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Aims: How people react to stressful and painful situations, such as suffering from a chronic disease like multiple sclerosis, is influenced by various factors, one of which is self-compassion. Self-compassion has a psychological nature and significant efficiency in the care and treatment of chronic patients. Existing tools have not paid attention to the characteristics of people suffering from chronic diseases such as MS in using self-compassion for managing the disease. Therefore, the present study aimed to design a questionnaire to measure self-compassion in patients with MS.
Instrument & Methods: The present study is methodological research that was conducted in qualitative and quantitative parts. The items were designed based on the qualitative part of the study and review of texts. The validation characteristics of the questionnaire were performed using face validity, content, and structure. The reliability was assessed by internal consistency and stability methods.
Findings: In the qualitative phase, 78 initial items were extracted. After calculating the item impact score index with values higher than 1.5, content validity ratio higher than 0.51, and content validity index higher than 0.79, 73 items were selected. Confirmatory and then exploratory factor analysis was performed. The internal consistency of the questionnaire was 0.813, and the stability of the questionnaire was 0.778.
Conclusion: Self-compassion scale in patients with MS is a valid and reliable tool in Iranian society. Since Neff theory was used as a guiding framework in this study, the tool can also be used for non-Iranian patients to understand their perception and experiences.
 

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Aims: Caring for patients during the Covid-19 pandemic exposes nurses to both physical and emotional challenges. This study aimed to investigate the correlation between the professional quality of life and caring behaviors in nurses working during the Covid-19 pandemic.
Instruments & Methods: This cross-sectional and correlational study was conducted in 2022. The study population was all nurses working in selected hospitals of Iran University of Medical Sciences. 124 nurses working during the Covid-19 epidemic by using convenience method were selected. Data collected using the demographic and occupational information form, Stumm's professional quality of life questionnaire, and Wolf's caring behaviors of nurses’ questionnaire. All questionnaires were completed by self-report method. Data were analyzed by Spearman's correlation coefficient, Mann-Whitney U, Kruskal-Wallis, and linear regression tests using SPSS 22 software.
Findings: The overall mean score of caring behaviors was 82.06±7.76, and the overall mean score of professional quality of life was 106.18±18.31. There was a significant and direct correlation between the professional quality of life and caring behaviors of nurses working in the Covid-19 epidemic (r=0.435; p<0.001). The linear regression method also showed a significant and direct linear relationship between the two variables.
Conclusion: The professional quality of life is significantly and directly correlated with caring behaviors in nurses working during the Covid-19 pandemic. Therefore, improving the professional quality of life of employees can be effective in providing better and more quality care.
 

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Aims: We aimed to evaluate the effectiveness of a specially designed educational intervention on the rate of iron deficiency anemia (IDA) in pregnant women.
Materials & Methods: This two-arm parallel randomized clinical trial was conducted in 2020 in Khoy, West Azerbaijan, Iran. The study population included all pregnant women diagnosed with IDA. IDA was determined through laboratory tests. A total of 200 participants were selected using a simple random method. Using the quadruple block allocation method, participants were divided into two groups: an intervention group (n=100) and a control group (n=100). The control group received standard care. The intervention group participated in a program consisting of four 45-to-55-minute training sessions over four weeks. Each session included lectures, educational videos, question-and-answer segments, small group discussions, and individual counseling. Data were analyzed using descriptive and inferential statistical tests with SPSS version 16.
Findings: The age, education, employment status, health status, and gestational age of participants in both groups were similar (p>0.05). Additionally, the results of the Mann-Whitney and Kruskal-Wallis tests indicated that the designed program significantly affected the reduction of IDA among participants, particularly among those with diabetes (p<0.05).
Conclusion: The designed educational intervention is effective in reducing IDA among pregnant women.

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In this paper the dynamic behavior of a rotating system which includes rotor (shaft), ball bearing and disk in stationary condition and different speeds is investigated. There are nonlinear characteristics in these systems which cause the linear modeling is not sufficiently accurate. So, in this paper the nonlinear dynamic equations of the system are derived and solved. To derive the equations of the system, Hamiltonian method is used, and complex coordinate transform is used to reduce the number of equations. After solving the equations, to investigate the vibrational properties of the system, time response diagram, dynamic orbit, frequency response, and mode shape of the rotor is plotted. To validate the analytical results, finite element method by ANSYS (workbench) software is used.There is a good conformity betweenthe analytical results and finite element results in resonance frequencies of the system in the first three modes which indicates the sufficient accuracy in nonlinear modeling. It can be concluded from nonlinear modeling that the decay rate is negative for the all modes which is indicates the stability of the all modes. Also, the maximum vibration amplitude in the bearing and rotor occurs in third and second modes respectively. Unbalance phase difference of 90 degrees in two discs causes the excitation of all three frequency modes, whereas by unbalance phase difference of 0 or 180 degrees in two discs,only the odd modes (first and third) and the even modes (second) is excited respectively.

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The main objective is to improve Hilbert-Huang transform using the advantages of non-linear entropy-based features in the time and frequency domain to reduce noise effects. In addition, applying appropriate entropy-based features lead to restrict information redundancy and overcome the need for dimension reduction, in the fault detection of a rotating system. To modify the Hilbert-Huang method, the effect of added noise on various types of nonlinear entropy-based features is investigated for each intrinsic mode functions (IMFs) which extracted by ensemble empirical mode decomposition algorithm. Considering the approximate entropy (ApEn) sensitivity to noise, an evaluation index is presented for selecting the proper amplitude of the added noise based on the approximate entropy and mutual information coefficient of the different IMFs. Subsequently, taking into account the high capability of permutation entropy (PeEn) and marginal Hilbert spectrum entropy (MHE) in the signal characteristic, a threshold is determined for fault detection based on their values associated to the main IMF which has the highest value of mutual information coefficient. As a result, the permutation entropy values and marginal Hilbert spectrum entropy of the main IMF can be used for detection of any deviation from normal operation of the rotor bearings system, regardless of the fault type. Consequently, to determine the type of defect, the higher-order spectra have been used.The bi-spectrum of envelope is calculated. This bi-spectrum is employed to identify the coupling between the rotating frequency and fault-characteristic frequencies, for misalignment and unbalanced fault diagnosis of a rotating machinery vibration simulation system

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Powder metallurgy process is commonly used to manufacture nanocomposite products, in which the product quality of this process depends upon Composite of reinforcement nanoparticle and distribution. In this article Metal Matrix Nanocomposite (MMN) by powder metallurgy with a base material stainless steel 316L, a material that is widely used in the industry, and reinforcement particles mixture of Carbide Titanium (TiC) as carbon-based reinforcing particles, and Hexagonal Nitride Boron (hBN) particles as the self-lubricating material is prepared. The reinforcement powders were micro Sized and mixed in high ball milling to reach Nano-sized, after 30 h mixing powders in high ball milling reach to Nano-sized, and then reinforcement Nanoparticles with 2 and 10 Wt.% Mixed with stainless steel 316L for 5 hours and compacted at 400 Mpa and sintered at 1400 C temperature and 3 Hours. Scanning electron microscope (SEM), Energy-dispersive X-ray Spectroscopy (EDX) and X-ray Diffraction (XRD) tests are performed on Powders to identify the nanocomposite microstructure. The Mechanical Properties such as Microhardness, Wear, and Bending Strength Were Analyzed. These results Compare with Results of stainless steel 316L without Reinforcement. Microhardness and abrasion resistance of Nanocomposite material have improved and flexural strength improved at the sample with 2 wt.% reinforcement and reduced at the sample with 10 Wt.%.