bluuebunny/crossref_metadata_2025 huggingface dataset:
A study of the impact of digital financial inclusion on the resilience of the agricultural chain
Authors: Gao Xia , Gao Ruyun
Published: December 2024

Based on the panel data of 30 provinces (except Tibet) from 2011 to 2020, the article examines the impact of digital financial inclusion on the resilience of the agricultural industry chain as well as the mechanism of its action. The results show that: (1) Digital inclusive finance promotes the enhancement of the resilience of the agricultural industry chain, in which the degree of influence on “marketing resilience” and “distribution resilience” is much larger than that on “production resilience,” “processing resilience,” and “product resilience.” The degree of impact on “marketing resilience” and “distribution resilience” is much larger than “production resilience,” “processing resilience,” and “product resilience.” (2) Digital inclusive finance can promote the resilience of the agricultural industry chain through the promotion of agricultural technology innovation and the development of new agricultural business entities. (3) The enhancement of the resilience of the agricultural industry chain is more significant in the eastern region, where the level of digital inclusive finance is high. Based on the results of the study, the article puts forward relevant suggestions in terms of promoting the in-depth integration of digital inclusive finance and the agricultural industry chain, increasing the support for new agricultural business subjects, and strengthening agricultural technological innovation to promote sustainable development of the agricultural industry chain.
The carbon emission reduction effect of smart agricultural policy—evidence from China
Authors: Zhang Zheng , Jiang Shu , Shen Bing , Mei Yuan Yuan , Zhang Xia Ran
Published: December 2024

Background

Agricultural activities are the second largest source of its greenhouse gas emissions in China, making it imperative to prioritize the reduction of carbon emissions in agriculture to achieve carbon neutrality. Agricultural modernization is recognized as a key strategy for achieving this reduction. In response, China has introduced a smart agriculture policy to catalyze the progress of agricultural modernization. However, it is not clear that the policy will achieve agriculture carbon reductions.

Purpose

To examine the impact and mechanism of smart agriculture policy on agricultural carbon emissions, thereby facilitating the transition toward low-carbon practices in agriculture.

Methods

Using panel data across China’s 31 provinces from 2001 to 2020, this paper applies a multi-period differences-in-differences (DID) method to assess the impact and mechanisms of the smart agriculture policy on carbon reduction.

Results

① The smart agriculture policy exerts a significant positive influence on reducing agricultural carbon emission. ② The policy’s effectiveness is particularly notable in China’s central and western regions and non-grain producing areas, in contrast to the limited impact observed in the eastern provinces and grain-producing areas. ③ Smart agriculture policies have greatly reduced agricultural carbon emissions by promoting the agricultural-scale operation and the advancement of agricultural technology.

Conclusion

The paper reveals that smart agriculture policy has a positive effect on carbon emission reduction and provides relevant policy recommendations for the government. This has considerable implications for promoting sustainable agriculture.
Exploring bison producers' access to veterinary services in Ontario, Canada
Authors: Lambrou Natassia H. , Winder Charlotte B. , Spence Kelsey L.
Published: December 2024

Introduction

Access to veterinary services is integral for animals of all species. These services play a crucial role in maintaining their health and welfare and maintaining a healthy, safe, and sustainable food system. Research has consistently shown that rural communities face challenges accessing veterinary services, with livestock producers outlining several barriers including cost, inadequate infrastructure, and delays in receiving treatments. Research on bison producers' access to veterinary services is limited, prompting our investigation to address this gap in knowledge. This qualitative study aimed to describe Ontario bison producers' current access to veterinary services and identify how any barriers, as perceived by producers, might impact their herd health and management practices.

Methods

Ontario bison producers were invited to participate in virtual focus groups to share their perspectives on their access to veterinary services. Audio from the focus groups was recorded, transcribed verbatim, and analyzed using reflexive thematic analysis.

Results

Despite all participants indicating they had access to veterinary services, they also encountered obstacles and expressed concerns accessing and utilizing these services. Two overarching themes were identified: producers were concerned about the future stability and costs associated with bison farming, and they had a desire to improve bison-specific knowledge among veterinarians servicing their farms. Producers suggested several strategies to address these concerns, including improving collaborations with veterinary organizations, like the College of Veterinarians of Ontario, to increase veterinarians' exposure to bison, building stronger relationships between producers and their veterinarians, monetary incentives for established food animal veterinarians, and providing financial support to prospective food animal veterinarians.

Discussion

The findings of this study demonstrate that although bison producers in our sample had access to veterinarians, they may not fully utilize the services or find their access entirely beneficial. Future research into the veterinarian perspective would allow for greater insights into these barriers, adding additional value and contributing to a more wholistic understanding of the topic.
Experimental Study on the Drag Resistance of Tunnel Towing Navigation Facilitating Upstream and Downstream Connectivity in Mountainous River Bends
Authors: Wu Jun , Zeng Jingke , Tang Hao , Wang Wei
Published: December 2024

Navigable tunnels serve as an effective method to connect upstream and downstream navigation structures in mountainous regions with sharp bends. The towing resistance of ships in navigable tunnels, a key technical indicator for towing equipment development, demands focused research. Utilizing the innovative top towing method for tunnels, this study develops a physical model for towed navigable tunnels, conducts ship model tests, and measures and calculates the total resistance of ships towing through navigation under various conditions. Through resistance test results, it analyzes factors influencing the total resistance of ship navigation. The findings reveal: (1) regarding towing speed, at speeds exceeding 1.5 m/s, resistance spikes by 100 kN to 560 kN; (2) concerning water depth, at depths lower than 5.5 m, the impact on a ship navigation’s total resistance is pronounced, reaching 5 to 13 times that of calm water; (3) in terms of flow velocity, at velocities over 2 m/s, the impact on a ship navigation’s total resistance is substantial, amounting to 1.5 to 2 times the resistance at a flow velocity of 1.5 m/s; (4) in comparative analyses, the total resistance of ships towing through navigation in narrow tunnels is significantly higher than calculations based on existing formulas, increasing by 7 to 138 times.
Forecasting of Local Lightning Using Spatial–Channel-Enhanced Recurrent Convolutional Neural Network
Authors: Zhou Wei , Li Jinliang , Wang Hongjie , Zhang Donglai , Wang Xupeng
Published: December 2024

Lightning is a hazardous weather phenomenon, characterized by sudden occurrences and complex local distributions. It poses significant challenges for accurate forecasting, which is crucial for public safety and economic stability. Deep learning methods are often better than traditional numerical weather prediction (NWP) models at capturing the spatiotemporal predictors of lightning events. However, these methods struggle to integrate predictors from diverse data sources, which leads to lower accuracy and interpretability. To address these challenges, the Multi-Scale Spatial–Channel-Enhanced Recurrent Convolutional Neural Network (SCE-RCNN) is proposed to improve forecasting accuracy and timeliness by utilizing multi-source data and enhanced attention mechanisms. The proposed model incorporates a multi-scale spatial–channel attention module and a cross-scale fusion module, which facilitates the integration of data from diverse sources. The multi-scale spatial–channel attention module utilizes a multi-scale convolutional network to extract spatial features at different spatial scales and employs a spatial–channel attention mechanism to focus on the most relevant regions for lightning prediction. Experimental results show that the SCE-RCNN model achieved a critical success index (CSI) of 0.83, a probability of detection (POD) of 0.991, and a false alarm rate (FAR) reduced to 0.351, outperforming conventional deep learning models across multiple prediction metrics. This research provides reliable lightning forecasts to support real-time decision-making, making significant contributions to aviation safety, outdoor event planning, and disaster risk management. The model’s high accuracy and low false alarm rate highlight its value in both academic research and practical applications.
Heat-Resistant Protective Coatings Applied to Aircraft Turbine Blades by Supersonic Thermal Spraying and Diffusion-Aluminizing
Authors: Ułanowicz Leszek , Dudziński Andrzej
Published: December 2024

Aircraft engine turbine blades are covered with protective coatings. These coatings should have the best thermophysical convergence with the blade’s parent material. The aim is to create heat-resistant covering for aircraft engine turbine blades made of nickel superalloy. The results of tests on coatings are presented; the inner layer is an adhesive layer of the MeCrAlY type, applied to the blade by means of supersonic thermal spraying, and the outer layer is diffusion-aluminized in the first case using the Vapor Phase Aluminizing method, and in the second using the suspension method. The inner layer of the coating protects the blade material against high-temperature corrosion, and the outer layer against high-temperature fuel combustion product stream. The protective coatings applied to aircraft engine turbine blades were subjected to an engine test in test bench conditions and then to material tests. A protective coating with an internal layer of MeCrAlY type applied to the blade by supersonic spraying and an external layer aluminized by the Vapor Phase Aluminizing method protects the nickel superalloy against high-temperature diffusion changes, protects it against oxidation and provides it thermal insulation.
An Attention-Guided Spatio-Temporal Convolutional Network (AG-STCN) for Spatio-Temporal Characterization Analysis
Authors: Fu Yu , Zhang Chengbo , Li Chunsheng , Zhen Mei , Chen Wei , Ji Yingqi , Hua Haonan
Published: December 2024

Spatio-temporal characterization analysis plays a key role in spatio-temporal data mining tasks such as social relationship inference, traffic flow prediction, and spatio-temporal graph node classification. Although traditional numerical simulation methods are effective, they often struggle to accurately portray the complex characteristics of spatio-temporal data due to the intricacy of the modeling processes and the limitations of underlying assumptions. Models based on statistical learning and machine learning, such as ARIMA (AutoRegressive Integrated Moving Average Model) and SVM (Support Vector Machine), are capable of handling spatio-temporal data to a certain extent, but they are limited in their ability to characterize highly nonlinear data and can fail to effectively capture spatio-temporal correlations. To address these challenges, this paper introduces a new deep learning model, the Attention-Guided Spatio-Temporal Convolutional Network (AG-STCN). In the spatial dimension, the model captures spatial dependencies through an attention-guided soft pruning strategy and graph convolution operations. In the temporal dimension, it employs causal convolutions, gated linear units, and a self-attention mechanism to capture temporal dependencies. Experimental results demonstrate that the AG-STCN significantly outperforms existing baseline methods on real-world datasets for multiple spatio-temporal characterization analysis tasks.
Numerical Study on Heat Leakage, Thermal Stratification, and Self-Pressurization Characteristics in Liquid Helium Storage Tanks
Authors: Xu Jing , Liu Fa’an , Zhang Jianguo , Li Chao , Liu Qinghua , Li Changjun , Jia Wenlong , Fu Shixiong , Li Longjiang
Published: December 2024

During the operation of liquid-phase He-4 (LHe-4) storage tanks, heat leakage changes the thermophysical parameters and phase properties of the LHe-4 in the tanks, resulting in the thermal layering phenomenon. This phenomenon is characterized by the LHe-4 temperature gradient and pressure increase (self-pressurization) phenomena in the tanks. Based on the Layer-by-Layer model, a heat transfer model of a composite adiabatic structure with multilayer insulation and liquid nitrogen screen (LNCS) insulation was established, and the Neumann boundary heat flux of the thermal response model was determined. A numerical simulation model of the thermal response of a liquid helium storage tank was established. The spatial and temporal evolutions of the pressure distribution, natural convection characteristics, thermal stratification characteristics, and self-pressurization characteristics of the LHe-4 tank were investigated. Finally, the self-pressurization thermodynamic model of the LHe-4 storage tank was built based on the isothermal saturation and homogeneous model. It is shown that the predictive performance of the mLee model for the self-boosting characteristics (relative deviation of 14.32%) was significantly improved compared with that of the Lee model (relative deviation of 39.64%). The thermal stratification degree (TSD) of the tank increased with the operation time, with TSDs of 1.023, 1.028, and 1.036 at 1 h, 2 h, and 3 h, which exacerbated the self-pressurization of the tank. The wall surface in contact with the phase interface is a strong evaporation point, so the interfacial mass transfer rate maps show a pattern of high at both ends and low in the middle.
Localization and Diagnosis of Short-Circuit Faults in Transformer Windings Injected by Damped Oscillatory Wave
Authors: Hu Chengbo , Zhu Xueqiong , Lu Yongling , Liu Ziquan , Wang Zhen , Liu Zhengyu , Yin Kangyong
Published: December 2024

Power transformers, as critical components in regional power distribution and transmission systems, require early fault detection to ensure system reliability. This paper presents a scalable design capable of rapidly simulating winding faults in experimental transformers. By diagnosing three-phase transformer winding short-circuit faults using oscillatory shock voltages and numerical statistical methods, the relationship between the transfer function and winding short-circuit faults is investigated. The experimental results show that winding short-circuit faults cause significant changes in the transfer function curve. By analyzing transfer function variations across different phases, the location of a fault can be effectively determined. Furthermore, the correlation coefficient and absolute logarithmic deviation provide a clear indication of the fault severity. The transfer function of the high-voltage phase-to-phase is particularly sensitive to winding short-circuit faults. In non-fault phases, after the application of damped oscillatory waves, the transfer function correlation coefficient becomes negative and the absolute logarithmic deviation increases linearly with fault severity. These findings provide a rapid diagnostic solution for determining both the faulty phase and the severity of damage in three-phase transformer winding short-circuit faults.
Resilience Evaluation of the Forest Products Platform Supply Chain Based on Artificial Intelligence and Extension Theory
Authors: Lu Lin , Long Ping , Luo Xiaochun
Published: December 2024

Forestry has a profound impact on environmental protection, economic development, and social welfare. With the improvement of global environmental protection awareness, the construction of platform supply chain of forest products has become the core driving force to promote sustainable development of forestry. Studying the resilience of supply chain of platform of forest products is of great importance to solve the contradiction between economic development and natural ecosystem protection. However, the existing resilience evaluation methods are not suitable for the dynamic and complex performance evaluation of the current forest products platform supply chain. Therefore, in order to make up for this shortcoming, this paper evaluates and analyzes the supply chain resilience of the forest products platform based on AI recommendation and extension theory. Firstly, this paper combined the characteristics of forest products and used AI recommendation technology to build a forest products platform supply chain resilience performance evaluation index system. Secondly, the AHP method was used to calculate the index weight, and the resilience evaluation model of the platform supply chain of forest products was constructed. Finally, in order to ensure the authenticity and credibility of the evaluation results, three practical cases were analyzed to illustrate the resilience level of the platform supply chain of forest products, and the effectiveness of the application of AI recommendation and extension theory in the resilience performance evaluation of forest products platform supply chains was verified. The scientific value of this paper is that it provides a new idea and a new method for the resilience performance evaluation of the forest products platform supply chain and makes theoretical and practical contributions to the fruitful application of AI recommendation in the supply chain field. In addition, this study also provides a new practical guideline for protecting the natural environment and realizing the sustainable development of forestry.
Effect of Ethylene Oxide Exposure on Sleep Health: Using NHANES Data from 2015 to 2020
Authors: Choi Hansol , Choy Yoon-Soo
Published: December 2024

Background/Objectives: This study aims to investigate the effects of ethylene oxide (EO) exposure on sleep health, focusing on sleep duration and quality. Methods: The study analyzed data from the NHANES (National Health and Nutrition Examination Survey) 2015–2020 cycles, including 4268 participants aged 20 and older. EO exposure was measured using hemoglobin adducts of EO (HbEO), which serve as a reliable biomarker. Sleep health was assessed through self-reported questionnaires on sleep duration and quality. Participants were categorized based on sleep duration (<6 h, 6–9 h, >9 h) and symptoms of sleep disturbances. Statistical analyses employed survey-weighted logistic regression models to evaluate the associations between HbEO levels and sleep outcomes, adjusting for sociodemographic, health-related, and behavioral factors. Moreover, to examine whether the impact of ethylene oxide exposure on sleep quality and sleep duration varies by sociodemographic characteristics, stratified analyses were conducted based on gender, age, marital status, and employment type. Results: According to the results, higher EO exposure was associated with shorter sleep durations and increased likelihood of sleep disturbances. Moreover, according to sub-group analysis by sex, men with higher exposure to EO, were likely to have short sleep duration, and women with higher exposure to EO had higher risk of daytime sleepiness and sleep problems. Conclusions: The findings suggest that EO exposure may negatively impact sleep health, emphasizing the need for stricter EO exposure regulations and public health interventions to reduce associated risks.
Satellite Altimetry and Seasonal Circulation in the Ligurian Sea
Authors: Picco Paola , Nardini Roberto , Repetti Luca , Vignudelli Stefano
Published: December 2024

Satellite altimetry observations are checked against in situ measurements to assess the capability of this remote sensing technique to describe the surface circulation in the Ligurian Sea. CTD profiles were collected during five oceanographic campaigns from 2017 and 2024 along the satellite track Jason 044, crossing the Ligurian Sea from the Corsica Channel to the coast close to Genoa. Eight months of ADCP surface currents from a fixed mooring were also used for the comparison with altimetric-derived geostrophic currents. Moreover, the possible contribution of ICESat-2 to oceanographic studies in the area is investigated. Altimetric measurements successfully reproduce the basic circulation features of the region and their seasonal variation and, despite the different nature of the used systems, can be well integrated with in situ observations. The results from the direct comparison with daily mean values of ADCP surface currents reported an RMSD of the same order as the standard deviation, which is consistent with similar investigations in other areas but evidences the need to define more appropriate metrics and methods.
Evaluation of the Integration of Topological Optimisation in the Process Chain for Manufacturing Customised Orthopaedic Devices via Additive Manufacturing
Authors: Sala Francesca , D’Urso Gianluca Danilo , Giardini Claudio
Published: December 2024

Purpose: The effectiveness of the customised solutions compared to the conventional ones and the emergence of advanced production technologies, such as Additive Manufacturing (AM) techniques, strengthened the trend towards an enhanced individualization of the clinical treatments. In the present research, the value of topological optimisation (TO) in the manufacturing process of tailor-made orthopaedic appliance (upper-limb orthosis) was analysed. Methodology: From the morphology of a patient’s arm, orthotic models were developed. Nonparametric optimization (Simulia Tosca) was performed, based on the Finite Element Analysis (FEA) program (Abaqus), and contributed to the development of TO orthotic models with diverse levels of volume reduction fraction. The modelling and manufacturing framework for customising orthotic solutions was evaluated with a discussion on the feasibility of lightweight and high-performance products, encompassing production time and cost. Pilot products were produced with a Material Extrusion (MEX) printer. Findings: TO proved to be a practical and valuable approach for the advanced customisation of orthopaedic devices, offering lightweight solutions able to withstand stresses also during patient rehabilitation and remission. From the rapid prototyping perspective, specific strategies must be adopted to prevent the escalation of production costs and time. Originality: The research delves into the overall benefit of implementing an advanced modelling technique within the context of manufacturing highly customised orthoses, analysing how TO activity impacts the rapid prototyping process. Beyond product evaluation, the analysis explores broader implications, including the assessment of feasibility and the development of strategies for integrating the approach into clinical workflows and hospital settings.
Opportunistic Weather Sensing by Smart City Wireless Communication Networks
Authors: Ostrometzky Jonatan , Messer Hagit
Published: December 2024

This paper presents how the concept of opportunistic integrated sensing and communication (ISAC), focusing on weather sensing, is incorporated into wireless smart cities’ networks. The concept, first introduced in 2006, utilized standard signal level measurements from wireless backhaul cellular networks for rain monitoring. Since then, it has expanded to include technologies like satellite communication and smart cities’ networks. Opportunistic ISAC (OISAC) for weather involves transforming communication networks into virtual sensors by interpreting the signal attenuation caused by environmental factors, such as rain. These virtual sensors form the sensing layer of an IoT system, with built-in connectivity. In this paper, we present the recent advancements in the field, emphasizing the potential of current and future smart cities’ wireless networks for accurate rainfall monitoring. We also demonstrate a test case in the city of Rehovot in Israel, where high spatiotemporal resolution rain maps produced via the OISAC paradigm significantly outperform the spatial resolution achieved by modern weather radars. We also discuss the challenges and opportunities in applying this concept.
Emerging Sensing Technologies for Liquid Biopsy Applications: Steps Closer to Personalized Medicine
Authors: Kalligosfyri Panagiota M. , Lamprou Eleni , Kalogianni Despina P.
Published: December 2024

Liquid biopsy is an efficient diagnostic/prognostic tool for tumor-derived component detection in peripheral circulation and other body fluids. The rapid assessment of liquid biopsy techniques facilitates early cancer diagnosis and prognosis. Early and precise detection of tumor biomarkers provides crucial information about the tumor that guides clinicians towards effective personalized medicine. Point-of-care-testing remains still a great challenge in cancer diagnostics. Liquid biopsy is a promising alternative to tissue biopsy with the great advantages of less invasion and real-time monitoring of the disease, also providing information about tumor heterogeneity. The field is continuously and rapidly expanding. Numerous sophisticated biosensors have been developed targeting several biomarkers to achieve low detection limits, increased specificity and robustness. Current biosensors include mainly optical sensors, such as colorimetric, fluorescent, SPR, SERS and lateral flow assays. Electrochemical sensors have also been developed, providing very low detection limits. Colorimetric sensors exhibited simplicity in signal interpretation, while fluorescent sensors contributed to low analysis times, and SPR/SERS enabled label-free and rapid analysis. Novel target amplification and signal enhancement techniques have been exploited to increase the detectability of the sensors. In this context, this review is focused on the recent advances in biosensing technology for cutting-edge liquid biopsy applications towards point-of-care testing.
Tunable Characteristics of Optical Frequency Combs from InGaAs/GaAs Two-Section Mode-Locked Lasers
Authors: Weng Dengqun , Liang Yanbo , Qiao Zhongliang , Li Xiang , Sia Jia Xu Brian , Li Zaijin , Li Lin , Chen Hao , Zhao Zhibin , Qu Yi , Liu Guojun , Liu Chongyang , Wang Hong
Published: December 2024

We observed tunable characteristics of optical frequency combs (OFCs) generated from InGaAs/GaAs double quantum wells (DQWs) asymmetric waveguide two-section mode-locked lasers (TS-MLLs). This involves an asymmetric waveguide mode-locked semiconductor laser (AWML-SL) operating at a center wavelength of net modal gain of approximately 1.06 µm, which indicates a stable pulse shape, with the power-current(P-I) characteristic curve revealing a small difference between forward and reverse drive currents in the gain region. Under different operating conditions, the laser exhibits the characteristics of OFCs. And the pulse interval in the timing and the peak interval in the frequency domain show a periodic alternating change trend with the increase in the gain current. This tunable characteristic is reported for the first time. The study demonstrates the feasibility of generating tunable optical combs using a monolithic integrated two-section mode-locked semiconductor laser (MI-TS-MLL). This has important reference value for the application of OFCs generated from MI-TS-MLLs or integrated optical chips.
Deep Learning-Based Spectrum Sensing for Cognitive Radio Applications
Authors: Abdelbaset Sara , Kasem Hossam , Khalaf Ashraf , Hussein Amr , Kabeel Ahmed
Published: December 2024

In order for cognitive radios to identify and take advantage of unused frequency bands, spectrum sensing is essential. Conventional techniques for spectrum sensing rely on extracting features from received signals at specific locations. However, convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have recently demonstrated promise in improving the precision and efficacy of spectrum sensing. Our research introduces a groundbreaking approach to spectrum sensing by leveraging convolutional neural networks (CNNs) to significantly advance the precision and effectiveness of identifying unused frequency bands. We treat spectrum sensing as a classification task and train our model with diverse signal types and noise data, enabling unparalleled adaptability to novel signals. Our method surpasses traditional techniques such as the maximum–minimum eigenvalue ratio-based and frequency domain entropy-based methods, showcasing superior performance and adaptability. In particular, our CNN-based approach demonstrates exceptional accuracy, even outperforming established methods when faced with additive white Gaussian noise (AWGN).
Edge-Cloud Synergy for AI-Enhanced Sensor Network Data: A Real-Time Predictive Maintenance Framework
Authors: Sathupadi Kaushik , Achar Sandesh , Bhaskaran Shinoy Vengaramkode , Faruqui Nuruzzaman , Abdullah-Al-Wadud M. , Uddin Jia
Published: December 2024

Sensor networks generate vast amounts of data in real-time, which challenges existing predictive maintenance frameworks due to high latency, energy consumption, and bandwidth requirements. This research addresses these limitations by proposing an edge-cloud hybrid framework, leveraging edge devices for immediate anomaly detection and cloud servers for in-depth failure prediction. A K-Nearest Neighbors (KNNs) model is deployed on edge devices to detect anomalies in real-time, reducing the need for continuous data transfer to the cloud. Meanwhile, a Long Short-Term Memory (LSTM) model in the cloud analyzes time-series data for predictive failure analysis, enhancing maintenance scheduling and operational efficiency. The framework’s dynamic workload management algorithm optimizes task distribution between edge and cloud resources, balancing latency, bandwidth usage, and energy consumption. Experimental results show that the hybrid approach achieves a 35% reduction in latency, a 28% decrease in energy consumption, and a 60% reduction in bandwidth usage compared to cloud-only solutions. This framework offers a scalable, efficient solution for real-time predictive maintenance, making it highly applicable to resource-constrained, data-intensive environments.
A Ray-Tracing-Based Single-Site Localization Method for Non-Line-of-Sight Environments
Authors: Hu Shuo , Guo Lixin , Liu Zhongyu
Published: December 2024

Localization accuracy in non-line-of-sight (NLOS) scenarios is often hindered by the complex nature of multipath propagation. Traditional approaches typically focus on NLOS node identification and error mitigation techniques. However, the intricacies of NLOS localization are intrinsically tied to propagation challenges. In this paper, we propose a novel single-site localization method tailored for complex multipath NLOS environments, leveraging only angle-of-arrival (AOA) estimates in conjunction with a ray-tracing (RT) algorithm. The method transforms NLOS paths into equivalent line-of-sight (LOS) paths through the generation of generalized sources (GSs) via ray tracing. A novel weighting mechanism for GSs is introduced, which, when combined with an iteratively reweighted least squares (IRLS) estimator, significantly improves the localization accuracy of non-cooperative target sources. Furthermore, a multipath similarity displacement matrix (MSDM) is incorporated to enhance accuracy in regions with pronounced multipath fluctuations. Simulation results validate the efficacy of the proposed algorithm, achieving localization performance that approaches the Cramér–Rao lower bound (CRLB), even in challenging NLOS scenarios.
Advanced Trajectory Planning and Control for Autonomous Vehicles with Quintic Polynomials
Authors: Jin Ma , Qu Mingcheng , Gao Qingyang , Huang Zhuo , Su Tonghua , Liang Zhongchao
Published: December 2024

This paper focuses on the design of vehicle trajectories and their control systems. A method based on quintic polynomials is utilized to develop trajectories for intelligent vehicles, ensuring the smooth continuity of the trajectory and related state curves under varying conditions. The construction of lateral and longitudinal controllers is discussed, which includes a tracking error model derived from the two-degree-of-freedom dynamic model of a two-wheeled vehicle and the application of the Frenet coordinate system transformation. The vehicle tracking performance is regulated by these controllers. Experimental verification on a small intelligent vehicle platform operating on the Ackermann steering principle was conducted. The results confirm the tracking performance of the controllers under different conditions and validate the effectiveness and feasibility of the overall framework of the study.
Modeling of a Novel T-Core Sensor with an Air Gap for Applications in Eddy Current Nondestructive Evaluation
Authors: Zhang Siquan
Published: December 2024

Multi-layer conductive structures, especially those with features like bolt holes, are vulnerable to hidden corrosion and cracking, posing a serious threat to equipment integrity. Early defect detection is vital for implementing effective maintenance strategies. However, the subtle signals produced by these defects necessitate highly sensitive non-destructive testing (NDT) techniques. Analytical modeling plays a critical role in both enhancing defect-detection capabilities and guiding the design of highly sensitive sensors for these complex structures. Compared to the finite element method (FEM), analytical approaches offer advantages, such as faster computation and high accuracy, enabling a comprehensive analysis of how sensor and material parameters influence defect detection outcomes. This paper introduces a novel T-core eddy current sensor featuring a central air gap. Utilizing the vector magnetic potential method and a truncated region eigenfunction expansion (TREE) method, an analytical model was developed to investigate the sensor’s interaction with multi-layer conductive materials containing a hidden hole. The model yielded closed-form expressions for the induced eddy current density and coil impedance. A comparative study, implemented in Matlab, analyzed the eddy current distribution generated by T-core, E-core, I-core, and air core sensors under identical conditions. Furthermore, the study examined how the impedance of the T-core sensor changed at different excitation frequencies between 100 Hz and 10 kHz when positioned over a multi-layer conductor with a hidden air hole. These findings were then compared to those obtained from E-core, I-core, and air-core sensors. The analytical results were validated through finite element simulations and experimental measurements, exhibiting excellent agreement. The study further explored the influence of T-core design parameters, including the air gap radius, dome radius, core column height, and relative permeability of the T-core material, on the inspection sensitivity. Finally, the proposed T-core sensor was used to evaluate crack and hole defects in conductors, demonstrating its superior sensitivity compared to I-core and air core sensors. Although slightly less sensitive than the E-core sensor, the T-core sensor offers advantages, including a more compact design and reduced material requirements, making it well-suited for inspecting intricate and confined surfaces of the target object. This analytical model provides a valuable tool for designing advanced eddy current sensors, particularly for applications like detecting bolt hole defects or measuring the thickness of non-conductive coatings in multi-layer conductor structures.
Digital Transformation of National Audits and Regional Sustainable Development: Quasi-Natural Experiment on the Establishment of National Audit Digital Departments
Authors: Zhang Yuhan , Zhang Yuankang , Wang Zichen
Published: December 2024

Promoting the realization of sustainable development goals is an important aspect of the national audit’s work. In practice, the digital transformation of the national audit, which emphasizes the integrated development of digital technology and traditional auditing methods, has emerged as a significant tool for advancing regional sustainable development. Based on the quasi-natural experiment of the establishment of a national audit digital department, the impact of national audit digital transformation on the effectiveness of regional sustainable development and the mechanisms through which it operates is empirically tested in this study. The findings reveal that national audit digital transformation significantly promotes regional sustainable development. A mechanism analysis indicates that national audit digital transformation promotes regional sustainable development by improving the performance of the government’s environmental governance and corporate green innovation. A heterogeneity analysis further demonstrates that the positive effects of national audit digital transformation on regional sustainable development are more pronounced in regions characterized by a high reliance on secondary industries, intense government competition, and elevated public concern for environmental issues.
Dynamic Changes and Driving Force of Ecosystem Services Values Under Land Use/Land Cover Scenarios in Nanning, China
Authors: Hu Jinlong , Bin Zhenhong , Huang Tingting , Wang Yingxue , Fan Yaming
Published: December 2024

A comprehensive understanding of the spatial and temporal patterns of changes in ecosystem services, along with their driving factors, is crucial for managing ecosystems effectively and ensuring sustainable development in the area. Intense human activities and rapid urbanization have led to frequent land use/cover changes. Nanning, as a rapidly developing city, has received increasing attention for its ecosystem services. This research assesses the valuation of ecosystem services by employing the equivalent factor approach, utilizing land use data from Nanning City spanning the period from 2000 to 2020. It analyzes the spatial and temporal dynamics of these services and identifies key drivers through the application of a GeoDetector model. The results show the following: (1) Between 2000 and 2020, forests were the predominant ecosystem in Nanning City. During this period, significant land type conversions occurred, with the largest outflow from arable land and the largest inflow to construction land. (2) Over the 20 years, the aggregate value of ecosystem services (ESV) in Nanning exhibited a declining trend, culminating in a net reduction of USD 1056.8 million. Forest land ESVs constituted the largest proportion of the total value, exceeding 59%. Among the individual ESVs, hydrological regulation and climate regulation were the predominant services, collectively accounting for over 48% of the total value, with hydrological regulation contributing 20.7% and climate regulation 27.7%. (3) The ESVs in Nanning City exhibit pronounced spatial differentiation, with higher values concentrated in the central regions and lower values in peripheral zones. Notably, the high-value zones are experiencing a reduction in size, while the low-value zones are progressively expanding. (4) The findings from the GeoDetector analysis indicate that soil organic matter, urbanization rate, annual sunshine, financial expenditure, and population density are the primary determinants affecting ESV. Furthermore, the explanatory power of these influencing factors is substantially increased following interactive detection. This research provides a scientific basis for developing land management strategies and policies in Nanning City and holds substantial significance for advancing sustainable regional development.
Desperate Importance of Nuclear Renaissance for the Future of Japan
Authors:   Primakov National Research Institute of World ·Economy and International Relations RAS, Yurkov Sergey
Published: December 2024

Article explains current status of the Japanese nuclear industry as for mid 2023. It unveils political and economic factors and directions of its renaissance 12 year after the Fukushima Dai-Ichi NPP disaster in 2011. The contradiction of this process and the declared by the Japanese government Green Transformation (GX) initiative is underlined. Article lists limits of political, economic, technological and social nature which will define the actual success of the proposed nuclear program. Creation of the industrial and technological as well as political and social base of the fusion energy and fusion manufacturing industries is considered as the strategic reason for pushing the nuclear renaissance through these limits, as the way to the global leadership and domination of the Japanese ‘Society 5.0’. Special attention is paid to the strong will of the Japanese government to pragmatically sacrifices democratic values, concentrate all available resources, and set ambitious targets to win in this sumo fight and be first to build ‘thermonuclear Japan’. Also the specific side of the nuclear energy development — building up the Japanese nuclear weapon potential — is considered as the way to mitigate both geo- and technopolitical risks.
Prospective research of mixed saliva composition in implantology patients during menopause
Authors:   FSBEI HE Smolensk State Medical University of the Ministry of Health of Russia, Semenova Yu. A. , Morozov V. G. ,   FSBEI HE Smolensk State Medical University of the Ministry of Health of Russia, Degtyarev N. E. ,   FSAEI HE Peoples' Friendship University of Russia named after Patrice Lumumba, Samsonov V.V. ,   Federal State Budgetary Institution NMHC VMT named after A.A.Vishnevsky; of the Ministry of Defense of the Russian Federation, 3 Department of Emergency Surgery of the branch of the S.M.Kirov VMEDA
Published: December 2024

The effectiveness of dental implantation largely depends on the qualitative
and quantitative composition of the oral fluid, as well as the state of the bone structures of the upper and lower jaws. Endocrine rearrangement during menopause in
women leads to an imbalance of mineral metabolism, which can serve as an important
factor in the formation of postmenopausal osteoporosis and, as a consequence, the
inefficiency of dental implants.
Experimental and theoretical studies of malleable flange joints
Authors: Zemlyanukhin A. D. , Kashevarova G. G. , Son M. P.
Published: December 2024

This paper presents the results of a study of pliable flange connections in steel frame. Calculation of the load carrying capacity and ductility of flange connections is an important design stage and requires consideration of various factors such as analysis of mechanical properties of materials, geometry, operating conditions and type of loads. The main objective of structural design is to ensure structural safety while minimizing cost. For this purpose, it is necessary to use calculation schemes that correspond to the real physical operation of structures. In solving this problem, the following were used: full-scale experiments, finite element method and analytical method of limit equilibrium (LEM), which is traditionally used in structural fracture mechanics. The influence of bending stiffness of the flange on the process of joint failure is investigated. A new design solution of a pliable beam-column assembly is proposed, which is characterized by simplicity of calculation and assembly.
Nonlinear deformation model in the analysis of eccentri-cally compressed concrete-filled steel tube elements
Authors: Lazovsky D. N. , Khatkevich A. M. , Glukhov D. O.
Published: December 2024

This paper discusses the applicability of a nonlinear deformation model for determining the parameters of the stress-strain state of eccentrically compressed concrete-filled steel tube elements. Adjustments to the deformation diagrams of the concrete core and the steel tube were introduced to account for the complex stress state of the materials. The cross-section of the concretefilled steel tube element is considered as a collection of elementary areas. As the criterion for calculating compressive resistance at the ultimate strength stage, it is proposed to use the maximum force calculated based on the compatibility condition of steel and concrete deformations. The advantage of this criterion is that it eliminates the need to normalize the ultimate compressibility of concrete and accounts for the high degree of force redistribution in cross-sections. The method was verified by comparing the calculated and experimentally obtained ultimate forces on a sample of experimental data. The applicability of the nonlinear deformation model based on deformation diagrams, considering the multiaxial stress state of concrete and the steel tube, was confirmed for the analysis of eccentrically compressed concrete-filled steel tube elements.
Deflection to natural vibration frequency ratio in a three-layer CLT slab with variable thickness of layers
Authors: Troshin M. Yu. , Turkov A. V. , Troshina A. S.
Published: December 2024

This study examines the relationship between the maximum deflection and natural frequency of vibration in a three-layer cross-laminated timber (CLT) board at variable thicknesses of the inner and transverse plank layers under different boundary conditions. The finite element method (FEM), implemented in the SCAD++ computer complex by creating a finite element computational model in the form of a composite plate with anisotropic properties of the layers and rigid bonds between the plates, was used for the investigations. As a result, the values of maximum deflections and natural vibration frequencies for plates with different cross-sectional parameters were obtained. The obtained results are compared with the analytical values of the fundamental dependence obtained by Professor V.I. Korobko describing the relationship between the maximum deflection of a composite plate and the frequency of natural vibrations. It was found that the deviation of the numerical values of the K coefficient does not exceed 3% under different boundary conditions of structures and parameters of their cross-section.
Estudos da região nordeste acerca do Pibid e contribuições na formação docente
Authors: Ramos Kymberli Luana Santos , Pantoja Marília Seabra , Jucá Rosineide de Sousa de , Sá Pedro Franco de
Published: December 2024

O objetivo deste trabalho é revisar a literatura de estudos da região nordeste do Brasil acerca do Programa Institucional de Bolsas de Iniciação à Docência (Pibid) e suas contribuições na formação docente. Trata-se de uma revisão sistemática que se delineia em pesquisa bibliográfica, assim como parte dos procedimentos metodológicos foi feita a busca por meio dos estudos do Catálogo de Teses e Dissertações da Capes que passaram por etapas de levantamento, seleção e análise. Foram selecionados oito trabalhos que atendiam aos critérios de inclusão definidos para esta revisão. Na revisão, foram consideradas duas categorias de análise: as contribuições na formação inicial e continuada de bolsistas e supervisores do Pibid e as implicações na prática de professores iniciantes egressos desse Programa. De modo geral, por intermédio dos resultados encontrados, inferimos que o Pibid traz contribuições para a formação de professores e os estudos se debruçam sobre os principais pontos que permeiam essas contribuições.