Search Results (431)

The Mobile Reliable Opportunistic Routing (MROR) protocol improves data-forwarding reliability in Cognitive Radio Sensor Networks (CRSNs) through mobility-aware virtual contention groups and handover zoning. However, its heuristic decision logic is difficult to optimize under highly dynamic spectrum access and random node mobility. To address this limitation, we present DRL-MROR, a refined routing framework that incorporates deep reinforcement learning (DRL) to enable intelligent and adaptive forwarding decisions. In DRL-MROR, the secondary users (SUs) act as autonomous agents that observe local state information, including primary-user activity, link quality, residual energy, and neighbor-mobility patterns. Each agent learns a forwarding policy through a Deep Q-Network (DQN) optimized for long-term network utility in terms of throughput, delay, and energy efficiency. We formulate routing as a Markov Decision Process (MDP) and use experience replay with prioritized sampling to improve learning stability and convergence. The DQN used at each node is intentionally lightweight, requiring 5514 trainable parameters, about 21.5 kB of weight storage in 32-bit precision, and approximately 5.4k multiply-accumulate operations per inference, which supports practical deployment on edge-capable CRSN nodes. Extensive simulations show that DRL-MROR outperforms the original MROR protocol and representative AI-based routing baselines such as AIRoute under diverse operating conditions. The results indicate gains of up to 38% in throughput, 42% in goodput, a 29% reduction in energy consumed per packet, and an approximately 18% improvement in network lifetime, while maintaining high route stability and fairness. DRL-MROR also reduces control overhead by about 30% and average end-to-end delay by up to 32%, maintaining strong performance even under elevated PU activity and higher node mobility. These results show that augmenting opportunistic routing with lightweight DRL can substantially improve adaptability and efficiency in next-generation IoT-oriented CRSNs. Full article

Energy-efficient routing is critical for extending the operational lifespan of wireless sensor networks (WSNs). While the Power-Efficient Gathering in Sensor Information Systems (PEGASIS) protocol achieves high efficiency through chain-based data aggregation, its standard round-robin leader selection fails to account for dynamic node factors, such as residual energy and historical reliability. This often leads to premature energy depletion and network instability. To address these limitations, this paper proposes K-NN-PEGASIS, a data-driven machine learning framework that utilises a weighted k-nearest neighbours (K-NN) algorithm for intelligent leader selection. By processing a normalised feature vector comprising residual energy, distance to the base station (BS), node degree, and historical performance, the framework adaptively identifies optimal leaders in each round. Simulations conducted in MATLAB for networks ranging from 100 to 1000 nodes demonstrate that K-NN-PEGASIS improves network lifetime by up to 47.3% and reduces total energy dissipation by 52.8% compared to baseline algorithms. Furthermore, the framework provides passive resilience against routing attacks, reducing the selection of malicious leaders by 96% and maintaining a 32.3% higher packet delivery ratio under attack scenarios. Full article

Extensive evidence now confirms Lipoprotein(a) [Lp(a)] as a causal, independent risk factor for atherosclerotic cardiovascular disease. Elevated Lp(a) levels are detected in approximately 20% of the global population, positioning it as a major contributor to residual cardiovascular risk. Circulating Lp(a) levels are determined predominantly by genetic factors, so they are largely unresponsive to lifestyle modifications or conventional lipid-lowering therapies. Therefore, multiple international guidelines now endorse a one-time, lifetime measurement of Lp(a), as lowering Lp(a) concentrations is expected to have a positive impact on the reduction of cardiovascular risk. Currently, the therapeutic landscape of Lp(a) lowering drugs is rapidly evolving. Some RNA-based therapies (antisense oligonucleotides (ASOs) and small interfering RNAs (siRNAs)) have been demonstrated to reduce plasma Lp(a) concentrations by up to 98% in early-phase clinical trials. The efficacy and safety of these compounds are currently being evaluated in large-scale cardiovascular outcome trials. The results of these studies will be critical in validating the “Lp(a) hypothesis”: specific reduction of Lp(a) levels can lead to a measurable decrease in cardiovascular events. The purpose of this narrative review is to examine and discuss the available evidence on the role of Lp(a) as a risk factor and pharmacological target to provide a practical tool for decision-making in clinical practice. Full article

Introduction/Objectives: Although contemporary guidelines strongly support intensive low-density lipoprotein cholesterol (LDL-C) lowering and the use of advanced lipid biomarkers for cardiovascular risk stratification, implementation in daily clinical practice remains inconsistent. This study aimed to assess current practices, knowledge, and perceived barriers in dyslipidemia management across medical specialties. Methods: We conducted a cross-sectional, anonymous online survey from August to September 2025 among physicians actively involved in lipid management. The questionnaire evaluated the use of Systematic Coronary Risk Evaluation 2 (SCORE2)-based risk assessment, familiarity with LDL-C targets, treatment intensification strategies, awareness and use of apolipoprotein B (apoB) and lipoprotein(a) [Lp(a)], perceived barriers to LDL-C goal attainment, and responses to a standardized clinical vignette. Descriptive analyses and chi-square testing were conducted. Results: Ninety-five physicians completed the survey, the majority practicing in Europe (92.7%), including 83.2% from Portugal (41.1% general practice/family medicine; 14.7% cardiology; 14.7% internal medicine/geriatrics; 14.7% vascular surgery; 9.5% endocrinology). SCORE2 calculators were used “often” or “always” by 52.6%, with significant inter-specialty variation (p < 0.001). Familiarity with LDL-C targets was high (76.8%), and 89.4% reported frequent therapy intensification when goals were not achieved; however, consistent escalation (“always”) differed markedly across specialties (p < 0.001). Although 69.5% were aware of recommendations for lifetime assessment of apoB/non–HDL-C/Lp(a), only 17.9% implemented them routinely. Most clinicians reported never or rarely using advanced biomarkers for residual risk assessment, and in a clinical vignette only 12.6% would consistently intensify therapy despite elevated Lp(a) and apoB (p = 0.004). Patient non-adherence (86.3%) was the most frequently perceived barrier. Conclusions: Despite the widespread awareness of LDL-C targets, important gaps persist in the consistent application of guideline-directed therapy and in the use of advanced biomarkers. The underutilization of apoB and Lp(a), together with therapeutic inertia and structural barriers, limits effective residual risk management. Bridging this gap will require coordinated efforts focused on implementation, access, and multidisciplinary care. Full article

This paper proposes a Q-Learning-Based Distributed Energy-Efficient Routing (QDER) protocol for underwater acoustic sensor networks (UASNs). The routing problem is formulated as a Markov Decision Process (MDP) and a distributed Q-learning approach is proposed. Each sensor node is treated as an agent that independently selects its next-hop node based on a Q-table. The rewards function is designed that jointly considers node residual energy and depth information, enabling each node to learn an effective routing policy through distributed decision-making. Unlike centralized routing approaches that rely on extensive global information exchange, the proposed scheme allows nodes to make local decisions, thereby reducing communication overhead and energy consumption while maintaining efficient routing paths. In addition, link quality is designed in the reward to account for channel conditions, which improves the robustness of the routing strategy under noisy underwater acoustic environments. Simulation results demonstrate that the QDER achieves better system performance compared with Depth-Based Routing (DBR) and Deep Q-Network-Based Intelligent Routing (DQIR). Considering channel attenuation and noise, the proposed method with the link quality metric achieves improved network lifetime and energy efficiency. It also shows good robustness and adaptability under different signal-to-noise ratio (SNR) conditions. Full article

Additive manufacturing (AM) has rapidly evolved from a prototyping tool into an effective method for producing end-use components, thanks to its ability to produce complex, lightweight and customised parts. However, this technique requires a thorough understanding of the long-term behaviour and degradation mechanisms of components, especially when polymers are involved in the printing process. Unlike polymer components manufactured using traditional methods, polymers produced through AM exhibit unique microstructures, anisotropies, and interfacial characteristics due to the layer-by-layer fabrication process. These features can affect how these materials respond to thermal, mechanical and environmental stresses over time. Furthermore, technology-specific processing parameters directly govern porosity distribution, crystallinity evolution, interlayer bonding quality, and residual stress development, all of which are key factors for ensuring long-term performance. This review aims to support researchers in the development of durable additively manufactured polymer components by systematically analysing polymer degradation mechanisms, accelerated ageing and lifetime prediction methodologies. Following a PRISMA-based screening process, approximately 160 international standards relevant to polymer durability in additive manufacturing were selected from an initial corpus of about 620 documents for in-depth analysis. Processing–structure–property relationships specific to the AM processing of polymers, including the commonly used FFF (fused filament fabrication), SLA (stereolithography) and SLS (selective laser sintering), are examined in relation to crucial aspects for long-term structural integrity and degradation behaviour. Finally, limitations within the current normative framework are identified, emphasising the absence of process-aware durability assessment protocols and the need for dedicated standards tailored to additively manufactured polymer components. Full article

Wireless Sensor Networks (WSNs) commonly employ clustering to improve scalability and energy efficiency; however, cluster heads (CHs) located near the base station (BS) often suffer from excessive relay traffic, leading to rapid energy depletion and reduced network lifetime. This article proposes an Energy-Efficient Distance-Controlled Clustering (EEDC) scheme that adjusts CH density and transmission power according to each node’s distance from the BS. In EEDC, a higher number of CHs is deployed near the BS to balance forwarding loads, while fewer CHs are selected in distant regions to conserve energy. Additionally, CHs adapt their transmission power to enable distance-proportional communication. A mathematical model is developed to analyze the relationship between CH distribution, transmission power, and overall energy consumption. Performance evaluation is conducted through simulations and compared with LEACH, HEED, DEEC, SEP, and EECS. The results show that EEDC improves the stability period by up to 42%, extends network lifetime by 23%, increases average residual energy by 13–29%, enhances throughput by 16–44%, and achieves 23–61% higher packet delivery efficiency. Moreover, cumulative CH energy consumption is reduced by 5–21%, leading to more balanced energy distribution. These findings indicate that distance-controlled CH selection and adaptive transmission power effectively alleviate the BS energy bottleneck and enhance the energy efficiency and operational longevity of clustered WSNs. Full article

Power electronic converters are being pushed toward higher power density and switching frequency, turning both design and operation into multi-objective, multi-physics optimization problems. While analytical rules and gradient-based methods remain essential, they often struggle with non-convex, mixed-integer trade-offs that include thermal behavior, Electromagnetic Interference/Electromagnetic Compatibility (EMI/EMC), and reliability constraints. This review surveys intelligent optimization approaches for power electronics across design-time, commissioning-time, and run-time horizons. We propose a deployment-oriented taxonomy of intelligent optimization approaches covering metaheuristics, surrogate-assisted and learning-guided design, constrained optimization via model predictive control, reinforcement learning-based supervisory policies, and hybrid physics-informed methods. For each family, we summarize typical tasks, computational and data requirements, robustness, interpretability, and validation maturity, highlighting where intelligent methods provide clear benefits and where classical approaches remain preferable. Reliability- and diagnostics-oriented optimization is discussed with emphasis on residual-based monitoring, stress-aware operation, and lifetime proxies. Practical adoption barriers—model–reality mismatch, data scarcity, real-time determinism, and certification—are synthesized into recurring design patterns that improve deployability. Finally, a conceptual cognitive design framework is proposed that couples virtual engineering, physics-informed surrogates, human-in-the-loop validation, and knowledge reuse in a closed-loop workflow, offering a structured perspective on how intelligent optimization may be integrated more reliably into industrial design practice. Full article

The prediction of material lifetime is central to nanomaterial engineering and reliability analysis. We propose the Marshall–Olkin Power Half-Logistic (MOPHL) distribution, obtained by applying a Marshall–Olkin transform to the Power Half-Logistic baseline. We derive core properties—including moments, hazard rate characterization, and Rényi entropy—and develop inference under generalized progressive hybrid censoring. Estimation is carried out via maximum likelihood and Bayesian methods using a Metropolis–Hastings sampler. Asymptotic results, Fisher information, and corresponding confidence/credible intervals are provided. A Monte Carlo study assesses bias, the mean squared error, and coverage across censoring scenarios and hazard regimes. In a case study on hydroxylated fullerene degradation, MOPHL outperforms nine competing models in goodness-of-fit and predictive reliability. We also report the mean time to failure and mean residual life to support engineering decision-making. The proposed framework offers a tractable and robust tool for degradation analysis under censored data, with applicability to materials, mechanical components, biomedical devices, and environmental monitoring. Full article

Aortic valve disease is increasingly recognized as a chronic, progressive condition in which the initial valve intervention exerts a decisive influence on all subsequent therapeutic options. The persistence of prosthesis–patient mismatch (PPM), often driven by implantation of small surgical prostheses (≤21–23 mm), is associated with higher residual transvalvular gradients, attenuated left ventricular reverse remodeling, inferior long-term survival, and compromised outcomes following valve-in-valve (ViV) transcatheter procedures. Accumulating clinical and imaging evidence indicates that aortic annular enlargement (AAE), particularly using contemporary Y-incision and extended “roof” reconstruction techniques, can safely and reproducibly expand the annulus, sinuses of Valsalva, and sinotubular junction, thereby permitting implantation of larger prostheses and substantially reducing the risk of PPM. Insights from computational fluid dynamics further demonstrate that annular and root enlargement favorably alters postoperative flow dynamics, resulting in lower peak velocities, reduced pressure gradients, and more physiologic flow patterns in both primary surgical valve replacement and simulated ViV settings. From a lifetime management perspective, valve diameter optimization emerges as a critical determinant of both immediate hemodynamic performance and future procedural feasibility. Surgical programs that adopt a systematic approach to anatomic assessment, valve sizing strategy, PPM surveillance, and ViV preparedness may achieve meaningful improvements in short- and long-term outcomes. This review integrates anatomic, operative, hemodynamic, and quality-oriented evidence to support consideration of valve upsizing as a central principle in contemporary aortic valve replacement. Full article

Cluster Head (CH) selection is a crucial process in clustered Wireless Sensor Networks (WSNs) because it directly affects energy balance and network lifetime. However, CH selection is an NP-hard optimization problem, and many metaheuristic-based methods suffer from limited search diversity and premature convergence, leading to uneven energy dissipation. This paper formulates CH selection as a multi-criteria energy-aware optimization problem and proposes an Enhanced Secretary Bird Optimization Algorithm (ESBOA). The proposed ESBOA improves the original Secretary Bird Optimization Algorithm by integrating logistic chaotic map-based population initialization to enhance early-stage exploration and an iterative local search mechanism to strengthen solution refinement in later iterations. A multi-criteria fitness function considering residual energy, distance to the base station, and node degree explicitly guides the optimization toward energy-efficient clustering. The proposed method is implemented in a Python 3.11.9-based simulation framework using a first-order radio energy model and evaluated against standard SBOA, Crested Porcupine Optimization (CPO), and Dung Beetle Optimization (DBO). Simulation results demonstrate that ESBOA preserves more alive nodes, maintains higher residual energy, delivers more cumulative packets to the base station, and extends network lifetime, achieving approximately 3–13% improvement in last node death (LND) compared with the standard SBOA. Full article

The development of composite materials based on post-consumer polymers and agricultural residues is a pragmatic valorization approach that extends the lifetime of materials. This research aimed to analyze the selected physical and mechanical properties of post-consumer-polyethylene-based composites with lignocellulosic fillers. This study explores the ‘ready-to-use’ valorization of untreated oilseed pomaces. The polyethylene ratio was set at 30% and 40%. Wood particles were substituted with oilseed pomace from nigella, rapeseed and evening primrose. The content of the pomace replacing wood particles was 30%, 65% and 100%. The composites made of post-consumer polyethylene and wood particles were used as a reference. The manufacturing process utilized a hybrid approach, combining extrusion with flat pressing. Increasing pomace content generally reduced the modulus of rupture and modulus of elasticity. Surface roughness decreased with higher pomace addition, except for the 30% rapeseed content for the lower polyethylene ratio, i.e., 30%, which showed unusually high roughness. Higher pomace content improved surface wettability, particularly for nigella-based composites. Water absorption and thickness swelling after 2 h and 24 h of soaking were highest at 30% pomace content and decreased with increasing substitution levels. Evening primrose composites consistently exhibited the lowest water uptake and swelling. Full article

This paper introduces RLFS-OR, a reinforcement learning-based opportunistic routing protocol designed for energy-constrained and duty-cycled wireless sensor networks (WSNs). Unlike traditional opportunistic routing, which either relies on static metrics or requires nodes to remain continuously active, RLFS-OR integrates a Deep Q-Network (DQN) to dynamically select the most energy-efficient forwarder based on residual energy, hop distance, wake-up timing, and link quality. A realistic Castalia-derived radio model is incorporated, accounting for transmission, reception, idle listening, and path loss-dependent energy consumption. Through coordinated learning and asynchronous duty-cycle integration, RLFS-OR minimizes overhearing and unnecessary wake-ups. Simulation results demonstrate that RLFS-OR significantly outperforms two established protocols—ORW and FCM-OR—achieving 10–30% lower energy consumption and 10–45% longer network lifetime under diverse network densities and traffic loads. RLFS-OR also provides smoother node-death dynamics and optimal performance at low duty cycles. The findings confirm RLFS-OR as an efficient and scalable solution for long-lived WSN deployments. Full article

This paper develops two nonparametric test statistics for testing exponentiality against alternatives in the increasing failure rate average (IFRA) class. The proposed procedures are constructed using information-theoretic functionals, namely the cumulative residual extropy and the cumulative past extropy of the first-order statistic. Exploiting fundamental properties of IFRA distributions, we derive explicit inequality relations that motivate the test statistics and establish their asymptotic normality under mild regularity conditions. To facilitate practical implementation, scale-invariant versions of the proposed tests are introduced, ensuring that their limiting distributions do not depend on unknown scale parameters. A comprehensive Monte Carlo simulation study demonstrates that the proposed tests possess strong power properties and frequently outperform several established competitors, particularly for moderate to large sample sizes. The applicability and effectiveness of the methodology are further illustrated through analyses of real lifetime datasets arising in reliability studies. The proposed tests are shown to be particularly effective for moderate sample sizes and provide a competitive alternative to existing IFRA-based procedures. Full article

Wireless sensor networks (WSNs) are foundational to modern smart environments, supporting applications ranging from healthcare and precision agriculture to industrial control and disaster response. Despite their potential, WSNs remain constrained by a limited battery life, packet loss, variable throughput, latency, and security vulnerabilities. This paper extends Horse Herd Leadership Optimization (HHLO), a bio-inspired metaheuristic modeling herd leadership, synchronization, and exploration to drive energy-aware clustering and trust-aware routing. HHLO rotates cluster-head leadership in order to balance load, injects chaotic exploration in order to avoid premature convergence, and incorporates a continuously updated node trust score directly into the routing cost in order to exclude unreliable or malicious nodes. Extensive MATLAB simulations with 1000 nodes deployed over a 1000 m × 1000 m² field for 400 rounds, under both static and mobile settings, demonstrate HHLO’s effectiveness. Compared to baseline approaches, HHLO achieves residual energy improvement of 12–21%, throughput gains of 14–23%, Packet Delivery Ratio (PDR) increase of 6–12%, and network lifetime extension of 18–32%; it also achieves an energy balance factor (EBF) of 0.91 and a trust balance factor (TBF) of 0.88, reduces end-to-end latency by 8–10%, and reduces control overhead ratio (COR) by 10–12%. These improvements result from HHLO’s joint optimization of energy, congestion, mobility, and trust, yielding longer-lived and more reliable networks. By unifying security and optimization within a single framework, HHLO advances the development of sustainable, resilient, and environmentally conscious WSNs for next-generation IoT deployments. Full article