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engineering of topological tunneling transistors via quantum point contact

Physical Review Applied 2025年第3期23卷 034074-034074页

作者： Gongwei Hu Jiaqi Yang Haozhen Chen Min Liu Shuaiwei Fan Fobao Huang Qiao Chen Minjiang Dan Hubei Engineering Research Center of Weak Magnetic-field Detection College of Science China Three Gorges University Yichang 443002 China College of Hydraulic & Environmental Engineering China Three Gorges University Yichang 443002 China School of Microelectronics Northwestern Polytechnical University Xi’an 710072 China Yangtze River Delta Research Institute of NPU Northwestern Polytechnical University Taicang 215400 China School of Computational Science and Electronics the lnnovation Institute of Advanced Functional Materials Hunan Institute of Engineering Xiangtan 411104 China Joint Laboratory for Extreme Conditions Matter Properties School of Mathematics and Physics Southwest University of Science and Technology Mianyang 621010 China

Owing to the versatile transport process, tunneling transistors (TTs) have become important building blocks for modern electronic, optoelectronic, and magnetic technologies. Although various TTs have been developed, their device operation still has challenges in terms of stability and reliability due to the tunneling process being susceptible to material size and interface scatterings. The robust edge channels in a topological insulator (TI) hold novel tunneling physics, but their TTs are still lacking. In this theoretical work, we propose topological tunneling and resonant TTs by using edge states (ESs) as transport channels in the quantum point contact (QPC) constriction. The finite size effect is the key to adjusting the band structure and tunneling barrier height of ESs by controlling the QPC width. By using the Bernevig-Hughes-Zhang (BHZ) model, we explore the single-QPC tunneling and double-QPC resonant tunneling transport, as well as their device characteristics via the quantum transport Kwant package. Topological TTs based on a single QPC possess excellent gate-modulated capability as well as a size-dependent current sensitivity of 0.1nm−1, several orders of magnitude lower than the conventional TTs. The transport results show that the tunneling probability is significantly influenced by the QPC width (barrier height) but is weakly dependent on the QPC length (barrier width). Moreover, topological resonant TTs are designed in a double-QPC structure and show a negative differential resistance with a large peak-valley current ratio (>104). The transmission spectrum exhibits multiple peaks due to the resonant tunneling driven by quantum well states. The distance between two QPCs can shift these resonant peaks by modulating the transverse confined effect. This study opens a window for designing stable, reliable, and high-performance tunneling devices via emerging quantum materials with great potential for low-power topological electronics applications.

关键词： Semiconductor quantum wells

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Autonomous Decision-Making in Cyber-Physical Systems Using Double Deep Q-Learning and Multi-Agent Collaboration

Autonomous Decision-Making in Cyber-Physical Systems Using D...

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Advancement in Computation & Computer Technologies (InCACCT), International Conference on

作者： Divyabarathi P Anbarasan R. Vasanthi R B. Leena Akshya J M. Sundarrajan Mani Deepak Choudhry Department of Artificial Intelligence and Data Science Bannari Amman Institute of Technology Erode Tamil Nadu India Department of Information Technology KGiSL Institute of Technology Coimbatore Tamil Nadu India Department of Computer Science and Engineering Bharathidasan Engineering College Thirupathur Tamil Nadu India Department of Electronics and Communication Engineering KGiSL Institute of Technology Coimbatore Tamil Nadu India Department of Computational Intelligence SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Networking and Communications SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computing Technologies SRM Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

The autonomous decision-making capabilities of cyber-physical systems (CPS) establish key elements that optimize the management of resources while distributing tasks and maximizing system operational efficiency. Deep Q-Learning (DQN) represents one approach along with Twin Delayed Deep Deterministic Policy Gradient (TD3) and Multi-Agent Proximal Policy Optimization (PPO) that demonstrates capability in handling decision tasks. The existing models face multiple implementation challenges including inaccurate Q-value estimations weak multi-agent relationships and restricted scalability for dynamic systems. The inadequate performance of present methods for energy efficiency and latency alongside poor collaborative coordination led to the necessity of developing a new approach. The new framework DDQN-MAC takes double-deep Q-Learning and combines it with multi-agent collaboration technology to enhance decision-making abilities specific to challenging environments. The method solves overestimation bias through separate action selection and evaluation processes that use multi-agent collaboration for dynamic task distribution and resource control. The designed model demonstrates enhanced performance in multiple critical evaluation measures. Benchmarks indicate that DDQN-MAC attained a superior completion rate of 95.4% while demonstrating reduced latency performance at 12.5 ms versus the other methods DQN (18.7 ms) and Multi-Agent PPO (92.3%). The research provides fundamental groundwork to guide upcoming investigations in hybrid model development while advancing complex multi-agent system capabilities for enhanced autonomous navigational choices within variable operational environments.

关键词： Q-learning Decision making Collaboration Estimation Cyber-physical systems Dynamic scheduling Energy efficiency Resource management Vehicle dynamics Optimization

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Multi-routine-data driven spatio-temporal short-term predictions for surface ozone in China

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Air Quality, Atmosphere and Health 2025年 1-10页

作者： Zheng, Canju Shen, Hengqing Sun, Jianan Liu, Guangliang Cao, Haowei Zhang, Jie Gong, Xiang Xu, Da Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Environment Research Institute Shandong University Shandong Qingdao 266237 China Ningbo Yonghuanyuan Environmental Engineering and Technology Co. LTD Zhejiang Ningbo 315012 China School of Mathematics and Physics Qingdao University of Science and Technology Qingdao 266061 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Qingdao Innovation Center of Artificial Intelligence Ocean Technology Qingdao 266061 China

Ozone (O3) is a major atmospheric pollutant, and accurate prediction of its concentrations remains challenging due to its complex nonlinear relationships with precursor compounds. Existing machine learning methods hav... 详细信息

Ozone (O₃) is a major atmospheric pollutant, and accurate prediction of its concentrations remains challenging due to its complex nonlinear relationships with precursor compounds. Existing machine learning methods have mainly focused on single-site or spatial predictions, lacking research on spatio-temporal short-term predictions based on simple factors. To address this gap, the MRD-O₃former, a deep learning model driven by multi-routine data, was developed to predict short-term hourly spatio–temporal surface ozone concentrations over China. The model exhibits strong spatio–temporal consistency, achieving a high correlation coefficient (r2=0.85∼0.90) and low normalized mean biases (NMBs) between -15% and 15% at the national scale compared to reanalysis ozone data. Both ablation experiments and permutation importance analysis reveal that historical ozone levels play a primary role in next-day ozone prediction, while meteorological factors such as wind speed and temperature also make significant contributions. Regional validation confirms the model’s effectiveness in the Beijing-Tianjin-Hebei region. Moreover, the study investigates the differential impact of crucial factors in urban and rural areas, revealing that historical ozone levels and meteorological factors significantly influence rural areas. However, the influence of historical ozone levels on urban ozone prediction is relatively small, especially during the summer. This suggests that urban ozone undergoes rapid formation and removal processes. These findings highlight the promising potential of deep learning techniques in accurately predicting spatiotemporal short-term ozone concentrations and interpreting the mechanism and source for ozone pollution. © The Author(s), under exclusive licence to Springer Nature B.V. 2025.

关键词： Data-driven Multi-factor Ozone concentration Spatio-temporal prediction

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Finite Temperature Transition in Hyper Stealth Dark Matter using Möbius Domain Wall Fermions

arXiv

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arXiv 2025年

作者： Ayyar, Venkitesh Matsumoto, Nobuyuki Meyer, Aaron S. Park, Sungwoo Hariri Institute for Computing and Computational Science and Engineering Boston University BostonMA02215 United States Physical and Life Sciences Division Lawrence Livermore National Laboratory LivermoreCA94550 United States Nuclear Science Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States

The first-order confinement transition of a strongly coupled composite dark matter theory can provide a possible source of gravitational waves in the early universe. In this work, on behalf of the Lattice Strong Dynamics (LSD) Collaboration, we present our recent investigation on the finite temperature confinement transition of the one-flavor SU(4) dark gauge theory named Hyper Stealth Dark Matter (HSDM). The dark matter candidate in this theory is a composite bosonic baryon and can have a remarkably low mass of a few GeV. We expect the finite temperature transition to be first-order over at least in some finite range of fermionic masses and to be a potential source of observable gravitational radiation. The finite temperature simulation of one-flavor SU(4) is done by using Möbius Domain wall fermions. The order of the transition and its fermionic mass dependence are explored by monitoring the Polyakov loop, chiral condensate and topological charge using three lattice volumes at Nt = 8. © 2025, CC BY.

关键词： Fermions

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A Neural Network Architecture Based on Attention Gate Mechanism for 3D Magnetotelluric Forward Modeling

arXiv

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arXiv 2025年

作者： Zhong, Xin Lin, Weiwei Pan, Kejia Wu, Pinxia Zhang, Jiajing Zhan, Zhiliang Xiao, Wenbo School of information engineering Jiangxi University of Science and Technology Ganzhou341000 China Jiangxi College of Applied Technology Ganzhou341000 China Key Laboratory of Ionic Rare Earth Resources and Environment Ministry of Natural Resources Ganzhou341000 China Hunan Shaofeng Institute for Applied Mathematics Xiangtan411105 China School of Mathematics and Statistics HNP-LAMA Central South University Changsha410083 China National Key Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Beijing100088 China School of science Jiangxi University of Science and Technology Jiangxi Ganzhou341000 China

—Traditional three-dimensional magnetotelluric (MT) numerical forward modeling methods, such as the finite element method (FEM) and finite volume method (FVM), suffer from high computational costs and low efficiency due to limitations in mesh refinement and computational resources. We propose a novel neural network architecture named MTAGU-Net, which integrates an attention gating mechanism for 3D MT forward modeling. Specifically, a dual-path attention gating module is designed based on forward response data images and embedded in the skip connections between the encoder and decoder. This module enables the fusion of critical anomaly information from shallow feature maps during the decoding of deep feature maps, significantly enhancing the network’s capability to extract features from anomalous regions. Furthermore, we introduce a synthetic model generation method utilizing 3D Gaussian random field (GRF), which accurately replicates the electrical structures of real-world geological scenarios with high fidelity. Numerical experiments demonstrate that MTAGU-Net outperforms conventional 3D U-Net in terms of convergence stability and prediction accuracy, with the structural similarity index (SSIM) of the forward response data consistently exceeding 0.98. Moreover, the network can accurately predict forward response data on previously unseen datasets models, demonstrating its strong generalization ability and validating the feasibility and effectiveness of this method in practical applications. Copyright © 2025, The Authors. All rights reserved.

关键词： Decoding

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MHD analysis of couple stress nanofluid through a tapered non-uniform channel with porous media and slip-convective boundary effects

International Journal of Thermofluids

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International Journal of Thermofluids 2025年 27卷

作者： Deepalakshmi, P. Shankar, G. Siva, E.P. Tripathi, D. Bég, O. Anwar Department of Mathematics College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Centre for Computational Biology Easwari Engineering College Tamil Nadu Chennai600089 India Center for Research SRM TRP Engineering College Tamil Nadu Tiruchirappalli621105 India Department of Mathematics National Institute of Technology Uttarakhand 246174 India Multi-Physical Engineering Sciences Group Dept. Mechanical and Aeronautical Engineering Salford University Corrosion/Coatings Lab 3-08 SEE Building Manchester M54WT United Kingdom

The current research addresses the peristaltic transport mechanism that propels fluid through a conduit through rhythmic contraction and relaxation of the conduit walls, a phenomenon evident in numerous biological systems, including the gastrointestinal tract. Motivated by applications in nano-pharmacological drug delivery and thermo-biomagnetic therapy, a mathematical and computational analysis of radiative heat transfer in peristaltic pumping of a magnetohydrodynamic (MHD) couple stress nanofluid through a tapered asymmetric passage, with the influences of a porous medium and wall slip, is presented. Buongiorno's two-component nanoscale model is deployed and the Stokes couple stress non-Newtonian model utilized. Physically the porous medium is modelled with a drag force formulation and simulates the presence of obstructions and deposits in the gastric tract and blood vessels. The governing equations for the couple stress nanofluid are reduced by employing the long-wavelength approximation and the low Reynolds number condition, both standard approaches in fluid dynamics research. Analytical solutions are derived for axial velocity, temperature profile, nanoparticle concentration, stream function, and pressure gradient, providing a comprehensive understanding of the flow dynamics. Furthermore, numerical integration methods are utilized to calculate the average pressure increase (ΔP) and the heat transfer coefficient (Z). The impact of critical parameters namely the Hartmann number (M), Brownian motion parameter (Nb), thermophoresis parameter (Nt), Prandtl number (Pr), slip parameter (L) and radiation parameter (Rn) on fluid dynamics is examined through comprehensive graphical representations. The findings indicate that peristaltic pumping efficiency is superior in a uniform channel relative to a non-uniform channel, underscoring the influence of channel geometry on flow performance. Moreover, the synergistic effects of thermophoresis and Brownian motion result in a

关键词： Reynolds number

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A UNIFIED A POSTERIORI ERROR ANALYSIS FOR DISCONTINUOUS GALERKIN APPROXIMATIONS OF REACTIVE TRANSPORT EQUATIONS

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Journal of computational mathematics 2006年第3期24卷 425-434页

作者： Ji-ming Yang Yan-ping Chen Hunan Key Laboratory for Computation and Simulation in Science and Engineering Institute for Computational and Applied Mathematics and School of Mathematics and Computing Science Xiangtan University Xiangtan 4 11105 China

Four primal discontinuous Galerkin methods are applied to solve reactive transport problems, namely, Oden-BabuSka-Baumann DG （OBB-DG）, non-symmetric interior penalty Galerkin （NIPG）, symmetric interior penalty Galerkin （SIPG）, and incomplete interior penalty Galerkin （IIPG）. A unified a posteriori residual-type error estimation is derived explicitly for these methods. From the computed solution and given data, explicit estimators can be computed efficiently and directly, which can be used as error indicators for adaptation. Unlike in the reference [10], we obtain the error estimators in L^2 （L^2） norm by using duality techniques instead of in L^2（H^1） norm.

关键词： A posteriori error estimates Duality techniques Discontinuous Galerkin methods

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A POSTERIORI ERROR ESTIMATE OF THE DSD METHOD FOR FIRST-ORDER HYPERBOLIC EQUATIONS

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Applied mathematics and Mechanics(English Edition) 2002年第6期23卷 732-740页

作者： KANG Tong(康彤) YU De-hao(余德浩) State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and System Science Chinese Academy of Sciences Beijing 100080 P R China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and System Science Chinese Academy of Sciences Beijing 100080 P R China

A posteriori error estimate of the discontinuous-streamline diffusion method for first-order hyperbolic equations was presented, which can be used to adjust space mesh reasonably. A numerical example is given to illus... 详细信息

关键词： posteriori error estimate discontinuous-streamline diffusion method first-order hyperbolic equation

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Subgrid informed neural networks for high-resolution flood mapping

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Journal of Hydrology 2025年 660卷

作者： Herath, Herath Mudiyanselage Viraj Vidura Marshall, Lucy Saha, Abhishek Rasnayaka, Sanka Seneviratne, Sachith Faculty of Science and Engineering Macquarie University SydneyNSW Australia Faculty of Engineering The University of Sydney SydneyNSW Australia Delft Institute of Applied Mathematics Delft University of Technology Delft Netherlands Hydroinformatics Institute Singapore Department of Computer Science School of Computing National University of Singapore Singapore Transport Health and Urban Systems Research Lab Melbourne School of Design The University of Melbourne MelbourneVIC Australia Faculty of Engineering and Information Technology The University of Melbourne MelbourneVIC Australia

Physics-based hydrodynamic models are essential for accurate flood prediction but are computationally expensive, limiting their applicability for real-time forecasting and probabilistic analyses. Conversely, pure machine learning (ML) models offer both computational efficiency and accuracy but often lack interpretability. To address this gap, we propose SGUnet, a physics-informed ML model and a hybrid theory-guided data science approach, for rapid, high-resolution flood mapping. It utilizes a neural network with U-Net architecture and integrates subgrid-based coarse-grid hydrodynamic model predictions as initial estimates, upskilling them to achieve fine-grid model accuracy. Unlike traditional hydrodynamic models, the subgrid method embeds fine-scale topographic details within coarse-grid cells, enhancing both computational efficiency and predictive accuracy. SGUnet processes flood depth raster patches (512 × 512 pixels) and corresponding digital elevation models as inputs. It functions as a deep learning-based corrector, refining flood predictions from numerical simulators. Trained through supervised learning, SGUnet learns to correct deviations in coarse-grid predictions using fine-grid model outputs as target values. The model is evaluated across three large Australian watersheds—Wollombi, Chowilla, and Burnett River—using HEC-RAS flood simulations with subgrid formulation. SGUnet reduces root mean squared error by a factor of 4.5–5.3 compared to coarse-grid models, achieves a critical success index exceeding 0.9 for flood extent mapping, and delivers a 50x speed-up over fine-grid hydrodynamic models. Furthermore, SGUnet outperforms a state-of-the-art ML-based upskilling model in depth and extent predictions. By effectively correcting flood artifacts from coarse-grid models, SGUnet achieves near fine-grid accuracy with significantly reduced computational cost, demonstrating its potential for real-time flood risk assessment. © 2025 The Authors

关键词： Deep learning

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STOCHASTIC TRUST-REGION METHODS WITH TRUST-REGION RADIUS DEPENDING ON PROBABILISTIC MODELS

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Journal of computational mathematics 2022年第2期40卷 294-334页

作者： Xiaoyu Wang Ya-xiang Yuan Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China State Key Laboratory of Scientific/Engineering Computing Institute of Computational Mathematics and Scientific/Engineering ComputingAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China

We present a stochastic trust-region model-based framework in which its radius is related to the probabilistic ***,we propose a specific algorithm termed STRME,in which the trust-region radius depends linearly on the gradient used to define the latest *** complexity results of the STRME method in nonconvex,convex and strongly convex settings are presented,which match those of the existing algorithms based on probabilistic *** addition,several numerical experiments are carried out to reveal the benefits of the proposed methods compared to the existing stochastic trust-region methods and other relevant stochastic gradient methods.

关键词： Trust-region methods,Stochastic optimization Probabilistic models Trust-region radius Global convergence

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