Learning can be seen as a process of obtaining stable brain information that shows itself in thought, experiences, or behavior, and there is local memory for storing and retrieving information. In a nutshell, learning is a process that defines its output, memory. In this study, the behavior of learning and memory of ray was investigated along with dynamic analysis of signals. In this study, to create learning and memory processes, the Rey Auditory Verbal Learning Test (RAVLT) is used. In this study, 65 men and women with age range of 19-28 years and right hand have been used that 15 words in each stage are repeated for the subject and in each step one needs to repeat it and repeat the EEG signals during these eight stages and before and after these stages in non-task relaxation conditions and 5 sub-frequency bands are analyzed. The statistical multivariate model demonstrates a correlation between behavioral learning rate (RAVLT) and three key parameters: the Hurst exponent, Higuchi fractal dimension, and approximate entropy of brain signals. The regression model highlights that the most significant predictor of learning and memory formation is the Higuchi fractal dimension of the signals, achieving an accuracy of 78.33% in predicting the learning rate. A second multivariate model, designed to estimate the behavioral learning rate based on the fractal dimensions of the sub-bands, achieves an accuracy of 73.27%. In this second model, the delta and theta frequency sub-bands exhibit the greatest effect size and the largest coefficients for predicting the learning rate. The study of this research shows that learning enhancement and synchronization process simultaneously reduces the entropy of approximation and the fractal analysis followed by Higuchi model which is the result of organizing the brain in information processing.

According to the researches, it turns out that human's activities are the results of the internal-neural activities of their brain. The reflection of such activities which are propagated throughout the scalp can then be acquired and processed. In this regard, brain signals can be acquired and recorded by EEG (Electroencephalography). Researchers have applied different technqiues for acquiring, pre-processing, feature extrcation and reduction and classifying EEG signal. According to published papers by Iranian researchers until 2015, it has been found that most studies have been performed in medical applications and brain computer interface fields. Sampling and receiving EEG signals have been performed more in the central region than other regions. Statistical technqiues have more been used for feature extraction than other technqiues. Finally, the support vector machines are mostly used in the classification of brain signals. At the end, a study on anxiety and depression detection on fifty cases was performed in medical field. Simulation results show that our approach achieve an accuracy of up to 97 percents.

In this paper, an alternative approach in operational modal analysis is presented, utilizing image processing technique and transmissibility functions. Imaging sensors do not impose additional mass on the structure due to their non-contact nature, while transmissibility functions, independent of excitation type, can directly extract mode shapes. The innovation of this research lies in combining these two techniques to record dynamic responses and identify modal properties. To capture the temporal response history from video signals, the block-matching method with sub-pixel accuracy was employed. Validation was conducted by recording the response of the tip of a cantilevered steel beam subjected to impact excitation, using a high-speed camera and a laser vibrometer, simultaneously. The RMSE plots in the time domain and the PSD in the frequency domain indicate high accuracy of this method. Using this approach, the displacement time histories of various points on the structure were extracted from the video signals, and the modal properties, including natural frequencies, damping ratios, and mode shapes, were identified using the transmissibility matrix method. The results obtained from the proposed method were compared with the stochastic subspace identification (SSI) method and analytical solutions. The findings reveal the accuracy of the modal identification approach introduced in this article. The highest relative error in estimating the natural frequencies of the first and second modes, compared to the values from the laser method, are 0.19% and 0.13%, respectively, and in comparison to the analytical values, they are 0.34% and 1.5%, respectively.

In traditional speech processing, feature extraction and classification were conducted as separate steps. The advent of deep neural networks has enabled methods that simultaneously model the relationship between acoustic and phonetic characteristics of speech while classifying it directly from the raw waveform. The first convolutional layer in these networks acts as a filter bank. To enhance interpretability and reduce the number of parameters, researchers have explored the use of parametric filters, with the SincNet architecture being a notable advancement. In SincNet's initial convolutional layer, rectangular bandpass filters are learned instead of fully trainable filters. This approach allows for modeling with fewer parameters, thereby improving the network's convergence speed and accuracy. Analyzing the learned filter bank also provides valuable insights into the model's performance. The reduction in parameters, along with increased accuracy and interpretability, has led to the adoption of various parametric filters and deep architectures across diverse speech processing applications. This paper introduces different types of parametric filters and discusses their integration into various deep architectures. Additionally, it examines the specific applications in speech processing where these filters have proven effective.

Background: Cognitive control of brain regions can be determined by the tasks involving the cognitive control such as the color word Stroop task. Stroop task define the reduction in function in incongruent condition, which requires more attention and control of competitive responses. Objective: The purpose of this study was to evaluate the activity of brain using the Modified Conflict Stroop Task in Military Personnel. Material and Methods: In this applied experimental study, to specify the activity of different regions of brain in response to conflict Persian color-word Stroop task, 20 healthy persons participated in this study. To evaluate selective attention, the traditional color-word Stroop Task Model was modified, and the Stroop test was designed in high-and low-threat zones. We used functional magnetic resonance imaging (fMRI) to evaluate the brain activation during the Stroop task performance. The color-word Stroop task consists of incongruent, congruent, and neutral conditions, and the subjects were requested to carefully choose the correct answer. Results: The mean response time was longer in incongruent condition (867. 6± 193. 5ms) compared to congruent and neutral conditions. Analysis of neuroimaging data revealed that the brain conflict-related regions are activated by the Stroop interference. In incongruent trial, the superior frontal gyrus (SFG) and inferior frontal gyrus (IFG) showed the most active and stronger BOLD responses. In congruent trials, the activation in the brain was less and had difference compared with incongruent trials. Conclusion: Our result offers that the frontal cortex and the anterior cingulate cortex are sensitive to different trials of Persian Stroop task. Using modified Stroop task, we determined the brain responses to the selective attention test.

BACKGROUND AND OBJECTIVE: Varicocele is one of the causes of male infertility. It changes with temperature and drainage of testicular veins that due to spermatogenesis disturbance and decrease of fertility in these patients. However varicocelectomy is a treatment for varicocele, but there are other methods of treating this disease including assisted reproductive technology (ART), Sperm processing and swim up. METHODS: This interventional study was performed on 155 cases in fertility and infertility Center of Babol University of medical sciences in 2004. Sperm parameters in varicocele patients was assessed before and after sperm processing. FINDINGS: The mean age of patients was 31.8 years. Sperm motility before sperm processing in grade III and IV was 53.9% and 12.9%, respectively but after sperm processing in grade III and IV changed to 78.7% and 56.8%, respectively. Total motility before sperm processing was 40 % and changed after processing to 80%. CONCLUSION: The results show that with sperm processing and collection of appropriate sperms, we are able to increase fertility.

The collaborative BRAIN Initiative (Brain Research through Advancing Innovative Neurotechnologies) was launched according to the aims of Brain Activity Map (BAM) on April, 2, 2013, with the main goal of mapping the activity of every neuron and cell in the human brain. The Brain Initiative will be led in a synergistic activity by prominent scientific foundations and institutes in U.S.A, such as the National Institutes of Health (NIH) and the National Science Foundation (NSF), and the private sector. The ultimate goal of this initiative is “to accelerate the development and application of innovative technologies to construct a dynamic picture of brain function that integrates neuronal and circuit activity over time and space”. Undoubtedly, through the BRAIN Initiative we can understand how the interplay of fluctuating patterns of electrical and chemical activity flowing within neural circuits creates our unique cognitive and behavioral capabilities. This attempt of the initiative is to follow an interdisciplinary approach and the development and creation of novel neurotechnologies and neuroimaging tools. The outcome will be generation of big data and a huge cybernetic platform which might be transformative for better diagnostic and therapeutics for millions worldwide who suffer from brain disorders. The BRAIN Initiative has been linked to the successful Apollo Space and Human Genome Project.