Jim Pollard is bringing bison bones, fossilized mammoth poop, and more to Blue Earth's 3,000 residents. The Southern Minnesota Museum of Natural History features fossils that range from 7,000 to 7 million years-old. Breakthroughs, discoveries, and DIY tips sent every weekday. When Jim Pollard first laid eyes on the small town of Blue Earth, Minnesota, he was out on a fossil digging trip with his sons. The town on the border of Iowa is surrounded by potential discovery sites -Quarry Hill Nature Center, Shadow Falls Park, and Lilydale Regional Park are all within a few hours drive away.

This paper investigates the application of machine learning regression algorithms Kernel Ridge Regression (KRR), Huber Regressor (HR), and Gaussian Process Regression (GPR) for predicting sound power levels of gensets, offering significant value for marketing and sales teams during the early bidding process. When engine sizes and genset enclosure dimensions are tentative, and measured noise data is unavailable, these algorithms enable reliable noise level estimation for unbuilt gensets. The study utilizes high fidelity datasets from over 100 experiments conducted at Cummins Acoustics Technology Center (ATC) in a hemi-anechoic chamber, adhering to ISO 3744 standards. By using readily available information from the bidding and initial design stages, KRR predicts sound power with an average accuracy of within 5 dBA. While HR and GPR show slightly higher prediction errors, all models effectively capture the overall noise trends across various genset configurations. These findings present a promising method for early-stage noise estimation in genset design.

Accurate shape sensing, only achievable through distributed proprioception, is a key requirement for closed-loop control of soft robots. Low-cost power efficient optoelectronic sensors manufactured from flexible materials represent a natural choice as they can cope with the large deformations of soft robots without loss of performance. However, existing integration approaches are cumbersome and require manual steps and complex assembly. We propose a semi-automated printing process where plastic optical fibers are embedded with readout electronics in 3D printed flexures. The fibers become locked in place and the readout electronics remain optically coupled to them while the flexures undergo large bending deformations, creating a repeatable, monolithically manufactured bending transducer with only 10 minutes required in total for the manual embedding steps. We demonstrate the process by manufacturing multi-material 3D printed fingers and extensively evaluating the performance of each proprioceptive joint. The sensors achieve 70% linearity and 4.81{\deg} RMS error on average. Furthermore, the distributed architecture allows for maintaining an average fingertip position estimation accuracy of 12 mm in the presence of external static forces. To demonstrate the potential of the distributed sensor architecture in robotics applications, we build a data-driven model independent of actuation feedback to detect contact with objects in the environment.

Address matching is an important task for many businesses especially delivery and take out companies which help them to take out a certain address from their data warehouse. Existing solution uses similarity of strings, and edit distance algorithms to find out the similar addresses from the address database, but these algorithms could not work effectively with redundant, unstructured, or incomplete address data. This paper discuss semantic Address matching technique, by which we can find out a particular address from a list of possible addresses. We have also reviewed existing practices and their shortcoming. Semantic address matching is an essentially NLP task in the field of deep learning. Through this technique We have the ability to triumph the drawbacks of existing methods like redundant or abbreviated data problems. The solution uses the OCR on invoices to extract the address and create the data pool of addresses. Then this data is fed to the algorithm BM-25 for scoring the best matching entries. Then to observe the best result, this will pass through BERT for giving the best possible result from the similar queries. Our investigation exhibits that our methodology enormously improves both accuracy and review of cutting-edge technology existing techniques.

An automated Veterans Affairs system meant to help accelerate claims decisions actually helped contribute to inaccurate ratings on 27% of high blood pressure claims. A VA Office of the Inspector General (OIG) report published last week found that more than a quarter of the 60 reviewed high blood pressure claims that were handled by the Automated Benefits Delivery System resulted in wrongful claims decisions for veterans, according to a report from Military.com. The system was introduced in December 2021, ahead of what the VA believed was going to be a "flood" of disability applications as a result of the PACT Act, with Vietnam-era veterans filing high blood pressure claims under the act after their exposure to Agent Orange, an exposure linked to hypertension. 'WE'RE HUMAN': DELTA FORCE VETERAN REFLECTS ON BATTLE OF MOGADISHU 30 YEARS LATER The automated system was designed to pull blood pressure readings and other high blood pressure data from VA treatment recons and create a summary that is reviewed by VA staff, who make the final decision on the claim. But incomplete data compiled by the system led to several incorrect decisions, the IG's office found in its review, which recommended that the VA make improvements to the technology and the quality assurance process.

FOX Business correspondent Lydia Hu has the latest on jobs at risk as AI further develops on'America's Newsroom.' President Biden will meet with science and technology advisers on Wednesday to discuss the "risks and opportunities" that artificial intelligence technologies pose for Americans and national security. A White House official said the president would focus on discussing the importance of protecting rights and safety to ensure there are appropriate safeguards and innovation is responsible. Furthermore, Biden will call on Congress to pass bipartisan legislation to protect children and to limit the personal data tech companies collect. The Council of Advisors on Science and Technology, or PCAST, is a federal advisory committee composed of experts outside the federal government charged with making science, technology and innovation policy recommendations to the White House.

The robots were Joe McGillivray's idea. The first one arrived at Dynamic Group in Ramsey, Minn., by pickup truck in two cardboard boxes. With a mixture of excitement and trepidation, McGillivray watched as a vendor unpacked two silver tubes, assorted blue-and-gray joints and a touch screen and put them all together. When he was finished 10 minutes later, McGillivray beheld an arm that, had its segments not all been able to swivel 360 degrees, might have belonged to a very large N.B.A. player or a fairly small giant. Its "shoulder" was mounted to a waist-high pedestal on wheels. If it were to hail someone across the room, its "elbow" would reach eye level. Below its "wrist," which was triple-jointed for extra dexterity, there were sockets for various attachments. McGillivray, not sure yet if he wanted to keep the contraption, stuck a piece of clear tape to the wrist and drew a happy face on it, which made the arm look a bit as if it were putting on a puppet show. He hoped that this would help it look nonthreatening.

The patter of automated machinery fills the air inside wire-basket manufacturer Marlin Steel's bustling factory in a rugged industrial section of this city. Maxi Cifarelli, 25, of Baltimore, peers through safety goggles at a flat screen, her left knee bent and heel resting on her chair. Two years after earning a fine arts degree from Towson University with a specialty in interdisciplinary object design, she now spends her work days working with a personality-free machine with a name to match: a computer numerical control, or CNC, router. With automation poised to sweep through the economy, some fear that it will kill more jobs than it creates. But Cifarelli's experience is the opposite. She befriended automation, instead of fighting it, and she has a job because of it.

The patter of automated machinery fills the air inside wire-basket manufacturer Marlin Steel's bustling factory in a rugged industrial section of this city. Maxi Cifarelli, 25, of Baltimore, peers through safety goggles at a flat screen, her left knee bent and heel resting on her chair. Two years after earning a fine arts degree from Towson University with a specialty in interdisciplinary object design, she now spends her work days working with a personality-free machine with a name to match: a computer numerical control, or CNC, router. With automation poised to sweep through the economy, some fear that it will kill more jobs than it creates. But Cifarelli's experience is the opposite. She befriended automation, instead of fighting it, and she has a job because of it.