bluuebunny/crossref_metadata_2025 huggingface dataset:
Effect of Geometry and Size on Additively Manufactured Short-Fiber Carbon-Nylon Composite Under Tensile Loading
Authors: Kámán András , Meszlényi Armand , Jakab Miklós , Kovács András , Egedy Attila
Published: February 2025

As the articles relating to the study of 3D printing processes are picking up pace, the question of comparability and repeatability based on the geometry and size of the specimens arises, based on the fact that the widely used extrusion 3D printing processes inherently have a structure that is made up of extruded lines of various shapes and sizes. This study aimed to determine the impact the specimen geometry and size have on the final tensile strength. One of the most widely used engineering materials, chopped carbon-fiber-reinforced nylon was used for this study. The four main specimen groups examined were specimens containing only walls and specimens containing only infill printed with both a 0.4 mm and 0.8 mm nozzle (to determine that the size of the extrusion lines has any effect on the tensile strength with different specimen sizes) achieving a solid body with two different line structures. Contradictory to the initial expectations, the tests showed that the geometry and size of the specimens had not influenced the tensile strength of the specimens in any of the four specimen groups. However, the tests showed that the groups containing only walls were always stronger than their only-infill counterparts and the groups printed with a 0.4 mm nozzle were stronger than the groups printed with a 0.8 mm nozzle.
Cold-Drawn Wood-Filled Polybutylene Succinate Macro-Fibers as a Reinforcing Material for Concrete
Authors: Herz Jonas , Schusser Verena , Muscat Dirk , Strübbe Nicole
Published: February 2025

The corrosive behavior of steel reinforcements causes issues in the concrete industry. To overcome this issue, alternative noncorrosive reinforcements such as polymer fibers could be used. However, as environmental protection becomes more important, sustainability must also be considered in the solution. An alternative to polymers based on raw oil is bio-based polymers. This study investigates the suitability of polymer fibers produced from polybutylene succinate together with cellulose and wood fillers as concrete reinforcements. Different mixtures of polybutylene succinate, cellulose, and wood fillers were created, and fibers were produced using a multiple drawing process. The fibers were tested using tensile tests, a single-fiber pull-out test, contact angle measurements, reflected light microscopy, density measurements, and thermogravimetric analysis. The fillers were shown to decrease the mechanical properties as the particle size and filler amount increased, resulting in a reduction in Young’s modulus and tensile strength of 55% and 70%, respectively, while adhesion to concrete increased with particle size from 0.31 ± 0.02 N/mm2 without filler to 0.90 ± 0.10 N/mm2 for the best-performing material combination. Reflected light microscopy images show changes in the fiber surface before and after pull-out. The fiber density decreased from 1.26 ± 0.05 g/cm3 to 0.91 ± 0.04 g/cm3 with an increasing filler amount and particle size for a compound with 10 weight percent of wood filler 1. The fiber thermal stability decreased slightly with the addition of filler. The greatest effect was a reduction in the temperature to ≈58 °C at 1% weight loss when 10 weight percent of wood was added. This study proves the possibility of using bio-based materials as concrete reinforcements.
Polysaccharides: The Sweet and Bitter Impacts on Cardiovascular Risk
Authors: Kalisz Grzegorz , Popiolek-Kalisz Joanna
Published: February 2025

Cardiovascular risk is a clinical factor that represents the probability of developing cardiovascular diseases (CVDs). This risk is shaped by non-modifiable and modifiable factors, including dietary patterns, which are the main lifestyle factor influencing CVD. Dietary polysaccharides, integral to nutrition, have varying effects on cardiovascular health depending on their type and source. They include starches, non-starch polysaccharides, and prebiotic fibers, categorized further into soluble and insoluble fibers. Soluble fibers, found in oats, legumes, and fruits, dissolve in water, forming gels that help lower serum cholesterol and modulate blood glucose levels. Insoluble fibers, present in whole grains and vegetables, aid in bowel regularity. The cardiovascular benefits of polysaccharides are linked to their ability to bind bile acids, reducing cholesterol levels, and the production of short-chain fatty acids by gut microbiota, which have anti-inflammatory properties. However, not all polysaccharides are beneficial; refined starches can lead to adverse metabolic effects, and chitosan to mixed effects on gut microbiota. This review examines the dualistic nature of polysaccharides, highlighting their beneficial roles in reducing cardiovascular risk factors and the potential adverse effects of specific types.
Environmental Assessment and Eco-Efficiency Analysis of the Dividing Wall Distillation Column for Separating a Benzene–Toluene–Xylene Mixture
Authors: Figueiredo Fernanda Ribeiro , Prata Diego Martinez
Published: February 2025

The benzene–toluene–xylene (BTX) system represents an energy-intensive petrochemical process with various industrial applications. Global climate changes have forced modern industry to act toward environmental safety, which requires technological changes. Thus, the divided wall column (DWC) represents a significant advancement in multicomponent mixture separation. To assess the impact of the conventional BTX process and its intensification proposal based on DWC technology, it is necessary to integrate an eco-efficiency approach that jointly analyzes the economic and environmental variables influencing the system, such as water consumption, CO2 emissions, and utility costs. An auxiliary utility plant was also considered for more realistic results in terms of energy and water consumption, which was identified as a lack in many research studies that performed an overall sustainability analysis. The results showed that the DWC scheme is 37.5% more eco-efficient than the conventional counterpart, mainly due to a 15.6% and 30.3% savings on energy and water consumption, respectively, which provided a 15.5% and 16.7% reduction on CO2 emissions and utility costs, respectively. In addition, all other environmental and safety indicators based on the waste algorithm reduction (WAR) were reduced by approximately 16%. Thus, the DWC proved to be a convenient technology with economic attractiveness and environmental friendliness.
Advances in Polyaniline-Based Composites for Room-Temperature Chemiresistor Gas Sensors
Authors: Masemola Clinton M. , Moloto Nosipho , Tetana Zikhona , Linganiso Linda Z. , Motaung Tshwafo E. , Linganiso-Dziike Ella C.
Published: February 2025

The increasing rate of environmental pollution and the emergence of new infectious diseases have drawn much attention toward the area of gas sensors for air quality monitoring and early-stage disease diagnosis, respectively. Polyaniline (PANI) has become one of the extensively studied polymers in the area of chemical sensing due to its good conductivity and sensitivity at room temperature. The development of room-temperature gas sensors represents a significant leap forward in air quality monitoring by conserving energy and enhancing the feasibility of the commercial development of sensing technologies. New research shines a light on the advantages of using PANI with materials such as semiconductor metal chalcogenides, metal oxides, metal nanoparticles, and graphitic carbon materials to form composites that can sense chemicals selectively at room temperature. This review focuses on the advancements in PANI-based gas sensors, exploring the materials, mechanisms, and applications that make these sensors a promising solution for modern air quality monitoring challenges. By examining the latest research and innovations, we aim to highlight this critical technology’s potential and future directions, instilling hope and optimism in safeguarding public health and the environment.
Fault Diagnosis of Wire Disconnection in Heater Control System Using One-Dimensional Convolutional Neural Network
Authors: Guo Jiawei , Sun Linfeng , Kawaguchi Takahiro , Hashimoto Seiji
Published: February 2025

Heaters are critical components in various heating control systems, and their faults are often a primary cause of system failure, drawing significant attention from engineers and researchers. Early and accurate fault diagnosis is crucial to prevent cascading failures. Many diagnostic methods target faults under generally stable and simple operating conditions, such as constant load or steady-state temperature. However, real-world scenarios are often complex and variable, involving dynamic loads, nonlinear temperature rises, and other challenges, which limit diagnostic accuracy. To address this issue, this paper proposes an intelligent fault diagnosis model based on a one-dimensional convolutional neural network (CNN), using the heater’s current and voltage as the input to the neural network. The effectiveness and accuracy of the proposed model were validated through experimental data under two different conditions, achieving an average accuracy rate of 98%. The disconnection faults were generated during actual operation and occurred in the early stages, differing significantly from artificially simulated faults, thereby increasing the difficulty of accurate diagnosis. Analysis and comparison of the experimental results demonstrate the feasibility of the intelligent diagnostic model and its high diagnostic accuracy.
Does Dexamphetamine Cause Addiction? A Narrative Review
Authors: Khalifa Hazer , Hibbert Emily J. , Gauci Natalie , Poulton Alison
Published: February 2025

Dexamphetamine (DEX) is widely used in treatments, but concerns persist about its potential for addiction. This review aimed to assess the risks of DEX addiction by examining the existing literature. Originally planned as a systematic review, it was adjusted to a narrative review due to a lack of qualifying studies. A search of the literature on addiction to DEX was conducted on three databases including PubMed, Scopus, and Embase. Seven articles consisting of three reviews and four papers with original data were identified and reviewed. In total, four poorly documented cases of possible DEX addiction were found. The commonly cited papers on prescription DEX addiction did not hold up to scrutiny. Overall, the evidence that DEX is addictive is limited and inconclusive, highlighting the need for more rigorous research to clarify its true addiction risk.
The Duality of Toni Morrison’s Beloved: Postmodern Religious Symbols That Highlight the Inherited Legacy of the American South
Authors: Gibson Charity L.
Published: February 2025

Toni Morrison’s novel Beloved includes a namesake character representing both Christianity and African cosmology. Beloved is neither straightforwardly good nor evil but serves as a dualistic and spiritual symbol. Though one could interpret Morrison’s narrative to support a postmodern religious multiplicity of voices, the potentially problematic theology still allows the readers to engage in useful discussions about the spiritual and cultural inheritance of the American South. Morrison’s narrative is only compatible with a Christian or African religious lens through recognizing symbolization as a representation of cultural manifestations rather than an endorsement of multiple worldviews.
A Case for a Eucharistic Approach to Healthcare According to the Social Teachings of the Catholic Church
Authors: Aihiokhai SimonMary Asese
Published: February 2025

The Christian message evokes the motif of abundant life for all. It is a summon to take seriously the holistic approach to life that has meaning and that is relational in all its expressions and manifestations. It is on this note that one has to appreciate the link between healthcare and the social teachings of the Catholic Church. Such a link is grounded in a pneumatological turn that mandates the Church to explore ways that the common good can be expressed through affordable healthcare for all persons. This work makes a claim that a eucharistic consciousness is the proper way to understand the link between healthcare affordability and the social teachings of the Church. It is eucharistic because the theology of the eucharist is a summon for the Church to be a church for others while upending individualism. Also, a eucharistic disposition is what constitutes the vision of the social teachings of the Church. Thus, if the Church is to offer its own wisdom on how healthcare ought to be understood in our times, it necessarily must be rooted in a eucharistic stance that defines its vision of the common good.
Electrochemical Direct Lithium Extraction: A Review of Electrodialysis and Capacitive Deionization Technologies
Authors: Park Jeongbeen , Lee Juwon , Shim In-Tae , Kim Eunju , Nam Sook-Hyun , Koo Jae-Wuk , Hwang Tae-Mun
Published: February 2025

The rapid expansion of lithium-ion battery (LIB) markets for electric vehicles and renewable energy storage has exponentially increased lithium demand, driving research into sustainable extraction methods. Traditional lithium recovery from brine using evaporation ponds is resource intensive, consuming vast amounts of water and causing severe environmental issues. In response, Direct Lithium Extraction (DLE) technologies have emerged as more efficient, eco-friendly alternatives. This review explores two promising electrochemical DLE methods: Electrodialysis (ED) and Capacitive Deionization (CDI). ED employs ion-exchange membranes (IEMs), such as cation exchange membranes, to selectively transport lithium ions from sources like brine and seawater and achieves high recovery rates. IEMs utilize chemical and structural properties to enhance the selectivity of Li+ over competing ions like Mg2+ and Na+. However, ED faces challenges such as high energy consumption, membrane fouling, and reduced efficiency in ion-rich solutions. CDI uses electrostatic forces to adsorb lithium ions onto electrodes, offering low energy consumption and adaptability to varying lithium concentrations. Advanced variants, such as Membrane Capacitive Deionization (MCDI) and Flow Capacitive Deionization (FCDI), enhance ion selectivity and enable continuous operation. MCDI incorporates IEMs to reduce co-ion interference effects, while FCDI utilizes liquid electrodes to enhance scalability and operational flexibility. Advancements in electrode materials remain crucial to enhance selectivity and efficiency. Validating these methods at the pilot scale is crucial for assessing performance, scalability, and economic feasibility under real-world conditions. Future research should focus on reducing operational costs, developing more durable and selective electrodes, and creating integrated systems to enhance overall efficiency. By addressing these challenges, DLE technologies can provide sustainable solutions for lithium resource management, minimize environmental impact, and support a low-carbon future.
Evaluating Policy Frameworks and Their Role in the Sustainable Growth of Distributed Photovoltaic Generation
Authors: Schetinger Annelys Machado , de Lucena André Frossard Pereira
Published: February 2025

In response to the growing photovoltaic distributed generation market, this study investigates the evolution of energy policies and mechanisms driving the growth of photovoltaic distributed generation (DGPV). Analyzing the top ten countries in photovoltaic installations, it examines historical trends in capacity growth, installation costs, and stakeholder engagement to evaluate policy effectiveness. Eight policy categories are identified as follows: direct financial incentives, energy market regulation, government management, production incentives, performance-based feed-in tariffs, renewable energy obligations, research and development initiatives, and agreements and commitments. The research results emphasize the crucial influence of government management policies, direct financial incentives, and energy market regulation on promoting the growth of DGPV. Political will and effective governance are identified as key drivers in advancing technology and market development. Policies reducing installation costs and encouraging investment support the transition of photovoltaic systems from early adoption to market maturity. Despite these advances, disparities in policy implementation highlight the need for adaptable frameworks tailored to local contexts. By leveraging solar energy, an abundant and universally accessible resource, nations can enhance energy equity through effective policies and accelerate the shift toward sustainable energy systems. This analysis offers valuable insights for policymakers seeking to promote DGPV as a central strategy in combating climate change.
On the Curvature of the Bachelier Implied Volatility
Authors: Alòs Elisa , García-Lorite David
Published: February 2025

Our aim in this paper is to analytically compute the at-the-money second derivative of the Bachelier implied volatility curve as a function of the strike price for correlated stochastic volatility models. We also obtain an expression for the short-term limit of this second derivative in terms of the first and second Malliavin derivatives of the volatility process and the correlation parameter. Our analysis does not need the volatility to be Markovian and can be applied to the case of fractional volatility models, both with H<1/2 and H>1/2. More precisely, we start our analysis with an adequate decomposition formula of the curvature as the curvature in the uncorrelated case (where the Brownian motions describing asset price and volatility dynamics are uncorrelated) plus a term due to the correlation. Then, we compute the curvature in the uncorrelated case via Malliavin calculus. Finally, we add the corresponding correlation correction and we take limits as the time to maturity tends to zero. The presented results can be an interesting tool in financial modeling and in the computation of the corresponding Greeks. Moreover, they allow us to obtain general formulas that can be applied to a wide class of models. Finally, they provide us with a precise interpretation of the impact of the Hurst parameter H on this curvature.
Remote Sensing Cross-Modal Text-Image Retrieval Based on Attention Correction and Filtering
Authors: Yang Xiaoyu , Li Chao , Wang Zhiming , Xie Hao , Mao Junyi , Yin Guangqiang
Published: February 2025

Remote sensing cross-modal text-image retrieval constitutes a pivotal component of multi-modal retrieval in remote sensing, central to which is the process of learning integrated visual and textual representations. Prior research predominantly emphasized the overarching characteristics of remote sensing images, or employed attention mechanisms for meticulous alignment. However, these investigations, to some degree, overlooked the intricacies inherent in the textual descriptions accompanying remote sensing images. In this paper, we introduce a novel cross-modal retrieval model, specifically tailored for remote sensing image-text, leveraging attention correction and filtering mechanisms. The proposed model is architected around four primary components: an image feature extraction module, a text feature extraction module, an attention correction module, and an attention filtering module. Within the image feature extraction module, the Visual Graph Neural Network (VIG) serves as the principal encoder, augmented by a multi-tiered node feature fusion mechanism. This ensures a comprehensive understanding of remote sensing images. For text feature extraction, both the Bidirectional Gated Recurrent Unit (BGRU) and the Graph Attention Network (GAT) are employed as encoders, furnishing the model with an enriched understanding of the associated text. The attention correction segment minimizes potential misalignments in image-text pairings, specifically by modulating attention weightings in cases where there’s a unique correlation between visual area attributes and textual descriptors. Concurrently, the attention filtering segment diminishes the influence of extraneous visual sectors and terms in the image-text matching process, thereby enhancing the precision of cross-modal retrieval. Extensive experimentation carried out on both the RSICD and RSITMD datasets, yielded commendable results, attesting to the superior efficacy of the proposed methodology in the domain of remote sensing cross-modal text-image retrieval.
Preliminary Trajectory Analysis of CubeSats with Electric Thrusters in Nodal Flyby Missions for Asteroid Exploration
Authors: Quarta Alessandro A.
Published: February 2025

This paper studies the performance of an interplanetary CubeSat equipped with a continuous-thrust primary propulsion system in a heliocentric mission scenario, which models a nodal flyby with a potential near-Earth asteroid. In particular, the mathematical model discussed in this work considers a small array of (commercial) miniaturized electric thrusters installed onboard a typical CubeSat, whose power-generation system is based on the use of classic solar panels. The paper also discusses the impact of the size of thrusters’ array on the nominal performance of the transfer mission by analyzing the trajectory of the CubeSat from an optimization point of view. In this context, the propulsive characteristics of a commercial electric thruster which corresponds to a iodine-fueled gridded ion-propulsion system are considered in this study, while the proposed procedure can be easily extended to a generic continuous-thrust propulsion system whose variation in thrust magnitude and specific impulse as a function of the input electric power is a known analytic function. Using an indirect approach, the paper illustrates the optimal guidance law, which allows the interplanetary CubeSat to reach a given solar distance, with the minimum flight time, by starting from a circular (ecliptic) parking orbit of assigned radius. The mission scenario is purely two-dimensional and models a rapid nodal flyby with a near-Earth asteroid whose nodal distance coincides with the solar distance to be reached.
DHQ-DETR: Distributed and High-Quality Object Query for Enhanced Dense Detection in Remote Sensing
Authors: Li Chenglong , Zhang Jianwei , Huo Bihan , Xue Yingjian
Published: February 2025

With the widespread application of remote sensing technologies and UAV imagery in various fields, dense object detection has become a significant and challenging task in computer vision research. Existing end-to-end detection models, particularly those based on DETR, often face criticism due to their high computational demands, slow convergence rates, and inadequacy in managing dense, multi-scale objects. These challenges are especially acute in remote sensing applications, where accurate analysis of large-scale aerial and satellite imagery relies heavily on effective dense object detection. In this paper, we propose the DHQ-DETR framework, which addresses these issues by modeling bounding box offsets as distributions. DHQ-DETR incorporates the Distribution Focus Loss (DFL) to enhance residual learning, and introduces a High-Quality Query Selection (HQQS) module to effectively balance classification and regression tasks. Additionally, we propose an auxiliary detection head and a sample assignment strategy that complements the Hungarian algorithm to accelerate convergence. Our experimental results demonstrate the superior performance of DHQ-DETR, achieving an average precision (AP) of 53.7% on the COCO val2017 dataset, 54.3% on the DOTAv1.0, and 32.4% on Visdrone, underscoring its effectiveness for real-world dense object detection tasks.
Sea Surface Temperature Forecasting Using Foundational Models: A Novel Approach Assessed in the Caribbean Sea
Authors: Bustos Usta David Francisco , Rodríguez-López Lien , Torres Parra Rafael Ricardo , Bourrel Luc
Published: February 2025

Sea surface temperature (SST) plays a pivotal role in air–sea interactions, with implications for climate, weather, and marine ecosystems, particularly in regions like the Caribbean Sea, where upwelling and dynamic oceanographic processes significantly influence biodiversity and fisheries. This study evaluates the performance of foundational models, Chronos and Lag-Llama, in forecasting SST using 22 years (2002–2023) of high-resolution satellite-derived and in situ data. The Chronos model, leveraging zero-shot learning and tokenization methods, consistently outperformed Lag-Llama across all forecast horizons, demonstrating lower errors and greater stability, especially in regions of moderate SST variability. The Chronos model’s ability to forecast extreme upwelling events is assessed, and a description of such events is presented for two regions in the southern Caribbean upwelling system. The Chronos forecast resembles SST variability in upwelling regions for forecast horizons of up to 7 days, providing reliable short-term predictions. Beyond this, the model exhibits increased bias and error, particularly in regions with strong SST gradients and high variability associated with coastal upwelling processes. The findings highlight the advantages of foundational models, including reduced computational demands and adaptability across diverse tasks, while also underscoring their limitations in regions with complex physical oceanographic phenomena. This study establishes a benchmark for SST forecasting using foundational models and emphasizes the need for hybrid approaches integrating physical principles to improve accuracy in dynamic and ecologically critical regions.
Using Night-Time Drone-Acquired Thermal Imagery to Monitor Flying-Fox Productivity—A Proof of Concept
Authors: Meade Jessica , McCarthy Eliane D. , Yabsley Samantha H. , Grady Sienna C. , Martin John M. , Welbergen Justin A.
Published: February 2025

Accurate and precise monitoring of species abundance is essential for determining population trends and responses to environmental change. Species, such as bats, that have slow life histories, characterized by extended lifespans and low reproductive rates, are particularly vulnerable to environmental changes, stochastic events, and human activities. An accurate assessment of productivity can improve parameters for population modelling and provide insights into species’ capacity to recover from population perturbations, yet data on reproductive output are often lacking. Recently, advances in drone technology have allowed for the development of a drone-based thermal remote sensing technique to accurately and precisely count the numbers of flying-foxes (Pteropus spp.) in their tree roosts. Here, we extend that method and use a drone-borne thermal camera flown at night to count the number of flying-fox pups that are left alone in the roost whilst their mothers are out foraging. We show that this is an effective method of estimating flying-fox productivity on a per-colony basis, in a standardized fashion, and at a relatively low cost. When combined with a day-time drone flight used to estimate the number of adults in a colony, this can also provide an estimate of female reproductive performance, which is important for assessments of population health. These estimates can be related to changes in local food availability and weather conditions (including extreme heat events) and enable us to determine, for the first time, the impacts of disturbances from site-specific management actions on flying-fox population trajectories.
Development of a Novel One-Dimensional Nested U-Net Cloud-Classification Model (1D-CloudNet)
Authors: Deng Minjie , Han Yong , Liu Yan , Dong Li , Zhou Qicheng , Zhang Yurong , Deng Ximing , Lu Tianwei
Published: February 2025

Cloud classification is fundamental to advancing climate research and improving weather forecasting. However, existing cloud classification models are constrained by several limitations. For instance, simple statistical methods depend heavily on prior knowledge, leading to frequent misclassifications in regions with high latitudes or complex terrains. Machine learning approaches based on two-dimensional images face challenges such as data scarcity and high annotation costs, which hinder accurate pixel-level cloud identification. Additionally, single-pixel classification methods fail to effectively exploit the spatial correlations inherent in cloud structures. In this paper, we introduce the one-dimensional nested U-Net cloud-classification model (1D-CloudNet), which was developed using Himawari-8 and CloudSat data collected over two years (2016–2017), comprising a total of 27,688 samples. This model is explicitly tailored for the analysis of one-dimensional, multi-channel images. Experimental results indicate that 1D-CloudNet achieves an overall classification accuracy of 88.19% during the day and 87.40% at night. This represents a 3–4% improvement compared to traditional models. The model demonstrates robust performance for both daytime and nighttime applications, effectively addressing the absence of nighttime data in the Himawari-8 L2 product. In the future, 1D-CloudNet is expected to support regional climate research and extreme weather monitoring. Further optimization could enhance its adaptability to complex terrains.
Dual-Branch Seasonal Error Elimination Change Detection Framework Using Target Image Feature Fusion Generator
Authors: Zhu Hongming , Zhang Jipeng , Wang Zeju , Liu Xinyu , Liu Qin , Du Bowen
Published: February 2025

In change detection tasks, seasonal variations in spectral characteristics and surface cover can negatively impact performance when comparing image pairs from different seasons. Many existing change detection methods do not specifically address the performance degradation caused by seasonal errors. To tackle this issue, the Dual-Branch Seasonal Error Elimination Change Detection Framework using Target Image Feature Fusion Generator (DBSEE-CDF) is introduced. Specifically, the approach utilizes the Target Image Feature Fusion Generator (TIFFG), which incorporates spatial and channel attention mechanisms to extract features from target images and integrates them with deep features from input images using cross-attention. To avoid a severe loss of visual fidelity caused by the significant differences in texture and color features between snow-covered and snow-free images, as well as the different requirements for style transformation, different generators for snow-covered winter images and snow-free winter images are employed to produce intermediate images that eliminate seasonal errors before conducting change detection tasks. The experimental results demonstrate significant improvements in both quantitative and qualitative assessments of change detection tasks compared to directly performing change detection with various models, highlighting the effectiveness of the proposed DBSEE-CDF.
The Role of Sports in Building Resilience: A Machine Learning Approach to the Psychological Effects of the COVID-19 Pandemic on Children and Adolescents
Authors: Di Martino Giulia , della Valle Carlo , di Cagno Alessandra , Fiorilli Giovanni , Calcagno Giuseppe , Conte Daniele
Published: February 2025

(1) Background: This study evaluated whether physical activity and sports serve as a protective factor in mitigating COVID-19 pandemic long-term consequences. (2) Methods: A survey of 1702 participants (8–18 years) used the Impact of Event Scale (IES), Generalized Anxiety Disorder Scale (GAD-7), and Center for Epidemiological Studies Depression Scale for Children (CES-DC). Participants were categorized based on their engagement in sports, cultural activities, or sedentary behaviors. Statistical analysis included non-parametric tests, CHAID models, and clustering. (3) Results: In total, 65.5% of participants experienced minimal to mild anxiety, and 34.5% reported moderate to severe anxiety. The main predictor of depression is the sample age, followed by the training experience. Depressive symptoms were identified in 71.3% of participants (75.7% adolescents; 64% children). Adolescents with longer training experience (67.6%) showed lower depression compared to those with shorter experience (77.2%). For post-traumatic stress, 38% of children and 46% of adolescents exceeded cut-off scores for significant symptoms, with age and training level identified as key predictors. Among children, sport experience with low weekly frequency was associated with the lowest depression rates (59.5%). (4) Conclusions: Four years after the pandemic, a high percentage of anxiety and depression disorders is observed in the youth population, particularly among adolescents. Sports appear to slightly alleviate this serious condition.
ESG Performance Empowers Financial Flexibility in Manufacturing Firms—Empirical Evidence from China
Authors: Wei Jianzhi , He Xuesong , Wu Yawei
Published: February 2025

In the context of slowing economic growth and increasing uncertainty, enhancing the financial flexibility of manufacturing enterprises is a critical foundation for promoting the high-quality development of the real economy. This study selects a sample of Chinese A-share-listed manufacturing firms from Shanghai and Shenzhen, spanning the years 2012 to 2022, and constructs a fixed-effects model to examine the impact of ESG performance on the financial flexibility of these firms and its underlying mechanisms. The study finds that: (1) ESG performance significantly enhances corporate financial flexibility. (2) ESG performance promotes financial flexibility primarily through mechanisms such as alleviating financing constraints, improving competitive advantages, and attracting analysts’ attention. (3) Heterogeneity analysis reveals that the positive effect of ESG performance on financial flexibility is more pronounced in high-tech firms and non-heavily-polluted firms. (4) Sub-dimensional analysis shows that corporate governance has a more significant impact on financial flexibility enhancement than social responsibility, while environmental investment exerts an inhibitory effect on financial flexibility. (5) The uncertainty associated with ESG ratings weakens the contribution of ESG practices to the financial flexibility of manufacturing firms. Based on these findings, this paper suggests that enterprises should be encouraged to actively adopt ESG practices, accelerate the improvement of their ESG disclosure systems, and support firms with strong ESG performance to foster high-quality development.
The Impact of Green Inclusive Leadership on Green Innovation in Chinese SMEs: The Mediating Roles of Green Knowledge Sharing and Green Organizational Identity
Authors: Zhang Shuning , Li Yannan , Hong Ahreum
Published: February 2025

The growing urgency of climate change underscores the need for enhanced green management practices across organizations, particularly for small and medium enterprises (SMEs), the backbone of economic development. Green inclusive leadership (GIL) is defined as a leadership approach that emphasizes inclusivity, openness, and support for employees’ green ideas and contributions, fostering alignment with organizational environmental objectives. This study examines the influence of GIL on green knowledge sharing (GKS), green organizational identity (GOI), and green innovation (GI) within diverse Chinese industries, including manufacturing, services, media, and IT. Using data from 625 valid survey responses collected from 700 predominantly young, educated employees, this research demonstrates that GIL has a direct positive impact on GKS, GOI, and GI. The findings further reveal that GKS and GOI significantly contribute to GI, highlighting their roles as critical drivers of green innovation. Additionally, GKS and GOI partially mediate the relationship between GIL and GI, providing insights into how GIL promotes sustainable practices and innovation. This study underscores the importance of leadership and collaborative organizational practices in fostering a green-oriented organizational climate, supporting adopting green business models essential for competitive and resilient enterprises in a sustainable economy.
Optimizing Semantic Segmentation of Street Views with SP-UNet for Comprehensive Street Quality Evaluation
Authors: Hua Caijian , Lv Wei
Published: February 2025

Traditional street quality evaluations are often subjective and limited in scale, failing to capture the nuanced and dynamic aspects of urban environments. This paper presents a novel and data-driven approach for objective and comprehensive street quality evaluation using street view images and semantic segmentation. The proposed SP-UNet (Spatial Pyramid UNet) is a multi-scale segmentation model that leverages the power of VGG16, SimSPPF (Simultaneous Spatial and Channel Pyramid Pooling), and MLCA (Multi-Level Context Attention) attention mechanisms. This integration effectively enhances feature extraction, context aggregation, and detail preservation. The model’s average intersection over union, Mean Pixel Accuracy, and overall accuracy achieving improvements of 5.83%, 6.52%, and 2.37% in mIoU, Mean Pixel Accuracy (mPA), and overall accuracy, respectively. Further analysis using the CRITIC method highlights the model’s strengths in various street quality dimensions across different urban areas. The SP-UNet model not only improves the accuracy of street quality evaluation but also offers valuable insights for urban managers to enhance the livability and functionality of urban environments.
What Tackles Vehicle GHG Emissions in California: Regional Plan Adoption or Local Leadership?
Authors: Zandiatashbar Ahoura , Alexander Serena , Tatarevic Branka
Published: February 2025

The California Senate Bill No. 375 (SB 375) serves as a model policy for reducing greenhouse gas (GHG) emissions by integrating transportation and land-use planning through regional and local policies. The 18 California Metropolitan Planning Organizations (MPOs) are tasked with developing Sustainable Communities Strategies (SCS) to guide emissions reductions, often implemented locally through Climate Action Plans (CAPs). However, CAPs are voluntary, and misalignment with SCS objectives can undermine their effectiveness. This study examined 25 California cities using content analysis and regression modeling to explore whether independent local actions, supported by community engagement, activist strategies, and leadership, are more effective than regional alignment in reducing vehicle trips. The findings show that while aligning regional and local plans is important for equity and resource distribution, local activist leadership in addressing specific issues, such as parking and public education, achieves significant reductions in vehicle trips. These efforts lead to a 20% increase in non-auto commuting, even without a mandated regional alignment. Additionally, regional strategies such as climate-friendly infrastructure and mass transit are crucial for addressing resource disparities between lower-income communities with limited volunteer capacity and wealthier communities that benefit from robust regional plans and strong local leadership. This study provides critical evidence of the effectiveness of regional and local approaches, emphasizing the need for a balanced, multi-scalar framework to enhance transportation emission reductions and climate resilience.
Technological Innovation in Start-Ups on a Pathway to Achieving Sustainable Development Goal (SDG) 8: A Systematic Review
Authors: Danil Lilian , Jahroh Siti , Syarief Rizal , Taryana Asep
Published: February 2025

In a start-up, the level of technological innovation is crucial to the start-up’s competitiveness, especially in the digital age; as a result, high-tech start-ups stand a better chance of being more profitable than middle-tech and low-tech start-ups. The aim of this study is to identify and examine research papers regarding the role of technological innovation in advancing Sustainable Development Goal 8 (SDG) in the current context. This study intends to fill research gaps by performing a systematic literature review and meta-analysis following the PRISMA guidelines on the subject. To investigate advancements in the use of start-up technologies, scientific publications were obtained from the Scopus database, yielding a total of 384 entries for the preferred reporting items for systematic reviews and the meta-analyses identification stage. The findings indicate that high technology encompasses artificial intelligence (AI), blockchain, the Internet of Things (IoT), and collaborative robots; medium technology comprises mobile applications, big data, and cloud computing; and low technology consists of software and connectivity. Each of the technological innovations plays a significant role in advancing SDG 8, encompassing aspects such as economic growth, employment, productivity, creativity, innovation, entrepreneurship, development policies, and business growth.
Estimating Soil Carbon Sequestration Potential in Portuguese Agricultural Soils Through Land-Management and Land-Use Changes
Authors: Raposo Mariana , Canaveira Paulo , Domingos Tiago
Published: February 2025

Soil carbon sequestration (SCS) is a nature-based, low-cost climate mitigation strategy that also contributes to the climate adaptation of agricultural systems. Some land-use and land-management practices potentially lead to an enhancement of the soil organic carbon (SOC) sink, such as no-till, the use of cover crops, leaving residues on fields, improving the variety of legume species in grasslands and reducing grazing intensity. However, uncertainties remain both in estimating and measuring the impact of the application of certain practices, as these vary with the soil, climate and historic land use. IPCC (Intergovernmental Panel on Climate Change) guidelines are commonly used to estimate SOC and SOC sequestration potentials at different tiers. Here, the IPCC’s tier 1 methodology was applied to estimate (1) the sequestration potential of nine mitigation practices and (2) the emission or sequestration potential of four current land-change trends for n = 7092 unique agricultural sites in mainland Portugal. The conversion of irrigated crops to improved grasslands resulted in the highest average unit sequestration (1.05 tC ha−1 yr−1), while cropland conversion to poor degraded pasture (abandonment) resulted in the highest unit SOC loss (−0.08 tC ha−1 yr−1). The abandonment of cropland results in a national SOC loss of up to 0.09 MtC yr−1, while the improvement of poor degraded pastures has the highest national sequestration potential, equal to 0.6 MtC yr−1 (2.2 MtCO2eq yr−1), about 4% of Portugal’s emissions in 2021, if applied in all managed areas. The results enable a comparison between different practices and land uses; however, to enhance accuracy, a higher tier methodology tailored to the Portuguese context should be developed.
Blockchain-Enhanced Demand-Side Management for Improved Energy Efficiency and Decentralized Control
Authors: Boumaiza Ameni
Published: February 2025

Blockchain technology introduces significant advancements in demand-side management (DSM) by enabling decentralized, transparent, and secure data handling within energy systems. This study explores a blockchain-based approach aimed at improving electricity efficiency through enhanced DSM strategies. We present a comprehensive framework that integrates blockchain technology with real-time data analytics to optimize energy consumption, stimulate consumer participation, and support efficient energy utilization. The experimental analysis demonstrates that blockchain integration significantly strengthens DSM operations by reducing operational costs, increasing participation in demand response programs, and enhancing grid stability. The findings highlight the effectiveness and potential of the proposed approach as a forward-looking solution for energy management systems.
Can Membership in Intergovernmental Organizations Effectively Reduce CO2 Emissions?
Authors: Huang Taocheng , Li Xiaoyan
Published: February 2025

Since the Industrial Revolution, emissions of greenhouse gases such as CO2 have soared in various countries. Different kinds of intergovernmental organizations (IGOs) were established after World War II, which aim for sustainable development. We want to investigate whether membership in IGOs can effectively reduce carbon dioxide emissions. The study uses global panel data of 168 countries from 1960 to 2014 to carry out the fixed effect regression. The results of basic regression show that there is a negative relationship between membership in IGOs and CO2 emission. The greater the number of IGOs joined by the specific sample country, the lower the CO2 emissions produced by that country will be. We use social network analysis and find that CO2 emissions will be effectively reduced if the distance of the sample country from other countries in IGO networks is smaller. In addition, joining more social IGOs can reduce CO2 emissions more than joining political and economic IGOs. Countries should be encouraged to actively participate in IGOs. IGOs provide a good platform for consultation, communication, and rule-making in environmental governance among countries.
BCA: Besiege and Conquer Algorithm
Authors: Jiang Jianhua , Meng Xianqiu , Wu Jiaqi , Tian Jun , Xu Gaochao , Li Weihua
Published: February 2025

This paper introduces a bio-inspired meta-heuristic algorithm, the Besiege and Conquer Algorithm (BCA), developed to tackle complex and high-dimensional optimization problems. Drawing inspiration from the concept of symmetry and guerrilla warfare strategies, the BCA incorporates four core components: besiege, conquer, balance, and feedback. The besiege strategy strengthens exploration, while the conquer strategy enhances exploitation. Balance and feedback mechanisms maintain a dynamic equilibrium between these capabilities, ensuring robust optimization performance. The algorithm’s effectiveness is validated through benchmark test functions, demonstrating superior results in comparison with existing methods, supported by Friedman rankings and Wilcoxon signed-rank tests. Beyond theoretical and experimental validation, the BCA showcases its real-world relevance through applications in engineering design and classification problems, addressing practical challenges. These results underline the algorithm’s strong exploration, exploitation, and convergence capabilities and its potential to contribute meaningfully to diverse real-world domains.
Bézier Curves and Surfaces with the Blending (α, λ, s)-Bernstein Basis
Authors: Karakılıç İlhan , Karakılıç Sedef , Budakçı Gülter , Özger Faruk
Published: February 2025

This study presents a generalized approach to Bézier curves and surfaces by utilizing the blending (α,λ,s)-Bernstein basis. The (α,λ,s)-Bernstein basis introduces shape parameters α, λ, and s, which allow for more flexibility and control over the curve’s shape compared to the classical Bernstein basis. The paper explores the properties of these generalized curves and surfaces, demonstrating their ability to maintain essential geometric characteristics, such as convex hull containment and endpoint interpolation, while providing enhanced control over the shape. This work aims to contribute to the fields of computer-aided geometric design and related applications by offering a robust tool for curve and surface modeling.