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.

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.

Lettuce (Lactuca sativa L.) is the main leafy vegetable grown in Brazil. Its productivity and quality are limited by the growing season, the nearby environment and the type of cultivar adopted. The objective of this work was to verify at different times of the year the best planting environment for lettuce cultivation in a semi-humid tropical climate. For this purpose, an experiment was set up in three different seasons (October–November 2014, January–March, May–July 2015). The experimental design was randomized blocks, in a 3 × 3 × 2 factorial arrangement, consisting of three seasons, three cultivars (cvs. Vera^®, Tainá^® and Rafaela^®) and two growing environments (low tunnel with beds protected with mulching consisting of soil protection with plastic fabric covering, and beds without protection or conventional cultivation) and four replicates per treatment. Plant biomass, stem length, head diameter, number of leaves per head and crop productivity were evaluated as response parameters. The results showed that the May–July period favored biomass production, head diameter and productivity. Despite the similarity between varieties, the variety Vera^® is more productive in biomass, number of leaves per head, stem length and productivity. The low tunnel planting system with mulching is adequate under the conditions evaluated for lettuce cultivation. This system in the May–July period favors a superior development in the characteristics biomass, head diameter and productivity, if compared to conventional cultivation during the October–November period.

Urban trees are one of the valuable storage in metropolitan areas. Nowadays, a particular attention is paid to the trees and spends million dollars per year to their maintenance. Trees are often subjected to abiotic factors, such as fungi, bacteria, and insects, which lead to decline mechanical strength and wood properties. The objective of this study was to determine the potential degradation of Elm tree wood by Phellinus pomaceus fungi, and Biscogniauxia mediteranae endophyte. Biological decay tests were done according to EN 113 standard and impact bending test in accordance with ASTM-D256-04 standard. The results indicated that with longer incubation time, weight loss increased for both sapwood and heartwood. Fungal deterioration leads to changes in the impact bending. In order to manage street trees, knowing tree characteristics is very important and should be regularly monitored and evaluated in order to identify defects in the trees.

With the purpose of identifying the characteristics of variation in fruit size and seed production (potential and efficiency) of Cedrela odorata L. between sites and progenies established in the ejido La Balsa, municipality of Emiliano Zapata, Veracruz, fruits were harvested from 20 trees in February 2013, preserving the identity of each one. Fruit length and width were measured, seed was extracted and developed and aborted seeds were counted to calculate Seed Production Potential (SPP) and Seed Efficiency (SE). The results showed significant differences between sites and between progenies and for fruit length between sites. The mean values found were: 32.52 mm (fruit length), 18.73 mm (fruit width), 39.9 seeds per fruit (SPP) and 57.51% (SE). The seed of this species for its use should be selected taking into account the production characteristics of crops and outstanding individual trees, in addition, due to the current regulatory restrictions on seed collection, the establishment of trials and plantations for germplasm production is a viable option for forest management of the species.