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Recent years have seen a rapid increase in digital medicine research in an attempt to transform traditional healthcare systems to their modern, intelligent, and versatile equivalents that are adequately equipped to tackle contemporary challenges. This has led to a wave of applications that utilise AI technologies; first and foremost in the fields of medical imaging, but also in the use of wearables and other intelligent sensors. In comparison, computer audition can be seen to be lagging behind, at least in terms of commercial interest. Yet, audition has long been a staple assistant for medical practitioners, with the stethoscope being the quintessential sign of doctors around the world. Transforming this traditional technology with the use of AI entails a set of unique challenges. We categorise the advances needed in four key pillars: Hear, corresponding to the cornerstone technologies needed to analyse auditory signals in real-life conditions; Earlier, for the advances needed in computational and data efficiency; Attentively, for accounting to individual differences and handling the longitudinal nature of medical data; and, finally, Responsibly, for ensuring compliance to the ethical standards accorded to the field of medicine.
In 2020, the first quick commerce businesses in grocery retail emerged in the European market. Customers can order online and receive their groceries within 15 min in the best case. The ability to provide short lead times is, therefore, essential. However, the ambitious service promises of quick deliveries further complicate order fulfillment, and many retailers are struggling to achieve profitability. Quick commerce retailers need to establish an efficient network of micro-fulfillment centers (MFCs) in customer proximity, i.e., urban areas, to master these challenges. We address this strategic network problem and formulate it as a location routing problem. This enables us to define the number, location, type, and size of MFCs based on setup, replenishment, order processing, and transportation costs. We solve the problem using a cluster-first-route-second heuristic based on agglomerative clustering to approximate transportation costs. Our numerical experiments show that our heuristic solves the problem effectively and provides efficient decision support for quick commerce retailing. We generate managerial insights by analyzing key aspects of a quick commerce business, such as lead times and problem-specific cost factors. We show, for example, that allowing slightly higher delivery flexibility (e.g., offering extended lead times) enables bundling effects and results in cost savings of 50% or more of fulfillment costs. Furthermore, using multiple small MFCs is more efficient than larger, automated MFCs from a lead time and cost perspective.
