International Scientific Technical and Economic Research
International Scientific Technical and Economic Research (ISTAER) Print ISSN 2959-1309 International Scientific Technical and Economic Research (ISTAER) is a multidisciplinary, peer-reviewed, open-access journal dedicated to breaking down disciplinary barriers and advancing the interdisciplinary integration of natural sciences, engineering, and socioeconomic sciences.
Sichuan Knowledgeable Intelligent Sciences
Application of YOLOv10 Integrated with Attention Mechanism in the Senseless Monitoring of Students' Classroom Psychological State
This paper proposes a YOLOv10 model integrated with an attention mechanism for the senseless monitoring of students' psychological states in class, aiming to achieve high-precision, real-time, and non-invasive psychological state recognition. The method introduces a multi-layer attention module in both channel and spatial dimensions to enhance the representation ability of key features. At the same time, collaborative optimization of detection and mental state recognition is achieved by combining lightweight feature enhancement with an end-to-end mental state classification network. The model is validated on a large-scale real classroom dataset (561,200 images covering multiple disciplines, different lighting, and occlusion conditions). It achieves an mAP@0.5 of 0.873, a psychological state classification accuracy of 0.835, and an F1-score of 0.812, while maintaining a real-time performance of 69 FPS. Ablation experiments show that the attention module and the feature enhancement module contribute 4.4% and 5.3% to mAP, respectively, demonstrating the model's robustness in complex scenes. The stability and long-term monitoring capability of the system are further verified in 50 real classroom deployment experiments. The results show that this method achieves high-precision, real-time, and deployable monitoring of students' psychological states in intelligent education scenarios, providing quantifiable data support for classroom management and teaching optimization.
YOLOv10; Attention mechanism; Classroom psychological state; Senseless monitoring; Real-time target detection
Copyright (c) 2026 Shaochong Yao (Author) https://creativecommons.org/licenses/by/4.0
Real Time Adaptive Teaching Digital Human System Based on Large Language Model
With the diversification and personalization of educational needs, the traditional teaching model is facing many challenges, especially in meeting students' personalized learning needs and providing real-time feedback. This study proposes a real-time adaptive teaching digital human system based on a large language model, which aims to improve the quality of education and student engagement through intelligent technology. The system obtains students' learning status in real time through a variety of data acquisition devices (such as learning behavior tracking, question-answering records, and speech recognition) and uses the large language model to generate personalized teaching content and feedback. The system calculates a comprehensive learning status score by evaluating students' learning progress, answer accuracy, participation, and learning time, thereby dynamically adjusting the teaching content and learning path. Experimental results show that with the system, students' knowledge mastery rate increases by 15%, understanding depth by 17%, and learning interest by 20%. The system's response time is reduced from 5 seconds (in traditional systems) to 1.5 seconds, and its processing capacity is increased by 2.5 times, supporting more concurrent users. The successful implementation of the system provides a new solution for personalized education and has broad application prospects, especially in online education and distance learning.
Large language model; Real time adaptive; Teaching digital people; Individualized education; System optimization
Copyright (c) 2026 Wumeng Yang (Author) https://creativecommons.org/licenses/by/4.0
Research on Product Structure Identification and Design Optimization of Smart Furniture Based on ResNet Network
Aiming at the problems of insufficient accuracy in structure identification and low efficiency in design optimization for smart furniture design, this paper proposes an integrated method for structure identification and design optimization based on an improved residual network. First, multi-source heterogeneous furniture datasets are constructed, and an improved model integrating multi-scale structure perception and a spatial topology joint attention mechanism is designed to achieve efficient representation and relationship modeling of complex structural features. On this basis, the parametric structure representation and multi-objective optimization framework are introduced, and the automatic generation and optimization of design schemes are realized by combining the generative model. The experimental results show that the accuracy of the proposed method in the structure recognition task is 0.956, and the IoU is 0.903, which is about 3.1% higher than that of the benchmark model on average; In terms of design optimization, the maximum structural displacement is reduced by 39.7%, the material utilization rate is increased by 19.1%, and the design efficiency is improved by about 45.2%. In addition, the number of model parameters is reduced by 26.2%, and the reasoning time is reduced by 31.5%, which verifies the good balance between accuracy and efficiency. The results show that this method can effectively improve the recognition ability and design optimization performance of complex furniture structure, and provide a feasible and efficient technical path for intelligent furniture design automation.
Smart furniture design; Structural identification; Residual network; Multi-scale feature; Topology modeling
Copyright (c) 2026 Yinglin Liu (Author) https://creativecommons.org/licenses/by/4.0
Research on Multimodal Prediction Model of Sports Event Ticket Revenue Based on Transformer
To address the problem that sports event ticket revenue is affected by the coupling of multiple factors and exhibits significant dynamic changes, this paper proposes a multimodal prediction model based on Transformer. This method integrates historical ticketing data, event information, user behavior, and public opinion data, constructs a cross‑modal dynamic alignment Transformer architecture (CMDA‑Transformer), and introduces a multi‑scale time series modeling mechanism and a demand sensitivity enhancement module to improve the model's ability to capture complex temporal patterns and multi‑source information. Experiments were conducted on about 24,000 real event records. The results show that compared with traditional ARIMA, XGBoost, and deep learning models (LSTM, GRU, and standard Transformer), the RMSE of this method is 18.72, which is about 5.7% lower than that of the baseline Transformer, the MAPE is about 8.3% lower, and the coefficient of determination increases to 0.912. At the same time, it still maintains stable performance under different types of events and noise interference scenarios, which verifies the generalization ability and robustness of the model. The results show that this method can effectively improve the accuracy of ticket revenue prediction, and provide reliable support for dynamic pricing and ticket optimization.
Sports events; Ticket revenue forecasting; Multimodal learning; Transformer; Time series prediction
Copyright (c) 2026 Qian Zhang (Author) https://creativecommons.org/licenses/by/4.0
Research on the Fusion Model of DeepFM and XGBoost for Digital Consumer Behavior Prediction
To leverage the complementary characteristics of deep models and tree models in feature interaction modeling for digital consumer behavior prediction, this paper proposes a dual channel fusion model of DeepFM and XGBoost. In this model, an optimized DeepFM branch and an enhanced XGBoost branch are constructed using a feature shunting mechanism, and the dynamic weighted fusion and attention mechanism based on sample features are introduced to realize the adaptive combination of the two branch outputs. At the same time, a feature interaction enhancement algorithm is designed, which combines the depth implicit representation with the rule features of the tree model by multiplication, and further improves the depiction ability of high-order interaction. Experiments on real e-commerce user behavior and ad click through rate data sets show that the AUC of this model reaches 0.879, LogLoss drops to 0.342, which is 5.4% higher and 11.6% lower than DeepFM, and 8.3% higher and 14.7% lower than XGBoost, respectively. Ablation experiments verify the effectiveness of the dynamic weighted fusion and feature enhancement module, and the performance degradation is 6.7% and 10.5%, respectively. The robustness test showed that the AUC remained at 0.839 and 0.851 under the proportion of 30% missing features and 1% positive samples, and the click-through rate in online simulations increased to 4.93%, which was 1.6% higher than that of the industrial reference system. The proposed model has significant advantages in prediction accuracy and stability.
DeepFM; XGBoost; Fusion model; Consumer behavior prediction; Dynamic weighted fusion
Copyright (c) 2026 Haotian Zhou (Author) https://creativecommons.org/licenses/by/4.0
Research on Image Representation Learning Method Based on Self-Supervised Learning
Aiming at the problems of negative sample dependence, representation degradation, and insufficient cross-scale modeling in self-supervised image representation learning, this paper proposes a self-supervised learning framework that combines multi-view consistent learning and cross-scale feature fusion. This method constructs a multi-branch collaborative structure, introduces a non-negative sample optimization strategy and a feature distribution constraint mechanism, and achieves efficient mining and stable expression of image semantic information. On the ImageNet dataset, the accuracy of linear evaluation reached 77.8%, which was 8.5% and 2.5% higher than that of SimCLR and SwAV, respectively; In downstream tasks, the target detection mAP increased by about 2.5%, and the semantic segmentation mIoU increased by about 2.5%. At the same time, the accuracy improves by 7.5% under noise disturbance, demonstrating stronger robustness. The experimental results show that this method is superior to the existing mainstream methods in terms of characterization quality, generalization ability and training stability, and has good application potential.
Self-supervised learning; Image representation learning; Cross-scale feature fusion; Non-negative sample learning; Deep learning
Copyright (c) 2026 Juanpeng Zhang (Author) https://creativecommons.org/licenses/by/4.0
Research on Transformer-Based Action Sequence Modeling of Intangible Cultural Heritage Shadow Play Using Attention Mechanisms
Shadow puppet movements are characterized by long-range spatiotemporal dependence, pronounced stylization, and complex control and transmission relationships, these characteristics pose two major challenges to digital modeling: capturing long-range dependencies and preserving artistic style expression. This paper proposes an improved Transformer model incorporating a multi-level attention mechanism for modeling and generating action sequences of intangible cultural heritage shadow play. The model designs three types of collaborative attention modules: spatial attention introduces bone adjacency priors to enhance structural rationality; temporal attention captures cross-frame long-range dependencies; and style-aware attention adjusts local computations via global feature statistics to preserve genre-specific performance styles. Furthermore, an enhanced architecture alternately stacking graph convolution and Transformer is adopted, and sparse and hierarchical modeling strategies are introduced to reduce computational complexity from quadratic to approximately linear in sequence length. The experimental results show that the average joint position error of the proposed method in motion prediction tasks is 31.4, which is 11.8 lower than that of the standard Transformer; Style loss decreased by 24.6%; Under the extreme condition of 50% missing key points, the error ratio is 1.31, which is significantly better than the comparison method. The proposed model provides effective technical support for the digital protection and intelligent inheritance of intangible cultural heritage.
Intangible cultural heritage digitization; Shadow play; Action sequence modeling; Transformer; Multi-level attention mechanism
Copyright (c) 2026 Yuxiao Liu, Shuolei Feng, Mengyu Liu (Author) https://creativecommons.org/licenses/by/4.0
Research on Multi-Agent Collaborative Decision-Making Algorithm for Supply Chain Management
Addressing the key challenges of fuzzy credit allocation, low exploration efficiency, and insufficient robustness in multi-node collaborative decision-making in supply chain management, this paper proposes a hybrid local-global credit allocation multi-agent collaborative decision-making algorithm (HGA-MADDPG). This algorithm introduces a hierarchical graph attention mechanism to dynamically represent the state of the supply chain network topology. It quantifies the contribution of individual actions to sub-chain objectives and system-level indicators through local and global credit networks, respectively, and designs an adaptive fusion weight based on marginal returns to dynamically balance local and global credit. Furthermore, an adversarial disturbance and resilient training architecture is constructed, including modeling three types of disturbances: demand mutation, node failure, and transportation delay, as well as adversarial agent injection, a dynamic environment replay buffer, and a two-stage training strategy. In a baseline scenario of a four-level supply chain and a dynamic environment driven by real data based on SCDL and WSN, compared with eight baseline algorithms, experimental results show that HGA-MADDPG achieves a total cost reduction rate of 26.2%, a service level improvement rate of 42.8%, and a stockout rate controlled at 3.2%. In the extreme scenario of triple perturbation, the cost deviation rate (29.6%) and recovery time (58 hours) are significantly better than existing methods. It still maintains a cost reduction rate of 21.5% in a 120-node ultra-large-scale supply chain. Ablation experiments and scalability analysis further verify the effectiveness of each core module.
Supply chain management; Multi-agent collaboration; Credit allocation; Graph attention network; Multi-agent deep deterministic policy gradient
Copyright (c) 2026 Changgeng Li, Zixi Liu (Author) https://creativecommons.org/licenses/by/4.0
Research on Long Sequence Learning Behavior Modeling Based on Transformer-XL
In view of the difficulties in modeling long-sequence dependence and the high computational complexity of online learning behavior data, this paper proposes a long sequence learning behavior modeling method based on Transformer-XL. This method improves the performance of the model from the two levels of structure and information modeling by constructing multidimensional behavior feature representation, integrating dynamic memory enhancement mechanism, behavioral semantic perception attention and sparse long sequence modeling strategy. Experimental results on real educational datasets such as ASSISTments and EdNet show that the proposed model is superior to the mainstream methods in terms of AUC, ACC and RMSE. The AUC increases by about 4.2% and RMSE decreases by about 8.1%. Further ablation experiments and parameter analysis verify the effectiveness of each module. Cross dataset experiments and noise tests show that the model has good generalization ability and robustness. In addition, interpretability analysis shows that the model can effectively focus on key learning behaviors. The results show that this method has significant advantages in the long sequence learning behavior modeling task, and provides effective support for personalized recommendation and learning state evaluation in intelligent education system.
Transformer-XL; Long sequence modeling; Learning behavior analysis; Attention mechanism; Dynamic memory
Copyright (c) 2026 Yuxiao Qin (Author) https://creativecommons.org/licenses/by/4.0
Personalized Recommendation Algorithm of Piano Practice Scheme Based on Multi Objective Optimization
Aiming at the problem that traditional recommendation methods in piano practice struggle to balance multiple optimization objectives—such as skill improvement, time cost, user matching, and cognitive load—this paper proposes a personalized practice scheme recommendation algorithm based on multi-objective optimization. The algorithm first constructs a multi-dimensional user capability vector model and tracks the evolution of learners’ skill dimensions, such as rhythm control and fingering proficiency, in real time through a dynamic state update mechanism. On this basis, the exercise recommendation is formalized as a four-objective optimization problem, the Pareto optimal theory is used to solve the non-dominated solution set, and a heuristic search strategy integrating sequence dependencies is designed to generate a coherent exercise path. Based on the data set containing 1248 users and 120000 interactive records, the experimental verification shows that the skill improvement rate, recommendation efficiency, and user satisfaction index of this method reach 0.513, 0.603, and 0.624 respectively, and the comprehensive score is 10.8% higher than that of the optimal baseline NSGA-II. The ablation experiment further confirms that the multi-objective optimization module contributes the most (the performance is reduced by 9.9%). The proposed method effectively realizes the unification of multi-objective collaborative optimization and personalized recommendation.
Multi objective optimization; Personalized recommendation; Piano practice; Intelligent music education; Sequence modeling
Copyright (c) 2026 Chunping Feng (Author) https://creativecommons.org/licenses/by/4.0
Research on Interactive Interface Adaptive Design Model Based on Dynamic Cognitive Load Evaluation
With the increasing complexity of human-computer interaction systems, the cognitive load caused by interface information overload has become a key bottleneck affecting user experience and operational efficiency. Therefore, this paper proposes an interactive interface adaptive design model based on dynamic cognitive load evaluation and constructs a closed-loop optimization framework of “perception-evaluation-decision-execution”. Therefore, this paper proposes an interactive interface adaptive design model based on the dynamic evaluation of cognitive load, and constructs a closed-loop optimization framework of “perception evaluation decision execution”. First, a dynamic multimodal cognitive load assessment model is designed, which integrates behavioral, eye-movement, and physiological signals via a cross-modal attention mechanism, combined with time series modeling and uncertainty estimation, to achieve continuous and accurate perception of cognitive load. The root mean square error of prediction is 0.592. Second, a cognitive-driven interface adaptive decision-making framework is constructed, which uses the results of cognitive load assessment and task context as the basis for decision-making. On this basis, a cognitive-constrained reinforcement learning optimization algorithm is proposed. By introducing an upper-limit constraint on cognitive load and a strategy clipping mechanism, interaction efficiency is guaranteed and decision stability is improved. Experimental results show that the proposed method reduces task completion time by 22.7%, increases user satisfaction by 41.9%, decreases cognitive load by 25.0%, and keeps total response delay within 81 milliseconds. This study provides a systematic solution for the construction of intelligent adaptive interface with both evaluation accuracy and decision stability.
Dynamic cognitive load assessment; Adaptive interface; Multimodal fusion; Reinforcement learning; Human-computer interaction
Copyright (c) 2026 Mingxiang Yang (Author) https://creativecommons.org/licenses/by/4.0
A Deep Reinforcement Learning Signal Control Algorithm for Traffic Carbon Emission Optimization
Urban traffic congestion leads to frequent vehicle start-stop events and low-speed operation, which is one of the primary drivers of carbon emission growth. To address the problems of multi-objective conflict, training instability, and inadequate carbon emission modeling in existing traffic signal control methods for carbon emission optimization, this paper proposes a deep reinforcement learning signal control algorithm for carbon emission optimization. This method constructs a carbon-emission-aware dynamic reward mechanism and achieves collaborative optimization of traffic efficiency and emission reduction objectives through adaptive weight adjustment; Lagrange multiplier method is introduced to embed the carbon emission threshold as an explicit constraint into the strategy learning process to ensure that the emission level is controlled within an acceptable range; For multi-intersection scenarios, a distributed collaborative control framework based on parameter sharing and neighborhood information interaction is designed to enhance the model's ability to perceive the spatial propagation characteristics of traffic flow. Based on the SUMO simulation platform, experimental validation is conducted in three scenarios: a single intersection, a 4×4 grid network, and a real-world urban road network. The results show that compared with PPO algorithm, the average carbon emissions of this method are reduced by 11.3% to 12.8%, average delay is reduced by 15.7%, average speed is increased by 9.6%, and the comprehensive performance index is improved by 12.2%; During the training process, the fluctuation of strategy is reduced by about 50%, and the degradation rate of generalization performance is reduced by 34.2% compared with the comparison method. This study provides an effective intelligent solution for low-carbon-oriented urban traffic signal control.
Deep reinforcement learning; Traffic signal control; Carbon emission optimization; Multi objective optimization; Constraint reinforcement learning
Copyright (c) 2026 Hanyu Xu (Author) https://creativecommons.org/licenses/by/4.0
Comparison and Analysis of Multiple Entropy Feature Tests for Abnormal Signals in Fixed-Point Deformation Monitoring
Fixed-point deformation monitoring is essential for geological hazard early warning, and identifying abnormal signals remains a key challenge. To evaluate the effectiveness of five entropy methods for this purpose, a quantitative comparison was conducted. Abnormal deformation data were preprocessed to extract sample entropy (SE), fuzzy entropy (FE), distribution entropy (DE), permutation entropy (PE), and adaptive weighted multi-scale fusion entropy (AWM-FE). Their ability to distinguish abnormal signals was compared using the T-test, followed by the Kruskal‑Wallis test and post hoc multiple comparisons. Simulation experiments showed that AWM-FE exhibited stable, reliable performance and was well-suited for complex field environments with multi-scale analysis needs. Real deformation data analysis revealed that SE had an average T-test p-value of 0.0016, indicating significant distinction across six category pairs, while DE achieved an F-value of 74.7205 in ANOVA, reflecting the largest overall inter-group variation. This study provides a reference for feature selection in identifying abnormal signals in fixed-point deformation observations.
Sample entropy; Fuzzy entropy; Distribution entropy; Permutation entropy; Adaptive weighted multi-scale fusion entropy
Copyright (c) 2026 Sirui Liu, Cong Pang, Xin Wang, Meiping Song (Author) https://creativecommons.org/licenses/by/4.0
Research on Automatic Evaluation Algorithm of Students’ Sports Action Standardization Based on Computer Vision
Aiming at the problems of strong subjectivity, lack of accuracy and difficulty in large-scale evaluation of students' sports action standardization, this paper proposes an automatic evaluation algorithm based on computer vision. First, a multi-perspective sports action dataset is constructed and an expert scoring system is designed; Secondly, key point sequences are extracted using an improved pose estimation model, and a multi-scale motion representation method is introduced to integrate joint-level, limb-level, and global features; Furthermore, a bias-aware alignment network is proposed to achieve adaptive modeling of spatiotemporal errors; Finally, a multi-task scoring model based on the fusion of GCN and Transformer is constructed to realize the normative classification and regression prediction of actions. The experimental results show that on the self-built data set, the MAE of this method is reduced to 0.318, which represents an improvement of approximately 29.6% over mainstream methods, the classification accuracy is 91.6%, and the correlation coefficient with expert score is 0.94. At the same time, in the cross-scenario test, the performance decreased by only 2.8%, which was significantly better than the comparison method. Ablation experiments and statistical tests validate the effectiveness of each module. The results show that this method has obvious advantages in accuracy, generalization ability and interpretability, and can provide technical support for intelligent physical education teaching and automatic evaluation.
Computer vision; Sports movement evaluation; Attitude estimation; Multiscale feature; Graph convolution network; Action alignment; Deep learning
Copyright (c) 2026 Han Li, Yanan Sun (Author) https://creativecommons.org/licenses/by/4.0
Multi-Stage Network Optimization Model and Decomposition Algorithm for Improving the Efficiency of Enterprise Production System under the Background of Digital Economy
Driven by the digital economy, enterprise production systems feature multi-stage networking with deeply embedded data elements, posing challenges for traditional optimization methods in modeling coupling relationships and efficiency improvement. To address this, this paper constructs a multi-stage network optimization model (MSN-OPT) that integrates digital elements, abstracting the production system as a resource- and information-flow-driven directed network. The model maximizes overall system efficiency through collaborative optimization of resource allocation and data input, incorporating stage weights and digital input costs. Using Benders Decomposition and column generation, the high-dimensional coupling problem is transformed into an iterative master-subproblem framework, significantly reducing computational complexity. Numerical experiments on a 100-200 node production network show that the proposed method reduces computation time by 55%-67% while maintaining optimal solution consistency. Under various disturbance scenarios, system efficiency stabilizes within 12%-18%. Sensitivity analysis reveals that when digital element disturbances are controlled within ±10%, efficiency fluctuations remain below 3%, confirming the model's robustness and stability. This paper provides a computable modeling approach and efficient algorithmic support for production system optimization in the digital economy context.
Digital economy; Multi-stage network optimization; Enterprise production system; Efficiency improvement; Decomposition algorithm
Copyright (c) 2026 Yibing Hu, Peng Jiang (Author) https://creativecommons.org/licenses/by/4.0
Research on Intelligent Generation Algorithm of Interface Icon Based on Diffusion Model
To address the problems in interface icon generation, such as a lack of structural expression, difficulty in maintaining style consistency, and limited capability for multi-condition generation, this paper proposes a structure-aware intelligent icon generation method named IconDiff, which is based on a diffusion model. Based on the classical diffusion framework, this method introduces a structure-guided branching mechanism and a multimodal condition fusion mechanism to achieve collaborative modeling of text semantics, style features, and attribute information. It also enhances boundary clarity and semantic identifiability by designing an icon-specific loss function. At the same time, a multidimensional annotation data set containing 268000 icon samples is constructed, and a special evaluation index system for icon tasks is designed. Under a unified experimental setup, compared with various mainstream generation methods, the proposed method reduces the FID by approximately 25.2%, improves structural clarity by about 6.0%, enhances identifiability by about 6.8%, and increases style consistency by about 7.8%. In addition, ablation experiments verify the effectiveness of the key modules. Generalization and robustness analysis show that the model maintains stable performance even in the absence of semantic and style conditions. The research results show that the method in this paper has significantly improved the generation quality and controllability, and provides an effective solution for the automatic design of interface icons.
Diffusion model; Interface icon generation; Multimodal conditional control; Structure perception; Style consistency
Copyright (c) 2026 Lijun Liu (Author) https://creativecommons.org/licenses/by/4.0
Intelligent Delineation Algorithm of Urban Development Boundary Based on Graph Neural Network
The Urban Growth Boundary (UGB) is essential for controlling urban sprawl and optimizing land use, yet traditional delineation methods struggle with modeling complex spatial relationships and fusing multi-source data. This study proposes an intelligent UGB delineation algorithm based on a Graph Neural Network (GNN). The study area is discretized into uniform spatial units to construct an urban graph, with node features integrating remote sensing imagery, land use types, transportation networks, and population-economic data. A spatially constrained graph convolution structure with an improved attention mechanism is designed to jointly model spatial structures and expansion driving factors, enhanced by multi-scale feature aggregation and spatial consistency constraints. Experimental validation in a 1,250 km² urban area (5,024 nodes, approximately 3.8×10⁴ edges) demonstrates that the proposed model achieves 0.912 accuracy and 0.900 F1-score in UGB recognition—3.4% and 3.8% higher than traditional GCN—with a 7.2% improvement in spatial consistency. The model remains stable across 250–1000 m spatial scales, indicating strong generalization ability and spatial adaptability. This GNN-based UGB delineation method effectively captures urban spatial structure characteristics and expansion patterns, providing a high-precision, data-driven technical pathway for territorial spatial planning and sustainable urban growth management.
Urban growth boundary; Graph neural network; Urban expansion prediction; Spatial planning; Multi-source data fusion
Copyright (c) 2026 Yu Li, Xinzi Ma (Author) https://creativecommons.org/licenses/by/4.0
Research on Algorithm Improvement of ARIMA-LSTM Hybrid Model in Time Series Prediction of Inflation Rate
As a key indicator of macroeconomic performance, inflation trends significantly influence monetary policy, macroeconomic regulation, and financial market stability. However, macroeconomic time series often contain both linear trends and complex nonlinear fluctuations, which limit the accuracy and stability of traditional statistical models. To address this, the paper proposes an improved ARIMA-LSTM hybrid forecasting model for inflation rate prediction. The ARIMA component extracts the linear structure of the series, while the residual sequence captures unexplained nonlinear information. A multi-scale LSTM network then learns deep features from the residuals, and a dynamic weight fusion mechanism adaptively combines linear and nonlinear predictions. Experiments using CPI data from IMF, World Bank, and FRED databases show that the proposed model achieves an RMSE of 0.564 on the test set—12.1% lower than the traditional ARIMA-LSTM and 17.1% lower than ARIMA alone. It also outperforms models such as SVR, random forest, LSTM, and GRU in MAE and MAPE. In multi-step forecasting, error growth remains around 12% over six steps, notably lower than comparison models. Ablation studies and Diebold–Mariano tests confirm the effectiveness of the multi-scale module and dynamic fusion mechanism. Overall, the improved ARIMA-LSTM model enhances inflation prediction accuracy and stability, offering practical value for macroeconomic forecasting and policy analysis.
Inflation rate forecast; Time series analysis; ARIMA-LSTM hybrid model; Multi-scale LSTM; Dynamic weight fusion
Copyright (c) 2026 Guona Chen (Author) https://creativecommons.org/licenses/by/4.0
Research on the Mechanism and Path of Digitalization Driving the Supply Chain Resilience of Pharmaceutical and Health Industry
Against the backdrop of frequent global public health emergencies and a complex, volatile international trade environment, the stability and security of the pharmaceutical and healthcare industry supply chain have become a critical component of national strategic security. This paper explores how digital technologies can enhance the resilience of the pharmaceutical and healthcare supply chain, systematically analyzing its underlying mechanisms and implementation pathways. The study concludes that digitalization significantly improves the supply chain's predictability, adaptability, and resilience through four key mechanisms: information transparency, intelligent processes, data-driven decision-making, and network collaboration. The proposed implementation pathways include: establishing a comprehensive data-sharing platform across the entire supply chain, deepening the application of artificial intelligence and Internet of Things (IoT) technologies, developing flexible supply chain network models, and establishing a robust digital governance framework. This research aims to provide theoretical references and practical guidance for relevant enterprises and policymakers.
Copyright (c) 2025 International Scientific Technical and Economic Research https://creativecommons.org/licenses/by-nc-nd/4.0
Joint Prediction Model of Reservoir Parameters Based on Multimodal Transformer Graph Neural Operator Physical Constraint Network
Accurate prediction of reservoir parameters is the core of reservoir evaluation and development plan optimization, but traditional methods are difficult to effectively integrate multi-source heterogeneous data, depict complex spatial heterogeneity and ensure physical consistency. Therefore, this paper proposes a multimodal transformer graph neural operator physical constraint network (MT-GNO-PCN) to realize the joint high-precision prediction of reservoir parameters. Firstly, the multimodal transformer is used to integrate seismic attributes, logging curves and geological interpretation data to construct a unified semantic feature representation; Then the map neural operator is used to learn the continuous space mapping function and flexibly model the distribution law of reservoir parameters in irregular geometry and complex geological structure; Finally, the physical constraint loss term based on the relationship between Darcy's law and rock physics is introduced to enhance the physical rationality and generalization ability of the prediction results. Experiments on real and synthetic reservoir data sets show that the average mean square error of this method is about 46% lower than that of the traditional convolutional neural network and 33% lower than that of the model using only multimodal transformer in the prediction of porosity, permeability and water saturation; The average determination coefficient (R²) is 0.89, and the error increase is controlled within 165% under 15% noise interference, which is significantly better than the existing comparison model. The framework provides a new way for reservoir intelligent modeling driven by multi-source data with high accuracy, strong robustness and physical consistency.
Reservoir parameter prediction; Multimodal transformer; Graph neural operator; Physical constraint network; Multi source data fusion
Copyright (c) 2026 Yanyu Ding, Guoqing Chen (Author) https://creativecommons.org/licenses/by/4.0
Cross-Border Trade Fraud Detection via Integrated Heterogeneous Graph Neural Network and XGBoost
Because cross-border trade fraud involves multiple types of entities, multiple business relationships and complex interactive structures, it exhibits high heterogeneity and strong concealment, which has brought significant challenges to the traditional risk identification methods. Aiming at the problem that existing methods struggle to balance the ability of structural modeling and classification performance, this paper proposes a cross-border trade fraud detection framework based on heterogeneous graph neural network (HGNN) and gradient lifting tree model XGBoost. Firstly, the cross-border trade system is modeled as a heterogeneous graph of multi type entities and multi relationship interactions, and HGNN is used to learn the high-order structural semantic representation of entities in complex trade networks; Then, the graph embedding features and statistical features are input into XGBoost to achieve high-precision classification of fraud. The experimental results on the real cross-border trade data set show that the AUC of the proposed model on the test set reaches 0.966, which is 18.7% and 3.4% higher than using XGBoost and HGNN alone, and significantly improves the recall rate of fraud samples in a variety of typical fraud scenarios. Ablation experiments further verified the key role of heterogeneous relationship modeling, attention mechanism and integration strategy in performance improvement. The above results show that HGNN–XGBoost integration framework has good detection performance and engineering application potential in complex heterogeneous scenes.
Copyright (c) 2026 Xi Zeng (Author) https://creativecommons.org/licenses/by/4.0
Supply Chain Digital Integration and Operational Resilience: An Empirical Study Based on Matched Data Between Manufacturing and Logistics Firms in Central and Eastern Europe
Based on the resource-based view and dynamic capability theory, this study constructs a chain-mediated model to examine the impact of supply chain digital integration on the operational resilience of manufacturing enterprises. Through empirical analysis of 480 pairs of manufacturing enterprises and logistics service providers in seven Central and Eastern European countries, the study finds that: First, supply chain digital integration significantly improves enterprise operational resilience (β = 0.356-0.381, p < 0.01). Second, digital integration indirectly enhances operational resilience through two pathways: operational synergy (indirect effect = 0.197, 95% CI [0.152, 0.254]) and knowledge synergy (indirect effect = 0.170, 95% CI [0.128, 0.226]), with mediating effects accounting for 34.1% and 26.4% of the total effect, respectively. Furthermore, relationship stability (interaction term β = 0.129, p < 0.01) and technological capability matching (interaction term β = 0.144, p < 0.01) both significantly enhanced the role of digital integration in promoting resilience. Heterogeneity analysis further indicated that this effect was more pronounced in regions with weaker institutional environments, SMEs, and highly complex manufacturing industries. This study expands the theoretical framework of supply chain digitalization and resilience from a bilateral collaborative perspective and provides empirical evidence for manufacturing enterprises in Central and Eastern Europe to build systemic resilience through digital integration.
Air cargo demand forecasting model based on iceemdan multi scale decomposition and grey correlation
Copyright (c) 2026 Haiqiang Wang (Author) https://creativecommons.org/licenses/by/4.0
Forecasting Short-Term Export Volumes with Hybrid Models Integrating SARIMA with Attention-Based LSTM
The short-term export volume forecast is of great significance for international trade decision-making and macroeconomic regulation, but the time series of export volume usually contains significant seasonal, trend and nonlinear fluctuation characteristics at the same time, so it is difficult to obtain ideal results with a single forecast model. In order to improve the prediction accuracy and stability, this paper proposes a hybrid prediction method combining seasonal autoregressive moving average model (SARIMA) and attention based memory network (attention based LSTM). Firstly, SARIMA model is used to describe the linear structure and seasonal components in the export volume series, and its prediction residual is modeled; Then, the attention LSTM is used to learn the nonlinear dynamic characteristics in the residual sequence, and finally the prediction results are obtained by additive fusion. The experimental results based on the monthly export volume data of Poland show that compared with the traditional SARIMA, LSTM and other comparative models, the MAE, RMSE and MAPE of the SARIMA-Attention-LSTM (Proposed) on the test set are reduced, on average, by about 20%–35%, respectively. The prediction residual fluctuation converges significantly, and shows better stability and generalization ability in repeated experiments. The results show that the effective integration of statistical model and deep learning model can significantly improve the short-term export forecasting performance, and provide a feasible and efficient solution for the prediction of complex economic time series.
Copyright (c) 2026 Wenhao Wang, Siyi Shen, Ye Yuan (Author) https://creativecommons.org/licenses/by/4.0
MAPFusion: Enhancing RoBERTa for News Classification with Multi-Head Attention Pooling and Feature Fusion
This paper proposes MAPFusion, a novel approach to enhance RoBERTa for news text classification using multi-head attention pooling. Traditional methods often rely on static pooling or simple averaging of [CLS] tokens, which can overlook subtle contextual information across token positions. The proposed method addresses this limitation with multi-head attention pooling (MAP), which captures context-aware representations by aggregating token-level embeddings with learned attention weights across multiple representation subspaces. The outputs from multiple attention heads are then integrated through a feature fusion layer to create a comprehensive sentence representation. This component is seamlessly integrated into the output layer of RoBERTa, preserving its pre-trained weights and requiring minimal additional parameters during fine-tuning. Experiments on benchmark datasets demonstrate that MAP-Fusion consistently outperforms baseline models, achieving significant improvements in classification accuracy. The framework is computationally efficient, broadly applicable to various text classification tasks, and provides a principled approach to more effectively utilize the latent representations of RoBERTa. Our work highlights the importance of adaptive feature aggregation in Transformer-based models, providing insights for future representation learning research. The code for this paper is available by contacting the respective authors.
MAPFusion, News text classification, Multi-head attention pooling, RoberTa, Attention mechanism
Copyright (c) 2025 = https://creativecommons.org/licenses/by/4.0
Application of Smart Wearable Device Interaction Design Based on User Experience Map
This paper addresses the interaction design challenges faced by smart wearable devices in limited screen space and diverse usage scenarios, proposing and systematically demonstrating an interaction design method based on user experience maps. By constructing a closed-loop design framework of "user research - journey description - pain point analysis - solution generation - prototype verification," the user experience map is elevated from an analysis tool to a core navigator throughout the entire design process, effectively solving the problems of experience breakpoints and scenario disconnects in traditional design methods. In a case study of a smartwatch's sports and health monitoring function, this framework successfully guided the design process from identifying pain points such as inconvenient data viewing and cumbersome operation to implementing specific solutions such as interface simplification, gesture optimization, and voice feedback. Empirical data shows that after optimization, task operation time was reduced by 45%, the completion rate reached 100%, and the system usability scale improved to an excellent level. This research innovatively establishes a systematic experience design method applicable to smart wearable devices, not only providing an operable process guide for design practice but also laying a theoretical foundation for exploring the integration of user experience maps with new technologies such as artificial intelligence and promoting the development of adaptive interaction.
User experience map; Smart wearable devices; Interaction design; User research; Touchpoint optimization
Copyright (c) 2025 Jiahao Yang* (Author) https://creativecommons.org/licenses/by/4.0
Framework for the Application of Digital Twin Technology in Intelligent Production Line Condition Monitoring and Predictive Maintenance
This paper addresses the urgent needs of intelligent manufacturing for highly reliable equipment operation and precise maintenance and delves into the innovative application of digital twin technology in production line condition monitoring and predictive maintenance. By systematically reviewing the core theories of digital twins, multi-source heterogeneous data acquisition and processing technologies, and predictive maintenance methodologies, a five-dimensional integrated framework comprising a physical layer, data layer, model layer, functional layer, and application layer is constructed. This framework innovatively achieves real-time dynamic mapping and bidirectional interaction between physical and virtual spaces, establishes a data-model hybrid-driven mechanism for equipment health status assessment and remaining life prediction, and forms a closed-loop optimization system from condition perception and fault early warning to maintenance decision-making. To verify the effectiveness of the framework, this study conducts a case study using a precision CNC gear machining production line. The results show that the framework can control the latency of critical equipment condition monitoring within 200 milliseconds, improve the accuracy of remaining life prediction by approximately 15% compared to purely data-driven methods, successfully achieve early fault warning, reduce unplanned downtime by 65%, and save maintenance costs by 28%. The research findings provide theoretical guidance and practical examples for achieving precise and forward-looking equipment health management in the context of intelligent manufacturing and have important reference value for promoting the digital transformation of the manufacturing industry.
Digital twin; Intelligent production line; Condition monitoring; Predictive maintenance; Framework construction
Copyright (c) 2025 Yu Zhang*, Mengxi Wang, Jiayi Zhou (Author) https://creativecommons.org/licenses/by/4.0
An Exploration of the Application of Bionic Design Methods in the Design of Miniaturized Personal Vehicles
With the acceleration of urbanization and the deepening of the concept of sustainable development, miniaturized personal transportation vehicles have become a key carrier for solving the "last mile" travel demand. However, current market products generally face the dilemma of homogenized styling and a lack of emotional value. Meanwhile, biomimetic design, as an interdisciplinary innovation method, provides a new perspective for breaking through this bottleneck. This study aims to systematically explore the application value and practical path of biomimetic design methods in the styling of miniaturized personal transportation vehicles. Through literature review, case analysis, and morphological deconstruction, it deeply analyzes the internal logic and manifestations of three application modes: morphological biomimetic, functional biomimetic, and imagery biomimetic. The study finds that biomimetic design can endow products with natural beauty and vitality through organic forms, optimize aerodynamic performance and lightweight structure with biological prototypes, and evoke emotional resonance in users through abstract imagery. The study further proposes an integrated strategy of "form-function-emotion," emphasizing that the selection of biological prototypes should be deeply aligned with user cognition and usage scenarios. Despite the challenges of engineering implementation and over-mimicry, biomimetic design combining intelligent interaction and sustainable technologies will become an important evolutionary direction for future personalized mobility experiences. This study provides a theoretical basis and practical inspiration for constructing miniaturized transportation vehicles that combine aesthetic value, functional rationality, and emotional warmth.
Swinburne College of Shandong University of Science and Technology, Jinan, China
Applications of MEMS sensors in industrial equipment condition sensing and the Internet of Things
This paper systematically studies the integrated application of microelectromechanical systems (MEMS) sensors in industrial equipment condition sensing and the Internet of Things (IoT). The research shows that MEMS sensors, with their advantages of miniaturization, low cost, and low power consumption, effectively overcome the limitations of traditional monitoring technologies in terms of deployment density and economy, laying a solid foundation for building large-scale equipment condition sensing networks. By analyzing multi-dimensional parameters such as vibration, attitude, and pressure, and combining edge computing and data fusion technologies, accurate diagnosis and early warning of the operating status of industrial equipment are achieved. Further research indicates that when MEMS sensors are deeply integrated into the industrial IoT architecture, not only is real-time monitoring of equipment status realized, but the operation and maintenance model is also promoted from reactive maintenance to predictive maintenance, significantly improving equipment reliability, production efficiency, and safety management levels. Although challenges remain in measurement accuracy, environmental adaptability, and standardization, with the deep integration of new materials, new processes, and artificial intelligence technologies, MEMS sensors will play an increasingly important role in building intelligent and adaptive industrial ecosystems, providing key technological support for industrial digital transformation.
MEMS; MEMS sensors; Condition monitoring; Predictive maintenance; Industrial Internet of Things (IIoT)
Copyright (c) 2025 Wenjing Zhou* (Author) https://creativecommons.org/licenses/by/4.0
Construction of Self-Optimization and Quality Prediction Model for Intelligent Die Casting Unit Process Parameters
With the deepening of intelligent transformation in the manufacturing industry, the die-casting industry faces an urgent need to improve product quality, production efficiency, and stability. Traditional trial-and-error process debugging methods relying on manual experience are no longer adequate to meet this challenge. Therefore, this research aims to construct an intelligent die-casting system integrating quality prediction and parameter self-optimization to achieve closed-loop optimization of the production process. First, the system collects multi-source process parameters such as injections and thermal parameters in the die-casting unit through an industrial Internet of Things (IoT) architecture. Based on this, machine learning algorithms such as XGBoost, random forest, and artificial neural networks are compared and screened, successfully constructing a high-precision quality prediction model with internal porosity as the core indicator. Then, using this prediction model as a surrogate model, a process parameter self-optimization strategy is developed, combining a multi-objective genetic algorithm and a Bayesian optimization strategy, to automatically seek multiple objectives such as quality, efficiency, and energy consumption while meeting equipment and process constraints. Experimental results show that this system can not only effectively predict key quality characteristics, but the process parameter combinations recommended by its self-optimization module significantly improve the product qualification rate and reduce the single-piece production cycle compared to traditional methods in actual production. This study successfully combines data-driven methods with the die-casting process mechanism, forming a complete technical solution from perception and prediction to decision-making, providing a theoretical model and system framework with practical value for the digital and intelligent upgrading of die-casting production.
Intelligent die casting; Process parameter optimization; Quality prediction; Machine learning; Digital twin
Copyright (c) 2025 Hongfang Shan*, Yuwen Su, Nuo Xu (Author) https://creativecommons.org/licenses/by/4.0
Design of an AR-based assembly guidance and remote maintenance system for complex electromechanical equipment
This paper addresses the challenges of high precision requirements, complex operations, and scarce expert resources in the assembly and maintenance of complex electromechanical equipment. It designs and implements an integrated guidance and remote maintenance system based on augmented reality (AR) technology. The research first constructs a core technical solution integrating hybrid tracking and registration, dynamic information visualization, and real-time remote collaboration. Then, it elaborates on the system's layered architecture and modular design and completes the development of a prototype system using a precision reducer for industrial robots. Test results show that the system effectively improves the accuracy and efficiency of assembly operations and significantly enhances the sense of presence and effectiveness of remote collaborative maintenance, providing valuable theoretical reference and practical examples for the in-depth application of AR technology in the field of industrial precision operation.
Copyright (c) 2025 Yu Zhang*, Yongzhuang Li, Jiayi Zhou (Author) https://creativecommons.org/licenses/by/4.0