Abrupt changes in environmental temperature, wind and humidity can lead to great threats to human life safety. The Gansu marathon disaster of China highlights the importance of early warning of hypothermia from extremely low apparent temperature (AT). Here a deep convolutional neural network model together with a statistical downscaling framework is developed to forecast environmental factors for 1 to 12 h in advance to evaluate the effectiveness of deep learning for AT prediction at 1 km resolution. The experiments use data for temperature, wind speed and relative humidity in ERA-5 and the results show that the developed deep learning model can predict the upcoming extreme low temperature AT event in the Gansu marathon region several hours in advance with better accuracy than climatological and persistence forecasting methods. The hypothermia time estimated by the deep learning method with a heat loss model agrees well with the observed estimation at 3-hour lead. Therefore, the developed deep learning forecasting method is effective for short-term AT prediction and hypothermia warnings at local areas.

The objective of this work was to evaluate the effect of potassium concentrations applied via fertigation on the growth, yield and chemical composition of eggplant ‘Ciça’ in a distroferric red Latosol. The treatments were composed of five concentrations of K₂O (0, 36, 72, 108 and 144 kg ha^-1 supplied via fertigation), using potassium chloride as a source, divided into six applications. The irrigation system was of the drip type and irrigation management was done via a “Class A” evaporometer tank. Harvest started at 62 days after transplanting (DAT) and lasted for five months. The variables evaluated were: plant height, number of leaves, fresh fruit mass, number of fruits per plant, yield per plant, productivity and classification of the fruits according to their length and diameter. At 85 DAT, fruit were collected for characterization as to the percentage of lipids, proteins and fibers. Although the potassium fertigation in cover provided a reduction in the production and productivity, the concentrations of 36 kg ha^-1 and 72 kg ha^-1 of K₂O applied via fertigation, increased the physical-chemical characteristics of the fruits.

The objective of this study was to evaluate the growth of four lettuce cultivars in Southern Piauí to recommend the best ones for the region. The experiment was conducted in a greenhouse with randomized blocks, with evaluation in subdivided time plots, evaluated in six seasons (20, 24, 28, 32, 36, 40 days after sowing—DAS) and with treatments corresponding to four cultivars (Americana Rafaela^®, Grand Rapids TBR^®, Crespa Repolhuda^® and Repolhuda Todo ano^®) with five repetitions. Leaf area, number of leaves, collar diameter, aboveground fresh mass, aboveground dry mass, root dry mass and total and the physiological indices of growth analysis were evaluated. The lettuce cultivars interfered significantly in the studied parameters, being that Americana Rafaela^® and Repolhuda todo ano^®, in the conditions that they were submitted, presented better performances and bigger morphophysiological indexes, cultivated in pot. The cultivars Americana Rafaela^® and Repolhuda todo ano^® can be produced under the conditions of the south of Piauí.

Colorectal cancer is the fourth leading cause of death worldwide and the fifth leading cause of cancer death in Colombia. Magnetic resonance imaging is the ideal modality for the evaluation of colorectal cancer, since it allows staging by determining invasion beyond the muscularis propria, extension towards adjacent organs, identification of patients who are candidates for chemotherapy or pre-surgical radiotherapy and planning of the surgical procedure. The key point is based on the differentiation between T2 and T3 stages through the use of sequences with high-resolution T2 information. In addition to this, it allows the assessment of the size and morphology of the lymph nodes, and considerably increases the specificity for the detection of lymph node involvement. MRI is a technique with high specificity and high reproducibility.

Small watershed ecological compensation is an important economic means to solve the contradiction between protecting the ecological environment and developing the economy. Taking the Changtian small watershed in the Xixiu District of Anshun City as an example, this paper uses the ecological service function value method to roughly calculate the ecological service function value of the small watershed ecosystem: the ecological service function value of the Changtian small watershed is 913.586 million yuan, and the total amount of ecological compensation is 11.6245 million yuan, of which the farmland system compensation is 1.3194 million yuan, the forest system compensation is 7.5336 million yuan, and the water system compensation is 256,000 yuan, The compensation for the fruit forest system is 2,515,500 yuan. Based on the value of ecosystem service function, the compensated and non-compensated ecosystem service functions are distinguished, and the equivalent factors that different ecosystems can provide compensated ecosystem functions are expressed, so that the determination of ecological compensation amount is scientific and more accurate, and then provides a basis for the determination of ecological compensation standard of the small watershed.

The importance of improving industrial transformation processes for more efficient ones is part of the current challenges. Specifically, the development of more efficient processes in the production of biofuels, where the reaction and separation processes can be intensified, is of great interest to reduce the energy consumption associated with the process. In the case of Biodiesel, the process is defined by a chemical reaction and by the components associated to the process, where the thermochemical study seeks to develop calculations for the subsequent understanding of the reaction and purification process. Thus, the analysis of the mixture of the components using the process simulator Aspen Plus V9^® unravels the thermochemical study. The UNIFAC-DMD thermodynamic method was used to estimate the binary equilibrium parameters of the reagents using the simulator. The analyzed aspects present the behavior of the components in different temperature conditions, the azeotropic behavior and the determined thermochemical conditions.