1,722,178 research outputs found
Indicators of unemployment and low-wage traps (Marginal effective tax rates on labour)
Get PDFThis paper presents results of an on-going joint European Commission / OECD project, aimed at monitoring the direct influence of tax and benefit instruments on household incomes. Indicators of financial work incentives are needed for identifying any undesired influences of taxes and social transfers on people’s work decisions. Marginal effective tax rates (METRs) are calculated in order to show what part of a change in earnings is “taxed away” by the combined operation of taxes, social security contributions (SSCs), and any withdrawal of earnings related social benefits. Three different types of METRs are calculated in order to measure so-called low-wage,unemployment and inactivity traps, that is situations where incentives to work are low. The results allow the identification of countries and family types that face little financial incentive to increase work effort or to take up a job.Unemployment trap, incentive to work, METR, tax on labour Make work pay, unemployment benefits.
Indicators of unemployment and low-wage traps (marginal effective tax rates on labour)
Get PDFThis paper presents results of an on-going joint European Commission / OECD project, aimed at monitoring the direct influence of tax and benefit instruments on household incomes. Indicators of financial work incentives are needed for identifying any undesired influences of taxes and social transfers on people's work decisions. At the same time, a central part of recent tax and benefit reform strategies has been to reduce reliance on welfare by “making work payâ€, that is, to make work an economically attractive option relative to welfare. It is therefore desirable to monitor the effects of such policies as well as the potential for further reform..taxation, fiscal policy, household income, marginal effective tax rates, METR, unemployment trap, inactivity trap, low-wage trap, employment, Carone, Salom�ki, Immervoll, Paturot
1DCNN-BiLSTM-transformer hypertension risk prediction model based on APW
Get PDFIntroductionHypertension has a multifactorial etiology. Recent studies have revealed a link between hypertension and gut microbiota dysbiosis. Pulse wave analysis holds significant clinical value for hypertension risk assessment. While research on deep learning models utilizing photoplethysmography (PPG) for hypertension classification has advanced, limitations persist. PPG offers limited richness and accuracy for characterizing blood pressure-related pathological information. In contrast, Arterial Pressure Waveform (APW) provides richer pathological information and exhibit stronger correlations with clinically interpretable features. However, deep learning research using APW for hypertension classification remains limited, as existing studies focus primarily on local feature extraction and neglect global temporal dynamics.MethodsTo address these challenges, we propose a novel 1D-CNN-BiLSTM-Transformer architecture for hypertension risk assessment based on APW, where the 1D-CNN module extracts waveform morphology features from signals within individual pressure segments, the BiLSTM module models long-range temporal dependencies from signals within each segment, and the Transformer module explicitly captures nonlinear interaction from signals across different pressure segments through multi-head self-attention mechanisms.ResultsWe use the multi-channel APW database from the Population Health Data Archive (PHDA), containing hypertensive and non-hypertensive cases with APW signals acquired from six traditional Chinese medicine points (left-cun, left-guan, left-chi, right-cun, right-guan, and right-chi) to evaluate the model’s performance. The model outperforms the current state-of-the-art methods in accuracy, precision, recall, and F1 score across all six points.ConclusionThe proposed model enhances classification performance. The physiologically driven interpretable analysis demonstrates that APW can reflect pathophysiological features associated with gut microbiota dysbiosis. The model-driven interpretable analysis offers a decision-making basis for clinical diagnosis
1DCNN-BiLSTM-Transformer Hypertension Risk Prediction Model Based on APW
No full textIntroduction: Hypertension has a multifactorial etiology. Recent studies have revealed a link between hypertension and gut microbiota dysbiosis. Pulse wave analysis holds significant clinical value for hypertension risk assessment. While research on deep learning models utilizing photoplethysmography (PPG) for hypertension classification has advanced, limitations persist. PPG offers limited richness and accuracy for characterizing blood pressure-related pathological information. In contrast, Arterial Pressure Waveform (APW) provides richer pathological information and exhibit stronger correlations with clinically interpretable features. However, deep learning research using APW for hypertension classification remains limited, as existing studies focus primarily on local feature extraction and neglect global temporal dynamics.Methods: To address these challenges, we propose a novel 1D-CNN-BiLSTM-Transformer architecture for hypertension risk assessment based on APW, where the 1D-CNN module extracts waveform morphology features from signals within individual pressure segments, the BiLSTM module models long-range temporal dependencies from signals within each segment, and the Transformer module explicitly captures nonlinear interaction from signals across different pressure segments through multi-head self-attention mechanisms.Results: We use the multi-channel APW database from the Population Health Data Archive (PHDA), containing hypertensive and non-hypertensive cases with APW signals acquired from six traditional Chinese medicine points (left-cun, left-guan, left-chi, right-cun, right-guan, and right-chi) to evaluate the model’s performance. The model outperforms the current state-of-the-art methods in accuracy, precision, recall, and F1 score across all six points.Conclusion: The proposed model enhances classification performance. The physiologically driven interpretable analysis demonstrates that APW can reflect pathophysiological features associated with gut microbiota dysbiosis. The model-driven interpretable analysis offers a decision-making basis for clinical diagnosis.</p
1310 APW Job Corpsman Construction - Sam Houston National Forest 1966
No full textAPW Job Corpsman Construction - Sam Houston National Forest.
Photographer: Unknownhttps://scholarworks.sfasu.edu/nfgt_general/2603/thumbnail.jp
Etude sur les hypothèses d'avenir des provinces wallonnes
Get PDFRapport d'expertise réalisé à la demande de l'Association des Provinces Wallonnes (APW), présenté publiquement à Namur le 2 mai 2018 (431 pages + annexes)
Ankündigung: Acta Pacis Westphalicae (APW) digital
No full texthttp://bsb-mdz12-spiegel.bsb.lrz.de/~mdz/index.html?c=sammlungen&l=de Die „Acta Pacis Westphalicae“ (APW) sind eine für die politische Geschichte des frühneuzeitlichen Europa zentrale Quellenedition. Sie bieten ausgewählte Akten zur Geschichte des Westfälischen Friedenskongresses (1643–1649) dar, der den Dreißigjährigen Krieg abschloss. [...] In Kooperation zwischen der Vereinigung zur Erforschung der Neueren Geschichte, der Universität Bonn und der Bayerischen Staatsbibliothek werden die bi..
An attempt to improve the convergence of the conventional APW method
No full textNeste trabalho fizemos uma tentativa para melhorar a convergência do método APW convencional por meio de um parâmetro introduzido através de um principio variacional generalizado. Discutimos algumas consequências da expressão variacional estabelecida e fazemos a aplicação no modelo unidimensional Kroning - Penney. Estabelecemos certos critérios para a escolha do parâmetro que fornece a melhor convergência. Concluímos que a melhor escolha do parâmetro é aquele correspondente ao método APW convencional.In this work a variational parameter is introduced in the conventional APW method in order to improve it´s convergence. We discuss some consequences of the variational expression and also estabilish a criteria for the choice of the parametrer wich furnishes the best convergence. After making a test with the one dimensional Kroning - Penney model we conclude that the best choice among the possible values of the parameter corresponds to the conventional APW method
An attempt to improve the convergence of the conventional APW method
No full textNeste trabalho fizemos uma tentativa para melhorar a convergência do método APW convencional por meio de um parâmetro introduzido através de um principio variacional generalizado. Discutimos algumas consequências da expressão variacional estabelecida e fazemos a aplicação no modelo unidimensional Kroning - Penney. Estabelecemos certos critérios para a escolha do parâmetro que fornece a melhor convergência. Concluímos que a melhor escolha do parâmetro é aquele correspondente ao método APW convencional.In this work a variational parameter is introduced in the conventional APW method in order to improve it´s convergence. We discuss some consequences of the variational expression and also estabilish a criteria for the choice of the parametrer wich furnishes the best convergence. After making a test with the one dimensional Kroning - Penney model we conclude that the best choice among the possible values of the parameter corresponds to the conventional APW method
APW Calculation for Band Structure of Cadmium
No full text An APW calculation of the band structure of cadmium is reported. The calculated band structure is similar to that obtained by others from pseudopotential calculations and explains some of the observed peaks in the optical absorption spectrum in terms of direct transitions. </jats:p
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