This study evaluated the influence of distinct substrates on the mechanical fatigue behavior of adhesively cemented simplified restorations made of glass, polycrystalline or polymer infiltrated-ceramics. CAD/CAM ceramic blocks (feldspathic - FEL; lithium disilicate - LD; yttria-stabilized zirconia - YZ; and polymer-infiltrated ceramic network - PICN) were shaped into discs (n = 15, Ø = 10 mm; thickness = 1.0 mm), mimicking a simplified monolithic restoration. After, they were adhesively cemented onto different foundation substrates (epoxy resin - ER; or Ni-Cr metal alloy - MA) of the same shape (Ø = 10 mm; thickness = 2.0 mm). The assemblies were subjected to fatigue testing using a step-stress approach (200N-2800 N; step-size of 200 N; 10,000 cycles per step; 20 Hz) upon the occurrence of a radial crack or fracture. The data was submitted to two-way ANOVA (α = 0.05) to analyze differences considering 'ceramic material' and 'type of substrate' as factors. In addition, a survival analysis (Kaplan Meier with Mantel-Cox log-rank post-hoc tests; α = 0.05) was conducted to obtain the survival probability during the steps in the fatigue test. Fractographic and finite element (FEA) analyzes were also conducted. The factors 'ceramic material', 'type of substrate' and the interaction between both were verified to be statistically significant (p < .001). All evaluated ceramics presented higher fatigue failure load (FFL), cycles for failure (CFF) and survival probabilities when cemented to the metallic alloy substrate. Among the restorative materials, YZ and LD restorations presented the best fatigue behavior when adhesively cemented onto the metallic alloy substrate, while FEL obtained the lowest FFL and CFF for both substrates. The LD, PICN and YZ restorations showed similar fatigue performance considering the epoxy resin substrate. A more rigid foundation substrate improves the fatigue performance of adhesively cemented glass, polycrystalline and polymer infiltrated-ceramic simplified restorations.
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http://dx.doi.org/10.1016/j.jmbbm.2021.104391 | DOI Listing |
Br J Hosp Med (Lond)
January 2025
Department of Gastroenterology, Nantong First People's Hospital, Affiliated Hospital 2 of Nantong University, Nantong, Jiangsu, China.
Artificial intelligence (AI), with advantages such as automatic feature extraction and high data processing capacity and being unaffected by fatigue, can accurately analyze images obtained from colonoscopy, assess the quality of bowel preparation, and reduce the subjectivity of the operating physician, which may help to achieve standardization and normalization of colonoscopy. In this study, we aimed to explore the value of using an AI-driven intestinal image recognition model to evaluate intestinal preparation before colonoscopy. In this retrospective analysis, we analyzed the clinical data of 98 patients who underwent colonoscopy in Nantong First People's Hospital from May 2023 to October 2023.
View Article and Find Full Text PDFPolymers (Basel)
January 2025
Department of Mechanical Engineering, Hanyang University, 222 Wangsimri-ro, Seongdong-gu, Seoul 04763, Republic of Korea.
This study presents a methodology for characterizing the constituent properties of composite materials by back-calculating from the laminate behavior under fatigue loading. Composite materials consist of fiber reinforcements and a polymer matrix, with the fatigue performance of the laminate governed by the interaction between these constituents. Due to the challenges in directly measuring the properties of individual fibers and the polymer matrix, a reverse-engineering approach was employed.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Institute of Artificial Intelligence in Sports, Capital University of Physical Education and Sports, Beijing 100191, China.
This study investigates mental fatigue in sports activities by leveraging deep learning techniques, deviating from the conventional use of heart rate variability (HRV) feature analysis found in previous research. The study utilizes a hybrid deep neural network model, which integrates Residual Networks (ResNet) and Bidirectional Long Short-Term Memory (Bi-LSTM) for feature extraction, and a transformer for feature fusion. The model achieves an impressive accuracy of 95.
View Article and Find Full Text PDFJ Clin Med
January 2025
Department of Internal Medicine and Hematology, Semmelweis University, 1088 Budapest, Hungary.
Limited research has explored histamine intolerance from the perspective of primary caregivers. Our objective was to develop a practical symptom profile from the standpoint of general practice. We also aimed to gather data on the frequency and timing of disease progression and to establish a staging system.
View Article and Find Full Text PDFMaterials (Basel)
January 2025
College of Civil Engineering, Nanjing Forestry University, Nanjing 210037, China.
The global asphalt production growth rate exceeded 10% in the past decade, and over 90% of the world's road surfaces are generated from asphalt materials. Therefore, the issue of asphalt aging has been widely researched. In this study, the aging of asphalt thin films under various natural conditions was studied to prevent the distortion of indoor simulated aging and to prevent the extraction of asphalt samples from road surfaces from impacting the aged asphalt.
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