Simplest tasks have greatest dual task interference with balance in brain injured adults

Attention difficulties and poor balance are both common sequel following a brain injury. This study aimed to determine whether brain injured adults had greater difficulty than controls in performing a basic balance task while concurrently completing several different cognitive tasks varying in visuo-spatial attentional load and complexity. Twenty brain injured adults and 20 age-, sex- and education level-matched controls performed a balance-only task (step stance held for 30s), five cognitive-only tasks (simple and complex non-spatial, visuo-spatial, and a control articulation task), and both together (dual tasks). Brain injured adults showed a greater centre of pressure (COP) excursion and velocity in all conditions than controls. Brain injured adults also demonstrated greater interference with balance when concurrently performing two cognitive tasks than control subjects. These were the control articulation and the simple non-spatial task. It is likely that distractibility during these simple tasks contributed to an increase in COP motion and interference with postural stability in stance. Performing visuo-spatial tasks concurrently with the balance task did not result in any change in COP motion. Dual task interference in this group is thus unlikely to be due to structural interference. Similarly, as the more complex tasks did not uniformly result in increased interference, a reduction in attentional capacity in the brain injured population is unlikely to be the primary cause of dual task interference in this group. (C) 2004 Elsevier B.V. All rights reserved.

Three putative types of El Nino revealed by spatial variability in impact on Australian wheat yield

The El Nino-Southern Oscillation (ENSO) phenomenon significantly impacts rainfall and ensuing crop yields in many parts of the world. In Australia, El Nino events are often associated with severe drought conditions. However, El Nino events differ spatially and temporally in their manifestations and impacts, reducing the relevance of ENSO-based seasonal forecasts. In this analysis, three putative types of El Nino are identified among the 24 occurrences since the beginning of the twentieth century. The three types are based on coherent spatial patterns (footprints) found in the El Nino impact on Australian wheat yield. This bioindicator reveals aligned spatial patterns in rainfall anomalies, indicating linkage to atmospheric drivers. Analysis of the associated ocean-atmosphere dynamics identifies three types of El Nino differing in the timing of onset and location of major ocean temperature and atmospheric pressure anomalies. Potential causal mechanisms associated with these differences in anomaly patterns need to be investigated further using the increasing capabilities of general circulation models. Any improved predictability would be extremely valuable in forecasting effects of individual El Nino events on agricultural systems.

Efficiently computing weighted proximity relationships in spatial databases

Spatial data mining recently emerges from a number of real applications, such as real-estate marketing, urban planning, weather forecasting, medical image analysis, road traffic accident analysis, etc. It demands for efficient solutions for many new, expensive, and complicated problems. In this paper, we investigate the problem of evaluating the top k distinguished “features” for a “cluster” based on weighted proximity relationships between the cluster and features. We measure proximity in an average fashion to address possible nonuniform data distribution in a cluster. Combining a standard multi-step paradigm with new lower and upper proximity bounds, we presented an efficient algorithm to solve the problem. The algorithm is implemented in several different modes. Our experiment results not only give a comparison among them but also illustrate the efficiency of the algorithm.

Short-term electricity price forecasting using wavelet and SVM techniques

In deregulated electricity market, modeling and forecasting the spot price present a number of challenges. By applying wavelet and support vector machine techniques, a new time series model for short term electricity price forecasting has been developed in this paper. The model employs both historical price and other important information, such as load capacity and weather (temperature), to forecast the price of one or more time steps ahead. The developed model has been evaluated with the actual data from Australian National Electricity Market. The simulation results demonstrated that the forecast model is capable of forecasting the electricity price with a reasonable forecasting accuracy.