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This dissertation explores constructions of Chineseness in China Wind music, a trend that emerged in the 2000s in Chinese popular music. China Wind music typically references traditional Chinese culture and incorporates Chinese instruments into global pop genres, such as hip hop, R&B, rock, and ballad. The trend was popularized by artists based in Taiwan but also includes those from the People’s Republic of China (PRC), Hong Kong, and Singapore. China Wind music’s seemingly pro-China messages belie the diverse political and cultural realities of Chinese-speaking artists and consumers residing in different regions. To better understand the complex relationship that China Wind artists and consumers have with Chinese identity, culture, and politics, I suggest that China Wind artists are acting as cultural nationalists who construct “an imagined cultural China” based on a supposedly shared culture and history. The three approaches to analyzing China Wind music employed in this thesis are: examining the musical construction of Chineseness in China Wind songs through the use of pentatonicism and musical borrowing, surveying the social meanings of instruments (e.g. erhu, pipa) and their deployment in China Wind music, and analyzing the relationship between the song and visual narratives in China Wind music videos and their remediation of Beijing opera, calligraphy, and martial arts. These analyses reveal how China Wind music incorporates a mix of living traditions and invented traditions (Hobsbawm 1983) to evoke an ambiguous “ancient” Chineseness that fosters a sense of belonging and connects audiences from various locales to an imagined cultural China. / Music Theory

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Nonlinear Mixed effect models are often used to describe population pharmacokinetics (PK) and Pharmacodynamics (PD) and play an important part of drug development both from regulatory and industry point of view. However, they can be time consuming and computationally expensive to develop. This thesis is a part of a larger collaboration between Uppsala University and two pharmaceutical companies, with the aim to develop a suite of software that can automate the model building process with more efficiency. One aspect that is important during the model building process is to detect how much the population parameter estimates are influenced by particular individuals. The results of this might lead to reconsideration of the model structure, as well as exclusion of these individuals from the dataset. The current tools available to detect this use case deletion diagnostics (CDD) to run the model multiple times with each subject removed from the dataset to examine whether the population estimates alter when that individual is removed. Another important aspect is whether an individual is an outlier from the population parameter predictions, which is obtained from simulating the model and evaluating the residuals (simeval). Both of these tools are computationally expensive and can take a lot of time, in particular CDD. Therefore, we developed a tool using machine learning (ML) algorithms that can predict these individuals based on other criteria, which will decrease the runtime in an automated model building procedure, whilst maintaining the robustness of the current methods described above.  To create a training database for the ML models, predictors were extracted from 27 previously published models and the CDD and simeval diagnostic tools were run on these models to obtain that true values we want the ML model to predict. The database was then used to train two artificial neural networks (ANN) which is an efficient and powerful method in ML. To enable ‘on-the-fly’ predictions, the developed ANN models were deployed using tflite into pharmpy. The resulted ANNs were able to predict outlying individuals with 79% sensitivity, 83% precision, and 99.1% specificity. While the influential individuals ANN was able to predict with 58% sensitivity, 63% precision and 99.6% specificity. Both ANNs offered a rapid assessment of influential individuals and outlying individuals and were able to make predictions in a matter of sub-seconds compared to hours using traditional methods.

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The purpose of this study was to develop a commercial singing guide that could be used as an aid in teaching selected elements of commercial singing. It addressed itself specifically to the following problems: determining how the selected elements of the commercial vocal style are produced, developing a guide for teaching the production of this vocal style to trained and untrained singers and evaluating the effectiveness of the guide.

A motor-mouthed rapper with a perfect ear wants to turn a golden-voiced oddball into a music superstar but his popstar ex-lover threatens to blow up her meteoric rise.