872 resultados para Trial and error


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Uno de los procesos de desarrollo más comunes para llevar a cabo un proyecto arquitectónico es el ensayo y error. Un proceso de selección de pruebas que se suele abordar de dos maneras, o bien se efectúa con el fin de ir depurando una posición más óptima, o bien sirve para explorar nuevas vías de investigación. Con el fin de profundizar en esto, el artículo presenta el análisis de dos diferentes procesos de proyecto de viviendas desarrolladas por ensayo y error, obras referenciales en la historia de la arquitectura, la Villa Stonborough de Wittgenstein y la Villa Moller de Adolf Loos. Ambas aunque pertenecientes al mismo periodo histórico, están desarrolladas de maneras muy opuestas, casi enfrentadas. De su estudio se pretende localizar los conceptos que han impulsado sus diferentes vías de producción, para poder extrapolados a otros casos similares. ABSTRACT: One of the most common processes to develop an architectonic project is the trial and error method. The process of selection of tests is usually done on two different ways. Or it is done with the goal to find out the most optimized position, or it is used to explore new ways of research. In order to investigate this item, the article shows the analysis of two different processes of housing projects that have been done by trial and error. Constructions, that are references in the history of architecture, the Villa Stonborough by Wittgenstein and the Villa Moller by Adolf Loos. Although both of them belong to the same historical period, they are developed by different ways, almost confronted. Thanks to this analysis we will attempt to localize the concepts that drove into their different way of production and then we will try to extrapolate these properties to other similar cases.

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Vicarious trial-and-error (VTE) is a term that Muenzinger and Tolman used to describe the rat's conflict-like behavior before responding to choice. Recently, VTE was proposed as a mechanism alternative to the concept of "cognitive map" in accounts of hippocampal function. That is, many phenomena of impaired learning and memory related to hippocampal interventions may be explained by behavioral first principles: reduced conflicting, incipient, pre-choice tendencies to approach and avoid. The nonspatial black-white discrimination learning and VTE behavior of the rat were investigated. Hippocampal-lesioned and sham-lesioned animals were trained for 25 days (20 trials per day) starting at 60 days of age. Each movement of the head from one discriminative stimulus to the other was counted as a VTE instance. Lesioned rats had fewer VTEs than sham controls, and the former learned much more slowly or never learned. After learning, VTE frequency declined. Male and female rats showed no significant differences in VTE behavior or discrimination learning.

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Nutrition interventions in the form of both self-management education and individualised diet therapy are considered essential for the long-term management of type 2 diabetes mellitus (T2DM). The measurement of diet is essential to inform, support and evaluate nutrition interventions in the management of T2DM. Barriers inherent within health care settings and systems limit ongoing access to personnel and resources, while traditional prospective methods of assessing diet are burdensome for the individual and often result in changes in typical intake to facilitate recording. This thesis investigated the inclusion of information and communication technologies (ICT) to overcome limitations to current approaches in the nutritional management of T2DM, in particular the development, trial and evaluation of the Nutricam dietary assessment method (NuDAM) consisting of a mobile phone photo/voice application to assess nutrient intake in a free-living environment with older adults with T2DM. Study 1: Effectiveness of an automated telephone system in promoting change in dietary intake among adults with T2DM The effectiveness of an automated telephone system, Telephone-Linked Care (TLC) Diabetes, designed to deliver self-management education was evaluated in terms of promoting dietary change in adults with T2DM and sub-optimal glycaemic control. In this secondary data analysis independent of the larger randomised controlled trial, complete data was available for 95 adults (59 male; mean age(±SD)=56.8±8.1 years; mean(±SD)BMI=34.2±7.0kg/m2). The treatment effect showed a reduction in total fat of 1.4% and saturated fat of 0.9% energy intake, body weight of 0.7 kg and waist circumference of 2.0 cm. In addition, a significant increase in the nutrition self-efficacy score of 1.3 (p<0.05) was observed in the TLC group compared to the control group. The modest trends observed in this study indicate that the TLC Diabetes system does support the adoption of positive nutrition behaviours as a result of diabetes self-management education, however caution must be applied in the interpretation of results due to the inherent limitations of the dietary assessment method used. The decision to use a close-list FFQ with known bias may have influenced the accuracy of reporting dietary intake in this instance. This study provided an example of the methodological challenges experienced with measuring changes in absolute diet using a FFQ, and reaffirmed the need for novel prospective assessment methods capable of capturing natural variance in usual intakes. Study 2: The development and trial of NuDAM recording protocol The feasibility of the Nutricam mobile phone photo/voice dietary record was evaluated in 10 adults with T2DM (6 Male; age=64.7±3.8 years; BMI=33.9±7.0 kg/m2). Intake was recorded over a 3-day period using both Nutricam and a written estimated food record (EFR). Compared to the EFR, the Nutricam device was found to be acceptable among subjects, however, energy intake was under-recorded using Nutricam (-0.6±0.8 MJ/day; p<0.05). Beverages and snacks were the items most frequently not recorded using Nutricam; however forgotten meals contributed to the greatest difference in energy intake between records. In addition, the quality of dietary data recorded using Nutricam was unacceptable for just under one-third of entries. It was concluded that an additional mechanism was necessary to complement dietary information collected via Nutricam. Modifications to the method were made to allow for clarification of Nutricam entries and probing forgotten foods during a brief phone call to the subject the following morning. The revised recording protocol was evaluated in Study 4. Study 3: The development and trial of the NuDAM analysis protocol Part A explored the effect of the type of portion size estimation aid (PSEA) on the error associated with quantifying four portions of 15 single foods items contained in photographs. Seventeen dietetic students (1 male; age=24.7±9.1 years; BMI=21.1±1.9 kg/m2) estimated all food portions on two occasions: without aids and with aids (food models or reference food photographs). Overall, the use of a PSEA significantly reduced mean (±SD) group error between estimates compared to no aid (-2.5±11.5% vs. 19.0±28.8%; p<0.05). The type of PSEA (i.e. food models vs. reference food photograph) did not have a notable effect on the group estimation error (-6.7±14.9% vs. 1.4±5.9%, respectively; p=0.321). This exploratory study provided evidence that the use of aids in general, rather than the type, was more effective in reducing estimation error. Findings guided the development of the Dietary Estimation and Assessment Tool (DEAT) for use in the analysis of the Nutricam dietary record. Part B evaluated the effect of the DEAT on the error associated with the quantification of two 3-day Nutricam dietary records in a sample of 29 dietetic students (2 males; age=23.3±5.1 years; BMI=20.6±1.9 kg/m2). Subjects were randomised into two groups: Group A and Group B. For Record 1, the use of the DEAT (Group A) resulted in a smaller error compared to estimations made without the tool (Group B) (17.7±15.8%/day vs. 34.0±22.6%/day, p=0.331; respectively). In comparison, all subjects used the DEAT to estimate Record 2, with resultant error similar between Group A and B (21.2±19.2%/day vs. 25.8±13.6%/day; p=0.377 respectively). In general, the moderate estimation error associated with quantifying food items did not translate into clinically significant differences in the nutrient profile of the Nutricam dietary records, only amorphous foods were notably over-estimated in energy content without the use of the DEAT (57kJ/day vs. 274kJ/day; p<0.001). A large proportion (89.6%) of the group found the DEAT helpful when quantifying food items contained in the Nutricam dietary records. The use of the DEAT reduced quantification error, minimising any potential effect on the estimation of energy and macronutrient intake. Study 4: Evaluation of the NuDAM The accuracy and inter-rater reliability of the NuDAM to assess energy and macronutrient intake was evaluated in a sample of 10 adults (6 males; age=61.2±6.9 years; BMI=31.0±4.5 kg/m2). Intake recorded using both the NuDAM and a weighed food record (WFR) was coded by three dietitians and compared with an objective measure of total energy expenditure (TEE) obtained using the doubly labelled water technique. At the group level, energy intake (EI) was under-reported to a similar extent using both methods, with the ratio of EI:TEE was 0.76±0.20 for the NuDAM and 0.76±0.17 for the WFR. At the individual level, four subjects reported implausible levels of energy intake using the WFR method, compared to three using the NuDAM. Overall, moderate to high correlation coefficients (r=0.57-0.85) were found across energy and macronutrients except fat (r=0.24) between the two dietary measures. High agreement was observed between dietitians for estimates of energy and macronutrient derived for both the NuDAM (ICC=0.77-0.99; p<0.001) and WFR (ICC=0.82-0.99; p<0.001). All subjects preferred using the NuDAM over the WFR to record intake and were willing to use the novel method again over longer recording periods. This research program explored two novel approaches which utilised distinct technologies to aid in the nutritional management of adults with T2DM. In particular, this thesis makes a significant contribution to the evidence base surrounding the use of PhRs through the development, trial and evaluation of a novel mobile phone photo/voice dietary record. The NuDAM is an extremely promising advancement in the nutritional management of individuals with diabetes and other chronic conditions. Future applications lie in integrating the NuDAM with other technologies to facilitate practice across the remaining stages of the nutrition care process.

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Background: Medication errors are common in primary care and are associated with considerable risk of patient harm. We tested whether a pharmacist-led, information technology-based intervention was more effective than simple feedback in reducing the number of patients at risk of measures related to hazardous prescribing and inadequate blood-test monitoring of medicines 6 months after the intervention. Methods: In this pragmatic, cluster randomised trial general practices in the UK were stratified by research site and list size, and randomly assigned by a web-based randomisation service in block sizes of two or four to one of two groups. The practices were allocated to either computer-generated simple feedback for at-risk patients (control) or a pharmacist-led information technology intervention (PINCER), composed of feedback, educational outreach, and dedicated support. The allocation was masked to general practices, patients, pharmacists, researchers, and statisticians. Primary outcomes were the proportions of patients at 6 months after the intervention who had had any of three clinically important errors: non-selective non-steroidal anti-inflammatory drugs (NSAIDs) prescribed to those with a history of peptic ulcer without co-prescription of a proton-pump inhibitor; β blockers prescribed to those with a history of asthma; long-term prescription of angiotensin converting enzyme (ACE) inhibitor or loop diuretics to those 75 years or older without assessment of urea and electrolytes in the preceding 15 months. The cost per error avoided was estimated by incremental cost-eff ectiveness analysis. This study is registered with Controlled-Trials.com, number ISRCTN21785299. Findings: 72 general practices with a combined list size of 480 942 patients were randomised. At 6 months’ follow-up, patients in the PINCER group were significantly less likely to have been prescribed a non-selective NSAID if they had a history of peptic ulcer without gastroprotection (OR 0∙58, 95% CI 0∙38–0∙89); a β blocker if they had asthma (0∙73, 0∙58–0∙91); or an ACE inhibitor or loop diuretic without appropriate monitoring (0∙51, 0∙34–0∙78). PINCER has a 95% probability of being cost eff ective if the decision-maker’s ceiling willingness to pay reaches £75 per error avoided at 6 months. Interpretation: The PINCER intervention is an effective method for reducing a range of medication errors in general practices with computerised clinical records. Funding: Patient Safety Research Portfolio, Department of Health, England.

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This paper reports on Years 8, 9 and 10 students’ knowledge of percent problem types, use of diagrams, and type of solution strategy. Non- and semi-proficient students displayed the expected inflexible formula approach to solution but proficient students used a flexible mixture of estimation, number sense and trial and error instead of expected schema based methods.

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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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The relationships between business planning and performance have divided the entrepreneurship research community for decades (Brinckmann et al, 2010). One side of this debate is the assumption that business plans may lock the firm in a specific direction early on, impede the firm to adapt to the changing market conditions (Dencker et al., 2009) and eventually, cause escalation of commitments by introducing rigidity (Vesper, 1993). Conversely, feedback received from the production and presentation of business plans may also lead the firm to take corrective actions. However, the mechanisms underlying the relationships between changes in business ideas, business plans and the performance of nascent firms are still largely unknown. While too many business idea changes may confuse stakeholders, exhaust the firm’s resources and hinder the undergoing legitimization process, some flexibility during the early stages of the venture may be beneficial to cope with the uncertainties surrounding new venture creation (Knight, 1921; March, 1982; Stinchcombe, 1965; Weick, 1979). Previous research has emphasized adaptability and flexibility as key success factors through effectual logic and interaction with the market (Sarasvathy, 2001; 2007) or improvisation and trial-and-error (Miner et al, 2001). However, those studies did not specifically investigate the role of business planning. Our objective is to reconcile those seemingly opposing views (flexibility versus rigidity) by undertaking a more fine-grained analysis at the relationships between business planning and changes in business ideas on a large longitudinal sample of nascent firms.

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Recently, ‘business model’ and ‘business model innovation’ have gained substantial attention in management literature and practice. However, many firms lack the capability to develop a novel business model to capture the value from new technologies. Existing literature on business model innovation highlights the central role of ‘customer value’. Further, it suggests that firms need to experiment with different business models and engage in ‘trail-and-error’ learning when participating in business model innovation. Trial-and error processes and prototyping with tangible artifacts are a fundamental characteristic of design. This conceptual paper explores the role of design-led innovation in facilitating firms to conceive and prototype novel and meaningful business models. It provides a brief review of the conceptual discussion on business model innovation and highlights the opportunities for linking it with the research stream of design-led innovation. We propose design-led business model innovation as a future research area and highlight the role of design-led prototyping and new types of artifacts and prototypes play within it. We present six propositions in order to outline future research avenues.

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Deciding the appropriate population size and number of is- lands for distributed island-model genetic algorithms is often critical to the algorithm’s success. This paper outlines a method that automatically searches for good combinations of island population sizes and the number of islands. The method is based on a race between competing parameter sets, and collaborative seeding of new parameter sets. This method is applicable to any problem, and makes distributed genetic algorithms easier to use by reducing the number of user-set parameters. The experimental results show that the proposed method robustly and reliably finds population and islands settings that are comparable to those found with traditional trial-and-error approaches.

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An estuary is formed at the mouth of a river where the tides meet a freshwater flow and it may be classified as a function of the salinity distribution and density stratification. An overview of the broad characteristics of the estuaries of South-East Queensland(Australia) is presented herein, where the small peri-urban estuaries may provide an useful indicator of potential changes which might occur in larger systems with growing urbanisation. Small peri-urban estuaries exhibits many key hydrological features and associated with ecosystem types of larger estuaries, albeit at smaller scales, often with a greater extent of urban development as a proportion of catchment area. We explore the potential for some smaller peri-urban estuaries to be used as natural laboratories to gain some much needed information on the estuarine processes, although any dynamics similarity is presently limited by critical absence of in-depth physical investigation in larger estuarine systems. The absence of the detailed turbulence and sedimentary data hampers the understanding and modelling of the estuarine zones. The interactions between the various stake holders are likely to define the vision for the future of South-East Queensland's peri-urban estuaries. This will require a solid understanding of the bio-physical function and capacity of the peri-urban estuaries. Based upon the knowledge gap, it is recommended that an adaptive trial and error approach be adopted for the future of investigation and management strategies.

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An Artificial Neural Network (ANN) is a computational modeling tool which has found extensive acceptance in many disciplines for modeling complex real world problems. An ANN can model problems through learning by example, rather than by fully understanding the detailed characteristics and physics of the system. In the present study, the accuracy and predictive power of an ANN was evaluated in predicting kinetic viscosity of biodiesels over a wide range of temperatures typically encountered in diesel engine operation. In this model, temperature and chemical composition of biodiesel were used as input variables. In order to obtain the necessary data for model development, the chemical composition and temperature dependent fuel properties of ten different types of biodiesels were measured experimentally using laboratory standard testing equipments following internationally recognized testing procedures. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture was optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the absolute fraction of variance (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found that ANN is highly accurate in predicting the viscosity of biodiesel and demonstrates the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties at different temperature levels. Therefore the model developed in this study can be a useful tool in accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

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This paper describes research investigating expertise and the types of knowledge used by airport security screeners. It applies a multi method approach incorporating eye tracking, concurrent verbal protocol and interviews. Results show that novice and expert security screeners primarily access perceptual knowledge and experience little difficulty during routine situations. During non-routine situations however, experience was found to be a determining factor for effective interactions and problem solving. Experts were found to use strategic knowledge and demonstrated structured use of interface functions integrated into efficient problem solving sequences. Comparatively, novices experienced more knowledge limitations and uncertainty resulting in interaction breakdowns. These breakdowns were characterised by trial and error interaction sequences. This research suggests that the quality of knowledge security screeners have access to has implications on visual and physical interface interactions and their integration into problem solving sequences. Implications and recommendations for the design of interfaces used in the airport security screening context are discussed. The motivations of recommendations are to improve the integration of interactions into problem solving sequences, encourage development of problem scheme knowledge and to support the skills and knowledge of the personnel that interact with security screening systems.

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This paper describes an investigation of conceptions of learning held by 22 Aboriginal and Torres Strait Islander students from three universities in Queensland, Australia. Other areas investigated were students' experiences of informal learning, their reasons for studying and the strategies they used to learn. Research into conceptions of learning is gaining impetus and current beliefs include the premise that approaches to learning adopted by university students, and hence learning outcomes, are closely related to their conceptions of learning. There is substantial research focused on Aboriginal learning styles in early childhood and primary school which indicates that Aboriginal children prefer to learn in a practical way as well as through observation and imitation and trial and error. Very little research has focused specifically on Aboriginal university students' conceptions of learning. Results of this study found that these students view and approach formal university learning in much the same way as other university students and most hold quantitative conceptions of learning. The most interesting result was the difference between students' conceptions of formal learning and their experiences of informal learning. Many students' experiences of informal learning were grounded in practical activities or exhibited a cultural focus, however, most formal learning is not dependent upon practical or cultural knowledge. It is proposed that formal learning for Indigenous students recognise and include an Indigenous perspective such as integrating, where appropriate, practical strategies for learning. We also suggest that Indigenous students be helped to develop conceptions that will enable them to learn formal, theoretical material successfully.

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Pattern recognition is a promising approach for the identification of structural damage using measured dynamic data. Much of the research on pattern recognition has employed artificial neural networks (ANNs) and genetic algorithms as systematic ways of matching pattern features. The selection of a damage-sensitive and noise-insensitive pattern feature is important for all structural damage identification methods. Accordingly, a neural networks-based damage detection method using frequency response function (FRF) data is presented in this paper. This method can effectively consider uncertainties of measured data from which training patterns are generated. The proposed method reduces the dimension of the initial FRF data and transforms it into new damage indices and employs an ANN method for the actual damage localization and quantification using recognized damage patterns from the algorithm. In civil engineering applications, the measurement of dynamic response under field conditions always contains noise components from environmental factors. In order to evaluate the performance of the proposed strategy with noise polluted data, noise contaminated measurements are also introduced to the proposed algorithm. ANNs with optimal architecture give minimum training and testing errors and provide precise damage detection results. In order to maximize damage detection results, the optimal architecture of ANN is identified by defining the number of hidden layers and the number of neurons per hidden layer by a trial and error method. In real testing, the number of measurement points and the measurement locations to obtain the structure response are critical for damage detection. Therefore, optimal sensor placement to improve damage identification is also investigated herein. A finite element model of a two storey framed structure is used to train the neural network. It shows accurate performance and gives low error with simulated and noise-contaminated data for single and multiple damage cases. As a result, the proposed method can be used for structural health monitoring and damage detection, particularly for cases where the measurement data is very large. Furthermore, it is suggested that an optimal ANN architecture can detect damage occurrence with good accuracy and can provide damage quantification with reasonable accuracy under varying levels of damage.