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CleanMat 2024年第1期1卷 3-4页

作者： Liyuan Chai Yong Sik Ok Institute of Environmental Engineering School of Metallurgy and Environment Central South University Changsha China Chinese National Engineering Research Center for Control & Treatment of Heavy Metal Pollution Changsha China Korea Biochar Research Center APRU Sustainable Waste Management Program & Division of Environmental Science and Ecological Engineering Korea University Seoul South Korea

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An Architecture of Real-World Data Database 1

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5th International Conference on Innovative Computing, IC 2022

作者： Lee, Hsiu-An Hsu, Chien-Yeh Department of Computer Science and Information Engineering Tamkang University New Taipei City Taiwan Department of Information Management National Taipei University of Nursing and Health Sciences Taipei Taiwan Master Program in Global Health and Development Taipei Medical University Taipei Taiwan

ISBN: (数字)9789811941320

ISBN: (纸本)9789811941313

In the analysis of medical data, it is now important to use "Real-World Data (RWD)". In the past, clinical research in the medical field was often completed by clinical trials. Clinical trials are a kind of use of listed drugs and comfort according to research objectives. The process of comparing drugs or other medical treatments on subjects as a control group. The Real-world data research database is essential to precision health. In order to develop the value of clinical data, improve the medical care service, and promote precision medicine, the goal of this research is to design a Real-World Data Research Database, aiming at the wide range and standardization of data, the detail component including: *** standard Health Level 7-Fast Health Interoperability Resources (HL-7 FHIR).*** sorting and cleaning ***-based database research ***-based technology for identity verification and database authorization recognition mechanism. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Clinical research

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Prediction of Forest Fire Using Machine Learning Algorithms: The Search for the Better Algorithm 6

Prediction of Forest Fire Using Machine Learning Algorithms:...

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6th IEEE International Conference on Innovative Technologies in Intelligent System and Industrial Application, CITISIA 2021

作者： Rakshit, Pranati Sarkar, Srestha Khan, Sambit Saha, Pritam Bhattacharyya, Sonali Dey, Nilarpan Islam, Sardar M.N. Pal, Souvik JIS College of Engineering Computer Science of Engineerng Kalyani India Victoria University ISILC and Decision Sciences and Modeling Program Australia Global Institute of Management and Technology Computer Science of Engineerng Krishnanagar India

ISBN: (纸本)9781665417846

Forest fire has several devastating effects on the natural vegetation and the forest lives. The forest fire plays an important role in everyone's lives and also in our environment. Forest fire is an integral part of many ecosystems such as grassland, temperate forest etc. The ability to predict the area where the forest fire may occur will help in optimizing the situation. The paper presented the prediction of forest fire risk with the help of a machine learning algorithm by using meteorological data. From the existing literature and Limitations, we can show that Different studies have shown the amount of burnt area due to the forest fire, and many have proposed different models to predict forest fire. But there is no such literature which predicts the depth of risk for this forest fire specifically. For that reason, the objective of this work is to predict the risk of forest fire by identifying the particular area as highly prone, moderately prone, low prone and no fire prone area. As a Present Research, in this paper we have worked with different classification models to check which models work best to predict forest fire with greater accuracy. The results we have obtained with the help of various classifiers in machine learning are much better and reliable than the results obtained by traditional computing methods. Thus, this paper indicates a deeper investigation in the field of predicting forest fire risk through machine learning. As a contribution, in this paper we have used SVM, KNN, Decision Tree, Naive Bayes classifier for prediction purposes. The main objective of this paper is to predict the possibility of forest fire with its intensity in specific atmospheric conditions in a given location. We have made comparison of the performance analysis of the different machine learning classifiers. At the end of the abstract, we got the highest AUC value of 0.99 and classification accuracy of 0.98 using Decision Tree to predict the same. © 2021 IEEE.

关键词： Forecasting

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Zema Dataset: A Comprehensive Study of Yaredawi Zema with a Focus on Horologium Chants

arXiv

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arXiv 2024年

作者： Muluneh, Mequanent Argaw Peng, Yan-Tsung Degife, Worku Abebe Tadesse, Nigussie Abate Demeku, Aknachew Mebreku Su, Li Institute of Information Science Academia Sinica Taipei Taiwan Department of Information System University of Gondar Gondar Ethiopia Department of Computer Science National Chengchi University Taipei Taiwan Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Taiwan Department of Information Management National Taiwan University of Science and Technology Taipei Taiwan Department of Material Science and Engineering National Taiwan University of Science and Technology Taipei Taiwan

Computational music research plays a critical role in advancing music production, distribution, and understanding across various musical styles in the world. Despite the immense cultural and religious significance, the Ethiopian Orthodox Tewahedo Church (EOTC) chants are relatively underrepresented in computational music research. This paper contributes to this field by introducing a new dataset specifically tailored for the analysis of EOTC chants, also known as Yaredawi Zema. This work provides a comprehensive overview of a 10-hour dataset, 369 instances, creation, and curation process, including rigorous quality assurance measures. Our dataset has a detailed word-level temporal boundary and reading tone annotation along with the corresponding chanting mode label of audios. Moreover, we have also identified the chanting options associated with multiple chanting notations in the manuscript by annotating them accordingly. Our goal in making this dataset available to the public1 is to encourage more research and study of EOTC chants, including lyrics transcription, lyric-to-audio alignment, and music generation tasks. Such research work will advance knowledge and efforts to preserve this distinctive liturgical music, a priceless cultural artifact for the Ethiopian people. © 2024, CC BY.

关键词： Music

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Optimizing Task Execution in Robotic Swarms: Coordination Strategies for Enhanced Efficiency

Optimizing Task Execution in Robotic Swarms: Coordination St...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Vandna Bansla Inderjeet Kaur Rudramani Bhutia Girish Singh Bisht Khemraj Sharma Shivalik Engineering College Dehradun U.K India Department of Computer Science and Engineering Ajay Kumar Garg Engineering College Ghaziabad U.P. India Department of Computer Science & Engineering Koneru Lakshmaiah Education Foundation A.P. India Department of Computer Science & Engineering Tula’s Institute Dehradun India Department of Business Administration Doctoral Program in Management Sciences NANHUA UNIVERSITY Dalin Chiayi R.O.C

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

The “swarm robotics” is a relatively recent field of study that investigates the ways in which several robot systems interact with one another throughout the course of time. The organization is able to accomplish its goals by delegating work to a large number of robots that are capable of doing activities that are beyond the capabilities of a single robot. This is accomplished via the use of a broad variety of different methods. It is possible to make use of optimization algorithms in robotic swarms in order to discover answers to difficult trans disciplinary challenges that are encountered in real-world environments. The fact that algorithms for swarm intelligence are able to adjust to changing circumstances makes the potential of this happening a reality. In order to achieve the highest possible level of performance, this study analyses a broad range of different swarm optimization algorithms that may be used to robotic swarm scenarios. This category contains a variety of methodologies, including PSO, GPSO, CSO, ACO, and GSO, among others. Within this section, a comprehensive analysis is provided for each of the strategies that came before it, and an inquiry into the potential future course of action for swarm optimization is furthermore carried out. The ramifications of these tactics are also discussed in this section, which is included in the overall debate.

关键词： Robot kinematics Swarm robotics Organizations Planning Particle swarm optimization Informatics Optimization

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Tmf-Gnn: Temporal Matrix Factorization-Based Graph Neural Network for Multivariate Time Series Forecasting with Missing Values

SSRN

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SSRN 2023年

作者： Kim, Suhyeon Lee, Taek-Ho Lee, Junghye Graduate School of Data Science Kyungpook National University Daegu41566 Korea Republic of Technology Management Economics and Policy Program & Institute of Engineering Research Seoul National University Seoul08826 Korea Republic of Technology Management Economics and Policy Program & Graduate School of Engineering Practice Seoul National University Seoul08826 Korea Republic of

Missing data in multivariate time series (MTS) is a very common issue, often caused by unreliable sensors and data storage or transmission problems. Particularly, such missing data can cause some errors and biases in the MTS forecasting tasks of real-world applications, implying that proper handling of the missing data is essential. Therefore, in this study, we propose a new method for MTS forecasting with missing values, called a temporal matrix factorization-based graph neural network (TMF-GNN), to improve predictive performance outcomes. TMF-GNN basically uses the concept of TMF, which reconstructs partially observed MTS data. We newly present a data-adaptive regularization method for TMF based on graph-based and sequential deep learning algorithms to capture both the variable- and sample-wise information of MTS data affected by the concept known as missingness. We demonstrate the feasibility of the proposed method by conducting various experiments on three MTS datasets and show how it outperforms baseline methods. We believe that our study will have an impact on several MTS-related tasks and that it can be a useful alternative for handling missing values in MTS data. © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Carbon-negative cement-bonded biochar particleboards

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Biochar 2022年第1期4卷 219-227页

作者： Liang Chen Yuying Zhang Claudia Labianca Lei Wang Shaoqin Ruan Chi Sun Poon Yong Sik Ok Daniel C.W.Tsang Department of Civil and Environmental Engineering The Hong Kong Polytechnic UniversityHung HomKowloonHong KongChina State Key Laboratory of Clean Energy Utilization Zhejiang UniversityHangzhou 310027China College of Civil Engineering and Architecture Zhejiang UniversityHangzhou 310058China Research Centre for Resources Engineering Towards Carbon Neutrality The Hong Kong Polytechnic UniversityHung HomKowloonHong KongChina Korea Biochar Research Center APRU Sustainable Waste Management Program&Division of Environmental Science and Ecological EngineeringKorea UniversitySeoul 02841Korea

Biochar from bio-waste pyrolysis presents excellent CO_(2) sequestration *** study innovated the design of cement-bonded particleboards utilizing a substantial amount of 50-70 wt.%pre-soaked biochar to render the products *** investigated the roles of biochar in magnesium oxysulfate cement(MOSC)system and demonstrated good mechanical and functional properties of biochar cement *** the presence of biochar,the amounts of hydration products were enriched in the cement systems as illustrated by the thermogravi-metric analyses(TGA)and X-ray diffraction(XRD).We further incorporated supplementary cementitious materials(SCMs)and generated 5 Mg(OH)_(2)⋅MgSO_(4)·7H_(2)O(5-1-7)phase in the MOSC *** a result,our designs of biochar particleboards satisfied the standard requirements for flexural strength(>5.5 MPa)and thickness swelling(<2%).Moreover,our biochar particleboards presented a low thermal conductivity as the biochar pores disrupted thermal bridging within *** illustrated that the high dosage ratio of biochar could significantly offset the CO_(2) emissions of the particleboards(i.e.,carbon-negative)via life cycle *** economic profits could also be accomplished for the biochar *** instance,the 50BC-MOSC bonded particleboard(with 50 wt.%pre-soaked biochar as aggregate,50 wt.%MOSC as binder)with promising mechanical properties could store 137 kg CO_(2) tonne^(−1) and yield an overall economic profit of 92 to 116 USD m^(−3) depending on the carbon prices in different *** summary,our new designs of carbon-negative biochar particleboards could curtail carbon emissions in the construction materials and promote the realization of carbon neutrality and circular economy.

关键词： Biochar-cement composite Carbon neutrality Wood waste biochar Magnesium oxysulfate cement Climate-positive construction materials

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Two-phase cost-sensitive-learning-based framework on customer-side quality inspection for TFT-LCD industry

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Journal of Intelligent Manufacturing 2024年

作者： Shih, Ming-Sung Chen, James C. Chen, Tzu-Li Hsu, Ching-Lan International Intercollegiate Ph.D. Program National Tsing Hua University Hsinchu30013 Taiwan Department of Industrial Engineering and Engineering Management National Tsing Hua University Hsinchu30013 Taiwan Graduate Institute of Intelligent Manufacturing Technology National Taiwan University of Science and Technology Taipei106335 Taiwan

The Covid-19 outbreak in 2020 boosted the stay-at-home economy, causing a surge in electronics industry demand, especially benefiting the LCD panel sector. However, as the pandemic situation improved, countries revised policies, leading to the gradual discontinuation of remote work arrangements in various industries. This resulted in declining dividends for the stay-at-home economy. The decreased demand created intense competition within the TFT-LCD industry, urging panel companies to prioritize product quality enhancement to meet customer expectations. Panel quality inspection heavily relied on manual labor, causing varying inspection levels due to subjective judgments. Understanding and aligning with customer expectations regarding product quality inspections became imperative. Identifying defective products during inspection led to additional costs for the companies. Balancing customer product quality requirements and re-inspection costs became crucial for optimal benefits. This study addresses the binary classification problem of customer-side quality inspection through cost-sensitive learning. The predictive model considers panel process yield, production history, customer feedback, inspection capacity constraints, and cost minimization to predict panel quality as accepted or defective. To tackle the highly imbalanced data, a two-phase cost-sensitive-learning-based framework is proposed, combining data preprocessing methods and models, while considering re-inspection capacity constraints and costs to enhance accuracy. The model’s evaluation uses key performance indicators like AUC and G-mean. Actual inspection cost and defective parts per million (DPPM) are calculated based on the company’s practical assessment. Two products are used for experimentation to validate the proposed model, demonstrating over 50% reduction in inspection cost and over 10% improvement in DPPM. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Spr

关键词： Inspection

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Characterization of Nanocomposite Membrane Based Bacterial Cellulose Made of Pineapple Waste Reinforced by Graphite Nanoplatelets

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Journal of Renewable Materials 2022年第9期10卷 2465-2475页

作者： Heru Suryanto Bili Darnanto Susilo Jibril Maulana Aminnudin Uun Yanuhar Surjani Wonorahardjo Husni Wahyu Wijaya Abu Saad Ansari Center of Excellence for Cellulose Composite(CECCom) Department of Mechanical EngineeringUniversitas Negeri MalangMalang65145Indonesia Centre of Advanced Material for Renewable Energy(CAMRY) Universitas Negeri MalangMalang65145Indonesia Master Program of Mechanical Engineering Faculty of EngineeringUniversitas Negeri MalangMalang65145Indonesia Department of Aquatic Resources Management Faculty of Fisheries and Marine SciencesBrawijaya UniversityMalang65145Indonesia Department of Chemistry Faculty of Mathematics and Natural SciencesUniversitas Negeri MalangMalang65145Indonesia Department of Materials Science&Engineering Incheon National UniversityIncheon464-742South Korea

Waste is the main problem for the *** waste for various useful applications has a benefit for the *** work has been studied for handling pineapple peel waste to make composite film bacterial cellulose nanocomposite membrane(BCNM)with addition graphite nanoplatelet(GNP).The concentration of GNP in the membrane influence the membrane *** bacterial cellulose(BC)pellicle was synthesized by using media from pineapple peel waste *** pellicle is cleaned with water and NaOH solution to be free from *** is synthesized through the mechanical disintegration *** results of disintegration using high pressure homogenizer at 150 bar and five ***/GNP is synthesized with varying addition of GNP of 2.5,5.0,10 and 100 wt%of dry bacterial nanocellulose(BNC).The BC and GNP solution were dried in an oven for 14 h at 80℃.BCNM morphology was observed using *** is dispersed and distributed in the BC matrix as *** analysis shows many peaks of BNC less pronounced with increasing of *** higher concentration of GNP,the rougher of *** optimum tensile strength of BCNM was achieved after addition GNP of 2.5 wt%.

关键词： Bacterial nanocellulose graphite nanoplatelet membrane nanocomposite pineapple waste

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Spatiotemporal Analysis of Meteorological Drought Using Standardized Precipitation Index (SPI) in Gabiley Region, Somaliland

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Journal of Geoscience and Environment Protection 2023年第5期11卷 47-59页

作者： Abdisamad Abib Omar Jie-Lun Chiang Bashir Hassan Daud Department of Tropical Agriculture and International Cooperation National Pingtung University Science and Technology Taiwan Department of Soil and Water Conservation National Pingtung University Science and Technology Taiwan College of Engineering International Master Program on Natural Hazards Mitigation and Management National Cheng Kung University Taiwan

Drought is a common natural disaster worldwide, with varying durations, severity levels, and spatial extents. This study aimed to model the spatiotemporal variation of meteorological drought events in the Gabiley region of Somaliland. The study utilized primary data collected from the meteorological station in Gabiley and CHIRPS (Climate Hazards Group InfraRed Precipitation with Station) data to develop the standardized precipitation index (SPI) at a 3-month timescale. The results of the study revealed that the study area was characterized by drought and received inadequate precipitation, resulting in catastrophic droughts that negatively impacted the socioeconomic situation of the community. Mild-to-severe meteorological drought events occurred every two to three years, with the most severe droughts occurring in 1998, 2002, 2009, 2015, and 2017. Specifically, the year 2015 experienced the most severe meteorological drought in the region during the studied period. The predominant type of drought was a mild year in the study area. The SPI was found to potentially identify meteorological drought, making it a useful tool for policymakers as they develop drought adaptation and mitigation policies. This study provides valuable information that can benefit local authorities and policymakers in creating drought mitigation and adaptation strategies in the Gabiley region.

关键词： Meteorological Drought SPI CHRIPS Rainfall Gabiley

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