Collaborating Authors

Supply Chain Automation: Robotics & AI in the Supply Chain


Automation is coming to every industry, and nowhere is this more true than in the supply chain. Indeed, coming advances in automation technology could potentially reshape the entire supply chain--and supply chain management software--as we know it. Forward-thinking supply chain players are already preparing themselves for the new landscape. CIOs should be leading this charge to drive the best possible outcomes for their organizations, but it's important to understand the nature of robotics, AI, and automation in the supply chain first. Supply chain automation is a broad term that refers to technology that reduces or eliminates human involvement in supply chain activities.

Hyundai announces $400M AI, robotics institute – TechCrunch


When Hyundai acquired Boston Dynamics at the end of 2020, there were plenty of open questions. Chief among them was why we should assume this acquisition would be any different than the past few? The 30-year-old bleeding-edge robotics firm had been an uncomfortable fit for its last two owners, Google and SoftBank, but the Korean automotive giant insisted things would be different. The pairing has, thus far, been something of a mixed bag. As Boston Dynamics looks to pragmatic applications to commercialize robots like Spot and Stretch, Hyundai has used the technology for some wild sci-fi demos, including one at this year's CES featuring Spot hanging out on Mars as a metaverse avatar.

Artificial intelligence was supposed to transform health care. It hasn't. – POLITICO


Machine learning could improve medicine by analyzing data to improve diagnoses and target cures, but technological, bureaucratic, and regulatory …

White House Deputy CTO and National AI Director Lynne Parker to step down – FedScoop


Dr. Lynne Parker, Deputy Chief Technology Office and Director of the National Artificial Intelligence Initiative Office within the White House, …

How Fear Restructures the Mouse Brain


Neurons communicate via synapses--tiny, button-like protrusions that sprout from one neuron and connect it to the next. These minuscule structures are thought to be the backbone of learning and memory, changing in strength and number as we learn. At about 1/5,000th the width of a human hair, synapses can be hard to visualize, and researchers are just beginning to develop the tools necessary to do so. In a study published in Cell Reports on August 2, researchers at the Chinese Academy of Sciences and Shanghai University used a combination of deep learning algorithms and high-resolution electron microscopy to map out how frightful experiences rearrange brain connections. They found that when mice learn to fear the sound of a buzzer, neurons in their hippocampus form more connections with other neurons downstream and shuttle more mitochondria to synaptic sites.

Tibetan Plateau Water Stores Under Threat: Study

International Business Times

The Tibetan Plateau will experience significant water loss this century due to global warming, according to research published Monday that warns of severe supply stress in a climate change "hotspot". The reservoirs of the Tibetan Plateau, which covers much of southern China and northern India, are fed by monsoons and currently supply most of the water demand for nearly two billion people. But the plateau's complex terrain has made it difficult for scientists to predict how warming temperatures and altered weather patterns linked to climate change will affect the region's water stores. Researchers based in China and the United States used satellite-based measurements to determine the net change in water and ice mass over the past two decades. They added in direct measurements of glaciers, lakes and sub-surface water levels to estimate changes in the water mass, then used a machine learning technique to predict storage changes under scenarios such as higher air temperature and reduced cloud cover.

Deep Learning, Subtraction Technique Optimal for Coronary Stent Evaluation by CTA


According to ARRS' American Journal of Roentgenology (AJR), the combination of deep-learning reconstruction (DLR) and a subtraction technique yielded optimal diagnostic performance for the detection of in-stent restenosis by coronary CTA. Noting that these findings could guide patient selection for invasive coronary stent evaluation, combining DLR with a two-breath-hold subtraction technique "may help overcome challenges related to stent-related blooming artifact," added corresponding author Yi-Ning Wang from the State Key Laboratory of Complex Severe and Rare Diseases at China's Peking Union Medical College Hospital. Between March 2020 and August 2021, Wang and team studied 30 patients (22 men, 8 women; mean age, 63.6 years) with a total of 59 coronary stents who underwent coronary CTA using the two-breath-hold technique (i.e., noncontrast and contrast-enhanced acquisitions). Conventional and subtraction images were reconstructed for hybrid iterative reconstruction (HIR) and DLR, while maximum visible in-stent lumen diameter was measured. Two readers independently evaluated images for in-stent restenosis ( 50% stenosis).