Parks play a crucial role in enhancing the quality of life by providing recreational spaces and environmental benefits. Understanding the patterns of park usage, including the number of visitors and their activities, is essential for effective security measures, infrastructure maintenance, and resource allocation. Traditional methods rely on single-entry sensors that count total visits but fail to distinguish unique users, limiting their effectiveness due to manpower and cost constraints.With advancements in affordable video surveillance and networked processing, more comprehensive park usage analysis is now feasible. This study proposes a multi-agent system leveraging low-cost cameras in a distributed network to track and analyze unique users. As a case study, we deployed this system at the Jack A. Markell (JAM) Trail in Wilmington, Delaware, and Hall Trail in Newark, Delaware. The system captures video data, autonomously processes it using existing algorithms, and extracts user attributes such as speed, direction, activity type, clothing color, and gender. These attributes are shared across cameras to construct movement trails and accurately count unique visitors. Our approach was validated through comparison with manual human counts and simulated scenarios under various conditions. The results demonstrate a 72% success rate in identifying unique users, setting a benchmark in automated park activity monitoring. Despite challenges such as camera placement and environmental factors, our findings suggest that this system offers a scalable, cost-effective solution for real-time park usage analysis and visitor behavior tracking.

First son Hunter Biden will be sentenced on Nov. 13, the week after the general election, after he was found guilty on charges in the criminal case focused on his purchase of a handgun in 2018. Judge Maryellen Noreika, in a court order Friday, set the sentencing date for Wednesday, Nov. 13, at 10:00 a.m. at the J. Caleb Boggs Federal Building in Wilmington, Delaware. President Biden's son will learn his fate 8 days after the 2020 presidential election. Hunter Biden was found guilty in June of making a false statement in the purchase of a gun, making a false statement related to information required to be kept by a federally licensed gun dealer, and possession of a gun by a person who is an unlawful user of or addicted to a controlled substance. He faces a total maximum prison time of 25 years for the three charges.

The face-recognition technology behind smartphones, self-driving cars and diagnostic imaging in healthcare has made massive strides of late. These examples all use solutions that make sense of objects in front of them, hence the term "computer vision" -- these computers are able to make sense of what they "see." During a recent Data Lab meetup at CompassRed in downtown Wilmington, Delaware, Chandra Kambhamettu, professor and director of the Video/Image Modeling and Synthesis Lab in the Department of Computer and Information Sciences at the University of Delaware, and Dave Wallin, manager of innovations at The Archer Group, offered a high-level explanation of how image technology works along with the deep learning technology that powers it. Much of the innovation in image recognition relies on deep learning technology, an advanced type of machine learning and artificial intelligence. Typical machine learning takes in data, pushes it through algorithms and then makes a prediction, making it appear that the computer is "thinking" and coming to its own conclusions.

An algorithm is able to identify genetic syndromes in patients more accurately than doctors can -- just by looking at a picture of a patient's face. The results suggest AI could help diagnosis rare disorders. "This is a long-awaited breakthrough in medical genetics that has finally come to fruition," Karen Gripp, a medical geneticist at the Nemours Alfred I. duPont Hospital for Children in Wilmington, Delaware and co-author of the new paper, said in a statement. "With this study, we've shown that adding an automated facial analysis framework … to the clinical workflow can help achieve earlier diagnosis and treatment and promise an improved quality of life." Severe genetic syndromes affect about six percent of children born globally.

Global software provider Katabat has released Katabat Engage, which delivers data-driven debt collections powered by machine learning to consumer lenders. Katabat Engage helps lenders collect more dollars through a platform of personalized, digital communications tailored to customer preferences. Powered by a proprietary machine learning platform that has performed well in Google Kaggle competitions, Engage enables lenders to deploy customized e-mail and SMS text collection messages and continuously tune customer outreach and response strategies. "We are very excited to present Engage to our clients and the broader marketplace," said Katabat CEO Ray Peloso, who recently did an interview with Thrive Global about how machine learning can improve the customer journey. "Our data science team has built a mature and reliable data pipeline for machine learning and continues to demonstrate the power of the platform through its success in several Google Kaggle machine-learning competitions."

Backus was born in Philadelphia and grew up in nearby Wilmington, Del., where he was apparently an indifferent student, according to his biographical entry in the Wikipedia. After a stint in the U.S. Army (during which he was treated for a brain tumor), Backus ended up in New York City, where he gravitated toward mathematics. Earning a master's degree in the discipline in 1949, he joined International Business Machines the following year to work on the firm's Selective Sequence Electronic Calculator. The SSEC was one of the last of the large electromechanical computers ever built. It also was one of the first to run a stored program.