Recent Articles

Rural women play a critical role in the rural economies of both developed and developing countries. In most parts of the developing world they participate in crop production and livestock care, provide food, water and fuel for their families, and engage in off-farm activities to diversify their families’ livelihoods. In addition, they carry out vital reproductive functions in caring for children, older persons and the sick. To understand the situation of rural women, it is necessary to examine the full diversity of their experiences in the context of the changing rural economy, including their position within household and community structures. A multi-stage sampling method was employed to select 136 respondents. Primary data was collected through the use of questionnaires and interview schedule and were subjected to both descriptive and inferential statistics. The mean farming experience was 11.4years, while mean farm size was 1.4ha. The main source of agricultural information was radio. Regression analysis showed that level of education, age and marital status were significantly related with level of participation. The findings conclude that women voices and level of participation is influenced by level of education, marital status and age. Therefore, the study recommends that women participation in sustainable livelihoods can only be achieved by empowering them with training and access to land in order to raise their voices in agricultural sector.

The data-driven methods capable of understanding, mimicking and aiding the information processing tasks of Machine Learning (ML) have been applied in an increasing range over the past years in diverse areas at a very high rate, and had achieved great success in predicting and stratifying given data instances of a problem domain. There has been generalization on the performance of the classifier to be the optimal based on the existing performance benchmarks such as accuracy, speed, time to learn, number of features, comprehensibility, robustness, scalability and interpretability. However, these benchmarks alone do not guarantee the successful adoption of an algorithm for prediction and stratification since there may be an incurring risk in its adoption. Therefore, this paper aims at developing a logical approach for using Empirical Risk Minimization (ERM) technique to determine the machine learning classifier with the minimum risk function for data stratification. The generalization on the performance of optimal algorithm was tested on BayesNet, Multilayered perceptron, Projective Adaptive Resonance Theory (PART) and Logistic Model Trees algorithms based on existing performance benchmarks such as correctly classified instances, time to build, kappa statistics, sensitivity and specificity to determine the algorithms with great performances. The study showed that PART and Logistic Model Trees algorithms perform well than others. Hence, a logical approach to apply Empirical Risk Minimization technique on PART and Logistic Model Trees algorithms is shown to give a detailed procedure of determining their empirical risk function to aid the decision of choosing an algorithm to be the best fit classifier for data stratification. This therefore serves as a benchmark for selecting an optimal algorithm for stratification and prediction alongside other benchmarks.

To evaluate the nutritional status of children and adolescents monitored in MEC / SESu UFPE project. Cross-sectional descriptive study in Centro de Revitalização e Revalorização da Vida, in the community of Bode, Recife / PE, from August to September 2015. A total of 35 children and adolescents and observed 18 % overweight, being higher in males (22). A high waist circumference was found in 22 % male, 8 % female. For weight / age and height / age was not found deficits nor surpluses in the sample.