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.

Abstract: This study sought to answer the question of what are the causes of marital infidelity in the Iranian family. To Answer the Research Question, Qualitative Meta-Synthesis (QMS) Method Were Used. Then, 78 Scientific Papers Which Were Considered Marital Infidelity in Iran were identified and were Analyzed. The results showed that the factors of infidelity in Iran can be classified into 9 main categories, including: 1. Social factors, 2. Psychological factors, 3. Cultural factors, 4. Family factors, 5. Personal factors, 6. Relational factors, 7. Economic factors, 8. Media factors, 9. Contextual factors. Each of the main Categories is divided into Different Subcategories. The Social Factors of infidelity were including “Infidelity Opportunities”, “Friend groups” and “Environmental Conditions”. Psychological Factors of Infidelity Were Including “Personality Type”, “Attachment Styles”, and their “Love Schema”. Cultural Factors have Pointed to “Change in Beliefs and values” and “the level of Religious Education” of people. The Family Factors were including “Inappropriate Marriage”, “Family Conflict” and “the Kind of Family Background of people”. The Personal Factors are “Sexual Dissatisfaction”, “Emotional Dissatisfaction”, “the Feeling of Loneliness”, “the Spouse Unavailability”, “Weakness in Controlling Spouse”, “Domestic Violence”, “Revenge of the Spouse” and “the Role of the Third Person in the Relationship”. The Economic factors are including “the Economic Status of the person”, “the Role of Economic Problems”, “the motivation of entering into infidelity under the Pretext of Job promotion”. The media Factors Were Emphasized on “weakening Iranian Values through Social Media Networks and Foreign Satellite Channels” and “Producing of New Needs by Media”. Finally, the Contextual Factors were including the Role of “Age” and “Gender” in the Infidelity. Findings showed that Infidelity Occurred in the group of men and youth in Iran, more than other Social Groups.

Identifying the constraining factors of production and yield gap is very important. Therefore; this research was performed to identify the production constraining factors of local rice cultivars. All management practices from nursery preparation to harvesting stages for 100 paddy fields of local rice cultivars were recorded through field studies, in Sari, from 2015-2016. In the CPA, the actual and calculated potential yield were 4495 and 5703 kg/ha, respectively and the gap was 1221 kg/ha. The yield gap caused by number of top-dressing variables was 324 kg/ha, equal to 27% of the total yield gap. The yield gap related to previous year of legumes cultivation was 218 kg ha-1, equal to 18% of the total yield variation. Among the 10 variables entered in the CPA model, the effects of top-dress fertilizer application and its application frequency and foliar application were remarkable, which could compensate a significant part of the yield gap (444 kg/ha, 37% of total) in the farmers’ fields by managing these variables. According to boundary line analysis (BLA) finding, actual yield mean on the basis of optimal limit related to 12 variables under study was 5369 kg/ha, with 881 kg/ha yield gap . Mean relative yield and relative yield gap for 12 variables (transplanting date, seedling age, number of seedlings per hill, planting density, nitrogen and phosphorous per hectare, nitrogen before transplanting, harvesting date, lodging problem, pest problem, diseases problem and weeds problem) were 83.64 and 16.35 kg/ha, respectively. Based on the finding, it can be stated that the model precision is appropriate and can be applied for both estimation of the quantity of yield gap and determining the portion of each restricting yield variables.

The purpose of this research is to investigate the factors affecting the formation of leading schools, so that it can be used in Iran's education. For this purpose, first, according to the research background and theoretical foundations, a conceptual framework was presented in the field of the components of an efficient and progressive school. Due to the nature of the subject, this research is an applied and developmental research and its method is of a mixed type, and includes quantitative and qualitative methods. According to the obtained information, the statistical population is about 3000 people. The sample size is estimated to be 344 people using Morgan's table. Stratified sampling method was used for sampling in this research, and 84 managers and 260 teachers participated in this study. The tool of this research is a researcher-made questionnaire. To analyze the data and answer the research questions, the correlation coefficient test and factor analysis were used. The results of the research showed that the components of management and leadership, research factors, educational factors, human resources, budget and infrastructure, technology and equipment are the main components of the formation of leading schools, and the component of educational factors has the largest contribution.

ABSTRACT Despite a wide range of components and criteria affecting travel behavior presented through empirical research, the results of these studies are inconclusive, which could be due to the difference between these components and criteria in the study areas. Therefore, this research presented a method to determine which factors in different physical developments affect travel behavior due to the differences in various physical developments. The required information was collected through 271 questionnaires at the level of three neighborhoods of Monirieh, Koye Bimeh, and Koye Golestan in Tehran, Iran, as the old, conventional, and new neighborhoods, respectively. ANOVA test was exerted to analyze the significant difference between different development patterns in three neighborhoods. Dunnett's T3 was applied to determine which neighborhood caused the difference between groups. Also, the factors affecting travel behavior were obtained based on exploratory factor analysis indicators. Finally, by comparing the results of the ANOVA test and regression analysis, it was discovered that factors such as car ownership, dependence and pro-liking for private cars, density and access to educational centers and parks, access to medical and service centers, and variety and density of retail stores had been introduced as the factors affecting travel behavior due to the differences in development patterns. However, proximity to the public transportation station, accessibility preferences in choosing a residence, dependence, and pro-liking for other than a private car, having a license, number of children under five years old, and age have influenced travel behavior regardless of the variation between neighborhoods. Extended Abstract Introduction Finding factors affecting travel behavior has been one of the main concerns of transportation planners. However, in the last two decades, the importance of the influence of the features of the built environment, including land use, along with demographic-economic characteristics, travel behavior, and attitudes of people, has been raised by urban planners. Studies seek to find factors affecting travel behavior, especially land use characteristics. Despite presenting a wide range of components and criteria affecting travel behavior, the results of the studies are inconclusive, which could be due to the difference between these components and criteria in the study areas. Therefore, this research presented a method to determine which factors in different physical developments affect travel behavior due to the differences in various physical developments. In order to do this, it must first be determined whether the study areas/different development patterns have a significant difference in terms of travel behavior or not. In case of a positive answer to the previous question, the following question is which study areas caused this difference. The next question arises: -Which physical and non-physical characteristics affect travel behavior due to distinctions between different development patterns?   Methodology The present research method is analytical and experimental based on quantitative methods. This research chose the frequency of travel by private car, public transportation, and walking as the travel behavior. According to the research's purpose, indicators and criteria affecting travel behavior were extracted after reviewing the theoretical and experimental literature. Then, the required information was collected through 271 questionnaires at the level of three neighborhoods of Monirieh, Koye Bimeh, and Koye Golestan as the old, conventional, and new neighborhoods, respectively. The questionnaire was compiled as a Likert scale in five parts of travel information, demographic-economic characteristics, perceptual characteristics of land use, travel habits, and access preferences of people in choosing their residence. ANOVA test was used to analyze the significant difference between different groups of a characteristic (here, different development patterns or the three case studies). Dunnett T3 was exerted to determine which neighborhood caused the difference between groups. Also, the factors affecting travel behavior were obtained based on exploratory factor analysis indicators. Finally, by comparing the results of the ANOVA test and regression analysis, it was discovered which factors affecting travel behavior were due to the differences in study areas and which factors affect travel behavior regardless of development patterns.   Results and discussion This research aims to identify the factors affecting travel behavior due to the differences in development patterns. In this regard, the findings in line with the first research question show that the frequency of three modes of travel, by private car, transportation, and pedestrian, differ significantly in the three neighborhoods. Furthermore, ANOVA test results depict that there is a significant difference between these three neighborhoods in terms of factors affecting travel behavior, such as perceptually environmental characteristics of the neighborhood, dependence and pro-liking for personal cars, variety and density of retail stores, density and access to educational units and parks, access to medical and service centers, and car ownership. Finally, by comparing the results of the ANOVA test with the regression analysis assessing the relationship between physical and non-physical factors (the same indicators in the same study areas) with travel behavior, the factors affecting travel behavior owing to different development patterns were identified. Factors such as car ownership, dependence and pro-liking for private cars, density and access to educational units and parks, access to medical and service centers, and variety and density of retail stores have been introduced as the factors affecting travel behavior due to the differences in development patterns. However, proximity to the public transportation station, accessibility preferences in choosing a place of residence, dependence, and pro-liking for other than a private car, having a certificate, number of children under five years old, and age have influenced on travel behavior regardless of the variation between neighborhoods (different physical development patterns).   Conclusion In In order to discover the factors affecting travel behavior due to the differences in patterns of physical development, this research has provided a more detailed analysis of the factors affecting travel behavior. It has achieved more accurate components than previous studies in this regard. Detailed analysis of studies related to travel behavior and finding the main components affecting it, considering the extent of variables and data, can pave the way for professionals in transportation planning and urban planning, in addition to providing detailed methods and criteria in the related literature.   Funding There is no funding support.   Authors’ Contribution Authors contributed equally to the conceptualization and writing of the article. All of the authors approved thecontent of the manuscript and agreed on all aspects of the work declaration of competing interest none.   Conflict of Interest Authors declared no conflict of interest.   Acknowledgments  We are grateful to all the scientific consultants of this paper.

Space requirements in tourist destinations assess based on various models. A Factor is one of these models. Attraction, how to access, Activity, accommodations and amenities are most important and Effective factors On tourism development in all areas. Persian Gulf islands can provide sustainable tourism development because of their cluster and collection trait. This study, investigated affecting factors on Persian Gulf islands tourist activities, based on A factors analyze. This model, analyze the value of tourism destinations, based on five key factors Mentioned above. The research method in this paper is analytic, with emphasis on the evaluation of resources and authors expertise. To this end, we analyzed the successful experiences of islands tourism planning in the world. Then studied five-component factors of this model in Persian Gulf islands. Ultimately we designed a model to provide sustainable tourism development process.