Software Reliability Using SPRT: Burr Type III Process Model

Increased dependence on software systems elicited the assessment of their reliability, a crucial task in software development. Effective tools and mechanisms are required to facilitate the assessment of software reliability. Classical approaches like hypothesis testing are significantly time consuming as the conclusion can only be drawn after collecting huge amounts of data. Statistical method such as Sequential Analysis can be applied to arrive at a decision quickly. This paper implemented Sequential Probability Ratio Test (SPRT) for Burr Type III model based on time domain data. For this, parameters were estimated using Maximum Likelihood Estimation to apply SPRT on five real time software failure datasets borrowed from different software projects. The results exemplify that the adopted model has given a rejection decision for the used datasets

Predicting Power Consumption of Individual Household using Machine Learning Algorithms

Climate change, as known, is the dangerous environmental effect we are going to face in the near future and electricity contributes the majority of its part in overcoming climate change as per the trends. Usage of electricity is widely increasing all over the world mainly as an alternative to the use of fossil fuels. In households the usage is rapidly increasing day by day, owing to the increase in the number of devices running on electricity. As we have observed mainly after the relaxation of the lockdown the bills received by households, especially in cities were unhappy and have left most of the people aghast. It is evident that users have no idea about the power they consume. In this work, a model to forecast the electricity bill of household users based on the previous trends and usage patterns by making use of machine learning techniques has been proposed. The historical data of the user is studied and the learning is done iteratively to improve the accuracy of the model. The model can then be used to forecast the consumption beforehand

CROPUP – A Crop Yield Prediction and Recommendation System with Geographical Data using DNN and XGBoost

Agricultural management is significant in a populous country like India. Farmers must have advance knowledge about predicted crop production and crop condition within particular area to make economic and farming decisions. To generate yield, we consider factors like temperature, humidity, pressure, NDVI values, Latitude, Longitude etc. When cultivating a particular crop on a specific type of soil, there are a number of factors to be considered. A crop recommender system considers soil properties such as N, P, and K, as well as other factors like rainfall, humidity, and pH levels, to choose the best crop for the farm. This paper presents a predictive algorithm that would estimate crop yield using deep neural networks with geographical data. A recommendation system was built using machine learning algorithm like Xgboost to recommend the suitable crop. A user interface named CROPUP has been developed to scale up crop productivity and efficiency using the proposed algorithms

Diversity of mantids in tea plantation

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Knowledge, attitude and practice regarding generic drugs and branded drugs: a cross sectional study

Background: Medicines play a main role in the process of human development. The rational utilization of medicines can decrease morbidity and mortality as well as improve quality of life. In an era of steeply rising health care expenses, generic medicines provide a less expensive alternative to branded medicines. Use of generic drugs can contribute to substantial savings in medicines expenditure and the issue of access and affordability is thus addressed.Methods: A sample of 500 patients selected from out patient department was randomly selected. A self-instructed questionnaire was used for the study for the duration of 1 month. Data was analyzed using IBM SPSS statistics Version 20 New York, United States. Summary statistics were expressed using mean and standard deviation (SD) for numerical variables (median and interquartile ranges [IQRs] when skewed) and counts and percentages for categorical variables.Results: Participants reported with 17.23% knowledge score, 40.65% attitude score, and 7.96% attitude score.Conclusions: In the study it was found that there is significant correlation between knowledge and attitude whereas no correlation was found between knowledge and practice regarding usage of generic drugs

Role of tumor necrosis factor-α and its receptors in diesel exhaust particle-induced pulmonary inflammation

Inhalation of diesel exhaust particles (DEP) induces an inflammatory reaction in the lung. However, the underlying mechanisms remain to be elucidated. Tumor necrosis factor alpha (TNF-alpha) is a proinflammatory cytokine that operates by binding to tumor necrosis factor receptor 1 (TNFR1) and tumor necrosis factor receptor 2 (TNFR2). The role of TNF-alpha signaling and the importance of either TNFR1 or TNFR2 in the DEP-induced inflammatory response has not yet been elucidated. TNF-alpha knockout (KO), TNFR1 KO, TNFR2 KO, TNFR1/TNFR2 double KO (TNFR-DKO) and wild type (WT) mice were intratracheally exposed to saline or DEP. Pro-inflammatory cells and cytokines were assessed in the bronchoalveolar lavage fluid (BALF). Exposure to DEP induced a dose-dependent inflammation in the BALF in WT mice. In addition, levels of TNF-alpha and its soluble receptors were increased upon exposure to DEP. The DEP-induced inflammation in the BALF was decreased in TNF-alpha KO, TNFR-DKO and TNFR2 KO mice. In contrast, the inflammatory response in the BALF of DEP-exposed TNFR1 KO mice was largely comparable with WT controls. In conclusion, these data provide evidence for a regulatory role of TNF-alpha in DEP-induced pulmonary inflammation and identify TNFR2 as the most important receptor in mediating these inflammatory effects

Susceptibility baselines for the invasive mealybugs Phenacoccus manihoti and Paracoccus marginatus (Hemiptera: Pseudococcidae) in cassava ecosystem against selected neonicotinoid insecticides

In recent years, an invasive cassava mealybug Phenacoccus manihoti has been threatening cassava cultivation alongside another invasive papaya mealybug Paracoccus marginatus which invaded the country more than a decade ago. In order to evaluate their responses against the commonly used neonicotinoid insecticides: thiamethoxam 25 WG and imidacloprid 17.8 SL,  acute toxicity experiments to determine the susceptibility baselines in populations of two invasive mealybugs in the cassava agro-ecosystem, namely, cassava mealybug P. manihoti and papaya mealybug P. marginatus were performed upto 15 generations. A systemic uptake method was used for the bioassay. The LC50 values of thiamethoxam for F1 generation were 3.298 ppm whereas it was 1.066 ppm for F15 in cassava mealybug. The LC50 values of F1 generation were 2.014 ppm and that of F15 generation was 1.384 ppm when tested with imidacloprid. In the case of papaya mealybug, the LC50 values ranged from 6.138 ppm (F1) to 2.503 ppm (F15) for thiamethoxam and 7.457 ppm (F1) to 3.231 ppm (F15) for imidacloprid. All the susceptibility indices calculated were less than threefold. The rate of resistance development was negative in all cases showing that none of the tested populations harboured any resistance without insecticidal selection pressure. Tentative discriminating doses were fixed for both chemicals with the help of LC95 values obtained from the bioassay experiments, namely five ppm for both thiamethoxam and imidacloprid in the case of cassava mealybug and 10 ppm and 15 ppm, respectively, for thiamethoxam and imidacloprid in the case of papaya mealybug.          

A STUDY TO ASSESS KNOWLEDGE, ATTITUDE AND PRACTICE ON BREAST CANCER AMONG WOMEN IN GOVERNMENT GENERAL HOSPITAL

Objectives: Breast cancer is the most frequent cancer in women worldwide and it accounts for 27% of all cancer cases among women in India. This study aims to assess the awareness of the patients regarding the breast cancer and also to check their knowledge toward the symptoms of breast cancer as well as the breast self-examination process. This study also determines the attitude of patients regarding the breast cancer and breast self-examination. Methods: A prospective educational study was done using a pre-designed questionnaire on 523 patients in a tertiary care teaching hospital for a period of 6 months. All women greater than 20 years admitted in the Department of General Medicine and General Surgery in-patient female ward of SVRRGGH were included in the study. Results: Out of 523 women, a greater proportion respondents 515 (98%) had poor knowledge of breast cancer. Two hundred and eighty-one (53%) show positive attitude while 225 (43%) show neutral attitude and 17 (4%) show negative attitude toward breast cancer. Only 18 (0.3%) know how to perform breast self-examination while the remaining patients have never performed the breast self-examination. Two hundred and one (38%) have agreed to consult a doctor if they found any lumps in the breast whereas the remaining did not respond. After the counseling session with patients, their knowledge regarding the above problems related to breast cancer has significantly increased. Conclusion: Majority of the participants had poor knowledge of breast cancer as well as low level of practice of breast cancer screening procedures. However, patients do have a positive attitude toward the breast cancer which can help to detect the cancer in early stages. Hence, a greater focus on providing breast cancer education programs can create awareness among women in respect to screening programs which, in turn, can decrease the risk of death due to its late discovery