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The purpose of this study is to investigate whether the convergence of linguistic features between physicians and patients with chronic diseases facilitates the effectiveness of physician–patient communication in online health communities (OHCs). Drawing on communication accommodation theory (CAT), the authors develop a research model that illustrates how the convergence of semantic features (language concreteness and emotional intensity) and stylistic features (language style) influence patient satisfaction and compliance. The model also incorporates the moderating effects of the physician's social status and the patients' complications.

The data, collected from a prominent online health platform in China, include 15,448 consultation records over five years. The logistic regression is leveraged to test the hypotheses.

The findings reveal that convergent semantic features, such as language concreteness and emotional intensity, along with stylistic features like language style, enhance patient satisfaction, which in turn leads to increased compliance. Additionally, the physician’s social status strengthens the effect of convergent emotional intensity but weakens the effect of convergent language concreteness. The physician’s social status has no significant impact on the link between convergent language style and satisfaction. Patients' complications weaken the effect of satisfaction on their compliance.

This study contributes to the CAT and OHC literature by enhancing the understanding of the role of linguistic convergence in the effectiveness of online physician–patient communication and provides managerial implications for physicians on how to accommodate their communicative styles toward chronic patients to improve patient satisfaction and compliance.

The purpose of this paper is to examine the influence of intelligent manufacturing on audit quality and its underlying mechanism as well as the variation in this influence across different types of organizations.

This research utilizes a difference-in-differences (DID) method to examine how enterprises that apply intelligent manufacturing choose auditors and impact their audit work. The study is based on 15,228 observations of Chinese-listed A-shares from 2011 to 2020.

(1) There is a strong correlation between intelligent manufacturing and audit quality. (2) This positive correlation is statistically significant only in state-owned enterprises (SOEs), those that have steady institutional investors and where the roles of the CEO and chairman are distinct. (3) Enterprises that have implemented intelligent manufacturing are more inclined to employ auditors who possess extensive industry expertise. The auditor's industry expertise plays a crucial role in ensuring audit quality. (4) The adoption of intelligent manufacturing also leads to higher audit fees and longer audit delay periods.

This paper validates the beneficial impact of intelligent manufacturing on improving corporate governance. In addition, it is recommended that managers prioritize the involvement of skilled auditors with specialized knowledge in the industry to ensure the high audit quality and the transparency of information in intelligent manufacturing enterprises.

This study builds upon previous research that has shown the importance of artificial intelligence in enhancing audit procedures. It contributes to the existing body of knowledge by examining how enterprise intelligent manufacturing systems (IMS) enhance audit quality. Additionally, this study provides valuable information on how to improve audit quality in the field of intelligent manufacturing by strategically selecting auditors based on resource dependency theory.

Performance optimization algorithms based on node attributes are of great importance for sharding blockchain systems. Currently, existing studies on blockchain sharding algorithms consider only random selection sharding strategies. However, the random selection strategy does not perfectly utilize the performance of a node to break the bottleneck of blockchain performance.

This paper proposes a blockchain sharding algorithm called TOPSIS Optimization Sharding System (TOSS), which is based on entropy weight method, relative Euclidean distance and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). It defines a multi-attribute matrix to assess node performance and applies TOPSIS for scoring nodes. Then, an algorithm based on the TOPSIS method is proposed to calculate the performance score of each data node. In addition, an entropy weighting method is introduced to obtain the weights of each attribute to balance the impact of dimensional differences of attributes on the attribute weights. Nodes are ranked by composite scores to guide partitioning.

The effectiveness of the proposed algorithm in this paper is verified by comparing it with various comparative algorithms. The experimental results show that the TOSS algorithm outperforms the comparison algorithms in terms of performance improvement for the blockchain system, and the throughput metrics are improved by about 20% in comparison.

This study introduces a novel approach to blockchain sharding by incorporating the entropy weight method and relative Euclidean distance TOPSIS into the sharding process. This approach allows for a more effective utilization of node performance attributes, leading to significant improvements in system throughput and overall performance, addressing the limitations of the random selection strategy commonly used in existing algorithms.

Maximum k vertex cover problem is a significant combinatorial optimization problem with various applications, such as transportation planning, social networks and sensor placement. Up to now, no practical algorithm has ever been proposed to solve this problem. Therefore, this paper aims to present an efficient local search algorithm LSKVC combining three methods for it.

First, the quick incremental evaluation method is proposed to update the related vertex scores following each addition or removal incrementally rather than recalculating them, which can speed up the algorithm. Second, the configuration checking method forbids vertices whose configuration has not changed since the last removal from being added into the candidate solution again, which can avoid the cycling problem effectively. Third, the two-stage exchange method swaps the pairs of inside and outside vertices separately rather than simultaneously, which can guarantee the tradeoff between the accuracy and complexity of the algorithm.

The proposed algorithm LSKVC is compared with the traditional GRASP algorithm and the well-known commercial solver CPLEX on DIMACS and BHOSLIB benchmarks. For the best solutions, the LSKVC algorithm is significantly superior to GRASP and CPLEX on DIMACS instances and the CPLEX solver fails, and the LSKVC algorithm slightly outperforms GRASP on the BHOSLIB instances. In addition, we undertake comparative studies of the offered methodologies and demonstrate their efficacy.

In previous research, the focus on the maximum k-vertex cover problem primarily centered around exact algorithms and approximation algorithms, with limited application of heuristic algorithms. While heuristic algorithms have been well-explored for the closely related Minimum Vertex Cover problem, they have seen limited application in the context of the maximum k-vertex cover problem. Consequently, existing algorithms designed for the Minimum Vertex Cover problem do not exhibit satisfactory performance when applied to the maximum k-vertex cover problem. In response to this challenge, we have undertaken algorithmic improvements specifically tailored to address this issue.

The purpose of studying the impact of crude oil and natural gas prices on the Vietnamese stock market is to understand the relationship between energy prices and the overall performance of the financial markets. As Vietnam is an energy-dependent country, fluctuations in crude oil and natural gas prices can significantly affect various industries, including manufacturing, inflation, transportation, energy production and economic growth. These sectors are often sensitive to changes in energy costs, which can lead to shifts in corporate profitability and investor sentiment. By analyzing how crude oil and natural gas prices influence the Vietnamese stock market, policymakers and investors can provide deeper insights into the economic risks and opportunities related to energy price volatility. This paper can also provide valuable information for decision-making in sectors such as economic forecasting, risk management and investment strategies.

Using monthly data from January 2006 to March 2024, data were collected from the Vietnamese stock market and the OPEC organization for oil prices, while data on natural gas were obtained from the EIA. The data were analyzed using vector error correction (VEC) model, impulse response function, variance decomposition test and asymmetric reactions method; the study tries to ascertain the short-term and long-term dynamic relationships between the shocks of the crude oil price and natural gas prices and their effects on the movement of the stock price. In addition, the GARCH model is applied to measure the volatility of crude oil and natural gas prices.

Crude oil price shocks have a statistically significant impact on most Vietnamese real stock market indices, except for the utility and consumer indices and some energy companies. Conversely, natural gas price shocks do not significantly affect on Vietnamese stock market indices, except for the energy index and some energy companies. Some “important” of both crude oil price and natural gas price shocks tend to depress the stock returns of energy companies. An increase in both crude oil and natural gas volatility can lead to heightened speculation in certain indices, particularly the energy and industrial indices, as well as in some energy companies. This heightened speculation often results in elevated of their stock returns.

This study provides valuable insights into the field of study examining how fluctuations in the prices of oil and gas, particularly during major crisis periods such as global financial crisis, COVID-19 pandemic and the Russo-Ukrainian War, affect financial markets.