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.

Application of air quality combination forecasting to Bogota

The bulk of existing work on the statistical forecasting of air quality is based on either neural networks or linear regressions, which are both subject to important drawbacks. In particular, while neural networks are complicated and prone to in-sample overfitting, linear regressions are highly dependent on the specification of the regression function. The present paper shows how combining linear regression forecasts can be used to circumvent all of these problems. The usefulness of the proposed combination approach is verified using both Monte Carlo simulation and an extensive application to air quality in Bogota, one of the largest and most polluted cities in Latin America. © 2014 Elsevier Ltd.

Foreign direct investment and economic growth: A real relationship or wishful thinking?

Several theories have been advanced on the beneficial effect of foreign direct investment (FDI) on economic growth. However, mixed empirical findings have resulted in a long-standing debate. This study explores the global FDI-growth relationship through an 'informed' econometric analysis predicated on substantial guidance obtained from a detailed investigation of 880 estimates reported in 108 published studies. With model uncertainties alleviated and the core specification benchmarked against the aforementioned assessment, our econometric analysis, utilising a global sample of 140 countries in the period 1970 to 2009, conclusively documents that FDI positively affects economic growth. Moreover, we find that this association holds globally as strongly as in the developing world. Further, it is regional variation rather than within-country variation, and contemporaneous FDI rather than past FDI, which matters for growth. Finally, appropriate absorptive capacity indicators for positive growth are identified to be trade openness and financial development rather than schooling.

Specifying model transformations by direct manipulation using concrete visual notations and interactive recommendations

Model transformations are a crucial part of Model-Driven Engineering (MDE) technologies but are usually hard to specify and maintain for many engineers. Most current approaches use meta-model-driven transformation specification via textual scripting languages. These are often hard to specify, understand and maintain. We present a novel approach that instead allows domain experts to discover and specify transformation correspondences using concrete visualizations of example source and target models. From these example model correspondences, complex model transformation implementations are automatically generated. We also introduce a recommender system that helps domain experts and novice users find possible correspondences between large source and target model visualization elements. Correspondences are then specified by directly interacting with suggested recommendations or drag and drop of visual notational elements of source and target visualizations. We have implemented this approach in our prototype tool-set, CONVErT, and applied it to a variety of model transformation examples. Our evaluation of this approach includes a detailed user study of our tool and a quantitative analysis of the recommender system.

KBRE: a framework for knowledge-based requirements engineering

Detecting inconsistencies is a critical part of requirements engineering (RE) and has been a topic of interest for several decades. Domain knowledge and semantics of requirements not only play important roles in elaborating requirements but are also a crucial way to detect conflicts among them. In this paper, we present a novel knowledge-based RE framework (KBRE) in which domain knowledge and semantics of requirements are central to elaboration, structuring, and management of captured requirements. Moreover, we also show how they facilitate the identification of requirements inconsistencies and other-related problems. In our KBRE model, description logic (DL) is used as the fundamental logical system for requirements analysis and reasoning. In addition, the application of DL in the form of Manchester OWL Syntax brings simplicity to the formalization of requirements while preserving sufficient expressive power. A tool has been developed and applied to an industrial use case to validate our approach.

SecDSVL: a domain-specific visual language to support enterprise security modelling

Enterprise security management requires capturing different security and IT systems' details, analyzing and enforcing these security details, and improving employed security to meet new risks. Adopting structured models greatly helps in simplifying and organizing security specification and enforcement processes. However, existing security models are generally limited to specific security details and do not deliver a comprehensive security model. They also often do not have user-friendly notations, being complicated extensions of existing modeling languages (such as UML). In this paper, we introduce a comprehensive Security Domain Specific Visual Language (SecDSVL), which enables capturing of key security details to support enterprise systems security management process. We discuss our SecDSVL, tool support and the model-based enterprise security management approach it supports, give a usage example, and present evaluation experiments of SecDSVL.