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| 1 | Evidence of perineural inva- sion on prostate biopsy specimen and survival after radical prostatecto- my显示文摘 | DeLancey JO Wood DP Jr He C | 2013 | Urology2013，81，2： | 1 |
| 2 | Virtual environments for student fieldwork using networked components显示文摘 | Jason Dykes Kate Moore Jo Wood | 1999 | International Journal of Geographical Information Science1999，，4： | 1 |
| 3 | Where is helvellyn? Fuzziness of multi-scale landscape morphometry 显示文摘 | Peter fisher Jo wood Tao cheng | 2004 | Transactions Institute of British Geographers2004，29，1： | 1 |
| 4 | HSP60 gene sequences as universal targets for microbial species identification: studies with coagulase-negative staphylococci显示文摘 | Goh SH Potter S Wood JO | 1996 | Clin Microbiol1996，34，： | 1 |
| 5 | Using Java to Interact with Geo-referenced VRML within a Virtual Field Course显示文摘 | Kate Moore Jason Dybes Jo Wood | 1999 | Computers & Geosciences1999，25，： | 1 |
| 6 | Virtual environments for student fieldwork using networked components显示文摘 | Jason Dykes Kate Moore Jo Wood | 1999 | International Journal of Geographical Information Science1999，，4： | 1 |
| 7 | Using Java to interact with geo-referenced VRML within a virtual field course 显示文摘 | Moore Kate Dykes Jason Wood Jo | 1999 | Computers and Geosciences1999，25，10： | 1 |
| 8 | Oxidative stress and epilepsy:literature review显示文摘 | Aguiar CC Almeida AB Araújo PV de Abreu RN Chaves EM do Vale OC Mac(e)do DS Woods D J Fonteles MM Vasconcelos SM | | 0，，： | 1 |
| 9 | Air temperature variations on the Atlantic_Arctic boundary since 1802显示文摘 | Wood K R Overland J E Jo'nsson T | 2010 | Geophysical Research Letters2010，37，17： | 1 |
| 10 | A penetration-aspiration scale显示文摘 | John C. Rosenbek Jo Anne Robbins Ellen B. Roecker Jame L. Coyle Jennifer L. Wood | 1996 | Dysphagia1996，，2： | 1 |
| 11 | Machine Learning and Deep Learning Methods for Enhancing Building Energy Efficiency and Indoor Environmental Quality – A Review显示文摘The built environment sector is responsible for almost one-third of the world’s final energy consumption. Hence, seeking plausible solutions to minimise building energy demands and mitigate adverse environmental impacts is necessary. Artificial intelligence (AI) techniques such as machine and deep learning have been increasingly and successfully applied to develop solutions for the built environment. This review provided a critical summary of the existing literature on the machine and deep learning methods for the built environment over the past decade, with special reference to holistic approaches. Different AI-based techniques employed to resolve interconnected problems related to heating, ventilation and air conditioning (HVAC) systems and enhance building performances were reviewed, including energy forecasting and management, indoor air quality and occupancy comfort/satisfaction prediction, occupancy detection and recognition, and fault detection and diagnosis. The present study explored existing AI-based techniques focusing on the framework, methodology, and performance. The literature highlighted that selecting the most suitable machine learning and deep learning model for solving a problem could be challenging. The recent explosive growth experienced by the research area has led to hundreds of machine learning algorithms being applied to building performance-related studies. The literature showed that existing research studies considered a wide range of scope/scales (from an HVAC component to urban areas) and time scales (minute to year). This makes it difficult to find an optimal algorithm for a specific task or case. The studies also employed a wide range of evaluation metrics, adding to the challenge. Further developments and more specific guidelines are required for the built environment field to encourage best practices in evaluating and selecting models. The literature also showed that while machine and deep learning had been successfully applied in building energy efficiency research, most of the studies are still at the experimental or testing stage, and there are limited studies which implemented machine and deep learning strategies in actual buildings and conducted the post-occupancy evaluation. | Paige Wenbin Tien Shuangyu Wei Jo Darkwa Christopher Wood John Kaiser Calautit | 2022 | Energy and AI2022，10，4： | 0 |
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