Jahangirnagar University Journal of Science

Phytochemical investigations on the leaves of the plant Clerodendrum viscosum Vent.

Md. Ataur Rahman — 2026-01-11

One steroidal compound (22E, 24S)-stigmasta-5, 22, 25-trien-3β-ol and a non-steroidal compound, clerodin, have been isolated from the n-hexane extract of the leaves of the plant Clerodendrum viscosum Vent. The isolated compounds were characterized on the basis of
their physical properties and spectroscopic data analysis.

IT Freelancing and Remittance Earnings in Bangladesh: Opportunities and Challenges

Sabina Yasmin — 2026-01-11

In Bangladesh, IT freelancing is becoming a cornerstone of economic progress, creating jobs for a large and enthusiastic young and tech-savvy populations while also increasing foreign earnings through remittances. This study examines how IT freelancing enhances employability and contributes to economic development and foreign currency inflows. Using both primary and secondary data collected from a diverse set of stakeholders, including individual freelancers, IT training centers, etc., the study identifies rapid sector growth, with annual earnings of over $1 billion. Yet, challenges persist, such as a gender disparity with women making up only 9% of freelancers, regional wage differences, and skill shortages in areas like AI, cybersecurity, and web development. Further challenges stem from inadequate rural infrastructure and unreliable payment systems. The study recommends investments in digital infrastructure, specialized training programs, and policies promoting gender inclusivity and freelancing as a career. These measures could boost Bangladesh's global competitiveness and the economic potential of its IT freelancing sector.

Enhancing Forecast Accuracy for Damped Trend and Multiplicative Seasonality: A Hybrid Time-Series Framework with Simulation Study

Md. Kamrul Hasan — 2026-01-13

Forecasting time series characterized by a damped trend and multiplicative seasonality (DTMS) presents a significant challenge, as conventional models like ARIMA and ETS often fail to capture their complex, non-linear interactions. This study bridges this gap by developing and evaluating a novel hybrid forecasting framework that synergistically combines statistical and machine learning (ML) approaches. We hypothesize that while statistical models capture linear components, ML models excel at modeling non-linear residuals. Our methodology employs a comprehensive simulation study to systematically control data characteristics, alongside an empirical analysis of a real financial dataset from the Dhaka Stock Exchange. Results demonstrate that hybrid models, particularly SVR-ANN and SVR-ETS, significantly outperform individual and other hybrid models across all accuracy metrics (RMSE, MAE, MAPE, MASE). The simulation confirmed this superiority, with the top hybrids achieving a mean MASE of 0.701. This indicates that an ML model, especially SVR, is highly effective in modeling the complex residuals from a primary statistical forecast. We conclude that a hybrid paradigm integrating statistical and ML techniques offers a robust and superior solution for forecasting DTMS series, providing practitioners with evidence-based guidance for enhanced accuracy.