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In contemporary construction practice, concrete surfaces are commonly coated; however, this factor is often disregarded in durability assessments, particularly with respect to carbonation. Such omission may lead to overly conservative designs and unnecessary material consumption. This study evaluates the actual performance of traditional coatings applied to concrete, considering three types of concrete: ordinary Portland cement (OPC), high-volume fly ash (FA), and high-volume FA with a low water-to-binder ratio. The coatings investigated were mainly based on cement and hydrated lime, with the inclusion of a FA-based alternative. Accelerated carbonation tests were performed on coated and uncoated concretes, as well as on coating mortars, while a sensitivity analysis was undertaken using an empirical and semi-probabilistic model across different exposure classes to simulate real service conditions. The results demonstrate excellent performance, with coated concretes achieving on average more than 52% higher resistance compared with uncoated counterparts. These findings indicate that properly designed coatings can enable reductions in cement content while still satisfying durability requirements, thereby contributing to more sustainable reinforced concrete structures.

This research presents a lens-based light collection system that integrates a handmade glass conical waveguide (GCW) with a single silica multimodal optical fiber (SMMF) and a concentrator Fresnel lens (FL). The GCW functions as a secondary optical element (SOE), effectively expanding the fiber’s receptive area and enabling efficient coupling of concentrated light. Calibrated ray-tracing simulations confirm that the complete FL + GCW + SMMF configuration maintains low transmission losses, thereby validating efficient coupling into the SMMF. Experimental results demonstrated a maximum net optical efficiency of 41% at an FL numerical aperture (NA) of 0.08, with GCW transmission reaching 60% and splice losses to the SMMF around 34%. With a luminous flux input of 155 lumens, the system delivered up to 63 lumens at the fiber output. Importantly, the FL + GCW + SMMF configuration combines reproducible fabrication, straightforward assembly, and reliable characterization, establishing a scalable pathway for daylight harvesting. The major contribution of this work is the demonstration that a simple, manufacturable GCW can substantially expand the effective collection area of multimodal fibers while preserving low optical losses, thereby bridging practical design with efficient energy transfer for sustainable photonics applications.

Background and aim: Artificial intelligence (AI) is gaining increasing relevance in orthopaedic surgery, particularly in prosthetic surgery, due to its ability to support preoperative planning through advanced imaging analysis, implant size prediction, and outcome forecasting. However, recent literature shows considerable variability in employed models, evaluated outcomes, and clinical applicability. The objective of this scoping review is to map AI applications in preoperative planning for orthopaedic arthroplasties and to assess their impact on radiographic and clinical outcomes, also discussing key ethical and medicolegal implications within both Italian and international contexts. Materials and methods: A literature review was conducted following scoping review methodology. The bibliographic search (10 September 2025) was performed in PubMed and Scopus using the query “preoperative planning WITH artificial intelligence AND prosthesis orthopaedic surgery AND outcomes”, restricted to the years 2020–2025, English-language studies, and research focused specifically on real-world AI techniques applied to preoperative planning in prosthetic surgery, reporting radiographic and/or clinical outcomes related to planning. Exclusion criteria included intra/postoperative studies, non-orthopaedic applications, robotic surgery, studies lacking clinical outcomes, case reports, and articles without full-text availability. After PRISMA screening and selection, 42 primary studies were included. Results: Of the 42 studies included, 20 focused on the hip, 19 on the knee, and 3 on the shoulder. Available evidence indicates that AI may improve templating accuracy and prosthetic component positioning, with more robust results in hip and knee arthroplasty, while applications in shoulder arthroplasty remain emerging. Nonetheless, important methodological limitations persist, including algorithm heterogeneity. Discussion: Overall, the findings suggest a promising role for AI in preoperative planning; however, the heterogeneity and variable quality of the evidence call for caution in interpretation and highlight the need for more rigorous prospective research. These considerations also carry relevant medicolegal implications, as the reliability and standardisation of AI-based tools represent essential prerequisites for their safe and conscious integration within diverse regulatory frameworks. Conclusions: AI appears to be a promising tool in the preoperative planning of orthopaedic arthroplasties, although further clinical validation and methodological standardisation are required. The evidence gathered also provides a useful foundation for addressing the associated medicolegal and regulatory implications, particularly in light of evolving Italian and European regulations and their differences from U.S. models.