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
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
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
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
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
Three-dimensional ocean subsurface temperature and salinity structures(OST/OSS)in the South China Sea(SCS)play crucial roles in oceanic climate research and disaster mitigation.Traditionally,real-time OST and OSS are ...Three-dimensional ocean subsurface temperature and salinity structures(OST/OSS)in the South China Sea(SCS)play crucial roles in oceanic climate research and disaster mitigation.Traditionally,real-time OST and OSS are mainly obtained through in-situ ocean observations and simulation by ocean circulation models,which are usually challenging and costly.Recently,dynamical,statistical,or machine learning models have been proposed to invert the OST/OSS from sea surface information;however,these models mainly focused on the inversion of monthly OST and OSS.To address this issue,we apply clustering algorithms and employ a stacking strategy to ensemble three models(XGBoost,Random Forest,and LightGBM)to invert the real-time OST/OSS based on satellite-derived data and the Argo dataset.Subsequently,a fusion of temperature and salinity is employed to reconstruct OST and OSS.In the validation dataset,the depth-averaged Correlation(Corr)of the estimated OST(OSS)is 0.919(0.83),and the average Root-Mean-Square Error(RMSE)is0.639°C(0.087 psu),with a depth-averaged coefficient of determination(R~2)of 0.84(0.68).Notably,at the thermocline where the base models exhibit their maximum error,the stacking-based fusion model exhibited significant performance enhancement,with a maximum enhancement in OST and OSS inversion exceeding 10%.We further found that the estimated OST and OSS exhibit good agreement with the HYbrid Coordinate Ocean Model(HYCOM)data and BOA_Argo dataset during the passage of a mesoscale eddy.This study shows that the proposed model can effectively invert the real-time OST and OSS,potentially enhancing the understanding of multi-scale oceanic processes in the SCS.展开更多
Self-powered neutron detectors(SPNDs)play a critical role in monitoring the safety margins and overall health of reactors,directly affecting safe operation within the reactor.In this work,a novel fault identification ...Self-powered neutron detectors(SPNDs)play a critical role in monitoring the safety margins and overall health of reactors,directly affecting safe operation within the reactor.In this work,a novel fault identification method based on graph convolutional networks(GCN)and Stacking ensemble learning is proposed for SPNDs.The GCN is employed to extract the spatial neighborhood information of SPNDs at different positions,and residuals are obtained by nonlinear fitting of SPND signals.In order to completely extract the time-varying features from residual sequences,the Stacking fusion model,integrated with various algorithms,is developed and enables the identification of five conditions for SPNDs:normal,drift,bias,precision degradation,and complete failure.The results demonstrate that the integration of diverse base-learners in the GCN-Stacking model exhibits advantages over a single model as well as enhances the stability and reliability in fault identification.Additionally,the GCN-Stacking model maintains higher accuracy in identifying faults at different reactor power levels.展开更多
The intramolecular aromatic-ring stacking interaction of mixed- ligand complex Pd(A)(UTP)^(2-)in the system pd^(2+)-A-UTP^(4-)has been determined by ~1HNMR,where A=1,10-phenanthroline(phen),2,2'-bipyridyl(bpy)and ...The intramolecular aromatic-ring stacking interaction of mixed- ligand complex Pd(A)(UTP)^(2-)in the system pd^(2+)-A-UTP^(4-)has been determined by ~1HNMR,where A=1,10-phenanthroline(phen),2,2'-bipyridyl(bpy)and DL- tryptophan(trp^-);UTP^(4-)=uridine 5-triphosphate.The result indicates that it is the partial stacking between the uracil ring of UTP^(4-)and the heterocyclic ring of A that makes H(5),H(6)and H(1')in the UTP^(4-)shift upfield signifi- cantly.Accordingly,the order of aromatic-ring interaction in the mixed- ligand complex has been obtained as follows:Pd(phen)(UTP)^(2-)(?)Pd(bpy)(UTP)^(2-) Pd(trp)(UTP)^(3-).展开更多
Aromatic systems like phenol, diphenol, cyano benzene, chloro benzene, aniline etc shows effective π-π stacking interactions, long range van der Waals forces;ion-π interactions etc. and these forces of interactions...Aromatic systems like phenol, diphenol, cyano benzene, chloro benzene, aniline etc shows effective π-π stacking interactions, long range van der Waals forces;ion-π interactions etc. and these forces of interactions play an crucial role in the stability of stacked π-dimeric system. On the other hand, substituents and conformational change in the stacked dimmers of aromatic system may also change the stability of different stacked dimers. In this current study, stacked phenolic dimmers (both phenol and diphenol) have been taken for investigation of the stacking π-π interaction. But, the stacking interactions are also greatly affected by the conformational change with internal rotation (i.e. dihedral angle, φ) between the stacked dimers. It is generally accepted that larger basis sets are required for the highly accurate calculation of interaction energies for any stacked aromatic models. But, it has recently been reported that M062X/6-311++G(d,p) basis set is effectively better than that of B3LYP/6-311++G(d,p) for determining the interaction energies for any kind of long range interaction in aromatic systems. Therefore, all the calculations were carried out by using M062X/6-311++G(d,p) basis set. However, in most of the cases the calculated π-π stacking interaction energies show almost same result for both DFT and ab initio methods.展开更多
Cu,Cu-2.2%Al and Cu-4.5%Al with stacking fault energies（SFE） of 78,35 and 7 mJ/m2 respectively were processed by cold-rolling（CR） at liquid nitrogen temperature（77 K） after hot-rolling.X-ray diffraction measurem...Cu,Cu-2.2%Al and Cu-4.5%Al with stacking fault energies（SFE） of 78,35 and 7 mJ/m2 respectively were processed by cold-rolling（CR） at liquid nitrogen temperature（77 K） after hot-rolling.X-ray diffraction measurements indicate that a decrease in SFE leads to a decrease in crystallite size but increase in microstrain,dislocation and twin densities of the CR processed samples.Tensile tests at room temperature indicate that as the stacking fault energy decreases,the strength and ductility increase.The results indicate that decreasing stacking fault energy is an optimum method to improve the ductility without loss of strength.展开更多
The microstructure and mechanical properties of Mg94Zn2Y4 extruded alloy containing long-period stacking ordered structures were systematically investigated by SEM and TEM analyses. The results show that the 18R-LPSO ...The microstructure and mechanical properties of Mg94Zn2Y4 extruded alloy containing long-period stacking ordered structures were systematically investigated by SEM and TEM analyses. The results show that the 18R-LPSO structure and α-Mg phase are observed in cast Mg94Zn2Y4 alloy. After extrusion, the LPSO structures are delaminated and Mg-slices with width of 50-200 nm are generated. By ageing at 498 K for 36 h, the ageing peak is attained andβ′phase is precipitated. Due to this novel precipitation, the microhardness ofα-Mg matrix increases apparently from HV108.9 to HV129.7. While the microhardness for LPSO structure is stabilized at about HV145. TEM observations and SAED patterns indicate that the β′ phase has unique orientation relationships betweenα-Mg and LPSO structures, the direction in the close-packed planes ofβ′precipitates perpendicular to that ofα-Mg and LPSO structures. The ultimate tensile strength for the peak-aged alloy achieves 410.7 MPa and the significant strength originates from the coexistence ofβ′precipitates and 18R-LPSO structures.展开更多
The performances of analog circuits depend greatly on the layout parasitics and mismatches.Novel techniques are proposed for modeling the distributed parasitic capacitance,parasitic parameter mismatch due to process g...The performances of analog circuits depend greatly on the layout parasitics and mismatches.Novel techniques are proposed for modeling the distributed parasitic capacitance,parasitic parameter mismatch due to process gradient and the inner stack routing mismatch.Based on the proposed models,an optimal stack generation technique is developed to control the parasitics and mismatches,optimize the stack shape and ensure the generation of an Eulerian graph for a given CMOS analog module.An OPA circuit example is given to demonstrate that the circuit performances such as unit gain bandwidth and phase margin are enhanced by the proposed layout optimization method.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.
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 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 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 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.
基金jointly supported by the National Key Research and Development Program of China(2022YFC3104304)the National Natural Science Foundation of China(Grant No.41876011)+1 种基金the 2022 Research Program of Sanya Yazhou Bay Science and Technology City(SKJC-2022-01-001)the Hainan Province Science and Technology Special Fund(ZDYF2021SHFZ265)。
文摘Three-dimensional ocean subsurface temperature and salinity structures(OST/OSS)in the South China Sea(SCS)play crucial roles in oceanic climate research and disaster mitigation.Traditionally,real-time OST and OSS are mainly obtained through in-situ ocean observations and simulation by ocean circulation models,which are usually challenging and costly.Recently,dynamical,statistical,or machine learning models have been proposed to invert the OST/OSS from sea surface information;however,these models mainly focused on the inversion of monthly OST and OSS.To address this issue,we apply clustering algorithms and employ a stacking strategy to ensemble three models(XGBoost,Random Forest,and LightGBM)to invert the real-time OST/OSS based on satellite-derived data and the Argo dataset.Subsequently,a fusion of temperature and salinity is employed to reconstruct OST and OSS.In the validation dataset,the depth-averaged Correlation(Corr)of the estimated OST(OSS)is 0.919(0.83),and the average Root-Mean-Square Error(RMSE)is0.639°C(0.087 psu),with a depth-averaged coefficient of determination(R~2)of 0.84(0.68).Notably,at the thermocline where the base models exhibit their maximum error,the stacking-based fusion model exhibited significant performance enhancement,with a maximum enhancement in OST and OSS inversion exceeding 10%.We further found that the estimated OST and OSS exhibit good agreement with the HYbrid Coordinate Ocean Model(HYCOM)data and BOA_Argo dataset during the passage of a mesoscale eddy.This study shows that the proposed model can effectively invert the real-time OST and OSS,potentially enhancing the understanding of multi-scale oceanic processes in the SCS.
基金the Industry-University Cooperation Project in Fujian Province University(No.2022H6020)。
文摘Self-powered neutron detectors(SPNDs)play a critical role in monitoring the safety margins and overall health of reactors,directly affecting safe operation within the reactor.In this work,a novel fault identification method based on graph convolutional networks(GCN)and Stacking ensemble learning is proposed for SPNDs.The GCN is employed to extract the spatial neighborhood information of SPNDs at different positions,and residuals are obtained by nonlinear fitting of SPND signals.In order to completely extract the time-varying features from residual sequences,the Stacking fusion model,integrated with various algorithms,is developed and enables the identification of five conditions for SPNDs:normal,drift,bias,precision degradation,and complete failure.The results demonstrate that the integration of diverse base-learners in the GCN-Stacking model exhibits advantages over a single model as well as enhances the stability and reliability in fault identification.Additionally,the GCN-Stacking model maintains higher accuracy in identifying faults at different reactor power levels.
文摘The intramolecular aromatic-ring stacking interaction of mixed- ligand complex Pd(A)(UTP)^(2-)in the system pd^(2+)-A-UTP^(4-)has been determined by ~1HNMR,where A=1,10-phenanthroline(phen),2,2'-bipyridyl(bpy)and DL- tryptophan(trp^-);UTP^(4-)=uridine 5-triphosphate.The result indicates that it is the partial stacking between the uracil ring of UTP^(4-)and the heterocyclic ring of A that makes H(5),H(6)and H(1')in the UTP^(4-)shift upfield signifi- cantly.Accordingly,the order of aromatic-ring interaction in the mixed- ligand complex has been obtained as follows:Pd(phen)(UTP)^(2-)(?)Pd(bpy)(UTP)^(2-) Pd(trp)(UTP)^(3-).
文摘Aromatic systems like phenol, diphenol, cyano benzene, chloro benzene, aniline etc shows effective π-π stacking interactions, long range van der Waals forces;ion-π interactions etc. and these forces of interactions play an crucial role in the stability of stacked π-dimeric system. On the other hand, substituents and conformational change in the stacked dimmers of aromatic system may also change the stability of different stacked dimers. In this current study, stacked phenolic dimmers (both phenol and diphenol) have been taken for investigation of the stacking π-π interaction. But, the stacking interactions are also greatly affected by the conformational change with internal rotation (i.e. dihedral angle, φ) between the stacked dimers. It is generally accepted that larger basis sets are required for the highly accurate calculation of interaction energies for any stacked aromatic models. But, it has recently been reported that M062X/6-311++G(d,p) basis set is effectively better than that of B3LYP/6-311++G(d,p) for determining the interaction energies for any kind of long range interaction in aromatic systems. Therefore, all the calculations were carried out by using M062X/6-311++G(d,p) basis set. However, in most of the cases the calculated π-π stacking interaction energies show almost same result for both DFT and ab initio methods.
基金Project (50874056) supported by the National Natural Science Foundation of China
文摘Cu,Cu-2.2%Al and Cu-4.5%Al with stacking fault energies（SFE） of 78,35 and 7 mJ/m2 respectively were processed by cold-rolling（CR） at liquid nitrogen temperature（77 K） after hot-rolling.X-ray diffraction measurements indicate that a decrease in SFE leads to a decrease in crystallite size but increase in microstrain,dislocation and twin densities of the CR processed samples.Tensile tests at room temperature indicate that as the stacking fault energy decreases,the strength and ductility increase.The results indicate that decreasing stacking fault energy is an optimum method to improve the ductility without loss of strength.
基金Project (BK2010392) supported by the Natural Science Foundation of Jiangsu Province of ChinaProject (3212000502) supported by the Innovation Foundation of Southeast University,China
文摘The microstructure and mechanical properties of Mg94Zn2Y4 extruded alloy containing long-period stacking ordered structures were systematically investigated by SEM and TEM analyses. The results show that the 18R-LPSO structure and α-Mg phase are observed in cast Mg94Zn2Y4 alloy. After extrusion, the LPSO structures are delaminated and Mg-slices with width of 50-200 nm are generated. By ageing at 498 K for 36 h, the ageing peak is attained andβ′phase is precipitated. Due to this novel precipitation, the microhardness ofα-Mg matrix increases apparently from HV108.9 to HV129.7. While the microhardness for LPSO structure is stabilized at about HV145. TEM observations and SAED patterns indicate that the β′ phase has unique orientation relationships betweenα-Mg and LPSO structures, the direction in the close-packed planes ofβ′precipitates perpendicular to that ofα-Mg and LPSO structures. The ultimate tensile strength for the peak-aged alloy achieves 410.7 MPa and the significant strength originates from the coexistence ofβ′precipitates and 18R-LPSO structures.
文摘The performances of analog circuits depend greatly on the layout parasitics and mismatches.Novel techniques are proposed for modeling the distributed parasitic capacitance,parasitic parameter mismatch due to process gradient and the inner stack routing mismatch.Based on the proposed models,an optimal stack generation technique is developed to control the parasitics and mismatches,optimize the stack shape and ensure the generation of an Eulerian graph for a given CMOS analog module.An OPA circuit example is given to demonstrate that the circuit performances such as unit gain bandwidth and phase margin are enhanced by the proposed layout optimization method.
