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The Bassoon
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Biosensors and Their Applications
A biosensor is a device in which a bioactive layer lies in direct contact with a transducer whose responses to change in the bioactive layer generate eloctronic signals for interpretation. The bioactive layer may consist of membrane-bound enzymes, anti-bodies, or receptors. The potential of this blend of electronics and biotechnology includes the direct assay of clinically important substrates (e.g. blood glucose) and of substances too unstable for storage or whose concentrations fluctuate rapidly. Written by the leading researchers in the field, this book reflects the most current developments in successfully constructing a biosensor. Major applications are in the fields of pharmacology, molecular biology, virology and electronics.
Divadelni Ochotnik
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Doing Business 2012
Ninth in a series of annual reports comparing business regulations in 183 economies, Doing Business 2012 measures regulations affecting 11 areas of everyday business activity: starting a business dealing with construction permits employing workers registering property getting credit protecting investors paying taxes trading across borders enforcing contracts closing a business getting electricity The report updates all indicators as of June 1, 2011, ranks countries on their overall "ease of doing business", and analyzes reforms to business regulation identifying which countries are strengthening their business environment the most. Doing Business 2012 includes a new set of indicators on the ...
Recent Advances in Stochastic Modeling and Data Analysis
This volume presents the most recent applied and methodological issues in stochastic modeling and data analysis. The contributions cover various fields such as stochastic processes and applications, data analysis methods and techniques, Bayesian methods, biostatistics, econometrics, sampling, linear and nonlinear models, networks and queues, survival analysis, and time series. The volume presents new results with potential for solving real-life problems and provides novel methods for solving these problems by analyzing the relevant data. The use of recent advances in different fields are emphasized, especially new optimization and statistical methods, data warehouse, data mining and knowledge systems, neural computing, and bioinformatics.
Federated Learning
This book provides a comprehensive and self-contained introduction to federated learning, ranging from the basic knowledge and theories to various key applications. Privacy and incentive issues are the focus of this book. It is timely as federated learning is becoming popular after the release of the General Data Protection Regulation (GDPR). Since federated learning aims to enable a machine model to be collaboratively trained without each party exposing private data to others. This setting adheres to regulatory requirements of data privacy protection such as GDPR. This book contains three main parts. Firstly, it introduces different privacy-preserving methods for protecting a federated lear...
Dataset Shift in Machine Learning
An overview of recent efforts in the machine learning community to deal with dataset and covariate shift, which occurs when test and training inputs and outputs have different distributions. Dataset shift is a common problem in predictive modeling that occurs when the joint distribution of inputs and outputs differs between training and test stages. Covariate shift, a particular case of dataset shift, occurs when only the input distribution changes. Dataset shift is present in most practical applications, for reasons ranging from the bias introduced by experimental design to the irreproducibility of the testing conditions at training time. (An example is -email spam filtering, which may fail...
