The urgency of ecological problems has become increasingly complex, so responses from diverse parties are needed, including in the context of ecological citizenship. The general hypothesis proposed in this research is that the problem of climate change has an influence on the high level of attention of the global community, including academics, to environmental issues related to the active role of citizens demanding environmental justice and sustainable development. This study aims to explore globally published documents to provide an in-depth discussion concerning ecological citizenship. Bibliometric analysis was employed from the Scopus database. The main findings confirm the significant contribution of ecological citizenship in shaping global understanding of the role of individuals in maintaining environmental sustainability. The research theme mapping shows the diversity of issues that have been explored, with particular emphasis on environmental education and social justice, providing a basis for recommendations for future research. In particular, environmental education has been recognized as a critical element in shaping society’s understanding of environmental issues, while social justice underscores the importance of fair distribution and critical analysis of inequality in social and ecological contexts. Future research recommendations include the exploration of effective strategies in promoting the concept of ecological citizenship, developing a holistic environmental education curriculum, and more active research in the context of social justice in various regions, including Asia. This bibliometric analysis is expected to contribute substantially to formulating policies and practical actions that support the vision of inclusive ecological citizenship, which positively impacts overcoming global environmental challenges.

Plant growth-promoting rhizobacteria (PGPR) offer eco-friendly alternatives to chemical fertilizers, promoting sustainable agriculture by enhancing soil fertility, reducing pathogens, and aiding in stress resistance. In agriculture, they play a crucial role in plant growth promotion through the production of agroactive compounds and extracellular enzymes to promote plant health and protection against phytopathogens. In the rhizosphere, diverse microbial interactions, including those with bacteria and fungi, influence plant health by production of antimicrobial compounds. The antagonism displayed by rhizobacteria plays a crucial role in shaping microbial communities and has potential applications in developing a natural and environmentally friendly approach to pest control. The rhizospheric microbes showcase their ecological importance and potential for biotechnological applications in the context of plant-microbe interactions. The extracellular enzymes produced by rhizospheric microbes like amylases, chitinases, glucanases, cellulases, proteases, and ACC deaminase contribute to plant processes and stress response emphasizing their importance in sustainable agriculture. Moreover, this review highlights the new paradigm including artificial intelligence (AI) in sustainable horticulture and agriculture as a harmonious interaction between ecological networks for promoting soil health and microbial diversity that leads to a more robust and self-regulating agricultural system for protecting the environment in the future. Overall, this review emphasizes microbial interactions and the role of rhizospheric microbial extracellular enzymes which is crucial for developing eco-friendly approaches to enhance crop production and soil health.

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.

Cartography includes two major tasks: map making and map application, which is inextricably linked to artificial intelligence technology. The cartographic expert system experienced the intelligent expression of symbolism. After the spatial optimization decision of behaviorism intelligent expression, cartography faces the combination of deep learning under connectionism to improve the intelligent level of cartography. This paper discusses three problems about the proposition of “deep learning + cartography”. One is the consistency between the deep learning method and the map space problem solving strategy, based on gradient descent, local correlation, feature reduction and non-linear nature that answer the feasibility of the combination of “deep learning + cartography”; the second is to analyze the challenges faced by the combination of cartography from its unique disciplinary characteristics and technical environment, involving the non-standard organization of map data, professional requirements for sample establishment, the integration of geometric and geographical features, as well as the inherent spatial scale of the map; thirdly, the entry points and specific methods for integrating map making and map application into deep learning are discussed respectively.

Spectrum map is the foundation of spectrum resource management, security governance and spectrum warfare. Aiming at the problem that the traditional spectrum mapping is limited to two-dimensional space, a three-dimensional spectrum data acquisition and mapping system architecture for the integration of space, sky and earth is presented, and a spectrum map reconstruction scheme driven by propagation model is proposed, which can achieve high-precision three-dimensional spectrum map rendering under the condition of sparse sampling. The spectrum map reconstructed by this method in the case of single radiation source and multiple radiation sources is in good agreement with the theoretical results based on ray tracing method. In addition, the measured results of typical scenes further verify the feasibility of this method.