bluuebunny/crossref_metadata_2025 huggingface dataset:
Multipath Effects Mitigation in Offshore Construction Platform GNSS-RTK Displacement Monitoring Using Parametric Temporal Convolution Network
Authors: Jiang Yiyang , Guo Cheng , Wang Jinfeng , Xu Rongqiao
Published: February 2025

The Global Navigation Satellite System (GNSS), renowned for its high precision and automation, has shone brightly in the deformation monitoring of offshore facilities and sea-crossing bridges. However, antennas placed in these locations are often subject to signal interference from various reflective surfaces, such as rivers and oceans, which significantly compromises observation accuracy and reliability. Synthesizing previous research, we first propose a method for multipath dataset construction, which involves GNSS observation linear combinations, detailed mapping of the near-field reflector, and employed static solution residuals as reference. Subsequently, we construct and train a corresponding para-TCN (parametric Temporal Convolution Network) to enable real-time prediction of multipath prediction. Through time domain and frequency domain analysis, it has been demonstrated that the trained network can capture the main features of multipath models and suppress those components in both the data distribution and frequency band, effectively mitigating the interference of multipath errors in observations.
Improvement of FAPAR Estimation Under the Presence of Non-Green Vegetation Considering Fractional Vegetation Coverage
Authors: Li Rui , Li Baolin , Yuan Yecheng , Liu Wei , Zhu Jie , Qi Jiali , Liu Haijiang , Ma Guangwen , Jiang Yuhao , Li Ying , Tan Qiuyuan
Published: February 2025

The homogeneous turbid medium assumption inherent to the Beer-Lambert’s law can lead to a reduction in the shading effect between leaves when non-green vegetation canopies are present, resulting in an overestimation of the fraction of absorbed photosynthetically active radiation (FAPAR). This paper proposed a method to improve the FAPAR estimation (FAPARFVC) based on Beer-Lambert’s law by incorporating fractional vegetation coverage (FVC). Initially, the canopy-scale leaf area index (LAI) of the green canopy distribution area within the pixel (sample site) was determined based on the FVC. Subsequently, the canopy-scale FAPAR was calculated within the green canopy distribution area, adhering to the assumption of a homogeneous turbid medium in the Beer-Lambert’s law. Finally, the average FAPAR across the pixel (sample site) was calculated based on the FVC. This paper conducted a case study using measured data from the BigFoot Project and grass savanna in Senegal, West Africa, as well as Moderate Resolution Imaging Spectroradiometer (MODIS) LAI/FPAR products. The results indicated that the FAPARFVC approach demonstrated superior accuracy compared to the FAPAR determined by MODIS LAI, according to the Beer-Lambert’s law (FAPARLAI) and MODIS FPAR products (FAPARMOD). The mean absolute percentage error of FAPARFVC was 48.2%, which is 25.6% and 52.1% lower than that of FAPARLAI and FAPARMOD, respectively. The mean percentage error of FAPARFVC was 16.8%, which was 71.6% and 73.4% lower than that of FAPARLAI and FAPARMOD, respectively. The improvements in accuracy and the decrease in overestimation for FAPARFVC became more pronounced with increasing FVC compared to FAPARLAI. The findings suggested that the FAPARFVC method enhanced the accuracy of FAPAR estimation under the presence of non-green vegetation canopies. The method can be extended to regional scale FAPAR and gross primary production (GPP) estimations, thereby providing more accurate inputs for understanding its tempo-spatial patterns and drivers.
Satellite Remote Sensing Techniques and Limitations for Identifying Bare Soil
Authors: Delaney Beth , Tansey Kevin , Whelan Mick
Published: February 2025

Bare soil (BS) identification through satellite remote sensing can potentially play a critical role in understanding and managing soil properties essential for climate regulation and ecosystem services. From 191 papers, this review synthesises advancements in BS detection methodologies, such as threshold masking and classification algorithms, while highlighting persistent challenges such as spectral confusion and inconsistent validation practices. The analysis reveals an increasing reliance on satellite data for applications such as digital soil mapping, land use monitoring, and environmental impact mapping. While multispectral sensors like Landsat and Sentinel dominate current methodologies, limitations remain in distinguishing BS from spectrally similar surfaces, such as crop residues and urban areas. This review emphasises the critical need for robust validation practices to ensure reliable estimates. By integrating technological advancements with improved methodologies, the potential for accurate, large-scale BS detection can significantly contribute to combating land degradation and supporting global food security and climate resilience efforts.
Gravity Data Fusion and Imaging of Geological Structures in the Red River Fault Zone and Adjacent Areas
Authors: Wu Guiju , Yu Fei , Tan Hongbo , Wang Jiapei , Liu Weihua
Published: February 2025

The geological structure in the Red River fault zone (RRF) and adjacent areas is complex. Due to the lack of high-precision gravity data in the study area, it is difficult to obtain the distribution of materials within the Earth’s crust. In this study, a gravity data-fused method is proposed. The Moho depth model data are utilized to construct the gravity anomaly trend, and the mapping relation between the gravity field model data and the measured gravity data is established. Using 934 high-precision measured gravity data as control points, the bilinear interpolation method is used to calculate high-precision grid data of the RRF. Finally, the apparent density inversion method is used to obtain clear crustal density images across the RRF. The experimental results show that the fuses data not only reflect the regional anomaly trend but also maintain the local anomaly information; the root-mean-square error of the fused data is less than 5% and the correlation coefficient is greater than 90%. Through an in-depth comparative analysis of density images, it is found that the low-density anomalous zones, with depths of ~20 km in the northern and southern sections of the RRF, are shallower than those in the middle. The data-fused method provides a new way to process geophysical data more efficiently.
Federated Learning-Based Predictive Traffic Management Using a Contained Privacy-Preserving Scheme for Autonomous Vehicles
Authors: Alqubaysi Tariq , Asmari Abdullah Faiz Al , Alanazi Fayez , Almutairi Ahmed , Armghan Ammar
Published: February 2025

Intelligent Transport Systems (ITSs) are essential for secure and privacy-preserving communications in Autonomous Vehicles (AVs) and enhance facilities like connectivity and roadside assistance. Earlier research models used for traffic management compromised user privacy and exposed sensitive data to potential adversaries while handling real-time data from numerous vehicles. This research introduces a Federated Learning-based Predictive Traffic Management (FLPTM) system designed to optimize service access and privacy for Autonomous Vehicles (AVs) within an ITS. Moreover, a CPPS will provide strong security to mitigate adversarial threats through state modelling and authenticated access permissions for the integrity of vehicle communication networks from man-in-the-middle attacks. The suggested FLPTM system utilizes a Contained Privacy-Preserving Scheme (CPPS) that decentralizes data processing and allows vehicles to train local models without sharing raw data. The CPPS framework combines a classifier-based learning technique with state modelling and access permissions to protect user data against invasions and man-in-the-middle attacks. The proposed model leverages Federated Learning (FL) to enhance data security in collaborative machine learning processes by allowing updates that preserve privacy, enabling joint learning without exposing raw data. It addresses key challenges such as high communication costs, the impact of adversarial attacks, and access time inefficiencies. Using FL, the model reduces communication costs by 23.29%, mitigates adversarial effects by 16.1%, and improves access time by 18.95%, achieving significant cost savings and maintaining data privacy throughout the learning process.
Effective Thermal Diffusivity Measurement Using Through-Transmission Pulsed Thermography: Extending the Current Practice by Incorporating Multi-Parameter Optimisation
Authors: Ali Zain , Addepalli Sri , Zhao Yifan
Published: February 2025

Through-transmission pulsed thermography (PT) is an effective non-destructive testing (NDT) technique for assessing material thermal diffusivity. However, the current literature indicates that the technique has lagged behind the reflection mode in terms of technique development despite it offering better defect resolution and the detection of deeper subsurface defects. Existing thermal diffusivity measurement systems require costly setups, including temperature-controlled chambers, multiple calibrations, and strict sample size requirements. This study presents a simple and repeatable methodology for determining thermal diffusivity in a laboratory setting using the through-transmission approach by incorporating both finite element analysis (FEA) and laboratory experiments. A full-factorial design of experiments (DOE) was implemented to determine the optimum flash energy and sample thickness for a reliable estimation of thermal diffusivity. The thermal diffusivity is estimated using the already established Parker’s half-rise equation and the recently developed new least squares fitting (NLSF) algorithm. The latter not only estimates thermal diffusivity but also provides estimates for the input flash energy, reflection coefficient, and the time delay in data capture following the flash event. The results show that the NLSF is less susceptible to noise and offers more repeatable values for thermal diffusivity measurements compared to Parker, thereby establishing it as a more efficient and reliable technique.
Collision Avoidance Mechanism for Swarms of Drones
Authors: Marek Dariusz , Biernacki Piotr , Szyguła Jakub , Domański Adam , Paszkuta Marcin , Szczygieł Marta , Król Marcel , Wojciechowski Konrad
Published: February 2025

This article presents a new approach to collision avoidance in drone swarms, designed for operations in large drone swarms and dynamic environments. The mechanism uses distributed communication, where drones share information about their positions and planned trajectories to predict and avoid collisions. The proposed mechanism enables drones to autonomously cooperate and maintain safe distances in complex scenarios. It is based on the concept of repulsion vectors. The avoidance response is determined by the level of immersion in the protective sphere of obstacles, including other drones. The advantage of the algorithm lies in its simplicity and low computational complexity, allowing it to be used even in small and inexpensive drones. The algorithm was tested in a developed simulation environment, created to handle swarms of over 20 drones and to demonstrate the scalability of the proposed solution. Two scenarios were analyzed: (i) two swarms, each with nine drones, flying on a collision course; (ii) a swarm of 25 drones changing formation. The results showed that the mechanism is effective in avoiding collisions, maintaining safe distances and adapting to changing conditions. The proposed mechanism represents a significant advancement in swarm coordination, offering a robust and scalable solution for real-world applications.
Headspace Determination of Acetone and Aromatic Hydrocarbons in Soil Using a Homebuilt, Inexpensive Gas Chromatograph with Photoionization Detection
Authors: Shane Cameron , Kennell Carly M. , Geyer Michael J. , Loegel Thomas N. , Danielson Neil D.
Published: February 2025

We have designed, built, and characterized an inexpensive gas chromatograph with photoionization detection using readily commercially available components. These include a home carbonation CO2 cylinder for the carrier gas, headspace sampling using a low pressure six port valve, a standard 30 m wide-bore capillary column, a consumer convection oven including added insulation with a thermocouple, and a photoionization sensor enclosed in a flow cell. The chromatography retention time reproducibility is good due to temperature control within 0.5 °C. The headspace linearity response using a 0.5 mL injection of benzene was from 10 to 250 ppm, with the limit of detection estimated at 4 ppm. The application of this instrument to the headspace monitoring of four soil samples spiked with a commercial adhesive cleaner containing acetone, ethyl benzene, and xylenes was possible. The interaction of acetone with the two different potting soil samples, likely due to their high organic content, was persistent for at least 30 min, even above an ambient temperature (60 °C). A comparison of linear plots of the signal versus volatilization time over the first 20 min showed substantially larger slopes for the two yard soil samples, indicating the rapid volatilization of acetone from these lower organic content soils, as compared to the two potting soil samples. This GC-PID instrument can be considered portable because it can be easily taken apart, transported, and reassembled. One viable option for the extension of the applications of this instrument is to provide hands-on teaching of GC.
Responses of Artificial Intelligence Chatbots to Testosterone Replacement Therapy: Patients Beware!
Authors: Pabla Herleen , Lange Alyssa , Nadiminty Nagalakshmi , Sindhwani Puneet
Published: February 2025

Background/Objectives: Using chatbots to seek healthcare information is becoming more popular. Misinformation and gaps in knowledge exist regarding the risk and benefits of testosterone replacement therapy (TRT). We aimed to assess and compare the quality and readability of responses generated by four AI chatbots. Methods: ChatGPT, Google Bard, Bing Chat, and Perplexity AI were asked the same eleven questions regarding TRT. The responses were evaluated by four reviewers using DISCERN and Patient Education Materials Assessment Tool (PEMAT) questionnaires. Readability was assessed using the Readability Scoring system v2.0. to calculate the Flesch–Kincaid Reading Ease Score (FRES) and the Flesch–Kincaid Grade Level (FKGL). Kruskal–Wallis statistics were completed using GraphPad Prism V10.1.0. Results: Google Bard received the highest DISCERN (56.5) and PEMAT (96% understandability and 74% actionability), demonstrating the highest quality. The readability scores ranged from eleventh-grade level to college level, with Perplexity outperforming the other chatbots. Significant differences were found in understandability between Bing and Google Bard, DISCERN scores between Bing and Google Bard, FRES between ChatGPT and Perplexity, and FKGL scoring between ChatGPT and Perplexity AI. Conclusions: ChatGPT and Google Bard were the top performers based on their quality, understandability, and actionability. Despite Perplexity scoring higher in readability, the generated text still maintained an eleventh-grade complexity. Perplexity stood out for its extensive use of citations; however, it offered repetitive answers despite the diversity of questions posed to it. Google Bard demonstrated a high level of detail in its answers, offering additional value through visual aids. With improvements in technology, these AI chatbots may improve. Until then, patients and providers should be aware of the strengths and shortcomings of each.
A Novel Exploration of Women’s Pathways Through Prison and the Roles of Trauma, Addiction, and Mental Health
Authors: Carei Rain , Steely Smith Mollee K. , Landon Matthew , Church Haley , Bagdon-Cox Courtney , Cheong Chee Kay , Zielinski Melissa J.
Published: February 2025

Trauma, mental illnesses, and substance use disorders (SUD) are well-documented contributors leading to women’s incarceration; however, less is known about how these factors also influence women’s pathways through prison once incarcerated. To address this gap, we examined (1) women’s pathways to and through prison-based mental health services, (2) summarized their sociodemographic and diagnostic profiles, and (3) examined how mental health and addiction relate to indicators of within-prison functioning, intervention receipt, and recidivism. Data derived from routine administrative and treatment records of women incarcerated between January 2015 and December 2023 in the Washington Corrections Center for Women (WCCW). The full sample comprised 5775 women who entered WCCW during the study period. The majority (53.2%) of women admitted to prison in the study period had at least one mental health diagnosis requiring at least moderate mental health intervention. Substance use (62.73%), trauma-related (61.11%), and mood (47.71%) disorders were most common. Individuals with at least a diagnosis of psychosis, neurocognitive disorders, and personality disorders had greater rates of close observation stays, crisis events, and non-suicidal self-injury risk assessments. Psychosis was associated with the highest rate of crisis events, while personality disorders were associated with the highest rate of non-suicidal self-injury risk assessments. Three-year recidivism rates were highest amongst those with a diagnosis of psychosis or ADHD. Trauma-focused and substance use treatments were associated with lower rates of crisis interventions and other critical incidents in their post-treatment period, but no relationship was observed for reductions in post-release recidivism. Overwhelmingly, women enter prison with significant mental health care needs and require a high-level of care that is largely reflective of the nearly ubiquitous nature of trauma, psychological distress, and addiction. The level of care needed, in response to the varied and complicated diagnostic profile of incarcerated women (e.g., ADHD, psychosis, trauma), as well as the number of critical incidents stemming from symptoms, reflects the need for more clinical staff to expand reach along with training in a wide range of modalities.
Exploring Industrial Symbiotic Networks: Challenges, Opportunities, and Lessons for Future Implementations
Authors: Sellitto Miguel Afonso , de Lima Maria Soares , Ackermann Andres Eberhard Friedl , Kadel Nelson , Butturi Maria Angela
Published: February 2025

In industrial symbiotic networks, the waste from one operation becomes the raw material or fuel for another, addressing concerns about municipal solid waste management. The purpose of this article is to draw lessons from previous experiences that can benefit future implementations of industrial symbiosis (IS). A comprehensive literature review identified 23 challenges and 20 opportunities, which were classified into four categories: technical, economic, legal/regulatory, and social. From the challenges and opportunities, and relying on previous case studies, the article yields 13 actionable lessons designed to assist stakeholders, investors, managers, and practitioners in the successful implementation of IS networks. IS embraces three dimensions: political, technological, and social, which enable insights for promoting collaborations and formulating incentive policies, which pave the way for future research and practical applications. The primary implication of this article is the establishment of a framework aimed at enhancing the sustainability and resilience of future implementations within industrial ecosystems. The main impact of the lessons is that practitioners and stakeholders can now build a roadmap to future implementations of industrial symbiotic networks.
Research on Joint Operation of Flood Diversion and Storage Measures: A Case Study of Poyang Lake
Authors: Deng Shupan , Wang Zhichao , Wu Longhua , Wu Ting , Xia Yang , Liu Yue
Published: February 2025

In recent years, flood hazards have occurred increasingly worldwide, posing significant threats to the safety of life and property in lacustrine and riverine environments. To mitigate the devastating impacts of floods, it is crucial to explore optimal strategies for joint flood diversion of flood diversion and storage measures (FDSM). The FDSM management of Poyang Lake in China focuses on studying semi-restoration polder areas (SR Polders) and flood storage and detention areas (FS Detentions), which are subjects of ongoing research. Existing studies primarily focus on SR Polders or FS Detentions, with limited research on the joint flood diversion potential of these two measures, particularly regarding optimal scheduling. This study takes 185 SR Polders and the Kangshan flood storage and detention area (KS Detention) as the primary research objects. By integrating hydraulic theory, numerical simulation techniques, and survey data, we develop a hydraulic model for the SR Polders and a hydrodynamic model for the KS Detention to carry out flood diversion simulation. The 1998 flood is chosen as a typical case to simulate and analyze their flood diversion processes under various schemes. The results indicate that altering the operation criteria for FDSM influences both the maximum diversion discharge and the timing of the main diversion period. For the SR Polders, under the current flood control scheme, raising the operation water level (OWL) of SR Polders-I by 1.0 m increases the maximum diversion discharge by 894 m3/s. Additionally, raising the OWL of SR Polders-II by 0.37 m delays the main diversion period by one day. For the KS Detention, higher flood diversion water levels correspond to greater discharge capacities. Furthermore, a fuzzy optimization method is applied to optimize nine joint schemes of the SR Polders and KS Detention. The results indicate that the optimal joint flood diversion strategy for Poyang Lake is operating SR Polders-I, SR Polders-II, and KS Detention at a Hukou water level of 21.65 m, 22.05 m, and 22.50 m, respectively. Finally, the study provides insights and recommendations for flood control management at Poyang Lake. The results of this study not only have important guiding significance for flood control management of large plain lakes but also provide references for the joint operation of flood diversion and storage areas in other regions.
Study on the Regional Carbon Emissions Reduction Effect of Green Manufacturing—A Policy Experiment Based on the Construction of Green Parks in China
Authors: Miao Honghui , Quan Qingshuang , Yang Ming
Published: February 2025

Green manufacturing is an effective means for regions to reduce carbon emissions. It is a crucial approach for improving modern environmental governance and lays the foundation for the Chinese government’s push for green transformation, sustainable development, and the realization of carbon neutrality. This paper utilizes data from 277 cities at the prefecture level from 2009 to 2022, with the creation of green parks under the China Green Manufacturing System Demonstration Construction Project serving as a case study. It deeply explores the effect of green manufacturing on local carbon output. The study reveals that green manufacturing, represented by the establishment of green parks, significantly reduces urban carbon emissions. The mechanism analysis shows that enhancing industrial intelligence is a key channel for green manufacturing to curb urban carbon emissions, and the advancement stages of the digital economy, market unification, and financial innovation further amplify green manufacturing’s carbon reduction effects. The diversity analysis suggests that green park establishment exhibits a stronger effect in the eastern regions, areas with abundant factor endowments, superior institutional environments, and those with a non-industrial base. Further analysis shows that the green park demonstration projects also contribute to elevating regional green innovation levels. This paper explores the effect of green park establishments on reducing carbon emissions from the perspective of green manufacturing system construction, providing important theoretical and empirical insights for understanding how green manufacturing can enhance the levels of carbon emissions reduction and promote sustainable green advancement within dual-carbon objectives.
The Influences of Land Use and Economic Policy on Main Ecosystem Services in Rural East China
Authors: Zhang Kun , Sun Xuehui , Zhang Tingjing , Zhang Xiaozheng , Wang Renqing , Zheng Peiming , Wang Hui , Zhang Shuping
Published: February 2025

The growing need for food provision and materials challenges the maintenance of ecosystem services. Understanding the composition of ecosystem services and the factors that affect the services are critical to improving rural development. An assessment of ecosystem services in the densely populated rural areas of East China has been conducted. The results show the average value of rural ecosystem services was 34.99 thousand RMB/ha. The average value of provision services was 30.01 thousand RMB/ha, which was the main part of ecosystem services. The relationships between provision services and ecosystem services were complex. Provision (nutrition) services had no significant correlation with regulation services and provision (material) services. Provision services were mainly influenced by forest cover, proportion of arable land, and rural population (adjusted R2 = 0.36). Social factors and land use factors also had a significant impact on nutrition provision services and material provision services. Land and economic policies could regulate the rural ecosystem service value by changing land use types, population mobility, and rural income. Our findings may shed light on the synergetic development of ecosystem services, provision services, and village development in densely populated rural areas worldwide.
Site Selection for Elderly Care Facilities in the Context of Big Data: A Case Study of Xi’an, China
Authors: Gu Huangling , Shen Ruiwu , Chen Qianqian , Duan Mingzhuo , Zhao Xianchao
Published: February 2025

The accelerated aging of China’s population has made the optimization of elderly care facility locations a critical priority. The field of big data presents innovative approaches for determining the optimal site selection for such facilities. This study uses Xi’an City, in Shaanxi Province, China, as a case study to explore how big data and the ID3 decision tree model can enhance the optimization of elderly care service facility locations. The study begins with a comprehensive analysis of Xi’an’s aging demographics, focusing on the current aging trends and the distribution characteristics of existing elderly care facilities. Utilizing the Baidu Map API, the study collected Point of Interest (POI) data for Xi’an, which were spatially analyzed using ArcGIS 10.8 software to identify the distribution patterns of elderly care facilities and their relationships with other public amenities. The ID3 algorithm was then employed to construct a decision tree model to simulate and predict optimal sites for elderly care facilities in Xi’an. By classifying and filtering POI data and dividing Xi’an into 500 m × 500 m grid units, the model was trained and validated, achieving an accuracy of 85.8%. The findings suggest that suitable sites for elderly care facilities in Xi’an should prioritize proximity to government offices and medical institutions, which would better address the healthcare needs of the elderly population. The application of the ID3 algorithm in planning the locations of elderly care facilities helps mitigate human biases and provides valuable insights for the planning of other public amenities.
Potential of Candida bombicola URM 3718 Biosurfactant for Application in Removal of Motor Oil Adsorbed on Soils
Authors: Nascimento Pollyana P. , Meira Hugo M. , Silva Elias J. , Converti Attilio , Rufino Raquel D. , Sarubbo Leonie A.
Published: February 2025

Soil contamination by petroleum derivatives is a growing environmental issue that affects ecosystems and human health, since the hydrocarbons present in them are persistent and toxic, compromising soil quality and biodiversity. This study investigated the potential of a biosurfactant from Candida bombicola URM 3718, to be applied to remove oils from contaminated soils. After isolation, its main surface-active characteristics were evaluated. The biomolecule was then characterized by NMR and FTIR spectroscopy analyses, and its ability to remove motor oil adsorbed on soils with different particle sizes and its genotoxicity profile were determined. Tests to determine surfactant activities revealed a reduction in water surface tension to 30 mN/m with a critical micelle concentration (CMC) of 0.03 g/L. The surfactant was shown to have a glycolipid nature. The removal of burned engine oil sorbed on various kinds of soil was investigated in both static and kinetic assays using the biosurfactant at different concentrations, namely, ½ CMC (0.015 g/L), CMC (0.03 g/L), and 2 × CMC (0.06 g/L). In the static tests, the maximum removal percentage was 65.32% for burned engine oil adsorbed on sandy soil, 59.04% on silty soil, and 57.42% on clayey soil, while in the kinetic tests, this parameter reached 98.60%, 93.22%, and 92.55% for sandy, silty, and clayey soils, respectively. The genotoxicity profile evaluated in Allium cepa roots did not reveal necrosis or the occurrence of micronuclei in the plant root cap cells, demonstrating that the biomolecule thus produced is not toxic. Such findings, when taken together, indicate that the C. bombicola URM 3718 biosurfactant was effective in removing oils and could, therefore, be used as an alternative agent for remediating hydrocarbon-polluted soil.
Corporate Financialization and Innovation Investment in China: Disentangling the Crowding-Out Effect and Reservoir Effect Under Economic Policy Uncertainty
Authors: Tian Jing
Published: February 2025

This study investigates the relationship between corporate financialization and innovation investment in Chinese manufacturing firms under the backdrop of economic policy uncertainty (EPU). We systematically examine the dual mechanisms through which financialization simultaneously supports and constrains innovation via its reservoir and crowding-out effects. Our findings reveal a robust inverted U-shaped relationship between financialization and R&D expenditure, which is significantly moderated by EPU. Notably, financialization predominantly enhances R&D expenditure for most firms in our sample, challenging conventional perspectives on the financialization–real investment nexus. Further analyses indicate that alleviating financing constraints serves as a fundamental mechanism through which financialization fosters innovation investment. However, elevated EPU exacerbates financing constraints, attenuating the reservoir effects of financialization on innovation activities.
iEVEM: Big Data-Empowered Framework for Intelligent Electric Vehicle Energy Management
Authors: Guo Siyan , Zhao Cong
Published: February 2025

Recent years have witnessed an unprecedented boom of Electric Vehicles (EVs). However, EVs’ further development confronts critical bottlenecks due to EV Energy (EVE) issues like battery hazards, range anxiety, and charging inefficiency. Emerging data-driven EVE Management (EVEM) is a promising solution but still faces fundamental challenges, especially in terms of reliability and efficiency. This article presents iEVEM, the first big data-empowered intelligent EVEM framework, providing systematic support to the essential driver-, enterprise-, and social-level intelligent EVEM applications. Particularly, a layered data architecture from heterogeneous EVE data management to knowledge-enhanced intelligent solution design is provided, and an edge–cloud collaborative architecture for the networked system is proposed for reliable and efficient EVEM, respectively. We conducted a proof-of-concept case study on a typical EVEM task (i.e., EV energy consumption outlier detection) using real driving data from 4000+ EVs within three months. The experimental results show that iEVEM achieves a significant boost in reliability and efficiency (i.e., up to 47.48% higher in detection accuracy and at least 3.07× faster in response speed compared with the state-of-art approaches). As the first intelligent EVEM framework, iEVEM is expected to inspire more intelligent energy management applications exploiting skyrocketing EV big data.
Effect of Anti-Bending Bars on Vertical Vibrations of Passenger Carriage Body
Authors: Apostol Ioana-Izabela , Mazilu Traian , Dumitriu Mădălina
Published: February 2025

High-speed passenger carriages with a long and light carriage body are sensitive to vertical vibration because the bending mode eigenfrequency falls within the most sensible frequency interval for the human being. Anti-bending bars (ABBs) are a passive means to raise the eigenfrequency of the bending mode of the carriage body beyond the sensitive limit, ameliorating ride comfort. ABBs are two bars fixed via vertical supports under the carriage chassis on the longitudinal beams. ABBs resist the bending of the carriage body and can, therefore, increase the bending eigenfrequency beyond the sensitive limit, as necessary. In this paper, a new model for the ABBs, which takes into account the longitudinal stiffness of the ABBs, the three-direction stiffness of the fastening between the ABBs and the vertical supports and the vertical vibration modes of the ABBs via the Euler–Bernoulli beam theory and modal analysis, is incorporated in the 10 degrees of freedom model of a passenger carriage; this is to study the effect of the ABBs upon the running behaviour and ride comfort according to the specific regulations in the field. First, the frequency response functions (FRFs) of the passenger carriage with an ABB system are calculated and analysed, and then, the root mean square (r.m.s.) acceleration and the comfort index are evaluated in the carriage body centre in the context of a parametric study. The longitudinal stiffness of the fastening is critical to ensure the effectiveness of the ABB system. However, the effect of decreasing in the longitudinal stiffness of the fastening can be compensated by adopting longer ABBs.
Prevalent Per- and Polyfluoroalkyl Substances (PFASs) Pollution in Freshwater Basins in China: A Short Review
Authors: Zhang Jingjing , Liu Jiaoqin , Jin Riya , Qiao Yina , Mao Jipeng , Wang Zunyao
Published: February 2025

Organic pollutants like per- and polyfluoroalkyl substances (PFASs) exhibit persistence, bioaccumulation, resistance to degradation, and high toxicity, garnering significant attention from scholars worldwide. To better address and mitigate the environmental risks posed by PFASs, this paper employs bibliometric analysis to examine the literature on PFASs’ concentrations collected in the Web of Science (WoS) database between 2019 and 2024. The results show that the overall trend of PFASs’ pollution research is relatively stable and increasing. In addition, this study also summarizes the pollution status of traditional PFASs across different environmental media in typical freshwater basins. It analyzes PFASs’ concentrations in surface water, sediment, and aquatic organisms, elucidating their distribution characteristics and potential sources. While perfluorooctanoic acid (PFOA) and perfluorooctane sulfonic acid (PFOS) levels in water environments are declining annually, short-chain PFASs and their substitutes are emerging as primary pollutants. Short-chain PFASs are frequently detected in surface water, whereas long-chain PFASs tend to accumulate in sediments. In aquatic organisms, PFASs are more likely to concentrate in protein-rich organs and tissues. The environmental presence of PFASs is largely influenced by human activities, such as metal plating, fluoride industry development, and industrial wastewater discharge. Currently, the development of PFASs in China faces a complex dilemma, entangled by policy and legal constraints, industrial production demands, the production and use of new alternatives, and their regulation and restriction, creating a vicious cycle. Breaking this deadlock necessitates continuous and active scientific research on PFASs, particularly PFOS, with an emphasis on detailed investigations of environmental sources and sinks. Furthermore, ecological and health risk assessments were conducted using Risk Quotient (RQ) and Hazard Quotient (HQ) methods. Comprehensive comparison indicates that PFASs (such as PFOA) in the majority of freshwater basins are at a low-risk level (RQ < 0.1 or HQ < 0.2), PFOS in some freshwater basins is at a medium-risk level (0.1 < RQ < 1), and no freshwater basin is at a high-risk level. The adsorption and removal approaches of PFASs were also analyzed, revealing that the combination of multiple treatment technologies as a novel integrated treatment technology holds excellent prospects for the removal of PFASs.
An Update of Tetrodotoxins Toxicity and Risk Assessment Associated to Contaminated Seafood Consumption in Europe: A Systematic Review
Authors: Varini Carlo , Manganelli Maura , Scardala Simona , Antonelli Pietro , Losasso Carmen , Testai Emanuela
Published: February 2025

Following the occurrence of Tetrodotoxins (TTXs) in Europe—a group of neurotoxins identified in Asia, where fatalities occurred after the ingestion of contaminated pufferfish—the EFSA proposed a limit of 44 µg of TTX/kg of shellfish meat in mollusks in 2017, to protect heavy consumers. The limit was based on an acute reference dose (ARfD) derived from the few available data on TTX toxicity. TTX is expected to increase with sea-surface warming; indeed, it has been found in spring/summer in mollusks in Europe, with concentrations often exceeding this limit. Due to the numerous uncertainties of the EFSA’s ARfD, we conducted a systematic review to provide an update on TTX toxicity. Out of 12,741 articles retrieved from PubMed, Science Direct, and Scopus since 2017, only 17 were eligible for data extraction. Our results show that they are not sufficient to modify the EFSA’s conclusions. Furthermore, our analysis of occurrence data in European seafood, to assess the current risk of exposure to TTX, reveals several gaps, such as different LODs/LOQs and seasonal monitoring not allowing comparisons between areas and too few analyzed sites. However, the presence of positive samples exceeding the EFSA limit indicates a potential risk even for general consumers, highlighting the urgency to address these knowledge gaps.
Investigating the Correlations Between Weather Factors and Mycotoxin Contamination in Corn: Evidence from Long-Term Data
Authors: Platzer Alexander , Cherkaoui Younos , Novak Barbara , Schatzmayr Gerd
Published: February 2025

Mycotoxins are secondary metabolites produced by certain fungi, posing significant health risks to humans and animals through contaminated food and feed. These fungi, and consequently the mycotoxins which they produce, are strongly influenced by weather, and this shifts over time due to climate change, leading to more frequent and severe events, such as heat waves, storms, and heavy rainfall. This study investigates how long-term weather trends and climatic factors impacted mycotoxin levels in corn samples over a 17-year period (2006–2022) across 12 countries, with a focus on 136 specific weather features. Among all potential relationships, we found Aspergillus toxins and fumonisins to be positively correlated with temperature, while deoxynivalenol and zearalenone are negatively correlated. Additionally, the dew point, particularly its 90th percentile value, is positively correlated with Aspergillus mycotoxins. We also identified significant patterns associated with wind direction. Collectively, these findings offer a comprehensive overview of mycotoxin–weather correlations, which may also be projected into future scenarios.
Gravitational Wave Signatures Induced by Dark Fluid Accretion in Binary Systems
Authors: Paraskevas Evangelos Achilleas , Perivolaropoulos Leandros
Published: February 2025

We investigate the impact of dark fluid accretion on gravitational waveforms emitted by a compact binary system consisting of a supermassive black hole and a stellar-mass black hole. Using a Lagrangian framework with 1 PN and 2.5 PN corrections, we analyze the effects of the spherically symmetric accretion of a fluid with steady-state flow, including those characterized by an equation of state parameter resembling dark energy, on the binary’s dynamics. We validate our approach by comparing it with previous studies in the common region of validity and extend the analysis to include both local effects, such as dynamical friction, and global gravitational interactions with the stellar-mass black hole, focusing on their dependence on the fluid’s properties. Our analysis reveals that these interactions induce de-phasing in gravitational waveforms, with the phase shift influenced by the fluid’s equation of state and energy density. We also extend the study to sudden cosmological singularities, finding that, although they can deform the binary’s orbit from initially circular to elliptical, their effect on de-phasing is negligible for cosmologically relevant energy densities. By incorporating both the local and global gravitational interactions of a fluid on a two-body system into the equations of motion, this preliminary study provides a framework for understanding the interplay between fluid dynamics and gravitational wave emissions in astrophysical systems. It further reinforces the potential for probing the properties of astrophysically relevant fluids through gravitational wave observations.
Leveraging Electron Beam (eBeam) Technology for Advancing the Development of Inactivated Vaccines
Authors: Perera Ruvindu , Pillai Suresh D. , Alrubaye Adnan , Jesudhasan Palmy
Published: February 2025

This review provides an overview of electron beam (eBeam) technology and its applications across a wide variety of disciplines. More importantly, it discusses this technology’s advantages and its benefits in developing inactivated vaccines. eBeam technology is currently being used all around the world for a variety of industrial applications, extending from food pasteurization to the cross-linking of polymers in the wire and cable industries. It is a successful emerging alternative for developing vaccines against bacterial, protozoan, and viral pathogens. This review includes a descriptive account of the mechanism of action of eBeam and how this technology achieves the complete inactivation of pathogens while retaining the integrity of their surface epitopes. This unique advantage is crucial for the production of efficacious vaccines. This review provides a detailed account of the usage of eBeam technology for developing vaccines to protect a multitude of hosts against a wide range of pathogens. eBeam-inactivated vaccines are advantageous over live vaccines, RNA/subunit vaccines, and chemically inactivated vaccines mainly due to the complete inactivation of pathogens, and the presence of intact, highly antigenic epitopes. To conclude, this article descriptively highlights eBeam technology’s advantages over other means of vaccine development.
Exploring Aeromonas veronii in Migratory Mute Swans (Cygnus olor): A Debut Report and Genetic Characterization
Authors: Peng Zhifeng , Gao Chunyan , Qiao Hongxing , Zhang Han , Huang Huimin , Sheng Yamin , Zhang Xiaojie , Li Baojun , Chao Baoliang , Kang Jingjing , Bian Chuanzhou
Published: February 2025

Aeromonas veronii (A. veronii) is a ubiquitous bacterium in terrestrial and aquatic environments. It has a significant impact on animal and human health, with it becoming an emerging crucial pathogen worldwide. However, there have been no reports of mute swan infections. In the present study, after an observation of pathological changes, one bacterial strain isolated from a dead migratory mute swan was identified as A. veronii HNZZ-1/2022 based on its morphology, matrix-assisted laser desorption ionization–time of flight mass spectrometry (MALDI-TOF MS), and sequence analysis of the 16S rRNA and gyrB genes. To explore its pathogenicity, virulence gene detection and a gosling infection experiment were subsequently carried out, respectively. Six virulence genes for cytotonic enterotoxins (alt), lateral elastase (ela), lipase (lip), cytotoxic enterotoxin (act), aerolysin (aerA), and polar flagellin (fla) were present in the template DNA of A. veronii HNZZ-1/2022. Experimentally infected goslings exhibited hemorrhages of various different degrees in multiple organs. The half-maximal lethal dose (LD50) value of A. veronii strain HNZZ-1/2022 was estimated to be 3.48 × 108 colony forming units (CFUs) per mL for goslings. An antimicrobial susceptibility test showed that the A. veronii HNZZ-1/2022 strain was resistant to meropenem, ampicillin, and enrofloxacin. To date, this is the first report of A. veronii in migratory mute swans, thus expanding the currently known host spectrum. These results suggest that the migratory mute swan is a new host for A. veronii and demonstrate the need for extensive surveillance and research of A. veronii to minimize its transmission between animals, the environment, and humans.
Depositional Architecture of Aggrading Delta Front Distributary Channels and Corresponding Depositional Evolution Process in Ordos Basin: Implications for Deltaic Reservoir Prediction
Authors: Huang Yuhang , Yu Xinghe , Fu Chao
Published: February 2025

Distributary channels at the delta front of lacustrine basins play a crucial role in transporting terrigenous sediments and redistributing depositional facies along the basin margin. These channels are also significant reservoirs for oil and gas. This study investigates the Triassic Yanchang Formation in the Southeastern Ordos Basin (China), emphasizing the sedimentary characteristics, hydrodynamic processes, and evolutionary patterns of delta front distributary channels. Special focus is given to the response of sedimentary filling to paleotopographic changes along the basin margin to enhance reservoir prediction. Through field profiling and quantification of channel morphological parameters, two distinct topographic types were identified: transitions from gentle to steep slopes and from steep to gentle slopes. The findings reveal that the morphology, evolution, and distribution patterns of distributary channels were primarily influenced by the paleotopographic gradient, with sediment grain size playing a supplementary role. Detailed analysis highlights the topographic control on sediment transport mechanisms: in gentle terrain, friction-driven processes dominate (rolling/suspension), whereas in steep terrain, inertial forces prevail (rolling/saltation). Channel architecture correlates strongly with paleotopography: gentle to steep transitions form isolated, vertically stacked sand bodies with thick mouth bars, while steep to gentle transitions produce sheet-like sands with lateral migration features. This study establishes a predictive framework linking slope thresholds to reservoir morphology, offering prioritized targets for hydrocarbon exploration. The methodology is applicable to the margins of lacustrine basins in intracratonic settings, reducing subsurface uncertainty by integrating paleotopographic reconstructions with channel aspect ratios and migration rates.