Pedestrian accidents occurring on freeways: an investigation of causative factors, accident data collection and analysis. Volume I: executive summary. Final report, phases III and IV.

Federal Highway Administration, Washington, D.C.

Pedestrian accidents occurring on freeways: an investigation of causative factors, accident data collection and analysis. Volume II. Final report, phases III and IV.

Federal Highway Administration, Washington, D.C.

Causative factors and countermeasures for rural and suburban pedestrian accidents: accident data collection and analysis - appendices. Phase I and II.

Federal Highway Administration, Washington, D.C.

The use and analysis of recorded traffic accident data. Final report.

Texas State Department of Highways and Public Transportation, Transportation Planning Division, Austin

Social Support Networks and Self-Efficacy of Peruvian Women Diagnosed with Cancer: A Biocultural Analysis of Health Behavior Constructs

Thesis (Ph.D.)--University of Washington, 2016-06

Data flow and validation in workflow modelling

A complete workflow specification requires careful integration of many different process characteristics. Decisions must be made as to the definitions of individual activities, their scope, the order of execution that maintains the overall business process logic, the rules governing the discipline of work list scheduling to performers, identification of time constraints and more. The goal of this paper is to address an important issue in workflows modelling and specification, which is data flow, its modelling, specification and validation. Researchers have neglected this dimension of process analysis for some time, mainly focussing on structural considerations with limited verification checks. In this paper, we identify and justify the importance of data modelling in overall workflows specification and verification. We illustrate and define several potential data flow problems that, if not detected prior to workflow deployment may prevent the process from correct execution, execute process on inconsistent data or even lead to process suspension. A discussion on essential requirements of the workflow data model in order to support data validation is also given..

An introduction to analysis of variance (ANOVA) with special reference to data from clinical experiments in optometry

This article is aimed primarily at eye care practitioners who are undertaking advanced clinical research, and who wish to apply analysis of variance (ANOVA) to their data. ANOVA is a data analysis method of great utility and flexibility. This article describes why and how ANOVA was developed, the basic logic which underlies the method and the assumptions that the method makes for it to be validly applied to data from clinical experiments in optometry. The application of the method to the analysis of a simple data set is then described. In addition, the methods available for making planned comparisons between treatment means and for making post hoc tests are evaluated. The problem of determining the number of replicates or patients required in a given experimental situation is also discussed. Copyright (C) 2000 The College of Optometrists.

Quantitative neuropathology:data collection and statistical analysis

The use of quantitative methods has become increasingly important in the study of neuropathology and especially in neurodegenerative disease. Disorders such as Alzheimer's disease (AD) and the frontotemporal dementias (FTD) are characterized by the formation of discrete, microscopic, pathological lesions which play an important role in pathological diagnosis. This chapter reviews the advantages and limitations of the different methods of quantifying pathological lesions in histological sections including estimates of density, frequency, coverage, and the use of semi-quantitative scores. The sampling strategies by which these quantitative measures can be obtained from histological sections, including plot or quadrat sampling, transect sampling, and point-quarter sampling, are described. In addition, data analysis methods commonly used to analysis quantitative data in neuropathology, including analysis of variance (ANOVA), polynomial curve fitting, multiple regression, classification trees, and principal components analysis (PCA), are discussed. These methods are illustrated with reference to quantitative studies of a variety of neurodegenerative disorders.

Analysis and Data Mining of Lead-Zinc Ore Data

This paper presents the results of our data mining study of Pb-Zn (lead-zinc) ore assay records from a mine enterprise in Bulgaria. We examined the dataset, cleaned outliers, visualized the data, and created dataset statistics. A Pb-Zn cluster data mining model was created for segmentation and prediction of Pb-Zn ore assay data. The Pb-Zn cluster data model consists of five clusters and DMX queries. We analyzed the Pb-Zn cluster content, size, structure, and characteristics. The set of the DMX queries allows for browsing and managing the clusters, as well as predicting ore assay records. A testing and validation of the Pb-Zn cluster data mining model was developed in order to show its reasonable accuracy before beingused in a production environment. The Pb-Zn cluster data mining model can be used for changes of the mine grinding and floatation processing parameters in almost real-time, which is important for the efficiency of the Pb-Zn ore beneficiation process. ACM Computing Classification System (1998): H.2.8, H.3.3.

Bagging model with cost sensitive analysis on diabetes data

Diabetes patients might suffer from an unhealthy life, long-term treatment and chronic complicated diseases. The decreasing hospitalization rate is a crucial problem for health care centers. This study combines the bagging method with base classifier decision tree and costs-sensitive analysis for diabetes patients' classification purpose. Real patients' data collected from a regional hospital in Thailand were analyzed. The relevance factors were selected and used to construct base classifier decision tree models to classify diabetes and non-diabetes patients. The bagging method was then applied to improve accuracy. Finally, asymmetric classification cost matrices were used to give more alternative models for diabetes data analysis.

Cultural influence and learning to teach: Beginning teachers' perceptions of teacher preparation programs and school support systems

Beginning teachers in the field of English Language Arts and Reading are responsible for providing literacy instruction to students. Teachers need a broad background in teaching reading, writing, listening, speaking, and viewing, as well as critical thinking. In secondary schools in particular, beginning English Language Arts and Reading teachers are also faced with the challenge of preparing students to be proficient enough readers and writers to meet required State standards. Beginning teachers must navigate compelling challenges that exist during the first years of teaching. The school support systems available to new teachers are an integral part of their educational development. ^ This qualitative study was conceptualized as an in-depth examination of the experiences and perceptions of eight beginning teachers. They represented different racial/ethnic groups, attended different teacher preparation programs, and taught in different school cultures. The data were collected through formal and informal interviews and classroom observations. A qualitative system of data analysis was used to examine the patterns relating to the interrelationship between teacher preparation programs and school support systems. ^ The experiences of the beginning teachers in this study indicated that teacher education programs should provide preservice teachers with a critical knowledge base for teaching literature, language, and composition. A liberal arts background in English, followed by an extensive program focusing on pedagogy, seems to provide a thorough level of curriculum and instructional practices needed for teaching in 21st century classrooms. The data further suggested that a school support system should pair beginning teachers with mentor teachers and provide a caring, professional environment that seeks to nurture the teacher as she/he develops during the first years of teaching. ^

Perceptions of underprepared community college students regarding their educational achievement

The purpose of this study was to understand the perceptions of underprepared college students who had participated in learning communities and who persisted to complete developmental classes and earned at least 30 college-level credit hours to graduate and the perceptions of their peers who had dropped out of college. The theories posed by Tinto, Astin, and Freire formed the framework for this case study. The 22 participants were graduates or transfer students now attending a public university, currently-enrolled sophomores, and students no longer enrolled at the time of the study. Semi-structured individual interviews and a group interview provided narrative data which were transcribed, coded, and analyzed to gain insights into the experiences and perspectives of the participants. The group interview provided a form of member checking to increase accuracy in interpreting themes. A peer reviewer provided feedback on the researcher’s data analysis procedures. The analysis yielded four themes and 14 sub-themes which captured the essence of the participants’ experiences. The pre-college characteristics/traits theme described the students’ internal values and attributes acquired prior to college. The external college support/community influence theme described the encouragement to attend college the students received from family, friends, and high school teachers. The social involvement theme described the students’ participation in campus activities and their interactions with other members of the campus. The academic integration theme described students’ use of campus resources and their contacts with the faculty. The persisters reported strong family and peer support, a sense of responsibility, appreciation for dedicated and caring faculty, and a belief that an education can be a liberatory means to achieve their goals. The non-persisters did not report having the same sense of purpose, goal orientation, determination, obligation to meet family expectations, peer support, campus involvement, positive faculty experiences, and time management skills. The researcher offers an emerging model for understanding factors associated with persistence and three recommendations for enhancing the academic experience of underprepared college students: (a) include a critical pedagogy perspective in coursework where possible, (b) integrate co-curricular activities with the academic disciplines, and (c) increase student-faculty interaction.

Early career special education teachers' perceptions of school site induction support

Research highlights teacher attrition as one of the biggest challenges facing public schools and their attempts to provide a quality teacher for every student (Ingersoll & Smith, 2003). The teacher shortage is particularly daunting in special education where teachers are over twice as likely to leave the field. The first few years of teaching are the most critical in determining whether or not a beginning teacher will stay in the teaching profession (Whitaker, 2000). ^ A mixed-methods sequential explanatory design was utilized to examine research questions focused on the components of induction support that early career teachers received at their school site, including what they considered most valuable to their long-term retention in the classroom and their development as a quality teacher. Eighty seven early career special education teachers were surveyed during the first phase of the study and six participants were interviewed during the second phase. ^ Data analysis of the Likert-scale survey used in the study revealed that the majority of the respondents received at least 21 of the 25 listed induction components. Moreover, early career special education teachers indicated that they valued all 25 induction components. In addition, findings revealed that over two thirds of the respondents indicated a desire to remain a special education teacher. Overall, early career special education teachers felt confident in their abilities to teach students with disabilities; however, nearly half of the respondents did not feel satisfied with the induction they received. Independent t-tests showed a statistically significant difference between teachers who indicated a desire to remain in special education and those that did not on the level of satisfaction with their induction experience. ^ The six interviews provided elaboration and clarification of the survey responses. The participants expressed their passion for the art of teaching, their dedication to students with disabilities, and their frustration with being a beginning teacher. Furthermore, it was reported that the overall school culture was not very supportive. Participants offered relevant ideas for additional or alternate induction components that would be more effective.^

Formal modeling and analysis techniques for high level Petri nets

Petri Nets are a formal, graphical and executable modeling technique for the specification and analysis of concurrent and distributed systems and have been widely applied in computer science and many other engineering disciplines. Low level Petri nets are simple and useful for modeling control flows but not powerful enough to define data and system functionality. High level Petri nets (HLPNs) have been developed to support data and functionality definitions, such as using complex structured data as tokens and algebraic expressions as transition formulas. Compared to low level Petri nets, HLPNs result in compact system models that are easier to be understood. Therefore, HLPNs are more useful in modeling complex systems. ^ There are two issues in using HLPNs—modeling and analysis. Modeling concerns the abstracting and representing the systems under consideration using HLPNs, and analysis deals with effective ways study the behaviors and properties of the resulting HLPN models. In this dissertation, several modeling and analysis techniques for HLPNs are studied, which are integrated into a framework that is supported by a tool. ^ For modeling, this framework integrates two formal languages: a type of HLPNs called Predicate Transition Net (PrT Net) is used to model a system's behavior and a first-order linear time temporal logic (FOLTL) to specify the system's properties. The main contribution of this dissertation with regard to modeling is to develop a software tool to support the formal modeling capabilities in this framework. ^ For analysis, this framework combines three complementary techniques, simulation, explicit state model checking and bounded model checking (BMC). Simulation is a straightforward and speedy method, but only covers some execution paths in a HLPN model. Explicit state model checking covers all the execution paths but suffers from the state explosion problem. BMC is a tradeoff as it provides a certain level of coverage while more efficient than explicit state model checking. The main contribution of this dissertation with regard to analysis is adapting BMC to analyze HLPN models and integrating the three complementary analysis techniques in a software tool to support the formal analysis capabilities in this framework. ^ The SAMTools developed for this framework in this dissertation integrates three tools: PIPE+ for HLPNs behavioral modeling and simulation, SAMAT for hierarchical structural modeling and property specification, and PIPE+Verifier for behavioral verification.^

Options to advance co-ordination and analysis of climate data in Europe

Systematic, high-quality observations of the atmosphere, oceans and terrestrial environments are required to improve understanding of climate characteristics and the consequences of climate change. The overall aim of this report is to carry out a comparative assessment of approaches taken to addressing the state of European observations systems and related data analysis by some leading actors in the field. This research reports on approaches to climate observations and analyses in Ireland, Switzerland, Germany, The Netherlands and Austria and explores options for a more coordinated approach to national responses to climate observations in Europe. The key aspects addressed are: an assessment of approaches to develop GCOS and provision of analysis of GCOS data; an evaluation of how these countries are reporting development of GCOS; highlighting best practice in advancing GCOS implementation including analysis of Essential Climate Variables (ECVs); a comparative summary of the differences and synergies in terms of the reporting of climate observations; an overview of relevant European initiatives and recommendations on how identified gaps might be addressed in the short to medium term.