56016 research outputs found
Sort by
Bioinspired low-friction surface coating with lubricant-infused spherical cavities for sustainable drag reduction
No full textThe slippery low-friction surfaces have strong potentials in various applications, including drag reduction, antifouling, and anti-icing, etc. However, conventional low-friction surfaces, such as the superhydrophobic surface (SHS) and the lubricant-infused surface (LIS), lose their slippery properties when subjected to external stimuli. The low sustainability remains the major obstacle to their practical applications, despite many attempts to enhance the durability of low-friction surfaces. Here, a marine creature-inspired surface (MIS) having lubricant-infused spherical cavities with tiny opening is proposed as a promising low-friction surface for achieving sustainable turbulent drag reduction in marine environments. The unique surface topography enables MIS to efficiently retain the infused lubricant and enhance the reduction of hydrodynamic frictional drag. The exceptional low-friction capability of the proposed MIS is demonstrated by measuring its frictional drag at high-speed flows up to 12 m/s, corresponding to the cruising velocity of a large container ship. Even in such highly turbulent flow conditions, the proposed nature-inspired MIS reduces frictional drag by up to similar to 39% compared with the bare aluminum surface, which is the best performance compared with other LIS surfaces reported in the literature. Adoption of the proposed MIS coating on marine vehicles would result in significant energy savings and environmental protection
Recent Advances in Applying Machine Learning and Deep Learning to Detect Upper Gastrointestinal Tract Lesions
No full textThe clinical application of a real-time artificial intelligence (AI) image processing system to diagnose upper gastrointestinal (GI) malignancies remains an experimental research and engineering problem. Understanding these commonly used technical techniques is required to appreciate the scientific quality and novelty of AI studies. Clinicians frequently lack this technical background, and AI experts may be unaware of such clinical relevance and implications in daily practice. As a result, there is a growing need for a multidisciplinary, international assessment of how to conduct high-quality AI research in upper GI malignancy detection. This research will help endoscopists build approaches or models to increase diagnosis accuracy for upper GI malignancies despite variances in experience, education, personnel, and resources, as it offers real-time and retrospective chances to improve upper GI malignancy diagnosis and screening. This comprehensive review sheds light on potential enhancements to computer-aided diagnostic (CAD) systems for GI endoscopy. The survey includes 65 studies on automatic upper GI malignancy diagnosis and evaluation, which are compared by endoscopic modalities, image counts, models, validation methods, and results. The main goal of this research is to assess and compare each AI method???s current stage and potential improvement to boost performance, maturity, and the possibility to open new research areas for the application of a real-time AI image recognition system that diagnoses upper GI malignancies. The findings of this study suggest that Support Vector Machines (SVM) are frequently utilized in gastrointestinal (GI) image processing within the context of machine learning (ML). Moreover, the analysis reveals that CNN-based supervised learning object detection models are widely employed in GI image analysis within the deep learning (DL) context. The results of this study also suggest that RGB is the most commonly used image modality for GI analysis, with color playing a vital role in detecting bleeding locations. Researchers rely on public datasets from 2018-2019 to develop AI systems, but combining them is challenging due to their unique classes. To overcome the problem of insufficient data to train a new DL model, a standardized database is needed to hold different datasets for the development of AI-based GI endoscopy systems
Highly Stable n-i-p Structured Formamidinium Tin Triiodide Solar Cells through the Stabilization of Surface Sn2+ Cations
No full textImproving the performance, reproducibility, and stability of Sn-based perovskite solar cells (PSCs) with n-i-p structures is an important challenge. Spiro-OMeTAD [2,2 ',7,7 '-tetrakis(N,N-di-p-methoxyphenyl-amine)9,9 '-spirobifluorene], a hole transporting material (HTM) with n-i-p structure, requires the oxygen exposure after addition of Li-TFSI [Lithium bis(trifluoromethanesulfonyl)imide] as a dopant to increase the hole concentration. In Sn-based PSC, Sn2+ is easily oxidized to Sn4+ under such a condition, resulting in a sharp decrease in efficiency. Herein, a formamidinium tin triiodide (FASnI(3))-based PSCs fabricated using DPI-TPFB [4-Isopropyl-4 '-methyldiphenyliodonium tetrakis(pentafluorophenyl)borate] instead of Li-TFSI are reported as a dopant in Spiro-OMeTAD. The DPI-TPFB enables the fabrication of PSCs with an efficiency of up to 10.9%, the highest among FASnI(3)-based PSCs with n-i-p structures. Moreover, approximate to 80% of the initial efficiency is maintained even after 1,597 h under maximum power point tracking conditions. In particular, the encapsulated device does not show any decrease in efficiency even after holding for 50 h in the 85 degrees C/85% RH condition. The high efficiency and excellent stability of PSCs prepared by doping with DPI-TPFB are attributed to not only increasing electrical conductivity by acting as a Lewis acid, but also stabilizing Sn2+ through coordination with Sn2+ on the surface of FASnI(3)
Analysis of Differences in Electrochemical Performance Between Coin and Pouch Cells for Lithium-Ion Battery Applications
No full textSmall coin cell batteries are predominantly used for testing lithium-ion batteries (LIBs) in academia because they require small amounts of material and are easy to assemble. However, insufficient attention is given to difference in cell performance that arises from the differences in format between coin cells used by academic researchers and pouch or cylindrical cells which are used in industry. In this article, we compare coin cells and pouch cells of different size with exactly the same electrode materials, electrolyte, and electrochemical conditions. We show the battery impedance changes substantially depending on the cell format using techniques including Electrochemical Impedance Spectroscopy (EIS) and Galvanostatic Intermittent Titration Technique (GITT). Using full cell NCA-graphite LIBs, we demonstrate that this difference in impedance has important knock-on effects on the battery rate performance due to ohmic polarization and the battery life time due to Li metal plating on the anode. We hope this work will help researchers getting a better idea of how small coin cell formats impact the cell performance and help predicting improvements that can be achieved by implementing larger cell formats
Efficient homomorphic encryption framework for privacy-preserving regression
No full textHomomorphic encryption (HE) has recently attracted considerable attention as a key solution for privacy-preserving machine learning because HE can apply to various areas that require to delegate outsourcing computations of user???s data. Nevertheless, its computational inefficiency still hinders its wider application. In this study, we propose an alternative to bridge the gap between the privacy and efficiency of HE by encrypting only a small amount of private information. We first derive an exact solution to HE-friendly ridge regression with multiple private variables, while linearly reducing the computational complexity of this algorithm over the number of variables. The proposed method has the advantage that it can be implemented using any HE scheme. Moreover, we propose an adversarial perturbation method that can prevent potential attacks on private variables, which have rarely been explored in HE-based machine learning studies. An extensive experiment on real-world benchmarking datasets supports the effectiveness of our method
Study on biological function of Saccharomyces cerevisiae bromodomain-containing AAA+ ATPase Yta7 using single-molecule imaging
No full textThe Implications of Metaethical Theories on Scientific and Moral Progress
No full textIt is a normative judgment that some moral changes are progressive. So is the judgment that some scientific changes are progressive. It follows that the eight rival metaethical theories that we discussed in Chapter 12 and 13 have competing implications on the two normative judgments, and that neither moral progress nor scientific progress supports the choice of moral realism over other metaethical theories. Although it is a normative judgment that some scientific changes are progressive, it is a factual judgment that those changes involve increases in problem-solutions, verisimilitude, knowledge, understanding, and evidence. Thus, error theory implies that the normative judgment is false, but not that the factual judgment is false. Finally, moral antirealists could accept that some scientific changes involve getting closer to scientific truths, but not that some moral changes involve getting closer to moral truths