The deployment of deep learning models on resource-constrained embedded platforms presents significant challenges due to limited computational power, memory, and energy efficiency. To address this issue, this study proposes a novel quantization method tailored to accelerate object detection using a quantized version of the YOLOv5m model, called Q_YOLOv5m. This method reduces the model's computational complexity and memory footprint, allowing for faster inference and lower power consumption, making it ideal for real-time applications on embedded systems. This approach incorporates advanced weight and activation quantization techniques to balance performance with accuracy, dynamically adjusting precision based on hardware capabilities. The efficacy of Q_YOLOv5m was confirmed, exhibiting substantial enhancements in inference speed and a reduction in model size with negligible loss in object detection accuracy. The findings underscore the capability of Q_YOLOv5m for edge applications, including autonomous vehicles, intelligent surveillance, and IoT-based monitoring systems.

UAV-based remote sensing systems are increasingly deployed in smart surveillance, disaster response, environmental monitoring, and critical infrastructure inspection. In these applications, aerial sensing platforms must transmit telemetry, control commands, and observation data securely and reliably under strict latency, energy, and computational constraints. However, existing security approaches often fail to jointly provide lightweight payload confidentiality, quantum-resilient key establishment, and adaptive communication protection suitable for dynamic and resource-constrained aerial sensing environments. To address this challenge, this paper proposes an energy-aware post-quantum hybrid cryptographic framework for secure and low-latency UAV remote sensing communications in UAV–IoT mission networks. The proposed framework integrates Ascon-based authenticated encryption for low-overhead protection of remote sensing payloads and mission telemetry, ML-KEM-based post-quantum session-key establishment for long-term resilience against quantum-era threats, and an AI-driven adaptive rekeying mechanism that dynamically adjusts key-refresh decisions according to threat level, residual energy, mobility state, channel stability, anomaly density, traffic sensitivity, link type, and mission progression. Accordingly, rekeying is treated not as a static maintenance process but as an intelligent and context-aware cryptographic control function that adapts communication security to evolving mission and sensing conditions. The framework is evaluated across twenty progressively demanding scenarios involving different UAV counts, sensor densities, payload sizes, communication modes, and adversarial settings relevant to real-time remote sensing operations. Experimental results demonstrate a secure delivery rate of 99.2%, attack detection and mitigation effectiveness of 98.9%, end-to-end encryption latency of 8.7 ms, throughput of 5.03 Mbps, energy overhead of 11.6 mJ/session, rekeying overhead of 2.9 mJ/event, session resilience of 96.4%, and integrity verification success of 99.1%. These findings show that the proposed framework provides a practical and scalable contribution to post-quantum secure UAV remote sensing by unifying lightweight authenticated encryption, ML-KEM-based quantum-resilient key establishment, and AI-driven adaptive rekeying within a resilient aerial–terrestrial communication architecture.

Road networks are routinely exposed to partial capacity losses caused by incidents, work zones, flooding and pavement failures, and planners need fast, trustworthy estimates of how such losses propagate through route choice. This paper develops a framework for user-equilibrium (UE) traffic networks with degradable link capacities that combines variational analysis with a learned surrogate. Writing the Beckmann potential in the coordinates $u_a=c_a^{-p}$ makes it affine in u, so standard results (the concavity of a pointwise minimum of affine functions and Danskin’s theorem) show that the optimal potential is concave, non-decreasing and differentiable, with a gradient available from one equilibrium solution. The resulting criticality index equals $p/(p+1)$ times the congestion delay of each link; the theory adds to this classical indicator a derivative interpretation and certified bounds. We derive two-sided certificates on the potential change for any degradation, the identity $\mathrm{TSTT}=(p+1)V-p\mathrm{FFT}$, a solver-free certificate for the total system travel time (TSTT), and an additive certificate for restoring sets of links, whose per-link weight is the damaged-state delay of the link times a severity factor, and we use these quantities as features of an envelope-informed surrogate (EIGS), a gradient-boosted model on per-link envelope features diffused over the line graph. Experiments on the Sioux Falls and Anaheim benchmarks show where the results are useful and where they are not. The potential certificate is within a factor of 1.45 of the exact change at 25% capacity loss on Sioux Falls (1.03 on the 100 most critical Anaheim links) but loosens quickly with severity (factor 3.28 at 50% loss on Sioux Falls; 574 and 74 at 90% loss), and the solver-free TSTT bound exceeds the true change by a median factor of 3.8–12 in the multi-link Monte Carlo scenario sets. No single cheap indicator ranks link criticality best on both networks. On Sioux Falls, EIGS predicts the TSTT increase with $R^2=0.942$ (0.913–0.960 over ten splits) against 0.919 for ridge regression and 0.924 for gradient boosting on the diffused severities, and with $R^2=0.909$ against 0.862 on a spatially correlated test set generated after the model was fixed. Under shifts in demand ($\pm 15\%$) and nominal capacity, refreshing its envelope features from one equilibrium of the shifted network gives $R^2=0.86$–$0.91$; using the same equilibrium to rescale the predictions of raw-input baselines gives them $0.84$–$0.89$, so the gain from the per-link envelope features is modest. On Anaheim, whose losses are extremely heavy-tailed, EIGS ranks scenarios well (Spearman $\rho =0.92$) but does not predict their magnitude reliably, while ridge regression with a cross-validated penalty ranks them poorly ($\rho =0.16$). For restoring three of about fifteen damaged links, the certified envelope rule, which needs a single equilibrium, recovers on average 93.6% of the loss recovered by the exact optimum over all three-link sets.

This study presents a mathematical modeling and simulation framework for pavement crack maintenance under limited work zone energy availability. The framework combines crack evolution, thermal repair, interface behavior, and maintenance decision-making within a unified structural–thermal–energy formulation. A three-dimensional multilayer flexible pavement model is developed with an XFEM-enriched region in the aged asphalt surface layer, a localized repair zone, cohesive interface behavior, and wheel-loading stages before and after maintenance. Rather than assuming constant heating, the repair heat flux is governed by a photovoltaic battery-dependent amplitude function, allowing the model to reflect practical variations in available energy. The simulation generated a dataset containing crack geometry, pavement properties, repair zone dimensions, loading conditions, heat flux intensity, heating duration, bonding quality, XFEM damage status, stress response, temperature distribution, interface damage, energy feasibility, durability, and service life gain. These variables were used to develop the Crack–Energy–Repair Interaction Graph Network (CERIG-Net), which represents each maintenance case as a physics-guided heterogeneous graph and ranks delayed repair, crack sealing, localized patching, thin overlay, and deep repair. The thin overlay strategy reduced final crack length from 94.7 mm to 53.8 mm, lowered maximum stress from 3.85 MPa to 2.22 MPa, achieved a durability index of 0.86, and extended service life by 6.7 years.