Search Results (753)

With the expansion of unmanned aerial vehicles (UAVs) into complex three-dimensional (3D) terrains for reconnaissance, rescue, and related missions, traditional path planning methods struggle to meet multi-constraint and multi-objective requirements. Existing swarm intelligence algorithms, limited by the “no free lunch” theorem, also face challenges when the standard Information Acquisition Optimizer (IAO) is applied to such tasks, including low exploration efficiency in high-dimensional search spaces, rapid loss of population diversity, and improper boundary handling. To address these issues, this study proposes a Multi-Strategy Enhanced Information Acquisition Optimizer (MEIAO). First, a Levy Flight-based information collection strategy is introduced to leverage its combination of short-range local searches and long-distance jumps, thereby broadening global exploration. Second, an adaptive differential evolution operator is designed to dynamically balance exploration and exploitation via a variable mutation factor, while crossover and greedy selection mechanisms help maintain population diversity. Third, a globally guided boundary handling strategy adjusts out-of-bound dimensions to feasible regions, preventing the generation of low-quality paths. Performance was evaluated on the CEC2017 (dim = 30/50/100) and CEC2022 (dim = 10/20) benchmark suites by comparing MEIAO with eight algorithms, including VPPSO and IAO. Based on the mean, standard deviation, Friedman mean rank, and Wilcoxon rank-sum tests, MEIAO demonstrated superior performance in local exploitation of unimodal functions, global exploration of multimodal functions, and complex adaptation on composite functions while exhibiting stronger robustness. Finally, MEIAO was applied to 3D mountainous UAV path planning, where a cost model considering path length, altitude standard deviation, and turning smoothness was established. The experimental results show that MEIAO achieved an average path cost of 253.9190, a 25.7% reduction compared to IAO (341.9324), with the lowest standard deviation (60.6960) among all algorithms. The generated paths were smoother, collision-free, and achieved faster convergence, offering an efficient and reliable solution for UAV operations in complex environments. Full article

H9N2 avian influenza virus (AIV) is widely prevalent in poultry in China. To understand the genetic characteristics and evolution of H9N2 AIVs in Guangxi, southern China, the complete genomes of H9N2 AIVs from 1999–2023 were systematically analysed. Maximum likelihood (ML) trees indicated that H9N2 AIV gene sublineage diversity contributed to genotype diversity, yielding 17 genotypes (G1–G17). Since 2010, genotype G14 (also known as genotype S or G57) has become predominant in poultry in Guangxi. Phylogenetic analysis in the HA has resulted in the distancing of recent Guangxi isolates from the vaccine strains. This study also revealed that the genotypes of H9N2 AIVs infecting swine, equines and canines in Guangxi were consistent with those found in avian species at the same time, highlighting the capacity of H9N2 AIVs to be transmitted across species. The antigenic residues in the HA head region and NA protein of the Guangxi isolates from 2020–2023 changed significantly compared to the vaccine strains, suggesting possible antigenic drift in these viruses. Amino acid analysis of the HA protein revealed that 84.9% (73/86) of H9N2 AIV isolates from Guangxi, including those from live poultry markets, preferentially bound to α-2,6 sialic acid receptors. Considerable attention should be given to cross-species transmission of H9N2 AIV in the region. On the basis of these findings, strengthening the monitoring of H9N2 AIV in poultry in Guangxi is essential. Full article

In this paper, we study the detour eccentric sum index (DESI) to obtain the Quantitative Structure–Property Relationship (QSPR) for different molecular structures. We establish theoretical bounds for this index and compute its values across fundamental graph families. Through correlation analyses between the physicochemical properties of molecular structures representing anti-malarial and breast cancer drugs, we show the high predictive value of two topological parameters, detour diameter (DD) and detour radius (DR). Specifically, DR shows strong positive correlations with boiling point, enthalpy, and flash point (up to 0.94), while DD is highly correlated with properties such as molar volume, molar refraction, and polarizability (up to 0.97). The DESI was then selected for detailed curvilinear regression modeling and comparison against the established eccentric distance sum index. For anti-malarial drugs, the second-order model yields the best fit. The DESI provides optimal prediction for boiling point, enthalpy, and flash point. In breast cancer drugs, the second-order model is again favored for properties except for melting point, best described by a third-order model. The results highlight how well the index captures subtle structural characteristics. Full article

Optimizing the siting and servicing of urban facilities is a core operations research problem that must reconcile heterogeneous demand, spatial constraints, and network-realistic travel. We present GD-ARISE, a GIS-integrated and data analytics pipeline that maintains a pedestrian–road network metric from demand inference through siting to routing. The workflow has three modules: (i) GIS integration that unifies spatial layers on one network and distance metric; (ii) data analytics that builds multi-criteria suitability via the Analytic Hierarchy Process (AHP) and maps scores to adaptive service radii; (iii) optimal location-and-routing that selects nonoverlapping sites with a transparent greedy rule (SCASS) and computes depot-to-depot routes via simulated annealing on the same metric. A case study in Seoul’s Gangnam District yields a high-coverage portfolio and feasible collection routes. We add a theoretical framework that casts SCASS as a conflict-graph problem, document the AHP elicitation with consistency checks, and report robustness analyses including sensitivity to AHP weights and to radius bounds. Results indicate that core hotspots remain stable to weighting, whereas mid-range corridors shift as criteria priorities or spatial parameters change. Full article

Noisy permutation channels are applied in modeling biological storage systems and communication networks. For noisy permutation channels with strictly positive and full-rank square matrices, new achievability bounds are given in this paper, which are tighter than existing bounds. To derive this bound, we use the $ϵ$-packing with Kullback–Leibler divergence as a distance and introduce a novel way to illustrate the overlapping relationship of error events. This new bound shows analytically that for such a matrix W, the logarithm of the achievable code size with a given block n and error probability $ϵ$ is closely approximated by $\ell log\left({\displaystyle \frac{\sqrt{n}}{-{\Phi }^{-1}(ϵ/G)}}\right)+logV\left(W\right)$, where $\ell =\mathsf{rank}\left(W\right)-1$, $G=2\left({\displaystyle \genfrac{}{}{0pt}{}{\ell +1}{2}}\right)$, and $V\left(W\right)$ is a characteristic of the channel referred to as channel volume ratio. Our numerical results show that the new achievability bound significantly improves the lower bound of channel coding. Additionally, the Gaussian approximation can replace the complex computations of the new achievability bound over a wide range of relevant parameters. Full article

Bioabsorbable scaffolds from synthetic polyesters are widely used in the field of tissue engineering. However, their hydrophobic surface and lack of suitable functional groups are the main limitations related to cell attachment. The aim of this research was to modify the surface of polycaprolactone (PCL) scaffolds using a bioactive coating containing heparin bound via albumin spacer and platelet lysate over heparin. Porous scaffolds were produced by electrospinning from 10% PCL (w/w) solution in methylene chloride (25 kV voltage, 100 mm distance between electrodes and 4 mL/h feedrate), which demonstrated 5.5 ± 1.1 MPa Young’s modulus, 2.5 ± 0.4 MPa tensile strength and 321 ± 29% elongation at break. Bioactive coating does not change the structure and mechanical properties of the scaffolds. Treated scaffolds are biocompatible and have no cytotoxic effect in direct contact with cells. Functionalization also promotes the in vitro adhesion and proliferation of human adipose mesenchymal stromal cells. After 7 days of incubation, the PCL scaffold modified with the heparin–platelet lysate complex had a cell density of 185.6 ± 15.7 cells/mm² compared to 79.5 ± 7.8 cells/mm² for nontreated control. The intramuscular implantation of scaffolds revealed that immobilization of heparin alone prolongs the acute phase of the inflammatory reaction. However, subsequent treatment with platelet lysate minimizes the inflammatory reaction, slows the rate of implant absorption, and accelerates vascularization. The results obtained show that the developed bioactive coating improves the cellular properties of PCL electrospun scaffolds and can be used to form in vivo tissue-engineered constructs. Full article

To address the challenges of poor model adaptability and high computational complexity in complex orchard environments characterized by variable lighting, severe occlusion, and dense fruit clusters, an enhanced kiwifruit detection network, Kiwi-YOLO, is proposed based on YOLOv8. Firstly, replacing the main network with the MobileViTv1 module reduces computational load and parameters, thus enhancing inference efficiency for mobile deployment. Secondly, incorporating BiFPN into the model’s neck as a replacement for PANet improves feature distinguishability between background regions and target instances. Additionally, incorporating MCA module promotes cross-dimensional feature interactions, strengthening model robustness and generalization performance. Finally, the MPDIoU loss function is adopted to minimize bounding box vertex distances, mitigating detection box distortion caused by sample heterogeneity while accelerating convergence and enhancing localization accuracy. Experimental results indicate that the enhanced model achieves improvements of 2.1%, 1.5% and 0.3% in precision, recall, and mAP, respectively, over the baseline YOLOv8, while reducing parameters (Params) and computational complexity (GFLOPs) by 19.71 million and 2.8 billion operations. Moreover, it surpasses other comparative models in performance. Furthermore, in experiments detecting kiwifruit targets under complex lighting and occlusion conditions, the Kiwi-YOLO model demonstrated excellent adaptability and robustness. Its strong environmental adaptability provides technical guidance for advancing the practical application of unmanned intelligent kiwifruit harvesting. Full article

This paper presents a performance analysis of the reconfigurable intelligent surface (RIS)-aided ambient backscatter communication (AmBC) network. The system consists of a base station (BS), a backscatter device (BD), an RIS, and a destination (D). No direct link exists between the BS and RIS and between the BD and D. We propose a novel tight Jensen’s inequality. A new tighter upper bound is derived for the ergodic capacity, and we demonstrate that the proposed upper bound is much tighter than the existing bound. Monte Carlo simulations are performed to validate the analytical results. The tightened upper bound is found to be almost identical to that in the Monte Carlo simulation results, and the ergodic capacity significantly increases with the number of reflecting elements. In addition, the ergodic capacity improves when the RIS is placed close to the BD or D, and when the distance between the BS and BD is small, the ergodic capacity is severely affected. Full article

We provide a brief discussion regarding relativistic limits on the discretization and temporal resolution of time values in a quantum clock. Our clock is characterized by a time observable chosen to be the complement of a bounded and discrete Hamiltonian that can have an equally spaced or a generic spectrum. In the first case, the time observable can be described by a Hermitian operator, and we find a limit in the discretization for the time eigenvalues. Nevertheless, in both cases, the time observable can be described by a POVM, and, by increasing the number of time states, we show how the bound on the minimum time quantum can be reduced and identify the conditions under which the clock values can be treated as continuous. Finally, we find a limit for the temporal resolution of our time observable when the clock is used (together with light signals) in a relativistic framework for the measurement of spacetime distances. Full article

Unmanned aerial vehicle (UAV)-based object detection shows promising potential in intelligent transportation and disaster response. However, detecting small targets remains challenging due to inherent limitations (long-distance and low-resolution imaging) and environmental interference (complex backgrounds and occlusions). To address these issues, this paper proposes an enhanced small target detection model, DDF-YOLO, which achieves higher detection performance. First, a dynamic feature extraction module (C2f-DCNv4) employs deformable convolutions to effectively capture features from irregularly shaped objects. In addition, a dynamic upsampling module (DySample) optimizes multi-scale feature fusion by combining shallow spatial details with deep semantic features, preserving critical low-level information while enhancing generalization across scales. Finally, to balance rapid convergence with precise localization, an adaptive Focaler-ECIoU loss function dynamically adjusts training weights based on sample quality during bounding box regression. Extensive experiments on VisDrone2019 and UAVDT benchmarks demonstrate DDF-YOLO’s superiority. Compared to YOLOv8n, our model achieves gains of 8.6% and 4.8% in mAP50, along with improvements of 5.0% and 3.3% in mAP50-95, respectively. Furthermore, it exhibits superior efficiency, requiring only 7.3 GFLOPs and attaining an inference speed of 179 FPS. These results validate the model’s robustness for UAV-based detection, particularly in small-object scenarios. Full article

Bioavailable fractions of particulate-bound trace elements are key determinants of inhalation toxicity, yet air-quality assessments typically rely on total metal concentrations, which may underestimate health risks. This study integrates the exchangeable (F1) and reducible (F2) fractions of trace elements in fine (PM_2.5) and coarse (PM₁₀) particulate matter with multi-criteria decision-making (TOPSIS) and similarity-based classification (Czekanowski’s method). Archival weekly-integrated samples from the summer season were collected at eight industrially influenced sites in southern Poland. Sequential extraction (F1–F2) and ICP-MS were applied to quantify concentrations of cadmium, cobalt, chromium, nickel, and lead in PM_2.5 and PM₁₀. Aggregated hazard values were then derived with TOPSIS, and site similarity was explored using Czekanowski’s reordered distance matrices. Regulatory targets for cadmium and nickel, and the limit for lead in PM₁₀ were not exceeded, but F1/F2 profiles revealed pronounced site-to-site differences in potential mobility that were not evident from total concentrations. Rankings were consistent across size fractions, with site P₁ exhibiting the lowest hazard indices and P₈ the highest, while mid-rank sites formed reproducible similarity clusters. The proposed chemical-fractionation and multivariate framework provides a reproducible screening tool for multi-element exposure, complementing compliance checks and supporting prioritisation of sites for targeted investigation and environmental management. In the sustainability context, bioavailability-based indicators strengthen air-quality assessment by linking monitoring data with health-relevant and cost-effective management strategies, supporting efficient resource allocation and reducing exposure in vulnerable populations. Full article

Network interdiction problems involving edge deletion on shortest paths have wide applications. However, in many practical scenarios, the complete removal of edges is infeasible. The minimum-cost shortest-path interdiction problem for trees with the weighted $l_{\infty }$ norm (MCSPIT_∞) is studied in this paper. The goal is to upgrade selected edges at minimum total cost such that the shortest root–leaf distance is bounded below by a given value. We designed an $O(nlogn)$ algorithm based on greedy techniques combined with a binary search method to solve this problem efficiently. We then extended the framework to the minimum-cost shortest-path double interdiction problem for trees with the weighted $l_{\infty }$ norm, which imposes an additional requirement that the sum of root–leaf distances exceed a given threshold. Building upon the solution to (MCSPIT_∞), we developed an equally efficient $O(nlogn)$ algorithm for this variant. Finally, numerical experiments are presented to demonstrate both the effectiveness and practical performance of the proposed algorithms. Full article

LiDAR with direct time-of-flight (dToF) technology based on single-photon avalanche diode detectors (SPADs) has been widely adopted in various applications. However, a comprehensive theoretical understanding of its fundamental ranging performance bounds—particularly the degradation caused by pile-up effects due to system dead time and the potential benefits of photon-number-resolving detectors—remains incomplete and has not been systematically established in prior work. In this work, we present the first theoretical derivation of the Cramér–Rao lower bound (CRLB) for dToF systems explicitly accounting for dead time effects, generalize the analysis to SPADs with photon-number-resolving capabilities, and further validate the results through Monte Carlo simulations and maximum likelihood estimation. Our analysis reveals that pile-up not only reduces the information contained within individual ToF but also introduces a previously overlooked statistical coupling between distance and photon flux rate, further degrading ranging precision. The derived CRLB enables the determination of the optimal optical photon flux, laser pulse width (with FWHM of $\approx 0.56\tau$), and ToF quantization resolution that yield the best achievable ranging precision, showing that an optimal precision of approximately $0.53\tau /\sqrt{N}$ remains theoretically achievable, where $\tau$ is TDC resolution and N is the number of laser pulses. The analysis further quantifies the limited performance improvement enabled by increased photon-number resolution, which exhibits rapidly diminishing returns. Overall, these findings establish a unified theoretical framework for understanding the fundamental limits of SPAD-based dToF LiDAR, filling a gap left by earlier studies and providing concrete design guidelines for the selection of optimal operating points. Full article

We develop algorithms for computing permutation polynomials (PPs) using normalization, so-called F-maps and G-maps, and the Hermite criterion. This allows for a more efficient computation of PPs for larger degrees and for larger finite fields. We use this to improve some lower bounds for $M(n,D)$, the maximum number of permutations on n symbols with a pairwise Hamming distance of D. Full article

This paper presents a novel adversarial Convolutional Neural Network (ConvNet) method for generating adversarial perturbations in 3D point clouds, enabling gradient-free robustness evaluation of object detection systems at inference time. Unlike existing iterative gradient methods, our approach embeds the ConvNet directly into the detection pipeline at the voxel feature level. The ConvNet is trained to maximize detection loss while maintaining perturbations within sensor error bounds through multi-component loss constraints (intensity, bias, and imbalance terms). Evaluation on a Sparsely Embedded Convolutional Detection (SECOND) detector with the KITTI dataset shows 8% overall mean Average Precision (mAP) degradation, while CenterPoint on NuScenes exhibits 24% weighted mAP reduction across 10 object classes. Analysis reveals an inverse relationship between object size and adversarial vulnerability: smaller objects (pedestrians: 13%, cyclists: 14%) show higher vulnerability compared to larger vehicles (cars: 0.2%) on KITTI, with similar patterns on NuScenes, where barriers (68%) and pedestrians (32%) are most affected. Despite perturbations remaining within typical sensor error margins (mean L2 norm of 0.09% for KITTI, 0.05% for NuScenes, corresponding to 0.9–2.6 cm at typical urban distances), substantial detection failures occur. The key novelty is training a ConvNet to learn effective adversarial perturbations during a one-time training phase and then using the trained network for gradient-free robustness evaluation during inference, requiring only a forward pass through the ConvNet (1.2–2.0 ms overhead) instead of iterative gradient computation, making continuous vulnerability monitoring practical for autonomous driving safety assessment. Full article