Environmental factors influencing composition, abundance and distribution of water hyacinth

Relationships between nutrient concentrations and water hyacinth biomass and composition have been studied in the shallow inshore bays of lakes Victoria, Kyoga and Albert. Additional information was obtained from Victoria Nile, Albert Nile and Kagera River. In this section, seasonal changes in nutrients and oxygen concentrations are used to explain changes in water hyacinth composition, biomass and distribution in Lake Victoria. Lake Victoria is of particular interest because it experienced strong hyacinth infestations in 1995, a sink in 1998 and resurgence in 2001. The lake has also been extensively sampled and provides time series data in nutrient, oxygen, mixing and thermal stratification which provide an opportunity to relate water hyacinth distribution and biomass to environmental factors. The possible origins and impacts of nutrient loads into Lake Victoria are also discussed in relation to water hyacinth proliferation and distribution especially in relation to known 'hot-spots'.

Bayesian classification of acoustical waveforms under environmental variability

We present in this paper a new multivariate probabilistic approach to Acoustic Pulse Recognition (APR) for tangible interface applications. This model uses Principle Component Analysis (PCA) in a probabilistic framework to classify tapping pulses with a high degree of variability. It was found that this model, achieves a higher robustness to pulse variability than simpler template matching methods, specifically when allowed to train on data containing high variability. © 2011 IEEE.

Environmental priorities in strategic product development

This paper reports some results from a major research project on the integration of environmental issues into design in the electrical and electronic industry. Product development practice and ecodesign methods were examined along with qualitative data from 19 manufacturers, using interviews and an action research methodology. A four-stage framework for ecodesign practice resulted: first, to make an environmental assessment from a life cycle viewpoint; then to communicate the analysis and collect feedback; thirdly to prioritize the environmental issues and finally to complete the design using relevant tools and methods. This 'ARPI' framework (analyse, report, prioritize, improve) applies to both strategic and operational levels. Initial testing of the framework in collaborating companies highlighted difficulties with communication with design teams; the role of 'ecodesign champions' is explained. Other challenges are the development of customized tools and the training of designers. The study concludes that the prioritization step is critical, as it simplifies and clarifies the tasks in design that take place after formulation of the specification. Copyright (C) 2000 John Wiley and Sons, Ltd. and ERP Environment.

Civil and Environmental Engineering Challenges for Data Sensing and Analysis

The objective of this study was to identify challenges in civil and environmental engineering that can potentially be solved using data sensing and analysis research. The challenges were recognized through extensive literature review in all disciplines of civil and environmental engineering. The literature review included journal articles, reports, expert interviews, and magazine articles. The challenges were ranked by comparing their impact on cost, time, quality, environment and safety. The result of this literature review includes challenges such as improving construction safety and productivity, improving roof safety, reducing building energy consumption, solving traffic congestion, managing groundwater, mapping and monitoring the underground, estimating sea conditions, and solving soil erosion problems. These challenges suggest areas where researchers can apply data sensing and analysis research.

Fluid Dynamics in Physics, Engineering and Environmental Applications

The book contains invited lectures and selected contributions presented at the Enzo Levi and XVII Annual Meeting of the Fluid Dynamic Division of the Mexican Physical Society in 2011.

Temporal difference models describe higher-order learning in humans.

The ability to use environmental stimuli to predict impending harm is critical for survival. Such predictions should be available as early as they are reliable. In pavlovian conditioning, chains of successively earlier predictors are studied in terms of higher-order relationships, and have inspired computational theories such as temporal difference learning. However, there is at present no adequate neurobiological account of how this learning occurs. Here, in a functional magnetic resonance imaging (fMRI) study of higher-order aversive conditioning, we describe a key computational strategy that humans use to learn predictions about pain. We show that neural activity in the ventral striatum and the anterior insula displays a marked correspondence to the signals for sequential learning predicted by temporal difference models. This result reveals a flexible aversive learning process ideally suited to the changing and uncertain nature of real-world environments. Taken with existing data on reward learning, our results suggest a critical role for the ventral striatum in integrating complex appetitive and aversive predictions to coordinate behaviour.