Purpose In time-critical natural disaster scenarios, unmanned aerial vehicles (UAVs) are crucial for search and rescue. While mobile edge computing (MEC) enables real-time data processing for these UAVs, it introduces a significant challenge: balancing low-delay data analysis to locate survivors against the UAVs’ limited battery life. This paper aims to propose a solution to minimize task processing delay in dynamic rescue environments while conserving UAV energy. Design/methodology/approach To overcome this challenge, this study proposes the multi-queue Lyapunov-guided deep reinforcement learning (MQ-LyDRL) method to minimize task processing delay by jointly optimizing task offloading and resource allocation. This method innovatively integrates Lyapunov optimization with DRL. Specifically, by constructing Lyapunov functions based on queue stability and energy constraints, MQ-LyDRL decomposes the complex multistage stochastic optimization problem into a deterministic, per-time-slot subproblem. An adaptive DRL framework is then employed to solve this subproblem, enabling it to learn the optimal policy for real-time decision-making without requiring prior knowledge of the environment’s dynamics. Findings Extensive simulations demonstrate that MQ-LyDRL significantly outperforms existing methods. It maintains operational stability in fluctuating conditions and reduces average delay by at least 9.21% while adhering to an energy budget. This reduction translates to faster data-to-decision cycles, accelerating life-saving interventions by extending the operational time of UAVs. Originality/value This work’s primary value is providing a blueprint for intelligent and efficient edge computing systems in high-stakes scenarios. By combining stability theory with adaptive artificial intelligence (AI), this study offers a practical framework applicable to critical missions where performance and reliability are nonnegotiable.

In order to ensure stable movement of differential drive robots on slopes, a control algorithm for stable movement of differential drive robots on slopes in non flat terrain is proposed. By planning the wheel motion conversion process that adapts to changes in terrain slope, the differential drive robot generates wheel motion sequences that are suitable for different slopes. Construct an evaluation function based on stability constraints, select a state with good motion as the next motion state of the robot, and adjust the lateral position of the robot to meet the stability margin requirements. On this basis, a deep enhancement method based on RBF neural network and Q-learning is introduced. With the change of the current environmental state value of the differential drive robot, through continuous training and learning, the network weights and wheel motion parameters are updated in real time, and the optimal execution wheel motion mode is selected to achieve stable slope movement control of the differential drive robot in non flat terrain. The experimental results show that under non flat ground conditions, the proposed algorithm has small changes in the horizontal velocity of the center of mass, the energy curve is consistent with the ideal, the stability margin is greater than 0.60 units, and the success rate of wheel motion conversion is greater than 92%; On a gentle slope, with a stability margin of 0.75, a moving speed of 1.0 m/s, a wheel motion conversion success rate of 98%, a recovery time of 2.0 seconds, and an error of 0.1 meters, the differential drive robot can achieve stable movement in non flat terrain.

This study develops a rigorous analytic framework for solving the Cauchy problem of polyharmonic equations in , highlighting the crucial role of symmetry in the structure, stability, and solvability of solutions. Polyharmonic equations, as higher-order extensions of Laplace and biharmonic equations, frequently arise in elasticity, potential theory, and mathematical physics, yet their Cauchy problems are inherently ill-posed. Using hyperspherical harmonics and homogeneous harmonic polynomials, whose orthogonality reflects the underlying rotational and reflectional symmetries, the study constructs explicit, uniformly convergent series solutions. Through analytic continuation of integral representations, necessary and sufficient solvability criteria are established, ensuring convergence of all derivatives on compact domains. Furthermore, newly derived Green-type identities provide a systematic method to reconstruct boundary information and enforce stability constraints. This approach not only generalizes classical Laplace and biharmonic results to higher-order polyharmonic equations but also demonstrates how symmetry governs boundary data admissibility, convergence, and analytic structure, offering both theoretical insights and practical tools for elasticity, inverse problems, and mathematical physics.

This study proposes a novel distributed Model Predictive Control (MPC) framework incorporating virtual tube constraints to address the dual challenges of safe navigation and efficient target attainment for Unmanned Aerial Vehicle (UAV) swarms in complex low-altitude traffic environments. An adaptive neighbor interaction mechanism is proposed that dynamically adjusts based on real-time perception and communication capabilities, enabling effective swarm coordination regardless of communication availability. Then, a multi-objective optimization formulation is constructed integrating maximum velocity pursuit, trajectory tracking precision and lateral deviation minimization within virtual tube boundaries. To ensure the stability of terminal velocity, an adaptive speed weight adjustment mechanism was specifically designed. Thus, a computationally efficient distributed MPC is designed utilizing dynamic local state information on switching topology. Through rigorous Lyapunov-based stability analysis and constraint satisfaction proofs, we establish the theoretical guarantees for both asymptotic stability and recursive feasibility of the proposed algorithm. Extensive numerical simulations demonstrate superior performance in complex traffic scenarios, showing faster target convergence while maintaining safe inter-agent distances and lateral deviation margins across various communication-restricted conditions.

In the context of a high proportion of renewable energy integration, active splitting section search—one of the “three defense lines” of a power system—is crucial for the security, stability, and long-term sustainability of islanded grids. Addressing the random fluctuations of high-penetration wind power and the weakened voltage support capability caused by multi-infeed HVDC, this paper proposes a slow-coherency-based active splitting section optimization model that explicitly accounts for wind power uncertainty and multi-infeed DC stability constraints. First, a GMM-K-means method is applied to historical wind data to model, sample, and cluster scenarios, efficiently generating and reducing a representative set of typical wind outputs; this accurately captures wind uncertainty while lowering computational burden. Subsequently, an improved particle swarm optimizer enhanced by genetic operators is used to optimize a multi-dimensional coherency fitness function that incorporates a refined equivalent power index, frequency constraints, and connectivity requirements. Simulations on a modified New England 39-bus system demonstrate that the proposed model markedly enlarges the post-split voltage stability margin and effectively reduces power-flow shocks and power imbalance compared with existing methods. This research contributes to enhancing the sustainability and operational resilience of power systems under energy transition.

Salt stress is a major abiotic constraint in cucumber (Cucumis sativus L.), reducing biomass, photosynthesis, and genomic stability. Grafting onto salt-tolerant Cucurbita rootstocks is a promising strategy to enhance plant resilience. Recently, machine learning (ML) has provided new opportunities to capture complex trait interactions and identify key predictors of stress tolerance. We evaluated two cucumber cultivars (Cagla F1, Minimix F1) grafted onto four interspecific Cucurbita maxima × Cucurbita moschata rootstocks (TZ148, Devrim, Cremna, Kublai) under 0 vs. 100 mM NaCl for 30 days in a soilless fertigation system. Morphological, physiological, and molecular traits were evaluated, including biomass accumulation, chlorophyll content (SPAD) and incident photosynthetically active radiation (PAR), and genomic template stability (GTS) using ISSR markers. Salt stress reduced growth and biomass (leaf FW - 56%, root DW - 74%) and lowered SPAD and relative water content (RWC); grafting-especially with TZ148 (and to a lesser extent Kublai)-mitigated these losses by maintaining chlorophyll content (SPAD) and biomass under salinity. Grafted combinations, especially TZ148/Cagla, maintained higher stability (GTS: 88%, GC: 0.07), confirming the protective role of grafting. ML approaches, including Principal Component Analysis (PCA) and Random Forest (RF), clearly separated control vs. salinity and, while grafting types showed only partial separation, RF consistently ranked root/stem fresh weight, SPAD, leaf area, and fruit weight as top predictors. Grafting significantly improved cucumber tolerance to salinity by sustaining biomass, photosynthetic capacity proxies (SPAD), and genomic integrity. ML-based analyses added predictive power and biological interpretation, confirming grafting with appropriate rootstocks as a sustainable strategy for cucumber production in saline nutrient solution conditions.

Improving robotic milling efficiency enhances productivity and reduces costs. While feed rate and spindle speed critically influence efficiency, chatter instability complicates their optimization. Existing stability constraints ignore state-dependent regenerative mechanisms, while the nonlinear effects of feed further complicate optimization. Although adjusting the robotic configuration improves stability, operational constraints such as joint singularity should be considered. This work proposes a reinforcement learning (RL) method to jointly optimize feed rate, spindle speed, and robotic configuration. RL dynamically maximizes efficiency in high-dimensional space using a reward function integrating stability and operability. Simulation results validate the method's superior performance.

Off-grid renewable power-to-hydrogen (ReP2H) systems face stability and economic constraints driven by the variability of renewable resources. This paper presents a comparative analysis of grid-forming (GFM) service requirements under three approaches, i.e., centralized GFM battery energy storage system (BESS), GFM electrolyzers and coordinated multi-source GFM strategies. We first establish detailed GFM models for off-grid ReP2H systems under each approach and then conduct hardware-in-the-loop (HIL) real-time simulations. By evaluating both dynamic performance and cost, we identify the strengths and limitations of the three strategies and quantify the GFM capacity needed to ensure stable off-grid hydrogen production.

This study evaluates the implicit, energy-conserving, and decaying scheme of Mamouri et al. for dynamic buckling analysis of geometrically exact 2D Reissner beams under non-impulsive, long-duration loading. While explicit schemes such as the central difference method excel in fast-dynamic events, they become computationally prohibitive in slow-to-moderate regimes due to stability constraints requiring millions of tiny time steps. Classical implicit methods like Newmark are efficient for smooth dynamics with small steps but fail to converge under large time steps in highly nonlinear regimes, particularly in the presence of high-frequency noise. In contrast, the proposed scheme combines unconditional stability with selective dissipation of high-frequency modes, enabling accurate and robust simulations using large time steps and drastically reducing the number of increments. Applied to three limit-point problems involving snap-through and snap-back, it consistently demonstrates superior efficiency: explicit schemes can incur excessive computational cost over long durations, and the Newmark scheme loses convergence under large steps in highly nonlinear contexts. In contrast, the proposed scheme delivers remarkable efficiency, accuracy, and stability; enabling reliable long-duration dynamic buckling analysis in highly nonlinear systems.

Accurate control of integrating and non-self-regulating processes remains a persistent challenge in process industries due to their inherent open-loop instability, slow response characteristics, and high sensitivity to parametric uncertainties and external disturbances. These characteristics often render conventional proportional-integral-derivative (PID) controllers inadequate, particularly under varying operating conditions or fault scenarios. To address these limitations, this study introduces a novel hybrid PID tuning framework that integrates the global optimization capability of the Harris Hawks Optimization (HHO) algorithm with a neural network-based supervisory model. The neural network is trained to dynamically estimate admissible bounds for key performance indices such as integral absolute error (IAE), integral squared error (ISE), and integral time-weighted absolute error (ITAE) based on process behavior. These bounds serve as adaptive constraints in the optimization phase, allowing the tuning mechanism to remain process-specific and responsive to variations, faults, and non-linearities. The overall controller design is formulated as a constrained multi-objective optimization problem, where the objectives include minimizing control errors while simultaneously satisfying robustness, stability, and performance constraints. Unlike traditional fixed-rule or purely heuristic-based tuning techniques, the proposed approach enables online adaptation to disturbances and faults by leveraging real-time feedback from the neural network. This enhances both robustness and fault tolerance across a wide range of operating scenarios. The effectiveness of the proposed method is rigorously evaluated through extensive simulations on several benchmark integrating processes under both nominal and faulty conditions, including sensor and actuator faults. Comparative analysis with recent methods confirms that the proposed controller offers superior tracking accuracy, faster settling time, and enhanced robustness. In addition, the robustness of the closed-loop system is graphically validated using frequency-domain magnitude plots under multiplicative input and output uncertainties. These results confirm the practical value and innovative nature of the proposed intelligent hybrid tuning strategy.

Abstract While voltage-sourced converter based high-voltage direct current (VSC-HVDC) transmission provides an effective solution for power evacuation from large-scale renewable energy source, its transmission capacity is heavily dependent on parameter design. This paper proposes a parameter design methodology that comprehensively considers steady-state operation, transient stability, and equipment security constraints. A multi-objective optimization approach using the Non-dominated Sorting Genetic Algorithm II determines key system parameters, including optimal transmission capacity and DC voltage level. Validation through electromagnetic transient simulation of a three-terminal VSC-HVDC system in PSCAD/EMTDC demonstrates that the optimized parameters ensure stable system operation with reliable fault ride-through capability, effectively supporting the efficient grid integration of renewable energy from remote areas.

Multi-objective optimization and comprehensive assessment of CO2-based mixtures with thermal stability constraints for parabolic trough concentrated solar thermal power plants

Developmental dyslexia is a common neurodevelopmental learning disorder that severely impacts children's reading abilities and social adaptation. In recent years, brain network analysis based on functional magnetic resonance imaging has provided new insights into its neural mechanisms, yet it struggles to capture the temporal characteristics of dynamic brain interactions. To address this, this paper proposes a GAT-LSTM framework for high-precision classification of DD. This method first constructs a dynamic functional connectivity network based on the AAL90 brain atlas. It then employs GAT to adaptively learn spatial dependencies between brain regions within each time window, followed by LSTM to model the temporal evolution patterns of node embedding sequences. To further enhance the model's temporal consistency and discriminative power, dynamic graph stability constraints are introduced during training. Experimental results demonstrate that the proposed method achieves an 85.36% classification accuracy, significantly outperforming baseline models. This study not only provides a novel computational paradigm for the objective diagnosis of DD but also offers robust support for the application of brain network modeling in neurodevelopmental disorder research.

Traditional transient stability-constrained optimal power flow (TSCOPF) methods rely on solving complex nonlinear differential equations, resulting in high computational demands and lengthy processing times. To address these issues, this paper proposes a TSCOPF model based on a cascaded CatBoost model (CatBoost-DF) and an improved seagull optimization algorithm (ISOA). First, a TSCOPF model is constructed. Second, the CatBoost-DF model is developed to establish a mapping relationship between the dynamic characteristics of the power system and the power angle of generators. The trained CatBoost-DF model is then employed as a surrogate model to handle transient stability constraints, thereby avoiding the computation of complex differential-algebraic equations traditionally required in transient stability constraint analysis. Then, the ISOA is employed to iteratively solve the TSCOPF model. This enables timely adjustment of generator output when transient instability risks arise, preventing accidents while maintaining system economic efficiency. Finally, simulations conducted on the IEEE 39 bus system demonstrate that this method effectively safeguards both system security and economic performance.

Viral infections of the eye, including herpes simplex keratitis, varicella-zoster keratitis, and cytomegalovirus retinitis, are major causes of ocular morbidity and vision loss worldwide. Current antiviral eye drop formulations face significant challenges, including poor ocular bioavailability, rapid precorneal clearance, limited tissue penetration, and the need for frequent administration. These factors reduce therapeutic efficacy and patient adherence. This review summarizes recent advances in antiviral ophthalmic formulation strategies aimed at overcoming these limitations. Conventional approaches, such as viscosity‐enhancing agents and mucoadhesive polymers, can prolong precorneal residence and improve comfort, while in-situ gelling systems and drug–device combinations enable sustained release. Nanotechnology-based carriers—including liposomes, nanoparticles, and nanoemulsions—offer enhanced stability, targeted delivery, and potential penetration into both anterior and posterior ocular segments. Additionally, combination therapies and stimuli-responsive delivery platforms provide opportunities for personalized treatment and reduced dosing frequency. Despite promising preclinical and early clinical results, the translation of these innovative systems into marketed products remains limited due to manufacturing complexity, stability constraints, and regulatory challenges. Preservative-free formulations and advanced delivery devices are particularly important for minimizing ocular surface toxicity and improving patient outcomes. Future directions include the integration of nanocarriers with established antivirals, optimization of excipient profiles for safety and stability, and rigorous in vivo testing to confirm efficacy and tolerability. Interdisciplinary collaboration between formulation scientists, clinicians, and regulatory bodies will be essential to accelerate the development of next-generation antiviral eye drop products capable of providing safe, effective, and patient-friendly treatment for vision-threatening viral ocular diseases.

This study addresses the accurate estimation of safe driving speeds for multi-axle trucks negotiating curved road segments by explicitly incorporating dynamic axle load transfer and load-sensitive tire–road friction characteristics. Conventional standards that assume a constant friction coefficient fail to capture wheel-specific load variations, leading to underestimation of rollover and skidding risks. To overcome these limitations, a load-sensitive friction model is integrated with the friction ellipse and static rollover threshold (SRT), and a forward–backward algorithm is applied to compute dynamically feasible speed trajectories. The proposed framework is demonstrated through accident reconstruction of a ramp rollover scenario using TruckSim–Simulink co-simulation with reported geometric and vehicle parameters. The results reveal that neglecting load sensitivity systematically overestimates safe speeds and underestimates lateral deviation. Furthermore, SRT variation analysis illustrates a trade-off between structural stability and frictional constraints, where rollover dominates under low stability and skidding under high stability conditions. These findings emphasize the necessity of accounting for dynamic load distribution and load-sensitive friction in truck safety speed estimation, providing a foundation for autonomous truck speed control strategies and enhanced road design standards.

The accurate assessment of Available Transfer Capability (ATC) is critical for ensuring reliable and efficient operation of modern power systems. This paper presents a comprehensive comparison of ATC, the five distinct methodologies are analyzed: Power Transfer Distribution Factors (PTDF), AC Power Flow analysis, Dynamic Analysis incorporating time-domain simulations, Continuation Power Flow (CPF) for voltage stability assessment, and AI technique VARMAX statistical modeling for forecasting applications. Through extensive literature review and comparative analysis, this study evaluates the accuracy, computational complexity, and practical applications of each method. The paper includes detailed mathematical formulations, block diagrams, performance comparisons, and implementation guidelines. Results demonstrate that while PTDF methods offer computational efficiency for real-time applications, AC power flow provides superior accuracy for detailed analysis. Dynamic methods are essential for transient stability constraints, CPF excels in voltage stability studies, and VARMAX offers unique capabilities for statistical forecasting of transfer capabilities. The comprehensive analysis includes over 30 references from leading researchers in the field, providing a complete overview of current state-of-the-art methodologies.

Calculating Occultation Light Curves Using Wavelets: Exponential Atmospheres and the Constraints of Static Stability

Abstract In this paper a framework of quantile-based sequential optimization and reliability assessment (SORA) is extended to consider the global stability constraint in the optimization of plane frames. Uncertainty is considered on the structural side without any prior assumptions on their distribution information, and two novel stopping criteria with a reduction coefficient are employed to smoothly shift the constraint boundary for the next iteration. Force density method is introduced for the shape optimization of plane frames to avoid the existence of the melting nodes, and the geometrical stiffness matrix is also penalized to exclude pseudo local buckling modes. The numerical examples illustrate that with the help of reduction coefficients, the shifts of different constraint boundaries in SORA become smoother and the convergence of sequential optimization is improved, and due to shape optimization, the reliability of structural stability can be satisfied with limited increase of structural volume of the one without considering stability. Moreover, it is also shown in the cantilever beam and bridge examples that global structural stability can be enhanced by applying nodal displacement constraints with higher reliability.

This paper develops a probabilistic digital twin-based methodology for optimal control of a rotorcraft that has incurred damage during a flight. A sudden change in the health state of the rotorcraft can significantly impact flight control. In the proposed digital twin approach, a probabilistic rotorcraft dynamic surrogate model is trained for fast time series prediction of future rotorcraft states for different damage levels for the damage mode of interest. The current health state is diagnosed to provide a probabilistic damage estimate, and future rotorcraft states are predicted given the current position, heading, the updated health condition, and candidate control settings. The diagnosis uncertainty as well as the prognosis uncertainty are quantified and propagated through time to obtain stochastic future rotorcraft states. The updated health estimate and the probabilistic, damage-sensitive prognosis surrogate are used for optimization under uncertainty to obtain the pilot control inputs that minimize the error between the planned and predicted flight path while satisfying probabilistic stability constraints for the rotorcraft. The methodology is demonstrated by performing simulation experiments on a helicopter that incurs damage on its rear horizontal stabilizer during flight.