Search Results (3,473)

Monitoring cardiovascular health enables continuous and real-time risk assessment. This study utilized the Framingham Heart Study dataset to develop and evaluate machine-learning models for predicting mortality risk based on key cardiovascular parameters. Some machine-learning algorithms were applied to multiple machine-learning models. Among these, XGBoost achieved the highest predictive performance, each with an area under the curve (AUC) value of 0.83. Feature importance analysis revealed that coronary artery disease, glucose levels, and diastolic blood pressure (DIABP) were the most significant risk factors associated with mortality. The primary contribution of this research lies in its implications for public health and preventive medicine. By identifying key risk factors, it becomes possible to calculate individual and population-level risk scores and to design targeted early intervention strategies aimed at reducing cardiovascular-related mortality. Full article

Background: This study aimed to evaluate prognostic factors of early filtering blebs using anterior segment swept-source optical coherence tomography (AS SS-OCT) in patients with uveitic and neovascular glaucoma. Methods: This retrospective cohort study included 22 eyes from 22 patients who underwent trabeculectomy (11 eyes each with uveitic or neovascular glaucoma). Intrableb characteristics were assessed using AS SS-OCT at 1 month, postoperatively. Surgical success was defined as intraocular pressure (IOP) ≤ 18 mmHg and ≥30% IOP reduction without medication at 12 months. Logistic regression was used to identify the prognostic factors associated with IOP control. Results: Sixteen eyes (72.7%) achieved surgical success, while six (27.3%) were unsuccessful. Eyes with successful IOP control at 12 months showed thicker and less reflective bleb walls with microcysts compared with unsuccessful cases of IOP control, in the early postoperative phase (all p < 0.033). However, IOP at the time of OCT did not significantly differ between the groups (p = 0.083). Multivariate logistic regression analysis revealed that higher bleb wall reflectivity at 1-month post-trabeculectomy was significantly associated with a higher surgical failure rate at 12 months after trabeculectomy (hazard ratio = 1.072, p = 0.032). Conclusions: Early intrableb assessment using AS SS-OCT may be beneficial for managing filtering blebs after trabeculectomy in uveitic and neovascular glaucoma. Higher bleb wall reflectivity in the early post-trabeculectomy phase may indicate poor features of the filtering bleb, suggesting the need for timely interventions for refractory cases. Full article

Urban green infrastructure plays a vital role in the sustainable development of cities. As urban areas expand, green spaces are increasingly affected. The pressure from new developments leads to a reduction in vegetation and raises new public health risks. Addressing this challenge requires effective planning, maintenance, and continuous monitoring. To enhance traditional approaches, remote sensing is becoming a vital tool for city-wide observations. Publicly available large-scale data, combined with machine learning models, can improve our understanding. We explore the potential of Sentinel-2 to classify and extract meaningful features from urban landscapes. Using advanced machine learning techniques, we aim to develop a robust and scalable framework for classifying urban environments. The proposed models will assist in monitoring changes in green spaces across diverse urban settings, enabling timely and informed decisions to foster sustainable urban growth. Full article

Mental stress is a psychological or emotional strain that typically occurs because of threatening, challenging, and overwhelming conditions and affects human behavior. Various factors, such as professional, environmental, and personal pressures, often trigger it. In recent years, various deep learning (DL)-based schemes using electroencephalograms (EEGs) have been proposed. However, the effectiveness of DL-based schemes is challenging because of the intricate DL structure, class imbalance problems, poor feature representation, low-frequency resolution problems, and complexity of multi-channel signal processing. This paper presents a novel hybrid DL framework, BDDNet, which combines a deep convolutional neural network (DCNN), bidirectional long short-term memory (BiLSTM), and deep belief network (DBN). BDDNet provides superior spectral–temporal feature depiction and better long-term dependency on the local and global features of EEGs. BDDNet accepts multiple EEG features (MEFs) that provide the spectral and time-domain features of EEGs. A novel improved crow search algorithm (ICSA) was presented for channel selection to minimize the computational complexity of multichannel stress detection. Further, the novel employee optimization algorithm (EOA) is utilized for the hyper-parameter optimization of hybrid BDDNet to enhance the training performance. The outcomes of the novel BDDNet were assessed using a public DEAP dataset. The BDDNet-ICSA offers improved recall of 97.6%, precision of 97.6%, F1-score of 97.6%, selectivity of 96.9%, negative predictive value NPV of 96.9%, and accuracy of 97.3% to traditional techniques. Full article

Slope stability assessment is a critical component of engineering safety. Conventional analytical methods frequently struggle to integrate heterogeneous slope data and model intricate failure mechanisms, thereby constraining their efficacy in practical engineering scenarios. To tackle these issues, this study presents a novel slope stability classification model grounded in the Optuna-TPE-CatBoost framework. By leveraging the Tree-structured Parzen Estimator (TPE) within the Optuna framework, the model adaptively optimizes CatBoost hyperparameters, thus enhancing prediction accuracy and robustness. It incorporates six key features—slope height, slope angle, unit weight, cohesion, internal friction angle, and the pore pressure ratio—to establish a comprehensive and intelligent assessment system. Utilizing a dataset of 272 slope cases, the model was trained with k-fold cross-validation and dynamic class imbalance strategies to ensure its generalizability. The optimized model achieved impressive performance metrics: an area under the receiver operating characteristic curve (AUC) of 0.926, an accuracy of 0.901, a recall of 0.874, and an F1-score of 0.881, outperforming benchmark algorithms such as XGBoost, LightGBM, and the unoptimized CatBoost. Validation via engineering case studies confirms that the model accurately evaluates slope stability across diverse scenarios and effectively captures the complex interactions between key parameters. This model offers a reliable and interpretable solution for slope stability assessment under complex failure mechanisms. Full article

Preeclampsia with severe features presents major anesthetic challenges, particularly in category 1 cesarean sections, in which rapid, safe, and hemodynamically stable induction is critical. Neuraxial techniques may be controversial due to neurological symptoms, making general anesthesia a viable option. However, traditional general anesthesia may exacerbate hypertension and increase maternal and fetal risks. Two primigravida patients with elevated blood pressure and neurological symptoms underwent category 1 cesarean delivery under TIVA-TCI with propofol, using the Marsh model. Hemodynamic stability, drug dosing, and maternal–neonatal outcomes were monitored. Sufentanil was administered for analgesia; neuromuscular blockade was achieved with rocuronium and reversed with sugammadex. No BIS or TOF monitoring was available. Both patients maintained stable hemodynamics and oxygenation throughout surgery. Intubation was successfully performed at an effect-site concentration of 3.5 µg/mL. Neonatal Apgar scores were within acceptable limits. No major complications occurred intraoperatively or postoperatively. TCI allowed individualized dosing and smooth emergence. TIVA-TCI with propofol appears to be a viable alternative to volatile-based general anesthesia in category 1 emergencies for cesarean sections for patients with preeclampsia with severe features, especially when neuraxial anesthesia is controversial. It offers hemodynamic stability and controlled depth of anesthesia, though its use requires experience and may not be optimal in cases requiring ultra-rapid induction. Full article

The numerical simulation of unsteady, high-Reynolds-number incompressible flows governed by the Navier–Stokes (NS) equations presents significant challenges in computational fluid dynamics, primarily concerning numerical stability and computational efficiency. Standard Galerkin finite element methods often suffer from non-physical oscillations in convection-dominated regimes, while the multiscale nature of these flows demands prohibitively high computational resources for uniformly refined meshes. This paper proposes an improved Galerkin framework that synergistically integrates a Variational Multiscale Stabilization (VMS) method with an adaptive mesh refinement (AMR) strategy to overcome these dual challenges. Based on the Ritz–Galerkin formulation with the stable Taylor–Hood ($P_2-P_1$) element, a VMS term is introduced, derived from a generalized $\theta$-scheme. This explicitly constructs a subgrid-scale model to effectively suppress numerical oscillations without introducing excessive artificial diffusion. To enhance computational efficiency, a novel a posteriori error estimator is developed based on dual residuals. This estimator provides the robust and accurate localization of numerical errors by dynamically weighting the momentum and continuity residuals within each element, as well as the flux jumps across element boundaries. This error indicator guides an AMR algorithm that combines longest-edge bisection with local Delaunay re-triangulation, ensuring optimal mesh adaptation to complex flow features such as boundary layers and vortices. Furthermore, the stability of the Taylor–Hood element, essential for stable velocity–pressure coupling, is preserved within this integrated framework. Numerical experiments are presented to verify the effectiveness of the proposed method, demonstrating its ability to achieve stable, high-fidelity solutions on adaptively refined grids with a substantial reduction in computational cost. Full article

Skin autofluorescence (SAF) detection technology represents a noninvasive, convenient, and cost-effective optical detection approach. It can be employed for the differentiation of various diseases, including metabolic diseases and dermatitis, as well as for monitoring the treatment efficacy. Distinct from diffuse reflection signals, the autofluorescence signals of biological tissues are relatively weak, making them challenging to be captured by photoelectric sensors. Moreover, the absorption and scattering properties of biological tissues lead to a substantial attenuation of the autofluorescence of biological tissues, thereby worsening the signal-to-noise ratio. This has also imposed limitations on the development and application of compact-sized autofluorescence detection systems. In this study, a compact LED light source and a CMOS sensor were utilized as the excitation and detection devices for skin tissue autofluorescence, respectively, to construct a mobile and wireless skin tissue autofluorescence detection system. This system can achieve the detection of skin tissue autofluorescence with a high signal-to-noise ratio under the drive of a simple power supply and a single-chip microcontroller. The detection time is less than 0.1 s. To enhance the stability of the system, a pressure sensor was incorporated. This pressure sensor can monitor the pressure exerted by the skin on the detection system during the testing process, thereby improving the accuracy of the detection signal. The developed system features a compact structure, user-friendliness, and a favorable signal-to-noise ratio of the detection signal, holding significant application potential in future assessments of skin aging and the risk of diabetic complications. Full article

Accurately identifying coherent vortex structures (CVSs) in backward-facing step (BFS) flows remains a challenge, particularly in reconciling visual streamlines with mathematical criteria. In this study, high-resolution velocity fields were captured using particle image velocimetry (PIV) in a pressurized BFS setup. Instantaneous streamlines reveal distinct spiral patterns, vortex centers, and saddle points, consistent with physical definitions of vortices and offering intuitive guidance for CVS detection. However, conventional vortex identification methods often fail to reproduce these visual features. To address this, an improved Q-criterion method is proposed, based on the normalization of the velocity gradient tensor. This approach enhances the rotational contribution while suppressing shear effects, leading to improved agreement in vortex position and shape with those observed in streamlines. While the normalization process alters the representation of physical vortex strength, the method bridges qualitative visualization and quantitative analysis. This streamline-consistent identification framework facilitates robust CVS detection in separated flows and supports further investigations in vortex dynamics and turbulence control. Full article

A systematic sensitivity analysis using three-dimensional discrete element models with discrete fracture networks (DEM-DFN) was conducted to evaluate underground excavation support in jointed rock masses at the CLAB2 site in Southeastern Sweden. The site features a joint network comprising six distinct joint sets, each with unique geometrical properties. The study examined 10 DFNs and 19 rock bolt patterns, both conventional and unconventional. It covered 200 scenarios, including 10 unsupported and 190 supported cases. Technical and economic criteria for stability were assessed for each support system. The results indicated that increasing rock bolt length enhances stability up to a certain point. However, multi-length rock bolt patterns with similar consumption can yield significantly different stability outcomes. Notably, the arrangement and properties of rock bolts are crucial for stability, particularly in blocks between bolting sections. These blocks remain interlocked in unsupported areas due to the induced pressure from supported sections. Although equal-length rock bolt patterns are commonly used, the analysis revealed that triple-length rock bolts (3, 6, and 9 m) provided the most effective support across all ten DFN scenarios. Full article

The Santurbán paramo is a sensitive high-mountain ecosystem exposed to pressures from extractive and agricultural activities, as well as increasing tourism. In response, this study presents a context-aware recommendation system designed to support sustainable tourism through the integration of deep neural networks and ontology-based semantic modeling. The proposed system delivers personalized recommendations—such as activities, accommodations, and ecological routes—by processing user preferences, geolocation data, and contextual features, including cost and popularity. The architecture combines a trained TensorFlow Lite model with a domain ontology enriched with GeoSPARQL for geospatial reasoning. All inference operations are conducted locally on Android devices, supported by SQLite for offline data storage, which ensures functionality in connectivity-restricted environments and preserves user privacy. Additionally, the system employs geofencing to trigger real-time environmental notifications when users approach ecologically sensitive zones, promoting responsible behavior and biodiversity awareness. By incorporating structured semantic knowledge with adaptive machine learning, the system enables low-latency, personalized, and conservation-oriented recommendations. This approach contributes to the sustainable management of natural reserves by aligning individual tourism experiences with ecological protection objectives, particularly in remote areas like the Santurbán paramo. Full article

Aims: Sinus tachycardia after heart transplantation (HTX) due to cardiac graft denervation is associated with reduced post-transplant survival and requires adequate treatment. We analyzed the long-term effects of heart rate control with ivabradine or metoprolol succinate in HTX recipients. Methods: This observational retrospective single-center study analyzed the ten-year results of 110 patients receiving ivabradine (n = 54) or metoprolol succinate (n = 56) after HTX. Analysis included comparison of demographics, medications, heart rates, blood pressure values, echocardiographic features, cardiac catheterization data, cardiac biomarkers, and post-transplant survival including causes of death. Results: Both groups showed no significant differences concerning demographics or medications (except for ivabradine and metoprolol succinate). At 10-year follow-up, HTX recipients with ivabradine showed a significantly lower heart rate (72.7 ± 8.5 bpm) compared to baseline (88.8 ± 7.6 bpm; p < 0.001) and to metoprolol succinate (80.1 ± 8.1 bpm; p < 0.001), a significantly lower NT-proBNP level (588.4 ± 461.4 pg/mL) compared to baseline (3849.7 ± 1960.0 pg/mL; p < 0.001) and to metoprolol succinate (1229.0 ± 1098.6 pg/mL; p = 0.005), a significantly lower overall mortality (20.4% versus 46.4%; p = 0.004), and mortality due to graft failure (1.9% versus 21.4%; p = 0.001). Multivariate analysis showed a significantly decreased risk of death within 10 years after HTX in patients with post-transplant use of ivabradine (HR 0.374, CI 0.182–0.770; p = 0.008). Conclusions: In this single-center trial, patients with ivabradine revealed a significantly more pronounced heart rate reduction, a lower NT-proBNP level, and a superior 10-year survival after HTX. Full article

The rapid urbanization process has accelerated the degradation of ecosystem services (ESs) in peri-urban rural areas of the Yangtze River Delta (YRD), leading to increasing landscape ecological risks (LERs). Establishing a scientifically grounded landscape ecological risk assessment (LERA) system and corresponding control strategies is therefore imperative. Using rural areas of Jiangning District, Nanjing as a case study, this research proposes an optimized dual-dimensional coupling assessment framework that integrates ecosystem service value (ESV) and ecological risk probability. The spatiotemporal evolution of LER in 2000, 2010, and 2020 and its key driving factors were further studied by using spatial autocorrelation analysis and geodetector methods. The results show the following: (1) From 2000 to 2020, cultivated land remained dominant, but its proportion decreased by 10.87%, while construction land increased by 26.52%, with minimal changes in other land use types. (2) The total ESV increased by CNY 1.67 × 10⁹, with regulating services accounting for over 82%, among which water bodies contributed the most. (3) LER showed an overall increasing trend, with medium- to highest-risk areas expanding by 55.37%, lowest-risk areas increasing by 10.10%, and lower-risk areas decreasing by 65.48%. (4) Key driving factors include landscape vulnerability, vegetation coverage, and ecological land connectivity, with the influence of distance to road becoming increasingly significant. This study reveals the spatiotemporal evolution characteristics of LER in typical peri-urban villages. Based on the LERA results, combined with terrain features and ecological pressure intensity, the study area was divided into three ecological management zones: ecological conservation, ecological restoration, and ecological enhancement. Corresponding zoning strategies were proposed to guide rural ecological governance and support regional sustainable development. Full article

Vegetation dynamics are complexly influenced by multiple factors such as climate, human activities, and topography. In recent years, the frequency, intensity, and diversity of human activities have increased, placing substantial pressure on the growth of vegetation. Arid and semi-arid regions are particularly sensitive to climate change, and climate change and large-scale ecological restoration have led to significant changes in the dynamic of dryland vegetation. However, few studies have explored the nonlinear relationships between these factors and vegetation dynamic. In this study, we integrated trend analysis (using the Mann–Kendall test and Theil–Sen estimation) and machine learning algorithms (XGBoost-SHAP model) based on long time-series remote sensing data from 2001 to 2020 to quantify the nonlinear response patterns and threshold effects of bioclimatic variables, topographic features, soil attributes, and anthropogenic factors on vegetation dynamic. The results revealed the following key findings: (1) The kNDVI in the study area showed an overall significant increasing trend (p < 0.01) during the observation period, of which 26.7% of the area showed a significant increase. (2) The water content index (Bio 23, 19.6%), the change in land use (15.2%), multi-year average precipitation (pre, 15.0%), population density (13.2%), and rainfall seasonality (Bio 15, 10.9%) were the key factors driving the dynamic change of vegetation, with the combined contribution of natural factors amounting to 64.3%. (3) Among the topographic factors, altitude had a more significant effect on vegetation dynamics, with higher altitude regions less likely to experience vegetation greening. Both natural and anthropogenic factors exhibited nonlinear responses and interactive effects, contributing to the observed dynamic trends. This study provides valuable insights into the driving mechanisms behind the condition of vegetation in arid and semi-arid regions of China and, by extension, in other arid regions globally. Full article

HIV infection and hypertensive disorders of pregnancy (HDP), particularly preeclampsia (PE) with severe features, are leading causes of maternal mortality worldwide. This study investigates the role of asymmetric dimethylarginine (ADMA) and prostacyclin (PGI2) concentrations in endothelial impairment in normotensive pregnant versus PE women within an HIV endemic setting in KwaZulu-Natal Province, South Africa. The study population (n = 84) was grouped according to pregnancy type, i.e., normotensive (n = 42) and PE (n = 42), and further stratified by HIV status. Clinical factors were maternal age, weight, blood pressure (both systolic and diastolic) levels, and gestational age. Plasma concentrations of ADMA and PGI2 were measured using the enzyme-linked immunoassay (ELISA). Differences in outcomes were analyzed using the Mann–Whitney U and Kruskal–Wallis test together with Dunn’s multiple-comparison post hoc test. The non-parametric data were presented as medians and interquartile ranges. Gravidity, gestational age, and systolic and diastolic blood pressures were significantly different across the study groups where p < 0.05 was deemed significant. Furthermore, the concentration of ADMA was significantly elevated in PE HIV-positive vs. PE HIV-negative (p = 0.0174) groups. PGI2 did not show a significant difference in PE compared to normotensive pregnancies (p = 0.8826) but was significantly different across all groups (p = 0.0212). An increase in plasma ADMA levels was observed in the preeclampsia HIV-negative group compared to the normotensive HIV-negative group. This is linked to the role played by ADMA in endothelial impairment, a characteristic of PE development. PGI2 levels were decreased in PE compared to the normotensive group regardless of HIV status. These findings draw attention to the importance of endothelial indicators in pathogenesis and possibly early prediction of PE development. Full article