International logistics supply chain is an important guarantee to support the country to build a new development pattern, this paper aims to propose a new strategy to promote the development of international logistics supply chain through the case study of Ningbo City. On the basis of supply chain theory and international logistics theory, this paper constructs SWOT model to study the case of Ningbo City, and draws the following conclusions: The international logistics supply chain of Ningbo city has the advantages of superior geographical location, perfect logistics infrastructure and strong port resources, and the disadvantages of low logistics informatization level and logistics management mode to be optimized. At the same time, it faces the opportunities of “One Belt and One Road” initiative and the competitive threat of other logistics centers. Adopting strategies such as policy support, strengthening logistics informatization construction and optimizing logistics management mode can ensure the stable development of foreign trade, which is conducive to accelerating the construction of a new development pattern and modern economic system in which domestic and foreign cycles promote each other.

In the era of artificial intelligence, smart clothing, as a product of the interaction between fashion clothing and intelligent technology, has increasingly attracted the attention and affection of enterprises and consumers. However, to date, there is a lack of focus on the demand of silver-haired population’s consumers for smart clothing. To adapt to the rapidly aging modern society, this paper explores the influencing factors of silver-haired population’s demand for smart clothing and proposes a corresponding consumer-consumption-need theoretical model (CCNTM) to further promote the development of the smart clothing industry. Based on literature and theoretical research, using the technology acceptance model (TAM) and functional-expressive-aesthetic consumer needs model (FEAM) as the foundation, and introducing interactivity and risk perception as new external variables, a consumer-consumption-need theoretical model containing nine variables including perceived usefulness, perceived ease of use, functionality, expressiveness, aesthetics, interactivity, risk perception, purchase attitude, and purchase intention was constructed. A questionnaire survey was conducted among the Chinese silver-haired population aged 55–65 using the Questionnaire Star platform, with a total of 560 questionnaires issued. The results show that the functionality, expressiveness, interactivity, and perceived ease of use of smart clothing significantly positively affect perceived usefulness (P < 0.01); perceived usefulness, perceived ease of use, aesthetics, and interactivity significantly positively affect the purchase attitude of the silver-haired population (P < 0.01); perceived usefulness, aesthetics, interactivity, and purchase attitude significantly positively affect the purchase intention of the silver-haired population (P < 0.01); functionality and expressiveness significantly positively affect perceived ease of use (P < 0.01); risk perception significantly negatively affects purchase attitude (P < 0.01). Through the construction and empirical study of the smart clothing consumer-consumption-need theoretical model, this paper hopes to stimulate the purchasing behavior of silver-haired population’s consumers towards smart clothing and enable them to enjoy the benefits brought by scientific and technological advancements, which to live out their golden years in comfort, also, promote the rapid development of the smart clothing industry.

The COVID-19 epidemic has given rise to a new situation that requires the qualification and training of teachers to operate in educational crises. Amidst the pandemic, online training has emerged as the predominant approach for delivering teacher training. The COVID-19 pandemic has created potential opportunities and challenges for online training, which may have a long-lasting impact on online training procedures in the post-pandemic era. This study aims to determine the primary potential and constraints of online training as seen by instructors. The Technology Acceptance Model (TAM) identified online training opportunities and challenges by examining the to-be-applied behavioral intention variables that influence trainees. These variables include individual, system, social, and organizational factors. The study has applied the Phenomenological technique to address the research issues, using the Semi-structured interview tool to get a comprehensive knowledge of the online training phenomena amongst the pandemic. A total of seven participants were selected from a list of general education teachers at the Central Education Office of the Education Department in Bisha Governorate. These people were deliberately selected because of their high frequency of completing training sessions throughout the epidemic. A series of interviews was conducted with these participants. The findings indicated that the primary prospects included both equal opportunities and digital culture within the individual factors, enrollment in training programs and variation in training programs across organizational characteristics, the use of digital material and electronic archiving within the system variables, engaging in the exchange of personal experiences, providing constructive criticism, and fostering favorable communication within the realm of social factors. However, the primary obstacles included deficiencies in digital competencies, compatibility of trainees’ attributes, and dearth of desire as per individual factors, the temporal arrangement of training programs, as well as the lack of prior preparation and preparedness within the realm of organizational factors. Other challenges included the absence of trainer assessment, limited diversity of training exercises, and technological obstacles within the system factors, and ultimately the absence of engagement with the instructor, and lack of engagement with peers are within the social variable.

Retinal disorders, such as diabetic retinopathy, glaucoma, macular edema, and vein occlusions, are significant contributors to global vision impairment. These conditions frequently remain symptomless until patients suffer severe vision deterioration, underscoring the critical importance of early diagnosis. Fundus images serve as a valuable resource for identifying the initial indicators of these ailments, particularly by examining various characteristics of retinal blood vessels, such as their length, width, tortuosity, and branching patterns. Traditionally, healthcare practitioners often rely on manual retinal vessel segmentation, a process that is both time-consuming and intricate, demanding specialized expertise. However, this approach poses a notable challenge since its precision and consistency heavily rely on the availability of highly skilled professionals. To surmount these challenges, there is an urgent demand for an automatic and efficient method for retinal vessel segmentation and classification employing computer vision techniques, which form the foundation of biomedical imaging. Numerous researchers have put forth techniques for blood vessel segmentation, broadly categorized into machine learning, filtering-based, and model-based methods. Machine learning methods categorize pixels as either vessels or non-vessels, employing classifiers trained on hand-annotated images. Subsequently, these techniques extract features using 7D feature vectors and apply neural network classification. Additional post-processing steps are used to bridge gaps and eliminate isolated pixels. On the other hand, filtering-based approaches employ morphological operators within morphological image processing, capitalizing on predefined shapes to filter out objects from the background. However, this technique often treats larger blood vessels as cohesive structures. Model-based methods leverage vessel models to identify retinal blood vessels, but they are sensitive to parameter selection, necessitating careful choices to simultaneously detect thin and large vessels effectively. Our proposed research endeavors to conduct a thorough and empirical evaluation of the effectiveness of automated segmentation and classification techniques for identifying eye-related diseases, particularly diabetic retinopathy and glaucoma. This evaluation will involve various retinal image datasets, including DRIVE, REVIEW, STARE, HRF, and DRION. The methodologies under consideration encompass machine learning, filtering-based, and model-based approaches, with performance assessment based on a range of metrics, including true positive rate (TPR), true negative rate (TNR), positive predictive value (PPV), negative predictive value (NPV), false discovery rate (FDR), Matthews's correlation coefficient (MCC), and accuracy (ACC). The primary objective of this research is to scrutinize, assess, and compare the design and performance of different segmentation and classification techniques, encompassing both supervised and unsupervised learning methods. To attain this objective, we will refine existing techniques and develop new ones, ensuring a more streamlined and computationally efficient approach.

The urgency of adapting urban areas to the increasing impacts of climate change has prompted the scientific community to seek new approaches in partnership with public entities and civil society organizations. In Malaysia, Penang Island has developed a nature-based urban climate adaptation program (PNBCAP) seeking to increase urban resilience, reduce urban heat and flooding, strengthening social resilience, and build institutional capacity. The project includes a strong knowledge transfer component focused on encouraging other cities in the country to develop and implement adaptation policies, projects, and initiatives. This research develops a model adopting the most efficient processes to accelerate the transfer of knowledge to promote urban adaptation based on the PNBCAP. The methodology is developed based on a review of literature focused on innovation systems and change theories. The integration of success strategies in adaptation contributes to informing the creation of solutions around the alliance of local, state, and national government agencies, scientific institutions, and civil society organizations, in a new framework designated the Malaysian Adaptation Sharing Hub (MASH). MASH is structured in 3-steps and will function as an accelerator for the implementation of urban climate adaptation policies, with the target of creating 2 new adaptation-related policies to be adopted annually by each city member, based on knowledge gathered in the PNBCAP. It is concluded that, to speed up urban adaptation, it is necessary to reinforce and promote the sharing of knowledge resulting from or associated with pilot projects.

The purpose of the current study is to examine the mediating role of intercultural communicative competence on the relationship between teaching of English language and learning at Chinese higher vocational colleges. The convenience sampling technique was used to collect data from 668 teachers, teaching English language subjects in different public and private Chinese higher vocational colleges. Smart partial least squares-structural equation modeling on SmartPLS software version 4 was used to test the hypotheses. The result revealed the direct effect of English language teaching (ELT) is not significant on English language learning (ELL). However, the intercultural communicative competences (ICC) have been tested and proved to be a potential mediator between English language teaching and learning. Because the indirect effect of ELT on ELL is positive and significant through mediator ICC. Therefore, based on the findings of this study, it can be concluded that the inclusion of intercultural communication ability is a crucial component in the vocational education of college students. Policymakers should be cautious about promoting and expanding the availability of cultural teaching and learning across demographic conditions (e.g., linguistic and ethnic diversity, age, and gender) and various levels of language proficiency. In accordance with the effects of teacher education and professional development programs, the implementation of ICC content necessitates a harmonization of pedagogical approaches and assessment practices across designated levels in order to effectively achieve educational objectives. To promote ICC in English language education, there must be clear guidelines and communication to school leaders, educators, and administrators regarding the necessity and goals of cultural integration.