The existence of minimizers is proven using measure-theoretic tools.

Propensity scores are a statistical method for adjusting for baseline differences between study groups. The scores are based on the probability of a subject being in a particular group, conditional on that subject’s values on those independent variables though to influence group membership. Propensity scores with multivariable analysis produces a better adjustment for baseline differences than simply including potential confounders in a multivariable model predicting outcome. Propensity scores are also particularly helpful when outcomes are rare and the proportions of subjects in the independent groups are relatively equal. Another advantage of propensity scores is that they make no assumptions about the relationship between the individual confounders and outcome. The adequacy of a propensity score is judged by whether there is sufficient overlap between the groups and whether it balances the covariates.

There are four major ways you can use propensity scores: matching, weighting, stratified, as a covariate in a model predicting outcome.

This chapter examines the complex diplomacy between the United States and Israel during the administration of President George H. W. Bush and Prime Minister Yitzhak Shamir, with a particular focus on the road to the Madrid Peace Conference in 1991. It argues that US Secretary of State James Baker ultimately played a pivotal role in shaping the negotiations. Drawing on newly available archival materials from the George H. W. Bush Presidential Library, the Israel State Archives, and the American Jewish Archives, the chapter presents a detailed account of the tensions that characterized the period. It explores how emotions - alongside interests and strategy - shaped diplomatic behavior, particularly over the peace process and the request for US loan guarantees to support the absorption of Soviet Jewish immigrants in Israel. The chapter also investigates the parallel strains in US relations with American Jewish organizations. In contrast to accounts that treat this period as an aberration in the U.S.-Israel "special relationship," the chapter shows how it encapsulated the recurring frictions and deep-rooted affinities that have long defined the alliance. It also reflects on the broader historiographical and methodological implications of using newly declassified sources to reassess well-known diplomatic episodes.1

This book is about the potential of social work, and in particular the potential of critical social work. It is about what social work is, what social work can be and, from a critical perspective, what social work should be. We use the word ‘potential’ quite deliberately, as it implies that there are elements of uncertainty in endeavouring to make social work critical that are yet to be fully realised and never guaranteed. Yet, in the current context, the values and vision of critical social work are perhaps more relevant and important than ever before.

Although a natural process, human actions and extreme climatic events can accentuate slope instability, leading to disastrous slope failures and loss of life, like the one that occurred in the Brazilian city of Petrópolis on February 17, 2022. Over 200 people died in the mudflows, caused by intense rainfall (258 mm in three hours) and the deforestation of upslope areas. Understanding how and why materials move downslope helps geomorphologists to predict where and when future mass movement events may occur.

Except for perhaps volcanic eruptions and earthquakes, the most impressive (and deadly) geomorphic “events” involve the downslope movement of rock, debris, and sediment – referred to as mass movements because the material moves en masse. In their simplest sense, mass movements represent the downslope transport of rock and soil materials. Examples range from massive, fast-moving landslides and debris flows, to the inexorably slow process of soil creep.

Otto Niemeyer imposed the British model of central banking across the “formal” and “informal” parts of the empire. In the 1930s, he conducted a series of overseas advisory missions, during which he promoted the principles of economic orthodoxy, such as balanced budgets, free trade, and fixed-exchange rates. Through his negotiations, he persuaded foreign governments to accept his policy prescriptions by demonstrating how they aligned with prevailing national interests. While Australia and New Zealand both aimed to secure their financial independence, Brazil and Argentina sought to establish their authority after political revolutions. It was a combination of factors related to state legitimacy, economic stagnation, and interwar expertise that shaped the outcome of the Niemeyer missions.

The choice of multivariable model depends primarily on the type of outcome variable. Use multiple linear regression and analysis of variance for interval outcomes, multiple logistic regression and log-binomial regression with dichotomous outcomes, proportion odds regression with ordinal outcomes, multinomial logistic regression for nominal outcomes, proportional hazards analysis for time to outcome, Poisson regression and negative binomial regression for counts and for incidence rates. Each model has a different set of underlying assumptions. All of the models assume that there is only one observation of outcome for each subject.

As we think and act, the brain is constantly producing Big Data in the firing of its neurons and in the connections that are strengthened and weakened. This chapter discusses how we can study the brain and the Big Data that it creates. First, we discuss how clever behavioral tasks, looking at development and other species, and natural variation across people are our first tools for understanding the brain. Next, we delve into describing several popular brain imaging methods – direct recording, electroencephalography, magnetoencephalography, magnetic resonance imaging, and a few others. We discuss how to interpret the Big Data shown by brain maps, and some Big Data methods like multiple comparisons correction to consider when viewing this data. Finally, we end the chapter discussing the ethical question of whether such neuroimaging allows mindreading.

The powerful methods of dimensional analysis are introduced via the pi-theorem. The reader discovers that many of the results obtained in Chapters 3 and 4 can be arrived at using dimensional analysis alone. These include drag and pipe flow. Dynamical similarity is explained.

Taking E. M. Forster and Virginia Woolf as case studies, this chapter examines the relationships – both clear and opaque – among their lives, their writings, and social progress. From a bird’s-eye view, these interrelations seem clear and linear: sexual tolerance and freedom have expanded since Bloomsbury’s time, signal forms of progress to which these authors’ unconventional loves and sensitive writings contributed. But seen from more intimate angles, these interrelations betray lacunae and discontinuities worthy of modernism. Their lives and writings were often not in sync: they wrote about things they had not experienced (Forster on sexual intimacy), feared things that did not befall them (Woolf foreseeing marriage as a catastrophe), avoided taboo topics (Forster on homosexuality), or failed – due to their lack of vocabulary – to describe avant-garde lifestyles they were enjoying (Woolf on urban tribes, Forster on polyamory). For all their articulateness, it would require later generations, including Bloomsbury’s respondents such as Angelica Bell and Michael Cunningham, to clarify how Woolf and Forster contributed – for good or ill – to the ever-evolving phenomena of intimacy.

This chapter describes the important role of artificial intelligence (AI) in Big Data psychology research. First, we discuss the main goals of AI, and then delve into an example of machine learning and what is happening under the hood. The chapter then describes the Perceptron, a classic simple neural network, and how this has grown into deep learning AI which has become increasingly popular in recent years. Deep learning can be used both for prediction and generation, and has a multitude of applications for psychology and neuroscience. This chapter concludes with the ethical quandaries around fake data generated by AI and biases that exist in how we train systems, as well as some exciting clinical applications of AI relevant to psychology and neuroscience.

We define and explain the basic objects and structures of a causal fermion system.