LGBTQIA+ patients are an important patient population to highlight when discussing urban emergency medicine. There are a multitude of terms regarding gender expression and identity that emergency medicine providers should familiarize themselves with if they plan on taking care of this patient population. Within the LGBTQIA+ population, there are specific medical and psychological issues that are relevant to each subgroup. Providers are not expected to know everything about their patients, but they must remember to remain open-minded and non-judgmental as they take care of everyone with precision and dedication. If a provider feels that the patient needs help in ways they cannot be of service, then the provider should be able to point the patient in the right direction via resources and referrals.

This concluding chapter summarizes the main insights gained in this book. Emotion theories show substantial agreement in their working definition of emotions in terms of the typical and apparent properties of emotions (intensional format) and the prototypical emotions (divisio format) they want to see explained. Their constitutive explanations range from narrow to broad. The most pronounced differences reside in the causal-mechanistic explanations they propose. Five fault lines stand out centering on the questions whether (a) emotions are caused by a stimulus-driven or goal-directed process, (b) the origin of feelings is central or peripheral, (c) emotions are caused by special-purpose or general-purpose mechanisms, (d) emotions do or do not form a scientific set, and (e) the variety in the set of emotions is best captured with discrete emotions or dimensions. The chapter also examines whether there is any hope for a unifying theory integrating several theories or a general framework for conducting emotion research. Starting from a unifying theory that combines a maximum of mechanisms, a pruning stage is required based on empirical research. A speculation about the result of this pruning exercise, inspired by the goal-directed theory advocated in this book, is presented, ultimately leading to the demystification of emotion.

This chapter discusses evolutionary theories in psychology (Ekman & Codaro, 2011; Levenson, 2011; Panksepp, 2012; Keltner, Tracy, et al., 2019), also known as motivational theories in philosophy. Building further on the bridging work of McDougall (1908), these theories propose the following causal-mechanistic explanation of emotion. Stimuli activate affect programs (another word for instincts specific to emotions), which in turn give rise to somatic and (subtle and coarse) motor responses and feelings. Some evolutionary theories are hybrids with stimulus evaluation theories in that they postulate that the affect programs is preceded by a stimulus evaluation process. The pure emotions caused by affect programs get mixed with learning and computation, which create flexibility on the input side (by elaborating the number of stimuli that can trigger affect programs) and on the output side (via emotion regulation and planning). Evolutionary theories account for the properties of ontogenetic and phylogenetic continuity, phenomenality, bodily aspect, heat, control precedence, and irrationality, but the non-hybrid version has difficulty accounting for the right kind of (world-directed) Intentionality. Evolutionary theories partition the set of emotions into discrete emotions. Empirical evidence pro and contra the existence of affect programs is discussed.

Lagrangian discrete models are the most ancient and still most effective models used in mechanics to predict the behavior of complex systems. Their structure is presented here, in a very classical way, which follows the classical presentation given by Levi-Civita. Their flexibility is highlighted, together with their capacity to be used in very efficient numerical codes. They must be regarded as a preferred tool also in the formulation of problems in the theory of metamaterials.

The need for this chapter arises once we start considering the realistic case of more than one variate at a time, the multivariate case. We have already started dealing with this topic (in disguise) in the introductions to conditioning and mixing in Chapters 1 and 2, and in some of the exercises using these ideas in Chapter 4. Joint distributions are defined, and we explain their relation to the univariate distributions seen earlier and more generally to the distribution of subsets (marginal distributions). Joint densities are also defined. The independence of variates is defined in terms of their joint distribution. We also introduce the concept of copulas, linking the joint distribution to its marginals.