Search Results (4,432)

The Late Eocene Huizhou-A sandy conglomeratic system in the Pearl River Mouth Basin presents a highly heterogeneous reservoir system shaped by intense synsedimentary fault activity and variable depositional processes. Utilizing 3D seismic interpretation, well log analysis, and core calibration, this study reconstructs the tectono-sedimentary evolution, facies distribution, and diagenetic modifications controlling reservoir quality. Results show that the best reservoir quality is not confined to proximal fan-delta coarse-grained deposits near steep boundary faults, but occurs mainly in fan-delta front and braided-river-delta deposits, especially braided- and turbidite-channel microfacies. These reservoirs benefit from better sorting, favorable grain size, and higher textural maturity, whereas proximal clastic-flow deposits are poorer due to heterogeneity, poor sorting, and compaction. Reservoir quality is also depth-dependent: upper Enping reservoirs are mainly controlled by maturity, while lower Enping reservoirs are more influenced by grain size. Semi-quantitative analysis identifies the 7–11 km transport-distance zone as the optimal fairway for vertically stacked high-quality reservoirs. This approach not only guides exploration and development in the Huizhou Sag but also offers a transferable predictive model for similar steep slope lacustrine rift basins with comparable tectono-sedimentary settings worldwide. Full article

Accurate multiphase flow measurement in horizontal wells is fundamentally challenged by the antagonistic electrical responses of water and gas: Electrical Resistance Tomography (ERT) loses sensitivity to thin liquid films, while Electrical Capacitance Tomography (ECT) suffers signal saturation in conductive water, preventing either modality from covering the full operating envelope alone. This study proposes a physics-guided hybrid modeling framework that integrates multi-modal ERT/ECT sensing to achieve high-precision flowrate inversion. The framework utilizes a corrected multi-modal fusion algorithm, achieving a liquid holdup MAPE of 2.5 ± 0.5% representing a nearly two-fold improvement over the best single-modality system (Direct ERT, 4.5%). For velocity estimation, an optimized cross-correlation method yields results with ± 3.0% error, incorporating multi-sensor and multi-sequence fusion. A key finding is that deep neural networks exhibit Architectural Phase Specialization: multi-branch architectures (MB-DNN) perform strongly on localized, heterogeneous liquid structures (2.0% liquid error), whereas fully-connected architectures (FC-DNN) excel at capturing the global patterns of the continuous gas core (1.2% gas error). By hybridizing a calibrated drift-flux physical model with these phase-specialized DNNs, the framework achieves overall averaged errors of 1.8% for gas and 1.5% for liquid across the full experimental envelope. The proposed framework was evaluated on 444,313 experimental samples and subsequently validated in a three-month industrial trial at the Puguang gas field under extreme conditions (26 MPa, 80 °C), where it maintained a prediction error of ± 2.3%. This work establishes a scalable, physically consistent paradigm for intelligent hydrocarbon production monitoring. Full article

Ceramic additive manufacturing offers strong potential for fabricating geometrically complex and application-specific components, yet achieving reliable dimensional fidelity remains challenging because dimensional deviation is governed by highly coupled material, process, thermal, and environmental factors. To address this problem, this study proposes an uncertainty-aware computational framework for dimensional error prediction in ceramic 3D printing under variable material and process conditions. The contribution is positioned as a system-level integration of established learning, uncertainty estimation, calibration, and reliability-interpretation components within a ceramic additive manufacturing dimensional-error prediction workflow, rather than as a fundamental methodological breakthrough. The validation is conducted using the publicly available Ceramic 3D Printing Process Control Dataset, a 1000-sample tabular dataset, and the resulting findings are therefore interpreted as dataset-specific computational evidence rather than direct proof of industrial deployment readiness. The methodology begins with a structured data-driven preprocessing pipeline that transforms the Ceramic 3D Printing Process Control Dataset into a multi-condition feature space through data cleaning, one-hot material encoding, min–max normalization, and engineered descriptors capturing extrusion–speed balance, thermal gradients, cooling intensity, deposition density, and material-conditioned interactions. A multi-branch deep computational architecture is then developed to encode material, process, thermal-environmental, and engineered-feature streams separately, followed by adaptive cross-condition fusion to learn nonlinear dependencies across ceramic printing regimes. To improve reliability beyond deterministic regression, the framework jointly models aleatoric and epistemic uncertainty and incorporates calibration refinement to align predictive confidence with observed error behavior, thereby enabling preliminary reliability-oriented interpretation of stable and high-risk operating conditions. Experimental results demonstrate that the full model achieves the best overall within-dataset performance, with a test MAE of 0.0118, RMSE of 0.0172, $R^2=0.999$, MAPE of 1.74%, calibration error of 0.003, PICP of 0.996, reliability score of 0.992, and a stable prediction rate of 98.7%. Although these values indicate strong predictive behavior under the current structured dataset, the exceptionally high $R^2$ should be interpreted cautiously because external experimental validation, larger measured datasets, and cross-machine ceramic printing trials are still required. These findings show that the proposed framework provides an effective system-level computational strategy for dataset-specific reliability-aware dimensional quality prediction in ceramic additive manufacturing and offers a preliminary data-driven foundation for uncertainty-aware intelligent process optimization. Full article

Background: Accurate preoperative prediction of length of hospital stay (LOS) after surgery for colorectal liver metastases (CRLMs) could improve patient counselling and resource planning, yet reliable risk tools are lacking. We aimed to develop an interpretable machine learning model predicting LOS following first-time liver-directed surgery for CRLMs. Methods: In this multicenter cohort study, we included patients who underwent first-time liver resection, ablation, or a combination for CRLMs at three Danish hepatobiliary centers between 2016 and 2023. Preoperative features from two national registries were used to train Elastic Net, Random Forest, HistGradientBoosting, and Explainable Boosting Machine (EBM) algorithms. Hyperparameters were optimized using five-fold cross-validation. Performance was evaluated on a 20% hold-out test sample using mean absolute error (MAE) with bootstrapped 95% confidence intervals (CIs). Results: Among 915 patients, median LOS was 4.0 days (interquartile range (IQR) 3.0–6.0). All four algorithms achieved comparable prediction error (MAE 3.0–3.1 days). The EBM (MAE 3.1 days, 95% CI 2.6–4.3) algorithm was selected for its inherent interpretability. Surgical approach was the strongest predictor, where percutaneous and laparoscopic approaches were associated with reductions of 1.9 and 1.2 days, respectively. Tumor burden, including number of lesions and largest lesion diameter, showed progressive non-linear associations with longer stays. Nonetheless, overall explained variance was low (R² ≤ 0.10), and calibration showed systematic underestimation of stays beyond five days. Conclusions: An inherently interpretable machine learning model matched the predictive performance of opaque algorithms for LOS after CRLM surgery, although overall predictive accuracy was modest and longer stays were underestimated. Explainability analysis identified surgical approach and tumor burden as the most influential predictors. External validation in healthcare systems with different discharge practices is warranted. Full article

Chronic wounds represent a substantial and growing clinical burden, yet durable healing remains difficult to achieve in a large proportion of patients. The skin microbiome plays a central role in this challenge: in healthy tissue, resident microorganisms support barrier integrity and calibrate immune responses, whereas in chronic wounds, community disruption—often combined with persistent biofilm formation—drives non-resolving inflammation, impairs re-epithelialisation, and increases antimicrobial tolerance. As antibiotic resistance escalates, these features strengthen the rationale for microbiome-directed strategies that target wound ecology while reducing reliance on conventional antimicrobials. Current evidence is still dominated by mechanistic and preclinical studies, with only early clinical signals for selected approaches; therefore, next-generation probiotics, including Lactiplantibacillus/Lactobacillus spp., as well as defined prebiotic and postbiotic formulations, should be interpreted as promising adjuncts rather than clinically established therapies. Causal mechanisms, optimal formulations, reproducibility, and patient-level determinants of response remain insufficiently defined, representing a critical knowledge gap that limits translation. Here, we synthesise current evidence linking microbial ecology to key wound-healing pathways and propose a precision framework that integrates metagenomics, transcriptomics, metabolomics, and spatial profiling to map host–microbe interactions, identify predictive biomarkers, and guide stratified therapy. We further highlight combinatorial approaches pairing ecological engineering with biofilm-disruptive materials and immune-modulatory molecules. Realising the potential of these interventions will require mechanism-resolved clinical trials, standardised outcome frameworks, and patient stratification tools—advances that could improve chronic wound management while reducing selective pressure for antimicrobial resistance. Full article

Grain moisture content is a key variable for safe storage, drying control, and quality management. Microwave sensing is attractive because water strongly modulates the complex relative permittivity (ε* = ε′ – jε″) of granular agricultural products, thereby shaping broadband scattering-parameter spectra. This study presents a meter-referenced feasibility evaluation of an interpretable S-parameter–permittivity–moisture chain using a vector network analyzer over 2–18 GHz. Wheat, maize, and mung bean were prepared at six moisture levels, and the moisture values were referenced to two commercial grain moisture meters (MC_ref) to represent rapid on-site benchmarking rather than absolute gravimetric moisture determination. Therefore, the reported errors should be interpreted as commercial-meter-referenced calibration indicators rather than absolute gravimetric moisture prediction accuracy. Two free-space configurations were compared on the same platform: a two-horn transmission setup under controlled packing and a metal-backed double-pass reflection setup intended to represent single-sided access under loose bulk packing. After SOLT calibration and empty-holder background normalization, ε′ and ε″ were retrieved via complex-domain nonlinear least-squares fitting of physics-based slab models to measured S21 spectra. The results show that moisture-dependent dielectric responses were grain- and configuration-dependent. In particular, ε″ generally provided a more robust moisture-sensitive feature in the free-space transmission configuration, whereas the optimal single-parameter predictor in the metal-backed configuration differed among grains. A mid-band frequency window of approximately 8–16 GHz provided more stable inversion by avoiding low-frequency coupling artefacts and high-frequency signal-to-noise degradation. The metal-backed configuration preserved moisture trends but yielded lower effective ε′ values, likely due to increased air fraction under loose packing. These results indicate that packing state, grain type, and frequency-window selection are critical factors for transferring microwave moisture calibration from laboratory measurements to practical grain-handling scenarios. Full article

Context: Modern quantum-computing experimentation generates heterogeneous, context-dependent execution data whose scientific value depends on preserving calibration state, compilation decisions, and run outcomes in a traceable and repository-ready form. In the NISQ era, probabilistic outputs, time-varying hardware conditions, and opaque transpilation pipelines create a data-management problem that directly affects reproducibility, traceability, and long-term reuse of experimental records. Goal: This paper aims to address this gap by proposing a specialized metadata and schema model for managing quantum-circuit execution data as governed, machine-interpretable, and evolvable repository artifacts. Proposal: We propose QC-MM, a platform-agnostic metadata model for capturing, validating, and relating contextual evidence of quantum-circuit experiments. The model integrates time-indexed calibration binding, transpilation traceability, lightweight provenance links, validation rules, and controlled schema evolution through a JSON Schema specification. Results: The evaluation follows a multi-scenario protocol and shows that QC-MM captures dynamic calibration context in IBM Quantum Cloud, remains interoperable through a local SpinQ NMR device, and makes transpilation effects traceable through structured records. It also supports repeated-run statistical reporting and links compilation decisions to execution outcomes, including circuit-depth reductions and changes in an estimated fidelity proxy under different optimization settings. Conclusions: QC-MM provides a specialized data-modeling and schema-governance foundation for traceable quantum-experiment repositories. Beyond improving reproducibility-oriented reporting, the proposal contributes to metadata validation, controlled schema evolution, and repository-oriented management of contextual experimental data. Full article

Aiming at addressing the problem whereby the traditional time-optimal trajectory planning based on the steady-state torque–speed characteristic cannot fully exploit the short-term dynamic output performance of the servo permanent magnet synchronous motor (SPMSM), a time-optimal trajectory planning method for the SPMSM based on the short-term dynamic feasible region constraint is proposed to effectively improve the response speed. Firstly, the dynamic trapezoidal domain operation boundary is obtained by analyzing the motor working point variation curve and considering factors such as the working temperature and trajectory control, which constitutes the torque–speed value and the dynamic constraint mechanism of trajectory planning. Secondly, based on the energy consumption model, the average thermal power is used to represent the torque overload limit condition, and a dynamic constraint method based on the short-term dynamic torque–speed operation boundary is proposed. Then, in order to reduce the computational load in the online millisecond-level response, a time-optimal trajectory optimization algorithm based on sequential least squares is proposed to calibrate the positioning time of the time-optimal trajectory under different working temperatures and angles. Finally, a simulation and experimental comparisons of the time-optimal trajectories under different angles and working temperatures are carried out to verify the effectiveness of the proposed method. Full article

Background/Objectives: Established AECOPD prognostic tools (DECAF, BAP-65, PEARL) predict mortality or readmission rather than length of stay (LOS), and no admission-based instrument specifically targets prolonged hospitalization. We tested whether admission PaCO₂ and the Prognostic Nutritional Index (PNI), reflecting ventilatory failure and nutritional–immune reserve, are independently associated with prolonged LOS and examined their interaction. Methods: In this single-center retrospective cohort, 213 adults hospitalized exclusively for AECOPD were analyzed after excluding concomitant pneumonia, pulmonary embolism, decompensated heart failure, and in-hospital deaths. Prolonged hospitalization was pre-specified as LOS > 7 days. Multivariable logistic regression evaluated admission PaCO₂ (per +10 mmHg) and PNI (per +5 units) with a PaCO₂ × PNI interaction; continuous LOS was modeled by Gamma regression. Discrimination was compared with DECAF using DeLong’s test. Results: Prolonged hospitalization occurred in 83 patients (39.0%). Admission PaCO₂ was independently associated with prolonged LOS (OR 1.52, 95% CI 1.25–1.88; p < 0.001), and PNI showed a borderline association (OR 0.84, 95% CI 0.71–1.00; p = 0.049); their interaction was significant but exploratory (OR 1.16, 95% CI 1.02–1.32; p = 0.025). In Gamma regression, PaCO₂ (RR 1.18 per 10 mmHg) and PNI (RR 0.92 per 5 units) remained associated with LOS. The two-variable model achieved an AUC of 0.682, showing discrimination similar to DECAF in this cohort (AUC 0.695; DeLong p = 0.76), with optimism-corrected AUC 0.672 and calibration slope 0.96. Within moderate hypercapnia (PaCO₂ 45–60 mmHg), the prolonged-LOS rate was 44.4% in low-PNI versus 15.6% in high-PNI patients. Conclusions: In this single-center retrospective cohort of AECOPD patients surviving to discharge, admission PaCO₂ and PNI were jointly associated with prolonged hospitalization, reflecting acute ventilatory burden and nutritional–immune reserve. Using only two admission inputs, the framework showed discrimination similar to DECAF without meaningful reclassification gain (IDI −0.02; NRI 0.02). Given only moderate discrimination (AUC ~ 0.68), external validation is required before clinical use, with the main practical value likely in complementary stratification within moderate hypercapnia. Full article

During horizontal drilling in coal-rock gas reservoirs, the particle size distribution (PSD) of drilled cuttings directly affects drilling efficiency, hole cleaning, and wellbore stability. However, the evolution of cuttings PSD and its controlling mechanisms during coal-rock fragmentation remain insufficiently understood. In this study, a drill bit–coal-rock interaction model was established using the discrete element method (DEM) and calibrated against uniaxial compression experiments. The effects of weight on bit (WOB), rotational speed, and depth of cut (DOC) on cuttings PSD were quantitatively investigated. The results show that the relative influence on the maximum cutting size followed the order of DOC > WOB > rotational speed, whereas the influence on the average cutting size followed the order of rotational speed > WOB > DOC. Increasing DOC from 0.5 mm to 1.5 mm increased the maximum cutting size from 11.6 mm to 29.4 mm. Increasing WOB promoted the generation of medium- and large-sized cuttings, thereby increasing hole-cleaning requirements. Meanwhile, increasing rotational speed from 40 rpm to 90 rpm reduced the average cutting size and shifted the dominant cutting fraction from 4–6 mm to 1–4 mm. DEM observations reveal that cutting PSD evolution is jointly controlled by primary brittle fracture and secondary particle breakage through a five-stage fragmentation process involving stress concentration, microcrack initiation, crack propagation and coalescence, fragment detachment, and secondary fragmentation. Field validation using 146 cutting samples demonstrated the applicability of the proposed optimization strategy. Under the investigated drilling conditions, a DOC of approximately 0.5 mm and a rotational speed of 70–90 rpm were found to effectively limit oversized cutting generation. These findings improve the mechanistic understanding of cutting PSD evolution and provide practical guidance for drilling parameter optimization and hole-cleaning management in coal-rock gas horizontal wells. Full article

Background: Cardiovascular disease (CVD) remains the leading cause of mortality worldwide, with over 75% of deaths occurring in low- and middle-income countries, where conventional risk models often demonstrate poor calibration and limited generalizability. Objective: This study aimed to develop an interpretable, pharmacotherapy-informed machine learning model for cardiovascular risk prediction using national electronic health record (EHR) data from Jordan. Methods: A retrospective cohort study was conducted using approximately 600,000 individuals from the national Hakeem EHR system (2018–2022). Demographic, clinical, blood pressure, laboratory, and medication data were integrated to construct three datasets reflecting varying levels of feature completeness. Multiple machine learning models were benchmarked, followed by optimization, hybrid modeling, and probability calibration. Model interpretability was assessed using SHAP analysis. Results: The national cohort demonstrated a high cardiometabolic burden, with prevalence of hypertension (50.2%), hyperlipidemia (54.9%), and diabetes (47.9%). Antihypertensive and lipid-lowering therapies were more frequently used among CVD patients (56.9% and 49.6%, respectively). Treatment patterns were dominated by amlodipine (19.9%) and atorvastatin (74.4%). The final calibrated seed-bagged gradient boosting model achieved robust performance (ROC-AUC 0.844; PR-AUC 0.813) with consistent generalization across datasets. Key predictors included antihyperlipidemic therapy, systolic blood pressure variability, age, and sex. Conclusions: This study presents JoRisk, a calibrated and interpretable machine learning framework that integrates pharmacotherapy and clinical data for short-term cardiovascular risk prediction. The model demonstrates strong performance using routinely available EHR variables and offers a scalable decision-support tool for risk stratification in resource-constrained healthcare systems. Full article

Fatigue detection in remote tower air traffic controllers is important for maintaining operational safety under sustained visual monitoring and high cognitive workload. This study proposes MFD-Net, a dual-stream multimodal fusion framework using eye-tracking and electrocardiogram (ECG) signals. The model separately encodes eye-tracking and ECG-derived temporal inputs, incorporates an ECG-derived RMSSD expert feature, and performs lightweight late fusion for fatigue-state classification. Under the mixed-subject random-window protocol, MFD-Net achieved an Accuracy of 85.20%, a Recall of 83.33%, and an AUC of 0.9337. Because overlapping windows from the same participant and scenario could appear in both training and test sets, this result should be interpreted as a potentially optimistic within-distribution estimate. Under the stricter zero-shot leave-one-subject-out (LOSO) protocol, performance decreased substantially, with an Accuracy of $70.95\pm 21.59\%$, a Recall of $22.98\pm 36.30\%$, and an AUC of $0.6025\pm 0.2984$. This low zero-shot Recall indicates limited subject-independent fatigue-detection capability. Lightweight target-subject calibration and sequential probability aggregation improved adaptation and temporal stability, although the calibration results should be interpreted cautiously because random target-subject windows were used for fine-tuning. These findings suggest that eye-tracking and ECG fusion are promising under controlled conditions, while practical deployment requires deployment-oriented calibration protocols, recall-oriented optimization, and further real-world validation. Full article

Background/Objectives: The accuracy and reliability of automated clinical analyzers are fundamental to patient safety and effective medical decision-making. Traditional quality control (QC) methods, which rely on periodic manual calibration and reactive maintenance, are inherently limited by high latency and labor costs; furthermore, they fail to provide continuous, real-time monitoring. This paper introduces a novel hybrid ensemble learning framework for the automated quality inspection of medical devices through the analysis of time-series reaction curves. Methods: Our system integrates three heterogeneous anomaly detection paradigms: an Enhanced Dynamic Time Warping (DTW) detector for robust non-linear pattern matching, a Shape Template Matching (STM) detector that mimics expert clinical logic by analyzing morphological features in a normalized shape space, and a specialized Time-series Variational Autoencoder (TimeVAE) for deep representation learning. The outputs of these detectors are fused using a weighted ensemble strategy, which is specifically designed to prioritize the minimization of false negatives—a critical requirement in medical diagnostics. Results: We evaluate our framework on a comprehensive, multi-center real-world dataset comprising seven distinct biochemical assays. Experimental results demonstrate that our proposed method achieves superior performance, attaining a $0\%$ false negative rate on CRE and DBIL assays and outperforming all baseline methods on the other five datasets. An ablation study confirms the model’s robustness even with limited training data, and a comparative analysis against eight state-of-the-art baseline methods further validates the effectiveness of our domain-optimized ensemble approach. Conclusions: The system provides a robust, interpretable, and highly automated solution for transitioning from reactive maintenance to proactive, real-time quality assurance in clinical laboratories. Full article

Accurate real-time soil moisture monitoring is critical for optimizing water use and ensuring crop health and food security. This study aims to calibrate and integrate low-cost capacitive soil moisture sensors into a solar-powered sensor node for real-time soil moisture monitoring in a loamy sand soil. Three capacitive soil moisture sensors were calibrated in the laboratory under controlled volumetric water content conditions (0–40%) using a constrained linear regression approach. The system was tested in a limited pilot-scale in a drip-irrigated onion field at the IWAD farm, Yagaba (North-East Region, Ghana). The results showed good agreement of the sensor readings with the soil moisture obtained using the gravimetric method (R² of 0.92–0.94, RMSE of 0.40–0.52%, and MAE of 0.35–0.39%) demonstrating the successful transfer of the calibration functions to field conditions. Soil moisture data was successfully monitored and transmitted from the nodes to a LoRa gateway via LoRaWAN (433 MHz) and from the gateway to a Raspberry Pi edge server via Wi-Fi. Data was stored both locally in SQLite on the Raspberry Pi and on the InfluxDB cloud. These results suggest that the developed system, when extensively validated under field conditions, can be used to support decision-making for data-driven IoT-based irrigation scheduling. Full article

In Norway, agroclimatic zones (ACZs) are a valuable tool for national analyses in subject areas concerning the optimized management of agricultural land resources. However, current Norwegian ACZs have been criticized for having an outdated standard climate normal (1931–1960), a limited representation of the local climatic variation, a lack of important model parameters, and weak methodological documentation. Therefore, this paper presents new ACZs for Norway that address these weaknesses. The most significant methodological updates are the use of the standard climate normal of 1991–2020, additional weather data variables, the downscaling of weather data to 250 m hexagons, and the incorporation of phenological crop models for spring wheat, spring barley, and forage grass. The grass model was calibrated with the number of grass harvests at research stations, while the grain models were calibrated with subsidy claim data. The modeled zones for the three crops were combined into the general ACZs. Example maps of the crop zones and new ACZs for the selected regions and the whole country are presented. The new ACZs are more robust, agronomically relevant, and better aligned with the current climatic conditions in Norway. The deliberate exclusion of factors other than climate ensures the new ACZs’ national comparability and their applicability in policy development, land-use planning, climate adaptation, and agronomic assessments at the national scale. Full article