The rapid advancement of artificial intelligence(AI)has significantly increased the computational load on data centers.AI-related computational activities consume considerable electricity and result in substantial car...The rapid advancement of artificial intelligence(AI)has significantly increased the computational load on data centers.AI-related computational activities consume considerable electricity and result in substantial carbon emissions.To mitigate these emissions,future data centers should be strategically planned and operated to fully utilize renewable energy resources while meeting growing computational demands.This paper aims to investigate how much carbon emission reduction can be achieved by using a carbonoriented demand response to guide the optimal planning and operation of data centers.A carbon-oriented data center planning model is proposed that considers the carbon-oriented demand response of the AI load.In the planning model,future operation simulations comprehensively coordinate the temporal‒spatial flexibility of computational loads and the quality of service(QoS).An empirical study based on the proposed models is conducted on real-world data from China.The results from the empirical analysis show that newly constructed data centers are recommended to be built in Gansu Province,Ningxia Hui Autonomous Region,Sichuan Province,Inner Mongolia Autonomous Region,and Qinghai Province,accounting for 57%of the total national increase in server capacity.33%of the computational load from Eastern China should be transferred to the West,which could reduce the overall load carbon emissions by 26%.展开更多
In order to explore the reduction pathways of zinc oxide in LiCl molten salt and the optimal process,experiments were conducted in an alumina crucible using metallic lithium as the reducing agent and lithium chloride ...In order to explore the reduction pathways of zinc oxide in LiCl molten salt and the optimal process,experiments were conducted in an alumina crucible using metallic lithium as the reducing agent and lithium chloride molten salt as the reaction medium at 923 K.The study assessed the effects of lithium thermochemical reduction and electrolytic reduction of ZnO.The volatilization behavior of metal oxides in molten salts,the equivalent of a reducing agent,reduction time,amount of molten salt,stirring time,and the method of reduction feed were investigated for their impacts on the reduction yield and product composition.X-ray powder diffraction(XRD)analysis of the products showed that lithium reduction of ZnO not only produced metallic Zn but also formed a LiZn alloy.Electrolytic reduction can be used to obtain the metallic Zn product by controlling the potential below-2.2 V(vs Ag/Ag^(+)).Moreover,sintered oxides and higher electrode potentials could enhance the efficiency of electrolysis.Under the optimal reaction conditions determined experimentally,the lithium reduction experiment achieved a yield of 77.2%after a 12-h test,and the electrolytic reduction reached a yield of 85.4%after a 6-h test.展开更多
In the past decade,financial institutions have invested significant efforts in the development of accurate analytical credit scoring models.The evidence suggests that even small improvements in the accuracy of existin...In the past decade,financial institutions have invested significant efforts in the development of accurate analytical credit scoring models.The evidence suggests that even small improvements in the accuracy of existing credit-scoring models may optimize profits while effectively managing risk exposure.Despite continuing efforts,the majority of existing credit scoring models still include some judgment-based assumptions that are sometimes supported by the significant findings of previous studies but are not validated using the institution’s internal data.We argue that current studies related to the development of credit scoring models have largely ignored recent developments in statistical methods for sufficient dimension reduction.To contribute to the field of financial innovation,this study proposes a Dimension Reduction Assisted Credit Scoring(DRA-CS)method via distance covariance-based sufficient dimension reduction(DCOV-SDR)in Majorization-Minimization(MM)algorithm.First,in the presence of a large number of variables,the DRA-CS method results in greater dimension reduction and better prediction accuracy than the other methods used for dimension reduction.Second,when the DRA-CS method is employed with logistic regression,it outperforms existing methods based on different variable selection techniques.This study argues that the DRA-CS method should be used by financial institutions as a financial innovation tool to analyze high-dimensional customer datasets and improve the accuracy of existing credit scoring methods.展开更多
BFOSC and YFOSC are the most frequently used instruments in the Xinglong 2.16 m telescope and Lijiang 2.4 m telescope,respectively.We developed a software package named“BYSpec”(BFOSC and YFOSC Spectra Reduction Pack...BFOSC and YFOSC are the most frequently used instruments in the Xinglong 2.16 m telescope and Lijiang 2.4 m telescope,respectively.We developed a software package named“BYSpec”(BFOSC and YFOSC Spectra Reduction Package)dedicated to automatically reducing the long-slit and echelle spectra obtained by these two instruments.The package supports bias and flat-fielding correction,order location,background subtraction,automatic wavelength calibration,and absolute flux calibration.The optimal extraction method maximizes the signal-to-noise ratio and removes most of the cosmic rays imprinted in the spectra.A comparison with the 1D spectra reduced with IRAF verifies the reliability of the results.This open-source software is publicly available to the community.展开更多
This paper reports the preparation of three di‑iron complexes containing a thiazole moiety.Esterification of complex[Fe_(2)(CO)_(6)(μ‑SCH_(2)CH(CH_(2)OH)S)](1)with 4‑methylthiazole‑5‑carboxylic acid gave the correspo...This paper reports the preparation of three di‑iron complexes containing a thiazole moiety.Esterification of complex[Fe_(2)(CO)_(6)(μ‑SCH_(2)CH(CH_(2)OH)S)](1)with 4‑methylthiazole‑5‑carboxylic acid gave the corresponding ester[Fe_(2)(CO)_(6)(μ‑tedt)](2),where tedt=SCH_(2)CH(CH_(2)OOC(5‑C_(3)HNSCH_(3)))S.Further reactions of complex 2 with tri(ptolyl)phosphine(tp)or tris(4‑fluorophenyl)phosphine(fp)gave the phosphine‑substituted derivatives[Fe_(2)(CO)_(5)(tp)(μ‑tedt)](3)and[Fe_(2)(CO)_(5)(fp)(μ‑tedt)](4).The structures of the newly prepared complexes were elucidated by elemental analysis,NMR,IR,and X‑ray photoelectron spectroscopy.Moreover,single‑crystal X‑ray diffraction analysis confirmed their molecular structures,showing that they contain a di‑iron core ligated by a bridged dithiolate bearing a thiazole moiety and terminal carbonyls.The electrochemical and electrocatalytic proton reduction were probed by cyclic voltammetry,revealing that three complexes can catalyze the reduction of protons to H_(2) under the electrochemical conditions.For comparison,complex 4 possessed the best efficiency with a turnover frequency of 23.5 s^(-1)at 10 mmol·L^(-1)HOAc concentration.In addition,the fungicidal activity of these complexes was also investigated in this study.CCDC:2477511,2;2477512,3;2477513,4.展开更多
Experts and officials shared their insights on poverty reduction cooperation and sustainable development during the 2025 International Seminar on Global Poverty Reduction Partnerships.
Accurately assessing the relationship between tree growth and climatic factors is of great importance in dendrochronology.This study evaluated the consistency between alternative climate datasets(including station and...Accurately assessing the relationship between tree growth and climatic factors is of great importance in dendrochronology.This study evaluated the consistency between alternative climate datasets(including station and gridded data)and actual climate data(fixed-point observations near the sampling sites),in northeastern China’s warm temperate zone and analyzed differences in their correlations with tree-ring width index.The results were:(1)Gridded temperature data,as well as precipitation and relative humidity data from the Huailai meteorological station,was more consistent with the actual climate data;in contrast,gridded soil moisture content data showed significant discrepancies.(2)Horizontal distance had a greater impact on the representativeness of actual climate conditions than vertical elevation differences.(3)Differences in consistency between alternative and actual climate data also affected their correlations with tree-ring width indices.In some growing season months,correlation coefficients,both in magnitude and sign,differed significantly from those based on actual data.The selection of different alternative climate datasets can lead to biased results in assessing forest responses to climate change,which is detrimental to the management of forest ecosystems in harsh environments.Therefore,the scientific and rational selection of alternative climate data is essential for dendroecological and climatological research.展开更多
Photoacoustic-computed tomography is a novel imaging technique that combines high absorption contrast and deep tissue penetration capability,enabling comprehensive three-dimensional imaging of biological targets.Howev...Photoacoustic-computed tomography is a novel imaging technique that combines high absorption contrast and deep tissue penetration capability,enabling comprehensive three-dimensional imaging of biological targets.However,the increasing demand for higher resolution and real-time imaging results in significant data volume,limiting data storage,transmission and processing efficiency of system.Therefore,there is an urgent need for an effective method to compress the raw data without compromising image quality.This paper presents a photoacoustic-computed tomography 3D data compression method and system based on Wavelet-Transformer.This method is based on the cooperative compression framework that integrates wavelet hard coding with deep learning-based soft decoding.It combines the multiscale analysis capability of wavelet transforms with the global feature modeling advantage of Transformers,achieving high-quality data compression and reconstruction.Experimental results using k-wave simulation suggest that the proposed compression system has advantages under extreme compression conditions,achieving a raw data compression ratio of up to 1:40.Furthermore,three-dimensional data compression experiment using in vivo mouse demonstrated that the maximum peak signal-to-noise ratio(PSNR)and structural similarity index(SSIM)values of reconstructed images reached 38.60 and 0.9583,effectively overcoming detail loss and artifacts introduced by raw data compression.All the results suggest that the proposed system can significantly reduce storage requirements and hardware cost,enhancing computational efficiency and image quality.These advantages support the development of photoacoustic-computed tomography toward higher efficiency,real-time performance and intelligent functionality.展开更多
Controllable synthesis of ultrathin metallene nanosheets and rational design of their spatial arrangement in favor of electrochemical catalysis are critical for their renewable energy applications.Here,a biomimetic de...Controllable synthesis of ultrathin metallene nanosheets and rational design of their spatial arrangement in favor of electrochemical catalysis are critical for their renewable energy applications.Here,a biomimetic design of“Trunk-Branch-Leaf”strategy is proposed to prepare the ultrathin edge-riched Zn-ene“leaves”with a thickness of~2.5 nm,adjacent Zn-ene cross-linked with each other,which are supported by copper nanoneedle“branches”on copper mesh“trunks,”named as Zn-ene/Cu-CM.The resulting superstructure enables the formation of an interconnected network and multiple channels,which can be used as an electrocatalytic CO_(2) reduction reaction(CO_(2)RR)electrode to allow a fast charge and mass transfer as well as a large electrolyte reservoir.By virtue of the distinctive structure,the obtained Zn-ene/Cu-CM electrode exhibits excellent selectivity and activity toward CO production with a maximum Faradaic efficiency of 91.3%and incredible partial current density up to 40 mA cm^(−2),outperforming most of the state-of-the-art Zn-based electrodes for CO_(2) reduction.The phenolphthalein color probe combined with in situ attenuated total reflection-infrared spectroscopy uncovered the formation of the localized pseudo-alkaline microenvironment at the interface of the Zn-ene/Cu-CM electrode.Theoretical calculations confirmed that the localized pH as the origin is responsible for the adsorption of CO_(2) at the interface and the generation of *COOH and *CO intermediates.This study offers valuable insights into developing efficient electrodes through synergistic regulation of reaction microenvironments and active sites,thereby facilitating the electrolysis of practical CO_(2) conversion.展开更多
Heteroatom-doped carbon is considered a promising alternative to commercial Pt/C as an efficient catalyst for the oxygen reduction reaction(ORR).This study presents the synthesis of iron-loaded,sulfur and nitrogen co-...Heteroatom-doped carbon is considered a promising alternative to commercial Pt/C as an efficient catalyst for the oxygen reduction reaction(ORR).This study presents the synthesis of iron-loaded,sulfur and nitrogen co-doped carbon(Fe/SNC)via in situ incorporation of 2-aminothiazole molecules into zeolitic imidazolate framework-8(ZIF-8)through coordination between metal ions and organic ligands.Sulfur and nitrogen doping in carbon supports effectively modulates the electronic structure of the catalyst,increases the Brunauer-Emmett-Teller surface area,and exposes more Fe-N_(x)active centers.Fe-loaded,S and N co-doped carbon with Fe/S molar ratio of 1:10(Fe/SNC-10)exhibits a half-wave potential of 0.902 V vs.RHE.After 5000 cycles of cyclic voltammetry,its half-wave potential decreases by only 20 mV vs.RHE,indicating excellent stability.Due to sulfur s lower electronegativity,the electronic structure of the Fe-N_(x)active center is modulated.Additionally,the larger atomic radius of sulfur introduces defects into the carbon support.As a result,Fe/SNC-10 demonstrates superior ORR activity and stability in alkaline solution compared with Fe-loaded N-doped carbon(Fe/NC).Furthermore,the zinc-air battery assembled with the Fe/SNC-10 catalyst shows enhanced performance relative to those assembled with Fe/NC and Pt/C catalysts.This work offers a novel design strategy for advanced energy storage and conversion applications.展开更多
The development of Pt-free catalysts for the oxygen reduction reaction(ORR)is a great issue for meeting the cost challenges of proton exchange membrane fuel cells(PEMFCs)in commercial applications.In this work,a serie...The development of Pt-free catalysts for the oxygen reduction reaction(ORR)is a great issue for meeting the cost challenges of proton exchange membrane fuel cells(PEMFCs)in commercial applications.In this work,a series of RuCo/C catalysts were synthesized by NaBH4 reduction method under the premise that the total metal mass percentage was 20%.X-ray diffraction(XRD)patterns and scanning electron microscopy(SEM)confirmed the formation of single-phase nanoparticles with an average size of 33 nm.Cyclic voltammograms(CV)and linear sweep voltammograms(LSV)tests indicated that RuCo(2:1)/C catalyst had the optimal ORR properties.Additionally,the RuCo(2:1)/C catalyst remarkably sustained 98.1% of its activity even after 3000 cycles,surpassing the performance of Pt/C(84.8%).Analysis of the elemental state of the catalyst surface after cycling using X-ray photoelectron spectroscopy(XPS)revealed that the Ru^(0) percentage of RuCo(2:1)/C decreased by 2.2%(from 66.3% to 64.1%),while the Pt^(0) percentage of Pt/C decreased by 7.1%(from 53.3% to 46.2%).It is suggested that the synergy between Ru and Co holds the potential to pave the way for future low-cost and highly stable ORR catalysts,offering significant promise in the context of PEMFCs.展开更多
Thermochemical sulfate reduction(TSR)is an important organic-inorganic reaction that occurs within sedimentary basins and alters the original chemical compositions and isotopic structures of hydrocarbons in natural ga...Thermochemical sulfate reduction(TSR)is an important organic-inorganic reaction that occurs within sedimentary basins and alters the original chemical compositions and isotopic structures of hydrocarbons in natural gases.We used the GC-Py-GC-IRMS method to study TSR and obtained a novel finding related to intramolecular carbon isotope fractionation in natural propane.The results show that theΔC-T(δ^(13)C_(central)-13 C_(terminal))andδ^(13)C_(central)values significantly increased to 44.7‰and 11.9‰,respectively,with increasing TSR alteration.In contrast,the 13 C_(terminal)values of propane remained largely unaltered by the TSR reaction.This difference in position-specific isotope fractionation can be attributed to the central carbon’s reactivity being higher than that of terminal carbon during TSR.In sum,the results indicate that theδ^(13)C_(terminal)values of propane can serve as robust indicators for source rock identification of natural gas altered by post-generation reactions such as TSR and anaerobic microbial oxidation.展开更多
Seawater zinc-air batteries are promising energy storage devices due to their high energy density and utilization of seawater electrolytes.However,their efficiency is hindered by the sluggish oxygen reduction reaction...Seawater zinc-air batteries are promising energy storage devices due to their high energy density and utilization of seawater electrolytes.However,their efficiency is hindered by the sluggish oxygen reduction reaction(ORR)and chlorideinduced degradation over conventional catalysts.In this study,we proposed a universal synthetic strategy to construct heteroatom axially coordinated Fe–N_(4) single-atom seawater catalyst materials(Cl–Fe–N_(4) and S–Fe–N_(4)).X-ray absorption spectroscopy confirmed their five-coordinated square pyramidal structure.Systematic evaluation of catalytic activities revealed that compared with S–Fe–N_(4),Cl–Fe–N_(4) exhibits smaller electrochemical active surface area and specific surface area,yet demonstrates higher limiting current density(5.8 mA cm^(−2)).The assembled zinc-air batteries using Cl–Fe–N_(4) showed superior power density(187.7 mW cm^(−2) at 245.1 mA cm^(−2)),indicating that Cl axial coordination more effectively enhances the intrinsic ORR activity.Moreover,Cl–Fe–N_(4) demonstrates stronger Cl−poisoning resistance in seawater environments.Chronoamperometry tests and zinc-air battery cycling performance evaluations confirmed its enhanced stability.Density functional theory calculations revealed that the introduction of heteroatoms in the axial direction regulates the electron center of Fe single atom,leading to more active reaction intermediates and increased electron density of Fe single sites,thereby enhancing the reduction in adsorbed intermediates and hence the overall ORR catalytic activity.展开更多
Missing data presents a crucial challenge in data analysis,especially in high-dimensional datasets,where missing data often leads to biased conclusions and degraded model performance.In this study,we present a novel a...Missing data presents a crucial challenge in data analysis,especially in high-dimensional datasets,where missing data often leads to biased conclusions and degraded model performance.In this study,we present a novel autoencoder-based imputation framework that integrates a composite loss function to enhance robustness and precision.The proposed loss combines(i)a guided,masked mean squared error focusing on missing entries;(ii)a noise-aware regularization term to improve resilience against data corruption;and(iii)a variance penalty to encourage expressive yet stable reconstructions.We evaluate the proposed model across four missingness mechanisms,such as Missing Completely at Random,Missing at Random,Missing Not at Random,and Missing Not at Random with quantile censorship,under systematically varied feature counts,sample sizes,and missingness ratios ranging from 5%to 60%.Four publicly available real-world datasets(Stroke Prediction,Pima Indians Diabetes,Cardiovascular Disease,and Framingham Heart Study)were used,and the obtained results show that our proposed model consistently outperforms baseline methods,including traditional and deep learning-based techniques.An ablation study reveals the additive value of each component in the loss function.Additionally,we assessed the downstream utility of imputed data through classification tasks,where datasets imputed by the proposed method yielded the highest receiver operating characteristic area under the curve scores across all scenarios.The model demonstrates strong scalability and robustness,improving performance with larger datasets and higher feature counts.These results underscore the capacity of the proposed method to produce not only numerically accurate but also semantically useful imputations,making it a promising solution for robust data recovery in clinical applications.展开更多
With the accelerating aging process of China’s population,the demand for community elderly care services has shown diversified and personalized characteristics.However,problems such as insufficient total care service...With the accelerating aging process of China’s population,the demand for community elderly care services has shown diversified and personalized characteristics.However,problems such as insufficient total care service resources,uneven distribution,and prominent supply-demand contradictions have seriously affected service quality.Big data technology,with core advantages including data collection,analysis and mining,and accurate prediction,provides a new solution for the allocation of community elderly care service resources.This paper systematically studies the application value of big data technology in the allocation of community elderly care service resources from three aspects:resource allocation efficiency,service accuracy,and management intelligence.Combined with practical needs,it proposes optimal allocation strategies such as building a big data analysis platform and accurately grasping the elderly’s care needs,striving to provide operable path references for the construction of community elderly care service systems,promoting the early realization of the elderly care service goal of“adequate support and proper care for the elderly”,and boosting the high-quality development of China’s elderly care service industry.展开更多
Converting CO_(2) into methanol(CH_(3)OH),a high-value-added liquid-phase product,through efficient and highly selective photocatalysis remains a significant challenge.Herein,we present a straightforward cation exchan...Converting CO_(2) into methanol(CH_(3)OH),a high-value-added liquid-phase product,through efficient and highly selective photocatalysis remains a significant challenge.Herein,we present a straightforward cation exchange strategy for the in-situ growth of BiVO_(4) on an InVO_(4) substrate to generate a Z-scheme heterojunction of InVO_(4)/BiVO_(4) .This in-situ partial transformation approach endows the InVO_(4)/BiVO_(4) heterojunction with a tightly connected interface,resulting in a significant improvement in charge separation efficiency between InVO_(4) and BiVO_(4).Moreover,the construction of the heterojunction reduces the formation energy barrier of the ^(*)COOH intermediate during the photoreduction of CO_(2) and increases the desorption energy barrier of the ^(*)CO intermediate,facilitating the deep reduction of ^(*)CO.Consequently,the InVO_(4)/BiVO_(4) heterojunction is capable of photocatalytic CO_(2) reduction to CH_(3)OH with high efficiency and selectivity.Under conditions where water serves as the electron source and a light intensity of 100 m W/cm^(2),the yield of CH_(3)OH reaches 130.5 μmol g^(-1)h^(-1) with a selectivity of 92 %,outperforming photocatalysts reported under similar conditions.展开更多
Exploring high-performance electrocatalysts for the nitrate reduction reaction(NO_(3)RR)is crucial for environmental nitrate removal and ammonia synthesis.Single-atom collaboration with cluster can provide sufficient ...Exploring high-performance electrocatalysts for the nitrate reduction reaction(NO_(3)RR)is crucial for environmental nitrate removal and ammonia synthesis.Single-atom collaboration with cluster can provide sufficient active sites for catalysts to promote NO_(3)RR,yet the unclear synergistic effect between the two hinders their rational design.Herein,a series of Ir_(3)clusters and metal single atoms co-embedded in graphitic carbon nitride(g-CN)catalysts(Ir_(3)M1)were constructed,and the synergistic effects of Ir_(3)clusters and M1 single atoms on the NO_(3)RR catalytic mechanism and activity were systematically explored using density functional theory(DFT)calculations combined with machine learning.Comprehensive evaluations of structural stability and catalytic activity demonstrate that the synergy between single atoms and clusters effectively balances the adsorption energies of key intermediates,yielding exceptional catalytic performance(the limiting potential of Ir_(3)Ti_(1)can reach−0.22 V).Machine learning models further clarify the synergistic mechanism,where the geometric configurations of clusters serve as critical features for modulating the catalytic activity of single-atom sites,whereas the electronic structures of single atoms directly govern the reactivity of cluster sites.This DFT-machine learning approach provides theoretical guidelines for catalyst design and a predictive framework for efficient NO_(3)RR electrocatalysts.展开更多
Multivariate anomaly detection plays a critical role in maintaining the stable operation of information systems.However,in existing research,multivariate data are often influenced by various factors during the data co...Multivariate anomaly detection plays a critical role in maintaining the stable operation of information systems.However,in existing research,multivariate data are often influenced by various factors during the data collection process,resulting in temporal misalignment or displacement.Due to these factors,the node representations carry substantial noise,which reduces the adaptability of the multivariate coupled network structure and subsequently degrades anomaly detection performance.Accordingly,this study proposes a novel multivariate anomaly detection model grounded in graph structure learning.Firstly,a recommendation strategy is employed to identify strongly coupled variable pairs,which are then used to construct a recommendation-driven multivariate coupling network.Secondly,a multi-channel graph encoding layer is used to dynamically optimize the structural properties of the multivariate coupling network,while a multi-head attention mechanism enhances the spatial characteristics of the multivariate data.Finally,unsupervised anomaly detection is conducted using a dynamic threshold selection algorithm.Experimental results demonstrate that effectively integrating the structural and spatial features of multivariate data significantly mitigates anomalies caused by temporal dependency misalignment.展开更多
As an important resource in data link,time slots should be strategically allocated to enhance transmission efficiency and resist eavesdropping,especially considering the tremendous increase in the number of nodes and ...As an important resource in data link,time slots should be strategically allocated to enhance transmission efficiency and resist eavesdropping,especially considering the tremendous increase in the number of nodes and diverse communication needs.It is crucial to design control sequences with robust randomness and conflict-freeness to properly address differentiated access control in data link.In this paper,we propose a hierarchical access control scheme based on control sequences to achieve high utilization of time slots and differentiated access control.A theoretical bound of the hierarchical control sequence set is derived to characterize the constraints on the parameters of the sequence set.Moreover,two classes of optimal hierarchical control sequence sets satisfying the theoretical bound are constructed,both of which enable the scheme to achieve maximum utilization of time slots.Compared with the fixed time slot allocation scheme,our scheme reduces the symbol error rate by up to 9%,which indicates a significant improvement in anti-interference and eavesdropping capabilities.展开更多
Modern intrusion detection systems(MIDS)face persistent challenges in coping with the rapid evolution of cyber threats,high-volume network traffic,and imbalanced datasets.Traditional models often lack the robustness a...Modern intrusion detection systems(MIDS)face persistent challenges in coping with the rapid evolution of cyber threats,high-volume network traffic,and imbalanced datasets.Traditional models often lack the robustness and explainability required to detect novel and sophisticated attacks effectively.This study introduces an advanced,explainable machine learning framework for multi-class IDS using the KDD99 and IDS datasets,which reflects real-world network behavior through a blend of normal and diverse attack classes.The methodology begins with sophisticated data preprocessing,incorporating both RobustScaler and QuantileTransformer to address outliers and skewed feature distributions,ensuring standardized and model-ready inputs.Critical dimensionality reduction is achieved via the Harris Hawks Optimization(HHO)algorithm—a nature-inspired metaheuristic modeled on hawks’hunting strategies.HHO efficiently identifies the most informative features by optimizing a fitness function based on classification performance.Following feature selection,the SMOTE is applied to the training data to resolve class imbalance by synthetically augmenting underrepresented attack types.The stacked architecture is then employed,combining the strengths of XGBoost,SVM,and RF as base learners.This layered approach improves prediction robustness and generalization by balancing bias and variance across diverse classifiers.The model was evaluated using standard classification metrics:precision,recall,F1-score,and overall accuracy.The best overall performance was recorded with an accuracy of 99.44%for UNSW-NB15,demonstrating the model’s effectiveness.After balancing,the model demonstrated a clear improvement in detecting the attacks.We tested the model on four datasets to show the effectiveness of the proposed approach and performed the ablation study to check the effect of each parameter.Also,the proposed model is computationaly efficient.To support transparency and trust in decision-making,explainable AI(XAI)techniques are incorporated that provides both global and local insight into feature contributions,and offers intuitive visualizations for individual predictions.This makes it suitable for practical deployment in cybersecurity environments that demand both precision and accountability.展开更多
基金supported by the Scientific&Technical Project of the State Grid(5700--202490228A--1--1-ZN).
文摘The rapid advancement of artificial intelligence(AI)has significantly increased the computational load on data centers.AI-related computational activities consume considerable electricity and result in substantial carbon emissions.To mitigate these emissions,future data centers should be strategically planned and operated to fully utilize renewable energy resources while meeting growing computational demands.This paper aims to investigate how much carbon emission reduction can be achieved by using a carbonoriented demand response to guide the optimal planning and operation of data centers.A carbon-oriented data center planning model is proposed that considers the carbon-oriented demand response of the AI load.In the planning model,future operation simulations comprehensively coordinate the temporal‒spatial flexibility of computational loads and the quality of service(QoS).An empirical study based on the proposed models is conducted on real-world data from China.The results from the empirical analysis show that newly constructed data centers are recommended to be built in Gansu Province,Ningxia Hui Autonomous Region,Sichuan Province,Inner Mongolia Autonomous Region,and Qinghai Province,accounting for 57%of the total national increase in server capacity.33%of the computational load from Eastern China should be transferred to the West,which could reduce the overall load carbon emissions by 26%.
文摘In order to explore the reduction pathways of zinc oxide in LiCl molten salt and the optimal process,experiments were conducted in an alumina crucible using metallic lithium as the reducing agent and lithium chloride molten salt as the reaction medium at 923 K.The study assessed the effects of lithium thermochemical reduction and electrolytic reduction of ZnO.The volatilization behavior of metal oxides in molten salts,the equivalent of a reducing agent,reduction time,amount of molten salt,stirring time,and the method of reduction feed were investigated for their impacts on the reduction yield and product composition.X-ray powder diffraction(XRD)analysis of the products showed that lithium reduction of ZnO not only produced metallic Zn but also formed a LiZn alloy.Electrolytic reduction can be used to obtain the metallic Zn product by controlling the potential below-2.2 V(vs Ag/Ag^(+)).Moreover,sintered oxides and higher electrode potentials could enhance the efficiency of electrolysis.Under the optimal reaction conditions determined experimentally,the lithium reduction experiment achieved a yield of 77.2%after a 12-h test,and the electrolytic reduction reached a yield of 85.4%after a 6-h test.
文摘In the past decade,financial institutions have invested significant efforts in the development of accurate analytical credit scoring models.The evidence suggests that even small improvements in the accuracy of existing credit-scoring models may optimize profits while effectively managing risk exposure.Despite continuing efforts,the majority of existing credit scoring models still include some judgment-based assumptions that are sometimes supported by the significant findings of previous studies but are not validated using the institution’s internal data.We argue that current studies related to the development of credit scoring models have largely ignored recent developments in statistical methods for sufficient dimension reduction.To contribute to the field of financial innovation,this study proposes a Dimension Reduction Assisted Credit Scoring(DRA-CS)method via distance covariance-based sufficient dimension reduction(DCOV-SDR)in Majorization-Minimization(MM)algorithm.First,in the presence of a large number of variables,the DRA-CS method results in greater dimension reduction and better prediction accuracy than the other methods used for dimension reduction.Second,when the DRA-CS method is employed with logistic regression,it outperforms existing methods based on different variable selection techniques.This study argues that the DRA-CS method should be used by financial institutions as a financial innovation tool to analyze high-dimensional customer datasets and improve the accuracy of existing credit scoring methods.
基金supported by the National Natural Science Foundation of China under grant No.U2031144partially supported by the Open Project Program of the Key Laboratory of Optical Astronomy,National Astronomical Observatories,Chinese Academy of Sciences+5 种基金supported by the National Key R&D Program of China with No.2021YFA1600404the National Natural Science Foundation of China(12173082)the Yunnan Fundamental Research Projects(grant 202201AT070069)the Top-notch Young Talents Program of Yunnan Provincethe Light of West China Program provided by the Chinese Academy of Sciencesthe International Centre of Supernovae,Yunnan Key Laboratory(No.202302AN360001)。
文摘BFOSC and YFOSC are the most frequently used instruments in the Xinglong 2.16 m telescope and Lijiang 2.4 m telescope,respectively.We developed a software package named“BYSpec”(BFOSC and YFOSC Spectra Reduction Package)dedicated to automatically reducing the long-slit and echelle spectra obtained by these two instruments.The package supports bias and flat-fielding correction,order location,background subtraction,automatic wavelength calibration,and absolute flux calibration.The optimal extraction method maximizes the signal-to-noise ratio and removes most of the cosmic rays imprinted in the spectra.A comparison with the 1D spectra reduced with IRAF verifies the reliability of the results.This open-source software is publicly available to the community.
文摘This paper reports the preparation of three di‑iron complexes containing a thiazole moiety.Esterification of complex[Fe_(2)(CO)_(6)(μ‑SCH_(2)CH(CH_(2)OH)S)](1)with 4‑methylthiazole‑5‑carboxylic acid gave the corresponding ester[Fe_(2)(CO)_(6)(μ‑tedt)](2),where tedt=SCH_(2)CH(CH_(2)OOC(5‑C_(3)HNSCH_(3)))S.Further reactions of complex 2 with tri(ptolyl)phosphine(tp)or tris(4‑fluorophenyl)phosphine(fp)gave the phosphine‑substituted derivatives[Fe_(2)(CO)_(5)(tp)(μ‑tedt)](3)and[Fe_(2)(CO)_(5)(fp)(μ‑tedt)](4).The structures of the newly prepared complexes were elucidated by elemental analysis,NMR,IR,and X‑ray photoelectron spectroscopy.Moreover,single‑crystal X‑ray diffraction analysis confirmed their molecular structures,showing that they contain a di‑iron core ligated by a bridged dithiolate bearing a thiazole moiety and terminal carbonyls.The electrochemical and electrocatalytic proton reduction were probed by cyclic voltammetry,revealing that three complexes can catalyze the reduction of protons to H_(2) under the electrochemical conditions.For comparison,complex 4 possessed the best efficiency with a turnover frequency of 23.5 s^(-1)at 10 mmol·L^(-1)HOAc concentration.In addition,the fungicidal activity of these complexes was also investigated in this study.CCDC:2477511,2;2477512,3;2477513,4.
文摘Experts and officials shared their insights on poverty reduction cooperation and sustainable development during the 2025 International Seminar on Global Poverty Reduction Partnerships.
基金supported by the International Partnership program of the Chinese Academy of Sciences(170GJHZ2023074GC)National Natural Science Foundation of China(42425706 and 42488201)+1 种基金National Key Research and Development Program of China(2024YFF0807902)Beijing Natural Science Foundation(8242041),and China Postdoctoral Science Foundation(2025M770353).
文摘Accurately assessing the relationship between tree growth and climatic factors is of great importance in dendrochronology.This study evaluated the consistency between alternative climate datasets(including station and gridded data)and actual climate data(fixed-point observations near the sampling sites),in northeastern China’s warm temperate zone and analyzed differences in their correlations with tree-ring width index.The results were:(1)Gridded temperature data,as well as precipitation and relative humidity data from the Huailai meteorological station,was more consistent with the actual climate data;in contrast,gridded soil moisture content data showed significant discrepancies.(2)Horizontal distance had a greater impact on the representativeness of actual climate conditions than vertical elevation differences.(3)Differences in consistency between alternative and actual climate data also affected their correlations with tree-ring width indices.In some growing season months,correlation coefficients,both in magnitude and sign,differed significantly from those based on actual data.The selection of different alternative climate datasets can lead to biased results in assessing forest responses to climate change,which is detrimental to the management of forest ecosystems in harsh environments.Therefore,the scientific and rational selection of alternative climate data is essential for dendroecological and climatological research.
基金supported by the National Key R&D Program of China[Grant No.2023YFF0713600]the National Natural Science Foundation of China[Grant No.62275062]+3 种基金Project of Shandong Innovation and Startup Community of High-end Medical Apparatus and Instruments[Grant No.2023-SGTTXM-002 and 2024-SGTTXM-005]the Shandong Province Technology Innovation Guidance Plan(Central Leading Local Science and Technology Development Fund)[Grant No.YDZX2023115]the Taishan Scholar Special Funding Project of Shandong Provincethe Shandong Laboratory of Advanced Biomaterials and Medical Devices in Weihai[Grant No.ZL202402].
文摘Photoacoustic-computed tomography is a novel imaging technique that combines high absorption contrast and deep tissue penetration capability,enabling comprehensive three-dimensional imaging of biological targets.However,the increasing demand for higher resolution and real-time imaging results in significant data volume,limiting data storage,transmission and processing efficiency of system.Therefore,there is an urgent need for an effective method to compress the raw data without compromising image quality.This paper presents a photoacoustic-computed tomography 3D data compression method and system based on Wavelet-Transformer.This method is based on the cooperative compression framework that integrates wavelet hard coding with deep learning-based soft decoding.It combines the multiscale analysis capability of wavelet transforms with the global feature modeling advantage of Transformers,achieving high-quality data compression and reconstruction.Experimental results using k-wave simulation suggest that the proposed compression system has advantages under extreme compression conditions,achieving a raw data compression ratio of up to 1:40.Furthermore,three-dimensional data compression experiment using in vivo mouse demonstrated that the maximum peak signal-to-noise ratio(PSNR)and structural similarity index(SSIM)values of reconstructed images reached 38.60 and 0.9583,effectively overcoming detail loss and artifacts introduced by raw data compression.All the results suggest that the proposed system can significantly reduce storage requirements and hardware cost,enhancing computational efficiency and image quality.These advantages support the development of photoacoustic-computed tomography toward higher efficiency,real-time performance and intelligent functionality.
基金supports of the National Natural Science Foundation of China(NSFC)(52021004,52394202)key project of the Joint Fund for Innovation and Development of Chongqing Natural Science Foundation(CSTB2022NSCQ-LZX0013)+1 种基金the National Natural Science Foundation of China(NSFC)(52301232,and 52476056)the Natural Science Foundation of Chongqing Province(2024NSCQ-MSX1109).
文摘Controllable synthesis of ultrathin metallene nanosheets and rational design of their spatial arrangement in favor of electrochemical catalysis are critical for their renewable energy applications.Here,a biomimetic design of“Trunk-Branch-Leaf”strategy is proposed to prepare the ultrathin edge-riched Zn-ene“leaves”with a thickness of~2.5 nm,adjacent Zn-ene cross-linked with each other,which are supported by copper nanoneedle“branches”on copper mesh“trunks,”named as Zn-ene/Cu-CM.The resulting superstructure enables the formation of an interconnected network and multiple channels,which can be used as an electrocatalytic CO_(2) reduction reaction(CO_(2)RR)electrode to allow a fast charge and mass transfer as well as a large electrolyte reservoir.By virtue of the distinctive structure,the obtained Zn-ene/Cu-CM electrode exhibits excellent selectivity and activity toward CO production with a maximum Faradaic efficiency of 91.3%and incredible partial current density up to 40 mA cm^(−2),outperforming most of the state-of-the-art Zn-based electrodes for CO_(2) reduction.The phenolphthalein color probe combined with in situ attenuated total reflection-infrared spectroscopy uncovered the formation of the localized pseudo-alkaline microenvironment at the interface of the Zn-ene/Cu-CM electrode.Theoretical calculations confirmed that the localized pH as the origin is responsible for the adsorption of CO_(2) at the interface and the generation of *COOH and *CO intermediates.This study offers valuable insights into developing efficient electrodes through synergistic regulation of reaction microenvironments and active sites,thereby facilitating the electrolysis of practical CO_(2) conversion.
基金financial support of the National Natural Science Foundation of China(No.52472271)the National Key Research and Development Program of China(No.2023YFE0115800)。
文摘Heteroatom-doped carbon is considered a promising alternative to commercial Pt/C as an efficient catalyst for the oxygen reduction reaction(ORR).This study presents the synthesis of iron-loaded,sulfur and nitrogen co-doped carbon(Fe/SNC)via in situ incorporation of 2-aminothiazole molecules into zeolitic imidazolate framework-8(ZIF-8)through coordination between metal ions and organic ligands.Sulfur and nitrogen doping in carbon supports effectively modulates the electronic structure of the catalyst,increases the Brunauer-Emmett-Teller surface area,and exposes more Fe-N_(x)active centers.Fe-loaded,S and N co-doped carbon with Fe/S molar ratio of 1:10(Fe/SNC-10)exhibits a half-wave potential of 0.902 V vs.RHE.After 5000 cycles of cyclic voltammetry,its half-wave potential decreases by only 20 mV vs.RHE,indicating excellent stability.Due to sulfur s lower electronegativity,the electronic structure of the Fe-N_(x)active center is modulated.Additionally,the larger atomic radius of sulfur introduces defects into the carbon support.As a result,Fe/SNC-10 demonstrates superior ORR activity and stability in alkaline solution compared with Fe-loaded N-doped carbon(Fe/NC).Furthermore,the zinc-air battery assembled with the Fe/SNC-10 catalyst shows enhanced performance relative to those assembled with Fe/NC and Pt/C catalysts.This work offers a novel design strategy for advanced energy storage and conversion applications.
基金Funded by the 111 Project(No.B17034)Open Project of Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle(No.ZDSYS202212)+1 种基金Innovative Research Team Development Program of Ministry of Education of China(No.IRT_17R83)the Science and Technology Project of China Southern Power Grid Co.,Ltd.(No.GDKJXM20222546)。
文摘The development of Pt-free catalysts for the oxygen reduction reaction(ORR)is a great issue for meeting the cost challenges of proton exchange membrane fuel cells(PEMFCs)in commercial applications.In this work,a series of RuCo/C catalysts were synthesized by NaBH4 reduction method under the premise that the total metal mass percentage was 20%.X-ray diffraction(XRD)patterns and scanning electron microscopy(SEM)confirmed the formation of single-phase nanoparticles with an average size of 33 nm.Cyclic voltammograms(CV)and linear sweep voltammograms(LSV)tests indicated that RuCo(2:1)/C catalyst had the optimal ORR properties.Additionally,the RuCo(2:1)/C catalyst remarkably sustained 98.1% of its activity even after 3000 cycles,surpassing the performance of Pt/C(84.8%).Analysis of the elemental state of the catalyst surface after cycling using X-ray photoelectron spectroscopy(XPS)revealed that the Ru^(0) percentage of RuCo(2:1)/C decreased by 2.2%(from 66.3% to 64.1%),while the Pt^(0) percentage of Pt/C decreased by 7.1%(from 53.3% to 46.2%).It is suggested that the synergy between Ru and Co holds the potential to pave the way for future low-cost and highly stable ORR catalysts,offering significant promise in the context of PEMFCs.
基金supported by the Helium Enrichment and Detection in Natural Gas Reservoirs Related to Oil and Gas Fields project(Grant No.2025ZD1010500)the Deep Earth Probe and Mineral Resources Exploration―National Science and Technology Major Project.-。
文摘Thermochemical sulfate reduction(TSR)is an important organic-inorganic reaction that occurs within sedimentary basins and alters the original chemical compositions and isotopic structures of hydrocarbons in natural gases.We used the GC-Py-GC-IRMS method to study TSR and obtained a novel finding related to intramolecular carbon isotope fractionation in natural propane.The results show that theΔC-T(δ^(13)C_(central)-13 C_(terminal))andδ^(13)C_(central)values significantly increased to 44.7‰and 11.9‰,respectively,with increasing TSR alteration.In contrast,the 13 C_(terminal)values of propane remained largely unaltered by the TSR reaction.This difference in position-specific isotope fractionation can be attributed to the central carbon’s reactivity being higher than that of terminal carbon during TSR.In sum,the results indicate that theδ^(13)C_(terminal)values of propane can serve as robust indicators for source rock identification of natural gas altered by post-generation reactions such as TSR and anaerobic microbial oxidation.
基金funded by the Innovative Research Group Project of the National Natural Science Foundation of China(52121004)the Research Development Fund(No.RDF-21-02-060)by Xi’an Jiaotong-Liverpool University+1 种基金support received from the Suzhou Industrial Park High Quality Innovation Platform of Functional Molecular Materials and Devices(YZCXPT2023105)the XJTLU Advanced Materials Research Center(AMRC).
文摘Seawater zinc-air batteries are promising energy storage devices due to their high energy density and utilization of seawater electrolytes.However,their efficiency is hindered by the sluggish oxygen reduction reaction(ORR)and chlorideinduced degradation over conventional catalysts.In this study,we proposed a universal synthetic strategy to construct heteroatom axially coordinated Fe–N_(4) single-atom seawater catalyst materials(Cl–Fe–N_(4) and S–Fe–N_(4)).X-ray absorption spectroscopy confirmed their five-coordinated square pyramidal structure.Systematic evaluation of catalytic activities revealed that compared with S–Fe–N_(4),Cl–Fe–N_(4) exhibits smaller electrochemical active surface area and specific surface area,yet demonstrates higher limiting current density(5.8 mA cm^(−2)).The assembled zinc-air batteries using Cl–Fe–N_(4) showed superior power density(187.7 mW cm^(−2) at 245.1 mA cm^(−2)),indicating that Cl axial coordination more effectively enhances the intrinsic ORR activity.Moreover,Cl–Fe–N_(4) demonstrates stronger Cl−poisoning resistance in seawater environments.Chronoamperometry tests and zinc-air battery cycling performance evaluations confirmed its enhanced stability.Density functional theory calculations revealed that the introduction of heteroatoms in the axial direction regulates the electron center of Fe single atom,leading to more active reaction intermediates and increased electron density of Fe single sites,thereby enhancing the reduction in adsorbed intermediates and hence the overall ORR catalytic activity.
文摘Missing data presents a crucial challenge in data analysis,especially in high-dimensional datasets,where missing data often leads to biased conclusions and degraded model performance.In this study,we present a novel autoencoder-based imputation framework that integrates a composite loss function to enhance robustness and precision.The proposed loss combines(i)a guided,masked mean squared error focusing on missing entries;(ii)a noise-aware regularization term to improve resilience against data corruption;and(iii)a variance penalty to encourage expressive yet stable reconstructions.We evaluate the proposed model across four missingness mechanisms,such as Missing Completely at Random,Missing at Random,Missing Not at Random,and Missing Not at Random with quantile censorship,under systematically varied feature counts,sample sizes,and missingness ratios ranging from 5%to 60%.Four publicly available real-world datasets(Stroke Prediction,Pima Indians Diabetes,Cardiovascular Disease,and Framingham Heart Study)were used,and the obtained results show that our proposed model consistently outperforms baseline methods,including traditional and deep learning-based techniques.An ablation study reveals the additive value of each component in the loss function.Additionally,we assessed the downstream utility of imputed data through classification tasks,where datasets imputed by the proposed method yielded the highest receiver operating characteristic area under the curve scores across all scenarios.The model demonstrates strong scalability and robustness,improving performance with larger datasets and higher feature counts.These results underscore the capacity of the proposed method to produce not only numerically accurate but also semantically useful imputations,making it a promising solution for robust data recovery in clinical applications.
文摘With the accelerating aging process of China’s population,the demand for community elderly care services has shown diversified and personalized characteristics.However,problems such as insufficient total care service resources,uneven distribution,and prominent supply-demand contradictions have seriously affected service quality.Big data technology,with core advantages including data collection,analysis and mining,and accurate prediction,provides a new solution for the allocation of community elderly care service resources.This paper systematically studies the application value of big data technology in the allocation of community elderly care service resources from three aspects:resource allocation efficiency,service accuracy,and management intelligence.Combined with practical needs,it proposes optimal allocation strategies such as building a big data analysis platform and accurately grasping the elderly’s care needs,striving to provide operable path references for the construction of community elderly care service systems,promoting the early realization of the elderly care service goal of“adequate support and proper care for the elderly”,and boosting the high-quality development of China’s elderly care service industry.
基金financially supported the National Key R&D Program of China (No.2022YFA1502902)the National Natural Science Foundation of China (NSFC,Nos.22475152 and U21A20286)the 111 Project of China (No.D17003)。
文摘Converting CO_(2) into methanol(CH_(3)OH),a high-value-added liquid-phase product,through efficient and highly selective photocatalysis remains a significant challenge.Herein,we present a straightforward cation exchange strategy for the in-situ growth of BiVO_(4) on an InVO_(4) substrate to generate a Z-scheme heterojunction of InVO_(4)/BiVO_(4) .This in-situ partial transformation approach endows the InVO_(4)/BiVO_(4) heterojunction with a tightly connected interface,resulting in a significant improvement in charge separation efficiency between InVO_(4) and BiVO_(4).Moreover,the construction of the heterojunction reduces the formation energy barrier of the ^(*)COOH intermediate during the photoreduction of CO_(2) and increases the desorption energy barrier of the ^(*)CO intermediate,facilitating the deep reduction of ^(*)CO.Consequently,the InVO_(4)/BiVO_(4) heterojunction is capable of photocatalytic CO_(2) reduction to CH_(3)OH with high efficiency and selectivity.Under conditions where water serves as the electron source and a light intensity of 100 m W/cm^(2),the yield of CH_(3)OH reaches 130.5 μmol g^(-1)h^(-1) with a selectivity of 92 %,outperforming photocatalysts reported under similar conditions.
基金the financial support from the Shandong Province colleges and universities youth innovation technology plan innovation team project(2022KJ285)the Natural Science Foundation of Shandong Province(ZR2022QE076)+1 种基金the National Natural Science Foundation of China(52202092)the Science and Technology Support Plan for Youth Innovation of Colleges and Universities of Shandong Province of China(2023KJ104).
文摘Exploring high-performance electrocatalysts for the nitrate reduction reaction(NO_(3)RR)is crucial for environmental nitrate removal and ammonia synthesis.Single-atom collaboration with cluster can provide sufficient active sites for catalysts to promote NO_(3)RR,yet the unclear synergistic effect between the two hinders their rational design.Herein,a series of Ir_(3)clusters and metal single atoms co-embedded in graphitic carbon nitride(g-CN)catalysts(Ir_(3)M1)were constructed,and the synergistic effects of Ir_(3)clusters and M1 single atoms on the NO_(3)RR catalytic mechanism and activity were systematically explored using density functional theory(DFT)calculations combined with machine learning.Comprehensive evaluations of structural stability and catalytic activity demonstrate that the synergy between single atoms and clusters effectively balances the adsorption energies of key intermediates,yielding exceptional catalytic performance(the limiting potential of Ir_(3)Ti_(1)can reach−0.22 V).Machine learning models further clarify the synergistic mechanism,where the geometric configurations of clusters serve as critical features for modulating the catalytic activity of single-atom sites,whereas the electronic structures of single atoms directly govern the reactivity of cluster sites.This DFT-machine learning approach provides theoretical guidelines for catalyst design and a predictive framework for efficient NO_(3)RR electrocatalysts.
基金supported by Natural Science Foundation of Qinghai Province(2025-ZJ-994M)Scientific Research Innovation Capability Support Project for Young Faculty(SRICSPYF-BS2025007)National Natural Science Foundation of China(62566050).
文摘Multivariate anomaly detection plays a critical role in maintaining the stable operation of information systems.However,in existing research,multivariate data are often influenced by various factors during the data collection process,resulting in temporal misalignment or displacement.Due to these factors,the node representations carry substantial noise,which reduces the adaptability of the multivariate coupled network structure and subsequently degrades anomaly detection performance.Accordingly,this study proposes a novel multivariate anomaly detection model grounded in graph structure learning.Firstly,a recommendation strategy is employed to identify strongly coupled variable pairs,which are then used to construct a recommendation-driven multivariate coupling network.Secondly,a multi-channel graph encoding layer is used to dynamically optimize the structural properties of the multivariate coupling network,while a multi-head attention mechanism enhances the spatial characteristics of the multivariate data.Finally,unsupervised anomaly detection is conducted using a dynamic threshold selection algorithm.Experimental results demonstrate that effectively integrating the structural and spatial features of multivariate data significantly mitigates anomalies caused by temporal dependency misalignment.
基金supported by the National Science Foundation of China(No.62171387)the Science and Technology Program of Sichuan Province(No.2024NSFSC0468)the China Postdoctoral Science Foundation(No.2019M663475).
文摘As an important resource in data link,time slots should be strategically allocated to enhance transmission efficiency and resist eavesdropping,especially considering the tremendous increase in the number of nodes and diverse communication needs.It is crucial to design control sequences with robust randomness and conflict-freeness to properly address differentiated access control in data link.In this paper,we propose a hierarchical access control scheme based on control sequences to achieve high utilization of time slots and differentiated access control.A theoretical bound of the hierarchical control sequence set is derived to characterize the constraints on the parameters of the sequence set.Moreover,two classes of optimal hierarchical control sequence sets satisfying the theoretical bound are constructed,both of which enable the scheme to achieve maximum utilization of time slots.Compared with the fixed time slot allocation scheme,our scheme reduces the symbol error rate by up to 9%,which indicates a significant improvement in anti-interference and eavesdropping capabilities.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R104)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Modern intrusion detection systems(MIDS)face persistent challenges in coping with the rapid evolution of cyber threats,high-volume network traffic,and imbalanced datasets.Traditional models often lack the robustness and explainability required to detect novel and sophisticated attacks effectively.This study introduces an advanced,explainable machine learning framework for multi-class IDS using the KDD99 and IDS datasets,which reflects real-world network behavior through a blend of normal and diverse attack classes.The methodology begins with sophisticated data preprocessing,incorporating both RobustScaler and QuantileTransformer to address outliers and skewed feature distributions,ensuring standardized and model-ready inputs.Critical dimensionality reduction is achieved via the Harris Hawks Optimization(HHO)algorithm—a nature-inspired metaheuristic modeled on hawks’hunting strategies.HHO efficiently identifies the most informative features by optimizing a fitness function based on classification performance.Following feature selection,the SMOTE is applied to the training data to resolve class imbalance by synthetically augmenting underrepresented attack types.The stacked architecture is then employed,combining the strengths of XGBoost,SVM,and RF as base learners.This layered approach improves prediction robustness and generalization by balancing bias and variance across diverse classifiers.The model was evaluated using standard classification metrics:precision,recall,F1-score,and overall accuracy.The best overall performance was recorded with an accuracy of 99.44%for UNSW-NB15,demonstrating the model’s effectiveness.After balancing,the model demonstrated a clear improvement in detecting the attacks.We tested the model on four datasets to show the effectiveness of the proposed approach and performed the ablation study to check the effect of each parameter.Also,the proposed model is computationaly efficient.To support transparency and trust in decision-making,explainable AI(XAI)techniques are incorporated that provides both global and local insight into feature contributions,and offers intuitive visualizations for individual predictions.This makes it suitable for practical deployment in cybersecurity environments that demand both precision and accountability.
