This paper examines published data to develop a model of Logistic Regression for detecting factors associated with Fraudulent Financial Statement (FFS). After an exhaustive exploitation of prior work used financial ratios, 21 ratios are selected as potential predictors of FFS and a series of experiments have been conducted to determine the optimal parameters for Logistic model. Then, we propose an appropriate model for detecting FFS of listed companies in China and compare its predictive ability with other detecting models using a data set of 174 listed companies in China including 87 with FFS and 87 with non-FFS during the period 1993-2007.The results demonstrate that the predictive ability of the model proposed in this paper is higher than other models at about 10% by using the optimal parameters determined and indicate the importance of financial ratios, which could benefit both internal and external auditors, taxation and other state authorities.

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To meet the more demanding requirements of the modeling and simulation technology in various fields, more diverse modeling languages and methods are emerging, which in turn makes it more challenging to integrate and reuse heterogeneous models for field applications. Therefore, it is necessary to extract a constructional frameset of heterogeneous models based on a unified modeling language. This paper presents a method of constructional frameset extraction and feature quantization to address the issues of matching, classification, and composition of multi-domain heterogeneous models. Firstly, heterogeneous models are converted into unified models, which are described by X language. Then, the structural features of each X model are extracted to be incorporated into a predefined template. Then the template can be quantified in the form of a binary tree. The sequence obtained by inorder transversal of the binary tree can be a quantified feature of the template. All the model templates make up the constructional frameset of multi-domain heterogeneous models. The results show that the proposed method can abstract the features of multi-domain heterogeneous models and support a constructional frameset library to rapid modeling, integration, and reuse.

By integrating the Internet of Things, artificial intelligence, 5G, and other new-generation electronic information technologies, the fourth industrial Revolution represented by intelligent manufacturing and industrial internet is promoted, which is the era of comprehensive intelligent industry 4.0. As a key technology of the industrial Internet, the Internet of Things (IoT) connects intelligent manufacturing complex systems and machines with built-in sensors to the network for real-time data collection, transmission, processing, and feedback, to optimize device management and production efficiency. With the increasing number and variety of IoT devices, improving the scalability and maintainability of IoT systems is a challenging demand and requires continuous efforts. This paper proposes an architecture of IoT platform based on Model-Based Systems Engineering (MBSE). In this architecture, a modeling method based on Integrated Modeling language and a model-driven method for cloud-edge collaboration platform is further proposed. The standardization, readability, and reusability of the model are used to drive the device expansion and management. The characteristics of interaction behaviours between cloud and edges are extracted, and models of Holonomic System are built by an integrated modeling language, called X language. Block Definition Diagram (BDD) of X language is used to build the static models of IoT devices and drive the platform to manage the devices. State Machine Diagram (SMD) of X language is used to build the dynamic models of process between the edges and cloud, and drive the processes of the platform. Through experiments and analysis, the feasibility and effectiveness of the X-Language-driven IoT platform are verified.