Increased recycling and reuse rates are a central part of the objectives laid out by the COP21. Nonetheless, the practical implementation of what has been called the circular economy, as well as its true potential, are not easily established. This is because the impact and implementation time scales of any intervention depend on knowing the lifetime of products, which is frequently unknown. This is particularly true in construction, responsible for 39% of worldwide emissions, 11% of which are embodied. Most material flow analysis (MFA) models will simply assume a range of plausible life expectancies when bottom‐up data are lacking. In this work, we propose a novel method of identification using the high quality but highly aggregated trade data available and use it to establish a “mortality curve” for buildings and other long‐lasting products. This identification method is intended to provide more reliable inputs to existing MFA models. It is widely applicable because of the general availability of the underlying data. Using it on United Kingdom trade data, we identify product classes at 1 year for packaging/home scrap, 1 to around 10 years for vehicles/equipment, and around 50 years for construction. The identification approach was then validated by using classical approaches using bottom‐up data for vehicles.
Android applications may request for users’ sensitive information through the GUI. Developer guidelines for designing applications mandate that information must be masked/encrypted before storing or leaving the system. However not all applications adhere to the guidelines. As a prerequisite to tracking sensitive input data, it is essential to identify the widgets that request it. Previous research has focused on identifying the sensitive input widgets, but the extraction of all layouts, including images and unused layouts, is fundamental. In this paper, we propose an automated framework that finds sensitive user input widgets from Android application layouts and validates the masking of these inputs. Our design includes novel techniques for resolving the user semantics, extraction of resources, identification of potential data leaks and helping users to prioritize the sharing of sensitive information, resulting in significant improvement over prior work. We also train track the obtained sensitive input widgets and check for unencrypted transmission or storage of sensitive data. Based on a preliminary evaluation of our framework with some applications from the Google Play store, we observe notable improvement over prior work in this domain.
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