Under the new mode of labor division for global production, the method of calculating a country’s energy consumption and carbon emissions is based on a “production side” principle that disregards the embodied energy and carbon emissions caused by international trade. This method is unfair to China and other large, exporting countries. From the perspective of value-added trade, the multiregional input–output model based on the world input–output table and environmental account from the World Input–Output Database are used to measure the scale of China’s value-added trade; subsequently, the import and export net values of China’s foreigntraderelated embodied energy and carbon emissions are calculated. The results show that: (1) China’s value-added exports in 2009 amounted to US $1,045.37 billion, which constitutes 21% of China’s Gross Domestic Product (GDP) in that year. Nearly half of the value-added exports are to fulfill the final demand from North America and European Union countries; manufacturing and service are the main value-added export industries of China. (2) China has a relatively high unit coefficient for value-added energy consumption and carbon emissions, both representing a net export of embodied energy and embodied carbon emissions in foreign trade. In this regard, energy and mid-level technology manufacturing industries, such as coke, refined oil, and nuclear fuel processing, are the main exporters of embodied energy and embodied carbon.
The United Kingdom is the third-largest peer-to-peer (P2P) lending market in the world, which is surpassed only by the two dominant forces in P2P investing, China and the United States of America. As an innovative financial market in the UK, P2P lending brings not only many opportunities but also many risks, especially the loan default risk. In this context, this paper uses binary logistic regression and survival analysis to evaluate default risk and loan performance in UK P2P lending. The empirical results indicate that credit group, loan purpose for capital needs, sector type, loan amount, interest rate, loan term, and the age of the company all have a significant impact on the probability of loan default. Among them, the interest rate, loan term, and loan purpose for capital needs are the three most important determinants of the probability of loan defaults and survival time of loans.
A lookback option is a path-dependent option, offering a payoff that depends on the maximum or minimum value of the underlying asset price over the life of the option. This paper presents a new mean-reverting uncertain stock model with a floating interest rate to study the lookback option price, in which the processing of the interest rate is assumed to be the uncertain counterpart of the Cox–Ingersoll–Ross (CIR) model. The CIR model can reflect the fluctuations in the interest rate and ensure that such rate is positive. Subsequently, lookback option pricing formulas are derived through the α-path method and some mathematical properties of the uncertain option pricing formulas are discussed. In addition, several numerical examples are given to illustrate the effectiveness of the proposed model.
As an emerging economy, market distortions exist in China’s institutional adjustment during its economy transformation. However, the price distortion of capital and labor factors will lead to factor misallocation among provinces. This will eventually reduce the total factor productivity (TFP) at the national level. Based on Hsieh and Klenow’s  model framework, this paper aims to measure the degree of misallocation of capital and labor factors among provinces, and estimates the growth potential of China’s TFP by using input-output data from 1993 to 2017. The findings show that: First, the degree of inter-provincial labor misallocation is greater than that of capital. For example, in 2017, the degree of capital (labor) misallocation was 5.77% (10.25%), resulting in China’s TFP loss of 17.23%. Second, due to the factor marketization reforms, the degree of labor misallocation has declined while the degree of capital misallocation has intensified in recent years. Lastly, this paper introduces the time-varying elasticity production function model, finding that using the Cobb-Douglas production function will cause the factor misallocation to be underestimated by 5.91% due to the assumption of constant output elasticity.
The modern finance industry is composed of not only numerous financial intermediaries but also internet-based mechanisms which are operated by mobile phone users and online consumers daily. In the coming 10 years, estimates suggest that over half of banks’ functions will likely be replaced by high-tech artificial intelligence. Given the great ongoing shifts in contemporary financial systems, the transmission effects of internet-based finance practices have introduced an important yet unaddressed empirical question on the coupling relationship between the internet finance industry and economic policy uncertainty (EPU). This paper adopts the time-varying parameter vector autoregressive model with stochastic volatility (TVP-SV-VAR) model and novel data from Alibaba Corp. to investigate this relationship. We find: First, the impact of internet-based financial approaches on EPU is greater than the reversal effect, indicating that China’s gross domestic product (GDP) is largely influenced by the online finance industry. Second, the lag impacts are time varying and become stable after 2016, corresponding to the current Chinese government’s long-term strategic plan that emphasizes maintaining the economy’s overall stability. Lastly, additional evidence shows that the online financial approaches are positively correlated with consumers’ behaviors, implying that the online finance industry is gaining its momentum when people are using e-currency rather than real cash. After all, it takes time to observe the real effect of these macro policies. With internet and information technology developing, artificial intelligence is being used in the areas of big data, credit lending, and risk control. This largely reduces the data analyzing cost for internet finance companies and makes risk control more convenient.
This study explores the relationship between spatial agglomeration and innovation, taking Chinese manufacturing data as an example. Tractable model is built to explain the mechanism through which spatial concentration of firms in a city affects industrial innovation. Then in the empirical analysis, new agglomeration and innovation indicators are used to test the theoretical conclusions at the city-industry level. Results show that the geographical concentration of firms has significant negative effects on industrial innovation and growth. These overall effects can be divided into positive and negative categories after considering the interaction between the industrial labor scale and firm’s spatial agglomeration. Industries with a higher labor scale will bear more crowding effects of firms’ spatial agglomeration. These findings mean that moving to a less concentrated area might be a good choice for the labor-intensive firms which aim at innovation.
We conducted sentiment classification for news reports during the COVID-19 pandemic on Sino-Japanese relations from Chinese and Japanese media. Based on the Word2vec and Dynamic Topic Models, we analyzed the macroscopic dimension of theme distribution and the semantic change process of “Sino-Japanese” related words. The results indicate that Chinese media reports are mainly neutral, but Japanese ones are mainly negative. The meaning of the terms related to “Sino-Japanese” has not changed significantly, but there are minor differences in the content of reports on the same subject in different periods. During Suga’s administration, the overall stability of Sino-Japanese relations and the continued advancement of pragmatic cooperation are expected to continue.
With the vigorous development of information technology, the textual data of financial news have grown massively, and this ever-rich online news information can influence investors’ decision-making behavior, which affects the stock market. Thus, online news is an important factor affecting market volatility. Quantifying the sentiment of news media and applying it to stock-market prediction has become a popular research topic. In this study, a financial news sentiment lexicon and an auxiliary lexicon applicable to the financial field are constructed, and a sentiment index (SI) is constructed by defining the weight of semantic rules. Then, a comprehensive sentiment index (CSI) is constructed via principal component analysis of the sentiment index and structured stock-market trading data. Finally, these two sentiment indices are added to the generalized autoregressive conditional heteroscedastic (GARCH) and the Long short-term memory (LSTM) models to predict stock returns. The results indicate that the prediction results of LSTM models are better than those of GARCH models. Compared with general-purpose lexicons, the financial lexicons constructed in this study are more stable, and the inclusion of a comprehensive investor sentiment index improves the accuracy of measuring sentiment information. Thus, the proposed lexicons allow more comprehensive measurement of the effects of external sentiment factors on stock-market returns and can improve the prediction effect of stock-return models.
The construction of Hainan International Tourism Island, one of the national strategies in China, implemented in January 2010. A common concern is whether the policy has managed to raise farmers’ income and reduce the income gap between urban and rural residents. Therefore, this study aims to make counterfactual causal inferences based on data from 2005 to 2018 on Hainan Province. This study applies both the generalized principal component analysis method and the synthetic control method. According to the model results, the policy did not increase farmers’ income in Hainan or narrow the income gap between urban and rural areas in the first five years. However, after 2014, the result reversed. The increasing farmers’ income and narrowing gap indicate that the tourism-related Kuznets curve hypothesis is valid for the policy. The results of a placebo test verify the robustness of this conclusion.
In this study, a fixed effect model is established to empirically examine the effect of population aging on consumption in China. Research analyzing the influence mechanism of population aging on consumption quantity has yet to determine the effect of population aging on consumption quantity under various conditions. Panel data from 31 provinces and cities in China were analyzed, with the old-age dependency ratio as the main control variable, and considering income, economic development level (GDP), and consumer price index. The samples are divided into nationwide, as well as eastern, central, and western regions, for modeling and discussion. The results show that population aging negatively affects consumption quantity. In response to the negative impact, we propose relevant policies and suggestions for enhancing consumption quantity.
Sustainability is a major challenge in any plant factory, particularly those involving precision agriculture. In this study, an adaptive fertigation system in a three-tier nutrient film technique aquaponic system was developed using a non-destructive vision-based lettuce phenotype (VIPHLET) model integrated with an 18-rule Mamdani fuzzy inference system for nutrient valve control. Four lettuce phenes, that is, fresh weight, chlorophylls a and b, and vitamin C concentrations as outputted by the genetic programming-based VIPHLET model were optimized for each growth stage by injecting NPK nutrients into the mixing tank, as determined based on leaf canopy signatures. This novel adaptive fertigation system resulted in higher nutrient use efficiency (99.678%) and lower chemical waste emission (14.108 mg L-1) than that by manual fertigation (92.468%, 178.88 mg L-1). Overall, it can improve agricultural malpractices in relation to sustainable agriculture.
Seed varieties are often differentiated via the manual and subjective classification of their external textural, spectral, and morphological biosignatures. This traditional method of manually inspecting seeds is inefficient and unreliable for seed phenotyping. The application of computer vision is an ideal solution allied with computational intelligence. This study used Lactuca sativa seed variants, which are commercially known as grand rapid, Chinese loose-leaf, and iceberg (which serves as noise data for extended model evaluation), in determining their corresponding classifications based on the extended morphological phenes using computational intelligence. Red-green-blue (RGB) imaging was employed for individual kernels. Extended morphological phenes, that is, solidity, roundness, compactness, and shape factors, were computed based on seed architectural traits and used as predictors to discriminate among the three cultivars. The suitability of ANFIS, NB, and CT was explored using a limited dataset. A mean accuracy of 100% was manifested in ANFIS; thus, it was proved to be the most reliable model.
Spray drying is a rapid, continuous, cost-effective, reproducible, and scalable process for reducing the moisture content of a fluid material into a solid powder. To improve this process in juice powder production, automation can be applied to increase efficiency and productivity. Hence, fuzzy logic is used in this study as a control system in the spray-drying process of concentrated liquid bignay juice into juice powder, where the inlet temperature and carrier agent concentrations affecting the properties of the juice powder, such as moisture content and product yield, are considered. The proposed fuzzy system can provide feedback to the control variables, inlet temperature, and carrier agent concentration based on the moisture content and product yield of the juice powder.
Unlike a media-filled aquaponic system, the nutrient film technique (NFT) and deep water culture (DWC) require the installation of an external biofilter to provide sufficient area for nitrifying bacteria colonization, which is essential for the conversion of toxic ammonia from fish waste into nitrate that is easily assimilated by plants. Given the importance of biofilters, it is imperative to properly design this tank to effectively support the nitrification process. Several factors need to be considered for the biofilter design. Thus, an optimization algorithm can be used to obtain combinations of the design parameters. The genetic algorithm (GA) is a heuristic solution search or optimization technique based on the Darwinian principle of genetic selection. The main goal of this study was to obtain the optimal biofilter size for a given fishpond volume and the amount of ammonia to be treated. The conversion coefficient in the Michaelis–Menten equation was used as the fitness function in this study. The parameters optimized using GA include the hydraulic loading rate, height of the biofilter, and predicted ammonia concentration. For the given assumption of a 60 kg feed introduced to the system and a 1500 L fishpond, the hydraulic loading rate, biofilter height, and final concentration of ammonia were 0.17437 m, 0.58585 m, and 0.01026 ppm, respectively. Using the values obtained from running the GA, the optimum biofilter volume for the system was 0.4608 m3, whereas the water flow rate was 0.03 L/min. For recommendations, multiple objective GAs can be used to add cost-related variables in the optimization because they have not yet been considered in the computation.
Three-dimensional multiple fish tracking has gained significant research interest in quantifying fish behavior. However, most tracking techniques use a high frame rate, which is currently not viable for real-time tracking applications. This study discusses multiple fish-tracking techniques using low-frame-rate sampling of stereo video clips. The fish were tagged and tracked based on the absolute error of the predicted indices using past and present fish centroid locations and a deterministic frame index. In the predictor sub-system, linear regression and machine learning algorithms intended for nonlinear systems, such as the adaptive neuro-fuzzy inference system (ANFIS), symbolic regression, and Gaussian process regression (GPR), were investigated. The results showed that, in the context of tagging and tracking accuracy, the symbolic regression attained the best performance, followed by the GPR, that is, 74% to 100% and 81% to 91%, respectively. Considering the computation time, symbolic regression resulted in the highest computing lag of approximately 946 ms per iteration, whereas GPR achieved the lowest computing time of 39 ms.
To address the problem of demagnetization fault diagnosis of permanent magnet synchronous motor (PMSM) under inductance change, a demagnetization fault detection method based on an adaptive observer is proposed. First, the mathematical model of the demagnetization fault of PMSM in a synchronous rotating coordinate system is established, and the inductance disturbance is analyzed separately. Then, considering the different characteristics of the flux linkage fault and inductance disturbance, a new adaptive observer is proposed. Two adaptive laws are designed to ensure the accuracy of fault diagnosis and to eliminate the influence of inductance disturbance, thus achieving the robust diagnosis of demagnetization fault.
Recently, personal mobility has been researched and developed to make short-distance travel within the community more comfortable and convenient. However, from the viewpoint of personal mobility, there are problems such as difficulty in picking up items while shopping when operating the joystick for shopping and the inability to use hands freely. Accordingly, because the speed of personal mobility can be controlled by foot stepping like an accelerator pedal, we developed an electric wheelchair system that can control the speed by pedal operation. Furthermore, we developed a control system that considers the ride quality using an electric wheelchair with pedal control. In this study, the proposed method is detailed in three parts. Firstly, to develop the pedal mechanism, a potentiometer was used to detect the angle of the pedal mechanism, and a spring mechanism was designed for return to its original position after the pedal was pushed. Next, we propose a feedback control system that considers the ride quality of the operator. In addition, we integrated the system with a smart device-based robot system to realize the mobility as a service (MaaS). Finally, we present several examples of the system and discuss the applicability of the proposed system.
Aiming at a high-precision tracking performance of the control of a machine tool moving axis, this study established a system mathematical model considering the elastic deformation of the ball screw. Then, a sliding mode controller was designed to suppress the influence of uncertainty on the control performance. Next, an extended state observer was designed to observe the system state and disturbance and provide feedback to the sliding mode controller for position control. Finally, the correctness of the designed sliding mode control and extended state observer were proved by MATLAB simulation analysis.
Gait recognition is a biometric identification method that can be realized under long-distance and no-contact conditions. Its applications in criminal investigations and security inspections are thus broad. Most existing gait recognition methods adopted the gait energy image (GEI) for feature extraction. However, the GEI method ignores the dynamic information of gait, which causes the recognition performance to be greatly affected by viewing angle changes and the subject’s belongings and clothes. To solve these problems, in this paper a cross-view gait recognition method that uses a dual-stream network based on the fusion of dynamic and static features (FDSN) is proposed. First, the static features are extracted from the GEI and the dynamic features are extracted from the image sequence of the human’s lower limbs. Then, the two features are fused, and finally, a nearest neighbor classifier is used for classification. Comparative experiments on the CASIA-B dataset created by the Automation Institute of the Chinese Academy of Sciences showed that the FDSN achieves a higher recognition rate than a convolutional neural network (CNN) and Gaitset under changes in viewing angle or clothing. To meet our requirements, in this study a gait image dataset was collected and produced in a campus setting. The experimental results on this dataset show the effectiveness of the FDSN in terms of eliminating the effects of disruptive changes.