Search Results (54)

The molecular management of elite-relevant lines in clonally exploited crops requires more than broad genetic structure alone. In Stevia rebaudiana, breeding materials derived from cv. ‘Morita II’ may retain useful variation while also concentrating molecularly similar lines, increasing redundancy within selection pipelines. This study assessed whether a reduced five-locus SSR dataset could provide an operational molecular-affinity framework for redundancy screening and breeding-context interpretation of previously identified elite-relevant lines in a ‘Morita II’-derived breeding collection. A curated five-locus SSR dataset comprising 85 genotypes from a tropical breeding programme was analysed using the Wang relatedness estimator, operational molecular-affinity classes, UPGMA clustering based on Wang-derived dissimilarity and permutation-based assessment of mean Wang relatedness. The collection combined a broad fraction of comparisons showing no detectable positive molecular affinity with a relevant high-affinity component, and this pattern differed between the two reference molecular strata. One subset showed a compact high-affinity profile and higher mean Wang relatedness than expected under random reassignment, whereas the other was dominated by comparisons with no detectable positive molecular affinity. Importantly, the five-locus SSR framework is interpreted here as an operational, locally validated decision-support tool rather than as genome-wide or pedigree-level relatedness inference. These findings suggest that reduced SSR-derived molecular-affinity information can complement phenotypic, physiological and clonal evaluations by providing redundancy context for line retention, clonal advancement, and parental-diversification decisions in tropical stevia breeding. Full article

This study addresses a modified continuous multi-reservoir scheduling problem characterized by a high-dimensional continuous decision space, strong time-varying storage constraints, strict terminal storage closure requirements, and a highly nonconvex composite objective. To solve this challenging problem, a multi-stage collaborative memetic algorithm based on CVT-MAP-Elites and clustering gradient (MCMA-CCG) is proposed. The framework consists of three tightly coupled stages: an exploration stage based on CVT-MAP-Elites to preserve diverse high-potential elites, a clustering stage using DBSCAN with a customized noise-retention strategy, and a refinement stage that combines DE with gradient-enhanced SLSQP to perform accurate exploitation. Under a unified experimental setting, MCMA-CCG was evaluated against several representative optimization algorithms, including DE, GA, SAPHTLR, HBMO, MFA, and MBWOHHO, over 30 independent runs. The updated results show that MCMA-CCG consistently achieves the best overall performance in both the four-cycle and five-cycle reservoir scheduling scenarios while also exhibiting superior empirical runtime and feasibility behavior. In the four-cycle case, it attained a best value of 6.08 × 10³, an average of 5.97 × 10³, and a standard deviation of 4.62 × 10¹; meanwhile, it produced feasible solutions in 26 of 30 runs, achieved a mean feasibility distance of 1.20 × 10⁻³, and required only 54.91 s on average under the 30,000-function-evaluation budget. In the more challenging five-cycle case, it attained a best value of 7.60 × 10³, an average of 7.44 × 10³, and a standard deviation of 7.55 × 10¹; it still generated feasible solutions in 19 of 30 runs, with a mean feasibility distance of 4.84 × 10⁻³ and an average runtime of 93.80 s under the 50,000-function-evaluation budget. By contrast, all baseline algorithms produced no fully feasible runs under the same feasibility criterion and generally required longer wall-clock time. Ablation studies further demonstrate that the superior performance of MCMA-CCG does not arise from any single module, but from the effective synergy among quality-diversity exploration, cluster-guided seed extraction, and gradient-assisted local refinement. These results confirm both the numerical superiority and the physical interpretability of the proposed framework for complex continuous multi-reservoir scheduling problems. Full article

To address the imbalance between global exploration and local exploitation in the secretary bird optimization algorithm (SBOA), this paper presents a multi-strategy improved version termed MSISBOA. The proposed approach incorporates optimal Latin hypercube sampling during initialization to achieve a more uniform distribution of initial solutions. In the hunting phase, an adaptive Cauchy mutation factor and a boundary strategy are integrated to refine local search precision. To reduce the risk of stagnation in local optima during later iterations, a triangular walk strategy is utilized for mutation perturbation. Furthermore, the escape phase employs a combined Tent chaotic-Gaussian mutation factor and an elite retention strategy to maintain high-quality solutions while diversifying the population. The performance of MSISBOA was evaluated using the benchmark suites released for the IEEE Congress on Evolutionary Computation (CEC), including CEC-2017 and CEC-2022, against nine other swarm intelligence algorithms, with statistical results showing that MSISBOA achieved the highest average rank. Additionally, the algorithm was applied to 18 engineering optimization problems to assess its capability in solving practical constrained tasks. Experimental results indicate that MSISBOA provides competitive convergence characteristics and solution quality across the tested scenarios. Full article

In the scenario where renewable energy sources (RESs) are connected to the power system (PS) through a flexible high-voltage direct current (HVDC) transmission system, their output becomes highly intermittent and volatile due to meteorological factors like wind direction and speed. This variability poses significant challenges to the real-time power balance and control of the PS. To address the uncertainties in system operation and the challenges of RES consumption, this paper proposes an artificial intelligence (AI) algorithm-driven collaborative optimization strategy for virtual power plants (VPPs) considering RESs transmitted by flexible HVDC. Firstly, a self-attention mechanism and multiple gated structures are integrated into a long short-term memory (LSTM) deep learning model. This enhancement improves the model’s ability to capture multi-timescale characteristics of RESs, increasing forecasting accuracy and robustness. Based on these forecasts, a total cost optimization model for VPP operation is developed, which includes high penalty costs for wind and solar curtailment. By embedding economic constraints that prioritize RESs usage, the model can reduce waste caused by traditional cost-driven scheduling. Additionally, to solve the high-dimensional nonlinear optimization problem in VPP scheduling, an improved population-based incremental learning (PBIL) algorithm is introduced. It incorporates an elite retention strategy and an adaptive mutation operator to boost global search efficiency and convergence speed. Simulations based on an VPP incorporating typical offshore wind and solar RESs transmitted via flexible HVDC demonstrate that the improved LSTM reduces MAPE by 7.14% for wind and 4.27% for PV compared to classical LSTM, and the proposed method achieves the lowest curtailment rates (wind 10.74%, PV 10.23%) and total cost (43,752 RMB), outperforming GA, PSO, and GW by 10–18% in cost reduction. Simulation results show that the proposed strategy enhances RESs consumption while maintaining system economy under flexible HVDC transmission. This work offers theoretical and practical insights for optimizing PS with high RES penetration and supports the low-carbon transition of new-type PS. Full article

Soil salinity remains a major constraint to rice productivity, particularly during early developmental stages when plants are highly sensitive to osmotic and ionic stress. In this study, we evaluated 201 genetically diverse rice genotypes from the 3K Rice Diversity Panel to investigate stage-specific mechanisms of salinity tolerance and develop machine learning-based predictive models for rapid phenotypic screening. Morphological and physiological traits were measured under control and saline conditions at germination and early seedling stages to derive Stress Tolerance Indices (STIs). The average membership function value (AMFV), calculated from multi-trait STI profiles, effectively captured variation in salinity responses and enabled classification of genotypes into five tolerance categories. Genome-wide association analysis using high-density SNP markers identified 36 significant marker–trait associations, including potentially novel SNPs on chromosomes 1 and 12. Several loci co-localized with candidate genes (LTR1, LGF1, OsCPS4, OsNCX7, and OsNHX4), while functional SNPs within genes (OsDRP2C, RLCK168, and OsMed37_2) and non-synonymous variants (qSVII11.1 and qSNaK3.1) further supported their candidacy in salinity tolerance. Mining favourable SNPs of causal genes identified superior multilocus combinations consistent with STI-based phenotypic patterns, with genotype 91-382 emerging as the strongest performer, exhibiting enhanced Na⁺ exclusion, K⁺ retention, and biomass resilience across developmental stages. To address multicollinearity among STI traits, we applied cross-validated LASSO (germination) and Elastic Net (early seedling) models, achieving high predictive accuracy and revealing a developmental shift from biomass-driven tolerance at germination to ion-regulatory processes at the seedling stage. Independent validation showed strong agreement between predicted and observed AMFVs. By integrating physiological indices, GWAS-derived SNP signals, and regularized machine learning approaches, this study provides a robust framework for identifying elite donors and accelerating breeding for salt-tolerant rice. Full article

The issue of emergency facility location is a long-term strategic issue, and the complexity and diversity of the decision-making environment force decision-makers to focus on multiple objectives when making location decisions. We develop a multi-objective optimization system centered on cost-effectiveness, service balance, and fairness, targeting three core objectives: minimizing total costs, minimizing differences in service quality among demand points, and minimizing material shortage gaps between demand points. To address the issue of limited facility service capacity induced by material shortages, we establish a multi-objective optimization model for the reliable location of emergency facilities. By combining the model’s characteristics with the Non-Dominated Sorting Genetic Algorithm (NSGA-II) and an elite retention strategy, the Pareto frontier solution set of the multi-objective model is obtained, and the model’s feasibility is verified through various examples of different scales. Finally, sensitivity analysis was conducted on the reliability location model of emergency facilities under different disruption risks using the control variable method, and the topology structure of the reliability location allocation network for emergency facilities under different disruption situations is obtained. The research findings provide decision-makers with actionable references and technical support for selecting reliable locations for emergency facilities amid disruption risks. Full article

When a fixed-wing Unmanned Aerial Vehicle (UAV) conducts All-Weather Post-Disaster Coverage Path Planning (PDCPP), the commonly used Sequential Path Coverage (SPC) method tends to generate redundant flight distance during turning transitions between adjacent coverage paths, which in turn increases the UAV’s flight energy consumption and thereby compromises the timeliness of rescue information acquisition. To address these challenges, this paper proposes a Multi-Selector Genetic Algorithm with Reinforcement Learning (MSGA-RL). It enhances population diversity through a distance-priority heuristic greedy initialization strategy, employs a multi-selector crossover operator to improve both solution diversity and convergence speed, and integrates a reinforcement learning-based individual retention mechanism with an elite pool protection strategy to prevent premature convergence. To simulate post-disaster scenarios, the disaster-affected area is modeled as a convex polygonal region with obstacles, while the flight energy consumption and stability of MSGA-RL are evaluated under different numbers of coverage paths. Simulation results indicate that, across all coverage path settings, MSGA-RL consistently achieves lower flight energy consumption than SPC, the Genetic Algorithm (GA), and the Dubins-based Enhanced Genetic Algorithm (DEGA), while exhibiting superior stability. In particular, in the convex quadrilateral scenario with 50 coverage paths, the flight energy consumption of MSGA-RL is reduced by 52.80%, 32.06%, and 15.96% compared with SPC, GA, and DEGA, respectively. Full article

With the large-scale integration of renewable energy, the operational complexity of power systems has increased, placing higher demands on the accuracy of load forecasting. To address the nonlinear characteristics of load variations and improve feature utilization, this paper proposes an IAVOA–EATCN load forecasting model. In the feature engineering stage, an expand–reduce transformation is employed to reconstruct the original multi-feature inputs, and variational mode decomposition (VMD) is further applied to extract low- and high-frequency components, thereby compressing redundant features while preserving essential information structures. In terms of model architecture, the nonlinear representation capability of the temporal convolutional network (TCN) is enhanced by introducing the FlexSwish activation function, and an Efficient Channel Attention (ECA) mechanism is integrated to strengthen the perception of critical features. For parameter optimization, an improved African Vulture Optimization Algorithm (IAVOA) is proposed, which initializes the population using perturbation-enhanced dynamic Tent mapping, balances global exploration and local exploitation through adaptive parameter control, and incorporates elite retention and migration mechanisms to avoid premature convergence. Experimental results on real-world load data demonstrate that the proposed model achieves RMSE, R², and MAE values of 26.5544, 0.9804, and 18.5589, respectively, significantly outperforming benchmark methods and exhibiting strong generalization capability and practical potential for intelligent load forecasting. Full article

This paper presents the Circle-Mapping Transition and Weighted Red-Billed Blue Magpie Optimizer (CTWRBMO), designed to address significant challenges in 3D path planning for drones. Although the original Red-Billed Blue Magpie Optimizer (RBMO) algorithm features a simple structure, few parameters, and strong local search capability, making it well-suited for UAV path optimization, it suffers from insufficient population diversity, limited global search ability, and a tendency to fall into local optima in complex high-dimensional scenarios. To overcome these limitations, four enhancement strategies are introduced. Firstly, the Circle chaotic mapping strategy leverages the randomness and ergodicity of chaotic sequences to generate an initial population that is uniformly distributed. This enhancement improves population diversity from the beginning and provides a solid foundation for global optimization. Secondly, the ε parameter is dynamically adjusted to prioritize local refinement during the early stages of optimization. This adjustment enables rapid convergence toward potentially optimal areas. This parameter increases to enhance global search capabilities as the algorithm progresses, thereby broadening the optimization space and achieving a dynamic equilibrium. Additionally, a nonlinear dynamic weighting factor (wd) is incorporated into the position update formula. The algorithm’s ability to escape local optima is significantly improved by dynamically altering the weight ratio between historical optimal positions and the current position. Furthermore, an elite perturbation mechanism based on individual neighborhoods is implemented to generate candidate solutions using local information. This mechanism enhances the algorithm’s local exploration capabilities and improves the stability of preserving optimal solutions, supported by a greedy criterion for optimal retention. Experimental results show that the CTWRBMO algorithm significantly outperforms comparison algorithms in terms of optimization accuracy and convergence speed, demonstrating exceptional global optimization capabilities. Additional applications in UAV 3D path planning simulations evaluated paths based on length, threat avoidance efficiency, and smoothness. The results indicate that paths planned using CTWRBMO are shorter, safer, and smoother compared to those generated by the Harrier Hawks Optimization (HHO), African Vulture Optimization Algorithm (AVOA), Artificial Bee Colony (ABC) Algorithm, and the traditional Magpie Algorithm, effectively meeting practical engineering requirements for UAV 3D path planning. Full article

Background: Neurofeedback training (NFT) enhances athletic performance through alpha modulation, but optimal inter-session intervals and individual response variability remain poorly understood. Objective: This is the first randomized controlled trial to systematically compare neurofeedback periodization (2-day vs. 3-day inter-session intervals) on neurophysiological adaptations, strength performance, and retention in elite judo athletes. Methods: Thirty-one national-level judokas completed 15 alpha enhancement sessions in 2-day (n = 12), 3-day (n = 12), or control (n = 7) groups, receiving pseudo-neurofeedback with randomized, non-contingent feedback. Primary outcomes included Frontal Alpha Index changes (ΔFAI; frontal alpha power modulation ratio) and squat performance (35–100% 1RM), with secondary assessment of 48/72 h retention and response phenotypes. Results: Mean ΔFAI was modest (E15G-2d: 0.005 ± 0.205; E15G-3d: 0.052 ± 0.202), with early peak responses followed by stabilization. E15G-3d demonstrated superior retention (90.2 ± 3.4% at 72 h vs. 76.8 ± 4.1% at 48 h; p < 0.001) despite similar peaks. Both training groups showed significant strength improvements versus controls (E15G-2d: 2.37 ± 0.66 reps; E15G-3d: 2.00 ± 0.53 reps), yet neurophysiological-performance correlations were non-significant (p > 0.072), indicating strength adaptations via mechanisms independent of alpha modulation. Three response phenotypes emerged (high: 29.0%, moderate: 51.6%, low: 19.4%), representing the first empirical classification of neurofeedback responsiveness in athletes. Conclusions: Three-day intervals uniquely optimize retention through enhanced consolidation, establishing evidence-based periodization guidelines for elite athletes. The dissociation between neural and performance adaptations challenges traditional neurofeedback theory, while individual heterogeneity necessitates personalized protocols for optimal NFT periodization. Full article

Background: Despite many young players showing strong potential, only a small fraction succeeds in the critical transition from youth to elite senior football. This scoping review synthesizes research on the junior-to-senior transition in men’s football, identifying main topics related with barriers and facilitators in the transition. Methods: Searches were performed in four databases (PubMed, Scopus, SPORTDiscus, and Web of Science) according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA, 2020) guidelines, using the following keywords: “football*” OR football AND talent* OR “talent identification” OR “talent development” OR expert* OR gift* AND “junior-to-senior” OR “transition career” or “athlete career transition” OR “transition phase”. Original articles in English focused on the junior-to-senior process in male footballers were included. Results: From 5307 titles, 35 studies met eligibility criteria. The most examined themes were psychosocial factors, including social support, stressors, and resilience. The reviewed studies identified organizational structure and effective club communication as facilitators and emphasized the importance of physical attributes to meet senior-level demands. Conclusions: Overall, the junior-to-senior transition is multifaceted, shaped by psychosocial, organizational, and physical factors. Despite robust research, gaps remain; future longitudinal and interdisciplinary studies should inform evidence-based strategies for optimizing player development and retention. Full article

Under the environment of globalized energy restructuring and achieving the goal of “peak carbon and carbon neutrality”, this paper proposes an optimal scheduling method based on the improved cosine algorithm for island microgrids, which relies on diesel generators, resulting in high carbon emissions and high operating costs. First, an optimal scheduling model for island microgrids is established with the objective of minimizing the system operating cost, which comprehensively considers the carbon emission penalty, power balance constraints, equipment operation constraints, and the volatility of renewable energy sources. Secondly, the traditional sine–cosine algorithm is improved by introducing an adaptive adjustment factor, elite retention strategy and chaotic mapping initialization population in order to solve its shortcomings of falling into local optimums and insufficient convergence accuracy when solving high-dimensional complex problems. Finally, the effectiveness of the proposed method is verified by simulation experiments. The results show that the method in this paper reduces the total system cost to 2994.2 yuan (6.5% lower than the baseline scenario), reduces the carbon emission to 968.8 kg (55.1% lower), and improves the wind and light consumption rate to 98.84%, which is an obvious advantage and provides a theoretical basis and technical support for the realization of the low-carbon and economic operation of island microgrids. Full article

To address the “metering blind zone” problem in distribution networks caused by flood disasters, this paper proposes an optimal deployment strategy for low-voltage instrument transformers (LVITs) based on time-varying risk assessment. A comprehensive model quantifying real-time node importance during disaster progression is established, considering cascading faults and dynamic load fluctuations. A multi-objective optimization model minimizes deployment costs while maximizing fault coverage, incorporating dynamic response constraints. A Genetic-Greedy Hybrid Algorithm (GGHA) with intelligent initialization and elite retention mechanisms is proposed to solve the complex spatiotemporal coupling problem. Simulation results demonstrate that GGHA achieves solution quality of 0.847, outperforming PSO, GA, and GD by 7.5%, 11.7%, and 8.7%, respectively, with convergence stability within ±2.5%. The strategy maintains 100% normal coverage and 73.3–95.5% disaster coverage across flood severity levels, exhibiting strong feasibility and generalizability on IEEE 123-node and 33-node test systems. Full article

Purpose: The aim of this review is to provide an overview of the effects of commonly used dietary supplements on soccer performance and to bridge the gap between scientific evidence and their practical application by practitioners working with elite soccer players. Methods: Relevant literature involving dietary supplement use in soccer players was identified through searches of PubMed, EMBASE, Scopus, and Web of Science. Additionally, insights were gathered from a cross-sectional online questionnaire completed by practitioners (nutritionists, physicians, sport scientists, strength and conditioning coaches, and heads of performance) working with first-division men’s teams across five European leagues. Eligible respondents were over 18 years old with >2 years of experience in elite sport. The 20-question survey, designed on Qualtrics and pilot-tested for content validity, covered practitioner background, beliefs about supplementation, and real-world practices. The study was approved by the Ethical Independent Committee in Genoa, Italy (Ref. 2020/12). Results: Among performance-enhancing supplements, caffeine has been shown to improve endurance, sprint performance, power, and cognitive function, while creatine consistently enhances short-duration, high-intensity efforts. Beta-alanine and sodium bicarbonate help reduce the buildup of acidity in muscles during repeated high-intensity exercise, supporting repeated sprint performance. For hydration and endurance support, dietary nitrates improve blood flow and oxygen delivery to muscles, and glycerol enhances fluid retention in hot environments and during compressed match schedules, where players compete in multiple matches within a short recovery window. Regarding recovery aids, protein and tart cherry supplementation have been shown to accelerate recovery, reduce muscle damage, and support training adaptations. Field insights revealed that creatine and caffeine were widely adopted by practitioners (>90%), with protein powders also commonly recommended (>80%). In contrast, beta-alanine, tart cherry, and dietary nitrates were only partially integrated into daily practice (30%, 32%, and 48.5%, respectively), while sodium bicarbonate (24%) and glycerol (10.5%) were used by a minority. Conclusions: Although scientific evidence provides a strong foundation for the efficacy of dietary supplements, their translation into elite soccer practice is shaped by a range of practical factors, including cultural resistance, taste preferences, gastrointestinal side effects, established team routines, and individual player preferences. These findings highlight the importance of targeted education for players and staff, individualized supplementation plans, and close collaboration between nutritionists, coaches, and medical teams. However, our survey did not directly assess reasons for non-implementation. In addition to practical barriers reported by practitioners, unfamiliarity with current evidence likely contributes to this evidence–practice gap. Full article

The evolution of remote sensing technology has led to significant improvements in high-resolution and hyperspectral image acquisition, enhancing applications like environmental monitoring and disaster assessment. However, the high dimensionality, nonlinearity, and heterogeneity of these images pose challenges for intelligent interpretation. While deep learning models (e.g., CNN) require balancing efficiency and parameter optimization, meta-heuristic algorithms establish self-organizing, parallelized search mechanisms capable of achieving asymptotic approximation towards the global optimum of parameters without requiring gradient information. In this paper, we first propose an improved Gannet Optimization Algorithm (GOA), named TEGOA, which uses the T-distribution perturbation and elite retention to address CNN’s parameter dependency. The experiment on CEC2017 shows that TEGOA has a better performance on composition functions. Hence, it is suitable for solving complex optimization problems. Then, we propose a classification model TEGOA-CNN, which combines TEGOA with CNN to increase the accuracy and efficiency of remote sensing sence classification. The experiments of TEGOA-CNN on two well-known datasets, UCM and AID, showed a higher performance in classification accuracy of remote sensing images. Particularly, TEGOA-CNN achieves 100% classification accuracy on 10 out of the 21 surface categories of UCM. Full article