Combining data driven programming with component based software development:with applications in geovisualisation and dynamic data driven application systems

Software development methodologies are becoming increasingly abstract, progressing from low level assembly and implementation languages such as C and Ada, to component based approaches that can be used to assemble applications using technologies such as JavaBeans and the .NET framework. Meanwhile, model driven approaches emphasise the role of higher level models and notations, and embody a process of automatically deriving lower level representations and concrete software implementations. The relationship between data and software is also evolving. Modern data formats are becoming increasingly standardised, open and empowered in order to support a growing need to share data in both academia and industry. Many contemporary data formats, most notably those based on XML, are self-describing, able to specify valid data structure and content, and can also describe data manipulations and transformations. Furthermore, while applications of the past have made extensive use of data, the runtime behaviour of future applications may be driven by data, as demonstrated by the field of dynamic data driven application systems. The combination of empowered data formats and high level software development methodologies forms the basis of modern game development technologies, which drive software capabilities and runtime behaviour using empowered data formats describing game content. While low level libraries provide optimised runtime execution, content data is used to drive a wide variety of interactive and immersive experiences. This thesis describes the Fluid project, which combines component based software development and game development technologies in order to define novel component technologies for the description of data driven component based applications. The thesis makes explicit contributions to the fields of component based software development and visualisation of spatiotemporal scenes, and also describes potential implications for game development technologies. The thesis also proposes a number of developments in dynamic data driven application systems in order to further empower the role of data in this field.

Statistical analysis in microbiology:statnotes

This book is aimed primarily at microbiologists who are undertaking research and who require a basic knowledge of statistics to analyse their experimental data. Computer software employing a wide range of data analysis methods is widely available to experimental scientists. The availability of this software, however, makes it essential that investigators understand the basic principles of statistics. Statistical analysis of data can be complex with many different methods of approach, each of which applies in a particular experimental circumstance. Hence, it is possible to apply an incorrect statistical method to data and to draw the wrong conclusions from an experiment. The purpose of this book, which has its origin in a series of articles published in the Society for Applied Microbiology journal ‘The Microbiologist’, is an attempt to present the basic logic of statistics as clearly as possible and therefore, to dispel some of the myths that often surround the subject. The 28 ‘Statnotes’ deal with various topics that are likely to be encountered, including the nature of variables, the comparison of means of two or more groups, non-parametric statistics, analysis of variance, correlating variables, and more complex methods such as multiple linear regression and principal components analysis. In each case, the relevant statistical method is illustrated with examples drawn from experiments in microbiological research. The text incorporates a glossary of the most commonly used statistical terms and there are two appendices designed to aid the investigator in the selection of the most appropriate test.

Education and self-management for people newly diagnosed with type 2 diabetes: a qualitative study of patients' views.

OObjectives: We explored the perceptions, views and experiences of diabetes education in people with type 2 diabetes who were participating in a UK randomized controlled trial of methods of education. The intervention arm of the trial was based on DESMOND, a structured programme of group education sessions aimed at enabling self-management of diabetes, while the standard arm was usual care from general practices. Methods: Individual semi-structured interviews were conducted with 36 adult patients, of whom 19 had attended DESMOND education sessions and 17 had been randomized to receive usual care. Data analysis was based on the constant comparative method. Results: Four principal orientations towards diabetes and its management were identified: `resisters', `identity resisters, consequence accepters', `identity accepters, consequence resisters' and `accepters'. Participants offered varying accounts of the degree of personal responsibility that needed to be assumed in response to the diagnosis. Preferences for different styles of education were also expressed, with many reporting that they enjoyed and benefited from group education, although some reported ambivalence or disappointment with their experiences of education. It was difficult to identify striking thematic differences between accounts of people on different arms of the trial, although there was some very tentative evidence that those who attended DESMOND were more accepting of a changed identity and its implications for their management of diabetes. Discussion: No one single approach to education is likely to suit all people newly diagnosed with diabetes, although structured group education may suit many. This paper identifies varying orientations and preferences of people with diabetes towards forms of both education and self-management, which should be taken into account when planning approaches to education.

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.

Managing performance of organisational units with PIMsoft:a new DEA software

DEA literature continues apace but software has lagged behind. This session uses suitably selected data to present newly developed software which includes many of the most recent DEA models. The software enables the user to address a variety of issues not frequently found in existing DEA software such as: -Assessments under a variety of possible assumptions of returns to scale including NIRS and NDRS; -Scale elasticity computations; -Numerous Input/Output variables and truly unlimited number of assessment units (DMUs) -Panel data analysis -Analysis of categorical data (multiple categories) -Malmquist Index and its decompositions -Computations of Supper efficiency -Automated removal of super-efficient outliers under user-specified criteria; -Graphical presentation of results -Integrated statistical tests

Efficacy and safety of second-generation antipsychotic long-acting injections (SGA LAIs) in maintenance treatment of bipolar disorder:protocol for a systematic review and meta-analysis

INTRODUCTION: Bipolar disorder requires long-term treatment but non-adherence is a common problem. Antipsychotic long-acting injections (LAIs) have been suggested to improve adherence but none are licensed in the UK for bipolar. However, the use of second-generation antipsychotics (SGA) LAIs in bipolar is not uncommon albeit there is a lack of systematic review in this area. This study aims to systematically review safety and efficacy of SGA LAIs in the maintenance treatment of bipolar disorder. METHODS AND ANALYSIS: The protocol is based on Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) and will include only randomised controlled trials comparing SGA LAIs in bipolar. PubMed, EMBASE, CINAHL, Cochrane Library (CENTRAL), PsychINFO, LiLACS, http://www.clinicaltrials.gov will be searched, with no language restriction, from 2000 to January 2016 as first SGA LAIs came to the market after 2000. Manufacturers of SGA LAIs will also be contacted. Primary efficacy outcome is relapse rate or delayed time to relapse or reduction in hospitalisation and primary safety outcomes are drop-out rates, all-cause discontinuation and discontinuation due to adverse events. Qualitative reporting of evidence will be based on 21 items listed on standards for reporting qualitative research (SRQR) focusing on study quality (assessed using the Jadad score, allocation concealment and data analysis), risk of bias and effect size. Publication bias will be assessed using funnel plots. If sufficient data are available meta-analysis will be performed with primary effect size as relative risk presented with 95% CI. Sensitivity analysis, conditional on number of studies and sample size, will be carried out on manic versus depressive symptoms and monotherapy versus adjunctive therapy.

Knowledge workers and their relationships with organisations

The programme of research examines knowledge workers, their relationships with organisations, and perceptions of management practices through the development of a theoretical model and knowledge worker archetypes. Knowledge worker and non-knowledge worker archetypes were established through an analysis of the extant literature. After an exploratory study of knowledge workers in a small software development company the archetypes were refined to include occupational classification data and the findings from Study 1. The Knowledge Worker Characteristics Model (KWCM) was developed as a theoretical framework in order to analyse differences between the two archetypes within the IT sector. The KWCM comprises of the variables within the job characteristics model, creativity, goal orientation, identification and commitment. In Study 2, a global web based survey was conducted. There were insufficient non-knowledge worker responses and therefore a cluster analysis was conducted to interrogate the archetypes further. This demonstrated, unexpectedly, that that there were marked differences within the knowledge worker archetypes suggesting the need to granulate the archetype further. The theoretical framework and the archetypes were revised (as programmers and web developers) and the research study was refocused to examine occupational differences within knowledge work. Findings from Study 2 identified that there were significant differences between the archetypes in relation to the KWCM. 19 semi-structured interviews were conducted in Study 3 in order to deepen the analysis using qualitative data and to examine perceptions of people management practices. The findings from both studies demonstrate that there were significant differences between the two groups but also that job challenge, problem solving, intrinsic reward and team identification were of importance to both groups of knowledge workers. This thesis presents an examination of knowledge workers’ perceptions of work, organisations and people management practices in the granulation and differentiation of occupational archetypes.

Oxidiative lipidomics coming of age:advances in analysis of oxidized phospholipids in physiology and pathology

Significance: Oxidized phospholipids are now well-recognized as markers of biological oxidative stress and bioactive molecules with both pro-inflammatory and anti-inflammatory effects. While analytical methods continue to be developed for studies of generic lipid oxidation, mass spectrometry (MS) has underpinned the advances in knowledge of specific oxidized phospholipids by allowing their identification and characterization, and is responsible for the expansion of oxidative lipidomics. Recent Advances: Studies of oxidized phospholipids in biological samples, both from animal models and clinical samples, have been facilitated by the recent improvements in MS, especially targeted routines that depend on the fragmentation pattern of the parent molecular ion and improved resolution and mass accuracy. MS can be used to identify selectively individual compounds or groups of compounds with common features, which greatly improves the sensitivity and specificity of detection. Application of these methods have enabled important advances in understanding the mechanisms of inflammatory diseases such as atherosclerosis, steatohepatitis, leprosy and cystic fibrosis, and offer potential for developing biomarkers of molecular aspects of the diseases. Critical Issues and Future Directions: The future in this field will depend on development of improved MS technologies, such as ion mobility, novel enrichment methods and databases and software for data analysis, owing to the very large amount of data generated in these experiments. Imaging of oxidized phospholipids in tissue MS is an additional exciting direction emerging that can be expected to advance understanding of physiology and disease.

Cluster Analysis of Some Clases of Objects by Application of the Test Recognition Algorithms

The paper treats the task for cluster analysis of a given assembly of objects on the basis of the information contained in the description table of these objects. Various methods of cluster analysis are briefly considered. Heuristic method and rules for classification of the given assembly of objects are presented for the cases when their division into classes and the number of classes is not known. The algorithm is checked by a test example and two program products (PP) – learning systems and software for company management. Analysis of the results is presented.

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.

Manageable Workflows for Processing Parallel Sequencing Data

ACM Computing Classification System (1998): D.2.11, D.1.3, D.3.1, J.3, C.2.4.

Data Mining for Software Development Life Cycle Quality Management

Computer software plays an important role in business, government, society and sciences. To solve real-world problems, it is very important to measure the quality and reliability in the software development life cycle (SDLC). Software Engineering (SE) is the computing field concerned with designing, developing, implementing, maintaining and modifying software. The present paper gives an overview of the Data Mining (DM) techniques that can be applied to various types of SE data in order to solve the challenges posed by SE tasks such as programming, bug detection, debugging and maintenance. A specific DM software is discussed, namely one of the analytical tools for analyzing data and summarizing the relationships that have been identified. The paper concludes that the proposed techniques of DM within the domain of SE could be well applied in fields such as Customer Relationship Management (CRM), eCommerce and eGovernment. ACM Computing Classification System (1998): H.2.8.

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.

Perceptions and experiences of the implementation, management, use and optimisation of electronic prescribing systems in hospital settings:protocol for a systematic review of qualitative studies

Introduction: There is increasing evidence that electronic prescribing (ePrescribing) or computerised provider/physician order entry (CPOE) systems can improve the quality and safety of healthcare services. However, it has also become clear that their implementation is not straightforward and may create unintended or undesired consequences once in use. In this context, qualitative approaches have been particularly useful and their interpretative synthesis could make an important and timely contribution to the field. This review will aim to identify, appraise and synthesise qualitative studies on ePrescribing/CPOE in hospital settings, with or without clinical decision support. Methods and analysis: Data sources will include the following bibliographic databases: MEDLINE, MEDLINE In Process, EMBASE, PsycINFO, Social Policy and Practice via Ovid, CINAHL via EBSCO, The Cochrane Library (CDSR, DARE and CENTRAL databases), Nursing and Allied Health Sources, Applied Social Sciences Index and Abstracts via ProQuest and SCOPUS. In addition, other sources will be searched for ongoing studies (ClinicalTrials.gov) and grey literature: Healthcare Management Information Consortium, Conference Proceedings Citation Index (Web of Science) and Sociological abstracts. Studies will be independently screened for eligibility by 2 reviewers. Qualitative studies, either standalone or in the context of mixed-methods designs, reporting the perspectives of any actors involved in the implementation, management and use of ePrescribing/CPOE systems in hospital-based care settings will be included. Data extraction will be conducted by 2 reviewers using a piloted form. Quality appraisal will be based on criteria from the Critical Appraisal Skills Programme checklist and Standards for Reporting Qualitative Research. Studies will not be excluded based on quality assessment. A postsynthesis sensitivity analysis will be undertaken. Data analysis will follow the thematic synthesis method. Ethics and dissemination: The study does not require ethical approval as primary data will not be collected. The results of the study will be published in a peer-reviewed journal and presented at relevant conferences.

A qualitative case study of teacher perceptions and concerns during the implementation of literacy folders in a select elementary school in Broward County, Florida

Classroom teachers are often required to implement new procedures or practices in response to local or federal education mandates. Attempts to implement innovations, often do not take into account the personal side of change; the perceptions, concerns and needs of those required to implement the innovation. One innovation that was required by the School Board of Broward County, Florida for all elementary classroom teachers was the implementation of Literacy Folders. ^ This study attempted to address the personal side of change by identifying teacher concerns during the implementation of Literacy Folders in a select elementary school in Broward County Florida. The Concerns Based Adoption model (CBAM) for change was used as the conceptual framework for this qualitative case study. ^ Sources of data for this study included participant interviews, observations and analysis of documents. Informal conversations with the participants and unscheduled classroom visits were also sources of data. Seven classroom teachers were interviewed using a predesigned interview guide developed based on the CBAM of change, specifically the Stages of Concern Dimension. Participant responses were coded into two categories, (a) recollections of past perceptions, and (b) present perceptions regarding the innovation. ^ Data analysis resulted in the emergence of one major theme and two subordinate themes. The themes were related to time and purpose of the innovation. The researcher also discovered that the participants exhibited responses typically representative of the CBAM for individuals who are in the process of adjusting to a new innovation. ^ Recommendations based on participant concerns are made for improving the implementation of the innovation. Recommendations for alternatives to the innovation and suggestions regarding areas for further research in the field of educational change are also made. ^