Somatic cell count in buffalo milk using fuzzy clustering and image processing techniques.

This research communication presents an automatic method for the counting of somatic cells in buffalo milk, which includes the application of a fuzzy clustering method and image processing techniques (somatic cell count with fuzzy clustering and image processing|, SCCFCI). Somatic cell count (SCC) in milk is the main biomarker for assessing milk quality and it is traditionally performed by exhaustive methods consisting of the visual observation of cells in milk smears through a microscope, which generates uncertainties associated with human interpretation. Unlike other similar works, the proposed method applies the Fuzzy C-Means (FCM) method as a preprocessing step in order to separate the images (objects) of the cells into clusters according to the color intensity. This contributes signficantly to the performance of the subsequent processing steps (thresholding, segmentation and recognition/identification). Two methods of thresholding were evaluated and the Watershed Transform was used for the identification and separation of nearby cells. A detailed statistical analysis of the results showed that the SCCFCI method is able to provide results which are consistent with those obtained by conventional counting. This method therefore represents a viable alternative for quality control in buffalo milk production.

Forecasting of the unknown end-of-life tire flow for control and decision making in urban solid waste management: A case study.

Efficient urban planning requires managers' experience and knowledge of reverse logistics in solid urban waste processes. Forecasting tools are needed to control, select and manage municipal solid waste. This paper presents the application of dynamic modeling approaches, namely, a linear autoregressive seasonal model, a model based on a FeedForward Artificial Neural Network and a Recurrent Neural Networks model, in order to forecast the unknown flows of end-of-life tires 12 months ahead. The models were identified using a database comprising four years of historical series related to the unknown flows of end-of-life tires. These were obtained through an exploratory analysis based on the annual sales reports of new tires issued by the Brazilian Institute of Geography and Statistics and reports related to the number of vehicles in circulation issued by Brazil's National Traffic Department. The results show that the models are able to carry out consistent forecasts over the horizon of a year ahead and the predictions are capable of identifying seasonalities and supporting decision making in urban waste management.

A hybrid clustering approach for multivariate time series - A case study applied to failure analysis in a gas turbine.

A clustering problem involving multivariate time series (MTS) requires the selection of similarity metrics. This paper shows the limitations of the PCA similarity factor (SPCA) as a single metric in nonlinear problems where there are differences in magnitude of the same process variables due to expected changes in operation conditions. A novel method for clustering MTS based on a combination between SPCA and the average-based Euclidean distance (AED) within a fuzzy clustering approach is proposed. Case studies involving either simulated or real industrial data collected from a large scale gas turbine are used to illustrate that the hybrid approach enhances the ability to recognize normal and fault operating patterns. This paper also proposes an oversampling procedure to create synthetic multivariate time series that can be useful in commonly occurring situations involving unbalanced data sets.

Characterization and quantification of corticosteroid-binding globulin in a southern toad, Bufo terrestris, exposed to coal-combustion-waste.

Corticosteroid-binding globulin (CBG) is a plasma protein that binds corticosterone and may regulate access of hormone to tissues. The role of CBG during a stress response is not clear. At least two hypotheses have been proposed: 1) CBG levels may increase in response to a stressor, thereby decreasing the amount of circulating free corticosterone, or 2) CBG levels may decline, making corticosterone available for its role in increased metabolic needs during stress. In this study, southern toads, Bufo terrestris, were exposed to a chronic pollutant (coal-combustion-waste), to determine changes in CBG and free corticosterone levels. Since toads exposed to chronic pollutants in previous studies did not exhibit the predicted changes in metabolic rate and mass, but did experience a significant elevation in total corticosterone, we hypothesized that CBG would likewise increase and thus, mitigate the effects of a chronic (i.e. 2 months) pollutant stressor. To conduct this study, we first characterized the properties of CBG in southern toads. Toad CBG has a K(d)=20.6+/-1.0 nM and a B(max)=332.2+/-5.1 nmol/L plasma. The rank order potencies for steroid inhibition of tritiated corticosterone are: dihydrotestosterone > corticosterone >> progesterone=testosterone >>> estrogen=dexamethasone. After characterization, we monitored the changes in CBG, total corticosterone, and free corticosterone in male toads that were exposed to either coal-combustion-waste or control conditions. CBG increased in all groups throughout the experiment. Total corticosterone, on the other hand, was only significantly elevated at four weeks of exposure to coal-combustion-waste. The increase in CBG did not parallel the increase in total corticosterone; as a result, free corticosterone levels were not buffered by CBG, but showed a peak at four weeks similar to total corticosterone. This finding indicates that, in this species, CBG may not provide a protective mechanism during long-term pollution exposure.