Search Results (347)

The Diamond Open Access (OA) model—characterized by the absence of fees for both authors and readers—has gained increasing attention in recent years. A wide range of scholarly journals are using this model, as emerged while mapping the Diamond OA landscape worldwide; however, some still depend on hybrid revenue streams such as print sales, subscriptions, and marginal APCs. A number of recent initiatives underlined the need to increase quality assurance, sustainability, and cooperation within the Diamond OA ecosystem. Among them, the Diamond OA Standard (DOAS), a framework comprising detailed guidelines and a self-assessment tool to facilitate Diamond OA publishing practices, was created by the DIAMAS project, sponsored by the European Commission. Annali dell’Istituto Superiore di Sanità, the official journal of the Italian leading public health research institution, is a Diamond OA journal. To improve transparency and quality, the editorial team used the DOAS self-assessment tool to evaluate its compliance with the standards proposed by DIAMAS and to identify potential areas for improvement. This article presents the process and findings of the DOAS self-assessment tool conducted on Annali ISS, with the aim of sharing insights and support with other journals seeking to align with the DOAS framework. Full article

Multi Jet Fusion (MJF) is a leading technology for producing functional polymer parts. However, it still faces challenges with dimensional accuracy and removing unfused powder from complex internal geometries. First, dimensional accuracy was mapped by producing 45 identical PA12 specimens on an HP MJF 4200 printer in a 5 × 9 layout across five vertical layers. The analysis revealed a consistent pattern: parts located in the central positions of the build volume exhibited the poorest accuracy, while those near the perimeter were the most precise, regardless of their vertical height. This spatial variation is attributed to non-uniform thermal control from the printer’s adaptive lamp–thermal camera system. Second, the limits of powder removal from closed body-centered cubic (BCC) lattice structures were quantified. Using sandblasting and X-ray inspection, a strong inverse relationship was found between a lattice’s relative density and the maximum thickness that could be thoroughly cleaned of powder. For example, low-density structures (ρ = 0.07) could be cleaned up to five layers deep, whereas high-density structures (ρ = 0.39–0.47) were limited to only 1.5–1.7 layers. These findings offer actionable guidelines for optimizing part placement and designing internal lattice structures for MJF technology. The key findings are the spatial variation in dimensional accuracy in MJF printing, where the central parts are the least accurate and perimeter parts are the most precise, and the inverse relationship between a lattice’s relative density (ρ) and cleanable thickness. Specifically, low-density structures (ρ = 0.07) could be thoroughly cleaned up to five layers, while high-density ones (ρ = 0.39–0.47) were limited to approximately 1.5–1.7 layers. The layer thickness was a pre-designed parameter (2, 3, 4, and 5 layers), and powder removal was supported by using automated sandblasting followed by verification via industrial X-ray imaging. Full article

Small surface defects in printed circuit boards (PCBs) severely affect the reliability of electronic devices, making PCB surface defect detection crucial for ensuring the quality of electronic products. However, the existing detection methods often struggle with insufficient accuracy and the inherent trade-off between detection precision and inference speed. To address these problems, we propose a novel ESDM-HNN-YOLO (EH-YOLO) network based on the improved YOLOv10 for efficient detection of small PCB defects. Firstly, an enhanced spatial-depth module (ESDM) is designed, which transforms spatial-dimensional features into depth-dimensional representations while integrating spatial attention module (SAM) and channel attention module (CAM) to highlight critical features. This dual mechanism not only effectively suppresses feature loss in micro-defects but also significantly enhances detection accuracy. Secondly, a hybrid neck network (HNN) is designed, which optimizes the speed–accuracy balance through hierarchical architecture. The hierarchical structure uses a computationally efficient weighted bidirectional feature pyramid network (BiFPN) to enhance multi-scale feature fusion of small objects in the shallow layer and uses a path aggregation network (PAN) to prevent feature loss in the deeper layer. Comprehensive evaluations on benchmark datasets (PCB_DATASET and DeepPCB) demonstrate the superior performance of EH-YOLO, achieving mAP@50-95 scores of 45.3% and 78.8% with inference speeds of 166.67 FPS and 158.73 FPS, respectively. These results significantly outperform existing approaches in both accuracy and processing efficiency. Full article

This study aimed to evaluate the effect of resin type and printing design on the dimensional accuracy of three dimensional (3D) printed orthodontic models, considering their clinical relevance for applications such as in-house aligner fabrication. Since low-cost Liquid Crystal Display (LCD) printers have been increasingly adopted in practice but data on their trueness and precision with different resins and print designs were limited, the study sought to provide evidence-based insights into their reliability. A mandibular model was designed using Blenderfordental (B4D, version 1.1.2024; Dubai, United Arab Emirates) software and fabricated with the Anycubic Photon Mono 7 Pro 14K (Anycubic, Shenzhen, China) LCD printer. The model was printed in vertical orientation using three different print designs at two layer thicknesses (50 µm and 100 µm). Four resins (Elegoo, Anycubic, eSUN, and Phrozen) were used, and each resin was printed with all three designs, yielding 126 models per resin and a total of 504 printed models. Dimensional deviations between the printed and reference models were assessed using root mean square (RMS) values and color-coded deviation maps. Significant differences in trueness were found among resins and print designs at both layer thicknesses (p < 0.001). At a layer thickness of 50 µm, eSUN and Anycubic showed superior trueness, whereas Phrozen exhibited the highest deviations. At a layer thickness of 100 µm, Anycubic, eSUN, and Phrozen generally performed better than Elegoo. Overall, printing at 100 µm yielded better performance than at 50 µm. Precision analysis revealed resin-dependent differences, with eSUN showing significantly higher precision than Elegoo at both layer thicknesses (p = 0.006 at 100 µm, p < 0.001 at 50 µm) and superior precision compared to Phrozen at 50 µm (p = 0.019). Both resin selection and print design significantly affect the dimensional accuracy of 3D-printed dental models. Full article

Edible hydrogels are the central material class in 3D food printing because they reconcile two competing needs: (i) low resistance to flow under nozzle shear and (ii) fast recovery of elastic structure after deposition to preserve geometry. This review consolidates the recent years of progress on hydrogel formulations—gelatin, alginate, pectin, carrageenan, agar, starch-based gels, gellan, and cellulose derivatives, xanthan/konjac blends, protein–polysaccharide composites, and emulsion gels alongside a critical analysis of printing technologies relevant to food: extrusion, inkjet, binder jetting, and laser-based approaches. For each material, this review connects gelation triggers and compositional variables to rheology signatures that govern printability and then maps these to process windows and post-processing routes. This review consolidates a decision-oriented workflow for edible-hydrogel printability that links formulation variables, process parameters, and geometric fidelity through standardized test constructs (single line, bridge, thin wall) and rheology-anchored gates (e.g., yield stress and recovery). Building on these elements, a “printability map/window” is formalized to position inks within actionable operating regions, enabling recipe screening and process transfer. Compared with prior reviews, the emphasis is on decisions: what to measure, how to interpret it, and how to adjust inks and post-set enablers to meet target fidelity and texture. Reporting minima and a stability checklist are identified to close the loop from design to shelf. Full article

Ancient Chinese scripts—including oracle bone carvings, bronze inscriptions, stone steles, Dunhuang scrolls, and bamboo slips—are rich in historical value but often degraded due to centuries of erosion, damage, and stylistic variability. These issues severely hinder manual transcription and render conventional OCR techniques inadequate, as they are typically trained on modern printed or handwritten text and lack interpretability. To tackle these challenges, we propose FAIR-Net, a hybrid architecture that combines the unsupervised feature learning capacity of a deep autoencoder with the semantic transparency of a fuzzy rule-based classifier. In FAIR-Net, the deep autoencoder first compresses high-resolution character images into low-dimensional, noise-robust embeddings. These embeddings are then passed into a Fuzzy Neural Network (FNN), whose hidden layer leverages Fuzzy C-Means (FCM) clustering to model soft membership degrees and generate human-readable fuzzy rules. The output layer uses Iteratively Reweighted Least Squares Estimation (IRLSE) combined with a Softmax function to produce probabilistic predictions, with all weights constrained as linear mappings to maintain model transparency. We evaluate FAIR-Net on CASIA-HWDB1.0, HWDB1.1, and ICDAR 2013 CompetitionDB, where it achieves a recognition accuracy of 97.91%, significantly outperforming baseline CNNs (p < 0.01, Cohen’s d > 0.8) while maintaining the tightest confidence interval (96.88–98.94%) and lowest standard deviation (±1.03%). Additionally, FAIR-Net reduces inference time to 25 s, improving processing efficiency by 41.9% over AlexNet and up to 98.9% over CNN-Fujitsu, while preserving >97.5% accuracy across evaluations. To further assess generalization to historical scripts, FAIR-Net was tested on the Ancient Chinese Character Dataset (9233 classes; 979,907 images), achieving 83.25% accuracy—slightly higher than ResNet101 but 2.49% lower than SwinT-v2-small—while reducing training time by over 5.5× compared to transformer-based baselines. Fuzzy rule visualization confirms enhanced robustness to glyph ambiguities and erosion. Overall, FAIR-Net provides a practical, interpretable, and highly efficient solution for the digitization and preservation of ancient Chinese character corpora. Full article

Background: Spinal deformity correction surgery, particularly in scoliosis, often necessitates long fusion constructs and complex osteotomies that create significant structural bone defects. These defects threaten the integrity of spinal fusion, potentially compromising surgical outcomes. Bone grafting remains the cornerstone of addressing these defects, traditionally relying on autologous bone. However, limitations such as donor site morbidity and insufficient graft volume have made urgent the development and adoption of biologic substitutes and synthetic alternatives. Additionally, innovations in three-dimensional (3D) printing offer emerging solutions for graft customization and improved osseointegration. Objective: This scoping review maps the evidence of the effectiveness of the use of biologic and synthetic bone grafts in scoliosis surgery. It focusses on the role of novel technologies, particularly osteobiologics in combination with 3D-printed scaffolds, in enhancing graft performance and surgical outcomes. Methods: A comprehensive literature search was conducted using PubMed, Scopus, and the Cochrane Library to identify studies published within the last 15 years. Inclusion criteria focused on clinical and preclinical research involving biologic grafts (e.g., allografts, demineralized bone matrix-DBM, bone morphogenetic proteins-BMPs), synthetic substitutes (e.g., ceramics, polymers), and 3D-printed grafts in the context of scoliosis surgery. Data were extracted on graft type, clinical application, outcome measures, and complications. The review followed PRISMA-ScR guidelines and employed the Arksey and O’Malley methodological framework. Results: The included studies revealed diverse grafting strategies across pediatric and adult populations, with varying degrees of fusion success, incorporation rates, and complication profiles. It also included some anime studies. Emerging 3D technologies demonstrated promising preliminary results but require further validation. Conclusions: Osteobiologic and synthetic bone grafts, including those enhanced with 3D technologies, represent a growing area of interest in scoliosis surgery. Despite promising outcomes, more high-quality comparative clinical studies are needed to guide clinical decision-making and standardize practice. Full article

With the global concept of sustainable development gaining widespread acceptance, the resource utilization of solid waste has become an important research direction in the field of building materials. Geopolymer concrete (GPC), especially solid waste-based geopolymer concrete (SWGPC) prepared using various industrial solid wastes as precursors, has gradually become a frontier in green building material research due to its low carbon footprint, high strength, and excellent durability. However, the rapid expansion of literature calls for a systematic review to quantify the knowledge structure, evolution, and emerging trends in this field. Based on two thousand and thirty-nine (2039) relevant articles indexed in the Web of Science Core Collection database between 2008 and 2025, this study employs bibliometric methods and visualization tools such as VOSviewer and CiteSpace to systematically construct a knowledge map of this field. The research comprehensively reveals the developmental trajectory, research hotspots, and frontier dynamics of SWGPC from multiple dimensions, including publication trends, geographical and institutional distribution, mainstream journals, keyword clustering, and burst word analysis. The results indicate that the field has entered a rapid development stage since 2016, with research hotspots focusing on the synergistic utilization of multi-source solid waste, optimization of alkali-activation systems, enhancement of concrete durability, and environmental impact assessment. In recent years, the introduction of emerging technologies such as machine learning, 3D printing, and nano-modification has been driving a paradigm shift in research. This systematic analysis not only clarifies research development trends but also provides a theoretical basis and decision-making support for future interdisciplinary integration and engineering practice transformation. Full article

Additive manufacturing (AM) is gaining prominence in architecture, engineering, and construction (AEC). Within this context, robotic additive manufacturing (RAM) has emerged as a promising solution, offering enhanced flexibility and motion control for fabricating complex geometries and performing on-site production. However, it also introduces new, complex manufacturing processes that impact the design, making the control of manufacturing variables important for achieving accurate and feasible architectural results. In this sense, this study presents a systematic review of the state of the art in RAM for AEC, with a focus on extrusion-based 3D printing using flexible robotic arms and materials such as thermoplastics and paste-based mixtures (cementitious and earth-based compositions). This review includes 142 peer-reviewed journal and conference papers published between 2014 and 2025. It maps key research subfields, geographic trends, and RAM technology evolution, complemented by a bibliometric analysis of co-authorship and keyword networks. This review identifies four key areas of research: process, design, materials, and equipment. Most studies come from North America, Europe, and Asia, with clay emerging as a material receiving growing attention in construction within the RAM field. However, challenges like scalability, programming complexity, and AI integration still limit broader implementation. Full article

To address the challenge of detecting small defects in Printed Circuit Boards (PCBs), a YOLO-HDEW model based on the enhanced YOLOv8 architecture is proposed. A high-resolution detection layer is introduced at the P2 feature level to improve sensitivity to small targets. Depthwise Separable Convolution (DSConv) is used for downsampling, reducing parameter complexity. An Edge-enhanced Multi-scale Parallel Attention mechanism (EMP-Attention) is integrated to capture multi-scale and edge features. The EMP mechanism is incorporated into the C2f module to form the C2f-EMP module, and dynamic non-monotonic Wise-IoU (W-IoU) loss is employed to enhance bounding box regression. The model is evaluated on the PKU-Market-PCB, DeepPCB, and NEU-DET datasets, with experimental results showing that YOLO-HDEW achieves 98.1% accuracy, 91.6% recall, 90.3% mAP@0.5, and 61.7% mAP@0.5:0.95, surpassing YOLOv8 by 1.5%, 2.3%, 1.2%, and 1.9%, respectively. Additionally, the model demonstrates strong generalization performance on the DeePCB and NEU-DET datasets. These results indicate that YOLO-HDEW significantly improves detection accuracy while maintaining a manageable model size, offering an effective solution for PCB defect detection. Full article

This paper experimentally investigates the fretting fatigue behavior of metal additive-manufactured Ti-6Al-4V alloy specimens fabricated using the selective laser melting (SLM) method, focusing on damage characterization and fatigue life assessment. Based on the ASTM E466 standard, the test components were manufactured using metal 3D printing technology. Fretting fatigue tests were conducted under varying axial stress levels and contact loads, followed by microscopic examinations using scanning electron microscopy (SEM) to analyze damage mechanisms. A fretting map was developed based on SEM observations, providing insights into damage evolution under different loading conditions. These findings contribute to a better understanding of the relationship between fretting fatigue parameters and failure mechanisms. The developed fretting map and experimental observations provide a foundation for further studies aimed at enhancing the fretting fatigue life assessment of standard specimens for different test parameters. Finally, this paper includes design-oriented evaluation frameworks that can guide engineers in integrating AM components into safety-critical systems under fretting fatigue conditions. Full article

Introduction: The radiofrequency catheter ablation (RFCA) of ventricular arrhythmias (VAs) originating from the right ventricular outflow tract (RVOT) is a well-established therapy. Traditionally, RFCA is guided using electroanatomical 3D mapping systems involving manual catheter navigation within cardiac chambers. While effective, this approach may be time-consuming, and it carries a potential risk of cardiac wall perforation. Although the risk is low, it cannot be underestimated. Therefore, alternative mapping methods are sought to reduce procedural times and improve the overall efficiency of RVOT-VAs ablation. Aim: To evaluate the safety, feasibility, and efficacy of a universal RVOT 3D model implementation for the ablation of idiopathic RVOT-VAs. Methods: Consecutive patients undergoing VA ablation supported with a universal RVOT 3D model (3D-MODEL group) were included in the study. The RVOT universal model in this group was created by processing DICOM images for the improved segmentation of anatomical structures, followed by production using 3D printing technology. Patients who underwent classic endocardial electroanatomical mapping served as controls (EAM group). Results: A total of 228 patients were included in the study (143 women, age 50 ± 17 years): 149 in the 3D-MODEL group and 79 in the EAM group. The acute complete elimination of clinical VAs was achieved for 133 (89%) of patients in the 3D-MODEL group vs. 65 (82%) in the EAM group (p = 0.14). The procedural time was significantly shorter in the 3D-MODEL group compared to the EAM group (38 ± 14 min vs. 80 ± 39 min, p < 0.001). A significant difference was also observed in the radiofrequency time between the 3D-MODEL and EAM groups (251 ± 176 s vs. 503 ± 425 s, p < 0.001). No significant difference in fluoroscopy time was found between the groups (284 ± 167 s vs. 260 ± 327 s, p = 0.49). Two cases of cardiac tamponade occurred, both in patients from the EAM group. During follow-up, lasting 14 ± 10 months, 87% of patients in the 3D-MODEL group and 75% in the EAM group remained arrhythmia-free (p = 0.45). Conclusions: The use of universal RVOT 3D modeling is a feasible, safe, and effective alternative to classic electroanatomical mapping in the ablation of idiopathic RVOT-VAs. Full article

Introduction: Guided bone regeneration (GBR) is a key approach for managing alveolar ridge defects. Although titanium meshes are widely used for non-resorbable space maintenance, their limitations have prompted interest in alternative materials. Polyetheretherketone (PEEK), a high-performance thermoplastic, has emerged as a potential barrier due to its mechanical strength, radiolucency, and compatibility with digital workflows. Objective: To map the current evidence on the use of PEEK barriers in GBR, focusing on biological performance, mechanical properties, and clinical outcomes in animal and human studies. Methods: A scoping review was conducted following PRISMA-ScR guidelines. Eligible studies included in vivo animal models or clinical trials involving PEEK barriers for alveolar bone regeneration. Data on study design, defect type, barrier characteristics, surgical protocol, outcomes, and complications were extracted. Results: Five studies met the inclusion criteria: two animal models and three clinical trials. All reported successful space maintenance and bone gain with PEEK barriers, with outcomes comparable to titanium meshes. Customization through CAD/CAM or 3D printing was common. Complications such as soft tissue dehiscence and exposure occurred but generally did not affect regeneration. Evidence was limited by small sample sizes, short follow-up, and single-center designs. Conclusions: PEEK barriers show promise as customizable alternatives to traditional GBR membranes. However, current evidence is limited and geographically concentrated. Future multicenter studies with long-term follow-up and standardized outcome measures are needed to validate the clinical potential of PEEK in bone regeneration. Full article

This study explores the aeroacoustic influence of leading-edge serrations applied to stator blades subjected to turbulent inflow, which is representative of rotor–stator interaction in turbomachinery. A set of serrated geometries—75 mm span, with up to 9 teeth corresponding to 10% chord amplitude—was fabricated via 3D printing and tested experimentally in a dedicated aeroacoustic facility at COMOTI. The turbulent inflow was generated using a passive grid, and far-field acoustic data were acquired using a semicircular microphone array placed in multiple inclined planes covering 15°–90° elevation and 0–180° azimuthal angles. The analysis combined power spectral density and autocorrelation techniques to extract turbulence-related quantities, such as integral length scale and velocity fluctuations. Beamforming methods were applied to reconstruct spatial distributions of sound pressure level (SPL), complemented by polar directivity curves to assess angular effects. Compared to the reference case, configurations with serrations demonstrated broadband noise reductions between 2 and 6 dB in the mid- and high-frequency range (1–4 kHz), with spatial consistency observed across measurement planes. The results extend the existing literature by linking turbulence properties to spatially resolved acoustic maps, offering new insights into the directional effects of serrated stator blades. Full article

Sustainable aviation fuel (SAF) has demonstrated significant potential to reduce carbon emissions in the aviation industry. Multiple national and international initiatives have been launched to accelerate SAF adoption, yet large-scale commercialization continues to face technological, operational, and regulatory barriers. Industry 4.0 provides a suite of advanced technologies that can address these challenges and improve SAF operations across the supply chain. This study conducts an integrative literature review to identify and synthesize research on the application of Industry 4.0 technologies in the production and distribution of SAF. The findings highlight that technologies such as artificial intelligence (AI), Internet of Things (IoT), blockchain, digital twins, and 3D printing can enhance feedstock logistics, optimize conversion pathways, improve certification and compliance processes, and strengthen overall supply chain transparency and resilience. By mapping these applications to the six key workstreams of the SAF Grand Challenge, this study presents a practical framework linking technological innovation to both strategic and operational aspects of SAF commercialization. Integrating Industry 4.0 solutions into SAF production and supply chains contributes to reducing life cycle greenhouse gas (GHG) emissions, strengthens low-carbon energy systems, and supports the United Nations Sustainable Development Goal 13 (SDG 13). The findings from this research offer practical guidance to policymakers, industry practitioners, investors, and technology developers seeking to accelerate the global shift toward carbon neutrality in aviation. Full article