bluuebunny/crossref_metadata_2025 huggingface dataset:
Estimation, Spatiotemporal Dynamics, and Driving Factors of Grassland Biomass Carbon Storage Based on Machine Learning Methods: A Case Study of the Hulunbuir Grassland
Authors: Zhi Qiuying , Hu Xiaosheng , Wang Ping , Li Ming , Ding Yi , Wu Yuxuan , Peng Tiantian , Li Wenjie , Guan Xiao , Shi Xiaoming , Li Junsheng
Published: October 2024

Precisely estimating the grassland biomass carbon storage is vital for evaluating grassland carbon sequestration potential and the monitoring and management of grassland resources. With the increasing intensity of climate change (CC) and human activities (HA), it is necessary to explore spatiotemporal variations in biomass carbon storage and its response to CC and HA. In this study, we focused on the Hulunbuir Grassland, utilizing sample plots data, MODIS data, environmental factors (terrain, soil, and climate), location factor, and texture characteristics to assess the performance of four machine learning algorithms: random forest, support vector machine, gradient boosting decision tree, and extreme gradient boosting in estimating grassland aboveground biomass (AGB). Based on the optimal model combined with root-shoot ratio data, grassland distribution data, and carbon content coefficients, the spatiotemporal characteristics and driving factors of biomass carbon storage from 2001–2022 were analyzed. The results showed that (1) the random forest achieved the highest prediction accuracy for grassland AGB, making it appropriate for AGB estimation in the Hulunbuir Grassland. (2) The spectral indices were the key variables of the grassland AGB, especially the enhanced vegetation index and difference vegetation index. (3) The 22-year average total biomass (TB) of the study area was 1037.10 gC/m2, of which the 22-year average AGB was 48.73 gC/m2 and 22-year average belowground biomass was 988.37 gC/m2, showing a spatial distribution feature of gradual increase from west to east. (4) From 2001–2022, TB carbon storage showed an insignificant growth trend (p > 0.05). The 22-year average carbon storage of TB was 72.34 ± 18.07 gC. (5) Climate factors were the main driving factors for the spatial pattern of grassland TB carbon density, while the combined effects of CC and HA were the main contributors to the interannual increase in grassland TB carbon density.
A Low-Cost and Lightweight Real-Time Object-Detection Method Based on UAV Remote Sensing in Transportation Systems
Authors: Liu Ziye , Chen Chen , Huang Ziqin , Chang Yoong Choon , Liu Lei , Pei Qingqi
Published: October 2024

Accurate detection of transportation objects is pivotal for enhancing driving safety and operational efficiency. In the rapidly evolving domain of transportation systems, the utilization of unmanned aerial vehicles (UAVs) for low-altitude detection, leveraging remotely-sensed images and videos, has become increasingly vital. Addressing the growing demands for robust, real-time object-detection capabilities, this study introduces a lightweight, memory-efficient model specifically engineered for the constrained computational and power resources of UAV-embedded platforms. Incorporating the FasterNet-16 backbone, the model significantly enhances feature-processing efficiency, which is essential for real-time applications across diverse UAV operations. A novel multi-scale feature-fusion technique is employed to improve feature utilization while maintaining a compact architecture through passive integration methods. Extensive performance evaluations across various embedded platforms have demonstrated the model’s superior capabilities and robustness in real-time operations, thereby markedly advancing UAV deployment in crucial remote-sensing tasks and improving productivity and safety across multiple domains.
Vertical Distribution of Water Vapor During Haze Processes in Northeast China Based on Raman Lidar Measurements
Authors: Zhang Tianpei , Yin Zhenping , Wei Yubin , Dai Yaru , Wang Longlong , Dong Xiangyu , Gao Yuan , Wei Lude , Zhang Qixiong , Hu Di , Zhou Yifan
Published: October 2024

Haze refers to an atmospheric phenomenon with extremely low visibility, which has significant impacts on human health and safety. Water vapor alters the scattering properties of atmospheric particulate matter, thus affecting visibility. A comprehensive analysis of the role of water vapor in haze formation is of great scientific significance for forecasting severe pollution weather events. This study investigates the distribution characteristics and variations of water vapor during haze weather in Changchun City (44°N, 125.5°E) in autumn and winter seasons, aiming to reveal the relationship between haze and atmospheric water vapor content. Analysis of observational results for a period of two months (October to November 2023) from a three-wavelength Raman lidar deployed at the site reveals that atmospheric water vapor content is mainly concentrated below 5 km, accounting for 64% to 99% of the total water vapor below 10 km. Furthermore, water vapor content in air pollution exhibits distinct stratification characteristics with altitude, especially within the height range of 1–3 km, where significant water vapor variation layers exist, showing spatial consistency with inversion layers. Statistical analysis of haze events at the site indicates a high correlation between the concentration variations of PM2.5 and PM10 and the variations in average water vapor mixing ratio (WVMR). During haze episodes, the average WVMR within 3 km altitude is 3–4 times higher than that during clear weather. Analysis of spatiotemporal height maps of aerosols and water vapor during a typical haze event suggests that the relative stability of the atmospheric boundary layer may hinder the vertical transport and diffusion of aerosols. This, in turn, could lead to a sharp increase in aerosol extinction coefficients through hygroscopic growth, thereby possibly exacerbating haze processes. These observational findings indicate that water vapor might play a significant role in haze formation, emphasizing the potential importance of observing the vertical distribution of water vapor for better simulation and prediction of haze events.
A “Region-Specific Model Adaptation (RSMA)”-Based Training Data Method for Large-Scale Land Cover Mapping
Authors: Li Congcong , Xian George , Jin Suming
Published: October 2024

An accurate and historical land cover monitoring dataset for Alaska could provide fundamental information for a range of studies, such as conservation habitats, biogeochemical cycles, and climate systems, in this distinctive region. This research addresses challenges associated with the extraction of training data for timely and accurate land cover classifications in Alaska over longer time periods (e.g., greater than 10 years). Specifically, we designed the “Region-Specific Model Adaptation (RSMA)” method for training data. The method integrates land cover information from the National Land Cover Database (NLCD), LANDFIRE’s Existing Vegetation Type (EVT), and the National Wetlands Inventory (NWI) and machine learning techniques to generate robust training samples based on the Anderson Level II classification legend. The assumption of the method is that spectral signatures vary across regions because of diverse land surface compositions; however, despite these variations, there are consistent, collective land cover characteristics that span the entire region. Building upon this assumption, this research utilized the classification power of deep learning algorithms and the generalization ability of RSMA to construct a model for the RSMA method. Additionally, we interpreted existing vegetation plot information for land cover labels as validation data to reduce inconsistency in the human interpretation. Our validation results indicate that the RSMA method improved the quality of the training data derived solely from the NLCD by approximately 30% for the overall accuracy. The validation assessment also demonstrates that the RSMA method can generate reliable training data on large scales in regions that lack sufficient reliable data.
High-Precision Heterogeneous Satellite Image Manipulation Localization: Feature Point Rules and Semantic Similarity Measurement
Authors: Wu Ruijie , Guo Wei , Liu Yi , Sun Chenhao
Published: October 2024

Misusing image tampering software makes it easier to manipulate satellite images, leading to a crisis of trust and security concerns in society. This study compares the inconsistencies between heterogeneous images to locate tampered areas and proposes a high-precision heterogeneous satellite image manipulation localization (HSIML) framework to distinguish tampered from real landcover changes, such as artificial constructions, and pseudo-changes, such as seasonal variations. The model operates at the patch level and comprises three modules: The heterogeneous image preprocessing module aligns heterogeneous images and filters noisy data. The feature point constraint module mitigates the effects of lighting and seasonal variations in the images by performing feature point matching, applying filtering rules to conduct an initial screening to identify candidate tampered patches. The semantic similarity measurement module designs a classification network to assess RS image feature saliency. It determines image consistency based on the similarity of semantic features and implements IML using predefined classification rules. Additionally, a dataset for IML is constructed based on satellite images. Extensive experiments compared with existing SOTA models demonstrate that our method achieved the highest F1 score in both localization accuracy and robustness tests and demonstrates the capability for handling large-scale areas.
Three-Dimensional Reconstruction of Zebra Crossings in Vehicle-Mounted LiDAR Point Clouds
Authors: Zhao Zhenfeng , Gan Shu , Xiao Bo , Wang Xinpeng , Liu Chong
Published: October 2024

In the production of high-definition maps, it is necessary to achieve the three-dimensional instantiation of road furniture that is difficult to depict on traditional maps. The development of mobile laser measurement technology provides a new means for acquiring road furniture data. To address the issue of traffic marking extraction accuracy in practical production, which is affected by degradation, occlusion, and non-standard variations, this paper proposes a 3D reconstruction method based on energy functions and template matching, using zebra crossings in vehicle-mounted LiDAR point clouds as an example. First, regions of interest (RoIs) containing zebra crossings are obtained through manual selection. Candidate point sets are then obtained at fixed distances, and their neighborhood intensity features are calculated to determine the number of zebra stripes using non-maximum suppression. Next, the slice intensity feature of each zebra stripe is calculated, followed by outlier filtering to determine the optimized length. Finally, a matching template is selected, and an energy function composed of the average intensity of the point cloud within the template, the intensity information entropy, and the intensity gradient at the template boundary is constructed. The 3D reconstruction result is obtained by solving the energy function, performing mode statistics, and normalization. This method enables the complete 3D reconstruction of zebra stripes within the RoI, maintaining an average planar corner accuracy within 0.05 m and an elevation accuracy within 0.02 m. The matching and reconstruction time does not exceed 1 s, and it has been applied in practical production.
Effects of Extreme Climatic Events on the Autumn Phenology in Northern China Are Related to Vegetation Types and Background Climates
Authors: Gao Xinyue , Tao Zexing , Dai Junhu
Published: October 2024

The increased intensity and frequency of extreme climate events (ECEs) have significantly impacted vegetation phenology, further profoundly affecting the structure and functioning of terrestrial ecosystems. However, the mechanisms by which ECEs affect the end of the growing season (EOS), a crucial phenological phase, remain unclear. In this study, we first evaluated the temporal variations in the EOS anomalies in Northern China (NC) based on the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) from 2001 to 2018. We then used event coincidence analysis (ECA) to assess the susceptibility of EOS to four ECEs (i.e., extreme heat, extreme cold, extreme wet and extreme dry events). Finally, we examined the dependence of the response of EOS to ECEs on background climate conditions. Our results indicated a slight decrease in the proportion of areas experiencing extreme heat and dry events (1.10% and 0.66% per year, respectively) and a slight increase in the proportion of areas experiencing extreme wet events (0.77% per year) during the preseason period. Additionally, EOS exhibited a delaying trend at a rate of 0.25 days/a during the study period. The susceptibility of EOS to ECEs was closely related to local hydrothermal conditions, with higher susceptibility to extreme dry and extreme hot events in drier and warmer areas and higher susceptibility to extreme cold and extreme wet events in wetter regions. Grasslands, in contrast to forests, were more sensitive to extreme dry, hot and cold events due to their weaker resistance to water deficits and cold stress. This study sheds light on how phenology responds to ECEs across various ecosystems and hydrothermal conditions. Our results could also provide a valuable guide for ecosystem management in arid regions.
A Tunnel Fire Detection Method Based on an Improved Dempster-Shafer Evidence Theory
Authors: Wang Haiying , Shi Yuke , Chen Long , Zhang Xiaofeng
Published: October 2024

Tunnel fires are generally detected using various sensors, including measuring temperature, CO concentration, and smoke concentration. To address the ambiguity and inconsistency in multi-sensor data, this paper proposes a tunnel fire detection method based on an improved Dempster-Shafer (DS) evidence theory for multi-sensor data fusion. To solve the problem of evidence conflict in the DS theory, a two-level multi-sensor data fusion framework is adopted. The first level of fusion involves feature fusion of the same type of sensor data, removing ambiguous data to obtain characteristic data, and calculating the basic probability assignment (BPA) function through the feature interval. The second-level fusion derives basic probability numbers from the BPA, calculates the degree of evidence conflict, normalizes the BPA to obtain the relative conflict degree, and optimizes the BPA using the trust coefficient. The classical DS evidence theory is then used to integrate and obtain the probability of tunnel fire occurrence. Different heat release rates, tunnel wind speeds, and fire locations are set, forming six fire scenarios. Sensor monitoring data under each simulation condition are extracted and fused using the improved DS evidence theory. The results show that there is a 67.5%, 83.5%, 76.8%, 83%, 79.6%, and 84.1% probability of detecting fire when it occurs, respectively, and identifies fire occurrence in approximately 2.4 s, an improvement from 64.7% to 70% over traditional methods. This demonstrates the feasibility and superiority of the proposed method, highlighting its significant importance in ensuring personnel safety.
An Experimental Assessment Using Acoustic Emission Sensors to Effectively Detect Surface Deterioration on Steel Plates
Authors: Angelopoulos Nikolaos , Kappatos Vassilios
Published: October 2024

Acoustic emission (AE) testing is used for the continuous evaluation of structural integrity and the monitoring of damage evolution in structural components and materials. During operation, the environmental and loading conditions of metal structures can result in corrosion and surface wear damage. The early detection of surface degradation flaws is crucial, as they can serve as local stress concentration points, leading to crack initiation and failure. In this work, the effectiveness of AE in monitoring corrosion and surface wear flaw formation was experimentally evaluated. AE sensors were installed on steel test plates during the artificial induction of corrosion and surface wear in order to detect and record the generated AE signals. Corrosion-related AE signals typically exhibit low amplitude, count, and energy values. The direct detection of active corrosion can be challenging in noisy environments, but it can be carried out under certain conditions using dedicated AE sensor groups. Surface-wear-related AE signals exhibit high amplitude, energy, and count values, with long duration values that are associated with wear and grinding conditions. It was found that AE sensors can be utilised to detect corrosion and surface degradation events. The effectiveness of the AE method in detecting surface degradation in noisy environments can be improved by implementing a filtering methodology. This will limit the recording of noise-related signals that can mask out actual surface degradation AE events.
Transforming Motor Imagery Analysis: A Novel EEG Classification Framework Using AtSiftNet Method
Authors: Xu Haiqin , Haider Waseem , Aziz Muhammad Zulkifal , Sun Youchao , Yu Xiaojun
Published: October 2024

This paper presents an innovative approach for the Feature Extraction method using Self-Attention, incorporating various Feature Selection techniques known as the AtSiftNet method to enhance the classification performance of motor imaginary activities using electrophotography (EEG) signals. Initially, the EEG signals were sorted and then denoised using multiscale principal component analysis to obtain clean EEG signals. However, we also conducted a non-denoised experiment. Subsequently, the clean EEG signals underwent the Self-Attention feature extraction method to compute the features of each trial (i.e., 350×18). The best 1 or 15 features were then extracted through eight different feature selection techniques. Finally, five different machine learning and neural network classification models were employed to calculate the accuracy, sensitivity, and specificity of this approach. The BCI competition III dataset IV-a was utilized for all experiments, encompassing the datasets of five volunteers who participated in the competition. The experiment findings reveal that the average accuracy of classification is highest among ReliefF (i.e., 99.946%), Mutual Information (i.e., 98.902%), Independent Component Analysis (i.e., 99.62%), and Principal Component Analysis (i.e., 98.884%) for both 1 and 15 best-selected features from each trial. These accuracies were obtained for motor imagery using a Support Vector Machine (SVM) as a classifier. In addition, five-fold validation was performed in this paper to assess the fair performance estimation and robustness of the model. The average accuracy obtained through five-fold validation is 99.89%. The experiments’ findings indicate that the suggested framework provides a resilient biomarker with minimal computational complexity, making it a suitable choice for advancing Motor Imagery Brain–Computer Interfaces (BCI).
An Improved YOLOv8-Based Foreign Detection Algorithm for Transmission Lines
Authors: Duan Pingting , Liang Xiao
Published: October 2024

This research aims to overcome three major challenges in foreign object detection on power transmission lines: data scarcity, background noise, and high computational costs. In the improved YOLOv8 algorithm, the newly introduced lightweight GSCDown (Ghost Shuffle Channel Downsampling) module effectively captures subtle image features by combining 1 × 1 convolution and GSConv technology, thereby enhancing detection accuracy. CSPBlock (Cross-Stage Partial Block) fusion enhances the model’s accuracy and stability by strengthening feature expression and spatial perception while maintaining the algorithm’s lightweight nature and effectively mitigating the issue of vanishing gradients, making it suitable for efficient foreign object detection in complex power line environments. Additionally, PAM (pooling attention mechanism) effectively distinguishes between background and target without adding extra parameters, maintaining high accuracy even in the presence of background noise. Furthermore, AIGC (AI-generated content) technology is leveraged to produce high-quality images for training data augmentation, and lossless feature distillation ensures higher detection accuracy and reduces false positives. In conclusion, the improved architecture reduces the parameter count by 18% while improving the mAP@0.5 metric by a margin of 5.5 points when compared to YOLOv8n. Compared to state-of-the-art real-time object detection frameworks, our research demonstrates significant advantages in both model accuracy and parameter size.
On the Specific Diclofenac–Iron Cation Interaction for Selective Diclofenac Removal from a Water Solution
Authors: Frańska Magdalena , Grześkowiak Aleksandra
Published: October 2024

Diclofenac is one of the most common, commercially available, non-steroidal anti-inflammatory drugs (NSAIDs) in the world, with thousands of tons produced and consumed per year, which creates issues related to its presence in water bodies and the need for its removal from them. Diclofenac forms complexes with cations of each metal, which has inspired a study to check if the formation/precipitation of such complexes can be used for effective diclofenac removal from water solutions. It was found that iron salts, e.g., FeCl3, can be used to remove diclofenac from a water solution in the form a of precipitated complex, provided that a high excess of iron salt was used. It has been observed that the diclofenac initial concentration of 5 × 10−4 M, as a result of FeCl3 addition, after 48 h, decreased by two orders of magnitude. Salts of other metals were found less effective in reducing diclofenac concentration. The iron cation–diclofenac interaction was found to be specific, since the precipitation of other drugs by iron cations has not been observed. In order to quantitively analyze the diclofenac removal (precipitation) by iron and other metal cations, the HPLC/ESI-MS analyses were performed.
Age Discrimination of Senior Citizens in European Countries
Authors: Santha Agnes , Tóth-Batizán Emese Emőke
Published: October 2024

This paper addresses perceived age discrimination of older persons in European countries. This disturbing issue has become all the more prominent, having doubled over a relatively short period between 2018 and 2021. Strongly associated with bad health outcomes, low levels of wellbeing, and considerable social tensions, age-related discrimination against senior citizens is a sign of the lack of their social integration. Our study used secondary data analysis based on survey methodology. Statistical analysis was performed on data from the European Social Survey wave 10 (2021) subsample of people aged 65 and above to assess the senior population’s age-based discrimination experiences and its individual and macro-level determinants. The results indicate that in all European countries, low socioeconomic status, older age, and living alone are strongly associated with the experience of age discrimination. Having bad health and being functionally limited in everyday activities results in a state of dependency that is connected to a higher risk of perceived exposure to age discrimination. Smaller populations, such as those in villages, are protective against discrimination at older age. Finally, compared to other welfare regimes, in social democratic welfare states with egalitarian redistribution systems and high levels of social benefits, there is a significantly larger share of old people who reported to have been victims of age discrimination. Our results have implications for policy in the context of ageing societies.
Opinion of Ecuadorians on Migration in the Target Country: Benefits and Challenges
Authors: Torres-Toukoumidis Angel , De-Santis Andrea , Vintimilla-León Diego
Published: October 2024

Migration plays a crucial role in shaping destination countries’ social and economic landscapes, including in Ecuador. This study aims to explore the benefits and challenges of migration by analyzing the perceptions of Ecuadorians who have directly or indirectly encountered its effects. Using a mixed-methods approach, we surveyed 1048 Ecuadorians and conducted 27 in-depth interviews to capture various viewpoints. The findings reveal a complex perspective: 47% of respondents view migration positively, emphasizing economic opportunities and cultural enrichment, while 53% express concerns about issues such as employment mismatches and barriers to social integration. These insights highlight the need for further research, e.g., to explore tailored policy solutions and deepen the understanding of migration’s multifaceted impact on Ecuador’s society and economy. The results highlight the need to investigate how media perceptions of migration influence the attitudes and decisions of migrants and host communities to design more balanced and realistic information campaigns for informed decision-making.
Urban Green Infrastructure and Ecosystem Service Supply: A Study Concerning the Functional Urban Area of Cagliari, Italy
Authors: Isola Federica , Lai Sabrina , Leone Federica , Zoppi Corrado
Published: October 2024

Urban green infrastructure (UGI) is a network composed of natural and semi-natural areas, such as greenspaces, open areas, and water bodies, designed to enhance the provision of ecosystem services and to meet the needs and expectations of local communities. UGIs should be accessible and should improve the well-being and health of their users, protect and enhance biodiversity, and allow for the enjoyment of natural resources. The study proposes a methodological approach to defining a UGI, conceived as a network of areas connected by urban ecological corridors and suitable for providing climate regulation, flood risk mitigation, outdoor recreation, and biodiversity and habitat quality enhancement. The methodology is applied to the functional urban area (FUA) of the City of Cagliari, Italy. The analysis results show that areas with high values of climate regulation, carbon storage and sequestration, and habitat quality enhancement are particularly suitable to be part of a UGI. Although values for outdoor recreation appear to be less significant, the provision of this service is particularly relevant within the Cagliari FUA. However, areas characterized by high values of flood risk mitigation show a different behavior, which highlights how the presence of impermeable surface within urban areas is associated with a loss of patch connectivity.
Perceptions of Women’s Safety in Transient Environments and the Potential Role of AI in Enhancing Safety: An Inclusive Mobility Study in India
Authors: Torrao Guilhermina , Htait Amal , Wong Shun Ha Sylvia
Published: October 2024

Travel safety for women is a concern, particularly in India, where gender-based violence and harassment are significant issues. This study examines how the perception of safety influences women’s travel behaviour and assesses the potential of technology solutions to ensure their safety. Additionally, it explores how AI and machine learning techniques may be leveraged to enhance women’s travel safety. A comprehensive mobility survey was designed to uncover the complex relationship between travel behaviour, reasons for mode choice, built environment, feelings, future mobility, and technological solutions. The responses revealed that security and safety are the most critical factors affecting women’s travel mode choices, with 54% and 41%, respectively. Moreover, over 80% of women indicated a willingness to change their travel behaviour after experiencing fear, anxiety, or danger during their everyday journeys. Participants were 24% less willing to use ride-sharing services than ride-hailing services, which could affect the transition towards more sustainable transportation options. Furthermore, AI-based sentiment analysis revealed that 46% of the respondents exhibited signs of ‘anger’ regarding what could help women feel safer in transient environments. The practical implications of this study’s findings are discussed, highlighting the potential of AI to enhance travel safety and optimise future sustainable transport planning.
From Protectionist to Regulator: Policy-Driven Transformation of Digital Urban Networks in China’s Online Gaming Industry
Authors: Zhang Xu , Huang Yingmin , Zou Xiaohua
Published: October 2024

In the digital era, data-driven production organizes digital urban networks. This study explores the critical role of government policies in shaping these networks, focusing on China’s evolving policy contexts. While existing research has mainly emphasized qualitative analyses, this paper quantitatively assesses the impact of policy changes on digital urban networks, specifically through the lens of China’s online gaming industry. The study aimed to elucidate the relationship between the policy environment and digital urban networks. By examining China’s transition from protectionist to regulatory policies, this research employed a social network analysis and valued exponential random graph models (ERGMs) across two key phases: the competitive protection phase (2014–2017) and the systematic regulatory phase (2018–2022). The findings revealed a significant transformation in urban network structure, shifting from a centralized model dominated by a few core cities to a decentralized, multi-centered network. The key factors influencing this evolution include the institutional proximity and cross-regional collaborations. This study offers valuable insights into how policy shifts affect urban networks in the digital economy, contributing both theoretically and practically to future policy design.
Artificial Intelligence for Social Innovation: Beyond the Noise of Algorithms and Datafication
Authors: Calzada Igor
Published: October 2024

In an era of rapid technological advancement, decisions about the ownership and governance of emerging technologies like Artificial Intelligence will shape the future of both urban and rural environments in the Global North and South. This article explores how AI can move beyond the noise of algorithms by adopting a technological humanistic approach to enable Social Innovation, focusing on global inequalities and digital justice. Using a fieldwork Action Research methodology, based on the Smart Rural Communities project in Colombia and Mozambique, the study develops a framework for integrating AI with SI. Drawing on insights from the AI4SI International Summer School held in Donostia-San Sebastián in 2024, the article examines the role of decentralized Web3 technologies—such as Blockchain, Decentralized Autonomous Organizations, and Data Cooperatives—in enhancing data sovereignty and fostering inclusive and participatory governance. The results demonstrate how decentralization can empower marginalized communities in the Global South by promoting digital justice and addressing the imbalance of power in digital ecosystems. The conclusion emphasizes the potential for AI and decentralized technologies to bridge the digital divide, offering practical recommendations for scaling these innovations to support equitable, community-driven governance and address systemic inequalities across the Global North and South.
Smart City Construction, Artificial Intelligence Development, and the Quality of Export Products: A Study Based on Micro-Level Data of Chinese Enterprises
Authors: Ou Jiayu , Zheng Zhiqiang , Ou Xiaojing , Zhang Naili
Published: October 2024

Quality improvement is essential for a nation’s economy to transition from large to strong. In the 21st century, a new wave of quality development has emerged globally, and upgrading the quality of enterprise export products is a key measure for driving exports and supporting high-quality economic development. The development of artificial intelligence, as the new core engine driving technological revolution and industrial transformation, will profoundly alter various aspects of economic activities, including production, distribution, exchange, and consumption. Exploring and cultivating new artificial intelligence-driven momentum to enhance the quality of enterprise export products is inevitably a major theoretical and practical issue of common interest to governments, enterprises, and academia. This paper uses China, a major developing and export-oriented economy, as a case study to explore the policy measures for stimulating new momentum in artificial intelligence development and their effects and transmission mechanisms on improving the quality of enterprise export products. Specifically, it constructs a theoretical model to examine the relationship between smart city construction, artificial intelligence development, and the quality of enterprise export products. By considering the smart city construction projects launched by the Chinese government as a quasi-natural experiment to facilitate artificial intelligence development, the study employs matched city-enterprise data from 2007 to 2015 and utilizes a difference-in-differences (DID) methodology to empirically test the impact of smart city construction on enhancing the quality of enterprise export products. According to the study, the policy-driven nature of smart city construction significantly enhances the quality of enterprise export products. This beneficial impact is particularly evident in the eastern regions, as well as in labor-intensive and capital-intensive industries, and among foreign-invested and private enterprises. Mechanism tests and additional analyses indicate that artificial intelligence development is significantly more advanced in smart cities than in non-smart cities, with the gap between them steadily widening. The construction of smart cities significantly advances artificial intelligence development, which subsequently enhances the quality of enterprise export products. Furthermore, smart cities can substantially contribute to this improvement by facilitating a more efficient, market-oriented allocation of resources.
Toward the Sustainability of a Healthy Citizenry: A Small-Sample, Qualitative, Exploratory Study of the Experiences of Male Students with Visual Impairment in Saudi Arabian Higher Education
Authors: Abed Mohaned G. , Abed Lowai G. , Shackelford Todd K.
Published: October 2024

Citizens of a sustainably healthy population have the right to an education, and this right must be extended to citizens with disabilities. Visual impairment presents challenges to such sustainability insofar as it can negatively impact the social and educational experiences of affected students. The present study explores the challenges that students with visual impairments face at higher education institutions in Saudi Arabia. The study examines the impacts of visual impairment on the social and academic domains of these students’ lives which thereby threaten the sustainability of a healthy and well-functioning society. Exploring strategies and support mechanisms, the study’s aim is to improve the Saudi educational environment in universities to better accommodate the needs of students with visual impairments. The study employs a qualitative methodology to investigate the experiences of eight male students with visual impairment enrolled in a Saudi Arabian university. The findings highlight challenges faced by these students and provide a foundation of recommendations for improving educational support and facilities so that students with visual impairment are more likely to succeed academically and to make meaningful contributions to the broader community. In consequence, such students are better positioned to contribute to the sustainability of a healthy and well-functioning Saudi society.
CH4 and CO2 Reductions from Methanol Production Using Municipal Solid Waste Gasification with Hydrogen Enhancement
Authors: Ostadi Mohammad , Cohn Daniel R. , Zang Guiyan , Bromberg Leslie
Published: October 2024

This study evaluates the greenhouse gas (GHG) impacts of converting municipal solid waste (MSW) into methanol, focusing on both landfill methane (CH4) emission avoidance and the provision of cleaner liquid fuels with lower carbon intensity. We conduct a life cycle assessment (LCA) to assess potential GHG reductions from MSW gasification to methanol, enhanced with hydrogen produced via natural gas pyrolysis or water electrolysis. Hydrogen enhancement effectively doubles the methanol yield from a given amount of MSW. Special attention is given to hydrogen production through natural gas pyrolysis due to its potential for lower-cost hydrogen and reduced reliance on renewable electricity compared to electrolytic hydrogen. Our analysis uses a case study of methanol production from an oxygen-fired entrained flow gasifier fed with refuse-derived fuel (RDF) simulated in Aspen HYSYS. The LCA incorporates the significant impact of landfill methane avoidance, particularly when considering the 20-year global warming potential (GWP). Based on the LCA, the process has illustrative net GHG emissions of 183 and 709 kgCO2e/t MeOH using renewable electricity for electrolytic hydrogen and pyrolytic hydrogen, respectively, for the 100-year GWP. The net GHG emissions using 20-year GWP are −1222 and −434 kgCO2e/t MeOH, respectively. Additionally, we analyze the sensitivity of net GHG emissions to varying levels of fugitive methane emissions.
How Government Subsidies Facilitate the Digital Transformation of Suppliers
Authors: Hao Xionglei , Chen Xingyu , Wang Futao
Published: October 2024

Digital transformation is increasingly recognized as a key driver of sustainable development, enabling suppliers to improve efficiency, reduce resource consumption, and adapt to changing market demands. However, it remains a challenging process for suppliers, often hindered by resource and capacity constraints. This study investigates how government subsidies can facilitate supplier digital transformation, considering supply chain diffusion and local government competition dynamics. Using data from A-share listed companies in China between 2010 and 2021, our analysis reveals that government subsidies significantly promote supplier digital transformation by encouraging a more diversified downstream customer base. Moreover, customer digital transformation can facilitate supplier digital transformation, but spillover effects are higher within the same jurisdiction than across different jurisdictions. This study further identifies that the impact of government subsidies is more pronounced under higher opportunistic risk but is constrained by systemic risk. Additionally, suppliers with higher human capital and a smaller digital divide with customers exhibit greater effectiveness in adopting innovation diffusion. These findings provide valuable insights into optimizing local government subsidies policies to enhance supplier digital transformation and contribute to the broader goal of sustainable development.
Attitudes towards Climate Change among Wilderness Seekers
Authors: Próchniak Piotr , Ossowski Adam
Published: October 2024

The aim of the study was to explore attitudes towards climate change among wilderness seekers. The subjects were 273 (M = 23.15, SD = 7.72) adults. These included: 189 women, 80 men and 4 people who identified as non-binary. The respondents completed four questionnaires: Wilderness Novelty Seeking Scale, Wilderness Self-Efficacy Sale, Wilderness Courage Scale, and Attitude Towards Climate Changes Scale. There were three distinct profiles of the respondents: Curious, who are interested in the wilderness but lack the skills and courage to explore dangerous wilderness places; Adventurous, who actively seek experiences in dangerous wilderness places and have survival skills; and Indifferent, who have little interest in the wilderness. The participants in these profiles differed in terms of attitude toward climate change. The Curious and Adventurous groups were significantly more concerned about climate change. In addition, they were more likely to believe that climate change is already having a negative impact on the lives of people in the places where they live. Furthermore, The Curious group felt less positive about climate change than the Adventurous group. They also felt more negative about it. Finally, wilderness seekers (Curious and Adventurous) were statistically more likely to engage in pro-environmental behaviors in the context of climate change compared to the Indifferent group.
Environmental Assessment and Restoration of the Hunjiang River Basin Based on the DPSIR Framework
Authors: Tang Shiyu , Yang Hao , Li Yu
Published: October 2024

The Hunjiang River, a vital water system in northeastern China, has suffered severe ecological damage due to overexploitation. This study analyzes the basin’s environmental conditions from 2016 to 2020, identifies key restoration factors, and examines practical restoration projects. Investigating five major pollutants (permanganate index, chemical oxygen demand (COD), biochemical oxygen demand, ammonia nitrogen, total phosphorus) in eight sections, the study finds the Xicun section most polluted, mainly from Baishan City’s industrial and domestic discharges. The ammonia nitrogen concentration at the Zian section also shows deterioration. Using a DPSIR (Driving forces, Pressures, State, Impacts, Responses) framework, the study elucidates the relationship between environmental and socio-economic issues. Results indicate that population changes, industrial development, and water resource management have complex ecological impacts. Evaluating the urban water resource carrying capacity with the entropy weight method and correlation coefficient weighting method, the study finds that increasing forest coverage, improving wastewater treatment efficiency, and reducing COD emissions are crucial. Quantitative assessment of integrated protection and restoration projects involving mountains, rivers, forests, farmlands, lakes, and grasslands demonstrates their positive impact. This research reveals the interplay between the ecological environment and social factors, proposes practical restoration measures, and clarifies project effects, providing reliable decision-making schemes for policymakers.
A Study of the Influence Mechanism of Digital Technology Affordance on the Disruptive Innovation of Enterprises
Authors: Liu Haibing , Ruan Wei , Huang Tianwei , Zhang Hong
Published: October 2024

In the context of increasingly fierce global competition, utilizing digital technology to realize disruptive innovation is an effective way for enterprises to gain access to the mainstream market and form long-term competitive advantages. From the perspective of affordance, this study classifies digital technology affordance into cumulative affordance and variant affordance and explores the influence mechanism of digital technology affordance on enterprise disruptive innovation according to three core innovation phases: “fuzzy front-end–development–commercialization”. Based on 241 data points from different industries and types of enterprises, this empirical study found that (1) cumulative affordance and variant affordance have a significant positive impact on disruptive innovation and its “fuzzy front-end—development—commercialization” phases; (2) dynamic capabilities play a mediating role between digital technology affordance and disruptive innovation; and (3) the three aspects of dynamic capabilities—sensing, seizing, and reconfiguring—are greatly enhanced by cumulative affordance and variable affordance. These findings contribute to the research on the relationship between digital technology affordance and disruptive innovation and provide a micro-level perspective on how firms realize disruptive innovation through digital technology affordance.
Deep Learning-Based Dust Detection on Solar Panels: A Low-Cost Sustainable Solution for Increased Solar Power Generation
Authors: Alatwi Aadel Mohammed , Albalawi Hani , Wadood Abdul , Anwar Hafeez , El-Hageen Hazem M.
Published: October 2024

The world is shifting towards renewable energy sources due to the harmful effects of fossils fuel-based power generation in the form of global warming and climate change. When it comes to renewable energy sources, solar-based power generation remains on top of the list as a clean and carbon cutting alternative to the fossil fuels. Naturally, the sites chosen for installing solar parks to generate electricity are the ones that get maximum solar radiance throughout the year. Consequently, such sites offer challenges for the solar panels such as increased temperature, humidity and high dust levels that negatively affect their power generation capability. In this work, we are more concerned with the detection of dust from the images of the solar panels so that the cleaning process can be done in time to avoid power loses due to dust accumulation on the surface of solar panels. To this end, we utilize state-of-art deep learning-based image classification models and evaluate them on a publicly available dataset to identify the one that gives maximum classification accuracy for dusty solar panel detection. We utilize pre-trained models of 20 deep learning models to encode the images that are then used to train and validate four variants of a support vector machine. Among the 20 models, we get the maximum classification of 86.79% when the images are encoded with the pre-trained model of DenseNet169 and then use these encodings with a linear SVM for image classification.
Efficacy of Using a Vessel Dilator during Surgery to Evaluate Vein Diameter and Predict Radiocephalic Arteriovenous Fistula Maturation and Patency
Authors: Takai Kanako , Nojima Takehisa , Taguchi Hidehiko , Hasegawa Kosei , Yamauchi Takashi
Published: October 2024

Background: We use vessel dilators to assess the diameter of the target vein during surgery for arteriovenous fistula (AVF) creation in hemodialysis patients. This study investigates the efficacy of using vein diameter as measured using dilators (surgical diameter; SD) versus that as measured by preoperative ultrasonography (ultrasonographic diameter; UD) to predict postoperative complications and patency. Methods: Sixty-three patients who underwent radiocephalic AVF creation and had measurements of UD and SD were retrospectively analyzed. Cutoff values for UD and SD regarding complications were used to dichotomize the patients into high and low groups for comparisons. Results: The 2-year primary and secondary patency rates overall were 66.5% and 88.9%, respectively. The optimal UD and SD cutoff values were 2.4 and 3.5 mm, respectively. The 2-year primary patency rate was higher in the high-SD group than the low-SD group (88.2% vs. 58.6%; p = 0.0426). The 2-year secondary patency rate was significantly higher in the high-UD/high-SD group than the low-UD/low-SD group (91.7% vs. 68.4%; p = 0.0067). Conclusions: Intraoperative measurement of vein diameter using dilators during AVF creation might be a useful method of predicting patency, particularly when SD is used in combination with UD.
Bivariate Log-Symmetric Regression Models Applied to Newborn Data
Authors: Saulo Helton , Vila Roberto , Souza Rubens
Published: October 2024

This paper introduces bivariate log-symmetric models for analyzing the relationship between two variables, assuming a family of log-symmetric distributions. These models offer greater flexibility than the bivariate lognormal distribution, allowing for better representation of diverse distribution shapes and behaviors in the data. The log-symmetric distribution family is widely used in various scientific fields and includes distributions such as log-normal, log-Student-t, and log-Laplace, among others, providing several options for modeling different data types. However, there are few approaches to jointly model continuous positive and explanatory variables in regression analysis. Therefore, we propose a class of generalized linear model (GLM) regression models based on bivariate log-symmetric distributions, aiming to fill this gap. Furthermore, in the proposed model, covariates are used to describe its dispersion and correlation parameters. This study uses a dataset of anthropometric measurements of newborns to correlate them with various biological factors, proposing bivariate regression models to account for the relationships observed in the data. Such models are crucial for preventing and controlling public health issues.