Major and trace element analytical methods of the National Status and Trends Program: 2000-2006

This document contains analytical methods that detail the procedures for determining major and trace element concentrations in bivalve tissue and sediment samples collected as part of the National Status and Trends Program (NS&T) for the years 2000-2006. Previously published NOAA Technical Memoranda NOS ORCA 71 and 130 (Lauenstein and Cantillo, 1993; Lauenstein and Cantillo, 1998) detail trace element analyses for the years 1984-1992 and 1993-1996, respectively, and include ancillary, histopathology, and contaminant (organic and trace element) analytical methods. The methods presented in this document for trace element analysis were utilized by the NS&T Mussel Watch and Bioeffects Projects. The Mussel Watch Project has been monitoring contaminants in bivalves and sediment for over 20 years, and is the longest active contaminant monitoring program operating in U.S. costal waters. Approximately 280 Mussel Watch sites are monitored on biennial and decadal timescales using bivalve tissue and sediment, respectively. The Bioeffects Project applies the sediment quality approach, which uses sediment contamination measurements, toxicity tests and benthic macroinfauna quantification to characterize pollution in selected estuaries and coastal embayments. Contaminant assessment is a core function of both projects. Although only one contract laboratory was used by the NS&T Program during the specified time period, several analytical methods and instruments were employed. The specific analytical method, including instrumentation and detection limit, is noted for each measurement taken and can be found at http://NSandT.noaa.gov. The major and trace elements measured by the NS&T Program include: Al, Si, Cr, Mn, Fe, Ni, Cu, Zn, As, Se, Sn, Sb, Ag, Cd, Hg, Tl and Pb.

Research in development: learning from the CGIAR Research Program on Aquatic Agricultural Systems

This working paper aims to synthesize and share learning from the experience of adapting and operationalizing the Research in Development(RinD) approach to agricultural research in the five hubs under the The CGIAR Research Program on Aquatic Agricultural Systems. It seeks to share learning about how the approach is working in context and to explore the outcomes it is achieving through initial implementation over 3 ½ years. This learning can inform continuation of agricultural research in the second phase of the CGIAR research programs and will be useful to others aiming to implement research programs that seek to equitably build capacity to innovate in complex social-ecological systems. Each of the chapters in this working paper have shown that RinD has produced a range of outcomes that were often unexpected and broader in scope than might result from other approaches to agricultural research. RinD also produces innovations, and there is evidence that it builds capacity to innovate.

Behavior learning of a face robot based on the characteristics of human instruction

Computer simulation experiments were performed to examine the effectiveness of OR- and comparative-reinforcement learning algorithms. In the simulation, human rewards were given as +1 and -1. Two models of human instruction that determine which reward is to be given in every step of a human instruction were used. Results show that human instruction may have a possibility of including both model-A and model-B characteristics, and it can be expected that the comparative-reinforcement learning algorithm is more effective for learning by human instructions.