Exploring the Elements of Visionary Leadership: A Case Study of Faculty Members in Macro Universities of Medical Sciences in Region 1 in Iran

The purpose of this study is to investigate the components that make up visionary leadership among faculty members in medical science universities across Iran’s Region 1. In this study, a descriptive survey was conducted to gather information on faculty members from universities of medical sciences in Region 1. A total of 320 participants completed a questionnaire containing 91 questions. The collected data were analyzed using exploratory and confirmatory factor analysis tests. The findings indicated that Visionary Leadership consisted of two dimensions - individual and organizational - each with three components. The individual dimension pertained to the characteristics, skills, and behaviors of visionary leaders, while the organizational dimension included the thematic role, schematic role, and matric role of visionary leadership within the organization. The highest standard coefficient was related to the component of characteristics of visionary leaders for the individual dimension and the schematic role of visionary leaders in the organization for the organizational dimension. These findings can help managers and authorities of medical education institutions to train high-powered executives who are committed and motivated to implement the Strategic Declaration of the Supreme Leader. It is necessary to establish a national resolve in this field to improve the quality of medical education in Iran

Application of Finite-Element Model Updating in Damage Detection of Offshore Jacket Platforms using Particle Swarm Optimization with Noisy Modal Data

Offshore jacket platforms are one of the most motivating structures for damage detection due to their importance and productivity. Model updating, which is applied as a powerful tool for discovering damage intense and location in several kinds of structures, is a process for minimizing the difference between similar features of the model and real structure. In this study, the modal data including natural frequencies and mode shapes are intended as target features which can be extracted from sensors located in the structure. However, the measured data is expected to be noisy. To minimize the error, particle swarm optimization is used for its abilities in coping with complex search areas. The efficiency of this method is evaluated on several damage cases. The results show that this method can detect the damage of this structure satisfactorily even if modal data is not precisely obtained in the way that the accuracy of achieved results will diminish by higher noise levels

Structural Damage Detection Using Finite Element Model Updating Using Particle Swarm Optimization with Noisy Modal Data

In this paper the applicability of Particle Swarm Optimization (PSO) is investigated for damage detection on a simple beam using model updating method considering measuring errors. Model updating, which is applied as a powerful tool for discovering damage intense and location in several kinds of structures, is a process for minimizing the difference between similar features of the model and real structure. In this study, the modal data including natural frequencies and mode shapes are intended as target features which can be extracted from sensors located in the structure. However, the measured data is expected to be noisy. To minimize the error, particle swarm optimization is used for its abilities in coping with complex search areas. The efficiency of this method is evaluated on several damage cases. The results show that this method can detect the damage in studued structure satisfactorily even if modal data is not precisely obtained in the way that the accuracy of achieved results will diminish by higher noise levels

Damage detection in an offshore jacket platform using genetic algorithm based finite element model updating with noisy modal data

Offshore jacket platforms are one of the most motivating structures for damage detection due to their importance and productivity. In this study, the application of finite element model updating in damage detection of an offshore jacket platform is investigated. The objective function of this method is based on the measured and analytical modal data, including natural frequencies and mode shapes. However, the measured data is expected to be noisy. Also, to avoid obtaining false damage results, a penalty term is added to the objective function. To update the model, genetic algorithm is utilized as a robust global searching tool. Afterward, the efficiency of this method is evaluated on several damage cases in presence of 0, 1, 2 and 3 percent noise with measured modal data. The results show that this method can detect the damage of this kind of structure satisfactorily even if modal data is not precisely obtained

Application of genetic algorithm based finite element model updating in damage detection of offshore jacket platforms

Offshore jacket platforms are one of the most motivating structures for damage detection due to their importance and productivity. Model updating, which is applied as a powerful tool for discovering damage intense and location in several kinds of structures, is a process for minimizing the difference between similar features of the model and real structure. In this study, the modal data including natural frequencies and mode shapes are intended as target features which can be extracted from sensors located in the structure. To minimize the error, genetic algorithm is used for its abilities in coping with complex search areas in addition to its capabilities to use less simplification than conventional methods. Besides, to avoid obtaining false damage results, a penalty term is added to the objective function. This method is tested on several damage cases successfully. Furthermore, the effect of the application of different number of mode shapes is examined by using first two and five modal data in updating procedure. This examination shows that increasing the number of used modes can improve the efficiency of the method in some cases. Afterward, some useful considerations are suggested to apply this method practically

A Critique and Analysis of Reasons for Illegitimacy of the Religious Government during the Occultation Era

Some believe that a religious government during the Occultation Era does not have the necessary legitimacy. The proponents of the idea have presented reasons most of which refer to the absence of the twelfth  Imam  per  se  and  the  existence  of  narrations  that  have pictured  any  uprisings  or  restorative  movements  as  useless, inefficient  and  even  illegitimate.  The  present  study  is  aimed  at indicating that the absence of the twelfth Imam per se may not be a reason for the illegitimacy of the religious government but a religious government can set the stage for the messianic culture. It will also show  that  despite  the  existence  of  narrations  that  reject  the legitimacy  of  an  uprising  during  the  Occultation  Era,  there  are genuine narrations in the narrative reference books that refer to the legitimacy of the movement. By drawing a comparison between the two categories of narrations, it may be concluded that some of those uprisings, including Imam Khomeini’s uprising that led to an Islamic government, were legitimate in case of having the conditions of an uprising

Modelling the compressive strength of geopolymer recycled aggregate concrete using ensemble machine learning

•Developed an ML-based methodology for modeling the compressive strength of GRAC.•Employed TGAN for data augmentation to overcome the limitation of small datasets.•Achieved less than 6 % error in predicting GRAC strength with boosting ML models. In the quest to reduce the environmental impact of the construction sector, the adoption of sustainable and eco-friendly materials is imperative. Geopolymer recycled aggregate concrete (GRAC) emerges as a promising solution by substituting supplementary cementitious materials, including fly ash and slag cement, for ordinary Portland cement and utilizing recycled aggregates from construction and demolition waste, thus significantly lowering carbon emissions and resource consumption. Despite its potential, the widespread implementation of GRAC has been hindered by the lack of an effective mix design methodology. This study seeks to bridge this gap through a novel machine learning (ML)-based approach to accurately model the compressive strength (CS) of GRAC, a critical parameter for ensuring structural integrity and safety. By compiling a comprehensive database from existing literature and enhancing it with synthetic data generated through a tabular generative adversarial network, this research employs eight ensemble ML techniques, comprising three bagging and five boosting methods, to predict the CS of GRAC with high precision. The boosting models, notably extreme gradient boosting, light gradient boosting, gradient boosting, and categorical gradient boosting regressors, demonstrated superior performance, achieving a mean absolute percentage error of less than 6 %. This precision in prediction underscores the viability of ML in optimizing GRAC formulations for enhanced structural applications. The identification of testing age, natural fine aggregate content, and recycled aggregate ratio as pivotal factors offers valuable insights into the mix design process, facilitating more informed decisions in material selection and proportioning. Moreover, the development of a user-friendly graphical interface for CS prediction exemplifies the practical application of this research, potentially accelerating the adoption of GRAC in mainstream construction practices. By enabling the practical use of GRAC, this research contributes to the global effort to promote sustainable development within the construction industry

Five-Echelon Multiobjective Health Services Supply Chain Modeling under Disruption

Medicine unreliability problem is taken into consideration as one of the most important issues in health supply chain management. This research is associated with the development of a multiobjective optimization problem for the selection of suppliers and distributors. To achieve the purposes, the optimal quota allocation is determined with respect to disruption of suppliers in a five-echelon supply chain network and consideration of the distributor centers as a hub location-allocation mode. The objective of the optimization model is involved in simultaneous minimization of transactions costs dealing with suppliers, expected purchasing costs from suppliers, expected percentages of delayed and returned products in each distributor, as well as transportation cost in each echelon and fixed cost for distributor centers, and finally maximization of the expected scores for suppliers and high priority of product customers. The optimization problem is formulated as a mixed-integer nonlinear programming model. The proposed optimization model is utilized to investigate a numerical case study for asthma-specific medicines. The analyzing procedure is conducted based on the collected real data from Cobel Darou pharmaceutical company in 2019. Furthermore, a fuzzy multichoice goal programming model is considered to solve the proposed optimization model by R optimization solver. The numerical results confirmed the authenticity of the model