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A developmental validation was performed to demonstrate reliability, reproducibility, and robustness of the ANDE Rapid DNA Identification System for processing of crime scene and disaster victim identification (DVI) samples. A total of 1705 samples were evaluated, including blood, oral epithelial samples from drinking containers, samples on FTA and untreated paper, semen, bone, and soft tissues. This study was conducted to address the FBI’s Quality Assurance Standards on developmental validation and to accumulate data from a sufficient number of unique donors and sample types to meet NDIS submission requirements for acceptance of the ANDE Expert System for casework use. To date, no Expert System has been approved for such samples, but the results of this study demonstrated that the automated Expert System performs similarly to conventional laboratory data analysis. Furthermore, Rapid DNA analysis demonstrated accuracy, precision, resolution, concordance, and reproducibility that were comparable to conventional processing along with appropriate species specificity, limit of detection, performance in the presence of inhibitors. No lane‐to‐lane or run‐to‐run contamination was observed, and the system correctly identified the presence of mixtures. Taken together, the ANDE instrument, I‐Chip consumable, FlexPlex chemistry (a 27‐locus STR assay compatible with all widely used global loci, including the CODIS core 20 loci), and automated Expert System successfully processed and interpreted more than 1200 unique samples with over 99.99% concordant CODIS alleles. This extensive developmental validation data provides support for broad use of the system by agencies and accredited forensic laboratories in single‐source suspect‐evidence comparisons, local database searches, and DVI.

Label-free mass spectrometry (MS) has developed into an important tool applied in various fields of biological and life sciences. Several software exist to process the raw MS data into quantified protein abundances, including open source and commercial solutions. Each software includes a set of unique algorithms for different tasks of the MS data processing workflow. While many of these algorithms have been compared separately, a thorough and systematic evaluation of their overall performance is missing. Moreover, systematic information is lacking about the amount of missing values produced by the different proteomics software and the capabilities of different data imputation methods to account for them. In this study, we evaluated the performance of five popular quantitative label-free proteomics software workflows using four different spike-in data sets. Our extensive testing included the number of proteins quantified and the number of missing values produced by each workflow, the accuracy of detecting differential expression and logarithmic fold change and the effect of different imputation and filtering methods on the differential expression results. We found that the Progenesis software performed consistently well in the differential expression analysis and produced few missing values. The missing values produced by the other software decreased their performance, but this difference could be mitigated using proper data filtering or imputation methods. Among the imputation methods, we found that the local least squares (lls) regression imputation consistently increased the performance of the software in the differential expression analysis, and a combination of both data filtering and local least squares imputation increased performance the most in the tested data sets.

Machine learning methods and in particular random forests are promising approaches for prediction based on high dimensional omics data sets. They provide variable importance measures to rank predictors according to their predictive power. If building a prediction model is the main goal of a study, often a minimal set of variables with good prediction performance is selected. However, if the objective is the identification of involved variables to find active networks and pathways, approaches that aim to select all relevant variables should be preferred. We evaluated several variable selection procedures based on simulated data as well as publicly available experimental methylation and gene expression data. Our comparison included the Boruta algorithm, the Vita method, recurrent relative variable importance, a permutation approach and its parametric variant (Altmann) as well as recursive feature elimination (RFE). In our simulation studies, Boruta was the most powerful approach, followed closely by the Vita method. Both approaches demonstrated similar stability in variable selection, while Vita was the most robust approach under a pure null model without any predictor variables related to the outcome. In the analysis of the different experimental data sets, Vita demonstrated slightly better stability in variable selection and was less computationally intensive than Boruta. In conclusion, we recommend the Boruta and Vita approaches for the analysis of high-dimensional data sets. Vita is considerably faster than Boruta and thus more suitable for large data sets, but only Boruta can also be applied in low-dimensional settings.

The ability to collect, store and analyze massive amounts of molecular and clinical data is fundamentally transforming the scientific method and its application in translational medicine. Collecting observations has always been a prerequisite for discovery, and great leaps in scientific understanding are accompanied by an expansion of this ability. Particle physics, astronomy and climate science, for example, have all greatly benefited from the development of new technologies enabling the collection of larger and more diverse data. Unlike medicine, however, each of these fields also has a mature theoretical framework on which new data can be evaluated and incorporated—to say it another way, there are no ‘first principals’ from which a healthy human could be analytically derived. The worry, and it is a valid concern, is that, without a strong theoretical underpinning, the inundation of data will cause medical research to devolve into a haphazard enterprise without discipline or rigor. The Age of Big Data harbors tremendous opportunity for biomedical advances, but will also be treacherous and demanding on future scientists.

As meta-analysis results published by consortia of genome-wide association studies (GWASs) become increasingly available, many association summary statistics-based multi-locus tests have been developed to jointly evaluate multiple single-nucleotide polymorphisms (SNPs) to reveal novel genetic architectures of various complex traits. The validity of these approaches relies on the accurate estimate of z-score correlations at considered SNPs, which in turn requires knowledge on the set of SNPs assessed by each study participating in the meta-analysis. However, this exact SNP coverage information is usually unavailable from the meta-analysis results published by GWAS consortia. In the absence of the coverage information, researchers typically estimate the z-score correlations by making oversimplified coverage assumptions. We show through real studies that such a practice can generate highly inflated type I errors, and we demonstrate the proper way to incorporate correct coverage information into multi-locus analyses. We advocate that consortia should make SNP coverage information available when posting their meta-analysis results, and that investigators who develop analytic tools for joint analyses based on summary data should pay attention to the variation in SNP coverage and adjust for it appropriately.

Gaining knowledge and actionable insights from complex, high-dimensional and heterogeneous biomedical data remains a key challenge in transforming health care. Various types of data have been emerging in modern biomedical research, including electronic health records, imaging, -omics, sensor data and text, which are complex, heterogeneous, poorly annotated and generally unstructured. Traditional data mining and statistical learning approaches typically need to first perform feature engineering to obtain effective and more robust features from those data, and then build prediction or clustering models on top of them. There are lots of challenges on both steps in a scenario of complicated data and lacking of sufficient domain knowledge. The latest advances in deep learning technologies provide new effective paradigms to obtain end-to-end learning models from complex data. In this article, we review the recent literature on applying deep learning technologies to advance the health care domain. Based on the analyzed work, we suggest that deep learning approaches could be the vehicle for translating big biomedical data into improved human health. However, we also note limitations and needs for improved methods development and applications, especially in terms of ease-of-understanding for domain experts and citizen scientists. We discuss such challenges and suggest developing holistic and meaningful interpretable architectures to bridge deep learning models and human interpretability.

Altres ajuts: the ICN2 is funded by the CERCA Programme/Generalitat de Catalunya. The development of paper-based electroanalytical strips as powerful diagnostic tools has gained a lot of attention within the sensor community. In particular, the detection of nucleic acids in complex matrices represents a trending topic, especially when focused toward the development of emerging technologies, such as liquid biopsy. DNA-based biosensors have been largely applied in this direction, and currently, there are two main approaches based on target/probe hybridization reported in the literature, namely Signal ON and Signal OFF. In this technical note, the two approaches are evaluated in combination with paper-based electrodes, using a single strand DNA relative to H1047R (A3140G) missense mutation in exon 20 in breast cancer as the model target. A detailed comparison among the analytical performances, detection protocol, and cost associated with the two systems is provided, highlighting the advantages and drawbacks depending on the application. The present work is aimed to a wide audience, particularly for those in the field of point-of-care, and it is intended to provide the know-how to manage with the design and development stages, and to optimize the platform for the sensing of nucleic acids using a paper-based detection method.

As photoacoustic imaging (PAI) technology matures, computational modeling will increasingly represent a critical tool for facilitating clinical translation through predictive simulation of real-world performance under a wide range of device and biological conditions. While modeling currently offers a rapid, inexpensive tool for device development and prediction of fundamental image quality metrics (e.g., spatial resolution and contrast ratio), rigorous verification and validation will be required of models used to provide regulatory-grade data that effectively complements and/or replaces in vivo testing. To address methods for establishing model credibility, we developed an integrated computational model of PAI by coupling a previously developed three-dimensional Monte Carlo model of tissue light transport with a two-dimensional (2D) acoustic wave propagation model implemented in the well-known k-Wave toolbox. We then evaluated ability of the model to predict basic image quality metrics by applying standardized verification and validation principles for computational models. The model was verified against published simulation data and validated against phantom experiments using a custom PAI system. Furthermore, we used the model to conduct a parametric study of optical and acoustic design parameters. Results suggest that computationally economical 2D acoustic models can adequately predict spatial resolution, but metrics such as signal-to-noise ratio and penetration depth were difficult to replicate due to challenges in modeling strong clutter observed in experimental images. Parametric studies provided quantitative insight into complex relationships between transducer characteristics and image quality as well as optimal selection of optical beam geometry to ensure adequate image uniformity. Multidomain PAI simulation tools provide high-quality tools to aid device development and prediction of real-world performance, but further work is needed to improve model fidelity, especially in reproducing image noise and clutter.

Many drugs are derived from small molecules produced by microorganisms and plants, so-called natural products. Natural products have diverse chemical structures, but the biosynthetic pathways producing those compounds are often organized as biosynthetic gene clusters (BGCs) and follow a highly conserved biosynthetic logic. This allows for the identification of core biosynthetic enzymes using genome mining strategies that are based on the sequence similarity of the involved enzymes/genes. However, mining for a variety of BGCs quickly approaches a complexity level where manual analyses are no longer possible and require the use of automated genome mining pipelines, such as the antiSMASH software. In this review, we discuss the principles underlying the predictions of antiSMASH and other tools and provide practical advice for their application. Furthermore, we discuss important caveats such as rule-based BGC detection, sequence and annotation quality and cluster boundary prediction, which all have to be considered while planning for, performing and analyzing the results of genome mining studies.

Biclustering is a powerful data mining technique that allows clustering of rows and columns, simultaneously, in a matrix-format data set. It was first applied to gene expression data in 2000, aiming to identify co-expressed genes under a subset of all the conditions/samples. During the past 17 years, tens of biclustering algorithms and tools have been developed to enhance the ability to make sense out of large data sets generated in the wake of high-throughput omics technologies. These algorithms and tools have been applied to a wide variety of data types, including but not limited to, genomes, transcriptomes, exomes, epigenomes, phenomes and pharmacogenomes. However, there is still a considerable gap between biclustering methodology development and comprehensive data interpretation, mainly because of the lack of knowledge for the selection of appropriate biclustering tools and further supporting computational techniques in specific studies. Here, we first deliver a brief introduction to the existing biclustering algorithms and tools in public domain, and then systematically summarize the basic applications of biclustering for biological data and more advanced applications of biclustering for biomedical data. This review will assist researchers to effectively analyze their big data and generate valuable biological knowledge and novel insights with higher efficiency.

In the past decade, significant progress has been made in complex disease research across multiple omics layers from genome, transcriptome and proteome to metabolome. There is an increasing awareness of the importance of biological interconnections, and much success has been achieved using systems biology approaches. However, because of the typical focus on one single omics layer at a time, existing systems biology findings explain only a modest portion of complex disease. Recent advances in multi-omics data collection and sharing present us new opportunities for studying complex diseases in a more comprehensive fashion, and yet simultaneously create new challenges considering the unprecedented data dimensionality and diversity. Here, our goal is to review extant and emerging network approaches that can be applied across multiple biological layers to facilitate a more comprehensive and integrative multilayered omics analysis of complex diseases.

Drug-drug interactions (DDIs) constitute an important concern in drug development and postmarketing pharmacovigilance. They are considered the cause of many adverse drug effects exposing patients to higher risks and increasing public health system costs. Methods to follow-up and discover possible DDIs causing harm to the population are a primary aim of drug safety researchers. Here, we review different methodologies and recent advances using data mining to detect DDIs with impact on patients. We focus on data mining of different pharmacovigilance sources, such as the US Food and Drug Administration Adverse Event Reporting System and electronic health records from medical institutions, as well as on the diverse data mining studies that use narrative text available in the scientific biomedical literature and social media. We pay attention to the strengths but also further explain challenges related to these methods. Data mining has important applications in the analysis of DDIs showing the impact of the interactions as a cause of adverse effects, extracting interactions to create knowledge data sets and gold standards and in the discovery of novel and dangerous DDIs.

Questioned documents analysis includes: handwriting comparison, analysis of the ink and the printer used in the production of the documents, and the physical and chemical characterization of the cellulosic substrate (paper) of the documents. In many situations in life, for financial, social, and personal concerns, we depend on different documents. Therefore, over time, various analytical methods have been developed in order to determine their authenticity, source, and age or to differentiate various papers. In this study a quantitative analytical method for the determination of eight trace level chemical elements (Al, Ba, Fe, Mg, Mn, Pb, Sr, Zn) from document paper samples using inductively coupled plasma-mass spectrometry (ICP-MS) was validated and applied. The evaluation of the performance parameters of the method (applicability, fitness for purpose, linearity, working range, limit of detection and limit of quantification, sensitivity, accuracy, and precision) was accomplished. An overview of the validation parameters are presented and discussed in detail.

Receiver operating characteristic (ROC) analysis is a fundamental tool used for the evaluation and comparison of diagnostic systems that provides estimates of the combinations of sensitivity and specificity that can be achieved with a given technique. Along with critical considerations of practical limitations, such as throughput and time to availability of results, ROC analyses can be applied to provide meaningful assessments and comparisons of available biodosimetry methods. Accordingly, guidance from the Food and Drug Administration to evaluate biodosimetry devices recommends using ROC analysis. However, the existing literature for the numerous biodosimetry methods that have been developed to address the needs for triage either do not contain ROC analyses or present ROC analyses where the dose distributions of the study samples are not representative of the populations to be screened. The use of non-representative sample populations can result in a significant spectrum bias, where estimated performance metrics do not accurately characterize the true performance under real-world conditions. Particularly, in scenarios where a large group of people is screened because they were potentially exposed in a large-scale radiation event, directly measured population data do not exist. However, a number of complex simulations have been performed and reported in the literature that provide estimates of the required dose distributions. Based on these simulations and reported data about the output and uncertainties of biodosimetry assays, we illustrate how ROC curves can be generated that incorporate a realistic representative sample. A technique to generate ROC curves for biodosimetry data is presented along with representative ROC curves, summary statistics and discussion based on published data for triage-ready electron paramagnetic resonance in vivo tooth dosimetry, the dicentric chromosome assay and quantitative polymerase chain reaction assay. We argue that this methodology should be adopted generally to evaluate the performance of radiation biodosimetry screening assays so that they can be compared in the context of their intended use.

This article provides an integrative review of the literature on judgment-based predictions of performance time, often described as task duration predictions in psychology and as expert-based effort estimation in engineering and management science. We summarize results on the characteristics of performance time predictions, processes and strategies, the influence of task characteristics and contextual factors, and the relations between estimates and characteristics of the estimator. Although dependent on the type of study and the level of analysis, underestimation was more frequently reported than overestimation in studies from the engineering and management literature. However, this was not the case in studies from the psychology literature. Our summaries challenge earlier results regarding the effects of factors such as complexity/difficulty and experience. We also question the recurrent finding that small tasks are overestimated and large tasks are underestimated, as this to some extent can be a statistical artifact caused by random error. Several other influences on predictions are identified and discussed. These include various types of anchoring effects, performance and accuracy incentives, task decomposition, request formats, group estimation, revisions of initial ideal or incomplete estimates, level of abstraction, and superficial cues. We summarize similarities and differences between performance time predictions (e.g., number of work hours) and completion time predictions (e.g., delivery dates) because many studies fail to distinguish between these 2 types of predictions. Finally, we discuss methodological issues in time prediction research and implications for research and application.

Reducing energy consumption is a major challenge for "energy-intensive" industries such as papermaking. A commercially viable energy saving solution is to employ data-based optimization techniques to obtain a set of "optimized" operational settings that satisfy certain performance indices. The difficulties of this are: 1) the problems of this type are inherently multicriteria in the sense that improving one performance index might result in compromising the other important measures; 2) practical systems often exhibit unknown complex dynamics and several interconnections which make the modeling task difficult; and 3) as the models are acquired from the existing historical data, they are valid only locally and extrapolations incorporate risk of increasing process variability. To overcome these difficulties, this paper presents a new decision support system for robust multiobjective optimization of interconnected processes. The plant is first divided into serially connected units to model the process, product quality, energy consumption, and corresponding uncertainty measures. Then multiobjective gradient descent algorithm is used to solve the problem in line with user's preference information. Finally, the optimization results are visualized for analysis and decision making. In practice, if further iterations of the optimization algorithm are considered, validity of the local models must be checked prior to proceeding to further iterations. The method is implemented by a MATLAB-based interactive tool DataExplorer supporting a range of data analysis, modeling, and multiobjective optimization techniques. The proposed approach was tested in two U.K.-based commercial paper mills where the aim was reducing steam consumption and increasing productivity while maintaining the product quality by optimization of vacuum pressures in forming and press sections. The experimental results demonstrate the effectiveness of the method.

Aim Electronic formats (ePROs) of paper-based patient-reported outcomes (PROs) should be validated before they can be reliably used. This review aimed to examine studies investigating measurement equivalence between ePROs and their paper originals to identify methodologies used and to determine the extent of such validation. Methods Three databases (OvidSP, Web of Science and PubMed) were searched using a set of keywords. Results were examined for compliance with inclusion criteria. Articles or abstracts that directly compared screen-based electronic versions of PROs with their validated paper-based originals, with regard to their measurement equivalence, were included. Publications were excluded if the only instruments reported were stand-alone visual analogue scales or interactive voice response formats. Papers published before 2007 were excluded, as a previous meta-analysis examined papers published before this time. Results Fifty-five studies investigating 79 instruments met the inclusion criteria. 53 % of the 79 instruments studied were condition specific. Several instruments, such as the SF-36, were reported in more than one publication. The most frequently reported formats for ePROs were Web-based versions. In 78 % of the publications, there was evidence of equivalence or comparability between the two formats as judged by study authors. Of the 30 publications that provided preference data, 87 % found that overall participants preferred the electronic format. Conclusions When examining equivalence between paper and electronic versions of PROs, formats are usually judged by authors to be equivalent. Participants prefer electronic formats. This literature review gives encouragement to the further widespread development and use of ePROs.

Background: Measurements of parameters from electrocardiograms (ECGs) are still largely performed from paper ECG records. Recent guidelines from regulatory agencies and, in particular, the requirement of the Food and Drug Administration to enforce the digital submission of annotated ECGs have triggered significant efforts in the pharmaceutical industry, which, to comply with the new guidelines, is adopting digital ECG technology. At the same time, the new requirements justify the need for tools to convert existing paper ECG records into digital format, particularly for retrospective studies. Methods: This article presents ECGScan, a computer application developed for the conversion of paper ECG records to digital ECG files. An image processing engine is used to first detect the underlying grid and, subsequently, to extrapolate the ECG waveforms using a technique based on active contour modeling. Results: ECGScan was validated using a set of 60 ECGs for which both the original digital waveform and paper printouts were available. Sample-by-sample comparisons provided evidence of a robust wave reconstruction (root mean square value from 169 PQRST complexes was 16.8 F 11.8 lV). Semiautomatic measurements of QT intervals performed on 144 complexes also indicated a strong agreement between original and derived ECGs (DQT = 0.577 F 5.41 milliseconds). Conclusions: ECGScan provides a robust reconstruction of a digital ECG, both in waveform reconstruction and in QT measurements performed on original (digital) ECGs and on digitized ECGs from paper printouts. D 2005 Elsevier Inc. All rights reserved.

Recent advances in handheld computer hardware and software may provide alternatives to paper-based data collection methods. The authors compared data collected with paper forms to data collected with handheld computer-based forms in a field observation study of alcohol purchase attempts at 47 community festivals in a large metropolitan area. Agreement between data collected with paper forms and data collected with handheld computers was greater than 95%. Computer-based forms handled branching patterns better and yielded data that were immediately available for analyses. Paper forms handled written comments better. Handheld computers are a feasible alternative to paper forms for field data collection.

The ability to detect and non-destructively collect biological samples for DNA processing would benefit the forensic community by preserving the physical integrity of evidentiary items for more thorough evaluations by other forensic disciplines. The Electrostatic Detection Apparatus (ESDA®) was systemically evaluated for its ability to non-destructively collect DNA from latent fingerprints deposited on various paper substrates for short tandem repeat (STR) DNA profiling. Fingerprints were deposited on a variety of paper substrates that included resume paper, cotton paper, magazine paper, currency, copy paper, and newspaper. Three DNA collection techniques were performed: ESDA collection, dry swabbing, and substrate cutting. Efficacy of each collection technique was evaluated by the quantity of DNA present in each sample and the percent profile generated by each sample. Both the ESDA and dry swabbing non-destructive sampling techniques outperformed the destructive methodology of substrate cutting. A greater number of full profiles were generated from samples collected with the non-destructive dry swabbing collection technique than were generated from samples collected with the ESDA; however, the ESDA also allowed the user to visualize the area of interest while non-destructively collecting the biological material. The ability to visualize the biological material made sampling straightforward and eliminated the need for numerous, random swabbings/cuttings. Based on these results, the evaluated non-destructive ESDA collection technique has great potential for real-world forensic implementation.

This paper addresses the issues of neural network model development and maintenance in the context of a complex task taken from the papermaking industry. In particular, it describes a comparison study of early stopping techniques and model selection, both to optimise neural network models for generalisation performance. The results presented here show that early stopping via use of a Bayesian model evidence measure is a viable way of optimising performance while also making maximum use of all the data. In addition, they show that ten-fold cross-validation performs well as a model selector and as an estimator of prediction accuracy. These results are important in that they show how neural network models may be optimally trained and selected for highly complex industrial tasks where the data are noisy and limited in number.

Background Decisions concerning drug safety and efficacy are generally based on pivotal evidence provided by clinical trials. Unfortunately, finding the relevant clinical trials is difficult and their results are only available in text-based reports. Systematic reviews aim to provide a comprehensive overview of the evidence in a specific area, but may not provide the data required for decision making. Methods We review and analyze the existing information systems and standards for aggregate level clinical trials information from the perspective of systematic review and evidence-based decision making. Results The technology currently used has major shortcomings, which cause deficiencies in the transfer, traceability and availability of clinical trials information. Specifically, data available to decision makers is insufficiently structured, and consequently the decisions cannot be properly traced back to the underlying evidence. Regulatory submission, trial publication, trial registration, and systematic review produce unstructured datasets that are insufficient for supporting evidence-based decision making. Conclusions The current situation is a hindrance to policy decision makers as it prevents fully transparent decision making and the development of more advanced decision support systems. Addressing the identified deficiencies would enable more efficient, informed, and transparent evidence-based medical decision making.

This paper describes the software package KELVIN, which supports the PPL (posterior probability of linkage) framework for the measurement of statistical evidence in human (or more generally, diploid) genetic studies. In terms of scope, KELVIN supports two-point (trait-marker or marker-marker) and multipoint linkage analysis, based on either sex-averaged or sex-specific genetic maps, with an option to allow for imprinting; trait-marker linkage disequilibrium (LD), or association analysis, in case-control data, trio data, and/or multiplex family data, with options for joint linkage and trait-marker LD or conditional LD given linkage; dichotomous trait, quantitative trait and quantitative trait threshold models; and certain types of gene-gene interactions and covariate effects. Features and data (pedigree) structures can be freely mixed and matched within analyses. The statistical framework is specifically tailored to accumulate evidence in a mathematically rigorous way across multiple data sets or data subsets while allowing for multiple sources of heterogeneity, and KELVIN itself utilizes sophisticated software engineering to provide a powerful and robust platform for studying the genetics of complex disorders.

Date of Conference: 10-13 Nov. 2008 Conference name: 2008 IEEE/ACM International Conference on Computer-Aided Design Leveraging the power of scratchpad memories (SPMs) available in most embedded systems today is crucial to extract maximum performance from application programs. While regular accesses like scalar values and array expressions with affine subscript functions have been tractable for compiler analysis (to be prefetched into SPM), irregular accesses like pointer accesses and indexed array accesses have not been easily amenable for compiler analysis. This paper presents an SPM management technique using Markov chain based data access prediction for such irregular accesses. Our approach takes advantage of inherent, but hidden reuse in data accesses made by irregular references. We have implemented our proposed approach using an optimizing compiler. In this paper, we also present a thorough comparison of our different dynamic prediction schemes with other SPM management schemes. SPM management using our approaches produces 12.7% to 28.5% improvements in performance across a range of applications with both regular and irregular access patterns, with an average improvement of 20.8%.

We conducted a reliability study comparing single data entry (SE) into a Microsoft Excel spreadsheet to entry using the existing forms (EF) feature of the Teleforms software system, in which optical character recognition is used to capture data off of paper forms designed in non-Teleforms software programs. We compared the transcription of data from multiple paper forms from over 100 research participants representing almost 20,000 data entry fields. Error rates for SE were significantly lower than those for EF, so we chose SE for data entry in our study. Data transcription strategies from paper to electronic format should be chosen based on evidence from formal evaluations, and their design should be contemplated during the paper forms development stage.

Total order ranking methods are multicriteria decision making techniques used for the ranking of various alternatives on the basis of more than one criterion. The criteria, which are the standards by which the elements of the system are judged are not always in agreement, they can be conflicting, motivating the need to find an overall optimum that can deviate from the optima of one or more of the single criteria. Total order ranking methods are based on an aggregation of the criteria in a scalar function, i.e. an order or ranking index, which allow to sort elements according to its numerical value. Several evaluation methods which define a ranking parameter generating a total order ranking have been proposed in the literature. Four total order ranking methods are here described: Desirability functions, Utility functions, Dominance functions and Absolute Reference method. These methods have been compared to each other by applying them to a decision making problem in the paper industry. Various bleaching processes have been analysed and compared on the basis of multiple criteria, the aim being to find out best bleaching process among the ones proposed in the last years as alternative to chlorine bleaching process which is of high environmental impact due to the potential for chlorinated dioxin production.

The aim of this study was to develop an empirical model that provides accurate predictions of the biochemical oxygen demand of the output stream from the aerated lagoon at International Paper of Brazil, one of the major pulp and paper plants in Brazil. Predictive models were calculated from functional link neural networks (FLNNs), multiple linear regression, principal components regression, and partial least-squares regression (PLSR). Improvement in FLNN modeling capability was observed when the data were preprocessed using the PLSR technique. PLSR also proved to be a powerful linear regression technique for this problem, which presents operational data limitations.

Demand for better productivity of printing presses is one of the key topics in the publication industry. For a papermaker, this means increased demands for improved runnability of paper. Lots of measuring data is used for controlling the press but unfortunately this data is not normally stored for later usage. Paper mills do a huge amount of on-line and off-line measurements, which are normally used internally. In this study, a large amount of diversified printing press and paper mill measurements were collected and a database was created. The aim was to clarify the reasons for runnability problems occurring in the pressroom. Over 2000 customer reels were measured at a newspaper press. The software package created for data mining is introduced. Results from a long-term study, and the causes for runnability problems in the printing press, are presented in this paper.

A model for calculating the assessment or consultability of archives was developed. This model includes an archive sampling procedure, defining damage profiles, damage sub-profiles and the development of a classifier. Research showed that the whole model developed is reliable and can be carried out easily.

Demand for better productivity of printing presses is one of the key topics in the publication industry. For a papermaker, this means increased demands for improved runnability of paper. Lots of measuring data is used for controlling the press but unfortunately this data is not normally stored for later usage. Paper mills do a huge amount of on-line and off-line measurements, which are normally used internally. In this study, a large amount of diversified printing press and paper mill measurements were collected and a database was created. The aim was to clarify the reasons for runnability problems occurring in the pressroom. Over 2000 customer reels were measured at a newspaper press. The software package created for data mining is introduced. Results from a long-term study, and the causes for runnability problems in the printing press, are presented in this paper.

We propose a method of test selection based on formal specifications, for functional testing of object-oriented software. This method is based on rigorous theoretical foundations. To limit the size of test sets, we present several hypotheses specific to object-oriented systems, used to reduce an exhaustive test set into a pertinent test set while preserving its qualities: regularity hypotheses are used to constrain the shape of tests while uniformities, with or without subdomain decomposition, are used to limit the values of the variables appearing in the tests. Care is taken to provide a method for which operational methods can be implemented.