bluuebunny/crossref_metadata_2025 huggingface dataset:
Self-Supervised Learning with Trilateral Redundancy Reduction for Urban Functional Zone Identification Using Street-View Imagery
Authors: Zhao Kun , Li Juan , Xie Shuai , Zhou Lijian , He Wenbin , Chen Xiaolin
Published: February 2025

In recent years, the use of street-view images for urban analysis has received much attention. Despite the abundance of raw data, existing supervised learning methods heavily rely on large-scale and high-quality labels. Faced with the challenge of label scarcity in urban scene classification tasks, an innovative self-supervised learning framework, Trilateral Redundancy Reduction (Tri-ReD) is proposed. In this framework, a more restrictive loss, “trilateral loss”, is proposed. By compelling the embedding of positive samples to be highly correlated, it guides the pre-trained model to learn more essential representations without semantic labels. Furthermore, a novel data augmentation strategy, tri-branch mutually exclusive augmentation (Tri-MExA), is proposed. Its aim is to reduce the uncertainties introduced by traditional random augmentation methods. As a model pre-training method, Tri-ReD framework is architecture-agnostic, performing effectively on both CNNs and ViTs, which makes it adaptable for a wide variety of downstream tasks. In this paper, 116,491 unlabeled street-view images were used to pre-train models by Tri-ReD to obtain the general representation of urban scenes at the ground level. These pre-trained models were then fine-tuned using supervised data with semantic labels (17,600 images from BIC_GSV and 12,871 from BEAUTY) for the final classification task. Experimental results demonstrate that the proposed self-supervised pre-training method outperformed the direct supervised learning approaches for urban functional zone identification by 19% on average. It also surpassed the performance of models pre-trained on ImageNet by around 11%, achieving state-of-the-art (SOTA) results in self-supervised pre-training.
The Development and Validation of a Novel Smartphone Application to Detect Postural Instability
Authors: Hussain Shirin R. , Wright W. Geoffrey
Published: February 2025

Traditional assessments of balance and postural control often face challenges related to accessibility, cost, subjectivity, and inter-rater reliability. With advancements in technology, smartphones equipped with inertial measurement units (IMUs) are emerging as a promising tool for assessing postural control, measuring both static and dynamic motion. This study aimed to develop and validate a novel smartphone application by comparing it with research-grade posturography instruments, including motion capture and force plate systems to establish construct- and criterion-related validity. Twenty-two participants completed the quiet stance under varying visual (eyes open—EO; eyes closed—EC) and surface (Firm vs. Foam) conditions, with data collected from the smartphone, force plate, and motion capture systems. Intraclass correlation coefficients (ICCs) and Pearson correlation coefficients assessed the reliability and validity for all outcome measures (sway area and sway velocity). The results demonstrated reliability, with strong validity between the devices. A repeated-measures ANOVA found no significant differences between the devices. Postural outcomes revealed the significant main effects of both the visual (EO vs. EC) and surface (Firm vs. Foam) conditions. In conclusion, the study demonstrated the validity, sensitivity, and accuracy of the custom-designed smartphone app, offering the potential for bridging the gap between at-home and clinical balance assessments.
Pressure Sensors for Measuring Tibiofemoral Contact Mechanics in Meniscal Root Repair: A Systematic Review
Authors: Boksh Khalis , Bashabayev Beibit , Shepherd Duncan E. T. , Espino Daniel M. , Ghosh Arijit , Aujla Randeep , Boutefnouchet Tarek
Published: February 2025

Background: Tibiofemoral contact mechanics (TFCM) is an accepted biomechanical metrics for evaluating the meniscus in its intact, torn, and repaired states. Pressure sensors are increasingly used, with accuracy and repeatability influenced by test conditions, their design, and their properties. To identify factors optimising performance, we performed a systematic review of the literature on their use for measuring TFCM in posterior meniscal root tears. Methods: The Cochrane Controlled Register of Trials, PubMed, and Embase were used to perform a systematic review using the PRISMA criteria. As laboratory and surgical setup can influence sensor performance, we collected data on specimen preparation, repair techniques, hardware use, and biomechanical testing parameters. Results: 24 biomechanical studies were included. Specimen preparations were similar across studies with respect to femoral and tibial mounting. Single axial compressive forces were applied between 100 and 1800 N at varying flexion angles (0–90°). Tekscan (Boston, MA, USA) was the commonest sensor used to measure TFCM, followed by digital capacitive sensors and Fujifilm (Tokyo, Japan). Factors influencing their performance included fluid exposure, lack of adequate fixation, non-specific calibration protocols, load saturation exceeding calibration, damaged sensels and inappropriate pre-test conditioning. Conclusions: Understanding potential factors influencing pressure sensors may improve accuracy, area, and pressure distribution measurements.
Separation of Simultaneous Speakers with Acoustic Vector Sensor
Authors: Kotus Józef , Szwoch Grzegorz
Published: February 2025

This paper presents a method of sound source separation in live audio signals, based on sound intensity analysis. Sound pressure signals recorded with an acoustic vector sensor are analyzed, and the spectral distribution of sound intensity in two dimensions is calculated. Spectral components of the analyzed signal are selected based on the calculated source direction, which leads to a spatial filtration of the sound. The experiments were performed with test signals convolved with impulse responses of a real sensor, recorded for a varying sound source position. The experiments evaluated the proposed method’s ability to separate sound sources, depending on their position, spectral content, and signal-to-noise ratio, especially when multiple sources are active at the same time. The obtained results are presented and discussed. The proposed algorithm provided signal-to-distortion ratio (SDR) values 10–12 dB, and Short-Time Objective Intelligibility Measure (STOI) values in the range 0.86–0.94, an increase by 0.15–0.30 compared with the unprocessed speech signal. The proposed method is intended for applications in automated speech recognition systems, speaker diarization, and separation in the concurrent speech scenarios, using a small acoustic sensor.
Associations Between Clinical Inflammatory Risk Markers, Body Composition, Heart Rate Variability, and Accelerometer-Assessed Physical Activity in University Students with Overweight and Obesity
Authors: Hoffmann Sascha W. , Schierbauer Janis , Zimmermann Paul , Voit Thomas , Grothoff Auguste , Wachsmuth Nadine , Rössler Andreas , Niedrist Tobias , Lackner Helmut K. , Moser Othmar
Published: February 2025

This cross-sectional study aimed to identify associations between clinical inflammatory risk markers, body composition, heart rate variability (HRV), and self-reported and objectively assessed physical activity (PA) in university students with overweight and obesity. Seventeen participants (eight females) completed a screening visit following a randomized controlled four-arm crossover trial period with 8 h of uninterrupted prolonged sitting, alternate sitting and standing, continuous standing, and continuous slow walking, respectively. Clinical inflammatory risk markers were obtained from venous blood samples, and PA was assessed using the International Physical Activity Questionnaire (IPAQ-SF) and ActiGraph wGT3X-BT accelerometers. HRV was recorded over 24 h using the Faros 180 Holter electrocardiogram (ECG). White blood cell (WBC) counts were significantly correlated with fat mass (FM; p = 0.03) and visceral adipose tissue (VAT; p = 0.04) and inversely correlated with moderate PA (p = 0.02). Light-intensity PA (LIPA) and moderate-to-vigorous PA (MVPA) were correlated with HRV parameters (p = 0.02), and LIPA was inversely correlated with interleukin-6 (p = 0.003) and c-reactive protein (p = 0.04) during different trial conditions. In university students with overweight and obesity, higher values of FM were negatively correlated with WBC count, and integrating LIPA and MVPA in the students’ daily life strengthened their sympathetic–parasympathetic regulation and positively mediated anti-inflammatory mechanisms.
The Spatial Association Network Structure and Influencing Factors of Pollution Reduction and Carbon Emission Reduction Synergy Efficiency in the Yellow River Basin
Authors: Yang Fan , Zhen Jianghong , Chen Xiaolong
Published: February 2025

As a national strategic development area, the Yellow River Basin (YRB) has seen progress in research on the synergy efficiency of pollution reduction and carbon reduction (SEPCR). However, there are still notable gaps. The theoretical framework for this area is lacking, leading to diverse and inconsistent conclusions. Additionally, difficulties in data collection and processing, along with incomplete and inconsistent data, negatively affect the accuracy of research findings. Current studies tend to focus on single aspects and lack a comprehensive and systematic analysis of the SEPCR across the entire basin. There is insufficient understanding of key network nodes, connections, and overall structural characteristics. A scientific assessment of its spatial correlation structure has far-reaching implications for the national battle against pollution and the realization of “dual carbon” goals. This study is based on panel data from 75 cities in the YRB from 2006 to 2022. It employs an ultra-efficiency SBM model to measure the SEPCR. Additionally, it utilizes a modified gravity model and social network analysis to explore the spatial network correlation structure in depth. Furthermore, the QAP model is used to clarify the mechanisms of various influencing factors. The research findings indicate that there is an imbalance in the spatial and temporal distribution of the SEPCR in the YRB. Although there is a fluctuating upward trend over time, significant internal spatial disparities exist. While the gaps between regions are gradually narrowing, there are still evident research disparities. Moreover, the spatial connectivity of the SEPCR in the YRB is gradually strengthening, with overall network connectivity also improving, yet there remains a considerable distance from an ideal state. The network density shows a decreasing trend from the downstream to the midstream and then to the upstream regions, with significant differences in spatial network centrality among these areas, particularly pronounced between the midstream and upstream regions. Differences in economic development levels, technological development levels, and industrial structure development levels promote the formation of spatial correlations in SEPCR, while disparities in energy utilization have a suppressive effect.
Estimation of Nitrogen Content in Hevea Rubber Leaves Based on Hyperspectral Data Deep Feature Fusion
Authors: Hu Wenfeng , Zhang Longfei , Chen Zhouyang , Luo Xiaochuan , Qian Cheng
Published: February 2025

Leaf nitrogen content is a critical quantitative indicator for the growth of rubber trees, and accurately determining this content holds significant value for agricultural management and precision fertilization. This study introduces a novel feature extraction framework—SFS-CAE—that integrates the Sequential Feature Selection (SFS) method with Convolutional Autoencoder (CAE) technology to enhance the accuracy of nitrogen content estimation. Initially, the SFS algorithm was employed to select spectral bands from hyperspectral data collected from rubber tree leaves, thereby extracting feature information pertinent to nitrogen content. Subsequently, a CAE was utilized to further explore deep features within the dataset. Ultimately, the selected feature subset was concatenated with deep features to create a comprehensive input feature set, which was then analyzed using partial least squares regression (PLSR) for nitrogen content regression estimation. To validate the effectiveness of the proposed methodology, comparisons were made against commonly used competitive adaptive reweighted sampling (CARS), successive projection algorithm (SPA), and uninformative variable elimination (UVE) feature selection algorithms. The results indicate that SFS-CAE outperforms traditional SFS methods on the test set; notably, CARS-CAE achieved optimal performance with a coefficient of determination (R2) of 0.9064 and a root mean square error (RMSE) of 0.1405. This approach not only effectively integrates deep features derived from hyperspectral data but also optimizes both band selection and feature extraction processes, offering an innovative solution for the efficient estimation of nitrogen content in rubber tree leaves.
Development of an Environmentally Friendly Steel Structural Framework: Evaluation of Bending Stiffness and Yield Bending Moment of Cross-Laminated Timber Slab–H-Shaped Steel Composite Beams for Component Reuse
Authors: Furukawa Sachi , Iwami Ryohei , Kimura Yoshihiro
Published: February 2025

The building and construction sector accounts for nearly 40% of global greenhouse gas emissions, with steel-framed buildings being a significant contributor due to high CO2 emissions during production. To mitigate this issue, integrating Cross-Laminated Timber (CLT) into structural systems has emerged as a sustainable alternative. CLT, known for its carbon sequestration properties, offers an environmentally friendly replacement for reinforced-concrete slabs, particularly when paired with steel structures to enhance material reuse and reduce lifecycle impacts. This study focuses on hybrid systems combining H-shaped steel beams and CLT floor panels connected using high-strength friction bolts. A four-point bending test, simulating a secondary beam, was conducted, demonstrating that the composite effect significantly enhances flexural stiffness and strength. Additionally, a simplified method for evaluating the flexural stiffness and yielding strength of these composite beams, based on material and joint properties, was shown to successfully evaluate the test results.
Systematic Review of the Role of Arts Places in Fostering Urban Sustainability and Resilience
Authors: Zeng Xinyu , Ortner Frederick Peter , Tunçer Bige
Published: February 2025

This systematic review examines the role of arts places in fostering urban sustainability and resilience, investigating how these spaces contribute to long-term urban development goals. By synthesizing 79 peer-reviewed articles published between 2013 and 2024, this review identifies the mechanisms through which art spaces promote sustainability and resilience across economic, social, environmental, and cultural dimensions. Urban resilience is achieved through civic engagement and empowerment, as well as the repurposing the spaces to adapt to new uses within the community and strengthening cultural heritage and identity. Similarly, urban sustainability in different aspects, such as economic, environmental, and social, is fulfilled by new revenues from tourism income, as well as educational promotion on ecological issues through art. The findings reveal how arts places—ranging from permanent museums and galleries to temporary and public art installations—stimulate community engagement, economic revitalization, and environmental stewardship. This study provides a theoretical framework that illustrates the connections between the types of arts places, their initiators, and their intended outcomes. The results also highlight challenges such as gentrification and the tension between artistic and commercial purposes. Implications for urban planners and policymakers include integrating arts places into urban regeneration strategies. Future research should focus on quantitative measures of the long-term impact of arts places and their role in mitigating urban inequities.
Cyber Resilience in Digital Marketing Within the Framework of Sustainable Management
Authors: Gündüzyeli Bora
Published: February 2025

The increasing reliance on information and communication technologies (ICTs), particularly the Internet, in digital marketing systems has made them prime targets for cyberattacks. The fact that, without adequate cybersecurity measures, digital marketing systems may face serious cyber threats, such as data breaches, financial fraud, and reputational damage, highlights the importance of enhancing cyber resilience in digital marketing operations to ensure long-term sustainability. This research sought to answer the following pivotal question: “How can cyber resilience enhance sustainability in marketing operations?” To address this issue, a comprehensive literature review was conducted and relevant articles were selected for a narrative review. The findings emphasize the importance of integrating cyber resilience strategies into marketing practices, particularly in terms of protecting data, ensuring operational continuity, and fostering customer trust. This research contributes to the field by exploring the intersection between cyber resilience and sustainable marketing practices, offering insights that provide practical advantages to marketers, businesses, and policymakers.
Heavy Metal Accumulation in Maize and Wheat in Acidic Soil: A Comparative Study
Authors: Wang Xiaorou , Lv Pengchao , Zhang Fujin , Wang Weiyu , Liu Xunsheng , Zhang Qiang , Mu Jingjing , Huang Xianmin , Bai Liyong , Dai Jiulan
Published: February 2025

Maize and wheat grains are at significant risk of accumulating excessive heavy metals in acidic soils, but relatively few comparative studies have been conducted on the two. This study employed Spearman correlation analysis, random forest modeling, and structural equation modeling to systematically investigate the interdependences between soil properties and heavy metal accumulation (Cd, Cr, As, Pb, Hg) in cereal grains. The results revealed distinct patterns in heavy metal content sequences between wheat and maize grains: maize exhibited Cr > Pb > As > Cd > Hg, while wheat followed Cr > Pb > Cd > As > Hg. Bioconcentration factor (BCF) sequences differed significantly, showing Hg > Cd > As > Cr > Pb in maize versus Cd > Hg > As > Cr > Pb in wheat. Soil cation exchange capacity demonstrated the strongest influence on Cd accumulation in both crops. Arsenic accumulation mechanisms displayed species-specific dependencies, with wheat primarily regulated by soil organic matter and maize by clay content. Notably, a significant positive correlation (p ≤ 0.05) between As and Pb concentrations was observed in both crops, suggesting potential homologous pollution sources. These findings elucidate crop-specific heavy metal accumulation mechanisms governed by soil physicochemical properties. This study demonstrates that under similar growing conditions, maize (BCF = 0.068) exhibits a weaker ability to accumulate heavy metals compared to wheat (BCF = 0.467). Moreover, maize and wheat showed distinct responses to soil properties, particularly in their accumulation of As and Cd. By addressing heavy metal contamination in acidic soils, this work supports the development of safer agricultural practices and improved crop quality control.
Climate Change vs. Circular Economy: Challenges of the Most Common Route for Recycling Gold from WEEE
Authors: Fritz Benjamin , Schmidt Mario
Published: February 2025

Gold production poses significant environmental challenges, including resource depletion, CO2 emissions, and toxic chemical usage. Similarly, improper WEEE management harms the environment. However, WEEE contains valuable metals such as gold, making it central to circular economy (CE) strategies and an alternative to mining. This study assesses the climate impact of pyrometallurgical gold recovery from WEEE using life cycle assessment (LCA). The study found that the carbon footprint of producing gold pyrometallurgically from WEEE is 2000 kg CO2eq/kg. These emissions are largely tied to the carbon content of waste, meaning that low-carbon energy sources have a limited impact. This creates a conflict between CE goals and CO2 reduction. Scenario analysis shows that utilizing waste heat for district heating significantly lowers emissions. The other strategies used to improve the environmental performance include separating the plastic fraction before smelting, using biogenic plastic in WEEE, and carbon capture and storage (CCS). Transport accounts for just 10% of the total carbon footprint. Future regulations must address multiple factors—EEE production, waste management, smelter infrastructure, global socioeconomic dynamics, and consumer behavior—as higher recycling rates alone will not solve WEEE challenges.
Impact of Green Human Resources Management Practices on Sustainability Through Organizational Resilience and Organizational Learning in Pakistan’s Banking Sector
Authors: Gul Tayyaba , Karaatmaca Ayse Gozde , Raza Ali
Published: February 2025

This study investigates the impact of green human resource management (GHRM) practices on organizational resilience (OR) and organizational sustainability (ORS) in Pakistan’s banking sector, with a focus on the mediating and moderating roles of resilience and organizational learning (ORL), respectively. Utilizing a cross-sectional, quantitative research design and data analyzed through Partial Least Squares Structural Equation Modeling (PLS-SEM), the findings confirm that core GHRM practices, including green recruitment, training, compensation, performance appraisal, and employee involvement, significantly enhancing organizational adaptability and pro-environmental behavior. OR is shown to strongly contribute to ORS, underscoring resilience as a foundational element for sustainable performance. Additionally, ORL amplifies GHRM’s positive impact on OR, revealing that a culture of learning further strengthens the resilience benefits of GHRM. This study enriches the GHRM and sustainability literature by elucidating the synergistic roles of resilience and learning in fostering sustainability, and offers practical insights for HR managers aiming to integrate GHRM into strategic frameworks. The findings emphasize the critical role of green HR practices in bolstering resilience and adapting to environmental challenges in emerging markets.
Spatial Effects of New Quality Productivity on the Low-Carbon Transformation of Energy Consumption Structure—Evidence from Provincial Data in China
Authors: Yin Kaimin , Shen Xing
Published: February 2025

The low-carbon transformation of an energy consumption structure (ECS) is related to the sustainable development of society. New quality productivity (NQP) is an important means to promote the green transformation of an ECS. This paper examines the spatiotemporal evolution of NQP development and low-carbon transformation of an ECS, utilizing panel data from 30 Chinese provinces (2012–2020) through kernel density estimation and spatial analysis methods. The impact of NQP development on the ECS and its spatial spillover effects are empirically analyzed using a two-way fixed-effects model and a spatial Durbin model. The study reveals that NQP development in China is generally positively correlated with ECS, although significant regional disparities exist. A significant positive spatial correlation exists between NQP and ECS. NQP development significantly promotes the transformation of local ECS but exerts a negative spatial spillover effect on neighboring regions. Further analysis reveals that NQP exhibits an “inverted U-shaped” impact on the spatial spillover effects of local ECS. Spatial attenuation analysis indicates that the negative spillover effects of NQP on ECS diminish with geographical distance, with a maximum attenuation boundary of 600 km. The study found that China is currently facing significant regional development imbalances. Promoting the balanced development of NQP is crucial for achieving high-quality and sustainable development.
An Exploratory Study of the Association Between Green Bond Features and ESG Performance
Authors: Wu Jinhui , Raghupathi Wullianallur , Raghupathi Viju
Published: February 2025

As investors increasingly incorporate non-financial performance metrics into investment decisions, CSR has become valuable due to its implications for voluntary disclosures and third-party ratings. Building on this premise, our study examines how green-bond issuance signals environmental commitment and is associated with ESG performance and valuation. While other studies examine this association, we go a step further and identify the green-bond features which are associated with ESG ratings. Using the Bloomberg database, we downloaded corporate green-bond data for 2550 green bonds. We use signaling theory as the foundation of the study. We deploy regression to test the relationships. Our findings show that green-bond features are associated with enhanced environmental and ESG disclosure scores but not with reductions in CO2 emissions relative to sales. The findings show weak associations of ESG with green-bond features. Taken together, the results contradict ‘greenwashing’ claims. However, the findings confirm that companies effectively signal environmental commitment through green-bond issuance. These insights enhance the understanding of green bonds’ nature and dimensions while providing meaningful implications for corporate policy.
Decision Support for Infrastructure Management of Public Institutions
Authors: Jajac Nikša
Published: February 2025

The management of public institutions is focused not only on providing and improving public services but also on managing the physical infrastructure that these institutions use—buildings for provision of such services. The focus of this paper is on decision support to the management of individual buildings and the set of such buildings (portfolio) during the planning phase. More precisely, it is directed towards support towards both the decision-maker (DM) and decision-making process (DMP) when planning construction activities/projects such as maintenance, renovation, reconstruction, extension, construction, design/preparation of project-technical documentation, etc. The aforementioned DMP includes the processing of a large amount of diverse data (technical, economic, social, etc.) expressed differently—numerically or descriptively, as well as in different units of measurement, simultaneously taking into account the different wishes and attitudes of stakeholders (consequently meeting their often conflicting goals and criteria). The above indicates that it is a complex and ill-defined multi-criteria problem faced by the DM/planner. On top of that, and knowing that the DM usually does not have all the necessary knowledge and skills, this paper proposes how to overcome these issues by supporting the DM within the DMP during such a planning process. The proposed concept promotes an integral (considering relevant aspects of this management problem) and inclusive (taking into account the views of relevant stakeholders) approach to managing complex construction projects and their portfolios. It is methodologically based on the logic of decision support systems and multi-criteria analysis. The multi-criteria methods used include the Preference Ranking Organization METhod for Enrichment Evaluation (PROMETHEE) for the evaluation and comparison of alternatives in an integral manner, as well as the Analytic Hierarchy Process (AHP) for determining the weights of criteria and achieving an inclusive and consistent approach to relevant stakeholders (based on the goal tree approach). The concept was tested on the planning of infrastructure management at a university in the Republic of Croatia, and it was proven to be useful because it provided the DM with a basis for decision making. The usefulness of the concept was confirmed by the concordance of the plan obtained using the concept and the activities/projects actually realized.
The Costs and Benefits of Electric Trucks: A Synopsis of the U.S. Trucking Market
Authors: Anderson Anne-Marie , Kish Richard J.
Published: February 2025

Electric trucks (e-trucks) are a key component of the climate change initiatives implemented to reduce pollutants within the transportation sector, one of the major contributors of carbon dioxide and other harmful particulates. Due to the substantial volume of pollution emitted by the trucking sector, any reduction would present a promising step towards reducing emissions and advancing sustainable transportation. However, as this paper points out, many obstacles must be overcome before e-trucking can become a major player in climate change. Key challenges include range limitations, charging infrastructure, and higher upfront costs. Thus, continued investment in technology, infrastructure development, and supportive policies will be crucial to overcoming these challenges and realizing the full potential of e-trucks within the global transportation sector. Future research needs to analyze the trade-offs between e-trucking and other options, including diesel, fuel cell electric and plug-in hybrids, and alternative clean fuel sources such as hydrogen.
Evaluation of Promising Areas for Biogas Production by Indirect Assessment of Raw Materials Using Satellite Monitoring
Authors: Opryshko Oleksiy , Kiktev Nikolay , Shvorov Sergey , Hluhan Fedir , Polishchuk Roman , Murakhovskiy Maksym , Hutsol Taras , Glowacki Szymon , Nurek Tomasz , Sojak Mariusz
Published: February 2025

An important issue in the sustainable development of agricultural engineering today is the use of biogas plants for the production of electricity and heat from the organic waste of agricultural products and other low-quality products, which also contributes to the improvement of environmental safety. Traditional methods for assessing the apparent severity of the Roslynnytsia campaign based on statistics from the dominions proved to be ineffective. A hypothesis was proposed regarding the possibility of estimating the apparent biomass by averaging the indicators of depletion and assessing the CH4 and CO emissions based on satellite monitoring data. The aim of this work is to create a methodology for preparing a raw material base in united territorial communities to provide them with electrical and thermal energy using biogas plants. The achievement of this goal was based on solving the following tasks: monitoring biomethane emissions in the atmosphere as a result of rotting organic waste, and monitoring carbon monoxide emissions as a result of burning agricultural waste. Experimental studies were conducted using earth satellites on sites with geometric centers in the village of Gaishin in the Pereyaslav united territorial community, the city of Ovruch in the Zhytomyr region, the Oleshkovsky Sands National Park in the Kherson region (Ukraine), and the city of Jüterbog, which is located in the state of Brandenburg and is part of the Teltow-Fläming district (Germany). The most significant results of this research involve the methodology for the preparation of the raw material base in the united territorial communities for the production of biogas, based on indirect measurements of methane and carbon dioxide emissions using the process of remote sensing. Based on the use of the proposed scientific and methodological apparatus, it was found that the location of the territory with the center in the village of Gaishin has better prospects for collecting plant raw materials for biogas production than the location of the territorial district with the center in the city of Ovruch, the emissions in which are significantly lower. From March 2020–August 2023, a higher CO concentration was recorded on average by 0.0009 mol/m2, which is explained precisely by crop growing practices. In addition, as a result of the conducted studies, for the considered emissions of methane and carbon monoxide for monitoring promising raw materials, carbon monoxide has the best prospects, since methane emissions can also be caused by anthropogenic factors. Thus, in the desert (Oleshkivskie Pisky), large methane emissions were recorded throughout the year which could not be explained by crop growing practices or the livestock industry.
Extraction of Metallic Nanoparticles from Electronic Waste for Biomedical Applications: Pioneering Sustainable Technological Progress
Authors: Kumar Sunil , Singh Harbinder , Singh Harjeevan , Soni Himanshi , Bechelany Mikhael , Singh Jagpreet
Published: February 2025

The extraction of metallic nanoparticles (MNPs) from waste electrical and electronic equipment (WEEE) has gained extensive attention from researchers for eco-friendly, reliable, and sustainable alternative protocol over the traditional linear economic approach (make-use-dispose) for boosting the circular economy. A plethora of MNPs including metals/metal oxide nanoparticles having a size dimension ranging from 1–100 nanometers (nm) have been extracted from these WEEE by using different chemical, physical, and biological methods. Recovery of certain precious MNPs can be achieved by dismantling and recycling electronic waste items in the form of gold (Au), platinum (Pt), zinc oxide (ZnO), silver (Ag), and copper oxide (CuO). These MNPs provide a huge range of applications such as antibacterial, therapeutic, target drug delivery, and biotechnological applications. This comprehensive review provides in-depth knowledge of the synthesis of MNPs using different techniques from WEEE and delves into their potential applications in biomedical fields with in-depth mechanisms. This article also discussed global challenges and opportunities in this area for adopting the concept of circular economy to conserve natural resources for future generations and hence create a greener environment and protect our planet.
Dynamic Evolutionary Game on Travel Mode Choices Among Buses, Ride-Sharing Vehicles, and Driving Alone in Shared Bus Lane Scenarios
Authors: Xue Yunqiang , Bao Guangfa , Tan Caifeng , Chen Haibo , Liu Jiayu , He Tong , Qiu Yang , Zhang Boru , Li Junying , Guan Hongzhi
Published: February 2025

Sharing bus lanes with ride-sharing vehicles is beneficial for improving the utilization efficiency of these lanes and alleviating urban traffic pressure. This paper applies evolutionary game theory to explore the evolutionary game dynamics of three travel modes—buses, ride-sharing vehicles, and driving alone—under different sharing strategy scenarios for bus lanes. Before constructing the game model, various influencing factors such as travel costs, time costs, and the combined costs of ride-sharing are quantified to calculate the cumulative prospect values before travel. The gains and losses in the cumulative prospect values are defined as parameter variables in the game model, establishing a payoff matrix for the three travel modes: buses, ride-sharing vehicles, and private cars. During the model-solving process, the Lyapunov first method is used for stability analysis of the equilibrium points, resulting in three groups of asymptotically stable equilibrium points. By rotating the parameter values according to the actual circumstances of different sharing strategies, the model simulates and evaluates the impact of various sharing policies on the travel mode choices among the three options. The results indicate that the gain and loss values in the cumulative prospect values of travel modes are key factors influencing travelers’ mode choices. Under the synergistic effects of urban ride-sharing policies and traffic system optimization, when the cumulative prospect value of ride-sharing is a gain, travelers recognize its advantages and are willing to choose it. Conversely, when the cumulative prospect value indicates a loss, travelers are more inclined to choose bus travel or driving alone. This paper provides a theoretical foundation for the formulation of sharing policies for bus lanes with ride-sharing, contributing to improved utilization efficiency of these lanes and alleviating urban traffic pressure.
Sustainable Livestock Solutions: Addressing Carbon Footprint Challenges from Indian and Global Perspectives
Authors: Samad Hari Abdul , Kumar Eshwaran Vineeth , Muquit Suhana Parvin , Sharma Lokesh , Arumugam Hemavathi , Kant Lata , Fatima Zikra , Sharun Khan , Aradotlu Parameshwarappa Madhusoodan , Latheef Shyma Kanirawther , Chouhan Vikrant Singh , Maurya Vijay Prakash , Singh Gyanendra , Kaniyamattam Karun
Published: February 2025

The rising environmental temperatures and growing global demand for animal protein pose major challenges to sustainable livestock production, highlighting the urgent need for climate change mitigation strategies. The livestock system in different parts of the world, especially in developing and underdeveloped nations, holds a significant role in supporting the livelihoods and nutritional security of millions, yet climate change is jeopardizing its efficiency and exacerbating its carbon footprint. This increase in carbon footprint is an alarming challenge for global sustainability, which needs to be addressed meticulously with fruitful outcomes. As the world’s largest livestock hub, the Indian livestock system can be adopted as a model for understanding the challenges and opportunities within the livestock system to develop sustainable approaches. In 2022, India accounted for approximately 7% of global greenhouse gas emissions (GHGEs), with a total of 3.9 billion metric tons of CO2e. This review provides updated insights on the livestock-related carbon footprint, sustainability-enhancing technologies, GHG estimation models, and strategies for climate-neutral livestock production. Emission estimation models are categorized into source-based and whole-farm models for a comprehensive assessment of emissions. Mitigation strategies for cattle include rumen modification, nutritional approaches, efficient manure management, and precision livestock farming. India’s commitment to achieving net-zero emissions by 2070 is reflected in various initiatives aimed at promoting sustainable livestock systems. Future perspectives emphasize decision modeling and climate-resilient technologies to address environmental challenges in alignment with the UN’s sustainable development goals.
The Impact of Environmental Regulation on the Growth of Small and Micro Enterprises: Insights from China
Authors: Zhong Yufen , Yao Xingyuan , Lin Weiming
Published: February 2025

Small and micro enterprises (SMEs) make important contributions to economic development, innovation, and employment in every country. The increasingly strict environmental regulations have become a global trend, but the empirical literature that evaluates the impacts of environmental regulations on the SMEs’ growth based on their observational data is extremely rare. This study aims to investigate how city-level environmental regulations in China affect the SMEs’ growth, with a focus on identifying lag effects, heterogeneous impacts across regions/enterprise types, and the mediating roles of technological innovation and policy support, using unbalanced panel data from 2007 to 2016. Using a dynamic panel model and entropy-weighted assessment, the results show the following: (1) Stricter environmental regulations significantly impede SMEs’ growth, with this effect persisting for up to two years. Robustness tests confirm the stability of these findings. (2) Despite the overall negative impact, our analysis reveals that environmental regulations can stimulate SMEs’ growth by promoting technological innovation and increasing policy support. (3) Heterogeneity analysis shows that the regulatory effects vary by region, ownership structure, and tax status, with the most adverse impacts observed in private firms, small-scale taxpayers, and businesses outside the Yangtze River Economic Belt. These findings highlight the need for differentiated regulatory approaches to balance environmental objectives with SMEs’ growth. The study is limited by its focus on data from 2007 to 2016, not considering recent policy shifts, and may have limited generalizability to economies with decentralized environmental governance.
Performance Evaluation and Strategic Analysis of Logistics Development for China Railway Express: A Spatial Connectivity Perspective
Authors: Wang Guan , Chen Maowei
Published: February 2025

Amid global challenges like COVID-19 and trade wars, resilient logistics networks are crucial. The China Railway Express (CRE) offers a sustainable alternative to sea and air transport, supporting China’s national logistics strategy and strengthening links between China and Europe. This study applies a three-stage Social Network Analysis (SNA) to CRE using a “point–line–network” approach. It evaluates city logistics with the entropy weight method, modifies the gravity model to assess intercity logistical gravity, and constructs a weighted network to analyze centrality evolution through SNA. The results show that cities such as Zhengzhou, Wuhan, and Chongqing have emerged as central logistics hubs, benefiting from strategic investments in infrastructure and multimodal systems. However, regional disparities persist, with cities like Harbin, Lanzhou, and Urumqi facing challenges in integration due to infrastructure deficits and geographic constraints. Furthermore, inefficiencies in border logistics, inconsistent customs procedures, and limited multimodal integration hinder the CRE’s potential. Addressing these challenges through infrastructure investment, unified customs standards, multimodal hub development, and advanced technologies like IoT and blockchain is crucial for enhancing connectivity and competitiveness. The findings offer actionable recommendations for policymakers, logistics firms, and researchers, contributing to the sustainable optimization of the CRE within global supply chains.
Effects of Short-Term Traffic-Related Air Pollution Exposure on Nasal Microbiome in Young Healthy Adults: A Randomized Crossover Controlled Trial
Authors: Qin Luwei , Pan Jingqi , Feng Demin , Yu Bingqing , Li Shunyu , Liu Xingyu , Jin Yuefei , Zhu Shenshen , Wu Weidong , Yang Wenjie
Published: February 2025

Traffic-related air pollution (TRAP) remains a concern for public health. However, the exact mechanisms through which TRAP affects the respiratory system are still not fully understood. This study aimed to investigate the nasal microbiome change in healthy adults after short-term exposure to TRAP, contributing to the understanding of the adverse health effects associated with TRAP. A randomized crossover controlled trial was conducted from 9 March to 30 March 2024 among college students aged 19–24 years. Twenty healthy students were recruited through a baseline questionnaire survey and randomly assigned into two groups. One group followed a crowed-testing procedure: the park portion, a three-week washout period, and then the road portion, while the other group experienced the opposite procedure. Both groups were fully exposed to either a park environment or a road environment with high traffic volume. Nasal mucus samples were collected from the participants at the end of the trial, and then 16SrRNA sequencing was performed to analyze the differences in compositional structure and diversity of the nasal microbiome when volunteers were exposed to different levels of TRAP. The α-diversity indices, including the Chao1 index (p = 0.0097), observed species index (p = 0.0089), and Faith’s PD index (p = 0.0255), demonstrated a significant increase in the nasal microbiome of healthy adults following short-term exposure to TRAP. Visualization through a two-dimensional NMDS plot (stress value < 0.2) indicated that nasal bacterial species distribution became richer after TRAP exposure. Furthermore, the relative abundance of nasal Firmicutes (Bacillota), Bacteroidota, and Actinobacteriota phyla, especially Firmicutes phylum, exhibited a richer distribution after conducting the trial in the road environment with high levels of TRAP, which was shown in the significance test of signature species. Collectively, our study indicates that short-term exposure to TRAP can affect the composition of the nasal microbiota in healthy adults. These findings offer a scientific basis for understanding how TRAP causes respiratory diseases.
Spatially Resolved Metabolomics and Network Pharmacology Reveal Extract D Nephrotoxicity Mechanisms in Pleuropterus multiflorus Thunb.
Authors: Jiang Haiyan , Wang Ying , Duan Xiaoyan , Guo Shushu , Fan Xiaoyu , Zhou Tianyu , Li Jie , He Jiuming , Yang Jianbo , Jin Hongtao
Published: February 2025

As a traditional Chinese medicine, the adverse hepatotoxicity effects of Pleuropterus multiflorus (Thunb.) Nakai (PM) have been documented. However, nephrotoxicity has been neglected as studies related to kidney toxicity mechanisms are limited. Our previous research reported that extract D [95% ethanol (EtOH) elution, PM-D] in a 70% EtOH PM extract showed more significant hepatotoxicity than other extracts. In the current study, PM-D was continuously administered to mice for 7 days at a dose of 2 g/kg (equivalent to a human dose of 219.8 mg/kg), which increased renal biochemical indexes and caused pathological kidney injury, suggesting renal toxicity. Therefore, network pharmacology and spatially resolved metabolomics were conducted to explore nephrotoxicity mechanisms underpinning PM-D. Network pharmacology indicated that BCL2, HSP90, ESR1, and CTNNB1 genes were core targets, while the phosphoinositide 3-kinase (PI3K)/protein kinase B(AKT)/signaling pathway was significantly enriched. Spatially resolved metabolomics indicated heterogeneous metabolite distribution in the kidney, further indicating that PM-D nephrotoxic metabolic pathways were enriched for α-linolenic acid and linoleic acid metabolism, pyrimidine metabolism, carnitine synthesis, and branched-chain fatty acid oxidation. Our comprehensive analyses highlighted that nephrotoxicity mechanisms were related to oxidative stress and apoptosis induced by disordered energy metabolism, lipid metabolism issues, and imbalanced nucleotide metabolism, which provide a platform for further research into PM nephrotoxicity mechanisms.
The Association of Systemic Inflammatory Response with Hepatitis B Vaccine Unresponsiveness
Authors: Karabay Oguz , Hamarat Kaan Furkan , Guney Eskiler Gamze , Aydin Ayhan
Published: February 2025

(1) Background: Hepatitis B virus (HBV) infection remains a major health challenge. Although vaccination protects people from HBV infection, 5–10% of people at risk of HBV infection and associated liver diseases do not respond to vaccination. The association of hematological indices with vaccine response is a crucial contributing factor in HBV-associated liver damage and the outcome of patients. In this context, we clinically assessed the interaction between inflammatory parameters and Hepatitis B vaccine response for the first time. (2) Methods: In total, 90 volunteers (44 non-responders and 46 responders) were included in this retrospective study. The demographic data and the hemogram parameters of the volunteers were recorded and statistically analyzed. Additionally, systemic inflammation index (SII), platelet-to-lymphocyte ratio (PLR), and neutrophil-to-lymphocyte ratio (NLR) were calculated. (3) Results: Our results indicate that higher median levels of white blood cells (8.61), lymphocytes (2.37), neutrophils (5.71), and platelets (280) were determined in the non-responders compared to the responders. SII and NLR indices were significantly higher in the non-responders than in the responders (p < 0.05). (4) Conclusions: The non-responders exerted higher systemic inflammation indicators than the responders, and the NLR value partially distinguished Hepatitis B vaccine response. Nevertheless, further studies with larger cohorts are essential to confirm the clinical utility of systemic inflammatory response as a reliable criterion for predicting Hepatitis B vaccine responsiveness.