A panel vector error correction approach to forecasting demand in regional construction markets

Reliable forecasting as to the level of aggregate demand for construction is of vital importance to developers, builders and policymakers. Previous construction demand forecasting studies mainly focused on temporal estimating using national aggregate data. The construction market can be better represented by a group of interconnected regions or local markets rather than a national aggregate, and yet regional forecasting techniques have rarely been applied. Furthermore, limited research has applied regional variations in construction markets to construction demand modelling and forecasting. A new comprehensive method is used, a panel vector error correction approach, to forecast regional construction demand using Australia’s state-level data. The links between regional construction demand and general economic indicators are investigated by panel cointegration and causality analysis. The empirical results suggest that both long-run and causal links are found between regional construction demand and construction price, state income, population, unemployment rates and interest rates. The panel vector error correction model can provide reliable and robust forecasting with less than 10% of the mean absolute percentage error for a medium-term trend of regional construction demand and outperforms the conventional forecasting models (panel multiple regression and time series multiple regression model). The key macroeconomic factors of construction demand variations across regions in Australia are also presented. The findings and robust econometric techniques used are valuable to construction economists in examining future construction markets at a regional level.

Sociocultural influences on strategies to lose weight, gain weight, and increase muscles among ten cultural groups

This study determined how sociocultural messages to change one's body are perceived by adolescents from different cultural groups. In total, 4904 adolescents, including Australian, Chilean, Chinese, Indo-Fijian, Indigenous Fijian, Greek, Malaysian, Chinese Malaysian, Tongans in New Zealand, and Tongans in Tonga, were surveyed about messages from family, peers, and the media to lose weight, gain weight, and increase muscles. Groups were best differentiated by family pressure to gain weight. Girls were more likely to receive the messages from multiple sociocultural sources whereas boys were more likely to receive the messages from the family. Some participants in a cultural group indicated higher, and others lower, levels of these sociocultural messages. These findings highlight the differences in sociocultural messages across cultural groups, but also that adolescents receive contrasting messages within a cultural group. These results demonstrate the difficulty in representing a particular message as being characteristic of each cultural group.

Mixing vector construction for single channel semi-blind source separation using empirical mode decomposition

The Empirical Mode Decomposition (EMD) method is a commonly used method for solving the problem of single channel blind source separation (SCBSS) in signal processing. However, the mixing vector of SCBSS, which is the base of the EMD method, has not yet been effectively constructed. The mixing vector reflects the weights of original signal sources that form the single channel blind signal source. In this paper, we propose a novel method to construct a mixing vector for a single channel blind signal source to approximate the actual mixing vector in terms of keeping the same ratios between signal weights. The constructed mixing vector can be used to improve signal separations. Our method incorporates the adaptive filter, least square method, EMD method and signal source samples to construct the mixing vector. Experimental tests using audio signal evaluations were conducted and the results indicated that our method can improve the similar values of sources energy ratio from 0.2644 to 0.8366. This kind of recognition is very important in weak signal detection.

Support vector machine (SVM) based multiclass prediction with basic statistical analysis of plasminogen activators

Plasminogen (Pg), the precursor of the proteolytic and fibrinolytic enzyme of blood, is converted to the active enzyme plasmin (Pm) by different plasminogen activators (tissue plasminogen activators and urokinase), including the bacterial activators streptokinase and staphylokinase, which activate Pg to Pm and thus are used clinically for thrombolysis. The identification of Pg-activators is therefore an important step in understanding their functional mechanism and derives new therapies.

Stable feature selection for clinical prediction: Exploiting ICD tree structure using Tree-Lasso

Modern healthcare is getting reshaped by growing Electronic Medical Records (EMR). Recently, these records have been shown of great value towards building clinical prediction models. In EMR data, patients' diseases and hospital interventions are captured through a set of diagnoses and procedures codes. These codes are usually represented in a tree form (e.g. ICD-10 tree) and the codes within a tree branch may be highly correlated. These codes can be used as features to build a prediction model and an appropriate feature selection can inform a clinician about important risk factors for a disease. Traditional feature selection methods (e.g. Information Gain, T-test, etc.) consider each variable independently and usually end up having a long feature list. Recently, Lasso and related l1-penalty based feature selection methods have become popular due to their joint feature selection property. However, Lasso is known to have problems of selecting one feature of many correlated features randomly. This hinders the clinicians to arrive at a stable feature set, which is crucial for clinical decision making process. In this paper, we solve this problem by using a recently proposed Tree-Lasso model. Since, the stability behavior of Tree-Lasso is not well understood, we study the stability behavior of Tree-Lasso and compare it with other feature selection methods. Using a synthetic and two real-world datasets (Cancer and Acute Myocardial Infarction), we show that Tree-Lasso based feature selection is significantly more stable than Lasso and comparable to other methods e.g. Information Gain, ReliefF and T-test. We further show that, using different types of classifiers such as logistic regression, naive Bayes, support vector machines, decision trees and Random Forest, the classification performance of Tree-Lasso is comparable to Lasso and better than other methods. Our result has implications in identifying stable risk factors for many healthcare problems and therefore can potentially assist clinical decision making for accurate medical prognosis.

Excessive gestational weight gain: a psychosocial and behaviour change approach

Briony’s findings indicate that gaining excess weight during pregnancy can be influenced by depressive symptoms, body image, confidence, and motivation. Prevention of excessive pregnancy weight gain needs to be addressed by identifying women at risk and incorporating psychological and behaviour change intervention into broader health system and prevention programs.

Testing the feasibility of a mobile technology intervention promoting healthy gestational weight gain in pregnant women (txt4two) - study protocol for a randomised controlled trial.

BACKGROUND: Overweight, obesity and excess gestational weight gain (GWG) are associated with negative health outcomes for mother and child in pregnancy and across the life course. Interventions promoting GWG within guidelines report mixed results. Most are time and cost intensive, which limits scalability. Mobile technologies (mHealth) offer low cost, ready access and individually-tailored support. We aim to test the feasibility of an mHealth intervention promoting healthy nutrition, physical activity and GWG in women who begin pregnancy overweight or obese. METHODS/DESIGN: txt4two is a parallel randomised control trial pilot recruiting women with a singleton, live gestation between 10(+0) and 17(+6) weeks at the first hospital antenatal clinic visit. Inclusion criteria are pre-pregnancy BMI > 25 kg/m(2) and mobile phone ownership. One hundred consenting women will be randomised to intervention or control groups at a 1:1 ratio. All participants will receive standard antenatal care. In addition, the txt4two intervention will be delivered from baseline to 36 weeks gestation and consists of a tailored suite of theoretically-grounded, evidence-based intervention strategies focusing on healthy nutrition, physical activity and GWG. This includes: mobile phone interactive text messages promoting positive health behaviours, goal setting and self-monitoring; video messages; an information website; and a private moderated Facebook® chat forum. The primary outcome is the feasibility of the intervention. Secondary outcomes include GWG and participants' knowledge and behaviour regarding diet and physical activity during pregnancy. DISCUSSION: Findings will inform the development of larger-scale mHealth programmes to improve the delivery of healthy pregnancy nutrition, physical activity and GWG, that could be widely translated and disseminated. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry: ACTRNU111111544397 . Date of registration: 19 March 2014.

Prediction interval estimation for electricity price and demand using support vector machines

Uncertainty is known to be a concomitant factor of almost all the real world commodities such as oil prices, stock prices, sales and demand of products. As a consequence, forecasting problems are becoming more and more challenging and ridden with uncertainty. Such uncertainties are generally quantified by statistical tools such as prediction intervals (Pis). Pis quantify the uncertainty related to forecasts by estimating the ranges of the targeted quantities. Pis generated by traditional neural network based approaches are limited by high computational burden and impractical assumptions about the distribution of the data. A novel technique for constructing high quality Pis using support vector machines (SVMs) is being proposed in this paper. The proposed technique directly estimates the upper and lower bounds of the PI in a short time and without any assumptions about the data distribution. The SVM parameters are tuned using particle swarm optimization technique by minimization of a modified Pi-based objective function. Electricity price and demand data of the Ontario electricity market is used to validate the performance of the proposed technique. Several case studies for different months indicate the superior performance of the proposed method in terms of high quality PI generation and shorter computational times.

Understanding gestational weight gain: the role of weight-related expectations and knowledge

BACKGROUND: Health behaviour theories acknowledge that beliefs, attitudes and knowledge contribute to health behaviours, yet the role of these cognitions in predicting weight gain during pregnancy has not been widely researched. AIMS: To explore and compare the predictive nature of gestational weight gain (GWG) expectations and knowledge on weight gain during pregnancy. MATERIALS AND METHODS: One hundred and sixty-six women were tracked during pregnancy. Participants provided information on prepregnancy weight, height, GWG expectations and knowledge at 16-18 weeks' gestation (Time 1). To calculate gestational weight gain, prepregnancy weight was subtracted from weight at 36 weeks' gestation (collected at Time 2). Gestational weight gain above the Institute of Medicine's GWG recommendations was classified as excessive. A hierarchical regression examined the predictive nature of GWG expectations for actual GWG. Chi-square significance tests determined whether the accuracy of GWG knowledge differed depending on GWG status and prepregnancy BMI category. RESULTS: GWG expectations were a significant predictor of weight gain during pregnancy. Women who experienced excessive GWG were more likely to overestimate the minimum amount of weight that they needed to gain to have a healthy baby. CONCLUSIONS: GWG expectations are predictive of actual GWG, and GWG knowledge among women is generally poor. In particular, overestimating of the minimum amount of weight to gain during pregnancy is associated with excessive GWG. As such, it may be beneficial to design interventions to prevent excessive GWG that targets these cognitions.

Psychosocial risk factors for excessive gestational weight gain: a systematic review

BACKGROUND: Excessive weight gain during pregnancy can have adverse health outcomes for mother and infant throughout pregnancy. However, few studies have identified the psychosocial factors that contribute to women gaining excessive weight during pregnancy. AIM: To review the existing literature that explores the impact of psychosocial risk factors (psychological distress, body image dissatisfaction, social support, self-efficacy and self-esteem) on excessive gestational weight gain. METHODS: A systematic review of peer-reviewed English articles using Academic Search Complete, Cumulative Index to Nursing and Allied Health Literature, MEDLINE Complete, PsycINFO, Informit, Web of Science, and Scopus was conducted. Quantitative studies that investigated psychosocial factors of excessive GWG, published between 2000 and 2014 were included. Studies investigating mothers with a low risk of mental health issues and normally-developing foetuses were eligible for inclusion. From the total of 474 articles located, 12 articles were identified as relevant and were subsequently reviewed in full. FINDINGS: Significant associations were found between depression, body image dissatisfaction, and social support with excessive gestational weight gain. No significant relationships were reported between anxiety, stress, self-efficacy, or self-esteem and excessive gestational weight gain. CONCLUSION: The relationship between psychosocial factors and weight gain in pregnancy is complex; however depression, body dissatisfaction and social support appear to have a direct relationship with excessive gestational weight gain. Further research is needed to identify how screening for, and responding to, psychosocial risk factors for excessive gestational weight gain can be successfully incorporated into current antenatal care.

First aid recommendations for psychosis: using the Delphi method to gain consensus between mental health consumers, carers, and clinicians.

BACKGROUND: Members of the general public often lack the knowledge and skills to intervene effectively to help someone who may be developing a psychotic illness before appropriate professional help is received. METHODS: We used the Delphi method to determine recommendations on first aid for psychosis. An international panel of 157 mental health consumers, carers, and clinicians completed a 146-item questionnaire about how a member of the public could help someone who may be experiencing psychosis. The panel members rated each questionnaire item according to whether they believed the statement should be included in the first aid recommendations. The results were analyzed by comparing consensus rates across the 3 groups. Three rounds of ratings were required to consolidate consensus levels. RESULTS: Eighty-nine items were endorsed by >or=80% of panel members from all 3 groups as essential or important for psychosis first aid. These items were grouped under the following 9 headings: how to know if someone is experiencing psychosis; how to approach someone who may be experiencing psychosis; how to be supportive; how to deal with delusions and hallucinations; how to deal with communication difficulties; whether to encourage the person to seek professional help; what to do if the person does not want help; what to do in a crisis situation when the person has become acutely unwell; what to do if the person becomes aggressive. CONCLUSIONS: These recommendations will improve the provision of first aid to individuals who are developing a psychotic disorder by informing the content of training courses.

Problematic eating and food-related behaviours and excessive weight gain : why children in out-of-home care are at risk

Emerging evidence suggests that abuse and neglect in childhood may play a role in subsequent development of obesity. One population group particularly at risk is children and young people living in out-of-home care (OOHC). Given this population is already a vulnerable group, identifying potential mechanisms by which childhood abuse and neglect increases risk for obesity is essential. A possible explanation is that problematic eating and food-related behaviours (i.e., emotional eating, compulsive eating, overeating, binge eating, stealing or hoarding food) might mediate the association between adverse childhood experiences and obesity. Hence, the overall goal of this paper was to provide a narrative review of eating and food-related difficulties for children in care and their possible association with unhealthy and excessive weight gain. This review revealed a shortage of existing empirical papers and signalled particular need for further examination of the mediating effects of problematic eating.

Recombinant influenza viruses as a vaccine vector for HIV

 Live recombinant influenza viruses were successfully used as HIV vaccine vectors in a mouse model. Following intranasal prime-boost vaccination, HIV-specific CD8+ T cell responses were detected in the spleen, broncho-alveolar lavage, mediastinal and inguinal lymph nodes. HIV+α4β7+ CD8+ T cells contributed to protection in pseudo-challenge experiments using recombinant vaccinia virus expressing HIV antigens. This research highlights the importance of mucosal CD8+ T cells in viral immunity and emphasizes the need for additional studies to provide key insights to underpin future vaccine development.

Health coaching to prevent excessive gestational weight gain: a randomized-controlled trial

OBJECTIVES: The objectives of this study were to evaluate the efficacy of a health coaching (HC) intervention designed to prevent excessive gestational weight gain (GWG), and promote positive psychosocial and motivational outcomes in comparison with an Education Alone (EA) group. DESIGN: Randomized-controlled trial. METHODS: Two hundred and sixty-one women who were <18 weeks pregnant consented to take part. Those allocated to the HC group received a tailored HC intervention delivered by a Health Coach, whilst those in the EA group attended two education sessions. Women completed measures, including motivation, psychosocial variables, sleep quality, and knowledge, beliefs and expectations concerning GWG, at 15 weeks of gestation (Time 1) and 33 weeks of gestation (Time 2). Post-birth data were also collected at 2 months post-partum (Time 3). RESULTS: There was no intervention effect in relation to weight gained during pregnancy, rate of excessive GWG or birth outcomes. The only differences between HC and EA women were higher readiness (b = 0.29, 95% CIs = 0.03-0.55, p < .05) and the importance to achieve a healthy GWG (b = 0.27, 95% CIs = 0.02-0.52, p < .05), improved sleep quality (b = -0.22, 95% CIs = -0.44 to -0.03, p < .05), and increased knowledge for an appropriate amount of GWG that would be best for their baby's health (b = -1.75, 95% CI = -3.26 to -0.24, p < .05) reported by the HC at Time 2. CONCLUSIONS: Whilst the HC intervention was not successful in preventing excessive GWG, several implications for the design of future GWG interventions were identified, including the burden of the intervention commitment and the use of weight monitoring. Statement of contribution What is already known on the subject? Designing interventions to address gestational weight gain (GWG) continues to be a challenge. To date, health behaviour change factors have not been the focus of GWG interventions. What does this study add? Our health coaching (HC) intervention did not reduce GWG more so than education alone (EA). There was an intervention effect on readiness and importance to achieve healthy GWG. Yet there were no group differences regarding confidence to achieve healthy GWG post-intervention.

A novel aggregate gene selection method for microarray data classification

This paper introduces a novel method for gene selection based on a modification of analytic hierarchy process (AHP). The modified AHP (MAHP) is able to deal with quantitative factors that are statistics of five individual gene ranking methods: two-sample t-test, entropy test, receiver operating characteristic curve, Wilcoxon test, and signal to noise ratio. The most prominent discriminant genes serve as inputs to a range of classifiers including linear discriminant analysis, k-nearest neighbors, probabilistic neural network, support vector machine, and multilayer perceptron. Gene subsets selected by MAHP are compared with those of four competing approaches: information gain, symmetrical uncertainty, Bhattacharyya distance and ReliefF. Four benchmark microarray datasets: diffuse large B-cell lymphoma, leukemia cancer, prostate and colon are utilized for experiments. As the number of samples in microarray data datasets are limited, the leave one out cross validation strategy is applied rather than the traditional cross validation. Experimental results demonstrate the significant dominance of the proposed MAHP against the competing methods in terms of both accuracy and stability. With a benefit of inexpensive computational cost, MAHP is useful for cancer diagnosis using DNA gene expression profiles in the real clinical practice.