Integrating red culture into vocational education is an important way to fulfill the fundamental task of cultivating students' moral character and nurturing talent. It is an effective method to enrich revolutionary spirit and fine traditions, and an important measure to foster students' patriotic feelings. Considering the current development status of vocational education, we explore the existing deficiencies. Focusing on inheriting the red gene, promoting the revolutionary spirit, and cultivating skilled talents with both moral integrity and practical abilities, we aim to promote the high-quality development of vocational education. Addressing the practical needs of contemporary college students, such as career development planning, employment concepts, and shaping professional values, and conducting in-depth research on innovative integration models of red culture and vocational education. By analyzing typical cases, we propose practical and operable solutions, which will provide important theoretical basis and methodological references for the reform, innovation, and the enhancement of the connotation of vocational education.

In the context of the deep integration of digital technology revolution and new urbanization strategy, the construction of smart cities has become an important path to solve the "urban disease" and achieve modernization of governance. This study adopts literature research and case analysis methods to systematically sort out the connotation, characteristics, and development evolution of smart cities. Through analysis, the current status of smart city construction in China is obtained, and the shortcomings in the current process of smart city construction in China are pointed out. In response to the shortcomings, this study proposes a "five in one" improvement strategy of policy innovation and capital synergy, reconstruction of new infrastructure system, technological breakthroughs and achievement transformation, circulation of data elements, and upgrading of security system. It provides practical reference for the transformation of Chinese path to modernization cities, and also provides experience for the construction of smart cities in developing countries.

Under the background of "double carbon", China actively promotes the development of new energy vehicles, and charging piles and other infrastructures have become key. This study focuses on the rural areas of Shijiazhuang, aiming to analyze the current situation of charging piles, identify and solve problems, and help new energy vehicles to the countryside. The study adopts a survey questionnaire to investigate the rural population in ten counties and three county-level cities around Shijiazhuang, and finds that the layout of rural charging piles is unreasonable, the number is insufficient, and there are barriers to their use through text mining, descriptive statistics, cross-tabulation analysis, decision tree modeling, K-modes cluster analysis, and structural equation modeling. Users can be categorized into long-distance high-frequency fast-charging, close-distance convenience type, and medium-distance planning type, and the quality of facilities, convenience and other factors significantly affect satisfaction and willingness to use. Based on this, the study proposes optimizing the layout, making charging prices transparent, simplifying the APP, increasing the number, deepening the environmental protection publicity, improving the infrastructure, and exploring multi-dimensional use, in order to promote the development of charging piles and new energy vehicles in rural areas.

This paper delineates five historical stages of human understanding of the universe, tracing the evolution from ancient mythology to modern scientific theories. Initially, myths like "Pangu Creating the World" explained cosmic origins, followed by rational interpretations during the Warring States period, as seen in the works of Zhuangzi and Hui Shi. The geocentric and heliocentric models of Ptolemy and Copernicus marked further advancements. Modern materialism, exemplified by Marx and Engels, introduced dialectical perspectives, while Lemaître’s "Big Bang" theory and Hawking’s "singularity" hypothesis blended scientific speculation with mythological elements. The final stage presents a novel "materialist" theory positing that the universe comprises infinite hierarchical structures of infinitesimal particles, forming atomic, molecular, and cosmic scales. This framework challenges idealist cosmologies, refutes Einstein’s "black hole" paradox, and reinterprets gravitational sources as "vacuum energy." By synthesizing empirical observations with philosophical inquiry, the study asserts a deterministic, infinitely scalable universe, displacing metaphysical narratives with a unified materialist ontology.

Non-invasive diagnostic techniques have made significant strides in the early detection and management of cardiovascular diseases. Advances in imaging modalities, such as echocardiography, cardiac MRI (CMR), CT, and PET/SPECT, have enhanced diagnostic accuracy and allowed for detailed assessment of heart function and structure, while reducing the risks associated with invasive procedures. In parallel, the emergence of electrocardiography (ECG) and wearable devices has enabled real-time monitoring, aiding in the detection of arrhythmias and chronic disease management. Furthermore, blood-based biomarkers, including troponins, natriuretic peptides, and microRNAs, have shown promise for early detection, risk stratification, and prognosis prediction, although they often need to be integrated with imaging and ECG data for comprehensive evaluations. Artificial intelligence (AI) and machine learning are transforming cardiovascular diagnostics by improving image reconstruction, ECG interpretation, and multi-modal data integration, offering personalized assessments and enhanced diagnostic precision. However, challenges such as cost, accessibility, regulatory hurdles, and clinician adoption remain significant barriers. Future research should focus on improving the accessibility, affordability, and interoperability of diagnostic tools across healthcare systems. With continued innovation, non-invasive techniques are poised to play an increasingly pivotal role in early intervention and improved outcomes for patients with heart diseases.

With the rapid development of the digital era, the widespread adoption of the internet has been accompanied by a surge in cybersecurity threats. This study proposes a practical theoretical framework supported by data-driven analysis to enhance national cybersecurity governance.Task 1: Utilizing datasets from ITU, INTERPOL, and VCDB, preprocessed data (with outliers removed) were visualized through heatmaps to analyze the global distribution of cyber threats. Clustered bar charts and stacked histograms further dissected regional variations in cybersecurity incident success rates, prevention rates, prosecution rates, and reporting rates. Results reveal that the distribution of cyber threats correlates with a nation’s economic development level and cybersecurity infrastructure robustness.Task 2: The Global Cybersecurity Index (GCI) was employed to evaluate national cybersecurity capabilities. Missing values in GCI’s five pillars were addressed via Lagrangian interpolation. Principal Component Analysis (PCA) identified six countries with the highest contribution rates, while the entropy weight method optimized the pillars into two core dimensions: collaboration and technology. Incorporating policy time lag effects, this framework pinpointed effective policies for mitigating cybersecurity incidents.Task 3: A multivariate linear regression model quantified the impact of demographic factors (e.g., internet penetration rate and education level) on cyber threat distribution. Finally, a neural network-based multivariate time series model predicted the theoretical impact of policy interventions on threat dynamics, complemented by sensitivity analysis to objectively assess the framework’s strengths and limitations.

Electrical materials play a pivotal role in electrical engineering, yet conventional research methodologies relying on experimental approaches and theoretical calculations face challenges of low efficiency and high costs. The rapid advancement of artificial intelligence (AI) technology has introduced novel tools for electrical material research, significantly enhancing the efficiency of material design, performance prediction, and experimental optimization. This paper systematically reviews recent progress in AI applications across semiconductor, alloy, energy storage, and metallic materials. In semiconductor research, machine learning has improved analytical precision through optimization of Hubbard U parameters and prediction of perovskite defect transition levels. For alloy materials, AI combined with active learning strategies has accelerated the discovery and performance optimization of high-entropy alloys.Our analysis reveals that AI technology substantially reduces material development cycles and experimental costs through multi-source data integration, quantitative model construction, and multi-objective optimization strategies. Looking forward, with advancements in data science and computational capabilities, AI is poised to play an increasingly critical role in multi-scale material design, performance prediction, and novel material development for electrical engineering, thereby driving technological innovations in power and electronic systems.

This article conducts research on the ideological and political teaching practice of professional course teachers in vocational colleges, revealing their core contradictions and optimization directions. To solve the practical dilemma of vocational college teachers who generally recognize the value of ideological and political education in courses, but are limited by the single teaching content and methods, the integration of ideological and political elements into mechanization, and the lack of systematic training. This article first proposes an optimization path centered on "three-dimensional breakthroughs", which involves innovating modular teaching content that deeply integrates professional and ideological education, promoting diversified methods such as case teaching and situational practice, and constructing a normalized teacher training system and interdisciplinary collaboration mechanism; Secondly, the practice of using typical course ideological and political cases as the main focus, supplemented by dynamic evaluation feedback mechanisms, has proven that it can significantly improve teaching effectiveness. Curriculum ideological and political education is an important driving force for the connotative development of vocational education, and its long-term development needs to be promoted through institutional improvement and resource coordination.

This article reviews the research progress on the pathological mechanism and clinical impact of endometrial injury. Endometrial injury is a common gynecological disease, with a complex pathogenesis involving multiple aspects such as inflammatory response, fibrosis formation, and abnormal angiogenesis. This article explores in detail these pathological mechanisms and analyzes the serious impact of endometrial damage on female reproductive health, including infertility, recurrent miscarriage, and menstrual abnormalities. In addition, the article also introduces commonly used diagnostic methods and treatment strategies, providing reference for clinical practice. Finally, this article looks forward to future research directions and emphasizes the necessity of conducting in-depth studies on the mechanisms of endometrial injury and developing new treatment methods.

Background: Low back pain (LBP), affecting ~10% of the global population, is a major public health challenge, with elevated prevalence in China (20.88–29.88%). Lumbar disc herniation (LDH), a leading cause of LBP (12–43% of cases), involves nucleus pulposus displacement and annulus fibrosus compromise. While conservative therapies resolve symptoms in 75% of patients, refractory cases necessitate surgery. Percutaneous Endoscopic Lumbar Discectomy (PELD) is a minimally invasive option with favorable outcomes. This systematic review and meta-analysis evaluate the efficacy of minimally invasive surgeries for LDH, focusing on pain reduction, functional improvement, and recurrence.

Methods: Following PRISMA guidelines, PubMed, Scopus, and Web of Science were systematically searched using keywords related to LDH, minimally invasive surgery, and clinical outcomes. Eligible studies included confirmed LDH diagnoses, detailed surgical data, and postoperative outcome measures. Two researchers independently screened articles and extracted data. Meta-analyses (RevMan 5.4, STATA 17.0) employed random-effects models to calculate mean differences (MDs) and odds ratios (ORs). Sensitivity and publication bias analyses were conducted.

Results: Among 11,626 screened articles, 14 studies (1,108 patients) met inclusion criteria. All procedures significantly reduced back and leg pain at 3, 6, and 12 months postoperatively. Unilateral Biportal Endoscopic (UBE) surgery demonstrated the largest improvements in Visual Analog Scale (VAS) scores for back and leg pain across all intervals. PELD with annular suture yielded the highest Oswestry Disability Index (ODI) improvements (MD: 65.32 at 3 months; 70.93 at 12 months). UBE also outperformed other techniques in functional outcomes. Recurrence rates between Microendoscopic Lumbar Discectomy (MELD) and PELD were comparable (OR: 0.90; 95% CI: 0.37–2.22).

Conclusion: Minimally invasive surgeries, particularly UBE and PELD with annular suture, significantly improve pain and function in LDH patients. Despite methodological heterogeneity, results robustly support their efficacy. Personalized surgical selection and standardized protocols are critical to optimizing outcomes. Future research should prioritize identifying patient-specific predictors of success to guide precision interventions. This analysis provides evidence-based insights to enhance clinical decision-making and patient quality of life.

The capability of financial risk management directly impacts a company's competitiveness. Financial risks such as financing risks and investment risks inevitably exist during corporate operations. Financial risk control affects resource integration capabilities and may even lead to corporate bankruptcy, making it crucial for enterprises to strengthen financial risk management. Big data technology provides new approaches for financial risk management. This paper analyzes the current financial risk management status of Du Technology Co., Ltd., identifies existing issues through financial indicators, and proposes innovative management strategies for corporate financial risks in the big data era. These strategies include enhancing internal controls, improving internal auditing, establishing financial risk early-warning mechanisms, and promoting financial personnel transformation, aiming to strengthen operational stability.

The development of philanthropy plays a significant role in fostering a harmonious society and narrowing the wealth gap. This study focuses on Zhejiang Province and constructs a measurement indicator system for the willingness to participate in personal philanthropy. Using data collected through questionnaires and empirical analysis with Stata 18, the study examines the measurement of philanthropic willingness and its influencing factors. The results indicate significant differences in philanthropic willingness across various prefecture-level cities, with Jiaxing and Jinhua standing out. Factors such as moral values, trust in charitable organizations, and tax incentives have a significant positive impact on philanthropic willingness, while income level shows a negative relationship with philanthropic participation. This paper proposes recommendations to improve institutional frameworks, optimize incentives, promote philanthropic culture, and enhance platforms to strengthen public trust in philanthropy and increase public participation. These findings provide empirical support and policy references for the high-quality development of philanthropy in Zhejiang Province.

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.