Moinul Islam

Bio: Moinul Islam is an academic researcher from Barasat Government College. The author has contributed to research in topics: Camellia sinensis. The author has an hindex of 2, co-authored 3 publications receiving 7 citations.

Phytochemical analysis of mite-infested tea leaves of Darjeeling Hills, India

Abstract: Introduction Darjeeling tea of India is one of the most famous beverages globally. However, a large amount of tea crop is damaged every year by the attack of mites. Objectives The study aimed to determine the changes in different pigments and biochemical components of tea leaves due to mite infestation. Materials and methods We used UV-visible and Fourier-transform infrared (FTIR) spectroscopy simultaneously to understand the damages in pigment contents of the leaves of tea (Camellia sinensis (L.) Kuntze) due to attack of phytophagus mite, Oligonychus coffeae Nietner. Furthermore, chemical analysis of infested tea leaves was also performed to compare the nutrients of the plants, namely total phenol, protein and soluble sugar. Results The UV-visible study reveals severe reduction of the pigments such as chlorophyll (Chl), carotenoids and xanthophylls in the tea leaf due to mite infestation. The findings of the FTIR study, also shows variation in different physiochemical components in the leaf Chl. The sugar and protein content of the infested leaves have been reduced compared to uninfested ones. Results in the case of tea leaves plucked during first (March) and third (November) flushes show similar trends. Conclusion The increase in phenolic and alcoholic components and decrease in Chl contents may affect the quality of Darjeeling tea. The extent of damages done by the pests measured here could aid the pest management in tea gardens.

Search for trends of Covid-19 infection in India, China, Denmark, Brazil, France. Germany and the USA on the basis of power law scaling

Abstract: The corona virus (SARS-CoV-2) or Covid-19 pandemic is growing alarmingly throughout the whole world. Using the power law scaling we analyze the data of different countries and three states of India up to 1st April, 2020 and explain in terms of power law exponent. We find significant reduction in growth of infections in China and Denmark (γ reduced from approximately 2.18 to 0.05 and 11.41 to 6.95, respectively). Very slow reduction is also seen in Brazil and Germany (γ reduced from approximately 6 to 4 and 11 to 7, respectively). Infection in India is growing (γ=9.23) though lesser in number than that in the USA (highest γ of 16 approximately, studied so far), Italy and a few other countries. Among three Indian states the growth in West Bengal (γ=0.64) is much slower than other states like Maharashtra and Kerala (γ=3.23 and 3.32, respectively). Some future predictions, though not rigid, has also been incorporated in our analysis. The earlier lock-down and stricter measures from the Governments concerned are being thought to be the only possible solutions, in the present situation, to fight against this virus.

Search for the trend of COVID-19 infection following Farr's law, IDEA model and power law.

Abstract: Following power law, Farr9s law and IDEA model, we analyze the data of COVID-19 pandemic for India up to 2 May, 2020 and for Germany, France, Italy, the USA, Singapore, China and Denmark up to 26 April, 2020. The cumulative total number of infected persons as a function of elapsed time has been fitted with power law to find the scaling exponent (γ). The reduction in γ in different countries signals the reduction in the growth of infection, possibly, due to long-term Government intervention. The extent of infection and reproduction rate R_0 of the same are also examined using Farr9s law and IDEA model. The new cases per day with time assume Gaussian bell shaped curve, obeying the rule that faster rise follows faster decay. In India and Singapore, the peak of the bell shaped curve is still elusive. It is found that, till date, countries such as Denmark and India implementing sooner lockdown have underwent lower number of new cases of infection. Daily variation shows, R_0 of all the countries is reducing, ushering in fresh hopes to combat COVID-19. Finally, we try to make a prediction as to the date on which the different countries will come down to daily cases of infection as low as one hundred (100).

Predictive models of COVID-19 in India: A Rapid Review

Abstract: Background The mathematical modelling of coronavirus disease-19 (COVID-19) pandemic has been attempted by a wide range of researchers from the very beginning of cases in India Initial analysis of available models revealed large variations in scope, assumptions, predictions, course, effect of interventions, effect on health-care services, and so on Thus, a rapid review was conducted for narrative synthesis and to assess correlation between predicted and actual values of cases in India Methods A comprehensive, two-step search strategy was adopted, wherein the databases such as Medline, google scholar, MedRxiv, and BioRxiv were searched Later, hand searching for the articles and contacting known modelers for unpublished models was resorted The data from the included studies were extracted by the two investigators independently and checked by third researcher Results Based on the literature search, 30 articles were included in this review As narrative synthesis, data from the studies were summarized in terms of assumptions, model used, predictions, main recommendations, and findings The Pearson’s correlation coefficient (r) between predicted and actual values (n = 20) was 07 (p = 0002) with R2 = 049 For Susceptible, Infected, Recovered (SIR) and its variant models (n = 16) ‘r’ was 065 (p = 002) The correlation for long-term predictions could not be assessed due to paucity of information Conclusion Review has shown the importance of assumptions and strong correlation between short-term projections but uncertainties for long-term predictions Thus, short-term predictions may be revised as more and more data become available The assumptions too need to expand and firm up as the pandemic evolves

What influences COVID-19 infection rates: A statistical approach to identify promising factors applied to infection data from Germany

Abstract: The recent COVID-19 pandemic is of big and world-wide concern. There is an intense discussion and uncertainty which factors and sanctions can reduce infection rates. The overall aim is to prevent an overload of the medical system. Even within one country, there is frequently a strong local variability in both – political sanctions as well as other local factors – which may influence infection rates. The main focus of study is analysis and interpretation of recent temporal developments (infection rates). We present a statistical framework designed to identify local factors which reduce infection rates. The approach is robust with respect to the number of undetected infection cases. We apply the framework to spatio-temporal infection data from Germany. In particular, we demonstrate that (1) infection rates are in average significantly decreasing in Germany; (2) there is a high spatial variability of these rates, and (3) both, early emergence of first infections and high local infection densities has led to strong recent decays in infection rates, suggesting that psychological effects (such as awareness of danger) lead to behaviour changes that reduce infection rates. However, the full potential of the presented method cannot yet be exploited, since more precise spatio-temporal data, such as local cell phone-based mobility data, are not yet available. In the nearest future, the presented framework could be applied to data from other countries at any state of infection, even during the exponential phase of the growth of infection rates.