The threat of predictable and unpredictable pain: Differential effects on central nervous system processing?

Central nervous system performance is disrupted by pain and by the threat of pain. It is not known whether disruption caused by the threat of pain is dependent on the likelihood of pain occurring. We hypothesised that when a painful stimulus is possible but unpredictable central nervous system performance is reduced, but when the pain is predictable and unavoidable it is not. Sixteen healthy subjects performed a reaction time task during predictable and unpredictable conditions (100% and 50% probability of pain, respectively). Group data showed increased reaction time with the threat of pain by 50 ms (95% Cl 16 to 83 ms) for the predictable condition and 46 ms (95% CI 12 to 80 ms) for the unpredictable condition (p < 0.01 for both), but there was no difference between predictable and unpredictable conditions (p = 0.41). However, individual data showed that there was a differential effect in 75% of subjects (p < 0.05 for all) and that there was a greater effect of predictable pain for some subjects and a greater effect of unpredictable pain for others. Reaction time was related to reported anxiety (r = 0.49, p = 0.02 for both conditions). The predictability of a painful stimulus may have a differential effect on central nervous system performance within individuals, but anxiety about the impending pain appears to be important in determining this effect.

Microbial Fuel Cells: Methodology and Technology

Microbial fuel cell (MFC) research is a rapidly evolving field that lacks established terminology and methods for the analysis of system performance. This makes it difficult for researchers to compare devices on an equivalent basis. The construction and analysis of MFCs requires knowledge of different scientific and engineering fields, ranging from microbiology and electrochemistry to materials and environmental engineering. DescribingMFCsystems therefore involves an understanding of these different scientific and engineering principles. In this paper, we provide a review of the different materials and methods used to construct MFCs, techniques used to analyze system performance, and recommendations on what information to include in MFC studies and the most useful ways to present results.

Feasibility of controlling nitrification in predenitrification plants using DO, pH and ORP sensors

Experimental studies were carried out on a bench-scale nitrogen removal system with a predenitrification configuration to gain insights into the spatial and temporal variations of DO, pH and ORP in such systems. It is demonstrated that these signals correlate strongly with the operational states of the system, and could therefore be used as system performance indicators. The DO concentration in the first aerobic zone, when receiving constant aeration, and the net pH change between the last and first aerobic zones display strong correlations with the influent ammonia concentration for the domestic wastewater used in this study. The pH profile along the aerobic zones gives good indication on the extent of nitrification. The experimental results also showed a good correlation between ORP values in the last aerobic zone and effluent ammonia and nitrate concentrations, provided that DO in this zone is controlled at a constant level. These results suggest that the DO, pH and ORP sensors could potentially be used as alternatives to the on-line nutrient sensors for the control of continuous systems. An idea of using a fuzzy inference system to make an integrated use of these signals for on-line aeration control is presented and demonstrated on the bench-scale system with promising results. The use of these sensors has to date only been demonstrated in intermittent systems, such as sequencing batch reactor systems.

Optimal design of a regenerative dynamic dynamometer using genetic algorithms

This paper presents an approach for optimal design of a fully regenerative dynamic dynamometer using genetic algorithms. The proposed dynamometer system includes an energy storage mechanism to adaptively absorb the energy variations following the dynamometer transients. This allows the minimum power electronics requirement at the mains power supply grid to compensate for the losses. The overall dynamometer system is a dynamic complex system and design of the system is a multi-objective problem, which requires advanced optimisation techniques such as genetic algorithms. The case study of designing and simulation of the dynamometer system indicates that the genetic algorithm based approach is able to locate a best available solution in view of system performance and computational costs.