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Carl Czerny (1791-1857) was an Austrian piano teacher, composer, pianist, historian, and theorist, who made many contributions to the musical world. He wrote many pieces of music over the course of his life, the most familiar to us being his exercises. His other types of work are less known and studied nowadays for various reasons. Yet it cannot be denied that Czerny was a very important figure who should not be allowed to fade away. We must delve deeper into his life and uncover more of his pieces, to reveal aspects of his work that are unknown for us. This study concentrates on the forgotten piece Fantaisie sur des mélodies de Beethoven, Op. 752. This is one of the pieces that I included in a World Premiere release recording issued by Toccata Classics. The intent of the program for the recording was to change the traditional concept of Czerny's work and show that his compositions are worth adding to today's recital repertoire. The program included five forgotten pieces by Czerny, of which Op. 752 was the most complicated and virtuosic. The purpose of this study is to analyze the piece and illustrate practicing methods or solutions for the technical challenges of different types that it requires, thus constituting a performance guide. Furthermore, this dissertation also gives suggestions for using the piece in teaching. The aim is to help pianists and teachers to understand and successfully perform the piece on the modern piano. Moreover, I hope that this study will draw more attention to Czerny and inspire others to uncover more of his works.

High-quality drinking water and wastewater systems are essential to public health, business, and quality of life in the United States. Even though the current performance of these systems is moderate, the concern is about the future performance. Planning can be done for improvement once the current performance of utilities is evaluated, and areas with a scope of improvement are identified. Benchmarking and performance evaluation are key components in the process of continuous improvement for utility's performance. Benchmarking helps utilities make policies and programmatic decisions that reduce operational expenses and increase productivity by understanding areas of underperformance, understanding customer needs, developing future plans, and setting goals. This study establishes a strong case for implementing benchmarking methodologies among utilities to evaluate and improve performance. There are many initiatives on performance benchmarking of utilities but a few of them focuses on one or few area of performance. There are a few initiatives which use subjective indicators. Additionally, consultants visit the utilities for performance evaluation. This research focuses on creating a web-based benchmarking platform for performance evaluation using holistic and quantitative indicators. Practical and robust methodologies are used and the research presents the current performance comparisons among utilities for areas that impact overall utility's performance. Web based benchmarking consists of two major parts -- data collection and result visualization. A major contribution from this study is the creation of an online performance benchmarking database. With time more data will be collected which will provide utilities an access to a better database for performance evaluation. The future work in this research will be analyzing the data and results for each participant for each set of indicators, and finding possible reasons for under performance, followed by suggesting solutions for improvement using the best practices. / Master of Science

Advancements in data acquisition technologies across different domains, from genome sequencing to satellite and telescope imaging to large-scale physics simulations, are leading to an exponential growth in dataset sizes. Extracting knowledge from this wealth of data enables scientific discoveries at unprecedented scales. However, the sheer volume of the gathered datasets is a bottleneck for knowledge discovery. High-performance computing (HPC) provides a scalable infrastructure to extract knowledge from these massive datasets. However, multiple data management performance gaps exist between big data analytics software and HPC systems. These gaps arise from multiple factors, including the tradeoff between performance and programming productivity, data growth at a faster rate than memory capacity, and the high storage footprints of data analytics workflows. This dissertation bridges these gaps by combining productive data management interfaces with application-specific optimizations of data parallelism, memory operation, and storage management. First, we address the performance-productivity tradeoff by leveraging Spark and optimizing input data partitioning. Our solution optimizes programming productivity while achieving comparable performance to the Message Passing Interface (MPI) for scalable bioinformatics. Second, we address the operating system's kernel limitations for out-of-core data processing by autotuning memory management parameters in userspace. Finally, we address I/O and storage efficiency bottlenecks in data analytics workflows that iteratively and incrementally create and reuse persistent data structures such as graphs, data frames, and key-value datastores. / Doctor of Philosophy / Advancements in various fields, like genetics, satellite imaging, and physics simulations, are generating massive amounts of data. Analyzing this data can lead to groundbreaking scientific discoveries. However, the sheer size of these datasets presents a challenge. High-performance computing (HPC) offers a solution to process and understand this data efficiently. Still, several issues hinder the performance of big data analytics software on HPC systems. These problems include finding the right balance between performance and ease of programming, dealing with the challenges of handling massive amounts of data, and optimizing storage usage. This dissertation focuses on three areas to improve high-performance data analytics (HPDA). Firstly, it demonstrates how using Spark and optimized data partitioning can optimize programming productivity while achieving similar scalability as the Message Passing Interface (MPI) for scalable bioinformatics. Secondly, it addresses the limitations of the operating system's memory management for processing data that is too large to fit entirely in memory. Lastly, it tackles the efficiency issues related to input/output operations and storage when dealing with data structures like graphs, data frames, and key-value datastores in iterative and incremental workflows.

This research attempts to identify factors which may influence instrumentality beliefs in a merit pay environment. Specifically, the purpose was to investigate the relationship of: (1) of perceived performance appraisal accuracy, merit increase satisfaction, trust in top management, and trust in supervisor to instrumentality beliefs, (2) of performance appraisals, deviations between self and supervisor ratings of performance, and trust in supervisor to perceived performance appraisal accuracy, and (3) merit pay increases and perceived relative size of merit pay increase to merit increase satisfaction. The research site for this study was a large transit authority on the West Coast. Results of this study of 1,260 managerial, professional, and clerical employees suggest that individuals are more likely to believe that pay is tied to performance if they are satisfied with their merit pay increase and they trust top management. Findings also suggest that for individuals who receive a lower than expected performance appraisal, higher levels of perceived performance appraisal accuracy are likely to be associated with stronger beliefs that pay is tied to performance. Findings further suggest that individuals are more likely to perceive their performance appraisal to be accurate if the supervisor rating of performance is consistent with their self-rating of performance and they trust their supervisor. Also, the higher the performance appraisal, the more likely an individual is to perceive it to be accurate. Finally, individuals are more likely to be satisfied with their merit pay increase if they perceive it to be large relative to the average pay increase. In addition, the larger the merit pay increase, the more likely an individual is to be satisfied with it. Study findings in total suggest that a large number of employees are likely to believe that pay is not tied to performance. To the extent successful merit pay programs require strong beliefs that pay is tied to performance, findings cast doubt about the ability of merit pay to elicit improved job performance. Implications for compensation practice are discussed and suggestions for future research are presented. / Ph. D.

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Yes / This study examines the relationships between performance appraisal (PA) purposes and immediate and ultimate outcomes. Drawing upon expectancy theory and Greenberg's taxonomy, we explore the roles of multiple mediators as sets of person- and organization-referenced ratee reactions and reveal the multiple why-related aspects of the relationships between PA purposes and PA effectiveness. Our research is based on a questionnaire survey of 563 employees from the telecommunications sector of Pakistan. The results of structural equation modeling analysis suggest that individual-focused PA better serves the employee perspective, whereas position- and organization-focused PA better serves the organizational perspective. These findings indicate that inclusion of role definition and strategic purposes in the PA system is likely to render PA more effective and practical. The findings also corroborate that ratee reactions mediate the relationship between PA purposes and PA effectiveness, albeit to varying degrees. Our findings have theoretical and practical implications.

The large environmental issues that are confronting modern society underscore that a critical shift is needed and there is an increasing demand for sustainable solutions to tackle these challenges. Agenda 2030 for sustainable development, outlined by the United Nations, encompasses 17 universal goals and 169 transformative targets that address the global challenges and emphasise the urgency of environmental degradation. In addressing the substantial environmental challenges, radical solutions are needed where eco-innovations will play a pivotal role. Eco-innovations are characterized by novelty and often new to the market, representing a risky and costly activity for companies. Managing environmental and financial performance becomes complex where addressing both goals simultaneously becomes conflicting.      To address the identified research gap the purpose of this study is to analyse how companies manage environmental and financial performance in eco-innovation, especially early in the innovation process. Conflicting perspectives and scattered research underscore the need for a nuanced understanding of how companies manage the tensions inherent in financial and environmental performance in eco-innovation. Paradox theory served as a theoretical lens to gain a deeper understanding of how companies manage the conflicting perspectives that can arise when balancing environmental and financial performance simultaneously. To address the research question and understand the complex interplay that organisations working with eco-innovation are facing, a qualitative method with an inductive approach was chosen. Semi-structured interviews were held with companies mainly working on reducing environmental impact through innovative activities.  The data collected from the semi-structured interviews were analysed through thematic analysis, it resulted in two main strategies that companies use to balance environmental and financial performance in eco-innovation: (1) Buying time through money (2) Future-proof customer demand which we interpret with the resolution strategy from paradox theory. The result indicated that companies that are buying time through money are facing a tension between saving the budget or saving the planet. The tension arises from the absence of strict regulations resulting in a market where demand is lacking and is not yet ready to embrace the radical eco-innovation. This time gap resulting in a lack of demand and our findings indicated that companies are handling this time gap by finding external resources until the market is ready which is coherent with a separation strategy from paradox theory.   The second strategy of future-proof customer demand indicates that companies are facing tension between being commercial or being sustainable. Companies are facing a mature market with price sensitive customers, suffering from fear of regulations and regulations that are not fit for purpose. The solution is to secure sales with an end-customer to reduce the financial uncertainty to be able to balance environmental and financial performance early in the innovation process before (if at all) the innovation starts generating profit. This aligns with a synthesis strategy from paradox theory.

The Ambient Sound Engine is a complex and highly specialized computer utility built for work with interactive performance. It has been made possible with the dedicated contributions of numerous students and professors. My personal contributions to this project have resulted in a wide array of learning experiences. I have learned to work with database software, advanced coding environments, UI implementations in Java, and advanced sound manipulation. In the end I hope that I have created a unique and valuable product that wili provide function to the industry of interactive performance. The Ambient Sound Engine is a utility that will allow quickly adaptable environments to be simulated in surround sound. It required an interface that could respond quickly to required changes, could store a large amount of configuration and sound data, and an interface that could be navigated easily for the users to utilize.