The Trier Social Stress Test: principles and practice

Researchers interested in the neurobiology of the acute stress response in humans require a valid and reliable acute stressor that can be used under experimental conditions. The Trier Social Stress Test (TSST) provides such a testing platform. It induces stress by requiring participants to make an interview-style presentation, followed by a surprise mental arithmetic test, in front of an interview panel who do not provide feedback or encouragement. In this review, we outline the methodology of the TSST, and discuss key findings under conditions of health and stress-related disorder. The TSST has unveiled differences in males and females, as well as different age groups, in their neurobiological response to acute stress. The TSST has also deepened our understanding of how genotype may moderate the cognitive neurobiology of acute stress, and exciting new inroads have been made in understanding epigenetic contributions to the biological regulation of the acute stress response using the TSST. A number of innovative adaptations have been developed which allow for the TSST to be used in group settings, with children, in combination with brain imaging, and with virtual committees. Future applications may incorporate the emerging links between the gut microbiome and the stress response. Future research should also maximise use of behavioural data generated by the TSST. Alternative acute stress paradigms may have utility over the TSST in certain situations, such as those that require repeat testing. Nonetheless, we expect that the TSST remains the gold standard for examining the cognitive neurobiology of acute stress in humans.

Average-case analysis of power consumption in embedded systems

Power efficiency is one of the most important constraints in the design of embedded systems since such systems are generally driven by batteries with limited energy budget or restricted power supply. In every embedded system, there are one or more processor cores to run the software and interact with the other hardware components of the system. The power consumption of the processor core(s) has an important impact on the total power dissipated in the system. Hence, the processor power optimization is crucial in satisfying the power consumption constraints, and developing low-power embedded systems. A key aspect of research in processor power optimization and management is “power estimation”. Having a fast and accurate method for processor power estimation at design time helps the designer to explore a large space of design possibilities, to make the optimal choices for developing a power efficient processor. Likewise, understanding the processor power dissipation behaviour of a specific software/application is the key for choosing appropriate algorithms in order to write power efficient software. Simulation-based methods for measuring the processor power achieve very high accuracy, but are available only late in the design process, and are often quite slow. Therefore, the need has arisen for faster, higher-level power prediction methods that allow the system designer to explore many alternatives for developing powerefficient hardware and software. The aim of this thesis is to present fast and high-level power models for the prediction of processor power consumption. Power predictability in this work is achieved in two ways: first, using a design method to develop power predictable circuits; second, analysing the power of the functions in the code which repeat during execution, then building the power model based on average number of repetitions. In the first case, a design method called Asynchronous Charge Sharing Logic (ACSL) is used to implement the Arithmetic Logic Unit (ALU) for the 8051 microcontroller. The ACSL circuits are power predictable due to the independency of their power consumption to the input data. Based on this property, a fast prediction method is presented to estimate the power of ALU by analysing the software program, and extracting the number of ALU-related instructions. This method achieves less than 1% error in power estimation and more than 100 times speedup in comparison to conventional simulation-based methods. In the second case, an average-case processor energy model is developed for the Insertion sort algorithm based on the number of comparisons that take place in the execution of the algorithm. The average number of comparisons is calculated using a high level methodology called MOdular Quantitative Analysis (MOQA). The parameters of the energy model are measured for the LEON3 processor core, but the model is general and can be used for any processor. The model has been validated through the power measurement experiments, and offers high accuracy and orders of magnitude speedup over the simulation-based method.