Search

Abstract Background Expanding technologies of early detection of Alzheimer’s disease allow to identify individuals at risk of dementia in early and asymptomatic disease stages. Neuropsychiatric symptoms, such as anxiety and depression, are common in the course of AD and may be clinically observed many years before the onset of significant cognitive symptoms. To date, therapeutic interventions for AD focus on pharmacological and life style modification-based strategies. However, despite good evidence for psychotherapy in late-life depression, evidence for such therapeutic approaches to improve cognitive and emotional well-being and thereby reduce psychological risk factors in the course of AD are sparse. Methods A systematic review was conducted in PUBMED, PsycINFO, Web of Science, and Clinical Trials to summarize the state of evidence on psychotherapeutic and psychoeducational interventions for individuals at risk for Alzheimer’s dementia. Eligible articles needed to apply a manualized and standardized psychotherapeutic or psychoeducational content administered by trained professionals for individuals with subjective cognitive decline or mild cognitive impairment and measure mental health, quality of life or well-being. Results The literature search yielded 32 studies that were included in this narrative summary. The data illustrates heterogeneous therapeutic approaches with mostly small sample sizes and short follow-up monitoring. Strength of evidence from randomized-controlled studies for interventions that may improve mood and well-being is scarce. Qualitative data suggests positive impact on cognitive restructuring, and disease acceptance, including positive effects on quality of life. Specific therapeutic determinants of efficacy have not been identified to date. Conclusions This review underlines the need of specific psychotherapeutic and psychoeducational approaches for individuals at risk of Alzheimer’s dementia, particularly in terms of an early intervention aiming at improving mental health and well-being. One challenge is the modification of psychotherapeutic techniques according to the different stages of cognitive decline in the course of AD, which is needed to be sensitive to the individual needs.

BackgroundWhite matter hyperintensities (WMH), a biomarker of small vessel disease, are often found in Alzheimer’s disease (AD) and their advanced detection and quantification can be beneficial for research and clinical applications. To investigate WMH in large-scale multicenter studies on cognitive impairment and AD, appropriate automated WMH segmentation algorithms are required. This study aimed to compare the performance of segmentation tools and provide information on their application in multicenter research.MethodsWe used a pseudo-randomly selected dataset (n = 50) from the DZNE-multicenter observational Longitudinal Cognitive Impairment and Dementia Study (DELCODE) that included 3D fluid-attenuated inversion recovery (FLAIR) images from participants across the cognitive continuum. Performances of top-rated algorithms for automated WMH segmentation [Brain Intensity Abnormality Classification Algorithm (BIANCA), lesion segmentation toolbox (LST), lesion growth algorithm (LGA), LST lesion prediction algorithm (LPA), pgs, and sysu_media] were compared to manual reference segmentation (RS).ResultsAcross tools, segmentation performance was moderate for global WMH volume and number of detected lesions. After retraining on a DELCODE subset, the deep learning algorithm sysu_media showed the highest performances with an average Dice’s coefficient of 0.702 (±0.109 SD) for volume and a mean F1-score of 0.642 (±0.109 SD) for the number of lesions. The intra-class correlation was excellent for all algorithms (>0.9) but BIANCA (0.835). Performance improved with high WMH burden and varied across brain regions.ConclusionTo conclude, the deep learning algorithm, when retrained, performed well in the multicenter context. Nevertheless, the performance was close to traditional methods. We provide methodological recommendations for future studies using automated WMH segmentation to quantify and assess WMH along the continuum of cognitive impairment and AD dementia.

We conducted an observational exploratory study of distraction by digital devices in multiple different sections across three large undergraduate physics courses. We collected data from two different settings based on the type of devices used for classroom polling: lecture sections that required mobile devices for polling and those that used standalone clickers. Our analysis shows no difference in the average distraction level between the two settings. However, we did observe an overall lower level of distraction during active learning modes, as compared to passive learning modes. Based on there being no observable difference in distraction levels in the mobile polling and standalone clicker classrooms, we recommend that instructors should choose the polling technology that best suits their needs without worrying about the impact on student distraction. The observed difference in distraction between the active and passive learning modes is consistent with previous results from the literature, which reinforces support for the use of active learning modes as much as possible.

Background: Adding nanoparticles as filler aims to improve the mechanical properties of acrylic resin and reduce the incidence of fracture in them. This study aimed to investigate the effect of adding zirconium oxide and titanium oxide nanoparticles to acrylic resin on its flexural strength.Methods and Materials: In this in-vitro study, surface modification of ZrO2 and TiO2 nanoparticles have been done by combining with silane coupling agent (trimethoxysilylpropyl methacrylate), and 25 samples of acrylic resin with 2.5*10*65 mm dimensions have been made. 5 groups were divided into control, 1 and 5% TiO2, 1 and 5% ZrO2. Flexural strength was evaluated by universal testing machine. Data were analyzed by One-Way ANOVA and Tukey's post hoc test. P < 0.05 was considered as statistically significant.Results: A significant increase in the flexural strength of acrylic resin modified with 1% zirconium oxide compared to the control sample was observed, with an average difference of 22.082 MPa (p

Abstract Handgrip strength (HGS) is a non-invasive and reliable biomarker of overall health, physical function, mobility, and mortality. This study aimed to investigate the possible relationship between HGS and mortality in older adult patients hospitalized with COVID-19 in the intensive care unit (ICU) by Alpha (B.1.1.7) and Delta (B.1.617.2) variants. This retrospective cohort study was conducted on 472 COVID-19 patients (222 female and 250 male) aged 60–85 years admitted to the ICU. Demographic data, underlying comorbidities, COVID-19-related symptoms, as well as laboratory and computed tomography (CT) findings were obtained from the patient’s medical records. Using a JAMAR® hydraulic dynamometer, the average grip strength value (kg) after three measurements on the dominant side was recorded for subsequent analysis. Low grip strength (LGS) was defined as an arbitrary cut-off of two standard deviations below the gender-specific peak mean value of normative HGS in Iranian healthy population, i.e.

IntroductionThe development of biomarkers for Alzheimer's disease (AD) has allowed researchers to increase sample homogeneity and test candidate treatments earlier in the disease. The integration of biomarker "screening" criteria should be met with a parallel implementation of standardized methods to disclose biomarker testing results to research participants; however, the extent to which protocolized disclosure occurs in trials is unknown.MethodsWe reviewed the literature to identify prodromal AD trials published in the past 10 years. From these, we quantified the frequency of biomarker disclosure reporting and the depth of descriptions provided.ResultsOf 30 published trials using positron emission tomography or cerebrospinal fluid-based amyloid positivity as an eligibility criterion, only one mentioned disclosure, with no details on methods.DiscussionPossible reasons for and implications of this information gap are discussed. Recommendations are provided for trialists considering biomarker screening as part of intervention trials focused on prodromal AD.HighlightsFew prodromal Alzheimer's disease (AD) trial papers discuss biomarker disclosure. Disclosure has implications for participants, family members, and trial success. Disclosure must be consistently integrated and reported in prodromal AD trials. Best practice guidelines and training resources for disclosure are needed.

Kamal Khademvatani,1 Mir Hossein Seyyed-Mohammadzad,1 Mohammad Akbari,1 Yousef Rezaei,2 Ramin Eskandari,3 Alireza Rostamzadeh1 1Department of Cardiology, Seyyed-al-Shohada Heart Center, Urmia University of Medical Sciences, Urmia, Iran; 2Seyyed-al-Shohada Heart Center, Urmia University of Medical Sciences, Urmia, Iran; 3Department of Cardiology, Firoozgar Hospital, Iran University of Medial Sciences, Tehran, Iran Background: Vitamin D has been shown to have an anticoagulant effect. A decrease in 25-hydroxyvitamin D [25(OH)D] concentration has also been associated with an increased risk of venous thromboembolism. Hence, we sought to determine the relationship between 25(OH)D levels and idiopathic lower-extremity deep vein thrombosis (DVT). Methods: In a case control study, a total of 82 participants with idiopathic lower-extremity DVT were enrolled along with 85 sex- and age-matched healthy participants as controls. The plasma 25(OH)D levels were measured in all the studied samples. Results: The participants' mean age was 47.1±12.3 years. Baseline characteristics were not significantly different between the groups. The concentration of 25(OH)D was significantly lower in the DVT group compared to that of the control group (17.9±10.3 versus 23.1±12.5 ng/mL, P=0.004). The prevalence of participants with deficient 25(OH)D levels was significantly higher in the both DVT and control groups than those with sufficient 25(OH)D levels (68.3% versus 13.4%, and 49.4% versus 28.2%, respectively, P=0.027). In a multivariate analysis, 25(OH)D levels and sex were found to be the only independent predictors of DVT (odds ratio [OR] 1.05, 95% confidence interval [CI] 1.02–1.08, P=0.001 and OR 0.51, 95% CI 0.26–1.00, P=0.049, respectively). Conclusion: Low levels of 25(OH)D are associated with idiopathic lower-extremity DVT. Further investigation is needed to establish determinants and probable causative role of 25(OH)D. Keywords: vitamin D, venous thromboembolism, idiopathic deep vein thrombosis

Abstract Cancer survivors undergo meticulous examinations, including regular magnetic resonance imaging (MRI) scans, to monitor the risk of disease recurrence. The use of magnetic iron nanoparticles (MNPs) enhances MRI accuracy. However, post-injection, MNPs exhibit a notable affinity for binding with proteins and biomolecules, forming a dynamic protein coating called a protein corona (CORONA). While there are reports of its elimination in the liver and kidney metabolism system, patients undergoing this method have shown symptoms of liver problems and related enzyme alterations. This study aims to discern whether the impact of MNPs on liver enzymes significantly contributes to liver damage. The investigation focuses on the effects of magnetic nanoparticles (MNPs) on selected enzymes, including alanine aminotransferase (ALT), aspartate transaminase (AST), α-amylase, and lipase. Employing 104 experiments over a central composite design (CCD), the study evaluates the effects of agents on MNP and enzyme structure, stability, and properties: enzyme assay, electron microscopy, and circular dichroism of secondary structure after interaction with MNPs. The study’s findings unveil the intricate relationship between MNPs and liver enzymes, providing valuable insights for clinical practices and refining the safety profile of MRI. This comprehensive exploration contributes to our understanding of potential implications and aids in optimizing the use of MNPs in medical imaging for cancer survivors.

Abstract MicroRNAs (miRNAs) are small RNA molecules that regulate genes and are involved in various biological processes, including cancer development. Researchers have been exploring the potential of miRNAs as therapeutic agents in cancer treatment. Specifically, targeting the mammalian target of the rapamycin (mTOR) pathway with miRNAs has shown promise in improving the effectiveness of radiotherapy (RT), a common cancer treatment. This review provides an overview of the current understanding of miRNAs targeting mTOR as therapeutic agents to enhance RT outcomes in cancer patients. It emphasizes the importance of understanding the specific miRNAs that target mTOR and their impact on radiosensitivity for personalized cancer treatment approaches. The review also discusses the role of mTOR in cell homeostasis, cell proliferation, and immune response, as well as its association with oncogenesis. It highlights the different ways in which miRNAs can potentially affect the mTOR pathway and their implications in immune-related diseases. Preclinical findings suggest that combining mTOR modulators with RT can inhibit tumor growth through anti-angiogenic and anti-vascular effects, but further research and clinical trials are needed to validate the efficacy and safety of using miRNAs targeting mTOR as therapeutic agents in combination with RT. Overall, this review provides a comprehensive understanding of the potential of miRNAs targeting mTOR to enhance RT efficacy in cancer treatment and emphasizes the need for further research to translate these findings into improved clinical outcomes.

Abstract Cardiac ischemic preconditioning (Pre) reduces cardiac ischemia–reperfusion injury (IRI) by stimulating opioid receptors. Chronic use of opioids can alter the signaling pathways. We investigated the effects of chronic methadone use on IRI and Pre. The experiments were performed on isolated hearts of male Wistar rats in four groups: IRI, Methadone + IRI (M-IRI), Pre + IRI (Pre-IRI), Methadone + Pre + IRI (M-Pre-IRI). The infarct size (IS) in the Pre-IRI group was smaller than the IRI group (26.8% vs. 47.8%, P

IntroductionFood security is one of the most important issues in the world. The corona epidemic, Russia's attack on Ukraine, drought and many other factors have led to an increase in food prices. The sharp increase in global prices of basic food is considered a serious threat to global development, which leads to a significant increase in poverty, a decrease in the level of nutrition, and limited access to services such as education and health. The increase in the price of food in the world markets will also affect the domestic markets of the countries, especially for the countries that are large importers of food and have experienced heavy inflation in their countries. Iran is one of the countries that import food in the world, so higher global food price will undoubtedly affect various economic sectors of Iran. Considering the negative economic and social effects of the unbridled increase in food prices, the impact of global food prices on domestic prices has been the focus of recent studies. Materials and MethodsComputable General Equilibrium (CGE) model is a system of equations that explains an economy in its general form and the interaction between its components. One of the types of general equilibrium models is the GTAP model. This model is specific to global trade analysis and is multi-regional. This model is static (one-period) and includes several economic regions, several economic sectors, and a fixed amount of several production factors. The simulations will be done using the Run-GTAP software. This database has 129 districts and 57 economic sectors. In this study, two areas are considered; which includes Iran (IR) and the rest of the world (ROW). In the database software, all goods and economic sectors are grouped in 57 categories, which can be grouped in a maximum of ten categories or less. Here are four economic sectors: agriculture, industry and mining, oil and services, and three factors of production including labor, capital and land are considered. Results and DiscussionIn this study, the political experience of a 30 percent increase in the global price of agricultural goods will be examined. With a 30% increase in the global prices of agricultural products, the amount of demand reduction in the agricultural sector as a percentage of changes is equal to -1.37. As a result of the political shock the per capita utility of the private sector household has decreased by 1.32% and the per capita utility of the public sector has decreased by 2.14%. Also, the decrease in equivalent change as a welfare index is equal to -3349.05 million dollars. In a static model, the factors of population, technology and investment are assumed to be constant, so the amount of their change is zero, but in dynamic models, the positive or negative effects of these three factors can also be observed. Here (30% price shock on agricultural goods) efficient allocation is the most important factor affecting welfare (negatively). ConclusionIran is one of the importing countries in the past years in terms of grain demanding, and due to successive droughts and failure to observe a serious cultivation pattern in this area, it has faced the weakness of supplying part of its food needs. This forced Iran to import grains and food from other countries in the world, especially Russia. According to the increase in the food price index in the world, the possible effects of the increase in the global food price (30% shock) for Iran have been estimated using a global general equilibrium model. The results show that if the price of agricultural products in the world increases by 30%, the consumer price in Iran will increase in the agricultural sector and decrease in other sectors. Also, the amount of consumer goods will decrease in all sectors, while the amount of consumption of domestic goods will increase, except for the service sector, and the amount of consumption of imported goods will decrease in all sectors, especially the agricultural sector. In addition, the results show that the amount of production in the agricultural and oil sectors increases and has decreased in other sectors. Based on the EV welfare criterion, the welfare level of the private household decreases and the results of the welfare analysis show that the most important factor influencing the welfare is the effects of resource allocation (efficiency).

As the transport sector is responsible for the consumption of a vast proportion of the oil produced, it is mandatory to research feasible solutions to tackle this issue. The application of aerodynamic attachments for passive flow control and reducing resisting aerodynamic forces such as drag and lift is one of the most practicable ways to minimize vehicle energy consumption. The flaps are one of the most innovative aerodynamic attachments that can enhance the flow motion in the boundary layer at the trailing edge of the wings. In the present paper, the flap is designed and modeled for controlling the airflow at the roof-end of a 2D Ahmed body model, inspired by the schematic of the flap at the trailing edge of the wing. As a result, the flap's geometry and position from the roof-end of the car model are parameterized, which leads to having four design variables. The objective functions of the present study are the vehicle's drag coefficient and lift coefficient. 25 Design of Experiment (DOE) points are considered enabling the Box-Behnken method. Then, each DOE point is modeled in the computational domain, and the flow-field around the model is simulated using Ansys Fluent software. The results obtained for the DOE points are employed by different regressors, and the relation between design variables and objective functions is extracted using GMDH-ANN. The GMDH-ANN is then coupled with three types of optimization algorithms, among which the Genetic algorithm proves to have the most ideal coupling process for optimization. Finally, after analyzing the variations in the geometry and position of the roof flap from the car roof-end, the roof-flap with specifications of L = 0.1726 m, α = 5.0875°, H = 0.0188 m, and d = 0.241 m can optimize the car drag and lift coefficients by 21.27% and 19.91%, respectively. The present research discusses the opportunities and challenges of optimal design roof-flap geometry and its influence on car aerodynamic performance.

Scheduling issues are typically classified as constrained optimization problems that examine the allocation of machines and the sequence in which tasks are processed. Regarding the existence of one machine, identification of works processing sequence forms a complete time schedule. Therefore, following a review of previous works, the goal of the present study is designing a mathematical model for open shop scheduling (OSS) problems using different machines aiming at minimizing the maximum time required to complete the works using an artificial neural network (ANN) and genetic algorithm (GA). The research data were driven from a Shoe company carried out between the years 2019 and 2020. The GA and ANN methodologies were employed to analyze and forecast the scheduling of activities within the shoe manufacturing sector. The findings indicated that the probability associated with the third population of the GA was 0.15. Furthermore, an examination of the average values of standard error revealed that the neural network model outperformed in terms of predictive accuracy. The estimated minimum time necessary for task completion, as determined by the neural network, was calculated to be 0.96699, facilitating an optimal condition for meeting the established objectives.

The uncontrollable rise in energy consumption become a most significant issue in recent decades. One of the largest consumers of energy resources across all industries is the residential building sector. Researchers have suggested several strategies to reduce energy loss, including enclosing insulation in wall structures because air conditioning systems account for the majority of energy use inside homes. The main goal of this article is to increase residents' thermal comfort (Tc) while reducing their heating load (HL) and cooling load (CL). Using the EnergyPlus program, the building model was simulated in sample cities with various climatic conditions. For optimization, the first seven design variables were determined in Jeplus software and then multi-objective optimization was performed by the Non-dominated Sorting Genetic Algorithm (NSGA-Ⅱ) algorithm. As a result, Tc, HL, and CL values improved by 38–62, 61 to 100, and 17 to 39 percent, respectively.

In this work, the vapor-liquid equilibrium behavior of aqueous ternary systems composed of choline chloride (ChCl) and imidazolium-based ionic liquids has been investigated using the isopiestic method at 25 °C. The ionic liquids used are 1-butyl-3-methylimidazolium bromide ([C4mim][Br]) and 1-butyl-3-methylimidazolium trifluoromethane sulfonate ([C4mim][CF3SO3]). The effect of ionic liquid anion on water activity, vapor pressure, and isopiestic equilibrium concentrations has been evaluated. The deviation of water iso-activity curves from the linear isopiestic relation has been used as a benchmark to unveil the salting-out or salting-in effect of ChCl on the aqueous solution of ionic liquids. The results show that ChCl plays the role of co-solvent in the studied systems and increases the solubility of the ionic liquids in water, indicating the occurrence of the salting-in phenomenon.

Background & Aims: Premature coronary artery disease (CAD) is common in men and women under 45 and 55 years, respectively. It has been demonstrated that R202Q mutation of MEFV gene may increase the risk of cardiovascular disease in individuals with metabolic syndrome. The goal of the present investigation was to evaluate the frequency of MEFV gene mutation R202Q in exon 2 in Iranian patients with premature CAD (West Azerbaijan province of Iran). Materials & Methods: A total of 100 patients with premature CAD and 100 healthy individuals participated in this hospital-based study. Cases and controls were selected based on strict criteria, including a minimum of one documented angiography with at least 50% stenosis of the coronary artery. PvuII based PCR-RFLP technique was used for the detection of R202Q mutation in the tested samples. Results: R202Q mutation was not found in any of the healthy controls; whereas 12 out of 100 patients with premature CAD were heterozygote for R202Q mutation (12%) (12% vs. 0%). Considering the heterozygosity of the R202Q mutation in the patients, the allele frequency was 0.06 (12 out of 200 chromosomes). Conclusion: Our results indicate that the R202Q mutation in the MEFV gene is frequent in patients with premature CAD. Further studies are necessary to analyze more details regarding variable expressivity or incomplete penetrance of R202Q mutation in the tested population.

Abstract The purpose of this study was to design and validate the franchisor growth model in Iran insurance industry. To achieve this goal, descriptive-analytical research method and structural equation modeling were used. The statistical population included all managers of Alborz insurance company and its outlets in East Azarbaijan province that entire population was selected using census method as study sample (120 individuals). The results showed that 30 drivers on eight principle components explain 74.32% of the variance of franchisor growth drivers in Iran's insurance industry, and based on the model of franchisor growth drivers, characteristics of insurance business through the franchise characteristics, the franchise system dispersion and the brand reputation, and then through the reliability, knowledge sharing, and conflict management affect the intention to continue cooperation.Introduction Franchise as a business model, has many economic and social effects such as job creation, economic modernization, and entrepreneurship development (Naatu & Alon, 2019: 738). The franchise has played an important role in rebuilding the economies of various countries with its significant growth in the post-epidemic period. Based on the esimates of the International Franchise Association (IFA), franchise business returns in 2022 will improve by 4.9 percent to $826.6 billion and their share of GDP in the US economy will be 3 percent (International Franchise Association, 2022). Although franchise in practice, has shown its impact on the prospects of various businesses; however, there are some fundamental gaps in researchers and executive managers' perceptions about the force governing and drivers of franchise performance, as research efforts to identify the relationship between drivers and franchisor growth have led to conflicting evidence (Kang, Asare, Brashear-Alejandro, & Li, 2018: 196). Furthermore, the causal relationships between franchise growth drivers in different industries are not well understood (Kang et al., 2018: 202). Given that there are fundamental differences between the characteristics of business in different industries; it is expected that the drivers of franchise growth in each industry will be different and different causal relationships will be established between them. Due to the limited research on franchise in the insurance industry, the purpose of this study was allocated to design and validate the model of franchisor growth drivers in the Iranian insurance industry.Case Study Alborz insurance company and its outlets in East Azarbaijan province were studied in the present study.Materials and Methods Descriptive-analytical research method and structural equation modeling were used in this present study. The statistical population included all the managers of Alborz Insurance Company and the managers of the representative offices of this company in East Azarbaijan province, and due to the limited number of the statistical population, the census method was used to select the participants (N = 120). Initially, based on the content analysis of published articles, 34 drivers were identified and the initial questionnaire was designed. After examining the content validity, the importance of the items was evaluated based on the collected data using two decision tree algorithms (CRT and C5.0), and four items were removed. Then we used exploratory factor analysis to determine the principle components of growth drivers, DEMATEL technique to develop a theoretical framework of causal relationships between eight components of growth drivers based on the perspectives of 12 academic experts and experienced managers of insurance companies (management experience over 10 years) and structural equation modeling to validate the developed model.Results and DiscussionExploratory factor analysis showed that 30 drivers on eight principle components consist of franchise system dispersion (16.64%), brand reputation (10.96%), reliability (9.85%), knowledge sharing (9.57%), conflict management (7.98%), intention to continue cooperation (7.81%), franchise characteristics (7.33%) and business characteristics (4.17%) explained 74.32% of variance of the franchisor growth drivers in Iran insurance industry. In the present study, the franchise system dispersion represented three indicators of geographical dispersion, overseas activity and number of outlet, which played the most important role in explaining the franchisor growth in the insurance industry. In other words, the dispersion of the franchise system was the main driver of franchisor growth in Iran insurance industry. These findings are consistent with the evidence reported by Julian & Castrogiovanni (1995) and Dant et al. (2007) explaining franchisor growth based on geographical distribution and number of oulet (Dant et al., 2007: 169; Julian & Castrogiovanni, 1995). : 2). However, outlet growth rate, which covers the growth speed of the franchisor (annual increase in the number of outlet), in the present study, based on the results of decision tree algorithms, did not play a role in franchisor growth in the insurance industry, which is inconsistent with the conclusion of Castrogiovanni and Justice (2002) and Ongo (2007) who have introduced the outlet growth rate as a more appropriate indicator for the franchisor growth (Elango, 2007: 179; Castrogiovanni & Justis, 2002: 98; Shane 2001; 136). The reason for these discrepancies may be related to the different characteristics of industries and also the different characteristics of the studied markets.In the present study, to design a model of causal relationships between franchisor growth drivers, first the DEMATEL technique and expert opinions were used to develop a preliminary theoretical model, then the validity of the model were tested based on experimental data and structural equations modeling. The results showed that the structural relationships proposed in the initial theoretical model are supported based on experimental data (χ2 = 635.33, p = 0.051, SRMR = 0.063, CFI = 0.91, IFI = 0.89). In this model, the coefficients of all paths were positive and the component of business characteristics was the starting factor in the model, whereas the component of the intention to continue cooperation appeared as the final outcome. In the model of franchisor growth drivers in Iran insurance industry, the component of insurance business characteristics through franchise characteristics, franchise system dispersion and brand reputation, and then through reliability, knowledge sharing, conflict management impacted the intention to continue cooperation.ConclusionThe model of franchisor growth drivers in Iran insurance industry with eight components of franchise system dispersion, brand reputation, reliability, knowledge sharing, conflict management, intention to continue cooperation, franchise characteristics, and business characteristics is a valid model for recognizing franchisor growth drivers and causal relationships among franchisor growth drivers in Iran insurance industry. Therefore, the use of identified drivers and attention to the pattern of causal relationships between their main components can be used for the growth and development of franchise systems in the Iran insurance industry.

For conducting an analysis of the experimental data, it is imperative to establish a mathematical correlation between the input and output variables. This entails executing a curve fitting or regression procedure on the data, for which numerous methodologies exist. Within the scope of present investigation, the design variables encompass the solid volume fraction (φ) and temperature. Thermal conductivity (TC) of MWCNT-CuO-CeO2 (20-40-40)/water hybrid nanofluid (HNF) is also the objective function. Ten different types of regressors are utilized for regression operations which are Multiple Linear Regression (MLR), Decision Tree (D-Tree), Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), Extreme Learning Machine (ELM), Radial Basis Function (RBF), Adaptive Neuro-Fuzzy Inference System (ANFIS), Gaussian Process Regression (GPR), Multivariate Polynomial Regression (MPR) and Group Method of Data Handling (GMDH). Once the governing equations linking the design variables and the objective functions have been established, these equations can be employed to forecast the simulation data. By substituting the above input values into the equations, we can calculate the corresponding output values for the TC of the HNF. The results obtained from the MPR algorithm are compared to the experimental data. For the GPR, MLR, D-Tree, ELM, MPR, MLP, RBF, SVM, ANFIS, and GMDH algorithms, the maximum margin of error is found to be 0.031, 0.02579, 0.028946, 0.033889, 0.01568, 0.02515, 0.03485, 0.03, 0.0385, and 0.0178, respectively. Moreover, the kernel density estimation diagram indicates the gap between experimental data and data predicted by regression algorithms. Finally, it is evident that the MPR algorithm demonstrates to have a reduced residual dispersion, with the residuals approaching zero.