The application of BIM tools to explore the dynamic characteristics of smart materials in a contemporary Shanashil building design element

Traditional architecture is known for its crafted facade features that respond to environmental, social and cultural requirements. Contemporary architecture produced façade features that attempted to enhance local design identity and local culture. Despite the advantages of modern technology, architectural elements have difficulties in fulfilling the idea of sustainable elegance that once traditional elements provided. This problem calls for an interdisciplinary design approach to deliver sustainable design solutions that positively adapt to the surrounding environment as well as maintain the state of elegance in design. With this in mind, the research aims to explore the role of new glass technologies to improve the performance and at the same time maintain the design value of traditional façade element “shanashil” in Baghdadi buildings. This research utilises BIM tools and uses smart materials to restore the lost value in design, which mimics the dynamic characteristics observed in nature, inspired by biomimetics strategies. Such qualities are found in the characteristics of smart dynamic glazing material particularly in the switchable, reversible properties of transparency and coloration efficiency. The material characteristics are attached to a 3D digital prototype to visualise the difference between dynamic and static properties through the use of technology tools Revit plugin and smart glazing virtual reality prototype. This research concludes that the dynamic characteristics of smart glazing materials are effective in delivering a multifunctional design quality to collectively blend in harmony with the surrounding environment

Exploring the relationship between maternal iron status and offspring's blood pressure and adiposity: a Mendelian randomization study

Background: iron deficiency is the most common micronutrient deficiency worldwide. Experimental animal studies suggest that mothers deficient in iron during pregnancy are more likely to have offspring who become obese with high blood pressure. C282Y mutation carriers are more likely to have higher iron stores.Methods: we undertook an instrumental variable (IV) analysis, using maternal C282Y as an indicator for the mother’s iron status, to examine its association with offspring blood pressure (BP), waist circumference (WC), and body mass index (BMI), and compared the results to that of ordinary least squares (OLS) regression. Offspring of a sub-cohort of mothers from the UK Women’s Cohort Study (UKWCS) were recruited in 2009–2010 (n = 348, mean age = 41 years). Their blood pressure, height, and weight were measured at their local general medical practice, and they were asked to self-measure their waist circumference. About half were offspring of C282Y carriers. Maternal ferritin was used as a biomarker of maternal iron status.Results: maternal C282Y was strongly associated with maternal ferritin (mean difference per allele = 84 g/L, 95% confidence interval: 31–137, P = 0.002). Using IV analyses, maternal ferritin was not linked to offspring’s BP, BMI, or WC. The first stage F-statistic for the strength of the instrument was 10 (Kleibergen–Paap rk LM P = 0.009). Maternal ferritin was linked to offspring diastolic BP, WC, and BMI in univariable, but not in multivariable OLS analysis. There was no difference between the OLS and the IV models coefficients for any of the outcomes considered.Conclusion: we found no association between maternal iron status and adult offspring’s BP and adiposity using both multivariable OLS and IV modeling. To our knowledge, this is the first study examining this relationship. Further exploration in larger studies that have genetic variation assessed in both mother and offspring should be considere

Studying the Structural Behaviour of RC Beams with Circular Openings of Different Sizes and Locations Using FE Method

This paper aims to investigate the structural behaviour of RC beams with circular openings of different sizes and locations modelled using ABAQUS FEM software. Seven RC beams with the dimensions of 1200 mm×150 mm×150 mm were tested under threepoint loading. Group A consists of three RC beams incorporating circular openings with diameters of 40 mm, 55 mm and 65 mm in the shear zone. However, Group B consists of three RC beams incorporating circular openings with diameters of 40 mm, 55 mm and 65 mm in the flexural zone. The final RC beam did not have any openings, to provide a control beam for comparison. The results show that increasing the diameter of the openings increases the maximum deflection and the ultimate failure load decreases relative to the control beam. In the shear zone, the presence of the openings caused an increase in the maximum deflection ranging between 4% and 22% and a decrease in the ultimate failure load of between 26% and 36% compared to the control beam. However, the presence of the openings in the flexural zone caused an increase in the maximum deflection of between 1.5% and 19.7% and a decrease in the ultimate failure load of between 6% and 13% relative to the control beam. In this study, the optimum location for placing circular openings was found to be in the flexural zone of the beam with a diameter of less than 30% of the depth of the beam

Empirical analysis of rough set categorical clustering techniques based on rough purity and value set

Clustering a set of objects into homogeneous groups is a fundamental operation in data mining. Recently, attention has been put on categorical data clustering, where data objects are made up of non-numerical attributes. The implementation of several existing categorical clustering techniques is challenging as some are unable to handle uncertainty and others have stability issues. In the process of dealing with categorical data and handling uncertainty, the rough set theory has become well-established mechanism in a wide variety of applications including databases. The recent techniques such as Information-Theoretic Dependency Roughness (ITDR), Maximum Dependency Attribute (MDA) and Maximum Significance Attribute (MSA) outperformed their predecessor approaches like Bi-Clustering (BC), Total Roughness (TR), Min-Min Roughness (MMR), and standard-deviation roughness (SDR). This work explores the limitations and issues of ITDR, MDA and MSA techniques on data sets where these techniques fails to select or faces difficulty in selecting their best clustering attribute. Accordingly, two alternative techniques named Rough Purity Approach (RPA) and Maximum Value Attribute (MVA) are proposed. The novelty of both proposed approaches is that, the RPA presents a new uncertainty definition based on purity of rough relational data base whereas, the MVA unlike other rough set theory techniques uses the domain knowledge such as value set combined with number of clusters (NoC). To show the significance, mathematical and theoretical basis for proposed approaches, several propositions are illustrated. Moreover, the recent rough categorical techniques like MDA, MSA, ITDR and classical clustering technique like simple K-mean are used for comparison and the results are presented in tabular and graphical forms. For experiments, data sets from previously utilized research cases, a real supply base management (SBM) data set and UCI repository are utilized. The results reveal significant improvement by proposed techniques for categorical clustering in terms of purity (21%), entropy (9%), accuracy (16%), rough accuracy (11%), iterations (99%) and time (93%). vi

Defining and distinguishing infant behavioral states using acoustic cry analysis: is colic painful?

BackgroundTo characterize acoustic features of an infant's cry and use machine learning to provide an objective measurement of behavioral state in a cry-translator. To apply the cry-translation algorithm to colic hypothesizing that these cries sound painful.MethodsAssessment of 1000 cries in a mobile app (ChatterBabyTM). Training a cry-translation algorithm by evaluating >6000 acoustic features to predict whether infant cry was due to a pain (vaccinations, ear-piercings), fussy, or hunger states. Using the algorithm to predict the behavioral state of infants with reported colic.ResultsThe cry-translation algorithm was 90.7% accurate for identifying pain cries, and achieved 71.5% accuracy in discriminating cries from fussiness, hunger, or pain. The ChatterBaby cry-translation algorithm overwhelmingly predicted that colic cries were most likely from pain, compared to fussy and hungry states. Colic cries had average pain ratings of 73%, significantly greater than the pain measurements found in fussiness and hunger (p < 0.001, 2-sample t test). Colic cries outranked pain cries by measures of acoustic intensity, including energy, length of voiced periods, and fundamental frequency/pitch, while fussy and hungry cries showed reduced intensity measures compared to pain and colic.ConclusionsAcoustic features of cries are consistent across a diverse infant population and can be utilized as objective markers of pain, hunger, and fussiness. The ChatterBaby algorithm detected significant acoustic similarities between colic and painful cries, suggesting that they may share a neuronal pathway

Health-related quality of life as measured with EQ-5D among populations with and without specific chronic conditions: A population-based survey in Shaanxi province, China

© 2013 Tan et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Introduction: The aim of this study was to examine health-related quality of life (HRQoL) as measured by EQ-5D and to investigate the influence of chronic conditions and other risk factors on HRQoL based on a distributed sample located in Shaanxi Province, China. Methods: A multi-stage stratified cluster sampling method was performed to select subjects. EQ-5D was employed to measure the HRQoL. The likelihood that individuals with selected chronic diseases would report any problem in the EQ-5D dimensions was calculated and tested relative to that of each of the two reference groups. Multivariable linear regression models were used to investigate factors associated with EQ VAS. Results: The most frequently reported problems involved pain/discomfort (8.8%) and anxiety/depression (7.6%). Nearly half of the respondents who reported problems in any of the five dimensions were chronic patients. Higher EQ VAS scores were associated with the male gender, higher level of education, employment, younger age, an urban area of residence, access to free medical service and higher levels of physical activity. Except for anemia, all the selected chronic diseases were indicative of a negative EQ VAS score. The three leading risk factors were cerebrovascular disease, cancer and mental disease. Increases in age, number of chronic conditions and frequency of physical activity were found to have a gradient effect. Conclusion: The results of the present work add to the volume of knowledge regarding population health status in this area, apart from the known health status using mortality and morbidity data. Medical, policy, social and individual attention should be given to the management of chronic diseases and improvement of HRQoL. Longitudinal studies must be performed to monitor changes in HRQoL and to permit evaluation of the outcomes of chronic disease intervention programs. © 2013 Tan et al.National Nature Science Foundation (No. 8107239

Walks4work: Rationale and study design to investigate walking at lunchtime in the workplace setting

Background: Following recruitment of a private sector company, an 8week lunchtime walking intervention was implemented to examine the effect of the intervention on modifiable cardiovascular disease risk factors, and further to see if walking environment had any further effect on the cardiovascular disease risk factors. Methods. For phase 1 of the study participants were divided into three groups, two lunchtime walking intervention groups to walk around either an urban or natural environment twice a week during their lunch break over an 8week period. The third group was a waiting-list control who would be invited to join the walking groups after phase 1. In phase 2 all participants were encouraged to walk during their lunch break on self-selecting routes. Health checks were completed at baseline, end of phase 1 and end of phase 2 in order to measure the impact of the intervention on cardiovascular disease risk. The primary outcome variables of heart rate and heart rate variability were measured to assess autonomic function associated with cardiovascular disease. Secondary outcome variables (Body mass index, blood pressure, fitness, autonomic response to a stressor) related to cardiovascular disease were also measured. The efficacy of the intervention in increasing physical activity was objectively monitored throughout the 8-weeks using an accelerometer device. Discussion. The results of this study will help in developing interventions with low researcher input with high participant output that may be implemented in the workplace. If effective, this study will highlight the contribution that natural environments can make in the reduction of modifiable cardiovascular disease risk factors within the workplace. © 2012 Brown et al.; licensee BioMed Central Ltd

Survival analysis modeling with hidden censoring

There are well-established survival analysis methodologies for data sets that are complete, with accurate information on censoring. But what if they are not complete? In this article we consider how to analyze cases where “hidden censoring” occurs, where individuals have effectively left the study but the hospital is unaware of this. We develop a new Markov chain-based methodology for generating survival curves and hazard functions, and demonstrate this using a breast cancer data set from the Kurdistan region of Iraq