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Equipment for calibrating three earth pressure cells simultaneously

AIP Conference Proceedings 2018年第1期1967卷

作者： Jiequn Liu Jinlong Liu Lei Zhu 1Department of Computer Science and Technology Hefei University Hefei 230601 China 2Department of Civil Engineering Hefei University Hefei 230601 China

An equipment for calibrating three earth pressure cells simultaneously is proposed in this article, which including the first level drum, the second level drum, the third level drum, loading platform and weighting blocks. The earth pressure cell is placed in the circular placing groove of drum, and the signal transmission line of earth pressure cell is embedded in the wire slot of drum and extended to outside of drum. The second level drum is embedded in the upper of the first level drum, the third level drum is embedded in the upper of the second level drum, and the loading platform is embedded in the upper of the third level drum. Three earth pressure cells could be calibrated simultaneously by adding weighting blocks gradually on the loading platform.

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Mapping the Multiscale Proteomic Organization of Cellular and Disease Phenotypes

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Biomedical Data Science 1000年第1期7卷 369-389页

作者： Anthony Cesnik Leah V. Schaffer Ishan Gaur Mayank Jain Trey Ideker Emma Lundberg 1Department of Bioengineering Stanford University Stanford California USA email: emmalu@stanford.edu 2Department of Medicine University of California San Diego La Jolla California USA email: tideker@health.ucsd.edu 3Departments of Computer Science and Engineering and Bioengineering University of California San Diego La Jolla California USA 4Department of Pathology Stanford University Palo Alto California USA 5Science for Life Laboratory School of Engineering Sciences in Chemistry Biotechnology and Health KTH Royal Institute of Technology Stockholm Sweden 6Chan Zuckerberg Biohub San Francisco California USA

While the primary sequences of human proteins have been cataloged for over a decade, determining how these are organized into a dynamic collection of multiprotein assemblies, with structures and functions spanning biological scales, is an ongoing venture. Systematic and data-driven analyses of these higher-order structures are emerging, facilitating the discovery and understanding of cellular phenotypes. At present, knowledge of protein localization and function has been primarily derived from manual annotation and curation in resources such as the Gene Ontology, which are biased toward richly annotated genes in the literature. Here, we envision a future powered by data-driven mapping of protein assemblies. These maps can capture and decode cellular functions through the integration of protein expression, localization, and interaction data across length scales and timescales. In this review, we focus on progress toward constructing integrated cell maps that accelerate the life sciences and translational research.

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Predicting the severity of blood vessel tissue damage in retinal images using support vector machine classifier

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AIP Conference Proceedings 2019年第1期2112卷

作者： C. Murukesh P. Rayavel 1Department of Electrical and Electronics Engineering Velammal Engineering College Tamil nadu India 2Department of Computer Science and Engineering Sri Sai Ram Institute of Technology Tamil nadu India

In recent years many of the people are suffering from diabetics which may result damaging the human eye sights by damaging the blood vessels of the retinal by form exudates around the optic disc. In this paper, we intend to build retinal exudates from fundus image to predict the severity of diabetics resulting in blood vessels tissue damages. In order to analysis the blood vessels damage and diabetic severity initially we use appropriate image pre-processing techniques to remove any noise from the retinal funds image, to remove the noise in this paper we use wavelet transform and first order Gaussian derivative and matched filter to segment the fundus image by rotating the original image by certain angle. The filtered data is stored in the matched filter bank, then by applying k-NN clustering technique to identify minimum value in each filter bank and marking such minimum value center of k-nearest neighbor value. Further, Support vector machine a supervised learning algorithm is applied to the identified k-nearest neighbor values thereby predicting the severity of blood vessel tissue damage from the fundus image.

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Mature antibody immune response over SARS-CoV-2 as a pattern recognition with artificial immune system

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AIP Conference Proceedings 2022年第1期2481卷

作者： Manita Dhyani Dibyahash Bordoloi Anupriya Sharma Ghai 1Department of Computer Science and Engineering Graphic Era (Deemed to be) University Dehradun Uttarakhand India 248002 2Department of Computer Applications Graphic Era Hill University Dehradun India

A natural immune system has a remarkable ability to recognise self from nonself antigens in human body. The immune system has a natural ability to learn, recognise and respond accordingly to protect human body against viruses and diseases. B-cells produce detectable antibodies and immunoglobulins and are responsible for humoral antibody immunity (IgM, IgG). Memory cells play an important role in this entire reactive process. With the help of these memory cells, the immune system recognises and responds immediately to the second entry of infection.I propose a supervised machine learning method (Immunos -R) to give an approach to build an immune response model to get an accurate antibody immunological response to SARS-CoV-2 without doing in-vitro laboratory *** rate for antibody immunological response (B lymphocytes cells subset) is 96.0% at fourth split and lowest at 8th split 68.0%

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Methodical gain knowledge on categorizing 1-hop and 2 – hop set of connections

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AIP Conference Proceedings 2019年第1期2112卷

作者： Kittappa Thiagarajan Justin Gnana Dhas Jospin Jeya Ramalingam Deebalakshmi R. 1AcademicResearc-Professor Jeppiaar Engineering College Chennai Tamilnadu India 2Department Of Computer Science & Engineering Jeppiaar Engineering College Chennai Tamilnadu India 3Department Of Computer Science & Engineering Jeppiaar Engineering College Chennai Tamilnadu India

In this research study we have considered only regular network from Graph Theory, the following analytical study provides identification of 1- hop network and 2 -hop network. The bottleneck node can be observed or ide...

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Effectiveness of Image-Based Classifiers for On-Site Identification of Lichens in Reserved Forest

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SSRN 2024年

作者： Sankar, Karthikumar Rajaram, Shyam Kumar Karuppiah, Ponmurugan Periyasami, Govindasami Karthikeyan, Perumal Selvi, Chengathir M. Selvan, Basil Tamil E. Department of Computer Science and Engineering Mepco Schlenk Engineering College Tamil Nadu Sivakasi626005 India Department of Biotechnology Kamaraj College of Engineering and Technology S.P.G.C.Nagar K.Vellakulam Tamil Nadu Virudhunagar626001 India Department of Botany and Microbiology College of Science King Saud University P.O. Box 2455 Riyadh11451 Saudi Arabia 2Department of Chemistry College of Science King Saud University P.O. Box 2455 Riyadh11451 Saudi Arabia Department of Chemistry and Biochemistry Ohio State University 170A CBEC 151 Woodruff Avenue ColumbusOH43210 United States Department of Computer Science and Engineering Kamaraj College of Engineering and Technology S.P.G.C.Nagar K.Vellakulam Tamil Nadu Virudhunagar626001 India

The collection and identification of lichen samples within the reserved forest pose significant challenges, leading researchers to seek alternative methodologies. In this context, an in-depth study was conducted using two approaches to evaluate the efficacy of several network models for the identification of lichens in digital images. In the initial method, a total of 119 lichen images, comprising 15 images from experimental work and additional images from open sources, were employed for the traditional classification approach utilizing MATLAB. In which, 90 different features were extracted from various color models such as RGB, YCbCr, HSV, CMYK, LAB, YIQ and subjected for classification in ANN model, which resulted 76.6% accuracy for the class of lichen family. In another approach, each species of lichen images was augmented using generative augmentation tool and subjected for multiclass deep learning network models, precisely transfer learning model. In total, 10410 images comprising 5 thallus type, 8 order and 77 distinct lichen species were used to train three different network models (VGG16, VGG19, ***50). It was found that ***50 model exhibited highest sensitivity (>0.99) and precision. The class lichen thallus type achieved a maximum accuracy of 86.6%, while the order exhibited 80%. Overall, the lichen species under the order of Laccanorals with fruticose thallus type showed highest classification accuracy in all the three models attempted in this study. The study concluded that utilizing transfer learning with a multilayer perceptron model proved to be more effective and accurate for the lichen dataset, especially when dealing with greater complexity in picture information. The obtained results were comparable to those of other recent deep learning approaches. © 2024, The Authors. All rights reserved.

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RPM prediction model for reducing dimensions by combining PCA, K-means using MLAS in identifying a disease

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AIP Conference Proceedings 2022年第1期2519卷

作者： A. P. Bhuvaneswari R. Praveen Sam C. Shoba Bindu 1Department of Computer Science and Engineering Jawaharlal Nehru Technological University Anantapuramu Anantapur Andhra Pradesh India 2Department of Computer Science and Engineering G Pulla Reddy Engineering College Kurnool Andhra Pradesh India

A large population around the world is unaware of the health condition early in their life which can increase their life span. This paper presents an efficient Risk Prediction Model (RPM) for the accurate prediction of the diseases early enough. Medical datasets have a greater number of dimensions for diagnoses and prediction of disease accurately. PCA is applied to the given dataset, as a greater number of features will reduce the model accuracy. The reduced datasets are clustered using the popular K-means algorithm to remove the outliers. Supervised machine learning classification algorithms are used in calculating the accuracy for the clustered medical datasets. Our main goal is to detect the disease early in the life and analyze the risks associated with the disease for a longer span of life. The RPM comprises of PCA, K-means and machine classification algorithms. Experimental results show that RPM model attains an accuracy of 1.94% higher when compared with other published studies. The proposed RPM algorithm yielded outstanding results by automatically predicting the presence of disease with high accuracy.

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Electromagnetic analysis of 1MJ class of high temperature superconducting magnetic energy storage (SMES) coil to be used in power applications

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AIP Conference Proceedings 2018年第1期2005卷

作者： Abhinav Kumar Ramanjit Kaur 1School of Mechanical Engineering Lovely Professional University Phagwara Punjab India 2Department of Computer Science and Engineering Punjabi University Patiala Punjab India

Superconducting Magnetic Energy Storage (SMES) technology is attracting scientists as an alternative in energy storage technologies since superconducting materials incorporated in SMES have a potential to overcome challenges related to increase in electric load demand, power security, transmission control and stabilization. Due to fast response and high energy densities characteristics, SMES can work efficiently while stabilizing the power grid. Solenoidal configuration has been widely employed in the development of SMES prototypes as it is simpler to manufacture and allows an easier handling of the mechanical stresses imposed on the structure due to Lorentz forces. Moreover, it has been found that this configuration allows minimum wire consumption and represents the most cost effective solution for isotropic superconductors. In the present work, solenoidal configuration has been employed in the development of 1MJ SMES Magnet using 2G (SuperPower, YBCO having Tc=90K @0T) High Temperature Superconducting (HTS) tape. The superconducting tape has been cooled at 14K using conduction cooling. A reference field of 3.5T has been considered in the development of electromagnetic design of superconducting magnet and its effect on the total number of turns around the solenoidal magnet for various aspect ratios have been studied. An electromagnetic analysis has been done on 1MJ SMES having aspect ratio 5. The effect of maximum operating current (650A, 1300A and 1950A) on the length of superconductor has also been evaluated for a constant deliverable energy of 1MJ. Maximum Lorentz forces (N/m3) have been evaluated in the superconducting domain as these can result into structural instabilities. The extent of average force (N) imposed on the SC tape has also been evaluated. It has been concluded that it would be beneficial to operate at higher currents as it can reduce the total length of the superconductor.

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Hyperbolic-trigonometric tension B-spline Galerkin approach for the solution of Fisher equation

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AIP Conference Proceedings 2021年第1期2334卷

作者： Idris Dag Ozlem Ersoy Hepson 1Department of Computer Engineering Eskişehir Osmangazi University Eskişehir Turkey 2Department of Mathematics and Computer Sicence Eskişehir Osmangazi University Eskişehir Turkey

A Galerkin finite element method is constructed for computing solution of the Fisher equation. Hyperbolic-trigonometric tension (HTT) B-splines are used as weight and shape functions in the Galerkin approach. Fully discretization of the Fisher equation is fulfilled via Crank-Nicolson method in time and Galerkin method in space. Travelling wave front solution is studied to show robustness of the presented scheme on Fisher equation. The numerical results obtained with the present method are satisfactory with regard to its accuracy.

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Task scheduling in geo-distributed big data using ant colony optimization

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AIP Conference Proceedings 2022年第1期2405卷

作者： J. Rajkamal P. Ezhumalai 1Department of Computer Science and Engineering Sri Venkateswara Institute of Science and Technology Thiruvallur Tamil Nadu INDIA 2Department of Computer Science and Engineering R.M.D. Engineering College Chennai Tamil Nadu INDIA

Our commercial application main design depends on the big data in grid computing, distributed computing, and fog computing. The significant challenges facing big data is higher time consumption and cost consumption. Scheduling algorithm is used to save time and money to introduced in big data analytics. The proposed work deals with a significant challenge in task planning. The formidable Optimization challenge in the meta-heuristic algorithm is a considerable solution for cloud task planning. An effective task Manager includes the changes in the working of the task algorithm's task and schedule. Work consists of the cloud a task-based policy of the Ant Colony Optimization (ACO) listens to the various programming algorithm such as first in first out and Round Robin arbitrary algorithm. The purpose of this algorithm is to increase the efficiency of the scheduled task within cloud computing. ACO is an arbitrary Optimization technique that selects the various functions from the incoming jobs to virtual machines. Cloud-Sim toolkit package is used to simulate the proposed algorithm. Various experimental studies were carried out with cloud activities using first in first out and Round Robin based on the ACO technique.

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