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The past few years have witnessed an increase in the development of wearable sensors for health monitoring systems. This increase has been due to several factors such as development in sensor technology as well as directed efforts on political and stakeholder levels to promote projects which address the need for providing new methods for care given increasing challenges with an aging population. An important aspect of study in such system is how the data is treated and processed. This paper provides a recent review of the latest methods and algorithms used to analyze data from wearable sensors used for physiological monitoring of vital signs in healthcare services. In particular, the paper outlines the more common data mining tasks that have been applied such as anomaly detection, prediction and decision making when considering in particular continuous time series measurements. Moreover, the paper further details the suitability of particular data mining and machine learning methods used to process the physiological data and provides an overview of the properties of the data sets used in experimental validation. Finally, based on this literature review, a number of key challenges have been outlined for data mining methods in health monitoring systems ; Funding Agencies: SAAPHO (Secure Active Aging: Participation and Health for the Old) Vinnova Sweden's Innovation Funding Agency

This paper contributes to the literature on the impact of immigrants on native female labour supply. By segmenting the market by educational levels, we are able to investigate which native-born women are more affected by an increase of low-skilled immigrants working in the household service sector. We present a model of individual choice with home production and, using an harmonized dataset (CNEF), we test its main predictions. Our sample includes countries implementing different family policies. Our results suggest that the share of immigrants working in services in a given local labour market is positively associated with the probability of native-born women to increase their labour supply at the intensive margin (number of hours worked per week), if skilled, and at the extensive margin (participation decision), if unskilled. Moreover, they show that these effects are larger in countries with less family-supportive policies. ; Wir untersuchen die Auswirkungen von Zuwanderung auf das Arbeitsangebot einheimischer Frauen. Insbesondere betrachten wir die Auswirkung des Anstiegs geringqualifizierter Zuwanderer, die im Haushaltssektor arbeiten, auf hoch- und geringqualifizierte einheimische Frauen. Wir modellieren individuelle Entscheidungen über die Haushaltsproduktion und testen die wichtigsten Aussagen dieses Modells anhand eines harmonisierten Datensatzes (CNEF). Unsere Stichprobe enthält Länder mit unterschiedlich großzügiger Familienpolitik. Unsere Ergebnisse zeigen, dass mit einem höheren Anteil an Migranten im Dienstleistungssektor eines lokal begrenzten Arbeitsmarktes die einheimischen hochqualifizierten Frauen ihr Arbeitsangebot erhöhen. Gleichzeitig steigt die Partizipationsrate unqualifizierter einheimischer Frauen. Darüber hinaus zeigen wir, dass diese Effekte in Ländern mit restriktiverer Familienpolitik stärker ausfallen.

Some recent evidence on government finance statistics of European countries suggests that countries with public debt issues also show a low tax revenue-GDP ratio. In this paper we develop a small open economy model of endogenous growth in which the engine of growth is public spending. We assume that government can finance public expenditures by borrowing on imperfect international financial markets where her borrowing capacity is limited. In contrast to the existing literature, where debt is constrained by the stock of capital, the collaterals are based on GDP. The balanced growth path and the transitional dynamics are studied. First, we show that the economy may converge in a finite time to the regime with binding collateral constraint. Second, in such regime the steady state public expenditures-GDP ratio is greater than that of the models without collateral constraints and of the stock-based collaterals literature. Third, the model predictions are consistent with recent empirical literature: there exists a certain threshold of financial and institutional development and economic features that an economy needs to attain in order to benefit from financial liberalization. Finally, if the degree of financial markets imperfections is weak, technologically developed countries experience a higher long-run growth rate than that of the stock-based collaterals literature, otherwise the world interest rate need to be high enough.

Статья посвящена проблеме выявления и исследования основных тенденций в численном и национальном составе населения Республики Казахстан (РК) за годы независимости (1991 г. – настоящее время). На основе итогов общенациональных переписей 1999 и 2009 годов, промежуточных данных Агентства РК по статистике проводится сравнительный анализ изменений в численности населения и его этнического среза, акцентируется органическая взаимосвязь данных процессов с историческими особенностями развития страны. Характеризуются кратко основные пути руководства страны по достижению прогнозируемого численного роста и стабильного национального состава. Обосновывается значимость данной проблемы в контексте реализации важнейших социально-политических и экономических программ Республики Казахстан. ; The article is dedicated to the issue of revelation and research of main tendencies in national and Republic of Kazakhstan population staff during the years of independence (1991 – present). Based on the national census of 1999 up to 2009 results as well as on intermediate data of Kazakhstan statistics agencies, the comparative analysis of changes in the population numbers and its ethnic staff is carried out, the organic link of this process to the historic features of the country development is described. The main ways of the state management of the achieved numbers increase in accordance with the forecast and national complement stability prediction are characterized briefly. Basis of this issue significance is given in the presence of social, political and economical programs of Kazakhstan.

According to the World Health Organization, about 2 billion people drink alcohol. Excessive alcohol consumption can result in alcohol addiction, which is one of the most prevalent neuropsychiatric diseases afflicting our society today. Prevention and intervention of alcohol binging in adolescents and treatment of alcoholism are major unmet challenges affecting our health-care system and society alike. Our newly formed German SysMedAlcoholism consortium is using a new systems medicine approach and intends (1) to define individual neurobehavioral risk profiles in adolescents that are predictive of alcohol use disorders later in life and (2) to identify new pharmacological targets and molecules for the treatment of alcoholism. To achieve these goals, we will use omics-information from epigenomics, genetics transcriptomics, neurodynamics, global neurochemical connectomes and neuroimaging (IMAGEN; Schumann etal. ) to feed mathematical prediction modules provided by two Bernstein Centers for Computational Neurosciences (Berlin and Heidelberg/Mannheim), the results of which will subsequently be functionally validated in independent clinical samples and appropriate animal models. This approach will lead to new early intervention strategies and identify innovative molecules for relapse prevention that will be tested in experimental human studies. This research program will ultimately help in consolidating addiction research clusters in Germany that can effectively conduct large clinical trials, implement early intervention strategies and impact political and healthcare decision makers.

International Journal of Advanced Research in Artificial Intelligence(IJARAI), 2(12), 2013 ; Software has played an essential role in engineering, economic development, stock market growth and military applications. Mature software industry count on highly predictive software effort estimation models. Correct estimation of software effort lead to correct estimation of budget and development time. It also allows companies to develop appropriate time plan for marketing campaign. Now a day it became a great challenge to get these estimates due to the increasing number of attributes which affect the software development life cycle. Software cost estimation models should be able to provide sufficient confidence on its prediction capabilities. Recently, Computational Intelligence (CI) paradigms were explored to handle the software effort estimation problem with promising results. In this paper we evolve two new models for software effort estimation using Multigene Symbolic Regression Genetic Programming (GP). One model utilizes the Source Line Of Code (SLOC) as input variable to estimate the Effort (E); while the second model utilize the Inputs, Outputs, Files, and User Inquiries to estimate the Function Point (FP). The proposed GP models show better estimation capabilities compared to other reported models in the literature. The validation results are accepted based Albrecht data set. ; http://thesai.org/Downloads/IJARAI/Volume2No12/Paper_7-Evolving_Software_Effort_Estimation_Models_Using.pdf

Human impacts on marine fisheries go back many centuries or even thousands of years in some coastal areas. Full global exploitation of the most productive fish stocks probably occurred around 1990. Many stocks have been overexploited and the assessment and management required to rein this in and to combat other human pressures, such as pollution, has been slow to mature, but is showing positive trends. The need to protect marine ecosystems for their intrinsic value and for the services they provide has also been recognised and is being embodied in legislation and turned into operational tools. As with terrestrial systems, it will not be easy to find acceptable balances between food production and conservation objectives. Climate change imposes a new set of pressures on marine ecosystems; increasing temperature, reduced salinity in some enclosed seas and coastal areas, changing windfields and seasonality, acidification, deoxygenation and rising sea level will all affect the productivity and distribution of marine life. We can detect some of the consequences already but prediction is very difficult for a variety of reasons. In spite of these difficulties it is possible to map out robust guidance on the kind of research that will help us to adapt and on the development of practices and management that will insure against future change

In India E-retailing is still a relatively unknown and redundant entity, but now in current days with the governments open attitude towards progression, lot of infrastructure is dedicated to this area. Over the last few years, the retail sector has become an increasingly competitive and dynamic business environment worldwide In India where the internet users are growing at an alarming rate it will be helpful for the consumer as well as for the companies, to discuss on e-retailing models, payment methods, security features, future trends and benefits etc. This study makes the "An Analytical Study of Effectiveness of E- Tailing with Special Reference to Baramati Region". Predictions made on this study can be used as a basis to foresee the future level of business. People are hesitating to buy online due to confusions on security and payment methods. The respondents are mostly students, officers, and house wives who had access the internet. The growth of e-retailing in India would be directly proportional to the penetration of the internet. There are also frauds taking place in credit cards which can happen while it on the internet. Here researchers hypothesis suggest that the online shopping in Baramati region is not much effective but it has great future in India. Researcher is also discussing the e-tailings benefits and some recommendations to attract online customers.

Detection of change is the measure of the distinct data framework and thematic change information that can direct to more tangible insights into underlying process involving land cover and landuse changes. Monitoring the locations and distributions of land cover changes is important for establishing links between policy decisions, regulatory actions and subsequent landuse activities. Change detection is the process that helps in determining the changes associated with landuse and land cover properties with reference to geo-registered multi-temporal remote sensing information. It assists in identifying change between two or more dates that is uncharacterized of normal variation. After image to image registrations, the normalized difference vegetation index (NDVI), the transformed normalized difference vegetation index (TNDVI), the enhanced vegetation index (EVI) and the soil-adjusted vegetation index (SAVI) values were derived from Landsat ETM+ dataset and an image differencing algorithm was applied to detect changes. This paper presents an application of the use of multi-temporal Landsat ETM+ images and multi-spectral MODIS (Terra) EVI/NDVI time-series vegetation phenology metrics for the District Sargodha. The results can be utilized as a temporal land use change model for Punjab province of Pakistan to quantify the extent and nature of change and assist in future prediction studies. This will support environmental planning to develop sustainable landuse practices.

AbstractSpatial cognition and memory are critical cognitive skills underlying foraging behaviors for all primates. While the emergence of these skills has been the focus of much research on human children, little is known about ontogenetic patterns shaping spatial cognition in other species. Comparative developmental studies of nonhuman apes can illuminate which aspects of human spatial development are shared with other primates, versus which aspects are unique to our lineage. Here we present three studies examining spatial memory development in our closest living relatives, chimpanzees (Pan troglodytes) and bonobos (P. paniscus). We first compared memory in a naturalistic foraging task where apes had to recall the location of resources hidden in a large outdoor enclosure with a variety of landmarks (Studies 1 and 2). We then compared older apes using a matched memory choice paradigm (Study 3). We found that chimpanzees exhibited more accurate spatial memory than bonobos across contexts, supporting predictions from these species' different feeding ecologies. Furthermore, chimpanzees – but not bonobos – showed developmental improvements in spatial memory, indicating that bonobos exhibit cognitive paedomorphism (delays in developmental timing) in their spatial abilities relative to chimpanzees. Together, these results indicate that the development of spatial memory may differ even between closely related species. Moreover, changes in the spatial domain can emerge during nonhuman ape ontogeny, much like some changes seen in human children.

Abstract. Ensemble forecasts at kilometre scale of two severe storms over the Mediterranean region are verified against satellite observations. In complement to assessing the forecasts against ground-based measurements, brightness temperature (BT) images are computed from forecast fields and directly compared to BTs observed from satellite. The so-called model-to-satellite approach is very effective in identifying systematic errors in the prediction of cloud cover for BTs in the infrared window and in verifying the forecasted convective activity with BTs in the microwave range. This approach is combined with the calculation of meteorological scores for an objective evaluation of ensemble forecasts. The application of the approach is shown in the context of two Mediterranean case studies, a tropical-like storm and a heavy precipitating event. Assessment of cloud cover and convective activity using satellite observations in the infrared (10.8 μm) and microwave regions (183–191 GHz) provides results consistent with other traditional methods using rainfall measurements. In addition, for the tropical-like storm, differences among forecasts occur much earlier in terms of cloud cover and deep convective activity than they do in terms of deepening and track. Further, the underdispersion of the ensemble forecasts of the two high-impact weather events is easily identified with satellite diagnostics. This suggests that such an approach could be a useful method for verifying ensemble forecasts, particularly in data-sparse regions.

Abstract. The calibration and execution of large hydrological models, such as SWAT (soil and water assessment tool), developed for large areas, high resolution, and huge input data, need not only quite a long execution time but also high computation resources. SWAT hydrological model supports studies and predictions of the impact of land management practices on water, sediment, and agricultural chemical yields in complex watersheds. The paper presents the gSWAT application as a web practical solution for environmental specialists to calibrate extensive hydrological models and to run scenarios, by hiding the complex control of processes and heterogeneous resources across the grid based high computation infrastructure. The paper highlights the basic functionalities of the gSWAT platform, and the features of the graphical user interface. The presentation is concerned with the development of working sessions, interactive control of calibration, direct and basic editing of parameters, process monitoring, and graphical and interactive visualization of the results. The experiments performed on different SWAT models and the obtained results argue the benefits brought by the grid parallel and distributed environment as a solution for the processing platform. All the instances of SWAT models used in the reported experiments have been developed through the enviroGRIDS project, targeting the Black Sea catchment area.

The present study examined predictions of increased learning effectiveness in connection with WebQuest as a self-instructional learning environment in the context of political education. Analyses were based on the data of 502 students from nineth- and tenth-grade classes from middle-track
(German: Realschulen) and high-track (German: Gymnasien) German secondary schools. In the framework of the TEESAEC project, lessons employing WebQuest were compared with teacher-directed lessons in a total of 22 classes. This study does not support the effectiveness of new types of teaching
methods in comparison to traditional teaching methods. While girls clearly profited more from the lesson series than boys, there was no substantial influence of sex on prior knowledge. Adolescents with a Turkish migration background performed considerably poorer and those with a southern European
background somewhat poorer in the pre-test than adolescents without a migration background. With regard to political interest, it was found that while interest in politics in Germany exerted a positive influence on prior knowledge, specific interest in European politics did not explain any
unique variance. Pupils may generally profit from parents' cultural capital and too little from political education lessons. The results of the study suggest that motivation and academic self-concept represent factors that are more important than the specific methods applied when it comes
to lesson efficiency.

Vulnerability of human beings exposed to a catastrophic disaster is affected by multiple factors that include hazard intensity, environment, and individual characteristics. The traditional approach to vulnerability assessment, based on the aggregate‐area method and unsupervised learning, cannot incorporate spatial information; thus, vulnerability can be only roughly assessed. In this article, we propose Bayesian network (BN) and spatial analysis techniques to mine spatial data sets to evaluate the vulnerability of human beings. In our approach, spatial analysis is leveraged to preprocess the data; for example, kernel density analysis (KDA) and accumulative road cost surface modeling (ARCSM) are employed to quantify the influence of geofeatures on vulnerability and relate such influence to spatial distance. The knowledge‐ and data‐based BN provides a consistent platform to integrate a variety of factors, including those extracted by KDA and ARCSM to model vulnerability uncertainty. We also consider the model's uncertainty and use the Bayesian model average and Occam's Window to average the multiple models obtained by our approach to robust prediction of the risk and vulnerability. We compare our approach with other probabilistic models in the case study of seismic risk and conclude that our approach is a good means to mining spatial data sets for evaluating vulnerability.

This research extends the role incongruity analysis of employment-related gender bias by investigating the role of dispositional and situational antecedents, specifically political ideology and the salience of cues to the traditional female gender role. The prediction that conservatives would show an anti-female candidate bias and liberals would show a pro-female bias when the traditional female gender role is salient was tested across three experimental studies. In Study 1, 126 participants evaluated a male or a female job applicant with thoughts of the traditional female gender role activated or not. Results showed that when the gender role is salient, political ideology moderates evaluations of the female candidates such that conservatives evaluate her negatively and liberals evaluate her positively. Study 2 (89 participants) replicated this effect and showed that this political ideology-based bias does not occur when the non-traditional female gender role is made salient. Study 2 also demonstrated that the observed effects are not driven by liberals' and conservatives' differing perceptions regarding the female applicant's qualifications for the job. Finally, Study 3 (159 participants) both replicated the political ideology-based evaluation bias for female candidates and demonstrated that this bias is mediated by conservatives' and liberals' attitudes toward the roles of women in society.