Air pollutant index calendar-based graphics for visualizing trends profiling and analysis

Detection of air quality abnormality is important as an early warning system for air quality control and management. The information can raise citizens’ awareness towards current air quality status. By using time series plot, the data pattern can be identified but not able to exactly determine the abnormality due to overcrowded plot. Therefore, visualization data profiling was presented in this study by using seven years Malaysia daily air pollutant index to improve the detection. Result shown, the developed approach can simply identify the poor air quality across the month and year. Malaysia air quality was good and consistent between November and May. However, upward trend existed between June and October due to the forest fire happened in Sumatra. This visualization approach improved air pollution detection profiling and it is useful for related agencies to guide the control actions to be taken. This approach can be applied to any countries and data set to give more competent information

Air Pollutant Index Calendar-Based Graphics for Visualizing Trends Profiling and Analysis

Detection of air quality abnormality is important as an early warning system for air quality control and management. The information can raise citizens’ awareness towards current air quality status. By using time series plot, the data pattern can be identified but not able to exactly determine the abnormality due to overcrowded plot. Therefore, visualization data profiling was presented in this study by using seven years Malaysia daily air pollutant index to improve the detection. Result shown, the developed approach can simply identify the poor air quality across the month and year. Malaysia air quality was good and consistent between November and May. However, upward trend existed between June and October due to the forest fire happened in Sumatra. This visualization approach improved air pollution detection profiling and it is useful for related agencies to guide the control actions to be taken. This approach can be applied to any countries and data set to give more competent information

Air pollutant index calendar-based graphics for visualizing trends profiling and analysis

Detection of air quality abnormality is important as an early warning system for air quality control and management. The information can raise citizens' awareness towards current air quality status. By using time series plot, the data pattern can be identified but not able to exactly determine the abnormality due to overcrowded plot. Therefore, visualization data profiling was presented in this study by using seven years Malaysia daily air pollutant index to improve the detection. Result shown, the developed approach can simply identify the poor air quality across the month and year. Malaysia air quality was good and consistent between November and May. However, upward trend existed between June and October due to the forest fire happened in Sumatra. This visualization approach improved air pollution detection profiling and it is useful for related agencies to guide the control actions to be taken. This approach can be applied to any countries and data set to give more competent information

Data Visualization of Budgeting Assumptions: An Illustrative Case of Trans-disciplinary Applied Knowledge

Trans-disciplinary research combines different fields into new conceptual and methodological frameworks. In this study, the SECI model of knowledge creation, which consists of Socialization, Externalization, Combination, and Internalization conversion modes, is used to analyze the implementation of a structured budgeting visualization system by a trans-disciplinary team. Through applied research in implementing a global budgeting system, budgeting assumptions are made explicit through visualization, transforming the approach to the budgeting process and its accuracy. This visualization, in turn, is enabled by assumptions underlying revenue planning, business services and employee compensation, and a visual process. The system displays a stepped approach, indicated by icons, representing the tasks involved in the budget process. For example, the system requires uploading the previous year’s information, setting the assumptions, calculating the suggested figures based on assumptions, and amending the proposed outcome. As adapted by Rice and Rice (2005), SECI is applied as the socialization of tacit-to-tacit budgeting assumption knowledge is solidified during the design phase of this transformation exercise. The externalization phase, in which budgeting assumptions are transformed from tacit to explicit, is evidenced during the configuration phase of the new system. The systemic collaboration results in the explicit assumptions being collectively leveraged across the regions during and after the “go-live” phase of system development. Finally, the internalization phase involves the explicit assumptions being transformed into new tacit knowledge as the experts evolve new assumptions derived from the transformation process. Semiotics provides variance information through hue, with, for example, darker colours indicating higher variances. This trans-disciplinary communication provides the means for increased efficiency and effectiveness. The resulting budget framework is visually validated through a heatmap by comparing the budgeting accuracy and assumption complexity between the different regions where it was implemented. In summary, value is added in developing a new data visualization process, focusing on the role of budgeting assumptions and using planning process visualizations. This approach improves communication efficiency, effectiveness, and understanding of budgeting while enhancing accuracy

Design and Implement a New Remote Web-Based Visualization System for the Clinical Examination and Treatment of Skin Lesions. Evaluate the System Based on Interview Feedback

In the field of dermatology research, researchers commonly take pictures of the skin lesion area with traditional cameras and measure its size over time to determine the effectiveness of the treatment process. To revolutionize this current practice, along with the help of an application that takes 3D captures and AR measurements, the proposed web-based visualization system follows user-centric design principles to clean, re-structure, process, and present the collected raw data in an intuitive, interactive, simplistic, and responsive manner. The system couples state-of-the-art modern web development with a secure and robust logical server through application programming interfaces (API) designed following best practices in the industry. An evaluation study with five participants was conducted to assess certain design choices of the system. Subjective feedbacks on the system were positive overall, with suggestions toward certain detailed aspects of the system that can be implemented in future development.Master of Science in Information Scienc

Profiling and forecasting air pollutant index for Malaysia

Detection of poor air quality is important to provide an early warning system for air quality control and management. Thus, air pollutant index (API) is designed as a referential parameter in describing air pollution levels to provide information to enhance public awareness. This study aims to study API trend, time series forecasting methods, their performance evaluations and missing values effect for accurate early warning system using several approaches. First, a calendar grid visualization is introduced to effectively display API daily profiling for the whole of Malaysia in identifying the exact point of poor air quality. Second, comparisons between classical and modern forecasting methods, artificial neural network (ANN), fuzzy time series (FTS) and hybrid are carried out to identify the best model in Johor sampling stations; industrial, urban and suburban. Third, due to the issue of different perfect score in existing index measurement to evaluate forecast performance, a combination index measures is proposed alongside error magnitude measurement. Fourth, decomposition and spatial techniques are compared to find the effect of high accuracy imputations in API missing values. The finding presented that the air quality trend across the day, week, month and year are more significant due to the daily arrangement in the calendar grid visualization. The ANN model gives the best forecasting model of API for industrial and urban area while the hybrid model provide the best forecasting for suburban area. The forecasting performance for industrial and urban areas improve between 14% to 20% and 20% to 55% in error magnitude and index measurements, respectively when high accuracy missing values imputation is conducted. In conclusion, the profiling using calendar grid visualization is useful to guide the control actions of early warning system. Forecasting using modern methods give promising result in API and the improvements in measurements will assist in choosing the best forecasting method. Missing values imputation in data series can enhance the forecasting performance

Nurses’ Perceived Comparative Usefulness between an Icon-based Electronic Clinical Dashboard and an Integrated Clinical System

University of Minnesota Ph.D. dissertation. June 2019. Major: Nursing. Advisor: Connie White Delaney. 1 computer file (PDF); viii, 113 pages.Nurses place a high value on spending as much time as possible directly caring for patients. Optimizing the health system to allow nurses adequate patient-centered time is essential for improved patient experiences, improved health of populations, reducing the overall cost of healthcare, and improving the work life of health care clinicians and staff. As nurses are asked repeatedly to do more with less in a constantly changing and demanding work environment, it will be essential that technology is viewed by nurses as a partner. Pivotal to a successful integration of the technology is understanding nurses’ intentions to use the technology within their practice. The purpose of this research is to compare nurses’ perceived usefulness (PU), perceived ease of use (PEU), and workload burden for the Integrated Clinical System (ICS) and the icon-based electronic clinical dashboard system, INFUZE. The comparison of the nurses’ perceptions between the ICS and INFUZE, was conducted via a retrospective descriptive, comparative mixed-methods design using secondary data. Data from a private clinical database representing 189 registered nurses (RNs) practicing from September 2012 through December 2012 was used in the secondary data analysis. Data compared RNs’ perceptions of the current electronic health record (EHR) system and a home-grown (native) prototype called INFUZE. The dataset included quantitative measurement regarding usefulness, ease of use, and cognitive workload as measured by either a five-point (Technology Assessment Model [TAM]) or seven-point (NASA Task Load Index [TLX]) Likert scale. To complement and provide further insight, focus group data was also included and analyzed using a constant comparative and content analysis. The mixed-method design compared nurses’ perceptions of the availability of patient data between two systems and measured the need for timely access to pertinent patient data. New insights for clinical data use to support nurses were discovered. This descriptive, comparative mixed methods study compared nurses’ PU, PEU, and workload burden for the ICS and the icon-based electronic clinical dashboard system, INFUZE. The research approach used an extended conceptual framework, utilization the TAM and NASA TLX models and the inclusion of external variables of support resources, experience, demographics, and relevance to task. The secondary dataset included ICS (N=131) questionnaire data INFUZE (n=85) questionnaire data complete between September 19, 2012 and January 31, 2013. Transcripts of three voluntary focus groups were analyzed using content analysis methods to synthesize the feedback of 13 nurse participants. For PEU and PU, ICS was favored over INFUZE. For workload, INFUZE was favored over ICS. Focus group analysis revealed that there would be value in implementing an integrated dashboard interface if it is helpful in consuming actionable data rapidly; however, if it is not helpful, the interface would be irrelevant and/or burdensome. Furthermore, nurses considered the learning curve for new technology burdensome. In summary, the use of icons and/or dashboards tailored to the specific needs of nursing has potential to improve nurses’ experience, if the dashboard is a seamless part of the workflow and is integrated within existing technology. Further research is needed to understand human-computer interaction for specific interfaces in situ, toward the goal of developing an interface that is effective as an integrated and seamless companion to the core EHR