Scientific Innovation in Asia

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.

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.