Identifying the key barriers to promote sustainable construction in the United States: A principal component analysis

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly citedThe need to build more facilities has intensified the inherited adverse impacts of the construction industry on the triple bottom lines of sustainability (i.e., people, planet, and profit). The current practice of sustainability in the construction industry is far from reaching the targeted green goals. In order to foster these endeavors, this study aims to explore sustainable construction barriers in the United States. To achieve the objective, first, 12 sustainability barriers were identified based on an excessive and comprehensive literature review and solicitation of experts’ opinions to validate the barriers. Next, a questionnaire survey was developed and distributed among 135 industry professionals to evaluate the relative importance of factors. To offer a practical solution, principal component analysis (PCA) was used to analyze the data and find the most effective barriers. The results show that four major barriers, including (1) pre-construction constraints, (2) managerial constraints, (3) legislative constraints, and (4) financial and planning constraints are the most influential challenges that the industry faces to foster sustainable construction. Practical solutions are suggested to tackle sustainable construction barriers. The findings of this study are beneficial to the architecture, engineering, and construction (AEC) industry members along with owners and policymakers.ECU Open Access Publishing Support Fun

Lessons Learned From Using An Ill-Conceived Nonmonetary, Prepaid Incentive In A Self-Administered Survey Of College Students

An experiment was conducted to determine how effective a prepaid, nonmonetary incentive would be at inducing college students to participate in a self-administered survey. The experiment was conducted on two college campuses in Los Angeles County.  As students exited their campus library, an interviewer approached them for an interview.  Half of those approached were offered a prepaid, nonmonetary incentive; the other half were not. Contrary to expectations, the prepaid, nonmonetary incentive dampened the response rate to the survey. Explanations are offered as to why the incentive was counterproductive

Walker-Independent Features for Gait Recognition from Motion Capture Data

MoCap-based human identification, as a pattern recognition discipline, can be optimized using a machine learning approach. Yet in some applications such as video surveillance new identities can appear on the fly and labeled data for all encountered people may not always be available. This work introduces the concept of learning walker-independent gait features directly from raw joint coordinates by a modification of the Fisher’s Linear Discriminant Analysis with Maximum Margin Criterion. Our new approach shows not only that these features can discriminate different people than who they are learned on, but also that the number of learning identities can be much smaller than the number of walkers encountered in the real operation

Extraction of bodily features for gait recognition and gait attractiveness evaluation

This is the author's accepted manuscript. The final publication is available at Springer via http://dx.doi.org/10.1007/s11042-012-1319-2. Copyright @ 2012 Springer.Although there has been much previous research on which bodily features are most important in gait analysis, the questions of which features should be extracted from gait, and why these features in particular should be extracted, have not been convincingly answered. The primary goal of the study reported here was to take an analytical approach to answering these questions, in the context of identifying the features that are most important for gait recognition and gait attractiveness evaluation. Using precise 3D gait motion data obtained from motion capture, we analyzed the relative motions from different body segments to a root marker (located on the lower back) of 30 males by the fixed root method, and compared them with the original motions without fixing root. Some particular features were obtained by principal component analysis (PCA). The left lower arm, lower legs and hips were identified as important features for gait recognition. For gait attractiveness evaluation, the lower legs were recognized as important features.Dorothy Hodgkin Postgraduate Award and HEFCE

Sensor fault tolerant system using least square support vector regression

Many fault detection and fault tolerant systems are designed for processes in which there is an analytical model for the system. If a model is not available then data-driven approaches are considered as an alternative method. In this paper we propose a data driven approach for sensor fault detection and accommodation in dynamic systems. Least square support vector machine (LSSVM) is implemented and the system output is predicted and used in control loop to accommodate sensor fault. Using LSSVM regression, a function can be approximated by training the model with available training data. LSSVM is used in the structure of a recurrent predictive model of a sensor output which is used in fault detection and fault tolerance (FDT). A fault tolerant approach is proposed and tested on a three tank system in which the output of the system is controlled in the desired operating region in presence of a sensor fault. The proposed approach is successfully applied and tested on a three tank system model in case of abrupt sensor fault

Finite element modeling and dynamic stress analysis of the arterial wallwith physiological arterial pressure waveforms

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Characterizing the effect of substrate stiffness on the extravasation potential of breast cancer cells using a 3D microfluidic model.

Different biochemical and biomechanical cues from tumor microenvironment affect the extravasation of cancer cells to distant organs; among them, the mechanical signals are poorly understood. Although the effect of substrate stiffness on the primary migration of cancer cells has been previously probed, its role in regulating the extravasation ability of cancer cells is still vague. Herein, we used a microfluidic device to mimic the extravasation of tumor cells in a 3D microenvironment containing cancer cells, endothelial cells, and the biological matrix. The microfluidic-based extravasation model was utilized to probe the effect of substrate stiffness on the invasion ability of breast cancer cells. MCF7 and MDA-MB-231 cancer cells were cultured among substrates with different stiffness which followed by monitoring their extravasation capability through the microfluidic device. Our results demonstrated that acidic collagen at a concentration of 2.5 mg/ml promotes migration of cancer cells. Additionally, the substrate softening resulted in up to 46% reduction in the invasion of breast cancer cells. The substrate softening not only affected the number of extravasated cells but also reduced their migration distance up to 53%. We further investigated the secreted level of matrix metalloproteinase 9 (MMP9) and identified that there is a positive correlation between substrate stiffening, MMP9 concentration, and extravasation of cancer cells. These findings suggest that the substrate stiffness mediates the cancer cells extravasation in a microfluidic model. Changes in MMP9 level could be one of the possible underlying mechanisms which need more investigations to be addressed thoroughly

Residual stress and circumferential stress distribution in a finite element model of the arterial wall

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