Search Results (3,576)

To overcome the limitations of traditional multi-objective evolutionary algorithms—which often become trapped in local optima when addressing complex optimization problems and face challenges in balancing convergence efficiency with population diversity—this study proposes an enhanced NSGA-II algorithm that incorporates Lévy flight and simulated annealing strategies. The proposed algorithm enhances global exploration via Lévy flight mutation, improves local search precision through simulated annealing, and dynamically coordinates the search process using adaptive parameter strategies. Experiments conducted on the ZDT and DTLZ test function series demonstrated that the proposed algorithm achieves performance comparable to or better than that of NSGA-II and other benchmark algorithms, as measured by inverted generational distance and hypervolume metrics. It also exhibited superior convergence, distribution uniformity, and robustness. Furthermore, the algorithm was applied to the multi-objective optimization of electric winch trajectories for oil drilling rigs, which employed trajectory planning based on quintic polynomials. The simulation results demonstrated, compared to the pre-optimization baseline data, reductions of 6% in total operation time, 17.99% in energy consumption, and 27.4% in impact severity, thereby validating the method’s effectiveness and applicability in practical engineering scenarios. The comprehensive results demonstrate that the improved algorithm exhibits robust performance and excellent adaptability when addressing complex multi-objective optimization problems. Full article

This paper proposes a novel fixed-time super-twisting sliding mode control (ST-SMC) strategy for uncertain robotic arm systems, aiming to address the issues of control chattering and the uncontrollable upper bound of convergence time in traditional sliding mode control algorithms. The proposed approach enhances system robustness, suppresses chattering, and ensures that the convergence time of the robotic arm can be explicitly bounded. First, a sliding surface with fixed-time convergence characteristics is constructed to guarantee that the tracking errors on this surface converge to the origin within a prescribed time. Then, an immersion and invariance (I&I) disturbance observer with exponential convergence properties is designed to estimate large, time-varying disturbances in real time, thereby compensating for system uncertainties. Based on this observer, a new super-twisting sliding mode controller is developed to drive the trajectory tracking errors toward the sliding surface within fixed time, achieving global fixed-time convergence of the tracking errors. Simulation results demonstrate that, regardless of the initial conditions, the proposed controller ensures fixed-time convergence of the tracking errors, effectively eliminates control torque chattering, and achieves a tracking error accuracy as low as 2 × 10⁻⁹. These results validate the proposed method’s applicability and robustness for high-precision robotic systems. Full article

Background: Patients with multiple sclerosis (PwMS) frequently experience dysphagia, which affects their quality of life. The swallowing disturbance questionnaire (SDQ) has demonstrated potential in screening dysphagia in different disorders. The objective of this study was to evaluate the validity and reliability of the Persian version of SDQ in PwMS. Methods: In this cross-sectional study, 198 PwMS were enrolled. The translation of SDQ into Persian was performed using the forward–backward method. Participants completed both the SDQ and the Dysphagia in Multiple Sclerosis (DYMUS) questionnaires. Convergent validity was assessed using the Spearman correlation, construct validity was evaluated by principal component analysis (PCA), and reliability was assessed by Cronbach’s alpha. Screening ability was evaluated with receiver operating characteristic (ROC) curve analysis, using DYMUS as the reference measure. Results: The Persian SDQ showed high internal consistency (Cronbach’s alpha = 0.913) after removing one item. PCA revealed a single dominant factor accounting for 49.4% of the variance. The 14-item SDQ correlated strongly with both DYMUS (Spearman’s rho = 0.62, p < 0.001) and Expanded Disability Status Scale (EDSS) (Spearman’s rho = 0.388, p < 0.001). The area under the curve of 0.957 revealed high screening power with a sensitivity of 91.7% and a specificity of 88.9%. Conclusions: The Persian SDQ is a valid and reliable tool for early detection and quick monitoring of dysphagia in PwMS. Full article

Background/Objectives: Long non-coding RNAs (lncRNAs) are increasingly recognized as key regulators of immune pathways and may hold diagnostic and therapeutic relevance in autoimmune diseases such as Multiple Sclerosis (MS). However, research on lncRNAs in MS remains fragmented and geographically clustered. This systematic review aimed to collate and critically evaluate studies of lncRNA expression in MS, assess consistency of findings across studies, and synthesize proposed functional implications of the most frequently studied lncRNAs. Methods: This PROSPERO-registered review (CRD420250575938), conducted in accordance with PRISMA, searched PubMed, Scopus, Embase, and Web of Science (2010–2024) for studies evaluating lncRNA expression in adult MS (≥18 years of age). Eligible studies included ≥20 participants and assessed lncRNAs in blood, PBMCs, serum, plasma, or CSF using qRT-PCR, RNA-seq, or microarrays. Pediatric, review, animal, and in vitro studies were excluded. Two reviewers independently screened and extracted data, with risk of bias evaluated using QUADAS-2. Results: Narrative synthesis of 51 studies identified 77 unique lncRNAs. A limited set (MALAT1, GAS5, MEG3, H19) demonstrated consistent dysregulation in MS, whereas others (THRIL, IFNG-AS1, HOTAIR, TUG1) exhibited context-dependent expression influenced by treatment, relapse status, or demographics. Functional annotations converged on immune pathways, including NF-κB, STAT3, IFN-γ/Th1, and glucocorticoid signaling. Conclusions: This review identifies reproducible and context-specific lncRNA dysregulation in MS, emphasizing the need for transcriptome-wide approaches, standardized methods, and multi-center validation. Current evidence is constrained by geographic clustering, preselection bias, and methodological heterogeneity. Full article

Quality Infrastructure (QI) underpins safe, sustainable, and competitive markets through metrology, standardization, accreditation, conformity assessment, and market surveillance. While mandatory schemes address immediate safety concerns, voluntary conformity assessments offer strategic advantages for emerging technologies by enabling market differentiation, regulatory anticipation, and gradual adaptation without compliance burdens. Focusing on Brazil’s National Institute of Metrology, Quality, and Technology (Inmetro), this study addresses operational gaps in implementing voluntary schemes under the modernized regulatory framework introduced by Inmetro’s Ordinance No. 30/2022. Using electric mobility to illustrate sustainability pathways, we show how voluntary assessments can operationalize and enable measurement of environmental and social co-benefits. Our five-stage qualitative methodology integrated documentary analysis of Brazilian regulations; comparative examination of approaches in the European Union, the United States, and South Korea; development of a 14-step methodological roadmap aligned with ISO/IEC standards; expert validation through a structured questionnaire with twelve specialists from government, industry, academia, and certification bodies; and systematic consolidation of feedback. The roadmap provides operational guidance on product definition, technical requirements, certification processes, and continuous improvement, with optional modules for advanced technologies and ESG criteria. Expert validation confirmed viability while identifying barriers (costs, laboratory capacity, cultural limitations) and enablers (fiscal incentives, procurement recognition). When applied to electric mobility, voluntary battery certification enhances safety and performance, charging infrastructure assessment improves reliability, and component schemes enable circular economy principles, directly supporting the Sustainable Development Goals. We conclude that strategically designed voluntary conformity schemes can accelerate regulatory convergence, strengthen competitiveness, and contribute to sustainability outcomes in modernizing economies. Full article

Feature selection in high-dimensional datasets presents significant computational challenges, particularly in domains with large feature spaces and limited sample sizes. This paper introduces FL-SBA, a novel metaheuristic algorithm integrating fractional calculus enhancements with Laguerre operators into the Secretary Bird Optimization Algorithm framework for binary feature selection. The methodology incorporates fractional opposition-based learning utilizing Laguerre operators for enhanced population initialization with non-local memory characteristics, and a Laguerre-based binary transformation function replacing conventional sigmoid mechanisms through orthogonal polynomial approximation. Fractional calculus integration introduces memory effects that enable historical search information retention, while Laguerre polynomials provide superior approximation properties and computational stability. Comprehensive experimental validation across ten high-dimensional gene expression datasets compared FL-SBA against standard SBA and five contemporary methods including BinCOA, BAOA, BJSO, BGWO, and BMVO. Results demonstrate FL-SBA’s superior performance, achieving 96.06% average classification accuracy compared to 94.41% for standard SBA and 82.91% for BinCOA. The algorithm simultaneously maintained exceptional dimensionality reduction efficiency, selecting 29 features compared to 40 for competing methods, representing 27% improvement while achieving higher accuracy. Statistical analysis reveals consistently lower fitness values (0.04924 averages) and stable performance with minimal standard deviation. The integration addresses fundamental limitations in integer-based computations while enhancing convergence behavior. These findings suggest FL-SBA represents significant advancement in metaheuristic-based feature selection, offering theoretical innovation and practical performance improvements for high-dimensional optimization challenges. Full article

The Rapidly Exploring Random Tree (RRT) is widely employed in the field of intelligent vehicles, but traditional RRT has issues like inefficient blind expansion, tortuous/discontinuous paths, and slow convergence. Thus, a multi-dimensional optimized RRT is proposed. First, a heuristic search method is adopted to reduce blind sampling, guiding sampling toward the target and cutting irrelevant searches. Second, to fix RRT’s inability to adjust step size dynamically (limiting complex road adaptability), step size is optimized based on environmental information. Third, since treating vehicles as mass points leads to unreasonable paths, sampling points are expanded for practicality. Finally, redundant points are removed via a greedy strategy, and paths are smoothed with quasi-uniform cubic B-splines to meet ride comfort needs. MATLAB R2022b simulations validate the algorithm: in simple scenarios, optimized RRT reduces sampling points to 232 (24.4% of traditional RRT), runtime to 3.25 s (79.4% cut), path length to 673.84 m (15.6% reduction); in complex scenarios, 636 points (37.0%), 11.07 s runtime (58.8% cut), 699.61 m path (21.6% reduction), outperforming traditional RRT and Q-RRT*. Full article

Understanding the mechanisms that assemble diverse forest communities is a central goal in ecology. Phylogenetic analyses based on DNA barcodes have advanced this field, but their use of sequences evolving at constant rates may not capture adaptations to specific environmental drivers. Light is a critical factor shaping forest structure, particularly in the vertical dimension. This study introduces a novel phylogenetic approach using the blue-light receptor gene, cryptochrome (Cry), which is directly involved in plant light perception and adaptation. We reconstructed a Cry-based phylogeny for 96 tree species in a 20 ha subtropical forest dynamics plot and analyzed community structure using the net relatedness index (NRI) and nearest taxon index (NTI) across horizontal habitats, successional stages, and vertical canopy layers. Compared to traditional DNA barcoding, the Cry phylogeny revealed distinct patterns, showing consistent phylogenetic structure across different habitats—a finding indicative of convergent evolution in light-sensing systems. Furthermore, the Cry-based analysis demonstrated a stronger and more consistent signal in the forest’s vertical structure, with significant phylogenetic clustering in upper canopy layers, directly linking light adaptation to community stratification. Over time, both NRI and NTI values increased, suggesting succession leads to greater phylogenetic overdispersion and highlighting an increased role for environmental filtering among closely related taxa. Our results validate Cry as a powerful functional gene marker for phylogenetics, providing unique insights into how light environment filters species and shapes the vertical assembly of forest communities. Full article

Gastroesophageal cancers including esophageal and gastric cancer remain major causes of global cancer mortality, primarily due to late diagnosis and high recurrence rates after curative treatment. Current surveillance methods, such as endoscopy and imaging, are invasive, costly, and often inadequate for detection. Blood-based biomarkers (“liquid biopsies”) offer a minimally invasive alternative capable of real-time tumor monitoring. In this review, we summarize recent advances across all major classes of blood-derived biomarkers: circulating tumor DNA (ctDNA), methylated DNA, cell-free RNAs (microRNAs, lncRNAs, circRNAs), circulating proteins, autoantibodies, circulating tumor cells, extracellular vesicles, and metabolites. Reviewing the existing literature on gastroesophageal cancers, we highlight current evidence, validation phases, performance metrics, and limitations. Special attention is given to clinical trial evidence, including ctDNA monitoring studies, that demonstrated earlier recurrence detection compared to imaging. While blood-based biomarker analysis has not yet supplanted endoscopy as standard of care in gastroesophageal cancer surveillance, the convergence of multi-analyte assays, AI, and clinical validation trials positions liquid biopsy as a transformative tool in the surveillance of gastroesophageal cancers. Full article

In the present study, we aimed to derive analytical solutions of the homotopy analysis method (HAM) for the time-fractional Navier–Stokes equations in cylindrical coordinates in the form of a rapidly convergent series. In this work, we explore the time-fractional Navier–Stokes equations by replacing the standard time derivative with the Katugampola fractional derivative, expressed in the Caputo form. The homotopy analysis method is then employed to obtain an analytical solution for this time-fractional problem. The convergence of the proposed method to the solution is demonstrated. To validate the method’s accuracy and effectiveness, two examples of time-fractional Navier–Stokes equations modeling fluid flow in a pipe are presented. A comparison with existing results from previous studies is also provided. This method can be used as an alternative to obtain analytic and approximate solutions of different types of fractional differential equations applied in engineering mathematics. Full article

In the field of industrial fault detection, accurate and timely fault identification is crucial for ensuring production safety and efficiency. Effective feature selection (FS) methods can significantly enhance detection performance in this process. However, the recently proposed Black-winged Kite Algorithm (BKA) tends to suffer from premature convergence and local optima when handling high-dimensional feature spaces. To address these limitations, this paper proposes an improved Black-winged Kite Algorithm (IBKA). This algorithm integrates two novel enhancement mechanisms: First, the Stagnation-Triggered Diversification Mechanism monitors the algorithm’s convergence state and applies mild perturbations to the worst-performing individuals upon detecting stagnation, effectively preventing traps in local optima. Second, the Adaptive Weak Guidance Mechanism employs a conditional elite guidance strategy during the late optimization phase to provide subtle directional guidance to underperforming individuals, thereby improving convergence efficiency. We comprehensively evaluated the proposed IBKA across 26 benchmark functions. Results demonstrate superior performance in solution quality, convergence speed, and robustness compared to the original BKA and other advanced meta-heuristics. Furthermore, fault detection applications on public datasets validate the practical applicability of the binary version of the IBKA (bIBKA), showcasing significant improvements in detection accuracy and reliability. Experimental results confirm that these enhancement mechanisms effectively balance exploration and exploitation capabilities while preserving algorithmic simplicity and computational efficiency. Full article

Aerial robots are a well-established solution for exploration, monitoring, and inspection, thanks to their superior maneuverability and agility. However, in many environments, they risk crashing and sustaining damage after collisions. Traditional methods focus on avoiding obstacles entirely, but these approaches can be limiting, particularly in cluttered spaces or on weight- and computationally constrained platforms such as drones. This paper presents a novel approach to enhance drone robustness and autonomy by developing a path recovery and adjustment method for a high-speed collision-resilient aerial robot equipped with lightweight, distributed tactile sensors. The proposed system explicitly models collisions using pre-collision velocities, rates and tactile feedback to predict post-collision dynamics, improving state estimation accuracy. Additionally, we introduce a computationally efficient vector-field-based path representation that guarantees convergence to a user-specified path, while naturally avoiding known obstacles. Post-collision, contact point locations are incorporated into the vector field as a repulsive potential, enabling the drone to avoid obstacles while naturally returning to its path. The effectiveness of this method is validated through Monte Carlo simulations and demonstrated on a physical prototype, showing successful path following, collision recovery, and adjustment at speeds up to $3.7$$\mathrm{m}$/$\mathrm{s}$. Full article

Cold-bonded (CB) fly ash aggregate, an eco-friendly material derived from industrial by-products, is used to fully replace natural coarse aggregate in producing lightweight concrete (LWC-CB). This study systematically investigates the post-high-temperature mechanical properties and damage mechanisms of LWC-CB. Specimens exposed to ambient temperature (10 °C) and elevated temperatures (200 °C, 400 °C, 600 °C) underwent cubic compression tests, with surface deformation monitored via digital image correlation (DIC). Experimental results indicate that the strength retention of LWC-CB is approximately 6% superior to ordinary concrete below 500 °C, beyond which its performance converges. Damage analysis reveals a transition in failure mode: at ambient temperature, shear failure is governed by the low intrinsic strength of CB aggregates, while after high-temperature exposure, damage localizes within the mortar and the interfacial transition zone (ITZ) due to mortar micro-cracking and thermal mismatch. To elucidate these mechanisms, a three-dimensional mesoscale model was developed and validated, effectively characterizing the internal multiphase structure at room temperature. Furthermore, a homogenization model was established to analyze the macroscopic thermo-mechanical response. The numerical simulations show strong agreement with experimental data, with a maximum deviation of 15% at 10 °C and 3% after high-temperature exposure, confirming the model’s accuracy in capturing the performance evolution of LWC-CB. Full article

Clean energy applications widely recognize Proton Exchange Membrane Fuel Cells (PEMFCs) for their high efficiency and environmental compatibility. Accurate parameter identification of PEMFC models is essential for enhancing system performance and reliability, particularly under dynamic operating conditions. This paper presents a novel optimization-based approach called Heterogeneous Comprehensive Learning-Bald Eagle Search (HCLBES) with enhanced exploration and exploitation capabilities for the effective modeling of PEMFC. The algorithm combines the exploration strength of the Bald Eagle Search with comprehensive learning and heterogeneity mechanisms to achieve a balanced global and local search space. In this algorithm, the number of agents is divided into two subagents. Each subagent is assigned to focus solely on either exploration or exploitation. The comprehensive learning strategy generates exemplars for both subgroups. In the exploration sub-agent, exemplars are generated using the personal best experiences of agents within that same exploration space. The exploitation subagent generates the exemplars using the personal best experiences of all agents. This separation preserves exploration diversity even if exploitation converges prematurely. The algorithm is applied to optimize parameters of the 250 W and 500 W PEMFC models under varying conditions. Simulation results demonstrate the outperformance of the HCLBES algorithm in terms of convergence speed, estimation accuracy, and robustness compared to recent optimization algorithms. The effectiveness of HCLBES was also verified through statistical metrics and different commercial PEMFC models, including BCS 500 W stacks, Horizon 500, and NedStack PS6. Experimental validation confirms that the proposed algorithm effectively captures the nonlinear behaviours of PEMFCs under dynamic operating conditions. This research aligns with the Sustainable Development Goals (SDGs) by promoting clean and affordable energy (SDG 7) through the enhanced efficiency and reliability of PEMFCs, thereby supporting sustainable industrialization and innovation (SDG 9). Full article

The complex configuration of the internal flow field in aero-engines leads to limitations on sensor installation positions, and how to accurately identify the disturbances of the installation influence parameters under this constraint has long been a significant challenge. To address this issue, this study proposes an optimization algorithm to identify the optimal sensor layout. This is achieved by employing mutually distinct integer encoding, which ensures the uniqueness of each sensor position and prevents duplication. More importantly, an objective evaluation system incorporating tracking error, sensor comprehensiveness, and spatial coverage is integrated into the fitness function design, thereby overcoming the one-sidedness and limitations of single-indicator evaluation. Building upon this foundation, a sensor optimization scheme is proposed for identifying installation influence parameters. This scheme integrates the rapid search capability of the Tabu Bee Colony Random Variation Dimension Algorithm with the global optimization capability of an Improved Genetic Random Variation Dimension Algorithm, resulting in a Tabu Bee Colony–Improved Genetic Random Variation Dimension Optimization Algorithm (TBC-IG-RVDOA). For each installation influence parameter, different perturbation conditions were established, and the selected optimal sensor combination was then validated using the Extended Kalman Filter (EKF). Experimental studies show that, under all perturbation scenarios, the TBC-IG-RVDOA demonstrates strong convergence, high computational efficiency, and fitness function values consistently exceeding 0.92, thereby accurately capturing the changes in each installation influence parameter. Full article