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Relevant bibliographies by topics / Progressive data analysis

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Academic literature on the topic 'Progressive data analysis'

Author: Grafiati

Published: 25 May 2024

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Journal articles on the topic "Progressive data analysis"

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P, Veeramuthu. "Analysis of Progressive Duplicate Data Detection." Journal of Computational Mathematica 3, no. 2 (2019): 41–50. http://dx.doi.org/10.26524/cm53.

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Lee, Kyeong-Jun, Jae-Ik Lee, and Chan-Keun Park. "Analysis of generalized progressive hybrid censored competing risks data." Journal of the Korean Society of Marine Engineering 40, no. 2 (2016): 131–37. http://dx.doi.org/10.5916/jkosme.2016.40.2.131.

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Rinzivillo, Salvatore, Dino Pedreschi, Mirco Nanni, Fosca Giannotti, Natalia Andrienko, and Gennady Andrienko. "Visually driven analysis of movement data by progressive clustering." Information Visualization 7, no. 3-4 (2008): 225–39. http://dx.doi.org/10.1057/palgrave.ivs.9500183.

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The paper investigates the possibilities of using clustering techniques in visual exploration and analysis of large numbers of trajectories, that is, sequences of time-stamped locations of some moving entities. Trajectories are complex spatio-temporal constructs characterized by diverse non-trivial properties. To assess the degree of (dis)similarity between trajectories, specific methods (distance functions) are required. A single distance function accounting for all properties of trajectories, (1) is difficult to build, (2) would require much time to compute, and (3) might be difficult to understand and to use. We suggest the procedure of progressive clustering where a simple distance function with a clear meaning is applied on each step, which leads to easily interpretable outcomes. Successive application of several different functions enables sophisticated analyses through gradual refinement of earlier obtained results. Besides the advantages from the sense-making perspective, progressive clustering enables a rational work organization where time-consuming computations are applied to relatively small potentially interesting subsets obtained by means of ‘cheap’ distance functions producing quick results. We introduce the concept of progressive clustering by an example of analyzing a large real data set. We also review the existing clustering methods, describe the method OPTICS suitable for progressive clustering of trajectories, and briefly present several distance functions for trajectories.

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Wang, Tao, Lianbin Deng, Yuhong Li, and Hao Peng. "Progressive TIN Densification with Connection Analysis for Urban Lidar Data." Photogrammetric Engineering & Remote Sensing 87, no. 3 (2021): 205–13. http://dx.doi.org/10.14358/pers.87.3.207.

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Urban lidar data are advantageous for capturing the terrain surface of built-up areas, which can be directly used to provide digital surface models. Cloud points are classified into ground points to obtain digital terrain models. This study proposes a method to improve the progressive triangulated irregular network (TIN ) densification method using a TIN connection analysis algorithm, namely, connection analysis via slope analysis. The proposed method comprises five steps: selection of seed points, connection and slope analysis, increasing the seed points, construction of the TIN model of the seed points, and an iterative construction of the final TIN. Seven data sets from the International Society for Photogrammetry and Remote Sensing Working Group are used to test whether the proposed method can preserve discontinuities of landscapes and reduce omission and total errors by an average of 9% and 5%, respectively; achieving such results can reduce the amount of workload required for result modification during posttreatment, thus decreasing costs.

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Hassan, Amal S., Rana M. Mousa, and Mahmoud H. Abu-Moussa. "Analysis of Progressive Type-II Competing Risks Data, with Applications." Lobachevskii Journal of Mathematics 43, no. 9 (2022): 2479–92. http://dx.doi.org/10.1134/s1995080222120149.

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Azarang, Leyla, and Manuel,Oviedo,de,la Fuente. "idmTPreg: Regression Model for Progressive Illness Death Data." R Journal 10, no. 2 (2019): 317. http://dx.doi.org/10.32614/rj-2018-081.

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Koch, Marcus W., Jop Mostert, Bernard Uitdehaag, and Gary Cutter. "Clinical outcome measures in SPMS trials: An analysis of the IMPACT and ASCEND original trial data sets." Multiple Sclerosis Journal 26, no. 12 (2019): 1540–49. http://dx.doi.org/10.1177/1352458519876701.

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Background: Still too little is known about the natural history of clinical outcome measures beyond the Expanded Disability Status Scale (EDSS), such as the timed 25-foot walk (T25FW) and nine-hole peg test (9HPT) in secondary progressive multiple sclerosis (SPMS). Objective: To describe progression on the EDSS, T25FW, 9HPT, and their combinations. To investigate the association of the baseline characteristics age, sex, EDSS, T25FW, gadolinium-enhancing lesions, and relapse activity with EDSS and T25FW progression. Methods: Using original trial data from the placebo arms of the IMPACT and ASCEND randomized controlled trials, we describe disability progression (with and without 3- or 6-month confirmation). We investigated the association of selected baseline characteristics with EDSS and T25FW progression over 2 years using binary logistic regression. Results: T25FW was the single outcome measure with the largest proportion of patients progressing, followed by EDSS and 9HPT. EDSS and T25FW at baseline were associated with EDSS and T25FW progression in both data sets. Age and relapse activity were only mild and inconsistent predictors, while sex and gadolinium enhancement at baseline did not predict disability progression in either data set. Conclusion: Our analyses inform the selection of primary outcome measures as well as inclusion criteria for clinical trials in SPMS.

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Turkay, Cagatay, Erdem Kaya, Selim Balcisoy, and Helwig Hauser. "Designing Progressive and Interactive Analytics Processes for High-Dimensional Data Analysis." IEEE Transactions on Visualization and Computer Graphics 23, no. 1 (2017): 131–40. http://dx.doi.org/10.1109/tvcg.2016.2598470.

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Shilpa, Author, and Sunita Parashar. "Performance Analysis of Apriori Algorithm with Progressive Approach for Mining Data." International Journal of Computer Applications 31, no. 1 (2011): 13–18. http://dx.doi.org/10.5120/3788-5216.

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Al-Hossain, Abdullah Y. "Predictive Inference from the Exponentiated Weibull Model Given Adaptive Progressive Censored Data." Applied Mathematics & Information Sciences 10, no. 3 (2016): 1177–84. http://dx.doi.org/10.18576/amis/100336.

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Dissertations / Theses on the topic "Progressive data analysis"

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Morone, Daniel Justin Reese. "Progressive Collapse: Simplified Analysis Using Experimental Data." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1354602937.

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Larson, Michael Andrew. "A Progressive Refinement of Postural Human Balance Models Based on Experimental Data Using Topological Data Analysis." Miami University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=miami159620428141697.

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Amrani, Naoufal, Joan Serra-Sagrista, Miguel Hernandez-Cabronero, and Michael Marcellin. "Regression Wavelet Analysis for Progressive-Lossy-to-Lossless Coding of Remote-Sensing Data." IEEE, 2016. http://hdl.handle.net/10150/623190.

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Regression Wavelet Analysis (RWA) is a novel wavelet-based scheme for coding hyperspectral images that employs multiple regression analysis to exploit the relationships among spectral wavelet transformed components. The scheme is based on a pyramidal prediction, using different regression models, to increase the statistical independence in the wavelet domain For lossless coding, RWA has proven to be superior to other spectral transform like PCA and to the best and most recent coding standard in remote sensing, CCSDS-123.0. In this paper we show that RWA also allows progressive lossy-to-lossless (PLL) coding and that it attains a rate-distortion performance superior to those obtained with state-of-the-art schemes. To take into account the predictive significance of the spectral components, we propose a Prediction Weighting scheme for JPEG2000 that captures the contribution of each transformed component to the prediction process.

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Silvaroli, Antonio. "Design and Analysis of Erasure Correcting Codes in Blockchain Data Availability Problems." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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In questo lavoro viene affrontato il concetto di Blockchain e Bitcoin, con enfasi sugli attacchi alla disponibilità riguardanti le transazioni, nel caso in cui nella rete vengano considerati alcuni nodi detti "light nodes", che migliorano la scalabilità del sistema. Quindi, si analizza il funzionamento della Blockchain di Bitcoin quando la struttura dati "Merkle Tree" viene codificata, in modo da aumentare la probabilità dei light nodes di rilevare cancellazioni di transazioni, attuate da parte di nodi attaccanti. Attraverso una codifica con erasure codes, in particolare con codici low density parity check (LDPC), si riesce ad aumentare la probabilità di detection di una cancellazione e, grazie alla decodifica iterativa è possibile recuperare tale cancellazione. Viene affrontato il problema degli stopping sets, cioè quelle strutture che impediscono il recupero dei dati tramite decodifica iterativa e si progetta un algoritmo per l'enumerazione di tali strutture. Sono poi testate, in modo empirico, alcune soluzioni teoriche presenti in letteratura. Successivamente vengono progettati nuovi codici, seguendo un metodo di design diverso da quello presente in letteratura. Tali codici comportano un miglioramento delle performance, in quanto il minimo stopping set per tali codici risulta più alto di quello di codici già analizzati. In questo modo eventuali attacchi alla disponibilità risultano, in probabilità, più difficili. Come conseguenza, il throughput della rete risulta più stabile dato che, con minori attacchi che vanno a buon fine, la frequenza di generazione di nuovi codici, per un nuovo processo di codifica delle transazioni, tende ad essere più bassa. Infine vengono proposti dei possibili miglioramenti.

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Eller, Michael R. "Utilizing Economic and Environmental Data from the Desalination Industry as a Progressive Approach to Ocean Thermal Energy Conversion (OTEC) Commercialization." ScholarWorks@UNO, 2013. http://scholarworks.uno.edu/td/1733.

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Ocean Thermal Energy Conversion (OTEC) is a renewable energy technology that has to overcome several key challenges before achieving its ultimate goal of producing baseload power on a commercial scale. The economic challenge of deploying an OTEC plant remains the biggest barrier to implementation. Although small OTEC demonstration plants and recent advances in subsystem technologies have proven OTEC’s technical merits, the process still lacks the crucial operational data required to justify investments in large commercial OTEC plants on the order of 50-100 megawatts of net electrical power (MWe-net). A pre-commercial pilot plant on the order of 5-10 MWe-net is required for an OTEC market to evolve. In addition to the economic challenge,OTEC plants have potential for adverse environmental impacts from redistribution of nutrients and residual chemicals in the discharge plume. Although long-term operational records are not available for commercial sizeOTEC plants, synergistic operational data can be leveraged from the desalination industry to improve the potential for OTEC commercialization. Large capacity desalination plants primarily use membranes or thermal evaporator tubes to transform enormous amounts of seawater into freshwater. Thermal desalination plants in particular possess many of the same technical, economic, and environmental traits as a commercial scale OTEC plant. Substantial long-term economic data and environmental impact results are now widely available since commercial desalination began in the 1950s. Analysis of this data indicates that the evolution of the desalination industry could be akin to the potential future advancement of OTEC. Furthermore, certain scenarios exist where a combined OTEC-desalination plant provides a new opportunity for commercial plants. This paper seeks to utilize operational data from the desalination industry as a progressive approach towards OTEC commercialization.

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Vidal, Jules. "Progressivité en analyse topologique de données." Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS398.

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L’analyse topologique de donnés permet l’extraction générique et efficace de caractéristiques structurelles dans les données. Cependant, bien que ces techniques aient des complexités asymptotiques connues et raisonnables elles sont rarement interactives en pratique sur des jeux de données réels. Dans cette thèse, nous avons cherché à développer des méthodes progressives pour l’analyse topologique de données scalaires scientifiques, qui peuvent être interrompues pour fournir rapidement un résultat approché exploitable, et sont capables de l’affiner ensuite. Nous introduisons deux algorithmes progressifs pour le calcul des points critiques et du diagramme de persistance d’un champ scalaire. Ensuite, nous revisitons ce cadre progressif pour introduire un algorithme pour le calcul approché du diagramme de persistance d’un champ scalaire, avec des garanties sur l’erreur d’approximation associée. Enfin, afin d’effectuer une analyse visuelle de données d’ensemble, nous présentons un nouvel algorithme progressif pour le calcul du barycentre de Wasserstein d’un ensemble de diagrammes de persistance, une tâche notoirement coûteuse en calcul. Notre approche progressive permet d’approcher le barycentre de manière interactive. Nous étendons cette méthode à un algorithme de classification topologique de données d’ensemble, qui est progressif et capable de respecter une contrainte de temps<br>Topological Data Analysis (TDA) forms a collection of tools that enable the generic and efficient extraction of features in data. However, although most TDA algorithms have practicable asymptotic complexities, these methods are rarely interactive on real-life datasets, which limits their usability for interactive data analysis and visualization. In this thesis, we aimed at developing progressive methods for the TDA of scientific scalar data, that can be interrupted to swiftly provide a meaningful approximate output and that are able to refine it otherwise. First, we introduce two progressive algorithms for the computation of the critical points and the extremum-saddle persistence diagram of a scalar field. Next, we revisit this progressive framework to introduce an approximation algorithm for the persistence diagram of a scalar field, with strong guarantees on the related approximation error. Finally, in a effort to perform visual analysis of ensemble data, we present a novel progressive algorithm for the computation of the discrete Wasserstein barycenter of a set of persistence diagrams, a notoriously computationally intensive task. Our progressive approach enables the approximation of the barycenter within interactive times. We extend this method to a progressive, time-constraint, topological ensemble clustering algorithm

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Kmetzsch, Virgilio. "Multimodal analysis of neuroimaging and transcriptomic data in genetic frontotemporal dementia." Electronic Thesis or Diss., Sorbonne université, 2022. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2022SORUS279.pdf.

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La démence frontotemporale (DFT) représente le deuxième type de démence le plus fréquent chez les adultes de moins de 65 ans. Il n’existe aucun traitement capable de guérir cette maladie. Dans ce contexte, il est essentiel d’identifier des biomarqueurs capables d’évaluer la progression de la maladie. Cette thèse a deux objectifs. Premièrement, analyser les profils d’expression des microARNs circulants prélevés dans le plasma sanguin de participants, afin d’identifier si l’expression de certains microARNs est corrélée au statut mutationnel et à la progression de la maladie. Deuxièmement, proposer des méthodes pour intégrer des données transversales de type microARN et de neuroimagerie pour estimer la progression de la maladie. Nous avons mené trois études. D’abord, nous avons analysé des échantillons de plasma provenant de porteurs d’une expansion dans le gène C9orf72. Ensuite, nous avons testé toutes les signatures de microARNs identifiées dans la littérature comme biomarqueurs potentiels de la DFT ou de la sclérose latérale amyotrophique (SLA), dans deux cohortes indépendantes. Enfin, dans notre troisième étude, nous avons proposé une nouvelle méthode, utilisant un autoencodeur variationnel multimodal supervisé, qui estime à partir d’échantillons de petite taille un score de progression de la maladie en fonction de données transversales d’expression de microARNs et de neuroimagerie. Les travaux menés dans cette thèse interdisciplinaire ont montré qu’il est possible d’utiliser des biomarqueurs non invasifs, tels que les microARNs circulants et l’imagerie par résonance magnétique, pour évaluer la progression de maladies neurodégénératives rares telles que la DFT et la SLA<br>Frontotemporal dementia (FTD) represents the second most common type of dementia in adults under the age of 65. Currently, there are no treatments that can cure this condition. In this context, it is essential that biomarkers capable of assessing disease progression are identified. This thesis has two objectives. First, to analyze the expression patterns of microRNAs taken from blood samples of patients, asymptomatic individuals who have certain genetic mutations causing FTD, and controls, to identify whether the expressions of some microRNAs correlate with mutation status and disease progression. Second, this work aims at proposing methods for integrating cross-sectional data from microRNAs and neuroimaging to estimate disease progression. We conducted three studies. Initially, we focused on plasma samples from C9orf72 expansion carriers. We identified four microRNAs whose expressions correlated with the clinical status of the participants. Next, we tested all microRNA signatures identified in the literature as potential biomarkers of FTD or amyotrophic lateral sclerosis (ALS), in two groups of individuals. Finally, in our third work, we proposed a new approach, using a supervised multimodal variational autoencoder, that estimates a disease progression score from cross-sectional microRNA expression and neuroimaging datasets with small sample sizes. The work conducted in this interdisciplinary thesis showed that it is possible to use non-invasive biomarkers, such as circulating microRNAs and magnetic resonance imaging, to assess the progression of rare neurodegenerative diseases such as FTD and ALS

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Conway, Devon S. "Long-Term Benefits of Early Treatment in Multiple Sclerosis: An Investigation Utilizing a Novel Data Collection Technique." Case Western Reserve University School of Graduate Studies / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=case1307635721.

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Baneshi, Mohammad Reza. "Statistical models in prognostic modelling with many skewed variables and missing data : a case study in breast cancer." Thesis, University of Edinburgh, 2009. http://hdl.handle.net/1842/4191.

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Prognostic models have clinical appeal to aid therapeutic decision making. In the UK, the Nottingham Prognostic Index (NPI) has been used, for over two decades, to inform patient management. However, it has been commented that NPI is not capable of identifying a subgroup of patients with a prognosis so good that adjuvant therapy with potential harmful side effects can be withheld safely. Tissue Microarray Analysis (TMA) now makes possible measurement of biological tissue microarray features of frozen biopsies from breast cancer tumours. These give an insight to the biology of tumour and hence could have the potential to enhance prognostic modelling. I therefore wished to investigate whether biomarkers can add value to clinical predictors to provide improved prognostic stratification in terms of Recurrence Free Survival (RFS). However, there are very many biomarkers that could be measured, they usually exhibit skewed distribution and missing values are common. The statistical issues raised are thus number of variables being tested, form of the association, imputation of missing data, and assessment of the stability and internal validity of the model. Therefore the specific aim of this study was to develop and to demonstrate performance of statistical modelling techniques that will be useful in circumstances where there is a surfeit of explanatory variables and missing data; in particular to achieve useful and parsimonious models while guarding against instability and overfitting. I also sought to identify a subgroup of patients with a prognosis so good that a decision can be made to avoid adjuvant therapy. I aimed to provide statistically robust answers to a set of clinical question and develop strategies to be used in such data sets that would be useful and acceptable to clinicians. A unique data set of 401 Estrogen Receptor positive (ER+) tamoxifen treated breast cancer patients with measurement for a large panel of biomarkers (72 in total) was available. Taking a statistical approach, I applied a multi-faceted screening process to select a limited set of potentially informative variables and to detect the appropriate form of the association, followed by multiple imputations of missing data and bootstrapping. In comparison with the NPI, the final joint model derived assigned patients into more appropriate risk groups (14% of recurred and 4% of non-recurred cases). The actuarial 7-year RFS rate for patients in the lowest risk quartile was 95% (95% C.I.: 89%, 100%). To evaluate an alternative approach, biological knowledge was incorporated into the process of model development. Model building began with the use of biological expertise to divide the variables into substantive biomarker sets on the basis of presumed role in the pathway to cancer progression. For each biomarker family, an informative and parsimonious index was generated by combining family variables, to be offered to the final model as intermediate predictor. In comparison with NPI, patients into more appropriate risk groups (21% of recurred and 11% of non-recurred patients). This model identified a low-risk group with 7-year RFS rate at 98% (95% C.I.: 96%, 100%).

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Ivarsson, Adam. "Expediting Gathering and Labeling of Data from Zebrafish Models of Tumor Progression and Metastasis Using Bespoke Software." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-148691.

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In this paper I describe a set of algorithms used to partly automate the labeling and preparation of images of zebrafish embryos used as models of tumor progression and metastasis. These algorithms show promise for saving time for researchers using zebrafish in this way.

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Books on the topic "Progressive data analysis"

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Desimone, Leslie A. Use of computer programs STLK1 and STWT1 for analysis of stream-aquifer hydraulic interaction. U.S. Dept. of the Interior, U.S. Geological Survey, 1999.

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Desimone, Leslie A. Use of computer programs STLK1 and STWT1 for analysis of stream-aquifer hydraulic interaction. U.S. Dept. of the Interior, U.S. Geological Survey, 1999.

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Meier, Benjamin Mason, Ryan Cronk, Jeanne Luh, Jamie Bartram, and Catarina de Albuquerque. Monitoring the Progressive Realization of the Human Rights to Water and Sanitation. Edited by Ken Conca and Erika Weinthal. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199335084.013.21.

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The human rights to water and sanitation have developed dramatically under international human rights law over the past forty years, with international political declarations leading to specific state obligations. Yet despite this evolution of human rights under international law, there are few mechanisms to monitor the progressive realization of those rights in national practice. The Water, Sanitation, and Hygiene (WaSH) Performance Index employs frontier analysis to monitor human rights to water and sanitation, across countries and over time. Tracking rates of change in water and sanitation coverage, the WaSH Performance Index allows for measurements of the progressive realization of human rights, publishing quantitative indicators reflective of the human rights to water and sanitation. Such external monitoring of outcome measures, correlating national implementation efforts with water and sanitation coverage data, provides a basis for future research and advocacy to facilitate rights-based accountability for water and sanitation policy.

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Glanville, Peter John. Symmetry. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198792734.003.0005.

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Chapter 5 determines the semantic typology of patterns III and VI, sometimes termed the vowel-lengthening patterns. It asserts that verbs formed in these patterns are symmetrical predicates, denoting relations consisting of two complementary forces. It shows that the difference between the two patterns results from the interplay between an underlying symmetric relation and a figure–ground orientation in which one of the participant roles involved is made more prominent than the other. The chapter divides verbs formed in pattern III into verbs of resistance, risk, competition, interaction, and co-action, and those formed in pattern VI into reciprocal verbs, feigning verbs, chaining verbs, and verbs of progressive change. It argues that an account based on a common symmetric structure is able to unite this diverse range of verbs within one analysis, and it offers data from other languages to support this claim.

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Anderson, C. W. The Idea of Data, Documents, and Evidence in Early-Twentieth-Century Journalism. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190492335.003.0002.

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This chapter chronicles the manner by which data and documents were incorporated into journalism in the early twentieth century, examining the intersection between social movements, progressive politics, public relations, and journalism. It analyzes the MRFM, a religious movement that used the accumulation of empirical data about urban conditions as both a mobilizing and public relations tool in order to advance social causes.

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Brace, Paul. Aggregating Survey Data to Estimate Subnational Public Opinion. Edited by Lonna Rae Atkeson and R. Michael Alvarez. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780190213299.013.15.

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Public opinion’s role in shaping governmental actions is a central concern of democracy, yet the absence of systematic state-level survey data has inhibited analyses of public opinion at the subnational level. This essay traces the evolution of studies of public opinion at that level, first reviewing studies using surrogates derived from demographic variables. It next considers methodologies that develop state-level opinion from aggregated national samples. Finally, it discusses recent efforts to develop state-level opinion measures using post-sample stratification integrating limited survey data with demographic variables. There is evidence of significant cross-sectional and temporal variation in public opinion and policy across and within the states. Research on subnational public opinion once hinged on assumptions about opinion surrogates, but is now based on abundant and progressively rigorous opinion data. These studies reveal that public opinion plays an enormous role in subnational politics, with effects varying across issues, contexts, and conditions.

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Hughes, Jeremy. Proteinuria as a direct cause of progression. Edited by David J. Goldsmith. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199592548.003.0137.

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Proximal tubular cells reabsorb any filtered proteins during health via cell surface receptors such as megalin and cubulin so that very low levels of protein are present in the excreted urine. Significant proteinuria is a common finding in patients with many renal diseases. Proteinuria is a marker of glomerular damage and podocyte loss and injury in particular. The degree of proteinuria at presentation or during the course of the disease correlates with long-term outcome in many renal diseases. Proteinuria per se may be nephrotoxic and thus directly relevant to the progression of renal disease rather than simply acting as a marker of the severity of glomerular injury and podocytes loss. Seminal studies used the atypical renal anatomy of the axolotl to instill proteins directly into the tubular lumen without requiring passage through the glomerulus. This indicated that tubular protein could be cytotoxic and induce interstitial inflammation and fibrosis in the peritubular region. Cell culture studies demonstrate that exposure to proteins results in proximal tubular cell activation and the production of pro-inflammatory and pro-fibrotic mediators. Proximal tubular cell death occurred in some studies reinforcing the potential of protein to exert cytotoxic effects via oxidative stress or endoplasmic reticulum stress. Analysis of renal biopsy material from both experimental studies using models of proteinuric disease or patients with various proteinuric diseases provided evidence of activation of transcription factors and production of chemokines and pro-inflammatory mediators by proximal tubular cells. These data strongly suggest that although proteinuria is the result of glomerular disease it also represents an important cause of progression in patients with chronic kidney disease associated with proteinuria.

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Touchon, Justin C. Applied Statistics with R. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198869979.001.0001.

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Whether at the undergraduate, graduate, or post-graduate level, Applied Statistics with R: A Practical Guide for the Life Sciences teaches readers to properly analyze data in an efficient, accessible, plainspoken, frank, and occasionally humorous manner. Readers will come away with the knowledge of which analyses they should use and when they should use them, an important skill in an age when the statistical analyses used in the life-sciences are becoming increasingly advanced. This book uses the statistical language R, which is the choice of ecologists worldwide and is rapidly becoming the ‘go-to’ stats program throughout the life-sciences. Written around a single real-world dataset, Applied Statistics with R which encourages readers to become deeply familiar with an imperfect but realistic set of data, much like they themselves might collect. Early chapters are designed to teach basic data manipulation skills and build good habits in preparation for learning more advanced analyses. This approach also demonstrates the importance of viewing data through different lenses, facilitating an easy and natural progression from linear and generalized linear models through to mixed effects versions of those same analyses. Readers will also learn advanced plotting and data-wrangling techniques, and gain an introduction to writing their own functions. Applied Statistics with R is suitable for senior undergraduate and graduate students, professional researchers, and practitioners throughout the life-sciences, whether in the fields of ecology, evolution, environmental studies, or computational biology.

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Fox, Alistair. A Gay Boy Comes to Terms with his Sexuality: 50 Ways of Saying Fabulous (Stewart Main, 2005). Edinburgh University Press, 2018. http://dx.doi.org/10.3366/edinburgh/9781474429443.003.0014.

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This chapter discusses Stewart Main’s 50 Ways of Saying Fabulous as the first, and to date only, New Zealand fiction feature made for theatrical release that deals with the adolescent experience of a boy who comes to understand that he is gay. Comparing the film with the source novel by Graeme Aitken, the analysis focuses on the gender inversions of the two main characters, the boy protagonist Billy, and his girl cousin Lou, each of who adopt fantasy identities as a character of the opposite sex in the TV show Lost in Space. The discussion also foregrounds both the romanticizing of the hypermasculine stereotype in the film, and also its progressive deconstruction.

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Strand, Vibeke, Jeremy Sokolove, and Alvina D. Chu. Design of clinical trials in rheumatology. Oxford University Press, 2013. http://dx.doi.org/10.1093/med/9780199642489.003.0030.

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Development of new therapies for rheumatic diseases requires a series of randomized controlled trials (RCTs) progressing from phase 1, 'first-in-human' to generate initial safety, pharmacokinetic (PK) and pharmacodynamic (PD) data; to phase 2, proof of concept for efficacy with safety and PK/PD data; and phase 3, designed to demonstrate definitive efficacy and safety to support regulatory approval. Important aspects of RCT designs include sample size estimations, treatment allocation, rescue, blinding, and statistical analyses of prespecified endpoints to preserve trial integrity. Over the past 15 years, significant progress has been made in the design of RCTs in rheumatoid arthritis (RA). Similarly, development and validation of composite outcome measures in psoriatic arthritis, ankylosing spondylitis, gout, and osteoarthritis have furthered trial design and treatment approvals. RCTs in systemic lupus erythematosus and other multisystem, heterogeneous diseases pose more challenges. Trial design will continue to evolve as promising therapies are introduced into the clinic.

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Book chapters on the topic "Progressive data analysis"

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Cramer, Erhard, and George Iliopoulos. "Adaptive Progressive Censoring." In Ordered Data Analysis, Modeling and Health Research Methods. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25433-3_5.

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Scippacercola, Sergio. "The Progressive Single Linkage Algorithm Based on Minkowski Ultrametrics." In Data Analysis and Classification. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03739-9_7.

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Reizer, Aiala, and Jonathan Reizer. "Progressive Multiple Alignment of Protein Sequences and the Construction of Phylogenetic Trees." In Computer Analysis of Sequence Data. Humana Press, 1994. http://dx.doi.org/10.1385/0-89603-276-0:319.

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Angelini, Marco, and Giorgio Cazzetta. "Progressive Visualization of Epidemiological Models for COVID-19 Visual Analysis." In Advanced Visual Interfaces. Supporting Artificial Intelligence and Big Data Applications. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68007-7_10.

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Zhou, Weipeng, and Gang Luo. "Parameter Sensitivity Analysis for the Progressive Sampling-Based Bayesian Optimization Method for Automated Machine Learning Model Selection." In Heterogeneous Data Management, Polystores, and Analytics for Healthcare. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71055-2_17.

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Farneti, Elisabetta, Nicola Cavalagli, Mario Costantini, et al. "Remote Sensing Satellite Data and Progressive Collapse Analysis for Structural Monitoring of Multi-span Bridges." In Lecture Notes in Civil Engineering. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-07258-1_39.

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Song, Xin, Cuirong Wang, Yanjun Chen, and Jing Gao. "A Massive Sensor Data Streams Multi-dimensional Analysis Strategy Using Progressive Logarithmic Tilted Time Frame for Cloud-Based Monitoring Application." In Advances in Neural Networks – ISNN 2014. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12436-0_61.

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Gordon, Steven Lawrence. "Immigration Policy in South Africa: Public Opinion, Xenophobia and the Search for Progress." In IMISCOE Research Series. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-92114-9_5.

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AbstractDeveloping progressive migration policy and legislation in South Africa is significantly hampered by anti-immigrant sentiment amongst the general public. Despite the recommendations of experts, the country has not adopted a clear and coherent immigration integration policy. Moreover, xenophobic violence presents a clear threat to the current South African migration regime. Why has this young democracy struggled to develop meaningful policy solutions to deal with migration? The paper explores this important question through the unique lens of mass opinion. Data from the South African Social Attitudes Survey for the period 2003–2018 will be used for this study. The outcomes of this analysis offer new insights into the obstacles facing attempts to implement both progressive immigration policy as well as anti-xenophobia strategies in South Africa. The paper concludes by outlining the implications of this work for academic attempts to understand address anti-immigrantism in the country. Recommendations for future research are also presented for discussion.

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Absolu, Brandt, Tao Li, and Mitsunori Ogihara. "Analysis of Chord Progression Data." In Advances in Music Information Retrieval. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11674-2_8.

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McCamish, Ben, and Arash Termehchy. "Progressive Interactions Between Data Sources." In Heterogeneous Data Management, Polystores, and Analytics for Healthcare. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14177-6_3.

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Conference papers on the topic "Progressive data analysis"

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Ai, Bo, Tinghua Ai, Xinming Tang, and Zhen Li. "Progressive transmission of road network." In International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, edited by Yaolin Liu and Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.838304.

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Hu, Peng, and Li Yang. "DEM's digital progressive generalization and multi-scale visualization of contour lines." In International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, edited by Yaolin Liu and Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.838337.

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Ranjan, Sudhanshu, Dheeraj Mekala, and Jingbo Shang. "Progressive Sentiment Analysis for Code-Switched Text Data." In Findings of the Association for Computational Linguistics: EMNLP 2022. Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.findings-emnlp.82.

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Venkat, Aniketh, Duong Hoang, Attila Gyulassy, et al. "High-Quality Progressive Alignment of Large 3D Microscopy Data." In 2022 IEEE 12th Symposium on Large Data Analysis and Visualization (LDAV). IEEE, 2022. http://dx.doi.org/10.1109/ldav57265.2022.9966406.

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Jo, Jaemin, Jinwook Seo, and Jean-Daniel Fekete. "A progressive k-d tree for approximate k-nearest neighbors." In 2017 IEEE Workshop on Data Systems for Interactive Analysis (DSIA). IEEE, 2017. http://dx.doi.org/10.1109/dsia.2017.8339084.

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Roeder, Jan. "Alternative Data for Credit Risk Management: An Analysis of the Current State of Research." In Digital Support from Crisis to Progressive Change. University of Maribor Press, 2021. http://dx.doi.org/10.18690/978-961-286-485-9.13.

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Determining credit risk is important for banks and nonbanks alike. For credit risk management, the heterogeneous data generated today can potentially complement the established data such as balance sheet ratios. It has not yet been clearly shown which alternative data sources, such as social media or satellite data, provide added value and how this value can be extracted effectively. This review provides an overview of the intersection between these areas and develops a research agenda. The analysis of the 29 identified papers shows that the use of financial news is analyzed most frequently. Social media has also been used to some extent. The use of other alternative data sets, such as geospatial data, has been analyzed infrequently. The empirical evidence suggests that alternative data can provide both explanatory and predictive benefits in credit risk management. Convergence in terms of analytical approaches and evaluation offers the potential to advance the field.

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Abbas, Antragama Ewa, Wirawan Agahari, Montijn van de Ven, Anneke Zuiderwijk, and Mark de Reuver. "Business Data Sharing through Data Marketplaces: A Systematic Literature Review." In Digital Support from Crisis to Progressive Change. University of Maribor Press, 2021. http://dx.doi.org/10.18690/978-961-286-485-9.6.

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Data marketplaces are expected to play a crucial role in tomorrow’s data economy but hardly achieve commercial exploitation. Currently, there is no clear understanding of the knowledge gaps in data marketplace research, especially neglected research topics that may contribute to advancing data marketplaces towards commercialization. This study provides an overview of the state of the art of data marketplace research. We employ a Systematic Literature Review (SLR) approach and structure our analysis using the Service-TechnologyOrganization-Finance (STOF) model. We find that the extant data marketplace literature is primarily dominated by technical research, such as discussions about computational pricing and architecture. To move past the first stage of the platform’s lifecycle (i.e., platform design) to the second stage (i.e., platform adoption), we call for empirical research in non-technological areas, such as customer expected value and market segmentation.

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Rouseau, Carl, Stephen P. Engelstad, and Stephen B. Clay. "Data Requirements for Progressive Damage Analysis of Composite Structures." In 58th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference. American Institute of Aeronautics and Astronautics, 2017. http://dx.doi.org/10.2514/6.2017-0196.

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Usher, Will, Landon Dyken, and Sidharth Kumar. "Speculative Progressive Raycasting for Memory Constrained Isosurface Visualization of Massive Volumes." In 2023 IEEE 13th Symposium on Large Data Analysis and Visualization (LDAV). IEEE, 2023. http://dx.doi.org/10.1109/ldav60332.2023.00007.

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Ferenček, Aljaž. "Impact Assesment of Open Government Data." In Digital Support from Crisis to Progressive Change. University of Maribor Press, 2021. http://dx.doi.org/10.18690/978-961-286-485-9.56.

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Public sector organizations produce and process increasing amounts of data and the number of research and initiatives on open data is also increasing. Defining the true value of OGD is challenging without knowing how it impacts society and its economy. While the analysis of the economic benefits of open data is one way to describe the effect of government openness, the impact of open data is measured also in social and political context. Feedback mechanisms that are currently used are mostly surveys, while the number of OGD use cases is increasing. This paper proposes a preliminary model for research on assessing impact areas of OGD in an automated manner by using text mining techniques on existing use cases.

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Reports on the topic "Progressive data analysis"

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Touhami, Abdelkhalek, and Dorothee Boccanfuso. Is the Moroccan Fiscal System Progressive ? A Shapley Decomposition. CIRANO, 2023. http://dx.doi.org/10.54932/wrzq6217.

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Public policies, particularly those related to tax policy and subsidies, should help reduce poverty and inequality. However, the combination of the components of these two systems, as implemented, leads sometimes to an increase in poverty and/or inequality without this being necessarily visible. In this paper, based on data from the 2019 wave of the ONDH Household Panel Survey from Morocco, we first highlight the ifluence of taxes and subsidies on household incomes. We then derive the income variations relating to the tax burden and gains from subsidies for the different population groups. We then characterize taxes and subsidies in terms of their progressiveness and regressiveness. Finally, using a Shapley decomposition, we determine the contribution of each tax and subsidy to poverty and inequality measures. This analysis is done separately for rural and urban areas, useful to formulate recommendations on this basis. Our results show that the tax and subsidy system, taken all together, is redistributive. We can also conclude unambiguously that this system reduces poverty and inequality. However, the value-added tax (VAT) is regressive in its current form, unlike income tax, which is progressive. Finally, subsidies for primary and secondary education are highly progressive, while those for higher education are regressive,benefiting the wealthiest quintiles.

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Go, Eugenia, Sam Hill, Maria Hanna Jaber, Yothin Jinjarak, Donghyun Park, and Anton Ragos. Developing Asia’s Fiscal Landscape and Challenges. Asian Development Bank, 2022. http://dx.doi.org/10.22617/wps220267-2.

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This paper analyzes how substantial fiscal policy stimulus in response to COVID-19, combined with the impact of the downturn on revenues, has severely weakened public finances in many developing Asian economies. Analysis drawing on newly compiled data shows that while tax revenues in developing Asia steadily rose in the 2 decades before the COVID–19 pandemic, they continued to lag well behind high-income economies and some developing peers. The region relies on indirect taxes, creating a relatively efficient but less progressive tax structure, while government expenditures on education and health were comparatively modest.

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von Schiller, Armin. Party System Institutionalization and Reliance on Personal Income Tax in Developing Countries. Inter-American Development Bank, 2015. http://dx.doi.org/10.18235/0011710.

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This paper explores the effect of party system institutionalization on the relevance of the personal income tax in the tax composition. Based on a fiscal contractualism approach, it is argued that institutionalized political party systems increase the capacity of political actors to credibly commit to fiscal contracts agreed with wealthy taxpayers. Consequently, in countries characterized by institutionalized political party systems wealthy taxpayers accept paying a bigger share of the tax burden, as reflected in a greater relevance of progressive tax types. The analysis of panel data for more than 90 countries from 1990 to 2010 supports this hypothesis, showing that party system institutionalization has an especially significant and strong positive effect on the relevance of the personal income tax where bureaucratic capacity is low. At high levels of bureaucratic capacity the effect disappears. The findings strongly support the claim that, particularly in developing countries, where bureaucratic capacity tends to be limited, taxation is best understood as a problem of credible commitment.

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Duong, Bich-Hang, and Joan DeJaeghere. From Student-Centered to Competency-Based Reform: Exploring Teachers’ Perspective of Meaningful Participation. Research on Improving Systems of Education (RISE), 2022. http://dx.doi.org/10.35489/bsg-rise-wp_2022/089.

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Student-centered pedagogy has been widely advocated in many contexts with student active participation in learning being a central element. Vietnam has adopted innovative pedagogies including child-centered and competency-based teaching to further active learning and develop students’ full potential. This study explores Vietnamese teachers’ views about student participation and teaching roles as they implement these progressive reforms. It also examines pedagogical practices that teachers planned to use and actually employed to support student learning through meaningful participation. Drawing on qualitative analysis of interviews and classroom observations conducted over three years with 47 secondary-level literature teachers throughout Vietnam, we found that student participation as expected by teachers broadly falls into three categories: participation as attention; participation as contribution and collaboration; and participation as autonomy and engagement. Each of these modes characterizes what teachers’ envision of students’ overall engagement, but these modes coexisted in the data in classroom practices. Our analysis shows how ‘hybrid pedagogy,’ a mix of teacher-directed and student-centered approaches, was most used to support students’ active contribution and collaboration. This research contributes to the literature on student-centered learning and student participation in transitional contexts, highlighting the complex processes of how teachers perceive and enact these pedagogical reforms.

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Vaughan, Tanya, Sarah Richardson, Toby Carslake, et al. Building capacity for Quality Teaching Rounds – Victoria. Final report. Australian Council for Educational Research, 2023. http://dx.doi.org/10.37517/978-1-74286-713-7.

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The Australian Council for Educational Research (ACER) was commissioned by the Teachers and Teaching Research Centre (TTRC) at the University of Newcastle to conduct an independent randomised controlled trial (RCT), with the goal of examining effects of Quality Teaching Rounds (QTR) on student outcomes and teachers’ practice in Victorian high schools. A total of 19 schools participated in Quality Teaching Rounds in 2022, with 20 schools in the wait list control. Data were gathered in an ongoing manner during the evaluation with: Progressive Assessment Tests in Mathematics (PAT-M) and reading (PAT-R) – baseline and follow up; student self-efficacy and aspiration surveys – baseline and follow up; teacher surveys – one questionnaire administered every term; implementation fidelity check surveys for teachers to complete for each QT Round; and implementation fidelity checks with onsite visits from ACER staff for 33% of the treatment schools. Key findings include: The mixed model analysis showed that treatment was not a significant predictor of PAT-R and PAT-M outcomes. Differences in student responses to the self-efficacy and aspiration surveys were identified. The control group showed a significant increase in the level of education that they aspired to complete (p = 0.037). Teachers in the control group had statistically significant growth in teacher efficacy, while those in the treatment group showed statistically significant lower teacher student support. Within the QTR process, the longest time was spent on discussing the coding and the individual coding process. Key observations identified from analysis of the fidelity check data are: teacher stress due to high rates of absenteeism, varied use of the Classroom Practice Guide, and analytical conversations about some elements and terms.

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Chronopoulos, Ilias, Katerina Chrysikou, George Kapetanios, James Mitchell, and Aristeidis Raftapostolos. Deep Neural Network Estimation in Panel Data Models. Federal Reserve Bank of Cleveland, 2023. http://dx.doi.org/10.26509/frbc-wp-202315.

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In this paper we study neural networks and their approximating power in panel data models. We provide asymptotic guarantees on deep feed-forward neural network estimation of the conditional mean, building on the work of Farrell et al. (2021), and explore latent patterns in the cross-section. We use the proposed estimators to forecast the progression of new COVID-19 cases across the G7 countries during the pandemic. We find significant forecasting gains over both linear panel and nonlinear time-series models. Containment or lockdown policies, as instigated at the national level by governments, are found to have out-of-sample predictive power for new COVID-19 cases. We illustrate how the use of partial derivatives can help open the "black box" of neural networks and facilitate semi-structural analysis: school and workplace closures are found to have been effective policies at restricting the progression of the pandemic across the G7 countries. But our methods illustrate significant heterogeneity and time variation in the effectiveness of specific containment policies.

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Lippolis, Nicolas. Diagnostics for Industrialisation: Growth, Sectoral Selection, and Constraints on Firms. Digital Pathways at Oxford, 2022. http://dx.doi.org/10.35489/bsg-dp-wp_2022/03.

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This paper reviews methods that have been suggested by the development literature on diagnostics. We subdivide the variety of diagnostics into three types: revealing the most binding constraints to economy-wide growth; selecting sectors in which to diversify; and identifying sources of sectoral underperformance. Each diagnostic method is judged on whether it: provides a structured way of performing diagnostics; directs analysts towards the right questions; and is efficient in its use of data and resources. After reviewing a variety of methods, we argue that, with respect to growth diagnostics, the best approach is to combine Hausmann’s, Rodrik and Velasco’s Growth Diagnostics with more encompassing and forward-looking methods. In sectoral selection, Hausmann and Hidalgo’s 'Product Space' analysis can serve as an adequate base for choosing sectors in which to diversify, but this method must be tempered by a much more diverse set of indicators that matter for sectoral choice. Finally, diagnostics at the sectoral level can be performed through a sequence of methods: starting from easily collectable perceptions data and progressing to more data-heavy techniques, depending on the time and resources available.

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Torero, Máximo, and Jaime Saavedra-Chanduví. Labor Market Reforms and Their Impact on Formal Labor Demand and Job Market Turnover: The Case of Peru. Inter-American Development Bank, 2000. http://dx.doi.org/10.18235/0011243.

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This paper analyzes the effects of several aspects of labor legislation that were modified through successive waves of reform since 1991. Firing costs diminished sharply through the progressive elimination of job security regulations, the introduction of temporary contracts and changes in the severance payment structure. Simultaneously, non-wage labor costs increased. This paper assesses the effect of these changes on the level of formal employment, the effect of regulations changes on turnover, and compares employment duration data for the formal and informal sectors using empirical hazards and parametric estimations of hazard functions.

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Wang, Hao, and Yulai Xu. Chemopreventive Effects of Immunotrophic Preparations in the Development of Prostate Cancer: A Network Meta-Analysis of Randomized Controlled Trials. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2022. http://dx.doi.org/10.37766/inplasy2022.8.0037.

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Review question / Objective: After successful treatment with ADT, most patients with advanced disease eventually develop resistance and progress to castration-resistant PCa (CRPC), which remains an incurable disease.Low survival and high mortality of PCa are associated with the appearance of CRPC and subsequent metastatic disease. To advance the fight against PCa, it is necessary to continue basic and clinical research to improve testing, prevention and treatment practices. However, under current treatments, prevention should be seen as a basic strategy to reduce PCa morbidity and mortality. Epidemiological studies have shown that a healthy diet may significantly affect the occurrence and progression of prostate cancer. After promising preclinical testing, several natural compounds have been evaluated in the clinic. In this study, we compared data from clinical trials on several natural chemopreventive drugs as well as chemopreventive agents that have been tested for PCa chemoprevention. Provides some grounding support for preventing the progression of prostate cancer.

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Ma, Yunxing, Julia Brettschneider, and Joanna Collingwood. A systematic review and meta-analysis of cerebrospinal fluid amyloid and tau levels in patients progressing from Mild Cognitive Impairment to Alzheimer’s Disease. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2022. http://dx.doi.org/10.37766/inplasy2022.7.0020.

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Review question / Objective: Reported levels of amyloid-beta and tau in human cerebrospinal fluid (CSF) are evaluated to discover if these biochemical markers can predict the transition from Mild Cognitive Impairment (MCI) to Alzheimer’s disease (AD). A systematic review and quantitative meta-analyses are performed to test relationships between three potential biomarkers in CSF (Aβ(1-42), T-tau, and P-tau181) and the evolution of AD in longitudinal evaluations of levels relative to baseline, using prior-published experimental data. The primary focus of the analysis is on the period describing the transition of a patient from MCI to AD, where it is critical to discover the main biomarker characteristics that differentiate patient outcomes for those who have a stable form of MCI, and those who progress to a confirmed diagnosis of AD. A secondary purpose of the review was to examine the status of iron in CSF as a function of disease status.

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