Search Results (57)

Gait recognition is a reliable biometric approach that uniquely identifies individuals based on their natural walking patterns. It is widely used to recognize individuals who are challenging to camouflage and do not require a person’s cooperation. The general face-based person recognition system often fails to determine the offender’s identity when they conceal their face by wearing helmets and masks to evade identification. In such cases, gait-based recognition is ideal for identifying offenders, and most existing work leverages a deep learning (DL) model. However, a single model often fails to capture a comprehensive selection of refined patterns in input data when external factors are present, such as variation in viewing angle, clothing, and carrying conditions. In response to this, this paper introduces a fusion-based multi-model gait recognition framework that leverages the potential of convolutional neural networks (CNNs) and a vision transformer (ViT) in an ensemble manner to enhance gait recognition performance. Here, CNNs capture spatiotemporal features, and ViT features multiple attention layers that focus on a particular region of the gait image. The first step in this framework is to obtain the Gait Energy Image (GEI) by averaging a height-normalized gait silhouette sequence over a gait cycle, which can handle the left–right gait symmetry of the gait. After that, the GEI image is fed through multiple pre-trained models and fine-tuned precisely to extract the depth spatiotemporal feature. Later, three separate fusion strategies are conducted, and the first one is decision-level fusion (DLF), which takes each model’s decision and employs majority voting for the final decision. The second is feature-level fusion (FLF), which combines the features from individual models through pointwise addition before performing gait recognition. Finally, a hybrid fusion combines DLF and FLF for gait recognition. The performance of the multi-model fusion-based framework was evaluated on three publicly available gait databases: CASIA-B, OU-ISIR D, and the OU-ISIR Large Population dataset. The experimental results demonstrate that the fusion-enhanced framework achieves superior performance. Full article

Shiga-toxin-producing E. coli (STEC) is a leading causing of bacterial foodborne and zoonotic illnesses in the USA. Whole-genome sequencing (WGS) is a powerful tool used in public health and microbiology for the detection, surveillance, and outbreak investigation of STEC. In this study, we applied three WGS-based subtyping methods, high quality single-nucleotide polymorphism (hqSNP) analysis, whole genome multi-locus sequence typing using chromosome-associated loci [wgMLST (chrom)], and core genome multi-locus sequence typing (cgMLST), to isolate sequences from 11 STEC outbreaks. For each outbreak, we evaluated the concordance between subtyping methods using pairwise genomic differences (number of SNPs or alleles), linear regression models, and tanglegrams. Pairwise genomic differences were highly concordant between methods for all but one outbreak, which was associated with international travel. The slopes of the regressions for hqSNP vs. allele differences were 0.432 (cgMLST) and 0.966 wgMLST (chrom); the slope was 1.914 for cgMLST vs. wgMLST (chrom) differences. Tanglegrams comprised of outbreak and sporadic sequences showed moderate clustering concordance between methods, where Baker’s Gamma Indices (BGIs) ranged between 0.35 and 0.99 and Cophenetic Correlation Coefficients (CCCs) were ≥0.88 across all outbreaks. The K-means analysis using the Silhouette method showed the clear separation of outbreak groups with average silhouette widths ≥0.87 across all methods. This study validates the use of cgMLST for the national surveillance of STEC illness clusters using the PulseNet 2.0 system and demonstrates that hqSNP or wgMLST can be used for further resolution. Full article

Sewage composition analysis is important for understanding environmental impact and ensuring effective treatment processes. In this study, we employed multivariate analysis techniques to delve into the intricate composition of sewage. Specifically, we utilized Principal Component Analysis (PCA) and Detrended Correspondence Analysis (DCA) to uncover patterns and relationships among different types of sewage pollutants. Statistical analysis revealed that treatment stages did not consistently reduce all pollutant concentrations. Mechanical treatment failed to lower chlorides and sulfates, but was effective for ether extract and phenols. Moreover, total mechanical–biological treatment provided a significant, 91% reduction of the ether extract and phenols, while only reducing chlorides by 13% and sulfates by 22%. The multivariate analysis revealed significant differences between raw sewage and mechanically treated sewage. Totally treated sewage stood out as the key factor influencing the pollutants studied, particularly chlorides and sulfates. This finding emphasizes the critical role of comprehensive treatment processes in effective sewage management. Among the analysed substances, chlorides showed the strongest clustering potential, with an average Silhouette coefficient of 0.738, the highest observed. Phenols, on the other hand, exhibited lower Within-Cluster Sum of Squares (WCSS), suggesting their potential as an alternative parameter for evaluation. Full article

The vertical heterogeneous structures formed during the evolution of urban agglomerations, driven by globalization, pose challenges to traditional two-dimensional spatial analysis methods. This study addresses the vertical heterogeneity and spatial multiscale problem in three-dimensional urban space and proposes an adaptive framework that takes into account building height for multiscale clustering in urban areas. Firstly, we established a macro-, meso- and micro-level analysis system for the characteristics of urban spatial structures. Subsequently, we developed a parameter-adaptive model through a dynamic coupling mechanism of height thresholds and average elevations. Finally, we proposed a density-based clustering method that integrates the multiscale urban analysis with parameter adaptation to distinguish urban spatial features at different scales, thereby achieving multiscale urban regional delineation. The experimental results demonstrate that the proposed clustering framework outperforms traditional density-based and hierarchical clustering algorithms in terms of both the Silhouette Coefficient and the Davies–Bouldin Index, effectively resolving the problem of vertical density variation in urban clustering. Full article

This study conducts a case study on the characteristics of fixed deposit businesses in a Portuguese bank, analyzing the current customer data features and the limitations of marketing strategies. It also highlights the limitations of the traditional DBSCAN algorithm, including issues with parameter selection and a lack of diverse clustering metrics. Using machine learning techniques, the study explores the relationship between customer attribute features and fixed deposits. The proposed KM-DBSCAN algorithm, which combines K-means and DBSCAN, is used for customer segmentation. This method integrates both implicit and explicit customer indicators, incorporates weight factors, constructs a distance distribution matrix, and optimizes the process of selecting the neighborhood radius and density threshold parameters. As a result, the clustering accuracy of customer segmentation is improved by 15%. Based on the clustering results, customers are divided into four distinct groups, and personalized marketing strategies for customer deposits are proposed. Differentiated marketing plans are implemented, with a focus on customer relationship management and feedback. The model’s performance is evaluated using silhouette coefficients, accuracy, and F1 score. The model is then applied in a real-world scenario, leading to an average business revenue growth rate of 16.08% and a 4.5% increase in customer engagement. Full article

Resolving the genetic relationships between cultivars is one of the objectives of research in viticulture. To this end, both DNA markers and morphological analysis help to identify synonyms and homonyms and to determine the degree of relatedness between cultivars. Results of genetic analysis using single sequence repeats (SSRs) and single nucleotide polymorphisms (SNPs) point to Hebén as the female progenitor of many of the cultivars currently used in viticulture. Here, seed shape is compared between Hebén and genetically related cultivars. An average silhouette derived from seeds of Hebén was used as a model, and the comparisons were made visually and quantitatively by calculation of J-index values (percent similarity of the seeds and the model). Quantification of seed shape by J-index confirms the data of DNA markers supporting different levels of conservation of maternal seed shape in the varieties. Other seed morphological measurements help to explain the basis of the differences in shape between Hebén, genetically related groups and the external group of unrelated cultivars. Curvature analysis in seeds silhouettes confirms the relationship between Hebén and other cultivars and supports the utility of this technique in the analysis of parental relationships. Full article

The layout of emergency medical services (EMS) is of vital importance. A well-planned layout significantly impacts the timeliness of response and operational efficiency, which are crucial for saving lives and mitigating injury severity. This paper presents a novel decision support framework for optimizing EMS station layout. Employing the k-means clustering algorithm in combination with the elbow method and silhouette coefficient method, we conduct a clustering analysis on a patient call record dataset. Comprising 166,161 emergency center call records in the Shanghai area over one year, this dataset serves as the basis for our analysis. The analysis results are applied to determine EMS station locations, with the average ambulance patient pickup time as the evaluation criterion. A simulation model is utilized to validate the effectiveness and reliability of the decision-making framework. An experimental analysis reveals that compared with the existing EMS station layout, the proposed framework reduces the average patient pickup time from 11.033 min to 9.661 min, marking a 12.441% decrease. Furthermore, a robustness test of the proposed scheme is carried out. The results indicate that even when some first-aid sites fail, the average response time can still be effectively controlled within 9.9 min. Through this robustness analysis, the effectiveness and reliability of the decision framework are demonstrated, offering more efficient and reliable support for the EMS system. Full article

Through advances in AI-based computer vision technology, the performance of modern image classification models has surpassed human perception, making them valuable in various fields. However, adversarial attacks, which involve small changes to images that are hard for humans to perceive, can cause classification models to misclassify images. Considering the availability of classification models that use neural networks, it is crucial to prevent adversarial attacks. Recent detection methods are only effective for specific attacks or cannot be applied to various models. Therefore, in this paper, we proposed an attention mechanism-based method for detecting adversarial attacks. We utilized a framework using an ensemble model, Grad-CAM and calculated the silhouette coefficient for detection. We applied this method to Resnet18, Mobilenetv2, and VGG16 classification models that were fine-tuned on the CIFAR-10 dataset. The average performance demonstrated that Mobilenetv2 achieved an F1-Score of 0.9022 and an accuracy of 0.9103, Resnet18 achieved an F1-Score of 0.9124 and an accuracy of 0.9302, and VGG16 achieved an F1-Score of 0.9185 and an accuracy of 0.9252. The results demonstrated that our method not only detects but also prevents adversarial attacks by mitigating their effects and effectively restoring labels. Full article

The elements of body composition and their correlation with physical activity, body image, and self-esteem are aspects that require in-depth studies. This link should be seen in the context of the percentage of adipose tissue, which can be modeled via physical activity. The objective of this study is to evaluate the relationships between the parameters that define body composition, self-esteem, body image, and physical activity according to gender. This study included 100 females and 100 males with an average age of 22 years. The evaluation included anthropometric parameters, body composition, self-esteem, physical activity index, and body image perception assessment. The results show that the males exhibited a higher percentage of exceeding the upper limit of normal and average weight; in contrast, there were two times more females exhibiting normal weight than those exceeding the upper limit. Therefore, obesity was higher among males. The average fitness score values were 69.07 for females and 76.53 for males, and the mean fitness was within normal limits. Regarding body image, according to the BSQ, we observed that both groups were not satisfied with and were concerned about their body shape. With respect to the Rosenberg self-esteem scale, the average score for females was 20.27, and for males, it was 19.60; the mean self-esteem value was 66% of the maximum value. In terms of the perceived ideal body size assessed with the Silhouette scale, most of the females were placed at level 3, and the males were placed at level 4. Regarding the physical activity index, females carried out on average 1.5 days of intense physical activity over 7 days, and males were involved in intense physical activity for 2 days. Conclusions: The degree of obesity and therefore the risk of developing cardiovascular disease and metabolic syndrome were higher in males than in females. Although males have a higher degree of obesity, self-esteem is quantified at a higher level of confidence. Full article

The seasonality of floods is a key factor affecting riparian agriculture. Flood season staging is the main means of identifying the seasonality of floods. In the process of staging the flood season, set pair analysis is a widely used method. However, the set pair analysis method (SPAM) cannot take into account the differences in and volatility of the staging indicators, and at the same time, the SPAM cannot provide corresponding staging schemes according to different scenarios. To address these problems, the improved set pair analysis method (ISPAM) is proposed. Kernel density estimation (KDE) is used to calculate the interval of the staging indicators to express their volatility. Based on the interval theory, the deviation method is improved, and the weights of the staging indicators are calculated to reflect the differences in different staging indicators. The theoretical correlation coefficient can be calculated by combining the weights and interval indicators and fitting the empirical connection coefficient corresponding to each time period. Finally, the ISPAM is established under different confidence levels to derive staging schemes under different scenarios. Based on the daily average precipitation flow data from 1961 to 2022 in the Nandujiang middle basin and surrounding areas in tropical island regions, the staging effect of the ISPAM was verified and compared using the SPAM, Fisher optimal segmentation method, and improved set pair analysis method without considering differences in the indicator weights (ISPAM-WCDIIW), and the improved set pair analysis method without considering indicator fluctuations (ISPAM-WCIF). According to the evaluation results from the silhouette coefficient method, it can be concluded that compared with the SPAM and ISPAM-WCIF, the ISPAM provided the optimal staging scheme for 100% of the years in the test set (2011–2022). Compared with the Fisher optimal segmentation method, the optimal staging scheme for more than 83% of the years (2011, 2013–2015, and 2017–2022) in the test set was provided by the ISPAM. Although the ISPAM-WCDIIW, like the ISPAM, can provide optimal staging schemes, the ISPAM-WCDIIW could not provide an exact staging scheme for more than 55% of the scenarios (the ISPAM-WCDIIW could not provide an exact staging scheme in scenarios (0.7, 0.6), (0.8, 0.6), (0.8, 0.9), (0.95, 0.6), and (0.95, 0.8)). The results show that the ISPAM model is more reasonable and credible compared with the SPAM, Fisher optimal segmentation method, ISPAM-WCDIIW, and ISPAM-WCIF. The purpose of this study is to provide a reference for flood season staging research during flood seasons. Full article

Roll-on/roll-off vessels (RO/RO vessels) are playing an increasingly critical role in international automobile transport, facilitating the efficient movement of vehicles and heavy machinery across continents. Despite this growing significance, there is still limited research specifically focused on the RO/RO shipping network and its impact on global trade. This paper studies the global RO/RO shipping network using AIS data on RO/RO vessels collected from 2020 to 2023. We construct a method based on the complex network theory and the graph feature extraction method to quantitatively assess the features of the RO/RO shipping network. This method assesses the complexity, sparsity, homogeneity, modularity, and hierarchy of the RO/RO shipping network across various ports and countries and employs the graph convolutional neural network (GCN) model to extract network features for community detection. This process enables the identification of port clusters that are frequently linked to RO/RO vessels, as well as regional transport modes. The paper’s findings support these conclusions: (1) From 2020 to 2023, the number of nodes in the RO/RO shipping network increased by 22%, primarily concentrated in African countries. The RO/RO shipping network underwent restructuring after the pandemic, with major complex network parameters showing an upward trend. (2) The RO/RO shipping network is complex, with a stable graph density of 0.106 from 2020 to 2023. The average degree increased by 7% to 4.224. Modularity decreased by 6.5% from 0.431 in 2022 to 0.403, while the hierarchy coefficient rose to 0.575, suggesting that post-pandemic, community routes have become more diverse, reflecting the reconstruction and maturation of the overall network. (3) The model yielded a silhouette coefficient of 0.548 and a Davies–Bouldin index of 0.559 using an improved automatic feature extraction method. In comparison between 2020 and 2023, the changes in the two indicators are small. This shows that GINs can effectively extract network features and give us results that we can understand for community detection. (4) In 2023, key communities divide the RO/RO shipping network, with one community handling 39% of global routes (primarily Europe–Asia), another community handling 23% (serving Asia–Pacific, Africa, and the Middle East), and a third community managing 38% (linking Asia, Europe, and South America). Full article

In dynamic scenarios, the status of a Radio Frequency Identification (RFID) system fluctuates with environmental changes. The key to improving system efficiency lies in the real-time monitoring and evaluation of the system status, along with adaptive adjustments to the system parameters and read algorithms. This paper focuses on the status changes in RFID systems in dynamic scenarios, aiming to enhance system robustness and reading performance, ensuring high link quality, reasonable resource scheduling, and real-time status evaluation under varying conditions. This paper comprehensively considers the system parameter settings in dynamic scenarios, integrating the interaction model between readers and tags. The system’s real-time status is evaluated from both the physical layer and the Medium Access Control (MAC) layer perspectives. For the physical layer, a link quality evaluation model based on Uniform Manifold Approximation and Projection (UMAP) and K-Means clustering is proposed from the link quality. For the MAC layer, a multi-criteria decision-making evaluation model based on combined weighting and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is proposed, which comprehensively considers both subjective and objective factors, utilizing the TOPSIS algorithm for an accurate evaluation of the MAC layer system status. For the RFID system, this paper proposes a real-time status evaluation model based on the Classification and Regression Tree (CART), which synthesizes the evaluation results of the physical layer and MAC layer. Finally, engineering tests and verification were conducted on the RFID robot system in mobile scenarios. The results showed that the clustering average silhouette coefficient of the physical layer link quality evaluation model based on K-Means was 0.70184, indicating a relatively good clustering effect. The system status evaluation model of the MAC layer, based on the combined weighting-TOPSIS method, demonstrated good flexibility and generalization. The real-time status evaluation model of the RFID system, based on CART, achieved a classification accuracy of 98.3%, with an algorithm runtime of 0.003 s. Compared with other algorithms, it had a higher classification accuracy and shorter runtime, making it well suited for the real-time evaluation of the RFID robot system’s status in dynamic scenarios. Full article

This paper analyzes the relationship between urban waste recycling and innovation systems in Europe. Data from the Global Innovation Index for 34 European countries in the period 2013–2022 were used. To analyze the characteristics of European countries in terms of waste recycling capacity, the k-Means algorithm optimized with the Elbow method and the Silhouette Coefficient was used. The results show that the optimal number of clusters is three. Panel data results show that waste recycling increases with domestic market scale, gross capital formation, and the diffusion of Information and Communication Technologies (ICTs), while it decreases with the infrastructure index, business sophistication index, and the average expenditure on research and development of large companies. Full article

Owing to the low detection accuracy of camera-based object detection models, various fusion techniques with Light Detection and Ranging (LiDAR) have been attempted. This has resulted in improved detection of objects that are difficult to detect due to partial occlusion by obstacles or unclear silhouettes. However, the detection performance remains limited in low-light environments where small pedestrians are located far from the sensor or pedestrians have difficult-to-estimate shapes. This study proposes an object detection model that employs a Gaussian–Sobel filter. This filter combines Gaussian blurring, which suppresses the effects of noise, and a Sobel mask, which accentuates object features, to effectively utilize depth maps generated by LiDAR for object detection. The model performs independent pedestrian detection using the real-time object detection model You Only Look Once v4, based on RGB images obtained using a camera and depth maps preprocessed by the Gaussian–Sobel filter, and estimates the optimal pedestrian location using non-maximum suppression. This enables accurate pedestrian detection while maintaining a high detection accuracy even in low-light or external-noise environments, where object features and contours are not well defined. The test evaluation results demonstrated that the proposed method achieved at least 1–7% higher average precision than the state-of-the-art models under various environments. Full article

Bridges, as vital infrastructure, require ongoing monitoring to maintain safety and functionality. This study introduces an innovative algorithm that refines bridge component performance assessment through the integration of modified K-means clustering, silhouette coefficient optimization, and cloud model theory. The purpose is to provide a reliable method for monitoring the safety and serviceability of critical infrastructure, particularly double-layer truss arch bridges. The algorithm processes large datasets to identify patterns and manage uncertainties in structural health monitoring (SHM). It includes field monitoring techniques and a model-driven approach for establishing assessment thresholds. The main findings, validated by case studies, show the algorithm’s effectiveness in enhancing clustering quality and accurately evaluating bridge performance using multiple indicators, such as statistical significance, cluster centroids, average silhouette coefficient, Davies–Bouldin index, average deviation, and Sign-Rank test p-values. The conclusions highlight the algorithm’s utility in assessing structural integrity and aiding data-driven maintenance decisions, offering scientific support for bridge preservation efforts. Full article