Search Results (2,331)

To address the lack of traversable region awareness in conventional path planning algorithms for obstacle-crossing robots, an adaptive path planning method is proposed. First, a traversal-aware environment model is constructed by introducing graded traversable regions with associated physical traversal costs. To effectively navigate this complex model, a hybrid Ant Colony Optimization (ACO) framework integrating Jump Point Search (JPS) and the Genetic Algorithm (GA) is developed. Specifically, a JPS-inspired pruning strategy is incorporated into the state transition process to significantly reduce redundant node expansion. Crucially, genetic operators—namely crossover and mutation—are embedded within the main ACO iterative loop to dynamically sustain population diversity and effectively mitigate stagnation in local optima. Correspondingly, the pheromone initialization, state transition mechanisms, and update rules are redesigned to incorporate the robot’s obstacle traversal capabilities. The framework is further complemented by path optimization operations that reduce unnecessary turning points. Extensive simulation experiments demonstrate that the proposed method outperforms conventional ACO-based and classical path planning algorithms. In particular, it achieves an average reduction of 11.1% in path length and 65.5% in the number of waypoints, while ensuring effective coordination with the robot’s physical traversal capabilities. These results validate the superior search efficiency, robustness, and practical applicability of the proposed approach. Full article

This case-control study investigated the association between polymorphisms in the dedicator of cytokinesis 2 (DOCK2) gene and susceptibility to COVID-19 in a Mexican population. Methods: Genotyping of five single-nucleotide polymorphisms (SNPs) in the DOCK2 gene (rs9307 A/G, rs1045176 G/T, rs1045168 C/T, rs2112703 A/C, and rs2287727 A/C) was performed using TaqMan assays in 248 COVID-19 patients and 288 healthy controls. Results: No significant differences were observed in the allelic or genotypic distributions of rs1045176 G/T and rs2287727 A/C between cases and controls. However, under multiple genetic inheritance models (co-dominant, dominant, recessive, heterozygous, and additive), the rs9307 A, rs1045168 C, and rs2112703 A alleles were significantly associated with a reduced risk of COVID-19 (p < 0.05). Furthermore, sub-analyses stratified by genotype in COVID-19 patients revealed that the rs9307 AA, rs1045168 CC, and rs2112703 AA genotypes correlated with altered plasma concentrations of lactic acid dehydrogenase (LDH), alanine aminotransferase (ALT), aspartate aminotransferase (AST), and ferritin. Conclusions: The DOCK2 SNPs rs9307 A/G, rs1045168 C/T, and rs2112703 A/C are associated with decreased susceptibility to COVID-19 in this population and influence plasma levels of LDH, ALT, AST, and ferritin, suggesting a potential role in disease pathogenesis and severity. Full article

Aging populations have intensified the demand for thermally comfortable and energy-efficient housing, particularly for elderly residents whose diminished thermoregulatory capacity renders them disproportionately vulnerable to indoor temperature fluctuations. Existing senior apartments in cold-climate regions frequently fail to meet age-specific thermal comfort standards, yet systematic retrofit optimization frameworks explicitly tailored to elderly occupants remain scarce. This study presents a data-driven multi-objective optimization framework for building envelope retrofitting, which is validated using on-site temperature measurements from a representative 1980s brick–concrete senior apartment building in Beijing. The framework integrates Latin Hypercube Sampling (LHS) for design space exploration, a Long Short-Term Memory (LSTM) surrogate model for simultaneous prediction of three performance objectives, and Non-dominated Sorting Genetic Algorithm II (NSGA-II) for Pareto-optimal solution generation, with final selection performed via a weighted Mahalanobis distance-based Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). Optimization targets—annual energy consumption, indoor thermal discomfort hours, and retrofit cost—are parameterized using the age-sensitive comfort thresholds specified in GB 50340-2016. The LSTM surrogate achieved R² values of 0.91–0.93 across all objectives with training–testing differences below 0.02. The optimal retrofit package—Polyvinyl Chloride (PVC) Low Emissivity (Low-E) double-glazed windows (5 + 6A + 5), glass fiber roof insulation (65.25 mm), and Extruded Polystyrene (XPS) external wall insulation (65.39 mm)—reduces annual energy consumption by 47.1% (from 40,867 to 21,626 kWh) and annual thermal discomfort hours by 62.4% (from 2454 °C·h to 923 °C·h). SHapley Additive exPlanations (SHAP)-based sensitivity analysis further identifies wall U-value and roof thickness as the dominant performance drivers. A reproducible and computationally efficient pathway is provided by the proposed framework for evidence-based envelope retrofit decision-making in existing senior residential buildings. Full article

Cancer exhibits pronounced heterogeneity at both spatial and cellular levels, contributing to variability in therapeutic responses and the emergence of treatment resistance. This heterogeneity is underscored by the diverse genetic, epigenetic, and phenotypic variations found within tumor cell populations. Cancer stem cells (CSCs), although representing a minor fraction of tumor cells, possess the capacity to self-renew and differentiate, thereby driving the dynamic evolution of tumor heterogeneity. CSCs interact intricately with various elements of the tumor microenvironment (TME), further amplifying this heterogeneity. Recent advancements in organoid technology have facilitated the development of CSC-derived organoid models that more faithfully recapitulate the TME and intratumoral heterogeneity, which conventional 2D culture systems fail to replicate. These CSC-derived organoid systems not only preserve the structural and genomic characteristics of tumors, but they also enable the exploration and evaluation of therapeutic strategies that reflect tumor complexity. However, CSC-derived organoid systems face several challenges, such as the rarity of CSCs, lack of standardized culture conditions, absence of TME components, limited predictive accuracy, and insufficient modeling of tumor heterogeneity. This review discusses these limitations and explores potential solutions, including the use of artificial intelligence (AI) to enhance treatment predictability. These innovations may improve the utility of organoid models for therapeutic evaluation and for targeting tumor heterogeneity. Ultimately, CSC-derived organoids may serve as a valuable platform for advancing precision medicine and cancer research. Full article

Background/Objectives: Operational DNA databases traditionally rely on static locus-count thresholds to determine search eligibility and report matches. While computationally straightforward, these rigid criteria routinely discard high-value investigative leads from degraded forensic profiles while simultaneously permitting adventitious matches when common alleles are involved. To overcome the limitations of static rules, this study introduces an automated framework for dynamic likelihood ratio (LR) thresholding. Methods: Utilizing a Fast Fourier Transform (FFT) algorithm, the system calculates the Probability Mass Function (PMF) for any specific combination of shared loci in real-time, natively incorporating the Balding–Nichols model to account for population substructure. Instead of applying an arbitrary locus count or fixed LR cutoff, the framework defines admissibility based on a user-defined maximum upper bound of acceptable false positives at a specified confidence (probability) level (e.g., 95%). Results: This empowers database custodians to precisely predict and adapt their search criteria to match an acceptable administrative workload, dynamically adjusting the required LR threshold to the exact size of the searched database. This approach was validated through massive-scale empirical simulations across five reference population groups. Receiver Operating Characteristic (ROC) and Poisson distribution analyses reveal that static thresholds inevitably collapse under the multiplicity effect of large-scale comparisons; for instance, a static locus rule that maintains safety within a small DNA database yields an unmanageable false positive risk when scaled to larger DNA databases or international networks like the Prüm DNA Exchange. Conclusions: By explicitly coupling the decision threshold to the database size and the genetic rarity of the evidence, this dynamic framework provides a mathematically rigorous and scalable solution. Most notably, it identifies rare, low-locus matches that static rules typically discard, offering a method to maintain a predefined expected false positive rate. Full article

The rapid accumulation of genome-scale data has transformed plant biology from descriptive genetics to predictive and increasingly mechanistic genomics. Longan (Dimocarpus longan Lour.) is an economically important subtropical fruit tree in China and Southeast Asia, but compared with model plants and major temperate fruit crops, its genomic resources and functional studies have developed relatively late. Here, we review recent progress in longan genomics with emphasis on three interrelated areas: genome assembly and annotation, transcriptomic resources, and representative gene family studies associated with flowering, somatic embryogenesis, and transporter-mediated stress tolerance. The progression from the first draft genome of ‘Honghezi’ to the chromosome-scale assemblies of ‘Jidanben’ and ‘Shixia’ has substantially improved contiguity and gene annotation, thereby enabling population-genomic analysis, genome-wide gene family identification, and candidate-gene discovery. Available transcriptomic datasets further support studies of reproductive development, stress responses, and embryogenic competence, although cross-study integration remains limited. We also summarize how gene family analyses have advanced the current understanding of floral induction, continuous flowering, somatic embryogenesis, mineral transport, and sugar transport in longan. Importantly, the field is still dominated by cataloguing and expression-based inference, whereas causal validation, pan-genomic analysis, and multi-omics integration remain insufficient. We therefore argue that future progress in longan molecular breeding will depend on integrating high-quality genomic resources with functional validation, standardized comparative annotation, and improved transformation or regeneration systems. Full article

Domestication has profoundly shaped the phenotypic differentiation and genetic architecture of Perilla. However, analyses of the morphological difference between its cultivated and weedy forms across its varieties remains incomplete. This study analyzed morphological variation, genetic diversity, population structure, and marker–trait associations of 45 accessions representing the cultivated and weedy forms of two Perilla varieties (P. frutescens var. frutescens and var. crispa) collected from South Korea and Japan. Analyses of ten qualitative and quantitative agronomic traits revealed clear domestication-related differentiation. Cultivated var. frutescens showed larger and heavier seeds, whereas cultivated var. crispa and the weedy accessions were characterized by longer inflorescences and higher floret numbers but smaller seeds. Strong positive correlations were observed among seed-related traits, particularly between seed size and seed weight (r = 0.932), indicating coordinated selection of seed traits. Genetic diversity analysis using 70 SSR markers identified 330 alleles consistent with domestication bottlenecks in cultivated forms while higher diversity was generally retained in the weedy accessions. Population structure, UPGMA clustering, and principal coordinate analyses broadly differentiated the cultivated and weedy accessions, although partial admixture indicated shared ancestry and historical gene flow. Association mapping using Q-based GLM and Q + K MLM models identified 23 significant marker–trait associations involving 16 SSR markers consistently detected across both models. Several markers were associated with multiple traits, implying pleiotropy or tight genetic linkage. Notably, five SSR markers (KNUPF192, KNUPF202, KNUPF207, KNUPF230, and KNUPF238) may represent potential candidate loci for marker-assisted selection to improve seed-related traits in var. frutescens and leaf-related traits in var. crispa. Full article

Improving the efficiency of distribution systems (DSs) through reactive power compensation using shunt capacitor banks is a widely applied practice, as it enhances the voltage profile and reduces operating costs. Owing to the nonlinear nature of DSs, heuristic algorithms—along with other optimization tools—are frequently employed to support techno-economic decision-making in DS design. In this study, we employ the neural population dynamics optimization algorithm (NPDOA), a recently developed heuristic approach inspired by brain neuroscience. The simulation and optimization model adopted in this research is based on quasi-static time-series analysis, which enables the planning problem and DS constraints to be examined from a probabilistic perspective. A comparative analysis with the genetic algorithm (GA) and the whale optimization algorithm (WOA) indicates that NPDOA provides a similar solution with comparable computational time. Specifically, the results show that NPDOA produces a solution only 0.02% higher than GA, with improvement probabilities of 27.42% and 10.94%, respectively. In comparison with WOA, NPDOA yields a solution that is 0.05% lower, with a corresponding probability of improvement of 10.76%. Furthermore, the installation of shunt capacitor banks optimized using NPDOA reduces the net present cost by 33%. Full article

A difficult NP-hard optimization problem, designing cryptographically robust substitution-boxes (S-boxes) necessitates a careful balancing act between several conflicting properties, such as differential uniformity and nonlinearity. Genetic Algorithms (GAs) have been widely used for this task; however, their performance is often limited by premature convergence and insufficient diversity during crossover operations. This primarily occurs because genetic algorithms commence with limited a priori knowledge. This sort of “blindness” and failure to utilize local knowledge results in diminished performance. In GA, the crossover operations facilitate the dissemination of robust candidates within the population. Conventionally, GA implements crossover for each pair of parents for diversity and a robust solution. However, this is not invariably the situation. To enhance children’s candidacy, parental diversity is quite crucial. This paper proposes a similarity-aware crossover strategy, integrated with a Siamese learning framework, to guide the genetic algorithm for improved S-box optimization with better diversity and faster convergence by utilizing parental local information. The proposed model is similarity-aware to guarantee that the GA improves parental diversity. When the parents exhibit excessive similarity, a “regressive” crossover is opted, which ensures the propagation of a parental couple with sufficient diversity to produce superior offspring. The proposed similarity-aware GA model is applied and evaluated to generate cryptographically robust and optimized S-boxes. To verify the robustness in terms of diversity, the model has been tested using three different loss functions: contrastive loss, KL divergence loss, and the suggested method of combining both loss functions to form a hybrid loss function. The effectiveness of the proposed approach is demonstrated through the generation of high-quality S-boxes with strong cryptographic properties. Full article

Sitka spruce (Picea sitchensis (Bong.) Carr.) is among the most commercially important tree species in European and North American forestry, and genetic improvement programmes are therefore essential for promoting its productivity and sustainability. This research emphasises the significance of the breeding programmes. The primary objective of this study was to provide more reliable information on family selection for the improvement programme of Sitka spruce by accounting for competition and environmental heterogeneity effects. Analyses in the present study were carried out on historical inventory data of height (HT) and diameter at breast height (DBH) from a half-sib progeny trial of Sitka spruce in Ireland. Tree measurement data were collected at ages 6, 12, 15 and 20 years. A mixed linear model incorporating spatial and competition terms was applied to estimate genetic parameters of the Sitka spruce population. The direct genetic effects of each family on its own phenotypes and the competition effect on its neighbour’s phenotype were examined over time. The study demonstrated an analytical approach for assessing both genetic as well as environmental aspects of competition in a Sitka spruce progeny trial. The combined model integrating competition and spatial terms (model CS) improved model fit compared with the basic model, which only included the random effects of genetic and experimental design factors (model B), with an AIC difference of up to 3609 between them. Residual error obtained from model CS was usually smaller than from model B, with the greatest reduction of 85%. Furthermore, model CS generally improved the estimation of heritability for growth traits, by up to 241, when compared with model B. In addition, genetic differences in competitive ability among families were also observed. Families with favourable combinations of direct genetic and competitive breeding values were suggested for selection in subsequent cycles of the breeding programme, i.e., families with relatively high direct genetic breeding value but low and consistent competitive breeding value over time. This work develops a practical framework to inform future family selection for Sitka spruce improvement programmes. Full article

Most glaucoma genetic data derive from European and East Asian cohorts, leaving high-consanguinity Middle Eastern populations under-characterized. This review synthesizes 33 Saudi-specific genetic studies (2014–2024, >9000 participants) to define a population-level glaucoma genetic architecture that diverges substantially from global models and carries direct precision medicine implications. Three findings distinguish the Saudi landscape. First, CYP1B1 functions as the dominant causal gene across both primary congenital glaucoma (PCG) and juvenile-onset open-angle glaucoma (JOAG), accounting for 76–86% of cases, with two founder alleles, p.G61E (penetrance 87.7%) and p.R469W (penetrance 93%), driving severe, early-onset phenotypes. Critically, MYOC and LTBP2, the primary JOAG genes in other populations, carry no pathogenic variants in Saudi cohorts, rendering standard multi-ethnic gene panels inadequate for this population. Second, adult-onset glaucoma follows a distinct polygenic architecture where APOE ε2 confers a near five-fold risk for primary angle-closure glaucoma (OR = 4.82), an effect absent or inconsistent in global datasets, and NOS3 variants associate with primary open-angle glaucoma specifically in men, a sex-stratified signal unreported outside Saudi cohorts. The MTHFR T/T genotype, common in European and Asian POAG patients, is entirely absent locally, indicating population-specific allelic distributions that alter folate-metabolism-related optic nerve susceptibility. Third, ACVR1 rs12997 associates across POAG, PACG, and pseudoexfoliation glaucoma (PXG), positioning BMP/TGF-β signaling as a shared mechanistic pathway spanning multiple subtypes. These findings argue for Saudi-specific genetic panels, CYP1B1-centered cascade testing in consanguineous families, and polygenic risk models incorporating local allele frequencies rather than globally derived weights. Full article

Given livestock’s crucial role in global food security and economic stability, the alarming threat of climate change calls for the implementation of effective mitigation strategies for climate-resilient livestock production. Management and nutritional strategies offer temporary relief, whereas genetic approaches represent a permanent solution. The role of genetic tools in enabling the development of climate-resilient livestock breeds is widely recognized. Genetic tools like microarrays, RNA-seq, omics, and GWAS can improve the understanding of livestock’s climate adaptability at a molecular level. These tools facilitate the identification of biomarkers for thermo-tolerance, bordering on climate-resilient livestock breeding. Among them, studies employing genome-wide association studies (GWAS) have increased in recent years. GWAS have the potential to improve the genetic basis of thermo-tolerance in heat-stressed livestock populations. GWAS have been used to identify candidate genes for complex and economically important traits in livestock. These include growth, reproduction, disease resistance, milk, meat, and wool production traits under heat stress conditions. This makes GWAS a useful tool for identifying biomarkers that can be incorporated in breeding programs through marker-assisted selection (MAS). The integration of these potential biomarkers into selection and breeding programs would allow GWAS to substantially refine breeding strategies, thereby advancing the climate-resilient potential and sustainability of the livestock sector. Furthermore, GWAS, when utilized along with emerging technologies like Artificial Intelligence (AI), machine learning (ML), and deep learning (DL) for genomic prediction, can predict genetic aspects of livestock adaptation more efficiently and precisely. Thus, future studies should focus on integrated modeling approaches for improving the climate resilience of livestock without jeopardizing their production potential. Such an effort will contribute to sustainable livestock production as well as ensure food security for the growing human population amid changing climate conditions. Full article

This paper addresses the NP-hard 2D irregular packing problem with non-convex geometric constraints. We propose a distributed hybrid metaheuristic based on an island population structure, integrating a genetic algorithm (GA), particle swarm optimization (PSO), simulated annealing (SA), and a grey wolf optimizer (GWO), with a novel Modular Adaptive Optimization Module (MAOM). The passivity and stability of the MAOM are rigorously proven via a Lyapunov energy function. The convergence rate of the island model is proven to be $O(T_{\max }/K)$, demonstrating linear speedup. Extensive experiments on 11 benchmark datasets show that the proposed algorithm achieves material utilization ranging from 61.73% to 79.42% with excellent stability ($\mathrm{CV}<0.03$). Statistical tests confirm significant improvements over traditional metaheuristics ($p<0.05$). This work provides a theoretically grounded and practically effective approach for 2D irregular nesting. Full article

Coronavirus disease-2019 COVID-19 exhibits marked inter-individual variability in susceptibility and clinical outcomes, suggesting a role for host genetic factors. Vitamin D exerts immunomodulatory effects through the vitamin D receptor (VDR), and genetic variation in the VDR gene may influence host responses to SARS-CoV-2 infection. This study aimed to investigate the association between VDR-gene polymorphisms—FokI (rs2228570), TaqI (rs731236), ApaI (rs7975232), and BsmI (rs1544410)—and COVID-19 susceptibility in the Kurdish population. The FokI polymorphism was significantly associated with COVID-19 susceptibility. Interestingly, the GG-genotype was more frequent among Patients than controls and was associated with increased odds of infection (OR = 9.00; 95% CI: 3.22–25.15; p < 0.0001), whereas the AG-genotype was associated with reduced susceptibility (OR = 0.33; 95% CI: 0.14–0.76; p = 0.001). Additionally, the G-allele was also more prevalent in Patients than controls (OR = 1.87; 95% CI: 1.21–2.89; p = 0.004). Similarly, the TaqI TT-genotype was more frequent among Patients and was associated with increased susceptibility (OR = 36.0; 95% CI: 11.2–115.8; p < 0.0001). In contrast, the ApaI AA-genotype was less frequent among Patients and was associated with reduced odds of COVID-19 susceptibility under a recessive model (OR = 0.15; 95% CI: 0.03–0.68; p = 0.003). Moreover, the BsmI polymorphism was monomorphic in both groups and therefore not informative. Genetic variation in the VDR gene, particularly at the FokI, TaqI, and ApaI loci, was associated with COVID-19 susceptibility in the case–control study, while BsmI showed no variations. These findings suggest that genetic variation in the VDR gene may contribute to inter-individual differences in susceptibility to SARS-CoV-2 infection in the Kurdish population. Larger studies incorporating functional validation and detailed clinical data are required to confirm these associations. Full article

Against the backdrop of high-quality urbanization in cities, the rapid expansion of metro networks has led to severe spatial mismatches in land use around station areas, which seriously restricts the full exertion of the comprehensive benefits of the transit-oriented development (TOD) model. Taking 139 operational metro stations in Xi’an in 2024 as the research sample, this study constructs a multi-objective land use optimization model with the richness of public services, transportation accessibility and population distribution balance as the three core maximization objectives. A hierarchically adaptive improved NSGA-III algorithm is proposed, with the following four key technical optimizations implemented: multi-dimensional adaptive reference point adjustment, design of real-integer hybrid coding genetic operators, construction of an enhanced multi-criteria environmental selection mechanism, and dynamic regulation of algorithm iteration. Experimental results show that the performance of the improved algorithm is significantly superior to that of the traditional NSGA-III algorithm: the values of the three core objectives are increased by 59.58%, 12.94% and 7.35% respectively compared with the original data; the algorithm achieves stable convergence after 25 iterations, with the convergence efficiency improved by 30%. The obtained Pareto optimal front features good uniformity (U = 0.92) and coverage (C = 0.95), and all the 80 non-dominated solutions meet all constraint conditions, with the solution set highly coupled with the urban functional zoning and spatial planning of Xi’an. This study proposes a zoned, prioritized and phased hierarchical land use optimization strategy for the areas around metro stations in Xi’an. The research findings provide a replicable research framework and methodological reference for the TOD practice and land use optimization of metro station areas in other rapidly urbanizing central cities in China and developing countries worldwide with the characteristic of rapid rail transit expansion. Full article