Frontiers in Applied Physics and Mathematics
Frontiers in Applied Physics and Mathematics is a peer-reviewed, open-access journal dedicated to publishing high-quality research in the fields of applied physics and mathematics. The journal aims to bridge the gap between theoretical advancements and practical applications, providing a platform for scientists, researchers, and academics to share their discoveries and innovations. With a focus on interdisciplinary studies, the journal covers a wide range of topics, including quantum mechanics, statistical physics, computational physics, materials science, applied mathematics, and mathematical modeling.
Tracking Pollution Sources Across Watersheds via Causal Graph Discovery
The accurate identification of pollution sources across complex watershed systems remains a fundamental challenge in environmental science. Traditional monitoring approaches relying on grab sampling and correlation statistics are frequently inadequate to capture the directional, time-lagged causal pathways through which contaminants propagate downstream. This paper proposes an integrated framework for tracking pollution sources across watersheds using causal graph discovery (CGD) techniques grounded in directed acyclic graphs (DAGs). The framework combines multivariate time-series observations from distributed water quality sensors with constraint-based and score-based structure learning algorithms, notably the Peter-Clark Momentary Conditional Independence (PCMCI) method and the Greedy Equivalence Search (GES) algorithm, to recover a sparse, interpretable causal network of pollutant transmission pathways. A structural equation model (SEM) is subsequently fitted to the recovered DAG to quantify causal effect strengths between upstream and downstream monitoring stations. Experimental validation on simulated watershed data demonstrates that the proposed approach correctly identifies the principal nonpoint source pollution (NPS) input nodes with a precision of 0.87 and recall of 0.82 under realistic observational noise conditions. Comparisons with Granger causality and transfer entropy (TE) baselines confirm the framework's superior robustness to confounded co-variation. The results underscore the transformative potential of causal graph methods for real-time, data-driven pollution source attribution in large, instrumented watershed systems.
Copyright (c) 2026 Zhaoyang Liu (Author) https://creativecommons.org/licenses/by/4.0
Meta-Reinforcement Learning for Cross-Domain Adaptation in Collaborative Decision Systems
Generalizing collaborative strategies across heterogeneous tasks remains a major challenge for reinforcement-driven systems. This work examines a meta-reinforcement learning framework that enables rapid adaptation of coordination policies across domains. The model employs model-agnostic meta-learning (MAML) combined with PPO to learn a shared initialization that can be fine-tuned with limited task-specific data. Experiments are conducted on a composite dataset of 11,400 tasks spanning scheduling, routing, and resource optimization domains. The approach achieves a 23.1% improvement in zero-shot performance and reduces adaptation steps by 34.6% compared to task-specific training. Furthermore, cross-domain performance variance is reduced by 20.8%, indicating more stable transfer. These findings suggest that meta-learning provides an effective mechanism for scaling collaborative decision systems to diverse environments.
Copyright (c) 2026 Jeroen van Dijk, Sander van der Meer, Thomas de Vries (Author) https://creativecommons.org/licenses/by/4.0
Neural Decoders Reduce Logical Error Rates in Surface Code Quantum Systems
Fault-tolerant quantum computation critically depends on the ability to detect and correct physical qubit errors faster than they accumulate. The surface code (SC) has emerged as the most experimentally promising quantum error correction (QEC) framework due to its high threshold and local stabilizer structure, yet its practical performance is bottlenecked by decoder efficiency. Traditional decoding approaches such as minimum-weight perfect matching (MWPM) achieve near-optimal accuracy under independent noise but struggle with correlated errors, circuit-level noise, and real-time latency constraints. This paper investigates how neural network (NN)-based decoders reduce logical error rates in SC quantum systems by learning complex syndrome-to-correction mappings from data. We propose a hybrid convolutional-recurrent neural architecture that processes stabilizer measurement syndromes across both spatial and temporal dimensions, enabling the decoder to capture correlated error patterns inherent in realistic hardware. Systematic simulations under depolarizing, biased, and circuit-level noise models demonstrate that the proposed neural decoder achieves a logical error rate reduction of up to 41.3% compared to MWPM at a physical error rate of p = 0.008, while maintaining sub-millisecond decoding latency compatible with superconducting qubit cycle times. These results establish that data-driven neural decoders represent a scalable and hardware-compatible pathway toward achieving the logical error rates required for practical fault-tolerant quantum computing.
Copyright (c) 2026 Lukas Schneider, Haruki Tanaka (Author) https://creativecommons.org/licenses/by/4.0/
Frequency-Domain Characterization of Memory Poisoning Propagation in Multi-Agent Collaborative Systems
Memory poisoning in multi-agent collaborative systems may exhibit structured propagation patterns across communication channels. This study analyzes poisoning diffusion using frequency-domain decomposition of inter-agent memory update signals. Memory state transitions are transformed via discrete Fourier analysis to identify anomalous high-frequency components introduced by adversarial perturbations. A spectral attenuation filter selectively suppresses abnormal frequency bands before synchronization. Experiments were conducted on 120-agent distributed coordination tasks over 10,000 update cycles. Poisoning injection at 15% intensity increased high-frequency spectral energy by 63.2% compared to clean baselines. Spectral filtering reduced contamination spread from 58.4% to 19.6% of agents and restored task performance to 87.3% of nominal levels. Frequency-domain modeling provides a novel perspective on poisoning detection and containment in collaborative agent systems.
Copyright (c) 2026 Wei Ming Tan, Cheryl Lim, Jonathan Lee (Author) https://creativecommons.org/licenses/by/4.0
Ledger-Guided Memory for Agentic Web RL: Structured Cost–Risk Accounting in Long-Horizon Decision Models
Long-horizon web tasks require consistent tracking of intermediate commitments (selected filters, filled fields, account states) and ongoing budgets (requests, latency, monetary fees) while avoiding repeated risky actions. We propose Ledger Memory Agent (LeMA), an agentic RL model equipped with a structured memory ledger that records (a) action commitments, (b) remaining multi-cost budgets, and (c) accumulated risk signals. The ledger is updated by a learned parser that extracts key-value state facts from the DOM/text and attaches each with provenance. Policy learning is augmented with (i) a ledger-consistency loss penalizing actions that contradict recorded commitments, and (ii) a budget–risk controller that modulates exploration based on remaining budgets and predicted risk spikes. Recommended evaluation uses 1,000–2,000 tasks with longer horizons (12–30 steps), comparing against standard recurrent/Transformer memory agents. Metrics include success, redundant action rate, cost overshoot, failure frequency, and ledger factual accuracy (F1 on extracted state facts). LeMA improves reliability by making cost–risk accounting explicit rather than implicit in hidden states.
Copyright (c) 2026 Andrew J. Patel, Emily R. Thompson, Benjamin K. Lee (Author) https://creativecommons.org/licenses/by/4.0
Pareto-Front Agentic RL with Dynamic Preference Conditioning for Cost–Risk–Success Trade-offs in Web Tasks
Web agent deployment requires navigating trade-offs among success rate, monetary/API costs, latency, and failure risk, which vary by user and scenario. We propose a preference-conditioned multi-objective RL framestudy that learns a Pareto set of policies for web tasks. The method trains a single agent conditioned on a preference vector w over (success, cost1_11, cost2_22, …, risk), using (i) multi-gradient conflict resolution to stabilize updates across objectives, and (ii) Pareto replay that balances samples from distinct regions of the frontier. To ensure tail-risk control, the risk objective is defined as quantile-based failure loss (e.g., 90th/95th percentile). We recommend benchmarking on 1,200+ tasks across 40–70 site templates, sweeping preference vectors to obtain a Pareto curve and reporting hypervolume improvement, frontier coverage, and policy switching stability. This approach enables “one model, many operating points,” supporting practical deployment where budgets and risk tolerance change dynamically.
Copyright (c) 2026 Marco Rossi, Giulia Bianchi, Alessandro Conti (Author) https://creativecommons.org/licenses/by/4.0
Beyond Interference: Estimating Causal Network Effects in Digital Advertising Markets
Digital advertising platforms operate within complex social networks where user responses to ads are not independent but are influenced by the actions and exposures of their peers. This interdependence, known as network interference, fundamentally violates the Stable Unit Treatment Value Assumption (SUTVA) of traditional causal inference models, leading to significant attribution bias and suboptimal budget allocation. This paper addresses the challenge of estimating the true causal effect of digital advertising expenditures, moving beyond direct response metrics to quantify the total demand generation effect, which includes both direct and socially mediated peer influences. We propose a novel, robust methodological framework that integrates high-dimensional network clustering with a two-stage least squares (2SLS) instrumental variable approach and a synthetic control method. Using peer adoption rates and platform algorithm shocks as instruments, we isolate exogenous variation in ad exposure. We then employ a synthetic control-based estimator to construct counterfactual outcomes for treated network segments, effectively absorbing the bias from spillover effects. Our empirical analysis, applied to a simulated marketplace mirroring real-world advertising dynamics, demonstrates that standard models underestimate the total return on ad spend by approximately 30-45%. The findings provide a scalable solution for advertisers to de-bias their attribution models and optimize campaigns in the presence of social interference, offering a significant advancement in the econometrics of digital marketing measurement.
Copyright (c) 2026 Yi Han (Author) https://creativecommons.org/licenses/by/4.0/
Multi-Agent Post-Co-Training of Large Language Models via Reinforcement Learning
This study introduces MAPoRL2, a post-training framework that enhances collaborative LLM performance through multi-agent reinforcement learning and structured discussion. Multiple LLM agents independently generate candidate solutions, engage in iterative discussion rounds, and are jointly optimized using verifier-based rewards that assess both correctness and corrective reasoning. Experiments across 5 reasoning and generation benchmarks with 4,500 training samples demonstrate improvements of 18.9% in answer accuracy and 22.4% in correction efficiency over single-agent post-training, highlighting the effectiveness of discussion-aware RL signals.
Copyright (c) 2026 James L. Carter, Yuxuan Liu, Thomas K. Lee (Author) https://creativecommons.org/licenses/by/4.0/
Radiomics-Prior SAM for Multi-Region Brain Tumor Substructure Segmentation
Different tumor subregions exhibit distinct morphological and radiological signatures, yet many segmentation models treat them uniformly. RP-SAM incorporates radiomics-derived priors that encode intensity, shape, and texture cues associated with edema, non-enhancing core, and enhancing tumor. These priors guide SAM’s mask generation process to favor subregion-consistent boundaries and reduce misclassification between visually similar regions. On BraTS2020 (369 subjects), RP-SAM improves enhancing-tumor Dice from 0.743 to 0.802 (+7.9%), tumor core Dice from 0.786 to 0.841 (+7.0%), and whole-tumor Dice from 0.892 to 0.923 (+3.1%).
Copyright (c) 2026 Emily R. Thompson, Jason M. Carter, Wei Zhang (Author) https://creativecommons.org/licenses/by/4.0
Low-Dose CT Image Reconstruction Using Uncertainty-Aware Convolution–Attention Networks
Reducing radiation dose in computed tomography often leads to increased noise and loss of structural detail. Motivated by hybrid convolution–attention denoising models such as CTLformer, this work investigates an uncertainty-aware reconstruction framework that combines local convolutional filtering with global self-attention. The model incorporates uncertainty estimation to adaptively balance fine-grained texture preservation and noise suppression. Experiments are conducted on two public low-dose CT datasets containing over 45,000 paired normal- and low-dose slices. Comparisons are performed against CNN-based methods (RED-CNN, DnCNN), transformer-based models, and recent hybrid architectures. Quantitative results show average improvements of 0.9–1.3 dB in PSNR and 1.5%–2.4% in SSIM under standard low-dose settings, with reduced variance across anatomical regions.
Copyright (c) 2026 Laura Gómez, Daniel Fernández (Author) https://creativecommons.org/licenses/by/4.0/
Fine-Grained Kernel Memory Isolation Using Hardware Protection Keys and Capability Mapping
Hardware protection keys (PKU/PKS) offer lightweight mechanisms for runtime memory access control, yet prior systems often apply them at coarse granularity. We introduce a capability-mapped isolation framework that partitions kernel subsystems into fine-grained memory regions with adaptive key assignments. Tested on Linux 6.1, the system enforces per-function memory boundaries with <3.5% performance overhead, blocking 94% of simulated invalid-write attacks. Stress tests using 22 historical CVE exploits demonstrate complete mitigation in 18 cases. Our results show that combining capability mapping with PKS significantly strengthens kernel memory integrity while remaining deployment-friendly.
Copyright (c) 2026 Hiroshi Nakamura, Yuki Matsumoto, Rina Takeda (Author) https://creativecommons.org/licenses/by/4.0
Spatiotemporal Graph Networks for Predicting Transformer Failures in Regional Power Grids
Power transformers are critical assets in regional electricity infrastructure, and their unexpected failures frequently trigger cascading outages with severe economic consequences. Traditional fault diagnosis approaches, including dissolved gas analysis (DGA) and periodic physical inspections, remain inadequate for proactive real-time monitoring across large and topologically complex grid networks. This paper proposes a spatiotemporal graph network (STGN) framework that jointly models the topological structure of a regional power grid and the temporal dynamics of transformer operational data to achieve early failure prediction. The architecture integrates spatio-temporal convolutional blocks, each comprising temporal gated convolution layers flanking a spatial graph convolution layer with gated linear unit activations, to capture both spatial propagation patterns between interconnected devices and time-varying deterioration signals at individual nodes. Panoramic state information, including dissolved gas concentrations, load data, and qualitative maintenance records, is encoded through sequential LSTM processing to support multi-horizon failure probability estimation. Experiments on a real-world regional transmission dataset demonstrate a precision of 91.4%, recall of 89.7%, and F1 score of 90.5%, surpassing support vector machine, LSTM, and standard convolutional neural network baselines by margins of 8 to 17 percentage points. Performance comparison across Correlation, CSI, FAR, and POD metrics at multiple prediction horizons confirms the sustained accuracy advantage of the spatiotemporal formulation over non-graph baselines.
Copyright (c) 2026 Marius Andersen, Chiara Lombardi (Author) https://creativecommons.org/licenses/by-sa/4.0
Causal Analysis of the Impact of Hotel Employee Training Investment on Turnover Rate
High employee turnover is a common human resource management challenge faced by the hotel industry, but whether training investment can effectively reduce turnover rate still lacks rigorous causal evidence. This study uses a large hotel group as the research subject to assess the actual impact of employee training investment on turnover behavior. The research sample covers the personnel, training, and performance records of 9,842 employees for 18 consecutive months. Variables include job type, training duration, performance rating, and attendance. Propensity score matching was used to construct a comparable sample, and difference analysis was combined to identify the net effect of training intervention. Empirical results show that employees who received systematic training had a significantly lower probability of leaving during the subsequent observation period, and the longer the training duration, the more significant the decrease in turnover rate. Further analysis shows that this effect is more prominent in front-line service positions. This study verifies the role of training in employee retention from a causal perspective, providing empirical support for the allocation of hotel training resources.
Copyright (c) 2026 Anna K. Schneider, Lukas H. Weber, Johannes M. Becker (Author) https://creativecommons.org/licenses/by-sa/4.0
Dynamic Response of Mooring Systems for Deep-Water Semi-Submersibles under Wave Fluid-Structure Interaction
The exploration of offshore hydrocarbon resources has increasingly moved towards deep and ultra-deep waters, necessitating robust floating production systems. Among these, semi-submersibles are widely utilized due to their favorable motion characteristics; however, their station-keeping relies heavily on complex mooring systems that are susceptible to fatigue and failure under extreme environmental loads. This paper presents a comprehensive investigation into the dynamic response of mooring systems for deep-water semi-submersibles, specifically focusing on the non-linear fluid-structure interaction (FSI) between the hull, the mooring lines, and the incident wave field. We employ a fully coupled time-domain analysis method that integrates diffraction-radiation theory for large-volume structures with a finite element model based on the lumped mass formulation for mooring dynamics. The study examines the impact of wave frequency, significant wave height, and current velocities on the tension variations and displacement vectors of the mooring lines. Results indicate that neglecting the fluid-structure interaction effects leads to a significant underestimation of peak tension loads, particularly in the splash zone and at the fairlead connections. Furthermore, the coupling effect induces low-frequency drift motions that amplify fatigue damage accumulation. This research provides critical insights for optimizing mooring designs and enhancing the safety margins of deep-water floating platforms.
Copyright (c) 2026 Robert Anderson, Laura Meyer (Author) https://creativecommons.org/licenses/by-sa/4.0
Autonomous Stereo Vision Parameter Optimization based on Nonlinear Conjugate Gradient
Stereo vision systems serve as a cornerstone for depth perception in autonomous robotics, advanced driver-assistance systems, and three-dimensional reconstruction. However, the performance of stereo matching algorithms is heavily contingent upon the precise tuning of hyperparameters, such as penalty coefficients, window sizes, and aggregation thresholds. Manual tuning of these parameters is laborious, subjective, and often fails to generalize across diverse environmental conditions. This paper introduces an automated framework for optimizing stereo vision parameters utilizing the Nonlinear Conjugate Gradient (NCG) method. By formulating the parameter selection process as a continuous optimization problem within a multidimensional cost landscape, we employ NCG to efficiently navigate towards optimal configurations. Unlike stochastic methods such as genetic algorithms, NCG leverages gradient approximation to achieve faster convergence while maintaining high solution quality. We validate our approach using standard benchmark datasets, demonstrating that the NCG-optimized parameters significantly reduce disparity error rates compared to baseline configurations and perform competitively against computationally expensive global search methods. The proposed framework offers a robust, time-efficient solution for adaptive stereo vision in dynamic environments.
Copyright (c) 2026 Wei Xie, Robert Sterling, Eleanor Vance (Author) https://creativecommons.org/licenses/by-sa/4.0
Learning Occlusion-Robust Pedestrian Representations via Uncertainty-Guided Feature Pruning
Occlusion and background interference remain major challenges for pedestrian re-identification in urban traffic environments. Inspired by uncertainty-aware CLIP-based frameworks, this paper introduces an uncertainty-guided feature selection mechanism that adjusts the contribution of local visual regions and semantic cues according to their estimated reliability. The proposed method is evaluated on two autonomous driving datasets with both real-world and synthetic occlusion patterns, covering occlusion ratios from 20% to 60%. Comparisons are conducted against attention-based and part-based ReID methods, including PCB, OSNet, and transformer-based attention models. The proposed approach achieves mAP improvements ranging from 4.5% to 6.2% under severe occlusion conditions, while maintaining comparable performance in fully visible settings.
Copyright (c) 2026 Lukas M. Schneider, Anna K. Vogel, Tobias R. Weber (Author) https://creativecommons.org/licenses/by-sa/4.0
Federated Learning Approaches to Collaborative Fraud Detection Across Financial Institutions
Financial fraud detection has emerged as a critical challenge for banking institutions worldwide, with billions of dollars lost annually to increasingly sophisticated fraudulent activities. Traditional centralized machine learning approaches face significant limitations due to data privacy regulations, institutional data silos, and the inability to leverage collective intelligence across organizations. This paper presents a comprehensive review of Federated Learning (FL) methodologies applied to collaborative fraud detection systems in the financial sector. FL enables multiple financial institutions to jointly train robust fraud detection models while maintaining strict data privacy and regulatory compliance. We examine the architectural frameworks, algorithmic innovations, and practical implementations of FL-based fraud detection systems, with particular emphasis on model initialization strategies, communication efficiency optimization, and handling data heterogeneity across institutions. Through analysis of recent developments, we demonstrate how FL addresses key challenges including non-independent and identically distributed data, communication overhead, and convergence stability. Our review reveals that FL-based approaches achieve detection accuracy improvements of up to 20% compared to isolated institutional models, while simultaneously ensuring compliance with data protection regulations such as GDPR and CCPA. We discuss the integration of advanced techniques including structured gradient compression, adaptive local training strategies, and initialization protocols that enhance both detection performance and system efficiency. The paper concludes by identifying emerging research directions and practical considerations for deploying FL systems in real-world financial environments.
Copyright (c) 2026 Camille Dupont, Niklas Bergmann (Author) https://creativecommons.org/licenses/by/4.0
Attention Mechanisms for Identifying Earnings Manipulation Signals in Corporate Financial Statements
The detection of earnings manipulation in corporate financial statements represents a critical challenge for investors, auditors, and regulatory bodies. Traditional statistical methods and rule-based systems often struggle to identify sophisticated manipulation schemes that leverage complex accounting techniques and temporal patterns. This paper proposes a novel approach that integrates attention mechanisms with deep learning architectures to enhance the detection of earnings manipulation signals. By leveraging the selective focus capabilities of attention layers, our framework automatically identifies the most relevant financial indicators and temporal dependencies that signal potential fraudulent activities. The methodology combines sequence-based neural networks with dual-stage attention mechanisms to analyze both cross-sectional financial ratios and longitudinal patterns in accounting data. Experimental results demonstrate that attention-enhanced models achieve superior performance compared to conventional fraud detection approaches, offering improved accuracy while maintaining interpretability through attention weight visualization. This research contributes to the growing intersection of artificial intelligence and forensic accounting, providing practitioners with advanced tools for financial statement analysis and fraud prevention.
Copyright (c) 2026 Federico Romano, Claudia Weiss (Author) https://creativecommons.org/licenses/by/4.0
Discussion on the System Reconstruction and Efficiency Enhancement of Oil and Gas Field Engineering Cost Management from the Perspective of Digital Empowerment
In the current complex and challenging economic environment, as well as in the context of increasingly fierce industry competition and the emergence of numerous new characteristics and requirements in industry development, many oil and gas field enterprises are actively and proactively promoting the continuous advancement of engineering cost management in their practical operations. However, inevitably, they will encounter a series of practical and urgent problems that are extremely difficult to handle. The existence of these problems and difficulties significantly hinders the smooth implementation of oil and gas field engineering cost management and the effective improvement of management level and quality. This paper will precisely and clearly focus its research perspective on the specific application of digital technology in the specific and crucial field of oil and gas field engineering cost management, and will conduct a comprehensive, in-depth, and meticulous discussion and research around this core theme. It is expected that through the comprehensive, in-depth, and meticulous research and analysis presented in this paper, it will provide useful references and guidance for oil and gas fields in their digital transformation of engineering cost management, and help them achieve orderly, stable, and efficient development during this transformation process.
Copyright (c) 2026 Xie Kunchao, Wu Jiangang, Zhang Long, Ku Yarong (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Balancing Detectability and Fluency in Neural Text Generation via Reinforcement-Guided Watermark Placement
The exponential growth of neural text generation systems has intensified concerns regarding content authenticity and model ownership protection in the artificial intelligence ecosystem. This research introduces a reinforcement learning framework that dynamically optimizes watermark placement during text generation to achieve superior balance between detection reliability and linguistic fluency. Traditional watermarking approaches apply uniform strategies across all generation contexts, resulting in either compromised text quality or insufficient watermark robustness. Our method employs a policy network trained through proximal policy optimization that learns context-aware placement decisions, identifying optimal positions and strengths for watermark injection based on local linguistic characteristics and downstream detectability requirements. The framework introduces a dual-objective reward function that simultaneously maximizes watermark detection confidence while minimizing perplexity degradation and semantic distortion. Through comprehensive evaluation on diverse text generation benchmarks including news article synthesis, dialogue generation, and creative writing tasks, we demonstrate that reinforcement-guided placement achieves 97.8% detection accuracy while maintaining perplexity within 1.9% of unwatermarked baselines, outperforming static watermarking strategies by 12.3% in quality-detectability trade-offs. The approach exhibits robust performance under adversarial conditions including paraphrasing attacks, text truncation, and insertion manipulations, with watermark retention rates exceeding 89% across attack scenarios. Our contributions establish reinforcement learning as a principled framework for adaptive watermarking in neural text generation systems, providing practical solutions for intellectual property protection in production language model deployments.
Copyright (c) 2026 Xingyu Liu, Haotian Ren, Giulia Ferraro, Lorenzo Bianchi (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Formal Verification and Compliance Release Control of Cloud-Native Avionics OTA Pipeline Based on GitOps
Avionics software is updated more often as OTA practices gain traction, yet current release processes must still meet strict DO-178C requirements. This study introduces a GitOps-based OTA pipeline that links a version-controlled repository with declarative deployment and formal checks. Each merge request triggers TLA+ model checking and automated DO-178C rule matching before rollout. The pipeline was tested in three simulated integration environments and one iron-bird platform, with a total of 420 release runs. The results show a 79.6% reduction in configuration drift, a 54.3% decrease in rollback events, and a rise in early compliance-defect detection to 92.1%. Release-preparation time dropped from 3.4 h to 1.2 h without adding additional reviewers. These findings show that a Git-driven, formally verified pipeline can support safe and repeatable OTA updates and provide a clear audit trail for certification. The approach offers a practical way to connect cloud-native DevOps workflows with avionics assurance needs.
Copyright (c) 2026 Ava Thompson, Michael Rangi, Liam McKenzie (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Adaptive Knowledge Tracing Through Multi-Agent Reinforcement Learning: A Framework for Personalized Learning Path Optimization
Personalized learning systems require accurate modeling of student knowledge states and adaptive curriculum sequencing to optimize learning outcomes. Traditional knowledge tracing approaches such as Bayesian Knowledge Tracing and recent deep learning methods like Deep Knowledge Tracing face limitations in coordinating multiple pedagogical objectives simultaneously. This paper proposes a novel multi-agent reinforcement learning framework that decomposes the adaptive learning problem into specialized agents responsible for knowledge estimation, content selection, difficulty calibration, and engagement optimization. The framework employs differentiable inter-agent communication protocols inspired by DIAL architecture and dueling network structures for robust Q-value estimation. Experimental results on the ASSISTments dataset demonstrate that the proposed MARL framework achieves 23.7% improvement in prediction accuracy (AUC 0.847 vs. 0.685) and 31.2% reduction in learning time compared to Deep Knowledge Tracing baselines, while maintaining high student engagement levels. The multi-agent coordination mechanism enables effective decomposition of complex educational objectives and provides interpretable insights into personalized learning path optimization strategies.
Copyright (c) 2026 Qianyu Sun, Bocheng Liu, Rachel Thompson (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
IoT-Based Real-Time Structural Health Monitoring for Civil Infrastructure using Edge Computing and Anomaly Detection Algorithms
The rapid deterioration of civil infrastructure, including bridges, dams, and high-rise buildings, presents a critical challenge to public safety and economic stability globally. Traditional Structural Health Monitoring systems often rely on manual inspections or centralized cloud-computing frameworks that suffer from high latency, significant bandwidth consumption, and connectivity dependence. This paper proposes a novel framework for real-time Structural Health Monitoring by integrating Internet of Things sensor networks with Edge Computing paradigms and advanced anomaly detection algorithms. By shifting data processing from centralized servers to the edge of the network, we demonstrate the ability to significantly reduce response times to structural anomalies while minimizing data transmission costs. The proposed architecture utilizes lightweight unsupervised learning models deployed directly on edge nodes to identify deviations in vibrational patterns and strain measurements. The results indicate that this decentralized approach maintains high detection accuracy while offering a robust solution for continuous, real-time integrity management of critical infrastructure assets.
Copyright (c) 2026 Jun-Ho Jeong, Ji-Woo Kang (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Design Optimization of Ultra-High-Speed Photonic Devices based on InP Technology using Genetic Algorithms
The exponential growth in global data traffic necessitates the development of next-generation optical communication systems capable of operating at symbol rates exceeding 100 Gbaud. Indium Phosphide (InP) technology stands at the forefront of this evolution due to its superior electron mobility and direct bandgap properties, which are critical for the realization of active high-speed photonic components. However, the design of such ultra-high-speed devices, particularly modulators and detectors, involves a complex interplay of optical and radio-frequency (RF) electrical constraints that renders traditional analytical design methodologies insufficient. This paper presents a comprehensive study on the application of Genetic Algorithms (GA) for the global optimization of InP-based photonic devices. We propose a robust optimization framework that couples evolutionary computation with full-wave electromagnetic solvers to navigate the multi-dimensional parameter space of photonic integrated circuits. By optimizing the geometric parameters of a Mach-Zehnder Modulator (MZM), we demonstrate a significant enhancement in electro-optic bandwidth and a reduction in insertion loss compared to standard designs. The results indicate that stochastic optimization methods can effectively overcome the limitations of manual tuning, paving the way for Terabit-scale optical interconnects.
Copyright (c) 2026 John Smith, Emily Davis, Sora Inoue (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Materials Property Prediction with Graph Transformers and Uncertainty-Calibrated Active Learning
The acceleration of materials discovery relies heavily on the ability to predict physicochemical properties of crystal structures with high accuracy and computational efficiency. While Density Functional Theory (DFT) provides precise ground-truth data, its cubic scaling with electron count renders it prohibitive for high-throughput screening of vast chemical spaces. Consequently, machine learning surrogates have emerged as a critical alternative. However, conventional Message Passing Neural Networks (MPNNs) often struggle to capture long-range atomic interactions and suffer from over-smoothing in deep architectures. Furthermore, standard active learning frameworks frequently rely on uncalibrated uncertainty estimates, leading to suboptimal sampling strategies and wasted computational resources. This paper introduces a novel framework: the Graph Transformer with Uncertainty-Calibrated Active Learning (GT-UCAL). We propose a geometric graph transformer architecture that integrates structural positional encodings to capture global topology, coupled with an evidential deep learning mechanism to quantify both aleatoric and epistemic uncertainties. Through rigorous experimentation on the Materials Project and JARVIS datasets, we demonstrate that GT-UCAL achieves state-of-the-art predictive performance while reducing the requisite labeled data by approximately 40% compared to random sampling.
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
Inverse Design of Metasurfaces Using Physics-Informed Diffusion Models with Spectral Constraints
The inverse design of metasurfaces constitutes a formidable challenge in computational electromagnetics and nanophotonics, primarily due to the non-uniqueness of the scattering problem and the high dimensionality of the design parameter space. Conventional optimization techniques, such as topology optimization and evolutionary algorithms, often succumb to high computational costs and convergence to local minima. Deep learning approaches, while promising in accelerating the design process, frequently struggle to strictly adhere to the governing Maxwell’s equations, leading to physically unrealizable or suboptimal structures. This paper introduces a novel framework: Physics-Informed Diffusion Models with Spectral Constraints (PIDM-SC). By integrating a pre-trained forward surrogate solver into the reverse diffusion process, we establish a generative mechanism that is explicitly guided by physical laws. The model is conditioned on desired spectral responses, ensuring that the generated meta-atoms not only exhibit high structural diversity but also strictly satisfy the target optical properties. Our approach utilizes a modified U-Net architecture capable of handling multi-modal data input, merging geometric features with spectral embeddings. Experimental validation on a dataset of silicon-on-insulator dielectric metasurfaces demonstrates that PIDM-SC outperforms state-of-the-art Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) in terms of spectral accuracy and fabrication feasibility. The results indicate a significant step forward in the reliable, data-driven design of complex nanophotonic devices.
Temporal Causal Discovery in Evolving Microservice Topologies under Distribution Shift
Microservice architectures exhibit highly dynamic behaviors where causal relationships between services evolve continuously as system configurations change and workload distributions shift. Existing causal discovery methods struggle with autocorrelated observational data and distribution shifts simultaneously, leading to unstable detection rates and spurious causal links. This paper proposes TDICD (Temporal Distribution-Invariant Causal Discovery), a framework that combines temporal dependency modeling, invariant pattern recognition across multiple system environments, and graph neural networks to discover stable causal structures in evolving microservice topologies. Our method addresses three key challenges: handling strong autocorrelation in time-series metrics, detecting causal links that remain invariant under environmental perturbations, and adapting to non-stationary distributions as system configurations evolve. Experimental evaluation on synthetic benchmarks and two real-world microservice applications demonstrates that TDICD achieves detection rates exceeding 85% for both weakly and strongly autocorrelated causal links while maintaining false positive rates below 8%. Compared to baseline methods including PCMCI, DYNOTEARS, and standard GNN approaches, TDICD shows 23% improvement in F1-score for contemporaneous link detection and 31% better invariance to distribution shifts.
Cross-System Transfer Learning for Root Cause Analysis via Domain-Invariant Graph Representations
Root cause analysis (RCA) in complex distributed systems faces significant challenges when diagnostic models need to be transferred across heterogeneous infrastructure environments. Traditional machine learning approaches for fault localization suffer from substantial performance degradation when applied to systems with different architectures, monitoring configurations, or operational characteristics. This paper introduces a novel cross-system transfer learning framework that leverages domain-invariant graph representations to enable effective knowledge transfer for RCA tasks. The proposed methodology constructs system behaviors as attributed graphs where nodes represent components and edges capture causal dependencies, then employs message passing neural networks to learn structural embeddings through adversarial feature alignment and graph contrastive learning. By disentangling system-agnostic causal patterns from domain-specific characteristics through domain-adversarial training with gradient reversal mechanisms, the framework maintains diagnostic accuracy when deploying models from well-instrumented source systems to target systems with limited training data. Experimental evaluations on production cloud infrastructure demonstrate that the approach achieves superior generalization performance compared to conventional transfer learning baselines, reducing diagnostic errors by 31% on average across heterogeneous system environments while maintaining computational efficiency suitable for real-time fault diagnosis.
Computational Frameworks for Optimizing the Trade-off Between Historic Preservation and Commercial Vitality in Urban Renewal
The tension between preserving historic commercial architecture and generating necessary economic vitality represents a complex multi-objective optimization problem in modern urban renewal. While traditional urban planning often relies on qualitative assessments and heuristic decision-making, this paper proposes a rigorous computational approach to resolve the dichotomy between heritage conservation and economic regeneration. We introduce a novel data-driven framework, the Adaptive Heritage-Economy Optimization (AHEO) model, which utilizes high-resolution spatial data, machine learning-based economic forecasting, and evolutionary algorithms to identify optimal intervention strategies. By parameterizing architectural significance alongside projected commercial revenue streams, we demonstrate how computational methods can navigate the non-linear trade-offs inherent in adaptive reuse projects. This research bridges the gap between computer science and urban heritage management, providing a scalable methodology for policymakers and developers. The results indicate that algorithmic mediation can secure higher rates of material preservation while simultaneously maximizing the functional utility and revenue potential of historic districts.
Copyright (c) 2026 Felix M. Hartmann (Author) https://creativecommons.org/licenses/by/4.0
Physics-Informed Graph Neural Networks for Supply Chain Disruption Prediction and Mitigation
Global supply chains face unprecedented challenges from multi-modal disruptions including natural disasters, geopolitical tensions, and market volatility. Traditional data-driven approaches for disruption prediction often fail to capture the underlying physical constraints and causal relationships governing supply chain dynamics. This paper introduces a novel Physics-Informed Graph Neural Network (PI-GNN) framework that integrates domain knowledge from supply chain theory with graph-based deep learning architectures for enhanced disruption prediction and mitigation strategies. The proposed methodology embeds physical laws such as conservation of flow, capacity constraints, and lead time dependencies directly into the neural network training process through custom loss functions and architectural constraints. We demonstrate that by incorporating physics-based regularization terms derived from supply chain fundamentals, the PI-GNN achieves superior predictive performance compared to purely data-driven GNNs, particularly in scenarios with limited historical data. Experimental results on real-world supply chain networks show that the PI-GNN framework reduces prediction error by 23% for disruption events and provides interpretable insights for proactive mitigation strategies. The framework facilitates real-time risk assessment across multi-tier supply networks while maintaining computational efficiency suitable for large-scale deployments.