ali mansouri

With the increasing use of the internet and the growing volume of electronically accessible documents on the web, automatic text classification has become a critical method for enhancing information retrieval and managing digital text collections. Text classification allows individuals to search for and retrieve information more accurately and quickly. The significance of automatic document classification lies in labeling documents into predefined classes so that documents within a class exhibit the highest similarity and the most remarkable dissimilarity with documents from other classes while utilizing semantic relationships. This study investigates the application of ontology and knowledge graphs in automatic text document classification.

This study reviewed research and documents related to applying semantic tools such as ontologies and knowledge graphs in text document classification. To collect texts, three domestic databases, including the "National Journal Database," the "Scientific Information Database of Jihad University," and "Marefate Danesh," along with three internal databases "Magiran," "SID" and "Civilica" and three external citation databases, such as "Web of Science", "Scopus" and "Google Scholar" It has been examined in both categories, regardless of the period.

Results of text exploration show that the vector space model does not consider the semantic relationships between words and disregards the word order in sentences. Neglecting the semantic and syntactic relationships between words in natural language provides a different representation of documents. However, ontologies and knowledge graphs help strengthen machine learning models by capturing the meaning of entities and classes. These tools act as an external reference during the classification process and provide domain knowledge for classification models. Using these tools generally allows machines to comprehend the meaning of the data they work with.

The application of ontologies and knowledge graphs in classifying textual documents can strengthen the results of machine learning algorithms through background knowledge. These tools can free the meanings of words from ambiguous sentences and solve problems related to natural language. Using ontology and knowledge graphs can effectively help classify textual documents and improve the accuracy and efficiency of classification models. However, constructing and integrating ontologies and knowledge graphs is a tedious, time-consuming, and complex task that limits the feasibility and practical application of these tools. In the Persian language, in addition to the problems raised in the application of ontologies and knowledge graphs in the classification of documents, there are limitations such as the specific features of the language in writing and technical limitations. Therefore, the use of ontology and knowledge graphs in discussing the classification of textual documents requires attention to linguistic limitations and technical complexity, and the need for further development and efforts is felt, especially in Persian.

Given the significance of universities, their rankings, research outputs, and the impact of leading researchers in the field, the research aims to address the following questions: 1. What factors do top researchers adhere to in order to achieve success in their endeavors? 2. What factors will the university follow, based on the experiences of top researchers, to ensure success in their research activities? 3. How can students be effective in their research activities based on the experiences of top researchers?

The current research is of an applied nature and utilizes the qualitative content analysis method. The research community included all the working and retired faculty members of Isfahan University. A total of 40 individuals (5 women and 35 men) were selected using purposeful and snowball sampling. Based on data from the SciVal database, the top 10 researchers at the university were identified in terms of the number of international outputs (ranging from 243 to 87) and citations (ranging from 3690 to 1037). These researchers come from various fields. Additionally, using the Research Specialists database, other top researchers at the university were identified through interviews conducted using the snowball method. These researchers were selected based on their quality research, social impact, effective scientific communication, fundraising abilities, collaboration with other institutions and organizations, engagement with the industry, mentorship of selected students, and effective management and entrepreneurship in the field of research. The interview was terminated when the data reached saturation and repetition, and no new points were extracted from the content of the subsequent interviews. A semi-structured face-to-face interview was conducted to gather the opinions of the participants in the research. The findings were analyzed in three stages: open coding (extraction of sub-components), central coding (formation of components), and selective coding (selection of concepts) using MAXQDA 2020 qualitative data analysis software.

The items mentioned by the participants in the interviews as factors influencing success in research activities can be categorized into three concepts based on their frequency and importance: factors related to faculty members (265 codes), university-related factors (244 codes), and factors related to students (12 codes). Each concept includes various components. The factors related to faculty members include students, individual characteristics and motivation, scientific communication, research orientation and purpose, research area, industry and society relationships, skills, research outputs, adherence to professional ethics, and thesis. The components related to the university factor include communication with industry and society, research policies, promotion regulations and policies, policies and programs, students, facilities and infrastructure, skill development, support and respect for faculty members, internationalization, financial issues, management, recruitment, research groups, administrative issues, and mission orientation. Skills and determination are also considered essential components of a student.

In order to promote research activities at both the individual and organizational levels, it is necessary to address them from various dimensions and provide comprehensive conditions. On the other hand, the university is considered a fundamental institution for this purpose, due to the expertise and knowledge of its faculty members and students. On the other hand, the activities of faculty members and students depend on the university platform. Despite the influence of the context and environmental conditions of each university in the planning process, the individual and organizational factors mentioned can be generalized to other individuals and universities facing similar conditions. These factors can also be adjusted to fit the specific context of each university. On the other hand, there are upstream factors that are policy-based and implemented at the macro level, which can be utilized and adapted to various academic institutions.

In addition to the distinction between the field of humanities and other sciences in terms of their nature and research goals, different disciplines within the humanities also possess distinct characteristics. This study aims to identify the disadvantages of evaluating humanities research outputs, with a specific focus on the fields of language and literature. It also aims to provide solutions to improve the evaluation of researchers in these areas. 

The present study is an applied type research with a qualitative content analysis method. The study's statistical population included faculty members from language and literature fields with various Persian and non-Persian orientations (English, Arabic, French, German, and Chinese) in Iran. The research sample consisted of 24 individuals, including 19 men and 5 women who were at least Assistant professors from 8 universities: Isfahan, Tarbiat Modarres, Tehran, Al-Zahra, Shahid Chamran Ahvaz, Shiraz, Allameh Tabatabaei, and Ferdowsi of Mashhad. The participants were selected using targeted sampling and the snowball method. The semi-structured interview was used as a research tool, and MAXQDA 2020 qualitative data analysis software was utilized for coding. Theoretical coding involves three stages: open coding, axial coding, and selective coding. To ensure credibility, the opinions of two coders were sought. Also, the results of the coding were sent to three interviewees who played a significant role in expressing injuries and proposing solutions. They were asked to comment on the compatibility of the extracted codes with their own experiences. The terms and details of the work are explained in detail within the plan for the transferability criterion. In order to measure dependability, all the stages and processes of the research were recorded and reported accurately from the beginning to the end, mentioning the details of the data analysis, and excerpts from the text of the interviews were presented. For confirmability, the findings were provided to 5 interviewees and peers to express their opinions about the results. 

Evaluation harms were identified through 62 codes. The concept of science production and the promotion of knowledge foundations were investigated in two general areas (humanities) and a specific area (language and literature) with different components. The general area includes the establishment of policies for evaluating research output, the inclusion of research output ratings in promotion regulations, and considerations of research ethics. The specific area includes the evaluation of research outputs in the promotion regulations, publications, international article publications, theoretical contributions, research style of faculty members, and literary and creative works. The concept of practicality and addressing societal issues also encompasses two aspects: disconnecting the relationship between language and literature fields and society, and teaching language and literature fields. The solutions were presented in the form of 34 codes. The concept of science production and the promotion of knowledge foundations were investigated in two general areas (humanities) and a specific area (language and literature) with different components. The general area includes the development of policies for evaluating research output, the inclusion of research output ratings in promotion regulations, and the improvement of research facilities and resources. The specific area includes the evaluation of research outputs in the promotion regulations, the research approach of faculty members, and research facilities. The concept of practicality and addressing societal issues also encompasses two aspects of applied research: the connection between the disciplines of language and literature and society. 

If the evaluation is conducted accurately and comprehensively, and it clearly indicates the gap between the current situation and the desired outcome, it will minimize trial and error in this field and reduce costs associated with ineffective and unnecessary tasks. It accelerates the steps towards success and progress. The essence of evaluation is optimizing performance. The competition to obtain the best scientific position among researchers, educational groups, and research institutes has gained great importance and momentum. The health of such a competition and its correct orientation will be possible through planning for accurate evaluation, and a review of the evaluation policy of academic faculty members of universities and higher education is one of the fundamental needs in this direction. The results of the present study can be utilized as an effective measure to enhance the current situation.

Human resources is considered one of the most important aspects of organizations as it has significant impact on performance. With the rapid growth of technologies and societal change, human resources has been influenced and has undergone rapid transformation. This study aimed to explore the entrepreneurial opportunities arising from the technological trends in the human resource ecosystem in Iran in the next 5 years. This exploratory mixed methods study was conducted using a quantitative-qualitative approach. First, 17 technological trends were identified in a literature search from over 40 sources. Then, a survey sought the opinions of 57 experts to priorities the trends. Then, semi-structured interviews with 21 technology and HR experts were conducted using MAXQDA software to extract 243 open codes for opportunities, which after synthesis led to 38 identified technology-based entrepreneurial opportunities, including developments such as interactive VR safety training platforms, AI-supported CV analyses and AI-based compensation systems. The identified opportunities are categorized into six key areas: talent acquisition, rewards, compensation and work experience, employee relations and risk management, HR information management, HR administration and shared services, talent management and development. The findings can assist entrepreneurs, employers, and HR professionals in recognizing and leveraging technology-driven opportunities in human resources.

Considering the function of humanities and its comparison with other sciences, this research seeks to examine the scientific outputs and the weight of each, evaluation criteria and indicators, and the relationship between each output and criteria in the evaluation of these sciences Especially in language and literature. In terms of purpose, the research is applied, and in terms of the research implementation process, it is of a sequential and exploratory type. The steps of the work are as follows: 1. Identification and weighting of scientific outputs through the three steps of the library method, conducting interviews, and the Analytical Hierarchy Process (AHP); 2. Identifying the evaluation criteria of scientific outputs with the library method; and 3. determining priorities for evaluating scientific outputs through concurrent function deployment (CFD). Based on the findings, scientific outputs were divided into two categories: 1. Formats: scientific journal article, conference article, book, research project, and dissertation/thesis; 2. Activity: in policy-making, social (scientific-promotional), social (media), and economic contexts. Evaluation criteria are also divided into 8 categories: platform for creation, presentation and publication; writing structure; content; impact in online environment; scientific impact; social impact; economic impact; and cultural impact, each of which includes indicators. Finally, outputs and criteria have been prioritized according to three approaches and goals: production of science and promotion of knowledge foundations, applicability and responsiveness to society’s problems, literary creation/creative literature. Considering the importance and necessity of different evaluation of each field and discipline according to the nature of each one as an effective factor on the application and effectiveness of their scientific outputs, research policy makers can use a new approach in decision-making and establishing policies for evaluating the scientific outputs of the language and literature fields and humanities according to the results.

Keyword extraction is one of the most important issues in text processing and analysis and provides a high-level and accurate summary of the text. Therefore, choosing the right method to extract keywords from the text is important. The aim of the present study was to compare the performance of three approaches in discovering and extracting the subject keywords of e-books using text mining and machine learning techniques. In this regard, three experimental approaches have been introduced and compared; including the successive implementation of the clustering process, improving the quality of clusters in terms of semantics and enriching the stop words of a specific field; Use of specialized keyword template; Finally, the use of important parts of the text in discovering and extracting key words and important topics of the text. The statistical population includes 1000 e-book titles from the subject fields of library and information science based on the congress classification system. bibliographic information of EBooks was obtained from the congress library database, then the original text was prepared. The extraction of topic keywords and clustering of training data was performed using the non-negative matrix factorization algorithm with three experimental approaches. The quality and performance of the subject clusters resulting from the implementation of three approaches in the automatic classification of experimental data were compared using a support vector machine. The findings showed that the Hamming loss (0.020) and in other words the error rate in the correct classification of experimental texts in the third approach is far less than the other two approaches. Also, the F1 score (0.82), which is the average of the two criteria of Precision (0.87) and recall (0.78) and is a reflection of the correct performance of the classification process in topic labeling of texts, is better in the third approach than the other two approaches. The results showed that the quality and semantic coherence of the subject clusters obtained from the third approach, ie the use of important parts of the text in discovering and extracting the subject, was better compared to the other two approaches. In this approach, by focusing on the main parts of the data, which represent the main content and theme of the text, more meaningful topic clusters were obtained. In addition, the keywords obtained from the topic cluster of the third approach can be used in unspecified and unknown collections in order to extract the unknown thematic content of the whole collection. The results of third approach also was better in terms of accuracy and readability (0.79) and the rate of classification error (0.020) of texts, in comparison of other two approaches.

Performance appraisal is the most important focal points of production units. In this study, the Casual Comparative approach applied to measure the performance of active poultry units in Zanjan province. Among the various performance measurement models, total productivity index of Malmquist Index was selected because of its greater effectiveness in measuring growth. In order to achieve a comprehensive model, all poultry farms in Zanjan province were considered for study; However, due to the lack of data required, only 30 poultry farms were selected for performance evaluation. In this study, the amount of initial hatching, feed consumption, energy costs, personnel costs and healthcare costs were selected as input variables and final slaughter weight and fertilizer weight were selected as output variables. Specialized software GAMS.34.1.0 and DEA SOLVER (V8) were used for data analysis. The result showed that among the existing decision units (DMUs), in the first period AL12 with 34.9% growth had the highest total productivity growth and AL14 with a decrease of 14.4% Has had the highest productivity decline. Meanwhile, AL14 with the changes in the method of feeding poultry in the second period has had the highest growth of total productivity. On the other hand, although the AL12 unit has had the highest growth in the first period, but due to lack of attention to costs Energy and non-optimal use of grain consumed in the second period are classified as low-growth decision-making units.

The primary purpose of the present study is to determine the life cycle and technology prediction based on patent bibliometric data using Markov hidden model. The study population included 50,915 patent licenses in medical equipment extracted from the US Patent and Trademark Office database from 1976 to 2015. The study findings revealed technology life cycle patterns in 21 medical equipment sub-areas. The status of medical device patent license indicators at different stages of the technology life cycle, alongside the likelihood of state transfer at different stages of the medical technology life cycle, were investigated. Ultimately, the results showed that "Drug delivery equipment, Disposable medical equipment, Oximeters, and Sharps (Medical instruments)" have more suitable areas for investment and commercialization. The current research results can also provide a good insight into technologies and can be used as a guide alongside experts and other decision-making methods.

In order to investigate the possibility of reducing the growth period and increasing the grain yield of different quinoa genotypes, in 2018, a factorial split plot experiment was conducted in a randomized complete block design with three replications in the research farm of Shahed University. The main factor included three different genotypes of quinoa (Titicaca, Giza 1 and Sajama) and the secondary factors included three models of planting methods (Transplanting, seed Priming with 1000 ppm Boric acid solution, and direct sowing) and boron foliar application at two levels (control and foliar application with 1000 ppm Boric acid solution). The results showed that the effect of genotype was significant on all studied traits. The highest plant height (123.38 cm) and grain yield (2860.228 kg/ha) were obtained in Giza 1 genotype. Also, the shortest period of growth and flowering was related to Titicaca genotype and the longest period was related to Sajama genotype. The effect of planting method on quinoa growth and yield traits was significant and transplanting was more effective than other methods. This method increased the plant height by 48.15% and grain yield by 176.69%, reduced the flowering (34.35%) and ripening period (37.97%). The priming was also able to improve the studied traits compared to the control. Of course, its effectiveness was not as high as transplanting. The effect of boron foliar application on grain yield was significant and increased this trait by 16.7%.

A factorial experiment was conducted in a completely randomized design with four replications to investigate the effect of boron priming on the chemical and antioxidant content of seedlings of different quinoa genotypes.Experimental factors included seed priming(at six levels of control,hydropriming,and boron concentrations of 1g/L،2g/L،3g/Land4g/L) and 3 quinoa genotypes(Titicaca,Giza1 and Sajama).The traits in this study included chlorophyll a, chlorophyll b, total chlorophyll, carotenoids, anthocyanins, saponins, total phenols, peroxidase,and superoxide dismutase.The results showed that boron priming has a significant effect on all traits.The highest levels of chlorophyll and carotenoids in Titicaca and Sajama genotypes were obtained by applying the level of boron concentrations of 2g/L,while the highest level of the mentioned traits was obtained by applying the level of 1g/L in the genotype of Giza1.In the studied genotypes,by increasing the concentration of boron solution,the saponin content increased(33.3%)and the highest level of saponin was observed in the Titicaca genotype(1.13 mg/gDW).The highest total phenol content in Titicaca and Sajama genotypes was obtained by applying boron concentrations of 1g/L,while boron priming reduced the total phenol content in Giza1 genotype,compared to hydropriming.In addition,the response of anthocyanin content to different levels of priming was different in different genotypes.The highest anthocyanin content was obtained in Titicaca genotype at 2g/L,in Giza1 genotype at 4g/L,and in Sajama genotype in control.At a boron concentration of 2g/L,the Titicaca and Sajama genotypes had the highest content of superoxide dismutase enzyme,while the genotype Giza1 had the highest level at a concentration of 1g/L.The highest peroxidase activity was observed in a boron concentration of 4g/L.

Clerkship in field is one of the important educational programs in the field of laboratory sciences and providing opportunities for students to learn in clerkship, improved the quality of health services. The aim of this study was to design a structured microbiology clerkship program from the perspective of case based discussion method among laboratory science students.

This was a single-group intervention study. The participants were from the field of laboratory sciences who participated in clerkship courses (99 participats). Educational intervention based on case-based discussion was designed as a formative evaluation method. Learners' performance was evaluated using a checklist in the stationary test and the students’ satisfaction was evaluated using a questionnaire. The data were analyzed using descriptive and inferential tests.

The students' learning scores after the intervention were in desirable level. The highest learning score related to blood culture evaluation was 8.04±1.54 and the lowest score related to wound culture evaluation was 6.26±1.78. There was no significant difference between the satisfaction scores of the course and the performance evaluation scores of students according to academic year. But the performance of senior year students was significantly better and the students’ satisfaction scores were significantly high and at a good level.

Using the discussion-based teaching method in the clerkship course increased the students' learning and this method can be effective in increasing the skills and learning of the trainees in the field. It is suggested the case based discussion was used in the instructional design of clerkship courses.

Identification of hot topics in research areas has always been of interest. Making smart decisions about what is needed to be studied is always a fundamental factor for researchers and can be challenging for them. The goal of this study is to identify hot topics and thematic trend analysis of articles indexed in Scopus database in the field of Knowledge and Information Science (KIS), between 2010 and 2019, by Text Mining techniques.The population consists of 50995 articles published in 249 journals indexed in Scopus database in the field of KIS from 2010 to 2019. To identify thematic clusters, algorithms of Latent Dirichlet Allocation (LDA) technique were used and the data were analyzed using libraries in Python software. To do this, by implementing the word weighting algorithm, using the TF-IDF method, and weighting all of the words and forming a text matrix, the topics in the documents and the coefficients for assigning each document to each topic (Theta) were determined. The output of the LDA algorithm led to the identification of the optimal number of 260 topics. Each topic was labeled based on the words with the highest weight assigned to each topic and with considering of experts’ opinions. Then, Topic clustering, keywords and topics identification were done. By performing calculations with 95% confidence, 63 topics were selected from 260 main topics. By calculating the average theta in years, 24 topics with a positive trend or slope (hot topic) and 39 topics with a negative trend or negative slope (cold topic) were determined. According to the results, measurement studies, e-management/ e-marketing, content retrieval, data analysis and e-skills, are considered as hot topics and training, archive, knowledge management, organization and librarians' health, were identified as cold topics in the field of KIS, in the period 2010 to 2019. The analysis of the findings shows that due to the interest of the most researchers in the last 10 years in using of emerging technologies, technology-based topics have attracted them more. In contrast, basic issues are less considered to be developed.

To investigate the effect of quinine seed priming on morpho-physiological and biochemical traits of seedlings, a factorial experiment was conducted in a completely randomized design with four replications at the seed processing laboratory of the Shahed University of Tehran. Experimental factors included two levels of seed age (one-year old and four-year old) and four priming treatments (control without priming, hydro-priming, salicylic acid with a concentration of 50 ppm and potassium nitrate with a concentration of 0.2 wt% by volume). The effect of seed age on all studied traits was significant at the level of 1% probability. With increasing seed age, germination percentage (45.6%), germination rate (63.43%), seedling length (59.21%), chlorophyll a content (53.1%), chlorophyll b content (67.4%), carotenoids (6 56.6%), soluble sugar (55.8%), protein (47%), anthocyanin (62%) and catalase activity (50%) decreased whereas the amount of electrolyte leakage increased by 25.9%. The effect of priming on all studied traits was significant at the level of one percent probability and improved the studied traits. The interaction effect of seed age and priming on some traits was also significant. Priming of four-year-old seeds with potassium nitrate as the best treatment composition increased Germination percentage (52.67%), Germination rate (about 490%), Shoot length (about 430%), Chlorophyll a content (77.4%), Chlorophyll b content (180%), Carotenoids (92.8%), Protein (200%) and Catalase activity (250%) and 40.2% reduction of Electrolyte leakage from the plasma membrane. According to the obtained results, it can be stated that the use of the potassium nitrate priming method can greatly reduce the effects of ageing and seed deterioration.

The high richness of food and the high resistance of quinoa to various environmental conditions and stresses have increased attention to its cultivation worldwide. The current research was carried out in the crop year of 2018-2019 in Kohdasht city in Lorestan province in the form of factorial split plots in randomized complete block design with three replications. The test factors include three genotypes (Titicaca, Giza 1, and Sajama) in the main plot and factorial levels including planting method (transplanting, seed priming with boric acid 1000ppm and direct sowing) and boron spraying (spraying with boric acid solution 1000ppm and no foliar spraying). The use of the transplanting method in planting Titicaca, Giza 1, and Sajama genotypes improves the studied traits such as increasing plant height (respectively 14.61, 11.28, and 50.55 percent), increasing leaf area index (respectively 24.28, 30.15 and 17.48 percent) reducing the time up to 50 percent of flowering (34.01, 34.2 and 38.83 days, respectively) and increasing seed yield (46.07, 34.45 and 28.82 percent, respectively) compared to the direct sowing method. The priming effect was also positive and significant on all traits of the studied genotypes, increased seed yield (14.8%), and reduced the ripening period (13 days). It was also found that the effect of boron foliar spraying on seed yield was significant and positive, and the method of transplanting increased its effects even more. Finally, Giza 1 genotype was recommended for planting in the Kohdasht region.

Undoubtedly, human societies have always faced the phenomenon of financial crimes as uninvited guests and are always exposed to harm. This research has been done using data mining tools and with the aim of increasing the efficiency and accuracy of financial crimes, with neural network and decision tree algorithms. Statement of Problem: The forthcoming issue indicates that the spread of cybercrime and, in parallel, the increase of recorded crime data, has confronted us with a large amount of data, each of which contains a large number of messages and information. If analyzed correctly, this data can help police organizations identify and track, as well as predict and prevent crime.

This study improves the efficiency and accuracy of financial crimes by data mining more than 4500 records of cyber financial crimes using data mining tools. The type of data mining method in this article is predictive. This study, by analyzing the data and crime variables taken from the FATA police database, using decision tree algorithms and neural networks, has created a new method to increase efficiency and accuracy in financial crimes, so that efficiency using From the neural network algorithm in the proposed method is 70. 11% and in the decision tree algorithm is 87.78%.

Using the existing tools in the field of data mining, it is possible to detect crimes or prevent their occurrence by using appropriate and optimal human resources of the police. It is hoped that police organizations can be assisted in identifying criminals and, more importantly, in predicting and preventing cybercrime, especially financial crime.

In order to develop the services of academic libraries and provide new services called Research Data Management (RDM) for helping faculty members and researchers in managing their own research data and benefiting from the research data of others, this article seeks to identify the components of RDM in academic libraries. The outcome can be used as a model for implementing RDM in a systematic and effective way.

This research was carried out in two steps. The first step was a qualitative content analysis to identify components of RDM. In the second step using the identified compontents, a questionnaire was composed and circulated in two Delphi rounds among 20 Iranian experts in the field for validation and refinement. To ensure the reliability of the questionnaire and the internal consistency of questions, Cronbach's alpha coefficient was calculated with a result of 0.97.

In the first step 69 components were identified for 6 steps of RDM process, 1) planning; 2) ethical and legal issues; 3) organizing, documenting, generating metadata, 4) storing and backing up, 5) preserving, protecting, deleting data, 6) sharing, and dissemination. Participants added two new steps 1) education and counseling and 2) technical to the previous 6 steps. Also, some components were removed, modified, merged, and moved, and finally 56 components were emphasized in 8 stages.

All identified components are important in the management of research data in academic libraries. In addition, the results showed that the two components of "formulation and development of research data management plan" and "generating metadata to data and files" are of particular importance. Developing data management plan helps individuals and research institutes estimate the material and human resources needed for projects in advance. Also, generating metadata by increasing percision, makes search, retrieval, discovery, access, and reuse of data by other researchers. Academic libraries should consider this in research data management activities.

Serological assay has critical role in defining immunity based on seroprevalence. Identifying the extend of seropositivity would determine the truly infected cases.

We conducted serologic testing for SARS-COV-2 antibodies in 492 health care workers (HCWs) before vaccination program in Shiraz, Iran (30 April 2020).

Based on job position, cases were divided into two categories: frontline staff (169, 34.34%) and non-front-line staff (323, 65.65%). Of all cases 40.65 were female and the rest of them were male. Mean age was estimated 47.97 ± 109.20 and 38.98 ± 8.76 in high risk and low risk population respectively (P = 0.1). In total 367 (74.59%) subjects reported to suffered at least one of the COVID-19 symptoms. More than half of cases (73.57) had history of COVID-19 disease within six months before this survey. No relation was seen between blood type and immunoglobulin positivity. Seropositivity prevalence in low risk and high-risk populations were estimated 8.0 (95% CI: 1.0-16.8) and 5.2(95% CI: 1.2-9.8) respectively. The immunoglobulin test for SARS-COV-2 revealed that 64 (64/492, 13%) and 37 (37/492, 7.52%) subjects had IgG and IgM antibodies against the virus. 14 (14/169, 8.28%) individuals who had IgM were frontline health workers, this is while 23 (14/323, 7.12%) were non front line

The findings of this study imply that seropositivity was lower in non-frontline health care staff. Furthermore, there was no association between blood type and seropositivity. Therefore, the knowledge of antibody status may help into overcome fears in clinical staff, and also more attention is need for interpreting the levels of anti-SARS-CoV-2 antibodies to make a clinical diagnosis of COVID-19, since the time of expose and time of test is an important point which should be considered in the issue.

Topic modeling is one of the text mining techniques that allows you to discover unknown topics in a collection of documents, interpret documents based on these topics, and use these interpretations to organize, summarize, and search for texts automatically. Familiarity with the concept and technique of topic modeling, and its application in discovering topics and organizing information is one of the main goals of this research.

The present study is a review-analytical type in which, while introducing topic modeling, it has categorized and reviewed the applications of this technique based on its performance and provided a sample of research that has used this technique.

Topic modeling algorithms is used not only in addition to the three main objectives of discovering hidden topics, interpreting documents based on topics, and finally organizing and classifying texts, but also is used in discovering hidden topics and relationships in the fields of science, information retrieval, categorizing documents based on topics, discovering outstanding patterns and emerging events, clustering the concepts of scientific fields, analyzing the course of conceptual evolution during historical periods, determining the hierarchical relationships of concepts. A specific scientific field or field and vocabulary enrichment.

Topic modeling based on machine learning and artificial intelligence knowledge has been proposed as one of the new approaches to organizing information resources and serious studies are being conducted in this field. Therefore, by using topic modeling algorithms in order to automate the extraction of the subject and discover the hidden issues in the source, it is possible to strengthen and update the new systems of organizing information resources.

Salinity is one of the most important environmental tensions and causes a significant reduction in yield in plants. In order to investigate the effect of priming with chitosan and potassium nitrate nanoparticles and salinity tension on the biochemical compounds of quinoa (Giza cultivar 1) in Hoagland growth medium, a factorial experiment was conducted in a completely randomized design with four replications in the research greenhouse of Shahed University of Tehran. Priming treatment at two levels including treatment A (solution containing 0/01 percent by weight- volume of chitosan nanoparticles plus 0/5 percent by weight- volume of potassium nitrate) and treatment B (solution containing 0/02 percent by weight- volume of chitosan nanoparticles plus 0/2 percent by weight-volume of potassium nitrate) and salinity tension factor at five levels (Hoagland solution salinity was 2 desi Siemens per meter as control, 8, 12, 16 and 20 desi Siemens per meter). The results showed that the effect of salinity on all studied traits except seedling dry matter and potassium content was significant. Also, the effect of priming treatment on all studied traits except potassium content was positive and significant. Treatment (A) increased seedling dry matter compared to the other treatment by 26/7 percent. Application of treatment (A) compared to treatment (B) at salinity of 20 desi Siemens per meter increased proline (11.8 percent), soluble sugar (20.6 percent) and catalase enzyme content (30.4 percent) and decreased sodium (34.2 percent) and a decrease in the ratio of sodium to potassium (27.8 percent). In general, it can be stated that the use of a solution containing 0.01 percent by weight -volume of chitosan nanoparticles plus 0.5 percent by weight- volume of potassium nitrate in quinoa seed priming can improve the biochemical content of quinoa seedlings under salinity tension conditions.

Wheat is the most important agricultural product in the world and plays an important role in ensuring human food security. According to the fact that Wheat is planted after Quinoa, it is feared that the presence of Quinoa in Wheat cultivation may cause negative allelopathic effects. To investigate the effect of aqueous extract of Quinoa on seed germination and early growth of Wheat seedling, a factorial experiment based on completely randomized design with four replications was conducted at Seed Technology Laboratory of Shahed University of Agriculture, Tehran, Iran. Experimental factors included organ type (inflorescence, leaf, stem and root) and aqueous extract concentration of Quinoa (0, 5, 25, 50, 75 and 100%). In this experiment traits such as germination percentage, shoot length, root length, plantlet length, plantlet dry weight, relative water content, alpha-amylase activity, soluble sugar content, total phenol and proline of Wheat seedling were studied. The results showed that low concentrations of the extract of Quinoa (5 and 25%) had not only negative effects on germination percentage, plantlet length, dry weight and relative water content of Wheat, but even improved these traits. High concentrations of the extract had negative effects on morphological traits. The negative effects of leaf and inflorescence extracts were more than the effects of stem and root extracts. Application of different extracts of Quinoa reduced the activity of alpha-amylase and increased soluble sugar, total phenol and amino acid proline in wheat plantlet.

this study seeks to determine the level of knowledge and skills of PhD students in publishing their resources in scholarly journals and assess the relationship between publishing skills in journals and variables of field, gender, age and also its relationship with the number of articles published in national and international journals. Finally, the impact of participating in related workshops is on the level of this skill. Methodology is an applied type with a quantitative and survey method. The statistical population includes all PhD students at the University of Isfahan in the number of 2432 people. The sampling was stratified random, the volume of which was estimated to be 332 using Krejcie and Morgan table, The skills rate of students was moderate and varied in different fields. But gender and age had no effect on its rate. The level of literacy was also positively correlated with the number of articles published in internal and international journals, and participation in related workshop increased its average score. Value: Since the students' skills were moderate, this research could be the beginning of a study that, like information literacy, creates a framework called publishing literacy in scholarly journals for novice researchers to benefit from. The results indicated the acquisition of this skill, the need to plan related training courses, the impact of the author's role in increasing this skill, the need to modify and pay attention to regulations and criteria for student admission, and the importance of holding related training workshops by universities.

Diabetes is a chronic disorder of the bodychr('39')s metabolism and is characterized by high blood sugar. One of the most common causes of coronavirus in Iran today is a dramatic increase in death anxiety in these patients. Training based on the BASNEF model on reducing death anxiety in Iran with the coronavirus was conducted.

The present study was an experimental study with a control group. The research samples included 70. Data collection tools in this study included demographic information questionnaire and death anxiety questionnaire. Before the intervention, the mentioned questionnaires were completed by the samples. Then the samples were randomly divided into intervention and control groups. Then, one session was held every week for the intervention group of 8 training sessions. There was no intervention in the control group. Data analysis was performed by SPSS software.

The mean score of death anxiety in the control group was 48.0 9 18.9 before the intervention and 21.0 41 41.10 after the intervention. Comparison of these two scores did not show a statistically significant difference (P = 0.11). Also, the mean score of death anxiety in the intervention group, before the intervention, was 74.0 21 21.10 and after the intervention was 39.0 81 81.5. Comparison of these two scores before and after the intervention showed a statistically significant difference in this group (P <0.0001).

According to the findings of the present study, it can be concluded that BASNEF educational model can be effective in reducing death anxiety. Therefore, it is recommended that regular educational programs with this method be developed for diabetic patients and other patients with chronic diseases and integrated into the health system.

In the steel plate shear wall, the thin steel plate is connected to the boundary elements, Therefore, forces transfer from the panel to the columns and increase the required cross sectional area of the columns. Separating the steel panel from the columns, reduces the seismic demands on the columns and prevent from the premature failure of the columns and the collapse of the entire structure. However, this technic decreases the lateral resistance of the system, therefore in such a case, using high yield steel (HYS) for the panel or utilizing a thicker steel plate may be required. In this paper, the seismic behavior of the steel plate shear walls connected to frame beams only (SSW-BO) is studied through nonlinear finite element analysis by ABAQUS software. After validating the numerical model against available experimental results, the effect of the edge stiffeners on the behavior of SSW-BO is explored. Based on the results, changing the edge stiffener cross section does not influence the seismic behavior, the stiffness, the lateral resistance and the dissipated energy of SSW-BO if the stiffeners are not connected to the top and bottom beams. However, connecting the stiffeners to the beams, drastically improve the seismic behavior and hysteretic characteristics of SSW-BO. It should be noted that, in such a case, greater forces may apply to the beams which should be considered in the seismic design of these members.

‎In this paper‎, ‎we investigate the existence of a solution for a pointwise defined‎, ‎multi-singular fractional differential equation in some regions with some boundary conditions‎.

In the conventional steel plate shear walls (SPSWs), the steel plate is connected to all boundary elements, but in the steel plate shear walls connected to frame beams only (SSW-BOs) the plate is not connected to columns. The separation of the plate from the columns decreases the flexural demands of the columns and prevents the premature failure of the columns. In this paper, the effect of opening properties on the seismic behavior of SSW-BOs is studied through nonlinear finite element modeling and analysis. Based on the obtained results, initial stiffness, lateral strength and dissipated energy were higher in the specimen containing square opening compared to the one with circular opening. Moreover, increasing the opening dimension altered the tension field path from a line along the diagonal of the panel to the corners of the opening and degraded the seismic performance of SSW-BO. The most critical opening location is the central one since it disturbs the lateral load path in the panel more than any other locations. Finally, the results of numerical simulations are used to propose a simple method for estimation of the lateral stiffness and strength of SSW-BOs with openings based on the stiffness and strength reduction factors and the corresponding values of the solid SSW-BOs.