Search Results (3,664)

Accurate representation of the pressure drop–flow rate (Δp–q) relationship of nozzle-type inflow control devices (ICDs) is critical for reliable reservoir–wellbore coupled simulation. Conventional ICD models in reservoir simulators rely primarily on empirical correlations or tabulated data, but commonly used formulations cannot consistently capture the linear behavior in the low-flow regime or the transition between flow regimes, which may reduce physical fidelity and numerical robustness. To overcome this limitation, this study proposes a unified characteristic-curve representation that integrates linear, transitional, and quadratic flow regimes into a single continuous and differentiable function through a physically constrained least-squares formulation, and further develops a physics-informed neural network (PINN) to learn the ICD pressure–flow relationship while enforcing physical consistency. The trained PINN model is embedded into a multi-segment well model within a reservoir–wellbore coupled simulation framework and evaluated using a mechanistic reservoir model containing permeability streaks with varying permeabilities. The results show that the proposed method improves numerical convergence and accurately reproduces ICD pressure–flow behavior across multiple flow regimes, providing a more physically consistent and robust representation of ICD performance for inflow control analysis and reservoir simulation. Full article

Background: The triglyceride–glucose (TyG) index has emerged as a simple surrogate marker of insulin resistance and metabolic disruption. In the context of ST-elevation myocardial infarction (STEMI), such disturbances have been associated with adverse cardiovascular outcomes, more complex angiographic profiles, and microvascular complications. However, data on the association between TyG and intracoronary thrombus burden (TB) in STEMI remain limited. Methods: In this prospective observational study, we included consecutive STEMI patients treated with primary percutaneous coronary intervention (pPCI). The TyG index was calculated using the following formula: ln [fasting triglycerides (mg/dL) × fasting glucose (mg/dL)/2]. TB was graded according to the modified thrombolysis in myocardial infarction (mTIMI) thrombus classification score after restoration of antegrade flow with a wire or small balloon when the culprit vessel was initially totally occluded. Patients were categorized as low-TB (LTB; mTIMI grades 1–3) and high-TB (HTB; mTIMI grade 4). The primary outcome was HTB; secondary outcomes were distal embolization and no-reflow. Associations between TyG and outcomes were assessed using univariable and multivariable logistic regression, restricted cubic spline analysis, and receiver operating characteristic (ROC) curves to evaluate incremental predictive value. Results: A total of 309 patients were analyzed. The TyG index was significantly higher in the HTB group compared with the LTB group (9.12 ± 0.62 vs. 8.92 ± 0.64, p = 0.004). In a stepwise multivariable model, TyG remained independently associated with HTB (adjusted odds ratio = 1.61; 95% confidence interval: 1.11–2.37; p = 0.014). Adding TyG to a baseline clinical model only numerically improved discrimination for HTB, as reflected by a small increase in ROC area under the curve. Restricted cubic spline analysis demonstrated a monotonic rise in the probability of HTB with higher TyG values. Higher TyG also showed non-significant trends toward increased odds of distal embolization and no-reflow. Conclusions: The TyG index was independently associated with HTB in STEMI patients undergoing pPCI and may serve as an accessible adjunctive marker for incremental risk stratification beyond conventional clinical and angiographic factors. Full article

Background: The C-reactive protein/albumin ratio (CAR) has increasingly attracted attention as a reliable predictive marker in patients with acute myocardial infarction (AMI). Purpose: This study aimed to evaluate the predictive value of the CAR for no-reflow development. Methods: A total of 201 patients with STEMI who underwent PCI were included in the study. Admission laboratory tests included CRP, albumin, CK, CK-MB, troponin T, and other biochemical parameters. The CAR was calculated as CRP divided by albumin ×100, while the CUAR was calculated as the base-10 logarithm of CRP × UA divided by albumin. Patients were then divided into two groups based on CAR levels. Results: A total of 201 STEMI patients were included: 106 (52.7%) in the low-CAR group (≤48.4) and 95 (47.3%) in the high-CAR group (>48.4). Significant differences between groups were observed for smoking, albumin, cholesterol, CRP, CUAR, and TIMI flow grade ≤ 2. Logistic regression analysis identified albumin, cholesterol, CRP, BUN, uric acid, CK-MB, CAR, and CUAR as significant predictors of TIMI flow grade. A receiver operating characteristic (ROC) curve of CRP, albumin, CAR, and CUAR was used to plot the true positive rate against the false positive rate across various cut-off points; the area under the curve (AUC) was 0.87 (95% CI, 0.81–0.94, p < 0.0001) for CRP, 0.73 (95% CI, 0.65–0.81, p < 0.0001) for albumin, 0.9 (95% CI, 0.84–0.95, p < 0.0001) for the CAR, and 0.94 (95%, 0.89–0.99, p < 0.0001) for the CUAR. The cut-off values were 2.11 for the CUAR, 48.4 for the CAR, 18 for CRP, and 38 for albumin. Conclusions: The ratio of C-reactive protein to albumin (CAR) may serve as a reliable and clinically accessible marker associated with the no-reflow phenomenon in STEMI patients undergoing PCI. A defined CAR cut-off has been proposed to help stratify patients at increased risk of no-reflow. Full article

Nature provides efficient strategies for fluid transport and thermal regulation through evolved structural features. This review summarizes recent progress in biomimetic thermal–fluid structures for enhancing fluid flow and heat transfer, with emphasis on the links among biological inspiration, engineering geometry, transport mechanisms, and application performance. Representative designs are classified into tree-like branching and fractal networks, compact hexagonal layouts, and bio-inspired curved morphologies, including riblets, grooves, fins, fluctuating channels, and TPMS structures. Their enhancement mechanisms involve flow redistribution, boundary-layer disturbance, secondary-flow and vortex generation, local acceleration, enlarged heat-transfer area, drag reduction, and compact flow organization. Applications using biomimetic structures are assessed in detail, such as in battery thermal management, electronic cooling, etc. The reviewed studies indicate that biomimetic structures can improve temperature uniformity, suppress hotspots, and enhance thermohydraulic performance, but the gains may be accompanied by pressure-drop or pumping-power penalties. Therefore, coupled thermal–hydraulic evaluation is essential for objective comparison. Key challenges of practical usage are identified in mechanism-based design, manufacturability, reliability, etc. This work establishes the guidance for translating biological forms into practical thermal–fluid structures with balanced efficacy. Full article

CRISPR-based diagnostics now detect viral, bacterial, and cancer-associated nucleic acids with sensitivities approaching quantitative PCR; however, their translation to decentralized care rests on computational design and interpretation that current datasets cannot sustain. Pandemic-era Cas12a assays reached 95% positive predictive agreement against reverse transcription quantitative PCR (RT-qPCR) at 10 copies/μL, and deep neural networks now design Cas13 detection assays spanning 1933 vertebrate-infecting viruses, ranking candidate guides at Spearman correlations of 0.69 to 0.84 across internal and external validation. Generative deep-learning systems improve single-nucleotide discrimination two- to three-fold, computer vision classifies lateral flow outputs at 96.5% accuracy, and multi-biomarker fusion reaches an area under the receiver operating characteristic curve (AUC) of 0.998 in lung cancer detection. These results mask a narrow data foundation. Cas13a guide prediction still draws from a single screening library of 19,209 guide–target pairs, Cas12a has one published diagnostic model, and signal classifiers almost uniformly validate on single-site cohorts. This review synthesizes mechanistic constraints, predictive and generative models, and point-of-care classifiers, and maps the path beyond this data ceiling. Evolutionary pretraining on RNA corpora and lab-in-the-loop agents that convert model failure into targeted data acquisition define the route forward. Full article

This paper proposes a data-driven framework for simulating turbocharger (TC) failure scenarios and modelling specific fuel oil consumption (SFOC) degradation in two-stroke low-speed marine diesel engines. A GaussianCopula model was fitted to the joint distribution of fifteen variables, using approximately eleven months of operational sensor data (n = 480 clean records, 4 h interval, January–December 2014) taken from a container ship. Three physically motivated failure scenarios were produced: turbine blade fouling, bearing wear and compressor surge. Predictive models trained on the real dataset achieved R² = 0.9998 for TC RPM and R² = 0.984 for fuel flow when using Gradient Boosting with 5-fold cross-validation. Feature importance analysis showed that the dominant determinants of TC speed were scavenging air intake pressure (35.3%) and engine power (MCR, 31.3%). Shaft power (45.5%) and TC RPM (19.3%) together explained most of the fuel consumption variance. Simulated failure scenarios produced SFOC increases of +6.6% (fouling), +9.6% (surge), and +13.3% (bearing wear) when compared to a normal operating baseline of 202 g/kWh, which is in line with published empirical data from MAN B&W engine performance curves. An IsolationForest anomaly detector trained only on normal operating samples flagged failure scenario records at a rate of 17.5–23.7%, which demonstrates that moderate-sensitivity early warning detection is feasible from routine sensor streams. The results show that TC condition monitoring could serve as a leading indicator of fuel-efficiency degradation. This has significant implications for condition-based maintenance planning and CII (Carbon Intensity Indicator) compliance. Full article

Milk flow dynamics during mechanical milking are strongly influenced by species-specific mammary anatomy, milk partitioning between cisternal and alveolar compartments, and milking management. The present study aimed to compare milkability traits across the main dairy species reared in central Italy using a large dataset collected over 20 years. A total of 7315 animals were included: dairy cows (1103), buffaloes (2870), goats (2399), sheep (754), and donkeys (189). Milk flow curves were recorded using a portable Lactocorder^® device. The following traits were analyzed: milk yield (MY), lag time (LT), milk ejection time (MET), total milking time (TMT), peak flow rate (PFR), average flow rate (AFR), plateau phase (PL), bimodal phase (BM), and bimodality incidence (Bimo). Marked interspecific differences emerged. Dairy cows showed the highest MY and PFR, with bimodality occurring in 23.7% of curves. Buffaloes exhibited lower flow rates, prolonged LT, and extended TMT, reflecting their strong dependence on oxytocin-mediated alveolar milk ejection. Sheep demonstrated short milking times and low bimodality (13.5%), consistent with their large cisternal milk fraction. Goats displayed breed-dependent variability, with specialized dairy breeds showing higher PFR and longer TMT. Donkeys produced low milk volumes but exhibited rapid and efficient milk flow, with the lowest incidence of bimodality (7.4%). Overall, milk flow patterns reflected species-specific udder morphology and physiological mechanisms of milk ejection. Although this field-based study faces inherent limitations in environmental and protocol standardization across farms, the resulting long-term dataset remains highly representative. These findings highlight the importance of tailoring milking machine settings and prestimulation protocols to species and breed characteristics to optimize milking efficiency, labor management, and animal welfare. Full article

Flood hazards are intensifying across Africa due to rapid urban expansion and hydro-climatic variability. This study develops a multi-metric geospatial framework combining extreme value analysis, hydrograph-based metrics, and dependence modelling to quantify flood magnitude, frequency, timing, and joint risk dynamics. Daily precipitation and streamflow reanalysis data (1985–2025) were analyzed for two contrasting Tanzanian catchments: the large Rufiji basin (RU) and the smaller Mirongo catchment (MW). Annual maxima were modelled using the Generalized Extreme Value (GEV) distribution, complemented by flow duration curves, peak-over-threshold detection, and regression-copula dependence analysis. Results reveal strong hydrological contrasts. RU exhibits amplified rare-event growth (design floods from ~2850 to 11,770 m³/s), extended recession persistence (>100 days), low flashiness, and long rainfall-runoff lags (~15 days), indicating storage-dominated behavior. MW shows smaller design floods (~80 to 370 m³/s), higher flashiness, and short lags (~4 days), reflecting rapid, rainfall-driven response. Gaussian copula parameters indicate moderate dependence in both basins (0.32 and 0.34), suggesting that joint dependence alone does not distinguish flood mechanisms without complementary metrics. The proposed framework improves basin-specific flood risk profiling and supports geospatial early-warning system design in data-scarce Sub-Saharan environments. Full article

To clarify the oil–gas multiphase flow behavior of natural gas/CO₂ composite flooding in the dual-medium system of the BZ26-6 fractured reservoir, systematic oil–gas relative permeability experiments were conducted under reservoir temperature and pressure conditions. Using the steady-state method, the effects of core type, gas composition, and reservoir pressure on relative permeability behavior were investigated. The results show that the relative permeability curves are characterized by relatively high oil-phase permeability and low gas-phase permeability. Increasing the CO₂ fraction generally enhances oil mobilization and displacement efficiency, whereas the two-phase co-flow zone may reach an optimum at an intermediate CO₂ fraction, depending on the core structure. Specifically, with increasing CO₂ fraction, displacement efficiency increased from 37.05% to 43.70% in fractured metamorphic cores and from 60.74% to 64.63% in fractured carbonate cores. In contrast, decreasing reservoir pressure may induce stress-sensitive fracture compression, narrow the co-flow zone, and reduce flow capacity. Oil–gas two-phase flow behavior is strongly controlled by reservoir structure, with fractured carbonate cores exhibiting higher displacement efficiency and a wider co-flow region than fractured metamorphic cores. Within the scope of this study, a CO₂ fraction of 40% appears to be a comparatively favorable composite-gas composition when both displacement performance and gas-source economics are considered. Full article

Background/Objectives: In laparoscopic and robot-assisted surgery, bleeding may rapidly impair operative-field readability and procedural safety. In the broader Robin Heart teleoperation framework, interpretation of such events is relevant not only for scene understanding but also as a potential prerequisite for future safety-oriented supervisory functions under communication-degraded conditions. The aim of this study was to assess whether a deep learning model for blood segmentation could provide outputs suitable for preliminary image-level temporal characterization of visible blood-region behavior in laparoscopic video. Methods: A U-Net-based binary blood-segmentation model was implemented in-house in PyTorch and evaluated on three paired image–mask datasets: a simulated bleeding dataset prepared under controlled laboratory conditions, an internal operative laparoscopic dataset, and an external-domain subset derived from the public GynSurg dataset. Segmentation performance was assessed using 5-fold cross-validation and reported using the Dice coefficient and Intersection over Union (IoU). Training dynamics were analyzed using training and validation loss and Dice curves. Additional baseline comparisons were performed on the internal operative dataset using U-Net++ and DeepLabV3+. Temporal analysis was performed on selected video fragments, including a low-motion reference sequence without active bleeding progression, internal bleeding-related sequences, and external-domain sequences, using mask-derived descriptors and auxiliary optical-flow-based motion descriptors computed after camera-motion compensation within the detected blood-related ROI. Results: In 5-fold cross-validation, the U-Net-based model achieved Dice coefficient and IoU values of 0.915 ± 0.012 and 0.851 ± 0.019 on the simulated dataset, 0.856 ± 0.013 and 0.756 ± 0.025 on the internal operative dataset, and 0.707 ± 0.053 and 0.570 ± 0.056 on the external-domain GynSurg subset, respectively. On the internal operative dataset, the proposed model performed comparably to U-Net++ and slightly above DeepLabV3+ under the same cross-validation protocol. The temporal descriptor set differentiated low-motion reference behavior, more spatially coherent progression, rapid coherent expansion, and dynamic or motion-active progression profiles. Peak dA/dt reflected abrupt visible blood-area expansion, temporal IoU described mask stability over time, and optical-flow-based descriptors provided additional information on local motion activity within the detected blood-related ROI. Conclusions: The results support the feasibility of combining deep-learning-based blood segmentation with temporal and optical-flow-based descriptors for exploratory image-level characterization of visible blood-region behavior in laparoscopic video. Within the Robin Heart development pathway, such descriptors may, in the future, serve as candidate components of image-analysis support modules for safety-oriented teleoperative scenarios. At this stage, they should be interpreted as exploratory image-derived indicators rather than clinically validated markers of bleeding severity. Full article

To address the lack of systematic quantitative characterization of oil bank dynamic evolution and unclear dominant controlling factors in polymer flooding, this study combines reservoir numerical simulation with Python-based quantitative analysis and a machine learning framework (random forest + SHAP). We established 1D and 2D reservoir models: the 1D model develops a precise quantitative characterization method for oil bank width (defined by front/rear edge saturation offsets Pf < 1.0% and Pb < 1.0%, fitted with a cubic polynomial, R² > 0.95) and height (derived from optimal oil saturation difference time curves and integral calculation); the 2D model investigates the regulatory mechanism of reservoir heterogeneity. Based on 15,000 sets of physically consistent simulation data, the random forest model achieves high prediction accuracy (R² = 0.98). Sensitivity analysis reveals that main flow direction permeability, reservoir temperature, and water-phase exponent (nw) of the Corey model are the dominant controlling parameters, exhibiting substantially higher sensitivity than polymer adsorption capacity and residual resistance coefficient. The oil bank height shows a negative correlation with the first two parameters, while it displays a peak-type variation with the water-phase exponent. Under heterogeneous conditions, permeability anisotropy amplifies the regulatory effect of relative permeability exponents, leading to unbalanced oil bank migration (quantified by front ratio R). This study breaks through the limitations of traditional qualitative characterization, elucidates the spatiotemporal evolution laws and heterogeneous regulatory mechanisms of the oil bank, and provides reliable theoretical and dataset support for optimizing polymer flooding schemes. Full article

Shale oil is a vital unconventional resource. Large-scale hydraulic fracturing serves as the core technology for the efficient development of shale oil reservoirs. Well testing can be applied to characterize the reservoir parameters of fractured shale formations. Nevertheless, conventional well testing approaches fail to account for numerous discrete fractures and complex formation geometries. Based on the embedded discrete fracture model (EDFM)—an effective tool for simulating flow in discrete fractures—this work proposes a numerical well testing approach for horizontal wells in hydraulically fractured shale reservoirs. The effects of fracture permeability, number of fracture clusters, matrix permeability, and water saturation on well testing curves are also investigated. The results showed that the parameters such as the main fracture permeability, the number of fracture clusters, and the matrix permeability all have significant effects on the well test curves. When the permeability of main fractures exceeds 20D, radial flow characteristics appear in Stage V. For the distance between fracturing intervals and pressure monitoring points within 0 m to 200 m, it imposes the most significant impact on Stage I and Stage II. The half-length of main fractures, the SRV extent in the Y-direction, and boundary conditions mainly affect Stage VI and Stage VII. Full article

The power flow problem is one of the most challenging tasks in power systems, affecting both generation cost and energy quality. Optimal power flow (OPF) further complicates this task by requiring the optimal adjustment of system variables and parameters. This paper adapts the Modified Effective Butterfly Optimizer (MEBO) to solve multi-objective optimal power flow (MOOPF) problems with the contribution of optimized weighting using multiple Pareto archives. MEBO is an advanced optimization algorithm that utilizes population reduction and parameter learning to guide subsequent searches for unconstrained problems. The proposed technique has been tested on IEEE 30 and 57 bus test systems, and the results have been compared with existing methods reported in the literature. In the paper, four single-objective functions, namely generator cost, active power loss, fuel emission, and voltage deviation, are used to construct four multi-objective (MO) problems: cost–loss, cost–voltage, cost-emission, and emission–loss. For the cost-emission case, the proposed MEBO achieved compromised solutions of 791.1951 $/h fuel cost with 0.10873 ton/h emission and 801.8172 $/h fuel cost with 0.10044 ton/h emission under different Pareto-based optimization metrics. In the emission–loss case, the algorithm obtained 0.20539 ton/h emission with 3.1403 MW/h power loss, demonstrating the effectiveness of the proposed approach in balancing conflicting objectives. The Pareto curves of MEBO in achieving MO problems are presented, along with the suggested compromised solutions acquired from the literature. In the literature, this is the first application of MEBO for solving MOOPF problems. The results demonstrate that MEBO performs better than most other alternatives; this shows potential for further improvements with respect to the MOOPF problem. Full article

Background/Objectives: Halitosis in geriatric patients is multifactorial, but the joint contribution of prosthetic rehabilitation type and polypharmacy after routine dental procedures has rarely been quantified. We investigated how prosthesis type, polypharmacy, and salivary function were associated with volatile sulfur compound (VSC) burden and self-perceived halitosis in elderly dental patients. Methods: This cross-sectional study enrolled 88 patients aged ≥65 years, four weeks after completing routine dental procedures. Participants were stratified into three groups: complete denture wearers (n = 30), partial removable denture wearers (n = 28), and fixed prostheses/implants (n = 30). We measured unstimulated salivary flow rate (uSFR), tongue coating index (TCI), denture biofilm index, total VSCs (Halimeter^®), organoleptic score (0–5), and self-perceived halitosis. Polypharmacy, comorbidities, and the Geriatric Oral Health Assessment Index (GOHAI) were recorded. Analyses included one- and two-way ANOVA, Spearman correlations, theory-informed multivariable linear and logistic regression, exploratory mediation analysis, and ROC curves. Results: Forty-two participants (47.7%) reported halitosis. Mean VSC differed across groups (complete dentures 278.2 ± 38.6 ppb; partial 211.2 ± 46.3 ppb; fixed 164.4 ± 43.9 ppb; ANOVA p < 0.001). uSFR correlated inversely with VSC (ρ = −0.61, p < 0.001) and TCI correlated positively (ρ = 0.56, p < 0.001). A significant prosthesis × polypharmacy interaction was observed (F = 3.74, p = 0.029, η²_p = 0.082): polypharmacy was associated with higher VSC most clearly among partial and fixed prostheses wearers, whereas complete denture wearers showed high VSC levels regardless of polypharmacy status. Exploratory mediation findings were consistent with partial indirect association, with 45.9% of the polypharmacy–VSC association statistically explained by reduced uSFR; however, the cross-sectional design precludes causal or temporal interpretation. The full multivariable model showed apparent discrimination for self-perceived halitosis (AUC = 0.92), while the simplified four-item chairside composite model showed AUC = 0.89; neither estimate was optimism-corrected or externally validated. Conclusions: In elderly post-procedure patients, complete denture wearing, polypharmacy, and salivary hypofunction were independently and jointly associated with higher halitosis burden. Reduced salivary flow was consistent with a partial indirect statistical pathway in the polypharmacy–VSC association, supporting hydration counseling and meticulous prosthesis hygiene as low-cost geriatric interventions. Sensitivity analyses excluding implant-supported restorations, participants with MMSE scores of 24–26, and expanded mediation models including TCI and biofilm/plaque did not materially change the main inference. Full article

The ecological consequences of hydraulic engineering on riverine environments have intensified the need for scientifically grounded ecological flow regimes. To ensure habitat suitability during critical fish spawning periods, this study developed habitat preference curves by correlating physiological parameters with key hydro-environmental drivers. A habitat suitability index (HSI) model was established using fuzzy logic, integrated with a genetic algorithm (GA) to simultaneously optimize fuzzy membership functions and inference rules. This model was applied to simulate the relationship between the weighted usable area (WUA) and discharge for various fish egg types in the reach downstream of the Fengman Dam, ultimately facilitating the determination of an optimized ecological pulse flow hydrograph. The results reveal distinct hydro-environmental preference variations among species. Specifically, drifting eggs require specific hatching cycles supported by higher flow magnitudes and velocities. Conversely, adhesive eggs experience a significant reduction in suitable habitat area under high-flow and high-velocity conditions. These findings suggest that reservoir water resource allocation must be tailored to the life-history requirements of target species to maximize spawning success. This study provides a robust scientific framework for eco-friendly reservoir scheduling and the conservation of regulated river ecosystems. Full article