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Correction to: Contaminants of emerging concerns (CECs) in a municipal wastewater treatment plant in Indonesia

Environmental science and pollution research international 2023年第8期30卷 21533-21534页

作者： Maryani Paramita Astuti Suprihanto Notodarmojo Cindy Rianti Priadi Lokesh P Padhye Department of Civil and Environmental Engineering The University of Auckland Auckland New Zealand. Environmental Engineering Study Program Faculty of Engineering President University Cikarang Indonesia. Environmental Engineering Department Faculty of Civil and Environmental Engineering Bandung Institute of Technology (ITB) Bandung Indonesia. Environmental Engineering Study Program Civil Engineering Department Engineering Faculty University of Indonesia (UI) Depok Indonesia. Department of Civil and Environmental Engineering The University of Auckland Auckland New Zealand. l.padhye@auckland.ac.nz.

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Mapping the Literature of Artificial Intelligence in Medical Education: A Scientometric Analysis 7th

Mapping the Literature of Artificial Intelligence in Medical...

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7th International Conference on Inventive Communication and Computaional Technologies, ICICCT 2023

作者： Maulana, Fairuz Iqbal Zaın, Muhammad Yasır Lestari, Dian Purnomo, Agung Adi, Puput Dani Prasetyo Computer Science Department Bina Nusantara University Jakarta11480 Indonesia Informatics Engineering Study Program University of Madura Pamekasan Indonesia Faculty of Medicine Universitas Airlangga Surabaya60132 Indonesia Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta Jakarta11480 Indonesia Telecommunication Research Center National Research and Innovation Agency Jakarta11480 Indonesia

ISBN: (纸本)9789819951659

This study aims to investigate the use of artificial intelligence in health education in the last ten years from 2012 to 2022 using the Scopus database. Researchers use bibliometric analysis combined with the quantification method of Vosviewer and Rstudio software used for literature analysis. The results of the article that were flushed showed data such as the year of publication, journal, country, keywords, and authors, to the highest number of citations. The general keywords used by researchers are artificial intelligence, medical, and education. Researchers limit findings through keywords as in the last ten years and English only so that 1681 articles were obtained. According to the study's findings, McGill University affiliation had the greatest number of papers, with 69 pieces, and medicine had a proportion of 39.7% (n = 1067). This bibliometric study will be useful for other researchers to examine the development of research on artificial intelligence in health education in the last ten years. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Medical education

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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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Enhancing Localized Surface Plasmon Resonance Response for Albumin Detection by Optimizing the Lateral Size of Hexagonal Gold Nanoparticles

Enhancing Localized Surface Plasmon Resonance Response for A...

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IEEE International Conference on Semiconductor Electronics (ICSE)

作者： Silva Nurfasha Chandra Wulandari Lilik Hasanah Ahmad Aminuddin Yanurita Dwi Hapsari Mohammad Arifin Roer Eka Pawinanto Budi Mulyanti Study Program of Physics Universitas Pendidikan Indonesia Bandung Indonesia Study Program of Physics Institut Teknologi Bandung Bandung Indonesia Department of Physics Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Surabaya Indonesia Study Program of Industrial Automation and Robotics Engineering Education Universitas Pendidikan Indonesia Bandung Indonesia Study Program of Electrical Engineering Universitas Pendidikan Indonesia Bandung Indonesia

ISBN: (数字)9798350378313

ISBN: (纸本)9798350378320

The kidney is a human organ with numerous vital functions, mainly for blood filtration. Damage to the kidneys can be detected from albumin levels in urine, higher levels of albumin in urine indicate the kidney's filtration function is impaired. The latest breakthrough in the medical field is the use of localized sensor plasma resonance (LSPR) in the field of biosensors to detect albumin protein in urine. Using Finite-Difference Time-Domain (FDTD) simulation, the lateral size of hexagonal gold nanoparticles (AuNPs) is varied to increase the optimal LSPR. AuNPs are well-known as the most promising material for plasmonic applications. In several studies, the hexagonal geometry was reported to increase the surface area of nanoparticles as well as enhance the LSPR signal. Varying the geometry of the hexagonal AuNPs will affect their sensitivity and optical properties. In this study, the lateral size of the hexagonal AuNPs varied from 10 nm to 60 nm, with a controlled thickness of 30 nm. The absorption graph blue shifts with the peak decreasing until the graph has almost no peak as the lateral size decreases. The lateral size of 40 nm at a thickness of 30 nm was found to have the highest shifting response towards the change of refractive index, indicating good sensitivity for sensors. Simulation of hexagonal AuNPs-based LSPR detection of albumin concentrations of 0.35 –5.71 mM produces a sensitivity of 2.59 nm/mM, equivalent to 245.96 nm/RIU. The competitive performance and ease of detection procedures in real applications show the potential of the hexagonal AuNPs-based LSPR sensor to be further explored in developing alternative albumin detection techniques.

关键词： Nanoparticles Geometry Gold Sensitivity Filtration Absorption Refractive index

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YoBagi's User Experience Evaluation using User Experience Questionnaire

YoBagi's User Experience Evaluation using User Experience Qu...

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International Conference on informatics and Computing (ICIC)

作者： Fransiskus Panca Juniawan Dwi Yuny Sylfania Rendy Rian Chrisna Putra Henderi Program Study of Technology Information University of Bangka Belitung Pangkalpinang Indonesia Program Study of Informatics Engineering Institut Sains dan Bisnis Atma Luhur Pangkalpinang Indonesia Informatics Engineering Faculty of Science and Technology University of Raharja Tangerang Indonesia

ISBN: (纸本)9798350345728

The pandemic has brought many negative impacts on human life. One of the negative impacts is economic downturn of Micro, Small, and Medium Enterprise (MSME) actors in Bangka Belitung Province. To solve this problem, a website-based marketplace platform called Yobagi was created. As we know, a new software product must have a good user experience. By having a good user experience means that the product has met the needs of its users. For this reason, an evaluation of the user experience is necessary. This study aims to measure the user experience of using Yobagi. The evaluation uses the User Experience Questionnaire (UEQ) tool, which consists of six scales: Attractiveness, Efficiency, Perspicuity, Dependability, Stimulation, and Novelty. There were 40 respondents who were taken from the top of MSME actors. As the UX evaluation results, it is known that the final score of the six scales is above 0.8 and at the excellent level. This means that Yobagi users have a very good user experience in using Yobagi. In addition, Yobagi has met the criteria for good software by having an excellent user experience value.

关键词： Economics System testing Pandemics Entrepreneurship User experience Software informatics

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Effective Alzheimer’s Stage Classification Using Transfer Learning Based on MRI Images

Effective Alzheimer’s Stage Classification Using Transfer L...

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Vocational Education and Electrical engineering, International Conference on

作者： Hamdani Hamdani Anindita Septiarini Theresia Amelia Pawitra Aldion Sumampouw Andi Tejawati Tri Basuki Kurniawan Dept. of Informatics Faculty of Engineering Mulawarman University Samarinda Indonesia Dept. of Industrial Engineering Faculty of Engineering Mulawarman University Samarinda Indonesia Magister Information of Technology Postgraduate Program Universitas Bina Darma Palembang Indonesia

ISBN: (数字)9798331505103

ISBN: (纸本)9798331505110

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and the most prevalent cause of dementia, significantly impacting cognitive functions and daily activities. Accurate and timely classification of AD stages is of paramount importance for the effective treatment and management of the disease. The aim of this research is to employ transfer learning in a convolutional neural network (CNN) to classify the stages of AD using magnetic resonance imaging (MRI) brain images, building on the aforementioned foundational principles. The CNN model employed in this study is MobileNetV3-Small, which has been trained on a comprehensive dataset of labeled MRI scans with the objective of accurately distinguishing the various stages of AD. The dataset, referred to as the “Augmented Alzheimer MRI Dataset,” comprises $\mathbf{3 3, 9 8 4}$ augmented and $\mathbf{6, 4 0 0}$ original MRI images, which have been categorized into four stages: Very Mild Demented, Moderate Demented, Mild Demented, and NonDemented. The methodology comprises three principal stages: data preparation, model training, and model evaluation. A systematic assessment of various hyperparameters, including split ratio, learning rate, batch size, and dropout rate, was conducted to optimize model performance. The results demonstrate that fine-tuning the model significantly enhances classification accuracy, achieving a maximum accuracy of $\mathbf{9 6 \%}$ compared to $\mathbf{7 2 \%}$ without fine-tuning. A smaller learning rate was found to improve performance metrics by preventing the model from converging to local minima. These findings highlight the potential of transfer learning and hyperparameter optimization in developing robust models for the early detection and classification of AD, contributing to better patient outcomes and advancing the field of medical image analysis.

关键词： Training Analytical models Accuracy Systematics Magnetic resonance imaging Computational modeling Transfer learning Data models Convolutional neural networks Alzheimer's disease

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Automated Diagnosis of Heart-Lung Diseases in Chest X-ray Images 5

Automated Diagnosis of Heart-Lung Diseases in Chest X-ray Im...

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5th International Conference on engineering Technology and its Applications, IICETA 2022

作者： Alslatie, Mohammad Alquran, Hiam Mustafa, Wan Azani Abu-Qasmieh, Isam Alqudah, Ali Mohammad Alkhayyat, Ahmed The Institute of Biomedical Technology King Hussein Medical Center Royal Jordanian Medical Service Amman Jordan Yarmouk University Biomedical Systems and Medical Informatics Engineering Irbid Jordan Arau Faculty of Electrical Engineering Technology Perlis Malaysia University of Manitoba Biomedical Engineering Program Department of Electrical and Computer Engineering Canada The Islamic University Faculty of Engineering Najaf Iraq

ISBN: (纸本)9781665472159

The state of the art of artificial intelligence (AI) for various medical imaging applications leads to enhanced accuracy, analysis, visualization, and interpretation of chest Xray (CXR) images for diagnosis. Many diseases are diagnosed based on CXR images. In this paper, two types of abnormalities are diagnosed based on AI techniques. The two classes are atelectasis and cardiomegaly. The acquired images are segmented to localize the chest region and then enhanced using gray-level transformation methods. The enhanced images are passed to two pretrained convolutional neural networks (CNNs): shuffle and mobile net. The transfer learning approach is utilized in this stage. The automated features are extracted from the last fully connected layer. Each CNN deserves to have the two most representative features for the two classes. These four features are passed to support the vector machine classifier. The training accuracy reached 100% and the test accuracy was 96.7%. The proposed method can be extended to be a milestone in the classification of all heart-lung diseases that can be diagnosed using chest X-ray images. © 2022 IEEE.

关键词： Convolutional neural networks

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Supervised Learning Models for Early Detection of Albuminuria Risk in Type-2 Diabetes Mellitus Patients 10

Supervised Learning Models for Early Detection of Albuminuri...

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10th International Conference on Advanced informatics: Concept, Theory and Application, ICAICTA 2023

作者： Muharram, Arief Purnama Tahapary, Dicky Levenus Lestari, Yeni Dwi Sarayar, Randy Dirjayanto, Valerie Josephine School of Electrical Engineering and Informatics Master Program of Informatics Institut Teknologi Bandung Bandung Indonesia Indonesia Medical Education and Research Institute Universitas Indonesia Metabolic Disorder Cardiovascular and Aging Research Center Faculty of Medicine Jakarta Indonesia Universitas Indonesia Faculty of Medicine Cipto Mangunkusumo General Hospital Ophthalmology Department Jakarta Indonesia Universitas Indonesia Faculty of Medicine Jakarta Indonesia

ISBN: (纸本)9798350329919

Diabetes, especially T2DM, continues to be a significant health problem. One of the major concerns associated with diabetes is the development of its complications. Diabetic nephropathy, one of the chronic complication of diabetes, adversely affects the kidneys, leading to kidney damage. Diagnosing diabetic nephropathy involves considering various criteria, one of which is the presence of a pathologically significant quantity of albumin in urine, known as albuminuria. Thus, early prediction of albuminuria in diabetic patients holds the potential for timely preventive measures. This study aimed to develop a supervised learning model to predict the risk of developing albuminuria in T2DM patients. The selected supervised learning algorithms included Naive Bayes, Support Vector Machine (SVM), decision tree, random forest, AdaBoost, XGBoost, and Multi-Layer Perceptron (MLP). Our private dataset, comprising 184 entries of diabetes complications risk factors, was used to train the algorithms. It consisted of 10 attributes as features and 1 attribute as the target (albuminuria). Upon conducting the experiments, the MLP demonstrated superior performance compared to the other algorithms. It achieved accuracy and f1-score values as high as 0.74 and 0.75, respectively, making it suitable for screening purposes in predicting albuminuria in T2DM. Nonetheless, further studies are warranted to enhance the model's performance. © 2023 IEEE.

关键词： Forecasting

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Methodology for the design of a nanosatellite telecommunication system in S-Band based on Software for Digital Mission engineering

Methodology for the design of a nanosatellite telecommunicat...

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International Conference on AeroSpace Science and Technology 2023, ICASST 2023

作者： García-Guillén, L.A. Sanchéz-Sanchéz, J.L. Ramírez-Aguilar, J.A. Ferrer-Pérez, J.A. Romo-Fuentes, J.C. Farah-Simón, A. Chávez-Moreno, R.G. Sánchez Hernández, D.C. Sánchez-Guzmán, J.A. Valdés-Galicia, J.F. Master's and Doctorate Programm in Engineering Engineering Faculty Universidad Nacional Autónoma de México CDMX Mexico Bachelor's in Aero Systems Electronics Engineering Universidad Aeronáutica en Querétaro Queretaro Mexico Advanced Technology Unit School of Engineering UNAM Queretaro Juriquilla Mexico Astronomy Institute Universidad Nacional Autónoma de México CDMX Mexico Faculty of Informatics Autonomous University of Queretaro Queretaro Juriquilla Mexico Space Program University Universidad Nacional Autónoma de México CDMX Mexico

A trend in the development of nanosatellite platforms for technology demonstration has increased in both private industry and academic projects. Communication link design plays a crucial role in the success of space missions, enabling efficient and reliable data transmission between satellites and ground stations. In this context, the use of advanced tools such as System Tool Kit (STK) has gained significant importance by enabling a comprehensive analysis of the technical and operational aspects of the communication link. In an effort to make the future design of the communications subsystem payload on a nanosatellite more efficient and accessible, this paper shows a methodology for the efficient use of STK to perform link analysis. © Published under licence by IOP Publishing Ltd.

关键词： Nanosatellites

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Hydrodynamic Properties of a Moored Floating Breakwater using CFD Approach 8

Hydrodynamic Properties of a Moored Floating Breakwater usin...

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8th International Seminar on Ocean and Coastal engineering, Environmental and Natural Disaster Management, ISOCEEN 2020

作者： Abdullah, S.F. Fitriadhy, A. Mohd, M.H. Rahman, M.A.A. Suntoyo Kurniawan, A. Pratikto, W. Program of Maritime Technology Faculty Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Malaysia Surabaya Indonesia Program of Ocean Engineering Faculty of Civil and Environmental Engineering Institut Teknologi Bandung Indonesia

In presence of complex-hydrodynamic interaction between water wave and moving structure, a reliable method that can analyse nonlinear phenomena is necessarily required. This paper presents numerical investigation of a moored floating breakwater using computational fluid dynamic (CFD) approach. The mathematical model is based on the extended Reynolds Average Navier-Stokes (RANS) solver for solid-porous obstacle. A high amplitude wave with several wave periods were deliberately considered in the simulation to allow nonlinear wave effects on the floating structure such as wave breaking, overtopping, including viscous friction. Here, the two fluid calculation method for interface boundary between water and air is proposed to capture the complex free surface changes. In addition, the fractional average volume obstacle representation (FAVOR) using partial cell treatment method is employed to simulate the motion of breakwater boundary on the free surface. Approximations and validations on the hydrodynamic properties of the structure have been carried out which include wave transmission coefficient, sway, heave, pitch, and mooring forces. The results show that the CFD model can fairly simulate well on hydrodynamics of the floating breakwater. The discrepancies between numerical and experimental data can be partly attributed to the nonlinearity in the incident wave definition. © Published under licence by IOP Publishing Ltd.

关键词： Computational fluid dynamics

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