Scientific Innovation in Asia

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.