The main purpose of blasting in open pit mines is to produce the feed for crushing stage with the optimum dimensions from in situ rocks. The size distribution of muck pile indicates the efficiency of blasting pattern ...The main purpose of blasting in open pit mines is to produce the feed for crushing stage with the optimum dimensions from in situ rocks. The size distribution of muck pile indicates the efficiency of blasting pattern to reach the required optimum sizes. Nevertheless, there is no mature model to predict fragmentation distribution to date that can be used in various open pit mines. Therefore, a new framework to evaluate and predict fragmentation distribution is presented based on the image analysis approach. For this purpose, the data collected from Jajarm bauxite mine in Iran were used as the sources in this study. The image analysis process was performed by Split-Desktop software to find out fragmentation distribution, uniformity index and average size of the fragmented rocks. Then, two different approaches including the multivariate regression method and the decision-making trial and evaluation laboratory(DEMATEL) technique were incorporated to develop new models of the uniformity index and the average size to improve the Rosin-Rammler function. The performances of the proposed models were evaluated in four blasting operation sites. The results obtained indicate that the regression model possesses a better performance in prediction of the uniformity index and the average size and subsequently the fragmentation distribution in comparison with DEMATEL and conventional Rosin-Rammler models.展开更多
With the development of central-private enterprises integration,selecting suitable key suppliers are able to provide core components for smart complex equipment.We consider selecting suitable key suppliers from matchi...With the development of central-private enterprises integration,selecting suitable key suppliers are able to provide core components for smart complex equipment.We consider selecting suitable key suppliers from matching perspective,for it not only satisfies natural development of smart complex equipment,it is also a good implementation of equipment project in central-private enterprises integration context.In in this paper,we carry out two parts of research,one is evaluation attributes based on comprehensive analysis,and the other is matching process between key suppliers and core components based on the matching attribute.In practical analysis process,we employ comprehensive evaluated analysis methods to acquire relevant attributes for the matching process that follows.In the analysis process,we adopt entropy-maximum deviation method(MDM)-decision-making trial and evaluation laboratory(DEMATEL)-technique for order preference by similarity to an ideal solution(TOPSIS)to obtain a comprehensive analysis.The entropy-MDM is applied to get weight value,DEMATEL is utilized to obtain internal relations,and TOPSIS is adopted to get ideal evaluated solution.We consider aggregating two types of evaluation information according to similarities of smart complex equipment based on the combination between geometric mean and arithmetic mean.Moreover,based on the aforementioned attributes and generalized power Heronian mean operator,we aggregate preference information to acquire relevant satisfaction degree,then combine the constructed matching model to get suitable key supplier.Through comprehensive analysis of selecting suitable suppliers,we know that two-sided matching and information aggregation can provide more research perspectives for smart complex equipment.Through analysis for relevant factors,we find that leading role and service level are also significant for the smart complex equipment development process.展开更多
Cryptocurrency adoption has gained significant attention across various fields owing to its disruptive potential and associated challenges.However,companies'adoption of cryptocurrencies remains relatively low.This...Cryptocurrency adoption has gained significant attention across various fields owing to its disruptive potential and associated challenges.However,companies'adoption of cryptocurrencies remains relatively low.This study aims to comprehensively examine the factors influencing cryptocurrency adoption,their interrelationships,and their relative importance.To achieve this objective,we employ a Decision-Making Trial and Evaluation Laboratory(DEMATEL)approach coupled with network analysis tools.By adopting a practical approach rather than a purely theoretical one,our unique contribution lies in the valuable insights derived from experienced Chief Financial Officers(CFOs)of various companies with experience in both traditional finance and cryptocurrencies.Furthermore,the unique blend of analytical rigor and industry expertise supports the study's relevance,offering nuanced insights that are not only academically robust but also immediately applicable in the corporate landscape.Our findings highlight the paramount importance of safety in transactions and trust in the chosen platform for companies considering cryptocurrency adoption.Additionally,criteria such as faster transactions without geographical limitations,lower transaction fees,seamless integration with existing systems,and potential cost savings are identified as crucial drivers.Both the DEMATEL approach and network analysis reveal strong interconnections among the criteria,emphasizing their interdependence and,notably,their reliance on transactional safety.Furthermore,our causes and effects analysis indicates that CFOs perceive company-led cryptocurrency adoption to positively impact the broader cryptocurrency market.展开更多
ldentifying risks and prioritizing is important for payment service provider(PSP)companies to get banking projects and gain more market share.However,studies regarding the identification of risks and causal relationsh...ldentifying risks and prioritizing is important for payment service provider(PSP)companies to get banking projects and gain more market share.However,studies regarding the identification of risks and causal relationships are insufficient in the lranian PSP industry and the industry is unique because of its characteristics.In this study,30 experts involved with PSP companies are employed as the research sample.Eleven key risks and 46 sub-risks are also ientified.Subsequently,the fuzzy decision-making trial and evaluation laboratory technique is applied to determine the effective and affected risks and the severity of their effects on each other.Finally,all risks are ranked.Due to the interal interrelationships of the main risks,the weight of each risk is calculated via the fuzy analytic network process.As the second-level risks have no significant interrelationships,they are ranked via the fuzzy analytical hierarchy process.Moreover,the best-worst method is used to ensure that the obtained rankings are reliable.This study identifies the risks affecting the loss of banking projects and determines the impacts of these risks on each.A sensitivity analysis is then conducted on the weights of the criteria,and the results are compared.展开更多
文摘The main purpose of blasting in open pit mines is to produce the feed for crushing stage with the optimum dimensions from in situ rocks. The size distribution of muck pile indicates the efficiency of blasting pattern to reach the required optimum sizes. Nevertheless, there is no mature model to predict fragmentation distribution to date that can be used in various open pit mines. Therefore, a new framework to evaluate and predict fragmentation distribution is presented based on the image analysis approach. For this purpose, the data collected from Jajarm bauxite mine in Iran were used as the sources in this study. The image analysis process was performed by Split-Desktop software to find out fragmentation distribution, uniformity index and average size of the fragmented rocks. Then, two different approaches including the multivariate regression method and the decision-making trial and evaluation laboratory(DEMATEL) technique were incorporated to develop new models of the uniformity index and the average size to improve the Rosin-Rammler function. The performances of the proposed models were evaluated in four blasting operation sites. The results obtained indicate that the regression model possesses a better performance in prediction of the uniformity index and the average size and subsequently the fragmentation distribution in comparison with DEMATEL and conventional Rosin-Rammler models.
文摘With the development of central-private enterprises integration,selecting suitable key suppliers are able to provide core components for smart complex equipment.We consider selecting suitable key suppliers from matching perspective,for it not only satisfies natural development of smart complex equipment,it is also a good implementation of equipment project in central-private enterprises integration context.In in this paper,we carry out two parts of research,one is evaluation attributes based on comprehensive analysis,and the other is matching process between key suppliers and core components based on the matching attribute.In practical analysis process,we employ comprehensive evaluated analysis methods to acquire relevant attributes for the matching process that follows.In the analysis process,we adopt entropy-maximum deviation method(MDM)-decision-making trial and evaluation laboratory(DEMATEL)-technique for order preference by similarity to an ideal solution(TOPSIS)to obtain a comprehensive analysis.The entropy-MDM is applied to get weight value,DEMATEL is utilized to obtain internal relations,and TOPSIS is adopted to get ideal evaluated solution.We consider aggregating two types of evaluation information according to similarities of smart complex equipment based on the combination between geometric mean and arithmetic mean.Moreover,based on the aforementioned attributes and generalized power Heronian mean operator,we aggregate preference information to acquire relevant satisfaction degree,then combine the constructed matching model to get suitable key supplier.Through comprehensive analysis of selecting suitable suppliers,we know that two-sided matching and information aggregation can provide more research perspectives for smart complex equipment.Through analysis for relevant factors,we find that leading role and service level are also significant for the smart complex equipment development process.
基金the financial support of Fundacao para a Ciencia e Tecnologia through the project number PTDC/EGE-ECO/7493/2020.
文摘Cryptocurrency adoption has gained significant attention across various fields owing to its disruptive potential and associated challenges.However,companies'adoption of cryptocurrencies remains relatively low.This study aims to comprehensively examine the factors influencing cryptocurrency adoption,their interrelationships,and their relative importance.To achieve this objective,we employ a Decision-Making Trial and Evaluation Laboratory(DEMATEL)approach coupled with network analysis tools.By adopting a practical approach rather than a purely theoretical one,our unique contribution lies in the valuable insights derived from experienced Chief Financial Officers(CFOs)of various companies with experience in both traditional finance and cryptocurrencies.Furthermore,the unique blend of analytical rigor and industry expertise supports the study's relevance,offering nuanced insights that are not only academically robust but also immediately applicable in the corporate landscape.Our findings highlight the paramount importance of safety in transactions and trust in the chosen platform for companies considering cryptocurrency adoption.Additionally,criteria such as faster transactions without geographical limitations,lower transaction fees,seamless integration with existing systems,and potential cost savings are identified as crucial drivers.Both the DEMATEL approach and network analysis reveal strong interconnections among the criteria,emphasizing their interdependence and,notably,their reliance on transactional safety.Furthermore,our causes and effects analysis indicates that CFOs perceive company-led cryptocurrency adoption to positively impact the broader cryptocurrency market.
文摘ldentifying risks and prioritizing is important for payment service provider(PSP)companies to get banking projects and gain more market share.However,studies regarding the identification of risks and causal relationships are insufficient in the lranian PSP industry and the industry is unique because of its characteristics.In this study,30 experts involved with PSP companies are employed as the research sample.Eleven key risks and 46 sub-risks are also ientified.Subsequently,the fuzzy decision-making trial and evaluation laboratory technique is applied to determine the effective and affected risks and the severity of their effects on each other.Finally,all risks are ranked.Due to the interal interrelationships of the main risks,the weight of each risk is calculated via the fuzy analytic network process.As the second-level risks have no significant interrelationships,they are ranked via the fuzzy analytical hierarchy process.Moreover,the best-worst method is used to ensure that the obtained rankings are reliable.This study identifies the risks affecting the loss of banking projects and determines the impacts of these risks on each.A sensitivity analysis is then conducted on the weights of the criteria,and the results are compared.
