Journal of Computer Science and Frontier Technologies
Journal of Computer Science and Frontier Technologies ISSN Print: 3104-4204 ISSN Online: 3104-4212 CD ISSN: 3104-4220
AGV Assignment Strategy in Express Distribution Center Based on Closed Queueing Model
A rational assignment strategy can effectively reduce Automated Guided Vehicle (AGV) driving distance and improve resource utilisation. Firstly, we propose a partition assignment strategy considering the driving distance of AGV. On this basis, we propose task balanced partition assignment strategy by considering the utilisation balance of the pick-up platform. And we develop a closed queueing model to evaluate the performance of the AGV system. Secondly, we design experiments to verify the effectiveness of the theoretical model. Finally, we analyse and compare the performance of random assignment strategy, partition assignment strategy and task balanced partition assignment strategy. The results show as (1) The closed queueing model can effectively evaluate system performance. (2) Task balanced partition assignment strategy can obviously improve the system throughput. (3) The partition assignment strategy caused resource congestion when parcels arrive unevenly, while the task balanced partition assignment strategy can obviously balance the utilisation rate of the pick-up platform.
Copyright (c) 2026 by author(s) and Erytis Publishing Limited https://creativecommons.org/licenses/by-sa/4.0
Design and Implementation of an Intelligent Learning Assistance System: A Full-Stack Web Application with React and FastAPI
This study presents the design and implementation of an Intelligent Learning Assistance System (ILAS), a full-stack web application that integrates machine learning services for student risk prediction, profiling, knowledge tracing, and personalized recommendations. The system adopts a four-tier architecture comprising a React 18 frontend with TypeScript, an Nginx gateway, a FastAPI backend with SQLAlchemy ORM, and a dedicated AI model service layer. The frontend implements 14 page routes with ECharts-based visualizations. The backend exposes 45+ RESTful API endpoints across six service modules, with JWT-based authentication and role-based access control. Key technical contributions include a Singleton Model Manager pattern for heterogeneous AI model lifecycle management, a three-strategy recommendation engine combining BKT-based weakness targeting (weight 0.5), BERT semantic matching (weight 0.3), and collaborative filtering (weight 0.2), and an asynchronous Celery-based LLM report generation pipeline. Performance evaluation demonstrates that database indexing improves query performance by 78% to 92%, and the system maintains an average response time of 312ms under 150 concurrent users with a 0.12% error rate.
Multimodal Data Fusion for Intelligent Early Warning System of Cucumber Diseases
To address the limitations of traditional cucumber disease recognition relying solely on image information with insufficient early warning accuracy, this paper proposes an intelligent early warning system for cucumber diseases based on multimodal data fusion. The system integrates leaf image data with environmental sensor data (temperature, humidity, light intensity, CO₂ concentration) and constructs an Attention Mechanism-based Convolutional Neural Network (AM-CNN). A multimodal dataset containing 12,000 samples across 7 disease categories was built by collecting cucumber leaf images and corresponding environmental parameters. Experimental results demonstrate that the AM-CNN model with multimodal fusion achieves an average recognition accuracy of 94.7%, which is 2.4 percentage points higher than the single-modal CNN model and 15.6-20.8 percentage points higher than traditional machine learning algorithms. The end-to-end response time is 1.6 seconds, meeting real-time monitoring requirements. This research provides a novel technical solution for precision disease warning in facility agriculture, with significant theoretical and practical value.
Comparing Chinese–English Translation Performance of Large Language Models: A Multidimensional Evaluation of Financial, Technological, and Political Texts
Large language models (LLMs) are increasingly used for machine translation; however, their reliability in professional Chinese–English translation, particularly for domain-specific texts, remains insufficiently examined. This challenge is especially evident in texts containing specialized terminology, numerical information, complex logical relations, and formal institutional phrasing. To address this issue, this study conducts a controlled comparison of six LLMs: ChatGPT-5.0, Claude Sonnet 4.5, DeepSeek-R1, GLM-4.6, Qwen3-MAX, and Grok 4.0. Three representative Chinese texts from the financial, technological, and political domains were selected and translated under three prompt conditions: zero-shot prompting, domain-enhanced prompting, and glossary-based prompting. Translation quality was assessed through a multidimensional evaluation framework. Standard reference-based metrics, including BLEU, ChrF++, TER, ROUGE, BERTScore, and COMET, were used to measure automatic translation quality. In addition, lexical richness and syntactic-discourse indicators were incorporated to examine stylistic and structural differences across model outputs. The results reveal a clear domain effect, indicating that financial, technological, and political texts present different levels and types of translation difficulty for LLMs. By contrast, prompt strategy did not produce significant main effects under the three tested conditions. At the model level, GLM-4.6 achieved the highest COMET-based ranking across all prompt settings, while DeepSeek-R1 showed comparatively stable second-place performance. Overall, the findings suggest that, within this controlled exploratory dataset, model type and text domain exert a stronger influence on translation performance than prompt modification alone. Rather than offering a definitive ranking of LLM translation capability, this study provides empirical evidence on how selected LLMs handle specialized Chinese–English translation under comparable experimental conditions. Future research should expand the dataset, incorporate human evaluation, and further investigate the role of terminology constraints in improving translation accuracy and stylistic appropriateness.
Flow Experience or Algorithm Trust? A Dual-Path Study of How AI Empowerment Drives Students’ Online Learning Stickiness
Now that artificial intelligence is available to all, it has changed how we learn online. Now many excellent systems that use artificial intelligence are available to provide personalised learning paths, intelligent tutoring, etc. Learning stickiness is the long-term intention of students to use a learning platform and stay engaged in learning over time, so it has become an important indicator of the sustainability of online education. Based on the experience of flow and algorithm trust, a two-way theoretical model is put forward in this paper to explore how the empowerment of artificial intelligence affects the stickiness of students' online learning in different ways. According to the theories of flow and trust, a total of 426 students were selected as the subjects of the questionnaire survey on the AI online learning platform in this paper. According to the results of the experiment, both the flow experience and trust in the algorithm were found to be moderators of the effect of AI empowerment on learning stickiness. The road of experience and flow is relatively more popular among adolescent students, and at the same time, the rational path of algorithm trust is used more frequently by adults. Personalisation of the platform, interactive feedback and data transparency are the three reasons for the appearance of the two mediating states. This paper studies the two motives behind AI-enabled online learning and offers some practical optimization ideas for the Design of learning platforms, Algorithm improvement, etc.
HMCO-AT: A Hierarchical Memory - Compute Orchestrated Adaptive Training Framework for Resource-Efficient and Adversarially Robust Large Models in Cloud–Edge Systems
Adversarial training has emerged as the most effective paradigm for improving the robustness of deep neural networks; however, it imposes substantial computational and memory demands, particularly during the adversarial example generation process. These challenges become more critical in cloud-edge collaborative learning scenarios, where edge devices have limited resources and communication bandwidth is highly variable. To address this problem, we propose HMCO-AT, a Hierarchical Memory-Compute Orchestrated Adaptive Training framework designed specifically for resource-efficient adversarial training of large-scale models. HMCO-AT explicitly models the memory footprint and computation peaks induced by adversarial sample generation and dynamically schedules model layers, training stages, and communication operations across cloud and edge nodes. Our framework integrates hierarchical parameter partitioning, memory-aware activation recomputation, quantized gradient transmission, and an RL-based adaptive scheduler that jointly optimizes memory consumption, computational load, and robustness performance. HMCO-AT is validated on large-scale language-modeling corpora including OpenWebText, WikiCorpus, and filtered C4 under realistic cloud–edge conditions. Experiments show that HMCO-AT reduces peak edge memory by 64.8% and lowers computation cost and latency by 35.8% and 26.7%, respectively. It also improves adversarial robustness, achieving 7.2% higher FGSM and 9.3% higher PGD-10 accuracy than strong baselines. These results confirm HMCO-AT as an efficient and robust framework for large-model cloud–edge training.
A Private Web-Based Pipeline for Converting Images and PDF Pages into Editable PowerPoint Shapes
Rasterized slide images and PDF pages are easy to distribute but difficult to reuse because text, layout objects, borders, and background regions are flattened into pixels. This paper presents a private web-based pipeline that converts images and PDF pages into editable PowerPoint files. The proposed system emphasizes local deployment, backend-centered image analysis, optical character recognition, text-region removal, geometric shape reconstruction, an intermediate Shape JSON representation, and .pptx export. The method first normalizes input pages, detects text regions, estimates background colors, extracts primary layout shapes through color clustering and contour analysis, and then reconstructs editable PowerPoint shapes with fill colors, borders, layer order, and placeholder text fields. A lightweight browser interface supports upload, preview, basic shape correction, and re-export, while computation remains on the local server. The current prototype completes the core conversion loop from raster input to editable PowerPoint output and provides a foundation for improved text removal, manual correction, batch PDF processing, and reusable private templates. The study demonstrates a practical engineering approach for transforming static visual documents into reusable presentation assets without relying on public cloud processing.
Copyright (c) 2026 by author(s) and Erytis Publishing Limited. https://creativecommons.org/licenses/by-sa/4.0
GIMeT: A Multimodal Transformer Framework for Automated Gastrointestinal Disease Diagnosis Using Endoscopic Images and Physiological Signals
Accurate and efficient diagnosis of gastrointestinal (GI) diseases is essential for timely clinical decision-making, yet traditional diagnostic workflows rely heavily on manual interpretation of endoscopic images and disconnected physiological measurements. These limitations often lead to variability in diagnosis and increased clinician workload. To address these challenges, we propose GIMeT (GastroIntestinal Multimodal Transformer), a novel deep learning framework that integrates endoscopic imaging, physiological time-series data, and optional clinical text information for automated GI disease classification and lesion segmentation. GIMeT consists of three modality-specific encoders—a CNN-based image encoder, a temporal encoder for physiological signals, and a BERT-based text encoder—followed by a Cross-Modal Transformer (CMT) to achieve fine-grained feature alignment and joint representation learning. A FiLM-conditioned U-Net decoder further enhances pixel-level lesion segmentation using multimodal cues. We evaluate GIMeT on a multi-center dataset comprising endoscopic images, electro-physiological measurements, and symptom descriptions. Experimental results show that GIMeT consistently surpasses all unimodal and multimodal baselines across both classification and segmentation tasks. The model achieves improvements of approximately 3-4% in accuracy and F1-score, and a 3% gain in lesion Dice coefficient. Ablation studies further validate the contribution of multimodal fusion and cross-modal attention, demonstrating GIMeT’s robustness and strong potential for computer-assisted gastrointestinal diagnosis.
Driver Fatigue Detection Method Based on Improved RT-DETR
To address the problem of poor robustness in driver fatigue detection under complex scenarios such as low illumination and motion blur, this paper proposes an optimization method based on hard sample augmentation. A hard sample dataset containing low illumination, motion blur, and their compound interference is constructed, and a progressive two-stage training strategy is designed to guide the model to adapt to various degradations step by step. Furthermore, a spatial-channel interleaved robustness module (SCIRM) is proposed to enhance the response of eye and mouth features, while the normalized Wasserstein distance (NWD) loss function is adopted to improve the localization accuracy of small targets. The improved RT-DETR model achieves mAP50 of 95.03%, 95.62%, and 94.56% under low illumination, motion blur, and compound interference scenarios, respectively, significantly outperforming the baseline model, while its performance on clear scenes remains almost unchanged. This method effectively enhances the robustness of fatigue detection under complex imaging conditions.
IoT Anomaly Traffic Detection Method Based on Transformer Encoder Architecture
The widespread deployment of IoT devices has made heterogeneous network traffic highly complex, making abnormal traffic detection vital for IoT security. This paper presents a Transformer-based IoT abnormal traffic detection method tailored to the long-range temporal correlation of IoT traffic and the drawbacks of existing approaches. It first builds traffic embeddings with learnable positional encoding to project raw traffic features into a high-dimensional space, then leverages Transformer multi-head self-attention to capture long-range dependencies by modeling temporal correlations in traffic sequences. Feature compression and residual enhancement modules are further adopted to lower model complexity and inference delay for resource-limited IoT terminals. Experiments verify that the scheme accurately identifies normal traffic and diverse anomalous attacks with outstanding detection accuracy and stability.
MUAD-Net: A Multi-scale URL Anomaly Detection Network for Malicious URL Identification
With the continuous evolution of cyber attack techniques, the propagation of malicious links has become a major threat in cybersecurity. Attackers induce users to visit target resources by constructing malicious URLs, leading to information theft and malware distribution. Effectively identifying malicious URLs in complex network environments has become an important research issue. To address this problem, this paper adopts an attack life-cycle perspective and proposes a deep learning-based malicious URL detection method. Considering the complex structure, short lifecycle, and fast generation speed of malicious URLs, the method first normalizes URL strings and encodes them as character sequences, then maps characters into vector representations through an embedding layer. A multi-scale convolutional structure is used to extract local pattern features from URL character sequences, combined with a sequence modeling mechanism to capture contextual dependencies, achieving automatic learning of malicious URL features. Experimental results show that the proposed method achieves high detection performance and good generalization ability. Through multi-scale feature extraction and model optimization, this paper effectively improves malicious URL detection performance, demonstrating good application value in real-world network scenarios.
CloudPayGuard: Hardware-Aware Real-Time Fraud Detection for Cloud-Native Credit Systems with OoO CPU Microarchitecture Optimization
Real-time fraud detection in cloud-native credit payment systems is a critical challenge due to the increasing complexity of transaction networks, the rapid evolution of fraudulent behaviors, and the high computational demands of modern deep learning models. To address these challenges, we propose CloudPayGuard, a hardware-aware framework that integrates Temporal Heterogeneous Graph Neural Networks (TH-GNN) for dynamic transaction modeling, Large Language Models (LLM) for automated security policy generation, and out-of-order (OoO) CPU microarchitecture performance prediction for hardware-accelerated inference. CloudPayGuard constructs multi-modal transaction graphs incorporating user behavior sequences, device fingerprints, and geolocation information, enabling real-time identification of suspicious activities with millisecond-level latency. The framework dynamically generates and verifies risk policies through LLM-based reasoning and constraint checking, ensuring trustworthy and adaptive deployment in cloud-native environments. To optimize inference performance, a deep learning-based CPU microarchitecture predictor estimates IPC and identifies potential bottlenecks in ROB, IQ, and LSQ resources, allowing dynamic adjustment of CPU parameters and task scheduling. Experiments on a large-scale financial transaction dataset show that CloudPayGuard achieves an F1-score of 0.91 and an average inference latency of 6 milliseconds, outperforming baseline TH-GNN and other models. The OoO CPU microarchitecture optimization reduces latency by 34–40%, while LLM-driven policy generation and TH-GNN-based graph modeling ensure accurate fraud detection. These results demonstrate CloudPayGuard’s efficiency, scalability, and effectiveness for real-time fraud detection in cloud-native credit systems.
Temporal Heterogeneous Graph Neural Network
Transmission Dynamics Model Incorporating Risk Perception and Behavioral Fatigue
The classical SIR model provides a foundational framework for studying transmission dynamics, but its standard form often assumes a fixed transmission rate and therefore cannot directly represent adaptive changes in human behavior. In real transmission processes, individuals may reduce contact when perceived risk rises, while prolonged protection may also generate behavioral fatigue and weaken adherence. To describe this coupled mechanism, this paper extends the classical SIR model by introducing two bounded behavioral variables: risk perception and behavioral fatigue. The effective transmission rate is specified as an exponential function of these variables, which guarantees positivity and captures two opposite effects: risk perception suppresses transmission, whereas fatigue amplifies transmission. Ideal numerical simulations are conducted to compare the classical SIR model, a risk-perception-only model, and a full risk-perception-fatigue model. The results show that risk perception substantially lowers the infection peak and final epidemic size, while behavioral fatigue partially offsets the protective effect and delays the infection peak. Sensitivity analysis further indicates that increasing the risk-perception suppression coefficient reduces the epidemic scale, whereas increasing the fatigue amplification coefficient produces stronger rebound effects. The model offers a compact and interpretable framework for studying behavioral feedback in transmission dynamics.
Design and Implementation of a Robotic Dynamic Grasping System Based on Im-proved YOLO and Fuzzy PID Control
Industrial and service robots increasingly need to grasp objects that are moving on conveyors, sliding down chutes, or being handed over by humans, yet most production-grade pipelines still assume static targets. Two pain points dominate: detection networks that drop precision when the target is small, partially occluded, or motion-blurred, and joint-level controllers whose gains are tuned offline and therefore fail to compensate for the time-varying dynamics introduced by chasing a moving object. This paper proposes an end-to-end dynamic grasping system that couples an improved YOLO detector with a fuzzy PID joint controller. The detector embeds a Ghost-bottleneck CSPDarknet backbone, a coordinate-attention module that emphasizes motion-salient regions, a BiFPN neck for multi-scale fusion, and an SIoU + α-CIoU regression objective that converges faster on tightly packed parts. The controller treats tracking error and its derivative as fuzzy variables, online tunes ΔKp, ΔKi, and ΔKd through a 49-rule Mamdani inference base, and feeds the corrected gains to each joint in real time. The two modules are connected by a Kalman-smoothed pose stream and a quintic on-line trajectory re-planner. We trained the detector on a 12,800-image conveyor dataset and evaluated the integrated system in 300 dynamic trials on a 6-DOF UR5 with parallel jaws. Compared with a YOLOv5s + classical-PID baseline, the proposed pipeline raises mAP@0.5 from 74.3 % to 86.4 %, cuts joint-tracking overshoot from 18.6 % to 3.7 %, and increases dynamic-grasp success rate from 65.4 % to 91.2 % at object speeds up to 0.30 m/s, while keeping inference at 129 FPS on a single RTX 3060.
Dental-DPMNet: A Latent Disease Progression Modeling Network for Predicting Pulpitis and Periapical Disease Evolution Using Longitudinal Dental Imaging and Clinical Indicators
Pulpal and periapical diseases exhibit a continuous and progressive pathological process driven by lesion evolution and inflammatory deterioration, yet most existing artificial intelligence approaches focus on static diagnosis and fail to model disease dynamics over time. In pharmaceutical research, Disease Progression Models (DPMs) have been widely adopted to characterize disease natural history using longitudinal biomarkers, enabling individualized prediction of disease trajectories. Inspired by this paradigm, this study proposes Dental-DPMNet, a latent disease progression modeling network for predicting the evolution of pulpitis and periapical diseases using longitudinal dental imaging and clinical indicators. Dental-DPMNet integrates cone-beam computed tomography and periapical radiographs with clinical examination records, including pain intensity, thermal test responses, and percussion sensitivity, to construct multimodal lesion evolution representations. A continuous latent disease state is introduced to capture the underlying severity of pulpal inflammation, and its temporal dynamics are explicitly modeled via a neural ordinary differential equation, allowing flexible handling of irregular follow-up intervals and patient-specific progression patterns. Multi-task decoders are employed to jointly reconstruct multimodal observations and estimate disease stage transition probabilities and future progression risks. Experiments on a retrospective longitudinal dental dataset demonstrate that Dental-DPMNet achieves an accuracy of 87.6% and an AUC of 0.91 in multi-stage prediction of pulpal and periapical disease progression. Compared with CNN-based, LSTM-based, and ODE-based baselines, the proposed model consistently shows superior performance. These results confirm the effectiveness of latent disease progression modeling for capturing lesion-driven evolution and supporting personalized endodontic decision-making.
FRAdRec: A Federated Real-Time Advertising Recommendation Framework Based on User Behavior Modeling and Cloud-Native Data Infrastructure
Advertising recommendation systems have become a core component of modern e-commerce and digital marketing platforms, where accurately capturing dynamic user preferences and supporting large-scale real-time recommendation services are critical for improving advertising effectiveness and platform revenue. However, traditional centralized recommendation frameworks often suffer from limited scalability, delayed interest modeling, insufficient optimization of business-oriented objectives, and increasing privacy risks caused by centralized user data collection. To address these challenges, this paper proposes FRAdRec, a federated real-time advertising recommendation framework based on user behavior modeling and cloud-native data infrastructure. The proposed framework integrates a Transformer-based sequential learning module with a Deep Interest Network (DIN) attention mechanism to capture both long-term and short-term user interests from click and purchase behavior sequences. Meanwhile, a federated learning strategy is introduced to enable decentralized model training without transmitting raw user data, thereby improving privacy preservation and reducing data leakage risks. Furthermore, a multi-task optimization mechanism jointly predicts click-through rate (CTR), conversion rate (CVR), and return on investment (ROI) to enhance advertising profitability. To support industrial-scale deployment, a cloud-native streaming architecture based on Kafka, Flink, Kubernetes, and Redis is designed for low-latency recommendation and online feature updating. Experimental results on the Criteo and Alibaba Taobao datasets demonstrate that FRAdRec achieves an AUC of 0.857 and reduces recommendation latency to 41 ms, outperforming several state-of-the-art recommendation models in both recommendation accuracy and real-time serving efficiency.
An sEMG-Controlled RHex-T3 Hexapod Mobile Grasping Robot for As-sistive Object Retrieval
Assistive robots have strong potential in object retrieval, delivery, and remote interaction, but their practical usability depends on natural and low-burden human-machine interfaces. This study aims to develop an sEMG-controlled RHex-T3 hexapod mobile grasping robot system for assistive object retrieval. The proposed system integrates a multimodal hexapod chassis, robotic arm, camera-based wireless visual transmission module, sEMG acquisition and recognition module, and host-computer control terminal. Existing sEMG gesture recognition methods are deployed as a task-level robotic control interface, and a gesture-to-action mapping mechanism with hierarchical mode switching is designed to convert discrete gesture labels into chassis locomotion, mode-switching, and robotic-arm commands. Experiments are designed for online recognition, chassis control, grasping execution, and composite task completion. The results indicate that the proposed system can complete basic mobility, approach, grasping, and delivery tasks, verifying the preliminary feasibility of using sEMG to control a hexapod mobile manipulator for assistive object retrieval.
A Knowledge Graph and Deep Learning-Based Semantic Recommendation Database System for Advertisement Retrieval and Personalization
In modern digital marketing, the growing complexity of advertisement data demands intelligent systems capable of understanding semantic relationships among products, audiences, and advertising content. To address this challenge, this paper proposes a Knowledge Graph and Deep Learning-Based Semantic Recommendation Database System (KGSR-ADS) for advertisement retrieval and personalization. The proposed framework integrates a heterogeneous Ad-Knowledge Graph (Ad-KG) that captures multi-relational semantics, a Semantic Embedding Layer that leverages large language models (LLMs) such as GPT and LLaMA to generate context-aware vector representations, a GNN + Attention Model that infers cross-entity dependencies, and a Database Optimization & Retrieval Layer based on vector indexing (FAISS/Milvus) for efficient semantic search. This layered architecture enables both accurate semantic matching and scalable retrieval, allowing personalized ad recommendations under large-scale heterogeneous workloads. Experiments were conducted on a large-scale real-world advertisement dataset containing approximately 1.2 million user profiles, 250,000 advertisements, and 20 million user-ad interactions. Experimental results demonstrate that the proposed KGSR-ADS model outperforms state-of-the-art baselines across all evaluation metrics. Compared with the strongest baseline GraphRec, KGSR-ADS achieves a 5.7% improvement in Precision@10, a 5.5% increase in Recall@10, a 6.3% gain in NDCG@10, and a 5.0% enhancement in MRR, while reducing average response latency by 23.9%. These results confirm the effectiveness and efficiency of integrating knowledge graph reasoning with semantic representation learning for personalized advertisement recommendation. These results demonstrate the system’s effectiveness and potential for next-generation semantic advertising platforms that integrate knowledge reasoning and deep learning for adaptive, real-time personalization.
EduRL-GPT: A Reinforcement Learning Optimized Generative AI Framework for Intelligent Teaching Content Generation and Personalized Feedback
The rapid advancement of generative artificial intelligence (Generative AI), particularly large language models such as GPT, Claude, and Gemini, has significantly reshaped the design of intelligent tutoring systems by enabling automatic generation of instructional content, adaptive assessments, and personalized learning feedback. However, most existing generative AI-based educational systems rely on static prompting strategies and lack mechanisms to continuously optimize feedback quality according to students’ evolving learning states, which limits their effectiveness in personalized education scenarios. To address this limitation, this paper proposes EduRL-GPT, a reinforcement learning optimized generative AI framework for intelligent teaching content generation and personalized feedback. EduRL-GPT integrates a state-aware generative teaching engine with a reinforcement learning-based feedback optimization module, allowing the system to dynamically generate explanations, quizzes, and learning suggestions tailored to individual students’ knowledge mastery, learning behaviors, and preferences. Specifically, a Proximal Policy Optimization (PPO) algorithm is employed to adaptively select pedagogically appropriate feedback strategies based on observed learning gains and engagement signals, forming a closed-loop intelligent tutoring process. The proposed framework is evaluated on an online learning dataset with real student interaction logs. Experimental results show that EduRL-GPT significantly outperforms strong baselines. Compared with an LLM-based one-shot tutoring system, EduRL-GPT achieves a 12.8% improvement in learning gain and approximately a 13.0% relative increase in student engagement, while also yielding higher feedback satisfaction and accuracy improvement. These results demonstrate that reinforcement learning-optimized generative AI can deliver more effective, stable, and personalized instructional feedback for intelligent tutoring and adaptive learning systems.
Quantitative Characterization of Pore–Fracture Structures in Deep Coal Reservoirs Based on the QSGS Method
This study aims to improve the characterization of multiscale pore structures in coal by constructing a more representative digital core model. Mercury intrusion porosimetry, micro-CT scanning, Avizo-based image processing, and the quartet structure generation set (QSGS) method were integrated to reconstruct the pore–fracture networks of four coal samples. Mercury intrusion data were used to determine porosity and pore-size distribution, while micro-CT images were applied to establish three-dimensional pore network models. To overcome the resolution limitation of CT imaging, the QSGS method was further used to supplement unresolved micropores. The results indicate that the proposed multiscale digital core can more accurately represent the pore structure of coal samples. Connectivity analysis based on Euler number, tortuosity, and coordination number shows clear differences in pore connectivity among the samples. This approach provides an effective method for pore structure characterization and offers a basis for permeability prediction and coalbed methane development.
CLASNet: A Cognitive Load–Aware CNN-LSTM-Attention Framework for Supply Chain Demand Forecasting and Adaptive Human–Computer Interaction
This paper proposes CLASNet, a Cognitive Load–Aware CNN-LSTM-Attention framework for supply chain demand forecasting and adaptive human–computer interaction. Unlike conventional deep learning forecasting approaches that primarily emphasize predictive accuracy while neglecting human cognitive constraints, CLASNet integrates demand prediction and cognitive load modeling within a unified end-to-end architecture to improve both forecasting performance and decision-support efficiency. The proposed framework combines a CNN-LSTM demand feature encoder for capturing local temporal patterns and long-term dependencies, a Cognitive Load Encoder (CLE) for modeling user interaction behaviors, a Cognitive-Aware Attention Fusion (CAAF) mechanism that dynamically adjusts temporal attention weights according to users’ cognitive states, and an Adaptive Interaction Decision Layer (AIDL) that optimizes visualization complexity and information density. Experiments were conducted on a real-world retail supply chain dataset containing three years of demand records and dashboard interaction logs collected from intelligent decision-support systems. Comparative results against ARIMA, LSTM, CNN-LSTM, and CNN-LSTM-Attention baselines demonstrate that CLASNet achieves superior forecasting accuracy, obtaining an RMSE of 14.55, MAE of 9.22, and MAPE of 10.9%, corresponding to an approximately 9.6% RMSE reduction compared with the strongest baseline. Ablation studies further verify the effectiveness of the cognitive load encoder, cognitive-aware attention mechanism, and adaptive interaction module. In addition, user-centered evaluations indicate that the proposed cognitive-aware adaptive interface significantly reduces task completion time and perceived cognitive load during demand analysis tasks. Overall, the results demonstrate that integrating cognitive awareness into deep learning–based supply chain forecasting systems can substantially enhance both predictive performance and human–AI collaborative decision-making efficiency.
ROI-Optimized Sequential Advertising Recommendation Framework with Cloud-Native Scalable Data Infrastructure
Online advertising recommendation systems are fundamental to large-scale e-commerce platforms, where accurate user targeting and efficient system deployment directly impact revenue generation and user experience. Existing approaches primarily optimize click-through rate (CTR) or conversion rate (CVR), which often fail to align with business-oriented objectives such as return on investment (ROI), while also lacking scalability under cloud-native, real-time environments. To address these challenges, we propose ROSA-Rec, a ROI-optimized sequential advertising recommendation framework that tightly integrates user behavior modeling with a cloud-native scalable data infrastructure. Specifically, ROSA-Rec employs a behavior-aware Transformer-based encoder to capture dynamic user interests from heterogeneous behavioral sequences, incorporating both action importance and temporal decay. A cross-attention interaction module is further designed to enhance fine-grained matching between users and advertisements. To directly optimize business value, we introduce a ROI-aware multi-task learning objective that jointly models CTR, CVR, and revenue signals. In addition, a hybrid online-offline serving architecture built on cloud-native technologies enables real-time feature updates and low-latency inference, ensuring scalability and consistency in large-scale production settings.Extensive experiments on benchmark and semi-simulated advertising datasets demonstrate that ROSA-Rec achieves superior performance, including approximately a 10.5% improvement in ROI over the strongest baseline (SASRec), while also improving CTR AUC from 0.826 to 0.854 and CVR-AUC from 0.748 to 0.781. These results confirm that ROSA-Rec effectively enhances both predictive accuracy and business-oriented metrics under large-scale advertising scenarios.
Copyright (c) 2026 by author(s) and Erytis Publishing Limited. https://creativecommons.org/licenses/by/4.0/
A White-box/Black-box Dual-modal Structured Query Method for Flood Disaster Spatiotemporal Knowledge Graph Question Answering
Flood disaster emergency question answering (QA) requires both structured fact retrieval and complex compositional reasoning. Existing large language model (LLM)-based knowledge graph QA methods still struggle to balance query flexibility and execution reliability. To address this contradiction, this paper proposes a white-box/black-box dual-modal structured query method for flood disaster spatiotemporal knowledge graph QA. The method takes the ReAct reasoning mechanism as its scheduling core, integrating LLM-generated dynamic Cypher queries (white-box path) and predefined scripted queries (black-box path) into a unified reasoning chain. For standardized, high-frequency fact queries, the system preferentially employs black-box scripts; for tasks involving causal chain tracing, trend statistics, and integrated decision-making, the appropriate query or mixed retrieval path is invoked based on question characteristics. A query routing strategy is designed, and a data flow/control flow separation mechanism is introduced to manage large-scale query results through a "summary–file bypass" approach, alleviating context bloat during multi-round reasoning. Based on a flood disaster spatiotemporal knowledge graph and emergency response plan corpus, 30 test questions are constructed around three complex task types—causal analysis, trend assessment, and integrated decision-making—with current status query cases used to validate the standard script path. Experimental results show that the dual-modal method achieves a 100% query success rate, 98.3% answer accuracy, and an average reasoning round count of 1.47, outperforming both the pure white-box and pure black-box methods overall.
DynSupplyNet: A Dynamic Graph Neural Network with Temporal Fusion for Supply Chain Risk Prediction and Propagation Analysis
Supply chain disruptions and credit contagion propagate through complex inter-firm networks, posing severe challenges to financial institutions, supply chain finance platforms, and national regulators. Traditional risk assessment models typically treat firms as independent entities and ignore the structural and temporal dependencies intrinsic to real-world supply chains. To address these limitations, we propose DynSupplyNet, a unified framework that models the supply chain as a dynamic graph sequence and integrates a Dynamic Graph Neural Network (GNN) Encoder with a Temporal Fusion Layer to simultaneously capture cross-firm relational dependencies and evolving temporal dynamics. The model incorporates heterogeneous node features—including financial indicators, transaction patterns, and sector attributes—and edge features representing supplier–buyer relationships, dependency ratios, and credit exposures. DynSupplyNet generates time-aware firm-level embeddings and predicts disruption probability, credit default risk, and risk-propagation paths through a task-specific prediction head and a propagation simulator. Experiments conducted on the large-scale supply chain dataset show that DynSupplyNet consistently outperforms all baselines, including static GCNs, temporal GNNs, and sequence models. On the test set, DynSupplyNet achieves an Accuracy of 0.821, Precision of 0.781, Recall of 0.752, and an AUROC of 0.874, representing improvements of 4–7% over the strongest competing model. These gains confirm the effectiveness of dynamic graph modeling and temporal fusion in capturing evolving inter-firm dependencies, demonstrating DynSupplyNet’s clear advantage for real-world supply chain risk prediction and propagation assessment.
CRE-CNN-LSTM-XAI: An Explainable Deep Learning Framework for Risk Assessment of Real Estate Collateralized Financial Products
Risk assessment of real estate collateralized financial products, such as mortgage-backed securities (MBS) and real estate investment trusts (REITs), is a core task in banking risk control, structured product issuance, and financial regulation. The strong dependence of these products on real estate price dynamics, interest rate fluctuations, and macroeconomic conditions poses significant challenges to traditional risk evaluation methods that rely on static indicators and linear assumptions. To address these limitations, this paper proposes CRE-CNN-LSTM-XAI, an explainable deep learning framework for AI-driven risk assessment of real estate collateralized financial products. The proposed model integrates a convolutional neural network (CNN) to capture short-term local fluctuations in multivariate real estate and macroeconomic time series with a long short-term memory (LSTM) network to model long-term dependencies and cyclical market trends. Based on the learned latent risk representations, the framework predicts the default probability of collateralized real estate financial assets. To enhance transparency and practical applicability, a SHAP-based explainable artificial intelligence module is incorporated to quantify the contribution of key risk factors, supporting regulatory compliance and decision-making in financial institutions. Experiments conducted on real estate market and mortgage-related datasets demonstrate that CRE-CNN-LSTM-XAI consistently outperforms benchmark models. The proposed framework achieves an AUC of 0.87, compared with 0.82 for a standalone LSTM and 0.79 for XGBoost. These results indicate that CRE-CNN-LSTM-XAI effectively captures complex temporal risk patterns while providing meaningful economic interpretability, making it well suited for practical applications in bank risk management and mortgage-backed securities issuance.
Similar-Case Retrieval for Government Hotlines via an Appeal-Driven Event Graph and Dual-Route Hybrid Retrieval
To address the colloquial expressions, redundant descriptions, and dispersed structural information commonly found in historical work orders from 12345 government hotlines, traditional keyword matching and purely semantic retrieval methods often fail to identify cases that are both semantically relevant and structurally comparable, limiting the effectiveness of similar-case retrieval in complex hotline scenarios. This paper proposes a similar-case retrieval method for government hotlines based on an appeal-driven event graph and dual-route hybrid retrieval. Specifically, key elements, including location, time, core issue, and core appeal, are extracted from historical work orders to construct an appeal-driven complaint event knowledge graph. On this basis, a dual-route hybrid retrieval mechanism that integrates dense vector recall and graph-based logical recall is designed, and a structure-aware fusion strategy with cross-encoder reranking is further introduced to improve candidate ranking quality. Experiments on a real government hotline complaint dataset and the public LeCaRD legal case retrieval dataset show that the proposed method outperforms multiple baseline methods. On the complaint dataset, it achieves a P@5 of 0.613 and a Mean Average Precision(MAP) of 0.633, while on LeCaRD it reaches a P@5 of 0.435 and a MAP of 0.487. The results demonstrate that the proposed method can effectively improve the accuracy and structural comparability of similar-case retrieval in complex government hotline scenarios.
Cloud-Edge Federated Incremental Learning Framework for PMSM Efficiency Optimization with Lightweight CNN-LSTM Models and OTA Differential Deployment
Permanent magnet synchronous motors (PMSMs) are critical components in industrial automation and energy-efficient drive systems, where dynamic operating conditions, load variations, and equipment aging can significantly affect efficiency. Traditional fixed-parameter controllers or static models often fail to maintain optimal performance under these time-varying conditions. Leveraging advances in cloud computing and distributed learning, this study proposes a cloud-edge federated incremental learning framework specifically designed for PMSM efficiency optimization. The framework integrates lightweight CNN-LSTM models deployed on resource-constrained motor controllers for real-time inference with a cloud-based model repository that performs incremental training and federated aggregation, enabling continuous adaptation to diverse operational scenarios. To ensure safe and efficient large-scale deployment, a secure over-the-air (OTA) differential update mechanism with version management and gray-scale rollout is implemented, minimizing communication overhead while supporting heterogeneous terminal devices. Experimental evaluations conducted on a large-scale PMSM dataset collected from 50 industrial motors show that the proposed framework achieves the best overall performance among all compared models. The Lightweight CNN-LSTM with Incremental Federated Learning and OTA Differential Deployment attains a mean absolute error of 0.412, an RMSE of 0.611, and an R^2 value of 0.973, outperforming both non-federated and conventional baseline models. Furthermore, the proposed architecture reduces parameter size to 109 K and achieves the lowest edge-side inference latency of 8.9 ms, demonstrating its suitability for large-scale deployment on resource-constrained PMSM controllers. These results indicate that the framework not only enables lifelong learning and self-adaptive optimization of PMSM control but also provides a scalable and reliable solution for distributed industrial applications, bridging cloud-edge collaboration, federated learning, and real-time embedded inference.
Research on static denial scene reconstruction and target recognition technology based on 3DGS
Aiming at the problem of limited perception of unmanned equipment in denial scenes, this paper proposes a technical framework of scene reconstruction and target recognition based on 3DGS (3D Gaussian Splatting), which realizes efficient modeling and accurate representation of static denial scenes. Specifically, the semantic attributes are extended in the 3D Gaussian primitives and the feature index mechanism is introduced to improve the semantic modeling ability of 3DGS in the case of low memory occupation, and the target recognition is carried out on this basis. Experiments show that the framework can effectively meet the actual functional requirements of scene reconstruction and target recognition for unmanned equipment in static denial scenarios.
Analysis and Prediction of Urban Traffic Accident Correlating Factors Based on SARIMAX Model
Nowadays, with the acceleration of urbanization and the rapid increase in population and motor vehicles, frequent traffic accidents pose a threat to public safety, forming the background of this study. Predicting accidents and their resulting losses to provide a basis for traffic management decisions is a key issue to be solved by society. Theoretically, in terms of research significance, the introduction of the SARIMAX model broadens the research path of traffic accidents and promotes interdisciplinary integration. In practice, it can help traffic departments optimize resource allocation and prevent accident risks. Visualized results can provide travel warnings for the public, reducing economic and human losses. The research focuses on urban traffic accident data. It first widely collects multi-dimensional raw data, followed by cleaning, seasonal decomposition, and stationarity testing. Exogenous variables such as weather and holidays are introduced to construct the SARIMAX model. Finally, the Gaode Maps API is used to visualize accident-prone areas. The research methods include literature research, empirical research, and interdisciplinary research methods. The literature method sorts out cutting-edge materials to lay a foundation; the empirical method relies on big data to verify according to the process; and the interdisciplinary method integrates multi-disciplinary expertise to solve problems, providing data support for regional control strategies.
Remaining Useful Life Prediction of Rolling Bearings Based on SSA-Optimized TCN-CBAM-BiLSTM Model
In the field of remaining useful life (RUL) prediction for rolling bearings, existing deep learning models suffer from issues such as insufficient capability to capture long-term temporal dependencies, poor focus on key degradation features, reliance on manual experience for hyperparameter tuning, and weak generalization ability under complex operating conditions. To address these challenges, this paper proposes a RUL prediction method for rolling bearings based on a Sparrow Search Algorithm (SSA)-optimized Temporal Convolutional Network-Convolutional Block Attention Module-Bidirectional Long Short-Term Memory Network (TCN-CBAM-BiLSTM). Firstly, a basic TCN-CBAM-BiLSTM prediction model is constructed. The dilated convolutions and residual connections in TCN are employed to capture long-span degradation correlations throughout the bearing's lifecycle. The CBAM's dual attention mechanism dynamically suppresses noise and enhances key degradation features, while BiLSTM's bidirectional temporal modeling capability fully extracts contextual information from sequences. Building on this foundation, SSA is utilized to adaptively optimize core hyperparameters such as the model's learning rate and the number of TCN convolutional kernels, resulting in the development of a high-precision SSA-TCN-CBAM-BiLSTM prediction model that resolves the subjectivity and inefficiency associated with manual parameter tuning. Multiple comparative experiments are conducted using the XJTU-SY rolling bearing full-lifecycle degradation dataset. The results demonstrate that the proposed model significantly reduces both the root mean square error and mean absolute error, two key metrics. Moreover, its predicted curves closely align with the actual RUL curves under multiple operating conditions, showcasing superior prediction accuracy and cross-condition generalization capability.
Bidirectional long short-term memory network