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.

Evaluating the cutting rate (CR) of stones is important in the cost estimation and the planning of the stone Processing plants. This research used regression models to estimate the stones’ CR based on their physico-mechanical characteristics. Stone Processing factories in Mahallat City (Markazi province, Iran) were visited, and the CR of diamond circular saws was recorded on six different travertine stones. Next, the stone block samples were collected from the quarries for laboratory tests. Stones’ porosity (n), uniaxial compressive strength (UCS), and Schmidt hammer hardness (SH) were determined in the laboratory as their physico-mechanical characteristics. Correlation relationships of CR with physico-mechanical characteristics were evaluated using simple and multiple regression analyses, and estimator models were developed. Results showed that multiple regression models are more reliable than simple regression for estimating the stones’ CR. The validity of the developed multiple regression models was verified with the published data of one researcher. The findings indicated that these models are accurate enough for estimating the CR of stones. Consequently, the multiple regression models provide practical advantages for estimating the CR and save time and cost during the planning and design of the stone Processing factories.

Background: Cilostazol is an anti-platelets drug with considerable antithrombotic effects in vivo. Therefore, it is widely used by elderly patients. However, it suffers from poor bioavailability due to its low aqueous solubility. The objective of this work was to enhance the dissolution of cilostazol with the aim of formulating fast dissolving tablets for geriatrics and those of swallowing difficulties. Methods: Ethanol-assisted co-grinding of cilostazol with sugar-based excipients was adopted. Sucralose and mannitol were used for this purpose as hydrophilic excipient as well as taste improving agents. The obtained products were investigated regarding differential scanning calorimetry (DSC), Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction, scanning electron microscope (SEM) and in vitro drug dissolution. Fast disintegrating tablets were prepared and evaluated. Results: Thermal behavior of the developed products reflected reduced crystallinity, it also suggested possible existence of new crystalline species with sucralose. Eutexia was also suggested for mannitol mixtures, that was supported by X-ray diffraction data. SEM indicated size reduction with the deposition of the drug as submicron particles over the excipient surface. Co-Processing markedly improved cilostazol dissolution compared to unprocessed drug. The optimized formulations were successively formulated into fast disintegrating tablets. Conclusion: This investigation introduced the wet grinding strategy with sugar excipients as a platform for the formulation of easy to use tablets with optimum drug release.

The heart rate characteristic (HeRO score) is a figure derived from the analysis of premature neonate’ s electrocardiogram signals, and can be used to detect infection before the onset of clinical symptoms. The United States and Europe accept this diagnostic technique, but we require more tests to prove its efficacy. This method is not accepted in other developed countries so far. The present study aimed to investigate changes in the heart characteristics of two neonates in Akbar Abadi Hospital in Tehran. Experts chose one newborn as a sepsis case, and the other neonate was healthy. The results were analyzed and compared with previous studies. In this research, a group of five neonates was selected randomly from the neonatal intensive care unit, and cardiac leads were attached to them for recording heart rates. We selected two neonates from the five cases, as a case (proven sepsis) and control, to analyze heart rate variability (HRV). Then, we compared the differences in the heart rate of both neonates. Analysis of HRV of these two neonates showed that the pattern of HRV is compatible with reports from US studies. Considering the results of this study, heart rates and their analysis can provide useful indicators for mathematical modeling before the onset of clinical symptoms in newborns.

Objectives: Apnea leads to respiratory arrest in premature infants, which decreases through the administration of caffeine by increasing the heart rate (HR). Nowadays, using electrocardiogram (ECG) signals, along with studying and comparing heart rate characteristics (HRC) in premature infants is considered as the most critical claim in the early detection of diseases, especially sepsis. Accordingly, this study investigated the effect of caffeine on HRC. Materials and Methods: To this end, the raw ECG data of infants were collected from the Akbarabadi neonatal intensive care unit section and then processed in time and statistical domain. Next, the effect of caffeine on their HRC was investigated, and finally, HRC signals were analyzed fifteen minutes before and immediately after caffeine administration. Results: Before caffeine administration, the probability distribution of inter-beat (RR) intervals and the probability distribution of the R2/R1 ratio were close to the normal distribution. According to previous studies, the irregularity of the signal in the diagram of the beat to beat RR interval indicates the infant health. However, these diagrams showed an abnormal distribution, and a specific uniformity was observed in the RR interval diagram after the administration of caffeine. Conclusions: Based on the results of this study, changes in the infant’ s HRC and its pattern should be identified after drug administration in order to evaluate the status of newborns, primarily through new methods of sepsis prediction in preterm infants. Eventually, the findings of this study enable clinicians to consider the drug effect as a confounding factor with a specific pattern in the signal without disconnecting diagnostic devices from infants for drug administration.