State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

Evidence synthesis on the occurrence, causes, consequences, prevention and management of bullying and harassment behaviours to inform decision making in the NHS

Background Workplace bullying is a persistent problem in the NHS with negative implications for individuals, teams, and organisations. Bullying is a complex phenomenon and there is a lack of evidence on the best approaches to manage the problem. Aims Research questions What is known about the occurrence, causes, consequences and management of bullying and inappropriate behaviour in the workplace? Objectives Summarise the reported prevalence of workplace bullying and inappropriate behaviour. Summarise the empirical evidence on the causes and consequences of workplace bullying and inappropriate behaviour. Describe any theoretical explanations of the causes and consequences of workplace bullying and inappropriate behaviour. Synthesise evidence on the preventative and management interventions that address workplace bullying interventions and inappropriate behaviour. Methods To fulfil a realist synthesis approach the study was designed across four interrelated component parts: Part 1: A narrative review of the prevalence, causes and consequences of workplace bullying Part 2: A systematic literature search and realist review of workplace bullying interventions Part 3: Consultation with international bullying experts and practitioners Part 4: Identification of case studies and examples of good practic

Cognitive visual tracking and camera control

Cognitive visual tracking is the process of observing and understanding the behaviour of a moving person. This paper presents an efficient solution to extract, in real-time, high-level information from an observed scene, and generate the most appropriate commands for a set of pan-tilt-zoom (PTZ) cameras in a surveillance scenario. Such a high-level feedback control loop, which is the main novelty of our work, will serve to reduce uncertainties in the observed scene and to maximize the amount of information extracted from it. It is implemented with a distributed camera system using SQL tables as virtual communication channels, and Situation Graph Trees for knowledge representation, inference and high-level camera control. A set of experiments in a surveillance scenario show the effectiveness of our approach and its potential for real applications of cognitive vision

Operational experience, improvements, and performance of the CDF Run II silicon vertex detector

The Collider Detector at Fermilab (CDF) pursues a broad physics program at Fermilab's Tevatron collider. Between Run II commissioning in early 2001 and the end of operations in September 2011, the Tevatron delivered 12 fb-1 of integrated luminosity of p-pbar collisions at sqrt(s)=1.96 TeV. Many physics analyses undertaken by CDF require heavy flavor tagging with large charged particle tracking acceptance. To realize these goals, in 2001 CDF installed eight layers of silicon microstrip detectors around its interaction region. These detectors were designed for 2--5 years of operation, radiation doses up to 2 Mrad (0.02 Gy), and were expected to be replaced in 2004. The sensors were not replaced, and the Tevatron run was extended for several years beyond its design, exposing the sensors and electronics to much higher radiation doses than anticipated. In this paper we describe the operational challenges encountered over the past 10 years of running the CDF silicon detectors, the preventive measures undertaken, and the improvements made along the way to ensure their optimal performance for collecting high quality physics data. In addition, we describe the quantities and methods used to monitor radiation damage in the sensors for optimal performance and summarize the detector performance quantities important to CDF's physics program, including vertex resolution, heavy flavor tagging, and silicon vertex trigger performance.Comment: Preprint accepted for publication in Nuclear Instruments and Methods A (07/31/2013

Artificial intelligence in construction asset management: a review of present status, challenges and future opportunities

The built environment is responsible for roughly 40% of global greenhouse emissions, making the sector a crucial factor for climate change and sustainability. Meanwhile, other sectors (like manufacturing) adopted Artificial Intelligence (AI) to solve complex, non-linear problems to reduce waste, inefficiency, and pollution. Therefore, many research efforts in the Architecture, Engineering, and Construction community have recently tried introducing AI into building asset management (AM) processes. Since AM encompasses a broad set of disciplines, an overview of several AI applications, current research gaps, and trends is needed. In this context, this study conducted the first state-of-the-art research on AI for building asset management. A total of 578 papers were analyzed with bibliometric tools to identify prominent institutions, topics, and journals. The quantitative analysis helped determine the most researched areas of AM and which AI techniques are applied. The areas were furtherly investigated by reading in-depth the 83 most relevant studies selected by screening the articles’ abstracts identified in the bibliometric analysis. The results reveal many applications for Energy Management, Condition assessment, Risk management, and Project management areas. Finally, the literature review identified three main trends that can be a reference point for future studies made by practitioners or researchers: Digital Twin, Generative Adversarial Networks (with synthetic images) for data augmentation, and Deep Reinforcement Learning

The clinical effectiveness of individual behaviour change interventions to reduce risky sexual behaviour after a negative human immunodeficiency virus test in men who have sex with men: systematic and realist reviews and intervention development

Background: Men who have sex with men (MSM) experience significant inequalities in health and well-being. They are the group in the UK at the highest risk of acquiring a human immunodeficiency virus (HIV) infection. Guidance relating to both HIV infection prevention, in general, and individual-level behaviour change interventions, in particular, is very limited. Objectives: To conduct an evidence synthesis of the clinical effectiveness of behaviour change interventions to reduce risky sexual behaviour among MSM after a negative HIV infection test. To identify effective components within interventions in reducing HIV risk-related behaviours and develop a candidate intervention. To host expert events addressing the implementation and optimisation of a candidate intervention. Data sources: All major electronic databases (British Education Index, BioMed Central, Cumulative Index to Nursing and Allied Health Literature, EMBASE, Educational Resource Index and Abstracts, Health and Medical Complete, MEDLINE, PsycARTICLES, PsycINFO, PubMed and Social Science Citation Index) were searched between January 2000 and December 2014. Review methods: A systematic review of the clinical effectiveness of individual behaviour change interventions was conducted. Interventions were examined using the behaviour change technique (BCT) taxonomy, theory coding assessment, mode of delivery and proximity to HIV infection testing. Data were summarised in narrative review and, when appropriate, meta-analysis was carried out. Supplemental analyses for the development of the candidate intervention focused on post hoc realist review method, the assessment of the sequential delivery and content of intervention components, and the social and historical context of primary studies. Expert panels reviewed the candidate intervention for issues of implementation and optimisation. Results: Overall, trials included in this review (n = 10) demonstrated that individual-level behaviour change interventions are effective in reducing key HIV infection risk-related behaviours. However, there was considerable clinical and methodological heterogeneity among the trials. Exploratory meta-analysis showed a statistically significant reduction in behaviours associated with high risk of HIV transmission (risk ratio 0.75, 95% confidence interval 0.62 to 0.91). Additional stratified analyses suggested that effectiveness may be enhanced through face-to-face contact immediately after testing, and that theory-based content and BCTs drawn from ‘goals and planning’ and ‘identity’ groups are important. All evidence collated in the review was synthesised to develop a candidate intervention. Experts highlighted overall acceptability of the intervention and outlined key ways that the candidate intervention could be optimised to enhance UK implementation. Limitations: There was a limited number of primary studies. All were from outside the UK and were subject to considerable clinical, methodological and statistical heterogeneity. The findings of the meta-analysis must therefore be treated with caution. The lack of detailed intervention manuals limited the assessment of intervention content, delivery and fidelity. Conclusions: Evidence regarding the effectiveness of behaviour change interventions suggests that they are effective in changing behaviour associated with HIV transmission. Exploratory stratified meta-analyses suggested that interventions should be delivered face to face and immediately after testing. There are uncertainties around the generalisability of these findings to the UK setting. However, UK experts found the intervention acceptable and provided ways of optimising the candidate intervention. Future work: There is a need for well-designed, UK-based trials of individual behaviour change interventions that clearly articulate intervention content and demonstrate intervention fidelity