A hybrid system for THz reflectometric imaging at 0.6 THz synchronizes a multiplied Gunn source with a femtosecond lasers for electro-optic detection and reaches 60 db dynamic range and 25 ms ...measurement time per pixel.
Objective: This study aimed to evaluate the volume, amount, and localization of root resorption in the maxillary first premolars using micro-computed tomography (micro-CT) after expansion with four ...different rapid maxillary expansion (RME) appliances. Methods: In total, 20 patients who required RME and extraction of the maxillary first premolars were recruited for this study. The patients were divided into four groups according to the appliance used: mini-implant-supported hybrid RME appliance, hyrax RME appliance, acrylic-bonded RME appliance, and full-coverage RME appliance. The same activation protocol (one activation daily) was implemented in all groups. For each group, the left and right maxillary first premolars were scanned using micro-CT, and each root were divided into six regions. Resorption craters in the six regions were analyzed using special CTAn software for direct volumetric measurements. Data were statistically analyzed using Kruskal-Wallis one-way analysis of variance and Mann-Whitney U test with Bonferroni adjustment. Results: The hybrid expansion appliance resulted in the lowest volume of root resorption and the smallest number of craters (p < 0.001). In terms of overall root resorption, no significant difference was found among the other groups (p > 0.05). Resorption was greater on the buccal surface than on the lingual surface in all groups except the hybrid appliance group (p < 0.05). Conclusions: The findings of this study suggest that all expansion appliances cause root resorption, with resorption craters generally concentrated on the buccal surface. However, the mini-implant-supported hybrid RME appliance causes lesser root resorption than do other conventional appliances.
To our knowledge, this is the first study that specifically aims to assess the readability and quality of online information about schizophrenia. The analysis is performed on 93 of 195 websites that ...appeared in an advanced Google search of the term "schizophrenia" performed on a single day. The websites were categorized as commercial, nonprofit, professional, and government. The websites were evaluated using the Health on the Net Foundation (HON) code certificate, DISCERN tool, and Journal of the American Medical Association ( JAMA ) benchmark criteria for quality and the Flesch Reading Ease Formula, Flesch-Kincaid Grade Level (FKGL) Formula, Simple Measure of Gobbledygook, and Gunning Fog indices for readability. A total of 21.5% of all websites had a HON code certificate, 50.5% were accepted as high quality ( JAMA score ≥3), and 25.8% reached the recommended readability level (FKGL ≤8). Only three websites scored at a fifth- to sixth-grade reading level. Commercial and government websites had significantly lower DISCERN scores. Commercial websites had significantly lower Flesch Reading Ease Score and FKGL score than nonprofit websites. In conclusion, the current findings indicate that the quality of online information on schizophrenia is generally acceptable, but the readability is insufficient. Website creators, physicians, and health authorities should be more sensitive to the readability of online information about schizophrenia, considering the poor cognitive capacity of the patients and the unique nature of the disease.
Machine learning has been densely used in most computer-aided medical diagnosis systems. These systems not only supported the physician's decision but also accelerate the necessitated procedures. ...Electroencephalography (EEG) is an essential device for measuring the brain's electrical activities. EEG is used to detect a series of brain disorders such as epilepsy, dementia, Parkinson's disease, and Schizophrenia (SZ). In this work, a novel method for detecting SZ using EEG recordings is suggested. Initially, the presented technique breaks down each channel of the input EEG recordings into EEG rhythms. The wavelet transform is employed to achieve this. The 1D local binary pattern (LBP) is then used to code the acquired rhythm signals. Each row of the input picture is formed by concatenating the uniform histograms of the 1D LBP coded beats. The rows of the images are formed from the channels of the input EEG signal, while the columns of the images are constructed from the rhythms. Extreme learning machines (ELM) based autoencoders (AE) are utilized at a data augmentation step. After data augmentation, the SZ and healthy cases are classified using well-known deep transfer learning. Deep transfer learning employs a variety of pre-trained deep Convolutional Neural Network (CNN) models. Various performance assessment indicators are used to evaluate the produced outcomes. An EEG dataset that Lomonosov Moscow State University released is used in experiments, and a 97.7% accuracy score is obtained. The obtained results are also compared with several recently published methods. The comparisons show that the proposed method outperforms the compared methods.
In this study, the thermodynamic and economic analysis of a geothermal energy assisted hydrogen production system was performed using real-time Artificial Neural Networks on Field Programmable Gate ...Array. During the modeling of the system in the computer environment, a liquid geothermal resource with a temperature of 200 °C and a flow rate of 100 kg/s was used for electricity generation, and this electricity was used as a work input in the electrolysis unit to split off water into the hydrogen and oxygen. In the designed system, the net work produced from the geothermal power cycle, the overall exergy efficiency of the system, the unit cost of the produced hydrogen and the simple payback period of the system were calculated as 7978 kW, 38.37%, 1.088 $/kg H2 and 4.074 years, respectively. In the second stage of the study, Feed-Forward Artificial Neural Networks model with a single hidden layer was used for modeling the system. The activation functions of the hidden layer and output layer were Tangent Sigmoid and Linear functions, respectively. The system was implemented on Field Programmable Gate Array using the Matlab-based model of the system as a reference. The maximum operating frequency and chip statistics of the designed unit of Field Programmable Gate Array based geothermal energy assisted hydrogen production system were presented. The result can be used to gain better knowledge and optimize hydrogen production systems.
•Analysis of Geothermal assisted hydrogen production system was performed.•System was implemented on FPGA using the Matlab-based FFANN.•FFANN model with a single hidden layer was used for modeling the system.•Energy and exergy efficiency of the system were calculated as 8.47% and 38.37%.•Hydrogen cost and system payback period were calculated as 1.088 $/kg and 4.074 yr.
Automatic detection of epileptic seizure from brain signal data (e.g. electroencephalogram (EEG)) is very crucial due to dynamic and complex nature of EEG signal (e.g. non-stationarity, aperiodic and ...chaotic). Owing to these natures, manual interpretation and detection of epileptic seizure is not reliable and efficient process. Hence, this study is intended to develop a new computer-aided detection system that can automatically and efficiently identify epileptic seizure from huge amount EEG data. In this study, Hermite Transform is introduced for extracting discriminating information from EEG data for the detection of epileptic seizure. The analysis is performed in three stages: EEG signal transformation into a new form by Hermite Transform; computation of three types of features, namely permutation entropy, histogram feature and statistical feature; and classification of obtained features by least square support vector machine. The classification outcomes reveal the presence of epileptic seizure. The proposed method is evaluated on a benchmark Epileptic EEG database (Bonn University data) and the performance of this method is compared with several state-of-art algorithms for the same database. The experimental results demonstrate that the proposed scheme has the ability to efficiently detect epileptic seizure from EEG data outperforming competing techniques in terms of overall classification accuracy.
Monitoring Power Quality Events (PQE) is a crucial task for sustainable and resilient smart grid. This paper proposes a fast and accurate algorithm for monitoring PQEs from a pattern recognition ...perspective. The proposed method consists of two stages: feature extraction (FE) and decision-making. In the first phase, this paper focuses on utilizing a histogram based method that can detect the majority of PQE classes while combining it with a Discrete Wavelet Transform (DWT) based technique that uses a multi-resolution analysis to boost its performance. In the decision stage, Extreme Learning Machine (ELM) classifies the PQE dataset, resulting in high detection performance. A real-world like PQE database is used for a thorough test performance analysis. Results of the study show that the proposed intelligent pattern recognition system makes the classification task accurately. For validation and comparison purposes, a classic neural network based classifier is applied.
This study aimed to evaluate the volume, amount, and localization of root resorption in the maxillary first premolars using micro-computed tomography (micro-CT) after expansion with four different ...rapid maxillary expansion (RME) appliances.
In total, 20 patients who required RME and extraction of the maxillary first premolars were recruited for this study. The patients were divided into four groups according to the appliance used: miniimplant- supported hybrid RME appliance, hyrax RME appliance, acrylic-bonded RME appliance, and full-coverage RME appliance. The same activation protocol (one activation daily) was implemented in all groups. For each group, the left and right maxillary first premolars were scanned using micro-CT, and each root were divided into six regions. Resorption craters in the six regions were analyzed using special CTAn software for direct volumetric measurements. Data were statistically analyzed using Kruskal-Wallis one-way analysis of variance and Mann-Whitney
test with Bonferroni adjustment.
The hybrid expansion appliance resulted in the lowest volume of root resorption and the smallest number of craters (
< 0.001). In terms of overall root resorption, no significant difference was found among the other groups (
> 0.05). Resorption was greater on the buccal surface than on the lingual surface in all groups except the hybrid appliance group (
< 0.05).
The findings of this study suggest that all expansion appliances cause root resorption, with resorption craters generally concentrated on the buccal surface. However, the mini-implant-supported hybrid RME appliance causes lesser root resorption than do other conventional appliances.
Aim
To assess the percentage volumes of filling materials and voids in oval‐shaped canals filled with either cold lateral compaction or warm compaction techniques, using micro‐computed tomography ...(micro‐CT).
Methodology
Twenty‐four single‐rooted maxillary premolar teeth with oval‐shaped canals were selected and the root canals prepared and assigned to two groups (n = 12), according to the filling technique: cold lateral compaction (CLC) or warm vertical compaction (WVC). Each specimen was scanned using a micro‐CT device at an isotropic resolution of 12.5 μm. Percentage volumes of root filling materials and voids were calculated, and data were statistically analysed using Student's t‐test and Friedman's test, with a significance level of 5%.
Results
Overall, mean percentage volumes of gutta‐percha, sealer and voids were 82.33 ± 3.14, 13.42 ± 2.91 and 4.26 ± 0.74 in the CLC group and 91.73 ± 4.48, 7.70 ± 4.44 and 0.57 ± 0.44 in the WVC group, respectively, with a statistically significant difference between groups (P < 0.05). At the apical level, differences in the percentage volumes of filling materials and voids between groups were not significant (P > 0.05).
Conclusions
No root fillings were void free. Warm vertical compaction produced a significantly greater volume of gutta‐percha and a significantly lower percentage of voids than those achieved with cold lateral compaction. Distribution of sealer and voids within the root canal space after root filling was unpredictable, irrespective of the technique used.
Mild cognitive impairment (MCI) can be an indicator representing the early stage of Alzheimier's disease (AD). AD, which is the most common form of dementia, is a major public health problem ...worldwide. Efficient detection of MCI is essential to identify the risks of AD and dementia. Currently Electroencephalography (EEG) is the most popular tool to investigate the presenence of MCI biomarkers. This study aims to develop a new framework that can use EEG data to automatically distinguish MCI patients from healthy control subjects. The proposed framework consists of noise removal (baseline drift and power line interference noises), segmentation, data compression, feature extraction, classification, and performance evaluation. This study introduces Piecewise Aggregate Approximation (PAA) for compressing massive volumes of EEG data for reliable analysis. Permutation entropy (PE) and auto-regressive (AR) model features are investigated to explore whether the changes in EEG signals can effectively distinguish MCI from healthy control subjects. Finally, three models are developed based on three modern machine learning techniques: Extreme Learning Machine (ELM); Support Vector Machine (SVM) and K-Nearest Neighbours (KNN) for the obtained feature sets. Our developed models are tested on a publicly available MCI EEG database and the robustness of our models is evaluated by using a 10-fold cross validation method. The results show that the proposed ELM based method achieves the highest classification accuracy (98.78%) with lower execution time (0.281 seconds) and also outperforms the existing methods. The experimental results suggest that our proposed framework could provide a robust biomarker for efficient detection of MCI patients.
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