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25th International Conference on Human-Computer Interaction, HCII 2023

作者： Islam, Ashraful Ahmed, Eshtiak Islam, Md Rakibul EMPATHY Lab Center for Computational and Data Sciences Independent University Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Independent University Bangladesh Dhaka Bangladesh Faculty of Information Technology and Communication Sciences Tampere University Tampere Finland Computer Science Department University of Wisconsin - Eau Claire Eau ClaireWI United States

ISBN: (纸本)9783031492112

Gestational Diabetes Mellitus (GDM) is a rising concern worldwide, particularly in low-resource countries such as Bangladesh, where access to healthcare facilities is limited and awareness of GDM management is inadequate. This work proposes an early-stage design of user interfaces (UIs) for a mobile health (mHealth) intervention using the Bengali language that allows users to input daily measures of blood glucose, weight, and other GDM-related health data. The intervention is designed to provide users with personalized feedback based on their input and the necessary coaching for managing GDM. It also reminds users to take medication if prescribed by their doctors. The app has six key features: blood glucose tracking, food logging, medication reminders, activity tracking, educational resources, and personalized recommendations. Overall, this work provides a valuable contribution to the field of mHealth-based maternal and child health in Bangladesh. However, further research is necessary to evaluate the effectiveness and feasibility of the intervention in real-world scenarios. Such evaluations will provide valuable insights into its potential impact and help refine the design to better serve the needs of GDM patients in Bangladesh. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： mHealth

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An SAV Method for Imaginary Time Gradient Flow Model in Density Functional Theory

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Advances in Applied Mathematics and Mechanics 2023年第3期15卷 719-736页

作者： Ting Wang Jie Zhou Guanghui Hu Department of Mathematics Faculty of Science and TechnologyUniversity of MacaoMacao SARChina School of Mathematics and Computational Science Xiangtan UniversityXiangtanHunan 411105China Zhuhai UM Science&Technology Research Institute ZhuhaiGuangdong 519030China Guangdong-Hong Kong-Macao Joint Laboratory for Data-Driven Fluid Mechanics and Engineering Applications University of MacaoMacao SARChina

In this paper,based on the imaginary time gradient flow model in the density functional theory,a scalar auxiliary variable(SAV)method is developed for the ground state calculation of a given electronic structure *** handle the orthonormality constraint on those wave functions,two kinds of penalty terms are introduced in designing the modified energy functional in SAV,i.e.,one for the norm preserving of each wave function,another for the orthogonality between each pair of different wave functions.A numerical method consisting of a designed scheme and a linear finite element method is used for the ***,the desired unconditional decay of modified energy can be obtained from our method,while computationally,both the original energy and modified energy decay behaviors can be observed successfully from a number of numerical *** importantly,numerical results show that the orthonormality among those wave functions can be automatically preserved,without explicitly preserving orthogonalization *** implies the potential of our method in large-scale simulations in density functional theory.

关键词： Density functional theory gradient flow scalar auxiliary variable unconditional energy stability orthonormalization free

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Pelvic bone region segmentation (PBRS) from X-ray image using convolutional neural network (CNN) 26

Pelvic bone region segmentation (PBRS) from X-ray image usin...

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26th International Conference on Computer and Information Technology, ICCIT 2023

作者： Ul Alam, Syed Alif Binte Alam, Saadia Saha, Sourav Haque, Mahmudul Rahman, Rashedur Kobashi, Syoji Independent University Bangladesh Department of Computer Science and Engineering Dhaka1229 Bangladesh Independent University Bangladesh Center for Computational & Data Sciences Dhaka1229 Bangladesh University of Hyogo Graduate School of Engineering Shosha Hyogo Himeji2167 Japan

ISBN: (纸本)9798350359015

Pelvic and hip fractures offer considerable public health risks with high morbidity and mortality rates. Because of the complicated bone structure of the pelvic bone region, detecting fractures is difficult. Though X-ray imaging is routinely utilised for detecting fractures, manual fracture diagnosis is prone to inaccuracies. This paper proposes the use of deep learning algorithms for automated segmentation of the pelvic bone region in X-ray images. In our work, we have investigated U-Net based pelvic area segmentation models with various convolutional neural network (CNN) backbones. The DenseNet121-based U-Net design emerged as the most optimal model, establishing a compromise between performance and computational efficiency. Although it had a modest loss in IoU and F1 scores when compared to InceptionNetV3, it had a remarkable 59.44% reduction in the number of parameters. © 2023 IEEE.

关键词： deep learning pelvic bone segmentation x-ray

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Ensemble Filter Scenario Based Control on Linearized Power System Manifold

Ensemble Filter Scenario Based Control on Linearized Power S...

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International Conference on System Reliability and science (ICSRS)

作者： Bahri Uzunoğlu Department of Electrical Engineering Computational and data-enabled science and engineering in energy systems Uppsala University Sweden Department of Mathematics Florida State University USA

ISBN: (数字)9798350354508

ISBN: (纸本)9798350354515

This article integrates the widely recognised ensemble filters that are employed for power system state estimation and their ensemble scenarios to controls for power systems linearised on manifolds. The approach has the effect of directly providing constraint scenarios for any optimal real-time fast power system management for any ensemble-based state estimator used in the power system, thereby reducing the computational task for both radial and meshed power systems and addressing the computational needs of stochastic external state parameter that is not controlled by the grid operator. New and unparalleled obstacles have surfaced in the recent past for power systems. Tighter guidelines for grid reliability and service quality, the growing spread of electric mobility that has given rise to a new class of intermittent loads with distinct spatiotemporal patterns, and the growing use of distributed microgenerators from renewable power sources-which are frequently characterised by unpredictable behavior-are some of these challenges. For proof of concept, the method is shown on a simple example.

关键词： Manifolds Renewable energy sources Uncertainty Decision making Transforms Power systems Spatiotemporal phenomena Kalman filters Reliability State estimation

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Optimization Models for Teaching Load Formation of University Teachers

Optimization Models for Teaching Load Formation of Universit...

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AITU: Digital Generation, IEEE

作者： Diana Chigambayeva Makar Goryakin Polina Batova Department of Computational and Data Science Astana IT University Astana Kazakhstan Depatment of Computer Engineering Astana IT University Astana Kazakhstan

ISBN: (数字)9798350364378

ISBN: (纸本)9798350364385

Functional and mathematical models for the distribution of academic workload at the stage of preparing the educational process at a university are considered, which make it possible to largely determine the uniformity of teachers' workload with various types of academic work when fulfilling the criteria and restrictions adopted for the distribution.

关键词： Training Optimization models Human-machine systems Education Load modeling Information systems

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Big data Analytics for Predictive Maintenance in Smart Grids and Energy Management Systems 8

Big Data Analytics for Predictive Maintenance in Smart Grids...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Nimma, Divya Malik, Sakshi Balakumar, A. University of Southern Mississippi Data Analyst in Ummc Computational Science United States O.P. Jindal Global University Jindal Global Business School Haryana Sonipat India K. Ramakrishnan College of Engineering Department of Ece Trichy India

ISBN: (纸本)9798350376425

The application of wind power within smart grids is crucial thereby mandating integrative energy change. Yet, the randomness of wind and technological nature of wind turbines lead to quite a number of problems regarding the dependability and performance of supplied energy. These issues can be managed through the help of predictive maintenance which reduces the time that is taken in the machinery's periodic maintenance while at the same time reduces the cost of the overall maintenance. The following research work puts forward a framework for developing deep learning models to improve the approaches to maintain the wind energy systems using LSTM networks. The framework is based on the Wind Turbine SCADA dataset which is a collection of time-series of various parameters of the turbine like wind speed, power output, rotor speed, temperature, and vibration and aims at predicting the probable equipment failures in future. The LSTM network used for the diagnosis of mechanical problems learns from the sensor data and watches for anomalies in real-time. Thus, owing to the organization of the maintenance activities according to such predictions, the framework minimizes the unexpected downtimes, and increases the lifespan of the turbine components, as well as the reliability of the energy production. In addition, the proposed business incorporates the predictive maintenance system into the smart grid system to facilitate energy management and the synchronization of the maintenance system with grid functionality. The research also responds to important issues: the fluctuation in the wind energy data and the adaptability of an LSTM model to large power stations. © 2024 IEEE.

关键词： Predictive maintenance

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Synergetic effects of multiple junction and surface hydroxyl in Cu/CuO/Cu2O/TiO2 heterostructures towards highly efficient photocatalysts for hydrogen generation

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Materials science for Energy Technologies 2025年 8卷 131-142页

作者： Subagyo, Riki Anindika, Garcelina Rizky Aufkani, Afif Akmal Zhang, Lei Ardhyananta, Hosta Burhan, R.Y. Perry Nugraheni, Zjahra Vianita Akhlus, Syafsir Bahruji, Hasliza Prasetyoko, Didik Wellia, Diana Vanda Ivansyah, Atthar Luqman Arramel Kusumawati, Yuly Department of Chemistry Faculty of Science and Analytic Data Institut Teknologi Sepuluh Nopember Surabaya60111 Indonesia Center of Excellence Applied Physics and Chemistry Nano Center Indonesia Jl PUSPIPTEK South Tangerang Banten15314 Indonesia Department of Physics National University of Singapore Singapore117551 Singapore Department of Materials and Metallurgical Engineering Faculty of Industrial Technology and Systems Engineering Institut Teknologi Sepuluh Nopember Surabaya60111 Indonesia Centre of Advanced Material and Energy Science Universiti Brunei Darussalam Jalan Tungku Link BE 1410 Brunei Department of Chemistry Faculty of Mathematics and Natural Sciences Universitas Andalas Padang25163 Indonesia Master Program in Computational Science Faculty of Mathematics and Natural Science Institut Teknologi Bandung Jalan Ganesha No. 10 Bandung40132 Indonesia Instrumentation and Computational Physics Research Group Department of Physics Faculty of Mathematics and Natural Sciences Institut Teknologi Bandung Jalan Ganesha No.10 West Java Bandung40132 Indonesia

The implementation of titanium dioxide (TiO2) as a photocatalyst material in hydrogen (H2) evolution reaction (HER) has embarked renewed interest in the past decade. Rapid electron-hole pairs recombination and wide band gap of a photo-sensitive material of TiO2 are detrimental toward the targeted catalytical reaction. In this study, we present the rational design, fabrication, photocatalytic performance of TiO2-Cu/CuO/Cu2O heterostructures (CuTi) using viable chemical reduction method. The Z-scheme and S-scheme are succesfully generated across the TiO2/CuO/Cu2O interfaces, while the Schottky junction arises on the Cu perimeters. This is evidenced from the blue shifted about 0.3 eV of Cu 2p core level determined by using X-ray photoemission spectroscopy (XPS), in combination with the formation of inverse V-shape of the Mott-Schottky plots. In addition, we find that Cu/CuO/Cu2O facilitates photon absorption range up to the visible region. The multiple heterojunction and the large number of OHsurface enhanced charge carrier transfer are associated to the suppression of photoluminescence (PL) intensity, high surface hydroxyl (OHsurface) density in CuTi probed by XPS, and fast electron transfer based on the electrochemical measurements. The presence of OHsurface inhibits the recombination of electron. A significant H2 photogeneration rate enhancement is achieved when an optimized 5 wt% Cu/CuO/Cu2O concentration is used on TiO2 to achieve 7,157.19 μmol·g−1 (1,789.30 μmol·g−1·h−1). Based on this finding, zero emission energy innitiative could be materialized under multiple heterojunctions in photocatalytic process is beneficial for enhancing the H2 production. © 2025 The Authors

关键词： Titanium dioxide

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Optimizing Network Management and Virtualization Using Machine Learning Approach: Network Slice Prediction

Optimizing Network Management and Virtualization Using Machi...

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2024 International Conference on science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Akinyemi, Lateef Adesola Oshinuga, Olamide Peter Oladejo, Sunday Oladayo Ekwe, Stephen Obono Sumbwanyanbe, Mbuyu Mnkandla, Ernest Shoewu, Oluwagbemiga Omotayo University of South Africa Departments of Computer Science and Elect. Engineering CSET Johannesburg South Africa Lagos State University Department of Electronic and Computer Engineering Lagos Nigeria Stellenbosch University School for Data Science and Computational Thinking Stellenbosch South Africa Cape Peninsula University of Technology Department of Elect. Electronic and Computer Engineering Cape Town South Africa University of South Africa Department of Electrical Engineering CSET Johannesburg South Africa University of South Africa Department for Computer Science CSET Johannesburg South Africa

ISBN: (纸本)9798350358155

This study demonstrates the utilization of Machine Learning (ML) for network slice prediction, enabling the optimization of resources for diverse network slices. Traditional methods for network slice prediction often lack efficiency and result in inaccuracies. By leveraging ML algorithms such as Naive Bayes and Random Forest, an intelligent framework that automates network slice prediction is developed. This framework enhances network virtualization and management, facilitating resource allocation. The ML algorithms take real-time network conditions and usages, such as packet delay and smart city, as input and output for selecting the most suitable network slice. data analysis is further conducted to reveal the connections between the input parameters and how these parameters influence the selection of the accurate network slice. Network slicing plays a crucial role as it enables the customization of services and facilitates efficient scaling to meet the specific needs of different applications and industries. The accuracy scores of the employed ML algorithms were generally perfect, except for the KNN and SVM classifiers, which achieved an accuracy of 94.30% and 92.16%, respectively, for the prediction of network slices based on incoming network connections and usages. © 2024 IEEE.

关键词： data Analysis Machine Learning Network Management Network Slicing Network Virtualization

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Damage Detection using Physics-based modeling and data-driven optimization

Damage Detection using Physics-based modeling and data-drive...

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Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2022

作者： Farnod, Borna R. Reinhart, Wesley F. Napolitano, Rebecca R. Department of Architectural Engineering State CollegePA16802 United States Department of Material Science Institute for Computational and Data Sciences State College16801 United States

ISBN: (数字)9781510649682

ISBN: (纸本)9781510649675

Environmental conditions, variability in building material, and fabrication error are several contributors that shorten a structure's service life. While infrastructure might not have visible exterior damage, there is always the risk for failure due to internal or obscured damage. There has been growing research in the area of Nondestructive testing. However, these methods often disregard uncertainties in the construction and maintenance processes. Nevertheless, providing an accurate and robust method for detecting, localizing, and estimating the severity of existing damage relies on parameterizing uncertainties surrounding material properties, composition, and loading history. This work combines conventional physics-based modeling with data-driven search strategies to infer unobservable information about the structure. The proposed approach uses Finite Element Analysis with automated adjoint calculation to efficiently obtain gradients in the solution with respect to the model parameters. The hybrid model updates the postulated material properties to match the mechanical compliance of a simulated structure with observations from a reference structure. Here we use a simple cantilever beam model to study and evaluate the effectiveness of the proposed approach. In addition, we analyze the results from multiple search strategies used to obtain the mechanical properties. We find that the benefit in search efficiency gained from the analytical evaluation of the gradients is offset by the additional computational cost of adjoint simulation compared to gradient-free search. However, we expect that the benefit of adjoint simulation would be more pronounced in problems with more degrees of freedom. © 2022 SPIE.

关键词： Damage detection

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SINO-CT-Fusion-Net: A Lightweight Deep Learning Framework for Detection and Classification of Intracranial Hemorrhages

SINO-CT-Fusion-Net: A Lightweight Deep Learning Framework fo...

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IEEE International Conference on Image Processing

作者： Chitimireddy Sindhura Phaneendra K. Yalavarthy Subrahmanyam Gorthi Department of Electrical Engineering Indian Institute of Technology Tirupati India Department of Computational and Data Sciences Indian Institute of Science Bengaluru India

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Deep learning has significantly improved medical diagnostics with its ability to learn the underlying complex patterns. A sinogram contains a sequence of X-ray projections of the patient into a lower dimensional space from different viewing angles, and a CT image is obtained as a result of applying reconstruction algorithms on the sinograms acquired by the scanner. While CT images are commonly used for automated diagnosis, recent developments have demonstrated that sinogram-based approaches can provide results on par with CT-based methods. This work leverages from the advantages of both approaches through the fusion of features learned from both those images. This paper presents a new lightweight deep learning model to detect and classify Intracranial Hemorrhages (ICH) through the fusion of high-level features learned from both sinogram and CT images. The proposed method is trained and evaluated on the publicly available RSNA ICH dataset. Furthermore, we analyze its multi-label classification capability in categorizing hemorrhages into five types. The proposed fusion model outperformed both CT-based and sinogram-based methods in general, it is particularly useful when there is less annotated training data and limited computational resources. The code and data can be found at https://***/sindhura234/SinoCTFusionNet

关键词： Deep learning Computed tomography computational modeling Training data Reconstruction algorithms Feature extraction data models

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