Search Results (359)

As Flying Ad Hoc Networks (FANETs) are highly vulnerable to security threats such as identity spoofing, session replay and man-in-the-middle attacks in open-air channels, it is crucial to design an authentication key agreement (AKA) scheme to ensure the security of unmanned aerial vehicle (UAV) swarm networking within FANETs. However, existing AKA schemes for FANETs often struggle to balance authentication efficiency and high dynamism within UAV swarms whilst meeting necessary security requirements. To address the issue, this paper proposes CUBAT-AKA (Collaborative UAV Batch Authentication and Tree-based Key Agreement), a lightweight UAV swarm authentication and key agreement scheme based on batch verification and a binary tree structure. The scheme constructs a secure and lightweight three-party authentication mechanism based on aggregated verification and the Chinese Remainder Theorem (CRT). By offloading computational tasks to the authentication center and aggregating authentication responses in batches, it significantly improves the efficiency of UAV access authentication in large-scale FANET scenarios. To address the dynamic nature of UAVs frequently joining and leaving clusters in FANETs, an improved binary tree-based key agreement method has been designed, reducing key update overhead to a logarithmic level and enabling lightweight session key distribution and updates for UAV clusters. Security analysis demonstrates that, under the random oracle model, CUBAT-AKA is resistant to eavesdropping, replay, man-in-the-middle, impersonation and collusion attacks, whilst ensuring forward and backward security during member changes. Performance analysis indicates that this scheme offers significant advantages over comparable solutions in terms of both UAV cluster access authentication efficiency and dynamic key agreement overhead. Full article

Phase-inversion in situ implants (PIISIs) represent a versatile polymer platform in which the rational choice of matrix former and solvent system directly governs the macroscopic properties of the resulting depot. This study applied a Quality by Design (QbD) approach to rationalize a bleached shellac–based PIISI, with particular focus on the physicochemical interactions between the polymer and the injection vehicle. Bleached shellac—a natural, low-cost, biodegradable oligomeric resin bearing –COOH, –OH, and ester functional groups—was selected as the matrix former and screened in seven neat solvents and five 1:1 binary combinations at 25% (m/m). Twelve formulations were evaluated against a predefined set of critical quality attributes, including injectability, phase-inversion kinetics, solvent diffusion volume, and implant structure (n = 5 per formulation; mean ± standard deviation (SD); one-way analysis of variance (ANOVA) with Tukey’s post hoc test, p < 0.05). Three lead solvent systems—propylene glycol/N-methylpyrrolidone (PG+NMP), PG/dimethyl sulfoxide (PG+DMSO), and DMSO/benzyl alcohol (DMSO+BA)—were identified as those providing an optimal balance between hydrogen-bond donor/acceptor solvation and controlled solvent extraction. In the second stage, shellac concentration (20–35%) was optimized, with 30% shellac in PG+NMP yielding the fastest phase inversion (~50 s), a structurally uniform matrix, and the lowest swelling (22%). A working mechanistic framework consistent with all observed critical quality attribute (CQA) trends in which solvent hydrogen-bond donor/acceptor balance and water miscibility govern implant architecture is proposed, and it is intended as a hypothesis-generating basis for the rational design of PIISI formulations; direct validation by spectroscopic, thermal-analytical, and biological methods is identified as the next step. The developed formulations are presented as a preliminary physicochemical platform; biological validation (in vitro cytocompatibility and inflammatory response assessment) is required before the system can be considered a validated formulation for dental drug delivery. Full article

Unmanned Aerial Vehicles (UAVs) are increasingly used in complex civil missions that require reliable operation under uncertainty, creating a need for formal methods to assess how artificial intelligence (AI) contributes to mission performance. This study develops and evaluates a unified modelling framework for AI-enabled UAV systems operating in autonomous and automatic modes on small- and medium-class platforms across different operational configurations, including both single-UAV and multi-UAV deployments. The framework combines a structured decomposition of mission tasks—Environmental Sensing and Monitoring, Situational Awareness, Communication and Sensing Interference Resilience, Hazard and Restricted-Zone Avoidance, and Mission Execution and Intervention—with binary set descriptions, Bayesian Networks (BN), and Reliability Block Diagrams (RBD). This integration enables consistent mapping between mission tasks, AI utilisation approaches, and system-level performance characteristics while accounting for environmental disturbances, communication degradation, and mission constraints. The results show that the framework supports scenario-based analytical evaluation of UAV effectiveness and enables assessment of how AI-enabled perception-stage performance influences mission-level success in a civil Chemical, Biological, Radiological, Nuclear, and Explosive (CBRNe) environment. The proposed framework provides a methodological basis for the design, analysis, and future experimental validation of AI-enabled UAV systems for safety-critical civil missions. Full article

Unmanned aerial vehicles (UAVs) are increasingly expected to support both wireless communication and logistics missions, creating a need for integrated operation strategies that jointly manage data collection and physical item handling. This paper investigates a UAV system that simultaneously performs uplink communication with multiple ground nodes (GNs) while completing time-constrained item-pickup tasks. To enhance both throughput and fairness across GNs, we maximize the proportional fair spectral efficiency of GNs while ensuring that all items are collected within the required mission duration under payload and geographical constraints. The resulting formulation constitutes a mixed-integer nonconvex optimization problem involving binary pickup assignments, binary communication scheduling, and trajectory-dependent channel coupling, making direct global optimization intractable. To address this challenge, we develop an iterative convexification framework that integrates the successive convex approximation and the penalty convex–concave procedure within a block coordinate descent structure, enabling efficient joint optimization of trajectory, pickup timing/sequence, and GN scheduling. Simulation results validate that the proposed scheme dynamically shapes the UAV trajectory to improve channel conditions without violating the pickup deadline and compensates disadvantaged GNs through proportional fair scheduling. As a result, it consistently outperforms the baseline strategies under various system parameters. Full article

Unmanned aerial vehicles (UAVs) are increasingly employed for parcel logistics while simultaneously serving as aerial communication platforms. However, jointly optimizing pickup-and-delivery operations and wireless communication raises a large-scale mixed-integer nonlinear programming problem due to the coupling of binary logistics decisions, trajectory planning, time allocation, user scheduling, and transmit-power control. This paper proposes a two-phase optimization framework that enables a dual-purpose UAV mission by jointly considering parcel pickup-and-delivery and downlink communication within a single framework. The key strength of the proposed approach is that it separates the logistics-dominated delivery stage from the communication-oriented service stage, thereby reducing the difficulty of directly handling the highly coupled MINLP while exploiting the residual mission time for communication enhancement. In Phase 1, a pickup-and-delivery optimization problem is formulated to minimize the delivery completion time by determining the UAV trajectory, time-slot lengths, and item handling sequence, where the binary pickup/drop-off decisions are relaxed and progressively enforced through a penalty convex–concave procedure. In Phase 2, communication performance is enhanced by optimizing user scheduling and transmit power over the entire mission horizon, together with residual flight trajectory refinement after delivery completion using successive convex approximation and block coordinate descent. Simulation results show that the proposed algorithm substantially improves the minimum average spectral efficiency among ground nodes while achieving early completion of logistics tasks. Compared with baseline strategies, the proposed method delivers consistent performance gains under various system parameters. In particular, it improves the minimum average spectral efficiency by up to $15\%$ compared with the baseline that removes the proposed post-delivery trajectory refinement, demonstrating the benefit of exploiting the residual flight trajectory for communication enhancement after delivery completion. Full article

Crashes involving freight and commercial vehicles impose substantial human and economic costs, yet most severity studies pool vehicle types or focus exclusively on heavy trucks, masking class-specific risk mechanisms. This study estimates separate Random Parameters Binary Logit models with heterogeneity in means and variances for three vehicle categories—heavy-duty multi-axle trucks (n = 6512), two-axle trucks (n = 2656), and light-duty pickup trucks (n = 23,477)—using 32,645 crash records from Thailand’s national highway network (May 2022–December 2024). Pairwise transferability tests rejected parameter transferability, with four of six comparisons exceeding the 97 percent confidence level (three of these above 99 percent; χ² = 85.38 to 240.01), confirming that disaggregate estimation is statistically warranted. Three core findings emerge: First, although barrier medians, cut-in-front maneuvers, and sideswipe crashes affect severity in consistent directions across all vehicle types, their magnitudes differ sharply: the protective effect of barrier medians is nearly six times larger for two-axle trucks (ME = −0.160) compared to heavy-duty trucks (ME = −0.028). Second, several determinants are class-specific: dark unlit conditions elevate severity only for two-axle trucks (ME = 0.128), flush medians only for heavy-duty trucks (ME = 0.040), and raised medians only for light-duty pickups (ME = 0.042). Third, no random parameter is common to all three models. Pooled models, therefore, impose misleading homogeneity assumptions; vehicle-type-specific estimation is essential for targeted safety policy. Full article

This paper presents the application of a micro unmanned aerial vehicle (UAV) that acts as a pollination agent in a controlled environment simulating greenhouse conditions. The micro-UAV system was integrated with a convolutional neural network (CNN) for autonomous flower detection and navigation. The custom Sequential CNN architecture was used on board to perform real-time binary classification, accurately distinguishing flowers from non-flower objects. The fusion of this deep learning-based detection with precise micro-UAV navigation enables efficient identification and approaches to target flowers within optimal operational distances. Experimental evaluations revealed that the micro-UAV’s onboard camera, combined with CNN processing, outperformed standard webcams in terms of detection speed and accuracy, demonstrating the benefits of specialized hardware. Within the experiment, the micro-UAV was pre-programmed to follow a ‘cross’-shaped flight pattern. Experimental results show that the proposed system successfully detects multiple flowers autonomously between distances of 30.5 cm and 91.5 cm within 149.1 s. Overall, this study validated the integration of neural network capabilities with micro-UAV navigation. These findings are crucial for highlighting the potential of neural network-enabled micro-UAVs as effective pollinators in enclosed agricultural environments and for addressing the challenges faced by natural pollinators in greenhouses. Full article

Risk identification in ramp merging is often compromised by complex vehicle interactions and ‘future information’ leakage. To resolve this, we decouple the process using an ‘observation-conflict’ mechanism. By extracting kinematic features solely from the merging preparation phase, the framework predicts continuous risks during actual execution without temporal bias. Furthermore, we stabilize risk detection by integrating cpTTC thresholds with duration constraints into a three-level risk labeling scheme, ensuring the results align with real-world physical dynamics. Multiple machine learning models are comparatively evaluated using group-based data partitioning. Results indicate that the XGBoost model achieves the best overall performance, yielding an overall accuracy of 0.8182 and a multiclass AUC (OvR) of 0.8898. Furthermore, time cross-domain validation under varying macroscopic traffic flow states demonstrates that the framework exhibits reliable statistical stability; when reconstructed into a binary classification task, it maintains a risk recall of 0.9978. These findings provide a reliable methodological basis and early-warning feature reference for dynamic traffic risk assessment in merging scenarios. Full article

Background/Objectives: A floating hip injury, defined as an ipsilateral fracture of the pelvis or acetabulum combined with a femoral fracture, represents a rare and devastating musculoskeletal injury resulting from high-energy trauma. Although Müller type A floating hip injuries comprising an acetabular fracture with an ipsilateral femoral fracture are recognized for their clinical complexity, the long-term prognostic factors influencing functional outcomes remain poorly elucidated. This study aimed to identify independent prognostic factors associated with unsatisfactory long-term functional outcomes in patients with Müller type A floating hip injuries. Methods: A retrospective study was performed on 68 consecutive patients with Müller type A floating hip injuries who underwent surgical fixation at a single tertiary trauma center, with a minimum follow-up period of 5 years. Functional outcomes were assessed using the Majeed score, and patients were dichotomized into satisfactory (n = 48; 70.6%) and unsatisfactory (n = 20; 29.4%) outcome groups. Acetabular fractures were classified according to the Judet–Letournel system, and femoral fractures were classified by fracture level (proximal, shaft, or distal). Radiological outcomes were evaluated using Matta’s radiological grading system. Demographic, injury-specific, and treatment-related variables were compared between groups using the Mann–Whitney U test and chi-square test with Bonferroni correction. A multivariate binary logistic regression model was constructed to determine independent predictors of unsatisfactory outcomes. Results: The mean age was 37.15 ± 12.07 years, with a male predominance (67.6%). The predominant mechanism of injury was pedestrian struck by vehicle (54.4%), followed by motor vehicle collision (27.9%) and fall from height (17.6%); collectively, high-energy vehicular trauma accounted for 82.3% of cases. In the univariate analysis, transverse with posterior wall acetabular fracture pattern (p = 0.001), proximal femur fracture level (p = 0.001), associated lower extremity fractures (p = 0.001), nerve damage (p = 0.001), higher body mass index (BMI) (p = 0.001), and lower Matta’s radiological scores (p = 0.001) were significantly associated with unsatisfactory outcomes. Three independent predictors emerged in the multivariate logistic regression: BMI (OR = 1.50; 95% CI: 1.05–2.15; p = 0.025), the presence of associated lower extremity fractures (OR = 29.02; 95% CI: 2.83–297.67; p = 0.005), and Matta’s radiological score (OR = 0.06; 95% CI: 0.01–0.56; p = 0.014). The model yielded internal discriminatory metrics within the acceptable range (overall accuracy 89.7%, sensitivity 95.8%, specificity 75.0%, Nagelkerke R² = 0.757); however, given the limited events-per-variable ratio (~6.7) and the wide confidence intervals observed for some predictors, these internal performance estimates are likely optimistic due to potential overfitting, and the findings should be interpreted as exploratory pending external validation. Conclusions: Elevated BMI, the presence of associated ipsilateral lower extremity fractures, and poor quality of acetabular reduction, assessed via Matta’s radiological criteria, are independent determinants of unsatisfactory long-term functional outcomes in Müller type A floating hip injuries. These findings underscore the critical importance of achieving anatomical reduction in the acetabulum and highlight the compounding effect of additional ipsilateral limb injuries on patient prognosis. Full article

The detection and identification of unmanned aerial vehicles (UAVs) using radio frequency (RF) signals becomes particularly challenging in congested spectral environments, where conventional approaches relying solely on spectral characteristics often prove limited. This work introduces a novel technique for both UAV detection and classification based on temporal representations derived directly from the envelope of received RF signals. The proposed system follows a two-stage architecture: first, binary detection of UAV presence in a given RF channel, and second, identification of the specific UAV model among several commercial platforms. For the first stage, two signal representation methodologies are employed—Gramian Angular Fields and Hilbert curves—both generated from short-time RF windows and subsequently used as inputs to convolutional neural networks. Experimental evaluation demonstrates that the detection stage achieves accuracy rates exceeding 94% for the non-UAV class and approaching 99% for the UAV class with both approaches. In the identification stage, the system attains an accuracy above 90% for most considered UAV models, reaching up to 100% for certain platforms. These results confirm the effectiveness of the envelope-based approach for analyzing UAV-related RF signals. Full article

Accurate electric vehicle (EV) charging load forecasting is essential for grid planning and resource allocation, yet existing approaches struggle with the inherent sparsity of charging data—a phenomenon characterized by excessive zeros representing periods of no charging activity. This paper addresses this challenge through a novel framework combining a Zero-Inflated Neural Network (ZINN) architecture with an Evolutionary Neural Architecture Search (ENAS) algorithm. ZINN explicitly decomposes the forecasting problem into binary classification (predicting charging occurrence) and regression (estimating energy magnitude conditioned on occurrence), enabling the model to learn distinct patterns for the absence and presence of charging events. Rather than relying on manually designed architectures, ENAS automatically discovers optimal encoder and decoder configurations from a comprehensive search space encompassing modern architectures (LSTM, GRU, Transformer, and iTransformer), layer configurations, activation functions, and hyperparameters. The evolutionary algorithm balances prediction accuracy with computational efficiency through multi-objective optimization. Extensive experiments on real-world EV charging data from 30 stations in Wuhan demonstrate that the ZINN+ENAS framework achieves the lowest prediction error compared to conventional baselines, with the discovered optimal configuration substantially outperforming hand-crafted designs. Comprehensive ablation studies reveal that the asymmetric dual-head architecture and adaptive regularization strategies are critical for handling data sparsity. These findings highlight the importance of explicit zero-inflation modeling and automated architecture discovery for specialized forecasting tasks, providing practitioners with an open-source framework for practical EV charging load prediction. Full article

To address the core challenges in air-to-ground target re-identification (ReID), including network focus on invalid background information, poor adaptability to nonlinear feature distribution, and insufficient cross-domain generalization, this paper proposes a novel air-to-ground ReID framework based on Neighborhood Feature Centralization Attention (NFCA). On the basis of Coordinate Attention, the framework introduces a parameter-free Neighborhood Feature Centralization mechanism to build a lightweight attention module, which enhances cross-feature semantic interaction and suppresses background noise while retaining precise position encoding. It achieves end-to-end direct optimization of sample pair similarity through binary cross-entropy loss, eliminating the proxy task bias of traditional classification loss and adapting to the nonlinear structure of feature space. A multi-source data-driven training strategy is constructed by fusing ReID datasets and general classification datasets, which expands the coverage of feature space and narrows the distribution gap between training data and real air-to-ground scenarios without additional manual annotation. Experiments show that the proposed method achieves leading mAP values on the self-developed UAV air-to-ground dataset JC-1, the public person ReID dataset Market-1501, and the public vehicle ReID dataset VehicleID. Sufficient statistical validation, ablation experiments and cross-domain tests verify the advancement, reliability and generalization of the proposed method in complex air-to-ground scenarios. Full article

The increasing deployment of underwater vehicles demands accurate and energy-efficient target tracking in sensor networks. However, existing approaches have largely addressed tracking accuracy and energy efficiency in isolation, and a system-level framework that jointly optimizes both remains lacking. To address this gap, this paper proposes a joint optimization framework with two main contributions. First, to improve tracking accuracy under complex maneuvering conditions, we develop an Interactive Multi-Model using Long Short-Term Memory Classification (IMM-LSTM-C) algorithm, which integrates multi-step model likelihoods into an LSTM network for precise motion classification, achieving a 7.1% accuracy improvement over IMM-BP. Second, to reduce network energy consumption while maintaining tracking performance, we introduce an Improved Binary Prairie Dog Optimization (IBPDO) algorithm for node selection, enhanced with Cauchy mutation and opposition-based learning. Simulation results show that IBPDO achieves 6.1–8.2% higher accuracy than BWOA and reduces energy consumption by 12% compared to LNS. Furthermore, the complete joint framework demonstrates synergistic effects, reducing tracking error by 19.3% and energy consumption by 15.4% over the IMM + LNS baseline. The proposed framework provides an effective balance between tracking accuracy and energy efficiency in underwater acoustic sensor networks. Full article

With the widespread adoption of various types of Unmanned Aerial Vehicles (UAVs), their non-compliant operations pose a severe challenge to public safety, necessitating the urgent identification and detection of UAV targets. However, in complex backgrounds, UAV targets exhibit small-scale dimensions and low contrast, coupled with extremely low signal-to-noise ratios. This forces conventional target detection methods to confront issues such as feature convergence, missed detections, and false alarms. To address these challenges, we propose a Multi-View Feature Fusion Network (MVFF) that achieves precise identification of small, low-contrast UAV targets by leveraging complementary multi-view information. First, we design a collaborative view alignment fusion module. This module employs a cross-map feature fusion attention mechanism to establish pixel-level mapping relationships and perform deep fusion, effectively resolving geometric distortion and semantic overlap caused by imaging angle differences. Furthermore, we introduce a view feature smoothing module that employs displacement operators to construct a lightweight long-range modeling mechanism. This overcomes the limitations of traditional convolutional local receptive fields, effectively eliminating ghosting artifacts and response discontinuities arising from multi-view fusion. Additionally, we developed a small object binary cross-entropy loss function. By incorporating scale-adaptive gain factors and confidence-aware weights, this function enhances the learning capability of edge features in small objects, significantly reducing prediction uncertainty caused by background noise. Comparative experiments conducted on a multi-perspective UAV dataset demonstrate that our approach consistently outperforms existing state-of-the-art methods across multiple performance metrics. Specifically, it achieves a Structure-measure of 91.50% and an F-measure of 85.14%, validating the effectiveness and superiority of the proposed method. Full article

Urban transportation systems face increasing sustainability challenges due to the dominance of private-car use, particularly for short-distance trips. This study investigates the potential of micromobility to replace private-car travel on short-distance journeys and evaluates the resulting impacts on urban transportation networks and environmental sustainability. The analysis focuses on the Bornova district of Izmir and is based on a face-to-face survey conducted with 502 private-vehicle users. Survey data were analyzed using descriptive statistics, chi-square tests and a binary logit regression model to identify factors influencing the willingness to adopt micromobility. Within the surveyed sample of private-car users, modal-shift rates were estimated as 35% for trips up to 5 km and 33% for trips between 5 and 10 km. These rates were applied to the private-car demand and distance matrices developed for the year 2030 within the scope of the Izmir Transportation Master Plan, resulting in a revised private-car demand matrix and a separate demand matrix representing potential micromobility users. Network assignments were performed in the PTV VISUM modeling environment. Assignment results demonstrate notable network-level changes following micromobility integration. The total length of road segments with micromobility traffic volumes exceeding a threshold of 10 veh/h was calculated at 292.5 km. Environmental impacts were evaluated using a life-cycle assessment (LCA) framework, revealing an approximate 5.5% reduction in total life-cycle CO₂ emissions. Overall, the findings provide quantitative evidence supporting micromobility as an effective component of sustainable urban transport strategies and offer guidance for local governments in infrastructure planning and policy development. Full article