Template-Type: ReDIF-Article 1.0 Author-Name: Sayed Majhab Hasan Amiri Title: The Impact of Islamic Educational Philosophy on Youth Development in Bangladesh Publication-Status: Published Abstract: Islamic educational philosophy has shaped moral landscapes, social identities, and civic values in Muslim-majority societies for over fourteen centuries. In Bangladesh, a nation where approximately 91% of the population identifies as Muslim (Bangladesh Bureau of Statistics, 2022) and where a dual educational system of modern secular and traditional Islamic (madrasah) institutions coexists, understanding how this philosophical tradition is associated with youth development is both academically urgent and socially consequential. This study investigates the relationship between Islamic educational philosophy and the moral, social, leadership, and identity development of Bangladeshi youth aged 14–25. Employing a convergent parallel mixed-methods design, the research draws on survey data collected from 320 students (160 male, 160 female) across six educational institutions in Dhaka, Comilla, and Sylhet, supplemented by semi-structured interviews with 24 educators and 12 Islamic scholars. Thematic analysis of qualitative data and descriptive-inferential statistical analysis of quantitative data reveal that engagement with Islamic educational philosophy is significantly and positively associated with moral reasoning, prosocial behaviour, and civic identity among Bangladeshi youth. Specifically, institutions integrating Quranic epistemology with modern pedagogy demonstrate stronger outcomes across all five measured developmental dimensions. However, persistent challenges, including curriculum rigidity, gendered access disparities, and a perceived tension between Islamic credentials and employment markets, constrain the full transformative potential of the tradition. The study concludes with evidence-based recommendations for curriculum reform, policy integration, and teacher professional development. Keywords: Islamic educational philosophy; youth development; Bangladesh; madrasah education; character formation; moral development; positive youth development Journal: Journal of Education and Development Lab Pages: 100-112 Volume: 1 Issue: 2 Year: 2026 Month: September DOI: 10.5281/zenodo.20026141 File-URL: https://jedl.us/index.php/jedl/article/view/7 File-Function: Abstract page File-Format: text/html File-URL: https://jedl.us/index.php/jedl/article/download/7/10 File-Function: Full text File-Format: application/pdf Handle: RePEc:jdk:jedl00:v:1:y:2026:i:2:id:7 Template-Type: ReDIF-Article 1.0 Author-Name: Sayed Mahbub Hasan Amiri Title: The Pillars of Faith: A Guide to the 28 Fundamental Seventh-day Adventist Beliefs Publication-Status: Published Abstract: The Seventh-day Adventist Church, a Christian denomination with a global presence, articulates its theology through a formal statement of 28 Fundamental Beliefs. This article, titled "The Pillars of Faith: A Look at the 28 Fundamental Beliefs of Adventists," seeks to demystify these doctrines by presenting them not as a rigid checklist, but as a cohesive and interconnected story. Drawing on the historical metaphor of "pillars" used by early Adventist pioneers, the article organizes the beliefs into thematic categories to enhance understanding. It explores the foundational doctrines of Scripture and the Godhead, the distinct Adventist understanding of humanity as a holistic unity, and the cosmic narrative of the "Great Controversy" between good and evil. The piece further examines the beliefs concerning salvation, the practical expressions of faith including the seventh-day Sabbath and stewardship and culminates in the eschatological hope centered on the Second Coming and the restoration of a New Earth. Ultimately, this article argues that the 28 beliefs are more than a list of theological propositions; they form a comprehensive description of a relationship with a creating, redeeming, and returning God, offering a framework for understanding human existence and destiny. The goal is to provide the curious reader with a clear, accessible roadmap to the core tenets of Adventist Christian faith. Keywords: Fundamental Beliefs; Adventist doctrine; Pillars of Adventism; SDA Church beliefs; Seventh-day Adventist beliefs Journal: Journal of Education and Development Lab Pages: 113-146 Volume: 1 Issue: 2 Year: 2026 Month: September DOI: 10.5281/zenodo.18862428 File-URL: https://jedl.us/index.php/jedl/article/view/8 File-Function: Abstract page File-Format: text/html File-URL: https://jedl.us/index.php/jedl/article/download/8/11 File-Function: Full text File-Format: application/pdf Handle: RePEc:jdk:jedl00:v:1:y:2026:i:2:id:8 Template-Type: ReDIF-Article 1.0 Author-Name: Atiar Zahan Title: Machine Learning Applications in Smartphone Healthcare Services and Fraud Detection: A Ten-Year Systematic Literature Review Publication-Status: Published Abstract: Bangladesh’s government-run health system is heavily subsidized yet remains underfunded. According to WHO (2010), health expenditure accounts for only about 3% of GDP. The government provides 34% of total health expenditure (THE), while individuals pay 66% through out-of-pocket (OOP) payments, resulting in substantial inequity in access to care. Drawing on secondary data, this review examines the strengths and weaknesses of Bangladesh’s health system. Bangladesh has made notable progress on health-related Millennium Development Goals (MDGs), particularly MDGs 4 and 5, but it still lacks a coherent health policy for system development despite a rapidly growing private sector focused on tertiary care. As smartphone ownership has become widespread, mobile applications are increasingly used to simplify daily life, creating demand for smartphone-based healthcare services. However, this digital health market is also vulnerable to fraud, especially medical insurance fraud. Manual detection of healthcare fraud is difficult, so machine learning and data mining have been used to automate the process. Many studies have applied these techniques to healthcare fraud detection, yet further research and more sophisticated machine-learning methods are needed to identify atypical patterns in health insurance usage. This review synthesizes literature from the past ten years on smartphone-based healthcare services and machine-learning-driven fraud detection for improving the healthcare system. Keywords: Healthcare Systems; Mobile application; Android; Machine learning; Deep learning Journal: Journal of Education and Development Lab Pages: 147-153 Volume: 1 Issue: 2 Year: 2026 Month: September DOI: 10.5281/zenodo.22728237 File-URL: https://jedl.us/index.php/jedl/article/view/9 File-Function: Abstract page File-Format: text/html File-URL: https://jedl.us/index.php/jedl/article/download/9/9 File-Function: Full text File-Format: application/pdf Handle: RePEc:jdk:jedl00:v:1:y:2026:i:2:id:9 Template-Type: ReDIF-Article 1.0 Author-Name: Faija Anjum Author-Name: L.M. Mahir Labib Title: Integrated Algal Bioadsorption for Removing Major Pollutants from Textile Effluents by Low-Cost Photobioreactor and Generating Valuable By-Products Publication-Status: Published Abstract: The increasing environmental strain of untreated or poorly treated textile effluents is one of the most critical issues facing the industrial ecology and the healthcare systems of the people in the world. The current research introduces an integrated system of phycoremediation that uses Cladophora algae as a primary biological treatment of textile wastewater, with strategic biomass valorization that will allow for the creation of an economic system. A cost-effective photobioreactor (PBR) was developed, built, and streamlined based on recyclable materials found locally to enable the effective growth of algae and the maximum utilization of the removal performance. The system aims to remove three key types of textile contaminants, such as azo dyes, heavy metals, and organic pollutants, by synergistically using bioadsorption and biodegradation processes. The experimental outcomes showed a removal efficiency of above 85 percent and removal of azo dye above 90 percent under optimum operating conditions. The algal biomass obtained was effectively used to produce two useful by-products, namely, high-performance bioadsorbents to be further used in the treatment process and natural textile dyes that can be easily converted into supplementary economic value at the same time, decreasing the total operation costs. In comparison to traditional wastewater treatment systems, it can be seen that the proposed solution can offer much lower energy use, a small amount of sludge, and improved sustainability indicators across a variety of environmental impacts. This model introduces a cost-efficient scaling solution that balances the environmental cleanup with the circular bioeconomy that is especially relevant in the textile sector of the developing economy, where the legacy treatment infrastructure is still too expensive to use. Keywords: Bioadsorption; Circular bioeconomy; Cladophora; Phycoremediation; Textile wastewater Journal: Journal of Education and Development Lab Pages: 154-166 Volume: 1 Issue: 2 Year: 2026 Month: September DOI: 10.5281/zenodo.22800705 File-URL: https://jedl.us/index.php/jedl/article/view/27 File-Function: Abstract page File-Format: text/html File-URL: https://jedl.us/index.php/jedl/article/download/27/12 File-Function: Full text File-Format: application/pdf Handle: RePEc:jdk:jedl00:v:1:y:2026:i:2:id:27 Template-Type: ReDIF-Article 1.0 Author-Name: S.M.Abtahi Noor Author-Name: Sarrinah Binta Reza Title: From Sensors to Insights: Leveraging Intelligent Data Mining for Precision Farming and Decision Support Publication-Status: Published Abstract: Climate change, resource scarcity, and growing weather variability make sustainable farm management increasingly difficult. This study presents a precision-agriculture framework that fuses real-time Internet of Things (IoT) measurements with satellite-based environmental information to support data-driven decisions. Low-cost wireless sensor nodes record soil moisture, pH, air temperature, relative humidity, water turbidity, and total dissolved solids, and we combine these field streams with NASA Earthdata, Landsat, and Agriculture and Water Management Data Pathfinder products. We clean, validate, normalize, and align the heterogeneous data in time and space before modeling. We use four task-specific models: YOLOv5 for plant disease and pest detection, a long short-term memory (LSTM) network for short-term ultraviolet (UV) index forecasting, a CNN2D-BGRU network for weather forecasting, and an RF-LSTM-Attention model for seasonal rainfall prediction to support flood and drought assessment. Outputs are delivered through a web-based decision-support platform with live visualizations and threshold-based alerts. The detector achieved 92.1% accuracy (95% CI 87.4–95.2%), 94.7% recall, and a Matthews correlation coefficient of 0.84. The UV model achieved a mean absolute error of 0.249 index units, and the rainfall model a mean absolute percentage error of 9.49%. A prevalence-aware analysis shows that at 10% field disease prevalence, only half of image-based alerts would be true positives, and that using IoT microclimate conditions as a prior can raise alert precision to about 79%. These results support multi-source fusion for precision agriculture, while sensor reliability, missing data, extreme weather, and model generalization remain open challenges. Keywords: Precision Agriculture; Internet of Things; Satellite Remote Sensing; Deep Learning; Time-series Forecasting; Decision Support Systems Journal: Journal of Education and Development Lab Pages: 167-179 Volume: 1 Issue: 2 Year: 2026 Month: September DOI: 10.5281/zenodo.23138969 File-URL: https://jedl.us/index.php/jedl/article/view/29 File-Function: Abstract page File-Format: text/html File-URL: https://jedl.us/index.php/jedl/article/download/29/13 File-Function: Full text File-Format: application/pdf Handle: RePEc:jdk:jedl00:v:1:y:2026:i:2:id:29