The development of an automatic recognition system for earmark and earprint comparisons

The value of earmarks as an efficient means of personal identification is still subject to debate. It has been argued that the field is lacking a firm systematic and structured data basis to help practitioners to form their conclusions. Typically, there is a paucity of research guiding as to the selectivity of the features used in the comparison process between an earmark and reference earprints taken from an individual. This study proposes a system for the automatic comparison of earprints and earmarks, operating without any manual extraction of key-points or manual annotations. For each donor, a model is created using multiple reference prints, hence capturing the donor within source variability. For each comparison between a mark and a model, images are automatically aligned and a proximity score, based on a normalized 2D correlation coefficient, is calculated. Appropriate use of this score allows deriving a likelihood ratio that can be explored under known state of affairs (both in cases where it is known that the mark has been left by the donor that gave the model and conversely in cases when it is established that the mark originates from a different source). To assess the system performance, a first dataset containing 1229 donors elaborated during the FearID research project was used. Based on these data, for mark-to-print comparisons, the system performed with an equal error rate (EER) of 2.3% and about 88% of marks are found in the first 3 positions of a hitlist. When performing print-to-print transactions, results show an equal error rate of 0.5%. The system was then tested using real-case data obtained from police forces.

Investigation of Negative Moment Reinforcing in Bridge Decks, TR-660, 2015

Multi-span pre-tensioned pre-stressed concrete beam (PPCB) bridges made continuous usually experience a negative live load moment region over the intermediate supports. Conventional thinking dictates that sufficient reinforcement must be provided in this region to satisfy the strength and serviceability requirements associated with the tensile stresses in the deck. The American Association of State Highway and Transportation Officials (AASHTO) Load and Resistance Factor Design (LRFD) Bridge Design Specifications recommend the negative moment reinforcement (b2 reinforcement) be extended beyond the inflection point. Based upon satisfactory previous performance and judgment, the Iowa Department of Transportation (DOT) Office of Bridges and Structures (OBS) currently terminates b2 reinforcement at 1/8 of the span length. Although the Iowa DOT policy results in approximately 50% shorter b2 reinforcement than the AASHTO LRFD specifications, the Iowa DOT has not experienced any significant deck cracking over the intermediate supports. The primary objective of this project was to investigate the Iowa DOT OBS policy regarding the required amount of b2 reinforcement to provide the continuity over bridge decks. Other parameters, such as termination length, termination pattern, and effects of the secondary moments, were also studied. Live load tests were carried out on five bridges. The data were used to calibrate three-dimensional finite element models of two bridges. Parametric studies were conducted on the bridges with an uncracked deck, a cracked deck, and a cracked deck with a cracked pier diaphragm for live load and shrinkage load. The general conclusions were as follows: -- The parametric study results show that an increased area of the b2 reinforcement slightly reduces the strain over the pier, whereas an increased length and staggered reinforcement pattern slightly reduce the strains of the deck at 1/8 of the span length. -- Finite element modeling results suggest that the transverse field cracks over the pier and at 1/8 of the span length are mainly due to deck shrinkage. -- Bridges with larger skew angles have lower strains over the intermediate supports. -- Secondary moments affect the behavior in the negative moment region. The impact may be significant enough such that no tensile stresses in the deck may be experienced.

La lombalgie aiguë en médecine de premier recours [Acute low back pain in general practice medicine].

The practioner's first concern is knowing how to single out from the immense majority of situations susceptible to a favourable spontaneous evolution those patients with a bad prognostic necessitating reference to a specialist. We present in this paper the clinical steps designed to meet this challenge and a reminder of certain principles of patient diagnosis and care.

Electron microscopic visualization of protein-DNA interactions at the estrogen responsive element and in the first intron of the Xenopus laevis vitellogenin gene.

Stable protein-DNA complexes can be assembled in vitro at the 5' end of Xenopus laevis vitellogenin genes using extracts of nuclei from estrogen-induced frog liver and visualized by electron microscopy. Complexes at the three following sites can be identified on the gene B2: the transcription initiation site, the estrogen responsive element (ERE) and in the first intron. The complex at the transcription initiation site is stabilized by dinucleotides and thus represents a ternary transcription complex. The formation of the complexes at the two other sites is enhanced by estrogen and is reduced by tamoxifen, an antagonist of estrogen, while this latter effect is reversed by adding an excess of hormone. No sequence homology is apparent between the site containing the ERE and the binding site in intron I and functional tests in MCF-7 cells suggest that these two sites are not equivalent. Finally, we made use of previously characterized deletion mutants of the 5' flanking region of the gene B1, a close relative of the gene B2, to demonstrate that the 13-bp palindromic core element of the ERE is involved in the formation of the complexes observed upstream of the transcription initiation site.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

Corruption in building the image of politics in television news broadcasts

This work analyses the political news of eight Spanish television channels in order to see what image is built of politics, and particularly how the news of corruption affects the image of politics in Spanish news broadcasts. Different cases of corruption such as Gürtel, Palma Arena and those associated with judge Baltasar Garzón in his final stage in office, occupy part of the study. A new methodology is therefore proposed that enables the quality of the political information emitted from inside and outside the political content of the news programmes to be observed. Particular attention is paid to the news broadcasts of Televisión Española and Cuatro as those which offer a more balanced view of politics, and channels such as La Sexta, which give priority to a narrative construction of politics in the news programmes around causes of corruption.

Comparison and validation of tissue modelization and statistical classification methods in T1-weighted MR brain images.

This paper presents a validation study on statistical nonsupervised brain tissue classification techniques in magnetic resonance (MR) images. Several image models assuming different hypotheses regarding the intensity distribution model, the spatial model and the number of classes are assessed. The methods are tested on simulated data for which the classification ground truth is known. Different noise and intensity nonuniformities are added to simulate real imaging conditions. No enhancement of the image quality is considered either before or during the classification process. This way, the accuracy of the methods and their robustness against image artifacts are tested. Classification is also performed on real data where a quantitative validation compares the methods' results with an estimated ground truth from manual segmentations by experts. Validity of the various classification methods in the labeling of the image as well as in the tissue volume is estimated with different local and global measures. Results demonstrate that methods relying on both intensity and spatial information are more robust to noise and field inhomogeneities. We also demonstrate that partial volume is not perfectly modeled, even though methods that account for mixture classes outperform methods that only consider pure Gaussian classes. Finally, we show that simulated data results can also be extended to real data.

Searching for sex-reversals to explain population demography and the evolution of sex chromosomes.

Sex determination can be purely genetic (as in mammals and birds), purely environmental (as in many reptiles), or genetic but reversible by environmental factors during a sensitive period in life, as in many fish and amphibians (Wallace et al. 1999; Baroiller et al. 2009a; Stelkens & Wedekind 2010). Such environmental sex reversal (ESR) can be induced, for example, by temperature changes or by exposure to hormone-active substances. ESR has long been recognized as a means to produce more profitable single-sex cultures in fish farms (Cnaani & Levavi-Sivan 2009), but we know very little about its prevalence in the wild. Obviously, induced feminization or masculinization may immediately distort population sex ratios, and distorted sex ratios are indeed reported from some amphibian and fish populations (Olsen et al. 2006; Alho et al. 2008; Brykov et al. 2008). However, sex ratios can also be skewed by, for example, segregation distorters or sex-specific mortality. Demonstrating ESR in the wild therefore requires the identification of sex-linked genetic markers (in the absence of heteromorphic sex chromosomes) followed by comparison of genotypes and phenotypes, or experimental crosses with individuals who seem sex reversed, followed by sexing of offspring after rearing under non-ESR conditions and at low mortality. In this issue, Alho et al. (2010) investigate the role of ESR in the common frog (Rana temporaria) and a population that has a distorted adult sex ratio. They developed new sex-linked microsatellite markers and tested wild-caught male and female adults for potential mismatches between phenotype and genotype. They found a significant proportion of phenotypic males with a female genotype. This suggests environmental masculinization, here with a prevalence of 9%. The authors then tested whether XX males naturally reproduce with XX females. They collected egg clutches and found that some had indeed a primary sex ratio of 100% daughters. Other clutches seemed to result from multi-male fertilizations of which at least one male had the female genotype. These results suggest that sex-reversed individuals affect the sex ratio in the following generation. But how relevant is ESR if its prevalence is rather low, and what are the implications of successful reproduction of sex-reversed individuals in the wild?

Molecular characterization of the Carma1-related protein card9

ABSTRACT : Fungal infections have become a major source of diseases in immuncompromised patients, but are quite benign in healthy individuals. As fungi are eukaryotes, and share many biological processes with humans, many antifungal drugs can cause toxicity in the patients. Therefore, the characterization of signaling pathways specific to the anti-fungal immune response is relevant for the better understanding of the disease and the development of new therapeutic approaches. Dectin-1 is the major mammalian pattern recognition receptor for the fungal component zymosan. Dectin-1 is an innate non-Toll-like receptor containing immunoreceptor tyrosine-based activation motifs (ITAMs). Card9, Bc110 and Maltl are proteins that have been shown to play a key role in the Dectin-l-induced signaliñg pathway by controlling Dectin-l-mediated cell activation, cytokine production and innate anti-fungal immunity in mice. Here we investigate the role of the Card9-Bc110-Maltl complex in humans using the monocytic cell line THP-1. We show that Card9 interacts with Bc110 through a CARD-CARD interaction and that interaction of Card9 with Bc110 is required for NF-xB activation. We further demonstrate that Card9 is phosphorylated in its C-terminal part on serine residues. The phosphorylation status of Card9 can influence its ability to active NF-xB, since mutation of the phosphorylation sites increases its ability to activate NF-xB. We find that Card9 is expressed in myeloid derived cells, such as the human monocytic cell lines THP1 and U937, and in human monocyte-enriched PBLs and monocyte-derived DCs. Our findings demonstrate that Card9 is implicated in anti-fungal responses, since silencing of Card9 as well as of Bc110 and Maltl diminishes the capacity of THP1 cells to produce TNF-a in response to zymosan. Interestingly, activation of the NF-xB and MAPK pathway remained normal and levels of TNF-a mRNA produced were also not affected in THP 1 cells silenced for the expression of Card9, Bc110 or Malt1. Using a Malt1 inhibitor, we provide evidence that the proteolytic activity of Malt1 is needed for zymosan-induced TNF-a production in THP 1 cells and bone marrow-derived macrophages of mice, but further experiments are required to confirm these findings and identify the substrate(s) of Malt1. In conclusion, our results reveal an important role for Card9 in the innate immune response of human macrophages to fungi. RÉSUMÉ : Les infections fongiques sont une source majeure de maladie chez les patients immunodéprimés, alors qu'elles sont plutôt bénignes chez les individus sains. Comme les champignons sont des eucaryotes et partagent beaucoup de processus biologiques avec les humains, les médicaments antifongiques peuvent être source de toxicité chez les patients. Il est donc important de mieux caractériser les voies de signalisation intracellulaire des réponses anti-fongiques pour pouvoir développer de nouvelles approches thérapeutiques. La protéine Dectin-1 est le récepteur principal du composé fongique zymosan. Les protéines Card9, Bc110 et Maltl ont été décrites comme jouant un rôle primordial dans les signaux d'activation induits par Dectin-l, en contrôlant l'activité cellulaire, la production de cytokines et la défense anti-fongique dans les souris. Dans cette étude, nous investiguons le rôle du complexe Card9-Bc110-Maltl dans la lignée monocytaire humaine THP1. Nous montrons que Card9 interagit avec Bc110 par une interaction CARD-CARD et que cette interaction est requise pour activer le facteur de transcription NF-xB. Nous observons que Card9 est phosphorylé dans sa partie C-terminale sur des résidus serine et que l'état de phosphorylation de Card9 influence sa capacité à activer NF-xB. En effet, sa capacité à activer NF-xB est augmentée, après mutation des sites de phosphorylation. La génération d'un anticorps spécifique dirigé contre Card9 nous a permis de démontrer que Card9 est exprimé dans des cellules myéloïdes comme les lignées cellulaires monocytiques THP-1 et U-937, ainsi que dans les cellules dendritiques humaines. Nos résultats démontrent que Card9 est impliqué dans la réponse immunitaire antifongique puisque la réduction de l'expression de Card9 ainsi que de Bc110 et de Malt1 diminue la capacité des THP-1 à produire du TNF-a en réponse au zymosan. Par contre, les voies de signalisation NF-xB et MAPK ainsi que les niveaux de mRNA de TNF-a produits en réponse au zymosan ne sont pas affectés dans ces cellules. En utilisant un inhibiteur de Malt1, nous montrons que l'activité protéolytique de Malt1 est nécessaire pour la production de TNF-a induite par le zymosan dans les cellules THP-1 ainsi que dans les macrophages de souris, mais d'autres expériences seront nécessaires pour confirmer cette observation et identifier le(s) substrat(s) de Malt1 responsables de cet effet. En conclusion, nos résultats révèlent un rôle important de la protéine Card9 dans la réponse immunitaire innée antifongique dans les macrophages humains.

Targeting Toll-Like Receptors: Promising Therapeutic Strategies for the Management of Sepsis-Associated Pathology and Infectious Diseases.

Toll-like receptors (TLRs) are pattern recognition receptors playing a fundamental role in sensing microbial invasion and initiating innate and adaptive immune responses. TLRs are also triggered by danger signals released by injured or stressed cells during sepsis. Here we focus on studies developing TLR agonists and antagonists for the treatment of infectious diseases and sepsis. Positioned at the cell surface, TLR4 is essential for sensing lipopolysaccharide of Gram-negative bacteria, TLR2 is involved in the recognition of a large panel of microbial ligands, while TLR5 recognizes flagellin. Endosomal TLR3, TLR7, TLR8, TLR9 are specialized in the sensing of nucleic acids produced notably during viral infections. TLR4 and TLR2 are favorite targets for developing anti-sepsis drugs, and antagonistic compounds have shown efficient protection from septic shock in pre-clinical models. Results from clinical trials evaluating anti-TLR4 and anti-TLR2 approaches are presented, discussing the challenges of study design in sepsis and future exploitation of these agents in infectious diseases. We also report results from studies suggesting that the TLR5 agonist flagellin may protect from infections of the gastrointestinal tract and that agonists of endosomal TLRs are very promising for treating chronic viral infections. Altogether, TLR-targeted therapies have a strong potential for prevention and intervention in infectious diseases, notably sepsis.

Retinal Image Analysis Using Machine Vision

The topic of this thesis is studying how lesions in retina caused by diabetic retinopathy can be detected from color fundus images by using machine vision methods. Methods for equalizing uneven illumination in fundus images, detecting regions of poor image quality due toinadequate illumination, and recognizing abnormal lesions were developed duringthe work. The developed methods exploit mainly the color information and simpleshape features to detect lesions. In addition, a graphical tool for collecting lesion data was developed. The tool was used by an ophthalmologist who marked lesions in the images to help method development and evaluation. The tool is a general purpose one, and thus it is possible to reuse the tool in similar projects.The developed methods were tested with a separate test set of 128 color fundus images. From test results it was calculated how accurately methods classify abnormal funduses as abnormal (sensitivity) and healthy funduses as normal (specificity). The sensitivity values were 92% for hemorrhages, 73% for red small dots (microaneurysms and small hemorrhages), and 77% for exudates (hard and soft exudates). The specificity values were 75% for hemorrhages, 70% for red small dots, and 50% for exudates. Thus, the developed methods detected hemorrhages accurately and microaneurysms and exudates moderately.

Self-Organising Maps and Correlation Analysis as a Tool to Explore Patterns in Excitation-Emission Matrix Data Sets and to Discriminate Dissolved Organic Matter Fluorescence Components

Dissolved organic matter (DOM) is a complex mixture of organic compounds, ubiquitous in marine and freshwater systems. Fluorescence spectroscopy, by means of Excitation-Emission Matrices (EEM), has become an indispensable tool to study DOM sources, transport and fate in aquatic ecosystems. However the statistical treatment of large and heterogeneous EEM data sets still represents an important challenge for biogeochemists. Recently, Self-Organising Maps (SOM) has been proposed as a tool to explore patterns in large EEM data sets. SOM is a pattern recognition method which clusterizes and reduces the dimensionality of input EEMs without relying on any assumption about the data structure. In this paper, we show how SOM, coupled with a correlation analysis of the component planes, can be used both to explore patterns among samples, as well as to identify individual fluorescence components. We analysed a large and heterogeneous EEM data set, including samples from a river catchment collected under a range of hydrological conditions, along a 60-km downstream gradient, and under the influence of different degrees of anthropogenic impact. According to our results, chemical industry effluents appeared to have unique and distinctive spectral characteristics. On the other hand, river samples collected under flash flood conditions showed homogeneous EEM shapes. The correlation analysis of the component planes suggested the presence of four fluorescence components, consistent with DOM components previously described in the literature. A remarkable strength of this methodology was that outlier samples appeared naturally integrated in the analysis. We conclude that SOM coupled with a correlation analysis procedure is a promising tool for studying large and heterogeneous EEM data sets.

The role of trust in business-to-business customer relationship

Pro gradu -tutkielman tavoitteena on selvittää, mikä on luottamuksen rooli B2B-asiakassuhteessa. Mitkä ovat B2B-suhteen ominaispiirteet, mikä on luottamuksen rooli ja luonne ja mikä on luottamuksen dynamiikka B2B-asiakassuhteessa. Tavoitteisiin on pyritty laadullisen tutkimuksen avulla. Aineisto kerättiin haastatteluilla ja analysointiin manuaalisesti teemoittain. Tutkimuksen tulokset osoittavat, että B2B-asiakassuhde on vaativa yhteistyömuoto, joka tarjoaa molemmille osapuolille hyötyjä sekä mahdollisuuksia kehittyä ja menestyä. Luottamus on suhteen ja menestyksellisen yhteistyön perusedellytys. Se perustuu hyvään mainee-seen, yhteiseen historiaan ja kokemuksiin ja sitä tarvitaan erityisesti viestinnässä, oppimisessa ja ongelmanratkaisussa. Henkilökohtaisten kontaktien ja partnereiden välisen henkilökemian lisäksi tehokkaimmat tavat rakentaa luottamusta ovat lupausten pitäminen jaerinomainen päivittäinen liiketoiminta asiakkaan kanssa.

Evaluation of an electronic nose to assess fruit ripeness

The main goal of our study was to see whether an artificial olfactory system can be used as a nondestructive instrument to measure fruit maturity. In order to make an objective comparison, samples measured with our electronic nose prototype were later characterized using fruit quality techniques. The cultivars chosen for the study were peaches, nectarines, apples, and pears. With peaches and nectarines, a PCA analysis on the electronic nose measurements helped to guess optimal harvest dates that were in good agreement with the ones obtained with fruit quality techniques. A good correlation between sensor signals and some fruit quality indicators was also found. With pears, the study addressed the possibility of classifying samples regarding their ripeness state after different cold storage and shelf-life periods. A PCA analysis showed good separation between samples measured after a shelf-life period of seven days and samples with four or less days. Finally, the electronic nose monitored the shelf-life ripening of apples. A good correlation between electronic nose signals and firmness, starch index, and acidity parameters was found. These results prove that electronic noses have the potential of becoming a reliable instrument to assess fruit ripeness.

Semantic Web applications: a framework for industry and business exploitation – What is needed for the adoption of the Semantic Web from the market and industry

In recent years, Semantic Web (SW) research has resulted in significant outcomes. Various industries have adopted SW technologies, while the ‘deep web’ is still pursuing the critical transformation point, in which the majority of data found on the deep web will be exploited through SW value layers. In this article we analyse the SW applications from a ‘market’ perspective. We are setting the key requirements for real-world information systems that are SW-enabled and we discuss the major difficulties for the SW uptake that has been delayed. This article contributes to the literature of SW and knowledge management providing a context for discourse towards best practices on SW-based information systems.