Search Results (100)

The aim was to investigate the genetic effects of a SNP located in the precursor region of gga-miR-3528. (1) Single-nucleotide polymorphisms within precursor regions of microRNAs play crucial biological roles. (2) Utilizing a Gushi–Anka F₂ resource population (n = 860), we screened and validated miRNA SNPs. A SNP mutation in the miR-3528 precursor region was identified. Specific primers were designed to amplify the polymorphic fragment. Genotyping was performed for this individual SNP across the population, using the MassArray system. Association analyses were conducted between this SNP and chicken growth and body measurement traits, carcass traits, meat quality traits, and serum enzyme activities. (3) The rs14098602 (+12 bp A > G) was identified within the precursor region of gga-miR-3528. Significant associations (p < 0.05) were observed between this SNP and chicken growth traits (body weight at the age of 0 day, body weight at the age of 2 weeks, and body weight at the age of 4 weeks), carcass traits (evisceration weight), meat quality traits (subcutaneous fat rate and pectoral muscle density), and serum enzyme activities (total protein, albumin, globulin, cholinesterase, and lactate dehydrogenase). (4) These findings suggest that the polymorphism at rs14098602 may influence chicken growth, meat quality, and serum biochemical indices, through specific mechanisms. The gga-miR-3528 gene likely plays an important role in chicken development. Therefore, this SNP can serve as a molecular marker for genetic breeding and auxiliary selection of growth-related traits, facilitating the rapid establishment of elite chicken populations with superior genetic resources. Full article

In lace textile manufacturing, the dyeing process in parallel machine environments faces challenges from sequence-dependent setup times due to color family transitions, machine eligibility constraints based on weight capacities, and probabilistic re-dyeing operations arising from quality inspection failures, which often lead to increased tardiness. To tackle this multi-constrained problem, a stochastic integer programming model is formulated to minimize total estimated tardiness. A novel symmetry-driven two-population collaborative differential evolution (TCDE) algorithm is then proposed. It features two symmetrically complementary subpopulations that achieve a balance between global exploration and local exploitation. One subpopulation employs chaotic parameter adaptation through a logistic map for symmetrically enhanced exploration, while the other adjusts parameters based on population diversity and convergence speed to facilitate symmetry-aware exploitation. Moreover, it also incorporates a symmetrical collaborative mechanism that includes the periodic migration of top individuals between subpopulations, along with elite-set guidance, to enhance both population diversity and convergence efficiency. Extensive computational experiments were conducted on 21 small-scale (optimally validated via CVX) and 15 large-scale synthetic datasets, as well as 21 small-scale (similarly validated) and 20 large-scale industrial datasets. These experiments demonstrate that TCDE significantly outperforms state-of-the-art comparative methods. Ablation studies also further verify the critical role of its symmetry-based components, with computational results confirming its superiority in solving the considered problem. Full article

Background: In recent years, comprehensive analyses using a genome-wide association study (GWAS) have been conducted to identify genetic factors related to athletic performance. In this study, we investigated the association between genetic variants and elite wrestling status across multiple ethnic groups using a genome-wide genotyping approach. Methods: This study included 168 elite wrestlers (64 Japanese, 67 Turkish, and 36 Russian), all of whom had competed in international tournaments, including the Olympic Games. Control groups consisted of 306 Japanese, 137 Turkish, and 173 Russian individuals without elite athletic backgrounds. We performed a GWAS comparing allele frequencies of single-nucleotide polymorphisms (SNPs) between elite wrestlers and controls in each ethnic cohort. Cross-population analysis comprised (1) identifying SNPs with nominal significance (p < 0.05) in all three groups, then (2) meta-analyzing overlapped SNPs to assess effect consistency and combined significance. Finally, we investigated whether the most significant SNPs were associated with gene expression in skeletal muscle in 23 physically active men. Results: The GWAS identified 328,388 (Japanese), 23,932 (Turkish), and 30,385 (Russian) SNPs reaching nominal significance. Meta-analysis revealed that the ATP2A3 rs6502758 and UNC5C rs265061 polymorphisms were associated (p < 0.0001) with elite wrestling status across all three populations. Both variants are located in intronic regions and influence the expression of their respective genes in skeletal muscle. Conclusions: This is the first study to investigate gene polymorphisms associated with elite wrestling status in a multi-ethnic cohort. ATP2A3 rs6502758 and UNC5C rs265061 polymorphisms may represent important genetic factors associated with achieving an elite status in wrestling, irrespective of ethnicity. Full article

Bacterial blight (BB) disease is a serious stress that affects up to 80% of rice yield. Utilizing an elite resistant variety was previously thought to be an alternative way to control disease outbreaks. The indigenous upland rice variety ULR207 is a high-potential donor for the BB resistance breeding program. However, the quantitative trait loci (QTLs) associated with bacterial blight resistance in this variety have not yet been discovered. Therefore, QTLs associated with BB resistance need to be identified. In this study, we identified the QTLs associated with BB resistance in the F_2:3 population crossed between the BB resistance variety ULR207 and Maled Phai, as well as a susceptible variety, via QTL-seq analysis and bulk-segregant analysis. We found a new QTL-associated BB resistance locus (qBBchr8) mapped on chromosome 8. Five positions were candidates, including Os08g0110700, Os08g0115200, Os08g0131300, Os08g0139500, and Os08g0163900. Afterwards, Kompetitive Allele-Specific PCR (KASP) markers specific to the SNP variant and the position of each gene were designed. These markers, associated with the disease lesion length phenotype, were validated with another 178 individual plants of the F₂ population via single-marker analysis. This analysis revealed that the position Os08g0110700 was the strongest locus, with a PVE of 15.00%. The results suggest that this KASP SNP marker could be used to improve elite rice for BB resistance. Full article

The honey badger algorithm (HBA) has gained significant attention as a metaheuristic optimization method; however, despite these design strengths, it still faces challenges such as premature convergence, suboptimal exploration–exploitation balance, and low population diversity. To address these limitations, we integrate a power-law degree distribution (PDD) topology into the HBA population structure. Three improved versions of the HBA are proposed, with each employing different population update strategies: PDDHBA-R, PDDHBA-B, and PDDHBA-H. In the PDDHBA-R strategy, individuals randomly select neighbours as references, promoting diversity and randomness. The PDDHBA-B strategy allows individuals to select the best neighbouring individual, speeding up convergence. The PDDHBA-H strategy combines both approaches, using random selection for elite individuals and best selection for non-elite individuals. These algorithms were tested on 30 benchmark functions from CEC2017, 21 real-world problems from CEC2011, and four constrained engineering problems. The experimental results show that all three improvements significantly improve the performance of the HBA, with PDDHBA-H delivering the best results across various tests. Further analysis of the parameter sensitivity, computational complexity, population diversity, and exploration–exploitation balance confirms the superiority of PDDHBA-H, highlighting its potential for use in complex optimization problems. Full article

Flower collection constitutes a critical segment of the flower logistics chain, and its efficiency significantly influences the industry. However, the energy consumption and carbon emissions that occur in the flower collection process present a great challenge for realizing efficient flower collection. To this end, this study proposes a green vehicle routing planning model that incorporates multiple factors, such as fixed costs, refrigeration costs, transportation costs, and so on, to minimize the total costs under hard time window constraints. Moreover, a Genetic Neighborhood Comprehensive Spider Wasp Algorithm (GN_CSWA) is proposed to find the solution to this problem. The random generation and the nearest neighbor algorithms are employed to construct the initial solution, followed by roulette selection, elite selection, and a best individual retention strategy to refine the population for the next iteration. A crossover operator is applied to facilitate global exploration, while six neighborhood search operators are applied to further enhance the quality of the solution. Moreover, to prevent the algorithm from converging to a local optimum, two mutation operators are introduced to generate new solutions. The effectiveness of the proposed optimizer is validated through extensive experimental results. Full article

A multi-strategy enhanced version of the escape algorithm (mESC, for short) is proposed to address the challenges of balancing exploration and development stages and low convergence accuracy in the escape algorithm (ESC). Firstly, an adaptive perturbation factor strategy was employed to maintain population diversity. Secondly, introducing a restart mechanism to enhance the exploration capability of mESC. Finally, a dynamic centroid reverse learning strategy was designed to balance local development. In addition, in order to accelerate the global convergence speed, a boundary adjustment strategy based on the elite pool is proposed, which selects elite individuals to replace bad individuals. Comparing mESC with the latest metaheuristic algorithm and high-performance winner algorithm in the CEC2022 testing suite, numerical results confirmed that mESC outperforms other competitors. Finally, the superiority of mESC in handling problems was verified through several classic real-world optimization problems. Full article

To address the challenges of low optimization efficiency and premature convergence in existing algorithms for unmanned aerial vehicle (UAV) 3D path planning under complex operational constraints, this study proposes an enhanced honey badger algorithm (LRMHBA). First, a three-dimensional terrain model incorporating threat sources and UAV constraints is constructed to reflect the actual operational environment. Second, LRMHBA improves global search efficiency by optimizing the initial population distribution through the integration of Latin hypercube sampling and an elite population strategy. Subsequently, a stochastic perturbation mechanism is introduced to facilitate the escape from local optima. Furthermore, to adapt to the evolving exploration requirements during the optimization process, LRMHBA employs a differential mutation strategy tailored to populations with different fitness values, utilizing elite individuals from the initialization stage to guide the mutation process. This design forms a two-population cooperative mechanism that enhances the balance between exploration and exploitation, thereby improving convergence accuracy. Experimental evaluations on the CEC2017 benchmark suite demonstrate the superiority of LRMHBA over 11 comparison algorithms. In the UAV 3D path planning task, LRMHBA consistently generated the shortest average path across three obstacle simulation scenarios of varying complexity, achieving the highest rank in the Friedman test. Full article

This paper addresses the time-dependent vehicle routing problem with drones in vehicle-restricted zones and no-fly zones (TDVRPD-VRZ-NFZ). The optimization model considers the impacts of vehicle-restricted zones, no-fly zones, and time-dependent road networks on delivery paths. The objective is to minimize the total cost, including vehicle dispatch costs, energy consumption costs for vehicles and drones, and time-window penalty costs. The model is verified for correctness using Gurobi. In response to the problem’s characteristics, a hybrid genetic algorithm and variable neighborhood search with a learning mechanism (HGAVNS-LM) is proposed to solve the problem. The algorithm starts by generating the initial population using a combination of logistic mapping and reverse learning. It then improves the genetic operators and variable neighborhood search operators to optimize the initial population. To improve the algorithm’s performance, an individual elite archive is used for knowledge learning, and a self-learning mechanism is established to dynamically adjust the algorithm’s key parameters. The solution obtained by HGAVNS-LM shows a deviation of −0.2% to −0.3% compared to Gurobi, but it saves 99.68% in solving time. Compared to the genetic neighborhood search algorithm and the hybrid genetic algorithm, the improvement rates are 5.1% and 13.0%, respectively. Through the analysis of multiple sets of test cases, it is concluded that time-varying road networks, vehicle-restricted zones and no-fly zones, and different detour rules all affect delivery costs and delivery plans. The research results provide a more scientific theoretical basis for logistics companies to customize delivery solutions. Full article

An equation-derived body fat estimator, namely the Clínica Universidad de Navarra Body Adiposity Estimator (CUN-BAE), was established to assess the body fat percentage in adults. However, its efficiency compared to that of the bioelectrical impedance analysis (BIA) approach remains under-researched. This study aimed to assess the agreement between the body fat percentages measured using a BIA and estimated using the CUN-BAE in a sample of Lithuanian professional athletes. A single cross-sectional study was conducted using the BIA technique to measure and the CUN-BAE equation to calculate the body fat percentages of 323 study participants. The Bland–Altman plot system was applied to comparing both the body fat percentages estimated using the CUN-BAE equation and those obtained via the BIA approach. The average values of the body fat percentages found in the total sample of elite athletes and estimated using the BIA and CUN-BAE equaled 18.4 ± 5.3% and 18.7 ± 6.6%, respectively (ICC: 0.91; 95% confidence interval (CI): 0.88; 0.93). This study found that the CUN-BAE method overestimated the BIA’s calculation of the body fat percentages by 2.7% on average. Meanwhile, the comparison of adiposity in the athletes using the CUN-BAE equation and the BIA methods demonstrated a similar, although not identical, accuracy. The BIA method cannot be replaced by the CUN-BAE equation in routine sports medicine practice due to moderately sized limits of agreement (95% CI: −6.5; 7.1), even when the access to body fat measurement devices is limited. From a public health perspective, the outcomes derived from the CUN-BAE equation can possibly be extrapolated to females and to individuals competing in strength–power sports, as well as to populations of adults. Full article

To balance the diversity and stringency of Pareto solutions in multi-objective optimization, this paper introduces a multi-objective White Shark Optimization algorithm (MONSWSO) tailored for multi-objective optimization. MONSWSO integrates non-dominated sorting and crowding distance into the White Shark Optimization framework to select the optimal solution within the population. The uniformity of the initial population is enhanced through a chaotic reverse initialization learning strategy. The adaptive updating of individual positions is facilitated by an elite-guided forgetting mechanism, which incorporates escape energy and eddy aggregation behavior inspired by marine organisms to improve exploration in key areas. To evaluate the effectiveness of MONSWSO, it is benchmarked against five state-of-the-art multi-objective algorithms using four metrics: inverse generation distance, spatial homogeneity, spatial distribution, and hypervolume on 27 typical problems, including 23 multi-objective functions and 4 multi-objective project examples. Furthermore, the practical application of MONSWSO is demonstrated through an example of optimizing the design of subway tunnel foundation pits. The comprehensive results reveal that MONSWSO outperforms the comparison algorithms, achieving impressive and satisfactory outcomes. Full article

This work effectively modifies APSM-jSO (a novel jSO variant with an adaptive parameter selection mechanism and a new external archive updating mechanism) to offer a new jSO (single objective real-parameter optimization: Algorithm jSO) version called NLAPSMjSO-EDA. There are three main distinctions between NLAPSMjSO-EDA and APSM-jSO. Firstly, in the linear population reduction strategy, the number of individuals eliminated in each generation is insufficient. This results in a higher number of inferior individuals remaining, and since the total number of iterations is fixed, these inferior individuals will also consume iteration counts for their evolution. Therefore, it is essential to allocate more iterations to the elite population to promote the emergence of superior individuals. The nonlinear population reduction strategy effectively addresses this issue. Secondly, we have introduced an Estimation of Distribution Algorithm (EDA) to sample and generate individuals from the elite population, aiming to produce higher-quality individuals that can drive the iterative evolution of the population. Furthermore, to enhance algorithmic diversity, we increased the number of individuals in the initial population during subsequent experiments to ensure a diverse early population while maintaining a constant total number of iterations. Symmetry plays an essential role in the design and performance of NLAPSMjSO-EDA. The nonlinear population reduction strategy inherently introduces a form of asymmetry that mimics natural evolutionary processes, favoring elite individuals while reducing the influence of inferior ones. This asymmetric yet balanced approach ensures a dynamic equilibrium between exploration and exploitation, aligning with the principles of symmetry and asymmetry in optimization. Additionally, the incorporation of EDA utilizes probabilistic symmetry in sampling from the elite population, maintaining structural coherence while promoting diversity. Such applications of symmetry in algorithm design not only improve performance but also provide insights into balancing diverse algorithmic components. NLAPSMjSO-EDA, evaluated on the CEC 2017 benchmark suite, significantly outperforms recent differential evolution algorithms. In conclusion, NLAPSMjSO-EDA effectively enhances the overall performance of APSM-jSO, establishing itself as an outstanding variant combining jSO and EDA algorithms. The algorithm code has been open-sourced. Full article

Origen’s theology is fundamentally rooted in the question of whether he upheld the pre-existence of the soul or focused more on the soul’s superiority over the body and its perfection. While inheriting many ideas from Plato, Origen adapted them in accordance with Christian doctrine. Both Origen and Plato emphasized that the soul governs the body and is superior to it in both status and importance. The image of God resides in human soul, not the body, guiding individuals toward the perfection of the soul and the attainment of the whole virtues. Origen’s tripartite distinction of spirit, soul, and body is intrinsically connected to Plato’s tripartite theory of the soul, with the intermediary of the incarnate soul corresponding to the ambiguous role of thumos (spiritedness) in Plato’s dialogue. This suggests that humans are capable of both good and evil, a potential grounded in free will rather than the sin of the body. Nevertheless, Origen assigned the body a more important role, asserting that the Incarnation not only depended on the body but also facilitated the practice of virtue, positioning the body as central to his theory of resurrection. Origen also adopted Plato’s epistemology, teleology of knowledge, and theory of participation. He emphasized that the perfection of the soul requires liberation from the dominance of the senses, using Plato’s dialectical method and drawing inspiration from the Holy Spirit to achieve comprehensive knowledge and spiritual maturity. Origen should not be viewed as merely a Platonist or an anti-Platonist. Both he and Plato shared concerns about the correct way of life and perfect knowledge, and both sought to bridge the gap between the majority and the minority, avoiding both elitism and populism. Full article

(1) Background: Populist radical right-wing parties and politicians have used conspiracy theories to perpetuate the antagonism between an evil elite conspiring against the good and unknowing people. Yet, less is known about whether and to what extent radical right-wing populism at the individual level is associated with different conspiracy beliefs. This analysis explores how the main components of radical right-wing populism—populist, nativist, and authoritarian attitudes—relate to both a general conspiracy mentality and specific conspiracy theories prevalent in political discourse. (2) Methods: Using data from an original 2023 online survey conducted in Austria, a stronghold of the populist radical right, this study includes new questions on immigration, COVID-19, and climate change, as well as a conspiracy mentality scale. (3) Results: The analyses reveal that all the main components are positively associated with different conspiracy beliefs, albeit to varying degrees. Across models, the strongest predictor is populism, followed by nativism and authoritarianism. Nativism varies the most across different conspiracy beliefs and is particularly associated with the belief in conspiracy theories related to immigration and climate change. (4) Conclusions: The results highlight the prevalence of radical right-wing populist attitudes across various conspiracy beliefs, reflecting how populist radical right-wing actors leverage conspiracy theories in their political discourse. Full article

To address the shortcomings of the Dung Beetle Optimization (DBO) algorithm in ship power-system fault reconfiguration, such as low population diversity and an imbalance between global exploration and local exploitation, the authors of this paper propose an improved Dung Beetle Optimization (LESSDBO) algorithm. The improvements include optimizing the initial population using Latin hypercube sampling and an elite population strategy, optimizing parameters with an improved sigmoid activation function, introducing the sine–cosine algorithm (SCA) for position update optimization, and performing multi-population mutation operations based on individual quality. The LESSDBO algorithm was applied to simulate the fault reconfiguration of a ship power system, and it was compared with the traditional DBO, Genetic Algorithm (GA), and Modified Particle Swarm Optimization (MSCPSO) methods. The simulation results showed that LESSDBO outperformed the other algorithms in terms of convergence accuracy, convergence speed, and global search capability. Specifically, in the reconfiguration under Fault 1, LESSDBO achieved optimal convergence in seven iterations, reducing convergence iterations by more than 30% compared with the other algorithms. In the reconfiguration under Fault 2, LESSDBO achieved optimal convergence in eight iterations, reducing convergence iterations by more than 23% compared with the other algorithms. Additionally, in the reconfiguration under Fault Condition 1, LESSDBO achieved a minimum of four switch actions, which is 33% fewer than the other algorithms, on average. In the reconfiguration under Fault Condition 2, LESSDBO achieved a minimum of eight switch actions, which is a 5.9% reduction compared with the other algorithms. Furthermore, LESSDBO obtained the optimal reconfiguration solution in all 50 trials for both Faults 1 and 2, demonstrating a 100% optimal convergence probability and significantly enhancing the reliability and stability of the algorithm. The proposed method effectively overcomes the limitations of the traditional DBO in fault reconfiguration, providing an efficient and stable solution for the intelligent topology reconfiguration of ship power systems. Full article