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A study of Galactic Plane Planck Galactic cold clumps observed by SCOPE and the JCMT Plane Survey

Monthly Notices of the Royal Astronomical Society 2024年第4期530卷 5192-5208页

作者： Eden, D.J. Liu, Tie Moore, T.J.T. Di Francesco, J. Fuller, G. Kim, Kee-Tae Li, Di Liu, S.-Y. Plume, R. Tatematsu, Ken'ichi Thompson, M.A. Wu, Y. Bronfman, L. Butner, H.M. Currie, M.J. Garay, G. Goldsmith, P.F. Hirano, N. Johnstone, D. Juvela, M. Lai, S.-P. Lee, C.W. Mannfors, E.E. Olguin, F. Pattle, K. Park, Geumsook Polychroni, D. Rawlings, M. Rigby, A.J. Sanhueza, P. Traficante, A. Urquhart, J.S. Weferling, B. White, G.J. Yadav, R.K. Armagh Observatory and Planetarium College Hill ArmaghBT61 9DB United Kingdom Shanghai Astronomical Observatory Chinese Academy of Sciences 80 Nandan Road Shanghai200030 China Astrophysics Research Institute Liverpool John Moores University Liverpool Science Park iC2 146 Brownlow Hill LiverpoolL3 5RF United Kingdom NRC Herzberg Astronomy and Astrophysics 5071 West Saanich Rd VictoriaBCV9E 2E7 Canada Department of Physics and Astronomy University of Victoria VictoriaBCV8W 2Y2 Canada Jodrell Bank Centre for Astrophysics School of Physics and Astronomy The University of Manchester Oxford Road ManchesterM13 9PL United Kingdom Korea Astronomy and Space Science Institute 776 Daedeokdae-ro Yuseong-gu Daejon34055 Korea Republic of 217 Gajeong-ro Yuseong-gu Daejeon34113 Korea Republic of NationalAstronomicalObservatories ChineseAcademyofSciences Beijing100012 China Key Laboratory of Radio Astronomy Chinese Academy of Science Nanjing210008 China Academia Sinica Institute of Astronomy and Astrophysics 11F of AS/NTU Astronomy – Mathematics Building No.1 Section 4 Roosevelt Rd Taipei10617 Taiwan Department of Physics and Astronomy University of Calgary 2500 University Drive NW CalgaryABT2N 1N4 Canada Nobeyama Radio Observatory National Astronomical Observatory of Japan National Institutes of Natural Sciences Nobeyama Minamimaki Minamisaku Nagano384-1305 Japan Astronomical Science Program Graduate Institute for Advanced Studies SOKENDAI 2-21-1 Osawa Mitaka Tokyo181-8588 Japan School of Physics and Astronomy University of Leeds LeedsLS2 9JT United Kingdom Department of Astronomy Peking University Beijing100871 China Astronomy Department Universidad de Chile Casilla 36-D Santiago7591245 Chile Department of Physics and Astronomy James Madison University MSC 4502 901 Carrier Drive HarrisonburgVA22807 United States RAL Space STFC Rutherford Appleton Laboratory Chilton Didcot OxfordshireOX11 0QX United Kingdom East Asian Obser

We have investigated the physical properties of Planck Galactic Cold Clumps (PGCCs) located in the Galactic Plane, using the JCMT Plane Survey (JPS) and the SCUBA-2 Continuum Observations of Pre-protostellar Evolution (SCOPE) survey. By utilizing a suite of molecular-line surveys, velocities, and distances were assigned to the compact sources within the PGCCs, placing them in a Galactic context. The properties of these compact sources show no large-scale variations with Galactic environment. Investigating the star-forming content of the sample, we find that the luminosity-to-mass ratio (L/M) is an order of magnitude lower than in other Galactic studies, indicating that these objects are hosting lower levels of star formation. Finally, by comparing ATLASGAL sources that are associated or are not associated with PGCCs, we find that those associated with PGCCs are typically colder, denser, and have a lower L/M ratio, hinting that PGCCs are a distinct population of Galactic Plane sources. © 2024 The Author(s).

关键词： Stars

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Author Correction: A shared disease-associated oligodendrocyte signature among multiple CNS pathologies

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Nature neuroscience 2023年 2023 Jan 31页

作者： Mor Kenigsbuch Pierre Bost Shahar Halevi Yuzhou Chang Shuo Chen Qin Ma Renana Hajbi Benno Schwikowski Bernd Bodenmiller Hongjun Fu Michal Schwartz Ido Amit Department of Neurobiology Weizmann Institute of Science Rehovot Israel. Department of Immunology Weizmann Institute of Science Rehovot Israel. Systems Biology Group Center for Bioinformatics Biostatistics and Integrative Biology Institut Pasteur CNRS Paris France. Sorbonne Université Complexité du vivant Paris France. University of Zurich Zurich Switzerland. Department of Biomedical Informatics College of Medicine Ohio State University Columbus OH USA. Biomedical Sciences Graduate Program Ohio State University Columbus OH USA. Department of Neuroscience College of Medicine Ohio State University Columbus OH USA. Department of Molecular Cell Biology Weizmann Institute of Science Rehovot Israel. Department of Neurobiology Weizmann Institute of Science Rehovot Israel. michal.schwartz@weizmann.ac.il. Department of Immunology Weizmann Institute of Science Rehovot Israel. ido.amit@weizmann.ac.il.

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Retracted: Effect of Types and Gap on Spark Plugs on Exhaust Emissions on the 110CC Double Wheel Vehicles and Its Dissemination Using Website

Retracted: Effect of Types and Gap on Spark Plugs on Exhaust...

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2018 Workshop on Multidisciplinary and Its Applications: Applied Mathematics, Computer Science, Information Systems, and Information Technology, WMA-Mathcomtech 2018

作者： Ginting, Tinus Manurung, Hotler Harahap, Astro Julida Ongko, Erianto Hartono, H. Abdullah, Dahlan Erliana, Cut Ita Saddhono, Kundharu Sriadhi, S. Supriyono, S. Iskandar, Akbar Saleh, A.A. Sudarsana, I. Ketut Department of Mechanical Engineering Akademi Teknologi Industri Immanuel Indonesia Department of Informatics Akademi Teknologi Industri Immanuel Indonesia Department of Computer Science STMIK IBBI Medan Indonesia Department of Informatics Universitas Malikussaleh Aceh Indonesia Department of Industrial Engineering Universitas Malikussaleh Aceh Indonesia Graduate Program Universitas Sebelas Maret Surakarta Indonesia Department of Electrical Engineering Universitas Negeri Medan Indonesia Department of Geography Education Universitas Prof Dr Hazairin SH Bengkulu Indonesia Department of Informatics STMIK AKBA Makassar Indonesia Statistics Financial and Social Sciences Research Group Indonesia Department of Religious Education Institut Hindu Dharma Negeri Denpasar Denpasar Indonesia

Based on the formulation of the problem to be discussed by the writer is to know: the influence of 0.4 mm spark gap variation, and standard spark plug type, to CO and HC, the influence of 0.5 mm spark plug gap variation, and variation of platinum spark plug on CO and HC, influence of spark gap variation 0,6mm, and variation of iridium spark plug type to CO and HC emission Method used to obtain data needed to support the writer that is research method with experiment, literature study method or literature study The result of this research concludes that there is spark gap influence 0.4 mm, with variations of standard plug, platinum and iridium spark plugs against CO and HC emissions, where the lowest CO value is 0.27% and HC 98 ppm, 0.5 mm spark plug with lowest CO 0.15% and HC 46 ppm, 0.6 mm spark plug gap the lowest CO value of 0.19% and HC 24 ppm. The results of this research will be disseminated using the website. © Published under licence by IOP Publishing Ltd.

关键词： Information systems

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Corrigendum to "Investigating nurses' acceptance of patients' bring your own device implementation in a clinical setting: A pilot study" [Asia-Pacific J Oncol Nurs 10 (2023) 100195]

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Asia-Pacific journal of oncology nursing 2024年第5期11卷 100437页

作者： Shuo-Chen Chien Chun-You Chen Chia-Hui Chien Usman Iqbal Hsuan-Chia Yang Huei-Chia Hsueh Shuen-Fu Weng Wen-Shan Jian Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Taiwan. Artificial Intelligence Research and Development Center Wan Fang Hospital Taipei Medical University Taipei Taiwan. International Center for Health Information and Technology College of Medical Science and Technology Taipei Medical University Taipei Taiwan. Department of Radiation Oncology Wan Fang Hospital Taipei Medical University Taipei Taiwan. Office of Public Affairs Taipei Medical University Taipei Taiwan. Health ICT Department of Health Tasmania Australia. Global Health and Health Security Department College of Public Health Taipei Medical University Taipei Taiwan. Department of Artificial Intelligence in Medicine Professional Master Program Taipei Medical University Taipei Taiwan. Department of Pharmacy Taipei Veterans General Hospital Taipei Taiwan. Division of Endocrinology and Metabolism Department of Internal Medicine Taipei Medical University Hospital Taipei Taiwan. Division of Endocrinology and Metabolism Department of Internal Medicine School of Medicine College of Medicine Taipei Medical University Taipei Taiwan. School of Health Care Administration Taipei Medical University Taipei Taiwan. School of Gerontology Health Management College of Nursing Taipei Medical University Taipei Taiwan. Research Center for Artificial Intelligence in Medicine Taipei Medical University Taipei Taiwan. Graduate Institute of Data Science Taipei Medical University Taipei Taiwan.

[This corrects the article DOI: 10.1016/***.2023.100195.].

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Measurement of activeness lecturers in SmartLecturer based learning using fuzzy Mamdani and Sugeno

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IOP Conference Series: Materials Science and Engineering 2020年第1期725卷

作者： Sfenrianto Sfenrianto Muhammad Yasin Kaman Nainggolan Mesra Betty Yel Information Systems Management Department BINUS Graduate Program – Master of Information Systems Management Bina Nusantara University Jakarta Indonesia 11480 Informatics Management Politeknik LP3I Jakarta Indonesia 10450 Master of Computer Science - Postgraduate Programs STMIK Nusa Mandiri Faculty of Computer Science STIKOM CKI Jakarta Indonesia

The success of learning in a classroom that uses supporting media demands lecturer activity. The level of activity of lecturers in class and using learning media such as Smart lecturer can be measured using a fuzzy logic approach. This study aims to measure the level of active lecturers using the approach, namely: Mamdani and Sugeno method. Stages of lecturer activity measurement by forming a fuzzy set, composition rules as many as 24 rules and the defuzzification process using the centroid method that produces the level of activity of each is low, medium and high. Based on 173 lecturer activity data, the results of the Mamdani method indicates low 66%, medium 31%, and high 3%. While the Sugeno method produces a level of activity low 75%, moderate 17%, and high 8%. Therefore, Mamdani method is more suitable for the calculation because the spread of results is relatively evenly distributed at each level.

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Publisher Correction: Immune landscape in invasive ductal and lobular breast cancer reveals a divergent macrophage-driven microenvironment

Nature cancer

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Nature cancer 2023年第4期4卷 582页

作者： Sayali Onkar Jian Cui Jian Zou Carly Cardello Anthony R Cillo Mostofa Rafid Uddin April Sagan Marion Joy Hatice U Osmanbeyoglu Katherine L Pogue-Geile Priscilla F McAuliffe Peter C Lucas George C Tseng Adrian V Lee Tullia C Bruno Steffi Oesterreich Dario A A Vignali Department of Immunology University of Pittsburgh School of Medicine Pittsburgh PA USA. Tumor Microenvironment Center UPMC Hillman Cancer Center Pittsburgh PA USA. Graduate Program in Microbiology and Immunology University of Pittsburgh School of Medicine Pittsburgh PA USA. Department of Biostatistics University of Pittsburgh Pittsburgh PA USA. Computational Biology Department Carnegie Mellon University Pittsburgh PA USA. Joint Carnegie Mellon University-University of Pittsburgh PhD Program in Computational Biology Pittsburgh PA USA. Department of Biomedical Informatics University of Pittsburgh Pittsburgh PA USA. UPMC Hillman Cancer Center Pittsburgh PA USA. NSABP Foundation Pittsburgh PA USA. Department of Bioengineering University of Pittsburgh Pittsburgh PA USA. Section of Breast Surgery Division of Surgical Oncology Department of Surgery University of Pittsburgh College of Medicine Magee Women's Hospital of UPMC Pittsburgh PA USA. Women's Cancer Research Center UPMC Hillman Cancer Center Magee-Womens Research Institute University of Pittsburgh Pittsburgh PA USA. Department of Pathology University of Pittsburgh Pittsburgh PA USA. Cancer Biology Program UPMC Hillman Cancer Center Pittsburgh PA USA. Department of Pharmacology and Chemical Biology University of Pittsburgh Pittsburgh PA USA. Cancer Immunology and Immunotherapy Program UPMC Hillman Cancer Center Pittsburgh PA USA. Women's Cancer Research Center UPMC Hillman Cancer Center Magee-Womens Research Institute University of Pittsburgh Pittsburgh PA USA. oesterreichs@upmc.edu. Cancer Biology Program UPMC Hillman Cancer Center Pittsburgh PA USA. oesterreichs@upmc.edu. Department of Pharmacology and Chemical Biology University of Pittsburgh Pittsburgh PA USA. oesterreichs@upmc.edu. Department of Immunology University of Pittsburgh School of Medicine Pittsburgh PA USA. dvignali@pitt.edu. Tumor Microenvironment Center UPMC Hillman Cancer Center Pittsburgh PA USA. dvignali@pi

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Using a Recurrent Neural Network Model for Classification of Tweets Conveyed Influenza-related Information 1

Using a Recurrent Neural Network Model for Classification of...

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1st International Workshop on Digital Disease Detection using Social Media, DDDSM 2017, co-located with the 8th International Joint Conference on Natural Language Processing, IJCNLP 2017

作者： Wang, Chen-Kai Singh, Onkar Tang, Zhao-Li Dai, Hong-Jie Graduate Institute of Biomedical Informatics Taipei Medical University Taipei Taiwan Bioinformatics Program Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei Taiwan Institute of Biomedical Informatics National Yang-Ming University Taipei Taiwan Department of Computer Science and Information Engineering National Taitung University Taitung Taiwan Interdisciplinary Program of Green and Information Technology National Taitung University Taitung Taiwan

ISBN: (纸本)9781948087070

Traditional disease surveillance systems depend on outpatient reporting and virological test results released by hospitals. These data have valid and accurate information about emerging outbreaks but it's often not timely. In recent years the exponential growth of users getting connected to social media provides immense knowledge about epidemics by sharing related information. Social media can now flag more immediate concerns related to outbreaks in real time. In this paper we apply the long short-term memory recurrent neural network (RNN) architecture to classify tweets conveyed influenza-related information and compare its performance with baseline algorithms including support vector machine (SVM), decision tree, naive Bayes, simple logistics, and naive Bayes multinomial. The developed RNN model achieved an F-score of 0.845 on the MedWeb task test set, which outperforms the F-score of SVM without applying the synthetic minority oversampling technique by 0.08. The F-score of the RNN model is within 1% of the highest score achieved by SVM with oversampling technique. © 2017 AFNLP

关键词： Support vector machines

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A proposed framework in an intelligent recommender system for the college student

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Journal of Physics: Conference Series 2019年第6期1402卷

作者： D Kurniadi E Abdurachman H L H S Warnars W Suparta Department of Computer Science BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Department of Informatics Sekolah Tinggi Teknologi Garut Garut Indonesia

This article aims to proposed framework an Intelligent Recommender System (IRS) for students in higher education institutions. This conceptual framework includes problems in predicting student performance, the possibility of graduating on time, and recommends choosing subjects according to performance, and career interests, which are useful for assisting pedagogical interventions in future student development. The success in the development and implementation of the proposed IRS framework is inseparable from using data mining and machine learning techniques in predicting and providing recommendations. Data analysis consisted of clustering techniques, association rules, and classification using Support Vector Machine (SVM), Naïve Bayes, and k-Nearest Neighbour (k-NN). These techniques are used to solve problems related to students and to provide appropriate recommendations. The result is an IRS conceptual framework for the college student that can be used as smart agents to provide student guidance and suggestions to support the process of education in higher education.

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Identification of gene expression signatures for psoriasis classification using machine learning techniques

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Medicine in Omics 2021年 1卷

作者： Nguyen Quoc Khanh Le Duyen Thi Do Trinh-Trung-Duong Nguyen Ngan Thi Kim Nguyen Truong Nguyen Khanh Hung Nguyen Thi Thu Trang Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei City 106 Taiwan Research Center for Artificial Intelligence in Medicine Taipei Medical University Taipei City 106 Taiwan Graduate Institute of Biomedical Informatics Taipei Medical University Taipei City 106 Taiwan Department of Computer Science and Engineering Yuan Ze University Chung-Li 32003 Taiwan School of Nutrition and Health Sciences Taipei Medical University Taipei City 110 Taiwan International Master/Ph.D. Program in Medicine College of Medicine Taipei Medical University Taipei City 110 Taiwan Orthopedic and Trauma Department Cho Ray Hospital Ho Chi Minh City Viet Nam International PhD Program for Cell Therapy and Regeneration Medicine College of Medicine Taipei Medical University Taipei City 110 Taiwan

Psoriasis classification requires the accurate identification of the lesional types for the early and effective diagnosis and it is worth interesting that the normal and psoriasis cell tissues exhibit different gene expression. Therefore, gene expression data is an effective source for psoriasis classification and there is a challenge regarding the selection of suitable gene signatures for its purpose. In this present study, the gene expression-based microarray data were used and 35 expression features linked with psoriasis were utilized to feed into our machine learning model. Overall, the performance of our model based on 35 mentioned-above features surpassed that of other state-of-the-art classifiers with an average accuracy of 98.3%, recall of 98.6%, and precision of 98% in 5-fold cross-validation tests. We also validate our model on two different sets of psoriasis and the performance results are significant. These results have suggested that our 35 expression signatures have been identified as key features for classifying samples between lesion and non-lesion. More specifically, the expression levels of few genes i.e., FABP5 , TGM1 , or BCAR3 are discovered as newly potential biomarkers for psoriasis classification and treatment with high confidence. This study, therefore, could shed light on developing the prediction models for psoriasis classification and treatment using gene expression profiles.

关键词： Psoriasis classification Random forest Gene expression Feature selection Machine learning FABP5 Dermatology

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Obtaining evidence of learning in digital games through a deep learning neural network to classify facial expressions of the players

Obtaining evidence of learning in digital games through a de...

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Frontiers in Education (FIE) Conference

作者： Heraclito A. Pereira Alberto F. De Souza Crediné S. De Menezes Post-graduate Program in Informatics Federal University of Espírito Santo Espírito Santo Brazil Education Faculty Federal University of Rio Grande do Sul Rio Grande do Sul Brazil

ISBN: (纸本)9781538611753;9781538611746

This Emotions play a significant role in determining human behavior, including the involvement and motivation of students, in the learning process. A digital game have become an important option, as a pedagogical tool, for the development of skills currently required for the insertion of an individual in labor market. This paper reports a research on a computational architecture, based on the techniques and methods of Learning Analytics, to assist educators, who use digital games as pedagogical tools, in assessing the impacts of a player's emotional states on his motivation to play and, consequently, in their learning. Such architecture aims to identify data on players' emotional states, through their facial expressions, and cross them with data on the players `actions, game events or states, which can evidence their learning. A deep learning neural network is used in the identification of the playerś facial expressions, through images of the faces of the students playing. The capture of these images is done by a webcam, installed on the computer where the game is playing, which is triggered by an intelligent software agent, according to pre-established conditions. This intelligent software agent also captures the data about the learning evidences. A software module, to analyze learning and the possible impacts of players' emotional states on their learning, completes the proposed computational architecture. The work was developed in the discipline on digital games applied to Education in a postgraduate course in Computer Science.

关键词： Games Face recognition Tools Deep learning Education Computer architecture Artificial neural networks

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