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This article attempts to contribute to medical dispute resolution by examining the adoption of medical judicial expertise opinions in determining medical malpractice responsibility and its coordination with the judge’s legal opinions.

This article examines the legal basis and empirical data to demonstrate the decisive effect of medical judicial experts’ opinions in allocating medical malpractice responsibility and corresponding dispute resolution effectiveness.

High reliance on medical judicial expertise in medical dispute litigation not only unifies the judicial standards but also limits judges’ discretion, which brings the risk of contradiction between factual and legal findings, which currently ends in judges’ compromise.

The current medical malpractice provisions neglect the divergence of medical judicial expertise and judges’ opinions in determining medical malpractice responsibility, which produces difficulties in harmonizing awarded compensations and parties’ expectations, leading to problematic medical dispute litigation in Mainland China.

The purpose of this paper is to optimize the degradation test for products subject to multiple types of inherent stresses and external random shocks. The mechanism that shows how the variables to be optimized influence the considered multiple objectives is also aimed to be explored by using the grey incidence analysis (GIA) model.

The Gamma process is employed to model the influences of different types of stresses and external random shocks. The GIA model is introduced to transfer multiple considered objectives as a comprehensive degree of grey incidence. The particle swarm optimization is integrated to search the globally optimal value of the characteristic variables to be optimized.

The acceleration of tested stresses and external random shocks both make the engineering systems become more vulnerable to the inherent degradation. And, the Kriging model can provide guidance of searching the optimal values of test characteristic variables and mitigate the computation burden. The grey incidence model can make the optimization focused and improve the optimality of objective values.

The proposed method can effectively overcome the drawbacks brought by the limitation of test data and can specify the dependence strength between the inherent degradation and external random shock. The computation cost and accuracy of optimization can be simultaneously ensured by the proposed model.

Effects of corrosion are very dire and mitigation of corrosion holds prime importance. Protective coatings play major role in preventing corrosion of metals and coating application is the most convenient, economical and quick solution. The purpose of the study is development of protective coatings to effectively mitigate corrosion of metal components.

A high-performance anticorrosion coating was prepared using multiple monomers and paste of functional and reinforcing fillers with extenders to protect metal components from corrosion in aggressive environmental conditions. The structures of copolymers synthesized with multiple monomers were studied by the NMR and FT-IR spectroscopic techniques. The percentage conversion of different proportions of various monomers was estimated using gas chromatography technique. The functional paste to impart superior anticorrosion properties was prepared using various functional and reinforcing fillers. The final coatings were prepared by mixing these resins with functional paste in various proportions.

The prepared anticorrosion coating was tested for high-performance mechanical and chemical properties and it was witnessed that the said coating offered desired performance properties needed for protecting metal components from corrosion.

As such it is overcoming drawbacks of two pack systems and thus has almost no limitations or implications for application on metal substrate.

Being formulated as a single pack, it is free from drawbacks otherwise involved in two pack system of conventional paints. The coating system developed is very easy to apply using conventional tools, namely, brush, spray and roller techniques. The formulation is made in such a way that it has fast-drying properties. Makes painting or coating operations cost effective and confirm the performance.

The findings of the research have anticorrosion nature that can enhance the life span of the substrates. It is specially designed for metal substrate and can protect metal substrate from corrosion in most aggressive conditions. Thus, it helps to reduce losses due corrosion and increase safety of metal structures and human being as well. As it is based on conventional material but with new formulation and technology, it has commercial possibilities to explore.

Unlike conventional protective coating systems, the said coating offered disruptive features like single pack systems and fast drying at ambient temperature along with high-performance properties. The coating formulation was observed to have a great importance in industry for effective corrosion mitigation and to reduce losses due to corrosion.

Learning from past construction accident reports is critical to reducing their occurrence. Digital technology provides feasibility for extracting risk factors from unstructured reports, but there are few related studies, and there is a limitation that textual contextual information cannot be considered during extraction, which tends to miss some important factors. Meanwhile, further analysis, assessment and control for the extracted factors are lacking. This paper aims to explore an integrated model that combines the advantages of multiple digital technologies to effectively solve the above problems.

A total of 1000 construction accident reports from Chinese government websites were used as the dataset of this paper. After text pre-processing, the risk factors related to accident causes were extracted using KeyBERT, and the accident texts were encoded into structured data. Tree-augmented naive (TAN) Bayes was used to learn the data and construct a visualized risk analysis network for construction accidents.

The use of KeyBERT successfully considered the textual contextual information, prompting the extracted risk factors to be more complete. The integrated TAN successfully further explored construction risk factors from multiple perspectives, including the identification of key risk factors, the coupling analysis of risk factors and the troubleshooting method of accident risk source. The area under curve (AUC) value of the model reaches up to 0.938 after 10-fold cross-validation, indicating good performance.

This paper presents a new machine-assisted integrated model for accident report mining and risk factor analysis, and the research findings can provide theoretical and practical support for accident safety management.

In the context of constructing an integrated national strategic system, collaborative innovation among enterprises is the current social focus. Therefore, in order to find the interest relationship between multiple game subjects, to explore the influencing factors of collaborative innovation of civil-military integration enterprises. This paper constructs a collaborative innovation mechanism for military–civilian integration involving four game subjects (military enterprises, private enterprises, local governments, and science and technology intermediaries). It aims to solve and reveal the evolutionary game relationship among the four parties.

To explore the mechanism of military–civilian collaborative innovation involving four players, this study employs game theory and constructs an evolutionary game model for collaborative innovation with the participation of military enterprises, civilian enterprises, local governments, and technology intermediaries. The model reveals the evolutionary game patterns among these four entities, analyzes the impact of various parameters on the evolutionary process of the game system, and numerical simulation is used to show these changes more specifically.

The research findings demonstrate that active government subsidies promote cooperation throughout the system. Moreover, increasing the input-output ratio of research and development (R&D), the rate of technological spillovers, and the R&D investment of civilian enterprises all facilitate the tendency toward cooperation within the system. However, when the government chooses to actively provide subsidies, increasing R&D investment in military enterprises may hinder the tendency toward cooperation. Furthermore, central transfer payments, government punishment from the central government, and an increase in the information conversion rate of technology intermediaries may suppress the rate of cooperation within the system.

Most of the previous studies on the collaborative innovation of military–civilian integration have been tripartite game models between military enterprises, private enterprises, and local governments. In contrast, this study adds science and technology intermediaries on this basis, reveals the evolution mechanism of collaborative innovation of civil-military integration enterprises from the perspective of four-party participation, and analyzes the factors influencing the cooperation of the whole system. The conclusion of this study not only enriches the collaborative innovation evolution mechanism of military–civilian integration enterprises from the perspective of multiple agents but also provides practical guidance for the innovation-driven development of military–civilian integration enterprises.

With the development of digitalization and interconnection, there is a growing need for enterprise customers to ensure the compatibility of the third-party components they are using in the manufacturing process, thus raising the integration requirements for the Industrial Internet platform and its third-party developers. Therefore, our study investigates the optimal integration decision of the Industrial Internet platform while considering its access price, the integration cost, and the net utility derived by enterprise customers from the third-party components.

We model a two-sided Industrial Internet platform that connects customers on the demand side to the developers on the supply side. We then explore the integration decision of the Industrial Internet platform and its important factors by solving the optimal profit function.

First, despite the high integration cost of third-party developers, the platform still chooses to integrate when enterprise customers derive high utility from the third-party components. Second, due to the compatibility effect, charging the enterprise customers a higher price may reduce the platform profits when these customers derive low utility from the third-party components. Third, the platform profits will increase along with the integration cost of third-party developers when it is low in the case where enterprise customers derive low utility from third-party components.

Our findings offer insightful takeaways for the Industrial Internet platform when making integration decisions.

This paper aims to provide a reference for the development of digital transformation from the perspective of manufacturing process links.

This paper applies canonical correlation analysis based on digital technology patents in the key links of manufacturing industries (product design, procurement, product manufacturing, warehousing and transportation, and wholesale and retail) and the related indicators of economic benefits of regions in China.

(1) The degree of digitalization of manufacturing process links is significantly correlated with economic benefits. (2) The improvement of the degree of digitalization in the “product design” link, the “warehousing and transportation” link, the “product manufacturing” link and the “wholesale and retail” link has significant impacts on the economic benefits of manufacturing industry. (3) The digital degree of the “procurement” link has no obvious influence on the economic benefits of manufacturing industry.

The research results can provide reference for the formulation and implementation of micro policies. The strategy of improving the level of digital transformation of key links of manufacturing industry is put forward to better promote both the digital transformation of manufacturing industry and economic development.

This paper innovatively studies the relationship between digitalization of manufacturing process links and economic benefits. The findings can provide theoretical and empirical support for the digital transformation of China's manufacturing industry and high-quality development of economy.

Using recent US regional data associated with food system operations, this study aims at building optimization and econometric models to incorporate varying influential factors on food hub location decisions and generate effective facility location solutions.

Mathematical optimization and econometric models have been commonly used to identify hub location decisions, and each is associated with specific strengths to handle uncertainty. This paper develops an optimization model and a hurdle model of the US fresh produce sector to compare the hub location solutions between these two modeling approaches.

Econometric modeling and mathematical optimization are complementary approaches. While there is a divergence between the results of the optimization model and the econometric model, the optimization solution is largely confirmed by the econometric solution. A combination of the results of the two models might lead to improved decision-making.

This study suggests a future direction in which model development can move forward, for example, to explore and expose how to make the existing modeling techniques easier to use and more accessible to decision-makers.

The models and results provide information that is currently limited and is useful to help inform sustainable decisions of various stakeholders interested in the development of regional food systems, regional infrastructure investment and operational strategies for food hubs.

This study sheds light on how the application of complementary modeling approaches improves the effectiveness of facility location solutions. This study offers new perspectives on elaborating key features to encompass facility location issues by applying interdisciplinary approaches.

This paper aims to try to develop new, environmentally friendly and efficient lubricating additives; study the compatibility of carbon-based additives with different base oils [Polyalphaolefin (PAO)-3, PAO-20 and NPE-2]; and explore the lubrication mechanism.

Oleylamine modified carbon nanoparticles (CNPs-OA) were prepared and the dispersion stability of CNPs-OA in PAO-3, PAO-20 and NPE-2 base oils was investigated by transmission electron microscopy, Fourier transform infrared, thermogravimetric analysis, energy dispersive spectroscopy and X-ray photoelectron spectroscopy. Universal Mechanical Tester (UMT) platform was used to carry out experiments on the effects of different additive concentrations on the lubricating properties of base oil.

The mean friction coefficient of PAO-3, PAO-20 and NPE-2 reduced by 32.8%, 10.1% and 11.4% when the adding concentration of CNPs-OA was 1.5, 2.0 and 0.5 Wt.%, respectively. Generally, The CNPs-OA exhibited the best friction-reducing and anti-wear performance in PAO-3.

The agglomeration phenomenon of carbon nanoparticles as lubricating additive was improved by surface modification, and the lubricating effect of carbon nanoparticles in three synthetic aviation lubricating base oils was compared.

This study aims to enhance the classification and processing of online appeals by employing a deep-learning-based method. This method is designed to meet the requirements for precise information categorization and decision support across various management departments.

This study leverages the ALBERT–TextCNN algorithm to determine the appropriate department for managing online appeals. ALBERT is selected for its advanced dynamic word representation capabilities, rooted in a multi-layer bidirectional transformer architecture and enriched text vector representation. TextCNN is integrated to facilitate the development of multi-label classification models.

Comparative experiments demonstrate the effectiveness of the proposed approach and its significant superiority over traditional classification methods in terms of accuracy.

The original contribution of this study lies in its utilization of the ALBERT–TextCNN algorithm for the classification of online appeals, resulting in a substantial improvement in accuracy. This research offers valuable insights for management departments, enabling enhanced understanding of public appeals and fostering more scientifically grounded and effective decision-making processes.