Search Results (5,117)

Large deformation runout is a key factor in assessing the hazards posed by underwater landslides. However, conventional kinematic analyses often neglect both the progressive degradation of slope materials and the hydrodynamic response accompanying the interaction between the moving mass and the overlying water. Taking the well-documented St. Niklausen underwater landslide as a representative case, this study employs a coupled Eulerian–Lagrangian (CEL) model to investigate the earthquake-triggered initiation, large deformation movement, and hydrodynamic response of the landslide. A Python 2.7.15-based stress mapping method is developed to establish an accurate initial geostatic stress field for the irregular slope profile. The numerical model reproduces the principal stages of landslide initiation, runout, and deposition. The results reveal a progressive retrogressive failure mechanism in which successive sliding masses interact through a high-strength compression zone. The rear sliding mass continuously transfers compressive work to the frontal mass, thereby maintaining its downslope movement and indirectly promoting basal erosion to a maximum depth of approximately 6.2 m. In addition, rapid landslide motion generates pronounced vortical flow in the overlying water. These flow structures reflect the hydrodynamic response induced by landslide motion, although their net influence on basal resistance and final runout cannot be isolated from the present coupled simulation. These findings clarify the internal mechanical evolution of underwater landslide movement and characterize the accompanying hydrodynamic response, providing a methodological basis for assessing landslide mobility and related underwater hazards. Full article

The carotid body (CB) is an interoceptive organ that transmits afferent information to the brain via the carotid sinus nerve (CSN) to maintain homeostasis, i.e., the regulation of internal equilibrium despite external changes. It functions as a complex polymodal receptor capable of sensing multiple stimuli, including blood flow, osmolarity, pO₂, pH, pCO₂, CO₂/H⁺, and temperature. In addition, the CB responds to a wide range of circulating molecules such as angiotensin II, endothelin-1, aldosterone, insulin, histamine, and leptin, and expresses receptors for interleukins (ILs) and tumor necrosis factor-α (TNF-α) (1). CB dysfunction has been associated with conditions such as obstructive sleep apnea (OSA), in which intermittent hypoxemia induces an inflammatory response mediated, among other mechanisms, by reactive oxygen species (ROS). This process contributes to alterations in respiratory drive and enhanced sympathetic nervous system activity. Following streptococcal toxic shock syndrome due to Streptococcus Pyogenes, severe abdominal septic shock, and multiple infectious complications, our patient developed an altered respiratory pattern and a hypercatabolic state that precluded weaning from mechanical ventilation (MV) despite respiratory physiotherapy. Given treatment failure, we hypothesized underlying carotid body (CB) hyperexcitability, likely pre-existing due to obstructive sleep apnea (OSA) and exacerbated by cytokine storm and severe systemic inflammation related to Strept. Pyogenes toxins and subsequent abdominal sepsis from colonic perforation. This may have contributed to sustained sympathetic overactivation and immune dysregulation. Clinically, the patient exhibited increased respiratory drive (30–35 breaths/min), excessive inspiratory effort, and marked patient–ventilator asynchrony in the absence of hypoxemia. Non-targeted physiotherapy may have acted as a second inflammatory hit, perpetuating the inflammatory cycle. Full article

Oscillating water column wave energy converters require self-rectifying turbines capable of maintaining stable performance under bidirectional airflow. This study numerically investigates the aerodynamic performance and internal flow characteristics of an axial-flow impulse turbine using OpenFOAM under both uniform and reciprocating airflow conditions. Rotor motion was modeled using the Multiple Reference Frame approach, and the numerical model was validated against experimental data for one-way flow at an inlet velocity of 8.71 m s⁻¹ and rotational speeds ranging from 300 to 1300 rpm. The CFD results successfully reproduced the experimental efficiency trend, yielding a peak efficiency of η = 0.4269 at 700 rpm and ϕ ≈ 0.95, which closely aligns with the experimental peak efficiency of η = 0.4425. Validation metrics demonstrated a high degree of accuracy, with an RMSE of 0.0219, a mean absolute error of 0.0197, a maximum absolute error of 0.0399, a squared Pearson correlation coefficient of 0.826, and a peak-efficiency difference of 3.5%. Flow-field analysis revealed that low rotational speeds resulted in high outlet velocities and incomplete energy extraction, whereas excessive rotational speeds caused flow misalignment, downstream vortex formation, and additional aerodynamic losses. Under reciprocating flow conditions, characterized by a sinusoidal velocity amplitude of 8.71 m s⁻¹ and periods of 0.5–2.0 s at 700 rpm, both input and torque coefficients exhibited hysteresis, with the strongest loops observed at the shortest periods. Examinations of streamline, pressure, and velocity distributions indicated that residual flow during flow reversal alters the effective inlet direction in the subsequent half-cycle, resulting in flow memory and a phase-dependent turbine response. As the present computational domain excludes the OWC chamber, these findings characterize turbine-level aerodynamic performance rather than the complete system power coefficient. Full article

Due to fluctuations in flow rate, pressure, and pump operating states, as well as environmental disturbances such as temperature variations and structural vibrations, pipeline leakage signals exhibit significant nonstationary characteristics. The traditional fixed sensor is limited by the layout position, resulting in suboptimal detection performance. For micro-leakage, it is even more difficult to achieve detection. With the advantages of small size and strong passing ability, the acoustic inner detector is well-suited to the task of comprehensive pipeline detection. Therefore, this paper carried out unsteady micro-leakage detection based on acoustic internal inspection signals. The unsteady micro-leakage simulation experiment of pipeline was carried out, and the leakage acoustic signal was collected for method verification. This paper investigates the integration of variational mode decomposition (VMD), random forest (RF) and least squares support vector machine (LSSVM) for signal processing and leakage classification. An unsteady micro-leakage detection method based on acoustic internal inspection signals was proposed, which is well-suited to the leakage detection task of pipelines. Experimental results indicated that the proposed method achieved a recognition accuracy of 95.31%, outperforming conventional leakage detection methods. Full article

In this study, the cooling performances of various nanofluids were compared under the operating conditions of a real automobile radiator, based on an internal combustion engine vehicle cooling system whose experiments had been previously completed. In the analyses, the radiator inlet fluid temperature was fixed at 70 °C, air inlet velocities were set to 6, 8, and 10 m/s, and fluid flow rates were taken as 17, 19, and 21 L/min. Under these conditions, the cooling capacities were evaluated for three different working fluids whose thermophysical properties were experimentally determined: 100% pure water, water-based 0.3% ZnO nanofluid, and water-based 0.3% ZnO + CuO hybrid nanofluid. Within the scope of this study, a Computational Fluid Dynamics (CFD) model was developed based on the aforementioned experimental parameters and validated with a maximum deviation of 6%. Using the validated model, additional CFD analyses were performed for water-based 0.3% Al₂O₃ and TiO₂ nanofluids, whose thermophysical properties were also experimentally determined, and their cooling performances were assessed. Based on the experimental and numerical results obtained, the highest cooling capacity was determined to be 20.8 kW in the 0.3% TiO₂ nanofluid, representing a 69.1% increase in cooling capacity compared to pure water. These findings clearly demonstrate that the use of nanofluids significantly enhances heat transfer performance in automotive cooling systems. Full article

Assuming the permanent charge density is much larger than the ion concentrations on two boundaries of an open ion channel, this paper studies the impacts of large permanent charges which are positioned along the channel wall and play a significant role in channel functioning. Through this work, the quasi-one-dimensional Poisson–Nernst–Planck (PNP) system is utilized to analyze the electrodiffusion properties of ionic flows through an ion channel. The geometric singular perturbation theory is applied to derive the existence and local uniqueness of solutions to the corresponding PNP system containing large permanent charges and two monovalent cations. The matching asymptotic expansions are conducted to generate the expansions of state variables in terms of the permanent charge density, from which the permanent charge effects on individual fluxes can be discussed by further using the regular perturbation analysis. The mechanisms of the inhibited role played by large permanent charge in the co-ions’ flow are also elucidated from the perspective of internal dynamics. We hope this work could provide more comprehensive information on large permanent charge effects on ion channels and display more realistic interactions between related physical parameters such as channel geometry, diffusion coefficients, boundary potential and boundary ion concentrations. Full article

Pin arrangement significantly affects rubber-melt mixing and extrusion in pin-barrel cold-feed extruders. However, internal flow details are difficult to observe experimentally, and efficient prediction of flow-field parameters remains unavailable. This study used a finite-element model preliminarily validated against measured temperatures, together with particle tracing, to compare configurations with 0, 2, 4, and 6 pins per group. A dataset of 140 pin arrangements was generated by Latin hypercube sampling and numerical simulation. A mind evolutionary algorithm-optimized back-propagation neural network (MEA-BP) was then developed to predict melt volume-averaged temperature and average shear rate. Pins increased melt velocity and shear heating and improved cross-sectional temperature uniformity. Among the four uniform configurations, the 4-pin-per-group configuration showed the fastest reduction in segregation scale with a moderate residence time, achieving a favorable balance between mixing adequacy and processing efficiency. Particle tracing indicated repeated fluid splitting and recombination, whereas further increases in the number of pins yielded limited benefits. Under identical data partitions, network settings, and evaluation conditions, MEA-BP achieved R² values of 0.957 and 0.872 for temperature and shear-rate prediction, respectively, outperforming GA-BP, PSO-BP, and conventional BP. Full article

Improving the performance of radial inflow turbines under coupled aerodynamic and mechanical constraints remains a key challenge in high-speed Organic Rankine Cycle (ORC) systems. In magnetically supported configurations, turbine design is restricted by axial thrust limitations, while maintaining the target mass flow rate is essential for stable system operation. To address these challenges, this study develops a physics-guided design approach for a high-speed ORC radial inflow turbine by integrating one-dimensional preliminary design, three-dimensional CFD-based optimization, and enthalpy gradient magnitude (EGM)-based flow diagnostics. The numerical model is validated against experimental data of a baseline turbine. Three key geometric parameters are optimized under coupled axial thrust and mass flow constraints. The optimized design increases the total-to-total isentropic efficiency from 74.7% to 87.1% while maintaining acceptable axial loading. EGM analysis shows that high-efficiency configurations exhibit more uniform spatial distributions of energy gradients within the impeller passages, whereas low-efficiency cases are characterized by localized high-gradient regions associated with flow separation and secondary flow structures. A volumetric average EGM parameter is further introduced for quantitative evaluation and exhibits a clear negative correlation with turbine efficiency. The optimized efficiency reported herein is a numerical prediction requiring future experimental validation. The results demonstrate that improved internal flow organization contributes significantly to turbine performance enhancement and provides diagnostic insights for design evaluation of high-speed ORC turbines. Full article

Institutional governance has traditionally been analyzed under the assumption that the space of potential violations is finite, enumerable, and progressively constrainable through rule refinement and calibrated enforcement. The rapid integration of large language models into strategic and documentary decision-making challenges this premise by transforming feasible deviation spaces from bounded sets into generative manifolds. This paper develops a formal simulation framework for examining institutional stability under algorithmically amplified strategic exploration. Regulatory rules are modeled as a constraint manifold characterized by effective dimensionality, while generative systems expand the behavioral strategy space through semantic recombination under detection and sanction constraints. Stability is defined through a minimum deterrence margin evaluated across the generatively reachable domain rather than only through historical violation catalogs. The study uses a 2014–2023 regulatory and violation corpus to initialize and calibrate the simulation and to conduct a limited historical hold-out check; the 250,000 LLM-generated scenarios are treated as synthetic stress-test proposals rather than observed violations. The computational specification reports the generator checkpoint, embedding model, decoding parameters, prompt templates, random seeds, filtering rules, and label partitions used in the simulation. The model introduces a dimensional dominance principle: systemic vulnerability may emerge in the simulation when the effective dimensionality of generative strategic search expands faster than the independent constraint dimensionality of the rule system. Under the reported baseline setting, the synthetic simulations show a pipeline-specific dimensional crossover, convergence limits in rule-consistency classification, and a nonlinear detection–sanction response surface. These outputs are interpreted as diagnostics of the stated computational pipeline, not as universal empirical laws about real institutions. The power-grid component is delimited accordingly: the paper does not simulate physical grid operation, power flow, dispatch, or relay-protection dynamics; it interprets the model at the documentary governance layer of power-grid enterprises, including procurement, construction supervision, maintenance records, dispatch-related documentation, customer-service reporting, and internal audit. The framework therefore provides a reproducible and cautiously delimited basis for analyzing text-mediated institutional resilience in the age of generative intelligence. Full article

Understanding which structural parameters govern flow stability and particle separation is essential for turbo air classifier design. In this study, the Y160L-6 turbo air classifier was used to examine whether different categories of spatial structural parameters influence classification performance through the same flow mechanism or play distinct roles in regulating the internal flow field. Two representative parameters, namely the spacing between the secondary air inlet and the rotor cage and the spacing between the secondary air inlet and the feed inlet, were analyzed using computational fluid dynamics (CFD) coupled with the RNG k–ε turbulence model and the discrete phase model (DPM). The results show that the two parameters affect the classifier through different mechanisms. Increasing the secondary air inlet–rotor cage spacing causes a non-monotonic variation in wall pressure and tangential velocity, indicating a strong influence on the global swirling structure. At a spacing of 1490 mm, the pressure distribution in the classification zone becomes more uniform, the tangential velocity reaches a relatively high level, and the intensity of the precessing vortex core (PVC) is reduced. Under this condition, the cumulative proportion of 2–5 μm particles at the fine powder outlet increases by 34.1% compared with the initial configuration. In contrast, variations in the secondary air inlet–feed inlet spacing exert only a limited influence on the overall flow structure and classification characteristics under relatively low feed inlet velocity conditions, indicating that this parameter mainly affects local flow disturbance rather than global flow stability. These findings demonstrate that structural parameters associated with the coupling between secondary airflow and rotor rotation dominate classifier performance, whereas parameters related to feed–air interaction exert only a secondary effect under low feed momentum conditions. These findings provide design guidance for the investigated Y160L-6 turbo air classifier and may serve as a reference for similar classifier structures under comparable operating conditions. Full article

Background: Ovarian cancer (OC) exhibits substantial tumor heterogeneity and an immunosuppressive tumor microenvironment (TME), both contributing to its unfavorable clinical outcomes. Recent studies have increasingly demonstrated that dysregulated glycosylation significantly impacts tumor progression and immune modulation. However, the specific functions and implications of glycosylation-associated regulators in OC remain poorly understood. This study integrates single-cell and bulk transcriptomic data to uncover crucial genes within the TME and investigates the potential role of Fucosyltransferase 8 (FUT8) in OC development. Methods: Single-cell RNA sequencing (scRNA-seq) data from OC and normal ovarian tissues (GSE184880, n = 12) were analyzed using Seurat and Harmony for clustering and annotation. Ro/e analysis identified B cells and plasma cells as enriched immune populations. Their marker genes were integrated with The Cancer Genome Atlas (TCGA) cohort as the training set, while internal testing and an independent external validation cohort (GSE63885) were used to construct and validate the prognostic model. FUT8 function was evaluated in OC cell lines using quantitative real-time PCR (qRT-PCR), cell counting kit-8 (CCK-8) assays, flow cytometry, and transcriptomic sequencing. Gene Set Variation Analysis (GSVA), Gene Set Enrichment Analysis (GSEA), and virtual knockout analyses were performed to explore FUT8-associated pathways. Results: We constructed a single-cell atlas consisting of 46,235 cells classified into seven principal cell populations, highlighting significant enrichment of B and plasma cells in OC tissues. The prognostic signature could stratify patients into high- and low-risk groups across training, internal validation, and external validation cohorts, showing consistent prognostic stratification capacity. FUT8 expression was elevated in OC samples and was associated with favorable overall survival (OS). Experimental overexpression of FUT8 in OC cell lines resulted in reduced cell proliferation and increased apoptosis. Both transcriptomic analyses and virtual knockout studies consistently associated FUT8 with pathways related to N-glycosylation. High-risk patients exhibited predicted activation of Wnt/β-catenin, Hedgehog, and Kras pathways, coupled with diminished immune cell infiltration. Conclusions: We developed a prognostic signature informed by single-cell data for OC and identified FUT8 as a potential regulator associated with N-glycosylation processes in OC. These results offer insights into OC molecular characteristics and highlight FUT8 as a candidate biomarker with potential prognostic relevance. Further experimental studies are necessary to validate and elucidate the precise molecular mechanisms involved. Full article

Carbon capture and storage (CCS) technology can play an important role in meeting net-zero ambitions; however, its successful deployment depends on the transport and storage infrastructure for CO₂, as they are the backbone of the carbon management industry. Among the key integrity threats for dense-phase and supercritical CO₂ pipelines, acid precipitation or dropout in CO₂-rich streams containing reactive impurities (SO_x, NO_x, H₂S, H₂O, O_2, etc.) is one of the most serious. These impurities can alter phase behavior, promote formation of highly acidic liquid-phase condensates, and trigger severe localized corrosion and rapid wall-thickness loss. This review focuses on understanding the effects of specific combinations of impurities on CO₂ phase envelopes, acid formation, and corrosion mechanisms in pipelines under realistic flow and operating conditions. It further assesses mitigation and design strategies, including impurity specification and control, deep dehydration, operational envelope management, corrosion-resistant alloys, internal linings and advanced coatings, and emerging modeling tools for predicting corrosive dropout. The knowledge gap in long-term performance under multi-impurity conditions, thermo-hydraulic transients, and coupled corrosion damage is highlighted. Additionally, the importance of future experimental, modeling, and standards development work to enable safe, cost-effective material solutions for CCS technology deployment is proposed. Full article

Background: Chronic obstructive pulmonary disease (COPD) is associated with substantial long-term morbidity and mortality. Peripheral blood flow cytometry may provide exploratory information regarding immune-cell distributions and activation-related markers. However, careful interpretation is required when flow cytometry outputs are derived from lymphocyte-gated percentages rather than marker-specific mean fluorescence intensity or sequential lineage-confirmed gating. We investigated whether specific lymphocyte-gated flow cytometry parameters are associated with mortality during follow-up in COPD patients. Methods: In this single-center observational cohort study, 51 consecutive clinically stable outpatients with COPD were enrolled in November 2023 and followed for 24 months. Baseline peripheral blood flow cytometry results were verified against archived original instrument reports. The principal exploratory flow cytometry-derived variables were CD45/SSC-defined lymphocyte-gate percentage and lymphocyte-gated CD138+ events; HLA-DR positivity was evaluated as a secondary exploratory variable. Group comparisons and descriptive receiver operating characteristic (ROC) analyses were performed. Multiplicity was assessed using a hierarchical Benjamini–Hochberg false discovery rate (FDR) framework that separated the two biologically prioritized principal variables from the remaining exploratory screening variables. For transparency, a more conservative pooled FDR correction across all ten flow cytometry-derived variables was also reported. A two-variable analysis was performed only as exploratory signal aggregation, with descriptive internal assessment using leave-one-out cross-validation (LOO-CV) and bootstrap optimism correction. Results: During the 24-month follow-up, 13 of 51 patients died (25.5%). In unadjusted analyses, non-survivors had lower arterial oxygen tension and nominally lower CD45/SSC-defined lymphocyte-gate percentages (median 13.08% vs. 22.63%, p = 0.008) and lymphocyte-gated CD138+ event percentages (median 0.07% vs. 0.39%, p = 0.026) than survivors. Within the hierarchical analytical-family framework, both CD45/SSC-defined lymphocyte-gate percentage and lymphocyte-gated CD138+ events retained significance in the principal-variable family (within-family q = 0.016 and 0.026), whereas no secondary-family parameter, including HLA-DR (within-family q = 0.50), did; significance was not retained under a single correction across all ten parameters (CD45 q = 0.081; CD138 q = 0.129). Descriptive AUCs were 0.749 for CD45/SSC-defined lymphocyte-gate percentage and 0.710 for CD138+ events. The two-variable signal-aggregation analysis yielded an apparent AUC of 0.858, an LOO-CV AUC of 0.796, and a bootstrap optimism-corrected AUC of 0.832. NLR was available for all 51 patients; NLR-adjusted analyses did not establish clinical incremental utility. Conclusions: Lower CD45/SSC-defined lymphocyte-gate percentage and lower lymphocyte-gated CD138+ event percentage showed within-cohort associations with 24-month mortality in this small COPD cohort. These observations should be regarded solely as hypothesis-generating signals. Neither principal finding was retained after pooled correction across all ten flow cytometry-derived parameters, and no incremental prognostic value beyond routine inflammatory indices or established clinical predictors was demonstrated. External validation was absent; prospective replication in larger, appropriately adjusted cohorts is required. Full article

This study introduces a novel empirical model to estimate the total pressure drag generated by trapped lee waves (TLW) and upward-propagating internal waves in moderate-to-strong non-hydrostatic, stratified flow over a mountain ridge, as a function of flow non-linearity. The core framework is based on a two-layer atmosphere characterized by a piecewise-constant Scorer parameter, l, where a lower layer of constant $l_1$ underlies an upper layer with $l_2<l_1$. This framework incorporates key features to extend beyond idealized assumptions, providing a reliable tool for predicting non-linear flow regimes over mountainous terrain, particularly those featuring realistic vertical profiles of the Scorer parameter. To develop the empirical formulation, a micro- to mesoscale numerical model is employed to simulate realistic, non-linear flows over steep topography. The proposed empirical model yields results that compare favorably with numerical simulations across a range of moderate-to-strong non-hydrostatic regimes, including complex cases derived from observational data and realistic vertical profiles of the Scorer parameter. The model demonstrates robust performance ranging from strongly to moderately non-hydrostatic regimes (the latter corresponding to dimensionless half-widths of approximately 5), and provides accurate drag estimates for non-linearities up to a dimensionless mountain height of approximately unity. Therefore, this empirical approach serves as a valuable foundation for improving drag parameterizations in weather prediction models, offering a computationally efficient alternative to high-resolution numerical downscaling over steep terrain. Full article

Tesla valves are passive hydraulic devices capable of producing directional flow resistance without moving components, making them attractive for applications in microfluidics, thermal systems, and high-reliability hydraulic circuits. Despite extensive experimental and numerical studies, an analytical formulation capable of describing the hydrodynamic behavior of Tesla valves under varying operating and geometric conditions remains limited. In this work, a comprehensive analytical model is developed to describe the pressure losses, flow redistribution, and diodicity behavior of Tesla valves through a physics-based formulation derived from conservation laws, dimensional analysis, and inertial scaling principles. The proposed model incorporates the influence of Reynolds number, flow partition, geometric ratios, branch inclination angle, and number of diode stages within a unified nonlinear framework. A closed structural equation is obtained that relates hydraulic losses and directional asymmetry to the internal geometry of the valve. The formulation reveals the existence of geometric and energetic constraints governing rectification efficiency, including bounds associated with stage number, channel scaling, and angular momentum exchange. The results show that Tesla valve performance emerges from a delicate balance between inertial amplification and dissipative mechanisms, providing an analytical framework for the design and optimization of Tesla-type hydraulic systems across multiple scales. Full article