Benchmarking Material Use Efficiency for Building Projects

Reducing the quantities of engineered materials provides a significant opportunity to mitigate the environmental impacts caused by material production and processing. Although the efficient use of materials in building construction has been emphasized, there has been little attention given to measuring the material use efficiency (MUE) of a project. This research fulfills this gap by using data envelopment analysis (DEA) as a benchmarking tool to generate an overall perspective on the MUE and to further compare its efficiency with that of peer projects, thereby promoting enhanced efficiency through target setting. In this research, MUE was measured by adopting the quantities of a variety of materials consumed during construction as input variables and the floor area of a built facility as an output variable. To generate a reliable MUE performance, a stepwise variable selection process was applied and then the performance was ranked based on evaluating cross-efficiency. In addition, clustering analysis and DEA were fused to enable a more realistic target to be set for each input, thereby determining practical targets for each underperforming project. It is anticipated that the proposed MUE benchmarking model would enable projects to recognize the gap with the best-performing projects and help them determine the targets to focus on to become efficient

Simplified Deterioration Modeling for Highway Sign Support Systems

Road sign support systems are not usually well managed, because bridges and pavements have budget and maintenance priority while the sign boards and sign supports are considered miscellaneous items. The authors of this paper developed a simple deterioration prediction model and a repair priority list for sign support systems. For asset management risk analysis, data were collected from the Connecticut Department of Transportation (CTDOT) asset management database. Asset ages, repair history, installation and repair costs, and other administrative information were collected. While there were many advanced and complicated structural deterioration prediction models, the Weibull function was used in this research because it is a simple probability distribution function that has been widely used in reliability theory. Assuming that the primary factors of the deterioration prediction are age and initial installation quality, the repair priority list was developed based on the risk analysis of the assets. Asset risks were calculated based on the deterioration prediction model and traffic impacts of the failure

SUMO-Specific Protease 2 (SENP2) Is an Important Regulator of Fatty Acid Metabolism in Skeletal Muscle

Small ubiquitin-like modifier (SUMO)-specific proteases (SENPs) that reverse protein modification by SUMO are involved in the control of numerous cellular processes, including transcription, cell division, and cancer development. However, the physiological function of SENPs in energy metabolism remains unclear. Here, we investigated the role of SENP2 in fatty acid metabolism in C2C12 myotubes and in vivo. In C2C12 myotubes, treatment with saturated fatty acids, like palmitate, led to nuclear factor-kB- mediated increase in the expression of SENP2. This increase promoted the recruitment of peroxisome proliferator- activated receptor (PPAR)d and PPARg, through desumoylation of PPARs, to the promoters of the genes involved in fatty acid oxidation (FAO), such as carnitinepalmitoyl transferase-1 (CPT1b) and long-chain acyl-CoA synthetase 1 (ACSL1). In addition, SENP2 overexpression substantially increased FAO in C2C12 myotubes. Consistent with the cell culture system, muscle-specific SENP2 overexpression led to a marked increase in the mRNA levels of CPT1b and ACSL1 and thereby in FAO in the skeletal muscle, which ultimately alleviated high-fat diet- induced obesity and insulin resistance. Collectively, these data identify SENP2 as an important regulator of fatty acid metabolism in skeletal muscle and further implicate that muscle SENP2 could be a novel therapeutic target for the treatment of obesity-linked metabolic disorders. © 2015 by the American Diabetes Association.1