Effect of dentin desensitizing procedures on methyl methacrylate diffusion through dentin

Background: Acrylic and bisacryl resins are widely used both during the temporization phase as well as for provisional restorations and the effect of external agents on dentin sensitivity can be reduced by the obliteration of the tubules.Objective: The purpose of this study was to evaluate diffusion of methyl methacrylate monomer through dentin by high performance liquid  chromatography (HPLC) after three different desensitizing procedures during the fabrication of two different provisional crown materials.Materials and Methods: Forty extracted restoration and caries free human premolar teeth were used in this study. Thermoplastic vacuum formed material was used as a matrix to fabricate provisional restorations for each tooth before crown preparation. Teeth were prepared for a metal supported ceramic crown with 1 mm shoulder margins and then crown parts were  separated from cementoenamel junction with a carborundum disk  perpendicular to the long axis of the teeth. To the cementoenamel junction of each tooth a polypropylene chamber was attached that contains 1.5 cm3of deionized distilled water. Prepared teeth were divided into four groups (n = 10) including control, desensitizing agent (DA) application,  neodymium.doped yttrium aluminum garnet (Nd: YAG) laser irradiation (LI), and LI after DA application groups. After application of DA (except control) each group were divided into two subgroups for fabrication of provisional restorations (n = 5). Two autopolymerizing provisional materials (Imident (Imicryl) and Systemp C and B (Ivoclar, vivadent)) were used to fabricate provisional restorations using the strips. Water elutes were analyzed by HPLC at 10 min and 24 h.Results: The monomer diffusion values varied statistically according to desensitizing procedures, provisional resin systems, and the time periods. Monomer diffusion through dentin surfaces desensitized with Nd: YAG LI after DA application was the lowest.Conclusions: Nd: YAG LI in association with DA application is an effective combination to eliminate monomer diffusion through dentin to pulpal chamber.Key words: Dentin hypersensitivity, dentin permeability, laser, monomer diffusion, provisional crow

Machine Learning-Based Fire Detection: A Comprehensive Review and Evaluation of Classification Models

Fires, regardless of their origin being natural events or human-induced, provide substantial economic and environmental hazards. Therefore, the development of efficient fire detection systems is of utmost importance. This study provides a comprehensive examination of the extant body of literature about studies on fire detection utilizing machine learning techniques. Significantly, the studies employed three distinct categories of datasets: pictures, data derived from Wireless Sensor Networks (WSNs), or a hybrid amalgamation of both. Our work mainly aims to categorize fire-related data utilizing four distinct classification models: Support Vector Machines (SVMs), Decision Trees, Logistic Regression, and Multi-Layer Perceptron (MLP). The model with the highest accuracy and ROC curve performance was identified through experimental analysis. The results of our study indicate that the MLP model exhibits the highest overall accuracy, achieving a score of 0.997. In this study, we analyze the learning curves to showcase the positive training dynamics of our model. Additionally, we explore the scalability of our model to ensure its suitability in real-world situations. In general, our research underscores the possibility of employing machine learning methodologies for fire detection, specifically emphasizing the effectiveness of the Multilayer Perceptron (MLP) model. This study contributes to the existing literature by offering valuable insights into the performance of several categorization models and conducting a comprehensive investigation of the Multilayer Perceptron (MLP) architecture. The results of our study have the potential to contribute to the advancement of fire detection systems, leading to enhanced accuracy and efficiency. This, in turn, may mitigate the adverse impacts of fires on both society and the environment

Machine Learning Based Fire Detection: A Comprehensive Review and Evaluation of Classification Models

—Fires, regardless of their origin being natural events or human-induced, provide substantial economic and environmental hazards. Therefore, the development of efficient fire detection systems is of utmost importance. This study provides a comprehensive examination of the extant body of literature about studies on fire detection utilizing machine learning techniques. Significantly, the studies employed three distinct categories of datasets: pictures, data derived from Wireless Sensor Networks (WSNs), or a hybrid amalgamation of both. Our work mainly aims to categorize fire-related data utilizing four distinct classification models: Support Vector Machines (SVMs), Decision Trees, Logistic Regression, and Multi-Layer Perceptron (MLP). The model with the highest accuracy and ROC curve performance was identified through experimental analysis. The results of our study indicate that the MLP model exhibits the highest overall accuracy, achieving a score of 0.997. In this study, we analyze the learning curves to showcase the positive training dynamics of our model. Additionally, we explore the scalability of our model to ensure its suitability in real-world situations. In general, our research underscores the possibility of employing machine learning methodologies for fire detection, specifically emphasizing the effectiveness of the Multilayer Perceptron (MLP) model. This study contributes to the existing literature by offering valuable insights into the performance of several categorization models and conducting a comprehensive investigation of the Multilayer Perceptron (MLP) architecture. The results of our study have the potential to contribute to the advancement of fire detection systems, leading to enhanced accuracy and efficiency. This, in turn, may mitigate the adverse impacts of fires on both society and the environment. © 2023, Politeknik Negeri Padang. All rights reserved.ACKNOWLEDGMENT The research leading to these results has received funding from the Conference Support Fund of A'Sharqiyah University in the Sultanate of Oman