This study was carried out to assess the generation and management of solid wastes in residential areas of some selected tertiary institutions in southwest Nigeria, to provide insights into existing waste management approaches, so as to provide sustainable avenues for institutional policy improvement. It was conducted for a period of six months (June to November 2012). The wastes were collected, sorted, weighed and classified according to their components. Also, questionnaires were administered to the unit heads in charge of waste management in the different institutions. The study shows that of the average waste generated per day in the residential areas of the institutions, food waste exhibited the highest percentage generation at 48%, 62% and 32% in the student, senior and junior staff residential areas, respectively. This was followed by plastic related materials with respective percentage generation at 18%, 7% and 19%. Other important waste materials identified in the study include e-waste, metals and textiles. The results also revealed that high income earners generate more wastes than low income earners. The high composition of non-biodegradable wastes from these results bears implication of the requirement for alternative waste management solutions for sustainable and environmental friendly waste management system in the university community.

This paper explores the use of support vector machines (SVM) for regression and genetic algorithm (GA) which may be referred to as SVMGA, to classify faults in low speed bearings over a specified speed range, with sinusoidal loads applied to the bearing along the radial and axial directions. GA is used as a heuristic tool to find an approximate solution to the difficult problem of solving the highly non-linear situation through the application of the principles of evolution by optimizing the statistical features selected for the SVM for regression training solution. It is used to determine the training parameters of SVM for regression which can optimize the model and thereby generate new features from the original dataset without prior knowledge of the probabilistic distribution. The fault recognition and the nonlinear regression is achieved by using SVM for regression. Classification is performed for three classes. In this work the GA is used to first optimize the statistical features for best performance before they are used to train the SVM for regression. Experimental studies using acoustic emission caused by bearing faults showed that SVMGA with a Gaussian kernel function better achieves classification on the bearings operated at low speeds, regardless the load type and, under different fault conditions, compared to the exponential kernel function and the other many kernel functions which also can be used for the same conditions.

This paper uses Bayesian robust new hidden Markov modeling (BRNHMM) for bearing fault detection and diagnosis based on its acoustic emission signal. A variational Bayesian approach is used that simultaneously approximates the distribution over the hidden states and parameters with simpler distribution hence using Bayesian inference for the estimation of the posterior HMM hyperparameters. This allows for online detection as small data sets can be used. Also, the Kullback-Leibler (KL) divergence is effectively used to access the divergence of the probability function of the BRNHMM, to find its lower bound approximation and by applying a linear transform to the maximum output probability parameter generation (MOPPG). The training set result obtained from BRNHMM is then compared to the result from artificial neural network (ANN) fault detection for same complex system of low speed and varying load conditions which are difficult from a diagnostic perspective, as found in rolling mills.

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