SKILL ACQUISITION FOR ENHANCING EMPLOYABILITY THROUGH MULTIPLE LEARNING EXPERIENCE INSTRUCTIONAL STRATEGY (MLEIS) – TOWARDS ENSURING INCLUSIVE AND EQUITABLE TECHNOLOGY EDUCATION

Matching skills to jobs has long been one of the important goals of education. The National Employability Report (2016) has highlighted the fact that engineering graduates do not fulfill the basic criteria of employability. It has been reported the current strategies do not address multiple modes and levels of numeracy, programming, computer literacy, algorithm and programming. It was revealed that students find certain subjects quite difficult and the objective based achievement test revealed failure to realize learning objectives and learning outcomes. Theory based instructional strategies and lecture mode of instructional delivery has been found to be not suited for engineering education [1]. In this study, the effectiveness of a Multiple Learning Experience based Instructional strategy (MLEIS) is explored. MLEIS is based on theories of learning, instruction design, learning styles and techno pedagogies. MLEIS envisages a skill based curricular strategy which addresses diversity, inclusiveness focusing on aspects like skill development, skill acquisition, professional competency and subject comprehension.  Article visualizations

IMPLEMENTATION OF AN AUTOMATIC LPGGAS DETECTION AND ALERT SYSTEM

Gas leakage is a major problem with industrialsector and residential premises. The safety workenvironment is important for every worker in theindustries, hotels, restaurants etc. and important tocreate a safer workplace. Gas accidents are vitalissues for all areas of life where precautions arevery important. One of the preventive methods tostop accident associated with the gas leakage is toinstall gas leakage detection kit at vulnerableplaces. The aim of this project is to present such adesign that can automatically detect, stop and alertgas leakage in vulnerable premises. Therefore animplementation of an automatic LPG gas detectionand alert system is proposed in our project. Proposed design can help us to avoid Gasleakage kind of problem in our daily life. The aimof this project is to monitor for liquid petroleum gas(LPG) leakage to avoid fire accidents providing safety feature where security has been animportant issue. The system detects the leakageofthe LPG using gas sensor and alerts the consumerabout the gas leakage by sending SMS

"Hey..! This medicine made me sick": Sentiment Analysis of User-Generated Drug Reviews using Machine Learning Techniques

Sentiment analysis has become increasingly important in healthcare, especially in the biomedical and pharmaceutical fields. The data generated by the general public on the effectiveness, side effects, and adverse drug reactions are goldmines for different agencies and medicine producers to understand the concerns and reactions of people. Despite the challenge of obtaining datasets on drug-related problems, sentiment analysis on this topic would be a significant boon to the field. This project proposes a drug review classification system that classifies user reviews on a particular drug into different classes, such as positive, negative, and neutral. This approach uses a dataset that is collected from publicly available sources containing drug reviews, such as drugs.com. The collected data is manually labeled and verified manually to ensure that the labels are correct. Three pre-trained language models, such as BERT, SciBERT, and BioBERT, are used to obtain embeddings, which were later used as features to different machine learning classifiers such as decision trees, support vector machines, random forests, and also deep learning algorithms such as recurrent neural networks. The performance of these classifiers is quantified using precision, recall, and f1-score, and the results show that the proposed approaches are useful in analyzing the sentiments of people on different drugs

A prospective, open label clinical study to evaluate the safety, efficacy and tolerability of azadvir herbal steam inhaler in asymptomatic, mildly symptomatic COVID-19 patients and health care workers posted to covid wards

Background: COVID-19 patients experience cytokine storm which cause pulmonary and extra-pulmonary complications even with currently available of standard of care. Additional antiviral and immune boosters are the need of hour to treat COVID-19 and to prevent post covid complications.Methods: In this study we enrolled 40 asymptomatic to mild COVID-19 patients to receive azadvir herbal steam inhaler along with standard of care. We evaluated the benefits of azadvir herbal steam inhaler by assessing RT-PCR conversion, clinical outcomes and improvement in immune markers (LDH, CRP, D-DIMER).Results: At the end of the study the immune markers improved significantly in study patients. In mild symptomatic cases IL-6 was 23.2 pg/ml on day 0 and 21.8 pg/ml on day 14. Reduction in IL-6 in mild symptomatic patients was statistically highly significant (p=0.0056). Mean IL-6 in asymptomatic patients was 22.3 pg/ml on day 0 and 21.1 pg/ml on day 14. Reduction in IL-6 in asymptomatic patients was statistically highly significant (p=0.0035).  Mean D-dimer was showing decreasing trend from day 0 to day 14 in mild symptomatic patients. In asymptomatic patients D dimer was 0.8 µg/ml on day 0 and 0.6 µg/ml on day 14. D-dimer decreased significantly from day 0 to day 14 (p value =0.0013). Mean LDH values on day 0 in mild symptomatic patients was 319.4 U/l and 219.3 on day 14. The reduction in LDH values in mild symptomatic patients is statistically significant (p value <0.0122). In asymptomatic patients mean LDH values on day 0 was 237 U/l and 194 U/l on day 14. The reduction in LDH values in asymptomatic group was statistically significant. Mean CRP values in mild symptomatic patients on day 0 was 12.2 mg/l and 3.8 mg/l on day 14. There was significant reduction in CRP values in mild symptomatic group which was statistically significant (p value =0.0546). Mean CRP values in asymptomatic patients on day 0 was 4.9 mg/l and 2.8 mg/l on day 14. There was significant reduction in mean CRP in asymptomatic patients which was statistically significant (p value =0.0446). In the present study all 40 patients (100%) cleared the virus and became negative for RT PCR test within 6 days. None of the patients progressed to severe COVID-19 and none of the patients succumbed to the disease.Conclusions: Azadvir accelerated recovery of COVID-19 patients by RT-PCR conversion, early improvement in clinical symptoms and immune markers in this study. This study results clearly indicates that azadvir has antiviral, immune booster activity and has definitive role in the management of asymptomatic to mild COVID-19 patients along with standard of care (CTRI no. CTRI/2020/06/026181)

Matrix-Bound PAI-1 Supports Cell Blebbing via RhoA/ROCK1 Signaling

The microenvironment of a tumor can influence both the morphology and the behavior of cancer cells which, in turn, can rapidly adapt to environmental changes. Increasing evidence points to the involvement of amoeboid cell migration and thus of cell blebbing in the metastatic process; however, the cues that promote amoeboid cell behavior in physiological and pathological conditions have not yet been clearly identified. Plasminogen Activator Inhibitor type-1 (PAI-1) is found in high amount in the microenvironment of aggressive tumors and is considered as an independent marker of bad prognosis. Here we show by immunoblotting, activity assay and immunofluorescence that, in SW620 human colorectal cancer cells, matrix-associated PAI-1 plays a role in the cell behavior needed for amoeboid migration by maintaining cell blebbing, localizing PDK1 and ROCK1 at the cell membrane and maintaining the RhoA/ROCK1/MLC-P pathway activation. The results obtained by modeling PAI-1 deposition around tumors indicate that matrix-bound PAI-1 is heterogeneously distributed at the tumor periphery and that, at certain spots, the elevated concentrations of matrix-bound PAI-1 needed for cancer cells to undergo the mesenchymal-amoeboid transition can be observed. Matrix-bound PAI-1, as a matricellular protein, could thus represent one of the physiopathological requirements to support metastatic formation

Implementation of Dynamical Nucleation Theory Effective Fragment Potentials Method for Modeling Aerosol Chemistry

In this work, the dynamical nucleation theory (DNT) model using the ab initio based effective fragment potential (EFP) is implemented for evaluating the evaporation rate constant and molecular properties of molecular clusters. Predicting the nucleation rates of aerosol particles in different chemical environments is a key step toward understanding the dynamics of complex aerosol chemistry. Therefore, molecular scale models of nanoclusters are required to understand the macroscopic nucleation process. On the basis of variational transition state theory, DNT provides an efficient approach to predict nucleation kinetics. While most DNT Monte Carlo simulations use analytic potentials to model critical sized clusters, or use ab initio potentials to model very small clusters, the DNTEFP Monte Carlo method presented here can treat up to critical sized clusters using the effective fragment potential (EFP), a rigorous nonempirical intermolecular model potential based on ab initio electronic structure theory calculations, improvable in a systematic manner. The DNTEFP method is applied to study the evaporation rates, energetics, and structure factors of multicomponent clusters containing water and isoprene. The most probable topology of the transition state characterizing the evaporation of one water molecule from a water hexamer at 243 K is predicted to be a conformer that contains six hydrogen bonds, with a topology that corresponds to two water molecules stacked on top of a quadrangular (H2O)4 cluster. For the water hexamer in the presence of isoprene, an increase in the cluster size and a 3-fold increase in the evaporation rate are predicted relative to the reaction in which one water molecule evaporates from a water hexamer cluster