Monitoring construction progress is crucial yet resource-intensive, prompting the exploration of computer-vision-based methodologies for enhanced efficiency and scalability. Traditional data acquisition methods, primarily focusing on indoor environments, falter in construction site's complex, cluttered, and dynamically changing conditions. This paper critically evaluates the application of two advanced 3D segmentation methods, Segment Anything Model (SAM) and Mask3D, in challenging outdoor and indoor conditions. Trained initially on indoor datasets, both models' adaptability and performance are assessed in real-world construction settings, highlighting the gap in current segmentation approaches due to the absence of benchmarks for outdoor scenarios. Through a comparative analysis, this study not only showcases the relative effectiveness of SAM and Mask3D but also addresses the critical need for tailored segmentation workflows capable of extracting actionable insights from construction site data, thereby advancing the field towards more automated and precise monitoring techniques.

The advancement of Connected and Automated Vehicles (CAVs) and Vehicle-to-Everything (V2X) offers significant potential for enhancing transportation safety, mobility, and sustainability. However, the integration and analysis of the diverse and voluminous V2X data, including Basic Safety Messages (BSMs) and Signal Phase and Timing (SPaT) data, present substantial challenges, especially on Connected Vehicle Corridors. These challenges include managing large data volumes, ensuring real-time data integration, and understanding complex traffic scenarios. Although these projects have developed an advanced CAV data pipeline that enables real-time communication between vehicles, infrastructure, and other road users for managing connected vehicle and roadside unit (RSU) data, significant hurdles in data comprehension and real-time scenario analysis and reasoning persist. To address these issues, we introduce the V2X-LLM framework, a novel enhancement to the existing CV data pipeline. V2X-LLM leverages Large Language Models (LLMs) to improve the understanding and real-time analysis of V2X data. The framework includes four key tasks: Scenario Explanation, offering detailed narratives of traffic conditions; V2X Data Description, detailing vehicle and infrastructure statuses; State Prediction, forecasting future traffic states; and Navigation Advisory, providing optimized routing instructions. By integrating LLM-driven reasoning with V2X data within the data pipeline, the V2X-LLM framework offers real-time feedback and decision support for traffic management. This integration enhances the accuracy of traffic analysis, safety, and traffic optimization. Demonstrations in a real-world urban corridor highlight the framework's potential to advance intelligent transportation systems.

The School of Engineering has announced that MIT has granted tenure to 14 members of its faculty in the departments of Biological Engineering, Civil and Environmental Engineering, Electrical Engineering and Computer Science (which reports jointly to the School of Engineering and MIT Schwarzman College of Computing), Materials Science and Engineering, and Mechanical Engineering. "I am truly amazed by our newest cohort of tenured faculty," says Anantha Chandrakasan, dean of the School of Engineering and the Vannevar Bush Professor of Electrical Engineering and Computer Science. "They are a diverse group of educators and scholars whose research and commitment to teaching has had a tremendous impact on our community, in the classroom, as well as in the lab." This year's newly tenured associate professors are: Guy Bresler, an associate professor of electrical engineering and computer science, conducts research at the interface of information theory, statistics, theoretical computer science, and probability. His work aims to understand the fundamental interplay between information properties, computational complexity, and combinatorial structure in modern statistical inference problems.

The grant, from the National Institute of Child Health and Human Development, extends the Duke Autism Center of Excellence research program for an additional 5 years. Geraldine Dawson, Ph.D., director of the Duke Center for Autism and Brain Development and professor of psychiatry and behavioral sciences, will lead a team of researchers that includes Duke faculty from psychiatry, pediatrics, biostatistics and bioinformatics, computer and electrical engineering, and civil and environmental engineering. "We are thrilled to receive this award, which allows Duke to remain at the forefront of autism research," Dawson said. "Our goal is to use advanced computational techniques to develop better methods for autism screening that will reduce known disparities in access to early diagnosis and intervention." In a project led by Dawson and Guillermo Sapiro, Ph.D., professor of electrical and computer engineering, researchers will test a digital app, used by parents at home on a smart phone, to videotape young children's behavior and interactions with their caregivers.

The difficulty and expense of collecting river water samples in remote areas has led to significant -- and in some cases, decades-long -- gaps in available water chemistry data, according to a Penn State-led team of researchers. The team is using artificial intelligence (AI) to predict water quality and fill the gaps in the data. Their efforts could lead to an improved understanding of how rivers react to human disturbances and climate change. The researchers developed a model that forecasts dissolved oxygen (DO), a key indicator of water's capability to support aquatic life, in lightly monitored watersheds across the United States. They published their results in Environmental Science & Technology.

In their 2019 AI Impact Challenge, Google asked nonprofits, social enterprises and research institutions around the world, "How would you use artificial intelligence (AI) for social good?" "We had a good idea that was looking for such an opportunity," said Chaopeng Shen, associate professor of civil and environmental engineering at Penn State and principal investigator of "deepLDB," one of 20 projects awarded funding by Google in the challenge last year. "Rainfall-induced landslides are a huge risk for people who live in mountainous areas, and we thought there was a possibility to use AI to better forecast them." Worldwide, landslides cause thousands of deaths and injuries and cost billions of dollars each year, according to the United States Geological Survey (USGS). The most frequent of these are induced by rainfall, often transforming into fast-moving debris flows like the Montecito, California mudslides in 2018. But Shen said that many of these events also go unreported, complicating efforts to study and eventually predict them.

USC Viterbi Professors Burcin Becerik-Gerber and Gale Lucas launch CENTIENTS, a center aimed at fostering research and collaboration toward human-centered design and integration of intelligent technologies into built environments. In the 1960s cartoon The Jetsons, the future was a world full of self-driving cars and sassy, meticulous robots. Individuals, like the patriarch George, could move through space--and the shower--without having to lift a finger. The mechanisms around him played a pivotal role in making decisions on his behalf, based on a learned understanding of his most basic preferences. For a while, this future seemed distant, but upon us now is an unprecedented opportunity to merge human behavior and preferences with automation to create a personalized, dynamic and improved daily reality for individuals at work and at home.

In the quest to better understand climate change, there is plenty we still don't know. But the question isn't whether or not climate change is happening. "What we sometimes hear on the news is political manufactured uncertainty," said Auroop Ganguly, a professor of civil & environmental engineering at Northeastern. Instead, real climate change uncertainty stems from the challenge of simulating the future. What will happen to Boston's electric grid under long-term extreme weather conditions?

Floods caused by extreme precipitation are one of the most frequent and widespread natural hazards. They are more costly and dangerous than ever, as population in urban areas increases and the global climate becomes more extreme and variable. Data shows that each year there are more than a hundred million people affected by flood events with a cost of more than $100 billion.1 While accurate precipitation monitoring is a key element for improving flood forecasting, traditional means of precipitation observation, such as ground-based gauges and radars, are limited in their spatial coverage. Recent advances in satellite remote sensing techniques have enabled precipitation observation in remote and ungauged regions to help hydrologists better forecast floods and manage water resources.

The standard way to measure the impact of drought is by its economic effect. Last year, for example, the severity California's four-year drought was broadly characterized by an estimate that it would cost the state's economy 2.7 billion and 21,000 jobs. However, there are many experts who feel economic measures alone are inadequate to fully assess the impact of this complex phenomenon, which affected more than one billion people worldwide in the last decade. They argue that there is an urgent need to come up with better methods for measuring the overall effects of drought because the duration and severity of droughts are widely expected to increase in the future due to global warming. To provide such a comprehensive view, a pair of Vanderbilt doctoral students has assembled a multi-disciplinary team of graduate students from around the country to conduct a multi-faceted study of how people are affected by and responding to drought conditions in the United States.