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Our aim is to examine how ethnic minority media emerged and became a factor in the development of media landscapes. What media is minority media? How important are societal contexts in different countries or regions? What key factors, forces and processes shape the minority media landscape? What differences and similarities can be identified, and explained? We discuss media produced by or for ethnic minorities, beginning with the foundation of minority language periodicals, and moving on to radio and television programming. We also discuss representation of ethnic minorities in mainstream media, and ethnic diversity - or lack thereof - in media companies. Finally, the emergence of broadcast media led to demands for minority programming, which has gradually expanded in the wake of migration and recognition of national minorities. Minority media, public or private, is vulnerable in times of economic crisis. Digitalization has opened new possibilities. But, the Internet is dominated by majority languages (Cunliff 2007). In the late 19th century Europe technological innovations and favorable conditions created flourishing press markets. Ethnic or nationalistic activists followed suit, founding the first periodicals. However, multiple changes in borders, state formations and central concepts make it difficult to determine which ethnic groups can be seen as minorities. There are no universal definitions of ‘minority’, ‘indigenous people’ or ’migrants’. The concept of ‘minority’ arose in the aftermath of WWI and signing of first minority treaties (Jackson 1998). But, how do we conceptualize ethnic groups in the 19th century multiethnic conglomerate states? Such states either didn’t have ethnic majorities, or people weren’t conceptualized in that manner. We use the term ‘ethnic minority’, to encompass migrants, national minorities and indigenous people. Some ethnic communities changed status from minority to majority, or vice versa. Estonians, Russians and Finns are examp

There is a short history of an evaluation of possibilities to calculate compound properties based on the content of ingredients. Methods included Neuronal Network Mathematics (NNM) and None Linear Regression (NLR) for example. A couple of patents granted on different compounds like recipes for color master batches but on rubber compounds as well. Testing a program based on NNM to predict the properties of a rubber compound formula failed even a larger database was created and implemented. On the other hand, the prediction of compound properties with a much smaller database created with Statistic Experimental Design (DoE) is successful but limited to the boundary conditions of this experiment. In a DoE is it called optimization and the result of the overlay plot. The analysis of the failure to predict even with a larger database is the intolerance of NNM to experimental error or mistype errors in the database and that the data in the database can not be corrected, respective excluded from the calculation. There is no ability to of tracking bag. Any regression model obtained from DoE can not handle any happen stance database. On the other hand most DoEs evaluated during investigations of ingredient / rubber property relation show clearly linear correlations, at least second order type of regression. In particular a more careful investigation of the statistics indicate a data fit with linear regression equations is sufficient. In most cases a none linear regression is indicated by the DoE-program used for evaluation, but in particular under consideration of measurement error in rubber testing and statistical noise in compound mixing it may be an over interpretation. As a consequence the prediction of a compound with its properties should be possible with sufficient precision if an approach is used based on linear regression in combination with iteration. This method is called Multi-Linear-Iteration (MLR). To demonstrate the precision of predictions based on MLR two exp, There is a short history of an evaluation of possibilities to calculate compound properties based on the content of ingredients. Methods included Neuronal Network Mathematics (NNM) and None Linear Regression (NLR) for example. A couple of patents granted on different compounds like recipes for color master batches but on rubber compounds as well. Testing a program based on NNM to predict the properties of a rubber compound formula failed even a larger database was created and implemented. On the other hand, the prediction of compound properties with a much smaller database created with Statistic Experimental Design (DoE) is successful but limited to the boundary conditions of this experiment. In a DoE is it called optimization and the result of the overlay plot. The analysis of the failure to predict even with a larger database is the intolerance of NNM to experimental error or mistype errors in the database and that the data in the database can not be corrected, respective excluded from the calculation. There is no ability to of tracking bag. Any regression model obtained from DoE can not handle any happen stance database. On the other hand most DoEs evaluated during investigations of ingredient / rubber property relation show clearly linear correlations, at least second order type of regression. In particular a more careful investigation of the statistics indicate a data fit with linear regression equations is sufficient. In most cases a none linear regression is indicated by the DoE-program used for evaluation, but in particular under consideration of measurement error in rubber testing and statistical noise in compound mixing it may be an over interpretation. As a consequence the prediction of a compound with its properties should be possible with sufficient precision if an approach is used based on linear regression in combination with iteration. This method is called Multi-Linear-Iteration (MLR). To demonstrate the precision of predictions based on MLR two exp

© 2016 Royal Meteorological Society. This article investigates the year-to-year variability of the onset of the South China Sea summer monsoon (SCSSM) and the possible influences exerted by the surface temperature anomalies over land and sea. Early and late monsoon onsets are related to the temperature anomalies in different regions. It is found that an early onset follows negative sea surface temperature (SST) anomalies in the central tropical Pacific (CP) Ocean during the preceding winter and spring, corresponding to a CP La Niña. In contrast, a late onset is preceded by the negative surface air temperature anomalies over land in the central Asian continent. Negative SST anomalies in the central-eastern equatorial Pacific Ocean and the associated warming in the western Pacific induce an anomalously enhanced Walker circulation. This anomalous Walker cell leads to an increase in convection, causing more latent heat release and a subsequent decrease of surface pressure. The anomalous Walker cell and the enhanced latent heat release weaken the Western North Pacific subtropical high and the Philippine Sea anticyclone, favouring a westerly flow from the Indian Ocean, resulting in an early SCSSM onset. On the other hand, negative land surface temperature anomalies cool the atmosphere over land, and locally modify the Hadley circulation, accompanied by the anomalous divergence in the low-level atmosphere over the western equatorial Pacific. This divergence anomaly reduces the latent heat release and strengthens the anticyclone in the Philippine Sea, thus preventing the westward extension of the westerlies from the Indian Ocean and causing a late SCSSM onset.