This research proposes an improved Puma optimization algorithm(IPuma)as a novel dynamic recon-figuration tool for a photovoltaic(PV)array linked in total-cross-tied(TCT).The proposed algorithm utilizes the Newton-Raph...This research proposes an improved Puma optimization algorithm(IPuma)as a novel dynamic recon-figuration tool for a photovoltaic(PV)array linked in total-cross-tied(TCT).The proposed algorithm utilizes the Newton-Raphson search rule(NRSR)to boost the exploration process,especially in search spaces with more local regions,and boost the exploitation with adaptive parameters alternating with random parameters in the original Puma.The effectiveness of the introduced IPuma is confirmed through comprehensive evaluations on the CEC’20 benchmark problems.It shows superior performance compared to both established and modern metaheuristic algorithms in terms of effectively navigating the search space and achieving convergence towards near-optimal regions.The findings indicated that the IPuma algorithm demonstrates considerable statistical promise and surpasses the performance of competing algorithms.In addition,the proposed IPuma is utilized to reconfigure a 9×9 PV array that operates under different shade patterns,such as lower triangular(LT),long wide(LW),and short wide(SW).In addition to other programmed approaches,such as the Whale optimization algorithm(WOA),grey wolf optimizer(GWO),Harris Hawks optimization(HHO),particle swarm optimization(PSO),gravitational search algorithm(GSA),biogeography-based optimization(BBO),sine cosine algorithm(SCA),equilibrium optimizer(EO),and original Puma,the indicated method is contrasted to the traditional configurations of TCT and Sudoku.In addition,the metrics of mismatch power loss,maximum efficiency improvement,efficiency improvement ratio,and peak-to-mean ratio are calculated to assess the effectiveness of the indicated approach.The proposed IPuma improved the generated power by 36.72%,28.03%,and 40.97%for SW,LW,and LT,respectively,outperforming the TCT configuration.In addition,it achieved the best maximum efficiency improvement among the algorithms considered,with 26.86%,21.89%,and 29.07%for the examined patterns.The results highlight the superiority and competence of the proposed approach in both convergence rates and stability,as well as applicability to dynamically reconfigure the PV system and enhance its harvested energy.展开更多
Bread wheat(Triticum aestivum L.)is a staple hexaploid crop with numerous wild relatives.However,domestication and modern breeding have significantly narrowed its genetic diversity,diminishing its capacity to adapt to...Bread wheat(Triticum aestivum L.)is a staple hexaploid crop with numerous wild relatives.However,domestication and modern breeding have significantly narrowed its genetic diversity,diminishing its capacity to adapt to climate change.Wild relatives of wheat serve as a vital reservoir of genetic diversity,offering traits thatenhance its resistance to various biotic and abiotic stresses.Over recent decades,remarkable progress has been made in utilizing superior genes from wild relatives to bolster wheat's defenses against diseases and pests,though the exploration of genes conferring abiotic stress tolerance has lagged behind.In this review,we summarize key advancements in the utilization of wild relatives for wheat enhancement over the past century,emphasizing both theoretical and technological innovations.Furthermore,we evaluate the potential contributions of wild relatives to address production challenges posed by climate change.We also explore strategies for isolating superior genes and developing prebreeding germplasm to support the future development of climate-resilient wheat varieties.展开更多
The ultracold neutron(UCN)transport code,MCUCN,designed initially for simulating UCN transportation from a solid deuterium(SD_2)source and neutron electric dipole moment experiments,could not simulate UCN storage and ...The ultracold neutron(UCN)transport code,MCUCN,designed initially for simulating UCN transportation from a solid deuterium(SD_2)source and neutron electric dipole moment experiments,could not simulate UCN storage and transportation in a superfluid^(4)He(SFHe,He-Ⅱ)source accurately.This limitation arose from the absence of an^(4)He upscattering mechanism and the absorption of^(3)He.And the provided source energy distribution in MCUCN is different from that in SFHe source.This study introduced enhancements to MCUCN to address these constraints,explicitly incorporating the^(4)He upscattering effect,the absorption of^(3)He,the loss caused by impurities on converter wall,UCN source energy distribution in SFHe,and the transmission through negative optical potential.Additionally,a Python-based visualization code for intermediate states and results was developed.To validate these enhancements,we systematically compared the simulation results of the Lujan Center Mark3 UCN system by MCUCN and the improved MCUCN code(iMCUCN)with UCNtransport simulations.Additionally,we compared the results of the SUN1 system simulated by MCUCN and iMCUCN with measurement results.The study demonstrates that iMCUCN effectively simulates the storage and transportation of ultracold neutrons in He-Ⅱ.展开更多
To address the issues of insufficient and imbalanced data samples in proton exchange membrane fuel cell(PEMFC)performance degradation prediction,this study proposes a data augmentation-based model to predict PEMFC per...To address the issues of insufficient and imbalanced data samples in proton exchange membrane fuel cell(PEMFC)performance degradation prediction,this study proposes a data augmentation-based model to predict PEMFC performance degradation.Firstly,an improved generative adversarial network(IGAN)with adaptive gradient penalty coefficient is proposed to address the problems of excessively fast gradient descent and insufficient diversity of generated samples.Then,the IGANis used to generate datawith a distribution analogous to real data,therebymitigating the insufficiency and imbalance of original PEMFC samples and providing the predictionmodel with training data rich in feature information.Finally,a convolutional neural network-bidirectional long short-termmemory(CNN-BiLSTM)model is adopted to predict PEMFC performance degradation.Experimental results show that the data generated by the proposed IGAN exhibits higher quality than that generated by the original GAN,and can fully characterize and enrich the original data’s features.Using the augmented data,the prediction accuracy of the CNN-BiLSTM model is significantly improved,rendering it applicable to tasks of predicting PEMFC performance degradation.展开更多
A performance improvement model of research and development(R&D)institutions based on evolutionary game and Bayesian network is proposed.First,the nature and performance factors of new R&D institutions are sys...A performance improvement model of research and development(R&D)institutions based on evolutionary game and Bayesian network is proposed.First,the nature and performance factors of new R&D institutions are systematically analyzed,the appropriate factor model is found,and the sharing of performance benefits between institutions and employees,the change in distribution proportion,and the risk of institutional improvement and employee cooperation are considered.Second,based on the mechanism improvement and employee cooperation,the payment matrix is given and evolutionary game analysis is carried out to obtain a stable and balanced institutional improvement probability and employee cooperation probability.These two probability values are substituted into the Bayesian network model of performance improvement of new R&D institutions,and the posterior probability of performance improvement is predicted by Bayesian network reasoning and diagnosis to find effective improvement measures.Finally,practical case analysis is given to verify the effectiveness and practicability of the proposed method.展开更多
The internal flow fields within a three-dimensional inward-tunning combined inlet are extremely complex,especially during the engine mode transition,where the tunnel changes may impact the flow fields significantly.To...The internal flow fields within a three-dimensional inward-tunning combined inlet are extremely complex,especially during the engine mode transition,where the tunnel changes may impact the flow fields significantly.To develop an efficient flow field reconstruction model for this,we present an Improved Conditional Denoising Diffusion Generative Adversarial Network(ICDDGAN),which integrates Conditional Denoising Diffusion Probabilistic Models(CDDPMs)with Style GAN,and introduce a reconstruction discrimination mechanism and dynamic loss weight learning strategy.We establish the Mach number flow field dataset by numerical simulation at various backpressures for the mode transition process from turbine mode to ejector ramjet mode at Mach number 2.5.The proposed ICDDGAN model,given only sparse parameter information,can rapidly generate high-quality Mach number flow fields without a large number of samples for training.The results show that ICDDGAN is superior to CDDGAN in terms of training convergence and stability.Moreover,the interpolation and extrapolation test results during backpressure conditions show that ICDDGAN can accurately and quickly reconstruct Mach number fields at various tunnel slice shapes,with a Structural Similarity Index Measure(SSIM)of over 0.96 and a Mean-Square Error(MSE)of 0.035%to actual flow fields,reducing time costs by 7-8 orders of magnitude compared to Computational Fluid Dynamics(CFD)calculations.This can provide an efficient means for rapid computation of complex flow fields.展开更多
Soil improvement is one of the most important issues in geotechnical engineering practice.The wide application of traditional improvement techniques(cement/chemical materials)are limited due to damage ecological en-vi...Soil improvement is one of the most important issues in geotechnical engineering practice.The wide application of traditional improvement techniques(cement/chemical materials)are limited due to damage ecological en-vironment and intensify carbon emissions.However,the use of microbially induced calcium carbonate pre-cipitation(MICP)to obtain bio-cement is a novel technique with the potential to induce soil stability,providing a low-carbon,environment-friendly,and sustainable integrated solution for some geotechnical engineering pro-blems in the environment.This paper presents a comprehensive review of the latest progress in soil improvement based on the MICP strategy.It systematically summarizes and overviews the mineralization mechanism,influ-encing factors,improved methods,engineering characteristics,and current field application status of the MICP.Additionally,it also explores the limitations and correspondingly proposes prospective applications via the MICP approach for soil improvement.This review indicates that the utilization of different environmental calcium-based wastes in MICP and combination of materials and MICP are conducive to meeting engineering and market demand.Furthermore,we recommend and encourage global collaborative study and practice with a view to commercializing MICP technique in the future.The current review purports to provide insights for engineers and interdisciplinary researchers,and guidance for future engineering applications.展开更多
Dear Editor,This letter focuses on how an attacker can design suitable improved zero-dynamics (ZD) attack signal based on state estimates of target system. Improved ZD attack is to change zero dynamic gain matrix of a...Dear Editor,This letter focuses on how an attacker can design suitable improved zero-dynamics (ZD) attack signal based on state estimates of target system. Improved ZD attack is to change zero dynamic gain matrix of attack signal to a matrix with determinant greater than 1.展开更多
Background Dynamic interpersonal therapy(DIT)is a short-term psychodynamic psychotherapy that has been shown to effectively reduce depressive symptoms in patients with major depressive disorder(MDD).In DIT,the depress...Background Dynamic interpersonal therapy(DIT)is a short-term psychodynamic psychotherapy that has been shown to effectively reduce depressive symptoms in patients with major depressive disorder(MDD).In DIT,the depressive symptoms are formulated as responses to impaired mentalisation.DIT aims to alleviate depressive symptoms by improving mentalising.Aims This study aimed to examine the effect of DIT on improving mentalising and the mediating effect of mentalising in changes in depressive symptoms.Methods Outpatients received either DIT combined with antidepressant medication treatment(DIT group)or antidepressant medication treatment alone(ADM group)for 16 weeks.The Hamilton Depression Rating Scale(HAMD),Patient Health Questionnaire(PHQ)and Reflective Functioning Questionnaire(RFQ)were used.The intention-to-treat principle,mixed linear models,multiple imputation,Pearson's correlation analysis and mediation analysis were conducted.The per-protocol principle was used as sensitivity analysis.Results The DIT group had significantly lower HAMD(least-squares(LS)mean difference=-3.756,p<0.001),PHQ(LS mean difference=-4.188,p<0.001),uncertainty about mental states in the RFQ(RFQ-U,LS mean difference=-2.116,p<0.001)and higher certainty about mental states in the RFQ(RFQ-C,LS mean difference=2.214,p=0.028)scores than the ADM group at post-treatment.The change in RFQ-C was marginally significantly correlated with the change in HAMD(r=-0.218,poretao=0.090),The change in RFQ-U was significantly correlated with the change in HAMD(r=-0.269,poroco-0.024)and the change in PHQ(r=-0.43,Peoretceo l<e0.001).When using RFQ-U as the mediating variable and PHQ as the dependent variable,a significant mediating effect was found(p=0.043,95% confidence interval 0.024 to 1.453).Conclusions The DIT group yielded better outcomes compared with the ADM group in reducing depressive symptoms and improving mentalising.Improvements in mentalising were associated with reductions in depressive symptoms.These findings support that mentalising may contribute to the therapeutic effects of DIT in MDD.展开更多
Objective The primary objective was to evaluate the impact of clinical decision support(CDS)tool integration into primary care visits on depression screening and follow-up rates and to assess whether CDS use improves ...Objective The primary objective was to evaluate the impact of clinical decision support(CDS)tool integration into primary care visits on depression screening and follow-up rates and to assess whether CDS use improves adherence to Health Resources and Services Administration(HRSA)guidelines for depression screening and follow-up.Design This quality improvement evaluation study employed quantitative and qualitative components conducted in parallel to provide complementary insights.Modified Poisson regression with generalised estimating equation(GEE)was used to assess the association between CDS tool use and meeting HRSA criteria for depression screening and follow-up.In addition,semi-structured interviews explored perspectives on the implementation and utility of CDS tools.Setting This study was conducted at a federally qualified health centre in Minnesota.Participant The dataset included 12338 patient encounters attributed to 8647 unique patients,covering 2 years of data.Five care providers were recruited through purposive sampling for the semi-structured interviews.Result CDS use was significantly associated with an increased likelihood of meeting HRSA depression screening and follow-up criteria(relative risk 1.44,95%CI 1.34 to 1.55;p<0.001).Qualitative findings suggested that while providers found CDS tools useful,workflow challenges and human-centred practices shaped their effectiveness.Conclusion Integrating CDS tools into primary care workflows can enhance adherence to depression screening and follow-up guidelines.However,their effectiveness relies on supportive person-centred approaches,including collaboration and previsit preparation.These findings highlight the need for a balanced approach that integrates technological interventions with human interaction to enhance clinical practices.Future research should investigate how CDS tools are used in practice,address barriers to their adoption and develop strategies to promote their broader use while fostering continued learning among providers.展开更多
Due to the lack of accurate data and complex parameterization,the prediction of groundwater depth is a chal-lenge for numerical models.Machine learning can effectively solve this issue and has been proven useful in th...Due to the lack of accurate data and complex parameterization,the prediction of groundwater depth is a chal-lenge for numerical models.Machine learning can effectively solve this issue and has been proven useful in the prediction of groundwater depth in many areas.In this study,two new models are applied to the prediction of groundwater depth in the Ningxia area,China.The two models combine the improved dung beetle optimizer(DBO)algorithm with two deep learning models:The Multi-head Attention-Convolution Neural Network-Long Short Term Memory networks(MH-CNN-LSTM)and the Multi-head Attention-Convolution Neural Network-Gated Recurrent Unit(MH-CNN-GRU).The models with DBO show better prediction performance,with larger R(correlation coefficient),RPD(residual prediction deviation),and lower RMSE(root-mean-square error).Com-pared with the models with the original DBO,the R and RPD of models with the improved DBO increase by over 1.5%,and the RMSE decreases by over 1.8%,indicating better prediction results.In addition,compared with the multiple linear regression model,a traditional statistical model,deep learning models have better prediction performance.展开更多
Photosynthesis is one the most important chemical reaction in plants,and it is the ultimate energy source of any living organisms.The light and dark reactions are two essential phases of photosynthesis.Light reaction ...Photosynthesis is one the most important chemical reaction in plants,and it is the ultimate energy source of any living organisms.The light and dark reactions are two essential phases of photosynthesis.Light reaction harvests light energy to synthesize ATP and NADPH through an electron transport chain,and as well as giving out O_(2);dark reaction fixes CO_(2) into six carbon sugars by utilizing NADPH and energy from ATP.Subsequently,plants convert optical energy into chemical energy for maintaining growth and development through absorbing light energy.Here,firstly,we highlighted the biological importance of photosynthesis,and hormones and metabolites,photosynthetic and regulating enzymes,and signaling components that collectively regulate photosynthesis in tomato.Next,we reviewed the advances in tomato photosynthesis,including two aspects of genetic basis and genetic improvement.Numerous genes regulating tomato photosynthesis are gradually uncovered,and the interaction network among those genes remains to be constructed.Finally,the photosynthesis occurring in fruit of tomato and the relationship between photosynthesis in leaf and fruit were discussed.Leaves and fruits are photosynthate sources and sinks of tomato respectively,and interaction between photosynthesis in leaf and fruit exists.Additionally,future perspectives that needs to be addressed on tomato photosynthesis were proposed.展开更多
Owing to the outstanding optoelectronic properties of perovskite materials,perovskite solar cells(PSCs)have been widely studied by academic organizations and industry corporations,with great potential to become the ne...Owing to the outstanding optoelectronic properties of perovskite materials,perovskite solar cells(PSCs)have been widely studied by academic organizations and industry corporations,with great potential to become the next-generation commercial solar cells.However,critical challenges remain in preserving high efficiency practical large-scale commercialized PSCs:a)the long-term stability of the cell materials and devices,b)lead leakage,and c)methods to scale the cells for larger area applications.This paper summarizes the prior-art strategies to address the above challenges,including the latest studies on the traditional glass-glass and thin-film encapsulation methods to better improve the reliability of PSCs,new technologies for preventing lead leakage,and geometric improvement strategies to enhance the reliability,efficiency,and performance of perovskite solar modules(PSMs).Through these strategies,the device achieved enhanced performance in long-term stability tests.The encapsulation resulted in a high lead leakage inhibition rate of up to 99%,and the PSMs possessed a geometric fill factor of 99.6%and a power conversion efficiency(PCE)of 20.7%.The dramatic improvement of efficiency and reliability of perovskite solar cells and modules indicate the great potential for mass production and commer-cialization of perovskite solar applications in the near future.展开更多
Aiming at the problem that the existing algorithms for vehicle detection in smart factories are difficult to detect partial occlusion of vehicles,vulnerable to background interference,lack of global vision,and excessi...Aiming at the problem that the existing algorithms for vehicle detection in smart factories are difficult to detect partial occlusion of vehicles,vulnerable to background interference,lack of global vision,and excessive suppression of real targets,which ultimately cause accuracy degradation.At the same time,to facilitate the subsequent positioning of vehicles in the factory,this paper proposes an improved YOLOv8 algorithm.Firstly,the RFCAConv module is combined to improve the original YOLOv8 backbone.Pay attention to the different features in the receptive field,and give priority to the spatial features of the receptive field to capture more vehicle feature information and solve the problem that the vehicle is partially occluded and difficult to detect.Secondly,the SFE module is added to the neck of v8,which improves the saliency of the target in the reasoning process and reduces the influence of background interference on vehicle detection.Finally,the head of the RT-DETR algorithm is used to replace the head in the original YOLOv8 algorithm,which avoids the excessive suppression of the real target while combining the context information.The experimental results show that compared with the original YOLOv8 algorithm,the detection accuracy of the improved YOLOv8 algorithm is improved by 4.6%on the self-made smart factory data set,and the detection speed also meets the real-time requirements of smart factory vehicle detection and subsequent vehicle positioning.展开更多
To improve the resilience of railway stations,a typical station was selected as the research object,and an isolation design was introduced.Twenty-four groups of near-fault pulse-like ground motions were selected.The s...To improve the resilience of railway stations,a typical station was selected as the research object,and an isolation design was introduced.Twenty-four groups of near-fault pulse-like ground motions were selected.The seismic resilience of the no-isolation railway stations(NIRS)and the isolation railway stations(IRS)were compared to provide a numerical result of the improvement in resilience.The results show that in the station isolation design,the station's functional requirements and structural characteristics should be considered and the appropriate placement of isolation bearings is under the waiting room.Under the action of a rare earthquake,the repair cost,repair time,rate of harm and death of the IRS were decreased by 8.04 million,18.30 days,6.93×10^(-3)and 1.21×10^(-3),respectively,when compared to the NIRS.The IRS received a seismic resilience grade of three-stars and the NIRS only one-star,indicating that rational isolation design improves the seismic resilience of stations.Thus,for the design of stations close to earthquake faults,it is suggested to utilize appropriate isolation techniques to improve their seismic resilience.展开更多
To overcome the challenges of poor real-time performance,limited scalability,and low intelligence in conventional jamming pattern recognition methods,this paper proposes a method based on Wavelet Packet Decomposition(...To overcome the challenges of poor real-time performance,limited scalability,and low intelligence in conventional jamming pattern recognition methods,this paper proposes a method based on Wavelet Packet Decomposition(WPD)and enhanced deep learning techniques.In the proposed method,an agent at the receiver processes the received signal using WPD to generate an initial Spectrogram Waterfall(SW),which is subsequently segmented using a sliding window to serve as the input for the jamming recognition network.The network employs a bilateral filter to preprocess the input SW,thereby enhancing the edge features of the jamming signals.To extract abstract features,depthwise separable convolution is utilized instead of traditional convolution,thereby reducing the network’s parameter count and enhancing real-time performance.A pyramid pooling layer is integrated before the fully connected layer to enable the network to process input SW of varying sizes,thus enhancing scalability.During network training,adaptive moment estimation is employed as the optimizer,allowing the network to dynamically adjust the learning rate and accelerate convergence.A comprehensive comparison between the proposed jamming recognition network and six other models is conducted,along with Ablation Experiments(AE)based on numerical simulations.Simulation results demonstrate that the proposed method based on WPD and enhanced deep learning achieves high-precision recognition of various jamming patterns while maintaining a favorable balance among prediction accuracy,network complexity,and prediction time.展开更多
To solve the problem of low detection accuracy for complex weld defects,the paper proposes a weld defects detection method based on improved YOLOv5s.To enhance the ability to focus on key information in feature maps,t...To solve the problem of low detection accuracy for complex weld defects,the paper proposes a weld defects detection method based on improved YOLOv5s.To enhance the ability to focus on key information in feature maps,the scSE attention mechanism is intro-duced into the backbone network of YOLOv5s.A Fusion-Block module and additional layers are added to the neck network of YOLOv5s to improve the effect of feature fusion,which is to meet the needs of complex object detection.To reduce the computation-al complexity of the model,the C3Ghost module is used to replace the CSP2_1 module in the neck network of YOLOv5s.The scSE-ASFF module is constructed and inserted between the neck network and the prediction end,which is to realize the fusion of features between the different layers.To address the issue of imbalanced sample quality in the dataset and improve the regression speed and accuracy of the loss function,the CIoU loss function in the YOLOv5s model is replaced with the Focal-EIoU loss function.Finally,ex-periments are conducted based on the collected weld defect dataset to verify the feasibility of the improved YOLOv5s for weld defects detection.The experimental results show that the precision and mAP of the improved YOLOv5s in detecting complex weld defects are as high as 83.4%and 76.1%,respectively,which are 2.5%and 7.6%higher than the traditional YOLOv5s model.The proposed weld defects detection method based on the improved YOLOv5s in this paper can effectively solve the problem of low weld defects detection accuracy.展开更多
This study tested the electrical conductivity and pressure sensitivity of lime⁃improved silty sand reinforced with Carbon Fiber Powder(CFP)as the conductive medium.The influence of CFP dosage,moisture content and curi...This study tested the electrical conductivity and pressure sensitivity of lime⁃improved silty sand reinforced with Carbon Fiber Powder(CFP)as the conductive medium.The influence of CFP dosage,moisture content and curing duration on the unconfined compressive strength,initial resistivity and pressure sensitivity of the improved soil was systematically analysed.The results showed that the unconfined compressive strength varied non⁃monotonically with increasing CFP dosage,reaching a peak at a dosage of 1.6%.Furthermore,the initial resistivity showed slight variations under different moisture conditions but eventually converged towards the conductive percolation threshold at a dosage of 2.4%.It is worth noting that CFP reinforced lime⁃improved silty sand(CRLS)exhibit a clear dynamic synchronization of strain with stress and resistivity rate of variation.The pressure sensitivity was optimized with CFP dosages ranging from 1.6%to 2.0%.Both insufficient and excessive dosages had a negative impact on pressure sensitivity.It is important to consider the weakening effect of high moisture content on the pressure sensitivity of the specimens in practical applications.展开更多
To improve the efficiency and accuracy of path planning for fan inspection tasks in thermal power plants,this paper proposes an intelligent inspection robot path planning scheme based on an improved A^(*)algorithm.The...To improve the efficiency and accuracy of path planning for fan inspection tasks in thermal power plants,this paper proposes an intelligent inspection robot path planning scheme based on an improved A^(*)algorithm.The inspection robot utilizes multiple sensors to monitor key parameters of the fans,such as vibration,noise,and bearing temperature,and upload the data to the monitoring center.The robot’s inspection path employs the improved A^(*)algorithm,incorporating obstacle penalty terms,path reconstruction,and smoothing optimization techniques,thereby achieving optimal path planning for the inspection robot in complex environments.Simulation results demonstrate that the improved A^(*)algorithm significantly outperforms the traditional A^(*)algorithm in terms of total path distance,smoothness,and detour rate,effectively improving the execution efficiency of inspection tasks.展开更多
When the maneuverability of a pursuer is not significantly higher than that of an evader,it will be difficult to intercept the evader with only one pursuer.Therefore,this article adopts a two-to-one differential game ...When the maneuverability of a pursuer is not significantly higher than that of an evader,it will be difficult to intercept the evader with only one pursuer.Therefore,this article adopts a two-to-one differential game strategy,the game of kind is generally considered to be angle-optimized,which allows unlimited turns,but these practices do not take into account the effect of acceleration,which does not correspond to the actual situation,thus,based on the angle-optimized,the acceleration optimization and the acceleration upper bound constraint are added into the game for consideration.A two-to-one differential game problem is proposed in the three-dimensional space,and an improved multi-objective grey wolf optimization(IMOGWO)algorithm is proposed to solve the optimal game point of this problem.With the equations that describe the relative motions between the pursuers and the evader in the three-dimensional space,a multi-objective function with constraints is given as the performance index to design an optimal strategy for the differential game.Then the optimal game point is solved by using the IMOGWO algorithm.It is proved based on Markov chains that with the IMOGWO,the Pareto solution set is the solution of the differential game.Finally,it is verified through simulations that the pursuers can capture the escapee,and via comparative experiments,it is shown that the IMOGWO algorithm performs well in terms of running time and memory usage.展开更多
基金funded by the Deanship of Scientific Research and Libraries,Princess Nourah bint Abdulrahman University,through the Program of Research Project Funding After Publication,grant No.(RPFAP-82-1445)。
文摘This research proposes an improved Puma optimization algorithm(IPuma)as a novel dynamic recon-figuration tool for a photovoltaic(PV)array linked in total-cross-tied(TCT).The proposed algorithm utilizes the Newton-Raphson search rule(NRSR)to boost the exploration process,especially in search spaces with more local regions,and boost the exploitation with adaptive parameters alternating with random parameters in the original Puma.The effectiveness of the introduced IPuma is confirmed through comprehensive evaluations on the CEC’20 benchmark problems.It shows superior performance compared to both established and modern metaheuristic algorithms in terms of effectively navigating the search space and achieving convergence towards near-optimal regions.The findings indicated that the IPuma algorithm demonstrates considerable statistical promise and surpasses the performance of competing algorithms.In addition,the proposed IPuma is utilized to reconfigure a 9×9 PV array that operates under different shade patterns,such as lower triangular(LT),long wide(LW),and short wide(SW).In addition to other programmed approaches,such as the Whale optimization algorithm(WOA),grey wolf optimizer(GWO),Harris Hawks optimization(HHO),particle swarm optimization(PSO),gravitational search algorithm(GSA),biogeography-based optimization(BBO),sine cosine algorithm(SCA),equilibrium optimizer(EO),and original Puma,the indicated method is contrasted to the traditional configurations of TCT and Sudoku.In addition,the metrics of mismatch power loss,maximum efficiency improvement,efficiency improvement ratio,and peak-to-mean ratio are calculated to assess the effectiveness of the indicated approach.The proposed IPuma improved the generated power by 36.72%,28.03%,and 40.97%for SW,LW,and LT,respectively,outperforming the TCT configuration.In addition,it achieved the best maximum efficiency improvement among the algorithms considered,with 26.86%,21.89%,and 29.07%for the examined patterns.The results highlight the superiority and competence of the proposed approach in both convergence rates and stability,as well as applicability to dynamically reconfigure the PV system and enhance its harvested energy.
基金supported by the Biological Breeding-National Science and Technology Major Project(2023ZD04071)the National Key Research and Development Program of China(2023YFF1000600)and the National Natural Science Foundation of China(32272084,32372089,and 31971887).
文摘Bread wheat(Triticum aestivum L.)is a staple hexaploid crop with numerous wild relatives.However,domestication and modern breeding have significantly narrowed its genetic diversity,diminishing its capacity to adapt to climate change.Wild relatives of wheat serve as a vital reservoir of genetic diversity,offering traits thatenhance its resistance to various biotic and abiotic stresses.Over recent decades,remarkable progress has been made in utilizing superior genes from wild relatives to bolster wheat's defenses against diseases and pests,though the exploration of genes conferring abiotic stress tolerance has lagged behind.In this review,we summarize key advancements in the utilization of wild relatives for wheat enhancement over the past century,emphasizing both theoretical and technological innovations.Furthermore,we evaluate the potential contributions of wild relatives to address production challenges posed by climate change.We also explore strategies for isolating superior genes and developing prebreeding germplasm to support the future development of climate-resilient wheat varieties.
基金the National Key R&D Program of China(No.2024YFE0110001)the National Natural Science Foundation of China(U1932219)the Mobility Programme endorsed by the Joint Committee of the Sino-German Center(M0728)。
文摘The ultracold neutron(UCN)transport code,MCUCN,designed initially for simulating UCN transportation from a solid deuterium(SD_2)source and neutron electric dipole moment experiments,could not simulate UCN storage and transportation in a superfluid^(4)He(SFHe,He-Ⅱ)source accurately.This limitation arose from the absence of an^(4)He upscattering mechanism and the absorption of^(3)He.And the provided source energy distribution in MCUCN is different from that in SFHe source.This study introduced enhancements to MCUCN to address these constraints,explicitly incorporating the^(4)He upscattering effect,the absorption of^(3)He,the loss caused by impurities on converter wall,UCN source energy distribution in SFHe,and the transmission through negative optical potential.Additionally,a Python-based visualization code for intermediate states and results was developed.To validate these enhancements,we systematically compared the simulation results of the Lujan Center Mark3 UCN system by MCUCN and the improved MCUCN code(iMCUCN)with UCNtransport simulations.Additionally,we compared the results of the SUN1 system simulated by MCUCN and iMCUCN with measurement results.The study demonstrates that iMCUCN effectively simulates the storage and transportation of ultracold neutrons in He-Ⅱ.
基金supported by the Jiangsu Engineering Research Center of the Key Technology for Intelligent Manufacturing Equipment and the Suqian Key Laboratory of Intelligent Manufacturing(Grant No.M202108).
文摘To address the issues of insufficient and imbalanced data samples in proton exchange membrane fuel cell(PEMFC)performance degradation prediction,this study proposes a data augmentation-based model to predict PEMFC performance degradation.Firstly,an improved generative adversarial network(IGAN)with adaptive gradient penalty coefficient is proposed to address the problems of excessively fast gradient descent and insufficient diversity of generated samples.Then,the IGANis used to generate datawith a distribution analogous to real data,therebymitigating the insufficiency and imbalance of original PEMFC samples and providing the predictionmodel with training data rich in feature information.Finally,a convolutional neural network-bidirectional long short-termmemory(CNN-BiLSTM)model is adopted to predict PEMFC performance degradation.Experimental results show that the data generated by the proposed IGAN exhibits higher quality than that generated by the original GAN,and can fully characterize and enrich the original data’s features.Using the augmented data,the prediction accuracy of the CNN-BiLSTM model is significantly improved,rendering it applicable to tasks of predicting PEMFC performance degradation.
基金supported by the National Natural Science Foundation of China(72071106)Jiangsu Provincial Social Science Fund(23EYA001)+1 种基金Jiangsu Provincial Education Science Planning Fund(Ba/2024/08)Jiangsu Higher Education Association Fund(24FYHLX090)。
文摘A performance improvement model of research and development(R&D)institutions based on evolutionary game and Bayesian network is proposed.First,the nature and performance factors of new R&D institutions are systematically analyzed,the appropriate factor model is found,and the sharing of performance benefits between institutions and employees,the change in distribution proportion,and the risk of institutional improvement and employee cooperation are considered.Second,based on the mechanism improvement and employee cooperation,the payment matrix is given and evolutionary game analysis is carried out to obtain a stable and balanced institutional improvement probability and employee cooperation probability.These two probability values are substituted into the Bayesian network model of performance improvement of new R&D institutions,and the posterior probability of performance improvement is predicted by Bayesian network reasoning and diagnosis to find effective improvement measures.Finally,practical case analysis is given to verify the effectiveness and practicability of the proposed method.
文摘The internal flow fields within a three-dimensional inward-tunning combined inlet are extremely complex,especially during the engine mode transition,where the tunnel changes may impact the flow fields significantly.To develop an efficient flow field reconstruction model for this,we present an Improved Conditional Denoising Diffusion Generative Adversarial Network(ICDDGAN),which integrates Conditional Denoising Diffusion Probabilistic Models(CDDPMs)with Style GAN,and introduce a reconstruction discrimination mechanism and dynamic loss weight learning strategy.We establish the Mach number flow field dataset by numerical simulation at various backpressures for the mode transition process from turbine mode to ejector ramjet mode at Mach number 2.5.The proposed ICDDGAN model,given only sparse parameter information,can rapidly generate high-quality Mach number flow fields without a large number of samples for training.The results show that ICDDGAN is superior to CDDGAN in terms of training convergence and stability.Moreover,the interpolation and extrapolation test results during backpressure conditions show that ICDDGAN can accurately and quickly reconstruct Mach number fields at various tunnel slice shapes,with a Structural Similarity Index Measure(SSIM)of over 0.96 and a Mean-Square Error(MSE)of 0.035%to actual flow fields,reducing time costs by 7-8 orders of magnitude compared to Computational Fluid Dynamics(CFD)calculations.This can provide an efficient means for rapid computation of complex flow fields.
基金funded by the National Natural Science Foundation of China(No.41962016)the Natural Science Foundation of NingXia(Nos.2023AAC02023,2023A1218,and 2021AAC02006).
文摘Soil improvement is one of the most important issues in geotechnical engineering practice.The wide application of traditional improvement techniques(cement/chemical materials)are limited due to damage ecological en-vironment and intensify carbon emissions.However,the use of microbially induced calcium carbonate pre-cipitation(MICP)to obtain bio-cement is a novel technique with the potential to induce soil stability,providing a low-carbon,environment-friendly,and sustainable integrated solution for some geotechnical engineering pro-blems in the environment.This paper presents a comprehensive review of the latest progress in soil improvement based on the MICP strategy.It systematically summarizes and overviews the mineralization mechanism,influ-encing factors,improved methods,engineering characteristics,and current field application status of the MICP.Additionally,it also explores the limitations and correspondingly proposes prospective applications via the MICP approach for soil improvement.This review indicates that the utilization of different environmental calcium-based wastes in MICP and combination of materials and MICP are conducive to meeting engineering and market demand.Furthermore,we recommend and encourage global collaborative study and practice with a view to commercializing MICP technique in the future.The current review purports to provide insights for engineers and interdisciplinary researchers,and guidance for future engineering applications.
基金supported in part by the National Natural Science Foundation of China(61873106,62303109)Start-Up Research Fund of Southeast University(RF1028623002)Shenzhen Science and Technology Program(JCYJ20230807114609019)
文摘Dear Editor,This letter focuses on how an attacker can design suitable improved zero-dynamics (ZD) attack signal based on state estimates of target system. Improved ZD attack is to change zero dynamic gain matrix of attack signal to a matrix with determinant greater than 1.
基金funded by Science and Technology Commission of Shanghai Municipality(No.21Y11905400)National Natural ScienceFoundationof China(General Program,No.82371555).
文摘Background Dynamic interpersonal therapy(DIT)is a short-term psychodynamic psychotherapy that has been shown to effectively reduce depressive symptoms in patients with major depressive disorder(MDD).In DIT,the depressive symptoms are formulated as responses to impaired mentalisation.DIT aims to alleviate depressive symptoms by improving mentalising.Aims This study aimed to examine the effect of DIT on improving mentalising and the mediating effect of mentalising in changes in depressive symptoms.Methods Outpatients received either DIT combined with antidepressant medication treatment(DIT group)or antidepressant medication treatment alone(ADM group)for 16 weeks.The Hamilton Depression Rating Scale(HAMD),Patient Health Questionnaire(PHQ)and Reflective Functioning Questionnaire(RFQ)were used.The intention-to-treat principle,mixed linear models,multiple imputation,Pearson's correlation analysis and mediation analysis were conducted.The per-protocol principle was used as sensitivity analysis.Results The DIT group had significantly lower HAMD(least-squares(LS)mean difference=-3.756,p<0.001),PHQ(LS mean difference=-4.188,p<0.001),uncertainty about mental states in the RFQ(RFQ-U,LS mean difference=-2.116,p<0.001)and higher certainty about mental states in the RFQ(RFQ-C,LS mean difference=2.214,p=0.028)scores than the ADM group at post-treatment.The change in RFQ-C was marginally significantly correlated with the change in HAMD(r=-0.218,poretao=0.090),The change in RFQ-U was significantly correlated with the change in HAMD(r=-0.269,poroco-0.024)and the change in PHQ(r=-0.43,Peoretceo l<e0.001).When using RFQ-U as the mediating variable and PHQ as the dependent variable,a significant mediating effect was found(p=0.043,95% confidence interval 0.024 to 1.453).Conclusions The DIT group yielded better outcomes compared with the ADM group in reducing depressive symptoms and improving mentalising.Improvements in mentalising were associated with reductions in depressive symptoms.These findings support that mentalising may contribute to the therapeutic effects of DIT in MDD.
文摘Objective The primary objective was to evaluate the impact of clinical decision support(CDS)tool integration into primary care visits on depression screening and follow-up rates and to assess whether CDS use improves adherence to Health Resources and Services Administration(HRSA)guidelines for depression screening and follow-up.Design This quality improvement evaluation study employed quantitative and qualitative components conducted in parallel to provide complementary insights.Modified Poisson regression with generalised estimating equation(GEE)was used to assess the association between CDS tool use and meeting HRSA criteria for depression screening and follow-up.In addition,semi-structured interviews explored perspectives on the implementation and utility of CDS tools.Setting This study was conducted at a federally qualified health centre in Minnesota.Participant The dataset included 12338 patient encounters attributed to 8647 unique patients,covering 2 years of data.Five care providers were recruited through purposive sampling for the semi-structured interviews.Result CDS use was significantly associated with an increased likelihood of meeting HRSA depression screening and follow-up criteria(relative risk 1.44,95%CI 1.34 to 1.55;p<0.001).Qualitative findings suggested that while providers found CDS tools useful,workflow challenges and human-centred practices shaped their effectiveness.Conclusion Integrating CDS tools into primary care workflows can enhance adherence to depression screening and follow-up guidelines.However,their effectiveness relies on supportive person-centred approaches,including collaboration and previsit preparation.These findings highlight the need for a balanced approach that integrates technological interventions with human interaction to enhance clinical practices.Future research should investigate how CDS tools are used in practice,address barriers to their adoption and develop strategies to promote their broader use while fostering continued learning among providers.
基金supported by the National Natural Science Foundation of China [grant numbers 42088101 and 42375048]。
文摘Due to the lack of accurate data and complex parameterization,the prediction of groundwater depth is a chal-lenge for numerical models.Machine learning can effectively solve this issue and has been proven useful in the prediction of groundwater depth in many areas.In this study,two new models are applied to the prediction of groundwater depth in the Ningxia area,China.The two models combine the improved dung beetle optimizer(DBO)algorithm with two deep learning models:The Multi-head Attention-Convolution Neural Network-Long Short Term Memory networks(MH-CNN-LSTM)and the Multi-head Attention-Convolution Neural Network-Gated Recurrent Unit(MH-CNN-GRU).The models with DBO show better prediction performance,with larger R(correlation coefficient),RPD(residual prediction deviation),and lower RMSE(root-mean-square error).Com-pared with the models with the original DBO,the R and RPD of models with the improved DBO increase by over 1.5%,and the RMSE decreases by over 1.8%,indicating better prediction results.In addition,compared with the multiple linear regression model,a traditional statistical model,deep learning models have better prediction performance.
基金supported by grants from the National Key Research&Development Plan(Grants Nos.2022YFF10030022022YFD1200502)+7 种基金National Natural Science Foundation of China(Grant Nos.3237269631991182)Wuhan Biological Breeding Major Project(Grant No.2022021302024852)Key Project of Hubei Hongshan Laboratory(2021hszd007)HZAU-AGIS Cooperation Fund(Grant No.SZYJY2023022)Funds for High Quality Development of Hubei Seed Industry(HBZY2023B004)Hubei Agriculture Research System(2023HBSTX4-06)Hubei Key Research&Development Plan(Grants Nos.2022BBA0066,2022BBA0062)。
文摘Photosynthesis is one the most important chemical reaction in plants,and it is the ultimate energy source of any living organisms.The light and dark reactions are two essential phases of photosynthesis.Light reaction harvests light energy to synthesize ATP and NADPH through an electron transport chain,and as well as giving out O_(2);dark reaction fixes CO_(2) into six carbon sugars by utilizing NADPH and energy from ATP.Subsequently,plants convert optical energy into chemical energy for maintaining growth and development through absorbing light energy.Here,firstly,we highlighted the biological importance of photosynthesis,and hormones and metabolites,photosynthetic and regulating enzymes,and signaling components that collectively regulate photosynthesis in tomato.Next,we reviewed the advances in tomato photosynthesis,including two aspects of genetic basis and genetic improvement.Numerous genes regulating tomato photosynthesis are gradually uncovered,and the interaction network among those genes remains to be constructed.Finally,the photosynthesis occurring in fruit of tomato and the relationship between photosynthesis in leaf and fruit were discussed.Leaves and fruits are photosynthate sources and sinks of tomato respectively,and interaction between photosynthesis in leaf and fruit exists.Additionally,future perspectives that needs to be addressed on tomato photosynthesis were proposed.
基金supported by the National Natural Science Foundation of China(No.62404041)the Natural Science Foundation of Jiangsu Province of China(No.BK20230830).
文摘Owing to the outstanding optoelectronic properties of perovskite materials,perovskite solar cells(PSCs)have been widely studied by academic organizations and industry corporations,with great potential to become the next-generation commercial solar cells.However,critical challenges remain in preserving high efficiency practical large-scale commercialized PSCs:a)the long-term stability of the cell materials and devices,b)lead leakage,and c)methods to scale the cells for larger area applications.This paper summarizes the prior-art strategies to address the above challenges,including the latest studies on the traditional glass-glass and thin-film encapsulation methods to better improve the reliability of PSCs,new technologies for preventing lead leakage,and geometric improvement strategies to enhance the reliability,efficiency,and performance of perovskite solar modules(PSMs).Through these strategies,the device achieved enhanced performance in long-term stability tests.The encapsulation resulted in a high lead leakage inhibition rate of up to 99%,and the PSMs possessed a geometric fill factor of 99.6%and a power conversion efficiency(PCE)of 20.7%.The dramatic improvement of efficiency and reliability of perovskite solar cells and modules indicate the great potential for mass production and commer-cialization of perovskite solar applications in the near future.
基金funded by Changzhou Science and Technology Project(No.CZ20230025)Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.XSJCX23_36).
文摘Aiming at the problem that the existing algorithms for vehicle detection in smart factories are difficult to detect partial occlusion of vehicles,vulnerable to background interference,lack of global vision,and excessive suppression of real targets,which ultimately cause accuracy degradation.At the same time,to facilitate the subsequent positioning of vehicles in the factory,this paper proposes an improved YOLOv8 algorithm.Firstly,the RFCAConv module is combined to improve the original YOLOv8 backbone.Pay attention to the different features in the receptive field,and give priority to the spatial features of the receptive field to capture more vehicle feature information and solve the problem that the vehicle is partially occluded and difficult to detect.Secondly,the SFE module is added to the neck of v8,which improves the saliency of the target in the reasoning process and reduces the influence of background interference on vehicle detection.Finally,the head of the RT-DETR algorithm is used to replace the head in the original YOLOv8 algorithm,which avoids the excessive suppression of the real target while combining the context information.The experimental results show that compared with the original YOLOv8 algorithm,the detection accuracy of the improved YOLOv8 algorithm is improved by 4.6%on the self-made smart factory data set,and the detection speed also meets the real-time requirements of smart factory vehicle detection and subsequent vehicle positioning.
基金National Natural Science Foundation of China under Grant No.52278534Sichuan Provincial Natural Science Foundation of China under Grant No.2022NSFSC0423。
文摘To improve the resilience of railway stations,a typical station was selected as the research object,and an isolation design was introduced.Twenty-four groups of near-fault pulse-like ground motions were selected.The seismic resilience of the no-isolation railway stations(NIRS)and the isolation railway stations(IRS)were compared to provide a numerical result of the improvement in resilience.The results show that in the station isolation design,the station's functional requirements and structural characteristics should be considered and the appropriate placement of isolation bearings is under the waiting room.Under the action of a rare earthquake,the repair cost,repair time,rate of harm and death of the IRS were decreased by 8.04 million,18.30 days,6.93×10^(-3)and 1.21×10^(-3),respectively,when compared to the NIRS.The IRS received a seismic resilience grade of three-stars and the NIRS only one-star,indicating that rational isolation design improves the seismic resilience of stations.Thus,for the design of stations close to earthquake faults,it is suggested to utilize appropriate isolation techniques to improve their seismic resilience.
基金supported by National Natural Science Foundation of China under Grant U23A20279China Electronics Tian’ao Innovation Theory and Technology Group Fund under Grand 20221193-04-04.
文摘To overcome the challenges of poor real-time performance,limited scalability,and low intelligence in conventional jamming pattern recognition methods,this paper proposes a method based on Wavelet Packet Decomposition(WPD)and enhanced deep learning techniques.In the proposed method,an agent at the receiver processes the received signal using WPD to generate an initial Spectrogram Waterfall(SW),which is subsequently segmented using a sliding window to serve as the input for the jamming recognition network.The network employs a bilateral filter to preprocess the input SW,thereby enhancing the edge features of the jamming signals.To extract abstract features,depthwise separable convolution is utilized instead of traditional convolution,thereby reducing the network’s parameter count and enhancing real-time performance.A pyramid pooling layer is integrated before the fully connected layer to enable the network to process input SW of varying sizes,thus enhancing scalability.During network training,adaptive moment estimation is employed as the optimizer,allowing the network to dynamically adjust the learning rate and accelerate convergence.A comprehensive comparison between the proposed jamming recognition network and six other models is conducted,along with Ablation Experiments(AE)based on numerical simulations.Simulation results demonstrate that the proposed method based on WPD and enhanced deep learning achieves high-precision recognition of various jamming patterns while maintaining a favorable balance among prediction accuracy,network complexity,and prediction time.
基金supported by Postgraduate Research&Practice Innovation Program of Jiangsu Province(Grant No.KYCX24_4084).
文摘To solve the problem of low detection accuracy for complex weld defects,the paper proposes a weld defects detection method based on improved YOLOv5s.To enhance the ability to focus on key information in feature maps,the scSE attention mechanism is intro-duced into the backbone network of YOLOv5s.A Fusion-Block module and additional layers are added to the neck network of YOLOv5s to improve the effect of feature fusion,which is to meet the needs of complex object detection.To reduce the computation-al complexity of the model,the C3Ghost module is used to replace the CSP2_1 module in the neck network of YOLOv5s.The scSE-ASFF module is constructed and inserted between the neck network and the prediction end,which is to realize the fusion of features between the different layers.To address the issue of imbalanced sample quality in the dataset and improve the regression speed and accuracy of the loss function,the CIoU loss function in the YOLOv5s model is replaced with the Focal-EIoU loss function.Finally,ex-periments are conducted based on the collected weld defect dataset to verify the feasibility of the improved YOLOv5s for weld defects detection.The experimental results show that the precision and mAP of the improved YOLOv5s in detecting complex weld defects are as high as 83.4%and 76.1%,respectively,which are 2.5%and 7.6%higher than the traditional YOLOv5s model.The proposed weld defects detection method based on the improved YOLOv5s in this paper can effectively solve the problem of low weld defects detection accuracy.
基金Sponsored by Jilin Provincial Department of Education Scientific Research Project(Grant Nos.JJKH20190875KJ,JJKH20230348KJ).
文摘This study tested the electrical conductivity and pressure sensitivity of lime⁃improved silty sand reinforced with Carbon Fiber Powder(CFP)as the conductive medium.The influence of CFP dosage,moisture content and curing duration on the unconfined compressive strength,initial resistivity and pressure sensitivity of the improved soil was systematically analysed.The results showed that the unconfined compressive strength varied non⁃monotonically with increasing CFP dosage,reaching a peak at a dosage of 1.6%.Furthermore,the initial resistivity showed slight variations under different moisture conditions but eventually converged towards the conductive percolation threshold at a dosage of 2.4%.It is worth noting that CFP reinforced lime⁃improved silty sand(CRLS)exhibit a clear dynamic synchronization of strain with stress and resistivity rate of variation.The pressure sensitivity was optimized with CFP dosages ranging from 1.6%to 2.0%.Both insufficient and excessive dosages had a negative impact on pressure sensitivity.It is important to consider the weakening effect of high moisture content on the pressure sensitivity of the specimens in practical applications.
文摘To improve the efficiency and accuracy of path planning for fan inspection tasks in thermal power plants,this paper proposes an intelligent inspection robot path planning scheme based on an improved A^(*)algorithm.The inspection robot utilizes multiple sensors to monitor key parameters of the fans,such as vibration,noise,and bearing temperature,and upload the data to the monitoring center.The robot’s inspection path employs the improved A^(*)algorithm,incorporating obstacle penalty terms,path reconstruction,and smoothing optimization techniques,thereby achieving optimal path planning for the inspection robot in complex environments.Simulation results demonstrate that the improved A^(*)algorithm significantly outperforms the traditional A^(*)algorithm in terms of total path distance,smoothness,and detour rate,effectively improving the execution efficiency of inspection tasks.
基金National Natural Science Foundation of China(NSFC61773142,NSFC62303136)。
文摘When the maneuverability of a pursuer is not significantly higher than that of an evader,it will be difficult to intercept the evader with only one pursuer.Therefore,this article adopts a two-to-one differential game strategy,the game of kind is generally considered to be angle-optimized,which allows unlimited turns,but these practices do not take into account the effect of acceleration,which does not correspond to the actual situation,thus,based on the angle-optimized,the acceleration optimization and the acceleration upper bound constraint are added into the game for consideration.A two-to-one differential game problem is proposed in the three-dimensional space,and an improved multi-objective grey wolf optimization(IMOGWO)algorithm is proposed to solve the optimal game point of this problem.With the equations that describe the relative motions between the pursuers and the evader in the three-dimensional space,a multi-objective function with constraints is given as the performance index to design an optimal strategy for the differential game.Then the optimal game point is solved by using the IMOGWO algorithm.It is proved based on Markov chains that with the IMOGWO,the Pareto solution set is the solution of the differential game.Finally,it is verified through simulations that the pursuers can capture the escapee,and via comparative experiments,it is shown that the IMOGWO algorithm performs well in terms of running time and memory usage.
