Jahangirnagar University Journal of Science

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

2024-11-12T11:57:34+06:00

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

2025-07-29T20:34:59+06:00

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

2025-08-14T20:09:30+06:00

Accurate forecasting of time series exhibiting damped trends with multiplicative seasonality (DTMS) is a critical yet challenging task, with significant implications for fields such as finance, environmental science, and supply chain management. This study evaluates and enhances forecasting performance by proposing a hybrid framework that integrates conventional statistical models with machine learning (ML) techniques, validated through both real-world and simulated data. We investigate the effectiveness of three widely used models: Autoregressive Integrated Moving Average (ARIMA), Exponential Smoothing State Space (ETS), and Artificial Neural Networks (ANN), using both real-world data from Renata PLC and simulated datasets. To address multiplicative seasonality inherent in DTMS data, we incorporate seasonal adjustment via Seasonal-Trend decomposition using LOESS (STL). Forecast accuracy is assessed through multiple criteria, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE).

Results indicate that for real data, the hybrid TBATS+ANN model achieves superior performance (RMSE = 143.39, MAE = 121.45, MAPE = 9.62) compared to other hybrid approaches. Conversely, in simulated environments, STL+ETS (RMSE = 958.56, MAE = 808.25, MAPE = 62.45) and ETS+ARIMA (RMSE = 1469.64, MAE = 1188.51, MAPE = 76.01) outperform other models in different scenarios. Moreover, ETS+ARIMA demonstrates robust forecasting for certain DTMS multiplicative seasonality conditions (RMSE = 1512.21, MAE = 1232.12, MAPE = 70.13) across real and simulated data, though TBATS+ANN consistently shows competitive or superior accuracy (RMSE = 1515.82, MAE = 1230.33, MAPE = 65.98).

The findings underscore the value of hybrid modeling frameworks, especially those combining state-of-the-art statistical and machine learning approaches, for capturing the nuanced dynamics of DTMS data. This study provides comprehensive insights into model selection and sequencing, facilitating enhanced forecasting accuracy in challenging time series contexts. The results have practical implications for industries reliant on time-sensitive forecasts under multiplicative seasonal influences, and contribute to advancing methodologies for predictive analytics in complex trend environments. Therefore, this research contributes to the broader discourse on time-series forecasting by addressing gaps in DTMS modeling, with implications for decision-making in volatile or seasonal domains.