Advanced Technologies and Systems Quarterly
Advanced Technologies and Systems Quarterly (ATSQ) is a comprehensive, peer-reviewed, open-access journal publishing high-quality research across all broad disciplines of engineering, applied sciences, and information technology. Published quarterly in Berlin by the European Node of Call Press Scholarly Group, ATSQ serves as a unified platform for rapid dissemination of technological innovations. The journal welcomes rigorous multi-disciplinary submissions, ranging from software engineering, artificial intelligence, and virtual environments, to hardware architecture, quantitative algorithms, and integrated automation systems. Our mission is to accelerate global technological development by breaking down traditional domain silos.
Continual De-Raining, Defect Detection, Multimodal Fusion, and Confidence-Aware Infrastructure Analytics
Resilient infrastructure analytics requires robust visual perception, defect detection, multimodal fusion, and reliable model interpretation. Continual rain removal improves image quality under changing rainy conditions, while domain adaptation and dual prompt learning address visual degradation in outdoor scenes. NeRF-based defect detection contributes a 3D representation approach for identifying structural anomalies. Infrared-visible image fusion and LiDAR-guided hyperspectral cross-attention provide complementary multimodal sensing strategies for target detection and image classification. Battery diagnostics and power electronics research address the energy reliability of sensing platforms. Pavement friction prediction connects visual indicators to transportation safety. LLM confidence research is relevant because infrastructure analytics systems must communicate uncertainty clearly when supporting maintenance or emergency decisions. This literature cluster supports safety-critical monitoring frameworks that integrate weather-robust vision, structural defect detection, multimodal sensing, energy diagnostics, and confidence-aware AI.
Copyright (c) 2026 Advanced Technologies and Systems Quarterly
AI-Driven Infrastructure Health, Energy Diagnostics, Hemodynamic Simulation, and Visual Sensing
AI-driven infrastructure and engineering systems require reliable sensing, diagnostics, simulation, and model interpretation. Continual de-raining and rainy-condition segmentation improve outdoor visual monitoring under adverse weather, while LiDAR-guided hyperspectral fusion and infrared-visible image fusion support multimodal image classification and target-aware supervision. Battery state-of-health prediction and AI applications in power electronics contribute diagnostic methods for energy systems that support infrastructure monitoring. Pavement friction prediction links image-based indicators to road safety, and hemodynamic modeling of aortic arch aneurysm treatment provides a biomedical simulation example where virtual modeling supports decision-making. LLM confidence research is relevant across these systems because automated outputs must be interpreted with appropriate uncertainty. This literature cluster connects visual sensing, energy reliability, road safety, biomedical simulation, and confidence-aware AI in safety-critical environments.
Visual Infrastructure Monitoring With NeRF-Based Defect Detection and Urban Sensing
Visual infrastructure monitoring increasingly relies on robust image processing, 3D representation, and multimodal sensing. NeRF-based defect detection provides a neural representation approach for identifying structural anomalies, while rain adaptation and continual rain removal improve outdoor image reliability. LiDAR-guided hyperspectral band selection and image classification add another sensing pathway for urban monitoring, especially where spatial-spectral information can support environmental or infrastructure assessment. Flood relocation research provides the climate adaptation context, showing why resilient urban monitoring matters for hazard-prone communities. Urban sub-center studies identify where infrastructure and adaptation interventions may be most important within metropolitan areas. Web3 governance research contributes a civic coordination perspective by examining how decentralized communities form social capital and respond to collective issues. This literature cluster supports integrated visual infrastructure systems combining defect detection, weather-robust imaging, multimodal sensing, and civic governance.
Inflammatory Modulation and Neonatal Brain Injury Evidence in Risk Analytics
Inflammatory modulation in neonatal brain injury provides a biological example of how early exposure conditions can alter long-term risk trajectories. This topic centers on neuroprotective effects of lipopolysaccharides in preterm hypoxia-ischemia and connects this biomedical evidence with weak-signal detection and flood relocation modeling. In neonatal injury, inflammatory signals may have context-dependent protective or harmful effects. In hyperspectral meat contamination detection, weak spectral signals require early identification before contamination becomes visually obvious. In flood relocation planning, early hazard and behavioral signals shape preventive decisions before disaster losses occur. Across these domains, the literature emphasizes timing, uncertainty, heterogeneity, and intervention design. This integrated view supports risk analytics frameworks that combine biological, environmental, and computational evidence.
Neuroprotective Mechanisms in Preterm Hypoxia-Ischemia and Flood-Related Vulnerability Modeling
Neuroprotective mechanisms in preterm hypoxia-ischemia are relevant to broader vulnerability modeling because early biological insults and environmental hazards both produce long-term consequences that depend on timing, exposure, and adaptive response. This topic combines neonatal brain injury evidence with flood relocation and equity analysis. Lipopolysaccharide-related neuroprotection in a dual-hit rat pup model highlights the complexity of inflammatory modulation in early brain injury. Flood relocation modeling, by contrast, examines how households respond to environmental exposure before disaster damage occurs. Hyperspectral meat contamination detection adds a weak-signal benchmark perspective, linking biomedical, food safety, and environmental risk contexts through early detection and preventive intervention. The literature direction supports cross-domain vulnerability analysis that connects biological injury, household risk, and evidence-based prevention.
Federal-Local Incentive Design for Equitable Flood Relocation
Federal-local incentive design plays a major role in determining whether flood relocation programs reduce risk equitably. Cost-sharing arrangements affect local participation, administrative capacity, homeowner incentives, and the distribution of benefits across communities. This topic examines flood relocation through both game-theoretic and agent-based lenses. Game-theoretic analysis clarifies strategic choices among federal agencies, local governments, and households, while agent-based modeling captures heterogeneous relocation decisions under changing flood risk. Hyperspectral weak-signal meat contamination detection is included as a parallel benchmark-driven early warning domain, highlighting the broader challenge of acting on uncertain evidence before visible crisis occurs. The literature structure supports policy analysis that treats equity, incentives, and behavioral response as central design elements in flood adaptation programs.
Climate Adaptation Fairness in Flood-Prone Communities
Climate adaptation fairness requires attention to who receives protection, who is asked to relocate, and who bears the financial and social costs of risk reduction. Flood-prone communities often contain heterogeneous households with different incomes, property values, tenure security, social networks, and relocation preferences. This topic combines pre-disaster relocation modeling with equity analysis of federal-local cost sharing in flood buyouts. Agent-based models help represent household responses to relocation incentives, while game-theoretic approaches capture strategic interactions among governments and homeowners. Hyperspectral weak-signal detection is included as an analogy for early intervention, where hidden risk must be identified before visible damage occurs. The literature direction emphasizes that adaptation policy should be evaluated not only by aggregate risk reduction but also by distributional consequences.
Game-Theoretic Modeling of Heterogeneous Homeowners in Flood Buyouts
Flood buyout outcomes depend on how homeowners respond to compensation offers, risk information, relocation costs, and expectations about future public investment. Game-theoretic modeling provides a framework for analyzing strategic behavior among federal agencies, local governments, and heterogeneous homeowners. This topic links game-theoretic flood buyout analysis with agent-based flood relocation models, allowing both strategic interaction and household-level heterogeneity to be represented. Hyperspectral weak-signal detection remains relevant as a comparative preventive-risk domain because both food contamination detection and flood buyout policy require timely action under uncertain evidence. The literature structure supports policy analysis that accounts for unequal household resources, local government capacity, and incentive compatibility in managed retreat programs.
Cross-Domain Trustworthy AI for Clinical, Visual, Urban, and Financial Risk Intelligence
Trustworthy AI across clinical, visual, urban, and financial domains requires evidence alignment, transparent reasoning, and domain-aware representation learning. This topic synthesizes evidence-intensive epilepsy reasoning, forensic vision-language compression, instance-aware super-resolution, financial fraud risk assessment, medical image interpretation, oncology evidence, neurodegenerative disease AI, medical device systems, and urban risk governance. The central challenge is to connect heterogeneous evidence without reducing domain-specific knowledge to generic prediction outputs. Clinical reasoning requires traceable medical evidence; visual analytics requires robust image restoration and token-efficient multimodal interpretation; urban governance requires cross-scale spatial and public health evidence; financial risk assessment requires explainable anomaly detection. By organizing these directions into a shared literature framework, this topic supports the development of cross-domain AI systems that remain interpretable, evidence-grounded, and operationally useful.
Evidence-Based Urban Resilience Planning With Public Health and Trauma Data
Urban resilience planning requires the integration of spatial structure, cross-scale governance, public health evidence, and emergency response data. This topic connects urban sub-center analysis and urban systems science with trauma epidemiology and evidence-intensive reasoning. Urban sub-centers influence access to medical services, emergency response capacity, transportation flows, and population exposure to risks. Cross-scale urban systems science provides a conceptual basis for linking local injury patterns with metropolitan planning and regional governance. Trauma epidemiology studies offer empirical evidence on geriatric and pediatric injury patterns, supporting the incorporation of public health data into resilience planning. Financial risk assessment contributes an additional framework for explainable risk modeling. This literature structure supports urban governance approaches that combine spatial analytics, health evidence, and risk-aware planning.
Sparse Computational Infrastructure for Urban Systems, Risk Governance, and Financial Fraud Assessment
Urban systems and financial risk governance increasingly depend on computational infrastructure capable of handling large-scale graph, spatial, and transactional data. This topic connects sparse matrix computation, urban systems science, urban sub-center analysis, and AI-assisted financial fraud risk assessment. IA-SpGEMM provides an example of input-aware auto-tuning for sparse matrix-matrix multiplication, which is relevant to graph analytics, spatial interaction modeling, and large-scale risk networks. Urban systems science frames cities as cross-scale dynamic systems, while urban sub-center analysis captures spatial heterogeneity in economic agglomeration. Financial fraud risk assessment contributes explainable AI methods for identifying abnormal risk patterns in complex data environments. Together, these references support a computationally grounded approach to urban and financial risk intelligence.
Integrated Urban Governance With Multimodal Remote Sensing and Vision-Language Evidence
Integrated urban governance benefits from multimodal evidence that links spatial observation, economic structure, visual interpretation, and policy decision-making. This topic connects urban systems science with hyperspectral-LiDAR fusion, forensic vision-language models, instance-aware super-resolution, and financial risk assessment. Remote sensing and vision-language models support observation of urban growth, infrastructure change, environmental conditions, and spatial inequality. Token-efficient image understanding reduces the computational burden of large-scale urban monitoring, while super-resolution improves visual detail in complex built environments. Urban sub-center analysis provides a framework for understanding spatial economic concentration, and cross-scale dynamics help connect neighborhood-level phenomena with metropolitan governance. Financial fraud risk modeling contributes an additional risk-analysis perspective for urban governance systems that require transparent and explainable decision support.
Cross-Scale Urban Health and Risk Modeling With Agglomeration Heterogeneity
Urban health and risk modeling requires attention to cross-scale dynamics, agglomeration heterogeneity, infrastructure distribution, and evidence-based governance. This topic connects urban sub-center analysis with integrated urban systems science and AI-enabled financial risk assessment. Urban sub-centers influence access to services, labor markets, mobility patterns, and economic concentration, while cross-scale urban systems science provides a framework for linking neighborhood, metropolitan, and regional dynamics. Financial fraud risk modeling contributes methods for structured risk assessment and explainable AI, which can be adapted to urban governance contexts involving resource allocation, infrastructure risk, and public service monitoring. Hyperspectral-LiDAR fusion adds a remote sensing perspective for observing urban form and environmental variation. This literature structure supports integrated urban analytics that connect spatial structure, economic behavior, governance systems, and risk intelligence.
Evidence-Intensive Clinical Reasoning With Knowledge Graphs for Epilepsy Decision Support
Epilepsy diagnosis and treatment require reasoning across heterogeneous evidence, including seizure semiology, electroencephalography, neuroimaging, genetics, medication response, and clinical guidelines. This article proposes a knowledge graph-based framework for evidence-intensive epilepsy decision support. The framework organizes epilepsy-related clinical concepts, patient observations, diagnostic categories, treatment options, and evidence sources into a structured graph representation. Large language models are used as reasoning interfaces, while graph retrieval provides grounded medical evidence to reduce hallucination and improve traceability. The framework also draws on recent advances in medical AI, neurodegenerative disease knowledge mapping, cognitive assessment review, transcriptomic regulation, ionic-stress sensing, and multimodal medical imaging. It supports tasks such as seizure classification, differential diagnosis, treatment recommendation, cognitive evaluation assistance, and explanation generation. The article emphasizes that clinical AI systems should not act as autonomous diagnostic agents but should provide evidence-linked support for clinicians. By combining knowledge graphs, retrieval-augmented reasoning, and explainable clinical decision support, the proposed approach addresses the need for reliable AI tools in complex neurological conditions.
Open-Source Cloud Security Compliance Based on Policy Evidence Graphs
Cloud infrastructure security depends on continuous monitoring of identity permissions, configuration baselines, runtime behavior, and infrastructure-as-code compliance. However, fragmented tools often make it difficult for security teams to connect policy violations with operational evidence. This article proposes an open-source cloud security compliance framework based on policy evidence graphs. The framework organizes cloud assets, identities, vulnerabilities, runtime alerts, and remediation actions into a graph-based structure. Policy engines evaluate infrastructure configurations against security baselines, while runtime monitoring tools detect suspicious activities in containerized environments. The evidence graph enables analysts to trace each security finding back to affected resources, violated policies, and recommended remediation steps. The article emphasizes open-source extensibility, multi-cloud adaptability, and explainable compliance reporting. By transforming fragmented security signals into structured policy evidence, the proposed approach improves audit readiness, remediation prioritization, and cloud governance transparency.
Explainable Security Risk Analytics for Embedded Networks and Cloud Infrastructure
Modern digital infrastructure faces increasingly complex security threats across embedded devices, cloud platforms, and large-scale network environments. This article proposes an explainable security risk analytics framework that integrates network anomaly detection, vulnerability quantification, and cloud infrastructure security assessment. The framework combines ensemble learning for traffic anomaly detection, CVSS-based vulnerability metrics for risk scoring, and policy-driven evidence collection for cloud environments. SHAP-based explanation is used to identify the most influential traffic and vulnerability features behind model outputs. The article argues that effective cybersecurity analytics must move beyond isolated detection models toward integrated, interpretable, and policy-aligned risk assessment. By combining embedded network monitoring with cloud vulnerability analysis, the proposed framework supports more transparent prioritization of remediation actions. The study contributes to cybersecurity research by linking explainable machine learning, vulnerability scoring, and cloud security governance into a unified risk analytics model.
Vision-Language Feature Fusion and Wavelet Attention for Fine-Grained Visual Recognition
Fine-grained visual recognition requires models to distinguish subtle differences among visually similar categories. Recent vision-language models have shown strong semantic representation ability, while wavelet-based convolutional networks improve sensitivity to frequency-domain details. This article proposes a hybrid feature fusion framework that combines semantic-guided visual encoding with multi-scale wavelet attention. The framework first extracts visual features through convolutional and transformer-based encoders, then enhances texture, contour, and lesion-related information through wavelet transform convolution. A semantic guidance module introduces category-level descriptions to improve discriminative learning and interpretation. Finally, an attention module refines channel and spatial features before classification or caption generation. The method is designed for fine-grained recognition tasks such as remote sensing scene analysis, melanoma image recognition, breast cancer DCE-MRI segmentation support, medical imaging interpretation, and industrial defect identification. The article highlights the complementary relationship between semantic representation and frequency-aware visual detail. By integrating these two feature sources, the framework aims to improve both recognition accuracy and interpretability in complex visual domains.
Federated Learning and Privacy-Preserving Stock Prediction: Secure Collaborative Learning Across Financial Institutions
The application of machine learning to stock prediction has demonstrated remarkable advances, with models such as the Stock State Space Graph achieving state-of-the-art performance through the integration of state space modeling and graph-based relational reasoning. However, the training of these models typically requires centralized access to large volumes of sensitive financial data, raising substantial privacy concerns and regulatory challenges. Financial institutions hold valuable proprietary trading data that could improve prediction models if shared, yet competitive sensitivities and data protection regulations such as GDPR and CCPA prohibit direct data sharing. This paper proposes a federated learning framework for privacy-preserving stock prediction that enables collaborative model training across multiple financial institutions without requiring raw data to leave individual institutions. Our approach, Graph-enhanced Federated Stock Prediction (GFedStock), extends the S3G architecture to a federated learning setting where institutions jointly train a shared model while keeping their data local. We incorporate differential privacy mechanisms and secure aggregation protocols to provide formal privacy guarantees, and analyze the privacy-utility tradeoff inherent in privacy-preserving approaches. Through extensive experiments on multi-institutional market datasets, we demonstrate that federated learning achieves 94-97% of the accuracy of centralized training while preserving data privacy, with the graph-enhanced architecture capturing cross-institutional market structure information. Our work contributes to the growing field of privacy-preserving machine learning in finance, providing a principled framework for secure collaborative learning in the financial services industry.
Contrastive Learning and Self-Supervised Pre-Training for Stock Prediction: A Graph-Based Representation Learning Approach
The scarcity of labeled financial data presents a fundamental challenge for supervised learning approaches to stock prediction, as the cost of obtaining expert-labeled data is prohibitively high while the amount of unlabeled market data grows exponentially. Self-supervised contrastive learning offers a principled solution by enabling models to learn useful representations from unlabeled data through the optimization of contrastive objectives that encourage augmented views of the same sample to be similar while views of different samples are dissimilar. This paper proposes a novel self-supervised pre-training framework for stock prediction based on contrastive learning over graph-augmented temporal views of financial time series. Our approach, Contrastive Stock State Space Graph (CS-S3G), builds upon the Stock State Space Graph architecture introduced by Lu, Hu, and Zhang, and integrates temporal and graph augmentation strategies to learn robust stock representations without relying on labeled data. We adapt explanation-based bias decoupling regularization principles from Zang and Liu's work on natural language inference to ensure that the learned representations capture genuine market dynamics rather than spurious correlations. Through extensive experiments on benchmark financial datasets, we demonstrate that self-supervised pre-training substantially improves downstream prediction accuracy, particularly in data-scarce scenarios, and that the learned representations transfer effectively across different markets and asset classes. Our work contributes to the growing body of research on self-supervised learning in finance, providing a principled framework for leveraging unlabeled data to improve prediction performance.
Neural Architecture Search and Automated Machine Learning for Stock Prediction: Discovering Optimal Graph-Based Architectures for Financial Markets
The design of neural network architectures for stock prediction has traditionally relied on manual experimentation and domain expertise, a process that is both time-consuming and potentially suboptimal. Different market conditions, asset classes, and prediction horizons may require different architectural configurations, yet the space of possible architectures remains largely unexplored. This paper proposes a Neural Architecture Search (NAS) framework tailored specifically for stock prediction, enabling the automated discovery of optimal graph-based neural network architectures without manual architecture engineering. Our approach builds upon the Stock State Space Graph architecture introduced by Lu, Hu, and Zhang, defining a novel search space that spans graph construction, message passing mechanisms, temporal aggregation, and state space configurations. We adapt explanation-based bias decoupling regularization principles from Zang and Liu's work on natural language inference to guide the architecture search toward configurations that are robust to overfitting and distribution shift. Through extensive experiments on benchmark financial datasets, we demonstrate that the NAS-discovered architectures substantially outperform manually designed baselines, achieving improvements of up to 18% in Sharpe ratio on the S&P 500 dataset. Furthermore, we show that the discovered architectures transfer effectively across different markets and asset classes, and that the architectures discovered for one prediction horizon can be adapted to others with minimal fine-tuning. Our work contributes to the growing body of research on AutoML in finance, providing a principled framework for automated architecture discovery that democratizes access to state-of-the-art prediction models.
Reinforcement Learning and Policy Optimization for Stock Trading: A Graph-Based State Space Approach to Sequential Decision Making
Stock trading is inherently a sequential decision-making problem wherein agents must balance immediate rewards against long-term portfolio growth while managing risk in an uncertain market environment. Traditional approaches to algorithmic trading rely on supervised learning for price prediction combined with heuristic rules for position sizing and risk management, failing to jointly optimize the complete trading pipeline in an end-to-end manner. This paper proposes a novel framework, RL-S3G (Reinforcement Learning Stock State Space Graph), that integrates reinforcement learning policy optimization with the Stock State Space Graph architecture for end-to-end learning of optimal trading policies. Our approach leverages the S3G model introduced by Lu, Hu, and Zhang as the environment representation within a Markov Decision Process framework, enabling an RL agent to learn trading strategies that directly optimize risk-adjusted returns. We adapt explanation-based bias decoupling regularization principles from Zang and Liu's work on natural language inference to improve the robustness of learned policies to market regime changes. Through extensive experiments on benchmark financial datasets, we demonstrate that RL-S3G substantially outperforms both supervised learning baselines and existing reinforcement learning trading agents in terms of Sharpe ratio, maximum drawdown, and risk-adjusted performance. Our work contributes to the growing body of research on deep reinforcement learning in quantitative finance, providing a principled framework for learning adaptive trading policies in complex market environments.
The Dynamics of Human-AI Judgment Complementarity: Disagreement, Appropriate Reliance, and the Organizational Future of Collaborative Decision-Making
The dominant narrative of artificial intelligence as a replacement for or augmentation of human judgment has obscured a more nuanced and productive possibility: that the greatest value of AI in organizational decision-making may emerge not from the accuracy of AI in isolation nor from human judgment alone, but from the structured interaction between them. Human-AI judgment complementarity—the capacity of human and AI decision-makers to make different but non-overlapping errors in ways that enable superior joint performance—represents a paradigm shift in how organizations should think about AI deployment. Yet realizing this complementary potential requires solving a set of deeply interconnected challenges: understanding when and why human and AI judgments diverge, enabling humans to appropriately rely on AI advice while retaining the ability to override it when necessary, calibrating trust in ways that reflect actual rather than miscalibrated AI reliability, and designing organizational processes that make productive disagreement rather than consensus the goal of human-AI collaboration. Drawing on three foundational references and twelve supplementary citations spanning human-AI teaming science, complementarity theory, appropriate reliance research, and organizational AI design, this paper develops a comprehensive framework for understanding and cultivating human-AI judgment complementarity in organizational settings. The analysis reveals that the metacognitive calibration crisis identified by the MIRROR benchmark, the structural auditing constraints revealed by the Verification Tax, and the practical demand for explainability in human resource analytics are not merely technical challenges but fundamental barriers to achieving the complementary potential of human-AI teams.
Tumor Immune Microenvironment Stratification by Viral Etiology: A Paradigm for Personalizing First-Line Immunotherapy and Targeted Therapy in Advanced Hepatocellular Carcinoma
Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide, with viral hepatitis—particularly hepatitis B virus (HBV) and hepatitis C virus (HCV)—accounting for the majority of cases globally. The advent of immune checkpoint inhibitor (ICI)-based combination therapies has transformed the first-line treatment landscape for advanced HCC, yet response rates remain heterogeneous and overall survival benefits are modest in unselected populations. A landmark network meta-analysis by Li and colleagues (2026), encompassing 24 randomized controlled trials and 13,572 patients, provided compelling evidence that viral etiology fundamentally shapes treatment response: significant heterogeneity was observed in efficacy outcomes across HBV-positive, HCV-positive, and non-viral subgroups for both immunotherapy and targeted therapy regimens. This finding carries profound implications for clinical trial design and therapeutic personalization. The tumor immune microenvironment (TIME) in viral-associated HCC is characterized by distinct immune infiltrates, T-cell exhaustion profiles, cytokine milieus, and molecular signatures that diverge substantially from non-viral HCC, providing a biological rationale for etiology-differentiated therapeutic approaches. This review argues that viral etiology should be elevated to the status of a primary framework for stratifying first-line treatment decisions in advanced HCC, integrated alongside established biomarkers such as PD-L1 expression and tumor mutational burden (TMB). We synthesize the current evidence linking viral etiology to TIME characteristics and differential treatment outcomes, propose a TIME-etiology clinical decision framework, examine safety considerations across etiologic subgroups, and outline critical directions for future research, including etiology-enriched trial designs, novel bispecific antibodies, antibody-drug conjugates (ADCs), and combination radiomics strategies.
Neuromorphic Computing and Spiking Neural Networks for Industrial Intelligence: Energy-Efficient Edge AI, Real-Time Sensory Processing, and the Neuromorphic Smart Factory
The proliferation of artificial intelligence in manufacturing — from process optimization and quality inspection to predictive maintenance and collaborative robotics — confronts a fundamental physical bottleneck: the energy consumption and inference latency of conventional GPU- and TPU-based AI accelerators cannot meet the demands of real-time, at-the-edge AI processing across thousands of sensors, machines, and robots in modern factories. Neuromorphic computing — computing architectures inspired by the structure and function of biological neural systems — and spiking neural networks (SNNs) — the third generation of neural networks that encode information in the timing of discrete spikes — have emerged as a transformative alternative, offering event-driven, massively parallel, and energy-efficient computation that is natively suited to the temporal dynamics of industrial sensor data. This review provides a comprehensive synthesis of neuromorphic computing and SNNs for industrial intelligence, examining the neuromorphic sensing-computation co-design paradigm, SNNs for real-time industrial quality inspection, neuromorphic edge AI for smart manufacturing, the integration of neuromorphic systems with industrial IoT platforms, and hybrid neuromorphic-classical architectures for complex manufacturing analytics. We further connect these advances to industrial optical sensing technologies — precision 3D surface metrology and four-dimensional thermal imaging — demonstrating how neuromorphic sensing-computation co-design enables ultra-low-latency, energy-efficient quality intelligence at the factory edge. A central contribution is the articulation of an integrated Neuromorphic Industrial Intelligence Architecture (NIIA) that unifies neuromorphic sensing, spiking neural network inference, and hybrid classical-neuromorphic computing for the next generation of energy-efficient, real-time smart manufacturing.
AI-Powered Quality Management Systems and Smart Factory Architecture for Manufacturing Excellence: Big Data Analytics, Real-Time Monitoring, and Integrated Quality Intelligence
Quality management is the cornerstone of modern manufacturing competitiveness, yet traditional quality management systems (QMS) — built on manual inspection, retrospective reporting, and fragmented data silos — are fundamentally inadequate for the complexity, speed, and precision demands of contemporary production environments. The proliferation of industrial IoT sensors, edge computing platforms, and artificial intelligence has catalyzed a transformation in quality management from a reactive, post-hoc function to a proactive, real-time, and predictive discipline. This review provides a comprehensive synthesis of AI-powered quality management systems (AI-QMS) and smart factory architecture for manufacturing excellence, examining big data analytics for manufacturing quality, machine learning algorithms for quality assurance and defect classification, real-time quality monitoring through edge computing and digital integration, predictive quality analytics and early warning systems, and the architectural frameworks that integrate AI-QMS into the broader smart factory ecosystem. We further connect these advances to industrial optical sensing technologies — precision 3D surface metrology and four-dimensional thermal imaging — demonstrating their roles as foundational data sources for AI-driven quality intelligence. A central contribution is the articulation of an integrated Quality Intelligence Architecture (QIA) that unifies real-time sensing, edge analytics, cloud-scale ML, and human decision support into a coherent quality management platform for the smart manufacturing era.
Meta-Learning and Autonomous Cognitive AI Agents for Adaptive Manufacturing: Few-Shot Model Adaptation, Continual Learning, and Foundation Model-Driven Rapid Deployment
Manufacturing environments are characterized by constant change — new product variants, shifted material suppliers, updated process parameters, evolving quality specifications — yet each change forces data-driven AI systems to undergo costly and time-consuming retraining cycles. The paradigm of meta-learning — learning to learn — offers a transformative solution: rather than training AI models from scratch on each new task, meta-learning enables models to adapt to novel manufacturing scenarios with minimal training data, by leveraging prior knowledge about the structure of learning problems across related tasks. Simultaneously, the emergence of foundation models — large-scale pre-trained AI models that can be adapted to diverse downstream tasks through fine-tuning or prompting — provides a new substrate for manufacturing AI, in which a single model pre-trained on broad industrial data can be rapidly specialized to specific production lines, products, or quality requirements. This review provides a comprehensive synthesis of meta-learning, few-shot adaptation, continual learning, and foundation model-driven AI for adaptive manufacturing, examining meta-learning algorithms and their manufacturing applications, few-shot adaptation for new product introduction and rapid deployment, continual learning for concept drift and production evolution, foundation model-driven manufacturing AI, autonomous cognitive AI agents for self-directed manufacturing intelligence, and the integration of meta-learning with the four preceding Yi Bao AI frameworks (RL-MPC, Adaptive Manipulation, Quality Intelligence Architecture, and Neuromorphic Industrial Intelligence Architecture). We further connect these advances to industrial optical sensing technologies — precision 3D surface metrology and four-dimensional thermal imaging — demonstrating how adaptive learning enables intelligent sensing systems to generalize across novel measurement tasks. A central contribution is the articulation of an integrated Adaptive Manufacturing Intelligence Architecture (AMIA) that unifies meta-learning adaptation, continual learning, foundation model fine-tuning, and autonomous cognitive agency for the next generation of self-improving manufacturing AI. Keywords: Meta-Learning; Few-Shot Learning; Continual Learning; Foundation Models; Manufacturing AI; Adaptive AI; Rapid Deployment; Lifelong Learning; Model Adaptation;
Knowledge Graphs and Neurosymbolic AI for Manufacturing Intelligence: Semantic Reasoning, Ontology-Driven Analytics, and the Integration of Symbolic and Neural Intelligence
The integration of artificial intelligence into manufacturing has generated powerful data-driven models — deep reinforcement learning policies for process control, convolutional networks for quality inspection, recurrent architectures for demand forecasting — yet these models operate as black boxes divorced from the rich domain knowledge that manufacturing engineers have accumulated over decades. Knowledge graphs — structured representations of entities (machines, products, materials, processes) and their relationships — and neurosymbolic AI — hybrid architectures that combine the pattern recognition power of neural networks with the logical reasoning capabilities of symbolic AI — have emerged as complementary paradigms that inject domain knowledge into AI systems, enabling reasoning, explanation, and causal understanding that purely data-driven approaches cannot provide. This review provides a comprehensive synthesis of knowledge graphs and neurosymbolic AI for manufacturing intelligence, examining knowledge graph construction and ontology engineering for manufacturing, graph neural networks for manufacturing analytics, neurosymbolic reasoning for fault diagnosis and process optimization, the integration of knowledge graphs with digital twin platforms, and the synthesis of knowledge-driven AI with the four preceding AI frameworks for smart manufacturing (Physics-Informed RL-MPC, Adaptive Manipulation, Quality Intelligence Architecture, and Neuromorphic Industrial Intelligence Architecture). We further connect these advances to industrial optical sensing technologies — precision 3D surface metrology and four-dimensional thermal imaging — demonstrating how structured knowledge and neural-symbolic integration enhance perceptual intelligence in manufacturing. A central contribution is the articulation of an integrated Knowledge-Augmented Manufacturing Intelligence (KAMI) framework that unifies knowledge graphs, neurosymbolic reasoning, and deep learning for trustworthy, explainable, and knowledge-driven manufacturing AI.
Multimodal Learning and Human Digital Twins for Industrial Safety Monitoring in Human-Robot Collaborative Environments
The transition from Industry 4.0 to Industry 5.0 marks a fundamental reorientation of manufacturing systems around human-centered collaboration, where workers and robots coexist and cooperate in shared workspaces. This paradigm shift introduces critical challenges in industrial safety monitoring: ensuring that collaborative robots respond safely and adaptively to human actions, that workers are protected from ergonomic risks and hazardous conditions, and that safety systems operate with the real-time reliability demanded by high-speed production environments. Traditional safety approaches—based on static rule-based logic and retrospective incident analysis—are fundamentally inadequate for the dynamic, unpredictable nature of human-robot collaboration. This review examines how multimodal learning—the integration of data from wearable sensors, computer vision systems, physiological monitors, and environmental sensors—combined with human digital twin architectures, is transforming industrial safety monitoring in human-robot collaborative environments. Drawing on twelve peer-reviewed works, we synthesize advances in human activity recognition (HAR) with wearable sensors, human intention recognition for real-time robot control, reinforcement learning for adaptive robotic manipulation, and worker safety digital twins for Industry 5.0. We further demonstrate how industrial sensing technologies—including four-dimensional thermal imaging, stereo phase-measuring deflectometry, and gesture-based robotic control—serve as critical sensor modalities within the multimodal safety monitoring framework. A central contribution of this review is the articulation of an integrated Human-Cobot Safety Intelligence (HCSI) paradigm that unifies multimodal perception, predictive safety analytics, and adaptive robot control for proactive, real-time industrial safety assurance.