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

Alfabetización del profesorado en aprendizaje de idiomas asistido por ordenador: un estudio comparativo en España e Irán

The application of technology in teacher education has received significant attention recently. It would be, somehow, impossible to deny the necessity of integrating technology in language education. The deficiency and illiteracy in delivering technology-based practices into instruction are assumed as key challenges of teachers in 21st-century education. This study aimed to explore the current level of Computer-Assisted Language Learning (CALL) literacy of language teachers in Iran and Spain. Moreover, the relationships between CALL literacy and their nationality are investigated. The study was based on a sample of 318 language teachers in Iran and Spain. Data collection was carried out through an online questionnaire. To make a sound decision, the researchers agreed to utilize the Delphi method so that appropriate experts were chosen in order to ensure a valid study. In the data analysis phase, descriptive, t-test, and one-way ANOVA analyses were performed to answer the research questions. The findings of the study revealed that there is no difference between CALL literacy of language teachers in terms of their nationality. Finally, pedagogical implications and recommendations for further research are presented.La aplicación de la tecnología en la formación del profesorado ha recibido una gran atención en los últimos tiempos. Sería de todo punto imposible negar la necesidad de integrar la tecnología en la enseñanza de idiomas. La deficiencia y la falta de formación a la horade aplicar prácticas basadas en la tecnología en la enseñanza se asumen como retos clave para el profesorado en la educación del siglo XXI. El objetivo de este estudio es explorar el nivel actual de alfabetización en aprendizaje de idiomas asistido por ordenador (CALL Computer Assisted Language Learning para sus siglas en inglés) de los profesores de idiomas en Irán y España. Además, se investigan las relaciones entre la alfabetización en el aprendizaje de idiomas asistido por ordenador y su nacionalidad. El estudio se basó en una muestra de 318 profesores de idiomas de Irán y España. La recogida de datos se llevó a cabo mediante un cuestionario en línea. El método Delphi fue utilizado para validar el instrumento diseñado ad hoc, para lo que se eligieron los expertos adecuados que garantizasen la validez de este trabajo. En la fase de análisis de datos, se realizaron análisis descriptivos, pruebas t y ANOVA para responder a las preguntas de investigación planteadas. Los resultados del estudio revelaron que no hay diferencias entre la alfabetización CALL de los profesores de idiomas en función de su nacionalidad. Por último, se presentan las implicaciones pedagógicas de este estudio y recomendaciones para futuras investigaciones

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

Cost-effectiveness of ivabradine for heart failure in the United States

Ivabradine is a heart rate-lowering agent approved to reduce the risk of hospitalization for worsening heart failure. This study assessed the cost-effectiveness of adding ivabradine to background therapy in the United States from the perspective of a commercial or Medicare Advantage payer.A cost-effectiveness, cohort-based Markov model using a state transition approach tracked a cohort of heart failure patients with heart rate ≥70 beats per minute in sinus rhythm who were treated with ivabradine+background therapy or background therapy alone. Model inputs, including adjusted hazard ratios, rates of hospitalization and mortality, adverse events, and utility-regression equations, were derived from a large US claims database and SHIFT (Systolic Heart failure treatment with the If inhibitor ivabradine Trial). In the commercial population, ivabradine+background therapy was associated with a cost savings of $8594 versus the cost of background therapy alone over a 10-year time horizon, primarily because of reduced hospitalization. Ivabradine was associated with an incremental benefit of 0.24 quality-adjusted life years over a 10-year time horizon. In the Medicare Advantage population, the incremental cost-effectiveness ratio for ivabradine was estimated to be$24 920/quality-adjusted life years.The cost-effectiveness model suggests that for a commercial population, the addition of ivabradine to background therapy was associated with cost savings and improved clinical outcomes. For a Medicare Advantage population, the analysis indicates that the clinical benefit of ivabradine can be achieved at a reasonable cost

Model of Cholera Forecasting Using Artificial Neural Network in Chabahar City, Iran

Background: Cholera as an endemic disease remains a health issue in Iran despite decrease in incidence. Since forecasting epidemic diseases provides appropriate preventive actions in disease spread, different forecasting methods including artificial neural networks have been developed to study parameters involved in incidence and spread of epidemic diseases such as cholera.Objectives: In this study, cholera in rural area of Chabahar, Iran was investigated to achieve a proper forecasting model.Materials and Methods: Data of cholera was gathered from 465 villages, of which 104 reported cholera during ten years period of study. Logistic regression modeling and correlate bivariate were used to determine risk factors and achieve possible predictive model one-hidden-layer perception neural network with backpropagation training algorithm and the sigmoid activation function was trained and tested between the two groups of infected and non-infected villages after preprocessing. For determining validity of prediction, the ROC diagram was used. The study variables included climate conditions and geographical parameters.Results: After determining significant variables of cholera incidence, the described artificial neural network model was capable of forecasting cholera event among villages of test group with accuracy up to 80%. The highest accuracy was achieved when model was trained with variables that were significant in statistical analysis describing that the two methods confirm the result of each other.Conclusions: Application of artificial neural networking assists forecasting cholera for adopting protective measures. For a more accurate prediction, comprehensive information is required including data on hygienic, social and demographic parameters