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The purpose of this paper is to study the existence and exponential stability of anti-periodic solutions of a class of shunting inhibitory cellular neural networks (SICNNs) with time-varying delays and continuously distributed delays.

The inequality technique and Lyapunov functional method are applied.

Sufficient conditions are obtained to ensure that all solutions of the networks converge exponentially to the anti-periodic solution, which are new and complement previously known results.

There are few papers that deal with the anti-periodic solutions of delayed SICNNs with the form negative feedback – a_ij(t)α_ij(x_ij(t)).

Economic interest groups such as seed, pesticide, feed, and food companies play an important role in supporting or preventing the production of genetically modified (GM) crops. The purpose of this paper is to examine firm managers’ attitudes toward GM technology, biotechnology R&D investment, and political lobbying activities.

Using data from surveys of 160 managers in the food, feed, chemical, and seed industries in 2013-2014, this paper employed three probit models to examine the determinants of managers’ attitudes, biotechnology R&D investment, and lobbying activities.

The results show that most Chinese agribusiness managers are concerned about GM foods and oppose its adoption. Nevertheless, one-third of the firms invest in biotechnology R&D and less than 15 percent of managers lobbied the government to change biotechnology policies. The econometric estimation results suggest that profit change expectation is the main factor affecting managers’ attitudes and biotechnology R&D investment decisions, whereas lobbying activities are significantly influenced by their attitudes and biotechnology R&D investment. In addition, managers’ attitudes toward GM foods also significantly influence firms’ decisions to invest in biotechnology R&D.

This paper has improved on previous research in two ways. First, it analyses the determinants of agribusiness firm managers’ attitudes toward GM technology, biotechnology R&D investment, and lobbying activities. Second, the methodology involves an analysis of agribusiness firm survey data in the food, feed, chemical, and seed industries, which is the first time to use such data to research on economic interest group in agricultural biotechnology field.

We examine the effect of institutional blockholders on the variability of firm performance.

We use OLS regression models to estimate the effect of institutional blockholders on within-firm, over-time variability of firm performance.

We find that firms with more institutional blockholders experience less variable firm performance. In particular, more institutional blockholders are associated with less variability of annual stock returns, ROA and the market-to-book ratio. We further explore several underlying mechanisms through with institutional blockholders reduce firm performance variability. We find that more institutional blockholders are associated with less variable capital expenditures and R&D investments, and less frequent acquisition activities.

A limitation of this paper is that our sample period only covers 1996–2006. Future studies can extend our research to a more recent period (e.g. 2009–2019) to test whether our findings remain valid in other periods.

We document a significant relation between institutional blockholders and firm performance variability in this paper. However, we do not make any judgment as to whether firms should increase their institutional blockholders as it is unclear whether the caused reduction in risk-taking is socially efficient. We argue that the value implication of institutional blockholders depends on the existing blockholder structure and the different levels of risk appetite between the CEO and shareholders. Thus, the decision on the increase or decrease of institutional blockholders should be carefully made based on a firm’s specific characteristics.

This paper is a first study which examines the impact of the presence of institutional blockholders on the variability of firm performance, while most prior studies focus on the stock ownership of institutional blockholders and examine its impact on the level of firm performance.

The purpose of this paper is to examine Chinese consumers’ attitudes toward genetically modified (GM) foods and the impact that consumers’ trust in different actors – GM scientists, non-GM scientists or individuals, the government and the media, has on their attitudes.

Consumers in Beijing were surveyed about their attitudes toward GM foods and their trust in different actors. The surveys were conducted from June to July of 2015. The sample size is 1,460 people. Given the potential endogeneity of trust variable, bivariate probit models are employed to estimate the impact of trust in different actors on consumers’ attitudes.

The results show that 55 percent of the Chinese consumers are opposed to GM foods and nearly 60 percent do not trust GM scientists. In total, 42 percent of Chinese consumers trust in the government and 39 percent trust the non-GM scientists or individuals. Around 35 percent of consumers believe the misinformation on GM technology that were provided by the media. Trust in the GM scientists and trust in the government have a significant positive impact on consumers’ acceptance of GM foods while trust in the non-GM scientists or individuals and believing the misinformation have a significant negative effect on the acceptance. Nearly 70 percent of Chinese consumers acquired information about GM food safety from the internet or via WeChat. Consumers who acquired GM technology information from the internet or via WeChat are less likely to embrace GM foods than those who obtain information from other sources.

Consumer trust plays a crucial role to accept biotech products in the market and it is crucial for producers, policy makers and consumers to have faith in new biotech products. The results of this study suggest that the government and GM scientists should make more effort to gain the trust and support of consumers, while the media should provide objective reports on GM products based on scientific evidence.

The purpose of this research is to compare several machine learning techniques on the task of Asian language text classification, such as Chinese and Japanese where no word boundary information is available in written text. The paper advocates a simple language modeling based approach for this task.

Naïve Bayes, maximum entropy model, support vector machines, and language modeling approaches were implemented and were applied to Chinese and Japanese text classification. To investigate the influence of word segmentation, different word segmentation approaches were investigated and applied to Chinese text. A segmentation‐based approach was compared with the non‐segmentation‐based approach.

There were two findings: the experiments show that statistical language modeling can significantly outperform standard techniques, given the same set of features; and it was found that classification with word level features normally yields improved classification performance, but that classification performance is not monotonically related to segmentation accuracy. In particular, classification performance may initially improve with increased segmentation accuracy, but eventually classification performance stops improving, and can in fact even decrease, after a certain level of segmentation accuracy.

Apply the findings to real web text classification is ongoing work.

The paper is very relevant to Chinese and Japanese information processing, e.g. webpage classification, web search.

The purpose of this paper is to develop a coated glass fiber fabric which can be used as the outer shell of firefighters' protective clothing and replace aramid fabric.

The silicone resin with excellent heat resistance was selected as the base solution. Silica nanoparticles, mica powder and ferric oxide were added into the coating solution, which was coated on the glass fiber fabrics. The vertical burning, thermal protective performance (TPP) value, second-degree burn time and water repellency of the coated fabrics were characterized.

Results showed that the dosages of the thickening filler were in the range 4%–6%; the coating solution has good viscosity. The optimal composition of fillers added in the silicone resin is silica nanoparticles 6%, ferric oxide 5% and mica powder 6%. The TPP value of the optimum coated fabric is 413 kW·s/m². The second-degree burn time is 4.98 s, which is obviously higher than that of the original glass fiber fabric (3.49 s) and that of the aramid fabric (3.82 s). The coated fabric has better thermal stability than aramid fabric.

The production cost of this coated glass fiber fabric was much lower than that of the aramid fabric.

Sparrow search algorithm (SSA) is a novel global optimization method, but it is easy to fall into local optimization, which leads to its poor search accuracy and stability. The purpose of this study is to propose an improved SSA algorithm, called levy flight and opposition-based learning (LOSSA), based on LOSSA strategy. The LOSSA shows better search accuracy, faster convergence speed and stronger stability.

To further enhance the optimization performance of the algorithm, The Levy flight operation is introduced into the producers search process of the original SSA to enhance the ability of the algorithm to jump out of the local optimum. The opposition-based learning strategy generates better solutions for SSA, which is beneficial to accelerate the convergence speed of the algorithm. On the one hand, the performance of the LOSSA is evaluated by a set of numerical experiments based on classical benchmark functions. On the other hand, the hyper-parameter optimization problem of the Support Vector Machine (SVM) is also used to test the ability of LOSSA to solve practical problems.

First of all, the effectiveness of the two improved methods is verified by Wilcoxon signed rank test. Second, the statistical results of the numerical experiment show the significant improvement of the LOSSA compared with the original algorithm and other natural heuristic algorithms. Finally, the feasibility and effectiveness of the LOSSA in solving the hyper-parameter optimization problem of machine learning algorithms are demonstrated.

An improved SSA based on LOSSA is proposed in this paper. The experimental results show that the overall performance of the LOSSA is satisfactory. Compared with the SSA and other natural heuristic algorithms, the LOSSA shows better search accuracy, faster convergence speed and stronger stability. Moreover, the LOSSA also showed great optimization performance in the hyper-parameter optimization of the SVM model.

Online health monitoring of large complex equipment has become a trend in the field of equipment diagnostics and prognostics due to the rapid development of sensing and computing technologies. The purpose of this paper is to construct a more accurate and stable grey model based on similar information fusion to predict the real-time remaining useful life (RUL) of aircraft engines.

First, a referential database is created by applying multiple linear regressions on historical samples. Then similarity matching is conducted between the monitored engine and historical samples. After that, an information fusion grey model is applied to predict the future degradation trajectory of the monitored engine considering the latest trend of monitored sensory data and long-term trends of several similar referential samples, and the real-time RUL is obtained correspondingly.

The results of comparative analysis reveal that the proposed model, which is called similarity-based information fusion grey model (SIFGM), could provide better RUL prediction from the early degradation stage. Furthermore, SIFGM is still able to predict system failures relatively accurately when only partial information of the referential samples is available, making the method a viable choice when the historical whole life cycle data are scarce.

The prediction of SIFGM method is based on a single monotonically changing health indicator (HI) synthesized from monitoring sensory signals, which is assumed to be highly relevant to the degradation processes of the engine.

The SIFGM can be used to predict the degradation trajectories and RULs of those online condition monitoring systems with similar irreversible degradation behaviors before failure occurs, such as aircraft engines and centrifugal pumps.

This paper introduces the similarity information into traditional GM(1,1) model to make it more suitable for long-term RUL prediction and also provide a solution of similarity-based RUL prediction with limited historical whole life cycle data.

Aero-engine components endure combined high and low cycle fatigue (CCF) loading during service, which has attracted more research attention in recent years. This study aims to construct a new framework for the prediction of probabilistic fatigue life and reliability evaluation of an aero-engine turbine shaft under CCF loading if considering the material uncertainty.

To study the CCF failure of the aero-engine turbine shaft, a CCF test is carried out. An improved damage accumulation model is first introduced to predict the CCF life and present high prediction accuracy in the CCF loading situation based on the test. Then, the probabilistic fatigue life of the turbine shaft is predicted based on the finite element analysis and Monte Carlo analysis, where the material uncertainty is taken into account. At last, the reliability evaluation of the turbine shaft is conducted by stress-strength interference models based on an improved damage accumulation model.

The results indicate that predictions agree well with the tested data. The improved damage accumulation model can accurately predict the CCF life because of interaction damage between low cycle fatigue loading and high cycle fatigue loading. As a result, a framework is available for accurate probabilistic fatigue life prediction and reliability evaluation.

The proposed framework and the presented testing in this study show high efficiency on probabilistic CCF fatigue life prediction and can provide technical support for fatigue optimization of the turbine shaft.

The novelty of this work is that CCF loading and material uncertainty are considered in probabilistic fatigue life prediction.

The sudden outbreak of COVID-19 has become a major public health emergency of global concern. Studying the Internet public opinion dissemination mechanism of public health emergencies is of great significance for creating a legalized network environment, and it is also helpful for managers to make scientific decisions when encountering Internet public opinion crisis.

Based on the analysis of the process of spreading the Internet public opinion in major epidemics, a dynamic model of the Internet public opinion spread system was constructed to study the interactive relationship among the public opinion events, network media, netizens and government and the spread of epidemic public opinion. The Shuanghuanglian event in COVID-19 in China was taken as a typical example to make simulation analysis.

Research results show three points: (1) the government credibility plays a decisive role in the spread of Internet public opinion; (2) it is the best time to intervene when Internet public opinion occurred at first time; (3) the management and control of social media are the key to public opinion governance. Besides, specific countermeasures are proposed to assist control of Internet public opinion dissemination.

The epidemic Internet public opinion risk evolution system is a complex nonlinear social system. The system dynamics model is used to carry out research to facilitate the analysis of the Internet public opinion propagation mechanism and explore the interrelationship of various factors.