Search Results (190)

Seeding constitutes a key crop-production operation that governs seed spatial arrangement, crop population structure, and potential yield formation, and forms the foundation of precise, efficient, and eco-friendly farming. However, field soil properties, regional climate, and crop agronomic requirements exhibit strong spatio-temporal heterogeneity. Conventional seeding operations based on manual experience and fixed preset parameters cannot meet the demands of large-scale precision agriculture. Enabled by progress in precision agriculture, intelligent sensing, artificial intelligence, and autonomous machinery, modern intelligent seeding systems integrate precision seed metering, high-precision environmental perception, and closed-loop dynamic self-regulation. Such systems can improve plant-spacing uniformity and enable precise seeding-depth control under standard open-field conditions, yet face noticeable performance limitations in GNSS-denied complex environments including dense crop canopies and greenhouses. This review outlines the evolutionary trajectory of seeding machinery and summarizes research progress regarding precision seeding, multi-functional equipment integration, multi-source information perception, and intelligent decision-making. Integrated design principles covering mechanical optimization, electronic control, and perception-driven decision systems are elaborated. Four developmental phases of seeding equipment are identified: mechanical precision operation, electronic intelligent regulation, multi-functional module integration, and intelligent cognitive integration. Current intelligent seeding technologies are constrained by limited adaptability to complex farmland conditions, unstable multi-source data fusion, insufficient long-term operational reliability, and high deployment costs across diverse scenarios, restricting their broad field-scale adoption. Future research should combine agronomic knowledge with artificial intelligence to improve environmental awareness and autonomous decision-making capability, develop low-cost, high-reliability integrated seeding equipment, and support the construction of intelligent agricultural machinery systems. Full article

This is the first empirical study to report on shyness in Black American children. Shyness is framed as serving the function of appeasement, that amid conditions of potential social conflict or power imbalance, the conciliatory nature of shyness engenders interpersonal cooperation to the benefit of the shy individual and the group to which he or she belongs. Peer nominations, teacher reports, and census data were used to explore age-related differences in the correlates of perceived shyness among 445 Black American students attending five urban elementary schools in the U.S. (grades 3–6; mean ages 9.4–12.3 years; 91% free or reduced lunch). Multilevel regression analysis yielded findings in at least partial support of four theoretically derived hypotheses. In partial support of the shyness as protection from environmental risk hypothesis (H₁), Black girls (but not boys) and older students (grades 5 and 6) who resided in more disadvantaged neighborhoods were more frequently perceived by their peers as being shy. Consistent with the ascendance of shyness as appeasement hypothesis (H₂), having low popularity was associated with higher levels of perceived shyness among students in grades 5 and 6, but not in grades 3 and 4. Consistent with the hedonic effects of shyness hypothesis (H₃), among students in grades 5 and 6 (but not grades 3 and 4), being widely perceived as shy was associated with higher levels of prosocial behavior. Consistent with the care-eliciting effects of shyness hypothesis (H₄), being widely perceived as shy was associated with having a closer relationship with the teacher, and this association was stronger among students in grades 5 and 6 than among students in grades 3 and 4. The study findings are discussed within a conceptual framework that integrates evolutionary, cultural, and ecological perspectives of human shyness. Full article

Classical vehicle routing models assume flat, symmetric road networks, yet mountain cities exhibit gravitational asymmetry and steep gradients that invalidate two-dimensional cost estimates and may produce mechanically infeasible routes. This study formalizes the Asymmetric Capacitated Vehicle Routing Problem with Topographic Constraints (ACVRP-TC) and introduces a generalized cost function (GCF) that linearizes direction-dependent energy consumption into an impedance metric within a mixed-integer linear programming (MILP) formulation. A hybrid memetic algorithm coupled with stochastic large neighborhood search (H-MA-LNS) is proposed to solve this NP-hard variant, combining evolutionary global exploration with structured local intensification. Benchmark validation yields a mean improvement of $9.4\%$ over the state of the art on flat reference instances. An extensive computational evaluation on 24 real geospatial instances from Seoul (Republic of Korea), enriched with satellite elevation data, reveals a topographic gap of 14.1% for this urban network (Wilcoxon: p = 0.002), quantifying the cost underestimation that orography imposes on theoretical planning in the studied setting. Furthermore, the feasibility-based arc pruning reduces the search space by $51\%$, accelerating convergence and inducing a shift toward radial route structures in high-density environments. Full article

Rapeseed (Brassica napus L.) is a globally vital oilseed crop for edible oil, biofuel and animal feed production, yet its yield and quality are severely threatened by a narrow genetic foundation, scarce germplasm and recurrent abiotic stresses including drought and salinity. Mediator subunits have been proven to be critical for abiotic stress tolerance in model plants, but systematic research on the Brassica napus Mediator (BnaMED) gene family and its stress regulatory mechanism in Brassica napus remains largely lacking. In this study, we identified 189 BnaMED members phylogenetically clustered into five subfamilies, with extensive family expansion mainly driven by whole-genome duplications, accompanied by functional differentiation and redundancy to bolster polyploid adaptability. Chromosomal mapping revealed 176 BnaMED genes unevenly distributed across A/C subgenomes. Through gene collinearity analysis among Brassica napus, Arabidopsis thaliana, Glycine max, Oryza sativa, and Zea mays, we found that Brassica napus shares high collinearity with dicotyledonous species, such as Glycine max and Arabidopsis thaliana, indicating evolutionary conservation specific to dicots. Protein physicochemical properties and promoter cis-element analysis uncovered diverse structural features and extensive involvement in light, hormone and stress signaling pathways. Tissue expression profiling demonstrated distinct subfamily specificity and functional divergence. The quantitative real-time PCR (qRT-PCR) analysis revealed that under salt stress, endogenous transcript levels of BnaMED family members BnaA09g34000D, BnaA09g23620D, BnaC03g31860D and BnaA06g10800D were up-regulated, whereas BnaA08g23170D and BnaA08g22420D were transcriptionally repressed. Following drought-stress treatment, all six genes (BnaA09g34000D, BnaA09g23620D, BnaA09g50540D, BnaA06g10800D, BnaA08g23170D and BnaA08g22420D) displayed markedly reduced expression. These observations reflect stress-specific regulatory strategies underlying abiotic-stress adaptation. In addition, several BnaMED genes showed dynamic expression during floral transition in the semi-winter Zhong Shuang 11 (ZS11) cultivar. This study provides a foundational insight into functional roles of BnaMED genes in rapeseed abiotic stress responses. Full article

North African oases constitute critical socio-ecosystems characterised by complex agro-biodiversity and millennia of traditional ecological knowledge (TEK). Nevertheless, such fragile landscapes are confronted with mounting pressures from climate change and an-thropogenic activities driven by agri-industrial modernisation. This systematic review, executed in accordance with the PRISMA 2020 (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, appraises the contemporary state of scientific literature concerning agrobiodiversity and local knowledge in North African oases, with the objective of identifying evolutionary trends and crucial research gaps. A comprehensive search of the Web of Science database yielded a substantial corpus of 270 studies, which were subsequently subjected to a rigorous, standardised analysis to ascertain the reliability and validity of the results. The results obtained demonstrate a significant structural imbalance in the distribution of research efforts. Geographically, there is a notable concentration of studies in the Maghreb region (Tunisia, Algeria, and Morocco), which collectively account for over 80% of the extant literature. In contrast, the Sahelo-Saharan arc remains significantly under-researched. The thematic orientation of re-search remains predominantly reductionist and species-centric, with a preponderance of studies focused on date palm (Phoenix dactylifera L.) and conventional agronomy. This narrow focus obscures the functional intricacies of the three-tiered stratified system and its agropastoral interconnections. Moreover, although the need to assess landscape degradation and knowledge erosion is urgent, the integration of geospatial technologies (GIS), accounting for a mere 1.5% of studies, with ethnobotanical approaches remains marginal. The necessity for a paradigm shift is emphasised by these findings. It is essential that future research adopts transdisciplinary frameworks, which should integrate advanced geospatial monitoring with the socio-economic revitalisation of TEK. This transition is required to occur from a utilitarian perspective to holistic, locally grounded conservation strategies. The latter should ensure the long-term resilience of such agroecosystems. Full article

This study develops an analytical–evolutionary framework for optimizing the dynamic efficiency of bidirectional porous functionally graded beams. Touratier’s higher-order shear deformation theory is coupled with a real-coded genetic algorithm. The material-gradation indices in the thickness and width directions, the porosity coefficient, and the cross-sectional aspect ratio are treated as four coupled design variables. Dynamic efficiency is defined as the first modal frequency per unit mass, J = f₁/m (Hz/kg), with f₁ evaluated at β₁ = π/L for the simply supported finite beam. Uniform and non-uniform porosity laws are examined under three admissible design domains. After correcting and consistently implementing the modified rule of mixtures, the restricted-domain efficiencies are 117.819 and 96.766 Hz/kg for uniform and non-uniform porosity, respectively. Extending the material-gradation bounds increases them to 920.823 and 328.627 Hz/kg, while extension of the geometric domain gives 2302.058 and 821.568 Hz/kg. Thirty independent GA runs for each case yield 100% success under a 0.5% tolerance. Deterministic corner and one-at-a-time sampled checks confirm the observed boundary-directed trends within the investigated boxes. The results are mathematical optima for the stated objective and constraints, not production-ready designs. Full article

Glycosyltransferases (GTs) are key enzymes that catalyze the transfer of sugar moieties to diverse acceptor molecules and play important roles in a wide range of biological processes, including plant growth, development, and environmental adaptation. However, glycosyltransferase family 4 (GT4), the second-largest GT family, remains poorly characterized in soybean. In this study, we identified 60 GT4 family members in the soybean genome based on sequence information from the CAZy database and systematically characterized their phylogenetic relationships, genomic organization, and expression patterns by integrating genomic and multidimensional transcriptomic data. Furthermore, using a previously established soybean ethyl methanesulfonate (EMS) mutant population, we identified GmSPS8 as a candidate gene potentially involved in the regulation of seed weight. Notably, natural variation and haplotype analyses revealed that GmSPS8 is located within a genomic region subject to domestication selection, suggesting its potential involvement in the evolutionary selection of soybean seed size. In sum, we systematically characterized the soybean GT4 gene family and identified GmSPS8 as a potential domestication-associated candidate gene for seed weight regulation, providing new insights into the functional characterization of soybean GT4 family members and the identification and utilization of genes associated with soybean yield-related traits. Full article

Background/Objectives: The 3′ untranslated region (3′ UTR) integrates cleavage and polyadenylation signals, microRNA targets, RNA-binding-protein sites, and RNA secondary structure, but the organizational levels that remain conserved during long-term sequence evolution are poorly understood. Methods: We analyzed the ras85D 3′ UTR in 37 drosophilid taxa. Substitution rates were estimated by maximum likelihood and RelTime; insertions and deletions were reconstructed with ARPIP and summarized as insertion–deletion evolutionary localizations (IELs). Mobile-element candidates were detected with CENSOR/Repbase, and evolutionarily conserved motifs (ECMs) with MEME/MAST. Functional and structural annotations were integrated for Drosophila melanogaster, Drosophila yakuba, and Drosophila virilis and tested using permutation-based coverage, distance, boundary-neighborhood, and multilayer architecture analyses. Results: The 2247-column alignment yielded 959 block events (710 deletions and 249 insertions). Among 63 positive-length ingroup branches, 16 were deletion-enriched, three were insertion-enriched, and one showed bidirectional turnover. Thirty-four IELs projected to 27 D. melanogaster loci and were associated with ECMs. The final registry contained 390 primary functional objects and 1135 RNAfold-predicted structural segments. Predicted weakly conserved miRNA target sites were depleted in ECM_15 and ECM_11, whereas none of 12 Functional Distance tests was significant. APA objects were enriched near predicted structural-segment boundaries (O/E = 5.21; FDR = 0.00761). SAME_MULTILOOP_INTERVAL showed reduced between-context variance (0.276× null; FDR = 0.0233), and the DIFFERENT_MULTILOOP_ARMS − SAME_MULTILOOP_INTERVAL contrast was significant (p = 0.00149; FDR = 0.00447). Conclusions: The ras85D 3′ UTR evolves as a mosaic system in which extensive deletion-biased sequence turnover coexists with conserved regulatory landmarks, recurrently remodeled local neighborhoods, and context-dependent structural–functional architectures. Full article

Sugarcane (Saccharum spp.) is a globally vital high-biomass sugar crop with a massive demand for potassium (K). Low-K⁺ stress severely restricts its yield and stress resistance. Plasma membrane intrinsic proteins (PIPs) play pivotal roles in transmembrane water transport and ion homeostasis; however, their evolutionary characteristics and molecular mechanisms underlying nutritional stress responses in the complex polyploid sugarcane remain poorly understood. In this study, genome-wide identification in the sugarcane cultivar XTT22 yielded 149 PIP gene family members (comprising 54 PIP1s and 95 PIP2s). Phylogenetic and chromosomal localization analyses demonstrated that the sugarcane PIP family underwent drastic paralogous expansion during evolution, with tandem duplication acting as the core driving force for the dramatic expansion of the PIP2 subfamily. Spatiotemporal expression profiling unveiled significant modular functional division among PIP genes, identifying a core co-expression group driving rapid early seedling elongation and a PIP2-specific expression cluster dedicated to the physiological homeostasis of mature stems. Notably, the core member ScPIP2-70 exhibited significant early-induced responses at both transcriptional and protein levels in roots under low-K⁺ stress. Functional complementation assays in the K⁺-uptake deficient yeast strain R5421 further confirmed that the heterologous expression of ScPIP2-70 effectively rescued the growth defects of yeast under low-K⁺ conditions, demonstrating its potential transmembrane K⁺ transport activity. This study not only comprehensively elucidates the evolutionary dynamics and spatiotemporal expression profiles of the sugarcane PIP gene family but also uncovers the novel pleiotropic function of ScPIP2-70 in mediating low-K⁺ stress tolerance, providing critical theoretical support and candidate gene resources for breeding “potassium-efficient” sugarcane cultivars via modern biotechnology. Full article

Training neural networks with gradient-based methods such as backpropagation is the dominant paradigm, but it depends on differentiable loss functions and is sensitive to initialization and local minima. Evolutionary algorithms offer a gradient-free alternative, yet the influence of their internal operators on training quality remains insufficiently characterized. This study presents a systematic comparison of backpropagation and ten variants of a genetic algorithm (GA) for training multi-layer perceptrons (MLPs), with particular focus on the role of crossover operators. The evaluation covers four MLP architectures and ten classification datasets from the UCI Machine Learning Repository, differing in sample size, dimensionality, and number of classes. Each configuration was assessed using stratified 4-fold cross-validation with 30 independent repetitions, and accuracy served as the primary performance metric, with macro-F1 reported to assess classifier behavior on class-imbalanced datasets. Backpropagation achieved higher mean accuracy than every GA variant on nine of the ten datasets, with the largest margins on high-dimensional problems. The genetic algorithm proved competitive on simpler, class-balanced datasets, where its better-performing variants matched the gradient-based baseline within one to two percentage points, and, on the Heart disease dataset, every GA variant reached a higher mean accuracy than backpropagation across all four architectures, though absolute performance remained modest on this five-class problem. Among crossover operators, BLX-$\alpha$ and BLX-$\alpha$-$\beta$ combined with tournament selection and a high crossover probability yielded the strongest configurations, while averaging crossover performed worst, as it restricts offspring to the midpoint of the parents and cannot explore beyond the range already present in the population. Tournament selection consistently led to higher mean accuracy than roulette-wheel selection, and shallow but moderately wide architectures, which encode fewer trainable parameters and thus a shorter chromosome, proved more amenable to evolutionary training than the two-layer alternative. These findings clarify when gradient-free training is competitive and which evolutionary operators drive its effectiveness. Full article

Electrospinning is a widely used nanofabrication technique capable of producing fibers with a range of diameters, morphologies, and porosities through the adjustment of experimental parameters. However, achieving reliable fiber diameter tuning remains challenging because of the complex, nonlinear interdependence among multiple electrospinning variables. In this study, a data-driven methodology is proposed that integrates predictive machine learning (ML) modeling with evolutionary algorithm-based optimization, specifically employing a genetic algorithm (GA), to predict fiber diameter and guide electrospinning parameter selection across nano- and microscale ranges. A curated dataset comprising 388 data points from 30 scientific publications was developed, focusing exclusively on polyacrylonitrile (PAN) dissolved in dimethylformamide (DMF). Multiple ML models were trained and tested to predict fiber diameter as a function of key electrospinning parameters. Among the evaluated ML models, the eXtreme gradient boosting (XGB) model achieved the highest predictive performance, yielding a coefficient of determination (R²) value of 0.93 with low prediction errors (root mean square error [RMSE]: 127.76 nm, mean absolute error [MAE]: 56.27 nm) on the test set. Experimental validation was performed by fabricating electrospun PAN nanofibers under one independent set of conditions, with scanning electron microscopy (SEM) showing close agreement between predicted and actual fiber diameters. The trained XGB model was subsequently integrated with a GA to identify electrospinning parameter sets for user-defined target fiber diameters ranging from 100 to 2000 nm. The evolutionary optimization process exhibited rapid convergence with low fitness error when evaluated using the trained predictive model. Overall, this study demonstrates the potential of a data-driven methodology to generate model-guided candidate conditions for target-driven PAN-DMF electrospinning, subject to broader experimental validation. Full article

Background: Mechanosensitive ion channels PIEZO1 and PIEZO2 are key mediators of mechanotransduction, which converts physical forces into cellular signals involved in proprioception, touch, vascular function, and other physiological processes. Mutations in human PIEZO proteins are linked to various diseases, such as hereditary xerocytosis, lymphatic dysplasia, and proprioceptive dysfunction. However, the role of intrinsic disorder in the regulation of these proteins and their susceptibility for disease-associated mutations remains unclear. Methods: We analyzed canonical human PIEZO1 and PIEZO2 protein sequences using machine learning, neural network, and energy-based disorder predictors, together with the prediction of disorder-mediated binding regions, phase separation propensity, interaction networks, evolutionary conservation, clinically annotated human variants, and peptide structural modeling. Results: Both proteins showed moderate intrinsic disorder, with PIEZO2 having slightly greater disorder propensity and higher predicted phase separation potential. Intrinsically disordered regions frequently overlapped binding-prone segments and post-translational modification sites, supporting regulatory functions. Evolutionary comparisons showed strong conservation of PIEZO proteins, while selected disordered regions retained disorder propensity despite greater sequence variability. Disease-causing variants mainly affected the ordered regions of both proteins, whereas disordered regions contained proportionally more benign variants and relatively few pathogenic mutations. The modeling of mutations within disordered hotspots showed altered local conformational tendencies, indicating that some disease variants may disrupt dynamic interaction interfaces rather than global structure. Interaction network analysis linked both proteins to enriched mechanotransduction, ion transport, and cytoskeletal pathways. Conclusions: Overall, our findings identify intrinsic disorder as an underappreciated feature of PIEZO channel biology and provide a framework for interpreting PIEZO-associated channelopathies. PIEZO proteins also perfectly illustrate the proteoform concept, where one gene yields a highly diverse kit of mechanosensitive molecular tools. While humans only have two primary PIEZO genes (PIEZO1 and PIEZO2), the body generates a vast array of functional variations. Full article

A large collection of real-world classification and regression problems can be addressed using machine learning tools such as, for example, radial basis function networks (RBF networks). However, the techniques used for training RBF networks often exhibit various problems, such as getting trapped in the local minima of the error function, or even encountering numerical issues when solving systems of linear equations in order to estimate the parameters of the RBF network. This paper presents a multi-stage evolutionary technique based on genetic algorithms for the effective training of RBF networks. In the first stage, the value ranges of the RBF network parameters are estimated using the K-Means algorithm. In the second stage, the chromosomes of the genetic algorithm are initialized within the parameter ranges determined in the first stage, followed by the execution of the genetic algorithm. Each chromosome of the genetic algorithm is considered a candidate parameter vector for the machine learning model. The centers and variances of the RBF network are estimated by the genetic algorithm, while the network weights are determined by solving a system of linear equations. This method was applied to a large set of classification and data-fitting problems, yielding excellent results. Full article

Reactive oxygen species (ROS) exert dual biological functions in plants. Though they form toxic byproducts of aerobic metabolism, ROS also serve as indispensable secondary messengers that orchestrate stress acclimation programs. For decades, plant physiologists operated under a pervasive assumption that constitutive and non-compartmentalized upregulation of antioxidant capacity would universally enhance abiotic stress tolerance. This long-standing dogma has now been thoroughly overturned. A growing body of evidence shows that sustained, global high antioxidant activity often impairs adaptation rather than helping it. In this review, we replace the simplistic “more antioxidants equal better tolerance” framework with a dynamic model of cellular redox homeostasis. We dissect three interconnected mechanisms though which unrestrained ROS scavenging generates deleterious phenotypic outcomes. First, indiscriminate clearance blunts transient ROS pulses and propagating ROS waves, the core signaling events acquired to trigger systemic acquired acclimation (SAA). Second, continuous antioxidant biosynthesis drains finite carbon skeletons, NADPH, and ATP pools, exacerbating evolutionary growth-defense resource trade-offs. Third, non-specific bulk ROS scavenging erases compartment-specific organellar retrograde signals, which rely on tightly controlled spatial and temporal ROS fluctuations. We concurrently define physiological boundary conditions where robust antioxidant activity remains vital for plant survival under extreme stress. Rather than advocating for the complete suppression of ROS detoxification, our analysis advocates context-dependent fine-tuning of redox signaling networks. We also summarize emerging precision redox monitoring and genetic engineering tools, and outline translational breeding pipelines to develop climate-resilient crops that balance stress survival and yield stability. This work delivers novel conceptual perspectives to advance fundamental plant redox biology. Full article

Simulation-dependent energy analysis has long dominated building retrofit research, yet this paradigm presents substantial barriers for non-expert building owners who lack technical software proficiency and detailed building documentation-a challenge compounded by the “curse of dimensionality” when multivariate analysis requires thousands of samples beyond available empirical records. Leveraging retrofit data accumulated through Korea’s Green Remodeling programs since 2017, this study proposes a Copula-Genetic Algorithm (Copula-GA) integrated framework that enables rational retrofit decision-making with minimal user inputs (construction year, floor area, structural type). From 178 documented retrofit cases, Gaussian copula-based multivariate sampling generated 10,000 synthetic records while preserving inter-variable dependency structures. Building physics constraints addressing vintage-thermal performance and capacity-efficiency relationships filtered implausible combinations, yielding 9898 valid cases with correlation matrix fidelity confirmed by a Frobenius norm deviation of 0.043. Evolutionary clustering employing a composite fitness function of Silhouette coefficient (0.68) and Davies-Bouldin Index (0.52) identified K = 16 as the optimal partition, categorizing outcomes into four reference model archetypes: Lightweight Structure (Type A, 27.0% reduction, 15.7-year payback), Masonry Structure (Type B, 29.0%, 14.8 years), RC Structure (Type C, 30.7%, 13.4 years), and Mixed Structure (Type D, 30.9%, 13.1 years). The proposed Copula-GA framework bridges the gap between advanced energy optimization methodologies and practical accessibility for non-expert building owners. By transforming limited empirical samples into reliable reference models, this research supports building-sector decarbonization. Using three minimal inputs, a building can be matched to one of the 16 standard models to obtain its expected saving rate, payback period, and recommended measures without detailed simulation. Full article