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arXiv 2022年

作者： Pati, Sarthak Baid, Ujjwal Edwards, Brandon Sheller, Micah Wang, Shih-Han Reina, G. Anthony Foley, Patrick Gruzdev, Alexey Karkada, Deepthi Davatzikos, Christos Sako, Chiharu Ghodasara, Satyam Bilello, Michel Mohan, Suyash Vollmuth, Philipp Brugnara, Gianluca Preetha, Chandrakanth J. Sahm, Felix Maier-Hein, Klaus Zenk, Maximilian Bendszus, Martin Wick, Wolfgang Calabrese, Evan Rudie, Jeffrey Villanueva-Meyer, Javier Cha, Soonmee Ingalhalikar, Madhura Jadhav, Manali Pandey, Umang Saini, Jitender Garrett, John Larson, Matthew Jeraj, Robert Currie, Stuart Frood, Russell Fatania, Kavi Huang, Raymond Y. Chang, Ken Balana, Carmen Capellades, Jaume Puig, Josep Trenkler, Johannes Pichler, Josef Necker, Georg Haunschmidt, Andreas Meckel, Stephan Shukla, Gaurav Liem, Spencer Alexander, Gregory S. Lombardo, Joseph Palmer, Joshua D. Flanders, Adam E. Dicker, Adam P. Sair, Haris I. Jones, Craig K. Venkataraman, Archana Jiang, Meirui So, Tiffany Y. Chen, Cheng Heng, Pheng Ann Dou, Qi Kozubek, Michal Lux, Filip Michálek, Jan Matula, Petr Keřkovský, Miloš Kopřivová, Tereza Dostál, Marek Vybíhal, Václav Vogelbaum, Michael A. Mitchell, J. Ross Farinhas, Joaquim Maldjian, Joseph A. Yogananda, Chandan Ganesh Bangalore Pinho, Marco C. Reddy, Divya Holcomb, James Wagner, Benjamin C. Ellingson, Benjamin M. Cloughesy, Timothy F. Raymond, Catalina Oughourlian, Talia Hagiwara, Akifumi Wang, Chencai To, Minh-Son Bhardwaj, Sargam Chong, Chee Agzarian, Marc Falcão, Alexandre Xavier Martins, Samuel B. Teixeira, Bernardo C.A. Sprenger, Flávia Menotti, David Lucio, Diego R. LaMontagne, Pamela Marcus, Daniel Wiestler, Benedikt Kofler, Florian Ezhov, Ivan Metz, Marie Jain, Rajan Lee, Matthew Lui, Yvonne W. McKinley, Richard Slotboom, Johannes Radojewski, Piotr Meier, Raphael Wiest, Roland Derrick, Murcia Fu, Eric Haas, Rourke Thompson, John Ormond, David Ryan Badve, Chaitra Sloan, Andrew E. Vadmal, Vachan Waite, Kristin Colen, Rivka R. Pei, Linmin Ak, Murat Srinivasan, Ashok Bapuraj, J. Rajiv Rao, Arvind Wang, Nicholas Yoshiaki, Ota Moritani, Tos University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Informatics Technical University of Munich Bavaria Munich Germany Intel Corporation Santa ClaraCA United States Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Heidelberg Germany Department of Neuropathology Heidelberg University Hospital Heidelberg Germany Division of Medical Image Computing German Cancer Research Center Heidelberg Germany Neurology Clinic Heidelberg University Hospital Heidelberg Germany Department of Radiology & Biomedical Imaging University of California San Francisco San FranciscoCA United States Symbiosis Center for Medical Image Analysis Symbiosis International University Maharashtra Pune India Department of Neuroimaging and Interventional Radiology National Institute of Mental Health and Neurosciences Karnataka Bangalore India Department of Radiology School of Medicine and Public Health University of Wisconsin MadisonWI United States Department of Medical Physics School of Medicine and Public Health University of Wisconsin MadisonWI United States Department of Neuroradiology Clinical Neuroscience Center University Hospital Zurich University of Zurich Zurich Switzerland Leeds Teaching Hospitals Trust Department of Radiology Leeds United Kingdom Department of Radiology Brigham and Women's Hospital Harvard Medical School BostonMA United States Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital CharlestownMA United States Catalan Institute of Oncology Badalona Spain Consorci MAR Parc de Salut de Barcelona Catalonia Spain Josep Trueta University Hospital Girona Spain

Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally sharing ample, and importantly diverse, data from multiple sites. However, such centralization is challenging to scale (or even not feasible) due to various limitations. Federated ML (FL) provides an alternative to train accurate and generalizable ML models, by only sharing numerical model updates. Here we present findings from the largest FL study to-date, involving data from 71 healthcare institutions across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, utilizing the largest dataset of such patients ever used in the literature (25,256 MRI scans from 6,314 patients). We demonstrate a 33% improvement over a publicly trained model to delineate the surgically targetable tumor, and 23% improvement over the tumor's entire extent. We anticipate our study to: 1) enable more studies in healthcare informed by large and diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further quantitative analyses for glioblastoma via performance optimization of our consensus model for eventual public release, and 3) demonstrate the effectiveness of FL at such scale and task complexity as a paradigm shift for multi-site collaborations, alleviating the need for data sharing. © 2022, CC BY.

关键词： Tumors

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Prediction of energy efficiency of iron-boron-silicon metallic glasses in aqueous solutions

Prediction of energy efficiency of iron-boron-silicon metall...

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Corrosion and Prevention 2018

作者： Habib, K. Jiang, W. Habib, A. Rahman, B. Emran, K. Materials Science and Photo-Electronics Lab. RE Program EBR Center KISR P.O.Box 24885 Safat13109 Kuwait Electrical Engineering Department Southeast University Nanjing Jiangsu China Parkview Heart Institute Fort WayneIN46845 United States Department of Electrical Electronic and Information Engineering City University London Northampton Square LondonEC1V 0HB United Kingdom Chemistry Department College of Sciences Taibah University Al-Madinah Al-Monawarah United Kingdom

A developed criterion of the energy efficiency of iron-boron-silicon metallic glasses was implemented in hydrochloric acid solutions. The criterion;lim (the conductivity of the metallic glass in aqueous solution to the conductivity of the metallic glass in air) =1 was applied to determine the energy efficiency of the metallic glass in the aqueous solution when the conductivity of a metallic glass in air became equal (decreased) to the steady conductivity of the metallic glass in aqueous solution as a function of time of the exposure of the metallic glass to the aqueous solution. The conductivity of the metallic glasses which corresponds to the energy of the metallic glasses can be obtained by EIS. In the meantime, the criterion takes into the account the worse scenario of metallic glasses that function (corrode) in acid solutions and compares the value of the conductivity of the metallic glasses in the acid solutions to the one in air. In addition, the criterion can be applied under diverse test conditions with a predetermined period of the operational life of the metallic glasses as functional materials. Furthermore, variations of the energy efficiency of the metallic glasses as a function of the acid concentration and time were produced by fitting the experimental data to a numerical model using a nonlinear regression method. The profiles of the metallic glasses exhibit a less conservative behaviour of the energy efficiency than the applied analytical criterion. © Corrosion and Prevention 2018. All Rights Reserved.

关键词： Silicon

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Limits on Broadband Absorption Enhancement in the Presence of Multiple Lossy materials

arXiv

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arXiv 2018年

作者： Raman, Aaswath Yu, Zongfu Fan, Shanhui Department of Materials Science and Engineering University of California Los AngelesCA90049 United States Department of Electrical and Computer Engineering University of Wisconsin MadisonWI53706 United States Department of Electrical Engineering Ginzton Laboratory Stanford University StanfordCA94305 United States

Enhancing the absorption and emission of electromagnetic waves over a broad range of wavelengths is a topic of fundamental and applied interest in photonics and energy research. In the context of light trapping in solar cells, for example, significant interest in the past decade has focused on overcoming limits in the ray-optics regime with nanophotonic structures. However, many such structures, in particular plasmonic structures, or PT-symmetric systems can posses multiple materials with varying values of intrinsic loss. Here, we rigorously determine the effect of parasitic loss on the achievable absorption enhancement in arbitrary electromagnetic structures. We show that the fundamental limit of broadband absorption enhancement, even in the presence of large parasitic loss in an alternate material, can exceed conventional ray-optics limits on light trapping and absorption enhancement. We numerically verify this behavior by determining the absorption enhancement factor of a canonical system, a metal-insulator-metal waveguide, whose core is a low-index organic semiconductor, in the presence of varying intrinsic loss values in the metal. Copyright © 2018, The Authors. All rights reserved.

关键词： Plasmonics

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Amplification of surface plasmons in graphene-black phosphorus injection laser heterostructures

arXiv

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arXiv 2019年

作者： Ryzhii, .V. Otsuji, .T. Ryzhii, .M. Dubinov, A.A. Aleshkin, V. Ya. Karasik, V.E. Shur, M.S. Research Institute of Electrical Communication Tohoku University Sendai980-8577 Japan Institute of Ultra High Frequency Semiconductor Electronics of Ras Moscow117105 Center for Photonics and Two-Dimensional Materials Moscow Institute of Physics Technology Dolgoprudny141700 Center for Photonics and Infrared Technology Bauman Moscow State Technical University Moscow111005 Department of Computer Science and Engineering University of Aizu Aizu-Wakamatsu965-8580 Japan Institute for Physics of Microstructures of Ras and Lobachevsky University of Nizhny Novgorod Nizhny Novgorod60395 Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States

We propose and evaluate the heterostructure based on the graphene-layer (GL) with the lateral electron injection from the side contacts and the hole vertical injection via the black phosphorus layer (PL) (p+PL-PL-GL heterostructure). Due to a relatively small energy of the holes injected from the PL into the GL (about 100 meV, smaller than the energy of optical phonons in the GL which is about 200 meV), the hole injection can effectively cool down the two-dimensional electron-hole plasma in the GL. This simplifies the realization of the interband population inversion and the achievement of the negative dynamic conductivity in the terahertz (THz) frequency range enabling the amplification of the surface plasmon modes. The later can lead to the plasmon lasing. The conversion of the plasmons into the output radiation can be used for a new types of the THz sources. Copyright © 2019, The Authors. All rights reserved.

关键词： Phosphorus

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Optical detection and manipulation of spontaneous gyrotropic electronic order in a transition-metal dichalcogenide semimetal

arXiv

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arXiv 2019年

作者： Xu, Su-Yang Ma, Qiong Gao, Yang Kogar, Anshul Zong, Alfred Mier Valdivia, Andrés M. Dinh, Thao H. Huang, Shin-Ming Singh, Bahadur Hsu, Chuang-Han Chang, Tay-Rong Ruff, Jacob P.C. Watanabe, Kenji Taniguchi, Takashi Lin, Hsin Karapetrov, Goran Xiao, Di Jarillo-Herrero, Pablo Gedik, Nuh Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics Carnegie Mellon University PittsburghPA15213 United States Department of Physics National Sun Yat-sen University Kaohsiung80424 Taiwan SZU-NUS Collaborative Center International Collaborative Laboratory of 2D Materials for Optoelectronic Science & Technology Engineering Technology Research Center for 2D Materials Information Functional Devices and Systems of Guangdong Province College of Optoelectronic Engineering Shenzhen University ShenZhen518060 China Department of Physics Northeastern University BostonMA02115 United States Department of Electrical and Computer Engineering National University of Singapore Singapore117683 Singapore Department of Physics National Cheng Kung University Tainan701 Taiwan CHESS Cornell University IthacaNY14853 United States National Institute for Materials Science Namiki 1 -1 Tsukuba Ibaraki305 -0044 Japan Institute of Physics Academia Sinica Taipei11529 Taiwan Department of Physics Department of Materials Science and Engineering Drexel University 3141 Chestnut St. PhiladelphiaPA19104 United States

The observation of chirality is ubiquitous in nature. Contrary to intuition, the population of opposite chiralities is surprisingly asymmetric at fundamental levels [1, 2]. Examples range from parity violation in the subatomic weak force [1] to the homochirality in essential biomolecules [2]. The ability to achieve chirality-selective synthesis (chiral induction) is of great importance in stereochemistry, molecular biology and pharmacology [2]. In condensed matter physics, a crystalline electronic system is geometrically chiral when it lacks any mirror plane, space inversion center or roto-inversion axis [3]. Typically, the geometrical chirality is predefined by a material’s chiral lattice structure, which is fixed upon the formation of the crystal. By contrast, a particularly unconventional scenario is the gyrotropic order [4–8], where chirality spontaneously emerges across a phase transition as the electron system breaks the relevant symmetries of an originally achiral lattice. Such a gyrotropic order, proposed as the quantum analogue of the cholesteric liquid crystals, has attracted significant interest [4–17]. However, to date, a clear observation and manipulation of the gyrotropic order remain challenging. We report the realization of optical chiral induction and the observation of a gyrotropically ordered phase in the transition-metal dichalcogenide semimetal 1T-TiSe2. We show that shining mid-infrared circularly polarized light near the critical temperature leads to the preferential formation of one chiral domain. As a result, we are able to observe an out-of-plane circular photogalvanic current, whose direction depends on the optical induction. Our study provides compelling evidence for the spontaneous emergence of chirality in the correlated semimetal TiSe2 [18]. Such chiral induction provides a new way of optical control over novel orders in quantum materials. Copyright © 2019, The Authors. All rights reserved.

关键词： Molecular biology

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A compact structure for realizing Lorentzian, Fano and EIT resonance lineshapes in a microring resonator

arXiv

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arXiv 2019年

作者： Gu, Linpeng Fang, Hanlin Li, Juntao Fang, Liang Chua, Soo Jin Zhao, Jianlin Gan, Xuetao MOE Key Laboratory of Material Physics and Chemistry under Extraordinary Conditions Shaanxi Key Laboratory of Optical Information Technology School of Science Northwestern Polytechnical University Xi'an710072 China State Key Laboratory of Optoelectronic Materials and Technologies School of Physics Sun Yat-Sen University Guangzhou510275 China Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore117583 Singapore

Microring resonators, as a fundamental building block of photonic integrated circuits, have been well developed into numerous functional devices, whose performances are strongly determined by microring's resonance lineshapes. We propose a compact structure to reliably realize Lorentzian, Fano, and electromagnetically induced transparency (EIT) resonance lineshapes in a microring. By simply inserting two air-holes in the side-coupled waveguide of a microring, a Fabry-Perot (FP) resonance is involved to couple with microring's resonant modes, showing Lorentzian, Fano, and EIT lineshapes over one free spectral range of the FP resonance. The quality factors, extinction ratios, and slope rates in different lineshapes are discussed. At microring's specific resonant wavelength, the lineshape could be tuned among these three types by controlling the FP cavity's length. Experiment results verify the theoretical analysis well and represent Fano lineshapes with extinction ratios of about 20 dB and slope rates over 280 dB/nm. The reliably and flexibly tunable lineshapes in the compact structure have potentials to improve microring-based devices and expand their application scopes. Copyright © 2019, The Authors. All rights reserved.

关键词： Light extinction

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Evolution of interlayer and intralayer magnetism in three atomically thin chromium trihalides

arXiv

引用

arXiv 2019年

作者： Kim, Hyun Ho Yang, Bowen Li, Siwen Jiang, Shengwei Jin, Chenhao Tao, Zui Nichols, George Sfigakis, Francois Zhong, Shazhou Li, Chenghe Tian, Shangjie Cory, David G. Miao, Guo-Xing Shan, Jie Mak, Kin Fai Lei, Hechang Sun, Kai Zhao, Liuyan Tsen, Adam W. Institute for Quantum Computing Department of Chemistry Department of Physics and Astronomy Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada Department of Physics University of Michigan 450 Church Street Ann ArborMI48109 United States School of Applied and Engineering Physics Department of Physics Kavli Institute at Cornell for Nanoscale Science IthacaNY14853 United States Department of Physics Beijing Key Laboratory of Opto-electronic Functional Materials & Micro-Nano Devices Renmin University of China Beijing100872 China

We conduct a comprehensive study of three different magnetic semiconductors, CrI3, CrBr3, and CrCl3, by incorporating both few- and bi-layer samples in van der Waals tunnel junctions. We find that the interlayer magnetic ordering, exchange gap, magnetic anisotropy, as well as magnon excitations evolve systematically with changing halogen atom. By fitting to a spin wave theory that accounts for nearest neighbor exchange interactions, we are able to further determine a simple spin Hamiltonian describing all three systems. These results extend the 2D magnetism platform to Ising, Heisenberg, and XY spin classes in a single material family. Using magneto-optical measurements, we additionally demonstrate that ferromagnetism can be stabilized down to monolayer in more isotropic CrBr3, with transition temperature still close to that of the bulk. Copyright © 2019, The Authors. All rights reserved.

关键词： Van der Waals forces

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The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI

arXiv

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arXiv 2023年

作者： Moawad, Ahmed W. Janas, Anastasia Baid, Ujjwal Ramakrishnan, Divya Saluja, Rachit Ashraf, Nader Jekel, Leon Amiruddin, Raisa Adewole, Maruf Albrecht, Jake Anazodo, Udunna Aneja, Sanjay Anwar, Syed Muhammad Bergquist, Timothy Calabrese, Evan Chiang, Veronica Chung, Verena Conte, Gian Marco Marco Dako, Farouk Eddy, James Ezhov, Ivan Familiar, Ariana Farahani, Keyvan Iglesias, Juan Eugenio Jiang, Zhifan Johanson, Elaine Kazerooni, Anahita Fathi Kofler, Florian Krantchev, Kiril LaBella, Dominic Van Leemput, Koen Li, Hongwei Bran Linguraru, Marius George Link, Katherine E. Liu, Xinyang Maleki, Nazanin Meier, Zeke Menze, Bjoern H. Moy, Harrison Osenberg, Klara Piraud, Marie Reitman, Zachary Shinohara, Russel Takeshi Tahon, Nourel hoda Nada, Ayman Velichko, Yuri S. Wang, Chunhao Wiestler, Benedikt Wiggins, Walter Shafique, Umber Willms, Klara Avesta, Arman Bousabarah, Khaled Chakrabarty, Satrajit Gennaro, Nicolo Holler, Wolfgang Kaur, Manpreet LaMontagne, Pamela Lin, MingDe Lost, Jan Marcus, Daniel S. Maresca, Ryan Merkaj, Sarah Nada, Ayaman Pedersen, Gabriel Cassinelli von Reppert, Marc Sotiras, Aristeidis Teytelboym, Oleg Tillmans, Niklas Westerhoff, Malte Youssef, Ayda Godfrey, Devon Floyd, Scott Rauschecker, Andreas Villanueva-Meyer, Javier Pflüger, Irada Cho, Jaeyoung Bendszus, Martin Brugnara, Gianluca Cramer, Justin Perez-Carillo, Gloria Guzman J. Johnson, Derek R. Kam, Anthony Kwan, Benjamin Yin Ming Lai, Lillian Lall, Neil U. Memon, Fatima Patro, Satya Narayana Petrovic, Bojan So, Tiffany Y. Thompson, Gerard Wu, Lei Schrickel, E. Brooke Bansal, Anu Barkhof, Frederik Besada, Cristina Chu, Sammy Druzgal, Jason Dusoi, Alexandru Farage, Luciano Feltrin, Fabricio Fong, Amy Fung, Steve H. Gray, R. Ian Ikuta, Ichiro Iv, Michael Postma, Alida A. Mahajan, Amit Joyner, David Krumpelman, Chase Letourneau-Guillon, Laurent Lincoln, Christie M. Maros, Mate E. Miller, Elka Morón, Fanny Nimchinsky, Esther A. Ozsarlak, Ozkan Patel, Uresh Rohatgi, Saurabh Saha, Atin Sayah, Anousheh Schwartz, Eric D. Shih, Robert Shiroishi, Mark Trinity health Mid Atlantic Hosiptals DarbyPA United States Department of Radiology and Biomedical Imaging Yale School of Medicine New HavenCT United States Division of Computational Pathology Department of Pathology and Laboratory Medicine School of Medicine Indiana University IndianapolisIN United States Department of Electical and Computer Engineering Cornell University Cornell Tech New YorkNY United States Department of Radiology Weill Cornell Medicine New YorkNY United States ImagineQuant Children’s Hospital of Philadelphia PhiladelphiaPA United States College of Medicine Alfaisal University Riyadh Saudi Arabia DKFZ Division of Translational Neurooncology The WTZ German Cancer Consortium DKTK Partner Site University Hospital Essen Essen Germany Medical Artificial Intelligence Lab Crestview Radiology Lagos Nigeria Sage Bionetworks SeattleWA United States Montreal Neurological Institute McGill University Montreal Canada lab Crestview Radiology Lagos Nigeria Department of Therapeutic Radiology Yale School of Medicine New HavenCT United States Sheikh Zayed Institute for Pediatric Surgical Innovation Children’s National Hospital WashingtonDC United States Department of Radiology Mayo Clinic RochesterMN United States Department of Radiology Duke University Medical Center DurhamNC United States Department of Neurosurgery Yale School of Medicine New HavenCT United States Center for Global Health Perelman School of Medicine University of Pennsylvania PA United States Department of Informatics Technical University Munich Germany Children’s Hospital of Philadelphia University of Pennsylvania PhiladelphiaPA United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD United States Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital BostonMA United States Children’s National Hospital WashingtonDC United States PrecisionFDA U.S. Food and Drug Administration S

The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms. Untreated brain metastases on standard anatomic MRI sequences (T1, T2, FLAIR, T1PG) from eight contributed international datasets were annotated in stepwise method: published UNET algorithms, student, neuroradiologist, final approver neuroradiologist. Segmentations were ranked based on lesion-wise Dice and Hausdorff distance (HD95) scores. False positives (FP) and false negatives (FN) were rigorously penalized, receiving a score of 0 for Dice and a fixed penalty of 374 for HD95. The mean scores for the teams were calculated. Eight datasets comprising 1303 studies were annotated, with 402 studies (3076 lesions) released on Synapse as publicly available datasets to challenge competitors. Additionally, 31 studies (139 lesions) were held out for validation, and 59 studies (218 lesions) were used for testing. Segmentation accuracy was measured as rank across subjects, with the winning team achieving a LesionWise mean score of 7.9. The Dice score for the winning team was 0.65 ± 0.25. Common errors among the leading teams included false negatives for small lesions and misregistration of masks in space. The Dice scores and lesion detection rates of all algorithms diminished with decreasing tumor size, particularly for tumors smaller than 100 mm3. In conclusion, algorithms for BM segmentation require further refinement to balance high sensitivity in lesion detection with the minimization of false positives and negatives. The BraTS-METS 2023 challenge successfully curated well-annotated, diverse d

关键词： Image segmentation

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Machine learning approaches for estimating building energy consumption

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IOP Conference Series: Earth and Environmental science 2020年第5期474卷

作者： Liangyu Liu Ningyi Liu Yilin Zhang Yumeng Li Xiaobo Rui Zi Yang Department of electrical and computer engineering The Ohio State University Columbus OH 43210 USA Department of statistics The Ohio State University Columbus OH 43210 USA Department of integrated systems engineering The Ohio State University Columbus OH 43210 USA State Key Laboratory of Precision Measuring Technology and Instruments Tianjin University Tianjin 300072 China Department of materials science and engineering The Ohio State University Columbus OH 43210 USA

Using building data and corresponding weather conditions provided by ASHRAE, a statistical method that carefully measures features and applies both linear regression and gradient boosting machine models to predict and analyse building energy consumption was developed. Comparison of the predicted and actual energy usage indicates our model can predict energy consumption within an acceptable error range. Such statistical model has the potential to be widely used to monitor energy consumption and measure energy savings for various kinds of buildings in the future. If additional data is available, this method will be more widely applicable to other sectors such as industrial facilities.

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Direct Imaging of Low-Dimensional Nanostructures

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Microscopy and Microanalysis 2018年第S1期24卷 90-91页

作者： Lupini, Andrew R Hudak, Bethany M Song, Jiaming Sims, Hunter Sharma, Yogesh Ward, T Zac Pantelides, Sokrates T Snijders, Paul C Materials Science and Technology Division Oak Ridge National Laboratory Oak Ridge TN USA The Institute for Functional Imaging of Materials Oak Ridge National Laboratory Oak Ridge TN USA Department of Physics and Astronomy and Department of Electrical Engineering and Computer Science Vanderbilt University Nashville Tennessee 37235 USA

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