Rethinking the development, dissemination and adoption of agricultural technologies

This paper focuses on the higher order factors affecting successful adoption of technologies. Drawing on the "actor-oriented perspective" in rural sociology, it is argued that successful examples of adoption at this higher level result from a complex conjunction of people and events, with outcomes that may have been quite unanticipated at the outset. From this perspective, research and extension projects and programs are viewed as arenas in which social actors–village leaders, farmers, researchers (local and international), aid officials, municipal agents, extension workers, and traders–pursue their own short- and long-term objectives and strategies. To this end, they maneuver, negotiate, organize, cooperate, participate, coerce, obstruct, form coalitions, adopt, adapt, and reject, all within a specific geographical and historical context.

An adaptive neural-wavelet model for short term load forecasting

This paper proposed a novel model for short term load forecast in the competitive electricity market. The prior electricity demand data are treated as time series. The forecast model is based on wavelet multi-resolution decomposition by autocorrelation shell representation and neural networks (multilayer perceptrons, or MLPs) modeling of wavelet coefficients. To minimize the influence of noisy low level coefficients, we applied the practical Bayesian method Automatic Relevance Determination (ARD) model to choose the size of MLPs, which are then trained to provide forecasts. The individual wavelet domain forecasts are recombined to form the accurate overall forecast. The proposed method is tested using Queensland electricity demand data from the Australian National Electricity Market. (C) 2001 Elsevier Science B.V. All rights reserved.

Neural adaptations to resistance training - Implications for movement control

It has long been believed that resistance training is accompanied by changes within the nervous system that play an important role in the development of strength. Many elements of the nervous system exhibit the potential for adaptation in response to resistance training, including supraspinal centres, descending neural tracts, spinal circuitry and the motor end plate connections between motoneurons and muscle fibres. Yet the specific sites of adaptation along the neuraxis have seldom been identified experimentally, and much of the evidence for neural adaptations following resistance training remains indirect. As a consequence of this current lack of knowledge, there exists uncertainty regarding the manner in which resistance training impacts upon the control and execution of functional movements. We aim to demonstrate that resistance training is likely to cause adaptations to many neural elements that are involved in the control of movement, and is therefore likely to affect movement execution during a wide range of tasks. We review a small number of experiments that provide evidence that resistance training affects the way in which muscles that have been engaged during training are recruited during related movement tasks. The concepts addressed in this article represent an important new approach to research on the effects of resistance training. They are also of considerable practical importance, since most individuals perform resistance training in the expectation that it will enhance their performance in-related functional tasks.

Neural influences on sprint running - Training adaptations and acute responses

Performance in sprint exercise is determined by the ability to accelerate, the magnitude of maximal velocity and the ability to maintain velocity against the onset of fatigue. These factors are strongly influenced by metabolic and anthropometric components. Improved temporal sequencing of muscle activation and/or improved fast twitch fibre recruitment may contribute to superior sprint performance. Speed of impulse transmission along the motor axon may also have implications on sprint performance. Nerve conduction velocity (NCV) has been shown to increase in response to a period of sprint training. However, it is difficult to determine if increased NCV is likely to contribute to improved sprint performance. An increase in motoneuron excitability, as measured by the Hoffman reflex (H-reflex), has been reported to produce a more powerful muscular contraction, hence maximising motoneuron excitability would be expected to benefit sprint performance. Motoneuron excitability can be raised acutely by an appropriate stimulus with obvious implications for sprint performance. However, at rest reflex has been reported to be lower in athletes trained for explosive events compared with endurance-trained athletes. This may be caused by the relatively high, fast twitch fibre percentage and the consequent high activation thresholds of such motor units in power-trained populations. In contrast, stretch reflexes appear to be enhanced in sprint athletes possibly because of increased muscle spindle sensitivity as a result of sprint training. With muscle in a contracted state, however, there is evidence to suggest greater reflex potentiation among both sprint and resistance-trained populations compared with controls. Again this may be indicative of the predominant types of motor units in these populations, but may also mean an enhanced reflex contribution to force production during running in sprint-trained athletes. Fatigue of neural origin both during and following sprint exercise has implications with respect to optimising training frequency and volume. Research suggests athletes are unable to maintain maximal firing frequencies for the full duration of, for example, a 100m sprint. Fatigue after a single training session may also have a neural manifestation with some athletes unable to voluntarily fully activate muscle or experiencing stretch reflex inhibition after heavy training. This may occur in conjunction with muscle damage. Research investigating the neural influences on sprint performance is limited. Further longitudinal research is necessary to improve our understanding of neural factors that contribute to training-induced improvements in sprint performance.