Building Information Modelling (BIM) application in relation to embodied energy and carbon (EEC) considerations during design: A practitioner perspective

Buildings’ carbon emission reduction efforts in buildings have mainly been focused on operational energy reduction and, as operational energy is reduced, embodied energy and carbon (EEC) becomes more significant. However, there is currently a lack of legislation and guidance relating to embodied carbon in buildings. This, together with the United Kingdom (UK) construction industry fragmentation, creates a significant barrier to dealing with EEC during building design. In this context, Building Information Modelling (BIM) empowers communications and stores information into one single digital model and has therefore potential to facilitate EEC considerations to be included in building design. This research takes a qualitative approach and looks at the design process in relation to EEC considerations and BIM application and how the latter can facilitate the inclusion of EEC in design considerations. Through semistructured interviews with the construction industry professionals, this research investigates BIM application in relation to EEC information during design. EEC’s current role in building design and the drivers and challenges EEC considerations are being mapped. EEC information processes and how BIM facilitates EEC information exchange and storage as well as the actors involved are revealed. The overall aim of this res

Developing a Data-driven Approach to inform Planning in County Health and Human Services Departments in the Context of a Case Study on Obesity

Since the 1970s, the obesity rate has steadily increased due to growing availability of food and declining physical activity. The existing environments within a community, including active recreation opportunities, access to healthy food options, the built environment, and transportation options, can moderate obesity. In Virginia, Fairfax County Health and Human Services (HHS) system is interested in developing the capacity for data-driven approaches to gain insights on current and future issues, such as obesity, to characterize factors at the county and sub-county level, and to use these insights to inform policy options. In exploring these questions, we developed statistical methods to combined data from a multitude of different sources including local administrative data (e.g., tax assessments, land use, student surveys), place-based data, and federal collections. Using synthetic data methods based on imputation, we recomputed American Community Survey statistics for non-Census tract geographic regions for political districts and high school attendance areas. We combined this with environmental factors, such as land dedicated to parks and recreation facilities, as well as measures of the density of healthy and unhealthy food locations to create a map of potentially obesogenic factors. Finally, we combined these data sources with Fairfax County’s youth survey and trained a random forest model to predict the effects of the environment on healthy food consumption and exercise. Our analysis highlights the need for (administrative) data at a fine scale and recommends policy changes concerning the recording and sharing of local data to better inform the policy and program development

THE USE OF NEAR INFRARED REFLECTANCE FOR EVALUATING COTTON FINENESS AND MATURITY

The U.S. Department of Agriculture has proposed to develop Ii new high speed, high volume technique to assess cotton quality. This goal has led us to investigate the feasibility of near infrared reflectance spectroscopy as a technique for evaluating cotton fiber perimeter size and wall thickness, two of the physical characteristics used in the evaluation of cotton fineness and maturity. In order to isolate the effects of perimeter size and wall thickness, nineteen cotton samples were selected on the basis of their having a nonsignificant correlation between these 2 measurements. The reflectance spectra from 1100 to 2500 nanometers was recorded at every other wavelength. The 700 independent variables were transformed by log (1/ reflectance). Due to the multicollinearity of the independent variables, the principle components were used in a multiple regression with data obtained from the reference method (arealometer) for the two dependent variables, perimeter size and wall thickness. The regression analysis of perimeter size and wall thickness on the principle components gave R2\u27S of 0.229 and 0.943 respectively

Building Information Modelling (BIM) application in relation to embodied energy and carbon (EEC) considerations during design: A practitioner perspective

Buildings’ carbon emission reduction efforts in buildings have mainly been focused on operational energy reduction and, as operational energy is reduced, embodied energy and carbon (EEC) becomes more significant. However, there is currently a lack of legislation and guidance relating to embodied carbon in buildings. This, together with the United Kingdom (UK) construction industry fragmentation, creates a significant barrier to dealing with EEC during building design. In this context, Building Information Modelling (BIM) empowers communications and stores information into one single digital model and has therefore potential to facilitate EEC considerations to be included in building design. This research takes a qualitative approach and looks at the design process in relation to EEC considerations and BIM application and how the latter can facilitate the inclusion of EEC in design considerations. Through semistructured interviews with the construction industry professionals, this research investigates BIM application in relation to EEC information during design. EEC’s current role in building design and the drivers and challenges EEC considerations are being mapped. EEC information processes and how BIM facilitates EEC information exchange and storage as well as the actors involved are revealed. The overall aim of this res

Leveraging U.S. Army Administrative Data for Individual and Team Performance

The Army possesses vast amounts of administrative (archival) data about Soldiers. These data sources include screening tests, personnel action codes, training scores, global assessments, physical fitness scores, and more. However, the Army has yet to integrate these data to create a holistic operating picture. Our research focuses on repurposing Army administrative data to (1) operationalize social constructs of interest to the Army (e.g., Army Values, Warrior Ethos) and (2) model the predictive relationship between these constructs and individual (i.e., Soldier) and team (i.e., unit) performance and readiness. The goal of the project is to provide people analytics models to Army leadership for the purposes of optimizing human capital management decisions. Our talk will describe the theoretical underpinnings of our human performance model, drawing on disciplines such as social and industrial/organizational psychology, as well as our experience gaining access to and working with Army administrative data sources. Access to the archival administrative data is provided through the Army Analytics Group (AAG), Person-event Data Environment (PDE). The PDE is a business intelligence platform that has two central functions: (1) to provide a secure repository for data sources on U.S. military personnel; and (2) to provide a secure collaborative work environment where researchers can access unclassified but sensitive military data

Leveraging U.S. Army Administrative Data for Individual and Team Performance

The Army possesses vast amounts of administrative (archival) data about Soldiers. These data sources include screening tests, personnel action codes, training scores, global assessments,  physical fitness scores, and more. However, the Army has yet to integrate these data to create a holistic operating picture. Our research focuses on repurposing Army administrative data to (1) operationalize social constructs of interest to the Army (e.g., Army Values, Warrior Ethos) and (2) model the predictive relationship between these constructs and individual (i.e., Soldier) and team (i.e., unit) performance and readiness. The goal of the project is to provide people analytics models to Army leadership for the purposes of optimizing human capital management decisions. Our talk will describe the theoretical underpinnings of our human performance model, drawing on disciplines such as social and industrial/organizational psychology, as well as our experience gaining access to and working with Army administrative data sources. Access to the archival administrative data is provided through the Army Analytics Group (AAG), Person-event Data Environment (PDE). The PDE is a business intelligence platform that has two central functions: (1) to provide a secure repository for data sources on U.S. military personnel; and (2) to provide a secure collaborative work environment where researchers can access unclassified but sensitive military data

A COMPARISON OF AFRICANIZED AND EUROPEAN DRONES : WEIGHTS, MUCUS GLAND AND SEMINAL VESICLE WEIGHTS, AND COUNTS OF SPERMATOZOA

International audienc