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
Using in-situ microstructure observations from 2010 to 2018,this study investigates the performance and generalization of machine learning models in parameterizing turbulent mixing in the northwestern South China Sea....Using in-situ microstructure observations from 2010 to 2018,this study investigates the performance and generalization of machine learning models in parameterizing turbulent mixing in the northwestern South China Sea.The results show that the data-driven extreme gradient boosting(XGBoost)performs better than the other four models,i.e.,random forest,neural network,linear regression and support vector machine regression.In order to further improve the generalization of machine learning-based parameterization method,we propose a physics-informed machine learning(PIML)that couples the MacKinnon-Gregg model(known as the MG model)and Osborn’s formula to the XGBoost model.The correlation coefficient(r)and root mean square error(RMSE)between the estimated and observed 1g(ε)(whereεdenotes the turbulent kinetic energy dissipation rate)from the PIML are improved by 14%and 16%,respectively.The results also show that PIML effectively improves the generalization of the XGBoost-based parameterization method,enhancing r and RMSE by 35%and 75%,respectively.展开更多
The accurate prediction of peak overpressure of explosion shockwaves is significant in fields such as explosion hazard assessment and structural protection, where explosion shockwaves serve as typical destructive elem...The accurate prediction of peak overpressure of explosion shockwaves is significant in fields such as explosion hazard assessment and structural protection, where explosion shockwaves serve as typical destructive elements. Aiming at the problem of insufficient accuracy of the existing physical models for predicting the peak overpressure of ground reflected waves, two physics-informed machine learning models are constructed. The results demonstrate that the machine learning models, which incorporate physical information by predicting the deviation between the physical model and actual values and adding a physical loss term in the loss function, can accurately predict both the training and out-oftraining dataset. Compared to existing physical models, the average relative error in the predicted training domain is reduced from 17.459%-48.588% to 2%, and the proportion of average relative error less than 20% increased from 0% to 59.4% to more than 99%. In addition, the relative average error outside the prediction training set range is reduced from 14.496%-29.389% to 5%, and the proportion of relative average error less than 20% increased from 0% to 71.39% to more than 99%. The inclusion of a physical loss term enforcing monotonicity in the loss function effectively improves the extrapolation performance of machine learning. The findings of this study provide valuable reference for explosion hazard assessment and anti-explosion structural design in various fields.展开更多
The safe and reliable operation of lithium-ion batteries necessitates the accurate prediction of remaining useful life(RUL).However,this task is challenging due to the diverse ageing mechanisms,various operating condi...The safe and reliable operation of lithium-ion batteries necessitates the accurate prediction of remaining useful life(RUL).However,this task is challenging due to the diverse ageing mechanisms,various operating conditions,and limited measured signals.Although data-driven methods are perceived as a promising solution,they ignore intrinsic battery physics,leading to compromised accuracy,low efficiency,and low interpretability.In response,this study integrates domain knowledge into deep learning to enhance the RUL prediction performance.We demonstrate accurate RUL prediction using only a single charging curve.First,a generalisable physics-based model is developed to extract ageing-correlated parameters that can describe and explain battery degradation from battery charging data.The parameters inform a deep neural network(DNN)to predict RUL with high accuracy and efficiency.The trained model is validated under 3 types of batteries working under 7 conditions,considering fully charged and partially charged cases.Using data from one cycle only,the proposed method achieves a root mean squared error(RMSE)of 11.42 cycles and a mean absolute relative error(MARE)of 3.19%on average,which are over45%and 44%lower compared to the two state-of-the-art data-driven methods,respectively.Besides its accuracy,the proposed method also outperforms existing methods in terms of efficiency,input burden,and robustness.The inherent relationship between the model parameters and the battery degradation mechanism is further revealed,substantiating the intrinsic superiority of the proposed method.展开更多
Landslide susceptibility mapping is a crucial tool for disaster prevention and management.The performance of conventional data-driven model is greatly influenced by the quality of the samples data.The random selection...Landslide susceptibility mapping is a crucial tool for disaster prevention and management.The performance of conventional data-driven model is greatly influenced by the quality of the samples data.The random selection of negative samples results in the lack of interpretability throughout the assessment process.To address this limitation and construct a high-quality negative samples database,this study introduces a physics-informed machine learning approach,combining the random forest model with Scoops 3D,to optimize the negative samples selection strategy and assess the landslide susceptibility of the study area.The Scoops 3D is employed to determine the factor of safety value leveraging Bishop’s simplified method.Instead of conventional random selection,negative samples are extracted from the areas with a high factor of safety value.Subsequently,the results of conventional random forest model and physics-informed data-driven model are analyzed and discussed,focusing on model performance and prediction uncertainty.In comparison to conventional methods,the physics-informed model,set with a safety area threshold of 3,demonstrates a noteworthy improvement in the mean AUC value by 36.7%,coupled with a reduced prediction uncertainty.It is evident that the determination of the safety area threshold exerts an impact on both prediction uncertainty and model performance.展开更多
Pressure swing adsorption(PSA)modeling remains a challenging task since it exhibits strong dynamic and cyclic behavior.This study presents a systematic physics-informed machine learning method that integrates transfer...Pressure swing adsorption(PSA)modeling remains a challenging task since it exhibits strong dynamic and cyclic behavior.This study presents a systematic physics-informed machine learning method that integrates transfer learning and labeled data to construct a spatiotemporal model of the PSA process.To approximate the latent solutions of partial differential equations(PDEs)in the specific steps of pressurization,adsorption,heavy reflux,counter-current depressurization,and light reflux,the system's network representation is decomposed into five lightweight sub-networks.On this basis,we propose a parameter-based transfer learning(TL)combined with domain decomposition to address the long-term integration of periodic PDEs and expedite the network training process.Moreover,to tackle challenges related to sharp adsorption fronts,our method allows for the inclusion of a specified amount of labeled data at the boundaries and/or within the system in the loss function.The results show that the proposed method closely matches the outcomes achieved through the conventional numerical method,effectively simulating all steps and cyclic behavior within the PSA processes.展开更多
The polarity of solvents plays a critical role in various research applications,particularly in their solubilities.Polarity is conveniently characterized by the Kamlet-Taft parameters that is,the hydrogen bonding acid...The polarity of solvents plays a critical role in various research applications,particularly in their solubilities.Polarity is conveniently characterized by the Kamlet-Taft parameters that is,the hydrogen bonding acidity(α),the basicity(β),and the polarizability(π^(*)).Obtaining Kamlet-Taft parameters is very important for designer solvents,namely ionic liquids(ILs)and deep eutectic solvents(DESs).However,given the unlimited theoretical number of combinations of ionic pairs in ILs and hydrogen-bond donor/acceptor pairs in DESs,experimental determination of their Kamlet-Taft parameters is impractical.To address this,the present study developed two different machine learning(ML)algorithms to predict Kamlet-Taft parameters for designer solvents using quantum chemically derived input features.The ML models developed in the present study showed accurate predictions with high determination coefficient(R^(2))and low root mean square error(RMSE)values.Further,in the context of present interest in the circular bioeconomy,the relationship between the basicities and acidities of designer solvents and their ability to dissolve lignin and carbon dioxide(CO_(2))is discussed.Our method thus guides the design of effective solvents with optimal Kamlet-Taft parameter values dissolving and converting biomass and CO_(2)into valuable chemicals.展开更多
Cyclohexene is an important raw material in the production of nylon.Selective hydrogenation of benzene is a key method for preparing cyclohexene.However,the Ru catalysts used in current industrial processes still face...Cyclohexene is an important raw material in the production of nylon.Selective hydrogenation of benzene is a key method for preparing cyclohexene.However,the Ru catalysts used in current industrial processes still face challenges,including high metal usage,high process costs,and low cyclohexene yield.This study utilizes existing literature data combined with machine learning methods to analyze the factors influencing benzene conversion,cyclohexene selectivity,and yield in the benzene hydrogenation to cyclohexene reaction.It constructs predictive models based on XGBoost and Random Forest algorithms.After analysis,it was found that reaction time,Ru content,and space velocity are key factors influencing cyclohexene yield,selectivity,and benzene conversion.Shapley Additive Explanations(SHAP)analysis and feature importance analysis further revealed the contribution of each variable to the reaction outcomes.Additionally,we randomly generated one million variable combinations using the Dirichlet distribution to attempt to predict high-yield catalyst formulations.This paper provides new insights into the application of machine learning in heterogeneous catalysis and offers some reference for further research.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
Single-phase ordered body-centered cubic or B2 multi-principal element intermetallics(MPEIs)have garnered significant attention due to their exceptional mechanical and functional properties.However,their discovery in ...Single-phase ordered body-centered cubic or B2 multi-principal element intermetallics(MPEIs)have garnered significant attention due to their exceptional mechanical and functional properties.However,their discovery in complex compositional spaces is challenging due to the lack of high-dimensional phase diagrams and the inefficiency of traditional trial-and-error methods.In this study,we developed a physics-informed machine learning(ML)framework that integrates a conditional variational autoencoder(CVAE)with an artificial neural network(ANN).This approach effectively addresses the challenges of data limitation and imbalance,enabling the high-throughput generation of B2 MPEIs.Using this framework,we successfully identified a wide range of B2 complex alloys,spanning quaternary to senary systems,with superior mechanical performance.This work not only demonstrates a significant advancement in the discovery of B2 MPEIs but also provides an accelerated pathway for their design and development.展开更多
The growing use of lithium-ion batteries in electric transportation and grid-scale storage systems has intensified the need for accurate and highly generalizable state-of-health(SOH)estimation.Conventional approaches ...The growing use of lithium-ion batteries in electric transportation and grid-scale storage systems has intensified the need for accurate and highly generalizable state-of-health(SOH)estimation.Conventional approaches often suffer from reduced accuracy under dynamically uncertain state-of-charge(SOC)operating ranges and heterogeneous aging stresses.This study presents a unified SOH estimation framework that integrates physics-informed modeling,subspace identification,and Transformer-based learning.A reduced-order model is derived from simplified electrochemical dynamics,providing an interpretable and computationally efficient representation of battery behavior.Subspace identification across a wide SOC and SOH range yields degradation-sensitive features,which the Transformer uses to capture long-range aging dynamics via multi-head self-attention.Experiments on LiFePO4 cells under joint-cell training show consistently accurate SOH estimation,with a maximum error of 1.39%,demonstrating the framework’s effectiveness in decoupling SOC and SOH effects.In cross-cell validation,where training and validation are performed on different cells,the model maintains a maximum error of 2.06%,confirming strong generalization to unseen aging trajectories.Comparative experiments on LiFePO_(4)and public LiCoO_(2)datasets confirm the framework’s cross-chemistry applicability.By extracting low-dimensional,physically interpretable features via subspace identification,the framework significantly reduces training cost while maintaining high SOH estimation accuracy,outperforming conventional data-driven models lacking physical guidance.展开更多
Accurate assessment of snowpack volumetric liquid water content and bulk density is essential for understanding snow hydrology,avalanche risk management,and monitoring cryosphere changes.This study presents a novel du...Accurate assessment of snowpack volumetric liquid water content and bulk density is essential for understanding snow hydrology,avalanche risk management,and monitoring cryosphere changes.This study presents a novel dual-parameter inversion framework that integrates synthetic electromagnetic modelling,dimensionality reduction,and machine learning algorithms to extract relative permittivity and log-resistivity from ground-penetrating radar(GPR)data.Traditional snowpack measurements are invasive,labor-intensive,and limited to point observations.To overcome these limitations,we developed a non-invasive,scalable,and data-driven framework that uses synthetic GPR datasets representing diverse snowpack conditions with variable moisture and density profiles.Synthetic 1D time series reflections(A-scans)are generated using finite-difference time-domain simulations in the state-of-the-art electromagnetic simulator gprMax.Principal component analysis(PCA)is applied to compress each A-scan while preserving key features,which significantly improved and enhanced the model training efficiency.Four machine learning models,including random forest,neural network,support vector machine,and eXtreme gradient boosting,are trained on PCA-reduced features.Among these,the neural network model achieved the best performance,with R^(2)>0.97 for permittivity and R 2>0.92 for resistivity.Gaussian noise(signal-to-noise ratio of 6 dB)is introduced to the synthetic data,and then targeted domain adaptation is employed to enhance generalization to field data.The framework is validated on two contrasting GPR transects in the Altay Mountains of the Chinese mainland,representing moist(T750)and wet(G125)snowpack conditions.The neural network model predictions are most consistent with the GPR derived estimates,Snowfork measurements,and snow pit data,achieving volumetric liquid water content deviation of≤1.5% and bulk density error within the range of 30-84 kg m^(-3).The results demonstrate that machine learning-based inversion,supported by realistic simulations and data augmentation enables scalable,non-invasive snowpack characterization with significant applications in hydrological forecasting,snow monitoring,and water resource management.展开更多
In the pharmaceutical field,machine learning can play an important role in drug development,production and treatment.Co-crystallization techniques have shown promising potential to enhance the properties of active pha...In the pharmaceutical field,machine learning can play an important role in drug development,production and treatment.Co-crystallization techniques have shown promising potential to enhance the properties of active pharmaceutical ingredients(APIs)such as solubility,permeability,and bioavailability,all without altering their chemical structure.This approach opens new avenues for developing natural products into effective drugs,especially those previously challenging in formulation.Emodin,an anthraquinone-based natural product,is a notable example due to its diverse biological activities;however,its physicochemical limitations,such as poor solubility and easy sublimation,restricted its clinical application.While various methods have improved emodin's physicochemical properties,research on its bioavailability remains limited.In our study,we summarize cocrystals and salts produced through co-crystallization technology and identify piperazine as a favorable coformer.Conflicting conclusions from computational chemistry and molecular modeling method and machine learning method regarding the formation of an emodin-piperazine cocrystal or salt led us to experimentally validate these possibilities.Ultimately,we successfully obtained the emodin-piperazine cocrystal,which were characterized and evaluated by several in vitro methods and pharmacokinetic studies.In addition,experiments have shown that emodin has a certain therapeutic effect on sepsis,so we also evaluated emodin-piperazine biological activity in a sepsis model.The results demonstrate that co-crystallization significantly enhances emodin's solubility,permeability,and bioavailability.Pharmacodynamic studies indicate that the emodin-piperazine cocrystal improves sepsis symptoms and provides protective effects against liver and kidney damage associated with sepsis.This study offers renewed hope for natural products with broad biological activities yet hindered by physicochemical limitations by advancing co-crystallization as a viable development approach.展开更多
Latest digital advancements have intensified the necessity for adaptive,data-driven and socially-centered learning ecosystems.This paper presents the formulation of a cross-platform,innovative,gamified and personalize...Latest digital advancements have intensified the necessity for adaptive,data-driven and socially-centered learning ecosystems.This paper presents the formulation of a cross-platform,innovative,gamified and personalized Learning Ecosystem,which integrates 3D/VR environments,as well as machine learning algorithms,and business intelligence frameworks to enhance learner-centered education and inferenced decision-making.This Learning System makes use of immersive,analytically assessed virtual learning spaces,therefore facilitating real-time monitoring of not just learning performance,but also overall engagement and behavioral patterns,via a comprehensive set of sustainability-oriented ESG-aligned Key Performance Indicators(KPIs).Machine learning models support predictive analysis,personalized feedback,and hybrid recommendation mechanisms,whilst dedicated dashboards translate complex educational data into actionable insights for all Use Cases of the System(Educational Institutions,Educators and Learners).Additionally,the presented Learning System introduces a structured Mentoring and Consulting Subsystem,thence reinforcing human-centered guidance alongside automated intelligence.The Platform’s modular architecture and simulation-centered evaluation approach actively support personalized,and continuously optimized learning pathways.Thence,it exemplifies a mature,adaptive Learning Ecosystem,supporting immersive technologies,analytics,and pedagogical support,hence,contributing to contemporary digital learning innovation and sociotechnical transformation in education.展开更多
Providing safe and quality food is crucial for every household and is of extreme significance in the growth of any society.It is a complex procedure that deals with all issues focusing on the development of food proce...Providing safe and quality food is crucial for every household and is of extreme significance in the growth of any society.It is a complex procedure that deals with all issues focusing on the development of food processing from seed to harvest,storage,preparation,and consumption.This current paper seeks to demystify the importance of artificial intelligence,machine learning(ML),deep learning(DL),and computer vision(CV)in ensuring food safety and quality.By stressing the importance of these technologies,the audience will feel reassured and confident in their potential.These are very handy for such problems,giving assurance over food safety.CV is incredibly noble in today's generation because it improves food processing quality and positively impacts firms and researchers.Thus,at the present production stage,rich in image processing and computer visioning is incorporated into all facets of food production.In this field,DL and ML are implemented to identify the type of food in addition to quality.Concerning data and result-oriented perceptions,one has found similarities regarding various approaches.As a result,the findings of this study will be helpful for scholars looking for a proper approach to identify the quality of food offered.It helps to indicate which food products have been discussed by other scholars and lets the reader know papers by other scholars inclined to research further.Also,DL is accurately integrated with identifying the quality and safety of foods in the market.This paper describes the current practices and concerns of ML,DL,and probable trends for its future development.展开更多
As urbanization continues to accelerate,the challenges associated with managing transportation in metropolitan areas become increasingly complex.The surge in population density contributes to traffic congestion,impact...As urbanization continues to accelerate,the challenges associated with managing transportation in metropolitan areas become increasingly complex.The surge in population density contributes to traffic congestion,impacting travel experiences and posing safety risks.Smart urban transportation management emerges as a strategic solution,conceptualized here as a multidimensional big data problem.The success of this strategy hinges on the effective collection of information from diverse,extensive,and heterogeneous data sources,necessitating the implementation of full⁃stack Information and Communication Technology(ICT)solutions.The main idea of the work is to investigate the current technologies of Intelligent Transportation Systems(ITS)and enhance the safety of urban transportation systems.Machine learning models,trained on historical data,can predict traffic congestion,allowing for the implementation of preventive measures.Deep learning architectures,with their ability to handle complex data representations,further refine traffic predictions,contributing to more accurate and dynamic transportation management.The background of this research underscores the challenges posed by traffic congestion in metropolitan areas and emphasizes the need for advanced technological solutions.By integrating GPS and GIS technologies with machine learning algorithms,this work aims to pay attention to the development of intelligent transportation systems that not only address current challenges but also pave the way for future advancements in urban transportation management.展开更多
Accurate prediction of rockburst intensity levels is crucial for ensuring the safety of deep hard rock engineering construction.This paper introduced an expert system for rockburst intensity level prediction that empl...Accurate prediction of rockburst intensity levels is crucial for ensuring the safety of deep hard rock engineering construction.This paper introduced an expert system for rockburst intensity level prediction that employs machine learning algorithms as the basis for its inference rules.The system comprises four modules:a database,a repository,an inference engine,and an interpreter.A database containing 1114 rockburst cases was used to construct 357 datasets that serve as the repository for the expert system.Additionally,19 types of machine learning algorithms were used to establish 6783 micro-models to construct cognitive rules within the inference engine.By integrating probability theory and marginal analysis,a fuzzy scoring method based on the SoftMax function was developed and applied to the interpreter for rockburst intensity level prediction,effectively restoring the continuity of rockburst characteristics.The research results indicate that ensemble algorithms based on decision trees are more effective in capturing the characteristics of rockburst.Key factors for accurate prediction of rockburst intensity include uniaxial compressive strength,elastic energy index,the maximum principal stress,tangential stress,and their composite indicators.The accuracy of the proposed rockburst intensity level prediction expert system was verified using 20 engineering rockburst cases,with predictions aligning closely with the actual rockburst intensity levels.展开更多
With the proliferation of Internet of Things(IoT)devices,securing these interconnected systems against cyberattacks has become a critical challenge.Traditional security paradigms often fail to cope with the scale and ...With the proliferation of Internet of Things(IoT)devices,securing these interconnected systems against cyberattacks has become a critical challenge.Traditional security paradigms often fail to cope with the scale and diversity of IoT network traffic.This paper presents a comparative benchmark of classic machine learning(ML)and state-of-the-art deep learning(DL)algorithms for IoT intrusion detection.Our methodology employs a twophased approach:a preliminary pilot study using a custom-generated dataset to establish baselines,followed by a comprehensive evaluation on the large-scale CICIoTDataset2023.We benchmarked algorithms including Random Forest,XGBoost,CNN,and StackedLSTM.The results indicate that while top-performingmodels frombothcategories achieve over 99%classification accuracy,this metric masks a crucial performance trade-off.We demonstrate that treebased ML ensembles exhibit superior precision(91%)in identifying benign traffic,making them effective at reducing false positives.Conversely,DL models demonstrate superior recall(96%),making them better suited for minimizing the interruption of legitimate traffic.We conclude that the selection of an optimal model is not merely a matter of maximizing accuracy but is a strategic choice dependent on the specific security priority either minimizing false alarms or ensuring service availability.Thiswork provides a practical framework for deploying context-aware security solutions in diverse IoT environments.展开更多
Accurate land surface temperature(LST)assessment is crucial for comprehending and reducing the impacts of climate change and understanding land use evolution.This study presents an innovative method by utilizing ensem...Accurate land surface temperature(LST)assessment is crucial for comprehending and reducing the impacts of climate change and understanding land use evolution.This study presents an innovative method by utilizing ensemble models,advanced correlation analysis,and trend analysis to investigate its environmental influences.Google Earth Engine(GEE)was utilized to process the datasets from Landsat-7 and Landsat-8 for the five big cities of Punjab,Pakistan,from 2001 to 2023.Results from this study show significant urban warming trends,and a strong correlation between environmental variables and LST was identified.The ensemble-based three machine learning models,including XGBoost,AdaBoost,and random forest(RF),were adopted to improve the accuracy of LST evaluation.Although XGBoost and AdaBoost attained modest levels of accuracy,with R^(2) values of 0.767 and 0.706,respectively,the RF model outperformed them by achieving an exceptional R^(2) of 0.796 and RMSE of 0.476.Moreover,Pearson correlation analysis revealed a negative relationship between LST and normalized difference latent heat index(NDLI)with r=-0.67,normalized difference vegetation index(NDVI)with r=-0.6,and modified normalized difference water index(MNDWI)with the value of r as -0.57.In addition,wavelet analysis showed that vegetation and water offer long-term LST cooling,lasting up to 64 months,while built-up areas and bare soil contribute to short-term warming,lasting 4 to 8 months.Latent heat indicated variable cooling periods,surpassing 60 months in cities.These findings enhance the understanding of LST changes and the impact of climate change on the environment.展开更多
Nasopharyngeal carcinoma(NPC)is a malignant tumor prevalent in southern China and Southeast Asia,where its early detection is crucial for improving patient prognosis and reducing mortality rates.However,existing scree...Nasopharyngeal carcinoma(NPC)is a malignant tumor prevalent in southern China and Southeast Asia,where its early detection is crucial for improving patient prognosis and reducing mortality rates.However,existing screening methods suffer from limitations in accuracy and accessibility,hindering their application in large-scale population screening.In this work,a surface-enhanced Raman spectroscopy(SERS)-based method was established to explore the profiles of different stratified components in saliva from NPC and healthy subjects after fractionation processing.The study findings indicate that all fractionated samples exhibit diseaseassociated molecular signaling differences,where small-molecule(molecular weight cut-offvalue is 10 kDa)demonstrating superior classification capabilities with sensitivity of 90.5%and speci-ficity of 75.6%,area under receiver operating characteristic(ROC)curve of 0:925±0:031.The primary objective of this study was to qualitatively explore patterns in saliva composition across groups.The proposed SERS detection strategy for fractionated saliva offers novel insights for enhancing the sensitivity and reliability of noninvasive NPC screening,laying the foundation for translational application in large-scale clinical settings.展开更多
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The National Science and Technology Major Project under contract No.2024YFC2817003the National Natural Science Foundation of China under contract Nos 42276019 and 42249911the Guangdong Ordinary University Innovation Team Project under contract No.2023KCXTD015.
文摘Using in-situ microstructure observations from 2010 to 2018,this study investigates the performance and generalization of machine learning models in parameterizing turbulent mixing in the northwestern South China Sea.The results show that the data-driven extreme gradient boosting(XGBoost)performs better than the other four models,i.e.,random forest,neural network,linear regression and support vector machine regression.In order to further improve the generalization of machine learning-based parameterization method,we propose a physics-informed machine learning(PIML)that couples the MacKinnon-Gregg model(known as the MG model)and Osborn’s formula to the XGBoost model.The correlation coefficient(r)and root mean square error(RMSE)between the estimated and observed 1g(ε)(whereεdenotes the turbulent kinetic energy dissipation rate)from the PIML are improved by 14%and 16%,respectively.The results also show that PIML effectively improves the generalization of the XGBoost-based parameterization method,enhancing r and RMSE by 35%and 75%,respectively.
文摘The accurate prediction of peak overpressure of explosion shockwaves is significant in fields such as explosion hazard assessment and structural protection, where explosion shockwaves serve as typical destructive elements. Aiming at the problem of insufficient accuracy of the existing physical models for predicting the peak overpressure of ground reflected waves, two physics-informed machine learning models are constructed. The results demonstrate that the machine learning models, which incorporate physical information by predicting the deviation between the physical model and actual values and adding a physical loss term in the loss function, can accurately predict both the training and out-oftraining dataset. Compared to existing physical models, the average relative error in the predicted training domain is reduced from 17.459%-48.588% to 2%, and the proportion of average relative error less than 20% increased from 0% to 59.4% to more than 99%. In addition, the relative average error outside the prediction training set range is reduced from 14.496%-29.389% to 5%, and the proportion of relative average error less than 20% increased from 0% to 71.39% to more than 99%. The inclusion of a physical loss term enforcing monotonicity in the loss function effectively improves the extrapolation performance of machine learning. The findings of this study provide valuable reference for explosion hazard assessment and anti-explosion structural design in various fields.
基金the financial support from the National Natural Science Foundation of China(52207229)the financial support from the China Scholarship Council(202207550010)。
文摘The safe and reliable operation of lithium-ion batteries necessitates the accurate prediction of remaining useful life(RUL).However,this task is challenging due to the diverse ageing mechanisms,various operating conditions,and limited measured signals.Although data-driven methods are perceived as a promising solution,they ignore intrinsic battery physics,leading to compromised accuracy,low efficiency,and low interpretability.In response,this study integrates domain knowledge into deep learning to enhance the RUL prediction performance.We demonstrate accurate RUL prediction using only a single charging curve.First,a generalisable physics-based model is developed to extract ageing-correlated parameters that can describe and explain battery degradation from battery charging data.The parameters inform a deep neural network(DNN)to predict RUL with high accuracy and efficiency.The trained model is validated under 3 types of batteries working under 7 conditions,considering fully charged and partially charged cases.Using data from one cycle only,the proposed method achieves a root mean squared error(RMSE)of 11.42 cycles and a mean absolute relative error(MARE)of 3.19%on average,which are over45%and 44%lower compared to the two state-of-the-art data-driven methods,respectively.Besides its accuracy,the proposed method also outperforms existing methods in terms of efficiency,input burden,and robustness.The inherent relationship between the model parameters and the battery degradation mechanism is further revealed,substantiating the intrinsic superiority of the proposed method.
基金Project(G2022165004L)supported by the High-end Foreign Expert Introduction Program,ChinaProject(2021XM3008)supported by the Special Foundation of Postdoctoral Support Program,Chongqing,China+1 种基金Project(2018-ZL-01)supported by the Sichuan Transportation Science and Technology Project,ChinaProject(HZ2021001)supported by the Chongqing Municipal Education Commission,China。
文摘Landslide susceptibility mapping is a crucial tool for disaster prevention and management.The performance of conventional data-driven model is greatly influenced by the quality of the samples data.The random selection of negative samples results in the lack of interpretability throughout the assessment process.To address this limitation and construct a high-quality negative samples database,this study introduces a physics-informed machine learning approach,combining the random forest model with Scoops 3D,to optimize the negative samples selection strategy and assess the landslide susceptibility of the study area.The Scoops 3D is employed to determine the factor of safety value leveraging Bishop’s simplified method.Instead of conventional random selection,negative samples are extracted from the areas with a high factor of safety value.Subsequently,the results of conventional random forest model and physics-informed data-driven model are analyzed and discussed,focusing on model performance and prediction uncertainty.In comparison to conventional methods,the physics-informed model,set with a safety area threshold of 3,demonstrates a noteworthy improvement in the mean AUC value by 36.7%,coupled with a reduced prediction uncertainty.It is evident that the determination of the safety area threshold exerts an impact on both prediction uncertainty and model performance.
基金supported by the National Natural Science Foundation of China(Nos.22078373 and 22078372).
文摘Pressure swing adsorption(PSA)modeling remains a challenging task since it exhibits strong dynamic and cyclic behavior.This study presents a systematic physics-informed machine learning method that integrates transfer learning and labeled data to construct a spatiotemporal model of the PSA process.To approximate the latent solutions of partial differential equations(PDEs)in the specific steps of pressurization,adsorption,heavy reflux,counter-current depressurization,and light reflux,the system's network representation is decomposed into five lightweight sub-networks.On this basis,we propose a parameter-based transfer learning(TL)combined with domain decomposition to address the long-term integration of periodic PDEs and expedite the network training process.Moreover,to tackle challenges related to sharp adsorption fronts,our method allows for the inclusion of a specified amount of labeled data at the boundaries and/or within the system in the loss function.The results show that the proposed method closely matches the outcomes achieved through the conventional numerical method,effectively simulating all steps and cyclic behavior within the PSA processes.
基金supported by the U.S.Department of Energy,Office of Science,Biological and Environmental Research Program under award#ERKP752,and the DOE Office of Science,Office of Basic Energy Sciences,Division of Chemical Sciences,Geosciences,and Biosciences(CSGB)(Award No.DE-SC0022214FWP 3ERKCG25)This manuscript has been authored by UT-Battelle,LLC,under contract DEAC05-00OR22725 with the US Department of Energy(DOE)。
文摘The polarity of solvents plays a critical role in various research applications,particularly in their solubilities.Polarity is conveniently characterized by the Kamlet-Taft parameters that is,the hydrogen bonding acidity(α),the basicity(β),and the polarizability(π^(*)).Obtaining Kamlet-Taft parameters is very important for designer solvents,namely ionic liquids(ILs)and deep eutectic solvents(DESs).However,given the unlimited theoretical number of combinations of ionic pairs in ILs and hydrogen-bond donor/acceptor pairs in DESs,experimental determination of their Kamlet-Taft parameters is impractical.To address this,the present study developed two different machine learning(ML)algorithms to predict Kamlet-Taft parameters for designer solvents using quantum chemically derived input features.The ML models developed in the present study showed accurate predictions with high determination coefficient(R^(2))and low root mean square error(RMSE)values.Further,in the context of present interest in the circular bioeconomy,the relationship between the basicities and acidities of designer solvents and their ability to dissolve lignin and carbon dioxide(CO_(2))is discussed.Our method thus guides the design of effective solvents with optimal Kamlet-Taft parameter values dissolving and converting biomass and CO_(2)into valuable chemicals.
基金Supported by CAS Basic and Interdisciplinary Frontier Scientific Research Pilot Project(XDB1190300,XDB1190302)Youth Innovation Promotion Association CAS(Y2021056)+1 种基金Joint Fund of the Yulin University and the Dalian National Laboratory for Clean Energy(YLU-DNL Fund 2022007)The special fund for Science and Technology Innovation Teams of Shanxi Province(202304051001007)。
文摘Cyclohexene is an important raw material in the production of nylon.Selective hydrogenation of benzene is a key method for preparing cyclohexene.However,the Ru catalysts used in current industrial processes still face challenges,including high metal usage,high process costs,and low cyclohexene yield.This study utilizes existing literature data combined with machine learning methods to analyze the factors influencing benzene conversion,cyclohexene selectivity,and yield in the benzene hydrogenation to cyclohexene reaction.It constructs predictive models based on XGBoost and Random Forest algorithms.After analysis,it was found that reaction time,Ru content,and space velocity are key factors influencing cyclohexene yield,selectivity,and benzene conversion.Shapley Additive Explanations(SHAP)analysis and feature importance analysis further revealed the contribution of each variable to the reaction outcomes.Additionally,we randomly generated one million variable combinations using the Dirichlet distribution to attempt to predict high-yield catalyst formulations.This paper provides new insights into the application of machine learning in heterogeneous catalysis and offers some reference for further research.
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supported by university grants council(RGC),the Hong Kong government,through the general research fund(GRF)with the grant numbers of CityU 11201721 and CityU 11202924supported by the National Natural Science Foundation of China(Grant No.U20A20236).
文摘Single-phase ordered body-centered cubic or B2 multi-principal element intermetallics(MPEIs)have garnered significant attention due to their exceptional mechanical and functional properties.However,their discovery in complex compositional spaces is challenging due to the lack of high-dimensional phase diagrams and the inefficiency of traditional trial-and-error methods.In this study,we developed a physics-informed machine learning(ML)framework that integrates a conditional variational autoencoder(CVAE)with an artificial neural network(ANN).This approach effectively addresses the challenges of data limitation and imbalance,enabling the high-throughput generation of B2 MPEIs.Using this framework,we successfully identified a wide range of B2 complex alloys,spanning quaternary to senary systems,with superior mechanical performance.This work not only demonstrates a significant advancement in the discovery of B2 MPEIs but also provides an accelerated pathway for their design and development.
基金supported by the National Natural Science Foundation of China(No.52207228)the Beijing Natural Science Foundation,China(No.3224070)the National Natural Science Foundation of China(No.52077208).
文摘The growing use of lithium-ion batteries in electric transportation and grid-scale storage systems has intensified the need for accurate and highly generalizable state-of-health(SOH)estimation.Conventional approaches often suffer from reduced accuracy under dynamically uncertain state-of-charge(SOC)operating ranges and heterogeneous aging stresses.This study presents a unified SOH estimation framework that integrates physics-informed modeling,subspace identification,and Transformer-based learning.A reduced-order model is derived from simplified electrochemical dynamics,providing an interpretable and computationally efficient representation of battery behavior.Subspace identification across a wide SOC and SOH range yields degradation-sensitive features,which the Transformer uses to capture long-range aging dynamics via multi-head self-attention.Experiments on LiFePO4 cells under joint-cell training show consistently accurate SOH estimation,with a maximum error of 1.39%,demonstrating the framework’s effectiveness in decoupling SOC and SOH effects.In cross-cell validation,where training and validation are performed on different cells,the model maintains a maximum error of 2.06%,confirming strong generalization to unseen aging trajectories.Comparative experiments on LiFePO_(4)and public LiCoO_(2)datasets confirm the framework’s cross-chemistry applicability.By extracting low-dimensional,physically interpretable features via subspace identification,the framework significantly reduces training cost while maintaining high SOH estimation accuracy,outperforming conventional data-driven models lacking physical guidance.
基金supported by the National Key R&D Program of China(Grant Nos.2023YFC3008300&2023YFC3008305)the National Natural Science Foundation of China(Grant No.42172320)+1 种基金the Key Laboratory of Mountain Hazards and Engineering Resilience,Institute of Mountain Hazards and Environment,Chinese Academy of Sciences(Grant Nos.KLMHER-Z06&KLMHER-T07)the Science and Technology Research Program of Institute of Mountain Hazards and Environment,Chinese Academy of Sciences(Grant No.IMHE-CXTD.04).
文摘Accurate assessment of snowpack volumetric liquid water content and bulk density is essential for understanding snow hydrology,avalanche risk management,and monitoring cryosphere changes.This study presents a novel dual-parameter inversion framework that integrates synthetic electromagnetic modelling,dimensionality reduction,and machine learning algorithms to extract relative permittivity and log-resistivity from ground-penetrating radar(GPR)data.Traditional snowpack measurements are invasive,labor-intensive,and limited to point observations.To overcome these limitations,we developed a non-invasive,scalable,and data-driven framework that uses synthetic GPR datasets representing diverse snowpack conditions with variable moisture and density profiles.Synthetic 1D time series reflections(A-scans)are generated using finite-difference time-domain simulations in the state-of-the-art electromagnetic simulator gprMax.Principal component analysis(PCA)is applied to compress each A-scan while preserving key features,which significantly improved and enhanced the model training efficiency.Four machine learning models,including random forest,neural network,support vector machine,and eXtreme gradient boosting,are trained on PCA-reduced features.Among these,the neural network model achieved the best performance,with R^(2)>0.97 for permittivity and R 2>0.92 for resistivity.Gaussian noise(signal-to-noise ratio of 6 dB)is introduced to the synthetic data,and then targeted domain adaptation is employed to enhance generalization to field data.The framework is validated on two contrasting GPR transects in the Altay Mountains of the Chinese mainland,representing moist(T750)and wet(G125)snowpack conditions.The neural network model predictions are most consistent with the GPR derived estimates,Snowfork measurements,and snow pit data,achieving volumetric liquid water content deviation of≤1.5% and bulk density error within the range of 30-84 kg m^(-3).The results demonstrate that machine learning-based inversion,supported by realistic simulations and data augmentation enables scalable,non-invasive snowpack characterization with significant applications in hydrological forecasting,snow monitoring,and water resource management.
基金funded by the National Natural Science Foundation of China(No.22278443)CAMS Innovation Fund for Medical Sciences(No.2022-I2M-1-015)+3 种基金the Key R&D Program of Shandong Province(No.2021ZDSYS26)Xinjiang Uygur Autonomous Region Innovation Environment Construction Special Fund and Technology Innovation Base Construction Key Laboratory Open Project(No.2023D04065)2023 Xinjiang Uygur Autonomous Region Innovation Tianchi Talent Introduction Program for financial supportthe Key Project of Natural Science of Bengbu Medical University(No.2024byzd138).
文摘In the pharmaceutical field,machine learning can play an important role in drug development,production and treatment.Co-crystallization techniques have shown promising potential to enhance the properties of active pharmaceutical ingredients(APIs)such as solubility,permeability,and bioavailability,all without altering their chemical structure.This approach opens new avenues for developing natural products into effective drugs,especially those previously challenging in formulation.Emodin,an anthraquinone-based natural product,is a notable example due to its diverse biological activities;however,its physicochemical limitations,such as poor solubility and easy sublimation,restricted its clinical application.While various methods have improved emodin's physicochemical properties,research on its bioavailability remains limited.In our study,we summarize cocrystals and salts produced through co-crystallization technology and identify piperazine as a favorable coformer.Conflicting conclusions from computational chemistry and molecular modeling method and machine learning method regarding the formation of an emodin-piperazine cocrystal or salt led us to experimentally validate these possibilities.Ultimately,we successfully obtained the emodin-piperazine cocrystal,which were characterized and evaluated by several in vitro methods and pharmacokinetic studies.In addition,experiments have shown that emodin has a certain therapeutic effect on sepsis,so we also evaluated emodin-piperazine biological activity in a sepsis model.The results demonstrate that co-crystallization significantly enhances emodin's solubility,permeability,and bioavailability.Pharmacodynamic studies indicate that the emodin-piperazine cocrystal improves sepsis symptoms and provides protective effects against liver and kidney damage associated with sepsis.This study offers renewed hope for natural products with broad biological activities yet hindered by physicochemical limitations by advancing co-crystallization as a viable development approach.
文摘Latest digital advancements have intensified the necessity for adaptive,data-driven and socially-centered learning ecosystems.This paper presents the formulation of a cross-platform,innovative,gamified and personalized Learning Ecosystem,which integrates 3D/VR environments,as well as machine learning algorithms,and business intelligence frameworks to enhance learner-centered education and inferenced decision-making.This Learning System makes use of immersive,analytically assessed virtual learning spaces,therefore facilitating real-time monitoring of not just learning performance,but also overall engagement and behavioral patterns,via a comprehensive set of sustainability-oriented ESG-aligned Key Performance Indicators(KPIs).Machine learning models support predictive analysis,personalized feedback,and hybrid recommendation mechanisms,whilst dedicated dashboards translate complex educational data into actionable insights for all Use Cases of the System(Educational Institutions,Educators and Learners).Additionally,the presented Learning System introduces a structured Mentoring and Consulting Subsystem,thence reinforcing human-centered guidance alongside automated intelligence.The Platform’s modular architecture and simulation-centered evaluation approach actively support personalized,and continuously optimized learning pathways.Thence,it exemplifies a mature,adaptive Learning Ecosystem,supporting immersive technologies,analytics,and pedagogical support,hence,contributing to contemporary digital learning innovation and sociotechnical transformation in education.
文摘Providing safe and quality food is crucial for every household and is of extreme significance in the growth of any society.It is a complex procedure that deals with all issues focusing on the development of food processing from seed to harvest,storage,preparation,and consumption.This current paper seeks to demystify the importance of artificial intelligence,machine learning(ML),deep learning(DL),and computer vision(CV)in ensuring food safety and quality.By stressing the importance of these technologies,the audience will feel reassured and confident in their potential.These are very handy for such problems,giving assurance over food safety.CV is incredibly noble in today's generation because it improves food processing quality and positively impacts firms and researchers.Thus,at the present production stage,rich in image processing and computer visioning is incorporated into all facets of food production.In this field,DL and ML are implemented to identify the type of food in addition to quality.Concerning data and result-oriented perceptions,one has found similarities regarding various approaches.As a result,the findings of this study will be helpful for scholars looking for a proper approach to identify the quality of food offered.It helps to indicate which food products have been discussed by other scholars and lets the reader know papers by other scholars inclined to research further.Also,DL is accurately integrated with identifying the quality and safety of foods in the market.This paper describes the current practices and concerns of ML,DL,and probable trends for its future development.
文摘As urbanization continues to accelerate,the challenges associated with managing transportation in metropolitan areas become increasingly complex.The surge in population density contributes to traffic congestion,impacting travel experiences and posing safety risks.Smart urban transportation management emerges as a strategic solution,conceptualized here as a multidimensional big data problem.The success of this strategy hinges on the effective collection of information from diverse,extensive,and heterogeneous data sources,necessitating the implementation of full⁃stack Information and Communication Technology(ICT)solutions.The main idea of the work is to investigate the current technologies of Intelligent Transportation Systems(ITS)and enhance the safety of urban transportation systems.Machine learning models,trained on historical data,can predict traffic congestion,allowing for the implementation of preventive measures.Deep learning architectures,with their ability to handle complex data representations,further refine traffic predictions,contributing to more accurate and dynamic transportation management.The background of this research underscores the challenges posed by traffic congestion in metropolitan areas and emphasizes the need for advanced technological solutions.By integrating GPS and GIS technologies with machine learning algorithms,this work aims to pay attention to the development of intelligent transportation systems that not only address current challenges but also pave the way for future advancements in urban transportation management.
基金Project(42077244)supported by the National Natural Science Foundation of ChinaProject(2020-05)supported by the Open Research Fund of Guangdong Provincial Key Laboratory of Deep Earth Sciences and Geothermal Energy Exploitation and Utilization,China。
文摘Accurate prediction of rockburst intensity levels is crucial for ensuring the safety of deep hard rock engineering construction.This paper introduced an expert system for rockburst intensity level prediction that employs machine learning algorithms as the basis for its inference rules.The system comprises four modules:a database,a repository,an inference engine,and an interpreter.A database containing 1114 rockburst cases was used to construct 357 datasets that serve as the repository for the expert system.Additionally,19 types of machine learning algorithms were used to establish 6783 micro-models to construct cognitive rules within the inference engine.By integrating probability theory and marginal analysis,a fuzzy scoring method based on the SoftMax function was developed and applied to the interpreter for rockburst intensity level prediction,effectively restoring the continuity of rockburst characteristics.The research results indicate that ensemble algorithms based on decision trees are more effective in capturing the characteristics of rockburst.Key factors for accurate prediction of rockburst intensity include uniaxial compressive strength,elastic energy index,the maximum principal stress,tangential stress,and their composite indicators.The accuracy of the proposed rockburst intensity level prediction expert system was verified using 20 engineering rockburst cases,with predictions aligning closely with the actual rockburst intensity levels.
文摘With the proliferation of Internet of Things(IoT)devices,securing these interconnected systems against cyberattacks has become a critical challenge.Traditional security paradigms often fail to cope with the scale and diversity of IoT network traffic.This paper presents a comparative benchmark of classic machine learning(ML)and state-of-the-art deep learning(DL)algorithms for IoT intrusion detection.Our methodology employs a twophased approach:a preliminary pilot study using a custom-generated dataset to establish baselines,followed by a comprehensive evaluation on the large-scale CICIoTDataset2023.We benchmarked algorithms including Random Forest,XGBoost,CNN,and StackedLSTM.The results indicate that while top-performingmodels frombothcategories achieve over 99%classification accuracy,this metric masks a crucial performance trade-off.We demonstrate that treebased ML ensembles exhibit superior precision(91%)in identifying benign traffic,making them effective at reducing false positives.Conversely,DL models demonstrate superior recall(96%),making them better suited for minimizing the interruption of legitimate traffic.We conclude that the selection of an optimal model is not merely a matter of maximizing accuracy but is a strategic choice dependent on the specific security priority either minimizing false alarms or ensuring service availability.Thiswork provides a practical framework for deploying context-aware security solutions in diverse IoT environments.
基金supported by the National Natural Science Foundation of China(Grant Nos.52479045,52279042)the Key Research and Development Program in Guangxi(Grant No.AB23026021)the Open Research Fund of Guangxi Key Laboratory of Water Engineering Materials and Structures,Guangxi Institute of Water Resources Research(Grant No.GXHRIWEMS-2022-07).
文摘Accurate land surface temperature(LST)assessment is crucial for comprehending and reducing the impacts of climate change and understanding land use evolution.This study presents an innovative method by utilizing ensemble models,advanced correlation analysis,and trend analysis to investigate its environmental influences.Google Earth Engine(GEE)was utilized to process the datasets from Landsat-7 and Landsat-8 for the five big cities of Punjab,Pakistan,from 2001 to 2023.Results from this study show significant urban warming trends,and a strong correlation between environmental variables and LST was identified.The ensemble-based three machine learning models,including XGBoost,AdaBoost,and random forest(RF),were adopted to improve the accuracy of LST evaluation.Although XGBoost and AdaBoost attained modest levels of accuracy,with R^(2) values of 0.767 and 0.706,respectively,the RF model outperformed them by achieving an exceptional R^(2) of 0.796 and RMSE of 0.476.Moreover,Pearson correlation analysis revealed a negative relationship between LST and normalized difference latent heat index(NDLI)with r=-0.67,normalized difference vegetation index(NDVI)with r=-0.6,and modified normalized difference water index(MNDWI)with the value of r as -0.57.In addition,wavelet analysis showed that vegetation and water offer long-term LST cooling,lasting up to 64 months,while built-up areas and bare soil contribute to short-term warming,lasting 4 to 8 months.Latent heat indicated variable cooling periods,surpassing 60 months in cities.These findings enhance the understanding of LST changes and the impact of climate change on the environment.
基金financially supported by National Natural Science Foundation ofChina(No.12374405)Provincial Science Foundation for Distinguished Young Scholars of Fujian(No.2024J010024)+1 种基金Natural Science Foundation of Fujian Province of China(No.2023J011267)Major Research Projects for Young and Middle-aged Researchers of Fujian Provincial Health Commission(No.2021ZQNZD010).
文摘Nasopharyngeal carcinoma(NPC)is a malignant tumor prevalent in southern China and Southeast Asia,where its early detection is crucial for improving patient prognosis and reducing mortality rates.However,existing screening methods suffer from limitations in accuracy and accessibility,hindering their application in large-scale population screening.In this work,a surface-enhanced Raman spectroscopy(SERS)-based method was established to explore the profiles of different stratified components in saliva from NPC and healthy subjects after fractionation processing.The study findings indicate that all fractionated samples exhibit diseaseassociated molecular signaling differences,where small-molecule(molecular weight cut-offvalue is 10 kDa)demonstrating superior classification capabilities with sensitivity of 90.5%and speci-ficity of 75.6%,area under receiver operating characteristic(ROC)curve of 0:925±0:031.The primary objective of this study was to qualitatively explore patterns in saliva composition across groups.The proposed SERS detection strategy for fractionated saliva offers novel insights for enhancing the sensitivity and reliability of noninvasive NPC screening,laying the foundation for translational application in large-scale clinical settings.
