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Die Frage «Wie weit kann die öffentliche Hand den Fremdenverkehr fördern?» unterstellt bereits die vollzogene Tatsache der staatlichen Förderung des Fremdenverkehrs. Sie scheint diese hinzunehmen, ja sogar davon auszugehen, dass der Staat den Fremdenverkehr fördern soll, vielleicht sogar muss, wobei es dann, nur noch einen Schritt bis zur Untersuchung und zum Entscheid darüber bedeuten würde, wie weit er iiberhaupt zu gehen imstande ist, um das apodiktisch feststehende Gebot der staatlichen Unterstüzung maximal zu erfüllen.

The purpose of the study is to classify the radiographic images into three categories such as fillings, cavity and implant to identify dental diseases because dental disease is a very common dental health problem for all people. The detection of dental issues and the selection of the most suitable method of treatment are both determined by the results of a radiological examination. Dental x-rays provide important information about the insides of teeth and their surrounding cells, which helps dentists detect dental issues that are not immediately visible. The analysis of dental x-rays, which is typically done by dentists, is a time-consuming process that can become an error-prone technique due to the wide variations in the structure of teeth and the dentist's lack of expertise. The workload of a dental professional and the chance of misinterpretation can be decreased by the availability of such a system, which can interpret the result of an x-ray automatically.

This study uses deep learning (DL) models to identify dental diseases in order to tackle this issue. Four different DL models, such as ResNet-101, Xception, DenseNet-201 and EfficientNet-B0, were evaluated in order to determine which one would be the most useful for the detection of dental diseases (such as fillings, cavity and implant).

Loss and accuracy curves have been used to analyze the model. However, the EfficientNet-B0 model performed better compared to Xception, DenseNet-201 and ResNet-101. The accuracy, recall, F1-score and AUC values for this model were 98.91, 98.91, 98.74 and 99.98%, respectively. The accuracy rates for the Xception, ResNet-101 and DenseNet-201 are 96.74, 93.48 and 95.65%, respectively.

The present study can benefit dentists from using the DL model to more accurately diagnose dental problems.

This study is conducted to evaluate dental diseases using Convolutional neural network (CNN) techniques to assist dentists in selecting the most effective technique for a particular clinical condition.

A perceived misalignment between socially responsible fans and football club management has recently led to a major crisis during the annual meeting in 2021 of Bayern Munich, one of the largest professional football teams in Europe. In an unprecedented scenario, Bayern Munich fans demanded that management drop one of its largest sponsors due to alleged violation of human rights. The goal of this paper is to examine this particular phenomenon, as it not only demonstrates a discrepancy between the social organizational identity and its image, but more importantly, how it impacts legitimation strategies and the fans' loyalty attitudes towards the club.

Using the underlying concepts of legitimacy and loyalty, this conceptual model paper proposes two frameworks for social responsibility in professional football clubs: (1) analyzing how the (mis-)alignment between organizational identity and image impacts fan loyalty and (2) depicting four different types of social responsibility strategies to align organizational identity and image.

The authors identify various theoretical concepts that influence organizational identity and image in and for social responsibility and combine the two critical concepts of legitimacy and loyalty to categorize the social responsibility strategies for professional football clubs.

Both frameworks advance the understanding of the decision-making behind social responsibility strategies and also synthesize the current literature to offer conceptual clarity regarding the varied implications and outcomes linked to the misalignment between organizational identity and image.