This paper analyzes the marketing strategies and growth of UNF, a youth-focused sportswear brand in China’s digital retail market. It highlights how UNF uses social commerce, influencer campaigns, and data-driven personalization to engage Gen Z consumers. The study identifies both strengths—such as niche positioning and digital agility—and challenges like licensing dependence and offline limitations. A phased strategy is proposed to support sustainable expansion.

This study, using a public childcare institution in Chengdu as a case example, analyzes parents' satisfaction with childcare services through questionnaires and interviews across six dimensions: management services, environmental facilities, health care, educational activities, teaching staff, and parent-child interaction. The research found that overall parental satisfaction is relatively high, but issues such as inflexible service management, suboptimal environmental facilities, and insufficient professional capabilities of the teaching staff still exist. To address these issues, strategies such as optimizing service management, upgrading.

As pipeline leaks will cause huge safety hidden trouble and early alert for pipeline leaks is urgently needed for the safety, efficiency operation of the industrial infrastructure. In order to address the problem mentioned above, this paper introduces an automatic pipeline leakage fault detection framework using Multi-Scale Attention CNN (MSA-CNN). (1)A multi-scale convolutional feature extraction module was designed to extract hierarchical spatial-temporal features from multi-source signals such as acoustics and pressure. (2) The channel-space dual attention mechanism is introduced to adaptively strengthen the important feature responses related to leakage at different scales and suppress irrelevant noise interference. (3) The residual fusion encoder structure is constructed to effectively integrate multi-layer feature representation to improve the model's ability to distinguish between small leakage signals and complex backgrounds. Compared with the traditional CNN and single-scale attention methods, the proposed model significantly enhances the sensitivity to small-scale leakage events and the robustness to complex environments while maintaining the computational efficiency, and has been deployed in a real-time detection platform that supports edge computing. Experimental results demonstrate that the detection accuracy of the model on the commonly used pipeline leaking detection dataset is 92.3%.

As one of the most important parts of rotating machinery, the early prediction of rolling bearing is very important for the safety of operation and cost reduction of maintenance. To improve the intelligent level of bearing fault recognition, this paper proposes an intelligent bearing fault diagnosis frame work based on a hybrid CNN-LSTM-GRU network, which combines the advantages of CNN, LSTM, and GRU, and makes structural innovation and optimization design on the basis of the existing deep learning model. The proposed model follows a three-segment hybrid structure, in the first segment, 1DCNN is employed to extract local time-domain feature from original vibration signal. Also, We feed the features that have been extracted by the CNN network as the inputs to the both LSTM network and GRU network in parallel in order to take care of the multiple dependencies between the sequence of data items and make the model optimize more efficiently, respectively. 5.fusion multi-head attention module is introduced to perform feature weighing for the dual-channel outputs, thus further enhancing the capability of identifying complicated failure modes. To prevent overfitting and coalescence while enhancing the generalization capability of the model, the final classification module is built upon Dropout regularized fully connected design with residual connection. Experiments on the CWRU bearing fault dataset demonstrate superior classification performance of the proposed network under different load and fault severities, providing an average accuracy of more than 94.2%.

It is important to diagnose rolling bearing, as it plays an important role in the safe operation and reliability of industrial rotatory machinery and equipment. In this work, the fault diagnosis of rolling bearings was investigated, and a fault diagnosis method of rolling bearings based on an improved SE-CNN was presented. The proposed method performs structure innovation and function enhancement on the classic Convolutional Neural Network (CNN) and Squeeze-and-Excitation (SE) attention mechanism. In particular, the proposed model includes a multi-scale convolution module for capturing both local and global fault feature information. The SE modules of both are intended to achieve adaptive strengthening of important information channels and spatial positions. Furthermore, the model has carried out hierarchical feature fusion, regarding shallow feature and deeper one between layers fusion, facilitating the robustness and expressive power of the model in multi working conditions and multi noise environment. Compared with original SE-CNN, the proposed model is of much higher structure complexity, more powerful cross-scale feature interaction and channel-adaptive crowd feature weight adjustment, hence it can more accurately represent the fault mode of bearings. Numerical experiments on the CWRU bearing dataset demonstrate that our methods outperform existing CNN-based methods in both accuracy of fault identification and generalization ability.

Under the dual background of accelerated development of digital economy and in-depth advancement of “dual-carbon” goal, the Beijing-Tianjin-Hebei region faces the challenges of regional empowerment of digital technology, transformation and upgrading of energy structure, synergistic development of regional economy and sustainable development of ecological environment. Based on the theory of digital-energy-economic-environmental coupling and coordination, this study constructs a spatio-temporal evaluation system with 32 indicators, employs the entropy weighting method to objectively assign weights, introduces the revised coupling coordination model (RCCC), and analyzes it based on the spatial autocorrelation model (Moran's I) combined with the spatial analysis tools of ArcGis and GeoDa to reveal the spatial and temporal evolution patterns of the Jing-Jin-Ji region in the period of 2006-2022. region's spatio-temporal evolution pattern from 2006 to 2022. The results show that the coupling coordination level of digital-energy-economy-environment in the Beijing-Tianjin-Hebei region shows a significant upward trend during the observation period, and at the end of the study period, the coupling coordination level of Beijing reaches the good coordination level, and the intermediate coordination level of Tianjin and Hebei respectively, but only Shijiazhuang and Langfang among the prefectural-level cities in Hebei province reach the intermediate coordination stage, while the rest are still in the primary coordination stage, and the region as a whole shows the The region as a whole shows a gradient difference of “Beijing leading-Tianjin catching up-Hebei lagging behind”, and there is an obvious spatial clustering effect among some regions.

Polyetherimide (PEI) is a high-performance thermoplastic engineering plastic. With excellent thermal stability, mechanical strength and chemical stability, it shows wide application potential in many fields. However, its stability at high temperatures and relatively low dielectric constant limit its application. In recent years, researchers have modified PEI by adding conductive fillers, inorganic nano-fillers and developing all-organic composite materials, effectively improving its dielectric properties. However, the balance between the thermal stability and dielectric properties of PEI, as well as the selection and optimization of fillers, remain urgent problems to be solved. In the future, in-depth research can be conducted in the directions of new matrix selection, optimization of filler types and forms, and structural improvement, in order to prepare dielectric materials with high temperature resistance and high energy storage density.

Zhaoqing, an important place where the peasant movement took place in Guangdong, is an area where the peasant movement was launched earlier, developed rapidly and had significant influence. The red heritage resources of the peasant movements in Zhaoqing are abundant, widely distributed, and the demonstration construction efforts are significant, which have educational value in cultivating students' spirit of hard work and perseverance, cultivating students' sense of patriotism and promoting the spirit of unity and cooperation. To better realize the educational function of the red heritage of the peasant movement in Zhaoqing, it can be achieved through integrating the red heritage resources of the peasant movement in Zhaoqing, leveraging the educational value of this red heritage, and enhancing the educational effectiveness of it.

Immune milk containing immunoglobulin A (IgA), immunoglobulin G (IgG), and lactoferrin (IF), as a nutritional supplement that can be added to foods, not only provides essential iron and amino acids but also has the potential to improve immune function and contribute to disease prevention. Methods: Between April 2023 and July 2023, a total of 279 randomly selected kindergarten children in Zhenhai District were included in the study and divided into three groups. These children consumed immune skim milk powder for 3 months. Children’s height, weight, BMI, and disease resistance capability were recorded. Results: The mean heights/weights for Groups 1-3 before intervention are 112.11±0.65/19.02±0.35, 118.84±0.53/21.97±0.44, 104.36±0.54/16.55±0.34, with height/weight increasing as:  1.70±0.05/0.36±0.14, 1.49±0.06/0.71±0.08, 1.52±0.10/0.50±0.08 at the end of the research. Statistical differences in the three groups between three-month post-intervention and pre-intervention were all significant (P<0.01), there were also significant differences were also observed in male and female children. The heights and weights of children are above the standard growth development levels. The health status of the 301 children, including 60.13% upper respiratory tract infections, 4.65% diarrhea, and 3.65% hand-foot-mouth disease, was lower than those reported in the literature. Conclusion: Immune milk containing IgG, IgA, and lactoferrin might enhance children’s immune capability and disease resistance. IgA, IgG, and LF have the potential to be used in both dietary supplements and medicines to improve the health of both humans and animals.

China's air transport volume has steadily ranked second globally and continues to grow rapidly. However, there is a significant gap in civil aviation infrastructure, and airports are reaching saturation. Developing strategies based on resilience theory has become an industry consensus. Aiming at the flight support operations in the airport movement area, we construct a complex network analysis model. In this model, we abstract nodes into an Activity On Vertex network (AOV net). We then employ the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) model to identify key nodes, providing data support for the cascading failure model. Focusing on the stability and recovery mechanisms of the flight support system, we establish a resilience evaluation system that incorporates metrics such as network efficiency, the relative scale of the largest connected component, etc., and conduct an empirical application of complex network theory. The conclusions drawn from this study guide the formulation of control strategies.

With the extensive use of intelligent monitoring systems in contemporary power system, it is becoming a significant challenge to make the power grid operate stably in achieving high robust fault diagnosis of transformers across operating conditions and environmental domains. In this work, a transfer learning enhanced deep CNN-LSTM cross-domain transformer fault diagnosis model (TL-DCNN-LSTM) is presented. The model, built on the classical CNN-LSTM, applies Convolutional Neural Network (CNN) to capture multi-scale spatial features, which are from the original current (or vibration) signal, between the circuit and gearbox, and employs Long Short-Term Memory Network (LSTM) to learn the time-dependent patterns of the fault signal. To address the distribution discrepancy between the source domain and the target domain, the paper presents a transfer learning mechanism, where the Maximum Mean Discrepancy (MMD) loss is embedded in the deep feature space to reduce feature distribution differences between different domains. Meanwhile, a domain-wise batch normalization (DSBN) block is introduced to maintain the discrepancy in domain. Furthermore, an auxiliary domain discriminator and adversarial training strategy are proposed to enhance the domain irrelevance and discrimination of the feature representation. The TL-DCNN-LSTM model obtains a cross-domain diagnosis accuracy of over 96.7%, which is substantially higher than traditional CNN, LSTM and common fine-tuning methods.

As the core competencies in English education and the new curriculum standards are progressively implemented, primary school English teachers have recognized the importance of integrating excellent traditional Chinese culture into their teaching. However, the integration of this culture in practice has not been as effective as expected. This paper explores the significance of incorporating excellent traditional Chinese culture into primary school English teaching, analyzes the current state and reasons for cultural integration, and investigates strategies to enhance the effective integration and penetration of this culture in teaching. The goal is to improve students' cross-cultural communication skills, strengthen their cultural and national confidence, and support the inheritance and promotion of excellent traditional Chinese culture.

Traditional Chinese medicine (TCM) culture is the crystallization of the wisdom of the Chinese nation over thousands of years, with a profound cultural heritage and a unique theoretical system. This paper analyzes classroom teaching of TCM culture from three aspects: the connotation and characteristics of TCM culture, the importance of TCM culture for international students of traditional Chinese medicine, and the current status of TCM culture teaching. Teaching methods and measures include online-offline blended teaching, integrating classic TCM texts and culture into clinical teaching, and improving the Chinese proficiency of international students, so as to enrich international students' knowledge of TCM culture and enhance their Chinese language level.

With the acceleration of digital transformation in the construction industry, the application of BIM technology in civil engineering teaching has become the key to improving the quality of talent cultivation. This article focuses on the application of BIM technology in civil engineering teaching, systematically elaborating on the specific implementation strategies of project-based teaching method, flipped classroom and blended online and offline teaching, cross disciplinary collaborative teaching and other practical paths; Taking a large-scale commercial complex construction project as a case study, this paper conducts an in-depth analysis of the BIM course teaching process, and evaluates the teaching effectiveness by comparing and analyzing the learning and practical ability data of students in 2021 and 2022. Research has shown that innovative teaching models based on BIM technology have significantly improved students' professional literacy and practical abilities, providing important references for the reform of teaching in civil engineering and the cultivation of composite talents in the industry.

Exoplanetary science stands as one of the most cutting-edge and promising research fields in modern astronomy, playing a pivotal role in unraveling fundamental scientific questions regarding planetary formation, evolution, and the origins of life. Among various detection methods, the transit method has emerged as one of the most successful techniques due to its high efficiency and reliability. This paper systematically elaborates on the fundamental principles of the transit method, which infers the existence of transiting exoplanets and their physical parameters (such as radius and orbital period) by detecting periodic dips in stellar brightness. This paper further presents current research developments of the transit method, while also examining the method's inherent limitations and prospective research directions. The high-precision detection capability of the transit method will continue to advance exoplanetary science, providing critical support in the search for habitable planets and potential biosignatures

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.