جستجوی مقالات مرتبط با کلیدواژه "modeling" در نشریات گروه "میان رشته ای"

Today, the development and use of information systems is essential for any organization. Business intelligence is a set of tools, processes, and technologies that transform raw data into information and extract knowledge from this information to facilitate and optimize the decision-making process. The purpose of this research was to evaluate the identification of effective drivers of intelligent systems in the field of business process modeling in a media content production company as a real case study; it was carried out with a descriptive-analytical approach in terms of practical purpose and based on library and field studies. To identify the conceptual indicators that explain the main drivers of intelligent systems and business processes, a purposeful documentary and the Delphi method were used. The sample population consisted of employees of the intelligent business system user and experts related to the research topic (30 people). The current research analyzed the components of intelligent systems and business processes by using the cross-effects analysis technique, which is a common and accepted method of forecasting using Micmac software. To achieve the objectives of the research, five components (operating dimension of the intelligent system, meeting the needs of users, meeting the needs of the organization, services, integration ability, and analysis ability) and 18 items regarding intelligent business systems using theoretical foundations were extracted. To analyze the data, the path analysis test using the Smart PLS structural equation model was used. The findings from the prioritization results showed the effective dimensions in intelligent business systems: meeting the needs of the organization (0.732) ranked first in importance, analysis ability (0.461) ranked second, followed by services and integration ability (0.325), operational dimension (0.199) and fulfillment Building users' needs (0.064). Finally, according to the results related to the analysis of cross-effects in identifying the drivers effective in intelligent business management systems, six key factors (system response speed, customer relationship management, mechanization of organization processes, comprehensive analysis, supply chain management, and system security) were identified. The results of this research led to the presentation of a framework for evaluating the performance of the intelligent business system and the optimal performance of this system in the studied company.

Due to the increase in population rates and the extensive growth of the construction industry in developing countries, the trend of occupational accidents in this sector has been increasing in recent years. In order to minimize such accidents, research must be conducted on the cause of occurrence and prevention methods of these accidents. Therefore, the current research was carried out with the aim of identifying and determining the impact of effective factors on the prevention of accidents at construction sites, using a descriptive-analytical method in terms of nature and practical in terms of purpose. The statistical population included supervisors and supervising engineers of construction sites with more than 10 years of experience working on construction sites in Kermanshah city. The primary information was collected through document-library review and based on the expert opinions, the effective factors in preventing accidents at construction sites were classified under three categories: managerial, individual and environmental. The primary research tool was a questionnaire, and SPSS and Smart PLS software were used to analyze the data. Model analysis was conducted by the structural equation modeling method with partial least squares approach. The results showed that the conceptual model of the research had a favorable fit. All relationships between sub-factors and factors were significant, and the managerial factor with a path coefficient of 0.724 had the greatest impact and the environmental factor with a path coefficient of 0.309 had the least impact on the prevention of accidents at construction sites.

The prediction of stocks on the stock market and how the symbols are changed are one of the most applied and popular researches. By predicting the symbols with the least error, a person can succeed in the stock market. In this paper, a new network including a neural-fuzzy sink function and an improved grasshopper optimization algorithm was used to predict the value of symbols. In this regard, to predict and model the stock symbols, black-box modeling and AR (Autoregressive) model were used. Model order was determined by using the gray wolf algorithm. To optimize the network’s linear parameters, a hybrid algorithm comprising of least square algorithm for initialization, recursive least square for online training, and a grasshopper optimization algorithm was used to optimize nonlinear parameters. The simulation illustrated that by providing a new structure, the gray wolf algorithm can determine the order of the model and the terms with the most impact on the steel symbol, effectively. In addition, the proposed network and algorithm had less error than other methods such as neural networks for predicting stock value, and the grasshopper optimization algorithm converged with the adaptive learning rate more rapidly.

Identification of mineral veins that do not have much data available, due to the importance of vein mines in the economy of the country, is one of the main issues in the explorations. In the eastern region of Iran, the largest magnesite deposits discovered are vein structures located along the strike-slip shear zones. At present, mining of this mineral is confined to the outcrops of veins at the surface. If surface reserves are exhausted, the need for this mineral will only be met by subsurface estimates. This study aimed to evaluate the deep magnesium expansion in the Kuh-e-Sefid mine concerning the geometry of the mineral veins in the Neogene folded conglomerate based on a combination of proposed methods including statistical analysis of the angles related to the intersection of structural elements and fuzzy logic to create a meaningful database before entering data into mineral modeling software. In this method, first all the angles between the structural strikes are weighted two by two based on their intersection. Then, the correlation map of the angles is determined and matched with the shear fractures of the Riedel model. Fuzzy logic is used to create better correlation models, especially in the narrower statistical populations. Field evaluation in the easternmost areas of the mine with the least raw data available confirms the early promising points obtained by this method. Therefore, using these two computational tools, one can convert meaningful data before entering raw data into modeling environments.

The purpose of this study is to analyze the accumulation and distribution of stress across fault planes using an analytical model based on the reflection of the detector spectrum on the roughness of the fault surfaces during the failure. Fault Modeling is based on asperities making half-spaces separated from each other. This model considers mechanical interaction between contact points and, therefore, can provide a physical explanation of how to redistribute stress at fail time at a locked fault plane. For a boundary condition, a certain displacement, normal and shear forces are calculated at contact points and compared to the failure criterion. If one or more locked surfaces of the fault do not work, the stresses are recalculated. The stress redistribution that is caused by the failure may cause other locked surfaces to fail. This process continues to achieve a stable state or complete failure of entire fault plane for analysis with detector spectrum. The distribution of stresses as well as the onset and progress of failures depend on the contact geometry and roughness.