Search Results (20,015)

The rapid deployment of autonomous haulage systems (AHSs) in open-pit mining has significantly altered haul road geometric design requirements, as autonomous trucks operate under strict kinematic constraints related to turning radius, gradient, and braking performance. Since haulage accounts for 50–60% of total mining costs, optimizing haul road geometry is critical for improving operational efficiency, energy consumption, and safety. This study presents a systematic review of 50 highly relevant studies selected from 81 candidate publications published between 2003 and 2025 through structured database searches and citation chaining. The review synthesizes current developments in haul road layout optimization, turning radius accommodation, gradient design, and safety integration for autonomous mining systems. The findings indicate that GIS-based and integrated optimization approaches consistently improve haulage performance, with reported productivity gains of 5–20%. Turning radius constraints emerged as the primary factor governing kinematic feasibility, while Hybrid A* and its advanced variants represent the dominant path-planning approaches. Recommended gradient limits of 8–12% remain important for balancing efficiency and safety, although emerging AHS-specific models suggest opportunities for controlled relaxation. The review identifies key research gaps in adaptive road design, integrated safety–geometry optimization, and field validation, providing a consolidated foundation for future AHS-compatible haul road design research. Full article

Background/Objectives: The common quail (Coturnix coturnix) is a game species facing conservation challenges, particularly hybridization with the Japanese quail (Coturnix japonica). To address this issue, one proposed measure is the urgent prohibition of releasing farmed quails into the wild. If authorized, mechanisms should be established to guarantee their genetic origin and prevent contamination of native populations. This work focuses on the development of a genetic tool based on Single-Nucleotide Polymorphism (SNP) markers that can differentiate between the two species and their hybrids. Our goal was to incorporate the selected markers into an OpenArray^® platform, to allow efficient, rapid, and cost-effective analysis. Methods: We tested two mitochondrial DNA SNPs (previously described in the literature) as diagnostic markers for species differentiation. We also assessed 24 nuclear DNA SNPs for compatibility with the OpenArray^® platform. Results: Of the 26 total SNPs, eight were excluded due to their limited utility. The remaining 18 SNPs achieved an overall genotyping success rate of 96.21%. Using the OpenArray^® platform with these 18 SNPs in a trial with samples from diverse Spanish field populations, we found 1.00% of C. japonica alleles (affecting 15.63% of individuals), suggesting introgression in the field. Population genetic analyses revealed strong differentiation between species and confirmed the presence of admixed individuals in field populations. Conclusions: This paper presents a new tool to differentiate between quail species and to identify foreign alleles in stocks and populations, by using an open platform system that optimizes the practical application of the diagnostic procedure based on the to-date most-reliable SNP markers for this goal. Full article

Mechanical damage remains a major constraint in low-damage harvesting and handling of the Korla fragrant pear, owing to its cultivar-specific bruise-sensitive traits (BSTs), namely its thin peel, crisp flesh, smooth epidermis, and high bruise sensitivity. This review synthesizes evidence from the Korla fragrant pear, other pear cultivars, apple, and related fresh produce to clarify damage mechanisms and engineering strategies for damage control. The reviewed studies show that injury is mainly governed by impact energy, compression load, contact stiffness, friction, fruit velocity, spacing, and transfer trajectory. Quasi-static compression and drop-impact tests provide essential thresholds, including elastic modulus, rupture force, absorbed energy, bruise area, and bruise volume, but Korla-specific data remain insufficient. Nondestructive techniques are complementary: RGB machine vision supports rapid surface screening, hyperspectral imaging improves early bruise detection, X-ray computed tomography quantifies internal bruising, and scanning electron microscopy verifies cellular damage mechanisms. FEM and DEM can predict stress distribution, deformation, collision behavior, and equipment-induced injury when calibrated with cultivar-specific parameters. Overall, apple- or general pear-based technologies require recalibration before application to the Korla fragrant pear. Future work should establish Korla-specific damage thresholds and validate detection, simulation, and conveying systems under real orchard and packing-line conditions. Full article

Spinal cord injury (SCI) is a devastating central nervous system disorder that causes irreversible loss of sensory and motor functions below the lesion, seriously compromising patients’ daily activities and quality of life. Due to the inherent shortcomings of existing therapeutic strategies and the rapid progress of material engineering, developing advanced functional materials has emerged as a promising approach for SCI treatment. This review comprehensively summarizes the applications of polymeric materials, inorganic materials, bioactive materials, and composite biomaterials for SCI treatment and regeneration, with a focus on their underlying mechanisms, therapeutic performance, and research trends. Cumulative evidence indicates that these materials possess versatile biological functions and great application potential in facilitating nerve regeneration and tissue reconstruction after SCI. In short, in-depth understanding of material-based therapeutic systems can offer innovative references for the optimization of SCI treatment regimens. Nevertheless, more preclinical and translational investigations are still indispensable to accelerate their clinical transformation and widespread practical use. Full article

Soil organic matter (SOM) stabilization is closely linked to microbial community structure and function, yet reliable biological indicators remain insufficiently defined. This study aimed to identify microbial and biochemical markers of SOM accumulation under different land use systems (cropland, mown with phytomass removal, mown without phytomass removal, and fallow) in gray forest soils. Soil profiles were investigated in four land use types (cropland, mown with phytomass removal, mown without phytomass removal, and fallow) in the Laishevsky District (Russia). Physicochemical properties, SOM fractions, basal respiration, substrate-induced respiration, Biolog EcoPlates, quantitative PCR, and metagenomic data were used to assess microbial diversity and activity. Microbial communities differed substantially among land use systems and soil horizons, with bacterial communities in fallow soils dominated by oligotrophic taxa, such as RB41, Candidatus Udaeobacter, and KD4-96, whereas arable and managed grassland soils showed increased relative abundance of copiotrophic genera, particularly Pseudomonas and Polaromonas. Fungal communities were primarily represented by Mortierella, Penicillium, Trechispora, and Metarhizium, while both bacterial and fungal diversity decreased with soil depth, and metabolic profiling indicated preferential utilization of carbohydrates and carboxylic acids across all land use types. The highest organic matter and total organic carbon (TOC) were in soils under mowing without phytomass removal and fallow land, while arable soils showed the lowest values. Microbial diversity decreased with soil depth across all variants. Hay meadow soils exhibited elevated metabolic activity and higher metabolic quotient (qCO₂), indicating intensified carbon turnover or microbial stress, whereas arable soils were characterized by reduced substrate utilization and simplified community structure. Oligotrophic bacterial taxa were associated with more stable SOM conditions, while copiotrophic dominance reflected rapid carbon turnover. The results demonstrate that microbial community composition, functional activity, and specific taxa (e.g., oligotrophic bacteria, saprotrophic fungi, arbuscular mycorrhizal fungi) can serve as sensitive indicators of SOM stabilization processes. These findings support the development of microbiome-based diagnostic tools for assessing soil carbon dynamics and guiding sustainable land management strategies. Full article

Flooding in the Kathmandu Valley has intensified in recent years due to rapid urbanization, unregulated land-use change, and insufficient drainage infrastructure. Existing flood hazard assessments are often based on low-resolution datasets and lack proper field validation. This study presents an integrated flood modeling framework that combines Unmanned Aerial Vehicle (UAV)-derived Digital Elevation Models (DEMs), field-based flood measurements, and hydrological simulations to assess urban flood hazards in the Bagmati-Nakkhu confluence, Nepal. High-resolution UAV-derived DEM and field survey data, including flood marks and high-water levels, were used as the foundation for the analysis. Hydrological modeling was conducted using the Hydrologic Engineering Center—Hydrologic Modeling System (HEC-HMS) to estimate the peak discharges of the Nakkhu River (2000–2024), which were then used to derive design flows for return periods of 5 to 150 years using the Gumbel distribution. These flows were used as boundary condition inputs for the Hydrologic Engineering Center—River Analysis System (HEC-RAS) to simulate flood depth and inundation extent under different scenarios. Flood extents for the 27 September 2024 event were derived from Sentinel-2 imagery and validated against surveyed flood marks. Additionally, land use/land cover (LULC) mapping based on UAV data was used to support flood impact analysis. The results show that flood depths ranged from approximately 0.5 m to 2.8 m, with inundation areas increasing by 35–50% under extreme rainfall. Model validation demonstrated strong agreement with simulated results, with deviations generally within ±0.3–0.5 m. Scenario analysis further indicates that urban expansion significantly increases runoff and flood extent, particularly in low-lying areas near the river confluence. Socio-economic exposure analysis for the 27 September 2024 event indicates that approximately 2569 residents (56.4% of the study zone population) and 4.011 km (77.42%) of the local road network were exposed to inundation. Overall, the results demonstrate that integrating high-resolution UAV data, field observations, and hydrological modeling greatly improves the accuracy and reliability of flood hazard assessments in data-scarce urban environments. Full article

Heavy metal ion pollution has emerged as a global issue. These contaminants are not only present in water sources but are also commonly detected in air, soil, food, and consumer products, posing serious risks to ecosystems and human health. Even at very low concentrations, heavy metal ions can exhibit substantial toxicity. Traditional methods for the detection of heavy metal ions typically require complex laboratory equipment and specialized technicians, making them inadequate for rapid on-site monitoring. Microfluidic technology, as an innovative platform capable of precisely controlling and manipulating minute volumes of fluid, has demonstrated enormous potential in analytical chemistry, biomedicine, and environmental monitoring. In the rapidly developing field of microfluidics, paper-based microfluidic platforms have become prominent due to their low cost, straightforward fabrication, and eco-friendly nature, offering powerful tools for the detection of heavy metal ions in diverse samples. This survey consolidates the major advances reported from 2015 to 2025 in utilizing paper-based microfluidic systems for identifying heavy metal ion pollutants in diverse sample types, including air, explosive residues, water sources, herbal supplements, skin-whitening cosmetics, environmental aerosols, urine, soil, gunshot residues, cucumber plants, and food. The review analyzes in detail the principles and applications of detection strategies based on colorimetric methods, fluorescent methods, electrochemical methods, dual-detection systems, and other methods, as well as the role of nanomaterials and selective recognition elements in improving detection sensitivity and specificity. These portable, low-cost, and easy-to-operate detection systems provide viable solutions for environmental and public health monitoring, particularly suitable for resource-limited regions and scenarios requiring rapid detection. Full article

The implementation of an artificial intelligence (AI)-based system for monitoring, forecasting, and learner performance support has been intensified by the rapid expansion of online education systems. Existing online educational platforms completely rely on learning analytics and machine learning to customize content delivery. On the other hand, these platforms fundamentally focus on behavioral and cognitive indicators, whereas the integration of affective computing into learning analytics and adaptive decision-making processes is lacking. During the learning process, emotions like engagement, boredom, and confusion play a vital role. Nonetheless, the integration of adaptive online learning systems is still fragmented and underdeveloped. The latest progress in affective computing and multimodal sensing technologies allow for the inference of the affective state of learners in real-time, which creates a range of potential opportunities to create emotionally sensitive learning spaces. Despite technological innovations, the existing studies do not have a conceptual framework that is unified, design-oriented, and clearly incorporates affective computing with AI-based learning analytics to inform real-time pedagogical adaptation. To address this gap, this study introduces a design-oriented conceptual framework for AI-based online education systems that incorporate real-time affective computing. This conceptual framework combines the theoretical foundation of learning analytics, affective computing, and adaptive learning systems. The suggested framework offers a clear and scalable basis of online learning environments that are affective-aware by offering a clear framework of development, assessment, and consequent empirical validation. Full article

Ion channels are validated drug targets, but they remain difficult to study as their pharmacology is influenced by rapid gating, conformational state transitions, cell-type-specific expression, and narrow safety margins. Recent advances in cryo-electron microscopy, structure-based in silico screening, machine-learning-guided prioritization, optical high-throughput screening, automated patch-clamp electrophysiology, and human-relevant organoid or microphysiological system (MPS) models are transforming this field. In this expanded review, we examine how these modalities can be integrated into a hybrid discovery pipeline that begins with computational triage, proceeds through scalable functional screening and state-aware electrophysiological validation, and concludes with multi-channel safety de-risking and translational analysis in complex human models. We also discuss disease-associated channel remodeling in cancer and inflammatory disorders, with an emphasis on transient receptor potential channels, voltage-gated potassium channel 1.3 (Kv1.3), Piezo channels, transmembrane protein 16A/anoctamin-1 (TMEM16A/ANO1), chloride channels, and proarrhythmic safety risks. Additionally, we highlight unresolved challenges, including bias in artificial intelligence models, incomplete conformational sampling, assay interference, organoid heterogeneity, and regulatory acceptance of MPS platforms. This review proposes a staged decision framework in which computational prioritization, scalable functional screening, direct electrophysiological confirmation, safety pharmacology, DMPK assessment, and disease-relevant human models serve as complementary filters rather than competing platforms for the identification of selective and translatable ion-channel therapeutics. Full article

The AMA Guides to the Evaluation of Permanent Impairment, Sixth Edition, have undergone a substantial transformation from a static publication to a continuously refined digital resource. This transition reflects both the rapid evolution of medical knowledge and longstanding concerns regarding the usability, consistency, and reproducibility of impairment ratings. Central to this transformation was the establishment of the AMA Guides Editorial Panel in 2019, which introduced a structured governance framework and evidence-based methodology for ongoing refinement. Unlike prior editions that relied on periodic print revisions, AMA Guides Digital permits continuous updating of individual chapters as new scientific evidence and clinical practices emerge. This narrative review examines the historical evolution of the AMA Guides, the development of AMA Guides Digital, the governance and methodological contributions of the Editorial Panel, and major system-specific refinements implemented between 2021 and 2025. Particular emphasis is placed on the Mental and Behavioral Disorders chapter (2021), the Nervous System chapter (2023), the Musculoskeletal chapters (2024), and the Pulmonary chapter (2025). These developments demonstrate a broader shift toward transparency, methodological rigor, harmonization across body systems, and alignment with contemporary clinical practice while maintaining continuity with the foundational principles of the Sixth Edition. The transition to a continuously refined digital model represents a paradigm shift in impairment evaluation with important implications for clinical, occupational, and medicolegal practice. Full article

Background: Traditional genotype-guided dosing often fails to predict real-time variability in the metabolic phenotype during complex polypharmacy. This secondary analysis of a retrospective cohort aims to elucidate mechanisms underlying real-time phenoconversion during antipsychotic therapy, focusing on homozygous loss-of-function CYP3A5*3/*3 non-expressors. Methods: Using an additive phenoconversion model that integrates a genotype-derived baseline with environmental modifiers for drug–drug interactions (DDI), systemic inflammation (CRP), and renal function (eGFR), we demonstrate that the expressed metabolic phenotype is a dynamic, context-dependent construct that can markedly diverge from the genotype-predicted state. Objectives: Our data show that patients with CYP3A5*3/*3 and CYP3A inhibitors (e.g., ritonavir) had a quetiapine plasma concentrations reached 1850 ng/mL, corresponding to 3.7-fold above the internationally accepted therapeutic reference range of 100–500 ng/mL. Acute systemic inflammation (CRP > 50 mg/L) induced a functional poor metabolizer phenotype (P_act < −0.9) in individuals with a genotypic normal metabolizer status. In contrast, strong inducers such as carbamazepine, phenytoin, and heavy smoking promoted an ultra-rapid metabolizer state (CL_ind > 4.0 L/h, quetiapine < 30 ng/mL), consistent with treatment failure. In this cohort, the additive P_act model showed a strong association with observed clearance and identified clinically relevant phenoconversion mechanisms not predicted from genotype alone. Conclusions: These results support a dynamic, multi-parametric approach that integrates pharmacogenomics, therapeutic drug monitoring, biomarker profiling (CRP, eGFR), and structured DDI assessment to enable higher-resolution, real-time phenotype tracking and more informed dose individualization in high-risk psychiatric polypharmacy. Full article

Background: The human visual system can extract object-category information from extremely brief visual input, and animate categories often show behavioral advantages over inanimate categories in rapid categorization, visual search, and change-detection tasks. Motivated by these behavioral findings, the present study asked whether, at the representational level, animate categories elicit more category-selective neural patterns than inanimate categories in the ventral visual cortex under brief input. Methods: Using fMRI and correlation-based multivoxel pattern analysis (MVPA), we examined whether activity patterns elicited by animate categories in the ventral occipitotemporal cortex under a 33 ms brief-presentation condition corresponded more selectively to same-category patterns under a 500 ms extended-viewing condition than did patterns elicited by inanimate categories. During scanning, participants viewed animate and inanimate stimuli, each comprising four basic-level subcategories, and performed a noise-detection task that did not require explicit category judgments. Results: Across multiple ROI-definition strategies, animate categories showed significantly higher cross-duration category information than inanimate categories. This effect also remained significant after excluding the human-head category, which contained human-face information. Stimulus-level image-feature analyses further showed that within-category visual homogeneity explained part of the variance in cross-duration category information, particularly in the full stimulus set that included human heads. However, a composite visual homogeneity index derived from HOG, Gabor, and ResNet50 features did not fully account for the higher cross-duration category information observed for animate categories. Conclusions: Overall, these results suggest that, when visual input is highly limited, animate categories elicit VOTC multivoxel patterns that correspond more selectively to same-category patterns under extended viewing. Full article

The hyperfine structure of the hydrogen atom provides a clean, experimentally relevant two-qubit platform in which the coupled electron and proton spins exhibit rich quantum behavior. We investigate the open-system dynamics of this system under the simultaneous influence of the intrinsic hyperfine coupling, an external static magnetic field (via the Zeeman interaction), and local Markovian dephasing noise. Employing the Lindblad master equation, we derive the exact time evolution of the density matrix for general X-shaped initial states and focus on two complementary measures of quantum coherence—the $L_1$-norm coherence $C_L\left(t\right)$ and the relative entropy of coherence $C_R\left(t\right)$—together with the state purity $P\left(t\right)$. Numerical results reveal that all three quantities display characteristic damped oscillatory evolution. For a vanishing magnetic field, the decay is relatively rapid and smooth, whereas increasing the proton magnetic parameter markedly raises the oscillation frequency and slows the overall envelope of both coherence and purity. Even under stronger dephasing rates, a suitably chosen external field can substantially postpone the loss of quantum features, acting effectively as a control knob that reshapes the coherent unitary dynamics to counteract dissipative effects. These findings underscore the delicate competition between intrinsic atomic interactions and environmental noise, while offering a practical route for protecting quantum resources in spin-based systems. Our work bridges fundamental atomic physics with resource-theoretic concepts and highlights promising strategies for coherence preservation in realistic, controllable quantum platforms. Full article

With the rapid development in generative artificial intelligence (GenAI) technologies, their application in STEAM education offers new possibilities for promoting interdisciplinary integration of technology and the arts. This study employs a systematic literature review method. Six databases—Google Scholar, Web of Science, PubMed, Taylor & Francis, Springer Link, and Scopus—were searched for publications from January 2021 to January 2026. After independent screening and review by two reviewers, 21 empirical studies out of 424 initial records were included. A comprehensive analysis was conducted using a combination of open and axial coding. The findings indicate that GenAI’s support for artistic creativity in STEAM education is primarily manifested in four dimensions: lowering the threshold for creation to enhance the accessibility of artistic creativity, stimulating interdisciplinary associations to strengthen subject integration, supporting critical artistic recreation to deepen cultural engagement, and building a human–GenAI collaborative creation ecosystem to foster reflexivity. Based on this, the study constructs a GCD (Guiding questioning–Co-refining–Deepening reflection) cyclic instructional framework, providing teachers with an actionable pedagogical pathway for using GenAI to cultivate students’ interdisciplinary artistic creativity across different educational stages. Furthermore, the study systematically analyzes ethical challenges such as technological dependency, cultural homogenization, educational equity, and originality, and proposes corresponding pedagogical strategies to address them. It should be noted that the current body of relevant empirical research is limited in quantity and exhibits substantial heterogeneity, and the GCD framework still requires further classroom-based practical validation. Future research could strengthen empirical longitudinal tracking of longterm effects, deepen the construction of support systems for teachers’ digital literacy, and continue to advance the exploration of ethical, equity, and cultural diversity issues in GenAI-based artistic creativity education. Full article

The rapid rise of antimicrobial resistance (AMR) has created an urgent need for innovative therapeutic strategies beyond conventional antibiotics. Smart nano-antibiotics have emerged as advanced antimicrobial systems capable of improving drug delivery, enhancing pathogen targeting, overcoming biofilm-associated resistance, and reducing systemic toxicity. This review discusses recent progress in stimuli-responsive nanoplatforms, including pH-responsive, enzyme-responsive, temperature-sensitive, and redox-activated systems for precision antimicrobial therapy. The role of artificial intelligence in nanomaterial design, toxicity prediction, drug release optimization, and personalized treatment development is also critically examined. Furthermore, the review highlights targeted antimicrobial delivery, multifunctional nano-drug combination systems, biosensor integration, and autonomous AI-driven therapeutic platforms for combating multidrug-resistant infections. Current challenges related to toxicity, regulatory limitations, scalability, and AI data reliability are discussed alongside emerging clinical and industrial developments. Smart nano-antibiotics represent a promising next-generation approach for improving precision antimicrobial therapy and addressing the growing global burden of antimicrobial resistance. Full article