Search Results (11,871)

The clearance of the nozzle vane significantly influences the aerodynamic performance of variable nozzle turbines (VNTs), often leading to increased flow losses and performance degradation. Although nozzle vane clearance often exhibits a non-uniform distribution due to corrosion, wear, machining tolerances, or assembly errors, the aerodynamic effects of such non-uniform clearance have rarely been investigated. This study aims to fill the research gap regarding the influence of non-uniform nozzle guide vane clearance on tip leakage flow and aerodynamic performance in a supersonic VNT. By systematically examining the flow field features under different clearance profiles via three-dimensional numerical simulations, this work seeks to identify a potential clearance configuration that can reduce flow loss and improve turbine efficiency. The flow losses, tip leakage vortex patterns, and the interaction between the leakage vortex and shock waves are analyzed in detail for different clearance profiles. The results indicate that for a rear-loaded vane profile, the shrinking clearance (SC) configuration yields a lower mass flow rate and higher aerodynamic efficiency compared to the expanding clearance (EC) and uniform clearance (UC) configurations. Specifically, the SC configuration effectively reduces leakage mass flow and vortex intensity. Consequently, the interaction between the leakage vortex and the shock wave is suppressed. This suppression significantly mitigates flow losses, which are primarily driven by the shock–vortex interaction rather than the interaction between the leakage flow and the main flow, thereby enhancing aerodynamic performance. These findings suggest that a rational design of non-uniform clearance profiles can substantially improve the aerodynamic performance of supersonic turbines. Full article

With the rapid development of Internet of Things (IoT) technology, the formation control of unmanned surface vehicles (USVs) has attracted increasing attention in marine applications. However, in practical marine missions, the implementation of affine formation maneuver control (AFMC) still faces challenges caused by limited communication resources and restricted interaction frequency of onboard communication devices. To address these issues, this paper proposes a hybrid event-driven interaction mechanism (HEIM) for AFMC of USVs. In this mechanism, a clock variable is first introduced to regulate the interaction process, such that excessively frequent information exchanges can be avoided and the interaction interval can satisfy the minimum communication interval (MCI) required by onboard hardware. However, when the clock variable reaches its lower bound, an interaction may be compulsorily triggered, which introduces a maximum interaction interval constraint. To remove this restriction, an additional threshold is further incorporated as a secondary interaction judgment condition. In this way, the proposed mechanism not only preserves an adjustable MCI but also avoids unnecessary interactions caused solely by the clock variable, thereby further improving communication efficiency. Theoretical analysis and simulation results demonstrate the effectiveness of the proposed method. Full article

This study evaluates the effect of thermal severity on the yield, structural evolution, and stability of biochar produced from rice husk via controlled pyrolysis. The raw biomass, containing 1.67 wt% moisture, 60.94 wt% volatiles, and 21.66-wt% fixed carbon, proved highly suitable for thermochemical conversion. Using a 3 × 3 factorial design (500–700 °C; 30–60 min), variance analysis revealed temperature as the dominant variable (F = 67.15; p < 0.001; ${\eta }_p^2$ = 0.88), alongside a significant temperature time interaction (F = 6.19; p = 0.003). Maximum biochar yield occurred at 500 °C and 60 min (59.4 ± 2.0 wt%), whereas heating to 700 °C reduced yields to 41.8–44.0 wt% via enhanced devolatilization and secondary cracking. Structurally, the biochar developed a predominantly mesoporous matrix with a maximum BET surface area of 56.23 m²/g and ~4.9 nm average pore diameters. Furthermore, FTIR and DSC analyses demonstrated that higher thermal severity reduced oxygen-containing functional groups while increasing thermal stability and aromatic reorganization. Ultimately, 700 °C (at the 45 min residence time evaluated for porosimetry) provided the greatest structural development (maximum BET surface area), while 700 °C/60 min provided the highest thermal-oxidative stability, and lower severities favored biochar yield, revealing a trade-off between mass recovery and structural/stability performance. These findings establish that thermal severity dictates the physicochemical evolution of rice husk biochar, offering vital criteria for optimizing energy and environmental applications. Full article

Transimpedance amplifiers (TIAs) are the critical components for current-to-voltage interfaces in optical receivers, LiDAR front-ends, biomedical sensors, and unconventional applications such as magnetic-resonance receiver-coil arrays and wide-bandgap ultraviolet detectors, and their CMOS design is governed by a fundamental gain-bandwidth-noise trade-off whose structure is rarely made explicit. This review introduces a unifying framework rooted in three explicit assumptions underlying the classical shunt-feedback TIA limit: a single-pole core amplifier (A1), a resistive feedback element (A2), and the full input capacitance loading the feedback summing node (A3). Relaxing one or more of these assumptions is shown to be the common structural thread behind every class of bandwidth or noise enhancement in the recent literature, and all surveyed architectures are organized into a six-tier taxonomy, from Tier 0 designs operating within the classical limit to Tier 5 topologies that bypass all three assumptions simultaneously. This taxonomy is supplemented by an orthogonal configurability axis spanning single- and dual-control reconfigurable, variable-gain, and dynamic-range-extension designs. We further show that stability is not removed by these relaxations but migrates with the tier, from the global phase margin of the classical loop to a local regulating loop, a group-delay-flatness constraint, an input-passivity condition, or a multi-loop interaction, so that each architecture carries a predictable stability locus. The taxonomy is cross-referenced with application domains and closed-form noise-floor boundary plots parametrized by input capacitance and amplifier gain-bandwidth product, and with the CMOS technology landscape, where we argue that the most advanced node is not universally optimal and that node and topology act as complementary rather than competing levers. A single consistent figure of merit, applied uniformly to a representative set of CMOS realizations from 0.6 $\mathsf{\mu }$m to 16 nm FinFET, shows no monotonic improvement with publication year or node and is presented as a diagnostic indicator rather than an absolute ranking. The review closes with an outlook on 200 Gb/s/lane links, wide-bandgap sensor integration, and the FinFET-to-gate-all-around device transition. Full article

Disentangling the effects of climate variability and forest management on catchment hydrology remains a major challenge in temperate plantation landscapes. We analyzed 21 hydrological years (1997/98–2017/18) of precipitation and runoff records from two experimental catchments in the Chilean Coastal Range with contrasting silvicultural histories to characterize changes in low-flow behavior. Hydroclimatic and low-flow indices were evaluated using the monotonic trends test, Sen’s slope estimates, change point detection, and standardized inter-catchment anomaly differences. Annual and seasonal precipitation indices, rainfall frequency, and maximum dry-spell duration showed no significant monotonic trends, whereas maximum 5-day precipitation declined at LP. The two catchments nevertheless exhibited divergent low-flow trajectories. Los Pinos, managed through partial harvesting and thinning within a forest mosaic, showed decreasing normalized low-flow availability and longer low-flow exposure during the latter part of the record. La Reina, clearcut in 1999/2000 and subsequently reforested, showed a progressive increase in low-flow magnitude and normalized low-flow availability, together with declining flow variability and fewer below-threshold events. Standardized inter-catchment comparisons confirmed a temporal divergence in low-flow behavior. They also revealed a concurrent shift in inter-catchment precipitation anomalies. These results indicate contrasting long-term hydrological trajectories that are consistent with differences in forest management histories; however, the non-paired study design, limited pre-harvest observations at La Reina, and differential precipitation forcing preclude formal attribution to silvicultural effects alone. This study highlights the value of long-term experimental catchments for evaluating low-flow dynamics under interacting climatic and land-management influences. Full article

This study presents a comprehensive analysis and predictive modeling framework for the axial compressive strength (f_cc) and ultimate axial strain (ε_cu) of concrete columns confined within fiber-reinforced polymer (FRP) systems. Large databases comprising 3312 samples for f_cc and 3319 for ε_cu were compiled from the literature, encompassing a wide range of key variables, including unconfined concrete strength from 7 MPa to 204 MPa and diverse FRP confinement configurations. The datasets were subjected to extensive statistical and multivariate analyses to identify the primary factors influencing axial behavior and guide feature selection for predictive modeling. Three groups of machine learning (ML) algorithms were subsequently considered: (i) artificial neural networks (including multilayer perceptrons with one and two hidden layers), (ii) kernel-based models (Gaussian process regression and support vector regression), and (iii) tree-based ensemble models (gradient boosting machine, eXtreme gradient boosting, and light gradient boosting machine). Hyperparameters were optimized using grid search cross-validation, while feature importance analyses were performed to quantify the contribution of each input variable. Among all ML models, eXtreme gradient boosting demonstrated superior predictive performance, effectively capturing the nonlinear and multivariate interactions governing confinement effectiveness. Comparative analysis with the top performing regression-based formulations further highlighted the accuracy, robustness, and generalization capability of the eXtreme gradient boosting model. The findings provide a data-driven and interpretable framework for the design and prediction of FRP-confined concrete columns. Full article

Coronary artery bypass grafting (CABG) remains one of the most effective treatments for advanced coronary artery disease; however, substantial variability persists in both perioperative and long-term outcomes despite advances in surgical technique, myocardial protection, and risk stratification. Oxidative stress is a central mediator of tissue injury during cardiac surgery, contributing to ischemia–reperfusion injury, endothelial dysfunction, systemic inflammation, and postoperative organ complications. At the same time, chronic exposure to ambient air pollution has emerged as an important environmental determinant of cardiovascular disease through mechanisms that converge on many of the same redox-sensitive pathways. We propose the concept of environmental oxidative priming, whereby long-term exposure to particulate matter, nitrogen oxides, ozone, and other pollutants establishes a persistent state of endothelial dysfunction, mitochondrial impairment, chronic inflammation, nitric oxide depletion, and reduced antioxidant reserve before surgery. Within this framework, CABG represents a second oxidative challenge superimposed on a pre-existing environmentally conditioned phenotype. We discuss the mechanistic overlap between air pollution-induced cardiovascular injury and cardiac surgical stress and examine how this interaction may contribute to postoperative complications, graft adaptation, major adverse cardiovascular events, and long-term survival. Recognition of air pollution as a modifier of biological resilience provides a novel framework for understanding outcome heterogeneity after CABG and may support future precision-based risk stratification and preventive strategies. Full article

Motor Imagery (MI) decoding based on electroencephalogram (EEG) is promising for Brain–Computer Interface (BCI) applications, yet existing methods generally suffer from two major limitations. (1) Global EEG signal analysis overlooks region-specific neural activity patterns, causing biased feature extraction and poor inter-subject generalization. (2) Few MI-EEG decoding studies adopt frequency decomposition for multi-rhythm feature extraction. Even when adopted, conventional methods rely on predefined frequency bands and suffer from mode mixing, failing to preserve the inherent fractal self-similarity and nonlinear characteristics of EEG signals, restricting the extraction of fine-grained specific features. To address these issues, we propose RMF-Net, a novel model integrating brain region division and Multi-variable Variational Mode Decomposition (MVMD). The model partitions EEG into functional brain regions based on MI neural mechanisms, performs dynamic modal feature extraction for each region via MVMD, and enables efficient cross-regional spatiotemporal feature interaction through an adaptive fusion. On the BCI Competition IV 2a open EEG MI dataset, our model achieves 80.06% accuracy in cross-session tasks and 63.05% in cross-subject tasks, outperforming other mainstream methods. Further analysis verifies that the cross-regional feature weight distribution of RMF-Net conforms to neuroanatomical principles. This work demonstrates that the spatiotemporal feature fusion framework combining brain region segmentation and fractal-aware multimodal signal decomposition is effective for EEG MI decoding tasks. Full article

Orthoflaviviruses comprise genetically diverse mosquito-borne viruses responsible for a broad spectrum of human diseases. Although naturally circulating clinical isolates exhibit biological variability, the extent to which they generate distinct early epithelial innate immune responses remains incompletely understood. Here, we characterized five clinical orthoflavivirus isolates obtained in Oaxaca, Mexico, using human HaCaT keratinocytes as an in vitro model of early infection. Productive infection was assessed by immunofluorescence microscopy, immunostained focus appearance under isolate-optimized assay conditions, and infectious virus production, whereas host responses were evaluated by transcriptional profiling and quantitative whole-slide single-cell immunofluorescence. All isolates established productive infection and exhibited different viral replication profiles. Temporal transcriptional analyses revealed variable expression of antiviral (IFNβ, Mx1, OAS1, PKR, IFITM3, Viperin, and RANTES) and inflammatory (TNF-α, IL-8, and MCP-1) genes. Quantitative whole-slide analysis provided complementary evidence of variable STAT1 and NF-κB signaling activation across the analyzed isolates. Within this limited panel, viral replication was not consistently aligned with the selected transcriptional and signaling readouts, although the exploratory nature of these comparisons precludes establishing independence between these variables. Together, the virological, transcriptional, and imaging analyses revealed distinct multidimensional functional profiles across the isolate panel. Overall, these findings demonstrate functional heterogeneity among the analyzed clinical orthoflavivirus isolates and highlight integrated functional phenotyping as a useful framework for examining virus–host interactions beyond viral replication alone. Full article

Sea surface temperature (SST) is a key variable regulating ocean–atmosphere interactions, yet the performance of global Level-4 (L4) SST products in complex coastal environments remains insufficiently evaluated. This study assesses four global L4 SST products (C3SSST, OSTIA, MGDSST, and OISST) in the coastal waters surrounding the Shandong Peninsula using both the iQuam dataset and regional moored buoy (MB) observations. Statistical metrics (COR, RMSE, MAE, BIAS) were employed to quantify accuracy. All four SST products show strong agreement with in situ observations, with correlation coefficients exceeding 0.97. Validation against iQuam showed that OISST achieved the lowest RMSE (~1.008 °C), while coastal buoy observations showed that C3SSST achieved the lowest RMSE at Zhifudao (RMSE ≈ 0.833 °C). Pronounced warm biases at Shidao station during summer suggest that L4 SST products may underestimate coastal upwelling along the Shandong Peninsula. The leave-one-out (LOO) ensemble analysis further revealed consistent differences among the four L4 SST products, with C3SSST and OSTIA showing relatively higher consistency, while OISST exhibited larger deviations from the other products. These results indicate that L4 SST performance depends on both product characteristics, including observational data sources, and local environmental conditions. These findings provide useful guidance for SST dataset selection in the Shandong Peninsula. Full article

Russia’s invasion of Ukraine has posed serious risks to global food security, by causing substantial crop yield losses since 2022. Accurate yield estimation helps policymakers to plan compensation, yet modelling yields in conflict regions remains challenging due to significant non-meteorological disruptions. This study proposes a novel framework to quantify war-related crop yield losses by comparing estimations derived from meteorological data, representing weather-driven yield variability, with those based on Earth observation (EO) data, reflecting actual crop conditions influenced by both weather and conflict. Thus, meteorologically based yield estimates are expected to exceed those derived from EO data, with the difference indicating war-related losses. Both, meteorological- and EO-based models, are developed using transfer learning (TL) to estimate yields of maize, winter wheat, and spring barley. Models are initially trained on EU country data and subsequently finetuned with Ukrainian data. Their performance is compared to two non-TL approaches: Extreme Gradient Boosting (XGB) and Artificial Neural Network (ANN) to test their reliability. Results show crop yield losses for maize; however, since we do not detect losses in the other crops, we conclude that simply comparing meteorological- and EO-based models proves insufficient to fully isolate conflict effects due to strong interactions of EO and meteorological data. Nevertheless, TL substantially enhances prediction accuracy (R² around 0.7), exceeding alternative models by 0.05–0.2 across crops. These findings demonstrate the value of TL for yield modelling in data-scarce environments and underscore the need for improved methodologies to quantify conflict-induced agricultural losses. Full article

Meat and meat products are highly susceptible to microbial spoilage, lipid oxidation, moisture loss, discoloration, and sensory deterioration, creating a need for effective, safe, and sustainable packaging solutions. Although previous studies have investigated biodegradable polymers, nanomaterials, and active packaging systems separately, an integrated assessment connecting material design, preservation mechanisms, safety, sustainability, and commercial feasibility remains limited. This review addresses this gap by critically evaluating recent advances in biodegradable nanocomposite films and coatings for meat preservation. Current evidence demonstrates that the incorporation of nanoscale reinforcements and bioactive agents into biopolymer matrices can enhance their mechanical performance, gas and moisture barrier properties, antimicrobial activity, antioxidant capacity, and controlled release behavior. However, these advantages are strongly influenced by the nanofiller characteristics, concentration, dispersion, polymer-nanofiller interactions, food matrix composition, and storage conditions. Excessive nanomaterial incorporation may promote aggregation, induce structural defects, reduce flexibility, and increase migration concerns. Despite promising preservation outcomes, most available studies remain limited to laboratory-scale investigations, variable testing protocols, and insufficient validation under real commercial conditions. Key challenges hindering industrial adoption include nanoparticle migration, long-term safety assessment, regulatory uncertainty, production costs, consumer acceptance, and limited life-cycle evaluation. Future research should focus on safe-by-design formulations, standardized real-food testing, scalable manufacturing approaches, controlled-release technologies, and integrated assessments of preservation efficiency, safety, economic feasibility, and environmental sustainability. Overall, biodegradable nanocomposite packaging represents a promising approach for extending meat shelf life; however, successful commercialization requires balancing enhanced preservation performance with safety assurance and industrial practicality. Full article

River networks influenced by tides represent dynamic, hydrologically complex systems where fluvial inflows interact with oceanic tidal forcing. Conventional flow-based indica-tors of hydrologic alteration (IHA) inadequately capture tidal-driven dynamics, including diurnal water level fluctuations, flow reversals, and tidal asymmetry. This study develops an augmented IHA (AIHA) framework comprising 96 indicators derived from hourly-resolution hydrodynamic simulations (1960–2019) in the Pearl River Delta (PRD), China. The AIHA extracts four daily series—ebb-peak flow and residual, highest, and lowest water levels—supplemented by tidal characteristic metrics. A moving t-test identified 1991 as the significant regime shift, enabling comparison of reference (1960–1991) and altered (1992–2019) periods. Three-dimensional alteration was assessed as follows: deviation from the range of variability (RVA), shift in central tendency (RCM), and change in dispersion (RCD). Indicator importance was integrated via CRITIC weighting and multi-site Borda scoring. Results show that range shifts and central-tendency shifts were generally dominated by low-intensity alteration, accounting for 58.3–71.9% and more than 85% of the indicators, respectively, whereas dispersion shifts were more pronounced, with medium- and high-alteration indicators accounting for an average of 42.4%. Water level indicators exhibited substantially greater alteration sensitivity than flow indicators, particularly in estuarine zones where the alteration degrees of some indicators exceeded 90%. Among hydrological elements, ebb-peak flow indicators responded more strongly in range shifts, with an average comprehensive alteration degree of 29.6%, while water level indicators showed more pronounced changes in central tendency and dispersion; the lowest water level indicators were especially sensitive, with average comprehensive alteration degrees of 19.6% and 77.3%, respectively. Spatially, center-of-distribution shifts (RCM) diverged: positive in western/northern tributaries (increased flows) versus negative in the eastern PRD (decreased flows). Integrated Borda scoring identified low-flow extremes during dry seasons, event timing, and tidal modulation as the most sensitive responses to hydrological stress. The AIHA framework demonstrates that tidal river alteration is characterized by intensified low-flow volatility and amplified tidal influence, with water level metrics providing a superior detection capacity than achieved by discharge alone. This process-integrated approach offers robust quantitative support for ecological flow management and estuarine restoration in tidal environments globally. Full article

Photodynamic therapy (PDT) is a minimally invasive therapeutic modality based on the interaction between a photosensitizer (PS), light, and molecular oxygen to generate reactive oxygen species (ROS) capable of inducing localized cytotoxicity. Natural products provide a chemically diverse source of photosensitizers, including curcumin, hypericin, hypocrellin, chlorin derivatives, alkaloids, flavonoids, anthraquinones, and other photoactive scaffolds. However, their translational development remains limited by poor solubility, aggregation, instability, variable purity, limited tissue penetration, suboptimal pharmacokinetics, and insufficient formulation readiness. In parallel, artificial intelligence (AI), including machine learning (ML), deep learning (DL), quantitative structure–activity relationship (QSAR) and quantitative structure–property relationship (QSPR) modeling, radiomics, and predictive analytics, is increasingly being applied to photosensitizer discovery, molecular property prediction, nanoformulation optimization, treatment planning, and precision PDT. This critical review evaluates the intersection between AI, natural photosensitizers, nanopharmaceutical development, and PDT, with emphasis on methodological strengths, current limitations, and translational priorities. A PRISMA 2020-inspired search strategy identified 27 studies for qualitative synthesis, comprising 11 review articles and 16 original investigations, while additional seminal references were used for historical and mechanistic contextualization. The analysis indicates that current AI applications in PDT are concentrated around molecular property prediction, QSAR/QSPR modeling, phototoxicity assessment, radiomics, image-guided therapy, and treatment-response prediction, whereas AI-guided exploration of natural photosensitizer chemical space and AI-assisted nanoformulation design remain comparatively underdeveloped. Key barriers include heterogeneous datasets, limited natural-product representation in predictive models, insufficient external validation, weak integration between formulation variables and photodynamic outcomes, and limited consideration of manufacturing and regulatory requirements. This review proposes an integrated AI-enabled translational framework connecting natural-product chemical diversity, photochemical prediction, nanocarrier optimization, precision PDT validation, and clinical implementation. Full article

Background: Postoperative hemoglobin (Hb) reduction reflects the physiologic extent of perioperative blood loss after total knee arthroplasty (TKA). Whereas previous studies have relied on transfusion as a binary endpoint, transfusion decisions are highly variable across institutions, obscuring the underlying hematologic trajectory. This study aimed to develop and interpret machine learning (ML) models to characterize and quantify the determinants of postoperative Hb reduction in a standardized cohort of non-transfused TKA patients. Methods: A retrospective cohort of 866 patients who underwent primary TKA under a standardized operative protocol—with identical cemented posterior-stabilized implants and uniform cementing technique—was analyzed (1 January 2014–31 March 2024). During the study period, a consistent 1 g intra-articular tranexamic acid (TXA) regimen administered through the drain was introduced and applied to a subset of patients, allowing TXA use to be modeled as a binary predictor. Four ML algorithms (Linear Regression, Random Forest, XGBoost, and Stacking Regressor) were trained using preoperative, demographic, and perioperative variables. Fivefold cross-validation assessed model performance, and SHapley Additive exPlanations (SHAP) values were used to identify influential predictors and enhance interpretability. Results: Across all ML models, preoperative Hb emerged as the strongest determinant of postoperative Hb reduction, followed by TXA use, body mass index (BMI), and platelet count. Ensemble models captured non-linear and interacting effects more effectively than linear regression. Test-set performance was modest (best R² = 0.330), consistent with the influence of unmeasured physiologic factors such as hidden blood loss, fluid dynamics, and inflammatory responses. Accordingly, the primary value of the framework lies in the exploratory and transparent assessment of determinant importance rather than in individual-level prediction. Conclusions: This study provides an interpretable, exploratory ML framework for identifying factors associated with percentage Hb reduction after TKA. Preoperative Hb was the dominant determinant, while TXA use and higher BMI were recurrently associated with smaller predicted percentage reductions. Given the modest test-set performance and the absence of external validation and clinical utility assessment, the models should not be interpreted as tools for individual-level prediction or clinical decision making. Full article