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a major impact on pig production), possibly detecting illness (such as a broken leg) or detecting the start of farrowing (turning on the heating system for newborns when farrowing starts). The objective of WaterNet [49] is to provide the users, namely drinking water authorities, with a suitable technology and it has demonstrated the usability and appropriateness of newly developed methods in real-life applications. The GlacsWeb [46] project is being developed in order to be able to monitor glacier behavior via different sensors and link them together into an intelligent web of resources. Probes are placed on and under glaciers and data collected from them by a base station on the surface. Measurements include temperature, pressure and subglacial movement. The main goal of Sustainable Bridges [9, 28] is to develop a cost effective solution for detecting structural defects and to better predict the remaining lifetime of the bridges by providing the necessary infrastructure and algorithms. Meteorology and hydrology in Yosemite National Park is monitored by Smart Mesh Weather Forecasting (SMWF) system. Results show how, in some years, snowmelt may occur quite uniformly over the Sierra, while in others it varies with elevation. The number of commercial applications will increase when algorithms and paradigms satisfy challenging requirements such as scalability, robustness or power ef ciency. The characteristics and the requirements of the above scenarios are summarized in Table 2.9. 2.5.6. Healthcare (H) Applications in this category include telemonitoring of human physiological data, tracking and monitoring of doctors and patients inside a hospital, drug administrators in hospitals, etc. Merging wireless sensor technology into health and medicine applications will make life much easier for doctors, disabled people and patients. They will also make diagnosis and consultancy processes faster by patient monitoring

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Research in data mining is highly interdisciplinary. Methods to tackle the tasks listed in the preceding section have been developed in a large variety of research areas including to name only the most important statistics, artificial intelligence, machine learning, and soft computing. As a consequence there is an arsenal of methods, based on a wide range of ideas, and thus there is no longer such a lack of tools. To give an overview, we list some of the more prominent data mining methods. Each list entry refers to a few publications on the method and points out for which data mining tasks the method is especially suited. Of course, this list is far from being complete. The references are necessarily incomplete and may not always be the best ones possible, since we are clearly not experts for all of these methods and since, obviously, we cannot name everyone who has contributed to the one or the other. classical statistics (discriminant analysis, time series analysis, etc.) [Larsen and Marx 2005, Everitt 2006, Witte and Witte 2006] [Freedman et al. 2007] classification, prediction, trend analysis decision/classification and regression trees [Breiman et al. 1984, Quinlan 1993, Rokach and Maimon 2008] classification, prediction naive Bayes classifiers [Good 1965, Duda and Hart 1973, Domingos and Pazzani 1997] classification, prediction probabilistic networks (Bayesian networks/Markov networks) [Lauritzen and Spiegelhalter 1988, Pearl 1988, Jensen and Nielsen 2007] classification, dependence analysis artificial neural networks [Anderson 1995, Bishop 1996, Rojas 1996, Haykin 2008] classification, prediction, clustering (Kohonen feature maps) support vector machines and kernel methods [Cristianini and Shawe-Taylor 2000, Sch lkopf and Smola 2001] o [Shawe-Taylor and Cristianini 2004, Abe 2005] classification, prediction

pKa HA;S pKa HA;R log ct H ;R 3 S log ct A ;R 3 S log ct HA;R 3 S 2:7 For acetic acid, the transfer activity coefficients of H+, A and HA are as follows:

DOWNSIZING AT ALL SIZES OF FIRMS Nearly half (44.7%) of all organizations in IOMA s outsourcing questionnaire, Best Practices in Outsourcing, Downsizing and Use of Consultants, reported that they have intentionally reduced the number of employees in their organizations in the past two years. However, recent downsizings are affecting a much smaller number of employees at each firm than in years past. The other striking finding of the study is that it is not just large organizations that are taking advantage of the immediate bottom-line boost of reductions in workforce: 43.1% of small- to medium-size organizations (up to 500 employees) and 47.8% of larger (more than 500 employees) have downsized (see Exhibit 14.1).

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