In order to facilitate spare parts management,an integrated approach of BP neural network and supportability analysis(SA)was proposed to evaluate the criticality of spare parts as well as to prioritize spare parts.Inf...In order to facilitate spare parts management,an integrated approach of BP neural network and supportability analysis(SA)was proposed to evaluate the criticality of spare parts as well as to prioritize spare parts.Influential factors of prioritizing spare parts were detailedly analyzed.Framework of the integrated method was established.The modelling process based on BP neural network was presented.As the input of the neural network,the values of influential factors were determined by supportability analysis data.Based on the presented method,spare parts could be automatically prioritized after supportability analysis for a new system.A case study results showed that the new method was applicable and effective.展开更多
This paper proposes a practical and framework-based approach to design an architecture transformation strategy and roadmap aiming to transform or modernize critical legacy enterprise systems.The approach is business v...This paper proposes a practical and framework-based approach to design an architecture transformation strategy and roadmap aiming to transform or modernize critical legacy enterprise systems.The approach is business value driven with IT supportability in terms of lower application operational and support costs,higher business value and shorter time to market of application delivery.The approach introduces a robust enterprise application architecture assessment framework with an emphasis on technical(internal)and strategic(external)perspectives to guide the application assessment and also a finance selfsupport transformation strategy to aid its transformation roadmap design.The approach was applied in multiple large enterprises successfully and received endorsements and positive feedback from the sponsors.The paper also presents a case study detailing the successful application of the approach to modernize an enterprise logistics transportation management system.展开更多
Andrew Wangota,a 48-year-old Ugandan farmer,has been using agrivoltaics technology,a solar technology that uses agricultural land for both food production and solar power generation,on his farm in Bunashimolo Parish,B...Andrew Wangota,a 48-year-old Ugandan farmer,has been using agrivoltaics technology,a solar technology that uses agricultural land for both food production and solar power generation,on his farm in Bunashimolo Parish,Bukyiende Subcounty in Uganda where he has been cultivating plantain,coffee and Irish potatoes for the past 16 years.展开更多
BACKGROUND Low anterior resection syndrome(LARS)is a prevalent and debilitating complication following sphincter-preserving surgery for rectal cancer.Evidence-based interventions for the concurrent psychological burde...BACKGROUND Low anterior resection syndrome(LARS)is a prevalent and debilitating complication following sphincter-preserving surgery for rectal cancer.Evidence-based interventions for the concurrent psychological burden are limited.Electroacupuncture has been proposed as a potential adjunctive therapy,but its psychological benefits remain inadequately studied.AIM To investigate the therapeutic effect of electroacupuncture on emotional recovery and gastrointestinal function in patients with moderate to severe LARS,and to explore its potential advantages in psychologically vulnerable subgroups.METHODS We conducted a retrospective,controlled study involving 100 patients with moderate to severe LARS(LARS score≥21)treated at two tertiary hospitals in China between January 2022 and December 2024.Patients received either standard postoperative care alone(n=50)or in combination with a standardized 4-week electroacupuncture protocol(n=50).Psychological and functional outcomes were assessed using validated instruments including Hospital Anxiety and Depression Scale(HADS),Body Image Scale(BIS),General Self-Efficacy Scale,Perceived Social Support Scale(PSSS),LARS score,and European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 at four time points.The primary endpoint was emotional remission,defined as a≥3-point reduction in HADS-Anxiety subscale(HADS-A).Analyses included repeated-measures comparisons,Kaplan-Meier survival curves,Cox regression models,and subgroup-interaction testing.RESULTS At baseline,demographic,surgical,and psychosocial characteristics were comparable among groups.By week 4,patients receiving electroacupuncture demonstrated significantly greater reductions in anxiety(HADS-A:4.8±2.6 vs 7.3±3.0;P<0.001),depression,and body-image disturbance(BIS:8.7±3.6 vs 11.9±4.2;P<0.001),alongside enhanced coping capacity(Brief Coping Orientation to Problems Experienced),perceived social support(PSSS),and bowel function(LARS score).Emotional remission-defined as a≥3-point HADS-A reduction-was achieved more rapidly in the electroacupuncture group,as confirmed by Kaplan-Meier analysis(log-rank P<0.001;odds ratio=4.7).Multivariate Cox regression identified higher baseline LARS and BIS scores as independent predictors of delayed emotional recovery.Subgroup analyses revealed significantly amplified treatment benefits in patients with high baseline anxiety(HADS-A≥8),elevated body-image disturbance(BIS≥12),or low perceived social support(PSSS<60),with consistent interaction effects(P for interaction<0.05 across subgroups).CONCLUSION Electroacupuncture may accelerate emotional recovery and improve functional and psychosocial outcomes in patients with LARS.Its integration into postoperative care may offer particular benefits for psychologically vulnerable subgroups.展开更多
An image processing and deep learning method for identifying different types of rock images was proposed.Preprocessing,such as rock image acquisition,gray scaling,Gaussian blurring,and feature dimensionality reduction...An image processing and deep learning method for identifying different types of rock images was proposed.Preprocessing,such as rock image acquisition,gray scaling,Gaussian blurring,and feature dimensionality reduction,was conducted to extract useful feature information and recognize and classify rock images using Tensor Flow-based convolutional neural network(CNN)and Py Qt5.A rock image dataset was established and separated into workouts,confirmation sets,and test sets.The framework was subsequently compiled and trained.The categorization approach was evaluated using image data from the validation and test datasets,and key metrics,such as accuracy,precision,and recall,were analyzed.Finally,the classification model conducted a probabilistic analysis of the measured data to determine the equivalent lithological type for each image.The experimental results indicated that the method combining deep learning,Tensor Flow-based CNN,and Py Qt5 to recognize and classify rock images has an accuracy rate of up to 98.8%,and can be successfully utilized for rock image recognition.The system can be extended to geological exploration,mine engineering,and other rock and mineral resource development to more efficiently and accurately recognize rock samples.Moreover,it can match them with the intelligent support design system to effectively improve the reliability and economy of the support scheme.The system can serve as a reference for supporting the design of other mining and underground space projects.展开更多
The year 2025 marks both the 25th anniversary of the Forum on China-Africa Cooperation(FOCAC)and the first year of implementation of the outcomes of the 2024 FOCAC Beijing Summit.Throughout the year,China has supporte...The year 2025 marks both the 25th anniversary of the Forum on China-Africa Cooperation(FOCAC)and the first year of implementation of the outcomes of the 2024 FOCAC Beijing Summit.Throughout the year,China has supported Africa in addressing historical injustices at diplomatic and political levels and firmly backed South Africa in hosting the G20 Leaders’Summit,further deepening China-Africa strategic mutual trust.展开更多
The major aim of stroke therapy is to stimulate brain repair and improve behavioral recovery after cerebral ischemia.One option is to stimulate endogenous neurogenesis in the subventricular zone and direct the newly f...The major aim of stroke therapy is to stimulate brain repair and improve behavioral recovery after cerebral ischemia.One option is to stimulate endogenous neurogenesis in the subventricular zone and direct the newly formed neurons to the damaged area.However,only a small percentage of these neurons survive,and many do not reach the damaged area,possibly because the corpus callosum impedes the migration of subventricular zone-derived stem cells into the lesioned cortex.A second major obstacle to stem cell therapy is the strong inflammatory reaction induced by cerebral ischemia,whereby the associated phagocytic activity of brain macrophages removes both therapeutic cells and/or cell-based drug carriers.To address these issues,neurogenesis was electrically stimulated in the subventricular zone,followed by isolation of proliferating cells,including newly formed neurons,which were subsequently mixed with a nutritional hydrogel.This mixture was then transferred to the stroke cavity of day 14 post-stroke mice.We found that the performance of the treated animals improved in behavioral tests,including novel object,open field,hole board,grooming,and“time-to-feel”adhesive tape tests.Furthermore,immunostaining revealed that the stem cell marker nestin,the neuroepithelial marker Mash1,and the immature neuronal marker doublecortin-positive cells survived in the transplanted area for 2 weeks,possibly due to reduced phagocytic activity and supportive angiogenesis.These results clearly indicate that the transplantation of committed subventricular zone stem cells combined with a protective nutritional gel directly into the infarct cavity after the peak of stroke-induced neuroinflammation represents a feasible approach to improve neurorestoration after cerebral ischemia.展开更多
Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex int...Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex internal chemical systems of LIBs and the nonlinear degradation of their performance,direct measurement of SOH and RUL is challenging.To address these issues,the Twin Support Vector Machine(TWSVM)method is proposed to predict SOH and RUL.Initially,the constant current charging time of the lithium battery is extracted as a health indicator(HI),decomposed using Variational Modal Decomposition(VMD),and feature correlations are computed using Importance of Random Forest Features(RF)to maximize the extraction of critical factors influencing battery performance degradation.Furthermore,to enhance the global search capability of the Convolution Optimization Algorithm(COA),improvements are made using Good Point Set theory and the Differential Evolution method.The Improved Convolution Optimization Algorithm(ICOA)is employed to optimize TWSVM parameters for constructing SOH and RUL prediction models.Finally,the proposed models are validated using NASA and CALCE lithium-ion battery datasets.Experimental results demonstrate that the proposed models achieve an RMSE not exceeding 0.007 and an MAPE not exceeding 0.0082 for SOH and RUL prediction,with a relative error in RUL prediction within the range of[-1.8%,2%].Compared to other models,the proposed model not only exhibits superior fitting capability but also demonstrates robust performance.展开更多
Latent heat thermal energy storage(TES)effectively reduces the mismatch between energy supply and demand of renewable energy sources by the utilization of phase change materials(PCMs).However,the low thermal conductiv...Latent heat thermal energy storage(TES)effectively reduces the mismatch between energy supply and demand of renewable energy sources by the utilization of phase change materials(PCMs).However,the low thermal conductivity and poor shape stability are the main drawbacks in realizing the large-scale application of PCMs.Promisingly,developing composite PCM(CPCM)based on porous supporting mate-rial provides a desirable solution to obtain performance-enhanced PCMs with improved effective thermal conductivity and shape stability.Among all the porous matrixes as supports for PCM,three-dimensional carbon-based porous supporting material has attracted considerable attention ascribing to its high ther-mal conductivity,desirable loading capacity of PCMs,and excellent chemical compatibility with various PCMs.Therefore,this work systemically reviews the CPCMs with three-dimensional carbon-based porous supporting materials.First,a concise rule for the fabrication of CPCMs is illustrated in detail.Next,the experimental and computational research of carbon nanotube-based support,graphene-based support,graphite-based support and amorphous carbon-based support are reviewed.Then,the applications of the shape-stabilized CPCMs including thermal management and thermal conversion are illustrated.Last but not least,the challenges and prospects of the CPCMs are discussed.To conclude,introducing carbon-based porous materials can solve the liquid leakage issue and essentially improve the thermal conductivity of PCMs.However,there is still a long way to further develop a desirable CPCM with higher latent heat capacity,higher thermal conductivity,and more excellent shape stability.展开更多
Rockbursts, which mainly affect mining roadways, are dynamic disasters arising from the surrounding rock under high stress. Understanding the interaction between supports and the surrounding rock is necessary for effe...Rockbursts, which mainly affect mining roadways, are dynamic disasters arising from the surrounding rock under high stress. Understanding the interaction between supports and the surrounding rock is necessary for effective rockburst control. In this study, the squeezing behavior of the surrounding rock is analyzed in rockburst roadways, and a mechanical model of rockbursts is established considering the dynamic support stress, thus deriving formulas and providing characteristic curves for describing the interaction between the support and surrounding rock. Design principles and parameters of supports for rockburst control are proposed. The results show that only when the geostress magnitude exceeds a critical value can it drive the formation of rockburst conditions. The main factors influencing the convergence response and rockburst occurrence around roadways are geostress, rock brittleness, uniaxial compressive strength, and roadway excavation size. Roadway support devices can play a role in controlling rockburst by suppressing the squeezing evolution of the surrounding rock towards instability points of rockburst. Further, the higher the strength and the longer the impact stroke of support devices with constant resistance, the more easily multiple balance points can be formed with the surrounding rock to control rockburst occurrence. Supports with long impact stroke allow adaptation to varying geostress levels around the roadway, aiding in rockburst control. The results offer a quantitative method for designing support systems for rockburst-prone roadways. The design criterion of supports is determined by the intersection between the convergence curve of the surrounding rock and the squeezing deformation curve of the support devices.展开更多
Landslide susceptibility prediction(LSP)is significantly affected by the uncertainty issue of landslide related conditioning factor selection.However,most of literature only performs comparative studies on a certain c...Landslide susceptibility prediction(LSP)is significantly affected by the uncertainty issue of landslide related conditioning factor selection.However,most of literature only performs comparative studies on a certain conditioning factor selection method rather than systematically study this uncertainty issue.Targeted,this study aims to systematically explore the influence rules of various commonly used conditioning factor selection methods on LSP,and on this basis to innovatively propose a principle with universal application for optimal selection of conditioning factors.An'yuan County in southern China is taken as example considering 431 landslides and 29 types of conditioning factors.Five commonly used factor selection methods,namely,the correlation analysis(CA),linear regression(LR),principal component analysis(PCA),rough set(RS)and artificial neural network(ANN),are applied to select the optimal factor combinations from the original 29 conditioning factors.The factor selection results are then used as inputs of four types of common machine learning models to construct 20 types of combined models,such as CA-multilayer perceptron,CA-random forest.Additionally,multifactor-based multilayer perceptron random forest models that selecting conditioning factors based on the proposed principle of“accurate data,rich types,clear significance,feasible operation and avoiding duplication”are constructed for comparisons.Finally,the LSP uncertainties are evaluated by the accuracy,susceptibility index distribution,etc.Results show that:(1)multifactor-based models have generally higher LSP performance and lower uncertainties than those of factors selection-based models;(2)Influence degree of different machine learning on LSP accuracy is greater than that of different factor selection methods.Conclusively,the above commonly used conditioning factor selection methods are not ideal for improving LSP performance and may complicate the LSP processes.In contrast,a satisfied combination of conditioning factors can be constructed according to the proposed principle.展开更多
To investigate the wind⁃induced vibration re⁃sponse characteristics of multispan double⁃layer cable photo⁃voltaic(PV)support structures,wind tunnel tests using an aeroelastic model were carried out to obtain the wind⁃...To investigate the wind⁃induced vibration re⁃sponse characteristics of multispan double⁃layer cable photo⁃voltaic(PV)support structures,wind tunnel tests using an aeroelastic model were carried out to obtain the wind⁃induced vibration response data of a three⁃span four⁃row double⁃layer cable PV support system.The wind⁃induced vibration characteristics with different PV module tilt angles,wind speeds,and wind direction angles were analyzed.The results showed that the double⁃layer cable large⁃span flexible PV support can effectively control the wind⁃induced vibration response and prevent the occur⁃rence of flutter under strong wind conditions.The maxi⁃mum value of the wind⁃induced vibration displacement of the flexible PV support system occurs in the windward first row.The upstream module has a significant shading effect on the downstream module,with a maximum effect of 23%.The most unfavorable wind direction angles of the structure are 0°and 180°.The change of the wind direction angle in the range of 0°to 30°has little effect on the wind vi⁃bration response.The change in the tilt angle of the PV modules has a greater impact on the wind vibration in the downwind direction and a smaller impact in the upwind di⁃rection.Special attention should be paid to the structural wind⁃resistant design of such systems in the upwind side span.展开更多
Diagnosing cardiac diseases relies heavily on electrocardiogram(ECG)analysis,but detecting myocardial infarction-related arrhythmias remains challenging due to irregular heartbeats and signal variations.Despite advanc...Diagnosing cardiac diseases relies heavily on electrocardiogram(ECG)analysis,but detecting myocardial infarction-related arrhythmias remains challenging due to irregular heartbeats and signal variations.Despite advancements in machine learning,achieving both high accuracy and low computational cost for arrhythmia classification remains a critical issue.Computer-aided diagnosis systems can play a key role in early detection,reducing mortality rates associated with cardiac disorders.This study proposes a fully automated approach for ECG arrhythmia classification using deep learning and machine learning techniques to improve diagnostic accuracy while minimizing processing time.The methodology consists of three stages:1)preprocessing,where ECG signals undergo noise reduction and feature extraction;2)feature Identification,where deep convolutional neural network(CNN)blocks,combined with data augmentation and transfer learning,extract key parameters;3)classification,where a hybrid CNN-SVM model is employed for arrhythmia recognition.CNN-extracted features were fed into a binary support vector machine(SVM)classifier,and model performance was assessed using five-fold cross-validation.Experimental findings demonstrated that the CNN2 model achieved 85.52%accuracy,while the hybrid CNN2-SVM approach significantly improved accuracy to 97.33%,outperforming conventional methods.This model enhances classification efficiency while reducing computational complexity.The proposed approach bridges the gap between accuracy and processing speed in ECG arrhythmia classification,offering a promising solution for real-time clinical applications.Its superior performance compared to nonlinear classifiers highlights its potential for improving automated cardiac diagnosis.展开更多
The application of machine learning for pyrite discrimination establishes a robust foundation for constructing the ore-forming history of multi-stage deposits;however,published models face challenges related to limite...The application of machine learning for pyrite discrimination establishes a robust foundation for constructing the ore-forming history of multi-stage deposits;however,published models face challenges related to limited,imbalanced datasets and oversampling.In this study,the dataset was expanded to approximately 500 samples for each type,including 508 sedimentary,573 orogenic gold,548 sedimentary exhalative(SEDEX)deposits,and 364 volcanogenic massive sulfides(VMS)pyrites,utilizing random forest(RF)and support vector machine(SVM)methodologies to enhance the reliability of the classifier models.The RF classifier achieved an overall accuracy of 99.8%,and the SVM classifier attained an overall accuracy of 100%.The model was evaluated by a five-fold cross-validation approach with 93.8%accuracy for the RF and 94.9%for the SVM classifier.These results demonstrate the strong feasibility of pyrite classification,supported by a relatively large,balanced dataset and high accuracy rates.The classifier was employed to reveal the genesis of the controversial Keketale Pb-Zn deposit in NW China,which has been inconclusive among SEDEX,VMS,or a SEDEX-VMS transition.Petrographic investigations indicated that the deposit comprises early fine-grained layered pyrite(Py1)and late recrystallized pyrite(Py2).The majority voting classified Py1 as the VMS type,with an accuracy of RF and SVM being 72.2%and 75%,respectively,and confirmed Py2 as an orogenic type with 74.3% and 77.1%accuracy,respectively.The new findings indicated that the Keketale deposit originated from a submarine VMS mineralization system,followed by late orogenic-type overprinting of metamorphism and deformation,which is consistent with the geological and geochemical observations.This study further emphasizes the advantages of Machine learning(ML)methods in accurately and directly discriminating the deposit types and reconstructing the formation history of multi-stage deposits.展开更多
Structural regulation of Pd-based electrocatalytic hydrodechlorination(EHDC)catalyst for constructing high-efficient cathode materials with low noble metal content and high atom utilization is crucial but still challe...Structural regulation of Pd-based electrocatalytic hydrodechlorination(EHDC)catalyst for constructing high-efficient cathode materials with low noble metal content and high atom utilization is crucial but still challenging.Herein,a support electron inductive effect of Pd-Mn/Ni foam catalyst was proposed via in-situ Mn doping to optimize the electronic structure of the Ni foam(NF),which can inductive regulation of Pd for improving the EHDC performance.The mass activity and current efficiency of Pd-Mn/NF catalyst are 2.91 and 1.34 times superior to that of Pd/NF with 2,4-dichlorophenol as model compound,respectively.The Mn-doped interlayer optimized the electronic structure of Pd by bringing the d-state closer to the Fermi level than Pd on the NF surface,which optimizied the binding of EHDC intermediates.Additionally,the Mn-doped interlayer acted as a promoter for generating H∗and accelerating the EHDC reaction.This work presents a simple and effective regulation strategy for constructing high-efficient cathode catalyst for the EHDC of chlorinated organic compounds.展开更多
BACKGROUND Hepatocellular carcinoma ranks among the most prevalent malignant neoplasms.Surgical intervention constitutes a critical therapeutic approach for this condition.Nonetheless,postoperative recovery is frequen...BACKGROUND Hepatocellular carcinoma ranks among the most prevalent malignant neoplasms.Surgical intervention constitutes a critical therapeutic approach for this condition.Nonetheless,postoperative recovery is frequently influenced by the patient's nutritional status and the quality of nursing care provided.AIM To examine the comprehensive impact of personalized nutritional support and nursing strategies on the postoperative rehabilitation of patients with liver cancer.METHODS In this study,a retrospective comparative analysis was conducted involving 60 post-operative liver cancer patients.The subjects were selected as subjects and divided into two groups based on differing nursing interventions,with each group comprising 30 patients.The control group received standard nutritional support and care,whereas the experimental group received individualized nutritional support and nursing strategies.The study aimed to evaluate the impact of individualized nutrition by comparing the rehabilitation indices,nutritional status,quality of life(QoL),and complication rates between the two groups.RESULTS The results showed that the recovery index of the experimental group was significantly better than that of the control group 2 weeks after surgery,and the average liver function recovery index of the experimental group was 85.significantly higher than that of the control group(73.67±7.19).In terms of nutritional status,the serum albumin level and body weight stabilization rate of the experimental group were also significantly higher than those of the control group,which were 42.33±2.4 g/L and 93.3%,respectively,compared with 36.01±3.85 g/L and 76.7%of the control group.In addition,the average QoL score of the experimental group was 84.66±3.7 points,which was significantly higher than that of the control group(70.92±4.28 points).At the psychological level,the average anxiety score of the experimental group was 1.17±0.29,and the average depression score was 1.47±0.4,which were significantly lower than the 2.26±0.42 and 2.57±0.45 of the control group.This showed that patients in the experimental group were better relieved of anxiety and depression under the individualized nutrition support and nursing strategy.More importantly,the complication rate in the experimental group was only 10%,much lower than the 33.3%in the control group.CONCLUSION Personalized nutritional support and tailored nursing strategies significantly enhance the postoperative rehabilitation of liver cancer patients.Consequently,it is recommended to implement and advocate for these individualized approaches to improve both the recovery outcomes and QoL for these patients.展开更多
Effective wildland fire management requires real-time access to comprehensive and distilled information from different data sources.The Digital Twin technology becomes a promising tool in optimizing the processes of w...Effective wildland fire management requires real-time access to comprehensive and distilled information from different data sources.The Digital Twin technology becomes a promising tool in optimizing the processes of wildfire pre-vention,monitoring,disaster response,and post-fire recovery.This review examines the potential utility of Digital Twin in wildfire management and aims to inspire further exploration and experimentation by researchers and practitioners in the fields of environment,forestry,fire ecology,and firefighting services.By creating virtual replicas of wildfire in the physical world,a Digital Twin platform facilitates data integration from multiple sources,such as remote sensing,weather forecast-ing,and ground-based sensors,providing a holistic view of emergency response and decision-making.Furthermore,Digital Twin can support simulation-based training and scenario testing for prescribed fire planning and firefighting to improve preparedness and response to evacuation and rescue.Successful applications of Digital Twin in wildfire management require horizontal collaboration among researchers,practitioners,and stakeholders,as well as enhanced resource sharing and data exchange.This review seeks a deeper understanding of future wildland fire management from a technological perspective and inspiration of future research and implementation.Further research should focus on refining and validating Digital Twin models and the integration into existing fire management operations,and then demonstrating them in real wildland fires.展开更多
In underground mining,especially in entry-type excavations,the instability of surrounding rock structures can lead to incalculable losses.As a crucial tool for stability analysis in entry-type excavations,the critical...In underground mining,especially in entry-type excavations,the instability of surrounding rock structures can lead to incalculable losses.As a crucial tool for stability analysis in entry-type excavations,the critical span graph must be updated to meet more stringent engineering requirements.Given this,this study introduces the support vector machine(SVM),along with multiple ensemble(bagging,adaptive boosting,and stacking)and optimization(Harris hawks optimization(HHO),cuckoo search(CS))techniques,to overcome the limitations of the traditional methods.The analysis indicates that the hybrid model combining SVM,bagging,and CS strategies has a good prediction performance,and its test accuracy reaches 0.86.Furthermore,the partition scheme of the critical span graph is adjusted based on the CS-BSVM model and 399 cases.Compared with previous empirical or semi-empirical methods,the new model overcomes the interference of subjective factors and possesses higher interpretability.Since relying solely on one technology cannot ensure prediction credibility,this study further introduces genetic programming(GP)and kriging interpolation techniques.The explicit expressions derived through GP can offer the stability probability value,and the kriging technique can provide interpolated definitions for two new subclasses.Finally,a prediction platform is developed based on the above three approaches,which can rapidly provide engineering feedback.展开更多
Traditional Chinese medicine(TCM)represents a paradigmatic approach to personalized medicine,developed through the systematic accumulation and refinement of clinical empirical data over more than 2000 years,and now en...Traditional Chinese medicine(TCM)represents a paradigmatic approach to personalized medicine,developed through the systematic accumulation and refinement of clinical empirical data over more than 2000 years,and now encompasses large-scale electronic medical records(EMR)and experimental molecular data.Artificial intelligence(AI)has demonstrated its utility in medicine through the development of various expert systems(e.g.,MYCIN)since the 1970s.With the emergence of deep learning and large language models(LLMs),AI’s potential in medicine shows considerable promise.Consequently,the integration of AI and TCM from both clinical and scientific perspectives presents a fundamental and promising research direction.This survey provides an insightful overview of TCM AI research,summarizing related research tasks from three perspectives:systems-level biological mechanism elucidation,real-world clinical evidence inference,and personalized clinical decision support.The review highlights representative AI methodologies alongside their applications in both TCM scientific inquiry and clinical practice.To critically assess the current state of the field,this work identifies major challenges and opportunities that constrain the development of robust research capabilities—particularly in the mechanistic understanding of TCM syndromes and herbal formulations,novel drug discovery,and the delivery of high-quality,patient-centered clinical care.The findings underscore that future advancements in AI-driven TCM research will rely on the development of high-quality,large-scale data repositories;the construction of comprehensive and domain-specific knowledge graphs(KGs);deeper insights into the biological mechanisms underpinning clinical efficacy;rigorous causal inference frameworks;and intelligent,personalized decision support systems.展开更多
文摘In order to facilitate spare parts management,an integrated approach of BP neural network and supportability analysis(SA)was proposed to evaluate the criticality of spare parts as well as to prioritize spare parts.Influential factors of prioritizing spare parts were detailedly analyzed.Framework of the integrated method was established.The modelling process based on BP neural network was presented.As the input of the neural network,the values of influential factors were determined by supportability analysis data.Based on the presented method,spare parts could be automatically prioritized after supportability analysis for a new system.A case study results showed that the new method was applicable and effective.
文摘This paper proposes a practical and framework-based approach to design an architecture transformation strategy and roadmap aiming to transform or modernize critical legacy enterprise systems.The approach is business value driven with IT supportability in terms of lower application operational and support costs,higher business value and shorter time to market of application delivery.The approach introduces a robust enterprise application architecture assessment framework with an emphasis on technical(internal)and strategic(external)perspectives to guide the application assessment and also a finance selfsupport transformation strategy to aid its transformation roadmap design.The approach was applied in multiple large enterprises successfully and received endorsements and positive feedback from the sponsors.The paper also presents a case study detailing the successful application of the approach to modernize an enterprise logistics transportation management system.
文摘Andrew Wangota,a 48-year-old Ugandan farmer,has been using agrivoltaics technology,a solar technology that uses agricultural land for both food production and solar power generation,on his farm in Bunashimolo Parish,Bukyiende Subcounty in Uganda where he has been cultivating plantain,coffee and Irish potatoes for the past 16 years.
文摘BACKGROUND Low anterior resection syndrome(LARS)is a prevalent and debilitating complication following sphincter-preserving surgery for rectal cancer.Evidence-based interventions for the concurrent psychological burden are limited.Electroacupuncture has been proposed as a potential adjunctive therapy,but its psychological benefits remain inadequately studied.AIM To investigate the therapeutic effect of electroacupuncture on emotional recovery and gastrointestinal function in patients with moderate to severe LARS,and to explore its potential advantages in psychologically vulnerable subgroups.METHODS We conducted a retrospective,controlled study involving 100 patients with moderate to severe LARS(LARS score≥21)treated at two tertiary hospitals in China between January 2022 and December 2024.Patients received either standard postoperative care alone(n=50)or in combination with a standardized 4-week electroacupuncture protocol(n=50).Psychological and functional outcomes were assessed using validated instruments including Hospital Anxiety and Depression Scale(HADS),Body Image Scale(BIS),General Self-Efficacy Scale,Perceived Social Support Scale(PSSS),LARS score,and European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 at four time points.The primary endpoint was emotional remission,defined as a≥3-point reduction in HADS-Anxiety subscale(HADS-A).Analyses included repeated-measures comparisons,Kaplan-Meier survival curves,Cox regression models,and subgroup-interaction testing.RESULTS At baseline,demographic,surgical,and psychosocial characteristics were comparable among groups.By week 4,patients receiving electroacupuncture demonstrated significantly greater reductions in anxiety(HADS-A:4.8±2.6 vs 7.3±3.0;P<0.001),depression,and body-image disturbance(BIS:8.7±3.6 vs 11.9±4.2;P<0.001),alongside enhanced coping capacity(Brief Coping Orientation to Problems Experienced),perceived social support(PSSS),and bowel function(LARS score).Emotional remission-defined as a≥3-point HADS-A reduction-was achieved more rapidly in the electroacupuncture group,as confirmed by Kaplan-Meier analysis(log-rank P<0.001;odds ratio=4.7).Multivariate Cox regression identified higher baseline LARS and BIS scores as independent predictors of delayed emotional recovery.Subgroup analyses revealed significantly amplified treatment benefits in patients with high baseline anxiety(HADS-A≥8),elevated body-image disturbance(BIS≥12),or low perceived social support(PSSS<60),with consistent interaction effects(P for interaction<0.05 across subgroups).CONCLUSION Electroacupuncture may accelerate emotional recovery and improve functional and psychosocial outcomes in patients with LARS.Its integration into postoperative care may offer particular benefits for psychologically vulnerable subgroups.
基金financially supported by the National Science and Technology Major Project——Deep Earth Probe and Mineral Resources Exploration(No.2024ZD1003701)the National Key R&D Program of China(No.2022YFC2905004)。
文摘An image processing and deep learning method for identifying different types of rock images was proposed.Preprocessing,such as rock image acquisition,gray scaling,Gaussian blurring,and feature dimensionality reduction,was conducted to extract useful feature information and recognize and classify rock images using Tensor Flow-based convolutional neural network(CNN)and Py Qt5.A rock image dataset was established and separated into workouts,confirmation sets,and test sets.The framework was subsequently compiled and trained.The categorization approach was evaluated using image data from the validation and test datasets,and key metrics,such as accuracy,precision,and recall,were analyzed.Finally,the classification model conducted a probabilistic analysis of the measured data to determine the equivalent lithological type for each image.The experimental results indicated that the method combining deep learning,Tensor Flow-based CNN,and Py Qt5 to recognize and classify rock images has an accuracy rate of up to 98.8%,and can be successfully utilized for rock image recognition.The system can be extended to geological exploration,mine engineering,and other rock and mineral resource development to more efficiently and accurately recognize rock samples.Moreover,it can match them with the intelligent support design system to effectively improve the reliability and economy of the support scheme.The system can serve as a reference for supporting the design of other mining and underground space projects.
文摘The year 2025 marks both the 25th anniversary of the Forum on China-Africa Cooperation(FOCAC)and the first year of implementation of the outcomes of the 2024 FOCAC Beijing Summit.Throughout the year,China has supported Africa in addressing historical injustices at diplomatic and political levels and firmly backed South Africa in hosting the G20 Leaders’Summit,further deepening China-Africa strategic mutual trust.
基金supported by European Union Funding Programme,PNRR,No. 760058(to DMH)the UEFISCDI Project,No. PN-III-P4-IDPCE-2020-059(to APW)
文摘The major aim of stroke therapy is to stimulate brain repair and improve behavioral recovery after cerebral ischemia.One option is to stimulate endogenous neurogenesis in the subventricular zone and direct the newly formed neurons to the damaged area.However,only a small percentage of these neurons survive,and many do not reach the damaged area,possibly because the corpus callosum impedes the migration of subventricular zone-derived stem cells into the lesioned cortex.A second major obstacle to stem cell therapy is the strong inflammatory reaction induced by cerebral ischemia,whereby the associated phagocytic activity of brain macrophages removes both therapeutic cells and/or cell-based drug carriers.To address these issues,neurogenesis was electrically stimulated in the subventricular zone,followed by isolation of proliferating cells,including newly formed neurons,which were subsequently mixed with a nutritional hydrogel.This mixture was then transferred to the stroke cavity of day 14 post-stroke mice.We found that the performance of the treated animals improved in behavioral tests,including novel object,open field,hole board,grooming,and“time-to-feel”adhesive tape tests.Furthermore,immunostaining revealed that the stem cell marker nestin,the neuroepithelial marker Mash1,and the immature neuronal marker doublecortin-positive cells survived in the transplanted area for 2 weeks,possibly due to reduced phagocytic activity and supportive angiogenesis.These results clearly indicate that the transplantation of committed subventricular zone stem cells combined with a protective nutritional gel directly into the infarct cavity after the peak of stroke-induced neuroinflammation represents a feasible approach to improve neurorestoration after cerebral ischemia.
基金funded by the Pyramid Talent Training Project of Beijing University of Civil Engineering and Architecture under Grant GJZJ20220802。
文摘Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex internal chemical systems of LIBs and the nonlinear degradation of their performance,direct measurement of SOH and RUL is challenging.To address these issues,the Twin Support Vector Machine(TWSVM)method is proposed to predict SOH and RUL.Initially,the constant current charging time of the lithium battery is extracted as a health indicator(HI),decomposed using Variational Modal Decomposition(VMD),and feature correlations are computed using Importance of Random Forest Features(RF)to maximize the extraction of critical factors influencing battery performance degradation.Furthermore,to enhance the global search capability of the Convolution Optimization Algorithm(COA),improvements are made using Good Point Set theory and the Differential Evolution method.The Improved Convolution Optimization Algorithm(ICOA)is employed to optimize TWSVM parameters for constructing SOH and RUL prediction models.Finally,the proposed models are validated using NASA and CALCE lithium-ion battery datasets.Experimental results demonstrate that the proposed models achieve an RMSE not exceeding 0.007 and an MAPE not exceeding 0.0082 for SOH and RUL prediction,with a relative error in RUL prediction within the range of[-1.8%,2%].Compared to other models,the proposed model not only exhibits superior fitting capability but also demonstrates robust performance.
基金supported by the National Natural Science Foundation of China(No.52127816),the National Key Research and Development Program of China(No.2020YFA0715000)the National Natural Science and Hong Kong Research Grant Council Joint Research Funding Project of China(No.5181101182)the NSFC/RGC Joint Research Scheme sponsored by the Research Grants Council of Hong Kong and the National Natural Science Foundation of China(No.N_PolyU513/18).
文摘Latent heat thermal energy storage(TES)effectively reduces the mismatch between energy supply and demand of renewable energy sources by the utilization of phase change materials(PCMs).However,the low thermal conductivity and poor shape stability are the main drawbacks in realizing the large-scale application of PCMs.Promisingly,developing composite PCM(CPCM)based on porous supporting mate-rial provides a desirable solution to obtain performance-enhanced PCMs with improved effective thermal conductivity and shape stability.Among all the porous matrixes as supports for PCM,three-dimensional carbon-based porous supporting material has attracted considerable attention ascribing to its high ther-mal conductivity,desirable loading capacity of PCMs,and excellent chemical compatibility with various PCMs.Therefore,this work systemically reviews the CPCMs with three-dimensional carbon-based porous supporting materials.First,a concise rule for the fabrication of CPCMs is illustrated in detail.Next,the experimental and computational research of carbon nanotube-based support,graphene-based support,graphite-based support and amorphous carbon-based support are reviewed.Then,the applications of the shape-stabilized CPCMs including thermal management and thermal conversion are illustrated.Last but not least,the challenges and prospects of the CPCMs are discussed.To conclude,introducing carbon-based porous materials can solve the liquid leakage issue and essentially improve the thermal conductivity of PCMs.However,there is still a long way to further develop a desirable CPCM with higher latent heat capacity,higher thermal conductivity,and more excellent shape stability.
基金funded by the National Natural Science Foundation of China (No. 52304133)the National Key R&D Program of China (No. 2022YFC3004605)the Department of Science and Technology of Liaoning Province (No. 2023-BS-083)。
文摘Rockbursts, which mainly affect mining roadways, are dynamic disasters arising from the surrounding rock under high stress. Understanding the interaction between supports and the surrounding rock is necessary for effective rockburst control. In this study, the squeezing behavior of the surrounding rock is analyzed in rockburst roadways, and a mechanical model of rockbursts is established considering the dynamic support stress, thus deriving formulas and providing characteristic curves for describing the interaction between the support and surrounding rock. Design principles and parameters of supports for rockburst control are proposed. The results show that only when the geostress magnitude exceeds a critical value can it drive the formation of rockburst conditions. The main factors influencing the convergence response and rockburst occurrence around roadways are geostress, rock brittleness, uniaxial compressive strength, and roadway excavation size. Roadway support devices can play a role in controlling rockburst by suppressing the squeezing evolution of the surrounding rock towards instability points of rockburst. Further, the higher the strength and the longer the impact stroke of support devices with constant resistance, the more easily multiple balance points can be formed with the surrounding rock to control rockburst occurrence. Supports with long impact stroke allow adaptation to varying geostress levels around the roadway, aiding in rockburst control. The results offer a quantitative method for designing support systems for rockburst-prone roadways. The design criterion of supports is determined by the intersection between the convergence curve of the surrounding rock and the squeezing deformation curve of the support devices.
基金funded by the Natural Science Foundation of China(Grant Nos.42377164 and 41972280)the Badong National Observation and Research Station of Geohazards(Grant No.BNORSG-202305).
文摘Landslide susceptibility prediction(LSP)is significantly affected by the uncertainty issue of landslide related conditioning factor selection.However,most of literature only performs comparative studies on a certain conditioning factor selection method rather than systematically study this uncertainty issue.Targeted,this study aims to systematically explore the influence rules of various commonly used conditioning factor selection methods on LSP,and on this basis to innovatively propose a principle with universal application for optimal selection of conditioning factors.An'yuan County in southern China is taken as example considering 431 landslides and 29 types of conditioning factors.Five commonly used factor selection methods,namely,the correlation analysis(CA),linear regression(LR),principal component analysis(PCA),rough set(RS)and artificial neural network(ANN),are applied to select the optimal factor combinations from the original 29 conditioning factors.The factor selection results are then used as inputs of four types of common machine learning models to construct 20 types of combined models,such as CA-multilayer perceptron,CA-random forest.Additionally,multifactor-based multilayer perceptron random forest models that selecting conditioning factors based on the proposed principle of“accurate data,rich types,clear significance,feasible operation and avoiding duplication”are constructed for comparisons.Finally,the LSP uncertainties are evaluated by the accuracy,susceptibility index distribution,etc.Results show that:(1)multifactor-based models have generally higher LSP performance and lower uncertainties than those of factors selection-based models;(2)Influence degree of different machine learning on LSP accuracy is greater than that of different factor selection methods.Conclusively,the above commonly used conditioning factor selection methods are not ideal for improving LSP performance and may complicate the LSP processes.In contrast,a satisfied combination of conditioning factors can be constructed according to the proposed principle.
基金The National Natural Science Foundation of China(No.52338011).
文摘To investigate the wind⁃induced vibration re⁃sponse characteristics of multispan double⁃layer cable photo⁃voltaic(PV)support structures,wind tunnel tests using an aeroelastic model were carried out to obtain the wind⁃induced vibration response data of a three⁃span four⁃row double⁃layer cable PV support system.The wind⁃induced vibration characteristics with different PV module tilt angles,wind speeds,and wind direction angles were analyzed.The results showed that the double⁃layer cable large⁃span flexible PV support can effectively control the wind⁃induced vibration response and prevent the occur⁃rence of flutter under strong wind conditions.The maxi⁃mum value of the wind⁃induced vibration displacement of the flexible PV support system occurs in the windward first row.The upstream module has a significant shading effect on the downstream module,with a maximum effect of 23%.The most unfavorable wind direction angles of the structure are 0°and 180°.The change of the wind direction angle in the range of 0°to 30°has little effect on the wind vi⁃bration response.The change in the tilt angle of the PV modules has a greater impact on the wind vibration in the downwind direction and a smaller impact in the upwind di⁃rection.Special attention should be paid to the structural wind⁃resistant design of such systems in the upwind side span.
文摘Diagnosing cardiac diseases relies heavily on electrocardiogram(ECG)analysis,but detecting myocardial infarction-related arrhythmias remains challenging due to irregular heartbeats and signal variations.Despite advancements in machine learning,achieving both high accuracy and low computational cost for arrhythmia classification remains a critical issue.Computer-aided diagnosis systems can play a key role in early detection,reducing mortality rates associated with cardiac disorders.This study proposes a fully automated approach for ECG arrhythmia classification using deep learning and machine learning techniques to improve diagnostic accuracy while minimizing processing time.The methodology consists of three stages:1)preprocessing,where ECG signals undergo noise reduction and feature extraction;2)feature Identification,where deep convolutional neural network(CNN)blocks,combined with data augmentation and transfer learning,extract key parameters;3)classification,where a hybrid CNN-SVM model is employed for arrhythmia recognition.CNN-extracted features were fed into a binary support vector machine(SVM)classifier,and model performance was assessed using five-fold cross-validation.Experimental findings demonstrated that the CNN2 model achieved 85.52%accuracy,while the hybrid CNN2-SVM approach significantly improved accuracy to 97.33%,outperforming conventional methods.This model enhances classification efficiency while reducing computational complexity.The proposed approach bridges the gap between accuracy and processing speed in ECG arrhythmia classification,offering a promising solution for real-time clinical applications.Its superior performance compared to nonlinear classifiers highlights its potential for improving automated cardiac diagnosis.
基金the National Key Research and Development Program of China(2021YFC2900300)the Natural Science Foundation of Guangdong Province(2024A1515030216)+2 种基金MOST Special Fund from State Key Laboratory of Geological Processes and Mineral Resources,China University of Geosciences(GPMR202437)the Guangdong Province Introduced of Innovative R&D Team(2021ZT09H399)the Third Xinjiang Scientific Expedition Program(2022xjkk1301).
文摘The application of machine learning for pyrite discrimination establishes a robust foundation for constructing the ore-forming history of multi-stage deposits;however,published models face challenges related to limited,imbalanced datasets and oversampling.In this study,the dataset was expanded to approximately 500 samples for each type,including 508 sedimentary,573 orogenic gold,548 sedimentary exhalative(SEDEX)deposits,and 364 volcanogenic massive sulfides(VMS)pyrites,utilizing random forest(RF)and support vector machine(SVM)methodologies to enhance the reliability of the classifier models.The RF classifier achieved an overall accuracy of 99.8%,and the SVM classifier attained an overall accuracy of 100%.The model was evaluated by a five-fold cross-validation approach with 93.8%accuracy for the RF and 94.9%for the SVM classifier.These results demonstrate the strong feasibility of pyrite classification,supported by a relatively large,balanced dataset and high accuracy rates.The classifier was employed to reveal the genesis of the controversial Keketale Pb-Zn deposit in NW China,which has been inconclusive among SEDEX,VMS,or a SEDEX-VMS transition.Petrographic investigations indicated that the deposit comprises early fine-grained layered pyrite(Py1)and late recrystallized pyrite(Py2).The majority voting classified Py1 as the VMS type,with an accuracy of RF and SVM being 72.2%and 75%,respectively,and confirmed Py2 as an orogenic type with 74.3% and 77.1%accuracy,respectively.The new findings indicated that the Keketale deposit originated from a submarine VMS mineralization system,followed by late orogenic-type overprinting of metamorphism and deformation,which is consistent with the geological and geochemical observations.This study further emphasizes the advantages of Machine learning(ML)methods in accurately and directly discriminating the deposit types and reconstructing the formation history of multi-stage deposits.
基金supported by the National Natural Science Foundation of China(Nos.22178388 and 22108306)Taishan Scholars Program of Shandong Province(No.tsqn201909065)Chongqing Science and Technology Bureau(No.cstc2019jscx-gksb X0032).
文摘Structural regulation of Pd-based electrocatalytic hydrodechlorination(EHDC)catalyst for constructing high-efficient cathode materials with low noble metal content and high atom utilization is crucial but still challenging.Herein,a support electron inductive effect of Pd-Mn/Ni foam catalyst was proposed via in-situ Mn doping to optimize the electronic structure of the Ni foam(NF),which can inductive regulation of Pd for improving the EHDC performance.The mass activity and current efficiency of Pd-Mn/NF catalyst are 2.91 and 1.34 times superior to that of Pd/NF with 2,4-dichlorophenol as model compound,respectively.The Mn-doped interlayer optimized the electronic structure of Pd by bringing the d-state closer to the Fermi level than Pd on the NF surface,which optimizied the binding of EHDC intermediates.Additionally,the Mn-doped interlayer acted as a promoter for generating H∗and accelerating the EHDC reaction.This work presents a simple and effective regulation strategy for constructing high-efficient cathode catalyst for the EHDC of chlorinated organic compounds.
文摘BACKGROUND Hepatocellular carcinoma ranks among the most prevalent malignant neoplasms.Surgical intervention constitutes a critical therapeutic approach for this condition.Nonetheless,postoperative recovery is frequently influenced by the patient's nutritional status and the quality of nursing care provided.AIM To examine the comprehensive impact of personalized nutritional support and nursing strategies on the postoperative rehabilitation of patients with liver cancer.METHODS In this study,a retrospective comparative analysis was conducted involving 60 post-operative liver cancer patients.The subjects were selected as subjects and divided into two groups based on differing nursing interventions,with each group comprising 30 patients.The control group received standard nutritional support and care,whereas the experimental group received individualized nutritional support and nursing strategies.The study aimed to evaluate the impact of individualized nutrition by comparing the rehabilitation indices,nutritional status,quality of life(QoL),and complication rates between the two groups.RESULTS The results showed that the recovery index of the experimental group was significantly better than that of the control group 2 weeks after surgery,and the average liver function recovery index of the experimental group was 85.significantly higher than that of the control group(73.67±7.19).In terms of nutritional status,the serum albumin level and body weight stabilization rate of the experimental group were also significantly higher than those of the control group,which were 42.33±2.4 g/L and 93.3%,respectively,compared with 36.01±3.85 g/L and 76.7%of the control group.In addition,the average QoL score of the experimental group was 84.66±3.7 points,which was significantly higher than that of the control group(70.92±4.28 points).At the psychological level,the average anxiety score of the experimental group was 1.17±0.29,and the average depression score was 1.47±0.4,which were significantly lower than the 2.26±0.42 and 2.57±0.45 of the control group.This showed that patients in the experimental group were better relieved of anxiety and depression under the individualized nutrition support and nursing strategy.More importantly,the complication rate in the experimental group was only 10%,much lower than the 33.3%in the control group.CONCLUSION Personalized nutritional support and tailored nursing strategies significantly enhance the postoperative rehabilitation of liver cancer patients.Consequently,it is recommended to implement and advocate for these individualized approaches to improve both the recovery outcomes and QoL for these patients.
基金funded by the National Natural Science Foundation of China(NSFC No.52322610)Hong Kong Research Grants Council Theme-based Research Scheme(T22-505/19-N).
文摘Effective wildland fire management requires real-time access to comprehensive and distilled information from different data sources.The Digital Twin technology becomes a promising tool in optimizing the processes of wildfire pre-vention,monitoring,disaster response,and post-fire recovery.This review examines the potential utility of Digital Twin in wildfire management and aims to inspire further exploration and experimentation by researchers and practitioners in the fields of environment,forestry,fire ecology,and firefighting services.By creating virtual replicas of wildfire in the physical world,a Digital Twin platform facilitates data integration from multiple sources,such as remote sensing,weather forecast-ing,and ground-based sensors,providing a holistic view of emergency response and decision-making.Furthermore,Digital Twin can support simulation-based training and scenario testing for prescribed fire planning and firefighting to improve preparedness and response to evacuation and rescue.Successful applications of Digital Twin in wildfire management require horizontal collaboration among researchers,practitioners,and stakeholders,as well as enhanced resource sharing and data exchange.This review seeks a deeper understanding of future wildland fire management from a technological perspective and inspiration of future research and implementation.Further research should focus on refining and validating Digital Twin models and the integration into existing fire management operations,and then demonstrating them in real wildland fires.
基金supported by the National Natural Science Foundation of China(Grant No.42177164)the Distinguished Youth Science Foundation of Hunan Province of China(Grant No.2022JJ10073)the Outstanding Youth Project of Hunan Provincial Department of Education,China(Grant No.23B0008).
文摘In underground mining,especially in entry-type excavations,the instability of surrounding rock structures can lead to incalculable losses.As a crucial tool for stability analysis in entry-type excavations,the critical span graph must be updated to meet more stringent engineering requirements.Given this,this study introduces the support vector machine(SVM),along with multiple ensemble(bagging,adaptive boosting,and stacking)and optimization(Harris hawks optimization(HHO),cuckoo search(CS))techniques,to overcome the limitations of the traditional methods.The analysis indicates that the hybrid model combining SVM,bagging,and CS strategies has a good prediction performance,and its test accuracy reaches 0.86.Furthermore,the partition scheme of the critical span graph is adjusted based on the CS-BSVM model and 399 cases.Compared with previous empirical or semi-empirical methods,the new model overcomes the interference of subjective factors and possesses higher interpretability.Since relying solely on one technology cannot ensure prediction credibility,this study further introduces genetic programming(GP)and kriging interpolation techniques.The explicit expressions derived through GP can offer the stability probability value,and the kriging technique can provide interpolated definitions for two new subclasses.Finally,a prediction platform is developed based on the above three approaches,which can rapidly provide engineering feedback.
基金supported by the National Key Research and Development Program (No.2023YFC3502604)the National Natural Science Foundation of China (Nos.U23B2062, 82274352,82174533, 82374302, 82204941)+3 种基金the Noncommunicable Chronic Diseases-National Science and Technology Major Project (No.2023ZD0505700)the Beijing-Tianjin-Hebei Basic Research Cooperation Project (No.22JCZXJC00070)the State Key Laboratory on Technologies for Chinese Medicine Pharmaceutical Process Control and Intelligent Manufacture (No.SKL2024Z0102)Key R&D project of Ningxia Autonomous Region (No.2022BEG02036).
文摘Traditional Chinese medicine(TCM)represents a paradigmatic approach to personalized medicine,developed through the systematic accumulation and refinement of clinical empirical data over more than 2000 years,and now encompasses large-scale electronic medical records(EMR)and experimental molecular data.Artificial intelligence(AI)has demonstrated its utility in medicine through the development of various expert systems(e.g.,MYCIN)since the 1970s.With the emergence of deep learning and large language models(LLMs),AI’s potential in medicine shows considerable promise.Consequently,the integration of AI and TCM from both clinical and scientific perspectives presents a fundamental and promising research direction.This survey provides an insightful overview of TCM AI research,summarizing related research tasks from three perspectives:systems-level biological mechanism elucidation,real-world clinical evidence inference,and personalized clinical decision support.The review highlights representative AI methodologies alongside their applications in both TCM scientific inquiry and clinical practice.To critically assess the current state of the field,this work identifies major challenges and opportunities that constrain the development of robust research capabilities—particularly in the mechanistic understanding of TCM syndromes and herbal formulations,novel drug discovery,and the delivery of high-quality,patient-centered clinical care.The findings underscore that future advancements in AI-driven TCM research will rely on the development of high-quality,large-scale data repositories;the construction of comprehensive and domain-specific knowledge graphs(KGs);deeper insights into the biological mechanisms underpinning clinical efficacy;rigorous causal inference frameworks;and intelligent,personalized decision support systems.
