Women’s mass media exposure and maternal health awareness in Ota, Nigeria

Maternal health has been an issue of priority to nations across the globe for years now. This study sought to: identify the sources of maternal health awareness; examine means of obtaining maternal health information; determine the frequency of mass media exposure; and analyse the influence of mass media exposure on maternal health awareness among the female residents. The Agendasetting theory and the descriptive (survey) research design, using the questionnaire as the research instrument, were utilized in this study. For this study, the purposive and haphazard sampling techniques were used. The internet (49%) was the topmost source of maternal health awareness; adverts/campaigns (30.6%) were the most common means of obtaining maternal health information; once in a month [27.6%] was the exposure frequency of most participants to the mass media while the least exposure frequency was once in two weeks [5.1%]. It was discovered that mass media exposure had a significant influence on maternal health awareness

Characteristics of Different Systems for the Solar Drying of Crops

Solar dryers are used to enable the preservation of agricultural crops, food processing industries for dehydration of fruits and vegetables, fish and meat drying, dairy industries for production of milk powder, seasoning of wood and timber, textile industries for drying of textile materials. The fundamental concepts and contexts of their use to dry crops is discussed in the chapter. It is shown that solar drying is the outcome of complex interactions particular between the intensity and duration of solar energy, the prevailing ambient relative humidity and temperature, the characteristics of the particular crop and its pre-preparation and the design and operation of the solar dryer

Automatic Extraction of Forests from Historical Maps Based on Unsupervised Classification in the CIELab Color Space

Part IIInternational audienceIn this chapter, we describe an automatic procedure to capture features on old maps. Early maps contain specific informations which allow us to reconstruct trajectories over time and space for land use/cover studies or urban area development. The most commonly used approach to extract these elements requires a user intervention for digitizing which widely limits its utilization. Therefore, it is essential to propose automatic methods in order to establish reproducible procedures. Capturing features automatically on scanned paper maps is a major challenge in GIS for many reasons: (1) many planimetric elements can be overlapped, (2) scanning procedure may conduct to a poor image quality, (3) lack of colors complicates the distinction of the elements. Based on a state of art, we propose a method based on color image segmentation and unsupervised classification (K-means algorithm) to extract forest features on the historical 'Map of France'. The first part of the procedure conducts to clean maps and eliminate elevation contour lines with filtering techniques. Then, we perform a color space conversion from RGB to L*a*b color space to improve uniformity of the image. To finish, a post processing step based on morphological operators and contextual rules is applied to clean-up features. Results show a high global accuracy of the proposed scheme for different excerpt of this historical map