Search Results (343)

Synthetic datasets are increasingly used in education research for methodological validation, privacy-preserving data sharing, and reproducible equity analysis; however, most generative approaches prioritize marginal distributional similarity without ensuring preservation of multilevel inferential properties. This limitation is consequential for repeated-measures data analyzed using intersectionality-focused hierarchical models, where conclusions depend on variance partitioning, partial pooling, and stratum-level heterogeneity. We introduce MAI-GAN, a hybrid generative framework that implements a structure–residual decomposition approach combining Bayesian longitudinal MAIHDA with conditional GAN-based residual generation. Inferential fidelity is operationalized with respect to multilevel intersectional models by explicitly targeting the preservation of fixed effects, variance components, and variance partitioning coefficients, while baseline composition is maintained via stratified bootstrap resampling. Applied to a six-semester undergraduate biology dataset (N = 2669 students), MAI-GAN was evaluated across multiple independent random seeds and consistently reproduced baseline-dependent residual structure and key inferential quantities. These results demonstrate that model-aligned generative strategies can produce synthetic longitudinal datasets that remain coherent under intersectionality-focused multilevel analysis, offering a principled foundation for equity-oriented synthetic data generation. Full article

This study demonstrates a hierarchical spectral modelling approach for predicting pasture nutrition metrics using TabPFN (Tabular Prior-Data Fitted Network), a transformer-based machine learning architecture. In the face of climate variability, aligning stocking rates with pasture resources is crucial for sustainable livestock grazing, requiring accurate assessments of both pasture biomass and nutrient composition. Our research, conducted across diverse growth stages at five tropical and subtropical savanna rangeland properties in Queensland, Australia, with native and introduced C4 grasses, employed a hierarchical sampling and modelling strategy that scales from laboratory spectroscopy to Sentinel-2 satellite predictions via uncrewed aerial vehicle (UAV) hyperspectral imaging. Spectral data were collected from leaf (laboratory spectroscopy) through field (point measurements), UAV hyperspectral imaging, and Sentinel-2 satellite imagery. Traditional laboratory wet chemistry methods determined plant leaf and stem nutrient content, from which crude protein (CP = total nitrogen (TN) × 6.25) and dry matter digestibility (DMD = 88.9–0.779 × acid detergent fibre (ADF)) were derived. TabPFN models were trained at each spatial scale, achieving validation ${\mathrm{R}}^2$ of 0.76 for crude protein at the leaf scale, 0.95 at the UAV scale, and 0.92 at the Sentinel-2 satellite scale. For dry matter digestibility, validation ${\mathrm{R}}^2$ was 0.88 at the UAV scale and 0.73 at the Sentinel-2 scale. A pasture classification masking approach using a deep neural network with 98.6% accuracy (7 classes) was implemented to focus predictions on productive pasture areas, excluding bare soil and woody vegetation. The Sentinel-2 models were trained on 462 samples from 19 site–date combinations across 11 field sites. The TabPFN architecture provided notable advantages over traditional neural networks: no hyperparameter tuning required, faster training, and superior generalisation from limited training samples. These results demonstrate the potential for accurate and efficient prediction and mapping of pasture quality across large areas (100 s–1000 s ${\mathrm{k}\mathrm{m}}^2$) using freely available satellite imagery and open-source machine learning frameworks. Full article

Introduction: Circadian misalignment has been proposed as a potential determinant of cardiometabolic risk. Chronotype, as an expression of individual circadian organization, has been associated with unfavorable metabolic profiles; however, the role of total and central adiposity as potential mediating mechanisms in this relationship remains incompletely understood. Objective: This study aimed to analyze the association between chronotype and cardiometabolic risk in adults and to evaluate the potential mediating role of body fat percentage (BF%) and waist-to-height ratio (WHtR). Methods: An observational study was conducted in 1462 adults from the general population. Chronotype was assessed using the Morningness–Eveningness Questionnaire (MEQ), and cardiometabolic risk was evaluated using a continuous cardiometabolic risk score (CMRS) derived from waist circumference (WC), systolic blood pressure (SBP), triglycerides (TG), fasting blood glucose (FBG), and total cholesterol (TC). Multiple linear regression models adjusted for covariates were used to examine the association between chronotype and CMRS, and hierarchical regression was performed to estimate the incremental contribution of adiposity indicators. Mediation analysis was conducted using the PROCESS macro (Model 4) with 95% bootstrap confidence intervals. Results: Chronotype was independently associated with CMRS after adjustment for covariates (β = 0.055; p = 0.030), although the effect size and explained variance were small. In hierarchical regression analysis, the inclusion of chronotype explained a small but significant increase in CMRS variance (ΔR² = 0.003; p = 0.030). The addition of adiposity indicators significantly increased the explained variance (ΔR² = 0.014; p < 0.001), with WHtR emerging as the most relevant predictor in the final model. Bootstrap mediation analysis did not reveal significant indirect effects of BF% or WHtR on the relationship between chronotype and CMRS. In sensitivity analyses excluding waist circumference from the CMRS, the association between chronotype and cardiometabolic risk was no longer significant (β = −0.001; p = 0.974). Conclusions: Chronotype showed a modest association with cardiometabolic risk in the primary analysis. However, sensitivity analyses indicated that this association may partly depend on the inclusion of waist circumference within the composite cardiometabolic risk score. These findings highlight the central role of abdominal adiposity in cardiometabolic health and suggest that the relationship between chronotype and cardiometabolic risk should be interpreted with caution. Full article

Dental implants have become common for restoring function and aesthetics after edentulism, with titanium (Ti) remaining the most widely used material due to its excellent mechanical properties and biocompatibility. Despite their clinical success, long-term performance is strongly influenced by surface characteristics, which regulate osseointegration and susceptibility to bacterial colonisation. Consequently, surface modification approaches have become critical strategies to enhance implant stability, bioactivity and longevity. This review critically evaluates conventional, advanced, and hybrid surface modification strategies. Subtractive methods, such as sandblasting and acid etching, increase microroughness (Ra 1.5–3 μm), enhancing osteoblast attachment and differentiation, but may promote bacterial adhesion and surface contamination. Combined treatments like SLA and SLActive generate hierarchical micro–nano topographies, improving protein adsorption, early-stage osteoblast proliferation (up to 2-fold), and clinical stability. Laser ablation and photofunctionalisation further modulate surface chemistry and wettability, accelerating osseointegration and epithelial cell adhesion. Coating approaches, including layer-by-layer self-assembly, nanospray drying, plasma spraying, and piezoelectric nanocomposites, introduce antimicrobial activity (>95% reduction in Escherichia coli or Staphylococcus aureus) and enhanced osteogenic differentiation with mechanical stability, with adhesion values reaching 49 MPa. Hybrid techniques such as sol–gel, hydrothermal, and anodisation provide controlled topography, chemical composition, and bioactivity, promoting early bone-to-implant contact (BIC increase of 10%–25%) in preclinical models. Notwithstanding promising in vitro and in vivo outcomes, variability in processing parameters and limited standardisation restrict large-scale clinical translation. Overall, contemporary Ti surface engineering emphasises a synergistic balance of topography, chemistry, wettability, and hierarchical structuring to optimise biological performance for dental implant applications. Full article

Enhancing the vibroacoustic performance of underwater vehicles remains a critical challenge in marine engineering. Increasing geometric stiffness is a conventional strategy to suppress vibration, yet its effectiveness in reducing underwater sound radiation can be practically limited. This paper presents a numerical investigation of the vibroacoustic response of composite grillage sandwich structures, with a focus on separating the contributions of geometric stiffening and core damping. A coupled acoustic structural model is developed based on the equivalent single layer theory and implemented in a finite element framework, then validated against analytical benchmark solutions. The parametric study reveals a stiffness saturation phenomenon in the acoustic domain. Although increasing rib height significantly reduces the mean square velocity, the radiated sound power reaches a saturation plateau and can even show a slight rebound at higher frequencies. This behavior is attributed to an increase in structural phase velocity that shifts modal components toward a more efficient radiation regime, thereby increasing radiation efficiency. To address this limitation, the damping modulation role of the core material is examined. The results show that introducing a high damping core into the grillage skeleton suppresses broadband noise and resonance peaks, without a comparable rise in radiation efficiency that may accompany geometric stiffening. The study indicates that a hierarchical synergistic design strategy that uses geometric stiffness for load bearing and low frequency control, while leveraging core damping to mitigate the acoustic saturation limit, provides useful physical insight into more efficient noise control approaches than purely stiffness based approaches. Full article

The continuous expansion of wind farms and the escalating demand for automated operation and maintenance have established the efficient and accurate performance of intelligent inspection systems for switching stations as a critical factor for ensuring power facility safety and stability. However, the intelligent inspection trolleys deployed in such settings are frequently hampered by suboptimal instrument detection accuracy and limited robustness, attributable primarily to environmental interference from fog, variable lighting conditions, or image noise. This paper proposes a multi-module-integrated real-time object detection model, termed HDC-RTDETR (HSAN + DBlockC3 + CGAFusion + RT-DETR). The model is grounded in the intelligent inspection principle of “clear visibility precedes efficient inspection”, with the primary objective of enabling reliable instrument identification under the influence of fog, changing lighting conditions or image noise. Specifically, building upon the RT-DETR architecture, we introduce three targeted enhancements: (1) the HSAN module adaptively fuses grayscale, edge, and color features to improve robustness against composite degradations (e.g., fog, illumination variations, noise) by enhancing target responses while suppressing background clutter; (2) DBlockC3 captures and integrates multi-scale contextual information, refining the discrimination of fine-grained instrument details under complex lighting; and (3) the CGAFusion module strengthens hierarchical feature integration within the encoder, effectively mitigating fog-induced blurring effects. Experimental validation on a Custom Dataset demonstrates that the proposed model achieves a mAP@50 of 95.566% (representing an improvement of 3.390 percentage points) and a precision of 90.557% (an increase of 11.20 percentage points). Furthermore, on an Industrial Instrument Needle Dataset, it attains a mAP@50 of 98.130% (+2.242%) and a precision of 95.130% (+4.269%). In addition, we validated its edge deployment capabilities on the Jetson AGX Orin, achieving real-time inference at 16.5 FPS, which meets the near-real-time video streaming processing requirements of many application scenarios. These results confirm that the HDC-RTDETR model exhibits superior detection performance and environmental adaptability in complex industrial scenarios, thereby establishing a high-confidence localization foundation for subsequent instrument reading extraction tasks. Full article

The hematite phase decorated with iron-doped cerium oxide nanoparticles (F@FC) was precipitated from cerium and iron oxalate intermediate products. The photocatalytic composite of graphitic carbon nitride (gCN) and F@FC was prepared by a simple method involving mixing the two components, followed by thermal treatment at 400 °C. According to electron microscopy, F@FC is composed of a submicron iron oxide (hematite) phase decorated with iron-doped cerium oxide nanoparticles deposited on gCN substrate. A hierarchically structured composite was observed instead of a simple mechanical mixture of α-Fe₂O₃, Fe-CeO₂, and gCN. To observe two types of degradation activity, photocatalytic and Photo-Fenton degradation activity, Rhodamine B (RhB) was applied as the model water pollutant. The influence of the amount of photocatalyst, the RhB concentration, the presence of cations and anions, the pH, and the effect of e⁻, h⁺, •OH, and •O₂⁻ scavenging reactants were studied. The Photo-Fenton degradation exhibited high efficiency across the entire tested pH range, whereas photocatalytic degradation showed comparable activity only at acidic pH. The F@FC-gCN composite catalyst exhibited a high degree of recyclability. The degradation pathways of photocatalytic and Photo-Fenton reactions were suggested by HPLC-MS analysis of the reaction products. A notable finding of this study was the observation that the green-yellow, fluorescent intermediate Rhodamine 110 was formed during the photocatalytic degradation of RhB. However, the high reactivity of the generated •OH radicals during Photo-Fenton degradation has been demonstrated to inhibit the formation of intermediate Rhodamine 110. Full article

Background and Objectives: Obesity affects over 1.9 billion adults globally, with a disproportionately higher prevalence in Saudi Arabia among women. While excessive adiposity is known to impair respiratory mechanics and lung function, its relationship with respiratory muscle strength and exercise energy expenditure remains inadequately elucidated. This study examined differences in respiratory muscle strength, metabolic equivalents (METs) of physical activity, and energy expenditure during exercise between adults with normal and high body fat percentage (BF%) and explored the statistical role of body fat as a potential mediator in the cross-sectional association between respiratory muscle strength and energy expenditure. Methods: In this cross-sectional study, 126 Saudi women aged 18–45 years (mean age: 21.7 ± 4.2 years) were stratified into normal (n = 63) and high (n = 63) BF% groups. Body composition was assessed via bioelectrical impedance analysis, and respiratory muscle strength (MIP and MEP) was measured using a MicroRPM device. Peak oxygen consumption (VO_2peak) and energy expenditure were obtained through the Bruce Submaximal Treadmill Protocol, and physical activity was self-reported via the IPAQ. Hierarchical regression and structural equation modeling were used to examine variable associations and explore statistical mediation patterns. Results: Participants with high body fat demonstrated significantly low MIP (−26%) and MEP (−31%), low VO_2peak (−13%), and approximately 26% high energy expenditure during exercise compared to the normal-BF group (all p < 0.001), despite comparable self-reported physical activity levels. Body fat percentage was the most strongly associated with energy expenditure (β = 0.078, R² = 0.329), with maximal inspiratory pressure contributing an additional 7.3% of explained variance in hierarchical regression (total R² = 0.414). Mediation analyses revealed that body fat percentage was statistically consistent with a partial mediation model in the relationship between MIP and energy expenditure (indirect association = −0.016, p = 0.033), accounting for 27% of the total association, and between MEP and energy expenditure (indirect association = −0.013, p = 0.035), accounting for 38% of the total association. Conclusions: High BF% is independently associated with low respiratory muscle strength and high exercise metabolic cost. Body fat is statistically associated with (and consistent with a mediating role in) an inverse relationship between respiratory muscle strength and energy expenditure. Alternative directional relationships and shared underlying factors may explain these observations. Full article

As complex system engineering involving multiple stakeholders, multi-objective collaboration, and multi-spatiotemporal scales, the components, logical structure, and functional mechanisms of production equipment operation management and control (PEOMC) can be generalized through functional modelling to support dynamic analysis and intelligent decision-making of PEOMC in the industrial internet environment. To address the diversity of scenarios and objectives of PEOMC, a hierarchical construction method for the functional model of PEOMC based on IDEF0 is proposed. By analysing relevant international standards, such as ISO 55010, ISO/IEC 62264, and OSA-CBM, the generic functional modules for the first and second layers of the functional model are identified and defined. On the basis of semi-supervised machine learning, topic clustering is used to extract the components, functional mechanisms, and logical relationships of production equipment operation management and control from approximately 200 standard texts and to construct a reference resource pool for the third-layer functional module. On this basis, an interface matching and recursive traversal algorithm for functional modules is designed, and a composition and orchestration strategy of functional modules for specific scenarios is provided to support the flexible construction of diversified and personalized PEOMC scenarios. The proposed construction and application method was validated through an engineering case study in an aero-engine transmission unit manufacturing workshop: the average process capability index of the enterprise’s production equipment steadily increased from 1.28 to approximately 1.60, the mean time to repair (MTTR) of production equipment failures significantly decreased from 8 h to 3 h, and the average overall equipment effectiveness (OEE) increased from 56.43% to a stable 68.57%, demonstrating its effectiveness and practicality. Full article

Climate change and rapid urbanization are intensifying flood risks in China, particularly in regions with complex terrain and dense populations. Traditional risk assessment methods often lack the flexibility to handle uncertainties in multi-dimensional risk systems. This study proposes a probabilistic flood risk assessment framework integrating Monte Carlo simulation with a composite indicator system from the perspective of disaster system theory. Taking Hunan Province as a case study, we constructed a hierarchical indicator system encompassing environmental susceptibility, hazard intensity, exposure vulnerability, and mitigation capacity. The analytic hierarchy process (AHP) and coefficient of variation (CV) methods were combined for indicator weighting, and Monte Carlo simulation was employed to quantify uncertainties and classify risk levels. Results reveal significant spatial heterogeneity in flood risk across the province, with high-risk areas concentrated in regions exhibiting intense rainfall, dense river networks, and insufficient mitigation infrastructure. The study provides a transferable, data-driven approach for spatially explicit flood risk zoning, offering evidence-based insights for land-use planning, resilient infrastructure development, and sustainable flood governance. This research contributes to the integration of probabilistic modeling into land system science, supporting disaster risk reduction and climate adaptation strategies aligned with SDG 11. This study also provides policy-relevant insights for regional flood governance by supporting risk-informed land-use planning, targeted infrastructure investment, and adaptive flood management strategies, thereby contributing to more resilient and sustainable land system development under increasing climate uncertainty. Full article

Construction sites implement various safety management activities, including toolbox meetings, risk assessments, and safety knowledge assessments, to reduce accidents. Multiple-choice question (MCQ)-based assessments are widely used to evaluate worker safety competencies. However, the effectiveness of MCQ assessments depends critically on distractor quality; incorrect options must be plausible enough to challenge uninformed respondents while remaining clearly distinguishable from knowledgeable ones. Manual distractor creation requires substantial expertise and is prone to inconsistency, whereas large language models (LLMs) often generate options that lack domain relevance. This paper proposes context-aware multipath adaptive safety scoring (CoMPASS), an algorithm that integrates construction safety domain knowledge with LLM capabilities for MCQ distractor generation. CoMPASS operates through two pathways: CoMPASS-H leverages a hierarchical hazard factor ontology for hazard identification questions, whereas CoMPASS-R uses hybrid retrieval-augmented generation (RAG) for risk control questions. An evaluation using 50 real construction accident cases with a robotic assessment test (RAT) using frontier LLMs as virtual examinees demonstrated that CoMPASS-R achieved a 90% quality pass rate, whereas all baseline methods failed to meet the composite quality criteria. The proposed framework provides a scalable approach to generating assessment content that supports effective safety management at construction sites. Full article

The widespread use of plastics has inevitably led to the accumulation of persistent plastic debris in aquatic systems, where gradual fragmentation generates microplastics (MPs) that threaten ecological and biological health. Their small size, chemical stability, and resistance to degradation make effective removal particularly challenging. In this work, a composite adsorbent was fabricated through the in situ solvothermal growth of Materials of Institute Lavoisier 100 (Iron) (MIL-100(Fe)) onto coconut shell-derived activated carbon (CSAC), yielding a monolithic material denoted as CSAC@MIL-100(Fe). The integration of porous C with a metal–organic framework created a hierarchically structured adsorbent rich in accessible binding sites. The composite achieved a maximum polystyrene (PS) removal efficiency of 97.4% and maintained 91.44% efficiency after seven regeneration cycles. Stable adsorption performance was observed across a broad pH range. Structural and chemical analyses (scanning electron microscopy (SEM), Brunauer–Emmett–Teller (BET), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS)) combined with adsorption modeling revealed heterogeneous multilayer adsorption behavior consistent with the Freundlich isotherm and pseudo-second-order kinetics. π–π interactions, electrostatic attraction, and coordination effects jointly governed PS capture. The Langmuir maximum adsorption capacity reached 746.27 mg/g. These findings demonstrate a practical and recyclable strategy for efficient MP remediation in aquatic environments. Full article

Developing advanced functional materials requires the synergistic integration of nanoscale reinforcements with tailored properties. In this work, composite films of cellulose nanocrystals (CNCs), cellulose nanofibrils (CNFs), and reduced graphene oxide (rGO) were synthesized using a combination of solution casting, high shear homogenization, vacuum filtration, and environmentally friendly chemical reduction. The resulting CNC/CNF/rGO films exhibited a robust hierarchical structure with strong interfacial interactions, enabling exceptional mechanical properties, specifically a tensile strength of 215 MPa and a Young’s modulus of 18 GPa, alongside a continuous conductive network confirmed by frequency-independent electrical conductivity up to 30 kHz. Comprehensive dielectric characterization revealed frequency-dependent permittivity and low dielectric loss, aligning with Maxwell–Wagner theoretical predictions for heterogeneous composites. The composites also demonstrated thermal stability, with electrical conductivity increasing monotonically from 0 °C to 200 °C. These findings highlighted the CNC/CNF/rGO films’ suitability for applications in flexible electronics, electromagnetic shielding, packaging, and high-performance structural materials. Future optimization and modeling approaches, including fractional calculus, are recommended to further enhance multifunctionality and exploit the unique synergistic interactions intrinsic to nanocellulose–graphene oxide platforms. Full article

Mobile Laser Scanning (MLS) integrated with Simultaneous Localization and Mapping (SLAM) has emerged as a key technology for digitizing GNSS-denied environments, such as underground mines. However, the automated interpretation of unstructured, high-density point clouds into semantic engineering models remains challenging due to extreme geometric anisotropy in point distributions and severe class imbalance inherent to narrow tunnel environments. To address these issues, this study proposes a highly automated scan-to-model framework for precise semantic segmentation and vectorized two-dimensional (2D) profile reconstruction. First, an enhanced hierarchical deep learning network tailored for point clouds is introduced. The architecture incorporates a context-aware sampling strategy with an expanded receptive field of up to 10 m to preserve axial continuity, coupled with a spatial–geometric dual-attention mechanism to refine boundary delineation. In addition, a composite Focal–Dice loss function is employed to alleviate the dominance of wall points during network training. Experimental validation on a field-collected dataset comprising 16 mine tunnels demonstrates that the proposed model achieves a mean Intersection over Union (mIoU) of 85.15% (±0.29%) and an Overall Accuracy (OA) of 95.13% (±0.13%). Building on this semantic foundation, a robust geometric modeling pipeline is established using curvature-guided filtering and density-adaptive B-spline fitting. The reconstructed profiles accurately recover the geometric mean surface of the tunnel wall, yielding an overall filtered Root Mean Square Error (RMSE) of 4.96 ± 0.48 cm. The proposed framework provides an efficient end-to-end solution for deformation analysis and digital twinning of underground mining infrastructure. Full article

Platelet-rich gel (PRG) is a fibrin-based biobased biomaterial generated by activating platelet-rich plasma (PRP), yet its biological characterization has commonly relied on univariate measurements of isolated mediators. This study aimed to define the multivariate biological organization of PRG and related hemocomponents (PRP, chemically induced platelet lysate (CIPL), and plasma) in a canine model under single exposure to non-steroidal anti-inflammatory drugs (NSAIDs). In a randomized crossover design (n = 6 dogs), hemocomponents were produced at baseline (0 h) and 6 h after administration of carprofen or firocoxib. Platelet and white blood cell (WBC) counts, growth factors (platelet-derived growth factor-BB (PDGF-BB) and transforming growth factor beta-1 (TGF-β1)), and cytokines (tumor necrosis factor alpha (TNF-α), interleukin-1 beta, and interleukin-10) were integrated using linear mixed-effects modeling, principal component analysis (PCA), and hierarchical clustering. PRG was derived from a leukocyte-poor PRP precursor with moderate platelet enrichment (~1.6-fold vs. whole blood) and a marked WBC reduction (~8–9-fold). In mixed-effects modeling, hemocomponent type significantly influenced the PDGF-BB:TNF-α log-ratio, with PRG (estimate −1.12; 95% CI −1.34 to −0.90) and plasma (−2.06; 95% CI −2.28 to −1.84) lower than PRP, while CIPL did not differ. Time and NSAID effects were not supported. PCA identified two orthogonal axes explaining 61.3% of total variance (PC1 = 43.7%, PC2 = 18.6%), separating a platelet/trophic dimension (log(PDGF-BB), log(TGF-β1), platelet count, PDGF-BB:TNF-α log-ratio) from an inflammatory dimension (log(TNF-α), log(IL-1β)). Overall, hemocomponent composition emerged as the primary determinant of mediator organization, supporting the interpretation of PRG as a structured, biomaterial defined by coordinated mediator networks. Full article