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 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.

Knowledge on sunflower seed physical characteristics can play an important role in the proper procedures during harvesting, transport, drying, sorting, seed peeling and storage. In order to study the effect of removing leaf and seed in different plant densities on seed physical and chemical properties of sunflower hybrids, an experiment was conducted as a factorial split plot based on completely randomized block design with three replications in 2009 at Aboureihan Research Field of University of Tehran. Azargol and new Iranian hybrid SHF81-90 and three plant densities 60000, 80000 and 100000 plants/ha were main plots and five levels of change in source and sink size (removed 50% lower leaves, removed 50% upper leaves, removed 50% seeds, removed 25% seeds) and control (without removing leaf and seed) were sub plots. Photographs were taken from the seeds, by a digital camera, and analyzed using MATLAB programming language. Some morphological features were extracted by this software. Results indicated that the hybrids had significant difference in Perimeter and Area, Major and Minor Axis, Elongation, Compactness, Roundness, Solidity, seed weight and seed coat to seed ratio at 5% and 1% probability levels. Also the effects of plant density and source and sink manipulation were significant on all traits. Oil percent increased along with the plant density increase. Correlation between oil percent with seed weight (r=+0.53) and seed coat to seed ratio (r=-0.27) was significant at 1% probability level. Also the results of this study showed that using image Processing technique, which is the new technique in agricultural researches, the physical properties of seed cultivars in different environmental conditions can be identified more accurately and this will be helpful in management of different planting, harvesting and postharvest stages.

Aim: The aim of this study was to determine the multiple relationships of focus, post-Processing and interpretation bias (related to yourself) with social anxiety in students in Isfahan. Methods: research method of the study was descriptive-correlational research design. The statistical population of the study was all of the university students of Isfahan in the 1396-1397 academic year. A sample of 237 students (96 men and 141 women) was randomly selected by simple cluster sampling among the universities in Isfahan. The participants were then asked to answer the questions of the Social Anxiety Questionnaire Kavner et al )2002), the Self-Focused Attention Questionnaire Woody, Cham bless & Glass )1997), the Post-Event Processing Questionnaire (PEPQ), the Revised Questionnaire of Butler et al. (1983). The descriptive statistics were used for descriptive analysis of data and the multiple regression and internal consistency were used to investigate the testing of the hypotheses. Results: The results indicated that the interpretation bias and Post-Event Processing with 13. 4% of the variance could predict the social anxiety. interpretation bias (β =0/24, P<0/01) and Post-Event Processing (β =0/14, P<0/05) They were able to predict social anxiety. Conclusions: Since postevent Processing and interpretation bias can be related to social anxiety. Therefore, for predicting and treating students' social anxiety, the mentioned variables can also be targeted and students can be taught ways to deal with social anxiety.

Background and Objectives: Graph Processing is increasingly gaining attention during era of big data. However graph Processing applications are highly memory intensive due to nature of graphs. Processing-in-memory (PIM) is an old idea which revisited recently with the advent of technology specifically the ability to manufacture 3D stacked chipsets. PIM puts forward to enrich memory units with computational capabilities to reduce the cost of data movement between processor and memory system. This approach seems to be a way of dealing with large-scale graph Processing, considering recent advances in the field. Methods: This paper explores real-world PIM technology to improve graph Processing efficiency by reducing irregular access patterns and improving temporal locality using HMC. We propose NodeFetch, a new method to access nodes and their neighbors while Processing a graph by adding a new command to HMC system. Results: Results of our simulation on a set of real-world graphs point out that the proposed idea can achieve 3. 3x speed up in average and 69% reduction of energy consumption over the baseline PIM architecture which is HMC. Conclusion: Most of the techniques in the field of Processing-in-memory, hire methods to reduce movement of data between processor and memory. This paper proposes a method to reduce graph Processing execution time and energy consumption by reducing cache misses while Processing a graph.