Prediction of crude protein and classification of the growth stage of wheat plant samples from NIR spectra

Near-infrared spectroscopy (NIRS) was used to analyse the crude protein content of dried and milled samples of wheat and to discriminate samples according to their stage of growth. A calibration set of 72 samples from three growth stages of wheat (tillering, heading and harvest) and a validation set of 28 samples was collected for this purpose. Principal components analysis (PCA) of the calibration set discriminated groups of samples according to the growth stage of the wheat. Based on these differences, a classification procedure (SIMCA) showed a very accurate classification of the validation set samples : all of them were successfully classified in each group using this procedure when both the residual and the leverage were used in the classification criteria. Looking only at the residuals all the samples were also correctly classified except one of tillering stage that was assigned to both tillering and heading stages. Finally, the determination of the crude protein content of these samples was considered in two ways: building up a global model for all the growth stages, and building up local models for each stage, separately. The best prediction results for crude protein were obtained using a global model for samples in the two first growth stages (tillering and heading), and using a local model for the harvest stage samples.

Integrating defeasible argumentation with fuzzy ART neural networks for pattern classification

Many classification systems rely on clustering techniques in which a collection of training examples is provided as an input, and a number of clusters c1,...cm modelling some concept C results as an output, such that every cluster ci is labelled as positive or negative. Given a new, unlabelled instance enew, the above classification is used to determine to which particular cluster ci this new instance belongs. In such a setting clusters can overlap, and a new unlabelled instance can be assigned to more than one cluster with conflicting labels. In the literature, such a case is usually solved non-deterministically by making a random choice. This paper presents a novel, hybrid approach to solve this situation by combining a neural network for classification along with a defeasible argumentation framework which models preference criteria for performing clustering.

Assessment of inflammatory Bowel Disease patient's needs and problems from a nursing perspective

BACKGROUND: In this study, we aimed at assessing Inflammatory Bowel Disease patients' needs and current nursing practice to investigate to what extent consensus statements (European Crohn's and Colitis Organization) on the nursing roles in caring for patients with IBD concur with local practice. METHODS: We used a mixed-method convergent design to combine quantitative data prospectively collected in the Swiss IBD cohort study and qualitative data from structured interviews with IBD healthcare experts. Symptoms, quality of life, and anxiety and depression scores were retrieved from physician charts and patient self-reported questionnaires. Descriptive analyses were performed based on quantitative and qualitative data. RESULTS: 230 patients of a single center were included, 60% of patients were males, and median age was 40 (range 18-85). The prevalence of abdominal pain was 42%. Self-reported data were obtained from 75 out of 230 patients. General health was perceived significantly lower compared with the general population (p < 0.001). Prevalence of tiredness was 73%; sleep problems, 78%; issues related to work, 20%; sexual constraints, 35%; diarrhea, 67%; being afraid of not finding a bathroom, 42%; depression, 11%; and anxiety symptoms, 23%. According to experts' interviews, the consensus statements are found mostly relevant with many recommendations that are not yet realized in clinical practice. CONCLUSION: Identified prevalence may help clinicians in detecting patients at risk and improve patient management. © 2015 S. Karger AG, Basel.

Evidence-based provisional clinical classification criteria for autoinflammatory periodic fevers.

The objective of this work was to develop and validate a set of clinical criteria for the classification of patients affected by periodic fevers. Patients with inherited periodic fevers (familial Mediterranean fever (FMF); mevalonate kinase deficiency (MKD); tumour necrosis factor receptor-associated periodic fever syndrome (TRAPS); cryopyrin-associated periodic syndromes (CAPS)) enrolled in the Eurofever Registry up until March 2013 were evaluated. Patients with periodic fever, aphthosis, pharyngitis and adenitis (PFAPA) syndrome were used as negative controls. For each genetic disease, patients were considered to be 'gold standard' on the basis of the presence of a confirmatory genetic analysis. Clinical criteria were formulated on the basis of univariate and multivariate analysis in an initial group of patients (training set) and validated in an independent set of patients (validation set). A total of 1215 consecutive patients with periodic fevers were identified, and 518 gold standard patients (291 FMF, 74 MKD, 86 TRAPS, 67 CAPS) and 199 patients with PFAPA as disease controls were evaluated. The univariate and multivariate analyses identified a number of clinical variables that correlated independently with each disease, and four provisional classification scores were created. Cut-off values of the classification scores were chosen using receiver operating characteristic curve analysis as those giving the highest sensitivity and specificity. The classification scores were then tested in an independent set of patients (validation set) with an area under the curve of 0.98 for FMF, 0.95 for TRAPS, 0.96 for MKD, and 0.99 for CAPS. In conclusion, evidence-based provisional clinical criteria with high sensitivity and specificity for the clinical classification of patients with inherited periodic fevers have been developed.

Generating highly balanced sudoku problems as hard problems

Sudoku problems are some of the most known and enjoyed pastimes, with a never diminishing popularity, but, for the last few years those problems have gone from an entertainment to an interesting research area, a twofold interesting area, in fact. On the one side Sudoku problems, being a variant of Gerechte Designs and Latin Squares, are being actively used for experimental design, as in [8, 44, 39, 9]. On the other hand, Sudoku problems, as simple as they seem, are really hard structured combinatorial search problems, and thanks to their characteristics and behavior, they can be used as benchmark problems for refining and testing solving algorithms and approaches. Also, thanks to their high inner structure, their study can contribute more than studies of random problems to our goal of solving real-world problems and applications and understanding problem characteristics that make them hard to solve. In this work we use two techniques for solving and modeling Sudoku problems, namely, Constraint Satisfaction Problem (CSP) and Satisfiability Problem (SAT) approaches. To this effect we define the Generalized Sudoku Problem (GSP), where regions can be of rectangular shape, problems can be of any order, and solution existence is not guaranteed. With respect to the worst-case complexity, we prove that GSP with block regions of m rows and n columns with m = n is NP-complete. For studying the empirical hardness of GSP, we define a series of instance generators, that differ in the balancing level they guarantee between the constraints of the problem, by finely controlling how the holes are distributed in the cells of the GSP. Experimentally, we show that the more balanced are the constraints, the higher the complexity of solving the GSP instances, and that GSP is harder than the Quasigroup Completion Problem (QCP), a problem generalized by GSP. Finally, we provide a study of the correlation between backbone variables – variables with the same value in all the solutions of an instance– and hardness of GSP.

Agency problems with non-smooth decision profiles: the case of monopoly under product quality

A method for dealing with monotonicity constraints in optimal control problems is used to generalize some results in the context of monopoly theory, also extending the generalization to a large family of principal-agent programs. Our main conclusion is that many results on diverse economic topics, achieved under assumptions of continuity and piecewise differentiability in connection with the endogenous variables of the problem, still remain valid after replacing such assumptions by two minimal requirements.

Perceptions of twelve-step mutual-help groups and their associations with motivation, treatment attendance and alcohol outcomes among chronically homeless individuals with alcohol problems.

BACKGROUND: Twelve-step mutual-help groups (TMGs) are among the most available forms of support for homeless individuals with alcohol problems. Qualitative research, however, has suggested that this population often has negative perceptions of these groups, which has been shown to be associated with low TMG attendance. It is important to understand this population's perceptions of TMGs and their association with alcohol outcomes to provide more appropriate and better tailored programming for this multiply affected population. The aims of this cross-sectional study were to (a) qualitatively examine perception of TMGs in this population and (b) quantitatively evaluate its association with motivation, treatment attendance and alcohol outcomes. METHODS: Participants (N=62) were chronically homeless individuals with alcohol problems who received single-site Housing First within a larger evaluation study. Perceptions of TMGs were captured using an open-ended item. Quantitative outcome variables were created from assessments of motivation, treatment attendance and alcohol outcomes. RESULTS: Findings indicated that perceptions of TMGs were primarily negative followed by positive and neutral perceptions, respectively. There were significant, positive associations between perceptions of TMGs and motivation and treatment attendance, whereas no association was found for alcohol outcomes. CONCLUSIONS: Although some individuals view TMGs positively, alternative forms of help are needed to engage the majority of chronically homeless individuals with alcohol problems.

Papaya pulp gelling: is it premature ripening or problems of water accumulation in the apoplast?

Gelled aspect in papaya fruit is typically confused with premature ripening. This research reports the characterization of this physiological disorder in the pulp of papaya fruit by measuring electrolyte leakage, Pi content, lipid peroxidation, pulp firmness, mineral contents (Ca, Mg and K - in pulp and seed tissues), and histological analysis of pulp tissue. The results showed that the gelled aspect of the papaya fruit pulp is not associated with tissue premature ripening. Data indicate a reduction of the vacuole water intake as the principal cause of the loss of cellular turgor; while the waterlogged aspect of the tissue may be due to water accumulation in the apoplast.

Intelligent Internet Data Searching

In this thesis author approaches the problem of automated text classification, which is one of basic tasks for building Intelligent Internet Search Agent. The work discusses various approaches to solving sub-problems of automated text classification, such as feature extraction and machine learning on text sources. Author also describes her own multiword approach to feature extraction and pres-ents the results of testing this approach using linear discriminant analysis based classifier, and classifier combining unsupervised learning for etalon extraction with supervised learning using common backpropagation algorithm for multilevel perceptron.