Science's Response to CoVID-19

CoVID-19 is a multi-symptomatic disease which has made a global impact due to its ability to spread rapidly, and its relatively high mortality rate. Beyond the heroic efforts to develop vaccines, which we do not discuss herein, the response of scientists and clinicians to this complex problem has reflected the need to detect CoVID-19 rapidly, to diagnose patients likely to show adverse symptoms, and to treat severe and critical CoVID-19. Here we aim to encapsulate these varied and sometimes conflicting approaches and the resulting data in terms of chemistry and biology. In the process we highlight emerging concepts, and potential future applications that may arise out of this immense effort.     

Toward methods for supporting the anticipation-feedback loop in user interface design

Recent research has compared different usability evaluation methods with respect to their effectiveness and efficiency. The paper analyses the impact of different usability evaluation methods on design problem-solving processes of individual designers and evaluators. It is proposed that usability evaluation methods have to be divided into two categories according to their fundamentally different way of supporting the design for usability: (1) guideline-based methods and (2) methods based on the mental generation of scenarios and anticipation of user goals. We present data from an experimental study that shows that these two types of methods entail differences in the perspective-taking processes. Furthermore, the results indicate that the methods have a differential impact on the general problem-solving strategy, i.e. whether to use a top-down, breadth-first or a depth-first approach. Possible implications for the development of techniques that support the design of usable systems are discussed.     

Unconstrained model predictive control and suboptimality estimates for nonlinear continuous-time systems

This paper presents a continuous-time version of recent results on unconstrained nonlinear model predictive control (MPC) schemes. Based on a controllability assumption and a corresponding infinite-dimensional optimization problem, performance estimates and stability conditions are derived in terms of the prediction horizon and the sampling time of the MPC controller. Moreover, improved estimates for small sampling times are discussed and a comparison to the application of the discrete-time results in a sampled-data context is provided.     

Capacity management under uncertainty with inter-process, intra-process and demand interdependencies in high-flexibility environments

In this paper, we develop models for capacity planning within the framework of stochastic processing times and stochastic demand for different process outcomes in high-flexibility environments. We particularly address stochastic interdependencies between processing times for different processes (inter-process correlation), interdependencies between the capacity consumption (task times) of different executions of the same task in a given production stage (intra-process correlation) as well as interdependencies between the demand for different process outcomes. After presenting the base model, we conduct extensive sensitivity analyses and analyze the main relationships between different model variables. We use process and demand data from the financial industry to demonstrate the applicability and relevance of our findings.