An innovative testing platform that more closely mimics what cancer encounters in the body may allow for more precise, personalized therapies by enabling the rapid study of multiple therapeutic combinations against tumor cells. The platform, which uses a three-dimensional environment to more closely mirror a tumor microenvironment, is demonstrated in research published in Communications Biology.
A number of vulnerabilities, known collectively as deep learning adversaries, hold artificial intelligence (AI) back from its full potential in applications like improving medical imaging quality and computer-aided diagnosis.
Accurate predictive simulations of the electrochemical reactions that power solar fuel generators, fuel cells, and batteries could advance these technologies through improved material design, and by preventing detrimental electrochemical processes, such as corrosion. However, electrochemical reactions are so complex that current computational tools can only model a fraction of all relevant factors at one time — with limited accuracy. This leaves researchers reliant on the trial and error of significant and expensive experimentation.
A new model, based on control theory, uses publicly available data to predict the minimal non-pharmaceutical intervention needed to control COVID-19 based on the vaccination rate in 381 metropolitan statistical areas — cities and their surrounding communities — across the country.
In the wake of the COVID-19 pandemic, restaurants throughout New York City and elsewhere use bespoke outdoor structures to offer safer dining experiences for their customers. However, many of these installations do not adequately protect servers, physically separate diners, provide thermal comfort, or easily disassemble if street maintenance is needed.
Artificial intelligence and machine learning are revolutionizing the ways in which we live, work, and spend our free time, from the smart devices in our homes to the tasks our phones can carry out. This transformation is being made possible by a surge in data and computing power that can help machine learning algorithms not only perform device-specific tasks, but also help them gain intelligence or knowledge over time.
TROY, N.Y. — Optoelectronic materials that are capable of converting the energy of light into electricity, and electricity into light, have promising applications as light-emitting, energy-harvesting, and sensing technologies. However, devices made of these materials are often plagued by inefficiency, losing significant useful energy as heat. To break the current limits of efficiency, new principles of light-electricity conversion are needed.
The future of quantum computing may depend on the further development and understanding of semiconductor materials known as transition metal dichalcogenides (TMDCs). These atomically thin materials develop unique and useful electrical, mechanical, and optical properties when they are manipulated by pressure, light, or temperature.
More strategic and coordinated travel restrictions likely could have reduced the spread of COVID-19 in the early stages of the pandemic. That’s according to new research published in Communications Physics. This finding stems from new modeling conducted by a multidisciplinary team of scientists and engineers at Rensselaer Polytechnic Institute.
The COVID-19 pandemic has exacerbated inequities in urban freight and the delivery of goods. This misalignment in the supply chain is perpetuating food insecurity, especially in areas where grocery store access is limited or non-existent and for those who have limited access to e-commerce.
