Volume 14, Issue 7, July 2023 Edition - IJSER Journal Publication
Publication for Volume 14, Issue 7, July 2023 Edition - IJSER Journal Publication
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Hazards Of Seismic Impact On Reinforced Structures Separated By a Seismic Gap [PDF]
Mhd Ghaith Mahmoud Maksoud, Amin Saleh Aly, Khaled Muhammad Ali MuhammadThis thesis is concerned with studying the collision of structures during earthquake shocks. Make an assessment of structures hazards due to earthquake loads and calculate collusion forces of adjacent buildings due to earthquake shocks that leads to the collapse of buildings that leads to serious human and material losses and comparison with the requirements of global and local codes. Make FORTRAN Program to calculate the seismic gap of the buildings under study and use the Excel program to find the mass and stiffness and use the ETABS program and compare the results in the two models to reach the most accurate solution. In comparison with the displacement results from the ETABS program, the results of the FORTRAN program were acceptable. Then calculating the seismic forces generated as a result of the collision during the earthquake through the ETABS program to reach the correct evaluation of these forces. Several adjacent buildings were modeled to serve each case to calculate the collision forces between them through the ETABS program, and the effect of the seismic gap distance on the collision forces and the effect of the mass of buildings on these forces were studied.
Upgrading the Future of Consciousness-The RASHA Dome and the Power of Base-12 Harmonization [PDF]
Dr. Jere Rivera-Dugenio, Ph.D.This document offers an in-depth exploration into the utilization of The RASHA Dome Technology. This innovative technology, created by Dr. Rivera-Dugenio, aims to accomplish a state of Base-12 harmonization in the brain, potentially opening doors to enhanced human performance and heightened mental abilities. At the heart of The RASHA Dome Technology is the use of Base-12 scalar energy sound frequency patterns, known for their potential to enhance consciousness coherence. This document presents the evidence that through this increased consciousness coherence, we can 12x harmonize the brain's left and right hemispheres. The resultant state is an upgraded state of consciousness coherence fostering improved human performance and enhanced mental capabilities. To elucidate the workings of The RASHA Dome technology, we first delve into the theoretical foundations of brain function and consciousness. Complex concepts of holography, the time-space dimension, quantum mechanics, and the consciousness matrix are discussed, providing a robust framework for understanding this groundbreaking technology. Dr. Rivera-Dugenio contemplates the potential uses of The RASHA Dome technology for enhancing sports performance and addressing mental health issues. Potential benefits include elevated cognition, faster recovery, and possible improvements in speed, agility, and strength. However, he cautions that we are only beginning to understand the complexity of this process. Further research is needed to fully understand and harness the revolutionary potential of The RASHA Dome Technology.
Evaluation and Comparison of Machine Learning Models for Autism Spectrum Disorder Prediction [PDF]
Narla Sai Vardhan Reddy, M.V.S Koushik, Gudipati VikasAutism Spectrum Disorder (ASD), a complex neurodevelopmental disorder, causes behavioral, social interaction, and communication difficulties. For those with ASD, early evaluation and treatment can improve results. Our investigation scrutinized six machine learning models - Random Forest, MLP, Naive Bayes, XGBoost, K-Nearest Neighbor, and Support Vector Machine - to ascertain their accuracy in predicting ASD. The dataset used for the present study included about 701 examples and 21 different attributes. Upon meticulous evaluation, we ascertained that all six machine learning models evinced remarkable accuracy in predicting ASD. Notably, the Random Forest model outperformed its counterparts, achieving an impressive accuracy rate of 99.30. These results demonstrate the important potential of machine learning models for aiding precise ASD prediction and advancing early detection and intervention efforts.
Determining the Remaining Useful Life of Lithium Ion Batteries using Machine Learning [PDF]
Sriya GuptaWith a dramatic shift towards renewable energy in the energy sector, the global demand for batteries has exponentially increased. It has become imperative to reliably assess and predict the remaining lifespan of the lithium ion batteries (LIB). This paper works towards examining the battery chemistry of lithium ion batteries and their working mechanism in addition to a brief literature review about them. Descriptions of popular ML algorithms like linear regression, decision trees and extreme gradient boosting (XGBoost Regressor) have also been presented and these models have then been used to predict the remaining useful life (RUL) of NMC-LCO batteries, a type of LIB, using a publicly-available dataset. It was found that, out of these three models, XGBoost Regressor performed the best and was able to predict values for RUL with an accuracy of 99.93%. The paper discusses these results and observations.
Mitigation of Train-Induced Vibrations with Developed In-Filled Coir Composite Wave Barriers [PDF]
Lekshmi chandran M, Jaya V, K BalanThe rapid urbanization and population growth in cities have led to the construction of buildings near train tracks. However, trains passing by these buildings generate ground vibrations that can potentially impact the structures and their foundations. Extensive research, including experiments, field studies, and finite element method analyses using computational software, is being conducted to identify effective mitigation techniques for reducing vibrations in buildings susceptible to train-induced vibrations. One crucial approach for vibration mitigation is the implementation of suitable wave barriers. This study focuses on evaluating the performance of in-filled coir composite barriers in isolating ground vibrations caused by trains. Coir composite, chosen as the fill material due to its favorable properties such as low density, low impedance, and low shear wave velocity, was utilized in the study. Prior to installing the coir composite barriers, a comprehensive field exploration was conducted to assess the site characteristics. The study findings highlight that the effectiveness of vibration mitigation depends on factors, including the type of infill coir composite barrier, and the distance between the building and the railway track. By considering these conditions, the most efficient vibration countermeasure can be determined. Overall, this study contributes to the understanding of the performance of in-filled coir composite barriers in reducing train-induced vibrations.