Search Results (5,042)

Pastures are the main feed source for beef cattle production, a sector in which Brazil plays a prominent global role. This study aimed to develop predictive models for dry matter yield (DM yield, kg ha⁻¹), crude protein (CP), and neutral detergent fiber (NDF) in Urochloa brizantha tropical pastures using laboratory VIS-NIR-SWIR spectroscopy, and to identify spectral patterns associated with these variables. Samples were collected from a commercial pasture area of approximately 200 ha, subdivided into 19 paddocks cultivated with Urochloa brizantha cv. Marandu and managed under rotational grazing during 2023. Forage samples were oven-dried, ground, and spectrally measured using a FieldSpec spectroradiometer (350–2500 nm). Partial least squares regression (PLSR) models were calibrated and evaluated using cross-validation, and informative wavelengths were identified using Variable Importance in Projection (VIP) scores. DM variability was mainly associated with near-infrared regions, CP with visible and near-infrared regions, and NDF with the visible region. Models calibrated with VIP-selected wavelengths achieved acceptable performance for CP (R²CV = 0.74) and NDF (R²CV = 0.72), whereas the general full-spectrum models showed moderate performance for CP (R²CV = 0.57) and acceptable performance for NDF (R²CV = 0.75). Temporal transferability varied among sampling periods, with greater robustness for CP and NDF than for DM. Overall, DM prediction remained limited and showed poor temporal transferability. Full article

The construction sector accounts for 34% of global energy-related CO₂ emissions, yet existing Digital Twin (DT) applications in construction remain largely descriptive, lacking the predictive and prescriptive capabilities required for proactive execution management. This study presents and evaluates, as a simulation-based proof of concept, a risk-informed DT framework integrating a formalized DT state transition model, a dual deep learning prediction engine (Long Short-Term Memory (LSTM) and Transformer), Monte Carlo-based probabilistic carbon quantification for lifecycle modules A1–A5 (S = 10,000), and a risk-adjusted, Non-dominated Sorting Genetic Algorithm II (NSGA-II) optimizer incorporating carbon variance, within a closed weekly feedback loop. The framework is evaluated across five European case studies (residential, education, and commercial office; the Netherlands, the UK, and France) anchored to published project data, with primary calibration on a hybrid real–synthetic mid-rise residential building (CS1; 7200 m², eight stories, the Netherlands) and a fully documented synthetic layer generating the execution records that the published sources do not provide. Under common random number simulation with 50 execution realizations per case, the full framework achieved simultaneous improvements of 5.8–13.2% in expected schedule duration and 10.4–16.4% in expected embodied carbon relative to a static Critical Path Method (CPM) baseline; for CS1, mean intensity fell from 476 to 426 kgCO₂e/m². Component ablation identified the bi-objective optimizer as the dominant contributor, producing an 11.7–20.2% carbon increase when removed, and the selected Pareto solution was invariant to the risk-aversion parameter λ over [0, 1], indicating that risk adjustment functions as a conservatism margin on the reported carbon target rather than a decision-altering preference. The framework advances construction DTs from descriptive monitoring tools toward risk-informed decision support, offering a transparent and reproducible benchmark that advances data-driven sustainability in construction scheduling and embodied carbon management. Full article

Background/Objectives: Postbiotics are increasingly recognized as biologically active products of microorganisms with emerging potential as microbiome-inspired therapeutic interventions. While most microbiome-based strategies focus on modifying microbial composition, restoration of microbial signaling has received comparatively less attention. This review examines postbiotics through the lens of microbial signaling restoration and proposes a unified Metabolite–Immune–Redox (MIR) axis linking microbial-derived signals with immune regulation, redox homeostasis, endothelial integrity, and host resilience. Methods: This narrative review synthesizes current evidence on postbiotics, microbial metabolites, structural microbial components, and extracellular vesicles, with emphasis on their roles in immunometabolic regulation, redox biology, endothelial function, and host-directed interventions. Results: Current evidence suggests that short-chain fatty acids, indole derivatives, bile acid metabolites, and microbial extracellular vesicles are important mediators of host–microbe communication. These signals influence interconnected pathways involving mitochondrial function, inflammasome activity, immune calibration, endothelial and glycocalyx homeostasis, and disease tolerance. The review highlights the endothelium as an underrecognized therapeutic target and discusses biomarkers, including soluble thrombomodulin, von Willebrand factor, and D-dimer, as potential tools for identifying patients most likely to benefit from host-directed interventions. Major translational challenges include product heterogeneity, incomplete mechanistic characterization, uncertain exposure–response relationships, and unresolved regulatory considerations. Conclusions: The proposed MIR axis provides a hypothesis-generating framework for understanding how restoration of microbial signaling may contribute to precision host-directed therapeutic strategies. Further mechanistic and clinical studies are needed to validate this concept and define its translational potential in inflammatory, infectious, and critical illness settings. Full article

This study presents a modular AI-assisted workflow for converting single 2D interior images into textured 3D assets and for evaluating those assets when ground-truth 3D meshes are unavailable. The proposed pipeline combines object detection, instance isolation, monocular-depth estimation, image-to-3D generation, texture synthesis, mesh export, and cloud-based execution to support early-stage interior-design and real-estate visualization tasks. A reference-free validation protocol is introduced, based on rendered multi-view comparisons, silhouette Intersection-over-Union, automated captioning, and multimodal embedding similarity, and is complemented by a composite validation framework that benchmarks reconstructed scenes against 200 panoramic indoor scenes from the Structured3D dataset using Hungarian-matched placement, size, recall, and relative-distance metrics. The workflow was implemented and tested using contemporary computer-vision and generative 3D components, with Hunyuan3D 2.0 used as the main reconstruction model. Proof-of-concept experiments on a representative corpus of 178 synthetically generated single-object images spanning a range of interior furniture categories show comparable silhouette IoU for textured and non-textured outputs and indicate that texture-preserving renderings improve visual and semantic similarity scores across CLIP-based evaluations. The 200-scene dataset evaluation reveals stable spatial localization (placement error ≈ 1.18 m, relative-distance error ≈ 0.54 m) alongside systematic over-prediction and size-calibration errors. Beyond the applied pipeline, the study contributes a reference-free, ground-truth-free protocol for 3D-asset evaluation and a first quantified account of where object-centric single-image reconstruction is reliable—spatial placement—and where it is not—object scale and spurious detection—at interior-scene scale. The results demonstrate the feasibility of integrating perception, 3D reconstruction, semantic assessment, and scalable deployment into a single applied pipeline, while remaining proof-of-concept and requiring extension to larger object and scene corpora, baselines, real-photograph evaluation, and human-centered assessment before broad claims about general interior-scene reconstruction can be made. Full article

Existing evaluations of sandstone-hosted uranium deposits commonly describe ore-controlling troughs and faults qualitatively, which limits the quantitative characterization of structural ore-controlling elements and the prediction of favorable mineralization areas. This study proposes a relative geological time (RGT)-constrained method for quantitatively computing intra-stratal trough attributes and a 3D seismic-based workflow for trough–fault coupled structural favorability prediction in sandstone-hosted uranium exploration and applies this framework to the Qianjiadian uranium mining area. An interpreted-horizon-constrained RGT volume was constructed to extract stratal two-way travel time (TWT) slices, and multi-scale trough attributes were calculated from local TWT relief differences. Fault attributes were constructed from laterally smoothed fault-scan results to improve fault-response continuity. Known mineralized wells were used to calibrate trough and fault responses and to construct trough ore-controlling index (TOI) and fault ore-controlling index (FOI) volumes. The calibrated TOI and FOI volumes were then integrated to generate a coupled favorability index (CFI) volume for structural favorability prediction. High CFI values occur mainly near major fault zones and in areas with moderate trough responses, indicating favorable trough–fault coupled structural conditions for uranium mineralization. Blind-well validation shows that 15 of 20 blind wells agree with the CFI prediction, yielding an overall accuracy of 75.0% and an AUC of 0.85. The proposed method converts qualitative trough and fault ore-control knowledge into computable, mineralization-calibrated, and blind-well-validated 3D structural favorability results, supporting favorable-area selection and drilling deployment in sandstone-hosted uranium exploration. Full article

Explainable intrusion detection systems often provide feature attributions without indicating whether a security action should be released, downgraded, or deferred. This paper investigates whether action-governed explanations can provide bounded triage evidence under traffic feature feasibility constraints. We present TRACE, a framework that maps calibrated detector outputs to a finite action ladder, constructs conformal action sets, selects actions via a utility-minimax rule, and releases high-severity actions only when compact support contracts remain stable under feasible perturbations, where feasibility is a property of the processed benchmark features and not of packet-level realizability. Ablations isolate the conformal set and release gate as the primary drivers of system behavior. Across 11 gated dataset–model pairs, TRACE produces non-degenerate action sets with zero full-set collapse and defer/block rates from 0.603 to 1.000. Under held-out sample split tuning, it achieves higher average proxy utility than unconditional release and release rate-matched random release on all 11 pairs. Against the strongest simple selective gate, however, it matches on 6 of 11 pairs and trails on the remaining 5. Robustness sweeps confirm positive all-row utility on all pairs, though pass-only utility becomes fragile in ultra-low-release regimes. Unlike display-only attribution summaries, the TRACE contract records the plausible action set, feasibility checks, stability summaries, and an explicit release rationale. The results support TRACE as a bounded evidentiary framework for action-governed XAI in IDS, rather than claiming superiority over all IDS/XAI methods or general deployment readiness. Full article

Background: Patients with stage I rectal cancer generally have favorable outcomes after curative surgery, but prognosis is not completely homogeneous. This study explored the prognostic association of preoperative inflammation–nutrition indices with overall survival and developed an interpretable internally validated machine learning survival model. Methods: We retrospectively included 475 patients with primary pathological stage I rectal adenocarcinoma who underwent curative-intent radical surgery at Sichuan University West China Hospital between 2018 and 2021. Patients downstaged to ypStage I after neoadjuvant therapy or treated by local transanal excision without lymph node dissection were excluded. Candidate predictors included age, sex, carcinoembryonic antigen, and routinely available preoperative inflammation–nutrition indices. LASSO-Cox regression was used for feature selection. Six survival models were developed and evaluated using 1000 bootstrap resamples with out-of-bag internal validation. Model performance was assessed at 60 months using time-dependent AUC, C-index, Brier score, calibration, and decision curve analysis. SHAP analysis was used for model interpretation. Results: During a median follow-up of 68 months, 30 deaths occurred. LASSO-Cox regression identified five predictors: age, lymphocyte-to-white blood cell ratio, fibrinogen-to-lymphocyte ratio, albumin-to-alkaline phosphatase ratio, and neutrophil-to-HDL cholesterol ratio. In bootstrap out-of-bag internal validation, XGBoost-Cox achieved the highest, although only marginally higher, discriminative performance among the evaluated models, with a 60-month time-dependent AUC of 0.783, a C-index of 0.774, and a Brier score of 0.0507. The calibration intercept and slope of XGBoost-Cox were 0.519 and 1.070, respectively. SHAP analysis identified age as the most influential predictor, followed by the selected inflammation–nutrition indices. Decision curve analysis suggested potential clinical utility within threshold probabilities from 1% to 20%, although this finding remains exploratory. Conclusions: Preoperative inflammation–nutrition indices may contribute to overall survival prognostic stratification in primary pathological stage I rectal cancer. External validation in larger multicenter cohorts is required before clinical application. Full article

Cerebrovascular computational fluid dynamics (CFD) models often rely on generic boundary conditions, which may limit their ability to represent subject-specific hemodynamics and cerebral autoregulation (CA). We propose a multimodal data-driven boundary calibration (MDBC) framework integrating transcranial color-coded Doppler and continuous blood pressure monitoring to optimize individualized outlet resistances. As a proof-of-concept, we evaluated the MDBC framework in a single healthy volunteer at resting baseline and enhanced external counterpulsation (EECP)—a hemodynamic perturbation potentially triggering CA. Compared with conventional open boundary (OB) and static Murray allocation boundary (SMAB) strategies, MDBC achieved closer agreement with in vivo middle cerebral artery (MCA) velocity waveforms under both states. At rest, MDBC’s left MCA relative root mean square error (rRMSE) was 7.19%, versus 22.85% (OB) and 30.89% (SMAB). During EECP, conventional models yielded rRMSEs > 32%, whereas MDBC maintained 11.24%. Meanwhile, MDBC reproduced inter-hemispheric perfusion imbalance, an EECP-induced flow surge in the right MCA, and pronounced wall shear stress increases that were masked by generic boundary strategies. Moreover, MDBC estimated a 25.8% increase in global cerebrovascular resistance during EECP, suggesting the capability of the framework to characterize subject-specific impedance adaptations potentially associated with CA during intervention. These single-subject findings support the technical feasibility of integrating multimodal physiological measurements into cerebrovascular CFD boundary calibration and warrant further validation in larger cohorts and patient populations. Full article

This study investigates the synergistic effects of compaction pressure and vapor-induced phase separation (VIPS) on the morphological, mechanical, and initial filtration properties of activated carbon/polyethersulfone composite block membranes. Membranes were fabricated using varying compaction pressures (5 and 10 kg/cm²) and VIPS exposure times (0 and 10 min) prior to direct non-solvent-induced phase separation (NIPS). Surface wettability analysis revealed that the optimized 50 wt.% activated carbon configurations were superhydrophilic (0° water contact angle), exhibiting instantaneous fluid absorption driven by strong capillary forces within the highly hygroscopic matrix. Morphological and gravimetric evaluations demonstrated that minimizing compaction (5 kg/cm²) and bypassing VIPS generated large macrovoids, resulting in the highest bulk internal porosity (61.05%) and maximum continuous gravity-driven water flux. Conversely, incorporating a 10-min VIPS exposure shifted the internal structure toward an interconnected sponge-like network. This structural transformation yielded the highest bovine serum albumin (BSA) rejection rate (12.97%) when paired with low pressure, as the network extended fluid residence time and maximized exposure to the activated carbon adsorption sites. Applying high compaction pressure (10 kg/cm²) to VIPS-treated membranes induced excessive polymer encapsulation of the active particles, significantly reducing separation efficiency while concurrently maximizing initial uniaxial tensile strength. Ultimately, these findings establish a foundational and highly tunable framework, demonstrating that calibrating mechanical compression alongside phase inversion dynamics balances permeability, adsorptive selectivity, and inter-particle binding cohesion for composite block membranes. Full article

Deep neural networks (DNNs) have shown outstanding performance in visual recognition tasks within vision sensor networks; however, they are still vulnerable to adversarial manipulations and imperceptible perturbations that can lead to erroneous predictions. To address that, this paper presents AEGIS, a semantic aware and uncertainty guided adversarial detection framework designed for robust image classification in vision sensors pipelines. At its core, a SemantiGAN module functions as a multi-class semantic discriminator, identifying and filtering visually inconsistent adversarial inputs before they propagate further in the pipeline. For inputs that pass this stage, a stochastic augmentation process generates test time variations, from which handcrafted instability metrics FlipScore, Prediction Inconsistency, Layerwise Cosine Similarity (early and mid layers), and Entropy are computed. These features are aggregated into a compact five dimensional vector and processed by an Evidential Deep Learning (EDL) classifier, which models output evidence using a Dirichlet distribution to yield both class predictions and calibrated uncertainty estimates. Evaluations on the Tiny ImageNet dataset across six categories clean, FGSM, PGD, patch-based, functional, and geometric attacks demonstrate the effectiveness of AEGIS. The proposed framework achieves an AUROC of 92.1%, an AUPRC of 90.2%, and an accuracy of 90.7%, outperforming conventional softmax-based detectors in terms of detection performance, robustness, interpretability, and uncertainty calibration. Full article

Residential buildings are central to decarbonisation because existing dwellings combine long service lives, high heating demand and heterogeneous constraints for retrofitting. This study investigates some retrofit strategies for a terraced house in Oxford, UK, to achieve the winter indoor air temperature set-point while reducing energy use, costs and CO₂ emissions. A calibrated dynamic simulation model was developed from on-site inspections, monitored temperatures, occupant schedules and energy-bill data. The analysis compares baseline configuration with scenarios including radiator power upgrading, envelope insulation, increased internal thermal mass and replacement of the condensing boiler with a high-temperature ground-source heat pump (GSHP). The results show that radiator upgrading enables the most critical rooms to reach the 20 °C set-point, while envelope insulation reduces heating energy and costs. Increased thermal mass improves night-time temperature stability, although its effect on annual energy demand is limited. The GSHP provides the largest primary energy reduction, lowering operational primary energy by 66.3% compared to the reference case and by 70.4% when combined with envelope and thermal-mass measures. Operational CO₂ emissions are reduced by 35.0–84.3%. The study highlights the need to evaluate the capacity of heat emitters, building envelope performance, thermal inertia and heat generator efficiency within a dynamic framework. Full article

Artificial intelligence (AI)-based 3D eye tracking is a fundamental enabling technology for human–computer interaction (HCI), extended reality (XR), and intelligent spatial computing. However, physical evaluation methodologies are heavily limited by non-repeatable human micro-movements, the lack of precise millimeter-level ground truth, and systemic camera calibration errors. Addressing these limitations, this paper delivers a methodological meta-contribution to the field by presenting a high-fidelity facial digital twin benchmark framework using NVIDIA Isaac Sim for the rigorous and repeatable evaluation of 3D eye-tracking algorithms. Rather than focusing on incremental algorithmic modifications, our framework establishes a standardized, hardware-free testing paradigm. By systematically sampling virtual facial poses under identical rendering configurations, it generates dense, fully repeatable trajectories with mathematically exact spatial ground truth across landmark-based, 3DMM-based, and direct regression architectures. To isolate intrinsic algorithmic capabilities from extrinsic calibration biases, we propose a self-referenced relative-motion evaluation protocol operating in a facial-centered local reference frame. Comprehensive diagnostics are performed across multiple key dimensions, including localization accuracy, temporal jitter, detection robustness, and pose sensitivity, culminating in a newly introduced Comprehensive Performance Index (CPI) to aggregate these multi-dimensional metrics. Statistical hypothesis testing reveals consistent performance hierarchies: landmark and 3DMM methods achieve superior geometric consistency and temporal stability by leveraging parametric shape constraints, whereas direct regression models exhibit severe tracking degradation and failure under extreme rotations. By resolving the long-standing benchmark replication bottleneck, this extensible digital twin platform establishes a standardized, reproducible methodology for developing and certifying trustworthy human-centric perception systems. Full article

Correlated color temperature (CCT) can substantially influence image-based color measurements, though its effects vary with the optical and physical properties of the meat surface being evaluated. This study investigated the effects of CCT on the image-based color properties of skinless chicken breast and skin-on drumstick samples. It evaluated the suitability of 6500 K as a reference condition. Seventeen skinless breast and seventeen skin-on drumstick samples were repeatedly photographed under 13 CCT conditions ranging from 2500 to 8500 K at 500 K intervals, producing 442 observations. Camera settings, imaging geometry, illuminance, and sample position were maintained constant. Image-derived L*, a*, and b* values were obtained using the Fiji freeware program, and chroma (C*), whiteness index (WI), yellowness index (YI), browning index (BI), and total color difference (ΔE*ab) relative to 6500 K were calculated. Data were analyzed using linear mixed models, and the common variation in L*, a*, and b* was summarized by principal component analysis. Sample type, CCT, and their interaction significantly affected all evaluated parameters (p < 0.001). Increasing CCT generally increased L* and WI, while a*, b*, C*, YI, and BI decreased markedly, indicating a transition toward a lighter and less chromatic image-derived appearance. The largest deviations from the 6500 K reference occurred at 2500 K, with ΔE*ab values of 42.86 and 43.57 for breast and drumstick samples, respectively. Even at 6000 K, only 500 K below the reference condition, ΔE*ab exceeded 4.0 in both breast and drumstick samples, indicating visually perceptible color differences. Visually relevant differences also persisted above 6500 K. The first principal component explained 86.89% of the total variance and described the coordinated increase in lightness and reduction in redness and yellowness with increasing CCT. These findings demonstrate that CCT is a major source of systematic variability in image-based poultry color analysis workflow; therefore, 6500 K should not be treated as a universal reference unless the complete illumination, camera, calibration, and image-processing protocol is standardized and validated for the specific poultry surface being examined. Full article

Accurate classification and efficient retrieval of histopathological images are essential for the diagnosis of lung adenocarcinoma (LUAD). Existing deep learning approaches for Content-Based Histopathological Image Retrieval (CBHIR) typically generate single-scale embeddings, missing the richer spatial context from earlier network stages. We propose a unified framework composed of: (1) a ConvNeXt V2 backbone with an integrated Convolutional Block Attention Module (CBAM) for multi-scale feature extraction, (2) an Adaptive Weighted Fusion Neck with learnable softmax-normalized weights, and (3) a novel Hybrid Representation Head producing an 18,496-dimensional descriptor by concatenating global, spatial, and attention-weighted features. Evaluated on the WSSS4LUAD dataset (10,087 patches, four tissue classes), our model achieves 85.03% accuracy (5-fold CV: 83.35 ± 0.93%), F1-score of 0.8116, mean Average Precision (MAP) of 0.8323 for retrieval, and an Expected Calibration Error (ECE) of 0.0378. Ablation experiments confirm that all proposed modules contribute positively, with the Attention Branch being the most impactful (Δ = −2.13%). The framework further provides Gradient-weighted Class Activation Mapping (Grad-CAM) explainability for clinical interpretability. Full article

Clothing pressure is a critical design parameter in compression garments, yet its estimation in three-dimensional (3D) digital environments remains challenging because it depends on fabric mechanics, garment deformation, body geometry, and garment–body contact. This study developed a calibrated 3D vector-projection method for estimating clothing pressure from digital garment-mesh deformation. The method was based on the mechanical premise that garment extension generates in-plane tensile forces, whereas interface pressure is associated with the component of those forces acting normal to the body surface. Accordingly, corresponding flat and deformed garment meshes from CLO 3D were used to calculate edge-length strain and internal force; resultant forces were projected onto local avatar-normal directions and normalized by vertex-associated surface area to obtain uncalibrated pressure-related values. Five tricot fabrics and two pattern-reduction levels were used to produce ten compression tops, and pressure measured at five body locations was used for modulus-group-specific linear calibration to account for stiffness-dependent differences in deformation-to-pressure conversion. Under leave-one-garment-out cross-validation, the final linear model achieved an overall R² of 0.563, an RMSE of 0.587 kPa, and an MAE of 0.427 kPa. A 1–15 mm contact-distance analysis identified 10 mm as a conservative numerical stabilization point, with normalized means remaining within ±2% of the 15 mm reference and adjacent-threshold changes below 0.1 from 10 to 15 mm. The proposed method provides a transparent, mechanics-informed mesh-level procedure that converts digital garment deformation into calibrated body-normal pressure estimates and 3D spatial maps without treating commercial virtual-fitting pressure maps as direct physical predictions. Full article