The present study was undertaken to evaluate the performance efficiency of an Effluent Treatment Plant (ETP) of a Textile industry located at Tongi, Bangladesh with biological treatment (BT) and Membrane Bio-Reactor (MBR) with an average inflow of 300 m3/hr. The effluent samples were collected from the inlet and outlet of the ETP on a weekly basis for a 4 weeks’ period and were analysed for key parameters such as colour, temperature, total suspended solids (TSS), Total Dissolved Solids (TDS), pH, Dissolved Oxygen (DO), Biological Oxygen Demand (BOD), and Chemical Oxygen Demand (COD). In this study, it was observed that the colour of the effluent in the inlet was dark blue and after multiple unit treatments of the colour’s final outlet the discharge, water colour was very light purple. The temperature was varied from 32.2⁰C to 34.33⁰C. The TDS was varied from 1252.5 mg/l to 1087.5 mg/l and the percentage removal efficiency of TDS was varied from 21.47% to 42.7%. The TSS was varied from 4 mg/l to 4.5 mg/l and the percentage removal efficiency of TSS was varied from 98.48% to 98.21%. The pH value was varied from 6.48 to 7.63. The DO value in the inlet was varied from 6.47 mg/l to 6.775 mg/l. The BOD was recorded from 12.75 mg/l to 17.75 mg/l and the percentage removal efficiency of BOD was varied from 89.92% to 87.24%. The COD was varied from 33.75 mg/l to 34.25 mg/l and the percentage removal efficiency of COD was varied from 91.11% to 90.5%. It is conjectured that the values of the measured parameters are seen to be within the permissible limit as per the standard of the Department of Environment (DoE) of Bangladesh.

The purpose of this study is to assess the nature of the job satisfaction level of private bank employees in Bangladesh. It used a semi-structured questionnaire which contains both pre-coded and open-ended questions. All questions were rated with the Likert 5-point scale. A Chi-square test was used to assess the relationship between independent and dependent variables. In this study, a significant inter-class relationship was observed between demographic characteristics namely sex, age, designation, salary, and family member, marital status, working environment, service period and family income, and job satisfaction indicators which are, participation in decision making, training facilities, autonomy in work, gender discrimination, working hour, chance of promotion, increase knowledge & capacity, the practice of MBO and surprisingly availability of tools and resources had no significant relation with any demographic factors. Employees who work as an officer (93.3%, p< .001) and withdraw salary 25,001-35,000 (88.2%, p< .000) cannot participate in decision making. But who works in the participative environment (83.9%, p< .000) get a proper training facility. Employees with <30-year age (83.3%, p< .016) cannot practice autonomy. Unmarried workers (53.8%, p< .006) face gender discrimination. Employees who work in an Autonomous environment (76.0%, p< .00001) do not get enough working hours. Employees with 30-35-year age (80.8%), p< .002) do not get a proper promotion. Male (98.0%, p< .001) report the organization increases its capacity and knowledge. Employees work in an autonomous environment (88.0%, p< .002) report that organization practices MBO. This study also shows that female employees are more satisfied than males. The overall situation can be improved by guaranteeing employees participation in decision making, regular training, providing appropriate increments and promotion, redesigning working hours and environment, and removing gender discrimination. Further Study is recommended.

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Breast cancer is a prevalent and potentially lifethreatening disease that affects millions of individuals worldwide. Early detection plays a crucial role in improving patient outcomes and increasing the chances of survival. In recent years, machine learning (ML) techniques have gained significant attention in the field of breast cancer detection and diagnosis due to their ability to analyze large and complex datasets, extract meaningful patterns, and facilitate accurate classification. This research focuses on leveraging ML algorithms and models to enhance breast cancer detection and provide more reliable diagnostic results in the real world. Two datasets from Kaggle have been used in this study and Decision tree (DT), Random Forest (RF), Logistic Regression (LR), K-Nearest Classifier (KNN) etc. are applied to identify potential breast cancer cases. On the first dataset, A, the test's accuracy using Logistic Regression, SVM, and Grid SearchCV was 95.614%, however in dataset B, the accuracy of Logistic Regression and Decision Tree increased to 99.270%. The accuracy of Boosting Decision Tree was 99.270% when compared to other algorithms. To defend the performances, various ensemble models are used. To assign the optimal parameters to each classifier, a hyper-parameter tweaking method is used. The experimental study examined the findings of recent studies and discovered that LRBO performed best, with the highest level of accuracy for predicting breast cancer being 95.614%.