Abstract
This paper provides a two-stage stochastic programming approach for the development of optimal offering strategies for wind power producers. Uncertainty is related to electricity market prices and wind power production. A hybrid intelligent approach, combining wavelet transform, particle swarm optimization and adaptive-network-based fuzzy inference system, is used in this paper to generate plausible scenarios. Also, risk aversion is explicitly modeled using the conditional value-at-risk methodology. Results from a realistic case study, based on a wind farm in Portugal, are provided and analyzed. Finally, conclusions are duly drawn.

Abstract
This paper, presents on the coordination of power system expansion planning including sustainable generation expansion planning (GEP) and transmission network expansion planning (TEP). For this purpose, a game theory based model is proposed to determine the dominant strategy of each investor entities in the generation and transmission sectors. In this model, all power producers compete with each other at the same level for maintaining the energy and reserve services. At this stage, after determining the Cournot equilibrium, the suggestions accepted in previous stage are evaluated in the transmission planning stage. Here, according to the existing units and the units proposed in the previous stage, transmission network expansion planning takes place based on cost, social welfare and reliability indices by independent system operator. Thereafter transmission expansion planning results announced, the accepted unit and not accepted units can offer their new strategies to the market. This iterative process continues until the dominant strategy of each entity is satisfied and the final equilibrium is obtained. To solve this game and finding its Nash equilibrium, the mixed integer non-linear programming (MINLP) optimization is used by all decision makers for optimizing their desirable strategies to invest in the power market. A conceptual test system is proposed to show the ability of the model. Simulation results verify the feasibility and capability of the proposed modeling of the long-term expansion planning. © Indian Society for Education and Environment (iSee).

Abstract
This paper presents an overview of the current state-of-the-art in mobile data stream mining. This area of mobile data stream mining is significant for a number of new application domains such as mobile crowd sensing and mobile activity recognition. The paper presents the strategies and techniques for adaptation that are essential in order to perform real-time, continuous data mining on mobile devices. We present an overview of the algorithms research in this area. Finally, we discuss the key toolkits, systems and applications of mobile data stream mining. © 2012 IEEE.

Abstract
In this paper we propose a framework for defining and discovering optimal association rules involving a numerical attribute A in the consequent. The consequent has the form of interval conditions (A < x, A >= x or A is an element of I where I is an interval or a set of intervals of the form [x(l), x(u))). The optimality is with respect to leverage, one well known association rule interest measure. The generated rules are called Maximal Leverage Rules (MLR) and are generated from Distribution Rules. The principle for finding the MLR is related to the Kolmogorov-Smirnov goodness of fit statistical test. We propose different methods for MLR generation, taking into account leverage optimallity and readability. We theoretically demonstrate the optimality of the main exact methods, and measure the leverage loss of approximate methods. We show empirically that the discovery process is scalable.

Abstract
Research on indicators related to the state of child well-being is a growing field that has experienced several changes over time. The growing supply of data on children, as well as the need to facilitate conclusions and to track trends, has led researchers to develop a number of child well-being indexes. This paper critically reviews the most recent and relevant child well-being indexes, i.e., the Index of Child and Youth Well-Being in the United States, the Child Well-being Index for the European Union, the Microdata Child Well-being Index, and the Deprivation Index. The study focuses primarily on the contributions and innovations the indexes have brought to the field, making a critical assessment of the methods used in the construction of the indexes and identifying their main limitations.

Abstract
This paper reflects on review of smart sensor activities for biomedical applications. The rise of biotechnology has provided innovative development of new therapies and detection methods for life threatening diseases. As a worldwide research focus, there is especially a strong interest in the use of microsystems in health care, particularly as smart implantable devices. Recent years have seen an increasing activity of hip and knee replacement and other type of implants, which are some of the most frequently performed surgical procedures in the world. Loosening of hip prosthesis is the dominant issue for many patients who undergo a hip arthroplasty. Artificial joints are subject to chronic infections associated with bacterial biofilms, which only can be eradicated by the traumatic removal of the implant followed by sustained intravenous antibiotic therapy. This review focuses on the clinical experience using all kinds of smart implants like orthopedic implants instrumented with strain gauges, retina implant system using image sensors. Technical design improvements will enhance function, quality of life, and longevity of total knee arthroplasty and all other kind of implants. Application of biocompatible nanomaterials in implantable biosensors for continuous monitoring of metabolites is an area of sustained scientific and technological interests. Crown Copyright