Use of a Generalized Additive Model to Investigate Key Abiotic Factors Affecting Microcystin Cellular Quotas in Heavy Bloom Areas of Lake Taihu

Lake Taihu is the third largest freshwater lake in China and is suffering from serious cyanobacterial blooms with the associated drinking water contamination by microcystin (MC) for millions of citizens. So far, most studies on MCs have been limited to two small bays, while systematic research on the whole lake is lacking. To explain the variations in MC concentrations during cyanobacterial bloom, a large-scale survey at 30 sites across the lake was conducted monthly in 2008. The health risks of MC exposure were high, especially in the northern area. Both Microcystis abundance and MC cellular quotas presented positive correlations with MC concentration in the bloom seasons, suggesting that the toxic risks during Microcystis proliferations were affected by variations in both Microcystis density and MC production per Microcystis cell. Use of a powerful predictive modeling tool named generalized additive model (GAM) helped visualize significant effects of abiotic factors related to carbon fixation and proliferation of Microcystis (conductivity, dissolved inorganic carbon (DIC), water temperature and pH) on MC cellular quotas from recruitment period of Microcystis to the bloom seasons, suggesting the possible use of these factors, in addition to Microcystis abundance, as warning signs to predict toxic events in the future. The interesting relationship between macrophytes and MC cellular quotas of Microcystis (i.e., high MC cellular quotas in the presence of macrophytes) needs further investigation

Pulsed laser cutting of mild steel and analysis of particles ejected during cutting

Abstract not available

Lifetime prediction of components for reuse: an overview

A state-of-the-art prediction of the remaining life of a product is discussed in detail in this paper on the basis of a literature study. The objective is to develop a robust model for estimating the remaining life of a product on the basis of data collected during its operating life. Very little research has been devoted to the remaining life prediction as a whole, but most research has been focused on predicting the mean time between failures (MTBF) and/or mean time to failure (MTTF). The authors of this paper have gathered this information to reflect the nature of the work of researchers to date and use it as a basis for model development

Determining the Reuse Potential of Components Based on Life Cycle Data

Reuse of components is one of the most efficient strategies for product recovery, which requires reliable methods for assessing the quality and the remaining life of used components. A new methodology, presented in this paper, is based on the trend analysis of lifetime monitoring data. Data with similar trends were grouped and a number of analysis techniques such as Linear Multiple Regression, Dynamic Ordinary Kriging, Universal Kriging and Neural Networks were applied in order to find the most suitable methodology for each group. The methodology was validated by using lifetime monitoring data from a consumer product