Does gender inequality reduce growth in sub-Saharan African and Arab countries?

This paper uses 1974 to 2001 panel data for 31 sub-Saharan African and 10 Arab countries and Arellano–Bond estimations to empirically assess the impact on growth of an important indicator associated with MDG 3; namely the ratio of 15–24-year-old literate females to males. Our findings indicate that gender inequalities in literacy have a statistically significant negative effect that is robust to changes in the specification. In addition, it seems that gender inequality has a stronger effect on growth in Arab countries. Interestingly, we find that the interaction between openness to trade and gender inequality has a positive impact. This result suggests that trade-induced growth may be accompanied by greater gender inequalities.

Ten years of environmental university building

The authors have been involved in over ten years of environmental consulting and research on university buildings. Numerous simulations and measurement studies have occurred over this period of time. The intentions have always been to improve and optimise the environmental performing aspects of a building. This paper is a reporting of the implemented strategies, their pre-building research investigation as well as their operational outcomes. Their successes and failures are discussed here. This research is intended as a feedback loop to future design specification, commissioning and maintenance improvements. In hindsight many of the environmental concepts, when executed as planned, were successful. However, often those requiring extensive control, such as lighting, ventilation and mechanical air-conditioning were a failure. The observations between simulation and actual performance are also noted. The paper includes discussion about some of the obstacles in building procurement which can hinder the result of a good environmental performing building.

Finding the beat : an analysis of the rhythmic elements of motion pictures

This paper presents a new study on the application of the framework of Computational Media Aesthetics to the problem of automated understanding of film. Leveraging Film Grammar as the means to closing the "semantic gap" in media analysis, we examine film rhythm, a powerful narrative concept used to endow structure and form to the film compositionally and enhance its lyrical quality experientially. The novelty of this paper lies in the specification and investigation of the rhythmic elements that are present in two cinematic devices; namely motion and editing patterns, and their potential usefulness to automated content annotation and management systems. In our rhythm model, motion behavior is classified as being either nonexistent, fluid or staccato for a given shot. Shot neighborhoods in movies are then grouped by proportional makeup of these motion behavioral classes to yield seven high-level rhythmic arrangements that prove to be adept at indicating likely scene content (e.g. dialogue or chase sequence) in our experiments. The second part of our investigation presents a computational model to detect editing patterns as either metric, accelerated, decelerated or free. Details of the algorithm for the extraction of these classes are presented, along with experimental results on real movie data. We show with an investigation of combined rhythmic patterns that, while detailed content identification via rhythm types alone is not possible by virtue of the fact that film is not codified to this level in terms of rhythmic elements, analysis of the combined motion/editing rhythms can allow us to determine that the content has changed and hypothesize as to why this is so. We present three such categories of change and demonstrate their efficacy for capturing useful film elements (e.g. scene change precipitated by plot event), by providing data support from five motion pictures.

The content validity of the behaviour support plan quality evaluation tool (BSP-QEII) and its potential application in accommodation and day-support services for adults with intellectual disability

Background The quality of support provided to people with disability who show challenging behaviour could be influenced by the quality of the behaviour support plans (BSPs) on which staff rely for direction. This study investigated the content validity of the Behaviour Support Plan Quality Evaluation tool (BSP-QEII), originally developed to guide the development of BSPs for children in school settings, and evaluated its application for use in accommodation and day-support services for adults with intellectual disability.

Method A three-round Delphi study involving a purposive sample of experienced behaviour support practitioners (n = 30) was conducted over an 8-week period. The analyses included deductive content analysis and descriptive statistics.

Results The 12 quality domains of the BSP-QEII were affirmed as valid for application in adult accommodation and day-support service settings. Two additional quality domains were suggested, relating to the provision of detailed background on the client and the need for plans to reflect contemporary service philosophy. Furthermore, the results suggest that some issues previously identified in the literature as being important for inclusion in BSPs might not currently be a priority for practitioners. These included: the importance of specifying replacement or alternative behaviours to be taught, descriptions of teaching strategies to be used, reinforcers, and the specification of objective goals against which to evaluate the success of the intervention programme.

Conclusions The BSP-QEII provides a potentially useful framework to guide and evaluate the development of BSPs in services for adults with intellectual disability. Further research is warranted to investigate why practitioners are potentially giving greater attention to some areas of intervention practice than others, even where research has demonstrated these others areas of practice could be important to achieving quality outcomes.

Enhancing fuzzy inference system based criterion-referenced assessment with an application

An important and difficult issue in designing a Fuzzy Inference System (FIS) is the specification of fuzzy sets, and fuzzy rules. The aim of this paper is to demonstrate how an additional qualitative information, i.e., monotonicity property, can be exploited and extended to be part of an FIS designing procedure (i.e., fuzzy sets and fuzzy rules design). In this paper, the FIS is employed as an alternative to the use of addition in aggregating the scores from test items/tasks in a Criterion-Referenced Assessment (CRA) model. In order to preserve the monotonicity property, the sufficient conditions of the FIS is proposed. Our proposed FIS based CRA procedure can be viewed as an enhancement for the FIS based CRA procedure, where monotonicity property is preserved. We demonstrate the applicability of the proposed approach with a case study related to a laboratory project assessment task at a university, and the results indicate the usefulness of the proposed approach in the CRA domain.

On monotonic sufficient conditions of fuzzy inference systems and their applications

An important and difficult issue in designing a Fuzzy Inference System (FIS) is the specification of fuzzy sets and fuzzy rules. In this paper, two useful qualitative properties of the FIS model, i.e., the monotonicity and sub-additivity properties, are studied. The monotonic sufficient conditions of the FIS model with Gaussian membership functions are further analyzed. The aim is to incorporate the sufficient conditions into the FIS modeling process, which serves as a simple (which can be easily understood by domain users), easy-to-use (which can be easily applied to or can be a part of the FIS model), and yet reliable (which has a sound mathematical foundation) method to preserve the monotonicity property of the FIS model. Another aim of this paper is to demonstrate how these additional qualitative information can be exploited and extended to be part of the FIS designing procedure (i.e., for fuzzy sets and fuzzy rules design) via the sufficient conditions (which act as a set of useful governing equations for designing the FIS model). The proposed approach is able to avoid the "trial and error" procedure in obtaining a monotonic FIS model. To assess the applicability of the proposed approach, two practical problems are examined. The first is an FIS-based model for water level control, while the second is an FIS-based Risk Priority Number (RPN) model in Failure Mode and Effect Analysis (FMEA). To further illustrate the importance of the sufficient conditions as the governing equations, an analysis on the consequences of violating the sufficient conditions of the FIS-based RPN model is presented.

Service-oriented wireless sensor networks and an energy-aware mesh routing algorithm

Service-oriented wireless sensor networks (WSNs) are being paid more and more attention because service computing can hide complexity of WSNs and enables simple and transparent access to individual sensor nodes. Existing WSNs mainly use IEEE 802.15.4 as their communication specification, however, this protocol suite cannot support IP-based routing and service-oriented access because it only specifies a set of physical- and MAC-layer protocols. For inosculating WSNs with IP networks, IEEE proposed a 6LoWPAN (IPv6 over LoW Power wireless Area Networks) as the adaptation layer between IP and MAC layers. However, it is still a challenging task how to discover and manage sensor resources, guarantee the security of WSNs and route messages over resource-restricted sensor nodes. This paper is set to address such three key issues. Firstly, we propose a service-oriented WSN architectural model based on 6LoWPAN and design a lightweight service middleware SOWAM (service-oriented WSN architecture middleware), where each sensor node provides a collection of services and is managed by our SOWAM. Secondly, we develop a security mechanism for the authentication and secure connection among users and sensor nodes. Finally, we propose an energyaware mesh routing protocol (EAMR) for message transmission in a WSN with multiple mobile sinks, aiming at prolonging the lifetime of WSNs as long as possible. In our EAMR, sensor nodes with the residual energy lower than a threshold do not forward messages for other nodes until the threshold is leveled down. As a result, the energy consumption is evened over sensor nodes significantly. The experimental results demonstrate the feasibility of our service-oriented approach and lightweight middleware SOWAM, as well as the effectiveness of our routing algorithm EAMR.

Pricing innovations in consumption growth: a re-evaluation of the recursive utility model

We re-evaluate the cross-sectional asset pricing implications of the recursive utility function of Epstein and Zin, 1989 and Epstein and Zin, 1991, using innovations in future consumption growth in our tests. Our empirical specification helps explain the size, value and momentum effects. Specifically, we find that (і) the beta associated with news about consumption growth has a systematic pattern: beta decreases along the size dimension and increases along the book-to-market and momentum dimensions, (іі) innovation in consumption growth is significantly priced in asset returns using both the Fama and MacBeth (1973) and the stochastic discount factor approaches, and (ііі) the model performs better than both the CAPM and Fama–French model.

Role of intensive training in the growth and maturation of artistic gymnasts

Short stature and later maturation of youth artistic gymnasts are often attributed to the effects of intensive training from a young age. Given limitations of available data, inadequate specification of training, failure to consider other factors affecting growth and maturation, and failure to address epidemiological criteria for causality, it has not been possible thus far to establish cause–effect relationships between training and the growth and maturation of young artistic gymnasts. In response to this ongoing debate, the Scientific Commission of the International Gymnastics Federation (FIG) convened a committee to review the current literature and address four questions: (1) Is there a negative effect of training on attained adult stature? (2) Is there a negative effect of training on growth of body segments? (3) Does training attenuate pubertal growth and maturation, specifically, the rate of growth and/or the timing and tempo of maturation? (4) Does training negatively influence the endocrine system, specifically hormones related to growth and pubertal maturation? The basic information for the review was derived from the active involvement of committee members in research on normal variation and clinical aspects of growth and maturation, and on the growth and maturation of artistic gymnasts and other youth athletes. The committee was thus thoroughly familiar with the literature on growth and maturation in general and of gymnasts and young athletes. Relevant data were more available for females than males. Youth who persisted in the sport were a highly select sample, who tended to be shorter for chronological age but who had appropriate weight-for-height. Data for secondary sex characteristics, skeletal age and age at peak height velocity indicated later maturation, but the maturity status of gymnasts overlapped the normal range of variability observed in the general population. Gymnasts as a group demonstrated a pattern of growth and maturation similar to that observed among short-, normal-, late-maturing individuals who were not athletes. Evidence for endocrine changes in gymnasts was inadequate for inferences relative to potential training effects. Allowing for noted limitations, the following conclusions were deemed acceptable: (1) Adult height or near adult height of female and male artistic gymnasts is not compromised by intensive gymnastics training. (2) Gymnastics training does not appear to attenuate growth of upper (sitting height) or lower (legs) body segment lengths. (3) Gymnastics training does not appear to attenuate pubertal growth and maturation, neither rate of growth nor the timing and tempo of the growth spurt. (4) Available data are inadequate to address the issue of intensive gymnastics training and alterations within the endocrine system.

Tissue specific roles for the Ribosome Biogenesis Factor Wdr43 in zebrafish development

During vertebrate craniofacial development, neural crest cells (NCCs) contribute to most of the craniofacial pharyngeal skeleton. Defects in NCC specification, migration and differentiation resulting in malformations in the craniofacial complex are associated with human craniofacial disorders including Treacher-Collins Syndrome, caused by mutations in TCOF1. It has been hypothesized that perturbed ribosome biogenesis and resulting p53 mediated neuroepithelial apoptosis results in NCC hypoplasia in mouse Tcof1 mutants. However, the underlying mechanisms linking ribosome biogenesis and NCC development remain poorly understood. Here we report a new zebrafish mutant, fantome (fan), which harbors a point mutation and predicted premature stop codon in zebrafish wdr43, the ortholog to yeast UTP5. Although wdr43 mRNA is widely expressed during early zebrafish development, and its deficiency triggers early neural, eye, heart and pharyngeal arch defects, later defects appear fairly restricted to NCC derived craniofacial cartilages. Here we show that the C-terminus of Wdr43, which is absent in fan mutant protein, is both necessary and sufficient to mediate its nucleolar localization and protein interactions in metazoans. We demonstrate that Wdr43 functions in ribosome biogenesis, and that defects observed in fan mutants are mediated by a p53 dependent pathway. Finally, we show that proper localization of a variety of nucleolar proteins, including TCOF1, is dependent on that of WDR43. Together, our findings provide new insight into roles for Wdr43 in development, ribosome biogenesis, and also ribosomopathy-induced craniofacial phenotypes including Treacher-Collins Syndrome.

Non-homogeneous volatility correlations in the bivariate multifractal model

In this paper, we consider an extension of the recently proposed bivariate Markov-switching multifractal model of Calvet, Fisher, and Thompson [2006. "Volatility Comovement: A Multifrequency Approach." Journal of Econometrics {131}: 179-215]. In particular, we allow correlations between volatility components to be non-homogeneous with two different parameters governing the volatility correlations at high and low frequencies. Specification tests confirm the added explanatory value of this specification. In order to explore its practical performance, we apply the model for computing value-at-risk statistics for different classes of financial assets and compare the results with the baseline, homogeneous bivariate multifractal model and the bivariate DCC-GARCH of Engle [2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models." Journal of Business & Economic Statistics 20 (3): 339-350]. As it turns out, the multifractal model with heterogeneous volatility correlations provides more reliable results than both the homogeneous benchmark and the DCC-GARCH model. © 2014 Taylor & Francis.

Beyond a definition: toward a framework for designing and specifying mentoring models

More than three decades of mentoring research has yet to converge on a unifying definition of mentoring; this is unsurprising given the diversity of relationships classified as mentoring. This article advances beyond a definition toward a common framework for specifying mentoring models. Sixteen design elements were identified from the literature and tested through specification of two different mentoring models from higher education contexts. This framework provides researchers and practitioners with a detailed yet concise method of communicating exactly what they mean when using the word mentoring; it may also act as a useful set of prompts for educators designing new mentoring interventions.

Does education reduce income inequality? A meta-regression analysis

This paper reexamines the effects of education on inequality through a comprehensive meta-regression analysis of the extant empirical literature. We find that education affects the two tails of the distribution of income: Education reduces the income share of top earners and increases the share of the bottom earners. Education has been particularly effective in reducing inequality in Africa. Some of the results suggest that secondary schooling appears to have a stronger effect than primary schooling, though this finding is not always robust. The heterogeneity in reported estimates can be largely explained by differences in the specification of the econometric model and measure of inequality and education. © 2013 John Wiley & Sons Ltd.

MicroRNA-23a has minimal effect on endurance exercise-induced adaptation of mouse skeletal muscle.

Skeletal muscles contain several subtypes of myofibers that differ in contractile and metabolic properties. Transcriptional control of fiber-type specification and adaptation has been intensively investigated over the past several decades. Recently, microRNA (miRNA)-mediated posttranscriptional gene regulation has attracted increasing attention. MiR-23a targets key molecules regulating contractile and metabolic properties of skeletal muscle, such as myosin heavy-chains and peroxisome proliferator-activated receptor gamma, coactivator 1 alpha (PGC-1α). In the present study, we analyzed the skeletal muscle phenotype of miR-23a transgenic (miR-23a Tg) mice to explore whether forced expression of miR-23a affects markers of mitochondrial content, muscle fiber composition, and muscle adaptations induced by 4 weeks of voluntary wheel running. When compared with wild-type mice, protein markers of mitochondrial content, including PGC-1α, and cytochrome c oxidase complex IV (COX IV), were significantly decreased in the slow soleus muscle, but not the fast plantaris muscle of miR-23a Tg mice. There was a decrease in type IId/x fibers only in the soleus muscle of the Tg mice. Following 4 weeks of voluntary wheel running, there was no difference in the endurance exercise capacity as well as in several muscle adaptive responses including an increase in muscle mass, capillary density, or the protein content of myosin heavy-chain IIa, PGC-1α, COX IV, and cytochrome c. These results show that miR-23a targets PGC-1α and regulates basal metabolic properties of slow but not fast twitch muscles. Elevated levels of miR-23a did not impact on whole body endurance capacity or exercise-induced muscle adaptations in the fast plantaris muscle.

Nonparametric discovery of learning patterns and autism subgroups from therapeutic data

Autism Spectrum Disorder (ASD) is growing at a staggering rate, but, little is known about the cause of this condition. Inferring learning patterns from therapeutic performance data, and subsequently clustering ASD children into subgroups, is important to understand this domain, and more importantly to inform evidence-based intervention. However, this data-driven task was difficult in the past due to insufficiency of data to perform reliable analysis. For the first time, using data from a recent application for early intervention in autism (TOBY Play pad), whose download count is now exceeding 4500, we present in this paper the automatic discovery of learning patterns across 32 skills in sensory, imitation and language. We use unsupervised learning methods for this task, but a notorious problem with existing methods is the correct specification of number of patterns in advance, which in our case is even more difficult due to complexity of the data. To this end, we appeal to recent Bayesian nonparametric methods, in particular the use of Bayesian Nonparametric Factor Analysis. This model uses Indian Buffet Process (IBP) as prior on a binary matrix of infinite columns to allocate groups of intervention skills to children. The optimal number of learning patterns as well as subgroup assignments are inferred automatically from data. Our experimental results follow an exploratory approach, present different newly discovered learning patterns. To provide quantitative results, we also report the clustering evaluation against K-means and Nonnegative matrix factorization (NMF). In addition to the novelty of this new problem, we were able to demonstrate the suitability of Bayesian nonparametric models over parametric rivals.