Search Results (12,989)

This study compares enhanced turbulence models in a natural river channel 3D simulation under extreme hydrometeorological conditions. Using ANSYS Fluent 2024 R1 and the Volume of Fluid scheme, five RANS closures were evaluated: realizable k–ε, Renormalization-Group k–ε, Shear Stress Transport k–ω, Generalized k–ω, and Baseline-Explicit Algebraic Reynolds Stress model. A segment of the Santa Catarina River in Monterrey, Mexico, defined the computational domain, which produced high-energy, non-repeatable real-world flow conditions where hydrometric data were not yet available. Empirical validation was conducted using surface velocity estimations obtained through high-resolution video analysis. Systematic bias was minimized through mesh-independent validation (<1% error) and a benchmarked reference closure, ensuring a fair basis for inter-model comparison. All models were realized on a validated polyhedral mesh with consistent boundary conditions, evaluating performance in terms of mean velocity, turbulent viscosity, strain rate, and vorticity. Mean velocity predictions matched the empirical value of 4.43 [m/s]. The Baseline model offered the highest overall fidelity in turbulent viscosity structure (up to 43 [kg/m·s]) and anisotropy representation. Simulation runtimes ranged from 10 to 16 h, reflecting a computational cost that increases with model complexity but justified by improved flow anisotropy representation. Results show that all models yielded similar mean flow predictions within a narrow error margin. However, they differed notably in resolving low-velocity zones, turbulence intensity, and anisotropy within a purely hydrodynamic framework that does not include sediment transport. Full article

Background: Hip fractures are common in the elderly and often require ICU admission post-surgery due to high ASA scores and comorbidities. Length of hospital stay after ICU is a crucial indicator affecting patient recovery, complication rates, and healthcare costs. This study aimed to develop and validate a machine learning-based model to predict the factors associated with extended hospital stay (>7 days from surgery to discharge) in hip fracture patients requiring postoperative ICU care. The findings could help clinicians optimize ICU bed utilization and improve patient management strategies. Methods: In this retrospective single-centre cohort study conducted in a tertiary ICU in Turkey (2017–2024), 366 ICU-admitted hip fracture patients were analysed. Conventional statistical analyses were performed using SPSS 29, including Mann–Whitney U and chi-squared tests. To identify independent predictors associated with extended hospital stay, Least Absolute Shrinkage and Selection Operator (LASSO) regression was applied for variable selection, followed by multivariate binary logistic regression analysis. In addition, machine learning models (binary logistic regression, random forest (RF), extreme gradient boosting (XGBoost) and decision tree (DT)) were trained to predict the likelihood of extended hospital stay, defined as the total number of days from the date of surgery until hospital discharge, including both ICU and subsequent ward stay. Model performance was evaluated using AUROC, F1 score, accuracy, precision, recall, and Brier score. SHAP (SHapley Additive exPlanations) values were used to interpret feature contributions in the XGBoost model. Results: The XGBoost model showed the best performance, except for precision. The XGBoost model gave an AUROC of 0.80, precision of 0.67, recall of 0.92, F1 score of 0.78, accuracy of 0.71 and Brier score of 0.18. According to SHAP analysis, time from fracture to surgery, hypoalbuminaemia and ASA score were the variables that most affected the length of stay of hospitalisation. Conclusions: The developed machine learning model successfully classified hip fracture patients into short and extended hospital stay groups following postoperative intensive care. This classification model has the potential to aid in patient flow management, resource allocation, and clinical decision support. External validation will further strengthen its applicability across different settings. Full article

The use of additive manufacturing (AM) has seen increased utilization over the last decade, thanks to well-documented advantages such as lower startup costs, reduced wastage, and the ability to rapidly prototype. The poor surface finish of unprocessed AM components is one of the major drawbacks of this technology, with the research literature suggesting a measurable impact on flow characteristics and burner operability. For instance, surface roughness has been shown to potentially increase resistance to boundary layer flashback—an area of high concern, particularly when utilizing fuels with high hydrogen content. A more detailed understanding of the underlying thermophysical mechanisms is, therefore, required. Computational fluid dynamics can help elucidate the impact of these roughness effects by enabling detailed data interrogation in locations not easily accessible experimentally. In this study, roughness effects on a generic gas turbine swirler were numerically modeled using a low-y+ detached eddy simulation (DES) approach. Three DES models were investigated utilizing a smooth reference case and two rough cases, the latter employing a literature-based and novel equivalent sand-grain roughness (k_s) correlation developed for this work. Existing experimental isothermal and CH₄ data were used to validate the numerical simulations. Detailed investigations into the effects of roughness on flow characteristics, such as swirl number and recirculation zone position, were subsequently performed. The results show that literature-based k_s correlations are unsuitable for the current application. The novel correlation yields more promising outcomes, though its effectiveness depends on the chosen turbulence model. Moreover, it was demonstrated that, for identical k_s values, while trends remained consistent, the extent to which they manifested differed under reacting and isothermal conditions. Full article

Global warming, driven by greenhouse gas (GHG) emissions, is accelerating globally and highlights the need for effective mitigation strategies. This study assesses the economic feasibility of rainbow trout aquaculture by incorporating GHG emissions into its analysis, thereby contributing to mitigation efforts in the fisheries sector. Focusing on two farming systems—recirculating aquaculture systems (RAS) and flow-through systems (FTS)—we estimated GHG emissions and conducted an economic evaluation using data collected through field surveys. The average GHG emission was 7.14 kg CO₂ eq per kilogram of trout produced, with RAS showing lower emissions than FTS. Electricity and feed were identified as the primary emission sources. The economic analysis revealed an average net present value (NPV) of USD 987,609 and an internal rate of return (IRR) of 18%, with RAS outperforming FTS in profitability. A sensitivity analysis under carbon pricing showed that economic feasibility was maintained, but the NPV declined by about 24% under the carbon tax scenario. Overall, these findings underscore the importance of balancing profitability and emission reduction for sustainable aquaculture management. Full article

This study explores the combustion behavior of three biomass pellet types—wood (W), sunflower husk (SH), and a mixture of wood and sunflower husks (W/SH)—in a residential hot water boiler. Experiments were carried out under two air supply regimes (40%/60% and 60%/40% primary to secondary air) to measure flue gas concentrations of oxygen (O₂), carbon monoxide (CO), and nitrogen oxides (NO_x). The results indicate that SH pellets generate the highest emissions (CO: 1095.3 mg/m³, NO_x: 679.3 mg/m³), while W pellets achieve the lowest (CO: 0.3 mg/m³, NO_x: 194.1 mg/m³). The mixed W/SH pellets produce intermediate values (CO: 148.7 mg/m³, NO_x: 201.8 mg/m³). Overall boiler efficiency for all tested fuels ranged from 90.3% to 91.4%. Numerical simulations using ANSYS CFX (2024 R2 (24.2)) were performed to analyze temperature distribution, flue gas composition, and flow fields, showing good agreement with experimental outlet temperature and emission trends. These findings emphasize that both pellet composition and air distribution significantly influence efficiency and emissions, offering guidance for optimizing small-scale biomass boiler operation. Full article

The delayed coking unit (DCU) is a critical technology in Mexican refineries for upgrading heavy crude oil into lighter, high-value products. Despite its economic relevance, the process is energy-intensive, generates substantial emissions, and produces significant coke, challenging its sustainability. This study proposes a multi-objective optimization framework to enhance DCU performance by integrating Aspen Plus^® v.12.1 simulations with sustainability metrics. Five key indicators were considered: Global Warming Potential (GWP), Specific Energy Intensity (SEI), Mass Intensity (MI), Reaction Mass Efficiency (RME), and Product Yield. A validated Aspen Plus^® model was combined with sensitivity analysis to identify critical decision variables, which were optimized through the $ϵ$-constraint method. Strategic adjustments in reflux flows, split ratios, and column operating conditions improved separation efficiency and reduced energy demand. Results show GWP reductions of 15–25% and SEI improvements of 5–18% for light and heavy gas oils, with smaller gains in MI and trade-offs in RME. Product yield was preserved under optimized conditions, ensuring economic feasibility. A key limitation is that this study did not model coking reactions; instead, optimization focused on the separation network, using reactor effluent as a fixed input. Despite this constraint, the methodology demonstrates a replicable path to improve refining sustainability. Full article

Isothermal hot compression tests were performed on an Al-4.8Cu-0.25Mg-0.32Mn-0.17Si alloy using a Gleeble-3500 thermomechanical simulator within the temperature range of 350–510 °C and strain rate range of 0.001–10 s⁻¹, achieving a true strain of 0.9. The constitutive equation and hot processing maps were established to predict the flow behavior of the alloy. The hot deformation mechanisms were investigated through microstructural characterization using inverse pole figure (IPF), grain boundary (GB), and grain orientation spread (GOS) analysis. The results demonstrate that both dynamic recovery (DRV) and dynamic recrystallization (DRX) occur during hot deformation. At high lnZ values (high strain rates and low deformation temperatures), discontinuous dynamic recrystallization (DDRX) dominates. Under middle lnZ conditions (low strain rate or high deformation temperature), both continuous dynamic recrystallization (CDRX) and DDRX are the primary mechanisms. Conversely, at low lnZ values (low strain rates and high temperatures), CDRX and geometric dynamic recrystallization (GDRX) become predominant. The DRX process in the Al-Cu-Mg alloy is controlled by the deformation temperature and strain rate. Full article

Background: Optical Coherence Tomography (OCT) is a high-resolution imaging technique capable of quantifying Blood Flow at Depth (BD) and the Attenuation Coefficient (AC). However, the clinical relevance of these parameters in burn assessment remains unclear. This study investigated whether OCT-derived metrics can differentiate between superficial and deep pediatric hand burns. Method: This prospective, single-center study analyzed 73 OCT scans from 37 children with thermal hand injuries. A structured algorithm was used to evaluate AC and BD. Results: The mean AC was 1.61 mm⁻¹ (SD ± 0.48), with significantly higher values in deep burns (2.11 mm⁻¹ ± 0.53) compared to superficial burns (1.49 mm⁻¹ ± 0.38; p < 0.001), reflecting increased optical density in more severe burns. BD did not differ significantly between burn depths, although superficial burns more often showed visible capillary networks. Conclusions: This is the first study to assess both AC and BD using OCT in pediatric hand burns. AC demonstrated potential as a diagnostic marker for burn depth, whereas BD had limited utility. Image quality limitations highlight the need for technical improvements to enhance OCT’s clinical application. Full article

Background: Coronary collateral circulation (CCC) plays a crucial protective role in patients with chronic total occlusion (CTO), mitigating ischemia and improving long-term outcomes. However, the degree of collateral vessel development varies substantially among individuals. Systemic inflammatory and nutritional status may influence this variability. The Naples Prognostic Score (NPS) is a composite index reflecting these parameters, yet its relationship with CCC remains incompletely defined. Methods: We retrospectively analyzed 324 patients with angiographically confirmed CTO at Selçuk University Faculty of Medicine between 2014 and 2025. Coronary collaterals were graded using the Rentrop classification, and patients were categorized as having poor (grades 0–1) or good (grades 2–3) collaterals. The NPS was calculated using serum albumin, cholesterol, neutrophil-to-lymphocyte ratio, and lymphocyte-to-monocyte ratio. Baseline clinical and laboratory data were compared between groups. Univariate and multiple binary logistic regression analyses were performed to identify independent predictors of collateral development. Results: Of the 324 patients, 208 (64.2%) had poor and 116 (35.8%) had good collateral circulation. Patients with good collaterals had higher body mass index, HDL Cholesterol (HDL-C), and triglyceride levels, and significantly lower NPS values compared with those with poor collaterals (p < 0.05 for all). In multiple binary logistic regression analysis, HDL-C (OR 1.035; 95% CI 1.008–1.063; p = 0.011) and NPS (OR 0.226; 95% CI 0.130–0.393; p < 0.001) emerged as independent predictors of well-developed collaterals. Conclusions: Both NPS and HDL-C are independently associated with the degree of coronary collateral circulation in CTO patients. These findings highlight the interplay between systemic inflammation, nutritional status, lipid metabolism, and vascular adaptation. As simple and routinely available measures, NPS and HDL-C may serve as practical tools for risk stratification and identifying patients at risk of inadequate collateral formation. Prospective studies with functional assessments of collateral flow are warranted to confirm these associations and explore potential therapeutic interventions. Full article

Recent advances in cardiovascular imaging have revolutionized the assessment and management of abdominal aortic aneurysm (AAA) through the integration of sophisticated haemodynamic biomarkers. This comprehensive review evaluates the clinical utility and mechanistic significance of multiple biomarkers in AAA pathogenesis, progression, and treatment outcomes. Advanced cardiac imaging modalities, including four-dimensional magnetic resonance imaging (4D MRI), computational fluid dynamics (CFD), and specialized echocardiography, enable precise quantification of critical haemodynamic parameters. Wall shear stress (WSS) emerges as a fundamental biomarker, with values below 0.4 Pa indicating pathological conditions and increased risk for aneurysm progression. Time-averaged wall shear stress (TAWSS), typically maintaining values above 1.5 Pa in healthy arterial segments, provides crucial information about sustained haemodynamic forces affecting the vessel wall. The oscillatory shear index (OSI), ranging from 0 (unidirectional flow) to 0.5 (purely oscillatory flow), quantifies directional changes in WSS during cardiac cycles. In AAA, elevated OSI values between 0.3 and 0.4 correlate with disturbed flow patterns and accelerated disease progression. The relative residence time (RRT), combining TAWSS and OSI, identifies regions prone to thrombosis, with values exceeding 2–3 Pa⁻¹ indicating increased risk. The endothelial cell activation potential (ECAP), calculated as OSI/TAWSS, serves as an integrated metric for endothelial dysfunction risk, with values above 0.2–0.3 Pa⁻¹ suggesting increased inflammatory activity. Additional biomarkers include the volumetric perivascular characterization index (VPCI), which assesses vessel wall inflammation through perivascular tissue analysis, and pulse wave velocity (PWV), measuring arterial stiffness. Central aortic systolic pressure and the aortic augmentation index provide essential information about cardiovascular load and arterial compliance. Novel parameters such as particle residence time, flow stagnation, and recirculation zones offer detailed insights into local haemodynamics and potential complications. Implementation challenges include the need for specialized equipment, standardized protocols, and expertise in data interpretation. However, the potential for improved patient outcomes through more precise risk stratification and personalized treatment planning justifies continued development and validation of these advanced assessment tools. Full article

The reverse circulation drill bit is the key component for the efficient and smooth implementation of the pneumatic hollow-through down-the-hole (DTH) hammer reverse circulation continuous coring (sampling) technology. To obtain the structural form of a reverse circulation drill bit with better reverse circulation performance, revealing its local flow fields by computational fluid dynamics (CFD) simulation is an effective approach. Taking the inner jet holes-type reverse circulation drill bit as the research object, three kinds of symmetrical and asymmetrical structures of inner jet holes were proposed. The CFD simulation results show that increasing the air volume supply and the number of inner jet holes leads to an increase in the velocity of air flow jet within the inner jet holes, an increase in the negative pressure formed in the central through channel below the inner jet holes, an enhancement of the reverse circulation performance and suction capacity formed by the reverse circulation drill bit, and an acceleration of the upward flow velocity of the rock cores (samples) located at the bottom of the borehole. Additionally, the reverse circulation performance formed by the reverse circulation drill bit with staggered arranged inner jet holes is superior to that of the reverse circulation drill bit with uniformly distributed inner jet holes. Under the same simulation conditions, the static pressure (i.e., negative pressure) and the upward flow velocity formed by the JB6 model are 2.34 kPa and 30.778 m/s higher than those formed by the JB3-3 model, while these two values formed by the JC6 model are 0.197 kPa and 3.689 m/s higher than those formed by the JB6 model, respectively. In conclusion, an asymmetric structural design would be more reasonable for the design of the inner jet holes-type reverse circulation drill bit. Full article

Planned events in urban public spaces often face design challenges, and consequent poor performance, due to limited consideration of spatial criteria during the planning process. Our previous work revealed that event designers tend to have no urban design, or similar, training. Consequently, this paper reports on a Virtual Reality (VR)/Mixed Reality (MR) tool developed as a ‘proof of concept’ to support event designers in evaluating and modifying event layouts using urban design principles. Building on a previous study that identified key design-based criteria, including pedestrian flow, permeability, and geometry, this research applies those criteria through interactive, immersive environments. A VR experiment involving three sessions with users demonstrated how the tool facilitates spatial analysis and encourages reflective design thinking. Insights from the sessions highlight the value of visual representation in decision-making and suggest directions for future tool development, such as expanding the criteria set and incorporating real-time data. The study concludes by proposing that immersive technologies can enhance collaborative and responsive temporary event design for public spaces. Full article

Diamond-like carbon (DLC) films are valued for their high hardness and wear resistance, but their application in harsh environments is limited by high internal stress and poor corrosion resistance. Co-doping with transition metals offers a promising route to overcome these drawbacks by tailoring microstructure and enhancing multifunctional performance. However, the synergistic effects of Ni and Cr co-doping in DLC remain underexplored. In this study, Ni and Cr co-doped DLC (NiCr-DLC) films were fabricated using filtered cathodic vacuum arc deposition (FCVAD). By varying the C₂H₂ flow rate, the carbon content and microstructure evolved from columnar to fine-grained and compact structures. The optimized film (F55) achieved an ultralow surface roughness (Sa = 0.26 nm), even smoother than the Si substrate. The Ni–Cr co-doping promoted a nanocomposite structure, yielding a maximum hardness of 15.56 GPa and excellent wear resistance (wear rate: 4.45 × 10⁻⁷ mm³/N·m). Electrochemical tests revealed significantly improved corrosion resistance compared to AISI 304L stainless steel, with F55 exhibiting the highest corrosion potential, the lowest current density, and the largest impedance modulus. This work demonstrates that Ni-Cr co-doping effectively enhances the mechanical and corrosion properties of DLC films while improving surface quality, providing a viable strategy for developing robust, multifunctional protective coatings for demanding applications in aerospace, automotive, and biomedical systems. Full article

Multiple myeloma (MM) is one of the most common hematological malignancies and remains incurable. However, the survival of multiple myeloma patients has significantly increased due to the implementation of novel therapies along with autologous stem cell transplantation, changing the natural history of the disease. Consequently, there is an unmet need for more sensitive response assessment techniques capable of quantifying minimal tumor burden to identify patients at higher risk of early relapse. Multiparameter flow cytometry (MFC) is an essential tool for diagnosing and monitoring patients with various hematological conditions and has recently gained prominence in identifying, characterizing, and monitoring malignant plasma cells. The implementation of Next-Generation Flow (NGF) by EuroFlow aims to overcome the pitfalls of conventional MFC, including lack of standardization and lower sensitivity, by offering standardized and optimized protocols for evaluating response depth. Both MFC and NGF have wide-ranging applications in MM for diagnosis and measurable residual disease (MRD) monitoring. Plasma cell identification and clonality evaluation through MFC and NGF assist in diagnostic workup and are routinely used to assess therapeutic response through MRD analysis. Additionally, flow cytometry is applied for circulating tumor plasma cell (CTPC) enumeration, which has demonstrated significant prognostic value. Immune composition studies through MFC may provide better understanding of disease biology. Furthermore, MFC provides additional information about other bone marrow cell populations, assessing cellularity, immunophenotypic characteristics of plasma cells, and possible hemodilution. This review explores the applications of MFC and NGF in MM, highlighting their roles in diagnosis, response assessment, and prognosis. Beyond their established use in MRD monitoring, flow cytometry-derived immunophenotypic profiles show strong potential as cost-effective prognostic tools. We advocate for future studies to validate and integrate these markers into risk stratification models, complementing cytogenetic analyses and guiding individualized treatment strategies. Full article

Background/Objectives: Cardiac allograft vasculopathy (CAV) remains a prevalent and serious long-term complication following orthotopic heart transplantation (OHT), contributing substantially to graft failure and patient mortality. Given the adverse prognostic impact of extensive coronary artery involvement, this study investigates whether myocardial work (MW) indices can serve as a non-invasive tool to detect OHT recipients with a high burden of coronary disease. Methods: In this prospective study, 55 OHT recipients underwent paired evaluations with coronary computed tomography angiography (CCTA) and transthoracic echocardiography (TTE) during routine follow-up. From the echocardiograms, global longitudinal strain (GLS) of the left ventricle (LV) and myocardial work (MW) indices were derived. Patients were classified into two groups according to CCTA findings: those without extensive coronary artery disease (disease affecting fewer than four coronary segments or none, OHT < 4) and those with extensive disease (disease of four or more coronary artery segments, OHT ≥ 4). Results: CCTA revealed extensive coronary disease in 38 OHT recipients, while 17 had involvement of fewer than four segments or none. Between-group comparisons showed significant differences in global wasted work (GWW, energy expended without generating forward flow) and global work efficiency (GWE, the percentage of constructive work relative to total work). Using the Youden Index, the optimal thresholds for identifying extensive disease were GWW > 88 mmHg% and GWE < 94%. Patients exceeding these thresholds had a markedly higher probability of having ≥ 4 affected segments, with ORs of 4.61 for pathological GWW and 3.68 for pathological GWE compared to those with normal values. Conclusions: GWW and GWE demonstrated the strongest performance for identifying OHT recipients with extensive coronary disease. If confirmed in larger cohorts, these indices could offer a practical, non-invasive approach for detecting extensive CAV. Full article