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Energy consumption of high-rise double skin façade buildings, a machine learning analysis

Journal of Building Engineering 2024年 89卷

作者： Rezaie, Maedeh Kariminia, Shahab Band, Shahab S. Ameri, Rasoul Farokhi, Maryam Pai, Hao-Ting Gocer, Ozgur Rismanchi, Behzad Shooshtarian, Salman Department of Architecture Advancement in Architecture and Urban Planning Research Center Najafabad Branch Islamic Azad University Najafabad Iran Future Technology Research Center National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Taiwan Department of Information Management National Yunlin University of Science and Technology Taiwan Bachelor Program of Big Data Applications in Business National Pingtung University Pingtung Taiwan School of Architecture Design and Planning The University of Sydney SydneyNSW Australia Renewable Energy and Energy Efficiency Group Department of Infrastructure Engineering Faculty of Engineering and Information Technology The University of Melbourne Australia School of Property Construction and Project Management RMIT University Melbourne Australia

Most energy exchanges take place through the building skin. The skin characteristics play a decisive role in the extent of these exchanges, but they are somewhat more varied in the double skin façade (DSF). Among these characteristics, cavity segmentation has a noticeable effect on the implementation of the DSF in different directions during the hot and cold seasons. The aim of this study was to investigate the role of DSF segmentation in energy consumption and natural ventilation of high-rise buildings in hot and dry climates. This study used DesignBuilder software to study sixty-four segmentation component scenarios in an eight-story residential building in Isfahan, Iran. Moreover, a proposed hybrid model utilizing the hybridization of the Hunger Game Search and Gradient Boosting (HGS-GB) algorithm was employed to estimate energy consumption in various scenarios involving lighting, heating, cooling, and total scenarios. The available outcomes revealed that the HGS-GB model had a better performance in comparison with other individual models, such as Gradient Boosting (GB), Random Forest (RF), and K-nearest neighbors (KNN). The R2 values for lighting, heating, cooling, and total energy estimation were 0.9993, 0.9958, 0.9991, and 0.9922, respectively. The findings of this study suggest the significance of DSF segmentation in energy consumption and natural ventilation in high-rise buildings in hot and dry climates. © 2024 Elsevier Ltd

关键词： Forestry

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Fabrication of PSf/P84-blended membranes with low P84 content: Characteristics and gas separation performance

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Case Studies in Chemical and Environmental Engineering 2024年 10卷

作者： Kayadoe, Victor Widiastuti, Nurul Gunawan, Triyanda Salleh, Wan Norhayati Wan Fansuri, Hamzah Romadiansyah, Taufik Qodar Pratama, Agus Wedi Department of Chemistry Faculty of Science and Data Analytical Institut Teknologi Sepuluh Nopember Chemistry Education Study Program Faculty of Teacher Training and Educational Pattimura University School of Chemical and Energy Engineering Faculty of Engineering Universiti Teknologi Malaysia Advanced Membrane Technology Research Center (AMTEC) Universiti Teknologi Malaysia

The demand for efficient and sustainable gas separation technologies is ever-increasing in various industries, including petrochemicals, natural gas processing, and carbon capture. Polymer-based membranes offer a promising solution due to their potential for high selectivity and energy efficiency. However, achieving optimal gas separation performance often requires overcoming the limitations of individual polymers through modifications such as polymer blending. In this study, PSf/P84 blended membranes with low P84 content were fabricated using the phase separation technique with a mixture of N-methyl-2-pyrrolidone and tetrahydrofuran as solvents. The effect of varying the mass ratio of PSf to P84 on membrane characteristics and gas separation performance was investigated. Characterization techniques included FTIR, XRD, SEM, TGA, Water Contact Angle (WCA) measurement, and mechanical property testing. Gas permeation tests were conducted with single gases at room temperature and 1 bar pressure. The results revealed that the addition of P84 increased membrane thickness, Young's modulus, and thermal stability, while decreasing d-spacing, dense layer thickness, hydrophilicity, tensile strength, and elongation. The F_index values, indicating the competence of the blended membranes compared to the ideal quality, demonstrated the positive impact of P84 addition on gas separation performance compared to pure PSf membranes. The optimal gas separation selectivity was achieved with the PSf/P84 1:0.025 blended membrane for H₂/CH₄ (11.28, 58 % increase), H₂/CO₂ (6.25, 193 % increase), and H₂/N₂ (5.25, 35 % increase). The highest N₂/CH₄ selectivity (6.29, 253 % increase) was observed with the 1:0.10 composition. Regarding commercially relevant separations based on the 2008 Robeson curve, the PSf/P84 1:0.20 blend showed promise for H₂/CO₂ separation, while the 1:0.

关键词： Findex Low P84 content PSf/P84 blended membrane Robeson curve

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Unsupervised News Topic Modelling with Doc2Vec and Spherical Clustering

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Procedia Computer science 2021年 179卷 40-46页

作者： Arif Budiarto Reza Rahutomo Hendra Novyantara Putra Tjeng Wawan Cenggoro Muhamad Fitra Kacamarga Bens Pardamean Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Information System Department School of Information System Bina Nusantara University Jakarta Indonesia 11480 Eureka AI Singapore Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

In the digital and Internet era, companies are racing to profile their target users based on their online activities. One of the reliable sources is the news articles they read that can represent their interests. However, extracting latent information from the news articles is not an easy task for a human. In this paper, we introduced a practical model to automatically extract latent information from news articles with pre-determined topics. Our proposed model used unsupervised learning, thus alleviating the need for humans to label news items manually. Doc2vec was used to generate word vectors for each article. Afterward, a spectral clustering algorithm was applied to group the data based on the similarity. A supervised Long Short Term Memory (LSTM) model was built to compare the clustering performance. The best 1, best 3, and best 5 scores were used to evaluate our model. The result showed that our model could not outperformed LSTM model for the best 1 score. However, the best 5 score result indicated that our model was sufficiently robust to cluster the articles based on topic similarity. Additionally, the proposed unsupervised model was implemented in both an on-premise server, and a cloud server. Surprisingly, our proposed method could run faster in the cloud server despite its less number of CPU cores.

关键词： Topic Modelling Latent Information News Article Document Embedding Spherical Clustering

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Inter-rater reliability of the infectious disease modeling reproducibility checklist (IDMRC) as applied to COVID-19 computational modeling research

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BMC Infectious Diseases 2023年第1期23卷 733-733页

作者： Pokutnaya, Darya Van Panhuis, Willem G Childers, Bruce Hawkins, Marquis S Arcury-Quandt, Alice E Matlack, Meghan Carpio, Kharlya Hochheiser, Harry Department of Epidemiology University of Pittsburgh Pittsburgh PA United States Office of Data Science and Emerging Technologies National Institute of Allergy and Infectious Diseases Rockville MD United States Department of Computer Science University of Pittsburgh Pittsburgh PA United States Department of Environmental and Occupational Health University of Pittsburgh Pittsburgh PA United States Department of Biomedical Informatics Intelligent Systems Program and Clinical and Translational Science Institute University of Pittsburgh Pittsburgh PA United States

Background: Infectious disease computational modeling studies have been widely published during the coronavirus disease 2019 (COVID-19) pandemic, yet they have limited reproducibility. Developed through an iterative testing process with multiple reviewers, the Infectious Disease Modeling Reproducibility Checklist (IDMRC) enumerates the minimal elements necessary to support reproducible infectious disease computational modeling publications. The primary objective of this study was to assess the reliability of the IDMRC and to identify which reproducibility elements were unreported in a sample of COVID-19 computational modeling publications. Methods: Four reviewers used the IDMRC to assess 46 preprint and peer reviewed COVID-19 modeling studies published between March 13th, 2020, and July 30th, 2020. The inter-rater reliability was evaluated by mean percent agreement and Fleiss’ kappa coefficients (κ). Papers were ranked based on the average number of reported reproducibility elements, and average proportion of papers that reported each checklist item were tabulated. Results: Questions related to the computational environment (mean κ = 0.90, range = 0.90–0.90), analytical software (mean κ = 0.74, range = 0.68–0.82), model description (mean κ = 0.71, range = 0.58–0.84), model implementation (mean κ = 0.68, range = 0.39–0.86), and experimental protocol (mean κ = 0.63, range = 0.58–0.69) had moderate or greater (κ > 0.41) inter-rater reliability. Questions related to data had the lowest values (mean κ = 0.37, range = 0.23–0.59). Reviewers ranked similar papers in the upper and lower quartiles based on the proportion of reproducibility elements each paper reported. While over 70% of the publications provided data used in their models, less than 30% provided the model implementation. Conclusions: The IDMRC is the first comprehensive, quality-assessed tool for guiding researchers in reporting reproducible infectious disease computational modeling studies. The inter-rater re

关键词： Coronavirus Disease 2019 COVID-19 Epidemiology Infectious Disease Modeling Reproducibility

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Correction: How Does ChatGPT Perform on the United States Medical Licensing Examination (USMLE)? The Implications of Large Language Models for Medical Education and Knowledge Assessment

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JMIR Medical Education 2024年 10卷 e57594页

作者： Gilson, Aidan Safranek, Conrad W. Huang, Thomas Socrates, Vimig Chi, Ling Taylor, Richard Andrew Chartash, David Section for Biomedical Informatics and Data Science Yale University School of Medicine New Haven CT United States Department of Emergency Medicine Yale University School of Medicine New Haven CT United States Program of Computational Biology and Bioinformatics Yale University New Haven CT United States School of Medicine University College Dublin National University of Ireland Dublin Dublin Ireland Section for Biomedical Informatics and Data Science Yale University School of Medicine 300 George Street Suite 501 New Haven 06511 CT United States

In “How Does ChatGPT Perform on the United States Medical Licensing Examination (USMLE)? The Implications of Large Language Models for Medical Education and Knowledge” (MIR Med Educ 2023;9:e45312) three additions were made to enhance discoverability. The title originally appeared as: How Does ChatGPT Perform on the United States Medical Licensing Examination? The Implications of Large Language Models for Medical Education and Knowledge Assessment And has been changed to: How Does ChatGPT Perform on the United States Medical Licensing Examination (USMLE)? The Implications of Large Language Models for Medical Education and Knowledge In the “Objective” section of the Abstract, the following sentence: This study aimed to evaluate the performance of ChatGPT on questions within the scope of the United States Medical Licensing Examination Step 1 and Step 2 exams, as well as to analyze responses for user interpretability. Has been changed to read as: This study aimed to evaluate the performance of ChatGPT on questions within the scope of the United States Medical Licensing Examination (USMLE) Step 1 and Step 2 exams, as well as to analyze responses for user interpretability. Finally, the abbreviation “USMLE” has been added to the Keywords section. The correction will appear in the online version of the paper on the JMIR Publications website on February 27, 2024 together with the publication of this correction notice. Because this was made after submission to PubMed, PubMed Central, and other full-text repositories, the corrected article has also been resubmitted to those repositories. © 2024 JMIR Publications Inc.. All rights reserved.

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Integrin αEβ7+ T cells direct intestinal stem cell fate decisions via adhesion signaling

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细胞研究（英文版） 2021年第12期31卷 1291-1307页

作者： Shiyang Chen Yajuan Zheng Xiaojuan Ran Hui Du Hua Feng Lei Yang Yating Wen Changdong Lin Shihui Wang Mengwen Huang Zhanjun Yan Dianqing Wu Hongyan Wang Gaoxiang Ge An Zeng Yi Arial Zeng Jianfeng Chen School of Life Science Hangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesHangzhouZhejiangChina State Key Laboratory of Cell Biology Center for Excellence in Molecular Cell ScienceShanghai Institute of Biochemistry and Cell BiologyChinese Academy of SciencesUniversity of Chinese Academy of SciencesShanghaiChina State Key Laboratory of Cell Biology Center for Excellence in Molecular Cell ScienceShanghai Institute of Biochemistry and Cell BiologyChinese Academy of SciencesUniversity of Chinese Academy of SciencesShanghaiChina Omics Core Bio-Med Big Data CenterShanghai Institute of Nutrition and HealthChinese Academy of SciencesShanghaiChina Department of Orthopedics Suzhou Ninth Peoples HospitalSoochow UniversitySuzhouJiangsuChina Department of Pharmacology Vascular Biology and Therapeutic ProgramYale School of MedicineNew HavenCTUSA

Intestinal stem cell (ISC) differentiation is regulated precisely by a niche in the crypt,where lymphocytes may interact with stem and transient amplifying (TA) ***,whether and how lymphocyte-stem/TA cell contact affects ISC differentiation is largely ***,we uncover a novel role of T cell-stem/TA cell contact in ISC fate *** show that intestinal lymphocyte depletion results in skewed ISC differentiation in mice,which can be rescued by T cell ***,integrin αEβ7 expressed on T cells binds to E-cadherin on ISCs and TA cells,triggering E-cadherin endocytosis and the consequent Wnt and Notch signaling *** αEβ7-E-cadherin adhesion suppresses Wnt signaling and promotes Notch signaling in ISCs and TA cells,leading to defective ISC ***,αEβ7+ T cells regulate ISC differentiation at single-cell level through cell-cell contact-mediated αEβ7-E-cadherin adhesion signaling,highlighting a critical role of the T cell-stem/TA cell contact in maintaining intestinal homeostasis.

关键词： Integrins Intestinal stem cells Life sciences general Cell Biology

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Developing Eye Tracking Exercise System for Treatment of Lazy Eye

Developing Eye Tracking Exercise System for Treatment of Laz...

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Medical Technologies National Conference (TIPTEKNO)

作者： Kaan Kurtel Melahat Kizil Umuthan Ercan Suzan E. Ozsimsek Hasan Baris Mihci Department of Software Engineering Izmir University of Economics Balcova Izmir Turkey Izmir University of Economics Paramedic Program Vocational School of Health Services Luleå University of Technology MSc. Student in Applied Artificial Intelligence E-commerce Project Manager Macquarie University MSc. Student in Data Science

The aim of this study is to develop a fun and effective computer application game for children with amblyopia and to prevent eye strain in people whose work or lifestyle involves constantly looking at screens. The main motivation is to use children’s enthusiasm for playing video games for the treatment of amblyopia. For this, hardware and software products were brought together, and a software was developed according to software engineering principles. The system was tested with a group of volunteers and necessary alterations were made.

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TWEET-FID: An Annotated dataset for Multiple Foodborne Illness Detection Tasks

arXiv

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arXiv 2022年

作者： Hu, Ruofan Zhang, Dongyu Tao, Dandan Hartvigsen, Thomas Feng, Hao Rundensteiner, Elke Data Science Program Worcester Polytechnic Institute WorcesterMA United States Vanke School of Public Health Tsinghua University Beijing China CSAIL Massachusetts Insititute of Technology CambridgeMA United States Dept. of Food Science & Human Nutrition Univ. of Illinois Urbana-Champaign UrbanaIL United States

Foodborne illness is a serious but preventable public health problem – with delays in detecting the associated outbreaks resulting in productivity loss, expensive recalls, public safety hazards, and even loss of life. While social media is a promising source for identifying unreported foodborne illnesses, there is a dearth of labeled datasets for developing effective outbreak detection models. To accelerate the development of machine learning-based models for foodborne outbreak detection, we thus present TWEET-FID (TWEET-Foodborne Illness Detection), the first publicly available annotated dataset for multiple foodborne illness incident detection tasks. TWEET-FID collected from Twitter is annotated with three facets: tweet class, entity type, and slot type, with labels produced by experts as well as by crowdsource workers. We introduce several domain tasks leveraging these three facets: text relevance classification (TRC), entity mention detection (EMD), and slot filling (SF). We describe the end-to-end methodology for dataset design, creation, and labeling for supporting model development for these tasks. A comprehensive set of results for these tasks leveraging state-of-the-art single- and multi-task deep learning methods on the TWEET-FID dataset are provided. This dataset opens opportunities for future research in foodborne outbreak detection. Copyright © 2022, The Authors. All rights reserved.

关键词： Crowdsourcing

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Joint Collaboration on Coupled data Assimilation and Modeling

Joint Collaboration on Coupled Data Assimilation and Modelin...

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作者： Sims, Jamese Lee, Tsengdar Koch, Dorothy Gross, Brian Stajner, Ivanka Considine, David Pawson, Steven Kleist, Daryl Gelaro, Ron Flampouris, Stylianos Jung, Youngsun Gasbarro, Marc NOAA/NWS/OSTI Modeling Program Silver SpringMD United States Weather Data for Operation and Assessment Program NASA PO WashingtonDC United States NOAA/OAR/Weather Program Office Silver SpringMD United States NOAA/NWS/EMC College ParkMD United States Modeling Analysis and Prediction Program NASA PO WashingtonDC United States Global Modeling and Assimilation Office NASA Goddard Space Flight Center GreenbeltMD United States Tomorrow.io BostonMA United States NOAA/NWS/OSTI Silver SpringMD United States NOAA/NWS/OSTI/Science and Technology Corp. Silver SpringMD United States

NOAA-NASA Coupled data Assimilation and Modeling Workshop: What: Scientists came together to discuss potential ideas and specific subjects on data assimilation and Earth system modeling. The workshop was organized by center leads and program managers from NOAA and NASA. When: 20 April 2021 Where: Online. © 2022 American Meteorological Society. All rights reserved.

关键词： NASA

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3D Liver and Tumor Segmentation in CT Scans with Robust Self-Paced Learning 7

3D Liver and Tumor Segmentation in CT Scans with Robust Self...

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7th International Conference on Information science and Control Engineering, ICISCE 2020

作者： Nartey, Obed Tettey Yang, Guowu Asare, Sarpong Kwadwo Wu, Jinzhao Agyapong, Dorothy Araba Yakoba Frempong, Lady Nadia University of Electronic Science and Technology of China Big Data Research Center School of Computer Science and Engineering Chengdu611731 China University of Electronic Science and Technology of China School of Electronic Science and Engineering Chengdu611731 China Guangxi University for Nationalities School of Mathematics and Physics Nanning530006 China Kwame Nkrumah University of Science and Technology Biomedical Engineering Program Kumasi Ghana

ISBN: (纸本)9781728164069

Liver segmentation is a challenging problem where fully supervised deep learning models require large amounts of voxel-wise labels, which are usually laborious, expensive, and time consuming to obtain. However, massive non-annotated 3D CT volumes are usually easily accessible. Most deep learning based works to address semi-supervised CT medical image segmentation problems have mostly been graph-based or adversarial-based. In sharp contrast, this work investigates the potential of a semi-supervised learning method;novel self-training framework utilizing self-paced learning strategy to generate and select pseudo-labels from easy-to-transfer(well represented classes) to hard-to-transfer(less represented classes) samples. The proposed scheme introduces an effective pseudo-label generation and selection algorithm to generate highly confident pseudo-labels, jointly learn and optimize a model which can be solved by alternatively generating pseudo labels on unlabeled target data and re-training the model with the generated labels. Further, we also propose a novel class balancing framework to avoid the gradual eminence of easy-to-transfer discriminative features on pseudo-label generation utilizing class-wise normalization of confidence scores. Comprehensive experiments conducted on publicly available datasets show that the proposed method outperformed other state-of-the-art semi-supervised and some fully supervised methods on the segmentation results of tumors and achieved very competitive performance for liver segmentation. © 2020 IEEE.

关键词： Image segmentation

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