Search Results (28,033)

Background: Obstructive sleep apnea (OSA) is prevalent and often underdiagnosed in cardiology. Worldwide, approximately 936 million adults aged 30–69 are affected by OSA, with the highest numbers in the USA, China, Brazil, and India. In cardiovascular clinics, OSA is found in about 40–80% of patients with hypertension, heart failure, coronary artery disease, atrial fibrillation, or stroke. Meta-analyses link OSA to nearly twice the risk of cardiovascular disease, stroke, and all-cause mortality. Continuous positive airway pressure (CPAP) therapy addresses the underlying mechanisms of OSA and enhances intermediate cardiovascular indicators. Materials and Methods: We conducted a narrative review using major medical search engines (PubMed, Embase, Cochrane) to examine recent statements, meta-analyses, large cohorts, and key trials. The review focused on the cardiovascular burden of sleep apnea and its pathophysiology—including arrhythmic, hemodynamic, vascular, and coagulation aspects—as well as the effects of CPAP on intermediate cardiovascular outcomes. We aimed to provide a synthesised overview of current cardiovascular evidence related to the burden and mechanisms of OSA, and to summarise the effects of continuous positive airway pressure (CPAP) on intermediate and clinical cardiovascular outcomes. Results: Intermittent hypoxia, sleep fragmentation, and major negative fluctuations in intrathoracic pressure create a clear pathway leading to adverse cardiovascular outcomes. This occurs through mechanisms like sympathetic activation, RAAS activation, endothelial dysfunction, oxidative stress, and inflammation, linking OSA to these health issues. Studies show that greater severity of OSA correlates with higher cardiovascular risk, including increased incidence and recurrence of AF, resistant hypertension, and new cases of heart failure. CPAP effectively lowers AHI and enhances nocturnal oxygen levels, as well as intermediate cardiovascular indicators such as blood pressure, sympathetic activity, and certain aspects of ventricular function, with clinical benefits most evident in adherent patients. Conclusions: OSA is a significant, modifiable risk factor for cardiovascular disease. Routine cardiovascular care should include targeted screening for OSA, especially in cases of resistant hypertension, atrial fibrillation, and heart failure, along with timely sleep testing and adherence-focused CPAP therapy, in addition to traditional risk-reduction methods. Full article

In industrial settings, defect detection using deep learning typically requires large numbers of defective samples. However, defective products are rare on production lines, creating a scarcity of defect samples and an overabundance of samples that contain only background. We introduce ImbDef-GAN, a sample imbalance generative framework, to address three persistent limitations in defect image generation: unnatural transitions at defect background boundaries, misalignment between defects and their masks, and out-of-bounds defect placement. The framework operates in two stages: (i) background image generation and (ii) defect image generation conditioned on the generated background. In the background image-generation stage, a lightweight StyleGAN3 variant jointly generates the background image and its segmentation mask. A Progress-coupled Gated Detail Injection module uses global scheduling driven by training progress and per-pixel gating to inject high-frequency information in a controlled manner, thereby enhancing background detail while preserving training stability. In the defect image-generation stage, the design augments the background generator with a residual branch that extracts defect features. By blending defect features with a smoothing coefficient, the resulting defect boundaries transition more naturally and gradually. A mask-aware matching discriminator enforces consistency between each defect image and its mask. In addition, an Edge Structure Loss and a Region Consistency Loss strengthen morphological fidelity and spatial constraints within the valid mask region. Extensive experiments on the MVTec AD dataset demonstrate that ImbDef-GAN surpasses existing methods in both the realism and diversity of generated defects. When the generated data are used to train a downstream detector, YOLOv11 achieves a 5.4% improvement in mAP@0.5, indicating that the proposed approach effectively improves detection accuracy under sample imbalance. Full article

The topic of the present study is the aerodynamic performance of a Natural Laminar Flow (NLF) wing for UAVs at low speed. The basis is a thoroughly tested NLF airfoil in the wind tunnel of NASA which is well-customized for light aircrafts. The aim of this work is the numerical verification that a typical wing design (tapered with moderate aspect ratio and wash-out), being constructed out of aerodynamically highly efficient NLF airfoils during cruise, can deliver high aerodynamic loading under minimal freestream turbulence as well as realistic atmospheric conditions of intermediate turbulence. Thus, high mission flexibility is achieved, e.g., short take off/landing capabilities on the deck of ship where moderate air turbulence is prevalent. Special attention is paid to the effect of the Wing Tip Vortex (WTV) under minimal inflow turbulence regimes. The flight conditions are take off or landing at moderate Reynolds number, i.e., one to two millions. The numerical simulation is based on an open source CFD code and parallel processing on a High Performance Computing (HPC) platform. The aim is the identification of both mean flow and turbulent structures around the wing and subsequently the formation of the wing tip vortex. Due to the purely three-dimensional character of the flow, the turbulence is resolved with advanced modeling, i.e., the Improved Delayed Detached Eddy Simulation (IDDES) which is well-customized to switch modes between Delayed Detached Eddy Simulation (DDES) and Wall-Modeled Large Eddy Simulation (WMLES), thus increasing the accuracy in the shear layer regions, the tip vortex and the wake, while at the same time keeping the computational cost at reasonable levels. IDDES also has the capability to resolve the transition of the boundary layer from laminar to turbulent, at least with engineering accuracy; thus, it serves as a high-fidelity turbulence model in this work. The study comprises an initial benchmarking of the code against wind tunnel measurements of the airfoil and verifies the adequacy of mesh density that is used for the simulation around the wing. Subsequently, the wing is positioned at near-stall conditions so that the aerodynamic loading, the kinematics of the flow and the turbulence regime in the wing vicinity, the wake and far downstream can be estimated. In terms of the kinematics of the WTV, a thorough examination is attempted which comprises its inception, i.e., the detachment of the boundary layer on the cut-off wing tip, the roll-up of the shear layer to form the wake and the motion of the wake downstream. Moreover, the effect of inflow turbulence of moderate intensity is investigated that verifies the bibliography with regard to the performance degradation of static airfoils in a turbulent atmospheric regime. Full article

This paper introduces a modified soft margin optimal hyperplane (MSMOH) algorithm, which enhances the linear separating properties of support vector machines (SVMs) by placing higher penalties on large misclassification errors. This approach improves margin symmetry in both balanced and asymmetric data distributions. The research is divided into two main stages. The first stage evaluates MSMOH for synthetic data classification and its application in heart disease diagnosis. In a cross-validation setting with unknown data, MSMOH demonstrated superior average performance compared to the standard soft margin optimal hyperplane (SMOH). Performance metrics confirmed that MSMOH maximizes the margin and reduces the number of support vectors (SVs), thus improving classification performance, generalization, and computational efficiency. The second stage applies MSMOH as a novel synthesis algorithm to design a neural associative memory (NAM) based on a recurrent neural network (RNN). This NAM is used for fault diagnosis in fossil electric power plants. By promoting more symmetric decision boundaries, MSMOH increases the accurate convergence of 1024 possible input elements. The results show that MSMOH effectively designs the NAM, leading to better performance than other synthesis algorithms like perceptron, optimal hyperplane (OH), and SMOH. Specifically, MSMOH achieved the highest number of converged input elements $\left(1019\right)$ and the smallest number of elements converging to spurious memories $\left(5\right)$. Full article

Dragonflies and damselflies are important indicator species for quality and health of (semi-)aquatic habitats. Hitherto, 78 species of Odonata have been reported for Austria. Ecological data, Red List assessments, and a dragonfly association index exist, but population- and species-level genetic data are largely lacking. In this study, we establish a comprehensive reference DNA barcode library for Austrian dragonflies and damselflies based on the standard barcoding marker COI. Because of the increasing significance of environmental DNA (eDNA) analyses, we also sequenced a segment of the mitochondrial 16S rRNA gene, a marker often used in eDNA metabarcoding approaches. In total, we provide 786 new COI barcode sequences and 867 new 16S sequences for future applications. Sequencing success was >90 percent for both markers. Identification success was similar for both markers and exceeded 90 percent. Difficulties were only encountered in the genera Anax Leach, 1815, Chalcolestes Kennedy, 1920, Coenagrion Kirby, 1890 and Somatochlora Selys, 1871, with low interspecific genetic distances and, consequently, BIN (barcode index number) sharing. In Anax, however, individual sequences clustered together in species-specific groups in the COI tree. Irrespective of these challenges, the results suggest that both markers perform well within most odonate families in terms of sequencing success and species identification and can be used for reliably delimiting Austrian species, monitoring, and eDNA approaches. Full article

Despite the advances on data analysis methodologies in the last decades, most of the traditional regression methods cannot be directly applied to large-scale data. Although aggregation methods are especially designed to deal with large-scale data, their performance may be strongly reduced in ill-conditioned problems (due to collinearity issues). This work compares the performance of a recent approach based on normalized entropy, a concept from information theory and info-metrics, with bagging and magging, two well-established aggregation methods in the literature, providing valuable insights for applications in regression analysis with large-scale data. While the results reveal a similar performance between methods in terms of prediction accuracy, the approach based on normalized entropy largely outperforms the other methods in terms of precision accuracy, even considering a smaller number of groups and observations per group, which represents an important advantage in inference problems with large-scale data. This work also alerts for the risk of using the OLS estimator, particularly under collinearity scenarios, knowing that data scientists frequently use linear models as a simplified view of the reality in big data analysis, and the OLS estimator is routinely used in practice. Beyond the promising findings of the simulation study, our estimation and aggregation strategies show strong potential for real-world applications in fields such as econometrics, genomics, environmental sciences, and machine learning, where data challenges such as noise and ill-conditioning are persistent. Full article

The need for new, more effective drugs to treat cancer, infectious diseases, various parasitic infestations, and metabolic disorders requires innovative approaches to the design of promising molecules. One of these areas is the creation of hybrid structures. Lupane triterpenoids are of significant interest for such research due to their high abundance in natural sources and their renewable nature, their molecular architecture, presence of several easily modifiable functional groups, enantiomeric purity, broad spectrum of biological activity, and low toxicity. Active research into the biological properties of new pentacyclic triterpenoid derivatives, not only of the lupane series but also of the oleonane and ursane series, is evidenced by the large number of reviews and experimental studies devoted to this topic. Our interest in the modification of lupanoids stems not only from the search for biologically active compounds but also from the development of functional materials. However, the materials science aspects of lupanoid applications are virtually unknown in literature. We have tried to fill this gap and examined the possibility of using betulin derivatives to create advanced materials. The high lipophilicity and nanoscale molecular structure of these compounds make them highly promising as chiral dopants in liquid crystal compositions and organogel components. Full article

In tightly coupled Industrial Control Systems (ICS), abnormal disturbances often propagate throughout the process, triggering a large number of time-correlated alarms that exceed the handling capacity of the operator. Consequently, a key challenge is how to leverage the directional and temporal characteristics of disturbance propagation to alleviate alarm overload. This paper proposes a delay-sensitive causal inference approach for industrial alarm analysis to address this problem. On the one hand, time delay estimation is introduced to precisely align the responses of two sensor sequences to disturbances, thereby improving the accuracy of causal relationship identification in the temporal domain. On the other hand, a multi-scale subgraph fusion strategy is designed to address the inconsistency in causal strength caused by disturbances of varying intensities. By integrating significant causal subgraphs from multiple scenarios into a unified graph, the method reveals the overall causal structure among alarm variables and provides guidance for alarm mitigation. To validate the proposed method, a case study is conducted on the Tennessee Eastman Process. The results demonstrate that the approach identifies causal relationships more accurately and reasonably and can effectively reduce the number of alarms by up to 51.6%. Full article

Monocular depth estimation is an essential component in computer vision that enables 3D scene understanding, with critical applications in autonomous driving and augmented reality. This paper proposes a lightweight self-supervised framework from single RGB images for multi-scale feature extraction and artifact elimination in monocular depth estimation (MsGf). The proposed framework first designs a Cross-Dimensional Multi-scale Feature Extraction (CDMs) module. The CDMs module combines parallel multi-scale convolution with sequential feature convolutions to achieve multi-scale feature extraction with minimal parameters. Additionally, a Sobel Edge Perception-Guided Filtering (SEGF) module is proposed. The SEGF module uses the Sobel operator to decompose the features into horizontal direction features and vertical direction features, and then generates the filter kernel through two steps of filtering to effectively suppress artifacts and better capture structural and edge features. A large number of ablation experiments and comparative experiments on the KITTI and Make3D datasets demonstrate that the MsGf with only 0.8 M parameters can achieve better performance than the current most advanced methods. Full article

Chronickidney disease (CKD) poses a major global public health challenge, driven by a complex pathogenesis involving multiple interconnected processes—including metabolic disturbances, chronic inflammation, oxidative stress, endoplasmic reticulum stress, and ferroptosis—which collectively contribute to progressive and often irreversible loss of renal function. Although current standard therapies can ameliorate CKD progression, a substantial number of patients still advance to end-stage renal disease, highlighting the urgent need for innovative treatment strategies. Natural products have shown great promise in the prevention and management of CKD, largely attributable to their multi-target and multi-pathway synergistic effects. This review systematically outlines the core pathogenic mechanisms underlying CKD and elucidates the molecular mechanisms through which bioactive natural compounds exert renoprotective effects. Despite robust preclinical evidence, the clinical translation of these compounds remains hindered by limitations such as poor bioavailability and a lack of large-scale clinical trials. Moving forward, research should prioritize clinical translation of these compounds, aiming to provide novel therapeutic perspectives for CKD management. Full article

(1) Background: Water buffaloes (Bubalus bubalis) are important for dairy and meat production. Up to now, genomic analysis has focused on female subjects, leaving the Y chromosome essentially unknown. Advances in third-generation sequencing (TGS) made it possible to improve the study of complex genome sequences, such as buffalo and other mammalian species including humans. (2) Methods: In this study, we applied TGS-based long-read sequencing to generate, in one step, high-quality whole-genome sequences, which can take full advantage of a rapid bioinformatic pipeline, such as that described in the companion paper. (3) Results: Five male buffalo genomes have been fully sequenced at relatively high depth (20–40×) which, combined with the read length typical of TGS, provide the basis for important insights into male-specific genetic traits, including those linked to meat and milk production. (4) Conclusions: With the use of TGS technologies, we offer a complete strategy for fast, one-step genome sequencing which can also be applied to other farm animals with a comparably large genome. This approach can help in revealing genetic features characteristic of an animal individual beyond the simple assessment of a number of SNPs or other known sequence variations, thus supporting improved genetic selection for dairy productivity and future research on genetic variability in buffalo breeds. Full article

In most vertebrates, males and females display distinct reproductive behaviors. Some fish can change their sexual phenotype at various life stages, which involves alterations in their gonadal sex and changes in their reproductive behavior to align with the new gonadal identity. Although the sex reversal phenomenon in reproductive behavior is well documented, the underlying mechanisms in the brains of these fish remain largely unknown. In the present study, we investigated the roles of the thyroid hormone (triiodothyronine (T3)) in the Mozambique tilapia as a potential regulator of male-specific nest-building behavior and gonadotropin-releasing hormone-3 (GnRH3) neurons, the regulatory neurons of male reproductive behavior, in the terminal nerve (TN) ganglion. T3 injection successfully induced nest-building behavior in mature female fish. T3 injection significantly elevated serum T3 concentrations in treated animals compared with those in controls. Through organotypic culture of brain slices that included the TN region, we demonstrated that T3 could stimulate an increase in the number of GnRH3 neurons, and the effect was inhibited by a thyroid hormone receptor (TR) inhibitor. Additionally, TRβ co-expression was observed in GnRH3 neurons. These findings highlight the crucial roles of T3 and GnRH3 in sex reversal processes within the fish brain. Full article

Climate change is increasing the frequency of extreme weather events, posing critical challenges for the resilience of specialized transportation services (STSs) that provide essential mobility for people with disabilities. In the South Korean context, heatwaves, cold spells, and heavy rainfall are particularly relevant because they directly affect health risks, trip demand, and operational reliability, making them central stressors for evaluating STS resilience in Busan. This study examines STS resilience in Busan, South Korea, focusing on three weather stressors: heatwaves, cold spells, and heavy rainfall. Large-scale operational data from the STSs of Busan were analyzed using the 4R (robustness, rapidity, redundancy, and resourcefulness) framework to classify daily service performance into distinct profiles. The analysis revealed that heatwaves coincided with reduced trip demand and shorter waiting times, yet this apparent stability reflected demand suppression rather than genuine robustness. Heavy rainfall produced the most severe disruptions, with longer and more variable waiting times that exacerbated inequities across users. Cold spells were associated with rapid recovery and the preservation of critical trips, although the small number of cases limits broader interpretation. These findings indicate that resilience in STSs is not uniform but event-specific, offering policy insights for strengthening operational stability and promoting equity in accessible transport. Full article

Air pollution poses a significant threat to public health and the environment, particularly fine particulate matter (PM2.5). Machine learning (ML) models have proven their accuracy in classifying and predicting air pollution levels. This research trains and compares the performance of eight machine learning regression models on a time series air quality dataset containing data from 12 dispersed air quality stations in Kuwait, to predict the PM2.5 Air Quality Index (AQI). After cleaning then trimming the large dataset to about 13.4% of its original size, we performed thorough data visualization and analysis of the dataset to identify important patterns. Next, in a set of five experiments exploring feature pruning, the tree-based models, namely Gradient Boosting and AdaBoost, generated mean square errors below 1.5 and $R^2$ numbers above 0.998, outperforming the other ML models. By integrating meteorological data, pollution source information, and geographical factors specific to Kuwait, these models provide a precise prediction of air quality levels. This research contributes to a deeper understanding and visualization of Kuwait’s air pollution challenges, and draws some public policy recommendations to mitigate environmental and health impacts. Full article

The Aloe vera industry discards large amounts of outer leaf tissue (“shavings”), creating an opportunity to repurpose this byproduct as a sustainable fertilizer. This study evaluated whether aloe shavings can serve as a plant-based alternative to compost in organic Aloe vera production. A field trial in the Lower Rio Grande Valley of Texas tested three treatments: aloe shavings (applied to supply 39 kg N ha⁻¹), organic compost (39 kg N ha⁻¹), and a non-fertilized control. Laboratory incubations further assessed nitrogen mineralization and microbial respiration. Aloe shavings significantly enhanced vegetative growth: leaf number increased from 5.7 to 12.3 leaves per plant (+115% over the season), and leaf length rose from 20 to 32 cm, with the greatest gains in September and March (p < 0.05). At harvest, plants receiving aloe shavings produced 456 g total leaf weight and 151 g gel weight per plant, compared to 375 g and 108 g in the control. Incubations showed initial nitrogen immobilization (negative mineralization) but subsequent slow release, while microbial respiration was higher in compost (2.3 mg CO₂-C kg⁻¹ day⁻¹) than aloe shavings (1.4 mg CO₂-C kg⁻¹ day⁻¹). These results highlight aloe shavings as a low-cost, slow-release organic amendment that reduces waste, supports circular economy practices, and enhances Aloe vera growth without mineral nitrogen addition. Full article