Search Results (1,102)

We recast cooperative localization and scheduling in heterogeneous multi-agent systems through the lens of symmetry and symmetry breaking. On the geometric side, the Fisher Information Matrix (FIM) objective is invariant to rigid Euclidean transformations of the global frame, while its maximization admits symmetric optimal sensor formations; on the algorithmic side, heterogeneity and task constraints break permutation symmetry across agents, requiring policies that are sensitive to role asymmetries. We model communication as a random graph and quantify structural symmetry via topology metrics (average path length, clustering, betweenness) and graph automorphism-related indices, connecting these to estimation uncertainty. We then design a hybrid reward for reinforcement learning (RL) that is equivariant to agent relabeling within roles yet intentionally introduces asymmetry through distance/FIM terms to avoid degenerate symmetric configurations with poor observability. Simulations show that (i) symmetry-aware, FIM-optimized path planning reduces localization error versus symmetric but non-informative placements; and (ii) controlled symmetry breaking in policy learning improves robustness and data rate–reward trade-offs over baselines. Our results position symmetry/asymmetry as first-class design principles that unify estimation-theoretic invariances with learning-based coordination in complex heterogeneous networks. Under DDPG training, the total data rate (SDR) reaches $6.63\pm 0.97$ and the average reward per step (ARPS) is $-80.70\pm 6.94$, representing improvements of approximately $11.8\%$ over the baseline $(5.93\pm 3.51)$ and $11.1\%$ over SAC $(5.97\pm 2.66)$, respectively. The network’s mean shortest-path length is $L=1.721$, and the average betweenness centrality of the coordination nodes is ≈0.098. Moreover, the FIM-optimized path-planning strategy achieves the lowest localization error among all evaluated policies. Full article

A collaborative control strategy combining the hyperbolic sine-cosine optimization (SCHO) algorithm with fuzzy adaptive linear active disturbance rejection control is proposed to address the nonlinearity and uncertainties in the hydraulic position servo system of shock absorber test benches. First, based on the dynamic characteristics of the shock absorber fatigue test bench and the tested shock absorber, a linearized model of the valve-controlled hydraulic cylinder and its load was established. The coupling mechanism of system parameter perturbation and disturbance was also analyzed. A third-order LADRC (Linear Active Disturbance Rejection Control) was designed considering the linear model characteristics of the test bench hydraulic servo system model to quickly estimate internal system disturbances and perform real-time compensation. Secondly, a multi-objective optimization function was constructed by integrating system performance indicators and incorporating controller and observer bandwidths into the optimization objectives. The SCHO algorithm was used for the global search and optimization of key LADRC parameters. To enhance the controller’s adaptive capability of modeling uncertainties and external disturbances, a fuzzy adaptive module was introduced to adjust control gains online according to errors and their rates of change, further improving system robustness and dynamic performance. The results show that compared with traditional PID, under different working conditions, the proposed method reduced the maximum tracking error, overshoot, and system response time by an average of 45%, from 15% to 5%, and by approximately 30%, respectively. Meanwhile, the parameter combination obtained via SCHO effectively avoids the limitations of manual parameter tuning, significantly improving control accuracy and energy utilization. The simulation results indicate that this method can significantly enhance position-tracking accuracy compared with traditional LADRC, providing an effective solution for position-tracking control in hydraulic servo testing systems. Full article

High-performance tracking control for the hydraulic manipulator should address the challenges of the uncertainties and unknowns associated with the electro-hydraulic servo system (EHSS). This paper presents an extended state observer-based chattering-free terminal sliding-mode (ESO-CFTSM) control scheme for hydraulic manipulators. A third-order integral chain model is developed to characterize the system dynamics, where uncertainties and unknowns are considered as disturbances and estimated by the ESO. Meanwhile, a full-order TSM manifold is designed to stabilize the closed-loop system in finite-time. For this proposed scheme, the feedforward compensation of disturbances is introduced in the equivalent control law. Furthermore, the composite reaching law and a low-pass filter are used to realize the chattering-free control. The singularity is avoided because there are no derivatives of terms with fractional powers in the control law. The stability of the overall system is proved by Lyapunov technique. The simulations using the physical model of a hydraulic manipulator with coupled dynamics show the effectiveness of the proposed scheme for trajectory tracking problems. Simulation results indicate that the proposed ESO-CFTSM can achieve superior performance without being affected by lumped disturbances. Full article

The Vehicular Internet of Things (V-IoT) has emerged as a cornerstone of next-generation intelligent transportation systems (ITSs), enabling applications ranging from safety-critical collision avoidance and cooperative awareness to infotainment and fleet management. These heterogeneous services impose stringent quality-of-service (QoS) demands for latency, reliability, and fairness while competing for limited and dynamically varying spectrum resources. Conventional schedulers, such as round-robin or static priority queues, lack adaptability, whereas deep reinforcement learning (DRL) solutions, though powerful, remain computationally intensive and unsuitable for real-time roadside unit (RSU) deployment. This paper proposes a lightweight and interpretable reinforcement learning (RL)-based spectrum management framework for Vehicular Internet of Things (V-IoT) networks. Two enhanced Q-Learning variants are introduced: a Value-Prioritized Action Double Q-Learning with Constraints (VPADQ-C) algorithm that enforces reliability and blocking constraints through a Constrained Markov Decision Process (CMDP) with online primal–dual optimization, and a contextual Q-Learning with Upper Confidence Bound (Q-UCB) method that integrates uncertainty-aware exploration and a Success-Rate Prior (SRP) to accelerate convergence. A Risk-Aware Heuristic baseline is also designed as a transparent, low-complexity benchmark to illustrate the interpretability–performance trade-off between rule-based and learning-driven approaches. A comprehensive simulation framework incorporating heterogeneous traffic classes, physical-layer fading, and energy-consumption dynamics is developed to evaluate throughput, delay, blocking probability, fairness, and energy efficiency. The results demonstrate that the proposed methods consistently outperform conventional Q-Learning and Double Q-Learning methods. VPADQ-C achieves the highest energy efficiency (≈$8.425\times {10}^7$ bits/J) and reduces interruption probability by over $60\%$, while Q-UCB achieves the fastest convergence (within ≈190 episodes), lowest blocking probability (≈0.0135), and lowest mean delay (≈0.351 ms). Both schemes maintain fairness near $0.364$, preserve throughput around 28 Mbps, and exhibit sublinear training-time scaling with $\mathcal{O}\left(1\right)$ per-update complexity and $\mathcal{O}\left(N^2\right)$ overall runtime growth. Scalability analysis confirms that the proposed frameworks sustain URLLC-grade latency (<0.2 ms) and reliability under dense vehicular loads, validating their suitability for real-time, large-scale V-IoT deployments. Full article

By 2050, the global population will be predominantly living in urban areas, and climate change mitigation planning will be crucial for addressing the climate emergency. Local governments are well-positioned to lead in adopting effective climate mitigation strategies. This systematic literature review examines the barriers, enablers, and systems that local governments will need to consider when implementing climate mitigation and strategies. A search across Scopus, Web of Science, and ProQuest databases yielded 411 results, from which 28 articles were selected for detailed analysis. Using Covidence and NVivo 14 software, the study employed a combination of deductive and inductive coding to identify key themes. The study identified themes specific to enablers, such as technology, collaboration, leadership, and management culture, as well as barrier themes, including short-term thinking, uncertainty avoidance, lack of knowledge among decision-makers, resource shortages, and organizational challenges. The findings underscore the importance of addressing organizational issues and allocating appropriate resources to bolster local-level systems change in support of climate change mitigation efforts. Full article

Atrial septal defect (ASD) is characterized by an abnormal opening between the left (LA) and right atria (RA). Even though it’s one of the most prevalent congenital heart defects, there are still many knowledge gaps and clinical uncertainties. This review aims to create a complex description of ASD and discuss its link with pulmonary arterial hypertension (PAH). An extensive literature search was conducted on sites such as PubMed, Google Scholar, and ScienceDirect. This study reviews the key findings of peer-reviewed articles with the keywords ASD, PAH, and congenital heart defects. The research showed that whilst there are multiple reviews, there is still a need for a deeper understanding, especially in areas of embryology, decision thresholds for closure, and pediatric-specific long-term outcomes. Current guidelines often extrapolate data only from adults or avoid certain topics. Those ambiguities often lead to variable medical practices, missed opportunities, and uncertainty for families. This review is divided into clear sections, creating a step-by-step characterization of the most important information, which can be useful to specialists. It touches on important subjects and controversies. It shows a clear timeline, starting with embryology, genetics, and anatomy, through pathophysiology and patient description, ending with recommendations and indications for specific treatment methods. Moreover, it creates a clear connection between ASD and PAH, outlining its danger and the need for prevention. Full article

This paper proposes a terminal area energy management (TAEM) guidance system using an online ground track predictor (GTP) for an uncrewed space vehicle (USV). Based on the current geometric range method for each separate phase, we establish a real-time range-to-go calculation method for generating reference commands online. The method ensures continuous range-to-go variation through status flags and an integrated range, thereby avoiding sudden command changes at subphase transitions, which may reduce longitudinal tracking stability. To enhance adaptability in an initial low-energy state, the system tracks the low-energy reference trajectory to provide an additional lift-to-drag margin, thus preventing an overly low terminal velocity. The results of numerical simulations with multiple uncertainties validate the proposed guidance strategy. Moreover, the flight test results confirm its ability to direct the USV to the target position with the desired energy state in real-world conditions. Full article

This paper presents a new robust control strategy for buck DC–DC converters that achieve fast and robust voltage regulation in the presence of load disturbances and model uncertainties. First, an adaptive state observer is designed to estimate the inductor current and capacitor voltage by utilizing the output measurement. The observer gains are tuned online via a Lyapunov-based adaptation law, ensuring that the estimation error remains uniformly bounded, even when the disturbances act on the system. Based on the state estimates, an integral sliding-mode controller is designed in order to eliminate the steady state error and ensure the finite time sliding. The detailed stability proofs for both the observer and the sliding-mode controller are derived showing the finite-time reaching of the sliding surface and exponential convergence of the voltage error. Simulation results under varying load profiles confirm that the proposed scheme outperforms traditional sliding-mode designs in terms of disturbance rejection and settling time, while avoiding excessive chattering. Full article

This paper proposes an enhanced hierarchical fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) integrated with the Analytic Network Process (ANP) for solving multi-criteria decision-making (MCDM) problems under uncertainty. Conventional fuzzy TOPSIS models often face significant challenges, such as restrictions to specific fuzzy number formats, difficulties in normalization when zero or very small values appear, and limited capacity to capture hierarchical interdependencies among criteria. To address these limitations, we develop a generalized fuzzy geometric mean approach for deriving weights from pairwise comparisons that can accommodate multiple fuzzy number types. Moreover, a novel normalization function is introduced, which ensures mathematically valid outcomes within the [0, 1] interval while avoiding division-by-zero and inconsistency issues. The proposed method is validated through both a numerical building selection problem and a practical supplier selection case study. Comparative analyses against established fuzzy MCDM models demonstrate the improved robustness, flexibility, and accuracy of the approach. Additionally, a sensitivity analysis confirms the stability of results with respect to variations in criteria weights, fuzzy number formats, and normalization techniques. These findings highlight the potential of the proposed fuzzy hierarchical TOPSIS-ANP framework as a reliable and practical decision support tool for complex real-world applications, including supply chain management and resource allocation under uncertainty. Full article

As global copper demand continues to grow, China, being the largest copper consumer, faces increasingly complex challenges in ensuring the security of its supply chain. However, a substantive gap remains: prevailing assessments rely on static index systems and discrete scenario analyses that seldom model uncertainty-driven, continuous-time strategic interactions, leaving the conditions for self-enforcing cooperation and the attendant policy trade-offs insufficiently identified. This study models the interaction between Chinese copper importers and foreign suppliers as a continuous-time stochastic differential game, with feedback Nash equilibria derived from a Hamilton–Jacobi–Bellman system. The supply security utility is specified as a diffusion process perturbed by Brownian shocks, while regulatory intensity and profit-sharing are treated as structural parameters shaping its drift and volatility—thereby delineating the parameter region for self-enforcing cooperation and clarifying how sudden disturbances reconfigure equilibrium security. The research findings reveal the following: (i) the mean and variance of supply security utility progressively strengthen over time under the influence of both parties’ maintenance efforts, while stochastic disturbances causing actual fluctuations remain controllable within the contract period; (ii) spontaneous cooperation can be achieved under scenarios featuring strong regulation of domestic importers, weak regulation of foreign suppliers, and a profit distribution ratio slightly favoring foreign suppliers, thereby reducing regulatory costs; this asymmetry is beneficial because stricter oversight of domestic importers curbs the primary deviation risk, lighter oversight of foreign suppliers avoids cross-border enforcement frictions, and a modest supplier-favored profit-sharing ratio sustains participation—together expanding the self-enforcing cooperation set; (iii) sudden events exert only short-term impacts on supply security with controllable long-term effects; however, an excessively stringent regulatory environment can paradoxically reduce long-term supply security. Security effort levels demonstrate positive correlation with supply security, while regulatory intensity must be maintained within a moderate range to balance incentives and constraints. Full article

Background and Clinical Significance: Tandem pathology at the dominant-hemisphere middle cerebral artery (MCA)—combining a wide-neck bifurcation aneurysm that shares the neck with both M2 origins and a short dorsal M1 fusiform dilation embedded in the lenticulostriate belt—compresses the therapeutic margin and complicates device-first pathways. We aimed to describe an anatomy-led, microscope-only sequence designed to secure an immediate branch-definitive result at the fork and to remodel dorsal M1 without perforator compromise, and to place these decisions within a pragmatic perioperative framework. Case Presentation: A 37-year-old right-handed man with reproducible, load-sensitive cortical association and capsulostriate signs underwent high-fidelity digital subtraction angiography (DSA) with 3D rotational reconstructions. Through a left pterional approach, vein-respecting Sylvian dissection achieved gravity relaxation. Reconstruction proceeded in sequence: a fenestrated straight clip across the bifurcation neck with the superior M2 encircled to preserve both M2 ostia, followed by a short longitudinal clip parallel to M1 to reshape the fusiform segment while keeping each lenticulostriate mouth visible and free. Temporary occlusion windows were brief (bifurcation 2 min 30 s; M1 < 2 min). No neuronavigation, intraoperative fluorescence, micro-Doppler, or intraoperative angiography was used. No perioperative antiplatelets or systemic anticoagulation were administered and venous thromboembolism prophylaxis followed institutional practice. The bifurcation dome collapsed immediately with round, mobile M2 orifices, and dorsal M1 regained near-cylindrical geometry with patent perforator ostia under direct inspection. Emergence was neurologically intact, headaches abated, and preoperative micro-asymmetries resolved without new deficits. The early course was uncomplicated. Non-contrast CT at three months showed structurally preserved dominant-hemisphere parenchyma without infarction or hemorrhage. Lumen confirmation was scheduled at 12 months. Conclusions: In dominant-hemisphere tandem MCA disease, staged, perforator-sparing clip reconstruction can restore physiologic branch and perforator behavior while avoiding prolonged antiplatelet exposure and device-related branch uncertainty. A future-facing pathway pairs subtle clinical latency metrics with high-fidelity angiography, reports outcomes in branch- and perforator-centric terms, and, where available, incorporates patient-specific hemodynamic simulation and noninvasive lumen surveillance to guide timing, technique, and follow-up. Full article

Progressive myopia in children is a highly prevalent condition in societies worldwide and is often treated with compounded low-dose atropine sulfate (AS) eye drops without preserving agents to avoid irritation/sensitisation. Surprisingly, there is a lack of data regarding the in-use stability of contamination-free LDPE dispenser units (CFDs) for this compounded multidose product, which causes uncertainty among prescribers and patients in Europe. Thus, our aim was to compare the effect of different dispenser types on the chemical and microbial stability of unpreserved AS eye drops (0.01% w/w). A dripping simulation was performed to obtain information on microbial stability over 4 weeks through plating and separately over 12 weeks through direct inoculation, HPLC and pH analysis. For CFDs, no contamination was found after 4, 8 or 12 weeks of use when stored at 23 or 4 °C as opposed to the control. AS content remained within 0.01 ± 0.0002% w/w after 12 weeks, with higher chemical stability at 4 °C despite decreasing pH. A stress test confirmed the validity of the CFD system. In conclusion, using CFDs and refrigerated storage was found to be safe for compounded unpreserved AS eye drops over 12 weeks of use. Full article

The frequency content of strong ground motion significantly affects the response of engineered systems under seismic excitation. Among some scalar parameters which exist in the literature, the mean period T_m has proved to be the most efficient. Ground Motion Predictive Equations (GMPEs) are usually developed for ground motion parameters through the calibration of coefficients of predefined functional forms, via linear or nonlinear regression, and based on recorded ground motion data. Such expressions of T_m are rare in the literature. Recently, the use of machine learning (ML) algorithms in earthquake engineering and engineering seismology has increased. The Artificial Neural Network (ANN) is an effective ML algorithm which has already been explored for the development of GMPEs for amplitude-based ground motion parameters. Within the work presented herein, multiple nonlinear regression (NLR)- and ANN-based GMPEs are developed for T_m using the latest strong motion database for shallow earthquakes in Greece. To the author’s knowledge, the implementation of ANN for producing GMPEs for T_m for shallow earthquake events has not been explored. Direct comparison between the NLR- and ANN-based GMPEs is performed, in terms of performance indexes, aleatory uncertainty, and working examples, as well as testing against earthquake events not included in the original dataset. The results reveal that the ANN-based GMPEs are useful in reducing aleatory uncertainty, although care should be taken in their implementation to avoid overfitting issues. Full article

Fixed-time coordination is critical for networked unmanned aerial vehicle (UAV) systems to accomplish time-sensitive missions such as rapid target encirclement, cooperative search, and emergency response. However, dynamic topology variations, caused by mission reassignment, obstacle avoidance, or communication disruptions, along with model uncertainties and external disturbances, present significant challenges to robust and timely coordination. To address these issues, this paper investigates the fixed-time bipartite containment tracking control problem of uncertain multi-UAV systems under switching communication topologies. A neuroadaptive robust containment tracking controller is developed to guarantee that all follower UAVs converge within a fixed time to the region spanned by multiple dynamic leaders, regardless of initial conditions. To handle unknown nonlinear dynamics, a neuroadaptive estimator is constructed using online parameter adaptation. A topology-dependent multiple Lyapunov function framework is employed to rigorously establish fixed-time convergence under switching topologies. Moreover, an explicit upper bound on the convergence time is analytically derived as a function of system parameters and dwell time constraints. Comparative analysis demonstrates that the proposed method reduces conservativeness in convergence time estimation and enhances robustness against frequent topology changes. Simulation results are provided to validate the effectiveness and advantages of the proposed control scheme. Full article

This manuscript presents a robust formation and collision-free containment control system designed for a quadrotor fleet operating under turbulent wind conditions. Emphasizing collision avoidance, we introduce a two-layer strategy in which a virtual leader defines a trajectory, and leaders and followers maintain their positions while avoiding collisions among them. A graph convention is used to illustrate the roles of leaders and followers, as well as their interactions. Inter-agent collision avoidance is proposed by expanding the desired distance relative to all neighboring agents, thereby guaranteeing the convergence stage. Moreover, the approach employs a class of adaptive sliding mode strategies to ensure finite-time convergence, as well as non-overestimation of the control gain in the presence of uncertainties and perturbations. A stability analysis demonstrates the practical finite-time stability of the system using the Lyapunov methodology. Results from the simulation underscore the effectiveness of our proposal in adhering to the desired time-varying trajectories and ensuring sensor-less inter-agent collision avoidance for the followers, even in the presence of turbulent wind conditions. Full article