bluuebunny/crossref_metadata_2025 huggingface dataset:
Accuracy Assessment of Four Land Cover Datasets at Urban, Rural and Metropolitan Area Level
Authors: Ursu Cosmina-Daniela , Benedek Jozsef , Temerdek-Ivan Kinga
Published: February 2025

Assessing land use/land cover changes currently represents an important avenue for achieving a better understanding of the urbanization phenomenon. Various free datasets based on satellite imagery are available, but the user should decide which one is the most suitable for their study area. The aim of the present paper is to perform an accuracy assessment of built-up areas using four datasets: Corine Land Cover Backbone (CLC Backbone), High Resolution Layers (HRL)–Imperviousness, Esri Land Cover and Dynamic World. The study case is represented by 12 major metropolitan areas (MAs) in Romania which have the most dynamic economic development and urban expansion. Confusion matrices were created, and the following metrics have been computed: overall accuracy (OA), kappa coefficient (k) and user accuracy (UA). The analysis was performed on three levels: for the entire surface of the MAs and separately for the urban and rural sides. The results at the metropolitan level show that even though CLC Backbone 2018 is the most suitable for extracting the built areas (0.85 overall accuracy), HRL and Esri Land Cover could also be used, as they share the same overall accuracy values (0.67). Significant differences exist between the urban and rural areas. CLC Backbone performed better in the rural areas (0.87) than in the urban areas (0.84). The other three datasets recorded major variations in the overall accuracy for the urban and rural areas. Esri Land Cover has the second greatest overall accuracy for the urban areas (0.81), while HRL is the second most accurate, after CLC Backbone, for assessing the rural areas (0.67). In conclusion, CLC Backbone has the best accuracy performance for all three levels of analysis. The significance of the study lies in the accuracy assessment results on the four datasets, performed at urban and rural levels. This paper aims to help researchers and decision makers choose the best dataset for assessing land use changes. Additionally, having a reliable dataset may help compute the indicators used to monitor the Sustainable Development Goals (SDGs).
Mapping Groundwater Potential Zones in the Widyan Basin, Al Qassim, KSA: Analytical Hierarchy Process-Based Analysis Using Sentinel-2, ASTER-DEM, and Conventional Data
Authors: El Sherbini Ragab A. , Ghazala Hosni H. , Ahmed Mohammed A. , Ibraheem Ismael M. , Al Ajmi Hussain F. , Genedi Mohamed A.
Published: February 2025

Groundwater availability in semi-arid regions like the Widyan Basin, the Kingdom of Saudi Arabia (KSA), is a critical challenge due to climatic, topographic, and hydrological variations. The accurate identification of groundwater zones is essential for sustainable development. Therefore, this study combines remote-sensing datasets (Sentinel-2 and ASTER-DEM) with conventional data using Geographic Information System (GIS) and analytical hierarchy process (AHP) techniques to delineate groundwater potential zones (GWPZs). The basin’s geology includes Pre-Cambrian rock units of the Arabian Shield in the southwest and Cambrian–Ordovician units in the northeast, with the Saq Formation serving as the main groundwater aquifer. Six soil types were identified: Haplic and Calcic Yermosols, Calcaric Regosols, Cambic Arenosols, Orthic Solonchaks, and Lithosols. The topography varies from steep areas in the southwest and northwest to nearly flat terrain in the northeast. Hydrologically, the basin is divided into 28 sub-basins with four stream orders. Using GIS-based AHP and weighted overlay methods, the GWPZs were mapped, achieving a model consistency ratio of 0.0956. The zones were categorized as excellent (15.21%), good (40.85%), fair (43.94%), and poor (0%). The GWPZ model was validated by analyzing data from 48 water wells distributed in the study area. These wells range from fresh water to primary saline water, with water depths varying between 13.98 and 130 m. Nine wells—with an average total dissolved solids (TDS) value of 597.2 mg/L—fall within the excellent zone, twenty-one wells are categorized in the good zone, fifteen wells are classified in the fair zone, and the remaining wells fall into the poor zone, with TDS values reaching up to 2177 mg/L. The results indicate that the central zone of the study area is suitable for drilling new water wells.
A Multipath Hemispherical Map with Strict Quality Control for Multipath Mitigation
Authors: Zhou Houxiang , Wang Xiaoya , Zhong Shengjian , Xi Kewei , Shen Hang
Published: February 2025

The multipath effect is a critical factor that prevents the Global Navigation Satellite System (GNSS) from achieving millimeter-level positioning accuracy. A multipath hemispherical map (MHM) is a popular approach to achieving real-time multipath error mitigation. The premise of the constructed MHM model is that the residuals in the grid only contain multipath errors and noise without any outliers. However, when there are numerous obvious outliers in each grid, the traditional quality control method is unable to detect them effectively. Therefore, we propose a multipath hemispherical map with strict quality control (MHM-S) to mitigate multipath errors. This method first uses the maximum phase delay to eliminate obvious outliers. Then, the 3-sigma rule and F-test are applied to remove the remaining few outliers in the grid. After applying the proposed MHM-S method, the experimental results show that when the PRN20 satellite is affected by outliers, the standard deviation (STD) reduction rate of the MHM-S residuals is 12.03% compared with the residual STDs of the MHM model. In addition, we evaluate the capabilities of MHM-S with carrier phase observation (MHM-SC) and carrier phase and pseudo-range observation (MHM-SCP) models in multipath error mitigation. Especially in the east direction, the positioning accuracy of the MHM-SCP model is improved by 48% compared with the MHM-SC model.
A Novel Dual-Branch Pansharpening Network with High-Frequency Component Enhancement and Multi-Scale Skip Connection
Authors: Huang Wei , Liu Yanyan , Sun Le , Chen Qiqiang , Gao Lu
Published: February 2025

In recent years, the pansharpening methods based on deep learning show great advantages. However, these methods are still inadequate in considering the differences and correlations between multispectral (MS) and panchromatic (PAN) images. In response to the issue, we propose a novel dual-branch pansharpening network with high-frequency component enhancement and a multi-scale skip connection. First, to enhance the correlations, the high-frequency branch consists of the high-frequency component enhancement module (HFCEM), which effectively enhances the high-frequency components through the multi-scale block (MSB), thereby obtaining the corresponding high-frequency weights to accurately capture the high-frequency information in MS and PAN images. Second, to address the differences, the low-frequency branch consists of the multi-scale skip connection module (MSSCM), which comprehensively captures the multi-scale features from coarse to fine through multi-scale convolution, and it effectively fuses these multilevel features through the designed skip connection mechanism to fully extract the low-frequency information from MS and PAN images. Finally, the qualitative and quantitative experiments are performed on the GaoFen-2, QuickBird, and WorldView-3 datasets. The results show that the proposed method outperforms the state-of-the-art pansharpening methods.
An Extension of Ozone Profile Retrievals from TROPOMI Based on the SAO2024 Algorithm
Authors: Bak Juseon , Liu Xiong , Abad Gonzalo González , Yang Kai
Published: February 2025

We investigate the retrieval of ozone (O3) profiles, with a particular focus on tropospheric O3, from backscattered ultraviolet radiances measured by the TROPOspheric Monitoring Instrument (TROPOMI), using the UV2 (300–332 nm) and UV3 (305–400 nm) channels independently. An optimal estimation retrieval algorithm, originally developed for the Ozone Monitoring Instrument (OMI), was extended as a preliminary step toward integrating multiple satellite ozone profile datasets. The UV2 and UV3 channels exhibit distinct radiometric and wavelength calibration uncertainties, leading to inconsistencies in retrieval accuracy and convergence stability. A yearly “soft” calibration mitigates overestimation and cross-track-dependent biases (“stripes”) in tropospheric ozone retrievals, enhancing retrieval consistency between UV2 and UV3. Convergence stability is ensured by optimizing the measurement error constraints for each channel. It is shown that our research product outperforms the standard product (UV1 and UV2 combined) in capturing the seasonal and long-term variabilities of tropospheric ozone. An agreement between the retrieved tropospheric ozone and ozonesonde measurements is observed within 0–3 DU ± 5.5 DU (R = 0.75), which is better than that of the standard product by a factor of two. Despite lacking Hartley ozone information in UV2 and UV3, the retrieved stratospheric ozone columns have good agreement with ozonesondes (R = 0.96).
The Spatiotemporal Evolution and Driving Forces of the Urban Heat Island in Shijiazhuang
Authors: Zhang Xia , Liu Yue , Chen Ruohan , Si Menglin , Zhang Ce , Tian Yiran , Shang Guofei
Published: February 2025

As a comprehensive reflection of the thermal characteristics of the urban environment, the urban heat island (UHI) effect has triggered a series of ecological and environmental issues. Existing studies on the UHI effect in Shijiazhuang, the capital of Hebei Province, China, have primarily focused on spatial–temporal distribution characteristics and migration trends, with less focus on the influences of other contributing factors. This study focuses on Shijiazhuang city, using Landsat ETM+/OLI data from 2000 to 2020 to analyze the spatiotemporal traits of the UHI effect. The mono-window algorithm (MW) was used to retrieve land surface temperatures (LSTs), and the seasonal autoregressive integrated moving average (SARIMA) model was used to predict LST trends. Key factors such as the normalized difference vegetation index (NDVI), digital elevation model (DEM), population (POP), precipitation (PPT), impervious surface (IPS), potential evapotranspiration (PET), particulate matter 2.5 (PM2.5), and night light (NL) were analyzed using spatial autocorrelation to explore their dynamic relationship with the UHI. Specifically, a multi-scale analysis model was developed to search for the optimum urban spatial scale, enabling a comprehensive assessment of the spatiotemporal evolution and drivers of the UHI in Shijiazhuang. The UHI showed pronounced spatial clustering, expanding annually by 44.288 km2, with a southeastward shift. Autumn exhibited the greatest reduction in UHI, while predictions suggested peak temperatures in summer 2027. According to the bivariate clustering analysis, the NDVI was the most influential factor in mitigating the UHI, while the IPS spatially showed the most significant enhancement in the UHI in the central urban areas. Other factors generally promoted the UHI after 2005. The multi-scale geographically weighted regression (MGWR) model was best fitted at a 3 km × 3 km scale. Considering the joint effects of multiple factors, the ranking of contributing factors to the model prediction is as follows: PET > DEM > NDVI > IPS > PPT > PM2.5 > NL > POP. The interactive effects, especially between the PET and DEM, reach a significant value of 0.72. These findings may address concerns regarding both future trends and mitigation indications for UHI variations in Shijiazhuang.
Correction: Berezowski et al. Comparison of Time-Lapse Ground-Penetrating Radar and Electrical Resistivity Tomography Surveys for Detecting Pig (Sus spp.) Cadaver Graves in an Australian Environment. Remote Sens. 2024, 16, 3498
Authors: Berezowski Victoria , Mallett Xanthé , Seckiner Dilan , Crebert Isabella , Ellis Justin , Rau Gabriel C. , Moffat Ian
Published: February 2025

In the original publication [...]
Mapping Urban Tree Species by Integrating Canopy Height Model with Multi-Temporal Sentinel-2 Data
Authors: Yao Yang , Wang Xiaoke , Qin Haiming , Wang Weimin , Zhou Weiqi
Published: February 2025

(1) Background: Urban tree species mapping is crucial for ecosystem service evaluation and sustainable urban strategy development. However, due to the spectral similarity among dominant urban tree species, spectral data alone are insufficient for high-accuracy classification. (2) Methods: We present an approach that integrates the high-precision Canopy Height Model (CHM), generated from Ziyuan-3 (ZY3) stereo images, with multi-temporal Sentinel-2 data, for mapping 23 dominant urban tree species in Shenzhen. We primarily employed a random forest classifier using combinations of spectral bands, vegetation indices, and structural features, with subsets refined through Variance Inflation Factor (VIF) screening. We compared different models with different combinations of features, with or without the inclusion of CHM data. (3) Results: This study found that integrating VIF-screened seasonal Sentinel-2 spectral data with vegetation indices and structural metrics (Cop_DEM and ZY3_Cop_CHM) yielded an overall accuracy of 89.2%. Notably, ZY3_Cop_CHM emerged as the most influential predictor in the model. Additionally, the incorporation of ZY3_CHM data enhanced the classification accuracy by 7.1% and improved the accuracy by 4.8% compared with the use of ALOS_CHM. The species-specific F1 accuracy of a tree varies under different models and feature combinations, which underscores the need for tailored model tuning and an increase in overall model performance. Conclusions: These results indicate that integrating the ZY3_CHM data with multi-temporal Sentinel-2 data can accurately map the dominant urban tree species, suggesting its potential applicability in other urban environments.
Cross-Visual Style Change Detection for Remote Sensing Images via Representation Consistency Deep Supervised Learning
Authors: Wei Jinjiang , Sun Kaimin , Li Wenzhuo , Li Wangbin , Gao Song , Miao Shunxia , Tan Yingjiao , Cui Wei , Duan Yu
Published: February 2025

Change detection techniques, which extract different regions of interest from bi-temporal remote sensing images, play a crucial role in various fields such as environmental protection, damage assessment, and urban planning. However, visual style interferences stemming from varying acquisition times, such as radiation, weather, and phenology changes, often lead to false detections. Existing methods struggle to robustly measure background similarity in the presence of such discrepancies and lack quantitative validation for assessing their effectiveness. To address these limitations, we propose Representation Consistency Change Detection (RCCD), a novel deep learning framework that enforces global style and local spatial consistency of features across encoding and decoding stages for robust cross-visual style change detection. RCCD leverages large-kernel convolutional supervision for local spatial context awareness and global content-aware style transfer for feature harmonization, effectively suppressing interference from background variations. Extensive evaluations on S2Looking and LEVIR-CD+ datasets demonstrate RCCD’s superior performance, achieving state-of-the-art F1-scores. Furthermore, on dedicated subsets with large visual style differences, RCCD exhibits more substantial improvements, highlighting its effectiveness in mitigating interference caused by visual style errors. The code has been open-sourced on GitHub.
Loosely Coupled PPP/Inertial/LiDAR Simultaneous Localization and Mapping (SLAM) Based on Graph Optimization
Authors: Zhang Baoxiang , Yang Cheng , Xiao Guorui , Li Peigong , Xiao Zhengyang , Wei Haopeng , Liu Jialin
Published: February 2025

Navigation services and high-precision positioning play a significant role in emerging fields such as self-driving and mobile robots. The performance of precise point positioning (PPP) may be seriously affected by signal interference and struggles to achieve continuous and accurate positioning in complex environments. LiDAR/inertial navigation can use spatial structure information to realize pose estimation but cannot solve the problem of cumulative error. This study proposes a PPP/inertial/LiDAR combined localization algorithm based on factor graph optimization. Firstly, the algorithm performed the spatial alignment by adding the initial yaw factor. Then, the PPP factor and anchor factor were constructed using PPP information. Finally, the global localization is estimated accurately and robustly based on the factor graph. The vehicle experiment shows that the proposed algorithm in this study can achieve meter-level accuracy in complex environments and can greatly enhance the accuracy, continuity, and reliability of attitude estimation.
Flow Raman Spectroscopy for the Detection and Identification of Small Microplastics
Authors: Kissel Alexander , Nogowski André , Kienle Alwin , Foschum Florian
Published: February 2025

The most commonly used methods for the detection and identification of small microplastics generally require a complex sample preparation procedure and only allow for static measurements. Quality control of food and drinking water therefore requires a lot of effort. Especially in view of the increasing amount of plastic waste in the environment, the rising public awareness of the issue and the indications for adverse effects of microplastics on human health, more sophisticated measuring methods are required. In this paper, we present a measuring setup for the detection and identification of microplastics using flow Raman spectroscopy. We demonstrate the ability to acquire Raman spectra of individual particles as small as about 4 µm, enabling the identification of their plastic type. We show measurements of differently generated and shaped particles and particles made of different plastic types, highlighting the observed challenges and differences. Finally, we show possible applications of the measuring method. We demonstrate that the measuring principle is suitable for detecting and identifying microplastic particles among other particles and that aged plastics can still be distinguished by their Raman spectra. Overall, our results show that flow Raman spectroscopy is a promising method that could significantly reduce the effort required to detect microplastics.
Mobile Phone Use and Women’s Empowerment in Bangladesh: An Analysis of the Bangladesh Financial Inclusion Insights Survey 2017
Authors: Nandi Ashim Kumar , Duvander Ann-Zofie
Published: February 2025

Despite steady economic and social development in Bangladesh, women are in an underprivileged situation in many ways. This study explores the association between the use of information and communication technology (ICT) and women’s empowerment in Bangladesh. This study employs ordinary least squares regression, the ordered logit model, and the multinomial logit model, using Financial Inclusion Insights (2017) Survey data, to investigate the relationship between mobile phone use and women’s empowerment. The study’s main result indicates that mobile phone use facilitates women’s empowerment in general, but the impact needs to be considered for different groups of women. Housewives who are restricted within the household are impacted less than non-housewives by mobile phone use, contrary to our expectations. Also heads of households and spouses of heads of households, who are in very different positions in a patriarchal family structure, are similarly impacted by mobile phone use. This result implies that ICT only affects the empowerment of women who are not restricted to the household, and we conclude that ICT access alone is not enough to ensure women’s awareness about their rights.
Toward Sustainable Land Management: Improving Ecological Risk Assessment by Incorporating Temporal–Spatial Activities of Protected Birds
Authors: Zhao Dan , Wu Weida , Zhang Wenqi , Yu Fang , Sun Qian
Published: February 2025

While methodologies for health risk assessment at contaminated sites are well established, ecological risk assessment remains in its exploratory stages. To explore suitable methods for evaluating the ecological risks of soil pollution, this study selected ten protected birds as risk receptors and evaluated the ecological risks posed by lead (Pb), arsenic (As), and total petroleum hydrocarbons (TPHs) at a petrochemical-contaminated site using a tiered approach. In the first tier, the risks of pollutants in soil and water were preliminarily evaluated based on existing ecological risk screening values, environmental quality standards, etc. The results indicated that pollutants in the soil and water at certain sampling points required attention. In the second tier, the ecological risks of multi-pathway exposure were assessed using the quotient method. This study improved the traditional exposure calculation model, which relied solely on bird body weight, by incorporating an exposure frequency adjustment coefficient. This coefficient accounts for the global activity patterns of birds, including the types of their activities (overwintering, breeding, migration, etc.) on the site, the temporal patterns of their activities, and the proportion of the site area relative to their annual activity range. Due to the low exposure frequency of birds on the site, ranging from 7.8 × 10−11 to 9.69 × 10−7, the total exposure levels of birds were relatively low and their risk quotients were below 1, indicating an acceptable level. This study demonstrates the rationality and feasibility of a tiered ecological risk assessment method based on an improved pollutant exposure model for birds, laying the foundation for developing soil pollution ecological risk assessment methodologies and standards in China and other countries while also supporting comprehensive and effective risk management and sustainable utilization of land.
Utilisation of Coal Clinker Ash in Transforming the Carbon Content of Sandy Soil
Authors: Dube Agnes Aron , Muhambi Mutsa , Tsubo Mitsuru , Sato Kuniaki , Nishihara Eiji
Published: February 2025

Soil carbon (C)’s importance in soil quality assessment and C sequestration is increasing. Sandy soils, due to their low C content, have low C sequestration potential and therefore require C enhancement. This study aimed to enhance sandy soils’ C content and sesame (Sesamum indicum L.) productivity by applying clinker ash (CA). A 3-year experiment with a completely randomised block design was conducted in an improvised field. Four treatments were replicated six times: Control (sandy only), CA1 (3 mm at 25 t ha−1), CA2 (5 mm at 25 t ha−1), and rice husk biochar as a positive control (14 t ha−1). Hairy vetch (Vicia villosa R.) was the source of plant nutrients incorporated as green manure. C content and fractions were determined from dry soil collected at each harvesting time. There were significant increases in the C content of sandy soil under CA1, with about 129 and 289% change compared to the control in season 1 and season 3, respectively. Also, the C fractions and CN ratio were significantly enhanced by CA1. CA2 improved the yield of sesame by 424 and 64% in season 2 and season 3, respectively. Applying CA to sandy soils improved the C content, increasing C sequestration capabilities, a key climate mitigation strategy.
Comparative Analysis of Carbon Tax and Carbon Market Strategies for Facilitating Carbon Neutrality in China’s Coal-Fired Electricity Sector
Authors: Li Yin , Wang Xu , Qin Qi
Published: February 2025

Carbon taxes and carbon markets both contribute to mitigating carbon emissions in China’s power industry. Nevertheless, the pricing mechanism within China’s national carbon market, confined solely to the power sector, faces challenges in accurately reflecting the diverse costs of emission-reduction efforts across various regions. Similarly, carbon taxes encounter difficulties in effectively harnessing the inherent emission-reduction capabilities of power enterprises. This study investigates which carbon-pricing mechanism—a carbon tax or the carbon market—can better promote carbon neutrality in China’s coal-based electricity industry. Using a stochastic electricity price model, we reveal the price shocks of both a carbon tax and the carbon market under different carbon-pricing goals. Taking the China Carbon Emission Trading Market as the research object, the results are as follows: First, both carbon tax and the carbon market could significantly trigger price volatility in the coal-based electricity industry, while the carbon market’s shock effect on the industry’s emissions is more significant than that of carbon tax. Second, through both carbon-pricing mechanisms, emissions could be reduced by as much as 20%—a key premise of achieving this goal is keeping the carbon price at the level of 100 yuan/ton. Third, the volatility range of the electricity price, which is policy based, does not manifest the incentivizing effect of economic instruments on emission reduction in the coal-based electricity industry. Policy allows for an upper limit of 15% in the floating electricity price. By clarifying the linkage between carbon-pricing tools and coal-based electricity costs, this study contributes to developing a carbon-pricing mechanism that could help China’s coal-based electricity industry achieve carbon neutrality in a timely manner.
Assessing Gender and Age Differences in the Adoption of Sustainable Diets: Insights from an Intervention of the Mediterranean Diet
Authors: Lombardo Mauro
Published: February 2025

Introduction: Sustainable nutrition is integral to environmental health and conservation. Gender and age differences influence dietary patterns, but their impact on the adoption of sustainable diets remains unclear. This study investigates the effects of a Mediterranean diet intervention on diet sustainability, focusing on gender and age differences. Methods: A cross-sectional study was conducted on 1666 participants (58.2% women) aged 18–75 years. Dietary preferences were assessed through food diaries and sustainability indices before and after the intervention. Participants followed a low-calorie Mediterranean diet for two months, with food diaries tracking their weekly consumption. Changes in sustainable and non-sustainable food consumption were analysed using paired t-tests and stratified by gender and age groups. Results: Men consumed significantly more meat and processed meat (p < 0.001), while women preferred low-fat yoghurt and whole grains (p < 0.01). Despite an increase in legume consumption (3.2%, p < 0.001), the overall sustainability index decreased due to a compensatory rise in meat consumption among men (p < 0.001), particularly in younger (<30) and older (>50) participants. Gender-related differences were observed, with female participants reporting a higher intake of foods classified as sustainable. However, significant cultural and palatability barriers limited the uptake of some sustainable foods such as tofu. Discussion: The intervention highlighted the challenges in promoting sustainable eating habits. Gender-targeted and age-specific strategies are critical to overcome barriers and support dietary improvements. Future interventions should incorporate cultural preferences and provide long-term support to achieve significant changes in behaviour. Conclusions: The Mediterranean diet intervention, conducted within an Italian cohort, highlighted both opportunities and challenges in promoting sustainable dietary transitions. Tailor-made approaches are essential to meet the different needs of various demographic groups. However, the effectiveness of nutrition programmes focusing on sustainability may depend on local food availability, cultural acceptance and dietary traditions.
Spatiotemporal Characteristics and Scale Effects of Ecosystem Service Bundles in the Xijiang River Basin: Implications for Territorial Spatial Planning and Sustainable Land Management
Authors: Zhang Longjiang , Chen Guoping , Zhao Junsan , Lin Yilin , Yang Haibo , He Jianhua
Published: February 2025

In-depth analysis of the evolution of ecosystem services (ESs) in the basin at different spatial scales, scientific identification of ecosystem service clusters, and revelation of their spatial and temporal characteristics as well as coupling mechanisms of interactions are the key prerequisites for effective implementation of ES management. This paper assessed the spatial and temporal changes of six key ESs covering food provisioning (FP), water yield (WY), soil retention (SR), water conservation (WC), habitat quality (HQ), and carbon sequestration (CS) in the Xijiang River Basin (XRB), China, between 2000 and 2020. Given that the scale effects of ESs and their spatial heterogeneity in the XRB are still subject to large uncertainties, a combination of Spearman correlation analysis and geographically weighted regression (GWR) modelling systematically revealed the trade-offs and synergistic relationships between ESs and the scale effects from a grid, watershed, and county perspective. Additionally, we applied the self-organizing mapping (SOM) method to identify multiple ecosystem service bundles (ESBs) and propose corresponding sustainable spatial planning and management strategies for each cluster. The results reveal the following key findings: (1) Spatial distribution and heterogeneity: The six ESs demonstrated pronounced spatial variability across the study area during the two-decade period from 2000 to 2020. The downstream areas had higher levels of ESs, while the upstream regions showed comparatively lower levels. This trend was particularly evident in areas with extensive arable land, higher population density, and more developed economic activity, where ESs levels were lower. (2) Trade-offs/synergies: The analysis highlighted the prevalence of synergistic effects among ESs, with food provisioning-related services exhibiting notable trade-offs. Trade-off/Synergistic effects were weaker at the grid scale but more pronounced at the sub-basin and county scales, with significant spatial heterogeneity. (3) Identification of ESBs: We identified five distinct ESBs: the HQ-CS synergy bundle (HCSB), the integrated ecological bundle (IEB), the agricultural bundle (AB), the key synergetic bundle lacking HQ (KSB), and the supply service bundle (SSB). These clusters suggest that the overall ecological environment of the study area has significantly improved, the supply functions have strengthened, and ecosystem vulnerability has been effectively mitigated. Building upon the identified multi-scale spatiotemporal heterogeneity patterns of ESBs in the XRB, this study proposes an integrated framework for territorial spatial planning and adaptive land management, aiming to optimize regional ecosystem service provisioning and enhance socio-ecological sustainability.
Integrating Machine Learning with Causal Inference to Improve Prediction of Ammonium Wet Deposition in the Pearl River Delta
Authors: Lin Rui , Liao Wenhui , Liu Haoming , Yang Liting , Chen Weihua , Wang Xuemei
Published: February 2025

Atmospheric nitrogen deposition is a vital component of the global nitrogen cycle, with significant implications for ecosystem health, pollution mitigation, and sustainable development. In the Pearl River Delta (PRD) region of China, high levels of ammonium (NHx) wet deposition, driven by abundant precipitation and intensive anthropogenic activities, pose significant challenges to ecological balance and environmental sustainability. However, accurately estimating NHx wet deposition flux is hindered by the complexity of nitrogen deposition processes and spatial heterogeneity in observational data. This study integrates machine learning and causal inference techniques to identify the spatial distribution patterns of NHx wet deposition and key drivers of its spatial heterogeneity. Based on these findings, four machine learning models were developed to estimate NHx wet deposition flux in the PRD region for the period 2012–2017. The results indicated that the integrated models significantly outperformed standard machine learning models (MSE = 0.486, R = 0.564), the FGCNN deep learning model (MSE = 0.454, R = 0.592), and the WRF-EMEP numerical model (MSE = 0.975, R = 0.334), achieving the highest average accuracy (MSE = 0.379, R = 0.610). This study emphasizes the importance of incorporating causal factors and spatial heterogeneity into estimation frameworks to improve the accuracy and stability of NHx wet deposition flux estimates. The findings provide actionable insights for targeted mitigation strategies, contributing to sustainable ecosystem management and pollution reduction in rapidly urbanizing regions.
From Marble Waste to Eco-Friendly Filament for 3D Printing to Help Renaturalization of Quarries
Authors: Fico Daniela , Rizzo Daniela , De Carolis Valentina , Lerario Francesca , Di Roma Annalisa , Esposito Corcione Carola
Published: February 2025

The excessive use of materials that are generally difficult to discard, such as stone materials, has caused growing ecological concern. Among these, marble is extracted from quarries, but when the raw material is exhausted, these places are deserted. For this reason, several measures have been adopted in recent years to requalify these areas. In addition, recent technological developments involve the creation of innovative green materials that privilege the circular economy and waste recycling. This research presents the development of innovative, sustainable filaments for the fused filament fabrication (FFF) printing technique from recycled marble waste (MW) and biocompostable and biodegradable polylactic acid (PLA) matrix. MW was added to the polymer in concentrations of 10 wt.%, 20 wt.%, and 30 wt.%, and the blends were extruded to develop innovative green filaments. The chemical/structural properties of the raw materials and the thermal and mechanical features of the new composites were investigated. Composites containing 10 and 20 wt.% of MW showed good printability. In contrast, extrusion and printing difficulties were observed with 30 wt.% of MW. Finally, this paper proposes a project to renaturalize and requalify a disused marble quarry located in Trani (Apulia, Italy) with 3D printing devices using the newly produced eco-filaments, which have better features. The main purpose of this article is to propose a concrete, economic, and sustainable application of 3D printing involving processes such as waste and by-product recycling and renaturalization of disused quarries, with both economic and environmental benefits.
Enhancing Pinus pinaster Wood Durability Through Citric Acid Impregnation
Authors: Cruz-Lopes Luísa , Sell Mariana , Lopes Rogério , Esteves Bruno
Published: February 2025

Citric acid (CA), a naturally occurring compound in fruits, mainly citrus, has gained attention for its eco-friendly potential in wood modification. Through esterification, citric acid reacts with wood polymers to form bonds that improve adhesion, dimensional stability, and durability while reducing moisture absorption and susceptibility to decay. This study evaluated the efficacy of CA as an eco-friendly wood treatment. Wood samples were treated with solutions at varying concentrations (5%, 10%, and 15%) and assessed for dimensional stability, mechanical properties, biological resistance, and ecotoxicity. CA treatments significantly improved dimensional stability, with higher concentrations yielding greater weight percent gain (WPG) and anti-swelling efficiency (ASE). Biological tests demonstrated exceptional termite resistance, with no survival and minimal mass loss in treated samples at higher concentrations. Similarly, fungal resistance improved, as citric acid inhibited fungal growth. Ecotoxicity tests showed relatively low phytotoxicity, with some decrease in germination indices (GI) at higher CA concentrations. These findings highlight CA as a sustainable wood treatment for enhanced durability and biodegradation resistance in construction and outdoor applications.
Agricultural Land, Sustainable Food and Crop Productivity: An Empirical Analysis on Environmental Sustainability as a Moderator from the Economy of China
Authors: Laghari Fahmida , Ahmed Farhan , Ansari Babar , Silveira Ferreira Paulo Jorge
Published: February 2025

The availability of agricultural land is central to stimulating reserves in sustainable food and crop production amidst accelerating economic sustainability and growth. Therefore, this article aims to investigate the influence of agricultural land (AGL) on food production (FP) and crop production (CP) with the linkage of environmental sustainability (ES) as a moderator from 1990 to 2021 for the economy of China with the autoregressive distributed lag (ARDL) bounds testing estimation model. Our findings showed that the ARDL model estimates the long-term and short-term joint matching relationships between agricultural land and the independent variables in the model, which is a statistically significant outcome. Therefore, in the long term, the food and crop production adjustment for speed to steadiness was huge as it was projected at 1.337%, 53.6%, 133.5%, and 37.4%, respectively, in all the models, which shows that the adjustment for speed of models is a good post-shock association process. We found evidence for a significant and positive relationship between agricultural land and food and crop production in ordinary least square (OLS) estimation, which also ensured the outcomes of the primary model. Furthermore, Toda–Yamamoto Granger causality test estimation found reverse causality between food production (FP) and crop production (CP) and showed evidence of the conservation hypothesis. We found bidirectional causality between food production and agricultural land and between crop production and agricultural land, which shows evidence of the feedback hypothesis. Additionally, the empirical findings of a robustness check with fully modified ordinary least square (FMOLS) and dynamic ordinary least square (DOLS) techniques showed consistency with the investigations of ARDL estimation in the long run, ensuring the validity and strength of the primary outcomes. Overall, the present paper brings fresh knowledge about agricultural land use, and food and crop production to promote environmental sustainability.
Optimization Analysis of Parameters for Carbon Fiber Composite Sucker Rod Pumping Systems Based on Finite Element Method
Authors: Zhu Wenming , Zhao Dong , Zhang Qiang , Zhao Shuai , Wei Rongjiang , Xu Zhi
Published: February 2025

Carbon fiber composite sucker rods represent a technological innovation in oil production systems, exhibiting excellent performance. This sucker rod not only improves oil production efficiency and reduces accidents, but also saves energy and lowers the operating costs of oil wells. However, the working conditions of the carbon fiber composite sucker rod oil extraction system are relatively complex. The carbon fiber composite sucker rod body adopts a symmetrical structure formed by one-time solidification of three layers of fiber (carbon/glass fiber) materials, requiring the use of steel sucker rods in combination, and the impact of various system parameters is not fully understood. This paper focuses on the carbon fiber composite sucker rod as the research object, analyzing the external loads of the carbon fiber composite sucker rod oil extraction system. It also establishes a mechanical model of carbon fiber composite sucker rods, adopts a new finite element modeling method for sucker rod pumping systems, conducts transient dynamic analysis on the lifting motion of carbon fiber composite sucker rods in oil wells, and optimizes system parameters. The example verifies the rationality and feasibility of the finite element model. The results show that the higher the dynamic viscosity of crude oil, the more polished rod dynamometer cars tend to approach a “parallelogram”, and the polished rod load becomes more stable during the lifting process. With larger strokes, the maximum polished rod load increases, the longitudinal vibration amplitude of the carbon fiber composite sucker rod increases, and the load variation becomes more unstable. As the number of strokes increases, the maximum polished rod load and the pump plunger stroke length both increase, leading to higher pump efficiency, but the fluctuation amplitude of the polished rod dynamometer cars also increases, which affects the stability of the sucker rod’s lifting motion. When the carbon fiber sucker rod ratio exceeds 0.5, the difference between the self-weight and polished rod load initially decreases, then increases. As the carbon fiber sucker rod ratio increases, the pump plunger stroke length gradually decreases, and pump efficiency declines.
Circumstellar and Circumbinary Discs in Multiple Stellar Systems
Authors: Cuello Nicolás , Alaguero Antoine , Poblete Pedro P.
Published: February 2025

The interplay between stellar multiplicity and protoplanetary discs represents a cornerstone of modern astrophysics, offering key insights into the processes behind planet formation. Protoplanetary discs act as cradles for planetary systems, yet their evolution and capacity to form planets are profoundly affected by gravitational forces within multiple stellar systems. This review synthesizes recent advancements in observational and theoretical studies to explore the rich diversity of circumstellar and circumbinary discs within multiple stellar systems. We examine how stellar companions shape disc morphology through truncation, spirals, and misalignment. We also outline how dust dynamics and planetesimal formation are impacted by stellar multiplicity. On top of this, observations at a high angular resolution reveal detailed disc structures, while simulations offer key insights into their evolution. Lastly, we consider the implications of stellar multiplicity for planetary system architectures, emphasizing the diversity of planetary outcomes in such environments. Looking ahead, coordinated efforts combining high-resolution observations with advanced numerical models will be critical for unraveling the role of multiple stellar systems in shaping planetary formation and evolution.
A Dynamic Shortest Travel Time Path Planning Algorithm with an Overtaking Function Based on VANET
Authors: Li Chunxiao , Fan Changhao , Wang Mu , Shen Jiajun , Liu Jiang
Published: February 2025

With the rapid development of the economy, urban road congestion has become more serious. The travel times for vehicles are becoming more uncontrollable, making it challenging to reach destinations on time. In order to find an optimal route and arrive at the destination with the shortest travel time, this paper proposes a dynamic shortest travel time path planning algorithm with an overtaking function (DSTTPP-OF) based on a vehicular ad hoc network (VANET) environment. Considering the uncertainty of driving vehicles, the target vehicle (vehicle for special tasks) is influenced by surrounding vehicles, leading to possible deadlock or congestion situations that extend travel time. Therefore, overtaking planning should be conducted through V2V communication, enabling surrounding vehicles to coordinate with the target vehicle to avoid deadlock and congestion through lane changing and overtaking. In the proposed DSTTPP-OF, vehicles may queue up at intersections, so we take into account the impact of traffic signals. We classify road segments into congested and non-congested sections, calculating travel times for each section separately. Subsequently, in front of each intersection, the improved Dijkstra algorithm is employed to find the shortest travel time path to the destination, and the overtaking function is used to prevent the target vehicle from entering a deadlocked state. The real-time traffic data essential for dynamic path planning were collected through a VANET of symmetrically deployed roadside units (RSUs) along the roadway. Finally, simulations were conducted using the SUMO simulator. Under different traffic flows, the proposed DSTTPP-OF demonstrates good performance; the target vehicle can travel smoothly without significant interruptions and experiences the fewest stops, thanks to the proposed algorithm. Compared to the shortest distance path planning (SDPP) algorithm, the travel time is reduced by approximately 36.9%, and the waiting time is reduced by about 83.2%. Compared to the dynamic minimum time path planning (DMTPP) algorithm, the travel time is reduced by around 18.2%, and the waiting time is reduced by approximately 65.6%.
Chaotic Real Number Generator with Quantum Wave Equation
Authors: Gençoğlu Muharrem Tuncay , Karaduman Özgür , Özkaynak Fatih
Published: February 2025

Randomness plays a crucial role in numerous applications, with cryptography being one of the most significant areas where its importance is evident. A major challenge in cryptographic applications is designing a reliable key generator that meets stringent security requirements. Existing methods often suffer from predictability and fail to provide robust randomness, necessitating novel mathematical approaches. In this study, we propose an innovative mathematical framework that integrates quantum wave functions with chaotic systems to enhance the unpredictability and security of random number generation. The proposed approach leverages the inherent uncertainty of quantum mechanics and the dynamic behavior of chaos to generate statistically strong random sequences. The analysis results confirm that the proposed generator successfully passes all standard statistical randomness tests, demonstrating its effectiveness in cryptographic applications. Additionally, we present a practical implementation of the proposed method as an image encryption algorithm, showcasing its potential for real-world information security solutions. The findings suggest that this approach can contribute significantly to secure communication systems, financial transactions, and other domains requiring high-level cryptographic security.
Novel Fractional-Order Chaotic System Applied to Mobile Robot Path Planning and Chaotic Path Synchronization
Authors: Cui Yan , Zheng Zexi
Published: February 2025

In this paper, a novel fractional-order chaotic system equipped with symmetric attractors was proposed for the full-coverage path-planning problem of mobile robots, especially in application scenarios where path privacy needs to be protected. By coupling this system with a kinematic model of a mobile robot, a novel path-planning algorithm was designed to realize encrypted full-coverage path planning. A predefined time-synchronization control strategy effectively resolved inconsistencies in the path caused by initial position, time delay, and uncertain disturbances. Numerical simulation results demonstrated that the proposed path-planning method, based on the novel chaotic system, significantly improved coverage and randomness, compared to existing studies. Moreover, it maintained accuracy and stability in path planning, even in the presence of time delays and uncertain disturbances.