Real-Time and Data-Driven Operation Optimization and Knowledge Discovery for an Enterprise Information System

An enterprise information system (EIS) is an integrated data-applications platform characterized by diverse, heterogeneous, and distributed data sources. For many enterprises, a number of business processes still depend heavily on static rule-based methods and extensive human expertise. Enterprises are faced with the need for optimizing operation scheduling, improving resource utilization, discovering useful knowledge, and making data-driven decisions.

This thesis research is focused on real-time optimization and knowledge discovery that addresses workflow optimization, resource allocation, as well as data-driven predictions of process-execution times, order fulfillment, and enterprise service-level performance. In contrast to prior work on data analytics techniques for enterprise performance optimization, the emphasis here is on realizing scalable and real-time enterprise intelligence based on a combination of heterogeneous system simulation, combinatorial optimization, machine-learning algorithms, and statistical methods.

On-demand digital-print service is a representative enterprise requiring a powerful EIS.We use real-life data from Reischling Press, Inc. (RPI), a digit-print-service provider (PSP), to evaluate our optimization algorithms.

In order to handle the increase in volume and diversity of demands, we first present a high-performance, scalable, and real-time production scheduling algorithm for production automation based on an incremental genetic algorithm (IGA). The objective of this algorithm is to optimize the order dispatching sequence and balance resource utilization. Compared to prior work, this solution is scalable for a high volume of orders and it provides fast scheduling solutions for orders that require complex fulfillment procedures. Experimental results highlight its potential benefit in reducing production inefficiencies and enhancing the productivity of an enterprise.

We next discuss analysis and prediction of different attributes involved in hierarchical components of an enterprise. We start from a study of the fundamental processes related to real-time prediction. Our process-execution time and process status prediction models integrate statistical methods with machine-learning algorithms. In addition to improved prediction accuracy compared to stand-alone machine-learning algorithms, it also performs a probabilistic estimation of the predicted status. An order generally consists of multiple series and parallel processes. We next introduce an order-fulfillment prediction model that combines advantages of multiple classification models by incorporating flexible decision-integration mechanisms. Experimental results show that adopting due dates recommended by the model can significantly reduce enterprise late-delivery ratio. Finally, we investigate service-level attributes that reflect the overall performance of an enterprise. We analyze and decompose time-series data into different components according to their hierarchical periodic nature, perform correlation analysis,

and develop univariate prediction models for each component as well as multivariate models for correlated components. Predictions for the original time series are aggregated from the predictions of its components. In addition to a significant increase in mid-term prediction accuracy, this distributed modeling strategy also improves short-term time-series prediction accuracy.

In summary, this thesis research has led to a set of characterization, optimization, and prediction tools for an EIS to derive insightful knowledge from data and use them as guidance for production management. It is expected to provide solutions for enterprises to increase reconfigurability, accomplish more automated procedures, and obtain data-driven recommendations or effective decisions.

The Jama legal narrative part II: A foray into concepts of improbability

In judicial decision making, the doctrine of chances takes explicitly into account the odds. There is more to forensic statistics, as well as various probabilistic approaches, which taken together form the object of an enduring controversy in the scholarship of legal evidence. In this paper, I reconsider the circumstances of the Jama murder and inquiry (dealt with in Part I of this paper: 'The JAMA Model and Narrative Interpretation Patterns'), to illustrate yet another kind of probability or improbability. What is improbable about the Jama story is actually a given, which contributes in terms of dramatic underlining. In literary theory, concepts of narratives being probable or improbable date back from the eighteenth century, when both prescientific and scientific probability were infiltrating several domains, including law. An understanding of such a backdrop throughout the history of ideas is, I claim, necessary for Artificial Intelligence (AI) researchers who may be tempted to apply statistical methods to legal evidence. The debate for or against probability (and especially Bayesian probability) in accounts of evidence has been flourishing among legal scholars; nowadays both the Bayesians (e.g. Peter Tillers) and the Bayesio-skeptics (e.g. Ron Allen), among those legal scholars who are involved in the controversy, are willing to give AI research a chance to prove itself and strive towards models of plausibility that would go beyond probability as narrowly meant. This debate within law, in turn, has illustrious precedents: take Voltaire, he was critical of the application of probability even to litigation in civil cases; take Boole, he was a starry-eyed believer in probability applications to judicial decision making. Not unlike Boole, the founding father of computing, nowadays computer scientists approaching the field may happen to do so without full awareness of the pitfalls. Hence, the usefulness of the conceptual landscape I sketch here.

Pl2 resistance gene differentiates the pathogenicity in four "Plasmopara halstedii" (sunflower downy mildew) races 304, 704, 314 and 714

In order to clarify the role of Pl2 resistance gene in differentiation the pathogenicity in Plasmopara halstedii (sunflower downy mildew), analyses were carried out in four pathotypes: isolates of races 304 and 314 that do not overcome Pl2 gene, and isolates of races 704 and 714 that can overcome Pl2 gene. Based on the reaction for the P. halstedii isolates to sunflower hybrids varying only in Pl resistance genes, isolates of races 704 and 714 were more virulent than isolates of races 304 and 314. Index of aggressiveness was calculated for pathogen isolates and revealed the presence of significant differences between isolates of races 304 and 314 (more aggressive) and isolates of races 704 and 714 (less aggressive). There were morphological and genetic variations for the four P. halstedii isolates without a correlation with pathogenic diversity. The importance of the Pl2 resistance gene to differentiate the pathogenicity in sunflower downy mildew was discussed.

Brief review of invariant texture analysis methods

This paper considers invariant texture analysis. Texture analysis approaches whose performances are not affected by translation, rotation, affine, and perspective transform are addressed. Existing invariant texture analysis algorithms are carefully studied and classified into three categories: statistical methods, model based methods, and structural methods. The importance of invariant texture analysis is presented first. Each approach is reviewed according to its classification, and its merits and drawbacks are outlined. The focus of possible future work is also suggested.

Psychology Statistics for Dummies

The quick, easy way to master all the statistics you'll ever need The bad news first: if you want a psychology degree you'll need to know statistics. Now for the good news: Psychology Statistics For Dummies. Featuring jargon-free explanations, step-by-step instructions and dozens of real-life examples, Psychology Statistics For Dummies makes the knotty world of statistics a lot less baffling. Rather than padding the text with concepts and procedures irrelevant to the task, the authors focus only on the statistics psychology students need to know. As an alternative to typical, lead-heavy statistics texts or supplements to assigned course reading, this is one book psychology students won't want to be without. Ease into statistics – start out with an introduction to how statistics are used by psychologists, including the types of variables they use and how they measure them Get your feet wet – quickly learn the basics of descriptive statistics, such as central tendency and measures of dispersion, along with common ways of graphically depicting information Meet your new best friend – learn the ins and outs of SPSS, the most popular statistics software package among psychology students, including how to input, manipulate and analyse data Analyse this – get up to speed on statistical analysis core concepts, such as probability and inference, hypothesis testing, distributions, Z-scores and effect sizes Correlate that – get the lowdown on common procedures for defining relationships between variables, including linear regressions, associations between categorical data and more Analyse by inference – master key methods in inferential statistics, including techniques for analysing independent groups designs and repeated-measures research designs Open the book and find: Ways to describe statistical data How to use SPSS statistical software Probability theory and statistical inference Descriptive statistics basics How to test hypotheses Correlations and other relationships between variables Core concepts in statistical analysis for psychology Analysing research designs Learn to: Use SPSS to analyse data Master statistical methods and procedures using psychology-based explanations and examples Create better reports Identify key concepts and pass your course

Assessment Method for a Power Analysis to Identify Differentially Expressed Pathways

Gene expression data can provide a very rich source of information for elucidating the biological function on the pathway level if the experimental design considers the needs of the statistical analysis methods. The purpose of this paper is to provide a comparative analysis of statistical methods for detecting the differentially expression of pathways (DEP). In contrast to many other studies conducted so far, we use three novel simulation types, producing a more realistic correlation structure than previous simulation methods. This includes also the generation of surrogate data from two large-scale microarray experiments from prostate cancer and ALL. As a result from our comprehensive analysis of 41,004 parameter configurations, we find that each method should only be applied if certain conditions of the data from a pathway are met. Further, we provide method-specific estimates for the optimal sample size for microarray experiments aiming to identify DEP in order to avoid an underpowered design. Our study highlights the sensitivity of the studied methods on the parameters of the system. © 2012 Tripahti and Emmert-Streib.

It's all about relationships:A qualitative study of health researchers' perspectives of conducting interdisciplinary health research

Background. Interdisciplinary research has been promoted as an optimal research paradigm in the health sciences, yet little is known about how researchers experience interdisciplinarity in practice. This study sought to determine how interdisciplinary research was conceptualized and operationalized from the researcher's perspective and to better understand how best to facilitate interdisciplinary research success. Methods. Key informant interviews were conducted with health researchers with expertise or experience in conducting interdisciplinary research. Interviews were completed either in person or over the telephone using a semi-structured interview guide. Data collection occurred simultaneously with data analysis so that emerging themes could be explored in subsequent interviews. A content analysis approach was used. Results. Nineteen researchers took part in this study. Interdisciplinary research was conceptualized disparately between participants, and there was modest attention towards operationalization of interdisciplinary research. There was one overriding theme, "It's all about relationships", that emerged from the data. Within this theme, there were four related subthemes: 1) Involvement in interdisciplinary research; 2) Why do I do interdisciplinary research?; 3) Managing and fostering interdisciplinary relationships; and 4) The prickly side to interdisciplinary research. Together, these themes suggest that the choice to conduct interdisciplinary research, though often driven by the research question, is highly influenced by interpersonal and relationship-related factors. In addition, researchers preferred to engage in interdisciplinary research with those that they had already established relationships and where their role in the research process was clearly articulated. A focus on relationship building was seen as a strong facilitator of interdisciplinary success. Conclusion. Many health researchers experienced mixed reactions towards their involvement in interdisciplinary research. A well thought-out rationale for interdisciplinary research, and strategies to utilize the contribution of each researcher involved were seen as facilitators towards maximizing the benefits that could be derived from interdisciplinary research. © 2008 Nair et al; licensee BioMed Central Ltd.

Harnessing the complexity of gene expression data from cancer: from single gene to structural pathway methods

High-dimensional gene expression data provide a rich source of information because they capture the expression level of genes in dynamic states that reflect the biological functioning of a cell. For this reason, such data are suitable to reveal systems related properties inside a cell, e.g., in order to elucidate molecular mechanisms of complex diseases like breast or prostate cancer. However, this is not only strongly dependent on the sample size and the correlation structure of a data set, but also on the statistical hypotheses tested. Many different approaches have been developed over the years to analyze gene expression data to (I) identify changes in single genes, (II) identify changes in gene sets or pathways, and (III) identify changes in the correlation structure in pathways. In this paper, we review statistical methods for all three types of approaches, including subtypes, in the context of cancer data and provide links to software implementations and tools and address also the general problem of multiple hypotheses testing. Further, we provide recommendations for the selection of such analysis methods.

Survey of challenges and best practice strategies for participant recruitment to research projects in northern ireland

Participant recruitment is understood to be one of the most difficult aspects of the research process. Researchers are now devoting increasing amounts of time and resources to understand how participants decide to take part in research and what researchers can do to make their work appeal to potential participants. The purpose of the study is to assess the problems experienced by researchers in Northern Ireland when recruiting human participants into trials and studies and to gain insight into how researchers handle and overcome these issues. The main research question being addressed by this research is to develop an understanding of the problems experienced by staff when recruiting human participants to research projects. Methods used to increase study recruitment were also examined. The participants in this research are investigators and other associated staff on research studies based in Northern Ireland. Potential participants were identified through contacts with research active organizations such as the academic researchers in Queen’s University Belfast and research physicians and clinical trialists employed by the Belfast Health and Social Care Trust. Each organization forwarded on the survey request via email or newsletters. Researchers willing to take part accessed the questionnaire through the Survey Monkey website. This study utilised a cross-sectional questionnaire design.

Comparison of methods used to calculate typical threshold values for potentially toxic elements in soil

The environmental quality of land can be assessed by calculating relevant threshold values, which differentiate between concentrations of elements resulting from geogenic and diffuse anthropogenic sources and concentrations generated by point sources of elements. A simple process allowing the calculation of these typical threshold values (TTVs) was applied across a region of highly complex geology (Northern Ireland) to six elements of interest; arsenic, chromium, copper, lead, nickel and vanadium. Three methods for identifying domains (areas where a readily identifiable factor can be shown to control the concentration of an element) were used: k-means cluster analysis, boxplots and empirical cumulative distribution functions (ECDF). The ECDF method was most efficient at determining areas of both elevated and reduced concentrations and was used to identify domains in this investigation. Two statistical methods for calculating normal background concentrations (NBCs) and upper limits of geochemical baseline variation (ULBLs), currently used in conjunction with legislative regimes in the UK and Finland respectively, were applied within each domain. The NBC methodology was constructed to run within a specific legislative framework, and its use on this soil geochemical data set was influenced by the presence of skewed distributions and outliers. In contrast, the ULBL methodology was found to calculate more appropriate TTVs that were generally more conservative than the NBCs. TTVs indicate what a "typical" concentration of an element would be within a defined geographical area and should be considered alongside the risk that each of the elements pose in these areas to determine potential risk to receptors.

Sediment changes upstream and downstream of the Altnahinch Dam in the River Bush in Northern Ireland: A preliminary study

The River Bush must reach a standard of good ecological potential (GEP) by 2015 due to the requirements of the water framework directive. The role of sediments within a water body is extremely important to all aspects of a river's regime. The aim of this research is to investigate the effects of Altnahinch Dam on sediment distribution in the River Bush (a heavily modified water body) with comparison made against the Glendun River (an unmodified water body). Samples collected from the rivers were analysed by physical (pebble count, sieve analysis) and statistical methods (ANOVA, GRADISTAT). An increase in fine sediments upstream of the dam provides evidence that the dam is impacting sediment distribution. Downstream effects are not shown to be significant. The output of this study also implies similar impacts at other drinking water storage impoundments. This research recommends that a sediment management plan be put in place for Altnahinch Dam and that further studies be carried-out concentrating on fine sediment distribution upstream of the dam. 

Any versus long-term prescribing of high risk medications in older people using 2012 Beers Criteria: results from three cross-sectional samples of primary care records for 2003/4, 2007/8 and 2011/12

Background: High risk medications are commonly prescribed to older US patients. Currently, less is known about high risk medication prescribing in other Western Countries, including the UK. We measured trends and correlates of high risk medication prescribing in a subset of the older UK population (community/institutionalized) to inform harm minimization efforts. Methods: Three cross-sectional samples from primary care electronic clinical records (UK Clinical Practice Research Datalink, CPRD) in fiscal years 2003/04, 2007/08 and 2011/12 were taken. This yielded a sample of 13,900 people aged 65 years or over from 504 UK general practices. High risk medications were defined by 2012 Beers Criteria adapted for the UK. Using descriptive statistical methods and regression modelling, prevalence of ‘any’ (drugs prescribed at least once per year) and ‘long-term’ (drugs prescribed all quarters of year) high risk medication prescribing and correlates were determined. Results: While polypharmacy rates have risen sharply, high risk medication prevalence has remained stable across a decade. A third of older (65+) people are exposed to high risk medications, but only half of the total prevalence was long-term (any = 38.4 % [95 % CI: 36.3, 40.5]; long-term = 17.4 % [15.9, 19.9] in 2011/12). Long-term but not any high risk medication exposure was associated with older ages (85 years or over). Women and people with higher polypharmacy burden were at greater risk of exposure; lower socio-economic status was not associated. Ten drugs/drug classes accounted for most of high risk medication prescribing in 2011/12. Conclusions: High risk medication prescribing has not increased over time against a background of increasing polypharmacy in the UK. Half of patients receiving high risk medications do so for less than a year. Reducing or optimising the use of a limited number of drugs could dramatically reduce high risk medications in older people. Further research is needed to investigate why the oldest old and women are at greater risk. Interventions to reduce high risk medications may need to target shorter and long-term use separately.

Development of a Piggybac based direct reprogramming system for derivation of integration free induced pluripotent stem cells

Induced pluripotent stem cells (iPSc) have great potential for applications in regenerative medicine, disease modeling and basic research. Several methods have been developed for their derivation. The original method of Takahashi and Yamanaka involved the use of retroviral vectors which result in insertional mutagenesis, presence in the genome of potential oncogenes and effects of residual transgene expression on differentiation bias of each particular iPSc line. Other methods have been developed, using different viral vectors (adenovirus and Sendai virus), transient plasmid transfection, mRNA transduction, protein transduction and use of small molecules. However, these methods suffer from low efficiencies; can be extremely labor intensive, or both. An additional method makes use of the piggybac transposon, which has the advantage of inserting its payload into the host genome and being perfectly excised upon re-expression of the transposon transposase. Briefly, a policistronic cassette expressing Oct4, Sox2, Klf4 and C-Myc flanked by piggybac terminal repeats is delivered to the cells along with a plasmid transiently expressing piggybac transposase. Once reprogramming occurs, the cells are re-transfected with transposase and subclones free of tranposon integrations screened for. The procedure is therefore very labor intensive, requiring multiple manipulations and successive rounds of cloning and screening. The original method for reprogramming with the the PiggyBac transposon was created by Woltjen et al in 2009 (schematized here) and describes a process with which it is possible to obtain insert-free iPSc. Insert-free iPSc enables the establishment of better cellular models of iPS and adds a new level of security to the use of these cells in regenerative medicine. Due to the fact that it was based on several low efficiency steps, the overall efficiency of the method is very low (<1%). Moreover, the stochastic transfection, integration, excision and the inexistence of an active way of selection leaves this method in need of extensive characterization and screening of the final clones. In this work we aime to develop a non-integrative iPSc derivation system in which integration and excision of the transgenes can be controlled by simple media manipulations, avoiding labor intensive and potentially mutagenic procedures. To reach our goal we developed a two vector system which is simultaneously delivered to original population of fibroblasts. The first vector, Remo I, carries the reprogramming cassette and GFP under the regulation of a constitutive promoter (CAG). The second vector, Eneas, carries the piggybac transposase associated with an estrogen receptor fragment (ERT2), regulated in a TET-OFF fashion, and its equivalent reverse trans-activator associated with a positive-negative selection cassette under a constitutive promoter. We tested its functionality in HEK 293T cells. The protocol is divided in two the following steps: 1) Obtaining acceptable transfection efficiency into human fibroblasts. 2) Testing the functionality of the construct 3) Determining the ideal concentration of DOX for repressing mPB-ERT2 expression 4) Determining the ideal concentration of TM for transposition into the genome 5) Determining the ideal Windows of no DOX/TM pulse for transposition into the genome 6) 3, 4 and 5) for transposition out of the genome 7) Determination of the ideal concentration of GCV for negative selection We successfully demonstrated that ENEAS behaved as expected in terms of DOX regulation of the expression of mPB-ERT2. We also demonstrated that by delivering the plasmid into 293T HEK cells and manipulating the levels of DOX and TM in the medium, we could obtain puromycin resistant lines. The number of puromycin resistant colonies obtained was significantly higher when DOX as absent, suggesting that the colonies resulted from transposition events. Presence of TM added an extra layer of regulation, albeit weaker. Our PCR analysis, while not a clean as would be desired, suggested that transposition was indeed occurring, although a background level of random integration could not be ruled out. Finally, our attempt to determine whether we could use GVC to select clones that had successfully mobilized PB out of the genome was unsuccessful. Unexpectedly, 293T HEK cells that had been transfected with ENEAS and selected for puromycin resistance were insensitive to GCV.

Contribuição da ecografia cerebral transfontanelar para a avaliação do crescimento cerebral do recém-nascido pré-termo

Tese de doutoramento, Medicina (Pediatria), Universidade de Lisboa, Faculdade de Medicina, 2013