International Scientific Technical and Economic Research
Call for Papers International Scientific Technical and Economic Research Dear Authors. We cordially invite you to submit your manuscripts to the journal International Scientific Technical and Economic Research. The journal is an international academic publication with the international issue number: ISSN 2959-1309. It is dedicated to the publication of high quality research in the fields of science, technology and economics. We welcome original research papers in all fields, including but not limited to the following topics: 1. scientific research: research results in the fields of physics, chemistry, biology, earth sciences, mathematics, etc; 2. technological developments: technological innovations in the fields of engineering, computer science, information technology, biotechnology, etc; 3. economic research: economic theory and empirical research in the fields of macroeconomics, microeconomics, international economics, finance, etc; 4. interdisciplinary: interdisciplinary research in multiple fields, such as the relationship between science and technology innovation and economic development, the impact of technology applications on society and the environment, etc. Please follow the following requirements for the call for papers: 1. Originality: Your submitted paper must be original, with a repetition rate of less than 20%, and not published or submitted in other journals or conferences. 2. Academic quality: We value academic rigor and quality, so please ensure that your research methods, data analysis, and paper structure meet academic standards. 3. Article format: Please write and format your paper according to the journal's author guidelines. We accept submissions in English language only. 4. Submission method: Submit your paper via our email address. email (istaer@126.com). 5. Collaboration and number of authors: We encourage collaborative research, but please ensure that all authors have substantial contributions and are clearly listed in the paper. 6. Review process: Our review process includes peer review to ensure fairness and anonymity of the review. Please wait patiently for the review results and make revisions based on the review comments, all of which will be returned in the email. 7. The journal charges a small page fee according to the quality of the paper; it is recommended to indicate the fund project to the teacher; if there is a fund project, the page fee will be significantly reduced and other publication and mailing costs will be charged, and the publication cycle will take about 3 months. We are committed to completing the review process in a short time and providing high quality publication services. Successfully published papers will be published in full in both the print and online versions of the journal, providing a valuable reference for the global academic community and industry. If you have any questions or require further information, please feel free to contact our editorial team. We look forward to receiving your valuable submissions! Good luck! Editorial Board of International Scientific Technical and Economic Research
Sichuan Knowledgeable Intelligent Sciences
This work is licensed under the Creative Commons Attribution International License (CC BY 4.0).
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
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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
Bridging the digital divide for seniors: Research on design principles for age-friendly touch interfaces
This study addresses the digital divide faced by the elderly and systematically explores the design principles and practical paths of aging-friendly touch interfaces. By integrating human-computer interaction theory, universal design principles, and cognitive psychology, an aging-friendly design framework with four dimensions, namely vision, interaction, function, and emotion, was constructed. The study adopted a mixed method, combining case analysis and user testing, to verify the effectiveness of innovative designs such as dynamic font size adjustment, multimodal feedback, and progressive learning guidance. The study found that successful aging-friendly design not only needs to solve usability issues at the operational level but should also pay attention to the psychological needs and trust building of elderly users. The "capability-centered" design concept and quantitative evaluation tools proposed in the study provide a systematic solution for the aging-friendly transformation of digital products and have important theoretical and practical value for promoting digital inclusion.
Human Failure Analysis and Fault-Tolerant Interaction Design of Deep-Sea Exploration Robots
Human-induced failures in deep-sea exploration robots are a key factor affecting the safety and reliability of deep-sea operations. This study systematically analyzes the mechanisms and influencing factors of human-induced failures in the extreme deep-sea environment and proposes a multi-layered fault-tolerant interaction design approach. By constructing a three-dimensional human-induced failure classification model, the authors reveal the coupled relationship between environmental pressure, task complexity, and operator cognitive characteristics. The developed layered fault-tolerant architecture provides comprehensive protection, from hardware redundancy to cognitive collaboration. Experimental results show that it can reduce the incidence of typical human-induced failures by 65%-78%. The study innovatively proposes the concept of "predictive fault tolerance," combining digital twins with operational model learning to enable the system to anticipate risks. Dynamic interface optimization and multimodal feedback strategies significantly enhance the operator's situational awareness. The research findings provide a systematic theoretical framework and practical guidance for the design of deep-sea exploration robots, promoting a paradigm shift from a "machine-centric" approach to a "human-machine collaborative" approach, and are of significant significance for improving the safety and efficiency of deep-sea operations.
Deep-sea exploration robots; Human error; Fault-tolerant design; Human-computer interaction; Digital twin; Multimodal feedback
Research on iterative design of human-machine interface of intelligent agricultural machinery based on digital twin
With the continuous development of digital twin technology, its application in the field of intelligent agricultural machinery has gradually attracted attention. This paper studies the iterative design of human-machine interfaces for intelligent agricultural machinery driven by digital twins. By analyzing the role of digital twins in intelligent agricultural machinery, key elements of iterative design, and interface optimization strategies, it explores how digital twin technology can promote interface design optimization and improve operational efficiency of intelligent agricultural machinery. The study shows that digital twin technology, through real-time data feedback and virtual simulation technology, can effectively support continuous interface optimization and significantly enhance the operator experience and agricultural machinery operation efficiency. Furthermore, experiments and case studies validate the application value of digital twin technology in interface design, providing theoretical basis and practical experience for the intelligent upgrade of intelligent agricultural machinery. However, the study also points out the challenges and limitations of digital twin technology in practical applications and envisions the promising future of digital twin technology in intelligent agricultural machinery interface design.
Digital twin; Smart agricultural machinery; Human-machine interface; Design iteration; Interface optimization
Privacy-Utility Tradeoff: Studying the Boundaries of Anonymization in Health Data Visualization Design
With the increasing prevalence of health data, how to maximize the utility of data while ensuring privacy protection has become a core issue in health data visualization design. This paper explores the issue of anonymization boundaries in health data visualization design, focusing on the trade-off between privacy and utility. Through theoretical analysis and experimental verification, the application effects of different anonymization techniques in visualization are studied, and the optimal balance between privacy protection and visualization utility is proposed. Experimental results show that under moderate anonymization boundary settings, privacy protection and data utility can be effectively balanced, preventing privacy leaks while retaining sufficient analytical value. This study provides theoretical support for the selection of privacy protection technologies and the setting of anonymization boundaries in health data visualization and offers new perspectives and directions for future research.
Health data; Privacy protection; Utility trade-off; Anonymization techniques; Visualization design; Differential privacy
Design and verification of an AI-based interactive tool for early screening of autism in children
With the increasing prevalence of autism spectrum disorder (ASD) among children, early screening has become an important tool for improving autism intervention outcomes. This paper proposes and designs an interactive tool for early screening of autism in children based on artificial intelligence (AI) technology. This tool uses an AI model to analyze children's behavioral data, such as facial expressions, body movements, and eye movements, to automatically identify potential features of autism. Through the design, development, and validation of this tool, this paper demonstrates its advantages in improving screening accuracy, reducing human error, and enhancing screening efficiency. Experimental results demonstrate that the tool has high accuracy and good user adaptability, providing effective early screening support for parents and physicians. Finally, this paper analyzes the limitations of the tool and proposes future optimization directions to provide more precise technical support for the early diagnosis of autism.
Artificial Intelligence; Childhood autism; Early screening; Behavioral analysis; Interactive tools; Deep Learning
Transformation Design and Human-Machine Scale Adaptability of Office Furniture in the Post-epidemic Era
In the post-pandemic era, the office environment has undergone profound changes, with flexible and remote working becoming mainstream work styles. With the diversification of working methods and changes in office space, the fixed design of traditional office furniture can no longer meet the needs of modern offices. This article explores the transformable design and ergonomic adaptability of office furniture in the post-pandemic era, focusing on how transformable design can improve furniture flexibility and space utilization, and the importance of ergonomics in furniture design. By analyzing outstanding office furniture cases both domestically and internationally, this article showcases innovative applications of transformable design and ergonomics, and based on user feedback, evaluates the effectiveness of these designs in actual use. The study found that the combination of transformable design and ergonomics can help improve the comfort, functionality, and adaptability of office furniture, but in practical application, it still faces challenges such as design complexity, stability, and personalized needs. Finally, the article looks forward to future trends in office furniture design, proposes development directions in terms of intelligence, personalization, and environmental protection, and provides theoretical basis and practical guidance for subsequent research.
Affective computing; Intelligent voice interaction; Emotion recognition; Speech synthesis; User experience; Human-computer interaction
Cultural Gene Translation: Semiotic Practice of Regional Elements in High-Speed Rail Interior Design
This paper explores the semiotic application of regional cultural elements in high-speed rail interior design, focusing on the application and challenges of cultural gene translation in design. As a modern means of transportation, high-speed rail interior design not only fulfills functional requirements but also serves as a crucial vehicle for cultural expression and emotional transmission. Drawing on a semiotic theoretical framework, this paper explores the process of cultural gene translation and reconstruction in high-speed rail interior design, emphasizing the selection, deconstruction, and innovation of regional cultural elements. By analyzing regional cultural symbols in high-speed rail design both domestically and internationally, this paper proposes modern strategies for the translation of cultural symbols and explores the balance between a sense of place and a global perspective in design. Furthermore, this paper explores challenges facing high-speed rail interior design, such as the misunderstanding and misuse of cultural symbols, the conflict between regional elements and modern design, and how to achieve the transmission of regional cultural identity in a globalized context. Finally, this paper explores future directions for cultural gene translation in high-speed rail interior design, particularly the integration of diverse cultures, technological innovation, and cultural heritage.
Cultural DNA translation; Regional elements; High-speed rail interior design; Semiotics; Cultural symbols; Design innovation; Globalization
Research on the Design of Intelligent Voice Interaction System Based on Affective Computing
With the rapid development of intelligent voice technology, traditional voice interaction systems have gradually been unable to meet users' needs for emotional interaction. To improve the naturalness and humanity of human-computer interaction, this study proposed and designed an intelligent voice interaction system based on emotional computing. The system accurately analyzes the user's emotional state through emotion recognition technology and combines speech synthesis technology to generate voice feedback that meets the user's emotional needs. The study first discussed the relevant technologies of emotion computing and voice interaction in detail, designed the system architecture, and implemented emotion recognition and speech generation algorithms. Through experimental evaluation and user feedback analysis, the superiority of the system in emotion recognition accuracy, voice interaction effect, and user experience were verified. Compared with traditional voice interaction systems, the system of this study significantly improved the user's emotional resonance and interaction satisfaction, reflecting the practical application value of emotion computing in voice interaction. Finally, the study discussed the limitations and future development directions of the system and proposed the possibilities and challenges for the further development of emotion computing and voice interaction.
Research on scientific research performance evaluation based on IAHP-ICRITIC combined weighting
In the field of scientific research, performance evaluation is a complex task with common and typical characteristics of the industry. Reasonable and effective performance evaluation can help motivate scientific researchers and optimize resource allocation. In order to give a reasonable performance distribution plan, it is necessary to evaluate the scientific research results of each employee first. Considering that the indicator data in the attachment are all positive indicators, the extreme value standardization method is first used to standardize the data. Then, the subjective indicator weights are obtained by the interval hierarchical analysis method, and the objective indicator weights are obtained by the improved CRITIC method. Based on the idea of ​​game theory, the combined weight value of the subjective and objective weights is obtained by solving the Nash equilibrium point. Finally, the performance scores of 20 scientific researchers are calculated according to the weights of each indicator, and the total bonus is distributed according to the performance score ratio. The first worker received the highest bonus of 74,321 yuan; the eighth worker received the lowest bonus of 72,330 yuan; the specific distribution plan is shown in Table 7.
IAHP; ICRITIC; Combination Empowerment; Scientific Research Performance Evaluation
Lightweight Design and Implementation of Machine Learning Models in Time Series Forecasting
With the surge in data volume and the continuous growth of computing requirements, the application of machine learning in time series forecasting faces the challenges of computing resource consumption and real-time requirements. In order to meet this demand, lightweight design has become a key technology to improve the efficiency of time series forecasting models. This paper deeply explores the lightweight design and implementation of machine learning models in time series forecasting, focusing on the application of lightweight technologies such as pruning, quantization, distillation, miniaturized neural networks, and hardware acceleration. By optimizing the network structure and reducing computing resource consumption, the lightweight model can not only improve real-time performance and inference speed, but also ensure high prediction accuracy. Studies have shown that lightweight technology has broad application prospects in fields such as finance, meteorology, and retail. This paper also proposes future research directions for lightweight design, including adaptive lightweight models, the combination of quantum computing and artificial intelligence, and efficient prediction on low-power devices. Finally, this paper looks forward to the optimization and application promotion of lightweight models. It is expected that with the development of technology, lightweight design will be widely used in more emerging fields.
Time series prediction; Machine learning; Lightweight design; Model compression; Real-time prediction
Algorithm acceleration technology of machine learning in financial time series forecasting
As the complexity of financial markets continues to increase, traditional financial time series forecasting methods face huge challenges. Machine learning, especially deep learning, has become a powerful tool to meet this challenge. However, these models often require a lot of computing resources when processing large-scale data, resulting in delays in the training and prediction process. In order to improve the efficiency and real-time performance of prediction, machine learning algorithm acceleration technology has emerged, mainly through hardware acceleration and software optimization to improve the speed of model training and reasoning. This study explores the application of machine learning algorithm acceleration technology in financial time series forecasting, analyzes how acceleration technology can help solve problems in data processing, real-time forecasting, and other aspects, and demonstrates the actual effects of these technologies in the stock market, foreign exchange market, and commodity market through case analysis. Despite the challenges of data diversity and hardware resource limitations, future research directions will focus on the combination of deep reinforcement learning, cloud computing, edge computing, and quantum computing to further promote the intelligent and efficient development of financial time series forecasting.
Machine Learning; Financial Time Series Forecasting; Algorithm Acceleration; GPU Acceleration; Deep Learning
Research on the promotion strategy of teachers' digital literacy under the background of education digital transformation
With the continuous advancement of the digital transformation of education, teachers' digital literacy has become an important factor affecting the quality of education and teaching innovation. Teachers' digital literacy not only includes technical operation ability, but also covers multiple dimensions such as information processing, educational innovation and teaching design. This paper conducts an in-depth study on the current situation, challenges and strategies of improving teachers' digital literacy under the background of digital transformation of education, and proposes specific countermeasures to promote the improvement of teachers' digital literacy. The study found that the improvement of teachers' digital literacy requires not only improving their information technology capabilities, but also changing traditional educational concepts and teaching models. By analyzing relevant policies and practical experiences at home and abroad, this paper proposes a phased and hierarchical training program, as well as a combination of practical operations and case teaching, aiming to provide scientific basis and practical guidance for the improvement of teachers' digital literacy. The research results show that systematic training, strong technical support, and policy guarantees are the key to the continuous improvement of teachers' digital literacy.
College of Preschool and Primary Education, China West Normal University, Sichuan, 637002, China
Practice of human-computer interaction behavior recognition and experience optimization in intelligent tour guide equipment
With the widespread application of smart devices, smart guide equipment has become a core component of major museums, tourist attractions, exhibitions and other places. How to improve user experience through effective human-computer interaction behavior recognition and experience optimization has become an important topic in the development of smart guide equipment. Based on behavior recognition technology, this study explores how to capture and analyze user behavior to achieve personalized content recommendation and dynamic interactive feedback, and further optimize the user experience of smart guide equipment. The study proposes optimization strategies for multiple terminals and multiple scenarios, focusing on analyzing the differences in user behavior in different devices and environments and their impact on experience. Through case analysis, this study demonstrates the innovative application and optimization practice of smart guide equipment in actual applications, and proposes potential directions for future development. The innovation of the study is to combine behavior recognition and experience optimization methods to provide a comprehensive design framework, providing theoretical support for the continuous innovation and development of smart guide equipment.
Intelligent Tour Guide Equipment; Human-Computer Interaction; Behavior Recognition; Experience Optimization; Personalized Recommendation
AI-assisted visual content creation process and optimization practice for short video platforms
With the rapid development of short video platforms, content creation has become an important factor in attracting users and increasing platform activity. However, creators face multiple challenges in creativity, efficiency, and quality in video production. The introduction of artificial intelligence (AI) technology provides a new solution for short video creation, especially in the creation and optimization of visual content. This paper studies the AI-assisted visual content creation process and optimization practice for short video platforms, and explores the application of AI in video editing, image processing, personalized recommendation and other fields and its optimization effect. Through the analysis of the AI-assisted creation technology framework, this paper reveals how AI can improve creation efficiency, optimize video quality, and promote the personalized and intelligent development of short video creation. In addition, this paper also analyzes the technical challenges, ethical and legal issues faced by AI-assisted creation, as well as the collaborative relationship between creators and AI. Finally, this paper looks forward to the future development trend of AI and short video creation, and puts forward relevant practical suggestions, providing theoretical guidance and technical support for creators and platforms.
Short Video Platform; Artificial Intelligence; Visual Content Creation; Automated Editing; Personalized Recommendations
Application research on multi-terminal visual communication optimization strategy based on user cognition
With the popularity of smart devices, more and more users obtain information through different terminals. How to achieve efficient and consistent visual communication on these devices has become a key issue in the design field. Based on user cognition theory, this study explores the optimization strategy of visual communication in a multi-terminal environment. By analyzing the differences in visual perception, information processing needs and cognitive load of users on different terminals, this study proposes a series of optimization strategies, including the adjustment of visual elements such as color, fonts, typography, and images, as well as terminal adaptation and responsive design methods. At the same time, the study emphasizes maintaining a consistent brand image and visual style between different devices to ensure that users have a consistent visual experience in various terminals. Finally, this study provides a theoretical framework and operational strategies for the design practice of multi-terminal visual communication, and promotes the deep integration of user cognition and visual design.
User Cognition; Multi-Terminal; Visual Communication; Optimization Strategy; Design Method
Stylistic differences and translation adaptation of Chinese and English reports from the perspective of corpus translation studies
With the acceleration of globalization, the translation demand for Chinese and English reports is increasing. There are significant style differences between Chinese and English in terms of grammatical structure, vocabulary usage, discourse organization, etc., which puts higher requirements on the translation process. From the perspective of corpus translation research, this paper explores the language style differences and translation adaptation strategies in the translation of Chinese and English reports. By constructing a bilingual corpus of Chinese and English reports and combining data analysis methods, the differences in grammar, vocabulary and discourse structure are deeply analyzed. The study shows that in the translation of Chinese and English reports, translators need to adopt multiple strategies such as cultural adaptation, linguistic adaptation and functional adaptation according to the cultural background and language characteristics of different languages ​​to ensure that the translated text can meet the style and cultural norms of the target language while faithfully conveying information. In addition, this paper also demonstrates the application of corpus translation research methods in translation practice, emphasizing the importance of big data analysis in revealing style differences and translation strategies. The research results provide theoretical support and practical guidance for improving the quality of Chinese and English report translation and promoting cross-cultural communication.
Chinese And English Reports; Style Differences; Translation Adaptation; Corpus Translation Research; Cultural Adaptation
A Study on the Intervention Effect of Discourse Coherence Theory in High School English Writing Teaching
This study aims to explore the application effect of discourse coherence theory in high school English writing teaching. With the teaching of high school English writing, students often face problems such as unclear logic and lack of coherence in articles. Traditional teaching methods are difficult to effectively solve these problems. As a linguistic theory, discourse coherence theory emphasizes the internal connection and logical connection between sentences and paragraphs in articles, which can effectively improve students' writing coherence. This study conducted an experiment on students in high school English writing courses and divided them into an experimental group and a control group. The experimental group applied discourse coherence theory in teaching to intervene, focusing on training students to use cohesive words, paragraph organization and information flow arrangement in writing. The results show that the writing coherence and overall writing quality of students in the experimental group have been significantly improved, especially in paragraph cohesion, sentence logic and orderly presentation of information flow. This study shows that the application of discourse coherence theory in high school English writing teaching can effectively improve students' writing ability and provide a useful reference for future teaching practice.
Discourse Coherence Theory; High School English Writing; Writing Coherence; Teaching Intervention; English Writing Teaching
Innovative application of virtual reality and human-computer interaction in the design of autonomous vehicles
With the rapid development of autonomous driving technology, the integration of VR and HCI technology has shown great potential in the design of autonomous driving vehicles. This study explores how VR and HCI technologies can work together to improve the efficiency, safety and driver experience of autonomous driving vehicle design. VR technology optimizes the design and functional testing of vehicles by creating an immersive test environment, while providing drivers with a simulation training platform to help them better cope with various driving situations. HCI technology enhances the trust and communication between drivers and autonomous driving systems through intuitive interactive interfaces and intelligent feedback mechanisms. In addition, this paper also analyzes the technical bottlenecks and challenges in the integration of VR and HCI technologies, and looks forward to the application prospects of these technologies in the field of autonomous driving in the future. In the future, with the continuous development of technology, VR and HCI technologies will further promote the intelligent, personalized and humanized design of autonomous driving vehicles, and improve the safety and efficiency of the overall transportation system.
Virtual Reality; Human-Computer Interaction; Autonomous Driving; Vehicle Design; Driver Experience
Analysis of the Differences and Formation Paths of Individual Achievement Motivation under Different Cultural Backgrounds
In the context of globalization and cultural integration, the differences in individual achievement motivation and its formation path have become important topics in psychological research. This study systematically analyzed the performance characteristics, influencing factors and dynamic evolution of achievement motivation under different cultural backgrounds. The study found that individualistic cultures tend to cultivate achievement motivation centered on self-realization, emphasizing personal goals and ability proof; while collectivist cultures pay more attention to social role fulfillment and group identity, and achievement motivation is often closely linked to family expectations and social evaluation. Through case comparison and quantitative research between Eastern and Western countries, it reveals how cultural values shape individual motivation patterns through family education, education system, social norms and other paths. In addition, with the acceleration of the process of globalization, immigrant groups and individuals with bicultural backgrounds show mixed achievement motivation, reflecting the complexity of cultural adaptation and psychological adjustment. This study not only expands the cultural perspective of traditional motivation theory, but also provides practical inspiration for cross-cultural education, corporate management and psychological counseling. Future research needs to pay more attention to non-Western cultural samples, the dynamic evolution of motivation and the new impact of the digital age on achievement motivation.
Achievement Motivation; Cultural Differences; Individualism; Collectivism; Cross-Cultural Research
Telecom Bank Card Fraud Analysis Based on Statistical Measures
In recent years, telecommunication network fraud has become increasingly severe, posing significant threats to financial security and social stability. This study aims to investigate the correlation between fraud occurrence and key indicators, including bank card usage and PIN authentication, to identify risk factors and protective measures. Statistical analyses were conducted on a comprehensive dataset of reported fraud cases, incorporating Kolmogorov Smirnov (K S) tests to examine the distribution characteristics of (Fraud), (Card), and (PIN) variables, followed by Spearman correlation analysis to assess their interdependencies. Visualization through pie charts revealed the proportional distribution of fraud types, with particular emphasis on differentiating telecom related bank card fraud from non-telecom cases. The results indicate that bank card transactions conducted via digital devices exhibit a higher susceptibility to fraudulent activities, whereas the implementation of PIN verification significantly mitigates fraud risk. These findings provide empirical insights for enhancing anti-fraud strategies, emphasizing the importance of secure authentication mechanisms in digital financial transactions.
Telecom Fraud; Spearman; K S Test; Normal Distribution; Pie Chart