bluuebunny/crossref_metadata_2025 huggingface dataset:
RDAU-Net: A U-Shaped Semantic Segmentation Network for Buildings near Rivers and Lakes Based on a Fusion Approach
Authors: Wang Yipeng , Wang Dongmei , Xu Teng , Shi Yifan , Liang Wenguang , Wang Yihong , Petropoulos George P. , Bao Yansong
Published: December 2024

The encroachment of buildings into the waters of rivers and lakes can lead to increased safety hazards, but current semantic segmentation algorithms have difficulty accurately segmenting buildings in such environments. The specular reflection of the water and boats with similar features to the buildings in the environment can greatly affect the performance of the algorithm. Effectively eliminating their influence on the model and further improving the segmentation accuracy of buildings near water will be of great help to the management of river and lake waters. To address the above issues, the present study proposes the design of a U-shaped segmentation network of buildings called RDAU-Net that works through extraction and fuses a convolutional neural network and a transformer to segment buildings. First, we designed a residual dynamic short-cut down-sampling (RDSC) module to minimize the interference of complex building shapes and building scale differences on the segmentation results; second, we reduced the semantic and resolution gaps between multi-scale features using a multi-channel cross fusion transformer module (MCCT); finally, a double-feature channel-wise fusion attention (DCF) was designed to improve the model’s ability to depict building edge details and to reduce the influence of similar features on the model. Additionally, an HRI Building dataset was constructed, comprising water-edge buildings situated in a riverine and lacustrine regulatory context. This dataset encompasses a plethora of water-edge building sample scenarios, offering a comprehensive representation of the subject matter. The experimental results indicated that the statistical metrics achieved by RDAU-Net using the HRI and WHU Building datasets are better than those of others, and that it can effectively solve the building segmentation problems in the management of river and lake waters.
Lightweight Detection and Counting of Maize Tassels in UAV RGB Images
Authors: Yang Hang , Wu Jiaji , Lu Yi , Huang Yuning , Yang Pinwei , Qian Yurong
Published: December 2024

By integrating unmanned aerial vehicle (UAV) remote sensing with advanced deep object detection techniques, it can achieve large-scale and high-throughput detection and counting of maize tassels. However, challenges arise from high sunlight, which can obscure features in reflective areas, and low sunlight, which hinders feature identification. Existing methods struggle to balance real-time performance and accuracy. In response to these challenges, we propose DLMNet, a lightweight network based on the YOLOv8 framework. DLMNet features: (1) an efficient channel and spatial attention mechanism (ECSA) that suppresses high sunlight reflection noise and enhances details under low sunlight conditions, and (2) a dynamic feature fusion module (DFFM) that improves tassel recognition through dynamic fusion of shallow and deep features. In addition, we built a maize tassel detection and counting dataset (MTDC-VS) with various sunlight conditions (low, normal, and high sunlight), containing 22,997 real maize tassel targets. Experimental results show that on the MTDC-VS dataset, DLMNet achieves a detection accuracy AP50 of 88.4%, which is 1.6% higher than the baseline YOLOv8 model, with a 31.3% reduction in the number of parameters. The counting metric R2 for DLMNet is 93.66%, which is 0.9% higher than YOLOv8. On the publicly available maize tassel detection and counting dataset (MTDC), DLMNet achieves an AP50 of 83.3%, which is 0.7% higher than YOLOv8, further demonstrating DLMNet’s excellent generalization ability. This study enhances the model’s adaptability to sunlight, enabling high performance under suboptimal conditions and offering insights for real-time intelligent agriculture monitoring with UAV technology.
BIPE: A Bi-Layer Predictive Ensemble Framework for Forest Fire Susceptibility Mapping in Germany
Authors: Hu Ling , Hochschild Volker , Neidhardt Harald , Schultz Michael , Khosravani Pegah , Shokati Hadi
Published: December 2024

Forest fires diminish forests’ ecological services, including carbon sequestration, water retention, air cooling, and recreation, while polluting the environment and endangering habitats. Despite considerable economic advancements, firefighting strategies remain less than optimal. This paper introduces the Bi-layer Predictive Ensemble (BIPE), an innovative machine learning model designed to enhance the accuracy and generalization of forest fire susceptibility mapping. BIPE integrates model-centric and data-driven strategies, employing automated methods such as 10-fold cross-validation and meta-learning to improve stability and generalization. During its 10-fold cross-validation, BIPE demonstrated excellent performance, with the Area Under the Curve (AUC) values ranging from 0.990 to 0.996 and accuracy levels consistently high, around 97%, underscoring its robust class separation ability and strong generalization across different datasets. Our results confirm that BIPE outperforms traditional high-performance models like Support Vector Machine (SVM), Multilayer Perceptron (MLP), Extreme Gradient Boosting (XGBoost), Deep Neural Network (DNN), and Convolutional Neural Network (CNN), showcasing its practical effectiveness and reliability on the data of nonlinear, high-dimensional, and complex interactions. Additionally, our forest fire susceptibility maps offer valuable complementary information for German forest fire management authorities, enhancing their ability to assess and manage fire risks more effectively.
Applications of Isosceles Triangular Coupling Structure in Optical Switching and Sensing
Authors: Zeng Lili , Zhang Xingjiao , Guo Qinghua , Fan Yang , Deng Yuanwen , Ma Zhengchao , Li Boxun
Published: December 2024

In the case of waveguide-based devices, once they are fabricated, their optical properties are already determined and cannot be dynamically controlled, which limits their applications in practice. In this paper, an isosceles triangular-coupling structure which consists of an isosceles triangle coupled with a two-bus waveguide is proposed and researched numerically and theoretically. The coupled mode theory (CMT) is introduced to verify the correctness of the simulation results, which are based on the finite difference time domain (FDTD). Due to the existence of the side mode and angular mode, the transmission spectrum presents two high transmittance peaks and two low transmittance peaks. In addition, the four transmission peaks exhibit different variation trends when the dimensions of the isosceles triangle are changed. The liquid crystal (LC) materials comprise anisotropic uniaxial crystal and exhibit a remarkable birefringence effect under the action of the external field. When the isosceles triangle coupling structure is filled with LC, the refractive index of the liquid crystal can be changed by changing the applied voltage, thereby achieving the function of an optical switch. Within a certain range, a linear relationship between refractive index and applied voltage can be obtained. Moreover, the proposed structure can be applied to biochemical sensing to detect glucose concentrations, and the sensitivity reaches as high as 0.283 nm·L/g, which is significantly higher than other values reported in the literature. The triangular coupling structure has advantages such as simple structure and ease of manufacturing, making it an ideal choice for the design of high-performance integrated plasmonic devices.
Systematic Evaluation of IMU Sensors for Application in Smart Glove System for Remote Monitoring of Hand Differences
Authors: Harrison Amy , Jester Andrea , Mouli Surej , Fratini Antonio , Jabran Ali
Published: December 2024

Human hands have over 20 degrees of freedom, enabled by a complex system of bones, muscles, and joints. Hand differences can significantly impair dexterity and independence in daily activities. Accurate assessment of hand function, particularly digit movement, is vital for effective intervention and rehabilitation. However, current clinical methods rely on subjective observations and limited tests. Smart gloves with inertial measurement unit (IMU) sensors have emerged as tools for capturing digit movements, yet their sensor accuracy remains underexplored. This study developed and validated an IMU-based smart glove system for measuring finger joint movements in individuals with hand differences. The glove measured 3D digit rotations and was evaluated against an industrial robotic arm. Tests included rotations around three axes at 1°, 10°, and 90°, simulating extension/flexion, supination/pronation, and abduction/adduction. The IMU sensors demonstrated high accuracy and reliability, with minimal systematic bias and strong positive correlations (p > 0.95 across all tests). Agreement matrices revealed high agreement (<1°) in 24 trials, moderate (1–10°) in 12 trials, and low (>10°) in only 4 trials. The Root Mean Square Error (RMSE) ranged from 1.357 to 5.262 for the 90° tests, 0.094 to 0.538 for the 10° tests, and 0.129 to 0.36 for the 1° tests. Likewise, mean absolute error (MAE) ranged from 0.967 to 4.679 for the 90° tests, 0.073 to 0.386 for the 10° tests, and 0.102 to 0.309 for the 1° tests. The sensor provided precise measurements of digit angles across 0–90° in multiple directions, enabling reliable clinical assessment, remote monitoring, and improved diagnosis, treatment, and rehabilitation for individuals with hand differences.
Reliable Disparity Estimation Using Multiocular Vision with Adjustable Baseline
Authors: Diaz-Ramirez Victor H. , Gonzalez-Ruiz Martin , Juarez-Salazar Rigoberto , Cazorla Miguel
Published: December 2024

Accurate estimation of three-dimensional (3D) information from captured images is essential in numerous computer vision applications. Although binocular stereo vision has been extensively investigated for this task, its reliability is conditioned by the baseline between cameras. A larger baseline improves the resolution of disparity estimation but increases the probability of matching errors. This research presents a reliable method for disparity estimation through progressive baseline increases in multiocular vision. First, a robust rectification method for multiocular images is introduced, satisfying epipolar constraints and minimizing induced distortion. This method can improve rectification error by 25% for binocular images and 80% for multiocular images compared to well-known existing methods. Next, a dense disparity map is estimated by stereo matching from the rectified images with the shortest baseline. Afterwards, the disparity map for the subsequent images with an extended baseline is estimated within a short optimized interval, minimizing the probability of matching errors and further error propagation. This process is iterated until the disparity map for the images with the longest baseline is obtained. The proposed method increases disparity estimation accuracy by 20% for multiocular images compared to a similar existing method. The proposed approach enables accurate scene characterization and spatial point computation from disparity maps with improved resolution. The effectiveness of the proposed method is verified through exhaustive evaluations using well-known multiocular image datasets and physical scenes, achieving superior performance over similar existing methods in terms of objective measures.
Understanding Cyclists’ Visual Behavior Using Eye-Tracking Technology: A Systematic Review
Authors: Kchour Fatima , Cafiso Salvatore , Pappalardo Giuseppina
Published: December 2024

Eye-tracking technologies are emerging in research aiming to understand the visual behavior of cyclists to improve their safety. These technologies gather real-time information to reveal what the cyclists look at and how they respond at a specific location and time. This systematic review investigates the use of eye-tracking systems to improve cyclist safety. An extensive search of the SCOPUS and WoS databases, following the PRISMA 2020 guidelines, found 610 studies published between 2010 and 2024. After filtering these studies according to predefined inclusion and exclusion criteria, 25 were selected for final review. The included studies were conducted in real traffic or virtual environments aiming to assess visual attention, workload, or hazard perception. Studies focusing on other types of road users or participants not involved in active cycling were excluded. Results reveal the important impact of road elements’ design, traffic density, and weather conditions on cyclists’ gaze patterns. Significant visual workload is imposed mainly by intersections. Along with the valuable insights into cyclist safety, potential biases related to small sample sizes and technological limitations were identified. Recommendations for future research are discussed to address these challenges through more diverse samples, advanced technologies, and a greater focus on peripheral vision.
Ensemble All Time-Scale Decomposition Method and Its Application in Bevel Gear Fault Diagnosis
Authors: Cheng Zhengyang , Yang Yu , Duan Chengcheng , Kang Xin , Cui Jianxin
Published: December 2024

All time-scale decomposition (ATD) is a non-parametric adaptive signal decomposition method, which relies on zero-crossing points and extreme points to jointly construct the baseline, achieving the suppression of modal mixing caused by the proximity of component frequencies. However, ATD is unable to solve mode mixing induced by noise. To improve this defect, a new noise-assisted signal decomposition method named ensemble all time-scale decomposition (EATD) is proposed in this paper. EATD introduces the noise-assisted technique of complementary ensemble empirical mode decomposition based on ATD, adding complementary noises to mask the noise interference in the signal. EATD not only overcomes mode mixing caused by noise but also preserves the capability of ATD to suppress mode mixing caused by the proximity of component frequencies. Simulation signals and bevel gear fault signals are utilized to validate EATD, and the results indicate that EATD can successfully overcome mode mixing induced by noise and can be effectively applied for gear fault diagnosis.
Predictive Maintenance and Fault Detection for Motor Drive Control Systems in Industrial Robots Using CNN-RNN-Based Observers
Authors: Eang Chanthol , Lee Seungjae
Published: December 2024

This research work presents an integrated method leveraging Convolutional Neural Networks and Recurrent Neural Networks (CNN-RNN) to enhance the accuracy of predictive maintenance and fault detection in DC motor drives of industrial robots. We propose a new hybrid deep learning framework that combines CNNs with RNNs to improve the accuracy of fault prediction that may occur on a DC motor drive during task processing. The CNN-RNN model determines the optimal maintenance strategy based on data collected from sensors, such as air temperature, process temperature, rotational speed, and so forth. The proposed AI model has the capacity to make highly accurate predictions and detect faults in DC motor drives, thus helping to ensure timely maintenance and reduce operational breakdowns. As a result, comparative analysis reveals that the proposed framework can achieve higher accuracy than the current existing method of combining CNN with Long Short-Term Memory networks (CNN-LSTM) as well as other CNNs, LSTMs, and traditional methods. The proposed CNN-RNN model can provide early fault detection for motor drives of industrial robots with a simpler architecture and lower complexity of the model compared to CNN-LSTM methods, which can enable the model to process faster than CNN-LSTM. It effectively extracts dynamic features and processes sequential data, achieving superior accuracy and precision in fault diagnosis, which can make it a practical and efficient solution for real-time fault detection in motor drive control systems of industrial robots.
Intelligent Sensor Software for Robust and Energy-Sustainable Decision-Making in Welding of Steel Reinforcement for Concrete
Authors: Ferreiro-Cabello Javier , Martinez-de-Pison Francisco Javier , Fraile-Garcia Esteban , Pernia-Espinoza Alpha , Divasón Jose
Published: December 2024

In today’s industrial landscape, optimizing energy consumption, reducing production times, and maintaining quality standards are critical challenges, particularly in energy-intensive processes like resistance spot welding (RSW). This study introduces an intelligent decision support system designed to optimize the RSW process for steel reinforcement bars. By creating robust machine learning models trained on limited datasets, the system generates interactive heat maps that provide real-time guidance to production engineers or intelligent systems, enabling dynamic adaptation to changing conditions and external factors such as fluctuating energy costs. These heat maps serve as a flexible and intuitive tool for identifying robust operational points that balance quality, energy efficiency, and productivity. The proposed methodology advances decision-making in welding processes by combining robust predictive modeling with innovative visualization techniques, offering a versatile solution for multiobjective optimization in real-world industrial applications.
Effects of Workplace Supervised Exercise on Psychological Capital: An Intervention Study
Authors: Salvador-Ferrer Carmen M. , Diaz-Fúnez Pedro A. , Carrera-Ruiz Álvaro , Monserrat-Hernández Montserrat , Artero Enrique G. , Mañas-Rodríguez Miguel A.
Published: December 2024

Public employees face a growing array of professional challenges, and psychological capital (PC) has emerged as a valuable resource to address them. Workplace supervised exercise (WSE) programs can improve physical fitness and personal resources, though their adoption in organizations is limited. This study examines the impact of a long-term WSE program on the psychological capital of public administration employees. Fifty-five participants completed PC questionnaires and attended at least 40% of the sessions; 49.1% were women, and 40% were over 50 years old (age range: 24–73 years). A quasi-experimental design without a control group was used with a longitudinal design, and a nine-month WSE program (from October to June) was implemented. The data show gender-specific variations in PC patterns. Among men, incremental increases were observed over the three measurement intervals, with statistical significance achieved only in self-efficacy and hope. Among women, an upward trend was observed between October and February without statistical significance, followed by a slight decline. The results suggest that the WSE program may enhance personal resources, particularly hope and self-efficacy, highlighting the importance of tailoring WSE programs to the specific characteristics of the target group.
Evaluation of Agricultural Measures to Safeguard the Vulnerable Karst Groundwater Habitat of the Black Olm (Proteus anguinus parkelj) from Nitrate Pollution
Authors: Glavan Matjaž , Cvejić Rozalija
Published: December 2024

The black olm (Proteus anguinus parkelj Sket & Arntzen) is an endemic species found exclusively in the Dobličica River subterranean water systems of the Dinaric karst in southern Slovenia. These unique habitats are vulnerable to contamination due to rapid water flow, primarily from nitrates from agricultural fertilisers and untreated urban wastewater. The safe limit of nitrate concentration for olms is 9.2 mg NO3−/L, yet measurements in karst springs have shown levels ranging from 3 mg to over 20 mg NO3−/L. The SWAT modelling tool assessed agri-environmental and land use scenarios for their impact on nitrate leaching. Using the model, we identified hotspots with high nitrogen leaching potential that require immediate attention and implementation of better agricultural practices for fertiliser use. For these hotspots, the most effective approach combines scenarios of cover crops (R2), reduced fertilisation (R3), crop rotation (R4), and conversion of cropland to grassland (E2, E4, E5), potentially decreasing nitrate leaching by up to 60%. Implementing the best scenarios is expected to reduce nitrogen levels below the limit value of 9.2 mg NO3−/L, essential for maintaining the black olm habitat.
The Influence of the Digital Economy on the Foreign Trade Competitiveness of Hunan Province in China
Authors: Yuan Minglan , Zhong Hui , Hao Zhijie , Tang Decai , Atsi Eugene Ray
Published: December 2024

With the evolution of the Internet, artificial intelligence, and other technologies, the era of the digital economy has quietly emerged. The digital economy’s growth is a major trend, and China is dedicated to building data power and strengthening foreign trade competitiveness (FTC). As an essential strategic region, Hunan Province should grasp the opportunities of the digital economy and constantly develop and increase its competitiveness. This research will explore the effect of Digital economy (DIG) on the FTC of Hunan Province. The study collected data from the region from 2013 to 2022 and Stata 17 software was applied to obtain the research outcomes. The findings indicate that DIG can significantly enhance the development of FTC in Hunan Province. This result remains consistent even after conducting robustness tests, showing its reliability. Furthermore, the positive impact is even more pronounced in urban agglomerations of major cities and larger areas. The paper’s main innovation lies in its focus on the Hunan Province region, which holds significant regional importance and plays a key role in enhancing China’s FTC.
A Proposed Circular Economy Model for Hospital Bio-Waste Management in Municipal Settings
Authors: Sepetis Anastasios , Georgantas Konstantinos , Nikolaou Ioannis
Published: December 2024

This paper presents a proposed circular economy (CE) model for hospital bio-waste management, using Athens as a case study to demonstrate its applicability in urban environments. The model incorporates waste segregation at the source, resource recovery methods such as composting and anaerobic digestion, and data-driven tools to enhance the efficiency and sustainability of healthcare waste management. The study investigates the transition from linear to CE practices, focusing on structured collection strategies, collaborative efforts between hospitals and municipal authorities, and continuous tracking of waste flows. A comprehensive analysis of bio-waste volumes from participating hospitals over a three-year period is conducted, utilizing multi-criteria decision-making (MCDM) tools such as TOPSIS to evaluate the system’s effectiveness. The results indicate a significant increase in hospital participation, improved waste separation, and optimized resource recovery, offering a scalable framework for other municipalities seeking to implement CE-based waste management practices in healthcare settings.
The Dynamics and Trends of International Research on Urban Carbon Risk
Authors: Yao Qiang , An Na , Ci Hai
Published: December 2024

Research on Urban Carbon Risk (RUCR) is crucial for understanding the impact mechanisms of carbon emissions on urban environments and health, particularly in rapidly urbanizing areas. This paper conducted a bibliometric analysis of 2012 studies on RUCR indexed in the Web of Science (WOS) database from 1991 to June 2023. It reached the following conclusions: (1) The annual publication volume of RUCR has steadily increased since 2005, mainly focusing on environmental science and public health. A co-citation analysis of the literature indicates that RUCR research content is centered on carbon sink assessment, risk factor analysis, and response strategies. (2) RUCR has undergone four developmental stages: singular exploration, evaluation and construction, innovative breakthroughs, and technological synergy. (3) The key research issues of RUCR include carbon reduction and sink enhancement, integrating qualitative and quantitative planning methods, and multidisciplinary collaboration. (4) Current research hotspots in RUCR focus on urban pollution and health risks, ecological environment and land use change, carbon emissions and energy utilization, and pollution monitoring technologies. Future research trends are anticipated to center on source apportionment and monitoring of carbon emissions, the relationship between air pollution and health risks, and the governance and mitigation of carbon emissions. (5) Based on the analysis of critical issues and trends, it is recommended that future research prioritize spatial identification and scenario simulation of urban carbon risk. The conclusions of this paper facilitate researchers’ quick understanding of the current status and development trends of RUCR and propose future research directions from urban planning.
Engineering Site Characterization of Al-Madinah Al-Munawarah, Saudi Arabia, for Sustainable Urban Development
Authors: Hazaea Bashar Y. , Alamri Abdullah M. , Fnais Mohammed S. , Abdelrahman Kamal
Published: December 2024

This study aims to estimate the shear wave velocity and identify the depth of the bedrock and the engineering site characterization utilizing the multichannel analysis of surface waves (MASW) method for sustainable urban development in the Al-Madinah Al-Munawarah area. Twenty-seven MASW profiles were carried out using Geode digital seismographs with a 24-geophone array of 4.5 Hz in the urban expansion area of Al-Madinah Al-Munawarah. The methodology entailed rigorous calibration during data collection, processing, and inversion to ensure precise shear velocity measurements. Results reflect subsurface conditions accurately where shear wave velocity (Vs) varies between 180 m/s and 1200 m/s across three main layers: alluvium deposits, which transfer laterally in some areas into vesicular basalt with Vs ranges from 180 to 360 m/s; fractured basalt where Vs varies between 360 and 760 m/s; and weathered basaltic rock with Vs that spans from 760 to 1200 m/s. Moreover, the average shear wave velocity of up to 30 m depth (Vs30) and ranging from 180–480 m/s indicate Site Class D (stiff soil) and Class C (soft rock and dense soil) according to National Earthquake Hazards Reduction Program (NEHRP). Furthermore, the depth of bedrock varies between 18 and 29 mm indicating the great thickness of soil deposits throughout the study area. These results provide civil engineers and urban planners with vital data about soil deposits characterization and geotechnical conditions in the area where alluvium deposits and vesicular basalt represent weak zones that require more attention during urban construction. Results will contribute as well, to a great extent, in achieving the sustainable development plans of Saudi Vision 2030.
Identification and Evaluation of Cultural Ecosystem Service Resources in the Territory of the Local Action Group Lednice–Valtice Area
Authors: Zourková Ilona , Hromková Lenka , Schneider Jiří , Fialová Jitka
Published: December 2024

The article focuses on the identification and mapping of cultural ecosystem service (CES) resources in the territory of the Local Action Group Lednice–Valtice Area (LAG LVA). The Consolidated Layer of Ecosystems (CLE) was used for mapping, and changes in the distribution and overall representation of cultural ecosystem service resources across the different CLE versions were monitored. A total of 27 ecosystem categories were identified in the region. Subsequently, the importance of cultural ecosystem services was assessed based on expert estimation. The classes of ecosystem services defined within the Common International Classification of Ecosystem Services (CICES) have been grouped into a total of four groups according to their common characteristics. Then, an expert estimation of all identified ecosystems in terms of their importance as sources of CESs was performed. It showed that ecosystems in the LAG LVA have a high potential to provide CES. As a result of the significant amount of CES resources, it is also an area that attracts a huge number of visitors annually. However, some places are more attractive and well known, causing an influx of tourists. The sustainability and protection of these resources, thus, depends on their appropriate management and integration into strategies and planning.
Modeling and Analyzing the Spatiotemporal Travel Patterns of Bike Sharing: A Case Study of Citi Bike in New York
Authors: Wen Zheng , Tian Dongwei , Wu Naiming
Published: December 2024

As the urban transportation demand continues to grow, the effective management and optimization of bike-sharing systems are of significant importance for urban planning and transportation engineering. This study aims to identify the spatiotemporal distribution of the peak-period departures and arrivals of bike sharing within Manhattan, New York, and to analyze the community clustering patterns and their underlying rules. Additionally, a comparative analysis across multiple time periods was conducted to enhance the research’s practical value. This study utilized GPS trajectory data from the New York City bike-sharing system for 2023. After analyzing the travel patterns throughout the year, we selected August, the month with the highest usage, to study the origin-destination (OD) travel aggregation patterns using flow models and the theoretical constructs of travel networks, measuring and analyzing travel characteristics. Subsequently, community detection algorithms were applied to analyze the clustering patterns and relationships among various neighborhoods. The findings revealed that the use of bike sharing in New York exhibits an overall trend of increasing and then decreasing throughout the year, with significantly higher usage in the spring and summer compared to the fall and winter. Notably, August saw the highest usage levels, with hotspots primarily concentrated in the southwestern part of Manhattan, which is also the economic center of New York City. The OD aggregation patterns across the upper, middle, and lower parts of August show distinct variations. Through community analysis, several strongly associated neighborhood clusters were identified, which exhibited both aggregation and dispersion trends over time. In southern Manhattan, a community with high modularity emerged, showcasing strong interconnections among neighborhoods. These findings provide valuable insights into the usage patterns of bike sharing in New York and the factors influencing them, offering significant implications for the optimization of bike-sharing system operations and planning.
Optimizing Timber Supply Chains: Exploring the Potential of Digital Collaboration
Authors: Ma Chenglin , Gao Xurui , Zhang Lin , Kang Wenchao
Published: December 2024

Digital intelligent supply chains strengthen industrial resilience and optimize economic efficiency in the timber industry. Information asymmetry and low collaboration efficiency remain key challenges across the timber supply chain. This study develops a three-party evolutionary game model examining digital collaboration between timber production and processing enterprises, finished product distribution and retail enterprises, and third-party service providers, introducing third-party service providers alongside traditional production and distribution enterprises. The model incorporates novel parameters including information sharing degree, value-added reliability gains, and free-riding coefficients to reflect real-world circumstances. Through equilibrium simulation and analysis, we identify four possible evolutionary states. The results demonstrate that successful digital collaboration in timber supply chains relies on three conditions: a high level of initial stakeholder involvement accelerates the formation of supply chain digital intelligent collaborative mechanisms, equitable benefit distribution maintains long-term cooperation, and integrated third-party services reduce implementation costs while improving information reliability. These findings provide a new perspective and reference for timber enterprises to implement digital transformation strategies.
Towards Perovskite Oxide-Based Electrocatalysts with Zero-Critical Elements for Sustainable Energy Production
Authors: Aliotta Chiara , Deganello Francesca
Published: December 2024

The well-being of the Earth and its inhabitants is compromised by the energy and climate crisis that has arisen from the prolonged and uncontrolled utilization of fossil fuels, which has caused a tremendous increase in anthropogenic CO2 and a consistent depletion of natural energy resources [...]
A New Adaptive Control Design of Permanent Magnet Synchronous Motor Systems with Uncertainties
Authors: Liu Yutang , Li Jiaojiao , Sun Zong-Yao , Chen Chih-Chiang
Published: December 2024

Symmetry is widely present in science and daily life. And the internal structure of surface-mounted permanent magnet synchronous motors (PMSMs) has good symmetry. This article is dedicated to studying the tracking problem of PMSMs with adaptive and backstepping control methods. The research objective of this study is to design new adaptive controllers Uq and Ud, which enable the state of the motor position servo system to asymptotically and stably track the given signals of the system. They can suppress the impact of changes in B, J, and TL and can also enhance the robustness of the system. (i) The strongly coupled current and speed, variation of parameters over time, and nonlinearity of motor torque objectively pose significant challenges in the design of adaptive tracking controllers for PMSMs. (ii) Adaptive control technology and backstepping control methods are used for designing controllers for the PMSMs. (iii) After rigorous reasoning, an intelligent adaptive tracking control strategy for the PMSMs has been derived, which is for the direct axis current and the angle. (iv) The new adaptive tracking controllers are superior to existing controllers in that they can strongly suppress the disturbance of system parameters J, TL, and B, make the system state asymptotically stable, and achieve good tracking performance for the given signals. The results of the simulation indicate the validity of the designed control strategy.
Lie Symmetries and Solutions for a Reaction–Diffusion–Advection SIS Model with Demographic Effects
Authors: Naz Rehana , Torrisi Mariano , Imran Ayesha
Published: December 2024

A reaction–diffusion susceptible–infectious–susceptible disease model with advection, vital dynamics (birth–death effects), and a standard incidence infection mechanism is carefully analyzed. Two distinct diffusion coefficients for the susceptible and infected populations are considered. The Lie symmetries and closed-form solutions for the RDA–SIS disease model are established. The derived solution allows to study dynamics of disease transmission. Our simulation clearly illustrates the evolution dynamics of the model by using the values of parameters from academic sources. A sensitivity analysis is performed, offering valuable perspectives that could inform future disease management policies.
Some Identities on (p,q)-Analog of the Generalized Hyperharmonic Polynomials of Order ν
Authors: Koparal Sibel
Published: December 2024

In this paper, p,q-analogues of the generalized harmonic polynomials and the generalized hyperharmonic polynomials of order ν are defined. We found new sums of these numbers by using combinatorial operations. We also obtained the generating function and used it to reproduce new equations. The applications of matrices are made in the end.
FGeo-Parser: Autoformalization and Solution of Plane Geometric Problems
Authors: Zhu Na , Zhang Xiaokai , Huang Qike , Zhu Fangzhen , Zeng Zhenbing , Leng Tuo
Published: December 2024

Automatic geometric problem-solving is an active and challenging subfield at the intersection of AI and mathematics, where geometric problem parsing plays a critical role. It involves converting geometric diagram and text into certain formal language. Due to the complexity of geometric shapes and the diversity of geometric relationships, geometric problem parsing demands that the parser exhibit cross-modal comprehension and reasoning capabilities. In this paper, we propose an enhanced geometric problem parsing method called FGeo-Parser, which converts problem diagrams and text into the formal language of the FormalGeo. It also supports reverse formalization to generate human-like solutions, reflecting the symmetry between parsing and generating. Specifically, diagram parser leverages the BLIP to generate the construction CDL and image CDL, while text parser employs the T5 to produce the text CDL and goal CDL where these neural networks are both based on a symmetric encoder–decoder architecture. With the assistance of a theorem predictor, these CDLs were automatically parsed and step-by-step reasoning was executed within FGPS. Finally, the reasoning process was input into a solution generator, which subsequently produced a human-like solution process. Additionally, we re-annotated problem diagrams and text based on the FormalGeo7K dataset. The formalization experiments on the new dataset achieved a match accuracy of 91.51% and a perfect accuracy of 56.47%, while the combination with the theorem predictor achieved a problem-solving accuracy of 63.45%.
Exploring Digital Transformation Journey Among Micro, Small-, and Medium-Sized Enterprises
Authors: Marolt Marjeta , Lenart Gregor , Kljajić Borštnar Mirjana , Pucihar Andreja
Published: December 2024

This paper aims to explore the patterns in micro, small-, and medium-sized enterprises’ (MSMEs) digital transformation journey during recent years. Using an emergent concurrent mixed-methods approach, we combined insights from six in-depth interviews with MSME managers and owners with survey data from 66 MSMEs. The findings reveal major inhibitors and accelerators of MSMEs’ digital transformation and demonstrate how they cope with them by engaging in digitalisation actions. This study also provides insights into how inhibitors, accelerators, and digitalisation actions vary across MSME sizes. While an increased adoption of digital technology was observed among the participating MSMEs, this study identifies three distinct digital transformation paths: necessary, experimental, and committed. Each path is shaped by a unique combination of inhibitors and accelerators. From the practical perspective, this research provides insights for MSME managers and owners on how to tailor their digital transformation efforts to their unique inhibitors and accelerators. In addition, our insights can help policy makers to promote the digital transformation of MSMEs through appropriate measures and support mechanisms tailored to the specific needs of smaller enterprises.
A Survey of Efficient Lightweight Cryptography for Power-Constrained Microcontrollers
Authors: Soto-Cruz Jesús , Ruiz-Ibarra Erica , Vázquez-Castillo Javier , Espinoza-Ruiz Adolfo , Castillo-Atoche Alejandro , Mass-Sanchez Joaquin
Published: December 2024

Protecting sensitive data, such as data collected from sensors, is crucial for ensuring the accurate assessment of sensing devices and preventing unauthorized access. In this regard, Internet of Things (IoT) devices offer a promising alternative for in situ monitoring. However, IoT sensing devices are often constrained by limited processing power and memory. Therefore, lightweight and efficient security algorithms are essential. This review paper assesses the implementation of lightweight cryptographic algorithms for power-constrained microcontrollers in IoT systems using symmetric cryptography. The implemented algorithms analyze the resource constraints of the IoT devices and compare their performance for the efficient implementation of secure monitoring systems. Experimental results demonstrate the performance of various lightweight encryption algorithms on low-power microcontrollers. The analysis reveals the comparative performance of these algorithms in terms of average power and energy consumption, memory usage, latency, and throughput.
Wrinkling Controlled Shear and Draping, Based on Hierarchical Textile Models, Weaving Kind and Yarn Properties
Authors: Orlik Julia , Neusius David , Krier Maxime , Steiner Konrad , Backes Sebastian , Bhat Shantanu , Gries Thomas
Published: December 2024

This paper covers the mechanical simulation of woven textiles on the yarn level with an investigation of the influence of the sliding between yarns and changing yarn cross-sections under loading. An experimental validation of the simulation tools for a range of chosen woven structures with different weaving types and thicknesses of multifilament glass fibre yarns is provided. The main focus of the paper is the classification of the folding mechanisms in textiles.
Development of a Predictive Model for N-Dealkylation of Amine Contaminants Based on Machine Learning Methods
Authors: Cheng Shiyang , Zhang Qihang , Min Hao , Jiang Wenhui , Liu Jueting , Liu Chunsheng , Wang Zehua
Published: December 2024

Amines are widespread environmental pollutants that may pose health risks. Specifically, the N-dealkylation of amines mediated by cytochrome P450 enzymes (P450) could influence their metabolic transformation safety. However, conventional experimental and computational chemistry methods make it difficult to conduct high-throughput screening of N-dealkylation of emerging amine contaminants. Machine learning has been widely used to identify sources of environmental pollutants and predict their toxicity. However, its application in screening critical biotransformation pathways for organic pollutants has been rarely reported. In this study, we first constructed a large dataset comprising 286 emerging amine pollutants through a thorough search of databases and literature. Then, we applied four machine learning methods—random forest, gradient boosting decision tree, extreme gradient boosting, and multi-layer perceptron—to develop binary classification models for N-dealkylation. These models were based on seven carefully selected molecular descriptors that represent reactivity-fit and structural-fit. Among the predictive models, the extreme gradient boosting shows the highest prediction accuracy of 81.0%. The SlogP_VSA2 descriptor is the primary factor influencing predictions of N-dealkylation metabolism. Then an ensemble model was generated that uses a consensus strategy to integrate three different algorithms, whose performance is generally better than any single algorithm, with an accuracy rate of 86.2%. Therefore, the classification model developed in this work can provide methodological support for the high-throughput screening of N-dealkylation of amine pollutants.
PS-MPs Induced Inflammation and Phosphorylation of Inflammatory Signalling Pathways in Liver
Authors: Ying Mengchao , Shao Naimin , Dong Cheng , Sha Yijie , Li Chen , Hong Xinyu , Ding Yu , Xu Jing , Qian Kelei , Tao Gonghua , Xiao Ping
Published: December 2024

As new pollutants, microplastics (MPs) have attracted much attention worldwide because they cause serious environmental pollution and pose potential health risks to humans. However, the toxic effects of MPs are still unclear. In this study, we analysed the inflammatory effects of 0.1 μm polystyrene microplastics (PS-MPs) on mouse and human liver cell lines. After 28 days of exposure to PS-MPs, the mice presented decreased liver index values and increased AST/ALT values. HL7702 and HepG2 were treated with PS-MPs for 24 h, and the cytotoxicity, the expression levels of inflammatory factors, and the phosphorylation of proteins in inflammation related pathways were confirmed. Compared with the control, the cell viability of these two cells significantly decreased after exposure to the PS-MPs at 1000 μm/cm2, and the BMD model also exhibited a similar dose. LDH leakage and AST also increased in a dose-dependent increase after PS-MPs exposure. The relative levels of chemokines such as GM-CSF, IL-6, IL-8, and IL-12p70 were significantly greater than those in the control. Furthermore, the PS-MPs can increase the expression levels of TLR4, MyD88, and NF-κB and activate the phosphorylation of NF-κB and STATs. Based on these results, exposure to PS-MPs can stimulate liver inflammation and activate the TLR4/MyD88/NF-κB and JAK-STAT pathways.
Active Moss Biomonitoring of Mercury in the Mine-Polluted Area of Abbadia San Salvatore (Mt. Amiata, Central Italy)
Authors: Meloni Federica , Calabrese Sergio , Vaselli Orlando , Capecchiacci Francesco , Ciani Francesco , Brusca Lorenzo , Bellomo Sergio , D’Alessandro Walter , Daskalopoulou Kyriaki , Venturi Stefania , Nisi Barbara , Rappuoli Daniele , Tassi Franco , Cabassi Jacopo
Published: December 2024

Active biomonitoring of mercury (Hg) using non-indigenous moss bags was performed for the first time within and around the former Hg mining area of Abbadia San Salvatore (Mt. Amiata, central Italy). The purpose was to discern the Hg spatial distribution, identify the most polluted areas, and evaluate the impacts of dry and wet deposition on mosses. The exposed moss bags consisted of a mixture of Sphagnum fuscum and Sphagnum tenellum from an external uncontaminated area. In each site, two different types of moss bags, one uncovered (to account for the wet + dry deposition) and one covered (to evaluate the dry deposition), were exposed. The behavior of arsenic (As) and antimony (Sb) in the mosses was investigated to assess the potential relationship with Hg. GEM (Gaseous Elemental Mercury) concentrations were also measured at the same sites where the mosses were exposed, although only as a reference in the initial stages of biomonitoring. The results revealed that the main Hg emissions sources were associated with the former mining area of Abbadia San Salvatore, in agreement with the measured GEM concentrations, while arsenic and antimony were related to soil enriched in As-Sb waste material. The three elements registered higher concentrations in uncovered mosses with respect to the covered ones, i.e., wet deposition was the key factor for their accumulation on the uncovered mosses, while dry deposition was especially important for the covered samples in the mining area. Hg was accumulated in the mosses via GEM adsorption, uptake of particulate Hg, and precipitation via raindrops/snowfall, with almost no loss and without post-deposition volatilization. The results testified that the chosen biomonitoring technique was an extremely useful tool for understanding Hg transport and fate in a contaminated area.