Abstract Intuition is one of the characteristics of entrepreneurs and an important factor in the decision-making processes of entrepreneurs. In recent years, at the same time as the development of entrepreneurship as an interdisciplinary science, intuition and providing different definitions of it have not been noticed by researchers and researchers in this field, and many researches have not been written in this field. This is despite the fact that in other fields of humanities, especially management (with which entrepreneurship is closely related), basic sciences and even medical sciences, there is a suitable background and theoretical literature about this. On the other hand, it seems that the theoretical and epistemological foundations of this concept have not been researched as much as necessary in the few researches that have been conducted, and very little research has been done in this field. In this article, firstly, the definitions and researches about intuition are reviewed, and secondly, the little researches that have been done in entrepreneurship and its relationship with other sciences are discussed.

In this paper, a novel risk-based, two-objective (technical and economical) Optimal reactive power dispatch method in a wind-integrated power system is proposed which is more consistent with operational criteria.  The technical objective includes the minimization of the new voltage instability risk index. The economical objective includes cost minimization of reactive power generation and active power loss. The proposed voltage instability risk employs a hybrid possibilistic (Delphi-Fuzzy)-probabilistic approach that takes into consideration the operator’s experience, the wind speed and demand forecast uncertainties when quantifying the risk index. The decision variables are the reactive power resources of the system. To solve the problem, the modified multi-objective particle swarm optimization algorithm with sine and cosine acceleration coefficients is utilized. The method is implemented on the modified IEEE 30-bus system. The proposed method is compared with those in the previously published literature, and the results confirm that the proposed risk index is better at estimating the voltage instability risk of the system, especially in cases with severe impact and low probability. In addition, according to the simulation results compared to typical security-based planning, the proposed risk-based planning may increase the security and economy of the system due to better utilization of system resources.

In this paper, the optimum position of Outriggers in a structure for minimizing the base moment under lateral loads is investigated. To do so, the base moment and top story displacement are formulated. Then, a Matlab code is employed to find the optimum positions of the Outriggers. Different kinds of lateral loads including concentrated, uniform, triangular and a combination of such loads are taken into account. Moreover, the effects of the variation of structural elements such as columns, Outriggers and core on the base moment and top story displacement are examined. The outcomes indicated that the optimum points of Outriggers get closer when the flexural rigidity of Outriggers increases. Furthermore, it is shown that the greatest decrease in the base moment occurs in a specific range which depends on the structure parameters such as axial rigidity of columns, the distance of columns, the flexural rigidity of Outriggers and height of the structure. The results show that when the wind load intensity increases, the optimum position of the Outriggers will move to the top of the structure.

About 80% of world transportation happens at sea. Therefore the safety of vessels, in particularduring vessels’ movement, is crucially important. As different contextual parameters affect vessels’movement, selecting Optimal contextual parameters is one of the main changes in vessels’ Context-Aware movement analysis. Toward this end, a Long Short-Term Memory (LSTM) network is usedfor wrapper feature selection to identify Optimal contextual parameters for vessels’ movementprediction. To do this, the Automatic Identification System (AIS) dataset from the eastern coast of theUnited States of America collected from December 2017 is used. All possible combinations of threecontextual parameters, including speed, course and vessels’ presence probability in different positionsat sea, were evaluated using the wrapper method in the LSTM network. In all evaluations, 70% ofdata was used for training and the remaining for cross-validation. The results selected speed andpresence probability as Optimal contextual parameters for vessel movement prediction. The modeltrained with Optimal contextual parameters is 26.98% more accurate than a model trained with allavailable contextual parameters and 16.14% better than a model without contextual parameters.Therefore, selecting Optimal parameters from available contextual parameters can help improve theaccuracy of vessels’ predictions. Keywords: Context-Aware, Long Short-Term Memory, AutomaticIdentification System, wrapper, Movement prediction, Context.

background: In the recent decade, due to the overwhelming importance of health and prevention of diseases, nurses, the greatest part of the health care system, are acting in any position of the health care delivery system; because nursing have a key role in promotion of health and health care everywhere. The objective of this research was to study the desired positions of nursing in the health care delivery system in Iran.methods: This was a triangulation study done on three steps during 2005-2007. At the first step, the positions of nurses were elicited out of library and internet sources. At the second step, the comments of 15 participants were collected using an open questionnaire. Thereafter, at the third step, using the collected data, a questionnaire was made for a poll (all over the country) on the Optimal positions of nursing in Iran, and 64 participants answered it. The results were analyzed using descriptive statistics.results: Derived positions were categorized in two groups: hospital, and community positions. The results showed that all positions were accepted more than 70%.conclusions: Considering positions of nursing and in order to promote the nursing itself and community health, it is suggested that proper planning should be implemented for nurses’ activities in these positions by health planners.

Guyed masts are slender, tall structures widely used to support various types of antennas in the field of telecommunications. These structures consist of a tall vertical mast laterally supported at several levels along its height by sets of inclined pre-tensioned cables. It is well known that natural frequencies are fundamental parameters that affect the dynamic behavior of structures. Optimizing natural frequencies of Guyed masts is an important issue in structural engineering. The main objective of this study presented in this paper is to obtain a design that will maximize the frequency of the Guyed masts. Meta-heuristic algorithms are applied in optimization problems in a variety of fields, including engineering, economics, and computer science. In this paper, three population-based meta-heuristic algorithms are employed for frequency optimization of Guyed masts. These algorithms consist of the Genetic Algorithm (GA), Firefly Algorithm (FA) and Particle Swarm Optimization Algorithm (PSO) aims to maximize the natural frequencies of Guyed masts while satisfying their constraints. The study on optimizing the position of the cable anchorages along the pole of a guyed tower using algorithms demonstrates that the Optimal values for these angles are approximately 35 degrees without pretension forces in the guys. Additionally, the optimum cable angle is 75 degrees when subjected to prestressed guy cables.

Objective Flexible Manufacturing Systems (FMS) are integrated workstations that utilize computer-controlled equipment components for production. These systems are managed by a central computer, which significantly enhances the efficiency and productivity of the production process. Accordingly, a case study is conducted on an FMS electrical manufacturing system with complex manufacturing processes, where automation on the production line is crucial. Selecting the Optimal number of advanced equipment is a challenging and vital issue for managers aiming to boost productivity and efficiently fulfill customer orders. It is a hard-to-change model, and replacing equipment incurs substantial costs.   Methods This study employs the two-stage D-Optimal method to optimize the combination of FMS elements and equipment. The D-Optimal response level input is derived from discrete-event simulation results. Depending on the conditions, various FMS equipment is allocated to each process. Each simulation result for element combinations serves as input for the experimental design. Additionally, the response level (y) of experiments from various FMS indexes is calculated using a weighting method. To reduce the number of experiments and increase data accuracy in a case study with hard-to-change parameters, all combinations are categorized based on the number of automated and manual equipment. The two stages of the D-Optimal design are defined as follows: In the first stage, all combinations within these categories are investigated. In the second stage, the optimized combination from the first stage is analyzed to determine the best configuration. Experiments in the top category from the first stage are simulated and further evaluated in the second stage of the D-Optimal method.   Results In the first phase, all advanced production equipment and FMS elements were considered. After selecting the best-calculated “y” value, which was 147,133.09 in this category, another D-Optimal design was optimized in the second phase to determine the best combination. This combination yielded a “y” value of 151,317.88, representing an improvement over the best category in the first phase of the D-Optimal design. Consequently, the optimized combination from the first phase was further refined. The results from the developed D-Optimal method and the second phase indicate that the Optimal combination of equipment involves using automated and FMS equipment for approximately 92.8% of the total components. Finally, a list of recommended FMS equipment is provided, and its productivity is compared with the productivity at the current level and a higher degree of automation for this production line.   Conclusion In summary, the results of the experimental design show that using advanced production systems does not necessarily improve system efficiency, and determining Optimal combinations requires accurate calculations.

In order to study the spatial structure of oriental beech (Fagus orientalis Lipsky) stand at Optimal stage, this research was carried out in the Caspian forests of Iran. The studied site consisted of a natural stand located at Sangdeh district (Mers-e-se) with three hectare area. It is located between 1900 and1950 m.a.s.l. Six sample plots each with 0.36 ha area, were systematically selected and all trees (1244 stem) within the plots were measured and recorded. Using increment borer, the mean age of the stand was estimated as 137 years; the age difference of trees was 40 years. Results showed that the stand has a closed canopy cover and distribution of stem number per diameter class was more or less homogenous (Bell shape) with a semi even-aged structure. Using Ripley's K function, the distribution of trees within the stand was random.