We are facing a global healthcare crisis today as the healthcare cost is ever climbing, but with the aging population, government fiscal revenue is ever dropping. To create a more efficient and effective healthcare system, three technical challenges immediately present themselves: healthcare access, healthcare equity, and healthcare efficiency. An autonomous mobile clinic solves the healthcare access problem by bringing healthcare services to the patient by the order of the patient's fingertips. Nevertheless, to enable a universal autonomous mobile clinic network, a three-stage technical roadmap needs to be achieved: In stage one, we focus on solving the inequity challenge in the existing healthcare system by combining autonomous mobility and telemedicine. In stage two, we develop an AI doctor for primary care, which we foster from infancy to adulthood with clean healthcare data. With the AI doctor, we can solve the inefficiency problem. In stage three, after we have proven that the autonomous mobile clinic network can truly solve the target clinical use cases, we shall open up the platform for all medical verticals, thus enabling universal healthcare through this whole new system.

If you happen to fall outside that specified range, navigating the internet, your community, even your own home, can become exponentially more difficult. But it doesn't have to be this way, argues artist, writer and design researcher Sara Hendren. In her new book, What Can a Body Do, Hendren examines the challenges that people with disabilities face on a daily basis in a world that often doesn't take their needs into account and shows that more inclusive design -- from cybernetic prosthetic arms and more accessible city streets to tactile doorbells for the deaf -- isn't just possible, it's already practical. In the excerpt below, Hendren looks at the Jaipur Foot, an unpowered, low-cost prosthetic that has helped nearly two million lower leg amputees in India and other countries regain their ability to walk. From WHAT CAN A BODY DO: How We Meet the Built World by Sara Hendren published on August 18, 2020 by Riverhead, an imprint of Penguin Publishing Group, a division of Penguin Random House LLC.

The Traveling Salesman Problem is one of the most intensively studied combinatorial optimization problems due both to its range of real-world applications and its computational complexity. When combined with the Set Covering Problem, it raises even more issues related to tractability and scalability. We study a combined Set Covering and Traveling Salesman problem and provide a mixed integer programming formulation to solve the problem. Motivated by applications where the optimal policy needs to be updated on a regular basis and repetitively solving this via MIP can be computationally expensive, we propose a machine learning approach to effectively deal with this problem by providing an opportunity to learn from historical optimal solutions that are derived from the MIP formulation. We also present a case study using the vaccine distribution chain of the World Health Organization, and provide numerical results with data derived from four countries in sub-Saharan Africa.

Tuberculosis is an issue close to home for Pune, India-based healthcare startup DeepTek. India has the world's highest prevalence of the disease -- accounting for over one-quarter of the 10 million new cases each year. It's a fitting first project for the company, whose founders hope to greatly improve global access to medical imaging diagnostics with an AI-powered radiology platform. India aims to eradicate TB by 2025, five years before the United Nations' global goal to end the epidemic by 2030. Chest X-ray imaging is the most sensitive screening tool for pulmonary TB, helping clinicians determine which patients should be referred for further lab testing.