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Floods are the deadly and catastrophic disasters, causing loss of life and harm to assets, farmland, and infrastructure. To address this, it is necessary to devise and employ an effective flood management system that can immediately identify flood areas to initiate relief measures as soon as possible. Therefore, this research work develops an effective flood detection method, named Anti- Corona-Shuffled Shepherd Optimization Algorithm-based Deep Quantum Neural Network (ACSSOA-based Deep QNN) for identifying the flooded areas. Here, the segmentation process is performed using Fuzzy C-Means with Spatial Constraint Multi-Kernel Distance (MKFCM_S) wherein the Fuzzy C-Means (FCM) is modified with Spatial Constraints Based on Kernel-Induced Distance (KFCM_S). For flood detection, Deep QNN has been used wherein the training progression of Deep QNN is done using designed optimization algorithm, called ACSSOA. Besides, the designed ACSSOA is newly formed by the hybridization of Anti Corona Virus Optimization (ACVO) and Shuffled Shepherd Optimization Algorithm (SSOA). The devised method was evaluated using the Kerala Floods database, and it acquires the segmentation accuracy, testing accuracy, sensitivity, and specificity with highest values of 0.904, 0.914, 0.927, and 0.920, respectively. [ABSTRACT FROM AUTHOR]

Severe acute respiratory syndrome coronavirus (SARS-COV) is a major family of viruses that cause infections in both animals and humans, including common cold, coronavirus disease (COVID-19), severe acute respiratory syndrome (SARS), and Middle East respiratory syndrome. This study primarily aims to predict the number of COVID-19 positive cases in 36 states of Nigeria using a long short-term memory (LSTM) algorithm of deep learning. The proposed approach employs K-means clustering to detect outliers and principal component analysis (PCA) to select important features from the dataset. The LSTM was chosen because of its non-linear characteristics to handle the dataset. As COVID-19 cases follow non-linear characteristics, LSTM is the most suitable algorithm for predicting their numbers. For comparison, several types of machine learning algorithms, such as naive Bayes, XG-boost, and SVM, were employed. After the comparison, LSTM was observed to be superior among all algorithms.

Cholera is a leading cause of mortality in Nigeria. The two most significant predictors of cholera are a lack of access to clean water and poor sanitary conditions. Other factors such as natural disasters, illiteracy, and internal conflicts that drive people to seek sanctuary in refugee camps may contribute to the spread of cholera in Nigeria. The aim of this research is to develop a cholera outbreak risk prediction (CORP) model using machine learning tools and data science. In this study, we developed a CORP model using design science perspectives and machine learning to detect cholera outbreaks in Nigeria. Nonnegative matrix factorization (NMF) was used for dimensionality reduction, and synthetic minority oversampling technique (SMOTE) was used for data balancing. Outliers were detected using density-based spatial clustering of applications with noise (DBSCAN) were removed improving the overall performance of the model, and the extreme-gradient boost algorithm was used for prediction. The findings revealed that the CORP model outcomes resulted in the best accuracy of 99.62%, Matthews's correlation coefficient of 0.976, and area under the curve of 99.2%, which were improved compared with the previous findings. The developed model can be helpful to healthcare providers in predicting possible cholera outbreaks. [ABSTRACT FROM AUTHOR]

Microclimatic alteration produced by mulching with different coloured plastic sheets, inside and outside the polyhouse and its effect on yield of tomato was studied during 2012-13 at Birsa Agricultural University, Ranchi. The mean weekly minimum and maximum air temperature during last week of December to 1st week of March were found to be higher by 2 to 9 0C inside the polyhouse than open field. Relative humidity was always higher in the open field during January to February by 2 to 7 % but it was higher inside the polyhouse in the months of March to May by 4 % at 7.00 AM. Almost a similar trend at 2.00 PM was also observed but during March to April relative humidity was higher by 10 % in polyhouse condition. The maximum available light intensity inside the polyhouse was about 30 to 40 % lower than that of the open field irrespective of growth stages. Average soil temperature was found to be higher by 2 to 5 0C under open field condition than inside the polyhouse. Leaf temperature of tomato grown under polyhouse was always lower than the open field condition. These microclimatic conditions inside the polyhouse favoured the performance of tomato and fruit yield obtain from the polyhouse was 65 t ha-1 against 33 t ha-1 from the open field

Purpose: Cisplatin is a cancer chemotherapeutic drug that has been extensively used in the treatment of a variety of cancers. However, the full usage of cisplatin is limited due to its treatment associated development of multiple side effects in the host. In the present study, the modulatory effect of rutin, a type of flavonoid, on the cisplatin mediated antitumor activity and allied genotoxicity in ascites Dalton's lymphoma (DL)-bearing mice were investigated. Methods: The antitumor activity was determined by calculating the percent increase in the life span of mice, cell viability and scanning electron microscopy (SEM) of DL cells. Further, the modulatory effect of rutin on the cisplatin-induced genotoxic effects in the same DL-bearing mice was assessed by the analysis of micronuclei, chromosomal aberration and sperm abnormality. Results: The combination treatment of mice with rutin and cisplatin showed a considerable increase in the life span of the DL-bearing mice depicting better antitumor efficacy. SEM of these DL cells showed severe membrane deformities in DL cells such as fusion of cell membrane, membrane blebbing, cell shrinkage, membrane folding and loss in microvilli from the tumor cell surface which may lead to cell death. Cisplatin alone treatment caused an increase in the frequency of chromosomal aberrations, micronuclei and sperms abnormality. However, the combination treatment of DL-bearing mice with rutin and cisplatin comparatively reduced these genotoxic effects. Conclusion: The overall findings suggest that rutin enhances the cisplatin-mediated antitumor activity and cytotoxicity against DL cells and at the same time diminishes the genotoxic effects induced by cisplatin in the DL-bearing mice. [ABSTRACT FROM AUTHOR]

Background: Spontaneous bacterial peritonitis is a fatal complication of liver cirrhosis. Ascitic fluid culture is positive in half of the cases. Materials and methods: The present cross-sectional study was conducted upon the patients admitted in Medicine ward of VIMS, Pawapuri. Bacterial examination of ascitic fluid, culture and antibiotic sensitivity was done. Results: 47.2% of SBP cases were culture positive. E. coli (46.2%), Klebsiella (26.9%), Acinetobacter (7.7%) and Pseudomonas aeruginosa (3.8%) were the common organisms isolated. Conclusion: As half of SBP cases have positive culture, ascitic fluid culture and antibiotic sensitivity testing is essential in such cases. [ABSTRACT FROM AUTHOR]