A comprehensive understanding of the relevance between molecular structure and passivation ability to screen efficient modifiers is essential for enhancing the performance of perovskite solar cells(PSCs).Here,three si...A comprehensive understanding of the relevance between molecular structure and passivation ability to screen efficient modifiers is essential for enhancing the performance of perovskite solar cells(PSCs).Here,three similarπ-πstacking molecules namely benzophenone(BPN),diphenyl sulfone(DPS),and diphenyl sulfoxide(DPSO)are used as back-interface modifiers in carbon-based CsPbBr_(3)PSCs.After investigation,the results demonstrate the positive effect of the p-πconjugation characteristic inπ-πstacking molecules on maximizing their passivation ability.The p-πco njugation of DPSO enables a higher coordinative activity of oxygen atom in its S=O group than that in 0=S=O group of DPS and C=O group of BPN,which gives a superior passivation effect of DPSO on defects of perovskite films.The modification of DPSO also significantly improves the p-type behavior of perovskite films and the back-interfacial energetics matching,inducing an increase of hole extraction and a decrease of energy loss.Finally,the unencapsulated carbon-based CsPbBr_(3)PSCs with DPSO achieve a maximum power conversion efficiency of 10.60%and outstanding long-term stability in high-temperature,high-humidity(85℃,85%relative humidity)air environment.This work provides insights into the influence of the structure ofπ-πstacking molecules on their ability to improve the perovskite films quality and therefore the PSCs performance.展开更多
Spectrum prediction is considered as a key technology to assist spectrum decision.Despite the great efforts that have been put on the construction of spectrum prediction,achieving accurate spectrum prediction emphasiz...Spectrum prediction is considered as a key technology to assist spectrum decision.Despite the great efforts that have been put on the construction of spectrum prediction,achieving accurate spectrum prediction emphasizes the need for more advanced solutions.In this paper,we propose a new multichannel multi-step spectrum prediction method using Transformer and stacked bidirectional LSTM(Bi-LSTM),named TSB.Specifically,we use multi-head attention and stacked Bi-LSTM to build a new Transformer based on encoder-decoder architecture.The self-attention mechanism composed of multiple layers of multi-head attention can continuously attend to all positions of the multichannel spectrum sequences.The stacked Bi-LSTM can learn these focused coding features by multi-head attention layer by layer.The advantage of this fusion mode is that it can deeply capture the long-term dependence of multichannel spectrum data.We have conducted extensive experiments on a dataset generated by a real simulation platform.The results show that the proposed algorithm performs better than the baselines.展开更多
High-density stacking faults(SFs)were introduced into a novel Ni-Co-based superalloy through warm rolling at 300-500°C,and the effects of SFs on its tensile properties at intermediate temperatures(650 and 750...High-density stacking faults(SFs)were introduced into a novel Ni-Co-based superalloy through warm rolling at 300-500°C,and the effects of SFs on its tensile properties at intermediate temperatures(650 and 750°C)were investigated.The results indicated that all warm rolled specimens have high-density SFs and Lomer-Cottrell locks compared with the initial specimens.Meanwhile,the simultaneous improvement of intermediate-temperature strength and ductility of alloys can be achieved by high-density SFs.In particular,the specimen rolled at 300°C exhibited a superior combination of high strength(yield and ultimate tensile strengths of(1311±18)and(1462±25)MPa respectively at 650°C,and(1180±17)and(1293±15)MPa respectively at 750°C)and high fracture elongation((26.7±2.5)%at 650°C and(10.7±1.3)%at 750°C).The high strengths and facture elongations of all warm-rolled specimens were primarily attributed to the interaction of pre-existingγ′phases,high-density SFs and Lomer-Cottrell locks with dislocations,as well as to the formation of high-density deformation nano-twins during tensile loading.展开更多
It is extremely difficult to introduce high-density nano twins during the solidification process of TiAl alloy.In this study,high-density nanotwins are inducted in the as-cast Ti48Al2Cr alloyed by adding Re element.Ph...It is extremely difficult to introduce high-density nano twins during the solidification process of TiAl alloy.In this study,high-density nanotwins are inducted in the as-cast Ti48Al2Cr alloyed by adding Re element.Phase transformation,morphology characteristics of nano twins,compressive and tensile proper-ties,and the related mechanisms have been studied.Results show that B2 phase enriched with Re tends to precipitate along theα_(2)/γinterface within lamellar colony.The stacking fault energy(SFE)ofγphase decreases from 43 mJ/m^(2) to 16 mJ/m^(2) as Re content increases from 0 at.%to 0.6 at.%,decreasing the crit-ical shear stress for twin formation.Compared to the mismatch value ofα_(2)/γinterface(0.004),which of B2/α_(2) and B2/γinterfaces increase to 0.247 and 0.149,respectively.Driven by high interfacial stress,high-density dislocations are generated at the B2/α_(2) interface,providing the dislocation slip channel for the formation of stacking faults(SFs)and nanotwins at the B2/γinterface.Therefore,the mechanism of inducting high-density nanotwins is to reduce the stacking fault energy ofγphase by Re and form highly mismatched B2/α_(2) interface.Compressive strength and the strain increase from 1723 MPa to 2398 MPa and 29%to 39%as Re content increases from 0 at.%to 0.6 at.%,respectively.Tensile strength increases from 356 MPa to 452 MPa without sacrificing plasticity.The improvement in strength and plasticity are attributed to the nano-twinning strengthening and interfacial thermal mismatch strengthening.Forming nanotwins during solidification process serve as the nucleation sites for newly formed twins during de-formation process,increasing the deformation tolerance of TiAl alloy.展开更多
Tailings produced by mining and ore smelting are a major source of soil pollution.Understanding the speciation of heavy metals(HMs)in tailings is essential for soil remediation and sustainable development.Given the co...Tailings produced by mining and ore smelting are a major source of soil pollution.Understanding the speciation of heavy metals(HMs)in tailings is essential for soil remediation and sustainable development.Given the complex and time-consuming nature of traditional sequential laboratory extraction methods for determining the forms of HMs in tailings,a rapid and precise identification approach is urgently required.To address this issue,a general empirical prediction method for HM occurrence was developed using machine learning(ML).The compositional information of the tailings,properties of the HMs,and sequential extraction steps were used as inputs to calculate the percentages of the seven forms of HMs.After the models were tuned and compared,extreme gradient boosting,gradient boosting decision tree,and categorical boosting methods were found to be the top three performing ML models,with the coefficient of determination(R^(2))values on the testing set exceeding 0.859.Feature importance analysis for these three optimal models indicated that electronegativity was the most important factor affecting the occurrence of HMs,with an average feature importance of 0.4522.The subsequent use of stacking as a model integration method enabled the ability of the ML models to predict HM occurrence forms to be further improved,and resulting in an increase of R^(2) to 0.879.Overall,this study developed a robust technique for predicting the occurrence forms in tailings and provides an important reference for the environmental assessment and recycling of tailings.展开更多
Carbonaceous material has attracted much attention in the application of sodium-ion batteries(SIBs)anode.However,sluggish reaction kinetics and structure stability impede the application.Therefore,a stacked layered su...Carbonaceous material has attracted much attention in the application of sodium-ion batteries(SIBs)anode.However,sluggish reaction kinetics and structure stability impede the application.Therefore,a stacked layered sulfur-carbon complex with long-chain C–S_(x)–C bond(M-SC-S)is prepared.The layered structure ensures structural stability,and long-chain C–S_(x)–C bond expanding interlayer spacing boosts facile Na+diffusion.When assembled into cells,a high-quality solid-electrolyte interphase film would be formed due to a good match between the M-SC-S electrode and ether electrolyte.Moreover,an electrochemical activation process would happen between the Cu current collector and proper S-doped electrode material to in-situ form Cu_(2)S.The formation of Cu_(2)S in active material can not only provide more active sites for sodium storage and enhance pseudo-capacitance,but also reinforce the electrode/current collector interface and decrease the interfacial transfer resistance for rapid Na+kinetics.The synergistic effect of structure design and interface engineering optimizes the sodium storage system.Thus,the M-SC-S electrode delivers an excellent cyclic performance(321.6 mAh g^(−1)after 1000 cycles at 2 A g^(−1)with a capacity retention rate of 97.4%)and good rate capability(282.8 mAh g^(−1)after 4000 cycles even at a high current density of 10 A g^(−1)).The full cell also has an impressive cyclic performance(151.4 mAh g^(−1)after 500 cycles at 0.5 A g^(−1)).展开更多
Deep Learning has been widely used to model soft sensors in modern industrial processes with nonlinear variables and uncertainty.Due to the outstanding ability for high-level feature extraction,stacked autoencoder(SAE...Deep Learning has been widely used to model soft sensors in modern industrial processes with nonlinear variables and uncertainty.Due to the outstanding ability for high-level feature extraction,stacked autoencoder(SAE)has been widely used to improve the model accuracy of soft sensors.However,with the increase of network layers,SAE may encounter serious information loss issues,which affect the modeling performance of soft sensors.Besides,there are typically very few labeled samples in the data set,which brings challenges to traditional neural networks to solve.In this paper,a multi-scale feature fused stacked autoencoder(MFF-SAE)is suggested for feature representation related to hierarchical output,where stacked autoencoder,mutual information(MI)and multi-scale feature fusion(MFF)strategies are integrated.Based on correlation analysis between output and input variables,critical hidden variables are extracted from the original variables in each autoencoder's input layer,which are correspondingly given varying weights.Besides,an integration strategy based on multi-scale feature fusion is adopted to mitigate the impact of information loss with the deepening of the network layers.Then,the MFF-SAE method is designed and stacked to form deep networks.Two practical industrial processes are utilized to evaluate the performance of MFF-SAE.Results from simulations indicate that in comparison to other cutting-edge techniques,the proposed method may considerably enhance the accuracy of soft sensor modeling,where the suggested method reduces the root mean square error(RMSE)by 71.8%,17.1%and 64.7%,15.1%,respectively.展开更多
This paper presents a space network emulation system based on a user-space network stack named Nos to solve space networks'unique architecture and routing issues and kernel stacks'inefficiency and development ...This paper presents a space network emulation system based on a user-space network stack named Nos to solve space networks'unique architecture and routing issues and kernel stacks'inefficiency and development complexity.Our low Earth orbit satellite scenario emulation verifies the dynamic routing function of the protocol stack.The proposed system uses technologies like Open vSwitch(OVS)and traffic control(TC)to emulate the space network's highly dynamic topology and time-varying link characteristics.The emulation results demonstrate the system's high reliability,and the user-space network stack reduces development complexity and debugging difficulty,providing convenience for the development of space network protocols and network functions.展开更多
Network Intrusion Detection System(NIDS)detection of minority class attacks is always a difficult task when dealing with attacks in complex network environments.To improve the detection capability of minority-class at...Network Intrusion Detection System(NIDS)detection of minority class attacks is always a difficult task when dealing with attacks in complex network environments.To improve the detection capability of minority-class attacks,this study proposes an intrusion detection method based on a two-layer structure.The first layer employs a CNN-BiLSTM model incorporating an attention mechanism to classify network traffic into normal traffic,majority class attacks,and merged minority class attacks.The second layer further segments the minority class attacks through Stacking ensemble learning.The datasets are selected from the generic network dataset CIC-IDS2017,NSL-KDD,and the industrial network dataset Mississippi Gas Pipeline dataset to enhance the generalization and practical applicability of the model.Experimental results show that the proposed model achieves an overall detection accuracy of 99%,99%,and 95%on the CIC-IDS2017,NSL-KDD,and industrial network datasets,respectively.It also significantly outperforms traditional methods in terms of detection accuracy and recall rate for minority class attacks.Compared with the single-layer deep learning model,the two-layer structure effectively reduces the false alarm rate while improving the minority-class attack detection performance.The research in this paper not only improves the adaptability of NIDS to complex network environments but also provides a new solution for minority-class attack detection in industrial network security.展开更多
Accurate short-term photovoltaic(PV)output forecasting is beneficial for increasing grid stabil-ity and enhancing the capacity for photovoltaic power absorption.In response to the challenges faced by commonly used pho...Accurate short-term photovoltaic(PV)output forecasting is beneficial for increasing grid stabil-ity and enhancing the capacity for photovoltaic power absorption.In response to the challenges faced by commonly used photovoltaic forecasting methods,which struggle to handle issues such as non-u-niform lengths of time series data for power generation and meteorological conditions,overlapping photovoltaic characteristics,and nonlinear correlations,an improved method that utilizes spectral clustering and dynamic time warping(DTW)for selecting similar days is proposed to optimize the dataset along the temporal dimension.Furthermore,XGBoost is employed for recursive feature selec-tion.On this basis,to address the issue that single forecasting models excel at capturing different data characteristics and tend to exhibit significant prediction errors under adverse meteorological con-ditions,an improved forecasting model based on Stacking and weighted fusion is proposed to reduce the independent bias and variance of individual models and enhance the predictive accuracy.Final-ly,experimental validation is carried out using real data from a photovoltaic power station in the Xi-aoshan District of Hangzhou,China,demonstrating that the proposed method can still achieve accu-rate and robust forecasting results even under conditions of significant meteorological fluctuations.展开更多
In today’s rapidly evolving digital landscape,web application security has become paramount as organizations face increasingly sophisticated cyber threats.This work presents a comprehensive methodology for implementi...In today’s rapidly evolving digital landscape,web application security has become paramount as organizations face increasingly sophisticated cyber threats.This work presents a comprehensive methodology for implementing robust security measures in modern web applications and the proof of the Methodology applied to Vue.js,Spring Boot,and MySQL architecture.The proposed approach addresses critical security challenges through a multi-layered framework that encompasses essential security dimensions including multi-factor authentication,fine-grained authorization controls,sophisticated session management,data confidentiality and integrity protection,secure logging mechanisms,comprehensive error handling,high availability strategies,advanced input validation,and security headers implementation.Significant contributions are made to the field of web application security.First,a detailed catalogue of security requirements specifically tailored to protect web applications against contemporary threats,backed by rigorous analysis and industry best practices.Second,the methodology is validated through a carefully designed proof-of-concept implementation in a controlled environment,demonstrating the practical effectiveness of the security measures.The validation process employs cutting-edge static and dynamic analysis tools for comprehensive dependency validation and vulnerability detection,ensuring robust security coverage.The validation results confirm the prevention and avoidance of security vulnerabilities of the methodology.A key innovation of this work is the seamless integration of DevSecOps practices throughout the secure Software Development Life Cycle(SSDLC),creating a security-first mindset from initial design to deployment.By combining proactive secure coding practices with defensive security approaches,a framework is established that not only strengthens application security but also fosters a culture of security awareness within development teams.This hybrid approach ensures that security considerations are woven into every aspect of the development process,rather than being treated as an afterthought.展开更多
The mechanical behaviour of Titanium-based Fiber Metal Laminates(FMLs)reinforced with Kevlar,Jute and the novel woven(Kevlar+Jute)fiber mat were evaluated through tensile,flexural,Charpy impact,and drop-weight tests.T...The mechanical behaviour of Titanium-based Fiber Metal Laminates(FMLs)reinforced with Kevlar,Jute and the novel woven(Kevlar+Jute)fiber mat were evaluated through tensile,flexural,Charpy impact,and drop-weight tests.The FMLs were fabricated with various stacking configurations(2/1,3/2,4/3,and 5/4)to examine their influence on mechanical properties.Kevlar-reinforced laminates consistently demonstrated superior tensile and flexural strengths,with the highest tensile strength of 772 MPa observed in the 3/2 configuration,attributed to Kevlar's excellent load-bearing capacity.Jute-reinforced laminates exhibited lower performance due to poor bonding and early delamination,while the FMLs reinforced with woven(Kevlar+Jute)fiber mat achieved a balance between mechanical strength and cost-effectiveness by attaining a tensile strength of 718 MPa in the 3/2 configuration.Impact energy absorption results revealed that Kevlar-reinforced FMLs provided the highest energy absorption under Charpy tests,reaching 13.5 J in the 3/2 configuration.The 4/3 configu ration exhibited superior resistance under drop-weight impacts,absorbing 104.7 J of energy.Failure analysis using SEM revealed key mechanisms such as fiber debonding,delamination,and fiber pull-out,with increased severity observed in laminates with a higher number of fiber-epoxy layers,especially in the 5/4 configuration.This study highlights the potential of Kevlar-Jute hybrid fiber-reinforced FMLs for applications requiring high mechanical performance and impact resistance.Future research should explore advanced surface treatments and the environmental durability of these laminates for aerospace and automotive applications.展开更多
基金financial supports from the Natural Science Foundation of Shandong Province(ZR2021ME037)the National Natural Science Foundation of China(52472259,22179051 and 61604143)+2 种基金the National Key Research and Development Program of China(2021YFE0111000)the Special Fund of Taishan Scholar Program of Shandong Province(tsqnz20221141)the Foundation of Key Laboratory of Advanced Technique&Preparation for Renewable Energy Materials,Ministry of Education,Yunnan Normal University(OF2022-02)。
文摘A comprehensive understanding of the relevance between molecular structure and passivation ability to screen efficient modifiers is essential for enhancing the performance of perovskite solar cells(PSCs).Here,three similarπ-πstacking molecules namely benzophenone(BPN),diphenyl sulfone(DPS),and diphenyl sulfoxide(DPSO)are used as back-interface modifiers in carbon-based CsPbBr_(3)PSCs.After investigation,the results demonstrate the positive effect of the p-πconjugation characteristic inπ-πstacking molecules on maximizing their passivation ability.The p-πco njugation of DPSO enables a higher coordinative activity of oxygen atom in its S=O group than that in 0=S=O group of DPS and C=O group of BPN,which gives a superior passivation effect of DPSO on defects of perovskite films.The modification of DPSO also significantly improves the p-type behavior of perovskite films and the back-interfacial energetics matching,inducing an increase of hole extraction and a decrease of energy loss.Finally,the unencapsulated carbon-based CsPbBr_(3)PSCs with DPSO achieve a maximum power conversion efficiency of 10.60%and outstanding long-term stability in high-temperature,high-humidity(85℃,85%relative humidity)air environment.This work provides insights into the influence of the structure ofπ-πstacking molecules on their ability to improve the perovskite films quality and therefore the PSCs performance.
文摘董志塬地区位于黄土高原中心地带,滑坡灾害频发,亟需明确滑坡易发性分区,以支持该区域滑坡隐患的科学防控。因此,本文以董志塬为研究区,选取高程、坡向和NDVI等12个影响因素作为评价因子,基于频率比(frequency ratio,FR)模型,结合随机森林(random forest,RF)与人工神经网络(artificial neural network,ANN)模型开展滑坡静态易发性评价,并分析各因子对评价精度的贡献。结果表明,FRRF和FR-ANN模型的曲线下面积(area under the curve,AUC)值分别为0.922和0.918,表明FR-RF模型在董志塬滑坡易发性评价中的精度更高。坡度、坡向和道路密度对滑坡易发性的贡献率分别为16.7%、15.3%和1.4%。为克服地形复杂和数据更新滞后的问题,本文将FR-RF模型的易发性结果与InSAR Stacking结果相结合,将静态滑坡易发性评价精度由6.9%提升到8.1%。动态易发性结果表明,董志塬滑坡高易发区主要分布于河流沿岸,占总面积的6.5%,该区域的滑坡数量占总滑坡数的23.6%,滑坡密度15.7个/km^(2)。低易发区主要位于远离河流的中部区域,占总面积的81.7%,滑坡数量占总滑坡数的57.8%,滑坡密度4.7个/km^(2)。本研究通过融合InSAR Stacking方法,解决了静态滑坡易发性评价数据更新滞后问题,减少了假阴性错误,为传统滑坡易发性评价赋予了时效性,可以实现董志塬滑坡易发性动态评价,为灾害防治提供了重要数据支持。
基金supported in part by the National Natural Science Foundation of China under Grants 62231015,62427801in part by Jiangsu Province Frontier Leading Technology Basic Research Project BK20232030.
文摘Spectrum prediction is considered as a key technology to assist spectrum decision.Despite the great efforts that have been put on the construction of spectrum prediction,achieving accurate spectrum prediction emphasizes the need for more advanced solutions.In this paper,we propose a new multichannel multi-step spectrum prediction method using Transformer and stacked bidirectional LSTM(Bi-LSTM),named TSB.Specifically,we use multi-head attention and stacked Bi-LSTM to build a new Transformer based on encoder-decoder architecture.The self-attention mechanism composed of multiple layers of multi-head attention can continuously attend to all positions of the multichannel spectrum sequences.The stacked Bi-LSTM can learn these focused coding features by multi-head attention layer by layer.The advantage of this fusion mode is that it can deeply capture the long-term dependence of multichannel spectrum data.We have conducted extensive experiments on a dataset generated by a real simulation platform.The results show that the proposed algorithm performs better than the baselines.
基金supported by the Program for Hong Liu Excellent Young Scholars by Lanzhou University of Technology in 2023,the National Key Research and Development Program of China(No.2017YFA0700703)the Major Science and Technology Project of Gansu Province,China(Nos.22ZD6GA008,23ZDGC002,23ZDGA010)+1 种基金the Gansu Provincial Key Research and Development Program,China(No.20YF3GA041)the Special Fund of National Key Laboratory of Ni&Co Associated Minerals Resources Development and Comprehensive Utilization by Jinchuan Group Co.,Ltd.,China(No.GZSYS-KY-2022-012).
文摘High-density stacking faults(SFs)were introduced into a novel Ni-Co-based superalloy through warm rolling at 300-500°C,and the effects of SFs on its tensile properties at intermediate temperatures(650 and 750°C)were investigated.The results indicated that all warm rolled specimens have high-density SFs and Lomer-Cottrell locks compared with the initial specimens.Meanwhile,the simultaneous improvement of intermediate-temperature strength and ductility of alloys can be achieved by high-density SFs.In particular,the specimen rolled at 300°C exhibited a superior combination of high strength(yield and ultimate tensile strengths of(1311±18)and(1462±25)MPa respectively at 650°C,and(1180±17)and(1293±15)MPa respectively at 750°C)and high fracture elongation((26.7±2.5)%at 650°C and(10.7±1.3)%at 750°C).The high strengths and facture elongations of all warm-rolled specimens were primarily attributed to the interaction of pre-existingγ′phases,high-density SFs and Lomer-Cottrell locks with dislocations,as well as to the formation of high-density deformation nano-twins during tensile loading.
基金supported by the National Natural Science Foundation of China(No.U21A2042)the National Nature Fund Youth Fund Project of China(No.52101038)Young Elite Scientists Sponsorship Program by CAST(No.2021QNRC001).
文摘It is extremely difficult to introduce high-density nano twins during the solidification process of TiAl alloy.In this study,high-density nanotwins are inducted in the as-cast Ti48Al2Cr alloyed by adding Re element.Phase transformation,morphology characteristics of nano twins,compressive and tensile proper-ties,and the related mechanisms have been studied.Results show that B2 phase enriched with Re tends to precipitate along theα_(2)/γinterface within lamellar colony.The stacking fault energy(SFE)ofγphase decreases from 43 mJ/m^(2) to 16 mJ/m^(2) as Re content increases from 0 at.%to 0.6 at.%,decreasing the crit-ical shear stress for twin formation.Compared to the mismatch value ofα_(2)/γinterface(0.004),which of B2/α_(2) and B2/γinterfaces increase to 0.247 and 0.149,respectively.Driven by high interfacial stress,high-density dislocations are generated at the B2/α_(2) interface,providing the dislocation slip channel for the formation of stacking faults(SFs)and nanotwins at the B2/γinterface.Therefore,the mechanism of inducting high-density nanotwins is to reduce the stacking fault energy ofγphase by Re and form highly mismatched B2/α_(2) interface.Compressive strength and the strain increase from 1723 MPa to 2398 MPa and 29%to 39%as Re content increases from 0 at.%to 0.6 at.%,respectively.Tensile strength increases from 356 MPa to 452 MPa without sacrificing plasticity.The improvement in strength and plasticity are attributed to the nano-twinning strengthening and interfacial thermal mismatch strengthening.Forming nanotwins during solidification process serve as the nucleation sites for newly formed twins during de-formation process,increasing the deformation tolerance of TiAl alloy.
基金financially supported by the Natural Science Foundation of Hunan Province,China(No.2024JJ2074)the National Natural Science Foundation of China(No.22376221)the Young Elite Scientists Sponsorship Program by CAST,China(No.2023QNRC001).
文摘Tailings produced by mining and ore smelting are a major source of soil pollution.Understanding the speciation of heavy metals(HMs)in tailings is essential for soil remediation and sustainable development.Given the complex and time-consuming nature of traditional sequential laboratory extraction methods for determining the forms of HMs in tailings,a rapid and precise identification approach is urgently required.To address this issue,a general empirical prediction method for HM occurrence was developed using machine learning(ML).The compositional information of the tailings,properties of the HMs,and sequential extraction steps were used as inputs to calculate the percentages of the seven forms of HMs.After the models were tuned and compared,extreme gradient boosting,gradient boosting decision tree,and categorical boosting methods were found to be the top three performing ML models,with the coefficient of determination(R^(2))values on the testing set exceeding 0.859.Feature importance analysis for these three optimal models indicated that electronegativity was the most important factor affecting the occurrence of HMs,with an average feature importance of 0.4522.The subsequent use of stacking as a model integration method enabled the ability of the ML models to predict HM occurrence forms to be further improved,and resulting in an increase of R^(2) to 0.879.Overall,this study developed a robust technique for predicting the occurrence forms in tailings and provides an important reference for the environmental assessment and recycling of tailings.
基金supported by the Key Research and Development Program of Wuhan(2025010102030005)the National Nature Science Foundation of Jiangsu Province(BK20221259)。
文摘Carbonaceous material has attracted much attention in the application of sodium-ion batteries(SIBs)anode.However,sluggish reaction kinetics and structure stability impede the application.Therefore,a stacked layered sulfur-carbon complex with long-chain C–S_(x)–C bond(M-SC-S)is prepared.The layered structure ensures structural stability,and long-chain C–S_(x)–C bond expanding interlayer spacing boosts facile Na+diffusion.When assembled into cells,a high-quality solid-electrolyte interphase film would be formed due to a good match between the M-SC-S electrode and ether electrolyte.Moreover,an electrochemical activation process would happen between the Cu current collector and proper S-doped electrode material to in-situ form Cu_(2)S.The formation of Cu_(2)S in active material can not only provide more active sites for sodium storage and enhance pseudo-capacitance,but also reinforce the electrode/current collector interface and decrease the interfacial transfer resistance for rapid Na+kinetics.The synergistic effect of structure design and interface engineering optimizes the sodium storage system.Thus,the M-SC-S electrode delivers an excellent cyclic performance(321.6 mAh g^(−1)after 1000 cycles at 2 A g^(−1)with a capacity retention rate of 97.4%)and good rate capability(282.8 mAh g^(−1)after 4000 cycles even at a high current density of 10 A g^(−1)).The full cell also has an impressive cyclic performance(151.4 mAh g^(−1)after 500 cycles at 0.5 A g^(−1)).
基金supported by the National Key Research and Development Program of China(2023YFB3307800)National Natural Science Foundation of China(62394343,62373155)+2 种基金Major Science and Technology Project of Xinjiang(No.2022A01006-4)State Key Laboratory of Industrial Control Technology,China(Grant No.ICT2024A26)Fundamental Research Funds for the Central Universities.
文摘Deep Learning has been widely used to model soft sensors in modern industrial processes with nonlinear variables and uncertainty.Due to the outstanding ability for high-level feature extraction,stacked autoencoder(SAE)has been widely used to improve the model accuracy of soft sensors.However,with the increase of network layers,SAE may encounter serious information loss issues,which affect the modeling performance of soft sensors.Besides,there are typically very few labeled samples in the data set,which brings challenges to traditional neural networks to solve.In this paper,a multi-scale feature fused stacked autoencoder(MFF-SAE)is suggested for feature representation related to hierarchical output,where stacked autoencoder,mutual information(MI)and multi-scale feature fusion(MFF)strategies are integrated.Based on correlation analysis between output and input variables,critical hidden variables are extracted from the original variables in each autoencoder's input layer,which are correspondingly given varying weights.Besides,an integration strategy based on multi-scale feature fusion is adopted to mitigate the impact of information loss with the deepening of the network layers.Then,the MFF-SAE method is designed and stacked to form deep networks.Two practical industrial processes are utilized to evaluate the performance of MFF-SAE.Results from simulations indicate that in comparison to other cutting-edge techniques,the proposed method may considerably enhance the accuracy of soft sensor modeling,where the suggested method reduces the root mean square error(RMSE)by 71.8%,17.1%and 64.7%,15.1%,respectively.
基金supported by the National Natural Science Foundation of China under Grant No.62131012ZTE Industry-University-Institute Cooperation Funds under Grant No.IA20230712005。
文摘This paper presents a space network emulation system based on a user-space network stack named Nos to solve space networks'unique architecture and routing issues and kernel stacks'inefficiency and development complexity.Our low Earth orbit satellite scenario emulation verifies the dynamic routing function of the protocol stack.The proposed system uses technologies like Open vSwitch(OVS)and traffic control(TC)to emulate the space network's highly dynamic topology and time-varying link characteristics.The emulation results demonstrate the system's high reliability,and the user-space network stack reduces development complexity and debugging difficulty,providing convenience for the development of space network protocols and network functions.
基金supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)—Innovative Human Resource Development for Local Intellectualization program grant funded by the Korea government(MSIT)(IITP-2025-RS-2022-00156334)in part by Liaoning Province Nature Fund Project(2024-BSLH-214).
文摘Network Intrusion Detection System(NIDS)detection of minority class attacks is always a difficult task when dealing with attacks in complex network environments.To improve the detection capability of minority-class attacks,this study proposes an intrusion detection method based on a two-layer structure.The first layer employs a CNN-BiLSTM model incorporating an attention mechanism to classify network traffic into normal traffic,majority class attacks,and merged minority class attacks.The second layer further segments the minority class attacks through Stacking ensemble learning.The datasets are selected from the generic network dataset CIC-IDS2017,NSL-KDD,and the industrial network dataset Mississippi Gas Pipeline dataset to enhance the generalization and practical applicability of the model.Experimental results show that the proposed model achieves an overall detection accuracy of 99%,99%,and 95%on the CIC-IDS2017,NSL-KDD,and industrial network datasets,respectively.It also significantly outperforms traditional methods in terms of detection accuracy and recall rate for minority class attacks.Compared with the single-layer deep learning model,the two-layer structure effectively reduces the false alarm rate while improving the minority-class attack detection performance.The research in this paper not only improves the adaptability of NIDS to complex network environments but also provides a new solution for minority-class attack detection in industrial network security.
基金Supported by the National Natural Science Foundation of China(No.52005442)the Technology Project of Zhejiang Huayun Information Technology Co.,Ltd.(No.HYJT/JS-2020-004).
文摘Accurate short-term photovoltaic(PV)output forecasting is beneficial for increasing grid stabil-ity and enhancing the capacity for photovoltaic power absorption.In response to the challenges faced by commonly used photovoltaic forecasting methods,which struggle to handle issues such as non-u-niform lengths of time series data for power generation and meteorological conditions,overlapping photovoltaic characteristics,and nonlinear correlations,an improved method that utilizes spectral clustering and dynamic time warping(DTW)for selecting similar days is proposed to optimize the dataset along the temporal dimension.Furthermore,XGBoost is employed for recursive feature selec-tion.On this basis,to address the issue that single forecasting models excel at capturing different data characteristics and tend to exhibit significant prediction errors under adverse meteorological con-ditions,an improved forecasting model based on Stacking and weighted fusion is proposed to reduce the independent bias and variance of individual models and enhance the predictive accuracy.Final-ly,experimental validation is carried out using real data from a photovoltaic power station in the Xi-aoshan District of Hangzhou,China,demonstrating that the proposed method can still achieve accu-rate and robust forecasting results even under conditions of significant meteorological fluctuations.
文摘In today’s rapidly evolving digital landscape,web application security has become paramount as organizations face increasingly sophisticated cyber threats.This work presents a comprehensive methodology for implementing robust security measures in modern web applications and the proof of the Methodology applied to Vue.js,Spring Boot,and MySQL architecture.The proposed approach addresses critical security challenges through a multi-layered framework that encompasses essential security dimensions including multi-factor authentication,fine-grained authorization controls,sophisticated session management,data confidentiality and integrity protection,secure logging mechanisms,comprehensive error handling,high availability strategies,advanced input validation,and security headers implementation.Significant contributions are made to the field of web application security.First,a detailed catalogue of security requirements specifically tailored to protect web applications against contemporary threats,backed by rigorous analysis and industry best practices.Second,the methodology is validated through a carefully designed proof-of-concept implementation in a controlled environment,demonstrating the practical effectiveness of the security measures.The validation process employs cutting-edge static and dynamic analysis tools for comprehensive dependency validation and vulnerability detection,ensuring robust security coverage.The validation results confirm the prevention and avoidance of security vulnerabilities of the methodology.A key innovation of this work is the seamless integration of DevSecOps practices throughout the secure Software Development Life Cycle(SSDLC),creating a security-first mindset from initial design to deployment.By combining proactive secure coding practices with defensive security approaches,a framework is established that not only strengthens application security but also fosters a culture of security awareness within development teams.This hybrid approach ensures that security considerations are woven into every aspect of the development process,rather than being treated as an afterthought.
基金the aid of Research and Development Fund-Seed Money provided by Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology。
文摘The mechanical behaviour of Titanium-based Fiber Metal Laminates(FMLs)reinforced with Kevlar,Jute and the novel woven(Kevlar+Jute)fiber mat were evaluated through tensile,flexural,Charpy impact,and drop-weight tests.The FMLs were fabricated with various stacking configurations(2/1,3/2,4/3,and 5/4)to examine their influence on mechanical properties.Kevlar-reinforced laminates consistently demonstrated superior tensile and flexural strengths,with the highest tensile strength of 772 MPa observed in the 3/2 configuration,attributed to Kevlar's excellent load-bearing capacity.Jute-reinforced laminates exhibited lower performance due to poor bonding and early delamination,while the FMLs reinforced with woven(Kevlar+Jute)fiber mat achieved a balance between mechanical strength and cost-effectiveness by attaining a tensile strength of 718 MPa in the 3/2 configuration.Impact energy absorption results revealed that Kevlar-reinforced FMLs provided the highest energy absorption under Charpy tests,reaching 13.5 J in the 3/2 configuration.The 4/3 configu ration exhibited superior resistance under drop-weight impacts,absorbing 104.7 J of energy.Failure analysis using SEM revealed key mechanisms such as fiber debonding,delamination,and fiber pull-out,with increased severity observed in laminates with a higher number of fiber-epoxy layers,especially in the 5/4 configuration.This study highlights the potential of Kevlar-Jute hybrid fiber-reinforced FMLs for applications requiring high mechanical performance and impact resistance.Future research should explore advanced surface treatments and the environmental durability of these laminates for aerospace and automotive applications.
