Search Results (37)

Sustainable irrigation planning under increasing water scarcity requires efficient allocation of limited water resources while simultaneously considering land suitability and agricultural productivity. In this study, we aim to identify optimal cropping patterns for sustainable irrigation management using an optimization-based decision-support framework applied to the Nam Mang 3 Irrigation Project in Lao PDR, based on data from 2022. Focusing on the dry season (November–April), we evaluated six major crops—rice, beans, maize, tomato, cucumber, and watermelon—under six irrigation scenarios to assess the impacts of land suitability and water availability. The analysis incorporated a water availability range from 17.70 to 18.10 mm³ to evaluate system robustness. Linear Programming (LP), the Genetic Algorithm (GA), and the African Vultures Optimization Algorithm (AVOA) were employed to determine optimal crop allocation. The proposed framework explicitly incorporates varied soil types and land-use constraints, providing a more realistic representation than conventional homogeneous assumptions. The results indicate that AVOA outperformed other models in terms of stability. Under the evaluated scenarios, the optimal cultivated area ranged from 3192 to 3200 ha, with total profits fluctuating between 34,125,930 and 34,314,900 US$. These findings demonstrate that integrating soil variability and sensitivity-based optimization significantly enhances irrigation planning, providing a practical, robust decision-support tool for planners to design adaptive and sustainable cropping strategies in water-scarce regions. Full article

Sea cucumber (Apostichopus japonicus) is a commercially important mariculture species in northern China. Papilla number has been recognized as a key economic trait in sea cucumbers. Notably, significant variation in papilla count exists among different populations. The genetic mechanisms controlling papilla development are not fully understood. In this study, 72 individuals from six geographically distinct sea cucumber populations (Group N1) and 35 individuals from their offspring (Group N2) were analyzed using reduced-representation genome sequencing (RRGS) and whole-genome resequencing (WGS), respectively. Genome-wide association studies (GWAS) and selective sweep analysis were conducted to identify the biological pathways and genetic basis underlying variation in papilla number. The GWAS analysis identified two single-nucleotide polymorphism (SNP) loci on chromosomes 4 and 14 in the Group N1 that were significantly associated with papilla number. Within the vicinity of two SNPs, 48 genes were annotated as putative candidate genes, six of which have been reported to be associated with growth in A. japonicus or other aquatic animals. Selective sweep analysis identified 23 candidate genes in the JZ vs. YT within Group N1 and 39 candidate genes in the G1 vs. G3 within Group N2. Notably, functional enrichment analysis revealed that the Calcium signaling pathway was significantly enriched in both Group N1 and Group N2. This pathway has been demonstrated to regulate key cellular processes such as cell proliferation and differentiation through the activation of downstream signaling cascades. The intersection of results from parental Group N1 and progeny Group N2 yielded a total of six key biological pathways, including biological process, cellular process, cellular anatomical entity, cellular component, membrane, and binding. Collectively, our findings contribute to a deeper understanding of the genetic mechanisms underlying papilla number variation in A. japonicus and provide valuable insights for genomic selection in breeding programs. Full article

Core germplasm, a strategically selected subset of the original germplasm, aims to maximize the representation of genetic diversity within the entire collection. Establishing a germplasm resource bank is essential for the effective management and sustainable utilization of genetic resources. This study developed a core germplasm repository for Hypsizygus marmoreus, a commercially important mushroom species, to capture the genetic diversity of the original collection with a minimal sample size. Genetic diversity and cluster analyses were conducted on 57 representative strains of H. marmoreus, including both cultivated and wild accessions from different regions, using 15 pairs of simple sequence repeat (SSR) markers. DNA molecular identity cards were generated for all germplasms, and cultivation trials with agronomic trait assessments were performed on 24 core accessions. A total of 115 distinct alleles were identified, with genetic similarity coefficients ranging from 0.70 to 1.00. Clustering at a similarity threshold of 0.76 classified the strains into five groups. The core germplasm panel, comprising 24 accessions (42.11% of the total collection), retained full allelic diversity and preserved the genetic and phenotypic variability of the original population, confirming its suitability for parental selection in breeding programs. unique molecular identity codes were developed for each H. marmoreus germplasm by integrating SSR marker profiles with data on geographical origin, fruiting body color, and cultivation traits. These were converted into DNA molecular ID codes, providing a reliable system for rapid identification and traceability of germplasm resources. The findings offer a valuable reference for breeding improvement and the protection of edible fungal varieties with independent intellectual property rights. Full article

We propose a hybrid framework that integrates instance clustering with Automatic Generation of Algorithms (AGA) to produce specialized algorithms for classes of Multidimensional Knapsack Problem (MKP) instances. This approach is highly relevant given the latest trends in AI, where Large Language Models (LLMs) are actively being used to automate and refine algorithm design through evolutionary frameworks. Our method utilizes a feature-based representation of 328 MKP instances and evaluates K-means, HDBSCAN, and random clustering to produce 11 clusters per method. For each cluster, a master optimization problem was solved using Genetic Programming, evolving algorithms encoded as syntax trees. Fitness was measured as relative error against known optima, a similar objective to those being tackled in LLM-driven optimization. Experimental and statistical analyses demonstrate that clustering-guided AGA significantly reduces average relative error and accelerates convergence compared with AGA trained on randomly grouped instances. K-means produced the most consistent cluster-specialization. Cross-cluster evaluation reveals a trade-off between specialization and generalization. The results demonstrate that clustering prior to AGA is a practical preprocessing step for designing automated algorithms in NP-hard combinatorial problems, paving the way for advanced methodologies that incorporate AI techniques. Full article

Product-service systems contribute to sustainable development through innovative service integration and novel customer value creation. However, the competitive advantage of sustainable product lifecycle service delivery hinges critically on the operational efficiency of service networks. This study addresses dynamic service facility location and allocation challenges in a time-varying demand environment, focusing on the strategic deployment of multiple comprehensive service centers (CSCs) and their dynamic customer allocation across planning horizons. In this study, we develop a 0–1 integer programming model and propose a novel co-evolutionary adaptive multi-objective genetic algorithm (CA-MOGA) with four key enhancements: (1) optimized chromosome representation, (2) adaptive strategy incorporation, (3) genetic operators with gene repair mechanisms, and (4) elite trans-generation migration. Through real-world case validation, CA-MOGA demonstrates significant improvements over conventional genetic algorithms in both convergence speed and solution quality. The performance and adaptability of the proposed algorithm suggest strong potential for customizable applications in solving diverse complex optimization problems. Full article

Accurate genomic prediction of complex phenotypes is crucial for accelerating genetic progress in swine breeding. However, conventional methods like Genomic Best Linear Unbiased Prediction (GBLUP) face limitations in capturing complex non-additive effects that contribute significantly to phenotypic variation, restricting the potential accuracy of phenotype prediction. To address this challenge, we introduce a novel framework based on a self-supervised, pre-trained encoder-only Transformer model. Its core novelty lies in tokenizing SNP sequences into non-overlapping 6-mers (sequences of 6 SNPs), enabling the model to directly learn local haplotype patterns instead of treating SNPs as independent markers. The model first undergoes self-supervised pre-training on the unlabeled version of the same SNP dataset used for subsequent fine-tuning, learning intrinsic genomic representations through a masked 6-mer prediction task. Subsequently, the pre-trained model is fine-tuned on labeled data to predict phenotypic values for specific economic traits. Experimental validation demonstrates that our proposed model consistently outperforms baseline methods, including GBLUP and a Transformer of the same architecture trained from scratch (without pre-training), in prediction accuracy across key economic traits. This outperformance suggests the model’s capacity to capture non-linear genetic signals missed by linear models. This research contributes not only a new, more accurate methodology for genomic phenotype prediction but also validates the potential of self-supervised learning to decipher complex genomic patterns for direct application in breeding programs. Ultimately, this approach offers a powerful new tool to enhance the rate of genetic gain in swine production by enabling more precise selection based on predicted phenotypes. Full article

Bunch compactness (BC) is a complex, multi-trait characteristic that has been studied mostly in the context of wine grapes, with table grapes being scarcely considered. As these groups have marked phenotypic and genetic differences, including BC, the study of this trait is reported here using a genetically diverse collection of 116 Vitis vinifera L. cultivars and lines enriched for table grapes over two seasons. For this, 3D scanning-based morphological data were combined with ground measurements of 14 BC-related traits, observing high correlations among both approaches (R² > 0.90–0.97). The multivariate analysis suggests that the attributes ‘berries per bunch’, ‘berry weight and width’, and ‘bunch weight and length’ could be considered as the main descriptors for BC, optimizing evaluation times. Then, GWASs based on a set of 70,335 SNPs revealed that GBS analysis in this same population enabled the detection of several SNPs associated with different sub-traits, with a locus for ‘berries per bunch’ in chromosome (chr) 18 being the most prominent. Enrichment analysis of significant and frequent SNPs found simultaneously in several traits and seasons revealed the over-representation of discrete functions such as alpha-linolenic acid metabolism and glycan degradation. In summary, the utility of 3D automated phenotyping was validated for table grape backgrounds, and new SNPs and candidate genes associated with the BC trait were detected. The latter could eventually become a selection tool for grapevine breeding programs. Full article

Objectives: This study investigates the prevalence, determinants, and educational implications of sensory disorders in Saudi Arabia. We hypothesize that sociodemographic factors (e.g., gender, marital status), genetic consanguinity, and regional disparities significantly influence sensory health outcomes, including vision, hearing, balance, and social participation, with consequences for learning environments and educational access. Participants: The primary data were analyzed data from 33,575 households across all administrative regions of Saudi Arabia. The sample includes Saudi nationals residing within the Kingdom and those temporarily abroad (e.g., for treatment, study, or tourism) who are considered household members. Households were selected via a stratified random sampling framework, drawing 25 households from each of 1300 statistical areas (out of 3600 total), ensuring nationwide representation aligned with the 2010 Population and Housing Census. Study Method: An observational analysis of secondary data from the nationally representative survey was conducted. Variables included vision, hearing, mobility, personal care, and communication disorders. Statistical methods encompassed chi-square tests for associations and Cramer’s V effect sizes, with regional, gender, and consanguinity-based sub-analyses. Findings: Males exhibited higher mild vision impairments (1.6% vs. 1.0% females; p < 0.001), while females had greater severe hearing disorders (2.3% vs. 1.8%; p < 0.001). Consanguineous groups showed autosomal recessive patterns (e.g., 91,512 mobility issues in first-degree relatives; Cramer’s V = 0.12). Regional disparities emerged, with rural Najran reporting elevated balance/motion deficits (3.1% vs. national 1.9%; p < 0.01). Never-married individuals faced extreme communication barriers (18.4% vs. 8.7% married; p < 0.001). Conclusions: Sensory disorders in Saudi Arabia are shaped by genetic, environmental, and sociocultural factors, with implications for educational access and inclusive learning environments. Gender-sensitive interventions, genetic counseling, and expanded sensory disability metrics are critical for equitable educational policies. Regional programs targeting trauma prevention, chronic disease management, and sensory-friendly accommodations in schools are recommended to address multisensory disorder burdens and enhance educational outcomes. Full article

The paper addresses the problem of the automatic design of sequential systems. For a complete description of the operation of the sequential system, a table of states or another representation of transition graphs describing possible changes in system states is necessary. This paper adopts a completely different approach, in which the description of the sequential system results from the study of the responses to signals given from outside and from an unknown system, which is treated as a black box. This approach may be useful when we want to recreate the internal structure of a given, unknown system or when we want to obtain a system based only on the information about the system’s reactions to given external signals, without going into the principles of its operation. The paper presents problems that arise when creating the data strings that describe the reactions of the designed system and ways for solving these problems, and it presents Multivalued Cartesian Genetic Programming (M-CGP)—a new approach used to design sequential circuits. Further research has developed a system based on this model. The paper presents examples of obtained sequential systems generated using the newly created system. Full article

This paper investigates the inland container transportation problem with a focus on multi-size containers, fuel consumption, and carbon emissions. To reflect a more realistic situation, the depot’s initial inventory of empty containers is also taken into consideration. To linearly model the constraints imposed by the multiple container sizes and the limited number of empty containers, a novel graphical representation is presented for the problem. Based on the graphical representation, a mixed-integer programming model is presented to minimize the total transportation cost, which includes fixed, fuel, and carbon emission costs. To efficiently solve the model, a tailored branch-and-price algorithm is designed, which is enhanced by improvement schemes including a heuristic label-setting algorithm, decremental state-space relaxation, and the introduction of a high-quality upper bound. Results from a series of computational experiments on randomly generated instances demonstrate that (1) the proposed branch-and-price algorithm demonstrates a superior performance compared to the tabu search algorithm and the genetic algorithm; (2) each additional empty container in the depot reduces the total transportation cost by less than 1%, with a diminishing marginal effect; (3) the rational configuration of different types of trucks improves scheduling flexibility and reduces fuel and carbon emission costs as well as the overall transportation cost; and (4) extending customer time windows also contributes to lower the total transportation cost. These findings not only deepen the theoretical understanding of inland container transportation optimization but also provide valuable insights for logistics companies and policymakers to improve efficiency and implement more sustainable operational practices. Additionally, our research paves the way for future investigations into the integration of dynamic factors and emerging technologies in this field. Full article

Due to the explosive growth of software quantity and the mixed ability of software developers, a large number of software defects emerge during the later stages of software maintenance. The search method based on genetic programming is one of the most popular in search algorithms, but it also has some issues. The single-objective approach to validate and select offspring patches without considering other constraints can affect the efficiency of patch generation. To address this issue, this paper proposes an automatic software repair method based on Multi-objective Genetic Programming (MGPRepair). Firstly, the method adopts a lightweight context analysis strategy to find suitable repair materials. Secondly, it decouples the replacement statements and insertion statements in the repair materials, using a lower-granularity patch representation method to encode the patches in the search space. Then, the automatic software defect repair is treated as a multi-objective search problem, and the NSGA-II multi-objective optimization algorithm is used to find simpler repair patches. Finally, the test case filtering technique is used to accelerate the patch validation process and generate correct patches. MGPRepair was experimentally evaluated on 395 real Java software defects from the Defects4J dataset. The experimental results show that MGPRepair can generate test case-passing patches for 51 defects, of which 35 defect patches are equivalent to manually generated patches. Its repair the efficiency and success rate are higher to other excellent automatic software defect repair methods such as jGenProg, RSRepair, ARJA, Nopol, Capgen, and SequenceR. Full article

The objective of this study was to evaluate the genetic diversity among traditional common bean accessions through morphological descriptors and molecular markers. Sixty-seven common bean accessions from the Germplasm bank of the Instituto Federal of Espírito Santo—Campus de Alegre were evaluated. For this, 25 specific morphological descriptors were used, namely 12 quantitative and 13 qualitative ones. A diversity analysis based on morphological descriptors was carried out using the Gower algorithm. For molecular characterization, 23 ISSR primers were used to estimate dissimilarity using the Jaccard Index. Based on the dendrograms obtained by the UPGMA method, for morphological and molecular characterization, high genetic variability was observed between the common bean genotypes studied, evidenced by cophenetic correlation values in the order of 0.99, indicating an accurate representation of the dissimilarity matrix by the UPGMA clustering. In the morphological characterization, high phenotypic diversity was observed between the accessions, with grains of different shapes, colors, and sizes, and the accessions were grouped into nine distinct groups. Molecular characterization was efficient in separating the genotypes in the Andean and Mesoamerican groups, with the 23 ISSR primers studied generating an average of 6.35 polymorphic bands. The work identified divergent accessions that can serve different market niches, which can be indicated as parents to form breeding programs in order to obtain progenies with high genetic variability. Full article

Genetic programming (GP) has a long-standing tradition in the evolution of computer programs, predominantly utilizing tree and linear paradigms, each with distinct advantages and limitations. Despite the rapid growth of the GP field, there have been disproportionately few attempts to evolve ’real’ Turing-like imperative programs (as contrasted with functional programming) from the ground up. Existing research focuses mainly on specific special cases where the structure of the solution is partly known. This paper explores the potential of integrating tree and linear GP paradigms to develop an encoding scheme that universally supports genetic operators without constraints and consistently generates syntactically correct Python programs from scratch. By blending the symmetrical structure of tree-based representations with the inherent asymmetry of linear sequences, we created a versatile environment for program evolution. Our approach was rigorously tested on 35 problems characterized by varying Halstead complexity metrics, to delineate the approach’s boundaries. While expected brute-force program solutions were observed, our method yielded more sophisticated strategies, such as optimizing a program by restricting the division trials to the values up to the square root of the number when counting its proper divisors. Despite the recent groundbreaking advancements in large language models, we assert that the GP field warrants continued research. GP embodies a fundamentally different computational paradigm, crucial for advancing our understanding of natural evolutionary processes. Full article

The Galilee region of Israel boasts a rich ethnic diversity within its Arab population, encompassing distinct Muslim, Christian, Druze, and Bedouin communities. This preliminary exploratory study uniquely examined potential ethnic and gender differences in ischemic stroke characteristics across these Arab subgroups, which are seldom investigated separately in Israel and are typically studied as a homogeneous “Arab” sector, despite significant variations in their ethnicity, culture, customs, and genetics. The current study aimed to comparatively evaluate stroke characteristics, including recurrence rates, severity, and subtypes, within and across these distinct ethnic groups and between genders. When examining the differences in stroke characteristics between ethnic groups, notable findings emerged. The Bedouin population exhibited significantly higher rates of recurrent strokes than Muslims (M = 0.55, SD = 0.85 vs. M = 0.25, SD = 0.56; p < 0.05). Large vessel strokes were significantly more prevalent among Christians (30%) than Druze (9.9%; p < 0.05). Regarding gender differences within each ethnic group, several disparities were observed. Druze women were six times more likely to experience moderate to severe strokes than their male counterparts (p < 0.05). Interestingly, Druze women also exhibited a higher representation of cardio-embolic stroke (19.8%) compared with Druze men (4.6%; p < 0.001). These findings on the heterogeneity in stroke characteristics across Arab ethnic subgroups and by gender underscore the need to reconsider the approach that views all ethnic groups comprising the Arab sector in Israel as a homogeneous population; instead, they should be investigated as distinct communities with unique stroke profiles, requiring tailored culturally aware community-based prevention programs and personalized therapeutic models. The identified patterns may guide future research to develop refined, individualized, and preventive treatment approaches targeting the distinct risk factors, healthcare contexts, and prevention needs of these diverse Arab populations. Full article

Business processes have undergone a significant transformation with the advent of the process-oriented view in organizations. The increasing complexity of business processes and the abundance of event data have driven the development and widespread adoption of process mining techniques. However, the size and noise of event logs pose challenges that require careful analysis. The inclusion of different sets of behaviors within the same business process further complicates data representation, highlighting the continued need for innovative solutions in the evolving field of process mining. Trace clustering is emerging as a solution to improve the interpretation of underlying business processes. Trace clustering offers benefits such as mitigating the impact of outliers, providing valuable insights, reducing data dimensionality, and serving as a preprocessing step in robust pipelines. However, designing an appropriate clustering pipeline can be challenging for non-experts due to the complexity of the process and the number of steps involved. For experts, it can be time-consuming and costly, requiring careful consideration of trade-offs. To address the challenge of pipeline creation, the paper proposes a genetic programming solution for trace clustering pipeline synthesis that optimizes a multi-objective function matching clustering and process quality metrics. The solution is applied to real event logs, and the results demonstrate improved performance in downstream tasks through the identification of sub-logs. Full article