Injury pattern of the Flash-Ball, a less-lethal weapon used for law enforcement: report of two cases and review of the literature.

Less-lethal weapons are used in law enforcement to neutralize combative individuals and to disperse riot crowds. Local police recently used such an impact weapon, the Flash-Ball, in two different situations. This gun fires large rubber bullets with kinetic energies around 200 J. Although it is designed to avoid skin penetration, impacts at such energies may still create major trauma with associated severe injuries to internal organs. This is a report of 2 patients shot with the Flash-Ball who required medical attention. One could be discharged quickly, but the other required hospitalization for heart and lung contusion. Both patients required advanced investigations including computed tomography (CT) scan. The medical literature on injuries induced by less-lethal impact weapons is reviewed. Impacts from the Flash-Ball can cause significant injury to internal organs, even without penetration. Investigations as for other high-energy blunt traumas are called for in these cases.

Prognostic significance of nailfold capillary microscopy in patients with Raynaud's phenomenon and scleroderma-pattern abnormalities. A six-year follow-up study.

The aim of this study was to assess the prognostic significance of scleroderma capillary pattern (SD-pattern) in patients with Raynaud's phenomenon. Thirty patients with a capillaroscopy examination suggestive of scleroderma (megacapillaries and/or avascularity) but without clinical criteria of scleroderma (ARA criteria) were reevaluated 6 years after the initial clinical and capillaroscopy examinations. SD-pattern abnormalities were classified according to a semiquantitative method. Eight out of the 28 evaluated patients (28%) developed a scleroderma spectrum disorder (SDS). The presence of avascularity and/or a mean of more than two megacapillaries digit greatly enhanced the percentage of evolution toward SDS (70%/88% respectively). Most of the patients with few enlarged capillaries and no capillary rarefaction at entry had primary acrocyanosis (11/15). None of them developed SDS. The microangiopathy disappeared during the follow-up period in most of these patients (14/15). These results confirm the prognostic value of SD-pattern capillary abnormalities for SDS. Primary acrocyanosis, a benign clinical entity should be considered in presence of few megacapillaries and without capillary rarefaction.

A Graphical Pattern Editor for Java Frameworks

Ohjelmistojen uudelleenkäyttö on hyvin tärkeä käsite ohjelmistotekniikan alueella.Ohjelmistojen uudelleenkäyttötekniikat parantavat ohjelmistokehitysprosessin laatua. Yleisiä ratkaisuja sekä ohjelmiston suunnittelun että arkkitehtuurin uudelleenkäyttöön ovat olio-ohjelmointi ja sovelluskehykset. Tähän asti ei ole ollut olemassa yleisiä tapoja sovelluskehysten erikoistamiseen. Monet nykyääntunnetuista sovelluskehyksistä ovat hyvin suuria ja mutkikkaita. Tällaisten sovelluskehyksien käyttö on monimutkaista myös kokeneille ohjelmoijille. Hyvin dokumentoidut uudelleenkäytettävät sovelluskehyksen rajapinnat parantavat kehyksen käytettävyyttä ja tehostavat myös erikoistamisprosessiakin sovelluskehyksen käyttäjille. Sovelluskehyseditori (framework editor, JavaFrames) on prototyyppityökalu, jota voidaan käyttää yksinkertaistamaan sovelluskehyksen käyttöä. Perusajatus JavaFrames lähestymistavassa ovat erikoistamismallit, joita käytetään kuvamaan sovelluskehyksen uudelleenkäytettäviä rajapintoja. Näihin malleihin perustuen JavaFrames tarjoaa automaattisen lähdekoodi generaattorin, dokumentoinninja arkkitehtuurisääntöjen tarkistuksen. Tämä opinnäyte koskee graafisen mallieditorin kehittämistä JavaFrames ympäristöön. Työssä on laadittu työkalu,jonka avulla voidaan esittää graafisesti erikoistamismalli. Editori sallii uusien mallien luomisen, vanhojen käyttämättä olevien poistamisen, kuten myös yhteyksien lisäämisen mallien välille. Tällainen graafinen tuki JavaFrames ympäristöönvoi huomattavasti yksinkertaistaa sen käyttöä ja tehdä sovellusten kehittämisprosessista joustavamman.

Image-Based Coral Reef Classification and Thematic Mapping

This paper presents a novel image classification scheme for benthic coral reef images that can be applied to both single image and composite mosaic datasets. The proposed method can be configured to the characteristics (e.g., the size of the dataset, number of classes, resolution of the samples, color information availability, class types, etc.) of individual datasets. The proposed method uses completed local binary pattern (CLBP), grey level co-occurrence matrix (GLCM), Gabor filter response, and opponent angle and hue channel color histograms as feature descriptors. For classification, either k-nearest neighbor (KNN), neural network (NN), support vector machine (SVM) or probability density weighted mean distance (PDWMD) is used. The combination of features and classifiers that attains the best results is presented together with the guidelines for selection. The accuracy and efficiency of our proposed method are compared with other state-of-the-art techniques using three benthic and three texture datasets. The proposed method achieves the highest overall classification accuracy of any of the tested methods and has moderate execution time. Finally, the proposed classification scheme is applied to a large-scale image mosaic of the Red Sea to create a completely classified thematic map of the reef benthos

Coordination pattern adaptability: energy cost of degenerate behaviors.

This study investigated behavioral adaptability, which could be defined as a blend between stability and flexibility of the limbs movement and their inter-limb coordination, when individuals received informational constraints. Seven expert breaststroke swimmers performed three 200-m in breaststroke at constant submaximal intensity. Each trial was performed randomly in a different coordination pattern: 'freely-chosen', 'maximal glide' and 'minimal glide'. Two underwater and four aerial cameras enabled 3D movement analysis in order to assess elbow and knee angles, elbow-knee pair coordination, intra-cyclic velocity variations of the center of mass, stroke rate and stroke length and inter-limb coordination. The energy cost of locomotion was calculated from gas exchanges and blood lactate concentration. The results showed significantly higher glide, intra-cyclic velocity variations and energy cost under 'maximal glide' compared to 'freely-chosen' instructional conditions, as well as higher reorganization of limb movement and inter-limb coordination (p<0.05). In the 'minimal glide' condition, the swimmers did not show significantly shorter glide and lower energy cost, but they exhibited significantly lower deceleration of the center of mass, as well as modified limb movement and inter-limb coordination (p<0.05). These results highlight that a variety of structural adaptations can functionally satisfy the task-goal.

Supervised localimage feature detection

This thesis is about detection of local image features. The research topic belongs to the wider area of object detection, which is a machine vision and pattern recognition problem where an object must be detected (located) in an image. State-of-the-art object detection methods often divide the problem into separate interest point detection and local image description steps, but in this thesis a different technique is used, leading to higher quality image features which enable more precise localization. Instead of using interest point detection the landmark positions are marked manually. Therefore, the quality of the image features is not limited by the interest point detection phase and the learning of image features is simplified. The approach combines both interest point detection and local description into one phase for detection. Computational efficiency of the descriptor is therefore important, leaving out many of the commonly used descriptors as unsuitably heavy. Multiresolution Gabor features has been the main descriptor in this thesis and improving their efficiency is a significant part. Actual image features are formed from descriptors by using a classifierwhich can then recognize similar looking patches in new images. The main classifier is based on Gaussian mixture models. Classifiers are used in one-class classifier configuration where there are only positive training samples without explicit background class. The local image feature detection method has been tested with two freely available face detection databases and a proprietary license plate database. The localization performance was very good in these experiments. Other applications applying the same under-lying techniques are also presented, including object categorization and fault detection.

Effect of temperature and moisture on the dormancy and activation pattern of Oxalis latifolia bulbs

Con objeto de buscar información para un mejor control de Oxalis latifolia Kunth, se ha estudiado el efecto que presentan la temperatura y la humedad sobre la activación de sus bulbos, tanto en la forma común como en la forma Cornwall de la misma. Los bulbos de la mala hierba se mantuvieron en un refrigerador a +4°C durante 13, 20, 27, 41, 48 y 55 días. Se sacaron tres grupos de 30 bulbos de cada forma en cada fecha de muestreo y se colocaron a 21°C; 15 de ellos se mantuvieron en condiciones de sequía —no se regaron— y otros 15 en condiciones de humedad —añadiendo el agua necesaria—. Los resultados muestran que la activación ocurre durante un período prolongado de tiempo en ambas formas, tanto en seco como en húmedo. También se observó que los bulbos secos se activaron antes que los húmedos y los de la forma común antes que los Cornwall. Se observaron dos patrones de activación: los bulbos secos de la forma común generalmente presentaron una activación que sigue un patrón logarítmico, mientras que sus bulbos humedecidos mostraron una tendencia linear; los bulbos Cornwall se activaron con una tendencia exponencial en la mayoría de los casos. El tiempo medio requerido para la activación después del almacenamiento en frío fue constante en la forma común, sin embargo la activación de Cornwall fue más rápida cuanto más tiempo permanecieron almacenados en frío.

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.

Diel pattern of Chironomids drift in the Llobregat river

Drift has recived considerable attention in recent times as a method to collect chironomid pupal exuviae (COFFMAN, 1973; LAVILLE, 1979, 1981) for taxonomie as well as water pollution studies (WILSON, 1977).

Keraamisten laattojen luokittelu väripiirteiden avulla

Vaikka keraamisten laattojen valmistusprosessi onkin täysin automatisoitu, viimeinen vaihe eli laaduntarkistus ja luokittelu tehdään yleensä ihmisvoimin. Automaattinen laaduntarkastus laattojen valmistuksessa voidaan perustella taloudellisuus- ja turvallisuusnäkökohtien avulla. Tämän työn tarkoituksena on kuvata tutkimusprojektia keraamisten laattojen luokittelusta erilaisten väripiirteiden avulla. Oleellisena osana tutkittiin RGB- ja spektrikuvien välistä eroa. Työn teoreettinen osuus käy läpi aiemmin aiheesta tehdyn tutkimuksen sekä antaa taustatietoa konenäöstä, hahmontunnistuksesta, luokittelijoista sekä väriteoriasta. Käytännön osan aineistona oli 25 keraamista laattaa, jotka olivat viidestä eri luokasta. Luokittelussa käytettiin apuna k:n lähimmän naapurin (k-NN) luokittelijaa sekä itseorganisoituvaa karttaa (SOM). Saatuja tuloksia verrattiin myös ihmisten tekemään luokitteluun. Neuraalilaskenta huomattiin tärkeäksi työkaluksi spektrianalyysissä. SOM:n ja spektraalisten piirteiden avulla saadut tulokset olivat lupaavia ja ainoastaan kromatisoidut RGB-piirteet olivat luokittelussa parempia kuin nämä.