This paper introduces a novel numerical method based on an energy-minimizing normalized residual network(EMNorm Res Net)to compute the ground-state solution of Bose-Einstein condensates at zero or low temperatures.Sta...This paper introduces a novel numerical method based on an energy-minimizing normalized residual network(EMNorm Res Net)to compute the ground-state solution of Bose-Einstein condensates at zero or low temperatures.Starting from the three-dimensional Gross-Pitaevskii equation(GPE),we reduce it to the 1D and 2D GPEs because of the radial symmetry and cylindrical symmetry.The ground-state solution is formulated by minimizing the energy functional under constraints,which is directly solved using the EM-Norm Res Net approach.The paper provides detailed solutions for the ground states in 1D,2D(with radial symmetry),and 3D(with cylindrical symmetry).We use the Thomas-Fermi approximation as the target function to pre-train the neural network.Then,the formal network is trained using the energy minimization method.In contrast to traditional numerical methods,our neural network approach introduces two key innovations:(i)a novel normalization technique designed for high-dimensional systems within an energy-based loss function;(ii)improved training efficiency and model robustness by incorporating gradient stabilization techniques into residual networks.Extensive numerical experiments validate the method's accuracy across different spatial dimensions.展开更多
The emergence of different computing methods such as cloud-,fog-,and edge-based Internet of Things(IoT)systems has provided the opportunity to develop intelligent systems for disease detection.Compared to other machin...The emergence of different computing methods such as cloud-,fog-,and edge-based Internet of Things(IoT)systems has provided the opportunity to develop intelligent systems for disease detection.Compared to other machine learning models,deep learning models have gained more attention from the research community,as they have shown better results with a large volume of data compared to shallow learning.However,no comprehensive survey has been conducted on integrated IoT-and computing-based systems that deploy deep learning for disease detection.This study evaluated different machine learning and deep learning algorithms and their hybrid and optimized algorithms for IoT-based disease detection,using the most recent papers on IoT-based disease detection systems that include computing approaches,such as cloud,edge,and fog.Their analysis focused on an IoT deep learning architecture suitable for disease detection.It also recognizes the different factors that require the attention of researchers to develop better IoT disease detection systems.This study can be helpful to researchers interested in developing better IoT-based disease detection and prediction systems based on deep learning using hybrid algorithms.展开更多
The functional properties of glasses are governed by their formation history and the complex relaxation processes they undergo.However,under extreme conditions,glass behaviors are still elusive.In this study,we employ...The functional properties of glasses are governed by their formation history and the complex relaxation processes they undergo.However,under extreme conditions,glass behaviors are still elusive.In this study,we employ simulations with varied protocols to evaluate the effectiveness of different descriptors in predicting mechanical properties across both low-and high-pressure regimes.Our findings demonstrate that conventional structural and configurational descriptors fail to correlate with the mechanical response following pressure release,whereas the activation energy descriptor exhibits robust linearity with shear modulus after correcting for pressure effects.Notably,the soft mode parameter emerges as an ideal and computationally efficient alternative for capturing this mechanical behavior.These findings provide critical insights into the influence of pressure on glassy properties,integrating the distinct features of compressed glasses into a unified theoretical framework.展开更多
Adversarial Reinforcement Learning(ARL)models for intelligent devices and Network Intrusion Detection Systems(NIDS)improve systemresilience against sophisticated cyber-attacks.As a core component of ARL,Adversarial Tr...Adversarial Reinforcement Learning(ARL)models for intelligent devices and Network Intrusion Detection Systems(NIDS)improve systemresilience against sophisticated cyber-attacks.As a core component of ARL,Adversarial Training(AT)enables NIDS agents to discover and prevent newattack paths by exposing them to competing examples,thereby increasing detection accuracy,reducing False Positives(FPs),and enhancing network security.To develop robust decision-making capabilities for real-world network disruptions and hostile activity,NIDS agents are trained in adversarial scenarios to monitor the current state and notify management of any abnormal or malicious activity.The accuracy and timeliness of the IDS were crucial to the network’s availability and reliability at this time.This paper analyzes ARL applications in NIDS,revealing State-of-The-Art(SoTA)methodology,issues,and future research prospects.This includes Reinforcement Machine Learning(RML)-based NIDS,which enables an agent to interact with the environment to achieve a goal,andDeep Reinforcement Learning(DRL)-based NIDS,which can solve complex decision-making problems.Additionally,this survey study addresses cybersecurity adversarial circumstances and their importance for ARL and NIDS.Architectural design,RL algorithms,feature representation,and training methodologies are examined in the ARL-NIDS study.This comprehensive study evaluates ARL for intelligent NIDS research,benefiting cybersecurity researchers,practitioners,and policymakers.The report promotes cybersecurity defense research and innovation.展开更多
Tropospheric zenith wet delay(ZWD)plays a vital role in the analysis of space geodetic observations.In recent years,machine learning methods have been increasingly applied to improve the accuracy of ZWD calculations.H...Tropospheric zenith wet delay(ZWD)plays a vital role in the analysis of space geodetic observations.In recent years,machine learning methods have been increasingly applied to improve the accuracy of ZWD calculations.However,a single machine learning model has limited generalization capabilities.To address these limitations,this study introduces a novel machine learning fusion(MLF)algorithm with stronger generalization capabilities to enhance ZWD modeling and prediction accuracy.The MLF algorithm utilizes a two-layer structure integrating extra trees(ET),backpropagation neural network(BPNN),and linear regression models.By comparing the root mean square error(RMSE)of these models,we found that both ET-based and MLF-based models outperform RF-based and BPNN-based models in terms of internal and external accuracy,across both surface meteorological data-based and blind models.The improvement in exte rnal accuracy is particularly significant in the blind models.Our re sults show that the MLF(with an RMSE of 3.93 cm)and ET(3.99 cm)models outperform the traditional GPT3model(4.07 cm),while the RF(4.21 cm)and BPNN(4.14 cm)have worse external accuracies than the GPT3 model.It is worth noting that the BPNN suffered from overfitting during external accuracy tests,which was avoided by the MLF.In summary,regardless of the availability of surface meteorological data,the MLF-based empirical models demonstrate superior internal and external accuracy compared to the other tested models in this study.展开更多
Convex feasibility problems are widely used in image reconstruction, sparse signal recovery, and other areas. This paper is devoted to considering a class of convex feasibility problem arising from sparse signal recov...Convex feasibility problems are widely used in image reconstruction, sparse signal recovery, and other areas. This paper is devoted to considering a class of convex feasibility problem arising from sparse signal recovery. We first derive the projection formulas for a vector onto the feasible sets. The centralized circumcentered-reflection method is designed to solve the convex feasibility problem. Some numerical experiments demonstrate the feasibility and effectiveness of the proposed algorithm, showing superior performance compared to conventional alternating projection methods.展开更多
In this paper,we first obtain the density of compactly supported bounded functions in anisotropic infinite dimensional Banach space-valued Musielak-Orlicz spaces.Then,we present the sufficient condition for the space ...In this paper,we first obtain the density of compactly supported bounded functions in anisotropic infinite dimensional Banach space-valued Musielak-Orlicz spaces.Then,we present the sufficient condition for the space of compactly supported smooth functions to be dense in anisotropic infinite dimensional Banach space-valued Musielak-Orlicz spaces.Moreover,the modular density is also given.展开更多
We report the development of the[Pt_(0.75)Ti_(0.25)/Co-Ni multilayer/Ta]_n superlattice with strong spin-orbit torque,large perpendicular magnetic anisotropy,and remarkably low switching current density.We demonstrate...We report the development of the[Pt_(0.75)Ti_(0.25)/Co-Ni multilayer/Ta]_n superlattice with strong spin-orbit torque,large perpendicular magnetic anisotropy,and remarkably low switching current density.We demonstrate that the efficiency of the spin-orbit torque increases nearly linearly with the repetition number n,which is in excellent agreement with the spin Hall effect of the Pt_(0.75)Ti_(0.25)being essentially the only source of the observed spin-orbit torque.The perpendicular magnetic anisotropy field is also substantially enhanced by more than a factor of 2 as n increases from 1 to6.The[Pt_(0.75)Ti_(0.25)/Co-Ni multilayers/Ta]_n superlattice additionally exhibits deterministic,low-current-density magnetization switching despite the very large total layer thicknesses.The unique combination of strong spin-orbit torque,robust perpendicular magnetic anisotropy,low-current-density switching,and excellent high thermal stability makes the[Pt_(0.75)Ti_(0.25)/Co-Ni multilayer/Ta]_n superlattice a highly compelling material candidate for ultrafast,energy-efficient,and long-data-retention spintronic technologies.展开更多
Zero-click attacks represent an advanced cybersecurity threat,capable of compromising devices without user interaction.High-profile examples such as Pegasus,Simjacker,Bluebugging,and Bluesnarfing exploit hidden vulner...Zero-click attacks represent an advanced cybersecurity threat,capable of compromising devices without user interaction.High-profile examples such as Pegasus,Simjacker,Bluebugging,and Bluesnarfing exploit hidden vulnerabilities in software and communication protocols to silently gain access,exfiltrate data,and enable long-term surveillance.Their stealth and ability to evade traditional defenses make detection and mitigation highly challenging.This paper addresses these threats by systematically mapping the tactics and techniques of zero-click attacks using the MITRE ATT&CK framework,a widely adopted standard for modeling adversarial behavior.Through this mapping,we categorize real-world attack vectors and better understand how such attacks operate across the cyber-kill chain.To support threat detection efforts,we propose an Active Learning-based method to efficiently label the Pegasus spyware dataset in alignment with the MITRE ATT&CK framework.This approach reduces the effort of manually annotating data while improving the quality of the labeled data,which is essential to train robust cybersecurity models.In addition,our analysis highlights the structured execution paths of zero-click attacks and reveals gaps in current defense strategies.The findings emphasize the importance of forward-looking strategies such as continuous surveillance,dynamic threat profiling,and security education.By bridging zero-click attack analysis with the MITRE ATT&CK framework and leveraging machine learning for dataset annotation,this work provides a foundation for more accurate threat detection and the development of more resilient and structured cybersecurity frameworks.展开更多
AdshtT Marine stratocumulus clouds profoundly affect Earth's energy budget by reflecting solar radiation over extensive oceanic areas.Yet,after using a large-eddy simulation(LES)and a Lagrangian microphysics schem...AdshtT Marine stratocumulus clouds profoundly affect Earth's energy budget by reflecting solar radiation over extensive oceanic areas.Yet,after using a large-eddy simulation(LES)and a Lagrangian microphysics scheme(Super-Droplet Method,SDM)for entrainment-mixing studies,uncertainty remains in the grid resolution and super-droplet number concentration(SDNC)required for accurate homogeneity capture.This study analyzes the homogeneous mixing degree(HMD)and the Damkohler number(Da)in stratocumulus using an LES with SDM,from microphysical and dynamical perspectives,respectively.Results show that HMD and Da both display a top-to-base gradient,with more intense inhomogeneity near the cloud top and relatively homogeneous conditions toward the base,although the upper region is more complex.Even at a fine horizontal resolution of 12.5 m and vertical resolution of 2.5 m,HMD remains sensitive and does not converge,whereas Da converges at coarser grid spacings(up to horizontal and vertical spacings of 25 m and 10 m,respectively)in the mid-cloud region.Similarly,HMD requires an SDNC well above 128 per cell for near-complete convergence,while Da converges once SDNC exceeds about 16 per cell.This difference arises because HMD depends on microphysical details,thereby demanding a high SDNC to capture local droplet inhomogeneities,whereas Da reflects turbulence-evaporation timescales that converge more readily once extreme droplet gradients are resolved.We further find that HMD and Da exhibit a significant negative correlation,with stronger anti-correlations emerging under finer spatial resolutions,reinforcing their complementary roles in diagnosing mixing regimes.Overall,these findings provide guidelines for selecting numerical configurations in entrainment-mixing simulations,ensuring that both turbulence-driven and microphysical processes are adequately resolved,.展开更多
Wave energy is a promising form of marine renewable energy that offers a sustainable pathway for electricity generation in coastal regions.Despite Malaysia’s extensive coastline,the exploration of wave energy in Sara...Wave energy is a promising form of marine renewable energy that offers a sustainable pathway for electricity generation in coastal regions.Despite Malaysia’s extensive coastline,the exploration of wave energy in Sarawak remains limited due to economic,technical,and environmental challenges that hinder its implementation.Compared to other renewable energy sources,wave energy is underutilized largely because of cost uncertainties and the lack of local performance data.This research aims to identify themost suitable coastal zone in Sarawak that achieves an optimal balance between energy potential,cost-effectiveness,and environmental impact,particularly in relation to infrastructure and regional development.The findings indicate that wave energy generation in Sarawak is technically feasible based on MOGA analysis.Among the studied sites,Bintulu emerged as the most balanced option,with a levelized cost of electricity(LCOE)of 0.778–0.864 USD/kWh and a CO_(2) emission factor as low as 0.019–0.020 CO_(2)/k Wh.Miri,while producing lower emissions than Sematan,recorded a higher LCOE of 1.045 USD/kWh with moderate emissions at 0.029 CO_(2)/kWh.Sematan,characterized by weaker wave conditions and higher installation penalties,resulted in the least favorable outcome,with an LCOE of 3.735 USD/kWh.Bintulu’s strategic location reduces CAPEX requirements,making it the most suitable site for large-scale wave energy deployment in Sarawak.展开更多
Self-trapped excitons(STEs),known for their unique radiative properties,have been harnessed in diverse photonic devices;however,their comprehensive understanding and manipulation remain elusive.In this study,we presen...Self-trapped excitons(STEs),known for their unique radiative properties,have been harnessed in diverse photonic devices;however,their comprehensive understanding and manipulation remain elusive.In this study,we present novel experimental and theoretical evidence revealing the hybrid nature and optical tunability of STE state in Cs_(2)Ag_(0.4)Na_(0.6)InCl_(6).The detection of the Fano resonance in laser energy-dependent Raman and photoluminescence spectra indicates the emergence of an exciton-phonon hybrid state,arising from robust quantum interference between the discrete phonon and continuum exciton states.Moreover,we demonstrate continuous tuning of this hybrid state with the energy and intensity of the laser field.These findings lay the foundation for a comprehensive understanding of the nature of STE and their potential for state control.展开更多
Soil contaminated with heavy metals is a global health hazard.Nanomaterials,with their unique physical and chemical properties,hold significant potential for the remediation of soil polluted with heavy metals.They eff...Soil contaminated with heavy metals is a global health hazard.Nanomaterials,with their unique physical and chemical properties,hold significant potential for the remediation of soil polluted with heavy metals.They effectively reduce the mobility and bioavailability of heavy metals through various mechanisms such as adsorption,precipitation,and oxidation-reduction.This paper provides an in-depth exploration of the cuttingedge applications of various nanomaterials,including nanometallic,nano non-metallic materials,nanoclay and mineral materials,and nano modified biochar materials,in the remediation of heavy metal-contaminated soils.It specifically focuses on the key factors influencing the remediation efficacy of these nanomaterials,as well as the underlying remediation mechanisms and methods for performance optimization.The aims of this paper are to provide guidance for the further application of nanomaterials in the field of soil heavy metal remediation,and to offer insights that could promote the effective control of soil heavy metal pollution.展开更多
Enhancing the kinetic stability of glasses typically requires deepening their thermodynamic stability,which increases structural rigidity and degrades ductility;decoupling these properties remains a major challenge.He...Enhancing the kinetic stability of glasses typically requires deepening their thermodynamic stability,which increases structural rigidity and degrades ductility;decoupling these properties remains a major challenge.Here,we demonstrate that spatial patterning in metallic glasses produces exceptional kinetic ultrastability that coexists with a thermodynamically metastable,high-energy state and excellent plasticity.Guided by atomistic simulations using replica exchange molecular dynamics and machine learning interatomic potentials,we reveal that oxygen,through reaction-diffusion-coupled pattern dynamics,self-organizes into oxygen-centered pinned structures(OPSs)that serve as localized kinetic constraints.These motifs drastically slow structural relaxation,delivering kinetic stability comparable to ultrastable glasses even as the system retains the high inherent energy of rapidly quenched states.The OPSs’topology yields a spatially uniform activation of plastic events,promoting strain delocalization under mechanical load.By geometrically tailoring oxygen patterns,we increase the glass transition onset temperature(Tonset)by about 200 K with negligible loss of deformability.Our findings establish a practicable paradigm for decoupling kinetic and thermodynamic stability and point to a scalable,additive route for designing amorphous materials that combine hyperstability with plasticity.展开更多
1.Introduction Artificial intelligence(AI)is rapidly reshaping geoscience,from Earth observation interpretation and hazard forecasting to subsurface characterisation and Earth system modelling(Kochupillai et al.,2022;...1.Introduction Artificial intelligence(AI)is rapidly reshaping geoscience,from Earth observation interpretation and hazard forecasting to subsurface characterisation and Earth system modelling(Kochupillai et al.,2022;Sun et al.,2024).These capabilities emerge at a time when geoscientific evidence is increasingly informing high-stakes decisions about climate adaptation,resource development,and disaster risk reduction(McGovern et al.,2022).展开更多
Halide solid-state electrolytes have gained significant attention in recent years due to their high ionic conductivity,making them promising candidates for future all-solid-state batteries.Recent studies have identifi...Halide solid-state electrolytes have gained significant attention in recent years due to their high ionic conductivity,making them promising candidates for future all-solid-state batteries.Recent studies have identified numerous crystal structures with the Li_(3)MX_(6)composition,although many remain unexplored across various chemical systems.In this research,we developed a comprehensive method to examine all conceivable space groups and structures within theLi-M-X system,where M includes In,Ga,and La,and X includes F,Cl,Br,and 1.Our findings revealed two metastable structures:Li_(3)InF_(6)with P3c1 symmetry and Li_(3)InI_(6)with C2/c symmetry,exhibiting ionic conductivities of 0.55 and 2.18mS/cm at 300K,respectively.Notably,the trigonal symmetry of Li3InF6 demonstrates that high ionic conductivities are not limited to monoclinic structures but can also be achieved with trigonal symmetries.The electrochemical stability windows,mechanical properties,and reaction energies of these materials with known cathodes suggest their potential for use in all-solid-state batteries.Additionally,we predicted the stability of novel materials,including Li_(5)InCl_(8),Li_(5)InBr_(8),Li_(5)InI_(8),LiIn_(2)Cl_(9),LiIn_(2)Br_(9),and LiIn_(2)I_(9).展开更多
With the widespread adoption of digital equipment in intelligent substations,testing digital signals in power systems has become an important role for relay protection test equipment.Testing and calibrating digital si...With the widespread adoption of digital equipment in intelligent substations,testing digital signals in power systems has become an important role for relay protection test equipment.Testing and calibrating digital signals require high accuracy.However,existing methods have low precision,cannot be calibrated at full range for all indexes,and have complex configuration,making them unsuitable for routine calibration work.To solve the above problems,a novel calibration method is designed and implemented using field programmable gate array(FPGA)to achieve accurate input and output time control.Accurate calibration relies on multiple forms of traceability including theoretical value traceability based on waveform comparison,time scale value traceability based on accurate time stamps,and algorithm traceability based on typical algorithms.Compared with other existing methods,the proposed approach reduces the mean absolute error of action time and time measurement by 92.88%,effectively addressing a key industry challenge and offering a valuable reference for further research,application,and standardization.展开更多
Pervasive IoT applications enable us to perceive,analyze,control,and optimize the traditional physical systems.Recently,security breaches in many IoT applications have indicated that IoT applications may put the physi...Pervasive IoT applications enable us to perceive,analyze,control,and optimize the traditional physical systems.Recently,security breaches in many IoT applications have indicated that IoT applications may put the physical systems at risk.Severe resource constraints and insufficient security design are two major causes of many security problems in IoT applications.As an extension of the cloud,the emerging edge computing with rich resources provides us a new venue to design and deploy novel security solutions for IoT applications.Although there are some research efforts in this area,edge-based security designs for IoT applications are still in its infancy.This paper aims to present a comprehensive survey of existing IoT security solutions at the edge layer as well as to inspire more edge-based IoT security designs.We first present an edge-centric IoT architecture.Then,we extensively review the edge-based IoT security research efforts in the context of security architecture designs,firewalls,intrusion detection systems,authentication and authorization protocols,and privacy-preserving mechanisms.Finally,we propose our insight into future research directions and open research issues.展开更多
The Spectral Statistical Interpolation (SSI) analysis system of NCEP is used to assimilate meteorological data from the Global Positioning Satellite System (GPS/MET) refraction angles with the variational technique. V...The Spectral Statistical Interpolation (SSI) analysis system of NCEP is used to assimilate meteorological data from the Global Positioning Satellite System (GPS/MET) refraction angles with the variational technique. Verified by radiosonde, including GPS/MET observations into the analysis makes an overall improvement to the analysis variables of temperature, winds, and water vapor. However, the variational model with the ray-tracing method is quite expensive for numerical weather prediction and climate research. For example, about 4 000 GPS/MET refraction angles need to be assimilated to produce an ideal global analysis. Just one iteration of minimization will take more than 24 hours CPU time on the NCEP's Cray C90 computer. Although efforts have been taken to reduce the computational cost, it is still prohibitive for operational data assimilation. In this paper, a parallel version of the three-dimensional variational data assimilation model of GPS/MET occultation measurement suitable for massive parallel processors architectures is developed. The divide-and-conquer strategy is used to achieve parallelism and is implemented by message passing. The authors present the principles for the code's design and examine the performance on the state-of-the-art parallel computers in China. The results show that this parallel model scales favorably as the number of processors is increased. With the Memory-IO technique implemented by the author, the wall clock time per iteration used for assimilating 1420 refraction angles is reduced from 45 s to 12 s using 1420 processors. This suggests that the new parallelized code has the potential to be useful in numerical weather prediction (NWP) and climate studies.展开更多
Nowadays,with the widespread application of the Internet of Things(IoT),mobile devices are renovating our lives.The data generated by mobile devices has reached a massive level.The traditional centralized processing i...Nowadays,with the widespread application of the Internet of Things(IoT),mobile devices are renovating our lives.The data generated by mobile devices has reached a massive level.The traditional centralized processing is not suitable for processing the data due to limited computing power and transmission load.Mobile Edge Computing(MEC)has been proposed to solve these problems.Because of limited computation ability and battery capacity,tasks can be executed in the MEC server.However,how to schedule those tasks becomes a challenge,and is the main topic of this piece.In this paper,we design an efficient intelligent algorithm to jointly optimize energy cost and computing resource allocation in MEC.In view of the advantages of deep learning,we propose a Deep Learning-Based Traffic Scheduling Approach(DLTSA).We translate the scheduling problem into a classification problem.Evaluation demonstrates that our DLTSA approach can reduce energy cost and have better performance compared to traditional scheduling algorithms.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.11971411)。
文摘This paper introduces a novel numerical method based on an energy-minimizing normalized residual network(EMNorm Res Net)to compute the ground-state solution of Bose-Einstein condensates at zero or low temperatures.Starting from the three-dimensional Gross-Pitaevskii equation(GPE),we reduce it to the 1D and 2D GPEs because of the radial symmetry and cylindrical symmetry.The ground-state solution is formulated by minimizing the energy functional under constraints,which is directly solved using the EM-Norm Res Net approach.The paper provides detailed solutions for the ground states in 1D,2D(with radial symmetry),and 3D(with cylindrical symmetry).We use the Thomas-Fermi approximation as the target function to pre-train the neural network.Then,the formal network is trained using the energy minimization method.In contrast to traditional numerical methods,our neural network approach introduces two key innovations:(i)a novel normalization technique designed for high-dimensional systems within an energy-based loss function;(ii)improved training efficiency and model robustness by incorporating gradient stabilization techniques into residual networks.Extensive numerical experiments validate the method's accuracy across different spatial dimensions.
文摘The emergence of different computing methods such as cloud-,fog-,and edge-based Internet of Things(IoT)systems has provided the opportunity to develop intelligent systems for disease detection.Compared to other machine learning models,deep learning models have gained more attention from the research community,as they have shown better results with a large volume of data compared to shallow learning.However,no comprehensive survey has been conducted on integrated IoT-and computing-based systems that deploy deep learning for disease detection.This study evaluated different machine learning and deep learning algorithms and their hybrid and optimized algorithms for IoT-based disease detection,using the most recent papers on IoT-based disease detection systems that include computing approaches,such as cloud,edge,and fog.Their analysis focused on an IoT deep learning architecture suitable for disease detection.It also recognizes the different factors that require the attention of researchers to develop better IoT disease detection systems.This study can be helpful to researchers interested in developing better IoT-based disease detection and prediction systems based on deep learning using hybrid algorithms.
基金supported by the National Natural Science Foundation of China (Grant Nos.T2325004 and 52161160330)the National Natural Science Foundation of China (Grants No.12504233)+2 种基金Advanced MaterialsNational Science and Technology Major Project (Grant No.2024ZD0606900)the Talent Hub for “AI+New Materials” Basic Researchthe Key Research and Development Program of Ningbo (Grant No.2025Z088)。
文摘The functional properties of glasses are governed by their formation history and the complex relaxation processes they undergo.However,under extreme conditions,glass behaviors are still elusive.In this study,we employ simulations with varied protocols to evaluate the effectiveness of different descriptors in predicting mechanical properties across both low-and high-pressure regimes.Our findings demonstrate that conventional structural and configurational descriptors fail to correlate with the mechanical response following pressure release,whereas the activation energy descriptor exhibits robust linearity with shear modulus after correcting for pressure effects.Notably,the soft mode parameter emerges as an ideal and computationally efficient alternative for capturing this mechanical behavior.These findings provide critical insights into the influence of pressure on glassy properties,integrating the distinct features of compressed glasses into a unified theoretical framework.
文摘Adversarial Reinforcement Learning(ARL)models for intelligent devices and Network Intrusion Detection Systems(NIDS)improve systemresilience against sophisticated cyber-attacks.As a core component of ARL,Adversarial Training(AT)enables NIDS agents to discover and prevent newattack paths by exposing them to competing examples,thereby increasing detection accuracy,reducing False Positives(FPs),and enhancing network security.To develop robust decision-making capabilities for real-world network disruptions and hostile activity,NIDS agents are trained in adversarial scenarios to monitor the current state and notify management of any abnormal or malicious activity.The accuracy and timeliness of the IDS were crucial to the network’s availability and reliability at this time.This paper analyzes ARL applications in NIDS,revealing State-of-The-Art(SoTA)methodology,issues,and future research prospects.This includes Reinforcement Machine Learning(RML)-based NIDS,which enables an agent to interact with the environment to achieve a goal,andDeep Reinforcement Learning(DRL)-based NIDS,which can solve complex decision-making problems.Additionally,this survey study addresses cybersecurity adversarial circumstances and their importance for ARL and NIDS.Architectural design,RL algorithms,feature representation,and training methodologies are examined in the ARL-NIDS study.This comprehensive study evaluates ARL for intelligent NIDS research,benefiting cybersecurity researchers,practitioners,and policymakers.The report promotes cybersecurity defense research and innovation.
基金funded by National Natural Science Foundation of China Key Program(12431014)Key Project of Hunan Education Department(22A0126)+1 种基金Natural Science Foundation of Hunan Province(2022JJ30555)Postgraduate Scientific Research Innovation Project of Xiangtan University(XDCX2024Y172)。
文摘Tropospheric zenith wet delay(ZWD)plays a vital role in the analysis of space geodetic observations.In recent years,machine learning methods have been increasingly applied to improve the accuracy of ZWD calculations.However,a single machine learning model has limited generalization capabilities.To address these limitations,this study introduces a novel machine learning fusion(MLF)algorithm with stronger generalization capabilities to enhance ZWD modeling and prediction accuracy.The MLF algorithm utilizes a two-layer structure integrating extra trees(ET),backpropagation neural network(BPNN),and linear regression models.By comparing the root mean square error(RMSE)of these models,we found that both ET-based and MLF-based models outperform RF-based and BPNN-based models in terms of internal and external accuracy,across both surface meteorological data-based and blind models.The improvement in exte rnal accuracy is particularly significant in the blind models.Our re sults show that the MLF(with an RMSE of 3.93 cm)and ET(3.99 cm)models outperform the traditional GPT3model(4.07 cm),while the RF(4.21 cm)and BPNN(4.14 cm)have worse external accuracies than the GPT3 model.It is worth noting that the BPNN suffered from overfitting during external accuracy tests,which was avoided by the MLF.In summary,regardless of the availability of surface meteorological data,the MLF-based empirical models demonstrate superior internal and external accuracy compared to the other tested models in this study.
基金Supported by the Natural Science Foundation of Guangxi Province(Grant Nos.2023GXNSFAA026067,2024GXN SFAA010521)the National Natural Science Foundation of China(Nos.12361079,12201149,12261026).
文摘Convex feasibility problems are widely used in image reconstruction, sparse signal recovery, and other areas. This paper is devoted to considering a class of convex feasibility problem arising from sparse signal recovery. We first derive the projection formulas for a vector onto the feasible sets. The centralized circumcentered-reflection method is designed to solve the convex feasibility problem. Some numerical experiments demonstrate the feasibility and effectiveness of the proposed algorithm, showing superior performance compared to conventional alternating projection methods.
基金Supported by the National Natural Science Foundation of China(Grant No.12161022)the Science and Technology Project of Guangxi(Grant No.Guike AD23023002).
文摘In this paper,we first obtain the density of compactly supported bounded functions in anisotropic infinite dimensional Banach space-valued Musielak-Orlicz spaces.Then,we present the sufficient condition for the space of compactly supported smooth functions to be dense in anisotropic infinite dimensional Banach space-valued Musielak-Orlicz spaces.Moreover,the modular density is also given.
基金supported by the Beijing Natural Science Foundation(Grant No.Z230006)the National Key Research and Development Program of China(Grant No.2022YFA1204000)the National Natural Science Foundation of China(Grant Nos.12274405 and 12393831)。
文摘We report the development of the[Pt_(0.75)Ti_(0.25)/Co-Ni multilayer/Ta]_n superlattice with strong spin-orbit torque,large perpendicular magnetic anisotropy,and remarkably low switching current density.We demonstrate that the efficiency of the spin-orbit torque increases nearly linearly with the repetition number n,which is in excellent agreement with the spin Hall effect of the Pt_(0.75)Ti_(0.25)being essentially the only source of the observed spin-orbit torque.The perpendicular magnetic anisotropy field is also substantially enhanced by more than a factor of 2 as n increases from 1 to6.The[Pt_(0.75)Ti_(0.25)/Co-Ni multilayers/Ta]_n superlattice additionally exhibits deterministic,low-current-density magnetization switching despite the very large total layer thicknesses.The unique combination of strong spin-orbit torque,robust perpendicular magnetic anisotropy,low-current-density switching,and excellent high thermal stability makes the[Pt_(0.75)Ti_(0.25)/Co-Ni multilayer/Ta]_n superlattice a highly compelling material candidate for ultrafast,energy-efficient,and long-data-retention spintronic technologies.
文摘Zero-click attacks represent an advanced cybersecurity threat,capable of compromising devices without user interaction.High-profile examples such as Pegasus,Simjacker,Bluebugging,and Bluesnarfing exploit hidden vulnerabilities in software and communication protocols to silently gain access,exfiltrate data,and enable long-term surveillance.Their stealth and ability to evade traditional defenses make detection and mitigation highly challenging.This paper addresses these threats by systematically mapping the tactics and techniques of zero-click attacks using the MITRE ATT&CK framework,a widely adopted standard for modeling adversarial behavior.Through this mapping,we categorize real-world attack vectors and better understand how such attacks operate across the cyber-kill chain.To support threat detection efforts,we propose an Active Learning-based method to efficiently label the Pegasus spyware dataset in alignment with the MITRE ATT&CK framework.This approach reduces the effort of manually annotating data while improving the quality of the labeled data,which is essential to train robust cybersecurity models.In addition,our analysis highlights the structured execution paths of zero-click attacks and reveals gaps in current defense strategies.The findings emphasize the importance of forward-looking strategies such as continuous surveillance,dynamic threat profiling,and security education.By bridging zero-click attack analysis with the MITRE ATT&CK framework and leveraging machine learning for dataset annotation,this work provides a foundation for more accurate threat detection and the development of more resilient and structured cybersecurity frameworks.
基金supported by the National Natural Science Foundation of China(Grant Nos.42325503 and 42230604)The appointment of Chunsong Lu at Nanjing University of Information Science&Technology is partially supported by the Jiangsu Specially-Appointed Professor(Grant No.R2024T01).This research partly used the computational resources of Hokkaido University through the HPCI System Research Project(project IDs:hp200078,hp210059,hp220062,hp230166,and hp240151)and the computer facilities of the Center for Cooperative Work on Data Science and Computational Science,University of Hyogo.Shin-ichiro SHIMA was supported by JSPS KAKENHI,Grant 20H00225 and 23H00149and JST(Moonshot R and D)(Grant JPMJMS2286).We acknowledge the High-Performance Computing Center of the Nanjing University of Information Science and Technology for their support of this work.This study was also supported by the National Key Scientific and Technological Infrastructure project“Earth System Science Numerical Simulator Facility”(EarthLab,2024-EL-PT-000615).Chongzhi YIN would like to thank Lei Zhu for his generous support and informative discussions.
文摘AdshtT Marine stratocumulus clouds profoundly affect Earth's energy budget by reflecting solar radiation over extensive oceanic areas.Yet,after using a large-eddy simulation(LES)and a Lagrangian microphysics scheme(Super-Droplet Method,SDM)for entrainment-mixing studies,uncertainty remains in the grid resolution and super-droplet number concentration(SDNC)required for accurate homogeneity capture.This study analyzes the homogeneous mixing degree(HMD)and the Damkohler number(Da)in stratocumulus using an LES with SDM,from microphysical and dynamical perspectives,respectively.Results show that HMD and Da both display a top-to-base gradient,with more intense inhomogeneity near the cloud top and relatively homogeneous conditions toward the base,although the upper region is more complex.Even at a fine horizontal resolution of 12.5 m and vertical resolution of 2.5 m,HMD remains sensitive and does not converge,whereas Da converges at coarser grid spacings(up to horizontal and vertical spacings of 25 m and 10 m,respectively)in the mid-cloud region.Similarly,HMD requires an SDNC well above 128 per cell for near-complete convergence,while Da converges once SDNC exceeds about 16 per cell.This difference arises because HMD depends on microphysical details,thereby demanding a high SDNC to capture local droplet inhomogeneities,whereas Da reflects turbulence-evaporation timescales that converge more readily once extreme droplet gradients are resolved.We further find that HMD and Da exhibit a significant negative correlation,with stronger anti-correlations emerging under finer spatial resolutions,reinforcing their complementary roles in diagnosing mixing regimes.Overall,these findings provide guidelines for selecting numerical configurations in entrainment-mixing simulations,ensuring that both turbulence-driven and microphysical processes are adequately resolved,.
基金supported by Swinburne University of Technology Sarawak Campus and Birmingham City University.
文摘Wave energy is a promising form of marine renewable energy that offers a sustainable pathway for electricity generation in coastal regions.Despite Malaysia’s extensive coastline,the exploration of wave energy in Sarawak remains limited due to economic,technical,and environmental challenges that hinder its implementation.Compared to other renewable energy sources,wave energy is underutilized largely because of cost uncertainties and the lack of local performance data.This research aims to identify themost suitable coastal zone in Sarawak that achieves an optimal balance between energy potential,cost-effectiveness,and environmental impact,particularly in relation to infrastructure and regional development.The findings indicate that wave energy generation in Sarawak is technically feasible based on MOGA analysis.Among the studied sites,Bintulu emerged as the most balanced option,with a levelized cost of electricity(LCOE)of 0.778–0.864 USD/kWh and a CO_(2) emission factor as low as 0.019–0.020 CO_(2)/k Wh.Miri,while producing lower emissions than Sematan,recorded a higher LCOE of 1.045 USD/kWh with moderate emissions at 0.029 CO_(2)/kWh.Sematan,characterized by weaker wave conditions and higher installation penalties,resulted in the least favorable outcome,with an LCOE of 3.735 USD/kWh.Bintulu’s strategic location reduces CAPEX requirements,making it the most suitable site for large-scale wave energy deployment in Sarawak.
基金funding support from the National Natural Science Foundation of China(Grant No.12525405)funding support from the National Natural Science Foundation of China(Grant No.12393831)the CAS Project for Young Scientists in Basic Research(Grant No.YSBR-120)。
文摘Self-trapped excitons(STEs),known for their unique radiative properties,have been harnessed in diverse photonic devices;however,their comprehensive understanding and manipulation remain elusive.In this study,we present novel experimental and theoretical evidence revealing the hybrid nature and optical tunability of STE state in Cs_(2)Ag_(0.4)Na_(0.6)InCl_(6).The detection of the Fano resonance in laser energy-dependent Raman and photoluminescence spectra indicates the emergence of an exciton-phonon hybrid state,arising from robust quantum interference between the discrete phonon and continuum exciton states.Moreover,we demonstrate continuous tuning of this hybrid state with the energy and intensity of the laser field.These findings lay the foundation for a comprehensive understanding of the nature of STE and their potential for state control.
基金the Natural Science Research Initiation Fund Project of China West Normal University(No.23KE001)the National Natural Science Foundation of China(Nos.42407186,42277033,and 42171045)+1 种基金the Basic Research Foundation of Yunnan Province(No.202401AT070304)the Central Public-interest Scientific Institution Basal Research Fund(No.Y2024QC28)for their financial support。
文摘Soil contaminated with heavy metals is a global health hazard.Nanomaterials,with their unique physical and chemical properties,hold significant potential for the remediation of soil polluted with heavy metals.They effectively reduce the mobility and bioavailability of heavy metals through various mechanisms such as adsorption,precipitation,and oxidation-reduction.This paper provides an in-depth exploration of the cuttingedge applications of various nanomaterials,including nanometallic,nano non-metallic materials,nanoclay and mineral materials,and nano modified biochar materials,in the remediation of heavy metal-contaminated soils.It specifically focuses on the key factors influencing the remediation efficacy of these nanomaterials,as well as the underlying remediation mechanisms and methods for performance optimization.The aims of this paper are to provide guidance for the further application of nanomaterials in the field of soil heavy metal remediation,and to offer insights that could promote the effective control of soil heavy metal pollution.
基金supported by the National Natural Science Foundation of China(Grants Nos.T2325004)the Advanced Materials-National Science and Technology Major Project(Grant No.2024ZD0606900)+3 种基金the Talent Hub for‘AI+New Materials’Basic Researchsupported by the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant Nos.XDB0620103 and XDB0510301)the National Natural Science Foundation of China(Grant No.12472112)R.S.acknowledges the Young Scientists Fund of the National Natural Science Foundation of China(51801046).
文摘Enhancing the kinetic stability of glasses typically requires deepening their thermodynamic stability,which increases structural rigidity and degrades ductility;decoupling these properties remains a major challenge.Here,we demonstrate that spatial patterning in metallic glasses produces exceptional kinetic ultrastability that coexists with a thermodynamically metastable,high-energy state and excellent plasticity.Guided by atomistic simulations using replica exchange molecular dynamics and machine learning interatomic potentials,we reveal that oxygen,through reaction-diffusion-coupled pattern dynamics,self-organizes into oxygen-centered pinned structures(OPSs)that serve as localized kinetic constraints.These motifs drastically slow structural relaxation,delivering kinetic stability comparable to ultrastable glasses even as the system retains the high inherent energy of rapidly quenched states.The OPSs’topology yields a spatially uniform activation of plastic events,promoting strain delocalization under mechanical load.By geometrically tailoring oxygen patterns,we increase the glass transition onset temperature(Tonset)by about 200 K with negligible loss of deformability.Our findings establish a practicable paradigm for decoupling kinetic and thermodynamic stability and point to a scalable,additive route for designing amorphous materials that combine hyperstability with plasticity.
基金supported by the Natural Science Foundation of Jiangsu Province,China(Grant No.BK20240937)the Natural Science Foundation of Shandong Province(Grant No.ZR2021QE187)+2 种基金the Shandong Higher Education“Young Entrepreneurship Talents Introduction and Cultivation Program”Project(Grant No.ZXQT20221228001)the Natural Science Foundation of China(Grant No.42502273)the Science and Technology Innovation Program of Hunan Province(Grant No.2022RC4028).
文摘1.Introduction Artificial intelligence(AI)is rapidly reshaping geoscience,from Earth observation interpretation and hazard forecasting to subsurface characterisation and Earth system modelling(Kochupillai et al.,2022;Sun et al.,2024).These capabilities emerge at a time when geoscientific evidence is increasingly informing high-stakes decisions about climate adaptation,resource development,and disaster risk reduction(McGovern et al.,2022).
基金supported by the Higher Education and Science Committee of Armenia in the frames of the research projects 20TTSG-2F010, 23AA-2F033 and ANSEF (EN-matsc-2660) grant.
文摘Halide solid-state electrolytes have gained significant attention in recent years due to their high ionic conductivity,making them promising candidates for future all-solid-state batteries.Recent studies have identified numerous crystal structures with the Li_(3)MX_(6)composition,although many remain unexplored across various chemical systems.In this research,we developed a comprehensive method to examine all conceivable space groups and structures within theLi-M-X system,where M includes In,Ga,and La,and X includes F,Cl,Br,and 1.Our findings revealed two metastable structures:Li_(3)InF_(6)with P3c1 symmetry and Li_(3)InI_(6)with C2/c symmetry,exhibiting ionic conductivities of 0.55 and 2.18mS/cm at 300K,respectively.Notably,the trigonal symmetry of Li3InF6 demonstrates that high ionic conductivities are not limited to monoclinic structures but can also be achieved with trigonal symmetries.The electrochemical stability windows,mechanical properties,and reaction energies of these materials with known cathodes suggest their potential for use in all-solid-state batteries.Additionally,we predicted the stability of novel materials,including Li_(5)InCl_(8),Li_(5)InBr_(8),Li_(5)InI_(8),LiIn_(2)Cl_(9),LiIn_(2)Br_(9),and LiIn_(2)I_(9).
基金supported by the Key Technologies R&D Program of Henan Province(No.242102211065)Postgraduate Education Reform and Quality Im-provement Project of Henan Province(No.YJS2025GZZ36).
文摘With the widespread adoption of digital equipment in intelligent substations,testing digital signals in power systems has become an important role for relay protection test equipment.Testing and calibrating digital signals require high accuracy.However,existing methods have low precision,cannot be calibrated at full range for all indexes,and have complex configuration,making them unsuitable for routine calibration work.To solve the above problems,a novel calibration method is designed and implemented using field programmable gate array(FPGA)to achieve accurate input and output time control.Accurate calibration relies on multiple forms of traceability including theoretical value traceability based on waveform comparison,time scale value traceability based on accurate time stamps,and algorithm traceability based on typical algorithms.Compared with other existing methods,the proposed approach reduces the mean absolute error of action time and time measurement by 92.88%,effectively addressing a key industry challenge and offering a valuable reference for further research,application,and standardization.
基金This research has been supported by the National Science Foundation(under grant#1723596)the National Security Agency(under grant#H98230-17-1-0355).
文摘Pervasive IoT applications enable us to perceive,analyze,control,and optimize the traditional physical systems.Recently,security breaches in many IoT applications have indicated that IoT applications may put the physical systems at risk.Severe resource constraints and insufficient security design are two major causes of many security problems in IoT applications.As an extension of the cloud,the emerging edge computing with rich resources provides us a new venue to design and deploy novel security solutions for IoT applications.Although there are some research efforts in this area,edge-based security designs for IoT applications are still in its infancy.This paper aims to present a comprehensive survey of existing IoT security solutions at the edge layer as well as to inspire more edge-based IoT security designs.We first present an edge-centric IoT architecture.Then,we extensively review the edge-based IoT security research efforts in the context of security architecture designs,firewalls,intrusion detection systems,authentication and authorization protocols,and privacy-preserving mechanisms.Finally,we propose our insight into future research directions and open research issues.
基金supported by the National Natural Science Eoundation of China under Grant No.40221503the China National Key Programme for Development Basic Sciences (Abbreviation:973 Project,Grant No.G1999032801)
文摘The Spectral Statistical Interpolation (SSI) analysis system of NCEP is used to assimilate meteorological data from the Global Positioning Satellite System (GPS/MET) refraction angles with the variational technique. Verified by radiosonde, including GPS/MET observations into the analysis makes an overall improvement to the analysis variables of temperature, winds, and water vapor. However, the variational model with the ray-tracing method is quite expensive for numerical weather prediction and climate research. For example, about 4 000 GPS/MET refraction angles need to be assimilated to produce an ideal global analysis. Just one iteration of minimization will take more than 24 hours CPU time on the NCEP's Cray C90 computer. Although efforts have been taken to reduce the computational cost, it is still prohibitive for operational data assimilation. In this paper, a parallel version of the three-dimensional variational data assimilation model of GPS/MET occultation measurement suitable for massive parallel processors architectures is developed. The divide-and-conquer strategy is used to achieve parallelism and is implemented by message passing. The authors present the principles for the code's design and examine the performance on the state-of-the-art parallel computers in China. The results show that this parallel model scales favorably as the number of processors is increased. With the Memory-IO technique implemented by the author, the wall clock time per iteration used for assimilating 1420 refraction angles is reduced from 45 s to 12 s using 1420 processors. This suggests that the new parallelized code has the potential to be useful in numerical weather prediction (NWP) and climate studies.
基金supported in part by the National Natural Science Foun-dation of China(61902029)R&D Program of Beijing Municipal Education Commission(No.KM202011232015)Project for Acceleration of University Classi cation Development(Nos.5112211036,5112211037,5112211038).
文摘Nowadays,with the widespread application of the Internet of Things(IoT),mobile devices are renovating our lives.The data generated by mobile devices has reached a massive level.The traditional centralized processing is not suitable for processing the data due to limited computing power and transmission load.Mobile Edge Computing(MEC)has been proposed to solve these problems.Because of limited computation ability and battery capacity,tasks can be executed in the MEC server.However,how to schedule those tasks becomes a challenge,and is the main topic of this piece.In this paper,we design an efficient intelligent algorithm to jointly optimize energy cost and computing resource allocation in MEC.In view of the advantages of deep learning,we propose a Deep Learning-Based Traffic Scheduling Approach(DLTSA).We translate the scheduling problem into a classification problem.Evaluation demonstrates that our DLTSA approach can reduce energy cost and have better performance compared to traditional scheduling algorithms.
