The COVID-19 pandemic is the defining health crisis of the world in 2020 and the world economy is affected as well. Bangladesh is also one of the impacted countries, which needs to conduct sufficient tests to identify patients and accordingly adopt measures to limit the massive outbreak of this viral infection. But due to economic drawbacks and also unavailability of testing equipment, Bangladesh is lagging critically behind in test numbers. This study shows a pool testing method named Conditional Cluster Sampling (CCS) that utilizes soft computing and data analysis techniques to reduce the expense of total testing equipment. The proposed method also demonstrates its effectiveness compared to the traditional individual testing method. Firstly, according to patients’ symptoms and severity of their conditions, they are classified into four classes- Minor, Moderate, Major, Critical. After that Random Forest Classifier (RFC) is used to predict the class. Then random sampling is done from each class according to CCS. Finally, using Monte Carlo Simulation (MCS) for 100 cycles, the effectiveness of CCS is demonstrated for different probability levels of infection. It is shown that the CCS method can save up to 22% of the test kits that can save a huge amount of money as well as testing time.
The effect of four controllable input process parameters of AISI 4140 steel, cross-feed, workpiece velocity, wheel velocity, and the depth of cut were experimentally investigated under dry and wet conditions. Three responses, contact temperature, material removal rate (MRR), and machining cost during surface grinding of AISI 4140 steel, were considered. The process was optimized using a recently developed combined methodology based on response surface methodology (RSM) and desirability functional approach (DFA). RSM generated the models of the responses for prediction while DFA solved these multi-response optimization problems. The DFA approach employed an objective function known as the desirability function, which converts an estimated response into a scale-free value known as desirability. The optimum parameter was attained at the maximum overall desirability. An analysis of variance (ANOVA) was conducted to confirm the model adequacy. From the results of the study, for equal weights of responses, the corresponding optimal values of the input parameters cross-feed, workpiece velocity, the wheel or cutting velocity and the depth of cut were found to be 6 mm/pass, 12 m/min, 15 m/s, and 0.095 mm respectively in wet conditions. The corresponding predicted output responses were: 134.55 °C for the temperature, and 7.366 BDT (Taka, Currency of Bangladesh) for the total cost with an overall desirability of 0.844. Confirmation testing of optimized parameters, i.e., checking the validity of optimal set of predicted responses with the real experimental run were conducted, and it was found that the experimental value for temperature and total cost were 140.854 °C and 8.36 BDT, respectively, with an overall desirability of 0.863. Errors of the predicted value from the experimental value for equal weightage scheme were 4.47% for the temperature and 7.37% for the total cost. It was also found that if the temperature was prioritized, then the wet condition dominated the overall desirability, which was expected. However, if the cost was given high weightage, dry condition achieved the highest overall desirability. This can be attributed to the cutting in the wet condition which was more expensive due to the application of cutting fluid. The proposed model was found to be new and highly flexible in the sense that there was always an option at hand to focus on a particular response if needed.
Measuring student performance based on both qualitative and quantitative factors is essential because many undergraduate students could not be able to complete their degree in the recent past. The first-year result of a student is very important because in the majority of cases this drives the students to be either motivated or demotivated. So, the first-year student performance of a renowned university in Bangladesh is investigated in this paper. This research is mainly based on finding the factors for students’ different types of results and then predicting students’ performance based on those 11 significant factors. For this purpose, 2 popular supervised machine learning algorithms have been used for classifying students’ different levels of results and predicting students’ performances, those are support vector machines (SVM) classifier and random forest classifier (RFC) which are tremendously used in classification and regression analysis. The input dataset for both training and testing were taken by merging the values obtained from 2 surveys done on students and experts using an adaptive neuro-fuzzy interference system (ANFIS). RF has outperformed SVM in predicting students’ performances. According to factor analysis, students’ effort (Factor-11) is the significant factor. This proposed model can also be applied to predict course-wise students’ performances and its precision can also be greatly improved by adding new factors. HIGHLIGHTS Identify the significant factors responsible for students’ different levels of performances Apply two machine learning algorithms to classify students’ results based on the factors Analyze the results obtained from the methods Compare the accuracy, and find the top five factors responsible for students’ academic results
Globalization and reflexive modernization are the main reasons for the development of the current social system. The discussion on women's demographic change in Bangladesh is not new rather than this must be voiced issue. Women are playing a significant role in various sectors in the country. Women are making themselves strongly changing socio-economic conditions, not only by indulging in household chores but also involving themselves in different sectors like RMG, banking, IT, teaching, and so on. By discussing some of the theories of Anthony Giddens and Ulrich Beck, presenting the current social context of the women of Bangladesh which has shown how they are self-conscious and self-reliant. These theoretical standpoints have been used to identify causes, consequences, and adaptation mechanisms to deal with the modern social system. This study highlights how relationship patterns and labor markets are changing due to globalization. It also analyzes the responsible elements and symptoms of the individualization of women in Bangladesh. The recent conflicting interests, love, and sexual life are investigated in this paper.
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