bluuebunny/crossref_metadata_2025 huggingface dataset:
Automatic Multi-Temporal Land Cover Mapping with Medium Spatial Resolution Using the Model Migration Method
Authors: Chen Ruijun , Chen Xidong , Ren Yu
Published: December 2024

Accurate land cover mapping plays a critical role in enhancing our understanding of Earth’s energy balance, carbon cycle, and ecosystem dynamics. However, existing methods for producing multi-epoch land cover products still heavily depend on manual intervention, limiting their efficiency and scalability. This study introduces an automated approach for multi-epoch land cover mapping using remote sensing imagery and the model migration strategy. Landsat ETM+ and OLI images with a 30 m resolution were utilized as the primary data sources. An automatic training sample extraction method based on prior multi-source land cover products was first utilized. Then, based on the generated training dataset and a random forest classifier, local adaptive land cover classification models of the reference year were developed. Finally, by migrating the classification model to the target epoch, multi-epoch land cover products were generated. Yuli County in Xinjiang and Linxi County in Inner Mongolia were used as test cases. The classification models were first generated in 2020 and then migrated to 2010 to test the effectiveness of automated land cover classification over multiple years. Our mapping results show high accuracy in both regions, with Yuli County achieving 92.52% in 2020 and 88.33% in 2010, and Linxi County achieving 90.28% in 2020 and 85.28% in 2010. These results demonstrate the reliability of our proposed automated land cover mapping strategy. Additionally, the uncertainty analysis of the model migration strategy indicated that land cover types such as water bodies, wetlands, and impervious surfaces, which exhibit significant spectral changes over time, were the least suitable for model migration. Our results can offer valuable insights for medium-resolution, multi-epoch land cover mapping, which could facilitate more efficient and accurate environmental assessments.
Enhanced Stochastic Models for VLBI Invariant Point Estimation and Axis Offset Analysis
Authors: Hong Chang-Ki , Bae Tae-Suk
Published: December 2024

The accuracy and stability of Very Long Baseline Interferometry (VLBI) systems are essential for maintaining global geodetic reference frames such as the International Terrestrial Reference Frame (ITRF). This study focuses on the precise determination of the VLBI Invariant Point (IVP) and the detection of antenna axis offset. Ground-based surveys were conducted at the Sejong Space Geodetic Observatory using high-precision instruments, including total station, to measure slant distances, as well as horizontal and vertical angles from fixed pillars to reflectors attached to the VLBI instrument. The reflectors comprised both prisms and reflective sheets to enhance redundancy and data reliability. A detailed stochastic model incorporating variance component estimation was employed to manage the varying precision of the observations. The analysis revealed significant measurement variability, particularly in slant distance measurements involving prisms. Iterative refinement of the variance components improved the reliability of the IVP and antenna axis offset estimates. The study identified an antenna axis offset of 5.6 mm, which was statistically validated through hypothesis testing, confirming its significance at a 0.01 significance level. This is a significance level corresponding to approximately a 2.576 sigma threshold, which represents a 99% confidence level. This study highlights the importance of accurate stochastic modeling in ensuring the precision and reliability of the estimated VLBI IVP and antenna axis offset. Additionally, the results can serve as a priori information for VLBI data analysis.
Tropospheric NO2: Anthropogenic Influence, Global Trends, Satellite Data, and Machine Learning Application
Authors: Ojeda-Castillo Valeria , Murillo-Tovar Mario Alfonso , Hernández-Mena Leonel , Saldarriaga-Noreña Hugo , Vargas-Amado María Elena , Herrera-López Enrique J. , Díaz Jesús
Published: December 2024

Nitrogen dioxide (NO2) is a critical air pollutant that has significant health and environmental impacts. Tropospheric NO2 refers specifically to the vertical column density of NO2, which is measured by satellites and serves as an indicator of anthropogenic NO2 sources. This pollutant is frequently assessed using satellite data owing to limitations in local monitoring. This investigation employs the Spectral Angle Mapper (SAM), a geometric machine-learning model, given its advantages in simplicity and computational efficiency, and OMI satellite measurements to carry out spatially supervised classification of tropospheric NO2 global patterns from 2005 to 2021. This study identifies four typical trends across developed urban centers, examining correlations with population growth, economic factors, and air quality policies. The results demonstrated regional variations, with a general downward trend in North America, Europe, and parts of Asia, underscoring the efficacy of stricter emission controls. However, upward trends persist in some Asian regions, reflecting varying policy implementations. This study revealed a pivotal inflection point around 2013, marking a shift in global NO2 dynamics. Although policies have led to improved air quality in some regions, achieving absolute decoupling of economic growth from NO2 emissions remains challenging. The COVID-19 pandemic has also exerted a significant influence, temporarily reducing emissions due to economic slowdowns. Overall, the SAM model effectively delineated NO2 patterns and provided insights for future policy and emission control strategies.
The Detection of Small-Scale Open-Burning Agriculture Fires Through Remote Sensing
Authors: Oliveira Eduardo R. , Silva Bárbara T. , Lopes Diogo , Corticeiro Sofia , Alves Fátima L. , Disperati Leonardo , Gama Carla
Published: December 2024

The open burning of agricultural residues is a widespread practice with significant environmental implications. This study explores the potential of satellite remote sensing to detect and analyze small-scale agricultural fires in Portugal, focusing on their spatial and temporal characteristics. Using active fire detection products from various satellite platforms, including VIIRS, MODIS, SLSTR, and SEVIRI, we conducted a detailed analysis across two local case studies and a national-scale assessment. This study evaluates both active fire detections and post-fire burned area estimations, using high-resolution satellite imagery to overcome the limitations associated with the small size and low intensity of these fires. The results indicate that while active fire detections are feasible for larger-scale burning, challenges remain for smaller fires due to resolution constraints. A systematic comparison with an agricultural burning request database further highlights the need for the enhancement of temporal and spatial precision in data to improve detection reliability. Despite these limitations, this work underscores the importance of remote sensing tools in monitoring agricultural burning practices and enhancing environmental management efforts.
Detection of the Optimal Temporal Windows for Mapping Paddy Rice Under a Double-Cropping System Using Sentinel-2 Imagery
Authors: Sheng Li , Lv Yuefeng , Ren Zhouqiao , Zhou Hongkui , Deng Xunfei
Published: December 2024

Accurately mapping paddy rice is crucial for food security, sustainable agricultural management and environmental protection. Recently, Sentinel-2 optical images with a spatial resolution of 10 m and a repeat cycle of five days have demonstrated enormous potential for mapping paddy fields. However, the influence of the temporal selection of Sentinel-2 optical images on mapping paddy rice is still unclear. In this study, the optimal temporal windows were detected by considering all possible temporal combinations during the growing stages from the constructed cloud-free 10-day time series and assessing the classification performances of all combination schemes on paddy rice mapping by F1_score. The results indicated that the combination of two or three phases is necessary for mapping early-cropping paddy rice (EP) and late-cropping paddy rice (LP), achieving the F1_score aim of 0.96. The detection of single-cropping paddy rice (SP) requires a combination of three to five phases and can obtain the F1_score aim of 0.94. Additionally, an automatic workflow for paddy rice mapping has been developed, which does not require any cloud removal but provides complete spatial coverage, suitable for regions with frequent rain and clouds. Through verification in the study area of Yiwu, China, the discrepancies between mapping results and agricultural statistics were within 5%, demonstrating the rationality and efficiency of the proposed framework.
Star Identification Algorithm Based on Dynamic Distance Ratio Matching
Authors: Dai Ya , Shi Chenguang , Ben Liyan , Zhu Hua , Zhang Rui , Wu Shufan , Shan Sixiang , Xu Yu , Zhou Wang
Published: December 2024

A star tracker is a widely used celestial sensor in astronomical navigation systems, which calculates the spacecraft’s high-precision attitude by observing stars in space to obtain several star vectors. Existing star identification algorithms typically require the selection of a specific anchor star (e.g., the nearest neighbor star), using the line connecting the target star and the anchor star as the rotational reference axis to achieve rotation invariance in the star identification algorithm. However, this approach makes the entire identification algorithm overly dependent on the anchor star, resulting in insufficient identification accuracy in cases of excessive positional noise or a high number of false stars. In this paper, we adopt the angles between any neighboring stars and the distances ratio between neighboring stars and the observed star as the initial matching criteria. We then calculate the matching with each navigation star using the accumulated angle in the counterclockwise direction based on this criterion. The navigation star with the highest matching is identified. Unlike other identification algorithms that require selecting the nearest neighbor star which can be easily affected by interference as the rotational reference axis, our method effectively achieves rotation invariance in star identification by leveraging angle information. Therefore, it exhibits better tolerance to positional noise, magnitude noise, and false stars, particularly demonstrating higher robustness against focal length variations.
Experimental Validation of a GNSS Receiver Antenna Absolute Field Calibration System
Authors: Tupek Antonio , Zrinjski Mladen , Špoljar Krunoslav , Stipetić Karlo
Published: December 2024

Carrier-phase measurements are essential in precise Global Navigation Satellite System (GNSS) positioning applications. The quality of those observations, as well as the final positioning result, is influenced by an extensive list of GNSS error sources, one of which is the receiver antenna phase center (PC) model. It has been well established that the antenna PC exhibits variability depending on the frequency, direction, and intensity of the incoming GNSS signal. To mitigate the corresponding range errors, phase center corrections (PCCs) are determined through a specialized procedure known as receiver antenna calibration and subsequently applied in data processing. In 2023, the Laboratory for Measurements and Measuring Technique (LMMT) of the Faculty of Geodesy, University of Zagreb, Croatia, initiated the development of a new robotic GNSS receiver antenna calibration system. The system implements absolute field calibration and PCC modeling through triple-difference (TD) carrier-phase observations and spherical harmonics (SH) expansion. This study presents and documents dual-frequency (L1 and L2) Global Positioning System (GPS) calibration results for several distinct receiver antennas. Furthermore, the main goals of this contribution are to evaluate the accuracy of dual-frequency GPS calibration results on the pattern level with respect to independent calibrations obtained from Geo++ GmbH and to extensively experimentally validate LMMT calibration results in the spatial (coordinate) domain, i.e., to investigate how the application of LMMT PPC models reflects on geodetic-grade GNSS positioning. Our experimental research results showed a submillimeter calibration accuracy, i.e., 0.36 mm for GPS L1 and 0.54 mm for the GPS L2 frequency. Furthermore, our field results confirmed that the application of LMMT PCC models significantly increases baseline accuracy and GNSS network solution accuracy when compared to type-mean PCC models of the International GNSS Service (IGS).
Preparation of Molecularly Imprinted Electrochemical Sensors and Analysis of the Doping of Epinephrine in Equine Blood
Authors: Wang Zhao , Li Yanqi , Xi Xiaoxue , Zou Qichao , Zhang Yuexing
Published: December 2024

In this paper, a novel molecularly imprinted polymer membrane modified glassy carbon electrode for electrochemical sensors (MIP-OH-MWCNTs-GCE) for epinephrine (EP) was successfully prepared by a gel-sol method using an optimized functional monomer oligosilsesquioxane-Al2O3 sol-ITO composite sol (ITO-POSS-Al2O3). Hydroxylated multi-walled carbon nanotubes (OH-MWCNTs) were introduced during the modification of the electrodes, and the electrochemical behavior of EP on the molecularly imprinted electrochemical sensors was probed by the differential pulse velocity (DPV) method. The experimental conditions were optimized. Under the optimized conditions, the response peak current values showed a good linear relationship with the epinephrine concentration in the range of 0.0014–2.12 μM, and the detection limit was 4.656 × 10−11 M. The prepared molecularly imprinted electrochemical sensor was successfully applied to the detection of actual samples of horse serum with recoveries of 94.97–101.36% (RSD), which indicated that the constructed molecularly imprinted membrane electrochemical sensor has a high detection accuracy for epinephrine in horse blood, and that it has a better value for practical application.
Bio-Inspired Algorithms for Efficient Clustering and Routing in Flying Ad Hoc Networks
Authors: Agrawal Juhi , Arafat Muhammad Yeasir
Published: December 2024

The high mobility and dynamic nature of unmanned aerial vehicles (UAVs) pose significant challenges to clustering and routing in flying ad hoc networks (FANETs). Traditional methods often fail to achieve stable networks with efficient resource utilization and low latency. To address these issues, we propose a hybrid bio-inspired algorithm, HMAO, combining the mountain gazelle optimizer (MGO) and the aquila optimizer (AO). HMAO improves cluster stability and enhances data delivery reliability in FANETs. The algorithm uses MGO for efficient cluster head (CH) selection, considering UAV energy levels, mobility patterns, intra-cluster distance, and one-hop neighbor density, thereby reducing re-clustering frequency and ensuring coordinated operations. For cluster maintenance, a congestion-based approach redistributes UAVs in overloaded or imbalanced clusters. The AO-based routing algorithm ensures reliable data transmission from CHs to the base station by leveraging predictive mobility data, load balancing, fault tolerance, and global insights from ferry nodes. According to the simulations conducted on the network simulator (NS-3.35), the HMAO technique exhibits improved cluster stability, packet delivery ratio, low delay, overhead, and reduced energy consumption compared to the existing methods.
A New Catalogue and Insights into the 2022 Adriatic Offshore Seismic Sequence Using a Machine Learning-Based Procedure
Authors: Costanzo Antonio
Published: December 2024

This paper presents a new catalogue of the 2022/2023 Adriatic Offshore Seismic Sequence obtained by machine learning-based processing. The procedure performs the automatic picking and association of phases starting from the analysis of the continuous waveforms recorded by 40 seismic stations of the Italian National Seismic Network and 5 stations of the SISMIKO emergency group network. The earthquakes were detected over a 3-month period, between 1 November 2022 and 31 January 2023. This new catalogue consists of 2780 earthquakes with a magnitude equal to or greater than ML 0.4, providing more information about lower-magnitude earthquakes in particular. The results make available, on the one hand, new insights into the offshore sequence, which can contribute to confirming the attribution of the earthquakes to the Adriatic Fault System, and in particular, the mainshocks to the Cornelia fault thrust, as also hypothesised by other works in the literature. Moreover, the work provides a further contribution in showing the great potential of using machine learning-based procedures to build catalogues with a greater degree of completeness, even in very particular cases such as the one represented by the Adriatic offshore sequence, for which the minimum distance from the epicentres is high and the azimuth coverage limited.
Delay Minimization for BAC-NOMA Offloading in UAV Networks
Authors: Li Haodong , Yin Zhengkai , Chen Changsheng
Published: December 2024

The rapid deployment and enhanced communication capabilities of unmanned aerial vehicles (UAVs) have enabled numerous real-time sensing applications. These scenarios often necessitate task offloading and execution under stringent transmission delay constraints, particularly for time-critical applications such as disaster rescue and environmental monitoring. This paper investigates the improvement of MEC-based task offloading services in energy-constrained UAV networks using backscatter communication (BackCom) with non-orthogonal multiple access (BAC-NOMA). The proposed BAC-NOMA protocol allows uplink UAVs to utilize downlink signals for backscattering tasks instead of transmitting through uplink NOMA. A resource allocation problem is formulated, aimed at minimizing offloading delays for uplink users. By converting the initially non-convex problem into a convex one, an iterative algorithm is developed to solve it. Simulation results demonstrate that the proposed protocol significantly reduces offloading delays relative to existing benchmarks.
HandFI: Multilevel Interacting Hand Reconstruction Based on Multilevel Feature Fusion in RGB Images
Authors: Pan Huimin , Cai Yuting , Yang Jiayi , Niu Shaojia , Gao Quanli , Wang Xihan
Published: December 2024

Interacting hand reconstruction presents significant opportunities in various applications. However, it currently faces challenges such as the difficulty in distinguishing the features of both hands, misalignment of hand meshes with input images, and modeling the complex spatial relationships between interacting hands. In this paper, we propose a multilevel feature fusion interactive network for hand reconstruction (HandFI). Within this network, the hand feature separation module utilizes attentional mechanisms and positional coding to distinguish between left-hand and right-hand features while maintaining the spatial relationship of the features. The hand fusion and attention module promotes the alignment of hand vertices with the image by integrating multi-scale hand features while introducing cross-attention to help determine the complex spatial relationships between interacting hands, thereby enhancing the accuracy of two-hand reconstruction. We evaluated our method with existing approaches using the InterHand 2.6M, RGB2Hands, and EgoHands datasets. Extensive experimental results demonstrated that our method outperformed other representative methods, with performance metrics of 9.38 mm for the MPJPE and 9.61 mm for the MPVPE. Additionally, the results obtained in real-world scenes further validated the generalization capability of our method.
Advancing a Vision Foundation Model for Ming-Style Furniture Image Segmentation: A New Dataset and Method
Authors: Wan Yingtong , Wang Wanru , Zhang Meng , Peng Wei , Tang He
Published: December 2024

This paper tackles the challenge of accurately segmenting images of Ming-style furniture, an important aspect of China’s cultural heritage, to aid in its preservation and analysis. Existing vision foundation models, like the segment anything model (SAM), struggle with the complex structures of Ming furniture due to the need for manual prompts and imprecise segmentation outputs. To address these limitations, we introduce two key innovations: the material attribute prompter (MAP), which automatically generates prompts based on the furniture’s material properties, and the structure refinement module (SRM), which enhances segmentation by combining high- and low-level features. Additionally, we present the MF2K dataset, which includes 2073 images annotated with pixel-level masks across eight materials and environments. Our experiments demonstrate that the proposed method significantly improves the segmentation accuracy, outperforming state-of-the-art models in terms of the mean intersection over union (mIoU). Ablation studies highlight the contributions of the MAP and SRM to both the performance and computational efficiency. This work offers a powerful automated solution for segmenting intricate furniture structures, facilitating digital preservation and in-depth analysis of Ming-style furniture.
CINet: A Constraint- and Interaction-Based Network for Remote Sensing Change Detection
Authors: Wei Geng , Shi Bingxian , Wang Cheng , Wang Junbo , Zhu Xiaolin
Published: December 2024

Remote sensing change detection (RSCD), which utilizes dual-temporal images to predict change locations, plays an essential role in long-term Earth observation missions. Although many deep learning based RSCD models perform well, challenges remain in effectively extracting change information between dual-temporal images and fully leveraging interactions between their feature maps. To address these challenges, a constraint- and interaction-based network (CINet) for RSCD is proposed. Firstly, a constraint mechanism is introduced that uses labels to control the backbone of the network during training to enhance the consistency of the unchanged regions and the differences between the changed regions in the extracted dual-temporal images. Secondly, a Cross-Spatial-Channel Attention (CSCA) module is proposed, which realizes the interaction of valid information between dual-temporal feature maps through channels and spatial attention and uses multi-level information for more accurate detection. The verification results show that compared with advanced parallel methods, CINet achieved the highest F1 scores on all six widely used remote sensing benchmark datasets, reaching a maximum of 92.00 (on LEVIR-CD dataset). These results highlight the excellent ability of CINet to detect changes in various practical scenarios, demonstrating the effectiveness and feasibility of the proposed constraint enhancement and CSCA module.
Photoelectron Spectroscopy Study of the Optical and Electrical Properties of Cr/Cu/Mn Tri-Doped Bismuth Niobate Pyrochlore
Authors: Zhuk Nadezhda A. , Sekushin Nikolay A. , Krzhizhanovskaya Maria G. , Selutin Artem A. , Koroleva Aleksandra V. , Badanina Ksenia A. , Nekipelov Sergey V. , Petrova Olga V. , Sivkov Victor N.
Published: December 2024

The multielement pyrochlore of the composition Bi1.57Mn1/3Cr1/3Cu1/3Nb2O9−Δ (sp. gr. Fd-3m:2, 10.4724 Å) containing transition element atoms—chromium, manganese and copper in equimolar amounts—was synthesized for the first time using the solid-phase reaction method. The microstructure of the ceramics is grainless and has low porosity. The sample is characterized by reflection in the red (705 nm) color region. The band gap for the direct allowed transition in the sample is 1.68 eV. The parameters of the Bi5d, Nb3d, Сr2p, Mn2p, and Cu2p X-ray photoelectron spectroscopy (XPS) spectra for the mixed pyrochlore are compared with the parameters of transition element oxides. For the complex pyrochlore, a characteristic shift in the Bi4f and Nb3d spectra to the region of lower energies by 0.15 and 0.60 eV, respectively, is observed. According to the XPS Cu2p and Mn2p spectra of pyrochlore, copper, and manganese cations are in a mixed charge state; they mainly have an effective charge of +2/+3, and the Cr2p spectrum is a superposition of the spectra of chromium ions in the charge state of +3, +4, +6. At 24 °С, the permittivity of the sample in the frequency range (104–106 Hz) weakly depends on the frequency and is equal to ~100, the dielectric loss tangent is 0.017. The activation energy of conductivity is equal to 0.41 eV. The specific electrical conductivity of Bi1.57Cr1/3Cu1/3Mn1/3Nb2O9−Δ increases with the temperature increasing from 1.8 × 10−5 Ohm−1·m−1 (24 °С) to 0.1 Ohm−1·m−1 (330 °С). Nyquist curves for the sample are modeled by equivalent electrical circuits.
Overcoming Barriers to Youth Economic Inclusion in Rural Morocco: A Case Study of Daït Aoua
Authors: Lahrech Mohamed Taha , Makhlouki Bochra , Bekkar Younes , Bounadi Imane , Harbouze Rachid , Maatala Nassreddine , Burte Julien
Published: December 2024

In rural areas like the Daït Aoua territory, young people often face economic marginalization due to limited access to land, financial resources, and socio-cultural constraints. This study examines the economic inclusion of youth in this region and identifies key factors influencing their participation in local economies. Using a participatory territorial approach, we combined quantitative and qualitative data through surveys, interviews, and statistical analyses. The findings highlight significant spatial inequalities restricting youth to low-income, precarious roles in agricultural labor or unpaid family work, primarily due to delayed intergenerational land transfers. Despite some efforts toward income diversification through entrepreneurship and seasonal migration, these strategies are often hindered by a lack of capital and market limitations. The research underscores the need for targeted policies to address these structural barriers, promote equitable access to resources, and foster sustainable economic development. Understanding these dynamics is essential for creating opportunities that empower rural youth, enabling them to contribute meaningfully to their communities’ growth.
Parental Reactions to Children’s Negative Emotions in France and the United States: Links to Preschoolers’ Socioemotional Outcomes
Authors: Li Kathryn , Hofer Claire , Williams Aya I. , Zhou Qing
Published: December 2024

Parental reactions to children’s negative emotions (PRCNEs), a type of emotion socialization, has been theorized to shape children’s socioemotional development. Few studies have examined cultural variations in PRCNE among Western/Minority World cultures. The present study used a cross-cultural, preschool-based sample of 101 parent–child dyads (child age = 36–79 months, 42% females; parent age = 19–58 years, 89% mothers) from two countries: U.S. (n = 33, 58% White/European American, 87% with college or above education) and France (n = 68, 61% French or other European origin, 40% with college or above education). Parents self-reported PRCNEs across 12 hypothetical scenarios. Children’s behavioral problems and prosocial behaviors were rated by parents and preschool teachers. Children’s emotion knowledge was measured by a behavioral task. We examined (1) cultural group variations in PRCNEs, and (2) the relations between PRCNE and children’s socioemotional outcomes. Controlling for demographics, French parents endorsed more non-supportive PRCNE than U.S. parents. Across both cultures, parents’ higher use of distress and punitive reactions were linked to lower parent-rated child prosocial behaviors. These findings revealed differences and similarities in PRCNEs between French and U.S. families and have implications for early childhood education practices promoting socioemotional competence.
Survival Times of Transplanted Kidneys Among Different Donor–Recipient Cohorts: The United States Registry Analysis from 1987 to 2018, Part 1: Gender and Ethnicity
Authors: Kachouie Nezamoddin N. , Despeignes Alain , Breininger Daniel
Published: December 2024

Over seven thousand people on average die each year in the United States waiting for an organ transplant due to the shortage of donated organs. With this alarming concern, efforts from the health organizations like the United Network Organ Sharing (UNOS) and government officials, by sharing the transplant data, inspire the investigation of the characteristics among donors and recipients that affects the longevity of donated organs. The goal of this study is to investigate the survival time of transplanted kidneys from 1987 to 2018 regarding the donors’ and the recipients’ characteristics. Survival analysis is performed to determine the characteristics associated with survival time of transplanted kidneys. Our results indicate that there is a noticeable correlation between the survival time and the matching ethnicity of donor and recipient. However, the optimal survival time was not necessarily associated with the matching genders of donor and recipient. It was observed that, on average, the male-to-female kidney transplant has a longer survival time. The premise of this study was the use of statistical analysis methods to identify general trends in survival times of transplanted kidneys among different patient cohorts available through the UNOS registry. We must emphasize that the context of this research is bounded within the domain of statistical analysis and within the scope of the methods that were employed in this study. The outcomes of this study are of statistical interest, with potential clinical significance.
Unraveling the Environmental Impacts of the Fashion Industry: A Fourier-Based Analysis of Pollution Dynamics and Causality Across Five Countries
Authors: Bildirici Melike , Türkkahraman Irmak , Ersin Özgür Ömer
Published: December 2024

The fashion industry, which stands out for its creativity and dynamism, has multidimensional impacts in terms of environmental sustainability from raw material extraction to waste management. The textile and fashion industries are criticized for posing significant threats to the ecosystem, biodiversity, and human health by negatively impacting air, water, and soil quality throughout the cycle, from production and distribution to consumption and disposal. By focusing on five emerging economies among the top ten textile exporters, this study focuses on an empirical examination of the nexus between the fashion and textile industry, energy consumption, economic growth, and carbon dioxide emissions. This study fills the existing quantitative research gap in the fashion sector. It provides a comprehensive review that analyzes the environmental impacts in the sector to adopt more sustainable and effective policies. After acknowledging the structural breaks in the sample covering 1980–2023, novel Fourier bootstrapping ARDL and Fourier Granger Causality methods are adopted to examine the long- and short-run interconnections and the directions of causality in a comparative setting for China, Türkiye, India, Bangladesh, and Vietnam. The results confirmed the positive effects of textiles and fashion as well as energy consumption and economic growth with varying magnitudes for the countries examined. The causality tests confirmed varying and complex unidirectional and bidirectional causality and feedback effects among the variables examined depending on the country analyzed, in addition to identifying common causal effects from textile and fashion to environmental degradation. The findings are of great importance and have significant policy implications.
Including Embodied Carbon in Assessing Renovation Options for Industrial Heritage Buildings: A Review and Case Studies
Authors: Huang Yidong , Wang Fan , Hiscock Alex Vidal , Satyarthi Jivantika , Smith Harry
Published: December 2024

Industrial buildings play vital roles in a society, from shaping the economic, technological, cultural, and social fabric of society to contributing to its growth, development, and resilience. Hence, often at the end of their lifespans, they are “preserved” for their historical value through renovation. Considerations for renovation often include their historical significance, structural integrity, adaptive reuse, social sustainability, financial viability, and environmental impacts. Among these considerations, the carbon emissions associated with a project are increasingly becoming a factor of relevance when a historical building is to be sensitively renovated so that it can continue to contribute to local sustainability. However, embodied carbon is often overshadowed by operational carbon and overlooked in the development of renovation options. This paper argues for the need to include embodied carbon in the consideration of any renovation process and for guidelines for doing so. The argument is built upon a systematic review of current practices in the renovation of industrial heritage buildings across selected representative countries from the Global South and the Global North, in the belief that the former could learn valuable lessons from the latter, which has more extensive experience in considering embodied carbon in such processes. The argument also shows the difference in policy between different countries and articulates how the inclusion of embodied carbon might support environmental targets in the Global South. Based on a quantitative comparison, this review explains why embodied carbon (EC) is missing in renovations of industrial heritage buildings in the Global South. This study estimates the proportion and value of EC within the total life cycle in renovations of industrial buildings to support the argument. Above all, a calculation using a standard life cycle assessment (LCA) tool (ISO14040 & 14044) applied to four successful examples and a quantitative comparison highlight the benefits of including embodied carbon in renovations of industrial buildings and the carbon savings in the Global South and further supports our argument.
Physical–Mechanical and Microstructural Properties of Non-Autoclaved Aerated Concrete with Ash-and-Slag Additives
Authors: Rudenko Olga , Beisekenov Nail , Sadenova Marzhan , Galkina Darya , Kulenova Natalya , Begentayev Meiram
Published: December 2024

Non-autoclaved aerated concrete (NAAC) is gaining attention for its strength-to-weight ratio and sustainability benefits. Produced by incorporating a blowing agent into a binder, aggregate, and water mixture, NAAC offers a lightweight and porous construction material. Ash and slag waste (ASW), primarily composed of silicon, aluminum, iron, and calcium oxides, presents significant potential as a sustainable additive. However, industrial-scale processing of ASW still needs to be explored in Kazakhstan. This study evaluates the feasibility of utilizing ASW from the Ust-Kamenogorsk Thermal Power Plant to produce earthquake-resistant NAAC. Incorporating 31.5% ASW by weight optimizes compressive strength, achieving 2.35 MPa and significantly improving the mechanical properties. Chemical and microstructural analyses confirm ASW’s suitability as a construction material. The study also introduces innovative processing methods and explores convolutional neural network models for predicting material structure changes, providing insights into optimizing production processes. The findings address the research objectives by confirming the viability of ASW in NAAC production and demonstrating its potential for sustainable construction. The results offer a pathway for industrial-scale applications, contributing to waste utilization and resource conservation.
Application of Virtual Reality Technology in Enhancing the Teaching Effectiveness of Coal Mine Disaster Prevention
Authors: Li Xuelong , Song Shuaifeng , Liu Shumin , Yin Dawei , Wang Rui , Gong Bin
Published: December 2024

Coal mine disaster prevention is a fundamental course within mining engineering and coal mine safety engineering curricula. Given the complexity and variability of coal mine disasters, it is crucial to cultivate students’ practical awareness to address the challenges encountered in this field. Virtual reality (VR) technology, with its highly realistic and reusable virtual environments, reduces the resource consumption required for on-site training. Additionally, it offers an effective solution for students to safely and efficiently understand coal mine disasters, master the common types of disasters and their causes, and enhance immersive learning, practical skills, and emergency response capabilities. This study integrates virtual simulation experiments with course content and utilizes VR technology to simulate mine environments and disaster processes, which allows students to experience disaster events in a safe virtual setting. By incorporating embodied cognition theory and VR gesture technology, an interactive learning system is developed to improve students’ learning efficiency and engagement. The results indicate that applying VR technology to teaching coal mine disaster prevention and control significantly stimulates students’ interest and facilitates a comprehensive, intuitive understanding of the causes, characteristics, and prevention measures associated with coal mine disasters. Employing virtual reality technology in education not only enhances the students’ awareness of coal mine safety but also provides strong support for the sustainable development of coal mine enterprises.
On the Road to Inclusion: A Multifaceted Examination of Transportation Challenges Faced by Individuals with Disabilities
Authors: Alçura Güzin Akyıldız
Published: December 2024

The Sustainable Development Goals (SDGs) set forth by the United Nations aim to eradicate poverty, protect the environment, and promote global prosperity by 2030. Within this framework, Goal 11 targets explicitly sustainable cities and communities, emphasizing the need for accessible, safe, and sustainable transportation systems for all individuals, including those with disabilities. However, despite these aspirations, individuals with disabilities often face unique challenges and barriers in accessing transportation services. This study delves into the complexities of transportation accessibility for people with disabilities, aiming to understand their perceptions and expectations of service quality regarding reliability, tangibles, cleanliness, safety, comfort, personnel, and stops. In a comprehensive survey involving 302 individuals with disabilities, data were collected considering strata such as visual impairment, hearing impairment, chronic illness, and physical disability. In the study where cluster analysis was applied to examine the common and unique assessments of individuals with disabilities, both demographic characteristics and transportation habits were evaluated to determine the most effective inputs. The optimal results were obtained using disability level, car ownership, access to stops, and frequency of service use, while the inclusion of other sociodemographic variables (such as age and income) negatively affected the quality of the clustering process. By analyzing service quality independently for each cluster, the study unveils potential variations in how people with disabilities perceive and evaluate transportation services. The findings shed light on the distinct evaluation approaches employed by people with disabilities based on their characteristics, highlighting the need for tailored transportation planning and policy-making solutions. For example, in the overall assessment of individuals with disabilities, vehicle ergonomics was not highlighted as an area for improvement, but it emerged as the aspect with the least satisfaction among individuals with higher levels of disability. By addressing these nuances, policymakers and stakeholders can better understand and meet the diverse needs of people with disabilities, contributing to the creation of more inclusive and accessible transportation systems in line with the SDGs.
Interactive Relationship and Coordinated Development of Sports Tourism and Ecological Civilization in the Yellow River Basin
Authors: Zhao Fanxiang , Han Joonyoung
Published: December 2024

In the context of economic structural transformation and upgrading in the Yellow River Basin, this study explored the interaction between sports tourism and ecological civilization in the region. By constructing an indicator system for sports tourism and ecological civilization and applying the entropy weight method, coupling coordination model, and using the obstacle degree model, sports tourism development and ecological civilization construction in each province of the Yellow River Basin were quantitatively analyzed. The results revealed upward trends in both sports tourism and ecological civilization construction in the Yellow River Basin. According to the coupling coordination model, the most significant growth in coupling coordination degree has been observed in Sichuan, Gansu, Shaanxi, and Henan provinces, whereas Qinghai and Inner Mongolia have seen the least change in their coordination level. Government spending on sporting events and per capita water resources was identified as the main factors influencing the development of sports tourism and ecological civilization in the Yellow River Basin. Based on these findings, policy recommendations are proposed to promote the coordinated development of sports tourism and ecological civilization in the Yellow River Basin.
Application of Life Cycle Assessment to Policy Environmental Impact Assessment—A Clean Energy Action Plan Case Study in Qinghai Region
Authors: Li Yuan , Gaffney Paul P. J. , Zhao Fang , Xu Xiangbo , Zhang Mingbo
Published: December 2024

Due to significant political and environmental decisions regarding clean energy, rapid adoption of solar photovoltaic (PV), wind power, and hydropower is taking place. In China, policy environmental impact assessment (EIA) plays an important role in pollution prevention, while its practice is relatively limited due to insufficient methodologies and weak legislative enforcement. Taking the clean energy action plan (CEAP) in the Qinghai region as a case study, this study explored the application of life cycle assessment (LCA) to evaluate the potential environmental impacts imposed by the installment capability of 70,000 MW solar PV in pristine areas. It was found that the CO2 emissions of solar PV are less than 3% of that of clean coal-fired power, while the emissions of pollutants such as sulfur dioxide, nitrogen oxides, and particulate matter only account for about 18~27% of coal-fired power. Meanwhile, from the full life cycle perspective, 4.61 million tons of solar PV panel waste will be generated, and 4172 square kilometers of land surface area will be occupied. Herein, implications for policy are proposed, including (1) advance planning of local waste disposal capacity and processing facilities, (2) the integration of clean energy planning with legal ecological environment protection schemes, and (3) rational planning of upstream and downstream solar PV industries.
The Impact of Weather Variability on Renewable Energy Consumption: Insights from Explainable Machine Learning Models
Authors: Qu Rong , Kou Ruibing , Zhang Tianyi
Published: December 2024

The pursuit of carbon neutrality is reshaping global energy systems, making the transition to renewable energy critical for mitigating climate change. However, unstable weather conditions continue to challenge energy consumption stability and grid reliability. This study investigates the effectiveness of various machine learning (ML) models at predicting energy consumption differences and employs the SHapley Additive Explanations (SHAP) interpretability tool to quantify the influence of key weather variables, using five years of data (2017–2022) and 196,776 observations collected across Europe. The dataset consists of hourly weather and energy consumption records, and key variables such as Global Horizontal Irradiance (GHI), sunlight duration, day length, cloud cover, and humidity are identified as critical predictors. The results demonstrate that the Random Forest (RF) model achieves the highest accuracy and stability (R2 = 0.92, RMSE = 360.17, MAE = 208.84), outperforming other models in predicting energy consumption differences. Through SHAP analysis, this study demonstrates the profound influence of GHI, which exhibits a correlation coefficient of 0.88 with energy consumption variance. Incorporating advanced data preprocessing and predictor selection techniques remains the RMSE of RF but reduces the RMSE by approximately 25% for the XGBoost model, underlining the importance of selecting appropriate input variables. Hyperparameter tuning further enhances model performance, particularly for less robust algorithms prone to overfitting. The study reveals the complex seasonal and regional effects of weather conditions on energy demands. These findings underscore the effectiveness of ML models at addressing the challenges of complex energy systems and provide valuable insights for policymakers and practitioners to optimize energy management strategies, integrate renewable energy sources, and achieve sustainable development objectives.
Sustainable Energy Management in Electric Vehicles Through a Fuzzy Logic-Based Strategy
Authors: Savran Efe , Karpat Esin , Karpat Fatih
Published: December 2024

The purpose of this study was to develop a fuzzy logic controller (FLC)-based energy management strategy for battery electric vehicles that enables them to reduce their energy consumption and carbon emission levels without sacrificing their performance. An electric vehicle model was developed in MATLAB/Simulink using a virtual battery and validated with real-world driving tests to save time and money. An in-depth investigation is conducted on both virtual and real vehicles to confirm the effectiveness of the proposed energy management strategy. This study shows that by using FLC-based energy management, an energy consumption advantage of 9.16% can be achieved while maintaining acceptable performance levels in real-world driving conditions. This advantage results in significant reductions annually: 1044.09 tons of CO2 emissions, USD 164,770.65 in savings for electric bus lines, and 5079 battery cycles. For European passenger electric vehicles, this corresponds to 405,657.6 tons of CO2 emissions reduced, USD 64,017,840 saved, and 5.071 battery cycles per vehicle. This strategy not only enhances energy efficiency but also contributes to long-term sustainability in public transportation systems, particularly for electric bus fleets, which play a critical role in urban mobility.