The development of the digital economy in the modern era significantly embodies a country’s comprehensive strength. This paper uses the data from China’s 2012 and 2017 input-output tables to analyze the linkage structure between the digital economy industry and other industries using the average propagation length model and social network analysis. The Weaver–Thomas model was used to obtain the industrial linkage matrix and network characteristics. An industrial chain map of the digital economy was depicted as the core. This paper is of practical significance in depicting the industrial chain structure and network characteristics, understanding the position of digital economy industry in the economic system and promoting the integrated development of the digital economy and traditional manufacturing industries, which allows them to incorporate the new generation of technology and industrial transformation with the digital technology as the core to promote their transformation and upgrading.

In China’s evolving market economy, household asset diversification and refinement have raised concerns about asset selection and rationalization for financial market development. Our research investigates the impact of individual heterogeneity factors, such as the “Big Three” personality traits and cognitive abilities, on household asset allocation behaviors, expanding upon the traditional focus on risky financial assets and the “Big Five” personality traits. We utilize data from the 2018 China Family Panel Studies and employ logistic and hurdle models to examine the breadth of household asset holdings. We utilize two-part models to explore the depth of household asset holdings. Our findings reveal the impact of different personality traits and cognitive abilities on the depth and breadth of different household assets. This study offers a more comprehensive understanding of household asset selection by considering various asset types and individual heterogeneity factors.

Economic downturns have increased in recent years, and the impact of COVID-19 has slowed economic growth. The market’s overall pessimism led to a contraction in the government’s fiscal revenue. The Chinese economy has always relied on government investments. Local governments’ productive investments have directly contributed to capital formation, which has promoted rapid economic growth. With the increase in expenditure pressure and the tightening of income caliber, will the continuous growth of the proportion of public service expenditure affect the established growth goals and hinder the efficiency of economic growth? This study explains this based on a general equilibrium model containing two types of government expenditure and uses China’s 2010–2019 provincial panel data for the empirical analysis. The results show that, in the current economic situation, increasing the proportion of public service expenditure is conducive to further economic growth.

The private placement of A-shares gained momentum with the release of the Administrative Measures for Securities Issuance of Listed Companies in 2006. This led to enhanced research on the impact of private placement on stock prices. In 2012, the Chinese government relaxed the requirements for directed issuance of listed companies, resulting in a surge of directed issuance since then. This study uses a sample of listed companies that conducted private placements in the A-share market between 2013 and 2021, to analyze the impact of investor sentiment on stock price differences after private placements from the perspective of short and long-term excess returns. This study constructs the non-main investor sentiment of individual stocks using high-frequency tick data of individual stocks and explores the relationship between this stock price anomaly and investor sentiment using multiple regression analysis. The results show a positive short-term announcement effect of A-share private placements, with the excess return rate occurring mainly before the plan announcement date. The stock price difference from the plan announcement date to ten trading days thereafter has a significantly negative relationship with the excess return rate. Furthermore, investor sentiment in private placements may negatively affect long-term stock performance. This study suggests that this phenomenon is caused by higher investor sentiment pushing stock prices upward in the short term, causing them to deviate from fundamentals, creating mispricing, and then driving them back to fundamentals, with information disclosure. After controlling for the severity of information disclosure, the effect of investor sentiment on long-term stock price performance becomes insignificant.

Objective: An interpretability model of enterprise digital transformation that integrates XGBoost and Shapley additive explanations (SHAP) is proposed to accurately identify the important factors that affect the digital transformation of enterprises and their mode of action, improve the digital capabilities and levels of enterprises, and prevent the risks of digital transformation of enterprises. Method: The annual report information of listed companies from 2009 to 2021 is used as the research object. First, the digital transformation index is constructed using the text mining method. Second, an enterprise digital transformation prediction model based on XGBoost is constructed and compared it with other mainstream algorithms, such as linear regression and random forest, to find a comprehensive optimal model. Finally, the SHAP interpretation framework is introduced to quantify and attribute the importance of each characteristic variable. Results: The results found that the XGBoost model outperformed the compared models in the mean absolute error and R² performance indicators. In addition, development capability, comprehensive capability, and solvency are important characteristics influencing the digital transformation of enterprises, and they differ in the way, direction, and strength of influence on the digital transformation of enterprises. Research value: This paper applies XGBoost integrated learning method to identify the factors of enterprise digital transformation, which enables enterprises to assess their digital transformation status, discover the key determinants of digital transformation, and adopt effective digital transformation modes for higher value.

A shuffle graph convolutional network (Shuffle-GCN) is proposed to recognize human action by analyzing skeleton data. It uses channel split and channel shuffle operations to process multi-feature channels of skeleton data, which reduces the computational cost of graph convolution operation. Compared with the classical two-stream adaptive graph convolutional network model, the proposed method achieves a higher precision with 1/3 of the floating-point operations (FLOPs). Even more, a channel-level topology modeling method is designed to extract more motion information of human skeleton by learning the graph topology from different channels dynamically. The performance of Shuffle-GCN is tested under 56,880 action clips from the NTU RGB+D dataset with the accuracy 96.0% and the computational complexity 12.8 GFLOPs. The proposed method offers feasible solutions for developing practical applications of action recognition.

This paper focuses on latecomers in technology standards alliances, a topic that has received limited attention in academia. Although latecomers have disadvantages in terms of technology and market access, they possess a strong desire to catch up technologically. As a fundamental characteristic of an alliance, alliance routines serve as a source of innovation for members and a basic unit of analysis, providing a new perspective for understanding and researching the technological catch-up of latecomers. In this paper, a questionnaire survey is conducted among 83 latecomer enterprises in a technology standards alliance. The survey covers strategic emerging industries, such as new energy, new-generation information technology, new materials, and high-end manufacturing. Then, hierarchical regression is performed for hypothesis testing. The research shows that the three dimensions of alliance routines significantly promote the utilization of technological catch-up by latecomer enterprises. However, the impact on the exploration technological catch-up varies, where the action logic promotes exploration technological catch-up while implicit norms hinder exploratory innovation with an inverted U-shaped impact. The absorptive capacity strengthens the positive impact of the three alliance routines on the exploitative innovation of latecomer enterprises and strengthens the inverted U-shaped relationship between mutual consensus and their exploratory innovation. Nonetheless, it fails to play a significant regulatory role in action logic or in the relationship between implicit norms and exploratory innovation of latecomers.

To ensure the safe operation of the power system, it is necessary to monitor and manage all kinds of power facilities. Real-time power monitoring can play a role in the early warning of geographical disasters. At the transmission end, the state detection and fault location of transmission lines are related to the normal operation of the power system. The precise time synchronization of the power system can be guaranteed through the satellite positioning time service, so that the time of various power operation actions is consistent. The accuracy can reach several microseconds, which provides a basis for the analysis of power grid operation. In this paper, by installing BeiDou functional modules in power equipment and developing the BeiDou operation platform for the distribution network, the precise management of power timing and positioning is realized by docking with the geographic information systems platform of the power grid. The three-dimensional sphere error probability (SEP) algorithm is selected to evaluate the positioning error data. Based on the three traditional SEP calculation methods, the maximum likelihood estimation algorithm is optimized and improved, which can achieve nanosecond synchronization accuracy. It can also be applied to other precision of synchronization scenarios of the power system to meet the development needs of the future clock system of the power system. Through the simulation of experimental data and comparison with other algorithms, the accuracy is verified and the reliable estimation of parameters is realized. Considering the information loss caused by dimension reduction evaluation, the simulation analysis in this paper is compared with other methods, and the accuracy is significantly improved. The power system positioning and timing module developed in this paper can not only be used in the power system but also has a certain reference and application value for other devices in the same industry that need timing and positioning services.

Blockchain is a technology that collects data in a blockchain and allows transactions securely and transparently. It is used in many fields. Bibliometric publications examining detailed research trends in blockchain and cryptocurrency are still rare, but one bibliometric report did include research on the topic. The present work is filling in the void that eludes the study of research in the blockchain area. In the current work, we have included a wide range of blockchain and cryptocurrency research efforts to date. This study used the Scopus database to extract 2,045 articles from 2014 to 2021. The goal was to examine blockchain and cryptocurrency growth in volume, trend, global distribution, significant journals, pioneering authors, leading nations, and dominant sectors. This study highlights many aspects of worldwide blockchain and cryptocurrency research by researchers from all over the world. This research also studies how consensus decision-making and its processes are used. This study highlights many aspects of global blockchain and cryptocurrency research. This study also examines their use and consensus methods.

In graph theory, the concept of domination is essential in a variety of domains. It has broad applications in diverse fields such as coding theory, computer network models, and school bus routing and facility location problems. If a fuzzy graph fails to obtain acceptable results, neutrosophic sets and neutrosophic graphs can be used to model uncertainty correlated with indeterminate and inconsistent information in arbitrary real-world scenario. In this study, we consider the concept of domination as it relates to single-valued neutrosophic incidence graphs (SVNIGs). Given the importance of domination and its utilization in numerous fields, we propose the application of dominating sets in SVNIG with valid edges. We present some relevant definitions such as those of valid edges, cardinality, and isolated vertices in SVNIG along with some examples. Furthermore, we also show a few significant sets connected to the dominating set in an SVNIG such as independent and irredundant sets. We also investigate a relationship between the concepts of dominating sets and domination numbers as well as irredundant and independence sets in SVNIGs. Finally, a real-life deployment of domination in SVNIGs is investigated in relation to COVID-19 vaccination locations as a practical application.

Articulation disorder is a condition in which the mouth, tongue, vocal cords, and other parts of the body that play an important role in producing voice are damaged, resulting in the inability to produce sound. To diagnose articulation disorders, the movement and shape of each organ concerned with pronunciation are examined. If necessary, the underlying disease or disorder should be managed properly. In it, a speech therapist tests your pronunciation. The observation of conversation and the examination of the pronunciation of each syllable are used to distinguish between mistakes and the degree of articulation disorder. However, these processes are time consuming and labor intensive and are subjective judgments by experts. Therefore, it is important to investigate the characteristics of vocal signals by acoustic analysis of speech objectively. In this study, we focused on fluctuations in the period and amplitude of speech signals and predicted a model for detecting abnormal articulations using fluctuation measurement of the voice data in six healthy subjects and nine patients with an articulation disorder. We used inverse probability of treatment weighting to match the variability for the two groups using the inverse of propensity scores. As the results, the classification performance area under the curve was 0.781 (sensitivity: 0.781, specificity: 0.680) for healthy subjects and patients. We conclude that acoustic analyzing techniques are useful for diagnosing and treating articulation disorders.

In order to improve the universality and security of the data management of the Internet of Things and avoid the single point of failure problem in the traditional management architecture, this paper proposes a massive connection management architecture based on blockchain. The blockchain is reconstructed according to the hierarchical structure of blockchain technology, and with reference to the attribute-based access control mechanism, a data exchange model is designed using gossip mode and TLS networking mode to ensure the safe transmission of data. The peer computer network technology, data signature, and consensus mechanism are used to complete the design of the data exchange network layer and realize the design of the blockchain-based massive connection management architecture, so as to effectively avoid the single point of failure problem in the traditional management architecture and greatly reduce the burden of the management center. The access requests of massive devices in 5G application scenarios were simulated, and the results showed that the blockchain-based access control architecture designed in this article can quickly process received requests, with information leakage below 10%, improving the security and processing performance of data connections, achieving efficient processing and management of large-scale data, and providing reliable guarantees for the development of the Internet era.

In response to escalating environmental uncertainty, corporate executives typically adjust their business strategy between diversification and refocusing. This study examines the effect of environmental uncertainty on idiosyncratic risk, as well as the moderating impact of diversification on this relationship. Utilizing a sample of companies listed on the Chinese stock market from 2005 to 2019, the results suggest that a significant positive correlation exists between uncertainty and idiosyncratic risk. As environmental uncertainty escalates, corporate managers increasingly opt for diversification before eventually refocusing their business strategy. Furthermore, while moderate diversification can mitigate the impact of uncertainty on idiosyncratic risk, excessive diversification intensifies this correlation. This research holds considerable significance for understanding the influence mechanism of idiosyncratic risk and ensuring the stability of the capital market.

In the digital world, automatic component classification is becoming increasingly essential for industrial and logistics applications. The ability to automatically classify various machine parts, such as bolts, nuts, locating pins, bearings, plugs, springs, and washers; using computer vision is challenging for image-based object recognition and classification. Despite varying shapes and classes, components are difficult to distinguish when they appear identical in several ways–particularly in images. This paper proposes identifying machine parts by a deep feature fusion classification model (DFFCM)-variance based designed through the convolutional neural network (CNN), by extracting features and forwarding them to an AdaBoost classifier. DFFCM-v extracts multilayered features from input images, including precise information from image edges, and processes them based on variance. The resulting deep vectors with higher variance are fused using weighted feature fusion to differentiate similar images and used as input to the ensemble AdaBoost classifier for classification. The proposed DFFCM-variance approach achieves the highest accuracy of 99.52% with 341,799 trainable parameters compared with the existing CNN and one-shot learning models, demonstrating its effectiveness in distinguishing similar images of machine components and accurately classifying them.

This paper proposes a two-step detector called segmented object detection, whose performance is improved by masking the background region. Previous single-stage object detection methods suffer from the problem of imbalance between foreground and background classes, where the background occupies more regions in the image than the foreground. Thus, the loss from the background is firmly incorporated into the training. RetinaNet addresses this problem with Focal Loss, which focuses on foreground loss. Therefore, we propose a method that generates probability maps using instance segmentation in the first step and feeds back the generated maps as background masks in the second step as prior knowledge to reduce the influence of the background and enhance foreground training. We confirm that the detector can improve the accuracy by adding instance segmentation information to both the input and output rather than only to the output results. On the Cityscapes dataset, our method outperforms the state-of-the-art methods.

With the increasing size of financial assets and the complexity of monetary patterns, countries around the world are gradually becoming more transparent in their monetary policies, using central bank monetary policy communication as a new type of monetary policy instrument. To measure central bank communication more accurately, this paper proposes a dynamic topic model, LDA-BP, based on branching processes, to construct the central bank communication index. At the same time, this paper does four things: it uses the constructed communication index as a proxy variable for the new monetary policy instrument; it builds a TVP-FAVAR model that can extract potential macroeconomic information from many variables, and its time-varying nature can better reflect the dynamic regulatory effect of monetary policy; it constructs a three-dimensional impulse response diagram; and it conducts a systematic analysis of macroeconomic impact. The experimental results of demonstrate its effectiveness on central bank monetary policy communication, as it captures timely information about conventional monetary policy instruments and immediately responds to changes in interest rates and money supply. All three monetary policy instruments are effective in smoothing output volatility, with monetary policy communication having a longer-term impact on the macroeconomy.

Information and communication technology-based monitoring services for the elderly can be improved by using a monitoring function that automatically detects cognitive decline. Therefore, we developed a system that provides a brief screening for mild cognitive impairment (MCI). This screening is performed by measuring the performance of two tasks completed simultaneously on a tablet device, such as drawing a spiral and counting color changes. First, we used this system to measure and analyze three groups: elderly persons suspected of having MCI, elderly persons with no cognitive impairment, and healthy young adults. We extracted evaluation parameters relevant for determining MCI through analysis. Furthermore, we developed a detection method using the evaluation parameters and performed an evaluation. Cross-validation revealed that the sensitivity and specificity of this method were 90.0% and 75.0%, respectively.

The field of human motion capture technology represents an emergent and multifaceted domain that encapsulates various disciplines, including but not limited to computer graphics, ergonomics, and communication technology. A distinct network platform within its domain has been established to ensure the reliability and stability of data transmission. Moreover, a sink node has been configured to facilitate sensor data reception through two distinct channels. Notably, the simplicity of the measurement system is directly proportional to the limited number of sensors used. This study focuses on accurately estimating uncertain human 3D movements via a sparse arrangement of wearable inertial sensors, utilizing only six sensors within the system. The methodology is based on a time series sequence throughout the motion process, wherein a series of discontinuous actions constitute the sequential motion. Deep learning methodologies, specifically recurrent neural networks, were employed to refine the regression parameters. Our approach integrated both historical and present sensor data to forecast future sensor data. These data were amalgamated into a superposed input vector, which was fed back into a shallow neural network to estimate human motion. Our experimental results demonstrate the viability of this approach: the six sensors could accurately replicate representative poses. This finding carries significant implications for advancing and applying wearable devices within the realm of motion capture, offering the potential for widespread adoption and implementation.

A three-dimensional (3D) loading device based on a pneumatic 3-universal-prismatic-universal (UPU) parallel manipulator (three chains with UPU joint each) is designed to apply time-varying multi-dimensional loads to a target. First, according to the principle of vector superposition and Newton–Raphson method, the inverse and forward kinematics of the loading device are analyzed. Second, based on the screw theory, the static mapping between the dynamic platform and actuations is derived. Third, a second-order mathematical model is established for pneumatic 3-UPU parallel mechanism based on proportional flow valve and a metal seal pneumatic cylinder. A referential inaccurate model is provided for the control algorithm during modeling. Fourth, based on active disturbance rejection control (ADRC) technique, a 3D loading control algorithm is proposed and dynamic loading control is realized. Experimental results show that the ADRC algorithm has good robustness against disturbances, the steady-state control accuracy is less than 2 N, and the mean square error in dynamic tracking (sinusoidal tracking at 0.2 Hz) is less than 10.5 N.

The rapid implementation of multi-task decoupling in restricted flight areas for unmanned aerial vehicle swarms is crucial to ensure swarm effectiveness. This study introduces a task-switching mechanism in the bio-inspired rule-based (Bio-RB) decision-making algorithm and establishes a mapping relationship from behavioral rules to task modes. A complete decision model is constructed for the cooperative search and package delivery tasks. To further improve the search efficiency of swarms in restricted areas, a boundary-handling strategy based on the combination of path prediction and virtual agents is proposed. The overall scheme is termed the task-driven rule-based (Task-RB) decision-making algorithm. The proposed Task-RB method is evaluated under full-flow simulation. Numerical experiments demonstrate the superior performance of the proposed Task-RB method against the Bio-RB method under different instances.

Differential privacy is a powerful technique that protects the privacy of individuals in a dataset by adding controlled randomness. With the increasing developments in smart sensors, the use of various biometric database is expanding. If privacy protections coexist with advanced use of the biometric database, wider utilization is expected. One of the promising approaches is to apply differential privacy to biometric information, which is attracting attention in use cases such as Google. By adding Laplace noise to biometric information, differential privacy can be added. Our aim is to focus on peak to peak interval of electrocardiogram. It is useful bio-information because it is possible to know not only heart disease but also various physical conditions such as exercise amount, activity amount, fatigue, sleep based on it. In this study, we demonstrated that differential privacy can be applied to obtain the sleep apnea index from PPIs with Laplace noise. The observed correlations were 0.96 to 0.99 for the corresponding PPIs with Laplace noise.

Edge computing is a new computing method, and task scheduling is challenging work. Using edge computing in intelligent buildings for managing smart home devices has gained popularity because it can reduce the delay and network congestion brought by cloud computing. Edge computing has the advantage of fast response speeds, but its computing capacity is limited. To solve this practical problem, a system framework of collaborative cloud and edge computing is constructed for intelligent buildings. First, the communication time, task completion time, and CPU energy consumption are considered comprehensively, and a mathematical model of the system is developed. Considering the compute-intensity task, the splitting ratio is determined for tasks to achieve the collaboration of cloud computing and edge computing. Then, the search mechanism of a single gene mutation in the genetic algorithm (GA) is introduced to compensate for the defects of the salp swarm algorithm (SSA), while focusing on the search ability and optimization efficiency. Finally, the proposed strategy is theoretically analyzed and experimentally evaluated. The simulation results show that the hybrid algorithm of SSA-GA has better performance than other algorithms, and the proposed collaborative cloud and edge computing task scheduling strategy demonstrated a lower delay and makespan.

In this study, the train platform rescheduling problem (TPRP) at a high-speed railway station is analyzed. The adjustments of the train track assignment and train arrival/departure times under train arrival delays are addressed in the TPRP. The problem is formulated as a mixed-integer nonlinear programming model that minimizes the weighted sum of total train delays and rescheduling costs. An improved genetic algorithm (GA) is proposed, and the individual is represented as a platform track assignment and train departure priority, which is a mixed encoding scheme with integers and permutations. The individual is decoded into a feasible schedule comprising the platform track assignment and arrival/departure times of trains using a rule-based method for conflict resolution in the platform tracks and arrival/departure routes. The proposed GA is compared with state-of-the-art evolutionary algorithms. The experimental results confirm the superiority of the GA, which uses the mixed encoding and rule-based decoding, in terms of constraint handling and solution quality.

Affective computing focuses on recognizing emotions using a combination of psychology, computer science, and biomedical engineering. With virtual reality (VR) becoming more widely accessible, affective computing has become increasingly important for supporting social interactions on online virtual platforms. However, accurately estimating a person’s emotional state in VR is challenging because it differs from real-world conditions, such as the unavailability of facial expressions. This research proposes a self-training method that uses unlabeled data and a reinforcement learning approach to select and label data more accurately. Experiments on a dataset of dialogues of VR players show that the proposed method achieved an accuracy of over 80% on dominance and arousal labels and outperformed previous techniques in the few-shot classification of emotions based on physiological signals.

Various fuzzy clustering algorithms have been proposed for vectorial data. However, most of these methods have not been applied to series data. This study presents three fuzzy clustering algorithms for series data based on shape-based distances. The first algorithm involves Shannon entropy regularization of the k-shape objective function. The second algorithm is similar to the revised Bezdek-type fuzzy c-means algorithm obtained by replacing the membership of the hard c-means objective function with its power. The third algorithm involves Tsallis entropy regularization of the objective function of the second algorithm. Theoretical observations revealed that the third algorithm is a generalization of the first and second algorithms, which was validated by numerical experiments. Furthermore, numerical experiments were performed using 11 benchmark datasets to demonstrate that the third algorithm outperforms the others in terms of accuracy.