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| 1 | Relevance of systems ap- proaches for implementing Integrated Coastal Zone Management principles in Europe 显示文摘 | REISA J STOJANOVICB T SMITHA H | 2014 | Marine Policy2014，43，： | 1 |
| 2 | Mapping the Geography of Online News 显示文摘 | Mike G Reisa K | 2008 | Canadian Journal of Communication2008，，： | 1 |
| 3 | Activation ofproinflammatory caspases by cathepsin B in focal cerebral ischemia 显示文摘 | Benchoua A Braudeau J ReisA | 2004 | J Cereb Blood FlowMetab2004，24，11： | 1 |
| 4 | Alzheimer’s disease research and development: a call for a new research roadmap显示文摘 | Howard H. Feldman Magali Haas Sam Gandy Darryle D. Schoepp Alan J. Cross Richard Mayeux Reisa A. Sperling Howard Fillit Diana L. Hoef Sonya Dougal Jeffrey S. Nye | 2014 | Ann NY Acad Sci2014，，1： | 1 |
| 5 | Toward defining the preclinical stages of Alzheimer's disease:Recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease 显示文摘 | Reisa A Sperling Paul S Aisen Laurel A | 2011 | Alzheimer''s & Dementia:The Journal of the Alzheimer''s Associa- tion2011，，： | 1 |
| 6 | Review of onsite temperature and solar forecasting models to enable better building design and operations显示文摘Advanced building controls and energy optimization for new constructions and retrofits rely on accurate weather data.Traditionally,most studies utilize airport weather information as the decision inputs.However,most buildings are in environments that are quite different than those at the airport miles away.Tree cover,adjacent buildings,and micro-climate effects caused by the larger surrounding area can all yield deviations in air temperature,humidity,solar irradiance,and wind that are large enough to influence design and operation decisions.In order to overcome this challenge,there are many prior studies on developing weather forecasting algorithms from micro-to meso-scales.This paper reviews and complies knowledge on common weather data resources,data processing methodologies and forecasting techniques of weather information.Commonly used statistical,machine learning and physical-based models are discussed and presented as two major categories:deterministic forecasting and probabilistic forecasting.Finally,evaluation metrics for forecasting errors are listed and discussed. | Bing Dong Reisa Widjaja Wenbo Wu Zhi Zhou | 2021 | Building Simulation2021，14，4： | 1 |
| 7 | The Evolution of Preclinical Alzheimer’s Disease: Implications for Prevention Trials显示文摘 | Reisa Sperling Elizabeth Mormino Keith Johnson | 2014 | Neuron2014，，： | 1 |
| 8 | Longtermuseoftopicaltacrolimus(FK506)inhigh-riskpenetratingkeratoplasty显示文摘 | BirnbaumF ReisA ReinhardT | 2009 | Cornea2009，28，6： | 1 |
| 9 | Functional abnormalities of the medial temporal lobe memory system in mild cognitive impairment and Alzheimer's disease:Insights from functional MRI studies显示文摘 | Bradford CD Reisa AS | 2008 | Neuropsychol2008，46，： | 1 |
| 10 | The effects of atmospheric ex-posure on the fracture properties of polymer concrete显示文摘 | Ferreira A J M | 2006 | Building and Environment2006，41，： | 1 |
| 11 | Recombinant antigen targets for serodiagnosis of African swine fever显示文摘 | Gallardo C ReisA L Kalema-Zikusoka G | 2009 | Clin Vaccine Immunol2009，6，： | 1 |
| 12 | Toward defining the preclinical stages of Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease显示文摘 | Reisa A. Sperling Paul S. Aisen Laurel A. Beckett David A. Bennett Suzanne Craft Anne M. Fagan Takeshi Iwatsubo Clifford R. Jack Jeffrey Kaye Thomas J. Montine Denise C. Park Eric M. Reiman Christopher C. Rowe Eric Siemers Yaakov Stern Kristine Yaffe Mari | 2011 | Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association2011，，3： | 1 |
| 13 | Vagetation based classification trees for rapid assessment of isolated wetland condition显示文摘 | Cohen M J Lane C R Reisa K C | 2005 | Ecological Indicators2005，5，3： | 1 |
| 14 | Introduction to the recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease显示文摘 | Clifford R. Jack Marilyn S. Albert David S. Knopman Guy M. McKhann Reisa A. Sperling Maria C. Carrillo Bill Thies Creighton H. Phelps | 2011 | Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association2011，，： | 1 |
| 15 | Spillovers and the competitive pressure for long-run innovation显示文摘 | ANA BALCAO REISA DANIEL A TRACA | 2008 | Euro- pean Economic Review2008，52，4： | 1 |
| 16 | Spcciation of chromium in river water samples contaminated with leather effluents by flame atom- ic absorption spectrometry after separation/preeoncentration by cloud point extraction显示文摘 | Matos G D dos Reisa E B Costa A C S | 2009 | Microchem J2009，92，2： | 1 |
| 17 | Serum vitamin D ,parathyroid hormone levels,and earotid atherosclerosis 显示文摘 | Jared P Reisa Denise von Mtlhlenb Erin D Miehos | 2009 | Atherosclerosis2009，，207： | 1 |
| 18 | A general spatial-temporal framework for short-term building temperature forecasting at arbitrary locations with crowdsourcing weather data显示文摘Weather forecasting has been a critical component to predict and control building energy consumption for better building energy management.Without accessibility to other data sources,the onsite observed temperatures or the airport temperatures are used in forecast models.In this paper,we present a novel approach by utilizing the crowdsourcing weather data from neighboring personal weather stations(PWS)to improve the weather forecast accuracy around buildings using a general spatial-temporal modeling framework.The final forecast is based on the ensemble of local forecasts for the target location using neighboring PWSs.Our approach is distinguished from existing literature in various aspects.First,we leverage the crowdsourcing weather data from PWS in addition to public data sources.In this way,the data is at much finer time resolution(e.g.,at 5-minute frequency)and spatial resolution(e.g.,arbitrary location vs grid).Second,our proposed model incorporates spatial-temporal correlation information of weather variables between the target building and a set of neighboring PWSs so that underlying correlations can be effectively captured to improve forecasting performance.We demonstrate the performance of the proposed framework by comparing to the benchmark models on temperature forecasting for a building located at an arbitrary location at San Antonio,Texas,USA.In general,the proposed model framework equipped with machine learning technique such as Random Forest can improve forecasting by 50%compares with persistent model and has 90%chance to outperform airport forecast in short-term forecasting.In a real-time setting,the proposed model framework can provide more accurate temperature forecasting results compared with using airport temperature forecast for most forecast horizon.Moreover,we analyze the sensitivity of model parameters to gain insights on how crowdsourcing data from the neighboring personal weather stations impacts forecasting performance.Finally,we implement our model in other cities such as Syracuse and Chicago to test the model’s performance in different landforms and climate types. | Reisa F.Widjaja Wenbo Wu Zhi Zhou Renhao Sun Hannah C.Fontenot Bing Dong | 2023 | Building Simulation2023，16，6： | 0 |
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