Search Results (9,812)

Previous characterisation of historic blue bricks (qingzhuan) from Beijing identified compositional and physical differences across periods, but their effect on early-stage freeze–thaw behaviour was untested. An adapted laboratory wetting–freezing–thawing procedure was applied to four Ming-attributed and four Qing-attributed Great Wall bricks from the Miyun section as the primary cohort, with Lingyue Temple and Guanyin Chanlin bricks as supporting cases. The 10-cycle endpoint is below the 15 cycles GB/T 2542-2012 prescribes for a frost-resistance rating, so the results index early-stage damage sensitivity, not freeze–thaw durability. Dry mass loss, colour difference (ΔE*_ab), gloss change and visible damage were evaluated. The lower-density, higher-absorption (Ming-attributed) group lost 1.22 ± 0.84% of its dry mass versus 0.37 ± 0.12% for the denser, lower-absorption (Qing-attributed) group, with complete rank separation that persists after normalising by estimated coupon surface area. At four bricks per group, the exact Mann–Whitney result (U = 16, p = 0.029) sits at the smallest attainable p-value, so the comparison is exploratory and hypothesis-generating and establishes no dynastic difference in frost resistance. Bulk properties predicted neither damage magnitude nor mode: a low-mass-loss coupon fractured through, and the metrics ranked specimens differently. Conservation assessment should report material loss, structural integrity and surface preservation as separate endpoints, and match repair material by water absorption, pore structure and freeze–thaw behaviour rather than colour and composition alone. Full article

In Fusion Deposition Modeling-based manufacturing, process parameters affect the mechanical and physical properties of the print. Considering these properties, accurately predicting print quality is essential. This is where machine learning (ML) models for three-dimensional (3D) print quality prediction come to the forefront. In this study, a dataset was prepared under strict operational measurement standards—utilizing the Interquartile Range (IQR) method for data sanitization—encompassing 10 material types, 2 printer types, and 4 printing parameters. Five hundred different sample combinations were prepared and printed in sets of three according to ISO 527-2 Type 4 standard dimensions. Tensile, hardness, and surface roughness tests were applied to the prepared samples. Using this validated dataset, a Hybrid Multi-Material Quality–Ensemble System–Stacking–Gradient Boosting Regressor (HMQ-ES-Stack-GBR) architecture is proposed as a diagnostic framework for multi-output quality prediction. Particularly in terms of quality outputs such as tensile strength, hardness, and surface roughness, while also providing a quantitative analysis of the effect of material type on print quality. Furthermore, a multi-objective optimization pipeline integrating three distinct meta-heuristic algorithms—Non-dominated Sorting Genetic Algorithm II (NSGA-II), Particle Swarm Optimization (PSO), and Grey Wolf Optimizer (GWO)—was coupled with the framework to systematically derive material-specific optimal processing parameter configurations. Furthermore, the study shows that open-system printers exhibit higher prediction errors than closed-system printers. Reflecting system-induced variability rather than full hardware independence. Although the study is limited to internal validation within the current experimental design and includes material imbalance across filament groups, the findings suggest that the proposed framework provides a promising diagnostic decision-support tool for pre-print quality estimation within the studied dataset. By accurately reflecting rather than physically overcoming manufacturing variability, it supports decision-making processes through pre-print quality estimation, thereby enabling proactive interventions that reduce raw material, time, and energy losses. Full article

Disturbance and vegetation-recovery monitoring is gaining growing attention in remote sensing-based studies, especially in studies focusing on protected areas. Despite this growth, the methodological diversity and the research themes addressed across these studies have not yet been systematically examined. This systematic review examines peer-reviewed studies published after 2015 to identify and document how disturbances and post-disturbance recovery are monitored using satellite Earth Observation data. We systematically reviewed 105 studies, following the PRISMA and PSALSAR guidelines, and classified the publications by disturbance type, ecosystem type, geographic region, satellite and auxiliary datasets, analytical methods, and validation approaches employed. The results reveal that the research is concentrated in a small group of countries and predominantly focused on forest ecosystems. Disturbance detection dominates the research literature, while recovery modeling and long-term predictive analysis remain underexplored. Validation practices are highly inconsistent, with limited use of standardized approaches. Overall, this review highlights substantial methodological progress over the past decade and identifies research gaps in disturbance and recovery assessment, multi-sensor integration, and alignment with emerging biodiversity and restoration policy frameworks. Full article

Traditional urban morphological analyses are structurally limited in terms of both systemic diversity and empirical scale due to reliance on manual methods. In contrast, decoding the complex fabric of rapidly growing cities necessitates data-driven, scalable approaches. To address this gap, this study proposes a multi-scale pipeline that classifies urban form systematically and reproducibly from open spatial data, and applies it comparatively to three Anatolian cities of contrasting typo-morphological character: Elazığ, Erzincan and Mardin. From street networks and building geometries, an integrated morphometric matrix was assembled by computing network topology and orientation metrics, space syntax configurational accessibility, morphological tessellation, coverage area ratio, building form–volume indicators, neighbourhood and adjacency measures, and Local indicators of spatial association (LISA) spatial autocorrelation. After transformation and standardisation, urban typologies were derived through Principal Component Analysis and grouped with spatially weighted k-means (Geo-KMeans). Three findings stand out. First, the cities trace a distinct morphological spectrum that runs from Mardin’s organic historical fabric to Erzincan’s relatively planned grid structure, with the radial polarised Elazığ occupying an intermediate, transitional position between the two. Second, accessibility and built density prove only weakly related (r = 0.05–0.25). Third, six, five and four morphological typologies emerged, triangulated against LISA hot spot clusters and space syntax maps. Overall, this reproducible framework offers planners a systematic, data-driven basis for exploratory morphological assessment rather than a definitive, universal typology. Full article

To address the limitations of existing studies on harmonic drive (HD) systems, such as the lack of systematic exploration of the influence of flexspline (FS) structural parameters on service lifetime and insufficient parameter optimization strategies, this study establishes a comprehensive research framework integrating finite element (FE) simulation, response surface optimization, and experimental validation. Three key structural parameters of FS, including cup length (CL), cup wall thickness (CT), and tooth width (TW), which were selected as design variables to investigate their effects on the displacement, von Mises stress, and fatigue lifetime of the HD system. A reliable FE model of the HD system was constructed, with mesh independence verified and experimental validation conducted using a servo motor-driven test platform. The results showed that the relative errors between simulated and measured displacement (4.7%) and stress (4.0%) were within the acceptable engineering range (<10%), confirming the model’s reliability. Systematic analysis revealed that the FS lifetime is jointly determined by global stress distribution and structural rigidity, rather than local tooth root stress alone. Increasing cup length reduces tooth root stress but increases cup wall bending stress, which ultimately dominates the fatigue failure process. Based on Box–Behnken Design (BBD) response surface analysis, the optimal parameter combination was determined as CL = 48 mm, TW = 6 mm, and CT = 0.64 mm, achieving the dual optimization of low system stress (77.7 MPa) and long FS lifetime (2950 h). Relative to the baseline control group (CL = 48 mm, CT = 0.46 mm, TW = 6 mm), the optimized configuration reduces system stress by 10.1% and extends fatigue lifetime by 1.7%. This study clarifies the multi-factor coupling mechanism of structural parameters regulating HD system performance, providing a robust theoretical basis and engineering reference for the structural design and performance improvement of HD systems. Full article

Field vibration measurements of quay-crane hoisting gearboxes are difficult to interpret because the measured response is generated under short, load-dependent cycles rather than stationary excitation. This study develops a PLC-guided method for constructing condition-tagged steady-state vibration samples from non-stationary field measurements. Multi-rate records are aligned on a common time basis; work cycles and action stages are identified from load and hoist-speed information; steady PLC candidates are selected using operating-context and local-steadiness criteria; and one-second vibration segments are fine-screened within the corresponding search intervals. The accepted segments are stored in a quality-controlled feature table and characterized by acceleration RMS, 10–1000 Hz velocity RMS, kurtosis, peak-to-peak acceleration, dominant frequency, and spectral entropy. Field data from four gearboxes yielded 1140 steady-state segments and 8912 quality-controlled segment-channel records. Stepwise CV analysis showed that action-only grouping reduced the coefficient of variation from 0.692 to 0.658, while full machine–action–channel grouping reduced it to 0.351, corresponding to a 49.35% reduction. Median acceleration RMS ranged from 0.494 to 4.805 m/s², and 10–1000 Hz velocity RMS ranged from 0.317 to 1.512 mm/s. The method provides a traceable basis for condition-aware baseline modelling and trend analysis without making unsupported fault-diagnostic claims. Full article

Quantitative phylogenetics in historical linguistics has relied almost entirely on lexical cognate data. This study asks a different question: how much genealogical signal can be recovered from structural features extracted from annotated corpora, and whether it survives at deep time depths. We compute 29 structural features—including Shannon entropies of dependency direction and of dependency-relation distributions, relation-specific directionality ratios, dependency-distance measures, and constructional ratios—across 25 Transeurasian languages from the five proposed groups (Turkic, Mongolic, Tungusic, Japonic, and Koreanic) and three outgroups (Chinese, Vietnamese, and Hindi), 28 languages in all. Most of the Tungusic and Mongolic languages have no running-text corpus, so we built new Universal Dependencies treebanks for them by glossing example sentences from reference grammars; thirteen are used here. Each feature was tested for phylogenetic signal (Pagel’s λ and Blomberg’s K, with FDR correction) under four competing reference topologies, and the features that passed were used for tree inference (Bayesian inference in MrBayes, with Neighbor-Joining as a check). The same pipeline was first run on Indo-European in a companion study, where it recovers only individual subgroups and does not resolve a stable tree. At the depth proposed for the Transeurasian family (a Proto-Transeurasian root of about 9000 years before present), the structural signal was not enough to reconstruct the family’s internal relationships. The signal tests favoured a flat three-way division of the major branches (7 strict/20 relaxed features) over any nested hypothesis (≤2 strict features each), and the strongest signal lay in core word-order parameters (e.g., object direction, λ = 1.00, K = 6.06). But both Bayesian and distance-based inference returned near-complete polytomies: although the chains converged (ASDSF < 0.01), no branch reached a posterior probability above 0.75, and none of the three multi-language branches (Turkic, Mongolic, or Tungusic) was recovered. The outgroup test made the reason clear: Hindi, which is Indo-European but SOV, grouped with the head-final Transeurasian languages rather than with the other two (head-initial) outgroups, so the features are tracking typological similarity, not shared descent, at this depth. The study contributes 13 new treebanks for poorly documented languages, a reproducible framework for testing how much genealogical signal structural features carry, and direct evidence that, at Transeurasian time depths, this signal reflects typology rather than genealogy. Full article

Background and Objectives: Head and neck cancer in patients aged 40 years or younger represents a rare and heterogeneous entity with conflicting data regarding prognosis and risk factors. This study aimed to evaluate oncological outcomes and independent prognostic factors in young patients treated with radio(chemo)therapy. Materials and Methods: This retrospective multi-center analysis included patients aged ≤ 40 years with histologically confirmed HNC who received definitive or adjuvant radio(chemo)therapy between 2002 and 2023 at the University Hospital Regensburg and affiliated partner hospitals. Overall survival (OS) and progression-free survival (PFS) were estimated using the Kaplan–Meier method. Independent prognostic factors were identified by Cox proportional hazard regression with backward stepwise elimination. Results: A total of 88 patients were included. The median age at diagnosis was 38.2 years (IQR 35.8–39.8). Median OS was 91.0 months in the adjuvant and 23.7 months in the definitive treatment group. In multivariate analysis, four independent predictors of OS were identified: nicotine abuse (HR 2.887; p = 0.004), pre-existing comorbidities (HR 2.871; p = 0.005), absence of complete remission 12 weeks after radiotherapy (HR 25.676; p < 0.001), and locoregional recurrence or distant metastases (HR 8.183; p < 0.001). Failure to achieve complete remission was the sole independent predictor of PFS (HR 4.479; p < 0.001). Conclusions: In young HNC patients, early treatment response and disease recurrence are the strongest determinants of survival, alongside modifiable lifestyle factors and comorbidity burden. These findings support the need for intensified response monitoring and tailored follow-up strategies in this patient population. Full article

Non-cooperative and manipulative behaviors are strategic actions adopted by experts for diverse reasons, such as rigid preference retention or self-interested exploitation of network structures. To address these challenges, this paper proposes multi-path trust aggregation with decayed trust propagation and a dynamic identification and adaptive penalty mechanism integrating multi-objective optimization to promote the consensus-reaching process in social network group decision-making (SNGDM). First, we propose a multi-path trust aggregation operator with decayed trust propagation, which incorporates a multi-dimensional path analysis framework that evaluates path reliability and structural diversity, thereby building a stable trust network for consensus feedback. Second, a tri-objective consensus optimization model is developed to provide optimal adjustment suggestions by balancing total adjustment cost, consensus degree, and cost fairness. Third, three types of abnormal behaviors are defined, including short-term non-cooperation, persistent non-cooperation, and manipulation. Based on these distinctions, differentiated management mechanisms are introduced. Specifically, a proportional trust decay strategy is applied to non-cooperative behaviors, while an influence-accelerated decay mechanism is designed for manipulative behaviors. In addition, the historical credit index is dynamically linked to unit cost coefficients to force uncooperative and manipulative experts to revise their opinions. Finally, experiments demonstrate the effectiveness of the proposed approach. Full article

Spinach (Spinacia oleracea L.) is an important leafy vegetable crop, and its leaf characteristics are important for spinach germplasm innovation and new variety breeding. In this study, spinach inbred lines P₁ and P₂ were used as parents to construct six-generation segregating populations. Based on multi-location trials, we adopted an improved analytical procedure for the major gene plus polygene mixed genetic model, and the genetic models governing leaf length, leaf width and petiole length in spinach were screened. Furthermore, a high-density genetic linkage map of spinach was constructed using re-sequencing data from 152 individuals of the F₂ population, and QTLs (quantitative trait loci) governing spinach leaf type were mapped to identify their candidate genes. The results showed that the optimal genetic model for leaf length was MX1-AD-ADI, for leaf width was MX1-AD-ADI, and for petiole length was MX2-ADI-AD. The genetic map developed from the F₂ segregating population contained 5900 bin markers assigned to six linkage groups, spanning a total genetic distance of 843.48 cM. One QTL associated with leaf length was mapped on chromosome 2 and designated qLL2-1, yet no candidate genes were identified within its candidate interval. Two QTLs controlling leaf width were mapped on chromosome 1 and chromosome 2, designated qLW1-1 and qLW2-1, respectively, and 15 genes were annotated in this candidate region. One QTL related to petiole length was located on chromosome 2 and named qPL2-1, with four genes annotated in its candidate interval. Combined with spinach genome annotation and expression-level analysis of candidate genes, the gene SOV2g023140 was identified as the candidate gene regulating spinach leaf width, which was named SpPINL2. Meanwhile, three genes regulating petiole length constituted a gene cluster FAR1-ULP-zf-GRF, designated SpFAR1-ULP-zf-GRF. Full article

Radar and geostationary satellite observations provide essential information for lightning identification and nowcasting. However, multi-source channels commonly exhibit correlation and redundancy, and directly using all channels increases computational cost and may degrade model performance. This paper focuses on lightning identification and 0–1 h nowcasting, integrating radar composite reflectivity, Himawari-8/9 AHI multi-channel observations, and VLF-LLN lightning location data to construct a multi-source spatiotemporally aligned dataset. Using baseline deep-learning-based identification and nowcasting models, this paper proposes a workflow for analyzing multi-source channel contribution differences and selecting key factors by integrating permutation feature importance (PFI), SHAP attribution, channel collinearity grouping, and validation-set wrapper search. The results show that radar composite reflectivity has a stable dominant contribution in both tasks. The final selected channels are radar composite reflectivity, $\Delta$Band 13, and Band 14 + Band 09 for the identification task, while they are radar composite reflectivity, Band 14, (Band 11 − Band 13) − (Band 13 − Band 15), and $\Delta$(Band 08 − Band 13) for the nowcasting task. Independent test results show that under fixed thresholds, the three-channel identification model achieves a CSI of 0.433, outperforming the full 28-channel model with a CSI of 0.415, while the four-channel nowcasting model achieves a CSI of 0.284, outperforming the full-channel model with a CSI of 0.274. These results indicate that the proposed method can reduce input dimensionality while maintaining or improving model performance, and it can support multi-source predictor selection, lightweight deployment, and operational interpretation in lightning nowcasting. Full article

This study aims to develop a lightweight, edge-deployable artificial intelligence (AI) system for real-time, non-contact recognition of cattle eating, standing, and lying behaviors in farm environments. Automated monitoring of these behaviors in cattle provides fundamental behavioral data for the future development of systems that analyze feeding duration, lying duration, and behavioral rhythms. Nevertheless, practical deployment on farms is hindered by data imbalance, dense animal groupings, scale variation, occlusion, and the need for low-cost edge computing. To address these challenges, we propose BoviFusionNet, a lightweight, edge-deployable AI system. A box balanced augmentation strategy rebalances training instances at the object level without altering the validation or test sets. Built upon YOLO11n, the model integrates three targeted enhancements: information-preserving downsampling (ADown), adaptive bidirectional feature fusion (BiFPN), and local window attention (C2CGA) to improve multi-scale representation and fine-grained behavior discrimination. Experimental results show that BoviFusionNet achieves 0.7851 recall, 0.7763 F1-score, 0.7976 mAP@0.50, and 0.6305 mAP@0.50:0.95, with only 5.4 GFLOPs and a 3.4 MB model size. Compared with the YOLO11n baseline, it improves mAP@0.50:0.95 by 9.92% and reduces the parameter count by 39.8%. After INT8 quantization and deployment on an RK3588S edge device, real-time inference reaches 28.08 frames per second (FPS). Therefore, BoviFusionNet offers an effective accuracy-complexity trade-off for on-farm edge AI applications. By enabling continuous, non-invasive monitoring of health-relevant behaviors, it provides fundamental behavioral data for the future development of veterinary health assessment tools without relying on cloud services or wearable sensors. Full article

Evaluating whether clinical classifiers perform equitably across patient subgroups is a central requirement for the responsible deployment of machine learning in medicine. Conventional approaches test one fairness metric at a time, such as sensitivity, positive predictive value, or area under the receiver operating characteristic curve, and therefore cannot detect disparities that manifest only in the joint structure of a group-specific confusion matrix. We develop a unified hypothesis-testing framework rooted in random matrix theory that compares demographic groups through the $L^2$ distance between their joint eigenvalue densities, yielding a scalar spectral divergence that is sensitive to every cell of the $2\times 2$ confusion matrix simultaneously. We derive the closed-form spectral divergence for Gaussian-approximated eigenvalue densities, prove almost-sure consistency of the empirical estimator via the delta method, and construct an extreme-value (Gumbel) test statistic with family-wise error rate control. Monte Carlo experiments comprising $\mathrm{10,000}$ replications across balanced, moderately imbalanced, and severely imbalanced group-size regimes show that the spectral test keeps Type I errors close to its nominal level while achieving power exceeding $90\%$ in complex and multi-dimensional violation scenarios, where the best single-metric competitor reaches at most $63\%$. Three clinical benchmark datasets from the UCI Machine Learning Repository utilised include Pima Indians Diabetes ($n=768$), Cleveland Heart Disease ($n=303$), and Heart Failure Clinical Records ($n=299$). Results confirm that the spectral method detects statistically significant ($p<0.001$) performance disparities missed by all three conventional tests. These results support eigenvalue-based divergence as a practical, model-agnostic diagnostic equity tool for clinical machine learning audits. Full article

Background/Objectives: Promoting positive mental well-being (PMW) among primary school-aged children can reduce future risks of developing adverse mental health. However, there is a dearth of appropriate psychometrically sound measures of PMW that school psychologists, educators, and allied health professionals can utilise with children. The present study sought to provide preliminary evidence pertaining to the suitability of the Warwick-Edinburgh Mental Well-being Scale (WEMWBS) for use with children. Methods: To conduct a preliminary structural validation of the factor structure of the WEMWBS, it was administered to 569 children (328 males, 238 females, 3 did not report their gender) aged 6 to 10 years of age from 14 Western Australian primary schools. Results: An exploratory factor analysis (EFA) from a stratified split-random sample (n = 281) yielded a single-factor solution. A confirmatory factor analysis (CFA) on the second half of the sample (n = 288) provided support for the hypothesised unidimensional structure of the measure. Multi-group CFA supported configural and metric invariance across gender, but invariance across individual school year levels was not clearly supported. Conclusions: While the findings provide preliminary support for the suitability of the WEMWBS as a measure of children’s PMW in real-world, ecologically valid settings like schools, caution is required for use with younger-aged children. Full article

Background: Personalized nutrition, also referred to as precision nutrition, is an emerging approach that integrates genetic, metabolic, phenotypic, behavioral, and environmental characteristics to develop individualized dietary strategies. Obesity, metabolic syndrome (MetS), and metabolic dysfunction-associated steatotic liver disease (MASLD) represent interconnected disorders with substantial inter-individual variability in disease development, metabolic risk, and response to dietary interventions. Although body mass index (BMI) remains widely used for obesity classification, it does not adequately capture differences in body composition, fat distribution, or metabolic health. Consequently, individuals with normal-weight obesity (NWO), characterized by excessive body fat accumulation despite a normal BMI, may remain unidentified despite increased cardiometabolic risk.This narrative review critically evaluates the current evidence on the potential role of personalized nutrition in the prevention and management of obesity, MetS, MASLD, and related cardiometabolic abnormalities. Particular attention is given to five major domains: nutrigenetics, gut microbiota, metabolic phenotyping, body composition assessment, and digital health technologies, with emphasis on their current clinical applicability and limitations. Methods: A structured narrative review was performed using PubMed, Scopus, and Web of Science to identify English-language studies (2003–2026) on personalized nutrition in obesity, normal-weight obesity, metabolic syndrome, and MASLD. Eligible studies were selected according to predefined inclusion and exclusion criteria, and 31 publications were included in the qualitative synthesis. Results: Current evidence suggests that personalized nutrition strategies may contribute to improvements in body weight regulation, insulin sensitivity, lipid metabolism, and liver-related outcomes; however, the magnitude and consistency of these effects remain variable. The integration of genetic, metabolic, microbiome, and phenotypic information may improve individual risk stratification and help identify high-risk groups, including individuals with NWO who may not be recognized through BMI-based assessment alone. Emerging approaches involving multi-omics technologies, microbiome profiling, wearable devices, continuous glucose monitoring, and artificial intelligence-based tools provide promising opportunities for individualized dietary interventions. Nevertheless, limitations related to methodological heterogeneity, insufficient standardization, limited external validation, and the scarcity of long-term pragmatic clinical trials currently restrict their routine implementation. Conclusions: Personalized nutrition represents a promising but still evolving approach for addressing obesity and its metabolic complications, including MetS and MASLD. While the integration of biological, phenotypic, and digital information may support more targeted dietary recommendations, current evidence does not yet fully establish the clinical effectiveness and cost-effectiveness of these approaches in routine care. Future large-scale, longitudinal, and well-designed randomized controlled trials are required to determine which personalized nutrition strategies provide clinically meaningful benefits and for which patient populations. Full article