In this paper, the clustering behavior of solid particles in a two-dimensional （2D） liquid-solid fluidized-bed was studied by using the charge coupled devices （CCD） imaging measuring and processing technique and w...In this paper, the clustering behavior of solid particles in a two-dimensional （2D） liquid-solid fluidized-bed was studied by using the charge coupled devices （CCD） imaging measuring and processing technique and was characterized by fractal analysis. CCD images show that the distribution of solid particles in the 2D liquid-solid fluidised-bed is not uniform and self-organization behavior of solid particles was observed under the present experimental conditions. The solid particles move up in the 2D fluidized-bed in groups or clusters whose configurations are often in the form of horizontal strands. The box fractal dimension of the cluster images in the 2D liquid-solid fluidized-bed increases with the rising of solid holdup and reduces with the increment of solid particle diameter and superficial liquid velocity. At given solid holdup and solid particle size, the lighter particles show smaller fractal dimensions.展开更多
In the era of Internet economics, e-business has become one of the most important strategic factors for enterprise development, so theoretical systems are needed to help enterprises develop e-business transformation s...In the era of Internet economics, e-business has become one of the most important strategic factors for enterprise development, so theoretical systems are needed to help enterprises develop e-business transformation strategies. A review of enterprise transformation theory identified five critical organization dimensions of e-business transformation, corporate strategy and vision transformation, organizational structure, product and market transformation, business process transformation, and corporate culture transformation. An e-business transformation process model was developed based on the five dimensions. This model can help enterprises to more effectively implement e-business transformation strategies.展开更多
Several organizations have migrated to the cloud for better quality in business engagements and security. Data quality is crucial in present-day activities. Information is generated and collected from data representin...Several organizations have migrated to the cloud for better quality in business engagements and security. Data quality is crucial in present-day activities. Information is generated and collected from data representing real-time facts and activities. Poor data quality affects the organizational decision-making policy and customer satisfaction, and influences the organization’s scheme of execution negatively. Data quality also has a massive influence on the accuracy, complexity and efficiency of the machine and deep learning tasks. There are several methods and tools to evaluate data quality to ensure smooth incorporation in model development. The bulk of data quality tools permit the assessment of sources of data only at a certain point in time, and the arrangement and automation are consequently an obligation of the user. In ensuring automatic data quality, several steps are involved in gathering data from different sources and monitoring data quality, and any problems with the data quality must be adequately addressed. There was a gap in the literature as no attempts have been made previously to collate all the advances in different dimensions of automatic data quality. This limited narrative review of existing literature sought to address this gap by correlating different steps and advancements related to the automatic data quality systems. The six crucial data quality dimensions in organizations were discussed, and big data were compared and classified. This review highlights existing data quality models and strategies that can contribute to the development of automatic data quality systems.展开更多
基金the National Natural Science Foundation of China(Grant No.20576091)Program for Changjiang Scholars and Innovative Research Team in University(PCSIRT).
文摘In this paper, the clustering behavior of solid particles in a two-dimensional （2D） liquid-solid fluidized-bed was studied by using the charge coupled devices （CCD） imaging measuring and processing technique and was characterized by fractal analysis. CCD images show that the distribution of solid particles in the 2D liquid-solid fluidised-bed is not uniform and self-organization behavior of solid particles was observed under the present experimental conditions. The solid particles move up in the 2D fluidized-bed in groups or clusters whose configurations are often in the form of horizontal strands. The box fractal dimension of the cluster images in the 2D liquid-solid fluidized-bed increases with the rising of solid holdup and reduces with the increment of solid particle diameter and superficial liquid velocity. At given solid holdup and solid particle size, the lighter particles show smaller fractal dimensions.
文摘In the era of Internet economics, e-business has become one of the most important strategic factors for enterprise development, so theoretical systems are needed to help enterprises develop e-business transformation strategies. A review of enterprise transformation theory identified five critical organization dimensions of e-business transformation, corporate strategy and vision transformation, organizational structure, product and market transformation, business process transformation, and corporate culture transformation. An e-business transformation process model was developed based on the five dimensions. This model can help enterprises to more effectively implement e-business transformation strategies.
文摘Several organizations have migrated to the cloud for better quality in business engagements and security. Data quality is crucial in present-day activities. Information is generated and collected from data representing real-time facts and activities. Poor data quality affects the organizational decision-making policy and customer satisfaction, and influences the organization’s scheme of execution negatively. Data quality also has a massive influence on the accuracy, complexity and efficiency of the machine and deep learning tasks. There are several methods and tools to evaluate data quality to ensure smooth incorporation in model development. The bulk of data quality tools permit the assessment of sources of data only at a certain point in time, and the arrangement and automation are consequently an obligation of the user. In ensuring automatic data quality, several steps are involved in gathering data from different sources and monitoring data quality, and any problems with the data quality must be adequately addressed. There was a gap in the literature as no attempts have been made previously to collate all the advances in different dimensions of automatic data quality. This limited narrative review of existing literature sought to address this gap by correlating different steps and advancements related to the automatic data quality systems. The six crucial data quality dimensions in organizations were discussed, and big data were compared and classified. This review highlights existing data quality models and strategies that can contribute to the development of automatic data quality systems.
