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This study analyses the metal recyclability from waste Printed Circuit Boards (PCBs) with three material recycling quoting approaches: Material Recycling Efficiency (MRE), Resource Recovery Efficiency (RRE), and Quotes for Environmentally Weighted Recyclability (QWERTY). The results indicate that MRE is likely inapplicable to quoting the metal recyclability of waste PCBs because it makes the recycling of any metal equal to each other (e.g. recycling of 1 kg of gold is as important as recycling of 1 kg of iron). RRE and QWERTY can overcome the poor yardstick of MRE because they concern not only the weight of recycled materials but also the contribution of recycled materials to the natural resource conservation and the environmental impact reduction, respectively. These two approaches, however, report an extremely different result, that makes the target stakeholders get confused with which material recycled. From the findings of the aforementioned analysis, this study proposes the Model for Evaluating Metal Recycling Efficiency from Complex Scraps (MEMRECS) as a new approach to quotes the metal recycling performance. MEMRECS allows the trade-offs between three criteria: mass, environmental impacts and natural resources conservation, hence it can provide the result in a sustainable sound manner. MEMRECS clearly models and enhances the role of natural resources conservation aspect rather than QWERTY does.

The variety of indoor architectures challenges for map matching. Unlike outdoor map, indoor pedestrian is even expected to walk on wide corridors, large open areas and irregular shapes of interior spaces. In the overview, most of existing digital maps are for GPS-based navigation systems. GPS does not need a previously estimated position to estimate the current one. But in dead reckoning, the present position is the result of propagating the previous estimated one. This makes most of previous outdoor map matching algorithms cannot be used for pedestrian directly without signification modifications. In this paper, we introduce our lightweight topological-based approach to assist pedestrian dead reckoning algorithm in term of reducing the error of determined position and improving user tracking task. We developed a practical complete, self-contained end-to-end system on a Smartphone. This methodology requires simple computations; hence it runs very fast. We conducted different scenario experiments to demonstrate the usability of our system in indoor environment. The evaluation results showed that the map matching error rate is 4.69% in a 42.6m x 29.6m testing area.