40 resultados para Biometric attributes


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Recent research (e.g. Barnes, Auburn & Lee, 2004) suggests that citizenship opportunities and resources may be afforded or denied to individuals according to their group memberships. We consider how the generic processes of intergroup differentiation by which groups are socially devalued and excluded can reflect divergent conceptualizations of citizenship among different groups. As part of a wider investigation of social exclusion, a combination of methods was used to investigate the relative intergroup perceptions of residents from more and less affluent areas in Limerick city, Ireland. Participants (n=214) completed the implicit association test and rated a fictional character on a series of citizenship-relevant dimensions. All participants displayed negative
implicit associations with designated disadvantaged areas in Limerick. The results of the explicit prejudice assessment illustrated that these negative associations are matched by a lower overall attribution of positive characteristics to residents from these areas relative to residents from a more affluent area. On examination of each group’s relative rating of traits, residents from less affluent
areas appear doubly disadvantaged as they are devalued in terms of both outgroup and ingroup understandings of citizenship attributes.

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The purpose of this study was to examine the challenges of integrating an asthma disease management (DM) program into a primary care setting from the perspective of primary care practitioners. A second goal was to examine whether barriers differed between urban-based and nonurban-based practices. Using a qualitative design, data were gathered using focus groups in primary care pediatric practices. A purposeful sample included an equal number of urban and nonurban practices. Participants represented all levels in the practice setting. Important themes that emerged from the data were coded and categorized. A total of 151 individuals, including physicians, advanced practice clinicians, registered nurses, other medical staff, and nonmedical staff participated in 16 focus groups that included 8 urban and 8 nonurban practices. Content analyses identified 4 primary factors influencing the implementation of a DM program in a primary care setting. They were related to providers, the organization, patients, and characteristics of the DM program. This study illustrates the complexity of the primary care environment and the challenge of changing practice in these settings. The results of this study identified areas in a primary care setting that influence the adoption of a DM program. These findings can assist in identifying effective strategies to change clinical behavior in primary care practices. © 2008 Mary Ann Liebert, Inc.

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Objective: To assess the contribution of organizational factors to implementation of 3 asthma quality measures: enrollment in a disease management program, development of a written treatment plan, and prescription of severity-appropriate anti-inflammatory therapy. Study design: A total of 138 pediatric clinicians and 247 office staff in 13 urban clinics and 23 nonurban private practices completed questionnaires about their practice's organizational characteristics (eg, leadership, communication, perceived effectiveness, job satisfaction). Results: 94% of the clinicians and 92% of the office staff completed questionnaires. When adjusted for confounders, greater practice activity and perceived effectiveness in meeting family needs were associated with higher rates of enrollment in the Easy Breathing program, whereas higher scores for 3 organizational characteristics-communication timeliness, decision authority, and job satisfaction-were associated with both higher enrollment and a greater number of written treatment plans. None of the organizational characteristics was associated with greater use of anti-inflammatory therapy. Conclusions: Three organizational characteristics predicted 2 quality asthma measures: use of a disease management program and creation of a written asthma treatment plan. If these organizational characteristics were amenable to change, then our findings could help focus interventions in areas of effective and acceptable organizational change. © 2009 Mosby, Inc. All rights reserved.

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We predicted that the probability of egg occurrence of salamander Salamandrina perspicillata depended on stream features and predation by native crayfish Austropotamobius fulcisianus and the introduced trout Salmo trutta. We assessed the presence of S. perspicillata at 54 sites within a natural reserve of southern Tuscany, Italy. Generalized linear models with binomial errors were constructed using egg presence/absence and altitude, stream mean size and slope, electrical conductivity, water pH and temperature, and a predation factor, defined according to the presence/absence of crayfish and trout. Some competing models also included an autocovariate term, which estimated how much the response variable at any one sampling point reflected response values at surrounding points. The resulting models were compared using Akaike's information criterion. Model selection led to a subset of 14 models with Delta AIC(c) <7 (i.e., models ranging from substantial support to considerably less support), and all but one of these included an effect of predation. Models with the autocovariate term had considerably more support than those without the term. According to multimodel inference, the presence of trout and crayfish reduced the probability of egg occurrence from a mean level of 0.90 (SE limits: 0.98-0.55) to 0.12 (SE limits: 0.34-0.04). The presence of crayfish alone had no detectable effects (SE limits: 0.86-0.39). The results suggest that introduced trout have a detrimental effect on the reproductive output of S. perspicillata and confirm the fundamental importance of distinguishing the roles of endogenous and exogenous forces that act on population distribution.

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Prior research has argued that use of optional properties in conceptual models results in loss of information about the semantics of the domains represented by the models. Empirical research undertaken to date supports this argument. Nevertheless, no systematic analysis has been done of whether use of optional properties is always problematic. Furthermore, prior empirical research might have deliberately or unwittingly employed models where use of optionality always causes problems. Accordingly, we examine analytically whether use of optional properties is always problematic. We employ our analytical results to inform the design of an experiment where we systematically examined the impact of optionality on users’ ability to understand domains represented by different types of conceptual models. We found evidence that use of optionality undermines users’ ability to understand the domain represented by a model but that this effect weakens when use of mandatory properties to replace optional properties leads to more-complex models.

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This paper estimates the marginal willingness-to-pay for attributes of a hypothetical HIV vaccine using discrete choice modeling. We use primary data from 326 respondents from Bangkok and Chiang Mai, Thailand, in 2008–2009, selected using purposive, venue-based sampling across two strata. Participants completed a structured questionnaire and full rank discrete choice modeling task administered using computer-assisted personal interviewing. The choice experiment was used to rank eight hypothetical HIV vaccine scenarios, with each scenario comprising seven attributes (including cost) each of which had two levels. The data were analyzed in two alternative specifications: (1) best-worst; and (2) full-rank, using logit likelihood functions estimated with custom routines in Gauss matrix programming language. In the full-rank specification, all vaccine attributes are significant predictors of probability of vaccine choice. The biomedical attributes of the hypothetical HIV vaccine (efficacy, absence of VISP, absence of side effects, and duration of effect) are the most important attributes for HIV vaccine choice. On average respondents are more than twice as likely to accept a vaccine with 99% efficacy, than a vaccine with 50% efficacy. This translates to a willingness to pay US$383 more for a high efficacy vaccine compared with the low efficacy vaccine. Knowledge of the relative importance of determinants of HIV vaccine acceptability is important to ensure the success of future vaccination programs. Future acceptability studies of hypothetical HIV vaccines should use more finely grained biomedical attributes, and could also improve the external validity of results by including more levels of the cost attribute.

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This paper argues that biometric verification evaluations can obscure vulnerabilities that increase the chances that an attacker could be falsely accepted. This can occur because existing evaluations implicitly assume that an imposter claiming a false identity would claim a random identity rather than consciously selecting a target to impersonate. This paper shows how an attacker can select a target with a similar biometric signature in order to increase their chances of false acceptance. It demonstrates this effect using a publicly available iris recognition algorithm. The evaluation shows that the system can be vulnerable to attackers targeting subjects who are enrolled with a smaller section of iris due to occlusion. The evaluation shows how the traditional DET curve analysis conceals this vulnerability. As a result, traditional analysis underestimates the importance of an existing score normalisation method for addressing occlusion. The paper concludes by evaluating how the targeted false acceptance rate increases with the number of available targets. Consistent with a previous investigation of targeted face verification performance, the experiment shows that the false acceptance rate can be modelled using the traditional FAR measure with an additional term that is proportional to the logarithm of the number of available targets.

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When applying biometric algorithms to forensic verification, false acceptance and false rejection can mean a failure to identify a criminal, or worse, lead to the prosecution of individuals for crimes they did not commit. It is therefore critical that biometric evaluations be performed as accurately as possible to determine their legitimacy as a forensic tool. This paper argues that, for forensic verification scenarios, traditional performance measures are insufficiently accurate. This inaccuracy occurs because existing verification evaluations implicitly assume that an imposter claiming a false identity would claim a random identity rather than consciously selecting a target to impersonate. In addition to describing this new vulnerability, the paper describes a novel Targeted.. FAR metric that combines the traditional False Acceptance Rate (FAR) measure with a term that indicates how performance degrades with the number of potential targets. The paper includes an evaluation of the effects of targeted impersonation on an existing academic face verification system. This evaluation reveals that even with a relatively small number of targets false acceptance rates can increase significantly, making the analysed biometric systems unreliable.

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Tomato is the second most widely grown vegetable crop across the globe and it is one of widely cultivated crops in Sri Lanka. However, tomato industry in Sri Lanka facing a problem of high postharvest loss (54%) during the glut coupled with heavy revenue loss to the country by importing processed products. The aim of this work is to develop shelf-stable tomato product with maximum quality characteristics using high pressure processing (HPP). Tomato juice with altered and unaltered pH was processed using HPP at 600 MPa for 1 min after blanching (90 oC/2 min). As a control tomato juice was subjected to thermal processing (TP) at 95 oC /20 min. Processed samples were stored under 20oC and 28oC for 9 month period and analysed for total viable count (TVC) and instrumental colour (L, a, b) value at 0,1,2 3, and 4 week and 2, 3, 6 and 9 months interval. The raw juice sample had initial 6.69 log10 CFU/ml and both TP and HPP caused a more than 4.69 log10 reduction in the TVC of juice and microbial numbers remained low throughout the storage period even at 3 months after storage irrespective of the storage temperature. Both TP and HPP treated samples had the redness ⤘a value’ of 14.44-17.15 just after processing and showed non-significant reduction with storage in all the treatments after 3 months. The storage study results and discussed in relation to the end goal and compared with the literature.

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Biometric systems provide a valuable service in helping to identify individuals from their stored personal details. Unfortunately, with the rapidly increasing use of such systems, there is a growing concern about the possible misuse of that information. To counteract the threat, the European Union (EU) has introduced comprehensive legislation that seeks to regulate data collection and help strengthen an individual’s right to privacy. This article looks at the implications of the legislation for biometric system deployment. After an initial consideration of current privacy concerns, it examines what is meant by ‘personal data’ and its protection, in legislation terms. Also covered are issues around the storage of biometric data, including its accuracy, its security, and justification for what is collected. Finally, the privacy issues are illustrated through three biometric use cases: border security, online bank access control and customer profiling in stores.

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Artificial neural network (ANN) methods are used to predict forest characteristics. The data source is the Southeast Alaska (SEAK) Grid Inventory, a ground survey compiled by the USDA Forest Service at several thousand sites. The main objective of this article is to predict characteristics at unsurveyed locations between grid sites. A secondary objective is to evaluate the relative performance of different ANNs. Data from the grid sites are used to train six ANNs: multilayer perceptron, fuzzy ARTMAP, probabilistic, generalized regression, radial basis function, and learning vector quantization. A classification and regression tree method is used for comparison. Topographic variables are used to construct models: latitude and longitude coordinates, elevation, slope, and aspect. The models classify three forest characteristics: crown closure, species land cover, and tree size/structure. Models are constructed using n-fold cross-validation. Predictive accuracy is calculated using a method that accounts for the influence of misclassification as well as measuring correct classifications. The probabilistic and generalized regression networks are found to be the most accurate. The predictions of the ANN models are compared with a classification of the Tongass national forest in southeast Alaska based on the interpretation of satellite imagery and are found to be of similar accuracy.