Impact of TiB2 Content and Sliding Velocity on Wear Performance of Aluminium Matrix Composites

600-605Particulates dispersed aluminium matrix composites are the major substitute for variety of application at present scenario due to its massive strength, ductile nature and great thermal conductivity. In this work, TiB2 micro-sized particulates dispersed aluminium matrix composites prepared with different weight percentages of TiB2 particles by using liquid state stir casting process. Present investigation influence the impact of TiB2 particles content (0, 3, 6 and 9 wt.%) and variation of sliding velocity (0.5, 1, 1.5 and 2 m/s) for a constant load 20N and sliding distance 1000 m on the wear performance of composite rubbing against EN31 steel disc. Wear analysis revealed that TiB2 content enhanced wear rate and reverse trend noticed in case of coefficient of friction. Similarly, wear rate deteriorated and enhanced COF as increasing sliding speed of counter plate rotation

Impact of TiB2 Content and Sliding Velocity on Wear Performance of Aluminium Matrix Composites

Particulates dispersed aluminium matrix composites are the major substitute for variety of application at present scenario due to its massive strength, ductile nature and great thermal conductivity. In this work, TiB2 micro-sized particulates dispersed aluminium matrix composites prepared with different weight percentages of TiB2 particles by using liquid state stir casting process. Present investigation influence the impact of TiB2 particles content (0, 3, 6 and 9 wt.%) and variation of sliding velocity (0.5, 1, 1.5 and 2 m/s) for a constant load 20N and sliding distance 1000 m on the wear performance of composite rubbing against EN31 steel disc. Wear analysis revealed that TiB2 content enhanced wear rate and reverse trend noticed in case of coefficient of friction. Similarly, wear rate deteriorated and enhanced COF as increasing sliding speed of counter plate rotation

Microstructure, mechanical and wear behaviour of Al7075/SiC aluminium matrix composite fabricated by stir casting

46-54Application of particulate reinforced composite is being emphasized day by day due to its modified strength, high hardness, less weight, and ductility. This paper deals with the behaviour of physical, mechanical and tribological properties of aluminium matrix composite reinforced by micro-sized silicon carbide particles (in a various quantity such as 0, 3, 6 and 9 wt.%) prepared by stir casting fabrication process. With the increase in weight percentage of SiC reinforcement, the density of composite improves from 2.76 gm/cc to 2.83 gm/cc, and the porosity of composite reduces from 1.78% to 0.56%. UTS and Hardness of SiC particle reinforced aluminium matrix composites developed from 140 MPa to 205 MPa and 66 HV to 84 HV respectively. Microstructure reveals that strong bonding develops between matrix and reinforcement in terms of strength and hardness in all the formed composites compared to its matrix material. Comparatively less wear is observed with enhancing SiC content conducted by the pin on disc dry sliding wear test for all the formed composites

Microstructure, mechanical and wear behaviour of Al7075/SiC aluminium matrix composite fabricated by stir casting

Application of particulate reinforced composite is being emphasized day by day due to its modified strength, high hardness, less weight, and ductility. This paper deals with the behaviour of physical, mechanical and tribological properties of aluminium matrix composite reinforced by micro-sized silicon carbide particles (in a various quantity such as 0, 3, 6 and 9 wt.%) prepared by stir casting fabrication process. With the increase in weight percentage of SiC reinforcement, the density of composite improves from 2.76 gm/cc to 2.83 gm/cc, and the porosity of composite reduces from 1.78% to 0.56%. UTS and Hardness of SiC particle reinforced aluminium matrix composites developed from 140 MPa to 205 MPa and 66 HV to 84 HV respectively. Microstructure reveals that strong bonding develops between matrix and reinforcement in terms of strength and hardness in all the formed composites compared to its matrix material. Comparatively less wear is observed with enhancing SiC content conducted by the pin on disc dry sliding wear test for all the formed composites

Aspect-based Sentiment Analysis Model for Evaluating Teachers' Performance from Students' Feedback

Evaluating teachers' performance is a fundamental pillar of educational enhancement, guiding the evolution of pedagogical practices and fostering enriched learning environments. This study pioneers an innovative approach by harnessing sentiment analysis within an aspect-based framework to decipher the intricate emotional nuances embedded within students' feedback. By categorizing sentiments as positive, negative, and neutral, we delve into the diverse perceptions of teaching aspects, offering a multifaceted portrait of educators' contributions. Through meticulous data collection, preprocessing, and a deep learning sentiment analysis model, we dissected student comments into distinct teaching aspects. The subsequent sentiment analysis unearthed positive, negative, and neutral sentiments. Positive sentiments highlighted strengths and effective communication, while negative sentiments illuminated areas for growth. Neutral sentiments provided contextual equilibrium, forming a holistic tapestry of teachers' performance. The proposed model achieved 86\% F1 score for classifying sentiments into three classes

Awareness to Deepfake: A resistance mechanism to Deepfake

The goal of this study is to find whether exposure to Deepfake videos makes people better at detecting Deepfake videos and whether it is a better strategy against fighting Deepfake. For this study a group of people from Bangladesh has volunteered. This group were exposed to a number of Deepfake videos and asked subsequent questions to verify improvement on their level of awareness and detection in context of Deepfake videos. This study has been performed in two phases, where second phase was performed to validate any generalization. The fake videos are tailored for the specific audience and where suited, are created from scratch. Finally, the results are analyzed, and the study’s goals are inferred from the obtained data

Evaluating Teachers’ Performance through Aspect-Based Sentiment Analysis

This research demonstrates a novel approach for evaluating teacher performance by conducting aspect-based sentiment analysis (ABSA) on student feedback. A large dataset of over 2 million student comments about teachers is analyzed using cutting-edge natural language processing and customized deep learning techniques. The methodology involves identifying positive, negative and neutral aspects of teaching using a BiLSTM model. Rigorous preprocessing, domain adaptation, and performance metrics ensure a robust and objective evaluation. The granular, nuanced insights obtained through this aspect-level sentiment analysis enable educational institutions to provide targeted and unbiased feedback to teachers on their strengths and areas needing improvement. Moreover, this work lays the foundation for detecting potentially fraudulent reviews in academic settings – a crucial capability for safeguarding assessment integrity. The detailed aspect-based analysis methodology presented here significantly advances subjective and holistic evaluation practices. This research has far-reaching implications for enriching teacher development while upholding the credibility of performance assessments through sentiment analysis innovations

A comprehensive dataset for aspect-based sentiment analysis in evaluating teacher performance

Teacher performance evaluation is an essential task in the field of education. In recent years, aspect-based sentiment analysis (ABSA) has emerged as a promising technique for evaluating teaching performance by providing a more nuanced analysis of student evaluations. This article presents a novel approach for creating a large-scale dataset for ABSA of teacher performance evaluation. The dataset was constructed by collecting student feedback from American International University-Bangladesh and then labeled by undergraduate-level students into three sentiment classes: positive, negative, and neutral. The dataset was carefully cleaned and preprocessed to ensure data quality and consistency. The final dataset contains over 2,000,000 student feedback instances related to teacher performance, making it one of the largest datasets for ABSA of teacher performance evaluation. This dataset can be used to develop and evaluate ABSA models for teacher performance evaluation, ultimately leading to better feedback and improvement for educators. The results of this study demonstrate the usefulness and effectiveness of ABSA in evaluating teacher performance and highlight the importance of creating high-quality datasets for this task