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The demographic structure of Latin American countries (LAC) is fast approaching that of developing countries, and the predicted prevalence of dementia in the former already exceeds the latter. Dementia has been declared a global challenge, yet regions around the world show differences in both the nature and magnitude of such a challenge. This article provides evidence and insights on barriers which, if overcome, would enable the harmonization of strategies to tackle the dementia challenge in LAC. First, we analyze the lack of available epidemiologic data, the need for standardizing clinical practice and improving physician training, and the existing barriers regarding resources, culture, and stigmas. We discuss how these are preventing timely care and research. Regarding specific health actions, most LAC have minimal mental health facilities and do not have specific mental health policies or budgets specific to dementia. In addition, local regulations may need to consider the regional context when developing treatment and prevention strategies. The support needed nationally and internationally to enable a smooth and timely transition of LAC to a position that integrates global strategies is highlighted. We focus on shared issues of poverty, cultural barriers, and socioeconomic vulnerability. We identify avenues for collaboration aimed to study unique populations, improve valid assessment methods, and generate opportunities for translational research, thus establishing a regional network. The issues identified here point to future specific actions aimed at tackling the dementia challenge in LAC.

A model of a reference wind farm is presented. It considers the wind and wave climatologies for a specific site and derives two different wind farm layouts. The layouts are then examined in terms of effective wake turbulence intensity for a given climatology, and a model for the influence on capital cost is proposed. An electrical design is suggested, and cable losses are calculated together with the energy yield. An operation and maintenance model is suggested, and the associated operating cost determined. The models are then combined in a term for levelized cost of energy allowing layouts to be compared. The data and models are available online and may serve as a baseline for benchmarking.

The last decades have witnessed fundamental changes in our understanding of both brain and language. In neurosciences, the static localisationist view of a 1:1 correspondence between circumscribed brain areas and specific cognitive functions gave way to a dynamic interaction of multiple networks, in which the same function can be distributed among many areas and the same area can be part of different networks. In parallel, the concept of language as an autonomous, “informationally encapsulated” module has been superseded by the notion of widely distributed language-related brain networks, going well beyond the traditional language areas and interacting with other aspects of cognition. These advances in the neuroscience of language have been of particular importance for our understanding of bilingualism. Firstly, we came to realise that different languages of a multilingual person cannot be reduced to static representations in isolated brain areas but are subject to parallel activation, inhibition, switching and monitoring within the same brain networks. Secondly, the tension between this parallel activation and a selective output necessary for successful communication constitutes a permanent training for frontal-executive functions. Accordingly, the cognitive effects of bilingualism transcend language itself, leading to a better performance on many executive tasks, particularly those requiring inhibition and switching. This mechanism offers an explanation for a number of recent empirical findings, in which bilinguals were show to be more resistant to cognitive ageing than monolinguals, develop dementia ca. 4 years later and show a significantly better cognitive recovery after stroke.

The main goal of this paper is to establish the present state of the art for wind farm control. The control area that will be focused on is the mechanical/aerodynamic part, which includes the wind turbines, their power production, fatigue and wakes affecting neighbouring wind turbines. The sub-objectives in this area of research are as follows: (i) maximizing the total wind farm power production; (ii) following a reference for the total wind farm active power; and (iii) doing this in a manner that minimizes fatigue loading for the wind turbines in the farm. Each of these sub-objectives is discussed, including the following important control issues: choice of input and output, control method and modelling used for controller design and simulation. The available literature from industry is also considered. Finally, a conclusion is presented discussing the established results, open challenges and necessary research.

Offshore wind farms are complex systems, influenced by boththe environment (e.g. wind, waves, current and seabed) andthe design characteristics of the equipment available for installation(e.g. turbine type, foundations, cabling and distanceto shore). These aspects govern the capital and operating expenditures,which, along with the energy produced, determinethe cost of energy. A better system-level understanding ofwind farms is hence of critical importance to the wind-energyindustry.

This paper introduce a new fault-tolerant control (FTC) method for discrete-time linear parameter varying (LPV) systems. Fault-tolerance is achieved without redesigning the nominal controller by inserting a reconfiguration block between the plant and the nominal controller. The reconfiguration block is realized by a virtual actuator and a virtual sensor. The signals from the faulty system are transformed such that its behavior is similar to that of the nominal system from the viewpoint of the controller. It transforms the controller output for the faulty system preserving the stability and performance. Input-to-state stabilizing LPV gains of the virtual actuator and sensor are obtained by solving LMIs. The gains guarantees the input-to-state stability (ISS) of the closed-loop reconfigured system. Moreover, we obtain performances in terms of the ISS gains for the virtual actuator, the virtual sensor, and their interconnection. Minimizing these performances is formulated as convex optimization problems subject to LMI constraints. The effectiveness of the method is demonstrated via a numerical example and stator current control of an induction motor.