456 resultados para UNKNOWN INPUTS
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The Cross-Entropy (CE) is an efficient method for the estimation of rare-event probabilities and combinatorial optimization. This work presents a novel approach of the CE for optimization of a Soft-Computing controller. A Fuzzy controller was designed to command an unmanned aerial system (UAS) for avoiding collision task. The only sensor used to accomplish this task was a forward camera. The CE is used to reach a near-optimal controller by modifying the scaling factors of the controller inputs. The optimization was realized using the ROS-Gazebo simulation system. In order to evaluate the optimization a big amount of tests were carried out with a real quadcopter.
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Walking through the city, passersby are occasionally jolted out of the mundane by surprises that seem out of place or incongruous with the expected urban function––a hidden cafe, or an unknown public art project. If you happen to be wandering through a major city on the third Friday of September each year, you might encounter a parking space that has been temporarily transformed into a "park" with green grass, a bench and an umbrella, perhaps a lemonade stand, a nursery, or an interactive space with a survey about local issues.
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Research has shown that a strong relationship exists between belongingness and depressive symptoms; however, the contribution of specific types of belongingness remains unknown. Participants (N=369) completed the sense of belonging instrument, psychological sense of organizational membership, and the depression scale of the depression anxiety stress scales. Factor analysis demonstrated that workplace and general belongingness are distinct constructs. When regressed onto depressive symptoms, these belongingness types made independent contributions, together accounting for 45% of variance, with no moderation effects evident. Hence, general belongingness and specific workplace belongingness appear to have strong additive links to depressive symptoms. These results add support to the belongingness hypothesis and sociometer theory and have significant implication for depression prevention and treatment
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Digital Stories are short autobiographical documentaries, often illustrated with personal photographs and narrated in the first person, and typically produced in group workshops. As a media form they offer ‘ordinary people’ the opportunity to represent themselves to audiences of their choosing; and this amplification of hitherto unheard voices has significant repercussions for their social participation. Many of the storytellers involved in the ‘Rainbow Family Tree’ case study that is the subject of this paper can be characterised as ‘everyday’ activists for their common desire to use their personal stories to increase social acceptance of marginalised identity categories. However, in conflict with their willingness to share their personal stories, many fear the risks and ramifications of distributing them in public spaces (especially online) to audiences both intimate and unknown. Additionally, while technologies for production and distribution of rich media products have become more accessible and user-friendly, many obstacles remain. For many people there are difficulties with technological access and aptitude, personal agency, cultural capital, and social isolation, not to mention availability of the time and energy requisite to Digital Storytelling. Additionally, workshop context, facilitation and distribution processes all influence the content of stories. This paper explores the many factors that make ‘authentic’ self-representation far from straight forward. I use qualitative data drawn from interviews, Digital Story texts and ethnographic observation of GLBTQIS participants in a Digital Storytelling initiative that combined face-to-face and online modes of participation. I consider mediating influences in practice and theory and draw on strategies put forth in cultural anthropology and narrative therapy to propose some practical tools for nuanced and sensitive facilitation of Digital Storytelling workshops and webspaces. Finally, I consider the implications of these facilitation strategies for voice, identity and social participation.
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The majority of distribution utilities do not have accurate information on the constituents of their loads. This information is very useful in managing and planning the network, adequately and economically. Customer loads are normally categorized in three main sectors: 1) residential; 2) industrial; and 3) commercial. In this paper, penalized least-squares regression and Euclidean distance methods are developed for this application to identify and quantify the makeup of a feeder load with unknown sectors/subsectors. This process is done on a monthly basis to account for seasonal and other load changes. The error between the actual and estimated load profiles are used as a benchmark of accuracy. This approach has shown to be accurate in identifying customer types in unknown load profiles, and is used in cross-validation of the results and initial assumptions.
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This paper is based on a practice-led research project I conducted into the artist’s ‘voice’ as part of my PhD. The artist’s ‘voice’ is, I argue, comprised of a dual motivation—'articulate' representation and ‘inarticulate’ affect—two things which do not necessarily derive from the artist; two things that are in effect, trans-subjective. Within this paper I will explore the ‘inarticulate’ through the later Lyotard’s affect-phrase, in conjunction with the example of my own painting and digital arts practice, to show just how this unknown can be mapped and understood as generative. As a visual artist my primary interest is in abstraction; I am curious about the emergence of pictorial significance and content from affect’s seemingly unknowable space. My studio practice occasions a sense of borderlessness, and uncertainty where each work or body of work ‘leaks’ into the next, exploring the unfamiliar through the powerful and restless discursive silence of affect. It is within this silence that is performed the disturbing yet generative disconnect that is the affect-phrase. This I contend is apparent in art’s manifest materiality that is, its degree of abstraction and muteness. For the later Lyotard, affect disrupts articulation by injuring or violating the rules of the genres of discourse. For this to be evident one needs to attend to the subtleties of how affect may ‘animate’ discourse. In other words how affect’s discursive disruption activates art’s resistance to definitive interpretation generating even demanding diverse ‘meaning’ creation for art, the abstract, and critical discourse.
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Reliable pollutant build-up prediction plays a critical role in the accuracy of urban stormwater quality modelling outcomes. However, water quality data collection is resource demanding compared to streamflow data monitoring, where a greater quantity of data is generally available. Consequently, available water quality data sets span only relatively short time scales unlike water quantity data. Therefore, the ability to take due consideration of the variability associated with pollutant processes and natural phenomena is constrained. This in turn gives rise to uncertainty in the modelling outcomes as research has shown that pollutant loadings on catchment surfaces and rainfall within an area can vary considerably over space and time scales. Therefore, the assessment of model uncertainty is an essential element of informed decision making in urban stormwater management. This paper presents the application of a range of regression approaches such as ordinary least squares regression, weighted least squares Regression and Bayesian Weighted Least Squares Regression for the estimation of uncertainty associated with pollutant build-up prediction using limited data sets. The study outcomes confirmed that the use of ordinary least squares regression with fixed model inputs and limited observational data may not provide realistic estimates. The stochastic nature of the dependent and independent variables need to be taken into consideration in pollutant build-up prediction. It was found that the use of the Bayesian approach along with the Monte Carlo simulation technique provides a powerful tool, which attempts to make the best use of the available knowledge in the prediction and thereby presents a practical solution to counteract the limitations which are otherwise imposed on water quality modelling.
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For the evaluation, design, and planning of traffic facilities and measures, traffic simulation packages are the de facto tools for consultants, policy makers, and researchers. However, the available commercial simulation packages do not always offer the desired work flow and flexibility for academic research. In many cases, researchers resort to designing and building their own dedicated models, without an intrinsic incentive (or the practical means) to make the results available in the public domain. To make matters worse, a substantial part of these efforts pertains to rebuilding basic functionality and, in many respects, reinventing the wheel. This problem not only affects the research community but adversely affects the entire traffic simulation community and frustrates the development of traffic simulation in general. For this problem to be addressed, this paper describes an open source approach, OpenTraffic, which is being developed as a collaborative effort between the Queensland University of Technology, Australia; the National Institute of Informatics, Tokyo; and the Technical University of Delft, the Netherlands. The OpenTraffic simulation framework enables academies from geographic areas and disciplines within the traffic domain to work together and contribute to a specific topic of interest, ranging from travel choice behavior to car following, and from response to intelligent transportation systems to activity planning. The modular approach enables users of the software to focus on their area of interest, whereas other functional modules can be regarded as black boxes. Specific attention is paid to a standardization of data inputs and outputs for traffic simulations. Such standardization will allow the sharing of data with many existing commercial simulation packages.
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Introduction QC and EQA are integral to good pathology laboratory practice. Medical Laboratory Science students undertake a project exploring internal QC and EQA procedures used in chemical pathology laboratories. Each student represents an individual lab and the class group represents the peer group of labs performing the same assay using the same method. Methods Using a manual BCG assay for serum albumin, normal and abnormal controls are run with a patient sample over 7 weeks. The QC results are assessed each week using calculated z-scores and both 2S & 3S control rules to determine whether a run is ‘in control’. At the end of the 7 weeks a completed LJ chart is assessed using the Westgard Multirules. Students investigate causes of error and the implications for both lab practice and patient care if runs are not ‘in control’. Twice in the 7 weeks two EQA samples (with target values unknown) are assayed alongside the weekly QC and patient samples. Results from each student are collated and form the basis of an EQA program. ALP are provided and students complete a Youden Plot, which is used to analyse the performance of each ‘lab’ and the method to identify bias. Students explore the concept of possible clinical implications of a biased method and address the actions that should be taken if a lab is not in consensus with the peer group. Conclusion This project is a model of ‘real world’ practice in which student demonstrate an understanding of the importance of QC procedures in a pathology laboratory, apply and interpret statistics and QC rules and charts, apply critical thinking and analytical skills to quality performance data to make recommendations for further practice and improve their technical competence and confidence.
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Background The mechanisms underlying socioeconomic inequalities in mortality from cardiovascular diseases (CVD) are largely unknown. We studied the contribution of childhood socioeconomic conditions and adulthood risk factors to inequalities in CVD mortality in adulthood. Methods The prospective GLOBE study was carried out in the Netherlands, with baseline data from 1991, and linked with the cause of death register in 2007. At baseline, participants reported on adulthood socioeconomic position (SEP) (own educational level), childhood socioeconomic conditions (occupational level of respondent’s father), and a broad range of adulthood risk factors (health behaviours, material circumstances, psychosocial factors). This present study is based on 5,395 men and 6,306 women, and the data were analysed using Cox regression models and hazard ratios (HR). Results A low adulthood SEP was associated with increased CVD mortality for men (HR 1.84; 95% CI: 1.41-2.39) and women (HR 1.80; 95%CI: 1.04-3.10). Those with poorer childhood socioeconomic conditions were more likely to die from CVD in adulthood, but this reached statistical significance only among men with the poorest childhood socioeconomic circumstances. About half of the investigated adulthood risk factors showed significant associations with CVD mortality among both men and women, namely renting a house, experiencing financial problems, smoking, physical activity and marital status. Alcohol consumption and BMI showed a U-shaped relationship with CVD mortality among women, with the risk being significantly greater for both abstainers and heavy drinkers, and among women who were underweight or obese. Among men, being single or divorced and using sleep/anxiety drugs increased the risk of CVD mortality. In explanatory models, the largest contributor to adulthood CVD inequalities were material conditions for men (42%; 95% CI: −73 to −20) and behavioural factors for women (55%; 95% CI: -191 to −28). Simultaneous adjustment for adulthood risk factors and childhood socioeconomic conditions attenuated the HR for the lowest adulthood SEP to 1.34 (95% CI: 0.99-1.82) for men and 1.19 (95% CI: 0.65-2.15) for women. Conclusions Adulthood material, behavioural and psychosocial factors played a major role in the explanation of adulthood SEP inequalities in CVD mortality. Childhood socioeconomic circumstances made a modest contribution, mainly via their association with adulthood risk factors. Policies and interventions to reduce health inequalities are likely to be most effective when considering the influence of socioeconomic circumstances across the entire life course and in particular, poor material conditions and unhealthy behaviours in adulthood.
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Bovine colostrum has been shown to influence the cytokine production of bovine leukocytes. However, it remains unknown whether processed bovine colostrum, a supplement popular among athletes to enhance immune function, is able to modulate cytokine secretion of human lymphocytes and monocytes. The aim of this investigation was to determine the influence of a commercially available bovine colostrum protein concentrate (CPC) to stimulate cytokine production by human peripheral blood mononuclear cells (PBMCs). Blood was sampled from four healthy male endurance athletes who had abstained from exercise for 48 h. PBMCs were separated and cultured with bovine CPC concentrations of 0 (control), 1.25, 2.5, and 5% with and without lipopolysaccharide (LPS) (3 microg/mL) and phytohemagglutinin (PHA) (2.5 microg/mL). Cell supernatants were collected at 6 and 24 h of culture for the determination of tumor necrosis factor (TNF), interferon (IFN)-gamma, interleukin (IL)-10, IL-6, IL-4, and IL-2 concentrations. Bovine CPC significantly stimulated the release of IFN-gamma, IL-10, and IL-2 (p < 0.03). The addition of LPS to PBMCs cocultured with bovine CPC significantly stimulated the release of IL-2 and inhibited the early release of TNF, IL-6, and IL-4 (p < 0.02). Phytohemagglutinin stimulation in combination with bovine CPC significantly increased the secretion of IL-10 and IL-2 at 6 h of culture and inhibited IFN-gamma and TNF (p < 0.05). This data show that a commercial bovine CPC is able to modulate in vitro cytokine production of human PBMCs. Alterations in cytokine secretion may be a potential mechanism for reported benefits associated with supplementation.
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Problem: Chlamydia trachomatis genital tract infections are easily treated with antibiotics, however the majority of infections are asymptomatic and therefore untreated, highlighting the need for a vaccine. Because most infections are asymptomatic, vaccination could potentially be administered to individuals who may have an acute infection at that time. In such individuals the effect of vaccination on the existing infection is unknown; however one potential outcome could be the development of a persistent infection. In vitro chlamydial persistence has been well characterized in various strains, however there have been no reported studies in C. muridarum. Method of Study: We performed ultrastructural characterization, and transcriptome analysis of selected genes. We then used the transcriptional profiles of the selected genes to examine whether intranasal immunization of mice during an active genital infection would induce persistence in the upper reproductive tract of female mice. Results and Conclusions: We found that persistence developed in the oviducts of mice as a result of immunization. This is a significant finding, not only because it is the first time that C. muridarum persistence has been characterized in vitro, but also due to the fact that there is minimal characterization of in vivo persistence of any chlamydial species. This highlights the importance of the timing of vaccination in individuals.
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Smartphones are getting increasingly popular and several malwares appeared targeting these devices. General countermeasures to smartphone malwares are currently limited to signature-based antivirus scanners which efficiently detect known malwares, but they have serious shortcomings with new and unknown malwares creating a window of opportunity for attackers. As smartphones become host for sensitive data and applications, extended malware detection mechanisms are necessary complying with the corresponding resource constraints. The contribution of this paper is twofold. First, we perform static analysis on the executables to extract their function calls in Android environment using the command readelf. Function call lists are compared with malware executables for classifying them with PART, Prism and Nearest Neighbor Algorithms. Second, we present a collaborative malware detection approach to extend these results. Corresponding simulation results are presented.
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Smartphones become very critical part of our lives as they offer advanced capabilities with PC-like functionalities. They are getting widely deployed while not only being used for classical voice-centric communication. New smartphone malwares keep emerging where most of them still target Symbian OS. In the case of Symbian OS, application signing seemed to be an appropriate measure for slowing down malware appearance. Unfortunately, latest examples showed that signing can be bypassed resulting in new malware outbreak. In this paper, we present a novel approach to static malware detection in resource-limited mobile environments. This approach can be used to extend currently used third-party application signing mechanisms for increasing malware detection capabilities. In our work, we extract function calls from binaries in order to apply our clustering mechanism, called centroid. This method is capable of detecting unknown malwares. Our results are promising where the employed mechanism might find application at distribution channels, like online application stores. Additionally, it seems suitable for directly being used on smartphones for (pre-)checking installed applications.
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A newly developed computational approach is proposed in the paper for the analysis of multiple crack problems based on the eigen crack opening displacement (COD) boundary integral equations. The eigen COD particularly refers to a crack in an infinite domain under fictitious traction acting on the crack surface. With the concept of eigen COD, the multiple cracks in great number can be solved by using the conventional displacement discontinuity boundary integral equations in an iterative fashion with a small size of system matrix to determine all the unknown CODs step by step. To deal with the interactions among cracks for multiple crack problems, all cracks in the problem are divided into two groups, namely the adjacent group and the far-field group, according to the distance to the current crack in consideration. The adjacent group contains cracks with relatively small distances but strong effects to the current crack, while the others, the cracks of far-field group are composed of those with relatively large distances. Correspondingly, the eigen COD of the current crack is computed in two parts. The first part is computed by using the fictitious tractions of adjacent cracks via the local Eshelby matrix derived from the traction boundary integral equations in discretized form, while the second part is computed by using those of far-field cracks so that the high computational efficiency can be achieved in the proposed approach. The numerical results of the proposed approach are compared not only with those using the dual boundary integral equations (D-BIE) and the BIE with numerical Green's functions (NGF) but also with those of the analytical solutions in literature. The effectiveness and the efficiency of the proposed approach is verified. Numerical examples are provided for the stress intensity factors of cracks, up to several thousands in number, in both the finite and infinite plates.