Comparative Financial Performance Analysis of Farmer Producer Companies in Eastern Uttar Pradesh

Farmer Producer Companies are emerging as an innovative model to transform agriculture into a profitable business venture by leveraging the collectives of small and marginal farmers through economies of scale with better bargaining power, better value addition facilities, better access to farming technologies, better input supply, and better market accessibility. Financial performance indicates the financial health and resource utilization efficiency of a business organization or a company over a certain period. In the present study, a comparative analysis of balance sheets and income statements was made for five different Farmer Producer Companies of Eastern Uttar Pradesh to identify the pattern of change in various financial components from 2019-20 to 2020-21. Based on the analysis, the important findings were that reserve and surpluses were significantly increased for most of the FPCs for future business expansion, three FPCs reported increased total liabilities, and all of the FPCs managed to grow their total assets compared to the previous year. The revenue growth rate for three FPCs was below par. The performance of the FPCs in respective to net profit after tax was asymmetric. Out of five, only four FPCs were able to meet all the operational expenses from the revenue generated out of business activities and hence, considered profit-making. Moreover, the overall financial performance of the FPCs was disrupted due to the wrath of the Covid-19 pandemic. © Economic Affairs (New Delhi).All rights reserved

Predictive Modeling of Stress in the Healthcare Industry During COVID-19: A Novel Approach Using XGBoost, SHAP Values, and Tree Explainer

There was a substantial medicine shortage and an increase in morbidity due to the second wave of the COVID-19 pandemic in India. This pandemic has also had a drastic impact on healthcare professionals' psychological health as they were surrounded by suffering, death, and isolation. Healthcare practitioners in North India were sent a self-administered questionnaire based on the COVID-19 Stress Scale (N = 436) from March to May 2021. With 10-fold cross-validation, extreme gradient boosting (XGBoost) was used to predict the individual stress levels. XGBoost classifier was applied, and classification accuracy was 88%. The results of this research show that approximately 52.6% of healthcare specialists in the dataset exceed the severe psychiatric morbidity standards. Further, to determine which attribute had a significant impact on stress prediction, advanced techniques (SHAP values), and tree explainer were applied. The two most significant stress predictors were found to be medicine shortage and trouble in concentrating.

Fungal infection: An unrecognized threat

The fungi are eukaryotes and of great interest to microbiologist. Fungi are heterotrophic organism that require organic compounds for nutrition. According to Hawksworth, only around 100 fungi cause diseases in humans and animals out of around 1.5 million existing in the universe. Fungal pathogenic infection may cause allergies, superficial infection, as well as invasive mycosis in severe cases. Public health can be significantly affected by zoonotic fungi that transmit naturally between animals and humans. Prevention of fungal infection arising out of zoonotes has received insufficient attention as it lacks mass awareness. A number of different fungal infections, their signs and symptoms, preventive measures, and treatment protocol are demonstrated in this chapter. Regarding the treatment of various fungal infections, azoles, fluoropyrimidines, polyenes, and echinocandins are the only four molecular classes of drugs available as on date to target fungal metabolic pathways despite years of drug discovery research. Few other promising molecules like morpholines and allylamines are useful antifungal but with poor efficacy and severe side effects when administered systematically. Development of resistance against most common antifungal drugs further aggravates the situation. Fungal infection like mucormycosis is observed in some parts of the world after a patient gets infected with COVID-19 as there is impairment in the immunity system. There is an urgent need to control this fungal infection as it poses serious threat silently. We can limit the spread of fungal infection by protecting susceptible population from being exposed. More efforts are needed from a global health perspective to aware the people regarding neglected fungal infection and its problem so that socioeconomic consequences and mortality can be better explained. An integrated platform of prevention and control strategies for the spread of fungal infection is the need of the hour. © 2023 Elsevier Inc. All rights reserved.

A multi-path delivery system with random refusal against online booking using Type-2 fuzzy logic-based fireworks algorithm

With the development of the internet and the availability of smart phones, online shopping and the number of digital buyers have increased enormously under the present social context (COVID-19). The major pitfall of this marketing system is the random non-availability of/refusal by customers. Nowadays, with the development of infrastructure, there are several route connections among the different locations worldwide. Considering these, we formulate some multi-path routing for goods delivery with refusal problems (MPRfGDwRPs) against online booking having different route connections between nodes (customers) and solved by a novel Type-2 fuzzy logic based fireworks algorithm (T2FLFWA) developed for discrete problems with probability-based selection and generation based prime mutation. In T2FLFWA, inferences are drawn to obtain the amplitude coefficient against the assumed spark number and iteration ratio. Some instances from TSPLIB are solved and supremacy of T2FLFWA is established through ANOVA test. In MPRfGDwRP, a delivery man starts with goods from a warehouse against customers' online demands under cash-on-delivery or cash-on bookings systems and delivers the appropriate goods to the respective customers located at different locations. The random refusal or non-availability of customers is considered. The optimum travel plan and appropriate routes between the nodes are selected for minimum total cost. The models are illustrated numerically through a real-life problem. Some managerial conclusions are drawn. The novelty of the investigation is that for the first time, T2FLFWA for discrete problems is developed, delivery man's routing plan with multiple paths between nodes with refusals is formulated and its solution using T2FLFWA is presented. © 2022 The Author(s)

Predictive Modeling of Stress in the Healthcare Industry during COVID-19: A Novel Approach Using XGBoost, SHAP Values, and Tree Explainer

There was a substantial medicine shortage and an increase in morbidity due to the second wave of the COVID-19 pandemic in India. This pandemic has also had a drastic impact on healthcare professionals' psychological health as they were surrounded by suffering, death, and isolation. Healthcare practitioners in North India were sent a self-administered questionnaire based on the COVID-19 Stress Scale (N = 436) from March to May 2021. With 10-fold cross-validation, extreme gradient boosting (XGBoost) was used to predict the individual stress levels. XGBoost classifier was applied, and classification accuracy was 88%. The results of this research show that approximately 52.6% of healthcare specialists in the dataset exceed the severe psychiatric morbidity standards. Further, to determine which attribute had a significant impact on stress prediction, advanced techniques (SHAP values), and tree explainer were applied. The two most significant stress predictors were found to be medicine shortage and trouble in concentrating. © 2022 IGI Global. All rights reserved.

A Detailed Scientometric Review of Coronavirus Research

A deadly novel coronavirus disease or severe acute respiratory syndrome (COVID-19 or SARS-CoV-2) has taken the entire globe in its grip and claimed over more than 0.1 million lives across the globe in barely four months of time. This has attracted researchers, medical practitioners, scientists, biologist's fraternity, etc. all over the world to join hands in fighting the pandemic. Therefore, a detailed study in the field of coronavirus, especially related to the research status and gaps under a common umbrella, will further help in understanding and improving the current scenario. In the present paper, the scientometric analysis technique was utilized for understanding the recent research activities, scientific trends, and global involvement in the research on coronavirus. Herein, Web of Science database was used for searching the documents. The "articles” in the "English” language were considered in the study. The VoSviewer software was used for carrying out the scientometric analysis. The scanning of the research publication status on a year-on-year basis suggested an increase in the field of research on coronavirus in the recent past. From 2000 till 2020, a total of 9257 number of research articles were published. Among all other countries, USA has the most number of documents published. Analysis of the journals, authors, organizations, funding agencies of the countries and their co-operation network were also analyzed based on citations. Further, co-occurrence analysis of the different keywords suggested that coronavirus related diseases are known to precipitate severe acute respiratory syndrome in the patients. This is also true in the case of COVID-19. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Omicron: A variant of public health concern

With the detection of Omicron, a new variant of severe acute respiratory syndrome coronavirus-2 in South Africa, a question arises regarding its implication in public health. The infectivity, ability of getting detected by available testing method and effectiveness of existing vaccine against this strain are not known properly. This Omicron variant can impact public health similarly or more than the Delta variant. The research is going on in many countries to get conclusion regarding the impact of the Omicron variant in public health.

Limitations and implications of doubling time approach in COVID-19 infection spreading study: A gradient smoothing technique

To control the spread of COVID-19, around the world, many countries imposed lockdowns. Numerous studies were reported on COVID-19 in different disciplines with various aspects. The doubling time is a mathematical technique to estimate the current rate of spread of the disease. Researchers used the doubling technique to address the COVID-19 pandemic situation. The larger doubling period represents a low spreading rate, whereas the smaller doubling period represents a high spreading rate. In other words, high infection implies the low doubling period and low infection implies the high doubling period. So, there is an inverse relationship between doubling time and the infection rate. But the real-life data does not follow such a rule properly in various domains. The data shows that after a certain time when the infection is high, the doubling period is also high, which misleads our general concept of doubling time. This chapter addressed this issue by investigating the real-time COVID-19 data. To overcome this limitation, a gradient smoothing technique has been proposed. © 2021, IGI Global. © 2021 by IGI Global.

Predictive Modeling of Healthcare Professional's Stress Based on XGBoost Model

In India, the second wave of the COVID-19 pandemic has resulted in a significant shortage of medicines and increased morbidity. COVID-19 has also had a profound influence on the psychological well-being of health professionals, who are surrounded by agony, death, and isolation as a result of the epidemic. The goal of this cross-sectional study is to look into the mental health of Indian healthcare workers during the second wave of the COVID-19 outbreak. From March 2021 to May 2021, a self-administered questionnaire based on the COVID-19 Stress Scale was delivered online to healthcare professionals (N = 836) in north India. An ensemble learning technique - Extreme Gradient Boosting (XGBoost) was applied to predict individual stress levels with 10-fold cross-validation. XGBoost had predicted stress with an average accuracy of 0.8889. According to the findings of this study, around 52.6 percent of healthcare professionals in the sample meet the threshold for severe psychiatric morbidity. In addition, advanced methodologies (SHAP values) were employed to determine which features had a significant impact on stress prediction. Medicine shortages and trouble concentrating were found to be the two most significant CSS predictors. © 2021 IEEE.

Potential applicability of Schiff bases and their metal complexes during COVID-19 pandemic - a review

The rapid growth and revolution in the area of emerging therapeutics has been able to save the life of millions of patients globally. Besides these developments, the microbes are consistently struggling for their own survival and hence becoming quite more sturdy and incurable to existing drugs. Covid-19 virus and Black Fungus are recent examples of failure of medical preparations and strength of these viruses beyond the imagination of medical practitioners. Henceforth the study has made an extensive survey of exiting literature on heterocyclic schiff bases and their transition metal complexes to look for their potential applicability as antimicrobial agents. The inherent physiognomies of the essential properties of these transition metal complexes including thermodynamic, kinetic and chelating are comparatively modifiable as per requirements. The study has found that the biological applications of these transition metal complexes are well suited to be used as antibacterial and antifungal agents.

Piece-wise linear regression: A new approach to predict COVID-19 spreading

The coronavirus disease 2019 (COVID-19) pandemic is the most rapidly evolving global emergency since March 2020 and one of the most exercised topics in all aspects of the world. So far there are numerous articles that have been published related to COVID-19 in various disciplines of science and social context. Since from the very beginning, researchers have been trying to address some fundamental questions like how long it will sustain when it will reach the peak point of spreading, what will be the population of infections, cure, or death in the future. To address such issues researchers have been used several mathematical models from the very beginning around the world. The goal of such predictions is to take strategic control of the disease. In most of the cases, the predictions have deviated from the real data. In this paper, a mathematical model has been used which is not explored earlier in the COVID-19 predictions. The contribution of the work is to present a variant of the linear regression model is the piecewise linear regression, which performs relatively better compared to the other existing models. In our study, the COVID-19 data set of several states of India has been used. © Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Published under licence by IOP Publishing Ltd