This research provides a novel framework to estimate the economic, environmental, and social values of electrifying public transit buses, for cities across the world, based on open-source data. Electric buses are a compelling candidate to replace diesel buses for the environmental and social benefits. However, the state-of-art models to evaluate the value of bus electrification are limited in applicability because they require granular and bespoke data on bus operation that can be difficult to procure. Our valuation tool uses General Transit Feed Specification, a standard data format used by transit agencies worldwide, to provide high-level guidance on developing a prioritization strategy for electrifying a bus fleet. We develop physics-informed machine learning models to evaluate the energy consumption, the carbon emissions, the health impacts, and the total cost of ownership for each transit route. We demonstrate the scalability of our tool with a case study of the bus lines in the Greater Boston and Milan metropolitan areas. Detailed Affiliation: U.Vijay, S.Woo, and S.J.Moura are at Department of Civil and Environmental Engineering, University of California-Berkeley, Davis Hall, Berkeley, California, 94720, USA. A.Jain is at Department of Electrical Engineering and Computer Sciences, University of California-Berkeley, Soda Hall, Berkeley, California, 94720, USA. D.Rodriguez and E.Mellekas are at Enel X, North America, Inc., One Marina Park Drive, Boston, 02210, MA, USA. S. Gambacorta is at Enel X, Innovation and Sustainability Global, Smart City, Viale Tor di Quinto, Rome, 00191, Italy. G.Ferrara is at Enel X, Innovation and Sustainability Global, Smart City, Passo Martino, Catania, 95121, Italy. L.Lanuzza is at Enel X, Innovation and Sustainability B2C & B2B Innovation Factory, Viale Tor di Quinto, Rome, 00191, Italy. C.Zulberti and C.Papa are at Enel Foundation, Via Bellini, Rome, 00198, Italy. Vehicle electrification is crucial for reducing the climate impact of the transportation sector, which currently accounts for 16.2% of the global greenhouse gas emissions [22]. Zero-emission electric vehicles can significantly improve the air quality, health, and environmental equity [23], [24].

With the fast-growing number of satellites orbiting Earth, the Space Operations field has become a prominent and thriving sector. As a consequence, the complexity of planning satellite operations is constantly increasing: Ground Stations have to handle communication with multiple satellites simultaneously while frequently engaged in Launch and Early Orbit Phase (LEOP) activities; Satellite Operators need to perform routine tasks and promptly react to contingencies while checking the status of the incoming and disseminated satellite's products. These actions are costly, require time, and are remarkably prone to human errors. Despite this, Satellite Operators still carry out many of these duties by relying on their technical expertise rather than leveraging modern machine learning tools. On the other hand, computers, hardware, and flight software are becoming more sophisticated with each passing day.

Urbanization and its problems require an in-depth and comprehensive understanding of urban dynamics, especially the complex and diversified lifestyles in modern cities. Digitally acquired data can accurately capture complex human activity, but it lacks the interpretability of demographic data. In this paper, we study a privacy-enhanced dataset of the mobility visitation patterns of 1.2 million people to 1.1 million places in 11 metro areas in the U.S. to detect the latent mobility behaviors and lifestyles in the largest American cities. Despite the considerable complexity of mobility visitations, we found that lifestyles can be automatically decomposed into only 12 latent interpretable activity behaviors on how people combine shopping, eating, working, or using their free time. Rather than describing individuals with a single lifestyle, we find that city dwellers' behavior is a mixture of those behaviors. Those detected latent activity behaviors are equally present across cities and cannot be fully explained by main demographic features. Finally, we find those latent behaviors are associated with dynamics like experienced income segregation, transportation, or healthy behaviors in cities, even after controlling for demographic features. Our results signal the importance of complementing traditional census data with activity behaviors to understand urban dynamics.

Information loss in numerical physics simulations can arise from various sources when solving discretized partial differential equations. In particular, errors related to numerical precision ("sub-precision errors") can accumulate in the quantities of interest when the simulations are performed using low-precision 16-bit floating-point arithmetic compared to an equivalent 64-bit simulation. On the other hand, low-precision computation is less resource intensive than high-precision computation. Several machine learning techniques proposed recently have been successful in correcting errors due to coarse spatial discretization. In this work, we extend these techniques to improve CFD simulations performed with low numerical precision. We quantify the precision-related errors accumulated in a Kolmogorov forced turbulence test case. Subsequently, we employ a Convolutional Neural Network together with a fully differentiable numerical solver performing 16-bit arithmetic to learn a tightly-coupled ML-CFD hybrid solver. Compared to the 16-bit solver, we demonstrate the efficacy of the hybrid solver towards improving various metrics pertaining to the statistical and pointwise accuracy of the simulation.

A new report from MIT and Deep Instinct seeks to dispel confusion in the cybersecurity market ... [ ] between artificial intelligence, machine learning, and deep learning. There are a lot of buzzwords in the world of cybersecurity marketing. When an emerging concept hits a certain viral tipping point, it seems like suddenly all vendors are using the same buzzword--which just makes everything more confusing. Artificial intelligence and machine learning are ubiquitous in cybersecurity marketing--and often confused with each other and with deep learning. A recent report from MIT clarifies the distinction between the three, and emphasizes the value of deep learning for more effective cybersecurity.

Humans have left more than 15,000 pounds of trash on Mars in the last 50 years and not a single person has ever stepped foot on the red planet. Cagri Kilic, a postdoctoral research fellow in robotics at West Virginia University, analyzed the mass of all rovers and orbiters sent to Mars and subtracted the weight of what is currently in operation, resulting in 15,694 pounds of debris. The trash includes discarded hardware, inactive spacecraft and those that crashed on the surface - specifically the Soviet Union's Mars orbiter 2 that made a crash landing in 1971. Not only are humans already polluting another planet, but scientists fear the debris could contaminate samples being collected by NASA's Perseverance rover that is currently searching for ancient life on Mars. A scientist calculates there is 15,694 pounds of trash on Mars. Most of it stems from discarded hardware like this thermal blanket that protected NASA's Perseverance survive its descent through the hellish atmosphere Much of the garbage is inevitable, as many of the parts have to be discarded in order to protect craft as it soars through the Red Planet's hellish atmosphere - including NASA's Perseverance that endured the seven minutes of hell when it landed in February 2021.

In the first-of-its kind, save-the-world experiment, NASA is about to clobber a small, harmless asteroid millions of miles away. A spacecraft named Dart will zero in on the asteroid Monday, intent on slamming it head-on at 14,000 mph (22,500 kph). The impact should be just enough to nudge the asteroid into a slightly tighter orbit around its companion space rock -- demonstrating that if a killer asteroid ever heads our way, we'd stand a fighting chance of diverting it. "This is stuff of science-fiction books and really corny episodes of "StarTrek" from when I was a kid, and now it's real," NASA program scientist Tom Statler said Thursday. Cameras and telescopes will watch the crash, but it will take days or even weeks to find out if it actually changed the orbit.

The software-defined nature of the HRL131 means it is situationally aware, with the ability to adapt its scan pattern depending on the driving scenario to maximize safety. It's critical that manufacturers be able to test and validate these performance modes and the product's performance in diverse situations, which NVIDIA DRIVE Sim will uniquely enable.

There is a lot of excitement in healthcare about the use of artificial intelligence (AI) to improve clinical decision-making. Pioneered by the likes of IBM Watson for Healthcare and DeepMinds Healthcare, AI promises to help specialists diagnose patients more accurately. Two years ago, McKinsey co-produced a report with the European Union's EIT Health to explore the potential for AI in healthcare. Among the key opportunities the report's authors found were in healthcare operations: diagnostics, clinical decision support, triage and diagnosis, care delivery, chronic care management and self-care. "First, solutions are likely to address the low-hanging fruit of routine, repetitive and largely administrative tasks, which absorb significant time of doctors and nurses, optimising healthcare operations and increasing adoption," they wrote.

Children with cerebral palsy might soon use technology to gain some independence. UC Riverside researchers are developing robotic sleeves that provide arm control to kids with cerebral palsy-related mobility issues. Rather than augment the arm like an exoskeleton, the technology will use voltage sensors to detect muscle contractions and predict what the wearer wants to do, like bend the elbow. Inflatable bladders will then push the arm toward the intended destination. Soft robotics will play an important role.