A study on the role of skill training in entrepreneurship of entrepreneurs and non-entrepreneurs of vocational rraining centers in Ardebil, Iran

This study was conducted to investigate the role of skill training in entrepreneurship of entrepreneurs and non-entrepreneurs through a descriptive correlational method. Population included all trainers from vocational training centers in Ardebil province, Iran. To determine the sample content Cochran formula was used, and 400 individuals were selected via classified random sampling method. To collect data, a researcher-made questionnaire was developed. Reliability of the questionnaire was found to be 89% by Cronbach’s alpha. Data were analyzed SPSS and through Mann-Whitney test. Findings showed that the role of skill training was not significantly different in entrepreneurship of entrepreneurs and non-entrepreneurs. But the role of skill training of graduate entrepreneurs and non-entrepreneurs was found to be different

A study on the role of skill training in entrepreneurship of entrepreneurs and non-entrepreneurs of vocational rraining centers in Ardebil, Iran

This study was conducted to investigate the role of skill training in entrepreneurship of entrepreneurs and non-entrepreneurs through a descriptive correlational method. Population included all trainers from vocational training centers in Ardebil province, Iran. To determine the sample content Cochran formula was used, and 400 individuals were selected via classified random sampling method. To collect data, a researcher-made questionnaire was developed. Reliability of the questionnaire was found to be 89% by Cronbach’s alpha. Data were analyzed SPSS and through Mann-Whitney test. Findings showed that the role of skill training was not significantly different in entrepreneurship of entrepreneurs and non-entrepreneurs. But the role of skill training of graduate entrepreneurs and non-entrepreneurs was found to be different

Psychometric analysis of the ambulatory care learning education environment measure (ACLEEM) in Iran

Background: Examining educational environment (academic and clinical) by means of a valid, reliable and comprehensive questionnaire is a major key in achieving a highly qualified student - oriented curricula. The Persian translation of Ambulatory Care Learning Education Environment Measure-ACLEEM questionnaire has been developed to support this goal, and its psychometrics has been explored in this administration in teaching hospitals affiliated to Tehran University of Medical Sciences. Methods: This descriptive - analytical study involved medical residents in four major clinics. In this study, the ACLEEM Questionnaire was conducted after translating and retranslating the questionnaire and examine the face and content validity, construct validity, test retest reliability and internal consistency coefficient. Results: In this study, 157 out of 192 residents completed the questionnaire (response rate 82). The mean age of the residents was 31.81 years. The final mean of the questionnaire was calculated as 110.91 out of 200 (with 95 confidence interval). Test - retest stability of the questionnaire was between 0.322 and 0.968. The face validity of the questionnaire was confirmed. The content validity ratio was 0.64; and content validity Index was 0.78. In Exploratory factor analysis, eight factors were confirmatory that changed the orientation of some questions. The Cronbach's alpha coefficient of the whole questionnaire was 0.936. Conclusion: According to the data, the Persian version of the ACLEEM questionnaire has sufficient psychometric reliability and validity to be used for conducting research, teaching and practicing the educational learning environment in ambulatory care in Iran

The global burden of cancer attributable to risk factors, 2010-19 : a systematic analysis for the Global Burden of Disease Study 2019

Background Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk-outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings Globally, in 2019, the risk factors included in this analysis accounted for 4.45 million (95% uncertainty interval 4.01-4.94) deaths and 105 million (95.0-116) DALYs for both sexes combined, representing 44.4% (41.3-48.4) of all cancer deaths and 42.0% (39.1-45.6) of all DALYs. There were 2.88 million (2.60-3.18) risk-attributable cancer deaths in males (50.6% [47.8-54.1] of all male cancer deaths) and 1.58 million (1.36-1.84) risk-attributable cancer deaths in females (36.3% [32.5-41.3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20.4% (12.6-28.4) and DALYs by 16.8% (8.8-25.0), with the greatest percentage increase in metabolic risks (34.7% [27.9-42.8] and 33.3% [25.8-42.0]). Interpretation The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.Peer reviewe

TECTAS : bridging the gap between collaborative tagging systems and structured data

Ontologies are core building block of the emerging semantic web, and taxonomies which contain class-subclass relationships between concepts are a key component of ontologies. A taxonomy that relates the tags in a collaborative tagging system makes the collaborative tagging system's underlying structure easier to understand. Automatic construction of taxonomies from various data sources such as text data and collaborative tagging systems has been an interesting topic in the field of data mining. This thesis introduces a new algorithm for building a taxonomy of keywords from tags in collaborative tagging systems. This algorithm is also capable of detecting has-a relationships between tags. Proposed method - the TECTAS algorithm - uses association rule mining to detect is-a relationships between tags and can be used in an automatic or semi-automatic framework. TECTAS algorithm is based on the hypothesis that users tend to assign both "child" and "parent" tags to a resource. Proposed method leverages association rule mining algorithms, bi-gram pruning using search engines, discovering relationships when pairs of tags have a common child, and lexico-syntactic patterns to detect meronyms. In addition to proposing the TECTAS algorithm, several experiments are reported using four real data sets: Del.icio.us, LibraryThing, CiteULike, and IMDb. Based on these experiments, the following topics are addressed in this thesis: (1) Verify the necessity of building domain specific taxonomies (2) Analyze tagging behavior of users in collaborative tagging systems (3) Verify the effectiveness of our algorithm compared to previous approaches (4) Use of additional quality and richness metrics for evaluation of automatically extracted taxonomies.Science, Faculty ofComputer Science, Department ofGraduat