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Introduction: It is unlikely that the population growth rate has stayed steady in recent years, given the present situation in Iran. There has been a slight rise in population growth over the past 60 years, according to birth, mortality, and census results from 1956 to 2016. Material & Methods: Mathematical modeling is a common element of computer programs used to predict population growth. This model was applied as a useful and instructive tool in the present research to predict Iran's population until 2041. Results: The standard mathematical modeling approach predicted the total population using the data from Iran's 1956–2016 census using different mathematical modeling methods. The descriptive population growth prediction model (exponential) outperformed the other techniques in terms of compatibility with historical population data. Furthermore, it proved to be highly effective and appropriate for projecting Iran's population in the future. For the year 2041, the following population estimates were projected: A total population of 113,215,189, a male population of 57,366,315, a female population of 55,848,874, an urban population of 83,786,814, a rural population of 29,428,375, and a total population of Iran exceeding 100 million people during the year 2031. Discussion & Conclusion: Combining the exponential and hyperbolic extrapolation models allows for the prediction that Iran's population will increase to 159,852,540 people by the year 2075, which is twice as many as that of the 2016 census. Moreover, it is also feasible to calculate the approximate total population that lived 100,000 years before the 1956 census using mathematical modeling. [ABSTRACT FROM AUTHOR]

Introduction: Considering the high prevalence and negative effects of borderline personality disorders, it is important to know as much as possible about this disorder and to identify the predictors and variables of this disorder. Aim: The present study was Mediating role of experiential avoidance in predicting borderline personality disorder based on childhood trauma. Method: The present study was a correlational description. The statistical population included all male patients diagnosed with borderline personality disorder admitted to psychiatric hospitals in Shiraz in 2022, which were selected using targeted sampling of 60 people. Data collection was done using the childhood trauma questionnaire (2003), the experiential avoidance questionnaire (2011) and the short scale of borderline personality disorder symptoms (2009). To analyze the data, path analysis test, structural equations and Smart PLS software were used. Results: The findings showed that there is a significant and positive relationship between childhood trauma and experiential avoidance (R2=0.711; p<0.05). Path analysis findings also showed that childhood trauma plays a significant role in predicting borderline personality disorder (path coefficient 0.546) and also the avoidance of experience plays a mediating role in the relationship between childhood trauma and borderline personality disorder, so that the path coefficient is 304. 0/ has shown a significant decrease (p<0.05). Conclusion: The results showed that experiential avoidance has a mediating role in predicting borderline personality disorder based on childhood trauma, so it is necessary to pay attention to the component of childhood trauma along with attention to experiential avoidance as a model to reduce the severity of psychological complications. [ABSTRACT FROM AUTHOR]

Introduction: Suicide is a tragic and complex public health problem that affects individuals, families, and communities. The current study aimed to predict suicidal ideation based on cognitive emotion regulation strategies, perceived social support, self-efficacy, perfectionism, and spiritual health in students at Ilam universities, Ilam, Iran. Material & Methods: The current study was a cross-sectional study conducted in 2021. A total of 463 working students studying in Ilam universities were selected and included in the study through the available sampling method. A questionnaire was used for data collection. To determine the relationship between the variables and suicidal thoughts, the Pearson correlation test was used, and to investigate the predictors associated with suicidal thoughts, stepwise regression analysis was performed. Findings: The research results showed a significant negative relationship between the variables of perceived social support, self-efficacy, spiritual health, and positive emotion regulation strategies (i.e., acceptance, positive reorientation, planning, positive appraisal, and underestimation) and a significant positive relationship between perfectionism variables and negative emotion regulation strategies (i.e., self-blame, rumination, catastrophizing, and blaming others) with suicidal thoughts. The highest correlation coefficient was related to perceived social support, which was the most important predictive variable in this study (r=-0.71). Perceived social support, existential health, rumination, perfectionism, self-efficacy, self-blame, and acceptance variables were able to become significant as predictive variables in the regression equation. In total, these factors explained 66% of the variance of suicidal thoughts. Discussion & Conclusion: Based on the research findings, the variables of cognitive emotion regulation strategies, perceived social support, self-efficacy, perfectionism, and spiritual health may be important components and predictors of suicidal thoughts. Therefore, it is important to pay attention to these components in suicide prevention programs and treatment and counseling methods. [ABSTRACT FROM AUTHOR]

Introduction: Corona virus is one of the viruses that cause respiratory infections. In addition, humans always experience fear and anxiety when faced with situations that are beyond their control . Aim: The purpose of the present study was to predict the anxiety of corona infection based on the cognitive regulation strategies of emotion, religious beliefs and resilience in students . Method: The descriptive research method is correlation type. The statistical population included all the students of Islamic Azad University, Shahrood branch in the academic year 2019 -2020, and 384 students were selected as a sample using available sampling method and according to Karjesi and Morgan's table. Corona Virus Anxiety Questionnaires (2019), Cognitive Emotion Regulation (2001), Allport and Ross Religious Beliefs (1967) and Connor and Davidson Resilience (2003) have been used. To analyze the data, Pearson's correlation coefficient and multiple regression analysis were used with SPSS software version 26 . Results: Based on the results, there is a negative relationship between cognitive emotion regulation and resilience (P<0.05) and a positive and significant relationship with religious orientation (P<0.05). Also, the regression analysis showed that 29.6% of the variance of the anxiety of contracting corona can be explained based on compatible and incompatible strategies of cognitive emotion regulation, internal and external religious orientation, and resilience . Conclusion: The results showed that the strategies of cognitive emotion regulation, religious orientation/beliefs and resilience have the ability to predict the anxiety of contracting corona virus in students and can reduce the anxiety of corona virus in students . [ABSTRACT FROM AUTHOR]

Analysis of the extraction modelling for natural compounds is essential for industrial application. In the present paper, ohmic-assisted extraction was investigated for the extraction of Falcaria vulgaris extract. This study was performed in order to express the effect of some pecific parameters (as: voltage gradiant, ethanol to water ratio, extraction time and temperature) on the extraction yield and total phenolic content (TPC). Kinetics models (first-order, second's-order and pelleg models) and artificial neural network were used for modeling ohmic-assisted extraction process. Kinetic study plays a very important role in evaluating the extraction process because it allows estimation of the cost-effectiveness of the process in saving time, money and energy. The results showed that the second-order and plleg's kinetic models respectively, were successfully predicted the value of the total phenol content of the extract and extraction yield in all experiments. The correlation coefficient between experimentally obtained extraction yield and total phenolic content and values predicted by artificial neural network (4-16-2) were 0.995 for training, 0.963 for validation, and 0.979 for testing, indicating the good predictive ability of the model. The artificial neural network model had a higher prediction efficiency than the kinetic models. Artificial neural network can reliably model the process with better predictive and estimation capabilities than the kinetic’s models. [ABSTRACT FROM AUTHOR]

Background and Objectives In recent years, medicine supply chain management has become more significant, especially after the Covid-19 pandemic. The most important issue is supply chain cost control. If the drug inventory is not properly managed, it will lead to issues such as the lack of inventory of certain drugs, provision of excess inventory, increased costs, and, finally, patient dissatisfaction. Materials and Methods In this study, an attempt has been made to predict and manage the pharmaceutical needs of hospitals using an efficient deep-learning algorithm. The drug consumption data for ten years of Besat General Hospital in Hamedan are extracted from the HIS database. As a case study, the accuracy of the predictive model is evaluated, especially for cefazolin. We use a deep model to analyze the medical time-series data efficiently. This model consists of a Long Short-Term Memory network, which can sufficiently recognize the change history in time-series prediction applications. The proposed model with many adjustable parameters in the deep architecture will bring good performance to overcome the complexities of the learning problem. Results Using the deep learning method can increase robustness by reducing the effects of complexity and uncertainty in medical data. The average forecasting error for the proposed method is 0.043, and the measured values for RMSE, MAE, and R² are 0.335, 0.260, and 0.851, respectively. Conclusion A comprehensive comparison between some other predictive methods and the implemented model shows the outperformance of the proposed approach. Additionally, the evaluation results indicate the efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

The Josephson junction is a device consisting of two superconducting electrodes connected by a weak junction such as a thin insulation coating. The Josephson junction has chaotic behavior parameters not desirable in high-frequency applications. In this paper, a model predictive control approach based on an ant colony optimization algorithm is proposed to synchronize two Josephson junction models with different parameters. Here, the Josephson junction is described with a nonlinear model, and the synchronization is obtained using the slave-master technique. For this purpose, an appropriate objective function is defined to assess the particles within the state space. This objective function minimizes simultaneously the tracking error, control effort, and control smoothness. The dynamic optimization problem is solved using an ant colony optimization algorithm. Numerical simulations are conducted to assess the efficiency of the proposed algorithm. Also, a Monte Carlo evaluation is achieved to compute the statistic performance of the suggested controller. In addition, sensitivity analysis to changes in the number of ants and the number of iteration of the inner loop of the algorithm was performed. The results show that the controller is significantly sensitive to reducing the number of iteration of the inner loop. [ABSTRACT FROM AUTHOR]

Background. The level of activity of behavioral brain systems is one of the effective factors in the occurrence or persistence psychopathic behaviors. Therefore, the aim of this study was to structurally model primary and secondary psychopathic disorder based on behavioral brain systems. Methods. This research is applied in terms of purpose and descriptive in terms of correlational research method. The statistical population of this study includes all medical students of Tabriz University of Medical Sciences. A total of 284 subjects were selected as a sample by convenience sampling method and they performed Lonson, Kiel and Fetispatrick Psychopathy Scale and the Carver and White Brain-Behavioral Systems Scale. Data were analyzed using Pearson correlation coefficient and structural equation modelling with SPSS software version 24 and LISREL version 8.80. Results. The results showed that the behavioral activation system has a positive and significant relationship with primary and secondary psychopathy and the behavioral inhibition system has a negative and significant relationship with primary and secondary psychopathy (P <0.01). The results of structural equation model showed that behavioral activator system and behavioral inhibition system have an effective role on primary and secondary psychopathy (P <0.01). Also, the results of structural equation model showed acceptable model (χ²/df= 2.50, RMSEA= 0.075, CFI= 0.96, NFI= 0.94, GFI= 0.94). Conclusion. According to the function of activating systems and behavioral inhibition, pathological personality traits can be oriented or corrected before the formation and stabilization of these traits. [ABSTRACT FROM AUTHOR]

This paper presents a control method based on model predictive control (MPC) to control the buck-type dynamic capacitor (DCAP) to improve power quality. In this method, the reference current consists of two distinct parts, i.e., reactive power compensator (RPC), and hai'monic current eliminator (HCE) and based on the load current and dynamically, it adapts itself to load changes. The proposed prediction model (PM) was extracted from the linearized differential equations of the DCAP. In the proposed method, vector decision variables in each cycle converter switching are updated only once during the prediction horizon, and the optimization problem related to predictive control is solved based on the particle swarm optimization (PSO) algorithm. This reduces the volume of calculations significantly. By considering the total harmonic distribution (THD) value and the power factor (PF) value, the prediction horizon is proposed. The proposed method, unlike the EHM method, has fewer control parameters and can be implemented more easily. The simulation results in MATLAB software showed the superiority of the proposed method in comparison with the EHM method on a single phase DCAP. [ABSTRACT FROM AUTHOR]

Background. The idea and attempt of suicide are highly predictive of deaths due to suicide and can lead to several negative consequences for both the individual and his family and for society. Therefore, this study was conducted with the aim of the role of pain, hopelessness, belonging and suicidal capacity in predicting suicide attempt in people with psychiatric disorders. Methods. The research method of this study was correlational and the statistical population included all individuals referred to psychiatric outpatient clinics of Razi Hospital in Tabriz in 2020-2021. Sample sizes According to Klein criterion and according to the 62-item questionnaire, 298 subjects were selected. Research tools also include the Suicide Attempt Scale, the Suicide Capacity Scale and to examine the three variables of pain, hopelessness and belonging to the Suicide Motivation Questionnaire were used. Structural equation modeling was used to analyze the data using Smart pls software version 2. Results. In this study, 63.3% of the respondents were men and 36.7% of the respondents were women. The mean and standard deviation of the age of the respondents was 37.26±10.88 years. According to the results of the path coefficients between hopelessness with suicide capacity (β= 0.63), sense of belonging with suicide capacity (β= 0.28) and suicide attempt (β= 0.21), suicide capacity with suicide attempt (β= 0.59) were at a significant level (P<0.05). On the other hand, suicidal capacity had a significant mediating role in the relationship between hopelessness and sense of belonging with suicide attempt. Conclusion. The results showed that the three-step model of suicide has the ability to predict suicide attempt in the community of patients with psychiatric disorders. Practical Implications. The results of this study are very important for professionals who work with people with psychiatric disorders to distinguish the idea of suicide from suicide attempt. [ABSTRACT FROM AUTHOR]

Background: Early outcome prediction of hospitalized patients is critical because the intensivists are constantly striving to improve patients' survival by taking effective medical decisions about ill patients in Intensive Care Units (ICUs). Despite rapid progress in medical treatments and intensive care technology, the analysis of outcomes, including mortality prediction, has been a challenge in ICUs. Hence, this study aims to predict the mortality of patients admitted to ICUs using data mining techniques. Methods: In this study, among the cases of patients who were admitted to ICUs of the Rasoul Akram and Firoozgar hospitals of Tehran City, Iran, from December 2017 to March 2018, the first 24 hours of the ICUs admission data of 874 cases were gathered. A new model based on the standard methodology CRISP was developed. In the modeling section, two well-known data mining techniques called artificial neural network (ANN), K nearest neighbor (KNN) and decision tree (DT) were used. WEKA 3.9.2 open-source software was implemented for data analysis. Finally, according to the accuracy, sensitivity, specificity criteria and AUC-ROC Curve, the superior model was introduced. Results: Based on the WEKA results, 19 variables had the most impact on the mortality prediction of patients admitted to ICUs including Glasgow Coma Scale (GCS), mechanical ventilation, surgical service at ICUs admission, gender, temperature, serum creatinine, diabetes, Blood urea nitrogen (BUN), age, addiction, International Normalized Ratio (INR), PH, Partial Thromboplastin Time (PTT), albumin, hemoglobin, glucose, pulse rate, hematocrit (HCT), PO2. Based on the created models, some rules have been extracted which can be used as a pattern to predict the probability of mortality. Although the AUC of the three models was acceptable (KNN 81.5%, ANN 77.5% and DT 74.3%), but the accuracy of decision tree J48 (74.2%) was higher. Conclusion: The study indicated that in the KNN model, the rules derived from it can be effective in mortality prediction in patients admitted to ICUs. [ABSTRACT FROM AUTHOR]

Background and Objectives: Pulmonary embolism is a potentially fatal and prevalent event that has led to a gradual increase in the number of hospitalizations in recent years. For this reason, it is one of the most challenging diseases for physicians. The main purpose of this paper was to report a research project to compare different data mining algorithms to select the most accurate model for predicting pulmonary embolism in hospitalized patients. This model would provide the knowledge needed by the medical staff fir better decision making. Methods: In this research, we designed a prediction model using different methods of machine learning that would best predict the probability of pulmonary embolism in patients at risk. Among data mining algorithms, Bayesian network, decisions tree (J48), logistic regression (LR), and sequential minimal optimization (SMO) were used. The data used in the study included risk factors and past history of patients admitted to the Lung Department of Shariati Hospital, Tehran, Iran. Results: The results showed that the accuracy and specificity of all prediction models were satisfactory. The Bayesian model had the highest sensitivity in predicting pulmonary embolism. Conclusion: Although the results showed a little difference in the performance of prediction models, the Bayesian model is a more appropriate tool to predict the occurrence of pulmonary embolism in hospitalized patients in this type of data. It can be considered a supportive approach along medical decisions to improve disease prediction.

Introduction: Self-handicapping is any action or choice of performance setting that enhances the opportunity to externalize failure and to internalize success. Aim: The aim of this study was to investigate the role of perfectionism and its various dimensions in predicting self-handicapping of Bojnourd university students. Method: The type of research was cross-sectional descriptive and a correlational type. The statistical population included undergraduate students of Bojnourd University in the academic year of 2013-2014. 200 people were selected by multistage cluster sampling and each of them answered to the Perfectionism Scale of Hill et al (2004) and the Self-Handicapping Scale of Jones and Rhodewalt (1982). Data were analyzed using Pearson correlation coefficient and multiple regression. SPSS v. 18 was also used in data analysis. Results: There was a significant positive and direct relationship between each of the eight dimensions of perfectionism and self-handicapping (P<0.05); In other words, with the increase of perfectionism, the use of self-handicapping strategies increases. None of the dimensions of perfectionism in the prediction model of self-handicapping were excluded from the regression and in the final model predicted 0.32 of the variance of self-handicapping (P<0.05). Conclusion: Perfectionism is a multidimensional construct that can affect students' selfhandicapping. Therefore, interventions are needed to systematically manage the components of student perfectionism. [ABSTRACT FROM AUTHOR]

The P-V characteristic of a photovoltaic system is a nonlinear characteristic. The location and the value of the maximum power point in a Photovoltaic (PV) system depends on environmental conditions such as the intensity of solar radiation and the ambient temperature. To extract maximum power, a system including a DC-DC converter and a control method is required. Such a system is called maximum power tracking (MPPT), which is an essential part of a photovoltaic system. Model predictive control (MPC) is a type of control method which is used in MPPT. The model-based predictive control method detects the maximum power point using the equations of the dc-dc converter. In MPC, the system equations are ideally formulated. However, the values of the elements in the dc -dc converter change over time. There are two solutions to this problem. The first is to write real system equations. As a result, the formulations become very complicated and take up a large amount of microprocessor time and memory. Also, most of the time the exact values of the elements in the circuit are unknown. The second method is the use of additional sensors, which increases the cost of the system. In this article, a Fuzzy Model Predictive Control (FMPC) method is proposed for a Cuk converter to implement MPPT in photovoltaic systems. The proposed method obtains an improved performance and eliminates the need for the actual values of the elements. The method has been simulated in the MATLAB/Simulink workspace and its performance superiority has been demonstrated by comparing it against existing MPC and FL- MPPT approaches. [ABSTRACT FROM AUTHOR]

Today, nondestructive methods are widely used to determine the quality of agricultural products. Meanwhile, visible and near-infrared (Vis/NIR) spectroscopy is regarded as one of the most widely used methods in the field of quality assessment of agricultural products. In this study, a system was developed to measure the Vis/NIR spectra of tomato fruit samples in the half-transmittance mode of measurement. Halogen lamps were used as the sources of irradiation and a portable fiber optic spectrometer was utilized to collect the spectra in the range of 400-100 nm. Immediately after collecting the spectra of 170 tomato samples, harvested at different stages of ripening, the firmness of the samples was measured using the standard Magness-Taylor penetration test. In order to eliminate the effects of irregular factors, different preprocessing methods were applied to the collected spectra. Then, partial least squares (PLS) regression was implemented to develop the firmness predictive models. Preliminary results showed two absorption peaks around 670 and 990 nm in the acquired spectra that were related to the chlorophyll and water contents of tomato, respectively. The spectroscopy method could nondestructively predict the tomato fruit firmness with a correlation coefficient (rp) of 0.920 and the root mean squares error of prediction (RMSEP) of 2.5 nm. [ABSTRACT FROM AUTHOR]

This study was performed with objective of predicting land cover change in the east of Mazandaran Province (Neka and Behshahr counties), using Artificial Neural Network in the GIS environment. Landsat imagery belonging to the years 1987 and 2001 was used for change detection. Then, using Multilayer Perceptron neural network transition potentials was implemented for 7 sub-models and finally, land cover change modeling for 2006 with 1987-2001 calibration period and by Markov Chain and hard prediction was run. The accuracy of the assessment model was determined by using the Null Successes, Hits, False Alarms, and Misses. Finally, land cover change prediction was done for 2015. The result showed that during 1987–2001, respectively, 1964 and 1197 ha from forest and orchard were reduced, and 1182 and 1978 ha agriculture and residential were added. The results showed high accuracy (67-89%) in all the sub-models. Total error prediction model was 9.98%. Furthermore, the prediction results showed that forest areas will be reduced and residential, agriculture and orchard will be increased in 2015 compared with 2006. [ABSTRACT FROM AUTHOR]