Search Results (48)

While foraging tunnels of the red imported fire ant, Solenopsis invicta, have been well studied, much less is known about the tunnels constructed between neighboring nests, despite their perceived importance in intra-colony exchange and collaboration. In this study, we investigated such tunnels by excavating 80 pairs of nests (with distances of <1 m between nests) located in different types of habitats. For each pair of nests, we recorded the number of inter-nest tunnels and observed their shape, diameter, subsurface depth, and ant presence within them. Moreover, we analyzed the relationships between the probability of constructing inter-nest tunnels and several nest/habitat characteristics, including distance between nests, colony social form, nest size, soil type, and vegetation cover, as well as the relationships between tunnel numbers and these factors. The results show that the number of inter-nest tunnels ranges from one to 11. These tunnels open to the two nests at terminals, are elliptical in cross-section, <1.5 cm in diameter, and mostly at 1–3 cm (range 1–12 cm) subsurface depth. Among the 36 pairs of nests possessing tunnels, 31 pairs (86.1%) had worker or alate ants within their tunnels. Polygynous colonies are more likely (52.4%) to construct inter-nest tunnels than monogynous colonies (17.6%). Nest pairs that have a small nest, located in habitats with higher vegetation cover and loamy or sandy loam soil, tend to have inter-nest tunnels. We also showed that the capacity of constructing inter-nest tunnels falls in the regime similar to foraging tunnels. As nests were treated with chemicals, 33 nests were relocated and 47 new nests resulted within 2 weeks, but no definite tunnels were constructed between original nests and corresponding new nests. Our results highlight the significance of including such tunnels when analyzing intra-colony exchange, collaboration, and adaptive strategies in S. invicta. Uses of tunnels by fire ants during nest relocation, and the requirement of destroying them during control program implementation, were discussed. Full article

Federated Clustering (FL clustering) aims to discover latent knowledge in multi-source distributed data through clustering algorithms while preserving data privacy. Federated learning is categorized into horizontal and vertical federated learning based on data partitioning scenarios. Horizontal federated learning is applicable to scenarios with overlapping feature spaces but different sample IDs across parties. Vertical federated learning facilitates cross-institutional feature complementarity, which is particularly suited for scenarios with highly overlapping sample IDs yet significantly divergent features. As a classic clustering algorithm, k-means has seen extensive improvements and applications in horizontal federated learning. However, its application in vertical federated learning remains insufficiently explored, with room for enhancement in privacy protection and communication efficiency. Simultaneously, client feature imbalance may lead to biased clustering results. To improve communication efficiency, this paper introduces Product Quantization (PQ) to compress high-dimensional data into low-dimensional codes by generating local codebooks. Leveraging the inherent k-means algorithm within PQ, local training preserves data structures while overcoming privacy risks associated with traditional PQ methods that require server-side data reconstruction (which may leak data distributions). To enhance privacy without compromising performance, Multidimensional Scaling (MDS) maps codebook cluster centers into distance-preserving indices. Only these indices are uploaded to the server, eliminating the need for data reconstruction. The server executes k-means on the indices to minimize intra-group similarity and maximize inter-group divergence. This scheme retains original codebooks locally for strict privacy protection.The nested application of PQ and MDS significantly reduces communication volume and frequency while effectively alleviating clustering bias caused by client feature dimension imbalance. Validation on the MNIST dataset confirms that the approach maintains k-means clustering performance while meeting federated learning requirements for privacy and efficiency. Full article

UAV-based small target detection is crucial in environmental monitoring, circuit detection, and related applications. However, UAV images often face challenges such as significant scale variation, dense small targets, high inter-class similarity, and intra-class diversity, which can lead to missed detections, thus reducing performance. To solve these problems, this study proposes a lightweight and high-precision model UAV-YOLO based on YOLOv8s. Firstly, a double separation convolution (DSC) module is designed to replace the Bottleneck structure in the C2f module with deep separable convolution and point-by-point convolution fusion, which can reduce the model parameters and calculation complexity while enhancing feature expression. Secondly, a new SPPL module is proposed, which combines spatial pyramid pooling rapid fusion (SPPF) with long-distance dependency modeling (LSKA) to improve the robustness of the model to multi-scale targets through cross-level feature association. Then, DyHead is used to replace the original detector head, and the discrimination ability of small targets in complex background is enhanced by adaptive weight allocation and cross-scale feature optimization fusion. Finally, the WIPIoU loss function is proposed, which integrates the advantages of Wise-IoU, MPDIoU and Inner-IoU, and incorporates the geometric center of bounding box, aspect ratio and overlap degree into a unified measure to improve the localization accuracy of small targets and accelerate the convergence. The experimental results on the VisDrone2019 dataset showed that compared to YOLOv8s, UAV-YOLO achieved an 8.9% improvement in the recall of mAP@0.5 and 6.8%, while the parameters and calculations were reduced by 23.4% and 40.7%, respectively. Additional evaluations of the DIOR, RSOD, and NWPU VHR-10 datasets demonstrate the generalization capability of the model. Full article

The sensitivity analysis of line-of-sight estimation to measurement errors in the L-shaped antenna array contributes to the deeper understanding of how the measurement errors affect a 3D localization system aimed to be used in the next generation of inter-satellite links. First, the proposed 3D localization model in the Cartesian coordinate system is given, where, for simplicity, the origin of the coordinate system is the origin of the L-shaped antenna array. The proposed localization method relies on three measurements: range measurement and line-of sight angles with the x- and y-axis, respectively. The sensitivity analysis revealed that the variation in the L-shaped antenna array geometry (variation of the antennas placements) has an impact on the 3D positioning precision: a misplaced antenna—placed closer than intended—will have a larger line-of-sight error for small distances/ranges in the presence of range measurement errors. Notably, a misplaced antenna will result in a larger line-of-sight error for large distances/ranges in the presence of phase measurement errors. Full article

The aim of this study was to determine the load-bearing capacity of Klein’s floors under fire conditions using analytical and numerical analyses. Analytical and numerical simulations were performed considering different structural variants of Klein’s floors. A numerical FEM model was developed in Abaqus to create temperature profiles of the beam-to-beam slabs and steel beams, and the fire load-bearing capacity of Klein’s celling after being exposed to fire for 30, 60, and 120 min was calculated analytically. The analytical method of assessing the fire load-bearing capacity of Klein’s floors uses temperature profiles, which allow for calculations to verify fire resistance in the time domain of different structural variants of Klein’s floors. (1) Introduction: The structural solutions of Klein’s floors are widely known, but no studies in the literature have addressed the mechanics of these elements under fire conditions. Both the beam-to-beam slab and steel beams are sensitive to high temperatures. Providing the required level of fire safety in buildings with Klein’s ceilings is a complex issue that requires detailed analysis. This often involves the assessment of technical and material solutions that are not currently used, and a verification of their fire resistance may be necessary to adapt existing buildings to the presently applicable technical and construction regulations. (2) Methodology: This study was prepared based on domestic and foreign sources, including standards presenting available methods for verifying the fire resistance of Klein’s ceilings in terms of their load-bearing capacity. A calculation scheme was indicated that takes into account the inter-beam slab, treated as a reinforced masonry element subjected to bending, and steel ceiling beams. In addition, this article presents an original method for determining the temperature profiles of individual elements of Klein’s ceilings, based on numerical methods, to determine their reduced values of material properties. The temperature profiles included in this study take into account both different construction variants of Klein’s ceilings and different ways of finishing the lower surface of these ceilings. The presented analytical method of fire load-bearing capacity assessment is supported by a calculation example. (3) Conclusions: The calculation methodology presented in this paper, which is part of the analysis of the fire load-bearing capacity of Klein’s ceilings, allows for a safe estimation of their durability in fire conditions. The presented temperature profiles of individual Klein’s ceiling elements allow for the verification of their fire resistance in terms of load-bearing capacity, in accordance with the literature on the subject. The temperature values of individual Klein’s ceiling elements, presented in the form of a table, depending on the fire duration, indicate that the applied structural solutions of the ceilings have a significant influence on the rate of temperature increase in the partition. Based on the conducted analyses, it was found that steel beams in an unplastered Klein’s ceiling lose their fire load-bearing capacity before the 30 min fire duration, defined by the standard temperature–time curve. The use of gypsum plaster with a thickness of at least 1.5 cm can provide fire resistance of the above elements for up to 120 min of fire duration. It was found that the quality of the plaster is important, influencing its adhesion to the lower surface of the ceiling. The fire resistance of the inter-beam slabs is significantly influenced by the temperature of their reinforcement, which largely depends on the distance of the reinforcement from the lower edge of the slab. Full article

This study analyzes inter-regional traffic patterns and network structures using origin–destination (OD) data. Block modeling, a method that clusters nodes performing similar roles within a network to identify functional regional structures, distinguishes passenger and freight patterns. Eigenvector centrality extracts central cities, while multiple regression analysis compares factors influencing flows in core areas. The findings reveal that (1) freight flows exhibit more active inter-regional movement than passenger flows, relying heavily on long-distance transport; (2) passenger hubs tend to be geographically central, whereas freight hubs are located in peripheral areas; and (3) passenger flows are shaped by regional characteristics, industrial structure, and infrastructure, while freight flows are influenced by regional characteristics, infrastructure, and land use patterns. Population density and industrial facilities significantly impact both flow types. This study provides a comprehensive understanding of the distinct characteristics of passenger and freight flows, bridging gaps in the existing research. Moreover, it offers practical insights for policymakers aiming to promote balanced development and sustainable regional growth, emphasizing the integration of underdeveloped areas into broader strategies to address disparities and foster connectivity. By combining advanced analytical methods, this study establishes a novel framework for enhancing regional planning and policy formulation. Full article

The structural and electronic features of the stimuli-responsive supramolecular inter-ionic charge-transfer material containing electron accepting N-benzylyridinium-4-oxime cation (BPA4⁺) and electron donating hexacyanoferrate (II) are reported. The study of reversible stimuli-induced transformation between hydrated reddish-brown (BPA4)₄[Fe(CN)₆]·10H₂O and anhydrous blue (BPA4)₄[Fe(CN)₆] revealed the origin of observed hydrochromic behavior. The comparison of the crystal structures of decahydrate and anhydrous phase showed that subsequent exclusion/inclusion of lattice water molecules induces structural relocation of one BPA4⁺ that alter the donor-to-acceptor charge-transfer states, resulting in chromotropism seen as reversible reddish-brown to blue color changes. The decreased donor-acceptor distance in (BPA4)₄[Fe(CN)₆] enhanced charge-transfer interaction allowing charge separation via one-electron transfer, as evidenced by in-situ ESR and FTIR spectroscopies. The reversibility of hydrochromic behavior was demonstrated by in-situ HT-XRPD, hot-stage microscopic and in situ diffuse-reflectance spectroscopic analyses. The insight into electronic structural features was obtained with density functional theory calculations, employed to elucidate electronic structure for both compounds. The electrical properties of the phases during dehydration process were investigated by temperature-dependent impedance spectroscopy. Full article

Population mobility between cities significantly affects traffic congestion, disease spread, and societal well-being. As globalization and urbanization accelerate, understanding the dynamics of population mobility becomes increasingly important. Traditional population migration models reveal the factors influencing migration, while machine learning methods provide effective tools for creating data-driven models to handle the nonlinear relationships between origin and destination characteristics and migration. To deepen the understanding of population mobility issues, this study presents GraviGBM, an expandable population mobility simulation model that combines the gravity model with machine learning, significantly enhancing simulation accuracy. By employing SHAPs (SHapley Additive exPlanations), we interpret the modeling results and explore the relationship between urban characteristics and population migration. Additionally, this study includes a case analysis of COVID-19, extending the model’s application during public health emergencies and evaluating the contribution of model variables in this context. The results show that GraviGBM performs exceptionally well in simulating inter-city population migration, with an RMSE of 4.28, far lower than the RMSE of the gravity model (45.32). This research indicates that distance emerged as the primary factor affecting mobility before the pandemic, with economic factors and population also playing significant roles. During the pandemic, distance remained dominant, but the significance of short distances gained importance. Pandemic-related indicators became prominent, while economics, population density, and transportation substantially lost their influence. A city-to-city flow analysis shows that when population sizes are comparable, economic factors prevail, but when economic profiles match, living conditions dictate migration. During the pandemic, residents from hard-hit areas moved to more distant cities, seeking normalcy. This research offers a comprehensive perspective on population mobility, yielding valuable insights for future urban planning, pandemic response, and decision-making processes. Full article

Heel marks (HMs) are a type of dirt stain consisting of polyester-based urethane rubber on polyvinyl chloride (PVC) floor surfaces. The rapid removal of HMs was achieved by using non-thermal atmospheric-pressure plasma technology. Mimetic HMs were prepared by coating PVC floor samples with HMs to a thickness of 13.9 μm. The removal area, thickness, and volume were measured after applying spark discharges at high voltage and a repetition rate of 50 kHz. The treated surfaces were analyzed by using X-ray photoelectron spectroscopy (XPS) and pyrolysis–gas chromatography with time-of-flight mass spectrometry (Py-GC/TOFMS). Removal rates of 20 mm²/min in area, 52 mm³/min in volume, and 7 μm/min in depth were achieved with an inter-electrode distance of 10.0 mm and an air flow rate of 20 standard liters per minute. A removal depth of 10 μm/min was achieved without air supply. The mechanism of stain removal by spark discharge was modeled by decomposing the original high-molecular-weight molecules in polyester-based urethane rubber into low-molecular-weight molecules, such as methylene diisocyanate (MDI) components. The results of this study may facilitate the development of a novel electric vacuum cleaner capable of removing floor stains. Full article

Vehicle-to-vehicle (V2V) communication, which plays an important role in intelligent transportation systems, has been statistically proven to improve traffic efficiency and reduce the probability of accidents. In real-world applications, it is critical to accurately estimate the path loss parameter in communication channels due to the variable and complex propagation environments often encountered in inter-vehicle communication scenarios. This paper presents a study on various machine learning methods to improve path loss estimation in V2V communication using a dataset (192,000 observations) obtained from field measurements of highway environments in the Trabzon and Gümüşhane provinces in Türkiye. For this purpose, path loss estimation was carried out with different machine learning algorithms such as Artificial Neural Networks, Random Forest, Linear Regression, Gradient Boosting, Support Vector Regression, and AdaBoost by using various environmental and system features. Then, performance comparisons were conducted between machine learning methods and traditional empirical approaches such as log-distance, two-ray, and log-ray. Examining the outputs reveals that machine learning methods outperform traditional methods and yield results quickly. As a result, the Random Forest and Gradient Boosting methods demonstrated the highest prediction performances, with R² values of 0.97 and 0.96, MAE values of 0.0557 and 0.0701, and RMSE values of 0.0774 and 0.0964, respectively, outperforming both empirical methods, other machine learning techniques, and the existing studies based on V2V. Overall, our study provides significant contributions to the existing literature by providing a comprehensive parameter set for highway environments, examining the path loss prediction performance of machine learning models with different capabilities, and comparing them with traditional methods. This study not only fills a critical gap in the existing literature but also highlights the necessity, efficiency, and originality of machine learning approaches for improving reliable V2V communication systems. Full article

Gearboxes operate in challenging environments, which leads to a heightened incidence of failures, and ambient noise further compromises the accuracy of fault diagnosis. To address this issue, we introduce a fault diagnosis method that employs singular value decomposition (SVD) and graph Fourier transform (GFT). Singular values, commonly employed in feature extraction and fault diagnosis, effectively encapsulate various fault states of mechanical equipment. However, prior methods neglect the inter-relationships among singular values, resulting in the loss of subtle fault information concealed within. To precisely and effectively extract subtle fault information from gear vibration signals, this study incorporates graph signal processing (GSP) technology. Following SVD of the original vibration signal, the method constructs a graph signal using singular values as inputs, enabling the capture of topological relationships among these values and the extraction of concealed fault information. Subsequently, the graph signal undergoes a transformation via GFT, facilitating the extraction of fault features from the graph spectral domain. Ultimately, by assessing the Mahalanobis distance between training and testing samples, distinct defect states are discerned and diagnosed. Experimental results on bearing and gear faults demonstrate that the proposed method exhibits enhanced robustness to noise, enabling accurate and effective diagnosis of gearbox faults in environments with substantial noise. Full article

To address the issue associated with poor accuracy rates for specific emitter identification (SEI) under low signal-to-noise ratio (SNR) conditions, where the single-dimension radar signal characteristics are severely affected by noise, we propose an attention-enhanced dual-branch residual network structure based on the adaptive large-margin Softmax (ALS). Initially, we designed a dual-branch network structure to extract features from one-dimensional intermediate frequency data and two-dimensional time–frequency images, respectively. By assigning different attention weights according to their importance, these features are fused into an enhanced joint feature for further training. This approach enables the model to extract distinctive features across multiple dimensions and achieve good recognition performance even when the signal is affected by noise. In addition, we have introduced L-Softmax to replace the original Softmax and propose the ALS. This approach adaptively calculates the classification margin decision parameter based on the angle between samples and the classification boundary and adjusts the margin values of the sample classification boundaries; it reduces the intra-class distance for the same class while increasing the inter-class distance between different classes without the need for cumbersome experiments to determine the optimal value of decision parameters. Our experimental findings revealed that, in comparison to alternative methods, our proposed approach markedly enhances the model’s capability to extract features from signals and classify them in low-SNR environments, thereby effectively diminishing the influence of noise. Notably, it achieves the highest recognition rate across a range of low-SNR conditions, registering an average increase in recognition rate of 4.8%. Full article

Imbalance classification is common in scenarios like fault diagnosis, intrusion detection, and medical diagnosis, where obtaining abnormal data is difficult. This article addresses a one-class problem, implementing and refining the One-Class Nearest-Neighbor (OCNN) algorithm. The original inter-quartile range mechanism is replaced with the K-means with outlier removal (KMOR) algorithm for efficient outlier identification in the target class. Parameters are optimized by treating these outliers as non-target-class samples. A new algorithm, the Location-based Nearest-Neighbor (LBNN) algorithm, clusters one-class training data using KMOR and calculates the farthest distance and percentile for each test data point to determine if it belongs to the target class. Experiments cover parameter studies, validation on eight standard imbalanced datasets from KEEL, and three applications on real medical imbalanced datasets. Results show superior performance in precision, recall, and G-means compared to traditional classification models, making it effective for handling imbalanced data challenges. Full article

Wildfires act as a selection force threatening the sustainability and diversity of forest genetic resources. Few studies have investigated the genetic effects of forest wildfires. Species with perennial canopy seed banks in serotinous cones and soil or with long-distance seed and pollen dispersion can preserve genetic diversity and population differentiation under normal fire regimes. To test this hypothesis, we characterised molecularly Pinus pinaster Aiton (maritime pine) seedlings produced from seeds sampled in post-fire, naturally regenerated populations that had been subject to different fire regimes in the North of Portugal using inter-simple sequence repeats (ISSRs). The sampled populations burned once (A), twice (B), or three (D) times or had no prior fire history (C, control). Given the globally low seed germination ability, only 104 plantlets regenerated and were described. These plantlets were grouped according to their origin population. Intra-group ISSR polymorphism ranged from 72.73% (B) to 89.41% (D), revealing genetic differentiation among groups originating from populations that had experienced different fire recurrence. Overall, the unaffected genetic diversity of the regenerated plantlets allowed us to accept the hypothesis. Our findings enhance our understanding of the species ability to withstand fire-induced challenges and their responses to wildfires, guiding conservation endeavours and forest management strategies to bolster ecosystem resilience. Full article

Metarhizium rileyi is an entomopathogenic fungus that naturally infects the larvae of Spodoptera frugiperda, and has biocontrol potential. To explore more natural entomopathogenic fungi resources, a total of 31 strains were isolated from 13 prefectures in Yunnan Province. All the strains were identified using morphology and molecular biology. The genetic diversity of the 31 isolates of M. rileyi was analyzed using inter-simple sequence repeat (ISSR) techniques. Seven primers with good polymorphism were selected, and fifty-four distinct amplification sites were obtained by polymerase chain reaction amplification. Among them, 50 were polymorphic sites, and the percentage of polymorphic sites was 94.44%. The thirty-one strains were divided into eight subpopulations according to the regions. The Nei’s gene diversity was 0.2945, and the Shannon information index was 0.4574, indicating that M. rileyi had rich genetic diversity. The average total genetic diversity of the subpopulations in the different regions was 0.2962, the gene diversity within the populations was 0.1931, the genetic differentiation coefficient was 0.3482 (>0.25), and the gene flow was 0.9360 (<1). The individual cluster analysis showed that there was no obvious correlation between the genetic diversity of the strains and their geographical origin, which also indicated that the virulence of the strains was not related to their phylogeny. Thus, the genetic distance of the different populations of M. rileyi in Yunnan Province was not related to the geographical distance. The virulence of those 32 strains against the 3rd-instar larvae of S. frugiperda were varied with the differences in geographical locations. On the 10th day of inoculation, seventeen strains had an insect mortality rate of 70.0%, and seven strains had an insect mortality rate of 100%. The half-lethal times of the M. rileyi SZCY201010, XSBN200920, and MDXZ200803 strains against the S. frugiperda larvae were less than 4 d. Thus, they have the potential to be developed into fungal insecticidal agents. Full article