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.
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.
Causal Discovery in Multi-Echelon Supply Networks: Leveraging Foundation Models for Demand Propagation Analysis
The complexity of modern multi-echelon supply networks presents significant challenges in understanding demand propagation patterns and causal relationships across network tiers. Traditional correlation-based approaches fail to capture the true causal mechanisms underlying supply chain disruptions and demand amplification phenomena. This research proposes a novel framework that integrates causal discovery methodologies with foundation models to analyze demand propagation in multi-echelon supply networks. By leveraging large-scale pre-trained models adapted for supply chain analytics, we develop a system capable of identifying causal relationships between demand signals, inventory decisions, and operational parameters across network echelons. The framework employs graph neural networks combined with causal inference algorithms to construct dynamic causal graphs that represent inter-echelon dependencies. Our approach addresses the limitations of existing methods by explicitly modeling directional causality rather than mere correlation, enabling more accurate root cause attribution and predictive capabilities. Empirical validation using supply chain simulation data demonstrates that network structural parameters significantly impact demand amplification, with high echelon configurations exhibiting peak amplification ratios exceeding fifty times baseline levels. The temporal evolution analysis reveals distinct propagation patterns across different network structures, validating the framework's ability to capture complex spatio-temporal dynamics. This research contributes to the emerging field of foundation models in supply chain management while advancing causal discovery techniques for complex network structures.
Evaluating the Incremental Value of E-commerce Promotional Campaigns: An Uplift Modeling Approach
E-commerce platforms frequently deploy promotional campaigns to stimulate customer purchases, yet accurately quantifying their true incremental impact remains challenging due to the presence of self-selection bias among customers. Traditional response models often fail to distinguish between customers who would purchase regardless of the campaign and those genuinely influenced by it. This study employs uplift modeling, a causal inference technique, to estimate the individual-level incremental effect of promotional campaigns on customer purchasing behavior. Using a real-world e-commerce dataset, we compare the performance of various uplift modeling algorithms, including the uplift random forest and causal forest, against conventional classification approaches. Our findings reveal that uplift models significantly outperform traditional methods in identifying persuadable customers—those whose purchase decisions are positively influenced by the campaign. Additionally, the results highlight the potential for cost savings by targeting only high-uplift segments, thereby avoiding wasteful spending on customers who are either immune or likely to purchase organically. This research underscores the strategic importance of uplift modeling in optimizing marketing resource allocation and enhancing return on investment for e-commerce promotions.
Copyright (c) 2025 Hongwei Wu, Yajing Zhu (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Predicting Consumer Confidence Index Using Social Media Sentiment Analysis
The Consumer Confidence Index (CCI) serves as a critical economic indicator, yet traditional survey-based methods for its measurement often entail delays and high costs. This study explores the potential of leveraging real-time social media data as an alternative approach to predict CCI trends. By employing sentiment analysis on a large dataset of user-generated content from platforms such as Twitter and Reddit, this research quantifies public sentiment and examines its correlation with official CCI values. A regression-based predictive model was developed, incorporating sentiment scores alongside macroeconomic variables for enhanced accuracy. The findings reveal a statistically significant relationship between aggregated social media sentiment and CCI movements, with the model demonstrating robust predictive performance, particularly in capturing short-term fluctuations. These results underscore the value of social media as a timely and cost-effective supplementary tool for forecasting consumer confidence. The study contributes to the growing body of literature on non-traditional data sources in economic forecasting and offers practical implications for policymakers and businesses seeking to anticipate economic trends.
Copyright (c) 2025 Yan Li, Xiaowei Wang , Jing Chen (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Parameter-Driven Efficiency Evaluation of Solar Thermal Collectors Using CFD–ML Integration
This research presents a comprehensive computational framework integrating Computational Fluid Dynamics (CFD) with Adaptive Neuro-Fuzzy Inference System (ANFIS) for parameter-driven efficiency evaluation of parabolic trough solar thermal collectors. The methodology combines high-fidelity CFD simulations with advanced ANFIS machine learning techniques to predict thermal performance parameters across diverse operational conditions. A validated CFD model was developed using ANSYS Fluent to simulate the complex thermal and fluid dynamic behavior within parabolic trough collector systems, generating 920 numerical datasets encompassing varying flow rates, inlet temperatures, solar irradiation levels, and geometric configurations. The ANFIS model demonstrated exceptional predictive capabilities, achieving coefficient of determination (R²) values of 0.94 for thermal efficiency predictions. The integrated CFD-ANFIS framework reduces computational time by 82% compared to traditional CFD approaches while maintaining prediction accuracy within 4.2% error bounds. Key findings indicate that solar irradiation intensity and heat transfer fluid mass flow rate are the most influential parameters affecting collector efficiency, with optimal performance achieved at Reynolds numbers between 10000-15000. The developed framework enables rapid parametric optimization and real-time performance assessment for parabolic trough solar thermal collector systems, providing significant advantages for design engineers and system operators in concentrating solar power applications.
Towards Sustainable Cold Storage: A Hybrid CFD–ML Approach to Temperature and Energy Optimization
This research presents an innovative hybrid computational framework that integrates Computational Fluid Dynamics (CFD) with Machine Learning (ML) algorithms to optimize temperature distribution and energy efficiency in cold storage facilities. The study addresses critical challenges in maintaining uniform thermal conditions while minimizing operational costs through advanced modeling techniques. A comprehensive CFD validation study demonstrates excellent agreement with established benchmarks, achieving correlation coefficients exceeding 0.95 for airflow distribution patterns. The hybrid approach employs systematic parameter analysis including surface velocity effects, temporal temperature variations, and multi-zone optimization strategies. Machine learning models trained on extensive CFD datasets achieve remarkable performance improvements, with temperature prediction accuracies reaching R² = 0.94 and energy consumption forecasting achieving R² = 0.91. Comparative analysis between optimized and conventional cold storage operations reveals significant improvements across multiple performance metrics. The optimized system demonstrates 23% reduction in energy consumption, 35% improvement in temperature uniformity, 28% decrease in product weight loss, and substantial reduction in transpiration rates. The framework successfully identifies optimal operational conditions including airflow velocities between 1.2-1.8 m/s and strategic evaporator positioning that enhances thermal performance. This integrated methodology provides a computationally efficient alternative to traditional approaches while maintaining high accuracy, enabling real-time optimization and intelligent control of cold storage systems.
Behavior Path Analysis for Blockchain Fraud Detection Using Graph Neural Architectures
With the deep penetration of blockchain technology across various fields, its security system faces severe challenges, and fraudulent activities are becoming increasingly frequent. This study focuses on the problem of fraud detection in blockchain and proposes an innovative model, FraudGNN, based on Graph Neural Networks (GNN). The model constructs a dynamic transaction graph, where transaction addresses are treated as nodes and asset transfer relationships as edges, incorporating time-series features. A Graph Attention Network (GAT) is used to extract behavioral features from node neighborhoods. In addition, a Bidirectional Long Short-Term Memory network (Bi-LSTM) is introduced to capture behavioral paths across block-level transactions, enabling accurate classification and prediction of abnormal accounts within blockchain networks. Experiments conducted on an Ethereum transaction dataset—containing approximately 3.6 million transaction records and 40,000 labeled addresses—show that the FraudGNN model significantly outperforms traditional methods such as Random Forest and Graph Convolutional Networks (GCN) in key metrics, achieving 91.2% precision, 87.5% recall, and an F1-score of 89.3%. In particular, the model demonstrates stronger generalization and reasoning capabilities when identifying previously unseen addresses, offering solid technical support for improving blockchain security systems.
Scalable Blockchain Fraud Detection Using Spatial-Temporal Graph Neural Networks
The increasing adoption of blockchain technology has led to a surge in financial fraud, including money laundering, Ponzi schemes, and illicit fund transfers. Traditional fraud detection techniques, such as rule-based systems and supervised machine learning models, struggle to handle the high-volume, high-velocity, and dynamically evolving nature of blockchain transactions. These limitations necessitate a scalable and adaptive approach to detect fraudulent activities efficiently. This study introduces a Spatial-Temporal Graph Neural Network (STGNN)-based fraud detection framework, specifically designed for scalable anomaly detection in large-scale blockchain networks. By modeling blockchain transactions as a spatial-temporal graph, the proposed system captures structural dependencies between wallets and temporal patterns of fund movements. The STGNN model employs graph convolutional networks (GCN) or graph attention networks (GAT) for spatial feature extraction and gated recurrent units (GRU) or temporal convolutional networks (TCN) for sequential fraud pattern recognition. Additionally, to ensure scalability, the framework incorporates graph partitioning techniques, parallelized mini-batch training, and distributed processing, enabling real-time fraud detection across high-throughput blockchain networks. Extensive experiments conducted on Bitcoin and Ethereum transaction datasets demonstrate that the STGNN model achieves higher accuracy, lower false positive rates, and improved computational efficiency compared to rule-based fraud detection systems, supervised ML models, and static GNNs. Case studies further confirm the model’s effectiveness in detecting large-scale fraud schemes, such as DeFi exploits, cross-chain laundering, and coordinated illicit transactions. This research highlights the potential of graph-based deep learning techniques in blockchain security, providing a foundation for future advancements in scalable fraud detection, cross-chain anomaly detection, and decentralized financial security monitoring.
Stochastic Processes in Applied Physics: From Brownian Motion to Financial Modeling
Stochastic processes are mathematical models used to describe systems that evolve over time with inherent randomness. This paper explores the application of stochastic processes in applied physics, focusing on their use in modeling phenomena such as Brownian motion and financial systems. The study begins with a foundational overview of stochastic processes, detailing their mathematical framework and key concepts. It then transitions to specific applications, including classical examples from statistical mechanics and modern applications in financial modeling. By examining the methodologies and results of various studies, this paper aims to highlight the versatility and significance of stochastic processes in understanding and predicting complex systems in physics and finance.
Copyright (c) 2024 Frontiers in Applied Physics and Mathematics