Reinforcement machine learning for predictive analytics in smart cities

The digitization of our lives cause a shift in the data production as well as in the required data management. Numerous nodes are capable of producing huge volumes of data in our everyday activities. Sensors, personal smart devices as well as the Internet of Things (IoT) paradigm lead to a vast infrastructure that covers all the aspects of activities in modern societies. In the most of the cases, the critical issue for public authorities (usually, local, like municipalities) is the efficient management of data towards the support of novel services. The reason is that analytics provided on top of the collected data could help in the delivery of new applications that will facilitate citizens’ lives. However, the provision of analytics demands intelligent techniques for the underlying data management. The most known technique is the separation of huge volumes of data into a number of parts and their parallel management to limit the required time for the delivery of analytics. Afterwards, analytics requests in the form of queries could be realized and derive the necessary knowledge for supporting intelligent applications. In this paper, we define the concept of a Query Controller ( QC ) that receives queries for analytics and assigns each of them to a processor placed in front of each data partition. We discuss an intelligent process for query assignments that adopts Machine Learning (ML). We adopt two learning schemes, i.e., Reinforcement Learning (RL) and clustering. We report on the comparison of the two schemes and elaborate on their combination. Our aim is to provide an efficient framework to support the decision making of the QC that should swiftly select the appropriate processor for each query. We provide mathematical formulations for the discussed problem and present simulation results. Through a comprehensive experimental evaluation, we reveal the advantages of the proposed models and describe the outcomes results while comparing them with a deterministic framework

Sub-Saharan Africa at a crossroads: a quantitative analysis of regional development

This repository item contains a single issue of The Pardee Papers, a series papers that began publishing in 2008 by the Boston University Frederick S. Pardee Center for the Study of the Longer-Range Future. The Pardee Papers series features working papers by Pardee Center Fellows and other invited authors. Papers in this series explore current and future challenges by anticipating the pathways to human progress, human development, and human well-being. This series includes papers on a wide range of topics, with a special emphasis on interdisciplinary perspectives and a development orientation.Sub-Saharan Africa is at a crossroads of development. Despite a quarter of a century of economic reforms propagated by national policies and international financial agencies and institutions, sub-Saharan Africa is still lagging in development. In this paper, the authors adopt two techniques using both qualitative (e.g. governance) and quantitative factors (e.g., GDP) to examine regional patterns of development in sub-Saharan Africa. More specifically, they examine and analyze similarities and differences among the countries in this region using a multivariate statistical technique, Principal Component Analysis (PCA), and a unsupervised neural network called Kohonen’s Self-Organizing Map (SOM) to cluster levels of development. PCA serves as a tool for determining regional patterns while SOM is more useful for determining continental patterns in development. Both PCA and SOM results show a “developed” cluster in Southern Africa (South Africa, Namibia, Botswana, and Gabon). SOM exhibits a cluster of least developed countries in southern Western Africa and western Central Africa. The results demonstrate that the applied techniques are highly effective to compress multidimensional qualitative and quantitative data sets to extract relevant information about development from a policy perspective. Our analysis indicates the significance of governance variables in some clusters while a combination of variables explains other regional clusters. Zachary Tyler works for a consulting firm in Massachusetts that conducts program evaluations for energy efficiency programs, and he continues to work on statistical and geospatial analyses of human development issues. In 2010, he will receive a master’s degree in energy and environmental analysis from Boston University. Sucharita Gopal is Professor and Director of Graduate Studies in the Department of Geography and Environment and a member of the Cognitive & Neural Systems (CNS) Technology Lab at Boston University. She teaches and conducts research in geographical information systems (GIS), spatial analysis and modeling, and remote sensing for environmental and public health applications. Her recent research includes the development of a marin integrated decision analysis system (MIDAS) for Belize, Panama, and Massachusetts, and a post-disaster geospatial risk model for Haiti. This paper is part of the Africa 2060 Project, a Pardee Center program of research, publications, and symposia exploring African futures in various aspects related to development on continental and regional scales. For more information, visit www-staging.bu.edu/pardee/research/

Global Management Effectiveness Study: Integrated Social and Ecological Report for Non-node and Node Sites

The purpose of this study is to provide a critical assessment of the implementation, impact, and performance of Marine Managed Area (MMA) projects to serve as a basis for improved planning and implementation of new MMA projects worldwide. The specific objectives of the study are (1) to determine the socioeconomic, governance and ecological effects of MMAs; (2) to determine the critical factors influencing MMA effects, as well as the impact of the timing of those factors on the effects of the MMA; and (3) to provide tools for predicting MMA effects based on ecological, socioeconomic and governance variable

Considering the shareholder perspective: value-based management systems and stock market performance

We empirically study the use of value-based management systems in listed German firms and examine implications for firms' stock market performance. Using a novel, hand-collected data set covering 1,083 firm years from 2002 to 2008, we find that value-based management systems become increasingly common. Specifically, in 2008 42% of our sample firms have implemented such a system. In the empirical analysis, we find that firms that implement value-based management systems earn statistically significant and economically substantial abnormal stock market returns measured within a two-year adoption phase. These excess returns are not jeopardized by poor post-adoption returns. In the analysis, we carefully control for risk and account for endogeneity concerns. Overall, our findings support the view that shareholders consider the adoption of a value-based management system as a credible signal that management will focus on shareholder interests and that such systems actually increase shareholder value. --value-based management,corporate governance,econometric analysis,Germany

Location Advantages, Governance Quality, Stock Market Development and Firm Characteristics as Antecedents of African M&As

This study explores firm- and country-specific antecedents of African M&As. We use one of the largest datasets to-date consisting of 1,490 unique African firms (11,183 firm-year observations) from 1996 to 2012. Our results suggest that improvements in time-varying country-level factors, including location advantages (market size, human capital and efficiency opportunities), national governance quality, and stock market development are associated with an increase in the volume of M&A activity. Consistent with the resource-curse paradox, high resource endowments are not associated with increased levels of M&A. In support of the management inefficiency but contrary to the traditional firm size hypotheses, African targets are generally characterised by declining stock returns and accounting profitability but are more likely to be larger firms; suggesting the presence of information asymmetry concerns in their selection. Notwithstanding, we find evidence of heterogeneity across countries with inconsistent support for established target prediction hypotheses. A model which combines firm- and country- specific factors better explains observed variations in African M&A activity

Democracy and Economic Development: a Fuzzy Classification Approach

The aim of this work is to (1) analyse whether countries differ on political indicators (democracy, rule of law, government effectiveness and corruption) and (2) study whether countries with different political profiles are associated with different levels of economic, human development and gender-related development indicators. Using a fuzzy classification approach (fuzzy k-means algorithm), we propose a typology of 124 countries based on 10 political variables. Six segments are identified; these political groups implicate the access to different levels of economic and human development. In this study evidence of a positive but not perfect relationship between democracy and economic and human development is observed, thus presenting new insights for the understanding of the heterogeneity of behaviors relatively to political indicators.Democracy, Economic Development, Fuzzy k-means

An exploratory trial implementing a community-based child oral health promotion intervention for Australian families from refugee and migrant backgrounds: a protocol paper for Teeth Tales

Introduction: Inequalities are evident in early childhood caries rates with the socially disadvantaged experiencing greater burden of disease. This study builds on formative qualitative research, conducted in the Moreland/Hume local government areas of Melbourne, Victoria 2006–2009, in response to community concerns for oral health of children from refugee and migrant backgrounds. Development of the community-based intervention described here extends the partnership approach to cogeneration of contemporary evidence with continued and meaningful involvement of investigators, community, cultural and government partners. This trial aims to establish a model for child oral health promotion for culturally diverse communities in Australia.<p></p> Methods and analysis: This is an exploratory trial implementing a community-based child oral health promotion intervention for Australian families from refugee and migrant backgrounds. Families from an Iraqi, Lebanese or Pakistani background with children aged 1–4 years, residing in metropolitan Melbourne, were invited to participate in the trial by peer educators from their respective communities using snowball and purposive sampling techniques. Target sample size was 600. Moreland, a culturally diverse, inner-urban metropolitan area of Melbourne, was chosen as the intervention site. The intervention comprised peer educator led community oral health education sessions and reorienting of dental health and family services through cultural Competency Organisational Review (CORe).<p></p> Ethics and dissemination: Ethics approval for this trial was granted by the University of Melbourne Human Research Ethics Committee and the Department of Education and Early Childhood Development Research Committee. Study progress and output will be disseminated via periodic newsletters, peer-reviewed research papers, reports, community seminars and at National and International conferences.<p></p&gt

International market selection of Parfois

Parfois has been internationalizing into many countries and would like to continue growing. This study focuses on the IMS of Parfois, comparing countries from the all around the world, in a quantitative analysis at first, followed by a qualitative analysis. The purpose of the project is to help Parfois select the optimal market for expansion. Through semi structured interviews, literature research, and country clustering and ranking methods South Korea was selected as the best market for expansion, followed by Israel