bluuebunny/crossref_metadata_2025 huggingface dataset:
Lateral Synodality: Academics of Asian Catholicism and Organizational Change
Authors: Chambon Michel , Asor Bubbles Beverly , Supan Renchillina Joy G.
Published: February 2025

As the Synod on Synodality has massively consulted Catholics around the globe for three years (2021–2024), this paper discusses ways in which academics with knowledge related to Asian Catholics have been involved in these processes. Focusing on this specific community of scholars, we highlight the paradoxical ways in which they contributed to these synodal conversations. They simultaneously illustrate the relatively new production of multidisciplinary knowledge on Asian Catholicism and the hesitations standing between ecclesial organizations and academia. While academics of Asian Catholicism produce scholarship not foreign to the principles of synodal listening and discernment, their involvement within synodal processes has often been indirect and filtered by their disciplinary background and ecclesial status (laity vs. clergy). Based on a survey conducted in May 2024, this paper shows that the production of academic knowledge on Asian Catholics is now driven chiefly by laity and shaped across various disciplines and places. Yet, a significantly higher proportion of scholars who directly engaged in synodal conversations were theologians belonging to the clergy. While other disciplines may have contributed indirectly, theologians were overrepresented. This creates a paradox in which synodal conversations have coexisted from a certain distance with a rich academic knowledge of Asian Catholics, and most academics of Asian Catholicism have remained outside of synodal efforts. Departing from existing theories that approach this Synod on Synodality as a vertical process, either as a bottom-up or a top-down reform of the Catholic Church, we highlight its restrained engagement with academia, a lateral community of listening and discernment.
Managerial Incentives and Firm Risk Taking: The Mediating Role of Corporate Social Responsibility
Authors: Yin Desheng , Wang Michael , Sun Yufan , Wang Haizhi , Zhen Xinting
Published: February 2025

In this study, we focus on managerial incentives provided by debt-like compensation and further investigate whether and to what extent such managerial incentives may affect CEOs’ decisions on risk management. Building on cumulative prospect theory and instrumental stakeholder theory, we propose that CEOs tend to have risk-reduction incentives if they are paid with debt in their own firms, and that firm engagement in corporate social responsibility (CSR) activities can mediate the relationship between debt-like compensation and firm risk taking. In addition, we posit that the mediated relationship between CEO debt-like compensation and firm risk taking is contingent, and we propose environmental dynamism and munificence as two such contingencies that moderate the mediated process. Using a large longitudinal dataset of nonfinancial U.S. firms, we document strong supportive evidence for these hypotheses.
U-MGA: A Multi-Module Unet Optimized with Multi-Scale Global Attention Mechanisms for Fine-Grained Segmentation of Cultivated Areas
Authors: Chen Yun , Xie Yiheng , Yao Weiyuan , Zhang Yu , Wang Xinhong , Yang Yanli , Tang Lingli
Published: February 2025

Arable land is fundamental to agricultural production and a crucial component of ecosystems. However, its complex texture and distribution in remote sensing images make it susceptible to interference from other land cover types, such as water bodies, roads, and buildings, complicating accurate identification. Building on previous research, this study proposes an efficient and lightweight CNN-based network, U-MGA, to address the challenges of feature similarity between arable and non-arable areas, insufficient fine-grained feature extraction, and the underutilization of multi-scale information. Specifically, a Multi-Scale Adaptive Segmentation (MSAS) is designed during the feature extraction phase to provide multi-scale and multi-feature information, supporting the model’s feature reconstruction stage. In the reconstruction phase, the introduction of the Multi-Scale Contextual Module (MCM) and Group Aggregation Bridge (GAB) significantly enhances the efficiency and accuracy of multi-scale and fine-grained feature utilization. The experiments conducted on an arable land dataset based on GF-2 imagery and a publicly available dataset show that U-MGA outperforms mainstream networks (Unet, A2FPN, Segformer, FTUnetformer, DCSwin, and TransUnet) across six evaluation metrics (Overall Accuracy (OA), Precision, Recall, F1-score, Intersection-over-Union (IoU), and Kappa coefficient). Thus, this study provides an efficient and precise solution for the arable land recognition task, which is of significant importance for agricultural resource monitoring and ecological environmental protection.
Flood Mud Index (FMI): A Rapid and Effective Tool for Mapping Muddy Areas After Floods—The Valencia Case
Authors: Alcaras Emanuele
Published: February 2025

Mapping flooded areas immediately after heavy rainfall is particularly challenging when sediment-laden floodwaters dominate the landscape. Traditional indices, such as the Normalized Difference Water Index (NDWI), are designed to detect water-covered areas but fail to identify muddy zones with high turbidity, which are common during extreme flood events. These muddy floodwaters often blend spectrally with surrounding land, leading to significant misclassifications. This study introduces the Flood Mud Index (FMI), a novel spectral index specifically developed to detect debris-laden flooded areas using only the red and blue bands. Landsat 8 imagery was utilized to validate the FMI, and its performance was evaluated through confusion matrices. The index achieved an overall accuracy of 97.86%, outperforming existing indices and demonstrating exceptional precision in delineating muddy floodplains. By relying solely on red and blue bands, the FMI is applicable to any platform equipped with RGB sensors, offering versatility for flood monitoring. Its compatibility with low-cost drones makes it especially valuable for rapid post-flood assessments, enabling immediate data collection even in scenarios with persistent cloud cover. The FMI addresses a critical gap in flood mapping, providing an effective tool for emergency response and management in sediment-rich environments.
Large-Scale Soil Organic Carbon Estimation via a Multisource Data Fusion Approach
Authors: Kalopesa Eleni , Tziolas Nikolaos , Tsakiridis Nikolaos L. , Safanelli José Lucas , Hengl Tomislav , Sanderman Jonathan
Published: February 2025

This study presents a methodological framework for predicting soil organic carbon (SOC) using laboratory spectral recordings from a handheld near-infrared (NIR, 1350–2550 nm) device combined with open geospatial data derived from remote sensing sensors related to landform, climate, and vegetation. Initial experiments proved the superiority of convolutional neural networks (CNNs) using only spectral data captured by the low-cost spectral devices reaching an R2 of 0.62, RMSE of 0.31 log-SOC, and an RPIQ of 1.87. Furthermore, the incorporation of geo-covariates with Neo-Spectra data substantially enhanced predictive capabilities, outperforming existing approaches. Although the CNN-derived spectral features had the greatest contribution to the model, the geo-covariates that were most informative to the model were primarily the rainfall data, the valley bottom flatness, and the snow probability. The results demonstrate that hybrid modeling approaches, particularly using CNNs to preprocess all features and fit prediction models with Extreme Gradient Boosting trees, CNN-XGBoost, significantly outperformed traditional machine learning methods, with a notable RMSE reduction, reaching an R2 of 0.72, and an RPIQ of 2.17. The findings of this study highlight the effectiveness of multimodal data integration and hybrid models in enhancing predictive accuracy for SOC assessments. Finally, the application of interpretable techniques elucidated the contributions of various climatic and topographical factors to predictions, as well as spectral information, underscoring the complex interactions affecting SOC variability.
Spatiotemporal Variations in Sea Ice Albedo: A Study of the Dynamics of Sea Ice Albedo in the Sea of Okhotsk
Authors: Zhou Yingzhen , Li Wei , Chen Nan , Toyota Takenobu , Fan Yongzhen , Tanikawa Tomonori , Stamnes Knut
Published: February 2025

This study utilizes a novel albedo retrieval framework combining radiative transfer modeling with scientific machine learning (RTM-SciML) to investigate sea ice dynamics in the Sea of Okhotsk. By validating albedo estimates derived from the MODIS sensor against in situ pyranometer measurements near the Hokkaido coast, we achieved a robust Pearson coefficient of 0.86 and an RMSE of 0.089 for all sea ice types, with even higher correlations for specific surfaces like snow-covered ice (Pearson-r = 0.89) and meltwater/open water (Pearson-r = 0.90). This confirms the framework’s efficacy across varying surface conditions. Cross-sensor comparisons between MODIS and the Second-Generation Global Imager (SGLI) further demonstrated its consistency, achieving an overall Pearson-r of 0.883 and RMSE of 0.036. Integrating these albedo estimates with sea ice concentration data from the Advanced Microwave Scanning Radiometer 2 (AMSR-2), we analyzed the complex role of the Sea of Okhotsk’s polynya systems and ice interactions in regional climate processes. Our results highlight the synergistic advantage of pairing optical sensors, like MODIS and SGLI, with microwave sensors, offering a more comprehensive understanding of evolving sea ice conditions and paving the way for future climate and cryosphere studies.
Improving Wheat Yield Prediction with Multi-Source Remote Sensing Data and Machine Learning in Arid Regions
Authors: Raza Aamir , Shahid Muhammad Adnan , Zaman Muhammad , Miao Yuxin , Huang Yanbo , Safdar Muhammad , Maqbool Sheraz , Muhammad Nalain E.
Published: February 2025

Wheat (Triticum aestivum L.) is one of the world’s primary food crops, and timely and accurate yield prediction is essential for ensuring food security. There has been a growing use of remote sensing, climate data, and their combination to estimate yields, but the optimal indices and time window for wheat yield prediction in arid regions remain unclear. This study was conducted to (1) assess the performance of widely recognized remote sensing indices to predict wheat yield at different growth stages, (2) evaluate the predictive accuracy of different yield predictive machine learning models, (3) determine the appropriate growth period for wheat yield prediction in arid regions, and (4) evaluate the impact of climate parameters on model accuracy. The vegetation indices, widely recognized due to their proven effectiveness, used in this study include the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Atmospheric Resistance Vegetation Index (ARVI). Moreover, four machine learning models, viz. Decision Trees (DTs), Random Forest (RF), Gradient Boosting (GB), and Bagging Trees (BTs), were evaluated to assess their predictive accuracy for wheat yield in the arid region. The whole wheat growth period was divided into three time windows: tillering to grain filling (December 15–March), stem elongation to grain filling (January 15–March), and heading to grain filling (February–March 15). The model was evaluated and developed in the Google Earth Engine (GEE), combining climate and remote sensing data. The results showed that the RF model with ARVI could accurately predict wheat yield at the grain filling and the maturity stages in arid regions with an R2 > 0.75 and yield error of less than 10%. The grain filling stage was identified as the optimal prediction window for wheat yield in arid regions. While RF with ARVI delivered the best results, GB with EVI showed slightly lower precision but still outperformed other models. It is concluded that combining multisource data and machine learning models is a promising approach for wheat yield prediction in arid regions.
Remote Sensing Identification and Analysis of Global Building Electrification (2012–2023)
Authors: Ou Shengya , Wu Mingquan , Niu Zheng , Chen Fang , Liu Jie , Wang Meng , Tian Dinghui
Published: February 2025

The accurate collection of spatially distributed electrification data is considered of great importance for tracking progress toward target 7.1 of the sustainable development goals (SDGs) and the formulation of policy decisions on electricity access issues. However, the existing datasets face severe limitations in terms of temporal discontinuity and restricted threshold selection. To effectively address these issues, in this work, an improved remote sensing method was proposed to monitor global building electrification. By integrating global land cover data, built-up area data, and annual NPP/VIIRS nighttime light images, a regional threshold method was used to identify electrified and unelectrified areas yearly, generating a global building electrification dataset for 2012–2023. Based on our analysis, we found the following: (1) The five assessment metrics of the product—Accuracy (0.9856), Precision (0.9734), Recall (0.9984), F1-score (0.9858), and Matthews Correlation Coefficient (0.9715)—all exceed 0.9, demonstrating that our method achieves high reliability in identifying electrified buildings. (2) In 2023, 91.88% of global building areas were electrified, with the unelectrified buildings being predominantly located in rural regions of developing countries. (3) Between 2012 and 2023, the global electrified building area increased by 2.4199 million km2, with rural areas experiencing a faster growth rate than town areas. The annual reduction rate of unelectrified building area was 0.62%. However, to achieve universal electricity access by 2030, this rate must nearly double. (4) External factors such as the COVID-19 pandemic, extreme weather events, and armed conflicts significantly affect global electrification progress, with developing countries being particularly vulnerable. In our work, remote sensing methodologies and datasets for monitoring electrification trends were refined, and a detailed spatial representation of unelectrified areas worldwide was provided.
Dimensionality Reduction and Electrode Arrangement Optimization for an Electric Field Source Seeking Surgical Navigation Method
Authors: Fang Yuxin , Yang Fan , He Wei , Li Xing , Li Xinheng
Published: February 2025

This study proposes a Dimensionality Reduction Electric Field Source Seeking (EFSS) method for real-time, high-precision navigation in intracranial puncture surgeries. The method integrates internal localization electrodes and external potential measurement electrodes to minimize surgical trauma while ensuring the accurate localization and guidance of surgical instruments. To optimize the electrode arrangement, two evaluation metrics—Mean Response Coefficient (MRC) and MRC-mean—were introduced. The simulation results demonstrated the effectiveness of these metrics, with the optimal arrangement achieving an average localization error below 2 mm and a 56% reduction in error after optimization. Experimental validation was conducted using a brain model with conductivity properties similar to those of human tissue. Localization experiments confirmed the robustness and accuracy of the EFSS method, with all results showing consistent repeatability and monotonic trends in performance across different electrode configurations. This study highlights the potential of the dimensionality reduction EFSS method as a novel and effective approach for navigation in minimally invasive intracranial surgeries.
A Review of Environmental Control Strategies and Models for Modern Agricultural Greenhouses
Authors: Chen Shuailiang , Liu Aolong , Tang Fei , Hou Pei , Lu Yanli , Yuan Pei
Published: February 2025

As crucial sites for optimizing crop growth conditions, greenhouses have gained increasing favor among scholars due to their potential to significantly enhance food production. Greenhouse control involves regulating environmental parameters such as temperature, humidity, light, and CO2 concentration to ensure an optimal growth environment for crops while conserving energy. This paper provides an overview of various strategies for controlling greenhouse environments, encompassing structural control, environmental parameter management, and control algorithms, and points out that the integration of artificial neural networks with various optimization algorithms is a future trend. Additionally, it delves into the exploration of greenhouse microclimate models and crop growth models, noting that current models focus on some of the internal environmental parameters and that the models rely on empirical parameters. Therefore, multi-scale coupling of greenhouse models is the way forward. Furthermore, it provides insights into how to achieve sustainable energy use in greenhouses, and the application of digital twin technology in greenhouses is promising.
Non-Uniformity Correction of Spatial Object Images Using Multi-Scale Residual Cycle Network (CycleMRSNet)
Authors: Jiang Chunfeng , Li Zhengwei , Wang Yubo , Chen Tao
Published: February 2025

Ground-based telescopes often encounter challenges such as stray light and vignetting when capturing space objects, leading to non-uniform image backgrounds. This not only weakens the signal-to-noise ratio for target tracking but also reduces the accuracy of recognition systems. To address this challenge, We have proposed a novel network architecture called CycleMRSNet, which is based on the CycleGAN framework and incorporates a multi-scale attention mechanism to enhance image processing capabilities. Specifically, we have introduced a multi-scale feature extraction module (MSFEM) at the front end of the generator and embedded an efficient multi-scale attention residual block (EMA-residual block) within the Resnet backbone network. This design improves the efficiency of feature extraction and increases the focus on multi-scale information in high-dimensional feature maps, enabling the network to more comprehensively understand and concentrate on key areas within images, thereby capably correcting non-uniform backgrounds. To evaluate the performance of CycleMRSNet, we trained the model using a small-scale dataset and conducted corrections on simulated and real images within the test set. Experimental results showed that our model achieved scores of PSNR 32.7923, SSIM 0.9814, and FID 1.9212 in the test set, outperforming other methods. These metrics suggest that our approach significantly improves the correction of non-uniform backgrounds and enhances the robustness of the system.
Robust Direction of Arrival and Polarization Parameter Estimation in Mutual Coupling Scenario with Non-Collocated Crossed Dipole Arrays
Authors: Chen Wenjiang , Lan Xiang , Wang Xianpeng
Published: February 2025

Traditional direction of arrival (DOA) and polarization parameter estimation algorithms generally perform well under ideal conditions. However, their performance degrades significantly in practical scenarios due to mutual coupling effects among array elements. This work introduces an innovative method based on a distributed crossed dipole array to jointly estimate DOA and polarization parameters in the presence of mutual coupling effects. This work firstly eliminates the mutual coupling matrix (MCM) through subarray selection, without requiring prior knowledge of the array’s mutual coupling. The DOA is then estimated using an improved high-resolution algorithm, followed by accurate estimation of the polarization parameters through parameter matching. The results from simulations confirm that the new method significantly improves the estimation accuracy in complex mutual coupling environments, showing notable potential for practical applications and robust performance.
Predictive Modeling of Electric Bicycle Battery Performance: Integrating Real-Time Sensor Data and Machine Learning Techniques
Authors: Rincón-Maya Catherine , Acosta-González Daniel , Guevara-Carazas Fernando , Hernández-Barajas Freddy , Patino-Rodríguez Carmen , Usuga-Manco Olga
Published: February 2025

In the field of sustainable mobility, this study highlights the importance of using machine learning for predictive modeling based on real traffic data collected from instrumented bicycles. The advent of advanced technologies like sustainable mobility apps, sensors, and advanced data analysis methods led to the ability to collect data from various sources, which enabled researchers to estimate battery state of charge (SOC) accurately. Most current research uses them in the lab experiments for data collection. In this work, we use real-time sensors data to construct data-driven models for lithium-ion battery SOC estimation. This research integrates both electric bicycle battery, environmental and route variables to achieve the following goals: (1) Collect a multimodal data set including operational, topography, vehicle, and external variables, (2) Preprocess data obtained from sensors installed on the electric bicycle battery, (3) Create models of lithium-ion battery SOC based on electric bicycle battery and environmental variables, and (4) Assess data-driven models and compare their performance for lithium-ion battery SOC with high accuracy. To achieve that, we conducted a real study to predict the Remaining Useful Life (RUL), as a measure of state of charge, of electric bicycle battery. The study was carried out on a 15 km cycle route in Medellín, Colombia, for 28 days. To estimate the RUL, we used four different machine learning algorithms: Long Short-Term Memory (LSTM), Support Vector Regression (SVR), AdaBoost, and Gradient Boost. Notably, data preprocessing techniques played a pivotal role, with a particular focus on smoothing sensor data using Convolutional Neural Networks (CNN). The results showed a significant improvement in prediction accuracy when using data preprocessing, confirming its importance in improving model performance. Furthermore, the comparison of network performance facilitated the selection of the most effective model for the test data. This study underscores the value of using real-world data to develop and validate predictive models in the pursuit of sustainable mobility solutions, and highlights the critical role of data-driven methodologies in addressing today’s urban transportation challenges.
Integration of Real Signals Acquired Through External Sensors into RoboDK Simulation of Robotic Industrial Applications
Authors: Cristoiu Cozmin , Ivan Andrei Mario
Published: February 2025

Ensuring synchronization between real-world sensor data and industrial robotic simulations remains a critical challenge in digital twin and virtual commissioning applications. This study proposes an innovative method for integrating real sensor signals into RoboDK simulations, bridging the gap between virtual models and real-world dynamics. The proposed system utilizes an Arduino-based data acquisition module and a custom Python script to establish real-time communication between physical sensors and RoboDK’s simulation environment. Unlike traditional simulations that rely on predefined simulated signals or manually triggered virtual inputs, our approach enables dynamic real-time interactions based on live sensor data. The system supports both analog and digital signals and is validated through latency measurements, demonstrating an average end-to-end delay of 23.97 ms. These results confirm the feasibility of real sensor integration into RoboDK, making the system adaptable to various industrial applications. This framework provides a scalable foundation for researchers and engineers to develop enhanced simulation environments that more accurately reflect real industrial conditions.
MRI-Based Meningioma Firmness Classification Using an Adversarial Feature Learning Approach
Authors: Murad Miada , Touir Ameur , Ben Ismail Mohamed Maher
Published: February 2025

The firmness of meningiomas is a critical factor that impacts the surgical approach recommended for patients. The conventional approaches that couple image processing techniques with radiologists’ visual assessments of magnetic resonance imaging (MRI) proved to be time-consuming and subjective to the physician’s judgment. Recently, machine learning-based methods have emerged to classify MRI instances into firm or soft categories. Typically, such solutions rely on hand-crafted attributes and/or feature engineering techniques to encode the visual content of patient MRIs. This research introduces a novel adversarial feature learning approach to tackle meningioma firmness classification. Specifically, we present two key contributions: (i) an unsupervised feature extraction approach utilizing the Bidirectional Generative Adversarial Network (BiGAN) and (ii) a depth-wise separable deep learning model were designed to map the relevant MRI features with the predefined meningioma firmness classes. The experiments demonstrated that associating the BiGAN encoder, for unsupervised feature extraction, with a depth-wise separable deep learning model enhances the classification performance. Moreover, the proposed pre-trained BiGAN encoder-based model outperformed relevant state-of-the-art methods in meningioma firmness classification. It achieved an accuracy of 94.7% and a weighted F1-score of 95.0%. This showcases the proposed model’s ability to extract discriminative features and accurately classify meningioma consistency.
Deep Learning Approach for Automatic Heartbeat Classification
Authors: Guerra Roger de T. , Yamaguchi Cristina K. , Stefenon Stefano F. , Coelho Leandro dos S. , Mariani Viviana C.
Published: February 2025

Arrhythmia is an irregularity in the rhythm of the heartbeat, and it is the primary method for detecting cardiac abnormalities. The electrocardiogram (ECG) identifies arrhythmias and is one of the methods used to diagnose cardiac issues. Traditional arrhythmia detection methods are time-consuming, error-prone, and often subjective, making it difficult for doctors to discern between distinct patterns of arrhythmia. To understand ECG signals, this study presents a multi-class classifier and an autoencoder with long short-term memory (LSTM) network layers for extracting signal properties on a dataset from the Massachusetts Institute of Technology and Boston’s Beth Israel Hospital (MIT-BIH). The suggested model had an accuracy rate of 98.57% on the arrhythmia dataset and 97.59% on the supraventricular dataset. In contrast to other deep learning models, the proposed model eliminates the problem of the gradient disappearing in classification tasks.
De Cero a Siempre: Reflections on the Colombian Early Childhood Policy from a Human Rights Perspective
Authors: Rojas Romero Cristian Jovan , Moreno Angarita Marisol , Guerrero Yeison , Prieto Cifuentes Laura Sofia
Published: February 2025

This article critically examines De Cero a Siempre (DCAS), a cross-sectoral policy established in Colombia in 2016 to provide coordinated and comprehensive early childhood development services. The policy aims to promote optimal conditions for children from pregnancy to six years of age by addressing their nutritional, health, educational, and social needs while fostering participation and citizenship. Designed with a differential approach, the DCAS integrates and adapts services considering gender, culture, income, ethnicity, displacement, and disability. This study evaluates the DCAS policy through the human rights approach. First, we examine the conceptual definitions Second, we identify specific programs and services Third, we assess the implementation and effectiveness of these services using the official System for Monitoring the Comprehensive Development of Early Childhood. The findings reveal a strong policy framework that values human dignity and equity but highlights critical gaps in coverage, particularly in rural areas, due to geographical and social challenges. The current landscape of disability services reveals significant systemic limitations in addressing individualized developmental needs. Our comprehensive analysis exposes critical challenges in designing comprehensive intervention strategies for CWDDs. The article concludes with recommendations to enhance policy implementation and improve service delivery, ensuring equitable access for all children.
Physical Activity Intensity and Learning Strategies in Students Aged 10 to 16: A Pilot Study
Authors: Solas-Martínez Jose Luis , Rusillo-Magdaleno Alba , Ruiz-Ariza Alberto , Martínez-López Emilio J.
Published: February 2025

This study analyzed the relationship between time spent on daily physical activity at different intensities (light, moderate, and vigorous) and learning strategies in adolescents. The sample included 147 Spanish adolescents (62% girls, 13.61 ± 1.95 years). Learning strategies were assessed using the Motivated Strategies for Learning Questionnaire (MSLQ), while physical activity intensity was recorded via the Xiaomi Mi Band 4 smartband, which measured heart rate (HR). HR was categorized into light (rest–50% max HR), moderate (50–70% max HR), and vigorous (70–85% max HR). Adolescents who engaged in moderate-intensity activities for 46–62 min daily showed the highest scores in elaboration, critical thinking, and metacognitive self-regulation, with girls also excelling in effort regulation (all p < 0.05). Among boys, 3–6 min of vigorous activity per day was linked to higher scores in elaboration, organization, critical thinking, peer learning, and rehearsal. Conversely, girls engaging in less than 3 min of vigorous activity obtained the highest scores in critical thinking and peer learning (all p < 0.05). It is recommended that students engage in 60 min of daily moderate-intensity physical activity. Additionally, short 5-min vigorous-intensity sessions should be encouraged in both boys and girls to enhance learning benefits and reduce social barriers to high-intensity exercise.
Regional Analysis of Socio-Economic Development: The Case of Poland
Authors: Smolińska-Bryza Kinga , Okereke Obinna , Anyebe Daniel
Published: February 2025

This article analyzes and evaluates the levels of socio-economic development of Polish voivodeships in two periods, 2010–2012 and 2020–2022. The purpose of this study was to outline regional disparities in development across the regions of Poland. To achieve the purpose of this study, 12 diagnostic characteristics were used, which were systematized by economic and social categories. Using the TOPSIS method, provinces were grouped, distinguishing between regions with the highest, high, low, and lowest levels of development. The approach adopted in this study identified changes in the processes of socio-economic development of Polish voivodeships in the analyzed years, highlighting clear spatial and structural differences. The findings can equip authorities in creating improved development plans and targeted responses to any shortcomings encountered.
Carbon Emission Reduction Strategies in Urban Water Sectors: A Case Study in Incheon Metropolitan City, South Korea
Authors: Lee Gyumin , Kim Hyunjung , Min Kyoungwon , Hwang Taemun , Kim Eunju , Lee Juwon , Kang Doosun
Published: February 2025

Achieving carbon neutrality is a priority in global environmental policies, and South Korea is committed to its 2050 carbon neutrality goal. This study explores methods to reduce carbon emissions in urban water cycle (UWC) systems, which are essential urban infrastructures that consume considerable energy. Focusing on Incheon Metropolitan City (IMC), the research identifies UWC components, estimates energy consumption, and calculates carbon emissions across eight administrative districts. The analysis comprises four water abstraction plants (WAPs), four water treatment plants (WTPs), and eleven wastewater treatment plants (WWTPs). Strategies for carbon reduction involve decreasing water and energy consumption and minimizing emissions from wastewater treatment. This study categorizes management targets as water, energy, and carbon, developing different carbon emissions reduction scenarios. A carbon emission calculation model for WTPs and WWTPs was developed to evaluate energy consumption and carbon emissions across scenarios. Notably, the scenario focusing on renewable energy development and energy efficiency improvements yielded the highest carbon emissions reductions, confirming that the government’s renewable energy initiatives are vital for achieving net-zero emissions in IMC’s UWC systems. Conversely, the scenario prioritizing water use reduction proved less effective, but excelled regarding investment costs. These findings can serve as a model for other cities managing UWC systems while striving for sustainability.
Evaluation of Management Effectiveness of an Outstanding Marine Protected Area in Southwest Coast of Türkiye: On the Road to 30 by 30
Authors: Kok Funda , Sisman-Aydin Goknur
Published: February 2025

The global increase in Marine Protected Areas (MPAs) is driven by international goals, like the Aichi Targets (2010) and the Kunming Global Biodiversity Framework (2023), which aim for 30% of land and sea to be protected by 2030. Despite these ambitious targets, experts agree that numerical goals alone are insufficient to address biodiversity loss and ensure ecosystem functionality. This has sparked significant research on the effective management of MPAs, highlighting the importance of biological, socio-economic, and managerial resilience. Various tools have been developed to support these aspects. However, even in areas with management plans, enforcement, and monitoring, stakeholder involvement in assessment processes is vital to equitable and sustainable management. Using Gökova MPA as a case study, this research evaluated its management over a three-year period by applying an assessment tool previously used by co-managing actors. Findings indicate that assessment results can vary depending on who conducts them, and structural challenges, such as coordination issues among governing bodies and overlapping authorities, further complicate management. These insights have been used to formulate recommendations for policymakers and conservation practitioners, emphasizing that reaching the “30 by 30” target requires not only increased area coverage but also improved management practices that incorporate stakeholder perspectives to achieve sustainable management of natural resources.
Physicochemical Characterization and Extrusion Effects on the Optimization of Agro-Industrial Byproduct Flour Formulation
Authors: Navia-Porras Diana Paola , Franco-Urbano Carolina , Torres-Valenzuela Laura Sofía , Plaza-Dorado José Luis , Hoyos-Concha José Luis
Published: February 2025

During the post-harvest of coffee and plantain, organic residues with high potential for utilization are generated. This work aimed to measure the effect of extrusion on the nutritional, physicochemical, and functional properties of mixtures of coffee pulp (CP), rejected plantain (RP), and plantain rachis (PR) flours. The residues were dehydrated, milled, and mixed according to the simplex reticular experimental design. Subsequently, the mixtures were extruded. The properties before and after extrusion were determined. It was found that the effect of extrusion reduced the crude fiber and lipid content composition, but protein and ash content were not changed. A positive relation was found between coffee pulp flour and rachis plantain flour in response to total phenolic content (TPC) and antioxidant activity (AA). Some blends increased the TPC and AA but others reduced it. At the same time, water activity and water and oil absorption capacity showed a significant extrusion effect, while the pH did not. It was determined that the optimum mixture extruded was 0.364:0.333:0.303 of CP, RP, and PR, respectively. Extrusion reduced all pasting properties of the optimized blend. The flours studied presented a relevant nutritional and functional contribution, which favors their viability for use in the food industry.
Sustainability Transition in the Visegrád Group: Shared Goals, Different Paths
Authors: Bolibok Piotr M. , Zinczuk Bartłomiej , Matras-Bolibok Anna
Published: February 2025

This paper aims to investigate the sustainability performance trajectories of the Visegrád Group (V4) countries since their accession to the European Union. Given their shared historical, geopolitical, and socio-economic backgrounds, this study explores common evolutionary patterns and convergence within the sustainable development goal framework. This research employs the Kml3D non-parametric clustering algorithm and standard beta and sigma convergence tests. The analysis covers the 2004–2023 data sourced from the European Sustainable Development Report 2023/24 by the Sustainable Development Solutions Network. The findings demonstrate that while each country follows a distinct path, Czechia and Slovakia exhibit notable similarities in the evolution of individual sustainability dimensions. This underscores the influence of path dependence, institutional inertia, and cross-border policy coordination in sustainable development. The results also suggest that although the V4 countries generally converge in socio-economic and infrastructural dimensions of sustainability, divergence persists in education, gender equality, nature preservation, and institutional performance.
Technological Innovations Shaping Sustainable Competitiveness—A Systematic Review
Authors: Hamdouna Muntaser , Khmelyarchuk Mariya
Published: February 2025

The concept of sustainable competitiveness is becoming increasingly relevant, as it combines the investigation of the factors that determine the competitive advantages of economic entities, as well as management strategies that ensure economic and environmental efficiency in the face of modern global requirements and challenges. The main hypothesis of the research is that technological innovations are key determinants of the sustainable competitiveness of economic entities, increase their resilience to various challenges and threats, and therefore contribute to sustainable competitiveness in the long run. Accordingly, the object of the research is a comprehensive science literature review at the intersection of the issues of competitiveness, technological innovations, and sustainable development using the Scopus database and PRISMA statement in order to substantiate the importance of technological innovation in ensuring sustainable competitiveness. The scientific research pinpoints three key questions shaping the scientific discussion: Are technological innovations key determinants of sustainable competitiveness? How do advanced technologies contribute across sectors? What strategies and measures stimulate sustainable competitiveness? By answering the research questions based on the methodology of nonempirical systematic scientific analysis, this review article provides scientific and practical insights for businesses and policymakers aiming to harness technological advancements to sustain their business in the long run.
Sustainable Marginal Water Resource Management: A Case Study of Brackish Water Irrigation on the Southern Coast of Laizhou Bay
Authors: Liu Wenquan , Lu Fang , Han Weitao
Published: February 2025

The secure and effective use of marginal water resources, such as brackish water, plays a crucial role in ensuring food security and promoting the sustainable development of agricultural land. This paper conducted indoor soil column experiments to simulate the infiltration of brackish water (0, 1, 3, and 5 g L−1) in order to study the effects of infiltration on the movement of soil water and salt, aiming to address the critical challenge of utilizing marginal water resources in coastal saline-alkali areas. The result showed that, as salt content increases, the movement speed of the moisture front and soil infiltration rate gradually decrease over the same period of time. The moisture front progress and infiltration volume showed a positive correlation. The moisture content of the soil profile gradually decreased, within the soil depth range of 0–40 cm, except for the 5 g L−1 saline water infiltration, and the Cl− content increased, while the other treatments showed a trend of first decreasing and then increasing. The higher salt content at the same depth, the higher the Na+ and Cl− contents. Under different irrigation water volume conditions, the soil profile conductivity shows a trend of first decreasing and then increasing. The research findings advance fundamental understanding of salinity-driven soil hydrological processes, offering theoretical support for the sustainable utilization of brackish water, balancing agricultural water demand and soil health in coastal areas.
Sustainable Biochar Fertiliser Production Using Melt Adsorption and Optimisation
Authors: Zhang Xia , Wang Yiyang , Su Panjie , Zeng Weida , Zhu Jingzhe , Cai Zongshou
Published: February 2025

The global production of coffee generates substantial waste in the form of coffee husks, presenting an opportunity for sustainable resource utilisation. This study investigates the conversion of coffee husks into biochar-based fertilisers using an optimised pyrolysis process and the melt adsorption method, with the aim of improving slow-release nutrient characteristics. Coffee husks were pyrolysed under controlled conditions, and the resulting biochar was blended with urea in a 1:1 ratio to produce the fertiliser. The nutrient release rates of the fertiliser were evaluated through soil column leaching experiments. For slow-release fertilisers, less release in a period means better performance, and the optimal nutrient release rate is the minimum in the experiment. Single-factor experiments assessed the effects of critical process parameters, including raw material particle size (0.25–2.8 mm), pyrolysis atmosphere (CO2 to N2 ratios ranging from 0% to 100%), pyrolysis temperature (400–800 °C), heating rate (10–30 °C min−1), and pyrolysis time (20–100 min). Orthogonal experiments were subsequently conducted to analyse the interactions between selected parameters of pyrolysis temperature, CO2 to N2 ratio, heating rate, and pyrolysis time using regression analysis. The optimal process conditions determined through MATLAB optimisation were a pyrolysis temperature of 591 °C, a heating rate of 15.4 °C∙min⁻1, and a pyrolysis time of 44.4 min, resulting in the minimum nutrient release rate of 40%. The sequence of process parameters influencing the slow-release characteristics was found to be heating rate > pyrolysis time > pyrolysis temperature > CO2 to N2 ratio. This study provides a framework for transforming agricultural residues into high-performance biochar-based fertilisers, aligning with sustainable resource management and pollution control strategies.
Landslide Hazard Assessment Based on Ensemble Learning Model and Bayesian Probability Statistics: Inference from Shaanxi Province, China
Authors: Shen Shuhan , Deng Longsheng , Tang Dong , Chen Jiale , Fang Ranke , Du Peng , Liang Xin
Published: February 2025

The geological and environmental conditions of the northern Shaanxi Loess Plateau are highly fragile, with frequent landslides and collapse disasters triggered by rainfall and human engineering activities. This research addresses the limitations of current landslide hazard assessment models, considers Zhuanyaowan Town in northern Shaanxi Province as a case study, and proposes an integrated model combining the information value model (IVM) with ensemble learning models (RF, XGBoost, and LightGBM) employed to derive the spatial probability of landslide occurrences. Adopting Pearson’s type-III distribution with the Bayesian theorem, we calculated rainfall-induced landslide hazard probabilities across multiple temporal scales and established a comprehensive regional landslide hazard assessment framework. The results indicated that the IVM coupled with the extreme gradient boosting (XGBoost) model achieved the highest prediction performance. The rainfall-induced hazard probabilities for the study area under 5-, 10-, 20-, and 50-year rainfall return periods are 0.31081, 0.34146, 0.4, and 0.53846, respectively. The quantitative calculation of regional landslide hazards revealed the variation trends in hazard values across different areas of the study region under varying rainfall conditions. The high-hazard zones were primarily distributed in a belt-like pattern along the Xichuan River and major transportation routes, progressively expanding outward as the rainfall return periods increased. This study presents a novel and robust methodology for regional landslide hazard assessment, demonstrating significant improvements in both the computational efficiency and predictive accuracy. These findings provide critical insights into regional landslide risk mitigation strategies and contribute substantially to the establishment of sustainable development practices in geologically vulnerable regions.
Global Trends in Community Energy Storage: A Comprehensive Analysis of the Current and Future Direction
Authors: Elliot Jake , Brown Jason , Mlilo Njabulo , Bowtell Les
Published: February 2025

Community Energy Storage (CES) is a rapidly evolving field with the potential to transform the modern energy landscape and enhance sustainability initiatives. This comprehensive review paper explores the multifaceted nature of CES, encompassing its diverse technologies, ownership models, regulatory frameworks, sharing paradigms, and applications. Technical aspects of various CES technologies, including batteries, flow batteries, pumped hydro storage, hydrogen-based systems, compressed air energy storage, flywheels, thermal storage, and future technology have been reviewed in detail. Additionally, different ownership models, ranging from private and community-owned to government-led initiatives have been examined. Regulatory frameworks, investment incentives, and grid integration standards are also explored, highlighting the importance of clear guidelines and international collaboration for the successful deployment of CES globally. Furthermore, diverse applications of CES, including increased self-sufficiency, lower energy bills, ancillary services, demand response, and bulk energy applications are discussed. By addressing the challenges and opportunities associated with CES, this review paper aims to contribute to the advancement and widespread adoption of this promising technology, ultimately fostering a more sustainable, resilient, and equitable energy future to meet global net-zero goals. This is achieved by summarising the future direction of CES and posing some yet unexplored research questions.
Inorganic Chemical Characteristics of PM2.5 During Pollution Events in Jinan, China
Authors: Li Jiarong , Li Kai , Wang Jinhe , Zhu Chao
Published: February 2025

A one-year observation campaign from December 2020 to December 2021 was carried out in Jinan, a city that previously suffered from severe fine particulate matter (PM2.5) pollution. The meteorological parameters and ambient concentrations of the air pollutants were recorded, and PM2.5 samples were collected during the campaign. The inorganic chemical compositions of the PM2.5 samples were measured, identifying 8 water-soluble ions (WSIs) and 15 trace metals. Pollution events, including 7 dust events (DEs) and 19 secondary inorganic events (SIEs), were identified and classified. The inorganic chemical compositions of the pollution events were characterized, summarized, and concluded. The low ratio of WSIs to PM2.5, high concentrations of Ca2+ and Mg2+, and significant increases in trace metals were the dominant characteristics of PM2.5 during DEs. The high concentrations of SO42−, NO3−, and NH4+; the high ratio of secondary ions to WSIs; and the high ratio of WSIs to PM2.5 were the dominant characteristics of PM2.5 during SIEs. Even though, recently, the PM2.5 pollution in Jinan has not been as severe as pollution from a dozen years ago, it still exists, just at lower frequencies and pollution levels. This investigation provides the characteristics of PM2.5 pollution under dramatically improved conditions and various ideas for the management and control of PM2.5 pollution.