Relevant bibliographies by topics / Mixed data types

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Mixed data types'

Author: Grafiati
Published: 9 March 2023
Last updated: 11 March 2023

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Journal articles on the topic "Mixed data types"

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Wang, Lu, Dongxiao Zhu, and Ming Dong. "Clustering over-dispersed data with mixed feature types." Statistical Analysis and Data Mining: The ASA Data Science Journal 11, no. 2 (January 10, 2018): 55–65. http://dx.doi.org/10.1002/sam.11369.

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Sánchez-Borrego, I., J. D. Opsomer, M. Rueda, and A. Arcos. "Nonparametric estimation with mixed data types in survey sampling." Revista Matemática Complutense 27, no. 2 (December 5, 2013): 685–700. http://dx.doi.org/10.1007/s13163-013-0142-2.

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Jørgensen, Bent, Søren Lundbye-Christensen, Peter Xue-Kun Song, and Li Sun. "State-space models for multivariate longitudinal data of mixed types." Canadian Journal of Statistics 24, no. 3 (September 1996): 385–402. http://dx.doi.org/10.2307/3315747.

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Einarsson, Bo. "Mixed language programming realization and the provision of data types." ACM SIGNUM Newsletter 21, no. 1-2 (April 1986): 2–9. http://dx.doi.org/10.1145/15983.15984.

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Yoon, Grace, Raymond J. Carroll, and Irina Gaynanova. "Sparse semiparametric canonical correlation analysis for data of mixed types." Biometrika 107, no. 3 (April 15, 2020): 609–25. http://dx.doi.org/10.1093/biomet/asaa007.

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Summary Canonical correlation analysis investigates linear relationships between two sets of variables, but it often works poorly on modern datasets because of high dimensionality and mixed data types such as continuous, binary and zero-inflated. To overcome these challenges, we propose a semiparametric approach to sparse canonical correlation analysis based on the Gaussian copula. The main result of this paper is a truncated latent Gaussian copula model for data with excess zeros, which allows us to derive a rank-based estimator of the latent correlation matrix for mixed variable types without estimation of marginal transformation functions. The resulting canonical correlation analysis method works well in high-dimensional settings, as demonstrated via numerical studies, and when applied to the analysis of association between gene expression and microRNA data from breast cancer patients.
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Amatya, Anup, and Hakan Demirtas. "Concurrent generation of multivariate mixed data with variables of dissimilar types." Journal of Statistical Computation and Simulation 86, no. 18 (April 22, 2016): 3595–607. http://dx.doi.org/10.1080/00949655.2016.1177530.

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Sun, Jinhui, Pang Du, Hongyu Miao, and Hua Liang. "Robust feature screening procedures for single and mixed types of data." Journal of Statistical Computation and Simulation 90, no. 7 (February 4, 2020): 1173–93. http://dx.doi.org/10.1080/00949655.2020.1719104.

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Vogl, Susanne. "Integrating and Consolidating Data in Mixed Methods Data Analysis: Examples From Focus Group Data With Children." Journal of Mixed Methods Research 13, no. 4 (August 31, 2018): 536–54. http://dx.doi.org/10.1177/1558689818796364.

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The challenge in data analysis often lies in accounting for the multidimensionality and complexity of the data while simultaneously discovering patterns. Integrating and consolidating different types of data during analysis can broaden the perspective and permit obtaining complementary views. This methodological research study on data collection illustrates how one type of data collection generates different types of data, which can be linked and consolidated to reach a better understanding of the topic. Procedures and practicalities are illustrated to offer a good practice example for data integration and consolidation. With the methodological reflection of research practice, I evaluate the consequences for the field of mixed methods research, in which the practicalities of an integrated mixed analysis still need to be elaborated.
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Harch, B. D., K. E. Basford, I. H. DeLacy, P. K. Lawrence, and A. Cruickshank. "Mixed data types and the use of pattern analysis on the Australian groundnut germplasm data." Genetic Resources and Crop Evolution 43, no. 4 (August 1996): 363–76. http://dx.doi.org/10.1007/bf00132957.

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Huang, Mingze, Christian Müller, and Irina Gaynanova. "latentcor: An R Package for estimating latent correlations from mixed data types." Journal of Open Source Software 6, no. 65 (September 21, 2021): 3634. http://dx.doi.org/10.21105/joss.03634.

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Dissertations / Theses on the topic "Mixed data types"

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Koomson, Obed. "Performance Assessment of The Extended Gower Coefficient on Mixed Data with Varying Types of Functional Data." Digital Commons @ East Tennessee State University, 2018. https://dc.etsu.edu/etd/3512.

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Clustering is a widely used technique in data mining applications to source, manage, analyze and extract vital information from large amounts of data. Most clustering procedures are limited in their performance when it comes to data with mixed attributes. In recent times, mixed data have evolved to include directional and functional data. In this study, we will give an introduction to clustering with an eye towards the application of the extended Gower coefficient by Hendrickson (2014). We will conduct a simulation study to assess the performance of this coefficient on mixed data whose functional component has strictly-decreasing signal curves and also those whose functional component has a mixture of strictly-decreasing signal curves and periodic tendencies. We will assess how four different hierarchical clustering algorithms perform on mixed data simulated under varying conditions with and without weights. The comparison of the various clustering solutions will be done using the Rand Index.
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Apitzsch, Cecilia, and Josefin Ryeng. "Cluster Analysis of Mixed Data Types in Credit Risk : A study of clustering algorithms to detect customer segments." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-172594.

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GIORDANI, ILARIA. "Relational clustering for knowledge discovery in life sciences." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2010. http://hdl.handle.net/10281/7830.

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Clustering is one of the most common machines learning technique, which has been widely applied in genomics, proteomics and more generally in Life Sciences. In particular, clustering is an unsupervised technique that, based on geometric concepts like distance or similarity, partitions objects into groups, such that objects with similar characteristics are clustered together and dissimilar objects are in different clusters. In many domains where clustering is applied, some background knowledge is available in different forms: labelled data (specifying the category to which an instance belongs); complementary information about "true" similarity between pairs of objects or about the relationships structure present in the input data; user preferences (for example specifying whether two instances should be in same or different clusters). In particular, in many real-world applications like biological data processing, social network analysis and text mining, data do not exist in isolation, but a rich structure of relationships subsists between them. A simple example can be viewed in biological domain, where there are al lot of relationships between genes and proteins based on many experimental conditions. Another example, maybe common, is the Web search domain where there are relations between documents and words in a text or web pages, search queries and web users. Our research is focalized on how this background knowledge can be incorporated into traditional clustering algorithms to optimize the process of pattern discovery (clustering) between instances. In this thesis, we first provide an overview of traditional clustering methods with some important distance measures and then we analyze three particular challenges that we try to overcome with different proposed methods: "feature selection" to reduce high dimensional input space and remove noise from data; "mixed data types" to handle in clustering procedure both numeric and categorical values, typically of life science applications; finally, "knowledge integration" in order to improve the semantic value of clustering incorporating the background knowledge. Regarding the first challenge we propose a novel approach based on using of genetic programming, an evolutionary algorithm-based methodology, in order to automatically perform feature selection. Different clustering algorithms are been investigated regarding the second challenge. A modify version of a particular algorithm is proposed and applied to clinical data. Particularly attention is given to the final challenge, the most important objective of this Thesis: the development of a new relational clustering framework in order to improve the semantic value of clustering taking into account in the clustering algorithm relationships learned from background knowledge. We investigate and classify existing clustering methods into two principal categories: - Structure driven approaches: that are bound to data structure. The data clustering problem is tackled from several dimensions: clustering concurrently columns and rows of a given dataset, like biclustering algorithm or vertical 3-D clustering. - Knowledge driven approaches: where domain information is used to drive the clustering process and interpret its results: semi-supervised clustering, that using both labelled and unlabeled data, has attracted significant attention. This kind of clustering algorithms represents the first step to implement the proposed general framework that it is classified into this category. In particular the thesis focuses on the development of a general framework for relational clustering instantiating it for three different life science applications: the first one with the aim of finding groups of gene with similar behaviour respect to their expression and regulatory profile. The second one is a pharmacogenomics application, in which the relational clustering framework is applied on a benchmark dataset (NCI60) to identify a drug treatment to a given cell line based both on drug activity pattern and gene expression profile. Finally, the proposed framework is applied on clinical data: a particular dataset containing different information about patients in anticoagulant therapy has been analyzed to find group of patients with similar behaviour and responses to the therapy.
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Abouzeid, Shadi. "A visual interactive grouping analysis tool (VIGAT) that takes mixed data types as input and provides visually interactive overlapping groups as output." Thesis, University of Strathclyde, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.401309.

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Sun, Jinhui. "Robust Feature Screening Procedures for Mixed Type of Data." Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/73709.

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High dimensional data have been frequently collected in many fields of scientific research and technological development. The traditional idea of best subset selection methods, which use penalized L_0 regularization, is computationally too expensive for many modern statistical applications. A large number of variable selection approaches via various forms of penalized least squares or likelihood have been developed to select significant variables and estimate their effects simultaneously in high dimensional statistical inference. However, in modern applications in areas such as genomics and proteomics, ultra-high dimensional data are often collected, where the dimension of data may grow exponentially with the sample size. In such problems, the regularization methods can become computationally unstable or even infeasible. To deal with the ultra-high dimensionality, Fan and Lv (2008) proposed a variable screening procedure via correlation learning to reduce dimensionality in sparse ultra-high dimensional models. Since then many authors further developed the procedure and applied to various statistical models. However, they all focused on single type of predictors, that is, the predictors are either all continuous or all discrete. In practice, we often collect mixed type of data, which contains both continuous and discrete predictors. For example, in genetic studies, we can collect information on both gene expression profiles and single nucleotide polymorphism (SNP) genotypes. Furthermore, outliers are often present in the observations due to experimental errors and other reasons. And the true trend underlying the data might not follow the parametric models assumed in many existing screening procedures. Hence a robust screening procedure against outliers and model misspecification is desired. In my dissertation, I shall propose a robust feature screening procedure for mixed type of data. To gain insights on screening for individual types of data, I first studied feature screening procedures for single type of data in Chapter 2 based on marginal quantities. For each type of data, new feature screening procedures are proposed and simulation studies are performed to compare their performances with existing procedures. The aim is to identify a best robust screening procedure for each type of data. In Chapter 3, I combine these best screening procedures to form the robust feature screening procedure for mixed type of data. Its performance will be assessed by simulation studies. I shall further illustrate the proposed procedure by the analysis of a real example.
Ph. D.
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Engardt, Sara. "Unsupervised learning with mixed type data : for detecting money laundering." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230891.

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The purpose of this master's thesis is to perform a cluster analysis on parts of Handelsbanken's customer database. The ambition is to explore if this could be of aid in identifying type customers within risk of illegal activities such as money laundering. A literature study is conducted to help determine which of the clustering methods described in the literature are most suitable for the current problem. The most important constraints of the problem are that the data consists of mixed type attributes (categorical and numerical) and the large presence of outliers in the data. An extension to the self-organising map as well as the k-prototypes algorithms were chosen for the clustering. It is concluded that clusters exist in the data, however in the presence of outliers. More work is needed on handling missing values in the dataset.
Syftet med denna masteruppsats är att utföra en klusteranalys på delar av Handelsbankens kunddatabas. Tanken är att undersöka ifall detta kan vara till hjälp i att identifiera typkunder inom olagliga aktiviteter såsom penningtvätt. Först genomförs en litteraturstudie för att undersöka vilken algoritm som är bäst lämpad för att lösa problemet. Kunddatabasen består av data med både numeriska och kategoriska attribut. Ett utökat Kohonen-nätverk (eng: self-organising map) samt k-prototyp algoritmen används för klustringen. Resultaten visar att det finns kluster i datat, men i närvaro av brus. Mer arbete behöver göras för att hantera tomma värden bland attributen.
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Codd, Casey. "A Review and Comparison of Models and Estimation Methods for Multivariate Longitudinal Data of Mixed Scale Type." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1398686513.

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Koufakou, Anna. "SCALABLE AND EFFICIENT OUTLIER DETECTION IN LARGE DISTRIBUTED DATA SETS WITH MIXED-TYPE ATTRIBUTES." Doctoral diss., University of Central Florida, 2009. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3431.

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An important problem that appears often when analyzing data involves identifying irregular or abnormal data points called outliers. This problem broadly arises under two scenarios: when outliers are to be removed from the data before analysis, and when useful information or knowledge can be extracted by the outliers themselves. Outlier Detection in the context of the second scenario is a research field that has attracted significant attention in a broad range of useful applications. For example, in credit card transaction data, outliers might indicate potential fraud; in network traffic data, outliers might represent potential intrusion attempts. The basis of deciding if a data point is an outlier is often some measure or notion of dissimilarity between the data point under consideration and the rest. Traditional outlier detection methods assume numerical or ordinal data, and compute pair-wise distances between data points. However, the notion of distance or similarity for categorical data is more difficult to define. Moreover, the size of currently available data sets dictates the need for fast and scalable outlier detection methods, thus precluding distance computations. Additionally, these methods must be applicable to data which might be distributed among different locations. In this work, we propose novel strategies to efficiently deal with large distributed data containing mixed-type attributes. Specifically, we first propose a fast and scalable algorithm for categorical data (AVF), and its parallel version based on MapReduce (MR-AVF). We extend AVF and introduce a fast outlier detection algorithm for large distributed data with mixed-type attributes (ODMAD). Finally, we modify ODMAD in order to deal with very high-dimensional categorical data. Experiments with large real-world and synthetic data show that the proposed methods exhibit large performance gains and high scalability compared to the state-of-the-art, while achieving similar accuracy detection rates.
Ph.D.
School of Electrical Engineering and Computer Science
Engineering and Computer Science
Computer Engineering PhD
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Chu, Shuyu. "Change Detection and Analysis of Data with Heterogeneous Structures." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/78613.

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Heterogeneous data with different characteristics are ubiquitous in the modern digital world. For example, the observations collected from a process may change on its mean or variance. In numerous applications, data are often of mixed types including both discrete and continuous variables. Heterogeneity also commonly arises in data when underlying models vary across different segments. Besides, the underlying pattern of data may change in different dimensions, such as in time and space. The diversity of heterogeneous data structures makes statistical modeling and analysis challenging. Detection of change-points in heterogeneous data has attracted great attention from a variety of application areas, such as quality control in manufacturing, protest event detection in social science, purchase likelihood prediction in business analytics, and organ state change in the biomedical engineering. However, due to the extraordinary diversity of the heterogeneous data structures and complexity of the underlying dynamic patterns, the change-detection and analysis of such data is quite challenging. This dissertation aims to develop novel statistical modeling methodologies to analyze four types of heterogeneous data and to find change-points efficiently. The proposed approaches have been applied to solve real-world problems and can be potentially applied to a broad range of areas.
Ph. D.
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Wahi, Rabbani Rash-ha. "Towards an understanding of the factors associated with severe injuries to cyclists in crashes with motor vehicles." Thesis, Queensland University of Technology, 2018. https://eprints.qut.edu.au/121426/1/Rabbani%20Rash-Ha_Wahi_Thesis.pdf.

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This thesis aimed to develop statistical models to overcome limitations in police-reported data to better understand the factors contributing to severe injuries in bicycle motor-vehicle crashes. In low-cycling countries such as Australia, collisions with motor vehicles are the major causes of severe injuries to cyclists and fear of collisions prevents many people from taking up cycling. The empirical results obtained from the models provide valuable insights to assist transport and enforcement agencies to improve cyclist safety.
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Books on the topic "Mixed data types"

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Okolelova, Tamara, Syergyey YEngashyev, and Ivan Yegorov. Poultry farming: current questions and answers. ru: Publishing Center RIOR, 2020. http://dx.doi.org/10.29039/02023-4.

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In the book in the form of questions and answers considerable attention is paid to data on the needs of all types of poultry in nutritional, mineral and biologically active substances, taking into account age of poultry. The characteristic of the main feed products is given, and the rational norms for including them in mixed feed for poultry are indicated. The role of vitamins, macro- and microelements, enzyme preparations, probiotics, prebiotics, antibiotics, organic acids, antioxidants, emulsifiers and other sources of biologically active substances in poultry nutrition is shown. Both nutritional factors that reduce the immune system and the causes of major feed diseases, which are related to the quality of feed, with violations in the rationing of nutrients and minerals, are indicated, and also biologically active substances, technologies for feeding and keeping poultry, methods of their prevention are given. It is addressed to specialists and managers of poultry farms, feed industry enterprises, researchers, postgraduates and students.
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Rossiĭskai︠a︡ akademii︠a︡ nauk. Kabardino-Balkarskiĭ nauchnyĭ t︠s︡entr. Mezhdunarodnyĭ Rossiĭsko-Abkhazskiĭ simpozium "Uravnenii︠a︡ smeshannogo tipa i rodstvennye problemy analiza i informatiki": VII Shkola molodykh uchenykh "Nelokalʹnye kraevye zadachi i problemy sovremennogo analiza i informatiki" : materialy Rossiĭsko-Abkhazskiĭ simpozium, Nalʹchik-Ėlʹbrus, 17-22 mai︠a︡ 2009 g. = International Russian-Abkhazian Symposium "Mixed type equations and related problems of analysis and informatics" : VII School of Young Scientists "Non-local boundary value problems and related problems of modern analysis and informatics" : proceedings. Nalʹchik: KBNT︠S︡ RAN, 2009.

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Watteeuw, Lieve, and Hannah Iterbeke, eds. Enclosed Gardens of Mechelen. NL Amsterdam: Amsterdam University Press, 2018. http://dx.doi.org/10.5117/9789463720724.

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During the Late Middle Ages a unique type of ‘mixed media’ recycled and remnant art arose in houses of religious women in the Low Countries: enclosed gardens. They date from the time of Emperor Charles V and are unique examples of ‘anonymous’ female art, devotion and spirituality. A hortus conclusus (or enclosed garden) represents an ideal, paradisiacal world. Enclosed Gardens are retables, sometimes with painted side panels, the central section filled not only with narrative sculpture, but also with all sorts of trinkets and hand-worked textiles.Adornments include relics, wax medallions, gemstones set in silver, pilgrimage souvenirs, parchment banderoles, flowers made from textiles with silk thread, semi-precious stones, pearls and quilling (a decorative technique using rolled paper). The ensemble is an impressive and one-of-a-kind display and presents as an intoxicating garden. The sixteenth-century horti conclusi of the Mechelen Hospital sisters are recognized Masterpieces and are extremely rare, not alone at a Belgian but even at a global level. They are of international significance as they provide evidence of devotion and spirituality in convent communities in the Southern Netherlands in the sixteenth century. They are an extraordinary tangible expression of a devotional tradition. The highly individual visual language of the enclosed gardens contributes to our understanding of what life was like in cloistered communities. They testify to a cultural identity closely linked with mystical traditions allowing us to enter a lost world very much part of the culture of the Southern Netherlands. This book is the first full survey of the enclosed gardens and is the result of year-long academic research.
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Miksza, Peter, and Kenneth Elpus. Design and Analysis of Experimental Research II. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199391905.003.0009.

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This chapter introduces the reader to more possibilities for thinking about causal questions and for laying the foundational concepts necessary for conducting data analyses that correspond to more complex experimental designs. The discussion of experimental design types presented in chapter 8 is expanded to include within-subjects designs, factorial designs, mixed designs, and designs for multivariate outcomes. Prototypical examples of each design type are presented along with the typical analysis tools used for testing the associated experimental hypotheses. Hypothetical examples of research designs that are suitable for illustrating analyses with repeated-measures ANOVA, factorial or multiway ANOVA, and MANOVA (multivariate analysis of variance).
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Atkins, David C., and Brian R. Baucom. Emerging Methodological and Statistical Techniques in Couple Research. Edited by Erika Lawrence and Kieran T. Sullivan. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199783267.013.16.

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Rapid changes in technology are altering some of the basic ways in which we interact with our world, as seen in the evolution of the telephone to mobile phone to smart phone. These technological changes are ushering in new methods of data collection and analysis, which also open up new types of research questions and designs for couple researchers. This chapter reviews current and emerging methods for data acquisition and analysis in relationship science. Data acquisition methods include mobile technology and context-specific ecological momentary assessment, as well as behavioral signal-processing techniques to quantify such data. Analytic methods cover mixed models and actor–partner interdependence models, as well as a broad introduction to machine learning techniques that are appropriate for massive datasets.
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Krishna, Anirudh. The Dynamics of Poverty. Edited by David Brady and Linda M. Burton. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199914050.013.14.

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This article examines the dynamics of poverty and explains why poverty dynamics studies are necessary: to estimate the risk of impoverishment and the probability of escaping poverty; to identify the reasons associated with poverty descents and escapes; to distinguish between transient and chronic poverty; and to elucidate the social mobility prospects of individuals in different economic situations. The article begins with an overview of three types of approaches used in investigating poverty dynamics: panel data studies, participatory poverty assessments and ethnographic studies, and mixed-method studies. This is followed by a discussion of key findings from poverty dynamics studies; one finding is that poverty creation and poverty reduction occur everywhere in tandem. The article concludes by outlining future directions for research into poverty dynamics.
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Doveton, John H. Principles of Mathematical Petrophysics. Oxford University Press, 2014. http://dx.doi.org/10.1093/oso/9780199978045.001.0001.

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The pioneering work of Gus Archie moved log interpretation into log analysis with the introduction of the equation that bears his name. Subsequent developments have mixed empiricism, physics, mathematical algorithms, and geological or engineering models as methods applied to petrophysical measurements in boreholes all over the world. Principles of Mathematical Petrophysics reviews the application of mathematics to petrophysics in a format that crystallizes the subject as a subdiscipline appropriate for the workstations of today. The subject matter is of wide interest to both academic and industrial professionals who work with subsurface data applied to energy, hydrology, and environmental issues. This book is the first of its kind, in that it addresses mathematical petrophysics as a distinct discipline. Other books in petrophysics are either extensive descriptions of tool design or interpretation techniques, typically in an ad hoc treatment. It covers mathematical methods that are applied to borehole and core petrophysical measurements to estimate rock properties of fluid saturation, pore types, permeability, mineralogy, facies, and reservoir characterization. These methods are demonstrated by a variety of case studies and summaries of applications. Principles of Mathematical Petrophysics is an invaluable resource for all people working with data related to petrophysics.
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Lindsey, Rose, John Mohan, Elizabeth Metcalfe, and Sarah Bulloch. Continuity and Change in Voluntary Action. Policy Press, 2018. http://dx.doi.org/10.1332/policypress/9781447324836.001.0001.

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This book provides a longitudinal perspective on change and continuity in voluntary action in recent decades in the UK. Drawing on more than 30 years of different quantitative and qualitative data, its longitudinal, mixed-methods approach offers insights into recent and contemporary British voluntary action. The book deploys a range of quantitative data sources on individual behaviour, both cross-sectional and longitudinal, to analyse aggregate trends in individual engagement in both formal and informal volunteering, in the level and frequency of engagement, the types of activities that volunteers carry out, their responses to questions concerning their motivation and the rewards they obtain from volunteering. These analyses are complemented, and given much greater depth, by the use of qualitative data from individuals who volunteer for the Mass Observation Project, through which they provide free-form written testimony about their daily lives. Tracking a subset of these individuals over time provides unique and novel insights into behaviour, motivation, and lifetime engagement. This source is also highly informative of individuals’ understandings of, and particularly their attitudes towards, voluntary action, and the balance between public and private responsibility for the provision of public services. The findings lead us to caution against any simplistic suggestions that levels of voluntary action can be increased significantly without policies that work with the grain of individuals’ everyday lives.
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Vogt, Manuel. Mobilization and Conflict in Multiethnic States. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780190065874.001.0001.

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Why are ethnic movements more likely to turn violent in some multiethnic countries than in others? Focusing on the long-term legacies of European colonialism, this book presents two ideal-typical logics of ethnic group mobilization—one of violent competition and another of nonviolent emancipatory opposition. The book’s theory first explains why ethnic grievances are translated into either violent or nonviolent forms of conflict as a function of distinct ethnic cleavage types, resulting from different colonial experiences. Violent intergroup conflict is least likely where settler colonialism resulted in persistent stratification, with ethnic groups organized as ethnoclasses. Such stratified societies are characterized by an equilibrium of inequality, in which historically marginalized groups lack both the organizational strength and the opportunities for armed rebellion. In contrast, where colonialism and decolonization divided ethnic groups into segmented, unranked subsocieties that feature distinct socioeconomic and cultural institutions, ethnic mobilization is more likely to trigger violent conflict. Second, the theory links this structural explanation to the political actors at the heart of ethnic movements—in particular, ethnic organizations. It elucidates how these organizations fuel the risk of civil conflict in segmented unranked societies, but peacefully promote the empowerment of historically marginalized groups in stratified societies. The book draws on an innovative mixed-methods design that combines large-n statistical analyses—using new data on the linguistic and religious segmentation of ethnic groups, as well as on ethnic organizations—with case studies based on original field research in four different countries in sub-Saharan Africa and Latin America.
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Keck, Paul E., and Susan L. McElroy. Pharmacological Treatments for Bipolar Disorder. Oxford University Press, 2015. http://dx.doi.org/10.1093/med:psych/9780199342211.003.0008.

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The majority of clinical trials in patients with bipolar disorders have been conducted in groups with bipolar I illness, although a few trials have included patients with bipolar II disorder. Pharmacological management of bipolar disorder involves the treatment of acute manic, hypomanic, mixed, and depressive episodes, as well as the prevention of further episodes and subsyndromal symptoms. Lithium, divalproex, carbamazepine, haloperidol, risperidone, olanzapine, quetiapine, ziprasidone, aripiprazole, and asenapine have demonstrated efficacy in the treatment of acute mania in randomized controlled (type 1) trials. Although the pharmacological treatment of acute bipolar depression remains understudied, data from randomized controlled trials indicate that lithium, olanzapine, olanzapine-fluoxetine, quetiapine, lurasidone, tricyclics, monoamine oxidase inhibitors, and fluoxetine have efficacy in this phase of the illness. Lithium, lamotrigine, olanzapine, aripiprazole, quetiapine, and risperidone (long-acting, injectable) have been shown to have efficacy in relapse prevention. Less extensive data suggest that divalproex and carbamazepine are also efficacious for prevention.
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Book chapters on the topic "Mixed data types"

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Vicente-Villardón, José L., and Julio C. Hernández-Sánchez. "External Logistic Biplots for Mixed Types of Data." In Studies in Classification, Data Analysis, and Knowledge Organization, 169–83. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3311-2_14.

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Beaton, Derek, Michael Kriegsman, Joseph Dunlop, Francesca M. Filbey, and Hervé Abdi. "Imaging Genetics with Partial Least Squares for Mixed-Data Types (MiMoPLS)." In Springer Proceedings in Mathematics & Statistics, 73–91. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40643-5_6.

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Abdullin, Artur, and Olfa Nasraoui. "A Seed-Based Inter-Domain Supervised Framework to Cluster Mixed Data Types." In Communications in Computer and Information Science, 37–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-54105-6_3.

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Wu, Yan, Weiqing Tao, Yingjie Zhang, and Xueting Li. "Mixed Communication Design of Phasor Data Concentrator in Distribution Network." In Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications, 84–92. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_10.

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AbstractPhase Data Concentrator (PDC) is an important part of Wide Area Measurement System (WAMS) and is widely used in transmission systems. WAMS technology will also be applied in smart distribution network, which has many nodes, complex architecture and various types of data transmission services, and a single communication mode cannot meet its needs. In order to solve this problem, this paper first introduces the composition of WAMS system, communication network mode, and discusses the access layer communication network mode. According to the main station, sub-station interaction process design a synchronous phase data set device that can carry out up-down communication and mix network by various means of communication. Finally, the experimental environment of Power Line Carrier (PLC) and twisted pair network communication is set up to verify.
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Feoli, E., and P. Ganis. "Autocorrelation for Measuring Predictivity in Community Ecology: An Example with Structural and Chorological Data from Mixed Forest Types of NE Italy." In Computer assisted vegetation analysis, 285–89. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3418-7_26.

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Montesinos López, Osval Antonio, Abelardo Montesinos López, and Jose Crossa. "General Elements of Genomic Selection and Statistical Learning." In Multivariate Statistical Machine Learning Methods for Genomic Prediction, 1–34. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89010-0_1.

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AbstractNowadays, huge data quantities are collected and analyzed for delivering deep insights into biological processes and human behavior. This chapter assesses the use of big data for prediction and estimation through statistical machine learning and its applications in agriculture and genetics in general, and specifically, for genome-based prediction and selection. First, we point out the importance of data and how the use of data is reshaping our way of living. We also provide the key elements of genomic selection and its potential for plant improvement. In addition, we analyze elements of modeling with machine learning methods applied to genomic selection and stress their importance as a predictive methodology. Two cultures of model building are analyzed and discussed: prediction and inference; by understanding modeling building, researchers will be able to select the best model/method for each circumstance. Within this context, we explain the differences between nonparametric models (predictors are constructed according to information derived from data) and parametric models (all the predictors take predetermined forms with the response) as well their type of effects: fixed, random, and mixed. Basic elements of linear algebra are provided to facilitate understanding the contents of the book. This chapter also contains examples of the different types of data using supervised, unsupervised, and semi-supervised learning methods.
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Jones, Kelly W., Allen Blackman, and Rodrigo Arriagada. "Methods to Advance Understanding of Tenure Security: Impact Evaluation for Rigorous Evidence on Tenure Interventions." In Land Tenure Security and Sustainable Development, 291–312. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-81881-4_14.

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AbstractThe impact of land tenure interventions on sustainable development outcomes is affected by political, social, economic, and environmental factors, and as a result, multiple types of evidence are needed to advance our understanding. This chapter discusses the use of counterfactual impact evaluation to identify causal relationships between tenure security and sustainable development outcomes. Rigorous evidence that tenure security leads to better outcomes for nature and people is thin and mixed. Using a theory of change as a conceptual model can help inform hypothesis testing and promote rigorous study design. Careful attention to data collection and use of experimental and quasi-experimental impact evaluation methods can advance understanding of causal connections between tenure security interventions and development outcomes.
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Montesinos López, Osval Antonio, Abelardo Montesinos López, and Jose Crossa. "Random Forest for Genomic Prediction." In Multivariate Statistical Machine Learning Methods for Genomic Prediction, 633–81. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89010-0_15.

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AbstractWe give a detailed description of random forest and exemplify its use with data from plant breeding and genomic selection. The motivations for using random forest in genomic-enabled prediction are explained. Then we describe the process of building decision trees, which are a key component for building random forest models. We give (1) the random forest algorithm, (2) the main hyperparameters that need to be tuned, and (3) different splitting rules that are key for implementing random forest models for continuous, binary, categorical, and count response variables. In addition, many examples are provided for training random forest models with different types of response variables with plant breeding data. The random forest algorithm for multivariate outcomes is provided and its most popular splitting rules are also explained. In this case, some examples are provided for illustrating its implementation even with mixed outcomes (continuous, binary, and categorical). Final comments about the pros and cons of random forest are provided.
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Gushev, Vesselin, and Geno Nikolov. "Some Cubature Formulae Using Mixed Type Data." In Recent Progress in Multivariate Approximation, 163–84. Basel: Birkhäuser Basel, 2001. http://dx.doi.org/10.1007/978-3-0348-8272-9_13.

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Behzadi, Sahar, Nikola S. Müller, Claudia Plant, and Christian Böhm. "Clustering of Mixed-Type Data Considering Concept Hierarchies." In Advances in Knowledge Discovery and Data Mining, 555–73. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16148-4_43.

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Conference papers on the topic "Mixed data types"

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Richards, John. "Analysis of mixed data types: the unresolved problem." In International Symposium on Remote Sensing, edited by Sebastiano B. Serpico. SPIE, 2002. http://dx.doi.org/10.1117/12.454146.

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Lazar, Alina, Ling Jin, C. Anna Spurlock, Kesheng Wu, and Alex Sim. "Data quality challenges with missing values and mixed types in joint sequence analysis." In 2017 IEEE International Conference on Big Data (Big Data). IEEE, 2017. http://dx.doi.org/10.1109/bigdata.2017.8258222.

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Nguyen, Hoang Vu, Emmanuel Müller, Periklis Andritsos, and Klemens Böhm. "Detecting correlated columns in relational databases with mixed data types." In the 26th International Conference. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2618243.2618251.

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Nguyen, Thy, and Tayo Obafemi-Ajayi. "Structured Iterative Hard Thresholding for Categorical and Mixed Data Types." In 2019 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2019. http://dx.doi.org/10.1109/ssci44817.2019.9002948.

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"A Semi-supervised Learning Framework to Cluster Mixed Data Types." In International Conference on Knowledge Discovery and Information Retrieval. SciTePress - Science and and Technology Publications, 2012. http://dx.doi.org/10.5220/0004134300450054.

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Seo, Seonguk, Jihye Ha, Jieun Shin, Sunah Kim, and Taeho Hwang. "A Unified Model for Bid Landscape Forecasting in the Mixed Auction Types of Real-Time Bidding." In 2022 IEEE International Conference on Big Data (Big Data). IEEE, 2022. http://dx.doi.org/10.1109/bigdata55660.2022.10020742.

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Tang, Ray. "Use Mixed Precision Data Types to Speed up Computation for Ultrasound Imaging Software." In 2022 7th International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS). IEEE, 2022. http://dx.doi.org/10.1109/iciibms55689.2022.9971490.

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Yi, Sung Gon, and Taesung Park. "Integrated analysis of the various types of microarray data using linear-mixed effects models." In 2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2010. http://dx.doi.org/10.1109/bibm.2010.5706594.

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Belyaev, Alexey O., Anton S. Boldyrev, Vladimir N. Podsvirov, Vladimir B. Podoprigora, and Sergey A. Sinutin. "Context-redefined language synthesis for energy consumption prediction using data from mixed remote sensors types." In Remote Sensing Technologies and Applications in Urban Environments, edited by Nektarios Chrysoulakis, Thilo Erbertseder, and Ying Zhang. SPIE, 2018. http://dx.doi.org/10.1117/12.2517147.

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Lin, Xuming, Ruifang Liu, Wenmei Hu, Yameng Li, Xin Zhou, and XiaoXin He. "A Deep Convolutional Network Demodulator for Mixed Signals with Different Modulation Types." In 2017 IEEE 15th Intl Conf on Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence and Computing, 3rd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech). IEEE, 2017. http://dx.doi.org/10.1109/dasc-picom-datacom-cyberscitec.2017.150.

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Reports on the topic "Mixed data types"

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Avis, William. Refugee and Mixed Migration Displacement from Afghanistan. Institute of Development Studies (IDS), August 2021. http://dx.doi.org/10.19088/k4d.2022.002.

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This rapid literature review summarises evidence and key lessons that exist regarding previous refugee and mixed migration displacement from Afghanistan to surrounding countries. The review identified a diverse literature that explored past refugee and mixed migration, with a range of quantitative and qualitative studies identified. A complex and fluid picture is presented with waves of mixed migration (both outflow and inflow) associated with key events including the: Soviet–Afghan War (1979–1989); Afghan Civil War (1992–96); Taliban Rule (1996–2001); War in Afghanistan (2001–2021). A contextual picture emerges of Afghans having a long history of using mobility as a survival strategy or as social, economic and political insurance for improving livelihoods or to escape conflict and natural disasters. Whilst violence has been a principal driver of population movements among Afghans, it is not the only cause. Migration has also been associated with natural disasters (primarily drought) which is considered a particular issue across much of the country – this is associated primarily with internal displacement. Further to this, COVID-19 is impacting upon and prompting migration to and from Afghanistan. Data on refugee and mixed migration movement is diverse and at times contradictory given the fluidity and the blurring of boundaries between types of movements. Various estimates exist for numbers of Afghanistan refugees globally. It is also important to note that migratory flows are often fluid involving settlement in neighbouring countries, return to Afghanistan. In many countries, Afghani migrants and refugees face uncertain political situations and have, in recent years, been ‘coerced’ into returning to Afghanistan with much discussion of a ‘return bias’ being evident in official policies. The literature identified in this report (a mix of academic, humanitarian agency and NGO) is predominantly focused on Pakistan and Iran with a less established evidence base on the scale of Afghan refugee and migrant communities in other countries in the region. . Whilst conflict has been a primary driver of displacement, it has intersected with drought conditions and poor adherence to COVID-19 mitigation protocols. Past efforts to address displacement internationally have affirmed return as the primary objective in relation to durable solutions; practically, efforts promoted improved programming interventions towards creating conditions for sustainable return and achieving improved reintegration prospects for those already returned to Afghanistan.
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Kholoshyn, Ihor, Svitlana Mantulenko, Accola Sharon Joyce, Daniel Sherick, Talgat Uvaliev, and Victoria Vedmitska. Geography of agricultural exports from Ukraine. EDP Sciences, June 2021. http://dx.doi.org/10.31812/123456789/4618.

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Based on the generalization and systematization of scientific and accounting data, the article considers agriculture as one of the leading sectors of the Ukrainian economy, providing 50% of foreign exchange earnings from exports of all goods from the country. The structure and geography of agricultural exports from Ukraine are analyzed. The existing export potential of the studied sector of the national economy and the level of self-sufficiency of Ukraine in agri-food products are revealed. Four specialization types of agricultural exports from Ukrainian regions are established: areas with dominance in export of animal origin products; areas with a dominance in the export of plant products; areas with a predominance in the export of animal or vegetable origin fats and oils; area, with mixed export specialization. Recommendations for the re-profiling of agricultural production in a number of the country’s regions in order to increase exports of this product type.
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Kholoshyn, Ihor, Svitlana Mantulenko, Accola Sharon Joyce, Daniel Sherick, Talgat Uvaliev, and Victoria Vedmitska. Geography of agricultural exports from Ukraine. EDP Sciences, June 2021. http://dx.doi.org/10.31812/123456789/4618.

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Based on the generalization and systematization of scientific and accounting data, the article considers agriculture as one of the leading sectors of the Ukrainian economy, providing 50% of foreign exchange earnings from exports of all goods from the country. The structure and geography of agricultural exports from Ukraine are analyzed. The existing export potential of the studied sector of the national economy and the level of self-sufficiency of Ukraine in agri-food products are revealed. Four specialization types of agricultural exports from Ukrainian regions are established: areas with dominance in export of animal origin products; areas with a dominance in the export of plant products; areas with a predominance in the export of animal or vegetable origin fats and oils; area, with mixed export specialization. Recommendations for the re-profiling of agricultural production in a number of the country’s regions in order to increase exports of this product type.
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Carney, Nancy, Tamara Cheney, Annette M. Totten, Rebecca Jungbauer, Matthew R. Neth, Chandler Weeks, Cynthia Davis-O'Reilly, et al. Prehospital Airway Management: A Systematic Review. Agency for Healthcare Research and Quality (AHRQ), June 2021. http://dx.doi.org/10.23970/ahrqepccer243.

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Objective. To assess the comparative benefits and harms across three airway management approaches (bag valve mask [BVM], supraglottic airway [SGA], and endotracheal intubation [ETI]) by emergency medical services in the prehospital setting, and how the benefits and harms differ based on patient characteristics, techniques, and devices. Data sources. We searched electronic citation databases (Ovid® MEDLINE®, CINAHL®, the Cochrane Central Register of Controlled Trials, the Cochrane Database of Systematic Reviews, and Scopus®) from 1990 to September 2020 and reference lists, and posted a Federal Register notice request for data. Review methods. Review methods followed Agency for Healthcare Research and Quality Evidence-based Practice Center Program methods guidance. Using pre-established criteria, studies were selected and dual reviewed, data were abstracted, and studies were evaluated for risk of bias. Meta-analyses using profile-likelihood random effects models were conducted when data were available from studies reporting on similar outcomes, with analyses stratified by study design, emergency type, and age. We qualitatively synthesized results when meta-analysis was not indicated. Strength of evidence (SOE) was assessed for primary outcomes (survival, neurological function, return of spontaneous circulation [ROSC], and successful advanced airway insertion [for SGA and ETI only]). Results. We included 99 studies (22 randomized controlled trials and 77 observational studies) involving 630,397 patients. Overall, we found few differences in primary outcomes when airway management approaches were compared. • For survival, there was moderate SOE for findings of no difference for BVM versus ETI in adult and mixed-age cardiac arrest patients. There was low SOE for no difference in these patients for BVM versus SGA and SGA versus ETI. There was low SOE for all three comparisons in pediatric cardiac arrest patients, and low SOE in adult trauma patients when BVM was compared with ETI. • For neurological function, there was moderate SOE for no difference for BVM compared with ETI in adults with cardiac arrest. There was low SOE for no difference in pediatric cardiac arrest for BVM versus ETI and SGA versus ETI. In adults with cardiac arrest, neurological function was better for BVM and ETI compared with SGA (both low SOE). • ROSC was applicable only in cardiac arrest. For adults, there was low SOE that ROSC was more frequent with SGA compared with ETI, and no difference for BVM versus SGA or BVM versus ETI. In pediatric patients there was low SOE of no difference for BVM versus ETI and SGA versus ETI. • For successful advanced airway insertion, low SOE supported better first-pass success with SGA in adult and pediatric cardiac arrest patients and adult patients in studies that mixed emergency types. Low SOE also supported no difference for first-pass success in adult medical patients. For overall success, there was moderate SOE of no difference for adults with cardiac arrest, medical, and mixed emergency types. • While harms were not always measured or reported, moderate SOE supported all available findings. There were no differences in harms for BVM versus SGA or ETI. When SGA was compared with ETI, there were no differences for aspiration, oral/airway trauma, and regurgitation; SGA was better for multiple insertion attempts; and ETI was better for inadequate ventilation. Conclusions. The most common findings, across emergency types and age groups, were of no differences in primary outcomes when prehospital airway management approaches were compared. As most of the included studies were observational, these findings may reflect study design and methodological limitations. Due to the dynamic nature of the prehospital environment, the results are susceptible to indication and survival biases as well as confounding; however, the current evidence does not favor more invasive airway approaches. No conclusion was supported by high SOE for any comparison and patient group. This supports the need for high-quality randomized controlled trials designed to account for the variability and dynamic nature of prehospital airway management to advance and inform clinical practice as well as emergency medical services education and policy, and to improve patient-centered outcomes.
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Evans, Julie, Kendra Sikes, and Jamie Ratchford. Vegetation classification at Lake Mead National Recreation Area, Mojave National Preserve, Castle Mountains National Monument, and Death Valley National Park: Final report (Revised with Cost Estimate). National Park Service, October 2020. http://dx.doi.org/10.36967/nrr-2279201.

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Vegetation inventory and mapping is a process to document the composition, distribution and abundance of vegetation types across the landscape. The National Park Service’s (NPS) Inventory and Monitoring (I&M) program has determined vegetation inventory and mapping to be an important resource for parks; it is one of 12 baseline inventories of natural resources to be completed for all 270 national parks within the NPS I&M program. The Mojave Desert Network Inventory & Monitoring (MOJN I&M) began its process of vegetation inventory in 2009 for four park units as follows: Lake Mead National Recreation Area (LAKE), Mojave National Preserve (MOJA), Castle Mountains National Monument (CAMO), and Death Valley National Park (DEVA). Mapping is a multi-step and multi-year process involving skills and interactions of several parties, including NPS, with a field ecology team, a classification team, and a mapping team. This process allows for compiling existing vegetation data, collecting new data to fill in gaps, and analyzing the data to develop a classification that then informs the mapping. The final products of this process include a vegetation classification, ecological descriptions and field keys of the vegetation types, and geospatial vegetation maps based on the classification. In this report, we present the narrative and results of the sampling and classification effort. In three other associated reports (Evens et al. 2020a, 2020b, 2020c) are the ecological descriptions and field keys. The resulting products of the vegetation mapping efforts are, or will be, presented in separate reports: mapping at LAKE was completed in 2016, mapping at MOJA and CAMO will be completed in 2020, and mapping at DEVA will occur in 2021. The California Native Plant Society (CNPS) and NatureServe, the classification team, have completed the vegetation classification for these four park units, with field keys and descriptions of the vegetation types developed at the alliance level per the U.S. National Vegetation Classification (USNVC). We have compiled approximately 9,000 existing and new vegetation data records into digital databases in Microsoft Access. The resulting classification and descriptions include approximately 105 alliances and landform types, and over 240 associations. CNPS also has assisted the mapping teams during map reconnaissance visits, follow-up on interpreting vegetation patterns, and general support for the geospatial vegetation maps being produced. A variety of alliances and associations occur in the four park units. Per park, the classification represents approximately 50 alliances at LAKE, 65 at MOJA and CAMO, and 85 at DEVA. Several riparian alliances or associations that are somewhat rare (ranked globally as G3) include shrublands of Pluchea sericea, meadow associations with Distichlis spicata and Juncus cooperi, and woodland associations of Salix laevigata and Prosopis pubescens along playas, streams, and springs. Other rare to somewhat rare types (G2 to G3) include shrubland stands with Eriogonum heermannii, Buddleja utahensis, Mortonia utahensis, and Salvia funerea on rocky calcareous slopes that occur sporadically in LAKE to MOJA and DEVA. Types that are globally rare (G1) include the associations of Swallenia alexandrae on sand dunes and Hecastocleis shockleyi on rocky calcareous slopes in DEVA. Two USNVC vegetation groups hold the highest number of alliances: 1) Warm Semi-Desert Shrub & Herb Dry Wash & Colluvial Slope Group (G541) has nine alliances, and 2) Mojave Mid-Elevation Mixed Desert Scrub Group (G296) has thirteen alliances. These two groups contribute significantly to the diversity of vegetation along alluvial washes and mid-elevation transition zones.
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Kira, Beatriz, Rutendo Tavengerwei, and Valary Mumbo. Points à examiner à l'approche des négociations de Phase II de la ZLECAf: enjeux de la politique commerciale numérique dans quatre pays d'Afrique subsaharienne. Digital Pathways at Oxford, March 2022. http://dx.doi.org/10.35489/bsg-dp-wp_2022/01.

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Realities such as the COVID-19 pandemic have expedited the move to online operations, highlighting the undeniable fact that the world is continuing to go digital. This emphasises the need for policymakers to regulate in a manner that allows them to harness digital trade benefits while also avoiding associated risk. However, given that digital trade remains unco-ordinated globally, with countries adopting different approaches to policy issues, national regulatory divergence on the matter continues, placing limits on the benefits that countries can obtain from digital trade. Given these disparities, ahead of the African Continental Free Trade Area (AfCFTA) Phase II Negotiations, African countries have been considering the best way to harmonise regulations on issues related to digital trade. To do this effectively, AfCFTA members need to identify where divergencies exist in their domestic regulatory systems. This will allow AfCFTA members to determine where harmonisation is possible, as well as what is needed to achieve such harmonisation. This report analyses the domestic regulations and policies of four focus countries – South Africa, Nigeria, Kenya and Senegal – comparing their regulatory approaches to five policy issues: i) regulation of online transactions; ii) cross-border data flows, data localisation, and personal data protection; iii) access to source code and technology transfer; iv) intermediary liability; and v) customs duties on electronic transmissions. The study highlights where divergencies exist in adopted approaches, indicating the need for the four countries – and AfCFTA members in general – to carefully consider the implications of the divergences, and determine where it is possible and beneficial to harmonise approaches. This was intended to encourage AfCFTA member states to take ownership of these issues and reflect on the reforms needed. As seen in Table 1 below, the study shows that the four countries diverge on most of the five policy issues. There are differences in how all four countries regulate online transactions – that is, e-signatures and online consumer protection. Nigeria was the only country out of the four to recognise all types of e-signatures as legally equivalent. Kenya and Senegal only recognise specific e-signatures, which are either issued or validated by a recognised institution, while South Africa adopts a mixed approach, where it recognises all e-signatures as legally valid, but provides higher evidentiary weight to certain types of e-signatures. Only South Africa and Senegal have specific regulations relating to online consumer protection, while Nigeria and Kenya do not have any clear rules. With regards to cross border data flows, data localisation, and personal data protection, the study shows that all four focus countries have regulations that consist of elements borrowed from the European Union (EU) General Data Protection Regulation (GDPR). In particular, this was regarding the need for the data subject's consent, and also the adequacy requirement. Interestingly, the study also shows that South Africa, Kenya and Nigeria also adopt data localisation measures, although at different levels of strictness. South Africa’s data localisation laws are mostly imposed on data that is considered critical – which is then required to be processed within South African borders – while Nigeria requires all data to be processed and stored locally, using local servers. Kenya imposes data localisation measures that are mostly linked to its priority for data privacy. Out of the four focus countries, Senegal is the only country that does not impose any data localisation laws. Although the study shows that all four countries share a position on customs duties on electronic transmissions, it is also interesting to note that none of the four countries currently have domestic regulations or policies on the subject. The report concludes by highlighting that, as the AfCFTA Phase II Negotiations aim to arrive at harmonisation and to improve intra-African trade and international trade, AfCFTA members should reflect on their national policies and domestic regulations to determine where harmonisation is needed, and whether AfCFTA is the right platform for achieving this efficiently.
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Lenhardt, Amanda. Evidence on the Effectiveness of Covid-19 International Travel Measures. Institute of Development Studies, January 2022. http://dx.doi.org/10.19088/k4d.2022.054.

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Evidence on the effectiveness of travel measures to prevent or slow the spread of Covid-19 and guidance on how and when to apply these measures is limited and the results are mixed. Given the social and economic disruptions that these measures can have, and their potential adverse effects on preventing the spread of the disease, WHO among others have cautioned implementing measures that are not supported by robust evidence. The scope of the search for this report was broad, covering all reported international travel restrictions to contain or slow the spread of Covid-19 and without geographical limitations. The results are therefore more general than restriction- or country-specific and more targeted studies may be omitted from the search due to these wide search parameters. There is general agreement across the literature that some form of travel restriction in the early stages of a disease or variant spread can lead to a slowing of the rate of infections in countries yet to be affected. The majority of studies conducted on travel restrictions adopt a modelling approach, and a systematic review conducted in December 2020 concluded that the quality of observational studies was low to very low (Bou-Karroum et al., 2021). Determining the specific effects of different travel measures is difficult as many studies examine the effects of combined measures. Few studies separate different types of restrictions and much of the modelling on international transmission rates uses data on the movements of people as a proxy for travel restrictions, therefore limiting the ability to observe how measures were implemented except for the timing of measures at different points in the pandemic. Limited country-level evidence was identified for this report and few global studies examine contextual factors that might affect the effectiveness of travel restriction measures. A targeted search for evidence on the effects of travel restrictions on different variants of Covid-19 did not reveal any studies making this distinction.
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Rodriguez, Dirk, and Cameron Williams. Channel Islands Nation Park: Terrestrial vegetation monitoring annual report - 2016. National Park Service, August 2022. http://dx.doi.org/10.36967/2293561.

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This report presents the data collected in 2016 as part of the long-term terrestrial vegetation monitoring program at Channel Islands National Park. The purposes of the monitoring program are to document the long-term trends in the major vegetation communities within the park. The data collected are from 30 m point-line intercept transects. In the past, each transect was sampled annually. However, beginning in 2012 the program began adding randomly located transects to improve the representativeness of the sampling, and transitioned to a rotating panel design. Now only a core subset of the transects are read annually. Non-core transects are assigned to one of four panels, and those transects are read only once every four years. A summary analysis of the 2016 data shows that: 165 transects were read. The 165 transects were distributed across all five islands: Santa Rosa Island (n = 87), Santa Cruz Island (n = 33), Santa Barbara Island (n = 18), Anacapa Island (n = 9) and San Miguel Island (n = 11). Relative native plant cover averaged 63% across all islands and sampled communities while absolute native plant cover averaged 32%. Among plant communities, relative percent native cover ranged from a low of 1% in seablite scrub to a high of 98% in oak woodland. In general, the number of vegetation data points recorded per transect positively correlates with average rainfall, which is reflected in the number of “hits” or transect points intersecting vegetation. When precipitation declined there is a corresponding drop in the number of hits. In 2016, however this was not the case. Even though rainfall increased as compared to the previous 4 years (18.99 inches in 2016 vs an average of 6.32 for the previous 4 years), the average number of hits was only 64. To put this into perspective, the highest average number of hits was 240 in 1993, an El Niño year of high precipitation. The number of vegetation communities sampled varied by island with the larger islands having more communities. In 2016, there were 15 communities sampled on Santa Rosa Island, 12 communities on Santa Cruz Island, 7 communities on San Miguel Island, 7 communities on Santa Barbara Island, and 7 communities on Anacapa Island. Twenty-six vegetation types were sampled in 2016. Of these, 13 occurred on more than one island. The most commonly shared community was Valley/Foothill grassland which was found in one form or another on all five islands within the park. The next most commonly shared communities were coastal sage scrub and coastal scrub, which were found on four islands. Coastal bluff scrub and coreopsis scrub were monitored on three islands. Four communities—ironwood, mixed woodland, oak woodland, riparian, and seacliff scrub—were monitored on two islands, and 12 communities—Torrey pine woodland, shrub savannah, seablite scrub, Santa Cruz Island pine, perennial iceplant, lupine scrub, fennel, coastal strand, coastal marsh, cactus scrub, boxthorn scrub, barren, and Baccharis scrub—were each monitored on one island.
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Blaisdell, George L., Terry D. Melendy, and Marin N. Blaisdell. Ballistic protection using snow. U.S. Army Engineer Research and Development Center, May 2022. http://dx.doi.org/10.21079/11681/44360.

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Small (5.56 mm, 7.62 mm and 9 mm) and medium (12.7 mm) arms rounds were fired at snow-filled 1.5m cubic gabions in a mid-winter condition in Fairbanks, Alaska. The rounds were excavated and penetration by each ammunition type was measured. A distribution and average of penetration depth was determined. All 320 rounds fired were captured within 1.5m after entering the snow barrier. Comparison with published models of ballistics penetration of snow showed mixed results with several matching our data within 10% and all but one within 32%. However, most of these models are simplistic in that they accommodate limited variables and therefore may not be expected to perform well in all settings. We conclude that snow-based ballistics protection structures can be quickly and efficiently erected in suitable environments and with minimal size, can provide reliable protection against small and medium arms fire.
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10

Dias, Rita, Miguel Rocha, Lucía Manrique, Sílvia Fraga, and Paula Meireles. HIV Prevention Combination Among Men who have Sex with Men: a scoping review protocol. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, October 2022. http://dx.doi.org/10.37766/inplasy2022.10.0071.

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Review question / Objective: To map the evidence regarding the combination of prevention strategies used by MSM to manage their HIV risk, assessing how this has been studied and how HIV prevention strategies are being combined by MSM. Eligibility criteria: We will include published scientific documents found through the search expression in the Web of Science, PubMed, and Scopus databases, about HIV prevention in HIV negative MSM. Studies about non-human animals and females will be excluded, as well as non-scientific documents and reports, editorials, opinion papers, comments, revisions, or journalistic material. Documents with no original data, such as reviews and systematic reviews, will also be excluded. Randomized controlled trials and control clinical trials will also be excluded since this type of study does not answer the scoping review questioning. Both quantitative and qualitative, as well as mixed-methods studies, will be included. The specificities of these studies will be properly acknowledged.
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