bluuebunny/crossref_metadata_2025 huggingface dataset:
Vertical Profiles of Aerosol Optical Properties (VIS/NIR) over Wetland Environment: POLIMOS-2018 Field Campaign
Authors: Chilinski Michal T. , Markowicz Krzysztof M. , Poczta Patryk , Chojnicki Bogdan H. , Harenda Kamila M. , Makuch Przemysław , Wang Dongxiang , Stachlewska Iwona S.
Published: December 2024

This study aims to present the benefits of unmanned aircraft systems (UAS) in atmospheric aerosol research, specifically to obtain information on the vertical variability of aerosol single-scattering properties in the lower troposphere. The results discussed in this paper were obtained during the Polish Radar and Lidar Mobile Observation System (POLIMOS) field campaign in 2018 at a wetland and rural site located in the Rzecin (Poland). UAS was equipped with miniaturised devices (low-cost aerosol optical counter, aethalometer AE-51, RS41 radiosonde) to measure aerosol properties (scattering and absorption coefficient) and air thermodynamic parameters. Typical UAS vertical profiles were conducted up to approximately 1000 m agl. During nighttime, UAS measurements show a very shallow inversion surface layer up to about 100–200 m agl, with significant enhancement of aerosol scattering and absorption coefficient. In this case, the Pearson correlation coefficient between aerosol single-scattering properties measured by ground-based equipment and UAS devices significantly decreases with altitude. In such conditions, aerosol properties at 200 m agl are independent of the ground-based observation. On the contrary, the ground observations are better correlated with UAS measurements at higher altitudes during daytime and under well-mixed conditions. During long-range transport of biomass burning from fire in North America, the aerosol absorption coefficient increases with altitude, probably due to entrainment of such particles into the PBL.
Deep Learning-Based Cloud Detection for Optical Remote Sensing Images: A Survey
Authors: Wang Zhengxin , Zhao Longlong , Meng Jintao , Han Yu , Li Xiaoli , Jiang Ruixia , Chen Jinsong , Li Hongzhong
Published: December 2024

In optical remote sensing images, the presence of clouds affects the completeness of the ground observation and further affects the accuracy and efficiency of remote sensing applications. Especially in quantitative analysis, the impact of cloud cover on the reliability of analysis results cannot be ignored. Therefore, high-precision cloud detection is an important step in the preprocessing of optical remote sensing images. In the past decade, with the continuous progress of artificial intelligence, algorithms based on deep learning have become one of the main methods for cloud detection. The rapid development of deep learning technology, especially the introduction of self-attention Transformer models, has greatly improved the accuracy of cloud detection tasks while achieving efficient processing of large-scale remote sensing images. This review provides a comprehensive overview of cloud detection algorithms based on deep learning from the perspective of semantic segmentation, and elaborates on the research progress, advantages, and limitations of different categories in this field. In addition, this paper introduces the publicly available datasets and accuracy evaluation indicators for cloud detection, compares the accuracy of mainstream deep learning models in cloud detection, and briefly summarizes the subsequent processing steps of cloud shadow detection and removal. Finally, this paper analyzes the current challenges faced by existing deep learning-based cloud detection algorithms and the future development direction of the field.
A Fish-Counting Method Using Fusion of Spatial Sensing and Temporal Information
Authors: Wu Zhaozhi , Zheng Xinze , Zhu Yi , Wu Longhao , Li Congcong , Tu Qiang , Yuan Fei
Published: December 2024

In modern aquaculture, accurate and efficient fish counting is crucial for the optimization of resource management and the enhancement of production profitability. Acoustic methods, known for their low energy consumption and extensive detection range, are widely utilized for underwater fish counting. However, traditional acoustic echo methods heavily rely on prior knowledge of fish schools and specific distribution models, leading to complexity and limited adaptability in practical applications. This paper introduces a fish-counting approach that integrates spatial sensing with temporal information. Initially, a spatial sensing matrix is constructed using ultrasonic Frequency-Modulated Continuous Wave (FMCW) technology, which facilitates the extraction of multidimensional features from fish echoes and reduces reliance on prior knowledge of fish schools. Subsequently, temporal information is extracted from echo signals using a Long Short-Term Memory (LSTM) network model, preventing missed detections caused by obstructions in single fish echoes during echo sessions. Finally, by fusing spatial and temporal feature information and employing a data-driven approach, we achieve fish counting while avoiding potential issues arising from improper selection of statistical distribution models. Tests on real fish datasets show that our proposed method consistently outperforms conventional statistical echo methods across all metrics, demonstrating its effectiveness in accurate fish counting.
Axial-UNet++ Power Line Detection Network Based on Gated Axial Attention Mechanism
Authors: Hu Ding , Zheng Zihao , Liu Yafei , Liu Chengkang , Zhang Xiaoguo
Published: December 2024

The segmentation and recognition of power lines are crucial for the UAV-based inspection of overhead power lines. To address the issues of class imbalance, low sample quantity, and long-range dependency in images, a specialized semantic segmentation network for power line segmentation called Axial-UNet++ is proposed. Firstly, to tackle the issue of long-range dependencies in images and low sample quantity, a gated axial attention mechanism is introduced to expand the receptive field and improve the capture of relative positional biases in small datasets, thereby proposing a novel feature extraction module termed axial-channel local normalization module. Secondly, to address the imbalance in training samples, a new loss function is developed by combining traditional binary cross-entropy loss with focal loss, enhancing the precision of image semantic segmentation. Lastly, ablation and comparative experiments on the PLDU and Mendeley datasets demonstrate that the proposed model achieves 54.7% IoU and 80.1% recall on the PLDU dataset, and 79.3% IoU and 93.1% recall on the Mendeley dataset, outperforming other listed models. Additionally, robustness experiments show the adaptability of the Axial-UNet++ model under extreme conditions and the augmented image dataset used in this study has been open sourced.
A Large-Scale Inter-Comparison and Evaluation of Spatial Feature Engineering Strategies for Forest Aboveground Biomass Estimation Using Landsat Satellite Imagery
Authors: Kilbride John B. , Kennedy Robert E.
Published: December 2024

Aboveground biomass (AGB) estimates derived from Landsat’s spectral bands are limited by spectral saturation when AGB densities exceed 150–300 Mg ha−1. Statistical features that characterize image texture have been proposed as a means to alleviate spectral saturation. However, apart from Gray Level Co-occurrence Matrix (GLCM) statistics, many spatial feature engineering techniques (e.g., morphological operations or edge detectors) have not been evaluated in the context of forest AGB estimation. Moreover, many prior investigations have been constrained by limited geographic domains and sample sizes. We utilize 176 lidar-derived AGB maps covering ∼9.3 million ha of forests in the Pacific Northwest of the United States to construct an expansive AGB modeling dataset that spans numerous biophysical gradients and contains AGB densities exceeding 1000 Mg ha−1. We conduct a large-scale inter-comparison of multiple spatial feature engineering techniques, including GLCMs, edge detectors, morphological operations, spatial buffers, neighborhood vectorization, and neighborhood similarity features. Our numerical experiments indicate that statistical features derived from GLCMs and spatial buffers yield the greatest improvement in AGB model performance out of the spatial feature engineering strategies considered. Including spatial features in Random Forest AGB models reduces the root mean squared error (RMSE) by 9.97 Mg ha−1. We contextualize this improvement model performance by comparing to AGB models developed with multi-temporal features derived from the LandTrendr and Continuous Change Detection and Classification algorithms. The inclusion of temporal features reduces the model RMSE by 18.41 Mg ha−1. When spatial and temporal features are both included in the model’s feature set, the RMSE decreases by 21.71 Mg ha−1. We conclude that spatial feature engineering strategies can yield nominal gains in model performance. However, this improvement came at the cost of increased model prediction bias.
Survival Risk Analysis for Four Endemic Ungulates on Grasslands of the Tibetan Plateau Based on the Grazing Pressure Index
Authors: Yan Lingyan , Kong Lingqiao , Ouyang Zhiyun , Hu Jinming , Zhang Li
Published: December 2024

Ungulates are essential for maintaining the health of grassland ecosystems on the Tibetan plateau. Increased livestock grazing has caused competition for food resources, threatening ungulates’ survival. The survival risk of food resources for ungulates can be quantified by the grazing pressure index, which requires accurate grassland carrying capacity. Previous research on the grazing pressure index has rarely taken into account the influence of wild ungulates, mainly due to the lack of precise spatial data on their quantity. In this study, we conducted field investigations to construct high-resolution spatial distributions for the four endemic ungulates on the Tibetan plateau. By factoring in the grazing consumption of these ungulates, we recalculated the grassland carrying capacity to obtain the grazing pressure index, which allowed us to assess the survival risks for each species. The results show: (1) Quantity estimates for Tibetan antelope (Pantholops hodgsonii), Tibetan wild donkey (Equus kiang), Tibetan gazelle (Procapra picticaudata), and wild yak (Bos mutus) of the Tibetan plateau are 24.57 × 104, 17.93 × 104, 7.16 × 104, and 1.88 × 104, respectively; they mainly distributed in the northern and western regions of the Tibetan plateau. (2) The grassland carrying capacity of the Tibetan plateau is 69.98 million sheep units, with ungulate grazing accounting for 5% of forage utilization. Alpine meadow and alpine steppe exhibit the highest grassland carrying capacity. (3) The grazing pressure index on the Tibetan plateau grasslands is 2.23, indicating a heightened grazing pressure in the southern and eastern regions. (4) The habitat survival risk analysis indicates that the high survival risk (the grazing pressure index exceeds 1.2) areas for the four ungulate species account for the following proportions of their total habitat areas: Tibetan wild donkeys (49.76%), Tibetan gazelles (47.00%), Tibetan antelopes (40.76%), and wild yaks (34.83%). These high-risk areas are primarily located within alpine meadow and temperate desert steppe. This study provides a quantitative assessment of survival risks for these four ungulate species on the Tibetan plateau grasslands and serves as a valuable reference for ungulate conservation and grassland ecosystem management.
Monthly Prediction of Pine Stress Probability Caused by Pine Shoot Beetle Infestation Using Sentinel-2 Satellite Data
Authors: Jia Wen , Meng Shili , Qin Xianlin , Pang Yong , Wu Honggan , Jin Jia , Zhang Yunteng
Published: December 2024

Due to the significant threat to forest health posed by beetle infestations on pine trees, timely and accurate predictions are crucial for effective forest management. This study developed a pine tree stress probability prediction workflow based on monthly cloud-free Sentinel-2 composite images to address this challenge. First, representative pine tree stress samples were selected by combining long-term forest disturbance data using the Continuous Change Detection and Classification (CCDC) algorithm with high-resolution remote sensing imagery. Monthly cloud-free Sentinel-2 images were then composited using the Multifactor Weighting (MFW) method. Finally, a Random Forest (RF) algorithm was employed to build the pine tree stress probability model and analyze the importance of spectral, topographic, and meteorological features. The model achieved prediction precisions of 0.876, 0.900, and 0.883, and overall accuracies of 89.5%, 91.6%, and 90.2% for January, February, and March 2023, respectively. The results indicate that spectral features, such as band reflectance and vegetation indices, ranked among the top five in importance (i.e., SWIR2, SWIR1, Red band, NDVI, and NBR). They more effectively reflected changes in canopy pigments and leaf moisture content under stress compared with topographic and meteorological features. Additionally, combining long-term stress disturbance data with high-resolution imagery to select training samples improved their spatial and temporal representativeness, enhancing the model’s predictive capability. This approach provides valuable insights for improving forest health monitoring and uncovers opportunities to predict future beetle outbreaks and take preventive measures.
HoloGaussian Digital Twin: Reconstructing 3D Scenes with Gaussian Splatting for Tabletop Hologram Visualization of Real Environments
Authors: Do Tam Le Phuc , Choi Jinwon , Le Viet Quoc , Gentet Philippe , Hwang Leehwan , Lee Seunghyun
Published: December 2024

Several studies have explored the use of hologram technology in architecture and urban design, demonstrating its feasibility. Holograms can represent 3D spatial data and offer an immersive experience, potentially replacing traditional methods such as physical 3D and offering a promising alternative to mixed-reality display technologies. Holograms can visualize realistic scenes such as buildings, cityscapes, and landscapes using the novel view synthesis technique. This study examines the suitability of spatial data collected through the Gaussian splatting method for tabletop hologram visualization. Recent advancements in Gaussian splatting algorithms allow for real-time spatial data collection of a higher quality compared to photogrammetry and neural radiance fields. Both hologram visualization and Gaussian splatting share similarities in that they recreate 3D scenes without the need for mesh reconstruction. In this research, unmanned aerial vehicle-acquired primary image data were processed for 3D reconstruction using Gaussian splatting techniques and subsequently visualized through holographic displays. Two experimental environments were used, namely, a building and a university campus. As a result, 3D Gaussian data have proven to be an ideal spatial data source for hologram visualization, offering new possibilities for real-time motion holograms of real environments and digital twins.
Real-Time Fatigue Detection Algorithms Using Machine Learning for Yawning and Eye State
Authors: Makhmudov Fazliddin , Turimov Dilmurod , Xamidov Munis , Nazarov Fayzullo , Cho Young-Im
Published: December 2024

Drowsiness while driving is a major factor contributing to traffic accidents, resulting in reduced cognitive performance and increased risk. This article gives a complete analysis of a real-time, non-intrusive sleepiness detection system based on convolutional neural networks (CNNs). The device analyses video data recorded from an in-vehicle camera to monitor drivers’ facial expressions and detect fatigue indicators such as yawning and eye states. The system is built on a strong architecture and was trained using a diversified dataset under varying lighting circumstances and facial angles. It uses Haar cascade classifiers for facial area extraction and advanced image processing algorithms for fatigue diagnosis. The results demonstrate that the system obtained a 96.54% testing accuracy, demonstrating the efficiency of using behavioural indicators such as yawning frequency and eye state detection to improve performance. The findings show that CNN-based architectures can address major public safety concerns, such as minimizing accidents caused by drowsy driving. This study not only emphasizes the need of deep learning in establishing dependable and practical driver monitoring systems, but it also lays the groundwork for future improvements, such as the incorporation of new behavioural and physiological measurements. The suggested solution is a big step towards increasing road safety and reducing the risks associated with driver weariness.
Performance of a Non-Invasive System for Monitoring Blood Glucose Levels Based on Near-Infrared Spectroscopy Technology (Glucube®)
Authors: Gómez-Peralta Fernando , Luque Romero Luis Gabriel , Puppo-Moreno Antonio , Riesgo Jesús
Published: December 2024

Background: The need for frequent blood glucose (BG) monitoring and the inconveniences associated with self-monitoring of BG (SMBG) have driven the development of non-invasive approaches. Methods: This prospective study aimed to investigate the accuracy of glucose level calculation using the near-infrared spectroscopy (NIRS) technology Glucube® system. People with Type 1 diabetes, Type 2 diabetes, prediabetes, and normal glucose metabolism were included. Over one week, individuals performed glucose measurements with the Glucube® system and capillary blood fingersticks with a standard glucometer (Ascensia Contour® Next). To assess the impact of the improvement in dexterity, the accuracy variables were compared with the point-of-care (POC) glucometer Accu-Chek® Inform II in a one-week sub-study. Results: Overall, 105 subjects (mean age 53.8 ± 13.8 years, 50.5% female) participated, resulting in 1914 paired glucose measurements between 49 and 331 mg/dL. Total mean absolute relative difference (MARD) was 20.3%, MARD for values >100 mg/dL was 18.3%, and mean absolute deviation (MAD) for values <100 mg/dL was 24.9%. A total of 97.3% of measurements fell within A+B Parkes zones, and 58.8%, 76.9%, and 88.1% within +−20%, +−30%, or +−40% error, respectively. On completion, 62 participants (59%) fulfilled the one-week prospective sub-study. In this subgroup, the total MARD was reduced between day 1 and day 8 from 22.8 to 18.3% (p = 0.068). The percentages within Zone A were 51.6 vs. 61.2%, Zone B 46.8 vs. 33.9%, and Zone C 1.6 vs. 4.8%, and the sum of Parkes Zones A+B was 98.4 vs. 95.2% (p = 0.311) for day 1 and day 8, respectively. Conclusions: Glucube® is a novel non-invasive system based on NIRS technology for monitoring blood glucose levels. Its promising capabilities support further research.
Design and Validation of an Obstacle Contact Sensor for Aerial Robots
Authors: Vigara-Puche Victor , Fernandez-Gonzalez Manuel J. , Fumagalli Matteo
Published: December 2024

Obstacle contact detection is not commonly employed in autonomous robots, which mainly depend on avoidance algorithms, limiting their effectiveness in cluttered environments. Current contact-detection techniques suffer from blind spots or discretized detection points, and rigid platforms further limit performance by merely detecting the presence of a collision without providing detailed feedback. To address these challenges, we propose an innovative contact sensor design that improves autonomous navigation through physical contact detection. The system features an elastic collision platform integrated with flex sensors to measure displacements during collisions. A neural network-based contact-detection algorithm converts the flex sensor data into actionable contact information. The collision system was validated with collisions through manual flights and autonomous contact-based missions, using sensor feedback for real-time collision recovery. The experimental results demonstrated the system’s capability to accurately detect contact events and estimate collision parameters, even under dynamic conditions. The proposed solution offers a robust approach to improving autonomous navigation in complex environments and provides a solid foundation for future research on contact-based navigation systems.
Optimising Concrete Crack Detection: A Study of Transfer Learning with Application on Nvidia Jetson Nano
Authors: Nguyen C. Long , Nguyen Andy , Brown Jason , Byrne Terry , Ngo Binh Thanh , Luong Chieu Xuan
Published: December 2024

The use of Artificial Intelligence (AI) to detect defects such as concrete cracks in civil and transport infrastructure has the potential to make inspections less expensive, quicker, safer and more objective by reducing the need for on-site human labour. One deployment scenario involves using a drone to carry an embedded device and camera, with the device making localised predictions at the edge about the existence of defects using a trained convolutional neural network (CNN) for image classification. In this paper, we trained six CNNs, namely Resnet18, Resnet50, GoogLeNet, MobileNetV2, MobileNetV3-Small and MobileNetV3-Large, using transfer learning technology to classify images of concrete structures as containing a crack or not. To enhance the model’s robustness, the original dataset, comprising 3000 images of concrete structures, was augmented using salt and pepper noise, as well as motion blur, separately. The results show that Resnet50 generally provides the highest validation accuracy (96% with the original dataset and a batch size of 16) and the highest validation F1-score (95% with the original dataset and a batch size of 16). The trained model was then deployed on an Nvidia Jetson Nano device for real-time inference, demonstrating its capability to accurately detect cracks in both laboratory and field settings. This study highlights the potential of using transfer learning on Edge AI devices for Structural Health Monitoring, providing a cost-effective and efficient solution for automated crack detection in concrete structures.
Design and Monitoring Application of an Adjustable Intelligent Bearing Based on Pressure Sensing
Authors: Li Shu , Zhang Zaiyu , Gan Luyi , Yin Jiheng , Fu Ming
Published: December 2024

Single-pier, dual-bearing bridges are susceptible to effects such as concrete creep, thermal expansion, and uneven foundation settlement. When combined with eccentric loading from heavy vehicles, these factors collectively can significantly increase the risk of bridge overturning. To address this risk, a comprehensive analysis of the bridge overturning mechanism was conducted. Considering the current limitations of health monitoring in bearing reaction force (BRF) measurement and risk mitigation, an adjustable intelligent bearing based on pressure sensing and self-locking principles was developed. Its mechanical performance was analyzed under the most unfavorable load conditions. To further validate the approach, a specific experimental bridge was used as a case study. The effectiveness of the force measurement and height adjustment functions was evaluated through moving load experiments. The results showed that the force measurement function accurately captured dynamic BRF changes within a precision range of ±0.1% FS and demonstrated high sensitivity to instantaneous impact effects. The height adjustment function achieved a reaction force change of up to 40 kN within the maximum adjustment range of 1.2 mm, significantly improving the load distribution of the bridge. These findings validated the reliability of the proposed intelligent bearing in real-time monitoring and proactive risk adjustment. This effectively overcomes the limitations of existing bearings, which only perform passive monitoring. Overall, it achieves the real-time monitoring of BRF and proactive control of bridge overturning risks.
Time-Lag Transiograms and Their Implications for Landscape Change Characterization
Authors: Li Xinba , Li Weidong , Zhang Chuanrong
Published: December 2024

Markov chain transition probability matrices (TPMs) have traditionally been used to characterize land use and land cover (LULC) changes and species succession. However, previous studies relied solely on TPMs or transition area matrices to describe overall class area/proportion changes, overlooking important time correlation features. This study introduces the concept of idealized time-lag transiograms and demonstrates how they can be computed from temporal TPMs, using illustrative examples. The primary objective is to explore the potential value and utility of idealized time-lag transiograms in revealing additional characteristics of landscape change. Specifically, we focus on computing idealized time-lag transiograms with a fixed starting point and highlighting their fundamental features, such as sills, practical correlation ranges, and curve shapes, along with peak positions and peak height ratios of peaked cross-transiograms. These features are identified and discussed in terms of their potential implications for characterizing LULC changes. While idealized time-lag transiograms with a fixed starting point may not precisely predict future LULC changes due to the assumptions of the Markov property and time homogeneity (i.e., stationarity), they provide new insights into future LULC dynamics, revealing aspects that traditional Markov chain analysis has overlooked.
Selection of Grape Varieties for Canned Processing Based on Peeling Performance, Sensory Quality, and Storage Stability
Authors: Feng Yabin , Wang Jiancheng , Chen Yihui , Pan Lifeng , Li Duo
Published: December 2024

The integration of grapes into canned food processing not only effectively extends their shelf life but also preserves their rich nutrition and delightful flavor. This marks a significant advancement toward value-added products and sustainability in the grape industry. This study aims to evaluate the appropriateness of different grape varieties for canned grape production, with a focus on peeling characteristics, sensory quality, and storage properties. Our findings reveal that Kyoho, Takatsuma, and Zuijinxiang grapes stand out as promising candidates, characterized by their ease of peeling, minimal peeling loss, and efficient peeling time. Subsequently, a fuzzy mathematical sensory evaluation approach was employed to assess the taste, flavor, texture, appearance, and size of the peeled grapes from nine grape varieties. Notably, Kyoho (3.87), Takatsuma (3.70), and Zuijinxiang (3.57) grapes exhibited superior sensory scores compared with the other varieties. Regarding storage quality, after 180 days of storage, Kyoho grapes exhibited lower color difference by 12.97–23.50%, higher brittleness by 13.77–19.17%, total phenolic content by 15.73–29.29%, total flavonoid content by 28.54–39.31%, anthocyanin content by 23.81–35.66%, and stronger antioxidant capacity (IC50 DPPH: 24.42–69.55%; IC50 ABTS: 13.27–57.43%) compared with Takatsuma and Zuijinxiang grapes. This comprehensive assessment highlights Kyoho grapes as the most suitable variety for canned grape production, followed by Takatsuma and Zuijinxiang grapes. Their exceptional peeling characteristics, sensory qualities, and notable storage resilience position them as promising candidates for commercialization, presenting substantial potential for widespread acceptance among consumers.
Analysis of the Selection of Suppliers of Loading and Transportation Equipment in Mining SMEs
Authors: Ramírez-Olivares Edison , Castillo-Vergara Mauricio , Olivares-Campusano Jovany , Tirado-Flores Matías
Published: December 2024

Small- and medium-sized enterprise (SME) mining firms contribute to Chile’s economy. However, more support is needed to improve decision-making, mainly in a context where it is necessary for mining to operate sustainably. Loading and transportation are essential unit operations in mining. Solution-focused supplier companies are joining the market, making selection more difficult. This study suggests a hierarchical analytical process-based multi-criteria analysis. Among what stands out are its simplicity and clarity. The Analytic Hierarchy Process (AHP), used in several fields, is a flexible multi-criteria analysis system for complex decision-making. Its development used Expert Choice® software. The results show that the most crucial criterion for selecting loading and transportation equipment suppliers is related to occupational safety and health. The most relevant components are the mortality, accident frequency, and severity rates. Operational indicators are the second most relevant criterion, enabling companies to be more productive and efficient in achieving their objectives. Sensitivity analysis demonstrates that, even with variations in the criterion preferences, the fatality rate remains at the top of the hierarchy, showing the robustness of the model used. Contrary to what might be expected, criteria such as the supplier profile do not stand out among the critical factors for the sector.
Multivariate Analysis of the Sustainable Development of the Silver Economy in the European Union Countries
Authors: Bieszk-Stolorz Beata , Dmytrów Krzysztof , Frąckiewicz Ewa
Published: December 2024

The ageing of societies is one of the biggest challenges of the modern world. The response to this new reality is the development of the silver economy. It is a multidimensional concept that is an extremely important factor in sustainable development. The aim of this article is to compare EU countries according to selected aspects of the development of the silver economy. This study covered the years 2009–2021. It was conducted on the basis of data from the Eurostat database. This study used multivariate statistical analysis methods: k-means, COmplex PRoportional ASsessment (COPRAS) in a dynamic approach, Dynamic Time Warping (DTW) and hierarchical clustering. EU countries differ in terms of the studied aspects of the silver economy in a north–south direction. In the period under study, there were significant changes in the country rankings and these changes were not the same for the EU countries. Also, most EU countries indicated improvements in terms of analyzed aspects of the silver economy. An innovative approach is the use of the COPRAS method in a dynamic approach, thanks to which it is possible to develop not only country rankings, but also to analyze the dynamics of changes.
The Sustainable Integration of AI in Higher Education: Analyzing ChatGPT Acceptance Factors Through an Extended UTAUT2 Framework in Peruvian Universities
Authors: Arbulú Ballesteros Marco Agustín , Acosta Enríquez Benicio Gonzalo , Ramos Farroñán Emma Verónica , García Juárez Hugo Daniel , Cruz Salinas Luis Edgardo , Blas Sánchez Julio Ernesto , Arbulú Castillo Julie Catherine , Licapa-Redolfo Gladys Sandi , Farfán Chilicaus Gary Christiam
Published: December 2024

ChatGPT, a large language model AI, has the potential to transform higher education by providing students with personalized learning support, assisting in writing tasks, and enhancing their level of engagement. This study examines the factors influencing the acceptance of ChatGPT among university students in Peru, following the extended UTAUT2 model with the addition of a construct called knowledge sharing (KS). A total of 772 students from seven universities in Lambayeque and La Libertad participated in an online survey, providing insights into their perceptions and experiences with using ChatGPT for academic purposes. The results from the structural equation model showed that effort expectancy, behavioral intention, and knowledge sharing positively influenced the actual use of ChatGPT. Furthermore, effort expectancy and performance expectancy were found to be determinants of the behavioral intention to use ChatGPT. The study also revealed that performance expectancy and behavioral intention serve as sequential mediating variables regarding the effect of effort expectancy on actual use. These findings suggest a positive adoption of ChatGPT among students, driven by individual and contextual factors, and highlight the importance of managing effort and performance expectations appropriately. This study represents a significant advancement in understanding the acceptance of ChatGPT in higher education and provides valuable guidance for practical implementation efforts, ensuring that this powerful AI tool is effectively leveraged to support student learning and success.
Optimizing Bus Bridging Service Considering Passenger Transfer and Reneging Behavior
Authors: Zhang Ziqi , Li Xuan , Zhang Jikang , Shi Yang
Published: December 2024

This paper addresses the design of bus bridging services in response to urban rail disruption, which plays a critical role in enhancing the resilience and sustainability of urban transportation systems. Specifically, it focuses on unplanned urban rail disruptions that result in temporary closure of line sections, including transfer stations. Under this “transfer scenario”, a heuristic-rule based method is firstly presented to generate candidate bus bridging routes. Non-parallel bridging routes are introduced to facilitate transfer passengers affected by the disruption. Meanwhile, the bridging stops visited by parallel routes are extended beyond the disrupted section, mitigating passenger congestion and bus bunching at turnover stations. Then, we propose an integrated optimization model that collaboratively addresses bus route selection and vehicle deployment issues. Capturing passenger reneging behavior, the model aims to maximize the number of served passengers with tolerable waiting times and minimize total passenger waiting times. A two-stage genetic algorithm is developed to solve the model, which incorporates a multi-agent simulation method to demonstrate dynamic passenger and bus flow within a time–space network. Finally, a case study is conducted to validate the effectiveness of the proposed methods. Sensitivity analyses are performed to explore the impacts of fleet size and route diversity on the overall bridging performance. The results offer valuable insights for transit agencies in designing bus bridging services under transfer scenarios, supporting sustainable urban mobility by promoting efficient public transit solutions that mitigate the social impacts of sudden service disruptions.
Regional Aspects of Transformations in Agriculture: The Case of the Republic of Bulgaria
Authors: Doitchinova Julia , Stoyanova Zornitsa
Published: December 2024

Transformations in agriculture not only change the ways in which natural resources and social capital are used but are also a prerequisite for different opportunities to create added value and improve the viability and sustainability of rural areas. The purpose of this article is to assess the differences and effects of transformations in agriculture in the Bulgarian regions. Based on statistical data from the Censuses of agricultural holdings in 2010 and 2020, the DEA method was applied. Six models (three input-oriented and three output-oriented) were constructed and tested, and the efficiency coefficients were assessed on a regional level. The degree of structural changes and the efficiency of the used production and other resources were evaluated, and conclusions were drawn. In the majority of the models, differences were observed between the Northern regions of the country and the South Western and South Central regions. The most significant are the efficiency coefficient of the labor force used, and the output produced, the gross value added, and the net mixed income. In the other models, multidirectional changes were observed. The North Western is the region in which all calculated efficiency coefficients increased, and in the North Eastern and South Eastern regions, the most calculated coefficients decreased. Based on the analysis, recommendations related to region-specific agricultural policies for better resource allocation and sustainable development are proposed.
Technology Characteristics for Sustainable Service Development in Intelligent Elderly Care Services
Authors: Fu Yi , Xu Chao , Cho Sung-Eui
Published: December 2024

Although the development of intelligent elderly care services is a critical challenge in an aging society, the role and impact of technology characteristics on customer satisfaction and the development of sustainable intelligent elderly care services remain unclear. This study examines how technological characteristics affect customer satisfaction and the development of sustainable intelligent elderly care services through an empirical study. The representative technology characteristics in intelligent elderly care services are defined with seven independent factors, including responsiveness, interactivity, technology readiness, customization, empathy, convenience, and security, and perceived value is adopted as a mediating factor, to analyze relationships with customer satisfaction. A total of 297 sets of data were collected from experienced consumers in the Chinese market, where intelligent elderly care services are broadly used. Exploratory factor analysis, multiple regression analysis, and mediation analysis were employed for statistical analyses. The findings showed that technology characteristics significantly impact customer satisfaction in intelligent elderly care services. The perceived value plays a meaningful mediating role in these relationships. This paper provides a valuable model for evaluating technology characteristics in intelligent elderly care services, taking a meaningful step toward developing relevant advanced services.
Foreign Direct Investment and Sustainable Development in Asia: Bibliometric Analysis and Systematic Literature Review
Authors: Rodríguez-Chávez Cristhina Aracelly , Oré-Evanán Luz Mirian , Zapata-Sánchez Giampierre Gerardo , Toribio-Lopez Alexander , Eguiguren-Eguigurem Germán Rafael
Published: December 2024

Through SDG 17, the United Nations 2030 Agenda highlights the importance of international cooperation to promote sustainable development. Following the COVID-19 pandemic, this cooperation has gained greater relevance, exacerbating the need for debt relief and financial assistance for developing countries. Although global Foreign Direct Investment (FDI) increased by 3% in 2023, investments in developing countries decreased by 9%, with Asia being the most affected region. This article addresses the research gap of a bibliometric study and systematic review on the contribution of FDI to sustainable development, covering economic, social and environmental dimensions. In order to examine the existing literature, a general bibliometric analysis and a systematic review were conducted, using databases such as Scopus and Web of Science, examining studies published between 2019 and early 2024. The bibliometric analysis revealed that Asia is the most studied region, while the systematic review of 48 articles showed that the economic and environmental dimensions are the most researched, with the social dimension being the least addressed. Accordingly, we suggest several directions for future research that could enrich and strengthen the field.
Data Augmentation Technique Based on Improved Time-Series Generative Adversarial Networks for Power Load Forecasting in Recirculating Aquaculture Systems
Authors: Li Jun , Zhang Xingzhao , Hu Qingsong , Zhang Fuxi , Gaida Oleg , Chen Leilei
Published: December 2024

Factory aquaculture faces a difficult situation due to its high running costs, with one of the main contributing factors being the high energy consumption of aquaculture workshops. Accurately predicting the power load of recirculating aquaculture systems (RAS) is critical to optimizing energy use, reducing energy consumption, and promoting the sustainable development of factory aquaculture. Adequate data can improve the accuracy of the prediction model. However, there are often missing and abnormal data in actual data detection. To solve this problem, this study uses a time-series convolutional network–temporal sequence generation adversarial network (TCN-TimeGAN) to synthesize multivariate RAS data and train a long short-term memory (LSTM) network on the original and generated data to predict future electricity loads. The experimental results show that the data generated based on the improved TCN-TimeGAN provide more comprehensive coverage of the original data distribution, with a lower discriminative score (0.2419) and a lower predictive score (0.0668) than the conventional TimeGAN. Using the generated data for prediction, the R2 reached 0.86, which represents a 19% improvement over the ARIMA model. Meanwhile, compared to LSTM and GRU without data augmentation, the mean absolute error (MAE) was reduced by 1.24 and 1.58, respectively. The model has good prediction performance and generalization ability, which benefits the RAS energy saving, production planning, and the long term sustainability of factory aquaculture.
Unlocking ESG Performance Through Intelligent Manufacturing: The Roles of Transparency, Green Innovation, and Supply Chain Collaboration
Authors: Huang Hui , Yang Jing , Ren Changman
Published: December 2024

With the advancement of global sustainable development goals and the introduction of the ‘dual-carbon’ strategy, intelligent manufacturing (IM) has become an important pathway to promote the transformation and upgrading of enterprises. However, the ways in which IM enhances environmental, social, and corporate governance (ESG) performance, along with its potential mechanisms, remain unexplored. This study employs a two-way fixed-effects model with panel data from 4417 Chinese listed firms spanning the period 2009–2022 to examine these relationships. It is found that IM significantly improves corporate ESG performance. Robustness tests confirm the reliability of these results, and mechanism analysis highlights the mediating effects of information transparency, green technology innovation, and supply chain collaborative innovation. Furthermore, the heterogeneity analysis indicates that IM has a notably stronger effect in high-carbon-emission sectors, state-owned enterprises, and high-tech industries. This suggests that policymakers should design differentiated policies based on industry and firm characteristics to promote the adoption of IM and foster sustainable development strategies. This research contributes to expanding the theoretical understanding of how IM affects ESG while also providing empirical evidence for enterprises and governments to promote green transformation.
Climate Risk and Its Impact on the Cost of Capital—A Systematic Literature Review
Authors: Meneses Cerón Luis Ángel , van Klyton Aaron , Rojas Albano , Muñoz Jefferson
Published: December 2024

As climate change intensifies the frequency and severity of its adverse events, there is growing concern about its impact on the economy, particularly in financial markets and corporate financial management. This study addresses this gap through a systematic literature review in Scopus according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method, aiming to analyse how climate risk affects the Weighted Average Cost of Capital (WACC) and, consequently, corporate investment and financing decisions. The findings reveal that companies with negative environmental profiles face higher capital costs and loan interest rates. The cost of capital is crucial in the fight against climate change, highlighting the need for sustainable financial policies and investments. Therefore, it is essential to implement policy interventions that reduce these costs, which requires a radical transformation of existing financial frameworks, the promotion of innovative instruments, and the strengthening of financial markets through an integrated approach that links climate finance with economic and social development.
Assessing Particulate Matter Deposition and Resuspension by Living Wall Systems in a Wind Tunnel Setup
Authors: Ysebaert Tess , Koch Kyra , Samson Roeland , Denys Siegfried
Published: December 2024

This study examines the particulate matter (PM) capture capacity of living wall systems (LWSs), focusing on leaf traits that facilitate PM deposition. Six LWS designs, differing in structure and substrate, were tested under constant airflow conditions with and without additional PM. Results showed that planter-based LWSs reduced PM0.1 by 2% and PM2.5 by 4%, while a textile LWS reduced PM0.1 by 23% and PM2.5 by 5%, though geotextile textile increased PM by 11% for both fractions. A moss substrate LWS worsened air quality, raising PM0.1 by 2% and PM2.5 by 5%. Magnetic analysis of leaf-deposited PM (SIRM) revealed species-specific differences (p < 0.001), with SIRM values ranging from 5 ± 1 µA to 260 ± 1 µA and higher PM accumulation in plants with lower specific leaf areas. No differences were observed in SIRM between deposition and resuspension phases, indicating the PM source lacked sufficient magnetisable particles. The findings highlight the potential of LWSs in urban environments for air quality improvement but underscore the importance of selecting suitable LWS structures and plant species.
Topology Optimization, Part Orientation, and Symmetry Operations as Elements of a Framework for Design and Production Planning Process in Additive Manufacturing L-PBF Technology
Authors: Malbašić Slobodan , Đorđević Aleksandar , Živković Srđan , Džunić Dragan , Sokolović Vlada
Published: December 2024

This paper investigates the possibility of the application of different optimization techniques in the design and production planning phase in the metal additive manufacturing process, specifically laser powder bed fusion (L-PBF) additive technology. This technology has a significant market share and belongs to the group of mature additive technology for the production of end-use metal parts. In the application of this technology, there is a space for additional cost/time reduction by simultaneously optimizing topology structure and part orientations. Simultaneous optimization reduces the production time and, indirectly, the cost of parts production, which is the goal of effective process planning. The novelty in this paper is the comparison of the part orientation solutions defined by the software algorithm and the experienced operator, where the optimal result was selected from the aspect of time and production costs. A feature recognition method together with symmetry operations in the part orientation process were also examined. A framework for the optimal additive manufacturing planning process has been proposed. This framework consists of design and production planning phases, within which there are several other activities: the redesign of the part, topological optimization, the creation of alternative build orientations (ABOs), and, as a final step, the selection of the optimal build orientation (OBO) using the multi-criteria decision method (MCDM). The results obtained after the MCDM hybrid method application clearly indicated that simultaneous topology optimization and part orientation has significant influence on the cost and time of the additive manufacturing process. The paper also proposed a further research direction that should take into consideration the mechanical as well as geometric, dimensioning and tolerances (GDT) characteristics of the part during the process of ABOs and OBO, as well as the uses of symmetry in these fields.