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1st Virtual Conference on engineering, Science and Technology, ViCEST 2020

作者： Esabella, Shinta Intan, Hikmawati Hidayatullah, Muhammad Andriani, Titi Gunawan, G. Informatics Department Faculty of Engineering Universitas Teknologi Sumbawa Indonesia Electrical Engineering Department Faculty of Engineering Universitas Teknologi Sumbawa Indonesia Physics Education Study Program Faculty of Teacher Training and Education Universitas Mataram Indonesia

Management of patient data at the Technical Implementation Unit of the Community Health Center (UPT. Puskesmas) of Rhee Sumbawa District has not been fully computerized. The need for the development of an Information System to store medical records (history) of patient data. The research aims to develop information systems with descriptive research methods. For software development methods using Waterfall Models and built with Microsoft Visual Studio C# software and databases stored in Microsoft SQL Server software. Software testing uses Black Box Testing, which is testing software in functional terms. The results of research into digital data technology products that function to enter, change, delete data and minimize the time to find the data needed to improve the effectiveness and efficiency of data management. © Published under licence by IOP Publishing Ltd.

关键词： Information management

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Electrochemical Performance of a Water-Based LiFePO4 Cathode for Li-Ion Batteries

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Defect and Diffusion Forum 2022年 417卷 163-168页

作者： Chanhaew, Araya Aranmala, Komsak Darmawan, Luthfi Mursid Nisa, Shofirul Sholikhatun Nurohmah, Anisa Raditya Meethong, Nonglak Materials Science and Nanotechnology Program Department of Physics Faculty of Science Khon Kaen University Muang District Khon Kaen40002 Thailand Department of Chemical Engineering Faculty of Engineering Universitas Sebelas Maret Central Java Surakarta57126 Indonesia Centre of Excellence for Electrical Energy Storage Technology Universitas Sebelas Maret Central Java Surakarta57146 Indonesia Khon Kaen University Muang District Khon Kaen40002 Thailand

Lithium iron phosphate (LiFePO4) has received much attention because it can provide high power density, abundant raw materials, safety, low toxicity, and high thermal stability. In general, the production of LiFePO4 cathode uses Polyvinylidene fluoride (PVDF) as a binder and N-Methyl-2-Pyrrolidone (NMP) as a solvent. These components are expensive, toxic, and can affect the environment. Therefore, to cover this shortcoming, the solvent and binder were replaced in this study. The solvent is replaced with water, which is more environmentally friendly and abundant, while the binder is replaced with water soluble binders. Suitable electrode formulations were investigated to obtain high performance cylindrical typed Li-ion batteries. As a result, Formula B (ratio of LiFePO4: AB: KS6: SBR: CMC = 90: 3: 3: 2.677: 1.333) has the highest specific discharge capacity of 120 mAh/g. The NMP-based cathode can give about 145 mAh/g. Therefore, the water-based cathode LiFePO4 gives a value that is slightly lower the NMP-based formular. © 2022 Trans Tech Publications Ltd, Switzerland.

关键词： Lithium compounds

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A Low Complexity ML-Based Methods for Malware Classification

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Computers, Materials & Continua 2024年第9期80卷 4833-4857页

作者： Mahmoud E.Farfoura Ahmad Alkhatib Deema Mohammed Alsekait Mohammad Alshinwan Sahar A.El-Rahman Didi Rosiyadi Diaa Salama Abd Elminaam Cybersecurity Department Al-Zaytoonah University of JordanAmman11733Jordan Department of Computer Science and Information Technology Applied CollegePrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Faculty of Information Technology Applied Science Private UniversityAmman11931Jordan Computer Systems Program-Electrical Engineering Department Faculty of Engineering-ShoubraBenha UniversityCairo11629Egypt Research Center for Artificial Intelligence and Cybersecurity Electronics and Informatics OrganizationNational Research and Innovation Agency(BRIN)KST Samaun SamadikunBandung40135Republic of Indonesia Jadara Research Center Jadara UniversityIrbid21110Jordan MEU Research Unit Middle East UniversityAmman11831Jordan

The article describes a new method for malware classification,based on a Machine Learning(ML)model architecture specifically designed for malware detection,enabling real-time and accurate malware *** an innovative feature dimensionality reduction technique called the Interpolation-based Feature Dimensionality Reduction Technique(IFDRT),the authors have significantly reduced the feature space while retaining critical information necessary for malware *** technique optimizes the model’s performance and reduces computational *** proposed method is demonstrated by applying it to the BODMAS malware dataset,which contains 57,293 malware samples and 77,142 benign samples,each with a 2381-feature *** the IFDRT method,the dataset is transformed,reducing the number of features while maintaining essential data for accurate *** evaluation results show outstanding performance,with an F1 score of 0.984 and a high accuracy of 98.5%using only two reduced *** demonstrates the method’s ability to classify malware samples accurately while minimizing processing *** method allows for improving computational efficiency by reducing the feature space,which decreases the memory and time requirements for training and *** new method’s effectiveness is confirmed by the calculations,which indicate significant improvements in malware classification accuracy and *** research results enhance existing malware detection techniques and can be applied in various cybersecurity applications,including real-timemalware detection on resource-constrained *** and scientific contribution lie in the development of the IFDRT method,which provides a robust and efficient solution for feature reduction in ML-based malware classification,paving the way for more effective and scalable cybersecurity measures.

关键词： Malware detection ML-based models dimensionality reduction feature engineering

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Capacity Planning of Access Point Deployment for Indoor Positioning System

Capacity Planning of Access Point Deployment for Indoor Posi...

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2021 International Conference on Power, Energy and Innovations, ICPEI 2021

作者： Thitinaruemit, Aphirak Sitjongsatapom, Suchada Prongnuch, Sethakarn Mahanakorn University of Technology The Electrical Engineering Graduate Program Faculty of Engineering and Technology Bangkok Thailand Faculty of Engineering and Technology Dept. of Electronic Engineering Bangkok Thailand Suan Sunandha Rajabhat University Faculty of Industrial Technology Dept. of Computer Engineering Bangkok Thailand

ISBN: (纸本)9781665402163

This paper present the network design and capacity planning of access point (AP) deployment for indoor positioning system. The estimation of WiFi distance is described briefly how to measure the signal strength of AP. Co-channel interference is a key for network design of access point installation for network capacity. The objective function for network capacity planning of AP deployment is to optimize the number of access point and location for indoor positioning system concerning with the minimized co-channel interference. Simulations are tested to install AP for lecture hall in university. Capacity planning design of wireless AP deployment is proposed for indoor position systems in terms of the received signal strength indications (RSSI). We assume the situation is supported for the crowded conference. The requirement of the results of wireless AP positioning in crowded conference rooms are placing the wireless AP in forms of overlap the signal in order to get the strong signal. Experimental results have been tested their received signal of WiFi APs with the 2.4GHz frequency using the receiver installed in laptops to check the signal. The empirical results shown that the strength of RSSI will be received a signal of -44 dBm at a distance of approximately 20 meters. © 2021 IEEE.

关键词： Signal receivers

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The Application of Artificial Neural Network Model to Predicting the Acid Mine Drainage from Long-Term Lab Scale Kinetic Test

arXiv

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arXiv 2024年

作者： Abfertiawan, Muhammad Sonny Kautsar, Muchammad Daniyal Hasan, Faiz Palinggi, Yoseph Pranoto, Kris Water and Wastewater Research Group Faculty of Civil and Environmental Engineering Bandung Institute of Technology Indonesia Department of Electrical and Information Engineering Universitas Gadjah Mada Indonesia Environmental Engineering Master Program Faculty of Civil and Environmental Engineering Bandung Institute of Technology Indonesia Environmental Department PT Kaltim Prima Coal Indonesia

Acid mine drainage (AMD) is one of the common environmental problems in the coal mining industry that was formed by the oxidation of sulfide minerals in the overburden or waste rock. The prediction of acid generation through AMD is important to do in overburden management and planning the post-mining land use. One of the methods used to predict AMD is a lab-scale kinetic test to determine the rate of acid formation over time using representative samples in the field. However, this test requires a long-time procedure and large amount of chemical reagents lead to inefficient cost. On the other hand, there is potential for machine learning to learn the pattern behind the lab-scale kinetic test data. This study describes an approach to use artificial neural network (ANN) modeling to predict the result from lab-scale kinetic tests. Various ANN model is used based on 83 weeks experiments of lab-scale kinetic tests with 100% potential acid-forming rock. The model approaches the monitoring of pH, ORP, conductivity, TDS, sulfate, and heavy metals (Fe and Mn). The overall Nash-Sutcliffe Efficiency (NSE) obtained in this study was 0.99 on training and validation data, indicating a strong correlation and accurate prediction compared to the actual lab-scale kinetic tests data. This show the ANN ability to learn patterns, trends, and seasonality from past data for accurate forecasting, thereby highlighting its significant contribution to solving AMD problems. This research is also expected to establish the foundation for a new approach to predict AMD, with time efficient, accurate, and cost-effectiveness in future applications. © 2024, CC BY-NC-SA.

关键词： Coal industry

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Review ofPractical Psychometrics: A Guide for Test Users

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Structural Equation Modeling: A Multidisciplinary Journal 2024年第5期31卷 932-934页

作者： Musyaffa’ Ahmad Muhammad Faisal a Graduate Program Department of Electrical Engineering and Information Technology Faculty of Engineering Universitas Gadjah Mada Jl. Grafika No. 2 UGM Campus Yogyakarta55281 Indonesia b Master of Mathematics Education Program Faculty of Mathematics and Natural Sciences Yogyakarta State University Jl. Colombo 1 Yogyakarta55281 Indonesia

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Advancing electrochemical impedance analysis through innovations in the distribution of relaxation times method

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Joule 2024年第7期8卷 1958-1981页

作者： Maradesa, Adeleke Py, Baptiste Huang, Jake Lu, Yang Iurilli, Pietro Mrozinski, Aleksander Law, Ho Mei Wang, Yuhao Wang, Zilong Li, Jingwei Xu, Shengjun Meyer, Quentin Liu, Jiapeng Brivio, Claudio Gavrilyuk, Alexander Kobayashi, Kiyoshi Bertei, Antonio Williams, Nicholas J. Zhao, Chuan Danzer, Michael Zic, Mark Wu, Phillip Yrjänä, Ville Pereverzyev, Sergei Chen, Yuhui Weber, André Kalinin, Sergei V. Schmidt, Jan Philipp Tsur, Yoed Boukamp, Bernard A. Zhang, Qiang Gaberšček, Miran O'Hayre, Ryan Ciucci, Francesco Department of Mechanical and Aerospace Engineering The Hong Kong University of Science and Technology Hong Kong Metallurgical and Materials Engineering Colorado School of Mines Golden80401 United States Beijing Key Laboratory of Green Chemical Reaction Engineering and Technology Department of Chemical Engineering Tsinghua University Beijing China Green Energy Storage Trento38123 Italy Department of Manufacturing and Production Engineering Faculty of Mechanical Engineering and Ship Technology Institute of Machine and Materials Technology Gdańsk University of Technology Gdańsk Poland Electrode Design for Electrochemical Energy Systems University of Bayreuth Bayreuth Germany School of Chemistry University of New South Wales Sydney Australia School of Advanced Energy Sun Yat-Sen University Shenzhen China Sustainable Energy Center CSEM Neuchâtel2002 Switzerland Interdisciplinary Faculty of Science and Engineering Shimane University Matsue Japan Centre for Electronic and Optical Materials National Institute for Materials Science Tsukuba Japan Department of Civil and Industrial Engineering University of Pisa Pisa Italy Department of Materials Imperial College London Exhibition Road LondonSW7 2AZ United Kingdom Department of Chemical Engineering Massachusetts Institute of Technology Cambridge02139 United States Electrical Energy Systems University of Bayreuth Bayreuth Germany Ruder Boskovic Institute Zagreb Croatia Department of Materials and Minerals Resources Engineering National Taipei University Taipei Taiwan Finland Johann Radon Institute for Computational and Applied Mathematics Linz Austria School of Science and Engineering Nanjing Tech. University Nanjing China Karlsruhe Germany Department of Materials Science and Engineering University of Tennessee KnoxvilleTN37996 United States Physical Science Division Pacific Northwest National Laboratory RichlandWA99354 United States Systems Engineering for Electrical Energy Storage University of B

Electrochemical impedance spectroscopy (EIS) is widely used in electrochemistry, energy sciences, biology, and beyond. Analyzing EIS data is crucial, but it often poses challenges because of the numerous possible equivalent circuit models, the need for accurate analytical models, the difficulties of nonlinear regression, and the necessity of managing large datasets within a unified framework. To overcome these challenges, non-parametric models, such as the distribution of relaxation times (DRT, also known as the distribution function of relaxation times, DFRT), have emerged as promising tools for EIS analysis. For example, the DRT can be used to generate equivalent circuit models, initialize regression parameters, provide a time-domain representation of EIS spectra, and identify electrochemical processes. However, mastering the DRT method poses challenges as it requires mathematical and programming proficiency, which may extend beyond experimentalists’ usual expertise. Post-inversion analysis of DRT data can be difficult, especially in accurately identifying electrochemical processes, leading to results that may not always meet expectations. This article examines non-parametric EIS analysis methods, outlining their strengths and limitations from theoretical, computational, and end-user perspectives, and provides guidelines for their future development. Moreover, insights from survey data emphasize the need to develop a large impedance database, akin to an impedance genome. In turn, software development should target one-click, fully automated DRT analysis for multidimensional EIS spectra interpretation, software validation, and reliability. Particularly, creating a collaborative ecosystem hinged on free software could promote innovation and catalyze the adoption of the DRT method throughout all fields that use impedance data. © 2024 Elsevier Inc.

关键词： Electrochemical impedance spectroscopy

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Stunting Classification in Toddlers: Implementation and Evaluation of the Decision Tree Algorithm

Stunting Classification in Toddlers: Implementation and Eval...

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Information Technology and Computing (ICITCOM), International Conference on

作者： Debby Alita Imam Ahmad Rhaishudin Jafar Rumandan Moh Erkamim Widyasmoro Faculty of Engineering and Computer Science Universitas Teknokrat Indonesia Bandarlampung Indonesia Islamic Education Management Study Program Institut Agama Islam Negeri Ambon Ambon Indonesia Smart City Information Systems Study Program Universitas Tunas Pembangunan Surakarta Indonesia Department of Electrical Engineering Faculty of Engineering Universitas Muhammadiyah Yogyakarta Yogyakarta Indonesia

ISBN: (数字)9798350379839

ISBN: (纸本)9798350379846

Stunting in toddlers is a chronic nutritional issue that affects the physical and cognitive development of children, with serious long-term consequences such as reduced cognitive function and an increased risk of chronic diseases in adulthood. Therefore, early identification and prevention efforts for stunting are crucial. Classifying toddlers into categories of at-risk for stunting or not is essential to provide timely and appropriate interventions. This study employs data mining techniques using the decision tree algorithm to expedite the stunting detection process and improve the accuracy of nutritional status classification in children. The results indicate that the constructed decision tree model can classify children's nutritional status with an accuracy of 83.26%. The decision tree achieves high accuracy in classifying stunting in toddlers due to its ability to handle complex data and identify significant patterns within the data.

关键词： Pediatrics Accuracy Computational modeling Prevention and mitigation Data models Classification algorithms Decision trees Data mining Information technology Diseases

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Digital Technology in Health Education Trend Mapping through Bibliometrics Analysis 4

Digital Technology in Health Education Trend Mapping through...

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4th International Conference on Informatics, Multimedia, Cyber and Information System, ICIMCIS 2022

作者： Maulana, Fairuz Iqbal Lestari, Dian Widartha, Vandha Pradwiyasma Haidar, Nirwana Dirgantara, Wahyu School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Airlangga University Faculty of Medicine Surabaya60132 Indonesia Telkom University Information System of Industrial Engineering Faculty Bandung Indonesia University of Madura Informatics Engineering Study Program Pamekasan Indonesia University of Merdeka Malang Department of Electrical Engineering Malang Indonesia

ISBN: (纸本)9781665473279

This study examines the mapping of research data on digital technology in the field of health education using bibliometric analysis method. Data was collected by identifying keywords in the Scopus database and sorting them out to sort the studies from the last 10 years (2012-2021). Through this step, a total of 1482 documents were obtained (articles, journals, proceedings, books, and others). The data is then processed using the VOSViewer instrument to obtain a visualization of the mapping analysis. This study also analyzes the network types of authors and co-authors through the VOSViewer instrument. The result of this study indicates that the most document types published within ten years are Articles (66.5%), Review (22.9%), Conference Paper (4.2%), and others. The most studied subjects are Medicine (55.7%), Nursing (9.0%), Health Professions (8.2%), Social Sciences (7.8%), engineering (3.9%), Computer Science (2.6%), Environmental Science (2.6%), Biochemistry-Genetics and Molecular Biology (2.2%), Psychology (1.6%), and Dentistry (1.3%). This study offers a written communication process and the nature and direction of developing descriptive means of counting and analyzing the various phases of communication as well as recognizing the authorship and direction of its symptoms in documents on the subject of digital technology in the health sector. © 2022 IEEE.

关键词： Mapping

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A multimodal integrative approach to model transcriptional addiction of thyroid cancer on RUNX2

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Cancer Communications 2022年第9期42卷 892-896页

作者： Emanuele Vitale Elisabetta Sauta Mila Gugnoni Federica Torricelli Veronica Manicardi Alessia Ciarrocchi Laboratory of Translational Research Azienda Unità Sanitaria Locale-IRCCS di Reggio EmiliaReggio Emilia 42123Italy Clinical and Experimental Medicine PhD Program University of Modena and Reggio EmiliaModena 41121Italy Department of Electrical Computer and Biomedical EngineeringUniversity of PaviaPavia 27100Italy

Dear Editor Aberrant gene expression sustains massive proliferation and stress adaptation under the regulation of oncogenic transcription factors(TFs)whose binding across the genome orchestrates in space and time *** dependency defines the addiction of cancer cells to TFs on top of the regulatory hierarchy governing cancer dysregulated programs[1].These factors and their dependent mechanisms are an attractive and unexplored reservoir of potential targets for new anticancer ***,while the transcriptional landscape of many cancer-supportive TFs has been revealed,this information remains purely descriptive and confined to in vitro *** foster transferability,new approaches that integrate clinical data into the transcriptional networks are needed.

关键词： addiction modal reservoir

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