The clinical competency of practicing and nonpracticing faculty of associate degree schools of nursing

This study assesses and describes the perception of clinical competency and the relationship to clinical practice of full-time nursing faculty in the associate degree nursing programs in the state of Florida. The study was developed around one major hypothesis and four research questions. The Hygiene-Motivators Theory proposed by Herzberg, Mausner, and Snyderman (1959) provided the conceptual framework to explain factors that would motivate a person to expand workload and maintain job satisfaction.^ Data were collected from the 244 faculty members teaching full-time at the 15 associate degree schools of nursing accredited by the National League for Nursing in the state of Florida. A total of 186 faculty (76%) responded and 175 (72%) cases were used for data analysis.^ Two instruments were modified and combined for the investigation. The instruments were the Faculty Perception of Practice Questionnaire (Parascenzo, 1983) and a three-part Attributes Deemed Necessary for Faculty to Proclaim Clinical Competency (Smith, 1991) scale. Computer analyses employing descriptive and inferential statistics were performed.^ The findings revealed that faculty were closely divided as to practice activities with more faculty nonpracticing than practicing. Factors identified as impediments to increased clinical practice were identified as teaching load and personal/family responsibilities that lead to a lack of time and lack of opportunity. Those faculty who practice did so as moonlighters in positions that would not require advanced training. Both the practicing and nonpracticing faculty reported a high level of satisfaction with their activities as a means of maintaining clinical practice. While both groups reported a high level of expertise, those practicing faculty perceived themselves to be more clinically competent on the attributes of knowledge, skills, and on the total attribute scale. It was further revealed that perception of competency declined with the length of time spent out of practice. There was no difference in the two groups on the attributes of values/attitude. ^

Generational change among women in rural Valencia

The demise of Generalissimo Francisco Franco in 1975 and the subsequent democratization of Spain and its inclusion in the European Community have profoundly altered the patriarchal traditions of Spanish society. This study focused on the changes that women in Moixent, a rural village in Valencia, Spain, have experienced as a result of this liberalization of government policies, modernization, and economic development. ^ The purpose of this research was to illuminate the changing lives of two generations of women and their families in rural Valencia. The qualitative research techniques of participant observation, in-depth interviewing, and narrative analysis were used to present the different frames of reference of the two generations. Young working women in this rural community have come to rely on the help and support of their mothers in their attempts to work outside the home and improve their standard of living. As they enter Spain's modernizing economy their consumption patterns increasingly mimic those promoted by the global media, and especially television. As these young women take jobs outside the home they are having fewer children and dramatically altering the nation's demographic profile. ^ The older generation of women, who lived through decades of deprivation during the Spanish Civil War and Franco's long regime, support their daughters' new independence by assuming the arduous tasks of providing informal day care for their grandchildren and performing a variety of unpaid services for their daughters, including shopping, cooking, and housecleaning. This older generation of grandmothers is assuming a more difficult and demanding workload in what otherwise would be their retirement years. Hence they are the true enablers of their daughters' economic progress and modern patterns of consumption. ^ Other influences from the outside world have altered family farming practices. The participation of women in the harvests has declined, and most harvesting is now done by migrant foreign workers. As young women enter the workforce grandmothers strive to impart traditional values to their grandchildren, in the face of a rapidly changing world. ^

An order batching and processing heuristic to address aisle congestion in a manual order picking warehouse with order due dates

A heuristic for batching orders in a manual order-picking warehouse has been developed. It prioritizes orders based on due time to prevent mixing of orders of different priority levels. The order density of aisles criterion is used to form batches. It also determines the number of pickers required and assigns batches to pickers such that there is a uniform workload per unit of time. The effectiveness of the heuristic was studied by observing computational time and aisle congestion for various numbers of total orders and number of orders that form a batch. An initial heuristic performed well for small number of orders, but for larger number of orders, a partitioning technique is computationally more efficient, needing only minutes to solve for thousands of orders, while preserving 90% of the batch quality obtained with the original heuristic. Comparative studies between the heuristic and other published heuristics are needed. ^

Improving storage performance through layout optimizations

Disk drives are the bottleneck in the processing of large amounts of data used in almost all common applications. File systems attempt to reduce this by storing data sequentially on the disk drives, thereby reducing the access latencies. Although this strategy is useful when data is retrieved sequentially, the access patterns in real world workloads is not necessarily sequential and this mismatch results in storage I/O performance degradation. This thesis demonstrates that one way to improve the storage performance is to reorganize data on disk drives in the same way in which it is mostly accessed. We identify two classes of accesses: static, where access patterns do not change over the lifetime of the data and dynamic, where access patterns frequently change over short durations of time, and propose, implement and evaluate layout strategies for each of these. Our strategies are implemented in a way that they can be seamlessly integrated or removed from the system as desired. We evaluate our layout strategies for static policies using tree-structured XML data where accesses to the storage device are mostly of two kinds—parent-to-child or child-to-sibling. Our results show that for a specific class of deep-focused queries, the existing file system layout policy performs better by 5–54X. For the non-deep-focused queries, our native layout mechanism shows an improvement of 3–127X. To improve performance of the dynamic access patterns, we implement a self-optimizing storage system that performs rearranges popular block accesses on a dedicated partition based on the observed workload characteristics. Our evaluation shows an improvement of over 80% in the disk busy times over a range of workloads. These results show that applying the knowledge of data access patterns for allocation decisions can substantially improve the I/O performance.

Development of a new student evaluation instrument of instructor effectiveness in online courses

The purpose of this study was to (a) develop an evaluation instrument capable of rating students' perceptions of the instructional quality of an online course and the instructor’s performance, and (b) validate the proposed instrument with a study conducted at a major public university. The instrument was based upon the Seven Principles of Good Practice for Undergraduate Education (Chickering & Gamson, 1987). The study examined four specific questions. 1. Is the underlying factor structure of the new instrument consistent with Chickering and Gamson's Seven Principles? 2. Is the factor structure of the new instrument invariant for male and female students? 3. Are the scores on the new instrument related students’ expected grades? 4. Are the scores on the new instrument related to the students' perceived course workload? ^ The instrument was designed to measure students’ levels of satisfaction with their instruction, and also gathered information concerning the students’ sex, the expected grade in the course, and the students’ perceptions of the amount of work required by the course. A cluster sample consisting of an array of online courses across the disciplines yielded a total 297 students who responded to the online survey. The students for each course selected were asked to rate their instructors with the newly developed instrument. ^ Question 1 was answered using exploratory factor analysis, and yielded a factor structure similar to the Seven Principles.^ Question 2 was answered by separately factor-analyzing the responses of male and female students and comparing the factor structures. The resulting factor structures for men and women were different. However, 14 items could be realigned under five factors that paralleled some of the Seven Principles. When the scores of only those 14 items were entered in two principal components factor analyses using only men and only women, respectively and restricting the factor structure to five factors, the factor structures were the same for men and women.^ A weak positive relationship between students’ expected grades and their scores on the instrument was found (Question 3). There was no relationship between students’ perceived workloads for the course and their scores on the instrument (Question 4).^

Improving Storage with Stackable Extensions

Storage is a central part of computing. Driven by exponentially increasing content generation rate and a widening performance gap between memory and secondary storage, researchers are in the perennial quest to push for further innovation. This has resulted in novel ways to "squeeze" more capacity and performance out of current and emerging storage technology. Adding intelligence and leveraging new types of storage devices has opened the door to a whole new class of optimizations to save cost, improve performance, and reduce energy consumption. In this dissertation, we first develop, analyze, and evaluate three storage extensions. Our first extension tracks application access patterns and writes data in the way individual applications most commonly access it to benefit from the sequential throughput of disks. Our second extension uses a lower power flash device as a cache to save energy and turn off the disk during idle periods. Our third extension is designed to leverage the characteristics of both disks and solid state devices by placing data in the most appropriate device to improve performance and save power. In developing these systems, we learned that extending the storage stack is a complex process. Implementing new ideas incurs a prolonged and cumbersome development process and requires developers to have advanced knowledge of the entire system to ensure that extensions accomplish their goal without compromising data recoverability. Futhermore, storage administrators are often reluctant to deploy specific storage extensions without understanding how they interact with other extensions and if the extension ultimately achieves the intended goal. We address these challenges by using a combination of approaches. First, we simplify the storage extension development process with system-level infrastructure that implements core functionality commonly needed for storage extension development. Second, we develop a formal theory to assist administrators deploy storage extensions while guaranteeing that the given high level goals are satisfied. There are, however, some cases for which our theory is inconclusive. For such scenarios we present an experimental methodology that allows administrators to pick an extension that performs best for a given workload. Our evaluation demostrates the benefits of both the infrastructure and the formal theory.

Improving caches in consolidated environments

Memory (cache, DRAM, and disk) is in charge of providing data and instructions to a computer's processor. In order to maximize performance, the speeds of the memory and the processor should be equal. However, using memory that always match the speed of the processor is prohibitively expensive. Computer hardware designers have managed to drastically lower the cost of the system with the use of memory caches by sacrificing some performance. A cache is a small piece of fast memory that stores popular data so it can be accessed faster. Modern computers have evolved into a hierarchy of caches, where a memory level is the cache for a larger and slower memory level immediately below it. Thus, by using caches, manufacturers are able to store terabytes of data at the cost of cheapest memory while achieving speeds close to the speed of the fastest one.^ The most important decision about managing a cache is what data to store in it. Failing to make good decisions can lead to performance overheads and over-provisioning. Surprisingly, caches choose data to store based on policies that have not changed in principle for decades. However, computing paradigms have changed radically leading to two noticeably different trends. First, caches are now consolidated across hundreds to even thousands of processes. And second, caching is being employed at new levels of the storage hierarchy due to the availability of high-performance flash-based persistent media. This brings four problems. First, as the workloads sharing a cache increase, it is more likely that they contain duplicated data. Second, consolidation creates contention for caches, and if not managed carefully, it translates to wasted space and sub-optimal performance. Third, as contented caches are shared by more workloads, administrators need to carefully estimate specific per-workload requirements across the entire memory hierarchy in order to meet per-workload performance goals. And finally, current cache write policies are unable to simultaneously provide performance and consistency guarantees for the new levels of the storage hierarchy.^ We addressed these problems by modeling their impact and by proposing solutions for each of them. First, we measured and modeled the amount of duplication at the buffer cache level and contention in real production systems. Second, we created a unified model of workload cache usage under contention to be used by administrators for provisioning, or by process schedulers to decide what processes to run together. Third, we proposed methods for removing cache duplication and to eliminate wasted space because of contention for space. And finally, we proposed a technique to improve the consistency guarantees of write-back caches while preserving their performance benefits.^

From the Grassroot to the Tree Tops: How One Library has become Embedded in Online Learning

This presentation will show how a grassroots initiative has budded into the Florida International University (FIU) Libraries being an instrumental part of online learning. It will describe some of the marketing and outreach efforts that have been successful and share ideas on how to build alliances and networks with online faculty and students. Along with outreach efforts, the presentation will demonstrate some of the successful tools used to meet the needs of online students. Some of the these tools include becoming embedded in courses, building course and program specific Libguides, using Adobe Connect to reach students, creating simple YouTube videos, and creating more professional videos with FIU Online. The presentation will conclude with sharing some tips on how to keep the workload manageable when distance-learning programs are growing at the same time as library budgets and resources are shrinking.

Fuzzy Modeling and Control Based Virtual Machine Resource Management

Virtual machines (VMs) are powerful platforms for building agile datacenters and emerging cloud systems. However, resource management for a VM-based system is still a challenging task. First, the complexity of application workloads as well as the interference among competing workloads makes it difficult to understand their VMs’ resource demands for meeting their Quality of Service (QoS) targets; Second, the dynamics in the applications and system makes it also difficult to maintain the desired QoS target while the environment changes; Third, the transparency of virtualization presents a hurdle for guest-layer application and host-layer VM scheduler to cooperate and improve application QoS and system efficiency. This dissertation proposes to address the above challenges through fuzzy modeling and control theory based VM resource management. First, a fuzzy-logic-based nonlinear modeling approach is proposed to accurately capture a VM’s complex demands of multiple types of resources automatically online based on the observed workload and resource usages. Second, to enable fast adaption for resource management, the fuzzy modeling approach is integrated with a predictive-control-based controller to form a new Fuzzy Modeling Predictive Control (FMPC) approach which can quickly track the applications’ QoS targets and optimize the resource allocations under dynamic changes in the system. Finally, to address the limitations of black-box-based resource management solutions, a cross-layer optimization approach is proposed to enable cooperation between a VM’s host and guest layers and further improve the application QoS and resource usage efficiency. The above proposed approaches are prototyped and evaluated on a Xen-based virtualized system and evaluated with representative benchmarks including TPC-H, RUBiS, and TerraFly. The results demonstrate that the fuzzy-modeling-based approach improves the accuracy in resource prediction by up to 31.4% compared to conventional regression approaches. The FMPC approach substantially outperforms the traditional linear-model-based predictive control approach in meeting application QoS targets for an oversubscribed system. It is able to manage dynamic VM resource allocations and migrations for over 100 concurrent VMs across multiple hosts with good efficiency. Finally, the cross-layer optimization approach further improves the performance of a virtualized application by up to 40% when the resources are contended by dynamic workloads.

Improving Storage Performance Through Layout Optimizations

Disk drives are the bottleneck in the processing of large amounts of data used in almost all common applications. File systems attempt to reduce this by storing data sequentially on the disk drives, thereby reducing the access latencies. Although this strategy is useful when data is retrieved sequentially, the access patterns in real world workloads is not necessarily sequential and this mismatch results in storage I/O performance degradation. This thesis demonstrates that one way to improve the storage performance is to reorganize data on disk drives in the same way in which it is mostly accessed. We identify two classes of accesses: static, where access patterns do not change over the lifetime of the data and dynamic, where access patterns frequently change over short durations of time, and propose, implement and evaluate layout strategies for each of these. Our strategies are implemented in a way that they can be seamlessly integrated or removed from the system as desired. We evaluate our layout strategies for static policies using tree-structured XML data where accesses to the storage device are mostly of two kinds - parent-tochild or child-to-sibling. Our results show that for a specific class of deep-focused queries, the existing file system layout policy performs better by 5-54X. For the non-deep-focused queries, our native layout mechanism shows an improvement of 3-127X. To improve performance of the dynamic access patterns, we implement a self-optimizing storage system that performs rearranges popular block accesses on a dedicated partition based on the observed workload characteristics. Our evaluation shows an improvement of over 80% in the disk busy times over a range of workloads. These results show that applying the knowledge of data access patterns for allocation decisions can substantially improve the I/O performance.

Geospatial Data Indexing Analysis and Visualization via Web Services with Autonomic Resource Management

With the exponential growth of the usage of web-based map services, the web GIS application has become more and more popular. Spatial data index, search, analysis, visualization and the resource management of such services are becoming increasingly important to deliver user-desired Quality of Service. First, spatial indexing is typically time-consuming and is not available to end-users. To address this, we introduce TerraFly sksOpen, an open-sourced an Online Indexing and Querying System for Big Geospatial Data. Integrated with the TerraFly Geospatial database [1-9], sksOpen is an efficient indexing and query engine for processing Top-k Spatial Boolean Queries. Further, we provide ergonomic visualization of query results on interactive maps to facilitate the user’s data analysis. Second, due to the highly complex and dynamic nature of GIS systems, it is quite challenging for the end users to quickly understand and analyze the spatial data, and to efficiently share their own data and analysis results with others. Built on the TerraFly Geo spatial database, TerraFly GeoCloud is an extra layer running upon the TerraFly map and can efficiently support many different visualization functions and spatial data analysis models. Furthermore, users can create unique URLs to visualize and share the analysis results. TerraFly GeoCloud also enables the MapQL technology to customize map visualization using SQL-like statements [10]. Third, map systems often serve dynamic web workloads and involve multiple CPU and I/O intensive tiers, which make it challenging to meet the response time targets of map requests while using the resources efficiently. Virtualization facilitates the deployment of web map services and improves their resource utilization through encapsulation and consolidation. Autonomic resource management allows resources to be automatically provisioned to a map service and its internal tiers on demand. v-TerraFly are techniques to predict the demand of map workloads online and optimize resource allocations, considering both response time and data freshness as the QoS target. The proposed v-TerraFly system is prototyped on TerraFly, a production web map service, and evaluated using real TerraFly workloads. The results show that v-TerraFly can accurately predict the workload demands: 18.91% more accurate; and efficiently allocate resources to meet the QoS target: improves the QoS by 26.19% and saves resource usages by 20.83% compared to traditional peak load-based resource allocation.