bluuebunny/crossref_metadata_2025 huggingface dataset:
MetaTrans-FSTSF: A Transformer-Based Meta-Learning Framework for Few-Shot Time Series Forecasting in Flood Prediction
Authors: Jiang Jiange , Chen Chen , Lackinger Anna , Li Huimin , Li Wan , Pei Qingqi , Dustdar Schahram
Published: January 2025

Time series forecasting, particularly within the Internet of Things (IoT) and hydrological domains, plays a critical role in predicting future events based on historical data, which is essential for strategic decision making. Effective flood forecasting is pivotal for optimal water resource management and for mitigating the adverse impacts of flood events. While deep learning methods have demonstrated exceptional performance in time series prediction through advanced feature extraction and pattern recognition, they encounter significant limitations when applied to scenarios with sparse data, especially in flood forecasting. The scarcity of historical data can severely hinder the generalization capabilities of traditional deep learning models, presenting a notable challenge in practical flood prediction applications. To address this issue, we introduce MetaTrans-FSTSF, a pioneering meta-learning framework that redefines few-shot time series forecasting. By innovatively integrating MAML and Transformer architectures, our framework provides a specialized solution tailored for the unique challenges of flood prediction, including data scarcity and complex temporal patterns. This framework goes beyond standard implementations, delivering significant improvements in predictive accuracy and adaptability. Our approach leverages Model-Agnostic Meta-Learning (MAML) to enable rapid adaptation to new forecasting tasks with minimal historical data. Our inner architecture is a Transformer-based meta-predictor capable of capturing intricate temporal dependencies inherent in flood time series data. Our framework was evaluated using diverse datasets, including a real-world hydrological dataset from a small catchment area in Wuyuan, China, and other benchmark time series datasets. These datasets were preprocessed to align with the meta-learning approach, ensuring their suitability for tasks with limited data availability. Through extensive evaluation, we demonstrate that MetaTrans-FSTSF substantially improves predictive accuracy, achieving a reduction of up to 16%, 19%, and 8% in MAE compared to state-of-the-art methods. This study highlights the efficacy of meta-learning techniques in overcoming the limitations posed by data scarcity and enhancing flood forecasting accuracy where historical data are limited.
Transformer-Based Ionospheric Prediction and Explainability Analysis for Enhanced GNSS Positioning
Authors: Wang He-Sheng , Jwo Dah-Jing , Lee Yu-Hsuan
Published: January 2025

This study aims to investigate the impact of ionospheric models on Global Navigation Satellite System (GNSS) positioning and proposes an ionospheric prediction method based on a Transformer deep learning model. We construct a Transformer-based deep learning model that utilizes global ionospheric maps as input to achieve spatiotemporal prediction of Total Electron Content (TEC). To gain a deeper understanding of the model’s prediction mechanism, we employ integrated gradients for explainability analysis. The results reveal the key ionospheric features that the model focuses on during prediction, providing guidance for further model optimization. This study demonstrates the efficacy of a Transformer-based model in predicting Vertical Total Electron Content (VTEC), achieving comparable accuracy to traditional methods while offering enhanced adaptability to spatial and temporal variations in ionospheric behavior. Furthermore, the application of advanced explainability techniques, particularly the Integrated Decision Gradient (IDG) method, provides unprecedented insights into the model’s decision-making process, revealing complex feature interactions and spatial dependencies in VTEC prediction, thus bridging the gap between deep learning capabilities and explainable scientific modeling in geophysical applications. The model achieved positioning accuracies of −1.775 m, −2.5720 m, and 2.6240 m in the East, North, and Up directions respectively, with standard deviations of 0.3399 m, 0.2971 m, and 1.3876 m. For VTEC prediction, the model successfully captured the diurnal variations of the Equatorial Ionization Anomaly (EIA), with differences between predicted and CORG VTEC values typically ranging from −6 to 6 TECU across the study region. The gradient score analysis revealed that solar activity indicators (F10.7 and sunspot number) showed the strongest correlations (0.7–0.8) with VTEC variations, while geomagnetic indices exhibited more localized impacts. The IDG method effectively identified feature importance variations across different spatial locations, demonstrating the model’s ability to adapt to regional ionospheric characteristics.
Asymmetric Distribution of Plasma Blobs During High Solar Activity in the Low- to Middle-Latitude Ionosphere
Authors: Huang Zhuo , Zhu Jia , Luo Weihua , Zhu Zhengping , Jia Guodong , Chang Shanshan
Published: January 2025

Using the data from the first satellite of the Republic of China (ROCSAT-1) obtained during high-solar-activity periods (2000–2003), the distributions of plasma density enhancement (plasma blobs) with local time, season and longitude were investigated. Some new features of plasma blobs can be concluded: (a) The distribution of plasma blobs shows remarkable seasonal and interhemispheric asymmetries, with the higher occurrence in June solstice months and in the winter hemisphere. (b) The occurrence of plasma blobs displays longitude dependence, more in the −180~−90°E, −60~0°E and 90~180°E longitude regions. (c) The seasonal and interhemispheric asymmetries of plasma blobs also depend on the longitude. Meridional wind plays an important role in the formation and evolution of low-latitude plasma blobs. Inclination and declination may control the longitudinal distribution of plasma blobs.
EventSegNet: Direct Sparse Semantic Segmentation from Event Data
Authors: Li Pengju , Fang Yuqiang , Qiu Jiayu , He Jun , Li Jishun , Zhu Qinyu , Wang Xia , Zhang Yasheng
Published: January 2025

Semantic segmentation tasks encompass various applications, such as autonomous driving, medical imaging, and robotics. Achieving accurate semantic information retrieval under conditions of high dynamic range and rapid scene changes remains a significant challenge for image-based algorithms. This challenge is primarily attributable to the limitations of conventional image sensors, which can experience motion blur or exposure artifacts. In contrast, event-based vision sensors, which asynchronously report changes in pixel intensity, offer a compelling solution by acquiring visual information at the same rate as the scene dynamics, thereby mitigating these limitations. However, we encounter a significant challenge in event-based semantic segmentation tasks: the need to expend time on converting event data into frame images to align with existing image-based semantic segmentation techniques. This approach squanders the inherently high temporal resolution of event data, compromising the accuracy and real-time performance of semantic segmentation tasks. To address these issues, this work explores a sparse semantic segmentation approach that directly addresses event data. We propose a network named EventSegNet that improves the ability to extract geometric features from event data by combining geometric feature enhancement operations and attention mechanisms. Based on this, we propose a large-scale event-based semantic segmentation dataset that provides labels for each event. Our approach achieved a new F1 score of 84.2% on the dataset. In addition, a lightweight and edge-oriented AI inference deployment technique was implemented for the network model. Compared to the baseline model, the optimized network model reduces the F1 score by 1.1% but is more than twice as fast computationally, enabling real-time inference on the NVIDIA AGX Xavier.
Research on the Detection Method of Martian Atmospheric Temperature and Pressure Profile Based on Laser Occultation Technology
Authors: Liu Ye , Kong Wei , Shen Xue , Chen Tao , Huang Genghua
Published: January 2025

As an important target of space exploration, Mars has attracted a lot of attention due to its unique geographical and atmospheric conditions. The detection of the vertical profiles of Mars atmospheric parameters provides deeper insights into the structure and composition of the Martian atmosphere. Meanwhile, it holds significant importance for the design and execution of Mars exploration missions. This paper presents a detection method for the Martian atmosphere utilizing laser occultation technology based on a network of high-orbit and low-orbit satellites around Mars. The measurement principle of Mars laser occultation is first introduced, which is that the atmospheric temperature and pressure are measured by analyzing the absorption spectrum characteristics of infrared carbon dioxide. Then, a detailed simulation process is described, including the establishment and validation of both the radiation intensity calculation model for laser occultation signals and the method for retrieving atmospheric parameters. A set of satellite payload parameters is also designed. The simulation results reveal that this method can accurately measure temperature and pressure at a vertical resolution of 100 m from 5 km to 50 km altitude of the Martian atmosphere with deviations of 0.43 K and 1.06%, respectively. It is indicated that the proposed laser occultation method can achieve effective detection of temperature and pressure and provide a promising approach for high vertical resolution profile detection of the Martian atmosphere in the future.
Disentangling the Roles of Climate Variables in Forest Fire Occurrences in China
Authors: Lian Chenqin , Feng Zhiming , Gu Hui , Gao Beilei
Published: January 2025

In the context of global warming, climate strongly affects forest fires. With long-term and strict fire prevention policies, China has become a unique test arena for comprehending the role of climatic variables in affecting forest fires. Here, using GIS spatial analysis, Pearson correlation, and geographical detector, the climate drivers of forest fires in China are revealed using the 2003–2022 active fire data from the MODIS C6 and climate products from CHELSA (Climatologies at high resolution for the Earth’s land surface areas). The main conclusions are as follows: (1) In total, 82% of forest fires were prevalent in the southern and southwestern forest regions (SR and SWR) in China, especially in winter and spring. (2) Forest fires were mainly distributed in areas with a mean annual temperature and annual precipitation of 14~22 °C (subtropical) and 800~2000 mm (humid zone), respectively. (3) Incidences of forest fires were positively correlated with temperature, potential evapotranspiration, surface downwelling shortwave flux, and near-surface wind speed but negatively correlated with precipitation and near-surface relative humidity. (4) Temperature and potential evapotranspiration dominated the roles in determining spatial variations of China’s forest fires, while the combination of climate variables complicated the spatial variation. This paper not only provides new insights on the impact of climate drives on forest fires, but also offers helpful guidance for fire management, prevention, and forecasting.
Soil Reflectance Composite for Digital Soil Mapping in a Mediterranean Cropland District
Authors: Zanini Monica , Heiden Uta , Pace Leonardo , Casa Raffaele , Priori Simone
Published: January 2025

Accurate soil maps are essential for soil protection, management, and digital agriculture. However, traditional soil maps often lack the detail required for local applications, while farm-scale surveys are often not economically viable. This study uses legacy soil data and digital soil mapping (DSM) to produce accurate, low-cost maps of key soil properties, namely clay, sand, total lime (CaCO3), organic carbon (SOC), total nitrogen (TN), and the cation-exchange capacity (CEC). The DSM procedure involved multivariate stepwise regression kriging that uses the terrain attributes and bare soil reflectance composite (SRC) from Sentinel-2 multitemporal images. The procedure to obtain the SRC was carried out following the Soil Composite Mapping Processor (SCMaP) methodology. The Sentinel-2 bands of the SRC showed strong correlations with soil features, making them very suitable explicative variables for regression kriging. In particular, the SWIR bands (b11 and b12) were important covariates in predicting clay, sand, and CEC maps. The accuracy of the regression models was very good for clay, sand, SOC, and CEC (R2 > 0.90), while CaCO3 showed lower accuracy (R2 = 0.67). Normalization of SOC, TN, and CaCO3 did not significantly improve the prediction accuracy, except for SOC, which showed a slight improvement. In addition, a supervised classification approach was applied to predict soil typological units (STUs) using the mapped soil attributes. This methodology demonstrates the potential of SRCs and regression kriging to produce detailed soil property maps to support precision agriculture and sustainable land management.
Review of Assimilating Spaceborne Global Navigation Satellite System Remote Sensing Data for Tropical Cyclone Forecasting
Authors: Bai Weihua , Wang Guanyi , Huang Feixiong , Sun Yueqiang , Du Qifei , Xia Junming , Wang Xianyi , Meng Xiangguang , Hu Peng , Yin Cong , Tan Guangyuan , Wu Ruhan
Published: January 2025

Global Navigation Satellite System (GNSS) Radio Occultation (RO) and GNSS Reflectometry (GNSS-R) are the two major spaceborne GNSS remote sensing (GNSS-RS) techniques, providing observations of atmospheric profiles and the Earth’s surface. With the rapid development of GNSS-RS techniques and spaceborne missions, many experiments and studies were conducted to assimilate those observational data into numerical weather-prediction models for tropical cyclone (TC) forecasts. GNSS RO data, known for its high precision and all-weather observation capability, is particularly effective in forecasting mid-to-upper atmospheric levels. GNSS-R, on the other hand, plays a significant role in improving TC track and intensity predictions by observing ocean surface winds under high precipitation in the inner core of TCs. Different methods were developed to assimilate these remote sensing data. This review summarizes the results of assimilation studies using GNSS-RS data for TC forecasting. It concludes that assimilating GNSS RO data mainly enhances the prediction of precipitation and humidity, while assimilating GNSS-R data improves forecasts of the TC track and intensity. In the future, it is promising to combine GNSS RO and GNSS-R data for joint retrieval and assimilation, exploring better effects for TC forecasting.
Non-Invasive Continuous Glucose Monitoring in Patients Without Diabetes: Use in Cardiovascular Prevention—A Systematic Review
Authors: Wilczek Filip , van der Stouwe Jan Gerrit , Petrasch Gloria , Niederseer David
Published: January 2025

Continuous glucose monitoring (CGM) might provide immediate feedback regarding lifestyle choices such as diet and physical activity (PA). The impact of dietary habits and physical activity can be demonstrated in real time by providing continuous data on glucose levels and enhancing patient engagement and adherence to lifestyle modifications. Originally developed for diabetic patients, its use has recently been extended to a non-diabetic population to improve cardiovascular health. However, since data in this population are scarce, the effect on cardiovascular outcomes is unclear. CGM may offer potential benefits for cardiovascular prevention in healthy individuals without diabetes. The aim of this systematic review is to evaluate the use of CGM in healthy non-diabetic individuals, focusing on its potential to guide lifestyle interventions in the context of cardiovascular prevention, which may ultimately reduce cardiovascular risk.
Vehicle Target Detection of Autonomous Driving Vehicles in Foggy Environments Based on an Improved YOLOX Network
Authors: Liu Zhaohui , Zhang Huiru , Lin Lifei
Published: January 2025

To address the problems that exist in the target detection of vehicle-mounted visual sensors in foggy environments, a vehicle target detection method based on an improved YOLOX network is proposed. Firstly, to address the issue of vehicle target feature loss in foggy traffic scene images, specific characteristics of fog-affected imagery are integrated into the network training process. This not only augments the training data but also improves the robustness of the network in foggy environments. Secondly, the YOLOX network is optimized by adding attention mechanisms and an image enhancement module to improve feature extraction and training. Additionally, by combining this with the characteristics of foggy environment images, the loss function is optimized to further improve the target detection performance of the network in foggy environments. Finally, transfer learning is applied during the training process, which not only accelerates network convergence and shortens the training time but also further improves the robustness of the network in different environments. Compared with YOLOv5, YOLOv7, and Faster R-CNN networks, the mAP of the improved network increased by 13.57%, 10.3%, and 9.74%, respectively. The results of the comparative experiments from different aspects illustrated that the proposed method significantly enhances the detection performance for vehicle targets in foggy environments.
Obfuscated Malware Detection and Classification in Network Traffic Leveraging Hybrid Large Language Models and Synthetic Data
Authors: Naseer Mehwish , Ullah Farhan , Ijaz Samia , Naeem Hamad , Alsirhani Amjad , Alwakid Ghadah Naif , Alomari Abdullah
Published: January 2025

Android malware detection remains a critical issue for mobile security. Cybercriminals target Android since it is the most popular smartphone operating system (OS). Malware detection, analysis, and classification have become diverse research areas. This paper presents a smart sensing model based on large language models (LLMs) for developing and classifying network traffic-based Android malware. The network traffic that constantly connects Android apps may contain harmful components that may damage these apps. However, one of the main challenges in developing smart sensing systems for malware analysis is the scarcity of traffic data due to privacy concerns. To overcome this, a two-step smart sensing model Syn-detect is proposed. The first step involves generating synthetic TCP malware traffic data with malicious content using GPT-2. These data are then preprocessed and used in the second step, which focuses on malware classification. This phase leverages a fine-tuned LLM, Bidirectional Encoder Representations from Transformers (BERT), with classification layers. BERT is responsible for tokenization, generating word embeddings, and classifying malware. The Syn-detect model was tested on two Android malware datasets: CIC-AndMal2017 and CIC-AAGM2017. The model achieved an accuracy of 99.8% on CIC-AndMal2017 and 99.3% on CIC-AAGM2017. The Matthew’s Correlation Coefficient (MCC) values for the predictions were 99% for CIC-AndMal2017 and 98% for CIC-AAGM2017. These results demonstrate the strong performance of the Syn-detect smart sensing model. Compared to the latest research in Android malware classification, the model outperformed other approaches, delivering promising results.
What Contributes to the Gender Gap? A Blinder–Oaxaca Decomposition Analysis of Hidden Workers in Australia
Authors: Lee Sora , Kang Woojin
Published: January 2025

(1) Hidden workers include those who are unemployed or underemployed or discouraged workers. This study analyses the probabilities of becoming a hidden worker between males and females in three age groups (youth, young adults, and mid-life) and identifies the extent to which these factors explain the gender gap among hidden workers in Australia. (2) Using the Oaxaca–Blinder decomposition approach, this study aims to contribute to the discussion by highlighting the impact of gender on the hidden worker population, further illustrating how the explained and unexplained impacts on hidden workers differ between the gender groups. (3) Results: Age not only raises the likelihood of being a hidden worker in both gender groups but also contributes to the widening gender gap among hidden workers, especially those in the child-rearing stage of their life. Human capital, including education, health endowment, and social capital, is negatively associated with the likelihood of being a hidden worker and reduces the gender gap. Childcare responsibilities and other care duties are associated with a greater likelihood of being a hidden worker for females and are among the greatest contributors to the gender gap. Local resources are associated with the likelihood of being a hidden worker and widen the gender gap within the 45–64-year old group. (4) Conclusions: The gender gap among hidden workers is widened by care responsibilities and locally available socio-economic resources. On the other hand, it is narrowed by education, individual health endowment, and social capital. The findings of this study corroborate the fact that a wide extent of the gender gap among hidden workers in Australia is socially and politically produced and is thus potentially avoidable through public policy. Greater attention from researchers and policy makers regarding hidden workers should be devoted to reducing this gender gap to prevent further social implications.
How Does Financial Support Affect Firms’ Innovation and Total Factor Productivity: A Quasi-Natural Experiment in China
Authors: Lu Guangyuan , Bai Xiong , Zhang Xiaoyun
Published: January 2025

Innovation and productivity improvements are essential drivers of economic growth, social stability, and sustainable development. As a high-risk, long-term activity, innovation requires external support, especially from the financial sector. In response, governments have introduced various financial support policies, yet their effectiveness remains debatable. Using panel data from Chinese listed firms between 2006 and 2022, we examined the impact of an innovation-oriented financial support initiative in China—the Technology and Finance Integration (TFI) pilot—on firm innovation and total factor productivity (TFP). This quasi-natural experiment effectively alleviated endogeneity and helped us establish the causality. Our results show that TFI significantly enhances both the quantity and quality of firms’ innovation, as well as TFP. Furthermore, we found that the policy effects are more pronounced in firms with higher perceived uncertainty, in private firms, and in those located in regions with advanced financial development. Improved liquidity conditions, increased R&D investment, and better asset allocation constitute plausible mechanisms for interpreting our results. Theoretically, this paper complements the research on the nexus between financial support and innovation activity, shedding light on the underlying mechanisms. Practically, our findings provide valuable insights for the formulation of financial policies to promote innovation, particularly in developing countries that lack sufficient R&D incentives and effective market mechanisms to drive technical upgrading and productivity growth.
A Cost–Benefit Model for Sustainable Product Reuse and Repurposing in Circular Remanufacturing
Authors: Psarommatis Foivos , May Gokan
Published: January 2025

This study developed and validated cost–benefit models to evaluate the economic feasibility of reuse and repurposing strategies in remanufacturing, utilizing activity-based costing to assess key financial factors and implications. The models provide a structured approach to compare reuse, repurposing, and recycling, focusing on identifying conditions that maximize cost savings and reduce environmental impact. Reuse strategies emphasize scenarios requiring minimal maintenance to extend product life, while repurposing explores transformations for new applications when direct reuse is not feasible. By quantifying reuse and repurposing costs, the models help manufacturers identify sustainable lifecycle extensions that support circular economy principles. The results demonstrate that reuse and repurposing are particularly advantageous when products retain significant remaining useful life. These models serve as practical tools for industries aiming to implement resource-efficient practices that enhance both economic resilience and environmental sustainability. Furthermore, these models can be adapted for specific industrial applications and enhanced with real-world validation, providing companies with actionable insights to further refine cost-saving and environmental impact predictions. This study addresses gaps in the current literature by presenting tailored cost assessment tools for circular remanufacturing, promoting informed decision making for sustainable manufacturing.
Balancing Growth and Preservation: Strategic Pathways for Sustainable Rural Tourism in China’s Environmental Landscape
Authors: Han Chenchen , Zhang Hongmei , Zhang Yechen
Published: January 2025

Sustainable rural tourism development is paramount for the socio-economic advancement of rural regions in China. This study systematically prioritizes the key drivers essential for the successful implementation of sustainable rural tourism development by employing the Interpretive Structural Modeling (ISM) and MICMAC analysis methodologies. Through an extensive literature review and expert consultations, we identify and categorize the critical drivers influencing sustainable rural tourism. Utilizing the ISM approach, we construct a hierarchical structure to elucidate the interrelationships among these drivers. Following this, MICMAC analysis is applied to assess their driving and dependence power. We find that government policy and support is the most powerful driver forming the basis for sustainable rural tourism development. Moreover, our findings uncover a complex network of interdependencies, with several drivers emerging as crucial for effective implementation. This research provides invaluable insights for policymakers, stakeholders, and practitioners, offering a robust framework to inform strategic decision-making in fostering sustainable rural tourism development in China.
Human Capital Investment, Technological Innovation, and Resilience of Chinese High-End Manufacturing Enterprises
Authors: Chao Kun , Wang Shixue , Wang Meijia
Published: January 2025

In the era of VUCA, cultivating and enhancing the resilience of high-end manufacturing enterprises is critical. Based on existing research, this paper defines enterprise resilience at the beginning and constructs an enterprise resilience evaluation index system that includes three segmented capabilities: recognition and resistance, adaptation and adjustment, and recovery and rebound. Finally, the relationship between human capital investment, technological innovation, and high-end enterprise resilience is empirically studied. The research results show that human capital investment positively affects the resilience of high-end manufacturing enterprises, with breakthrough innovation and progressive innovation playing a mediating role. Digital transformation positively moderates the impact of human capital investment on the resilience of high-end manufacturing enterprises. Further, there is a higher threshold between human capital investment and technological innovation in improving the resilience of high-end manufacturing enterprises. Human capital investment has a significantly positive effect on high-end manufacturing enterprises’ ability to resist risks and adapt to adjustments but has no significant impact on recovery and rebound ability. Breakthrough and progressive innovation partially mediate the impact of human capital investment on the ability to resist risks and adapt to adjustments, while breakthrough innovation has no significant impact on the recovery of the rebound ability; however, progressive innovation completely mediates the relationship between human capital investment and the recovery of rebound ability. Compared with Chinese non-state-owned enterprises, state-owned enterprises’ efforts to increase investment in human capital only positively impact their ability to resist risks. Compared with large-scale enterprises, the increase in human capital investment in small-scale enterprises only has a significant positive impact on the ability to resist risks. Based on the above, this paper suggests that high-end manufacturing enterprises should enhance their strategic focus and constantly strengthen their investment in human capital and technological innovation; at the same time, they should further optimize the structure of human capital investment and introduce and cultivate cutting-edge talents.
Parametric Study by Design of Experiments of Thermal Management of a Light-Emitting Diode Dissipator in a Cavity
Authors: Araoud Zouhour , Ben Abdelmlek Khaoula , Charrada Kamel , Zissis Georges , Canale Laurent
Published: January 2025

This paper presents the results of a numerical investigation by design of experiments of the thermal management of a symmetrical rectangular heat sink for LED lighting placed in a cavity with asymmetrical conditions at its opening. Our mathematical model is validated by an experiment we carried out for an LED placed in a cavity. According to the proposed design of experiments (24 factorial designs), we analyzed, by simulation modeling using COMSOL Multiphysics®, the influence of the different controllable parameters (the position A and width C of the openings, the inlet air velocity B, and the cavity height D) on the evolution of the junction temperature in order to optimize the thermal management of the proposed LED lamp. Using Minitab® manipulation software, a Pareto analysis and an analysis of variance (ANOVA) were carried out, and mathematical models were deduced to estimate the optimal junction temperature and the convective heat transfer coefficient of the proposed radiator and the surrounding air as a function of the controlled parameters. It was found that the position of the opening is the most influential factor on the junction temperature, with a contribution of 93.46%, followed by the factors velocity and width of the opening, with low contributions (3.22% and 1.24%). We also observe that the height of the cavity and the interactions (A × C, A × D, B × C, B × D, C × D) have no significant influence on the junction temperature.
Cosmological Solutions in Teleparallel F(T, B) Gravity
Authors: Gholami Fateme , Landry Alexandre
Published: January 2025

In this paper, we find several teleparallel F(T,B) solutions for a Robertson–Walker (TRW) cosmological spacetime. We first set and solve the F(T,B)-type field equations for a linear perfect fluid. Using similar techniques, we then find new F(T,B) solutions for non-linear perfect fluids with a weak quadratic correction term to the linear equation of state (EoS). Finally, we solve for new classes of F(T,B) solutions for a scalar field source by assuming a power-law scalar field and then an exponential scalar field in terms of the time coordinate. For flat cosmological cases (k=0 cases), we find new exact and approximate F(T,B) solutions. For non-flat cases (k=±1 cases), we only find new teleparallel F(T,B) solutions for some specific and well-defined cosmological expansion subcases. We conclude by briefly discussing the impact of these new teleparallel solutions on cosmological processes such as dark energy (DE) quintessence and phantom energy models.
Should It Always Be Central? Substitution Effects of Multi-Network Embeddedness on Absorptive Capacity
Authors: Xu Xiurui , Tian Shanwu
Published: January 2025

In the knowledge economy, organizations embedded within large network systems enhance their capability to identify, acquire, assimilate, and effectively utilize external knowledge. Given the diversity and complexity of these networks, examining the factors that influence organizational absorptive capacity from a multi-network perspective is both essential and timely. However, how organizations can strategically allocate positions across different networks to enhance absorptive capacity remains unclear. Drawing on social network theory and the knowledge-based view, this study proposes that the relational embeddedness of organizations across networks produces an interactive substitution effect on absorptive capacity. Using a composite database of patent, financial, and organizational data, we annually constructed multiple networks of listed companies in the global automobile and components industry and empirically tested the model through multiple stepwise regression analysis. The results indicate that relational embeddedness in both cooperation and knowledge networks positively affects absorptive capacity; nevertheless, relational embeddedness in knowledge networks can limit the positive effect of cooperation network embeddedness on organizational absorptive capacity. This interaction highlights the substitution effect between different network roles in shaping absorptive capacity.
Holistic Framework for Blockchain-Based Halal Compliance in Supply Chains Enabled by Artificial Intelligence
Authors: Sunmola Funlade , Baryannis George , Tan Albert , Co Kenneth , Papadakis Emmanuel
Published: January 2025

The global halal market is growing, driven by rising stakeholder populations and increasing consumer interest in ethical and sustainable food choices. This surge in demand necessitates robust halal compliance throughout complex supply chains. However, there are several challenges, including fragmented information, increased understanding of halal requirements among stakeholders, and difficulties in tracing product provenance. This paper proposes a holistic framework for halal certification and compliance, addressing these challenges through the integration of artificial intelligence (AI) and blockchain technologies. AI can automate halal compliance checks, identify potential irregularities in sourcing and composition, and facilitate risk management. The blockchain offers an ideal platform for tracking product provenance throughout the halal supply chain. This ensures trust and confidence among consumers by providing verifiable information on ingredient origin and production processes. This paper further strengthens the potential of this framework by presenting an illustrative example that utilises knowledge graphs, machine learning, and smart contracts. This exemplifies the potential application of the proposed framework in the context of halal pre-certification processes. By fostering transparency and streamlining compliance procedures, the proposed holistic framework, empowered by AI and the blockchain, can significantly enhance trust and confidence among stakeholders within the halal food industry.
Tuberculous Osteomyelitis of the Scapular Spine Revealing HIV-1 Infection
Authors: Idsaid Khaoula , Idalene Malika , Danaoui Khadija , Ait Driss Wiam , Elfargani Rania , Soraa Nabila , Tassi Noura
Published: January 2025

Tuberculosis is no longer confined to developing nations; it persists as a significant contributor to illness and death on a global scale. The subtle clinical manifestation and association with human immunodeficiency virus infection poses obstacles for early diagnosis and management. Tuberculosis manifesting at extrapulmonary sites is relatively rare. We herein present the case of a 26-year-old patient from Cameroon with a history of pleuropulmonary tuberculosis treated in 2008, who had been taking pre-exposure prophylaxis (PrEP). The patient presented with right shoulder pain of an inflammatory type. The case was diagnosed as tuberculous osteomyelitis of the scapular spine, complicated by a deltoid abscess. Diagnosis was confirmed using computed tomography and the MTB/RIF GeneXpert test on the abscess puncture. This rare form of tuberculosis with an exceptional site revealed a HIV infection with profound immunosuppression. The patient was initiated on anti-tubercular treatment according to Moroccan recommendations.
Radio Detection of UHE Cosmic Rays and Neutrinos off the Moon with Two 30 m Telescopes
Authors: Chen Linjie , Zhang Jianli , Geng Lihong , Cheng Lingmei
Published: January 2025

Due to the Askaryan effect, radio emissions will be produced when ultra-high-energy (UHE) cosmic rays and neutrinos impact the lunar regolith. Many experiments have been proposed and performed to detect such radio emissions from the lunar regolith. However, none of the Cherenkov radio signals has been detected in these experiments up to now. In order to improve the detectability of the UHE particles, we proposed an experiment to carry out the radio observations of the UHE cosmic rays and neutrinos with two 30 m telescopes for a far longer time than the present experiments. The expected sensitivity for the detection of such UHE particles has been calculated, both for cosmic rays and neutrinos. The results show that a few UHE particle events above 1020 eV could be detected with the expected observation time of several thousand hours.
Lane Detection Based on CycleGAN and Feature Fusion in Challenging Scenes
Authors: Lu Eric Hsueh-Chan , Chiu Wei-Chih
Published: January 2025

Lane detection is a pivotal technology of the intelligent driving system. By identifying the position and shape of the lane, the vehicle can stay in the correct lane and avoid accidents. Image-based deep learning is currently the most advanced method for lane detection. Models using this method already have a very good recognition ability in general daytime scenes, and can almost achieve real-time detection. However, these models often fail to accurately identify lanes in challenging scenarios such as night, dazzle, or shadows. Furthermore, the lack of diversity in the training data restricts the capacity of the models to handle different environments. This paper proposes a novel method to train CycleGAN with existing daytime and nighttime datasets. This method can extract features of different styles and multi-scales, thereby increasing the richness of model input. We use CycleGAN as a domain adaptation model combined with an image segmentation model to boost the model’s performance in different styles of scenes. The proposed consistent loss function is employed to mitigate performance disparities of the model in different scenarios. Experimental results indicate that our method enhances the detection performance of original lane detection models in challenging scenarios. This research helps improve the dependability and robustness of intelligent driving systems, ultimately making roads safer and enhancing the driving experience.
Anti-Stemness and Anti-Proliferative Effects of Cadmium on Bovine Mammary Epithelial Cells
Authors: Liu Penggang , Chen Xueli , Zhao Yuqing , Ali Waseem , Xu Tianle , Sun Jing , Liu Zongping
Published: January 2025

Cadmium accumulation in the body can damage a variety of organs and impair their development and functions. In the present study, we investigated the effect of cadmium on the stemness and proliferation of normal bovine mammary epithelial cells (BMECs). Normal bovine mammary epithelial cells treated with cadmium chloride were assessed for the expression of stemness-related proteins and cell proliferation. Western blotting results found that exposure to different concentrations of cadmium (0, 1.25, 2.5, and 5 μm) for 48 h significantly increased Gli1 expression but unexpectedly decreased the expression of downstream stem cell-related proteins including BMI1, SOX2, and ALDH. However, we also observed that treatment with 5 μm cadmium for 48 h inhibited mammosphere formation using microscopy. In this study, cadmium exposure significantly reduced cell viability and mobility. Flow cytometry detection found that cadmium decreased the percentage of cells in the G0 phase but increased the percentage of cells in the S phase and the apoptosis rate. Furthermore, cadmium exposure significantly increased the levels of caspase-8, caspase-3, and PARP cleavage as assessed by western blotting. Our study uncovers a previously unrecognized role of cadmium in mammary cell stemness and suggests that cadmium may affect breast development by impairing normal stem cell self-renewal and inducing their apoptosis. Therefore, this study provides important scientific significance regarding whether heavy metal cadmium affects normal breast development.
Inclusion of Ora-Pro-Nóbis (Pereskia aculeata) Leaf Meal in the Diet of Adult Nile Tilapia Improves Growth Performance and Intestinal Absorption Capacity Without Compromising Metabolic and Hematological Variables
Authors: Matos Émerson J. A. , Novodworski Jailson , Gonçalves Rafaela M. , Urbinati Elisabeth C. , Bombardelli Robie A. , Meurer Fábio
Published: January 2025

Corn and soybeans are commodities and ingredients of global interest, whose prices fluctuate based on global demands. In this sense, this study aimed to assess ora-pro-nóbis (Pereskia aculeata) leaf meal (OLM) as an alternative to be included in the diets of Nile tilapia (Oreochromis niloticus). The optimal inclusion level of OLM in tilapia diets is investigated herein, aiming to improve their growth performance and health. Five diet variations containing OLM (0%, 5%, 10%, 15%, and 20%) were tested. Feed conversion and protein efficiency rates in the 5% and 10% OLM groups were statistically similar to the control group (p ≤ 0.05) and lower in the 15% and 20% OLM groups. Fish fed 5% and 10% OLM diets showed better feed efficiency, while higher OLM levels (15% and 20%) led to reduced carcass protein and ether extract levels. Increasing OLM levels enhanced intestinal villi height and area, associated with improved nutrient absorption and decreased liver fat degeneration, suggesting dietary adaptation and healthier liver conditions. Thus, OLM can be included up to 10% in tilapia diets, improving their growth performance, feed efficiency, and intestinal absorptive capacity without adversely affecting other parameters.
Indigenous Knowledge in Watershed Management: The Case of the Mara River Basin, Tanzania
Authors: Ngonyani Happyness , Gambo Bello , Mourad Khaldoon A. , Rilwanu Tasi’u Yalwa , Berndtsson Ronny
Published: January 2025

Achieving sustainable water resource utilization requires a holistic management approach that recognizes the knowledge and values of different stakeholders through a comprehensive participation approach. This study evaluates the indigenous knowledge and technologies employed in watershed management by analyzing qualitative and quantitative data from six villages in the Mara River Basin area. Data were collected through interviews, focus group discussions, and household surveys. The findings show that although participants work in tandem with scientific knowledge, indigenous knowledge and technologies continue to be important in watershed management, and that indigenous groups are cognizant of the effects of climate change, with 76.6% of respondents noting a decrease in rainfall and 78.0% observing changes in the rainy season. The integration of scientific knowledge with indigenous knowledge yields significant benefits for watershed management, and it is evident that indigenous communities possess a wealth of knowledge. The results show that involving indigenous communities in watershed management will benefit both water managers and indigenous populations. This engagement can build trust and a sense of ownership, which will lead to sustainable natural resource management.
The Diversity of Geochemical and Ecotoxicological Indices of Alluvial Deposits Reflects the Pattern of Landforms: The Case of the Vistula River Valley in the Małopolski Gorge (Poland)
Authors: Kałmykow-Piwińska Agnieszka , Falkowska Ewa
Published: January 2025

This study aimed to (1) determine the environmental risk resulting from the contamination of river valley sediments with trace elements of anthropogenic origin, (2) assess the relationship between this environmental risk and the geomorphology of the valley, and (3) identify areas that may become a source of contamination. This research was conducted in the Vistula River Valley between Sulejów and Kazimierz Dolny (Poland). Geochemical and ecotoxicological indices (for fraction < 1 mm) were analyzed (EF, Igeo, PI, CF, Cd, PISum, PIAvg, PINemerow, PLI, ER, RI). Geomorphological mapping, supported by DEM and remote sensing analysis, was performed. High concentrations of trace elements in sediments, as determined by the ICP-OES and ICP-MS methods throughout the study area, indicate generally high environmental degradation and a moderate-to-considerable ecological risk. Contamination differs in the sediments of individual landforms: the highest levels are found in the sediments of the contemporary floodplain and oxbow lakes, while the lowest are observed in the Pleistocene terrace sediments. Only high concentrations of As, Pb, Zn, and Cd are of anthropogenic origin. Their source is probably the mining area of Upper Silesia (As, Pb, Zn) and agricultural activity (Cd). The differences in the values of geochemical indices in individual landforms confirm the influence of fluvial processes on the distribution of trace elements.
Effects of Atmospheric Particulate Matter on Microbial Communities in Wetland Ecosystems
Authors: Liu Ying , Zhang Zhenming
Published: January 2025

As an important component of urban ecosystems, changes in microbial communities in urban wetland ecosystems have a profound impact on human beings. In this paper, we studied the changes in microbial communities in urban wetland ecosystems (three major interfaces: atmosphere, foliage and water) under the background of atmospheric pollution by high-throughput techniques. The α-diversity of microorganisms at each interface showed that the species richness of the sample communities did not differ significantly at different levels of contamination and it was all at a high level. And the β-diversity showed a significantly larger between-group gap than within-group gap between the samples. The functions predicted a higher metabolic function in water samples and atmospheric samples, and a higher function of microorganisms harmful to humans in the microbial community on the leaf surface. Further analysis of the correlation between atmospheric particulate matter and environmental microorganisms revealed that the atmospheric microbial communities that were strongly negatively correlated with TSP, PM10, PM2.5, and PM1 were Actinobacteriota, Cyanobacteria, and Verrucomicrobiota. Among the microbial communities on the leaf surface, only Bacteroidota was strongly positively correlated with total suspended particle (TSP), particles with a diameter of 10 micrometers or less (PM10), particles with a diameter of 2.5 micrometers or less (PM2.5) and particles with a diameter of 1 micrometers or less (PM1). As for the microbial communities in the water column, Firmicutes, Bacteroidota, Campilobacterota, and Deferribacteres were strongly and positively correlated with the different particle sizes. There was no significant correlation between the functions of the three interfacial microorganisms and the particle size of the atmospheric particles. This paper studies the structure and function of microbial communities within three interfaces at three pollution levels and explores the resulting changes with the aim of providing directions for monitoring urban wetland ecosystems and for species diversity conservation.