Search Results (218)

In the booming digital economy, data circulation—particularly for massive multimodal data generated by IoT sensor networks—faces critical challenges: ambiguous ownership and broken cross-domain traceability. Traditional property rights theory, ill-suited to data’s non-rivalrous nature, leads to ownership fuzziness after multi-source fusion and traceability gaps in cross-organizational flows, hindering marketization. This study aims to establish native ownership confirmation capabilities in trusted IoT-driven data ecosystems. The approach involves a dual-factor system: the collaborative extraction of text (from sensor-generated inspection reports), numerical (from industrial sensor measurements), visual (from 3D scanning sensors), and spatio-temporal features (from GPS and IoT device logs) generates unique SHA-256 fingerprints (first factor), while RSA/ECDSA private key signatures (linked to sensor node identities) bind ownership (second factor). An intermediate state integrates these with metadata, supported by blockchain (consortium chain + IPFS) and cross-domain protocols optimized for IoT environments to ensure full-link traceability. This scheme, tailored to the characteristics of IoT sensor networks, breaks traditional ownership confirmation bottlenecks in multi-source fusion, demonstrating strong performance in ownership recognition, anti-tampering robustness, cross-domain traceability and encryption performance. It offers technical and theoretical support for standardized data components and the marketization of data elements within IoT ecosystems. Full article

Geophysical techniques are a core toolkit of modern archeology, thanks to their effectiveness in reconstructing important pieces of evidence for buried ruins, which are relics of the past usage of an inspected site. Some methodological approaches and advancements are proposed for investigating the site of Gela, which was one of the most important western Greek colonies, founded in 689–688 BC on the southern coast of Sicily, Italy. The ancient settlement was developed on a hill, mostly flat on the top, and over its sides. The archeological evidence discovered so far in the acropolis of the city can be attributed to two main architectural typologies: urban blocks and archaic temples. Based on these targets, a geophysical protocol has been tested, utilizing passive seismic, electrical resistivity tomography (ERT), and ground-penetrating radar (GPR) methods. Where the lowest physical contrast was expected among possible archeological remains and burying soil (close to the urban blocks area), the three geophysical techniques have been jointly applied, while an innovative support-to-interpretation approach for GPR datasets is proposed and developed over both kinds of archeological targets. Our experimental outcomes underline the effectiveness (and possible weaknesses) of the two geophysical investigation strategies against various targets producing different signal-to-noise responses, thanks to the synergistic contributions from multi-method and multi-depth approaches. The integrated use of GPR, ERT, and passive seismic methods allowed the reconstruction of complementary information, with each method compensating for the limitations of the others. This combined approach provided a more robust and comprehensive understanding of the subsurface features than would have been achieved through the application of any single technique. Full article

Food and nutrition services are critical to the health of long-term care home (LTCH) residents, yet little is known about how regulatory inspections detect non-compliance with Food, Nutrition, and Hydration (FNH) standards. We conducted a cross-sectional study of administrative inspection data from all licensed LTCHs in Ontario, Canada. One inspection report was randomly selected per LTCH, yielding a sample of 623 LTCHs. The data were collected for the period spanning 1 January 2024 to 31 December 2024. The primary exposure was use of the FNH inspection protocol, and the outcome was FNH non-compliance, defined as at least one Written Notification or Compliance Order. Statistical analyses included chi-square tests for categorical variables and independent samples t-tests (including Welch’s t-tests where appropriate) for continuous variables, with effect sizes (Φ, Cramer’s V, Cohen’s d) reported to complement p-values. This study did not require research ethics review under Western University policy, consistent with Canada’s Tri-Council Policy Statement (TCPS 2, Article 2.2) regarding use of publicly available data. FNH non-compliance was identified in 12.2% (n = 76) of all LTCHs, and in 43.7% of those using the FNH protocol. Use of the FNH protocol was associated with a higher likelihood of detecting FNH non-compliance compared with other inspection protocols (p < 0.001, Φ = 0.55). LTCH ownership and inspection type were also associated with detection patterns. This exploratory study provides the first province-wide analysis of FNH non-compliance in Ontario LTCHs. Findings suggest that inspection protocols influence detection of FNH issues, underscoring the need for further comparative and qualitative research to understand the organizational factors underlying non-compliance. Full article

Between April and May 2025, an outbreak of travel-associated Legionnaires’ disease (TALD) occurred, involving six cases at a hotel in Crete, Greece. Including two cases reported in 2023 and two additional cases from 2016 to 2017, ten cases were associated with this accommodation site. All TALD cases were reported by the European Legionnaires’ Disease Surveillance Network (ELDSNet). In compliance with the European Centre for Disease Prevention and Control (ECDC) surveillance and investigation protocols for hotels associated with the patient’s stay, local public health authorities conducted on-site inspections at the hotel by collecting water samples and performing risk assessments, while simultaneously recording the required epidemiological, environmental, and physicochemical data. A total of 181 statistically analyzed water samples showed positive rates for L. pneumophila of 12.71% (95% CI: 7.86–17.56) for (≥50 CFU/L) and 6.08% (95% CI: 2.60–9.56) for (≥1000 CFU/L). Risk assessments identified 18 stagnation points, systemic maintenance deficiencies, and high cumulative structural (30/52) and water (36/71) system risk scores. Low microbiological positivity of water samples does not necessarily equate to low risk, thus necessitating continuous risk assessment, implementation of Water Safety Plans (WSPs), and integrated monitoring by accommodation facilities to prevent LD cases. Full article

This study examines the effects of hailstorms on photovoltaic (PV) modules, focussing on damage mechanisms, testing standards, numerical simulations, damage detection techniques, and mitigation strategies. A comprehensive review of the recent literature (2017–2025), experimental results, and case studies is complemented by advanced simulation methods such as finite element analysis (FEA) and smoothed particle hydrodynamics (SPH). The research emphasises the crucial role of protective glass thickness, cell type, number of busbars, and quality of lamination in improving hail resistance. While international standards such as IEC 61215 specify test protocols, actual hail events often exceed these conditions, leading to glass breakage, micro-cracks, and electrical faults. Numerical simulations confirm that thicker glass and optimised module designs significantly reduce damage and power loss. Detection methods, including visual inspection, thermal imaging, electroluminescence, and AI-driven imaging, enable rapid identification of both visible and hidden damage. The study also addresses the financial risks associated with hail damage and emphasises the importance of insurance and preventative measures. Recommendations include the use of certified, robust modules, protective covers, optimised installation angles, and regular inspections to mitigate the effects of hail. Future research should develop lightweight, impact-resistant materials, improve simulation modelling to better reflect real-world hail conditions, and improve AI-based damage detection in conjunction with drone inspections. This integrated approach aims to improve the durability and reliability of PV modules in hail-prone regions and support the sustainable use of solar energy amidst increasing climatic challenges. Full article

Introduction: Preformed myofunctional appliances are increasingly being studied in orthodontics and are typically used to address oral function anomalies as well as malocclusions and development defects of the jaws. The aim of this study is to evaluate the efficacy of a protocol based on the use of preformed devices and myofunctional therapy for the correction of malocclusions. Materials and Methods: A retrospective study was conducted to evaluate the effectiveness of a preformed myofunctional devices in correcting certain orthodontic problems related to overbite, overjet, and cross-bite. Thirty-six patients in the mixed dentition phase were analyzed along with their clinical records, photos, and scans. Overjet, Overbite, and Crossbite were measured by analyzing the files exported in the Standard Tesselation Language format (Stl) of patients’ arches using Zeiss Inspect^® software (version 2025.1.0.1985). Results: The data analysis reveals a statistically significant improvement in the correction of deep bite, overjet, and crossbite. Specifically, regarding the overbite (OVB), the initial measurement at T0 showed an average of 2.52 mm. The average OVB decreased to 1.73 mm at T1. The overjet had an initial average of 3.59 mm at T0, which decreased to 1.77 mm at T1. In this case as well, the difference between the measurements at T0 and T1 was statistically significant. Finally, the crossbite was evaluated by comparing the difference between mandibular and maxillary intermolar widths at T0 and T1. The average difference decreased from 5.84 mm at T0, to 1.68 mm at T1. Conclusions: Preformed myofunctional appliances represent a valid alternative in interceptive orthodontics for correcting and preventing orthodontic issues, especially of mild severity. Full article

The inspection and maintenance of urban sewer infrastructure remain critical challenges for megacities, where conventional manual inspection approaches are labor-intensive, time-consuming, and prone to human error. Although deep learning has been increasingly applied to sewer inspection, the field lacks both a publicly available large-scale dataset and a systematic evaluation of CNN and transformer-based models on real sewer footage. The primary aim of this study is to systematically evaluate and compare state-of-the-art deep learning architectures for automated sewer defect detection using a newly introduced dataset. We present the Istanbul Sewer Defect Dataset (ISWDS), comprising 13,491 expert-annotated images collected from Istanbul’s wastewater network and covering eight defect categories that account for approximately 90% of reported failures. The scientific novelty of this work lies in both the introduction of the ISWDS and the first systematic benchmarking of YOLO (v8/11/12) and RT-DETR (v1/v2) architectures under identical protocols on real sewer inspection footage. Experimental results demonstrate that RT-DETR v2 achieves the best performance (F1: 79.03%, Recall: 81.10%), significantly outperforming the best YOLO variant. While transformer-based architectures excel in detecting partially occluded defects and complex operational conditions, YOLO models provide computational efficiency advantages for resource-constrained deployments. Furthermore, a QGIS-based inspection tool integrating the best-performing models was developed to enable real-time video analysis and automated reporting. Overall, this study highlights the trade-offs between accuracy and efficiency, demonstrating that RT-DETR v2 is most suitable for server-based processing. In contrast, compact YOLO variants are more appropriate for edge deployment. Full article

Automotive manufacturing quality control faces persistent challenges such as limited defect samples, cross-domain variability, and the demand for interpretable decision-making. This work presents an explainable few-shot anomaly detection framework that integrates EfficientNet-based feature extraction, adaptive prototype learning, and component-specific attention mechanisms to address these requirements. The system is designed for rapid adaptation to novel defect types while maintaining interpretability through a multi-modal explainable AI module that combines visual, quantitative, and textual outputs. Evaluation on automotive datasets demonstrates promising performance on evaluated automotive components, achieving 99.4% accuracy for engine wiring inspection and 98.8% for gear inspection, with improvements of 5.2–7.6% over state-of-the-art baselines, including traditional unsupervised methods (PaDiM, PatchCore), advanced approaches (FastFlow, CFA, DRAEM), and few-shot supervised methods (ProtoNet, MatchingNet, RelationNet, FEAT), and with only 0.63% cross-domain degradation between wiring and gear inspection tasks. The architecture operates under real-time industrial constraints, with an average inference time of 18.2 ms, throughput of 60 components per minute, and memory usage below 2 GB on RTX 3080 hardware. Ablation studies confirm the importance of prototype learning (−4.52%), component analyzers (−2.79%), and attention mechanisms (−2.21%), with K = 5 few-shot configuration providing the best trade-off between accuracy and adaptability. Beyond performance, the framework produces interpretable defect localization, root-cause analysis, and severity-based recommendations designed for manufacturing integration with execution systems via standardized industrial protocols. These results demonstrate a practical and scalable approach for intelligent quality control, enabling robust, interpretable, and adaptive inspection within the evaluated automotive components. Full article

The construction industry remains one of the most hazardous sectors despite its economic importance, with high-altitude fall accidents being the most prevalent and deadly type of incident. This paper aimed to study and analyze the accident data of the past accident cases in China and find out the key causes and rules of the accidents. This research analyzed 1223 Chinese accident reports (2014–2023) using Latent Dirichlet Allocation topic modeling to identify causal factors, followed by Apriori algorithm correlation analysis to reveal accident causation patterns. This study comprehensively uses topic model, association rules and visualization methods to systematically analyze the causes of high-altitude fall accidents. The research identified 24 distinct accident cause topics across personnel, equipment, management, and environmental dimensions. Key findings revealed that incorrect use of labor protective equipment, inadequate safety inspections, and failure to implement safety management protocols were persistent issues throughout the study period. Notably, the post COVID-19 pandemic introduced new safety challenges, with the intensity of topics related to “subject of responsibility for safety production has not been implemented” showing significant post-pandemic increases. These findings highlight the evolving nature of construction safety challenges and the need for targeted interventions to address persistent and emerging risks. Full article

Plant viruses have detrimental effects on commercial tomato cultivation leading to severe economic consequences. Viral metagenomics studies provide the opportunity to examine in depth the virome composition of a sample set without any pre-existing knowledge of the viral species that are present. In the present study, 101 plant samples were collected from commercial greenhouses in 13 countries in Europe, Africa, Asia, and North America between 2017 and 2024. All samples were processed with the VLP enrichment protocol NetoVIR and the obtained data were analyzed with the ViPER pipeline. Forty-three eukaryotic viral species were identified, with a median identification of 2 species per sample. The most prevalent viral species were pepino mosaic virus (PepMV), tomato brown rugose fruit virus (ToBRFV), and southern tomato virus (STV). The obtained genome sequences were used to study the diversity and phylogeny of these viruses. The three genotypes identified for PepMV showed low diversity within each genotype (96.2–99.0% nucleotide identity). Low isolate diversity was also found for ToBRFV and STV. No significant association could be found between STV identification and the presence of symptoms, questioning the pathogenic potential of STV. Three other pathogenic viral species of particular interest due to their effects on tomato cultivation or recent emergence, namely tomato torrado virus (ToTV), tomato fruit blotch virus (ToFBV), and cucumber mosaic virus (CMV), were part of the virome with low prevalence. Our study provided a comprehensive overview of the analyzed samples’ virome, as well as the possibility to inspect the genetic diversity of the identified viral genomes and to look into their potential role in symptom development. Full article

Endometrial hyperplasia, presenting without atypia (EH) or as atypical hyperplasia (AH), is considered a precursor of endometrial cancer and affects women of reproductive or perimenopausal age, posing a major public health concern. The aim of this study was to review and compare the most recently published influential guidelines providing recommendations on the management of endometrial hyperplasia. Thus, a comparative review of guidelines from the Royal College of Obstetricians and Gynecologists, the Society of Obstetricians and Gynecologists of Canada, and the American College of Obstetricians and Gynecologists was conducted. There is a consensus regarding the optimal management strategies for EH, with observation and medical treatment being the first-line options and surgical treatment with total hysterectomy offering a second line in specific cases. Moreover, there is agreement regarding patients with AH, with surgical treatment being the recommended approach, while medical therapy is preferred for women who seek fertility preservation. Notably, close surveillance with endometrial biopsies every 3 or 6 months is suggested unanimously, as well as long-term follow-up in high-risk patients. Controversy exists regarding the initial diagnostic approach, with RCOG and SOGC suggesting outpatient endometrial biopsy, while ACOG recommends diagnostic hysteroscopy, as well as the therapeutic regimens for the oral treatment of EH. Surgical techniques such as endometrial ablation, intraoperative frozen section analysis, intraoperative visual inspection of the uterus, and morcellation constitute areas of controversy among the reviewed guidelines, and the surveillance protocols for women with EH are addressed differently between RCOG and SOGC. Notably, RCOG is the only medical society offering recommendations regarding women under HRT and those on therapy for breast cancer. The development of consistent international practice protocols for timely management strategies and surveillance protocols is of paramount importance to safely guide clinical practice and subsequently improve women’s health. Full article

Surface roughness influences biofilm adhesion on denture base materials, impacting oral health. Despite advances in polymeric denture materials, the effects of common cleaning protocols on their surface texture remain inadequately characterized. This study investigated the influence of toothbrush abrasion on the surface texture of dimethyl methacrylate-based (DMA, printed: V-Print dentbase), polymethyl methacrylate (PMMA, milled: VITA Vionic Base, pressed: IvoBase Hybrid), polyamide (PA, pressed: Bre.flex), and polyether ether ketone (PEEK, milled: Juvora Disc). The specimens were fabricated as polished discs. The Vickers and Martens hardness, indentation modulus, elastic and plastic part of indentation work, and indentation creep were determined. Toothbrushing simulation and surface texture analysis were conducted in three steps: 1800, 1800, and 3600 cycles using water, dish detergent, or toothpaste slurry. The surface texture parameters Sa, Sal, Sdr, Sku, and Ssk were determined using confocal laser scanning microscopy and suitable filtering (S-F and S-L surface). Sa, Sal, and Sdr showed significant changes depending on the choice of medium and the material used. The duration had a small effect (three-way ANOVA; all p < 0.001). DMA showed minor surface changes. Milled and pressed PMMA exhibited similar surface deformities due to wide valleys that were not considered critical for biofilm adhesion. PA showed the lowest and PEEK the highest Vickers and Martens hardness. However, both PA and PEEK exhibited surface changes that could promote biofilm development. These findings suggest that denture cleaning recommendations should remain material-specific. Regular surface inspections and repolishing are necessary to reduce the risk of biofilm formation on PA or PEEK-containing dentures. Full article

Keel bone lesions (KBLs) represent a relevant welfare concern in laying hens, arising from complex interactions among genetics, housing systems, and management practices. This study presents the development of an image analysis system for the automated detection and classification of KBLs in slaughterhouse videos, enabling scalable and retrospective welfare assessment. In addition to lesion classification, the system can track and count individual carcasses, providing estimates of the total number of specimens with and without significant lesions. Videos of brown laying hens from a commercial slaughterhouse in northeastern Italy were recorded on the processing line using a smartphone. Six hundred frames were extracted and annotated by three independent observers using a three-scale scoring system. A dataset was constructed by combining the original frames with crops centered on the keel area. To address class imbalance, samples of class 1 (damaged keel bones) were augmented by a factor of nine, compared to a factor of three for class 0 (no or mild lesion). A YOLO-based model was trained for both detection and classification tasks. The model achieved an F1 score of 0.85 and a mAP@0.5 of 0.892. A BoT-SORT tracker was evaluated against human annotations on a 5 min video, achieving an F1 score of 0.882 for the classification task. Potential improvements include increasing the number and variability of annotated images, refining annotation protocols, and enhancing model performance under varying slaughterhouse lighting and positioning conditions. The model could be applied in routine slaughter inspections to support welfare assessment in large populations of animals. Full article

Bridges play an increasingly indispensable role in endorsing the economic and social development of societies by linking highways and facilitating the mobility of people and goods. Concurrently, they are susceptible to high traffic volumes and an intricate service environment over their lifespans, resulting in undergoing a progressive deterioration process. Hence, efficient measures of maintenance, repair, and rehabilitation planning are critical to boost the performance condition, safety, and structural integrity of bridges while evading less costly interventions. To this end, this research paper furnishes a mixed review method, comprising systematic literature and scientometric reviews, for the meticulous examination and analysis of the existing research work in relation with maintenance fund allocation models of bridges (BriMai_all). With that in mind, Scopus and Web of Science databases are harnessed collectively to retrieve peer-reviewed journal articles on the subject, culminating in 380 indexed journal articles over the study period (1990–2025). In this respect, VOSviewer and Bibliometrix R package are utilized to create a visualization network of the literature database, covering keyword co-occurrence analysis, country co-authorship analysis, institution co-authorship analysis, journal co-citation analysis, journal co-citation, core journal analysis, and temporal trends. Subsequently, a rigorous systematic literature review is rendered to synthesize the adopted tools and prominent trends of the relevant state of the art. Particularly, the conducted multi-dimensional review examines the six dominant methodical paradigms of bridge maintenance management: (1) multi-criteria decision making, (2) life cycle assessment, (3) digital twins, (4) inspection planning, (5) artificial intelligence, and (6) optimization. It can be argued that this research paper could assist asset managers with a practical guide and a protocol to plan maintenance expenditures and implement sustainable practices for bridges under deterioration. Full article

Fabric defect detection is essential for quality assurance in textile manufacturing, where manual inspection is inefficient and error-prone. This paper presents a real-time deep learning-based system leveraging YOLOv11 for detecting defects such as holes, color bleeding and creases on solid-colored, patternless cotton and linen fabrics using edge computing. The system runs on an NVIDIA Jetson Orin Nano platform and supports real-time inference, Message Queuing Telemetry (MQTT)-based defect reporting, and optional Real-Time Messaging Protocol (RTMP) video streaming or local recording storage. Each detected defect is logged with class, confidence score, location and unique ID in a Comma Separated Values (CSV) file for further analysis. The proposed solution operates with two RealSense cameras placed approximately 1 m from the fabric under controlled lighting conditions, tested in a real industrial setting. The system achieves a mean Average Precision (mAP@0.5) exceeding 82% across multiple synchronized video sources while maintaining low latency and consistent performance. The architecture is designed to be modular and scalable, supporting plug-and-play deployment in industrial environments. Its flexibility in integrating different camera sources, deep learning models, and output configurations makes it a robust platform for further enhancements, such as adaptive learning mechanisms, real-time alerts, or integration with Manufacturing Execution System/Enterprise Resource Planning (MES/ERP) pipelines. This approach advances automated textile inspection and reduces dependency on manual processes. Full article