To address the high costs and operational instability of distribution networks caused by the large-scale integration of distributed energy resources(DERs)(such as photovoltaic(PV)systems,wind turbines(WT),and energy s...To address the high costs and operational instability of distribution networks caused by the large-scale integration of distributed energy resources(DERs)(such as photovoltaic(PV)systems,wind turbines(WT),and energy storage(ES)devices),and the increased grid load fluctuations and safety risks due to uncoordinated electric vehicles(EVs)charging,this paper proposes a novel dual-scale hierarchical collaborative optimization strategy.This strategy decouples system-level economic dispatch from distributed EV agent control,effectively solving the resource coordination conflicts arising from the high computational complexity,poor scalability of existing centralized optimization,or the reliance on local information decision-making in fully decentralized frameworks.At the lower level,an EV charging and discharging model with a hybrid discrete-continuous action space is established,and optimized using an improved Parameterized Deep Q-Network(PDQN)algorithm,which directly handles mode selection and power regulation while embedding physical constraints to ensure safety.At the upper level,microgrid(MG)operators adopt a dynamic pricing strategy optimized through Deep Reinforcement Learning(DRL)to maximize economic benefits and achieve peak-valley shaving.Simulation results show that the proposed strategy outperforms traditional methods,reducing the total operating cost of the MG by 21.6%,decreasing the peak-to-valley load difference by 33.7%,reducing the number of voltage limit violations by 88.9%,and lowering the average electricity cost for EV users by 15.2%.This method brings a win-win result for operators and users,providing a reliable and efficient scheduling solution for distribution networks with high renewable energy penetration rates.展开更多
Efficient surface passivation is critical for achieving high-performance perovskite solar cells(PSCs),yet the discovery of optimal passivators remains a time-consuming,trial-and-error process.Here,we report a synergis...Efficient surface passivation is critical for achieving high-performance perovskite solar cells(PSCs),yet the discovery of optimal passivators remains a time-consuming,trial-and-error process.Here,we report a synergistic machine learning(ML)and density functional theory(DFT)approach that enables predictive and rapid identification of effective passivation materials.By training an XGBoost model(91.3%accuracy)with DFT-derived molecular descriptors and activity calculations,we identify 2-(4-aminophenyl)-3H-benzimidazol-5-amine(APBIA)as a promising passivator.Experimental validation demonstrates that APBIA effectively removes surface impurities and passivates defects within perovskite films,leading to a significant increase in power conversion efficiency(PCE)from 22.48%to 25.55%(certified as 25.02%).This ML-DFT framework provides a generalizable pathway for accelerating the development of advanced functional materials for photovoltaic applications.展开更多
Federated learning is a machine learning framework designed to protect privacy by keeping training data on clients’devices without sharing private data.It trains a global model through collaboration between clients a...Federated learning is a machine learning framework designed to protect privacy by keeping training data on clients’devices without sharing private data.It trains a global model through collaboration between clients and the server.However,the presence of data heterogeneity can lead to inefficient model training and even reduce the final model’s accuracy and generalization capability.Meanwhile,data scarcity can result in suboptimal cluster distributions for few-shot clients in centralized clustering tasks,and standalone personalization tasks may cause severe overfitting issues.To address these limitations,we introduce a federated learning dual optimization model based on clustering and personalization strategy(FedCPS).FedCPS adopts a decentralized approach,where clients identify their cluster membership locally without relying on a centralized clustering algorithm.Building on this,FedCPS introduces personalized training tasks locally,adding a regularization term to control deviations between local and cluster models.This improves the generalization ability of the final model while mitigating overfitting.The use of weight-sharing techniques also reduces the computational cost of central machines.Experimental results on MNIST,FMNIST,CIFAR10,and CIFAR100 datasets demonstrate that our method achieves better personalization effects compared to other personalized federated learning methods,with an average test accuracy improvement of 0.81%–2.96%.Meanwhile,we adjusted the proportion of few-shot clients to evaluate the impact on accuracy across different methods.The experiments show that FedCPS reduces accuracy by only 0.2%–3.7%,compared to 2.1%–10%for existing methods.Our method demonstrates its advantages across diverse data environments.展开更多
In the parallel steering coordination control strategy for path tracking,it is difficult to match the current driver steering model using the fixed parameters with the actual driver,and the designed steering coordinat...In the parallel steering coordination control strategy for path tracking,it is difficult to match the current driver steering model using the fixed parameters with the actual driver,and the designed steering coordination control strategy under a single objective and simple conditions is difficult to adapt to the multi-dimensional state variables’input.In this paper,we propose a deep reinforcement learning algorithm-based multi-objective parallel human-machine steering coordination strategy for path tracking considering driver misoperation and external disturbance.Firstly,the driver steering mathematical model is constructed based on the driver preview characteristics and steering delay response,and the driver characteristic parameters are fitted after collecting the actual driver driving data.Secondly,considering that the vehicle is susceptible to the influence of external disturbances during the driving process,the Tube MPC(Tube Model Predictive Control)based path tracking steering controller is designed based on the vehicle system dynamics error model.After verifying that the driver steering model meets the driver steering operation characteristics,DQN(Deep Q-network),DDPG(Deep Deterministic Policy Gradient)and TD3(Twin Delayed Deep Deterministic Policy Gradient)deep reinforcement learning algorithms are utilized to design a multi-objective parallel steering coordination strategy which satisfies the multi-dimensional state variables’input of the vehicle.Finally,the tracking accuracy,lateral safety,human-machine conflict and driver steering load evaluation index are designed in different driver operation states and different road environments,and the performance of the parallel steering coordination control strategies with different deep reinforcement learning algorithms and fuzzy algorithms are compared by simulations and hardware in the loop experiments.The results show that the parallel steering collaborative strategy based on a deep reinforcement learning algorithm can more effectively assist the driver in tracking the target path under lateral wind interference and driver misoperation,and the TD3-based coordination control strategy has better overall performance.展开更多
An intelligent endo-atmospheric penetration strategy based on generative adversarialreinforcement learning is proposed in this manuscript.Firstly,attack and defense adversarial mod-els are established,and missile mane...An intelligent endo-atmospheric penetration strategy based on generative adversarialreinforcement learning is proposed in this manuscript.Firstly,attack and defense adversarial mod-els are established,and missile maneuver penetration problem is transformed into an optimal con-trol problem,considering penetration,handover position and mid-terminal guidance velocityconstraints.Then,Radau Pseudospectral method is adopted to generate data samples consideringrandom perturbations.Furthermore,Generative Adversarial Imitation Learning Combined withDeep Deterministic Policy Gradient method(GAIL-DDPG)is designed,with internal processreward signals constructed to tackle long-term sparse reward in missile manuver penetration prob-lem.Finally,penetration strategy is trained and verified.Simulation shows that using generativeadversarial reinforcement learning,with sample library to learn expert experience in training earlystage,the proposed method can quickly converge.Also,performance is further optimized with rein-forcement learning exploration strategy in the later stage of training.Simulation shows that the pro-posed method has better engineering application ability compared with traditional reinforcementlearning method.展开更多
In Internet of Vehicles,VehicleInfrastructure-Cloud cooperation supports diverse intelligent driving and intelligent transportation applications.Federated Learning(FL)is the emerging computation paradigm to provide ef...In Internet of Vehicles,VehicleInfrastructure-Cloud cooperation supports diverse intelligent driving and intelligent transportation applications.Federated Learning(FL)is the emerging computation paradigm to provide efficient and privacypreserving collaborative learning.However,in Io V environment,federated learning faces the challenges introduced by high mobility of vehicles and nonIndependently Identically Distribution(non-IID)of data.High mobility causes FL clients quit and the communication offline.The non-IID data leads to slow and unstable convergence of global model and single global model's weak adaptability to clients with different localization characteristics.Accordingly,this paper proposes a personalized aggregation strategy for hierarchical Federated Learning in Io V environment,including Fed SA(Special Asynchronous Federated Learning with Self-adaptive Aggregation)for low-level FL between a Road Side Unit(RSU)and the vehicles within its coverage,and Fed Att(Federated Learning with Attention Mechanism)for high-level FL between a cloud server and multiple RSUs.Agents self-adaptively obtain model aggregation weight based on Advantage Actor-Critic(A2C)algorithm.Experiments show the proposed strategy encourages vehicles to participate in global aggregation,and outperforms existing methods in training performance.展开更多
In practical combat scenarios,Hypersonic Glide Vehicles(HGV)face the challenge of evading Successive Pursuers from the Same Direction while satisfying the Homing Constraint(SPSDHC).To address this problem,this paper p...In practical combat scenarios,Hypersonic Glide Vehicles(HGV)face the challenge of evading Successive Pursuers from the Same Direction while satisfying the Homing Constraint(SPSDHC).To address this problem,this paper proposes a parameterized evasion guidance algorithm based on reinforcement learning.The three-player optimal evasion strategy is firstly analyzed and approximated by parametrization.The switching acceleration command of HGV optimal evasion strategy considering the upper limit of missile acceleration command is analyzed based on the optimal control theory.The terminal miss of HGV in the case of evading two missiles is analyzed,which means that the three-player optimal evasion strategy is a linear combination of two one-toone strategies.Then,a velocity control algorithm is proposed to increase the terminal miss by actively controlling the flight speed of the HGV based on the parametrized evasion strategy.The reinforcement learning method is used to implement the strategy in real time and a reward function is designed by deducing homing strategy for the HGV to approach the target,which ensures that the HGV satisfies the homing constraint.Experimental results demonstrate the feasibility and robustness of the proposed parameterized evasion strategy,which enables the HGV to generate maximum terminal miss and satisfy homing constraint when facing single or double missiles.展开更多
In recent years,robotic arm grasping has become a pivotal task in the field of robotics,with applications spanning from industrial automation to healthcare.The optimization of grasping strategies plays a crucial role ...In recent years,robotic arm grasping has become a pivotal task in the field of robotics,with applications spanning from industrial automation to healthcare.The optimization of grasping strategies plays a crucial role in enhancing the effectiveness,efficiency,and reliability of robotic systems.This paper presents a novel approach to optimizing robotic arm grasping strategies based on deep reinforcement learning(DRL).Through the utilization of advanced DRL algorithms,such as Q-Learning,Deep Q-Networks(DQN),Policy Gradient Methods,and Proximal Policy Optimization(PPO),the study aims to improve the performance of robotic arms in grasping objects with varying shapes,sizes,and environmental conditions.The paper provides a detailed analysis of the various deep reinforcement learning methods used for grasping strategy optimization,emphasizing the strengths and weaknesses of each algorithm.It also presents a comprehensive framework for training the DRL models,including simulation environment setup,the optimization process,and the evaluation metrics for grasping success.The results demonstrate that the proposed approach significantly enhances the accuracy and stability of the robotic arm in performing grasping tasks.The study further explores the challenges in training deep reinforcement learning models for real-time robotic applications and offers solutions for improving the efficiency and reliability of grasping strategies.展开更多
The uplift resistance of the soil overlying shield tunnels significantly impacts their anti-floating stability.However,research on uplift resistance concerning special-shaped shield tunnels is limited.This study combi...The uplift resistance of the soil overlying shield tunnels significantly impacts their anti-floating stability.However,research on uplift resistance concerning special-shaped shield tunnels is limited.This study combines numerical simulation with machine learning techniques to explore this issue.It presents a summary of special-shaped tunnel geometries and introduces a shape coefficient.Through the finite element software,Plaxis3D,the study simulates six key parameters—shape coefficient,burial depth ratio,tunnel’s longest horizontal length,internal friction angle,cohesion,and soil submerged bulk density—that impact uplift resistance across different conditions.Employing XGBoost and ANN methods,the feature importance of each parameter was analyzed based on the numerical simulation results.The findings demonstrate that a tunnel shape more closely resembling a circle leads to reduced uplift resistance in the overlying soil,whereas other parameters exhibit the contrary effects.Furthermore,the study reveals a diminishing trend in the feature importance of buried depth ratio,internal friction angle,tunnel longest horizontal length,cohesion,soil submerged bulk density,and shape coefficient in influencing uplift resistance.展开更多
The purpose of this research is to analyze the causal mechanisms of learning difficulties of middle school students and use them to propose strategies to help them.This research is particularly valuable for its focus ...The purpose of this research is to analyze the causal mechanisms of learning difficulties of middle school students and use them to propose strategies to help them.This research is particularly valuable for its focus on middle school students.Research on this critical transition period is often lacking compared to primary and high school.Therefore,this research establishes a structured equation model and analyzes the data from the survey using the partial least squares method.The data were obtained from a 13,900 Wenzhou City,China students’questionnaire.The research found that learning strategies were the most significant influences on learning effectiveness,followed by learning motivation and learning relationships.Meanwhile,learning relationships had a significant impact on learning pressure.Therefore,this research proposes targeted support strategies.It aims to enhance learning motivation(Set achievable learning goals for each student with learning difficulties based on their actual situation),optimize learning strategies(Encourage students with learning difficulties to learn self-regulatory strategies such as goal setting,time management,and self-reflection),and improve learning relationships(Establish a good social network to promote positive interaction between students with learning difficulties and their peers).At the same time,it reduces students’learning pressure.Ultimately,the learning effectiveness of students with learning difficulties is improved.展开更多
The iterative continuation task(ICT)requires English as a foreign language(EFL)learners to read a segment and write a continuation that aligns with the preceding segment of an English novel with successive turns,offer...The iterative continuation task(ICT)requires English as a foreign language(EFL)learners to read a segment and write a continuation that aligns with the preceding segment of an English novel with successive turns,offering exposure to diverse grammatical structures and opportunities for contextualized usage.Given the importance of integrating technology into second language(L2)writing and the critical role that grammar plays in L2 writing development,automated written corrective feedback provided by Grammarly has gained significant attention.This study investigates the impact of Grammarly on grammar learning strategies,grammar grit,and grammar competence among EFL college students engaged in ICT.This study employed a mixed-methods sequential exploratory design;56 participants were divided into an experimental group(n=28),receiving Grammarly feedback for ICT,and a control group(n=28),completing ICT without Grammarly feedback.Quantitative results revealed that both groups showed improvements in L2 grammar learning strategies,grit and competence.For the experimental group,significant differences were observed across all variables of L2 grammar learning strategies,grit,and competence between pre-and post-tests.For the control group,significant differences were only observed in the affective dimension of grammar learning strategies,Consistency of Interest(COI)of grammar grit,and grammar competence.However,the control group presented a significantly higher improvement in grammar competence.Qualitative analysis showed both positive and negative perceptions of Grammarly.The pedagogical implications of integrating Grammarly and ICT for L2 grammar development are discussed.展开更多
Multi-constrained pipes conveying fluid,such as aircraft hydraulic control pipes,are susceptible to resonance fatigue in harsh vibration environments,which may lead to system failure and even catastrophic accidents.In...Multi-constrained pipes conveying fluid,such as aircraft hydraulic control pipes,are susceptible to resonance fatigue in harsh vibration environments,which may lead to system failure and even catastrophic accidents.In this study,a machine learning(ML)-assisted weak vibration design method under harsh environmental excitations is proposed.The dynamic model of a typical pipe is developed using the absolute nodal coordinate formulation(ANCF)to determine its vibrational characteristics.With the harsh vibration environments as the preserved frequency band(PFB),the safety design is defined by comparing the natural frequency with the PFB.By analyzing the safety design of pipes with different constraint parameters,the dataset of the absolute safety length and the absolute resonance length of the pipe is obtained.This dataset is then utilized to develop genetic programming(GP)algorithm-based ML models capable of producing explicit mathematical expressions of the pipe's absolute safety length and absolute resonance length with the location,stiffness,and total number of retaining clips as design variables.The proposed ML models effectively bridge the dataset with the prediction results.Thus,the ML model is utilized to stagger the natural frequency,and the PFB is utilized to achieve the weak vibration design.The findings of the present study provide valuable insights into the practical application of weak vibration design.展开更多
Countries around the world have been making efforts to reduce pollutant emissions. However, the response of global black carbon(BC) aging to emission changes remains unclear. Using the Community Atmosphere Model versi...Countries around the world have been making efforts to reduce pollutant emissions. However, the response of global black carbon(BC) aging to emission changes remains unclear. Using the Community Atmosphere Model version 6 with a machine-learning-integrated four-mode version of the Modal Aerosol Module, we quantify global BC aging responses to emission reductions for 2011–2018 and for 2050 and 2100 under carbon neutrality. During 2011–18, global trends in BC aging degree(mass ratio of coatings to BC, R_(BC)) exhibited marked regional disparities, with a significant increase in China(5.4% yr^(-1)), which contrasts with minimal changes in the USA, Europe, and India. The divergence is attributed to opposing trends in secondary organic aerosol(SOA) and sulfate coatings, driven by regional changes in the emission ratios of corresponding coating precursors to BC(volatile organic compounds-VOCs/BC and SO_(2)/BC). Projections under carbon neutrality reveal that R_(BC) will increase globally by 47%(118%) in 2050(2100), with strong convergent increases expected across major source regions. The R_(BC) increase, primarily driven by enhanced SOA coatings due to sharper BC reductions relative to VOCs, will enhance the global BC mass absorption cross-section(MAC) by 11%(17%) in 2050(2100).Consequently, although the global BC burden will decline sharply by 60%(76%), the enhanced MAC partially offsets the magnitude of the decline in the BC direct radiative effect, resulting in the moderation of global BC DRE decreases to 88%(92%) of the BC burden reductions in 2050(2100). This study highlights the globally enhanced BC aging and light absorption capacity under carbon neutrality, thereby partly offsetting the impact of BC direct emission reductions on future changes in BC radiative effects globally.展开更多
Anxiety,motivation,and strategy have long been seen as critical in second language acquisition.This study presents a systematic review of the literature on these variables in terms of their relationship with one anoth...Anxiety,motivation,and strategy have long been seen as critical in second language acquisition.This study presents a systematic review of the literature on these variables in terms of their relationship with one another,their effects on learning outcomes,and how they are affected by technology-assisted tools in the teaching of Chinese as a second language.This study includes 24 articles for the review study based on the criteria and process of the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocol(PRISMA-P)and the clustering techniques of VOSviewer.It is found that 1)anxiety,motivation,and strategy were interrelated,that is,motivation was negatively associated with anxiety but positively related to strategy,while strategy could positively predict anxiety;2)anxiety could both positively and negatively affect learning outcomes,while motivation and strategy could both positively and insignificantly influence learning outcomes;3)the technology-assisted tools used in the classroom could both positively and negatively affect the levels of these variables and learning outcomes in the L2 Chinese context.The need to explore more complicated relationships between language-specific individual variables themselves and other possible factors that affect these variables,such as cultural ones,are also discussed for future research.展开更多
Autonomous driving technology is constantly developing to a higher level of complex scenes,and there is a growing demand for the utilization of end-to-end data-driven control.However,the end-to-end path tracking proce...Autonomous driving technology is constantly developing to a higher level of complex scenes,and there is a growing demand for the utilization of end-to-end data-driven control.However,the end-to-end path tracking process often encounters challenges in learning efficiency and generalization.To address this issue,this paper designs a deep deterministic policy gradient(DDPG)-based reinforcement learning strategy that integrates imitation learning and feedforward exploration in the path following process.In imitation learning,the path tracking control data generated by the model predictive control(MPC)method is used to train an end-to-end steering control model of a deep neural network.Another feedforward exploration behavior is predicted by road curvature and vehicle speed,and adds it and imitation learning to the DDPG reinforcement learning to obtain decision-making experience and action prediction behavior of the path tracking process.In the reinforcement learning process,imitation learning is used to update the pre-training parameters of the actor network,and a feedforward steering technique with random noise is adopted for strategy exploration.In the reward function,a hierarchical progressive reward form and a constrained objective reward function referring to MPC are designed,and the actor-critic network architecture is determined.Finally,the path tracking performance of the designed method is verified by comparing various training results,simulations,and HIL tests.The results show that the designed method can effectively utilize pre-training and feedforward prior experience to obtain optimal path tracking performance of an autonomous vehicle,and has better generalization ability than other methods.This study provides an efficient control scheme for improving the end-to-end control performance of autonomous vehicles.展开更多
Federated Learning(FL)protects data privacy through a distributed training mechanism,yet its decentralized nature also introduces new security vulnerabilities.Backdoor attacks inject malicious triggers into the global...Federated Learning(FL)protects data privacy through a distributed training mechanism,yet its decentralized nature also introduces new security vulnerabilities.Backdoor attacks inject malicious triggers into the global model through compromised updates,posing significant threats to model integrity and becoming a key focus in FL security.Existing backdoor attack methods typically embed triggers directly into original images and consider only data heterogeneity,resulting in limited stealth and adaptability.To address the heterogeneity of malicious client devices,this paper proposes a novel backdoor attack method named Capability-Adaptive Shadow Backdoor Attack(CASBA).By incorporating measurements of clients’computational and communication capabilities,CASBA employs a dynamic hierarchical attack strategy that adaptively aligns attack intensity with available resources.Furthermore,an improved deep convolutional generative adversarial network(DCGAN)is integrated into the attack pipeline to embed triggers without modifying original data,significantly enhancing stealthiness.Comparative experiments with Shadow Backdoor Attack(SBA)across multiple scenarios demonstrate that CASBA dynamically adjusts resource consumption based on device capabilities,reducing average memory usage per iteration by 5.8%.CASBA improves resource efficiency while keeping the drop in attack success rate within 3%.Additionally,the effectiveness of CASBA against three robust FL algorithms is also validated.展开更多
The development of fungicides is time-consuming and costly.Introducing a fungicide-likeness assessment strategy at the early screening stage can help reduce development risks and improve the success rate.However,exist...The development of fungicides is time-consuming and costly.Introducing a fungicide-likeness assessment strategy at the early screening stage can help reduce development risks and improve the success rate.However,existing assessment methods are often plagued by low accuracy and poor generalization,while fragment-based design strategies commonly fail to account for synergistic effects between structural units.Therefore,based on a small-scale sample set,this study developed a more efficient global predictive model for fungicidal activity—-named APPf—by integrating multi-scale feature screening methods and machine learning algorithms,which also accounts for synergistic effects among different structural fragments.We utilized three independent external test sets for model validation:External Test Set 1 for general validation,External Test Set 2 for comparison with existing models,and External Test Set 3 for disease-specific fungicide evaluation.On External Test Set 1,the APPf model achieved a precision of 0.6454,a recall of 0.8535,and an F1 score of 0.7350,demonstrating its robust predictive performance.It also exhibited strong enrichment capability for positive samples in External Test Set 2.For External Test Set 3,APPf achieved a prediction accuracy exceeding 80%for each disease,suggesting its promising potential in practical fungicide development.Furthermore,we quantified the contribution of molecular descriptors to the model predictions using SHAP value analysis and identified nHdNH and NssssNp as strong indicative features for predicting fungicidal activity,thereby enhancing the interpretability of the model.APPf has been deployed on a public web server(http://pesticides.cau.edu.cn/APPf),providing a user-friendly online prediction service to support the discovery of novel fungicides.Meanwhile,we employed a molecular fragmentation strategy to analyze the co-occurrence relationships between fragments in fungicides and constructed a network map of fragment co-occurrence associated with fungicidal activity.This study provides both an active fragment library and a global fungicide-likeness assessment tool for AI-based de novo molecular generation aimed at discovering novel fungicidal leads,which is expected to enhance the efficiency of developing new fungicides.展开更多
Development of high performance,flexible piezoelectric nanogenerators(PENGs)is critical for advancing self-powered sensing and microelectronic applications.In this study,a hydrogen-bond substitute strategy was employe...Development of high performance,flexible piezoelectric nanogenerators(PENGs)is critical for advancing self-powered sensing and microelectronic applications.In this study,a hydrogen-bond substitute strategy was employed to fabricate a multi-layer PENG based on a cellulose/polyvinylidene fluoride(PVDF)blend film matrix,incorporating multi-phase BCZT(0.1BaZr_(0.2)Ti_(0.8)O_(3)-0.9Ba_(0.7)Ca_(0.3)TiO_(3))ceramic fillers.Structural characterization via SEM and TEM revealed that an intricate hydrogen-bond network facilitated the uniform dispersion of ceramic fillers within the composite film’s sub-layers.In order to study the effect of filler distribution on piezoelectric performance,the single-and double-layer composite films with varying BCZT configurations were produced and evaluated.The results demonstrated that double-layer PENGs exhibit significantly enhanced electrical output compared to their single-layer counterparts,with the D-L_(3)H_(7) configuration achieving an open circuit voltage(V_(OC))of 23.13 V and a short circuit current(I_(SC))of 8.32μA.This enhancement is attributed to increased inter-layer interfaces,which effectively suppressed charge injection and migration,leading to improved charge density.Additionally,the presence of sharp tipped hexagonal tetragonal phase nanoparticles induced an electric field enhancement effect,further optimizing performance.展开更多
Background Breed identification plays an important role in conserving indigenous breeds,managing genetic resources,and developing effective breeding strategies.However,researches on breed identification in livestock m...Background Breed identification plays an important role in conserving indigenous breeds,managing genetic resources,and developing effective breeding strategies.However,researches on breed identification in livestock mainly focused on purebreds,and they yielded lower predict accuracy in hybrid.In this study,we presented a Multi-Layer Perceptron(MLP)model with multi-output regression framework specifically designed for genomic breed composition prediction of purebred and hybrid in pigs.Results We utilized a total of 8,199 pigs from breeding farms in eight provinces in China,comprising Yorkshire,Landrace,Duroc and hybrids of Yorkshire×Landrace.All the animals were genotyped with 1K,50K and 100K SNP chips.Comparing with random forest(RF),support vector regression(SVR)and Admixture,our results from five replicates of fivefold cross validation demonstrated that MLP achieved a breed identification accuracy of 100%for both hybrid and purebreds in 50K and 100K SNP chips,SVR performed comparable with MLP,they both outperformed RF and Admixture.In the independent testing,MLP yielded accuracy of 100%for all three pure breeds and hybrid across all SNP chips and panel,while SVR yielded 0.026%–0.121%lower accuracy than MLP.Compared with classification-based framework,the new strategy of multi-output regression framework in this study was helpful to improve the predict accuracy.MLP,RF and SVR,achieved consistent improvements across all six SNP chips/panel,especially in hybrid identification.Our results showed the determination threshold for purebred had different effects,SVR,RF and Admixture were very sensitive to threshold values,their optimal threshold fluctuated in different scenarios,while MLP kept optimal threshold 0.75 in all cases.The threshold of 0.65–0.75 is ideal for accurate breed identification.Among different density of SNP chips,the 1K SNP chip was most cost-effective as yielding 100%accuracy with enlarging training set.Hybrid individuals in the training set were useful for both purebred and hybrid identification.Conclusions Our new MLP strategy demonstrated its high accuracy and robust applicability across low-,medium-,and high-density SNP chips.Multi-output regression framework could universally enhance prediction accuracy for ML methods.Our new strategy is also helpful for breed identification in other livestock.展开更多
基金supported in part by the Research on Key Technologies for the Development of an Active Balancing Cooperative Control Systemfor Distribution Networks and the National Natural Science Foundation of China under Grant 521532240029,Grant 62303006.
文摘To address the high costs and operational instability of distribution networks caused by the large-scale integration of distributed energy resources(DERs)(such as photovoltaic(PV)systems,wind turbines(WT),and energy storage(ES)devices),and the increased grid load fluctuations and safety risks due to uncoordinated electric vehicles(EVs)charging,this paper proposes a novel dual-scale hierarchical collaborative optimization strategy.This strategy decouples system-level economic dispatch from distributed EV agent control,effectively solving the resource coordination conflicts arising from the high computational complexity,poor scalability of existing centralized optimization,or the reliance on local information decision-making in fully decentralized frameworks.At the lower level,an EV charging and discharging model with a hybrid discrete-continuous action space is established,and optimized using an improved Parameterized Deep Q-Network(PDQN)algorithm,which directly handles mode selection and power regulation while embedding physical constraints to ensure safety.At the upper level,microgrid(MG)operators adopt a dynamic pricing strategy optimized through Deep Reinforcement Learning(DRL)to maximize economic benefits and achieve peak-valley shaving.Simulation results show that the proposed strategy outperforms traditional methods,reducing the total operating cost of the MG by 21.6%,decreasing the peak-to-valley load difference by 33.7%,reducing the number of voltage limit violations by 88.9%,and lowering the average electricity cost for EV users by 15.2%.This method brings a win-win result for operators and users,providing a reliable and efficient scheduling solution for distribution networks with high renewable energy penetration rates.
基金supported by the National Key Research and Development Program of China (Grant No. 2024YFB4205101)the National Natural Science Foundation of China (No. 62274098 and No. 62074084)+2 种基金the Natural Science Foundation of Tianjin (No.22JCYBJC01300, No. 23JCYBJC01620 and No. 21JCYBJC00270)the Overseas Expertise Introduction Project for Discipline Innovation of Higher Edu cation of China (Grant No. B16027)the Fundamental Research Funds for the Central Universities,Nankai University (No. 63241568)
文摘Efficient surface passivation is critical for achieving high-performance perovskite solar cells(PSCs),yet the discovery of optimal passivators remains a time-consuming,trial-and-error process.Here,we report a synergistic machine learning(ML)and density functional theory(DFT)approach that enables predictive and rapid identification of effective passivation materials.By training an XGBoost model(91.3%accuracy)with DFT-derived molecular descriptors and activity calculations,we identify 2-(4-aminophenyl)-3H-benzimidazol-5-amine(APBIA)as a promising passivator.Experimental validation demonstrates that APBIA effectively removes surface impurities and passivates defects within perovskite films,leading to a significant increase in power conversion efficiency(PCE)from 22.48%to 25.55%(certified as 25.02%).This ML-DFT framework provides a generalizable pathway for accelerating the development of advanced functional materials for photovoltaic applications.
基金supported by the Foundation of President of Hebei University(XZJJ202303).
文摘Federated learning is a machine learning framework designed to protect privacy by keeping training data on clients’devices without sharing private data.It trains a global model through collaboration between clients and the server.However,the presence of data heterogeneity can lead to inefficient model training and even reduce the final model’s accuracy and generalization capability.Meanwhile,data scarcity can result in suboptimal cluster distributions for few-shot clients in centralized clustering tasks,and standalone personalization tasks may cause severe overfitting issues.To address these limitations,we introduce a federated learning dual optimization model based on clustering and personalization strategy(FedCPS).FedCPS adopts a decentralized approach,where clients identify their cluster membership locally without relying on a centralized clustering algorithm.Building on this,FedCPS introduces personalized training tasks locally,adding a regularization term to control deviations between local and cluster models.This improves the generalization ability of the final model while mitigating overfitting.The use of weight-sharing techniques also reduces the computational cost of central machines.Experimental results on MNIST,FMNIST,CIFAR10,and CIFAR100 datasets demonstrate that our method achieves better personalization effects compared to other personalized federated learning methods,with an average test accuracy improvement of 0.81%–2.96%.Meanwhile,we adjusted the proportion of few-shot clients to evaluate the impact on accuracy across different methods.The experiments show that FedCPS reduces accuracy by only 0.2%–3.7%,compared to 2.1%–10%for existing methods.Our method demonstrates its advantages across diverse data environments.
基金Supported by National Natural Science Foundation of China(Grant Nos.U22A20246,52372382)Hefei Municipal Natural Science Foundation(Grant No.2022008)+1 种基金the Open Fund of State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures(Grant No.KF2023-06)S&T Program of Hebei(Grant No.225676162GH).
文摘In the parallel steering coordination control strategy for path tracking,it is difficult to match the current driver steering model using the fixed parameters with the actual driver,and the designed steering coordination control strategy under a single objective and simple conditions is difficult to adapt to the multi-dimensional state variables’input.In this paper,we propose a deep reinforcement learning algorithm-based multi-objective parallel human-machine steering coordination strategy for path tracking considering driver misoperation and external disturbance.Firstly,the driver steering mathematical model is constructed based on the driver preview characteristics and steering delay response,and the driver characteristic parameters are fitted after collecting the actual driver driving data.Secondly,considering that the vehicle is susceptible to the influence of external disturbances during the driving process,the Tube MPC(Tube Model Predictive Control)based path tracking steering controller is designed based on the vehicle system dynamics error model.After verifying that the driver steering model meets the driver steering operation characteristics,DQN(Deep Q-network),DDPG(Deep Deterministic Policy Gradient)and TD3(Twin Delayed Deep Deterministic Policy Gradient)deep reinforcement learning algorithms are utilized to design a multi-objective parallel steering coordination strategy which satisfies the multi-dimensional state variables’input of the vehicle.Finally,the tracking accuracy,lateral safety,human-machine conflict and driver steering load evaluation index are designed in different driver operation states and different road environments,and the performance of the parallel steering coordination control strategies with different deep reinforcement learning algorithms and fuzzy algorithms are compared by simulations and hardware in the loop experiments.The results show that the parallel steering collaborative strategy based on a deep reinforcement learning algorithm can more effectively assist the driver in tracking the target path under lateral wind interference and driver misoperation,and the TD3-based coordination control strategy has better overall performance.
文摘An intelligent endo-atmospheric penetration strategy based on generative adversarialreinforcement learning is proposed in this manuscript.Firstly,attack and defense adversarial mod-els are established,and missile maneuver penetration problem is transformed into an optimal con-trol problem,considering penetration,handover position and mid-terminal guidance velocityconstraints.Then,Radau Pseudospectral method is adopted to generate data samples consideringrandom perturbations.Furthermore,Generative Adversarial Imitation Learning Combined withDeep Deterministic Policy Gradient method(GAIL-DDPG)is designed,with internal processreward signals constructed to tackle long-term sparse reward in missile manuver penetration prob-lem.Finally,penetration strategy is trained and verified.Simulation shows that using generativeadversarial reinforcement learning,with sample library to learn expert experience in training earlystage,the proposed method can quickly converge.Also,performance is further optimized with rein-forcement learning exploration strategy in the later stage of training.Simulation shows that the pro-posed method has better engineering application ability compared with traditional reinforcementlearning method.
基金supported by the National Natural Science Foundation of China under Grant 61931005Beijing Natural Science Foundation under Grant L202018the Key Laboratory of Internet of Vehicle Technical Innovation and Testing(CAICT),Ministry of Industry and Information Technology under Grant No.KL-2023-001。
文摘In Internet of Vehicles,VehicleInfrastructure-Cloud cooperation supports diverse intelligent driving and intelligent transportation applications.Federated Learning(FL)is the emerging computation paradigm to provide efficient and privacypreserving collaborative learning.However,in Io V environment,federated learning faces the challenges introduced by high mobility of vehicles and nonIndependently Identically Distribution(non-IID)of data.High mobility causes FL clients quit and the communication offline.The non-IID data leads to slow and unstable convergence of global model and single global model's weak adaptability to clients with different localization characteristics.Accordingly,this paper proposes a personalized aggregation strategy for hierarchical Federated Learning in Io V environment,including Fed SA(Special Asynchronous Federated Learning with Self-adaptive Aggregation)for low-level FL between a Road Side Unit(RSU)and the vehicles within its coverage,and Fed Att(Federated Learning with Attention Mechanism)for high-level FL between a cloud server and multiple RSUs.Agents self-adaptively obtain model aggregation weight based on Advantage Actor-Critic(A2C)algorithm.Experiments show the proposed strategy encourages vehicles to participate in global aggregation,and outperforms existing methods in training performance.
基金supported by the National Natural Science Foundation of China(No.62103014)。
文摘In practical combat scenarios,Hypersonic Glide Vehicles(HGV)face the challenge of evading Successive Pursuers from the Same Direction while satisfying the Homing Constraint(SPSDHC).To address this problem,this paper proposes a parameterized evasion guidance algorithm based on reinforcement learning.The three-player optimal evasion strategy is firstly analyzed and approximated by parametrization.The switching acceleration command of HGV optimal evasion strategy considering the upper limit of missile acceleration command is analyzed based on the optimal control theory.The terminal miss of HGV in the case of evading two missiles is analyzed,which means that the three-player optimal evasion strategy is a linear combination of two one-toone strategies.Then,a velocity control algorithm is proposed to increase the terminal miss by actively controlling the flight speed of the HGV based on the parametrized evasion strategy.The reinforcement learning method is used to implement the strategy in real time and a reward function is designed by deducing homing strategy for the HGV to approach the target,which ensures that the HGV satisfies the homing constraint.Experimental results demonstrate the feasibility and robustness of the proposed parameterized evasion strategy,which enables the HGV to generate maximum terminal miss and satisfy homing constraint when facing single or double missiles.
文摘In recent years,robotic arm grasping has become a pivotal task in the field of robotics,with applications spanning from industrial automation to healthcare.The optimization of grasping strategies plays a crucial role in enhancing the effectiveness,efficiency,and reliability of robotic systems.This paper presents a novel approach to optimizing robotic arm grasping strategies based on deep reinforcement learning(DRL).Through the utilization of advanced DRL algorithms,such as Q-Learning,Deep Q-Networks(DQN),Policy Gradient Methods,and Proximal Policy Optimization(PPO),the study aims to improve the performance of robotic arms in grasping objects with varying shapes,sizes,and environmental conditions.The paper provides a detailed analysis of the various deep reinforcement learning methods used for grasping strategy optimization,emphasizing the strengths and weaknesses of each algorithm.It also presents a comprehensive framework for training the DRL models,including simulation environment setup,the optimization process,and the evaluation metrics for grasping success.The results demonstrate that the proposed approach significantly enhances the accuracy and stability of the robotic arm in performing grasping tasks.The study further explores the challenges in training deep reinforcement learning models for real-time robotic applications and offers solutions for improving the efficiency and reliability of grasping strategies.
基金Guangzhou Metro Scientific Research Project(No.JT204-100111-23001)Chongqing Municipal Special Project for Technological Innovation and Application Development(No.CSTB2022TIAD-KPX0101)Science and Technology Research and Development Program of China State Railway Group Co.,Ltd.(No.N2023G045)。
文摘The uplift resistance of the soil overlying shield tunnels significantly impacts their anti-floating stability.However,research on uplift resistance concerning special-shaped shield tunnels is limited.This study combines numerical simulation with machine learning techniques to explore this issue.It presents a summary of special-shaped tunnel geometries and introduces a shape coefficient.Through the finite element software,Plaxis3D,the study simulates six key parameters—shape coefficient,burial depth ratio,tunnel’s longest horizontal length,internal friction angle,cohesion,and soil submerged bulk density—that impact uplift resistance across different conditions.Employing XGBoost and ANN methods,the feature importance of each parameter was analyzed based on the numerical simulation results.The findings demonstrate that a tunnel shape more closely resembling a circle leads to reduced uplift resistance in the overlying soil,whereas other parameters exhibit the contrary effects.Furthermore,the study reveals a diminishing trend in the feature importance of buried depth ratio,internal friction angle,tunnel longest horizontal length,cohesion,soil submerged bulk density,and shape coefficient in influencing uplift resistance.
基金2025 Wenzhou Key Research Base of Philosophy and Social Science(Wenzhou University Learning Science and Technology Research Centre)Research Project:Investigation and Strategy Research on the Causes of Middle School Students’Learning Difficulties in the Context of the Leading Country in Education.
文摘The purpose of this research is to analyze the causal mechanisms of learning difficulties of middle school students and use them to propose strategies to help them.This research is particularly valuable for its focus on middle school students.Research on this critical transition period is often lacking compared to primary and high school.Therefore,this research establishes a structured equation model and analyzes the data from the survey using the partial least squares method.The data were obtained from a 13,900 Wenzhou City,China students’questionnaire.The research found that learning strategies were the most significant influences on learning effectiveness,followed by learning motivation and learning relationships.Meanwhile,learning relationships had a significant impact on learning pressure.Therefore,this research proposes targeted support strategies.It aims to enhance learning motivation(Set achievable learning goals for each student with learning difficulties based on their actual situation),optimize learning strategies(Encourage students with learning difficulties to learn self-regulatory strategies such as goal setting,time management,and self-reflection),and improve learning relationships(Establish a good social network to promote positive interaction between students with learning difficulties and their peers).At the same time,it reduces students’learning pressure.Ultimately,the learning effectiveness of students with learning difficulties is improved.
文摘The iterative continuation task(ICT)requires English as a foreign language(EFL)learners to read a segment and write a continuation that aligns with the preceding segment of an English novel with successive turns,offering exposure to diverse grammatical structures and opportunities for contextualized usage.Given the importance of integrating technology into second language(L2)writing and the critical role that grammar plays in L2 writing development,automated written corrective feedback provided by Grammarly has gained significant attention.This study investigates the impact of Grammarly on grammar learning strategies,grammar grit,and grammar competence among EFL college students engaged in ICT.This study employed a mixed-methods sequential exploratory design;56 participants were divided into an experimental group(n=28),receiving Grammarly feedback for ICT,and a control group(n=28),completing ICT without Grammarly feedback.Quantitative results revealed that both groups showed improvements in L2 grammar learning strategies,grit and competence.For the experimental group,significant differences were observed across all variables of L2 grammar learning strategies,grit,and competence between pre-and post-tests.For the control group,significant differences were only observed in the affective dimension of grammar learning strategies,Consistency of Interest(COI)of grammar grit,and grammar competence.However,the control group presented a significantly higher improvement in grammar competence.Qualitative analysis showed both positive and negative perceptions of Grammarly.The pedagogical implications of integrating Grammarly and ICT for L2 grammar development are discussed.
基金Project supported by the Foundation for Innovative Research Groups of the National Natural Science Foundation of China(No.12421002)the National Science Funds for Distinguished Young Scholars of China(No.12025204)+1 种基金the National Natural Science Foundation of China(No.12372015)China Scholarship Council(No.202206890065)。
文摘Multi-constrained pipes conveying fluid,such as aircraft hydraulic control pipes,are susceptible to resonance fatigue in harsh vibration environments,which may lead to system failure and even catastrophic accidents.In this study,a machine learning(ML)-assisted weak vibration design method under harsh environmental excitations is proposed.The dynamic model of a typical pipe is developed using the absolute nodal coordinate formulation(ANCF)to determine its vibrational characteristics.With the harsh vibration environments as the preserved frequency band(PFB),the safety design is defined by comparing the natural frequency with the PFB.By analyzing the safety design of pipes with different constraint parameters,the dataset of the absolute safety length and the absolute resonance length of the pipe is obtained.This dataset is then utilized to develop genetic programming(GP)algorithm-based ML models capable of producing explicit mathematical expressions of the pipe's absolute safety length and absolute resonance length with the location,stiffness,and total number of retaining clips as design variables.The proposed ML models effectively bridge the dataset with the prediction results.Thus,the ML model is utilized to stagger the natural frequency,and the PFB is utilized to achieve the weak vibration design.The findings of the present study provide valuable insights into the practical application of weak vibration design.
基金supported by the National Natural Science Foundation of China (42505149,41925023,U2342223,42105069,and 91744208)the China Postdoctoral Science Foundation (2025M770303)+1 种基金the Fundamental Research Funds for the Central Universities (14380230)the Jiangsu Funding Program for Excellent Postdoctoral Talent,and Jiangsu Collaborative Innovation Center of Climate Change。
文摘Countries around the world have been making efforts to reduce pollutant emissions. However, the response of global black carbon(BC) aging to emission changes remains unclear. Using the Community Atmosphere Model version 6 with a machine-learning-integrated four-mode version of the Modal Aerosol Module, we quantify global BC aging responses to emission reductions for 2011–2018 and for 2050 and 2100 under carbon neutrality. During 2011–18, global trends in BC aging degree(mass ratio of coatings to BC, R_(BC)) exhibited marked regional disparities, with a significant increase in China(5.4% yr^(-1)), which contrasts with minimal changes in the USA, Europe, and India. The divergence is attributed to opposing trends in secondary organic aerosol(SOA) and sulfate coatings, driven by regional changes in the emission ratios of corresponding coating precursors to BC(volatile organic compounds-VOCs/BC and SO_(2)/BC). Projections under carbon neutrality reveal that R_(BC) will increase globally by 47%(118%) in 2050(2100), with strong convergent increases expected across major source regions. The R_(BC) increase, primarily driven by enhanced SOA coatings due to sharper BC reductions relative to VOCs, will enhance the global BC mass absorption cross-section(MAC) by 11%(17%) in 2050(2100).Consequently, although the global BC burden will decline sharply by 60%(76%), the enhanced MAC partially offsets the magnitude of the decline in the BC direct radiative effect, resulting in the moderation of global BC DRE decreases to 88%(92%) of the BC burden reductions in 2050(2100). This study highlights the globally enhanced BC aging and light absorption capacity under carbon neutrality, thereby partly offsetting the impact of BC direct emission reductions on future changes in BC radiative effects globally.
文摘Anxiety,motivation,and strategy have long been seen as critical in second language acquisition.This study presents a systematic review of the literature on these variables in terms of their relationship with one another,their effects on learning outcomes,and how they are affected by technology-assisted tools in the teaching of Chinese as a second language.This study includes 24 articles for the review study based on the criteria and process of the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocol(PRISMA-P)and the clustering techniques of VOSviewer.It is found that 1)anxiety,motivation,and strategy were interrelated,that is,motivation was negatively associated with anxiety but positively related to strategy,while strategy could positively predict anxiety;2)anxiety could both positively and negatively affect learning outcomes,while motivation and strategy could both positively and insignificantly influence learning outcomes;3)the technology-assisted tools used in the classroom could both positively and negatively affect the levels of these variables and learning outcomes in the L2 Chinese context.The need to explore more complicated relationships between language-specific individual variables themselves and other possible factors that affect these variables,such as cultural ones,are also discussed for future research.
基金Supported by National Natural Science Foundation of China(Grant No.52405104)Jiangxi Provincial Natural Science Foundation(Grant Nos.20242BAB20249 and 20232BAB204041)Science and Technology Project of Department of Transportation of Jiangxi Province(Grant No.2025QN003).
文摘Autonomous driving technology is constantly developing to a higher level of complex scenes,and there is a growing demand for the utilization of end-to-end data-driven control.However,the end-to-end path tracking process often encounters challenges in learning efficiency and generalization.To address this issue,this paper designs a deep deterministic policy gradient(DDPG)-based reinforcement learning strategy that integrates imitation learning and feedforward exploration in the path following process.In imitation learning,the path tracking control data generated by the model predictive control(MPC)method is used to train an end-to-end steering control model of a deep neural network.Another feedforward exploration behavior is predicted by road curvature and vehicle speed,and adds it and imitation learning to the DDPG reinforcement learning to obtain decision-making experience and action prediction behavior of the path tracking process.In the reinforcement learning process,imitation learning is used to update the pre-training parameters of the actor network,and a feedforward steering technique with random noise is adopted for strategy exploration.In the reward function,a hierarchical progressive reward form and a constrained objective reward function referring to MPC are designed,and the actor-critic network architecture is determined.Finally,the path tracking performance of the designed method is verified by comparing various training results,simulations,and HIL tests.The results show that the designed method can effectively utilize pre-training and feedforward prior experience to obtain optimal path tracking performance of an autonomous vehicle,and has better generalization ability than other methods.This study provides an efficient control scheme for improving the end-to-end control performance of autonomous vehicles.
基金supported by the National Natural Science Foundation of China(Grant No.62172123)the Key Research and Development Program of Heilongjiang Province,China(GrantNo.2022ZX01A36).
文摘Federated Learning(FL)protects data privacy through a distributed training mechanism,yet its decentralized nature also introduces new security vulnerabilities.Backdoor attacks inject malicious triggers into the global model through compromised updates,posing significant threats to model integrity and becoming a key focus in FL security.Existing backdoor attack methods typically embed triggers directly into original images and consider only data heterogeneity,resulting in limited stealth and adaptability.To address the heterogeneity of malicious client devices,this paper proposes a novel backdoor attack method named Capability-Adaptive Shadow Backdoor Attack(CASBA).By incorporating measurements of clients’computational and communication capabilities,CASBA employs a dynamic hierarchical attack strategy that adaptively aligns attack intensity with available resources.Furthermore,an improved deep convolutional generative adversarial network(DCGAN)is integrated into the attack pipeline to embed triggers without modifying original data,significantly enhancing stealthiness.Comparative experiments with Shadow Backdoor Attack(SBA)across multiple scenarios demonstrate that CASBA dynamically adjusts resource consumption based on device capabilities,reducing average memory usage per iteration by 5.8%.CASBA improves resource efficiency while keeping the drop in attack success rate within 3%.Additionally,the effectiveness of CASBA against three robust FL algorithms is also validated.
基金the National Key R&D Program of China(No.2022YFD1700200).
文摘The development of fungicides is time-consuming and costly.Introducing a fungicide-likeness assessment strategy at the early screening stage can help reduce development risks and improve the success rate.However,existing assessment methods are often plagued by low accuracy and poor generalization,while fragment-based design strategies commonly fail to account for synergistic effects between structural units.Therefore,based on a small-scale sample set,this study developed a more efficient global predictive model for fungicidal activity—-named APPf—by integrating multi-scale feature screening methods and machine learning algorithms,which also accounts for synergistic effects among different structural fragments.We utilized three independent external test sets for model validation:External Test Set 1 for general validation,External Test Set 2 for comparison with existing models,and External Test Set 3 for disease-specific fungicide evaluation.On External Test Set 1,the APPf model achieved a precision of 0.6454,a recall of 0.8535,and an F1 score of 0.7350,demonstrating its robust predictive performance.It also exhibited strong enrichment capability for positive samples in External Test Set 2.For External Test Set 3,APPf achieved a prediction accuracy exceeding 80%for each disease,suggesting its promising potential in practical fungicide development.Furthermore,we quantified the contribution of molecular descriptors to the model predictions using SHAP value analysis and identified nHdNH and NssssNp as strong indicative features for predicting fungicidal activity,thereby enhancing the interpretability of the model.APPf has been deployed on a public web server(http://pesticides.cau.edu.cn/APPf),providing a user-friendly online prediction service to support the discovery of novel fungicides.Meanwhile,we employed a molecular fragmentation strategy to analyze the co-occurrence relationships between fragments in fungicides and constructed a network map of fragment co-occurrence associated with fungicidal activity.This study provides both an active fragment library and a global fungicide-likeness assessment tool for AI-based de novo molecular generation aimed at discovering novel fungicidal leads,which is expected to enhance the efficiency of developing new fungicides.
基金National Natural Science Foundation of China(52472132)Opening Project of Engineering Research Center of Eco-friendly Polymeric Materials,Ministry of Education(EFP-KF2403)Innovation Service Capability Support Plan of Xianyang(Science and Technology Innovation Talents)。
文摘Development of high performance,flexible piezoelectric nanogenerators(PENGs)is critical for advancing self-powered sensing and microelectronic applications.In this study,a hydrogen-bond substitute strategy was employed to fabricate a multi-layer PENG based on a cellulose/polyvinylidene fluoride(PVDF)blend film matrix,incorporating multi-phase BCZT(0.1BaZr_(0.2)Ti_(0.8)O_(3)-0.9Ba_(0.7)Ca_(0.3)TiO_(3))ceramic fillers.Structural characterization via SEM and TEM revealed that an intricate hydrogen-bond network facilitated the uniform dispersion of ceramic fillers within the composite film’s sub-layers.In order to study the effect of filler distribution on piezoelectric performance,the single-and double-layer composite films with varying BCZT configurations were produced and evaluated.The results demonstrated that double-layer PENGs exhibit significantly enhanced electrical output compared to their single-layer counterparts,with the D-L_(3)H_(7) configuration achieving an open circuit voltage(V_(OC))of 23.13 V and a short circuit current(I_(SC))of 8.32μA.This enhancement is attributed to increased inter-layer interfaces,which effectively suppressed charge injection and migration,leading to improved charge density.Additionally,the presence of sharp tipped hexagonal tetragonal phase nanoparticles induced an electric field enhancement effect,further optimizing performance.
基金supported by grants from Key R&D Program of Shandong Province(2022LZGC003)China Agriculture Research System of MOF and MARA,the National Key Research and Development Project(2023YFD1300200 and 2023YFF1001104)+1 种基金the Science and Technology Program of Sichuan Province(2024ZHCG0109)the 2115 Talent Development Program of China Agricultural University.
文摘Background Breed identification plays an important role in conserving indigenous breeds,managing genetic resources,and developing effective breeding strategies.However,researches on breed identification in livestock mainly focused on purebreds,and they yielded lower predict accuracy in hybrid.In this study,we presented a Multi-Layer Perceptron(MLP)model with multi-output regression framework specifically designed for genomic breed composition prediction of purebred and hybrid in pigs.Results We utilized a total of 8,199 pigs from breeding farms in eight provinces in China,comprising Yorkshire,Landrace,Duroc and hybrids of Yorkshire×Landrace.All the animals were genotyped with 1K,50K and 100K SNP chips.Comparing with random forest(RF),support vector regression(SVR)and Admixture,our results from five replicates of fivefold cross validation demonstrated that MLP achieved a breed identification accuracy of 100%for both hybrid and purebreds in 50K and 100K SNP chips,SVR performed comparable with MLP,they both outperformed RF and Admixture.In the independent testing,MLP yielded accuracy of 100%for all three pure breeds and hybrid across all SNP chips and panel,while SVR yielded 0.026%–0.121%lower accuracy than MLP.Compared with classification-based framework,the new strategy of multi-output regression framework in this study was helpful to improve the predict accuracy.MLP,RF and SVR,achieved consistent improvements across all six SNP chips/panel,especially in hybrid identification.Our results showed the determination threshold for purebred had different effects,SVR,RF and Admixture were very sensitive to threshold values,their optimal threshold fluctuated in different scenarios,while MLP kept optimal threshold 0.75 in all cases.The threshold of 0.65–0.75 is ideal for accurate breed identification.Among different density of SNP chips,the 1K SNP chip was most cost-effective as yielding 100%accuracy with enlarging training set.Hybrid individuals in the training set were useful for both purebred and hybrid identification.Conclusions Our new MLP strategy demonstrated its high accuracy and robust applicability across low-,medium-,and high-density SNP chips.Multi-output regression framework could universally enhance prediction accuracy for ML methods.Our new strategy is also helpful for breed identification in other livestock.
