Single-molecule dynamic triangulation

A proposal for using single molecules as nanoprobes capable of detecting the trajectory of an elementary charge is discussed in detail. Presented numerical simulations prove that this singlemolecule technique allows determination of a three-dimensional single-electron displacement within a few seconds with an accurocy better than 0.006 nm. Surprisingly, this significantly exceeds the accuracy with which the probe;, molecule itself can be localized (given the same measuring time by means of single-molecule microscopy. It is also shown that the optimal concentration of probe molecules in the vicinity of:the electron (i.e. the concentration which provides the best accuracy of the inferred electron displacement) is of the order of 10(-5) m.

Stochastic dynamic programming for election timing: A game theory approach

In this paper, we consider dynamic programming for the election timing in the majoritarian parliamentary system such as in Australia, where the government has a constitutional right to call an early election. This right can give the government an advantage to remain in power for as long as possible by calling an election, when its popularity is high. On the other hand, the opposition's natural objective is to gain power, and it will apply controls termed as "boosts" to reduce the chance of the government being re-elected by introducing policy and economic responses. In this paper, we explore equilibrium solutions to the government, and the opposition strategies in a political game using stochastic dynamic programming. Results are given in terms of the expected remaining life in power, call and boost probabilities at each time at any level of popularity.

The effects of varying time under tension and volume load on acute neuromuscular responses

The purpose of this study was to examine the effects of different methods of measuring training volume, controlled in different ways, on selected variables that reflect acute neuromuscular responses. Eighteen resistance-trained males performed three fatiguing protocols of dynamic constant external resistance exercise, involving elbow flexors, that manipulated either time-under-tension (TUT) or volume load (VL), defined as the product of training load and repetitions. Protocol A provided a standard for TUT and VL. Protocol B involved the same VL as Protocol A but only 40% concentric TUT; Protocol C was equated to Protocol A for TUT but only involved 50% VL. Fatigue was assessed by changes in maximum voluntary isometric contraction (MVIC), interpolated doublet (ID), muscle twitch characteristics (peak twitch, time to peak twitch, 0.5 relaxation time, and mean rates of force development and twitch relaxation). All protocols produced significant changes (P

A bang-bang PLL employing dynamic gain control for low jitter and fast lock times

Bang-bang phase detector based PLLs are simple to design, suffer no systematic phase error, and can run at the highest speed a process can make a working flip-flop. For these reasons designers are employing them in the design of very high speed Clock Data Recovery (CDR) architectures. The major drawback of this class of PLL is the inherent jitter due to quantized phase and frequency corrections. Reducing loop gain can proportionally improve jitter performance, but also reduces locking time and pull-in range. This paper presents a novel PLL design that dynamically scales its gain in order to achieve fast lock times while improving fitter performance in lock. Under certain circumstances the design also demonstrates improved capture range. This paper also analyses the behaviour of a bang-bang type PLL when far from lock, and demonstrates that the pull-in range is proportional to the square root of the PLL loop gain.

Functional analysis of a real-time protocol for networked control systems

Traditional real-time control systems are tightly integrated into the industrial processes they govern. Now, however, there is increasing interest in networked control systems. These provide greater flexibility and cost savings by allowing real-time controllers to interact with industrial processes over existing communications networks. New data packet queuing protocols are currently being developed to enable precise real-time control over a network with variable propagation delays. We show how one such protocol was formally modelled using timed automata, and how model checking was used to reveal subtle aspects of the control system's dynamic behaviour.

Spatially localized distortions of event time

A fundamental question about the perception of time is whether the neural mechanisms underlying temporal judgements are universal and centralized in the brain or modality specific and distributed []. Time perception has traditionally been thought to be entirely dissociated from spatial vision. Here we show that the apparent duration of a dynamic stimulus can be manipulated in a local region of visual space by adapting to oscillatory motion or flicker. This implicates spatially localized temporal mechanisms in duration perception. We do not see concomitant changes in the time of onset or offset of the test patterns, demonstrating a direct local effect on duration perception rather than an indirect effect on the time course of neural processing. The effects of adaptation on duration perception can also be dissociated from motion or flicker perception per se. Although 20 Hz adaptation reduces both the apparent temporal frequency and duration of a 10 Hz test stimulus, 5 Hz adaptation increases apparent temporal frequency but has little effect on duration perception. We conclude that there is a peripheral, spatially localized, essentially visual component involved in sensing the duration of visual events.

A low-cost, easy-to-use, real-time power system simulator

We present the design rationale and basic workings of a low-cost, easy-to-use power system simulator developed to support investigations into human interface design for a hydropower plant. The power system simulator is based on three important components: models of power system components, a data repository, and human interface elements. Dynamic Data Exchange (DDE) allows simulator components to communicate with each other within the simulator. To construct the modules of the simulator we have combined the advantages of commercial software such as Matlab/Simulink, ActiveX Control, Visual Basic and Excel and integrated them in the simulator. An important advantage of our approach is that further components of the simulator now can be developed independently. An initial assessment of the simulator indicates it is fit for intended purpose.

The impact of supply chain performance measurement systems on dynamic behaviour in supply chains

The amplification of demand variation up a supply chain widely termed ‘the Bullwhip Effect’ is disruptive, costly and something that supply chain management generally seeks to minimise. Originally attributed to poor system design; deficiencies in policies, organisation structure and delays in material and information flow all lead to sub-optimal reorder point calculation. It has since been attributed to exogenous random factors such as: uncertainties in demand, supply and distribution lead time but these causes are not exclusive as academic and operational studies since have shown that orders and/or inventories can exhibit significant variability even if customer demand and lead time are deterministic. This increase in the range of possible causes of dynamic behaviour indicates that our understanding of the phenomenon is far from complete. One possible, yet previously unexplored, factor that may influence dynamic behaviour in supply chains is the application and operation of supply chain performance measures. Organisations monitoring and responding to their adopted key performance metrics will make operational changes and this action may influence the level of dynamics within the supply chain, possibly degrading the performance of the very system they were intended to measure. In order to explore this a plausible abstraction of the operational responses to the Supply Chain Council’s SCOR® (Supply Chain Operations Reference) model was incorporated into a classic Beer Game distribution representation, using the dynamic discrete event simulation software Simul8. During the simulation the five SCOR Supply Chain Performance Attributes: Reliability, Responsiveness, Flexibility, Cost and Utilisation were continuously monitored and compared to established targets. Operational adjustments to the; reorder point, transportation modes and production capacity (where appropriate) for three independent supply chain roles were made and the degree of dynamic behaviour in the Supply Chain measured, using the ratio of the standard deviation of upstream demand relative to the standard deviation of the downstream demand. Factors employed to build the detailed model include: variable retail demand, order transmission, transportation delays, production delays, capacity constraints demand multipliers and demand averaging periods. Five dimensions of supply chain performance were monitored independently in three autonomous supply chain roles and operational settings adjusted accordingly. Uniqueness of this research stems from the application of the five SCOR performance attributes with modelled operational responses in a dynamic discrete event simulation model. This project makes its primary contribution to knowledge by measuring the impact, on supply chain dynamics, of applying a representative performance measurement system.

Useful ideas for exploiting time to engineer representations

Commentary on target article "From simple associations to systematic reasoning: a connectionist representation of rules, variables, and dynamic bindings using temporal synchrony", by L. Shastri and V. Ajjangadde, pp. 417-494

Tracking of non-stationary time-series using resource allocating RBF networks

In this paper, we discuss some practical implications for implementing adaptable network algorithms applied to non-stationary time series problems. Using electricity load data and training with the extended Kalman filter, we demonstrate that the dynamic model-order increment procedure of the resource allocating RBF network (RAN) is highly sensitive to the parameters of the novelty criterion. We investigate the use of system noise and forgetting factors for increasing the plasticity of the Kalman filter training algorithm, and discuss the consequences for on-line model order selection. We also find that a recently-proposed alternative novelty criterion, found to be more robust in stationary environments, does not fare so well in the non-stationary case due to the need for filter adaptability during training.

Towards a framework for combining stochastic and deterministic descriptions of nonstationary financial time series

We present in this paper ideas to tackle the problem of analysing and forecasting nonstationary time series within the financial domain. Accepting the stochastic nature of the underlying data generator we assume that the evolution of the generator's parameters is restricted on a deterministic manifold. Therefore we propose methods for determining the characteristics of the time-localised distribution. Starting with the assumption of a static normal distribution we refine this hypothesis according to the empirical results obtained with the methods anc conclude with the indication of a dynamic non-Gaussian behaviour with varying dependency for the time series under consideration.

Modelling financial time series with switching state space models

The deficiencies of stationary models applied to financial time series are well documented. A special form of non-stationarity, where the underlying generator switches between (approximately) stationary regimes, seems particularly appropriate for financial markets. We use a dynamic switching (modelled by a hidden Markov model) combined with a linear dynamical system in a hybrid switching state space model (SSSM) and discuss the practical details of training such models with a variational EM algorithm due to [Ghahramani and Hilton,1998]. The performance of the SSSM is evaluated on several financial data sets and it is shown to improve on a number of existing benchmark methods.

Dynamical local models for segmentation and prediction of financial time series

In the analysis and prediction of many real-world time series, the assumption of stationarity is not valid. A special form of non-stationarity, where the underlying generator switches between (approximately) stationary regimes, seems particularly appropriate for financial markets. We introduce a new model which combines a dynamic switching (controlled by a hidden Markov model) and a non-linear dynamical system. We show how to train this hybrid model in a maximum likelihood approach and evaluate its performance on both synthetic and financial data.

Strategy, IT and dynamic change in enterprises

This paper explores the importance of collaboration between different types of organizations within an enterprise. To achieve successful collaboration requires both endogenous and exogenous factors of each organization to be considered and a shared meta-strategy supported by shared cross-organizational processes and technology. A rolling business plan would periodically review, assess and reposition each organization within this meta-strategy according to how well they have contributed. We show that recent technological advances have made organizational structures more agile, organizational infra-structure more connected and the sharing of real-time information an operational reality; we also discuss the challenges and risks.