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Welcome Message from the Dean, faculty of applied science and technology (FAST)

Journal of Physics: Conference Series 2019年第1期1373卷

作者： Azhari, Imam Faculty of Applied Science and Technology Universitas Ahmad Dahlan

On behalf of the faculty member of FAST, Universitas Ahmad Dahlan, we are delighted to welcome you to The 2019 Conference on Fundamental and applied science for Advanced technology (ConFAST 2019). The Conference in this series has been successfully organized in the previous events starting from 2012 (ICGWBt), 2014 (ICGWBT) and 2016 (ConFAST). Since then, ConFAST has become a 'trademark' scientific forum for our faculty to interact with local, regional and international partners. I would like to express my sincere gratitude to the distinguished keynote speakers for sharing their expertise in our forum, especially for Prof. Setiawan from Universitas Brawijaya, Dr. Warsito P. Taruno from CTECH Laboratory, Prof. Afroz Ahmad Shah from Universiti Brunei Darussalam, Dr. Abhishek Kumar from Solar Energy Research Institute of Singapore, and Prof. Kitichate Sridith from Prince of Songkla University. I hope this conference can serve as initial step stone for a productive collaboration in the near future. Gratitude also goes to all presenters and participant for their active contribution to the conference, we hope to seeing you again in the next series of ConFAST 2021. Also, I would like to congratulate the organizing committee and editorial boards for their rigorous efforts in preparation, execution, and post publication of the conference. Last but not least, I sincerely hope that all the presenters and participants will benefit from the technical contents, as well as genuine strategic networking forged through this conference. Sincerely, Imam Azhari, ***. Dean, faculty of applied science and technology Universitas Ahmad Dahlan List of image is available in this pdf.

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Factors Analysis Affecting Academic Achievement of Undergraduate Student: A study on faculty of Information technology Students at applied science Private University 2

Factors Analysis Affecting Academic Achievement of Undergrad...

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2nd International Conference on Business Analytics for technology and Security, ICBATS 2023

作者： Alkayed, Manal Almasalha, Fadi Hijjawi, Mohammad Qutqut, Mahmoud H Applied Science Private University Faculty of Information Technology Jordan

ISBN: (纸本)9798350335644

The analysis of students' educational achievements at a university is beneficial for the university, society and students. This study aims to search for the various attributes related to academic student's achievements. The subjects of the study were 3509 graduated students at the applied science Private University in Jordan from all department programs of the faculty of Information technology. The study is intended to analyze students profile and find its relation to and association with their university GPA (Grade Point Average) by selecting four main attributes related to the students: department, secondary certificate grade, secondary certificate city, and gender. Then it is intended to find the high effecting value for each attribute, and create main association rules through using data mining classifier like (Decision tree, naïve bayse and association rules). Finally, the result of this study may help current and new faculty students to predict the possible obtained GPA to control their performance and try to enhance it. © 2023 IEEE.

关键词： Students

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Factors Analysis Affecting Academic Achievement of Undergraduate Student: A study on faculty of Information technology Students at applied science Private University

Factors Analysis Affecting Academic Achievement of Undergrad...

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Business Analytics for technology and Security (ICBATS), International Conference on

作者： Manal Alkayed Fadi Almasalha Mohammad Hijjawi Mahmoud H Qutqut Faculty of Information Technology Applied Science Private University

The analysis of students’ educational achievements at a university is beneficial for the university, society and students. This study aims to search for the various attributes related to academic student’s achievements. The subjects of the study were 3509 graduated students at the applied science Private University in Jordan from all department programs of the faculty of Information technology. The study is intended to analyze students profile and find its relation to and association with their university GPA (Grade Point Average) by selecting four main attributes related to the students: department, secondary certificate grade, secondary certificate city, and gender. Then it is intended to find the high effecting value for each attribute, and create main association rules through using data mining classifier like (Decision tree, naïve bayse and association rules). Finally, the result of this study may help current and new faculty students to predict the possible obtained GPA to control their performance and try to enhance it.

关键词： Analytical models Urban areas Data models Data mining Decision trees Security Business intelligence

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SOME PROPERTIES OF MINIMAL AND MAXIMAL OPERATORS IN AN ABSTRACT FRAMEWORK

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UPB Scientific Bulletin, Series A: applied Mathematics and Physics 2025年第1期87卷 29-40页

作者： Catană, Viorel Georgescu, Horia-George Department of Mathematics-Informatics Faculty of Applied Sciences National University of Science and Technology POLITEHNICA Bucharest Romania

The main goal of this paper is to give a two-parameter abstract framework in which we build a theory of minimal and maximal operators associated to a linear operator A: D(A) ⊂ X → X, with dense domain D(A), where X is a complex Banach space. We prove an analogue of the Agmon-Douglis-Nirenberg inequality for pseudodifferential operators in our abstract setting. Using this inequality, we show that the minimal and maximal operators of the operator A are equal under suitable hypotheses on the complex Banach space X and on the operator A. As an application, we study the existence and regularity of weak solutions of the linear equations Au = f on the reflexive complex Banach space X. Further, we prove a perturbation result regarding the Agmon-Douglis-Nirenberg estimate when the operator A is perturbed by a potential operator with some suitable properties. Moreover, an application to strongly continuous semigroups of contractions generated by the operator A is given. Finally, we prove that the minimal operator of the operator A is Fredholm under suitable hypotheses. © 2025, Politechnica University of Bucharest. All rights reserved.

关键词： Banach spaces

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Effective removal of As5+ from aqueous medium using date palm fiber biochar/chitosan/glutamine nanocomposite: kinetic and thermodynamic studies

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Environmental science and Pollution Research 2025年第7期32卷 3760-3775页

作者： Khalifa, Al Isaee Alshandoudi, Laila M. Hassan, Asaad F. Braish, Amany G. Science Department Rustaq College of Education University of Technology and Applied Sciences Rustaq Oman Chemistry Department Faculty of Science Damanhour University Damanhour Egypt

In the current work, three adsorbent materials were developed: biochar derived from date palm fiber (C), date palm fiber biochar/chitosan nanoparticles (CCS), and biochar/chitosan nanoparticle composite supplemented with glutamine (CCSG). These compounds were used as solid adsorbents to remove As5+ from polluted water. Several characterization approaches were used to investigate all the synthesized solid adsorbents, including thermogravimetric analysis, N2 adsorption/desorption isotherm, scanning electron microscopy, transmission electron microscopy (TEM), attenuated total reflectance with Fourier transform infrared, and zeta potential. Date palm fiber biochar/chitosan/glutamine nanocomposite (CCSG) demonstrated good thermal stability, with a maximum specific surface area of 518.69 m2/g, a mesoporous size of 2.06 nm, total pore volume of 0.25 cm3/g, TEM average particle size of 38 nm, and pHPZC of 6.9. Contact time (5–60 min), pH (1–9), starting As5+ concentration (50–500 mg/L), adsorbent dose (0.1–2.0 g/L), temperature (27–45 °C), and ionic strength (0.05–0.40 mol/L) were among the sorption parameters that were investigated in order to improve the adsorption conditions. It is observed that the modified samples were effectively able to remove As5+ (CCS;256.0 and CCSG;376.0 mg/g) than unmodified ones (C;150.5 mg/g). The As5+ removal procedure corresponded well with Langmuir isotherm model. Thermodynamic and kinetic experiments show that the Elovich, pseudo-first order, and Van’t Hoff plot with endothermic, spontaneous, and physisorption nature are the best fitted models. EDTA has the highest desorption efficiency percentage (98.8%). CCSG demonstrated enhanced reusability after six application cycles of As5+ adsorption/desorption, with only a 4% decrease in the efficiency of adsorption. This work shows that adding glutamine to the DPF biochar/chitosan composite reinforces it, resulting in the fabrication of a solid adsorbent that shows promise for use in water remedia

关键词： Thermogravimetric analysis

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EFFICIENT MODELING OF POLARIZED REFLECTION SPECTRA IN METASURFACES USING DEEP LEARNING FRAMEWORKS

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UPB Scientific Bulletin, Series A: applied Mathematics and Physics 2025年第1期87卷 189-198页

作者： Ilie, Alexandra Mănăilă-Maximean, Doina Dănilă, Octavian Faculty of Engineering in Foreign Languages National University of Science and Technology POLITEHNICA of Bucharest Romania Dept. of Physics National University of Science and Technology POLITEHNICA of Bucharest Romania Academy of Romanian Scientists Romania Faculty of Applied Sciences National University of Science and Technology POLITEHNICA of Bucharest Romania

Modeling the electromagnetic responses of metasurfaces, advanced materials engineered to manipulate electromagnetic waves, remains computationally intensive, particularly for complex geometries such as polygonal and ringlike designs. This study explores the potential of frequency-domain prediction, followed by spectral transformation, as a viable approach for capturing x-and y-polarized reflection spectra of metasurfaces. To this end, a deep learning framework is developed, combining Variational Autoencoders (VAEs) and Convolutional Neural Networks (CNNs). The VAE effectively encodes spectral features in a low-dimensional latent space, while the CNN utilizes residual connections to directly map metasurface patterns to their spectral responses. By integrating frequency-domain representations with inverse Fourier transforms, the framework ensures compatibility with traditional time-domain data while maintaining computational efficiency. The proposed models achieve high predictive accuracy, with the VAE outperforming the CNN across multiple error metrics. This dual focus on methodological development and performance evaluation demonstrates the feasibility and advantages of integrating machine learning for rapid and precise metasurface analysis and design. © 2025, Politechnica University of Bucharest. All rights reserved.

关键词： Inverse problems

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Advanced Machine Learning Approaches for Breast Cancer Detection with Neutrosophic Sets

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Neutrosophic Sets and Systems 2025年 81卷 273-284页

作者： Elbehiery, Hussam Fathi, Hanaa Eassa, Mohamed Abdelhafeez, Ahmed Abdellah, Mohamed Refaat Mahmoud, Hadeer Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt College of Information Technology Amman Arab University Amman Jordan Applied Science Research Center Applied Science Private University Amman Jordan The Department of Computer Science College of Information Technology Misr University for Science and Technology Cairo Egypt Department of Artificial Intelligence Faculty of Computers and Artificial Intelligence Modern University for Technology & Information Cairo Egypt

Breast Cancer (BC) remains a significant health challenge for women and is one of the leading causes of mortality worldwide. Accurate diagnosis is critical for successful therapy and increased survival rates. Recent advances in medical imaging and computational technologies have enabled more precise methods of detecting and evaluating breast cancer. Accurate analysis and diagnosis utilizing medical imaging have developed as essential research topics, providing important help in clinical decision-making for various illnesses, including breast cancer. Machine learning (ML) can accurately predict breast cancer. But the breast cancer data has vague and uncertainty information. So, the neutrosophic sets (NSs) are used in this study to deal with uncertainty data. We convert the original dataset into neutrosophic data with three components such as truth, indeterminacy, and falsity values. Then we applied four ML models with N-data such as logistic regression, gradient boosting (GB), k-nearest neighbor (KNN), and support vector machines (SVM), to improve diagnostic accuracy. Then we compared the ML models with and without using N-data. The results show the logistic regression has higher accuracy with 98.6% with the N-data and 95.80% without N-data. So, the NSs can improve the accuracy of ML models. © 2025, University of New Mexico. All rights reserved.

关键词： Lung cancer

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Recent advances in the modification of melamine sponge for oil-water separation

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材料科学技术（英文版） 2025年第4期207卷 209-224页

作者： Xing Zhou Dexiang Li Lili Wang Qi Wang Zhen Wang Qing Jing Rinderer Marisol Lu Li Faculty of Printing Packaging and Digital Media TechnologyXi'an University of TechnologyXi'an 710048China School of Physics and Materials Science Nanchang UniversityNanchang 330096China Key Laboratory of Auxiliary Chemistry and Technology for Chemical Industry Ministry of EducationShaanxi University of Science and TechnologyXi'an 710021China Faculty of Printing Packaging and Digital Media TechnologyXi'an University of TechnologyXi'an 710048China Faculty of Printing Packaging and Digital Media TechnologyXi'an University of TechnologyXi'an 710048China School of Physics and Materials Science Nanchang UniversityNanchang 330096China Faculty of Printing Packaging and Digital Media TechnologyXi'an University of TechnologyXi'an 710048China Faculty of Print and Media Stuttgart University of Applied ScienceStuttgart70569Germany Key Laboratory of Auxiliary Chemistry and Technology for Chemical Industry Ministry of EducationShaanxi University of Science and TechnologyXi'an 710021China

Melamine sponge is a major concern for oil-water separation due to its lightweight,high porosity(＞99％),cost-effectiveness,impressive mechanical properties,and chemical/thermal ***,its amphiphilic nature hinders selective oil absorption in *** strategies to enhance hydrophobic-ity are reviewed,including synthetic methods and materials,with comprehensive explanations of the mechanisms driven by surface energy and *** performance indicators for MS in oil-water separation,including adsorption capacity,wettability,stability,emulsion separation,reversible wettability switching,flame retardancy,mechanical properties,and recyclability,are thoroughly *** conclu-sion,this review provides insights into the future potential and direction of functional melamine sponges in oil-water separation.

关键词： Melamine sponge Hydrophobicity Lipophilicity Oil-water separation Modification

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Single-Valued Neutrosophic Graph with Heptapartitioend Structure

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Neutrosophic Sets and Systems 2025年 80卷 728-748页

作者： Fathi, Hanaa Myvizhi, M. Abdelhafeez, Ahmed Abdellah, Mohamed Refaat Eassa, Mohamed Sawah, Mohamed S. Elbehiery, Hussam College of Information Technology Amman Arab University Amman Jordan KPR Institute of Engineering and Technology Tamilnadu Coimbatore India Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan The Department of Computer Science College of Information Technology Misr University for Science and Technology Cairo Egypt Department of Computer Science Faculty of Information Technology Ajloun National University P.O.43 Ajloun26810 Jordan

This study applies single-valued neutrosophic sets, which extend the frameworks of fuzzy and intuitionistic fuzzy sets, to graph theory. We introduce a new category of graphs called Single-Valued Heptapartitioned Neutrosophic Graphs (SVHNG) and investigate their characteristics through comprehensive proofs and illustrative examples. © 2025, University of New Mexico. All rights reserved.

关键词： Heptapartitioend Neutrosophic Set Neutrosophic Graph Neutrosophic Sets SVHN-Graph

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Epileptic seizure detection in EEG signals via an enhanced hybrid CNN with an integrated attention mechanism

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Mathematical Biosciences and Engineering 2025年第1期22卷 73-105页

作者： Mekruksavanich, Sakorn Phaphan, Wikanda Jitpattanakul, Anuchit Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao56000 Thailand Department of Applied Statistics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Science and Technology Research Institute King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand

Epileptic seizures, a prevalent neurological condition, necessitate precise and prompt identification for optimal care. Nevertheless, the intricate characteristics of electroencephalography (EEG) signals, noise, and the want for real-time analysis require enhancement in the creation of dependable detection approaches. Despite advances in machine learning and deep learning, capturing the intricate spatial and temporal patterns in EEG data remains challenging. This study introduced a novel deep learning framework combining a convolutional neural network (CNN), bidirectional gated recurrent unit (BiGRU), and convolutional block attention module (CBAM). The CNN extracts spatial features, the BiGRU captures long-term temporal dependencies, and the CBAM emphasizes critical spatial and temporal regions, creating a hybrid architecture optimized for EEG pattern recognition. Evaluation of a public EEG dataset revealed superior performance compared to existing methods. The model achieved 99.00% accuracy in binary classification, 96.20% in three-class tasks, 92.00% in four-class scenarios, and 89.00% in five-class classification. High sensitivity (89.00–99.00%) and specificity (89.63–99.00%) across all tasks highlighted the model’s robust ability to identify diverse EEG patterns. This approach supports healthcare professionals in diagnosing epileptic seizures accurately and promptly, improving patient outcomes and quality of life. © 2025 American Institute of Mathematical sciences. All rights reserved.

关键词： Electroencephalography

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