891 resultados para Curve Matching


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De Groot, D. (2016). Flexibele Leerroutes voor Propedeusestudenten: Grounded Theory Onderzoek naar het Identificeren van Studentkenmerken in de Matching, ten behoeve van een Vraaggerichte, Gepersonaliseerde Leerroute in de Propedeuse Social Work. Juli, 26, 2016, Heerlen, Nederland: Open Universiteit.

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Understanding the overall catalytic activity trend for rational catalyst design is one of the core goals in heterogeneous catalysis. In the past two decades, the development of density functional theory (DFT) and surface kinetics make it feasible to theoretically evaluate and predict the catalytic activity variation of catalysts within a descriptor-based framework. Thereinto, the concept of the volcano curve, which reveals the general activity trend, usually constitutes the basic foundation of catalyst screening. However, although it is a widely accepted concept in heterogeneous catalysis, its origin lacks a clear physical picture and definite interpretation. Herein, starting with a brief review of the development of the catalyst screening framework, we use a two-step kinetic model to refine and clarify the origin of the volcano curve with a full analytical analysis by integrating the surface kinetics and the results of first-principles calculations. It is mathematically demonstrated that the volcano curve is an essential property in catalysis, which results from the self-poisoning effect accompanying the catalytic adsorption process. Specifically, when adsorption is strong, it is the rapid decrease of surface free sites rather than the augmentation of energy barriers that inhibits the overall reaction rate and results in the volcano curve. Some interesting points and implications in assisting catalyst screening are also discussed based on the kinetic derivation. Moreover, recent applications of the volcano curve for catalyst design in two important photoelectrocatalytic processes (the hydrogen evolution reaction and dye-sensitized solar cells) are also briefly discussed.

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La tesi si prefigge di definire la molteplicità dell’intersezione tra due curve algebriche piane. La trattazione sarà sviluppata in termini algebrici, per mezzo dello studio degli anelli locali. In seguito, saranno discusse alcune proprietà e sarà proposto qualche esempio di calcolo. Nel terzo capitolo, l’interesse volgerà all’intersezione tra una varietà e un’ipersuperficie di uno spazio proiettivo n-dimensionale. Verrà definita un’ulteriore di molteplicità dell’intersezione, che costituirà una generalizzazione di quella menzionata nei primi due capitoli. A partire da questa definizione, sarà possibile enunciare una versione estesa del Teorema di Bezout. L’ultimo capitolo focalizza l’attenzione nuovamente sulle curve piane, con l’intento di studiarne la topologia in un intorno di un punto singolare. Si introduce, in particolare, l’importante nozione di link di un punto singolare.

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Nel presente lavoro è affrontato lo studio delle curve ellittiche viste come curve algebriche piane, più precisamente come cubiche lisce nel piano proiettivo complesso. Dopo aver introdotto nella prima parte le nozioni di Superfici compatte e orientabili e curve algebriche, tramite il teorema di classificazione delle Superfici compatte, se ne fornisce una preliminare classificazione basata sul genere della superficie e della curva, rispettivamente. Da qui, segue la definizione di curve ellittiche e uno studio più dettagliato delle loro pricipali proprietà, quali la possibilità di definirle tramite un'equazione affine nota come equazione di Weierstrass e la loro struttura intrinseca di gruppo abeliano. Si fornisce quindi un'ulteriore classificazione delle cubiche lisce, totalmente differente da quella precedente, che si basa invece sul modulo della cubica, invariante per trasformazioni proiettive. Infine, si considera un aspetto computazionale delle curve ellittiche, ovvero la loro applicazione nel campo della Crittografia. Grazie alla struttura che esse assumono sui campi finiti, sotto opportune ipotesi, i crittosistemi a chiave pubblica basati sul problema del logaritmo discreto definiti sulle curve ellittiche, a parità di sicurezza rispetto ai crittosistemi classici, permettono l'utilizzo di chiavi più corte, e quindi meno costose computazionalmente. Si forniscono quindi le definizioni di problema del logaritmo discreto classico e sulle curve ellittiche, ed alcuni esempi di algoritmi crittografici classici definiti su quest'ultime.

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Nell'elaborato, dopo una breve descrizione di come vengono suddivise le macchine elettriche a seconda che vi siano o meno parti in movimento al loro interno, vengono esaminati inizialmente gli aspetti teorici che riguardano le macchine sincrone a poli lisci ed a poli salienti prendendo in esame anche quelli che sono i provvedimenti necessari a ridurre il contributo dei campi armonici di ordine superiore. Per questo tipo di macchine, spesso utilizzate in centrale per la pruduzione dell'energia elettrica, risultano di fondamentale importanza le curve a "V" e le curve di "Capability". Esse sono strumenti che permettono di valutare le prestazioni di tali macchine una volta che siano noti i dati di targa delle stesse. Lo scopo della tesi è pertanto quello di sviluppare un software in ambiente Matlab che permetta il calcolo automatico e parametrico di tali curve al fine di poter ottimizzare la scelta di una macchina a seconda delle esigenze. Nel corso dell'eleaborato vengono altresì proposti dei confronti su come varino tali curve, e pertanto i limiti di funzionamento ad esse associati, al variare di alcuni parametri fondamentali come il fattore di potenza, la reattanza sincrona o, nel caso di macchine a poli salienti, il rapporto di riluttanza. Le curve di cui sopra sono state costruite a partire da considerazioni fatte sul diagramma di Behn-Eschemburg per le macchine isotrope o sul diagramma di Arnold e Blondel per le macchine anisotrope.

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The Pennsylvania Adoption Exchange (PAE) helps case workers who represent children in state custody by recommending prospective families for adoption. We describe PAE's operational challenges using case worker surveys and analyze child outcomes through a regression analysis of data collected over multiple years. A match recommendation spreadsheet tool implemented by PAE incorporates insights from this analysis and allows PAE managers to better utilize available information. Using a discrete-event simulation of PAE, we justify the value of a statewide adoption network and demonstrate the importance of better information about family preferences for increasing the percentage of children who are successfully adopted. Finally, we detail a series of simple improvements that PAE achieved through collecting more valuable information and aligning incentives for families to provide useful preference information.

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Negli ultimi anni, è aumentato notevolmente l'interesse per piante e prodotti vegetali, e composti da essi derivati od estratti, in alternativa ai conservanti chimici per prevenire o ritardare lo sviluppo microbico negli alimenti. Questo deriva dalla percezione negativa, ormai diffusa a livello pubblico, nei confronti di sostanze di sintesi che sono ampiamente utilizzate come conservanti nell’industria alimentare. Sono stati effettuati diversi studi sull’attività antimicrobica di questi composti negli alimenti, anche se il loro utilizzo a livello industriale è limitato. Ciò dipende dalla difficile standardizzazione di queste sostanze, dovuta alla variabilità della matrice alimentare che ne può alterarne l’attività antimicrobica. In questa sperimentazione si sono utilizzati l’olio essenziale di Sateureja montana e l’estratto di Cotinus coggygria e sono state fatte delle prove preliminari, determinandone le componenti volatili tramite gas-cromatografia abbinata a microestrazione in fase solida. Sono stati selezionati un ceppo di Listeria monocytogenes (Scott A) e uno di Saccharomyces cerevisiae (SPA), e sono stati utilizzati per realizzare curve di morte termica in sistema modello e in sistema reale. Dai risultati ottenuti si può affermare che Satureja montana e Cotinus coggygria possono essere presi in considerazione come antimicrobici naturali da impiegare per la stabilizzazione di alimenti, nonché per ridurre l’entità dei trattamenti termici atti a salvaguardare le proprietà nutrizionali ed organolettiche di alimenti, come ad esempio succhi di frutta, garantendone la sicurezza e qualità microbiologica.

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Objective. Minimally invasive video-assisted thyroidectomy (MIVAT) is a technically demanding procedure and requires a surgical team skilled in both endocrine and endoscopic surgery. A time consuming learning and training period is mandatory at the beginning of the experience. The aim of our report is to focus some aspects of the learning curve of the surgeon who practices video-assisted thyroid procedures for the first time, through the analysis of our preliminary series of 36 cases. Patients and methods. From September 2004 to April 2005 we selected 36 patients for minimally invasive video-assisted surgery of the thyroid. The patients were considered eligible if they presented with a nodule not exceeding 35mm in maximum diameter; total thyroid volume within normal range; absence of biochemical and echographic signs of thyroiditis. We analyzed surgical results, conversion rate, operating time, post-operative complications, hospital stay, cosmetic outcome of the series. Results. We performed 36 total thyroidectomy. The procedure was successfully carried out in 33/36 cases. Post-operative complications included 3 transient recurrent nerve palsies and 2 transient hypocalcemias; no definitive hypoparathyroidism was registered. All patients were discharged 2 days after operation. The cosmetic result was considered excellent by most patients. Conclusions. Advances in skills and technology have enabled surgeons to reproduce most open surgical techniques with video-assistance or laparoscopically. Training is essential to acquire any new surgical technique and it should be organized in detail to exploit it completely.

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The goal of image retrieval and matching is to find and locate object instances in images from a large-scale image database. While visual features are abundant, how to combine them to improve performance by individual features remains a challenging task. In this work, we focus on leveraging multiple features for accurate and efficient image retrieval and matching. We first propose two graph-based approaches to rerank initially retrieved images for generic image retrieval. In the graph, vertices are images while edges are similarities between image pairs. Our first approach employs a mixture Markov model based on a random walk model on multiple graphs to fuse graphs. We introduce a probabilistic model to compute the importance of each feature for graph fusion under a naive Bayesian formulation, which requires statistics of similarities from a manually labeled dataset containing irrelevant images. To reduce human labeling, we further propose a fully unsupervised reranking algorithm based on a submodular objective function that can be efficiently optimized by greedy algorithm. By maximizing an information gain term over the graph, our submodular function favors a subset of database images that are similar to query images and resemble each other. The function also exploits the rank relationships of images from multiple ranked lists obtained by different features. We then study a more well-defined application, person re-identification, where the database contains labeled images of human bodies captured by multiple cameras. Re-identifications from multiple cameras are regarded as related tasks to exploit shared information. We apply a novel multi-task learning algorithm using both low level features and attributes. A low rank attribute embedding is joint learned within the multi-task learning formulation to embed original binary attributes to a continuous attribute space, where incorrect and incomplete attributes are rectified and recovered. To locate objects in images, we design an object detector based on object proposals and deep convolutional neural networks (CNN) in view of the emergence of deep networks. We improve a Fast RCNN framework and investigate two new strategies to detect objects accurately and efficiently: scale-dependent pooling (SDP) and cascaded rejection classifiers (CRC). The SDP improves detection accuracy by exploiting appropriate convolutional features depending on the scale of input object proposals. The CRC effectively utilizes convolutional features and greatly eliminates negative proposals in a cascaded manner, while maintaining a high recall for true objects. The two strategies together improve the detection accuracy and reduce the computational cost.

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The major drawback of Ka band, operating frequency of the AltiKa altimeter on board SARAL, is its sensitivity to atmospheric liquid water. Even light rain or heavy clouds can strongly attenuate the signal and distort the signal leading to erroneous geophysical parameters estimates. A good detection of the samples affected by atmospheric liquid water is crucial. As AltiKa operates at a single frequency, a new technique based on the detection by a Matching Pursuit algorithm of short scale variations of the slope of the echo waveform plateau has been developed and implemented prelaunch in the ground segment. As the parameterization of the detection algorithm was defined using Jason-1 data, the parameters were re-estimated during the cal-val phase, during which the algorithm was also updated. The measured sensor signal-to-noise ratio is significantly better than planned, the data loss due to attenuation by rain is significantly smaller than expected (<0.1%). For cycles 2 to 9, the flag detects about 9% of 1Hz data, 5.5% as rainy and 3.5 % as backscatter bloom (or sigma0 bloom). The results of the flagging process are compared to independent rain data from microwave radiometers to evaluate its performances in term of detection and false alarms.

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Stated-preference valuation techniques are often used to assess consumers' willingness-to-pay for food items produced in farming systems that adopt a sustainable use of pesticides (SUP). We propose an innovative valuation methodology in which dichotomous-choice contingent valuation is used to estimate the demand curve (price-quantity relationship) for such food items where price means price premium for the SUP output, quantity is the probability of choosing SUP and the conventional food product is kept available in the market at the current market price. This methodology can be used to evaluate market differentiation as a policy option to promote the SUP. The methodology is tested with data from a sample of urban consumers of fruits and vegetables in Portugal. The estimated demand curve is used to define the price level maximizing the total premium revenue for the SUP sector as a whole. This optimal level of the price premium is €77.55 (or 163% of the value of the monthly basket of fruits and vegetables at current prices). Adopting the optimal price premium will decrease the number of consumers of SUP food by 54%. The reduction is even higher for low income consumers (80%) leaving them more exposed to the risks of pesticide use.

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This paper provides a new reading of a classical economic relation: the short-run Phillips curve. Our point is that, when dealing with inflation and unemployment, policy-making can be understood as a multicriteria decisionmaking problem. Hence, we use so-called multiobjective programming in connection with a computable general equilibrium (CGE) model to determine the combinations of policy instruments that provide efficient combinations of inflation and unemployment. This approach results in an alternative version of the Phillips curve labelled as efficient Phillips curve. Our aim is to present an application of CGE models to a new area of research that can be especially useful when addressing policy exercises with real data. We apply our methodological proposal within a particular regional economy, Andalusia, in the south of Spain. This tool can give some keys for policy advice and policy implementation in the fight against unemployment and inflation.

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Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnoloigia, 2016.

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The main aim of this study was to analyze evidence of an environmental Kuznets curve for water pollution in the developing and developed countries. The study was conducted based on a panel data set of 54 countries – that were categorized into six groups of “developed countries”, “developing countries”, “developed countries with low income”, “developed countries with high income” and “coastal countries”- between the years 1995 to 2006. The results do not confirm the inverted U-shape of EKC curve for the developed countries with low income. Based on the estimated turning points and the average GDP per capita, the study revealed at which point of the EKC the countries are. Furthermore, impacts of capital-and-labor ratio as well as trade openness are drawn by estimating different models for the EKC. The magnitude role of each explanatory variable on BOD was calculated by estimating the associated elasticity.