Recent advances in multimodal large language models (MLLMs) have significantly improved medical AI, enabling it to unify the understanding of visual and textual information. However, as medical knowledge continues to evolve, it is critical to allow these models to efficiently update outdated or incorrect information without retraining from scratch. Although textual knowledge editing has been widely studied, there is still a lack of systematic benchmarks for multimodal medical knowledge editing involving image and text modalities. To fill this gap, we present MedMKEB, the first comprehensive benchmark designed to evaluate the reliability, generality, locality, portability, and robustness of knowledge editing in medical multimodal large language models. MedMKEB is built on a high-quality medical visual question-answering dataset and enriched with carefully constructed editing tasks, including counterfactual correction, semantic generalization, knowledge transfer, and adversarial robustness. We incorporate human expert validation to ensure the accuracy and reliability of the benchmark. Extensive experiments on state-of-the-art general and medical MLLMs demonstrate the limitations of existing knowledge editing methods in the medical domain, highlighting the need to develop specialized editing strategies.

The in-depth exploration and development of knowledge organization methodologies tailored to the unique characteristics of traditional Chinese medicine (TCM) has become a pivotal issue in advancing the modernization of TCM knowledge systems and fostering scientific innovation. Through systematic review and analysis of existing TCM knowledge organization research, this study identifies current limitations. It proposes exploring integrated approaches for integrating ancient and contemporary TCM knowledge within existing classical text repositories, constructing theoretical knowledge bases, conducting ontological mapping studies between historical and modern TCM knowledge, and developing efficient human-machine collaborative annotation methods to build knowledge graphs. These initiatives aim to provide actionable strategies for optimizing TCM knowledge organization frameworks.

Objective To develop a unified and standardised metadata framework for ancient books of traditional Chinese medicine, aiming to achieve standardised and regulated management of such resources. This initiative seeks to enhance the efficiency of managing and utilising ancient books of traditional Chinese medicine while providing a scientific guideline for their description. Methods Using literature review, comparative analysis, and expert consultation methods, this study analyzes and compares the current state of ancient books of traditional Chinese medicine metadata and relevant domestic and international standards. Based on the Dublin Core Element Set (DC) as the basic framework and tailored to the characteristics of ancient books of traditional Chinese medicine, it proposes a research idea and framework for ancient books of traditional Chinese medicine metadata standards. Results This study proposes the research ideas and principles of metadata standards for ancient Chinese medicine books centered on the knowledge system of traditional Chinese medicine, clearly stipulates the objects and information sources of ancient Chinese medicine book resources, constructs a metadata structure model and hierarchical relationship including core metadata, extended metadata and element modifiers, and defines the core metadata elements. Conclusion The metadata standard for ancient books of traditional Chinese medicine will facilitate the integration and sharing of ancient books of traditional Chinese medicine resources, thereby advancing the inheritance and development of traditional Chinese medicine knowledge.

Objective To systematically evaluate and analyze the prescriptions for treating Xiongbi (Chest Bi-Syndrome) in ancient Traditional Chinese Medicine (TCM) books by integrating evidence-based valuation of ancient TCM literature and data mining methods. Methods First, the screened vidence of TCM prescriptions for Xiongbi from ancient books was evaluated and graded using the Evaluation and Grading Scale for Evidence of Disease Prevention and Treatment in Ancient TCM Books. For high-level evidence, the “Ancient and Modern Medical Records Cloud Platform” was applied to analyze indicators including drug use frequency, efficacy categories, nature, taste, and meridian tropism of drugs. Additionally, association rule analysis and cluster analysis were conducted. Results A total of 155 pieces of evidence from ancient TCM books for treating Xiongbi were finally included. Through evaluation, 31 prescriptions (e.g., Renshen Decoction, Fuling Xingren Gancao Decoction, Gualou Xiebai Banxia Decoction) were identified as high-level evidence. Data mining results showed that, among the high-level evidence for Xiongbi treatment in ancient books, the main drug efficacies were dispelling cold to relieve pain, tonifying fire to support yang, and warming and unblocking meridians; warm-natured drugs were most frequently used; pungent was the dominant taste of drugs; and the spleen meridian was the primary meridian tropism. Association rule analysis revealed that Cinnamomi Cortex (cinnamon) was a common drug for treating Xiongbi with heart pain, and the “Ginseng-Cinnamomi Cortex” pair was the most frequently used drug combination for Xiongbi. Cluster analysis yielded 4 core prescription groups, each reflecting distinct treatment strategies. Conclusion This study conducts quality evaluation and in-depth mining analysis of prescriptions for Xiongbi in ancient TCM books using evidence-based evaluation of ancient TCM literature and data mining. The results not only provide more reliable evidence from ancient books for the clinical treatment of Xiongbi in TCM but also offer sufficient ancient book-based evidence support for the formulation of TCM clinical decisions.

Existing large language model (LLM) agents for automating data science show promise, but they remain constrained by narrow task scopes, limited generalization across tasks and models, and over-reliance on state-of-the-art (SOTA) LLMs.We introduce DatawiseAgent 1 , a notebook-centric LLM agent framework for adaptive and robust data science automation.Inspired by how human data scientists work in computational notebooks, DatawiseAgent introduces a unified interaction representation and a multi-stage architecture based on finitestate transducers (FSTs).This design enables flexible long-horizon planning, progressive solution development, and robust recovery from execution failures.Extensive experiments across diverse data science scenarios and models show that DatawiseAgent consistently achieves SOTA performance by surpassing strong baselines such as AutoGen and TaskWeaver, demonstrating superior effectiveness and adaptability.Further evaluations reveal graceful performance degradation under weaker or smaller models, underscoring the robustness and scalability.

Flying Ad Hoc Networks (FANETs) formed by Unmanned Aerial Vehicles (UAVs) with multiple heterogeneous radios are increasingly being adopted for various applications. However, the presence of asymmetric links caused by hardware differences poses challenges to common routing protocols designed for homogeneous FANETs. Ignoring asymmetric links may lead to inefficient resource utilization and compromise network connectivity, resulting in a significant decline in the performance of existing protocols. How to fully utilize the abundant link resources to enhance the network performance and offer more routing options for data with diversified service requirements, is a challenging task. In this paper, we propose an asymmetric link-based routing protocol in heterogeneous multi-radio FANETs, named ARHet. To efficiently exploit the asymmetric links, we first propose an asymmetric link discovery and information feedback mechanism. Then, to satisfy the specific service requirement of each type of data, we propose a traffic-differentiated routing strategy that can establish the appropriate paths for them. Experimental results show that ARHet can improve the delivery ratio by 20 %, increase the throughput by 31 %, and reduce the overhead by 69 % compared to the other benchmarks. In addition, ARHet can achieve a better differentiated service performance.

To analyze the discrimination and treatment of "Cold-Dampness Depression Lung Syndrome" of COVID-19 in TCM classics. Methods: Using the mathematical statistics and data mining methods to sort and analyze information of prescriptions treating "Cold-Dampness Depression Lung Syndrome" of COVID-19 in TCM classics. Results: 50 ancient prescriptions with therapeutic effects were selected, contain contain 125 traditional Chinese medicines, and the top 5 are Gancao (Glycyrrhizae Radix Rhizoma), Banxia(Pinelliae Rhizoma), Renshen (Ginseng Radix Et Rhizoma), Baizhu(Atractylodis Macrocephalae Rhizoma) and Chenpi(Citri Reticulatae Pericarpium) in order of frequency of use. The meridians of the medicines are mainly lung meridian, spleen meridian and stomach meridian, and the properties of the medicines are mostly warm, followed by mlid and lukewarm. The main medicinal pairs are Jiegeng (platycodonis Radix)-Gancao (Glycyrrhizae Radix Rhizoma), Baishao (Paeoniae Radix Alba)-Gancao (Glycyrrhizae Radix Rhizoma),Mahuang (Ephedrae Herba)-Gancao (Glycyrrhizae Radix Rhizoma),Chuanxiong (Chuanxiong Rhizoma)- Gancao (Glycyrrhizae Radix Rhizoma) and Cangnzhu (Atractylodis Rhizoma)-Gancao (Glycyrrhizae Radix Rhizoma).Conclusion: By analysing the ancient prescriptions with potential treatment for "Cold-Dampness Depression Lung Syndrome" of COVID-19, we found high-frequency medicines and medicinal pairs, and had a more comprehensive understanding of the treatment of COVID-19, can provide a reference for the research of COVID-19 specific medicines.

Objective To address the issues in named entity recognition (NER) in the field of traditional Chinese medicine (TCM), this study proposes a method for identifying entities in TCM herbal literature; Methods We identify and describe the types of knowledge entities and entity relationships involved in herbal literature. We apply the BIO sequence labeling method to generate a training corpus dataset and use our self-developed CNLP text annotation system for text annotation. The Bert model is employed for recognizing named entities; Results The Bert model achieved entity recognition results for various entities in TCM herbal literature with precision (P) of 71.49%, recall (R) of 72.33%, and F1 score of 71.91%; Conclusion The Bert model demonstrates a certain level of applicability in recognizing various entities in TCM herbal literature. This model is helpful in extracting valuable structured information from a large volume of text data.

Objective: To analyze the differentiation and treatment principles of Swollen-head Infection in TCM classics. Methods: Ancient medical case data related to warm diseases were selected as the data source, and the standard principles of data extraction were formulated. Data mining methods such as mathematical statistics, factor analysis, cluster analysis, and association rules were used to systematically sort out and analyze the etiology, location, syndrome, treatment, formulations and other information of Swollen-head Infection. Results: Swollen-head Infection is primarily attributed to pathogenic wind and heat toxins. The significance of "Li Qi" (Epidemic pathogen) should be emphasized.The disease primarily affects the head, and the pathogenic factors tend to linger in the lung-defense. The clinical manifestations are closely related to the affected area of the head, often accompanied by other systemic symptoms. The treatment approach commonly involves combining internal and external therapies. Combinations of herbs such as Xuanshen (Scrophulariae Radix)-Lianqiao (Forsythiae Fructus), Xuanshen (Scrophulariae Radix)-Huangqin(Scutellariae Radix), Jiegeng(Platycodonis Radix)-Lianqiao (Forsythiae Fructus), Jiegeng(Platycodonis Radix)-Huangqin(Scutellariae Radix), Chaihu(Bupleuri Radix)- Jiegeng(Platycodonis Radix), and Chaihu(Bupleuri Radix)- Huanglian(Coptidis Rhizoma) are notable for their abilities to clear heat, detoxify, disperse wind, and eliminate pathogenic factors. Additionally, Puji Xiaodu Yin and its modifications are considered essential medications for treating Swollen-head Infection. Conclusion: Through the data mining of the rules of syndrome and prescription of Swollen-head Infection in ancient books of warm diseases, to provide reference for the differentiation and treatment of head and face swelling and poison infectious diseases.

Traditional Chinese medicine (TCM) has accumulated thousands years of knowledge in herbal therapy, but the use of herbal formulas is still characterized by reliance on personal experience. Due to the complex mechanism of herbal actions, it is challenging to discover effective herbal formulas for diseases by integrating the traditional experiences and modern pharmacological mechanisms of multi-target interactions. In this study, we propose a herbal formula prediction approach (TCMFP) combined therapy experience of TCM, artificial intelligence and network science algorithms to screen optimal herbal formula for diseases efficiently, which integrates a herb score (Hscore) based on the importance of network targets, a pair score (Pscore) based on empirical learning and herbal formula predictive score (FmapScore) based on intelligent optimization and genetic algorithm. The validity of Hscore, Pscore and FmapScore was verified by functional similarity and network topological evaluation. Moreover, TCMFP was used successfully to generate herbal formulae for three diseases, i.e. the Alzheimer's disease, asthma and atherosclerosis. Functional enrichment and network analysis indicates the efficacy of targets for the predicted optimal herbal formula. The proposed TCMFP may provides a new strategy for the optimization of herbal formula, TCM herbs therapy and drug development.

Objective; To analyze the discrimination and treatment of acute larynx ulcer in TCM classics. Methods: Using the mathematical statistics and data mining methods to sort and analyze information of the scarlet fever in TCM classics, such as etiology, disease-bit, treatment and formulas. Results: The causes of the scarlet fever involved warms up when poison, weakened body resistance and pidemic pathogen with li gas, and its site of cerebral apoplexy located in throat, skin and stomach. TCM syndrome of the scarlet fever included pathogenic factors invade lung and surface, toxic obstructing qi aspect, pathogenic factors invade the ying blood and pathogenic factors invade liver and kidney. For the treatment of the scarlet fever, the main method is clearing heat and detoxification, and external treatment is emphasized, such as external application, laryngeal blowing and removing corruption. Niuhuang (Bovis Calculus)-Zhenzhu (Margarita), Bingpian (Borneolum Syntheticum)-Daqingye (Isatidis Folium), Niuxi (Achyranthis Bidentatae Radix)-Tuniuxigen (Achyranthes aspera), Bingpian (Borneolum Syntheticum)-Niuhuang (Bovis Calculus), Bingpian (Borneolum Syntheticum)-Xionghuang (Realgar), Bingpian (Borneolum Syntheticum)-Shexiang (Moschus) combination has the characteristics of scarlet fever treatment. Conclusion: By summarizing and excavating the rules of syndrome differentiation and treatment of scarlet fever, it can provide a reference for the treatment of modern acute respiratory infectious diseases.

Business process recommendation can be used to simplify the working procedures of enterprises, avoid unnecessary expenses, and promote the development of enterprises. In the process of process recommendation, there are a lot of activities that are similar in structure and difficult to choose. Here, a process recommendation method based on cost constraints is proposed to solve the problem of difficult to distinguish similar processes. First, the business process is transformed into a labelled Petri net, and the execution probability of each transition is calculated according to the business process log. Then, the matrix used to represent Petri nets is constructed according to the adjacent relationship between transitions, and the matrix is made into the same dimension, and the similarity between matrices is calculated by biggest–smallest approach degree, and the set of Petri nets with similar structure is established. Finally, a cost constraint-based process recommendation method is proposed to find lower service cost items in similar process sets. In the experimental part, the feasibility of the method is compared and verified.

Objective The prescription rules of traditional Chinese medicine (TCM) for knee osteoarthritis (KOA) in ancient medical classics were explored based on Traditional Chinese Medicine Inheritance Computer System (TCMICS) to provide reference and evidence for modern clinical treatment. Methods The contents of TCM treatment for KOA in ancient medical classics were comprehensively collected and then logged in TCMICS after screening, where frequency of formulae, medicinals and medicinal combination, as well as properties, flavors and channel tropism of frequently-used medicinals, etc. were counted. Results A total of 510 items were selected from ancient medical classics, including 221 formulae with specific names, and 25 medicinals with a frequency of more than 40. The top three formulae which were most frequently used included Da Fangfeng Tang (Major Ledebouriella Decoction), Wutou Tang (Aconite Decoction), and Duhuo Jisheng Tang (Pubescent Angelica and Mistletoe Decoction). The top five medicinals which were most frequently used included Danggui (Angelicae Sinensis Radix), Niuxi (Achyranthis Bidentatae Radix), Fangfeng (Saposhnikoviae Radix), Chuanxiong (Chuanxiong Rhizoma), and Gancao (Glycyrrhizae Radix et Rhizoma). It was also found through analysis that the core formula was modification of Da Fangfeng Tang. Conclusion The main formulae used for KOA are Da Fangfeng Tang, Wutou Tang, and Duhuo Jisheng Tang, while the selection of medicinals highlights the actions of nourishing the liver and kidney, boosting qi and blood, or activating blood and removing blood stasis, and warming the channels and dissipating cold, which can provide reference for clinical treatment.

Objective: Based on the integrated pharmacology platform of Chinese medicine V2.0 (TCMIP V2.0), this paper explores the molecular mechanism and quality markers of Tanyu Tongzhi Recipe against myocardial ischemia reperfusion injury (MIRI). Methods: Use TCMIP V2.0 to collect the drug components, targets, and MIRI disease targets of the recipe for phlegm and blood stasis, construct a drug-disease-target interaction network, screen drugs and disease common targets, analyze the biological process of the common targets, and finally establish a “component-target-pathway-pharmacological action” multi-dimensional network analysis and analysis of the core components in the network. Results: A total of 157 medicinal chemical components, such as Pinellia, Red Peony, Chuanxiong, Licorice, etc. were collected from the prescription of phlegm and blood stasis, with 272 corresponding targets. A total of 290 MIRI disease targets were collected. Through "Traditional Chinese Medicine Association Network Mining," the first 100 targets in the core targets were analyzed and 31 targets shared by drugs and diseases. Analysis of these shared targets revealed that the shared targets are mainly involved in the positive regulation of tube formation, angiogenesis, positive regulation of autophagy, inflammation, and negative regulation of apoptosis. KEGG enrichment analysis shows that these common targets are mainly enriched in the FoxO signaling pathway, HIF-1 signaling pathway, TNF signaling pathway, PI3K-Akt signaling pathway, VEGF signaling pathways and other signal pathways. Further analysis of the multi-dimensional network revealed that 46 components of Tanyu Tongzhi Prescription affect the above five signal pathways to exert anti-MIRI effects by interacting with 19 shared targets. Among them, quercetin, kaempferol, naringenin, and baicalein are intervened. AKT affects five signal pathways involved in the occurrence and development of MIRI. Conclusion: Quercetin, kaempferol, naringenin, and baicalein in Tanyu Tongzhi Recipe can resist MIRI by regulating inflammation, autophagy and apoptosis, oxidative stress, angiogenesis, and other processes.

Objective: To analyze medication rule of the treatment for spring warm disease in case records of Qing Dynasty (1636-1912) based on data mining. Methods: Ancient case records of warm disease were selected as the data source and the standard principle of data extraction was established. TCM Miner was used for collecting data and analyzing association rules. Results: A total of 225 case records of spring warm disease are recorded in 20 ancient medical classics, including 37 formulas and 219 Chinese medicines. The main formulas include Baihu Tang (White Tiger Decoction), Fumai Tang (Pulse-Restorative Decoction) and Liuyi San (Six-to-One Powder), which fall into the category of heat-clearing formula, tonifying formula and phlegm-expelling formula respectively. The main Chinese medicines include Lianqiao (Fructus Forsythiae), Fuling (Poria) and Shichangpu (Rhizoma Acori Tatarinowii), which are capable of relieving cough and panting and resolving phlegm, promoting urination and eliminating dampness, as well as tonifying. The properties are mainly warm, cold and neutral; the flavors are mainly sweet, bitter and acrid; and the medicines mainly enter meridians of the lung, stomach and liver respectively. The frequently-used medicinal combinations include Xingren ((Armeniacae Amarum)) -Beimu (Bulbus Fritillariae), Jupi (Citri Exocarpium)-Xingren, Lianqiao-Jinyinhua (Flos Lonicerae Japonicae) -Xuanshen (Radix Scrophulariae), and Lianqiao-Xuanshen-Shichangpu. Conclusion: The formulas and medications in the treatment of spring warm disease in ancient case records focus on clearing heat that is assisted by eliminating exterior pathogen and protecting yin fluid.