Search Results (1,007)

Detecting Escherichia coli on food preparation and processing surfaces is critical for ensuring food safety and preventing foodborne illness. This study focuses on detecting E. coli contamination on common food processing surfaces using UV-C fluorescence imaging and deep learning. Four concentrations of E. coli (0, 10⁵, 10⁷, and 10⁸ colony forming units (CFU)/mL) and two egg solutions (white and yolk) were applied to stainless steel and white rubber to simulate realistic contamination with organic interference. For each concentration level, 256 droplets were inoculated in 16 groups, and fluorescence videos were captured. Droplet regions were extracted from the video frames, subdivided into quadrants, and augmented to generate a robust dataset, ensuring 3–4 droplets per sample. Wavelet-based denoising further improved image quality, with Haar wavelets producing the highest Peak Signal-to-Noise Ratio (PSNR) values, up to 51.0 dB on white rubber and 48.2 dB on stainless steel. Using this dataset, multiple deep learning (DL) models, including ConvNeXtBase, EfficientNetV2L, and five YOLO11-cls variants, were trained to classify E. coli concentration levels. Additionally, Eigen-CAM heatmaps were used to visualize model attention to bacterial fluorescence regions. Across four dataset groupings, YOLO11-cls models achieved consistently high performance, with peak test accuracies of 100% on white rubber and 99.60% on stainless steel, even in the presence of egg substances. YOLO11s-cls provided the best balance of accuracy (up to 98.88%) and inference speed (4–5 ms) whilst having a compact size (11 MB), outperforming larger models such as EfficientNetV2L. Classical machine learning models lagged significantly behind, with Random Forest reaching 89.65% accuracy and SVM only 67.62%. Overall, the results highlight the potential of combining UV-C fluorescence imaging with deep learning for rapid and reliable detection of E. coli on stainless steel and rubber conveyor belt surfaces. Additionally, this approach could support the design of effective interventions to remove E. coli from food processing environments. Full article

This study aims to gather a sustainable understanding of additive manufacturing and other Manufacturing 4.0 approaches like horizontal and vertical integration and cloud computing techniques with a focus on industrial applications. The DfAM will apply 4.0 tools to gather product feasibility and execution, with CAE—FEM analysis and CAM. This publication focuses on the redesign of a vehicle suspension arm. The main objective is to apply innovative design techniques that optimize component performance while minimizing cost and time. The IDeS method and TRIZ methodology were used, resulting in a composite element, aiming to make the FDM-sourced process a viable option, with a weight reduction of more than 80%, with less material consumption and, hence, less vehicle energy consumption. The part obtained is holistically sustainable as it was obtained by reducing the overall labor used and material/scrap generated, and the IDES data sharing minimized rework and optimized the overall production time. Full article

This paper presents a low-cost, fully on-premise Edge Artificial Intelligence (AI) system designed to support real-time pest and disease detection in open-field chili pepper cultivation. The proposed architecture integrates AI-Thinker ESP32-CAM module (ESP32-CAM) image acquisition nodes (“Sticks”) with a Raspberry Pi 5–based edge server (“Module”), forming a plug-and-play Internet of Things (IoT) pipeline that enables autonomous operation upon simple power-up, making it suitable for aging farmers and resource-limited environments. A Leaf-First 2-Stage vision model was developed by combining YOLOv8n-based leaf detection with a lightweight ResNet-18 classifier to improve the diagnostic accuracy for small lesions commonly occurring in dense pepper foliage. To address network instability, which is a major challenge in open-field agriculture, the system adopted a dual-protocol communication design using Hyper Text Transfer Protocol (HTTP) for Joint Photographic Experts Group (JPEG) transmission and Message Queuing Telemetry Transport (MQTT) for event-driven feedback, enhanced by Redis-based asynchronous buffering and state recovery. Deployment-oriented experiments under controlled conditions demonstrated an average end-to-end latency of 0.86 s from image capture to Light Emitting Diode (LED) alert, validating the system’s suitability for real-time decision support in crop management. Compared to heavier models (e.g., YOLOv11 and ResNet-50), the lightweight architecture reduced the computational cost by more than 60%, with minimal loss in detection accuracy. This study highlights the practical feasibility of resource-constrained Edge AI systems for open-field smart farming by emphasizing system-level integration, robustness, and real-time operability, and provides a deployment-oriented framework for future extension to other crops. Full article

This in vitro study evaluated the shear bond strength (SBS) of four CAD/CAM (Computer aided design/Computer aided manufacturing) polymer-based indirect composites bonded to dentin and microhybrid composite substrates using two resin cements. Gradia Plus (GP), Ceramage (Ce), Tescera ATL (TA), and Lava Ultimate (LA) were fabricated into cylindrical specimens (3 × 3 mm). Dentin substrates were obtained from extracted molars, while composite substrates were prepared from Filtek Z250 (4 mm × 2 mm). Bonding was performed using either a self-adhesive resin cement (RelyX U200; RU200) or a dual-cure adhesive resin cement (RelyX Ultimate; RU), resulting in 16 experimental groups (n = 12 per group). SBS was measured using a universal testing machine (1 mm/min), and failure modes were assessed under stereomicroscopy. Bond strength was significantly higher on composite substrates than on dentin (p < 0.001), primarily due to favorable polymer–polymer compatibility and matrix interdiffusion, which improved stress accommodation at the adhesive interface. TA and Ce showed superior adhesion when combined with RU, while LA exhibited the lowest values, particularly on dentin bonded with RU200. Overall, the dual-cure adhesive system provided stronger bonding than the self-adhesive system (p < 0.05). These findings highlight the influence of substrate type, composite architecture, and cement chemistry on interfacial performance in indirect polymer-based restorations. Full article

The measured antenna temperature of microwave radiometers differs from the true brightness temperature due to antenna pattern effects. Corrections for the antenna pattern effects constitutes an essential component of microwave radiometer calibration. The Compact Atmospheric Microwave Sounder (CAMS) is a cross-track scanning microwave designed for small satellites. It adopts full-circle sampling on the scan plane. Leveraging its special scan geometry, a new method of antenna pattern correction (APC) is developed. This method utilizes adjacent samplings from consecutive scans to obtain APC coefficients, and correct antenna temperature to the pixel level brightness temperature. For the first time, real samplings from beyond the Earth swath are introduced to assist APC near the swath edges. The performance of the method are analyzed through scenarios of coastlines and Earth swath edges. Analysis in the coastline scenarios demonstrates that the proposed method is more effective in correcting antenna pattern effects and detecting brightness temperature variations than traditional APC approaches in heterogeneous Earth scenarios. Comparative analysis of the method at Earth swath edges demonstrates that the introduction of samplings outside the swath effectively enhances the precision of corrected brightness temperature at swath edges. This method provides a reference for antenna pattern correction and sampling strategy in other microwave radiometers. Full article

Background: Lingual orthodontic systems have recently advanced with the introduction of fully customized CAD/CAM-based designs featuring self-ligating (SL) mechanisms. This study aimed to evaluate the three-dimensional accuracy of a customized SL lingual system in reproducing digitally planned tooth positions. Methods: A total of 280 teeth were analyzed following treatment with a fully customized self-ligating lingual system (Harmony^®, Aso International Inc., Tokyo, Japan). Digital models obtained before treatment (T0), from the setup (TS), and after treatment (T1) were superimposed using a best fit algorithm in GOM Inspect. Tooth movements were quantified across seven biomechanically relevant parameters including tip, torque, rotation, buccolingual, mesiodistal, vertical, and overall displacement. Predicted and achieved movements were compared using paired t tests and Bland–Altman analysis. Results: The fully customized SL lingual appliance achieved an overall dentition accuracy of 92.1%. Mean accuracy for linear tooth movements was 94.5% ± 2.1% in the maxilla and 93.8% ± 2.5% in the mandible. For angular movements, mean accuracy was 90.8% ± 3.4% in the maxilla and 89.3% ± 3.9% in the mandible. The highest precision was observed in anterior teeth for mesiodistal (96.2%) and buccolingual (95.8%) movements, whereas the lowest accuracy occurred in rotational movements of the posterior segments (87.1%). No statistically significant differences were found between predicted and achieved movements for most parameters (p > 0.05). Conclusions: The fully customized SL lingual orthodontic system demonstrated high accuracy in reproducing digitally planned tooth movements, particularly in the anterior segments. Although accuracy was slightly lower in the posterior regions, the overall outcomes remained mechanically and clinically acceptable across all evaluated dimensions. Full article

Automated pollen identification has become an increasingly important tool for palaeoecological research; however, its application to fossil pollen assemblages remains challenging due to complex backgrounds, morphological variability, and taxonomic similarity among pollen types. In this study, we propose Pollen-YOLO, a deep learning-based object detection framework designed for automated pollen identification from microscopic images, and evaluate its performance using the TPPOL23 dataset. The model integrates a tailored backbone architecture with attention-based feature enhancement and class-specific data augmentation strategies to address the characteristics of fossil pollen images. Experimental results indicate that Pollen-YOLO achieves stable and competitive detection performance for most pollen taxa under the tested conditions, particularly for dominant taxa with distinctive morphological features. Model behavior is further examined through ablation experiments and Grad-CAM-based interpretability analysis, which provide insights into feature learning and classification mechanisms. The applicability of the framework is explored using a fossil pollen sequence from the Shaqu profile on the Tibetan Plateau. Automated results show a high level of agreement with manual identification in capturing major stratigraphic trends and vegetation succession patterns, while discrepancies persist for morphologically similar or low-abundance taxa. Overall, this study suggests that object detection-based deep learning approaches have the potential to support fossil pollen analysis and palaeoecological reconstruction. Rather than replacing expert identification, Pollen-YOLO is intended as a complementary, high-throughput tool that may assist large-scale pollen analysis under appropriate quality control when combined with expert verification. Full article

Background: Reconstruction of the maxilla and midface remains one of the most demanding challenges in craniofacial surgery, requiring precise planning and a clear understanding of defect geometry to achieve functional and esthetic restoration. Advances in computer-assisted surgery (CAS) and virtual surgical planning (VSP), based on 3D segmentation of radiologic imaging, have significantly improved the management of maxillary deformities, allowing for further knowledge of patient-specific information, including anatomy, pathology, surgical planning, and reconstructive issues. The integration of computer-aided design and manufacturing (CAD/CAM) and 3D printing has further transformed reconstruction through customized titanium meshes, implants, and surgical guides. Methods:This systematic review, conducted following PRISMA 2020 guidelines, synthesizes evidence from clinical studies on CAD/CAM-assisted reconstruction of maxillary and midfacial defects of congenital, acquired, or post-resection origin. It highlights the advantages and drawbacks of maxillary reconstruction with patient-specific implants (PSISs). Primary outcomes are represented by accuracy in VSP reproduction, while secondary outcomes included esthetic results, functions, and assessment of complications. Results: Of the 44 identified articles, 10 met inclusion criteria with a time frame from April 2013 to July 2022. The outcomes of 24 treated patients are reported. CAD/CAM-guided techniques seemed to improve osteotomy accuracy, flap contouring, and implant adaptation. Conclusions: Although current data support the efficacy and safety of CAD/CAM-based approaches, limitations persist, including high costs, technological dependency, and variable long-term outcome data. This article critically evaluates the role of PSISs in maxillofacial reconstruction and outlines future directions for its standardization and broader adoption in clinical practice. Full article

Atlantoaxial instability (AAI) in toy- and small-breed dogs remains a significant clinical challenge, as the restricted anatomical space and risk of complications complicate the selection of implants. This study aimed to compare three patient-specific Ti-6Al-4V stabilizers for the C1–C2 region: a clinically used ventral C1–C3 plate, a shortened ventral C1–C2 plate, and a dorsal C1–C2 implant. Computed tomography, segmentation, virtual reduction, CAD/CAM design, and finite element analysis were employed to evaluate the linear-static mechanical behavior of each construct under loading ranging from 5 to 25 N, with a focus on displacements, von Mises stresses, and peri-screw bone strains. Additionally, cadaver procedures were performed in nine small-breed dogs using custom drill guides and additively manufactured implants to evaluate procedural feasibility and implantation time. Finite element models demonstrated that all stabilizers operated within material and biological safety limits. The C1–C3 plate exhibited the highest implant stresses, while the C1–C2 plate demonstrated an intermediate response, and the dorsal implant minimized implant stresses, albeit by increasing bone stresses. Cadaver experiments revealed that dorsal fixation required less implantation time than ventral fixation. Collectively, the findings indicate that all evaluated constructs represent safe stabilization options, and the choice of implant should reflect the preferred load-transfer pathway as well as anatomical or surgical constraints that may limit ventral access. Full article

Objectives: The aim of this study was to present and describe a digital workflow integrating Digital Smile Design (DSD) with computer-aided design/computer-aided manufacturing (CAD/CAM) and additive manufacturing technologies for the fabrication of dental preparation guides, focusing on workflow feasibility, design reproducibility, and clinical handling. Materials and Methods: A digital workflow was implemented using intraoral scanning and Exocad DentalCAD 3.1 Elefsina software to design dental preparation guides based on digitally planned restorations. Preparation margins, insertion paths, and minimal material thickness were defined virtually. The guides were fabricated using both subtractive (PMMA milling) and additive (stereolithographic-based 3D printing) manufacturing techniques. Post-processing included chemical cleaning, support removal, additional light curing, and manual finishing. The evaluation was qualitative and descriptive, based on visual inspection, workflow performance, and guide adaptation to printed models. Results: The proposed digital workflow was associated with consistent fabrication of preparation guides and predictable transfer of the virtual design to the manufactured guides. Digital planning facilitated clear visualization of preparation margins and insertion axes, supporting controlled and minimally invasive tooth preparation. The workflow demonstrated good reproducibility and efficient communication between clinician and dental technician. No quantitative measurements or statistical analyses were performed. Conclusions: Within the limitations of this qualitative feasibility study, the integration of DSD with CAD/CAM and 3D printing technologies represents a viable digital approach for designing and fabricating dental preparation guides. The workflow shows potential for improving predictability and communication in restorative dentistry. Full article

Patients with severe structural tooth wear present significant restorative challenges, including compromised oral function and the loss of essential anatomical landmarks such as marginal ridges, incisal edges, cusps, occlusal planes, and vertical dimension of occlusion (VDO). Successful management requires meticulous diagnosis, comprehensive treatment planning, and careful selection of restorative materials with appropriate biomechanical properties. Digital technologies have become integral to this process, particularly for enhancing diagnostic accuracy, material selection, and tooth preparation design within a fully digital workflow. This clinical case report illustrates a complete digital approach, beginning with an initial intraoral scan merged with a digital wax-up STL file featuring varying translucency dimensions to guide tooth preparation. This workflow enabled precise planning of tooth reduction, accurate assessment of available interocclusal space, and determination of material thickness requirements prior to irreversible procedures. Additionally, the integration of digital visualization improved patient communication, treatment predictability, and interdisciplinary collaboration. Overall, this case highlights the value of CAD/CAM technology in supporting complex oral rehabilitation for patients with advanced tooth wear, demonstrating its capacity to enhance efficiency, precision, and outcome quality in full-mouth zirconia ceramic restorations. Full article

Background/Objectives: The clinical application of CAD/CAM restorative materials continues to evolve due to increasing demand for aesthetic, durable, and minimally invasive indirect restorations. Hybrid nanoceramics, such as Grandio disc (VOCO GmbH, Cuxhaven, Germany), are increasingly used in indirect restorative dentistry due to their favourable combination of mechanical strength, polishability, wear resistance, and bonding potential. One challenge associated with adhesive protocols for CAD/CAM materials lies in achieving durable bonds with resin cements. Extensive post-polymerization during fabrication reduces the number of unreacted monomers available for chemical interaction, thereby limiting the effectiveness of traditional adhesive strategies and necessitating specific surface conditioning approaches. This study aimed to evaluate, in a preliminary, non-inferential manner, the influence of several combined conditioning protocols on surface micromorphology, elemental composition, and descriptive SBS trends of a CAD/CAM hybrid nanoceramic. This work was designed as a preliminary pilot feasibility study. Due to the limited number of specimens (two discs per protocol, each providing two independent enamel bonding measurements), all bond strength outcomes were interpreted descriptively, without inferential statistical testing. This in vitro study investigated the effects of various surface conditioning protocols on the adhesive performance of CAD/CAM hybrid nanoceramics (Grandio disc, VOCO GmbH, Cuxhaven, Germany) to dental enamel. Hydrofluoric acid (HF) etching was performed to improve adhesion to indirect resin-based materials using two commercially available gels: 9.5% Porcelain Etchant (Bisco, Inc., Schaumburg, IL, USA) and 4.5% IPS Ceramic Etching Gel (Ivoclar Vivadent, Schaan, Liechtenstein), in combination with airborne-particle abrasion (APA), silanization, and universal adhesive application. HF may selectively dissolve the inorganic phase, while APA increases surface texture and micromechanical retention. However, existing literature reports inconsistent results regarding the optimal conditioning method for hybrid composites and nanoceramics, and the relationship between micromorphology, elemental surface changes, and adhesion remains insufficiently clarified. Methods: A total of ten composite specimens were subjected to five conditioning protocols combining airborne-particle abrasion with varying hydrofluoric acid (HF) concentrations and etching times. Bonding was performed using a dual-cure resin cement (BiFix QM) and evaluated by shear bond strength (SBS) testing. Surface morphology was examined through environmental scanning electron microscopy (ESEM), and elemental composition was analyzed via energy-dispersive X-ray spectroscopy (EDS). Results: indicated that dual treatment with HF and sandblasting showed descriptively higher SBS, with values ranging from 5.01 to 6.14 MPa, compared to 1.85 MPa in the sandblasting-only group. ESEM revealed that higher HF concentrations (10%) created more porous and irregular surfaces, while EDS indicated an increased fluorine presence trend and silicon reduction, indicating deeper chemical activation. However, extending HF exposure beyond 20 s did not further improve bonding, suggesting the importance of protocol optimization. Conclusions: The preliminary observations suggest a synergistic effect of mechanical and chemical conditioning on hybrid ceramic adhesion, but values should be interpreted qualitatively due to the pilot nature of the study. Manufacturer-recommended air abrasion alone may provide limited adhesion under high-stress conditions, although this requires confirmation in studies with larger sample sizes and ageing simulations. Future studies should address long-term durability and extend the comparison to other hybrid CAD/CAM materials and to other etching protocols. Full article

This narrative review examines contemporary applications of polymeric materials in dentistry from 2020 to 2025, spanning prosthodontics, restorative dentistry, orthodontics, endodontics, implantology, diagnostics, and emerging technologies. We searched PubMed, Scopus, Web of Science, and Embase for peer reviewed English language articles and synthesized evidence on polymer classes, processing routes, mechanical and chemical behavior, and clinical performance. Approximately 116 articles were included. Polymers remain central to clinical practice: poly methyl methacrylate (PMMA) is still widely used for dentures, high performance systems such as polyether ether ketone (PEEK) are expanding framework and implant-related indications, and resin composites and adhesives continue to evolve through nanofillers and bioactive formulations aimed at improved durability and reduced secondary caries. Thermoplastic polyurethane and copolyester systems drive clear aligner therapy, while polymer-based obturation materials and fiber-reinforced posts support endodontic rehabilitation. Additive manufacturing and computer aided design computer aided manufacturing (CAD CAM) enable customized prostheses and surgical guides, and sustainability trends are accelerating interest in biodegradable or recyclable dental polymers. Across domains, evidence remains heterogeneous and clinical translation depends on balancing strength, esthetics, biocompatibility, aging behavior, and workflow constraints. Full article

Electroencephalography (EEG)-based motor imagery (MI) brain–computer interfaces (BCIs) hold considerable potential for applications in neuro-rehabilitation and assistive technologies. Yet, their development remains constrained by challenges such as low spatial resolution, vulnerability to noise and artifacts, and pronounced inter-subject variability. Conventional approaches, including common spatial patterns (CSP) and convolutional neural networks (CNNs), often exhibit limited robustness, weak generalization, and reduced interpretability. To overcome these limitations, we introduce EEG-GCIRNet, a Gaussian connectivity-driven EEG imaging representation network coupled with a regularized LeNet architecture for MI classification. Our method integrates raw EEG signals with topographic maps derived from functional connectivity into a unified variational autoencoder framework. The network is trained with a multi-objective loss that jointly optimizes reconstruction fidelity, classification accuracy, and latent space regularization. The model’s interpretability is enhanced through its variational autoencoder design, allowing for qualitative validation of its learned representations. Experimental evaluations demonstrate that EEG-GCIRNet outperforms state-of-the-art methods, achieving the highest average accuracy (81.82%) and lowest variability (±10.15) in binary classification. Most notably, it effectively mitigates BCI illiteracy by completely eliminating the “Bad” performance group (<60% accuracy), yielding substantial gains of ∼22% for these challenging users. Furthermore, the framework demonstrates good scalability in complex 5-class scenarios, performing competitive classification accuracy (75.20% ± 4.63) with notable statistical superiority (p = 0.002) against advanced baselines. Extensive interpretability analyses, including analysis of the reconstructed connectivity maps, latent space visualizations, Grad-CAM++ and functional connectivity patterns, confirm that the model captures genuine neurophysiological mechanisms, correctly identifying integrated fronto-centro-parietal networks in high performers and compensatory midline circuits in mid-performers. These findings suggest that EEG-GCIRNet provides a robust and interpretable end-to-end framework for EEG-based BCIs, advancing the development of reliable neurotechnology for rehabilitation and assistive applications. Full article

Real-time fine-grained human activity recognition (HAR) remains a challenging problem due to rapid spatial–temporal variations, subtle motion differences, and dynamic environmental conditions. Addressing this difficulty, we propose NovAc-DL, a unified deep learning framework designed to accurately classify short human-like actions, specifically, “pour” and “stir” from sequential video data. The framework integrates adaptive time-distributed convolutional encoding with temporal reasoning modules to enable robust recognition under realistic robotic-interaction conditions. A balanced dataset of 2000 videos was curated and processed through a consistent spatiotemporal pipeline. Three architectures, LRCN, CNN-TD, and ConvLSTM, were systematically evaluated. CNN-TD achieved the best performance, reaching 98.68% accuracy with the lowest test loss (0.0236), outperforming the other models in convergence speed, generalization, and computational efficiency. Grad-CAM visualizations further confirm that NovAc-DL reliably attends to motion-salient regions relevant to pouring and stirring gestures. These results establish NovAc-DL as a high-precision real-time-capable solution for deployment in healthcare monitoring, industrial automation, and collaborative robotics. Full article