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Air pollution exerts a profound impact on both public health and the natural environment. In India, festivals like Diwali also contaminate the air by releasing pollutants into the atmosphere. It is essential to identify the most polluted region by estimating these pollutants. Since air quality assessment involves multiple air pollutants, there may be inherent uncertainty associated with data. This study employs a fuzzy Multi Attribute Decision Making (MADM) framework fuzzy Analytical Hierarchy Process-Entropy-fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje (FAHP-Entropy-FVIKOR) to model the impact of air pollution as a decision-making problem to address the uncertainty and assess the air quality during the Diwali festival from 2019 to 2021 in Tamil Nadu, India. An integrated weighting approach is utilised to determine the weights of the air pollutants using a fuzzy Analytical Hierarchy Process and Entropy methods. Mainly, the fuzzy VIKOR approach is employed to rank the polluted regions. The validation of the proposed model is established through a comparative analysis using Spearman's rank correlation with two other existing fuzzy MADM methods. Furthermore, a sensitivity analysis is conducted to evaluate the influence of priority weights and the interdependence of pollutants in determining regional rankings. The results conclude that a strong positive correlation is attained between the proposed and existing methods and the highest levels of air pollution during the festival period are observed in Gandhi Nagar (2019), Rayapuram (2020), T. Nagar, Sowcarpet and Triplicane (2021) in their respective years. These findings substantiate the consistency and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

BACKGROUND: HMIS will incorporate a paradigm shift in health such as removing manual records and transformation of data through the complex structure of health departments in Tamilnadu. Thus developing a model of technology acceptance in HMIS contest is important and necessary in order to promote usage of the HMIS in rural health care system.OBJECTIVE: The papers purpose is to formulate a model of technology acceptance of Health Management Information System (HMIS) by generating and validating a research model that best describes rural health care workers usage behavior and behavior intention. METHODS: This research proposes a theoretical framework which is comprised of key determinants that influence usage behavior of HMIS together with moderator. It has been tested through different parametric test and confirmatory factor analysis. RESULTS: Data analysis shows that health workers innovativeness and voluntariness have a direct and positive influence on Technology Acceptance level and that the basic TAM hypotheses are fulfilled. HMIS usage behavior and behavior intention can be increased with factors that are effecting the successful implementation of HMIS when it remains high. CONCLUSIONS: This research enables health departments to know which aspects of their HMIS components and variables to improve. This shows that HMIS usage and health workers/staffs acceptance level are key tools in the success of HMIS. This research has seemed to be done at the right time and in the right place to the best of its kind. [ABSTRACT FROM AUTHOR]

In this study, we objectively focus on the relationship between the number of coronavirus (COVID-19) cases and key climate variables. We found that the risk of COVID-19 was approximately doubled during warm summer months when the aerosol molecules are likely stimulated by temperature and rainfall. Given that India is currently emerging as the new epicenter for the third and fourth outbreaks of COVID-19, we selected four key hotspot states-Maharashtra, Andhra Pradesh, Kerala, and Tamil Nadu - to closely look into the impact of climate variables on the spread of COVID-19 infected cases during 2020 and 2021. We found that COVID-19 is most active in temperature between 27°C and 32°C, while it is active in monthly average rainfall between 250 mm and 350 mm. This study further confirms that, although temperature and rainfall are not the initial triggers of COVID-19, both variables seem to play significant roles in spreading COVID-19 in India, especially during the summer season of 2020 and 2021, when the Indian summer monsoon was stronger in these four states. [ABSTRACT FROM AUTHOR]

This research synthesises two evaluations of a local level climate change adaptation and planning process in the southernmost coastal region, Kanyakumari, India which was supported through community-based appraisals. First, understanding the public perception of climate change factors is crucial in generating support for climate change response. Second, generating enough grass-root level support from the local population to assess the long-term climate change impacts on decision-making to design the adaptation strategies for sea-level rise. These two results highlight the level of understanding and awareness of climate change's impact by the local population in the coastal region of Kanyakumari. A well-designed questionnaire with structured interviews, to collect data on local knowledge and local perception of climate change and sea-level rise from 100 respondents was distributed each in 44 coastal villages in the study area. The finding showed that 20.7% of the local population have knowledge of climate change and its impacts and 18.4% of the population agreed that the impact of sea-level rise is experienced in the coastal region. This study is to provide insight into the communities' knowledge of the adaptive capacity of the local population to cope with climate change towards the treaty of sea-level rise. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) are consistently gathering environmental weather data from sensor nodes on a random basis. The wireless sensor node sends the data via the base station to the cloud server, which frequently consumes immoderate power consumption during transmission. In distribution mode, WSN typically produces imprecise measurable or missing data and redundant data that influence the whole network of WSN. To overcome this complexity, an effective data prediction model was developed for decentralized photovoltaic plants using hybrid Harris Hawk Optimization with Random Forest algorithm (HHO-RF) primarily based on the ensemble learning approach. This work is proposed to predict the precise data and minimization of error in WSN Node. An efficient model for data reduction is proposed based on the Principal Component Analysis (PCA) for processing data from the sensor network. The datasets were gathered from the Tamil Nadu photovoltaic power plant, India. A low cost portable wireless sensor node was developed for collecting PV plant weather data using Internet of Things (IoT). The experimental outcomes of the proposed hybrid HHO-RF approach were compared with the other four algorithms, namely: Linear Regression (LR), Support Vector Machine (SVM), Random Forest (RF) and Long Short Term Memory (LSTM) algorithm. Results show that the determination coefficient (R2), Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values of the HHO-RF model are 0.9987, 0.0693, 0.2336 and 0.15881, respectively. For the prediction of air temperature, the RMSE of the proposed model is 3.82 %, 3.84% and 6.92% model in the lowest, average and highest weather days. The experimental outcomes of the proposed hybrid HHO-RF model have better performance compared to the existing algorithms. [ABSTRACT FROM AUTHOR]