Search Results (768)

Wireless sensor networks (WSNs) are the backbone of IoT-enabled smart manufacturing, environmental monitoring, and industrial automation. However, their broadcast nature makes communication links vulnerable to eavesdropping, routing manipulation, and denial-of-service attacks. Strategically placing monitor nodes to check each link is an effective approach to protect against attacks, but energy, connectivity, and capacity constraints should be considered while picking monitor nodes. In this paper, we tackle the Minimum-Weighted Connected Capacitated Vertex Cover (MWCCVC) problem, which minimizes monitoring costs, ensures backbone connectivity, and adheres to per-node capacity constraints. Unlike prior works that consider weighted vertex cover, connectivity constraints, or capacitated variants separately, the proposed MWCCVC model jointly integrates all three dimensions within a single vertex cover-based monitoring framework. We first provide a Branch-and-Bound (B&B) solver with linear programming relaxation bounds and constraint-based pruning strategies that produces optimum solutions. Three constructive greedy heuristics (GD, GR, GW) and two hybrid genetic algorithms (HGA, HGA-v2) that combine parameterized greedy decoders with evolutionary search are proposed; all methods guarantee full edge coverage, induced-subgraph connectivity, and max-flow-validated capacity feasibility. Tests on 130 small, 160 medium, and 19 large benchmark instances show that HGA matches B&B optima on every small instance, beats the time-limited B&B by 6.6% on medium instances, where the percentage is computed based on the relative difference in average total weight with respect to B&B, and stays the best on large graphs with up to 1000 nodes. The HGA-v2 tries to balance the quality and speed, with only a 3.1% difference at $10\times$ faster execution. Full article

Achieving resilient consensus in adversarial environments often requires extending the W-MSR algorithm to multi-hop communication. While the robustness guarantees of multi-hop W-MSR are now well understood, the problem of how to determine the minimal hop radius $h^{*}$ that ensures these guarantees has remained largely unaddressed. Existing work typically assumes a fixed h, leaving practitioners without a systematic way to balance robustness requirements against communication and computational cost. This paper introduces a new hop-selection framework that identifies the smallest communication horizon capable of satisfying the robustness assumptions underlying MW-MSR consensus. The framework combines exact robustness verification—when tractable—with a hierarchy of computationally efficient proxy tests based on local feasibility, normalized algebraic connectivity, and adversary-dilution criteria. These components provide a practical and scalable mechanism for establishing $h^{*}$ in both synchronous and bounded-delay asynchronous settings. Design-time and runtime procedures, complexity analysis, and validation on IEEE 14-, 30-, and 57-bus networks demonstrate that the proposed approach reliably detects resilience thresholds and substantially improves consensus behavior under stealthy and burst-type adversaries. The results show that systematic hop selection is essential for avoiding failure at small h while preventing unnecessary communication overhead at large h. The framework thus offers an implementable and deployment-oriented strategy for resilient distributed coordination in sparse and adversarial multi-agent networks. Full article

Active distribution networks require precise real-time monitoring and control despite measurement outliers and rapid load dynamics. Conventional robust estimators frequently fail to distinguish between transient measurement corruption and genuine physical state mutations, leading to estimation lag or erroneous control actions. To address this, we propose a resilient cyber–physical framework that jointly optimizes robust dynamic state estimation and collaborative voltage control. At the estimation layer, a novel Persistence-Based Robust Extended Kalman Filter (PB-REKF) is developed, which employs a temporal persistence counter to adaptively switch between Huber M-estimation for sporadic outlier suppression and covariance inflation for rapid tracking of persistent state mutations. At the control layer, a chance-constrained Second-Order Cone Programming (SOCP) strategy directly embeds the real-time posterior covariance from the PB-REKF into the voltage safety constraints, creating a data-quality-adaptive security buffer that provides a $95\%$ probabilistic voltage guarantee. Simulations on 5-bus and IEEE 33-bus systems demonstrate that the proposed framework achieves a $29.5\%$ reduction in global RMSE and a $72.8\%$ reduction in peak outlier-window estimation error relative to the standard EKF, while reducing the voltage violation rate from $8.8\%$ to $3.8\%$. The complete estimation and control pipeline requires $1.341$ ms per update step, confirming real-time feasibility. Full article

Aiming at the difficult problem of floor heave control in deep coal mine roadways, this paper took the 1224 transportation roadway of Shuguang Coal Mine in Shanxi as the engineering background and carried out the first underground industrial test of floor-slotting pressure relief technology by using special slotting equipment. The aim is to reveal the energy transfer law of the floor rock mass during slotting pressure relief and clarify its inherent connection with stress redistribution and floor heave deformation control. The research adopts a combination of theoretical analysis, numerical simulation, and field tests to systematically explore the energy accumulation characteristics of the floor and the induced mechanism of floor heave. Results show that the maximum energy accumulated in the floor after roadway excavation reaches 6.0 × 10⁵ J, which is the fundamental cause of floor heave. After optimizing the slotting parameters (depth 2.5 m, width 0.2 m), numerical simulation indicates that the surrounding rock stress concentration zone migrates to the deep part, the energy peak shifts down by 2.5 m, the floor plastic zone expands, and the range of the high-energy zone shrinks. Field test results show that the floor heave amount decreases from 30 cm to 20 cm, with a reduction rate of 33%. This study reveals the synergistic mechanism of “energy transfer–stress regulation–deformation control”, verifies the effectiveness and feasibility of the slotting pressure relief technology in the floor heave control of deep, high-stress roadways, and provides a guarantee for the safe and efficient advancement of the working face. Full article

Modern instant messaging systems require end-to-end (E2E) security guarantees while operating over server-mediated infrastructures that cannot be fully trusted. At the same time, the impending transition to post-quantum cryptography raises nontrivial challenges for the design of secure messaging protocols that preserve these guarantees. In this work, we present the design of a post-quantum end-to-end secure protocol for instant messaging applications under an untrusted relay model. The proposed construction relies on lattice-based primitives standardized by NIST, namely ML-KEM for key establishment and ML-DSA for authentication, and follows a Double-KEM pattern combined with explicit context binding to derive an E2E session key known only to the communicating clients. The server acts solely as an authenticated relay and never gains access to plaintext messages or session keys. In addition to the protocol design, we complement the protocol description with an automated symbolic verification using ProVerif, establishing injective mutual authentication and session-key secrecy under a Dolev–Yao adversary model. Finally, we characterize the computational cost of different authentication and verification policies and evaluate the performance of the handshake on heterogeneous cloud-based architectures. The results provide practical insight into the feasibility of deploying post-quantum end-to-end secure protocols within existing instant messaging infrastructures. Full article

Wireless sensor networks increasingly support time-critical monitoring applications, where coverage optimization must often be performed under limited computational resources. This work addresses a previously underexplored WSN coverage problem involving range-free, angular-limited sensors with transmitter-induced sensing degradation and discrete sector orientation. We formulate a mixed combinatorial problem that jointly optimizes K-out-of-N sensor activation and sector assignment under strict feasibility constraints. A constraint-aware genetic algorithm with repair-based feasibility enforcement is proposed and validated against the global optimum obtained via exhaustive enumeration, enabling direct quantification of optimality. The repair mechanism corrects infeasible offspring after each genetic operation to guarantee that exactly K sensors remain active, eliminating the need for penalty-based constraint handling. A brute-force search is used to establish the global optimum of our small-scale scenario, serving as a ground-truth optimality benchmark for evaluating the proposed method. The purpose of this comparison is not to assess competitiveness against other metaheuristic algorithms, but to quantify how closely the proposed approach approximates the true optimal solution under strict problem constraints. The constraint-aware genetic algorithm is developed using an integer chromosome encoding, two initialization strategies, two crossover pairing schemes, elitism, and per-gene mutation, combined with alternative constraint-handling strategies. Two experimental series evaluate the impact of population size, crossover method, mutation probability, and constraint handling using problem-specific metrics, alongside convergence and fitness statistics. The proposed algorithm reliably reaches near-optimal solutions with significantly reduced computational cost when compared to exhaustive search. By integrating problem-specific constraints directly into the process, the proposed evolutionary optimization method effectively balances solution quality and execution time, making it well suited for scenarios requiring rapid sensor reconfiguration. Full article

Per- and polyfluoroalkyl substances (PFAS) have found wide application in proton exchange membrane fuel cells (PEMFCs) and water electrolysers (PEMELs), thanks to their exceptional chemical and thermal stability. However, their environmental persistence and growing regulatory pressure—particularly from the European Union—have made the transition to PFAS-free components a priority. This work reviews current advancements in alternative materials that can guarantee the same performance or maybe improve it. Although several non-fluorinated materials have demonstrated initial performance close to PFAS-based benchmarks, significant challenges remain. These include limited long-term stability, difficulties for new materials to fit into existing stack architectures, and the lack of standardized testing protocols. Nevertheless, recent efforts have successfully demonstrated a PFAS-free PEM electrolyser stack at TRL 4, validating the technical feasibility of full PFAS substitution. Achieving commercial readiness will require parallel progress in materials development and industrial scalability. This review highlights the possibility that hydrogen technologies, such as fuel cells and electrolysers, which are called upon to support the energy transition towards a more sustainable future, are themselves truly environmentally friendly, thus making their use as green as possible. Full article

Physical coupling, nonlinearity and uncertainty degrade the dynamic performance of electro-hydraulic servo systems, particularly under conditions involving input delays, leading to reduced trajectory tracking accuracy or even system instability. These factors often fail to meet the high-precision requirements of engineering applications. To effectively address these difficulties, this paper proposes a novel adaptive control protocol for networked electro-hydraulic servo systems. For the minimal communication delay problem of networked electro-hydraulic servo systems, Laplace transform algorithm together with Padé approximation is adopted in this study to remove the delay term from the mathematical system model. Moreover, the matched modeling parametric uncertainty of systems is estimated and compensated by the neural network adaptive method to improve the dynamical performance of the system during the steady state. The controller is designed on the basis of recursive backstepping strategy and the finite-time stability theorem, which can handle system nonlinearity and guarantee transient response. The validity of the proposed theoretical results is proved by Lyapunov stability and the feasibility and superiority are verified via physical simulation. Full article

Equitable prioritization of public investments is increasingly critical as municipalities face constrained budgets, heterogeneous neighborhood needs, and demands for transparent decisions. This paper proposes a fairness-aware group multi-criteria decision-making (MCDM) framework for ranking municipal infrastructure investments when budgets are constrained, and neighborhood needs differ. Six alternatives are assessed in the Istanbul case study: flood risk mitigation, inclusive public realm and cooling, smart and energy-efficient municipal assets, walking and cycling infrastructure, healthcare access improvements, and seismic retrofitting of public buildings. The criteria system combines efficiency, implementability, socio-environmental performance, and equity-oriented priorities through five main dimensions and 23 sub-criteria. In addition to cost, feasibility, and service effectiveness, the framework incorporates fairness-related criteria such as baseline need and deficit severity, vulnerability-targeting effectiveness, minimum service guarantee for the worst-off, and priority for low-accessibility centers. Public acceptance and environmental performance are also included. Stakeholder panels provide expert judgments using intuitionistic fuzzy sets, capturing membership, non-membership, and hesitation to reflect uncertainty. Criteria weights are derived with Intuitionistic Fuzzy Step-wise Weight Assessment Ratio Analysis (IF-SWARA), enabling importance elicitation and group aggregation without forcing crisp consensus. Alternatives are then ranked using Intuitionistic Fuzzy Combined Compromise Solution (IF-CoCoSo), which blends additive and multiplicative compromise solutions to balance overall performance with equity objectives. Robustness is assessed through sensitivity analysis by varying the γ parameter within the IF-CoCoSo procedure. A municipal case study demonstrates that healthcare access improvements achieve the highest compromise performance, followed by flood risk mitigation and seismic retrofitting of public buildings, while smart and energy-efficient municipal assets rank last. The findings confirm that explicitly embedding fairness criteria can shift municipal priorities toward alternatives that more directly reduce deprivation, risk, and spatial inequality. The main contribution of this study is not merely empirical application, but the development of a fairness-aware group MCDM framework that operationalizes distributive justice in municipal investment prioritization through a structured set of criteria. Full article

With the growing volume of sensitive data stored and processed in cloud environments, conventional security models are no longer sufficient to guarantee privacy, integrity, and trust. This paper proposes a blockchain-based framework that integrates Zero-Knowledge Proofs (ZKPs) and homomorphic encryption (HE) to enable secure and privacy-preserving data sharing. ZKPs are employed to verify user access rights without exposing identities or underlying information, while HE allows computations to be performed directly on encrypted data, ensuring confidentiality is preserved throughout the data lifecycle. The proposed framework addresses the limitations of existing approaches that either lack encrypted computation capabilities or expose sensitive data during processing. Formal and informal analyses demonstrate the feasibility of the model in terms of encryption time, ZKP verification latency, and computation overhead. The framework is designed to be applied initially in the healthcare sector and aligns with national digital transformation initiatives such as Saudi Vision 2030. Full article

Autonomous path planning in obstacle-dense environments remains challenging for swarm intelligence methods due to infeasible initialization, insufficient exploration–exploitation balance, and poor trajectory smoothness for real-robot execution. To address these issues, this paper proposes a Kinematic-Aware Improved Hippo Optimization algorithm (KA-IHO) for mobile robot path planning. The proposed method integrates four components: an elite safety pool initialization strategy to improve feasible solution generation in dense maps, a hierarchical elite-scout update mechanism to better balance global exploration and local exploitation, anti-stagnation mechanisms including a Population Stagnation Restart strategy and a 10-Direction Radial Micro-Search to guarantee high feasibility rates across all map complexities, and a late-stage Laplacian Line-of-Sight Ironing Operator to reduce path redundancy and improve trajectory smoothness. Comparative experiments are conducted on five reproducible grid maps with different complexity levels ($40\times 40$ and $80\times 80$), where KA-IHO is evaluated against six representative algorithms, including HO, SBOA, PSO, GWO, ARO, and INFO, over 20 independent runs. The results show that KA-IHO consistently achieves collision-free planning and obtains lower mean fitness values with smaller standard deviations than the compared methods, indicating improved robustness and solution quality. In addition, hardware closed-loop experiments on a differential-drive mobile robot demonstrate that the planned paths can be executed reliably in real environments, with trajectory tracking errors controlled within $\pm 4$ cm. Full article

The quasi-clique enumeration problem (degree-based quasi-cliques) is substantially more challenging than the classical clique problem because quasi-cliques do not satisfy the hereditary property, leading to exponential search spaces even in dense graphs. We introduce the Split-and-Reduce (SR) algorithm, an exact deterministic method that derives a family of tight lower bounds M_k(N,r) on k-wise neighbourhood intersections directly from the pigeonhole principle. By integrating critical-chain inclusion theorems, recursive graph decomposition, weakest-first candidate ordering, and dynamically updated upper bounds on the maximum feasible quasi-clique size, SR prunes invalid vertices, pairs, and triplets far earlier than existing methods while remaining completely insensitive to vertex ordering. We prove that SR yields strictly stronger theoretical upper bounds on the maximum quasi-clique size than all previously known results. A single-threaded implementation outperforms the state-of-the-art QUICK algorithm by up to two orders of magnitude with guaranteed completeness and zero false positives. For the first time, SR computes maximum quasi-cliques in graphs containing billions of edges (Friendster, Orkut, DBLP), revealing unexpectedly small stable quasi-clique sizes (<150 vertices even at moderate r) in real-world networks. These advances substantially strengthen the mathematical foundation for exact dense-subgraph enumeration and provide powerful new tools for structural graph theory. Full article

Municipal solid waste (MSW) generation is a global problem affecting the environment and public health. The current landfill’s useful life is reaching its end, making new site selection a priority to guarantee proper MSW management. This research evaluated the suitability of the metropolitan area of the municipalities of Chihuahua, Aldama, and Aquiles Serdan, using Spatial Decision Support Systems (SDSS) integrated with Multi-criteria Decision-making (MCDM) and hierarchical analysis, and Geographic Information Systems (GIS) to determine potential sites for new Metropolitan landfill development in a semi-arid region. Results showed that 44.7% of the areas studied present a high suitability level, while 29.52% corresponds to a very high suitability level. These areas are located mainly in the north and center zones of the Chihuahua and Aldama municipalities, with some isolated areas in Aquiles Serdan. The key selection criteria were airport distance, land slope, and proximity to the intermunicipal boundary, which enabled the identification of sites with lower environmental impact and greater technical and economic feasibility. This study demonstrates that SDSS and GIS are efficient tools for identifying potential landfill sites. The results highlight the importance of integrating technical, environmental, and social criteria into MSW management planning to achieve sustainable, efficient management in the region. Full article

This paper develops a rigorous operator-theoretic framework for learning-augmented quasi-gradient methods in constrained optimization. We consider the minimization of an objective function over a closed convex feasible set, where feasibility is enforced via projection and directional updates may incorporate data-driven corrections. Such settings arise naturally in modern optimization algorithms that integrate artificial intelligence components under structural constraints. The proposed formulation introduces an explicit contraction–bias–variance decomposition of the iterative dynamics. Curvature induces deterministic contraction, alignment distortion—quantified by a geometric parameter—modifies the effective contraction margin, and stochastic learning components inject controlled dispersion. Explicit error recursions yield convergence guarantees under strong convexity, the Polyak–Łojasiewicz condition, and smooth nonconvexity. The analysis establishes that stability regions and first-order complexity bounds are preserved whenever alignment distortion remains below unity and bounded second-moment conditions hold. A fully reproducible computational study provides quantitative validation: the empirically observed steady-state error closely matches the theoretical prediction proportional to ${\sigma }^2/\mu (1-\eta )$. Comparative experiments with gradient, stochastic gradient, and momentum methods confirm that the proposed operator retains classical stability margins and conditioning sensitivity while enabling principled integration of learned directional components. The results provide a transparent mathematical bridge between stochastic approximation theory and contemporary AI-enhanced constrained optimization. Full article

The demand for food has increased due to the world’s expanding population, which has also put pressure on vital resources like water, land, and nutrients. Therefore, in order to guarantee food security, it is imperative to establish alternative, sustainable, and dependable strategies. In recent decades, researchers have developed novel food production methods that collectively enhance the efficiency and sustainability of food systems. Among these, aquaponics stands out as an advanced and eco-friendly agricultural technology that integrates aquaculture and hydroponics. In this system, fish waste from the aquaculture unit is utilized as a nutrient medium in the hydroponic subsystem to grow edible plants. This review aims to assess the potential of aquaponics to produce high-quality fruits, vegetables, and fish while minimizing environmental impacts without relying on chemical fertilizers. The study focuses on system design, nutrient cycling, and productivity parameters to assess its feasibility under Potohar conditions. The expected outcome is to demonstrate that aquaponics can enhance food quality, conserve resources, and uplift the socio-economic status of farming communities by alleviating poverty. Full article