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NetEPD: A Network-Based Essential Protein Discovery Platform

Tsinghua science and Technology 2020年第4期25卷 542-552页

作者： Jiashuai Zhang Wenkai Li Min Zeng Xiangmao Meng Lukasz Kurgan Fang-Xiang Wu Min Li the School of Computer Science and Engineering Central South UniversityChangsha 410083China the Department of Computer Science Virginia Common-wealth UniversityRichmondVA 23284-2512USA the Division of Biomedical Engineering and Department of Mechanical Engineering University of SaskatchewanSaskatoonS7N 5A9Canada

Proteins drive virtually all cellular-level *** proteins that are critical to cell proliferation and survival are defined as *** essential proteins are implicated in key metabolic and regulatory networks,and are important in the context of rational drug design *** computational identification of the essential proteins benefits from the proliferation of publicly available protein interaction *** have developed several algorithms that use these interaction datasets to predict essential ***,a comprehensive web platform that facilitates the analysis and prediction of essential proteins is *** this study,we design,implement,and release Net EPD:a network-based essential protein discovery *** resource integrates data on Protein–Protein Interaction(PPI)networks,gene expression,subcellular localization,and a native set of essential *** also computes a variety of node centrality measures,evaluates the predictions of essential proteins,and visualizes PPI *** comprehensive platform functions by implementing four activities,which include the collection of datasets,computation of centrality measures,evaluation,and *** results produced by Net EPD are visualized on its website,and sent to a user-provided email,and they are available to download in a parsable *** platform is freely available at http://***/netepd.

关键词： essential proteins centrality data integration evaluation visualization

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Machine Learning Techniques for Predictive Maintenance in Industrial IoT Systems

Machine Learning Techniques for Predictive Maintenance in In...

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science, Technology, engineering and Management (ICSTEM), International Conference on

作者： G. Nixon Samuel Vijayakumar Vipin Sharma S. Pradeep Pratheeba C T. Suresh Balakrishnan Ramprasad Jat Department of Physics R.M.K. Engineering College Kavaraipettai Tamil Nadu India Department of Mechanical Engineering Medi-Caps University Indore Madhya Pradesh India Department of Electronics and Communication Engineering S. A. Engineering College Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Nandha Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Computer Engineering Government Engineering College Jhalawar Rajasthan India

ISBN: (数字)9798350376913

ISBN: (纸本)9798350388473

The purpose of this exploration is to probe implicit operations of machine literacy ways for prophetic conservation within the environment of Industrial Internet of Effects (IIoT) grounded systems. As a result of the explosion of detector data in artificial settings, prophetic conservation has developed as an essential system for reducing time-out and maximizing the performance of means. The purpose of this study is to probe a variety of machine literacy algorithms, including retrogression, bracket, and anomaly discovery, which are specifically designed to examine streaming detector data and anticipate faults in outfits before they do. In addition, it investigates the difficulties that are connected with the perpetration of prophetic conservation results in IIoT systems. These difficulties include problems with data quality, the interpretability of models, and the scalability of the results. The purpose of this study is to give perceptivity into the utility and limitations of colorful machine learning approaches in the process of perfecting the trustability and effectiveness of artificial ministry. These perceptivity are handed through a complete examination of being exploration and case studies.

关键词： Scalability Machine learning Detectors Explosions Internet Environmental management Probes

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Machine Learning-based Prediction of FGFR Kinase Inhibitors: A comparative study and implications for Targeted Cancer Treatment

Machine Learning-based Prediction of FGFR Kinase Inhibitors:...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Md Adil Ganesh Karnati Sujitha D Mechanical Engineering National Institute of Technology Kurukshetra Kurukshetra India Department of Biotechnology Manipal Institute of Technology Manipal India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

Fibroblast Growth Factor receptor alterations are particularly prevalent in certain cancer types, such as breast cancer, lung cancer, and cholangiocarcinoma. In some cases, specific FGFR alterations have been identified as therapeutic targets. FGFR kinase inhibitors poses a promising role for targeted cancer therapy in patients with Fibro Blast Growth Receptor alterations, predicting ligands inhibiting receptor is a crucial task in drug discovery. In this study we present different Machine Learning techniques to screen the compounds along various databases and to predict the pIC50 values based on highly performed Machine Learning technique. The studies reveal that the usage of Random Forest gives best yield over classification and regression compared to different techniques.

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An Automatic Measurement Method of Laser-Cladding Coating Defects Based on Deep Learning

SSRN

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SSRN 2024年

作者： Luo, Longmei Lian, Guofu Zhang, Xueming Feng, Meiyan Lan, Ruqing School of Mechanical and Automotive Engineering Fujian University of Technology Fujian Fuzhou350118 China School of Computer Science and Mathematics Fujian University of Technology Fujian Fuzhou350118 China

A DeepSA-UNet model for automatic recognition and segmentation of defects in laser cladding coatings was proposed in the work. This model integrated dual-attention residual and deep guidance modules. First, a dual-attention residual module was introduced at the encoder end's bottleneck layer. This addressed the issue of ignored detailed information due to the encoder's continuous pooling and downsampling. This enhancement significantly improved the network's capability for feature representation. Second, a deep guidance module was introduced to prevent the loss of semantic information like defect location and category during transmission in the original network. This module integrated deep semantic information into the shallow feature layer. Third, a feature fusion module was introduced in the decoder to balance deep and shallow feature differences. This module increased the feature maps' ability to express details and location information. Finally, a joint optimization strategy was adopted using Dice loss and Focal loss functions. This strategy addressed the imbalance between background and defect area proportions. Experimental results showed that the model achieved 94.79% of MIOU, 96.87% of MR, and 97.64% of MP in defect recognition. MIOU, MR, and MP improved by 2.02, 2.01, and 2.78%, respectively, compared to the original UNet network. An automatic measurement method for coating defect data was designed based on the DeepSA-UNet model. This method converted the predicted label map's pixel information into defect rate data. The results indicated an analysis accuracy above 95%, with significantly increased measurement efficiency. This method provides a fast, accurate, and intelligent solution for automatically measuring and analyzing laser cladding coating defects. © 2024, The Authors. All rights reserved.

关键词： Laser cladding

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Cable-Driven Parallel Robot for Warehouse Monitoring Tasks

Cable-Driven Parallel Robot for Warehouse Monitoring Tasks

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IEEE Annual Computing and Communication Workshop and Conference (CCWC)

作者： Armaun Zargari Zahir Abram Castrejon Dongbin Kim Paul Y. Oh Department of Mechanical Engineering University of Nevada Las Vegas Las Vegas NV USA Department of Electrical Engineering and Computer Science United States Military Academy West Point NY USA

Present manufacturing processes demand innovative methods for inventory management. Unmanned aerial and ground vehicles (UAVs and UGVs) have been shown to be potential solutions as heightened mobility and wide areas of operation are provided. However, they struggle with battery life, physical restrictions, and highly trained operators. A feasible alternative is offered through cable driven parallel robots. They provide low inertia, high payload-to-weight ratios, and a large operation volume. In order to design any given CDPR system, the static behavior must be understood and is presented. This paper also integrates actuation, camera stabilization mechanisms, and computer vision into a singular CDPR system. Being modular and reconfigurable, this system may quickly and accurately scan storage racks within a warehouse.

关键词： Parallel robots computer vision Manufacturing processes Conferences Robot vision systems Inventory management Land vehicles

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Image Stabilization for Hololens Camera in Remote Collaboration

arXiv

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arXiv 2023年

作者： Senthil, Gowtham Krishnan, Siva Vignesh Lakshmanan, Annamalai Kissling, Florence Department of Information Technology & Electrical Engineering ETH Zürich Switzerland Department of Mechanical & Process Engineering ETH Zürich Switzerland Department of Computer Science ETH Zürich Switzerland

With the advent of new technologies, Augmented Reality (AR) has become an effective tool in remote collaboration. Narrow field-of-view (FoV) and motion blur can offer an unpleasant experience with limited cognition for remote viewers of AR headsets. In this article, we propose a two-stage pipeline to tackle this issue and ensure a stable viewing experience with a larger FoV. The solution involves an offline 3D reconstruction of the indoor environment, followed by enhanced rendering using only the live poses of AR device. We experiment with and evaluate the two different 3D reconstruction methods, RGB-D geometric approach and Neural Radiance Fields (NeRF), based on their data requirements, reconstruction quality, rendering, and training times. The generated sequences from these methods had smoother transitions and provided a better perspective of the environment. The geometry-based enhanced FoV method had better renderings as it lacked blurry outputs making it better than the other attempted approaches. Structural Similarity Index (SSIM) and Peak Signal to Noise Ratio (PSNR) metrics were used to quantitatively show that the rendering quality using the geometry-based enhanced FoV method is better. Link to the code repository - https://***/MixedRealityETHZ/ImageStabilization © 2023, CC BY.

关键词： Augmented reality

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Research and design of capacitance plug-in assembly manipulator 2

Research and design of capacitance plug-in assembly manipula...

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2nd International Conference on Electronics, Electrical and Information engineering, ICEEIE 2022

作者： Qin, Jiangtao Wang, Jirong Xiao, Yihao Li, Jun College of Mechanical and Electrical Engineering Qingdao University Qingdao266071 China College of Computer Science and Technology Qingdao University Qingdao266071 China Weihai Innovation Research Institute Qingdao University Weihai264200 China

In order to improve the efficiency of PCB (Printed Circuit Board) capacitor plug-in, reduce labor input and improve the accuracy of plug-in, a capacitor plug-in assembly manipulator was designed. The manipulator was mainly composed of X-axis module, Y-axis module and Z-axis module. The mechanical gripper of Z-axis module could adjust the distance between two mechanical grippers to meet the needs of different capacitance sizes. By adopting double z-axis design, the work efficiency of rectangular coordinate manipulator was improved. The addition of the X-ray axis improved the synchronization of the movement at both ends of the X and enhanced the rigidity of the structure. Siemens PLC S7-200 motion library function controlled manipulator grasp, positioning and plug-in. The efficiency of the plug-in unit was 2.5 s/board, and the manipulator could complete capacitor plug-in efficiently and accurately. The whole system has the advantages of high control accuracy, simple operation, stable operation and low noise, and has important practical significance for enterprises. © Published under licence by IOP Publishing Ltd.

关键词： Capacitance

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Estimation of Tropical Cyclone Intensity from Satellite Data Using Self Attentive – TCNN-BiGRU Approach

Estimation of Tropical Cyclone Intensity from Satellite Data...

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International Conference on Inventive Research in Computing Applications (ICIRCA)

作者： Trilok Suthar Tejas Shah Hemant Singh Pokhariya S. Ranichandra V. Kalpana K.P. Yuvaraj Gujarat Technological University Ahmedabad India L D College of Engineering Ahmedabad India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Science Dhanalakshmi Srinivasan College of Arts and Science for Women(Autonomous) Perambalur Tamilnadu India Department of Science and Humanities RMK College of Engineering and Technology Puduvoyal Thiruvallur Tamilnadu India Department of Mechanical Engineering Sri Krishna College of Engineering and Technology Coimbatore Tamilnadu India

Predicting a tropical cyclone's intensity is difficult since it need for human involvement at every stage of the process, from feature extraction through pre-processing to the integration of data from many satellites. There are many reasons why intensity estimation could be difficult, including inconsistent results, a huge quantity of data preprocessing, a difficult problem area, and concerns regarding generalizability. For the purpose of developing a hurricane classification system, this approach presents a GPU-based TCNN-BiGRU architecture. The proposed model achieves better accuracy and root-mean-square error compared to state-of-the-art methods when only satellite photos are available. To further elucidate the learning procedure, they also provide visual representations of learned features and related deconvolutions at many depths. The suggested method utilizes normalization for preprocessing, allowing for faster computations and higher quality results. PCA is used to lessen the number of dimensions and prevent model overfitting. The SA-TCNN-BiGRU Model is used to analyze the results. When compared to the CNN and GRU models, it fares exceptionally well.

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Rapid Identification of Boiling Crisis with Event-Based Visual Streaming Analysis

SSRN

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SSRN 2023年

作者： Lu, Dale Suh, Youngjoon Won, Yoonjin Department of Computer Science University of California 3019 Donald Bren Hall IrvineCA92697-3425 United States Department of Mechanical and Aerospace Engineering University of California 5200 Engineering Hall IrvineCA92617-2700 United States Department of Engineering and Computer Science University of California 4410 Engineering Hall IrvineCA92697-2700 United States

The formation of a stable vapor layer during boiling processes triggers the boiling crisis known as the critical heat flux. While efforts have been made to build a predictive model for critical heat flux on live images, images captured by conventional frame-cameras show limitations when performing real-time digital inference due to significant unnecessary information. A new type of imaging technique called neuromorphic event imaging captures individual pixel-wise brightness changes, thereby recording motion-based phenomena in a sparse and efficient manner. In this study, we develop neural network models capable of predicting the critical heat flux by utilizing neuromorphic-simulated event data. We reveal that event-based models demonstrate their proficiency and time efficiency in learning patterns of the boiling process from relatively small datasets. The event-based models also prove the generalizability to unseen surfaces by showing the potential for transfer learning. The results suggest that an extension of this work may prove effective in developing forecasting models that detect the incipient boiling crisis before it damages boiling surfaces. These findings highlight that event data can be effectively applied to real-time visual streaming analysis for rapid nucleation processes. © 2023, The Authors. All rights reserved.

关键词： Heat flux

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An Effective Approach to Detect and Classify Human Monkey Pox using Inception V3

An Effective Approach to Detect and Classify Human Monkey Po...

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Delhi Section Conference (DELCON), IEEE

作者： Ritu Shree Kalyan Acharjya Khushmeen Kaur Brar M. N. Nachappa Mohammed I. Habelalmateen Saurabh Aggarwal Computer Science & Application Vivekananda Global University Jaipur India Maharishi School of Engineering & Technology MUIT Uttar Pradesh India Department of Electronics and Communication Engineering Chandigarh University Mohali Punjab India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India The Islamic university Najaf Iraq Department of Mechanical Engineering Uttaranchal Institute of Technology Uttaranchal University Dehradun India

ISBN: (数字)9798331518592

ISBN: (纸本)9798331518608

The virus which generates monkeypox is now emerging as a new pandemic menace as humans overcome from COVID-19. Despite reports of incidents involving monkeypox continue to flood in on every single day, it is not believed to be as harmful or as widespread as COVID-19. The human skin infected with the monkeypox virus and that impacted region might be taken for detecting the disease early since a photograph might be utilized for learning additional information about the ailment. But as of now, there hasn't been an accurate accessible Monkeypox dataset which can be utilized for training and testing DL models. The pictures in this collection can be employed for developing and employing DL techniques with greater certainty. Additionally, we suggested and assessed a methodology, an improved DL based CNN model. The present research proposed a deep CNN model that could accurately identify monkeypox disease with accuracies of 88.82%, 75% and 85.29% for bayes net, DT and RF respectively considering the original dataset. Precision, recall and f score values for class 0 are 0.87, 0.94 and 0.908 in case of RF. While for class 1, the values are 0.90, 0.81 and 0.85 respectively.

关键词： Radio frequency COVID-19 Training Accuracy Pandemics Neural networks Skin Bayes methods Diseases Testing

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