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.

In this study, we utilized a convolutional neural network (CNN) trained on microscopic images encompassing the SARS-CoV-2 virus, the protozoan parasite “plasmodium falciparum” (causing of malaria in humans), the bacterium “vibrio cholerae” (which produces the cholera disease) and non-infected samples (healthy persons) to effectively classify and predict epidemics. The findings showed promising results in both classification and prediction tasks. We quantitatively compared the obtained results by using CNN with those attained employing the support vector machine. Notably, the accuracy in prediction reached 97.5% when using convolutional neural network algorithms.

In this investigation the effect of collection seasons of explants (winter, spring and summer), type of explants (leaf disc and intermodal segments) and length of explants (0.5, 1.0 and 1.5 cm) for callusing in low-chill peach were standardized. The maximum callus induction (97.78%) in the low-chill peach was obtained from the intermodal segments of 0.5 cm in length used as an explant collected during spring season. The structural changes on the surface of the callus (5–7 weeks old yellowish green compact callus) during the progress of somatic embryogenesis of low-chill peach from the both intermodal segment as well as leaf disc derived callus were also examined with the use of scanning electron microscope (SEM). The SEM studies indicated that callus derived from internodal segment explant had the highest frequency of somatic embryos than callus from leaf discs. The SEM investigation, also demonstrated the sequential events/steps leading to low-chill peach somatic embryogenesis which was originating from somatic embryo mother cells through one unicellular pathway. Two types of calli were morphologically distinguished in both leaf disc and intermodal segment generated callus and these were the compact, well organized yellowish green embryogenic callus, containing large number of small, rich cytoplasmic, starch containing meristematic cells and soft and unorganized non-embryogenic callus containing sparsely cytoplasmic, vacuolated, and large cells devoid of metabolic reserves. The present SEM studies clearly demonstrated that somatic cells from peach explants generated callus could develop into fully differentiated somatic embryos through the characteristic embryological patterns of differentiation.

This study adopts a discursive and analytical perspective to explore how technological advances are reconfiguring the dynamics of the global labour market, with special attention to the phenomenon of microwork. Microwork, characterised by short, fragmented tasks carried out through digital platforms and geographically distributed, has seen exponential growth, particularly in nations with lower economic development. This type of work shows a growing distinction between tasks of a complex and creative nature and those of a repetitive and monotonous nature that do not require advanced skills to perform. This differentiation can intensify wage disparities between developed and developing countries, as well as contribute to the precariousness of work in activities considered less complex and valued. The article highlights the emergence of unstable and poorly paid jobs that do not require specific qualifications and discusses their impact on social security systems in countries where labour regulations are insufficient. Using a theoretical-methodological approach, the research examines the role of artificial intelligence in the rise of micro-labour and its socio-economic implications. It concludes that despite the flexibility and short-term earning opportunities offered by microwork, it poses considerable challenges in terms of income security, workers’ rights, and social protection, emphasising the need for regulatory measures to mitigate its adverse effects on vulnerable communities.

Entrepreneurial motivation has been one psychological factor that determines the success of MSMEs as it interacts with external factors. However, this has been scarcely studied in relation to women and the success of MSMEs. This study aims to analyze the effects of motivation and external factors on the success of women running MSMEs in the province of Jambi, Indonesia. A survey research methodology through the distribution of questionnaires on the motivation scale and the success of the MSMEs scale that were constructed by the authors was applied. The rating scale of the questionnaires was rated on a five-point Likert scale. A total of 325 women running MSMEs in Jambi City were given and returned the questionnaires. The results showed that motivation, which is comprised of self-confidence, risk-taking, results-orientedness, intelligence, and skills, has a significant effect on the success of MSMEs. Meanwhile, the success of MSMEs is significantly more affected by time and length of effort than loans or funds due to the decreasing trend of demand for goods and services.