arXiv subjects

Jun Cai

Publications and source records attributed to Jun Cai.

At least 19 recordsLinked to original sources

arXiv2026

From State Synchronization to Cognitive Self-Evolution: An Operational Architecture for Cognitive Digital Twins

As Digital Twin (DT) systems evolve beyond state synchronization toward task-oriented and knowledge-driven operation, Cognitive Digital Twins (CDTs) have emerged as an extension that incorporates cognitive capabilities into twin operation. Existing CDT studies often focus on specific enabling techniques, such as learning modules, knowledge graphs, and large language models, while providing limited insight into how cognition can be systematically integrated into DT architectures. To address this issue, this paper proposes a four-layer CDT architecture consisting of the physical layer, digital-twin layer, cognitive layer, and task layer. The proposed architecture establishes a self-evolving closed operational loop spanning these four layers, in which physical states are synchronized into digital representations, cognition constructs task-specific cognitive models through knowledge, memory, and attention, and task-level decisions are generated under practical constraints. Operational feedback further refines cognitive experience and updates relationships and annotations in the digital representation, enabling subsequent task interpretation, initiation, and reasoning to evolve with system operation. Based on this framework, two representative operation modes are characterized: user-request-driven cognition and self-driven cognition. We further discuss key enabling mechanisms and deployment challenges associated with semantic communication, knowledge querying, task orchestration, and closed-loop synchronization. A lightweight simulation study illustrates reliable closed-loop task feasibility under limited semantic information and improved operational efficiency through accumulated task experience. The proposed framework provides a structured foundation for the design and development of future CDT systems.

cs.AI↗

arXiv2026

Self-Driven Atomic Dispersion in Graphitic Layers

Carbon-supported single-atom catalysts maximize metal utilization, but how metal nanoparticles transform into isolated atoms within carbon remains unclear. We show that metal nanoparticles can undergo a self-driven dispersion process under hydrocarbon oxidation conditions, transforming into single atoms that are confined in carbon matrix. Using Pt-catalysed hydrocarbon oxidation as a model, we combine operando electron microscopy, near-ambient-pressure X-ray photoelectron spectroscopy and mass spectrometry to track coupled structural and chemical evolution. Graphitic carbon grows at step edges of Pt nanoparticle, continuously reconstructing Pt surface and generating undercoordinated sites for atom release. In-situ generated CO accumulates at the metal-carbon interface, weakening bonding and facilitating self-amplified atom release and migration. Defective carbon overlayers then trap, stabilize and transport liberated atoms, while oxidative etching preserves interfacial access of reaction-gas. Similar behaviour across other metals suggests a general atomization pathway for single-atom catalyst synthesis, yielding products with electrocatalytic hydrogen production activity beyond standard commercial benchmarks.

cond-mat.mtrl-sci↗

arXiv2026

How is Water released in Hydrogen-Based Metal Oxide Reduction? Unraveling the Kinetic Bottleneck in Sustainable Metal Production

Hydrogen-based direct reduction of metal oxides is a ubiquitous solid-gas redox process central to geophysics, sustainable metallurgy, redox energy cycles and catalysis. During this process, hydrogen removes lattice oxygen to form water, yet product water has long been regarded as a passive exhaust, and its nanoscale formation, trapping and removal remain poorly understood. Here, we directly observe redox-product water release from iron oxide during hydrogen-based direct reduction. Because water removal emerges from coupled structural, chemical and crystallographic evolution across multiple length-scales under realistic non-equilibrium reaction-conditions, we establish a correlative multiscale in-situ approach that links pore evolution, molecular water signatures, phase transformation and chemical-state evolution during hematite reduction. We uncover a mechanism in which oxygen removal induces closed nanopores spatially delocalized from reaction surfaces, causing transient trapping of water vapor. Water is released only when these pores coalesce into a percolating network connected to the surface, coinciding with and accelerating the onset of the hematite-to-magnetite transformation. These findings show that dynamically evolving pore topology governs mass transport and redox kinetics in solid-gas reactions, closing a critical mechanistic gap in product-water removal and providing nanoscale guidance for hydrogen-based metal extraction, reactor design, and sustainable redox energy technologies under practical conditions.

physics.chem-ph↗

arXiv2026

Joint Optimization of Training and Inference in Federated Edge Learning via Constrained Multi-Objective Deep Reinforcement Learning

Federated edge learning (FEEL) has recently emerged as a promising paradigm for achieving edge intelligence (EI) via enabling collaborative model training across edge devices while protecting data privacy. In this paper, we put forth an online optimization framework that jointly manages federated training and inference on resource-constrained edge devices. We introduce a tandem-queue-inspired conversion mechanism that bridges inference requests and training data, and further incorporate both data and model freshness into the accuracy formulation to capture temporal dynamics in real-world environments. To maximize inference accuracy while minimizing latency and energy consumption, the mode selections, communication, and computation resource allocations of edge devices are jointly optimized. We formulate this optimization as a multi-objective optimization problem, which is NP-hard and further complicated by the online setting. To address these challenges, we transform the problem into a multi-objective Markov decision process (MOMDP) and develop a \underline{c}onstrained \underline{m}ulti-\underline{o}bjective \underline{p}roximal \underline{p}olicy \underline{o}ptimization (C-MOPPO) algorithm. Specifically, C-MOPPO first learns a set of policies with different preferences across three objectives, then leverages constrained policy optimization to enrich the Pareto front and obtain high-quality, dense solutions. Extensive experiments demonstrate that C-MOPPO achieves well-balanced trade-offs among objectives and significantly outperforms baselines under various system configurations.

cs.LG↗

arXiv2026

Mechanical Origin of High-Temperature Thermal Stability in Platinum Oxides

Platinum oxides are vital catalysts, but their limited thermal stability hinders applications. Recent studies have uncovered a structural transition in two-dimensional platinum oxides that significantly enhances their thermal resilience by several hundred Kelvin. Herein, we demonstrate that this enhanced stability stems from the mechanical robustness of the elastic network at the atomic scale. Prior to the transition, an over-constrained lattice generates localized states of self-stress through an incommensurate Moir\'{e} pattern with the platinum substrate, reducing thermal endurance. After the transition, the oxide shifts to a mechanically flexible structure with balanced degrees of freedom and constraints. The isostatic network, together with the platinum substrate, forms a commensurate Moir\'{e} superlattice that relaxes elastic energy and enhances stability. These findings highlight the fundamental role of network connectivity in governing thermal stability, and provide a design principle for catalysts in extreme environments.

cond-mat.mtrl-sci↗

arXiv2026

Scaling Two-Dimensional Semiconductor Nanoribbons for High-Performance Electronics

As silicon transistors scale toward future technology nodes, three-dimensional architectures -- including gate-all-around (GAA) nanoribbon and complementary field-effect transistors (CFETs) -- require channel widths in the tens of nanometers to meet density targets. Monolayer transition metal dichalcogenides (TMDs), with their atomically thin bodies, are promising channel materials for these architectures, yet most TMD-based FETs remain limited to micrometer-scale widths. Here, we show that channel width scaling of monolayer MoS2 nanoribbon transistors not only preserves but also enhances device performance. Reducing the channel width from hundreds of nanometers to $\sim$30--40 nm increases the median on-current density by $\sim$42% and reduces the median subthreshold swing by $\sim$16%, with a champion device reaching 995 $\mu$A $\mu$m$^{-1}$ at a drain-to-source voltage of 1 V and an overdrive voltage of 2.5 V. We attribute these improvements to three mechanisms: minimal edge-induced disorder, enhanced gate electrostatics at ribbon edges, and more efficient side-contact injection, together reducing contact resistance from $\sim$860 $\Omega$ $\mu$m to $\sim$270 $\Omega$ $\mu$m. Extending the platform to n-type WS2 and p-type WSe2 FETs, we achieve WSe2 p-FET on-currents of 357 $\mu$A $\mu$m$^{-1}$. These findings suggest that monolayer TMD nanoribbon FETs are promising candidates for future ultra-scaled electronics.

cond-mat.mtrl-sci↗

arXiv2025

Enhancing Oxygen Reduction Reaction on Pt-Based Electrocatalysts through Surface Decoration for Improved OH Reduction Equilibrium and Reduced H2O Adsorption

Electrochemical energy and substance conversion devices involve complex electrode processes, characterized by multiple charge transfer steps, competing pathways, and various intermediates. Such complexity makes it challenging to enhance electrocatalytic activity. The prevailing strategy typically focuses on optimizing the geometric and electronic structures of the electrocatalysts to align the adsorption energies of reaction intermediates with the peak of the activity Volcano curve. In this study, we demonstrate that surface decoration can effectively shape the micro reaction environment for the model system of oxygen reduction reaction (ORR) on Pt electrodes. By applying a partial hydrophobic I* adlayer on the Pt surface, we can shift the equilibrium of OH* reduction and weaken H2O* adsorption, which significantly enhances ORR kinetics. With in situ scan tunneling microscopy (STM) and theoretical calculations, our study reveals the formation of isolated Pt2 surface units situated in a hydrophobic valley surrounded by adsorbed iodine atoms. This minimalist Pt2 active unit exhibits significantly greater activity for ORR compared to an extended Pt surface. This strategy could pave the way for developing highly efficient catalysts with potential applications in fuel cell technology and metal air batteries and extension to other electrochemical conversion reactions such as ammonia synthesis and CO2 reduction.

physics.chem-ph↗

arXiv2025

OH$^-$-Enhanced Alkaline Hydrogen Evolution Reaction at the Au(111) Electrode

The hydrogen evolution reaction (HER) in alkaline media suffers from sluggish kinetics but the origin of the pH-dependent activity remains debated. This study investigates the role of hydroxide ions (OH) in enhancing the alkaline HER at Au(111) by systematically varying the pH and the NaOH concentration both with and without fixing the total Na concentration.Contrary to conventional cation-centric models of alkaline HER, we demonstrate a notable anion effect by showing that the HER activity increases monotonically with pH and OH concentration, even at extremely high NaOH concentrations(up to 9 M).Tafel slopes decrease from 181 mV/dec at pH=10 to 124 mV/dec at pH=13 and to 111 mV/dec for the case with 9 M NaOH, indicating accelerated kinetics.Infrared spectroscopy reveals that interfacial OH strengthens the hydrogen-bond network, which is expected to lower the activation energy for the Volmer step,the rate-determining step of HER on Au(111). Hence,OH enhances alkaline HER kinetics by strengthening the hydrogen bond network and its connectivity at the electrochemical interface;this allows us to propose a unified mechanism for the electrolyte effects on alkaline HER where structure-making ions (Li,K,and OH) improve the reaction kinetics by optimizing the interfacial hydrogen bond network.

physics.chem-ph↗

arXiv2025

A Novel Collaborative Framework for Efficient Synchronization in Split Federated Learning over Wireless Networks

Split Federated Learning (SFL) offers a promising approach for distributed model training in wireless networks, combining the layer-partitioning advantages of split learning with the federated aggregation that ensures global convergence. However, in heterogeneous wireless environments, disparities in device capabilities and channel conditions make strict round-based synchronization heavily straggler-dominated, thereby limiting both efficiency and scalability. To address this challenge, we propose a new framework, called Collaborative Split Federated Learning (CSFL), that redefines workload redistribution through device-to-device collaboration. Building on the flexibility of model partitioning, CSFL enables efficient devices, after completing their own forward propagation, to seamlessly take over the unfinished layers of bottleneck devices. This collaborative process, supported by D2D communications, allows bottleneck devices to offload computation earlier while maintaining synchronized progression across the network. Beyond the system design, we highlight key technical enablers such as privacy protection, multi-perspective matching, and incentive mechanisms, and discuss practical challenges including matching balance, privacy risks, and incentive sustainability. A case study demonstrates that CSFL significantly reduces training latency without compromising convergence speed or accuracy, underscoring collaboration as a key enabler for synchronization-efficient learning in next-generation wireless networks.

cs.LG↗

arXiv2025

Extending Ambient Pressure X-ray Photoelectron Spectroscopy to Plasma Studies: A novel and flexible plasma gun approach

The characterization of the electronic structure and chemical states of gases, solids, and liquids can be effectively performed using ambient pressure X-ray photoelectron spectroscopy (AP-XPS). However, the acquisition of electronic and chemical information under plasma conditions poses significant challenges. In this study, we have developed an advanced experimental system capable of garnering electronic information amidst plasma environments, alongside providing detailed surface chemical states of samples subjected to plasma conditions. By designing a customized plasma generation apparatus, we successfully integrated it with a traditional AP-XPS system. This novel plasma-AP-XPS system confined plasma proximal to the sample area, with adjustable intensity parameters controlled by either modifying the distance between the plasma source and the sample surface or adjusting the voltage applied. This configuration permitted the direct detection of electrons in the plasma via the XPS electron detector. To substantiate the efficacy and versatility of this setup, it was applied to two distinct studies: the plasma etching of graphene and plasma oxidation of platinum (Pt). The investigations confirmed that argon (Ar) plasma facilitates the etching of graphene, a phenomenon clearly evidenced by the XPS spectra. Similarly, the exposure of the Pt surface to oxygen plasma was found to induce effective oxidation. This developed system significantly extends the utility of AP-XPS, enhancing its application for in-depth studies of plasma-enhanced reactions under operando conditions, thereby holding promise for the advancement in material science and chemical engineering fields.

physics.chem-ph↗

arXiv2025

Personalized Class Incremental Context-Aware Food Classification for Food Intake Monitoring Systems

Accurate food intake monitoring is crucial for maintaining a healthy diet and preventing nutrition-related diseases. With the diverse range of foods consumed across various cultures, classic food classification models have limitations due to their reliance on fixed-sized food datasets. Studies show that people consume only a small range of foods across the existing ones, each consuming a unique set of foods. Existing class-incremental models have low accuracy for the new classes and lack personalization. This paper introduces a personalized, class-incremental food classification model designed to overcome these challenges and improve the performance of food intake monitoring systems. Our approach adapts itself to the new array of food classes, maintaining applicability and accuracy, both for new and existing classes by using personalization. Our model's primary focus is personalization, which improves classification accuracy by prioritizing a subset of foods based on an individual's eating habits, including meal frequency, times, and locations. A modified version of DSN is utilized to expand on the appearance of new food classes. Additionally, we propose a comprehensive framework that integrates this model into a food intake monitoring system. This system analyzes meal images provided by users, makes use of a smart scale to estimate food weight, utilizes a nutrient content database to calculate the amount of each macro-nutrient, and creates a dietary user profile through a mobile application. Finally, experimental evaluations on two new benchmark datasets FOOD101-Personal and VFN-Personal, personalized versions of well-known datasets for food classification, are conducted to demonstrate the effectiveness of our model in improving the classification accuracy of both new and existing classes, addressing the limitations of both conventional and class-incremental food classification models.

cs.CV↗

arXiv2025

Towards Intelligent Transportation with Pedestrians and Vehicles In-the-Loop: A Surveillance Video-Assisted Federated Digital Twin Framework

In intelligent transportation systems (ITSs), incorporating pedestrians and vehicles in-the-loop is crucial for developing realistic and safe traffic management solutions. However, there is falls short of simulating complex real-world ITS scenarios, primarily due to the lack of a digital twin implementation framework for characterizing interactions between pedestrians and vehicles at different locations in different traffic environments. In this article, we propose a surveillance video assisted federated digital twin (SV-FDT) framework to empower ITSs with pedestrians and vehicles in-the-loop. Specifically, SVFDT builds comprehensive pedestrian-vehicle interaction models by leveraging multi-source traffic surveillance videos. Its architecture consists of three layers: (i) the end layer, which collects traffic surveillance videos from multiple sources; (ii) the edge layer, responsible for semantic segmentation-based visual understanding, twin agent-based interaction modeling, and local digital twin system (LDTS) creation in local regions; and (iii) the cloud layer, which integrates LDTSs across different regions to construct a global DT model in realtime. We analyze key design requirements and challenges and present core guidelines for SVFDT's system implementation. A testbed evaluation demonstrates its effectiveness in optimizing traffic management. Comparisons with traditional terminal-server frameworks highlight SV-FDT's advantages in mirroring delays, recognition accuracy, and subjective evaluation. Finally, we identify some open challenges and discuss future research directions.

cs.ET↗

arXiv2024

SCKF-LSTM Based Trajectory Tracking for Electricity-Gas Integrated Energy System

This paper introduces a novel approach for tracking the dynamic trajectories of integrated natural gas and power systems, leveraging a Kalman filter-based structure. To predict the states of the system, the Holt's exponential smoothing techniques and nonlinear dynamic equations of gas pipelines are applied to establish the power and gas system equations, respectively. The square-root cubature Kalman filter algorithm is utilized to address the numerical challenges posed by the strongly nonlinear system equations. The boundary conditions in the gas system include the flow balances at sink nodes, and the mass flow rates of loads have to be predicted at each computation step. For the prediction of load mass flows, the long short-term memory network is employed, known for its effectiveness in time series prediction. Consequently, a combined method based on the square-root cubature Kalman filter and the long short-term memory network is proposed for tracking integrated gas and power systems. To evaluate the tracking performances of the proposed method, the IEEE-39 bus power system and GasLib-40 node gas system are used to form the testing system. Simulation results demonstrate high precision in tracking the dynamic states of power and gas systems. Two indexes are introduced for a numerical analysis of the tracking results, indicating that the accuracy of this method surpasses that of traditional measurements.

eess.SY↗

arXiv2024

Demand-Aware Beam Hopping and Power Allocation for Load Balancing in Digital Twin empowered LEO Satellite Networks

Low-Earth orbit (LEO) satellites utilizing beam hopping (BH) technology offer extensive coverage, low latency, high bandwidth, and significant flexibility. However, the uneven geographical distribution and temporal variability of ground traffic demands, combined with the high mobility of LEO satellites, present significant challenges for efficient beam resource utilization. Traditional BH methods based on GEO satellites fail to address issues such as satellite interference, overlapping coverage, and mobility. This paper explores a Digital Twin (DT)-based collaborative resource allocation network for multiple LEO satellites with overlapping coverage areas. A two-tier optimization problem, focusing on load balancing and cell service fairness, is proposed to maximize throughput and minimize inter-cell service delay. The DT layer optimizes the allocation of overlapping coverage cells by designing BH patterns for each satellite, while the LEO layer optimizes power allocation for each selected service cell. At the DT layer, an Actor-Critic network is deployed on each agent, with a global critic network in the cloud center. The A3C algorithm is employed to optimize the DT layer. Concurrently, the LEO layer optimization is performed using a Multi-Agent Reinforcement Learning algorithm, where each beam functions as an independent agent. The simulation results show that this method reduces satellite load disparity by about 72.5% and decreases the average delay to 12ms. Additionally, our approach outperforms other benchmarks in terms of throughput, ensuring a better alignment between offered and requested data.

eess.SP↗

arXiv2024

Theory of vibrational Stark effect for adsorbates and diatomic molecules

Nowadays the vibrational Stark effect (VSE) of adsorbates at the electrochemical interfaces is generally investigated using the Lambert theory, in which the strong electric field across the interfaces can be treated as some kind of perturbation. Lambert found that the VSE arises mainly from the classical effect, and the quantum effect is negligible. This idea is accepted by almost all current first-principle calculations for this issue. Here we revisit this problem by addressing the fundamental question that to what extent the quantum effect is important for VSE, and if it is observable, then which physical quantity determines this effect. We use the Morse, Lennard-Jones and Dunham potentials as basic potentials to explore this problem using quantum perturbation theory. We define the relative difference between quantum and classical VSE slopes to define the quantum effect, $\eta$, and show that for CO, $\eta \sim $ 2 - 3\%, while for adsorbed hydrogen on Pt electrode, $\eta \sim$ 8 - 10\%, using the experimental data. We find that $\eta$ is determined by the anharmonic coefficient $\chi_e$. Without results we present a new understanding of the VSE as a function of electric field and potential in electrochemical experiments, showing that the nonlinear slope of VSE as a function of potential should arise from the nonlinear relation between electric field and potential across the interfaces, which may resolve the long-standing controversial in experiments.

cond-mat.mtrl-sci↗

arXiv2024

Worst-case values of target semi-variances with applications to robust portfolio selection

The expected regret and target semi-variance are two of the most important risk measures for downside risk. When the distribution of a loss is uncertain, and only partial information of the loss is known, their worst-case values play important roles in robust risk management for finance, insurance, and many other fields. Jagannathan (1977) derived the worst-case expected regrets when only the mean and variance of a loss are known and the loss is arbitrary, symmetric, or non-negative. While Chen et al. (2011) obtained the worst-case target semi-variances under similar conditions but focusing on arbitrary losses. In this paper, we first complement the study of Chen et al. (2011) on the worst-case target semi-variances and derive the closed-form expressions for the worst-case target semi-variance when only the mean and variance of a loss are known and the loss is symmetric or non-negative. Then, we investigate worst-case target semi-variances over uncertainty sets that represent undesirable scenarios faced by an investors. Our methods for deriving these worst-case values are different from those used in Jagannathan (1977) and Chen et al. (2011). As applications of the results derived in this paper, we propose robust portfolio selection methods that minimize the worst-case target semi-variance of a portfolio loss over different uncertainty sets. To explore the insights of our robust portfolio selection methods, we conduct numerical experiments with real financial data and compare our portfolio selection methods with several existing portfolio selection models related to the models proposed in this paper.

q-fin.RM↗

arXiv2024

Edge Computing Enabled Real-Time Video Analysis via Adaptive Spatial-Temporal Semantic Filtering

This paper proposes a novel edge computing enabled real-time video analysis system for intelligent visual devices. The proposed system consists of a tracking-assisted object detection module (TAODM) and a region of interesting module (ROIM). TAODM adaptively determines the offloading decision to process each video frame locally with a tracking algorithm or to offload it to the edge server inferred by an object detection model. ROIM determines each offloading frame's resolution and detection model configuration to ensure that the analysis results can return in time. TAODM and ROIM interact jointly to filter the repetitive spatial-temporal semantic information to maximize the processing rate while ensuring high video analysis accuracy. Unlike most existing works, this paper investigates the real-time video analysis systems where the intelligent visual device connects to the edge server through a wireless network with fluctuating network conditions. We decompose the real-time video analysis problem into the offloading decision and configurations selection sub-problems. To solve these two sub-problems, we introduce a double deep Q network (DDQN) based offloading approach and a contextual multi-armed bandit (CMAB) based adaptive configurations selection approach, respectively. A DDQN-CMAB reinforcement learning (DCRL) training framework is further developed to integrate these two approaches to improve the overall video analyzing performance. Extensive simulations are conducted to evaluate the performance of the proposed solution, and demonstrate its superiority over counterparts.

cs.CV↗

arXiv2024

Energy-Efficient UAV Swarm Assisted MEC with Dynamic Clustering and Scheduling

In this paper, the energy-efficient unmanned aerial vehicle (UAV) swarm assisted mobile edge computing (MEC) with dynamic clustering and scheduling is studied. In the considered system model, UAVs are divided into multiple swarms, with each swarm consisting of a leader UAV and several follower UAVs to provide computing services to end-users. Unlike existing work, we allow UAVs to dynamically cluster into different swarms, i.e., each follower UAV can change its leader based on the time-varying spatial positions, updated application placement, etc. in a dynamic manner. Meanwhile, UAVs are required to dynamically schedule their energy replenishment, application placement, trajectory planning and task delegation. With the aim of maximizing the long-term energy efficiency of the UAV swarm assisted MEC system, a joint optimization problem of dynamic clustering and scheduling is formulated. Taking into account the underlying cooperation and competition among intelligent UAVs, we further reformulate this optimization problem as a combination of a series of strongly coupled multi-agent stochastic games, and then propose a novel reinforcement learning-based UAV swarm dynamic coordination (RLDC) algorithm for obtaining the equilibrium. Simulations are conducted to evaluate the performance of the RLDC algorithm and demonstrate its superiority over counterparts.

cs.NI↗