Background: Tracheal stenosis is still a serious consequence of endotracheal intubation. Previous classification Systems are commonly descriptive and are not intended to deal with management approach. The aim of this study was to present a classification System for post intubation tracheal stenosis and evaluate its efficacy in distinguishing critically ill patients who need surgical intervention.Materials and Methods: This classification System was developed based on size and type of stenosis and associated clinical signs and symptoms. Stenosis was graded based on the results of clinical examination and rigid bronchoscopy. All patients received surgical or conservative treatment based on the judgment of a surgeon experienced in management of post-intubation tracheal stenosis without considering their score. ROC curve analysis was done and cut-off point was established based on the greatest Youden index.Results: Sixty patients were studied. Resection and anastomosis were done for 49 patients. The mean score for all samples was 9.18 (range 8.77-9.45). Chosen cutoff point was 8.5 and calculated sensitivity and specificity were 89% and 42%, respectively. Positive and negative predictive values were 83.7% and 54.5%, respectively. A reasonable agreement between the estimated score and surgeon’s clinical judgment (kappa=0.78) was observed. A statistically significant relationship was observed between scores greater than 8.5 and need for surgical intervention (P=0.007).Conclusion: We presented a scoring System for post-intubation and tracheostomy tracheal stenosis using main factors influencing diagnosis and treatment and its efficacy was evaluated prospectively. It seems that this System would be capable of assimilating the treatment interventions and comparing them.

Introduction: The quality of agricultural products is associated with their color, size and health, Grading of fruits is regarded as an important step in post-harvest processing. In most cases, manual sorting inspections depends on available manpower, time consuming and their accuracy could not be guaranteed. Machine Vision is known to be a useful tool for external features measurement (e.g. size, shape, color and defects) and in recent century, Machine Vision technology has been used for shape sorting.....

Background and Aim: Chronic gastritis is one of the most common gastrointestinal disorders and detection of its etiology especially Helicobacter pylori and risk factors of malignancy (atrophy and intestinal metaplasia) is, to a great extent, based on histological assessment. Therefore, a System which could sustain good reproducibility in reporting observations is very important. Today, the most frequently used System for assessment and Grading of chronic gastritis is the Updated Sydney System. The aim of this study was to find out inter-observer agreement on each criterion of this System (i.e reproducibility rate).Materials and Methods: In this descriptive analytical study, 44 endoscopic gastric biopsy samples (with hematoxylin, eosin, and geimsa stains) were reviewed by two pathologists in turn. Each pathologist graded any of Sydney System criteria including chronic inflammation and H.P density level from 0-3 (absent, mild, moderate and severe); glandular atrophy and intestinal metaplasia from 0-1 (absence and presence). In addition, neutrophilic infiltration (activity of disease) was also graded from 0-3. Finally; results of the observation were analaysed by means of McNemar statistical test and K coefficient.Results: The most/best inter-observer agreement was found on intestinal metaplasia (K=0.84) representing. Good interobserver agreement was observed on chronic inflammation (K=0.67), H.P density (K=0.62) and neutrophilic infiltration (K=0. 68). For glandular atrophy, agreement was the least (K=0.32) Conclusion: Updated Sydney System is a useful System for the assessment and Grading of chronic gastritis. In most cases there was good agreement between the observers. Reproducibility is evaluative differences in Grading a specific feature and lack of standardized diagnostic criteria to charactrize its presence and degree.

Statement of Problem: The extraction versus non-extraction debate is almost as old as the advent of orthodontic practice and up to now, this dilemma remains. Recently, the American Board of Orthodontics (ABO) has developed a method by the name of Objective Grading System (OGS) in order to evaluate the results of orthodontic treatment. Aim: The aim of the present study was to evaluate and compare the patients’ final occlusion after extraction and non-extraction therapy using the OGS. Materials and Methods: Sixty sex-matched cases with an age range of 15-20 year old were selected and evenly divided into 2 groups as follows: 30 patients were treated by extraction of 4 premolars and 30 received a non-extraction treatment. All patients had class 1 malocclusion before treatment and were well treated with the standard edgewise System in a private clinic. With the aid of an ABO measuring gauge, 8 parameters of occlusion were measured 3 times, each. Reproducibility of the measurements were evaluated by use of the Phi correlation coefficient and the total OGS scores between the two groups were compared using Levene`s test and Student t- test with the significant level at 95%. Results: The mean OSG scores were significantly more negative in the non–extraction group (-6.58 ± 8.63) as compared to the extraction group (-28.65 ± 6.67, p < 0.004). Acceptable occlusion was observed in 73.4% of the extraction and 43.4% of the non-extraction cases.Conclusion: In this study according to the ABO Grading System (OGS), the final occlusion of patients treated with extraction seemed more acceptable than non-extracted cases.

This study was conducted to achieve effective and low-cost technology for non-destructive Grading of unshelled almonds in real time. A laboratory prototype of an intelligent online impact-acoustic System composed of a feeding unit, acoustical recognition unit, and pneumatic separator with an electronic controller unit was constructed and tested. To evaluate System operation according to almond variety and class (hard, semi-soft, and soft), the effect of an acoustic signal generated by dropping the nuts onto a steel plate was captured by microphone and the amplitude, phase, and power spectral density were extracted from analysis of the sound signal in the time and frequency domains by means of fast Fourier transform. A multilayer perceptron neural network with a LM training function were used in all experiments. The classification accuracy using validation data was about 96. 2% in the offline mode, but accuracy decreased to 88% in the online mode. This decrease in accuracy was probably the result of a difference in size and mass of the almond samples in the hard and semi-soft classes.

Plum is one of the nutritious and popular fruits in Iran. Depending on the quality of the fresh fruit before harvesting and the drying process, different quality grades of this fruit are produced as dried plums. In this research, a computer vision System and machine learning algorithms were used to classify dried plums into three different quality grades. Different color, shape, and texture features were extracted from the images of dried plum samples, and were used separately and in combination with each other for developing classification algorithms of Multilayer Perceptron (MLP) neural networks, Support Vector Machine (SVM), Linear Discrimination Analysis (LDA) and Decision Tree (DT). In order to reduce the number of features and to extract more important features, the Correlation-based Feature Selection (CFS) method was used. Results showed that the combination of different image-extracted features increases the classification accuracy, compared to individual color, shape, or texture features. In this regard, the Random Forest (RF) DT model using the combination of image features and CFS feature selection algorithm had the highest classification accuracy in the training and evaluation stages. The values of Root Mean Squared Error (RMSE) and accuracy of this model were obtained equal to 0.1958 and 93.75% in the training stage, and equal to 0.2110 and 91.67% in the evaluation phase, respectively. Considering these performance parameters and the nature of machine vision Systems, the results of this research can be used to develop an accurate, fast, and inexpensive System for the quality Grading of dried plums.

An electronically automated System for Grading fruits was designed and its performance was evaluated. The machine comprised of two electronic and mechanical sections; the fruit passed through them and was graded by weight using a 30 N load cell and a supported control. Then The effect of four independent variables on machine performance was investigated. The independent variables in the first test were fruit type (two types), input slope (30o, 40o, 50o), and sensing time (1, 1.5, 2, 2.5, 3 sec). In the second test, the independent factors were fruit shape (apple, kiwi, -fruit) and output slope (10o, 30o, 50o). Three main factors were statistically significant for machine performance (fruits/sec). It was found that the most important factor was the timing of the load cell. Optimum correction of the timers by calculating the timing of fruit passing the sections and gates increased the performance of the machine. Results showed that maximum capacity was achieved for kiwi fruit with an input slope of 50o and 1 sec of time for the load cell, totaling 872.4 fruits/hr.