Introduction: The aim of this study is to determine some of the maternal Risk Factors that lead to preterm delivery. Recognizing these Risk Factors makes it possible to prevent preterm labors as the most frequent cause of neonatal morbidity and mortality. Materials and Methods: This is a descriptive-analytical research study. The data were collected from the recorded information in the files of mothers and preterm neonates that had been born in Bahman 22nd and Shahrivar 17th Hospitals in Mashhad during 2007-2009. The case group consists of 360 preterm deliveries (before week 37th of gestation). The control group consists of 360 term deliveries (after week 37th of gestation). In this study, some of the maternal Risk Factors leading to preterm labor were taken into consideration. The criteria selected were: maternity age, previous abortions, number of previous pregnancies, preeclampsia, the number and gender of fetuses, diabetes and bacterial vaginitis. Also, UTI, the premature rupture of membrane (PROM) was extracted from the registration records. Using SPSS, the two groups were compared and the data were analyzed with the method of chi- square test.Results: In this study, the prevalence rate of preterm labor was estimated as 6.1%. The findings suggest that maternal Risk Factors, such as diabetes, UTI, maternity age, the number of previous pregnancies and the gender of fetus did not cause any significant difference between the groups of the study, but we found significant p-value for the Risk Factors, such as bacterial vaginitis, PROM, preeclampsia, previous abortions, and the number of fetuses. Conclusion: We can reduce the incidence of preterm labor as a result of which the rate of morbidity and mortality of preterm neonates would decrease. This can be materialize  through early diagnosis and treatment of maternal genitourinary infections, prevention of premature rupture (PROM) and preeclampsia as well as preventing repeated abortions and iatrogenic multiple gestation with induction of ovulation.

Background & Aims: supracondylar humerus fracture is one of the most common pediatric elbow fractures accounting for about 16% of the pediatric fractures. Loss of reduction is one of the complications after closed and percutaneous fixation. This research is aimed to investigate the Factors involved in the loss of reduction. Method: In this descriptive study, the children with supracondylar humerus fractures were examined. 175 children under the age of 10 with type 3 and 4 supracondylar humerus fracture fractures who underwent closed fixation and pinning were entered into the study. The studied Factors were analyzed by a logistic regression method. Results: in this study 175 children under the age of 10 including 71 girls and 104 boys (59. 4%) with the average age of 5. 4± 2. 4 were investigated. In 153 cases, (87. 4%) the fixation was anatomical and stable during the follow-up; while 22 cases (12. 6%) had the loss of fixation. The most important anatomical disorder was internal rotation displacement in the medial distal part of fracture in these 22 cases. Pinning technique and obesity were among the effective Factors in the failure of the anatomical reduction. In a way that obese children (above 85 percentile or IBM>25) and the lateral pinning location were divergent but they crossed in the fracture site and were effective with odd ratios of 1. 3 (CI 95% 0. 8-2. 3) and 1. 8 (CI 95% 0. 9-2. 3), respectively. In 14 cases (63. 6%) the failure was due to the place of fracture cross and in 8 cases (36. 6%) the complication occurred in the obese children. The fracture type had no effect on the loss of reduction as no difference was observed. Conclusions: Obesity and fixation technique are among the effective Factors in the loss of reduction in pediatric supracondylar humerus fractures. In obese children, cross-suitable fixation should be considered to prevent complications.

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.

The world is embroiled in a pandemic of a new coronavirus at the end of 2019 and early in 2020, with such a large scale that it has severely involved the political, economic, and social systems in most countries around the world. The scale of the crisis is so widespread that many experts believe that the post-corona world will be substantially different from the pre-corona world. In the face of this massive epidemic, how people perceive Risk and how to Risk is communicated, plays a central role in deciding and choosing people's behavior. Fighting with this emerging phenomenon requires mobilizing all the possibilities of society and helping all people in the field of empathy and altruism. In this article, we briefly review aspects of the important concept of Risk perception and communication in this pervasive crisis.

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.

Introduction: Preterm Labor, which is defined from 22 to 37 weeks of pregnancy, is the cause of 75% of fetal Mortality and morbidity. The survivals carry the Risk of cerebral palzy, visul and audio deficiencies and respiratory disease. The post neonatol cost is considerably higher than usual. The main aim of this study is to clarify the incidence and Risk Factors of preterm labor which intern can help to discover the ways of control its accordance. The impacts are to increase the health standards in the society. Patients and Methods: The type of study is cross sectional as the total number of preterm labor "within the periods of 1/10/78 to 30/3/79" is 257, therefore to study Risk factor, 293 women were considered as control groups. The results show that lack of prenatal care, multifetal pregnancy, the history of infertility, use of ovulation induction, breech presentation, fetal growth restriction, history of preterm labor, PIH, oligo hydroamnious and hydroamnious, male gender, IUFD and low apgar have meaningfully relation with preterm labor. Finally it is concluded that preterm labor is multifactorial event. Knowing the Risk Factors, proper women cooperation and advancing their knowledge can decrease the rate of peterm labor.  

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.