The OLMAR method, which stands for the on-line moving average reversion method, is reported to be one of the most powerful among portfolio selection algorithms in the stock markets. In this research, we use intensive statistical and simulation analyses of long-term data on stock market changes to uncover the secrets of why and when the superiority appears. We find that there have been long-lasting fluctuations in the stock markets and that the OLMAR method actively makes use of such characteristics. In this paper, we analyze long-term stock data from Japan and the United States. The analyses confirm the following points. 1) The OLMAR method yields superior returns. 2) By using the moving average divergence rate provided by the OLMAR method, it is possible to detect specific fluctuation characteristics in long-term stock data from Japan and the United States. 3) Superior returns cannot be obtained from data in which specific fluctuation characteristics have been corrected.

How to reduce environmental pollution is fundamental for current civilization. Increasing in R&D investment may reduce the environmental pollution, yet whether and how R&D investment influence the environmental pollution needs further discussion and verification. Considering that the R&D investment can directly and also indirectly influence the environmental pollution by affecting the economic growth, and the fact that there is an obvious discontinuity for economic growth during the observed period, this paper firstly proposes an integrated approach based on panel moderated mediation analysis and regression discontinuity. It examines the R&D impact on environmental pollution on the basis of province-level data in China and uses the integrated approach to test its direct and indirect effect. Finally conclusion is made according to the findings.

Measurement of tumor position is important for the radiotherapy of lung tumors with respiratory motion. Although tumors can be observed using X-ray fluoroscopy during radiotherapy, it is often difficult to measure tumor position from X-ray image sequences accurately because of overlapping organs. To measure tumor position accurately, a method for extracting tumor intensities from X-ray image sequences using a hidden Markov model (HMM) has been proposed. However, the performance of tumor intensity extraction depends on limited knowledge regarding the tumor motion observed in the four-dimensional computed tomography (4DCT) data used to construct the HMM. In this study, we attempted to improve the performance of tumor intensity extraction by augmenting 4DCT data. The proposed method was tested using simulated datasets of X-ray image sequences. The experimental results indicated that the HMM using the augmentation method could improve tumor-tracking performance when the range of tumor movement during treatment differed from that in the 4DCT data.

Support vector regression-based fuzzy c-means algorithm (SVR-FCM) clusters data according to their relationship among attributes, which can provide competitive clustering results for the dataset having functional relationship among attributes. In this paper, we study the performance of SVR-FCM on incomplete data clustering. The conventional incomplete data clustering strategies of fuzzy c-means algorithm (FCM) are first applied to SVR-FCM, and a new strategy named MIS strategy is designed to assist SVR-FCM handle incomplete data as well. A number of synthetic datasets are used to study the effect of data missing rate and missing attribute numbers on the performance of SVR-FCM based on different incomplete data clustering strategies. Several engineering datasets are used to test the performance of the current and proposed incomplete data clustering strategies for SVR-FCM. The results indicate that SVR-FCM can provide better clustering results than FCM for the dataset having functional relationship among attributes even if it has missing values, and the proposed MIS strategy can assist SVR-FCM to achieve the best clustering results for most datasets.

This paper presents an adaptive compensation control strategy for packet losses, time delays, and exogenous disturbances in a networked control system. The structure consists of five parts: a plant, a Luenberger observer, an equivalent-input-disturbance (EID) estimator, an adaptive model predictive controller (AMPC), and a network. The AMPC in the local main control room produces an adaptive tracking gain, which can ensure the effective tracking of the reference signal in the presence of uncertainty and time delays in the plant. The EID estimator at the local site compensates for packet losses and exogenous disturbances through an independently designed state observer and a low-pass filter. A practical application case results show the effectiveness of the presented method compared with the conventional EID approach.

Reinforcement learning can lead to autonomous behavior depending on the environment. However, in complex and high-dimensional environments, such as real environments, a large number of trials are required for learning. In this paper, we propose a solution for the learning problem using local learning to select an action based on the surrounding environmental information. Simulation experiments were conducted using maze problems, pitfall problems, and environments with random agents. The actions that did not contribute to task accomplishment were compared between the proposed method and ordinary reinforcement learning method.

This paper presents a method that uses a web questionnaire to create a corpus containing spontaneous utterances of natural ideas, which may contain grammatical mistakes. In an experimental implementation of the method, the subjects were informed that they were receiving nursing care from a person, and they were required to answer a web-based questionnaire in which their responses were recorded as speech utterances. Compared to the Wizard of Oz approach and interview-based corpus-creation methods, the presented method simplifies the collection of utterances. Furthermore, we conducted a two-fold assessment to verify the effectiveness of the presented method. First, the approach exhibited a significant reduction in workload compared to interview-style utterance collection. Second, we compared the variety of expressions collected when subjects were informed that they were talking to a person with those collected when they were informed that they were communicating with a nursing robot. The results indicate that, although the number of utterances was larger for a robot than for a person, in terms of other metrics such as time efficiency index, the total number of morphemes, the average number of morphemes per utterance, the number of unique morphemes, and coefficient of variation, the utterances were larger for a human speech target than for a robot.

Social robots are increasingly being adopted as companions in educational scenarios. Self-efficacy, a viable construct for comprehending performance, particularly on academic tasks, has lately received great attention. In this study, participants completed four sections of the Wisconsin Card-Sorting Task (WCST) with a social robot Kebbi. The robot performed four kinds of expressions consisting of different combinations of Laban-theory-based motion with a positive voice designed to point out the mistakes the participant made. The impressions of the robot were reported in the post-experimental questionnaires while the bio-signals of the participant including heart rate and brainwave were collected by wearable devices. The results demonstrated that the participants tended to find the robot with the designed motion more likable, and they were less likely to feel frustrated and experienced lower levels of stress when the robot communicated with motion and voice simultaneously.

After the financial crisis in 2008, finance has been considered a significant cause of the fluctuations in the business cycle. This study introduces the expected monetary policy into a dynamic stochastic general equilibrium (DSGE) model that includes a mechanism of finance amplifying the fluctuations in the business cycle. It compares and analyzes the effects of expected and unexpected monetary policy on financial variables that comprise real estate price and credit. The results show that regarding the shock of reinforcing monetary policy, the expected and unexpected monetary policies have resulted in the reduction of real estate price and credit. However, the unexpected monetary policy has resulted in a greater effect on real estate price and credit. The rapid decline in real estate price and credit may cause severe fluctuations in the business cycle owing to the role of financial accelerators. Therefore, the expected monetary policy can better stabilize finance. Moreover, the expected duration of monetary policy by the entities in the Chinese economic market is found to be one-quarter. Central bank should try to avoid releasing sudden and unexpected monetary policy news to the public, as this will result in severe fluctuations in credit and real estate price, which can further result in severe fluctuations in the business cycle. Central bank should also adopt the monetary policy of expectation management to stabilize finance to further mitigate the magnifying effect of financial factors on fluctuations in the business cycle and prevent systemic financial risks.

In the past year, video blogs (vlogs) seem to have become a major form of information acquisition and entertainment socializing for the Internet users. The vlog, which is essentially a digital diary, is an emerging video content form and is touted by multimedia as the next mainstay of the video blogging era. Many creators have achieved self-branding and self-promotion through the continuous output of vlogs. Vlog visibility has greatly increased and has become a new territory for promotion. Therefore, analyzing the impact of vlog promotional content on consumer attitudes is important to provide companies with a theoretical basis for developing vlog promotions. Based on a large amount of literature and book research, this study proposes three attitude components in reference to the ABC attitude model: cognitive attitude, affective attitude, and behavioral tendency. Based on this, a questionnaire was designed to determine the influence of vlog promotion on consumer attitudes, and correlation and regression analyses were conducted on the three outcome and six predictor variables according to the results. All six predictors were determined to have different degrees of influence on consumer attitudes, with the vlogger’s (who provides the vlog) understanding of the advertised products having the most influence on cognitive attitudes. This was also the most influential factors in affective attitudes and behavioral tendency; the second-most influential factors for these are the degree of fan interactivity with the vlogger and vlog content, respectively.

With the development of economy and society, the utilization of the Internet in all walks of life is booming, and corporate social responsibility (CSR) has also evolved from a traditional offline model to a combination of online and offline models. The use of social media for value co-creation is called virtual value co-creation. China’s Alipay platform launched a public welfare activity Ant Forest, which is an example of virtual value co-creation. Behind this seemingly simple activity is a complex value co-creation behavior. This article considers Ant Forest as an example, guided by the theory of value co-creation, and selects the post-90s as the research object to explore the motivation of users to participate in virtual value co-creation.

The new industrial revolution featuring artificial intelligence (AI) as its core is flourishing globally. However, there are many controversies surrounding the impact of AI on productivity owing to the different understandings of its development. Thus, this study adopts a text mining method to construct indicators for measuring the intelligent development of enterprises based on the information obtained from the annual reports of listed Chinese manufacturing companies from 2009 to 2019. To explore the impact of intelligent development on the total factor productivity (TFP) of enterprises, fixed-effect regression and panel threshold models are employed to empirically prove its overall and threshold effects. The result reveals that the impact of intelligent development on TFP of enterprises is significantly positive at the aggregate level. Regarding the stage characteristics, “Solow’s paradox” exists in the development of intelligence. The effect of intelligence development on TFP is not significant at its early stage; moreover, the rapid development of intelligence exerts a “promotion effect.” However, at the extreme stage (when intelligent development crosses the critical value), it exerts a negative effect on the TFP of enterprises.

One of the challenges in reinforcement learning is regarding the partially observable Markov decision process (POMDP). In this case, an agent cannot observe the true state of the environment and perceive different states to be the same. Our proposed method uses the agent’s time-series information to deal with this imperfect perception problem. In particular, the proposed method uses reservoir computing to transform the time-series of observation information into a non-linear state. A typical model of reservoir computing, the echo state network (ESN), transforms raw observations into reservoir states. The proposed method is named dual ESNs reinforcement learning, which uses two ESNs specialized for observation and action information. The experimental results show the effectiveness of the proposed method in environments where imperfect perception problems occur.

In this study, the reconnaissance and confirmation task planning of multiple fixed-wing unmanned aerial vehicles (UAV) with specific payloads, which is an NP-hard problem with strong constraints and mixed variables, is decomposed into two subproblems, task allocation with “payload-target” matching constraints, and fast path planning of the UAV group, for which two mathematical models are respectively established. A bi-layer collaborative solution framework is also proposed. The outer layer optimizes the allocation scheme between the UAVs and targets, whereas the inner layer generates the UAV path and evaluates the outer scheme. In the outer layer, a unified encoding based on the grouping and pairing relationship between UAVs and targets is proposed. The corresponding combinatorial mutation operators are then designed for the representative NSGA-II, MOEA/D-AWA, and DMOEA-ϵC algorithms. In the inner layer, an efficient heuristic algorithm is used to solve the path planning of each UAV group. The simulation results verify the effectiveness of the cooperative bi-layer solution scheme and the combined mutation operators. At the same time, compared with the NSGA-II and MOEA/D-AWA, DMOEA-ϵC can obtain a significantly better Pareto front and can weigh the assigned number of UAVs and the total task completion time to generate more diversified reconnaissance confirmation execution schemes.

This study uses two main monetary policy tools, the real interest rate and M2, to investigate and analyze the correlation between monetary policy tools and housing prices. The sample period runs from 2006 to 2021 and the data frequency is monthly. The analysis explores the effects of the real interest rate and M2 on real estate prices through the integration of short- and long-term perspectives. The findings show that the real interest rate has no asymmetric effect on housing prices in the short term, but has a significant asymmetric effect in the long term. Monetary supply is asymmetric in both the short and long terms. In the short term, monetary supply tools are invalid for adjusting housing prices, while increasing the real interest rate could inhibit the rise in housing prices; in the long term, decreasing the real interest rate and monetary supply could inhibit the rise in housing prices.

In gas transmission, the regulator needs to adjust the gas pressure from high to low. The pressure energy can be then recovered by an expander, and the expander can drive a generator to produce electricity. However, the gas pressure regulator system and generator torque process often present difficult adjustment of PI parameters, and strong non-linearity of the hysteresis comparator and switching table in the traditional direct torque control (DTC) cause difficulties in the controller design and lead to large fluctuations of the generator torque. This paper designs a model predictive controller (MPC) for the gas pressure regulator process to reduce generator torque fluctuations. Simultaneously, a fuzzy PI controller is designed for the generator rotational speed process, and an MPC controller is exploited for the torque process; they operate in a cascaded manner. The fuzzy PI controller is used to calculate the torque set point. And the MPC controller is designed to obtain the optimal voltage vector of the generator for improving control performance through time delay compensation. The simulation experimental results highlight that the fluctuation of the regulator outlet gas pressure is reduced by 7.9% and 8.1%, and the output torque range is reduced by 3.4% and 2.1% compared with the traditional PI control and fuzzy PI control, respectively. The generator torque fluctuation range is reduced by 82.3%, the rotational speed fluctuation range is reduced by 76.9%, and the three-phase current fluctuation range is reduced by 76.6% compared with the traditional DTC.

To predict stock prices, this paper proposes a CNN-GRUA-FC model based on multi-factor analysis for time series forecasting. First, we use the random forest algorithm to evaluate the importance of the factor series, selecting the factor series of greater importance as the input of the subsequent prediction model. We then use the convolutional neural network (CNN) to extract the spatial characteristics of stock data for prediction, taking advantage of the gated recurrent unit (GRU) neural network to extract the dynamic characteristics of stock data and make prediction. Additionally, we combine an attention mechanism with the GRU neural network (GRUA) to improve its prediction performance. Finally, the prediction results of the two different sub-models are passed through the fully connected (FC) layer to obtain the final predictions. The results show that the prediction accuracy of the CNN-GRUA-FC prediction model proposed in this paper is higher than that of other models.

This study decomposes export trade into three dimensions: size, structure, and mode in 1987–2018 China’s relevant financial development and export trade. The study applies regression in the corresponding empirical analysis method and finds that excessive financial development reduced the growth of international trade scale, had less effect on the structure of export trade, and reduced the proportion of processing trade. Its marginal contribution lies in a comprehensive and accurate understanding of the impact of financial development on export trade. Financial development should be kept stable; otherwise, it will reduce export trade or even hinder the development of export trade. Therefore, China should maintain the coordinated development of finance and export trade, not excessively pursue the expansion of the scale of financial development, maintain a reasonable financial development structure, and improve the efficiency of financial development. China should pay attention to the phenomenon of diminishing marginal efficiency of financial development.

Taking the technical standards alliance (TSA) of strategic emerging industries as the sample, including China’s new energy vehicles, new-generation information technology, new materials, and high-end equipment manufacturing, the article empirically verified the impact of the external cooperation network and internal knowledge network of late-developing enterprises on their dual catch-up. The results showed that the appropriate centrality of the cooperative network promotes the exploitative catch-up and exploratory catch-up of late-developing enterprises. However, the excessive centrality blocked its exploratory catch-up. The structural hole of the cooperation network was conducive to the exploratory catch-up of late-developing enterprises, but not to their exploitative catch-up. The comprehensive cohesiveness of the knowledge network strengthened the positive impact of the centrality of the cooperation network on exploitative catch-up of late-developing enterprises, damaging the negative impact of the structural hole of the cooperation network on exploitative catch-up. The partial cohesiveness of the knowledge network positively adjusted the centrality of the cooperative network, the relationship between the structural hole and exploratory catch-up, and negatively adjusted the relationship between the centrality of the network and exploitative catch-up. By analyzing the differential impact of the dual network on two types of technology catch-up strategies of late-developing enterprises, the article deepened the theory of organizational duality. Meanwhile, the article contained innovation activities of late-developing enterprises in the TSA, which provided a new theoretical perspective and empirical basis for the combination of standardization of cooperation and innovation management theory.

Based on the China Family Panel Studies (CFPS2016) data, this study empirically tests the impact of population aging on household consumption. The results show that population aging promotes the basic living consumption expenditure of most households and inhibits the enjoyment or development consumption expenditure of most households. At the same time, low-level consumer families are more affected by population aging and income than high-level consumer families. Moreover, the impact of population aging on developmental consumption varies among families with different income levels, especially in medical and health expenditures. The higher the family income level, the smaller the impact. Finally, this study puts forward suggestions such as improving the social security mechanism, improving the level of education, releasing the consumption potential of the elderly, improving the consumption level, and promoting consumption upgrading.

Consumer behavior is embedded in a certain social structure and social networks, and the scale and density of household social networks will be likely to affect consumption expenditure. To explore the impact of social networks and institutional embeddedness on household consumption, this study constructs a model of consumption influencing factors, and devises an empirical study using the data of China Household Finance Survey (CHFS). The results show some innovation. (1) The impact of household social networks on total household consumption is significant. A 1% increase in social networks spending boosts household consumption spending by 0.364%. (2) The institutional embeddedness will affect household consumption. Every 1% increase of social security account balance (the proxy variable of institutional embeddedness) can boost household consumption by 0.196%. This proves that the social insurance institution can enhance consumer confidence and promote current consumption growth. (3) The results of the robustness test confirmed that even after replacing the dependent variable with “the proportion of developmental consumption in total household consumption,” the influence of social networks and institutional embeddedness on consumption is still significant. Using the variable “communication expenses” instead of “gift income and expenditure” as the proxy variable of social networks, the estimation result is still robust. (4) Social networks have a significant influence on all types of household consumption except medical care consumption, but the degree of influence is different. Further discussion revealed that the estimation results are different for different regions in China, but the coefficients of core independent variables are not significantly different. This conclusion is different from people’s intuition, which holds that people in regions with low economic development rely more on social communication and spend more on social communication to maintain a certain social status. The conclusion of this paper is of great significance for formulating policies and institutions affecting residents’ consumption.

Factor mismatch is considered to be an important restriction on the growth of total factor productivity. Based on the panel data of 30 Chinese provinces from 2013 to 2019, this work first measures the digital economy development index of each Chinese province by using a particle swarm optimization projection pursuit model, followed by a panel econometric model, to verify the effect of the digital economy and artificial intelligence manufacturing on the labor-resource mismatch. The results show that, from 2013 to 2019, China’s digital economy generally showed a trend of steady progress, with an average annual growth rate of 12.10%. The mismatch index of the labor force dropped by 1.46% every year, and the situation of insufficient or surplus allocation of labor force resources in China was alleviated. The fitting results of the spatial econometric model show that the digital economy can reduce the labor mismatch index, and this conclusion has remained valid after a series of robustness tests. The intermediary mechanism shows that intelligent manufacturing plays a masking role in the process of alleviating labor misallocation in the digital economy. Artificial intelligence cannot alleviate labor mismatches, but it strengthens the corrective function of the digital economy.