Ensuring worker safety remains a critical challenge in modern manufacturing environments. Industry 5.0 reorients the prevailing manufacturing paradigm toward more human-centric operations. Using a design science research methodology, we identify three essential requirements for next-generation safety training systems: high accuracy, low latency, and low cost. We introduce a multimodal chatbot powered by large language models that meets these design requirements. The chatbot uses retrieval-augmented generation to ground its responses in curated regulatory and technical documentation. To evaluate our solution, we developed a domain-specific benchmark of expert-validated question and answer pairs for three representative machines: a Bridgeport manual mill, a Haas TL-1 CNC lathe, and a Universal Robots UR5e collaborative robot. We tested 24 RAG configurations using a full-factorial design and assessed them with automated evaluations of correctness, latency, and cost. Our top 2 configurations were then evaluated by ten industry experts and academic researchers. Our results show that retrieval strategy and model configuration have a significant impact on performance. The top configuration (selected for chatbot deployment) achieved an accuracy of 86.66%, an average latency of 10.04 seconds, and an average cost of $0.005 per query. Overall, our work provides three contributions: an open-source, domain-grounded safety training chatbot; a validated benchmark for evaluating AI-assisted safety instruction; and a systematic methodology for designing and assessing AI-enabled instructional and immersive safety training systems for Industry 5.0 environments.

Machine learning (ML), and deep learning (DL) in particular, is currently one of the most common data analysis approaches in applications. Deep models handle large amounts of input data, training many layers, but in most cases, their functioning is not transparent. In this regard they are often called black boxes [Saleem et al., 2022]. Decision-making process in such deep architectures is difficult to explain, raising concerns about the trustworthiness of such models and the security of their deployment. The problem of explainability of artificial intelligence (AI) models has received much attention [Baehrens et al., 2010, Lipton, 2018, Samek et al., 2017, Simonyan et al., 2014], and made eXplainable Artificial Intelligence (XAI) an important area of AI [Nauta et al., 2023]. Major goals of XAI are to develop approaches capable of uncovering the grounds behind model decision-making, and, more profoundly, to develop interpretable and logically explainable models. XAI explanations must be understandable, reliable, whereas the explained models must retain predictive accuracy [Saleem et al., 2022].

Background: While deep learning technology, which has the capability of obtaining latent representations based on large-scale data, can be a potential solution for the discovery of a novel aging biomarker, existing deep learning methods for biological age estimation usually depend on chronological ages and lack of consideration of mortality and morbidity that are the most significant outcomes of aging. Methods: This paper proposes a novel deep learning model to learn latent representations of biological aging in regard to subjects' morbidity and mortality. The model utilizes health check-up data in addition to morbidity and mortality information to learn the complex relationships between aging and measured clinical attributes. Findings: The proposed model is evaluated on a large dataset of general populations compared with KDM and other learning-based models. Results demonstrate that biological ages obtained by the proposed model have superior discriminability of subjects' morbidity and mortality.

We're starting today's 3D Printing News Briefs with a new system announcement, as Farsoon just introduced its FS200M 2 platform to the AMEA and North America AM market. Moving on, Senvol and Northrop Grumman presented together at RAPID about using machine learning to improve process parameter optimization. Finally, United Performance Metals announced a new Additive Manufacturing Solutions Center, and a new Innovation Centre for advanced materials & digitalization was established by TWI and Manchester Metropolitan University. Farsoon Technologies has introduced the latest addition to its medium-size metal LPBF line--the FS200M 2 platform, with a powerful dual 500-watt laser configuration and 425 x 230 x 300 mm build volume. The company says the versatile, compact printer offers maximized productivity and turn-over rates, and is well-suited for medium to high volume metal series production and prototyping.

It's a cloudy day in early October and I'm circling my rented Jeep Wrangler around a maze of industrial buildings in Hamilton, Ohio. Hamilton is a small city 30 miles north of Cincinnati with a population of just over 62,000 people. Like much of Ohio, farming is important here. I'm on my way to a farm called 80 Acres, but it isn't the sprawling midwestern wheat field you're picturing in your mind. This tech-centric farm is indoors, housed entirely in a nondescript 10,000-square-foot warehouse.

AgFunderNews can reveal that the deal was worth more than $40 million in equity capital, according to sources close to the deal. The funding will go towards the completion of 80 Acres' Hamilton, Ohio facility, which was announced last year and is set to be partially operational in the next few months. It will be automated from seeding to growing to harvesting featuring handling robotics, artificial intelligence, data analytics, and around-the-clock monitoring sensors and control systems to optimize every aspect of growing produce indoors. The company also has facilities -- that are more manually operated -- in Arkansas, North Carolina and Alabama from which it serves local major national grocers, local retailers, restaurants, and food service companies with leafy greens, tomatoes, micro greens and herbs. It expects to add grapes and strawberries in the near future.