No abstract

This paper proposes three nonlinear controllers such as fuzzy logic controller (FLC), static nonlinear controller (SNC), and adaptive-network-based fuzzy inference system (ANFIS)based variable resistive-type fault current limiter (VR-FCL) to augment the transient stability of a large-scale hybrid power system consisting of a doubly fed induction generator (DFIG)-based wind farm, a photovoltaic (PV) plant, and a synchronous generator (SG). Appropriate resistance generation of the VR-FCL during a grid fault to provide better transient stability is the main contribution of the work. The effectiveness of the proposed control methods in improving the transient stability of the hybrid power network is verified by applying both balanced and unbalanced faults in one of the double circuit transmission lines connected to the system. Simulation results show that the proposed FLC-, SNC-, or ANFIS-based VR-FCL are effective in improving the transient stability of the studied hybrid system. Moreover, all the proposed methods exhibit almost similar performance. Therefore, any of the methods can be chosen for the transient stability enhancement of the hybrid power system. Index Terms-Adaptive-network-based fuzzy inference system (ANFIS), doubly fed induction generator (DFIG), duty ratio (d), fuzzy logic controller (FLC), photovoltaic (PV), static nonlinear controller (SNC), synchronous generator (SG), transient stability, variable resistive-type fault current limiter (VR-FCL).

Two northwestern districts of Bangladesh with a population of 629,752 were surveyed from June 1991 to March 1993 to detect and follow up lathyrism patients. Out of 2,567 neurological patients, 882 were diagnosed as having lathyrism, giving a prevalence rate of 14.0/10,000. This prevalence was higher among young males: only 12.9% of the patients were female, and only 19.3% of the patients were over 30 years of age at onset of the disease. The average family size was 4.6 members. In the surveyed area, 730 families were evaluated: 622 families had only 1 affected member, and 108 families had 2–8 affected members. Most of the patients were working, while only 4% were not. The majority of the patients had a very low intake of animal protein. A few cases of human T-lymphotropic virus infection and osteolathyrism were found during this study.

Coronavirus disease, COVID-19, has touched every country globally except five countries (North Korea, Turkmenistan, Tonga, Tuvalu and Nauru). Vaccination is the most effective method to protect against infectious diseases. The objective is to ensure that everyone has access to a COVID-19 vaccine. The conventional vaccine development platforms are complex and time-consuming to obtain desired approved vaccine candidates through rigorous regulatory pathways. These safeguards guarantee that the optimized vaccine product is safe and efficacious for various demographic populations prior to it being approved for general use. Nucleic acid vaccines employ genetic material from a pathogen, such as a virus or bacteria, to induce an immune response against it. Based on the vaccination, the genetic material might be DNA or RNA; as such, it offers instructions for producing a specific pathogen protein that the immune system will perceive as foreign and mount an immune response. Nucleic acid vaccines for multiple antigens might be made in the same facility, lowering costs even more. Most traditional vaccine regimens do not allow for this. Herein, we demonstrate the recent understanding and advances in nucleic acid vaccines (DNA and mRNA based) against COVID-19, specifically those in human clinical trials.

No abstract

No abstract

Purpose There is a strong prerequisite for organizations to analyze customer review behavior to evaluate the competitive business environment. The purpose of this study is to analyze and predict customer reviews of halal restaurants using machine learning (ML) approaches. Design/methodology/approach The authors collected customer review data from the Yelp website. The authors filtered the reviews of only halal restaurants from the original data set. Following cleaning, the filtered review texts were classified as positive, neutral or negative sentiments, and those sentiments were scored using the AFINN and VADER sentiment algorithms. Also, the current study applies four machine learning methods to classify each review toward halal restaurants into its sentiment class. Findings The experiment showed that most of the customer reviews toward halal restaurants were positive. The authors also discovered that all of the methods (decision tree, linear support vector machine, logistic regression and random forest classifier) can correctly classify the review text into sentiment class, but logistic regression outperforms the others in terms of accuracy. Practical implications The results facilitate halal restaurateurs in identifying customer review behavior. Social implications Sentiment and emotions, according to appraisal theory, form the basis for all interactions, facilitating cognitive functions and supporting prospective customers in making sense of experiences. Emotion theory also describes human affective states that determine motives and actions. The study looks at how potential customers might react to a halal restaurant’s consensus on social media based on reviewers’ opinions of halal restaurants because emotions can be conveyed through reviews. Originality/value This study applies machine learning approaches to analyze and predict customer sentiment based on the review texts toward halal restaurants.

Purpose Due to the rapid surge in the number of COVID-19 cases in India, the health-care supply chain (HCSC) disruptions and uncertainties have increased manifold posing severe challenges to health-care facilities and significantly hampering the functioning of the health industry. This study aims to propose a hierarchical structural model of enablers of HCSC in the COVID-19 outbreak and identifies inter-relationships among them in the health-care market. Design/methodology/approach Enablers of emergency HCSC have been identified through extensive literature review and experts’ opinions. Subsequently, total interpretive structural modeling (TISM) and cross-impact matrix-multiplication (MICMAC) analysis have been implemented to determine the hierarchical inter-relationships among enablers and classify them according to their contribution to the overall system. Findings The research has identified and validated 15 enablers of the emergency supply chain in health-care businesses. The study resulted in a seven-level hierarchical structural model based on enabler’s driving and dependence powers. Further, the application of MICMAC analysis resulted in the classification of enablers into four groups, namely, autonomous, dependent, linkage and independent group. Research limitations/implications This study would help health professionals, policymakers and academia to implement the theoretical model constructed to alleviate the effect of COVID-19 by improving the HCSC performances in pandemic situations. This study has social and economic implications in terms of cost-effective and efficient delivery of care services in health emergencies. Originality/value The proposed theoretical model constructed is a new effort addressing the issues of HCSC in the COVID-19 crisis. Procedural implementation of TISM and MICMAC analysis in this study would help researchers to grasp concepts in a very lucid manner. The present study is one of the very few studies analyzing enablers in pandemic situations by implementing the TISM approach.