RESUMO
Background: Mosquito Control Programs are articulated to control Mosquito Borne Diseases and success of such programs depends on the activities of field workers, and their adherence to the standard operating procedures (SOP's) is governed by their knowledge, attitudes, and practices (KAP). Present study was intended to assess the KAP of mosquito control workers of Pakistan to get an exact depiction of prevailing situation. Methods: A cross-sectional descriptive study was conducted in March-April 2020. Questionnaire containing 30 closed and open-ended questions were administrated to participants. Knowledge and practices were evaluated using a scoring system i.e., by giving 1 point to each correct answer while attitude questions were analyzed individually and expressed in percentage for each response. Results: Total 639 workers were interviewed, mean age was 29.8 (SD ±7.87) years, majority (65.1%) was in age group of 18-30 years. Mean knowledge score was 6.96±1.28 (range 0-9) with 77.36% correct answers (P= 0.073). Mean practices score was 7.00±1.62 (range 2-9) with 77.83% appropriate answers (P< 0.001). Both knowledge and practices scores were higher for permanent employees, practices score increased with increase in job experience. Very weak positive correlation (r= 0.127) was observed between knowledge and practice scores. Conclusion: Appropriate practice correlates with better knowledge and positive attitude towards control activities. Hence, training on protection and protective measures for having a positive attitude among healthcare workers is necessary against the fight with mosquitoes.
RESUMO
An unexpected outbreak of deadly Covid-19 in later part of 2019 not only endangered the economies of the world but also posed threats to the cultural, social and psychological barriers of mankind. As soon as the virus emerged, scientists and researchers from all over the world started investigating the dynamics of this disease. Despite extensive investments in research, no cure has been officially found to date. This uncertain situation rises severe threats to the survival of mankind. An ultimate need of the time is to investigate the course of disease transfer and suggest a future projection of the disease transfer to be enabled to effectively tackle the always evolving situations ahead. In the present study daily new cases of COVID-19 was predicted using different forecasting techniques; Autoregressive Integrated Moving Average (ARIMA), Exponential Smoothing/Error Trend Seasonality (ETS), Artificial Neural Network Models (ANN), Gene Expression Programming (GEP), and Long Short-Term Memory (LSTM) in four countries; Pakistan, USA, India and Brazil. The dataset of new daily confirmed cases of COVID-19 from the date on which first case was registered in the respective country to 30 November 2020 is analyzed through these five forecasting models to forecast the new daily cases up to 31st January 2020. The forecasting efficiency of each model was evaluated using well known statistical parameters R 2, RMSE, and NSE. A comparative analysis of all above-mentioned models was performed. Finally, the study concluded that Long Short-Term Memory (LSTM) neural network-based forecasting model projected the future cases of COVID-19 pandemic best in all the selected four stations. The accuracy of the model ranges from coefficient of determination value of 0.85 in Brazil to 0.96 in Pakistan. NSE value for the model in India is 0. 99, 0.98 in USA and Pakistan and 0.97 in Brazil. This high-accuracy forecast of COVID-19 cases enables the projection of possible peaks in near future in the aforementioned countries and, therefore, prove to be helpful in formulating strategies to get prepared for the potential hard times ahead.
RESUMO
Carbon dioxide (CO2) concentration in greenhouses is sub-optimal for vegetable production. Many techniques have been used to increase CO2 concentration in greenhouses but most of them are expensive with certain limitations and drawbacks. We adopted a new strategy to elevate CO2 concentration in the greenhouse throughout the day via crop residues and animal manure composting (CRAM). During the whole cultivation period, CRAM-treated greenhouse had doubled CO2 concentration which significantly increased the yield of cherry tomatoes (Lycopersiconesculentum L.) i.e. up to 38%. The influence of CRAM procedure on cherry tomato quality was also investigated and the concentrations of total soluble solids (TSS) and soluble sugar were found to be significantly higher in cherry tomatoes grown under composting greenhouse than that of non-composting greenhouse. Additionally, CRAM-CO2 enrichment also resulted in increased concentrations of ascorbic acid (Vitamin C) and titrate acid as compared with the control. In contrast, the concentration of nitrate was considerably decreased in cherry tomato grown under CO2 enriched condition than that of control. The increase in active oxygen metabolisms such as POD, CAT and SOD while a decrease in MDA, as well as APX was observed for cherry tomatoes grown under CO2 enriched condition. Hence, CO2 fertilization by using CRAM in greenhouse significantly improved quality and increased the yield of cherry tomatoes.
