-- Collaborative robots (cobots) increasingly operate alongside humans, demanding robust real-time safeguarding. Current safety standards (e.g., ISO 10218, ANSI/RIA 15.06, ISO/TS 15066) require risk assessments but offer limited guidance for real-time responses. We propose a virtual fencing approach that detects and predicts human motion, ensuring safe cobot operation. Safety and performance tradeoffs are modeled as an optimization problem and solved via sequential quadratic programming. Experimental validation shows that our method minimizes operational pauses while maintaining safety, providing a modular solution for human-robot collaboration. I. INTRODUCTION Cobots, short for collaborative robots, have gained significant traction in various fields, such as manufacturing, assembly, service, education, and healthcare, due to their ability to seamlessly interact with humans while ensuring their physical and mental well-being [1]-[3].

Investigating the public experience of urgent care facilities is essential for promoting community healthcare development. Traditional survey methods often fall short due to limited scope, time, and spatial coverage. Crowdsourcing through online reviews or social media offers a valuable approach to gaining such insights. With recent advancements in large language models (LLMs), extracting nuanced perceptions from reviews has become feasible. This study collects Google Maps reviews across the DMV and Florida areas and conducts prompt engineering with the GPT model to analyze the aspect-based sentiment of urgent care. We first analyze the geospatial patterns of various aspects, including interpersonal factors, operational efficiency, technical quality, finances, and facilities. Next, we determine Census Block Group(CBG)-level characteristics underpinning differences in public perception, including population density, median income, GINI Index, rent-to-income ratio, household below poverty rate, no insurance rate, and unemployment rate. Our results show that interpersonal factors and operational efficiency emerge as the strongest determinants of patient satisfaction in urgent care, while technical quality, finances, and facilities show no significant independent effects when adjusted for in multivariate models. Among socioeconomic and demographic factors, only population density demonstrates a significant but modest association with patient ratings, while the remaining factors exhibit no significant correlations. Overall, this study highlights the potential of crowdsourcing to uncover the key factors that matter to residents and provide valuable insights for stakeholders to improve public satisfaction with urgent care.

As telecommunications networks become increasingly complex, the integration of advanced technologies such as network digital twins and generative artificial intelligence (AI) emerges as a pivotal solution to enhance network operations and resilience. This paper explores the synergy between network digital twins, which provide a dynamic virtual representation of physical networks, and generative AI, particularly focusing on Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). We propose a novel architectural framework that incorporates these technologies to significantly improve predictive maintenance, network scenario simulation, and real-time data-driven decision-making. Through extensive simulations, we demonstrate how generative AI can enhance the accuracy and operational efficiency of network digital twins, effectively handling real-world complexities such as unpredictable traffic loads and network failures. The findings suggest that this integration not only boosts the capability of digital twins in scenario forecasting and anomaly detection but also facilitates a more adaptive and intelligent network management system.

Supply chain and logistics industries worldwide lose over $1 trillion a year due to out-of-stock or overstocked items1. Shifting demands and shipping difficulties make the situation worse. Challenges in inventory management, demand forecasting, price optimization, and more can result in missed opportunities and lost revenue. The retail marketplace has become increasingly complex and competitive. Keeping pace with the connected consumer, embracing emerging trends in shopping, or staying ahead of the competition--these challenges bear down on retailers and manufacturers greater than ever before.

In the financial services industry, banks, insurers, asset managers and fintech companies are increasing the speed at which they deploy artificial intelligence (AI)-enabled applications, confident that AI will help them assess risk more accurately, enable operational efficiencies, and reduce costs, results from a new study by American tech firm Nvidia show. The 2023 State of AI in Financial Services report, released on February 02, 2023, draws on a survey of nearly 500 global financial services professionals that sought to understand AI trends in the sector, as well as the opportunities perceived and challenges faced by the industry. Results from the study show that the adoption of AI in the finance sector is accelerating at a fast pace, with over half of the respondents indicating having deployed three or more of the 21 different AI-enabled use cases analyzed by the survey. A fifth of respondents said they had six or more use cases in market. Accelerated adoption of AI in the sector comes on the back of increased awareness of the imperative among executive leadership teams.

The world of video analytics has come a long way in the past few years. What started as a complementary security surveillance technology, has evolved into a critical decision-making solution for stakeholders beyond law enforcement and public safety. Powered by AI and deep learning, today's sophisticated video analytics have far-reaching and impactful applications, from accelerating investigations for criminal or commercial claims to increasing operational productivity across industries and end users, delivering cost efficiency, enhanced safety, and elevated experiences. These applications only continue to gain strength, and in this article, I'll walk you through some examples of diverse industries innovatively supporting operational and business decision making with the power of data-driven intelligence derived from video analytics. But first, a quick word on how it works: Video intelligence software detects and extracts objects in video, identifies each object based on trained Deep Neural Networks, and classifies each object to enable intelligent video analysis through search and filtering, alerting, data aggregation, and visualisation capabilities.

Are you considering how to use AI in telecommunications for your business? In this article, we explore the potential of AI and its implications for digital transformation. Discover how using AI can help your business reach new heights and stay ahead of the competition in an ever-evolving world of technology. What is AI and what does it do? AI is a process of programming computers to make decisions for themselves.

Opinions abound on what's right and wrong with our U.S. healthcare system, but there's one thing most can agree on: There's a need to transform the experience for patients, providers and payers. The Covid-19 pandemic served as a catalyst for us to relook at and reimagine the digitization of the healthcare system. The strategic adoption of artificial intelligence (AI) could be transformational, but technology leaders at healthcare organizations are constrained by stringent compliance requirements and security concerns. And their fears aren't unfounded--any data breach could be catastrophic. Trust in AI doesn't come easily--one must tread cautiously, particularly in this industry.

The new funding will enable Ubie to accelerate its growth and strengthen its presence in the U.S., following strong interest and traction in that market. To date, Ubie has raised $76 million in total. It will also focus on expanding its business to the U.S. in order to apply the technology it has developed in Japan, one of the leading countries in the medical field. This is the second overseas corporation following Singapore. The decision follows the fact that the number of users has been increasing steadily since the release of the AI symptom checker in April 2022 in the U.S. The establishment of the local subsidiary will further strengthen the partnership with U.S. pharmaceutical companies, as well as the products, including hiring local people in the U.S. Ubie is a Japanese health-tech startup founded by a medical doctor and an engineer in 2017.

Automation represents perhaps the clearest embodiment of Benjamin Franklin's legendary "time is money" aphorism -- and artificial intelligence (AI)-driven networks are one area where it's relatively easy to see the near-term benefits that give new meaning to Franklin's simple phrase. Network automation simplifies operations for network teams and reduces configuration errors. So, it stands to reason that greater automation through AI will deliver a more predictable and reliable network that seemingly can speed up time while saving lots of money. We turned to the CIO Experts Network of IT professionals and industry analysts to collect their views on AI-driven networks and how the technology is likely to change the lives of network teams. "I think of an AI-driven network as one that can be prepared in advance of a catastrophe or breach by capturing and saving critical data prior to a network outage or cyber event," says Scott Schober (@ScottBVS), President/CEO at Berkeley Varitronics Systems, Inc. "When this is an integrated part of the network, troubleshooting time is reduced delivering improved efficiencies for network teams. Still, like all things AI, it's necessary to sort what's real from what's hype, experts say. Hyped up AI technologies are often rolled out as the solution to all problems, observes Nicki Doble, Chief Transformation Officer AIA Philippines. "I don't buy into the hype," she says. "However, I absolutely agree that an AI-driven network helps in detecting new and never seen before threats.