Convolutional Neural Network (CNN) is the most widely used algorithm model in computer vision problems , and has achieved remarkable results. In this paper, for the Fruits 360 image dataset on Kaggle, we use the algorithm based on The pre-trained network of Resnet-50, constructs the algorithm to build a convolutional neural network model. The difference between ResNet and traditional convolutional neural networks is that it adds some shortcuts to the network, i.e. direct channels are able to skip certain layers to optimize the network.ResNet introduces the idea of residual function F(x), which is set as: H(x)=F(x)+x. Specifically, if F(x)=0, then the residual functions form an identity map H(x)=x. It is easier for a neural network to fit the residuals.

Character animation, which aims to generate dynamic character videos from static images, has gained significant attention in recent years. Despite the advances in diffusion models, which have established themselves as the leading approach in visual generation tasks due to their strong generative capabilities, challenges remain in the domain of image-to-video synthesis, particularly in character animation. The preservation of temporal consistency and the retention of fine-grained character details across frames continue to pose significant obstacles. In this work, we propose a novel framework specifically designed for character animation, leveraging the potential of diffusion models. To address the challenge of maintaining intricate appearance details from the reference image, we introduce ReferenceNet, a network that integrates detailed features using spatial attention mechanisms. To enhance controllability and ensure smooth motion transitions, we present an efficient pose guide that directs the character's movements and incorporate an effective temporal modeling strategy to facilitate seamless inter-frame consistency. Our framework is capable of animating arbitrary characters by expanding the training data, outperforming existing image-to-video methods in character animation tasks. Experimental evaluations on benchmark image animation datasets demonstrate that our approach achieves state-of-the-art performance, setting a new standard for this domain.

This paper discusses the new problems and new ways of protecting the legal rights and interests of rural women under the background of digital technology, based on the Marxist view of women. The Marxist view on women reveals that private ownership is the root of women's oppression, emphasizes the importance of women's participation in social production labor, and puts forward that women's liberation is a long process. Under the background of digital technology, the protection of rural women's rights and interests is faced with new problems: the gender digital divide restricts the expansion and acquisition of rights and interests, the shadow of digital technology brings potential risks of rights infringement and loss, and the lack of system and protection leads to the absence of laws and regulations for rights and interests protection. In order to protect the rights and interests of rural women, the principles of rights protection should be clarified, including equal rights of men and women, protection and promotion of women's development in accordance with the law; Narrowing the gender digital divide, strengthening digital infrastructure, upgrading digital skills and education, and eliminating gender bias and discrimination; Guard against the dark side of digital technology, increase awareness of privacy protection, combat digital gender-based violence, and eliminate algorithmic bias; Improve systems and safeguards, improve laws and regulations, strengthen law enforcement and supervision, and improve the social security system.

English language education is undergoing a transformative shift, propelled by advancements in technology. This research explores the integration of Generative Artificial Intelligence (Generative AI) and interactive learning environments in the context of English language education, with a focus on developing a personalized oral assessment method. The proposed method leverages Generative AI's language generation capabilities within an interactive learning space to create a dynamic, adaptive learning environment. The study addresses historical challenges in traditional teaching methodologies, emphasizing the need for AI-driven approaches. The research objectives encompass a comprehensive exploration of the historical context, challenges, and existing technological interventions in English language education. A novel, technology-driven oral assessment method is designed, implemented, and rigorously evaluated using datasets such as Librispeech and L2Arctic. The ablation study investigates the impact of training dataset proportions and model learning rates on the method's performance. Results from the study highlight the importance of maintaining a balance in dataset proportions, selecting an optimal learning rate, and considering model depth to achieve optimal performance.

This paper explores the transformation and challenges of grassroots social governance mechanisms in the context of technological innovation. The paper first analyzes the driving role of technological innovation in grassroots social governance, highlighting its key features, including intelligent participation, information application, and agile disposal. Through the example of COVID-19 prevention and control, the paper deeply analyzes the manifestation of these features in practice and their improvement space. Then, the paper reveals the difficulties faced in the upgrading of grassroots social governance, such as the unevenness of intelligent participation, the problem of information data applicability, and the special challenges of agile disposal. Finally, based on these problems, the paper proposes new mechanisms for grassroots social governance in the context of China's modernization, including building a balanced mechanism for intelligent participation, innovating to maximize the suitability of governance technologies, and optimizing the decision-making mechanism for flexible responses.

Computer Science is a subject with a high degree of logical complexity and extremely abstract and complex specialised concepts. Computer science students need to deal with a large amount of complex information when learning. Cognitive load becomes an important factor that affects students' learning outcomes and hinders the establishment and retention of their knowledge systems. My present study takes clarifying the specific role of cognitive load on the construction of computer science students' knowledge system as a starting point to find ways that can help students regulate cognitive load effectively. My main focus this time was to investigate real and effective practical ways for students to reduce their cognitive load while studying computer science by using qualitative interviews. I conducted in-depth interviews with 10 separate computer science students and used thematic analysis to carefully and deeply analyse the data collected. The study showed that students were able to effectively reduce cognitive load and improve their understanding and long-term memory effects of complex concepts through segmented processing, visual aids, and teamwork. This study points out the importance of managing students‘ cognitive load in computer science education practice, and provides useful references for educators to improve teaching methods and enhance students’ learning outcomes.

In the new era of civil engineering industry, we are in urgent need of transformation and upgrading, this paper aims to integrate the traditional industry technology and digital twin technology, with building information model (BIM) as the carrier, in order to achieve the smart site, smart site as the goal, in the project construction stage to achieve technology integration and innovation, Finally complete the transformation and upgrading of the civil engineering industry to intelligence and digitalization in the new era.

This paper intends to explore the function and approaches of intelligent construction in reducing the risks of railway engineering construction. By means of literature review and risk analysis, effective measures for lowering construction risks through the organic combination of intelligent construction and railway engineering are put forward, including data-driven decision support, real-time monitoring and early-warning systems, digital construction technology, intelligent equipment and automated construction, collaborative working platforms, etc. Finally, the prospects and potential of intelligent construction in reducing the risks of railway engineering construction are indicated.

This paper explores the relationship between human resource (HR) investment decisions and financial performance in Singaporean companies, with a focus on real-world case studies. By analyzing the HR strategies of three prominent companies—Singtel, DBS Bank, and Singapore Airlines—the study highlights how HR investments in leadership development, employee well-being, and talent management contribute to financial success. The paper adopts a qualitative approach, utilizing company reports and public data to assess the impact of these HR practices. Key findings demonstrate that strategic HR investments aligned with organizational goals improve productivity, profitability, and resilience in the face of external challenges. The research also discusses the importance of measuring HR’s return on investment (ROI) and calls for a closer partnership between HR and finance departments. While econometrics is mentioned as a potential tool for future research, this paper relies on qualitative insights to provide actionable recommendations for companies looking to enhance their HR strategies. The study concludes by emphasizing the need for flexible, data-driven HR policies and further empirical research to strengthen the understanding of HR’s role in organizational financial performance.

This study investigates the evolving trends in employee training and skill development in Singaporean companies from 2018 to 2023, focusing on key areas such as digital skills, leadership, innovation, and employee engagement. Using publicly available annual reports from 12 prominent companies across five industries—Finance, Technology, Healthcare, Manufacturing, and Real Estate—keyword frequency analysis and industry comparisons were conducted to identify patterns in training priorities. The results indicate a notable increase in the emphasis on digital skills and leadership development, particularly within the Finance and Technology sectors, reflecting the broader trend of digital transformation and innovation. While employee engagement showed varying trends, particularly spiking in Healthcare during the pandemic, the focus on innovation remained crucial across all sectors, especially in Manufacturing. The findings suggest that Singaporean companies are adapting their training strategies to meet industry-specific challenges and global trends. The study offers practical recommendations for businesses to prioritize digital skills and leadership training and highlights the need for targeted strategies to enhance employee engagement and innovation.

Under the "dual carbon" vision, the concept of green transportation has become a key factor to promote the sustainable development of the transportation economy, especially in the field of railway transportation. This paper explores how new green transportation technologies can drive the innovation path of safety management and sustainable economic development of railway transportation. This paper analyzes the sustainable development path of railway transportation under the concept of green transportation, identifies the challenges that may be faced in the process of realizing green railway transportation, and proposes a series of coping strategies such as strengthening the research and development and application of energy-saving and emission reduction technologies, accelerating the pace of infrastructure renovation, and improving the level of informatization and intelligence. At the same time, this paper also pays attention to the basic position of railway transportation safety management, and emphasizes the importance of railway safety to the personal and property safety of the public. Under the guidance of the "dual carbon" goal, this paper aims to provide valuable reference for policymakers, industry practitioners and the public, provide theoretical support and practical guidance for promoting the green, safe and efficient development of railway transportation, and look forward to the future development direction.

This study investigates the effects of hydrogen sulfide (H2S) molecules on the Nrf2 (nuclear factor E2-related factor 2)/HO-1 (heme oxygenase-1) pathway in a mouse model of Parkinson's disease (PD), aiming to elucidate the neuroprotective mechanism of hydrogen sulfide therapy for PD. Real-time fluorescence quantitative PCR was employed to measure the expression levels of glutathione peroxidase (GSH-Px) mRNA, superoxide dismutase (SOD), and Nrf2 in the striatum of the mice. Additionally, Western blot analysis was conducted to assess the protein expression levels of TH and the Nrf2/HO-1 pathway in the striatal tissue. The results indicated that the expression levels of CAT, GSH-Px mRNA, SOD, and Nrf2 showed significant differences between the experimental group and the control group, with notable increases in TH and Nrf2/HO-1 expression (P<0.05). In conclusion, hydrogen sulfide can upregulate the expression levels of Nrf2/HO-1 in the cerebral striatum of PD mice, contributing to its therapeutic effects.

With the development of the Internet and science and technology, smart phones have been evolving rapidly. They provide primary school students with massive learning resources (such as e-books, online courses, encyclopedic knowledge, and cross-cultural communication), facilitating knowledge acquisition. However, excessive use is prone to cause distractions, reduced learning efficiency, weakened social skills, gaming addiction, visual impairment, psychological problems, and value deviations. To balance this "double-edged sword", it is essential to prioritize both information literacy cultivation and prevention of Internet addiction, which requires the collaborative efforts of families, schools, technology platforms, and the government.

Background: Exosomes derived from plant embryo cells have emerged as key players in mediating stress responses in plants. This study investigates the functional roles of exosomes from Arabidopsis thaliana embryo cells in enhancing plant tolerance to drought and salinity stress. Understanding these roles could lead to innovative applications in agriculture for improving crop resilience.

Methods: Exosomes were isolated from Arabidopsis thaliana embryo cells using ultracentrifugation. The characterization of exosome cargo included proteomic and lipidomic analyses to identify key molecules involved in stress responses. Plant stress tolerance was assessed through exosome treatment under controlled conditions of drought and salinity. Parameters such as relative water content (RWC), chlorophyll content, and expression levels of stress-responsive genes (DREB1A, RD29A) were measured to evaluate the effectiveness of the treatment.

Results: Exosome treatment significantly improved plant tolerance to drought and salinity, evidenced by increased RWC and chlorophyll levels compared to untreated controls. Molecular analysis revealed that exosomes contained bioactive molecules, including small RNAs and stress-related proteins, which were associated with the upregulation of stress-responsive genes. The findings suggest that exosomes enhance stress tolerance by delivering regulatory molecules that activate stress response pathways.

Conclusions: The study highlights the potential of exosomes derived from plant embryo cells to improve plant resilience against environmental stressors. The integration of exosome-based technologies in agriculture could provide a sustainable approach to enhancing crop performance under adverse conditions. Future research should focus on optimizing exosome production and assessing their efficacy in field settings to fully leverage their benefits in agricultural practices.

Big data in education has become a driving force for educational transformation and innovation, emerging as a prominent area of focus in educational technology research. In the context of big data in higher education, student performance analysis is a key issue that has garnered significant attention from researchers and institutions alike. This review provides a systematic overview of student performance analysis from the perspective of educational data mining. The process of student performance analysis is categorized into three steps: the acquisition and preprocessing of educational big data, methods for analyzing student performance, and the visualization and application of data analysis results. Various methods for student performance analysis and their applications are introduced in detail, summarizing existing research and providing insights into future research directions.

This document describes in detail the design and modeling scheme of the towering tower structure in the seventh national college students' structural design competition in Hebei Province. For the towering tower structure with horizontal vibration, this paper firstly interprets the competition problem and clarifies the design requirements and loading conditions, including three levels of loading: horizontally excited vibration, hydrostatic calm load and horizontally excited vibration of higher frequency and amplitude. Subsequently, the final model scheme was optimized and determined through material performance testing, component design and testing, structural modeling and analysis. In terms of materials, bamboo veneer and bamboo strips were used as the main materials in this paper, and their mechanical properties were tested in detail. The structure was modeled and analyzed by Midas finite element software, and the strength, stiffness and stability of the structure were comprehensively evaluated. During the modeling process, the focus was placed on the connection method of nodes, material treatment and detail construction to ensure that the model can safely carry and meet the design requirements during actual loading. After several rounds of design optimization and loading tests, the model of the towering tower structure in the form of truss structure is finally determined, and the model shows good force performance and load carrying capacity at all levels of loading conditions. The whole process of model design, fabrication and testing is recorded in detail in this paper, which provides valuable experience for the design of similar structures.

This paper aims to explore the use of "Internet+" technology to flip the traditional classroom teaching model in order to better enhance students’learning outcomes in the fields of medical microbiology and immunology. Through research and practice, it has been found that the flipped classroom approach can stimulate students’ interest in learning, enhance their ability for self-directed learning, and promote deep learning. This paper will combine specific cases with the latest research findings to propose more concrete teaching strategies and suggestions, and discuss how to effectively evaluate the application effects of the flipped classroom in the teaching of medical microbiology and immunology. The goal is to provide reference and inspiration for educational reform.

 Since the 1970s, road disease detection at home and abroad has become a research hotspot. Intelligent detection of road diseases can effectively reduce traffic safety accidents and maintain social safety. Based on R-CNN and YOLO models, our team innovatively proposed a detection method of many algorithms, which further improved the detection efficiency. In terms of hardware, micro high-speed industrial camera, LPMS series and iAHRS-MO attitude sensor are adopted. At present, the team has completed a complete set of detection methods and structures, which can adapt to many future needs and changes.

Objective To investigate the effects of wogonin on the proliferation and migration of nasopharyngeal carcinoma cells C666-1 and HK-1. Methods The inhibitory effect of wogonin on nasopharyngeal carcinoma cells C666-1 and HK-1 was determined by CCK8 assay, and IC50 was calculated. The effects of wogonin on the proliferation and independent viability of nasopharyngeal carcinoma cell C666-1 were investigated by plate colony formation test. The effects of wogonin on the migration ability of nasopharyngeal carcinoma cells were studied by scratch test and Transwell migration test. Results The results of CCK8 experiment showed that wogonin significantly inhibited the activity of C666-1 and HK-1 cells, and the inhibitory effect increased with the increase of drug concentration. Under the same drug concentration, the inhibitory effect for 48 h was higher than that for 24 h, and the inhibitory effect was dose-time dependent. The IC50 values of wogonin treated C666-1 cells were 37.15 μmol/L for 24 h and 20.75 μmol/L for 48 h. The IC50 values of HK-1 cells treated with wogonin were 29.09 μmol/L for 24 h and 20.46 μmol/L for 48 h. The results of plate colony experiment showed that after the intervention of wogonin, the number of visible colonies of nasopharyngeal carcinoma cells C666-1 was less and the volume was smaller. Scratch test and Transwell migration test showed that the migration ability of C666-1 and HK-1 cells was significantly inhibited, and the higher the drug concentration, the stronger the inhibition effect. Conclusion Wogonin can inhibit the activity, proliferation and migration of nasopharyngeal carcinoma cells.

This paper delves into strategies to elevate the global influence of China's western region's economy amidst current global economic dynamics. Recognizing the necessity for economic advancement in this region, the study initiates with an analysis of the prevailing economic development trends both globally and within China, acknowledging the smooth operation of China's economy against a backdrop of global economic recuperation challenges. Through a detailed exploration of the theoretical and practical significance, the paper underscores the criticality of this research. It reviews the existing scholarly discourse, noting a domestic focus on digital economy enhancements in the western region, contrasted with international analyses rooted in broader assessments of China's economic landscape. The document culminates in a comprehensive review, positing the study's contributions towards understanding and improving the economic stature of China's western region on the world stage.