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Crop yield forecasting is critical for enhancing food security and ensuring an appropriate food supply. It is critical to complete this activity with high precision at the regional and national levels to facilitate speedy decision-making. Tea is a big cash crop that contributes significantly to economic development, with a market of USD 200 billion in 2020 that is expected to reach over USD 318 billion by 2025. As a developing country, Bangladesh can be a greater part of this industry and increase its exports through its tea yield and production with favorable climatic features and land quality. Regrettably, the tea yield in Bangladesh has not increased significantly since 2008 like many other countries, despite having suitable climatic and land conditions, which is why quantifying the yield is imperative. This study developed a novel spatiotemporal hybrid DRS–RF model with a dragonfly optimization (DR) algorithm and support vector regression (S) as a feature selection approach. This study used satellite-derived hydro-meteorological variables between 1981 and 2020 from twenty stations across Bangladesh to address the spatiotemporal dependency of the predictor variables for the tea yield (Y). The results illustrated that the proposed DRS–RF hybrid model improved tea yield forecasting over other standalone machine learning approaches, with the least relative error value (11%). This study indicates that integrating the random forest model with the dragonfly algorithm and SVR-based feature selection improves prediction performance. This hybrid approach can help combat food risk and management for other countries.

Background: Schizophrenia is a psychosis characterized by delusions and hallucinations occurring in clear consciousness. Studies have shown that the cytokines may modulate dopaminergic metabolism and schizophrenic symptomatology in schizophrenia. Cytokine involvement in periodontal disease is also well documented. To date, however, there has been relatively little research assessing periodontal status of patients with schizophrenia. The present study was therefore mainly intended to understand the exact link, if any, between periodontal disease and schizophrenia. Materials and Methods: A total of 250 schizophrenic patients (140 males and 110 females), between 25 and 55 years of age, were selected from the out patient department of National Institute of Mental Health and Neural Sciences, Bangalore and their periodontal status was assessed as part of this cross-sectional epidemiological survey. Results: ANOVA showed that there was increased evidence of poor periodontal condition, as evidenced by gingival index and plaque index in patients who had been schizophrenic for a longer duration of time (P < 0.001). So also, higher probing pocket depths were found in schizophrenics suffering from a longer period of time than others (P < 0.001). Conclusions: Although oral neglect might be a cause of poor periodontal health in schizophrenics, the possible link between periodontal diseases giving rise to schizophrenia cannot be overlooked due to the presence of cytokine activity which is present both in schizophrenia and periodontal disease.