International Scientific Technical and Economic Research
International Scientific Technical and Economic Research (ISTAER) 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
This work is licensed under the Creative Commons Attribution International License (CC 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 International Scientific Technical and Economic Research https://creativecommons.org/licenses/by-nc-nd/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
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
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
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.
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
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.
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
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
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
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
The practical value, implementation approaches, and effectiveness of integrating Chengdu's red culture into ideological and political education courses at local universities
This paper, grounded in the contemporary imperative of "cultivating virtue and nurturing talent," explores strategies to integrate Chengdu's rich revolutionary cultural heritage into practical teaching of ideological and political theory courses at local universities. The study first analyzes the distinctive connotations and educational value of Chengdu's revolutionary culture. It then systematically examines three primary implementation models: the "classroom narrative + resource immersion" approach, the "on-site teaching + emotional resonance" model, and the "project-driven + knowledge-action integration" framework. Through reflective analysis of practical outcomes, the paper identifies challenges in resource integration, content adaptation, and evaluation mechanisms. It proposes optimization recommendations including establishing a collaborative "university-local-library" education mechanism, developing digital teaching resources, and refining formative assessment systems. These insights aim to provide actionable strategies for enhancing the appeal, engagement, and effectiveness of ideological and political education in regional universities.
Chengdu Red Culture; ideological and political course
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)
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
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.
Research and Development of Robotic Grinding and Polishing Process Parameter Adaptive Control System
With the increasing demands for surface quality in intelligent manufacturing, robotic grinding and polishing technology has become a key solution due to its flexibility. However, traditional robotic grinding and polishing based on fixed parameters struggles to cope with process disturbances such as workpiece geometric errors and tool wear, leading to unstable processing quality. Therefore, this study focuses on the research and development of an adaptive control system for robotic grinding and polishing process parameters. This paper first analyzes the grinding and polishing process mechanism and adaptive control theory, constructing a robot-workpiece-tool interactive dynamics model and a process model based on contact force. Then, a core algorithm based on high-precision force or position hybrid control and incorporating a fuzzy adaptive strategy is designed, realizing the dynamic optimization of key parameter control for feed rate and spindle speed based on real-time force feedback. At the system implementation level, a hardware platform is constructed by integrating an industrial robot, a six-dimensional force sensor, and a grinding and polishing spindle, and a software system with real-time control and human-machine interaction functions is developed. Experimental results show that, compared with fixed parameter grinding and polishing, this system can reduce the range of surface roughness (Ra) by about 40%, and exhibits excellent material removal uniformity and strong robustness to changes in working conditions in the processing of complex curved surfaces, effectively verifying the advanced nature and engineering application potential of the proposed method and system.
Robotic grinding and polishing; Process parameters; Adaptive control; Force control; Intelligent regulation; System development
Research on Lightweight Design and Performance Optimization of Lattice Structures Based on Additive Manufacturing
Addressing the urgent need for lightweight and functionally integrated structures in aerospace, transportation, and other fields, this paper focuses on the systematic design and performance optimization of additive manufacturing-based lattice structures. To overcome the core challenge of the disconnect between theoretical models and manufacturing practice in lattice structure design, this study constructs a comprehensive research system encompassing theoretical modeling, process adaptation, performance optimization, and experimental verification. First, the equivalent mechanical properties of lattice structures are explored in depth, and process adaptability design criteria for additive manufacturing are established to address manufacturability issues. Then, a multi-objective optimization method is used to synergistically improve the static and dynamic mechanical properties and energy absorption characteristics of the lattice structures. Finally, experimental prototypes are prepared by carefully selecting additive manufacturing process parameters, and systematic mechanical performance tests are conducted to verify the accuracy of the numerical model and the effectiveness of the optimized design. The research results not only demonstrate the enormous potential of additively manufactured lattice structures in terms of lightweighting and multifunctional load-bearing capacity but also provide a closed-loop methodology covering design, manufacturing, and performance evaluation for their engineering applications, possessing significant theoretical and engineering reference value.
Lattice structure; Additive manufacturing; Lightweight design; Performance optimization; Process adaptability
Research on the Application Logic and Ethical Boundaries of Artificial Intelligence in Systemic Financial Risk Early Warning
With the deep penetration of artificial intelligence (AI) technology into the field of financial risk control, its application in systemic risk early warning is attracting widespread attention from academia and regulatory agencies. This study focuses on the dual characteristics of AI technology in financial risk early warning: on the one hand, through multi-source data fusion, complex algorithm models, and intelligent decision support, AI technology has significantly improved its ability to identify high-dimensional nonlinear risks, realizing a paradigm shift from static assessment to dynamic early warning; on the other hand, ethical issues such as algorithmic bias, black-box decision-making, and data privacy violations are becoming increasingly prominent, and may even give rise to new systemic risks due to model homogeneity. This paper constructs a three-dimensional analysis system of "technology application-ethical boundaries-governance framework," and uses case analysis and comparative research methods to demonstrate the necessity of establishing a responsible AI early warning mechanism. The study finds that effective risk governance requires the organic combination of technological empowerment and ethical regulation. By developing explainable artificial intelligence, constructing an algorithm audit system, and improving cross-departmental collaborative supervision, while leveraging the early warning advantages of AI technology, it is essential to ensure that its application complies with the fundamental requirements of financial stability and fairness and justice. This provides an important reference for building a new paradigm of intelligent financial supervision in the digital age.
Artificial intelligence; Systemic financial risk; Risk warning; Machine learning; Ethical boundaries
Research on Business Models and Risk Governance Frameworks for Fintech-Enabled Financing of Micro and Small Enterprises
This study focuses on the core proposition of "financial technology empowering the financing of micro and small enterprises," systematically exploring its business model innovation and risk governance framework construction. The paper first analyzes the theoretical logic and practical motivations for financial technology to solve the financing difficulties of micro and small enterprises, pointing out that it effectively alleviates the two core obstacles of information asymmetry and excessively high transaction costs by reconstructing risk pricing models and service processes. The study further deconstructs the value creation mechanisms and operational characteristics of typical business models such as digital lending, supply chain finance, and big data risk control, finding that while these models improve the accessibility and efficiency of financial services, they also generate complex challenges such as technological risks, new business risks, compliance risks, and systemic risks. Based on an in-depth analysis of risk characteristics, this paper innovatively proposes a multi-layered, dynamic risk governance framework. This framework emphasizes the organic unity of internal governance, external supervision, and ecosystem collaboration: at the micro level, it requires fintech companies to establish a full-process risk control system and compliant technology applications; at the meso level, it advocates adaptive regulatory models such as regulatory sandboxes and penetrating supervision; and at the macro level, it requires improving infrastructure such as credit information sharing and multi-party co-governance mechanisms. The case study of MYbank validated the practical value of this governance framework. The study ultimately concluded that the sustainable development of fintech must be built upon the simultaneous improvement of business model innovation and risk governance capabilities, requiring the construction of a dynamic governance ecosystem that balances innovation incentives and risk control.
Fintech; SME financing; Business model; Risk governance; Inclusive finance; Regulatory technology
Solving the NIPT optimal timing problem based on NSGA-II
This study addresses the optimal timing of noninvasive prenatal testing (NIPT) testing. A multi-objective optimization model centered on minimizing testing error and maternal and fetal risk was established and solved using the NSGA-II algorithm. Results demonstrated that the model effectively reflects the optimal gestational age distribution across populations with varying BMIs (BMIs). The optimal timing for NIPT testing was 18.63 weeks for those with a BMI ≥ 38.3, 23.74 weeks for those with a BMI 28.6–34.5, and 24.72 weeks for those with a BMI 20.0-28.6. With increasing iterations, the uniformity and diversity of the Pareto front significantly improved, and the HV index continued to rise. Monte Carlo perturbation and sensitivity analyses validated the model's stability and robustness, with BMI having the greatest impact on the results. Overall, the NSGA-II-based model effectively addresses the optimal timing of NIPT testing, providing accurate and reliable results and providing an effective quantitative basis for stratified and personalized testing.
Non-invasive prenatal testing; Multi-objective optimization; NSGA-II algorithm; Optimal testing time; body mass index
Research on MRI Object Detection Using an Enhanced YOLOv8 Framework
Early diagnosis and accurate localization of brain tumors are crucial for improving patient survival rates. Among these, automated detection methods based on deep learning have become a research hotspot. However, tumor detection, especially for complex shapes and small targets, remains challenging. To address this, this study proposes an improved YOLO model—YOLO-CDF—aimed at enhancing the detection accuracy for complex and small targets in brain tumor MRI images. The model builds upon YOLO by incorporating the BRASPPF module (A combination of bi-level routing attention mechanism and spatial pyramid pooling), dilated convolution, and small object detection layers. Experimental results show that the YOLOv8-CDF model achieves a good balance between precision and recall, with an overall mAP@0.5 of 0.929 and an F1 score reaching 0.90, demonstrating excellent detection performance. When detecting tumors, the model's precision values are 0.974, 0.964, and 0.851, respectively. Validation results show that the model can provide accurate predictions at both high and low confidence levels, with strong detection capabilities and good generalization ability, making a significant contribution to the identification of brain tumors.
YOLO; MRI; Deep learning; Comparative experiment; Image processing
Machine Learning Approaches for Detecting Abnormalities in Female Fetuses
Non-invasive prenatal testing (NIPT) plays a vital role in the early detection of female fetal abnormalities, which is essential for birth defect prevention. In this study, clinical data containing Z-scores of chromosomes 21, 18, and 13, GC content, X chromosome concentration, read count ratio, and maternal BMI were analyzed. To address the class imbalance caused by the limited number of abnormal cases, the Synthetic Minority Over-sampling Technique (SMOTE) was applied, and stratified sampling was used to divide the dataset into training, validation, and testing sets (7:2:1). Multiple machine learning models, including XGBoost, Decision Tree, CNN, MLP, SVM, and Random Forest, were developed and evaluated with accuracy, precision, recall, F1-score, and AUC-ROC metrics. Results demonstrated that Random Forest outperformed other models, achieving an AUC of 0.997 with strong stability and generalization. These findings highlight the effectiveness of machine learning combined with proper data preprocessing in enhancing female fetal abnormality detection.
Female fetal abnormality; NIPT; Machine learning; Random Forest; SMOTE
Study on the Agricultural System in a Certain Region Based on the LSTM Model and Genetic Algorithms
In response to the two core demands in agricultural production, namely precision irrigation and sustainable transformation, this study conducts two key research tasks. First, it predicts the crop irrigation demand based on meteorological data. Initially, the 3σ principle is adopted to identify and label outliers isolated outliers are filled using linear interpolation, while consecutive outliers are filled with average value. Subsequently, two types of models are constructed: on the one hand, three evapotranspiration formulas (Hargreaves, Priestley-Taylor, and Makkink) are integrated, and the evapotranspiration (ET) amount is calculated through weighted fusion to establish the ET formula; on the other hand, a Long Short-Term Memory (LSTM) model is built via a two-layer architecture. These two models are then compared. Second, it carries out research on the transformation of organic agriculture based on farm economic and environmental data. First, farms are classified into geographical types (Plain, hilly, and mountainous). Then, a multi-objective optimization model is constructed with the goals of maximizing economic benefits and maximizing environmental benefits. Combined with constraints on transformation ratio and annual transformation, the genetic algorithm is used to solve the model.
3σ Principle; LSTM Model; ET Formula; Multi-Objective Optimization; Genetic Algorithm
Research on regulated biological regularity LED light source based on normality test algorithm
In this paper, the optimal channel weight combination and the key parameters of the synthesized spectrum are obtained respectively for the two scenes of daytime illumination mode and night sleep aid mode. The question of whether optimal lighting has a beneficial effect on sleep quality compared to normal light and dark environment, we first used genetic algorithms (GA), a multichannel spectral synthesis model is established, and then five key performance indexes are solved and optimized. Then, the overnight sleep data of 11 subjects in three environments were processed. Six groups of data including total sleep time, sleep efficiency, sleep latency, deep sleep proportion, rem sleep proportion and wake times were extracted. Next, using the normality test model, we pass the KS test and SW test, concluded that optimal light does not improve sleep quality compared with normal light and dark environment.
Intelligent optimization algorithm; Genetic algorithms (GA); Normality test model; KS test; SW test
Design and evaluation system construction of VR cultural heritage products from the perspective of user experience
In the digital age, virtual reality (VR) technology offers innovative avenues for cultural heritage preservation, but its development still faces a disconnect between user experience and the transmission of cultural values. This paper systematically constructs a design and evaluation system for VR cultural heritage products from a user experience perspective. First, by analyzing the connotations and types of VR cultural heritage products and integrating user experience theory with cultural communication theory, a design framework is proposed with the core goal of "integrating cultural value transmission with deep user experience." This multi-layered design strategy encompasses strategic, content, interaction, and sensory layers. Second, a comprehensive evaluation model is established encompassing four dimensions: system usability, experience quality, emotion and cognition, and behavioral intention. This model integrates subjective and objective evaluation methods to form a closed-loop optimization mechanism. Finally, implementation recommendations are proposed for typical application scenarios such as museum guided tours, intangible cultural heritage teaching, and historical restoration. This study aims to provide theoretical guidance and practical tools for the development of VR cultural heritage products, promoting their transformation from a technology-oriented to a user-value-oriented approach.
User experience (UX); Virtual reality; Cultural heritage; Design framework; Evaluation model
Online music personalized recommendation algorithm based on KKBox large-scale real-world dataset and integrated behavior sequence transformer
To address the user selection overload and decision making difficulties caused by the explosive growth of online music platforms, this paper proposes and validates a deep learning recommendation model that incorporates the Behavior Sequence Transformer, aiming to significantly improve music recommendation accuracy, diversity, and novelty. We use the BST Behavior Sequence Transformer to perform multi head self attention modeling on user listening sequences and compare it with a LightGBM boosted tree and logistic regression baseline model. Experiments use AUC, accuracy, and RMSE as core metrics, and use an 80/20 train-test split and early stopping to control overfitting. The BST model achieved an AUC of 0.7662 and an accuracy of 0.7551 on the test set, significantly outperforming LightGBM and logistic regression . Feature importance analysis shows that user genre cross features, contextual entry points, and sequence position encoding contribute most to the model. The recommendation algorithm based on the Behavior Sequence Transformer effectively captures the dynamic evolution of user interests, alleviates information silos, and possesses good scalability and engineering potential. Future research into multimodal fusion and reinforcement learning can further enhance diversity and interpretability. **************** ACKNOWLEDGEMENTS**************** This work was supported by Jiangxi Provincial College Students' Innovation and Entrepreneurship Program (S202510421047).
Music recommendation system; Behavior Sequence Transformer; Attention Mechanism; LightGBM; KKBox dataset; Deep Learning
Trust building mechanism of multimodal interaction in autonomous driving system
Multimodal interaction technology, as a key means of enhancing trust in autonomous driving systems, integrates multi-channel information such as vision, voice, and touch to build a transparent and explainable human-machine collaborative mechanism. This study systematically explored the role of multimodal interaction in building trust, analyzed interaction design strategies for different driving scenarios, and experimentally verified the significant effect of multimodal feedback on improving user trust. The results show that reasonable multimodal interaction design can enhance users' understanding and prediction of system behavior, reduce uncertainty anxiety, and accelerate trust restoration in abnormal situations. At the same time, the study also revealed issues such as sensor fusion accuracy and ethical privacy faced by current technologies and envisioned future development directions such as anthropomorphic interaction and contextual awareness. The results of this study provide a theoretical basis and practical guidance for the interaction design of autonomous driving systems, which is of great value in promoting the commercialization of the technology.
Multimodal interaction; Autonomous driving; Trust building; Human-computer interaction; Explainability; User experience
Application of modular design strategy in the life cycle of household appliances under the goal of carbon neutrality
Modular design, a key strategy for achieving carbon neutrality in home appliances, provides a systematic solution for the industry's green transformation by restructuring carbon footprint management across all stages of the product lifecycle. This study systematically analyzes the application mechanisms of modular design in the design, production, use, and recycling stages of home appliances, revealing its carbon reduction benefits through three pathways: extending product lifespan, improving resource utilization efficiency, and promoting a circular economy. The study shows that home appliances employing modular design can reduce carbon emissions over their entire lifecycle by over 30%, with reductions of 15% in production, 10% in use, and 5% in recycling. The study further explores challenges facing modular design, including technical standards, market acceptance, and policy support, and proposes strategies such as establishing an open modular platform, innovating service models, and strengthening the standard system. Finally, the study explores the potential of modular design, enabled by digital technology, intelligent carbon management, business model innovation, and the construction of cross-industry circular systems, providing theoretical basis and practical guidance for the home appliance industry's path to carbon neutrality.
Modular design; Carbon neutrality; Home appliances; Life cycle assessment; Circular economy; Carbon emission reduction
The impact of social media live streaming on fashion brands’ sales performance: A case study of TikTok
With the rapid development of mobile internet and social media, livestreaming sales have become a crucial channel for fashion brands to expand their markets and boost sales performance. Douyin and TikTok, with their vast user bases and sophisticated content distribution mechanisms, have become core platforms for livestreaming e-commerce. This paper, focusing on the impact of livestreaming sales on the sales performance of fashion brands, systematically analyzes the operational models, impact mechanisms, and pathways of livestreaming sales, drawing on case studies of fast fashion and luxury brands for comparative study. The study finds that livestreaming sales forms a complete conversion cycle through "traffic acquisition, trust building, and consumer stimulation," effectively driving brand exposure and sales. Furthermore, livestreaming strategies vary significantly across fashion brands: fast fashion brands prioritize traffic volume and price advantages, luxury brands emphasize brand storytelling and high-end experiences, and emerging brands rely on differentiation and emotional resonance to achieve breakthroughs. The paper also points out that while livestreaming sales presents significant opportunities, it also carries potential risks, such as reliance on low prices, brand image dilution, and operational pressure. Based on the research findings, this paper proposes management recommendations, including optimizing brand strategies, improving platform mechanisms, and forecasting future trends. Overall, live streaming sales on social media is not only a new e-commerce model, but also a strategic location for fashion brands in the process of digital transformation. It is of great significance to both short-term sales and long-term brand value.
Social media; Livestreaming; Fashion management; TikTok; Sales performance