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.

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.

The purpose of this article is to determine the equitability of airport and university allocations throughout Ethiopian regional states based on the number of airports and institutions per 1 million people. According to the sample, the majority of respondents believed that university allocation in Ethiopia is equitable. In contrast, the majority of respondents who were asked about airports stated that there is an uneven distribution of airports across Ethiopia’s regional states. Hence, both interviewees and focus group discussants stated that there is a lack of equitable distribution of universities and airports across Ethiopia’s regional states. This paper contributes a lesson on how to create a comprehensive set of determining factors for equitable infrastructure allocation. It also provides a methodological improvement for assessing infrastructure equity and other broader implications across Ethiopian regional states.

The propagation of plant material in the arracacha crop is commonly done vegetatively through asexual seed, this activity has allowed its multiplication and conservation over time. The plant material available is of low quality, affecting the development and potential yield of the crop and therefore the producer’s income. The objective of the research was to comparatively analyze two technologies for the production of arracacha seed: local technology and Agrosavia technology. The information for the local technology was obtained from surveys applied to farmers and the selection was made using the deterministic sampling technique, and for the Agrosavia technology through the recording of data and production costs in research lots at commercial scale. Descriptive statistics and calculation of economic return indicators were applied for the two situations. The results show that the use of quality seed allows obtaining higher seed production (251,559 unit ha^-1) and tuberous roots (25,875 kg ha^-1), being superior to local technology by 14% and 28% respectively; thus, the arracacha producer acquires greater economic efficiency by obtaining lower unit cost per kilo produced and better net income with a marginal rate of return of 316.45. The results achieved are useful for farmers, companies and entities that wish to produce quality seed and support the arracacha production system in Colombia.