For agencies in the health and human services field, data reporting is the primary source of information from which progress and organizational outcomes are measured. Often, front-line staff are responsible for collecting these data and yet are often ill-equipped to collect data accurately and consistently leading to a performance deficit affecting the strength and success of the program. Evaluating and addressing performance issues in the human services field has long been a challenge due to limited resources. Finding an effective and efficient method of evaluation that leads to a function-based intervention would improve performance and ultimately improve organizational outcomes. This study evaluated the Performance Diagnostic Checklist – Human Services (PDC-HS) as means of assessing the performance deficit of inaccurate data reporting in a community-based adult autism services program. A multiple-baseline design was used across four participants to evaluate the effects of the interventions indicated by the PDC-HS. Two participants received performance feedback and two received additional training following the behavior skills training model. Results indicated that both interventions were effective in increasing accurate data reporting across participants. Social validity measures also indicated the PDC-HS was easy to use and could be completed in a timely manner. / Applied Behavioral Analysis

We develop and analyze a class of CAC algorithms for resource management in wireless networks with the goal not only to satisfy QoS constraints, but also to maximize a value or reward objective function specified by the system. We demonstrate through analytical modeling and simulation validation that the CAC algorithms developed in this research for resource management can greatly improve the system reward obtainable with QoS guarantees, when compared with existing CAC algorithms designed for QoS satisfaction only. We design hybrid partitioning-threshold, spillover and elastic CAC algorithms based on the design techniques of partitioning, setting thresholds and probabilistic call acceptance to use channel resources for servicing distinct QoS classes. For each CAC algorithm developed, we identify optimal resource management policies in terms of partitioning or threshold settings to use channel resources. By comparing these CAC algorithms head-to-head under identical conditions, we determine the best algorithm to be used at runtime to maximize system reward with QoS guarantees for servicing multiple service classes in wireless networks. We study solution correctness, solution optimality and solution efficiency of the class of CAC algorithms developed. We ensure solution optimality by comparing optimal solutions achieved with those obtained by ideal CAC algorithms via exhaustive search. We study solution efficiency properties by performing complexity analyses and ensure solution correctness by simulation validation based on real human mobility data. Further, we analyze the tradeoff between solution optimality vs. solution efficiency and suggest the best CAC algorithm used to best tradeoff solution optimality for solution efficiency, or vice versa, to satisfy the system's solution requirements. Moreover, we develop design principles that remain applicable despite rapidly evolving wireless network technologies since they can be generalized to deal with management of 'resources' (e.g., wireless channel bandwidth), 'cells' (e.g., cellular networks), "connections" (e.g., service calls with QoS constraints), and "reward optimization" (e.g., revenue optimization in optimal pricing determination) for future wireless service networks. To apply the CAC algorithms developed, we propose an application framework consisting of three stages: workload characterization, call admission control, and application deployment. We demonstrate the applicability with the optimal pricing determination application and the intelligent switch routing application. / Ph. D.

Increasing size and prevalence of real time information have become important characteristics of databases found on the internet. Due to changing information, the relevancy ranking of the search results also changes. Current methods in information retrieval, which are based on offline indexing, are not efficient in such dynamic search spaces and cannot quickly provide the most current results. Due to the explosive growth of the internet, stove-piped approaches for dealing with dynamism by simply employing large computational resources are ultimately not scalable. A new processing methodology that incorporates intelligent resource allocation strategies is required. Also, modeling the dynamism in the search space in real time is essential for effective resource allocation. In order to support multi-grained dynamic resource allocation, we propose to use a partial processing approach that uses anytime algorithms to process the documents in multiple steps. At each successive step, a more accurate approximation of the final similarity values of the documents is produced. Resource allocation algorithm use these partial results to select documents for processing, decide on the number of processing steps and the computation time allocated for each step. We validate the processing paradigm by demonstrating its viability with image documents. We design an anytime image algorithm that uses a combination of wavelet transforms and machine learning techniques to map low level visual features to higher level concepts. Experimental validation is done by implementing the image algorithm within an established multiagent information retrieval framework called I-FGM. We also formulate a multiagent resource allocation framework for design and performance analysis of resource allocation with partial processing. A key aspect of the framework is modeling changes in the search space as external and internal dynamism using a grid-based search space model. The search space model divides the documents or candidates into groups based on its partial-value and portion processed. Hence the changes in the search space can be effectively represented in the search space model as flow of agents and candidates between the grids. Using comparative experimental studies and detailed statistical analysis we validate the search space model and demonstrate the effectiveness of the resource allocation framework. / Ph. D.

Visualization tools allow scientists to comprehend very large data sets and to discover relationships which are otherwise difficult to detect. Unfortunately, not all types of data can be visualized easily using existing tools. In particular, long sequences of nonnumeric data cannot be visualized adequately. Examples of this type of data include trace files of computer performance information, the nucleotides in a genetic sequence, a record of stocks traded over a period of years, and the sequence of words in this document. The term categorical time series is defined and used to describe this family of data. When visualizations designed for numerical time series are applied to categorical time series, the distortions which result from the arbitrary conversion of unordered categorical values to totally ordered numerical values can be profound. Examples of this phenomenon are presented and explained. Several new, general purpose techniques for visualizing categorical time series data have been developed as part of this work and have been incorporated into the Chitra perfor- mance analysis and visualization system. All of these new visualizations can be produced in O(n) time. The new visualizations for categorical time series provide general purpose techniques for visualizing aspects of categorical data which are commonly of interest. These include periodicity, stationarity, cross-correlation, autocorrelation, and the detection of recurring patterns. The effective use of these visualizations is demonstrated in a number of application domains, including performance analysis, World Wide Web traffic analysis, network routing simulations, document comparison, pattern detection, and the analysis of the performance of genetic algorithms. / Ph. D.

Supply chain management has been recently introduced to address the integration of organizational functions ranging from the ordering and receipt of raw materials throughout the manufacturing processes, to the distribution and delivery of products to the customer. Its application demonstrates that this idea enables organizations to achieve higher quality products, better customer service, and lower inventory cost. In order to achieve high performance, supply chain functions must operate in an integrated and coordinated manner. Several challenging problems associated with integrated supply chain design are: (1) how to model and coordinate the supply chain business processes, specifically in the area of supply chain workflows; (2) how to analyze the performance of an integrated supply chain network so that optimization techniques can be employed to improve customer service and reduce inventory cost; and (3) how to evaluate dynamic supply chain networks and obtain a comprehensive understanding of decision-making issues related to supply network configurations. These problems are most representative in the supply chain theory's research and applications. There are three major objectives for this research. The first objective is to develop viable modeling methodologies and analyzing algorithms for supply chain business processes so that the logic properties of supply chain process models can be analyzed and verified. This problem has not been studied in integrated supply chain literature to date. To facilitate the modeling and verification analysis of supply chain workflows, an object-oriented Petri nets based modular modeling and analyzing approach is presented. The proposed, structured, process-modeling algorithm provides an effective way to design structured supply chain business processes. The second objective is to develop a network of inventory-queue models for the performance analysis and optimization of an integrated supply network with inventory control at all sites. An inventory-queue is a queueing model that incorporates an inventory replenishment policy for the output store. This dissertation extends the previous work done on the supply network model with base-stock control and service requirements. Instead of one-for-one base stock policy, batch-ordering policy and lot-sizing problems are considered. To determine the replenishment lead times of items at the stores, a fixed-batch target-level production authorization mechanism is employed to explicitly obtain performance measures of the supply chain queueing model. The validity of the proposed model is illustrated by comparing the results from the analytical performance evaluation model and those obtained from the simulation study. The third objective is to develop simulation models for understanding decision-making issues of the supply chain network configuration in an integrated environment. Simulation studies investigate multi-echelon distribution systems with installation stock reorder policy and echelon stock reorder policy. The results show that, depending on the structure of multi-echelon distribution systems, either echelon stock or installation stock policy may be advantageous. This dissertation presents a new transshipment policy, called "alternate transshipment policy," to improve supply chain performance. In an integrated supply chain network that considers both the distribution function and the manufacturing function, the impacts of component commonality on network performance are also evaluated. The results of analysis-of-variance and Tukey's tests reveal that there is a significant difference in performance measures, such as delivery time and order fill rates, when comparing an integrated supply chain with higher component commonality to an integrated supply chain with lower component commonality. Several supply chain network examples are employed to substantiate the effectiveness of the proposed methodologies and algorithms. / Ph. D.

Efficient usage of file systems poses a major challenge for highly scalable parallel applications. The performance of even the most sophisticated I/O subsystems lags behind the compute capabilities of current processors. To improve the utilization of I/O subsystems, several libraries, such as HDF5, facilitate the implementation of parallel I/O operations. These libraries abstract from low-level I/O interfaces (for instance, POSIX I/O) and may internally interact with additional I/O libraries. While improving usability, I/O libraries also add complexity and impede the analysis and optimization of application I/O performance. This thesis proposes a methodology to investigate application I/O behavior in detail. In contrast to existing approaches, this methodology captures I/O activities on multiple layers of the I/O software stack, correlates these activities across all layers explicitly, and identifies interactions between multiple layers of the I/O software stack. This allows users to identify inefficiencies at individual layers of the I/O software stack as well as to detect possible conflicts in the interplay between these layers. Therefor, a monitoring infrastructure observes an application and records information about I/O activities of the application during its execution. This work describes options to monitor applications and generate event logs reflecting their behavior. Additionally, it introduces concepts to store information about I/O activities in event logs that preserve hierarchical relations between I/O operations across all layers of the I/O software stack. In combination with the introduced methodology for multi-layer I/O performance analysis, this work provides the foundation for application I/O tuning by exposing patterns in the usage of I/O routines. This contribution includes the definition of I/O access patterns observable in the event logs of parallel scientific applications. These access patterns originate either directly from the application or from utilized I/O libraries. The introduced patterns reflect inefficiencies in the usage of I/O routines or reveal optimization strategies for I/O accesses. Software developers can use these patterns as a guideline for performance analysis to investigate the I/O behavior of their applications and verify the effectiveness of internal optimizations applied by high-level I/O libraries. After focusing on the analysis of individual applications, this work widens the scope to investigations of coordinated sequences of applications by introducing a top-down approach for performance analysis of entire scientific workflows. The approach provides summarized performance metrics covering different workflow perspectives, from general overview to individual jobs and their job steps. These summaries allow users to identify inefficiencies and determine the responsible job steps. In addition, the approach utilizes the methodology for performance analysis of applications using multi-layer I/O to record detailed performance data about job steps, enabling a fine-grained analysis of the associated execution to exactly pinpoint performance issues. The introduced top-down performance analysis methodology presents a powerful tool for comprehensive performance analysis of complex workflows. On top of their theoretical formulation, this thesis provides implementations of all proposed methodologies. For this purpose, an established performance monitoring infrastructure is enhanced by features to record I/O activities. These contributions complement existing functionality and provide a holistic performance analysis for parallel scientific applications covering computation, communication, and I/O operations. Evaluations with synthetic case studies, benchmarks, and real-world applications demonstrate the effectiveness of the proposed methodologies. The results of this work are distributed as open-source software. For instance, the measurement infrastructure including improvements introduced in this thesis is available for download and used in computing centers world-wide. Furthermore, research projects already employ the outcomes of this work.

Yes / The implementation of Reverse Osmosis (RO) technology is noticeably increased to produce freshwater from brackish and seawater resources. In this work, performance analysis of a multistage multi pass medium-sized spiral wound brackish water RO (BWRO) desalination plant (1200 m³/day) of Arab Potash Company (APC) located in Jordan is evaluated using modelling and simulation. For this purpose, a mathematical model for the spiral wound RO process based on the principles of solution diffusion model is developed. The model is then used to simulate the operating conditions of low-salinity brackish water RO (BWRO) desalination plant. The results obtained are then compared against the real industrial data of BWRO desalination plant of APC which shows a high-level of consistency. Finally, the model is used to analysis the impact of the operating parameters such as salinity, pressure, temperature, and flow rate on the plant performance. The sensitivity analysis confirms that both feed flow rate and operating pressure as the critical parameters that positively affect the product salinity.

From flickering films to microlearning in 4K, the field known as Instructional Design and Technology (IDT) today has a long history, marked by constant evolution since the 1920s. The field has been thrust into the spotlight by the COVID-19 pandemic. This bibliometric analysis examines research articles from the Educational Technology Research and Development (ETRandD) journal spanning 1957 to 2023. The study addresses the challenges posed by the influx of new IDT professionals from diverse backgrounds, as well as the opportunity presented by the occasion of the centennial celebration of the Association for Educational Communications and Technology (AECT), the key professional organization in the field. Data for this study was primarily obtained from the Web of Science, supplemented by SCOPUS and Dimensions. The analysis utilized Biblioshiny to evaluate the journal's performance and impact, identifying influential works and authors. Additionally, VOSviewer was employed to map the scientific landscape, tracing the evolution of research themes and the collaborative networks among authors, institutions, and countries. While the analysis navigates through the limitations inherent in focusing on a single journal, majorly employing a specific dataset, and adhering to an English-only corpus, it successfully illuminates the core tendencies and transformative shifts within IDT. The findings emphasize the field's enduring commitment to leveraging technological advancements within a theoretically grounded framework to enhance learning outcomes. As the field continues to evolve in response to new entrants from diverse backgrounds, emerging technologies and shifting learners' contexts, this study provides a useful reference point for future research. / Doctor of Philosophy / The field of Instructional Design and Technology (IDT) has evolved dramatically from its early days in the 1920s, using simple film-based instruction, to today's sophisticated 4K microlearning environments. The field has been thrust into the limelight by the unprecedented challenges of the COVID-19 pandemic. This study is a bibliometric analysis of articles published in the Educational Technology Research and Development journal from 1957 to 2023. The research addresses the challenge of the entry of professionals from varied backgrounds into the IDT field and celebrates a century of contributions by the Association for Educational Communications and Technology (AECT), a leading organization in this discipline. Using databases like the Web of Science, SCOPUS, and Dimensions, and tools such as Biblioshiny and VOSviewer, the study maps the development of IDT research themes, identifies key works, and authors, and explores the collaborative networks that span authors, institutions, and countries. Despite focusing on a single journal and an English-only dataset, the analysis reveals significant trends and shifts in IDT. It underscores the field's commitment to integrating new technologies within a solid theoretical framework to improve educational outcomes. This study not only reflects on the past but also sets the stage for future research as IDT continues to adapt to the needs of diverse learners and emerging technologies.

In this work, we design parallel Monte Carlo algorithms for the Ising spin model on a hierarchical cluster. A hierarchical cluster can be considered as a cluster of homogeneous nodes which are partitioned into multiple supernodes such that communication across homogenous clusters is represented by a supernode topological network. We consider different data layouts and provide equations for choosing the best data layout under such a network paradigm. We show that the data layouts designed for a homogeneous cluster will not yield results as good as layouts designed for a hierarchical cluster. We derive theoretical results on the performance of the algorithms on a modified version of the LogP model that represents such tiered networking, and present simulation results to analyze the utility of the theoretical design and analysis. Furthermore, we consider the 3-D Ising model and design parallel algorithms for sweep spin selection on both homogeneous and hierarchical clusters. We also discuss the simulation of hierarchical clusters on a homogeneous set of machines, and the efficient implementation of the parallel Ising model on such clusters. / Master of Science

Throughout her career, the artist and singer Lady Gaga has portrayed a great number of personas in her performances. This thesis uses Gaga’s performances of ”Paparazzi”, ”The Edge of Glory”, and ”Million Reasons” to evaluate how her musical personas have been expressed between 2009 and 2018. Philip Auslander’s theories on musical personas are applied as a tool to examine and analyze Lady Gaga’s use of personas in nine performances. The results of the analysis suggest that, in addition to genre conventions, performance contexts also impacted the adaptations of Gaga’s musical personas. Furthermore, Gaga’s Born This Way Ball tour showcased how a musician can adopt a temporary character tailored for a specific tour plot, suggesting the idea of a “tour persona” as a distinct entity separate from musical personas and song characters. The conclusion drawn from the case study suggests that the term ”persona adjustment” may be more suitable than ”persona development” or ”evolvement” when analyzing changes in musical personas over time. Finally, Gaga's changing personas in this case study challenge Auslander's idea that a performer's persona development relies on longevity and audience negotiation, as Gaga's artistic image is sustained through unpredictable fashion choices and diverse mannerisms.