Post-partum follicular dynamics in beef cows calving during spring and autumn in southern Brazil.

Ovarian activity early post-partum in beef cows with intermediate body condition scores that calved during spring and autumn and treated with either 48 h of temporary weaning or exogenous hormones was investigated. Calving cows were given body condition scores and their ovaries were ultrasonographically scanned daily starting on day ten postpartum. The number and size of the follicles were recorded. Upon detection of a dominant follicle (>9 mm), the animals were distributed to different treatments. Over 80% of the animals (41/49) in both seasons presented a dominant follicle during the second or third week post-partum. The percentage of cows ovulating within seven days after treatment varied from 30% (3/10) for control cows to 60% (6/10) for MAP+GnRH treated cows for both spring and autumn calving cows. A reduction of 16% and 19% in body condition score was observed during the post-partum period studied for both spring and autumn calving cows, respectively. The decrease in body condition score was accompanied by a reduction in the follicular population of 43% during the fifth week post-partum only in those calving during autumn. In the spring calving cows, no change was detected in the follicular population despite the decrease in body condition score. Irrespective of the differences in environmental conditions between the two breeding seasons, cows present large follicles in their ovaries that are capable of responding to hormonal treatments, during the early post-partum period.Doc 1. Disponível em: . Acesso em: 27 ago. 2018

Fetal development and blood hematological-biochemical parameters in Campeiro and Pantaneiro foals.

Made available in DSpace on 2019-01-02T23:37:38Z (GMT). No. of bitstreams: 1 2018Vieiraetal..pdf: 293128 bytes, checksum: 55472a23573b40b9efd496c73b492ee6 (MD5) Previous issue date: 2019-01-02bitstream/item/189575/1/2018-Vieira-et-al.-.pd

SmartIX: A database indexing agent based on reinforcement learning

Configuring databases for efficient querying is a complex task, often carried out by a database administrator. Solving the problem of building indexes that truly optimize database access requires a substantial amount of database and domain knowledge, the lack of which often results in wasted space and memory for irrelevant indexes, possibly jeopardizing database performance for querying and certainly degrading performance for updating. In this paper, we develop the SMARTIX architecture to solve the problem of automatically indexing a database by using reinforcement learning to optimize queries by indexing data throughout the lifetime of a database. We train and evaluate SMARTIX performance using TPC-H, a standard, and scalable database benchmark. Our empirical evaluation shows that SMARTIX converges to indexing configurations with superior performance compared to standard baselines we define and other reinforcement learning methods used in related work

Feasibility and Environmental Sustainability of a 103.5 kWp floating Photovoltaic Electrical System with a Case Study in a Hydroelectric Power Plant, Santa Clara Hpp, Located in the South of Brazil Region

Typical environmental problems associated with the implementation of solar photovoltaic systems for the generation of peak electrical energy, on a larger scale, such as on the order of 1 MWp, is in the occupied area, usually more than 3 km2. This can be minimized by the use of water parks or water dam’s reservoir, small and large hydroelectric power plants dams. Both the terrestrial and aquatic systems can impact the site, the first one, for the need to promote earthworks, removal of extensive green areas in the surroundings, installation of new transmission line, among others; and the second, despite the fact that a flat surface is already used and that there is no need for new civil procedures for its installation and can normally take advantage of the existing power transmission line, may cause changes in the biota of the reservoir, depending on the shading areas on the surface of the lake. Due to these facts, this research was proposed to investigate, parameterize and tropicalize an electric power generation system based on floating silicon photovoltaic cell panels installed in the Santa Clara HPP reservoir, in terms of peak power, durability, aspects and environmental impacts, with the study of possible evolutionary improvements of the project such as "tracking" or solar tracking, as well as dynamism of the structure, allowing the shadow area to be shifted over time, minimizing its effects in the local biota

ACO-RR: Ant Colony Optimization Ridge Regression in Reuse of Smart City System

© 2019, Springer Nature Switzerland AG. With the rapid development of artificial intelligence, governments of different countries have been focusing on building smart cities. To build a smart city is a system construction process which not only requires a lot of human and material resources, but also takes a long period of time. Due to the lack of enough human and material resources, it is a key challenge for lots of small and medium-sized cities to develop the intelligent construction, compared with the large cities with abundant resources. Reusing the existing smart city system to assist the intelligent construction of the small and medium-sizes cities is a reasonable way to solve this challenge. Following this idea, we propose a model of Ant Colony Optimization Ridge Regression (ACO-RR), which is a smart city evaluation method based on the ridge regression. The model helps small and medium-sized cities to select and reuse the existing smart city systems according to their personalized characteristics from different successful stories. Furthermore, the proposed model tackles the limitation of ridge parameters’ selection affecting the stability and generalization ability, because the parameters of the traditional ridge regression is manually random selected. To evaluate our model performance, we conduct experiments on real-world smart city data set. The experimental results demonstrate that our model outperforms the baseline methods, such as support vector machine and neural network

Effect of prepartum somatotropin injection in late-pregnant Holstein heifers on metabolism, milk production and postpartum resumption of ovulation.

O objetivo deste estudo foi determinar o efeito do pré-parto injeção de somatotropina em novilhas late-Holstein em grávidas metabolismo de produção de leite, e a retomada da ovulação pós-parto