The relationship between heaven and humanity is one of the fundamental philosophical foundations of ecological ethics in ancient Chinese Confucian thought.As a master of Confucian philosophy of mind,Wang Yangming inte...The relationship between heaven and humanity is one of the fundamental philosophical foundations of ecological ethics in ancient Chinese Confucian thought.As a master of Confucian philosophy of mind,Wang Yangming integrated the traditional Confucian discourse on the relationship between heaven and humanity into the principles of the philosophy of mind.Building on the traditional doctrine of benevolence centered on moral concern,he further developed an ecological view of‘benevolence as the unity of heaven and earth’.In his work Inquiry on the Great Learning,Wang Yangming systematically elaborated on this notion,emphasizing the philosophical expression of the relationship between humans and nature within an ethical framework and outlining the new implications of traditional Confucian ecological thought.This paper aims to analyze Wang Yangming’s ecological view of‘benevolence as the unity of heaven and earth’by examining the ecological ideas in his Inquiry on the Great Learning.On this basis,it seeks to refine the valuable achievements of traditional Chinese ecological civilization thought and strengthen the theoretical foundation of contemporary ecological ideas with Chinese characteristics.展开更多
The solar cycle(SC),a phenomenon caused by the quasi-periodic regular activities in the Sun,occurs approximately every 11 years.Intense solar activity can disrupt the Earth’s ionosphere,affecting communication and na...The solar cycle(SC),a phenomenon caused by the quasi-periodic regular activities in the Sun,occurs approximately every 11 years.Intense solar activity can disrupt the Earth’s ionosphere,affecting communication and navigation systems.Consequently,accurately predicting the intensity of the SC holds great significance,but predicting the SC involves a long-term time series,and many existing time series forecasting methods have fallen short in terms of accuracy and efficiency.The Time-series Dense Encoder model is a deep learning solution tailored for long time series prediction.Based on a multi-layer perceptron structure,it outperforms the best previously existing models in accuracy,while being efficiently trainable on general datasets.We propose a method based on this model for SC forecasting.Using a trained model,we predict the test set from SC 19 to SC 25 with an average mean absolute percentage error of 32.02,root mean square error of 30.3,mean absolute error of 23.32,and R^(2)(coefficient of determination)of 0.76,outperforming other deep learning models in terms of accuracy and training efficiency on sunspot number datasets.Subsequently,we use it to predict the peaks of SC 25 and SC 26.For SC 25,the peak time has ended,but a stronger peak is predicted for SC 26,of 199.3,within a range of 170.8-221.9,projected to occur during April 2034.展开更多
Lead(Pb)is a typical low-melting-point ductile metal and serves as an important model material in the study of dynamic responses.Under shock-wave loading,its dynamic mechanical behavior comprises two key phenomena:pla...Lead(Pb)is a typical low-melting-point ductile metal and serves as an important model material in the study of dynamic responses.Under shock-wave loading,its dynamic mechanical behavior comprises two key phenomena:plastic deformation and shock-induced phase transitions.The underlying mechanisms of these processes are still poorly understood.Revealing these mechanisms remains challenging for experimental approaches.Non-equilibrium molecular dynamics(NEMD)simulations are an alternative theoretical tool for studying dynamic responses,as they capture atomic-scale mechanisms such as defect evolution and deformation pathways.However,due to the limited accuracy of empirical interatomic potentials,the reliability of previous NEMD studies has been questioned.Using our newly developed machine learning potential for Pb-Sn alloys,we revisited the microstructural evolution in response to shock loading under various shock orientations.The results reveal that shock loading along the[001]orientation of Pb exhibits a fast,reversible,and massive phase transition and stacking-fault evolution.The behavior of Pb differs from previous studies by the absence of twinning during plastic deformation.Loading along the[011]orientation leads to slow,irreversible plastic deformation,and a localized FCC-BCC phase transition in the Pitsch orientation relationship.This study provides crucial theoretical insights into the dynamic mechanical response of Pb,offering a theoretical input for understanding the microstructure-performance relationship under extreme conditions.展开更多
Nondestructive measurement technology of phenotype can provide substantial phenotypic data support for applications such as seedling breeding,management,and quality testing.The current method of measuring seedling phe...Nondestructive measurement technology of phenotype can provide substantial phenotypic data support for applications such as seedling breeding,management,and quality testing.The current method of measuring seedling phenotypes mainly relies on manual measurement which is inefficient,subjective and destroys samples.Therefore,the paper proposes a nondestructive measurement method for the canopy phenotype of the watermelon plug seedlings based on deep learning.The Azure Kinect was used to shoot canopy color images,depth images,and RGB-D images of the watermelon plug seedlings.The Mask-RCNN network was used to classify,segment,and count the canopy leaves of the watermelon plug seedlings.To reduce the error of leaf area measurement caused by mutual occlusion of leaves,the leaves were repaired by CycleGAN,and the depth images were restored by image processing.Then,the Delaunay triangulation was adopted to measure the leaf area in the leaf point cloud.The YOLOX target detection network was used to identify the growing point position of each seedling on the plug tray.Then the depth differences between the growing point and the upper surface of the plug tray were calculated to obtain plant height.The experiment results show that the nondestructive measurement algorithm proposed in this paper achieves good measurement performance for the watermelon plug seedlings from the 1 true-leaf to 3 true-leaf stages.The average relative error of measurement is 2.33%for the number of true leaves,4.59%for the number of cotyledons,8.37%for the leaf area,and 3.27%for the plant height.The experiment results demonstrate that the proposed algorithm in this paper provides an effective solution for the nondestructive measurement of the canopy phenotype of the plug seedlings.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
The S38C railway axle undergoes induction hardening,resulting in a gradient-distributed microstructure and mechanical properties.The accurate identification of gradient-distributed plastic parameters for the S38C axle...The S38C railway axle undergoes induction hardening,resulting in a gradient-distributed microstructure and mechanical properties.The accurate identification of gradient-distributed plastic parameters for the S38C axle remains a challenging task.To tackle this challenge,the present study proposes a novel approach for identifying the gradient-distributed plastic parameters for the S38C axle by integrating nano-indentation techniques with the machine learning method.Firstly,nano-indentation tests are conducted along the radial direction of the S38C axle to obtain the gradient-distributed load-displacement curves,nano-hardness,and elastic modulus.Subsequently,the dimensionless analysis is performed to obtain the representative stress,strain,and yield stress from load-displacement curves.These parameters are then incorporated into the machine learning method as physical information to identify the gradient-distributed plastic parameters of the S38C axle.The results indicate that the proposed method based on the physics-informed neural network and multi-fidelity neural network successfully identifies the gradient-distributed plastic parameters of the S38C axles and demonstrates superior prediction accuracy and generalization compared with the purely data-driven machine learning method.展开更多
Oxide dispersion strengthened(ODS)alloys are extensively used owing to high thermostability and creep strength contributed from uniformly dispersed fine oxides particles.However,the existence of these strengthening pa...Oxide dispersion strengthened(ODS)alloys are extensively used owing to high thermostability and creep strength contributed from uniformly dispersed fine oxides particles.However,the existence of these strengthening particles also deteriorates the processability and it is of great importance to establish accurate processing maps to guide the thermomechanical processes to enhance the formability.In this study,we performed particle swarm optimization-based back propagation artificial neural network model to predict the high temperature flow behavior of 0.25wt%Al2O3 particle-reinforced Cu alloys,and compared the accuracy with that of derived by Arrhenius-type constitutive model and back propagation artificial neural network model.To train these models,we obtained the raw data by fabricating ODS Cu alloys using the internal oxidation and reduction method,and conducting systematic hot compression tests between 400 and800℃with strain rates of 10^(-2)-10 S^(-1).At last,processing maps for ODS Cu alloys were proposed by combining processing parameters,mechanical behavior,microstructure characterization,and the modeling results achieved a coefficient of determination higher than>99%.展开更多
An image processing and deep learning method for identifying different types of rock images was proposed.Preprocessing,such as rock image acquisition,gray scaling,Gaussian blurring,and feature dimensionality reduction...An image processing and deep learning method for identifying different types of rock images was proposed.Preprocessing,such as rock image acquisition,gray scaling,Gaussian blurring,and feature dimensionality reduction,was conducted to extract useful feature information and recognize and classify rock images using Tensor Flow-based convolutional neural network(CNN)and Py Qt5.A rock image dataset was established and separated into workouts,confirmation sets,and test sets.The framework was subsequently compiled and trained.The categorization approach was evaluated using image data from the validation and test datasets,and key metrics,such as accuracy,precision,and recall,were analyzed.Finally,the classification model conducted a probabilistic analysis of the measured data to determine the equivalent lithological type for each image.The experimental results indicated that the method combining deep learning,Tensor Flow-based CNN,and Py Qt5 to recognize and classify rock images has an accuracy rate of up to 98.8%,and can be successfully utilized for rock image recognition.The system can be extended to geological exploration,mine engineering,and other rock and mineral resource development to more efficiently and accurately recognize rock samples.Moreover,it can match them with the intelligent support design system to effectively improve the reliability and economy of the support scheme.The system can serve as a reference for supporting the design of other mining and underground space projects.展开更多
To curb the worsening tropospheric ozone(O_(3))pollution problem in China,a rapid and accurate identification of O_(3)-precursor sensitivity(OPS)is a crucial prerequisite for formulating effective contingency O_(3) po...To curb the worsening tropospheric ozone(O_(3))pollution problem in China,a rapid and accurate identification of O_(3)-precursor sensitivity(OPS)is a crucial prerequisite for formulating effective contingency O_(3) pollution control strategies.However,currently widely-used methods,such as statistical models and numerical models,exhibit inherent limitations in identifying OPS in a timely and accurate manner.In this study,we developed a novel approach to identify OPS based on eXtreme Gradient Boosting model,Shapley additive explanation(SHAP)al-gorithm,and volatile organic compound(VOC)photochemical decay adjustment,using the meteorology and speciated pollutant monitoring data as the input.By comparing the difference in SHAP values between base sce-nario and precursor reduction scenario for nitrogen oxides(NO_(x))and VOCs,OPS was divided into NO_(x)-limited,VOCs-limited and transition regime.Using the long-lasting O_(3) pollution episode in the autumn of 2022 at the Guangdong-Hong Kong-Macao Greater Bay Area(GBA)as an example,we demonstrated large spatiotemporal heterogeneities of OPS over the GBA,which were generally shifted from NO_(x)-limited to VOCs-limited from September to October and more inclined to be VOCs-limited at the central and NO_(x)-limited in the peripheral areas.This study developed an innovative OPS identification method by comparing the difference in SHAP value before and after precursor emission reduction.Our method enables the accurate identification of OPS in the time scale of seconds,thereby providing a state-of-the-art tool for the rapid guidance of spatial-specific O_(3) control strategies.展开更多
Ferroptosis,a type of cell death that mainly involves iron metabolism imbalance and lipid peroxidation,is strongly correlated with the phagocytic response caused by bleeding after spinal cord injury.Thus,in this study...Ferroptosis,a type of cell death that mainly involves iron metabolism imbalance and lipid peroxidation,is strongly correlated with the phagocytic response caused by bleeding after spinal cord injury.Thus,in this study,bulk RNA sequencing data(GSE47681 and GSE5296)and single-cell RNA sequencing data(GSE162610)were acquired from gene expression databases.We then conducted differential analysis and immune infiltration analysis.Atf3 and Piezo1 were identified as key ferroptosis genes through random forest and least absolute shrinkage and selection operator algorithms.Further analysis of single-cell RNA sequencing data revealed a close relationship between ferroptosis and cell types such as macrophages/microglia and their intrinsic state transition processes.Differences in transcription factor regulation and intercellular communication networks were found in ferroptosis-related cells,confirming the high expression of Atf3 and Piezo1 in these cells.Molecular docking analysis confirmed that the proteins encoded by these genes can bind cycloheximide.In a mouse model of T8 spinal cord injury,low-dose cycloheximide treatment was found to improve neurological function,decrease levels of the pro-inflammatory cytokine inducible nitric oxide synthase,and increase levels of the anti-inflammatory cytokine arginase 1.Correspondingly,the expression of the ferroptosis-related gene Gpx4 increased in macrophages/microglia,while the expression of Acsl4 decreased.Our findings reveal the important role of ferroptosis in the treatment of spinal cord injury,identify the key cell types and genes involved in ferroptosis after spinal cord injury,and validate the efficacy of potential drug therapies,pointing to new directions in the treatment of spinal cord injury.展开更多
With the rapid development of artificial intelligence,the Internet of Things(IoT)can deploy various machine learning algorithms for network and application management.In the IoT environment,many sensors and devices ge...With the rapid development of artificial intelligence,the Internet of Things(IoT)can deploy various machine learning algorithms for network and application management.In the IoT environment,many sensors and devices generatemassive data,but data security and privacy protection have become a serious challenge.Federated learning(FL)can achieve many intelligent IoT applications by training models on local devices and allowing AI training on distributed IoT devices without data sharing.This review aims to deeply explore the combination of FL and the IoT,and analyze the application of federated learning in the IoT from the aspects of security and privacy protection.In this paper,we first describe the potential advantages of FL and the challenges faced by current IoT systems in the fields of network burden and privacy security.Next,we focus on exploring and analyzing the advantages of the combination of FL on the Internet,including privacy security,attack detection,efficient communication of the IoT,and enhanced learning quality.We also list various application scenarios of FL on the IoT.Finally,we propose several open research challenges and possible solutions.展开更多
The current deep learning models for braced excavation cannot predict deformation from the beginning of excavation due to the need for a substantial corpus of sufficient historical data for training purposes.To addres...The current deep learning models for braced excavation cannot predict deformation from the beginning of excavation due to the need for a substantial corpus of sufficient historical data for training purposes.To address this issue,this study proposes a transfer learning model based on a sequence-to-sequence twodimensional(2D)convolutional long short-term memory neural network(S2SCL2D).The model can use the existing data from other adjacent similar excavations to achieve wall deflection prediction once a limited amount of monitoring data from the target excavation has been recorded.In the absence of adjacent excavation data,numerical simulation data from the target project can be employed instead.A weight update strategy is proposed to improve the prediction accuracy by integrating the stochastic gradient masking with an early stopping mechanism.To illustrate the proposed methodology,an excavation project in Hangzhou,China is adopted.The proposed deep transfer learning model,which uses either adjacent excavation data or numerical simulation data as the source domain,shows a significant improvement in performance when compared to the non-transfer learning model.Using the simulation data from the target project even leads to better prediction performance than using the actual monitoring data from other adjacent excavations.The results demonstrate that the proposed model can reasonably predict the deformation with limited data from the target project.展开更多
As AI systems scale, the limitations of cloud-based architectures, including latency, bandwidth, and privacy concerns, demand decentralized alternatives. Federated learning (FL) and Edge AI provide a paradigm shift by...As AI systems scale, the limitations of cloud-based architectures, including latency, bandwidth, and privacy concerns, demand decentralized alternatives. Federated learning (FL) and Edge AI provide a paradigm shift by combining privacy preserving training with efficient, on device computation. This paper introduces a cutting-edge FL-edge integration framework, achieving a 10% to 15% increase in model accuracy and reducing communication costs by 25% in heterogeneous environments. Blockchain based secure aggregation ensures robust and tamper-proof model updates, while exploratory quantum AI techniques enhance computational efficiency. By addressing key challenges such as device variability and non-IID data, this work sets the stage for the next generation of adaptive, privacy-first AI systems, with applications in IoT, healthcare, and autonomous systems.展开更多
Predicting the productivity of multistage fractured horizontal wells plays an important role in exploiting unconventional resources.In recent years,machine learning(ML)models have emerged as a new approach for such st...Predicting the productivity of multistage fractured horizontal wells plays an important role in exploiting unconventional resources.In recent years,machine learning(ML)models have emerged as a new approach for such studies.However,the scarcity of sufficient real data for model training often leads to imprecise predictions,even though the models trained with real data better characterize geological and engineering features.To tackle this issue,we propose an ML model that can obtain reliable results even with a small amount of data samples.Our model integrates the synthetic minority oversampling technique(SMOTE)to expand the data volume,the support vector machine(SVM)for model training,and the particle swarm optimization(PSO)algorithm for optimizing hyperparameters.To enhance the model performance,we conduct feature fusion and dimensionality reduction.Additionally,we examine the influences of different sample sizes and ML models for training.The proposed model demonstrates higher prediction accuracy and generalization ability,achieving a predicted R^(2)value of up to 0.9 for the test set,compared to the traditional ML techniques with an R^(2)of 0.13.This model accurately predicts the production of fractured horizontal wells even with limited samples,supplying an efficient tool for optimizing the production of unconventional resources.Importantly,the model holds the potential applicability to address similar challenges in other fields constrained by scarce data samples.展开更多
The historicity of China's first state-level government(the Xia Dynasty),during which a Great Flood is claimed to have swept through the core of northern China,remains a well-known yet unresolved issue.Archaeologi...The historicity of China's first state-level government(the Xia Dynasty),during which a Great Flood is claimed to have swept through the core of northern China,remains a well-known yet unresolved issue.Archaeologists hypothesize a connection between the legendary events of the Xia Dynasty and archaeological discoveries in the Central China Plains cultural area,encompassing late Neolithic and Bronze Age cultures such as Henan's Longshan,Xinzhai,Erlitou,and Erligang.The authenticity of these speculations has been challenging to substantiate due to the lack of systematic evidence for the Great Flood in the middle to lower Yellow River(YR)Basin.In this paper,we present high-resolution hydrological environmental proxy data,sedimentological remains,and paleontological evidence from the central North China Plain.Our findings provide isochronous evidence of the termination of a hundred-year-long flood period dated to approximately 2080±216 BC,consistent with the observations from lower YR flood plain and marginal marine sediments.These findings both spatially and temporally overlap with the framework of the Great Flood described in the Chinese classics.The alignment between the geoscientific and archaeological evidence and the information in the Chinese classics provides robust confirmation that the Great Flood occurred in the middle to lower YR region during the late Longshan era.展开更多
Arsenic(As)pollution in soils is a pervasive environmental issue.Biochar immobilization offers a promising solution for addressing soil As contamination.The efficiency of biochar in immobilizing As in soils primarily ...Arsenic(As)pollution in soils is a pervasive environmental issue.Biochar immobilization offers a promising solution for addressing soil As contamination.The efficiency of biochar in immobilizing As in soils primarily hinges on the characteristics of both the soil and the biochar.However,the influence of a specific property on As immobilization varies among different studies,and the development and application of arsenic passivation materials based on biochar often rely on empirical knowledge.To enhance immobilization efficiency and reduce labor and time costs,a machine learning(ML)model was employed to predict As immobilization efficiency before biochar application.In this study,we collected a dataset comprising 182 data points on As immobilization efficiency from 17 publications to construct three ML models.The results demonstrated that the random forest(RF)model outperformed gradient boost regression tree and support vector regression models in predictive performance.Relative importance analysis and partial dependence plots based on the RF model were conducted to identify the most crucial factors influencing As immobilization.These findings highlighted the significant roles of biochar application time and biochar pH in As immobilization efficiency in soils.Furthermore,the study revealed that Fe-modified biochar exhibited a substantial improvement in As immobilization.These insights can facilitate targeted biochar property design and optimization of biochar application conditions to enhance As immobilization efficiency.展开更多
General anesthesia,pivotal for surgical procedures,requires precise depth monitoring to mitigate risks ranging from intraoperative awareness to postoperative cognitive impairments.Traditional assessment methods,relyin...General anesthesia,pivotal for surgical procedures,requires precise depth monitoring to mitigate risks ranging from intraoperative awareness to postoperative cognitive impairments.Traditional assessment methods,relying on physiological indicators or behavioral responses,fall short of accurately capturing the nuanced states of unconsciousness.This study introduces a machine learning-based approach to decode anesthesia depth,leveraging EEG data across different anesthesia states induced by propofol and esketamine in rats.Our findings demonstrate the model’s robust predictive accuracy,underscored by a novel intrasubject dataset partitioning and a 5-fold cross-validation method.The research diverges from conventional monitoring by utilizing anesthetic infusion rates as objective indicators of anesthesia states,highlighting distinct EEG patterns and enhancing prediction accuracy.Moreover,the model’s ability to generalize across individuals suggests its potential for broad clinical application,distinguishing between anesthetic agents and their depths.Despite relying on rat EEG data,which poses questions about real-world applicability,our approach marks a significant advance in anesthesia monitoring.展开更多
Numerous intermediate to felsic igneous rocks are present in both subduction and collisional orogens.However,porphyry copper deposits(PCDs)are comparatively rare.The underlying factors that differentiate fertile magma...Numerous intermediate to felsic igneous rocks are present in both subduction and collisional orogens.However,porphyry copper deposits(PCDs)are comparatively rare.The underlying factors that differentiate fertile magmas,which give rise to PCDs,from barren magmas in a specific geological setting are not well understood.In this study,three supervised machine learning algorithms:random forest(RF),logistic regression(LR)and support vector machine(SVM)were employed to classify metallogenic fertility in southeastern Tibet,Sanjiang orogenic belt,based on whole-rock trace element and Sr-Nd isotopic ratios.The performance of the RF model is better than LR and SVM models.Feature importance analysis of the models reveals that the concentration of Y,Eu,and Th,along with Sr-Nd isotope compositions are crucial variables in distinguishing fertile and barren samples.However,when the optimized models were applied to predict the datasets of Miocene Gangdese porphyry copper belt and Jurassic Gangdese arc representing collision and subduction settings respectively,a marked decline in metrics occurred in all three models,particularly on the subduction dataset.This substantial decrease indicates the compositional characteristics of intrusions across different tectonic settings could be diverse in a multidimensional space,highlighting the complex interplay of geological factors influencing PCD’s formation.展开更多
Hydrocracking technology represents a crucial position in the conversion of heavy oil and the transformation development from oil refining to the chemical industry.The properties of catalysts are one of the key factor...Hydrocracking technology represents a crucial position in the conversion of heavy oil and the transformation development from oil refining to the chemical industry.The properties of catalysts are one of the key factors in the hydrocracking process.As the main acidic component of hydrocracking catalyst,the influence of zeolite properties on the reaction performance has been the focus of research.In this study,a series of NiMo/Al_(2)O_(3)-Y catalysts were prepared using different Y zeolites as acidic components,and their performances in the hydrocracking of n-C_(10)were also evaluated.The structure-activity relationship between Y zeolite and the cracking performance of n-C_(10)was investigated with machine learning.First,a database of the physical and chemical properties of Y zeolite and their performance was established,and the correlation analysis was also conducted.Parameters such as the cell constant,acid content,acid strength,B/L ratio,mesopore volume,micropore volume of Y zeolite,and the reaction temperature were selected as independent variables.The conversion of n-C_(10)and the ratios of products C_(3)/C_(7)and i-C_(4)/n-C_(4)were selected as dependent variables.A model was established by the random forest algorithm and a new zeolite was predicted based on it.The results of model prediction were in good agreement with the experimental results.The R^(2)of the n-C_(10)conversion,C_(3)/C_(7)ratio,and i-C_(4)/n-C_(4)ratio were 0.9866,0.9845,and 0.9922,and the minimum root mean square error values were 0.0163,0.101,and 0.0211,respectively.These results can provide reference for the development of high performance hydrocracking catalyst and technology.展开更多
In order to address the limited mechanical properties of silicon-based materials,this study designed 12 B-site mixed-valence perovskites with s^(0)+s^(2)electronic configurations.Five machine learning models were used...In order to address the limited mechanical properties of silicon-based materials,this study designed 12 B-site mixed-valence perovskites with s^(0)+s^(2)electronic configurations.Five machine learning models were used to predict the bandgap values of candidate materials,and Cs_(2)AgSbCl_(6)was selected as the optimal light absorbing material.By using first principles calculations under stress and strain,it has been determined that micro-strains can achieve the goals of reducing material strength,enhancing flexible characteristics,directionally adjusting the anisotropy of stress concentration areas,improving thermodynamic properties,and enhancing sound insulation ability without significantly affecting photoelectric properties.According to device simulations,tensile strain can effectively increase the theoretical efficiency of solar cells.This work elucidates the mechanism of mechanical property changes under stress and strain,offering insights into new materials for solar energy conversion and accelerating the development of high-performance photovoltaic devices.展开更多
文摘The relationship between heaven and humanity is one of the fundamental philosophical foundations of ecological ethics in ancient Chinese Confucian thought.As a master of Confucian philosophy of mind,Wang Yangming integrated the traditional Confucian discourse on the relationship between heaven and humanity into the principles of the philosophy of mind.Building on the traditional doctrine of benevolence centered on moral concern,he further developed an ecological view of‘benevolence as the unity of heaven and earth’.In his work Inquiry on the Great Learning,Wang Yangming systematically elaborated on this notion,emphasizing the philosophical expression of the relationship between humans and nature within an ethical framework and outlining the new implications of traditional Confucian ecological thought.This paper aims to analyze Wang Yangming’s ecological view of‘benevolence as the unity of heaven and earth’by examining the ecological ideas in his Inquiry on the Great Learning.On this basis,it seeks to refine the valuable achievements of traditional Chinese ecological civilization thought and strengthen the theoretical foundation of contemporary ecological ideas with Chinese characteristics.
基金supported by the Academic Research Projects of Beijing Union University(ZK20202204)the National Natural Science Foundation of China(12250005,12073040,12273059,11973056,12003051,11573037,12073041,11427901,11572005,11611530679 and 12473052)+1 种基金the Strategic Priority Research Program of the China Academy of Sciences(XDB0560000,XDA15052200,XDB09040200,XDA15010700,XDB0560301,and XDA15320102)the Chinese Meridian Project(CMP).
文摘The solar cycle(SC),a phenomenon caused by the quasi-periodic regular activities in the Sun,occurs approximately every 11 years.Intense solar activity can disrupt the Earth’s ionosphere,affecting communication and navigation systems.Consequently,accurately predicting the intensity of the SC holds great significance,but predicting the SC involves a long-term time series,and many existing time series forecasting methods have fallen short in terms of accuracy and efficiency.The Time-series Dense Encoder model is a deep learning solution tailored for long time series prediction.Based on a multi-layer perceptron structure,it outperforms the best previously existing models in accuracy,while being efficiently trainable on general datasets.We propose a method based on this model for SC forecasting.Using a trained model,we predict the test set from SC 19 to SC 25 with an average mean absolute percentage error of 32.02,root mean square error of 30.3,mean absolute error of 23.32,and R^(2)(coefficient of determination)of 0.76,outperforming other deep learning models in terms of accuracy and training efficiency on sunspot number datasets.Subsequently,we use it to predict the peaks of SC 25 and SC 26.For SC 25,the peak time has ended,but a stronger peak is predicted for SC 26,of 199.3,within a range of 170.8-221.9,projected to occur during April 2034.
基金supported by the National Key R&D Program of China(Grant No.2022YFA1004300)the National Natural Science Foundation of China(Grant No.12404004)。
文摘Lead(Pb)is a typical low-melting-point ductile metal and serves as an important model material in the study of dynamic responses.Under shock-wave loading,its dynamic mechanical behavior comprises two key phenomena:plastic deformation and shock-induced phase transitions.The underlying mechanisms of these processes are still poorly understood.Revealing these mechanisms remains challenging for experimental approaches.Non-equilibrium molecular dynamics(NEMD)simulations are an alternative theoretical tool for studying dynamic responses,as they capture atomic-scale mechanisms such as defect evolution and deformation pathways.However,due to the limited accuracy of empirical interatomic potentials,the reliability of previous NEMD studies has been questioned.Using our newly developed machine learning potential for Pb-Sn alloys,we revisited the microstructural evolution in response to shock loading under various shock orientations.The results reveal that shock loading along the[001]orientation of Pb exhibits a fast,reversible,and massive phase transition and stacking-fault evolution.The behavior of Pb differs from previous studies by the absence of twinning during plastic deformation.Loading along the[011]orientation leads to slow,irreversible plastic deformation,and a localized FCC-BCC phase transition in the Pitsch orientation relationship.This study provides crucial theoretical insights into the dynamic mechanical response of Pb,offering a theoretical input for understanding the microstructure-performance relationship under extreme conditions.
基金funded by the National Key Research and Development Program of China(Grant No.2019YFD1001900)the HZAU-AGIS Cooperation Fund(Grant No.SZYJY2022006).
文摘Nondestructive measurement technology of phenotype can provide substantial phenotypic data support for applications such as seedling breeding,management,and quality testing.The current method of measuring seedling phenotypes mainly relies on manual measurement which is inefficient,subjective and destroys samples.Therefore,the paper proposes a nondestructive measurement method for the canopy phenotype of the watermelon plug seedlings based on deep learning.The Azure Kinect was used to shoot canopy color images,depth images,and RGB-D images of the watermelon plug seedlings.The Mask-RCNN network was used to classify,segment,and count the canopy leaves of the watermelon plug seedlings.To reduce the error of leaf area measurement caused by mutual occlusion of leaves,the leaves were repaired by CycleGAN,and the depth images were restored by image processing.Then,the Delaunay triangulation was adopted to measure the leaf area in the leaf point cloud.The YOLOX target detection network was used to identify the growing point position of each seedling on the plug tray.Then the depth differences between the growing point and the upper surface of the plug tray were calculated to obtain plant height.The experiment results show that the nondestructive measurement algorithm proposed in this paper achieves good measurement performance for the watermelon plug seedlings from the 1 true-leaf to 3 true-leaf stages.The average relative error of measurement is 2.33%for the number of true leaves,4.59%for the number of cotyledons,8.37%for the leaf area,and 3.27%for the plant height.The experiment results demonstrate that the proposed algorithm in this paper provides an effective solution for the nondestructive measurement of the canopy phenotype of the plug seedlings.
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supported by the National Key Research and Development Plan(Grant No.2022YFB3401901)the National Natural Science Foundation of China(Grant Nos.12192210,12192214,12072295,and 12222209)+1 种基金Independent Project of State Key Laboratory of Rail Transit Vehicle System(Grant No.2023TPL-T03)Fundamental Research Funds for the Central Universities(Grant No.2682023CG004).
文摘The S38C railway axle undergoes induction hardening,resulting in a gradient-distributed microstructure and mechanical properties.The accurate identification of gradient-distributed plastic parameters for the S38C axle remains a challenging task.To tackle this challenge,the present study proposes a novel approach for identifying the gradient-distributed plastic parameters for the S38C axle by integrating nano-indentation techniques with the machine learning method.Firstly,nano-indentation tests are conducted along the radial direction of the S38C axle to obtain the gradient-distributed load-displacement curves,nano-hardness,and elastic modulus.Subsequently,the dimensionless analysis is performed to obtain the representative stress,strain,and yield stress from load-displacement curves.These parameters are then incorporated into the machine learning method as physical information to identify the gradient-distributed plastic parameters of the S38C axle.The results indicate that the proposed method based on the physics-informed neural network and multi-fidelity neural network successfully identifies the gradient-distributed plastic parameters of the S38C axles and demonstrates superior prediction accuracy and generalization compared with the purely data-driven machine learning method.
基金financial support of the National Natural Science Foundation of China(No.52371103)the Fundamental Research Funds for the Central Universities,China(No.2242023K40028)+1 种基金the Open Research Fund of Jiangsu Key Laboratory for Advanced Metallic Materials,China(No.AMM2023B01).financial support of the Research Fund of Shihezi Key Laboratory of AluminumBased Advanced Materials,China(No.2023PT02)financial support of Guangdong Province Science and Technology Major Project,China(No.2021B0301030005)。
文摘Oxide dispersion strengthened(ODS)alloys are extensively used owing to high thermostability and creep strength contributed from uniformly dispersed fine oxides particles.However,the existence of these strengthening particles also deteriorates the processability and it is of great importance to establish accurate processing maps to guide the thermomechanical processes to enhance the formability.In this study,we performed particle swarm optimization-based back propagation artificial neural network model to predict the high temperature flow behavior of 0.25wt%Al2O3 particle-reinforced Cu alloys,and compared the accuracy with that of derived by Arrhenius-type constitutive model and back propagation artificial neural network model.To train these models,we obtained the raw data by fabricating ODS Cu alloys using the internal oxidation and reduction method,and conducting systematic hot compression tests between 400 and800℃with strain rates of 10^(-2)-10 S^(-1).At last,processing maps for ODS Cu alloys were proposed by combining processing parameters,mechanical behavior,microstructure characterization,and the modeling results achieved a coefficient of determination higher than>99%.
基金financially supported by the National Science and Technology Major Project——Deep Earth Probe and Mineral Resources Exploration(No.2024ZD1003701)the National Key R&D Program of China(No.2022YFC2905004)。
文摘An image processing and deep learning method for identifying different types of rock images was proposed.Preprocessing,such as rock image acquisition,gray scaling,Gaussian blurring,and feature dimensionality reduction,was conducted to extract useful feature information and recognize and classify rock images using Tensor Flow-based convolutional neural network(CNN)and Py Qt5.A rock image dataset was established and separated into workouts,confirmation sets,and test sets.The framework was subsequently compiled and trained.The categorization approach was evaluated using image data from the validation and test datasets,and key metrics,such as accuracy,precision,and recall,were analyzed.Finally,the classification model conducted a probabilistic analysis of the measured data to determine the equivalent lithological type for each image.The experimental results indicated that the method combining deep learning,Tensor Flow-based CNN,and Py Qt5 to recognize and classify rock images has an accuracy rate of up to 98.8%,and can be successfully utilized for rock image recognition.The system can be extended to geological exploration,mine engineering,and other rock and mineral resource development to more efficiently and accurately recognize rock samples.Moreover,it can match them with the intelligent support design system to effectively improve the reliability and economy of the support scheme.The system can serve as a reference for supporting the design of other mining and underground space projects.
基金supported by the Key-Area Research and Development Program of Guangdong Province(No.2020B1111360003)the National Natural Science Foundation of China(Nos.42465008 and 42105164)+2 种基金Yunnan Science and Technology Department Project(No.202501AT070239)Yunnan Science and Technology Department Youth Project(No.202401AU070202)Xianyang Rapid Response Decision Support Project for Ozone(No.YZ2024-ZB019).
文摘To curb the worsening tropospheric ozone(O_(3))pollution problem in China,a rapid and accurate identification of O_(3)-precursor sensitivity(OPS)is a crucial prerequisite for formulating effective contingency O_(3) pollution control strategies.However,currently widely-used methods,such as statistical models and numerical models,exhibit inherent limitations in identifying OPS in a timely and accurate manner.In this study,we developed a novel approach to identify OPS based on eXtreme Gradient Boosting model,Shapley additive explanation(SHAP)al-gorithm,and volatile organic compound(VOC)photochemical decay adjustment,using the meteorology and speciated pollutant monitoring data as the input.By comparing the difference in SHAP values between base sce-nario and precursor reduction scenario for nitrogen oxides(NO_(x))and VOCs,OPS was divided into NO_(x)-limited,VOCs-limited and transition regime.Using the long-lasting O_(3) pollution episode in the autumn of 2022 at the Guangdong-Hong Kong-Macao Greater Bay Area(GBA)as an example,we demonstrated large spatiotemporal heterogeneities of OPS over the GBA,which were generally shifted from NO_(x)-limited to VOCs-limited from September to October and more inclined to be VOCs-limited at the central and NO_(x)-limited in the peripheral areas.This study developed an innovative OPS identification method by comparing the difference in SHAP value before and after precursor emission reduction.Our method enables the accurate identification of OPS in the time scale of seconds,thereby providing a state-of-the-art tool for the rapid guidance of spatial-specific O_(3) control strategies.
基金supported by the National Natural Science Foundation of China,No.81972073(to HZ)a grant from the Taishan Scholars Program ofShandong Province-Young Taishan Scholars,No.tsqn201909197(to HZ)+1 种基金a grant from Tianjin Key Medical Discipline(Specialty)Construct Project,No.TJYXZDXK-027A(to SF)a grant from Academic Expert International Innovation Summit,No.22JRRCRC00010(to SF).
文摘Ferroptosis,a type of cell death that mainly involves iron metabolism imbalance and lipid peroxidation,is strongly correlated with the phagocytic response caused by bleeding after spinal cord injury.Thus,in this study,bulk RNA sequencing data(GSE47681 and GSE5296)and single-cell RNA sequencing data(GSE162610)were acquired from gene expression databases.We then conducted differential analysis and immune infiltration analysis.Atf3 and Piezo1 were identified as key ferroptosis genes through random forest and least absolute shrinkage and selection operator algorithms.Further analysis of single-cell RNA sequencing data revealed a close relationship between ferroptosis and cell types such as macrophages/microglia and their intrinsic state transition processes.Differences in transcription factor regulation and intercellular communication networks were found in ferroptosis-related cells,confirming the high expression of Atf3 and Piezo1 in these cells.Molecular docking analysis confirmed that the proteins encoded by these genes can bind cycloheximide.In a mouse model of T8 spinal cord injury,low-dose cycloheximide treatment was found to improve neurological function,decrease levels of the pro-inflammatory cytokine inducible nitric oxide synthase,and increase levels of the anti-inflammatory cytokine arginase 1.Correspondingly,the expression of the ferroptosis-related gene Gpx4 increased in macrophages/microglia,while the expression of Acsl4 decreased.Our findings reveal the important role of ferroptosis in the treatment of spinal cord injury,identify the key cell types and genes involved in ferroptosis after spinal cord injury,and validate the efficacy of potential drug therapies,pointing to new directions in the treatment of spinal cord injury.
基金supported by the Shandong Province Science and Technology Project(2023TSGC0509,2022TSGC2234)Qingdao Science and Technology Plan Project(23-1-5-yqpy-2-qy)Open Topic Grants of Anhui Province Key Laboratory of Intelligent Building&Building Energy Saving,Anhui Jianzhu University(IBES2024KF08).
文摘With the rapid development of artificial intelligence,the Internet of Things(IoT)can deploy various machine learning algorithms for network and application management.In the IoT environment,many sensors and devices generatemassive data,but data security and privacy protection have become a serious challenge.Federated learning(FL)can achieve many intelligent IoT applications by training models on local devices and allowing AI training on distributed IoT devices without data sharing.This review aims to deeply explore the combination of FL and the IoT,and analyze the application of federated learning in the IoT from the aspects of security and privacy protection.In this paper,we first describe the potential advantages of FL and the challenges faced by current IoT systems in the fields of network burden and privacy security.Next,we focus on exploring and analyzing the advantages of the combination of FL on the Internet,including privacy security,attack detection,efficient communication of the IoT,and enhanced learning quality.We also list various application scenarios of FL on the IoT.Finally,we propose several open research challenges and possible solutions.
基金supported by the National Key Research and Development Program of China(Grant No.2023YFC3009400)the National Natural Science Foundation of China(Grant Nos.42307218 and U2239251).
文摘The current deep learning models for braced excavation cannot predict deformation from the beginning of excavation due to the need for a substantial corpus of sufficient historical data for training purposes.To address this issue,this study proposes a transfer learning model based on a sequence-to-sequence twodimensional(2D)convolutional long short-term memory neural network(S2SCL2D).The model can use the existing data from other adjacent similar excavations to achieve wall deflection prediction once a limited amount of monitoring data from the target excavation has been recorded.In the absence of adjacent excavation data,numerical simulation data from the target project can be employed instead.A weight update strategy is proposed to improve the prediction accuracy by integrating the stochastic gradient masking with an early stopping mechanism.To illustrate the proposed methodology,an excavation project in Hangzhou,China is adopted.The proposed deep transfer learning model,which uses either adjacent excavation data or numerical simulation data as the source domain,shows a significant improvement in performance when compared to the non-transfer learning model.Using the simulation data from the target project even leads to better prediction performance than using the actual monitoring data from other adjacent excavations.The results demonstrate that the proposed model can reasonably predict the deformation with limited data from the target project.
文摘As AI systems scale, the limitations of cloud-based architectures, including latency, bandwidth, and privacy concerns, demand decentralized alternatives. Federated learning (FL) and Edge AI provide a paradigm shift by combining privacy preserving training with efficient, on device computation. This paper introduces a cutting-edge FL-edge integration framework, achieving a 10% to 15% increase in model accuracy and reducing communication costs by 25% in heterogeneous environments. Blockchain based secure aggregation ensures robust and tamper-proof model updates, while exploratory quantum AI techniques enhance computational efficiency. By addressing key challenges such as device variability and non-IID data, this work sets the stage for the next generation of adaptive, privacy-first AI systems, with applications in IoT, healthcare, and autonomous systems.
基金supported by the National Natural Science Foundation of China(52274055)the Shandong Provincial Natural Science Foundation(ZR2022YQ50)the Taishan Scholar Program of Shandong Province(tsqn202408088)。
文摘Predicting the productivity of multistage fractured horizontal wells plays an important role in exploiting unconventional resources.In recent years,machine learning(ML)models have emerged as a new approach for such studies.However,the scarcity of sufficient real data for model training often leads to imprecise predictions,even though the models trained with real data better characterize geological and engineering features.To tackle this issue,we propose an ML model that can obtain reliable results even with a small amount of data samples.Our model integrates the synthetic minority oversampling technique(SMOTE)to expand the data volume,the support vector machine(SVM)for model training,and the particle swarm optimization(PSO)algorithm for optimizing hyperparameters.To enhance the model performance,we conduct feature fusion and dimensionality reduction.Additionally,we examine the influences of different sample sizes and ML models for training.The proposed model demonstrates higher prediction accuracy and generalization ability,achieving a predicted R^(2)value of up to 0.9 for the test set,compared to the traditional ML techniques with an R^(2)of 0.13.This model accurately predicts the production of fractured horizontal wells even with limited samples,supplying an efficient tool for optimizing the production of unconventional resources.Importantly,the model holds the potential applicability to address similar challenges in other fields constrained by scarce data samples.
基金Strategic Priority Research Program of the Chinese Academy of Sciences,No.XDB26000000National Natural Science Foundation of China,No.41888101,No.42072209Geological Survey Projects of China,No.DD20189629,No.DD20190370。
文摘The historicity of China's first state-level government(the Xia Dynasty),during which a Great Flood is claimed to have swept through the core of northern China,remains a well-known yet unresolved issue.Archaeologists hypothesize a connection between the legendary events of the Xia Dynasty and archaeological discoveries in the Central China Plains cultural area,encompassing late Neolithic and Bronze Age cultures such as Henan's Longshan,Xinzhai,Erlitou,and Erligang.The authenticity of these speculations has been challenging to substantiate due to the lack of systematic evidence for the Great Flood in the middle to lower Yellow River(YR)Basin.In this paper,we present high-resolution hydrological environmental proxy data,sedimentological remains,and paleontological evidence from the central North China Plain.Our findings provide isochronous evidence of the termination of a hundred-year-long flood period dated to approximately 2080±216 BC,consistent with the observations from lower YR flood plain and marginal marine sediments.These findings both spatially and temporally overlap with the framework of the Great Flood described in the Chinese classics.The alignment between the geoscientific and archaeological evidence and the information in the Chinese classics provides robust confirmation that the Great Flood occurred in the middle to lower YR region during the late Longshan era.
基金supported by the National Key Research and Development Program of China(No.2020YFC1808701).
文摘Arsenic(As)pollution in soils is a pervasive environmental issue.Biochar immobilization offers a promising solution for addressing soil As contamination.The efficiency of biochar in immobilizing As in soils primarily hinges on the characteristics of both the soil and the biochar.However,the influence of a specific property on As immobilization varies among different studies,and the development and application of arsenic passivation materials based on biochar often rely on empirical knowledge.To enhance immobilization efficiency and reduce labor and time costs,a machine learning(ML)model was employed to predict As immobilization efficiency before biochar application.In this study,we collected a dataset comprising 182 data points on As immobilization efficiency from 17 publications to construct three ML models.The results demonstrated that the random forest(RF)model outperformed gradient boost regression tree and support vector regression models in predictive performance.Relative importance analysis and partial dependence plots based on the RF model were conducted to identify the most crucial factors influencing As immobilization.These findings highlighted the significant roles of biochar application time and biochar pH in As immobilization efficiency in soils.Furthermore,the study revealed that Fe-modified biochar exhibited a substantial improvement in As immobilization.These insights can facilitate targeted biochar property design and optimization of biochar application conditions to enhance As immobilization efficiency.
基金supported by grants from the Shanghai Municipal Health Commission(2023ZDFC0203)the National Natural Science Foundation of China(32171044).
文摘General anesthesia,pivotal for surgical procedures,requires precise depth monitoring to mitigate risks ranging from intraoperative awareness to postoperative cognitive impairments.Traditional assessment methods,relying on physiological indicators or behavioral responses,fall short of accurately capturing the nuanced states of unconsciousness.This study introduces a machine learning-based approach to decode anesthesia depth,leveraging EEG data across different anesthesia states induced by propofol and esketamine in rats.Our findings demonstrate the model’s robust predictive accuracy,underscored by a novel intrasubject dataset partitioning and a 5-fold cross-validation method.The research diverges from conventional monitoring by utilizing anesthetic infusion rates as objective indicators of anesthesia states,highlighting distinct EEG patterns and enhancing prediction accuracy.Moreover,the model’s ability to generalize across individuals suggests its potential for broad clinical application,distinguishing between anesthetic agents and their depths.Despite relying on rat EEG data,which poses questions about real-world applicability,our approach marks a significant advance in anesthesia monitoring.
基金financially supported by the National Key Research and Development Program of China(2019YFA0708602,2022YFF0800903)National Natural Science Foundation of China(42472112,U2244217,41973045)+1 种基金Basic Science and Technology Research Fundings of the Institute of Geology,CAGS(JKYZD202312)Geological Survey Projects of the China Geological Survey(DD20242878,DD20243512).
文摘Numerous intermediate to felsic igneous rocks are present in both subduction and collisional orogens.However,porphyry copper deposits(PCDs)are comparatively rare.The underlying factors that differentiate fertile magmas,which give rise to PCDs,from barren magmas in a specific geological setting are not well understood.In this study,three supervised machine learning algorithms:random forest(RF),logistic regression(LR)and support vector machine(SVM)were employed to classify metallogenic fertility in southeastern Tibet,Sanjiang orogenic belt,based on whole-rock trace element and Sr-Nd isotopic ratios.The performance of the RF model is better than LR and SVM models.Feature importance analysis of the models reveals that the concentration of Y,Eu,and Th,along with Sr-Nd isotope compositions are crucial variables in distinguishing fertile and barren samples.However,when the optimized models were applied to predict the datasets of Miocene Gangdese porphyry copper belt and Jurassic Gangdese arc representing collision and subduction settings respectively,a marked decline in metrics occurred in all three models,particularly on the subduction dataset.This substantial decrease indicates the compositional characteristics of intrusions across different tectonic settings could be diverse in a multidimensional space,highlighting the complex interplay of geological factors influencing PCD’s formation.
文摘Hydrocracking technology represents a crucial position in the conversion of heavy oil and the transformation development from oil refining to the chemical industry.The properties of catalysts are one of the key factors in the hydrocracking process.As the main acidic component of hydrocracking catalyst,the influence of zeolite properties on the reaction performance has been the focus of research.In this study,a series of NiMo/Al_(2)O_(3)-Y catalysts were prepared using different Y zeolites as acidic components,and their performances in the hydrocracking of n-C_(10)were also evaluated.The structure-activity relationship between Y zeolite and the cracking performance of n-C_(10)was investigated with machine learning.First,a database of the physical and chemical properties of Y zeolite and their performance was established,and the correlation analysis was also conducted.Parameters such as the cell constant,acid content,acid strength,B/L ratio,mesopore volume,micropore volume of Y zeolite,and the reaction temperature were selected as independent variables.The conversion of n-C_(10)and the ratios of products C_(3)/C_(7)and i-C_(4)/n-C_(4)were selected as dependent variables.A model was established by the random forest algorithm and a new zeolite was predicted based on it.The results of model prediction were in good agreement with the experimental results.The R^(2)of the n-C_(10)conversion,C_(3)/C_(7)ratio,and i-C_(4)/n-C_(4)ratio were 0.9866,0.9845,and 0.9922,and the minimum root mean square error values were 0.0163,0.101,and 0.0211,respectively.These results can provide reference for the development of high performance hydrocracking catalyst and technology.
基金financially supported by the National Key Research and Development Program Projects of China(No.2023YFB3608901)the Xi'an University of Architecture and Technology Branch of Xi'an Computing Center.
文摘In order to address the limited mechanical properties of silicon-based materials,this study designed 12 B-site mixed-valence perovskites with s^(0)+s^(2)electronic configurations.Five machine learning models were used to predict the bandgap values of candidate materials,and Cs_(2)AgSbCl_(6)was selected as the optimal light absorbing material.By using first principles calculations under stress and strain,it has been determined that micro-strains can achieve the goals of reducing material strength,enhancing flexible characteristics,directionally adjusting the anisotropy of stress concentration areas,improving thermodynamic properties,and enhancing sound insulation ability without significantly affecting photoelectric properties.According to device simulations,tensile strain can effectively increase the theoretical efficiency of solar cells.This work elucidates the mechanism of mechanical property changes under stress and strain,offering insights into new materials for solar energy conversion and accelerating the development of high-performance photovoltaic devices.
