Performance Prediction of Data-Driven Knowledge summarization of High Entropy Alloys (HEAs) literature implementing Natural Language Processing algorithms

The ability to interpret spoken language is connected to natural language processing. It involves teaching the AI how words relate to one another, how they are meant to be used, and in what settings. The goal of natural language processing (NLP) is to get a machine intelligence to process words the same way a human brain does. This enables machine intelligence to interpret, arrange, and comprehend textual data by processing the natural language. The technology can comprehend what is communicated, whether it be through speech or writing because AI pro-cesses language more quickly than humans can. In the present study, five NLP algorithms, namely, Geneism, Sumy, Luhn, Latent Semantic Analysis (LSA), and Kull-back-Liebler (KL) al-gorithm, are implemented for the first time for the knowledge summarization purpose of the High Entropy Alloys (HEAs). The performance prediction of these algorithms is made by using the BLEU score and ROUGE score. The results showed that the Luhn algorithm has the highest accuracy score for the knowledge summarization tasks compared to the other used algorithms

Assessment of Personal Care Product Use and Perceptions of Use in a Sample of US Adults Affiliated with a University in the Northeast

Evidence supports unequal burdens of chemical exposures from personal care products (PCPs) among some groups, namely femme-identifying and racial and ethnic minorities. In this study, we implemented an online questionnaire to assess PCP purchasing and usage behaviors and perceptions of use among a sample of US adults recruited at a Northeastern university. We collected PCP use across seven product categories (hair, beauty, skincare, perfumes/colognes, feminine hygiene, oral care, other), and behaviors, attitudes, and perceptions of use and safety across sociodemographic factors to evaluate relationships between sociodemographic factors and the total number of products used within the prior 24–48 h using multivariable models. We also summarized participants’ perceptions and attitudes. Among 591 adults (20.0% Asian American/Pacific Islander [AAPI], 5.9% Hispanic, 9.6% non-Hispanic Black [NHB], 54.6% non-Hispanic White [NHW], and 9.9% multiracial or other), the average number of PCPs used within the prior 24–48 h was 15.6 ± 7.7. PCP use was greater among females than males (19.0 vs. 7.9, P \u3c 0.01) and varied by race and ethnicity among females. Relative to NHWs, AAPI females used fewer hair products (2.5 vs. 3.1) and more feminine hygiene products (1.5 vs. 1.1), NHB females used more hair products (3.8 vs. 3.1), perfumes (1.0 vs. 0.6), oral care (2.3 vs. 1.9), and feminine hygiene products (1.8 vs. 1.1), and multiracial or other females used more oral care (2.2 vs. 1.9) and feminine hygiene products (1.5 vs. 1.1) (P-values \u3c0.05). Generally, study participants reported moderate concern about exposures and health effects from using PCPs, with few differences by gender, race, and ethnicity. These findings add to the extant literature on PCP use across sociodemographic characteristics. Improving the understanding of patterns of use for specific products and their chemical ingredients is critical for developing interventions to reduce these exposures, especially in vulnerable groups with an unequal burden of exposure

Pattern recognition in the landscape of seemingly random chimeric transcripts

The molecular and functional diversity generated by chimeric transcripts (CTs) that are derived from two genes is indicated to contribute to tumor cell survival. Several gaps yet exist. The present research is a systematic study of the spectrum of CTs identified in RNA sequencing datasets of 160 ovarian cancer samples in the The Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov). Structural annotation revealed complexities emerging from chromosomal localization of partner genes, differential splicing and inclusion of regulatory, untranslated regions. Identification of phenotype-specific associations further resolved a dynamically modulated mesenchymal signature during transformation. On an evolutionary background, protein-coding CTs were indicated to be highly conserved, while non-coding CTs may have evolved more recently. We also realized that the current premise postulating structural alterations or neighbouring gene readthrough generating CTs is not valid in instances wherein the parental genes are genomically distanced. In addressing this lacuna, we identified the essentiality of specific spatiotemporal arrangements mediated gene proximities in 3D space for the generation of CTs. All these features together suggest non-random mechanisms towards increasing the molecular diversity in a cell through chimera formation either in parallel or with cross-talks with the indigenous regulatory network

3rd National Conference on Image Processing, Computing, Communication, Networking and Data Analytics

This volume contains contributed articles presented in the conference NCICCNDA 2018, organized by the Department of Computer Science and Engineering, GSSS Institute of Engineering and Technology for Women, Mysore, Karnataka (India) on 28th April 2018

Abstracts of National Conference on Research and Developments in Material Processing, Modelling and Characterization 2020

This book presents the abstracts of the papers presented to the Online National Conference on Research and Developments in Material Processing, Modelling and Characterization 2020 (RDMPMC-2020) held on 26th and 27th August 2020 organized by the Department of Metallurgical and Materials Science in Association with the Department of Production and Industrial Engineering, National Institute of Technology Jamshedpur, Jharkhand, India. Conference Title: National Conference on Research and Developments in Material Processing, Modelling and Characterization 2020Conference Acronym: RDMPMC-2020Conference Date: 26–27 August 2020Conference Location: Online (Virtual Mode)Conference Organizer: Department of Metallurgical and Materials Engineering, National Institute of Technology JamshedpurCo-organizer: Department of Production and Industrial Engineering, National Institute of Technology Jamshedpur, Jharkhand, IndiaConference Sponsor: TEQIP-