bluuebunny/crossref_metadata_2025 huggingface dataset:
Integration of the Adaptive Approach in HVAC System Operation: A Case Study
Authors: Aparicio-Ruiz Pablo , Ragel-Bonilla J. C. , Barbadilla-Martín Elena , Guadix José
Published: January 2025

Although different investigations have been carried out on the analysis of adaptive thermal comfort in naturally ventilated buildings, fewer have focused on mixed mode operation. Moreover, there is limited research as for the implementation of adaptive comfort models into the control system of buildings. Therefore, this paper investigates how the application of a setpoint based on adaptive comfort control (ACC) would affect occupants’ comfort considering mixed mode operation and based on the results of a longitudinal field study in an academic office building of a tertiary educational institution in southern Spain. The manuscript analyses the Thermal Preference Vote over 12 months in a mixed mode room with an HVAC system whose setpoint is adjusted with a previously calculated adaptive algorithm for the building. For that, a thorough analysis was conducted in which users identified situations regarding thermal comfort and the operation of the conditioning system was collected. The results indicate that it is possible to develop adaptive comfort models that ensure the thermal well-being of occupants. Moreover, this study highlights the need for further research to assess the implications of ACC in terms of comfort and energy consumption as well as addressing the future improvements and the limitations of the work carried out.
A Novel Data Obfuscation Framework Integrating Probability Density and Information Entropy for Privacy Preservation
Authors: Cheng Haolan , Qiang Chenyi , Cong Lin , Xiao Jingze , Liu Shiya , Zhou Xingyu , Wang Huijun , Ruan Mingzhuo , Lv Chunli
Published: January 2025

Data privacy protection is increasingly critical in fields like healthcare and finance, yet existing methods, such as Fully Homomorphic Encryption (FHE), differential privacy (DP), and federated learning (FL), face limitations like high computational complexity, noise interference, and communication overhead. This paper proposes a novel data obfuscation method based on probability density and information entropy, leveraging a probability density extraction module for global data distribution modeling and an information entropy fusion module for dynamically adjusting the obfuscation intensity. In medical image classification, the method achieved precision, recall, and accuracy of 0.93, 0.89, and 0.91, respectively, with a throughput of 57 FPS, significantly outperforming FHE (0.82, 23 FPS) and DP (0.84, 25 FPS). Similarly, in financial prediction tasks, it achieved precision, recall, and accuracy of 0.95, 0.91, and 0.93, with a throughput of 54 FPS, surpassing traditional approaches. These results highlight the method’s ability to balance privacy protection and task performance effectively, offering a robust solution for advancing privacy-preserving technologies.
Enhancing News Articles: Automatic SEO Linked Data Injection for Semantic Web Integration
Authors: Salem Hamza , Salloum Hadi , Orabi Osama , Sabbagh Kamil , Mazzara Manuel
Published: January 2025

This paper presents a novel solution aimed at enhancing news web pages for seamless integration into the Semantic Web. By utilizing advanced pattern mining techniques alongside OpenAI’s GPT-3, we rewrite news articles to improve their readability and accessibility for Google News aggregators. Our approach is characterized by its methodological rigour and is evaluated through quantitative metrics, validated using Google’s Rich Results Test API to confirm adherence to Google’s structured data guidelines. In this process, a “Pass” in the Rich Results Test is taken as an indication of eligibility for rich results, demonstrating the effectiveness of our generated structured data. The impact of our work is threefold: it advances the technological integration of a substantial segment of the web into the Semantic Web, promotes the adoption of Semantic Web technologies within the news sector, and significantly enhances the discoverability of news articles in aggregator platforms. Furthermore, our solution facilitates the broader dissemination of news content to diverse audiences. This submission introduces an innovative solution substantiated by empirical evidence of its impact and methodological soundness, thereby making a significant contribution to the field of Semantic Web research, particularly in the context of news and media articles.
Cybersecurity Requirements for Industrial Machine Control Systems
Authors: Kasprzyczak Leszek , Manowska Anna , Dźwiarek Marek
Published: January 2025

The first part of this paper discusses the research context, taking a closer look at the development of Industry 4.0 and the growing importance of the IIoT, which entails new cybersecurity challenges. The issue of cyber threats and the need to increase the level of protection in machine control systems, which are particularly vulnerable to attacks due to their connection to the network, is also presented. The Introduction concludes with a presentation of the article’s objective, which is to analyze the requirements of security levels (SLs) and the implementation of relevant international standards. The next section reviews the current research on cybersecurity in machine control systems. This section also points out the research gaps that the article aims to fill. The next section presents the risk assessment used to ensure safety during machine operations based on ISO 12100. The article describes safety functions implemented in machine control systems, including the SIL (safety integrity level) and PL (performance level) specifications. An important part of the article is the creation of a relationship between PL and SL, showing how the safety functions of systems are related to protection against cyber threats. The last part of the article gives a case study in the form of examples of machines and their control systems performing safety functions, which require various SLs depending on the PLs.
Adaptive Impedance Control of a Human–Robotic System Based on Motion Intention Estimation and Output Constraints
Authors: Ma Junjie , Chen Hongjun , Liu Xinglan , Yang Yong , Huang Deqing
Published: January 2025

The rehabilitation exoskeleton represents a typical human–robot system featuring complex nonlinear dynamics. This paper is devoted to proposing an adaptive impedance control strategy for a rehabilitation exoskelton. The patient’s motion intention is estimated online by the neural network (NN) to cope with the intervention of the patient’s subjective motor awareness in the late stage of rehabilitation training. Due to the differences in impedance parameters for training tasks in individual patients and periods, the least square method was used to learn the impedance parameters of the patient. Considering the uncertainties of the exoskeleton and the safety of rehabilitation training, an adaptive neural network impedance controller with output constraints was designed. The NN was applied to approximate the unknown dynamics and the barrier Lyapunov function was applied to prevent the system from violating the output rules. The feasibility and effectiveness of the proposed strategy were verified by simulation.
Patterns and Practices in the Use of Endodontic Materials: Insights from Romanian Dental Practices
Authors: Marian Diana , Popovici Ramona Amina , Olariu Iustin , Pitic (Cot) Dana Emanuela , Marta Maria-Monica , Veja (Ilyes) Ioana
Published: January 2025

The success of endodontic treatment depends on the correct use of materials during the cleaning and filling of the root canal system. The field of endodontics is constantly evolving with the introduction of new procedures and materials. Despite the continuous development of a wide range of chemical solutions and the introduction of new materials in endodontics, driven by the advances in state-of-the-art technologies, there is still a lack of data on how these advances are adapted to the daily practice of Romanian dentists in this field. The aim of this cross-sectional questionnaire-based study was the evaluation of current trends in endodontic practice, focusing on the materials used by dentists throughout Romania, performed by a number of dentists who graduated from universities in the Northern, Southern, Eastern, Western, and Central zones of Romania. The questions were about the irrigants used, the interim medication, the type of sealant, the filling technique, and the number of endodontic treatment sessions. The majority of participants in the research were general practitioners and endodontists, with some dentists of different specialties performing endodontic treatments in their practices. Statistical analyses were performed using DATAtab version 2024 software. The results obtained from this study provide a valuable resource and database for researchers to access a wide range of information and an apparent trend towards high-performance endodontic materials used in Romania.
Combination of Phase Change Composite Material and Liquid-Cooled Plate Prevents Thermal Runaway Propagation of High-Specific-Energy Battery
Authors: Ji Weigao , Dang Yongchun , Yu Yongchao , Zhou Xunli , Li Lei
Published: January 2025

Ternary lithium-ion batteries (LIBs) have the advantages of high energy density and high charging efficiency, and they are the preferred energy source for long-life new energy vehicles. However, when thermal runaway (TR) occurs in the ternary LIB, an open flame is easily produced. The burning phenomenon is intense, and the rapid of TR propagation is high; consequently, vehicle-level fire accidents are easily induced. These accidents have become the biggest obstacle restricting the batteries’ development. Therefore, this study investigates the TR behavior of ternary LIBs at the cell and module levels. The addition of an insulation layer alone, including ceramic nano fibers, glass fiber aerogel, and phase-change composite materials, cannot prevent TR propagation. To completely block the TR propagation, we developed a safety prevention strategy, combining the phase-change composite materials with a commercial liquid cooling plate. This approach provides a three-level TR protection mechanism that includes heat absorption, heat conduction, and heat insulation. The use of a 2 mm thick phase change composite material combined with a liquid cooling plate effectively prevents the TR propagation between60 Ah ternary LIBs with 100%SOCs.. The front surface temperature of the adjacent cell is maintained near 90 °C, with its maximum temperature consistently stays below 100 °C. This study successfully demonstrates the blockage of TR propagation and offers valuable insights for the thermal safety design of high-specific-energy LIBs; the aim is to improve the overall safety of battery packs in practical applications.
Parameter Study and Engineering Verification of the Hardening Soil Model with Small-Strain Stiffness for Loess in the Xi’an Area
Authors: Hu Jiayuan , Du Qinwen
Published: January 2025

With the advancement of the construction of urban underground spaces, it is inevitable that new tunnels will pass through existing pipelines. To ensure the safety and stability of these pipelines, it is essential to strictly control the impact of shield tunneling. The hardening soil model with small-strain stiffness (HSS) comprehensively accounts for the small-strain behavior of soil, and the calculated results are closer to the values measured in engineering compared to those of other models. Consequently, it has been widely adopted in the development and utilization of underground spaces. The selection of parameters for the HSS model is particularly critical when performing numerical simulations. This article establishes the proportional relationships between the small-strain moduli of the HSS model in the loess region of Xi’an through standard consolidation tests, triaxial consolidation drained shear tests, and triaxial consolidation drained loading−unloading shear tests. Additionally, an empirical formula for the static lateral pressure coefficient applicable to loess was derived and validated through engineering examples.
Cluster Set vs. Traditional Set in Plyometric Training: Effect on the Athletic Performance of Youth Football Players
Authors: Öztürk Barışcan , Adıgüzel Niyazi Sıdkı , Koç Murat , Karaçam Aydın , Canlı Umut , Engin Hakan , Orhan Bekir Erhan , Bartik Peter , Sagat Peter , Pérez Jason , Isip Maria , Prieto-González Pablo
Published: January 2025

Aim: This study evaluated the effects of plyometric training with different set configurations on sprint speed, change of direction (COD), jump performance, and perceived exertion in youth football players. Method: Twenty-four U-19 players were randomized into three groups: Cluster Set (CLS, n = 8), Traditional Set (TRD, n = 8), and Control (CON, n = 8). CLS performed 8–10 sets of 2–3 repetitions, while TRD completed 2–3 sets of 8–10 repetitions in an 8-week plyometric program (2 sessions/week). The CON group did not train. Performance measures included 10 m, 20 m, and 30 m sprints, COD, Counter Movement Jump (CMJ), Reactive Strength Index (RSI), and Rate of Perceived Exertion (RPE). A repeated measures ANOVA analyzed group*time interactions. Results: Significant improvements were observed in CLS and TRD groups for sprints (10 m: f = 21.44; 20 m: f = 19.40; 30 m: f = 49.56; p < 0.001), COD (f = 14.66; p < 0.001), CMJ (f = 51.50; p < 0.001), and RSI (f = 24.91; p < 0.001). No changes occurred in CON (p > 0.05). Conclusions: CLS and TRD plyometric training improved sprint speed, COD, and jump performance, with CLS showing slightly superior results and better fatigue management.
Research on the Spiral Rolling Gait of High-Voltage Power Line Serpentine Robots Based on Improved Hopf-CPGs Model
Authors: Yang Zhiyong , Fang Zhen , Yang Shengze , Xiong Yuhong , Zhang Daode
Published: January 2025

The efficiency of helical locomotion in snake-like robots along high-voltage transmission lines is often hindered by low motion efficiency, high joint signal noise, and challenges in traversing obstacles. This study aims to address these issues by proposing a gait generation method that leverages a standardized Central Pattern Generator (CPG). We modify the traditional Hopf-CPG model by incorporating constraint functions and a frequency-tuning mechanism to regulate the oscillator, which allows for the generation of asymmetric waveform signals for deflection joints and facilitates rapid convergence. The method begins by determining initial and obstacle-crossing state parameters, such as deflection angles and helical radii of the snake-like robot, using the backbone curve method and the Frenet–Serret framework. Subsequently, a CPG neural network is constructed based on Hopf oscillators, with a limit cycle convergent speed adjustment factor and amplitude bias signals to establish a fully connected matrix model for calculating multi-joint output signals. Simulation analysis using Simulink–CoppeliaSim evaluates the robot’s obstacle-crossing ability and the optimization of deflection joint signal noise. The results indicate a 55.70% increase in the robot’s average speed during cable traversal, a 57.53% reduction in deflection joint noise disturbance, and successful crossing of the vibration damper. This gait generation method significantly enhances locomotion efficiency and noise suppression in snake-like robots, offering substantial advantages over traditional approaches.
Research on Negative Road Obstacle Detection Based on Multimodal Feature Enhancement and Fusion
Authors: Huo Guanglei , Cao Chuqing , Li Yaxin , Lin Wenwei , Zhang Chentao
Published: January 2025

To address the issues of low recognition rates and poor detection accuracy for road negative obstacles caused by insufficient feature representation, we propose a novel detection framework: the Negative Road Obstacles Segmentation Network (NROSegNet). The detection performance of the algorithm is improved through a data enhancement strategy based on feature splicing and an adaptive feature enhancement module. Specifically, the data augmentation strategy introduces negative obstacle features into other datasets through geometric transformations and random splicing, effectively increasing the diversity of training data. This can solve the problem of an uneven distribution of data features while improving the performance of the model in capturing illumination changes and local details. The framework further adopts a dynamic multi-scale feature enhancement module to improve the perception of local details and global semantics. A robust multimodal data fusion mechanism and edge-aware optimization strategy are designed to effectively alleviate the problems of noise interference and boundary blur. The experimental results show that the NROSegNet proposed in this paper achieves 70.6 and 83.0 in mIoU and mF1, respectively, which is 2.8 percentage points and 2.9 percentage points higher than other methods. The results fully demonstrate its excellent performance in precise segmentation and boundary detail processing.
E2-VINS: An Event-Enhanced Visual–Inertial SLAM Scheme for Dynamic Environments
Authors: Huang Jiafeng , Zhao Shengjie , Zhang Lin
Published: January 2025

Simultaneous Localization and Mapping (SLAM) technology has garnered significant interest in the robotic vision community over the past few decades. The rapid development of SLAM technology has resulted in its widespread application across various fields, including autonomous driving, robot navigation, and virtual reality. Although SLAM, especially Visual–Inertial SLAM (VI-SLAM), has made substantial progress, most classic algorithms in this field are designed based on the assumption that the observed scene is static. In complex real-world environments, the presence of dynamic objects such as pedestrians and vehicles can seriously affect the robustness and accuracy of such systems. Event cameras, which use recently introduced motion-sensitive biomimetic sensors, efficiently capture scene changes (referred to as “events”) with high temporal resolution, offering new opportunities to enhance VI-SLAM performance in dynamic environments. Integrating this kind of innovative sensor, we propose the first event-enhanced Visual–Inertial SLAM framework specifically designed for dynamic environments, termed E2-VINS. Specifically, the system uses visual–inertial alignment strategy to estimate IMU biases and correct IMU measurements. The calibrated IMU measurements are used to assist in motion compensation, achieving spatiotemporal alignment of events. The event-based dynamicity metrics, which measure the dynamicity of each pixel, are then generated on these aligned events. Based on these metrics, the visual residual terms of different pixels are adaptively assigned weights, namely, dynamicity weights. Subsequently, E2-VINS jointly and alternately optimizes the system state (camera poses and map points) and dynamicity weights, effectively filtering out dynamic features through a soft-threshold mechanism. Our scheme enhances the robustness of classic VI-SLAM against dynamic features, which significantly enhances VI-SLAM performance in dynamic environments, resulting in an average improvement of 1.884% in the mean position error compared to state-of-the-art methods. The superior performance of E2-VINS is validated through both qualitative and quantitative experimental results. To ensure that our results are fully reproducible, all the relevant data and codes have been released.
Nutritional Potential and Low Heavy Metals Content of Oryctes monoceros (Olivier, 1789) and Rhynchophorus phoenicis (Fabricius, 1801) Adults, Two Coleopteran Species Consumed in Togo
Authors: Badanaro Fègbawè , Barsan Narcis , Mélila Mamatchi , Ouro-Sama Kamilou , Améyran Koami , Nedeff Florin-Marian , Irimia Oana , Nedeff Valentin
Published: January 2025

Oryctes monoceros (Olivier, 1789) (Scarabaeidae) and Rhynchophorus phoenicis (Fabricius, 1801) (Curculionidae) are two insects generally known as formidable pests of oil palms and coconuts trees. Although little known, different developmental stages of these insects are consumed. The aim of this study is to determine the composition of these adult Coleopteran species in order to promote their consumption as a strategy for enhancing food security. Chemical analyses were carried out on adults of both species. Samples of O. monoceros and R. phoenicis were collected in three localities in Togo. The ash, protein, vitamin, and lipid contents were determined according to the AOAC reference methods. The fiber contents were obtained by the method of Weende. The minerals and heavy metals were analyzed by atomic absorption spectrophotometry and colorimetry. Fatty acid composition was determined by gas chromatography. The results showed the average protein content ranges from 44.32 ± 0.83 to 45.89 ± 0.83%. The lipid level is between 15.06 ± 0.28% and 14.64 ± 0.54. Their lipids contain unsaturated fatty acids, notably oleic (40.84 ± 0.112 vs. 40.84 ± 0.11%), linoleic (4.49 ± 0.00 vs. 5.07 ± 0.02%), and α-linolenic (5.07 ± 0.02 vs. 6.35 ± 0.01%) acid. They are excellent sources of minerals and vitamins. They are also free of heavy metals. These species could, therefore, contribute to the nutritional balance of consumers. They deserve to be better promoted for human consumption, as they could make a significant contribution to the fight against malnutrition and constitute a novel food source.
Related Keyframe Optimization Gaussian–Simultaneous Localization and Mapping: A 3D Gaussian Splatting-Based Simultaneous Localization and Mapping with Related Keyframe Optimization
Authors: Ma Xiasheng , Song Ci , Ji Yimin , Zhong Shanlin
Published: January 2025

Simultaneous localization and mapping (SLAM) is the basis for intelligent robots to explore the world. As a promising method for 3D reconstruction, 3D Gaussian splatting (3DGS) integrated with SLAM systems has shown significant potential. However, due to environmental uncertainties, errors in the tracking process with 3D Gaussians can negatively impact SLAM systems. This paper introduces a novel dense RGB-D SLAM system based on 3DGS that refines Gaussians through sub-Gaussians in the camera coordinate system. Additionally, we propose an algorithm to select keyframes closely related to the current frame, optimizing the scene map and pose of the current keyframe. This approach effectively enhances both the tracking and mapping performance. Experiments on high-quality synthetic scenes (Replica dataset) and low-quality real-world scenes (TUM-RGBD and ScanNet datasets) demonstrate that our system achieves competitive performance in tracking and mapping.
Tibetan Judicial Event Extraction Based on Deep Word Representation and Hybrid Neural Networks
Authors: Gao Lu , Zhao Xiaobing
Published: January 2025

For the extraction of judicial events for Tibetan, a low-resource language, traditional simple neural network approaches struggle to adequately capture the deep semantics and features of the texts because Tibetan texts are usually lengthy and contain numerous judicial-related entities. To overcome this limitation, this research presents an event extraction model combining deep word representation with hybrid neural networks for the Tibetan judicial domain. The model introduces the Chinese minority pre-trained language model (CINO), which generates dynamic word vector representations, addressing the challenge of modeling the deep semantics inherent in Tibetan texts. During feature extraction, a bidirectional long short-term memory network (BiLSTM) is applied to extract the temporal and contextual dependencies, while a convolutional neural network (CNN) is utilized to capture the local semantic features to construct a comprehensive global semantic representation. Finally, the sequences are decoded through conditional random field (CRF) to generate optimal prediction results, thus achieving the efficient extraction of Tibetan judicial events. The experimental findings indicate that the model outperforms the baselines by achieving F1 scores of 70.47% for trigger detection and 62.99% for argument recognition, with improvements of 16.6% and 16.42%, respectively. These results confirm the effectiveness and superiority of the proposed model.
Evaluating Changes in the VOC Profile of Different Types of Food Products After Electron Beam Irradiation
Authors: Oprunenko Anastasia , Bolotnik Timofey , Ikhalaynen Yuri , Ipatova Victoria , Bliznyuk Ulyana , Borshchegovskaya Polina , Yurov Dmitry , Bolotnik Nadezhda , Kozlova Elena , Chernyaev Alexander , Ananieva Irina , Rodin Igor
Published: January 2025

During the development of food radiation processing protocols, one of the aims is to find an optimal dose range for a specific type of product in which pathogenic microflora are inhibited while biochemical and organoleptic properties are not disturbed. When various food products are exposed to ionizing radiation, volatile organic compounds (VOCs) are formed. Depending on the radiation dose, the list of VOCs and their content change, so they could be considered marker compounds for the description of irradiation-related processes. This work proposes a universal way to study and compare the profile of volatile compounds in products of animal and plant origin using GC-MS in combination with various data representation techniques, including unsupervised machine learning methods. The VOC profiles of beef, chicken, turkey, fish, and potatoes were examined.
Environmentally Friendly Green O-Alkylation Reaction for Ethenzamide Synthesis
Authors: Niedziejko-Ćwiertnia Paulina , Drabczyk Anna Karolina , Kułaga Damian , Podobińska Patrycja , Bachowski Wojciech , Zeńczak-Tomera Kamila , Michorczyk Piotr , Sheng Ruilong , Jaśkowska Jolanta
Published: January 2025

Ethenzamide (2-ethoxybenzamide), besides acetylsalicylic acid, is one of the mostly used salicylic acid derivatives in pharmaceuticals. It has analgesic and anti-inflammatory effects that originate from the inhibition of cyclooxygenase (COX-1) activity, thus blocking prostaglandin synthesis. In this work, efficient and eco-friendly methods were developed for the synthesis of ethenzamide via the O-alkylation reaction of salicylamide. The reactions were carried out under conventional conditions in a solvent-free system using variant solvents and different phase transfer catalysts (PTC) in the presence of microwave radiation or ultrasonic conditions. It was shown that in solvent-free conditions using TBAB as a catalyst, ethenzamide can be obtained within 15 min at 80 °C with 79% yield. Meanwhile, using microwave radiation under the same conditions, the reaction time can be shortened to 90 s with 92% yield. Notably, high yields can be achieved under PTC in water (or organic solvent-free) conditions using microwave radiation (2 min, 94%) or ultrasound (10 min, 95% efficiency). The studies prove that the PTC synthesis process of ethenzamide can be conducted under mild conditions, with a shorter reaction time and remarkably lower energy consumption in comparison to conventional processes, thus actualizing “green chemistry” for practical ethenzamide preparation.
Advancing Data Quality Assurance with Machine Learning: A Case Study on Wind Vane Stalling Detection
Authors: de Feiter Vincent S. , Strickland Jessica M. I. , Garcia-Marti Irene
Published: January 2025

High-quality observational datasets are essential for climate research and models, but validating and filtering decades of meteorological measurements is an enormous task. Advances in machine learning provide opportunities to expedite and improve quality control while offering insight into non-linear interactions between the meteorological variables. The Cabauw Experimental Site for Atmospheric Research in the Netherlands, known for its 213 m observation mast, has provided in situ observations for over 50 years. Despite high-quality instrumentation, measurement errors or non-representative data are inevitable. We explore machine-learning-assisted quality control, focusing on wind vane stalling at 10 m height. Wind vane stalling is treated as a binary classification problem as we evaluate five supervised methods (Logistic Regression, K-Nearest Neighbour, Random Forest, Gaussian Naive Bayes, Support Vector Machine) and one semi-supervised method (One-Class Support Vector Machine). Our analysis determines that wind vane stalling occurred 4.54% of the time annually over 20 years, often during stably stratified nocturnal conditions. The K-Nearest Neighbour and Random Forest methods performed the best, identifying stalling with approximately 75% accuracy, while others were more affected by data imbalance (more non-stalling than stalling data points). The semi-supervised method, avoiding the effects of the inherent data imbalance, also yielded promising results towards advancing data quality assurance.
Applying Deep Learning for Wildfire Identification: Economical and Accessible Solutions Leveraging Small Datasets
Authors: Shrivastava Aarav M. , Shrivastava Manish
Published: January 2025

Wildfires significantly impact human health, air quality, visibility, weather, and climate change and cause substantial economic losses. While state and county-operated air quality monitors provide critical insights during wildfires, they are not available in all regions. This highlights the need for affordable, accessible tools that allow the general public to assess air quality impacts. In this study, we apply machine learning with deep neural networks to diagnose air quality rapidly from sky images taken at the Pacific Northwest National Laboratory in Richland, WA, USA. Using a convolutional neural network (CNN) framework, we trained a deep learning model to classify air quality indices based on sky images. By leveraging transfer learning, our approach fine-tunes a pre-trained model on a small dataset of sky images, significantly reducing training time while maintaining high accuracy. Our results demonstrate the potential of deep learning to provide rapid air quality diagnostics during wildfire episodes, offering early warnings to the public and enabling timely mitigation strategies, particularly for vulnerable populations. Additionally, we show that lower respiratory infections pose the highest health risk during acute smoke exposures. Reactive oxygen species (ROS) from wildfire particles further exacerbate health risks by triggering inflammation and other adverse effects.
Modeling Return Levels of Non-Stationary Rainfall Extremes Due to Climate Change
Authors: Razi Ghalavand Mahin , Farajzadeh Manuchehr , Ghavidel Rahimi Yousef
Published: January 2025

Global warming increases evaporation and atmospheric water vapor, leading to more extreme events in both spatial and temporal domains. This study conducts a non-stationary extreme value analysis of the annual daily maximum at 36 meteorological stations over Iran from 1960 to 2021. We applied stationary and non-stationary Generalized Extreme Value (GEV) models within a Bayesian framework to estimate return levels for rainfall extremes, along with 90% confidence intervals. Our findings indicate that non-stationary models are not prominently evident based on AIC at most stations; however, non-stationary Generalized Extreme Value (GEV) models outperform stationary models based on RMSE and NSE evaluation criteria that sufficiently capture variations in extremes. Furthermore, most observed changes in extreme events exhibit a non-stationary pattern. Non-stationary analysis indicates that the frequency and severity of rainfall extremes have shown both increasing and decreasing trends, characterized by inconsistent spatial patterns.
Detection of Atmospheric NO2 Using Scheimpflug DIAL with a Blue External Cavity Diode Laser Source
Authors: Yao Cheng , Luo Weixuan , Xiao Anping , Peng Xiqing , Zhang Bin , Wang Longlong , Ling Qiang , Zhou Yan , Guan Zuguang , Chen Daru
Published: January 2025

Nitrogen dioxide (NO2) is broadly acknowledged as one of the six key air pollutants, posing a significant threat to environmental stability and human health. The profile of atmospheric nitrogen dioxide is required for quantifying NO2 emissions from fossil fuel combustion and industry. In continuous-wave differential absorption lidar (CW-DIAL) systems, the laser sources employed are subject to the issues of varying output characteristics and poor instability. This study presents a CW-DIAL system for remote sensing of atmospheric NO2 that employs a compact grating-based external cavity diode laser (ECDL) and Scheimpflug imaging. The laser in this system utilizes a piezoelectric transducer (PZT) for precise wavelength tuning, emitting at 448.1 nm and 449.7 nm with an output power of 2.97 W and a narrow linewidth of 0.16 nm. Signal capturing was achieved through a Newtonian telescope with a diameter of 200 mm and a 45° inclined CCD image sensor, satisfying the Scheimpflug principle. A case study near road traffic was used to verify the feasibility of ECDL-DIAL, which took place from 1 October to 2 October 2023 over an industrial park. The system generates precise NO2 distribution maps with sub-50 m resolution over 3 km, updating every 10 min.
Method for Testing Combinational Circuits by Multiple Diagnostic Features Using Weight-Based Sum Codes Properties
Authors: Efanov Dmitry V. , Pivovarov Dmitry V. , Cortegoso Vissio Nicolás , Kuptsov Alexander O. , Egorov Daniil E.
Published: January 2025

The paper puts forth a methodology for the arrangement of calculation control at the outputs of combinational digital devices based on utilizing multiple diagnostic features. The first diagnostic feature is the belonging of the formed codewords to a pre-selected weight-based sum code. The second and third diagnostic features are the control of calculations by the belonging of calculated functions describing data and checking bits of sum codes to the class of self-dual and related functions. The arrangement of calculation control by multiple diagnostic features is founded upon the implementation of Boolean signal correction during the synthesis of the embedded control circuit. This is conducted in consideration of the established characteristics of weight-based sum codes, wherein the functions describing their checking bits exhibit identical values on pairs of data vectors inversed in all bits (these are the so-called self-quasidual functions). The utilization of Boolean signal correction on the orthogonal to all input variable combinations enables the additional determination of correction function values. This ensures the self-duality of functions describing data bits and, consequently, the self-quasiduality of functions describing checking bits. The paper proposes a structure for arranging the calculation control in accordance with the three specified diagnostic features. Furthermore, it develops the algorithm of correction function additional determination that underlies the synthesis of the embedded control circuit. It should be noted that the algorithm does not require the analysis of the values of functions calculated at the outputs of the object of diagnostics; instead, it automatically allows for the performance of additional determinations. This facilitates its utilization when integrated with computer-aided logic design systems. The paper presents a case study of the process of obtaining correction functions and simulates the operation of the synthesized self-checking device, thereby demonstrating the effectiveness of the proposed method.
Muscle-Driven Total Knee Replacement Stability with Virtual Ligaments
Authors: Galley Alexandre , Donnelly Emma , Borukhov Ilya , Lanting Brent , Willing Ryan
Published: January 2025

Knee joint stability comprises passive (ligaments), active (muscles), and static (articular congruency) contributors. The stability of total knee replacement (TKR) implants can be assessed pre-clinically using joint motion simulators. However, contemporary testing methods with these platforms do not accurately reproduce the biomechanical contributions of passive stabilizers, active stabilizers, or both. A key component of joint stability is therefore missing from laxity tests. A recently developed muscle actuator system (MAS) pairs the quadriceps-driven motion capabilities of an Oxford knee simulator with the prescribed displacements and laxity testing methods of a VIVO robotic knee testing system, which also includes virtual ligament capabilities. Using a TKR-embedded non-cadaveric joint analogue, TKR with two different virtual ligament models were compared to TKR with no active ligaments. Laxity limits were then obtained for both developed models using the conventional style of laxity testing (the VIVO’s force/displacement control) and compared with results obtained under similar conditions with the MAS (gravity-dependent muscle control). Differences in joint control methods identified the need for muscle forces providing active joint stability, while differences in the effects of the virtual ligament models identified the importance of physiological representations of collateral ligaments during testing.
Identification of an Immunoglobulin Paratope Binding to Keratan Sulfate and Expression of a Single-Chain Derivative for Imaging
Authors: Boyraz Burak , Tauber Rudolf , Dernedde Jens
Published: January 2025

Keratan sulfate (KS) is a negatively charged carbohydrate linked to proteins. Several KS-bearing structural glycosaminoglycans participate to maintain the homeostasis of a functional extracellular matrix. Dysfunction of its biochemical composition and structure might therefore lead to pathological situations. For this reason, imaging of KS in tissues is an important diagnostic tool. Here, we describe the identification of the KS paratope derived from the ancestral anti-KS IgG mAb MZ15, as well as the engineering, functional recombinant expression in E. coli, and purification of an anti-KS single-chain variable fragment (ScFv). The ScFv enabled in vitro imaging of KS in cryosections of rat cornea by immunofluorescence microscopy comparable to the ancestral IgG MZ15.
Cold vs. Room Temperature: A Comparative Analysis of Platelet Functionality in Cold Storage
Authors: Drossos Panagiotis V. , Fortis Sotirios P. , Anastasiadi Alkmini T. , Pavlou Efthymia G. , Tsantes Andreas G. , Spyratos Gerasimos A. , Papageorgiou Effie G. , Nomikou Efrosyni G. , Stamoulis Konstantinos E. , Dryllis Georgios , Tzounakas Vassilis L. , Politou Marianna , Valsami Serena , Kriebardis Anastasios G.
Published: January 2025

Background: The platelet functionality of cold-stored platelets remains a subject of debate. Our aim was to investigate the effect of temperature on the hemostatic properties of stored platelets. Methods: Ten split pooled platelets stored at cold and at room temperature were evaluated in vitro on storage days 1, 5, 10, and 15 for metabolic, physiological, and vesiculation parameters, as well as their hemostatic profile using rotational thromboelastometry (ROTEM®). Results: The integrity profile was better preserved in the cold-stored platelets, as lower lactate dehydrogenase levels were documented (e.g., day 10: 261 ± 46 vs. 572 ± 220 U/L, 4 vs. 22 °C, p = 0.004). A time-dependent decrease in hemostatic capacity was evident regardless of the temperature, but the cold-stored units were linked to shorter clot initiation times and increased elasticity, strength, and firmness parameters, especially during extended storage (e.g., maximum clot firmness, INTEM day 15: 81 ± 2 vs. 19 ± 4 mm, 4 vs. 22 °C, p = 0.0008). Additionally, the aggregation of cold-stored platelets was superior after the addition of any agonist tested. Regarding vesiculation parameters, the extracellular vesicles of the units at 4 °C were characterized by a larger size from day 10 onwards, when they also presented higher procoagulant activity (e.g., phospholipid-dependent clotting time of day 15: 21.4 ± 2.3 vs. 25.0 ± 3.0 s, 4 vs. 22 °C, p = 0.016). Conclusion: Our results indicate that cold-stored platelets perform better than those stored at room temperature, demonstrating superior clot formation and stability. This suggests that cold storage may more effectively preserve platelet function, potentially offering advantages for transfusion therapy and the extension of shelf-life. However, the clinical relevance of these findings requires further investigation.
The Relationship Between Soleus H-Reflex Following Standing GVS and Postural Control Responses on Firm and Foam Surfaces: An Exploratory Study
Authors: Mitsutake Tsubasa , Taniguchi Takanori , Nakazono Hisato , Shiozaki Tomoyuki , Yoshizuka Hisayoshi , Sakamoto Maiko
Published: January 2025

Background: The vestibular postural control system affects standing stability on an unstable surface. However, it is unclear whether maintaining a standing position on different surfaces alters lateral vestibulospinal tract (LVST) excitability and body control responses following vestibular stimulation. This study aimed to investigate the relationship between the soleus H-reflex following galvanic vestibular stimulation (GVSH), a measure of LVST, and post-stimulus body movement responses while standing with eyes closed on different surfaces. Methods: Twelve healthy volunteers (mean age 20.4 ± 0.5 years, 7 females) performed eyes-closed standing GVSH on firm and foam surfaces. Body control responses in each condition were evaluated using an inertial measurement unit to monitor neck and pelvic movements, along with surface electromyography to assess muscle activity in the tibialis anterior and soleus muscles. Body responses to the GVS were averaged over up to a second after tibial nerve stimulation. Results: We observed a significant negative correlation between the H-wave amplitude of the GVSH on the firm surface and the tibialis anterior muscle activity following stimulation (r = −0.666, p = 0.018). No significant differences were observed during the eyes-closed standing GVSH on either firm or foam surfaces (p = 0.568). Conclusions: Postural maintenance in response to vestibular stimulation may contribute to body stability by regulating tibialis anterior muscle contraction via the LVST. Our findings may help elucidate the neural activity of vestibular function-related standing postural control responses.
Reflection or Dependence: How AI Awareness Affects Employees’ In-Role and Extra-Role Performance?
Authors: Zhao Heng , Ye Long , Guo Ming , Deng Yanfang
Published: January 2025

To address the challenges posed by AI technologies, an increasing number of organizations encourage or require employees to integrate AI into their work processes. Despite the extensive research that has explored AI applications in the workplace, limited attention has been paid to the role of AI awareness in shaping employees’ cognition, interaction behaviors with AI, and subsequent impacts. Drawing on self-construal theory, this study investigates how AI awareness influences employees’ in-role and extra-role performance. A multi-time-point analysis of data from 353 questionnaires reveals that employees’ AI awareness affects their perceived overqualification, which subsequently influences reflection on AI usage and dependence on AI usage, ultimately shaping their in-role and extra-role performance. Furthermore, employee–AI collaboration moderates the relationship between AI awareness and perceived overqualification. This study elucidates the mechanisms and boundary conditions through which AI awareness impacts employees’ performance, offering a more comprehensive perspective on AI awareness research and providing practical implications for promoting its positive effects while mitigating its negative consequences.
Building a Positive Work Environment: The Role of Psychological Empowerment in Engagement and Intention to Leave
Authors: Chompukum Pachsiry , Vanichbuncha Tita
Published: January 2025

This study examines the relationships between psychological empowerment, employee engagement, and intention to leave. Employing structural equation modeling, this study analyzes survey data from both academic and non-academic staff in Thailand. Findings reveal that psychological empowerment positively relates to employee engagement, which, in turn, negatively relates to the intention to leave. In addition, psychological empowerment directly affects the intention to leave. This highlights the potential of empowerment-focused interventions as an innovative strategy for improving employee retention, particularly relevant in today’s dynamic and challenging work environments. Our study contributes to the growing body of knowledge on positive organizational psychology by demonstrating the efficacy of empowerment initiatives in a non-Western context. Furthermore, it offers practical implications for organizations seeking nonmonetary rewards to foster a thriving workforce by cultivating a sense of meaning, competence, autonomy, and impact among their employees. Future research can explore the broader applicability of empowerment-based strategies across diverse organizational contexts.
What Is the Link of Closeness and Jealousy in Romantic Relationships?
Authors: Fernández Ana María , Barbato Maria Teresa , Barone Pamela , Zavalla Belén , Rivera-Ottenberger Diana , Guzmán-González Mónica
Published: January 2025

From an evolutionary perspective, love and attachment foster closeness, while jealousy ensures exclusivity in romantic relationships. This study examined the links between jealousy and affective aspects of love, hypothesizing positive associations despite their apparent opposition. An online sample of 265 individuals in Chile and Spain completed measures of digital jealousy, closeness, love, felt loved, and attachment. Results revealed higher jealousy in Chile than in Spain. Across both countries, anxious attachment and closeness were significant predictors of jealousy, explaining nearly 30% of its variance. In Chile, feeling loved negatively predicted jealousy, suggesting that reassurance of the romantic bond may reduce jealousy in this cultural context. Notably, affective closeness—conceptualized as the inclusion of the self in the other—emerged as a novel predictor of jealousy, extending beyond the established role of anxious attachment. These findings underscore the nuanced interplay between cultural context, affective closeness, and attachment in shaping jealousy.
Multi-Criteria Optimal Operation Strategy for Photovoltaic Systems in Large-Scale Logistics Parks Concerning Climate Impact
Authors: Peng Kai , Ma Mingzhu , Zhao Wenxuan , Zhang Rongpeng
Published: January 2025

Solar power is widely regarded as one of the most promising renewable resources for generating electricity and reducing building energy consumption. Logistics parks, with their low-rise buildings and extensive rooftop areas, offer significant advantages for solar energy utilization via rooftop photovoltaics (PVs). However, limited research has been conducted on the proper operational principles and optimized control strategies for the PV systems of logistics parks, particularly regarding the mismatch between power generation and the loads of various building types under varying climatic conditions. This study proposes four optimal PV operation strategies for large-scale logistics parks across diverse climatic regions, developed using a multi-criteria optimization approach. The strategies optimize the azimuth and tilt angles of PV panels under four adjustment frequencies: annual, semi-annual, seasonal, and monthly. The investigated strategies are validated in a 5500 m2 logistics park, comprising refrigerated storage, warehouses, sorting centers, and other facilities. The results indicate that the proposed strategies outperform conventional fixed-angle approaches, with the monthly adjustment strategy delivering the best performance. Economic costs are reduced by 9.26–17.02%, while self-sufficiency can be improved by 2.00–7.08%. Cold regions with high solar radiation show particularly significant benefits, with self-consumption increasing by 82.44–359.04%. This study provides valuable insights and practical guidelines for optimizing PV system operations in logistics parks, offering enhanced energy efficiency and cost-effectiveness.