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Left ventricular global longitudinal strain and diagnostic yield of genetic testing in hypertrophic cardiomyopathy in a multicenter registry analysis
Owl airfoil aerodynamic noise sources and performance compared to hawk and NACA0012 airfoils for low Reynolds applications
Abstract The investigation of low Reynolds number flows is crucial, particularly for applications such as wind turbines and small-scale UAVs. This study compares the owl airfoil with the NACA0012 and hawk airfoils through wind tunnel testing, utilizing pressure sensors and force balance to examine the aerodynamic noise sources and aerodynamic performance of the airfoils. A total of six airfoils were investigated at various Reynolds numbers from 44 × 103 to 160 × 103, considering the glide flight envelop for various owl species. Wind tunnel test results showed higher Cl and L/D ratio for the owl airfoil, outperforming the NACA0012 and hawk airfoils by up to 6.7% and 44.1%, respectively. This is attributed to the optimal camber of the owl airfoil compared to the two other airfoils, and its lower relative thickness too. This helps flight with this airfoil at lower AOA, which reduces noise. In addition, the stall angle for owl airfoil was ranging from 8° to 15° higher than NACA0012 airfoil, which stalled at 10°-11°, and higher than 6° to 12° hawk stall angle. This feature allowed owls to perform efficient flights in glide phase at lower AOA that minimized the main aerodynamic noise sources such as the separations and pressure fluctuations. Pressure measurements represented the initiation of LSB for the owl airfoil at around AOA = 6° to 10° at different Reynolds numbers, while the hawk airfoil shown the presence of LSB starting from AOA = 0°. A detailed analysis of the pressure fluctuations showed that the owl airfoil had fewer sources of aerodynamic noise, such as LSB, stall phenomena, and separated shear layers, on both its upper and lower surfaces, compared to other types of airfoils. Additionally, an analysis in the frequency domain showed that the amplitude of FFT for NACA0012 and hawk airfoil is generally higher compared with the owl airfoil. These findings shed light on the aerodynamic characteristics and noise generation mechanisms of owl airfoil for future research and design considerations.
Age associated SARS-CoV-2 immune responses provide insights into population immunity over four years since the COVID-19 pandemic
Quantitative description of internal 3D structure of a geological sample using algebraic topology methods
Dual smart sensor data-based deep learning network for premature infant hypoglycemia detection
Airport microgrid control using integral-of-absolute-error having high renewable penetration
Abstract The serious concern about the continuous depletion of fossil fuels and their environmental impact has drawn the focus of researchers worldwide, towards the renewable energy sector. Renewable resources are being penetrated into microgrids on a larger scale in order to manage sustainable financial and environmental viability. Increased penetration of renewable resources has increased the operational challenges associated with it. Moreover, the stochastic nature of renewable resources with the combined effect of load disturbances, causes frequency deviation at a considerable scale. Minimization of frequency deviation is a crucial task for maintaining the stability of airport microgrid (AP $${\mu }G_{d}$$ ). To deal with the aforementioned operational challenges, in this article, frequency deviation is managed by designing the PID controller employing integral absolute error (IAE) for an AP $${\mu }G_{d}$$ system. Firstly, the overall transfer function (OATrFn) for AP $${\mu }G_{d}$$ system is obtained by modeling and combining each component. For easier and more efficient analytical study cum controller design, the first order plus delay time (FOPDT) model is obtained for the AP $${\mu }G_{d}$$ system. A detailed analysis in terms of frequency deviation and controller effort is carried out for AP $${\mu }G_{d}$$ system with and without a PID controller to validate the impact of a PID controller in maintaining the frequency stability of AP $${\mu }G_{d}$$ system. Further, a comparative study for the same system is performed considering the integral time absolute error (ITAE) as a main design criterion. Tabular data and various plots validate the superiority of IAE driven PID controller over ITAE-PID controller to maintain frequency stability. Furthermore, a bar plot is plotted to provide a comparative analysis among various error indices in the form of frequency deviations.
A multi-modal graph-based framework for Alzheimer’s disease detection
Abstract We propose a compositional graph-based Machine Learning (ML) framework for Alzheimer’s disease (AD) detection that constructs complex ML predictors from modular components. In our directed computational graph, datasets are represented as nodes $$n_i$$ , and deep learning (DL) models are represented as directed edges $$n_i \rightarrow n_j$$ , allowing us to model complex image-processing pipelines $$n_1 \rightarrow n_2 \rightarrow n_3... \rightarrow n_T$$ as end-to-end DL predictors. Each directed path in the graph functions as a DL predictor, supporting both forward propagation for transforming data representations, as well as backpropagation for model finetuning, saliency map computation, and input data optimization. We demonstrate our model on Alzheimer’s disease prediction, a complex problem that requires integrating multimodal data containing scans of different modalities and contrasts, genetic data and cognitive tests. We built a graph of 11 nodes (data) and 14 edges (ML models), where each model has been trained on handling a specific task (e.g. skull-stripping MRI scans, AD detection,image2image translation, ...). By using a modular and adaptive approach, our framework effectively integrates diverse data types, handles distribution shifts, and scales to arbitrary complexity, offering a practical tool that remains accurate even when modalities are missing for advancing Alzheimer’s disease diagnosis and potentially other complex medical prediction tasks.
Optimal control analysis to reduce the health complexity of co-infection with COVID-19 and kidney disease
Anticipatory and reactive mechanisms of habituation to visual distractors
Abstract Human attention can rapidly habituate to irrelevant and repetitive visual distractors. Although this phenomenon is well-documented in behavioral studies, the neural mechanisms involved remain largely unknown. In this study, we investigated the neural correlates of attentional habituation using scalp electroencephalography (EEG). Participants performed a visual discrimination task while intermittently presented with salient distractor stimuli. The cost in reaction times (RT) associated with the distractor exhibited the typical time course of habituation, decreasing as a function of repeated exposure to the distractor. We found that this habituation coincided with both reactive and proactive changes in EEG activity. Post-distractor reactive EEG components emerged gradually over the course of the experiment, likely reflecting the operation of an inhibitory network aimed at suppressing distractor interference in the main task. Pre-stimulus α rhythms gradually tuned their power peaks to the anticipated moment of the distractor, suggesting the involvement of predictive inhibitory models based on prior experience with the distractor. Collectively, our findings suggest that attentional habituation involves multi-stage interacting mechanisms that anticipate the occurrence of a distractor and facilitate the rapid reallocation of attentional resources away from the distractor.
Endothelial cells expressing CPE and vWF are involved in the Immunopathogenesis of primary biliary cholangitis
L-Ascorbic acid preferentially kills KRAS mutant pancreatic cancer cells through DNA damage
ESG ratings empower high-quality development of logistics enterprises through digital transformation and green innovation
Smart traceable framework for transportation of transplantable organs using IPFS, iot, and smart contracts
Termite antimicrobial defense through interaction with symbiotic microorganisms in nest materials
Abstract Social insects build robust nests to physically defend their colonies against attacks by predators and the intrusion of parasites and pathogens. While many previous studies on termite nests have focused on their physical defense functions, their nests also harbor various microorganisms that play a role in maintaining the colony’s hygienic environment. In this study, we report a dynamic defense mechanism of termite nests, where termites bury pathogen-infected corpses into the nest material, enhancing the antimicrobial defense provided by symbiotic bacteria inhabiting the nest. Termites buried pathogen-infected corpses, which could pose a high pathogenic risk, into the nest material, while they cannibalized corpses that were non-infected. In nest material where corpses were buried, the abundance of Streptomyces , antibiotic-producing bacteria, increased and enhanced the antifungal activity of the nest material. Furthermore, this Streptomyces inhibited the growth of termite pathogens and improved worker survival rates in the presence of these pathogens. These results suggest that the interaction between termites and nest-associated symbiotic bacteria, facilitated by corpse burial, contributes to the continuous maintenance of nest hygiene. This study elucidates the function of the nest as a 'living defensive wall’ and enhances our understanding of the dynamic pathogen-defense systems employed by social insects.
A 10-year retrospective study on the prevalence and biometric characteristics of high myopia in Chinese cataract surgery candidates
Advanced thermal management in X-ray systems using magnetohydrodynamic nanofluids and Cattaneo-Christov heat flux model
Nanofabrication of all-dielectric metasurfaces through pulsed thermal scanning probe lithography
Scalable geometric learning with correlation-based functional brain networks
A comprehensive evaluation of life sciences data resources reveals significant accessibility barriers
Abstract Individuals with disabilities participate notably less in the scientific workforce. While the reasons for this discrepancy are multifaceted, accessibility of knowledge is likely a factor. In the life sciences, digital resources play an important role in gaining new knowledge and conducting data-driven research. However, there is little data on how accessible essential life sciences resources are for people with disabilities. Our work is the first to comprehensively evaluate the accessibility of life sciences resources. To understand the current state of accessibility of digital data resources in the life sciences, we pose three research questions: (1) What are the most common accessibility issues?; (2) What factors may have contributed to the current state of accessibility?; and (3) What is the potential impact of accessibility issues in real-world use cases? To answer these questions, we collected large-scale accessibility data about two essential resources: data portals (n = 3,112) and journal websites (n = 5,099). Our analysis shows that many life sciences resources contain severe accessibility issues (74.8% of data portals and 69.1% of journal websites) and are significantly less accessible than US government websites, which we used as a baseline. Focusing on visual impairment, we further conducted a preliminary study to evaluate three data portals in-depth with a blind user, unveiling the practical impact of the identified accessibility issues on common tasks (53.3% success rate), such as data discovery tasks. Based on our results, we find that simply implementing accessibility standards does not guarantee real-world accessibility of life sciences data resources. We believe that our data and analysis results bring insights into how the scientific community can address critical accessibility barriers and increase awareness of accessibility, leading to more inclusive life sciences research and education. Our analysis results are publicly available at http://inscidar.org/.