Integrating Facility Management Information into Building Information Modelling using COBie: Current Status and Future Directions

Building Information Modelling (BIM) has been implemented by architecture, engineering and construction (AEC) firms since it effectively manages and integrates information throughout the building life cycle bringing long term benefits compared to existing practices. BIM models serve as communication between design and construction phases. These models are used to determine geometry details, resolve constructability problems, track the types of materials, represent functional characteristics, and produce workflows among others. However, the benefits of BIM have not been fully extended to the operations and maintenance phase. Once the building is finished, the owner still needs the handover of paper documents to complete the information required to support the facility management phase. Therefore, to help owners extend information support throughout the building life cycle, this study proposes a process model to collect information required for facility management and incorporate it into BIM. A Construction Operations Building Information Exchange (COBie) approach is used to capture essential information to support facility management as it is created in design and construction. By capturing all the information, owners may be able to directly import data into maintenance management systems to support facility operations. Future directions are also discussed

An Experimental Investigation of the Integration of Smart Building Components with Building Information Model (BIM)

Building Information Modeling (BIM) is a methodology to digitally represent all the physical and functional characteristics of a building. Importantly, in smart buildings smart components that are enabled with sensing and actuation need to be modeled accurately within the BIM model. This data representation needs to include multiple status of the smart component based on their performance to guide the design and construction process. However, currently there is not a clear methodology or guideline on how to embed smart components in the BIM model. Visualization techniques have been developed based on CAD technology to integrate smart components in the building model but these techniques have not been applied to BIM environment. To accurately model smart components, the component must be more than a single status representation and must contain complete and accurate dynamic data of the smart component. In this research, data properties, visualization techniques, and categorization of smart components is investigated. Then, through an experimental investigation, nine smart components across five building disciplines are modeled and embedded in a BIM model of a smart space. The model includes parameters that facilitate the data representation of the smart components. Data properties, data organization, and simulation of the smart component within the building model is explained. Challenges and future research is discussed

Targeted kinase inhibition relieves slowness and tremor in a Drosophila model of LRRK2 Parkinson’s disease

Disease models: A reflex reaction A simple reflex in flies can be used to test the effectiveness of therapies that slow neurodegeneration in Parkinson’s disease (PD). Christopher Elliott and colleagues at the University of York in the United Kingdom investigated the contraction of the proboscis muscle which mediates a taste behavior response and is regulated by a single dopaminergic neuron. Flies bearing particular mutations in the PD-associated gene leucine-rich repeat kinase 2 (LRRK2) in dopaminergic neurons lost their ability to feed on a sweet solution. This was due to the movement of the proboscis muscle becoming slower and stiffer, hallmark features of PD. The authors rescued the impaired reflex reaction by feeding the flies l-DOPA or LRRK2 inhibitors. These findings highlight the proboscis extension response as a useful tool to identify other PD-associated mutations and test potential therapeutic compounds

An Introduction to EEG Source Analysis with an illustration of a study on Error-Related Potentials

International audienceOver the last twenty years blind source separation (BSS) has become a fundamental signal processing tool in the study of human electroencephalography (EEG), other biological data, as well as in many other signal processing domains such as speech, images, geophysics and wireless communication (Comon and Jutten, 2010). Without relying on head modeling BSS aims at estimating both the waveform and the scalp spatial pattern of the intracranial dipolar current responsible of the observed EEG, increasing the sensitivity and specificity of the signal received from the electrodes on the scalp. This chapter begins with a short review of brain volume conduction theory, demonstrating that BSS modeling is grounded on current physiological knowledge. We then illustrate a general BSS scheme requiring the estimation of second-order statistics (SOS) only. A simple and efficient implementation based on the approximate joint diagonalization of covariance matrices (AJDC) is described. The method operates in the same way in the time or frequency domain (or both at the same time) and is capable of modeling explicitly physiological and experimental source of variations with remarkable flexibility. Finally, we provide a specific example illustrating the analysis of a new experimental study on error-related potentials

Differential Geometry for Model Independent Analysis of Images and Other Non-Euclidean Data: Recent Developments

This article provides an exposition of recent methodologies for nonparametric analysis of digital observations on images and other non-Euclidean objects. Fr\'echet means of distributions on metric spaces, such as manifolds and stratified spaces, have played an important role in this endeavor. Apart from theoretical issues of uniqueness of the Fr\'echet minimizer and the asymptotic distribution of the sample Fr\'echet mean under uniqueness, applications to image analysis are highlighted. In addition, nonparametric Bayes theory is brought to bear on the problems of density estimation and classification on manifolds

Inferring causal molecular networks: empirical assessment through a community-based effort.

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense

Inferring causal molecular networks: empirical assessment through a community-based effort

Inferring molecular networks is a central challenge in computational biology. However, it has remained unclear whether causal, rather than merely correlational, relationships can be effectively inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge that focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results constitute the most comprehensive assessment of causal network inference in a mammalian setting carried out to date and suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess the causal validity of inferred molecular networks

Inferring causal molecular networks: empirical assessment through a community-based effort

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense