Chronic kidney disease(CKD)is an increasingly prevalent medical condition associated with high mortality and cardiovascular complications.The intricate interplay between kidney dysfunction and subsequent metabolic dis...Chronic kidney disease(CKD)is an increasingly prevalent medical condition associated with high mortality and cardiovascular complications.The intricate interplay between kidney dysfunction and subsequent metabolic disturbances may provide insights into the underlying mechanisms driving CKD onset and progression.Herein,we proposed a large-scale plasma metabolite identification and quantification system that combines the strengths of targeted and untargeted metabolomics technologies,i.e.,widely-targeted metabolomics(WT-Met)approach.WT-Met method enables large-scale identification and accurate quantification of thousands of metabolites.We collected plasma samples from 21 healthy controls and 62CKD patients,categorized into different stages(22 in stages 1-3,20 in stage 4,and 20 in stage 5).Using LC-MS-based WT-Met approach,we were able to effectively annotate and quantify a total of 1431metabolites from the plasma samples.Focusing on the 539 endogenous metabolites,we identified 399significantly altered metabolites and depicted their changing patterns from healthy controls to end-stage CKD.Furthermore,we employed machine-learning to identify the optimal combination of metabolites for predicting different stages of CKD.We generated a multiclass classifier consisting of 7 metabolites by machine-learning,which exhibited an average AUC of 0.99 for the test set.In general,amino acids,nucleotides,organic acids,and their metabolites emerged as the most significantly altered metabolites.However,their patterns of change varied across different stages of CKD.The 7-metabolite panel demonstrates promising potential as biomarker candidates for CKD.Further exploration of these metabolites can provide valuable insights into their roles in the etiology and progression of CKD.展开更多
Objective: Postoperative complications adversely affected the prognosis in patients with gastric cancer. This study intends to investigate the feasibility of using machine-learning model to predict surgical outcomes i...Objective: Postoperative complications adversely affected the prognosis in patients with gastric cancer. This study intends to investigate the feasibility of using machine-learning model to predict surgical outcomes in patients undergoing gastrectomy.Methods: In this study, cancer patients who underwent gastrectomy at Shanghai Rui Jin Hospital in 2017 were randomly assigned to a development or validation cohort in a 9:1 ratio. A support vector classification(SVC) model to predict surgical outcomes in patients undergoing gastrectomy was developed and further validated.Results: A total of 321 patients with 32 features were collected. The positive and negative outcomes of postoperative complication after gastrectomy appeared in 100(31.2%) and 221(68.8%) patients, respectively. The SVC model was constructed to predict surgical outcomes in patients undergoing gastrectomy. The accuracy of 10-fold cross validation and external verification was 78.17% and 78.12%, respectively. Further, an online web server has been developed to share the SVC model for machine-learning-assisted prediction of surgical outcomes in patients undergoing gastrectomy in the future procedures, which is accessible at the web address:http://47.100.47.97:5005/r_model_prediction.Conclusions: The SVC model was a useful predictor for measuring the risk of postoperative complications after gastrectomy, which may help stratify patients with different overall status for choice of surgical procedure or other treatments. It can be expected that machine-learning models in cancer informatics research are possibly shareable and accessible via web address all over the world.展开更多
The composite exciter and the CaO to Na_(2)SO_(4) dosing ratios are known to have a strong impact on the mechanical strength offly-ash concrete.In the present study a hybrid approach relying on experiments and a machi...The composite exciter and the CaO to Na_(2)SO_(4) dosing ratios are known to have a strong impact on the mechanical strength offly-ash concrete.In the present study a hybrid approach relying on experiments and a machine-learn-ing technique has been used to tackle this problem.The tests have shown that the optimal admixture of CaO and Na_(2)SO_(4) alone is 8%.The best 3D mechanical strength offly-ash concrete is achieved at 8%of the compound activator;If the 28-day mechanical strength is considered,then,the best performances are obtained at 4%of the compound activator.Moreover,the 3D mechanical strength offly-ash concrete is better when the dosing ratio of CaO to Na_(2)SO_(4) in the compound activator is 1:1;the maximum strength offly-ash concrete at 28-day can be achieved for a 1:1 ratio of CaO to Na_(2)SO_(4) by considering a 4%compound activator.In this case,the compressive andflexural strengths are 260 MPa and 53.6 MPa,respectively;the mechanical strength offly-ash concrete at 28-day can be improved by a 4:1 ratio of CaO to Na_(2)SO_(4) by considering 8%and 12%compound excitants.It is shown that the predictions based on the aforementioned machine-learning approach are accurate and reliable.展开更多
The coronavirus 3C-like(3CL)protease,a cysteine protease,plays an important role in viral infection and immune escape.However,there is still a lack of effective tools for determining the cleavage sites of the 3CL prot...The coronavirus 3C-like(3CL)protease,a cysteine protease,plays an important role in viral infection and immune escape.However,there is still a lack of effective tools for determining the cleavage sites of the 3CL protease.This study systematically investigated the diversity of the cleavage sites of the coronavirus 3CL protease on the viral polyprotein,and found that the cleavage motif were highly conserved for viruses in the genera of Alphacoronavirus,Betacoronavirus and Gammacoronavirus.Strong residue preferences were observed at the neighboring positions of the cleavage sites.A random forest(RF)model was built to predict the cleavage sites of the coronavirus 3CL protease based on the representation of residues in cleavage motifs by amino acid indexes,and the model achieved an AUC of 0.96 in cross-validations.The RF model was further tested on an independent test dataset which were composed of cleavage sites on 99 proteins from multiple coronavirus hosts.It achieved an AUC of 0.95 and predicted correctly 80%of the cleavage sites.Then,1,352 human proteins were predicted to be cleaved by the 3CL protease by the RF model.These proteins were enriched in several GO terms related to the cytoskeleton,such as the microtubule,actin and tubulin.Finally,a webserver named 3CLP was built to predict the cleavage sites of the coronavirus 3CL protease based on the RF model.Overall,the study provides an effective tool for identifying cleavage sites of the 3CL protease and provides insights into the molecular mechanism underlying the pathogenicity of coronaviruses.展开更多
Anomaly detection is becoming increasingly significant in industrial cyber security,and different machine-learning algorithms have been generally acknowledged as various effective intrusion detection engines to succes...Anomaly detection is becoming increasingly significant in industrial cyber security,and different machine-learning algorithms have been generally acknowledged as various effective intrusion detection engines to successfully identify cyber attacks.However,different machine-learning algorithms may exhibit their own detection effects even if they analyze the same feature samples.As a sequence,after developing one feature generation approach,the most effective and applicable detection engines should be desperately selected by comparing distinct properties of each machine-learning algorithm.Based on process control features generated by directed function transition diagrams,this paper introduces five different machine-learning algorithms as alternative detection engines to discuss their matching abilities.Furthermore,this paper not only describes some qualitative properties to compare their advantages and disadvantages,but also gives an in-depth and meticulous research on their detection accuracies and consuming time.In the verified experiments,two attack models and four different attack intensities are defined to facilitate all quantitative comparisons,and the impacts of detection accuracy caused by the feature parameter are also comparatively analyzed.All experimental results can clearly explain that SVM(Support Vector Machine)and WNN(Wavelet Neural Network)are suggested as two applicable detection engines under differing cases.展开更多
Drug addiction can cause abnormal brain activation changes,which are the root cause of drug craving and brain function errors.This study enrolled drug abusers to determine the effects of different drugs on brain activ...Drug addiction can cause abnormal brain activation changes,which are the root cause of drug craving and brain function errors.This study enrolled drug abusers to determine the effects of different drugs on brain activation.A functional near-infrared spectroscopy(fNIRS)device was used for the research.This study was designed with an experimental paradigm that included the induction of resting and drug addiction cravings.We collected the fNIRS data of 30 drug users,including 10 who used heroin,10 who used Methamphetamine,and 10 who used mixed drugs.First,using Statistical Analysis,the study analyzed the activations of eight functional areas of the left and right hemispheres of the prefrontal cortex of drug addicts who respectively used heroin,Methamphetamine,and mixed drugs,including Left/Right-Dorsolateral prefrontal cortex(L/R-DLPFC),Left/Right-Ventrolateral prefrontal cortex(L/R-VLPFC),Left/Right-Fronto-polar prefrontal cortex(L/R-FPC),and Left/Right Orbitofrontal Cortex(L/R-OFC).Second,referencing the degrees of activation of oxyhaemoglobin concentration(HbO2),the study made an analysis and got the specific activation patterns of each group of the addicts.Finally,after taking out data which are related to the addicts who recorded high degrees of activation among the three groups of addicts,and which had the same channel numbers,the paper classified the different drug abusers using the data as the input data for Convolutional Neural Networks(CNNs).The average three-class accuracy is 67.13%.It is of great significance for the analysis of brain function errors and personalized rehabilitation.展开更多
Reliable sales forecasts are important to the garment industry. In recent years, the global climate is warming, the weather changes frequently, and clothing sales are affected by weather fluctuations. The purpose of t...Reliable sales forecasts are important to the garment industry. In recent years, the global climate is warming, the weather changes frequently, and clothing sales are affected by weather fluctuations. The purpose of this study is to investigate whether weather data can improve the accuracy of product sales and to establish a corresponding clothing sales forecasting model. This model uses the basic attributes of clothing product data, historical sales data, and weather data. It is based on a random forest, XGB, and GBDT adopting a stacking strategy. We found that weather information is not useful for basic clothing sales forecasts, but it did improve the accuracy of seasonal clothing sales forecasts. The MSE of the dresses, down jackets, and shirts are reduced by 86.03%, 80.14%, and 41.49% on average. In addition, we found that the stacking strategy model outperformed the voting strategy model, with an average MSE reduction of 49.28%. Clothing managers can use this model to forecast their sales when they make sales plans based on weather information.展开更多
The outstanding comprehensive mechanical properties of newly developed hybrid lattice structures make them useful in engineering applications for bearing multiple mechanical loads.Additive-manufacturing technologies m...The outstanding comprehensive mechanical properties of newly developed hybrid lattice structures make them useful in engineering applications for bearing multiple mechanical loads.Additive-manufacturing technologies make it possible to fabricate these highly spatially programmable structures and greatly enhance the freedom in their design.However,traditional analytical methods do not sufficiently reflect the actual vibration-damping mechanism of lattice structures and are limited by their high computational cost.In this study,a hybrid lattice structure consisting of various cells was designed based on quasi-static and vibration experiments.Subsequently,a novel parametric design method based on a data-driven approach was developed for hybrid lattices with engineered properties.The response surface method was adopted to define the sensitive optimization target.A prediction model for the lattice geometric parameters and vibration properties was established using a backpropagation neural network.Then,it was integrated into the genetic algorithm to create the optimal hybrid lattice with varying geometric features and the required wide-band vibration-damping characteristics.Validation experiments were conducted,demonstrating that the optimized hybrid lattice can achieve the target properties.In addition,the data-driven parametric design method can reduce computation time and be widely applied to complex structural designs when analytical and empirical solutions are unavailable.展开更多
Chemical short-range order(SRO),a phenomenon at the atomic scale resulting from inhomogeneities in the local chemical environment,is usually studied using machine learning force field-based molecular dynamics simulati...Chemical short-range order(SRO),a phenomenon at the atomic scale resulting from inhomogeneities in the local chemical environment,is usually studied using machine learning force field-based molecular dynamics simulations due to the limitations of experimental methods.To promote the reliable application of machine potentials in high-entropy alloy simulations,first,this work uses NEP models trained on two different datasets to predict the SRO coefficients of NbMoTaW.The results show that within the same machine learning framework,there are significant differences in the prediction of SRO coefficients for the Nb-Nb atomic pair.Subsequently,this work predicts the SRO coefficients of NbMoTaW using the NEP model and the SNAP model,both of which are trained on the same dataset.The results reveal significant discrepancies in SRO predictions for like-element pairs(e.g.,Nb-Nb and W-W)between the two potentials,despite the identical training data.The findings of this study indicate that discrepancies in the prediction results of SRO coefficients can arise from either the same machine learning framework trained on different datasets or different learning frameworks trained on the same dataset.This reflects possible incompleteness in the current training set's coverage of local chemical environments at the atomic scale.Future research should establish unified evaluation standards to assess the capability of training sets to accurately describe complex atomic-scale behaviors such as SRO.展开更多
Liquid metals demonstrate significant potential for applications in thermal management and flexible electronic circuits, necessitating a comprehensive understanding of their transport properties for technological adva...Liquid metals demonstrate significant potential for applications in thermal management and flexible electronic circuits, necessitating a comprehensive understanding of their transport properties for technological advancements. Experimental measurement of these properties presents challenges due to factors like cost, corrosion and impurity control. Consequently, accurate computational simulations become essential for predicting the physical properties of these materials. In this research, molecular dynamics(MD) simulations were employed to model several properties of gallium(Ga), indium(In) and Ga–In alloys, including lattice structural parameters, radial distribution functions(RDF), structure factors, selfdiffusion coefficients and viscosity. Due to the difficulty of traditional interatomic potentials in capturing the short-range interactions directly related to the mechanical behavior of liquid atoms, machine-learning interatomic potentials(MLIPs)have been constructed to precisely describe the liquid metals Ga, In, and Ga–In alloys. This was achieved by utilizing the moment tensor potential(MTP) framework in combination with an active learning strategy. MTP was trained using a comprehensive database generated from DFT and MD simulations, which include a variety of crystal structures, point defects and liquid structures. The calculations of physical properties in this research have shown strong consistency with experimental data, demonstrating that the MTP can accurately describe the interatomic interactions between Ga–Ga, In–In and Ga–In. Our work has established a novel paradigm for investigating the physical properties of various liquid metal systems, offering valuable insights and references for future research.展开更多
In recent years,physical unclonable function(PUF)has emerged as a lightweight solution in the Internet of Things security.However,conventional PUFs based on complementary metal oxide semiconductor(CMOS)present challen...In recent years,physical unclonable function(PUF)has emerged as a lightweight solution in the Internet of Things security.However,conventional PUFs based on complementary metal oxide semiconductor(CMOS)present challenges such as insufficient randomness,significant power and area overhead,and vulnerability to environmental factors,leading to reduced reliability.In this study,we realize a strong,highly reliable and reconfigurable PUF with resistance against machine-learning attacks in a 1 kb spinorbit torque magnetic random access memory fabricated using a 180 nm CMOS process.This strong PUF achieves a challenge-response pair capacity of 10^(9) through a computing-in-memory approach.The results demonstrate that the proposed PUF exhibits near-ideal performance metrics:50.07% uniformity,50% diffuseness,49.89% uniqueness,and a bit error rate of 0%,even in a 375 K environment.The reconfigurability of PUF is demonstrated by a reconfigurable Hamming distance of 49.31% and a correlation coefficient of less than 0.2,making it difficult to extract output keys through side-channel analysis.Furthermore,resistance to machine-learning modeling attacks is confirmed by achieving an ideal accuracy prediction of approximately 50% in the test set.展开更多
As the demands for environmental sustainability and the requirements to lower carbon emissions have escalated,New Energy Vehicles(NEVs)have emerged as a compelling substitute for fossil-fuel-run automobiles.Hence,a sm...As the demands for environmental sustainability and the requirements to lower carbon emissions have escalated,New Energy Vehicles(NEVs)have emerged as a compelling substitute for fossil-fuel-run automobiles.Hence,a smart energy management strategy has been developed to enhance the performance of NEVs,maximizing the sustainability of transportation systems and minimizing environmental impacts.The system combines different power reserves,includ-ing a photovoltaic(PV)generator,fuel cell(FC),and battery system,to provide a continuous energy supply,even when the vehicle is running.The Multi-Directional Power Transfer converter for the battery provides the required energy ad-aptation between the input and output.The FC and PV systems are all connected through a direct current/direct current converter to effectively charge the battery whenever excess energy is present.The new energy management technique called Optimized Ant Colony Algorithm is proposed to dynamically allocate power among the different power sources,improving system efficiency.Unlike traditional methods,the suggested approach actively optimizes energy flow accord-ing to actual demand and availability,minimizing energy losses and enhancing sustainability.The MATLAB/Simulink tool was used to simulate the energetic performance of an electric car utilizing the suggested approach.The performance of this multi-source power system is assessed by contrasting the energy the PV and FC generating devices offer,and the energy generation of each recharge system.Additionally,the battery power comparison validates the cost-effectiveness and sustainability of the proposed model in NEVs.Results designate a significant improvement in energy efficiency and overall NEV environmental sustainability within contemporary transportation networks.展开更多
The belief rule-based(BRB)system has been popular in complexity system modeling due to its good interpretability.However,the current mainstream optimization methods of the BRB systems only focus on modeling accuracy b...The belief rule-based(BRB)system has been popular in complexity system modeling due to its good interpretability.However,the current mainstream optimization methods of the BRB systems only focus on modeling accuracy but ignore the interpretability.The single-objective optimization strategy has been applied in the interpretability-accuracy trade-off by inte-grating accuracy and interpretability into an optimization objec-tive.But the integration has a greater impact on optimization results with strong subjectivity.Thus,a multi-objective optimiza-tion framework in the modeling of BRB systems with inter-pretability-accuracy trade-off is proposed in this paper.Firstly,complexity and accuracy are taken as two independent opti-mization goals,and uniformity as a constraint to give the mathe-matical description.Secondly,a classical multi-objective opti-mization algorithm,nondominated sorting genetic algorithm II(NSGA-II),is utilized as an optimization tool to give a set of BRB systems with different accuracy and complexity.Finally,a pipeline leakage detection case is studied to verify the feasibility and effectiveness of the developed multi-objective optimization.The comparison illustrates that the proposed multi-objective optimization framework can effectively avoid the subjectivity of single-objective optimization,and has capability of joint optimiz-ing the structure and parameters of BRB systems with inter-pretability-accuracy trade-off.展开更多
Satellite Precipitation Products(SPPs) face challenges in detecting Extreme Precipitation Events(EPEs). Hence, the primary objective of this research is to introduce a novel framework termed Machine-Learning Clusterin...Satellite Precipitation Products(SPPs) face challenges in detecting Extreme Precipitation Events(EPEs). Hence, the primary objective of this research is to introduce a novel framework termed Machine-Learning Clustering-Merging Algorithms(ML-CMAs) to evaluate EPEs using SPPs and Auxiliary Data(AD). Daily precipitation measurements were utilized for training and evaluating EPE estimates over Iran, which is comprised of arid and semi-arid regions. Statistical analysis and evaluation of five SPPs demonstrated that during EPE occurrences, all products face challenges in precipitation estimation, and using these products individually is not recommended. Among the SPPs, Multi-Source Weighted-Ensemble Precipitation(MSWEP) performed best for heavy(>20 mm d–1) and extreme(>40 mm d–1)precipitation events, followed by Global Satellite Mapping of Precipitation(GSMa P), Integrated Multi-Satellite Retrievals for Global Precipitation Measurement(IMERG), Climate Prediction Center morphing technique(CMORPH), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Dynamic Infrared-Rain Rate(PERSIANN-PDIR). The findings indicate that all proposed methods based on ML-CMAs could estimate precipitation rates more accurately than SPPs and improve statistical indices. The seasonal assessment and spatial analysis of statistical metrics of the overall daily precipitation results for all periods and climates revealed that all methods based on ML-CMAs performed well in all seasons and at nearly all measurement stations. Using unsupervised K-means++ classification for clustering EPEs and Deep Neural Network(DNN) and Convolutional Neural Network(CNN) methods for merging the MLCMAs reduced the error rate of SPPs in EPE estimation by approximately 50%. Therefore, incorporating ML-CMAs along with PWV as AD can significantly improve the performance of SPPs in evaluating EPEs over the study region.展开更多
Lipid metabolism play an essential role in occurrence and development of asthma,and it can be disturbed by phthalate esters(PAEs)and organophosphate fame retardants(OPFRs).As a chronic infammatory respiratory disease,...Lipid metabolism play an essential role in occurrence and development of asthma,and it can be disturbed by phthalate esters(PAEs)and organophosphate fame retardants(OPFRs).As a chronic infammatory respiratory disease,the occurrence risk of childhood asthma is increased by PAEs and OPFRs exposure,but it remains not entirely clear how PAEs and OPFRs contribute the onset and progress of the disease.We have profiled the serum levels of PAEs and OPFRs congeners by liquid chromatography coupled with mass spectrometry,and its relationships with the dysregulation of lipid metabolism in asthmatic,bronchitic(acute infammation)and healthy(non-infammation)children.Eight PAEs and nine OPFRs congeners were found in the serum of children(1–5 years old)from Shenzhen,and their total median levels were 615.16 ng/m L and 17.06 ng/m L,respectively.Moreover,the serum levels of mono-methyl phthalate(MMP),tri-propyl phosphate(TPP)and tri-n-butyl phosphate(TNBP)were significant higher in asthmatic children than in healthy and bronchitic children as control.Thirty-one characteristic lipids and fatty acids of asthma were screened by machine-learning random forest model based on serum lipidome data,and the alterations of infammatory characteristic lipids and fatty acids including palmitic acids,12,13-Di HODE,14,21-Di HDHA,prostaglandin D2 and Lyso PA(18:2)showed significant correlated with high serum levels of MMP,TPP and TNBP.These results imply PAEs and OPFRs promote the occurrence of childhood asthma via disrupting infammatory lipid and fatty acid metabolism,and provide a novel sight for better understanding the effects of plastic additives on childhood asthma.展开更多
The photovoltaic(PV)water electrolysis method currently stands as the most promising approach for green hydrogen production.The rapid iteration of photovoltaic technologies has significantly affected on the technical ...The photovoltaic(PV)water electrolysis method currently stands as the most promising approach for green hydrogen production.The rapid iteration of photovoltaic technologies has significantly affected on the technical and economic evaluation for photovoltaic hydrogen production.In this work,the photovoltaic hydrogen production of three most advanced silicon photovoltaic technologies is systematically compared for the first time under the climatic conditions of the Kucha region.All-weather stable hydrogen production control system with optimal charging and discharging strategies is constructed to realize stable and efficient hydrogen energy production.Seven machine learning(ML)algorithms are used to forecast the performance in power generation and hydrogen production of a 100 MW photovoltaic hydrogen production and energy storage(PH-S)system throughout its operational life.The long short-term memory(LSTM)algorithm exhibits the best performance,achieving mean absolute error(MAE)of 0.0415,root mean square error(RMSE)of 0.0891,and coefficient of determination(R2)of 0.8402.In terms of cost-effectiveness,heterojunction with intrinsic thin layer(HJT)PV technology achieves the lowest levelized cost of electricity(LCOE)and hydrogen(LCOH)at 0.025$/kWh and 6.95$/kg,respectively.According to the sensitivity analysis,when the cost of proton exchange membrane electrolysis(PEMEC)reduced 50%,the LCOH for PH-S system decreased 21.40%.This study provides valuable insights for the practical implementation of large-scale photovoltaic hydrogen production and cost reduction in PH-S systems.展开更多
This study describes improving network security by implementing and assessing an intrusion detection system(IDS)based on deep neural networks(DNNs).The paper investigates contemporary technical ways for enhancing intr...This study describes improving network security by implementing and assessing an intrusion detection system(IDS)based on deep neural networks(DNNs).The paper investigates contemporary technical ways for enhancing intrusion detection performance,given the vital relevance of safeguarding computer networks against harmful activity.The DNN-based IDS is trained and validated by the model using the NSL-KDD dataset,a popular benchmark for IDS research.The model performs well in both the training and validation stages,with 91.30%training accuracy and 94.38%validation accuracy.Thus,the model shows good learning and generalization capabilities with minor losses of 0.22 in training and 0.1553 in validation.Furthermore,for both macro and micro averages across class 0(normal)and class 1(anomalous)data,the study evaluates the model using a variety of assessment measures,such as accuracy scores,precision,recall,and F1 scores.The macro-average recall is 0.9422,the macro-average precision is 0.9482,and the accuracy scores are 0.942.Furthermore,macro-averaged F1 scores of 0.9245 for class 1 and 0.9434 for class 0 demonstrate the model’s ability to precisely identify anomalies precisely.The research also highlights how real-time threat monitoring and enhanced resistance against new online attacks may be achieved byDNN-based intrusion detection systems,which can significantly improve network security.The study underscores the critical function ofDNN-based IDS in contemporary cybersecurity procedures by setting the foundation for further developments in this field.Upcoming research aims to enhance intrusion detection systems by examining cooperative learning techniques and integrating up-to-date threat knowledge.展开更多
Intrusion detection systems(IDS)are essential in the field of cybersecurity because they protect networks from a wide range of online threats.The goal of this research is to meet the urgent need for small-footprint,hi...Intrusion detection systems(IDS)are essential in the field of cybersecurity because they protect networks from a wide range of online threats.The goal of this research is to meet the urgent need for small-footprint,highly-adaptable Network Intrusion Detection Systems(NIDS)that can identify anomalies.The NSL-KDD dataset is used in the study;it is a sizable collection comprising 43 variables with the label’s“attack”and“level.”It proposes a novel approach to intrusion detection based on the combination of channel attention and convolutional neural networks(CNN).Furthermore,this dataset makes it easier to conduct a thorough assessment of the suggested intrusion detection strategy.Furthermore,maintaining operating efficiency while improving detection accuracy is the primary goal of this work.Moreover,typical NIDS examines both risky and typical behavior using a variety of techniques.On the NSL-KDD dataset,our CNN-based approach achieves an astounding 99.728%accuracy rate when paired with channel attention.Compared to previous approaches such as ensemble learning,CNN,RBM(Boltzmann machine),ANN,hybrid auto-encoders with CNN,MCNN,and ANN,and adaptive algorithms,our solution significantly improves intrusion detection performance.Moreover,the results highlight the effectiveness of our suggested method in improving intrusion detection precision,signifying a noteworthy advancement in this field.Subsequent efforts will focus on strengthening and expanding our approach in order to counteract growing cyberthreats and adjust to changing network circumstances.展开更多
Machine-learning interatomic potentials have revolutionized materials modeling at the atomic scale.Thanks to these,it is now indeed possible to perform simulations of ab initio quality over very large time and length ...Machine-learning interatomic potentials have revolutionized materials modeling at the atomic scale.Thanks to these,it is now indeed possible to perform simulations of ab initio quality over very large time and length scales.More recently,various universal machine-learning models have been proposed as an out-of-box approach avoiding the need to train and validate specific potentials for each particular material of interest.In this paper,we review and evaluate four different universal machine-learning interatomic potentials(uMLIPs),all based on graph neural network architectures which have demonstrated transferability from one chemical system to another.The evaluation procedure relies on data both from a recent verification study of density-functional-theory implementations and from the Materials Project.Through this comprehensive evaluation,we aim to provide guidance to materials scientists in selecting suitable models for their specific research problems,offer recommendations for model selection and optimization,and stimulate discussion on potential areas for improvement in current machinelearning methodologies in materials science.展开更多
基金supported by the National Key R&D Program of China(Nos.2022YFC3400700,2022YFA0806600)the Key Research and Development Project of Hubei Province(No.2023BCB094)+1 种基金the Interdisciplinary Innovative Talents Foundation from Renmin Hospital of Wuhan University(No.JCRCGW-2022-008)the Key Laboratory of Hubei Province(No.2021KFY005)。
文摘Chronic kidney disease(CKD)is an increasingly prevalent medical condition associated with high mortality and cardiovascular complications.The intricate interplay between kidney dysfunction and subsequent metabolic disturbances may provide insights into the underlying mechanisms driving CKD onset and progression.Herein,we proposed a large-scale plasma metabolite identification and quantification system that combines the strengths of targeted and untargeted metabolomics technologies,i.e.,widely-targeted metabolomics(WT-Met)approach.WT-Met method enables large-scale identification and accurate quantification of thousands of metabolites.We collected plasma samples from 21 healthy controls and 62CKD patients,categorized into different stages(22 in stages 1-3,20 in stage 4,and 20 in stage 5).Using LC-MS-based WT-Met approach,we were able to effectively annotate and quantify a total of 1431metabolites from the plasma samples.Focusing on the 539 endogenous metabolites,we identified 399significantly altered metabolites and depicted their changing patterns from healthy controls to end-stage CKD.Furthermore,we employed machine-learning to identify the optimal combination of metabolites for predicting different stages of CKD.We generated a multiclass classifier consisting of 7 metabolites by machine-learning,which exhibited an average AUC of 0.99 for the test set.In general,amino acids,nucleotides,organic acids,and their metabolites emerged as the most significantly altered metabolites.However,their patterns of change varied across different stages of CKD.The 7-metabolite panel demonstrates promising potential as biomarker candidates for CKD.Further exploration of these metabolites can provide valuable insights into their roles in the etiology and progression of CKD.
文摘Objective: Postoperative complications adversely affected the prognosis in patients with gastric cancer. This study intends to investigate the feasibility of using machine-learning model to predict surgical outcomes in patients undergoing gastrectomy.Methods: In this study, cancer patients who underwent gastrectomy at Shanghai Rui Jin Hospital in 2017 were randomly assigned to a development or validation cohort in a 9:1 ratio. A support vector classification(SVC) model to predict surgical outcomes in patients undergoing gastrectomy was developed and further validated.Results: A total of 321 patients with 32 features were collected. The positive and negative outcomes of postoperative complication after gastrectomy appeared in 100(31.2%) and 221(68.8%) patients, respectively. The SVC model was constructed to predict surgical outcomes in patients undergoing gastrectomy. The accuracy of 10-fold cross validation and external verification was 78.17% and 78.12%, respectively. Further, an online web server has been developed to share the SVC model for machine-learning-assisted prediction of surgical outcomes in patients undergoing gastrectomy in the future procedures, which is accessible at the web address:http://47.100.47.97:5005/r_model_prediction.Conclusions: The SVC model was a useful predictor for measuring the risk of postoperative complications after gastrectomy, which may help stratify patients with different overall status for choice of surgical procedure or other treatments. It can be expected that machine-learning models in cancer informatics research are possibly shareable and accessible via web address all over the world.
基金supported by the Scientific Research Fund Project of Yunnan Education Department(Grant Numbers 2023J1974 and 2023J1976)the Yunnan University Professional Degree Graduate Student Practical Innovation Fund Project(Grant Number ZC-22222374)also supported by the Yunnan Provincial Education Department Fund(Grant No.2022Y286).
文摘The composite exciter and the CaO to Na_(2)SO_(4) dosing ratios are known to have a strong impact on the mechanical strength offly-ash concrete.In the present study a hybrid approach relying on experiments and a machine-learn-ing technique has been used to tackle this problem.The tests have shown that the optimal admixture of CaO and Na_(2)SO_(4) alone is 8%.The best 3D mechanical strength offly-ash concrete is achieved at 8%of the compound activator;If the 28-day mechanical strength is considered,then,the best performances are obtained at 4%of the compound activator.Moreover,the 3D mechanical strength offly-ash concrete is better when the dosing ratio of CaO to Na_(2)SO_(4) in the compound activator is 1:1;the maximum strength offly-ash concrete at 28-day can be achieved for a 1:1 ratio of CaO to Na_(2)SO_(4) by considering a 4%compound activator.In this case,the compressive andflexural strengths are 260 MPa and 53.6 MPa,respectively;the mechanical strength offly-ash concrete at 28-day can be improved by a 4:1 ratio of CaO to Na_(2)SO_(4) by considering 8%and 12%compound excitants.It is shown that the predictions based on the aforementioned machine-learning approach are accurate and reliable.
基金supported by the National Key Plan for Scientific Research and Development of China(2016YFD0500300)National Natural Science Foundation of China(32170651)Hunan Provincial Natural Science Foundation of China(2020JJ3006)。
文摘The coronavirus 3C-like(3CL)protease,a cysteine protease,plays an important role in viral infection and immune escape.However,there is still a lack of effective tools for determining the cleavage sites of the 3CL protease.This study systematically investigated the diversity of the cleavage sites of the coronavirus 3CL protease on the viral polyprotein,and found that the cleavage motif were highly conserved for viruses in the genera of Alphacoronavirus,Betacoronavirus and Gammacoronavirus.Strong residue preferences were observed at the neighboring positions of the cleavage sites.A random forest(RF)model was built to predict the cleavage sites of the coronavirus 3CL protease based on the representation of residues in cleavage motifs by amino acid indexes,and the model achieved an AUC of 0.96 in cross-validations.The RF model was further tested on an independent test dataset which were composed of cleavage sites on 99 proteins from multiple coronavirus hosts.It achieved an AUC of 0.95 and predicted correctly 80%of the cleavage sites.Then,1,352 human proteins were predicted to be cleaved by the 3CL protease by the RF model.These proteins were enriched in several GO terms related to the cytoskeleton,such as the microtubule,actin and tubulin.Finally,a webserver named 3CLP was built to predict the cleavage sites of the coronavirus 3CL protease based on the RF model.Overall,the study provides an effective tool for identifying cleavage sites of the 3CL protease and provides insights into the molecular mechanism underlying the pathogenicity of coronaviruses.
基金This work is supported by the Scientific Research Project of Educational Department of Liaoning Province(Grant No.LJKZ0082)the Program of Hainan Association for Science and Technology Plans to Youth R&D Innovation(Grant No.QCXM201910)+2 种基金the National Natural Science Foundation of China(Grant Nos.61802092 and 92067110)the Hainan Provincial Natural Science Foundation of China(Grant No.620RC562)2020 Industrial Internet Innovation and Development Project-Industrial Internet Identification Data Interaction Middleware and Resource Pool Service Platform Project,Ministry of Industry and Information Technology of the People’s Republic of China.
文摘Anomaly detection is becoming increasingly significant in industrial cyber security,and different machine-learning algorithms have been generally acknowledged as various effective intrusion detection engines to successfully identify cyber attacks.However,different machine-learning algorithms may exhibit their own detection effects even if they analyze the same feature samples.As a sequence,after developing one feature generation approach,the most effective and applicable detection engines should be desperately selected by comparing distinct properties of each machine-learning algorithm.Based on process control features generated by directed function transition diagrams,this paper introduces five different machine-learning algorithms as alternative detection engines to discuss their matching abilities.Furthermore,this paper not only describes some qualitative properties to compare their advantages and disadvantages,but also gives an in-depth and meticulous research on their detection accuracies and consuming time.In the verified experiments,two attack models and four different attack intensities are defined to facilitate all quantitative comparisons,and the impacts of detection accuracy caused by the feature parameter are also comparatively analyzed.All experimental results can clearly explain that SVM(Support Vector Machine)and WNN(Wavelet Neural Network)are suggested as two applicable detection engines under differing cases.
基金This work was supported by the National Natural Science Foundation of China(No.61976133)Shanghai Industrial Collaborative Technology Innovation Project(No.2021-cyxt1-kj14)+2 种基金Major Scienti¯c and Technological Innovation Projects of Shan Dong Province(No.2019JZZY021010)Science and Technology Innovation Base Project of Shanghai Science and Technology Commission(19DZ2255200)Defense Industrial Technology Development Program(JCKY2019413D002).
文摘Drug addiction can cause abnormal brain activation changes,which are the root cause of drug craving and brain function errors.This study enrolled drug abusers to determine the effects of different drugs on brain activation.A functional near-infrared spectroscopy(fNIRS)device was used for the research.This study was designed with an experimental paradigm that included the induction of resting and drug addiction cravings.We collected the fNIRS data of 30 drug users,including 10 who used heroin,10 who used Methamphetamine,and 10 who used mixed drugs.First,using Statistical Analysis,the study analyzed the activations of eight functional areas of the left and right hemispheres of the prefrontal cortex of drug addicts who respectively used heroin,Methamphetamine,and mixed drugs,including Left/Right-Dorsolateral prefrontal cortex(L/R-DLPFC),Left/Right-Ventrolateral prefrontal cortex(L/R-VLPFC),Left/Right-Fronto-polar prefrontal cortex(L/R-FPC),and Left/Right Orbitofrontal Cortex(L/R-OFC).Second,referencing the degrees of activation of oxyhaemoglobin concentration(HbO2),the study made an analysis and got the specific activation patterns of each group of the addicts.Finally,after taking out data which are related to the addicts who recorded high degrees of activation among the three groups of addicts,and which had the same channel numbers,the paper classified the different drug abusers using the data as the input data for Convolutional Neural Networks(CNNs).The average three-class accuracy is 67.13%.It is of great significance for the analysis of brain function errors and personalized rehabilitation.
文摘Reliable sales forecasts are important to the garment industry. In recent years, the global climate is warming, the weather changes frequently, and clothing sales are affected by weather fluctuations. The purpose of this study is to investigate whether weather data can improve the accuracy of product sales and to establish a corresponding clothing sales forecasting model. This model uses the basic attributes of clothing product data, historical sales data, and weather data. It is based on a random forest, XGB, and GBDT adopting a stacking strategy. We found that weather information is not useful for basic clothing sales forecasts, but it did improve the accuracy of seasonal clothing sales forecasts. The MSE of the dresses, down jackets, and shirts are reduced by 86.03%, 80.14%, and 41.49% on average. In addition, we found that the stacking strategy model outperformed the voting strategy model, with an average MSE reduction of 49.28%. Clothing managers can use this model to forecast their sales when they make sales plans based on weather information.
基金supported by National Natural Science Foundation of China(Grant No.52375380)National Key R&D Program of China(Grant No.2022YFB3402200)the Key Project of National Natural Science Foundation of China(Grant No.12032018).
文摘The outstanding comprehensive mechanical properties of newly developed hybrid lattice structures make them useful in engineering applications for bearing multiple mechanical loads.Additive-manufacturing technologies make it possible to fabricate these highly spatially programmable structures and greatly enhance the freedom in their design.However,traditional analytical methods do not sufficiently reflect the actual vibration-damping mechanism of lattice structures and are limited by their high computational cost.In this study,a hybrid lattice structure consisting of various cells was designed based on quasi-static and vibration experiments.Subsequently,a novel parametric design method based on a data-driven approach was developed for hybrid lattices with engineered properties.The response surface method was adopted to define the sensitive optimization target.A prediction model for the lattice geometric parameters and vibration properties was established using a backpropagation neural network.Then,it was integrated into the genetic algorithm to create the optimal hybrid lattice with varying geometric features and the required wide-band vibration-damping characteristics.Validation experiments were conducted,demonstrating that the optimized hybrid lattice can achieve the target properties.In addition,the data-driven parametric design method can reduce computation time and be widely applied to complex structural designs when analytical and empirical solutions are unavailable.
基金Project supported by the Hunan Provincial Natural Science Foundation(Grant Nos.2024JJ6190 and 2024JK2007-1)。
文摘Chemical short-range order(SRO),a phenomenon at the atomic scale resulting from inhomogeneities in the local chemical environment,is usually studied using machine learning force field-based molecular dynamics simulations due to the limitations of experimental methods.To promote the reliable application of machine potentials in high-entropy alloy simulations,first,this work uses NEP models trained on two different datasets to predict the SRO coefficients of NbMoTaW.The results show that within the same machine learning framework,there are significant differences in the prediction of SRO coefficients for the Nb-Nb atomic pair.Subsequently,this work predicts the SRO coefficients of NbMoTaW using the NEP model and the SNAP model,both of which are trained on the same dataset.The results reveal significant discrepancies in SRO predictions for like-element pairs(e.g.,Nb-Nb and W-W)between the two potentials,despite the identical training data.The findings of this study indicate that discrepancies in the prediction results of SRO coefficients can arise from either the same machine learning framework trained on different datasets or different learning frameworks trained on the same dataset.This reflects possible incompleteness in the current training set's coverage of local chemical environments at the atomic scale.Future research should establish unified evaluation standards to assess the capability of training sets to accurately describe complex atomic-scale behaviors such as SRO.
基金Project supported by the National Natural Science Foundation of China (Grant Nos. 12202159 and 12472216)。
文摘Liquid metals demonstrate significant potential for applications in thermal management and flexible electronic circuits, necessitating a comprehensive understanding of their transport properties for technological advancements. Experimental measurement of these properties presents challenges due to factors like cost, corrosion and impurity control. Consequently, accurate computational simulations become essential for predicting the physical properties of these materials. In this research, molecular dynamics(MD) simulations were employed to model several properties of gallium(Ga), indium(In) and Ga–In alloys, including lattice structural parameters, radial distribution functions(RDF), structure factors, selfdiffusion coefficients and viscosity. Due to the difficulty of traditional interatomic potentials in capturing the short-range interactions directly related to the mechanical behavior of liquid atoms, machine-learning interatomic potentials(MLIPs)have been constructed to precisely describe the liquid metals Ga, In, and Ga–In alloys. This was achieved by utilizing the moment tensor potential(MTP) framework in combination with an active learning strategy. MTP was trained using a comprehensive database generated from DFT and MD simulations, which include a variety of crystal structures, point defects and liquid structures. The calculations of physical properties in this research have shown strong consistency with experimental data, demonstrating that the MTP can accurately describe the interatomic interactions between Ga–Ga, In–In and Ga–In. Our work has established a novel paradigm for investigating the physical properties of various liquid metal systems, offering valuable insights and references for future research.
基金supported by the National Natural Science Foundation of China(92164206,52261145694,T2394474,T2394470,623B2015,62271026,62401026,and 62404013)the National Key Research and Development Program of China(2022YFB4400200)+1 种基金the New Cornerstone Science Foundation through the XPLORER PRIZE,the National Postdoctoral Program for Innovative Talents(BX20220374 and BX20240455)the China Postdoctoral Science Foundation Funded Project(2023M740177 and 2022M720345).
文摘In recent years,physical unclonable function(PUF)has emerged as a lightweight solution in the Internet of Things security.However,conventional PUFs based on complementary metal oxide semiconductor(CMOS)present challenges such as insufficient randomness,significant power and area overhead,and vulnerability to environmental factors,leading to reduced reliability.In this study,we realize a strong,highly reliable and reconfigurable PUF with resistance against machine-learning attacks in a 1 kb spinorbit torque magnetic random access memory fabricated using a 180 nm CMOS process.This strong PUF achieves a challenge-response pair capacity of 10^(9) through a computing-in-memory approach.The results demonstrate that the proposed PUF exhibits near-ideal performance metrics:50.07% uniformity,50% diffuseness,49.89% uniqueness,and a bit error rate of 0%,even in a 375 K environment.The reconfigurability of PUF is demonstrated by a reconfigurable Hamming distance of 49.31% and a correlation coefficient of less than 0.2,making it difficult to extract output keys through side-channel analysis.Furthermore,resistance to machine-learning modeling attacks is confirmed by achieving an ideal accuracy prediction of approximately 50% in the test set.
文摘As the demands for environmental sustainability and the requirements to lower carbon emissions have escalated,New Energy Vehicles(NEVs)have emerged as a compelling substitute for fossil-fuel-run automobiles.Hence,a smart energy management strategy has been developed to enhance the performance of NEVs,maximizing the sustainability of transportation systems and minimizing environmental impacts.The system combines different power reserves,includ-ing a photovoltaic(PV)generator,fuel cell(FC),and battery system,to provide a continuous energy supply,even when the vehicle is running.The Multi-Directional Power Transfer converter for the battery provides the required energy ad-aptation between the input and output.The FC and PV systems are all connected through a direct current/direct current converter to effectively charge the battery whenever excess energy is present.The new energy management technique called Optimized Ant Colony Algorithm is proposed to dynamically allocate power among the different power sources,improving system efficiency.Unlike traditional methods,the suggested approach actively optimizes energy flow accord-ing to actual demand and availability,minimizing energy losses and enhancing sustainability.The MATLAB/Simulink tool was used to simulate the energetic performance of an electric car utilizing the suggested approach.The performance of this multi-source power system is assessed by contrasting the energy the PV and FC generating devices offer,and the energy generation of each recharge system.Additionally,the battery power comparison validates the cost-effectiveness and sustainability of the proposed model in NEVs.Results designate a significant improvement in energy efficiency and overall NEV environmental sustainability within contemporary transportation networks.
基金supported by the National Natural Science Foundation of China(71901212)the Science and Technology Innovation Program of Hunan Province(2020RC4046).
文摘The belief rule-based(BRB)system has been popular in complexity system modeling due to its good interpretability.However,the current mainstream optimization methods of the BRB systems only focus on modeling accuracy but ignore the interpretability.The single-objective optimization strategy has been applied in the interpretability-accuracy trade-off by inte-grating accuracy and interpretability into an optimization objec-tive.But the integration has a greater impact on optimization results with strong subjectivity.Thus,a multi-objective optimiza-tion framework in the modeling of BRB systems with inter-pretability-accuracy trade-off is proposed in this paper.Firstly,complexity and accuracy are taken as two independent opti-mization goals,and uniformity as a constraint to give the mathe-matical description.Secondly,a classical multi-objective opti-mization algorithm,nondominated sorting genetic algorithm II(NSGA-II),is utilized as an optimization tool to give a set of BRB systems with different accuracy and complexity.Finally,a pipeline leakage detection case is studied to verify the feasibility and effectiveness of the developed multi-objective optimization.The comparison illustrates that the proposed multi-objective optimization framework can effectively avoid the subjectivity of single-objective optimization,and has capability of joint optimiz-ing the structure and parameters of BRB systems with inter-pretability-accuracy trade-off.
文摘Satellite Precipitation Products(SPPs) face challenges in detecting Extreme Precipitation Events(EPEs). Hence, the primary objective of this research is to introduce a novel framework termed Machine-Learning Clustering-Merging Algorithms(ML-CMAs) to evaluate EPEs using SPPs and Auxiliary Data(AD). Daily precipitation measurements were utilized for training and evaluating EPE estimates over Iran, which is comprised of arid and semi-arid regions. Statistical analysis and evaluation of five SPPs demonstrated that during EPE occurrences, all products face challenges in precipitation estimation, and using these products individually is not recommended. Among the SPPs, Multi-Source Weighted-Ensemble Precipitation(MSWEP) performed best for heavy(>20 mm d–1) and extreme(>40 mm d–1)precipitation events, followed by Global Satellite Mapping of Precipitation(GSMa P), Integrated Multi-Satellite Retrievals for Global Precipitation Measurement(IMERG), Climate Prediction Center morphing technique(CMORPH), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Dynamic Infrared-Rain Rate(PERSIANN-PDIR). The findings indicate that all proposed methods based on ML-CMAs could estimate precipitation rates more accurately than SPPs and improve statistical indices. The seasonal assessment and spatial analysis of statistical metrics of the overall daily precipitation results for all periods and climates revealed that all methods based on ML-CMAs performed well in all seasons and at nearly all measurement stations. Using unsupervised K-means++ classification for clustering EPEs and Deep Neural Network(DNN) and Convolutional Neural Network(CNN) methods for merging the MLCMAs reduced the error rate of SPPs in EPE estimation by approximately 50%. Therefore, incorporating ML-CMAs along with PWV as AD can significantly improve the performance of SPPs in evaluating EPEs over the study region.
基金supported by the National Natural Science Foundation of China (Nos.22076197,21707149 and 82127801)the Scientific Instrument Developing Project of the Chinese Academy of Sciences (No.YJKYYQ20200034)+1 种基金Shenzhen Science and Technology Research Funding (Nos.JCYJ20210324115811031 and JCYJ20200109115405930)Guangdong Basic and Applied Basic Research Foundation (No.2020B1515120080)。
文摘Lipid metabolism play an essential role in occurrence and development of asthma,and it can be disturbed by phthalate esters(PAEs)and organophosphate fame retardants(OPFRs).As a chronic infammatory respiratory disease,the occurrence risk of childhood asthma is increased by PAEs and OPFRs exposure,but it remains not entirely clear how PAEs and OPFRs contribute the onset and progress of the disease.We have profiled the serum levels of PAEs and OPFRs congeners by liquid chromatography coupled with mass spectrometry,and its relationships with the dysregulation of lipid metabolism in asthmatic,bronchitic(acute infammation)and healthy(non-infammation)children.Eight PAEs and nine OPFRs congeners were found in the serum of children(1–5 years old)from Shenzhen,and their total median levels were 615.16 ng/m L and 17.06 ng/m L,respectively.Moreover,the serum levels of mono-methyl phthalate(MMP),tri-propyl phosphate(TPP)and tri-n-butyl phosphate(TNBP)were significant higher in asthmatic children than in healthy and bronchitic children as control.Thirty-one characteristic lipids and fatty acids of asthma were screened by machine-learning random forest model based on serum lipidome data,and the alterations of infammatory characteristic lipids and fatty acids including palmitic acids,12,13-Di HODE,14,21-Di HDHA,prostaglandin D2 and Lyso PA(18:2)showed significant correlated with high serum levels of MMP,TPP and TNBP.These results imply PAEs and OPFRs promote the occurrence of childhood asthma via disrupting infammatory lipid and fatty acid metabolism,and provide a novel sight for better understanding the effects of plastic additives on childhood asthma.
基金support from the Sichuan Science and Technology Program(2022NSFSC0226)the Production-Education Integration Demonstration Project of Sichuan Province“Photovoltaic Industry Production-Education Integration Comprehensive Demonstration Base of Sichuan Province(Sichuan Financial Education[2022]No.106)”+2 种基金National Key Research and Development Program of China(2022YFB3803300,2023YFE0116800)Natural Science Foundation of Sichuan(2022NSFSC0023,23NSFSC0112)Supported by Sichuan Science and Technology Program(No.2023ZYD0163).
文摘The photovoltaic(PV)water electrolysis method currently stands as the most promising approach for green hydrogen production.The rapid iteration of photovoltaic technologies has significantly affected on the technical and economic evaluation for photovoltaic hydrogen production.In this work,the photovoltaic hydrogen production of three most advanced silicon photovoltaic technologies is systematically compared for the first time under the climatic conditions of the Kucha region.All-weather stable hydrogen production control system with optimal charging and discharging strategies is constructed to realize stable and efficient hydrogen energy production.Seven machine learning(ML)algorithms are used to forecast the performance in power generation and hydrogen production of a 100 MW photovoltaic hydrogen production and energy storage(PH-S)system throughout its operational life.The long short-term memory(LSTM)algorithm exhibits the best performance,achieving mean absolute error(MAE)of 0.0415,root mean square error(RMSE)of 0.0891,and coefficient of determination(R2)of 0.8402.In terms of cost-effectiveness,heterojunction with intrinsic thin layer(HJT)PV technology achieves the lowest levelized cost of electricity(LCOE)and hydrogen(LCOH)at 0.025$/kWh and 6.95$/kg,respectively.According to the sensitivity analysis,when the cost of proton exchange membrane electrolysis(PEMEC)reduced 50%,the LCOH for PH-S system decreased 21.40%.This study provides valuable insights for the practical implementation of large-scale photovoltaic hydrogen production and cost reduction in PH-S systems.
基金Princess Nourah bint Abdulrahman University for funding this project through the Researchers Supporting Project(PNURSP2024R319)funded by the Prince Sultan University,Riyadh,Saudi Arabia.
文摘This study describes improving network security by implementing and assessing an intrusion detection system(IDS)based on deep neural networks(DNNs).The paper investigates contemporary technical ways for enhancing intrusion detection performance,given the vital relevance of safeguarding computer networks against harmful activity.The DNN-based IDS is trained and validated by the model using the NSL-KDD dataset,a popular benchmark for IDS research.The model performs well in both the training and validation stages,with 91.30%training accuracy and 94.38%validation accuracy.Thus,the model shows good learning and generalization capabilities with minor losses of 0.22 in training and 0.1553 in validation.Furthermore,for both macro and micro averages across class 0(normal)and class 1(anomalous)data,the study evaluates the model using a variety of assessment measures,such as accuracy scores,precision,recall,and F1 scores.The macro-average recall is 0.9422,the macro-average precision is 0.9482,and the accuracy scores are 0.942.Furthermore,macro-averaged F1 scores of 0.9245 for class 1 and 0.9434 for class 0 demonstrate the model’s ability to precisely identify anomalies precisely.The research also highlights how real-time threat monitoring and enhanced resistance against new online attacks may be achieved byDNN-based intrusion detection systems,which can significantly improve network security.The study underscores the critical function ofDNN-based IDS in contemporary cybersecurity procedures by setting the foundation for further developments in this field.Upcoming research aims to enhance intrusion detection systems by examining cooperative learning techniques and integrating up-to-date threat knowledge.
基金The authors would like to thank Princess Nourah bint Abdulrahman University for funding this project through the Researchers Supporting Project(PNURSP2023R319)this research was funded by the Prince Sultan University,Riyadh,Saudi Arabia.
文摘Intrusion detection systems(IDS)are essential in the field of cybersecurity because they protect networks from a wide range of online threats.The goal of this research is to meet the urgent need for small-footprint,highly-adaptable Network Intrusion Detection Systems(NIDS)that can identify anomalies.The NSL-KDD dataset is used in the study;it is a sizable collection comprising 43 variables with the label’s“attack”and“level.”It proposes a novel approach to intrusion detection based on the combination of channel attention and convolutional neural networks(CNN).Furthermore,this dataset makes it easier to conduct a thorough assessment of the suggested intrusion detection strategy.Furthermore,maintaining operating efficiency while improving detection accuracy is the primary goal of this work.Moreover,typical NIDS examines both risky and typical behavior using a variety of techniques.On the NSL-KDD dataset,our CNN-based approach achieves an astounding 99.728%accuracy rate when paired with channel attention.Compared to previous approaches such as ensemble learning,CNN,RBM(Boltzmann machine),ANN,hybrid auto-encoders with CNN,MCNN,and ANN,and adaptive algorithms,our solution significantly improves intrusion detection performance.Moreover,the results highlight the effectiveness of our suggested method in improving intrusion detection precision,signifying a noteworthy advancement in this field.Subsequent efforts will focus on strengthening and expanding our approach in order to counteract growing cyberthreats and adjust to changing network circumstances.
基金supported by the National Key Research and Development Program of China(2022YFE0141100 and 2023YFB3003005).
文摘Machine-learning interatomic potentials have revolutionized materials modeling at the atomic scale.Thanks to these,it is now indeed possible to perform simulations of ab initio quality over very large time and length scales.More recently,various universal machine-learning models have been proposed as an out-of-box approach avoiding the need to train and validate specific potentials for each particular material of interest.In this paper,we review and evaluate four different universal machine-learning interatomic potentials(uMLIPs),all based on graph neural network architectures which have demonstrated transferability from one chemical system to another.The evaluation procedure relies on data both from a recent verification study of density-functional-theory implementations and from the Materials Project.Through this comprehensive evaluation,we aim to provide guidance to materials scientists in selecting suitable models for their specific research problems,offer recommendations for model selection and optimization,and stimulate discussion on potential areas for improvement in current machinelearning methodologies in materials science.
