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The lack of and need for modern quality medical equipment in developing African countries has steadily increased with increasing population, leaving these countries to depend heavily on donated medical equipment from developed countries. Most of these second-hand and old medical devices which are most often not suitable for or suited to use in developing countries have a short life span and this has resulted in the large stockpiles of abandoned medical equipment waiting to be discarded as waste. The need to adopt a more sustainable approach for the management of this ever-increasing waste stream in many hospitals in most developing countries cannot be over emphasized. The recovery and utilization of discarded medical equipment for the remanufacture of medical products stands to significantly minimize environmental pollution and the amount of waste materials to be sent to the landfills. This study focused on assessing the current management practices of medical waste and the potential for the implementation of medical equipment remanufacture in Nigeria. We will be using Nigeria's premier teaching Hospital – University College Hospital as a case study and highlighting barriers such as poor infrastructure, and safety approval standards and processes, and other key factors that could influence the implementation of remanufacture within the Nigerian context, where local knowledge and understanding of needs, context and available resources could be more effectively incorporated into designs and implementation plans for a sustainable and robust remanufacture scheme.

Deep neural networks have been successfully used in diverse emerging domains to solve real world complex problems with may more deep learning(DL) architectures, being developed to date. To achieve these state-of-the-art performances, the DL architectures use activation functions (AFs), to perform diverse computations between the hidden layers and the output layers of any given DL architecture. This paper presents a survey on the existing AFs used in deep learning applications and highlights the recent trends in the use of the activation functions for deep learning applications. The novelty of this paper is that it compiles majority of the AFs used in DL and outlines the current trends in the applications and usage of these functions in practical deep learning deployments against the state-of-the-art research results. This compilation will aid in making effective decisions in the choice of the most suitable and appropriate activation function for any given application, ready for deployment. This paper is timely because most research papers on AF highlights similar works and results while this paper will be the first, to compile the trends in AF applications in practice against the research results from literature, found in deep learning research to date., 20 pages, 6 figures, 2 tables

Deep Learning has emerged as a state-of-the-art learning technique across a wide range of applications, including image recognition, localisation, natural language processing, prediction and forecasting systems. With significant applicability, Deep Learning is continually seeking other new fronts of applications for these techniques. This research is the first to apply Deep Learning algorithm to inspection in remanufacturing. Inspection is a key process in remanufacturing, which is currently an expensive manual operation in the remanufacturing process that depends on human operator expertise, in most cases. This research further proposes an automation framework based on Deep Learning algorithm for automating this inspection process. The proposed technique offers the potential to eliminate human factors in inspection, save cost, increase throughput and improve precision. This paper presents a novel vision-based inspection system on Deep Convolution Neural Network (DCNN) for three types of defects, namely pitting, surface abrasion and cracks by distinguishing between these surface defected parts. The materials used for this feasibility study were 100cm x 150cm mild steel plate material, purchased locally, and captured using a web webcam USB camera of 0.3 megapixels. The performance of this preliminary study indicates that the DCNN can classify with up to 100% accuracy on validation data and above 96% accuracy on a live video feed, by using 80% of the sample dataset for training and the remaining 20% for testing. Therefore, in the remanufacturing parts inspection, the DCNN approach has high potential as a method that could surpass the current technologies, especially for accuracy and speed. This preliminary study demonstrates that Deep Learning techniques have the potential to revolutionise inspection in remanufacturing. This research offers valuable insight into these opportunities, serving as a starting point for future applications of Deep Learning algorithms to remanufacturing.