The objective of this paper is to apply the Translog Stochastic Frontier production model (SFA) and Data Envelopment Analysis (DEA) to estimate efficiencies over time and the Total Factor Productivity (TFP) growth rate for Bangladeshi rice crops (Aus, Aman and Boro) throughout the most recent data available comprising the period 1989–2008. Results indicate that technical efficiency was observed as higher for Boro among the three types of rice, but the overall technical efficiency of rice production was found around 50%. Although positive changes exist in TFP for the sample analyzed, the average growth rate of TFP for rice production was estimated at almost the same levels for both Translog SFA with half normal distribution and DEA. Estimated TFP from SFA is forecasted with ARIMA (2, 0, 0) model. ARIMA (1, 0, 0) model is used to forecast TFP of Aman from DEA estimation.

Purpose: To identify the financial stress and associate factors of tertiary level student.Design/methodology/approach: To identify the financial stress and associate factors, a structured questionnaire was developed to collect data from 571 students of different universities of Bangladesh through Google form. Chi square and Cramer's V were applied to find the associated factors with financial stress of students, whereas ordinal regression was performed to evaluate the influence of the associated factors on financial stress.Findings: Gender, living status, borrowing loan factors have positive association with financial stress. Tuition fee dues have significant positive association with financial stress. A student who has tuition fee dues feels 1.48 times more stress compared to student who does not have dues. Females were found less likely to be financially stressed (AOR = 1.536) than male students. Students who borrowed loans were more likely to have financial stress than non-borrowed loan groups of students.Practical implications: Results indicate that financial stress has a strong impact on participation in different academic and personal activities of a student. Therefore, parents, university administration and student counselor should consider the factor to assess stress of a student.Originality/value: This study identified factors responsible for financial stress of Bangladeshi students of tertiary level.

Background: Among university students, large numbers are involved with smoking and suffer from many chronic diseases. This study examined tobacco smoking prevalence and potential predictors among university students in Sylhet Division, Bangladesh. Methods: A total of 416 students were selected for face-to-face interviews. Logistic regression analysis was used to fulfil the specific objectives of the study. Results: It was discovered that the prevalence of smoking among university students in Sylhet Division was 37% and almost half of current male students were smokers. Mother’s occupation and peer smoking status were found to be significant factors. Although data were collected using multistage sampling, the stages (type of university, universities, departments and academic levels) were insignificant with smoking status. There is a 2.1 times greater likelihood of a student becoming a smoker if a close friend is a smoker. The adjusted living expenses (>$100) was a proactive factor, though individually it was a significant factor. Conclusions: This study and previous studies found that smoking initiation among female students is negligible, so gender-specific health promotion and intervention is needed in Bangladesh. Tobacco control awareness programs and ‘No smoking’ signage should be displayed within university campuses and sanctioned by university authorities.

Purpose: Students’ progressing to tertiary (undergrad or higher) level education often faces problems due to the change in the educational settings that may directly or indirectly affect their studies and interim results. In addition, they may suffer from low self-esteem, which in turn may result from another set of issues such as negative peers, past poor results, unable to get into a school of choice and so on. Those who have to live away from home to attend a university could have their own set of problems. To provide support many universities have counseling offices to help the students tackle their problems. The counseling officers often give repeated appointments in an attempt to help solve these problems. At the university where the research was conducted, more than 75 percent of the students come from a rural background. These are students who, for the purposes of acquiring their tertiary-level education, have had to move to the city, away from their family. These students are known to have problems such as adjusting to urban life, missing home, managing all household chores by themselves, even a culture shock given the nature of urban life and so on, all of which may affect their studies. Unfortunately, the university under study does not employ a student counselor to look after students coming from rural areas. Therefore, to support such students it was thought that a peer counseling session might help the students help themselves.Design/methodology/approach: A workshop using a collaborative approach to help students solve their own problems was designed and conducted. From three different departments, 78 students with a low Cumulative Grade Point Average (CGPA < 2.50 out of 4.0) participated in the problem-solving workshop. The students were guided in a collaborative environment where individual problems were first identified anonymously by working in pairs and possible solutions were presented by the students themselves by working in collaborative groups.Findings: The paper proposes a performance-improving model to reduce the dropout rate and ensure graduation with a better CGPA. After six semesters of the intervention, the performance of the participating students was compared against the students with similar profile who did not attend the workshop. The perception of effectiveness of the collaborative guidance model was also measured by taking feedback from students.Research limitations/implications: The collaborative approach seems to benefit students by empowering them to think on their own to solve study-related problems by themselves. To become a standardized procedure, however, more rigorous testing across a greater cross section and number of subjects would be helpful. This paper provides an initial study of the methodology for further study and use.Originality/value: As the collaborative approach purely involves peers, it benefits students by empowering them to solve study-related problems by themselves and indirectly help self-esteem development.

INTRODUCTION: Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the leading cause of dementia. Early diagnosis is vital. We developed an interpretable machine learning (ML) model for early AD prediction using open clinical data.METHODS: Data from 2149 adults (60–90 years) were obtained from Kaggle. After preprocessing and feature engineering, tree‐based models were trained. A stacking ensemble model combining Gradient Boosting and XGBoost was trained, with Logistic Regression as the meta‐learner. SHapley Additive exPlanations (SHAP) provided interpretability. Performance was measured by accuracy, precision, recall, F1 score, ROC and AUC.RESULTS: The stacked ensemble achieved 97% accuracy (AUC 0.97), with 0.97 precision, 0.94 recall, and 0.96 F1 score for AD. SHAP identified memory complaints, Mini‐Mental State Examination (MMSE), functional assessment, behavioral symptoms, cholesterol, and lifestyle factors (activity, diet, sleep) as top predictors.CONCLUSION: The ensemble model, enhanced by SHAP analysis, provides accurate and interpretable AD risk predictions with potential applicability in future clinical decision support systems.Highlights: Developed an ensemble machine learning (ML) model for early Alzheimer's disease (AD) prediction. Achieved 97% accuracy using stacked XGBoost and Gradient Boosting. SHapley Additive exPlanations (SHAP) analysis identified key cognitive and lifestyle‐related risk factors. Model interprets AD risk using explainable artificial intelligence (AI) for clinical applicability. Utilized open‐access dataset to ensure reproducibility and transparency.