In recent years, China has witnessed the rapid development in housing finance, and there have emerged constantly real estate finance innovations; however, there exists no relevant index for measuring the innovations of China's real estate finance. Based on the perspectives of the governments, enterprises and the public, this paper constructs the "innovation index of real estate finance" on a quarterly basis from 2009 to 2019, with the method of empowerment which combines the subjective method (analytic hierarchy process) and the objective one (range coefficient method). It clearly and concretely depicts the innovations in housing finance and the related temporal-spatial characteristics in China since the outbreak of the financial crisis in 2008. The index covers 30 provinces, autonomous regions and municipalities directly under the central government, and analyzes its temporal and spatial characteristics. The findings show that there exist a strong spatial autocorrelation and a big regional difference in innovations.

The development of digital technology and the construction of smart cities urge service enterprises to seek competitive advantages by building smart service brands. However, there are few studies explore the brand value, brand strategies, and corresponding business strategies of smart service providers from the financial perspective. This paper selects listed property companies from China as the sample and explores the value of the smart community service brand of property enterprises based on the observation data. This research introduces the market value measurement index (Tobin q) and discounted cash flow model (DCF) to explore the influence of diversified brand strategies through combining smart brand strategy with naming strategies and business strategies on brand value. The results show that smart community service brand has a significant impact on firms' market value. Compared with the brand extension strategy, the adoption of brand renewal strategy will significantly affect market value. Further, the development of smart value-added services by enterprises will exert a positive impact on their market value. However, the stakeholders are not optimistic about smart technical services by property companies, which could reduce shareholders' expectations of the market value of enterprises.

Alternating direction method of multipliers (ADMM) receives much attention in the recent years due to various demands from machine learning and big data related optimization. In 2013, Ouyang et al. extend the ADMM to the stochastic setting for solving some stochastic optimization problems, inspired by the structural risk minimization principle. In this paper, we consider a stochastic variant of symmetric ADMM, named symmetric stochastic linearized ADMM (SSL-ADMM). In particular, using the framework of variational inequality, we analyze the convergence properties of SSL-ADMM. Moreover, we show that, with high probability, SSL-ADMM has O((ln N)·N^-1/2) constraint violation bound and objective error bound for convex problems, and has O((ln N)·N^-1/2) constraint violation bound and objective error bound for strongly convex problems, where N is the iteration number. Symmetric ADMM can improve the algorithmic performance compared to classical ADMM, numerical experiments for statistical machine learning show that such an improvement is also present in the stochastic setting.

Generative adversarial imitation learning (GAIL) directly imitates the behavior of experts from human demonstration instead of designing explicit reward signals like reinforcement learning. Meanwhile, GAIL overcomes the defects of traditional imitation learning by using a generative adversary network framework and shows excellent performance in many fields. However, GAIL directly acts on immediate rewards, a feature that is reflected in the value function after a period of accumulation. Thus, when faced with complex practical problems, the learning efficiency of GAIL is often extremely low and the policy may be slow to learn. One way to solve this problem is to directly guide the action (policy) in the agents' learning process, such as the control sharing (CS) method. This paper combines reinforcement learning and imitation learning and proposes a novel GAIL framework called generative adversarial imitation learning based on control sharing policy (GACS). GACS learns model constraints from expert samples and uses adversarial networks to guide learning directly. The actions are produced by adversarial networks and are used to optimize the policy and effectively improve learning efficiency. Experiments in the autonomous driving environment and the real-time strategy game breakout show that GACS has better generalization capabilities, more efficient imitation of the behavior of experts, and can learn better policies relative to other frameworks.

As a legal currency with the credit endorsement of the Chinese government, DC/EP has many advantages and special characteristics. Based on the application of DC/EP in the financing of small- and medium-sized enterprises (SMEs), this paper studies the optimal financing decisions of SMEs under both the traditional financing mode and the DC/EP financing mode. Considering the construction and use costs of DC/EP and the role of DC/EP credit traceability in improving mortgage rates, this paper explores the feasible range for SMEs to select DC/EP financing. It is found that mortgage rates affect the returns of SMEs when they take on loans; under different credit strategies, the pledge rate set by the bank will affect the financing willingness of SMEs. The participants had different optimal mortgage rates to optimize the returns of borrowers and banks. High-quality SMEs are more willing to use the DC/EP platform for financing. With the increase in default costs, banks will be more inclined to use DC/EP, while enterprises will be inclined to utilize the traditional financing mode. Through the comparative analysis of the two financing modes, the default cost range that neither banks nor borrowers are willing to use DC/EP is found. This study provides theoretical support and management inspiration for scientific decision-making to solve the financing problems of SMEs by using DC/EP.

Grey prediction is vital in statistical prediction with wide applications. However, most grey prediction methods focus on annual predictions of the monotonic time series instead of the seasonal time series. The paper uses the extended model of the grey GM(1, 1) model to predict the seasonal time series. Some improvements have been made in two aspects to improve the prediction accuracy of the model. 1) We introduce seasonal multiple factors to transform the original time series, which improves the adaptability of the seasonal data to the model. The transformed series conforms to the law presented by the model. 2) The seasonal data are in superimposed sine and cosine fluctuations with tendencies. Therefore, the paper extends the grey action quantity of the traditional GM(1, 1) model. The newly extended grey model is called the GM(1, 1, exp×sin, exp×cos) model, which is provided with the parameter optimization methods and time response equations. According to the proposed modeling method, we establish a GM(1, 1, exp×sin, exp×cos) model for China's quarterly gross domestic product (GDP) with high accuracy.

As one of the most common social relationships among college students, the dormitory interpersonal relationship has important effects on students' psychology and behavior. For quantitative analysis of the dormitory interpersonal influence on college students' emergency evacuation behavior, an evacuation simulation experiment of college students carried out and coupling questionnaire survey, measurement and social force model of society to visualize the normal dormitory interpersonal relationship and emergency evacuation following relationship of college students, then simulation experiment is used to explore the impact of different types of dormitory relationship structure on emergency evacuation. The results show that dormitory interpersonal relationship is an important component of college students' interpersonal network and has an important impact on emergency evacuation behavior. The close and united dormitory relationship has a good promotion effect on the emergency evacuation efficiency. When the emergency occurs, the dormitory interpersonal relationship of college students will partly transform into the leader-following behavior relationship, and the evacuation efficiency will decrease. The influence of dormitory interpersonal relationship on evacuation behavior is related to gender and grade which is higher for female students than male students, and is higher for junior students than senior students.

Since Bitcoin came into the world, modelling and analyzing the underlying characteristics of Bitcoin has attracted increasing attention. This paper uses a framework including decomposition, reconstruction and extraction method (DRE) to analyze price fluctuations based on ultra-high-frequency data from Dec.1, 2019, to Nov.30, 2021. First, the ensemble mode decomposition (EMD) is employed to decompose the Bitcoin hourly spot price into 13 intrinsic mode functions (IMF) plus a residual. Second, the IMFs are reconstructed into high-frequency components, low-frequency components and a trend based on fine-to-coarse reconstruction. Furthermore, the intraday volatility analysis based on LM test is applied on 15-minutes frequency data to detect discontinuous jump arrivals and extract jump from realized quadratic variation. Empirical results show that three components of reconstruction can be identified as short term fluctuations process caused by microstructure noise, the shocks affected by major events, and a long-term trend based on inelastic supply and rigid demand. We find that approximately 40% of jumps can be matched with the news from the public news database (Factiva), and the jump sizes are larger than that of stock markets. This finding indicates that the Bitcoin market has more irregularly noise and unforeseen shocks from unscheduled events.

The resumption of production after the "suspension" caused by the COVID-19 has emerged as an urgent problem for many enterprises and the government. The resumption of production is actually a dynamic evolution problem from 0 to 1 (100%). This paper constructs a general game model and a dynamic replication system for the resumption of production and government support, and gives theorems for the construction of the model. It analyzes the evolution mechanism and scenario conditions for the convergence of enterprise strategies to the "resumption of production" strategy, takes the resumption of production of hog farmers as an example to carry out a study on the regulation of countermeasures to resume hog production, and explores systemic countermeasures and suggestions for the rapid convergence of farmers' strategies to the "resumption of work and production" strategy. The study found that the production resuming behavior system dynamics evolution game regulation model provides a systematic model and method for the study of resumption countermeasures, a general regulation model for the resumption ratio from 0 to 1 (100%), and a systematic idea, method and model for exploring the "precise strategy" system to promote the rapid resumption of production.

Based on heterogeneity extraction, this paper analyzes four potential characteristics of the supervisory board, they are Individual Heterogeneity of the Supervisory Member (Internal Heterogeneity), Organization Size of the Supervisory Board (Organization Size), Structural Characteristics of the Supervisory Board (Structural Characteristics) and Identity Background of the Supervisory Board (Identity Background); and verifies the impact and action path of the potential characteristics on irregularities. Then, systematically evaluates the micro enterprise organization construction and corporate governance behavior by using the methods of factor analysis and Heckman two-stage model. Empirical research shows that the scale of corporate assets does have an important impact on corporate irregularities and the governance of the board of supervisors. Under the regulation of the company scale, the three potential characteristics: Organization Size, Identity Background and Structural Characteristics have played a significant inhibitory role on irregularities, and the Internal Heterogeneity has no significant effect. When using violation behavior as an alternative variable of supervision performance, the sample selection deviation will be caused by the lack of information disclosure. This paper suggests that we should pay attention to the team of the board of supervisors scientifically and reasonably, weaken the appropriate personalized differences within the board of supervisors, and comprehensively consider the interaction between the company scale, asset quality and the performance of the board of supervisors when formulating the corporate internal management system.

Contrastive learning, a self-supervised learning method, is widely used in image representation learning. The core idea is to close the distance between positive sample pairs and increase the distance between negative sample pairs in the representation space. Siamese networks are the most common structure among various current contrastive learning models. However, contrastive learning using positive and negative sample pairs on large datasets is computationally expensive. In addition, there are cases where positive samples are mislabeled as negative samples. Contrastive learning without negative sample pairs can still learn good representations. In this paper, we propose a simple framework for contrastive learning of image classification (SimCLIC). SimCLIC simplifies the Siamese network and is able to learn the representation of an image without negative sample pairs and momentum encoders. It is mainly by perturbing the image representation generated by the encoder to generate different contrastive views. We apply three representation perturbation methods, namely, history representation, representation dropoput, and representation noise. We conducted experiments on several benchmark datasets to compare with current popular models, using image classification accuracy as a measure, and the results show that our SimCLIC is competitive. Finally, we did ablation experiments to verify the effect of different hyperparameters and structures on the model effectiveness.

It is a hot issue to allocate resources using auction mechanisms in vehicular fog computing (VFC) with cloud and edge collaboration. However, most current research faces the limitation of only considering single type resource allocation, which cannot satisfy the resource requirements of users. In addition, the resource requirements of users are satisfied with a fixed amount of resources during the usage time, which may result in high cost of users and even cause a waste of resources. In fact, the actual resource requirements of users may change with time. Besides, existing allocation algorithms in the VFC of cloud and edge collaboration cannot be directly applied to time-varying multidimensional resource allocation. Therefore, in order to minimize the cost of users, we propose a reverse auction mechanism for the time-varying multidimensional resource allocation problem (TMRAP) in VFC with cloud and edge collaboration based on VFC parking assistance and transform the resource allocation problem into an integer programming (IP) model. And we also design a heuristic resource allocation algorithm to approximate the solution of the model. We apply a dominant-resource-based strategy for resource allocation to improve resource utilization and obtain the lowest cost of users for resource pricing. Furthermore, we prove that the algorithm satisfies individual rationality and truthfulness, and can minimize the cost of users and improve resource utilization through comparison with other similar methods. Above all, we combine VFC smart parking assistance with reverse auction mechanisms to encourage resource providers to offer resources, so that more vehicle users can obtain services at lower prices and relieve traffic pressure.

The conventional grey GM(2, 1) model built for the fast growing time sequence generally has big errors. To improve the modeling precision, the paper improves from the following two aspects: First, the paper transforms the accumulated generating sequence of original time sequence quantitatively to make the transformed time sequence have the better adaptability to the model; second, the paper extends the conventional grey GM(2, 1) model's structure to make the extended model meet the variation law of fast growing sequence better. The extended grey model is called the GM(2, 1, Σexp(ct)) model. The paper offers the parameter optimization method and the solving method of time response sequence of GM(2, 1, Σexp(ct)) model. Using the model and methods proposed, the paper builds the GM(2, 1, Σexp(ct)) models for the natural gas consumption of China and Chongqing City, China, respectively. Results show that the models built have high simulation precision and prediction precision.

In recent years, various speech embedding methods based on deep learning have been proposed and have shown better performance in speaker verification. Those new technologies will inevitably promote the development of forensic speaker verification. We propose a new forensic speaker verification method based on embeddings trained with loss function called generalized end-to-end (GE2E) loss. First, a long short-term memory (LSTM) based deep neural network (DNN) is trained as the embedding extractor, then the cosine similarity scores between embeddings from same speaker comparison pairs and different speaker comparison pairs are trained to represent within-speaker model and between-speaker model respectively, and finally, the cosine similarity scores between the questioned embeddings and enrolled embeddings are evaluated in the above two models to get the likelihood ratio (LR) value. On the subset of LibriSpeech, test-other-500, we achieve a new state of the art. Both all the same speaker comparison pairs and different speaker comparison pairs get correct results and can provide considerable strong evidence strength for courts.

Establishing a modern central banking system in China necessitates the deployment of a novel suite of monetary policy instruments and unencumbering of the channels through which these policies are transmitted. A critical aspect of evaluating the soundness and efficacy of monetary policy is to examine its capacity for tempering non-stationary volatility in the bond market. We use a synthetic difference in differences model (SynthDid), which draws upon panel data from eight countries spanning October 2011 to June 2022 period, to accurately determine the efficiency of the transmission of these monetary policy instruments. The Medium-term Lending Facility (MLF) can mitigate fluctuations in both medium- and long-term bond markets. Implementing a unified lending cycle of one year and expanding MLF collateral enhance the transmission efficiency of the newly established monetary policy instruments to the bond market. Additionally, the utilization of the Standing Lending Facility (SLF) substantially reduces the risk associated with short- and medium-term bond markets. Nevertheless, the efficacy of monetary policy transmission via different instruments varies in different periods.

Since China began reforming and opening up its economy, and especially since the launch of development projects in western China, province A has attracted an increasing amount of investment, which is the main driving force for provincial economic growth. Hence, this study uses a state space model to examine how government investment has affected economic growth in province A in western China, and explains whether there is a crowding-in effect or a crowding-out effect of local government investment on private investment. The findings indicate that both government and private investments have a positive, stimulating influence on economic growth in province A, with the latter being more impactful than the former. Productive and non-productive investments have different effects on province A's economic growth. From the perspective of the trajectory of government investment elasticity, the elasticity of government and private investments in province A presents a very large spatio-temporal change. That is, from 1994 to 2009, government investment in province A had a crowding-in effect on private investment, but from 2010 to 2017, a crowding-out effect was observed.

This paper proposes a hybrid Monte Carlo simulation method for pricing European options under the stochastic volatility model and three-factor model. First, the European options are expressed as a conditional expectation formula, which can be used not only for reducing variance of simulations, but also for calculating the value of Greeks easily, due to the elimination of the weak singularity for the payoff of the option. Then, in order to reduce variance further, the authors also construct a new explicit regression based control variate under Heston model and three-factor model respectively. Numerical results of experiments show that the proposed method can greatly reduce the variance of simulation for pricing European option, and is easy to complement for the calculation of Greeks.

The popularization of smartphones and the acceleration of their replacement lead to a surge in mobile phone disposal. How to recycle waste mobile phones efficiently becomes a major problem in today's society. The "Internet +" recycling mode is an effective way to solve this problem. The recycling process of waste mobile phones involves retailers, manufacturers, third parties and other recycling parts. Retailers have natural advantages compared with other parts because of their perfect sales network and logistics system. The system dynamics model for "Internet +" recycling of waste mobile phones dominated by retailers is constructed, and the Vensim software is used to simulate the influence of changes in two key factors in "Internet +" recycling environment: Annual operating cost of online platform and offline unit logistics cost on retailers' recycling volume and recycling profit. The results show that the investment of online platform operation cost is conducive to the increase of retailers' online waste mobile phone recycling volume and recycling profit, while the investment of offline logistics cost increases retailers' online waste mobile phone recycling volume, but reduces the recycling profit.

In the context of adulteration by suppliers, downstream firms need to choose between incentives and regulation to ensure product quality. Studies have shown that the adulteration behavior of suppliers increases with the degree of dispersion of suppliers, that is, the number of suppliers increases. Therefore, based on the assumption that the number of suppliers impacts quality uncertainty, this paper further introduces the number of suppliers into the incentive model to investigate the relationship between supply chain dispersion, that is, the number of suppliers, social integrity, and incentive strength. The study finds that the optimal number of suppliers depends on social integrity, regulatory cost, and incentive strength. There is a positive correlation between social integrity and the number of suppliers, while regulatory costs and incentive strength have a negative correlation with the number of suppliers. That means, the higher the social integrity, the lower the regulatory cost; and the lower the incentive intensity, the more optimal suppliers can be selected.

This paper aims to develop and validate a deep learning-based short-term mortality risk prediction model for critically ill patients by using routinely collected data in a large Chinese cohort and explore the explainability of the model decision. A total of 10925 critically ill patients between January 2014 and June 2020 are included in this study. Data routinely collected in the electronic health records (EHRs) system are extracted and used to develop a short-term mortality risk prediction model based on a deep artificial neural network (ANN). The features include demographic characteristics, vital signs, laboratory tests, and the daily dose of intravenous medications. The developed deep learning model (AUROC: 0.88, AUPRC: 0.63, Brier score: 0.108) is superior to the model based on APACHE Ⅱ scores (AUROC: 0.78, AURPC: 0.52, Brier score: 0.124) in the prediction of hospital mortality for critically ill patients. Further attribution analysis based on the integrated gradients method shows that measurements observed at a later time seem to have a more significant influence on mortality, while earlier usage of amiodarone or dexmedetomidine contributed to lower mortality. This well-performing and interpretable model may have practical implications for improving the quality of care for critically ill patients.

Advertising-driven demand is very common in practice. This paper considers pricing and advertising strategies issues in a two-echelon supply chain involving a manufacturer and a retailer. According to who undertakes the advertising expenditure, both the retailer-advertising case and the manufacturer-advertising case are analyzed under the Stackelberg framework. The crucial factor that affects the advertising strategy and the optimal profit for each participant is revealed. Furthermore, we compare profits of the two participants under different situations. It is demonstrated that both the manufacturer and the retailer gain more profits in the retailer-advertising case than those in the manufacturer-advertising case. In other words, the retailer has more incentive to advertise the product for the sake of maximizing its profit. Finally, a numerical illustration is presented to examine the change of the profit for each participant under different marginal demands and promotion degrees.

With the continuous development of economic globalization, especially in the face of expanding COVID-19 pandemic, the supply shortage of suppliers will directly affect the ordering strategy of enterprises, which will cause price fluctuations in the commodity market and corporate profits. We assume that the demand for the product in the market is constant, and the supply determines the product price. An EOQ model is constructed with a supply shortage under the additive supply. We find that the optimal order quantity is consistent with the classic EOQ without considering product price changes; after introducing the price function, we analyze the relationship between the product's market price and the seller's optimal profit and the supplier uncertainty. The research results show that the product's market price will increase with the increase of the out-of-stock quantity. At the same time, the uncertainty will decrease, and the seller's optimal profit will decrease as the average stock-out and uncertainty increase.

The online review function helps consumers grasp more real product information and reduce the possibility of returning, but it may also damage firms' reputations or profits. However, few studies considered the relationship between online reviews and consumer returns. Based on this, we develop an e-commerce supply chain (E-SC) game model consisting of a single manufacturer and a single e-platform, aiming to explore the relationship between consumer returns and online reviews and to analyze the impact on both the decision-making of E-SC members and their profits. We find that there is a negative relationship between consumer returns and online reviews of product quality, and consumer returns make the pricing decisions in the two scenarios of yes/no online reviews move toward two different directions. Only when the online review is positive and higher than a certain threshold will it have a positive impact on sales and E-SC members' profits. Finally, we design a new "commission joint returns and quality improvement costs sharing" contract to optimize the decentralized model with online reviews, and we find that the higher the accuracy of product information, the less conducive the contract applied to E-SC.

A critical problem plaguing regulators in promoting pharmaceutical innovation is to design and select efficient incentive policies. In this study, we develop a stylized model comprising a regulator and two representative drug producers to evaluate the effects of three incentive policies: Innovation subsides, inclusion new drugs in the health insurance plan, and the combination of the above two policies (also called hybrid policy). Our analysis shows that innovation subsidies and inclusion of new drugs in the health insurance plan can both promote pharmaceutical innovation, but their incentive effects vary in different policy objectives. Specifically, if the regulator aims to improve patient welfare, he should incorporate new drugs into the health insurance plan to expand the accessibility of new drug when the copayment level is low. However, if the regulator aims to improve social welfare, he should choose innovation subsidies when the copayment level is high, and the hybrid policy when the copayment level is low. In particular, with a sufficiently low copayment level, the hybrid policy allows the new drug producer, patients and the regulator to achieve Pareto improvement due to a lower regulator's innovation subsidy expenditure, higher profits of the new drug producer and consumer surplus.

In this paper, we discuss the development process of local government special bonds, and the role channels of local government special debt investment in driving China's economic growth. Based on the specific decomposition of Xinjiang local government special bond investment, this paper uses the non-competitive input-output model for the first time to analyze the net pulling effect of Xinjiang local government special bond investment on Xinjiang's GDP and employment in 2020. Two measure calibers are set in this paper based on whether the financing costs are considered or not; in addition, we set up four scenarios based on two conditions: Whether to consider retained fun and whether to consider using special-purpose bond investment to leverage social capital. The results show that: 1) when financing costs are not considered, the RMB77.4 billion local government special-purpose bonds can push the GDP of Xinjiang to grow by RMB42.27 billion, RMB35.12 billion, RMB77.548 billion and RMB69.34 billion respectively under the four scenarios; 2) when financing costs are not considered, the number of jobs driven by the RMB77.4 billion local government special-purpose bonds was respectively 372, 300, 324, 500, 718, 500 and 601, 300 in the four scenarios; 3) when financing costs are considered, the RMB77.4 billion local government special-purpose bonds can push the GDP of Xinjiang to grow by RMB71.876 billion and RMB64.268 billion under scenario 3) and scenario 4).

Aiming at the lack of classification and good standard corpus in the task of joint entity and relationship extraction in the current Chinese academic field, this paper builds a dataset in management science that can be used for joint entity and relationship extraction, and establishes a deep learning model to extract entity and relationship information from scientific texts. With the definition of entity and relation classification, we build a Chinese scientific text corpus dataset based on the abstract texts of projects funded by the National Natural Science Foundation of China (NSFC) in 2018-2019. By combining the word2vec features with the clue word feature which is a kind of special style in scientific documents, we establish a joint entity relationship extraction model based on the BiLSTM-CNN-CRF model for scientific information extraction. The dataset we constructed contains 13060 entities (not duplicated) and 9728 entity relation labels. In terms of entity prediction effect, the accuracy rate of the constructed model reaches 69.15%, the recall rate reaches 61.03%, and the F1 value reaches 64.83%. In terms of relationship prediction effect, the accuracy rate is higher than that of entity prediction, which reflects the effectiveness of the input mixed features and the integration of local features with CNN layer in the model.

Industrial relocation and ecological subsidy are viewed as effective ways to overcome transboundary industrial pollution. In this paper, we study the transboundary industrial pollution control problem in the Beijing-Tianjin-Hebei region in the context of industrial relocation. Firstly, we construct an economic model of pollution control with relevant variables such as environmental tax and environmental damage coefficient. Secondly, we solve the economic contributions by using the proportional split-off solution. Finally, we compare the optimal relocation quantity and welfare functions in both cooperative and non-cooperative cases. Our research finds that: 1) The optimal strategy is closely related to utility coefficient, environmental loss coefficient and incentive intensity coefficient. 2) The welfare function and the optimal relocation quantity in the cooperative case are significantly greater than those in the non-cooperative case. Based on the analysis, some suggestions are provided for transboundary industrial pollution management.

To explore the factors and paths that influence the willingness to use sharing new energy autonomous vehicles (SNEAVs), this paper incorporates the unified theory of acceptance and use of technology (UTAUT2) as the basic frameworks, with gender serving as a moderating variable. Seven psychological latent variables, including performance expectancy, social influence, hedonic motivation, price sensitivity, perceived risk, trust in technology, and innovativeness, are considered to examine their effects on behavioral intention. Quantitative data (n = 1082) was collected via an online questionnaire in Beijing. The ordered logit model was used to preliminarily demonstrate the significant impact of gender, with further parameter fitting confirming the good fit of the psychological latent variable model. Path analysis results reveals that gender influences the willingness to use SNEAVs in multiple aspects. Specially, females are more significantly influenced by hedonic motivation, whereas males prioritize performance expectation. Furthermore, price sensitivity positively has a positive impact on male behavioral intention, but a negative effect on female behavioral intention. Additionally, trust in technology plays a more important role for women compared to men. These findings are crucial in promoting the development of SNEAVs.

The presence and effective implementation of the intellectual potential of construction enterprises is important for creating unique competitive advantages as a response to challenges caused by globalization, the era of the knowledge economy, as well as the development of communication and information technologies. The purpose of the study is to provide a comprehensive understanding of the factors that contribute to the development of intellectual capital in construction enterprises; development of an algorithm for thorough response by the enterprise to the action of determinants. The following methods were used: Analysis and synthesis, induction and deduction when studying the variety of determinants of the development of intellectual capital; grouping in the process of classifying determinants; statistical methods in the process of researching the reporting of construction enterprises; systematic approach in the process of forming an algorithm of actions aimed at ensuring effective management of the intellectual capital of construction enterprises; abstraction to generalize research results and outline significant trends in changing indicators that reflect the development of individual components of intellectual capital. In the paper, the determinants of the development of intellectual capital are grouped according to the classification features important for Ukrainian construction enterprises. The indicators, the monitoring of which is necessary in the process of implementing a conscious response to the action of the determinants of the development of the intellectual capital of construction enterprises, are studied on the example of Ukrainian construction enterprises.

The space-temporal evolution of economic inequality is examined with Markov chain test method, and the dynamic interrelationships among environmental quality, energy consumption, and economic inequality in China from the province-level are tested by focusing on accounting for structural shifts in causal linkages in this paper. We first employ the Toda-Yamamoto causality framework and then augment it with a Fourier approximation which captures structural changes as a smooth process. The empirical findings show that taking into account smooth structural shifts is important for the causal linkages between economic inequality and energy consumption, and also between environmental quality and energy consumption. The causality analysis with structural changes provides a causal linkage between economic inequality and energy consumption in 26 out 30 provinces and a causal linkage between environmental quality and energy consumption in 7 out 30 provinces, while the quantities are 22 out 30 and 5 out 30 respectively when not accounting for structural shifts. These findings are consistent with the fact that provincial economics in China have experienced structural changes in economy-environment-energy sectors. We also conduct additional analyses which point out that regional and cyclical dependency matter for the causal relationships, and the method of HP filtering can not effectively solve the problem of smooth shifts in economy-environment-energy causality.

This paper proposes a new time-varying parameter distributed lag (DL) model. In contrast to the existing methods, which assume parameters to be random walks or regime shifts, our method allows time-varying coefficients of lagged explanatory variables to be conditional on past information. Furthermore, a test for constant-parameter DL model is introduced. The model is then applied to examine time-varying causal effect of inventory on crude oil price and forecast weekly crude oil price. Time-varying causal effect of US commercial crude oil inventory on crude oil price return is presented. In particular, the causal effect of inventory is occasionally positive, which is contrary to some previous research. It's also shown that the proposed model yields the best in and out-of-sample performances compared to seven alternative models including RW, ARMA, VAR, DL, autoregressive-distributed lag (ADL), time-varying parameter ADL (TVP-ADL) and DCB (dynamic conditional beta) models.

Cities generate more than 60% of carbon emissions and are the main battleground for achieving the target. However, there is no unified and standardized measurement methods of carbon emissions in cities. In this paper, we took Xi'an as an example and started by measuring carbon emissions with the new standards. Then, the decoupling of economic development from carbon emissions was studied according to the Tapio decoupling theory. Based on the generalized Divisia index method, the decoupling effort model was proposed to study the impact of carbon emission factors contributing to carbon reduction. The results show: (ⅰ) During the period 1995–2021, the carbon emissions of Xi'an increased rapidly, with an average annual growth rate of 6.06%, due to the accelerating pace of urbanization and industrialization. (ⅱ) The energy consumption sector accounted for the largest share of carbon emissions, ranging from 77.38% to 89.46%. Xi'an's energy structure is primarily based on fossil fuels, especially coal, which holds a significant proportion. To achieve the "double carbon" goal, it is crucial to reduce the dependence on fossil fuels. (ⅲ) The 10th Five-Year Plan was in the state of "expansive coupling", while other periods were in the "weak decoupling" state from the 9th to 14th Five-Year Plan periods. After the carbon peak year in the 15th Five-Year Plan, it would be in a state of "strong decoupling". The agricultural production account was the first to achieve a "strong decoupling" state. (ⅳ) The government of Xi'an made efforts to decouple, but these were not enough. Technological innovation played a crucial role in the carbon reduction of Xi'an, and was a key factor in achieving the "double carbon" goal.

The bond market is an important market for investment and financing in China's economic sectors, and also an important part of the monetary policy framework. The internal transmission of bond market is an important part of market interest rate transmission, which iscritical to the effectiveness of monetary policy. However, few scholars have studied the characteristics of interest rate transmission in China. An in-depth study of the interest rate transmission mechanism and its dynamic evolution between different bond markets is conducive to clarify the pulse of transmission within Chinese bond market and to further unblock the transmission mechanism of monetary policy. From the perspective of system theory and based on the analysis method of Granger causality complex network, this paper finds that the interest rate transmission among various varieties in China's bond market is relatively significant. Treasury bonds and CDB bonds are the two core bond varieties of interest rate transmission in the bond market. Simultaneously, this study concludes that the medium and long-term interest rate played a dominant role in the transmission of market interest rate during the easing phase of monetary policy, while the short-term interest rate played a dominant role in the transmission of market interest rate during the tightening phase of monetary policy. This paper also gives enlightenment and suggestions.

In this paper, stochastic global optimization algorithms, specifically, genetic algorithm and simulated annealing are used for the problem of calibrating the dynamic option pricing model under stochastic volatility to market prices by adopting a hybrid programming approach. The performance of this dynamic option pricing model under the obtained optimal parameters is also discussed. To enhance the model throughput and reduce latency, a heterogeneous hybrid programming approach on GPU was adopted which emphasized a data-parallel implementation of the dynamic option pricing model on a GPU-based system. Kernel offloading to the GPU of the compute-intensive segments of the pricing algorithms was done in OpenCL. The GPU approach was found to significantly reduce latency by an optimum of 541 times faster than a parallel implementation approach on the CPU, reducing the computation time from 46.24 minutes to 5.12 seconds.

Project portfolio management is a major challenge for some organizations. In most organizations, there are a large number of projects active at the same time, some not necessarily delivering value or not aligned with their strategic goals. Also universities face a lot of uncertainties when selecting and prioritizing the projects that make up their portfolio. In addition, the achievement of those who are aligned with the strategy of the university becomes a great challenge. So to ensure good project portfolio management, the implementation of selection and prioritization methods and processes becomes important. For the project portfolio management to be effective, it is necessary to establish a structured method adapted to the needs and strategy of the university. In this context, this paper proposes a method for selecting and prioritizing projects within the framework of the portfolio management dedicated to universities, which can promote harmony between the university's strategy, the needs and the priority objectives for enable better decision-making. This method is based on the processes of the COBIT 5 good practice framework, and on the multi-criteria decision-making methods AHP, TOPSIS and the WSM technique, thus, it proposes seven project selection criteria based on the five axes IT governance strategies and two catalysts derived from COBIT 5 enablers. The evaluation and validation of this method was applied in the portfolio management of the Abdelmalek Essaadi Moroccan University (AUE). The result shows that this proposed method has made it possible to make a better selection and prioritization of the portfolio of projects of Abdelmleek Essaadi University having the most value.

We conduct an empirical analysis of Shanghai-Hong Kong Stock Connect to reveal the dynamic impacts of stock connect trading activity on the stock pool's Amihud illiquidity proxy, index return, and CNY-HKD exchange rate. From pairwise conditional g causality analysis, we note a mutual significant causal connection between northbound net buying volume and Shanghai stock exchange return on all frequency levels. Meanwhile, we find a significant causal impact on the Shanghai portfolio's liquidity from northbound net buying volume. And there is a significant causal impact from the southbound net buying volume on Hang Seng Index return. Both are significant at the low-frequency level. In particular, northbound trading activity stimulates the Shanghai portfolio's liquidity in the low trading activity regime from the threshold VAR analysis. In robust analysis, we find similar significant dynamic causal connection and stimulation effects for the northbound trades when replacing Amihud illiquidity with the turnover rate. The result might relate to the investment behaviors looking for opportunity in the low trading activity regime. In contrast, the investors' beliefs may vary in the high trading activity regime, which weakens the connection between trading activities and other factors like liquidity.

Based on the different premium volatility characteristics of various systematic factors in the A-share market, this paper constructs six representative high-frequency volatility prediction models that consider multiple complex risk structures. On this basis, a detailed comparative analysis of the differences in volatility characteristics among various factors is conducted, and the optimal prediction and early warning framework for the A-share market is proposed. Research shows that: 1) The volatility research results only for individual market indexes are not universally representative. 2) The fluctuation characteristics among different systematic factors and their respective optimal prediction model frameworks generally have significant differences, that is, there is no single fixed combination of model parameters. 3) Complex risk characteristics such as long memory, measurement errors, and high-frequency jump fluctuations obviously exist in the A-share market. The optimal forecast and early warning framework for the A-share market can be constructed by a combination of models that consider one or more of the above risk characteristics. The above conclusions have important practical reference value for the risk warning and prevention of the A-share market and the formulation of related policies.

To analyze the influencing factors on the growth of economic and management talents in China's Sichuan Province, this paper constructs an analysis framework for the growth of economic and management talents, takes 340 economic management scholars in 53 higher education institutions in Sichuan Province as the research objects, uses CV analysis to organize their CVs and information, and constructs an evaluation index system combined with system science theory for the influencing factors from five dimensions: Educational experience, work experience, research experience, part-time experience, and award experience. Correlation analysis and structural equation model are used to systematically analyze the influencing factors on the growth of economic and management talents. The experimental results show that work experience, research experience, and award experience have a direct positive significant influence on talent growth; research experience and award experience play mediating roles in the influence of talent growth. This paper enriches the theoretical dimensions of this research field and explores the interactions among these factors. It also helps to improve the cultivation and development mode of economic and management talents in the western region. Furthermore, it provides guidance and reference for the role of talents in promoting economic growth, industrial upgrading, and sustainable development.

To analyze the spatial influence mechanism of talent policy on population flow, this study compares the government work reports of 31 provinces between 2008 and 2020, and quantifies regional talent policies in nine aspects, including talent evaluation and incentives, utilizing a comprehensive, standardized, and continuous approach. Additionally, this paper develops a spatial econometric analysis model and expands on the conventional neighborhood, distance, and economic matrices by constructing a spatial weight matrix that reflects talent flow. The findings indicate that population movement exhibits spatial clustering patterns. The regional government's talent policy, primarily based on talent evaluation and incentives, positively influences population inflow. Moreover, during the implementation of talent policies, local governments demonstrate cooperative relationships. The inter-regional spillover effect between talent evaluation and talent incentives is significantly positive. In other words, a stronger local talent evaluation policy, along with robust talent incentives, encourages population inflow from neighboring provinces. However, this conclusion may vary in different regions and over time. Recently, the spatial spillover effect of population inflow and the impact of talent policies have not shown significant results. Additionally, the attractiveness of talent evaluation in the eastern region surpasses that of talent incentives, while the opposite holds true for the central and western regions. This study investigates the impact of local government talent policies on population inflow and its spatial spillover effect, offering theoretical support for intergovernmental cooperation.

A three-dimensional boundary-spanning technology search model including search depth, scope and height is established, and a quantitative calculation method is proposed to dynamically describe an organisation's technology search behaviour and demand characteristics. Organisations are clustered by types as technical, comprehensive, or professional using k-means based on technology search behaviour. Recommendation strategies for various types of organisations are proposed based on this, and the search and supply libraries of each organisation are built by considering their type and search contents. The semantic similarity between patents in different libraries is calculated using a Word2Vec and TextRank model to achieve patent recommendations. An empirical study of the robotics field shows a recommendation accuracy of 0.751, and the accuracy of the technical, comprehensive, and professional types is 0.8282, 0.5389 and 0.7723, respectively. This study considers an organisation's dynamic search behaviour and makes class-based recommendations, with a low computational complexity and strong interpretability.