Prediction of Corona Virus Disease Outcome and Future Forecast the Trend of COVID-19 in India: Observational Study

This work is motivated by the disease caused by the novel corona virus Covid-19, rapid spread in India. An encyclopaedic search from India and worldwide social networking sites was performed between 1 March 2020 and 20 Jun 2020. Nowadays social network platform plays a vital role to track spreading behaviour of many diseases earlier then government agencies. Here we introduced the approach to predict and future forecast the disease outcome spread through corona virus in society to give earlier warning to save from life threats. We compiled daily data of Covid-19 incidence from all state regions in India. Five states (Maharashtra, Delhi, Gujarat, Rajasthan and Madhya-Pradesh) with higher incidence and other states considered for time series analysis to construct a predictive model based on daily incidence training data. In this study we have applied the predictive model building approaches like k-nearest neighbour technique, Random-Forest technique and stochastic gradient boosting technique in COVID-19 dataset and the simulated outcome compared with the observed outcome to validate model and measure the performance of model by accuracy (ACC) and Kappa measures. Further forecast the future trends in number of cases of corona virus deceased patients using the Holt Winters Method. Time series analysis is effective tool for predict the outcome of corona virus disease. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Health detection system for COVID19 patients using IoT

Currently, the entire planet is terrified of a virus known as COVID-19 (coronavirus). Its effects are so deadly that the whole world has been placed on lockdown. Vaccines for this virus are being developed by scientists and physicians all over the world. Machine learning, the Internet of Things (IoT), and artificial intelligence all play a role in detecting people who have been affected by coronavirus. We have also operated in this direction and developed a system called "Health Detection System for COVID-19 Patients using IoT" which can identify coronavirus-infected people and create a database for easy monitoring. Our system named as "Health Detection System for COVID-19 Patients using IoT" can detect corona by measuring the temperature and oxygen level of the patient. The system will detect the temperature of person with the help of DHT sensor and the oxygen level with the help of MAX30100, which are interfaced with NodeMCU. Data will be uploaded on ThingSpeak server (cloud) through which it can be monitored. The system is quite simple and very effective, especially at the hospital (ICU) where doctors can monitor patient from a distant place. Complete system cost around Rs 1,000/- (Rupees One Thousand Only). © 2022 Scrivener Publishing LLC.

Dynamic connectedness in commodity futures markets during Covid-19 in India: New evidence from a TVP-VAR extended joint connectedness approach

This paper presents a unique time-varying parameter vector autoregression (TVP-VAR) based extended joint connectedness approach to quantify the connectedness and transmission mechanism of shocks of nine commodities futures returns (namely;Gold and Silver from the category of precious metals;Copper, Lead, Zinc, Nickel and Aluminium from the category of base or industry metals;Natural Gas and Brent Crude Oil from energy sector) obtained from Multi Commodity Exchange of India Limited (MCX) from January 1, 2018 to December 31, 2021. This paper employs Balcilar et al. (2021)'s TVP-VAR extended joint connectedness approach, which combines the TVP-VAR connectedness approach of Antonakakis et al. (2020) with the joint spillover approach of Lastrapes and Wiesen (2021), to investigate the dynamic connectedness among the select commodity futures of interest. Our findings show that system-wide dynamic connectedness varies over time and is driven by economic events. The pandemic shocks appear to have an impact on system-wide dynamic connectedness, which peaks during the COVID-19 pandemic. Crude oil and zinc are the primary net shock transmitters, whereas gold and silver are the primary net shock receivers. We also discovered that the role of aluminum in shock transmitters and shock receivers changed during the course of the investigation. Pairwise connectivity, on the other hand, shows that Zinc, Copper, Nickel, and Crude oil are the key drivers of gold price changes, explaining the network's high degree of interconnectivity. During the study period, it was also discovered that silver has a significant influence on gold. Furthermore, in comparison to natural gas, gold's spillover activity is still relatively modest (on a scale), indicating that gold is less sensitive to market innovations. © 2023 Elsevier Ltd

Study of corona epidemic: Predictive mathematical model

The outbreak of SARS-CoV-2 (Covid-19) is one of the most unprecedented and devastating events that the world has witnessed so far. It was manifested in Wuhan, China in December 2019 and has spread worldwide. The rapidity at which Covid-19 is transmitted has become one of the major concerns regarding the safety of mankind. The similarity of symptoms between Covid-19 and normal flu, like cough, body ache and headache, makes it difficult to ascertain a case to be of normal flu or of Covid. Consequently, many Covid cases are unreported which further increases the risk of spread of infection. In the present chapter, by using three mathematical models, we aim to give an outline of the spread of Covid-19 in West Bengal and how lockdown has helped to reduce the number of Covid cases. The first model is an exponential model;the second model is based on Geometric Progression which shows spread of coronavirus using a tree chart. The third model, named as Model for Stay at Home, shows that due to lockdown, the number of cases is gradually attaining a constant level instead of growing exponentially;thus urging each citizen to stay at home during lockdown unless an unavoidable situation arises. © 2022 Scrivener Publishing LLC.

Organization citizenship behaviour among indian nurses during SARS-COV-2: A direct effect moderation model

The pandemic COVID has engulfed the entire world in general and India in particular. Almost the entire country is under lockdown now and then forcing the people to stay inside their homes for the safety of themselves and others. There is one section, i.e. Indian nurses, which is braving against all odds to ensure the proper functioning of the health care system and educate and persuade the patients and their relatives. This has necessitated the nurses to go an extra mile reflecting a sense of responsibility towards patients, colleagues, hospitals, society, and nation and discharge their duties performing activities beyond the formal job descriptions, formal reward system, or direct and explicit recognition. In the present study, the researchers have empirically investigated the nature, extent, and mechanism of the impact of variables transformational leadership, job satisfaction, and emotional intelligence that lead to nurses displaying the organizational citizenship behaviour at this unprecedented juncture of time in India. © 2022, The Author(s), under exclusive licence to Springer Nature B.V.

Infection profile in children with acute lymphoblastic leukemia during maintenance chemotherapy: A study in Dr M R Khan Shishu Hospital and ICH, Dhaka

Background: Major bulk of treatment-related morbidity and mortality in childhood acute lymphoblastic leukemia (ALL) is attributed to infection. The study aimed to analyze the infection profile in children with ALL during maintenance chemotherapy. Method(s): In this 18-month prospective study, clinical and laboratory data of infection profile were collected and analyzed in children with ALL undergoing maintenance chemotherapy at M R Khan Shishu Hospital and Institute of child health, Dhaka, Bangladesh in collaboration with TATA Medical Center, Kolkata, India. Result(s): Of the 42 children, the male to female ratio was 1:1 with the median age of 7.8 years (IQR 4-12.8). Among them, 81% (n=34) had precursor B-ALL and 72% (n=28) had high-risk (HR) disease. Twenty-eight patients (67%) developed infection for a total of 72 episodes and febrile neutropenia (25%) was most common. Major site of infection was lung;21% had upper respiratory tract infection and 8% had radiologically proven pneumonia. Other episodes of infection commonly seen were fever without neutropenia (19%), COVID-19 infection (7%), diarrhoea (4%). Blood culture was positive in only three (4%) infective episodes. Two-third episodes (65%) required hospital admission with no PICU support and infection related mortality. No significant association was found in terms of age, sex, type of ALL, NCI- Risk and type of maintenance therapy with development of infections. Conclusion(s): Although maintenance therapy is considered mild, a significant proportion of children with ALL in maintenance therapy had infective episodes and two-third of them requiring hospital admission. However, no significant association was seen in terms of age, sex, type of ALL, NCI Risk and type of maintenance therapy and infection occurrence. Copyright © 2022

The value of firm flexibility under extreme positive demand shocks: COVID-19 and toilet paper panic purchases

We estimate the relationship between firms' production flexibility and profitability under extreme positive demand shocks using the European toilet paper manufacturing industry in 2020 as a case study. This industry faced extreme increases in demand due to consumers panic purchasing toilet paper after the outbreak of the COVID-19 pandemic. Our estimates show that flexibility and profitability are positively related. This is even more pronounced in times of an extreme positive demand shock when a ten percent improvement in flexibility is associated with a five percent increase in profitability. © 2022 Elsevier B.V.

Examining the role of Emotional Intelligence as a mediator for the impact of Virtual Communication on decision making effectiveness during the covid-19 crisis

Across the world global pandemic Covid-19 has opened a new approach towards life. It has carved a novel arena of working where organizations have re-structured their operations through Virtual Communication. Assuming the current situation as perennial, it has opened both opportunities and challenges to the organizations. We have witnessed a paradigm shift in the organization culture and working dynamics where the new normal is working via virtual mode. The present study assumes that Emotional Intelligence plays a key role in understanding relationship of Virtual communication and Decision-Making Effectiveness. Task technology fit theory lays the foundation of this study. Implications of this study is applicable to academicians, trainers, coaches, consultants, policy makers and all professionals who relied on virtual communication as an important platform during covid. © 2022 University of Stockholm. All rights reserved.

Predicting the antecedents of consumers' intention toward purchase of mutual funds: A hybrid PLS-SEM-neural network approach

The current study intends to identify the behavioural antecedents of investors' attitude and investment intention toward mutual funds using a robust SEM-ANN approach. It focuses on novel factors in the purview of the COVID-19 pandemic, increasing digitalization and social media usage. The research outcome indicates that attitude (ATB), awareness (AW) and investment decision involvement (IDI) have a significant positive relation with investment intention (BI). In contrast, perceived barrier (PBR) negatively relates to investment intention. Herd behaviour (HB) and social media influence (SMI) do not influence investment intention toward mutual funds. Moreover, all the tested predictors share direct relation with the attitude toward mutual fund investment, barring perceived risk (PR), which has an inverse relationship. As per the outcome of ANN sensitivity analysis, attitude is the most crucial determinant of investment intention. It is followed by awareness (AW), perceived barriers (PBR) and investment decision involvement (IDI). Among the significant determinants of attitude, self-efficacy (SE) is the most important determinant, followed by perceived usefulness (PU), perceived emergency (PEMER), subjective norms (SN) and perceived risk (PR). © 2022 John Wiley & Sons Ltd.

Emergency responses of Indian smart cities during the COVID-19 pandemic

This paper presents the current status of Indian smart cities and examines their preparedness and response to the COVID-19 outbreak. The study focuses on implemented & ongoing projects under the Smart City Mission of the Government of India, which have contributed significantly to controlling the pandemic along with other channels. The study finds that modern cities in both developed and developing countries were not well-prepared to deal with the emergency situations and struggled in providing a satisfactory response during the pandemic. The analysis of primary and secondary data has shown that digital surveillance and movement control through integrated control command centers (ICCC) were the most useful projects in monitoring the COVID-19 cases. However, the lack of technology integration in smart cities hinders the effective usage of implemented projects. Thus, the study recommends integrated network-based applications that include healthcare, essential services, mobility, and movement across smart cities in India. The proposed framework is expected to provide the much-needed alignment at the policy, objective, and implementation levels of smart city framework designs. © 2022 Urban Affairs Association.

Analysis of Sentiments on the Onset of COVID-19 Using Machine Learning Techniques

The novel coronavirus (COVID-19) pandemic has struck the whole world and is one of the most striking topics on social media platforms. Sentiment outbreak on social media enduring various thoughts, opinions, and emotions about the COVID-19 disease, expressing views they are feeling presently. Analyzing sentiments helps to yield better results. Gathering data from different blogging sites like Facebook, Twitter, Weibo, YouTube, Instagram, etc., and Twitter is the largest repository. Videos, text, and audio were also collected from repositories. Sentiment analysis uses opinion mining to acquire the sentiments of its users and categorizes them accordingly as positive, negative, and neutral. Analytical and machine learning classification is implemented to 3586 tweets collected in different time frames. In this paper, sentiment analysis was performed on tweets accumulated during the COVID-19 pandemic, Coronavirus disease. Tweets are collected from the Twitter database using Hydrator a web-based application. Data-preprocessing removes all the noise, outliers from the raw data. With Natural Language Toolkit (NLTK), text classification for sentiment analysis and calculate the score subjective polarity, counts, and sentiment distribution. N-gram is used in textual mining -and Natural Language Processing for a continuous sequence of words in a text or document applying uni-gram, bi-gram, and tri-gram for statistical computation. Term frequency and Inverse document frequency (TF-IDF) is a feature extraction technique that converts textual data into numeric form. Vectorize data feed to our model to obtain insights from linguistic data. Linear SVC, MultinomialNB, GBM, and Random Forest classifier with Tfidf classification model applied to our proposed model. Linear Support Vector classification performs better than the other two classifiers. Results depict that RF performs better.

Risk analysis in road construction using failure mode and effect analysis

Risk management is defined as process of identification of risk factors, their assessment as well as prioritization of risks along with economical application of resources to minimize and control the occurrence of the risk events. The overall aim of this study is to analyze the practice of risk management, significant risk factors and its ranking in road construction in Sindhupal chowk district with the perspective of global pandemic of coronavirus disease (COVID-19). This research is conducted through questionnaire survey to collect the primary data. Response obtained from respondents is rated on a 5 point Likert scale and analyzed through MS Excel. The data are analyzed to find out mean and ranking of each risk factors for the severity of risk based on FMEA model regarding their probability of occurrence, its consequence and its detectability. Major risk factors based on risk priority number are time overrun risk, Safety Health and Environmental risk, cost overrun risk, financial and economic risk, force majeure and ecological risk, political legal and social risk, organizational risk, contractual risk, quality risk and design and specification risk in descending order of risk severity.

Organization citizenship behaviour among indian nurses during SARS-COV-2: A direct effect moderation model

The pandemic COVID has engulfed the entire world in general and India in particular. Almost the entire country is under lockdown now and then forcing the people to stay inside their homes for the safety of themselves and others. There is one section, i.e. Indian nurses, which is braving against all odds to ensure the proper functioning of the health care system and educate and persuade the patients and their relatives. This has necessitated the nurses to go an extra mile reflecting a sense of responsibility towards patients, colleagues, hospitals, society, and nation and discharge their duties performing activities beyond the formal job descriptions, formal reward system, or direct and explicit recognition. In the present study, the researchers have empirically investigated the nature, extent, and mechanism of the impact of variables transformational leadership, job satisfaction, and emotional intelligence that lead to nurses displaying the organizational citizenship behaviour at this unprecedented juncture of time in India. © 2022, The Author(s), under exclusive licence to Springer Nature B.V.

Harnessing the capability of CADD methods in the prediction of anti-COVID drug likeliness

The COVID-19 pandemic has claimed many lives and added to the social, economic, and psychological distress. The contagious disease has quickly spread to almost 200 countries following the regional outbreak in China. As the number of infected populations increases exponentially, there is a pressing demand for anti-COVID drugs and vaccines. Virtual screening provides possible leads while extensively cutting down the time and resources required for ab-initio drug design. The chapter aims to highlight the various computer-aided drug design methods to predict an anti-COVID drug molecule. © 2021, IGI Global.

A review of the presentation and outcome of left ventricular thrombus in coronavirus disease 2019 infection

Background: Cardiovascular complications of the coronavirus disease 2019 (COVID-19), which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS- CoV-2), have been documented both in the acute phase and in convalescence. One such complication is the formation of the left ventricular (LV) thrombus. There is a lack of clarity regarding the incidence, risk factors, and management of this complication. Aim: The aim of the study is to identify the clinical presentation, risk factors and outcome of COVID-19 patients with left ventricular thrombus (LVT). Methods: A literature search was conducted to identify all case reports of COVID-19 with LVT in PubMed/Medline, Embase, Web of Science, and Google Scholar. Results: Among the 65 patients identified, 60 had LVT, either at admission, or during the acute phase of the illness. Six patients with mild symptoms during the acute phase of viral illness had only the COVID-19 antibody test positivity at the time LV thrombus was detected. Few of the patients (23.1%) had no comorbidities. The mean age of the patients was 52.8 years, and the youngest patient was 4 years old. This suggests that LVT formation can occur in young COVID-19 patients with no co-morbid conditions. Most of the patients (69.2%) had more than one site of thrombosis. A mortality rate of 23.1% was observed in our review, and ST-elevation myocardial infarction (STEMI) was diagnosed in 33.3% of those who died. Conclusions: A high degree of suspicion for LVT must be maintained in patients with known cardiac disease and those with new-onset arterial or venous thromboembolism, and such patients may benefit from a screening echocardiography at admission. Relevance for Patients: The patients with preexisting cardiovascular disease must take added precautions to prevent acquiring COVID-19 infection as there is a higher risk of developing LV thrombus. In patients who develop LVT in COVID-19, mortality rate is higher.

A review of the presentation and outcome of sarcoidosis in coronavirus disease 2019

Background: In the setting of the current pandemic, concerns have arisen regarding the multisystemic involvement of sarcoidosis and the possible exacerbations in response to the exposure to severe acute respiratory syndrome coronavirus 2. Aim: This study aims to compare the differences in clinical presentation, management, and outcome of coronavirus disease 2019 (COVID-19) between patients with sarcoidosis and those in the general population. Methods: A literature search was conducted by reviewing original research articles such as case reports, case series, observational studies, and questionnaire-based surveys published in PubMed/Medline, Web of Science, and Google scholar. Data from individual patients in case series and case reports have been pooled to create a data set that was compared with larger such cohorts obtained from several other observational studies. Results: Twenty-seven patients were identified from 14 original articles. No significant differences were found in the clinical manifestations of patients with sarcoidosis presenting with COVID-19 as compared to the general population. The rate of hospitalization in our study was found to be 48.1%. The overall mortality in our study was 7.4%, which is higher than the global average of 2.1%. Conclusion: Our observations have reinforced the hypothesis that the presence of additional medical comorbidities is associated with a higher risk of intensive care unit admission. Furthermore, the presence of moderate to a severe limitation in pulmonary functions is an additional risk factor associated with increased hospital admissions and mortality in sarcoidosis. However, neither the diagnosis of sarcoidosis nor ongoing treatment with steroids, methotrexate, or other immunosuppressants was associated with a poorer prognosis in patients with sarcoidosis. Relevance for patients: Patients with sarcoidosis must take added precautions to mitigate the risk of acquiring COVID-19 infection in view of the COVID-19-related mortality rate in this group of patients. Specifically, immunocompromised patients (on immunomodulator drugs and high dose steroids) have been found to have an increased risk of contracting COVID-19. Overall impact on prognostication and outcome in cases requiring hospitalization remains yet to be determined.

Limited usage of prednisolone: Severe alcoholic hepatitis beyond steroids, a path to be explored!

Background: Steroids are mainstay of treatment in severe alcoholic hepatitis and steroid ineligibility is major cause of mortality, specially in the absence of transplant option. There is paucity of data on the proportion and causes of steroid ineligibility, and the natural history and outcome in such patients.This data would help in planning alternative therapeutic strategies.We aimed to study the factors responsible for steroid ineligibility and clinical course of these patients. Methods: Consecutive patients of SAH admitted to the Institute of Liver and Biliary Sciences, between April 2017 to February 2021 were screened for steroid eligibility. All patients who left against medical advice or had likelihood of death within 24 hours of admission, were excluded. The criteria for ineligibility were MDF >90,Acute kidney injury, sepsis, extrahepatic organ failures or severe comorbidities. The patients who developed an event in the hospital or were discharged were followed for 90days survival. Results: Altogether,522 patients were admitted with SAH,138(26.4%) were steroid eligible and 384(73.6%) were ineligible(figure 1).The main reasons for steroid ineligibility were: one or more organ failures in 179(46.7%) patient, with 37(9.6%), 71(18.4%), 28(7.2%) and 43(11.1%) patients having 4,3,2 and 1 organ failures respectively. Specifically, 28.3%(109) patients had AKI, 31.8% (122) had respiratory failure, 33.5% (129) had circulatory failure, 39.3%(159) had grade 3 or more encephalopathy. In addition, 32(8.3%) patients had a DF of >90, 27(7.0%) patients had recent upper GI bleed,25/384(6.5%) had superadded acute viral hepatitis ,20(5.2%) patients had severe alcohol withdrawal and 7(1.8%) had SBP. 15(4%) patients were not given steroid due to spontaneous reduction in mDF to <32 after admission. Further, 42(10.9%) patients were either found to spontaneously improve during the period of evaluation and hence were not taken up for steroid therapy. 37(9.6%) patients were not given steroids due urinary infection, continuous fever, cellulitis, mild COVID and active tuberculosis. The overall survival in the steroid ineligible cohort at day 90 was 54.4%, while the 28 day survival was 58.5%. On multivariate Analysis the presence of any organ failure, Age, albumin, AARC score, Presence of variceal bleed increase the risk of mortality (table1). 42 patients had spontaneously resolving jaundice were not given steroids, out of them 6 patients were readmitted and required the therapy. None of them died on follow up at day 90. Twenty patients had alcohol withdrawal at presentation;6 of them subsequently received steroids. Conclusion: Almost two-third patients with SAH are not eligible to receive steroids and carry a high 90 days mortality. The age, albumin, AARC score, variceal bleed and presence of organ failure predict mortality in steroid non exposed SAH patients. RCT's are needed for comparing various therapies used in such patients. .

In silico screening of some naturally occurring bioactive compounds predicts potential inhibitors against SARS-COV-2 (COVID-19) protease

The reports pertaining to the mitigation and treatment of the COVID-19 pandemic are still lacking. Compatibility of the natural products and availability of the modern computational techniques motivated us to carry out In Silico investigations on some bioactive natural compounds reportedly found in the fruits and leaves of Anthocephalus Cadamba commonly known as Kadam or Kadamb Tree, aiming to predict the potential inhibitors against the aforesaid virus. Having modeled the ground state ligand structure of the nine natural compounds applying density functional theory at B3LYP/631+G (d, p) level, we have performed their molecular docking with SARS-COV-2 protease to calculate the binding affinity as well as to screen the binding at S-protein site during ligand-protein interactions. Out of these nine studied naturally occurring compounds;Oleanic Acid has been appeared to be a potential inhibitor for COVID-19 followed by Ursolic Acid, Iso-Vallesiachotamine, Vallesiachotamine, Cadambine, Vincosamide-N-Oxide, Isodihydroamino-cadambine, Pentyle Ester of Chlorogenic Acid and D-Myo-Inositol. Hence, these bioactive natural compounds or their structural analogs may be explored as an anti-COVID-19 drug agents. The solubility and solvent-effect related to the phytochemicals may be the point of concern. In vivo investigations on these proposed natural compounds or their structural analogs are invited for designing and developing the potential medicine/vaccine for the treatment of COVID-19 pandemic.

An Assessment of Popular Virtual Platforms for Online Education in COVID-19 Pandemic in India: A Study

Online education is not new to the twenty-first century, especially to higher education. It is known since early 2000 and continues. Traditional education is offered in all government institutions in India. However, in March 2020, Education turned 360° immediately after the COVID-19 pandemic, which allegedly originated in Wuhan city of China in late December 2019. Since then, all the Educational Institutions including schools, colleges, universities, coaching centers insisted to shut down for an unknown time by the government to maintain social distancing and mitigating COVID-19 spread. Due to this, the traditional system was bound to shift to Online Education (OE). In this chapter, challenges that occurred due to the immediate shift to the online education system are discussed. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Drug discovery and treatment of an emerging pandemic infection covid-19

In the last decades, two serious coronaviruses have appeared in humans and animals, SARS-CoV (severe acute respiratory syndrome) and MERS-CoV (middle east respiratory syndrome) are also contagious viruses that causes ARDS stands for acute respiratory distress syndrome. SARS-CoV in 2003 and MERS-CoV in 2012 are characterized as a high mortality rate. This novel human respiratory coronaviruses, also known as SARS n-CoV or SARS-CoV-2, are needed to rapidly provide therapeutic options to reduce and prevent the spread of this outbreak. For such infections, there are currently no scientifically approved precautions or anti-viral products authorized.;therefore, effective remedial and preventive strategies have to be developed that can be easily applied to this newly emerging epidemic. In addition to all these, some therapeutic options are also being evaluated that are used for Covid-19, which includes inhibitory virus molecules or molecules that target specific replication and transcription enzymes. Since the drugs remdesivir, lopinavir-ritonavir, favipiravir, camostat mesylate, chloroquine, and hydroxychloroquine were originate closely stand high successful in controlling infection, Covid-19 looks promising. The research on Covid-19, discuss the efficacy of the several drug and vaccines against Covid-19 and previous outbreaks of SARS and MERS, and provide recommendations for new modes of treatment, assessment, and clinical research on such terrible epidemic. This paper will summarize and discuss the main biological characteristics of SARS-CoV-2 and the current scenario of emerging Covid-19 infections, as well as explain the current therapeutic medications treating Covid-19, based on the clinical trial data. © 2021, Turkish Chemical Society. All rights reserved.