UAVs, also known as unmanned aerial vehicles, have emerged as an efficient and flexible system for offering a rapid and cost-effective solution. In recent years, large-scale mapping using UAV photogrammetry has gained significant popularity and has been widely adopted in academia as well as the private sector. This study aims to investigate the technical aspects of this field, provide insights into the procedural steps involved, and present a case study conducted in Cesme, Izmir. The findings derived from the case study are thoroughly discussed, and the potential applications of UAV photogrammetry in large-scale mapping are examined. The study area is divided into 12 blocks. The flight plans and the distribution of ground control point (GCP) locations were determined based on these blocks. As a result of the data processing procedure, average GCP positional errors ranging from 1 to 18 cm have been obtained for the blocks.

Detailed record and analysis of a psychological crisis intervention and counseling process of college students during the "epidemic period": understanding the basic situation of students, problem analysis and judgment that students is in a state of psychological crisis, Analyzing five factors: students' family economic difficulties, stressful life events (their father died in a car accident), poor academic performance, lack of social support and staying at home during the epidemic, A targeted psychological crisis intervention, At the same time, to strengthen social support, Improving the family environment; Psychological counseling is conducted in a planned way, Set the phased psychological counseling goals and achieve them gradually, Focusing on the two topics of "meaning of life" and "self-denial", carry out psychological counseling, Finally, guide the students to clarify the meaning behind the psychological behavior, Put down the burden and go on lightly; In summary, this case has achieved a good guidance effect in the remission, breakthrough period and consolidation period, However, it is still worth paying attention to and discussing on the limitations of student psychological counseling and the boundary of daily counselors.

Low integrity is a challenge for any organization. However, most organizations emphasize integrity without explaining what is required of an individual with high integrity. Exhibiting high integrity is necessary for academics; yet, the level of academic integrity remains unclear. Therefore, the purpose of this study is to examine the integrity level of academicians in a Malaysian public university. This paper shares the findings on the level of integrity of academics based on a questionnaire completed by 213 academicians. Data were collected by survey questionnaire and was analyzed using descriptive and inferential statistics. An overall mean score of 9.45 from a possible 10.0 indicated a high level of integrity among academics. The self-evaluation results by academics also demonstrated that they have attained integrity at a high level for their generic task, teaching and learning, research and publications and service for community with a mean score between 9.36 and 9.49. The value with the highest mean score was for “service to community”, whereas the lowest was for “research and publication”. These findings show that the university has successfully instilled values of integrity among academicians. Nevertheless, the university must continue to enhance academic integrity by exploring religiosity. Using Google Scholar, a literature search identified an Islam-based academic integrity model to explain the quantitative findings. Finally, a mixed method approach and involving all universities in Malaysia are recommended to further the findings of this study.

We studied the role of industry-academic collaboration (IAC) in the enhancement of educational opportunities and outcomes under the digital driven Industry 4.0 using research and development, the patenting of products/knowledge, curriculum development, and artificial intelligence as proxies for IAC. Relevant conceptual, theoretical, and empirical literature were reviewed to provide a background for this research. The investigator used mainly principal (primary) data from a sample of 230 respondents. The primary statistics were acquired through a questionnaire. The statistics were evaluated using the structural equation model (SEM) and Stata version 13.0 as the statistical software. The findings indicate that the direct total effect of Artificial intelligence (Aint) on educational opportunities (EduOp) is substantial (Coef. 0.2519916) and statistically significant (p < 0.05), implying that changes in Aint have a pronounced influence on EduOp. Additionally, considering the indirect effects through intermediate variables, Research and Development (Res_dev) and Product Patenting (Patenting) play crucial roles, exhibiting significant indirect effects on EduOp. Res_dev exhibits a negative indirect effect (Coef = −0.009969, p = 0.000) suggesting that increased research and development may dampen the impact of Aint on EduOp against a priori expectation while Patenting has a positive indirect effect (Coef = 0.146621, p = 0.000), indicating that innovation, as reflected by patenting, amplifies the effect of Aint on EduOp. Notably, Curriculum development (Curr_dev) demonstrates a remarkable positive indirect effect (Coef = 0.8079605, p = 0.000) underscoring the strong role of current development activities in enhancing the influence of Aint on EduOp. The study contributes to knowledge on the effective deployment of artificial intelligence, which has been shown to enhance educational opportunities and outcomes under the digital driven Industry 4.0 in the study area.

Recognizing the discipline category of the abstract text is of great significance for automatic text recommendation and knowledge mining. Therefore, this study obtained the abstract text of social science and natural science in the Web of Science 2010-2020, and used the machine learning model SVM and deep learning model TextCNN and SCI-BERT models constructed a discipline classification model. It was found that the SCI-BERT model had the best performance. The precision, recall, and F1 were 86.54%, 86.89%, and 86.71%, respectively, and the F1 is 6.61% and 4.05% higher than SVM and TextCNN. The construction of this model can effectively identify the discipline categories of abstracts, and provide effective support for automatic indexing of subjects.

This study explores the intricate relationship between family functioning, emotional bonding, parent-child contact, and academic success among students through a serial mediation analysis. The research, conducted on a sample of 200 participants, sheds light on the indirect pathways through which family dynamics influence academic achievements, emphasizing the significance of emotional connections and parent-child interactions. The findings affirm the positive association between family functioning and academic achievement, in alignment with prior research. Additionally, the study identifies parent-child bonds and contact as partial mediators in this relationship, reinforcing previous findings. A noteworthy discovery is the full complementary sequential mediation effect, revealing that family functioning’s influence on academic success becomes substantial when emotional bonds foster increased parent-child contact. In conclusion, this research underscores the importance of emotional bonds and parent-child contact as sequential mediators, emphasizing their role in translating family dynamics into academic achievements among students. While providing valuable insights, the study acknowledges limitations such as sample size, potential sampling bias, self-reported measures, and a cross-sectional design. Addressing these limitations and expanding the scope of outcomes in future research will contribute to a more comprehensive understanding of the complex dynamics within family and educational institutions relationships and their profound impacts on students’ academic success.