Modern businesses are obligated to conform to regulations to prevent physical injuries and ill health for anyone present on a site under their responsibility, such as customers, employees and visitors. Safety officers (SOs) are engineers, who perform site audits to businesses, record observations regarding possible safety issues and make appropriate recommendations. In this work, we develop a multimodal machine-learning architecture for the analysis and categorization of safety observations, given textual descriptions and images taken from the location sites. For this, we utilize a new multimodal dataset, Safety4All, which contains 5344 safety-related observations created by 86 SOs in 486 sites. An observation consists of a short issue description, written by the SOs, accompanied with images where the issue is shown, relevant metadata and a priority score. Our proposed architecture is based on the joint fine tuning of large pretrained language and image neural network models. Specifically, we propose the use of a joint task and contrastive loss, which aligns the text and vision representations in a joint multimodal space. The contrastive loss ensures that inter-modality representation distances are maintained, so that vision and language representations for similar samples are close in the shared multimodal space. We evaluate the proposed model on three tasks, namely, priority classification of input observations, observation assessment and observation categorization. Our experiments show that inspection scene images and textual descriptions provide complementary information, signifying the importance of both modalities. Furthermore, the use of the joint contrastive loss produces strong multimodal representations and outperforms a baseline simple model in tasks fusion. In addition, we train and release a large transformer-based language model for the Greek language based on the Electra architecture.

A significant challenge for Maintenance Management professionals is related to the need for them to possess a truly multi-disciplinary set of competencies. This paper presents an analysis of Maintenance Management training and competence assessment requirements, including results of a user survey conducted in EU countries. On the basis of this, a maintenance management curriculum has been defined. The design principles for a e-learning and self-assessment tool for maintenance management training and competencies assessment are outlined. The iLearn2Main tool has been developed based on an open source Learning Management System and is currently delivered in five European languages. It has been pilot-tested by user groups from industry and academia. The evaluation results analysis show high acceptance rates for the education tools, visually-appealing and well-structured content are crucial for the user acceptance of maintenance management e-training.

Large language models (LLMs) are increasingly used to interpret mechanical engineering drawings, yet the main risk is a wrong answer that reads as correct. We report an exploratory empirical study of how prompt design and evaluation shape the reliability of this interpretation, in two connected stages over the same twenty drawings. Stage 1 runs one prose prompting protocol on two model families and finds that progressive enrichment does not improve accuracy; beyond a threshold, it raises confident fabrication. Stage 2 compares six structured prompt architectures at thirty repetitions per cell, producing 3540 generations scored under four judge conditions. Across both stages, identification accuracy does not improve, while the rate of claims the drawing does not support varies by nearly an order of magnitude with prompt architecture. Only a judge that sees the drawing can measure it; a text-only judge inflates confident fabrication three to four times, making evaluator modality a first-order variable. Without a ground-truth answer, an image-only check can test only whether claims are visible; it recovers about half of the reference flags, enough for human-in-the-loop triage. A judge from another model family reproduces the ranking. We distill five practical recommendations for this setting.