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Background and Aim: Stuttering contains speech disruption or observed behaviors and inner reactions of speech- associated or emotional, disruptive, coping, and attitudinal reactions. Inner events can be reported by the person who stutters but can not be reliably defined by listener perceptions. Both the disorder's directly observable and inner elements and their interactive effects need to be fully assessed to achieve to clinically deals. This study investigates validity and reliability of the tests about inner events in stuttering children.
Materials & Methods: This descriptive and analytical study was performed on 96 stuttering and non stuttering students in 6-11 (similar in age and sex). After Persian equalization of questionnaire and verifying, tests validity are scored by 10 experts. In order to tests reliability, children performed tests in two stages (25 days interval). Three methods were used: 1.comparsion between the mean tests scores in two stage 2.Investigation of correlation between two stages. 3. Investigation of results reliability (ICC). The data were analyzed by parametric and non-parametric statistical tests (paired t-test, spearman and pearson).Independent t-test were used in order to compare the mean tests scores between stuttering and non- stuttering children.
Results: Although there was different between the mean BCL and CAT scores of stuttering and non stuttering children but this between group difference is not statistically significant (p>0.05) . The mean BCL and CAT scores between two group (stuttering and non-stuttering) was statistically significant (p<0.05).
Conclusion: Behavioral checklist and communication attitude tests changed in stuttering children at 6-11 age. Both are valid and reliable and these tests distinguish between stuttering and non stuttering children.

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Background and Aim: Knee joint injuries are the most common injuries in routine life and stirring sports. The most common injuries in knee joint are meniscus injuries, anterior cruciate ligament rupture and kind of tears of above structures. Diagnosis of meniscus tear is generally clinically and by magnetic resonance imaging (MRI: Magnetic Resonance Imaging). In this study, meniscuc tear was recognized by recorded the knee vibration signals (VAG: Vibroarthrography).

Materials and Methods: Forty subjects (20 normal and 20 abnormal) with meniscus tear were selected and recorded the signals by electrostethoscope, 3 times in 15 sec. Testimonial form was taken from all of participants. 

Results: After recording, the signals were processed and reduced the noise by singular value decomposition algorithm (SVD: Singular Value Decomposition), four parameters of these signals were extracted in energy and frequency domain. These were included energy parameter (EP: Energy Parameter), energy spread parameter (ESP: Energy Spread Prameter), frequency parameter (FP: Frequency Parameter) and frequency spread parameter (FSP: Frequency Spread Parameter). Mean and standard deviation of each feature were considered and analyzed eight features of the signals. Statistical analyzes showed the P-Value less than 0.05 ( ) for each feature. Three methods for data classification were used. ) Mean and standard deviation of the parameters were obtained as below: Multi Layer Perceptron (MLP: Multi Layer Perceptron), Support Vector Machine (SVM: Support Vector Machine) and K- Nearest Neighbor (KNN: K-Nearest Neighbor) with ( ), ( ) and ( . K-nearest neighbor method (K=5) has the highest percentage of accuracy.

Conclusion: Knee signals processing (VAG signals) is a suitable and non-invasive method for diagnosis of meniscus tear which can save the time and reduce the costs.

Keywords: Articular pathology, Meniscus tear, VAG signals, Singular Value Decomposition (SVD), Time- frequency distribution.