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This study explores the use of advanced 3D imaging and printing technologies to digitally document and physically replicate cultural artifacts from the Archaeological Museum of Alexandroupolis. By employing structured light scanning and additive manufacturing techniques, detailed digital models and precise physical replicas of two significant artifacts were created—a humanoid ceramic vessel and a glass cup. A handheld 3D scanner was utilized for capturing intricate surface details, with post-processing methods to refine and colorize the digital models. Regarding 3D printing, both Fused Deposition Modeling (FDM) and Stereolithography (SLA) were employed, tailored to the artifacts’ unique requirements for resolution and material properties. This dual approach supports heritage preservation by generating tangible educational resources and providing alternative exhibits to safeguard original artifacts. Our results demonstrate that integrating 3D scanning and printing effectively enhances the accessibility, durability, and educational utility of cultural heritage assets, offering a sustainable model for artifact preservation and study.

The digitization of ancient artifacts and the fabrication of sustainable 3D-printed replicas present a promising solution for enhancing the accessibility to cultural heritage sites for visitors with disabilities. This article focuses on the case study of the Piraeus Archaeological Museum. The study investigates the process of digitizing a selection of ancient artifacts from the museum’s collection and utilizing 3D printing technology to produce tactile replicas from recycled Polylactic Acid (PLA) material that provide a multisensory experience for individuals with disabilities like vision impairment. The research examines the technical challenges and considerations faced by the authors’ team during the 3D scanning process of the artifacts, the manufacturing of raw material from 3D printing waste, as well as the optimization of 3D printing parameters to ensure the creation of high-quality 3D-printed replicas. Furthermore, the article points out the positive future impact that the 3D-printed replicas will have on the engagement and comprehension of vision-impaired visitors, highlighting the potential of this approach in promoting inclusivity and fostering a connection with cultural heritage.

The emergence of additive manufacturing technologies has brought about a significant transformation in several industries. Among these technologies, Fused Deposition Modeling/Fused Filament Fabrication (FDM/FFF) 3D printing has gained prominence as a rapid prototyping and small-scale production technique. The potential of FDM/FFF for applications that require improved mechanical, thermal, and electrical properties has been restricted due to the limited range of materials that are suitable for this process. This study explores the integration of various reinforcements, including carbon fibers, glass fibers, and nanoparticles, into the polymer matrix of FDM/FFF filaments. The utilization of advanced materials for reinforcing the filaments has led to the enhancement in mechanical strength, stiffness, and toughness of the 3D-printed parts in comparison to their pure polymer counterparts. Furthermore, the incorporation of fillers facilitates improved thermal conductivity, electrical conductivity, and flame retardancy, thereby broadening the scope of potential applications for FDM/FFF 3D-printed components. Additionally, the article underscores the difficulties linked with the utilization of filled filaments in FDM/FFF 3D printing, including but not limited to filament extrusion stability, nozzle clogging, and interfacial adhesion between the reinforcement and matrix. Ultimately, a variety of pragmatic implementations are showcased, wherein filled filaments have exhibited noteworthy benefits in comparison to standard FDM/FFF raw materials. The aforementioned applications encompass a wide range of industries, such as aerospace, automotive, medical, electronics, and tooling. The article explores the possibility of future progress and the incorporation of innovative reinforcement materials. It presents a plan for the ongoing growth and application of advanced composite materials in FDM/FFF 3D printing.

3D scanning, 3D printing, and CAD design software are considered important tools in Industry 4.0 product development processes. Each one of them has seen widespread use in a variety of scientific and commercial fields. This work aims to depict the added value of their combined use in a proposed workflow where a customized product needs to be developed. More specifically, the geometry of an existing physical item’s geometry needs to be defined in order to fabricate and seamlessly integrate an additional component. In this instance, a 3D scanning technique was used to digitize an e-bike’s frame geometry. This was essential for creating a peripheral component (in this case, a rear rack) that would be integrated into the frame of the bicycle. In lieu of just developing a tail rack from scratch, a CAD generative design process was chosen in order to produce a design that favored both light weight and optimal mechanical behaviors. FDM 3D printing was utilized to build the final design using ABS-CF10 materials, which, although being a thermoplastic ABS-based material, was introduced as a metal replacement for lighter and more ergonomic component production. Consequently, the component was manufactured in this manner and successfully mounted onto the frame of the e-bike. The proposed process is not limited to the manufacturing of this component, but may be used in the future for the fabrication of additional peripheral components and tooling.

Soil maps are essential for managing Earth’s resources, but the accuracy of widely used global and pan-European digital soil maps in heterogeneous landscapes remains a critical concern. This study provides a comprehensive evaluation of two prominent datasets, ISRIC-SoilGrids and the European Soil Data Centre (ESDAC), by comparing their soil texture predictions against the detailed Greek National Soil Map, which is based on over 10,000 field samples. The results from statistical and spatial analyses reveal significant discrepancies and weak correlations, with a very low overall accuracy for soil texture class prediction (19–21%) and high Root Mean Square Error (RMSE) values ranging from 13% to 19%. The global models failed to capture local variability, showing very low explanatory power (R2 < 0.2) and systematically underrepresenting soils with extreme textures. Furthermore, these prediction errors are not entirely random but are significantly clustered in hot spots linked to distinct parent materials and geomorphological features. Our findings demonstrate that while invaluable for large-scale assessments, the direct application of global soil databases for regional policy or precision agriculture in a geologically complex country like Greece is subject to considerable uncertainty, highlighting the critical need for local calibration and the integration of national datasets to improve the reliability of soil information.

AgERA5 (ECMWF) is a relatively new climate dataset specifically designed for agricultural applications. MERRA-2 (NASA) is also used in agricultural applications; however, it was not specifically designed for this purpose. Despite the proven value of these datasets in assessing global climate patterns, their effectiveness in small-scale agricultural contexts remains unclear. This research aims to fill this gap by assessing the suitability and performance of AgERA5 and MERRA-2 in precision irrigation management, which is crucial for regions with limited ground data availability. The wine-making region of Nemea, Greece, with its complex and challenging terrain is used as a characteristic case study. The datasets are assessed for key weather variables and for irrigation planning, using detailed local meteorological station data as a reference. The results reveal that both products have serious limitations in small scale irrigation scheduling applications in contrast to what was reported in previous studies for other regions. The uneven performance of global datasets in different regions due to lack of sufficient observation data for reanalysis data calibration was also indicated. Comparing the two datasets, AgERA5 outperforms MERRA-2, especially in precipitation and reference evapotranspiration. MERRA-2 shows comparable potential in irrigation planning, as it occasionally matches or exceeds AgERA5’s performance. The study findings underscore the importance of evaluating metanalysis datasets in the application area before their use for precision agriculture, particularly in regions with complex topography.

Wildfires are frequently observed in watersheds with a Mediterranean climate and seriously affect vegetation, soil, hydrology, and ecosystems as they cause abrupt changes in land cover. Assessing wildfire effects, as well as the recovery process, is critical for mitigating their impacts. This paper presents a geospatial analysis approach that enables the investigation of wildfire effects on vegetation, soil, and hydrology. The prediction of regeneration potential and the period needed for the restoration of hydrological behavior to pre-fire conditions is also presented. To this end, the catastrophic wildfire that occurred in August 2021 in the wider area of Varybobi, north of Athens, Greece, is used as an example. First, an analysis of the extent and severity of the fire and its effect on the vegetation of the area is conducted using satellite imagery. The history of fires in the specific area is then analyzed using remote sensing data and a regrowth model is developed. The effect on the hydrological behavior of the affected area was then systematically analyzed. The analysis is conducted in a spatially distributed form in order to delineate the critical areas in which immediate interventions are required for the rapid restoration of the hydrological behavior of the basin. The period required for the restoration of the hydrological response is then estimated based on the developed vegetation regrowth models. Curve Numbers and post-fire runoff response estimations were found to be quite similar to those derived from measured data. This alignment shows that the SCS-CN method effectively reflects post-fire runoff conditions in this Mediterranean watershed, which supports its use in assessing hydrological changes in wildfire-affected areas. The results of the proposed approach can provide important data for the restoration and protection of wildfire-affected areas.

Sensors are prone to malfunction, leading to blank or erroneous measurements that cannot be ignored in most practical applications. Therefore, data users are always looking for efficient methods to substitute missing values with accurate estimations. Traditionally, empirical methods have been used for this purpose, but with the increasing accessibility and effectiveness of Machine Learning (ML) methods, it is plausible that the former will be replaced by the latter. In this study, we aimed to provide some insights on the state of this question using the network of meteorological stations installed and operated by the GIS Research Unit of the Agricultural University of Athens in Nemea, Greece as a test site for the estimation of daily average solar radiation. Routine weather parameters from ten stations in a period spanning 1,548 days were collected, curated, and used for the training, calibration, and validation of different iterations of two empirical equations and three iterations each of Random Forest (RF) and Recurrent Neural Networks (RNN). The results indicated that while ML methods, and especially RNNs, are in general more accurate than their empirical counterparts, the investment in technical knowledge, time, and processing capacity they require for their implementation cannot constitute them as a panacea, as such selection for the best method is case-sensitive. Future research directions could include the examination of more location-specific models or the integration of readily available spatiotemporal indicators to increase model generalization.