979 resultados para Multiple use of vehicles
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Mode of access: Internet.
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Cette thèse porte sur les problèmes de tournées de véhicules avec fenêtres de temps où un gain est associé à chaque client et où l'objectif est de maximiser la somme des gains recueillis moins les coûts de transport. De plus, un même véhicule peut effectuer plusieurs tournées durant l'horizon de planification. Ce problème a été relativement peu étudié en dépit de son importance en pratique. Par exemple, dans le domaine de la livraison de denrées périssables, plusieurs tournées de courte durée doivent être combinées afin de former des journées complètes de travail. Nous croyons que ce type de problème aura une importance de plus en plus grande dans le futur avec l'avènement du commerce électronique, comme les épiceries électroniques, où les clients peuvent commander des produits par internet pour la livraison à domicile. Dans le premier chapitre de cette thèse, nous présentons d'abord une revue de la littérature consacrée aux problèmes de tournées de véhicules avec gains ainsi qu'aux problèmes permettant une réutilisation des véhicules. Nous présentons les méthodologies générales adoptées pour les résoudre, soit les méthodes exactes, les méthodes heuristiques et les méta-heuristiques. Nous discutons enfin des problèmes de tournées dynamiques où certaines données sur le problème ne sont pas connues à l'avance. Dans le second chapitre, nous décrivons un algorithme exact pour résoudre un problème de tournées avec fenêtres de temps et réutilisation de véhicules où l'objectif premier est de maximiser le nombre de clients desservis. Pour ce faire, le problème est modélisé comme un problème de tournées avec gains. L'algorithme exact est basé sur une méthode de génération de colonnes couplée avec un algorithme de plus court chemin élémentaire avec contraintes de ressources. Pour résoudre des instances de taille réaliste dans des temps de calcul raisonnables, une approche de résolution de nature heuristique est requise. Le troisième chapitre propose donc une méthode de recherche adaptative à grand voisinage qui exploite les différents niveaux hiérarchiques du problème (soit les journées complètes de travail des véhicules, les routes qui composent ces journées et les clients qui composent les routes). Dans le quatrième chapitre, qui traite du cas dynamique, une stratégie d'acceptation et de refus des nouvelles requêtes de service est proposée, basée sur une anticipation des requêtes à venir. L'approche repose sur la génération de scénarios pour différentes réalisations possibles des requêtes futures. Le coût d'opportunité de servir une nouvelle requête est basé sur une évaluation des scénarios avec et sans cette nouvelle requête. Enfin, le dernier chapitre résume les contributions de cette thèse et propose quelques avenues de recherche future.
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Inductively coupled plasma optical emission spectrometers (ICP DES) allow fast simultaneous measurements of several spectral lines for multiple elements. The combination of signal intensities of two or more emission lines for each element may bring such advantages as improvement of the precision, the minimization of systematic errors caused by spectral interferences and matrix effects. In this work, signal intensities for several spectral lines were combined for the determination of Al, Cd, Co, Cr, Mn, Pb, and Zn in water. Afterwards, parameters for evaluation of the calibration model were calculated to select the combination of emission lines leading to the best accuracy (lowest values of PRESS-Predicted error sum of squares and RMSEP-Root means square error of prediction). Limits of detection (LOD) obtained using multiple lines were 7.1, 0.5, 4.4, 0.042, 3.3, 28 and 6.7 mu g L(-1) (n = 10) for Al, Cd. Co, Cr, Mn, Pb and Zn, respectively, in the presence of concomitants. On the other hand, the LOD established for the most intense emission line were 16. 0.7, 8.4, 0.074. 23, 26 and 9.6 mu g L(-1) (n = 10) for these same elements in the presence of concomitants. The accuracy of the developed procedure was demonstrated using water certified reference material. The use of multiple lines improved the sensitivity making feasible the determination of these analytes according to the target values required for the current environmental legislation for water samples and it was also demonstrated that measurements in multiple lines can also be employed as a tool to verify the accuracy of an analytical procedure in ICP DES. (C) 2009 Elsevier B.V. All rights reserved.
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Dysplasia and esophageal adenocarcinoma may arise in patients with Barrett`s esophagus after fundoplication esophageal pH monitoring showing no acid in esophagus. This suggests the need to develop methodology to evaluate the occurrence of ultra-distal reflux (1 cm above the LES). The objective of the study was to compare acid exposition in three different levels: 5 cm above the upper border of the LES, 1 cm above the LES and in the intrasphincteric region. Eleven patients with Barrett`s esophagus after Nissen fundoplication with no clinical, endoscopic and radiologic evidence of reflux were selected. Four-channel pH monitoring took place: channel A, 5 cm above the upper border of the LES; channel B, 1 cm above the LES; channel C, intrasphincteric; channel D, intragastric. The results of channels A, B and C were compared. There was significant increase in number of reflux episodes and a higher fraction of time with pH <4.0 in channel B compared to channel A. There was significant decrease in fraction of time with pH <4.0 in channel B compared to channel C. Two cases of esophageal adenocarcinoma were diagnosed in the studied patients. The region 1 cm above the upper border of the LES is more exposed to acid than the region 5 cm above the upper border of the LES, although this exposure occurred in reduced levels. The region 1 cm above the upper border of the LES is less exposed to acid than the intrasphincteric region.
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The aim of this study was to assess the variation between neuropathologists in the diagnosis of common dementia syndromes when multiple published protocols are applied. Fourteen out of 18 Australian neuropathologists participated in diagnosing 20 cases (16 cases of dementia, 4 age-matched controls) using consensus diagnostic methods. Diagnostic criteria, clinical synopses and slides from multiple brain regions were sent to participants who were asked for case diagnoses. Diagnostic sensitivity, specificity, predictive value, accuracy and variability were determined using percentage agreement and kappa statistics. Using CERAD criteria, there was a high inter-rater agreement for cases with probable and definite Alzheimer's disease but low agreement for cases with possible Alzheimer's disease. Braak staging and the application of criteria for dementia with Lewy bodies also resulted in high inter-rater agreement. There was poor agreement for the diagnosis of frontotemporal dementia and for identifying small vessel disease. Participants rarely diagnosed more than one disease in any case. To improve efficiency when applying multiple diagnostic criteria, several simplifications were proposed and tested on 5 of the original 210 cases. Inter-rater reliability for the diagnosis of Alzheimer's disease and dementia with Lewy bodies significantly improved. Further development of simple and accurate methods to identify small vessel lesions and diagnose frontotemporal dementia is warranted.
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The introduction of electricity markets and integration of Distributed Generation (DG) have been influencing the power system’s structure change. Recently, the smart grid concept has been introduced, to guarantee a more efficient operation of the power system using the advantages of this new paradigm. Basically, a smart grid is a structure that integrates different players, considering constant communication between them to improve power system operation and management. One of the players revealing a big importance in this context is the Virtual Power Player (VPP). In the transportation sector the Electric Vehicle (EV) is arising as an alternative to conventional vehicles propel by fossil fuels. The power system can benefit from this massive introduction of EVs, taking advantage on EVs’ ability to connect to the electric network to charge, and on the future expectation of EVs ability to discharge to the network using the Vehicle-to-Grid (V2G) capacity. This thesis proposes alternative strategies to control these two EV modes with the objective of enhancing the management of the power system. Moreover, power system must ensure the trips of EVs that will be connected to the electric network. The EV user specifies a certain amount of energy that will be necessary to charge, in order to ensure the distance to travel. The introduction of EVs in the power system turns the Energy Resource Management (ERM) under a smart grid environment, into a complex problem that can take several minutes or hours to reach the optimal solution. Adequate optimization techniques are required to accommodate this kind of complexity while solving the ERM problem in a reasonable execution time. This thesis presents a tool that solves the ERM considering the intensive use of EVs in the smart grid context. The objective is to obtain the minimum cost of ERM considering: the operation cost of DG, the cost of the energy acquired to external suppliers, the EV users payments and remuneration and penalty costs. This tool is directed to VPPs that manage specific network areas, where a high penetration level of EVs is expected to be connected in these areas. The ERM is solved using two methodologies: the adaptation of a deterministic technique proposed in a previous work, and the adaptation of the Simulated Annealing (SA) technique. With the purpose of improving the SA performance for this case, three heuristics are additionally proposed, taking advantage on the particularities and specificities of an ERM with these characteristics. A set of case studies are presented in this thesis, considering a 32 bus distribution network and up to 3000 EVs. The first case study solves the scheduling without considering EVs, to be used as a reference case for comparisons with the proposed approaches. The second case study evaluates the complexity of the ERM with the integration of EVs. The third case study evaluates the performance of scheduling with different control modes for EVs. These control modes, combined with the proposed SA approach and with the developed heuristics, aim at improving the quality of the ERM, while reducing drastically its execution time. The proposed control modes are: uncoordinated charging, smart charging and V2G capability. The fourth and final case study presents the ERM approach applied to consecutive days.
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The massification of electric vehicles (EVs) can have a significant impact on the power system, requiring a new approach for the energy resource management. The energy resource management has the objective to obtain the optimal scheduling of the available resources considering distributed generators, storage units, demand response and EVs. The large number of resources causes more complexity in the energy resource management, taking several hours to reach the optimal solution which requires a quick solution for the next day. Therefore, it is necessary to use adequate optimization techniques to determine the best solution in a reasonable amount of time. This paper presents a hybrid artificial intelligence technique to solve a complex energy resource management problem with a large number of resources, including EVs, connected to the electric network. The hybrid approach combines simulated annealing (SA) and ant colony optimization (ACO) techniques. The case study concerns different EVs penetration levels. Comparisons with a previous SA approach and a deterministic technique are also presented. For 2000 EVs scenario, the proposed hybrid approach found a solution better than the previous SA version, resulting in a cost reduction of 1.94%. For this scenario, the proposed approach is approximately 94 times faster than the deterministic approach.
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Energy resource scheduling is becoming increasingly important, as the use of distributed resources is intensified and of massive electric vehicle is envisaged. The present paper proposes a methodology for day-ahead energy resource scheduling for smart grids considering the intensive use of distributed generation and Vehicle-to-Grid (V2G). This method considers that the energy resources are managed by a Virtual Power Player (VPP) which established contracts with their owners. It takes into account these contracts, the users' requirements subjected to the VPP, and several discharge price steps. The full AC power flow calculation included in the model takes into account network constraints. The influence of the successive day requirements on the day-ahead optimal solution is discussed and considered in the proposed model. A case study with a 33-bus distribution network and V2G is used to illustrate the good performance of the proposed method.
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The main serological marker for the diagnosis of recent toxoplasmosis is the specific IgM antibody, along with IgG antibodies of low avidity. However, in some patients these antibodies may persist long after the acute/recent phase, contributing to misdiagnosis in suspected cases of toxoplasmosis. In the present study, the diagnostic efficiency of ELISA was evaluated, with the use of peptides derived from T. gondii ESA antigens, named SAG-1, GRA-1 and GRA-7. In the assay referred to, we studied each of these peptides individually, as well as in four different combinations, as Multiple Antigen Peptides (MAP), aiming to establish a reliable profile for the acute/recent toxoplasmosis with only one patient serum sample. The diagnostic performance of the assay using MAP1, with the combination of SAG-1, GRA-1 and GRA-7 peptides, demonstrated better discrimination of the acute/recent phase from non acute/recent phase of toxoplasmosis. Our results show that IgM antibodies to MAP1 may be useful as a serological marker, enhancing the diagnostic efficiency of the assay for acute/recent phase of toxoplasmosis.
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In health related research it is common to have multiple outcomes of interest in a single study. These outcomes are often analysed separately, ignoring the correlation between them. One would expect that a multivariate approach would be a more efficient alternative to individual analyses of each outcome. Surprisingly, this is not always the case. In this article we discuss different settings of linear models and compare the multivariate and univariate approaches. We show that for linear regression models, the estimates of the regression parameters associated with covariates that are shared across the outcomes are the same for the multivariate and univariate models while for outcome-specific covariates the multivariate model performs better in terms of efficiency.
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Context: Understanding the process through which adolescents and young adults are trying legal and illegal substances is a crucial point for the development of tailored prevention and treatment programs. However, patterns of substance first use can be very complex when multiple substances are considered, requiring reduction into a few meaningful number of categories. Data: We used data from a survey on adolescent and young adult health conducted in 2002 in Switzerland. Answers from 2212 subjects aged 19 and 20 were included. The first consumption ever of 10 substances (tobacco, cannabis, medicine to get high, sniff (volatile substances, and inhalants), ecstasy, GHB, LSD, cocaine, methadone, and heroin) was considered for a grand total of 516 different patterns. Methods: In a first step, automatic clustering was used to decrease the number of patterns to 50. Then, two groups of substance use experts, three social field workers, and three toxicologists and health professionals, were asked to reduce them into a maximum of 10 meaningful categories. Results: Classifications obtained through our methodology are of practical interest by revealing associations invisible to purely automatic algorithms. The article includes a detailed analysis of both final classifications, and a discussion on the advantages and limitations of our approach.
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This trial was aimed to explore the efficacy of pegfilgrastim to accelerate neutrophil engraftment after stem cell autotransplant. Twenty patients with multiple myeloma and 20 with lymphoma received pegfilgrastim 6 mg on day +1. Forty cases treated with daily filgrastim starting at median day +7 (5-7), matched by age, sex, diagnosis, high-dose chemotherapy schedule, CD34 + cell-dose, and prior therapy lines, were used for comparison. Median time to neutrophil engraftment was 9.5 vs. 11 days for pegfilgrastim and filgrastim, respectively (p < 0.0001). Likewise, duration of neutropenia, intravenous antibiotic use, and hospitalization favored pegfilgrastim, while platelet engraftment, transfusion requirement, and fever duration were equivalent in both groups. No grade ≥ 3 toxicities were observed. Patients with lymphoma performed similarly to the entire cohort, while patients with myeloma showed faster neutrophil engraftment and shorter neutropenia but not shorter hospitalization and antibiotic use. The possibility of different outcomes for lymphoma and myeloma suggests that stratification by diagnosis may be useful in future phase III studies.
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Customer knowledge management (CKM) practices enable organizations to create customer competence with systematic use of customer information that is integrated throughout the organization. Nonetheless, organizations are not able to fully exploit the vast amount of data available. Previous research on use of customer information is limited especially in a multichannel environment. The aim of this study was to identify the main obstacles for utilizing customer information efficiently across multiple sales channels. The study was conducted as a single case study in order to gain deeper understanding of the research problem. The empirical findings indicate that lack of CKM practices and a common goal are major challenges obstructing effective utilization of customer information. Furthermore, decentralized organizational structure and insufficient analytical skills create obstacles for information sharing and capabilities to process information and create new knowledge. The implications of the study suggest that in order to create customer competence organizations should shift their focus from technology to the organizational factors affecting use of information and implement CKM practices throughout the organization.