Bingyin Lei

Background/Objective: This study investigates the implementation and evolution of intelligent medical question-answering (QA) systems in healthcare to enhance service efficiency and quality. Methods: Through an integrated literature review and bibliometric analysis using CiteSpace 6.3.R1(64-bit) Basic software, we systematically evaluated core concepts, frameworks, and applications within medical QA systems, analyzing literature from 2018 to 2025 to identify research trends. Results: Significant applications were revealed across clinical decision support, medical knowledge retrieval, traditional Chinese medicine (TCM) formulation development, medical imaging report analysis, medical record quality control, mental health monitoring, and emotion recognition, demonstrating optimized resource allocation and service efficiency. Persistent challenges include system accuracy limitations, multimodal interaction capabilities, user trust barriers, and privacy protection concerns. Conclusion: Future research should prioritize multimodal diagnostic imaging, TCM-specific AI agents, and virtual-reality-assisted surgical exploration. Contributions: This work consolidates current achievements while establishing theoretical–practical foundations for innovation and large-scale implementation, advancing intelligent healthcare transformation.

In response to the growing demand for healthcare and the increasing importance people place on medical services, efficiently meeting these needs within the constraints of limited healthcare resources is of great social and economic benefit. Therefore, research into applying Large Language Models in the healthcare sector holds significant importance. This paper provides a review of the research progress on the application of Large Language Models in the healthcare field. First, the basic framework of Large Language Models is summarized, and the training process of healthcare-specific language models is systematically reviewed. Next, six specific application areas of Large Language Models in healthcare are reviewed: disease diagnosis and decision support, dissemination of medical knowledge, medical assistance, medical image analysis, biomedicine, and medical education. Then, several representative healthcare-specific Large Language Models are discussed, along with their performance analysis. Following this, the challenges faced by Large Language Models in healthcare are summarized, and relevant suggestions are provided. The future development trends of healthcare-related Large Language Models are also explored. Finally, a bibliometric analysis is performed. Through the literature review, we found: (1) After pretraining, Large Language Models are widely adaptable to downstream tasks, significantly enhancing processing performance and efficiency; (2) Healthcare-specific large models possess multiple capabilities and can handle multi-modal data; (3) Bibliometric analysis shows that researchers are paying increasing attention to the application of Large Language Models in healthcare; (4) Further research is needed in optimizing, improving reliability, and expanding practical applications of large healthcare models.

Aero-engines are highly complex machines, and their reliability and lifespan directly impact flight safety. As a result, aero-engine reliability has become a significant research field. This paper aims to comprehensively summarize the progress in aero-engine reliability research. First, we analyzed the existing structures of aircraft engines, as well as the current research on reliability and commonly used estimation methods. Second, we reviewed the techniques employed for monitoring operational status and diagnosing faults in aircraft engines. Third, we summarized the optimization methods frequently used in fault diagnosis and lifespan prediction. Fourth, we conducted a bibliometric analysis. Finally, we identified potential challenges for future research. Based on our literature review, we found that: (1) mathematical modeling-based and data-driven approaches are currently the most commonly used methods for performance monitoring; (2) neural networks and deep learning are the most popular methods for fault diagnosis and lifespan prediction; (3) the bibliometric analysis reveals that China’s interest in aero-engine reliability research techniques is higher than that of other countries; and (4) further development is needed in the areas of overall system reliability, real-world usage scenario studies, and advanced simulation and modeling.

China’s economic development has achieved great success in recent years, but the problems of energy scarcity and environmental pollution have become increasingly serious. To enhance the reliability and efficiency between energy, the environment and the economy, sustainable development is an inevitable choice. In the context of measuring sustainability efficiency, a network data envelopment analysis model is proposed to formulate the two-stage process of energy use and operations management. A double frontier is derived to optimize the available energy for sustainable development. Due to nonlinearity, previous linear methods are not directly applicable to identify the double frontier and calculate stage efficiencies for inefficient decision-making units. To address this problem, this study develops the primal-dual relationship between multiplicative and envelopment network models based on the Lagrange duality principle of parametric linear programming. The newly developed approach is used to evaluate the sustainability efficiency of 30 administrative regions in China. The results show that insufficient sustainability efficiency is a systemic problem. Different regions should take different measures to conserve energy and reduce pollutant emissions for sustainable development. To increase sustainability efficiency, regions should support energy-saving and emission-reducing technologies in production processes and strengthen their capacity for technological innovation. Compared with energy use efficiency, operations management efficiency in China has a wider range of changes. During the operations management stage, there is not much difference between the capacity and quantity of each region. Based on benchmark regions at the efficiency frontier, there is an opportunity to improve operations management in the near future. Blockchain technology can effectively improve energy allocation efficiency.

In the face of an increasingly high-demand environment for outpatients, achieving a balance between allocation of limited medical resources and patient satisfaction has considerable social and economic benefits. Therefore, appointment scheduling (AS) system operation is used in clinics and hospitals, and its operation optimization research is of great significance. This study reviews the research progress on appointment scheduling system optimization. Firstly, we classify and conclude the existing appointment scheduling system structures and decision-making frameworks. Subsequently, we summarize the system reliability optimization framework from three aspects: appointment scheduling system optimization objectives, decision variables and constraints. Following that, we methodically review the most applied system optimization algorithms in different appointment scheduling systems. Lastly, a literature bibliometric analysis is provided. During our review of the literature, we observe that (1) optimization methods in ASs predominantly involve the application of genetic algorithms and simulation optimization algorithms; (2) neural networks and deep learning methods are core technologies in health management optimization; (3) a bibliometric analysis reveals a heightened interest in the optimization technology of ASs within China compared to other nations; and (4) further advancements are essential in the comprehensive optimization of the system, exploration of practical usage scenarios, and the application of advanced simulation and modeling techniques in this research.

Clean and renewable energy is the only way to achieve sustainable energy development, with considerable social and economic benefits. As a key technology for clean and renewable energy, it is very important to research the reliability optimization of microgrids. This paper reviews the research progress in microgrid reliability optimization. This paper first classifies and summarizes the existing research on microgrid control strategies and reliability assessment. Then, the system reliability optimization framework is summarized in terms of both microgrid systems and optimization objectives. Next, we summarize the most commonly used optimization algorithms for microgrid reliability for different microgrid systems. Finally, we provide a bibliometric analysis of the literature on the reliability research of microgrids. In addition, we propose some research challenges in the future for the reliability of microgrids.

Most factory production processes are completed by machines and workers on production lines. The operation schedule is arranged to reduce the cost of the enterprises to obtain the maximum economic profit for sustainable running. Previous studies usually investigated the working time while only considering the workers’ conditions. This study proposed a method to optimize the operation schedule by jointly considering the workers’ fatigue states and the operation states of machines. This method was proposed based on a system structure called the multistate consecutively connected system (MCCS), which has been widely applied in many areas, such as electronic communications. This structure is also an analogy of the production line. The corresponding model is constructed based on the universal generating function (UGF) since it is a powerful tool in modeling a consecutively connected system. The proposed model can be used to evaluate the different productivities of different types of workers in different states and to realize the screening of the whole scheme through simulation. According to the proposed method, we obtained the optimal operation schedule, including the working time, rest time and allocation strategy for a production line system. Some examples are provided to illustrate the proposed method.

Mosquito control is very important, in particular, for tropical countries. The purpose of mosquito control is to decrease the number of mosquitos such that the mosquitos transmitted diseases can be reduced. However, mosquito control can be costly, thus there is a trade-off between the cost for mosquito control and the cost for mosquitos transmitted diseases. A model is proposed based on renewal theory in this paper to describe the process of mosquitos’ growth, with consideration of the mosquitos transmitted diseases growth process and the corresponding diseases treatment cost. Through this model, the total mosquitos control cost of different strategies can be estimated. The optimal mosquito control strategy that minimizes the expected total cost is studied. A numerical example and corresponding sensitivity analyses are proposed to illustrate the applications.

With the development of science and technology, the structure of engineering system has become increasingly large and complex. In order to ensure the safety and stability of the system in operation, the reliability evaluation of complex system has become an important research field. However, previous studies on the reliability of multi-state systems did not take into account the multi-level performance sharing in the system, and it has been unable to accurately assess the reliability of such complex systems. Therefore, based on the actual engineering system, this paper proposes a multi-state system with multi-level performance sharing mechanism. On this basis, we established a system reliability evaluation model using universal generating function technique. Through numerical examples, the application of the model and analyze the influence of different parameters on system reliability are demonstrated. In addition, we also use genetic algorithm to optimize the allocation of components in the system, so as to improve the reliability of the system. Different from the previous studies on system reliability with common bus performance sharing, the system proposed in this study is more general.