bluuebunny/crossref_metadata_2025 huggingface dataset:
Thermal Power and the Structural Parameters of a Wind Turbine Permanent Magnet Eddy Current Heater
Authors: Lu Honglei , Zhou Ce , Hong Wenpeng , Ding Haoshu
Published: October 2024

Permanent magnet eddy current heating as a new type of wind energy utilization method, which is energy-saving, is zero-emission, and involves no pollution and a high utilization of wind energy, has attracted more and more attention. This paper deals with the simulation and optimal design of a permanent magnet eddy current heater (PMECH) driven by wind. Solid steel, closed-slot, and open-slot PMECH are proposed, and corresponding 2D finite element method (FEM) models are established. Using the skin depth concept, numerical analyses are conducted on the influence of the number, size, and position of copper strips on the thermal power of closed-slot and open-slot PMECHs, and the thermal power growth compared to solid steel PMECH. The results showed that there is an optimal value for stator wall thickness. When the air-gap length is 0.5 mm and the rotation speed is 200 and 1000 rpm, the optimal stator wall thickness is 16 and 9 mm, respectively. Compared to the influence of conductivity on thermal power, the influence of permeability is more significant. Compared with solid steel PMECH, both closed-slot and open-slot PMECH in a low-speed region can effectively improve thermal power, and the open slot has more obvious advantages. The maximum values of the thermal power growth (TPG) and thermal power growth rate (TPGR) of the closed-slot PMECH are 1.57 kW and 120.15%, respectively. The maximums of TPG and TPGR of the open-slot PMECH are 2.58 kW and 175.08%, respectively. The experimental results prove the validity of the analytical calculation.
Thermal Oxidation Characteristics and Kinetics of Micron Aluminum Powder in Different Ambient Oxygen Concentration Atmospheres
Authors: Zhang Xiaoliang , Guo Ronghan , Feng Xiao , Fang Jiawei , Xu Jiaqiang , Wang Xuehui , Zhang Jun
Published: October 2024

This paper examines the effect of oxygen concentration on the oxidation process and properties of aluminum particles, providing valuable insights for production and storage. Micron-grade aluminum powders were tested at heating rates of 5, 7.5, 10, and 15 K/min under oxygen concentrations of 7, 11, 15, 21, and 30 vol%. Results indicate a two-step mass gain oxidation process, with less pronounced mass gain at lower oxygen concentrations. SEM and XRD characterized the morphological and crystalline changes during oxidation. Lowering oxygen concentration from 30 vol% to 7 vol% increased the onset oxidation temperature by 17.1 °C. Increasing the proportion of inert gas in the atmosphere increases the ignition temperature of aluminum powder. The mathematical modeling approach of AKTS was used to decouple and analyze the thermal effects of simultaneous melting and oxidation, using the Friedman method to show that the apparent activation energy is about 350 kJ/mol in low-oxygen atmospheres (7 vol% and 11 vol%). The kinetics of aluminum oxidation were found to be closely related to the oxygen concentration, and based on the kinetics parameter, it is possible to predict a minimum limiting oxygen concentration.
Effective Machine Learning Techniques for Dealing with Poor Credit Data
Authors: Nkambule Dumisani Selby , Twala Bhekisipho , Pretorius Jan Harm Christiaan
Published: October 2024

Credit risk is a crucial component of daily financial services operations; it measures the likelihood that a borrower will default on a loan, incurring an economic loss. By analysing historical data for assessment of the creditworthiness of a borrower, lenders can reduce credit risk. Data are vital at the core of the credit decision-making processes. Decision-making depends heavily on accurate, complete data, and failure to harness high-quality data would impact credit lenders when assessing the loan applicants’ risk profiles. In this paper, an empirical comparison of the robustness of seven machine learning algorithms to credit risk, namely support vector machines (SVMs), naïve base, decision trees (DT), random forest (RF), gradient boosting (GB), K-nearest neighbour (K-NN), and logistic regression (LR), is carried out using the Lending Club credit data from Kaggle. This task uses seven performance measures, including the F1 Score (recall, accuracy, and precision), ROC-AUC, and HL and MCC metrics. Then, the harnessing of generative adversarial networks (GANs) simulation to enhance the robustness of the single machine learning classifiers for predicting credit risk is proposed. The results show that when GANs imputation is incorporated, the decision tree is the best-performing classifier with an accuracy rate of 93.01%, followed by random forest (92.92%), gradient boosting (92.33%), support vector machine (90.83%), logistic regression (90.76%), and naïve Bayes (89.29%), respectively. The classifier is the worst-performing method with a k-NN (88.68%) accuracy rate. Subsequently, when GANs are optimised, the accuracy rate of the naïve Bayes classifier improves significantly to (90%) accuracy rate. Additionally, the average error rate for these classifiers is over 9%, which implies that the estimates are not far from the actual values. In summary, most individual classifiers are more robust to missing data when GANs are used as an imputation technique. The differences in performance of all seven machine learning algorithms are significant at the 95% level.
Research and Design of BPM Shortwave Time Signal Modulation Technology Based on Chirp
Authors: Yuan Jiangbin , Li Shifeng , Yan Wenhe , Song Yuhang , Yang Chaozhong , Hu Zhaopeng , Yang Dafeng , Hua Yu
Published: October 2024

The shortwave time service system is a vital land-based wireless time service solution, serving as a supplement and backup to the global navigation satellite system. It ensures that time users can access reliable timings, especially in extreme situations. However, the current BPM shortwave time service signal in China faces issues such as insufficient anti-interference reception capabilities and poor timing accuracy. This paper capitalizes on the advantages of Chirp signals and explores a new modulation technology for BPM shortwave time signals that is compatible with the existing modulation system. A Dual Chirp Time-Division Combined Modulation (DCTDCM) scheme is proposed for broadcasting two time signals: Coordinated Universal Time (UTC) and Universal Time 1 (UT1). Furthermore, an in-depth study of the receiving method for this scheme is conducted, with detailed design of its parameters. The designed DCTDCM signals offer a spread spectrum gain of 24 dB and a multipath resolution capability of at least 125 μs, significantly enhancing the anti-interference reception and anti-multipath attenuation capabilities of shortwave time signals. As a result, the availability and timing accuracy of shortwave time signals are substantially improved. Finally, extensive comparative experiments on reception performance validate the effectiveness of this approach.
An Adaptive Unmixing Method Based on Iterative Multi-Objective Optimization for Surface Water Fraction Mapping (IMOSWFM) Using Zhuhai-1 Hyperspectral Images
Authors: Lei Cong , Liu Rong , Kuang Zhiyuan , Deng Ruru
Published: October 2024

Surface water fraction mapping is an essential preprocessing step for the subpixel mapping (SPM) of surface water, providing valuable prior knowledge about surface water distribution at the subpixel level. In recent years, spectral mixture analysis (SMA) has been extensively applied to estimate surface water fractions in multispectral images by decomposing each mixed pixel into endmembers and their corresponding fractions using linear or nonlinear spectral mixture models. However, challenges emerge when introducing existing surface water fraction mapping methods to hyperspectral images (HSIs) due to insufficient exploration of spectral information. Additionally, inaccurate extraction of endmembers can result in unsatisfactory water fraction estimations. To address these issues, this paper proposes an adaptive unmixing method based on iterative multi-objective optimization for surface water fraction mapping (IMOSWFM) using Zhuhai-1 HSIs. In IMOSWFM, a modified normalized difference water fraction index (MNDWFI) was developed to fully exploit the spectral information. Furthermore, an iterative unmixing framework was adopted to dynamically extract high-quality endmembers and estimate their corresponding water fractions. Experimental results on the Zhuhai-1 HSIs from three test sites around Nanyi Lake indicate that water fraction maps obtained by IMOSWFM are closest to the reference maps compared with the other three SMA-based surface water fraction estimation methods, with the highest overall accuracy (OA) of 91.74%, 93.12%, and 89.73% in terms of pure water extraction and the lowest root-mean-square errors (RMSE) of 0.2506, 0.2403, and 0.2265 in terms of water fraction estimation. This research provides a reference for adapting existing surface water fraction mapping methods to HSIs.
ES-L2-VGG16 Model for Artificial Intelligent Identification of Ice Avalanche Hidden Danger
Authors: Guo Daojing , Tang Minggao , Xu Qiang , Wu Guangjian , Li Guang , Yang Wei , Long Zhihang , Zhao Huanle , Ren Yu
Published: October 2024

Ice avalanche (IA) has a strong concealment and sudden characteristics, which can cause severe disasters. The early identification of IA hidden danger is of great value for disaster prevention and mitigation. However, it is very difficult, and there is poor efficiency in identifying it by site investigation or manual remote sensing. So, an artificial intelligence method for the identification of IA hidden dangers using a deep learning model has been proposed, with the glacier area of the Yarlung Tsangpo River Gorge in Nyingchi selected for identification and validation. First, through engineering geological investigations, three key identification indices for IA hidden dangers are established, glacier source, slope angle, and cracks. Sentinel-2A satellite data, Google Earth, and ArcGIS are used to extract these indices and construct a feature dataset for the study and validation area. Next, key performance metrics, such as training accuracy, validation accuracy, test accuracy, and loss rates, are compared to assess the performance of the ResNet50 (Residual Neural Network 50) and VGG16 (Visual Geometry Group 16) models. The VGG16 model (96.09% training accuracy) is selected and optimized, using Early Stopping (ES) to prevent overfitting and L2 regularization techniques (L2) to add weight penalties, which constrained model complexity and enhanced simplicity and generalization, ultimately developing the ES-L2-VGG16 (Early Stopping—L2 Norm Regularization Techniques—Visual Geometry Group 16) model (98.61% training accuracy). Lastly, during the validation phase, the model is applied to the Yarlung Tsangpo River Gorge glacier area on the Tibetan Plateau (TP), identifying a total of 100 IA hidden danger areas, with average slopes ranging between 34° and 48°. The ES-L2-VGG16 model achieves an accuracy of 96% in identifying these hidden danger areas, ensuring the precise identification of IA dangers. This study offers a new intelligent technical method for identifying IA hidden danger, with clear advantages and promising application prospects.
Estimation of Malondialdehyde Content in Medicago truncatula under Salt Stress Based on Multi-Order Spectral Transformation Characteristics
Authors: Zhang Jiaxin , Zhang Jingyu , Wang Juan , Zhang Aiwu , Deng Xiong
Published: October 2024

Salt stress is a significant abiotic factor affecting the growth and development of alfalfa. Malondialdehyde (MDA) serves as a critical biomarker for assessing alfalfa’s salt tolerance. Traditional methods for measuring MDA are often time-consuming and labor-intensive. Recent advances in remote sensing technology have made non-destructive estimation of metabolites feasible, positioning the accurate estimation of MDA content in alfalfa as a key focus in intelligent breeding. To address the challenge of detecting subtle changes in MDA content, this study developed a partial least squares regression (PLSR) model specifically for Medicago truncatula. This study utilized leaf reflectance hyperspectral data across the visible near-infrared–shortwave infrared (VIR-NIR-SWIR) spectrum, applying multi-order spectral transformation methods, including continuous wavelet transform (CWT), fractional differential (FD), and multi-granularity spectral segmentation (MGSS). Feature selection techniques, such as sequential forward selection (SFS), Least-Squares Boosting (LSBoost), and feature selection using neighborhood component analysis for regression (FSRNCA), were employed to enhance the efficiency of the MDA estimation. The findings revealed that the optimal PLSR model for MDA estimation was achieved by integrating CWT features across orders 1–30 with the SFS method. This model demonstrated robust estimation capabilities under varying salt stress conditions, significantly outperforming the original spectral data (R2 = 0.654, RMSE = 22.567 vs. R2 = 0.242, RMSE = 33.411). A comparative analysis of feature selection methods confirmed that SFS was the most effective for estimating MDA content in alfalfa. These results provide valuable insights and methodologies for MDA estimation and evaluating salt tolerance in alfalfa.
Center-Highlighted Multiscale CNN for Classification of Hyperspectral Images
Authors: Zhu Xing-Hui , Li Kai-Run , Deng Yang-Jun , Long Chen-Feng , Wang Wei-Ye , Tan Si-Qiao
Published: October 2024

Hyperspectral images (HSIs) capture a wide range of spectral features across multiple bands of light, from visible to near-infrared. Hyperspectral image classification technology enables researchers to accurately identify and analyze the composition and distribution of surface materials. Current mainstream deep learning methods typically use block sampling to capture spatial features for the model. However, this approach can affect classification results due to the influence of neighboring features within the sample block. To improve the model’s focus on the center of the sampling block, this study proposes a center highlight with multiscale CNN for hyperspectral image classification (CHMSC). The network utilizes an automatic channel selector (Auto-CHS) to fully consider every channel feature and capture the correlation between the channels. Then, CHMSC enhances the model’s ability to concentrate on the central features of the sampling block utilizing structures such as the center highlight. Finally, before outputting the prediction results, an SENet is employed to further refine the features and learn associate interactions between different scales of spatial features and spectral features. Experimental results from three hyperspectral datasets validate the effectiveness of the proposed method. Specifically, when 15 samples from each class are selected for training, CHMSC achieves the highest overall accuracy (OA) of 90.05%, 92.78%, and 90.15% on the three datasets, outperforming other methods with increases of more than 3.11%, 1.8%, and 2.01% in OA, respectively.
Co-Seismic and Post-Seismic Slip Properties Associated with the 2024 M 7.5 Noto Peninsula, Japan Earthquake Determined by GNSS Observations
Authors: Xiang Yunfei , Qin Ming , Chen Yuanyuan , Xing Yin , Bian Yankai
Published: October 2024

Based on GNSS observations, the co-seismic and post-seismic slip of the 2024 Noto Peninsula earthquake and the spatio-temporal pattern of afterslip are investigated in this paper. The co-seismic slip is mainly distributed in the depth range of 2 to 15 km with the maximum value of 5.94 m. Compared with the co-seismic rupture pattern, a shallow afterslip can be observed after the earthquake, and the afterslip patch is formed northeast of the epicenter. The maximum value of afterslip during the post-seismic 180 days is 1.13 m, which is situated at the longitude of 137.53°, latitude of 37.75°, and epth of 5.43 km. The spatio-temporal evolution of afterslip indicates that the fault activity has continued throughout the post-seismic 180 days, and the coverage and magnitude of afterslip have gradually increased. As time goes on, the fault activity tends to weaken, as evidenced by a decrease in slip rate. The daily images of afterslip demonstrate that the fault activity is particularly strong in the early time period following the earthquake. The maximum value of afterslip in the first week accounts for about 18% of that in the post-seismic 180 days, and the maximum slip rate reaches 0.043 m/day. In addition, the Coulomb stress analysis indicates that afterslip and most aftershocks appear in the positive Coulomb stress region, suggesting that co-seismic Coulomb stress changes may be the driving mechanism of afterslip and aftershocks.
Evaluating the Effects of Wetland Restoration on Ecosystem Services Using InVEST and Geostatistics: A Case Study of Dongting Lake in China
Authors: Zhou Jiaying , Wang Zijian , He Ying , Liu Peizhong , Xu Jie , Lu Cai , Lei Guangchun , Wen Li
Published: October 2024

Ecological restoration projects aim to recover ecosystems that have been degraded, damaged, or destroyed. The effective assessment of these projects is crucial for ensuring that restoration goals are met and for guiding future efforts. The Poplar Ecological Restoration Project (PERP), focused on removing floodplain poplar plantations, is a key environmental initiative in China to preserve the ecological functions and services of the Dongting Lake freshwater wetland complex. Understanding the dynamic changes in ecosystem services and their trade-offs/synergies before and after the project is crucial for ensuring that ecosystem services are maintained and enhanced. This study selected three key ecosystem services, water conservation, carbon storage, and habitat quality, as indicators to assess the PERP. Using the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model and geostatistical methods, including the Spearman correlation analysis and spatial autocorrelation analysis, we explored the ecological outcomes of the project. The results indicated that since its implementation in 2014, the PERP has significantly altered the land use and land cover (LULC) in the lake. Most of the former plantations have been converted into reedbeds (17,404 ha), followed by sedgelands (3841 ha), mudflats (443 ha), shallow waters (382 ha), and deep waters (301 ha). During this period, water conservation and habitat quality initially decreased and then increased, while carbon storage continuously declined. Overall, all assessed services showed an increasing trend except for carbon storage, resulting in a slight decrease in the integrated ecosystem services. Moreover, there were strong bivariate associations among the three investigated ecosystem services, and the synergies were strengthened during the project. Importantly, these synergies became positive after the PERP, meaning that the improvement of one service enhanced others, potentially leading to a more sustainable and resilient ecosystem.
A Heatmap-Supplemented R-CNN Trained Using an Inflated IoU for Small Object Detection
Authors: Butler Justin , Leung Henry
Published: October 2024

Object detection architectures struggle to detect small objects across applications including remote sensing and autonomous vehicles. Specifically, for unmanned aerial vehicles, poor detection of small objects directly limits this technology’s applicability. Objects both appear smaller than they are in large-scale images captured in aerial imagery and are represented by reduced information in high-altitude imagery. This paper presents a new architecture, CR-CNN, which predicts independent regions of interest from two unique prediction branches within the first stage of the network: a conventional R-CNN convolutional backbone and an hourglass backbone. Utilizing two independent sources within the first stage, our approach leads to an increase in successful predictions of regions that contain smaller objects. Anchor-based methods such as R-CNNs also utilize less than half the number of small objects compared to larger ones during training due to the poor intersection over union (IoU) scores between the generated anchors and the groundtruth—further reducing their performance on small objects. Therefore, we also propose artificially inflating the IoU of smaller objects during training using a simple, size-based Gaussian multiplier—leading to an increase in the quantity of small objects seen per training cycle based on an increase in the number of anchor–object pairs during training. This architecture and training strategy led to improved detection overall on two challenging aerial-based datasets heavily composed of small objects while predicting fewer false positives compared to Mask R-CNN. These results suggest that while new and unique architectures will continue to play a part in advancing the field of object detection, the training methodologies and strategies used will also play a valuable role.
Artificial Intelligence-Based Precipitation Estimation Method Using Fengyun-4B Satellite Data
Authors: Liu Nianqing , Jiang Jianying , Mao Dongyan , Fang Meng , Li Yun , Han Bowei , Ren Suling
Published: October 2024

This paper proposes a novel precipitation estimation method based on FY-4B meteorological satellite data (FY-4B_AI). This method facilitates the spatiotemporal matching of 125 features derived from the multi-temporal and multi-channel data of the FY-4B satellite with precipitation data at stations. Subsequently, a precipitation model was constructed using the light gradient boosting machine (LGBM) algorithm. A comparative analysis of FY-4B_AI and GPM/IMERG-L products for over 450 million station cases throughout 2023 revealed the following: (1) The results demonstrate that FY-4B_AI is more accurate than GPM/IMERG-L. Six of the eight evaluation indices exhibit superior performance for FY-4B_AI in comparison to GPM/IMERG-L. These indices include the mean absolute error (MAE), root mean square error (RMSE), relative error (RE), correlation coefficient (CC), probability of detection (POD), and critical success index (CSI). As for the MAE, the results are 1.67 (FY-4B_AI) and 1.92 (GPM/IMERG-L), respectively. The RMSEs are 3.68 and 4.07, respectively. The REs are 17.72% and 26.28%, respectively. The CCs are 0.44 and 0.36, respectively. The PODs are 61.84% and 47.31%, respectively. The CSIs are 0.30 and 0.27, respectively. However, with regard to the mean errors (MEs) and false alarm rates (FARs), FY-4B_AI (−0.88 and 62.85%, respectively) displays a slight degree of inferiority in comparison to GPM/IMERG-L (−0.80 and 62.21%, respectively). (2) An evaluation of two strong weather events to represent the spatial distribution of precipitation in different climatic zones revealed that both FY-4B_AI and GPM/IMERG-L are equally capable of accurately representing these phenomena, irrespective of whether the region in question is humid, as is the case in the southeast, or dry, as is the case in the northwest.
What Is Beyond Hyperbola Detection and Characterization in Ground-Penetrating Radar Data?—Implications from the Archaeological Site of Goting, Germany
Authors: Wunderlich Tina , Majchczack Bente S. , Wilken Dennis , Segschneider Martin , Rabbel Wolfgang
Published: October 2024

Hyperbolae in radargrams are caused by a variety of small subsurface objects. The analysis of their curvature enables the determination of propagation velocity in the subsurface, which is important for exact time-to-depth conversion and migration and also yields information on the water content of the soil. Using deep learning methods and fitting (DLF) algorithms, it is possible to automatically detect and analyze large numbers of hyperbola in 3D Ground-Penetrating Radar (GPR) datasets. As a result, a 3D velocity model can be established. Combining the hyperbola locations and the 3D velocity model with reflection depth sections and timeslices leads to improved archaeological interpretation due to (1) correct time-to-depth conversion through migration with the 3D velocity model, (2) creation of depthslices following the topography, (3) evaluation of the spatial distribution of hyperbolae, and (4) derivation of a 3D water content model of the site. In an exemplary study, we applied DLF to a 3D GPR dataset from the multi-phased (2nd to 12th century CE) archaeological site of Goting on the island of Föhr, Northern Germany. Using RetinaNet, we detected 38,490 hyperbolae in an area of 1.76 ha and created a 3D velocity model. The velocities ranged from approximately 0.12 m/ns at the surface to 0.07 m/ns at approx. 3 m depth in the vertical direction; in the lateral direction, the maximum velocity variation was ±0.048 m/ns. The 2D-migrated radargrams and subsequently created depthslices revealed the remains of a longhouse, which was not known beforehand and had not been visible in the unmigrated timeslices. We found hyperbola apex points aligned along linear strong reflections. They can be interpreted as stones contained in ditch fills. The hyperbola points help to differentiate between ditches and processing artifacts that have a similar appearance as the ditches in time-/depthslices. From the derived 3D water content model, we could identify the thickness of the archaeologically relevant layer across the whole site. The layer contains a lot of humus and has a high water retention capability, leading to a higher water content compared to the underlying glacial moraine sand, which is well-drained.
Phytoremediation of Mercury Contamination: Bibliometric Analysis
Authors: Mosquera Chaverra Lina , Paredes Cuervo Diego , López Gutiérrez Ana , Arias Carlos A. , Carvalho Pedro N.
Published: October 2024

Mercury is classified as one of the world’s most toxic and dangerous pollutants as it tends to bioaccumulate and biomagnify within the trophic chain and is persistent. Various approaches are available to remediate Hg-affected sites including phytoremediation, which includes the use of plants to clean up contaminated environments. The phytoremediation of mercury contamination is attracting increasing attention because of its advantages: it is environmentally friendly, inexpensive, simple, and can improve soil fertility. In this report, VOSviewer (version 1.6.1) and Bibliometrix (version 4.16) software were used to analyze 457 and 697 documents published from 2000 to 2023, retrieved from the databases WoS and Scopus, respectively. China, India, the United States, and Spain were the top four most productive countries. The largest topic area was environmental sciences, and the Chinese Academy of Sciences was the organization that contributed the most to the overall number of publications. The keywords with the highest frequency excluding phytoremediation and mercury in WoS were heavy metals, accumulation, cadmium, soils, and phytoextraction. In Scopus, the most frequent keywords were bioremediation, heavy metals, soil pollution, bioaccumulation, biodegradation, and environmental. From the above analysis, we concluded that future research should focus on (1) finding native plants, (2) genetic engineering applications, (3) increasing remediation ability through assisted phytoremediation, and (4) the detoxification mechanism of mercury. This study provides insights into trending themes and serves as a reference for future research.
Scaling the Food Mountain: Implications for Addressing the Determinants of Consumer Food Waste Practices
Authors: Wakefield Alysia , Axon Stephen
Published: October 2024

The growing food waste phenomenon is recognised as a global issue with significant social, economic, and environmental burdens. This is a major concern in developed nations, where consumers are the largest contributors to the total volume of food waste production. As a leading cause of food and water insecurity, economic inequality, and environmental degradation, preventing and minimising consumer food waste is a key objective for policymakers and practitioners. Due to the complex consumer behaviours and practices associated with food waste generation, current understandings of why food waste occurs remain scattered. The purpose of this review is therefore to map the history and development of consumer food waste research over time, highlighting key themes and inconsistencies within the existing literature. Adopting a narrative approach, the literature is organised into three distinct themes to explore and identify the various internal and external determinants of consumer food waste. Our analysis highlights consumer food waste as a complex and multi-faceted challenge which cannot be attributed to one single variable, but rather a combination of behaviours determined by various societal, individual, and behavioural factors. While previous research tends to frame food waste as mainly a consumer issue, this review identifies several collective actors who are central to the problem. These findings call for a holistic view across the food supply chain to help identify opportunities for multi-stakeholder actions that prevent and reduce food waste at the consumer level. Drawing upon these new insights, we provide practical recommendations to assist policymakers, retailers, and consumers in mitigating consumer-related food waste.
Review of Bridge Structure Damping Model and Identification Method
Authors: Qu Chunxu , Tu Guikai , Gao Fuzhong , Sun Li , Pan Shengshan , Chen Dongsheng
Published: October 2024

Damping is a fundamental characteristic of bridge structures, reflecting their ability to dissipate energy during vibration. In the design and maintenance of bridges, the damping ratio has a direct impact on the safety and service life of the structure, thus affecting its sustainability. Currently, there is no suitable theoretical method for estimating structural damping at the design stage. Therefore, the modal damping ratio of a completed or under-construction bridge can only be obtained through field dynamic tests to ensure compliance with design specifications. To summarize the latest research findings on bridge structure damping models and identification methods, and to advance the development of damping identification techniques, this paper provides an in-depth review from several perspectives: Firstly, it offers a comprehensive analysis of the theoretical framework for structural damping. Secondly, it summarizes the damping models proposed by researchers from various countries. Thirdly, it reviews the research progress on identifying the modal damping ratio of bridge structures using time domain, frequency domain, and time-frequency domain methods based on environmental excitation. It also summarizes the methods and current status of identifying the modal damping ratio using artificial excitation. Finally, the future prospects and conclusions are discussed from three aspects: damping theory, test and identification method and data processing. This research and summary provide a solid theoretical foundation for advancing bridge structural damping theory and identification methods and offer valuable references for bridge operation and maintenance, as well as damage identification. From the perspective of modal parameter identification, it provides a theoretical basis for the sustainable development of bridges.
Beyond Illumination: Stakeholder Perspectives on Urban Lighting Master Planning for Chiang Mai Old City, Thailand
Authors: Navaraj Preechaya , Inkarojrit Vorapat
Published: October 2024

Chiang Mai Old City, a cultural heritage site and major tourist destination in Thailand, has significant cultural capital but lacks a well-designed urban lighting system, limiting its nighttime development potential. This issue arises from the absence of an urban lighting master plan, a crucial tool for guiding the city’s growth. The challenge lies in reconciling the diverse perspectives of stakeholders to create a comprehensive lighting master plan that meets shared goals. This research proposes a system dynamics approach to analyze stakeholder complexity. A qualitative, multi-stage method was employed, through in-depth interviews and focus groups with 60 stakeholders from three groups: government, professionals, and end users, to prioritize critical factors. Causal Loop Diagrams (CLDs) were used to illustrate the interrelations among those factors, leading to alternative scenarios for the lighting master plan’s development. The findings demonstrate that safety and security is the top priority, followed by cultural and economic factors. Eleven reinforcing loops and two balancing loops are proposed through CLD visualization. This framework highlights the importance of a participatory process, advocating for a systematic and holistic approach where all stakeholders with diverse perspective collaborate side-by-side in the development of the urban lighting master plan for Chiang Mai Old City.
A Semantic and Optimized Focused Crawler Based on Semantic Graph and Genetic Algorithm
Authors: Huang Wenhao , Li Xiaoyan , Zhou Xiao , Qi Deyu , Xi Jianqing , Liu Wenjun , Zhao Feiyu
Published: October 2024

A focused crawler automatically retrieves, organizes, and extracts specific topic-related information from the internet for analysis and application. Currently, most focused crawlers assess the relevance of web pages to a given topic through methods such as keyword matching, semantic analysis, and link structures. However, these existing focused crawlers suffer from issues such as misleading directions and reduced accuracy due to the lack of semantic analysis of topic terms, as well as biased computation of topic relevance caused by the absence of effective weighting factors. To solve the above-mentioned problems, this study proposes a semantic and optimized focused crawler based on Semantic Graph and Genetic Algorithm. The proposed crawler eliminates ambiguous terms by constructing a semantic graph, optimizes the weighting factors of topic relevance with asymmetry by using a genetic algorithm, and combines both above two points to predict the priority of each unvisited hyperlink. The experiment results indicate that the proposed SG-GA Crawler improves the evaluation indicators compared with the other three focused crawlers, including VSM Crawler, SSRM Crawler, and SG Crawler. More specifically, the percentage improvement achieved by the proposed method exceeds 19%, 19%, and 13% in terms of three evaluation indicators, including the number of relevant web pages, acquisition rate, and average relevance, respectively. In conclusion, the proposed focused crawler can grab more quantity and higher quality topic-related web pages from the Internet.
Supersymmetric Integrable Hamiltonian Systems, Conformal Lie Superalgebras K(1, N = 1, 2, 3), and Their Factorized Semi-Supersymmetric Generalizations
Authors: Prykarpatski Anatolij K. , Dilnyi Volodymyr M. , Pukach Petro Ya. , Vovk Myroslava I.
Published: October 2024

We successively reanalyzed modern Lie-algebraic approaches lying in the background of effective constructions of integrable super-Hamiltonian systems on functional N=1,2,3- supermanifolds, possessing rich supersymmetries and endowed with suitably related compatible Poisson structures. As an application, we describe countable hierarchies of new nonlinear Lax-type integrable N=2,3-semi-supersymmetric dynamical systems and constructed their central extended superconformal Lie superalgebra K(1|3) and its finite-dimensional coadjoint orbits, generated by the related Casimir functionals. Moreover, we generalized these results subject to the suitably factorized super-pseudo-differential Lax-type representations and present the related super-Poisson brackets and compatible suitably factorized Hamiltonian superflows. As an interesting point, we succeeded in the algorithmic construction of integrable super-Hamiltonian factorized systems generated by Casimir invariants of the centrally extended super-pseudo-differential operator Lie superalgebras on the N=1,2,3-supercircle.
Applications of a q-Integral Operator to a Certain Class of Analytic Functions Associated with a Symmetric Domain
Authors: Ahmad Adeel , Louati Hanen , Rasheed Akhter , Ali Asad , Hussain Saqib , Hilali Shreefa O. , Al-Rezami Afrah Y.
Published: October 2024

In this article, our objective is to define and study a new subclass of analytic functions associated with the q-analogue of the sine function, operating in conjunction with a convolution operator. By manipulating the parameter q, we observe that the image of the unit disc under the q-sine function exhibits a visually appealing resemblance to a figure-eight shape that is symmetric about the real axis. Additionally, we investigate some important geometrical problems like necessary and sufficient conditions, coefficient bounds, Fekete-Szegö inequality, and partial sum results for the functions belonging to this newly defined subclass.
Asymmetry and Symmetry in New Three-Dimensional Chaotic Map with Commensurate and Incommensurate Fractional Orders
Authors: Al-Taani Hussein , Abu Hammad Ma’mon , Abudayah Mohammad , Diabi Louiza , Ouannas Adel
Published: October 2024

According to recent research, discrete-time fractional-order models have greater potential to investigate behaviors, and chaotic maps with fractional derivative values exhibit rich dynamics. This manuscript studies the dynamics of a new fractional chaotic map-based three functions. We analyze the behaviors in commensurate and incommensurate orders, revealing their impact on dynamics. Through the maximum Lyapunov exponent (LEmax), phase portraits, and bifurcation charts. In addition, we assess the complexity and confirm the chaotic features in the map using the approximation entropy ApEn and C0 complexity. Studies show that the commensurate and incommensurate derivative values influence the fractional chaotic map-based three functions, which exhibit a variety of dynamical behaviors, such as hidden attractors, asymmetry, and symmetry. Moreover, the new system’s stabilizing employing a 3D nonlinear controller is introduced. Finally, our study validates the research results using the simulation MATLAB R2024a.
Modeling and Analyzing Carbon Emission Market Volatility and Impact: Evidence from Guangdong Province, China
Authors: Tan Kangye , Wu Yumeng , Xu Fang , Ji Xuanyu , Li Chunsheng
Published: October 2024

This research investigates the volatility of carbon prices in Guangdong’s emission trading market, a critical element of China’s broader climate strategy aimed at reducing greenhouse gas emissions and promoting sustainable development. This study applies ensemble empirical mode decomposition (EEMD) to analyze the complex interactions between carbon price fluctuations and various economic factors, including energy prices and environmental regulations. By decomposing the data, we identify key trends and cycles within the market, providing a clearer understanding of both short-term volatility and long-term market trends. Our findings reveal that regulatory policies play a pivotal role in shaping carbon market dynamics, with shifts in regulations leading to significant price volatility. Additionally, fluctuations in global energy prices, especially oil and coal, are found to have a considerable impact on carbon price movements, further complicating the market’s stability. This underscores the interconnected nature of the carbon trading market with broader economic and environmental factors, both domestic and international. The findings provide valuable insights for policymakers and market participants, underscoring the importance of stable carbon markets for promoting the transition to a low-carbon economy and achieving broader sustainability goals.
Exploring Urbanization Strategies by Dissecting Aggregate Crowd Behaviors: A Case Study in China
Authors: Li Yanbin , Song Xingyao , Sun Leilei , Zhuang Castiel Chen , Liu Jiayi , Yang Meng
Published: October 2024

Town development, a crucial stage of urbanization, has been increasingly prioritized in recent sustainable socio-economic growth strategies. Vitality, especially the one measured by aggregate crowd behaviors, is widely recognized as a crucial development element. Conducting comprehensive assessments of the drivers of town vitality, particularly crowd vitality, is thus essential for addressing challenges and monitoring progress. This study examines representative towns in China and employs multiple datasets along with XGBoost-SHAP to investigate the mechanisms of development environment factors on aggregate crowd vitality. Key findings highlight the study’s novelty and broader implications: (1) The degree of industrial agglomeration is the most significant factor impacting the dependent measures, providing new data-driven insights into the role of economic clustering in town development. (2) Other indicators, such as the minimum distance to the town center, the enclosure, and car and pedestrian friendliness, can effectively predict town vitality, offering practical considerations for town planning. (3) Industrial innovation and diversification, rational planning of living circles, and enhancement of town conditions emerge as three crucial strategies for promoting urbanization. This study enhances empirical insights with strategies for addressing urbanization challenges, emphasizing how crowd data can be used to inform urbanization policies and planning practices, aiding urban planners in building more sustainable systems.
Prediction and Evaluation of Industrial Land Intensive Use: Adding Policy Impact to System Dynamics Model
Authors: Luo Jiaojiao , Wu Yuzhe , Zhang Liang
Published: October 2024

Nowadays, urban development prioritizes improving quality over mere quantitative expansion, which requires a shift in land use from previous extensive use patterns to a more refined and efficient intensive use pattern. The intensive use of industrial land (IUIL) is a resource-utilization strategy for industrial land that aims at maximizing the dual growth of ecological and economic benefits by minimizing resource consumption and factor costs. In this research, IUIL is deconstructed into four aspects: input intensity, utilization intensity, economic benefits, and environmental benefits. Grounded in a qualitative analysis of the interconnected casual feedback among the various factors influencing IUIL, a system dynamics model with 36 indicators for IUIL evaluation and prediction is constructed. Policy impacts on IUIL from the supply side, utilization side, and supervision side are analyzed and embedded for a scenario simulation based on the theory of the entire life cycle of land use. The simulation shows its efficiency in reporting the variation tendency of industrial land use with varied policy impact strength. The results reveal a dynamic understanding of the evolving patterns of land intensification and present different characteristics of IUIL. This study further proposes policy recommendations to provide experience and references for the authorities to promote high-quality urbanization development in practice.
Clinical Effectiveness of Bee Venom Acupuncture for Bone Fractures and Potential Mechanisms: A Narrative Overview
Authors: Kim Jung-Hyun , Seo Byung-Kwan
Published: October 2024

Bee venom acupuncture, a type of herbal acupuncture, combines the pharmacological actions of bioactive compounds from bee venom with the mechanical stimulation of meridian points. Bee venom acupuncture is gaining popularity, particularly in the Republic of Korea, primarily for pain relief of various conditions. This study aimed to summarize and evaluate the available evidence on the use of bee venom acupuncture for recovery after bone fractures. Electronic literature searches for experimental studies and clinical trials were conducted using the PubMed, China Academic Journals (CAJ), and OASIS databases. The search revealed 31 studies, of which six met our criteria. These studies demonstrated that bee venom acupuncture can be effective in treating bone fractures, suggesting a promising area for future research. However, evidence supporting its efficacy in this context is limited. Rigorous trials with large sample sizes and robust designs are needed to clarify the role of bee venom acupuncture for these indications. In addition, future studies should explore the optimal dosage and concentration of bee venom acupuncture.
Peptide Toxin Diversity and a Novel Antimicrobial Peptide from the Spider Oxyopes forcipiformis
Authors: Wang Kexin , Mwangi James , Cao Kaixun , Wang Yi , Gao Jinai , Yang Min , Michira Brenda B. , Lu Qiumin , Li Juan
Published: October 2024

Spider venoms are emerging as a rich source of bioactive peptide toxins with therapeutic potential. Lynx spiders of the genus Oxyopes are small, cursorial hunters that employ complex venom to subdue arthropod prey. However, extracting crude venom from these diminutive arachnids poses significant challenges. This study presents a transcriptome analysis of venom glands from an undescribed Oxyopes forcipiformis species, revealing 339 putative protein and peptide toxin sequences categorized into seven functional groups. The venom composition was dominated by membrane-active peptides (40.71%), venom auxiliary proteins (22.71%), neurotoxins (15.63%), channel active peptides (7.08%) and uncharacterized components (13.87%). Additionally, phylogenetic analysis of 65 disulfide-bond-rich peptides yielded six distinct families based on sequence homology and cysteine framework. Finally, a novel antimicrobial peptide, GK37, was identified using in silico and homology analyses. Our data suggested that GK37 presented significant antibacterial activity against Gram-positive bacteria Staphylococcus aureus with a minimum inhibitory concentration (MIC) of 1.552 µM by disrupting bacterial membranes. At 4× MICs, GK37 almost showed no hemolytic activity on blood cells or toxicity against Hek293T cells. Our findings provided a basis for targeted studies of the diversity and pharmacological effects of lynx spider peptide. We elucidated a valuable high-throughput approach for obtaining proteins and peptides from small-group spiders.
Phenotypic Characterization of Subtype A and Recombinant AC Transmitted/Founder Viruses from a Rwandan HIV-1 Heterosexual Transmission Cohort
Authors: Yue Ling , Xu Rui , Mclnally Samantha , Qin Qianhong , Rhodes Jake W. , Muok Erick , Umviligihozo Gisele , Brooks Kelsie , Zhang Jiayi , Qin Zhaohui , Bizimana Jean , Hare Jonathan , Price Matthew A. , Allen Susan A. , Karita Etienne , Hunter Eric
Published: October 2024

HIV-1 subtypes have distinct geographical distributions, with subtypes A, C, and D and inter-subtype recombinants circulating in sub-Saharan Africa. Historically, individuals living with subtype A viruses exhibit slower CD4 decline and progression to AIDS diagnosis. Despite this, there are few authentic infectious molecular clones (IMCs) of subtype A or AC recombinant transmitted founder (TF) viruses with which to investigate viral impacts on pathogenesis. In this study, we constructed 16 authentic subtype A1 and 4 A1C recombinant IMCs from the IAVI Rwandan Protocol C acute infection cohort and characterized these viruses phenotypically. The virus replicative capacity (RC) scores varied over 50-fold, but the natural substitution of non-consensus amino acids in the p17(MA) domain of Gag was generally linked to higher RC levels. Sensitivity to a panel of broadly neutralizing antibodies (bNAbs) showed that all but one TF was sensitive to N6, which targets the CD4 binding site, while bNAbs PG16 and PGT 128 had a similar level of potency but reduced breadth against our panel of viruses. In contrast, bNAb 10E8V4 revealed high breadth but much lower potency. This panel of well-characterized, authentic subtype A and AC recombinant IMCs provides a resource for studies on the role of the virus subtype in HIV-1 transmission, pathogenesis, and vaccine design.