: Seki Keisuke , Shiratsuchi Koji , Toki Arata , Kamimoto Atsushi , Hagiwara Yoshiyuki
Published: January 2025

Background/Objectives: For dental implant treatment to be successful, esthetics, functionality, and cleanability are all required of the superstructure, which is the final prosthesis. Screw fixation and cementation have been the conventional methods of choice for the crown prosthesis of implants, but these individual methods cannot fulfill all the requirements. Methods: As a solution to this problem, we have devised a new implant superstructure restoration method called the inlay covering esthetic technique, which uses computer-aided design/computer-aided manufacturing inlays. Results: It involves the placement of an inlay covering the access hole in a highly translucent partially stabilized zirconia crown. Conclusions: This technique, demonstrated in this clinical case study, expands the indications for implant treatment and improves the oral quality of life of patients. This case report describes a novel esthetic technique for restoring dental implant access holes.
Microbiological and Ergonomic Effects of Three Prototypes of a Device to Reduce Aerosol Dispersion in Dental Care During the COVID-19 Pandemic: A Randomized Controlled Clinical Trial
Authors: Baldasso Camila N. , Teichert-Filho Ruy , Marinowic Daniel R. , Campos Maria M. , Gomes Maximiliano S.
Published: January 2025

Objectives: This randomized clinical trial evaluated the microbiological efficacy and the ergonomic impact of three prototypes of a device to reduce aerosol dispersion during dental procedures. Methods: Sixty patients undergoing dental care using high-speed turbines and/or ultrasonic tips were randomly assigned to 4 groups (n = 15): CG: control group, with standard personal protective equipment (PPE); G1: PPE + acrylic device (AD) with aspiration; G2: PPE + AD without aspiration; and G3: PPE + polyvinyl chloride device. The device prototypes consisted of a rigid translucent acrylic structure (G1 and G2), or a rigid PVC tube structure surrounded by layers of translucent flexible PVC films (G3), adjusted to the dental chair, involving the patient’s head, neck and chest. The main outcome was the microbiological analysis (mean Δ of CFU at 10 different sites), and the secondary outcome was the ergonomic evaluation (questionnaire to dentists and patients). Results: The final sample comprised 59 participants (mean age 38.6 ± 11.4 years, 55.2% male). The overall mean time for dental procedures was 32.4 ± 16.9 min, with no differences between groups (p = 0.348). Microbiological analyses showed that the use of the device significantly reduced contamination in the light reflector (01.46 ± 4.43 ΔCFU in G2 vs. 19.25 ± 36.50 ΔCFU in CG; p = 0.028), apron (09.11 ± 12.05 ΔCFU in G3 vs. 21.14 ± 43.41 ΔCFU in GC; p = 0.044), and face shield (08.80 ± 32.70 ΔCFU in G1 vs. 56.78 ± 76.64 ΔCFU in the GC; p = 0.017). The device was well accepted by patients and increased the dentists‘ perception of safety and protection (p < 0.001), but significantly decreased ergonomics related to the clinical view, space, agility and access to the patient, and ease of performing procedures (p < 0.001). Conclusions: The tested device can be an additional tool for infection prevention and control in dentistry, not only during the COVID-19 pandemic, but also for the control of future infectious diseases and epidemics.
Temperature Gradients as a Data Storage Principle
Authors: Schoenmaker Jeroen , Martins Pâmella Gonçalves , Teixeira Julio Carlos
Published: January 2025

In this work, we analyze the thermodynamic principles underlying modern data storage systems, including Random Access Memory (RAM), hard disk drive (HDD), flash memory, magnetic RAM (MRAM), ferroelectric RAM (FeRAM), and phase-change RAM (PCRAM), as well as other less well-known data storage mechanisms. The analysis is conducted in the context of data storage and processing in relation to Landauer’s principle, with special emphasis on hysteresis. Analogous to how heat engines are characterized by thermodynamic cycles, data storage systems are examined in terms of the hysteresis loop of their fundamental data unit. We explore the role of heat in data storage systems. Afterward, we introduce the concept of temperature gradient memory (TeGraM) along with a detailed layout of a realizable device. Experimental results demonstrating this technology are also presented.
Mathematical Theory of Seismic Activity and Its Specific Cases: Gutenberg–Richter Law, Omori Law, Roll-Off Effect, and Negative Binomial Distribution
Authors: Borisov Roumen , Vitanov Nikolay K.
Published: January 2025

We discuss a model of seismic activity that is based on the concept of energy in a cluster of sources of seismic activity. We show that specific cases of the studied model lead to the Gutenberg–Richter relationship and the Omori law. These laws are valid for earthquakes that happen in a single cluster of sources of seismic activity. Further, we discuss the distribution of earthquakes for several clusters containing sources of seismic activity. This distribution contains, as a specific case, a version of the negative binomial distribution. We show that at least a part of the roll-off effect connected to the parameter b of the Gutenberg– Richter law occurs because one records earthquakes that happen in more than one cluster of sources of seismic activity.
Application of Fuzzy Discount Factors in Behavioural Decision-Making for Financial Market Modelling
Authors: Siwek Joanna , Żywica Patryk
Published: January 2025

This paper presents an innovative approach to financial market modelling by integrating fuzzy discount factors into the decision-making process, thereby reflecting the complexities of human behaviour. Traditional financial models often fail to account for market dynamics’ psychological factors. The proposed method utilizes fuzzy logic to encapsulate the uncertainty and subjective judgment inherent in financial decisions. By representing financial variables as fuzzy numbers, the model better simulates the way humans assess information and make decisions under uncertainty. The incorporation of fuzzy discount factors marks a significant shift from deterministic to a more realistic representation of financial markets, suitable for practical application. This methodology offers a nuanced investment strategy that balances theoretical rigour with real-world applicability, appealing to a broad spectrum of investors. The aim of the following paper is to introduce an alternative to price modelling with the use of fuzzy return rates, which results in some errors in the mathematical model. The solution has the form of introducing fuzzy discount factors (FDFs) that retain the advantages of the fuzzy approach (e.g., encompassing subjectivity and imprecision) while preserving the shape of the fuzzy number modelling a price.
The Impact of Programs Aimed at Raising Awareness About Children with Intellectual and Developmental Disabilities in Schools: A Systematic Review
Authors: Uria-Olaizola Naroa , Yurrebaso Atutxa Garazi , León-Guereño Patxi
Published: January 2025

The awareness and inclusion of school children with intellectual and developmental disabilities are garnering growing attention in educational research. Schools are increasingly focused on creating inclusive environments, and programs aimed at improving students’ attitudes toward peers with disabilities are key to this effort. This article reviews studies on the impact of these programs on primary and secondary education. A systematic search of the WOS, Scopus, PubMed, and ERIC databases was conducted using the PRISMA statement. This yielded nine relevant articles published either in Spanish or English. This review found that awareness programs had a generally positive effect on students’ perceptions of their peers with disabilities. However, the awareness of intellectual and developmental disabilities remained lower than that of physical, visual, or hearing disabilities. The most effective strategies involved direct interaction and information sharing, with simulation techniques also having positive outcomes. Despite some successful results, challenges remain, particularly involving the need to engage educational staff and ensure the long-term sustainability of these programs within the curriculum. Future research should investigate the long-term impact of these interventions and their effectiveness across different educational settings.
It Is Not the Huge Enemy: Preservice Teachers’ Evolving Perspectives on AI
Authors: Uwosomah Ese Emmanuel , Dooly Melinda
Published: January 2025

The application of Artificial Intelligence (AI) to teacher training is a rather recent phenomenon and there is a need for more research on its use in teacher education. This paper examines the use and interpretation of AI by student language teachers during a 10-week telecollaborative course between students from two universities, one in the USA and the other in Spain (n = 46). The course focused on Technology-Enhanced Project-Based Language Learning (TePBLL) and was divided into different ‘technological blocks’. This article is centered around the AI technology block. The analysis is based on three exit tickets (reflection prompts) that demonstrate participants’ thoughts and changing perspectives towards AI. Through thematic analysis of the open-ended responses, this study shows that participants initially appeared skeptical before moving to tentative optimism after first studying theory and examples of the application of AI, followed by the creation of AI-based lessons and activities. The student teachers identify AI as a means to personalize and make language learning more efficient while expressing concerns related to its overuse, ethical issues and potential for undermining critical thinking and creativity. This small study looks at the evolution of the student teachers’ concepts about and perspectives towards AI-enhanced language teaching and learning before, during and after they engage in the technology block. The findings suggest that hands-on training that includes lesson design helps student teachers view AI as a complementary tool for many aspects of their teaching, although this can only be achieved through an adequate pedagogical application.
Linguistic and Communicative Diversity in Inclusive Settings—Effects, Challenges, and Opportunities
Authors: Stitzinger Ulrich
Published: January 2025

Diversity is understood as a significant advantage for inclusive learning. However, the questions of whether linguistic and communicative diversity in the classroom is truly beneficial and whether mutually linguistic support is effective between students with and without language impairments have not yet been answered comprehensively. Therefore, within the research project ‘LINUS—Learners in Inclusive Settings’, interactions between children (n = 48; aged 6 y 7 m to 7 y 7 m) in dyads in primary school were videotaped and micro-sequentially analyzed. The pairs of children were engaged in a 15 min playful framework within a quasi-experimental design. Based on quantitative language corpus analyses, the occurrence of linguistic elements was compared between pairs of children with (a) typical age-appropriate language acquisition, (b) developmental language disorders, and (c) heterogeneous settings with respect to backgrounds of language acquisition. Additionally, quantitative coding was used to analyze how often linguistic elements were productively imitated from one child to the other in different peer settings. The results show that there is a higher potential for mutually motivated communication and more resources for linguistic support in mixed dyads. From this study, specific principles for teachers and students can be drawn to foster linguistic support between children in the context of diversity.
A Novel Non-Line-of-Sight Error Mitigation Algorithm Using Double Extended Kalman Filter for Ultra-Wide Band Ranging Technology
Authors: Xu Sheng , Liu Qianyun , Lin Min , Wang Qing , Chen Kaile
Published: January 2025

In complex indoor environments, target tracking performance is impacted by non-line-of-sight (NLOS) noises and other measurement errors. In order to fix NLOS errors, a Double Extended Kalman filter (DEKF) algorithm is proposed, which refers to a kind of cascaded structure composed of two Kalman filters. In the proposed algorithm, the first filter is a classic Kalman filter (KF) and the second is an Extended Kalman filter (EKF). The time of arrival (TOA) measurements collected by multiple stationary ultra-wide band (UWB) sensors are used. Residual errors between the measured TOA and the prediction from the first KF are used to adjust the covariance of the first KF accordingly. Then, we use the estimated distance state of the first KF as a measurement vector of the second EKF in order to obtain a smoother observation. One of the advantages of the proposed algorithm is that it is able to perform target tracking with a good accuracy even without or with only one line-of-sight(LOS) TOA measurement for a period of time without prior information of the NLOS noise, which may be difficult to obtain in practical applications. Another advantage is that the accuracy does not significantly decrease when NLOS noises persist for a long period of time. Finally, the proposed DEKF can maintain high-precision positioning characteristics in both the constant velocity (CV) model and the constant acceleration (CA) model for LOS/NLOS environments. In the case of mixed LOS/NLOS environments, the RMSE of the proposed algorithm can be kept within 5 cm, while the RMSEs of other compared algorithms are easily beyond tens of centimeters. At the same time, when the confidence of RMSE is set to 95% for 1000 MC simulations, the confidence interval of the proposed algorithm is the smallest, and the mean value is 3–5 times closer to the true value compared to other algorithms. Simulation and experimental results show that the proposed algorithm performs much better than other state-of-the-art algorithms, particularly in severe mixed LOS/NLOS environments.
Overcoming Printed Circuit Board Limitations in an Energy Harvester with Amplitude Shift Keying and Pulse Width Modulation Communication Decoder Using Practical Design Solutions
Authors: Al Sabbagh Mohamad , Amaya Rony E. , Yagoub Mustapha Chérif-Eddine , Almohaimeed Abdullah M.
Published: January 2025

This paper presents PCB design solutions for implementing a radiative-field RF energy harvester with an ASK-PWM decoding communication scheme using available commercial components. The paper provides the design approach and tackles key challenges such as the impact of inductive parasitic effects at the output of the harvester, how to maintain the PCE at a constant value regardless of the time constant at the output of the communication path’s rectifier, and the difficulty of changing the aspect ratio of the discrete inverter used for PWM decoding. These challenges are addressed by using multiple capacitors connected in parallel at the output of the rectifier to reduce inductive parasitic effects, adding a series resistor in the communication path’s rectifier to isolate its loading from impacting the PCE, and utilizing a potentiometer in the inverter to realize PWM decoding on PCB. The system was manufactured using FR-4 substrate material with a size of 5 cm × 4 cm × 0.6 cm, harvesting energy at the ISM frequency of 924 MHz with a PCE of 42.12% at a bit rate of 15 Kbps. Moreover, the system consumes only 355 μW of power and maintains correct harvesting and decoding operation in the antenna separation range of 6–12 cm. This work aims to provide an alternative to IC realization by implementing the system entirely using commercial discrete components, reducing costs, adding flexibility, reducing development time, and allowing for simple debugging.
A Practical Human-Centric Risk Management (HRM) Methodology
Authors: Kioskli Kitty , Seralidou Eleni , Polemi Nineta
Published: January 2025

Various standards (e.g., ISO 27000x, ISO 31000:2018) and methodologies (e.g., NIST SP 800-53, NIST SP 800-37, NIST SP 800-161, ETSI TS 102 165-1, NISTIR 8286) are available for risk assessment. However, these standards often overlook the human element. Studies have shown that adversary profiles (AP), which detail the maturity of attackers, significantly affect vulnerability assessments and risk calculations. Similarly, the maturity of the users interacting with the Information and Communication Technologies (ICT) system in adopting security practices impacts risk calculations. In this paper, we identify and estimate the maturity of user profiles (UP) and propose an enhanced risk assessment methodology, HRM (based on ISO 27001), that incorporates the human element into the risk evaluation. Social measures, such as awareness programs, training, and behavioral interventions, alongside technical controls, are included in the Human-Centric Risk Management (HRM) risk treatment phase. These measures enhance user security hygiene and resilience, reducing risks and ensuring comprehensive security strategies in SMEs.
Intelligent Upgrading of the Localized GNSS Monitoring System: Profound Integration of Blockchain and AI
Authors: Lu Tianzeng , Sun Yanan , Zhu Qinglin , Zhou Xiaolin , Li Qiaoyang , Liu Jianan
Published: January 2025

With the extensive application of the Global Navigation Satellite System (GNSS), the intelligent upgrading of the GNSS monitoring system is of particular significance. Traditional GNSS monitoring systems typically rely on a centralized architecture, which possesses certain drawbacks when it comes to data tampering, fault tolerance, and data sharing. This paper presents an intelligently upgraded localized GNSS monitoring system that integrates blockchain and artificial intelligence (AI) technology to achieve the deep integration of security, transparency, and intelligent processing of monitoring data. Firstly, this paper employs blockchain technology to guarantee the integrity and tamper-resistance of GNSS monitoring data and utilizes a distributed ledger structure to realize the decentralization of data storage and transmission, thereby enhancing the anti-attack capability and reliability of the system. Secondly, the LSTM model is utilized to analyze and predict the vast amount of monitoring data in real-time, enabling the intelligent detection of GNSS signal anomalies and deviations and providing real-time early warnings to optimize the monitoring effect. Based on this architecture, we also combine the trained model with smart contracts to realize real-time monitoring and early warnings of GNSS satellites. By integrating the security guarantee of blockchain and the intelligent analysis ability of AI, the localized GNSS monitoring system can offer more efficient and accurate data monitoring and management services. In the study, we constructed a prototype system and tested it in both simulated and real environments. The results indicate that the system can effectively identify and respond to GNSS signal anomalies, and enhance the monitoring accuracy and response speed. Additionally, the application of blockchain enhances the immutability and traceability of data, providing a solid foundation for the long-term storage and auditing of GNSS data. The introduction of AI algorithms, especially the application of the Long Short-Term Memory (LSTM) network in anomaly detection, has significantly enhanced the system’s ability to recognize complex patterns.
A Hybrid Intention Recognition Framework with Semantic Inference for Financial Customer Service
Authors: Cai Nian , Li Shishan , Xu Jiajie , Tian Yinfeng , Zhou Yinghong , Liao Jiacheng
Published: January 2025

Automatic intention recognition in financial service scenarios faces challenges such as limited corpus size, high colloquialism, and ambiguous intentions. This paper proposes a hybrid intention recognition framework for financial customer service, which involves semi-supervised learning data augmentation, label semantic inference, and text classification. A semi-supervised learning method is designed to augment the limited corpus data obtained from the Chinese financial service scenario, which combines back-translation with BERT models. Then, a K-means-based semantic inference method is introduced to extract label semantic information from categorized corpus data, serving as constraints for subsequent text classification. Finally, a BERT-based text classification network is designed to recognize the intentions in financial customer service, involving a multi-level feature fusion for corpus information and label semantic information. During the multi-level feature fusion, a shallow-to-deep (StD) mechanism is designed to alleviate feature collapse. To validate our hybrid framework, 2977 corpus texts about loan service are provided by a financial company in China. Experimental results demonstrate that our hybrid framework outperforms existing deep learning methods in financial customer service intention recognition, achieving an accuracy of 89.06%, precision of 90.27%, recall of 90.40%, and an F1 score of 90.07%. This study demonstrates the potential of the hybrid framework to automatic intention recognition in financial customer service, which is beneficial for the improvement of the financial service quality.
Enhancing Driving Control via Speech Recognition Utilizing Influential Parameters in Deep Learning Techniques
Authors: Hussein Hasan H. , Karan Oguz , Kurnaz Sefer
Published: January 2025

This study investigates the enhancement of automated driving and command control through speech recognition using a Deep Neural Network (DNN). The method depends on some sequential stages such as noise removal, feature extraction from the audio file, and their classification using a neural network. In the proposed approach, the variables that affect the results in the hidden layers were extracted and stored in a vector to classify them and issue the most influential ones for feedback to the hidden layers in the neural network to increase the accuracy of the result. The result was 93% in terms of accuracy and with a very good response time of 0.75 s, with PSNR 78 dB. The proposed method is considered promising and is highly satisfactory to users. The results encouraged the use of more commands, more data processing, more future exploration, and the addition of sensors to increase the efficiency of the system and obtain more efficient and safe driving, which is the main goal of this research.
YOLOFLY: A Consumer-Centric Framework for Efficient Object Detection in UAV Imagery
Authors: Ma Pengwei , Fei Hongmei , Jia Dingyi , Sun Zheng , Lian Nan , Wei Jingyi , Zhou Jie
Published: January 2025

As an emerging edge device aimed at consumers, Unmanned Aerial Vehicles (UAVs) have attracted significant attention in the consumer electronics market, particularly for intelligent imaging applications. However, aerial image detection tasks face two major challenges: first, there are numerous small and overlapping objects that are difficult to identify from an aerial perspective, and second, if the detection frame rate is not high enough, missed detections may occur when the UAV is moving quickly, which can negatively impact the user experience by reducing detection accuracy, increasing the likelihood of collision-avoidance failures, and potentially causing unsafe flight behavior. To address these challenges, this paper proposes a novel YOLO (you only look once) framework, named YOLOFLY, which includes a C4f feature extraction module and a DWcDetect head to make the model lightweight, as well as an MPSA attention mechanism and an ACIoU loss function, aimed at improving detection accuracy and performance for consumer-grade UAVs. Extensive experiments on the public VisDrone2019 dataset demonstrate that YOLOFLY outperforms the latest state-of-the-art model, YOLOv11n, by 3.2% in mAP50-95, reduces detection time by 27.2 ms, decreases the number of parameters by 0.6 M, and cuts floating-point operations by 1.8 B. Finally, testing YOLOFLY in real-world environments also yielded the best results, including a 3.75% reduction in missed detections at high speeds. These findings validate the superiority and effectiveness of YOLOFLY.
Supervised Face Tampering Detection Based on Spatial Channel Attention Mechanism
Authors: Wang Xinyi , Song Wanru , Hao Chuanyan , Liu Sijiang , Liu Feng
Published: January 2025

Face images hold exceptional significance in contemporary society, serving as direct identifiers due to their rich personal attributes, enhancing daily life and work efficiency. However, advancements in deep learning and image processing have led to the proliferation of sophisticated face forgery software, rendering detection increasingly challenging. We propose a novel face tampering detection method utilizing a spatial attention-enhanced bidirectional convolutional neural network to address this. This approach synergizes the strengths of dense convolutional and depthwise separable networks for superior image feature extraction, thereby improving the accuracy of authentic and manipulated face detection. Furthermore, the network is trained to initially localize tampered regions within face images by integrating a spatial channel-based attention module as supervisory input. On three widely used public face forgery datasets, our method achieves an AUC of no less than 96.45%. The experimental results validate the effectiveness of our method in accurately detecting and initially localizing face tampering.
Exploring the Feasibility of Head-Tracking Data for Cybersickness Prediction in Virtual Reality
Authors: Ramaseri-Chandra Ananth N. , Reza Hassan , Pothana Prasad
Published: January 2025

Cybersickness remains a significant barrier to the widespread adoption of virtual reality (VR) technology. Traditional methods for predicting cybersickness rely on self-reported questionnaires or physiological signals from specialized sensors, which have their limitations. This study explores the potential of using real-time, easily acquired head-tracking data (HTD) from standard VR headsets as a scalable alternative for estimating cybersickness. Twelve participants engaged in a VR session using an Oculus Quest 2 headset while their HTD were recorded. Kinematic metrics such as linear and angular velocity, acceleration, and jerk were computed from the HTD, including positional and angular parameters. Participants’ cybersickness levels were assessed using the Virtual Reality Sickness Questionnaire. While exploratory data analysis revealed no significant direct correlation between individual kinematic variables and cybersickness scores, machine learning models were employed to identify predictive patterns. Subsequently, four regression models, including Random Forest, Gradient Boosting, K-Nearest Neighbors, and Support Vector Machines, were trained and evaluated using the computed kinematic features to predict the cybersickness score. Among these, the Gradient Boosting model demonstrated superior performance, accurately predicting cybersickness scores with normalized differences less than 3.08% on unseen data. This approach offers a scalable and practical solution for real-time cybersickness prediction in VR applications and compliments other techniques that rely on physiological sensors, hardware, or user profiles.
An Efficient and Lightweight Surface Defect Detection Method for Micro-Motor Commutators in Complex Industrial Scenarios Based on the CLS-YOLO Network
Authors: Chen Qipeng , Xiong Qiaoqiao , Huang Haisong , Tang Saihong
Published: January 2025

Existing surface defect detection methods for micro-motor commutators suffer from low detection accuracy, poor real-time performance, and high false detection and missed detection rates for small targets. To address these issues, this paper proposes a high-performance and robust commutator surface defect detection model (CLS-YOLO), using YOLOv11-n as the baseline model. First, a lightweight Cross-Scale Feature Fusion Module (CCFM) is introduced to integrate features from different scales, enhancing the model’s adaptability to scale variations and ability to detect small objects. This approach reduces model parameters and improves detection speed without compromising detection accuracy. Second, a Large Separable Kernel Attention (LSKA) module is incorporated into the detection head to strengthen feature understanding and capture, reducing interference from complex surface patterns on the commutator and significantly improving adaptability to various target types. Finally, to address issues related to the center point location, aspect ratio, angle, and sample imbalance in bounding boxes, SIoU Loss replaces the CIoU Loss in the original network, overcoming limitations of the original loss function and enhancing overall detection performance. Model performance was evaluated and compared on a commutator surface defect detection dataset, with additional experiments designed to verify the model’s effectiveness and feasibility. Experimental results show that, compared to YOLOv11-n, the CLS-YOLO model achieves a 2.08% improvement in mAP@0.5. This demonstrates that CLS-YOLO can accurately detect large defect targets while maintaining accuracy for tiny defects. Additionally, CLS-YOLO outperforms most YOLO-series models, including YOLOv8-n and YOLOv10-n. The model’s parameter count is only 1.860 million, lower than YOLOv11-n, with a detection speed increase of 8.34%, making it suitable for deployment on resource-limited terminal devices in complex industrial scenarios.
Readout System for Multipurpose Real-Time and Portable Spectrometer
Authors: Real Diego , Ballester Jose , Calvo David , Manzaneda Mario , Moreno Alberto , Albiol Francisco , Alonso Luis
Published: January 2025

A ready-to-use spectrometer-based product, which focuses on data acquisition using a BeagleBone board and a Hamamatsu C12666MA spectrometer module, is presented. The device meets stringent requirements, including the ability to measure the visible light spectrum over a wide range of intensities, being compact and lightweight, and having customizable electronics to suit different application needs. The system’s primary component is a Hamamatsu C12666MA spectrometer module with a measurement range of 341 nm to 780 nm, which is supplemented by supporting electronics such as a microcontroller and an analog-to-digital converter. The development encompasses hardware design, the fabrication of a control board, and software development for spectral acquisition and visualization. The software controls the spectral measurement process and facilitates data processing and analysis. The results demonstrate that the designed system can accurately capture spectra and fulfill the specified requirements. Additionally, this work investigates and evaluates the potential migration of the data acquisition system to Field-Programmable Gate Array technology. Such a migration offers several advantages, including real-time processing, parallel data handling capabilities, reduced latency, and greater flexibility in adapting to various spectrometer configurations, as well as the possibility to work in a synchronized way with other devices. These improvements would significantly expand the system’s potential applications in real-time spectroscopy and other demanding optical measurement tasks. The proposed system thus provides a foundation for future enhancements, which could exploit Field-Programmable Gate Array technology, potentially revolutionizing the efficiency and application scope of portable spectrometry devices.
Design of a Current-Mode Trapezoidal Waveform Generator in High-Voltage SOI Technology with Modifications Based on Safe Operating Area Limits
Authors: Jankowski Mariusz
Published: January 2025

Integrated circuits are the core building components of virtually all communication systems. Wireless communication systems are becoming increasingly common. They require specialized transmission components to reduce electromagnetic interference. This paper presents the design of a trapezoidal waveform generator intended for generation of waveforms with limited level and spectrum of radiated interference This limitation is important because the discussed circuit is a high-voltage function block that can drive the output antenna with relatively high-power pulses. The introduced design is based on a mix of low- and high-voltage devices; however, most of them operate in low-voltage steady and near steady conditions. The implemented design flow includes safe operating area controls, which result in the implementation of a set of overvoltage devices. The designed generator provides means of frequency and slew rate control and can produce high-quality output waveforms. The results show that this type of design can be further optimized for generating waveforms with a limited range of slew rate values. Moreover, this paper presents some operational aspects and phenomena that must be addressed to provide a design that can be practically implemented in modern high-voltage integrated circuits.
An Intelligent Multi-Criteria Decision Approach for Selecting the Optimal Operating System for Educational Environments
Authors: Marinović Minja , Viduka Dejan , Lavrnić Igor , Stojčetović Bojan , Skulić Aleksandar , Bašić Ana , Balaban Petra , Rastovac Dragan
Published: January 2025

The selection of an appropriate operating system (OS) in educational environments is a critical decision that impacts the functionality, user experience, and overall efficiency. Financial factors, along with the availability and functionality of tools used on these systems, play a crucial role in this selection. Furthermore, the OS affects the user experience, security, and adaptability to learning. Previous research in this area is scarce, and this paper contributes to a better understanding of the OS selection process in education. This paper proposes a novel multi-criteria decision-making approach to evaluate and select the most suitable OS for educational institutions. The methodology integrates the PIPRECIA method for assessing weighted criteria, alongside utility analysis (NWA), enabling a balanced decision-making process between institutional management and IT experts. The evaluation considered the following criteria: performance, cost, security, usability, implementation, support, and documentation. The study compared three popular operating systems: Microsoft Windows 11, GNU Linux Ubuntu 22.04 LTS, and Apple macOS 12 using the proposed approach. The results, based on the integrated evaluation of all criteria, indicate that GNU Linux Ubuntu 22.04 LTS (0.562) ranked highest, followed by Microsoft Windows 11 (0.553) and Apple macOS 12 (0.543). This paper emphasizes the importance of objective, group-based decision-making in selecting an OS for education, providing practical insights and guidelines for effective technology integration in academic settings.
Towards a Unified Temporal and Event Logic Paradigm for Multi-Hop Path Reasoning in Knowledge Graphs
Authors: Zeng Yajian , Hou Xiaorong , Wang Xinrui , Li Junying
Published: January 2025

Path reasoning in knowledge graphs is a pivotal task for uncovering complex relational patterns and facilitating advanced inference processes. It also holds significant potential in domains such as power electronics, where real-time reasoning over dynamic, evolving data is essential for advancing topology design and application systems. Despite its importance, traditional approaches often encounter substantial limitations when applied to dynamic, time-sensitive scenarios. These models typically fail to adequately capture intricate logical dependencies and demonstrate suboptimal performance in data-constrained environments. To address these challenges, we introduce Path-Reasoning Logic (PRlogic), an innovative framework that seamlessly integrates rule-based logical reasoning with cutting-edge neural network methodologies. PRlogic enhances path inference by leveraging a context-aware logical association network adept at handling temporal and event-driven attributes, enabling improved reasoning for dynamic systems such as IoT-based power electronics and smart grids. This adaptability allows the framework to better accommodate evolving knowledge structures, significantly improving reasoning accuracy under resource-scarce conditions. Furthermore, PRlogic employs a multi-stage refinement strategy, harmonizing logic-based rules with learned contextual representations to achieve heightened robustness and scalability. Comprehensive experiments on widely-recognized benchmark datasets validate the superiority of PRlogic, demonstrating its consistent outperformance of existing models in path reasoning tasks. These results underscore the efficacy of incorporating logic-driven mechanisms into knowledge graph reasoning and highlight PRlogic’s potential as a powerful solution for applications in dynamic data environments.
Development of New Composite Beds for Enhancing the Heat Transfer in Adsorption Cooling Systems
Authors: Mika Łukasz , Bujok Tomasz , Sztekler Karol , Kalawa Wojciech , Radomska Ewelina , Mlonka-Mędrala Agata , Čespiva Jakub , Boruta Piotr
Published: January 2025

Adsorption chillers are distinguished by their low electricity consumption, lack of moving parts and exceptional reliability. However, their considerable weight, due to the low sorption capacity of conventional adsorbents, remains a key limitation. This study investigates the effect of introducing thermally conductive additives—aluminium powder, copper powder and graphite flakes—at 5, 15 and 25 wt.% to silica-gel-based adsorbent beds on the enhancement of heat transfer. In contrast to other works, this study also includes a novel analysis of the thermal properties of dry sorbents, since the moisture content affects the thermal conductivity. Additives improve the thermal conductivity, as measured by the laser flash method (LFA), of the bed by up to 20.7% while maintaining a reasonable sorption capacity, as measured by the dynamic vapor sorption (DVS). Additions of copper at 5–15 wt.% and graphite flakes at 15–25 wt.% provide an optimal compromise between thermal conductivity and sorption capacity. Aluminium powder, on the other hand, offers flexibility over a wider range (5–25 wt.%). The increased thermal conductivity of these modified materials is expected to lead to more efficient heat transport, which suggests the hypothesis that it could reduce the cycle time and increase the efficiency of adsorption chillers.
A Predictive Model for Traffic Noise Reduction Effects of Street Green Spaces with Variable Widths of Coniferous Vegetation
Authors: Meng Qi , Evgrafova Olga , Li Mengmeng
Published: January 2025

Street green spaces can effectively attenuate traffic noise, but the crucial role of coniferous trees and shrubs in the reduction in green space noise has not been systematically explored. Therefore, in this study, the aim was to determine the mechanism of the influence of plant morphological characteristics and planting forms on the noise reduction effect using field measurements of the noise reduction effect of 36 street green spaces planted with coniferous trees and shrubs. It was found that for the same width of street green spaces, the noise reduction effects of planting single and multiple trees were significantly different, and this difference increased with an increase in street green space width. The noise reduction effect of planting low shrubs in street green spaces was significantly different from that of planting common shrubs of the same width, and their difference increased with an increase in the street green space width. The factors that significantly affected the noise reduction effect of the 5 m wide street green space were tree height, crown width, and DBH, and all of them were positively correlated. In addition, the noise reduction effect of the street green space planted with conifers was affected by the road and pavement widths. Finally, in this study, a stepwise regression model was constructed for the noise reduction effect of street green spaces based on plant morphological parameters, planting methods, and physical characteristics of the road to quantify the crucial role of each factor in the noise reduction effect of street green spaces. The results of this study can provide plant noise reduction strategies for urban landscape planning and design to create a healthy urban acoustic environment.
Examining the Drivers and Economic and Social Impacts of Cryptocurrency Adoption
Authors: Guo Yongsheng , Yousef Ezaddin , Naseer Mirza Muhammad
Published: January 2025

This study investigates the key drivers and the economic and social impacts of cryptocurrency adoption. Based on panel data across 37 countries from 2020 to 2023, this research examines the interplay between cryptocurrency adoption and technology development, monetary policies, and economic and social development. Employing a mixed-methods approach, the research incorporates panel data analysis across multiple countries to explore correlations and causal relationships between these variables. The study found that technology development, measured by the Network Readiness Index (NRI) enables cryptocurrency adoption. Economic conditions measured by higher national inflation rates and monetary policy indicators, including lower interest and exchange rates are the key drivers for cryptocurrency adoption. The empirical findings reveal that cryptocurrency adoption has negative relationships with economic development measured by the GDP growth rate, unemployment rate, and social development represented by the governance quality corruption index. It implies that cryptocurrency is used as a virtual anchor (digital gold) for national inflation. Findings reveal how network readiness, economic conditions, and monetary policies contribute to fostering cryptocurrency adoption, while resulting in impacts on economic growth, labour markets, and governance. The research contributes to the literature by integrating technological, economic, and governance perspectives to elucidate the role of cryptocurrency in reshaping the global economic and social systems.
Fuel Resistance of Firefighting Surfactant Foam Formulations
Authors: Ateş Ayşenur , Qiao Rui , Lattimer Brian Y.
Published: January 2025

Aqueous film-forming foam (AFFF) is widely recognized for its excellent fire-extinguishing capabilities, yet the specific roles of its components remain insufficiently understood. AFFF typically consists of fluorocarbon and hydrocarbon surfactants, as well as organic solvents such as diethylene glycol butyl ether (DGBE), which can significantly influence foam performance. This study investigates the effects of surfactant mixtures and the DGBE additive on foam stability and fuel resistance at room temperature and ambient humidity. Static foam ignition experiments were conducted to assess fuel transport through foams using various hydrocarbon fuels, including n-octane, iso-octane, n-heptane, methylcyclohexane, methylcyclopentane, and a mixture of 25% trimethylbenzene with 75% n-heptane. Methylcyclopentane, with its higher vapor pressure and solubility, led to the shortest ignition times, indicating faster fuel transport. The addition of DGBE increased ignition times by a factor of 1.2 to 3.7 for individual surfactants, while the Capstone+Glucopon mixture improved ignition times by a factor of 2.4 to 5.5 compared to the individual surfactants. Further enhancement was observed with Capstone+Glucopon+DGBE, increasing ignition times by a factor of 3 to 7.3 compared to the individual surfactants. Additionally, combining DGBE with surfactant mixtures reduced fuel concentration in the bulk solution by over 60% compared to individual surfactants, significantly enhancing fuel resistance. Interface experiments showed that fuel presence, particularly methylcyclopentane and n-octane, altered the foam structure and accelerated drainage at the foam/fuel interface, impacting foam stability and fuel transport. These findings demonstrate that surfactant mixtures and DGBE-enhanced formulations substantially improve foam stability and fuel resistance.
A Simulation Analysis of the Coverage and Demand Suitability of the Firefighting Capacity in Complex Commercial Areas
Authors: Xie Wenhan , Jiang Yongqing , Wang Bo , Sun Chao , Yu Peilun , Xie Yanqi
Published: January 2025

The initial firefighting capacity of complex commercial areas depends on the service level and the location of micro firefighting facilities. In response to the issue of coverage by micro firefighting facilities in complex commercial areas, a graded progressive coverage model is established. This model includes distance-progressive coverage and firefighting service level attenuation coverage. The former integrates fairness and efficiency in fire planning, while the latter considers the adaptability between the demand points and the fire service levels. The objectives include maximizing the coverage matching degree, effective coverage rate, medium- and high-risk coverage rate, and overall coverage rate of the fire service point and the demand points. A genetic algorithm is designed to solve the model, where the construction level and the number of micro fire stations are varied to analyze changes in various indicators. Central Street, characterized by complex buildings and high pedestrian traffic, is selected as a case for the experiment. The results show that simply adjusting the construction level of micro fire stations increases the effective coverage rate by 2.5%. The graded progressive coverage model shows a turning point in the effective coverage rate and the overall coverage rate when the number of new micro fire stations is 14 or 10, with coverage rates of 76.1% and 93.2%, respectively. The maximum progressive coverage model shows a turning point in the overall coverage rate when 9 new stations are added, which is 10.5% lower than that of the graded coverage model; when 10 stations are added, the overall coverage rate is 11.2% lower. These findings demonstrate the effectiveness of the graded progressive coverage model.