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Background and aims: Patients with low back pain like other chronic diseases, may experience psychological symptoms in addition to physical symptoms which leads to the exacerbation of the condition of these patients and reduce their quality of life. Due to lack of clinical studies in this field, this study was conducted to investigate the relationship between pain and severity of depression and anxiety in patients with nonspecific chronic low back pain. Methods: This analytic- descriptive study was performed on 250 patients with nonspecific low back pain referring to the orthopedic clinic in Shahrekord selected by convenience sampling. Demographic questionnaire, the visual analogue scale of pain, Beck Depression Inventory and Beck Anxiety Inventory were used to collect the patient’s information, pain severity, anxiety and depression. Stata software 14 was used for data analysis. Results: Mean ± SD of pain in women was 4.6±1.21 and higher than in men (4.1±1.25). Pain severity showed a significant difference between the groups of patients with different levels of anxiety and depression (P

BACKGROUND: Metabolic syndrome which underlies the increased prevalence of cardiovascular disease and Type 2 diabetes is considered as a group of metabolic abnormalities including central obesity, hypertriglyceridemia, glucose intolerance, hypertension, and dyslipidemia. Recently, artificial intelligence based health-care systems are highly regarded because of its success in diagnosis, prediction, and choice of treatment. This study employs machine learning technics for predict the metabolic syndrome. METHODS: This study aims to employ decision tree and support vector machine (SVM) to predict the 7-year incidence of metabolic syndrome. This research is a practical one in which data from 2107 participants of Isfahan Cohort Study has been utilized. The subjects without metabolic syndrome according to the ATPIII criteria were selected. The features that have been used in this data set include: gender, age, weight, body mass index, waist circumference, waist-to-hip ratio, hip circumference, physical activity, smoking, hypertension, antihypertensive medication use, systolic blood pressure (BP), diastolic BP, fasting blood sugar, 2-hour blood glucose, triglycerides (TGs), total cholesterol, low-density lipoprotein, high density lipoprotein-cholesterol, mean corpuscular volume, and mean corpuscular hemoglobin. Metabolic syndrome was diagnosed based on ATPIII criteria and two methods of decision tree and SVM were selected to predict the metabolic syndrome. The criteria of sensitivity, specificity and accuracy were used for validation. RESULTS: SVM and decision tree methods were examined according to the criteria of sensitivity, specificity and accuracy. Sensitivity, specificity and accuracy were 0.774 (0.758), 0.74 (0.72) and 0.757 (0.739) in SVM (decision tree) method. CONCLUSION: The results show that SVM method sensitivity, specificity and accuracy is more efficient than decision tree. The results of decision tree method show that the TG is the most important feature in predicting metabolic syndrome. According to this study, in cases where only the final result of the decision is regarded significant, SVM method can be used with acceptable accuracy in decision making medical issues. This method has not been implemented in the previous research.&nbsp

Among the most basic requirements of any healthy tax system in the community is to promote and develop the tax culture. In term of this issue, one of the most fundamental problems is tax compliance which includes tax collection. This problem will be just resolved based on public culture and tax culture. The aim of this research is to identify and explain the factors affecting tax compliance (A Case Study of State Tax Administration of West Tehran). Methodology: The present research is applied research according to its purpose and on in term of nature and method it is considered a descriptive research. The population consisted of all tax payers to the General Administration of West Tehran. Total of 250 cases were selected as sample using simple random sampling method. In the present study, the income level of the taxpayer, tax rates, tax fines, Article 272 Q of tax auditing, tax attitudes, dominant social norms, perceived justice, tax knowledge and awareness, the complexity of tax laws, the tax service quality and social responsibility, were considered as the independent variables and their relationship with the tax culture was examined. The extent of relationship between tax culture and tax compliance was studied. In addition, the analysis was done using structural equation model and LISREL software (Lisrel). Results: The results indicate that all variables have positive and significant relationship with the tax culture and in the view of taxpayers. Conclusion: tax culture has a positive and significant relationship with tax compliance.

In this paper, we present a formal model, named PobSAM (Policy-based Self-Adaptive Model), for modelingself-adaptive systems. In this model, policies are used as a mechanism to direct and adapt the behaviorof self-adaptive systems. A PobSAM model consists of a set of self-managed modules(SMM). An SMMis a collection of autonomous managers and managed actors. Managed actors are dedicated to functionalbehavior while autonomous managers govern the behavior of managed actors by enforcing suitable policies.To adapt SMM behavior in response to changes, policies governing an SMM are adjusted, i.e., dynamicpolicies are used to govern and adapt system behavior. We employ the combination of an algebraic formalismand an actor-based model to specify this model formally. Managers are modeled as meta-actors whosepolicies are described using an algebra. Managed actors are expressed by an actor model. Furthermore, weprovide an operational semantics for PobSAM described using labeled transition systems. Keywords: Adaptive systems; Policy-based Computing; Component-based Design; Algebra; Actor Models.