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.

In view of the large energy consumption of the regeneration process in the chemical absorption decarburization process, on the basis of the enrichment classification flow process, the nanoscale ceramic film is used as a new heat exchanger between the enriched liquid and the regeneration gas. The porous ceramic film is capable of coupling thermal-mass transfer to effectively recover part of the water vapor and the heat carried in the regeneration gas, so as to reduce the regenerative energy consumption of the system. The effects of parameters such as regeneration temperature, flow rate, molar fraction of water vapor, and MEA enrichment temperature, flow rate, and MEA concentration of shunt on the hydrothermal recovery effect of ceramic membranes of different pore sizes and lengths were studied by using the heat recovery flux and water recovery rate as the indicators. The results show that the hydrothermal recovery performance of the ceramic membrane increases with the increase of MEA enrichment flow, but decreases significantly with the increase of the enrichment temperature. At the same time, with the increase of regenerative gas velocity and the molar fraction of water vapor in the regenerative gas, the heat recovery flux will increase. The heat recovery performance of the 10 nm ceramic membrane is better than that of the 20 nm ceramic membrane.

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.