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Workshop on Communication Networks and Power Systems (WCNPS)

作者： Domingos Sávio de O. Santos Douglas A. P. Dantas Paulo S. G. de Mattos Neto Hugo Valadares Siqueira Aranildo R. Lima Francisco Madeiro João. F. L. de Oliveira Alex C. Pereira Manoel H. N. Marinho Escola Politécnica de Pernambuco Universidade de Pernambuco Recife PE Brazil Centro de Informática Universidade Federal de Pernambuco Recife PE Brazil Graduate Program in Electrical Engineering Federal University of Technology-Paraná Ponta Grossa PR Brazil Aquatic Informatics Vancouver Canada São Francisco Hydroelectric Company (Chesf) Recife PE Brazil

Multi-step solar irradiance forecast is an important part of a successful power grid management. However that forecast can be difficult due to nonlinear patterns and random fluctuation. This work applies statistical and machine learning methods to forecast solar irradiance time series from three different locations of Brazil. The applied models are: Autoregressive Integrated Moving Average (ARIMA), Deep Belief Network (DBN), Extreme Learning Machine (ELM), Multi Layer Perceptron neural network (MLP), Random Forest (RF), and Support Vector Regression (SVR). Taking into account these methods, an ensemble of them is proposed, which has the objective of reducing: the uncertainty of selecting a single model, overfitting, and underfitting. Compared with single models and based on two performance metrics, the ensemble found good overall results when applied in the time series with horizons of 6, 12 and 24 hours ahead.

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Glucose-mediated mitochondrial reprogramming by cholesterol export at TM4SF5-enriched mitochondria-lysosome contact sites

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Cancer Communications 2024年第1期44卷 47-75页

作者： Ji Eon Kim So-Young Park Chulhwan Kwak Yoonji Lee Dae-Geun Song Jae Woo Jung Haesong Lee Eun-Ae Shin Yangie Pinanga Kyung-hee Pyo Eun Hae Lee Wonsik Kim Soyeon Kim Chang-Duck Jun Jeanho Yun Sun Choi Hyun-Woo Rhee Kwang-Hyeon Liu Jung Weon Lee Department of Pharmacy College of PharmacySeoul National UniversitySeoulRepublic of Korea Research Institute of Pharmaceutical Sciences College of PharmacySeoul National UniversitySeoulRepublic of Korea BK21 FOUR Community-Based Intelligent Novel Drug Discovery Education Unit College of Pharmacy and Research Institute of Pharmaceutical SciencesKyungpook National UniversityDaeguRepublic of Korea Department of Chemistry Seoul National UniversitySeoulRepublic of Korea College of Pharmacy Chung-Ang UniversitySeoulRepublic of Korea Natural Product Informatics Research Center Korea Institute of Science and Technology(KIST)Gangneung-siGangwon-doRepublic of Korea School of Life Sciences Gwangju Institute of Science and Technology(GIST)GwangjuRepublic of Korea Department of Biochemistry College of MedicineDong-A UniversityBusanRepublic of Korea Global AI Drug Discovery Center College of Pharmacy and Graduate School of Pharmaceutical SciencesEwha Womans UniversitySeoulRepublic of Korea Interdisciplinary Program in Genetic Engineering Seoul National UniversitySeoulRepublic of Korea

Background:Transmembrane 4 L six family member 5(TM4SF5)translocates subcellularly and functions metabolically,although it is unclear how intracellu-lar TM4SF5 translocation is linked to metabolic *** is thus of interests to understand how the traffic dynamics of TM4SF5 to subcellular endosomal membranes are correlated to regulatory roles of ***:Here,we explored the metabolic significance of TM4SF5 localization at mitochondria-lysosome contact sites(MLCSs),using in vitro cells and in vivo animal systems,via approaches by immunofluorescence,proximity labelling based proteomics analysis,organelle reconstitution ***:Upon extracellular glucose repletion following depletion,TM4SF5 became enriched at MLCSs via an interaction between mitochondrial FK506-binding protein 8(FKBP8)and lysosomal *** labeling showed molecular clustering of phospho-dynamic-related protein I(DRP1)and certain mitophagy receptors at TM4SF5-enriched MLCSs,leading to mitochondrial fis-sion and ***4SF5 bound NPC intracellular cholesterol transporter 1(NPC1)and free cholesterol,and mediated export of lysosomal cholesterol to mitochondria,leading to impaired oxidative phosphorylation but intact tri-carboxylic acid(TCA)cycle andβ-*** mouse models,hepatocyte Tm4sf5 promoted mitophagy and cholesterol transport to mitochondria,both with positive relations to liver ***:Our findings suggested that TM4SF5-enriched MLCSs regu-late glucose catabolism by facilitating cholesterol export for mitochondrial reprogramming,presumably while hepatocellular carcinogenesis,recapitulating aspects for hepatocellular carcinoma metabolism with mitochondrial repro-gramming to support biomolecule synthesis in addition to glycolytic energetics.

关键词： cholesterol fluorescent imaging glucose catabolism hepatocellular carcinogenesis mem-brane contact sites mitochondria function mitophagy oxidative phosphorylation protein-protein interaction tetraspanin

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Contour Detection from Ultrasound Kidney Images with A Coarse-to-Fine Approach

Contour Detection from Ultrasound Kidney Images with A Coars...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Peng, Tao Gu, Yidong Xu, Yanqing Wang, Caishan Zhang, Lei Cai, Jing School of Future Science and Engineering Soochow University Suzhou China UT Southwestern Medical Center Department of Radiation Oncology DallasTX United States The Affiliated Suzhou Hospital of Nanjing Medical University Department of Medical Ultrasound Suzhou Municipal Hospital Jiangsu Suzhou China University of Texas Southwestern Medical Center McDermott Center for Human Growth and Development DallasTX United States The Second Affiliated Hospital of Soochow University Department of Ultrasound Suzhou China Duke Kunshan University Graduate Program of Medical Physics and Data Science Research Center Jiangsu Kunshan China The Hong Kong Polytechnic University Department of Health Technology and Informatics Hong Kong Hong Kong

ISBN: (纸本)9798350337020

Ultrasound kidney image segmentation presents significant challenges due to missing or ambiguous boundaries. In this study, we introduce a coarse-to-refinement approach incorporating four novel aspects. Firstly, we leverage the properties of a principal curve (PC) to automatically fine-tune the curve shape and employ a neural network's learning ability to reduce model error. Secondly, a deep fusion learning network is utilized for the coarse segmentation step, incorporating a parallel architecture to enhance deep-learning performance. Thirdly, addressing the limitation of standard PC-based methods in determining the number of vertices automatically, we propose an automatic searching polygon tracking method using a mean shift clustering-based approach to replace the projection and vertex extension step in standard PC-based methods. Lastly, we develop an explainable mathematical map function for the kidney contour, as denoted by the neural network output (i.e., optimized vertices), which aligns well with the ground truth contour. We conducted various experiments to evaluate our method's performance, demonstrating its effectiveness in ultrasound kidney image segmentation. © 2023 IEEE.

关键词： Artificial intelligence Explainable mathematical model Ultrasound kidney segmentation

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Hybrid Intelligent-Annotation Organ Segmentation on Medical Datasets

Hybrid Intelligent-Annotation Organ Segmentation on Medical ...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Peng, Tao Zhao, Jing Gu, Yidong Di, Gongye Zhang, Lei Cai, Jing School of Future Science and Engineering Soochow University Suzhou China Ut Southwestern Medical Center Department of Radiation Oncology DallasTX United States Tsinghua University Affiliated Beijing Tsinghua Changgung Hospital Department of Ultrasound Beijing China The Affiliated Suzhou Hospital of Nanjing Medical University Suzhou Municipal Hospital Department of Medical Ultrasound Jiangsu Suzhou China The Affiliated Taizhou People's Hospital of Nanjing Medical University Department of Ultrasonic Taizhou China Duke Kunshan University Graduate Program of Medical Physics Data Science Research Center Jiangsu Kunshan China The Hong Kong Polytechnic University Department of Health Technology and Informatics Hong Kong

ISBN: (纸本)9798350337020

Ultrasound image segmentation is crucial for early disease detection and treatment planning but remains a challenging task due to the low contrast of organ boundaries and varying image quality. Current methods often require manual intervention or have limited accuracy. In this paper, we propose a novel hybrid framework that combines an automatic option polygon segment (AOPS) algorithm and a distributed- and memory-based evolution (DME) algorithm for precise ultrasound organ segmentation. Our pipeline consists of two cascaded stages: (1) a coarse segmentation step using the AOPS algorithm, which determines the number of vertices/clusters without human intervention, and (2) a refinement step using the DME algorithm to hunt for the optimal neural network, which is then used to represent a smooth, explainable mathematical expression of the organ boundary. We employ the fractional backpropagation learning network with L2 regularization (FBLN) for training and use the scaled exponential linear unit (SELU) activation function to address the vanishing gradient problem. This is a new attempt such a hybrid framework is applied to ultrasound organ segmentation tasks, and it demonstrates significant contributions in terms of accuracy, smoothness, and computational efficiency. © 2023 IEEE.

关键词： distributed-evolution learning network Multi-organs segmentation principal curve SEL U-based explainable mathematical model ultrasound

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Instruction Multi-Constraint Molecular Generation Using a Teacher-Student Large Language Model

arXiv

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arXiv 2024年

作者： Zhou, Peng Wang, Jianmin Li, Chunyan Wang, Zixu Liu, Yiping Sun, Siqi Lin, Jianxin Wei, Leyi Cai, Xibao Lai, Houtim Liu, Wei Wang, Longyue Liu, Yuansheng Zeng, Xiangxiang College of Information Science and Engineering Hunan University Changsha China The Interdisciplinary Graduate Program in Integrative Biotechnology Yonsei University Incheon21983 Korea Republic of School of Informatics Yunnan Normal University Kunming650500 China Department of Computer Science University of Tsukuba Tsukuba3058577 Japan Research Institute of Intelligent Complex Systems Fudan University Shanghai200433 China Shanghai AI Laboratory Shanghai200232 China Centre for Artificial Intelligence driven Drug Discovery Faculty of Applied Science Macao Polytechnic University China School of Informatics Xiamen University Xiamen China Tencent AI Lab China

While various models and computational tools have been proposed for structure and property analysis of molecules, generating molecules that conform to all desired structures and properties remains a challenge. Here, we introduce a multi-constraint molecular generation large language model, TSMMG, which, akin to a student, incorporates knowledge from various small models and tools, namely, the 'teachers'. To train TSMMG, we construct a large set of text-molecule pairs by extracting molecular knowledge from these 'teachers', enabling it to generate novel molecules that conform to the descriptions through various text prompts. We experimentally show that TSMMG remarkably performs in generating molecules meeting complex, natural language-described property requirements across two-, three-, and four-constraint tasks, with an average molecular validity of over 99% and success ratio of 82.58%, 68.03%, and 67.48%, respectively. The model also exhibits adaptability through zero-shot testing, creating molecules that satisfy combinations of properties that have not been encountered. It can comprehend text inputs with various language styles, extending beyond the confines of outlined prompts, as confirmed through empirical validation. Additionally, the knowledge distillation feature of TSMMG contributes to the continuous enhancement of small models, while the innovative approach to dataset construction effectively addresses the issues of data scarcity and quality, which positions TSMMG as a promising tool in the domains of drug discovery and materials science. Copyright © 2024, The Authors. All rights reserved.

关键词： Students

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Exploring Tourist Feedback on Riau Attractions Through Indonesian Language YouTube Opinion Using Naïve Bayes Algorithm

Exploring Tourist Feedback on Riau Attractions Through Indon...

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IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)

作者： Rahmad Kurniawan Iwan Iskandar Fitra Lestari Habibi Al Rasyid Harpizon Ilyas Husti Department of Computer Science Faculty of Mathematics and Natural Sciences Universitas Riau Pekanbaru Indonesia Department of Informatics Engineering Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia Department of Industrial Engineering Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Selangor Malaysia Graduate Program Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia

YouTube is a widely-used platform in Indonesia, with 93.8% of its users. As such, it presents a valuable opportunity for marketing tourist destinations, particularly in Riau province, which aims to become Indonesia’s top Halal travel destination. Tourism is a vital contributor to the economic growth of regions, and each province in Indonesia competes to promote its tourist attractions to attract more visitors every year. However, the large volume of data can challenge the manual analysis of feedback from YouTube’s features, such as likes, dislikes, and comments. A literature review suggests that the Naive Bayes algorithm, which uses machine learning, is helpful for sentiment analysis. Therefore, this study aims to analyze public sentiment toward tourist destinations in Riau province by analyzing YouTube comments using the Naïve Bayes algorithm. The study used 1680 opinions collected from 10 YouTube videos showcasing tourist destinations in Riau. The Naive Bayes algorithm classified 60% of the comments as positive, 32% as neutral, and 8% as negative. The experimental results indicated an accuracy and precision of 73%, a recall of 94%, and an F-1 Score of 82%. The study used the word frequency technique to reveal that Riau could become a popular halal tourist destination based on several frequently occurring words in the comments.

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Development of Educational Game for Indonesian History Lesson Focusing on Indonesia Pre-Literacy Relics 'Prehistoria' for Grade X High School Student

Development of Educational Game for Indonesian History Lesso...

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2020 International Conference on Computer engineering, Network, and Intelligent Multimedia, CENIM 2020

作者： Nugroho, Supeno Mardi Susiki Rahman, Muhammad Daffa Abiyyu Hariadi, Mochamad Arief, Muhammad Faculty of Intelligent Electrical and Informatics Technology Sepuluh Nopember Institute of Technology Computer Engineering Department Surabaya Indonesia Tokyo University of Technology Media Science Program Graduate School of Bionics Computer and Media Sciences Tokyo Japan

ISBN: (纸本)9781728182834

The development of game technology has opened up new possibilities in learning methods through educational games. However, in Indonesia, the application of this concept is lacking. One lesson that can benefit from educational games is Indonesian History in class X, with a focus on history in the pre-literacy period. This study aims to create a puzzle-Adventure type of digital game that can help educate students about the pre-literacy period, developed with Unity, and uses WebGL as a target device while referring to Indonesia's revised 2013 national curriculum. The result of this research is the creation of a digital game that can be played on average of 254.36 seconds, with potential for educational use. A survey result shows that many of the ancient objects and some of the ages are well-recognized. However, the survey also shows many of the gameplay aspects, such as UI, gameplay features, and adventure elements, need improvements. © 2020 IEEE.

关键词： Surveys

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Comparison of Fetal Heart Rate Detection Using Envelope Extraction and Shannon Energy

Comparison of Fetal Heart Rate Detection Using Envelope Extr...

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2019 International Seminar on Application for Technology of Information and Communication, iSemantic 2019

作者： Setiawan, Agung W. Sujatmiko, Wahyu Biomedical Engineering Research Group School of Electrical Engineering and Informatics ITB Bandung Indonesia Graduate Program in Electrical Engineering School of Electrical Engineering and Informatics ITB Bandung Indonesia

ISBN: (纸本)9781728138329

Heart rate monitoring is widely used as a general procedure to examine fetal health condition. Fetal heart rate can be used to identified possibilities of fetal anomaly and warn the obstetrician to provide immediate intervention before any further abnormalities. The purpose of this research is to compare fetal heart rate detection result using envelope extraction and Shannon energy envelope method. There are 115 fetal phonocardiography and maternal phonocardiography that is used in this research. The dataset was obtained from Hafez Hospital, Shiraz University of Medical Science. Envelope extraction method can be used to detect fetal heart rate signal with 89.2% accuracy for all fetal phonocardiography signal while Shannon energy envelope method can be used to detect fetal heart rate signal with 88.3% accuracy for all fetal phonocardiography signal. © 2019 IEEE.

关键词： Phonocardiography

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Estimating action plans for smart poultry houses

arXiv

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arXiv 2020年

作者： Klotz, Darlan Felipe Ribeiro, Richardson Enembreck, Fabrcio Denardin, Gustavo Barbosa, Marco Casanova, Dalcimar Teixeira, Marcelo Graduate Program in Electrical Engineering Federal University of Technology Paran Pato Branco Brazil Graduate Program in Informatics of the Pontifical Catholic University of Paran Curitiba Brazil

In poultry farming, the systematic choice, update, and implementation of periodic (t) action plans define the feed conversion rate (FCRhti), which is an acceptable measure for successful production. Appropriate action plans provide tailored resources for broilers, allowing them to grow within the so-called thermal comfort zone, without wast or lack of resources. Although the implementation of an action plan is automatic, its configuration depends on the knowledge of the specialist, tending to be inefficient and error-prone, besides to result in different FCRhti for each poultry house. In this article, we claim that the specialist’s perception can be reproduced, to some extent, by computational intelligence. By combining deep learning and genetic algorithm techniques, we show how action plans can adapt their performance over the time, based on previous well succeeded plans. We also implement a distributed network infrastructure that allows to replicate our method over distributed poultry houses, for their smart, interconnected, and adaptive control. A supervision system is provided as interface to users. Experiments conducted over real data show that our method improves 5% on the performance of the most productive specialist, staying very close to the optimal FCR(t).MSC Codes 68T35 (Primary) 93C40 (Secondary) Copyright © 2020, The Authors. All rights reserved.

关键词： Adaptive control systems

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Genomic data processing with GenomeFlow

arXiv

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arXiv 2025年

作者： Park, Junseok Maury, Eduardo A. Oh, Changhoon Shin, Donghoon Denisko, Danielle Lee, Eunjung Alice Division of Genetics and Genomics Boston Children’s Hospital 3 Blackfan Circle BostonMA United States Department of Pediatrics Harvard Medical School 25 Shattuck St BostonMA United States Bioinformatics Integrative Genomics Program Harvard/MIT MD-PHD Program Harvard Medical School BostonMA United States Program in Medical and Population Genetics Broad Institute of MIT and Harvard CambridgeMA United States Graduate School of Information Yonsei University 50 Yonsei-ro Seodaemun-gu Seoul Korea Republic of Department of Human Centered Design and Engineering University of Washington SeattleWA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States

Advances in genome sequencing technologies generate massive amounts of sequence data that are increasingly analyzed and shared through public repositories. On-demand infrastructure services on cloud computing platforms enable the processing of such large-scale genomic sequence data in distributed processing environments with a significant reduction in analysis time. However, parallel processing on cloud computing platforms presents many challenges to researchers, even skillful bioinformaticians. In particular, it is difficult to design a computing architecture optimized to reduce the cost of computing and disk storage as genomic data analysis pipelines often employ many heterogeneous tools with different resource requirements. To address these issues, we developed GenomeFlow, a tool for automated development of computing architecture and resource optimization on Google Cloud Platform, which allows users to process a large number of samples at minimal cost. We outline multiple use cases of GenomeFlow demonstrating its utility to significantly reduce computing time and cost associated with analyzing genomic and transcriptomic data from hundreds to tens of thousands of samples from several consortia. Here, we describe a step-by-step protocol on how to use GenomeFlow for a common genomic data processing task. We introduce this example protocol geared toward a bioinformatician with little experience in cloud computing. © 2025, CC BY.

关键词： Cloud platforms

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