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Decision Support System for Deciding Eligible Journals to be Published in Majalah Kedokteran Nusantara Using the Fuzzy Logic Method

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Journal of Physics: Conference Series 2019年第1期1363卷

作者： A M H Pardede Silvia Lestari Sirait Muhammad Zarlis Tulus Dian Rianita Rosida Tiurma Manurung Eko Sumartono Kundharu Saddhono Arlis Dewi Kuraesin Nuning Kurniasih Abdussakir Indrya Mulyaningsih Rahmadani Sitepu Dian Hendrawan Yudie Irawan Edy Winarno STMIK Kaputama Binjai Sumatera Utara Indonesia Department of Computer Science Universitas Sumatera Utara Medan Indonesia Department of Mathematics Universitas Sumatera Utara Medan Indonesia Department of Public Administrations Universitas Lancang Kuning Indonesia Graduate Program in Scientific Psychology Universitas Kristen Maranatha Faculty of Agriculture Universitas Bengkulu Bengkulu Indonesia Graduate Program Universitas Sebelas Maret Surakarta Indonesia STIE Muhammadiyah Jakarta Indonesia Faculty of Communication Sciences Library and Information Science Program Universitas Padjadjaran Bandung Indonesia Department of Mathematics Education State Islamic University of Malang MalangIndonesia Department of Indonesian Language Teaching Institut Agama Islam Negeri Syekh Nurjati Cirebon Indonesia Department of Public Health Faculty of Medicine Universitas Islam Sumatera Utara Medan Indonesia Department of Agribisnis Faculty of Agriculture Universitas Islam Sumatera Utara Medan Indonesia Department of Information System Universitas Muria Kudus Indonesia Faculty of Information Technology Universitas Stikubank Semarang Indonesia

The decision to determine whether or not a journal to be published in Majalah Kedokteran Nusantara can be decided by using the Decision Support System with certain methods. One of methods used is fuzzy logic method and is applied to the Microsoft Visual Basic programming language. From the results of the research, the first step that must be done in determining a journal is published was to identify the problem and to determine how to evaluate the quality of the journal which is done by calculating the parameter values of the variables that have been determined by the editorial board. The parameters used in the calculation were title of articles, abstract, background, content, conclusions and bibliography. The conclusions obtained from the research is to building a decision support system to determine a journal published in Majalah Kedokteran Nusantara using fuzzy logic method was not so difficult to do, because it utilized Fis Sugeno Matlab application and by using fuzzy logic methods and utilizing fuzzyfication to get linguistic variables were very helpful in determining how a journal is published.

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Combined effects of hydrometeorological hazards and urbanisation on dengue risk in Brazil: a spatiotemporal modelling study

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The Lancet Planetary Health 2021年第4期5卷 e209-e219页

作者： Lowe, Rachel Lee, Sophie A Brady, Oliver J Bastos, Leonardo Carrasco-Escobar, Gabriel de Castro Catão, Rafael Colón-González, Felipe J Barcellos, Christovam Carvalho, Marilia Sá Blangiardo, Marta Rue, Håvard Gasparrini, Antonio Centre for Mathematical Modelling of Infectious Diseases London School of Hygiene & Tropical Medicine London United Kingdom Centre on Climate Change and Planetary Health London School of Hygiene & Tropical Medicine London United Kingdom Department of Infectious Disease Epidemiology London School of Hygiene & Tropical Medicine London United Kingdom Department of Public Health Environments and Society London School of Hygiene & Tropical Medicine London United Kingdom Centre for Statistical Modelling London School of Hygiene & Tropical Medicine London United Kingdom Scientific Computing Program Oswaldo Cruz Foundation Rio de Janeiro Brazil Health Information and Communication Institute Oswaldo Cruz Foundation Rio de Janeiro Brazil Scripps Institution of Oceanography University of California San Diego San Diego CA United States Health Innovation Laboratory Institute of Tropical Medicine Alexander von Humboldt Universidad Peruana Cayetano Heredia Lima Peru Department of Geography Federal University of Espírito Santo Vitória Brazil MRC Centre for Environment and Health Department of Epidemiology and Biostatistics Imperial College London London United Kingdom Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia

Background: Temperature and rainfall patterns are known to influence seasonal patterns of dengue transmission. However, the effect of severe drought and extremely wet conditions on the timing and intensity of dengue epidemics is poorly understood. In this study, we aimed to quantify the non-linear and delayed effects of extreme hydrometeorological hazards on dengue risk by level of urbanisation in Brazil using a spatiotemporal model. Methods: We combined distributed lag non-linear models with a spatiotemporal Bayesian hierarchical model framework to determine the exposure-lag-response association between the relative risk (RR) of dengue and a drought severity index. We fit the model to monthly dengue case data for the 558 microregions of Brazil between January, 2001, and January, 2019, accounting for unobserved confounding factors, spatial autocorrelation, seasonality, and interannual variability. We assessed the variation in RR by level of urbanisation through an interaction between the drought severity index and urbanisation. We also assessed the effect of hydrometeorological hazards on dengue risk in areas with a high frequency of water supply shortages. Findings: The dataset included 12 895 293 dengue cases reported between 2001 and 2019 in Brazil. Overall, the risk of dengue increased between 0–3 months after extremely wet conditions (maximum RR at 1 month lag 1·56 [95% CI 1·41–1·73]) and 3–5 months after drought conditions (maximum RR at 4 months lag 1·43 [1·22–1·67]). Including a linear interaction between the drought severity index and level of urbanisation improved the model fit and showed the risk of dengue was higher in more rural areas than highly urbanised areas during extremely wet conditions (maximum RR 1·77 [1·32–2·37] at 0 months lag vs maximum RR 1·58 [1·39–1·81] at 2 months lag), but higher in highly urbanised areas than rural areas after extreme drought (maximum RR 1·60 [1·33–1·92] vs 1·15 [1·08–1·22], both at 4 months lag). We also found the den

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Corrigendum to "A Gene Module-Based eQTL Analysis Prioritizing Disease Genes and Pathways in Kidney Cancer"

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Computational and structural biotechnology journal 2018年 16卷 196页

作者： M Q Yang D Li W Yang Y Zhang J Liu W Tong Joint Bioinformatics Graduate Program Department of Information Science George W. Donaghey College of Engineering and Information Technology University of Arkansas at Little Rock USA. University of Arkansas for Medical Sciences 2801 S. University Ave Little Rock AR 72204 USA. School of Computer Science Carnegie Mellon University 5000 Forbes Ave Pittsburgh PA 15213 USA. Department of Statistics Harvard University Cambridge MA 02138 USA. Divisions of Bioinformatics and Biostatistics National Center for Toxicological Research US Food and Drug Administration 3900 NCTR Road Jefferson AR 72079 USA.

[This corrects the article DOI: 10.1016/***.2017.09.003.].

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Artificial Intelligence, Bioinformatics, and Pathology

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Advances in Molecular Pathology 2022年第1期5卷 e1-e24页

作者： Joshua Levy Yunrui Lu Marietta Montivero Ojas Ramwala Jason McFadden Carly Miles Adam Gilbert Diamond Ramya Reddy Ram Reddy Taylor Hudson Zarif Azher Akash Pamal Sameer Gabbita Tess Cronin Abdol Aziz Ould Ismail Tarushii Goel Sanjay Jacob Anish Suvarna Sumanth Ratna Jason Zavras Louis Vaickus Department of Pathology and Laboratory Medicine Emerging Diagnostic and Investigative Technologies Dartmouth Health Lebanon NH USA Department of Dermatology Dartmouth Health Lebanon NH USA Department of Epidemiology Dartmouth College Geisel School of Medicine Hanover NH USA Program in Quantitative Biomedical Sciences Dartmouth College Geisel School of Medicine Hanover NH USA Dartmouth College Geisel School of Medicine Hanover NH USA Department of Biomedical Informatics and Medical Education University of Washington Seattle WA USA Undiagnosed Diseases Program National Human Genome Research Institute Bethesda MD USA Department of Computer Science University of Michigan Ann Arbor MI USA Hanover High School Hanover NH USA Thomas Jefferson High School for Science and Technology Alexandria VA USA Department of Computer Science University of Virginia Charlottesville VA USA University of New Hampshire Durham NH USA White River Junction VA Medical Center White River Junction VT USA Massachusetts Institute of Technology Cambridge MA USA Case Western Reserve University Cleveland OH USA Yale University New Haven CT USA Broad Institute of MIT and Harvard Cambridge MA USA

The advent of complex molecular assays and anatomic imaging platforms has enabled unprecedented characterization of disease pathogenesis. However, challenges remain in formulating diagnostic and prognostic tests that can effectively navigate and synthesize the massive treasure troves of information now available in modern pathology laboratories. Artificial intelligence (AI), and, in particular, machine learning, are pattern mining algorithms that can efficiently uncover biomarkers and risk factors personalized to the individual. These technologies will also have the benefit of providing efficiency gains, resolving ambiguity, and reducing the unnecessary resource expenditures in the Pathology setting. Adoption of AI technologies in these settings can be improved by engaging pathologists on how AI can be meaningfully used in their practice and through dissemination and communication of these core concepts and their potential benefits. In this work, we summarize emerging application areas for AI in the fields of Anatomic and Molecular Pathology.

关键词： Artificial intelligence technologies Artificial neural networks Machine learning algorithms Virtual staining Spatial omics Bioinformatics Image analysis

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Input optics systems of the KAGRA detector during O3GK (vol 2023, 023F01, 2023)

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PROGRESS OF THEORETICAL AND EXPERIMENTAL PHYSICS 2023年第5期2023卷

作者： Akutsu, T. Ando, M. Arai, K. Arai, Y. Araki, S. Araya, A. Aritomi, N. Asada, H. Aso, Y. Bae, S. Bae, Y. Baiotti, L. Bajpai, R. Barton, M. A. Cannon, K. Cao, Z. Capocasa, E. Chan, M. Chen, C. Chen, K. Chen, Y. Chiang, C-I Chu, H. Chu, Y-K Eguchi, S. Enomoto, Y. Flaminio, R. Fujii, Y. Fujikawa, Y. Fukunaga, M. Fukushima, M. Furuhata, T. Gao, D. Ge, G-G Ha, S. Hagiwara, A. Haino, S. Han, W-B Hasegawa, K. Hattori, K. Hayakawa, H. Hayama, K. Himemoto, Y. Hiranuma, Y. Hirata, N. Hirose, E. Hong, Z. Hsieh, B-H Huang, G-Z Huang, H-Y Huang, P. Huang, Y-C Huang, Y-J Hui, D. C. Y. Ide, S. Ikenoue, B. Imam, S. Inayoshi, K. Inoue, Y. Ioka, K. Ito, K. Itoh, Y. Izumi, K. Jeon, C. Jin, H-B Jung, K. Jung, P. Kaihotsu, K. Kajita, T. Kakizaki, M. Kamiizumi, M. Kanbara, S. Kanda, N. Kang, G. Kataoka, Y. Kawaguchi, K. Kawai, N. Kawasaki, T. Kim, C. Kim, J. Kim, J. C. Kim, Ws Kim, Y-M Kimura, N. Kita, N. Kitazawa, H. Kojima, Y. Kokeyama, K. Komori, K. Kong, A. K. H. Kotake, K. Kozakai, C. Kozu, R. Kumar, R. Kume, J. Kuo, C. Kuo, H-S Kuromiya, Y. Kuroyanagi, S. Kusayanagi, K. Kwak, K. Lee, H. K. Lee, H. W. Lee, R. Leonardi, M. Li, K. L. Lin, L. C-C Lin, C-Y Lin, F-K Lin, F-L Lin, H. L. Liu, G. C. Luo, L-W Majorana, E. Marchio, M. Michimura, Y. Mio, N. Miyakawa, O. Miyamoto, A. Miyazaki, Y. Miyo, K. Miyoki, S. Mori, Y. Morisaki, S. Moriwaki, Y. Nagano, K. Nagano, S. Nakamura, K. Nakano, H. Nakano, M. Nakashima, R. Nakayama, Y. Narikawa, T. Naticchioni, L. Negishi, R. Quynh, L. Nguyen Ni, W-T Nishizawa, A. Nozaki, S. Obuchi, Y. Ogaki, W. Oh, J. J. Oh, S. H. Ohashi, M. Ohishi, N. Ohkawa, M. Ohta, H. Okutani, Y. Okutomi, K. Oohara, K. Ooi, C. Oshino, S. Otabe, S. Pan, K-C Pang, H. Parisi, A. Park, J. Arellano, F. E. Pena Pinto, I. Sago, N. Saito, S. Saito, Y. Sakai, K. Sakai, Y. Sakuno, Y. Sato, S. Sato, T. Sawada, T. Sekiguchi, T. Sekiguchi, Y. Shao, L. Shibagaki, S. Shimizu, R. Shimoda, T. Shimode, K. Shinkai, H. Shishido, T. Shoda, A. Somiya, K. Son, E. J. Sotani, H. Sugimoto, R. Suresh, J. Suzuki, T. Suzuki, T. Tagoshi, H. Takahashi, H Gravitational Wave Science Project National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Department of Physics The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan Research Center for the Early Universe The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan Institute for Cosmic Ray Research KAGRA Observatory The University of Tokyo 5-1-5 Kashiwa-no-Ha Kashiwa City Chiba 277-8582 Japan Accelerator Laboratory High Energy Accelerator Research Organization (KEK) 1-1 Oho Tsukuba City Ibaraki 305-0801 Japan Earthquake Research Institute The University of Tokyo 1-1-1 Yayoi Bunkyo-ku Tokyo 113-0032 Japan Department of Mathematics and Physics Graduate School of Science and Technology Hirosaki University 3 Bunkyo-cho Hirosaki Aomori 036-8561 Japan Kamioka Branch National Astronomical Observatory of Japan 238 Higashi-Mozumi Kamioka-cho Hida City Gifu 506-1205 Japan The Graduate University for Advanced Studies (SOKENDAI) 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Korea Institute of Science and Technology Information 245 Daehak-ro Yuseong-gu Daejeon 34141 Republic of Korea National Institute for Mathematical Sciences 70 Yuseong-daero 1689 Beon-gil Yuseong-gu Daejeon 34047 Republic of Korea International College Osaka University 1-1 Machikaneyama-cho Toyonaka City Osaka 560-0043 Japan School of High Energy Accelerator Science The Graduate University for Advanced Studies (SOKENDAI) 1-1 Oho Tsukuba City Ibaraki 305-0801 Japan Department of Astronomy Beijing Normal University Xinjiekouwai Street 19 Haidian District Beijing 100875 China Department of Applied Physics Fukuoka University 8-19-1 Nanakuma Jonan Fukuoka City Fukuoka 814-0180 Japan Department of Physics Tamkang University No. 151 Yingzhuan Road Danshui Dist. New Taipei City 25137 Taiwan Department of Physics

KAGRA, the underground and cryogenic gravitational-wave detector, was operated for its solo observation from February 25 to March 10, 2020, and its first joint observation with the GEO 600 detector from April 7 to April 21, 2020 (O3GK). This study presents an overview of the input optics systems of the KAGRA detector, which consist of various optical systems, such as a laser source, its intensity and frequency stabilization systems, modulators, a Faraday isolator, mode-matching telescopes, and a high-power beam dump. These optics were successfully delivered to the KAGRA interferometer and operated stably during the observations. The laser frequency noise was observed to limit the detector sensitivity above a few kilohertz, whereas the laser intensity did not significantly limit the detector sensitivity.

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The Biodiversity of Flora and Fauna in the Re-vegetation Area in The Post-Mining Area of Pongkor

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IOP Conference Series: Earth and Environmental science 2020年第1期501卷

作者： A Kusumoarto A Gunawan Machfud A Hikmat Doctoral Student Program Natural Resource and Environmental Management Science School of Post-Graduate IPB University Jl. Raya Pajajaran Bogor Indonesia 16143 Lecturer in Architecture Study Program Faculty of Technic and Computer Science Indraprasta PGRI University Jl. Raya Nangka No. 58C RT.5/RW.5 Tanjung Barat Kecamatan Jagakarsa Kota Jakarta Selatan Daerah Khusus Ibukota Jakarta 12530 Lecturer in Landscape Architecture Department IPB University Jl. Raya Darmaga Bogor Indonesia 16680 Lecturer in AgroIndustrial Technology Department IPB University Jl.Raya Darmaga Bogor Indonesia 16680 Lecturer in Conservation of Forest and Ecotourism Department IPB University Jl. Raya Darmaga Bogor Indonesia 16680

The Pongkor post-mining area is designated as one of the ecotourism areas in Bogor. This area has biodiversity of flora and fauna. This study aims to: (1) analysis the importance value index (IVI) of flora types; (2) analysis species richness index, species diversity index, and relative species abundance index; (3) analysis the beauty, the uniqueness and the rarity of the flora and fauna types. The method used in this research is quantitative descriptive. Based on the identification results, 185 species of flora are distributed in this region. During the rainy season, 15 species of herpetofauna were identified, 32 types of aves, and 4 types of mammals, while in the dry season 19 species of herpetofauna were identified, 23 types of aves, and 2 types of mammals. Based on the results of the analysis conducted on these flora and fauna, there are flora species that have high IVI values and some beautiful and unique flora species that are in the category of endangered species. Besides that, there are also fauna species which have a value of species richness index, species diversity index, and relative species abundance index, as well as some beautiful, unique, and fauna species that are in the category of endangered species.

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Classification of datasets with imputed missing values: does imputation quality matter?

arXiv

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arXiv 2022年

作者： Shadbahr, Tolou Roberts, Michael Stanczuk, Jan Gilbey, Julian Teare, Philip Dittmer, Sören Thorpe, Matthew Torné, Ramon Viñas Sala, Evis Lió, Pietro Patel, Mishal Rudd, James H.F. Mirtti, Tuomas Rannikko, Antti Sakari Aston, John A.D. Tang, Jing Schönlieb, Carola-Bibiane Selby, Ian Breger, Anna Weir-McCall, Jonathan R. Gkrania-Klotsas, Effrossyni Korhonen, Anna Jefferson, Emily Langs, Georg Yang, Guang Prosch, Helmut Preller, Jacobus Stanczuk, Jan Babar, Judith Sánchez, Lorena Escudero Wassin, Marcel Holzer, Markus Walton, Nicholas Research Program in Systems Oncology Faculty of Medicine University of Helsinki Helsinki Finland Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom Data Science & Artificial Intelligence AstraZeneca Cambridge United Kingdom Department of Mathematics University of Manchester Manchester United Kingdom Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom Department of Radiology University of Cambridge Cambridge United Kingdom Clinical Pharmacology & Safety Sciences AstraZeneca Cambridge United Kingdom Department of Medicine University of Cambridge Cambridge United Kingdom Department of Pathology University of Helsinki Helsinki University Hospital Finland iCAN-Digital Precision Cancer Medicine Flagship Helsinki Finland Department of Urology University of Helsinki Helsinki University Hospital Helsinki Finland Department of Pure Mathematics and Mathematical Statistics University of Cambridge Cambridge United Kingdom ZeTeM University of Bremen Bremen Germany Faculty of Mathematics University of Vienna Austria Royal Papworth Hospital Cambridge Royal Papworth Hospital NHS Foundation Trust Cambridge United Kingdom Addenbrooke’s Hospital Cambridge University Hospitals NHS Trust Cambridge United Kingdom Language Technology Laboratory University of Cambridge Cambridge United Kingdom Population Health and Genomics School of Medicine University of Dundee Dundee United Kingdom Department of Biomedical Imaging and Image-guided Therapy Computational Imaging Research Lab Medical University of Vienna Vienna Austria National Heart and Lung Institute Imperial College London London United Kingdom Contextflow GmbH Vienna Austria Institute of Astronomy University of Cambridge Cambridge United Kingdom

Classifying samples in incomplete datasets is a common aim for machine learning practitioners, but is non-trivial. Missing data is found in most real-world datasets and these missing values are typically imputed using established methods, followed by classification of the now complete, imputed, samples. The focus of the machine learning researcher is then to optimise the downstream classification performance. In this study, we highlight that it is imperative to consider the quality of the imputation. We demonstrate how the commonly used measures for assessing quality are flawed and propose a new class of discrepancy scores which focus on how well the method recreates the overall distribution of the data. To conclude, we highlight the compromised interpretability of classifier models trained using poorly imputed data. All code and data used in this paper are also released publicly at [inserted upon publication]. © 2022, CC BY.

关键词： Classification (of information)

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Author Correction: Predicting the efficiency of prime editing guide RNAs in human cells

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Nature biotechnology 2024年第3期42卷 529页

作者： Hui Kwon Kim Goosang Yu Jinman Park Seonwoo Min Sungtae Lee Sungroh Yoon Hyongbum Henry Kim Department of Pharmacology Yonsei University College of Medicine Seoul Republic of Korea. Brain Korea 21 Plus Project for Medical Sciences Yonsei University College of Medicine Seoul Republic of Korea. Center for Nanomedicine Institute for Basic Science (IBS) Seoul Republic of Korea. Graduate Program of Nano Biomedical Engineering (NanoBME) Advanced Science Institute Yonsei University Seoul Republic of Korea. Electrical and Computer Engineering Seoul National University Seoul Republic of Korea. Interdisciplinary Program in Bioinformatics Seoul National University Seoul Republic of Korea. Graduate School of Data Science Seoul National University Seoul Republic of Korea. Department of Pharmacology Yonsei University College of Medicine Seoul Republic of Korea. hkim1@yuhs.ac. Brain Korea 21 Plus Project for Medical Sciences Yonsei University College of Medicine Seoul Republic of Korea. hkim1@yuhs.ac. Center for Nanomedicine Institute for Basic Science (IBS) Seoul Republic of Korea. hkim1@yuhs.ac. Graduate Program of Nano Biomedical Engineering (NanoBME) Advanced Science Institute Yonsei University Seoul Republic of Korea. hkim1@yuhs.ac. Severance Biomedical Science Institute Yonsei University College of Medicine Seoul Republic of Korea. hkim1@yuhs.ac. Graduate Program of NanoScience and Technology Yonsei University Seoul Republic of Korea. hkim1@yuhs.ac.

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The 2022 report of the Lancet Countdown on health and climate change: health at the mercy of fossil fuels (vol 400, pg 1619, 2022)

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LANCET 2022年第10365期400卷 1766-1766页

作者： Romanello, M. Di Napoli, C. Drummond, P. Institute for Global Health University College London London UK School of Agriculture Policy and Development University of Reading Reading UK Institute for Sustainable Resources University College London London UK Department of Global Health Centre for Health and the Global Environment University of Washington Seattle WA USA UCL Energy Institute University College London London UK Department of Health Sciences University of York York UK Department of Meteorology University of Reading Reading UK Institute for Risk and Disaster Reduction University College London London UK School of Earth and Environment University of Leeds Leeds UK Centre on Climate Change and Planetary Health London School of Hygiene & Tropical Medicine London UK School of Population Health University of Melbourne Melbourne VIC Australia Department of Earth System Science Tsinghua University Beijing China Mercator Research Institute on Global Commons and Climate Change Berlin Germany Department of Environment Climate Change and Health World Health Organization Geneva Switzerland Institute of Environmental Sciences University of Geneva Geneva Switzerland Cardiovascular Epidemiology Unit Department of Public Health & Primary Care University of Cambridge Cambridge UK School of Government University of Birmingham Birmingham UK Economic Analysis of Climate Impacts and Policy Division Centro Euro-Mediterraneo sui Cambiamenti Climatici Venice Italy Institute for Environmental Design and Engineering University College London London UK Natural Resources Institute University of Greenwich London UK Department of Environmental Health Sciences and Yale Center on Climate Change and Health Yale University New Haven CT USA Department of Civil and Environmental Engineering Northeastern University Boston MA USA Department of Fish and Wildlife Conservation Virginia Polytechnic Institute and State University Blacksburg VA USA Department of Geography University College London London UK NU

With advancements in the science of detection and attribution studies, the influence of climate change over many events has now been quantified. Because of the rapidly increasing temperatures, vulnerable populations (adults older than 65 years, and children younger than one year of age) were exposed to 3·7 billion more heatwave days in 2021 than annually in 1986–2005 (indicator 1.1.2), and heat-related deaths increased by 68% between 2000–04 and 2017–21 (indicator 1.1.5), a death toll that was significantly exacerbated by the confluence of the COVID-19 pandemic. Despite the local cooling and overall health benefits of urban greenspaces, only 277 (27%) of 1038 global urban centres were at least moderately green in 2021 (indicator 2.2.3), and the number of households with air conditioning increased by 66% from 2000 to 2020, a maladaptive response that worsens the energy crisis and further increases urban heat, air pollution, and greenhouse gas emissions. [...]millions of people do not have access to the energy needed to keep their homes at healthy temperatures, preserve food and medication, and meet the seventh Sustainable Development Goal (to ensure access to affordable, reliable, sustainable, and modern energy for all). Aggravating this situation even further, governments continue to incentivise fossil fuel production and consumption: 69 (80%) of 86 countries reviewed had net-negative carbon prices (ie, provided a net subsidy to fossil fuels) for a net total of US$400 billion

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Photonic Crystals: Light‐Patterned Crystallographic Direction of a Self‐Organized 3D Soft Photonic Crystal (Adv. Mater. 42/2017)

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Advanced Materials 2017年第42期29卷

作者： Zhi‐Gang Zheng Cong‐Long Yuan Wei Hu Hari Krishna Bisoyi Ming‐Jie Tang Zhen Liu Pei‐Zhi Sun Wei‐Qiang Yang Xiao‐Qian Wang Dong Shen Yannian Li Fangfu Ye Yan‐Qing Lu Guoqiang Li Quan Li Department of Physics East China University of Science and Technology Shanghai 200237 China Liquid Crystal Institute and Chemical Physics Interdisciplinary Program Kent State University Kent OH 44242 USA National Laboratory of Solid State Microstructures Collaborative Innovation Center of Advanced Microstructures and College of Engineering and Applied Sciences Nanjing University Nanjing 210093 China Beijing National Laboratory for Condensed Matter Physics and CAS Key Laboratory of Soft Matter Physics Institute of Physics Chinese Academy of Sciences Beijing 100190 China Visual and Biomedical Optics Lab Department of Ophthalmology and Visual Science and Department of Electrical and Computer Engineering Ohio State University Columbus OH 43212 USA

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