Objective expertise evaluation of individuals,as a prerequisite stage for team formation,has been a long-term desideratum in large software development companies.With the rapid advancements in machine learning methods...Objective expertise evaluation of individuals,as a prerequisite stage for team formation,has been a long-term desideratum in large software development companies.With the rapid advancements in machine learning methods,based on reliable existing data stored in project management tools’datasets,automating this evaluation process becomes a natural step forward.In this context,our approach focuses on quantifying software developer expertise by using metadata from the task-tracking systems.For this,we mathematically formalize two categories of expertise:technology-specific expertise,which denotes the skills required for a particular technology,and general expertise,which encapsulates overall knowledge in the software industry.Afterward,we automatically classify the zones of expertise associated with each task a developer has worked on using Bidirectional Encoder Representations from Transformers(BERT)-like transformers to handle the unique characteristics of project tool datasets effectively.Finally,our method evaluates the proficiency of each software specialist across already completed projects from both technology-specific and general perspectives.The method was experimentally validated,yielding promising results.展开更多
Generating dynamically feasible trajectory for fixed-wing Unmanned Aerial Vehicles(UAVs)in dense obstacle environments remains computationally intractable.This paper proposes a Safe Flight Corridor constrained Sequent...Generating dynamically feasible trajectory for fixed-wing Unmanned Aerial Vehicles(UAVs)in dense obstacle environments remains computationally intractable.This paper proposes a Safe Flight Corridor constrained Sequential Convex Programming(SFC-SCP)to improve the computation efficiency and reliability of trajectory generation.SFC-SCP combines the front-end convex polyhedron SFC construction and back-end SCP-based trajectory optimization.A Sparse A^(*)Search(SAS)driven SFC construction method is designed to efficiently generate polyhedron SFC according to the geometric relation among obstacles and collision-free waypoints.Via transforming the nonconvex obstacle-avoidance constraints to linear inequality constraints,SFC can mitigate infeasibility of trajectory planning and reduce computation complexity.Then,SCP casts the nonlinear trajectory optimization subject to SFC into convex programming subproblems to decrease the problem complexity.In addition,a convex optimizer based on interior point method is customized,where the search direction is calculated via successive elimination to further improve efficiency.Simulation experiments on dense obstacle scenarios show that SFC-SCP can generate dynamically feasible safe trajectory rapidly.Comparative studies with state-of-the-art SCP-based methods demonstrate the efficiency and reliability merits of SFC-SCP.Besides,the customized convex optimizer outperforms off-the-shelf optimizers in terms of computation time.展开更多
The research and development of new traditional Chinese medicine(TCM)drugs have progressively established a novel system founded on the integration of TCM theory,human experience,and clinical trials(termed the“Three ...The research and development of new traditional Chinese medicine(TCM)drugs have progressively established a novel system founded on the integration of TCM theory,human experience,and clinical trials(termed the“Three Combinations”).However,considering TCM's distinctive features of“syndrome differentiation and treatment”and“multicomponent formulations and complex mechanisms”,current TCM drug development faces challenges such as insufficient understanding of the material basis and the overall mechanism of action and an incomplete evidence chain system.Moreover,significant obstacles persist in gathering human experience data,evaluating clinical efficacy,and controlling the quality of active ingredients,which impede the innovation process in TCM drug development.Network pharmacology,centered on the“network targets”theory,transcends the limitations of the conventional“single target”reductionist research model.It emphasizes the comprehensive effects of disease or syndrome biological networks as targets to elucidate the overall regulatory mechanism of TCM prescriptions.This approach aligns with the holistic perspective of TCM,offering a novel method consistent with TCM's holistic view for investigating the complex mechanisms of TCM and developing new TCM drugs.It is internationally recognized as a“next-generation drug research model”.To advance the research of new tools,methods,and standards for TCM evaluation and to overcome fundamental,critical,and cutting-edge technical challenges in TCM regulation,this consensus aims to explore the characteristics,progress,challenges,applicable pathways,and specific applications of network pharmacology as a new theory,method,and tool in TCM drug development.The goal is to enhance the quality of TCM drug research and development and accelerate the efficiency of developing new TCM products.展开更多
Drug development remains a critical issue in the field of biomedicine.With the rapid advancement of information technologies such as artificial intelligence(AI)and the advent of the big data era,AI-assisted drug devel...Drug development remains a critical issue in the field of biomedicine.With the rapid advancement of information technologies such as artificial intelligence(AI)and the advent of the big data era,AI-assisted drug development has become a new trend,particularly in predicting drug-target associations.To address the challenge of drug-target prediction,AI-driven models have emerged as powerful tools,offering innovative solutions by effectively extracting features from complex biological data,accurately modeling molecular interactions,and precisely predicting potential drug-target outcomes.Traditional machine learning(ML),network-based,and advanced deep learning architectures such as convolutional neural networks(CNNs),graph convolutional networks(GCNs),and transformers play a pivotal role.This review systematically compiles and evaluates AI algorithms for drug-and drug combination-target predictions,highlighting their theoretical frameworks,strengths,and limitations.CNNs effectively identify spatial patterns and molecular features critical for drug-target interactions.GCNs provide deep insights into molecular interactions via relational data,whereas transformers increase prediction accuracy by capturing complex dependencies within biological sequences.Network-based models offer a systematic perspective by integrating diverse data sources,and traditional ML efficiently handles large datasets to improve overall predictive accuracy.Collectively,these AI-driven methods are transforming drug-target predictions and advancing the development of personalized therapy.This review summarizes the application of AI in drug development,particularly in drug-target prediction,and offers recommendations on models and algorithms for researchers engaged in biomedical research.It also provides typical cases to better illustrate how AI can further accelerate development in the fields of biomedicine and drug discovery.展开更多
Exo-atmospheric vehicles are constrained by limited maneuverability,which leads to the contradiction between evasive maneuver and precision strike.To address the problem of Integrated Evasion and Impact(IEI)decision u...Exo-atmospheric vehicles are constrained by limited maneuverability,which leads to the contradiction between evasive maneuver and precision strike.To address the problem of Integrated Evasion and Impact(IEI)decision under multi-constraint conditions,a hierarchical intelligent decision-making method based on Deep Reinforcement Learning(DRL)was proposed.First,an intelligent decision-making framework of“DRL evasion decision”+“impact prediction guidance decision”was established:it takes the impact point deviation correction ability as the constraint and the maximum miss distance as the objective,and effectively solves the problem of poor decisionmaking effect caused by the large IEI decision space.Second,to solve the sparse reward problem faced by evasion decision-making,a hierarchical decision-making method consisting of maneuver timing decision and maneuver duration decision was proposed,and the corresponding Markov Decision Process(MDP)was designed.A detailed simulation experiment was designed to analyze the advantages and computational complexity of the proposed method.Simulation results show that the proposed model has good performance and low computational resource requirement.The minimum miss distance is 21.3 m under the condition of guaranteeing the impact point accuracy,and the single decision-making time is 4.086 ms on an STM32F407 single-chip microcomputer,which has engineering application value.展开更多
In scenarios such as vehicle radiation monitoring and unmanned aerial vehicle radiation detection,rapid measurements using a NaI(Tl)detector often result in low photon counts,weak characteristic peaks,and significant ...In scenarios such as vehicle radiation monitoring and unmanned aerial vehicle radiation detection,rapid measurements using a NaI(Tl)detector often result in low photon counts,weak characteristic peaks,and significant statistical fluctuations.These issues can lead to potential failures in peak-searching-based identification methods.To address the low precision associated with short-duration measurements of radionuclides,this paper proposes an identification algorithm that leverages heterogeneous spectral transfer to develop a low-count energy spectral identification model.Comparative experiments demonstrated that transferring samples from 26 classes of simulated heterogeneous gamma spectra aids in creating a reliable model for measured gamma spectra.With only 10%of target domain samples used for training,the accuracy on real low-count spectral samples was 95.56%.This performance shows a significant improvement over widely employed full-spectrum analysis methods trained on target domain samples.The proposed method also exhibits strong generalization capabilities,effectively mitigating overfitting issues in low-count energy spectral classification under short-duration measurements.展开更多
Deep-time Earth research plays a pivotal role in deciphering the rates,patterns,and mechanisms of Earth's evolutionary processes throughout geological history,providing essential scientific foundations for climate...Deep-time Earth research plays a pivotal role in deciphering the rates,patterns,and mechanisms of Earth's evolutionary processes throughout geological history,providing essential scientific foundations for climate prediction,natural resource exploration,and sustainable planetary stewardship.To advance Deep-time Earth research in the era of big data and artificial intelligence,the International Union of Geological Sciences initiated the“Deeptime Digital Earth International Big Science Program”(DDE)in 2019.At the core of this ambitious program lies the development of geoscience knowledge graphs,serving as a transformative knowledge infrastructure that enables the integration,sharing,mining,and analysis of heterogeneous geoscience big data.The DDE knowledge graph initiative has made significant strides in three critical dimensions:(1)establishing a unified knowledge structure across geoscience disciplines that ensures consistent representation of geological entities and their interrelationships through standardized ontologies and semantic frameworks;(2)developing a robust and scalable software infrastructure capable of supporting both expert-driven and machine-assisted knowledge engineering for large-scale graph construction and management;(3)implementing a comprehensive three-tiered architecture encompassing basic,discipline-specific,and application-oriented knowledge graphs,spanning approximately 20 geoscience disciplines.Through its open knowledge framework and international collaborative network,this initiative has fostered multinational research collaborations,establishing a robust foundation for next-generation geoscience research while propelling the discipline toward FAIR(Findable,Accessible,Interoperable,Reusable)data practices in deep-time Earth systems research.展开更多
Non-technical losses(NTL)of electric power are a serious problem for electric distribution companies.The solution determines the cost,stability,reliability,and quality of the supplied electricity.The widespread use of...Non-technical losses(NTL)of electric power are a serious problem for electric distribution companies.The solution determines the cost,stability,reliability,and quality of the supplied electricity.The widespread use of advanced metering infrastructure(AMI)and Smart Grid allows all participants in the distribution grid to store and track electricity consumption.During the research,a machine learning model is developed that allows analyzing and predicting the probability of NTL for each consumer of the distribution grid based on daily electricity consumption readings.This model is an ensemble meta-algorithm(stacking)that generalizes the algorithms of random forest,LightGBM,and a homogeneous ensemble of artificial neural networks.The best accuracy of the proposed meta-algorithm in comparison to basic classifiers is experimentally confirmed on the test sample.Such a model,due to good accuracy indicators(ROC-AUC-0.88),can be used as a methodological basis for a decision support system,the purpose of which is to form a sample of suspected NTL sources.The use of such a sample will allow the top management of electric distribution companies to increase the efficiency of raids by performers,making them targeted and accurate,which should contribute to the fight against NTL and the sustainable development of the electric power industry.展开更多
In the face of the large number of people with motor function disabilities,rehabilitation robots have attracted more and more attention.In order to promote the active participation of the user's motion intention i...In the face of the large number of people with motor function disabilities,rehabilitation robots have attracted more and more attention.In order to promote the active participation of the user's motion intention in the assisted rehabilitation process of the robots,it is crucial to establish the human motion prediction model.In this paper,a hybrid prediction model built on long short-term memory(LSTM)neural network using surface electromyography(sEMG)is applied to predict the elbow motion of the users in advance.This model includes two sub-models:a back-propagation neural network and an LSTM network.The former extracts a preliminary prediction of the elbow motion,and the latter corrects this prediction to increase accuracy.The proposed model takes time series data as input,which includes the sEMG signals measured by electrodes and the continuous angles from inertial measurement units.The offline and online tests were carried out to verify the established hybrid model.Finally,average root mean square errors of 3.52°and 4.18°were reached respectively for offline and online tests,and the correlation coefficients for both were above 0.98.展开更多
As future ship system,hybrid energy ship system has a wide range of application prospects for solving the serious energy crisis.However,current optimization scheduling works lack the consideration of sea conditions an...As future ship system,hybrid energy ship system has a wide range of application prospects for solving the serious energy crisis.However,current optimization scheduling works lack the consideration of sea conditions and navigational circumstances.There-fore,this paper aims at establishing a two-stage optimization framework for hybrid energy ship power system.The proposed framework considers multiple optimizations of route,speed planning,and energy management under the constraints of sea conditions during navigation.First,a complex hybrid ship power model consisting of diesel generation system,propulsion system,energy storage system,photovoltaic power generation system,and electric boiler system is established,where sea state information and ship resistance model are considered.With objective optimization functions of cost and greenhouse gas(GHG)emissions,a two-stage optimization framework consisting of route planning,speed scheduling,and energy management is constructed.Wherein the improved A-star algorithm and grey wolf optimization algorithm are introduced to obtain the optimal solutions for route,speed,and energy optimization scheduling.Finally,simulation cases are employed to verify that the proposed two-stage optimization scheduling model can reduce load energy consumption,operating costs,and carbon emissions by 17.8%,17.39%,and 13.04%,respectively,compared with the non-optimal control group.展开更多
In complex water environments,search tasks often involve multiple Autonomous Underwater Vehicles(AUVs),and a single centralized control cannot handle the complexity and computational burden of large-scale systems.Targ...In complex water environments,search tasks often involve multiple Autonomous Underwater Vehicles(AUVs),and a single centralized control cannot handle the complexity and computational burden of large-scale systems.Target search in complex water environments has always been a major challenge in the field of underwater robots.To address this problem,this paper proposes a multi-biomimetic robot fish collaborative target search method based on Distributed Model Predictive Control(DMPC).First,we established a bionic robot fish kinematic model and a multi-biomimetic robot fish communication model;second,this paper proposed a distributed model predictive control algorithm based on the distributed search theory framework,so that the bionic robot fish can dynamically adjust their search path according to each other’s position information and search status,avoid repeated coverage or missing areas,and thus improve the search efficiency;third,we conducted simulation experiments based on DMPC,and the results showed that the proposed method has a target search success rate of more than 90%in static targets,dynamic targets,and obstacle environments.Finally,we compared this method with Centralized Model Predictive Control(CMPC)and Random Walk(RW)algorithms.The DMPC approach demonstrates significant advantages,achieving a remarkable target search success rate of 94.17%.These findings comprehensively validate the effectiveness and superiority of the proposed methodology.It can be seen that DMPC can effectively dispatch multiple bionic robot fish to work together to achieve efficient search of vast waters.It can significantly improve the flexibility,scalability,robustness and cooperation efficiency of the system and has broad application prospects.展开更多
Accurately soft sensing of the mechanical properties of hot-rolled strips is essential to ensure product quality,optimize production,and reduce costs.However,it faces the difficulty caused by limited labeled samples,f...Accurately soft sensing of the mechanical properties of hot-rolled strips is essential to ensure product quality,optimize production,and reduce costs.However,it faces the difficulty caused by limited labeled samples,for which co-training based semi-supervised learning offers a potential solution.So in this paper,a novel soft sensing method for mechanical properties based on improved co-training(ICO)is proposed.Compared with the existing co-training framework,the proposed ICO introduces improvements from the aspects of multiple view partition,confidence estimation,and pseudo-label assignment.Specifically,(ⅰ)in the stage of multiple view partition,ICO integrates metallurgical mechanisms of hot rolling processes and statistical mutual information to achieve a balance between view sufficiency and independence,which improves model performance and interpretability;(ⅱ)in the stage of confidence estimation,ICO evaluates the confidence of unlabeled samples at the cluster level rather than at the level of a single sample,which facilitates the exploration of sample distribution and the selection of representative samples;(ⅲ)in the pseudo-label assignment stage,ICO adopts a safe pseudo-label algorithm(which is called SAFER by its author and originally used for each single sample)to assign pseudo-labels for cluster of samples with the highest confidence determined in the previous step stage,to take advantage of the merit of handling unlabeled samples at the cluster level mentioned above on one hand,and the merit of SAFER in enhancing the quality of pseudo-labels on the other hand.The proposed soft sensing method effectively predicts mechanical properties on the real hot rolling dataset,achieving approximately 5%improvement in R~2 compared to traditional supervised learning.展开更多
A lifespan prediction model was developed based on a few samples to provide decision-making information for operation and maintenance,as well as improve the economy and safety of nuclear power plant(NPP)operations.Thi...A lifespan prediction model was developed based on a few samples to provide decision-making information for operation and maintenance,as well as improve the economy and safety of nuclear power plant(NPP)operations.This paper applies a Weibull model to forecast the lifespan of electronic cards with a few samples in NPPs.Relationship between the lifespan prediction of electronic cards and the ambient temperature is revealed using the Arrhenius equation.Censored samples are used to compensate for the lack of fault electronic card data.Scale parameter and shape parameter of the Weibull model are optimized by adjusting the weight ratio between the censored data and the fault data.Characteristic life is then obtained using the rank regression fitting equation.Parameters of the Arrhenius equation can be calculated by dividing the samples into groups according to the ambient temperature.A case study of the intermediate range high-voltage electric card of ex-core neutron detectors demonstrates that the lifespan prediction of electronic cards in NPPs can be successfully predicted with a few samples by combining the Weibull model and the Arrhenius model.This can help provide preventive maintenance recommendations for electronic cards.Finally,operation suggestions for the electronic card’s ambient temperature can be made by utilizing the temperature-life model.展开更多
This paper addresses the design problem of an embedded mobile stage location system based on a two-dimensional laser radar.The mobile stage is a piece of important performance equipment in art performances,and the pos...This paper addresses the design problem of an embedded mobile stage location system based on a two-dimensional laser radar.The mobile stage is a piece of important performance equipment in art performances,and the positioning problem is one of the key issues in the control of the mobile stage.Both hardware and software are developed for the mobile stage.The hardware of the location system consists of a laser radar,an embedded control board,a wireless router,and a motion control unit.The software of the location system includes embedded software and human-machine interface(HMI),and they are designed to achieve the functions of real-time positioning and monitoring.First,a novel landmark identification method is presented based on the landmark reflection intensities and shapes.Then,the initial pose of the mobile stage is calculated by using the triangle matching algorithm and the least squares method.A distributed fusion Kalman filtering algorithm is applied to fuse landmark information and odometer information to achieve real-time positioning of the mobile stage.The designed system has been implemented in a practical mobile stage,and the results demonstrate that the location system can achieve a high positioning precision in both the stationary and moving scenarios.展开更多
The development of renewable energy power generation for carbon neutrality and energy transition has been increasing worldwide,leading to an increasing demand for high-power conversion.Compared with traditional interl...The development of renewable energy power generation for carbon neutrality and energy transition has been increasing worldwide,leading to an increasing demand for high-power conversion.Compared with traditional interleaved paralleling,the integrated paralleling of three-level inverters can further reduce the output harmonics.Moreover,a well-designed switching sequence ensures that the average circulating current is zero,which provides a superior and feasible solution to satisfy the demands of high-power operations.However,a large instantaneous loop current exists between shunt converters,which leads to disadvantages such as higher switching device stress and loss.In this study,by utilizing the state-distribution redundancy provided by the integrated modulation process,a new design for switch-ing sequences is suggested for the integrated modulation of shunt three-level converters.This design aims to reduce the circulating current while better preserving the same output current harmonics than traditional parallel methods.The proposal includes an in-depth analysis and explanation of the implementation process.Finally,the proposed method is validated through simulations and prototype experi-ments.The results indicate that compared with traditional methods,the adoption of the improved switching sequence presented in this study leads to an average reduction of 3.2%in the total harmonic distortion of the inverter’s output and an average decrease of 32%in the amplitude of the circulating current.Both the output harmonics and circulating currents are significantly suppressed across various modulation indices.展开更多
In actual industrial scenarios,the variation of operating conditions,the existence of data noise,and failure of measurement equipment will inevitably affect the distribution of perceptive data.Deep learning-based faul...In actual industrial scenarios,the variation of operating conditions,the existence of data noise,and failure of measurement equipment will inevitably affect the distribution of perceptive data.Deep learning-based fault diagnosis algorithms strongly rely on the assumption that source and target data are independent and identically distributed,and the learned diagnosis knowledge is difficult to generalize to out-of-distribution data.Domain generalization(DG)aims to achieve the generalization of arbitrary target domain data by using only limited source domain data for diagnosis model training.The research of DG for fault diagnosis has made remarkable progress in recent years and lots of achievements have been obtained.In this article,for the first time a comprehensive literature review on DG for fault diagnosis from a learning mechanism-oriented perspective is provided to summarize the development in recent years.Specifically,we first conduct a comprehensive review on existing methods based on the similarity of basic principles and design motivations.Then,the recent trend of DG for fault diagnosis is also analyzed.Finally,the existing problems and future prospect is performed.展开更多
Metal–organic frameworks(MOFs)hold great potential for gas separation and storage,and graph neural networks have proven to be a powerful tool for exploring material structure–property relationships and discovering n...Metal–organic frameworks(MOFs)hold great potential for gas separation and storage,and graph neural networks have proven to be a powerful tool for exploring material structure–property relationships and discovering new materials.Unlike traditional molecular graphs,crystal graphs require consideration of periodic invariance and modes.In addition,MOF structures such as covalent bonds,functional groups,and global structures impact adsorption performance in different ways.However,redundant atomic interactions can disrupt training accuracy,potentially leading to overfitting.In this paper,we propose a multi-scale crystal graph for describing periodic crystal structures,modeling interatomic interactions at different scales while preserving periodicity invariance.We also propose a multi-head attention crystal graph network in multi-scale graphs(MHACGN-MS),which learns structural characteristics by focusing on interatomic interactions at different scales,thereby reducing interference from redundant interactions.Using MOF adsorption for gases as an example,we demonstrate that MHACGN-MS outperforms traditional graph neural networks in predicting multi-component gas adsorption.We also visualize attention scores to validate effective learning and demonstrate the model’s interpretability.展开更多
The output regulation approach has effectively addressed the speed tracking and disturbance rejection problem of permanent magnet synchronous motor(PMSM).Although accurate speed tracking under time-varying load torque...The output regulation approach has effectively addressed the speed tracking and disturbance rejection problem of permanent magnet synchronous motor(PMSM).Although accurate speed tracking under time-varying load torque disturbance has been achieved,the number of disturbance frequencies should be known.In this paper,an adaptive observer-based error feedback control method is proposed,which can solve the speed tracking control problem of PMSM subject to completely unknown multi-frequency sinusoidal load torque disturbance,requiring only the upper bound of the number of disturbance frequencies.The design steps of this method can be divided into the following three steps.In step one,a filtered transformation is applied to convert the observer canonical form of the error system and the transformed exosystem into an adaptive observer form.In step two,an adaptive observer is designed to estimate the unknown parameters of the exosystem and states of the adaptive observer form.In step three,an adaptive observer-based error feedback controller is designed to solve this control problem.The effectiveness of the proposed method is demonstrated by experimental results.展开更多
Background:Skin photoaging is a physiological or pathological process caused by multiple factors.Developing anti-skin photoaging drugs is a hot topic in cosmetology research fields.The purpose of this study was to exp...Background:Skin photoaging is a physiological or pathological process caused by multiple factors.Developing anti-skin photoaging drugs is a hot topic in cosmetology research fields.The purpose of this study was to explore the therapeutic effect of Dendrobium officinale(D.officinale)on skin aging.Methods:The ingredients of D.officinale were detected by UHPLC-Q-TOF/MS.The targets of D.officinale were screened by Swiss Target Prediction database.GeneCards,NCBI,and OMIM databases were utilized to find out the targets associated with skin photoaging.Overlapping targets of D.officinale and skin photoaging were obtained by Venn analysis.The ingredient-disease target network and protein-protein interaction network were constructed by using the STRING database and Cytoscape software.The key compounds and hub genes were obtained by analyzing networks.The DAVID database was applied for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis of the overlapping targets.Autodock Vina software was used to simulate molecular docking and the results were visualized using Pymol.Finally,the skin photoaging models of cells and mice were established to validate the results predicted by network pharmacology.Results:In D.officinale,a total of 59 compounds and 595 targets were detected,of which 59 proteins were intersectional with skin photoaging targets.The top 10 active ingredients(Dendrophenol,Herbacetin,Lyoniresinol,Trans-ferulaldehyde,Naringenin,and so on)and 8 hub genes(AKT1,TNF,VEGFA,MAPK3,CASP3,MMP9,CTNNB1,and EGFR)were identified.All the key active compounds could bind well with core protein targets(binding energy<-5 kcal/mol).The potential therapeutic targets were related to the response to reactive oxygen species,collagen catabolic process,extracellular matrix organization,and apoptotic process,mainly.We also found that D.officinale could enhance the cell viability and activity of anti-oxidases,reduce reactive oxygen species and MMP9 levels,and stable mitochondrial membrane potential.Furthermore,D.officinale could alleviate skin photoaging injury and reduce malondialdehyde level in mice.Conclusion:D.officinale alleviated skin photoaging via regulating oxidative stress,apoptosis,and collagen catabolic process.展开更多
Radiation-induced lung injury(RILI)is a common complication of cancer radiotherapy,yet effective treatments remain elusive.Compound Kushen injection(CKI),a traditional Chinese medicine(TCM)formula,is widely used in cl...Radiation-induced lung injury(RILI)is a common complication of cancer radiotherapy,yet effective treatments remain elusive.Compound Kushen injection(CKI),a traditional Chinese medicine(TCM)formula,is widely used in clinical practice for treating radiation-related diseases and as an adjunct therapy for cancer and has demonstrated some effectiveness.However,the mechanisms underlying CKI intervention in RILI and its role in cancer adjunctive therapy remain unclear.In this study,we refined previous statistical approaches and successfully integrated quantitative data on the compounds in CKI.We constructed a network-based holistic target model and developed modular biological networks to explore the modular regulatory effects of CKI in RILI.Through this network-based analysis,we identified specific alkaloid components of CKI that contribute to its therapeutic effect in alleviating RILI.Furthermore,through transcriptomic analysis,we confirmed that oxidative stress plays a central role in the treatment of RILI by CKI.The modular regulatory effects of CKI have been validated in animal models of irradiation,demonstrating the ability of CKI to alleviate oxidative stress,reduce inflammation,regulate immune responses,and inhibit apoptosis.In addition,we demonstrated that nuclear factor erythroid 2-related factor 2(NRF2)serves as a key mediator of the antioxidant effects of CKI.Matrine and sophoridine,representative alkaloids in CKI,exhibit binding interactions with NRF2.CKI promotes the nuclear translocation of NRF2,and NRF2 activates its downstream targets,such as heme oxygenase-1(HO-1)and NAD(P)H quinone dehydrogenase 1(NQO1),to suppress oxidative stress in RILI.This,in turn,inhibits the expression of inflammatory molecules,including interleukin(IL)-6,tumor necrosis factor(TNF)-α,and inducible nitric oxide synthase(iNOS),while promoting the activity of antioxidants such as superoxide dismutase(SOD)and glutathione peroxidase-4(GPX-4),thereby exerting therapeutic effects on RILI.展开更多
基金supported by the project“Romanian Hub for Artificial Intelligence-HRIA”,Smart Growth,Digitization and Financial Instruments Program,2021–2027,MySMIS No.334906.
文摘Objective expertise evaluation of individuals,as a prerequisite stage for team formation,has been a long-term desideratum in large software development companies.With the rapid advancements in machine learning methods,based on reliable existing data stored in project management tools’datasets,automating this evaluation process becomes a natural step forward.In this context,our approach focuses on quantifying software developer expertise by using metadata from the task-tracking systems.For this,we mathematically formalize two categories of expertise:technology-specific expertise,which denotes the skills required for a particular technology,and general expertise,which encapsulates overall knowledge in the software industry.Afterward,we automatically classify the zones of expertise associated with each task a developer has worked on using Bidirectional Encoder Representations from Transformers(BERT)-like transformers to handle the unique characteristics of project tool datasets effectively.Finally,our method evaluates the proficiency of each software specialist across already completed projects from both technology-specific and general perspectives.The method was experimentally validated,yielding promising results.
基金supported by the National Natural Science Foundation of China(No.62203256)。
文摘Generating dynamically feasible trajectory for fixed-wing Unmanned Aerial Vehicles(UAVs)in dense obstacle environments remains computationally intractable.This paper proposes a Safe Flight Corridor constrained Sequential Convex Programming(SFC-SCP)to improve the computation efficiency and reliability of trajectory generation.SFC-SCP combines the front-end convex polyhedron SFC construction and back-end SCP-based trajectory optimization.A Sparse A^(*)Search(SAS)driven SFC construction method is designed to efficiently generate polyhedron SFC according to the geometric relation among obstacles and collision-free waypoints.Via transforming the nonconvex obstacle-avoidance constraints to linear inequality constraints,SFC can mitigate infeasibility of trajectory planning and reduce computation complexity.Then,SCP casts the nonlinear trajectory optimization subject to SFC into convex programming subproblems to decrease the problem complexity.In addition,a convex optimizer based on interior point method is customized,where the search direction is calculated via successive elimination to further improve efficiency.Simulation experiments on dense obstacle scenarios show that SFC-SCP can generate dynamically feasible safe trajectory rapidly.Comparative studies with state-of-the-art SCP-based methods demonstrate the efficiency and reliability merits of SFC-SCP.Besides,the customized convex optimizer outperforms off-the-shelf optimizers in terms of computation time.
基金supported by the National Medical Products Administration Commissioned Research Project (No.20211440216)the National Administration of Traditional Chinese Medicine Science and Technology Project (No.GZY-KJS-2024-03)+3 种基金the State Key Laboratory of Drug Regulatory Science Project (No.2023SKLDRS0104)the Basic Research Program Natural Science Fund-Frontier Leading Technology Basic Research Special Project of Jiangsu Province (No.BK20232014)the Programs Foundation for Leading Talents in National Administration of Traditional Chinese Medicine of China“Qihuang scholars”Projectthe Tianjin Administration for Market Regulation Science and Technology Key Projects (No.2022-W35)。
文摘The research and development of new traditional Chinese medicine(TCM)drugs have progressively established a novel system founded on the integration of TCM theory,human experience,and clinical trials(termed the“Three Combinations”).However,considering TCM's distinctive features of“syndrome differentiation and treatment”and“multicomponent formulations and complex mechanisms”,current TCM drug development faces challenges such as insufficient understanding of the material basis and the overall mechanism of action and an incomplete evidence chain system.Moreover,significant obstacles persist in gathering human experience data,evaluating clinical efficacy,and controlling the quality of active ingredients,which impede the innovation process in TCM drug development.Network pharmacology,centered on the“network targets”theory,transcends the limitations of the conventional“single target”reductionist research model.It emphasizes the comprehensive effects of disease or syndrome biological networks as targets to elucidate the overall regulatory mechanism of TCM prescriptions.This approach aligns with the holistic perspective of TCM,offering a novel method consistent with TCM's holistic view for investigating the complex mechanisms of TCM and developing new TCM drugs.It is internationally recognized as a“next-generation drug research model”.To advance the research of new tools,methods,and standards for TCM evaluation and to overcome fundamental,critical,and cutting-edge technical challenges in TCM regulation,this consensus aims to explore the characteristics,progress,challenges,applicable pathways,and specific applications of network pharmacology as a new theory,method,and tool in TCM drug development.The goal is to enhance the quality of TCM drug research and development and accelerate the efficiency of developing new TCM products.
基金supported by grants from the National Natural Science Foundation of China(Grant No.:T2341008)Intelligent and Precise Research on TCM for Spleen and Stomach Diseases(20233930063).
文摘Drug development remains a critical issue in the field of biomedicine.With the rapid advancement of information technologies such as artificial intelligence(AI)and the advent of the big data era,AI-assisted drug development has become a new trend,particularly in predicting drug-target associations.To address the challenge of drug-target prediction,AI-driven models have emerged as powerful tools,offering innovative solutions by effectively extracting features from complex biological data,accurately modeling molecular interactions,and precisely predicting potential drug-target outcomes.Traditional machine learning(ML),network-based,and advanced deep learning architectures such as convolutional neural networks(CNNs),graph convolutional networks(GCNs),and transformers play a pivotal role.This review systematically compiles and evaluates AI algorithms for drug-and drug combination-target predictions,highlighting their theoretical frameworks,strengths,and limitations.CNNs effectively identify spatial patterns and molecular features critical for drug-target interactions.GCNs provide deep insights into molecular interactions via relational data,whereas transformers increase prediction accuracy by capturing complex dependencies within biological sequences.Network-based models offer a systematic perspective by integrating diverse data sources,and traditional ML efficiently handles large datasets to improve overall predictive accuracy.Collectively,these AI-driven methods are transforming drug-target predictions and advancing the development of personalized therapy.This review summarizes the application of AI in drug development,particularly in drug-target prediction,and offers recommendations on models and algorithms for researchers engaged in biomedical research.It also provides typical cases to better illustrate how AI can further accelerate development in the fields of biomedicine and drug discovery.
基金co-supported by the National Natural Science Foundation of China(No.62103432)the China Postdoctoral Science Foundation(No.284881)the Young Talent fund of University Association for Science and Technology in Shaanxi,China(No.20210108)。
文摘Exo-atmospheric vehicles are constrained by limited maneuverability,which leads to the contradiction between evasive maneuver and precision strike.To address the problem of Integrated Evasion and Impact(IEI)decision under multi-constraint conditions,a hierarchical intelligent decision-making method based on Deep Reinforcement Learning(DRL)was proposed.First,an intelligent decision-making framework of“DRL evasion decision”+“impact prediction guidance decision”was established:it takes the impact point deviation correction ability as the constraint and the maximum miss distance as the objective,and effectively solves the problem of poor decisionmaking effect caused by the large IEI decision space.Second,to solve the sparse reward problem faced by evasion decision-making,a hierarchical decision-making method consisting of maneuver timing decision and maneuver duration decision was proposed,and the corresponding Markov Decision Process(MDP)was designed.A detailed simulation experiment was designed to analyze the advantages and computational complexity of the proposed method.Simulation results show that the proposed model has good performance and low computational resource requirement.The minimum miss distance is 21.3 m under the condition of guaranteeing the impact point accuracy,and the single decision-making time is 4.086 ms on an STM32F407 single-chip microcomputer,which has engineering application value.
基金supported by the National Defense Fundamental Research Project(No.JCKY2022404C005)the Nuclear Energy Development Project(No.23ZG6106)+1 种基金the Sichuan Scientific and Technological Achievements Transfer and Transformation Demonstration Project(No.2023ZHCG0026)the Mianyang Applied Technology Research and Development Project(No.2021ZYZF1005)。
文摘In scenarios such as vehicle radiation monitoring and unmanned aerial vehicle radiation detection,rapid measurements using a NaI(Tl)detector often result in low photon counts,weak characteristic peaks,and significant statistical fluctuations.These issues can lead to potential failures in peak-searching-based identification methods.To address the low precision associated with short-duration measurements of radionuclides,this paper proposes an identification algorithm that leverages heterogeneous spectral transfer to develop a low-count energy spectral identification model.Comparative experiments demonstrated that transferring samples from 26 classes of simulated heterogeneous gamma spectra aids in creating a reliable model for measured gamma spectra.With only 10%of target domain samples used for training,the accuracy on real low-count spectral samples was 95.56%.This performance shows a significant improvement over widely employed full-spectrum analysis methods trained on target domain samples.The proposed method also exhibits strong generalization capabilities,effectively mitigating overfitting issues in low-count energy spectral classification under short-duration measurements.
基金Strategic Priority Research Program of the Chinese Academy of Sciences,No.XDB0740000National Key Research and Development Program of China,No.2022YFB3904200,No.2022YFF0711601+1 种基金Key Project of Innovation LREIS,No.PI009National Natural Science Foundation of China,No.42471503。
文摘Deep-time Earth research plays a pivotal role in deciphering the rates,patterns,and mechanisms of Earth's evolutionary processes throughout geological history,providing essential scientific foundations for climate prediction,natural resource exploration,and sustainable planetary stewardship.To advance Deep-time Earth research in the era of big data and artificial intelligence,the International Union of Geological Sciences initiated the“Deeptime Digital Earth International Big Science Program”(DDE)in 2019.At the core of this ambitious program lies the development of geoscience knowledge graphs,serving as a transformative knowledge infrastructure that enables the integration,sharing,mining,and analysis of heterogeneous geoscience big data.The DDE knowledge graph initiative has made significant strides in three critical dimensions:(1)establishing a unified knowledge structure across geoscience disciplines that ensures consistent representation of geological entities and their interrelationships through standardized ontologies and semantic frameworks;(2)developing a robust and scalable software infrastructure capable of supporting both expert-driven and machine-assisted knowledge engineering for large-scale graph construction and management;(3)implementing a comprehensive three-tiered architecture encompassing basic,discipline-specific,and application-oriented knowledge graphs,spanning approximately 20 geoscience disciplines.Through its open knowledge framework and international collaborative network,this initiative has fostered multinational research collaborations,establishing a robust foundation for next-generation geoscience research while propelling the discipline toward FAIR(Findable,Accessible,Interoperable,Reusable)data practices in deep-time Earth systems research.
文摘Non-technical losses(NTL)of electric power are a serious problem for electric distribution companies.The solution determines the cost,stability,reliability,and quality of the supplied electricity.The widespread use of advanced metering infrastructure(AMI)and Smart Grid allows all participants in the distribution grid to store and track electricity consumption.During the research,a machine learning model is developed that allows analyzing and predicting the probability of NTL for each consumer of the distribution grid based on daily electricity consumption readings.This model is an ensemble meta-algorithm(stacking)that generalizes the algorithms of random forest,LightGBM,and a homogeneous ensemble of artificial neural networks.The best accuracy of the proposed meta-algorithm in comparison to basic classifiers is experimentally confirmed on the test sample.Such a model,due to good accuracy indicators(ROC-AUC-0.88),can be used as a methodological basis for a decision support system,the purpose of which is to form a sample of suspected NTL sources.The use of such a sample will allow the top management of electric distribution companies to increase the efficiency of raids by performers,making them targeted and accurate,which should contribute to the fight against NTL and the sustainable development of the electric power industry.
基金the National Key Research and Development Program of China(No.2020YFC2007500)the Science and Technology Commission of Shanghai Municipality(No.20DZ2220400)。
文摘In the face of the large number of people with motor function disabilities,rehabilitation robots have attracted more and more attention.In order to promote the active participation of the user's motion intention in the assisted rehabilitation process of the robots,it is crucial to establish the human motion prediction model.In this paper,a hybrid prediction model built on long short-term memory(LSTM)neural network using surface electromyography(sEMG)is applied to predict the elbow motion of the users in advance.This model includes two sub-models:a back-propagation neural network and an LSTM network.The former extracts a preliminary prediction of the elbow motion,and the latter corrects this prediction to increase accuracy.The proposed model takes time series data as input,which includes the sEMG signals measured by electrodes and the continuous angles from inertial measurement units.The offline and online tests were carried out to verify the established hybrid model.Finally,average root mean square errors of 3.52°and 4.18°were reached respectively for offline and online tests,and the correlation coefficients for both were above 0.98.
基金supported by the National Natural Science Foundation of China under Grant 62473328by the Open Research Fund of Jiangsu Collaborative Innovation Center for Smart Distribution Network,Nanjing Institute of Technology under No.XTCX202203.
文摘As future ship system,hybrid energy ship system has a wide range of application prospects for solving the serious energy crisis.However,current optimization scheduling works lack the consideration of sea conditions and navigational circumstances.There-fore,this paper aims at establishing a two-stage optimization framework for hybrid energy ship power system.The proposed framework considers multiple optimizations of route,speed planning,and energy management under the constraints of sea conditions during navigation.First,a complex hybrid ship power model consisting of diesel generation system,propulsion system,energy storage system,photovoltaic power generation system,and electric boiler system is established,where sea state information and ship resistance model are considered.With objective optimization functions of cost and greenhouse gas(GHG)emissions,a two-stage optimization framework consisting of route planning,speed scheduling,and energy management is constructed.Wherein the improved A-star algorithm and grey wolf optimization algorithm are introduced to obtain the optimal solutions for route,speed,and energy optimization scheduling.Finally,simulation cases are employed to verify that the proposed two-stage optimization scheduling model can reduce load energy consumption,operating costs,and carbon emissions by 17.8%,17.39%,and 13.04%,respectively,compared with the non-optimal control group.
基金funded by National Natural Science Foundation of China(Nos.62473236,62073196).
文摘In complex water environments,search tasks often involve multiple Autonomous Underwater Vehicles(AUVs),and a single centralized control cannot handle the complexity and computational burden of large-scale systems.Target search in complex water environments has always been a major challenge in the field of underwater robots.To address this problem,this paper proposes a multi-biomimetic robot fish collaborative target search method based on Distributed Model Predictive Control(DMPC).First,we established a bionic robot fish kinematic model and a multi-biomimetic robot fish communication model;second,this paper proposed a distributed model predictive control algorithm based on the distributed search theory framework,so that the bionic robot fish can dynamically adjust their search path according to each other’s position information and search status,avoid repeated coverage or missing areas,and thus improve the search efficiency;third,we conducted simulation experiments based on DMPC,and the results showed that the proposed method has a target search success rate of more than 90%in static targets,dynamic targets,and obstacle environments.Finally,we compared this method with Centralized Model Predictive Control(CMPC)and Random Walk(RW)algorithms.The DMPC approach demonstrates significant advantages,achieving a remarkable target search success rate of 94.17%.These findings comprehensively validate the effectiveness and superiority of the proposed methodology.It can be seen that DMPC can effectively dispatch multiple bionic robot fish to work together to achieve efficient search of vast waters.It can significantly improve the flexibility,scalability,robustness and cooperation efficiency of the system and has broad application prospects.
基金supported in part by National Key Research&Development Program of China(2021YFB3301200)in part by the National Natural Science Foundation of China(61933015)。
文摘Accurately soft sensing of the mechanical properties of hot-rolled strips is essential to ensure product quality,optimize production,and reduce costs.However,it faces the difficulty caused by limited labeled samples,for which co-training based semi-supervised learning offers a potential solution.So in this paper,a novel soft sensing method for mechanical properties based on improved co-training(ICO)is proposed.Compared with the existing co-training framework,the proposed ICO introduces improvements from the aspects of multiple view partition,confidence estimation,and pseudo-label assignment.Specifically,(ⅰ)in the stage of multiple view partition,ICO integrates metallurgical mechanisms of hot rolling processes and statistical mutual information to achieve a balance between view sufficiency and independence,which improves model performance and interpretability;(ⅱ)in the stage of confidence estimation,ICO evaluates the confidence of unlabeled samples at the cluster level rather than at the level of a single sample,which facilitates the exploration of sample distribution and the selection of representative samples;(ⅲ)in the pseudo-label assignment stage,ICO adopts a safe pseudo-label algorithm(which is called SAFER by its author and originally used for each single sample)to assign pseudo-labels for cluster of samples with the highest confidence determined in the previous step stage,to take advantage of the merit of handling unlabeled samples at the cluster level mentioned above on one hand,and the merit of SAFER in enhancing the quality of pseudo-labels on the other hand.The proposed soft sensing method effectively predicts mechanical properties on the real hot rolling dataset,achieving approximately 5%improvement in R~2 compared to traditional supervised learning.
基金the Major Scientific Instrument Research of National Natural Science Foundation of China(No.61627810)the National Science and Technology Major Program of China(No.2018YFB1305003)。
文摘A lifespan prediction model was developed based on a few samples to provide decision-making information for operation and maintenance,as well as improve the economy and safety of nuclear power plant(NPP)operations.This paper applies a Weibull model to forecast the lifespan of electronic cards with a few samples in NPPs.Relationship between the lifespan prediction of electronic cards and the ambient temperature is revealed using the Arrhenius equation.Censored samples are used to compensate for the lack of fault electronic card data.Scale parameter and shape parameter of the Weibull model are optimized by adjusting the weight ratio between the censored data and the fault data.Characteristic life is then obtained using the rank regression fitting equation.Parameters of the Arrhenius equation can be calculated by dividing the samples into groups according to the ambient temperature.A case study of the intermediate range high-voltage electric card of ex-core neutron detectors demonstrates that the lifespan prediction of electronic cards in NPPs can be successfully predicted with a few samples by combining the Weibull model and the Arrhenius model.This can help provide preventive maintenance recommendations for electronic cards.Finally,operation suggestions for the electronic card’s ambient temperature can be made by utilizing the temperature-life model.
基金Supported by the Philosophy and Social Sciences Planning Project of Zhejiang Province(No.23NDJC374YB)the Major Humanities and Social Sciences Research Projects in Zhejiang Higher Education Institutions(No.2024QN067).
文摘This paper addresses the design problem of an embedded mobile stage location system based on a two-dimensional laser radar.The mobile stage is a piece of important performance equipment in art performances,and the positioning problem is one of the key issues in the control of the mobile stage.Both hardware and software are developed for the mobile stage.The hardware of the location system consists of a laser radar,an embedded control board,a wireless router,and a motion control unit.The software of the location system includes embedded software and human-machine interface(HMI),and they are designed to achieve the functions of real-time positioning and monitoring.First,a novel landmark identification method is presented based on the landmark reflection intensities and shapes.Then,the initial pose of the mobile stage is calculated by using the triangle matching algorithm and the least squares method.A distributed fusion Kalman filtering algorithm is applied to fuse landmark information and odometer information to achieve real-time positioning of the mobile stage.The designed system has been implemented in a practical mobile stage,and the results demonstrate that the location system can achieve a high positioning precision in both the stationary and moving scenarios.
基金supported by the National Natural Science Foundation of China(Grant No.51977046)Wuxi University Research Start-up Fund for Introduced Talent(2022r021).
文摘The development of renewable energy power generation for carbon neutrality and energy transition has been increasing worldwide,leading to an increasing demand for high-power conversion.Compared with traditional interleaved paralleling,the integrated paralleling of three-level inverters can further reduce the output harmonics.Moreover,a well-designed switching sequence ensures that the average circulating current is zero,which provides a superior and feasible solution to satisfy the demands of high-power operations.However,a large instantaneous loop current exists between shunt converters,which leads to disadvantages such as higher switching device stress and loss.In this study,by utilizing the state-distribution redundancy provided by the integrated modulation process,a new design for switch-ing sequences is suggested for the integrated modulation of shunt three-level converters.This design aims to reduce the circulating current while better preserving the same output current harmonics than traditional parallel methods.The proposal includes an in-depth analysis and explanation of the implementation process.Finally,the proposed method is validated through simulations and prototype experi-ments.The results indicate that compared with traditional methods,the adoption of the improved switching sequence presented in this study leads to an average reduction of 3.2%in the total harmonic distortion of the inverter’s output and an average decrease of 32%in the amplitude of the circulating current.Both the output harmonics and circulating currents are significantly suppressed across various modulation indices.
基金supported by the National Natural Science Foundation of China(62322315,61873237)the Zhejiang Provincial Natural Science Foundation of China(LR22F030003)+1 种基金supported by Research Grant Council of Hong Kong(11201023,11202224)Hong Kong Innovation and Technology Commission(InnoHK Project CIMDA).
文摘In actual industrial scenarios,the variation of operating conditions,the existence of data noise,and failure of measurement equipment will inevitably affect the distribution of perceptive data.Deep learning-based fault diagnosis algorithms strongly rely on the assumption that source and target data are independent and identically distributed,and the learned diagnosis knowledge is difficult to generalize to out-of-distribution data.Domain generalization(DG)aims to achieve the generalization of arbitrary target domain data by using only limited source domain data for diagnosis model training.The research of DG for fault diagnosis has made remarkable progress in recent years and lots of achievements have been obtained.In this article,for the first time a comprehensive literature review on DG for fault diagnosis from a learning mechanism-oriented perspective is provided to summarize the development in recent years.Specifically,we first conduct a comprehensive review on existing methods based on the similarity of basic principles and design motivations.Then,the recent trend of DG for fault diagnosis is also analyzed.Finally,the existing problems and future prospect is performed.
基金supported by the National Natural Science Foundation of China(NSFC)(61821005).
文摘Metal–organic frameworks(MOFs)hold great potential for gas separation and storage,and graph neural networks have proven to be a powerful tool for exploring material structure–property relationships and discovering new materials.Unlike traditional molecular graphs,crystal graphs require consideration of periodic invariance and modes.In addition,MOF structures such as covalent bonds,functional groups,and global structures impact adsorption performance in different ways.However,redundant atomic interactions can disrupt training accuracy,potentially leading to overfitting.In this paper,we propose a multi-scale crystal graph for describing periodic crystal structures,modeling interatomic interactions at different scales while preserving periodicity invariance.We also propose a multi-head attention crystal graph network in multi-scale graphs(MHACGN-MS),which learns structural characteristics by focusing on interatomic interactions at different scales,thereby reducing interference from redundant interactions.Using MOF adsorption for gases as an example,we demonstrate that MHACGN-MS outperforms traditional graph neural networks in predicting multi-component gas adsorption.We also visualize attention scores to validate effective learning and demonstrate the model’s interpretability.
基金supported by the National Natural Science Foundation of China(Nos.62273127 and 62073217)the Dreams Foundation of Jianghuai Advance Technology Center(No.2023-ZM01J006)the Anhui Provincial Key Research and Development Project(No.2022a05020025).
文摘The output regulation approach has effectively addressed the speed tracking and disturbance rejection problem of permanent magnet synchronous motor(PMSM).Although accurate speed tracking under time-varying load torque disturbance has been achieved,the number of disturbance frequencies should be known.In this paper,an adaptive observer-based error feedback control method is proposed,which can solve the speed tracking control problem of PMSM subject to completely unknown multi-frequency sinusoidal load torque disturbance,requiring only the upper bound of the number of disturbance frequencies.The design steps of this method can be divided into the following three steps.In step one,a filtered transformation is applied to convert the observer canonical form of the error system and the transformed exosystem into an adaptive observer form.In step two,an adaptive observer is designed to estimate the unknown parameters of the exosystem and states of the adaptive observer form.In step three,an adaptive observer-based error feedback controller is designed to solve this control problem.The effectiveness of the proposed method is demonstrated by experimental results.
文摘Background:Skin photoaging is a physiological or pathological process caused by multiple factors.Developing anti-skin photoaging drugs is a hot topic in cosmetology research fields.The purpose of this study was to explore the therapeutic effect of Dendrobium officinale(D.officinale)on skin aging.Methods:The ingredients of D.officinale were detected by UHPLC-Q-TOF/MS.The targets of D.officinale were screened by Swiss Target Prediction database.GeneCards,NCBI,and OMIM databases were utilized to find out the targets associated with skin photoaging.Overlapping targets of D.officinale and skin photoaging were obtained by Venn analysis.The ingredient-disease target network and protein-protein interaction network were constructed by using the STRING database and Cytoscape software.The key compounds and hub genes were obtained by analyzing networks.The DAVID database was applied for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis of the overlapping targets.Autodock Vina software was used to simulate molecular docking and the results were visualized using Pymol.Finally,the skin photoaging models of cells and mice were established to validate the results predicted by network pharmacology.Results:In D.officinale,a total of 59 compounds and 595 targets were detected,of which 59 proteins were intersectional with skin photoaging targets.The top 10 active ingredients(Dendrophenol,Herbacetin,Lyoniresinol,Trans-ferulaldehyde,Naringenin,and so on)and 8 hub genes(AKT1,TNF,VEGFA,MAPK3,CASP3,MMP9,CTNNB1,and EGFR)were identified.All the key active compounds could bind well with core protein targets(binding energy<-5 kcal/mol).The potential therapeutic targets were related to the response to reactive oxygen species,collagen catabolic process,extracellular matrix organization,and apoptotic process,mainly.We also found that D.officinale could enhance the cell viability and activity of anti-oxidases,reduce reactive oxygen species and MMP9 levels,and stable mitochondrial membrane potential.Furthermore,D.officinale could alleviate skin photoaging injury and reduce malondialdehyde level in mice.Conclusion:D.officinale alleviated skin photoaging via regulating oxidative stress,apoptosis,and collagen catabolic process.
基金supported by the Innovation Team and Talent Support Program Project of Traditional Chinese Medicine(ZYYCXTD-D-202405)from National Administration of Traditional Chinese Medicine,the National Natural Science Foundation of China(T2341008)the Pilot Project for Disciplinary Breakthroughs of Ministry of Education(Prevention and Treatment of Multi-System Comorbid Diseases with Traditional Chinese Medicine).
文摘Radiation-induced lung injury(RILI)is a common complication of cancer radiotherapy,yet effective treatments remain elusive.Compound Kushen injection(CKI),a traditional Chinese medicine(TCM)formula,is widely used in clinical practice for treating radiation-related diseases and as an adjunct therapy for cancer and has demonstrated some effectiveness.However,the mechanisms underlying CKI intervention in RILI and its role in cancer adjunctive therapy remain unclear.In this study,we refined previous statistical approaches and successfully integrated quantitative data on the compounds in CKI.We constructed a network-based holistic target model and developed modular biological networks to explore the modular regulatory effects of CKI in RILI.Through this network-based analysis,we identified specific alkaloid components of CKI that contribute to its therapeutic effect in alleviating RILI.Furthermore,through transcriptomic analysis,we confirmed that oxidative stress plays a central role in the treatment of RILI by CKI.The modular regulatory effects of CKI have been validated in animal models of irradiation,demonstrating the ability of CKI to alleviate oxidative stress,reduce inflammation,regulate immune responses,and inhibit apoptosis.In addition,we demonstrated that nuclear factor erythroid 2-related factor 2(NRF2)serves as a key mediator of the antioxidant effects of CKI.Matrine and sophoridine,representative alkaloids in CKI,exhibit binding interactions with NRF2.CKI promotes the nuclear translocation of NRF2,and NRF2 activates its downstream targets,such as heme oxygenase-1(HO-1)and NAD(P)H quinone dehydrogenase 1(NQO1),to suppress oxidative stress in RILI.This,in turn,inhibits the expression of inflammatory molecules,including interleukin(IL)-6,tumor necrosis factor(TNF)-α,and inducible nitric oxide synthase(iNOS),while promoting the activity of antioxidants such as superoxide dismutase(SOD)and glutathione peroxidase-4(GPX-4),thereby exerting therapeutic effects on RILI.
