A study on the use of pesticides in market-gardening production was carried out on 108 market-gardeners in the rural city of Tori-Bossito in Southern Benin. The objective of the study was to characterize the potential risks of pesticides usage by farmers and the impacts on their health and on the environment. Two risk indexes were calculated for each pesticide: an environmental risk index (ERI) and a health risk index (HRI). First stage larva of the mosquito Aedes aegypti were used as bio-indicator for detecting insecticide residue in vegetable before their harvesting on the farms. The highest ERI were obtained for carbofuran, chlorpyriphos ethyl and endosulfan. Pesticide residues were found in 42% of the samples of leaves of eggplant, cucumber, amaranth and solanum. Vegetables growers used pesticides that may be highly hazardous and which were not registered in most cases. These situations could have unexpected consequences including the exposure of consumers to health hazards

Abstract-We investigate the unsupervised K-means clustering and the semi-supervised hidden Markov model (HMM) to automatically detect anomalous motion patterns in groups of people (crowds). Anomalous motion patterns are typically people merging into a dense group, followed by disturbances or threatening situations within the group. The application of Kmeans clustering and HMM are illustrated with datasets from four surveillance scenarios. The results indicate that by investigating the group of people in a systematic way with different K values, analyze cluster density, cluster quality and changes in cluster shape we can automatically detect anomalous motion patterns. The results correspond well with the events in the datasets. The results also indicate that very accurate detections of the people in the dense group would not be necessary. The clustering and HMM results will be very much the same also with some increased uncertainty in the detections.

From 20 provenances of field-collected actinorhizae of Myrica gale, more than 3000 test tubes were inoculated with OsO4-treated nodules. Only 30 Frankia strains were isolated from 6 provenances. Most isolates showed an extremely slow growth in the various isolation media tested. For 12 strains whose growth was sufficient, the Frankia nature of isolates was verified by morphological characterization and biochemical analysis, using gas chromatography for the presence of 2-O-methyl-D-mannose. All 12 strains showed infectivity on M. gale and Alnus glutinosa. Five of those strains were ineffective on A. glutinosa but effective on Elaeagnus angustifolia and Hippophaë rhamnoides.

In uncontrolled environments, with dynamic background and lighting changes, performing efficient and real-time foreground − background segmentation is very challenging. This work is based on the hypothesis that the combination of long wave infrared (LWIR) (8-12 µm) and colour cameras can significantly improve the robustness of moving objects extraction. Pros and cons of colour and thermal imagers in outdoor video monitoring applications are discussed. In order to fuse information from both sensors, we favoured an approach based on "analytical fusion" rather than "representative fusion". Starting from a state-of-the-art algorithm for moving objects extraction in colour video (non-parametric codebook model [1]), we first adapted the method for processing of "Red-Green-Blue-Thermal" video format. A preliminary objective performance evaluation of detection accuracy is presented. Original image sequences grabbed with co-aligned thermal and visible fields of view was used. Finally, some improvements to the original codebook model are proposed.

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