Analysis of TIG Welding Process on Mechanical Properties and Microstructure of Aa6063 Aluminum Alloy Joints

Present work pertains to testing of mechanical properties and microstructure of AA 6063 Aluminium alloy joints by Tungsten inert gas (TIG) welding. By varying weld current and maintaining all parameters constant hardness, impact and tensile strength of the welded joints are tested and finally, microstructure details are analyzed. The results showed mechanical properties linearly varies with increase in weld current

Structural Equation Modeling to Detect Correlates of Childhood Vaccination: A Moderated Mediation Analysis

OBJECTIVES: This study used a health belief theory derived framework and structural equation model to examine moderators, mediators, and direct and indirect predictors of childhood vaccination. METHODS: A secondary analysis was conducted using data collected from a cross-sectional survey of a random sample of 1599 parents living in urban and rural areas of Mysore district, India. Applying two-stage probability proportionate-to-size sampling, adolescent girls attending 7th through 10th grades in 23 schools were selected to take home a questionnaire to be answered by their parents to primarily assess HPV vaccine intentions. Parents were also asked whether their children had received one dose of Bacillus Calmette-Guérin; three doses of Diphtheria, Pertussis, Tetanus; three doses of oral Polio vaccine; and one dose of Measles vaccine. In addition, parents were asked about their attitudes towards childhood vaccination. RESULTS: Out of the 1599 parents, 52.2% reported that their children had received all the routine vaccines (fully vaccinated); 42.7% reported their children had missed at least one routine vaccine, and 5.2% reported that their children had missed all routine vaccinations. Perceptions about the benefits/facilitators to childhood vaccination significantly predicted the full vaccination rate (standardized regression coefficient (β) = 0.29) directly and mediated the effect of parental education (β = 0.11) and employment (β = -0.06) on the rate of full vaccination. Parental education was significantly associated indirectly with higher rates of full vaccination (β = 0.11). Parental employment was significantly associated indirectly with decreasing rates of full vaccination (β = -0.05). Area of residence moderated the role of religion (β = 0.24) and the \u27number of children\u27 in a family (β = 0.33) on parental perceptions about barriers to childhood vaccination. The model to data fit was acceptable (Root Mean Square Error of Approximation = 0.02, 95% CI 0.018 to 0.023; Comparative Fit Index = 0.92; Tucker-Lewis Index = 0.91). CONCLUSIONS: Full vaccination rate was relatively low among children in Mysore, especially among parents who were unsure about the benefits of routine vaccination and those with low educational levels. Interventions increasing awareness of the benefits of childhood vaccination that target rural parents with lower levels of education may help increase the rate of full childhood vaccination in India

Development and Validation of Enantiomeric Purity of Montelukast by SFC Method on Amylose Based Stationary Phase

A new Supercritical fluid liquid chromatographic (SFC) method has been developed in normal-phase conditions for the determination of enantiomeric purity of Montelukast sodium (S,E)-2-(1-((1-3-(2-(7-chloroquinolin-2-yl)vinyl)phenyl)-3-(2-(2-hydroxypropan-2-yl)phenyl)propylthio)ethyl) cyclopropyl) acetic acid (R-isomer) (Anti asthmatic drug) in bulk drugs and in dosage forms. The sample was screened on the analytical SFC to determine the best column for the separation. The screening conditions are Column: Chiralpak AS-H (250 mm x 4.6 mm, 5 μm) column using a mobile phase system containing Supercritical fluid (Co2) and 2-Propanol in the ratio (85:15% v/v). The mobile-phase compositions and the differences in separation capability of the method is noted. The resolution between two enantiomers is found to be greater than 1.5. The SFC method for the separation of enantiomers of Montelukast is proved Accurate, Precise, Linear and robust. Relative standard deviation of retention times and peak areas were better than 0.2% and 0.4%, respectively, for precision.Â

Exploratory Data Analysis on Autopilot: Python's Automatic Solutions

Python has gained immense popularity in the fields of data science and machine learning due to its extensive libraries and efficient coding capabilities, enabling time-saving solutions. This article presents a comprehensive tutorial on Automatic Exploratory Data Analysis (EDA) using Python. By leveraging Python libraries, we can swiftly extract valuable insights and statistical information from datasets, reducing the manual effort involved in data exploration. The article aims to equip readers with the knowledge and tools to efficiently analyze data, revealing hidden patterns and trends, all accomplished through just a few lines of code. By the end of this article, readers will have a clear understanding of how Python's automated EDA techniques can revolutionize the data analysis process, maximizing efficiency and productivity

From Novice to Expert: A Journey into Training Machine Learning Models

Machine learning has evolved into a priceless asset for tackling complex obstacles across a wide range of disciplines, including Computer Vision(CV), Natural Language Processing(NLP), healthcare, and finance. At the core of machine learning lies the training process, wherein model parameters are optimized to make precise predictions on unseen data. For beginners venturing into this domain, it is crucial to grasp the fundamentals of training machine learning models. This article serves as a comprehensive guide, specifically focusing on training machine learning models using Python. Step-by-step instructions and explanations are provided to facilitate a thorough understanding of the training process. By following this article, beginners will gain practical knowledge and confidence in training their own machine learning models

Data Driven Exploration: Unleashing Topic Modelling Using Python

Topic Modeling is a crucial technique in natural language processing that involves assigning topic labels to a set of text documents. The primary objective of topic modeling is to unveil the latent themes or subjects present within the textual data. This article serves as a comprehensive guide for individuals seeking to acquire knowledge on performing topic modeling using machine learning algorithms with the aid of Python. Through this article, readers will gain insights into the fundamental concepts of topic modeling, various machine learning techniques used in the process, and a step-by-step implementation using Python programming language. By the end of this article, readers will have a solid foundation in topic modeling and the necessary skills to explore and extract meaningful topics from their own text data

Numeric Metamorphosis: Converting Categorical Features With Python

In data preprocessing for machine learning, converting categorical features to numerical values is a crucial step. Python offers various techniques to achieve this transformation. One common approach is Label Encoding, where each category is assigned a unique integer. This method is suitable when there's a meaningful ordinal relationship between categories. Alternatively, One-Hot Encoding can be used to create binary columns for each category, which is ideal when there's no inherent order among them. These conversions enable machine learning algorithms to work with categorical data efficiently, making them an essential part of the data preparation process, ultimately leading to more accurate and effective predictive models

A Forest of Possibilities: Decision Trees and Beyond

Decision trees are fundamental in machine learning due to their interpretability and versatility. They are hierarchical structures used for classification and regression tasks, making decisions by recursively splitting data based on features. This abstract explores decision tree algorithms, tree construction, pruning to prevent overfitting, and ensemble methods like Random Forests. Additionally, it covers handling categorical data, imbalanced datasets, missing values, and hyperparameter tuning. Decision trees are valuable for feature selection and model interpretability. However, they have drawbacks, such as overfitting and sensitivity to data variations. Nevertheless, they find applications in fields like finance, medicine, and natural language processing, making them a critical topic in machine learning

Python's AutoTS: Your Co-Pilot for Time Series Analysis

AutoTS is a powerful automatic machine learning library designed specifically for automatic time series forecasting in Python. With its intuitive functionality and versatility, this library empowers users to effortlessly engage in various time series forecasting tasks, such as predicting stock prices for a specified number of days ahead. In this article, we present a comprehensive tutorial on utilizing the AutoTS library in Python. By following this guide, readers will gain insights into the library's capabilities, enabling them to harness its potential for accurate and efficient time series forecasting in their projects. Whether you are a seasoned data scientist or a beginner in the field, AutoTS offers a seamless and accessible solution for tackling complex time series analysis with ease