The business world is currently undergoing a significant shift towards sustainability and intelligent automation, which presents both promising prospects and formidable hurdles for business owners. The increasing demand for sustainable goods and services, driven by pressing social and environmental issues, opens doors for entrepreneurs to establish companies that address these concerns. Moreover, automation and technological advancements have revolutionized the operational landscape of firms, providing entrepreneurs with novel opportunities to enhance efficiency and foster creativity. However, thriving in this dynamic environment necessitates a fresh skill set and innovative approaches. Entrepreneurs must actively acquire the requisite technological expertise to leverage the potential of intelligent automation while navigating the intricate legislative and social frameworks surrounding sustainability. Furthermore, they must demonstrate agility and adaptability, adept at pivoting strategies and offerings to align with the evolving business panorama. This study’s exploration of the intersection of automation and entrepreneurship resonates deeply with the principles of sustainability. By dissecting the challenges and strategies entrepreneurs use to embrace automation, the research contributes valuable insights to the ongoing discourse on feasible business practices within the context of burgeoning sustainability. The findings will assist policymakers by providing useful information to cultivate an environment conducive to sustainable, technology-based entrepreneurship.

Competency-based education is one of the many important educational objectives in the cultivation of senior vocational talents. In the past education model, the importance of achievement is greater than ability. Teachers rely on the scores of test papers to classify students' grades. Competency-based education has changed this situation very well, paying special attention to students' ability training. This paper mainly studies how to better promote the reform and innovation of English teaching in higher vocational colleges and strengthen students' learning ability and vocational skills while ensuring students' ability development.

Species of the Moraceae family are of great economic, medicinal and ecological importance in Amazonia. However, there are few studies on their diversity and population dynamics in residual forests. The objective was to determine the composition, structure and ecological importance of Moraceae in a residual forest. The applied method was descriptive and consisted of establishing 16 plots of 20 m × 50 m (0.10 ha), in a residual forest of the Alexánder von Humboldt substation of the National Institute of Agrarian Innovation-INIA, Pucallpa, department of Ucayali, where individuals of arboreal or hemi-epiphytic habit, with DBH ≥ 2.50 cm, were evaluated. The floristic composition was represented by 33 species, distributed in 12 genera; five species not recorded for Ucayali were found. Structurally, the family was represented by 138 individuals/ha with a horizontal distribution similar to an irregular inverted “J”. However, there were different horizontal structures among species. It was determined that 85% of the species were in diameter class I (2.50 to 9.99 cm), being the most abundant Pseudolmedia laevis (Ruiz & Pav.) J.F. Macbr. (41.88 individuals/ha); and the most dominant were Brosimum utile (Kunth) Oken (1.71 m²∕ha) and Brosimum alicastrum subsp. bolivarense (Pittier) C.C.Berg (0.90 m²/ha). Likewise, P. laevis and B. utile were the most ecologically important. The information from the present research will allow the establishment of a baseline, which can be used to propose the management of Moraceae in residual forests in the same study area.

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.