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Frontiers in Sustainable Science and Technology
Published by CV. Science Tech Group
ISSN : -     EISSN : 30892767     DOI : 10.69930
Core Subject : Health, Science, Agriculture, Social, Engineering,
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Medicine & Pharmacology Social Sciences
Frontiers in Sustainable Science and Technology (FSST) is a peer-reviewed, open-access international journal committed to publishing cutting-edge research on the convergence of sustainability, science, health, technology, and engineering. FSST aims to provide an interdisciplinary platform for global scientists, researchers, and practitioners to share, discuss, and disseminate novel insights and innovations that address the pressing challenges related to sustainability. The journal welcomes contributions that focus on both foundational research and its application in solving real-world problems in environmental, technological, and industrial contexts. The overarching goal of FSST is to foster the development of sustainable solutions that promote sciences, engineering, and technology.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 22 Documents
 1   2   3 
Analysis of the Effectiveness of Red Andong Leaf Extract (Cordyline fruticosa) as an Alternative to 2% Eosin in the Examination of Soil-Transmitted Helminth Eggs Marsyah; Asriyani Ridwan; Andi Harmawati Novriani
Frontiers in Sustainable Science and Technology Vol. 3 No. 2 (2026): Available online
Publisher : CV. Science Tech Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69930/fsst.v3i2.917

Abstract

Microscopic examination of fecal specimens is an important method for detecting Soil-Transmitted Helminth (STH) eggs. Eosin 2% staining is standardly used to provide high diagnostic contrast; however, natural dye alternatives are increasingly sought to advance eco-friendly and safer laboratory diagnostics. This preliminary laboratory study aimed to evaluate the effectiveness of red andong leaf (Cordyline fruticosa) extract as a natural alternative to 2% eosin in direct slide examinations of STH eggs. The extract was prepared by 70% ethanol maceration, acidified with 0.1 N HCl, and concentrated at 45–50°C. Three concentrations (40%, 60%, and 80%) were evaluated without independent experimental replication, alongside distilled water (negative control) and 2% eosin (positive control). Preparations were microscopically assessed at 10× and 40× objective magnifications using a five-parameter operational scoring rubric on a 1–4 scale. Results demonstrated that 40%, 60%, and 80% extract concentrations failed to produce adequate staining contrast, with preparations remaining largely transparent. Although 0.1 N HCl maintained the visible red color of the extract, it did not produce sufficient microscopic contrast for clear visualization of the STH eggs. The mean staining score was the same across all extract concentrations (1.8), compared with 2.0 for distilled water and 4.0 for 2% eosin. As a preliminary descriptive observation, red andong leaf extract did not demonstrate adequate staining performance as a direct substitute for 2% eosin under the tested conditions. Further studies should optimize the extraction solvent, pH, staining time, pigment concentration, and the interaction between plant pigments and biological structures, with appropriate independent experimental replication. This study may contribute to the exploration of plant-derived staining materials in support of SDG 3 (Good Health and Well-Being) and SDG 12 (Responsible Consumption and Production); however, the potential health and environmental benefits of the extract require further evaluation.
Machine Learning-Based Agricultural Crop Yield Prediction for Sustainable Economic Development in Afghanistan Abdul Hadi Mukhtar; Zabihullah Rahmani; Nasratullah Mahboob; Shirullah Rahmani
Frontiers in Sustainable Science and Technology Vol. 3 No. 2 (2026): Available online
Publisher : CV. Science Tech Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69930/fsst.v3i2.921

Abstract

Afghanistan's economy remains heavily dependent on rain-fed and irrigated agriculture, a sector that employs the majority of the rural workforce yet is increasingly threatened by rising temperatures, erratic precipitation, and recurrent drought, so timely and reliable crop yield forecasts are essential for food-security planning, market stabilization, and rural income protection, even though conventional statistical and agronomic estimation methods struggle to capture the nonlinear interactions among climatic, soil, and management variables that drive yield variability. This paper develops a conceptual machine learning (ML)-based framework for crop yield prediction adapted to the data, infrastructure, and institutional conditions of Afghanistan, using a narrative synthesis of recent literature on machine learning and deep learning approaches to crop yield estimation together with Afghanistan-specific studies on climate change and agricultural productivity. The synthesis yields a four-layer framework, comprising data, processing, model, and decision layers, together with an implementation pipeline spanning data collection, preprocessing, feature selection, model training, validation, and advisory deployment, accommodating heterogeneous inputs including meteorological records, satellite-derived vegetation indices, soil parameters, and historical yield statistics, and comparing ensemble methods (Random Forest, XGBoost), kernel-based methods (Support Vector Machines), and deep architectures (CNN, LSTM, and CNN-LSTM hybrids) reported in the literature. The discussion shows how such a framework could support the Ministry of Agriculture, Irrigation and Livestock and development partners in strengthening early-warning systems, guiding input allocation, and advancing Sustainable Development Goal 2 in a fragile, data-scarce environment; because the paper is conceptual and does not report primary empirical results, it concludes with a discussion of implementation barriers, including connectivity, data governance, and capacity constraints, and directions for future empirical validation using field-level Afghan agricultural data. By improving the potential timeliness and spatial specificity of crop-yield information, the framework could support evidence-based planning related to sustainable food production, agricultural resilience, and climate-risk management. These potential contributions are aligned particularly with SDG 2 (Zero Hunger), while their actual magnitude remains subject to future empirical validation using Afghan agricultural data.

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