5 resultados para Laplace transforms

em Dalarna University College Electronic Archive


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The emergence of social movements’ global politics Globalization not only transforms capital, media and technology, but also creates conditions for global politics, beyond ”international politics”. New transnational public arenas emerge, where a broad range of actors articulate demands and interests. A globalized political infrastructure arise from the combination of the (1) internal transnational mobilization within two opposing global networks: movements’ World Social Forum and political economy elites’ World Economic Forum; and a global connection with (2) regular dramatic street protests during multilateral regime summits; and (3) a permanent and virtual network of information communication technology that enables new forms of action, organization and mobilization. Together these arenas make participatory and global politics possible for social movements. Regime confrontations are formed by the new global media of ICT in a way that transforms the struggle into a political drama, where activists’ diversity of tactics – The Majority Drama, The Carnival, and The David-Goliath Drama – creates both competition and collaboration. These arenas are only emerging and this new form of global political structure creates both possibilities and problems. Still, a unique potential to democratize politics is created.

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This paper presents a two-step pseudo likelihood estimation technique for generalized linear mixed models with the random effects being correlated between groups. The core idea is to deal with the intractable integrals in the likelihood function by multivariate Taylor's approximation. The accuracy of the estimation technique is assessed in a Monte-Carlo study. An application of it with a binary response variable is presented using a real data set on credit defaults from two Swedish banks. Thanks to the use of two-step estimation technique, the proposed algorithm outperforms conventional pseudo likelihood algorithms in terms of computational time.

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Background: Voice processing in real-time is challenging. A drawback of previous work for Hypokinetic Dysarthria (HKD) recognition is the requirement of controlled settings in a laboratory environment. A personal digital assistant (PDA) has been developed for home assessment of PD patients. The PDA offers sound processing capabilities, which allow for developing a module for recognition and quantification HKD. Objective: To compose an algorithm for assessment of PD speech severity in the home environment based on a review synthesis. Methods: A two-tier review methodology is utilized. The first tier focuses on real-time problems in speech detection. In the second tier, acoustics features that are robust to medication changes in Levodopa-responsive patients are investigated for HKD recognition. Keywords such as Hypokinetic Dysarthria , and Speech recognition in real time were used in the search engines. IEEE explorer produced the most useful search hits as compared to Google Scholar, ELIN, EBRARY, PubMed and LIBRIS. Results: Vowel and consonant formants are the most relevant acoustic parameters to reflect PD medication changes. Since relevant speech segments (consonants and vowels) contains minority of speech energy, intelligibility can be improved by amplifying the voice signal using amplitude compression. Pause detection and peak to average power rate calculations for voice segmentation produce rich voice features in real time. Enhancements in voice segmentation can be done by inducing Zero-Crossing rate (ZCR). Consonants have high ZCR whereas vowels have low ZCR. Wavelet transform is found promising for voice analysis since it quantizes non-stationary voice signals over time-series using scale and translation parameters. In this way voice intelligibility in the waveforms can be analyzed in each time frame. Conclusions: This review evaluated HKD recognition algorithms to develop a tool for PD speech home-assessment using modern mobile technology. An algorithm that tackles realtime constraints in HKD recognition based on the review synthesis is proposed. We suggest that speech features may be further processed using wavelet transforms and used with a neural network for detection and quantification of speech anomalies related to PD. Based on this model, patients' speech can be automatically categorized according to UPDRS speech ratings.

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In the home of others: exploring new sites and methods when investigating the doings of gender, class and ethnicity What role does the experience of being in and observing other people’s home play informing one’s gender and class identities and family aspirations? And how can it be explored? Through the traditions of socialization theory the everyday/-night experiences of family life are objectified into an institution (the family) with abstracted relations (mother-father-child) and functions (”primary socialization”). This is a view directly related to ruling relations through which the family is institutionalized, by rules and regulations, and made accountable as such. Hereby the question of experiences of other sites (and localities!) and other relations when forming one’s gender and family aspirations are not raised. In this article it is argued that when using an alternative approach (the method of inquiry proposed by Dorothy E. Smith) and alternative methods (memory work) the door to other homes is opened. Using experience stories a picture is drawn where new sites and relations are made visible as crucial contexts where gender and family life is explored and learned. By illuminating the ”work knowledge” of family life another way of mapping is presented, a way that extends and transforms the traditions within family research.

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AIMS/HYPOTHESIS: Soluble tumor necrosis factor receptors 1 and 2 (sTNFR1 and sTNFR2) contribute to experimental diabetic kidney disease, a condition with substantially increased cardiovascular risk when present in patients. Therefore, we aimed to explore the levels of sTNFRs, and their association with prevalent kidney disease, incident cardiovascular disease, and risk of mortality independently of baseline kidney function and microalbuminuria in a cohort of patients with type 2 diabetes. In pre-defined secondary analyses we also investigated whether the sTNFRs predict adverse outcome in the absence of diabetic kidney disease. METHODS: The CARDIPP study, a cohort study of 607 diabetes patients [mean age 61 years, 44 % women, 45 cardiovascular events (fatal/non-fatal myocardial infarction or stroke) and 44 deaths during follow-up (mean 7.6 years)] was used. RESULTS: Higher sTNFR1 and sTNFR2 were associated with higher odds of prevalent kidney disease [odd ratio (OR) per standard deviation (SD) increase 1.60, 95 % confidence interval (CI) 1.32-1.93, p < 0.001 and OR 1.54, 95 % CI 1.21-1.97, p = 0.001, respectively]. In Cox regression models adjusting for age, sex, glomerular filtration rate and urinary albumin/creatinine ratio, higher sTNFR1 and sTNFR2 predicted incident cardiovascular events [hazard ratio (HR) per SD increase, 1.66, 95 % CI 1.29-2.174, p < 0.001 and HR 1.47, 95 % CI 1.13-1.91, p = 0.004, respectively]. Results were similar in separate models with adjustments for inflammatory markers, HbA1c, or established cardiovascular risk factors, or when participants with diabetic kidney disease at baseline were excluded (p < 0.01 for all). Both sTNFRs were associated with mortality. CONCLUSIONS/INTERPRETATIONS: Higher circulating sTNFR1 and sTNFR2 are associated with diabetic kidney disease, and predicts incident cardiovascular disease and mortality independently of microalbuminuria and kidney function, even in those without kidney disease. Our findings support the clinical utility of sTNFRs as prognostic markers in type 2 diabetes.