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Background: Gaming is a new phenomenon and there are increasing concerns over its negative impact. After the official recognition by diagnostic and statistical manual-5 and International Classification of Disorder-11, much of the research is directed toward understanding the different constructs of the gambling disorder. Peer influence is an established factor in substance use but less researched in gaming. Aims of the Study: The aim of this study is to explore and understand the influence of peer pressure on Internet gaming. Materials and Methods: Individuals between 15 and 35 years were asked to participate after explaining the study objectives and assessed on the Peer Pressure Scale and Internet Gaming Disorder (IGD) Scale. Responses were analyzed using the R language with R studio as the integrated development environment. Generalized additive model was used to identify the best-fit model. The Akaike information criterion was used to select the best model. Results: Battle Ground Mobile India is the most played game. Peer pressure alone accounts for 46.2% of the variability in Internet gaming and along with factors such as student; real friends; strategy games; online mode of playing, night time playing; and hours spent playing, increasing it to 56%. Peer pressure is positively correlated with IGD (P < 0.001, R = 0.65). Conclusions: Peer pressure is directly associated and plays a role in developing Internet gaming along with factors such as being a student; playing with real friends; strategy game genre; online mode of playing games; and nightly playing. Substance use such as tobacco may influence peer pressure and Internet gaming, acting as a co-factor for both. [ABSTRACT FROM AUTHOR]

This study aimed to explore the short-term health effects of ambient air pollutants P M 2.5 , P M 10 , S O 2 , N O 2 , O 3 , and C O on COVID-19 daily new cases and COVID-19 daily new deaths. A time-series design used in this study. Data were obtained from 1 April 2020 to 31 December 2020 in the National Capital Territory (NCT) of Delhi, India. The generalized additive models (GAMs) were applied to explore the associations of six air pollutants with COVID-19 daily new cases and COVID-19 daily new deaths. The GAMs revealed statistically significant associations of ambient air pollutants with COVID-19 daily new cases and COVID-19 daily new deaths. These findings suggest that governments need to give greater considerations to regions with higher concentrations of P M 2.5 , P M 10 , S O 2 , N O 2 , O 3 , and C O , since these areas may experience a more serious COVID-19 pandemic or, in general, any respiratory disease. [ABSTRACT FROM AUTHOR]

The impact of COVID-19 on the Global scale is tremendously drastic. There are several types of research going on across the world simultaneously to understand and overcome this dire pandemic outbreak. This paper is purely a statistical study on a distinct set of datasets regarding COVID-19 in India. The motivation of this study is to provide an insight into the rapid growth of confirmed COVID-19 cases in India. The rapid growth of COVID-19 cases in India started in March 2020. The main objective of this paper is to provide a solid statistical model for the policymaker to handle this kind of pandemic situation in the near future with nonlinear data. In this paper, the data was got from 1st April to 29th November 2020. To come up with a solid statistical model, various nonlinear data such as confirmed COVID-19 cases, maximum temperature, minimum temperature, the total population (state-wise), the total area in km2 (state-wise), and the total rural and urban population count (state-wise) have been analyzed. In this paper, six different Generalized Additive Models (GAM) was identified after a thorough analysis of other researchers' (Xie and Zhu, 2020; Prata et al., 2020) findings. In all perspectives, the results were identified and analyzed. The GAM model regarding total COVID-19 confirmed cases, total population, and the total rural population provides the best average fit of R2 value of 0.934. As the population value is quite high, the author has concise it using logarithm to provide the best p -value of 0.000542 and 0.001407 for a relation between the total number of COVID-19 cases regarding the total population and total rural population respectively. [ABSTRACT FROM AUTHOR]

The contribution of four variables, namely Chlorophyll-a (Chl-a), Sea Surface Temperature (SST), diffuse attenuation coefficient (Kd_490 or Kd), and Photosynthetically Active Radiation (PAR), in predicting the catches of major pelagic fish species (Indian mackerel, horse mackerel, Bombay duck, oil sardine, and other sardines) was evaluated using Canonical Correlation Analysis (CCA). The outcome of the analysis was compared with those obtained by using the following models and methods: the Generalized Linear Model (GLM), the Generalized Additive Model (GAM), connection weight methods, and the explanatory methods of Artificial Neural Networks (ANNs). Both the sets of results were in agreement. Neither the GAM nor the ANNs method showed any clear advantage over each other, although the GAM performed better than the GLM. [ABSTRACT FROM AUTHOR]

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease represents the causative agent with a potentially fatal risk which is having great global human health concern. Earlier studies suggested that air pollutants and meteorological factors were considered as the risk factors for acute respiratory infection, which carries harmful pathogens and affects the immunity. The study intended to explore the correlation between air pollutants, meteorological factors, and the daily reported infected cases caused by novel coronavirus in India. The daily positive infected cases, concentrations of air pollutants, and meteorological factors in 288 districts were collected from January 30, 2020, to April 23, 2020, in India. Spearman's correlation and generalized additive model (GAM) were applied to investigate the correlations of four air pollutants (PM2.5, PM10, NO2, and SO2) and eight meteorological factors (Temp, DTR, RH, AH, AP, RF, WS, and WD) with COVID-19-infected cases. The study indicated that a 10 μg/m3 increase during (Lag0-14) in PM2.5, PM10, and NO2 resulted in 2.21% (95%CI: 1.13 to 3.29), 2.67% (95% CI: 0.33 to 5.01), and 4.56 (95% CI: 2.22 to 6.90) increase in daily counts of Coronavirus Disease 2019 (COVID 19)-infected cases respectively. However, only 1 unit increase in meteorological factor levels in case of daily mean temperature and DTR during (Lag0-14) associated with 3.78% (95%CI: 1.81 to 5.75) and 1.82% (95% CI: -1.74 to 5.38) rise of COVID-19-infected cases respectively. In addition, SO2 and relative humidity were negatively associated with COVID-19-infected cases at Lag0-14 with decrease of 7.23% (95% CI: -10.99 to -3.47) and 1.11% (95% CI: -3.45 to 1.23) for SO2 and for relative humidity respectively. The study recommended that there are significant correlations between air pollutants and meteorological factors with COVID-19-infected cases, which substantially explain the effect of national lockdown and suggested positive implications for control and prevention of the spread of SARS-CoV-2 disease. [ABSTRACT FROM AUTHOR]

Research studies connecting air pollution and its effect on health are much needed by the policymakers in India. Generalized additive model (GAM) was applied to quantify the effect of air pollutants on hospital visits related to respiratory diseases for an Indian city, Guwahati. A total of 6335 patients visited the only medical college and hospital in the city for respiratory diseases during 2016-2018. The multi-pollutant GAM models indicate the existence of synergistic effects of particulate matter less than 2.5 µm in diameter (PM2.5), nitrogen dioxide (NO2), and carbon monoxide (CO) on hospital visits of the patients due to respiratory diseases. The ambient PM2.5 concentrations have a strong link with hospital visits in all 6 single lag days. However, the effect of CO on hospital visits due to respiratory diseases was more robust. The percent change of hospital visits associated with each 1 unit increase in CO concentrations at lag 1 day was 15.7% (RR = 1.157; 95% CI 0.929, 1.442), which was enhanced to as high as 40.8% (RR = 1.408; 1.130,1.755) at lag3. The rise of each 10 units of concentrations of PM2.5 and 1 unit of CO at cumulative lag0-6 day was associated with the percentage change in hospital visits of 6.67% and 62.36% respectively. Single-pollutant GAM models indicate that the risk percentage of PM2.5 causing respiratory disease increased in combination with other pollutants. [ABSTRACT FROM AUTHOR]

We analyzed a national database on plant richness with 7761 species from 15,565 nested quadrants to offer the maiden and indicative plant richness pattern of India across its latitudinal and longitudinal gradient. We observed two peaks in the species richness curves along both latitudinal and longitudinal gradients, due to harbouring of higher species richness in the Western Ghats and the Himalayan hot spots. The bands at 10°–11°N latitude and 76°–77°E longitude accommodate maximum of 703 and 864 number of plant species respectively, which could be explained due to cumulative contribution of (i) the Western Ghats, Eastern Ghats, Lakshadweep and Andaman and Nicobar islands along latitudinal; and (ii) the Western Ghats, central India and the Western Himalaya along longitudinal gradients. On dissection of the relationship between plant richness to environment (i.e., geographic area, topography and vegetation types) using Generalized Additive Model (GAM), we observed varied explanations for latitude and longitude. While geographic area and topography explained (98.8% deviance) to the species richness pattern across longitude, all three explained (99% deviance) to the distribution pattern along latitude. We also found that the species richness and vegetation types are positively correlated. Environmental heterogeneity, especially geographic area, topography and disturbance explain the distribution pattern of plant richness in India. Knowing the spatial pattern of plant richness could help in formulating large-scale conservation measures for India. It is hoped that the study would attract larger readership, particularly the Indian bio-geographers. [ABSTRACT FROM AUTHOR]

Fish frequently shift their distribution ranges as a result of changes in preferred environmental factors. Knowledge on distribution of fish in relation to their environmental optima is crucial for improving the understanding of fishing grounds and planning sustainable exploitation. This study investigated the monthly variability in environmental factors impacting the catch rate and the spatio-temporal distribution patterns of fish along northwest coast of India (NWCI) from 2017 to 2019. The time series images of sea surface temperature (SST), sea surface height anomaly (SSHa), chlorophyll-a concentration (CHL), and euphotic depth (Zeu) indicate close coupling between bio-physical variables in the region. Generalized Additive Model (GAM) applied to the catch rate of mid-water trawlers found that CHL and Zeu were the most influential variables for ribbonfish distribution, while CHL and SST influence squid distribution and SST and SSHa influence cuttlefish distribution. The total deviation explained 37.70 %, 10.70 %, and 22.28 % in the final model for simulating the spatio-temporal distribution of ribbonfish, squid, and cuttlefish, respectively. Environmental factors were significant in the final GAM model (p < 0.05). Spatio-temporal distribution patterns of fishery resources from mid-water trawlers showed that they were related to biophysical changes in the northwest coast of India. The high catch rate was observed in the northern latitudes during the post-monsoon (October), the major fishing season in the region. However, it shrank and shifted to southern latitudes during the winter (January). This study could be adapted to nearby countries in the Arabian Sea region for an effective and useful tool to monitor and manage the fishery resources. Furthermore, it could aid in decision-making for policymakers and resource managers to formulate strategies for holistic marine fishery management and sustainable resource exploitation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Background and Aims: As, the COVID-19 has been deemed a pandemic by World Health Organization (WHO), and since it spreads everywhere throughout the world, investigation in relation to this disease is very much essential. Investigation of pattern in the occurrence of COVID-19, to check the influence of different meteorological factors on the incidence of COVID-19 and prediction of incidence of COVID-19 are the objectives of this paper., Methods: For trend analysis, Sen's Slope and Man-Kendall test have been used, Generalized Additive Model (GAM) of regression has been used to check the influence of different meteorological factors on the incidence and to predict the frequency of COVID-19, and Verhulst (Logistic) Population Model has been used., Results: Statistically significant linear trend found for the daily-confirmed cases of COVID-19. The regression analysis indicates that there is some influence of the interaction of average temperature (AT) and average relative humidity (ARH) on the incidence of COVID-19. However, this result is not consistent throughout the study area. The projections have been made up to 21st May, 2020., Conclusions: Trend and regression analysis give an idea of the incidence of COVID-19 in India while projection made by Verhulst (Logistic) Population Model for the confirmed cases of the study area are encouraging as the sample prediction is as same as the actual number of confirmed COVID-19 cases., Competing Interests: Declaration of competing interest The authors declare that there is no known competing interest, which could have influence in this paper., (Copyright © 2020 Diabetes India. Published by Elsevier Ltd. All rights reserved.)