Frontiers in Interdisciplinary Applied Science
Frontiers in Interdisciplinary Applied Science is a dynamic, peer-reviewed journal dedicated to the publication of high-quality research that spans multiple scientific disciplines. It emphasizes innovative approaches and solutions that address complex challenges through the integration of knowledge and methodologies from diverse fields. The journal covers a broad spectrum of topics, including but not limited to biotechnology, environmental sciences, computational modeling, materials science, and engineering. By fostering interdisciplinary collaboration, Frontiers in Interdisciplinary Applied Science aims to advance scientific understanding and facilitate the development of novel technologies and applications with significant practical impact.
Selective State Propagation for Real-Time Crypto Asset Forecasting with Subquadratic Attention Alternatives
Cryptocurrency markets present unique challenges for real-time forecasting due to their high volatility, continuous trading cycles, and sensitivity to external information flows. Traditional attention-based models, while effective in capturing long-range dependencies, suffer from quadratic computational complexity that hinders real-time deployment. This paper introduces a novel selective state propagation mechanism combined with subquadratic attention alternatives for efficient crypto asset price forecasting. Our approach leverages state space models (SSM) with selective gating to dynamically filter relevant historical information while maintaining computational efficiency. The proposed architecture achieves O(N log N) complexity compared to O(N²) in standard transformers, enabling microsecond-level inference suitable for high-frequency trading environments. We evaluate our method on five major cryptocurrencies over 24 months, demonstrating 18.3% improvement in mean absolute percentage error (MAPE) and 23.7% reduction in inference latency compared to transformer baselines. The selective propagation mechanism shows particular strength in volatile market conditions, accurately capturing regime shifts and flash crash patterns. Our findings suggest that computational efficiency and predictive accuracy need not be mutually exclusive in financial forecasting applications, opening pathways for deploying sophisticated models in latency-critical trading systems.
Copyright (c) 2026 Frontiers in Interdisciplinary Applied Science
Process Optimization in Smart Manufacturing via Data-Driven Approaches and Digital Twin Simulations
The advent of Industry 4.0 has necessitated a paradigm shift in manufacturing operations, moving from static, linear production lines to dynamic, interconnected ecosystems. This paper explores the integration of data-driven methodologies with Digital Twin (DT) technology to achieve process optimization in smart manufacturing environments. By leveraging real-time data acquisition, advanced analytics, and high-fidelity virtual replications, we propose a comprehensive framework for predictive maintenance, resource allocation, and anomaly detection. The research delineates a methodology for constructing a bidirectional data flow between the physical shop floor and its digital counterpart, enabling instantaneous feedback loops that enhance decision-making capabilities. Through a detailed implementation study involving a discrete manufacturing assembly line, we demonstrate that this synergistic approach significantly reduces operational downtime and energy consumption while improving throughput. The findings suggest that the convergence of machine learning algorithms and digital twin simulations provides a robust solution to the stochastic challenges inherent in modern production systems.
Evaluating the Long-Term Credit-Risk Effects of Cross-Selling Promotions Using Longitudinal Causal Modeling
This research assesses the long-term credit-risk implications of cross-selling promotions that encourage customers to open additional credit products. A dataset of 1.1 million accounts observed over 36 months was analyzed using a longitudinal causal model incorporating marginal structural models and time-dependent confounder adjustments. Results indicate that customers targeted by cross-selling promotions show a 14.6% higher cumulative probability of 180-day default over three years. Sequential mediation analysis finds that 63.4% of this effect is driven by increases in total credit exposure following promotion uptake. The study provides a comprehensive quantification of the delayed risk consequences of promotional strategies.
Graph Neural Anomaly Detection via Multi-Scale Temporal Subgraph Contrastive Learning
The detection of anomalous patterns in dynamic graph structures is a pivotal challenge in modern data mining, with critical applications ranging from financial fraud detection to cybersecurity and social network analysis. While static graph neural networks have achieved remarkable success in identifying structural irregularities, they often fail to capture the temporal evolution of anomalies that manifest only over extended periods. Existing dynamic approaches attempt to bridge this gap but frequently suffer from a trade-off between local structural sensitivity and long-term temporal dependency modeling. This paper introduces a novel framework, Graph Neural Anomaly Detection via Multi-Scale Temporal Subgraph Contrastive Learning (MSTS-CL). Our approach leverages a multi-scale subgraph sampling strategy to capture structural features at varying granularities, integrated with a temporal attention mechanism that highlights significant historical snapshots. Furthermore, we propose a self-supervised contrastive learning objective designed to maximize the mutual information between local temporal embeddings and global context representations, thereby mitigating the scarcity of labeled anomaly data. Extensive experiments on three benchmark datasets demonstrate that MSTS-CL outperforms state-of-the-art baselines by a significant margin, offering robust detection capabilities even in the presence of noise and structural sparsity.
Hierarchical Memory Networks for Multi-Hop Reasoning in Large-Scale Knowledge Bases
Knowledge graph reasoning has emerged as a critical task in artificial intelligence, enabling systems to infer missing information and answer complex queries through multi-hop reasoning. Traditional memory network architectures, while effective for single-hop reasoning tasks, struggle to capture the hierarchical relationships and long-range dependencies inherent in large-scale knowledge bases. This paper proposes a novel Hierarchical Memory Network (HMN) framework that addresses these limitations by introducing a multi-layered memory architecture with hierarchical attention mechanisms. The HMN framework decomposes complex multi-hop reasoning into a structured hierarchical process, where each layer progressively refines the reasoning path by attending to relevant knowledge at different levels of abstraction. Our approach integrates three key innovations: a hierarchical memory organization that explicitly models knowledge at multiple granularities, a progressive attention mechanism that enables iterative refinement of reasoning paths, and a dynamic memory retrieval strategy that efficiently scales to knowledge bases containing millions of entities and relations. Experimental evaluation on multiple benchmark datasets demonstrates that HMN achieves superior performance compared to existing state-of-the-art methods in multi-hop question answering and knowledge graph completion tasks. The hierarchical architecture not only improves reasoning accuracy but also enhances interpretability by providing explicit attention patterns at each reasoning step. Our findings suggest that explicitly modeling hierarchical structures in memory-augmented neural networks is essential for achieving robust multi-hop reasoning in large-scale knowledge-intensive applications.
Fire Risks in High-Rise Buildings: A Critical Review of Mechanisms, Impacts, and Technical Management Challenges
The growing size and complexity of high-rise buildings pose significant challenges to fire safety worldwide. This review synthesizes recent global trends in high-rise buildings and analyzes the evolving risk landscape associated with fire incidents. It systematically explores the mechanisms of fire development and vertical spread, followed by a detailed assessment of the multi-faceted impact of building fires on life safety, structural integrity, property safety and environmental health. Based on a combination of statistical data and the latest literature, this paper identifies key technical management deficiencies in the construction and operational phases that lead to the accumulation of fire risk. Despite advancements in smart fire protection technology, there is still a lack of a comprehensive technical management framework based on the life cycle. This paper concludes by highlighting the need for future research, particularly the development of integrated strategies for fire prevention, early detection, coordinated evacuation, and post-event recovery in high-rise environments.
Copyright (c) 2025 Frontiers in Interdisciplinary Applied Science
Research on the Application of Virtual Reality Technology in Digital Interaction of Tourism Exhibition Halls
The digitization of cultural heritage and tourism resources has become a pivotal area of research within computer science and human-computer interaction. As physical boundaries in tourism are increasingly challenged by global events and sustainability concerns, Virtual Reality (VR) offers a transformative solution for remote presence and immersive exploration. This paper investigates the comprehensive application of VR technology in the digital interaction of tourism exhibition halls. Specifically, we propose a novel framework, the Adaptive Gaze-Contingent Rendering System (AGCRS), designed to optimize rendering performance while maintaining high visual fidelity in resource-constrained environments. By integrating real-time user behavior analysis with dynamic Level of Detail (LOD) management, the system addresses common latency and motion sickness issues inherent in current VR tourism applications. We evaluate the proposed method through extensive experiments comparing it against traditional static rendering pipelines. The results demonstrate a significant reduction in rendering latency and a marked improvement in user immersion scores. This study contributes to the theoretical understanding of digital presence and provides practical architectural guidelines for the next generation of smart tourism exhibitions.
Big-Data Multimodal Power System Analysis with Markov Temporal Modeling for Community Detection
Modern power systems generate massive volumes of heterogeneous data from sensors, meters, supervisory devices, market platforms, and environmental services. Extracting stable structural patterns—especially community structures—from such big-data environments is essential for understanding grid behavior, enhancing situational awareness, and supporting reliable operation. However, existing community detection approaches often fail to capture the non-stationary temporal dependencies and multimodal heterogeneity inherent in large-scale power networks. To address these challenges, this study proposes a big-data multimodal learning framework for power system community detection, integrating electrical measurements, topological descriptors, environmental indicators, and operational event logs into a unified representation. A Markov temporal prior is employed to model sequential dependencies and suppress unstable transitions across evolving power system states, thereby improving temporal consistency. The framework further incorporates scalable data processing, adaptive modality fusion, and topology-aware clustering techniques tailored for large and complex power grids. Experiments conducted on both real-world and synthetic datasets demonstrate that the proposed framework achieves superior accuracy, robustness, and temporal stability compared with state-of-the-art baselines. The detected community structures align closely with physical grid regions, operational partitions, and disturbance propagation patterns, confirming the method’s practical interpretability. This work provides a scalable and data-driven solution for community detection in large power systems, offering valuable insights for grid monitoring, anomaly localization, reliability assessment, and intelligent decision-making.
Temporal Causal Inference for Web Application Monitoring: Structure Learning from Sequential Performance Data with Delayed Effects
Web application monitoring systems collect vast amounts of sequential performance data, yet traditional approaches struggle to identify complex temporal causal relationships, particularly when effects manifest with delays. This paper introduces a novel framework for temporal causal inference in web application monitoring that addresses the challenge of learning causal structures from sequential performance data exhibiting delayed effects. Our approach integrates Granger causality principles with modern structure learning techniques to construct Directed Acyclic Graphs (DAGs) that capture both instantaneous and lagged causal relationships among performance metrics. We propose a hybrid methodology combining constraint-based and score-based methods specifically designed to handle the non-stationary nature of web performance data and the presence of time-varying confounders. The framework employs a sliding window approach for dynamic causal structure discovery, enabling real-time adaptation to changing system behaviors. Experimental validation using both synthetic datasets and real-world web application traces demonstrates that our method achieves superior performance in identifying true causal relationships compared to baseline approaches, with particular improvements in detecting delayed causal effects. The proposed framework reduces false discovery rates by approximately 35% while maintaining high sensitivity for genuine causal links, even under conditions of high-dimensional data and limited sample sizes. These findings have significant implications for automated root cause analysis, predictive maintenance, and performance optimization in modern web infrastructure.
Risk Assessment Model for Prefabricated Construction Projects Using Multi-Factor Evaluation
This study develops a multi-factor risk assessment model for prefabricated construction projects, covering production capacity, transportation limits, on-site conditions, and worker skills. A scoring system was tested on 21 projects across three provinces. Results show that transportation delays and inaccurate component scheduling were the most significant risk contributors. The model provides a structured framework for predicting project uncertainty and improving planning reliability during the early design stage.
Hybrid VAE-LSTM Framework for Multi-Asset Implied Volatility Forecasting with Cross-Sectional Constraints
Accurate forecasting of implied volatility surfaces across multiple assets remains a critical challenge in financial risk management and derivatives pricing. This paper proposes a novel hybrid framework that integrates Variational Autoencoders (VAE) with Long Short-Term Memory (LSTM) networks to forecast multi-asset implied volatility while enforcing cross-sectional arbitrage-free constraints. The VAE component learns a low-dimensional latent representation of the volatility surface structure, capturing complex non-linear relationships and ensuring consistency across strikes and maturities. The LSTM component, enhanced with attention mechanisms, models temporal dynamics and long-range dependencies in volatility evolution. We incorporate cross-sectional constraints through a regularization mechanism that preserves the no-arbitrage conditions inherent in option pricing theory. Empirical results on S&P 500 index options and multiple equity options demonstrate that our hybrid VAE-LSTM framework significantly outperforms traditional econometric models and standalone deep learning approaches, achieving a reduction in mean absolute error of approximately 23% compared to benchmark methods. The framework successfully maintains arbitrage-free properties while providing superior predictive accuracy for both short-term and long-term forecasting horizons.
Forecasting Commodity Futures Prices Under Macroeconomic Uncertainty
This study examines the predictability of commodity futures prices in the context of heightened macroeconomic uncertainty. Commodity markets are increasingly influenced by global economic fluctuations, policy shifts, and geopolitical risks, which complicate accurate price forecasting. The primary objective of this research is to evaluate the efficacy of advanced econometric models, including vector autoregression (VAR) and machine learning algorithms, in forecasting commodity futures prices under such uncertain conditions. Using high-frequency data from major commodity exchanges and macroeconomic indicators from 2000 to 2023, the analysis reveals that models incorporating uncertainty proxies—such as the Global Economic Policy Uncertainty Index—significantly enhance forecasting accuracy. Key findings indicate that macroeconomic uncertainty not only amplifies price volatility but also alters the predictive power of traditional financial variables. These results underscore the importance of integrating uncertainty measures into commodity pricing models, offering valuable insights for investors, policymakers, and risk managers in mitigating exposure to volatile markets.
Research on Spatio-Temporal Prediction and Rebalancing Optimization for Bike-Sharing Supply-Demand Imbalance
Bike-sharing systems have emerged as a pivotal component of urban sustainable transportation, yet they frequently face challenges related to supply-demand imbalances across spatial and temporal dimensions. This study aims to address these inefficiencies by integrating spatio-temporal prediction with rebalancing optimization strategies. The proposed methodology employs a hybrid deep learning model combining convolutional neural networks (CNN) and long short-term memory (LSTM) networks to forecast short-term bike demand and supply patterns across stations. Subsequently, a rebalancing optimization model is formulated using integer programming to minimize operational costs while ensuring service availability. Experimental results on real-world bike-sharing data demonstrate that the hybrid prediction model achieves superior accuracy in capturing spatio-temporal dependencies, and the optimization approach significantly reduces imbalance rates by 18.7% compared to baseline methods. The findings underscore the importance of data-driven decision-making for enhancing system efficiency and user satisfaction in shared mobility services. This research provides actionable insights for urban planners and bike-sharing operators to improve resource allocation and operational sustainability.
A Comparative Study of Machine Learning Algorithms for Predicting Housing Prices in Chinese Cities
This study conducts a comparative analysis of machine learning algorithms for predicting housing prices in major Chinese cities, addressing the growing complexity and economic significance of the real estate market. With housing affordability becoming a critical socioeconomic issue, accurate price prediction models are essential for policymakers, investors, and urban planners. The research evaluates the performance of four algorithms—Linear Regression, Decision Trees, Random Forests, and Gradient Boosting Machines—using a dataset comprising historical transaction records, macroeconomic indicators, and location-based features from 10 major Chinese cities. Results indicate that ensemble methods, particularly Gradient Boosting Machines, achieve the highest predictive accuracy, with a mean absolute error reduction of up to 18% compared to traditional linear models. The findings underscore the importance of incorporating non-linear relationships and feature interactions in housing price modeling. This study not only provides a practical framework for real estate valuation but also highlights the potential of advanced machine learning techniques in addressing urban economic challenges.
Explainable AI for CPU Resource Scheduling in Cloud Operating Systems
Cloud computing environments require intelligent and efficient resource scheduling to manage dynamic workloads and meet service-level objectives. Traditional rule-based scheduling algorithms often fall short in handling the complexity and scale of modern cloud systems. This paper introduces a novel framework that leverages Explainable Artificial Intelligence (XAI) techniques to optimize CPU resource scheduling in cloud operating systems. By integrating interpretable models such as decision trees and SHAP (SHapley Additive exPlanations) values with deep learning-based schedulers, the framework not only enhances scheduling accuracy but also offers transparency in decision-making processes. Experimental results on synthetic and real-world workloads demonstrate the effectiveness of the proposed framework in improving system performance while providing human-understandable insights into scheduling logic.
Hierarchical Multi-Task Learning for Fine-Grained and Coarse Text Classification
Text classification tasks often vary in granularity, with coarse labels capturing general topics and fine-grained labels capturing nuanced subcategories or sentiments. Traditional models trained separately on these classification levels struggle to leverage the hierarchical relationships between them. In this paper, we propose a hierarchical multi-task learning (HMTL) framework that jointly models coarse and fine-grained text classification tasks by aligning shared and task-specific layers in a hierarchical architecture. Our model exploits the inherent semantic dependencies between classification layers, enabling better generalization and improved performance on both tasks. Evaluations on benchmark datasets demonstrate that HMTL outperforms single-task baselines and flat multi-task models, particularly in domains with rich label hierarchies. The proposed framework provides a scalable and effective approach for tasks requiring contextual depth and label interdependence.
A Brief Discussion on Modern Landscape Design under the Influence of Ecologism
The deterioration of the global ecological environment makes people begin to redefine the relationship between man and nature, and the idea of ecologism gradually rises. Some landscape architects realize the importance of ecological protection in landscape design, thus opening the exploration and practice of ecological landscape design. This paper illustrates that it is because of the environmental crisis that modern landscape design came into being, and now it has entered a new period of ecological development. The expression forms of modern ecological design thought are listed. And the connotation of ecological landscape and design methods to do a comprehensive and in-depth analysis; This paper summarizes the basic principles of ecological landscape design, and introduces the practice of "ecological" methodology combined with specific cases.
Guyi Garden in Historical Transition: The Interpenetration and Transformation of Classical Gardens and Public Parks
Guyi Garden, located in Nanxiang Ancient Town in Jiading District, Shanghai, is one of the five major classical gardens in the city. Following the opening of Shanghai to foreign trade, local gardens underwent significant transformations under the influence of commerce and cultural exchange. This paper traces the evolution of Guyi Garden, interpreting the sociocultural context behind its renaming over time, and examines the conceptual shift from “classical garden” to “public park” as a reflection of broader societal changes. Through an analysis of this specific Jiangnan classical garden, the study aims to reveal the underlying mechanisms that led to the transformation of classical gardens in modern Shanghai. Based on historical and social developments, the transformation of Guyi Garden is divided into four major phases: the stable development period of a private garden in the Ming Dynasty; a rapid transformation during the commercial boom and temple-centered expansion; the incursion of public park culture following Shanghai’s opening; and the expansion into a public tourist attraction after the founding of the People’s Republic of China. Furthermore, the paper explores changes in garden aesthetics and the interplay between Chinese and Western garden cultures, offering insights for the preservation and transmission of classical garden heritage in contemporary times.
Towards an Open Block Space: An Analysis of Derivative Problems of Super-Large Closed Communities
In the wake of an extensive, growth-driven urban development paradigm, countless super-sized gated residential compounds have sprung up. As China’s cities have expanded rapidly, the attendant problems associated with these enclaves have become increasingly acute. Responding to the State Council’s directive that “new residential developments should in principle adopt a block-based layout and refrain from constructing walled compounds,” this paper reviews and clarifies the concept, attributes and significance of the “open block,” and traces the historical evolution of Chinese residential districts. It argues that Chinese society has consistently favoured open collective-housing models. After synthesising the manifold drawbacks of today’s mega-scale gated communities, the study examines, from the perspectives of block scale, boundary walls and design codes, the inevitability of China’s transition toward open urban blocks. Finally, it summarises the principal difficulties and challenges encountered in the implementation of open-block schemes.
A Case Study on Planning and Design of Hot Spring Tourism Resources: Taking Nanjing Tangshan Hot Spring Resort as an Example
Tangshan, Nanjing, is famous for its hot springs. The rise of hot spring tourism this year has brought opportunities to Tangshan. This paper takes Tangshan Hot Spring Resort in Nanjing as an example to analyze the planning and design of hot spring tourism resources. Through the analysis and sorting of the local natural and cultural conditions and the classification of hot springs, understand the concept and principles of local tourism planning, and focus on the analysis of Tangshan Hot Spring Health Care Town, a key tourism planning project. This paper proposes that local development may be affected by traditional tourism development models, and over development, homogeneity of tourism products and other issues may restrict local tourism development, and puts forward suggestions and countermeasures for the above issues.
A Preliminary Study on the Renewal Experience of Japan's Old Communities from the Perspective of Aging — Take the Japanese Hanada Community as an example
This paper takes Hanada community in Japan as a case study to discuss the experience and enlightenment of how to renew the old community from the perspective of adaptation to the elderly. The research shows that the renewal of old communities should start from housing, transportation, medical care, welfare and other aspects to improve the quality of life of elderly residents. In the renewal practice of Huatian Community, community residents actively participate in community planning and decision-making, the government and social groups provide necessary support and services for the community, and also adopt advanced scientific and technological means, such as intelligent facilities, to improve the functionality and comfort of the community. In addition, the study also put forward a number of recommendations, such as focusing on the accessibility and mutual assistance of the community, and strengthening the connection with the surrounding communities, so as to promote the renewal and sustainable development of the old community. In summary, this study provides useful experience and enlightenment for the renewal of age-appropriate communities.
A Study on the Construction of an Integrated Intelligent Curriculum System for Basic Anatomy Based on the Integration of Teaching, Learning, Assessment, and Improvement
Objective To explore the construction and effectiveness of the basic anatomical wisdom curriculum system based on the integration of teaching, learning, evaluation and reform. Methods A total of 303 students majoring in higher vocational nursing were randomly selected for the 2023 fall class as the survey objects, and the independent learning ability, critical thinking ability, and learning of smart courses, offline final theoretical results, and teaching satisfaction were evaluated before and after teaching. Results 97% of the students had a completion rate of 100% in the browsing of teaching materials, and the total learning score of the digital human stem7.0 platform was (83.09±5.48). The final theory test score is (86.70±6.87); After teaching, the scores of learning motivation, self-management, learning cooperation, and information literacy in students' self-directed learning ability were higher than those before teaching, and the differences were statistically significant(P<0. 05）. After the teaching of the basic anatomy curriculum system based on the integration of teaching, learning, evaluation and reform, the scores of truth-seeking, open-mindedness, analytical ability, systematization ability, self-confidence, intellectual curiosity and cognitive maturity of critical thinking were higher than those before teaching, and the differences were statistically significant (P). <0. 05）. 97% of the students gave a high level of satisfaction. Conclusion The basic anatomy wisdom curriculum system based on the integration of teaching, learning, evaluation and reform can effectively improve students' learning interest and participation in the basic anatomy course, and promote the improvement of their independent learning ability and critical thinking ability. At the same time, the students were also satisfied with the exploration of the construction of the intelligent curriculum system of the basic anatomy course. Therefore, this study believes that the basic anatomy wisdom curriculum system based on the integration of teaching, learning, evaluation and reform has significant promotion value, which can provide a useful reference for the teaching reform of smart anatomy.
Real-Time 3D Intelligent Detection System for Railway Fasteners
This study addresses the challenges of low efficiency in manual railway fastener inspection and susceptibility to lighting interference in image-based detection by proposing a line structured light-based 3D reconstruction and fault detection method, enabling precise identification of fastener anomalies and loosening. Key research contributions include: 3D Point Cloud Reconstruction Developed an improved region-growing algorithm for segmenting fastener component point clouds, constructing high-precision 3D models.For Type I resilient fasteners, a host computer software was implemented for scanning and reconstruction. Experimental results demonstrated 40% reduction in point cloud noise and 35% improvement in model completeness. Anomaly Detection Algorithm Proposed an ensemble classifier model to detect six categories of anomalies, including nut loss and resilient fastener missing.At a detection speed of 40 km/h, the system achieved 95% detection accuracy with only 3% false alarm rate. Loosening Detection Method Utilized resilient fastener registration algorithms to calculate gap distances and established a conversion relationship between nut loosening values and fastener displacement.Field tests confirmed <1 mm measurement error and RMS error of 0.32 mm, meeting high-speed railway maintenance standards.The developed detection system has been validated through experiments, showing 8× efficiency improvement over manual inspection, providing critical technical support for intelligent railway maintenance.
A Review of Domestic Language Service Research under the Perspective of Bibliometrics
This paper applies the bibliometric method, based on CNKI database, to screen and statistic the related literature published in the three major core journals system of China in the field of language service from 2008 to 2024, and statistically analyzes the literature in terms of the distribution of time, authors, journals, institutions, keywords and other aspects. It is found that under the current discipline system of language economics, language services have achieved certain research results. And the research results are analyzed and thought.
Research Status of High-Temperature Fiber-Optic Sensors Based on Fabry-Perot Interferometry
The measurement of physical parameters under high-temperature environments is widely needed in fields such as aerospace engines (e.g., ramjet engines and supersonic combustion ramjet engines), gas turbines, and hypersonic vehicles. Monitoring and evaluating combustion instability phenomena through pulsating pressure measurements is beneficial for studying the mechanisms behind combustion instability, providing a basis for physical structural improvements of combustion chambers as well as for engine design and evaluation. It also contributes to improving engine control performance and operational safety. Fiber-optic sensing technology based on Fabry-Perot (FP) interferometry has attracted significant attention due to its advantages of small size and high-temperature resistance, which enable it to measure physical parameters in harsh environments such as in aerospace engines. This paper reviews domestic and international research on fiber-optic sensors based on FP interferometry. The focus is on analyzing the advantages and disadvantages of various fabrication methods for fiber-optic sensors, with the aim of summarizing the characteristics of each technique and providing new ideas for the engineering application of high-temperature fiber-optic FP sensing technology.
Research on the construction of gene library of tie-dyeing technology based on AI-aided design
Tie-dyeing is an important part of traditional Chinese culture and has profound historical heritage and artistic value. With the advancement of modern industrialization, traditional tie-dye techniques are facing the dilemma of inheritance, especially the depletion of dye resources, the lack of process innovation and the reduction of market demand. With the rapid development of artificial intelligence (AI) technology, especially breakthroughs in the fields of image generation, deep learning and data mining, AI technology has brought new opportunities for the protection, inheritance and innovation of tie-dye skills. In this paper, we propose a framework for the construction of a gene database of tie-dye technology based on AI-assisted design, combined with generative adversarial network (GAN), convolutional neural network (CNN) and other technologies, to explore how to establish a tie-dye pattern database, optimize the tie-dye process, preserve natural dye genes, and promote the digital protection and global promotion of tie-dye technology. Through the elaboration of the details of technical realization, the feasibility and practical path of AI in the innovation and inheritance of tie-dyeing skills are proposed.
Graph Neural Networks for Blockchain Security: A Deep Learning Approach to Anomaly Detection
The rapid expansion of blockchain technology has led to increased security challenges, particularly in detecting fraudulent transactions and malicious activities within decentralized networks. Traditional anomaly detection techniques, including rule-based heuristics and supervised learning models, struggle to adapt to the dynamic and complex nature of blockchain transactions. This paper introduces a graph neural network (GNN)-based anomaly detection framework designed to improve blockchain security by leveraging the inherent graph structure of transaction networks. The proposed approach models blockchain transactions as a directed graph, where nodes represent wallet addresses and edges correspond to transaction flows. By applying spatial and temporal graph learning techniques, the framework captures both network topology and transaction evolution over time, allowing for the identification of anomalous activities such as money laundering, phishing scams, and Ponzi schemes. The GNN model incorporates graph convolutional networks (GCN), graph attention networks (GAT), and gated recurrent units (GRU) to learn both spatial dependencies and sequential patterns within blockchain transactions. Experiments conducted on Bitcoin and Ethereum transaction datasets demonstrate that the GNN-based framework outperforms conventional fraud detection methods in terms of precision, recall, and false positive reduction. The model successfully detects fraudulent transactions with an F1-score of 0.92, showing its effectiveness in identifying emerging threats in blockchain networks. These results highlight the potential of deep learning-based anomaly detection in enhancing blockchain security, providing a scalable and adaptive solution for detecting fraud in decentralized financial ecosystems.
Literature Review: Personalized Learning Recommendation System in Educational Scenarios: XAI-Driven Student Behavior Understanding and Teacher Collaboration Mechanism
This literature review delves into personalized learning recommendation systems (PLRSs) within educational contexts. It places a significant emphasis on the understanding of student behavior that is driven by Explainable AI (XAI). Additionally, it focuses on the mechanisms of teacher collaboration. The traditional educational models are not without their drawbacks. These limitations have instigated a transition towards personalized learning. This movement has, in turn, propelled the development of PLRSs. These systems are designed with the dual objectives of boosting learning efficiency and enhancing learning outcomes. To accomplish these goals, they offer customized learning resources and strategies. There are key trends. One trend is dynamic and adaptive recommendation strategies. Another trend is the use of explainable AI (XAI). XAI builds trust. XAI also builds transparency. In - depth student behavior understanding is a trend. Performance modeling is also a trend. Advanced content understanding is a trend. Semantic analysis is a trend as well. The application of collaborative filtering is a trend. The application of hybrid approaches is a trend. The emphasis on teacher collaboration is a trend. The emphasis on human - AI interaction is a trend too. Dynamic systems can adapt to students' changing needs. XAI makes AI - driven recommendations understandable and trustworthy. Precise student models improve the relevance of recommendations. Semantic analysis of educational content does the same. Hybrid approaches enhance the performance of collaborative filtering. Teacher - AI collaboration is important for the effective implementation of PLRSs. However, there are several challenges. Future research should focus on different things. It should develop more comprehensive student models. It should enhance XAI techniques for educational contexts. It should empirically study the impact of XAI on learning outcomes. It should design effective teacher collaboration mechanisms. It should create human - AI interaction strategies. It should address ethical issues and fairness in personalized learning. It should explore the integration of multimodal data and learning analytics. By dealing with these challenges, PLRSs can be optimized. This can create more intelligent, transparent, and human - centered personalized learning environments. In the end, this will enhance learning outcomes. It will also empower students and educators.
Personalized Learning Recommendation System
Literature Review: Designing for Explainability in Financial Credit Assessment: XAI Interaction Strategies for Non-expert Users
This review focuses on XAI interaction strategies for non-expert users in financial credit assessment. As AI models gain traction in finance, especially in credit scoring, explainability becomes crucial for trust, fairness, and user understanding. Regulatory, ethical, and practical needs drive XAI development. LIME and SHAP are key techniques for explaining complex models. However, XAI's success in financial credit assessment relies on user - centered design. Principles like contextualization and interactivity are important for effective explanation experiences. User - centric evaluation methods are essential to gauge XAI's impact on users. There are still challenges, such as balancing explanation complexity and user comprehension, and addressing ethical issues. Future research should focus on user - adaptive interfaces, causal explanations, and integrating XAI with financial education. Overall, XAI design in financial credit assessment is a human - centered problem. Optimizing user experience and following research directions can revolutionize financial credit assessment and build a more reliable financial future.
An Invariant and Balanced Deep Learning Approach for Financial Risk Assessment
Financial risk assessment is a fundamental process in banking, insurance, and investment sectors, enabling institutions to quantify and manage exposure to credit, operational, and market risks. Traditional financial risk models rely on statistical learning techniques, such as logistic regression and decision trees, which are limited in handling non-linear financial dependencies, class imbalance, and distributional biases. Recent advances in deep learning (DL) and artificial intelligence (AI) have introduced more powerful models capable of capturing complex risk patterns and improving prediction accuracy. However, DL models often suffer from data imbalance issues and lack of invariance across economic conditions, leading to biased and inconsistent financial risk predictions. This study proposes an invariant and balanced DL-based financial risk assessment framework, integrating graph neural networks (GNNs) for relational financial modeling, adversarial domain adaptation for bias mitigation, and cost-sensitive learning techniques for class imbalance correction. The model enhances risk prediction accuracy while ensuring fairness, robustness, and generalizability across different financial environments. Additionally, an explainability layer is incorporated to improve regulatory transparency and model interpretability. Experiments on real-world financial datasets demonstrate that the proposed framework outperforms traditional financial risk models, achieving higher recall for high-risk entities, improved invariance across economic shifts, and reduced disparities in risk classification. The findings highlight the potential of DL-based risk assessment systems to offer more balanced, fair, and adaptive risk management strategies, ensuring more reliable financial decision-making in banking and investment sectors.