In intelligent confrontation games, how to estimate the detection effectiveness of the early-warning detection (EWD) system of systems (SoS) is the most important issue that has been studied in hopes of a breakthrough for the long time. The conventional approaches to effectiveness estimation have been reductionism or the linear estimation methods, which are not suitable for the estimation of the effectiveness of EWD SoS. Effectiveness estimation methods and ideas based on complex networks have been proposed and studied, which can inspire that the logic relationship in the SoS can be analyzed by using the network thoughts. As a development of some research based on the Data mode, data and visualization analysis methods have been proposed. In these approaches, the multi-angle data and visualization analysis methods can be utilized to directly show some significant relationships in the SoS from the different aspects, especially from different angles of sight. These statistics and suggested results can be employed to analyze the situation of SoS, so they have potentially important capabilities in terms of the estimation of EWD SoS. Therefore, in this paper, these new ideas are introduced into the study and solution of the problem of the detection effectiveness estimation of EWD SoS. On the basis that the running characteristics of the detecting work of the EWD SoS are described, the data method and idea with multi-angles for EWD SoS are proposed and discussed, and the visualization analysis method and ideas about EWD SoS are suggested and analyzed. Furthermore, a typical application is employed to estimate the detection effectiveness of EWD SoS, based on the data and visualization analysis methods stated in this paper. As the results of the estimate are generally consistent with the actual situation, the validity of the proposed methods is considered proven. The main work in this paper can provide new ideas on the study of the issue of the detection effectiveness estimation of EWD SoS, and it also helpful for SoS analysis and other estimations of effectiveness.

Clustering by fast search and find of density peak (CFSFDP) is a simple and crisp density-clustering algorithm. The original algorithm is not suitable for direct application to anomaly detection. Its clustering results have a high level of redundant density information. If used directly as behavior profiles, the computation and storage costs of anomaly detection are high. Therefore, an improved algorithm based on CFSFDP is proposed for anomaly detection. The improved algorithm uses a few data points and their radius to support behavior profiles, and deletes the redundant data points without supporting profiles. This method not only reduces the large amount of data storage and distance calculation in the process of generating profiles, but also reduces the search space of profiles in the detection process. Numerous experiments show that the improved algorithm generates profiles faster than density-based spatial clustering of application with noise (DBSCAN), and has better profile precision than adaptive real-time anomaly detection with incremental clustering (ADWICE). The improved algorithm inherits the arbitrary shape clusters of CFSFDP, and improves the storage and computation performance. Compared with DBSCAN and ADWICE, the improved anomaly-detection algorithm based on CFSFDP has more balanced detection precision and real-time performance.

Based on the panel data from 1990 to 2018, this paper analyzes the regional difference in the impact of changes in price terms of trade and changes in income terms of trade on inflation by establishing the Hybrid NKPC model under the open economy. The empirical results show that the changes in price terms of trade and the changes in expected price have a significant negative and positive impact on the current inflation rate for each region. The changes in income terms of trade and the changes in expected income terms of trade have significant negative and positive effects on the inflation for each region. There is a significant difference in the degree of impact on the regional inflation and the degree of impact will further strengthen. Therefore, the change in terms of trade is an important determinant of the level and trend of domestic inflation in both the short term and long term.

Objectives: This study aimed to validate the application of the 3 × 2 achievement goal model in sports. Motivations: In order to offer new perspectives on achievement goals, this study explores 3 × 2 achievement goals used in competitive sports, and the prediction of passion and psychological well-being for sports. Methods: The study sample consists of 406 college and university athletes, including 230 males and 176 females. Average age of the subjects was 20.34 years. Average length of years of sports participation was 8.23 years. Data were collected with a questionnaire that incorporated a 3 × 2 achievement goal scale, a sports passion scale, and the Psychological Well-Being Scale. Statistical Methods: Data were analyzed using descriptive statistics for fuzzy data, fuzzy correlation coefficients, and fuzzy regression models. Finding: 1. There was a correlation between every two of task-approach, task-avoidance, self-approach, self-avoidance, other-approach, other-avoidance, harmonious passion, obsessive passion, and psychological well-being. 2. Among college and university athletes, task-approach and self-approach positively influence harmonious passion; task-approach, self-approach, other-approach, and other-avoidance positively influence obsessive passion; task-avoidance negatively influences obsessive passion; task-approach and self-approach positively influence psychological well-being, and task-avoidance negatively influences psychological well-being. Innovations: Use of the 3 × 2 achievement goal scale is applicable to college sportsmen in Taiwan, and the research method uses fuzzy statistical analysis, which breaks through the barriers of traditional psychological survey methods, and will improve the research quality of the sample survey. This study provides new techniques for research on psychological trends in sports. Value: In the future, coaches and athletes should focus on task-approach and self-approach goals in order to enhance the college or university athletes’ harmonious passion for a positive impact on their psychological well-being when they engage in sports through their own free will.

No consensus exists in the literature on whether stock prices can be predicted, with most existing studies employing point forecasting to predict returns. By contrast, this study adopts the new perspective of distribution forecasting to investigate the predictability of the stock market using the model combination strategy. Specifically, the Shanghai Composite Index and the Shenzhen Component Index are selected as research objects. Seven models – GARCH-norm, GARCH-sstd, EGARCH-sstd, EGARCH-sstd-M, one-component Beta-t-EGARCH, two-component Beta-t-EGARCH, and the EWMA-based nonparametric model – are employed to perform distribution forecasting of the returns. The results of out-of-sample forecasting evaluation show that none of the individual models is “qualified” in terms of predictive power. Therefore, three combinations of individual models were constructed: equal weight combination, log-likelihood score combination, and continuous ranked probability score combination. The latter two combinations were found to always have significant directional predictability and excess profitability, which indicates that the two combined models may be closer to the real data generation process; from the perspective of economic evaluation, they may have a predictive effect on the conditional return distribution in China’s stock market.

In this paper, the time window in which aquatic products must be delivered and the uncertainty of road conditions that affect the time at which customers are able to receive the goods are added as constraints in the optimization model of the Vehicle Routing Problem. The use of pheromones in the original ant colony algorithm was improved, and the waiting factor was added into the state transition rules to limit the information range. The improved ant colony algorithm was used to simulate the model with the example of aquatic product transportation route planning in Zhoushan city. The results show that this algorithm can optimize the transportation and distribution routes of aquatic products more effectively.

We developed two models in this study: one to show the distribution of heat for pans of different shapes, and the other to select the best type of pan to maximize the number of pans that can fit in the oven and to maximize even heat distribution in the pans. We constructed a model of heat distribution. The uneven distribution of heat is mainly caused by heat conduction. We established a differential equation for heat conduction according to Fourier’s law. The finite-difference method and Gauss-Seidel iteration were used to solve the equation, and MATLAB was used to draw the corresponding heat-distribution chart. We built a quantitative model of the shape optimization with an evaluation equation. Using the permutation and combination method, we calculated the maximum number of pans and the utilization rate of area. Finally, we determined that the optimal pan type is a round square, which achieved the best state.

Based on the time-varying elasticity production function model, we calculate factor price distortions, and study their influence on the rationalization and optimization of industrial structure. We find that the impact coefficient of capital, and labor factor price distortions on the rationalization of industrial structure are −1.2087 and −0.3147 respectively. Additionally, the impact coefficients on the optimization of industrial structure are −0.2333 and −0.0718 respectively. These results demonstrate that capital and labor factor price distortions are significantly negative for the rationalization and optimization of industrial structure. Therefore, it is imperative to reduce factor price distortions, and support industrial structure upgrades to promote supply-side structural reform.

At the present time, consumers often disclose their privacy when using online platforms to receive personalized recommendation information and services, but they are also highly concerned whether their privacy is being violated. “Privacy paradox” is becoming a hot topic of research. What are the potential impacts of individual cognitive differences and situational cues on privacy decision-making? How to balance the internal causes of the “privacy paradox” so that consumers are more willing to accept personalized recommendation services based on users’ privacy data? Can the transparency of privacy rights ease user conflict perceptions and promote disclosure intentions? These questions are inconclusive. Therefore, the purpose of this our research was to explore consumer privacy paradoxical behaviors from a novel perspective of the characteristics of authorization cues, and to clarify the internal relationship between individual cognitive processing and privacy authorization cues. This study suggests that the big data platform, when collecting or using user information, should try to reduce the behaviors that induce users’ resistance. It also provides a reference for how to better achieve benign interaction in personalized recommendation between Internet companies and users. The event-related potential technique is adopted to explore the matching relationship between individual cognitive processing and privacy authorization cues and to analyze the internal neural mechanism of the personalized recommendation user in the authorization decision. The experiment simulated the privacy authorization situation, and adopted a 2 × 2 × 2 hybrid experimental design: authority sensitivity (high/low) * authorization transparency (with/without permission) * cognitive style (field dependent/field independent). The experimental results show that: (1) Authorization transparency, authority sensitivity and their interactions will affect the user’s privacy authorization behaviors, and the interaction of the two cues has a greater impact on the behavior than the role of a single cue; (2) The cognitive style will affect the individual’s attention resource allocation in the authorization scenario, which, limited by cognitive resources, will result in selective attention to contextual cues: Compared with the field-independent group with self-characterization as a reference, the field-dependent group induced a greater P2 amplitude; (3) When the two-cue valences in the authoritative scenario are inconsistent, the amplitude of the N2 component is greater than that when the valences are consistent, and the amplitude of the N2 induced by the field-dependent group is more affected by the scenario cue valence; (4) Regardless of whether it is a field-dependent group or a field-independent group, there is no salient difference in the amplitude of LPP components induced in each scenario. According to the results of this study, even if privacy authorization involves high risks, individuals tend to selectively seek supportive cues or avoid obtaining information that is inconsistent with their cognition. This research reveals the differences of neural mechanisms in users’ actual decision-making, provides the possibility for further exploration of the black box behind users’ attitudes and behaviors, and opens up new ideas for the study of the “privacy paradox.”

The impact of capital deepening on total factor productivity (TFP) is a significant and controversial issue. Based on the calculation of relevant indicators, this study adopts a Bayesian time-varying parameter model, Bayesian quantile regression, and adaptive Bayesian quantile models for in-depth statistical analysis. TFP was found to have a complex non-linear structure, and physical and human capital deepening indicators show a significant upward trend. The deepening of physical capital has a negative impact on TFP, while the deepening of human capital has a positive impact. In the capital deepening structure, the level of TFP has been improved and its structure optimized. Primary human and non-production physical capital deepening has no significant effect on TFP, while secondary human capital deepening has some significant effects on TFP. Tertiary and productive human capital deepening of TFP present two different forms of significant effect: the influence coefficient of the former declines in the increasing quantile and the change is larger, while the latter has a stable negative impact. The results of this study provide insights in terms of the improvement of China’s productivity.

Terrain analysis is essential to flood disaster risk evaluation. It is a complicated evaluation process, involving both quantitative and qualitative data analysis. However, quantitative and qualitative data cannot be put into operation directly. Based on stochastic and fuzzy mathematics, cloud models allow interchange between qualitative and quantitative data, dealing with randomness and ambiguity. Two- or multi-dimensional cloud models can solve the problem of multivariable analysis. This study used absolute elevation and neighborhood elevation standard deviation as main factors. Using the model, it demonstrated the construction of qualitative conditions and risk evaluation clouds and established a set of two-dimensional cloud reasoning rules to calculate the joint certainties with all the grids in reasoning rules. By selecting the highest certainty of cloud reasoning, preliminary evaluation results were obtained. For more accurate results, the model algorithm was improved, and further iterations were performed. The results of two-dimensional cloud reasoning showed better dispersion and precision than traditional methods did. The terrain risk distribution of Chaohu Basin, China, agreed with reality with great detail. A new method regarding the risk assessment of flood disaster was also proposed.

A common method for encouraging a user to tidy up his/her office or school desk is to provide oral instructions or displaying posters. Some researchers have proposed a robot system to encourage users to tidy up. However, little attention has been paid to performing a comparative analysis of the various methods for motivating users to tidy up. In this study, we investigated the effects of motivating participants using verbal reminders, posters, and robots. Our results showed that urging users using vibrations produced by a robot is more effective than using oral instructions or posters. Particularly, using a robot is effective in reducing microslips and maintaining the motivation for tidying up.

In recent years, with the rapid development of science and technology, dynamic characterization and control of the research circuit system has become not only theoretical but also practical consideration in academic research and practical engineering applications. Therefore, the complex behavior of a research circuit system has become a hot spot in the theoretical field. This thesis is aimed toward the stability criterion and bifurcation of the fractional-order Chua’s circuit system. Despite numerous studies relating to the Chua’s system, most of them focus on its sum of delays. Different from traditional bifurcation analysis of Chua’s circuit system, the parameters are chosen as the bifurcation parameters in this paper such that the stability and bifurcation of the fractional-order Chua’s system is analyzed from a new angle. Then, the conditions of the existence for Hopf bifurcations are achieved by analyzing its characteristic equation. Finally, the validity and rationality of the theory are verified by numerical simulation.

An approach to N-best hypotheses re-ranking using a sequence-labeling model is applied to resolve the data deficiency problem in Grammatical Error Correction (GEC). Multiple candidate sentences are generated using a Neural Machine Translation (NMT) model; thereafter, these sentences are re-ranked via a stacked Transformer following a Bidirectional Long Short-Term Memory (BiLSTM) with Conditional Random Field (CRF). Correlations within the sentences are extracted using the sequence-labeling model based on the Transformer, which is particularly suitable for long sentences. Meanwhile, the knowledge from a large amount of unlabeled data is acquired through the pre-trained structure. Thus, completely revised sentences are adopted instead of partially modified sentences. Compared with conventional NMT, experiments on the NUCLE and FCE datasets demonstrate that the model improves the F_0.5 score by 8.22% and 2.09%, respectively. As an advantage, the proposed re-ranking method has the advantage of only requires a small set of easily computed features that do not need linguistic inputs.

A method is proposed to detect the filling flow status for automatic filling of thick liquid food. The method is based on a convolutional neural network algorithm and it solves the problem of poor accuracy in traditional flow detection devices. An adaptive threshold segmentation algorithm was first used to extract the region of interest for the acquired level image. Next, normalization and augmentation treatment were performed on the extracted images to construct a flow status dataset. A VGG-16 network trained on an ImageNet dataset was then used for isomorphic data-oriented feature migration and parameter tuning to automatically extract features and train the model. The identification accuracy and error rate of the network were verified and the advantages and disadvantages of the proposed method were compared to those of other methods. The experimental results demonstrated that the algorithm effectively detects multi-category flow status information and complies with the requirements for actual production.