Professional identity among faculty members in private higher education institutions plays a vital role in shaping the quality and sustainability of these institutions. This research aims to investigate the factors influencing the professional identity of teachers in Chengdu's private higher education institutions. The study employs a theoretical framework centered on "identification" with behavior intention, behavior attitude, and sense of belonging as fundamental dimensions. Data were collected through questionnaire surveys and analyzed using SPSS 23.0. The study hypothesizes that behavior intention, behavior attitude, and sense of belonging have a significant positive impact on professional identity among faculty members. Additionally, behavior attitude, subjective norms, and perceived behavioral control are expected to have a significant positive influence on behavior intention, and subjective norms and perceived usefulness may positively affect sense of belonging. The results are expected to provide valuable insights for enhancing the professional satisfaction and educational quality of faculty in private higher education institutions.

To achieve the electrification of private vehicles, it is urgent to develop public charging infrastructure. However, choosing the most beneficial type of public charging infrastructure for the development of a country or region remains challenging. The municipal decision’s implementation requires considering various perspectives. An important aspect of energy development involves effectively integrating and evaluating public charging infrastructure. While car charging facilities have been thoroughly studied, motorcycle charging facilities have been neglected despite motorcycles being a vital mode of transportation in many countries. The study created a hybrid decision-making model to evaluate electric motorcycle charging infrastructure. Firstly, a framework for evaluating electric motorcycle charging infrastructure was effectively constructed through a literature survey and expert experience. Secondly, decision-makers’ opinions were gathered and integrated using Bayesian BWM to reach a group consensus. Thirdly, the performance of the alternative solutions was evaluated by exploring the gaps between them and the aspiration level through modified VIKOR. An empirical analysis was conducted using examples of regions/countries with very high rates of motorcycle ownership worldwide. Finally, comparative and sensitivity analyses were conducted to demonstrate the practicality of the proposed model. The study’s findings will aid in addressing municipal issues and achieving low-carbon development objectives in the area.

The economy, unemployment, and job creation of South Africa heavily depend on the growth of the agricultural sector. With a growing population of 60 million, there are approximately 4 million small-scale farmers (SSF) number, and about 36,000 commercial farmers which serve South Africa. The agricultural sector in South Africa faces challenges such as climate change, lack of access to infrastructure and training, high labour costs, limited access to modern technology, and resource constraints. Precision agriculture (PA) using AI can address many of these issues for small-scale farmers by improving access to technology, reducing production costs, enhancing skills and training, improving data management, and providing better irrigation infrastructure and transport access. However, there is a dearth of research on the application of precision agriculture using artificial intelligence (AI) by small scale farmers (SSF) in South Africa and Africa at large. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) and Bibliometric analysis guidelines were used to investigate the adoption of precision agriculture and its socio-economic implications for small-scale farmers in South Africa or the systematic literature review (SLR) compared various challenges and the use of PA and AI for small-scale farmers. The incorporation of AI-driven PA offers a significant increase in productivity and efficiency. Through a detailed systematic review of existing literature from inception to date, this study examines 182 articles synthesized from two major databases (Scopus and Web of Science). The systematic review was conducted using the machine learning tool R Studio. The study analyzed the literature review articled identified, challenges, and potential societal impact of AI-driven precision agriculture.

The most important issue of economic development is the question of the real reasons for the growth of labor productivity based on innovative equipment and technologies or “closing technologies”, both directly and in the sphere of organization and management of economic systems. Organizational innovations can also be classified as “closing technologies”. For example, the creation of strategic institution, alliances and associations capable of changing the situation in the global economy, likely World Bank (WB), World Health Organization (WHO), International association Brazil, Russia, India, China, South Africa (BRICS) etc. This approach involves the formation of fundamental innovative solutions at all levels of the management hierarchy. The imperfection of the existing ideological and methodological paradigm, ignoring the mathematical constants of the Universe when designing economic supersystems or economic systems as integral distributed systems with complex dynamics similar to natural systems, the inefficiency of institutional intervention is the main reason for the impossibility of minimizing the structural and functional instability of the state economic system. The consequence of this is systemic violations and disproportions in the economy, risks associated with changes in the structure of the world economy and a colossal difference in the level of economic security of states and the phenomenon of crisis transfer.

Traditional shipping plays a crucial role in the national sea transportation system, serving inland areas, remote areas, and outer islands that are widely distributed throughout the country. However, there is still limited research on the problems of traditional shipping empowerment and its implementation. This research aims not only to analyze the obstacles encountered in empowering traditional shipping but also the implementation of the traditional shipping grant program. This study employed a quantitative descriptive approach, utilizing a likert scale, to analyze the issues that arise in the empowerment of traditional shipping. Additionally, for policy implementation analysis, the Hellmut-Wollmann policy analysis was used. The findings indicate that the most significant issues arise in the area of human resource development, such as a lack of competent teaching staff, insufficient short courses, complicated testing procedures, and the lack of crew certification. In the ex-ante stage, the variable of empowering traditional shipping transportation programs experienced the highest implementation rate. During the ongoing stage, the variable empowering traditional shipping services achieved the highest implementation score. And in the ex-post stage, traditional shipping services had the highest implementation score. This paper emphasizes the significance of collaboration and coordination among all levels of government, from the central to the local, in order to effectively implement the traditional shipping empowerment program. These findings also highlight the necessity of extending the traditional shipping grant program while making improvements in areas such as ship safety management regulations, the management and supply of traditional shipping terminals, the division of transportation types, and route determination policies.

Preserving roads involves regularly evaluating government policy through advanced assessments using vehicles with specialized capabilities and high-resolution scanning technology. However, the cost is often not affordable due to a limited budget. Road surface surveys are highly expected to use low-cost tools and methods capable of being carried out comprehensively. This research aims to create a road damage detection application system by identifying and qualifying precisely the type of damage that occurs using a single CNN to detect objects in real time. Especially for the type of pothole, further analysis is to measure the volume or dimensions of the hole with a LiDAR smartphone. The study area is 38 province’s representative area in Indonesia. This research resulted in the iRodd (intelligent-road damage detection) for detection and classification per type of road damage in real-time object detection. Especially for the type of pothole damage, further analysis is carried out to obtain a damage volume calculation model and 3D visualization. The resulting iRodd model contributes in terms of completion (analyzing the parameters needed to be related to the road damage detection process), accuracy (precision), reliability (the level of reliability has high precision and is still within the limits of cost-effective), correct prediction (four-fifths of all positive objects that should be identified), efficient (object detection models strike a good balance between being able to recognize objects with high precision and being able to capture most objects that would otherwise be detected-high sensitivity), meanwhile, in the calculation of pothole volume, where the precision level is established according to the volume error value, comparing the derived data to the reference data with an average error of 5.35% with an RMSE value of 6.47 mm. The advanced iRodd model with LiDAR smartphone devices can present visualization and precision in efficiently calculating the volume of asphalt damage (potholes).