Assessment and Characterization of Airborne Dust in Coal Surface Mine

Now a day’s dust pollution is the major environmental issue inside an opencast mine, which has various effects on human life. There are a number of fugitive sources, and activities which cause dust pollution inside an opencast mine eg. Drilling, transportation, blasting, crushing, conveying, overburden face, haul road etc. Among these dust, there are some toxic and carcinogenic dust which are when exposed to the workers that lead to different serious health effects like silicosis and lungs cancer. So measurement of these dust concentration is necessary to know the impact of various mining activity on the surrounding environment. From the above view, this current project mainly focuses on the dust sampling by using high volume dust sampler i.e. Envirotech APM 460 NL and Envirotech APM 550, measuring the personal dust exposure of different workmen at different mining sites by using Personal Dust Sampler (Model Arelco Ineris CIP 10), and characterization of the dust collected from the filter paper by using FTIR (Fourier Transform Infra-Red spectroscopy). For this purpose Lajkura Opencast Project was chosen which produces 30 MT of coal per year for convenience, because as it is a large opencast mine so better knowledge can be gained from this mine regarding the concentration and effects of the dust. The dust sampling and monitoring was conducted during the month of March 2016 to get a good assess of dust. From the measurement through Envirotech APM 460NL the dust concentration was found out to be 1074µg/m3 and 984 µg/m3, and through Envirotech APM 550 dust concentration is found out to be 196 µg/m3. Personal dust exposure is also measured and the measured concentration was found to vary between 0.8mg/m3 to 1.3 mg/m3. From the characterization of the dust sample the compound that we found are Silica, Sulfates, Sulfoxide, and Carboxylates etc

Are paid tools worth the cost? A prospective cross-over study to find the right tool for plagiarism detection

Background: The increasing pressure to publish research has led to a rise in plagiarism incidents, creating a need for effective plagiarism detection software. The importance of this study lies in the high cost variation amongst the available options for plagiarism detection. By uncovering the advantages of these low-cost or free alternatives, researchers could access the appropriate tools for plagiarism detection. This is the first study to compare four plagiarism detection tools and assess factors impacting their effectiveness in identifying plagiarism in AI-generated articles. Methodology: A prospective cross-over study was conducted with the primary objective to compare Overall Similarity Index(OSI) of four plagiarism detection software(iThenticate, Grammarly, Small SEO Tools, and DupliChecker) on AI-generated articles. ChatGPT was used to generate 100 articles, ten from each of ten general domains affecting various aspects of life. These were run through four software, recording the OSI. Flesch Reading Ease Score(FRES), Gunning Fog Index(GFI), and Flesch-Kincaid Grade Level(FKGL) were used to assess how factors, such as article length and language complexity, impact plagiarism detection. Results: The study found significant variation in OSI(p < 0.001) among the four software, with Grammarly having the highest mean rank(3.56) and Small SEO Tools having the lowest(1.67). Pairwise analyses revealed significant differences(p < 0.001) between all pairs except for Small SEO Tools-DupliChecker. Number of words showed a significant correlation with OSI for iThenticate(p < 0.05) but not for the other three. FRES had a positive correlation, and GFI had a negative correlation with OSI by DupliChecker. FKGL negatively correlated with OSI by Small SEO Tools and DupliChecker. Conclusion: Grammarly is unexpectedly most effective in detecting plagiarism in AI-generated articles compared to the other tools. This could be due to different softwares using diverse data sources. This highlights the potential for lower-cost plagiarism detection tools to be utilized by researchers