Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal International Journal of Applied Mathematics and Computing. Green Engineering: Journal of Engineering and Applied Science
Syeda Azwa Asif
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Mathematics

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Evaluating the Impact of Distributed Solar-Battery Systems on Urban Electricity Resilience and Community Carbon Emissions Reduction Idi Jang Acik; Soleman; Syeda Azwa Asif
Green Engineering: International Journal of Engineering and Applied Science Vol. 2 No. 1 (2025): January: Green Engineering: International Journal of Engineering and Applied Sc
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/greenengineering.v2i1.272

Abstract

This study evaluates the impact of distributed solar-battery systems on urban electricity resilience and community carbon emissions reduction. As urban areas continue to grow, the demand for electricity has placed considerable strain on traditional centralized grids, resulting in increased vulnerabilities. The integration of decentralized energy resources (DERs), particularly solar photovoltaic (PV) systems paired with battery energy storage systems (BESS), has emerged as a promising solution to enhance grid resilience, reduce carbon emissions, and support the transition to more sustainable energy systems. This research uses a simulation-based approach to model the integration of solar-battery systems into residential blocks, assessing their impact on grid reliability, downtime reduction, and the frequency of power outages. Additionally, the study estimates the reduction in carbon dioxide (CO₂) emissions achieved by shifting from fossil-fuel-based energy generation to renewable sources such as solar PV. The results demonstrate that solar-battery systems significantly improve electricity reliability by providing backup power during outages, while also reducing CO₂ emissions by decreasing reliance on conventional grids. The study also discusses the technical and financial challenges associated with the integration of these systems, such as energy storage capacity, system efficiency, and upfront installation costs. Policy recommendations emphasize the importance of government incentives, grid modernization, and long-term financial benefits to encourage the adoption of decentralized energy solutions. Finally, the study highlights areas for future research, including advanced storage technologies and the integration of electric vehicles with solar-battery systems to further enhance energy resilience and sustainability.
Adaptive Algorithmic Simulation for Nonlinear Eigenvalue Problems in Mathematical Physics Abid Nurhuda; Ali Anhar Syi’bul Huda; Syeda Azwa Asif
International Journal of Applied Mathematics and Computing Vol. 2 No. 2 (2025): April: International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v2i2.265

Abstract

Nonlinear eigenvalue problems (NEPs) pose significant challenges in mathematical physics and other computational applications due to their nonlinear nature, which makes analytical solutions difficult to obtain. NEPs are encountered in various scientific and engineering fields, including signal processing, electronic structure calculations, and structural optimization. This study aims to explore the application of adaptive algorithms in solving nonlinear eigenvalue problems, with a primary focus on improving accuracy and computational efficiency. The proposed method combines an iterative solver with adaptive step-size adjustment, where the step size is dynamically adjusted during the iteration based on error estimates calculated at each step. This approach enables faster convergence and significant reductions in computational time without compromising accuracy. In experiments conducted on large-scale problems, the adaptive algorithm reduced computational time by 40% faster compared to fixed-step iterative methods. The comparison between the adaptive algorithm and traditional methods showed that the adaptive algorithm is not only more efficient but also more robust when dealing with high-complexity problems. Additionally, the adaptive algorithm provides more accurate error estimates, allowing better error control throughout the iteration process. Overall, this study concludes that adaptive algorithms offer a more effective and efficient solution for complex nonlinear eigenvalue problems and can be adapted to various types of problems in scientific and engineering applications. Further research could focus on optimizing the implementation of this algorithm for larger and more complex scales.
Optimization of Numerical Algorithms for Solving Large Linear Equation Systems in Industrial Mathematical Computing M Bastian; Putry Wahyu Setyaningsih; Syeda Azwa Asif
International Journal of Applied Mathematics and Computing Vol. 1 No. 4 (2024): October: International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v1i4.275

Abstract

The rapid advancement of modern computing has driven extensive research on numerical algorithms for solving large-scale systems of linear equations. Classical methods such as LU decomposition, Jacobi, and Gauss–Seidel have been revisited and optimized to leverage parallel architectures, GPUs, and even quantum platforms. Recent studies demonstrate that optimized algorithms can reduce computation time by more than 50% while maintaining high accuracy in solving high-dimensional problems. LU decomposition, particularly in its parallel and GPU-based implementations, has shown superior performance in batch processing and industrial-scale simulations. Meanwhile, iterative methods such as Jacobi and Gauss–Seidel remain relevant due to their flexibility in numerical modeling, with further developments for block matrix systems, finite element applications, and FPGA architectures. The integration of these enhanced algorithms is not only beneficial for the advancement of scientific software development but also supports practical applications in engineering simulations, large-scale data optimization, and machine learning. Therefore, an integrative review of modern numerical algorithm developments is crucial in bridging the gap between industrial demands and research progress in scientific computing.
Co-Authors Abid Nurhuda Ali Anhar Syi’bul Huda Idi Jang Acik M Bastian Putry Wahyu Setyaningsih Soleman