bluuebunny/crossref_metadata_2025 huggingface dataset:
Spatial and Temporal Changes and Assessment of Multi-Species Habitat in Hainan Jianfengling Protected Area
Authors: Ma Yong , Liu Lixi , Yao Wutao , Zeng Zhigao , Zhang Mingjun , Shang Erping , Zhang Shuyan , Yang Jing
Published: December 2024

The loss and fragmentation of wildlife habitats is a major threat to their survival and expansion, and protected areas (PAs) are the main tool for conserving biodiversity and protecting habitats. However, most current studies focus on analyzing suitable habitats for species and rarely analyze the spatial and temporal changes in multi-species habitats in protected areas and the effectiveness of conservation. In this study, we analyzed changes in the suitable habitats of five focal mammal species before and after the incorporation of the Hainan Jianfengling protected area into China’s national parks. We utilized the ensemble species distribution model (ESDM) to assess these changes, based on multi-species infrared camera monitoring data from 2015 to 2016 and 2020 to 2021. Furthermore, we evaluated differences in conservation effectiveness before and after the establishment of the national parks. The results showed that there were some differences in habitat changes among all the species included in this study, and all of them showed the phenomenon of the migration from suitable habitats to the central area. The environmental changes in and around the protected area suggest that the changes are closely related to the increase in anthropogenic activities around the protected area, and it is recommended that the protected area should be better managed at its edges to minimize the impact of anthropogenic disturbances on the species and their habitats.
XCO2 Data Full-Coverage Mapping in China Based on Random Forest Models
Authors: Chen Ruizhi , Wang Zhongting , Zhou Chunyan , Zhang Ruijie , Xie Huizhen , Li Huayou
Published: December 2024

Carbon dioxide (CO2) is a key driver of global climate change. Since the Industrial Revolution, the rapid rise in atmospheric CO2 levels has significantly intensified global warming and climate-related issues. To accurately and promptly monitor changes in CO2 concentrations and to support the development of climate policies, this study proposes a method based on random forest models to generate a continuous monthly dataset of CO2 column concentration (XCO2) across the entire Chinese region from 2004 to 2023. The study integrates XCO2 satellite observations from SCIAMACHY, GOSAT, OCO-2, and GF-5B, alongside nighttime light remote sensing data, meteorological parameters, vegetation indices, and CO2 profile data. Using the random forest algorithm, a complex relationship model was established between XCO2 concentrations and various environmental variables. The goal of this model is to provide XCO2 estimates with enhanced spatial coverage and accuracy. The XCO2 concentrations predicted by the model show a high level of consistency with satellite observations, achieving a correlation coefficient (R-value) of 0.9959 and a root mean square error (RMSE) of 1.1631 ppm. This indicates that the model offers strong predictive accuracy and generalization ability. Additionally, ground-based validation further confirmed the model’s effectiveness, with a correlation coefficient (R-value) of 0.956 when compared with TCCON site observation data.
Tracking the Dynamics of Salt Marsh Including Mixed-Vegetation Zones Employing Sentinel-1 and Sentinel-2 Time-Series Images
Authors: Yi Yujun , Chen Kebing , Xu Jiaxin , Luo Qiyong
Published: December 2024

Salt marshes, as one of the most productive ecosystems on earth, have experienced fragmentation, degradation, and losses due to the impacts of climate change and human overexploitation. Accurate monitoring of vegetation distribution and composition is crucial for salt marsh protection. However, achieving accurate mapping has posed a challenge. Leveraging the high spatiotemporal resolution of the Sentinel series data, this study developed a method for high-accuracy mapping based on monthly changes across the vegetation life cycle, utilizing the random forest algorithm. This method was applied to identify Phragmites australis, Suaeda salsa, Spartina alterniflora, and the mixed-vegetation zones of Tamarix chinensis in the Yellow River Delta, and to analyze the key features of the model. The results indicate that: (1) integrating Sentinel-1 and Sentinel-2 satellite data achieved superior mapping accuracy (OA = 90.7%) compared to using either satellite individually; (2) the inclusion of SAR data significantly enhanced the classification accuracy within the mixed-vegetation zone, with “SARdivi” in July emerging as the pivotal distinguishing feature; and (3) the overall extent of salt marsh vegetation in the Yellow River Delta remained relatively stable from 2018 to 2022, with the largest area recorded in 2020 (265.69 km2). These results demonstrate the robustness of integrating Sentinel-1 and Sentinel-2 features for mapping salt marsh, particularly in complex mixed-vegetation zones. Such insights offer valuable guidance for the conservation and management of salt marsh ecosystems.
High-Precision Sub-Wavelength Motion Compensation Technique for 3D Down-Looking Imaging Sonar Based on an Acoustic Calibration System
Authors: Wang Jun , Liang Peihui , Song Junqiang , Xu Pan , Hu Yongming , Zhang Peng , Lou Kang , Ren Rongyao , Tang Wusheng
Published: December 2024

Three-dimensional hydro-acoustic imaging is a research hot spot in the underwater acoustic signal processing field, which has a wide range of application prospects in marine environmental resource surveying, seabed topography and geomorphological mapping, and underwater early warning and monitoring. To solve the problem that the resolution of the current imaging sonar reduces rapidly with increase in distance and a scanning gap exists in side-scan sonar, we designed a down-looking 3D-imaging sonar with a linear array structure. The imaging scheme adopts a time-domain spatial beam-forming method with the Back Projection (BP) algorithm as the core, and the formation of a virtual plane array can effectively improve the along-track resolution. To cope with the interference of the carrier motion error on the imaging, we proposed a high-precision sub-wavelength motion compensation method based on a real-time acoustic calibration system. Simulation and real data experiments show that the motion compensation method can effectively eliminate the influence of motion error and make the imaging energy more focused, leading to higher-quality acoustic images. Under equal average energy, the maximum superimposed sound intensity values in the imaging results increased by 20.75 dB and 6.57 dB, respectively, for simulation and real data. After motion compensation, the resolution of this imaging system reached 3 cm × 3 cm × 2.5 cm @ Depth = 17 m, TBP = 30 s · Hz.
Comprehensive Assessment of Sustainable Development of Terrestrial Ecosystem Based on SDG 15—A Case Study of Guilin City
Authors: Pan Hongyu , Liu Guang , Muller Jan-Peter , Sun Zhongchang , Yao Yuefeng , Chang Yao , Xiong Zesen , Zhang Yuchen
Published: December 2024

Sustainable Development Goal 15 (SDG 15) specifically targets the protection, restoration, and sustainable use of terrestrial ecosystems, including forests, wetlands, mountains, and drylands, along with their biodiversity. This study localizes the SDG 15 indicator system and integrates geospatial and statistical data to construct an enhanced evaluation framework for assessing the sustainable development of terrestrial ecosystems at the county level. The proposed system encompasses key indicators such as forest coverage rate, terrestrial biodiversity, sustainable forest management, land degradation neutrality, mountain biodiversity, and mountain green cover index. Using Guilin City as a study area, the ecological status of each county was assessed over the period 2010 to 2020, providing valuable insights to guide ecological conservation and sustainable development efforts. The main results are as follows: (1) Spatial heterogeneity is evident in the distribution of key biodiversity areas, which are concentrated in the northern and southeastern mountainous regions of Guilin. (2) Land degradation during the assessment period is notably smaller than during the baseline period, though a significant gap remains toward achieving land degradation neutrality. (3) Sustainable development scores for terrestrial ecosystems show an overall upward trend across counties, but the poor performance in sustainable forest management affects the comprehensive sustainable development of terrestrial ecosystems in Guilin. The localized SDG 15 indicator system proposed in this paper can effectively quantify changes in terrestrial ecosystems and visualize their spatial distribution, and can effectively serve as a model for other sustainable development areas.
Reconstruction of Optical Coherence Tomography Images from Wavelength Space Using Deep Learning
Authors: Viqar Maryam , Sahin Erdem , Stoykova Elena , Madjarova Violeta
Published: December 2024

Conventional Fourier domain Optical Coherence Tomography (FD-OCT) systems depend on resampling into a wavenumber (k) domain to extract the depth profile. This either necessitates additional hardware resources or amplifies the existing computational complexity. Moreover, the OCT images also suffer from speckle noise, due to systemic reliance on low-coherence interferometry. We propose a streamlined and computationally efficient approach based on Deep Learning (DL) which enables reconstructing speckle-reduced OCT images directly from the wavelength (λ) domain. For reconstruction, two encoder–decoder styled networks, namely Spatial Domain Convolution Neural Network (SD-CNN) and Fourier Domain CNN (FD-CNN), are used sequentially. The SD-CNN exploits the highly degraded images obtained by Fourier transforming the (λ) domain fringes to reconstruct the deteriorated morphological structures along with suppression of unwanted noise. The FD-CNN leverages this output to enhance the image quality further by optimization in the Fourier domain (FD). We quantitatively and visually demonstrate the efficacy of the method in obtaining high-quality OCT images. Furthermore, we illustrate the computational complexity reduction by harnessing the power of DL models. We believe that this work lays the framework for further innovations in the realm of OCT image reconstruction.
Running-In Behavior and Failure Mechanism Between AgCuNi Alloy and Au-Electroplated Layer
Authors: Wu Hongjian , Zhang Ya’nan , Jia Qingjian , Cao Hui , Li Han , Ma Ming
Published: December 2024

To avoid wear and tear of the slip ring due to electrical corrosion, the slip ring needs to undergo the running-in process under atmospheric conditions without current after assembly. To address the urgent demand for long-service capability space conductive slip rings in the aerospace field, the running-in behavior and failure mechanism between the AgCuNi alloy and Au-electroplated layer are investigated using a ball-on-disc tribometer in this paper. The results show that the transfer film composed of Au plays an important role in modifying the friction during the sliding process. With the accumulation of wear debris composed of Ag on the disc, the contact material of the friction pair changed from Au and Au to Au, Ag and Au, so the surface roughness of wear tracks increased. Finally, the transfer film broke, which made the layer fail. This paper reveals the key element failure mechanism that causes transfer film failure in the running-in contact area, which is used to reveal the friction behavior and failure mechanism of slip ring friction pair materials, and provides a basis for the selection of running-in parameters during the running-in process of slip rings before power-on operation.
Elevating Student Voice and Levelling Traditional Power Hierarchies Through Open Textbook Co-Creation: What Do Students Say?
Authors: Masuku Bianca , Cox Glenda , Willmers Michelle
Published: December 2024

There are calls for the democratisation of higher education in line with the principles of social justice. Collaboration with students offers the potential for creating a more inclusive higher education environment, and open textbook development initiatives can be a vehicle for change. This paper focuses on the experiences of students as co-creators in open textbook initiatives at the University of Cape Town, South Africa. Drawing on interviews with 11 open textbook collaborators, this paper utilises Nancy Fraser’s social justice framework to explore students’ perspectives on injustices, challenges of collaboration and co-creation, and power dynamics in student–staff partnerships. The study shows that students experience and navigate various injustices in their classroom contexts related to economic maldistribution, cultural misrecognition and political misrepresentation. It reveals a complex interrelationship between student voice, power dynamics in the classroom, and the power of student–staff partnerships to build confidence and flatten hierarchies in open textbook co-creation. The student views presented here provide powerful evidence of a range of benefits they experience when the traditional hierarchies between student and lecturer are levelled through collaborative open textbook development processes. Results indicate that co-creation activities enabled them to have a voice through the power of publication and own their academic journeys.
Game-Theoretic Approaches for Power-Generation Companies’ Decision-Making in the Emerging Green Certificate Market
Authors: Cheng Lefeng , Zhang Mengya , Huang Pengrong , Lu Wentian
Published: December 2024

This study examines the decision-making optimization of Power-Generation Enterprises (PGEs) in the green certificate market, with a focus on balancing bidding strategies and carbon-reduction targets. Given the increasing complexity of the green certificate market, the research employs Bayesian games, evolutionary games, and Stackelberg games to systematically analyze the strategic behavior of PGEs and their interactions within the market framework. The findings demonstrate that game theory facilitates cost structure optimization and enhances adaptability to market dynamics under policy-driven incentives and penalties. Additionally, the study explores the integration of stochastic modeling and machine learning techniques to address market uncertainties. These results provide theoretical support for policymakers in designing efficient green electricity market regulations and offer strategic insights for PGEs aligning with carbon neutrality objectives. This work bridges theoretical modeling and practical application, contributing to the advancement of sustainable energy policies and the development of green electricity markets.
Impact of the Mediating and Moderating Roles of Sustainable Entrepreneurial Intentions on Business Performance in Chinese SMEs
Authors: Anwar Rana Salman , Mahmood Shanayyara , Ramzan Muhammad , Adeeb Hina , Ahmed Alsadig , Antohi Valentin Marian , Fortea Costinela , Zlati Monica Laura , Meca Alina , Pricope Laurentiu Nicolae
Published: December 2024

Purpose: This research aims to explore the impact of sustainable entrepreneurial intentions on business performance within small- and medium-sized enterprises (SMEs) in China. It investigates how sustainable entrepreneurship mediates this relationship and examines the moderating roles of behavioral intention and environmental sustainability orientation. Method: The study utilized a quantitative approach, collecting data from 239 employees working across various SMEs in China through a structured survey. The measurement scales were adopted from established research to ensure reliability and validity. Structural equation modeling (SEM) was employed using Stata-SEM for data analysis, allowing for a comprehensive examination of direct, indirect, and moderating effects. Findings: The results indicate that sustainable entrepreneurial intentions significantly influence business performance, with sustainable entrepreneurship serving as a crucial mediator in this relationship. Behavioral intentions and environmental sustainability orientation were found to significantly moderate the impact of sustainable entrepreneurial intentions on business performance. These findings highlight the importance of integrating sustainability practices and personal commitment into business strategies. Originality/Implications: This research contributes to the theoretical and practical understanding of sustainable entrepreneurship by providing empirical evidence on the mediating and moderating effects within SMEs. It offers actionable insights for entrepreneurs, managers, and policymakers to enhance business performance through strategic sustainability efforts. The study also suggests avenues for future research to further explore these dynamics across different contexts.
How Does the Urban Built Environment Affect the Accessibility of Public Electric-Vehicle Charging Stations? A Perspective on Spatial Heterogeneity and a Non-Linear Relationship
Authors: Sheng Jie , Xiang Zhenhai , Ban Pengfei , Bao Chuang
Published: December 2024

The deployment of electric vehicle charging stations (EVCSs) is crucial for the large-scale adoption of electric vehicles and the sustainable energy development of global cities. However, existing research on the spatial distribution of EVCSs has provided limited analysis of spatial equity from the perspective of supply–demand relationships. Furthermore, studies examining the influence of the built environment on EVCS accessibility are scarce, and often rely on single methods and perspectives. To explore the spatial characteristics of EVCS accessibility and its influencing factors, using multi-source urban spatial data, this study initially employs the Gaussian two-step floating catchment area (G2SFCA) method to measure and analyze the spatial distribution characteristics of EVCS accessibility in Guangzhou, China, with consideration of supply–demand relationships. Subsequently, it integrates the MGWR and random forest (RF) models to comprehensively investigate the impact mechanism of the built environment on EVCS accessibility from the perspectives of spatial heterogeneity and non-linear relationship. The results show that the EVCS accessibility exhibits a “ higher in the west and lower in the east, with extreme core concentration” distribution pattern, and has significant spatial autocorrelation. The built-environment variables exhibit different scale effects and spatial non-stationarity, with widespread non-linear effects. Among them, the auto service, distance to regional center, and distance to subway station play important roles in influencing EVCS accessibility. These findings offer important guidance for the efficient and equitable layout of EVCSs in high-density cities.
Attitudes Toward Aging: A Sustainability Psychology Perspective on the Perceptions of Undergraduate Students
Authors: Elvira-Zorzo María Natividad , Álvarez María Laura Vergara , Lorenzo Miguel
Published: December 2024

The aging of the population is a key challenge for society today, with important implications for psychological and social sustainability. The persistence of negative attitudes toward older people leads to ageism, a form of discrimination that hinders an inclusive and equitable society. This study aims to develop a scale to assess attitudes toward old age and to analyze its psychometric properties in university students. The sample consisted of 185 university students aged between 19 and 44 years. A 35-item questionnaire was used, divided into two scales measuring positive and negative attitudes toward old age, each with three factors. The results indicate that the Attitudes toward Aging Scale (AAS) is suitable for measuring both attitudes and is a valid and reliable tool for diagnosing both negative and positive attitudes, as well as facilitating prevention and intervention strategies against ageism. From a sustainability psychology perspective, the use of the AAS contributes to a more equitable and sustainable society by promoting inclusive attitudes toward aging. This approach allows population aging to be seen not only as a demographic challenge, but also as an opportunity to promote intergenerational well-being and social cohesion, key elements for long-term sustainability. The limitations of this study include the small sample size, which could affect the generalizability of the results, but are justified by the contextual relevance of the sample, focusing on a key university group for the formation of future social and educational attitudes.
Characteristics of Noise Caused by Trains Passing on Urban Rail Transit Viaducts
Authors: Song Lizhong , Zhang Jiong , Liu Quanmin , Zhang Liangtao , Wu Xiaolong
Published: December 2024

With the large-scale construction of urban rail transit viaducts in China, the noise problem caused by trains traversing these sections has become increasingly prominent and a key technical challenge that restricts the sustainable development of rail transit. There are two main noise sources when trains pass on rail transit viaducts, namely, wheel-rail noise (WRN) and bridge-borne noise (BBN). However, most of the existing rail transit viaduct noise prediction models consider only a single noise source. In this study, a total noise prediction model incorporating both WRN and BBN was established using the finite element method (FEM), the boundary element method (BEM), and statistical energy analysis (SEA). The viaducts of Wuhan Metro Line 2 were selected as the research object, and noise tests of trains passing on the viaducts were carried out to validate the total noise prediction model. Based on the validated model, the spatial distribution characteristics and attenuation laws of the total noise were investigated, along with the influence of train speed on the total noise. The results show that the prediction model accurately simulated the total noise caused by trains passing on viaducts. When a train passed on the viaduct at a speed of 60 km/h, the total noise near the viaduct reached 88 dB(A) and decreased with the increase in the distance; at 120 m from the track centerline, the total noise decreased to less than 57 dB(A). As the distance increased, the total noise diminished across the entire frequency spectrum. Notably, low-frequency noise decayed at a slower rate than high-frequency noise. As the distance from the track centerline doubled, the total noise decreased by about 4.23 dB(A). The total noise increased with train speed. When the train speed doubled, the total noise at 30 m and 120 m from the track centerline increased by 6.32 dB(A) and 5.96 dB(A), respectively. The reason for this phenomenon is that the wheel-rail forces increase with the increase in train speed. This study will have important guiding significance and scientific value for the sustainable development of urban rail transit.
Activated Biochar from Pineapple Crown Biomass: A High-Efficiency Adsorbent for Organic Dye Removal
Authors: Cano Francisco J. , Reyes-Vallejo Odín , Sánchez-Albores Rocío Magdalena , Sebastian Pathiyamattom Joseph , Cruz-Salomón Abumalé , Hernández-Cruz Maritza del Carmen , Montejo-López Wilber , González Reyes Mayram , Serrano Ramirez Rocío del Pilar , Torres-Ventura Héctor Hiram
Published: December 2024

Renowned for its versatility in environmental applications, biochar exhibits substantial potential to enhance anaerobic digestion, facilitate carbon sequestration, and improve water treatment through its highly efficient adsorption mechanisms. This study focuses on biochar derived from pineapple crown biomass, produced through slow pyrolysis, and its efficiency in removing organic dyes from contaminated water. The structural, morphological, and surface properties of both biochar and chemically activated biochar samples were comprehensively characterized using a range of techniques, including XRD, FTIR, XPS, BET surface area analysis, and SEM microscopy. The adsorption performance was evaluated using methylene blue (MB), rhodamine B (RhB), and malachite green (MG) dyes as model contaminants, with particular emphasis on the contact time on dye removal efficiency. Initial results showed removal rates of 10.8%, 37.5%, and 88.4% for RhB, MB, and MG, respectively. Notably, chemical activation significantly enhanced the adsorption efficiency, achieving complete (100%) removal of all tested dyes. Complete adsorption of MB and MG occurred within 9 min, indicating rapid adsorption kinetics. Adsorption data fit well with pseudo-second-order kinetics (R2 = 0.9748–0.9999), and the Langmuir isotherm (R2 = 0.9770–0.9998) suggested monolayer adsorption with chemical interactions between dyes and biochar. The intraparticle diffusion model further clarified the adsorption mechanisms. These findings demonstrate the efficacy of activated biochar for dye removal and highlight the potential of pineapple crown biomass in environmental remediation.
Construction of a Corporate Carbon Disclosure Indicator System and Quality Evaluation: Evidence from Resource-Based Listed Companies
Authors: Li Tian , Zeng Shihong , Wu Shaomin , Peng Qiao
Published: December 2024

Resource-based companies are key players in reducing carbon emissions and play a central role in achieving China’s dual-carbon goal. Establishing and improving an objective carbon information disclosure mechanism for companies and evaluating the quality of carbon information disclosure in a scientific and reasonable manner have significant reference value for rationally shaping the way to realize carbon peak and carbon neutrality. In view of this, this paper develops an evaluation index system based on four dimensions based on the corporate social responsibility reports of listed companies from 2018 to 2022. After excluding firms with a high degree of greenwashing, the combined weighting-TOPSIS method was used to evaluate the carbon disclosure quality of companies. The research results show that, although the quality of carbon disclosure of resource-based companies has indeed improved since the 2020 dual-carbon goal was proposed, there are differences in the quality of carbon disclosure of companies between different subsectors and regions, and relevant policy recommendations are proposed.
Impact of Climate Change on Culex pipiens Mosquito Distribution in the United States
Authors: Ragab Sanad H. , Alkhaibari Abeer Mousa , Alharbi Jalal , Areshi Sultan Mohammed , Mashlawi Abadi M. , Embaby Doaa M. , Tyshenko Michael G. , Selim Tharwat A. , Kamel Mohamed
Published: December 2024

Culex pipiens (Diptera: Culicidae) is a disease vector for the West Nile virus (WNV). Climate change impacts the disease transmission carried by mosquitoes, and it is partly to blame for the resurgence of mosquitoes as important disease vector insects. This study assessed the geographic range of Culex pipiens in the United States under both present and projected climatic scenarios, identifying the primary environmental factors influencing its distribution. Employing species distribution modeling (MaxEnt, Version 3.4.1) and occurrence data from the Global Biodiversity Information Facility, we examined the effects of key variables, including altitude, temperature seasonality (bio4), and annual precipitation (bio12), on habitat suitability for C. pipiens. The analysis revealed that altitude accounted for 60.3% of the model’s explanatory power, followed by temperature seasonality (31%) and annual precipitation (8.7%). Areas having low elevation and moderate temperature fluctuations were the most favorable for C. pipiens, with a predicted range extending across the Midwest and southeastern United States under present variables. Future projections for 2050 and 2070 under Representative Concentration Pathway (climatic change) scenarios suggest possible northward expansion in response to rising temperatures and altered precipitation patterns. The study highlights some shifts in C. pipiens distribution and the potential for increased disease transmission into new areas. This study serves as a catalyst for decision-makers to coordinate their management reaction and create more resilient and comprehensive strategies to safeguard human health.
Changes in the Structure of Agriculture in Central and Eastern Europe in the Light of the European Green Deal
Authors: Radlińska Kamila
Published: December 2024

The economic transformation following the collapse of the Eastern Bloc and the accession of Central and Eastern European (CEE) countries to the European Union (EU) are milestones that have set the current course for their agricultural sectors. With their accession to the EU, the CEE countries obliged, among other things, to adapt their agricultural activities to the requirements of the common organization of agricultural market and the Common Agricultural Policy (CAP). In 2004, the agricultural sectors of Central and Eastern European countries (the Czech Republic, Estonia, Hungary, Lithuania, Latvia, Poland and Slovakia), and in 2007, the agricultural sectors of Bulgaria and Romania, were significantly different from those of the EU countries. After almost two decades in EU structures and in the face of climate challenges, it is interesting to discuss the assessment of changes that have taken place in the agricultural sectors of CEE countries and their future. Based on FADN data, the structure of agricultural sectors in Central and Eastern Europe and selected EU countries for 2004–2021 was estimated in terms of types of farming and economic size classes. The agricultural sectors of CEE countries have become similar to those of EU countries in terms of agricultural production structure, and in 2021 will be characterized by a higher level of production specialization than when they accessed the EU. There was also an increase in the economic size of their production, but it was still significantly lower compared to the economic size of farms in EU countries. In light of the goals of the European Green Deal (EGD), this observation may be useful for moving into the next stage of development of the agricultural sector in Central and Eastern Europe. Indeed, achieving the EGD’s goals will require setting adopted development directions, i.e., formulating agricultural practices that facilitate the transition to more sustainable agriculture, such as practices based on the concept of sustainable intensification or ecological intensification.
A Study on the Impact of Digital Financial Literacy on Household Entrepreneurship—Evidence from China
Authors: Xie Yumei , Chen Taoke
Published: December 2024

With the rapid development of digital finance, digital financial literacy is becoming increasingly important. This study examines the impact of digital financial literacy on household entrepreneurship and finds that it significantly promotes the occurrence of household entrepreneurship. To address potential endogeneity concerns, the study employs an instrumental variable approach, and the results remain robust after a series of checks. Heterogeneity tests reveal that the effect of digital financial literacy on household entrepreneurship is particularly pronounced in rural areas, underdeveloped regions, and lower-tier cities. Further analysis indicates that digital financial literacy also enhances household entrepreneurial investment levels, with a more significant impact on survival entrepreneurship investments. In mechanism tests, the study shows that access to formal finance partially mediates the relationship between digital financial literacy and household entrepreneurship. Additionally, our research also found that digital financial literacy can promote entrepreneurship among marginalized groups, such as women and individuals with lower levels of education. Our findings offer valuable insights for policymakers aiming to foster sustainable and inclusive development. In line with the United Nations’ 2030 Agenda for Sustainable Development, entrepreneurship is recognized as a key driver for achieving sustainable economic growth. Our study emphasizes the importance of promoting entrepreneurship by enhancing digital financial literacy among residents in underdeveloped areas, which is crucial for fostering inclusive growth, creating jobs, and reducing regional disparities, all of which contribute to long-term sustainable development.
A Bargaining with Negotiation Cost for Water Use and Pollution Conflict Management
Authors: Fan Zhipeng , Fu Xiang , Zhao Xiaodan
Published: December 2024

The intensifying overexploitation of water resources and the increasing pollution discharge have exacerbated conflicts in water resource utilization, making it urgent to effectively reconcile the contradiction between water resource utilization and environmental protection. This study developed a Cost-Inclusive Multi-Objective Bargaining Methodology (CIMB), coupled with a Compromise Programming (CP) method, to address conflicts between water use and pollution discharge, considering the economic benefits and the sustainable development of water resources. A deterministic multi-objective bargaining approach was employed, with two players representing the maximization of water use benefits and the minimization of total pollution discharge. This study takes the middle and lower reaches of the Han River region as an example to optimize water resource allocation in ten cities in this area. Using the CIMB-CP model, the water use and pollution discharge for different cities were obtained, and the impact of various factors on the game outcomes was analyzed. The model results indicate that negotiation cost have a significant impact on the Nash equilibrium solution. Compared to the Cost-Exclusive Multi-Objective Bargaining Methodology (CEMB) model, the Nash equilibrium solution of the CIMB-CP model shows an approximately 0.1% decrease in economic benefits and an approximately 0.3% decrease in pollution discharge. The risk attitudes of the participants have a significant impact on the game outcomes, and decision-makers need to formulate corresponding negotiation strategies based on their own risk preferences.
Research Status and Development Trends of Inorganic Salt Lake Resource Extraction Based on Bibliometric Analysis
Authors: Li Leiming , Ge Fei , Jiang Yingying , An Zhao , Li Na , Zhang Zherui , Liu Haining , Li Jiansen , Liang Dan
Published: December 2024

Salt lake resources are unique and valuable minerals on Earth associated with specific elements. The advancement of technology and the rise of new industries are progressively showcasing their strategic significance for economic development. This study used bibliometrics and visualization techniques to analyze the current state and developmental trends of research on salt lake resource exploitation, both domestically and globally. A total of 760 articles from Science Citation Index Expanded (SCIE) were analyzed. The research findings reveal that the processes of salt lake separation and extraction have progressed through three distinct stages: the germination stage, the stable development stage, and the rapid development stage. China has offered robust policy support for research in this domain at the national level. China possesses a centrality score of 1.08 in the separation and extraction of salt lakes, with 50% of the 10 most active nations in this domain situated in Asia and South America. The prominent institutions comprise the Chinese Academy of Sciences (centrality score of 0.32), the Qinghai Salt Lake Study Institute (centrality score of 0.22), and the University of the Chinese Academy of Sciences (centrality score of 0.14), encompassing a diverse array of study subjects. Keywords from 2003 signify the initial advancement of lithium extraction from saline lakes, whereas those from 2011 underscore the heightened focus on integrated resource utilization and multidisciplinary study. Keywords from 2015 indicate an intensified emphasis on the extraction of lithium and other elements. The terms “tributyl phosphate” (citation strength of 6.05) and “nanofiltration” (citation strength of 4.29) exhibit significant interest in magnesium–lithium separation research and water treatment technologies employed in salt lake separation and extraction, receiving the highest number of citations. The persistent emphasis on “lithium ions” signifies the increasing demand for raw materials propelled by advancements in the new energy sector. Research trend analysis indicates that sodium resource utilization has stabilized, whereas magnesium, a byproduct of lithium extraction, is presently a key focus for downstream product applications. Rare elements remain at the experimental research stage. The industrialization of salt lake resources, including potassium, lithium, and boron, is notably advanced. Future research should focus on the mineralization and enrichment patterns of potassium resources, developing improved extraction methods for lithium, and advancing technologies for the cost-effective and environmentally friendly separation of boron resources. The future objective for resource extraction in salt lakes is to transition from a crude methodology to a refined, sustainable, and intelligent development framework.
Linkage Academia–Industry/Innovative High-Performance Systems: A Pathway to Strengthen Technological Capabilities for Innovation in Public Research Centers in Mexico
Authors: Rodríguez-Salazar Adela Eugenia , Torres-Huerta Aidé Minerva , Licona-Aguilar Ángeles Iveth , Gutiérrez-Galicia Francisco , Hernández-Alvarado Margarita Josefina , Nivón-Pellón Alejandra , Domínguez-Crespo Miguel Antonio
Published: December 2024

This study aims to create a reference framework to evaluate the technological capabilities (TCs) of public research centers in Mexico through their characterization, measurement, and statistical analysis for decision making on technological strengthening. Additionally, the study seeks to understand the context in which innovation and linkage activities occur within the research system and highlight the importance of integrating such studies into academic institutions. Using this generic framework, public research centers (PRCs), in addition to identifying weaknesses in the development of capabilities at the organizational level, could determine the level of development at which their TCs are located to address social demands and promote collaboration models between university and industry. To achieve this objective, 228 surveys were carried out per stage among researchers from the PRCs in Mexico. Each stage was composed of one questionnaire. Questionnaire 1 considered aspects of investment capabilities, assimilation, modification, support, and linkage. Questionnaire 2 was used to collect data on scientific productivity based on evaluation criteria established by the National Council of Humanities, Sciences and Technologies (CONAHCyT). The aspects covered included infrastructure, scientific publications, intellectual property, postgraduate programs, and collaborative projects. The results indicate that the majority of Mexican PRCs have developed basic and intermediate TCs, with 77% involved in applied research and technological development, but from the total research projects, only 8% present higher levels of technological maturity. The originality of the study lies in the quantitative measurement of TCs within the Mexican PRCs for the benefit of innovative and high-performance work systems, addressing a gap in the existing literature, and could be extrapolated to other universities of developing countries.
Promoting Sustainable Transportation: How People Trust and Accept Autonomous Vehicles—Focusing on the Different Levels of Collaboration Between Human Drivers and Artificial Intelligence—An Empirical Study with Partial Least Squares Structural Equation Modeling and Multi-Group Analysis
Authors: Yang Yi , Kim Min-Yong
Published: December 2024

Despite the advancement in autonomous vehicles, public trust and acceptance are crucial for AV’s widespread adoption. This study examines how different collaboration levels between human drivers and artificial intelligence influence users’ trust and acceptance of AVs. Using an extended Technology Acceptance Model, this study incorporates psychological factors and technological attitudes such as perceived safety, perceived risk, AI literacy, and AI technophobia. Data collected from 392 vehicle owners across 11 Chinese cities were analyzed using Partial Least Squares Structural Equation Modeling and Multi-Group Analysis. The findings reveal that at the fully manual level, perceived ease of use significantly influences perceived usefulness, while trust remains grounded in mechanical reliability rather than AI systems. In contrast, as AI assumes driving responsibilities at collaborative automation levels, the findings show that AI literacy significantly increases perceived trust and ease of use, while AI technophobia decreases them, with these effects varying across different driving automation levels. As AI takes on greater driving responsibilities, perceived ease of use becomes less critical, and perceived trust increasingly influences users’ acceptance. These findings highlight the need for targeted public education and phased automation strategies, offering guidance for AV developers to address user concerns and build trust in autonomous technologies. By enhancing public trust and acceptance, this study contributes to sustainable development by promoting safer roads and enabling more efficient, resource-conscious transportation systems. Gradually integrating AVs into urban mobility also supports smart city initiatives, fostering more sustainable urban environments.
Assessment of Ecological Carrying Capacity in Xilingol League Based on Three-Dimensional Ecological Footprint Model
Authors: Wu Jimuji , Yang Xia
Published: December 2024

The real-time assessment of ecological conditions in arid and semi-arid regions is essential for global ecological sustainability. This study utilizes an improved three-dimensional ecological footprint model to assess the ecological footprint, ecological carrying capacity, and footprint depth of Xilingol League, analyzing its spatiotemporal evolution characteristics. An ecological pressure index is developed to evaluate ecological security, and two-way ANOVA and M-K mutation tests are employed to analyze the effects of county-level and year-level factors on these indicators and their evolving trends. The results show that the following: (1) From 2011 to 2021, the ecological footprint and footprint depth in Xilingol League first increased and then decreased, while its ecological carrying capacity slightly improved, leading to an overall reduction in ecological deficit. Spatially, ecological footprint and carrying capacity were higher in the northeast than in the southwest, and ecological pressure was higher in the eastern and western regions compared to the central area. (2) Grassland ecological footprint and footprint depth were relatively high, with a higher carrying capacity than other land types; however, the ecological deficit was severe, reaching a minimum value of −5 in 2019. (3) County-level factors had a significant impact on ecological footprint, carrying capacity, ecological balance, footprint depth, and ecological pressure, while year-level factors significantly influenced footprint depth and ecological pressure. (4) From 2013 to 2021, the ecological footprint significantly increased, while ecological carrying capacity notably improved from 2016 to 2021; however, ecological balance remained in a deficit. Footprint depth showed a significant increase from 2011 to 2016 and a significant decrease from 2016 to 2021, while ecological pressure significantly increased from 2011 to 2018 and then significantly decreased from 2018 to 2021. This study highlights the spatiotemporal dynamics of the ecological system in Xilingol League and provides scientific support for ecological management in arid and semi-arid regions.
Towards Human-Centric Manufacturing: Exploring the Role of Human Digital Twins in Industry 5.0
Authors: Bucci Ilaria , Fani Virginia , Bandinelli Romeo
Published: December 2024

Human-centricity, a cornerstone of Industry 5.0, emphasizes the central role of human needs and capabilities in the technological landscape of modern manufacturing. As Digital Twins (DTs) become a core technology of Industry 4.0, the evolution towards Human Digital Twins (HDTs) marks a significant shift to enhance human-system integration. HDTs serve as digital replicas that mirror human characteristics directly in system design and performance, facilitating a more nuanced approach to smart manufacturing. This paper addresses the critical need for deeper investigation into HDTs to fully leverage their potential in promoting human-centric manufacturing. Through a comprehensive review, the current state and rapid evolution of HDT frameworks and architectures within Industry 5.0 settings are explored. The enabling technologies that underpin HDTs, their applications across various industrial scenarios, and the challenges in their development are discussed. The analysis not only underscores the importance of HDTs in meeting the diverse needs of workers but also outlines future research directions to further empower individuals within the adaptive and intelligent manufacturing systems shaped by Industry 5.0.
Detecting Plant Diseases Using Machine Learning Models
Authors: Kohut Nazar , Basystiuk Oleh , Shakhovska Nataliya , Melnykova Nataliia
Published: December 2024

Sustainable agriculture is pivotal to global food security and economic stability, with plant disease detection being a key challenge to ensuring healthy crop production. The early and accurate identification of plant diseases can significantly enhance agricultural practices, minimize crop losses, and reduce the environmental impacts. This paper presents an innovative approach to sustainable development by leveraging machine learning models to detect plant diseases, focusing on tomato crops—a vital and globally significant agricultural product. Advanced object detection models including YOLOv8 (minor and nano variants), Roboflow 3.0 (Fast), EfficientDetV2 (with EfficientNetB0 backbone), and Faster R-CNN (with ResNet50 backbone) were evaluated for their precision, efficiency, and suitability for mobile and field applications. YOLOv8 nano emerged as the optimal choice, offering a mean average precision (MAP) of 98.6% with minimal computational requirements, facilitating its integration into mobile applications for real-time support to farmers. This research underscores the potential of machine learning in advancing sustainable agriculture and highlights future opportunities to integrate these models with drone technology, Internet of Things (IoT)-based irrigation, and disease management systems. Expanding datasets and exploring alternative models could enhance this technology’s efficacy and adaptability to diverse agricultural contexts.