bluuebunny/crossref_metadata_2025 huggingface dataset:
Formulation, E-Beam Crosslinking, and Comprehensive Characterisation of Lavender Oil-Enriched Hydrogels
Authors: Demeter Maria , Călina Ion , Scărișoreanu Anca , Nemțanu Monica R. , Brașoveanu Mirela , Micutz Marin , Dumitru Marius
Published: November 2024

This study focused on the formulation, electron beam (e-beam) crosslinking, and characterisation of hydrogels enriched with lavender oil (LO) to enhance their structural and functional properties for biomedical applications. Stable hydrogels were synthesised using water-soluble polymers and suitable ratios of Tween 80 and Isopropyl alcohol (IPA) as surfactant and co-surfactant, respectively, via e-beam irradiation at doses up to 70 kGy. The most effective crosslinking was achieved with a radiation dose of 30 kGy, depending on the concentrations of surfactants and LO. LO-enriched hydrogels exhibited enhanced superabsorbent swelling (7700% to 18,000%) and faster equilibrium rates than the control hydrogel. Structural analysis revealed a flexible spongiform porous architecture with larger mesh sizes (156 nm to 246 nm) and adequate elastic moduli (130 to 308 Pa). Degradation tests aligned with swelling data, demonstrating a degradation rate of 12% after 35 days, indicating an appropriate balance of stability and degradation. These findings suggest that e-beam technology, in conjunction with LO and surfactant addition, can effectively tailor hydrogel properties for biomedical applications, making them promising candidates for further research in wound care, drug delivery systems, and other biological applications.
Three-Dimensionally-Printed Polymer and Composite Materials for Dental Applications with Focus on Orthodontics
Authors: Tichá Daniela , Tomášik Juraj , Oravcová Ľubica , Thurzo Andrej
Published: November 2024

Three-dimensional printing has transformed dentistry by enabling the production of customized dental restorations, aligners, surgical guides, and implants. A variety of polymers and composites are used, each with distinct properties. This review explores materials used in 3D printing for dental applications, focusing on trends identified through a literature search in PubMed, Scopus, and the Web of Science. The most studied areas include 3D-printed crowns, bridges, removable prostheses, surgical guides, and aligners. The development of new materials is still ongoing and also holds great promise in terms of environmentally friendly technologies. Modern manufacturing technologies have a promising future in all areas of dentistry: prosthetics, periodontology, dental and oral surgery, implantology, orthodontics, and regenerative dentistry. However, further studies are needed to safely introduce the latest materials, such as nanodiamond-reinforced PMMA, PLA reinforced with nanohydroxyapatite or magnesium, PLGA composites with tricalcium phosphate and magnesium, and PEEK reinforced with hydroxyapatite or titanium into clinical practice.
Renewable Distillation of Spent Nuclear Fuel
Authors: Böhm Dominik , Czerski Konrad , Weißbach Daniel , Gottlieb Stephan , Huke Armin , Ruprecht Götz
Published: November 2024

Nuclear waste is one of the most important environmental problems of nuclear power plants. A novel renewable distillation method has been proposed for the direct on-site recycling of spent nuclear fuel and the separation of its valuable components from fissile isotopes, which is especially applicable for reactors using liquid fuels. This dry separation technique can be applied in two single, parallel total-reflux columns with integrated separation stages for chlorinated nuclear waste. According to theoretical calculations, high separation accuracy of the UCl4-NpCl4, PuCl3-UCl3, CmCl3-SmCl3, and EuCl3-CsCl fractions could be achieved using twenty-six separation stages and five total-reflux repetitions, demonstrating the high efficiency of the method proposed. A scheme of the future pyroprocessing separation plant is also presented.
Temperature Effect of Cocoa (Theobroma cacao L.) Drying on Energy Consumption, Bioactive Composition and Vibrational Changes
Authors: Jiménez-Rodríguez David J. , García-Alamilla Pedro , Márquez-Rocha Facundo J. , Vázquez-Medina Rubén , Carrera-Lanestosa Areli , González-Alejo Fanny A. , Sánchez-Ramos Carlos A. , Ruiz-Santiago Franco L.
Published: November 2024

Cocoa drying is the post-harvest thermal process used to condition the beans to a moisture content between 6.5 and 7% for storage and further processing. Convective drying is an energy-intensive process where time and temperature are considered critical factors for the degradation of bioactive compounds in edible products. In the present study, the energy parameters, vibrational spectroscopy, and changes in bioactive compounds of cocoa beans were studied during thin-layer hot air drying at 50 °C, 60 °C, and 70 °C. Moisture loss, specific energy consumption (SEC), energy efficiency, total phenolics (TPs), total flavonoids (TFs), and antioxidant activity (DPPH) were determined. Fourier transform infrared (FT-IR) spectroscopy with attenuated total reflectance (ATR) was used to characterize the samples, and a multivariate analysis was applied to find interactions among the components. The obtained SEC was 18,947.30–24,469.51 kJ/kg, and the energy efficiency was 9.73–12.31%. When the temperature was 70 °C, the best values for SEC and energy efficiency were obtained. The results also showed that the convective drying generated changes in the TP levels for the three temperatures, mainly after 300 min, with maximum levels between 360 and 600 min, at 70 °C; however, it does not have a clear relationship with the TFs and the antioxidant activity. The FT-IR and the multivariate analysis revealed changes in several signals in the 1800 to 400 cm−1 range, confirming the variation in the associated signal with phenolic compounds.
Correction: Romano (2024). Unveiling Superstition in Vieste: Popular Culture and Ecclesiastical Tribunals in the 18th-Century Kingdom of Naples. Religions 15: 1202
Authors: Romano Francesca Vera
Published: November 2024

There was an error in the original publication (Romano 2024) [...]
Research on Airborne Ground-Penetrating Radar Imaging Technology in Complex Terrain
Authors: Chi Yuelong , Pang Su , Mao Lifeng , Zhou Qiang , Chi Yuehua
Published: November 2024

The integration of ground-penetrating radar (GPR) with unmanned aerial vehicles (UAVs) enables efficient non-contact detection, performing exceptionally well in complex terrains and extreme environments. However, challenges in data processing and interpretation remain significant obstacles to fully utilizing this technology. To mitigate the effects of numerical dispersion, this paper develops a high-order finite-difference time-domain (FDTD(2,4)) three-dimensional code suitable for airborne GPR numerical simulations. The simulation results are compared with traditional FDTD methods, validating the accuracy of the proposed approach. Additionally, a Kirchhoff migration algorithm that considers the influence of the air layer is developed for airborne GPR. Different processing strategies are applied to flat and undulating terrain models, significantly improving the identification of shallowly buried targets. Particularly under undulating terrain conditions, the energy ratio method is introduced, effectively suppressing the interference of surface reflections caused by terrain variations. This innovative approach offers a new technical pathway for efficient GPR data processing in complex terrains. The study provides new insights and methods for the practical application of airborne GPR.
Remote Inspection of Bridges with the Integration of Scanning Total Station and Unmanned Aerial Vehicle Data
Authors: Olaszek Piotr , Maciejewski Edgar , Rakoczy Anna , Cabral Rafael , Santos Ricardo , Ribeiro Diogo
Published: November 2024

Remote visual inspections are valuable tools for maintaining bridges in safe operation. In the case of old structures with incomplete documentation, the verification of dimensions is also an essential aspect. This paper presents an attempt to use a Scanning Total Station (STS) and Unmanned Aerial Vehicle (UAV) for the inspection and inventory of bridge dimensions. The STS’s measurements are conducted by applying two methods: the direct method using a total station (TS) and advanced geometric analyses of the collected point cloud. The UAV’s measurements use a Structure from Motion (SfM) method. Verification tests were conducted on a steel truss railway bridge over the largest river in Poland. The measurements concerned both the basic dimensions of the bridge and the details of a selected truss connection. The STS identified a significant deviation in the actual geometry of the measured connection and the design documentation. The UAV’s inspection confirmed these findings. The integration of STS and UAV technologies has demonstrated significant advantages, including STS’s high accuracy in direct measurements, with deviations within acceptable engineering tolerances (below a few mm), and the UAV’s efficiency in covering large areas, achieving over 90% compliance with reference dimensions. This combined approach not only reduces operating costs and enhances safety by minimizing the need for heavy machinery or scaffolding but also provides a more comprehensive understanding of the structural condition.
Evaluation of the Accuracy of Interactive Multisensor Snow and Ice Mapping System (IMS) 1 km Product Using Ground Snow Depth Data Across China
Authors: Chen Aijun , Wang Yaxuan , Zheng Zhaojun , Wang Jiangeng
Published: November 2024

This study evaluates the accuracy of the U.S. interactive multisensor snow and ice mapping system (IMS) 1 km product with recall, precision, false alarm rate, and overall accuracy by utilizing ground-observed snow depth data from meteorological stations in China over six snow seasons spanning from 2015 to 2020. Furthermore, it examines the correlation between recall and snow depth, as well as the correlation between overall accuracy and snow depth. The results reveal the following: The precision typically peaks during the middle of the snow season, predominantly exceeding 80% but rarely reaching 100%, with minimal spatial variation. The precision generally ranges from 75% to 80%, exhibiting slight spatial variations during this period. The overestimation rate remains below 5%, seldom exceeding 10%. The overall accuracy mostly exceeds 95%, showing no significant spatial variations. Across both China and its five regions, the recall demonstrates a significant logarithmic correlation with snow depth, while the overall accuracy exhibits an almost horizontal linear correlation with snow depth once it exceeds 3 cm.
Advances in Optical and Thermal Remote Sensing of Vegetative Drought and Phenology
Authors: Li Ting , Zhong Shaobo
Published: November 2024

In recent decades, remote sensing of vegetative drought and phenology has gained considerable attention from researchers, leading to a significant increase in research activity in this area. While new drought indices are being proposed, there is also growing attention on how variations in phenology affect drought detection. This review begins by exploring the crucial role of satellite optical and thermal remote sensing technologies in monitoring vegetative drought. It presents common methods after revisiting the foundational concepts. Then, the review examines remote sensing of land surface phenology (LSP) due to its strong connection with vegetative drought. Subsequently, we investigate vegetative drought detection techniques that consider phenological variability and recommend approaches to improve the detection of vegetative drought, emphasizing the necessity to incorporate phenological metrics. Finally, we suggest potential future work and directions. Unlike other review papers on remote sensing of vegetative drought, this review uniquely surveys the comprehensive advancements in both detecting vegetative drought and estimating LSP through optical and thermal remote sensing. It also highlights the necessity and potential applications for these practices.
SAM-Net: Spatio-Temporal Sequence Typhoon Cloud Image Prediction Net with Self-Attention Memory
Authors: Ren Yanzhao , Ye Jinyuan , Wang Xiaochuan , Xiao Fengjin , Liu Ruijun
Published: November 2024

Cloud image prediction is a spatio-temporal sequence prediction task, similar to video prediction. Spatio-temporal sequence prediction involves learning from historical data and using the learned features to generate future images. In this process, the changes in time and space are crucial for spatio-temporal sequence prediction models. However, most models now rely on stacking convolutional layers to obtain local spatial features. In response to the complex changes in cloud position and shape in cloud images, the prediction module of the model needs to be able to extract both global and local spatial features from the cloud images. In addition, for irregular cloud motion, more attention should be paid to the spatio-temporal sequence features between input cloud image frames in the temporal sequence prediction module, considering the extraction of temporal features with long temporal dependencies, so that the spatio-temporal sequence prediction network can learn cloud motion trends more accurately. To address these issues, we have introduced an innovative model called SAM-Net. The self-attention module of this model aims to extract an inner image frame’s spatial features of global and local dependencies. In addition, a memory mechanism has been added to the self-attention module to extract interframe features with long temporal and spatial dependencies. Our method shows better performance than the PredRNN-v2 model on publicly available datasets such as MovingMNIST and KTH. We achieved the best performance in both the 4-time-step and 10-time-step typhoon cloud image predictions. On a cloud dataset consisting of 10 time steps, we observed a decrease in MSE of 180.58, a decrease in LPIPS of 0.064, an increase in SSIM of 0.351, and a significant improvement in PSNR of 5.56 compared to PredRNN-v2.
Assessment of CCMP in Capturing High Winds with Respect to Individual Satellite Datasets
Authors: Rong Pingping , Su Hui
Published: November 2024

High-wind structures were identified in the Cross-Calibrated Multi-Platform (CCMP) ocean wind vector reanalysis for comparison with winds measured by satellite radiometers, scatterometers, and synthetic aperture radar (SAR) instruments from February to October 2023. The comparison aims to evaluate bias, uncertainty, and spatial correlations with the goal of enhancing the accuracy of ocean wind datasets during tropical cyclones (TCs). In 10° longitude × 10° latitude blocks, each containing a TC, Soil Moisture Active Passive (SMAP) and Advanced Microwave Scanning Radiometer 2 (AMSR2) winds are 6.5 and 4.8% higher than CCMP, while Advanced Scatterometer (ASCATB) is 0.8% lower. For extratropical cyclones, AMSR2 and SMAP also show stronger winds with a 5% difference, and ASCATB is about 0.3% weaker compared to CCMP. The comparison between SAR and CCMP for TC winds, sampled at the locations and time frames of SAR tiles, indicates that SAR winds around TCs are about 9% higher than CCMP winds. Using empirically defined TC structural indices, we find that the TCs observed by CCMP are shifted in locations and lack a compact core region. A Random Forest (RF) regressor was applied to TCs in CCMP with corresponding SAR observations, nearly correcting the full magnitude of low bias in CCMP statistically, with a 15 m/s correction in the core region. The hierarchy of importance among the predictors is as follows: CCMP wind speed (62%), distance of SAR pixels to the eye region (21%) and eye center (7%), and distance of CCMP pixels to the eye region (5%) and eye center (5%).
Landslide Susceptibility Mapping Based on Ensemble Learning in the Jiuzhaigou Region, Sichuan, China
Authors: An Bangsheng , Zhang Zhijie , Xiong Shenqing , Zhang Wanchang , Yi Yaning , Liu Zhixin , Liu Chuanqi
Published: November 2024

Accurate landslide susceptibility mapping is vital for disaster forecasting and risk management. To address the problem of limited accuracy of individual classifiers and lack of model interpretability in machine learning-based models, a coupled multi-model framework for landslide susceptibility mapping is proposed. Using Jiuzhaigou County, Sichuan Province, as a case study, we developed an evaluation index system incorporating 14 factors. We employed three base models—logistic regression, support vector machine, and Gaussian Naive Bayes—assessed through four ensemble methods: Stacking, Voting, Bagging, and Boosting. The decision mechanisms of these models were explained via a SHAP (SHapley Additive exPlanations) analysis. Results demonstrate that integrating machine learning with ensemble learning and SHAP yields more reliable landslide susceptibility mapping and enhances model interpretability. This approach effectively addresses the challenges of unreliable landslide susceptibility mapping in complex environments.
Improvement of Coal Mining-Induced Subsidence-Affected (MISA) Zone Irregular Boundary Delineation by MT-InSAR Techniques, UAV Photogrammetry, and Field Investigation
Authors: Liu Linan , Xu Nengxiong , Zhou Wendy , Qin Yan , Luan Shilong
Published: November 2024

Coal mining-induced ground subsidence is a severe hazard that can damage property, infrastructure, and the environment in the vicinity when the deformation is not negligible. The boundary of a mining-induced subsidence-affected zone refers to the area beyond which the ground subsidence is less concerned. Accurately measuring mining-induced ground deformation is essential for delineating the irregular boundary of the impacted area. This study employs multitemporal interferometric synthetic aperture radar (MT-InSAR) techniques, including differential InSAR (DInSAR), InSAR stacking, and interferometric point target analysis (IPTA), to analyze coal mine subsidence and delineate the boundaries of the mining-impacted zones. DInSAR accurately reconstructs, locates, and detects the trend in mining-induced subsidence and correlates well with documented mining operations. The InSAR stacking method maps the spatial variation of the ground’s average line-of-sight (LOS) velocity over the mining area, delineating the boundary of the impacted zone. IPTA analysis combining multilook and single-pixel phases achieves millimeter-level surface measurement above tunnel alignments and measures unevenly distributed deformation fields. This study considers an average of 4 cm per year of surface deformation in the LOS direction as the subsidence threshold value for delineating the boundary of the mining-induced subsidence-affected (MISA) zone during the active coal mining stage. Interestingly, there are twin transportation tunnels near the mining area. The twin tunnels completed before the coal mining activities started were functioning well, but damage was observed after the mining began. Our study reveals the tunnels are located within the InSAR-derived MISA zone, although the tunnels approach the MISA boundary. As direct signs of subsidence, ground fissures have been identified near the tunnels via field investigations and UAV photogrammetry. Furthermore, the derived distribution of ground fissures validates and verifies InSAR measurements. The integrated approach of MT-InSAR, UVA photogrammetry, and field investigation developed in this study can be applied to delineate the irregular boundary of the MISA zone and study the accumulating effects of mining-induced subsidence on the performance of infrastructure in areas proximate to coal mining activities.
Construction Method of a Digital-Twin Simulation System for SCARA Robots Based on Modular Communication
Authors: Zhang Zihan , Guo Qihui , Grigorev Maksim A. , Kholodilin Ivan
Published: November 2024

Due to the high cost of robots, the algorithm testing cost for physical robots is high, and the construction of motion control programs is complex, with low operation fault tolerance. To address this issue, this paper proposes a low-cost, cross-platform SCARA robot digital-twin simulation system based on the concept of digital twins. This method establishes a 5D architecture based on the characteristics of different platforms, classifies data and integrates functions, and designs a data-processing layer for motion trajectory calculation and data storage for a virtual-reality robot. To address the complexity of data interaction under different cross-platform communication forms, an editable, modular, cross-platform communication system is constructed, and various control commands are encapsulated into simple programming statements for easy invocation. Experimental results showed that, based on modular communication control, users can accurately control data communication and synchronous motion between virtual and physical models using simple command statements, reducing the development cost of control algorithms. Meanwhile, the virtual-robot simulation system, as a data mapping of the real experimental platform, accurately simulated the physical robot’s operating state and spatial environment. The robot algorithms tested using the virtual simulation system can be successfully applied to real robot platforms, accurately reproducing the operating results of the virtual system.
Pulse-Driven MEMS NO2 Sensors Based on Hierarchical In2O3 Nanostructures for Sensitive and Ultra-Low Power Detection
Authors: Mei Haixia , Zhang Fuyun , Zhou Tingting , Zhang Tong
Published: November 2024

As the mainstream type of gas sensors, metal oxide semiconductor (MOS) gas sensors have garnered widespread attention due to their high sensitivity, fast response time, broad detection spectrum, long lifetime, low cost, and simple structure. However, the high power consumption due to the high operating temperature limits its application in some application scenarios such as mobile and wearable devices. At the same time, highly sensitive and low-power gas sensors are becoming more necessary and indispensable in response to the growth of the environmental problems and development of miniaturized sensing technologies. In this work, hierarchical indium oxide (In2O3) sensing materials were designed and the pulse-driven microelectromechanical system (MEMS) gas sensors were also fabricated. The hierarchical In2O3 assembled with the mass of nanosheets possess abundant accessible active sites. In addition, compared with the traditional direct current (DC) heating mode, the pulse-driven MEMS sensor appears to have the higher sensitivity for the detection of low-concentrations of nitrogen dioxide (NO2). The limit of detection (LOD) is as low as 100 ppb. It is worth mentioning that the average power consumption of the sensor is as low as 0.075 mW which is one three-hundredth of that in the DC heating mode. The enhanced sensing performances are attributed to loose and porous structures and the reducing desorption of the target gas driven by pulse heating. The combination of morphology design and pulse-driven strategy makes the MEMS sensors highly attractive for portable equipment and wearable devices.
A Multi-Class ECG Signal Classifier Using a Binarized Depthwise Separable CNN with the Merged Convolution–Pooling Method
Authors: Zhang Rui , Zhou Ranran , Zhong Zuting , Qi Haifeng , Wang Yong
Published: November 2024

Binarized convolutional neural networks (bCNNs) are favored for the design of low-storage, low-power cardiac arrhythmia classifiers owing to their high weight compression rate. However, multi-class classification of ECG signals based on bCNNs is challenging due to the accuracy loss introduced by the binarization operation. In this paper, an effective multi-classifier system is proposed for electrocardiogram (ECG) signals using a binarized depthwise separable convolutional neural network (bDSCNN) with the merged convolution–pooling (MCP) method. The binarized depthwise separable convolution layer is adopted to reduce the increased number of parameters in multi-classification systems. Instead of operating convolution and pooling sequentially as in a traditional convolutional neural network (CNN), the MCP method merges pooling together with convolution layers to reduce the number of computations. To further reduce hardware resources, this work employs blockwise incremental calculation to eliminate redundant storage with computations. In addition, the R peak interval data are integrated with P-QRS-T features to improve the classification accuracy. The proposed bDSCNN model is evaluated on an Intel DE1-SoC field-programmable gate array (FPGA), and the experimental results demonstrate that the proposed system achieves a five-class classification accuracy of 96.61% and a macro-F1 score of 89.08%, along with a dynamic power dissipation of 20 μW for five-category ECG signal classification. The hardware resource usage of BRAM and LUTs plus REGs is reduced by at least 2.94 and 1.74 times, respectively, compared with existing ECG classifiers using bCNN methods.
AER-Net: Attention-Enhanced Residual Refinement Network for Nuclei Segmentation and Classification in Histology Images
Authors: Cao Ruifen , Meng Qingbin , Tan Dayu , Wei Pijing , Ding Yun , Zheng Chunhou
Published: November 2024

The acurate segmentation and classification of nuclei in histological images are crucial for the diagnosis and treatment of colorectal cancer. However, the aggregation of nuclei and intra-class variability in histology images present significant challenges for nuclei segmentation and classification. In addition, the imbalance of various nuclei classes exacerbates the difficulty of nuclei classification and segmentation using deep learning models. To address these challenges, we present a novel attention-enhanced residual refinement network (AER-Net), which consists of one encoder and three decoder branches that have same network structure. In addition to the nuclei instance segmentation branch and nuclei classification branch, one branch is used to predict the vertical and horizontal distance from each pixel to its nuclear center, which is combined with output by the segmentation branch to improve the final segmentation results. The AER-Net utilizes an attention-enhanced encoder module to focus on more valuable features. To further refine predictions and achieve more accurate results, an attention-enhancing residual refinement module is employed at the end of each encoder branch. Moreover, the coarse predictions and refined predictions are combined by using a loss function that employs cross-entropy loss and generalized dice loss to efficiently tackle the challenge of class imbalance among nuclei in histology images. Compared with other state-of-the-art methods on two colorectal cancer datasets and a pan-cancer dataset, AER-Net demonstrates outstanding performance, validating its effectiveness in nuclear segmentation and classification.
Three-Dimensional Instance Segmentation Using the Generalized Hough Transform and the Adaptive n-Shifted Shuffle Attention
Authors: Mulindwa Desire Burume , Du Shengzhi , Liu Qingxue
Published: November 2024

The progress of 3D instance segmentation techniques has made it essential for several applications, such as augmented reality, autonomous driving, and robotics. Traditional methods usually have challenges with complex indoor scenes made of multiple objects with different occlusions and orientations. In this work, the authors present an innovative model that integrates a new adaptive n-shifted shuffle (ANSS) attention mechanism with the Generalized Hough Transform (GHT) for robust 3D instance segmentation of indoor scenes. The proposed technique leverages the n-shifted sigmoid activation function, which improves the adaptive shuffle attention mechanism, permitting the network to dynamically focus on relevant features across various regions. A learnable shuffling pattern is produced through the proposed ANSS attention mechanism to spatially rearrange the relevant features, thus augmenting the model’s ability to capture the object boundaries and their fine-grained details. The integration of GHT furnishes a vigorous framework to localize and detect objects in the 3D space, even when heavy noise and partial occlusions are present. The authors evaluate the proposed method on the challenging Stanford 3D Indoor Spaces Dataset (S3DIS), where it establishes its superiority over existing methods. The proposed approach achieves state-of-the-art performance in both mean Intersection over Union (IoU) and overall accuracy, showcasing its potential for practical deployment in real-world scenarios. These results illustrate that the integration of the ANSS and the GHT yields a robust solution for 3D instance segmentation tasks.
Heuristic Optimal Scheduling for Road Traffic Incident Detection Under Computational Constraints
Authors: Wu Hao , Yang Jiahao , Yuan Ming-Dong , Li Xin
Published: November 2024

The intelligent monitoring of road surveillance videos is a crucial tool for detecting and predicting traffic anomalies, swiftly identifying road safety risks, rapidly addressing potential hazards, and preventing accidents or secondary incidents. With the vast number of surveillance cameras in operation, conducting traditional real-time video analysis across all cameras at once requires substantial computational resources. Alternatively, methods that employ periodic camera patrol analysis frequently overlook a significant number of anomalous traffic events, thereby hindering the effectiveness of traffic event detection. To overcome these challenges, this paper introduces a heuristic optimal scheduling approach designed to enhance traffic event detection efficiency while operating within limited computational resources. This method leverages historical data and prior knowledge to compute a weighted event feature value for each camera, providing a quantitative measure of its detection efficiency. To optimize resource allocation, a cyclic elimination mechanism is implemented to exclude low-performing cameras, enabling the dynamic reallocation of resources to higher-performing cameras, thereby enhancing overall detection performance. Finally, the effectiveness of the proposed method is validated through a case study conducted in a representative region of a major metropolitan city in China. The results revealed a substantial improvement in traffic event detection efficiency, with increases of 40%, 28%, 17%, and 28% across different time periods when compared to the pre-optimized state. Furthermore, the proposed method outperformed existing resource scheduling algorithms in terms of average load degree, load balance degree, and higher computational resource utilization. By avoiding the common issues of resource wastage and insufficiency often found in static allocation models, this approach offers greater flexibility and adaptability in computational resource scheduling, thereby effectively addressing the practical demands of traffic anomaly detection and early warning systems.
Leveraging Urban Water Distribution Systems with Smart Sensors for Sustainable Cities
Authors: García Baigorri Anaraida , Parada Raúl , Monzon Baeza Victor , Monzo Carlos
Published: November 2024

Optimizing urban water distribution systems is essential for reducing economic losses, minimizing water wastage, and addressing resource access gaps, particularly in drought-prone regions impacted by climate change. We apply advanced artificial intelligence (AI) techniques and the Internet of Things (IoT) to optimize water networks in Spain using simulation. By employing EPANET for hydraulic modeling and a linear regression-based algorithm for optimization, we achieved up to 96.62% system efficiency with a mean absolute error of 0.049. Our approach demonstrates the potential to conserve up to 648,000 L of water daily at high-demand nodes, contributing to substantial resource savings across urban water networks. We propose a global architecture utilizing Low Power Wide Area Network and Low Earth Orbit solutions for widespread deployment. This study underscores the potential of AI in water network optimization and suggests future research avenues for implementing the proposed architecture in real urban water systems.
Calculation of Fringe Angle with Enhanced Phase Sensitivity and 3D Reconstruction
Authors: Wang Hongyang , He Xin , Wei Zhonghui , Lv Zhuang , Zhang Qiwen , Wang Jun , He Jiawei
Published: November 2024

In the field of fringe projection profilometry, phase sensitivity is a critical factor influencing the precision of object measurements. Traditional techniques that employ basic horizontal or vertical fringe projection often do not achieve optimal levels of phase sensitivity. The identification of the fringe angle that exhibits optimal phase sensitivity has been a significant area of research. The present study introduces a novel method for determining the optimal fringe angle, facilitating 3D reconstruction without the need for equipment adjustments. Initially, the optimal fringe is derived through mathematical analysis, and the system’s position within each coordinate system is standardized, leading to the determination of the optimal fringe angle in the world coordinate system. Subsequently, an optimal fringe pattern, akin to that produced by a rotating projector, is generated based on the concept of rotation around a central point, with corresponding adjustments made to the calibration parameters. Finally, the optimal fringe is projected onto the target object for 3D reconstruction, thereby validating the proposed method. The experimental results demonstrate that this approach accurately identifies the optimal fringe angle, significantly enhancing both phase sensitivity and measurement accuracy. The accuracy of the measurement is significantly greater, by an order of magnitude, compared to the traditional method, with the error being approximately 50% of that associated with the currently established improved method.
Digital Government Transformation and Sustainable Development Goals: To What Extent Are They Interconnected? Bibliometric Analysis Results
Authors: Sanina Anna , Styrin Evgeny , Vigoda-Gadot Eran , Yudina Maria , Semenova Anna
Published: November 2024

The increasing prevalence of digital technologies has sparked interest in the relationship between digital transformation and sustainable development, leading to a surge in theoretical and practical research on this topic. This study conducts a bibliometric analysis of publications in the Web of Science database to examine the extent to which sustainable development goals are integrated into digital government transformation discourses. Using statistical properties of the citation network and the main path approach to identify key publication clusters, the analysis reveals limited research explicitly focusing on the link between digital government transformation and sustainable development goals. Furthermore, articles exploring this interconnection are notably scarce in public administration journals, indicating a significant gap in the literature that requires further investigation.
Green Finance Mechanisms for Sustainable Development: Evidence from Panel Data
Authors: Xing Licong , Chang Bisharat Hussain , Aldawsari Salem Hamad
Published: November 2024

The nexus between environmental degradation, green finance, and sustainable development has been analyzed in a number of studies. Despite attempts by different studies to fill the gap in the existing literature, they have all failed to do so. This study further extends the existing literature by applying robust techniques such as the system-GMM method and applying various proxies to measure green finance, which other studies have failed to examine. For this purpose, we employ panel data for the period of 1985–2021. Our findings support our hypothesis: while green funding tends to have a positive effect on sustainable development, environmental degradation has exactly the opposite effect. These findings are supported by a wide range of statistical methods, including the system-GMM technique. Our work underlines the major contribution bound to be made by green resources toward legislative frameworks in an attempt to aid the effort of reducing the negative impact of environmental degradation and building a sustainable development path.
Outsourcing of Agricultural Machinery Operation Services and the Sustainability of Farmland Transfer Market: Promoting or Inhibiting?
Authors: Lu Yangxiao , Wei Suhao
Published: November 2024

The agricultural machinery operation services (AMOS) market and the farmland transfer market are regarded by policymakers as complementary goals for promoting agricultural development in China. Nonetheless, the farmland transfer market in China is confronted with the threat of sustainable development. The relationship between AMOS and farmland transfer is not always complementary. To analyze the relationship between the AMOS market and the sustainability of the farmland transfer market, methods such as conditional mixed process, the Heckman two-step method, and the Sobel test were employed to explore the influence of AMOS on farmland transfer and its underlying mechanisms. The empirical results show the following: (i) AMOS inhibits farmland transfer-out but promotes farmland transfer-in, which will intensify the market competition of farmland transfer. This result remains valid after correcting for the potential endogenous bias and selective bias and is consistent across different variables and samples. This conclusion suggests that AMOS has emerged as a factor restricting the sustainable development of the farmland transfer market in China. (ii) The heterogeneity analysis results indicate that AMOS has a greater inhibitory effect on the farmland transfer-out of small-scale farmers, part-time farmers, and elderly farmers, and a greater incentive effect on the farmland transfer-in of large-scale farmers, professional farmers, and non-elderly farmers. (iii) Labor allocation and agricultural capital allocation are the potential mechanisms for AMOS to affect farmland transfer. AMOS indirectly inhibits farmland transfer-out and promotes farmland transfer-in by farmland operation ability of labor force and benefits of farmland operation. These results imply that there is a complementary relationship and substitution relationship between the AMOS market and the farmland transfer market. The substitution relationship may sometimes frustrate policies aimed at stimulating the farmland transfer market. The Chinese government is required to be wary of the potential menace that AMOS brings to the sustainability of China’s farmland transfer market.
Are We on the Same Page? Chinese General Visitors’ Perception of the Role of Museums in Sustainable Development
Authors: Zhao Xingyu , Mao Ruohan , Ai Jingfang
Published: November 2024

The issue of sustainability has emerged as a focal point within the museum sector. This article aims to investigate the perceptions and attitudes of Chinese general visitors towards museums and sustainability. To achieve this, we employed a visitor evaluation approach, with inhabitants of the Chinese mainland serving as the target population. We conducted a survey using an online questionnaire, yielding a total of 1260 valid samples. The study finds that most museum visitors in mainland China see a strong link between museums and sustainable development, with factors like age, gender, education, familiarity with sustainable development, and museum interaction shaping these perceptions. The results indicate that large segments of the Chinese visitors hold a favourable perception of the significance of museums in terms of environmental, social, economic, and cultural sustainability. However, the visitor generally does not wish to sacrifice their own visiting experience to enhance museums’ sustainable development capacities. The article examines the relationship between museums and sustainability and offers recommendations for museum practice and policymaking in China and beyond.
Determine the Profiles of Power Consumption in Commercial Buildings in a Very Hot Humid Climate Using a Temporary Series
Authors: Vallejo-Coral E. Catalina , Garzón Ricardo , Ortega López Miguel Darío , Martínez-Gómez Javier , Moya Marcelo
Published: November 2024

With the growth of the nations, the commercial and public services sectors have recently seen an increase in their electricity usage. This demonstrates how crucial it is to understand a building’s behavior in order to lower its usage. This requires on-site data collection by qualified professionals and specialized equipment, which represents high costs. However, multiple studies have demonstrated that it is possible to find electricity-saving strategies from the study of electricity usage, recorded in an hourly period or less, captured by smart meters. In this context, the present study applies a methodology to determine useful information on the operation and characteristics of public buildings on the Ecuadorian coast based on the data gathered over a period of five consecutive months from smart meters. The methodology consists of four steps: (1) data cleaning and filling, (2) time-series decomposition, (3) the generation of consumption profile and (4) the identification of the temperature influence. According to the results, the pre-cooling of spaces accounts for 5% of all electricity used in the commercial buildings, while prolonged shutdown uses 10%. Approximately USD 1100 per month would be spent on the main building and USD 78 on the agency as a result.
Exploring How to Optimise Transformative Pro-Environmental Behaviour Changes via Nudging on Shared Values Crystallisation
Authors: Tening Rahel N. , Ebido Chike C. , Harder Marie K.
Published: November 2024

Transformative learning processes that can trigger deep and long-lasting behaviour changes are highly sought after for targeted improvements ranging from human diet and health to pro-environmental behaviours. A step forward was the reporting of a method that reliably produces transformative learning outcomes (TLOs) as an (incidental) effect of group shared values crystallisation, but the theme of the TLOs could not be targeted, e.g., for pro-environmental behaviours. A recent exploratory study bolted on environmentally themed pre-Nudging and unexpectedly produced a heavy bias towards pro-environmental behaviour changes. Here, we investigated more systematically the influences of different Nudging types upon TLO themes produced using two further case study designs created for comparability with earlier studies and using the same process (WeValue InSitu) and post-event data collection of TLOs categorised as environmental/not and behavioural/not. Our findings show that most Nudging had no effect, including raising the profile of environmental photos and the materials used in the crystallisation process, having participants reflect on their environmental identities, or emphasising environmental topics before going home. However, Nudging which involved answering written questions on specific personal pro-environmental actions such as recycling, applied early on, was linked to desired results. This has pragmatic significance for sustainability practitioners and raises questions for further research on the mechanisms of both active learning and Nudging.
Disaster Risk Reduction Education Through Digital Technologies in the Context of Education for Sustainable Development: A Curricula Analysis of Security and Defense Studies in Serbia
Authors: Rokvić Vanja , Stanojević Petar
Published: November 2024

This study examines the integration of disaster risk reduction (DRR) into security and defense studies curricula at Serbian universities, focusing on public and private institutions. As climate change accelerates and natural disasters become more frequent, addressing these risks is critical for national security and sustainable development. This research evaluates the extent of DRR incorporation in curricula and the use of emerging technologies in DRR education. A qualitative analysis of programs at institutions such as the Faculty of Security Studies at the University of Belgrade, the Military Academy, the University of Criminal Investigation and Police Studies, and private universities like Singidunum and Educons University reveals that public institutions have made significant progress. However, private universities still need comprehensive DRR-focused courses and technological integration. This study recommends fostering collaboration between public and private universities, expanding access to the National Simulation Center, and incorporating modern technologies and active learning strategies across curricula to bridge existing gaps. These steps equip future security professionals with the practical skills and interdisciplinary knowledge necessary for effective disaster management in an increasingly complex risk environment.
The Transformative Power of Generative Artificial Intelligence for Achieving the Sustainable Development Goal of Quality Education
Authors: Nedungadi Prema , Tang Kai-Yu , Raman Raghu
Published: November 2024

This study explored the transformative potential of generative artificial intelligence (GAI) for achieving the UN Sustainable Development Goal on Quality Education (SDG4), emphasizing its interconnectedness with the other SDGs. A proprietary algorithm and cocitation network analysis were used to identify and analyze the network of SDG features in GAI research publications (n = 1501). By examining GAI’s implications for ten SDG4 targets, the findings advocate for a collaborative, ethical approach to integrating GAI, emphasizing policy and practice developments that ensure that technological advancements align with the overarching goals of SDG4. The results highlight the multifaceted impact of GAI on the SDGs. First, this paper outlines a framework that leverages GAI to enhance educational equity, quality, and lifelong learning opportunities. By highlighting the synergy between GAI and the SDGs, such as reducing inequalities (SDG10) and promoting gender equality (SDG5), this study underscores the need for an integrated approach to utilizing GAI. Moreover, it advocates for personalized learning, equitable technology access, adherence to ethical AI principles, and fostering global citizenship, proposing a strategic alignment of GAI applications with the broader SDG agenda. Next, the results highlight that GAI introduces significant challenges, including ethical concerns, data privacy, and the risk of exacerbating the digital divide. Overall, our findings underscore the critical role of policy reforms and innovative practices in navigating the challenges and harnessing the opportunities presented by GAI in education, thereby contributing to a comprehensive discourse on technology’s role in advancing global education and sustainable development.
Textual Analysis of China’s Environmental Policies from the Perspective of Policy Instruments
Authors: Yang Jian , Ding Xin
Published: November 2024

Environmental governance cannot be separated from scientific and rational policy guidelines, and content analysis of environmental governance policy texts helps to understand the characteristics of existing policies and improve policy design. Using policy bibliometric and content analysis, this study analyses the environmental governance policy texts issued by China’s central government from 2018 to 2023, constructs an analytical framework that combines types of policy instruments and environmental governance cycles, and is devoted to exploring the key hotspots, design features, and development logics of the policy system. The study finds that China’s environmental governance policy instruments show characteristics of the synergistic development of diversified regulatory instruments, but focus on the use of environmentally based policy instruments, with relatively few supply-based and demand-based policy instruments. Furthermore, they focus on the prevention of environmental pollution in advance, with relatively little attention paid to post-pollution governance and the construction of the governance guarantee system, and the use of economic instruments remains limited. Accordingly, this paper puts forward relevant policy recommendations to provide valuable reference and guidance for the future development of China’s environmental governance.