712 resultados para Architektur in China


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The hepatitis B virus (HBV) is among the leading causes of chronic hepatitis, cirrhosis and hepatocellular carcinoma. In Brazil, genotype A is the most frequent, followed by genotypes D and F. Genotypes B and C are found in Brazil exclusively among Asian patients and their descendants. The aim of this study was to sequence the entire HBV genome of a Caucasian patient infected with HBV/C2 and to infer the origin of the virus based on sequencing analysis. The sequence of this Brazilian isolate was grouped with four other sequences described in China. The sequence of this patient is the first complete genome of HBV/C2 reported in Brazil.

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AIM: Circular stapled mucosectomy is the standard therapy for the treatment of symptomatic third-degree haemorrhoids and mucosal prolapse. Recently, new staplers made in China have entered the market offering an alternative to the PPH stapling devices. The aim of this prospective randomized study was to compare the safety and efficacy of these new devices. METHODS: Fifty patients with symptomatic third-degree haemorrhoids were randomized to mucosectomy either by using stapler A (CPH32; Frankenman International Ltd, Hong Kong, China; n = 25) or stapler B (PPH03; Ethicon Endo-Surgery, Spreitenbach, Switzerland; n = 25). All procedures were performed by two experienced surgeons. After the stapler was fired by one surgeon, the other surgeon, who was blinded for stapler type, evaluated the stapler line. Postoperative outcome including pain, complications and patient satisfaction were analysed. RESULTS: Demographic and clinical features were no different between the groups. There was no significant difference regarding venous bleeding (P = 0.55), but arterial bleeding was significantly more frequent when stapler B was used (P < 0.001). This led to significantly more suture ligations (P = 0.002). However, no differences regarding operation time (P = 0.99), weight of the resected mucosa (P = 0.81) and height of the stapler line (anterior, P = 0.18; posterior, P = 0.65) were detected. Postoperative pain scores (visual analogue scale) and patient satisfaction were no different either (P = 0.91 and P = 0.78, respectively). No recurrence or incontinence occurred during follow-up. CONCLUSIONS: CPH32 required significantly fewer sutures for bleeding control along the stapler line after circular mucosectomy. However, operation time, rate of postoperative complications and patient satisfaction were similar in both groups.

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In China, with the cost of improved technology rising, surplus labor shrinking, and demand for food quality and safety increasing, it will be just a matter of time before the country’s hog production sector will be commercialized like that of developed countries. However, even if China’s cost of production converges to international levels, as shown in this case study, China may continue to retain some competitive advantage because of the labor-intensive nature of the marketing services involved in hog processing and meat distribution. The supply of variety meats offers the most promising market opportunity for foreign suppliers in China. The market may open further if the tariff rate for variety meats is reduced from 20% and harmonized with the pork muscle meat rate of 12%, and if the value-added tax of 13% is applied equally to both imported and domestic products. The fast-growing Western-style family restaurant and higher-end dining sector is another market opportunity for high-quality imported pork.

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La forte teneur en elements siderophiles des sediments de la limite Cretace-Tertiaire suggere que les principaJes disparitions d'especes ont ete provoquees par des catastrophes cosmiques. Cette hypothese pourrait etre confirmee par la decouverte d'une anomaJie similaire it la limite Permien-Trias, caracterisee par la plus grave crise biologique du Phanerozoique. L'etude du site de Meishan, en Republique populaire de Chine, n'apporte aucune confirmation de ce scenario. Aucune trace d'iridium, Ie meilleur traceur de la matiere extraterrestre, n'a ete trouvee dans les 18 echantillons preleves au voisinage de la transition Permien-Trias. Toute relation entre la crise biologique du Permien-Trias et une catastrophe cosmique doit donc, pour l'instant, etre consideree comme hypothetique. The presence of siderophile-enriched material at the Cretaceous-Tertiary boundary suggests that the major extinctions of living species could result from cosmic catastrophes. The finding of the same kind of material at the Permian-Triassic boundary would be important to confirm the influence of cosmic phenomena on extinctions. The study of the M eishan section, in China, does not provide any support to this view. Iridium, the best tracer of cosmic material, has not been detected in any of the 18 samples collected around the boundary. A relation between the Permian-Triassic extinction and a cosmic collision therefore remains hypothetical.

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The aim of this study was to analyze the cross-cultural generalizability of the alternative Five-Factor Model (AFFM). The total sample was made up of 9,152 subjects from six countries: China, Germany, Italy, Spain, Switzerland, and the United States. The internal consistencies for all countries were generally similar to those found for the normative American sample. Factor analyses within cultures showed that the normative American structure was replicated in all cultures, however the congruence coefficients were slightly lower in China and Italy. A similar analysis at the facet level confirmed the high cross-cultural replicability of the AFFM. Mean-level comparisons did not always show the hypothesized effects. The mean score differences across countries were very small.

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This research project focuses on the role of English and Spanish as linguae francae. More specifically, the research attempts to answer the following questions: (i) What is the place of English and Spanish as linguae francae in the world, in general, and in China, in particular? (ii) What kinds of foreign language teaching/learning attitudes and practices are characteristic of the Chinese educational system? (iii) What are the motivations, expectations and experience of Chinese students in study abroad programmes, in general, and in the programme of the University of Lleida, in particular? The study constitutes an attempt to answer each of these questions in two ways: a review of the literature and a pilot study with 26 Chinese students at UdL. The research reveals that even though English is a very dominant foreign language in China, Spanish is a language on the rise and mainly for economic reasons. The results of the study also point at the impact of the dominance of the grammar-translation method in the perspective of Chinese students about language learning. Finally, the study shows the relevance of taking part in a SA programme for Chinese students as well as their experience of them.

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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.

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R. solanacearum was ranked in a recent survey the second most important bacterial plant pathogen, following the widely used research model Pseudomonas syringae (Mansfield et al., 2012). The main reason is that bacterial wilt caused by R. solanacearum is the world"s most devastating bacterial plant disease (http://faostat.fao.org), threatening food safety in tropical and subtropical agriculture, especially in China, Bangladesh, Bolivia and Uganda (Martin and French, 1985). This is due to the unusually wide host range of the bacterium, its high persistence and because resistant crop varieties are unavailable. In addition, R. solanacearum has been established as a model bacterium for plant pathology thanks to pioneering molecular and genomic studies (Boucher et al., 1985; Cunnac et al., 2004b; Mukaihara et al., 2010; Occhialini et al., 2005; Salanoubat et al., 2002). As for many bacterial pathogens, the main virulence determinant in R. solanacearum is the type III secretion system (T3SS) (Boucher et al., 1994), which injects a number of effector proteins into plant cells causing disease in hosts or an hypersensitive response in resistant plants. In this article we discuss the current state in the study of the R. solanacearum T3SS, stressing the latest findings and future perspectives.

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The Internet is becoming more and more popular among drug users. The use of websites and forums to obtain illicit drugs and relevant information about the means of consumption is a growing phenomenon mainly for new synthetic drugs. Gamma Butyrolactone (GBL), a chemical precursor of Gamma Hydroxy Butyric acid (GHB), is used as a "club drug" and also in drug facilitated sexual assaults. Its market takes place mainly on the Internet through online websites but the structure of the market remains unknown. This research aims to combine digital, physical and chemical information to help understand the distribution routes and the structure of the GBL market. Based on an Internet monitoring process, thirty-nine websites selling GBL, mainly in the Netherlands, were detected between January 2010 and December 2011. Seventeen websites were categorized into six groups based on digital traces (e.g. IP addresses and contact information). In parallel, twenty-five bulk GBL specimens were purchased from sixteen websites for packaging comparisons and carbon isotopic measurements. Packaging information showed a high correlation with digital data confirming the links previously established whereas chemical information revealed undetected links and provided complementary information. Indeed, while digital and packaging data give relevant information about the retailers, the supply routes and the distribution close to the consumer, the carbon isotopic data provides upstream information about the production level and in particular the synthesis pathways and the chemical precursors. A three-level structured market has been thereby identified with a production level mainly located in China and in Germany, an online distribution level mainly hosted in the Netherlands and the customers who order on the Internet.

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Tämän tutkielman tavoitteena on tarkastella Kiinan osakemarkkinoiden tehokkuutta ja random walk -hypoteesin voimassaoloa. Tavoitteena on myös selvittää esiintyykö viikonpäiväanomalia Kiinan osakemarkkinoilla. Tutkimusaineistona käytetään Shanghain osakepörssin A-sarjan,B-sarjan ja yhdistelmä-sarjan ja Shenzhenin yhdistelmä-sarjan indeksien päivittäisiä logaritmisoituja tuottoja ajalta 21.2.1992-30.12.2005 sekä Shenzhenin osakepörssin A-sarjan ja B-sarjan indeksien päivittäisiä logaritmisoituja tuottoja ajalta 5.10.1992-30.12.2005. Tutkimusmenetelminä käytetään neljä tilastollista menetelmää, mukaan lukien autokorrelaatiotestiä, epäparametrista runs-testiä, varianssisuhdetestiä sekä Augmented Dickey-Fullerin yksikköjuuritestiä. Viikonpäiväanomalian esiintymistä tutkitaan käyttämällä pienimmän neliösumman menetelmää (OLS). Testejä tehdään sekä koko aineistolla että kolmella erillisellä ajanjaksolla. Tämän tutkielman empiiriset tulokset tukevat aikaisempia tutkimuksia Kiinan osakemarkkinoiden tehottomuudesta. Lukuun ottamatta yksikköjuuritestien saatuja tuloksia, autokorrelaatio-, runs- ja varianssisuhdetestien perusteella random walk-hypoteesi hylättiin molempien Kiinan osakemarkkinoiden kohdalla. Tutkimustulokset osoittavat, että molemmilla osakepörssillä B-sarjan indeksien käyttäytyminenon ollut huomattavasti enemmän random walk -hypoteesin vastainen kuin A-sarjan indeksit. Paitsi B-sarjan markkinat, molempien Kiinan osakemarkkinoiden tehokkuus näytti myös paranevan vuoden 2001 markkinabuumin jälkeen. Tutkimustulokset osoittavat myös viikonpäiväanomalian esiintyvän Shanghain osakepörssillä, muttei kuitenkaan Shenzhenin osakepörssillä koko tarkasteluajanjaksolla.

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This paper shows that in a stylized model with two countries, characterized by different levels of financial development, the following facts can be replicated: 1) persistent current account surpluses and 2) high TFP growth in China. Under autarky, entrepreneurs in the emerging country overinvest in short-term projects and underinvest in long-term projects because short-term assets help them secure long-term investments in the presence of credit constraints. This creates an aggregate misallocation of capital. When financial markets integrate, entrepreneurs with long-term projects can have access to cheaper short-term assets abroad, which leaves them more resources to invest in their projects. This both reduces capital misallocations and generates capital outflows.

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Tutkielman tavoitteena oli lähestyä Kiinan markkinoita suomalaisten pienten ja keskisuurten yritysten (pk-yritys) näkökulmasta. Tutkielman päätavoitteena oli käydä läpi niitä tekijöitä, joiden tiedostaminen auttaa suomalaisia pk-yrityksiä etabloitumaan Kiinan markkinoille. Tutkielman tarkoituksena oli myös tuottaa oleellista yleistietoa Kiinasta liiketoimintaympäristönä, minkä etsiminen on usein hyvin aikaakuluttavaa. Yleistiedon pohjalta pk-yritykset voivat punnita soveltuuko uusi ja houkutteleva markkina-alue niille. Tutkielma on luonteeltaan lähinnä kuvaileva markkinatutkimus, joka perustuu jo olemassa olevaan tietoon Kiinan markkinoista. Tutkielma toteutettiin ns. kirjoituspöytätutkimuksena ja suurin osa tiedosta on sekundaarista, yleistietoa liiketoimintaympäristöstä. Tutkielman lähdeaineistona käytettiin mahdollisimman uutta koti- ja ulkomaista kirjallisuutta, artikkeleita sekä seminaareissa esitettyjä tutkimuspapereita. Lähdeaineiston käsittely perustui aineistolähtöiseen analyysiin. Analyysin avulla pyrittiin tiivistämään aineisto ja kasvattamaan sen informaatioarvoa luomalla hajanaisesta aineistosta selkeää ja mielekästä, kadottamatta silti sen sisältämää informaatiota. Tässä tutkielmassa esiin tulleiden tekijöiden perusteella vaikuttaa siltä, että suomalaisilla pk-yrityksillä on huomattavia kehitys- ja kasvumahdollisuuksia Kiinan markkinoilla. Parhaat mahdollisuudet menestyä Kiinan markkinoilla on niillä yrityksillä, jotka omaavat kansainvälistä kokemusta, ovat teknologiaorientoituneita ja pitkälle erikoistuneita omalla toimialallaan. Menestyminen edellyttää myös hyvää paikallista markkinatuntemusta, kykyä solmia hyviä kontakteja paikallisiin viranomaisiin ja markkinoilla toimijoihin sekä valmiutta sitoutua pitkällä aikajänteellä Kiinan markkinoille.

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Already in ancient Greece, Hippocrates postulated that disease showed a seasonal pattern characterised by excess winter mortality. Since then, several studies have confirmed this finding, and it was generally accepted that the increase in winter mortality was mostly due to respiratory infections and seasonal influenza. More recently, it was shown that cardiovascular disease (CVD) mortality also displayed such seasonality, and that the magnitude of the seasonal effect increased from the poles to the equator. The recent study by Yang et al assessed CVD mortality attributable to ambient temperature using daily data from 15 cities in China for years 2007-2013, including nearly two million CVD deaths. A high temperature variability between and within cities can be observed (figure 1). They used sophisticated statistical methodology to account for the complex temperature-mortality relationship; first, distributed lag non-linear models combined with quasi-Poisson regression to obtain city-specific estimates, taking into account temperature, relative humidity and atmospheric pressure; then, a meta-analysis to obtain the pooled estimates. The results confirm the winter excess mortality as reported by the Eurowinter3 and other4 groups, but they show that the magnitude of ambient temperature.