Epidemiology and outcomes of pediatric autosomal recessive polycystic kidney disease in the Middle East and North Africa

The incidence of rare diseases is expected to be comparatively higher in the Middle East and North Africa (MENA) region than in other parts of the world, attributed to the high prevalence of consanguinity. Most MENA countries share social and economic statuses, cultural relativism, religious beliefs, and healthcare policies. Polycystic kidney diseases (PKDs) are the most common genetic causes of kidney failure, accounting for nearly 8.0% of dialysis cases. The development of PKDs is linked to variants in several genes, including PKD1, PKD2, PKHD1, DZIP1L, and CYS1. Autosomal recessive PKD (ARPKD) is the less common yet aggressive form of PKD. ARPKD has an estimated incidence between 1:10,000 and 1:40,000. Most patients with ARPKD require kidney replacement therapy earlier than patients with autosomal dominant polycystic kidney disease (ADPKD), often in their early years of life. This review gathered data from published research studies and reviews of ARPKD, highlighting the epidemiology, phenotypic presentation, investigations, genetic analysis, outcomes, and management. Although limited data are available, the published literature suggests that the incidence of ARPKD may be higher in the MENA region due to consanguineous marriages. Patients with ARPKD from the MENA region usually present at a later disease stage and have a relatively short time to progress to kidney failure. Limited data are available regarding the management practice in the region, which warrants further investigations

STOCK MARKET DEVELOPMENT AND ECONOMIC GROWTH: THE CAUSAL LINKAGE

This paper addresses the question: does stock market development cause growth? It examines the causal linkage between stock market development, financial development and economic growth. The argument is that any inference that financial liberalisation causes savings or investment or growth, or that financial intermediation causes growth, drawn from bivariate causality tests may be invalid, as invalid causality inferences can result from omitting an important variable. The empirical part of this study exploits techniques recently developed by Toda and Yamamoto (1995) to test for causality in VARs, and emphasises the possibility of omitted variable bias. The evidence obtained from a sample of seven countries suggests that a well-developed stock market can foster economic growth in the long run. It also provides support to theories according to which well-functioning stock markets can promote economic development by fuelling the engine of growth through faster capital accumulation, and by tuning it through better resource allocation.Financial Development, Economic Growth, Stock Market, Causality Testing, VARs, Incomplete Systems

The Long-term Relationship Between Enterprise Risk Management and bank Performance : the missing link in Nigeria

This study investigates the relationship between Enterprise Risk Management adoption and implementation, and the performance of banks using a sample of four out of the seven Strategically Important Banks (SIB) listed on the Nigerian Stock Exchange covering the period from 2005 q1 to 2015 q2. In this study, we determined a measure for Enterprise Risk Management (ERM) adoption or implementation (ERM index) using an integrated Enterprise Risk Management measurement model for the banking sector suggested by Soliman and Mukhtar (2017). A time series Johansen’s cointegration test was used to obtain evidence of the long-term association between ERM and performance, while Vector Error Correction Model (VECM) analysis was performed to gather evidence of causality relationship between ERM and performance. Finally, Generalized Impulse Response Function was used to obtain evidence of how performance responds to the introduction of a shock on Enterprise Risk Management. This study makes significant contributions to the existing body of knowledge, as it yields the first Enterprise Risk Management-performance-based empirical results that indicate a long-term relationship, causation effects, in addition to responding to performance ERM

The role of subsidiaries in Global Value Chains (GVCs): an institutional voids perspective on LVC upgrading and integration

We explore the process through which MNE subsidiaries engage and retain a critical mass of small suppliers in Global Value Chains (GVCs) while addressing institutional voids in emerging markets. Using evidence from an interpretive inductive longitudinal case study in agribusiness, we draw on the GVC and institutional voids literatures to: (1) extend the GVC literature by offering a subsidiary-focused view of GVCs; and (2) demonstrate the dynamic process of void engagement through complementary institutional bridging activities. Our temporal sequencing of subsidiary institutional agency in response to different modalities of voids demonstrates a constellation of bridging activities that results from a dynamic interplay between voids and practice

On Using Active Learning and Self-Training when Mining Performance Discussions on Stack Overflow

Abundant data is the key to successful machine learning. However, supervised learning requires annotated data that are often hard to obtain. In a classification task with limited resources, Active Learning (AL) promises to guide annotators to examples that bring the most value for a classifier. AL can be successfully combined with self-training, i.e., extending a training set with the unlabelled examples for which a classifier is the most certain. We report our experiences on using AL in a systematic manner to train an SVM classifier for Stack Overflow posts discussing performance of software components. We show that the training examples deemed as the most valuable to the classifier are also the most difficult for humans to annotate. Despite carefully evolved annotation criteria, we report low inter-rater agreement, but we also propose mitigation strategies. Finally, based on one annotator's work, we show that self-training can improve the classification accuracy. We conclude the paper by discussing implication for future text miners aspiring to use AL and self-training.Comment: Preprint of paper accepted for the Proc. of the 21st International Conference on Evaluation and Assessment in Software Engineering, 201

Effect of Building Configuration on Seismic Response Parameters

To contribute to the available information on the inelastic performance of irregular structures, the investigation of four building characteristics on it seismic response was initiated. These characteristics are column height, beam-to-column capacity, stiffness distribution in elevation and set-backs and non-symmetric elevation configuration. The parametric study presented in the paper is intended to be more indicative than comprehensive, since simplifications in the modeling of structures were necessary

A Phenomenological Analysis of Managerial Capability in SMEs in Nigeria

As concluded by researchers change process of transformation involves implementation of better strategies to have sustainable competitive advantage. Non financing strategies should be adopted rather than solely relying on financial indices as observed in most Nigerian SMEs. This research argues that managerial capabilities which include HR competencies and skills are vital for business survival as such employers should develop and execute strategies to prioritise capabilities. The aim of this study is to evaluate phenomenologically an interpretation of managerial capability as a non-financial dimension of Organisational Health in relation to the growth of SMEs in the Nigerian context. It is pertinent to know that managerial capability which is one of the soft factor dimensions has already identified skills and competencies in related literature. This interpretive phenomenological study adopted an inductive approach using the philosophical paradigm of constructivism and semi-structured interviews for data collection. Nvivo software was used for the data analysis. The study’s key findings show that managerial capability has the highest percentage and seems to be the ultimate foundation of Organisational Health. It further shows that connectivity and relationship exist between dimensions of Organisational Health especially capability and leadership on the one hand and accountability and reporting structure on the other hand which all have influence on the growth of SMEs. However, capability and accountability are found to have stronger influence on SMEs' growth compared to leadership and reporting structure. This study reveals that the successes of SMEs are closely tied to socio-psychological processes and not only to financing support. DOI: 10.5901/mjss.2016.v7n3p12

Operational Aspect of the Policy Coordination for Financial Stability: Role of Jeffreys-Lindley’s Paradox in Operations Research

This study analyses the implications of Jeffery-Lindley’s paradox and Global Financial Crisis (GFC) for the operational aspect of macroeconomic policy coordination for financial stability. Using a Bayesian Vector Auto-regressive (BVAR) model and data from Jan 1985 to June 2016, our key findings suggest that the claim of macroeconomic policy interaction, interdependence and significance of coordinated policy operations for the financial stability holds its ground. The argument in the support for policy coordination for financial stability was found to be robust against the Jeffreys-Lindley’s paradox and in the Post-GFC era. A profound practical, operational and philosophical implication of this study is the positive aspects of Jeffreys-Lindley’s paradox and the possibility of employing the Frequentist and Bayesian estimation techniques as complementing rather competing frameworks

Potato Classification Using Deep Learning

Abstract: Potatoes are edible tubers, available worldwide and all year long. They are relatively cheap to grow, rich in nutrients, and they can make a delicious treat. The humble potato has fallen in popularity in recent years, due to the interest in low-carb foods. However, the fiber, vitamins, minerals, and phytochemicals it provides can help ward off disease and benefit human health. They are an important staple food in many countries around the world. There are an estimated 200 varieties of potatoes, which can be classified into a number of categories based on the cooked texture and ingredient functionality. Using a public dataset of 2400 images of potatoes, we trained a deep convolutional neural network to identify 4 types (Red, Red Washed, Sweet, and White).The trained model achieved an accuracy of 99.5% of test set, demonstrating the feasibility of this approach