Search Results (3,512)

In air jet looms, relay nozzles are critical components in governing airflow velocity and air consumption during the weft insertion process. Although computational fluid dynamics (CFD) offers high-fidelity simulation for aerodynamic analysis, its computational burden hinders its practicality in iterative aerodynamic design of relay nozzles. To address the challenge, this study proposes a data-driven framework integrating a Chebyshev polynomial Kolmogorov–Arnold Network (Chebyshev KAN) surrogate model with an Improved Multi-objective Red-billed Blue Magpie Optimizer (IMORBMO). The accuracy of the Chebyshev KAN model was benchmarked against conventional multilayer perceptrons (MLP), convolutional neural networks (CNN), and the standard Kolmogorov–Arnold Network (KAN). Experimental results demonstrate that the Chebyshev KAN model achieves the lowest mean absolute error (MAE) of 0.103 for airflow velocity and 0.115 for air consumption. Building upon the non-dominated sorting and crowding distance strategies, IMORBMO was developed, incorporating an adaptive mutation mechanism by information entropy for improvement of convergence, diversity, and uniformity of the Pareto-optimal solutions. Comprehensive evaluations on the ZDT and WFG benchmark suites confirm that the IMORBMO consistently attains the best and highly competitive performance, yielding the lowest generation distance (GD), inverted generational distance (IGD) values and the highest hypervolume (HV). Applied to the aerodynamic optimization of a relay nozzle, the proposed framework delivers an optimal aerodynamic design that increases airflow velocity by 10.5% while reducing air consumption by 15.4%, as verified by CFD simulation. The steady-state flow field was simulated by solving the Reynolds-Average NavierStokes equations with the k−ω turbulent model, utilizing Fluent 2025.R2. No-slip wall, inlet pressure and outlet pressures are boundary conditions to the relay nozzle surfaces. This work establishes a computationally efficient and accurate optimization paradigm that holds significant promise for aerodynamic design and other complex real-world engineering applications. Full article

Background: Tartary buckwheat (Fagopyrum tataricum) serves as an excellent model for studying plant water adaptation mechanisms due to its exceptional drought tolerance. While aquaporins (AQPs) mediate the transmembrane transport of water and solutes in plants, their fine-tuned regulatory networks underlying stress resilience in Tartary buckwheat remain largely elusive. Methods: Here, we combined bioinformatics and transcriptomics to systematically identify 30 highly conserved FtAQP genes at the genome-wide level. Results: Cross-validated by qRT-PCR, our analysis revealed their distinct expression patterns across different organs. Based on our transcriptomic data, we hypothesize that FtAQP family members potentially participate in a coordinated whole-plant water management network through differential spatiotemporal expression. Specifically, the robust transcription of FtAQP8, FtAQP12, and FtAQP28 in roots is associated with the initial water uptake process. As water undergoes long-distance transport, the synergistic upregulation of FtAQP13, FtAQP17, FtAQP20, and FtAQP29 in the stem suggests a potential role in facilitating critical lateral water flow. Furthermore, during reproductive development, FtAQP27 exhibits extreme tissue specificity in floral organs, implying its possible involvement in maintaining local osmotic homeostasis. Furthermore, the promoter regions of FtAQPs are highly enriched with cis-acting elements responsive to light, abscisic acid (ABA), and cold stress, suggesting they are intimately regulated by a coupling of endogenous phytohormones and environmental cues. Conclusions: Ultimately, this study provides valuable insights into the potential molecular basis of multidimensional water regulation in Tartary buckwheat, and identifies candidate genetic targets for improving water use efficiency in dryland agriculture through the precise manipulation of aquaporins. Collectively, while these observational findings provide valuable predictive models, future in vivo experimental validations are required to confirm their exact biological functions. Full article

In semi-arid regions, sustainable groundwater management for irrigation is critical for agricultural productivity and food security. This study presents an integrated methodological framework combining hydrochemical characterization, machine learning (ML) modeling, and explainable artificial intelligence (XAI) to predict the Irrigation Water Quality Index (IWQI) in the Ain Oussera plain, Djelfa Province, Algeria. A total of 191 groundwater samples were collected from November 2023 to September 2024 and analyzed for major ions and physicochemical parameters. Multiple irrigation suitability indices were calculated, including Sodium Adsorption Ratio (SAR), Sodium Percentage (Na%), Magnesium Hazard (MH), Permeability Index (PI), Residual Sodium Carbonate (RSC), Soluble Sodium Percentage (SSP), and Kelly’s Ratio (KR). Five ML models were developed and evaluated for IWQI prediction: Random Forest, Gradient Boosting, XGBoost, K-Nearest Neighbors, and Support Vector Regression. Results showed that 55% of groundwater samples exhibited low to no restrictions for irrigation use, while 19% required high to severe restrictions. The XGBoost model demonstrated superior performance, with the highest R² (0.95) and the lowest RMSE (3.22) among all tested algorithms. SHAP (SHapley Additive exPlanations) analysis provided a transparent interpretation of model predictions, identifying electrical conductivity and Sodium Adsorption Ratio as the most influential parameters affecting IWQI, while chloride, sodium, total hardness, and magnesium had minimal impact. Spatial mapping using Inverse Distance Weighting (IDW) interpolation in ArcGIS 10.8 revealed considerable spatial variability in water quality throughout s the plain. This research addresses a critical gap in North African groundwater management by integrating ML predictive capabilities with XAI transparency, providing water resource managers and agricultural stakeholders with interpretable, data-driven tools for sustainable irrigation planning in water-stressed semi-arid environments. Full article

The European Middle Pleistocene represents a critical spatiotemporal interval in human evolution, marked by increasing morphological variability and ongoing debate regarding the evolutionary processes leading to the emergence of Neandertals. In particular, it remains unclear whether this variability reflects the coexistence of multiple evolutionary lineages within Europe or structured variation within a single, evolving lineage. Within this context, the site of Bilzingsleben (Thuringia, Germany) provides a key contribution to discussions of European Middle Pleistocene population structure. This study presents a detailed morphological assessment of the Bilzingsleben E7 mandibular fragment, integrating qualitative anatomical observations with quantitative analyses of discrete characters. The Bilzingsleben mandible was examined directly and evaluated within a broad comparative framework including European Middle Pleistocene hominins, Neandertals, and selected African and Asian specimens. Multivariate analyses, including Principal Coordinates Analysis (PCoA) and neighbor-joining cluster analysis based on Gower distances, were used to explore patterns of morphological affinity. Qualitative analysis indicates that the Bilzingsleben mandible exhibits a mosaic combination of predominantly primitive features—such as multiple mental foramina, marked lateral relief of the corpus, and a weakly developed submandibular fossa—together with a limited number of incipiently derived traits, including posterior extension of the corpus and a downward orientation of the digastric fossae. Quantitative results consistently place Bilzingsleben within the morphological variability of European Middle Pleistocene hominins but outside the compact Neandertal cluster. In the PCoA, Bilzingsleben occupies an intermediate (PCo1) and peripheral position (PCo2), contrasting with more centrally positioned specimens such as Mauer. Taken together, these results support an interpretation of Bilzingsleben as part of a European Middle Pleistocene set of populations exhibiting mosaic morphology, rather than considering Bilzingsleben as evidence for a distinct evolutionary lineage. When integrated with evidence from other anatomical elements from Bilzingsleben, the mandibular morphology supports interpreting this population within the broader evolutionary context of the Neandertal lineage. Full article

Metal artifacts generated by dental implants significantly degrade cone-beam computed tomography (CBCT) volumes, obscuring critical anatomical structures and compromising diagnostic precision. To address this, an unsupervised deep learning framework has been proposed for Metal Artifact Reduction (MAR) utilizing a Cycle-Consistent Adversarial Network (CycleGAN) optimized for high-fidelity restoration. Unlike supervised methods that rely on unattainable voxel-aligned paired datasets, the proposed approach leverages an unpaired dataset of approximately 4000 images, curated from the public ToothFairy dataset. The architecture integrates U-Net-based generators and PatchGAN discriminators, specifically tuned to mitigate generative hallucinations and preserve morphological integrity. Quantitative benchmarking on a held-out test set demonstrates a 34.6% improvement in the Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) score, a substantial reduction in Fréchet Inception Distance (FID) from 207.03 to 157.04, and a superior Structural Similarity Index Measure (SSIM) of 0.9105. The framework achieves real-time efficiency with a 3.03 ms inference time per slice, effectively suppressing artifacts while preserving anatomical detail. Expert validation confirms high fidelity; however, to ensure reliability in extreme cases, the architecture is recommended as a clinical decision-support tool under human-in-the-loop oversight. By enhancing diagnostic clarity via a scalable software pipeline, this study provides a robust solution for high-fidelity dental implant imaging. Full article

Global freshwater resources face severe water quality degradation, with chlorophyll-a (Chl-a) concentration serving as a critical eutrophication indicator. While deep learning methods enable accurate Chl-a retrieval from remote sensing reflectance (Rrs) spectra, the scarcity of paired Rrs-Chl-a samples limits model generalization and causes overfitting, particularly in optically complex inland waters. To address this data bottleneck, we propose a physics-constrained latent diffusion model for synthesizing high-fidelity paired Rrs-Chl-a data to augment limited training sets for deep learning-based water quality retrieval. Our framework integrates three key innovations: (1) a lightweight variational autoencoder achieving 8.6:1 latent space compression, reducing computational overhead while preserving spectral features; (2) band-selective attention mechanisms targeting chlorophyll-sensitive wavelengths (440, 550, 680, and 700–750 nm) based on bio-optical principles; and (3) physics-guided conditional encoding that captures concentration-dependent spectral responses across oligotrophic to eutrophic regimes. Evaluated on the GLORIA dataset, our model demonstrates superior performance in spectral similarity (0.535), sample diversity (0.072), and distribution matching (Fréchet distance 0.0008) compared to conventional generative models. When applied to data augmentation, synthetic spectra improved downstream Chl-a retrieval from R²= 0.75 to 0.91, reducing RMSE by 39%. This physics-informed generative approach addresses data scarcity in aquatic remote sensing research, supporting global needs for enhanced understanding of inland and coastal water quality dynamics in data-limited regions. Full article

Energy-efficient routing is critical for extending the operational lifespan of wireless sensor networks (WSNs). While the Power-Efficient Gathering in Sensor Information Systems (PEGASIS) protocol achieves high efficiency through chain-based data aggregation, its standard round-robin leader selection fails to account for dynamic node factors, such as residual energy and historical reliability. This often leads to premature energy depletion and network instability. To address these limitations, this paper proposes K-NN-PEGASIS, a data-driven machine learning framework that utilises a weighted k-nearest neighbours (K-NN) algorithm for intelligent leader selection. By processing a normalised feature vector comprising residual energy, distance to the base station (BS), node degree, and historical performance, the framework adaptively identifies optimal leaders in each round. Simulations conducted in MATLAB for networks ranging from 100 to 1000 nodes demonstrate that K-NN-PEGASIS improves network lifetime by up to 47.3% and reduces total energy dissipation by 52.8% compared to baseline algorithms. Furthermore, the framework provides passive resilience against routing attacks, reducing the selection of malicious leaders by 96% and maintaining a 32.3% higher packet delivery ratio under attack scenarios. Full article

This paper introduces the concept of the Point of No Return as a physically grounded safety indicator for rear-end vehicle conflicts, addressing fundamental limitations of the widely used time-to-collision metric. Unlike purely kinematic approaches, the proposed formulation incorporates braking capability and reaction constraints, enabling a direct assessment of whether a collision can still be avoided. To illustrate the applicability of the concept, a vision-based framework using a single camera is developed based on dashcam data, combining YOLO-based object detection, Kalman-filter tracking, and geometric distance estimation derived from bounding-box features and camera projection models. The estimated distance is further processed to obtain relative motion, allowing a unified analysis of time to collision and the Point of No Return within the same evaluation pipeline. Experimental results on real-world driving sequences show that the Point of No Return consistently precedes critical conditions identified by time to collision and provides a more stable and physically interpretable characterization of the transition toward collision inevitability. The results also highlight the sensitivity of the proposed indicator to braking capability, while showing lower sensitivity to variations in relative speed. Overall, this study demonstrates the relevance of the Point of No Return as a complementary indicator for collision risk assessment, offering a physically meaningful basis for decision-making in driver assistance systems and improving the interpretation of critical traffic situations. The proposed approach supports sustainable urban mobility by enabling earlier and more reliable intervention strategies, contributing to improved traffic safety, smoother traffic flow, and reduced environmental impact. Full article

Lodgepole pine (Pinus contorta Dougl.) exhibits pronounced morphological variation across its range, historically attributed to allopatric differentiation during the Wisconsin glaciation. However, whether genetic divergence aligns with morphological differentiation—a fundamental prediction of allopatric speciation theory—remains untested. We conducted a comprehensive phylogeographic analysis of chloroplast DNA (trnL intron and trnL/trnF spacer) and mitochondrial DNA (nad1 b/c intron) across 31 populations representing all four recognized subspecies to test hypotheses of refugial isolation and to evaluate the genetic basis of current taxonomic classification. Contrary to predictions of allopatric divergence, both organellar genomes showed striking genetic uniformity (π = 0.000178–0.000186; intersubspecific genetic distances: 1.06 × 10⁻⁴ to 3.96 × 10⁻⁴) with no phylogenetic structure corresponding to morphological boundaries. Significant negative neutrality test values (Tajima’s D = −2.26, p < 0.02; Fu and Li’s D* = −4.52, p < 0.02) suggest recent demographic expansion rather than equilibrium divergence. A distinctive 5 bp indel in coastal populations provides molecular evidence for a northern Pacific refugium, and its occurrence in interior populations is consistent with post-glacial pollen-mediated gene flow, though this directionality remains inferential pending nuclear genomic confirmation. These findings suggest that morphological divergence reflects rapid adaptive evolution in heterogeneous environments rather than deep phylogenetic divisions. This pattern exemplifies gene flow-selection balance, in which divergent selection maintains local adaptation despite extensive gene flow—supporting an ecotypic rather than a phylogenetic interpretation of intraspecific diversity. The persistence of morphological variation despite genetic homogeneity indicates strong selection on ecologically important traits, likely driven by variation in fire regimes, differential seed predation, and climate gradients. These results have critical implications for understanding adaptive evolution rates in widespread conifers and for developing conservation strategies that emphasize adaptive processes over taxonomic categories. Full article

Silane-based hydrophobic coatings are widely used to improve the durability of cement-based materials in aggressive environments such as marine and hydraulic structures. However, their long-term effectiveness is strongly influenced by interfacial adhesion degradation under humid conditions, which remains a critical challenge in engineering applications. From a scientific perspective, the fundamental mechanisms governing how silane-based coatings interact with cement hydration products, particularly under varying moisture conditions, are still not fully understood. In particular, the role of interfacial water in regulating bonding strength and intermolecular force transfer at the nanoscale has not been quantitatively clarified. To address these issues, this study investigates the interfacial debonding behavior of polydimethylsiloxane (PDMS), a representative silane-based hydrophobic component, on calcium silicate hydrate (C–S–H) substrates using molecular dynamics simulations under controlled hydration states. The results show that the interfacial interaction is dominated by van der Waals forces, with a calculated binding energy of approximately 357 kcal/m². As the interfacial water content increases from dry to high-humidity conditions, the maximum debonding force (F_max) decreases from approximately 1.6 × 10³ pN to 1.3 × 10³ pN, corresponding to a reduction of about 18–20%. Similarly, the debonding work (W_max) shows a consistent decreasing trend, indicating reduced energy required for interface separation. This reduction is attributed to the formation of a continuous water film, which increases the interfacial separation distance and reduces the efficiency of intermolecular force transfer. These findings demonstrate the humidity-dependent weakening of interfacial adhesion and provide new insights into the nanoscale mechanisms governing the performance of silane-based coatings. The results offer a theoretical basis for optimizing the durability and reliability of hydrophobic treatments in cement-based materials under realistic service conditions. Full article

Transit connectivity is a critical indicator for evaluating the transfer efficiency and network performance of hub airports within integrated transport systems. However, conventional connectivity models primarily rely on flight frequency and schedule coordination, while passenger path choice behavior and multimodal competition effects are often overlooked. To address this limitation, this study develops an enhanced transit connectivity evaluation framework that incorporates passenger path choice preferences and air–rail intermodal effects. A novel air–rail intermodal gain coefficient is introduced to capture the context-dependent interplay between aviation and high-speed rail, quantifying synergistic effects when HSR complements air transfer and substitution effects when it competes with it. The proposed model integrates direct transfer connectivity (C^d) and indirect transfer connectivity (C^ind) within a unified quantitative framework, embedding transfer time compliance and detour factor constraints to improve behavioral realism and operational applicability. A case study of Xi’an Xianyang International Airport demonstrates that the introduction of the intermodal gain mechanism increases overall transit connectivity from 3606.3 to 3664.1, with the gain concentrated in the 500 to 800 km distance band where HSR journey times are most competitive with door-to-door air travel. The results reveal strong polarization in direct transfer connectivity and the limited effectiveness of indirect transfer routes due to transfer time constraints. The proposed framework offers a replicable assessment tool for hub airport network connectivity and multimodal transport planning, with potential for broader application across hub airports operating within integrated air–rail networks. Full article

The rapid development of machine learning, large language models, and related technologies has greatly increased the demand for data storage capacity. Therefore, the role of distributed storage systems in such applications has become more prominent. However, it is inevitable that a single node fails in a distributed storage system during long-term use. Being able to repair failed nodes in a timely manner is extremely important for the stable operation of distributed storage systems, and a specific encoding scheme is required to meet the needs of efficiently repairing failed nodes. This research presents a novel family of binary locally repairable codes (LRCs) developed using multiple disjoint recovery sets constructed based on mutually orthogonal Latin squares (MOLS). The proposed constructions achieve distance optimality under the Singleton-like bound for LRCs with availability. Specifically, the codes are parameterized as $(n=r^2+tr,k=r^2,r,t)$ and $(n=rm+tm,k=rm,r,t)$, where n is the block length, k is the dimension, r is the locality, and t is the availability. These codes achieve minimum distance $d=t+1$, guaranteeing efficient recovery with t disjoint repair sets, each of size r. Compared to existing constructions, the proposed codes offer significant improvements in terms of code rate $\mathcal{R}={\displaystyle \frac{r}{r+t}}$, support for larger block lengths, and reduced finite field size requirements (field size $q=2$). Additionally, a method is introduced to improve the minimum distance of codes with even availability t, constructing codes with parameters $(n+1,k,r,t)$ and $d=t+2$, while preserving optimality. These properties make the proposed codes particularly suitable for distributed storage systems, where efficient and parallel repair of failed nodes is critical. Full article

DBSCAN is widely used to identify structured regions in unlabeled data, but its performance depends critically on the selection of the neighborhood parameter $\epsilon$. Traditional heuristics for estimating $\epsilon$ often become unreliable in high-dimensional or varying-density settings because they rely heavily on local geometric criteria and may fail under smooth transitions or topological ambiguity. This work presents a three-level perspective on DBSCAN hyperparameter selection. At the algorithmic level, $\epsilon$ controls neighborhood connectivity and structural transitions in clustering. At the modeling level, the ordered k-distance signal is approximated through a surrogate dynamical estimation framework inspired by a mass–spring–damper system. At the causal level, the resulting estimator is interpreted through interventions on its internal threshold-selection mechanism. The proposed method models the variation of $\epsilon$ using ordinary differential equations defined on the ordered k-distance signal, enabling analysis of structural transitions in density organization via a surrogate dynamical representation. System identification is performed using L-BFGS-B optimization on the smoothed k-distance curve, while the system dynamics are solved with the fourth-order Runge–Kutta method. The resulting estimator identifies transition regions that are structurally informative for $\epsilon$ selection in DBSCAN. To analyze the estimator at the intervention level, Pearl’s do-calculus is used to compute the Average Causal Effect (ACE). The method was evaluated on synthetic benchmarks and on the Covtype dataset, including scenarios with multi-density overlap and dimensionality up to ${\mathbb{R}}^{10}$. The resulting ACE values, $+0.9352$, $+0.5148$, and $+0.9246$, indicate that the proposed estimator improves intervention-based $\epsilon$ selection relative to the geometric baseline across the evaluated datasets. Its practical computational cost is dominated by nearest-neighbor search, behaving approximately as $O(NlogN)$ under favorable indexing conditions and degrading toward $O\left(N^2\right)$ in high-dimensional or weak-pruning regimes. Full article

Background: Global positioning system (GPS) technology is widely used to quantify external training load (ETL) in youth soccer. Despite its extensive application in training and match contexts, considerable heterogeneity is present in the selection, definition, and interpretation of GPS-derived variables, limiting comparability between studies and practical implementation by coaches. Objective: This narrative review aimed to summarize and critically evaluate the current literature on GPS-based ETL monitoring in youth soccer players, with a focus on commonly used variables, methodological considerations, and practical applications in training and match contexts. Methods: A narrative literature search was conducted using PubMed, SPORTDiscus, and Scopus databases. Peer-reviewed studies published in English between the years of 2012 and 2025 were included. Data were extracted on participant characteristics, GPS technology, monitored ETL variables, and contextual settings. Results: The 34 reviewed studies primarily reported total distance (TD; m), high-speed running distance (HSR; m), sprint distance (SD; m), distance per minute (m·min⁻¹), peak speed (km·h⁻¹), and acceleration- and deceleration-based (ACC, DEC; count) ETL variables. Substantial variability was observed in speed thresholds, acceleration definitions, and data processing methods. Positional roles, training formats (e.g., small-sided games), and seasonal phase influenced ETL demands, although methodological inconsistencies limited cross-study comparisons. Conclusion: GPS technology provides valuable insights into the ETL demands of youth soccer. The lack of standardized variable definitions and thresholds remains a major limitation. Greater methodological consistency and clearer reporting standards are required to enhance the practical usefulness of GPS monitoring for coaches in youth soccer. Full article

High Andean lagoons in southern Peru have critical hydrological and ecological functions; however, long-term time series integrating trophic, integral quality, and metal contamination metrics to support adaptive management are lacking. A total of 1846 records (2015–2024) from four systems (3100–4600 m a.s.l.) were analyzed using seven indices assessing trophic status (TSItsr, TRIX), general water quality (OWQI, WQIHA, CCME-WQI), and metal contamination (HPI, CD). Temporal trends were assessed using Mann–Kendall and Theil–Sen slope; spatial heterogeneity using Kruskal–Wallis and Dunn–Bonferroni comparisons; controlling factors using distance-based redundancy analysis (999 permutations); and functional typology using Ward’s hierarchical clustering on Z-standardized data. 93% of the series lacked monotonic trends (52/56 lagoon–stratum × index combinations), demonstrating high interannual stability; spatial variance was marked (ε² = 0.73 in CCME-WQI). Distance-based redundancy analysis (db-RDA) explained 24.6% of total variability, with lake identity as the dominant driver (~45%), followed by temporal change (~8%). Four functional archetypes emerged, including a metal-eutrophic hotspot (HPI ≈ 213; CD ≈ 19) and recovering reservoirs with intermediate water quality indicators. Joint thresholds (TSItsr ≥ 60 + HPI ≥ 100) establish early-warning criteria, with Paucarani (HPI = 213) approaching the critical domain where metal-driven stress may facilitate cyanobacterial dominance. Systems show temporal resilience but strong spatial divergence induced by local pressures. The proposed typology and thresholds provide an operational basis for early warnings and prioritization of remediation actions in high-mountain ecosystems subject to increasing anthropogenic stress. Full article