A Bibliometric Analysis of Impact Energy Absorption System to Enhance Vehicle Crashworthiness

In automotive engineering, crashworthiness is defined as an automobile\u27s functionality to shield its occupants from critical harm or death just in case of accidents of a given proportion. The comprehensive study observed composite materials exhibit a high specific energy absorption rate in a controlled manner while crushing. Crashworthiness research has also captured attention, especially to evaluate the energy absorbing capacity of different components made from composite material while undergoing deformation. Composite materials may be custom designed to show that specific energy absorption abilities are better than the metal structures. The present study will benefit the community of engineers resulting in a sturdy automotive system. It is observed that a total of 1458 articles are published in different forms by past researchers. Following the trend of publications in the concerned area, the last six years are the point of significant contribution, and in the year 2016, a maximum of 263 articles are published worldwide. The detailed survey revealed that a maximum of journal articles are published compared to the other relevant sources. The United States is the leading country in the concerned research area publications, followed by China and Germany. Different energy absorbing system has shown promising attributes for reducing the fatality of accidents during a collision. Still, it has a long way to achieve a system that can absorb the total energy generated during a crash

A Bibliometric Analysis of Variable Displacement Pump for Optimal Control of Operating Parameters

A centralized lubrication system or automatic lubrication system (ALS) is a system that delivers controlled amounts of lubricant to multiple locations on a machine while the machine is operating as per machine requirement. Lubrication occurs while the machinery is in operation, causing the lubricant to be equally distributed within the bearing and increasing the machine’s availability. Proper lubrication of critical components ensures the safe operation of the machinery. Less wear on the elements results in extension of component life, lower breakdowns, reduced downtime, reduced replacement costs, and reduced maintenance costs. If we can measure lubrication amounts, we can control the wasted lubricant supplied in excess to machine components, resulting in lowering energy consumption. The advantages of this new technology are transparent, although the heart of the automated lubrication system is the variable displacement pump. It is observed that a total of 1554 articles are published in different forms by past researchers. Following the trend of publications in the concerned area, the last seven years are the point of significant contribution, and in the year 2020, a maximum of 109 articles are published worldwide. The detailed survey revealed that a maximum of journal articles is published compared to the other relevant sources. China is the leading country in the concerned research area publications, followed by the United States and Italy

Prediction of groundwater level fluctuations by artificial neural network models : A case study

In the last few years, due to excessive exploitation of groundwater resources, the water level has gone down in many parts of India. According to a data from the Central Ground Water Board (CGWB), the average groundwater level in India has decreased by 61 per cent between 2007 and 2017, due to which the availability and quality of water are taking serious problems day-by-day. According to the CGWB (2017), 313 blocks out of a total of 6881 blocks are Critical, 1186 blocks are over exploited, and 100 blocks fall into the category of saline. And there are 94 such blocks where the availability of groundwater is significantly less. The overall development and management of groundwater resources are critical because of the water crisis arising from the declining level of groundwater resources and its immense importance in the social and economic growth of the country. Accurate forecasting of groundwater levels is essential for efficient management of groundwater resources and sustainable development of ecosystems. In this study, Multi-Layer Perceptron (MLP) artificial neural network (ANN) was used to predict groundwater levels in a selected aquifer system. The development of the ANN model included data on rainfall, temperature and river water level and groundwater level. The ANN model was trained using the gradient decant algorithm (GDM). The said model was applied and validated at seven different observation locations, and the estimated capacity of the ANN model was developed for each location. This was assessed using four statistical indicators [Bias, RMSE, NSE and MSE] as well as visual testing. Based on the results of this study, the Neural Network Model (ANN) was found to be efficient for forecasting monthly groundwater levels at almost all selected observation locations. The study concluded that artificial neural network techniques (ANNs) could be used efficiently to predict groundwater level fluctuations, especially in data-scarce situations

Prediction of groundwater level fluctuations by artificial neural network models : A case study

151-158In the last few years, due to excessive exploitation of groundwater resources, the water level has gone down in many parts of India. According to a data from the Central Ground Water Board (CGWB), the average groundwater level in India has decreased by 61 per cent between 2007 and 2017, due to which the availability and quality of water are taking serious problems day-by-day. According to the CGWB (2017), 313 blocks out of a total of 6881 blocks are Critical, 1186 blocks are over exploited, and 100 blocks fall into the category of saline. And there are 94 such blocks where the availability of groundwater is significantly less. The overall development and management of groundwater resources are critical because of the water crisis arising from the declining level of groundwater resources and its immense importance in the social and economic growth of the country. Accurate forecasting of groundwater levels is essential for efficient management of groundwater resources and sustainable development of ecosystems. In this study, Multi-Layer Perceptron (MLP) artificial neural network (ANN) was used to predict groundwater levels in a selected aquifer system. The development of the ANN model included data on rainfall, temperature and river water level and groundwater level. The ANN model was trained using the gradient decant algorithm (GDM). The said model was applied and validated at seven different observation locations, and the estimated capacity of the ANN model was developed for each location. This was assessed using four statistical indicators [Bias, RMSE, NSE and MSE] as well as visual testing. Based on the results of this study, the Neural Network Model (ANN) was found to be efficient for forecasting monthly groundwater levels at almost all selected observation locations. The study concluded that artificial neural network techniques (ANNs) could be used efficiently to predict groundwater level fluctuations, especially in data-scarce situations

Road Rage Menace: A Cross-sectional Study to Assess Driver Anger Level in Public Motor Vehicle Drivers in a City in Central India

Introduction: Road rage and aggressive driving is a prevalent condition in today’s society due to motorists’ frustrations during heavy traffic volumes. Objective: This study was done to assess the level of anger amongst the drivers of public transport vehicles in Indore, using Driving Anger Scale (DAS by Deffenbacher et. al.) and various factors affecting it. Material and Methods: A cross-sectional study was conducted among 135 drivers of Public transport vehicle drivers (Star bus, City-van and star cab drivers) in Indore to assess their anger level using Driving Anger Scale. The participants were required to record the amount of anger they would experience in response to each item in the scale (1=not at all angry, 2=a little angry, 3=some anger, 4=much anger, 5=very much angry). Results: The mean DAS score in Indore was found to be 3.013 and in the three organizations namely Star bus drivers, City van drivers and Star cab drivers was 2.92, 3.08 and 3.04 respectively. The DAS score of drivers with respect to the 6 sub-scales were: hostile gestures (Star bus -3.42,City van -3.67,Star cab -3.38), slow driving (Star bus -2.73,City van driv-2.78,Star cab-3.17), traffic obstructions (Star bus-2.85,City van -3.25,Star cab-3.18), discourtesy (Star bus -3.23,City van-3.33,Star cab -3.25)and police presence (Star bus -2.15,City van -1.99,Star cab -2.78), illegal driving (Star bus -3.04,City van -3.14,Star cab -2.89). The DAS scores of the drivers did not vary significantly with age group, experience, and educational qualification. Conclusion: Though DAS scores did not vary between the three groups of drivers, however average level anger for various given circumstances commonly found in the Indian traffic scenario was on the higher side

DEVELOPMENT AND VLAIDATION OF SIMPLE UV-SPECTROPHOTOMETRIC METHOD OF QUANTIZATION OF DIAZEPAM IN BULK DRUG AND SOLID DOSAGE FORMULATION USING MIXED SOLVENCY CONCEPT

Objective: Commonly used organic solvents for spectrophotometric analysis of water insoluble drugs are methanol, ethanol, chloroform, benzene, toluene etc. The main drawbacks of organic solvents include high cost, toxicity, and pollution. Organic solvents have numerous adverse effects caused by single exposure like dermatitis, headache, drowsiness, nausea, eye irritation and long term exposure causes serious effects such as neurological disorder, chronic renal failure, and liver damage. They should be replaced by other ecofriendly alternative sources.Methods: The present study is an attempt to show that solid can also be used to act as solvent precluding the use of organic solvents. A simple, safe and sensitive method of spectrophotometric determination of diazepam obeyed beers law in the concentration range of 5-25 mcg/ml at 306 nm.Results: The results of analyses have been validated statistically for Linearity, accuracy, precision, LOD and LOQ. The results of validation parameters also indicated that proposed method was found to be accurate, precise, reproducible, sensitive, and suitable for routine quality control analysis for estimation of diazepam in bulk drug and solid dosage formulation.Conclusion: A rapid, simple, and non toxic UV spectrophotometric method has been developed for the determination and quantification of diazepam. The present method also validated as per ICH guidelines for linearity, precision, accuracy

Landmine Detection and Discrimination using High-Pressure Waterjets

Methods of locating and identifying buried landmines using high-pressure waterjets were investigated. Methods were based on the sound produced when the waterjet strikes a buried object. Three classification techniques were studied, based on temporal, spectral, and a combination of temporal and spectral approaches using weighted density distribution functions, a maximum likelihood approach, and hidden Markov models, respectively. Methods were tested with laboratory data from low-metal content simulants and with field data from inert real landmines. Results show that the sound made when the waterjet hit a buried object could be classified with a 90% detection rate and an 18% false alarm rate. In a blind field test using 3 types of harmless objects and 7 types of landmines, buried objects could be accurately classified as harmful or harmless 60%-90% of the time. High-pressure waterjets may serve as a useful companion to conventional detection and classification methods

Coronavirus Disease 2019: Knowledge, Attitude, Practice, and Perceived Barriers among Health care Workers at Cairo University Children Hospital, Egypt

BACKGROUND: Insufficient knowledge and negative attitude toward coronavirus disease 2019 (COVID-19) among health care workers (HCWs) could lead to faulty practices resulting in delayed diagnosis and spread of the disease. AIM: this study was conducted to assess the knowledge, attitude, practice, and perceived barriers to infection control toward COVID-19 among Egyptian HCWs. METHODS: A cross-sectional study was conducted in Cairo University Children Hospital, with 537 HCWs (doctors and nurses) enrolled. RESULTS: HCWs had an overall good knowledge level about COVID-19 where 61% had a knowledge score of ≥18 points (out of 23). Doctors were more knowledgeable than nurses. About 64% of HCWs were considered as having positive attitude (scored ≥10 out of 13 points), with a significantly higher positive attitude among nurses. The mean practice score of HCWs was 1.0 ± 2.0 with a significantly higher good practice among nurses. Younger age, being a doctor, and higher qualification were the significant positive predictors of acquiring knowledge about the disease. The most commonly perceived barriers for applying infection control measures in hospitals were overcrowdings in health-care facilities (78.2%) and insufficient infection control policies (62.6%). CONCLUSION: HCWs in general expressed good knowledge, positive attitude, and good practice toward COVID-19 despite some gaps that were detected in specific items. Proper planning of educational programs that are directed according to the needs of different groups of HCWs is crucial. Effective policies should be established to overcome the barriers for applying infection control in health facilities