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Scientific Journal of Engineering Research
Published by PT. Teknologi Futuristik Indonesia
ISSN : -     EISSN : 31091725     DOI : https://doi.org/10.64539/sjer
Core Subject : Engineering,
Engineering
The Scientific Journal of Engineering Research (SJER) is a peer-reviewed and open-access scientific journal, managed and published by PT. Teknologi Futuristik Indonesia in collaboration with Universitas Qamarul Huda Badaruddin Bagu and Peneliti Teknologi Teknik Indonesia. The journal is committed to publishing high-quality articles in all fundamental and interdisciplinary areas of engineering, with a particular emphasis on advancements in Information Technology. It encourages submissions that explore emerging fields such as Machine Learning, Internet of Things (IoT), Deep Learning, Artificial Intelligence (AI), Blockchain, and Big Data, which are at the forefront of innovation and engineering transformation. SJER welcomes original research articles, review papers, and studies involving simulation and practical applications that contribute to advancements in engineering. It encourages research that integrates these technologies across various engineering disciplines. The scope of the journal includes, but is not limited to: Mechanical Engineering Electrical Engineering Electronic Engineering Civil Engineering Architectural Engineering Chemical Engineering Mechatronics and Robotics Computer Engineering Industrial Engineering Environmental Engineering Materials Engineering Energy Engineering All fields related to engineering By fostering innovation and bridging knowledge gaps, SJER aims to contribute to the development of sustainable and intelligent engineering systems for the modern era.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 4 Documents
 1 
Search results for , issue "Vol. 2 No. 2 (2026): June Article in Process" : 4 Documents clear
Sustainable Construction Practices: Integrating Renewable Energy for Carbon Footprint Reduction Alabi, Oluwaseyi Omotayo; Laoye, Adeoti Oyegbori
Scientific Journal of Engineering Research Vol. 2 No. 2 (2026): June Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v2i2.2026.386

Abstract

The construction sector is a major contributor to resource depletion and greenhouse gas emissions, underscoring the importance of adopting sustainable practices to meet environmental and climate goals. However, current assessments often underestimate impacts because of narrow system boundaries and insufficiently localized material inventory data, creating a critical research gap in accurately evaluating building sustainability. This study therefore applies to a comprehensive Life Cycle Assessment (LCA) framework to evaluate the environmental performance of key construction materials and to investigate strategies for integrating circular design and renewable energy to reduce carbon footprints. The results reveal that medium-term environmental impacts are approximately 20–30% higher than previously reported, while the Global Warming Potential of conventional brick increases by about 23% when additional life-cycle stages are considered. Furthermore, the analysis demonstrates that design-for-disassembly and recycling-oriented approaches can significantly enhance material recovery and reduce waste. These findings imply that developing harmonized, region-specific material databases and promoting circular construction alongside renewable energy integration are essential for improving LCA accuracy and achieving meaningful reductions in the environmental footprint of buildings.
Federated Temporal Graph Learning for Weakly Supervised Bearing Anomaly Detection Darlami, Khagendra; Awasthi, Lalit
Scientific Journal of Engineering Research Vol. 2 No. 2 (2026): June Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v2i2.2026.413

Abstract

Industrial bearing health monitoring is hindered by four interrelated challenges: high class imbalance, the absence of fault-type annotations, stringent data privacy constraints prohibiting centralized aggregation, and non-independent and identically distributed (non-IID) degradation dynamics across geographically dispersed assets. To address these, we propose Fed-TGCN, a novel weakly supervised federated learning framework grounded in temporal graph neural networks. Each client represents a leave-one-bearing-out fold, comprising two training bearings, one validation bearing, and one held-out test bearing, constructs a hybrid spatio-temporal graph from six physics-informed statistical features derived from raw vibration signals; edges encode both sequential dependencies and feature-space similarity via k-nearest neighbors. Pseudo-anomaly labels are generated locally through adaptive thresholding of a degradation score using exponentially weighted moving average, eliminating reliance on expert annotations. Under a strict leave-one-bearing-out protocol on the NASA IMS dataset (12 bearings), local Temporal Graph Convolutional Networks are trained in isolation and aggregated globally via FedAvg. Our method achieves an Average Precision of 0.675 ± 0.276 and Matthews Correlation Coefficient of 0.636 ± 0.285, maintains stronger performance consistency across heterogeneous bearing conditions than isolated and non-graph baselines (ΔMCC = +0.130, p < 0.01). Ablation studies confirm the necessity of temporal modeling (MCC drops by 0.069 without GRU). To the best of our knowledge, this is the first work integrating weakly supervised, graph-based federated learning for bearing prognostics under, demonstrating that parameter coordination but not the data sharing which enables degradation-invariant representation learning across heterogeneous assets.
Numerical and Experimental Investigation of a Vortex Head for Back-Pressure Suppression in Petroleum Pumping Systems Effiom, Samuel Oliver; Enoh, Maria Kaka Etete; Willie, Godwin Effiong; Effiom, Precious-Chibuzo
Scientific Journal of Engineering Research Vol. 2 No. 2 (2026): June Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v2i2.2026.422

Abstract

Back-pressure accumulation and transient pressure surges remain critical operational challenges in petroleum pumping systems, particularly during high-rate product transfer into storage tanks. Conventional mitigation strategies such as pressure relief valves, surge vessels, and bypass lines are largely reactive, energy-intensive, and maintenance-dependent. Despite advances in computational fluid dynamics (CFD), limited research has addressed passive inlet-based hydrodynamic conditioning for petroleum storage tanks with full-scale industrial validation. This study presents a combined numerical and experimental investigation of a passive vortex head (VH) designed to suppress back pressure through vortex-induced flow redistribution at the tank inlet. Three-dimensional CFD simulations were performed using ANSYS Fluent with the realizable k–ε turbulence model to analyze pressure distribution, velocity fields, turbulence characteristics, and vortex formation. Controlled experimental validation was conducted using a prototype system under normalized inlet pressure conditions (0.07 bar) in an industrial petroleum storage facility. The results demonstrate that the vortex head induces a stable swirling flow that promotes gradual momentum dissipation and reduces localized pressure buildup near full capacity. Compared with a conventional straight inlet configuration, the vortex head reduced peak back pressure by approximately 20–30%, while decreasing total tank filling time by about 15% under identical flow conditions. CFD predictions agreed with experimental measurements within ±5%. The findings establish passive vortex-based inlet conditioning as a practical, energy-efficient strategy for preventive back-pressure suppression in petroleum storage infrastructure.
LSKD: Lightweight Self-Knowledge Distillation Framework for Fast and Robust Crowd Counting Raza, Muhammad; Ling, Miaogen; Ur Rahman, Atta; Pallewatta, Pandula; Hersi, Aboubakar Abdinur; Beruwalage, Shehan Maxwell; Kannangara, Deshan Sachintha
Scientific Journal of Engineering Research Vol. 2 No. 2 (2026): June Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v2i2.2026.436

Abstract

Crowd counting plays an important role in the surveillance of the safety of the people, traffic, and intelligent surveillance systems. However, the exact density estimations remain hard to achieve in highly congested scenes due to the tough occlusion, large-scale variance, and complicated background. Although the recent deep-learning methods have high performance, several of them do not need computationally efficient underlying backbone networks, and rather, they employ an external teacher-student distillation architecture, which can limit their use in resource-constrained applications. To avoid this problem, we introduce LSKD, a lightweight self-knowledge distillation network that is density map regression-specific. Unlike other conventional teacher-dependent processes, LSKD can also independently carry out internal multi-level feature alignment within a single small network that is not in need of an external teacher model. The structure integrates a Feature Matching Block (FMB) and a Context Fusion (CoFuse) block to enhance the hierarchical match of features and global awareness of context. The large experiments demonstrate that LSKD obtain competitive performance using the number of parameters as 2.65 million and GFLOPs as 10.23. Particularly, it has 63.17 MAE on ShanghaiTech Part A, 8.94 on ShanghaiTech Part B, 143.7 on UCF-QNRF, and 223.88 on UCF-CC-50, which is a good ratio between the accuracy and the efficiency of the calculations. Such results indicate that LSKD has an implementable and efficient solution to the real-time counting of crowds at the edge devices.

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