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Quantum Secure Multiparty Computation:Bridging Privacy,Security,and Scalability in the Post-Quantum Era 认领 引用
1
作者 Sghaier Guizani Tehseen Mazhar Habib Hamam 《Computers, Materials & Continua》 SCIE EI 2026年第4期1-25,共25页
The advent of quantum computing poses a significant challenge to traditional cryptographic protocols,particularly those used in SecureMultiparty Computation(MPC),a fundamental cryptographic primitive for privacypreser... The advent of quantum computing poses a significant challenge to traditional cryptographic protocols,particularly those used in SecureMultiparty Computation(MPC),a fundamental cryptographic primitive for privacypreserving computation.Classical MPC relies on cryptographic techniques such as homomorphic encryption,secret sharing,and oblivious transfer,which may become vulnerable in the post-quantum era due to the computational power of quantum adversaries.This study presents a review of 140 peer-reviewed articles published between 2000 and 2025 that used different databases like MDPI,IEEE Explore,Springer,and Elsevier,examining the applications,types,and security issues with the solution of Quantum computing in different fields.This review explores the impact of quantum computing on MPC security,assesses emerging quantum-resistant MPC protocols,and examines hybrid classicalquantum approaches aimed at mitigating quantum threats.We analyze the role of Quantum Key Distribution(QKD),post-quantum cryptography(PQC),and quantum homomorphic encryption in securing multiparty computations.Additionally,we discuss the challenges of scalability,computational efficiency,and practical deployment of quantumsecure MPC frameworks in real-world applications such as privacy-preserving AI,secure blockchain transactions,and confidential data analysis.This review provides insights into the future research directions and open challenges in ensuring secure,scalable,and quantum-resistant multiparty computation. 展开更多
关键词 Quantum computing secure multiparty computation(MPC) post-quantum cryptography(PQC) quantum key distribution(QKD) privacy-preserving computation quantum homomorphic encryption quantum network security federated learning blockchain security quantum cryptography
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Numerical simulation and analysis of risk factors leading to pancreaticobiliary reflux:Insights from a computational fluid dynamics study to idealized models 认领 引用 被引量：1
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作者 Nian-Zong Hou Zhao-Guang Wang +5 位作者 Guan-Qing Xiong Ming-Zhe Li Yan Hou Hai Hu Kai Wang Yu-Long Yang 《Hepatobiliary & Pancreatic Diseases International》 SCIE CAS CSCD 2026年第2期198-211,共14页
Background:The analysis and prediction of pancreaticobiliary reflux(PBR)play a crucial role in planning surgical interventions for hepato-biliary-pancreatic diseases,considering the uncertain mechanism behind it.Howev... Background:The analysis and prediction of pancreaticobiliary reflux(PBR)play a crucial role in planning surgical interventions for hepato-biliary-pancreatic diseases,considering the uncertain mechanism behind it.However,current practices are limited by fragmented clinical observations,making it challenging to visualize the complex phenomenon in the pancreaticobiliary junction(PBJ)through imaging and radiography experiments.This study aimed to comprehensively describe the retrograde flow characteristics in various PBR scenarios and assess the factors leading to PBR using simulations based on idealized geometry and boundary conditions.Methods:By Cadence Pointwise,we developed a computational fluid dynamics(CFD)model using an idealized PBJ system.Standard parameters such as pressure and viscosity were applied,along with typical assumptions relevant to fluid dynamic modeling.Subsequently,based on the aforementioned basic idealized model,we analyzed 8 hypothetical PBR conditions,covering a range of high(shorter)and low(longer)values or different positions for each specific parameter,at a representative stage of a peristaltic propagation cycle of the Oddi's sphincter.Results:We modeled a two-dimensional PBJ with the propagation of a peristaltic wave.These findings demonstrated that the shortened septum,the extended ampulla,the increased wavelength and enhanced amplitude of the Oddi's sphincterial peristalsis,the widened diameter difference and the increased pressure difference between the common bile duct(CBD)and the main pancreatic duct(MPD),as well as the gravitational effect(position),strongly impacted PBR,while the viscosity of bile and pancreatic juice had a weaker influence.Additionally,an inequality incorporating these risk factors was developed for the evaluation of whether reflux occurs.Conclusions:Numerical simulation can be used to describe the reflux flow field,offering the possibility to visualize and analyze PBR,which has the potential to significantly revolutionize the understanding of PBR and improve clinical decision-making.Future work should focus on bridging the gap between CFD and clinical practice. 展开更多
关键词 Pancreaticobiliary reflux Computational fluid dynamics
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A Computational Modeling Framework for Verifiable Computation Offloading in Resource-Constrained IoT Smart Contract Systems Using Zero-Knowledge and Fuzzy Logic 认领 引用
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作者 Hong Min Yousef Ibrahim Daradkeh +2 位作者 Jung Taek Seo Mohd Anjum Sana Shahab 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第6期1488-1520,共33页
This study presents a computational modeling framework for efficient and secure computation offloading in Internet of Things(IoT)-enabled smart contract systems.The integration of IoT,edge computing,and blockchain int... This study presents a computational modeling framework for efficient and secure computation offloading in Internet of Things(IoT)-enabled smart contract systems.The integration of IoT,edge computing,and blockchain introduces significant challenges,including limited device capacity,high verification cost,and scalability constraints.Existing blockchain verification approaches depend on computationally intensive cryptographic operations that are inefficient for resource-constrained IoT devices,resulting in increased latency,energy consumption,and transaction costs.To address these issues,this study proposes the Zero-Knowledge Fuzzy Logic Offloading and Rollup(Z-FLOR)framework,an adaptive and energy-efficient model designed to enable secure and verifiable computation in IoT-based smart contract systems.The proposed framework integrates three key components.First,a zero-knowledge proof-based verification model using the Grothl6 zkSNARK module generates compact and privacy-preserving proofs that enable fast and reliable verification.Second,a Fuzzy Logic-Driven Energy-Aware Offloading module dynamically allocates computational tasks between IoT devices,edge servers,and cloud platforms based on energy availability,network delay,and device reliability.Third,an Optimistic Rollup Verification module aggregates proofs off-chain and submits them in batches to reduce gas costs and enhance scalability.Extensive simulation and experimental evaluation across diverse IoT scenarios demonstrate the effectiveness of the proposed computational framework.Results indicate that Z-FLOR achieves 99.7%verification accuracy and 98.9%proof compression efficiency,while gas cost analysis indicates gas cost reductions in the range of 80%-98%.Z-FLOR additionally achieves a 44.0%reduction in latency,5l.0%savings in gas costs,and 38.0%energy consumption compared to baseline approaches.These findings highlight the capability of the proposed approach to serve as a scalable and energy-efficient modeling solution for secure IoT smart contract execution in decentralized environments. 展开更多
关键词 Zero-knowledge proofs zkSNARK IoT smart contracts verifiable computation blockchain scalability edge computing rollup architecture
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DRL-Based Cross-Regional Computation Offloading Algorithm 认领 引用
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作者 Lincong Zhang Yuqing Liu +2 位作者 Kefeng Wei Weinan Zhao Bo Qian 《Computers, Materials & Continua》 SCIE EI 2026年第1期901-918,共18页
In the field of edge computing,achieving low-latency computational task offloading with limited resources is a critical research challenge,particularly in resource-constrained and latency-sensitive vehicular network e... In the field of edge computing,achieving low-latency computational task offloading with limited resources is a critical research challenge,particularly in resource-constrained and latency-sensitive vehicular network environments where rapid response is mandatory for safety-critical applications.In scenarios where edge servers are sparsely deployed,the lack of coordination and information sharing often leads to load imbalance,thereby increasing system latency.Furthermore,in regions without edge server coverage,tasks must be processed locally,which further exacerbates latency issues.To address these challenges,we propose a novel and efficient Deep Reinforcement Learning(DRL)-based approach aimed at minimizing average task latency.The proposed method incorporates three offloading strategies:local computation,direct offloading to the edge server in local region,and device-to-device(D2D)-assisted offloading to edge servers in other regions.We formulate the task offloading process as a complex latency minimization optimization problem.To solve it,we propose an advanced algorithm based on the Dueling Double Deep Q-Network(D3QN)architecture and incorporating the Prioritized Experience Replay(PER)mechanism.Experimental results demonstrate that,compared with existing offloading algorithms,the proposed method significantly reduces average task latency,enhances user experience,and offers an effective strategy for latency optimization in future edge computing systems under dynamic workloads. 展开更多
关键词 Edge computing computational task offloading deep reinforcement learning D3QN device-to-device communication system latency optimization
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A Multi-Objective Deep Reinforcement Learning Algorithm for Computation Offloading in Internet of Vehicles 认领 引用
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作者 Junjun Ren Guoqiang Chen +1 位作者 Zheng-Yi Chai Dong Yuan 《Computers, Materials & Continua》 SCIE EI 2026年第1期2111-2136,共26页
Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrain... Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrained onboard devices to nearby Roadside Unit(RSU),thereby achieving lower delay and energy consumption.However,due to the limited storage capacity and energy budget of RSUs,it is challenging to meet the demands of the highly dynamic Internet of Vehicles(IoV)environment.Therefore,determining reasonable service caching and computation offloading strategies is crucial.To address this,this paper proposes a joint service caching scheme for cloud-edge collaborative IoV computation offloading.By modeling the dynamic optimization problem using Markov Decision Processes(MDP),the scheme jointly optimizes task delay,energy consumption,load balancing,and privacy entropy to achieve better quality of service.Additionally,a dynamic adaptive multi-objective deep reinforcement learning algorithm is proposed.Each Double Deep Q-Network(DDQN)agent obtains rewards for different objectives based on distinct reward functions and dynamically updates the objective weights by learning the value changes between objectives using Radial Basis Function Networks(RBFN),thereby efficiently approximating the Pareto-optimal decisions for multiple objectives.Extensive experiments demonstrate that the proposed algorithm can better coordinate the three-tier computing resources of cloud,edge,and vehicles.Compared to existing algorithms,the proposed method reduces task delay and energy consumption by 10.64%and 5.1%,respectively. 展开更多
关键词 Deep reinforcement learning internet of vehicles multi-objective optimization cloud-edge computing computation offloading service caching
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An Adaptive Intrusion Detection Framework for IoT: Balancing Accuracy and Computational Efficiency 认领 引用
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作者 Abdulaziz A.Alsulami Badraddin Alturki +2 位作者 Ahmad J.Tayeb Rayan A.Alsemmeari Raed Alsini 《Computers, Materials & Continua》 SCIE EI 2026年第6期1214-1241,共28页
Intrusion Detection Systems(IDS)play a critical role in protecting networked environments from cyberattacks.They have become increasingly important in smart environments such as the Internet of Things(IoT)systems.Howe... Intrusion Detection Systems(IDS)play a critical role in protecting networked environments from cyberattacks.They have become increasingly important in smart environments such as the Internet of Things(IoT)systems.However,IDS for IoT networks face critical challenges due to hardware constraints,including limited computational resources and storage capacity,which lead to high feature dimensionality,prediction uncertainty,and increased processing cost.These factors make many conventional detection approaches unsuitable for real-time IoT deployment.To address these challenges,this paper proposes an adaptive intrusion detection framework that intelligently balances detection accuracy and computational efficiency.The proposed framework integrates mutual information(MI)feature selection model,deep contextual embeddings,and an adaptive decision mechanism.The MI model identifies and retains the most informative features,which reduces dimensionality while maintaining high detection accuracy.The adaptive decision dynamically selects between multiple inference paths to ensure that additional computation is needed only when the uncertainty level is high.Experimental evaluations on benchmark IoT datasets namely RT-IoT-2022,CIC-IoT-2023 and CIC-IoMT-2024 show that the proposed framework achieves F1-score of 99.92%,96.66%,and 99.84%,respectively,with an average inference time of approximately 0.105 ms per sample.These results demonstrate that the framework effectively adapts inference complexity to data uncertainty,which provides an intelligent,interpretable and efficient solution for real-world IoT intrusion detection. 展开更多
关键词 Adaptive IDS cyberattack detection computational cost IoT security
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High-throughput computational screening of functionalized MOFs for energy-efficient CO2capture:Balancing selective CO2adsorption performance and energy inputs 认领 引用
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作者 Sen Liu Zhe Sun +8 位作者 Bo Liao Huili Zhang Ling Zhang Yuchen Huang Lin Wan Maohuai Wang Shuxian Wei Baojun Wei Xiaoqing Lu 《Journal of Energy Chemistry》 SCIE EI CAS CSCD 2026年第3期136-145,共10页
The rational design of high-performance CO2adsorbents remains a critical challenge in addressing global carbon emissions,with metal-organic frameworks(MOFs)emerging as promising candidates due to their tunable pore... The rational design of high-performance CO2adsorbents remains a critical challenge in addressing global carbon emissions,with metal-organic frameworks(MOFs)emerging as promising candidates due to their tunable pore environments.However,the lack of systematic guidelines for functional group selection has hindered their practical implementation in carbon capture applications.Here,this gap was addressed by developing a comprehensive design framework through high-throughput computational screening.Through construction of a topology-directed database of 4797,integrating 10 metal centers with 144 functionalized ligands(18 ligands modified by–NH2,–NO2,–CH3,–CF3,–SH2,–SO2,–OH,and–OLi)across 36 topologies,the fundamental structure–property relationships governing CO2capture performance was established.Multi-metric evaluation reveals that–NO2,–SO2,and–OLi dramatically enhance CO2selectivity over CH_4/N2via selectivity(Sads),working capacity(ΔN),adsorbent performance score(APS),sorbent selection parameter(Ssp),and renewability R.Specially,ΔN rises from 2.34(pristine)to 5.91–7.94 mmol g-1and Sadssurges from 24.94/40.36 to 121.11/176.87(–NO2),149.94/215.54(–SO2),and 58.64/267.44(–OLi).Besides,the critical trade-off between adsorption strength and renewability demonstrates that enhanced performance comes at the cost of reduced renewability,where stronger CO2affinity(isosteric heat of-29.15,-29.96,and-30.09 for–NO2,–SO2,and–OLi)compromises renewability(R reduced by -50%).To resolve this trade-off,a novel energy efficiency(η)metric was introduced,which holistically evaluates both adsorption performance(Sads,ΔN,APS,Ssp,and R)and energy inputs(desorption heat,pressure-swing energy,net loss).This leads to the identification of–SO2as the optimal functional group that balances exceptional CO2capture(η=6.17/12.78 for CO2over CH_4/N2),surpassing the second higher of 4.74/8.80 in–CF3and 0.99/2.18 in non-functionalized counterparts.Adopting high-throughput computational screening methods,this work provides both fundamental insights into host–guest interactions in functionalized MOFs and a practical framework for designing next-generation adsorbents,bridging the gap between materials discovery and process engineering considerations in carbon capture technologies. 展开更多
关键词 Metal-organic frameworks High-throughput computational screening Selective CO2adsorption Functional group engineering Energy efficiency
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Infrared-visible computational sensor with rapid and relaxation photoresponses 认领 引用
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作者 Zhu-An Li Yuekun Yang +10 位作者 Chen Pan Pengfei Wang Wentao Yu Zhoujie Zeng Yuxuan Yuan Yanwei Cui Xingchen Liu Yinzhi Huang Sicheng Chen Bin Cheng Shi-Jun Liang 《International Journal of Extreme Manufacturing》 SCIE EI CAS CSCD 2026年第2期1-4,共4页
Conventional machine vision sensors send raw data frame by frame to the backend,generating substantial redundant data movement and causing severe decision latency.This bottleneck becomes much more critical in resource... Conventional machine vision sensors send raw data frame by frame to the backend,generating substantial redundant data movement and causing severe decision latency.This bottleneck becomes much more critical in resource-constrained edges and motion-detection scenarios.Two-dimensional(2D)materials exhibit a range of novel properties in electronic transport,band engineering,and interfacial physics[1-3]. 展开更多
关键词 raw data backend interfacial physics relaxation photoresponse electronic transportband engineeringand rapid photoresponse conventional machine vision sensors infrared visible computational sensor
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Data-Driven Materials Science Using Machine Learning and Computational Modeling 认领 引用
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作者 Manjodh Kaur Princy Randhawa +4 位作者 Jitendra Jaiswal Deepak Dubal Ravindra N.Bulakhe Deepanraj Balakrishnan Nithesh Naik 《Computers, Materials & Continua》 SCIE EI 2026年第8期90-124,共35页
This review emphasizes the growing role of artificial intelligence(AI)in transforming the materials discovery process into a data-driven and autonomous approach.It systematically traces the evolution of scientific par... This review emphasizes the growing role of artificial intelligence(AI)in transforming the materials discovery process into a data-driven and autonomous approach.It systematically traces the evolution of scientific paradigms in materials science and examines how machine learning,generative models,and AI agents are revolutionizing the design,screening,and optimization of materials.A key contribution is a detailed,step-by-step machine learning framework that guides researchers through data collection,preprocessing,feature engineering,model development,and validation,utilizing publicly available materials databases and computational tools.Additionally,the review discusses the latest advances in generative AI and autonomous research systems,highlighting their potential to enable inverse design and closed-loop experiments.It includes a tutorial case study on sodium-ion battery materials to demonstrate practical application in formation energy prediction via machine learning,along with comparisons to high-throughput screening accuracy using density functional theory(DFT).The article also addresses current challenges such as data limitations,model interpretability,and physics-based approaches.Overall,this publication serves as both a conceptual and practical guide for integrating AI into materials research,aiming to accelerate the discovery process and improve efficiency. 展开更多
关键词 Artificial intelligence materials science materials discovery energy materials computational modeling
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Data-driven computing ligament loading mechanisms:integration of the computational ligament mechanics models with deep learning 认领 引用
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作者 Datao Xu Huiyu Zhou +7 位作者 Yi Yuan Zanni Zhang Tianle Jie Zhifeng Zhou Zixiang Gao Liangliang Xiang Meizi Wang Yaodong Gu 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第5期628-672,共45页
Ankle injuries account for more than 25%of sports-related injuries.However,there is a lack of computational mechanics modeling and assessment tools for the ligament loading mechanism(LLM)caused by ankle injury.This st... Ankle injuries account for more than 25%of sports-related injuries.However,there is a lack of computational mechanics modeling and assessment tools for the ligament loading mechanism(LLM)caused by ankle injury.This study combines medical imaging data to construct the subject-specific ankle musculoskeletal model,which considers the subject's individualized characteristics and ligamentous attributes.Furthermore,we developed the structural constitutive model to restore the nonlinear short-term viscoelastic properties of the ligament-dense connective tissue,which can more realistically revert the LLM and reveal the mechanical properties of ankle injury.Based on the computational ligament mechanics(CLM)model,we developed a deep learning-based prediction model to predict LLM by CLM data-driven modeling.The modeling simulation results are highly consistent with the calculation results from the dual fluoroscopic imaging system,which demonstrated that the CLM model has high accuracy.The data-driven modeling performs exceptionally well in predicting ligament loading forces.The findings indicate that the constructed CLM data-driven model has the potential to enhance the accuracy and safety of ankle rehabilitation robots,while also providing personalized,dynamically adjusted rehabilitation training programs.The proposed comprehensive solutions would bring benefits to more patients with sports injuries and the general rehabilitation population,and promote the development and advancement of the research field of CLM and biomechanical variable prediction. 展开更多
关键词 Computational ligament mechanics Subject-specific musculoskeletal model Structural constitutive model Ankle ligament injury mechanisms Biomechanical variable prediction
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Joint computation offloading and service downloading in satellite edge computing networks 认领 引用
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作者 Wu Qi Zhu Lidong 《China Communications》 SCIE EI CSCD 2026年第4期238-258,共21页
The limited onboard cache and computing resources significantly constrain the computational service capabilities of individual edge satellites in hotspot regions.To address this challenge,we propose a twotier cloud-ed... The limited onboard cache and computing resources significantly constrain the computational service capabilities of individual edge satellites in hotspot regions.To address this challenge,we propose a twotier cloud-edge computing architecture that organizes edge satellites and their associated ground clouds into multiple collaborative domains.Within each domain,we formulate a joint optimization problem for computation offloading and service downloading under the constraints of edge satellites’service deployment and caching space,aiming to minimize the sum of weighted energy consumption and latency.The originally non-convex problem is transformed into a more tractable convex optimization formulation through variable relaxation.Subsequently,we develop an alternating direction method of multipliers(ADMM)-based distributed optimization framework that enables cooperative decision-making among domain satellites for the optimization of computational offloading,service downloading,and service deleting variables.Additionally,we propose an innovative binary variable recovery algorithm that ensures feasible conversion from continuous solutions to discrete decision variables while preserving constraint satisfaction.Extensive simulations demonstrate that our approach achieves lower task execution cost and packet loss rate compared with benchmarks. 展开更多
关键词 alternating direction method of multipliers(ADMMs) cloud-edge computing architecture computation offloading edge satellites service downloading
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Coupled computational fluiddynamics–discrete-element method investigation of soil contact erosion under cyclic hydraulic loading:A multiscale perspective 认领 引用
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作者 Xianfeng Diao Guoqing Cai +3 位作者 Tao Liu Peiyun Zhang Yanlin Su Bangzheng Rao 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第6期4812-4830,共19页
Tidal waves,intermittent rainfall,and fluctuations in water levels,which create cyclic hydraulic gradients,can exacerbate the migration of fine particles within soils and lead to deterioration in stability.However,mac... Tidal waves,intermittent rainfall,and fluctuations in water levels,which create cyclic hydraulic gradients,can exacerbate the migration of fine particles within soils and lead to deterioration in stability.However,macroscale experimental methods struggle to capture the microscopic deformations that occur during seepage-induced erosion.Therefore,this study,which is based on the coupled computational fluid dynamics–discrete element method(CFD–DEM)coupling method,investigates the contact mechanical mechanisms that induce macroscopic deformation under cyclic hydraulic gradients by considering the effects of different amplitudes and frequencies.The results show that the erosion mass of fine particles increases in a stepwise manner,with a multipeak variation in the erosion rate,and both the erosion amount and intensity are greater under constant gradient conditions.Fine particles erode primarily near the contact surface and,after migration,accumulate mainly in the coarse particle layers close to the contact surface.Increasing the amplitude and frequency of the cyclic hydraulic gradient leads to more fine particle blockages within the coarse particle layer.The cyclic hydraulic gradient causes the contact force chain network to repeatedly break and reorganize,reducing the shear strengths of the soil and resulting in more pronounced anisotropy in the contact force distribution. 展开更多
关键词 Computational fluid dynamics–discrete element method(CFD–DEM)coupling Contact erosion Cyclic hydraulic gradients Macroscopic deformation Micromechanism
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A Combined Asymptotic and Characteristic-Based Computational Framework for Exit-Plane Disturbance Response in Solid Rocket Motor Chambers 认领 引用
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作者 Abdelkarim Hegab Faisal Albatati +3 位作者 Ahmad Hussain Asad A.Zaidi Abdullah Abuhabaya Ragab A.El-Sehiemy 《Fluid Dynamics & Materials Processing》 EI 2026年第6期106-136,共31页
A combined asymptotic and characteristic-based computational framework is developed to investigate unsteady compressible flow response in solid rocket motor(SRM)chambers subjected to exit-plane disturbances and steady... A combined asymptotic and characteristic-based computational framework is developed to investigate unsteady compressible flow response in solid rocket motor(SRM)chambers subjected to exit-plane disturbances and steady sidewall mass injection.The formulation integrates a low-Mach-number asymptotic reduction of the governing equations with a time-accurate numerical solution of the parabolized Navier-Stokes equations,employing characteristic-based boundary conditions to ensure physically consistent wave reflection and transmission at chamber boundaries.Controlled exit-plane pressure forcing is imposed under non-resonant and near-resonant conditions to examine acoustic-vorticity coupling mechanisms within slender SRM geometries.The computational framework is verified and validated against analytical solutions and available experimental measurements for canonical duct configurations,demonstrating accurate pressure wave prediction and stable long-time integration over multiple acoustic cycles.The validated model is subsequently applied to configurations with sidewall mass injection,where interaction between injected flow and acoustic oscillations generates rotational structures that progressively penetrate the chamber cross-section.Parametric investigations reveal strong Reynolds-number dependence of vorticity amplitude,wave penetration depth,and transient flow reversal behavior.Increasing Reynolds number reduces viscous attenuation and promotes sustained wave-vorticity interaction,while weakly nonlinear modulation introduces higher harmonic content in the acoustic response.Despite complex velocity and vorticity structures,transverse pressure gradients remain negligible for slender chamber configurations,consistent with asymptotic predictions. 展开更多
关键词 Solid rocket motor aeroacoustics computational fluid dynamics characteristic boundary conditions acoustic-vorticity interaction Reynolds-number effects exit-plane forcing unsteady compressible flow
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Cognitive Computational Neuroscience:The Next Decade 认领 引用
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作者 Yixuan Ku 《Brain Science Advances》 2026年第2期25-29,共5页
As an emerging interdisciplinary field,Cognitive Computational Neuroscience aims to bridge traditional disciplinary boundaries(Figure 1).Its core vision is to uncover the computational principles underlying complex co... As an emerging interdisciplinary field,Cognitive Computational Neuroscience aims to bridge traditional disciplinary boundaries(Figure 1).Its core vision is to uncover the computational principles underlying complex cognitive processes and their neural implementation in the physical brain.This field lies at the deep intersection of cognitive science,psychology,computer science,and neurobiology. 展开更多
关键词 Cognitive computational neuroscience computational principles cognitive modules working memory distributed representations Gene-Brain-Environment-Behavior close-loop
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From cells to patients:Multiscale computational pathology in the era of foundation models and vision-language systems 认领 引用
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作者 Zeyu Gao Jiusong Ge +8 位作者 Junbo Lu Lei Wu Hannah Clayton Di Zhang Jiashuai Liu Inês Prata Machado Yan Chen Mireia Crispín‑Ortuzar Chen Li 《Intelligent Oncology》 CAS 2026年第3期66-75,共10页
Computational pathology is fundamentally defined by its inherent hierarchical structure,spanning from nuclear morphology and cellular interactions to tissue microenvironments,ultimately integrating into whole-slide im... Computational pathology is fundamentally defined by its inherent hierarchical structure,spanning from nuclear morphology and cellular interactions to tissue microenvironments,ultimately integrating into whole-slide images for patient-level prognostic profiling.While traditional deep learning approaches have achieved remarkable success in specific tasks,the recent emergence of large-scale foundation models and vision-language models has precipitated a paradigm shift in the field.These data-driven systems,characterized by their robust representation learning and semantic reasoning capabilities,are redefining how we analyze pathological data across diverse spatial scales.In this review,we provide a comprehensive synthesis of this transformation through a multiscale lens.We systematically survey the application of foundation models and vision-language models in deciphering biological complexity,ranging from cell-level segmentation and tissue phenotyping to whole-slide image-level prediction and multimodal integration.Furthermore,we critically analyze the limitations of current approaches,such as interpretability,computational efficiency,and data bias,then outline promising future directions for developing holistic,context-aware systems that bridge the gap between pixel-level features and patient-centric clinical decision-making. 展开更多
关键词 Computational pathology Foundation model Multiscale modeling
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Deep Learning Kp Computational Prediction Combined Multimodal Data of Solar Wind and EUV Images 认领 引用
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作者 GUO Dalei LI Xiaokun XUE Bingsen 《空间科学学报》 CAS CSCD 北大核心 2026年第3期604-617,共14页
This study presents a deep learning framework for multi-scale prediction of the geomagnetic Kp index by integrating multi-modal solar data.Unlike models that rely solely on upstream solar wind measurements from the Ad... This study presents a deep learning framework for multi-scale prediction of the geomagnetic Kp index by integrating multi-modal solar data.Unlike models that rely solely on upstream solar wind measurements from the Advanced Composition Explorer(ACE),which offer a short-term forecast horizon of 0.5~2 h,our approach synergistically incorporates Extreme Ultraviolet(EUV)images of coronal holes from the Solar Dynamics Observatory(SDO).These images provide precursor signals of solar activity,enabling medium-term forecasts.This study constructed a multi-year dataset(2011-2019)with carefully designed features to ensure physical meaning and sample balance.And several models were developed and evaluated:a direct solar wind model,an indirect multi-scale model using EUV images,and a model incorporating coronal hole location information.While all models showed similar and superior performance in 3-hour-ahead predictions,the model incorporating coronal hole location information demonstrated a significant advantage at extended forecast horizons,maintaining a Correlation Coefficient(CC)of approximately 0.23 even at a 72 h lead time.This work underscores the critical value of fusing solar source images with in-situ measurements to achieve reliable,long-lead space weather forecasts. 展开更多
关键词 Geomagnetic index Deep learning Computational prediction Coronal hole Multi-modal data Space weather forecasting
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Guest Editorial for the Special Issue on Advanced Computational Modeling and Simulations for Engineering Structures and Multifunctional Materials:Bridging Theory and Practice 认领 引用
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作者 S.M.Anas Rayeh Nasr Al-Dala’ien 《Computers, Materials & Continua》 SCIE EI 2026年第7期2268-2273,共6页
1 Introduction The growing use of computational modelling, simulation tools, and data-driven methods has changedhe way engineering structures and advanced materials are studied and designed. With the increasing availa... 1 Introduction The growing use of computational modelling, simulation tools, and data-driven methods has changedhe way engineering structures and advanced materials are studied and designed. With the increasing availability of high-performance computing, artificial intelligence, and multi-scale simulation techniques,computational modelling is no longer limited to purely theoretical studies. It has now emerged as a practical design aid, allowing researchers to predict material behavior, understand complex interactions, and support engineering decisions across different material and structural scales. These developments have helped in narrowing the gap between theoretical studies and practical engineering applications. 展开更多
关键词 engineering structures practical design aid advanced materials understand complex inte computational modelling simulation tools predict material behavior artificial intelligence
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Methods,Algorithms,Codes,and Software for Theoretical and Computational Chemistry 认领 引用
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作者 Bin Jiang 《Chinese Journal of Chemical Physics》 SCIE EI CAS CSCD 2026年第3期301-302,共2页
Theoretical and computational chemistry has profoundly impacted a wide range of disciplines,from chemistry and physics to biology and materials science.In recent years,remarkable advances in electronic structure theor... Theoretical and computational chemistry has profoundly impacted a wide range of disciplines,from chemistry and physics to biology and materials science.In recent years,remarkable advances in electronic structure theory,molecular dynamics,and machine learning methods——coupled with increasingly powerful algorithms and software—have equipped chemists with an unprecedented arsenal of tools to tackle complex chemical problems. 展开更多
关键词 theoretical computational chemistry electronic structure theorymolecular dynamicsand algorithms codes software methods materials sciencein theoretical chemistry
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Editorial:Quantum algorithms in computational mechanics 认领 引用
19
《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第6期1-3,共3页
Computational mechanics,as a cornerstone of modern engineering and scientific research,has driven transforma-tive advances across aerospace,energy,biomedical,and other related fields over the past decades.However,the ... Computational mechanics,as a cornerstone of modern engineering and scientific research,has driven transforma-tive advances across aerospace,energy,biomedical,and other related fields over the past decades.However,the ever-increasing demand for high-fidelity simulations of complex systems has pushed classical computing archi-tectures to their performance limits.The inherent ex-ponential complexity of multiscale,multiphysics problems often leads to prohibitive computational costs,creating a bottleneck for next-generation engineering innovation. 展开更多
关键词 multiscale problems complex systems classical computing computational mechanicsas engineering innovation scientific researchhas computational mechanics quantum algorithms
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Deep learning-driven computational imaging for light field microscopy 认领 引用 被引量：1
20
作者 Wenshuang Liang Beibei Gao +2 位作者 Lu Gao Wei Ge Fu Wang 《Journal of Innovative Optical Health Sciences》 SCIE EI CSCD 2026年第4期47-62,共16页
Lightfield microscopy(LFM),with its snapshot-based three-dimensional imaging capability,has become a vital tool for observing dynamic living specimens.To overcome limitations in resolution and reconstruction speed inh... Lightfield microscopy(LFM),with its snapshot-based three-dimensional imaging capability,has become a vital tool for observing dynamic living specimens.To overcome limitations in resolution and reconstruction speed inherent in traditional algorithms,the application of deep learning to lightfield microscopic imaging has emerged as a key development direction.This review will introduce the basic theory and classical algorithms of LFM,survey the application of deep learning-based methods in computational image enhancement and analysis,and discuss the associated challenges and future research directions. 展开更多
关键词 Lightfield deep learning reconstruction algorithm computational imaging
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