bluuebunny/crossref_metadata_2025 huggingface dataset:
Study and Analysis of Corrosion Rate, Hot Tensile Properties, and Metallurgical Changes of SSDS 2507 and AISI 316 Dissimilar Weldments
Authors: Vemulawada Mamatha , Dinka Megersa Olumana , Agarwal Abhishek , Ilunga Masengo , Yelamasetti Balram , K Srinivasa Vadayar , P Naveen Kumar
Published: January 2025

This research study aims to study and investigate the corrosion rate, hot tensile properties, and microstructures of SSDS 2507 and AISI 316 gas tungsten arc dissimilar weldments. Three separate samples were developed with frequencies of 2, 4, and 6 Hz using the pulse arc mode technique. The tensile characteristics were assessed at two distinct temperatures (27 °C and 350 °C) in order to examine the behavior of the welded structure. Mechanical characterization such as hardness measurement and corrosion behavior were studied. The metallurgical characteristics of pulsed and continuous current weldments were examined using microscopes (optical and scanning), revealing variations across different zones. At the 4 Hz pulse frequency, the material exhibited improved tensile qualities compared to constant arc welding. The microstructures indicated that the fusion zone in the pulsed arc weldment consisted of a balanced mixture of inter-granular austenite and ferrite phases. A better corrosion resistance rate of 0.0487 mm/year was observed in the pulsed arc weldment compared to both the SSDS2507 base metal and the constant arc weldment. Specifically, at a temperature of 27 °C, the ultimate tensile strength was 695 MPa, whereas at a temperature of 350 °C, the tensile strength was 475 MPa. The weld strength of the pulsed arc weldment exhibited a 15.8% improvement in comparison to the constant arc weldment. The surface hardness value increased to 240 HV compared to the constant arc weldment, which had an HV of 225.
Editorial: Examining the Complicated Nature of Religious Solidarity
Authors: Zalta Anja , Jurekovič Igor
Published: January 2025

As we were sketching out aims for our Special Issue, we decided to focus on societal crisis and the role that religious communities (and religion more broadly) may play in bridging the conflicts of our age [...]
Effectiveness Trade-Off Between Green Spaces and Built-Up Land: Evaluating Trade-Off Efficiency and Its Drivers in an Expanding City
Authors: Dong Xinyu , Ye Yanmei , Zhou Tao , Haase Dagmar , Lausch Angela
Published: January 2025

Urban expansion encroaches on green spaces and weakens ecosystem services, potentially leading to a trade-off between ecological conditions and socio-economic growth. Effectively coordinating the two elements is essential for achieving sustainable development goals at the urban scale. However, few studies have measured urban–ecological linkage in terms of trade-off. In this study, we propose a framework by linking the degraded ecological conditions and urban land use efficiency from a return on investment perspective. Taking a rapidly expanding city as a case study, we comprehensively quantified urban–ecological conditions in four aspects: urban heat island, flood regulating service, habitat quality, and carbon sequestration. These conditions were assessed on 1 km2 grids, along with urban land use efficiency at the same spatial scale. We employed the slack-based measure model to evaluate trade-off efficiency and applied the geo-detector method to identify its driving factors. Our findings reveal that while urban–ecological conditions in Zhengzhou’s periphery degraded over the past two decades, the inner city showed improvement in urban heat island and carbon sequestration. Trade-off efficiency exhibited an overall upward trend during 2000–2020, despite initial declines in some inner city areas. Interaction detection demonstrates significant synergistic effects between pairs of drivers, such as the Normalized Difference Vegetation Index and building height, and the number of patches of green spaces and the patch cohesion index of built-up land, with q-values of 0.298 and 0.137, respectively. In light of the spatiotemporal trend of trade-off efficiency and its drivers, we propose adaptive management strategies. The framework could serve as guidance to assist decision-makers and urban planners in monitoring urban–ecological conditions in the context of urban expansion.
Object-Level Contrastive-Learning-Based Multi-Branch Network for Building Change Detection from Bi-Temporal Remote Sensing Images
Authors: Li Shiming , Yan Fengtao , Liao Cheng , Hu Qingfeng , Ma Kaifeng , Wang Wei , Zhang Hui
Published: January 2025

Buildings are fundamental elements of human environments, and detecting changes in them is crucial for land cover studies, urban expansion monitoring, and the detection of illegal construction activities. Existing methods primarily focus on pixel-level differences in bi-temporal remote sensing imagery. However, pseudo-changes, such as variations in non-building areas caused by differences in illumination, seasonal changes, and other factors, pose significant challenges for reliable building change detection. To address these issues, we propose a novel object-level contrastive-learning-based multi-branch network (OCL-Net) for detecting building changes by integrating bi-temporal remote sensing images. First, we design a multi-head decoder to separately extract more distinguishable building change features and auxiliary semantic features from bi-temporal images, effectively leveraging building-specific priors. Second, an object-level contrastive learning loss is designed and jointly optimized with a pixel-level similarity loss to ensure the global consistency of buildings. Finally, an attention-based discriminative feature generation and fusion block is designed to enhance the representation of multi-scale change features. We validate the effectiveness of the proposed method through comparative experiments on the publicly available WHU-CD and S2Looking datasets. Our approach achieves IoU values of 88.54% and 51.94%, respectively, surpassing state-of-the-art methods for building change detection.
High-Resolution Satellite Reveals the Methane Emissions from China’s Coal Mines
Authors: Li Xingyu , Cheng Tianhai , Zhu Hao , Ye Xiaotong , Fan Donghao , Tang Tao , Tong Haoran , Yin Shizhe , Xiong Jingyu
Published: January 2025

Coal mines are a major global source of methane emissions, accounting for 10% of global methane emissions. As the world’s largest coal producer and consumer, China has various coal mine types, yet significant uncertainty exists in its methane emissions due to a lack of systematic ground-based data. Therefore, accurately quantifying methane emissions from coal mining activities is crucial. Existing inventories struggle to capture complex and anomalous emissions, while medium-resolution satellites lack facility-level precision. High-spatial-resolution satellite observations offer detailed insights. With a spatial resolution of 60 m and spectral channels from 381 to 2493 nm, the EMIT satellite can finely characterize facility-level methane plumes. This study uses data from 88 methane emission plumes captured by the EMIT satellite to quantify the methane emission characteristics of 32 coal mines located in Inner Mongolia, Ningxia Hui Autonomous Region, and Shanxi Province, China. Principal Component Analysis reveals that mine size, coal type, and processing stage are key factors influencing methane emissions, with emission rates varying significantly under different conditions. Data indicate varying methane emission rates across production stages. The median methane emission rate in gas treatment/utilization is double that of ventilation shafts and chemical plants. Larger coal mines show a decreasing trend in the unit methane emission rate with scale increase, with super-large mines emitting only one-tenth that of medium-sized mines. For large coal mines, bituminous coal mines emit nearly double that of anthracite coal mines. Bottom-up emission inventory evaluation results for the 32 coal mines studied show that EDGAR v8.0 and GFEI v2 underestimated annual methane total emissions, capturing only about half of the emissions quantified through satellite observations. The average emission intensity of the 32 coal mines estimated by satellite data is 0.48 kg/GJ, which is higher than the emission intensities reported by EDGAR v8.0 (0.24 kg/GJ) and GFEI v2 (0.18 kg/GJ). Overall, high-resolution satellite data offer new insights into facility-level emissions, revealing the complexity of methane emissions from coal mines and underscoring the need for tailored mitigation strategies that consider different mine types and operational stages.
Long-Term Evaluation of GCOM-C/SGLI Reflectance and Water Quality Products: Variability Among JAXA G-Portal and JASMES
Authors: Salem Salem Ibrahim , Toratani Mitsuhiro , Higa Hiroto , Son SeungHyun , Siswanto Eko , Ishizaka Joji
Published: January 2025

The Global Change Observation Mission-Climate (GCOM-C) satellite, launched in December 2017, is equipped with the Second-generation Global Imager (SGLI) sensor, featuring a moderate spatial resolution of 250 m and 19 spectral bands, including the unique 380 nm band. After six years in orbit, a comprehensive evaluation of SGLI products and their temporal consistency is needed. Remote sensing reflectance (Rrs) is the primary product for monitoring water quality, forming the basis for deriving key oceanic constituents such as chlorophyll-a (Chla) and total suspended matter (TSM). The Japan Aerospace Exploration Agency (JAXA) provides Rrs products through two platforms, G-Portal and JASMES, each employing different atmospheric correction methodologies and assumptions. This study aims to evaluate the SGLI full-resolution Rrs products from G-Portal and JASMES at regional scales (Japan and East Asia) and assess G-Portal Rrs products globally between January 2018 and December 2023. The evaluation employs in situ matchups from NASA’s Aerosol Robotic Network-Ocean Color (AERONET-OC) and cruise measurements. We also assess the retrieval accuracy of two water quality indices, Chla and TSM. The AERONET-OC data analysis reveals that JASMES systematically underestimates Rrs values at shorter wavelengths, particularly at 412 nm. While the Rrs accuracy at 412 nm is relatively low, G-Portal’s Rrs products perform better than JASMES at shorter wavelengths, showing lower errors and stronger correlations with AERONET-OC data. Both G-Portal and JASMES show lower agreement with AERONET-OC and cruise datasets at shorter wavelengths but demonstrate improved agreement at longer wavelengths (530 nm, 565 nm, and 670 nm). JASMES generates approximately 12% more matchup data points than G-Portal, likely due to G-Portal’s stricter atmospheric correction thresholds that exclude pixels with high reflectance. In situ measurements indicate that G-Portal provides better overall agreement, particularly at lower Rrs magnitudes and Chla concentrations below 5 mg/m3. This evaluation underscores the complexities and challenges of atmospheric correction, particularly in optically complex coastal waters (Case 2 waters), which may require tailored atmospheric correction methods different from the standard approach. The assessment of temporal consistency and seasonal variations in Rrs data shows that both platforms effectively capture interannual trends and maintain temporal stability, particularly from the 490 nm band onward, underscoring the potential of SGLI data for long-term monitoring of coastal and oceanic environments.
GNSS Signal Extraction Using CEEMDAN–WPD for Deformation Monitoring of Ropeway Pillars
Authors: Zhang Song , Yang Yuntao , Xie Yilin , Tang Haoran , Li Haiyang , Yao Lianbi , Yang Yin
Published: January 2025

Traditional surveying methods have various drawbacks in monitoring cable-stayed bridge deformations. Global Navigation Satellite System (GNSS) technology is increasingly recognized for its critical role in structural deformation monitoring, providing precise measurements for various structural applications. Accurate signal extraction is essential for reliable deformation monitoring, as it directly influences the quality of the detected structural changes. However, effective signal extraction from GNSS data remains a challenging task due to the presence of noise and complex signal components. This study integrates Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and wavelet packet decomposition (WPD) to extract GNSS deformation monitoring signals for the ropeway pillar. The proposed approach effectively mitigates high-frequency noise interference and modal mixing in GNSS signals, thereby enhancing the accuracy and reliability of deformation measurements. Simulation experiments and real-world scenario applications with operational field data processing demonstrate the effectiveness of the proposed method. This research contributes to advancing GNSS-based deformation monitoring techniques, offering a robust solution for detecting and analyzing subtle structural changes in various engineering contexts.
Lightweight Multi-Scale Network for Segmentation of Riverbank Sand Mining Area in Satellite Images
Authors: Zhang Hongyang , Liu Shuo , Liu Huamei
Published: January 2025

Riverbank sand overexploitation is threatening the ecology and shipping safety of rivers. The rapid identification of riverbank sand mining areas from satellite images is extremely important for ecological protection and shipping management. Image segmentation methods based on AI technology are gradually becoming popular in academia and industry. However, traditional neural networks have complex structures and numerous parameters, making them unsuitable for meeting the needs of rapid extraction in large areas. To improve efficiency, we proposed a lightweight multi-scale network (LMS Net), which uses a lightweight multi-scale (LMS) block in both the encoder and decoder. The lightweight multi-scale block combines parallel computing and depthwise convolution to reduce the parameters of the network and enhance its multi-scale extraction ability. We created a benchmark dataset to validate the accuracy and efficiency improvements of our network. Comparative experiments and ablation studies proved that our LMS Net is more efficient than traditional methods like Unet and more accurate than typical lightweight methods like Ghostnet and other more recent methods. The performance of our proposed network meets the requirements of river management.
Study on the Influence of Rural Highway Landscape Green Vision Rate on Driving Load Based on Factor Analysis
Authors: Li Hao , Yang Jiabao , Jiang Heng
Published: January 2025

The green vision rate of rural highway greening landscape is a key factor affecting the driver’s visual load. Based on this, this paper uses the eye tracking method to study the visual characteristics of drivers in different green vision environments on rural highways in Xianning County. Based on the HSV color space model, this paper obtains four sections of rural highway with a green vision rate of 10~20%, green vision rate of 20~30%, green vision rate of 30~40%, and green vision rate of 40~50%. Through the real car test, the pupil area, fixation time, saccade time, saccade angle, saccade speed, and other visual indicators of the driver’s green vision rate in each section were obtained. The visual load quantization model was combined with factor analysis to explore the influence degree of the green vision rate in each section on the driver’s visual load. The results show that the visual load of the driver in the four segments with different green vision rate is as follows: Z10~20% > Z20~30% > Z30~40% > Z40~50%. When the green vision rate is 10~20%, the driver’s fixation time becomes longer, the pupil area becomes larger, the visual load is the highest, and the driving is unstable. When the green vision rate is 40% to 50%, the driver’s fixation time and pupil area reach the minimum, the visual load is the lowest, and the driving stability is the highest. The research results can provide theoretical support for the design of rural highway landscape green vision rate and help to promote the theoretical research of traffic safety.
Modeling of Measuring Transducers for Relay Protection Systems of Electrical Installations
Authors: Iliev Iliya , Kryukov Andrey , Suslov Konstantin , Kodolov Nikolay , Kryukov Aleksandr , Beloev Ivan , Valeeva Yulia
Published: January 2025

The process of establishing relay protection and automation (RPA) settings for electric power systems (EPSs) entails complex calculations of operating modes. Traditionally, these calculations are based on symmetrical components, which require the building of equivalent circuits of various sequences. This approach can lead to errors both when identifying the operating modes and when modeling the RPA devices. Proper modeling of measuring transformers (MTs), symmetrical component filters (SCFs), and circuits connected to them effectively solves this problem, enabling the configuration of relay protection and automation systems. The methods of modeling the EPS in phase coordinates are proposed to simultaneously determine the operating modes of high-voltage networks and secondary circuits connected to the current and voltage transformers. The MT and SCF models are developed to concurrently identify the operating modes of secondary wiring circuits and calculate the power flow in the controlled EPS segments. This method is effective in addressing practical problems related to the configuration of the relay protection and automation systems. It can also be used when establishing cyber–physical power systems. For a comprehensive check of the adequacy of the MT models, 140 modes of the electric power system were determined which corresponded to time-varying traction loads. Based on the results of calculating the complexes of currents and voltages at the MT terminals, parametric identification of the power transmission line was performed. Based on this, the model of this transmission line was adjusted; repeated modeling was carried out, and errors were calculated. The modeling results showed a high accuracy when calculating the modules and phases of voltages using the identified model. The average error value for current modules was 0.6%, and for angles, it was 0.26°.
A Novel Mechanism-Equivalence-Based Tweedie Exponential Dispersion Process for Adaptive Degradation Modeling and Life Prediction
Authors: Wu Jiayue , Liu Yujie , Wang Han , Ma Xiaobing , Zhao Yu
Published: January 2025

Accurately predicting the remaining useful life (RUL) of critical mechanical components is a central challenge in reliability engineering. Stochastic processes, which are capable of modeling uncertainties, are widely used in RUL prediction. However, conventional stochastic process models face two major limitations: (1) the reliance on strict assumptions during model formulation, restricting their applicability to a narrow range of degradation processes, and (2) the inability to account for potential variations in the degradation mechanism during modeling and prediction. To address these issues, we propose a novel mechanism-equivalence-based Tweedie exponential dispersion process (ME-based TEDP) for adaptive degradation modeling and RUL prediction of mechanical components. The proposed model enhances the original Tweedie exponential dispersion process (TEDP) by incorporating degradation mechanism equivalence, effectively capturing the correlation between model parameters. Furthermore, it improves prediction accuracy and interpretability by employing a dynamic testing–modeling–predicting strategy. Application of the ME-based TEDP model to high-speed rail bogie systems demonstrates its effectiveness and superiority over existing approaches. This study advances the theory of degradation modeling and significantly improves the precision of RUL predictions.
Obstacle-Aware Crowd Surveillance with Mobile Robots in Transportation Stations
Authors: Choi Yumin , Kim Hyunbum
Published: January 2025

Recent transportation systems are operated by cooperative factors including mobile robots, smart vehicles, and intelligent management. It is highly anticipated that the surveillance using mobile robots can be utilized in complex transportation areas where the high accuracy is required. In this paper, we introduce a crowd surveillance system using mobile robots and intelligent vehicles to provide obstacle avoidance in transportation stations with a consideration of different moving strategies of the robots in an existing 2D area supported by line-based barriers and surveillance formations. Then, we formally define a problem that aims to minimize the distance traveled by a mobile robot, while also considering the speed of the mobile robot and avoiding the risk of collisions when the mobile robot moves to specific locations to fulfill crowd surveillance. To solve this problem, we propose two different schemes to provide improved surveillance that can be used even when considering speed. After that, various ideas are gathered to define conditions, set various settings, and modify them to evaluate their performances.
Mounting Angle Prediction for Automotive Radar Using Complex-Valued Convolutional Neural Network
Authors: Moon Sunghoon , Kim Younglok
Published: January 2025

In advanced driver-assistance systems (ADASs), the misalignment of the mounting angle of the automotive radar significantly affects the accuracy of object detection and tracking, impacting system safety and performance. This paper introduces the Automotive Radar Alignment Detection Network (AutoRAD-Net), a novel model that leverages complex-valued convolutional neural network (CV-CNN) to address azimuth misalignment challenges in automotive radars. By utilizing complex-valued inputs, AutoRAD-Net effectively learns the physical properties of the radar data, enabling precise azimuth alignment. The model was trained and validated using mounting angle offsets ranging from −3° to +3° and exhibited errors no greater than 0.15° across all tested offsets. Moreover, it demonstrated reliable predictions even for unseen offsets, such as −1.7°, showcasing its generalization capability. The predicted offsets can then be used for physical radar alignment or integrated into compensation algorithms to enhance data interpretation accuracy in ADAS applications. This paper presents AutoRAD-Net as a practical solution for azimuth alignment, advancing radar reliability and performance in autonomous driving systems.
Enhancement of Spurious Signal Suppression in Microstrip Mixers by Load Resistor Termination
Authors: Apriono Catur , Ruhiyat Abdurrasyid , Kurniawan Farohaji , Alphones Arokiaswami , Zulkifli Fitri Yuli
Published: January 2025

The mixer is an essential component in RF transceiver subsystems. It has a role in shifting the signal frequency for more convenient processing of up-conversion as well as down-conversion. Despite generating the desired signal, the mixer also generates spurious noise that wastes power and reduces the performance of the overall system. This research proposes the design of a microstrip mixer that can enhance the suppression of spurious signals by utilizing the characteristics of the load resistor termination. The simulation results show that by adding the load resistor, the parasitic electrical field on the diode and the reflection signal inside the mixer can be reduced. The measurement result also validates that the suppression of the spurious signal is enhanced, where the signal-to-noise ratio can reach above 50 dB on the single-balanced mixer and above 60 dB on the double-balanced mixer.
DynamicVLN: Incorporating Dynamics into Vision-and-Language Navigation Scenarios
Authors: Sun Yanjun , Qiu Yue , Aoki Yoshimitsu
Published: January 2025

Traditional Vision-and-Language Navigation (VLN) tasks require an agent to navigate static environments using natural language instructions. However, real-world road conditions such as vehicle movements, traffic signal fluctuations, pedestrian activity, and weather variations are dynamic and continually changing. These factors significantly impact an agent’s decision-making ability, underscoring the limitations of current VLN models, which do not accurately reflect the complexities of real-world navigation. To bridge this gap, we propose a novel task called Dynamic Vision-and-Language Navigation (DynamicVLN), incorporating various dynamic scenarios to enhance the agent’s decision-making abilities and adaptability. By redefining the VLN task, we emphasize that a robust and generalizable agent should not rely solely on predefined instructions but must also demonstrate reasoning skills and adaptability to unforeseen events. Specifically, we have designed ten scenarios that simulate the challenges of dynamic navigation and developed a dedicated dataset of 11,261 instances using the CARLA simulator (ver.0.9.13) and large language model to provide realistic training conditions. Additionally, we introduce a baseline model that integrates advanced perception and decision-making modules, enabling effective navigation and interpretation of the complexities of dynamic road conditions. This model showcases the ability to follow natural language instructions while dynamically adapting to environmental cues. Our approach establishes a benchmark for developing agents capable of functioning in real-world, dynamic environments and extending beyond the limitations of static VLN tasks to more practical and versatile applications.
Beyond Profession: The Impact of the Job on Probation Personnel
Authors: Eryalçın Münevver , Eryalçın Talip
Published: January 2025

This study aims to assess the impact of work on managers, specialists, and probation officers in Türkiye. The research seeks to enhance understanding of how work life affects personnel and to propose policy recommendations from a social work perspective to improve professional well-being. A qualitative research design utilizing a phenomenological approach was adopted. Data collection involved an individual information form and a semi-structured in-depth interview form, developed by the researchers, to evaluate work-related impacts. Through purposive quota sampling, 30 participants, including specialists, managers, and probation officers, were recruited from probation offices with the highest workload across Türkiye’s seven regions. The data were descriptively analyzed using the Maxqda 2020 software. The findings reveal that professional, individual, and institutional stress factors significantly impact the well-being of probation personnel. While staff report deriving satisfaction from helping individuals break the cycle of crime and receiving positive feedback, they encounter challenges such as emotional labor, lack of professional recognition, and inadequate working conditions. Security concerns related to working with probationers were also emphasized. During the pandemic, disruptions in rehabilitation programs, increased recidivism, and perceptions of impunity were noted. The study underscores the necessity for improved working conditions, benefits, and professional respect to enhance the welfare of probation personnel.
Mosaicos de la Comunidad (Mosaics of the Community): Community-Engaged Participatory Muraling with Madres Emprendedoras
Authors: Fernández Jesica Siham , Nichols Laura A.
Published: January 2025

In this paper, we describe a collaborative community-based research project that centered on community members’ lived experiences, which led to the identification of key community issues that resulted in a representative art project in the form of a public mural. Eleven mothers who were long-time residents of the community were the drivers of the issue identification and mural creation. The issues identified, and subsequently depicted in the mural, revealed the importance of the environment in neighborhoods, with residents dealing with encampments, illegal dumping, prostitution, eviction, and gentrification. In the mural Mosaicos de la Comunidad (Mosaics of the Community), a group of madres (mothers) sought to emphasize their shared admiration of art as a form of remembrance of ample food, clean air, and beautiful spaces to live and raise their children. Drawing from the madres’ reflections and written testimonials, this paper describes the collective mural-making process; moving from research-based issues identified by the madres into the mural design stage, including the identification of symbols and their meanings, to the creation and painting, and the culmination with a mural-unveiling celebration. The paper ends with a description of the value of community-based art as a form of resistance and as a reminder to concretize the environmental justice issues and values that are central to community members.
Development and Validation of a Survey on Inclusive Judo: Judo Teachers’ Attitudes Towards Including Participants with Intellectual Developmental Disorders (J-TAID)
Authors: Descamps Gaston , Massart Alain , Rizzo Terry , Oblak Viktorija Pečnikar , Campos Maria João
Published: January 2025

This study developed and refined the Judo Teachers’ Attitudes Towards Including Participants with Intellectual Developmental Disorders (J-TAID) survey, addressing the need to assess attitudes, subjective norms, perceived behavioral control, and intention regarding inclusion, and grounded in the Theory of Planned Behavior. The survey, translated into English, Portuguese, French, and Slovenian, was administered to 163 participants in order to assess its reliability and validity using Cronbach’s alpha, Principal Component Analysis (PCA), Confirmatory Factor Analysis (CFA), and Exploratory Factor Analysis (EFA). Internal consistency regarding attitudes, subjective norms, and perceived behavioral Constructs ranged from 0,79 to 0.80, with test–retest reliability improving, demonstrating moderate to strong temporal stability (α = 0.679–0.813). The PCA and CFA identified a robust three-factor structure explaining 74% of the variance, with good model fit (RMSEA = 0.048, CFI = 0.978). Pearson correlations supported the TPB constructs. The refined J-TAID demonstrates validity and reliability for its intended purpose, although the results are still preliminary, and the limitations that were observed suggest a need for further validation.
Ecological Sensitivity of the Mata Allo Sub-Watershed, South Sulawesi: A Spatial Analysis Using Principal Component Analysis
Authors: Rijal Syamsu , Samsuri  , Masruroh Heni , Nursaputra Munajat , A Chairil , Ardi Nur Zamzam Putri
Published: January 2025

Watersheds are critical ecosystems that provide essential services, but they face increasing threats from deforestation, land use changes, and climate variability. The Mata Allo Sub-Watershed, which is characterized by steep topography and high rainfall, is particularly vulnerable to erosion, landslides, and habitat loss, necessitating robust conservation strategies. This study used principal component analysis (PCA) to assess ecological sensitivity, focusing on slope, rainfall, vegetation density, and land cover. The PCA results identified land cover as the most influential positive factor in F1 (loading value: 0.588), increasing sensitivity due to human-induced land use changes, while rainfall contributed most negatively (−0.638) by potentially mitigating extreme ecological risks. These contrasting roles underscore the complexity of interactions shaping watershed sensitivity. Slope strongly influenced F2 (−0.795), explaining 26.48% of the variance and highlighting the critical role of steep slopes in exacerbating erosion risks. Vegetation density in F3 (−0.679) and rainfall in F4 (−0.724) played significant roles in stabilizing soil and mitigating ecological risks, emphasizing their importance in reducing watershed sensitivity. The “Extremely Sensitive” class covers 48.79% of the watershed, primarily in areas with steep slopes and sparse vegetation, while “High Sensitivity” areas occupy 34.93%. Projections for 2032 suggest a reduction in “Extremely Sensitive” zones to 41.00%, reflecting improvements from targeted management interventions. These findings provide a foundation for promoting sustainable watershed management, enhancing climate resilience, and supporting biodiversity conservation efforts in vulnerable regions.
A Mini-Review of Full-Scale Drinking Water Treatment Plants for Per- and Polyfluoroalkyl Substances (PFAS) Removal: Possible Solutions and Future Directions
Authors: Jafarinejad Shahryar
Published: January 2025

The United States Environmental Protection Agency (US EPA) recently finalized the enforceable maximum contaminant levels for some per- and polyfluoroalkyl substances (PFAS) in drinking water which intends to substantially decrease their level in it. Conventional processes in full-scale drinking water treatment plants (DWTPs) are usually inefficient in PFAS removal from source water (i.e., groundwater and surface water). There is an increasing interest in investigating/evaluating advanced treatment technologies for PFAS removal from PFAS-contaminated water to help generate a number of potential solutions to this water engineering design challenge/problem. While numerous excellent research studies have been carried out and reported in the literature on the efficiency of several treatment processes in removing PFAS from PFAS-contaminated water, mostly at lab- and pilot-scales, full-scale DWTP investigations still need further attention. This study reviews the US EPA’s PFAS water quality guidelines/regulations, remediation technologies for PFAS in water, and PFAS removal studies on full-scale DWTPs. Then, it discusses some configurations of DWTP for PFAS removal from source water (i.e., groundwater and surface water) as well as suggesting future directions. Further research on the effect of environmental factors (e.g., organic matter) on PFAS removal, the effective elimination of short-chain PFAS from real PFAS-contaminated source water using cost-effective and industrially applicable remediation technologies, the efficiency/performance of full-scale treatment trains including innovative advanced technologies in long-term for PFAS removal from source water to produce drinking water and the associated costs, as well as cost reduction/minimization via process optimization is still of interest.
The Urban Intersection Accident Detection Method Based on the GAN-XGBoost and Shapley Additive Explanations Hybrid Model
Authors: Shi Zhongji , Wang Yingping , Guo Dong , Jiao Fangtong , Zhang Hu , Sun Feng
Published: January 2025

Traffic accidents at urban intersections may lead to severe traffic congestion, necessitating effective detection and timely intervention. To achieve real-time traffic accident monitoring at intersections more effectively, this paper proposes an urban road intersection accident detection method based on Generative Adversarial Networks (GANs), Extreme Gradient Boosting (XGBoost), and the SHAP interpretability framework. Data extraction and processing methods are described, and a brief analysis of accident impact features is provided. To address the issue of data imbalance, GAN is used to generate synthetic accident samples. The XGBoost model is then trained on the balanced dataset, and its accident detection performance is validated. In addition, SHAP is employed to interpret the results and analyze the importance of individual features. The results indicate that the accident samples generated by GAN not only retain the characteristics of real data but also enhance sample diversity, improving the AUC value of the XGBoost model by 7.1% to reach 0.844. Compared with the benchmark models mentioned in the study, the AUC value shows an average improvement of 7%. Additionally, the SHAP model confirms that the time–vehicle ratio and average speed are key factors influencing the model’s detection results. These findings provide a reliable method for urban road intersection accident detection, and accurate accident location detection can assist urban planners in formulating comprehensive emergency management strategies for intersections, ensuring the sustainable operation of traffic flow.
Research on Urbanization and Ecological Environmental Response: A Case Study of Zhengzhou City
Authors: Feng Haoran , Wang Dian , Ji Qiyan
Published: January 2025

The relationship between the urbanization process and the ecological environment is key to regional development. As a typical Chinese city undergoing rapid urban development, Zhengzhou is an important representative of the urbanization process and the changes in the ecological environment. In this study, we explored the response relationship between urban development and the ecological environment in Zhengzhou, using night light data, Landsat satellite imagery, and population data from this city. The analysis of the NTL data showed that there were three stages of development in Zhengzhou from 2000 to 2021: the slow expansion stage from 2000 to 2003, the steady expansion stage from 2004 to 2011, and the rapid expansion stage from 2012 to 2021. The multi-year average RSEI value of Zhengzhou was less than 0.4, and it showed a trend of first increasing and then decreasing, indicating that the quality of the city’s ecological environment was poor and indirectly indicating that the urbanization degree of the region was significant. The changes in the NTL and RSEI indicate that urban development has significantly reduced the quality of the city’s ecological environment, particularly after Zhengzhou entered the stage of rapid expansion. The coupling degree (C) and coupling coordination degree (D) between urbanization and the ecological environment showed a decreasing trend, and the average value was lower than 0.3. This indicates that the ecological environment in Zhengzhou has been seriously affected by the process of urbanization, and the natural ecology has been strongly impacted by human activity. C and D also showed a decreasing trend from 2000 to 2015 but increased from 2016 to 2021, indicating that the ecological environment in Zhengzhou has gradually improved. The degree of coordination D between urbanization and the ecological environment in Zhengzhou had a strong negative correlation with the population size and growth rate but a positive correlation with the Moran value, indicating that an increase in the population increases the burden on the ecological environment. However, a reasonable spatial population distribution is conducive to improving regional urban–ecological coordination.
The Intersection Between Food Literacy and Sustainability: A Systematic Quantitative Literature Review
Authors: McManus Sarah , Pendergast Donna , Kanasa Harry
Published: January 2025

Achieving a sustainable global society is dependent on effective modern food systems and sustainable food literacy competency. This study aimed to make visible the nature of the intersection between food literacy and sustainability (sustainable food literacy) by employing a systematic quantitative literature review reported through PRISMA principles. It explored the representation and intersectionality of these research concepts to reveal the knowledge, skill, and attitude/behaviour dimensions of sustainable food literacy and Education for Sustainable Development to support SDG 4: Quality Education. The inclusion criteria of English language, peer-reviewed, full-text articles where food literacy and sustainability intersect through minor, major, or focus article links yielded N = 114 articles. Although experiencing rapid growth between 2013 and 2022, sustainable food literacy research was identified as fragmented and incomplete. This review reveals and conceptualises 19 sustainable food literacy research topics, including knowledge, skills, and attitudes/behaviours (86%), education (51%), food systems (45%), consumers/consumption (44%), and sustainable diets (34%). This study confirms that education with foundational and critical knowledge, skills, and attitudes/behaviours is pivotal to developing sustainable food literacy competency. Eight propositions are provided to guide further research to support SDG 4: Quality Education, focusing on exploring teacher capacity, successful educational approaches, and supportive policy.
Optimizing Performance of a Solar Flat Plate Collector for Sustainable Operation Using Box–Behnken Design (BBD)
Authors: Chitharaj Ramesh , Perumal Hariprasad , Almeshaal Mohammed , Manoj Kumar P.
Published: January 2025

This study investigated the performance optimization of nickel-cobalt (Ni-Co)-coated absorber panels in solar flat plate collectors (SFPCs) using response surface methodology for sustainable operation and optimized performance. Ni-Co coatings, applied through an electroplating process, represent a novel approach by offering superior thermal conductivity, durability, and environmental benefits compared to conventional black chrome coatings, addressing critical concerns related to ecological impact and long-term reliability. Experiments were conducted to evaluate the thermal efficiency of Ni-Co-coated panels with and without reflectors under varying flow rates, collector angles, and reflector angles. The thermal efficiency was calculated based on the inlet and outlet water temperatures, solar radiation intensity, and panel area. The results showed that the SFPC achieved average efficiencies of 50.9% without reflectors and 59.0% with reflectors, demonstrating the effectiveness of the coatings in enhancing solar energy absorption and heat transfer. A validated quadratic regression model (R2 = 0.9941) predicted efficiency based on the process variables, revealing significant individual and interaction effects. Optimization using the Box–Behnken design identified the optimal parameter settings for maximum efficiency: a flow rate of 1.32 L/min, collector angle of 46.91°, and reflector angle of 42.34°, yielding a predicted efficiency of 79.2%. These findings highlight the potential of Ni-Co coatings and reflectors for enhancing SFPC performance and provide valuable insights into the sustainable operation of solar thermal systems. Furthermore, the introduction of Ni-Co coatings offers a sustainable alternative to black chrome, reducing environmental risks while enhancing efficiency, thereby contributing to the advancement of renewable energy technologies.
Correction: Wang, L.; Dai, S. Carbon Footprint Accounting and Verification of Seven Major Urban Agglomerations in China Based on Dynamic Emission Factor Model. Sustainability 2024, 16, 9817
Authors: Wang Lingling , Dai Shufen
Published: January 2025

The authors would like to make the following corrections to the published paper [...]
The Impact of Market-Based Environmental Regulation on Carbon Emission Intensity: An Analysis Based on Policy Texts
Authors: Wu Jianzu , Yu Zhipiao
Published: January 2025

Market-based environmental regulations play a crucial role in promoting local carbon emissions reduction under the context of achieving “carbon peaking and carbon neutrality goals” goals. Previous studies have mainly focused on the emission reduction effects of single policy instruments, lacking systematic measurement of market-based environmental regulation. Based on panel data from Chinese prefecture-level cities during 2011–2020, this study constructs a market-based environmental regulation index using a policy text analysis method to empirically examine its impact on carbon emission intensity and the underlying mechanisms. The research findings are as follows: (1) The impact of market-based environmental regulations on carbon emission intensity shows an inverted U-shaped relationship, indicating increased carbon emissions in the short term but favorable carbon reduction in the long term. (2) Mechanism tests reveal that market-based environmental regulations primarily influence carbon emission intensity through enterprise green innovation, showing an inverted U-shaped relationship, suggesting that such regulations may suppress enterprise innovation vitality in the short term but effectively promote green innovation in the long term. (3) The carbon reduction effect of market-based environmental regulations varies significantly across regions with different official characteristics. This study provides an important theoretical basis and policy implications for improving market-based environmental regulation policy design and enhancing carbon reduction effectiveness.
The Impact of Governmental Regulations and Environmental Activities on Innovation Efficiency
Authors: Knapp Daniel , Bayrle-Kelso Niklas , Nigg-Stock Arabella , Brecht Leo
Published: January 2025

Innovation efficiency plays a key role in ensuring that sustainability efforts are implemented effectively and yield maximum benefits. This study emphasizes the impact of government regulations and environmental resource-saving approaches on innovation efficiency. Within this study, environmental resource-saving approaches include activities against air pollution, water pollution, energy consumption, waste, and hazardous substances. By analyzing data from 1196 German companies statistically, this study examines the importance of governmental and legal regulations as well as engagement in environmental activities for green innovation. The findings reveal that companies prioritizing regulations and environmental practices achieve higher innovation output from the same innovation input than other firms, leading to improved innovation efficiency. In a developed country, companies focusing on sustainability aspects exhibit a higher level of innovation efficiency. For them, embracing regulations and environmental practices are firm resources that raise the possibility of competitive advantage. Companies embracing these aspects hence contribute to both their own success and the well-being of the environment.
The Impact of Pairing Local Food and Wine on the Sustainability of Hospitality Businesses in the Wine Region of Srem (Vojvodina, R. Serbia)
Authors: Stošić Tamara , Tešanović Dragan , Kalenjuk Pivarski Bojana , Paunić Maja , Gagić Jaraković Snježana , Lazarević Jasmina , Ivanović Velibor , Šmugović Stefan
Published: January 2025

This research examined how food and wine pairing affects the sustainability in local hospitality settings. This study focused on hospitality employees’ attitudes in Srem, where it explored the importance of integrating local produce, the economic and environmental benefits of reduced food miles, and customer satisfaction through authentic experiences. Key research questions investigated the perceived value of food–wine pairing, factors that influenced sustainable practices, and employee motivation. Data were gathered via a structured survey that highlighted challenges like limited ingredient availability and high costs. The results show that the employees recognized the importance of sustainability but displayed limited motivation for further initiatives. The findings suggest that enhancing local sourcing can bolster economic resilience and cultural preservation, promoting a sustainable tourism model for the region. This study recommends additional research on ecological impacts and local community benefits to further embed sustainability in hospitality practices.