Search Results (291)

This study evaluated the genetic parameters for growth and Vibrio parahaemolyticus (Vp_AHPND) resistance in both the introduced MK strain and the self-constructed GK strain of Penaeus vannamei, investigating the impact of genotyped female parents on trait estimates under a single-parent nested mating design. A total of 32 families from the MK strain and 44 families from the GK strain were analyzed. Fifty-four female parents from both strains were genotyped using the “Yellow Sea Chip No. 1” containing 10.0 K SNPs. In the MK strain, heritability estimates ranged from 0.439 to 0.458 for body weight (Bw) and from 0.308 to 0.489 for survival time (ST) and survival rates at 36 h (36 SR), 50% mortality (SS₅₀), and 60 h (60 SR). In the GK strain, heritability for Bw ranged from 0.724 to 0.726, while ST, 36 SR, SS₅₀, and 60 SR had heritability estimates between 0.370 and 0.593. Genetic correlations between Bw and ST were 0.601 to 0.622 in the MK strain and 0.742 to 0.744 in the GK strain. For Bw and survival rates, correlations ranged from 0.120 to 0.547 in the MK strain and from 0.426 to 0.906 in the GK strain. The genetic correlation between ST and survival rates was not significantly different from 1 (p > 0.05) in both strains. High Pearson correlations (0.853 to 0.997, p < 0.01) were observed among survival rates at different points. Predictive accuracies for Bw, ST, and survival rates using single-step genomic best linear unbiased prediction (ssGBLUP) were comparable to pedigree-based best linear unbiased prediction (pBLUP) in the MK strain, while in the GK strain, ssGBLUP improved predictive accuracies for Bw, ST, and SS₅₀ by 0.20%, 0.32%, and 0.38%, respectively. The results indicate that both growth and Vp_AHPND resistance have significant breeding potential. Although the genetic correlation between weight and resistance varies across different populations, there is a positive genetic correlation between these traits, supporting the feasibility of multi-trait selection. To enhance genetic accuracy, breeding programs should include more genotyped progeny. These findings also suggest that infection frequency and observation time influence resistance performance and breeding selection, emphasizing the need for a tailored resistance evaluation program to improve breeding efficiency and reduce costs. Full article

Desiccation-induced cracking in clay soils significantly affects the structural performance and durability of geotechnical systems. This study presents a data-driven approach to predict the Crack and Shrinkage Intensity Factor (CSIF), a comprehensive index quantifying both crack formation and shrinkage behavior in drying soils. A database of 100 controlled desiccation tests was developed using five clay mixtures with varying plasticity indices, which were subjected to a range of drying rates, soil thicknesses and initial conditions. Four predictive models—Multiple Linear Regression (MLR), Classification and Regression Random Forest (CRRF), Artificial Neural Network (ANN) and Genetic Programming (GP)—were evaluated. The ANN model using Bayesian Regularization demonstrated superior performance (R = 0.99, MAE = 5.44), followed by CRRF and symbolic GP equations. Sensitivity analysis identified drying rate and soil thickness as the most influential parameters, while initial moisture content and ambient conditions were found to be redundant when the drying rate was included. This study not only advances the predictive modeling of desiccation cracking but also introduces interpretable equations for practical engineering uses. The developed models offer valuable tools for crack risk assessment in clay liners, soil covers and expansive soil foundations. Full article

The improvement of carcass traits is a key focus in pig genetic breeding programs. To identify quantitative trait loci (QTLs) and genes linked to key carcass traits, we conducted a genome-wide association study (GWAS) using whole-genome sequencing data from 1118 commercial pigs (Duroc sires and Yorkshire/Landrace F1 dams). This study focused on six phenotypes: iodine value, belly firmness, belly side fat, total side thickness (belly SThK), belly subcutaneous fat (Subq), and belly seam. Phenotypes were measured using image analysis, DEXA, and fatty acid profiling, and genotyping was performed using low-pass sequencing (SkimSeq). After quality control, 18,911,793 single nucleotide polymorphisms (SNPs) were retained for further analysis. A GWAS was conducted using a linear mixed model implemented in GCTA. Key findings include a significant QTL on SSC15 (110.83–112.23 Mb), which is associated with the iodine value, containing genes such as COX15, CHUK, SCD, and HIF1AN, which have known roles in fatty acid metabolism. Additionally, PNKD, VIL1, and PRKAG3 (120.74–121.88 Mb on SSC15) were linked to belly firmness, influencing muscle structure and fat composition. Three QTLs for belly side fat were identified on SSC1, SSC2, and SSC3, highlighting genes like SLC22A18, PHLDA2, and OSBPL5, which regulate fat deposition and lipid metabolism. The results provide novel molecular markers that can be incorporated into selective breeding programs to improve pork quality, fat distribution, and meat composition. These findings enhance our understanding of the genetic mechanisms underlying carcass belly traits while offering tools to improve pork quality, optimize fat composition, and align with consumer preferences in the meat production industry. Full article

With the rapid development of industrial internet, blockchain, and other new-generation information technology, the shared manufacturing model provides a new way to address the problems of low resource utilization of the traditional manufacturing industry and serious duplication of construction through the mechanism of collaborative resource sharing. Concurrently, to meet the requirements of sustainable development, manufacturing enterprises need to balance economic efficiency with production efficiency in their production practices. This study investigates an identical parallel machine offline scheduling problem with rental costs and shared service costs of shared machines. In machine renting, manufacturers with a certain number of identical parallel machines will incur fixed rental costs, unit variable rental costs, and shared service costs when renting the shared machines. The objective is to minimize the sum of the makespan and total sharing costs. To address this problem, an integer linear programming model is established, and several properties of the optimal solution are provided. A heuristic algorithm based on the number of rented machines is designed. Finally, numerical simulation experiments are conducted to compare the proposed heuristic algorithm with a genetic algorithm and the longest processing time (LPT) rule. The results demonstrate the effectiveness of the proposed heuristic algorithm in terms of calculation accuracy and efficiency. Additionally, the experimental findings reveal that the renting and scheduling results of the machines are influenced by various factors, such as the manufacturer’s production conditions, the characteristics of the jobs to be processed, production objectives, rental costs, and shared service costs. Full article

Effective route and speed decision-making can significantly reduce vessel operating costs and emissions. However, existing optimization methods developed for conventional fuel-powered vessels are inadequate for application to methanol dual-fuel ships, which represent a new energy vessel type. To address this gap, this study investigates the operational characteristics of methanol dual-fuel liners and develops a mixed-integer nonlinear programming (MINLP) model aimed at minimizing operating costs. Furthermore, an improved genetic algorithm (GA) integrated with the Nonlinear Programming Branch-and-Bound (NLP-BB) method is proposed to solve the model. The case study results demonstrate that the proposed approach can reduce operating costs by more than 15% compared to conventional route and speed strategies while also effectively decreasing emissions of CO₂, NOx, SOx, PM, and CO. Additionally, comparative experiments reveal that the designed algorithm outperforms both the GA and the Linear Interactive and General Optimizer (LINGO) solver for identifying optimal route and speed solutions. This research provides critical insights into the operational dynamics of methanol dual-fuel vessels, demonstrating that traditional route and speed optimization strategies for conventional fuel vessels are not directly applicable. This study provides critical insights into the optimization of voyage decision-making for methanol dual-fuel vessels, demonstrating that traditional route and speed optimization strategies designed for conventional fuel vessels are not directly applicable. It further elucidates the impact of methanol fuel tank capacity on voyage planning, revealing that larger tank capacities offer greater operational flexibility and improved economic performance. These findings provide valuable guidance for shipping companies in strategically planning methanol dual-fuel operations, enhancing economic efficiency while reducing vessel emissions. Full article

This study proposes a nonlinear 0-1 mixed-integer programming model for optimizing the location of logistics distribution centers within the Hohhot–Baotou–Ordos–Ulanqab urban agglomeration, integrating transportation costs, carbon emissions, and operational coefficients. The optimization problem is solved using a genetic algorithm (GA), whose robustness is systematically validated through comparative analyses with linear programming (LP) and alternative heuristic optimization methods including simulated annealing (SA) and particle swarm optimization (PSO). Comprehensive sensitivity analyses are conducted on critical parameters—including transportation costs, demand fluctuations, carbon pricing mechanisms, the logistics center capacity, land use impact, and water resource constraints—to evaluate the model’s adaptability under diverse operational scenarios. The research methodology incorporates environmental impact factors, including carbon emission costs, land resource utilization, and water resource management, thereby extending traditional optimization frameworks to address region-specific ecological sensitivity concerns. The empirical results demonstrate that the optimized location configuration significantly reduces logistics operational costs while simultaneously enhancing both the economic efficiency and environmental sustainability, thus fostering regional economic coordination. This study makes several key contributions: (1) developing an integrated decision-making framework that balances economic efficiency and environmental sustainability; (2) systematically incorporating environmental impact factors into the optimization model; (3) establishing calibration methods specifically tailored for ecologically sensitive regions; and (4) demonstrating the potential for the synergistic optimization of economic and environmental objectives through strategic logistics network planning. Full article

Husk number (HN) trait is an important factor affecting maize kernel dehydration rate after the physiological maturity stage. In general, a reasonable reduction in HN is a key target sought for breeding maize varieties that are suitable for mechanized harvesting. In this study, the HN of a maize natural population panel containing 232 inbred lines was analyzed, and the results showed a broad-sense heritability of 0.89, along with a wide range of phenotypic variation. With the best linear unbiased prediction (BLUP) values across the three environments, a genome-wide association study (GWAS) was conducted using 995,106 single-nucleotide polymorphism (SNP) markers. A total of 16 SNPs significantly associated with HN were identified by the mixed linear model and general linear model using the TASSEL 5.0 software program. A local linkage disequilibrium (LD) study was performed to infer the candidate interval around the lead SNPs. A total of 19 functionally annotated genes were identified. The candidate genes were divided into multiple functional types, including transcriptional regulation, signal transduction, and metabolic and cellular transport. These results provide hints for the understanding of the genetic basis of the HN trait and for the breeding of maize varieties with fewer HN and faster dehydration rate. Full article

Earth observation satellites, particularly agile Earth observation satellites (AEOSs) with enhanced attitude maneuverability, have become increasingly crucial in emergency response and disaster monitoring operations. Efficient mission planning for densely distributed ground targets with diverse priorities poses significant challenges, especially when considering strict attitude maneuver constraints and time-sensitive requirements. To address these challenges, this paper proposes a target clusters and dual-timeline optimization (TCDO) framework that integrates priority-based geographical clustering with temporal–spatial coordination mechanisms for efficient mission planning. The proposed approach effectively maintains satellite maneuver constraints while achieving significant improvements in priority-based target acquisition and computational efficiency. Experimental results demonstrate the framework’s superior performance, achieving a 94% coverage rate and a 99.5% reduction in computation time compared to traditional scheduling methods, such as linear programming and genetic algorithms. Full article

The transition from the use of heavy fuel oil (HFO) to the use of green fuels (e.g., methanol) for container liner shipping presents a significant challenge for liner shipping system design (LSSD) in terms of achieving emission reductions. While methanol, including both green and gray methanol, offers environmental benefits, its lower energy density introduces operational complexities. Motivated by the aforementioned background, we establish a bi-level programming model. This model integrates liner speed management and bunker fuel management strategies (i.e., bunkering port selection and bunkering amount determination) with traditional network design decision (i.e., fleet deployment, shipping network design, and slot allocation) optimization. Specifically, the upper-level model optimizes the number of liners deployed in the fleet and shipping network structure, whereas the lower-level model coordinates decisions associated with liner sailing speed management, bunker fuel management, and slot allocation. Moreover, we propose an adaptive piecewise linearization approach combined with a genetic algorithm, which can efficiently solve large-scale instances. Sensitivity analyses of fuel types and fuel prices are conducted to demonstrate the effectiveness of the model and algorithm. Overall, our paper offers valuable insights for policymakers in designing customized emission reduction policies to support the green fuel transition in the maritime industry. Full article

This paper presents a genetic algorithm-based methodology to address the Site-Specific Management Zone (SSMZ) delineation problem. A SSMZ is a subregion of a field that is homogeneous with respect to a soil or crop property, enabling farmers to apply customized management strategies for optimizing resource use. The algorithm generates optimized field partitions using rectangular zones, applicable to both regular and irregularly shaped fields. To the best of our knowledge, the Genetic Algorithm for Zone Delineation (GAZD) is the first approach to handle the rectangular SSMZ delineation problem in irregular-shaped lands without introducing non-real data. The algorithm’s performance is compared with an exact solution based on integer linear programming. Experimental tests conducted on real-field and generated irregular-shaped instances show that while the GAZD requires longer execution times than the exact approach, it proves to be functional and robust in solving the SSMZ problem. Furthermore, the GAZD offers a set of “good enough” solutions that can be evaluated for feasibility and practical convenience, making it a valuable tool for decision-making processes. Moreover, strategies such as implementation in a compiled language and parallel processing can be used to improve the execution time performance of the algorithm. Full article

Harvest weight uniformity is a critical economic trait in the production of Pacific white shrimp (Penaeus vannamei). Social interactions among individuals can significantly influence both uniformity and productivity in aquaculture. To improve harvest weight uniformity through selective breeding, it is essential to accurately partition the genetic component of social effects, known as an indirect genetic effect (IGE), from purely environmental factors. Since IGEs cannot be estimated when all individuals are kept in a single group, a specialized experimental design, such as the grouping design with three families per group (3FAM), is required. With this experimental design, the shrimp population is divided into multiple groups (cages), each containing three families. Individuals from each family are then evenly subdivided and placed in three cages, thereby enabling the estimation of both direct and social genetic effects. Additionally, integrating genomic information instead of relying solely on pedigree data improves the accuracy of genetic relatedness among individuals, leading to more precise genetic evaluation. This study employed a 3FAM experimental design involving 40 families (36 individuals per family) to estimate the contribution of direct and indirect genetic effects on harvest weight uniformity. The genotypes of all tested individuals obtained using the 55K SNP panel were incorporated into a hierarchical generalized linear model to predict direct genetic effects and indirect genetic effects (IGE) separately. The results revealed that the heritability of harvest weight uniformity was low (0.005 to 0.017). However, the genetic coefficient of variation (0.340 to 0.528) indicates that using the residual variance in harvest weight as a selection criterion for improving uniformity is feasible. Incorporating IGE into the model increased heritability estimates for uniformity by 150% to 240% and genetic coefficient of variation for uniformity by 32.11% to 55.29%, compared to the model without IGE. Moreover, the genetic correlation between harvest weight and its uniformity shifted from a strongly negative value (−0.862 to −0.683) to a weakly positive value (0.203 to 0.117), suggesting an improvement in the genetic relationship between the traits and better separation of genetic and environmental effects. The inclusion of genomic data enhanced the prediction ability of single-step best linear unbiased prediction for both harvest weight and uniformity by 6.35% and 10.53%, respectively, compared to the pedigree-based best linear unbiased prediction. These findings highlight the importance of incorporating IGE and utilizing genomic selection methods to enhance selection accuracy for obtaining harvest weight uniformity. This approach provides a theoretical foundation for guiding uniformity improvements in shrimp breeding programs and offers potential applications in other food production systems. Full article

Driven by policy incentives and economic pressures, energy-intensive industries are increasingly focusing on energy cost reductions amid the rapid adoption of renewable energy. However, the existing studies often isolate photovoltaic-energy storage system (PV-ESS) configurations from detailed load scheduling, limiting industrial park energy management. To address this, we propose a two-layer cooperative optimization approach (TLCOA). The upper layer employs a genetic algorithm (GA) to optimize the PV capacity and energy storage sizing through natural selection and crossover operations, while the lower layer utilizes mixed integer linear programming (MILP) to derive cost-minimized scheduling strategies under time-of-use tariffs. Multi-process parallel computing accelerates the fitness evaluations, resolving high-dimensional industrial data challenges. Multi-process parallel computing is introduced to accelerate fitness evaluations, effectively addressing the challenges posed by high-dimensional industrial data. Validated with real power market data, the TLCOA demonstrated rapid adaptation to load fluctuations while achieving a 23.68% improvement in computational efficiency, 1.73% reduction in investment costs, 7.55% decrease in power purchase costs, and 8.79% enhancement in renewable energy utilization compared to traditional methods. This integrated framework enables cost-effective PV-ESS deployment and adaptive energy management in industrial facilities, offering actionable insights for renewable integration and scalable energy optimization. Full article

The boll opening rate (BOR) is an early maturity trait that plays a crucial role in cotton production in China, as BOR has a significant effect on defoliant spraying and picking time of unginned cotton, ultimately determining yield and fiber quality. Therefore, elucidating the genetic basis of BOR and identifying stably associated loci, elite alleles, and potential candidate genes can effectively accelerate the molecular breeding process. In this study, we utilized the mixed linear model (MLM) algorithm to perform a genome-wide association study (GWAS) based on 4,452,629 single-nucleotide polymorphisms (SNPs) obtained through whole-genome resequencing of a natural population of 418 upland cotton accessions and phenotypic BOR data acquired from five environments. A total of 18 SNP loci were identified on chromosome D11 that are stable and significantly associated with BOR in multiple environments. Moreover, a significant SNP peak (23.703–23.826 Mb) was identified, and a GH-D11G2034 gene and favorable allelic variation (GG) related to BOR were found in this genomic region, significantly increasing cotton BOR. Evolutionary studies have shown that GH-D11G2034 may have been subjected to artificial selection throughout the variety selection process. This study provides valuable insights and suggests that the GH-D11G2034 gene and its favorable allelic variation (GG) could be potential targets for molecular breeding to improve BOR in upland cotton. However, further research is needed to validate the function of this gene and explore its potential applications in cotton breeding programs. Overall, this study contributes to the advancement of genetic improvement in early maturity and has important implications for the sustainable development of the cotton industry. Full article

Competitive dynamics in dry bulk terminals necessitate efficient planning and scheduling to optimize operations. This study focuses on the productivity of stackers and reclaimers by developing a mathematical optimization model to enhance scheduling efficiency. A mixed-integer linear programming (MILP) model was formulated to minimize the maximum completion time (makespan) of operations while ensuring smooth material flow and resource utilization. Given the computational complexity of real-world scenarios, a novel hybrid genetic algorithm (GA) was proposed. This algorithm integrates tabu search to generate a high-quality initial population size and employs innovative chromosome designs that respect operational constraints, such as equipment availability, material flow continuity, and sequencing restrictions. This hybrid approach balances exploration and exploitation, improving solution convergence and robustness. Computational experiments using real data from a Moroccan dry bulk terminal validated the algorithm’s efficiency and effectiveness. Performance indicators such as makespan reduction, equipment utilization, and computational efficiency were analyzed. The results demonstrate that the hybrid GA significantly reduced processing times and improved resource efficiency compared to conventional methods. Additionally, the algorithm showed scalability across different operational scenarios, confirming its adaptability to dynamic terminal conditions. These findings highlight the potential of advanced optimization techniques to enhance decision making and improve operational productivity in dry bulk terminals. Full article

The university course scheduling problem (UCSP) is a challenging combinatorial optimization problem that requires optimization of the quality of the schedule and resource utilization while meeting multiple constraints involving courses, teachers, students, and classrooms. Although various algorithms have been applied to solve the UCSP, most of the existing methods are limited to scheduling independent courses, neglecting the impact of joint courses on the overall scheduling results. To address this limitation, this paper proposed an innovative mixed-integer linear programming model capable of handling the complex constraints of both joint and independent courses simultaneously. To improve the computational efficiency and solution quality, a hybrid method combining a genetic algorithm and dynamic programming, named POGA-DP, was designed. Compared to the traditional algorithms, POGA-DP introduced exchange operations based on a judgment mechanism and mutation operations with a forced repair mechanism to effectively avoid local optima. Additionally, by incorporating a greedy algorithm for classroom allocation, the utilization of classroom resources was further enhanced. To verify the performance of the new method, this study not only tested it on real UCSP instances at Beijing Forestry University but also conducted comparative experiments with several classic algorithms, including a traditional GA, Ant Colony Optimization (ACO), the Producer–Scrounger Method (PSM), and particle swarm optimization (PSO). The results showed that POGA-DP improved the scheduling quality by 46.99% compared to that of the traditional GA and reduced classroom usage by up to 29.27%. Furthermore, POGA-DP increased the classroom utilization by 0.989% compared to that with the traditional GA and demonstrated an outstanding performance in solving joint course scheduling problems. This study also analyzed the stability of the scheduling results, revealing that POGA-DP maintained a high level of consistency in scheduling across adjacent weeks, proving its feasibility and stability in practical applications. In conclusion, POGA-DP outperformed the existing algorithms in the UCSP, making it particularly suitable for efficient scheduling under complex constraints. Full article