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Computational discovery of plant-derived flavonoids as potential amyloid-β fibril disaggregating agents for alzheimer's disease

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Brain Disorders 2025年 18卷 100233-100233页

作者： Rakesh, Uthirapathi Logeswari Kumar, Golla Anil Panneerselvam, Theivendren Pavadai, Parasuraman Veerachamy, Suganthan Palanisamy, Ponnusamy Bandral, SunilKumar Kunjiappan, Selvaraj Department of Biotechnology Kalasalingam Academy of Research and Education Tamilnadu Krishnankoil 626126 India Department of Pharmaceutical Chemistry & Analysis School of Pharmaceutical Sciences Vels Institute of Science Technology & Advanced Studies Pallavaram Tamilnadu Chennai 600117 India Department of Pharmaceutical Chemistry Faculty of Pharmacy M.S. Ramaiah University of Applied Sciences Bengaluru 560054 India School of Electronics Engineering Vellore Institute of Technology Tamilnadu Vellore 632014 India School of Mechanical Engineering Vellore Institute of Technology Tamilnadu Vellore 632014 India

The neurological hallmark of Alzheimer's disease (AD) is a pathogenic deposition of amyloid-β peptide in the brain. Amyloid-β aggregation is neurotoxic and ultimately results in dysfunction of the nervous system. The present study aims to find potential amyloid-β fibrils disaggregating molecules from plant sources through molecular modeling techniques, which seems to be a promising and attractive therapeutic approach. Here, 500 flavonoids from various plants were considered, initially undergoing ADME studies to screen molecules that could cross the blood-brain barrier. Later, potential Amyloid-β-disaggregating molecules were predicted by molecular docking and molecular dynamics studies. Five molecules, prenylmethoxy flavonol (-7.3 kcal × mol^-1), isopentenyl flavonol (-7.3 kcal × mol^-1), 7,3′-Dihydroxyflavone (-7.2 kcal × mol^-1), 7-Hydroxy-5-methyl-4′-methoxyflavone (-7.2 kcal × mol^-1), 8‑hydroxy-7-methoxyflavone (-7 kcal × mol^-1) exhibited top binding score against Alzheimer's Aβ (1–42) fibrils, and these are very close to the standard drug (Donepezil) (-7.90 kcal × mol^-1). Further, the MD simulation studies confirmed the stability of the five selected ligands-Amyloid-β oligomer protein complex. Based on these findings, the selected five compounds might be used as potential Amyloid-β fibril disaggregating agents, and in vitro and in vivo studies are necessary to confirm the promising therapeutic capability. © 2025 The Author(s)

关键词： Alzheimer's disease Amyloid-β fibrils Bioinformatics Blood-brain barrier Disaggregation Flavonoids In silico studies

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Genetic algorithm-based road network design for optimising the vehicle travel distance

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International Journal of Vehicle Information and Communication Systems 2019年第4期4卷 355-374页

作者： Shanmugasundaram, N. Sushita, K. Kumar, S. Pradeep Ganesh, E.N. Department of Electrical and Electronics Engineering School of Engineering VELS Institute of Science Technology and Advanced Studies Chennai Tamil Nadu600117 India

One-way road networks can be designed with the aid of computer program. However, the use of genetic algorithm in such design is new. This research paper presents a new approach to search out and determine appropriate travel directions for the one-way road network. The main objective is to minimise the total distance travelled by vehicles. To calculate total distance, a branch-and-bound technique with breadth-first search for is applied to search the shortest path between the start points and the end points. Results demonstrate that genetic algorithm approach can be successfully applied to one-way road network design for case studies. In the future, the genetic algorithm can be applied to design the one-way road network in general. Copyright © 2019 Inderscience Enterprises Ltd.

关键词： Genetic algorithms

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Mechanical and Dynamic Properties of Banana Fiber-Reinforced Polyester Composites: A Multi-Analytical Characterization Study

Engineering Reports

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engineering Reports 2025年第6期7卷

作者： G. Sathish Kumar R. Sridhar A. Parthiban S. Sivabalan T. Sathish Jayant Giri J. Isaac Joshua Ramesh Lalvani Ahmad O. Hourani Nidhal Becheikh Boutheyna Belhaj Bettaieb Department of Mechanical Engineering Vels Institute of Science Technology & Advanced Studies Chennai India Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu India Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur Maharashtra India Division of Research and Development Lovely Professional University Phagwara Punjab India Faculty of Mechanical Engineering Arba Minch Institute of Technology Arba Minch University Arba Minch Ethiopia Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan Mining Research Center Northern Border University Arar Saudi Arabia Engineering School (ENIG) RL Processes Energetic Environment and Electric Systems (PEESE) University of Gabes Gabes Tunisia

The principal objective of this investigation is to assess the damping properties of polyester composites reinforced with short banana fibers. The analysis includes evaluating mechanical characteristics, conducting free vibration examinations, measuring damping efficiency (tan δ), and utilizing Scanning Electron Microscopy (SEM). A comprehensive examination assesses Tensile Strength, Flexural Strength, Impact Strength, Hardness, free vibration attributes, damping coefficients, and SEM imagery from the fractured tensile sample surface. The composite samples are fabricated using a hand layup procedure with randomly arranged 10 mm-long fibers. Banana fiber content varies at 5, 10, 15, and 20 wt.%. Experimental results demonstrate a direct relationship between fiber quantity and improvements in Tensile Strength, Flexural Strength, Impact Strength, Hardness, Natural Frequency, and Damping ratio. Composites containing 15 wt.% banana fibers show the most significant improvements across various temperature and frequency ranges. Simultaneously, the presence of fibers reduces peak values in the damping curves (tan δ). The most notable improvement occurs in composites with 15 wt.% banana fibers, exhibiting characteristics similar to 100% polyester. This empirical study identifies a clear association between increasing fiber content and enhanced natural frequency and damping properties of the composite material.

关键词： damping factor (tan δ) and SEM mechanical properties natural fiber polyester resin

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Facial Expression Classification using KNN and Decision Tree Classifiers

Facial Expression Classification using KNN and Decision Tree...

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International Conference on computer, Communication and Signal Processing (ICCCSP)

作者： M Murugappan A M Mutawa Sai Sruthi Aya Hassouneh Ali Abdulsalam Jerritta S Ranjana R Intelligent Signal Processing Research Lab Kuwait College of Science and Technology (A Private University) Doha Kuwait College of Engineering and Petroleum Kuwait University Kuwait Kuwait Technical College (K-Tech) Kuwait Vels Institute of Science and Technology and Advanced Studies (VISTAS) Tamilnadu India

ISBN: (数字)9781728165097

ISBN: (纸本)9781728165103

In recent years, facial emotional expression recognition attracts several researchers for developing intelligent human-machine interface (HMI) system. This present work classify six different facial expressions (happiness, sadness, anger, fear, disgust, and surprise) using two classifiers namely, K Nearest Neighbor (KNN) and Decision Tree (DT) classifiers. Fifty-five undergraduate university students (35 male and 20 female) with a mean age of 23.9 years voluntarily participated in the experiment to acquire six facial emotional expressions using ten virtual markers called Facial Action Units (FAUs). Firstly, Haar-like features are used for detecting the face and eyes in a video-frame using Viola-Jones adaboost classification method. These FAUs are placed on specific location on the subject's face based on facial action coding system (FACS) using a mathematical model. Lucas-Kande optical flow algorithm is used to continuously track the markers positions. Here, the distance between the FAU at the center of the subject face to other markers are calculated and used as a feature for facial expression classification. The one-way analysis of variance with a significance level of p

关键词： Face recognition Emotion recognition Databases Classification algorithms Feature extraction Real-time systems Optical flow

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Deep learning enabled smart charging technology for electric vehicles

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AIP Conference Proceedings 2022年第1期2527卷

作者： T. Blesslin Sheeba C. Sharanya C. Nayanatara S. K. Indumathi K. Kalins G. Ignisha Rajathi 1Department of ECE R.M.K. Engineering College Chennai India 2Department of ECE Vels Institute of Science Technology and Advanced Studies Chennai India 3Department of EEE Sri Sairam Engineering College Chennai India 4Centre for Automation and Robotics Hindustan Institute of Technology and Science Chennai India 5Department of Science and Humanities Sri Krishna College of Engineering and Technology Coimbatore India 6Department of Computer Science and Business Systems Sri Krishna College of Engineering and Technology Coimbatore India

Reliability, efficiency, and cost-effectiveness of smart grids are enhanced with power demand softening by means of efficient load management in electric vehicles. In such initiatives, the involvement of EV users may reduce due to the lack of adaptable user-centric approaches. During the connection sessions, the EV charging time is determined using a deep learning algorithm-based smart charging strategy proposed in this paper. Here, the total energy cost of the vehicle is minimized by making charging decisions considering demand time series, pricing, environment, driving, and other auxiliary data. The memorization technique is used for the estimation of the optimal solution of the existing connection sessions in the initial stage. The deep learning models are trained with this existing data and optimal decisions to make suitable decisions in real-time scenarios where car usage or future energy price values are undetermined. A significant reduction in the charging cost is observed by training the neural network with the proposed model. The results obtained are compared to the optimal charging costs computed and are found to be closely similar.

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Influence of microstructure and mechanical properties of TiC reinforced magnesium nano composites

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Materials Today: Proceedings 2020年 27卷 1530-1534页

作者： G. Anbuchezhiyan B. Mohan S. Kathiresan R. Pugazenthi Department of Mechanical Engineering SRM Valliammai Engineering College SRM Nagar Kattankulathur Chennai 603203 Tamil Nadu India Department of Mechanical Engineering CEG Campus Anna University Chennai 600025 Tamil Nadu India Departments of Mechanical Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai 600127 India Department of Mechanical Engineering Vels Institute of Science Technology & Advanced Studies Chennai 600117 Tamil Nadu India

In the present study, an effort has been made to synthesize TiC reinforced magnesium nanocomposites by varying its weight percentage (3%, 6% and 9%) using powder metallurgy for marine applications. The compaction pressure was maintained at 350 MPa for 15 min at 250 °C followed by sintering process. Sintering temperature was maintained at 650 °C in argon protected atmosphere for 8 hrs. Optical microscope and scanning electron microscopy (SEM) have been used to study the microstructure of magnesium nanocomposites. Vickers micro hardness test and ASTM-E9 standards have been used to evaluate the hardness and compressive properties of magnesium nanocomposites. The microstructure of synthesized magnesium nanocomposites revealed that the reinforcement particles are homogeneously distributed in the magnesium matrix without indication of cluster and they appeared as a lamellar precipitates during diffusion. The increase in percentage of reinforcement increases its compressive strength and hardness of magnesium nanocomposites due to the inclusion of stiffer and stronger reinforcement in matrix alloy. B117 salt spray technique was utilized to study the corrosion behavior of magnesium nanocomposites. It has been observed that the corrosion properties of magnesium nanocomposites significantly increased to a maximum of 46% compared with monolithic AZ91D magnesium material.

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Comparison Performance Evaluation of Different Classifiers on Segmented Mammogram Clusters

SSRN

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SSRN 2019年

作者： Venmathi, A.R. Ganesh, E.N. Kumararatharan, N. ECE dept Kings Engineering College chennai India VELS Institute of Science Technology & Advanced Studies Formerly VELS University ECE dept Sri Venkateswara College of Engineering chennai India

In image processing technique a high resolution images are needed for segmentation and detection and, to interpret the regions in image a high technical tool and skilled personal is needed. Recently many improvements are achieved in this area but still there are some challenges remaining in this direction for better algorithms in detection and segmentation process. The image segmentation method is often used in the processing of medical images. The recent image segmentation procedures incorporate region based segmentation;edge detection segmentation;segmentation depends on clustering. And there also exists number of algorithms to determine the segmented region for detection. This paper deals with a novel technique to compare some special algorithms. The segmented images are undergone for performance evaluation with different algorithms to compare their detection ability. These analysis and summary evaluates and estimates the plus and minus of different algorithms in the image segmentation process. Finally, we can make out the accurate outcome of the development trend in image segmentation with the combination of these algorithms. © 2019, The Authors. All rights reserved.

关键词： Image segmentation

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Advance synthetic aperture radar images for characterization of oil spills disaster in ocean using Daubechies analysis

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Journal of Physics: Conference Series 2020年第1期1432卷

作者： Mukta Jagdish Department of Computer Science and Engineering Vels Institute of Science Technology & Advance Studies VISTAS Chennai India

Background: Researchers have developed various algorithms to identify the occurrence of an oil spill in these oceans. However, knowing the type of oil that is spilled in the ocean is important to assess and plan the restoration process. To predict the type of oil that is spilled in the ocean by using machine learning techniques. Fifty satellite images of three types of an oil spill, namely petroleum, crude oil, and diesel were examined to identify the type of oil spill over the affected area. The oil spills were initially identified from the images using K-Nearest Neighbor algorithm. Color-based, Statistical, Textural and Geographical features are extracted after applying various types of wavelets to obtain the features relevant to the physical parameters and type of oil in the ocean. The features were then trained and classified using K-Nearest Neighbor algorithm to identifying the type of oil. Methods: For wavelet analysis (Daubechies analysis db1, db2, db3, db4, db5, db6, db7, db8, db9, db10) and machine learning (k-nearest neighbor algorithm) applied to optimize the oil spill feature sets. Features included color-based, statistical, texture and geological features. This experiment was conducted on SAR images. The features were classified using a k-nearest neighbor algorithm. Seventy percent of features used for training and thirty percent for testing. Results: The results show that oil spill classification achieved by wavelet transforms and machine learning algorithms outperformed very well with similar parameter settings, especially with 70% training data and 30% testing data using confusion matrix. It also represents 99% accuracy for petrol oil using Daubechies 5 analysis which indicates better characterization of oil spills. Results denote oil spill detection using Synthetic Aperture Radar (SAR) remote sensing which provides an excellent tool in oil spill characterization various features can be extracted from SAR data set.

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Magnesium alloy machining and its methodology: A systematic review and analyses

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AIP Conference Proceedings 2022年第1期2473卷

作者： R. Venkatesh S. SivaChandran T. Maridurai S. Baskar N. Sivashankar R. Arivazhagan 1Department of Mechanical Engineering Saveetha School of Engineering Chennai Tamilnadu India 2Department of Mechanical Engineering Sri Sairam Engineering College Chennai Tamilnadu India 3Department of Automobile Engineering Vels Institute of Science Technology and Advanced Studies Pallavaram Chennai Tamilnadu India 4Department of Mechanical Engineering Kongunadu College of Engineering and Technology Trichy Tamilnadu India

Magnesium alloy has proved its role in various applications because of its lightest structure and strength. To demonstrate its specific characteristics, number of research work was carried out. Machining of Magnesium alloy in minimum lubrication condition, dry machining, milling, drilling, optimization of Mg alloy machining parameters, study about chip formation, surface roughness, joining of Mg alloys, corrosion resistance, cutting force measurement etc. are the major research area in which research are carried out in recent days. Mg Alloys application was extended in structural applications in aircraft, gear box, laptops, mobile applications, chain saws etc. It has replaced some engineering plastics because of its higher stiffness. So optimization is much needed to obtain perfect results. This paper will indicate the possible research methodology and its results of Mg alloys under various circumstances.

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RETRACTED ARTICLE: Detection of malware on the internet of things and its applications depends on long short-term memory network

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Journal of Ambient Intelligence and Humanized Computing 2021年第1期13卷 31-31页

作者： Priyadarsini, K. Mishra, Nilamadhab Prasad, M. Gupta, Varun Khasim, Syed Department of CSE Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai India School of Computing Science and Engineering VIT Bhopal University Bhopal India School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Chennai India Department of Electronics and Instrumentation Engineering KIET Group of Institutions Delhi-NCR Ghaziabad India Department of Computer Science and Engineering Dr.Samuel George Institute of Engineering and Technology Markapur India

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