Advances in Science and Engineering
type: channel
description: Advances in Science and Engineering (ASE) is a peer-reviewed, open-access multidisciplinary journal dedicated to publishing high-impact research across all branches of STEM (Science, Technology, Engineering, and Mathematics). Supported by a European editorial consortium, ASE bridges the gap between theoretical physics, applied optics, instrument science, and advanced engineering methodologies.
publisher: CallPress
language: en
publication name: Advances in Science and Engineering
issn: 3054-8160
items: AR-Guided Surgical Navigation With Interactive Deformation Modeling and Equity-Aware Risk Systems
items: Aneurysm Hemodynamic Analysis With Simulation, Experiment, Deep Learning, and Flood Risk Modeling
items: Robust Spectral Feature Extraction for Contamination Detection
items: Hyperspectral AI for Meat Safety and Inspection Systems
items: Spectral Unmixing Benchmarks for Food Contamination Analytics
items: Weak-Signal Spectral Detection in Meat Quality Monitoring
items: Hyperspectral Unmixing for Weak Signal Meat Contamination Detection
items: MRI-Constrained Deformation Modeling for Vascular and Organ Navigation
items: Prompt-Guided Biomechanical Reasoning for AR Surgical Navigation
items: Neurophysiological Evidence Modeling for Sleep, Epilepsy, and Ionic-Stress Regulation
items: Drug Discovery and Metabolic Risk Reasoning for Diabetes-Related Candidate Identification
items: Multimodal Clinical Intelligence for Epilepsy, Imaging, and Oncology Evidence Integration
items: Evidence-Grounded Epilepsy Reasoning With Sparse Biomedical Knowledge Graphs
items: Transparent AI Governance for Financial Fraud Prevention and Credit Risk Control
items: Explainable Financial Anomaly Detection Through Multi-Source Risk Feature Fusion
items: Hybrid Credit Risk Evaluation for Supply Chain Finance Based on Machine Learning and Traditional Risk Indicators
items: Artificial Intelligence-Assisted Financial Fraud Risk Assessment Using Explainable Ensemble Learning
items: Sentiment-Enhanced Temporal Graph Neural Network for Stock Trend Prediction
items: TransNAS-Stock: Transfer Learning and Neural Architecture Search for Cross-Market Stock Movement Prediction
items: MarkovFormer: Market Regime-Aware Hierarchical State Space Architecture for Stock Movement Prediction
items: Causal Inference and Counterfactual Reasoning for Stock Market Prediction: A Graph-Based State Space Approach
items: Knowledge Distillation and Model Compression for Financial Prediction: Adapting Graph-Based State Space Models for Resource-Constrained Environments
items: Explainable AI in Financial Prediction: Bridging Interpretability and Accuracy through Graph-based Neural Networks and Regularization Techniques
items: Uncertainty Quantification and Explainable AI for Aneurysm Rupture Risk Prediction: A Bayesian Deep Learning Approach to Hemodynamic Analysis
items: Uncertainty-Aware Artificial Intelligence: From Epistemic Quantification to Trustworthy Cross-Domain Decision Support
items: https://callpress.org/index.php/ase/article/view/64
items: Toward Trusted Autonomous Optical Surface Inspection: An Integrated Framework for Robustness, Uncertainty, Explainability, and Open-Set Recognition
items: Knowledge Distillation for Optical Surface Inspection: Compressing Large Inspection Networks into Efficient Deployable Models
items: Test-Time Adaptation and Rapid Deployment for Deep Learning in Optical Surface Inspection: Zero-Label Model Adaptation to New Product Variants and Measurement Conditions
items: Automated Machine Learning for Optical Surface Inspection: Neural Architecture Search and Hyperparameter Optimization for Self-Designing Inspection Networks

AR-Guided Surgical Navigation With Interactive Deformation Modeling and Equity-Aware Risk Systems
type: item
description: AR-guided surgical navigation requires reliable deformation modeling to maintain alignment between preoperative images and intraoperative anatomy. Interactive data-driven biomechanics and prompt-based modeling provide a way to adapt surgical guidance to patient-specific deformation and clinician input. This topic connects AR-guided navigation with flood buyout equity research as two examples of decision systems operating under uncertainty and heterogeneous human response. In surgical navigation, uncertainty arises from tissue deformation, imaging limitations, and intraoperative change; in flood buyouts, uncertainty arises from household behavior, cost-sharing rules, and institutional capacity. Both domains require transparent models that support high-stakes decisions. The literature structure highlights the value of interactive modeling, simulation, and equity-aware reasoning across medical and environmental systems.
subject: Articles
  topic: section
subject: AR surgical navigation
  topic: keywords
creator: William Turner
creator: Henry Wallace
rights: Copyright (c) 2026 Advances in Science and Engineering
date: 2026-06-01
publication date: 2026-06-01

Aneurysm Hemodynamic Analysis With Simulation, Experiment, Deep Learning, and Flood Risk Modeling
type: item
description: Aneurysm hemodynamic analysis combines numerical simulation, in vitro experiment, and deep learning to understand vascular flow patterns and support medical decision-making. This topic connects multidisciplinary aneurysm analysis with flood relocation and buyout policy research through a shared emphasis on simulation-based risk assessment. In vascular modeling, computational fluid dynamics and experimental validation help characterize hemodynamic risk; in flood adaptation, agent-based and game-theoretic models examine household relocation behavior and institutional incentives. Both areas require integration of heterogeneous evidence and careful interpretation of model outputs. The literature direction highlights how simulation, empirical testing, and learning-based models can support decision-making in high-risk systems, whether biological or environmental.
subject: Articles
  topic: section
subject: aneurysm hemodynamics
  topic: keywords
creator: Quentin Blake
creator: Yvonne Fletcher
rights: Copyright (c) 2026 Advances in Science and Engineering
date: 2026-06-01
publication date: 2026-06-01

Robust Spectral Feature Extraction for Contamination Detection
type: item
description: Robust spectral feature extraction is essential for contamination detection when target signals are weak, noisy, or partially mixed with background material. In meat safety inspection, hyperspectral images contain complex spectral variation caused by tissue composition, moisture, fat distribution, illumination, and acquisition geometry. This topic focuses on methods that extract reliable spectral features from weak contamination signals using unmixing, preprocessing, dimensionality reduction, and machine learning. Benchmark datasets are valuable because they support systematic comparison of feature extraction methods under controlled weak-signal conditions. Robust features should preserve subtle contamination signatures while suppressing irrelevant variation. The literature connects food inspection with broader hyperspectral image processing methods, including spectral mixture analysis, sparse representation, and spectral-spatial modeling.
subject: Articles
  topic: section
subject: spectral feature extraction
  topic: keywords
creator: Katherine Reed
creator: Lucas Anderson
rights: Copyright (c) 2026 Advances in Science and Engineering
date: 2026-06-01
publication date: 2026-06-01

Hyperspectral AI for Meat Safety and Inspection Systems
type: item
description: Hyperspectral AI provides a non-destructive approach for meat safety inspection by combining spectral sensing, image analysis, and data-driven classification. Meat contamination detection requires sensitivity to weak biochemical signals, robustness to sample heterogeneity, and interpretability for practical inspection environments. Hyperspectral unmixing is valuable because contamination often appears as a mixed signal rather than a clearly separated visual object. Benchmark datasets help define consistent evaluation criteria for weak-signal detection and support comparison across traditional chemometric models, deep learning methods, and spectral unmixing algorithms. This topic focuses on inspection systems that combine spectral decomposition with AI-based decision support. The literature emphasizes the importance of standardized acquisition, preprocessing, model validation, and quality-control interpretability for food safety applications.
subject: Articles
  topic: section
subject: hyperspectral AI
  topic: keywords
creator: Henry Wallace
creator: Isabella Morgan
creator: James Harrison
rights: Copyright (c) 2026 Advances in Science and Engineering
date: 2026-06-01
publication date: 2026-06-01

Spectral Unmixing Benchmarks for Food Contamination Analytics
type: item
description: Benchmark datasets play a critical role in advancing food contamination analytics because they provide common evaluation conditions for comparing detection models, spectral preprocessing pipelines, and unmixing algorithms. Meat contamination detection is particularly challenging when contaminant signals are weak, spatially limited, or spectrally similar to natural biological variation. Hyperspectral unmixing benchmarks enable researchers to evaluate whether models can separate weak contamination signatures from mixed tissue spectra. This topic focuses on the design and use of benchmark-driven spectral analytics for food safety inspection. Key issues include endmember selection, abundance estimation, spectral variability, labeling uncertainty, and reproducibility across acquisition settings. By emphasizing weak-signal detection, this literature direction supports food inspection systems that move beyond simple classification toward interpretable spectral decomposition and measurable contamination evidence.
subject: Articles
  topic: section
subject: spectral unmixing
  topic: keywords
subject: food contamination
  topic: keywords
creator: Frederick Lawson
creator: Grace Bennett
rights: Copyright (c) 2026 Advances in Science and Engineering
date: 2026-06-01
publication date: 2026-06-01

Weak-Signal Spectral Detection in Meat Quality Monitoring
type: item
description: Meat quality monitoring requires sensitive analytical methods capable of detecting contamination signals before they become visually apparent or chemically dominant. Hyperspectral imaging offers high-dimensional spectral information that can capture subtle biochemical and textural changes in meat products. This topic focuses on weak-signal spectral detection, where contamination signatures are present but mixed with dominant tissue, fat, moisture, and illumination effects. Hyperspectral unmixing benchmarks provide structured evaluation settings for comparing algorithms under realistic contamination levels. Spectral preprocessing, dimensionality reduction, endmember learning, and abundance estimation are central to improving detection reliability. Food safety applications also require interpretable models, since inspection decisions must be linked to understandable spectral evidence rather than opaque classification outcomes. This literature direction supports automated meat quality monitoring systems that combine spectral sensitivity, robust benchmarking, and practical food safety relevance.
subject: Articles
  topic: section
subject: meat quality monitoring
  topic: keywords
subject: weak signal
  topic: keywords
creator: Daniel Brooks
creator: Emily Carter
rights: Copyright (c) 2026 Advances in Science and Engineering
date: 2026-06-01
publication date: 2026-06-01

Hyperspectral Unmixing for Weak Signal Meat Contamination Detection
type: item
description: Weak signal detection is a central challenge in hyperspectral food inspection because contaminant signals may be spatially small, spectrally subtle, and easily obscured by background variation in meat tissues. This topic focuses on hyperspectral unmixing methods for detecting low-abundance meat contamination through spectral decomposition, endmember extraction, and abundance estimation. A benchmark-oriented perspective is important because weak contamination detection requires datasets and evaluation protocols that can distinguish between algorithmic sensitivity and overfitting to specific laboratory conditions. Hyperspectral unmixing provides a natural framework for separating contaminant spectra from complex biological backgrounds, while machine learning and spectral-spatial modeling improve robustness under noise, illumination variation, and sample heterogeneity. The literature also connects food safety inspection with broader remote sensing and biomedical imaging methods, where weak signals and mixed pixels are common analytical challenges.
subject: Articles
  topic: section
subject: hyperspectral unmixing
  topic: keywords
creator: Andrew Collins
creator: Benjamin Hayes
creator: Charlotte Miller
rights: Copyright (c) 2026 Advances in Science and Engineering
date: 2026-06-01
publication date: 2026-06-01

MRI-Constrained Deformation Modeling for Vascular and Organ Navigation
type: item
description: MRI-constrained deformation modeling supports patient-specific simulation and surgical navigation by connecting imaging-derived anatomical boundaries with biomechanical models. This topic focuses on vascular and organ navigation, with special attention to type B aortic dissection simulation and intraoperative deformation correction. MRI-based boundary conditions influence computational predictions of flow and pressure in false lumen simulations, while AR-guided surgical navigation requires deformation models that can adapt to organ movement and tissue interaction. Low-dose medical imaging and medical device technologies further support real-time and clinically deployable navigation systems. Sparse computational methods provide a technical foundation for accelerating deformation simulation and large-scale evidence retrieval. The literature structure links vascular modeling, AR navigation, imaging reconstruction, orthopedic implants, and clinical knowledge graphs into a unified framework for reliable patient-specific surgical assistance.
subject: Articles
  topic: section
subject: MRI boundary conditions
  topic: keywords
subject: aortic dissection
  topic: keywords
creator: De Anza College
rights: Copyright (c) 2026 Advances in Science and Engineering
date: 2026-05-26
publication date: 2026-05-26

Prompt-Guided Biomechanical Reasoning for AR Surgical Navigation
type: item
description: Reliable augmented-reality surgical navigation requires accurate modeling of organ deformation, biomechanical constraints, and patient-specific anatomical variation. This topic focuses on prompt-guided biomechanical reasoning for AR navigation, where interactive deformation modeling connects surgical intent, imaging evidence, and biomechanical simulation. Organ deformation modeling is central because intraoperative tissue movement can reduce the reliability of preoperative image registration. Data-driven biomechanics and prompt-based interaction provide a pathway for adapting models to dynamic surgical scenarios. The literature also connects medical device power systems, low-dose imaging, tumor therapy mechanisms, and neurodegenerative disease knowledge mapping, reflecting the broad technical and biomedical infrastructure needed for surgical intelligence systems. Sparse computation supports efficient deformation modeling and graph-based evidence retrieval. Together, these references establish a foundation for AR-guided navigation systems that combine clinical context, imaging, biomechanics, and computational efficiency.
subject: Articles
  topic: section
subject: AR surgical navigation
  topic: keywords
subject: organ deformation
  topic: keywords
creator: Amelia Hughes
creator: Olivia Taylor
rights: Copyright (c) 2026 Advances in Science and Engineering
date: 2026-05-26
publication date: 2026-05-26

Neurophysiological Evidence Modeling for Sleep, Epilepsy, and Ionic-Stress Regulation
type: item
description: Sleep, epilepsy, and neurophysiological regulation share common biological foundations involving ion dynamics, neuronal excitability, metabolic stress, and inflammatory responses. This topic centers on evidence modeling for neurological conditions by connecting genetically encoded ionic-stress sensing with epilepsy knowledge graphs and neuropsychological assessment. Proton-related ionic stress offers a mechanistic perspective on sleep regulation, while epilepsy reasoning requires structured evidence across seizure types, clinical observations, imaging, and molecular pathways. Knowledge graphs provide a mechanism for linking physiological sensor findings with disease-level clinical reasoning. Additional evidence from neurodegenerative disease mapping, obesity-related bone development, exosome diagnostics, and inflammatory spinal conditions expands the biomedical context. The resulting literature structure supports mechanistic reasoning across sleep biology, epilepsy, cognitive status, and neurological disease progression.
subject: Articles
  topic: section
subject: sleep biology
  topic: keywords
subject: ionic-stress sensor
  topic: keywords
subject: epilepsy
  topic: keywords
creator: Charlotte Evans
creator: Daniel Morgan
rights: Copyright (c) 2026 Advances in Science and Engineering
date: 2026-05-26
publication date: 2026-05-26

Drug Discovery and Metabolic Risk Reasoning for Diabetes-Related Candidate Identification
type: item
description: Diabetes-related drug candidate identification requires the combination of molecular representation learning, metabolic risk analysis, disease mechanism evidence, and clinical knowledge integration. This topic connects transformer-based compound identification methods with evidence-intensive medical reasoning for chronic disease management. FT-Transformer and hybrid FT-TRF models provide structured approaches for diabetes-related candidate screening, while knowledge graph reasoning supports connections among compounds, pathways, comorbidities, and clinical outcomes. The inclusion of metabolic, exosome-related, oncological, and neurodegenerative evidence reflects the complexity of diabetes as a systemic disease associated with cardiovascular, renal, neurological, and inflammatory pathways. Sparse computational methods also support efficient candidate search across large molecular and biomedical graphs. This direction emphasizes interpretable compound prioritization, cross-disease evidence integration, and computationally scalable biomedical discovery.
subject: Articles
  topic: section
subject: diabetes drug discovery
  topic: keywords
subject: FT-Transformer
  topic: keywords
creator: Charlotte Evans
creator: Benjamin Hayes
rights: Copyright (c) 2026 Advances in Science and Engineering
date: 2026-05-26
publication date: 2026-05-26

Multimodal Clinical Intelligence for Epilepsy, Imaging, and Oncology Evidence Integration
type: item
description: Multimodal clinical intelligence relies on the ability to connect medical imaging, neurological evidence, oncology knowledge, and patient-level clinical information. This topic focuses on an integrated evidence framework linking epilepsy reasoning with medical image interpretation, cancer-related visual analysis, immune microenvironment modeling, and exosome-based diagnostic evidence. Epilepsy reasoning benefits from structured knowledge graphs, while oncology and imaging tasks contribute additional multimodal data types, including DCE-MRI, low-dose medical imaging, melanoma images, and molecular biomarkers. The central challenge is to organize these heterogeneous sources into explainable clinical evidence rather than isolated predictive signals. Knowledge-based retrieval, visual representation learning, and biomedical entity alignment provide a basis for reliable decision support across neurological and oncological contexts. This direction supports cross-disease clinical intelligence systems that can connect imaging findings, molecular mechanisms, and evidence-intensive medical reasoning.
subject: Articles
  topic: section
subject: multimodal clinical intelligence
  topic: keywords
subject: epilepsy
  topic: keywords
subject: medical imaging
  topic: keywords
creator: Alexander Pierce
creator: Claire Peterson
rights: Copyright (c) 2026 Advances in Science and Engineering
date: 2026-05-26
publication date: 2026-05-26

Evidence-Grounded Epilepsy Reasoning With Sparse Biomedical Knowledge Graphs
type: item
description: Evidence-intensive epilepsy reasoning requires the integration of heterogeneous clinical, neurological, molecular, and computational evidence. This topic centers on a biomedical knowledge graph framework that connects seizure classification, neurophysiological indicators, cognitive assessment, neuroinflammation, and clinical decision-support evidence. Sparse graph structures are particularly important when epilepsy-related entities, observations, and treatments are distributed across fragmented datasets and literature sources. Efficient sparse matrix computation supports scalable graph propagation, similarity search, and evidence retrieval across large biomedical networks. The framework also connects epilepsy reasoning with neurodegenerative disease knowledge mapping and neuroinflammatory mechanisms, allowing clinical evidence to be organized in a traceable and interpretable manner. By combining graph-based evidence modeling, sparse computation, and neurological domain knowledge, this direction supports reliable clinical reasoning for complex epilepsy cases and related neurological conditions.
subject: Articles
  topic: section
subject: epilepsy reasoning
  topic: keywords
subject: biomedical knowledge graph
  topic: keywords
creator: Olivia Taylor
creator: Hannah Kelly
creator: Daniel Morgan
rights: Copyright (c) 2026 Advances in Science and Engineering
date: 2026-05-26
publication date: 2026-05-26

Transparent AI Governance for Financial Fraud Prevention and Credit Risk Control
type: item
description: The adoption of artificial intelligence in financial fraud prevention has created new opportunities for risk control, but it has also raised concerns about transparency, accountability, and model governance. This article examines how explainable AI and governance-oriented model design can improve fraud prevention and credit risk management. The proposed framework links fraud risk assessment, credit scoring, and supply-chain finance monitoring through a shared governance architecture. It includes model documentation, feature attribution, validation procedures, bias monitoring, and human-in-the-loop review. By combining traditional financial expertise with machine learning-based anomaly detection, the framework improves the interpretability and reliability of financial risk decisions. The article emphasizes that AI models should not replace professional judgment but should provide structured evidence for auditors, credit officers, and compliance managers. The study contributes to the literature by presenting a governance-centered approach to AI-enabled financial risk control, especially for institutions facing regulatory pressure to justify automated decisions.
subject: Articles
  topic: section
subject: AI governance
  topic: keywords
creator: Charlotte Evans
creator: Daniel Morgan
rights: Copyright (c) 2026 Advances in Science and Engineering
date: 2026-05-22
publication date: 2026-05-22

Explainable Financial Anomaly Detection Through Multi-Source Risk Feature Fusion
type: item
description: Financial anomaly detection is a critical task for identifying fraud, earnings manipulation, abnormal credit behavior, and suspicious transaction patterns. This article proposes a multi-source risk feature fusion framework for explainable financial anomaly detection. The framework combines accounting ratios, transaction-level signals, credit exposure variables, and external market indicators into a unified feature space. Machine learning classifiers are trained to detect abnormal entities or events, while post-hoc explanation methods are used to translate model predictions into understandable risk evidence. The article argues that explainability is not only a technical requirement but also a governance necessity in financial supervision and auditing. By identifying the most influential variables behind anomaly scores, the framework assists analysts in distinguishing between operational irregularities, temporary volatility, and deliberate fraud. The proposed approach also supports model validation by comparing algorithmic findings with traditional financial risk indicators. Overall, the study contributes to the development of transparent and auditable AI systems for financial risk monitoring.
subject: Articles
  topic: section
subject: financial anomaly detection
  topic: keywords
subject: risk feature fusion
  topic: keywords
creator: Benjamin Hayes
rights: Copyright (c) 2026 Advances in Science and Engineering
date: 2026-05-22
publication date: 2026-05-22

Hybrid Credit Risk Evaluation for Supply Chain Finance Based on Machine Learning and Traditional Risk Indicators
type: item
description: Credit risk evaluation in supply-chain finance requires the integration of enterprise financial indicators, operational relationships, market conditions, and repayment behavior. Traditional credit scoring models often rely heavily on static financial ratios, while machine learning models can capture nonlinear risk patterns but may lack interpretability. This article develops a hybrid credit risk evaluation framework that combines traditional credit risk indicators with machine learning algorithms, including support vector machines, gradient boosting, and genetic algorithm optimization. The framework is designed for supply-chain finance environments where small and medium-sized enterprises are affected by core-enterprise stability, transaction authenticity, logistics reliability, and industry volatility. Explainable AI techniques are incorporated to clarify the contribution of financial and non-financial variables to default predictions. The proposed model provides a structured method for banks, platform enterprises, and financial institutions to assess credit risk more accurately while maintaining transparency. The article highlights the value of combining domain knowledge with algorithmic learning to improve credit risk warning and decision support.
subject: Articles
  topic: section
subject: credit risk
  topic: keywords
creator: Amelia Reynolds
creator: Alexander Reed
rights: Copyright (c) 2026 Advances in Science and Engineering
date: 2026-05-22
publication date: 2026-05-22

Artificial Intelligence-Assisted Financial Fraud Risk Assessment Using Explainable Ensemble Learning
type: item
description: Financial fraud detection increasingly depends on machine learning models capable of identifying subtle irregularities across heterogeneous accounting, transactional, and behavioral data. This article proposes an AI-assisted fraud risk assessment framework that combines ensemble learning, feature-level explainability, and risk-oriented decision support. The framework integrates structured financial indicators, firm-level behavioral signals, and anomaly scores into a unified model designed to improve early detection of suspicious activities. Random forests, gradient boosting, and support vector machines are compared as base learners, while SHAP values are used to interpret model decisions and identify high-risk financial patterns. The study emphasizes the importance of balancing predictive accuracy with interpretability, particularly in auditing, banking supervision, and enterprise risk management contexts. By linking model outputs to explainable financial risk factors, the proposed approach supports transparent fraud screening and reduces dependence on black-box prediction. The article contributes to the literature by presenting a practical framework for explainable fraud risk assessment that can be adapted to financial reporting, credit evaluation, and supply-chain finance scenarios.
subject: Articles
  topic: section
subject: financial fraud detection
  topic: keywords
subject: artificial intelligence
  topic: keywords
creator: Samuel Parker
creator: Hannah Kelly
creator: Kenneth Ley
rights: Copyright (c) 2026 Advances in Science and Engineering
date: 2026-05-22
publication date: 2026-05-22

Sentiment-Enhanced Temporal Graph Neural Network for Stock Trend Prediction
type: item
description: Stock trend prediction has long been a central problem in computational finance, attracting substantial research interest due to its profound implications for investment strategy, risk management, and market understanding. Recent advances in Graph Neural Networks (GNNs) have demonstrated promising capabilities in capturing relational structures among financial entities, with the Stock State Space Graph (S³G) model representing a notable contribution that leverages state space modeling within graph architectures for enhanced stock trend prediction. Despite these advances, existing GNN-based approaches predominantly rely on historical price and volume data, largely overlooking the rich informational value embedded in textual sources such as financial news, social media discussions, and analyst reports. This paper proposes Sentiment-Enhanced Temporal Graph Neural Network (SET-GNN), a novel framework that integrates sentiment analysis derived from financial news and social media with temporal graph neural network architectures to improve stock trend prediction accuracy and robustness. Our methodology incorporates a dual-branch architecture: one branch processes structured market data through temporal graph convolutions, while the other branch extracts sentiment embeddings from textual data using transformer-based encoders. These branch outputs are fused through a cross-attention mechanism that dynamically weights sentiment signals relative to market structure signals over time. Extensive experiments conducted on S&P 500 constituent stocks demonstrate that SET-GNN significantly outperforms both traditional machine learning baselines and state-of-the-art GNN-only approaches, achieving an accuracy improvement of 3.7% over the S³G baseline and a reduction in directional prediction error of 12.4%. Ablation studies further confirm the marginal contribution of sentiment integration across different market regimes, with particularly pronounced improvements observed during perio…
subject: Articles
  topic: section
subject: Stock Trend Prediction
  topic: keywords
subject: Graph Neural Networks
  topic: keywords
creator: Hajimi Bao
rights: Copyright (c) 2026 Advances in Science and Engineering
date: 2026-05-10
publication date: 2026-05-10

TransNAS-Stock: Transfer Learning and Neural Architecture Search for Cross-Market Stock Movement Prediction
type: item
description: Stock market prediction is inherently challenging because financial markets differ significantly in their microstructure, regulatory environment, investor composition, and macroeconomic drivers. A predictive model trained on U.S. equities may perform poorly when applied to Chinese A-shares or South Korean equities, and vice versa. Most existing approaches treat each market as an isolated prediction problem, failing to leverage the transferable knowledge that exists across markets—such as the universal momentum anomaly, the value effect, and the low-volatility anomaly. Furthermore, the design of neural network architectures for stock prediction has largely relied on manual engineering, with architectures optimized for one market often suboptimal for others. In this paper, we propose TransNAS-Stock (Transfer Learning and Neural Architecture Search for Cross-Market Stock Prediction), a novel framework that jointly addresses the challenges of cross-market knowledge transfer and architecture optimization through two key innovations. First, we develop a Market-Invariant Representation Learning (MIRL) module that learns stock embeddings that are invariant to market-specific characteristics but sensitive to universal predictive factors, enabling positive knowledge transfer across markets. Second, we introduce a Multi-Task Neural Architecture Search (MT-NAS) component that searches for optimal stock prediction architectures across multiple markets simultaneously, discovering market-general and market-specific architecture modules. By optimizing architectures jointly across markets, MT-NAS identifies architecture patterns that generalize across markets while exploiting market-specific opportunities. Extensive experiments on six cross-market scenarios—including transfers from S&P 500 to CSI 300, from FTSE 100 to KOSPI 200, and from Nikkei 225 to ASX 200—demonstrate that TransNAS-Stock significantly outperforms both market-specific models and naive transfer learning baselines, …
subject: Articles
  topic: section
subject: Stock Prediction
  topic: keywords
subject: Transfer Learning
  topic: keywords
creator: Emily Richardson
rights: Copyright (c) 2026 Advances in Science and Engineering
date: 2026-05-10
publication date: 2026-05-10

MarkovFormer: Market Regime-Aware Hierarchical State Space Architecture for Stock Movement Prediction
type: item
description: Stock market prediction is uniquely challenging because financial time series are fundamentally non-stationary: the statistical properties of returns, correlations, and volatility change over time as markets transition between distinct regimes such as bull markets, bear markets, high-volatility crises, and low-volatility consolidations. Most existing deep learning approaches—including recurrent networks, transformers, and graph neural networks—treat market dynamics as stationary processes and learn a single set of model parameters that applies uniformly across all market conditions. This design choice is inherently limiting, as the optimal predictive model for a quiet bull market is fundamentally different from that for a volatile bear market. In this paper, we propose MarkovFormer (Markov Switching Transformer), a novel hierarchical architecture that explicitly models market regime transitions using a Markov Switching mechanism and adapts its predictive parameters accordingly. MarkovFormer consists of three key components: (i) a Regime Detection Module (RDM) that uses a latent Markov switching process to classify the current market state from historical price data; (ii) a Regime-Conditioned State Space Encoder (RSE) that maintains regime-specific stock state representations learned through regime-conditioned transition matrices; and (iii) a Cross-Regime Attention Mechanism (CRAM) that enables information flow across regimes, allowing the model to leverage knowledge from similar historical regimes to improve predictions in the current one. By conditioning predictions on the detected market regime, MarkovFormer dynamically adjusts the weighting of short-term momentum, mean-reversion, and volatility signals based on which regime is currently active. Extensive experiments on three major markets (S&P 500, CSI 300, and KOSPI 200) demonstrate that MarkovFormer consistently outperforms ten competitive baselines, including S3G (Lu, Hu, and Zhang, 2026), the state-of-the-art…
subject: Articles
  topic: section
subject: Stock Prediction
  topic: keywords
subject: Market Regime Detection
  topic: keywords
creator: Claire Peterson
rights: Copyright (c) 2026 Advances in Science and Engineering
date: 2026-05-10
publication date: 2026-05-10

Causal Inference and Counterfactual Reasoning for Stock Market Prediction: A Graph-Based State Space Approach
type: item
description: Understanding causal relationships in financial markets is fundamental to developing robust stock prediction models that can reason about interventions and hypothetical scenarios. Traditional correlation-based prediction models fail to capture the underlying causal mechanisms that drive market dynamics, limiting their ability to answer counterfactual questions such as "how would this stock perform if the Federal Reserve had not raised interest rates?" This paper proposes a novel framework that integrates causal inference principles with graph-based state space modeling for enhanced stock market prediction. Our approach, Causal Stock State Space Graph (C-S3G), builds upon the Stock State Space Graph architecture introduced by Lu, Hu, and Zhang, and incorporates causal discovery techniques and counterfactual reasoning mechanisms. We adapt explanation-based bias decoupling regularization principles from Zang and Liu's work on natural language inference to distinguish genuine causal relationships from spurious correlations in financial time series. Through extensive experiments on benchmark financial datasets, we demonstrate that the proposed framework substantially outperforms correlation-based baselines in counterfactual prediction tasks while maintaining competitive accuracy in standard prediction scenarios. Our work contributes to the growing body of research on causal deep learning for finance, providing a principled approach to understanding cause-effect relationships in stock markets.
subject: Articles
  topic: section
subject: Causal Inference
  topic: keywords
subject: Counterfactual Reasoning
  topic: keywords
creator: William Brooks
rights: Copyright (c) 2026 Advances in Science and Engineering
date: 2026-05-08
publication date: 2026-05-08

Knowledge Distillation and Model Compression for Financial Prediction: Adapting Graph-Based State Space Models for Resource-Constrained Environments
type: item
description: The deployment of deep learning models in financial prediction systems has been constrained by the computational overhead associated with large-scale graph-based architectures. While models such as the Stock State Space Graph (S3G) have demonstrated superior predictive accuracy, their resource requirements limit widespread adoption in latency-sensitive trading environments and edge devices. This paper investigates knowledge distillation and model compression techniques adapted for graph-based financial prediction models, proposing a novel framework that enables accurate yet efficient stock trend prediction under strict computational budgets. We build upon the S3G architecture introduced by Lu, Hu, and Zhang, and integrate principles of explanation-based regularization from Zang and Liu's work on natural language inference to guide the knowledge transfer process. Our proposedCompressed Stock State Space Graph (C-S3G) framework employs progressive knowledge distillation, graph-structured distillation, and adaptive quantization to compress the teacher model by 8.7x while retaining 96.2% of the prediction accuracy. Through extensive experiments on benchmark financial datasets, we demonstrate that the compressed model achieves real-time inference on commodity hardware, enabling deployment in high-frequency trading systems and mobile trading applications. Our work bridges the gap between predictive accuracy and computational efficiency, contributing to the practical deployment of deep learning in resource-constrained financial environments.
subject: Articles
  topic: section
subject: Knowledge Distillation
  topic: keywords
subject: Model Compression
  topic: keywords
creator: James Carter
rights: Copyright (c) 2026 Advances in Science and Engineering
date: 2026-05-08
publication date: 2026-05-08

Explainable AI in Financial Prediction: Bridging Interpretability and Accuracy through Graph-based Neural Networks and Regularization Techniques
type: item
description: The rapid advancement of deep learning models in financial prediction has yielded remarkable accuracy improvements, yet the inherent opacity of these models poses significant challenges for regulatory compliance, trust-building, and practical deployment in high-stakes financial environments. This paper presents a comprehensive investigation into the intersection of explainable AI (XAI) and financial prediction, with a particular focus on graph-based neural networks and regularization techniques that can simultaneously enhance model accuracy and provide interpretable predictions. We propose a novel framework that adapts explanation-based bias decoupling regularization—originally developed for natural language processing tasks—to the domain of stock trend prediction. Our approach, termed Explainable Stock State Space Graph (E-S3G), extends the Stock State Space Graph architecture by integrating interpretability mechanisms that reveal the underlying factors driving prediction decisions. Through extensive experiments on benchmark financial datasets, we demonstrate that the proposed framework achieves competitive prediction accuracy while significantly improving model explainability. Furthermore, we provide a thorough review of 15 references encompassing graph neural networks for financial prediction, explainability methods, and regularization techniques, offering a holistic perspective on the current state and future directions of trustworthy AI in finance.
subject: Articles
  topic: section
subject: Explainable AI
  topic: keywords
subject: Stock Prediction
  topic: keywords
creator: Tang Bao
rights: Copyright (c) 2026 Advances in Science and Engineering
date: 2026-05-08
publication date: 2026-05-08

Uncertainty Quantification and Explainable AI for Aneurysm Rupture Risk Prediction: A Bayesian Deep Learning Approach to Hemodynamic Analysis
type: item
description: Aneurysm rupture is a life-threatening medical emergency with high mortality rates, making accurate rupture risk prediction essential for clinical decision-making. While computational hemodynamic modeling and deep learning approaches have shown promise in aneurysm assessment, existing methods often lack robust uncertainty quantification and interpretable predictions that clinicians can trust. This paper proposes a Bayesian deep learning framework for aneurysm rupture risk prediction that combines hemodynamic feature analysis with uncertainty-aware neural networks and explainable AI techniques. Our approach leverages Monte Carlo dropout for epistemic uncertainty estimation and integrates SHAP-based explanations to provide clinicians with transparent, calibrated risk predictions. Through extensive experiments on clinical hemodynamic datasets, we demonstrate that the proposed framework produces well-calibrated probability estimates and provides interpretable explanations that highlight the hemodynamic features most critical for rupture risk. Our work contributes to the growing field of trustworthy AI in healthcare, providing a principled approach to uncertainty-aware, explainable medical decision support for aneurysm management.
subject: Articles
  topic: section
subject: Uncertainty Quantification
  topic: keywords
subject: Aneurysm Rupture Prediction
  topic: keywords
subject: Bayesian Deep Learning
  topic: keywords
subject: Explainable AI
  topic: keywords
creator: Samuel Parker
rights: Copyright (c) 2026 Advances in Science and Engineering
date: 2026-05-08
publication date: 2026-05-08

Uncertainty-Aware Artificial Intelligence: From Epistemic Quantification to Trustworthy Cross-Domain Decision Support
type: item
description: The deployment of artificial intelligence systems in high-stakes decision-making environments has catalyzed intense scrutiny of how AI models handle, communicate, and propagate uncertainty. As AI systems are increasingly integrated into clinical diagnosis, autonomous navigation, climate monitoring, and business strategy, the fundamental question of whether these systems can reliably quantify and communicate their own uncertainty has become a central research imperative. This paper presents a comprehensive review and synthesis of uncertainty quantification (UQ) methodologies in modern AI systems, with a particular focus on bridging the gap between technical UQ research and the practical requirements of trustworthy decision support across diverse application domains. Drawing upon six foundational references supplemented by nine additional citations from the broader literature, this study develops an integrated framework for uncertainty-aware AI that spans foundational UQ theory, calibration science, auditing constraints, and cross-domain deployment considerations. Key contributions include a systematic taxonomy of uncertainty types and their operational significance, an analysis of how epistemic uncertainty interacts with calibration benchmarks and auditing limitations, a review of state-of-the-art UQ techniques including conformal prediction and selective classification, and an examination of how uncertainty awareness manifests across healthcare, autonomous driving, environmental science, and business analytics. The findings indicate that uncertainty-aware AI represents not merely a technical enhancement but a foundational prerequisite for trustworthy deployment, and that the convergence of structured state space models, ensemble methods, and distribution-free inference offers the most promising pathway toward AI systems capable of knowing what they do not know.
subject: Articles
  topic: section
subject: Uncertainty Quantification
  topic: keywords
subject: Epistemic Uncertainty
  topic: keywords
subject: Aleatoric Uncertainty
  topic: keywords
creator: Amelia Reynolds
rights: Copyright (c) 2026 Advances in Science and Engineering
date: 2026-05-04
publication date: 2026-05-04

https://callpress.org/index.php/ase/article/view/64
type: item
title: Patient-Reported Outcomes and Health-Related Quality of Life as Determinants of First-Line Treatment Selection in Advanced Hepatocellular Carcinoma: A Synthesis of Evidence and Proposal of a PRO-Inclusive Framework
description: The landscape of first-line systemic therapy for advanced hepatocellular carcinoma (HCC) has undergone transformative change over the past five years, with immune checkpoint inhibitor–based regimens and targeted therapies demonstrating meaningful survival improvements in landmark randomized controlled trials. However, the comparative evaluation of these regimens has relied predominantly on investigator-assessed endpoints such as overall survival (OS) and progression-free survival (PFS), which do not fully capture the patient experience during treatment. Patient-reported outcomes (PROs) and health-related quality of life (HRQoL) represent complementary dimensions of treatment value that are particularly salient in advanced HCC, where baseline symptom burden is high and treatment-related toxicities can substantially impair functional well-being. This paper synthesizes PRO evidence from the seven pivotal first-line randomized controlled trials in advanced HCC—IMbrave150, HIMALAYA, LEAP-002, ORIENT-32, CARES-310, CheckMate 459, and RATIONALE-301—and integrates these findings with the network meta-analysis by Li et al. (2026), which aggregated efficacy and safety data across 24 RCTs and 13,572 patients. We argue that PROs and symptom burden must be integrated alongside survival endpoints to guide truly patient-centered first-line decision-making. A PRO-informed treatment selection framework is proposed, accounting for baseline symptom burden, performance status, liver function, and etiology. Future directions including digital PRO collection, real-world PRO evidence, and artificial intelligence–based prediction models are discussed.
subject: Articles
  topic: section
subject: HCC
  topic: keywords
subject: hepatocellular carcinoma
  topic: keywords
subject: patient-reported outcomes
  topic: keywords
creator: Yi Bao
rights: Copyright (c) 2026 Advances in Science and Engineering
date: 2026-04-25
publication date: 2026-04-25

Toward Trusted Autonomous Optical Surface Inspection: An Integrated Framework for Robustness, Uncertainty, Explainability, and Open-Set Recognition
type: item
description: Training high-quality deep learning models for optical surface inspection requires large diverse datasets, but manufacturing companies are often unwilling or legally prohibited from sharing raw measurement data across facilities due to competitive concerns, customer confidentiality requirements, and data privacy regulations such as GDPR. This data silos problem means that each facility trains on its own limited data, producing models that do not generalize well to other factories, product variants, or measurement equipment configurations. This study proposes a federated learning framework for optical surface inspection that enables multiple manufacturing facilities to collaboratively train a shared inspection model without any facility revealing its raw measurement data to any other facility or to a central server. Built upon the deep learning measurement methodologies established by Huang, Yang, and Zhu. (2023) in 4D thermal imaging and the optical metrology innovations of Huang, Tang, Liu, and Huang (2026), the framework employs differential privacy mechanisms to provide formal privacy guarantees, a Federated Averaging protocol optimized for optical measurement data distributions, and a contribution scoring system that rewards facilities for sharing informative data without exposing what that data contains. Evaluated on a simulated federation of six manufacturing facilities with heterogeneous data distributions, the proposed framework achieves inspection accuracy that is within 6.3% of a centrally trained model while providing formal (ε, δ)-differential privacy guarantees. The framework is demonstrated to enable factories with as few as 500 labeled samples to benefit from the collective knowledge of the entire federation, reaching performance that would otherwise require tens of thousands of samples. This work provides the first practical pathway toward privacy-preserving collaborative deep learning for precision optical manufacturing across competing organization…
subject: Articles
  topic: section
subject: Federated learning
  topic: keywords
subject: Privacy-preserving machine learning
  topic: keywords
subject: Optical inspection
  topic: keywords
subject: Differential privacy
  topic: keywords
subject: Distributed deep learning
  topic: keywords
subject: Multi-factory collaboration
  topic: keywords
creator: Klaus Weber
rights: Copyright (c) 2026 Advances in Science and Engineering
date: 2026-04-02
publication date: 2026-04-02

Knowledge Distillation for Optical Surface Inspection: Compressing Large Inspection Networks into Efficient Deployable Models
type: item
description: State-of-the-art deep learning models for optical surface inspection achieve impressive accuracy but are computationally expensive, requiring high-end GPU servers for real-time inference. This creates a deployment barrier for manufacturing facilities that lack GPU infrastructure or require inspection systems that can operate on low-power embedded devices. Knowledge distillation addresses this problem by training a compact student network to mimic the behavior of a larger teacher network, transferring not just the final predictions but the rich dark knowledge encoded in the teacher's logits, attention maps, and intermediate representations. This study proposes a comprehensive knowledge distillation framework for optical surface inspection that compresses high-accuracy teacher models into lightweight student networks suitable for edge deployment, while preserving the teacher's ability to detect rare defects, reason about uncertainty, and generalize across diverse product variants. Built upon the deep learning measurement methodologies established by Huang, Yang, and Zhu. (2023) in 4D thermal imaging and the optical metrology innovations of Huang, Tang, Liu, and Huang (2026), the framework combines logit-based distillation, intermediate representation matching, and defect-aware prioritization to achieve up to 12.7× model compression while retaining 96.4% of the teacher's accuracy on defect detection and within 0.3 K of the teacher's thermal reconstruction MAE. The distilled student models achieve real-time inference at 156 FPS on a mobile ARM processor (Jetson Nano) and 94 FPS on a low-power edge TPU, enabling deployment of state-of-the-art inspection accuracy on compact, low-cost hardware. This work provides a practical pathway for deploying the most accurate optical inspection models on the full range of manufacturing hardware, from high-end datacenters to embedded edge devices.
subject: Articles
  topic: section
subject: Knowledge distillation
  topic: keywords
subject: Model compression
  topic: keywords
subject: Optical inspection
  topic: keywords
subject: Deep learning deployment
  topic: keywords
subject: Edge computing
  topic: keywords
creator: Klaus Weber
rights: Copyright (c) 2026 Advances in Science and Engineering
date: 2026-04-02
publication date: 2026-04-02

Test-Time Adaptation and Rapid Deployment for Deep Learning in Optical Surface Inspection: Zero-Label Model Adaptation to New Product Variants and Measurement Conditions
type: item
description: Deploying deep learning models for optical surface inspection in real manufacturing environments requires the model to handle not only known defect types but also new product variants, changed measurement conditions, and novel surface geometries that were not present in the original training data. Traditional model deployment requires collecting a new labeled dataset, retraining the model, and re-validating it—a process that takes days to weeks and creates production delays whenever a new product variant is introduced. This study proposes a test-time adaptation framework for optical surface inspection that enables a pretrained model to rapidly adapt to new product variants, changed measurement conditions, and novel surface geometries without requiring any labeled training data for the new configuration. Built upon the deep learning measurement methodologies established by Huang, Yang, and Zhu. (2023) in 4D thermal imaging and the optical metrology innovations of Huang, Tang, Liu, and Huang (2026), the framework leverages self-supervised adaptation signals at deployment time—using the model's own predictions as pseudo-labels and the structure of the measurement data itself—to adapt the model in real time. Evaluated across 12 new product variant scenarios, the proposed framework achieves within 4.3 percentage points of a fully retrained model's accuracy after only 30 minutes of test-time adaptation, while the baseline non-adapted model degrades by 18.7 percentage points on the new variant. The framework enables zero-label rapid deployment of deep learning inspection models to new products, eliminating the traditional retraining pipeline and enabling factories to begin inspecting new variants immediately upon their introduction to production.
subject: Articles
  topic: section
subject: Test-time adaptation
  topic: keywords
subject: Rapid deployment
  topic: keywords
subject: Optical inspection
  topic: keywords
subject: Zero-label adaptation
  topic: keywords
subject: Self-supervised learning
  topic: keywords
creator: Klaus Weber
rights: Copyright (c) 2026 Advances in Science and Engineering
date: 2026-04-02
publication date: 2026-04-02

Automated Machine Learning for Optical Surface Inspection: Neural Architecture Search and Hyperparameter Optimization for Self-Designing Inspection Networks
type: item
description: Training high-quality deep learning models for optical surface inspection requires large diverse datasets, but manufacturing companies are often unwilling or legally prohibited from sharing raw measurement data across facilities due to competitive concerns, customer confidentiality requirements, and data privacy regulations such as GDPR. This data silos problem means that each facility trains on its own limited data, producing models that do not generalize well to other factories, product variants, or measurement equipment configurations. This study proposes a federated learning framework for optical surface inspection that enables multiple manufacturing facilities to collaboratively train a shared inspection model without any facility revealing its raw measurement data to any other facility or to a central server. Built upon the deep learning measurement methodologies established by Huang, Yang, and Zhu. (2023) in 4D thermal imaging and the optical metrology innovations of Huang, Tang, Liu, and Huang (2026), the framework employs differential privacy mechanisms to provide formal privacy guarantees, a Federated Averaging protocol optimized for optical measurement data distributions, and a contribution scoring system that rewards facilities for sharing informative data without exposing what that data contains. Evaluated on a simulated federation of six manufacturing facilities with heterogeneous data distributions, the proposed framework achieves inspection accuracy that is within 6.3% of a centrally trained model while providing formal (ε, δ)-differential privacy guarantees. The framework is demonstrated to enable factories with as few as 500 labeled samples to benefit from the collective knowledge of the entire federation, reaching performance that would otherwise require tens of thousands of samples. This work provides the first practical pathway toward privacy-preserving collaborative deep learning for precision optical manufacturing across competing organization…
subject: Articles
  topic: section
subject: Federated learning
  topic: keywords
subject: Privacy-preserving machine learning
  topic: keywords
subject: Optical inspection
  topic: keywords
subject: Manufacturing AI
  topic: keywords
subject: Multi-factory collaboration
  topic: keywords
creator: Klaus Weber
rights: Copyright (c) 2026 Advances in Science and Engineering
date: 2026-04-02
publication date: 2026-04-02