Emerging technologies for learning (volume 1)

Collection of 5 articles on emerging technologies and trend

Antigen-driven T-cell turnover.

A mathematical model is developed to characterize the distribution of cell turnover rates within a population of T lymphocytes. Previous models of T-cell dynamics have assumed a constant uniform turnover rate; here we consider turnover in a cell pool subject to clonal proliferation in response to diverse and repeated antigenic stimulation. A basic framework is defined for T-cell proliferation in response to antigen, which explicitly describes the cell cycle during antigenic stimulation and subsequent cell division. The distribution of T-cell turnover rates is then calculated based on the history of random exposures to antigens. This distribution is found to be bimodal, with peaks in cell frequencies in the slow turnover (quiescent) and rapid turnover (activated) states. This distribution can be used to calculate the overall turnover for the cell pool, as well as individual contributions to turnover from quiescent and activated cells. The impact of heterogeneous turnover on the dynamics of CD4(+) T-cell infection by HIV is explored. We show that our model can resolve the paradox of high levels of viral replication occurring while only a small fraction of cells are infected

Integrating soil and plant tissue tests and using an artificial intelligence method for data modelling is likely to improve decisions for in-season nitrogen management

This paper hypothesizes that there is value in combining soil, climate and plant tissue data to give more reliable advice on nitrogen top-ups in-season when compared with models that are currently available. The benefit of soil and climate data is to factor in N mineralisation and potential yield while plant test data is a more direct approach of yield estimates when considering firstly plant N uptake from the whole soil profile and secondly biomass (important yield component). Plant test data are closer to yield in time and space than soil test data, shortening the time period for any yield prognosis by about 2-3 months, depending when plant testing occurred. A positive side-effect of plant testing is to check whether any other nutrients, apart from nitrogen, are limiting yield or an N response. Secondly, this paper explores an AI method as a comparison to the traditional modelling technique to further improve the accuracy and to turn the model into a self-calibrating model. Unlike a statistical autoregression technique, the tested AI method has dynamic functions that can be used not only on time series data but also on data such as used here

Impact of Lime and Gypsum on Wheat Yield, Soil and Solution Properties in the Short and Long Term

Subsoil aluminium (Al) toxicity or soils with AlCaCl2 content \u3e 2.5 mg Al kg-1 in the soil layers below 10 cm is a significant problem in south Western Australia. Both lime and gypsum can be used to treat subsoils Al toxicity because these products decrease the toxic effect of soil Al leading to an increase in crop grain yields. In this presentation, results are reported from three field lime by gypsum rate experiments located in the east of Merredin

Experimental Testing and Mathematical Modeling of the Interconnected Hydragas Suspension System

The Moulton Hydragas suspension system improves small car ride quality by interconnecting the front and rear wheel on each side of the vehicle via a hydraulic fluid pipe between the front and rear dampers. A Hydragas system from a Rover Group MGF sports car was statically and dynamically tested to generate stiffness and damping coefficient matrices. The goal was to develop the simplest possible model of the system for use in ride quality studies. A linear model showed reasonable accuracy over restricted frequency ranges. A second model used bilinear spring and damping constants, and was more accurate for predicting force at both the front and rear units for frequencies from 1 to 8 Hz. The Hydragas system static stiffness parameters, when used in the model, caused peak force underprediction in the jounce direction. The bilinear model required increased jounce stiffness to account for hysteresis in the rubber elements of the system, and dynamic fluid flow phenomena