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Mega Novita
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asset@upgris.ac.id
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Advance Sustainable Science, Environmental Engineering and Technology (ASSET) Jl. Sidodadi Timur No.24, Karangtempel, Kec. Semarang Tim., Kota Semarang, Jawa Tengah 50232
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Kota semarang,
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INDONESIA
Advance Sustainable Science, Engineering and Technology (ASSET)
Published by Universitas PGRI Semarang
ISSN : -     EISSN : 27154211     DOI : https://doi.org/10.26877/asset
Core Subject : Science, Engineering,
Chemistry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering
Advance Sustainable Science, Engineering and Technology (ASSET) is a peer-reviewed open-access international scientific journal dedicated to the latest advancements in sciences, applied sciences and engineering, as well as relating sustainable technology. This journal aims to provide a platform for scientists and academicians all over the world to promote, share, and discuss various new issues and developments in different areas of sciences, engineering, and technology. The Scope of ASSET Journal is: Biology and Application Chemistry and Application Mechanical Engineering Physics and Application Information Technology Electrical Engineering Mathematics Pharmacy Statistics
Arjuna Subject : Teknik Kimia (semua kategori) - Teknik Kimia (Lain-Lain)
Articles 272 Documents
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Performance of Self-Compacting Concrete Mixed and Cured with Magnetized Water Alshirah, Majed; Zahid, Mohd Zulham Affandi Bin Mohd; Abu Bakar, Badorul Hisham Bin; Jaafar, Zul Fahmi Bin Mohamed
Advance Sustainable Science Engineering and Technology Vol. 8 No. 1 (2026): November - January
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i1.2825

Abstract

This study investigates the effect of magnetized water (MW) on the mechanical and microstructural properties of self-compacting concrete (SCC). Four mixes with identical proportions were prepared to isolate the influence of MW at different stages: SCNTN (normal water used in both mixing and curing), SCNTM (normal water in mixing and MW in curing), SCMTN (MW in mixing and normal water in curing), and SCMTM (MW used in both mixing and curing). Compressive strength tests were conducted at 7 and 28 days, and microstructural characterization was performed using X-ray diffraction (XRD) and Fourier-transform infrared spectroscopy (FTIR). The results showed that using MW during mixing enhances early-age strength through improved dispersion and hydration of cement particles, while using MW during curing contributes more significantly to long-term hydration. The combined use of MW in both mixing and curing (SCMTM) achieved the highest strength values, with improvements of 25.8% at 7 days and 14.2% at 28 days compared to the control mix. Microstructural findings confirmed denser calcium–silicate–hydrate (C–S–H) gel formation and reduced unhydrated phases in MW-treated concrete. These results indicate that MW has a positive influence on both hydration and strength development in SCC.
Deep Learning-Based Classification of Cognitive Workload Using Functional Connectivity Features Vineeta Khemchandani; Alok Singh Chauhan; Shahnaz Fatima; Jalauk Singh Maurya; Abhay Singh Rathaur; Kumar Sharma, Narendra; Daya Shankar Srivastava; Vugar Abdullayev
Advance Sustainable Science Engineering and Technology Vol. 8 No. 1 (2026): November - January
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i1.2833

Abstract

Cognitive workload plays a vital role in tasks that demand dynamic decision-making, especially under high-risk and time-sensitive conditions. An excessive workload can lead to unexpected and disproportionate risks, whereas insufficient workload may cause disengagement, undermining task performance. This underscores the importance of maintaining an optimal level of mental focus in high-pressure situations to ensure successful task execution. This study leverages deep learning methods alongside functional connectivity measures to classify cognitive workload levels. Using the N-back EEG dataset, functional connectivity metrics such as Phase Locking Value (PLV), Phase Lagging Index (PLI), and Coherency are extracted after data pre-processing. These metrics, characterized as directed or non-directed, enable efficient computational analysis. A convolutional neural network (CNN) classifier is employed to categorize cognitive workload into three levels: low (0-back), medium (2-back), and high (3-back). The CNN-A architecture achieves peak performance with an accuracy of 93.75% using PLV, 87.5% using Coherency, and 68.75% using PLI.

Page 28 of 28 | Total Record : 272

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