521 resultados para selection methods


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Identification of hot spots, also known as the sites with promise, black spots, accident-prone locations, or priority investigation locations, is an important and routine activity for improving the overall safety of roadway networks. Extensive literature focuses on methods for hot spot identification (HSID). A subset of this considerable literature is dedicated to conducting performance assessments of various HSID methods. A central issue in comparing HSID methods is the development and selection of quantitative and qualitative performance measures or criteria. The authors contend that currently employed HSID assessment criteria—namely false positives and false negatives—are necessary but not sufficient, and additional criteria are needed to exploit the ordinal nature of site ranking data. With the intent to equip road safety professionals and researchers with more useful tools to compare the performances of various HSID methods and to improve the level of HSID assessments, this paper proposes four quantitative HSID evaluation tests that are, to the authors’ knowledge, new and unique. These tests evaluate different aspects of HSID method performance, including reliability of results, ranking consistency, and false identification consistency and reliability. It is intended that road safety professionals apply these different evaluation tests in addition to existing tests to compare the performances of various HSID methods, and then select the most appropriate HSID method to screen road networks to identify sites that require further analysis. This work demonstrates four new criteria using 3 years of Arizona road section accident data and four commonly applied HSID methods [accident frequency ranking, accident rate ranking, accident reduction potential, and empirical Bayes (EB)]. The EB HSID method reveals itself as the superior method in most of the evaluation tests. In contrast, identifying hot spots using accident rate rankings performs the least well among the tests. The accident frequency and accident reduction potential methods perform similarly, with slight differences explained. The authors believe that the four new evaluation tests offer insight into HSID performance heretofore unavailable to analysts and researchers.

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The selection of projects and programs of work is a key function of both public and private sector organisations. Ideally, projects and programs that are selected to be undertaken are consistent with strategic objectives for the organisation; will provide value for money and return on investment; will be adequately resourced and prioritised; will not compete with general operations for resources and not restrict the ability of operations to provide income to the organisation; will match the capacity and capability of the organisation to deliver; and will produce outputs that are willingly accepted by end users and customers. Unfortunately,this is not always the case. Possible inhibitors to optimal project portfolio selection include: processes that are inconsistent with the needs of the organisation; reluctance to use an approach that may not produce predetermined preferences; loss of control and perceived decision making power; reliance on quantitative methods rather than qualitative methods for justification; ineffective project and program sponsorship; unclear project governance, processes and linkage to business strategies; ignorance, taboos and perceived effectiveness; inadequate education and training about the processes and their importance.

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The traditional searching method for model-order selection in linear regression is a nested full-parameters-set searching procedure over the desired orders, which we call full-model order selection. On the other hand, a method for model-selection searches for the best sub-model within each order. In this paper, we propose using the model-selection searching method for model-order selection, which we call partial-model order selection. We show by simulations that the proposed searching method gives better accuracies than the traditional one, especially for low signal-to-noise ratios over a wide range of model-order selection criteria (both information theoretic based and bootstrap-based). Also, we show that for some models the performance of the bootstrap-based criterion improves significantly by using the proposed partial-model selection searching method. Index Terms— Model order estimation, model selection, information theoretic criteria, bootstrap 1. INTRODUCTION Several model-order selection criteria can be applied to find the optimal order. Some of the more commonly used information theoretic-based procedures include Akaike’s information criterion (AIC) [1], corrected Akaike (AICc) [2], minimum description length (MDL) [3], normalized maximum likelihood (NML) [4], Hannan-Quinn criterion (HQC) [5], conditional model-order estimation (CME) [6], and the efficient detection criterion (EDC) [7]. From a practical point of view, it is difficult to decide which model order selection criterion to use. Many of them perform reasonably well when the signal-to-noise ratio (SNR) is high. The discrepancies in their performance, however, become more evident when the SNR is low. In those situations, the performance of the given technique is not only determined by the model structure (say a polynomial trend versus a Fourier series) but, more importantly, by the relative values of the parameters within the model. This makes the comparison between the model-order selection algorithms difficult as within the same model with a given order one could find an example for which one of the methods performs favourably well or fails [6, 8]. Our aim is to improve the performance of the model order selection criteria in cases where the SNR is low by considering a model-selection searching procedure that takes into account not only the full-model order search but also a partial model order search within the given model order. Understandably, the improvement in the performance of the model order estimation is at the expense of additional computational complexity.

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Evaluation, selection and finally decision making are all among important issues, which engineers face in long run of projects. Engineers implement mathematical and nonmathematical methods to make accurate and correct decisions, whenever needed. As extensive as these methods are, effects of any selected method on outputs achieved and decisions made are still suspicious. This is more controversial and challengeable, where evaluation is made among non-quantitative alternatives. In civil engineering and construction management problems, criteria include both quantitative and qualitative ones, such as aesthetic, construction duration, building and operation costs, and environmental considerations. As the result, decision making frequently takes place among non-quantitative alternatives. It should be noted that traditional comparison methods, including clear-cut and inflexible mathematics, have always been criticized. This paper demonstrates a brief review of traditional methods of evaluating alternatives. It also offers a new decision making method using, fuzzy calculations. The main focus of this research is some engineering issues, which have flexible nature and vague borders. Suggested method provides analyzability of evaluation for decision makers. It is also capable to overcome multi criteria and multi-referees problems. In order to ease calculations, a program named DeMA is introduced.

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We study model selection strategies based on penalized empirical loss minimization. We point out a tight relationship between error estimation and data-based complexity penalization: any good error estimate may be converted into a data-based penalty function and the performance of the estimate is governed by the quality of the error estimate. We consider several penalty functions, involving error estimates on independent test data, empirical VC dimension, empirical VC entropy, and margin-based quantities. We also consider the maximal difference between the error on the first half of the training data and the second half, and the expected maximal discrepancy, a closely related capacity estimate that can be calculated by Monte Carlo integration. Maximal discrepancy penalty functions are appealing for pattern classification problems, since their computation is equivalent to empirical risk minimization over the training data with some labels flipped.

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This paper presents a strategy for delayed research method selection in a qualitative interpretivist research. An exemplary case details how explorative interviews were designed and conducted in accordance with a paradigm prior to deciding whether to adopt grounded theory or phenomenology for data analysis. The focus here is to determine the most appropriate research strategy in this case the methodological framing to conduct research and represent findings, both of which are detailed. Research addressing current management issues requires both a flexible framework and the capability to consider the research problem from various angles, to derive tangible results for academia with immediate application to business demands. Researchers, and in particular novices, often struggle to decide on an appropriate research method suitable to address their research problem. This often applies to interpretative qualitative research where it is not always immediately clear which is the most appropriate method to use, as the research objectives shift and crystallize over time. This paper uses an exemplary case to reveal how the strategy for delayed research method selection contributes to deciding whether to adopt grounded theory or phenomenology in the initial phase of a PhD research project. In this case, semi-structured interviews were used for data generation framed in an interpretivist approach, situated in a business context. Research questions for this study were thoroughly defined and carefully framed in accordance with the research paradigm‟s principles, while at the same time ensuring that the requirements of both potential research methods were met. The grounded theory and phenomenology methods were compared and contrasted to determine their suitability and whether they meet the research objectives based on a pilot study. The strategy proposed in this paper is an alternative to the more „traditional‟ approach, which initially selects the methodological formulation, followed by data generation. In conclusion, the suggested strategy for delayed research method selection intends to help researchers identify and apply the most appropriate method to their research. This strategy is based on explorations of data generation and analysis in order to derive faithful results from the data generated.

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The favourable scaffold for bone tissue engineering should have desired characteristic features, such as adequate mechanical strength and three-dimensional open porosity, which guarantee a suitable environment for tissue regeneration. In fact, the design of such complex structures like bone scaffolds is a challenge for investigators. One of the aims is to achieve the best possible mechanical strength-degradation rate ratio. In this paper we attempt to use numerical modelling to evaluate material properties for designing bone tissue engineering scaffold fabricated via the fused deposition modelling technique. For our studies the standard genetic algorithm was used, which is an efficient method of discrete optimization. For the fused deposition modelling scaffold, each individual strut is scrutinized for its role in the architecture and structural support it provides for the scaffold, and its contribution to the overall scaffold was studied. The goal of the study was to create a numerical tool that could help to acquire the desired behaviour of tissue engineered scaffolds and our results showed that this could be achieved efficiently by using different materials for individual struts. To represent a great number of ways in which scaffold mechanical function loss could proceed, the exemplary set of different desirable scaffold stiffness loss function was chosen. © 2012 John Wiley & Sons, Ltd.

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The Texas Department of Transportation (TxDOT) is concerned about the widening gap between pavement preservation needs and available funding. Thus, the TxDOT Austin District Pavement Engineer (DPE) has investigated methods to strategically allocate available pavement funding to potential projects that improve the overall performance of the District and Texas highway systems. The primary objective of the study presented in this paper is to develop a network-level project screening and ranking method that supports the Austin District 4-year pavement management plan development. The study developed candidate project selection and ranking algorithms that evaluated pavement conditions of each project candidate using data contained in the Pavement Management Information system (PMIS) database and incorporated insights from Austin District pavement experts; and implemented the developed method and supporting algorithm. This process previously required weeks to complete, but now requires about 10 minutes including data preparation and running the analysis algorithm, which enables the Austin DPE to devote more time and resources to conducting field visits, performing project-level evaluation and testing candidate projects. The case study results showed that the proposed method assisted the DPE in evaluating and prioritizing projects and allocating funds to the right projects at the right time.

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The inquiries to return predictability are traditionally limited to conditional mean, while literature on portfolio selection is replete with moment-based analysis with up to the fourth moment being considered. This paper develops a distribution-based framework for both return prediction and portfolio selection. More specifically, a time-varying return distribution is modeled through quantile regressions and copulas, using quantile regressions to extract information in marginal distributions and copulas to capture dependence structure. A preference function which captures higher moments is proposed for portfolio selection. An empirical application highlights the additional information provided by the distributional approach which cannot be captured by the traditional moment-based methods.

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Background and Objectives Obesity and some dietary related diseases are emerging health problems among Chinese immigrants and their children in developed countries. These health problems are closely linked to eating habits, which are established in the early years of life. Young children’s eating habits are likely to persist into later childhood and youth. Family environment and parental feeding practices have a strong effect on young children’s eating habits. Little information is available on the early feeding practices of Chinese mothers in Australia. The aim of this study was to understand the dietary beliefs, feeding attitudes and practices of Chinese mothers with young children who were recent immigrants to Australia. Methods Using a sequential explanatory design, this mixed methods study consisted of two distinct phases. Phase 1 (quantitative): 254 Chinese immigrant mothers of children aged 12 to 59 months completed a cross-sectional survey. The psychometric properties and factor structure of a Chinese version of the Child Feeding Questionnaire (CFQ, by Birch et al. 2001) were assessed and used to measure specific maternal feeding attitudes and controlling feeding practices. Other questions were developed from the literature and used to explore maternal traditional dietary beliefs and feeding practices related to their beliefs, perceptions of picky eating in children and a range of socioeconomic and acculturation factors. Phase 2 (qualitative): 21 mothers took part in a follow-up telephone interview to assist in explaining and interpreting some significant findings obtained in the first phase. Results Chinese mothers held strong traditional dietary beliefs and fed their children according to these beliefs. However, children’s consumption of non-core foods was high. Both traditional Chinese and Australian style foods were consumed by their children. Confirmatory factor analysis revealed that the original 7-factor model of the CFQ provided an acceptable fit to the data with minor modification. However, an alternative model with eight constructs in which two items related to using food rewards were separated from the original restriction construct, not only provided an acceptable fit to the data, but also improved the conceptual clarity of the constructs. The latter model included 24 items loading onto the following eight constructs: restriction, pressure to eat, monitoring, use of food rewards, perceived responsibility, perception of own weight, perception of child’s weight, and concern about child becoming overweight. The internal consistency of the constructs was acceptable or desirable (Cronbach’s α = .60 - .93). Mothers reported low levels of concern about their child overeating or becoming overweight, but high levels of controlling feeding practices: restriction, monitoring, pressure to eat and use of food rewards. More than one quarter of mothers misinterpreted their child’s weight status (based on mothers’ self-reported data). In addition, mothers’ controlling feeding practices independently predicted half of the variance and explained 16% of the variance in child weight status: pressuring the child to eat was negatively associated with child weight status (β = -0.30, p < .01) and using food rewards was positively associated with child weight status (β = 0.20, p < .05) after adjusting for maternal and child covariates. Monitoring and restriction were not associated with child weight status. Mothers’ perceptions of their child’s weight were positively associated with child weight status (β = 0.33, p < .01). Moreover, mothers reported that they mostly decided what (65%) and how much (80%) food their child ate. Mothers who decided what food their child ate were more likely to monitor (β = -0.17, p < .05) and restrict (β = -0.17, p < .05) their child’s food consumption. Mothers who let their child decide how much food their child ate were less likely to pressure their child to eat (β = -0.38, p < .01) and use food rewards (β = -0.24, p < .01). Mothers’ perceptions of picky eating behaviour were positively associated with their use of pressure (β = 0.21, p < .01) and negatively associated with monitoring (β = -0.16, p < .05) and perceptions of their child’s weight status (β = -0.13, p < .05). Qualitative data showed that pressuring to eat, monitoring and restriction of the child’s food consumption were common practices among these mothers. However, mothers stated that their motivation for monitoring and restricting was to ensure the child’s general health. Mothers’ understandings of picky eating behaviour in their children were consistent with the literature and they reported multiple feeding strategies to deal with it. Conclusion Chinese immigrant mothers demonstrated strong traditional dietary beliefs, a low level of concern for child weight, misperceptions of child weight status, and a high overall level of control in child feeding in this study. The Chinese version of the CFQ, which consists of eight constructs and distinguishes between the constructs using food rewards and restriction, is an appropriate instrument to assess feeding attitudes and controlling feeding practices among Chinese immigrant mothers of young children in Australia. Mothers’ feeding attitudes and practices were associated with children’s weight status and mothers’ perceptions of picky eating behaviour in children after adjusting for a range of socio-demographic maternal and child characteristics. Monitoring and restriction of children’s food consumption according to food selection may be positive feeding practices, whereas pressuring to eat and using food rewards appeared to be negative feeding practices in this study. In addition, the results suggest that these young children have high exposure to energy-dense, nutrient-poor food. There is a need to develop and implement nutrition interventions to improve maternal feeding practices and the dietary quality among children of Chinese immigrant mothers in Australia.

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This paper details the processes and challenges involved in collecting inventory data from smallholder and community woodlots on Leyte Island, Philippines. Over the period from 2005 through to 2012, 253 woodlots at 170 sites were sampled as part of a large multidisciplinary project, resulting in a substantial timber inventory database. The inventory was undertaken to provide information for three separate but interrelated studies, namely (1) tree growth, performance and timber availability from private smallholder woodlots on Leyte Island; (2) tree growth and performance of mixed-species plantings of native species; and (3) the assessment of reforestation outcomes from various forms of reforestation. A common procedure for establishing plots within each site was developed and applied in each study, although the basis of site selection varied. A two-stage probability proportion to size sampling framework was developed to select smallholder woodlots for inclusion in the inventory. In contrast, community-based forestry woodlots were selected using stratified random sampling. Challenges encountered in undertaking the inventory were mostly associated with the need to consult widely before the commencement of the inventory and problems in identifying woodlots for inclusion. Most smallholder woodlots were only capable of producing merchantable volumes of less than 44 % of the site potential due to a lack of appropriate silviculture. There was a clear bimodal distribution of proportion that the woodlots comprised of the total smallholding area. This bimodality reflects two major motivations for smallholders to establish woodlots, namely timber production and to secure land tenure.

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Purpose: Data from two randomized phase III trials were analyzed to evaluate prognostic factors and treatment selection in the first-line management of advanced non-small cell lung cancer patients with performance status (PS) 2. Patients and Methods: Patients randomized to combination chemotherapy (carboplatin and paclitaxel) in one trial and single-agent therapy (gemcitabine or vinorelbine) in the second were included in these analyses. Both studies had identical eligibility criteria and were conducted simultaneously. Comparison of efficacy and safety was performed between the two cohorts. A regression analysis identified prognostic factors and subgroups of patients that may benefit from combination or single-agent therapy. Results: Two hundred one patients were treated with combination and 190 with single-agent therapy. Objective responses were 37 and 15%, respectively. Median time to progression was 4.6 months in the combination arm and 3.5 months in the single-agent arm (p < 0.001). Median survival imes were 8.0 and 6.6 months, and 1-year survival rates were 31 and 26%, respectively. Albumin <3.5 g, extrathoracic metastases, lactate dehydrogenase ≥200 IU, and 2 comorbid conditions predicted outcome. Patients with 0-2 risk factors had similar outcomes independent of treatment, whereas patients with 3-4 factors had a nonsignificant improvement in median survival with combination chemotherapy. Conclusion: Our results show that PS2 non-small cell lung cancer patients are a heterogeneous group who have significantly different outcomes. Patients treated with first-line combination chemotherapy had a higher response and longer time to progression, whereas overall survival did not appear significantly different. A prognostic model may be helpful in selecting PS 2 patients for either treatment strategy. © 2009 by the International Association for the Study of Lung Cancer.

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A large number of methods have been published that aim to evaluate various components of multi-view geometry systems. Most of these have focused on the feature extraction, description and matching stages (the visual front end), since geometry computation can be evaluated through simulation. Many data sets are constrained to small scale scenes or planar scenes that are not challenging to new algorithms, or require special equipment. This paper presents a method for automatically generating geometry ground truth and challenging test cases from high spatio-temporal resolution video. The objective of the system is to enable data collection at any physical scale, in any location and in various parts of the electromagnetic spectrum. The data generation process consists of collecting high resolution video, computing accurate sparse 3D reconstruction, video frame culling and down sampling, and test case selection. The evaluation process consists of applying a test 2-view geometry method to every test case and comparing the results to the ground truth. This system facilitates the evaluation of the whole geometry computation process or any part thereof against data compatible with a realistic application. A collection of example data sets and evaluations is included to demonstrate the range of applications of the proposed system.

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Background: Self-selection-whether individuals inclined to walk more seek to live in walkable environments-must be accounted for when studying built environment influences on walking. The way neighborhoods are marketed to future residents has the potential to sway residential location choice, and may consequently affect measures of self-selection related to location preferences. We assessed how walking opportunities are promoted to potential buyers, by examining walkability attributes in marketing materials for housing developments. Methods: A content analysis of marketing materials for 32 new housing developments in Perth, Australia was undertaken, to assess how walking was promoted in the text and pictures. Housing developments designed to be pedestrian-friendly (LDs) were compared with conventional developments (CDs). Results: Compared with CDs, LD marketing materials had significantly more references to 'public transport,' small home sites,' walkable parks/open space,' ease of cycling,' safe environment,' and 'boardwalks.' Other walkability attributes approached significance. Conclusion: Findings suggest the way neighborhoods are marketed may contribute to self-reported reasons for choosing particular neighborhoods, especially when attributes are not present at the time of purchase. The marketing of housing developments may be an important factor to consider when measuring self-selection, and its influence on the built environment and walking relationship.

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Use of appropriate nursery environments will maximize gain from selection for yield of wheat (Triticum aestivum L.) in the target population of environments of a breeding program. The objective of this study was to investigate how well-irrigated (low-stress) nursery environments predict yield of lines in target environments that varied in degree of water limitation. Fifteen lines were sampled from the preliminary yield evaluation stage of the Queensland wheat breeding program and tested in 26 trials under on-farm conditions (Target Environments) across nine years (1985 to 1993) and also in 27 trials conducted at three research stations (Nursery Environments) in three years (1987 to 1989). The nursery environments were structured to impose different levels of water and nitrogen (N) limitation, whereas the target environments represented a random sample of on-farm conditions from the target population of environments. Indirect selection and pattern analysis methods were used to investigate selection for yield in the nursery environments and gain from selection in the target environments. Yield under low-stress nursery conditions was an effective predictor of yield under similar low-stress target environments (r = 0.89, P < 0.01). However, the value of the low-stress nursery as a predictor of yield in the water-limited target environments decreased with increasing water stress (moderate stress r = 0.53, P < 0.05, to r = 0.38, P > 0.05; severe stress r = -0.08, P > 0.05). Yield in the stress nurseries was a poor predictor of yield in the target environments. Until there is a clear understanding of the physiological-genetic basis of variation for adaptation of wheat to the water-limited environments in Queensland, yield improvement can best be achieved by selection for a combination of yield potential in an irrigated low-stress nursery and yield in on-farm trials that sample the range of water-limited environments of the target population of environments.