Search Results (711)

This paper introduces a Hybrid Tyrannosaurus Rex Optimization Algorithm (Hybrid TROA) combined with Variable Neighborhood Search (VNS), two variations of the Path Relinking strategy, and a randomized Nawaz–Enscore–Ham (NEH) heuristic to address the Permutation Flowshop Scheduling Problem (PFSP). The TROA is a novel bio-inspired meta-heuristic algorithm modeled on the hunting behavior of the prehistoric Tyrannosaurus Rex. Leveraging the potential of this newly developed and efficient algorithm, we propose a framework in which an initial population of solutions is generated using the randomized NEH heuristic. These solutions are then further optimized through VNS and Path Relinking, yielding highly satisfactory results for the PFSP. First, we consider two optimization criteria separately: the makespan and the total flow time. Next, we conduct a comparative study of the Hybrid TROA against other prominent meta-heuristics, along with a statistical analysis using non-parametric tests, to determine the best-performing method for each objective. According to our findings, the Hybrid TROA proves to be the most suitable method in this study for minimizing both targets. Finally, recognizing that contemporary industry demands both high productivity and energy efficiency, we propose an energy-efficient version of the classic PFSP, simultaneously considering two criteria for optimization: the makespan and total energy consumption. Our study introduces a novel objective function that achieves balanced optimization by integrating both criteria. Full article

Metaheuristic optimization algorithms have attracted considerable research interest for solving complex optimization problems, yet many existing algorithms suffer from premature convergence and an inadequate balance between exploration and exploitation. The Coati Optimization Algorithm (COA) is a recently proposed nature-inspired metaheuristic that models the hunting and escape behaviors of coatis; however, it exhibits limited search diversity and tends to stagnate in local optima on high-dimensional, multimodal landscapes. This paper proposes an Improved Coati Optimization Algorithm (ICOA) that integrates four complementary enhancement strategies: (1) a Dynamic Adaptive Step-Size strategy that combines Lévy flights with Student’s t-distribution perturbations for heavy-tailed exploration; (2) a Population-Adaptive Dynamic Perturbation strategy that incorporates differential evolution operators with fitness-proportional scaling; (3) an Iterative-Cyclic Differential Perturbation strategy that employs sinusoidal scheduling and population-differential guidance; and (4) a Cosine-Adaptive Gaussian Perturbation strategy for refined exploitation with time-decaying intensity. ICOA is evaluated on 29 CEC2017, 10 CEC2020, and 12 CEC2022 benchmark functions across dimensions ranging from 10 to 100, compared against seven state-of-the-art algorithms in each benchmark suite. A statistical analysis using the Friedman test and the Wilcoxon rank-sum test confirms that ICOA achieves overall rank 1 on all three benchmark suites, with Friedman mean ranks of 1.207 (CEC2017, $D=100$), 1.000 (CEC2020, $D=10$), and 2.208 (CEC2022, $D=10$); the CEC2020 result should be interpreted in the context of its low dimensionality. A scalability analysis across four dimensionalities (10D, 30D, 50D, 100D) demonstrates consistent first-place rankings with mean ranks between 1.000 and 1.207. An ablation study and a sensitivity analysis of the strategy activation probability validate the contribution of each individual strategy and the optimality of the 50% activation setting. Furthermore, ICOA achieves the best results on all six constrained engineering design problems tested, with all improvements confirmed as statistically significant ($p<0.05$). Full article

Particle swarm optimization (PSO) improves classification performance and reduces computational complexity in feature selection. However, it frequently experiences from premature convergence and insufficient exploration. To address these constraints, this paper suggests an adaptive binary PSO (ABPSO) algorithm specifically designed for feature selection. First, an adaptive transfer function and two adaptive learning coefficients are introduced to achieve a better balance between exploration and exploitation during the search process. Second, a hybrid learning mechanism that integrates personal best, global best, and elite solutions is utilized to enhance population diversity. Finally, a simulated annealing (SA)–based local search strategy is employed to further refine candidate solutions and improve convergence behavior. Experimental results demonstrate that ABPSO outperforms binary PSO (BPSO), harris hawks optimization (HHO), whale optimization algorithm (WOA), and ant colony optimization (ACO) in classification accuracy. In particular, ABPSO achieves the lowest classification error rates on the Dermatology (0.0106), Ionosphere (0.0705), Lung (0.1521), Sonar (0.0996), Spambase (0.0758), Statlog (0.1446), and Wine (0.0280) datasets. Full article

About half the elements heavier than iron in the universe, like silver and gold, are created in the rapid neutron-capture (r-)process. However, today, almost 70 years after the theoretical prediction of this process, it is still highly debated in what type of stellar explosions it can take place. One of the best places to search for answers is in ancient, metal-poor stars formed from the enriched gas. Their chemical makeup is like a time capsule, a direct fingerprint of the elements produced by the stellar generations that came before them. Since the first highly r-process-enhanced star, CS 22892-052 was discovered more than 30 years ago, multiple projects like the Hamburg/ESO r-Process Enhanced Star (HERES) survey, the Chemical Evolution of r-process Elements in Stars (CERES) project, and the r-Process Alliance (RPA) have searched for more r-process-enriched stars in the Milky Way. At the same time, numerous r-process-enriched stars have been discovered in stellar streams and dwarf galaxies. Here we present an overview of recent advances in finding r-process-enriched metal-poor stars and what the detailed chemo-dynamical analysis of these stars can tell us about heavy element nucleosynthesis and the astrophysical site(s) of the r-process. Full article

Background/Objectives: Renal denervation (RDN) has re-emerged as an adjunctive treatment option for patients with uncontrolled or resistant hypertension, with contemporary sham-controlled trials showing a modest but reproducible reduction in out-of-office blood pressure. However, in routine practice, apparent treatment resistance often reflects pseudoresistance caused by the white-coat effect, poor measurement quality, therapeutic inertia, or nonadherence. This review aimed to summarize the contemporary evidence on renal denervation in uncontrolled or resistant hypertension and to propose a pragmatic, measurement-first framework for patient selection, integration into routine care, and a structured post-procedural response assessment. Methods: This article is a narrative, implementation-focused review. A structured search of PubMed, Embase, Cochrane CENTRAL, and Web of Science was performed from database inception through January 2026. We prioritized the randomized sham-controlled RDN trials, major meta-analyses, guidelines, consensus documents, and studies addressing ABPM, HBPM, medication adherence, and telemonitoring. Results: The contemporary sham-controlled trials support RDN as an adjunctive option with a modest blood pressure-lowering effect, which is best assessed by out-of-office measurements. The placebo-adjusted reductions in ambulatory systolic blood pressure were generally in the 4–6 mmHg range. Appropriate use requires the confirmation of sustained uncontrolled hypertension, the exclusion of pseudoresistance, the optimization of treatment, and an adherence assessment. We identified three phenotypes most likely to benefit and proposed a three-axis framework for a response assessment at 3 and 6 months. Conclusions: RDN should be viewed not as a substitute for antihypertensive therapy but as a program-based adjunct for carefully selected patients. The measurement-first care pathway presented here should be interpreted as a pragmatic clinical model intended to operationalize the available trial and guideline evidence in routine care, rather than as a prospectively validated algorithm or formal consensus recommendation. Full article

Eupatorium fortunei Turcz. (E. fortunei), a member of the Asteraceae family, is a widely utilized traditional medicinal herb in China. Historically, it has been employed to treat conditions such as influenza, nausea, anorexia, and various ailments associated with “pathogenic dampness”. To the best of our knowledge, this study presents the first systematic review of recent research on E. fortunei, based on a comprehensive literature search across both Chinese and international databases, including Web of Science, PubMed, SciFinder, and CNKI. The review encompasses its botanical characteristics, traditional applications, phytochemical composition, pharmacological properties, and toxicological profiles. Current research reveals a diverse array of phytochemicals in E. fortunei, with 162 compounds identified to date, including thymol derivatives, terpenoids, alkaloids, benzofurans, fatty acids, and other bioactive constituents. These compounds exhibit a broad spectrum of pharmacological activities, encompassing anti-cancer, anti-viral, anti-fungal, anti-inflammatory, and anti-diabetic effects. Among these, thymol derivatives and benzofurans emerge as the most prominent bioactive compounds, demonstrating potent cytotoxic effects against various tumor cell lines. Although E. fortunei is generally considered safe, certain pyrrolizidine alkaloids (PAs) present potential hepatotoxic risks, which can be mitigated through appropriate dosage control and formulation optimization. As a valuable traditional Chinese medicinal herb, E. fortunei exhibits substantial therapeutic potential. In conclusion, this review provides a comprehensive and systematic overview of current research on E. fortunei, offering scientific evidence and guidance for its rational development and clinical application. Full article

Metaheuristic algorithms have become indispensable for solving high-dimensional, non-convex, and constrained optimization problems arising in science and engineering. However, no single method can simultaneously provide strong global exploration, accurate local exploitation, and robust performance across diverse problem classes. This paper proposes JADEFLO, a new hybrid nature-inspired metaheuristic that couples Adaptive Differential Evolution with Optional External Archive (JADE) and Frilled Lizard Optimization (FLO) in a two-stage search framework. In the first stage, JADE drives global exploration using p-best mutation, an external archive, and adaptive control of the mutation factor and crossover rate to maintain population diversity. In the second stage, FLO performs intensive local refinement by mimicking the hunting and tree-climbing behaviors of frilled lizards through dedicated exploration and exploitation moves. The resulting algorithm has linear time complexity with respect to the population size, dimensionality, and number of iterations. JADEFLO is evaluated on the IEEE CEC 2022 single-objective benchmark suite (F1–F12) and three constrained engineering design problems (Pressure Vessel, tension/compression spring, and speed reducer), using 30 independent runs and comparisons against more than thirty state-of-the-art metaheuristics, including GA, PSO, DE variants, GWO, WOA, MFO, and FLO. The results show that JADEFLO attains the best overall rank on the CEC functions, delivers faster convergence and higher accuracy on most test cases, and matches or improves the best-known designs with markedly reduced variance. These findings indicate that JADEFLO is a promising general-purpose optimizer and a flexible foundation for future extensions to multi-objective and large-scale optimization. Full article

The porcine epidemic diarrhea virus (PEDV) causes a gastrointestinal disease generating mortality rates approaching 100% in piglets worldwide. The S glycoprotein of PEDV is the main target for the development of vaccines. Two vaccines approved by the Ministry of Agriculture and Rural Development are used in Mexico: the first vaccine is based on an inactivated virus isolated more than a decade ago, whereas the second vaccine is based on mRNA technology. The most important tool for controlling PEDV outbreaks is vaccination; however, coronaviruses are characterized by the accumulation of multiple mutations, which compromise the immune response elicited by outdated vaccines. In this work, we classified the Mexican strains of PEDV reported so far in GenBank, according to their genotypes. Subsequently, we searched for B and T cell epitopes conserved in Mexican PEDV strains using bioinformatic tools. In addition, we explored whether these epitopes can induce allergies, autoimmunity, and/or toxic effects. Next, we determined the localization of B cell epitopes in the S glycoprotein using the protein crystal and protein modeling of several S glycoproteins. Finally, we carried out molecular docking analysis to assess whether these T cell epitopes could interact with the peptide-binding groove of the Swine Leukocyte Antigens (SLAs). Five conserved B cell epitopes were found to be exposed on the surface of the S glycoprotein, whereas several promiscuous CTL and HTL epitopes were bound, with low free energy, to the peptide-binding grooves of SLA-I and SLA-II, respectively. The best epitopes were used to generate a plasmid carrying the sequence to produce a recombinant protein. This plasmid was used for transfection experiments in PK-15 cell culture. The B cell epitopes reported here were recognized by the sera from pigs infected with PEDV but not by the sera from uninfected animals. These results justify future evaluations of the ability of these epitopes to stimulate cytokine production by T cells, antibody generation, and their neutralizing activity. Full article

Reactive power compensation devices (RPCDs) are crucial for improving the efficiency of energy systems. Distribution systems are commonly modeled under the simplifying assumption of balanced operation, which does not accurately represent real operating conditions. Motivated by the need to develop an effective computational tool for the proper selection of RPCDs, this paper proposes the application of the experience exchange strategy (EES) to the coordinated design of RPCDs. To the best of the authors’ knowledge, this is the first study to employ EES for this purpose. The proposed methodology is validated through two case studies. In the first case, an extensive exploration of the search space is performed by repeating the optimization process, resulting in a solution with a high probability of being the global optimum. Under this scenario, a comparative analysis shows that EES outperforms the genetic algorithm by 7.4%. In the second case, EES is compared with other popular heuristic techniques, including particle swarm optimization (PSO), without performing a deep exploration of the search space, observing that EES ranks in the middle, with a difference of 11.9% relative to PSO. Overall, the results confirm that the proposed EES-based framework constitutes a reliable and efficient approach. Full article

In complex ground environments, conventional RRT* often suffers from low planning efficiency and poor path quality for robot path planning. This paper proposes BGSE-RRT* (Bi-tree Cooperative, Goal-guided, low-discrepancy Sampling, multi-sector Expansion). First, BGSE-RRT* constructs a nonlinear switching probability via bi-tree cooperative adaptive switching, together with KD-Tree nearest-neighbor acceleration and multi-condition triggering, to adaptively balance global exploration and local convergence. Meanwhile, a goal-guided expansion with dynamic target binding and adaptive step size, under a multi-constraint feasibility check, accelerates the convergence of the two trees. When the goal-guided expansion becomes blocked, BGSE-RRT* generates candidate points in local multi-sector regions using a 2D Halton low-discrepancy sequence and selects the best candidate for expansion; if the multi-sector expansion still fails, a sampling-point-guided expansion is activated to continue advancing and search for a feasible path. Second, B-spline smoothing is applied to improve trajectory continuity. Finally, in five simulation environments and ROS/real-robot joint validation, compared with GB-RRT*, BI-RRT*, BI-APF-RRT*, and BAI-RRT*, BGSE-RRT* reduces planning time by up to 84.71%, shortens path length by 2.94–6.88%, and improves safety distance by 20.68–48.33%. In ROS/real-robot validation, the trajectory-tracking success rate reaches 100%. Full article

Background and Objectives: Corneal allogeneic intrastromal ring segments (CAIRS) are designed to decrease and stabilize the extent of corneal ectasia in keratoconus patients. This systematic review and meta-analysis evaluate the effectiveness of different surgical techniques for CAIRS preparation and the adjunctive use of corneal cross-linking. Materials and Methods: Following the PRISMA statement and checklist, a comprehensive search was conducted in Embase, Medline, and the Cochrane Controlled Trials Register, through the use of a systematic search approach in accordance with the Cochrane Collaboration guidelines. Results: Eighteen studies, involving 567 eyes of 459 patients, met the inclusion criteria. At one month postoperatively, CAIRS implantation significantly improved uncorrected visual acuity (UCVA) (−0.45 logMAR, 95% CI [−0.59 to −0.31], p < 0.001) and best corrected visual acuity (BCVA) (−0.36 logMAR, 95% CI [−0.46 to −0.25], p < 0.001). These improvements remained significant after one year (UCVA: −0.39 logMAR, 95% CI [−0.48 to −0.30], p < 0.001; BCVA: −0.34 logMAR, 95% CI [−0.50 to −0.18], p < 0.001). Similarly, mean simulated keratometry (Kmean) decreased by −4.42 D (95% CI [−5.94 to −2.90], p < 0.001) and maximum keratometry (Kmax) by −3.88 D (95% CI [−6.71 to −1.05], p < 0.001) at one month, with sustained reductions at one year (−3.59 D, 95% CI [−4.35 to −2.84], p < 0.001 and −3.73 D, 95% CI [−4.91 to −2.55], p < 0.001). No significant differences in surgical outcome have been observed between the different surgical techniques. Conclusions: CAIRS implantation appears to be an effective treatment option for keratoconus, regardless of the technique used for segment preparation or the addition of corneal cross-linking. No approach demonstrated clear clinical superiority over others in the first year after surgery. Full article

Background/Objectives: Natural medicine analysis remains challenging due to chemical diversity. To the best of our knowledge, the comprehensive identification of multiple chemical constituents in Xiaoban Kangfu (XBKF) capsules has not been reported. Therefore, a combined approach utilizing ultra-high-performance liquid chromatography quadrupole-Exactive Orbitrap mass spectrometry (UHPLC-Q-Exactive Orbitrap MS) and molecular network analysis needs to be developed to comprehensively characterize the chemical constituents of XBK capsules in heat-clearing and toxin-eliminating granules, thereby enhancing annotation accuracy and enabling visualization. Methods: Firstly, chromatographic and mass spectrometry conditions were optimized to achieve good separation and a rich signal response. Subsequently, the literature searches, database consultations, and reference standards were employed to enhance annotation reliability. Finally, the raw data acquired under optimized conditions were uploaded to Global Natural Products Social (GNPSs), enabling component visualization by linking precursor ions of similar structural features with identical colors. Results: A total of 170 compounds were identified from this medicinal resource for the first time, including 50 flavonoids, 34 phenolic acids, 16 terpenoids, 14 quinones, 14 organic acids, eight coumarins, ive carbohydrates, and 29 other compounds. Conclusions: This study establishes a robust UHPLC-Q-Exactive Orbitrap MS/MS strategy for the comprehensive chemical profiling of XBKF capsules. The use of the presented validated analytical method for the comprehensive quality control of XBKF capsules is highly promising, offering fast, highly sensitive, and reliable analysis. Full article

Phenomenological crystal plasticity (CP) models are widely used in Integrated Computational Materials Engineering (ICME) to link microstructural features with engineering-scale mechanical behaviour. Their practical use, however, is limited by the high computational cost of physics-based simulations and the labour-intensive nature of parameter calibration, challenges that are amplified in additively manufactured materials with location-dependent properties. To address these obstacles, we first developed deep neural network (DNN) surrogate models of physics simulations to predict the stress–strain response of an additively manufactured AlSi10Mg alloy. Twenty-five experimentally derived scenarios (five microstructures × five sets of grain orientations) were used for training 25 separate DNNs, with datasets for validated material behaviour generated using the Düsseldorf Advanced Material Simulation Kit (DAMASK) platform and a Fast Fourier Transform (FFT)-based solver. Once trained, the DNNs produced stress–strain curves almost instantaneously, enabling an exhaustive grid-search exploration of a vast parameter space. Our approach yielded significant efficiency gains, which were comprehensively quantified. The best-fit CP parameters obtained through this approach are expected to be more accurate than those derived from conventional trial-and-error calibration, which is restricted to a limited number of candidate values. In addition, the minimum number of CP-FFT simulations required to train the DNNs with sufficient accuracy was identified, reducing the need for costly physics simulations in future studies. The proposed framework enhances the practical utility of CP models for simulation-informed materials engineering and optimisation and is broadly applicable to parameter identification in phenomenological models of other domains. Full article

This paper proposes to estimate the travel time at each edge of an urban street network using Artificial Neural Networks (ANNs). To improve the ANN performance and minimize errors in manual design, an Algorithm Auto-Design ANN Topology (A-DANNT) is introduced that automatically determines the most suitable architecture for regression problems. The methodology implements an algorithm based on Tabu Search, called the Best R-Value Determination algorithm (BR-vD), which optimizes the topology obtaining a lower Mean Square Error (MSE) and a higher correlation coefficient. The process is developed in three phases: first, the variables that impact the travel time are analyzed; then, the proposed algorithm is used to find the best topology; and finally, the travel times are estimated. The proposal is evaluated in two case studies: in the first, the algorithm automatically designs the architecture, and a 0.99366 correlation coefficient is achieved between the results and the objectives. In the second case, the performance of the algorithm is compared with a fuzzy travel time model, achieving a 0.99898 correlation coefficient. In both cases, the proposed algorithm is capable of designing topologies with coefficients greater than 0.99 and Mean Absolute Errors (MAEs) of 3.2765 and 0.6957 s, respectively. Full article

Constrained multi-objective optimization problems (CMOPs) are widely encountered in practical engineering and scientific applications. To address these issues, this paper proposes a Constraint-Tightening Feasible-Trajectory-Guided Two-Stage Evolutionary Algorithm (CT-FTREA). By dividing the optimization process into a feasible-region-guided stage and a constrained Pareto front (CPF)-focused search stage, the algorithm effectively improves search efficiency and solution quality under complex constraints. In the first stage, CT-FTREA introduces an adaptive constraint boundary tightening strategy based on the number of function evaluations. By gradually reducing the $\epsilon$-constraint boundaries, the population is guided from a relaxed search space toward the feasible region. This stage also employs objective-space reference points and a weighted fitness evaluation mechanism to select and evolve individuals, while an elite archive strategy preserves obtained feasible solutions, thereby enhancing the population’s ability to advance toward the feasible Pareto front. In the second stage, CT-FTREA exchanges the roles of the population and the archive, shifting the search focus to the fine-grained approximation of the CPF. An improved elite selection strategy combined with a differential evolution operator is used to generate offspring, adaptively balancing the exploration and exploitation capabilities of the population. The computational complexity of CT-FTREA is $O(F{E}_{max}\times N)$, where $F{E}_{max}$ is the maximum number of function evaluations and N is the population size. Extensive experiments on 28 benchmark instances and four real-world engineering problems show that CT-FTREA outperforms seven state-of-the-art algorithms. Specifically, it achieves the best IGD result with a 54% improvement and the best HV result with a 50% improvement over competing methods on the test problems. The algorithm also demonstrates statistically significant advantages in terms of convergence, solution quality, and robustness (Wilcoxon rank-sum test at a 0.05 significance level). CT-FTREA algorithm offers an efficient and robust solution to CMOPs, with competitive performance on both benchmark and real-world problems. Full article