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This paper aims to introduce a multiscale computational method for structural failure analysis with inheriting simulation of moving trans-scale boundary (MTB). This method is motivated from the error in domain bridging caused by cross-scale damage evolution, which is common in structural failure induced by damage accumulation.

Within the method, vulnerable regions with high stress level are described by continuum damage mechanics, while elastic structural theory is sufficient for the rest, dividing the structural model into two scale domains. The two domains are bridged to generate mixed dimensional finite element equation of the whole system. Inheriting simulation is developed to make the computation of MTB sustainable.

Numerical tests of a notched three-point bending beam and a steel frame show that this MTB method can improve efficiency and ensure accuracy while capturing the effect of material damage on deterioration of components and structure.

The proposed MTB method with inheriting simulation is an extension of multiscale simulation to structural failure analysis. Most importantly, it can deal with cross-scale damage evolution and improve computation efficiency significantly.

The function, utilization, and resource allocation of sport facilities in residential areas have attracted increasing attention in China along with the country's rapid economic development and the popularization of the health concept. To optimize the allocation of public sports resources and develop functions for sports facilities in China, this study analyzed existing policies concerning the planning and construction of sports facilities in residential areas and actual problems in the operation, management, and construction of sports facilities in the country. The analysis was conducted using document, investigation and expert consultation methods. Corresponding improvement measures were proposed by combining actual situations of urban sport participation of residents. Sports facilities in urban residential areas in China are facing various problems, such as inadequate quantity, uneven layout, poor management, and imperfect related laws and regulations as well as planning policies. Formulating sports facility planning policies and updating existing sports facilities are beneficial in relieving the sports facility shortage caused by the increased, diversified, and actual demands of residents. These measures could also guide the future construction of sports facilities in residential areas in high-density cities in China.

This study aims to investigate the initial corrosion behavior in aqueous solution of 20# seamless steel under (CO₂/aqueous solution) gas–liquid two-phase stratified flow conditions.

The initial corrosion behavior was studied through the weight loss methods, scanning electron microscopy with energy-dispersive x-ray spectroscopy and x-ray diffraction.

The corrosion rate of 20# steel obviously increases with the increasing gas pressure at different corrosion time when the CO₂ pressure is less than 0.11 MPa, and the increase of corrosion rate tends to be steady when the pressure exceeds 0.11 MPa. With the increase of CO₂ pressure, the corrosion products changed from flocculent to acicular, granular and scaly. A four-stage model for the growth of the corrosion product layer was proposed, namely, the diffusion reaction stage, the local film formation stage, the complete film formation stage and the densification stage of the product film.

A four-stage model for the growth of the corrosion product layer on the pipe wall surface under this condition was proposed, namely, the diffusion reaction stage, the local film formation stage, the complete film formation stage and the densification stage of the product film. The growing process and densification mechanism of corrosion products layer were discussed.

The purpose of this paper is to analyze the reasons that plagiarism in online literature is so hard to control in China, and it will conclude with a clear solution for the future.

This paper begins its research with the statistics and analysis of plagiarism data and a review of expert interviews regarding online literature publishing. All of these data materials were collected from anti-plagiarism platforms, online literature websites, news report websites and judiciary office websites.

The paper provides empirical insights into why the plagiarism is so rampant in the publishing of online literature in China. It suggests that the current task of controlling network literature plagiarism is arguably created by the literary production platform, which leads to the problem of the validity of the “self-monitoring model.” In fact, controlling plagiarism must be emphasized by means of external monitoring, because strict supervision and various external punitive measurements for committing plagiarism can force literature-generating platforms to strengthen their own internal monitoring.

Online plagiarism occurs almost constantly, but it rarely results in court cases over copyright because of the lack of a robust copyright ecology in China. This paper considers large amounts of data and cases from self-publishing media platforms.

The paper includes implications for the development of plagiarism management in online literature publishing from the publishing Association, media and government.

This paper suggests to online literature users that plagiarism will be controlled when certain active measures against it are taken. The authors hope that this view will promote the development of original online literature.

This paper points out that China must strengthen supervision that comes from outside the online literature generate platforms to control the current rampant plagiarism that occurs on these platforms.

With the rapid development in wired and wireless networks, the demand for network security system is rising rapidly due to more and more new applications introduced. The main factors that rate the encryption algorithms are its ability to secure and protect data against attacks, its speed and efficiency. In this paper, a reconfigurable network security design using multi-mode data encryption standard (DES) algorithm has been implemented with low complexity and low cost, which will also reduce the speed. The paper aims to discuss these issues.

The design can be easily reconfigured to 3DES (triple DES) which is more secure and more powerful in encryption and decryption, as one of the trick in designing 3DES is to reuse three instances of DES. The design can be used for wired and wireless network applications, and it has been described using VHDL and implemented in a reconfigurable Programmable System-on-Chip (PSoC). The hardware implementation has targeted Xilinx Spartan XC3S700-AN FPGA device.

The main idea of reducing the complexity for the hardware implementation is by optimizing the number of logic gates and LUTs of the design. The number of logic gates can be decreased by changing the way of writing the VHDL code and by optimizing the size of the chip.

The design has been tested in simulation and hardware levels, and the simulation results and performance are discussed.

Limitations encountered with the models developed in the previous studies had occurrences of global minima; due to which this study developed a new intelligent ubiquitous computational model that learns with gradient descent learning rule and operates with auto-encoders and decoders to attain better energy optimization. Ubiquitous machine learning computational model process performs training in a better way than regular supervised learning or unsupervised learning computational models with deep learning techniques, resulting in better learning and optimization for the considered problem domain of cloud-based internet-of-things (IOTs). This study aims to improve the network quality and improve the data accuracy rate during the network transmission process using the developed ubiquitous deep learning computational model.

In this research study, a novel intelligent ubiquitous machine learning computational model is designed and modelled to maintain the optimal energy level of cloud IOTs in sensor network domains. A new intelligent ubiquitous computational model that learns with gradient descent learning rule and operates with auto-encoders and decoders to attain better energy optimization is developed. A new unified deterministic sine-cosine algorithm has been developed in this study for parameter optimization of weight factors in the ubiquitous machine learning model.

The newly developed ubiquitous model is used for finding network energy and performing its optimization in the considered sensor network model. At the time of progressive simulation, residual energy, network overhead, end-to-end delay, network lifetime and a number of live nodes are evaluated. It is elucidated from the results attained, that the ubiquitous deep learning model resulted in better metrics based on its appropriate cluster selection and minimized route selection mechanism.

In this research study, a novel ubiquitous computing model derived from a new optimization algorithm called a unified deterministic sine-cosine algorithm and deep learning technique was derived and applied for maintaining the optimal energy level of cloud IOTs in sensor networks. The deterministic levy flight concept is applied for developing the new optimization technique and this tends to determine the parametric weight values for the deep learning model. The ubiquitous deep learning model is designed with auto-encoders and decoders and their corresponding layers weights are determined for optimal values with the optimization algorithm. The modelled ubiquitous deep learning approach was applied in this study to determine the network energy consumption rate and thereby optimize the energy level by increasing the lifetime of the sensor network model considered. For all the considered network metrics, the ubiquitous computing model has proved to be effective and versatile than previous approaches from early research studies.

The developed ubiquitous computing model with deep learning techniques can be applied for any type of cloud-assisted IOTs in respect of wireless sensor networks, ad hoc networks, radio access technology networks, heterogeneous networks, etc. Practically, the developed model facilitates computing the optimal energy level of the cloud IOTs for any considered network models and this helps in maintaining a better network lifetime and reducing the end-to-end delay of the networks.

The social implication of the proposed research study is that it helps in reducing energy consumption and increases the network lifetime of the cloud IOT based sensor network models. This approach helps the people in large to have a better transmission rate with minimized energy consumption and also reduces the delay in transmission.

In this research study, the network optimization of cloud-assisted IOTs of sensor network models is modelled and analysed using machine learning models as a kind of ubiquitous computing system. Ubiquitous computing models with machine learning techniques develop intelligent systems and enhances the users to make better and faster decisions. In the communication domain, the use of predictive and optimization models created with machine learning accelerates new ways to determine solutions to problems. Considering the importance of learning techniques, the ubiquitous computing model is designed based on a deep learning strategy and the learning mechanism adapts itself to attain a better network optimization model.

The purpose of this paper is to exploit the effect of equity financing constraints on the firm’s investment in research and development (R&D) by utilizing a quasi-experiment in China.

A difference-in-difference identification strategy is employed to test the treatment effect of the IPO suspension in China. For robustness testing, the authors incorporate cross-sectional variation in external financial dependence to identify the different influences an IPO suspension has on R&D investments for firms across multiple industries through the difference-in-difference-in-difference approach and the authors also adopt a matching approach to check the parallel trend assumption. Moreover, the authors introduce a placebo test to further verify the empirical results.

Through the empirical analysis, the authors find that the firms subjecting to the IPO suspension are more likely to reduce their investments in R&D than those not affected by the IPO suspension. Besides, the negative effect of equity financing constraints on R&D investments is concentrated on external financial dependent industries. The firms in industries with larger external financial dependence tend to decrease more in their R&D expenditures when they experience the equity financing constraints.

This paper provides a new negative evidence on equity financing constraints on R&D investments link so that contributes to the debate about the effect of the financing constraints on R&D investments.

The study provides a meaningful suggestion for governments to diminish the frictions in the financial market and to improve enterprises’ public financing circumstances by finding that equity financing constraints have negative impacts on firms’ R&D investments.

There are some apprehensions around previous empirical studies investigating the impact of financing constraints on R&D investments as the existing literature is unable to provide an unambiguous method to exactly distinguish and measure the degree of financing constraints the firms confronting with. This paper solves this problem by first utilizing an IPO suspension in China as a quasi-experiment to examine the effect of equity financing constraints. Therefore, it gives insight to solve the problem of measuring financing constraints in future researches.