Irrigant flow characteristics in the root canal with internal root resorption: a computational fluid dynamics evaluation.

Irrigation dynamics of syringe irrigation with different needle designs (side-vented, double side-vented, notched) and ultrasonic irrigation in the root canal with internal root resorption were evaluated using a computational fluid dynamics model. A micro-CT scanned mandibular premolar was used for modeling internal root resorption. The needles and the ultrasonic tip were positioned at 2, 4, and 5 mm from the working length. The insertion depth and the irrigation model were found influential on the shear stress and the irrigant extension. The extension of the irrigant increased toward 2-5 mm from the working length. Ultrasonic irrigation revealed the highest shear stress values regardless of the insertion depth. The shear stress distribution on the resorption cavity walls gradually increased when the needles were positioned coronally. The residence time of the irrigant in the canal was affected by the needle position relative to the internal root resorption cavity and the needle type.

Higher heating value estimation of wastes and fuels from ultimate and proximate analysis by using artificial neural networks.

Higher heating value (HHV) is one of the most important parameters in determining the quality of the fuels. In this study, comparatively large datasets of ultimate and proximate analysis are constructed to be used in HHV estimation of several classes of fuels, including char & fossil fuels, agricultural wastes, manure (chicken, cow, horse, sheep, llama, and pig), sludge (like paper, paper-mil, sewage, and pulp), micro/macro-algae's, wastes (RDF and MSW), treated woods, untreated woods, and others (non-fossil pyrolysis oils) between the HHV range of 4.22-55.55 MJ/kg. The relationships of carbon, hydrogen, and oxygen atomic ratios for fuel classes are illustrated by using ternary plots, and the effects of elemental composition on HHV was analyzed with the extensive dataset. Then, the ultimate (U) and ultimate & proximate (UP) datasets were utilized separately to estimate the HHV by using artificial neural networks (ANN). Hyperparameter optimization was carried out and the best performing ANNs were determined for each dataset, which yielded R2 values of 0.9719 and 0.9715, respectively. The results indicated that while ANNs trained by both datasets perform remarkably well, utilization of U dataset is sufficient for HHV estimation. Finally, the best performing ANN models for both U and UP datasets are given in a directly utilizable format enabling the accurate estimation of HHV of any fuel for optimization of fuel processing and waste management operations.

Efficiency of oral keratinized gingiva detection and measurement based on convolutional neural network.

BACKGROUND: With recent advances in artificial intelligence, the use of this technology has begun to facilitate comprehensive tissue evaluation and planning of interventions. This study aimed to assess different convolutional neural networks (CNN) in deep learning algorithms to detect keratinized gingiva based on intraoral photos and evaluate the ability of networks to measure keratinized gingiva width. METHODS: Six hundred of 1200 photographs taken before and after applying a disclosing agent were used to compare the neural networks in segmenting the keratinized gingiva. Segmentation performances of networks were evaluated using accuracy, intersection over union, and F1 score. Keratinized gingiva width from a reference point was measured from ground truth images and compared with the measurements of clinicians and the DeepLab image that was generated from the ResNet50 model. The effect of measurement operators, phenotype, and jaw on differences in measurements was evaluated by three-factor mixed-design analysis of variance (ANOVA). RESULTS: Among the compared networks, ResNet50 distinguished keratinized gingiva at the highest accuracy rate of 91.4%. The measurements between deep learning and clinicians were in excellent agreement according to jaw and phenotype. When analyzing the influence of the measurement operators, phenotype, and jaw on the measurements performed according to the ground truth, there were statistically significant differences in measurement operators and jaw (p < 0.05). CONCLUSIONS: Automated keratinized gingiva segmentation with the ResNet50 model might be a feasible method for assisting professionals. The measurement results promise a potentially high performance of the model as it requires less time and experience. PLAIN LANGUAGE SUMMARY: With recent advances in artificial intelligence (AI), it is now possible to use this technology to evaluate tissues and plan medical procedures thoroughly. This study focused on testing different AI models, specifically CNN, to identify and measure a specific type of gum tissue called keratinized gingiva using photos taken inside the mouth. Out of 1200 photos, 600 were used in the study to compare the performance of different CNN in identifying gingival tissue. The accuracy and effectiveness of these models were measured and compared to human clinician ratings. The study found that the ResNet50 model was the most accurate, correctly identifying gingival tissue 91.4% of the time. When the AI model and clinicians' measurements of gum tissue width were compared, the results were very similar, especially when accounting for different jaws and gum structures. The study also analyzed the effect of various factors on the measurements and found significant differences based on who took the measurements and jaw type. In conclusion, using the ResNet50 model to identify and measure gum tissue automatically could be a practical tool for dental professionals, saving time and requiring less expertise.

Prediction of Pseudomonas spp. Population in Food Products and Culture Media Using Machine Learning-Based Regression Methods.

Machine learning approaches are alternative modelling techniques to traditional modelling equations used in predictive food microbiology and utilise algorithms to analyse large datasets that contain information about microbial growth or survival in various food matrices. These approaches leverage the power of algorithms to extract insights from the data and make predictions regarding the behaviour of microorganisms in different food environments. The objective of this study was to apply various machine learning-based regression methods, including support vector regression (SVR), Gaussian process regression (GPR), decision tree regression (DTR), and random forest regression (RFR), to estimate bacterial populations. In order to achieve this, a total of 5618 data points for Pseudomonas spp. present in food products (beef, pork, and poultry) and culture media were gathered from the ComBase database. The machine learning algorithms were applied to predict the growth or survival behaviour of Pseudomonas spp. in food products and culture media by considering predictor variables such as temperature, salt concentration, water activity, and acidity. The suitability of the algorithms was assessed using statistical measures such as coefficient of determination (R2), root mean square error (RMSE), bias factor (Bf), and accuracy (Af). Each of the regression algorithms showed appropriate estimation capabilities with R2 ranging from 0.886 to 0.913, RMSE from 0.724 to 0.899, Bf from 1.012 to 1.020, and Af from 1.086 to 1.101 for each food product and culture medium. Since the predictive capability of RFR was the best among the algorithms, externally collected data from the literature were used for RFR. The external validation process showed statistical indices of Bf ranging from 0.951 to 1.040 and Af ranging from 1.091 to 1.130, indicating that RFR can be used for predicting the survival and growth of microorganisms in food products. Therefore, machine learning approaches can be considered as an alternative to conventional modelling methods in predictive microbiology. However, it is important to highlight that the prediction power of the machine learning regression method directly depends on the dataset size, and it requires a large dataset to be employed for modelling. Therefore, the modelling work of this study can only be used for the prediction of Pseudomonas spp. in specific food products (beef, pork, and poultry) and culture medium with certain conditions where a large dataset is available.

Investigation of Structural, Morphological, and Optical Properties of Novel Electrospun Mg-Doped TiO2 Nanofibers as an Electron Transport Material for Perovskite Solar Cells.

Perovskite solar cells (PSCs) are quickly becoming efficient solar cells due to the effective physicochemical properties of the absorber layer. This layer should ideally be placed between a stable hole transport material (HTM) layer and a conductive electron transport material (ETM) layer. These outer layers play a critical role in the current densities and cell voltages of solar cells. In this work, we successfully fabricated Mg-doped TiO2 nanofibers as ETM layers via electrospinning. This study systematically investigates the morphological and optical features of Mg-doped nanofibers as mesoporous ETM layers. The existence of the Mg element in the lattice was confirmed by XRD and XPS. These optical characterizations indicated that Mg doping widened the energy band gap and shifted the edge of the conduction band minimum upward, which enhanced the open circuit voltage (Voc) and short current density (Jsc). The electron-hole recombination rate was lowered, and separation efficiency increased with Mg doping. The results have demonstrated the possibility of improving the efficiency of PSCs with the use of Mg-doped nanofibers as an ETM layer.

Development of prediction software to describe total mesophilic bacteria in spinach using a machine learning-based regression approach.

The purpose of this study was to create a tool for predicting the growth of total mesophilic bacteria in spinach using machine learning-based regression models such as support vector regression, decision tree regression, and Gaussian process regression. The performance of these models was compared to traditionally used models (modified Gompertz, Baranyi, and Huang models) using statistical indices like the coefficient of determination (R2) and root mean square error (RMSE). The results showed that the machine learning-based regression models provided more accurate predictions with an R2 of at least 0.960 and an RMSE of at most 0.154, indicating that they can be used as an alternative to traditional approaches for predictive total mesophilic. Therefore, the developed software in this work has a significant potential to be used as an alternative simulation method to traditionally used approach in the predictive food microbiology field.