Sleep paralysis is when you're awake but powerless to move. Although the majority of occurrences are linked to extreme terror and some potentially clinically significant sufferings are connected with the case;litt...
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Scene text image super-resolution (STISR) aims at enhancing the visual clarity of a low-resolution text image for human perception or tasks like text recognition. In recent STISR work, various visual and semantic clue...
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Background: Ajuga integrifolia (Armagusa) is used as a decoction to treat high blood pressure and diabetes, widely in Ethiopia. Specific compounds for anti-hypertension activity were not identified so far. This study ...
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Background: Ajuga integrifolia (Armagusa) is used as a decoction to treat high blood pressure and diabetes, widely in Ethiopia. Specific compounds for anti-hypertension activity were not identified so far. This study aims to provide a scientific basis for the therapeutic use of A. integrifolia as an antihypertension agent. Methods: In silico studies were used to evaluate the antihypertensive components of A. integrifolia. Flavonoids identified using HPLC analysis and iridoid glycosides isolated from A. integrifolia in this study and those isolated from synonyms (A. remota and A. bractosa) were considered in the molecular docking study. Interactions were studied by using Autodock vina (1.2) on PyRx 0.8 and visualizing in 2D and 3D using ligPlot+ and Discovery studio software. Activities like vasoprotection and druglikeness properties were predicted using online servers. Results: Flavonoids such as quercetin, myricetin, and rutin were identified and quantified by HPLC analysis from different extracts of A. integrifolia. Reptoside and 8-O-acetylharpgide isolated from the aerial part of A. integrifolia. The binding energies of all 17 candidates considered in this study range from −10.2 kcal/mol to −7.5 kcal/mol and are lower than enalapril (reference drug: −5.9 kcal/ mol). The binding energies, in most case, constitute hydrogen bonding. Biological activity predicted using PASS test also showed that the flavonoids have more probability of activity than the iridoid glycosides. Druglikeness properties of the candidate molecules showed that most follow the Lipinski rule of five with few violations. Conclusion: Lower binding energies involving hydrogen bonding and predicted activities concerning hypertension confirm the traditional use of the aerial part of the medicinal plant concerned. Flavonoids: rutin, myricetin, quercetin, and kaempferol take the leading role in the antihypertensive activity of the aerial part of A. integrifolia. The iridoid glycosides studied are almos
Fortran compilers that provide support for Fortran's native parallel features often do so with a runtime library that depends on details of both the compiler implementation and the communication library, while oth...
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Graphical User Interface (GUI) testing has been a significant topic in the software engineering community. Most existing GUI testing frameworks are intrusive and can only support some specific platforms, which are qui...
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ISBN:
(纸本)9798400702174
Graphical User Interface (GUI) testing has been a significant topic in the software engineering community. Most existing GUI testing frameworks are intrusive and can only support some specific platforms, which are quite limited. With the development of dis-tinct scenarios, diverse embedded systems or customized operating systems on different devices do not support existing intrusive GUI testing frameworks. Some approaches adopt robotic arms to re-place the interface invoking of mobile apps under test and use computer vision technologies to identify GUI elements. However, some challenges remain unsolved with such approaches. First, existing approaches assume that GUI screens are fixed so that they cannot be adapted to diverse systems with different screen conditions. Second, existing approaches use XY-plane robotic arm system, which cannot flexibly simulate human testing operations. Third, existing approaches ignore the compatibility bugs of apps and only focus on the crash bugs. To sum up, a more practical approach is required for the non-intrusive scenario. In order to solve the remaining challenges, we propose a practi-cal non-intrusive GUI testing framework with visual-based robotic arms, namely ROBOTEST. ROBOTEST integrates a set of novel GUI screen and widget detection algorithm that is adaptive to detecting screens of different sizes and then to extracting GUI widgets from the detected screens. Then, a complete set of widely-used testing operations are applied with a 4-DOF robotic arm, which can more effectively and flexibly simulate human testing operations. During the app exploration, ROBOTEST integrates the specially designed Principle of Proximity-guided (PoP-guided) exploration strategy, which chooses close widgets of the previous operation targets to reduce the robotic arm movement overhead and improve exploration efficiency. Moreover, ROBOTEST can effectively detect some compat-ibility bugs beyond crash bugs with a GUI comparison on different devices of
Essential proteins with biological functions are necessary for the survival of organisms. computational recognition methods of essential proteins can reduce the workload and provide candidate proteins for biologists. ...
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Essential proteins with biological functions are necessary for the survival of organisms. computational recognition methods of essential proteins can reduce the workload and provide candidate proteins for biologists. However, existing methods fail to efficiently identify essential proteins, and generally do not fully use amino acid sequence information to improve the performance of essential protein recognition. In this work, we propose an end-to-end deep contextual representation learning framework called DeepIEP to automatically learn biological discriminative features without prior knowledge based on protein network heterogeneous information. Specifically, the model attaches amino acid sequences as the attributes of each protein node in the protein interaction network, and then automatically learns topological features from protein interaction networks by graph embedding algorithms. Next, multi-scale convolutions and gated recurrent unit networks are used to extract contextual features from gene expression profiles. The extensive experiments confirm that our DeepIEP is an effective and efficient feature learning framework for identifying essential proteins and contextual features of protein sequences can improve the recognition performance of essential proteins.
Recently,deep learning has yielded transformative success across optics and photonics,especially in optical *** neural networks (DNNs) with a fully convolutional architecture (e.g.,U-Net and its derivatives) have been...
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Recently,deep learning has yielded transformative success across optics and photonics,especially in optical *** neural networks (DNNs) with a fully convolutional architecture (e.g.,U-Net and its derivatives) have been widely implemented in an end-to-end manner to accomplish various optical metrology tasks,such as fringe denoising,phase unwrapping,and fringe ***,the task of training a DNN to accurately identify an image-to-image transform from massive input and output data pairs seems at best naive,as the physical laws governing the image formation or other domain expertise pertaining to the measurement have not yet been fully exploited in current deep learning *** this end,we introduce a physics-informed deep learning method for fringe pattern analysis (PI-FPA) to overcome this limit by integrating a lightweight DNN with a learning-enhanced Fourier transform profilometry (Le FTP) *** parameterizing conventional phase retrieval methods,the Le FTP module embeds the prior knowledge in the network structure and the loss function to directly provide reliable phase results for new types of samples,while circumventing the requirement of collecting a large amount of high-quality data in supervised learning *** by the initial phase from Le FTP,the phase recovery ability of the lightweight DNN is enhanced to further improve the phase accuracy at a low computational cost compared with existing end-to-end *** results demonstrate that PI-FPA enables more accurate and computationally efficient single-shot phase retrieval,exhibiting its excellent generalization to various unseen objects during *** proposed PI-FPA presents that challenging issues in optical metrology can be potentially overcome through the synergy of physics-priors-based traditional tools and data-driven learning approaches,opening new avenues to achieve fast and accurate single-shot 3D imaging.
Time delays occur in various engineering applications because they may be inherent in the plants or caused by networks. In this paper, we investigate the safety verification problem of time-delay systems modeled by no...
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Time delays occur in various engineering applications because they may be inherent in the plants or caused by networks. In this paper, we investigate the safety verification problem of time-delay systems modeled by nonlinear delay differential equations subject to control inputs and disturbances in their dynamics. Building upon classical control barrier functionals, we develop the notions of input-to-state safety and input-to-state safe control barrier functionals, in which input-to-state safe control barrier functionals are used to guarantee the safety of time-delay systems with control inputs and disturbances. Three examples are provided to demonstrate the proposed approach.
With the popularity and widespread use of social media platforms, such as Twitter and Facebook, massive amounts of text and image information posted by a variety of users have flooded these social media platforms. Thu...
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Detection and assessment of micro vibrations are crucial tasks in both industrial settings and daily life. However, vibration sensors attached to the target vibrator may introduce potential resonance, and wireless det...
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