This study compared the surface detail reproduction and dimensional accuracy of stone models obtained from molds disinfected with 2% sodium hypochlorite, 2% chlorhexidine digluconate or 0.2% peracetic acid to models produced using molds which were not disinfected, with 3 alginate materials (Cavex ColorChange, Hydrogum 5 and Jeltrate Plus). The molds were prepared over matrix containing 20-, 50-, and 75-µm lines, performed under pressure with perforated metal tray. The molds were removed following gelation and either disinfected (using one of the solutions by spraying followed by storage in closed jars for 15 min) or not disinfected. The samples were divided into 12 groups (n=5). Molds were filled with dental gypsum Durone IV and 1 h after the start of the stone mixing the models were separated from the tray. Surface detail reproduction and dimensional accuracy were evaluated using optical microscopy on the 50-µm line with 25 mm in length, in accordance with the ISO 1563 standard. The dimensional accuracy results (%) were subjected to ANOVA. The 50 µm-line was completely reproduced by all alginate impression materials regardless of the disinfection procedure. There was no statistically significant difference in the mean values of dimensional accuracy in combinations between disinfectant procedure and alginate impression material (p=0.2130) or for independent factors. The disinfectant solutions and alginate materials used in this study are no factors of choice regarding the surface detail reproduction and dimensional accuracy of stone models.
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http://dx.doi.org/10.1590/s0103-64402012000400018 | DOI Listing |
Sensors (Basel)
December 2024
Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications, Institute of Photonics Technology, Jinan University, Guangzhou 510630, China.
Real-time online monitoring of track deformation during railway construction is crucial for ensuring the safe operation of trains. However, existing monitoring technologies struggle to effectively monitor both static and dynamic events, often resulting in high false alarm rates. This paper presents a monitoring technology for track deformation during railway construction based on dynamic Brillouin optical time-domain reflectometry (Dy-BOTDR), which effectively meets requirements in the monitoring of both static and dynamic events of track deformation.
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December 2024
Department of Biomedical Engineering, Price Faculty of Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada.
Monitoring cerebral oxygenation and metabolism, using a combination of invasive and non-invasive sensors, is vital due to frequent disruptions in hemodynamic regulation across various diseases. These sensors generate continuous high-frequency data streams, including intracranial pressure (ICP) and cerebral perfusion pressure (CPP), providing real-time insights into cerebral function. Analyzing these signals is crucial for understanding complex brain processes, identifying subtle patterns, and detecting anomalies.
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December 2024
Engineering Surveying Department, College of Engineering Sciences, Omdurman Islamic University, Khartoum 11111, Sudan.
The objective of our research is to produce a digital elevation model (DEM) in a real-time domain. For this purpose, GNSS measurements are obtained from a kinematic trajectory in a clear location in New Aswan City, Egypt. Different real-time processing solutions are employed, including real-time precise point positioning (RT-PPP) and real-time kinematics (RTK); additionally, the widely used post-processed precise point positioning (PPP) processing scenario is used.
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December 2024
School of Artificial Intelligence and Computer Science, Nantong University, Nantong 226019, China.
With the growing prominence of autonomous driving, the demand for accurate and efficient lane detection has increased significantly. Beyond ensuring accuracy, achieving high detection speed is crucial to maintaining real-time performance, stability, and safety. To address this challenge, this study proposes the ECBAM_ASPP model, which integrates the Efficient Convolutional Block Attention Module (ECBAM) with the Atrous Spatial Pyramid Pooling (ASPP) module.
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December 2024
National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University, Changchun 130025, China.
Depth completion is widely employed in Simultaneous Localization and Mapping (SLAM) and Structure from Motion (SfM), which are of great significance to the development of autonomous driving. Recently, the methods based on the fusion of vision transformer (ViT) and convolution have brought the accuracy to a new level. However, there are still two shortcomings that need to be solved.
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