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 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.

Introduction: Given the heterogeneous nature and inherent complexity of forensic medical expertise, the expert (medical professional or related areas) must make the best use of the technical and technological tools at his disposal. Imaging, referring to the set of techniques that allow obtaining images of the human body for clinical or scientific purposes, in any of its techniques, is a powerful support tool for establishing facts or technical evidence in the legal field. Objective: To analyze the use of magnetic resonance and computed tomography in postmortem diagnosis. Methodology: information was searched in the databases PubMed, Science Direct, Springer Journal and in the search engine Google Scholar, using the terms “X-Ray Computed Tomography”, “Magnetic Resonance Spectroscopy”, “Autopsy” and “Forensic Medicine” published in the period 2008–2015. Results: MRI is useful for the detailed study of soft tissues and organs, while computed tomography allows the identification of fractures, calcifications, implants and trauma. Conclusions: In the reports found in the literature search, regarding the use of nuclear magnetic resonance and computed tomography in postmortem cases, named by the genesis of the trauma, correlation was found between the use of imaging and the correct expert diagnosis at autopsy.

The wide distribution of the common beech (Fagus sylvatica) in Europe reveals its great adaptation to diverse conditions of temperature and humidity. This interesting aspect explains the context of the main objective of this work: to carry out a dendroclimatic analysis of the species Fagus sylvatica in the Polaciones valley (Cantabria), an area of transition with environmental conditions from a characteristic Atlantic type to more Mediterranean, at the southern limit of its growth. The methodology developed is based on the analysis of 25 local chronologies of growth rings sampled at different altitudes along the valley, generating a reference chronology for the study area. Subsequently, the patterns of growth and response to climatic variations are estimated through the response and correlation function, and the most significant monthly variables in the annual growth of the species are obtained. Finally, these are introduced into a Geographic Information System (GIS) where they are cartographically modeled in the altitudinal gradient through multivariate analysis, taking into account the different geographic and topographic variables that influence the zonal variability of the species response. The results of the analyses and cartographic models show which variables are most determinant in the annual growth of the species and the distribution of its climatic response according to the variables considered.

In November 2018, the sample plot survey method was used to analyze the population characteristics of Lithocarpus polystachyus in the natural secondary forest with different disturbance intensity in Jianning, Fujian Province, and compile its population static life table. The results showed that the number of individuals in the population was small, but it was clustered. With the increase of interference intensity, the first and second age seedlings and young trees decreased. The population types affected by human disturbance are all lacking level V trees, and the population type belongs to primary population (N₁); The undisturbed population lacks level I and II seedlings and young trees, but there are level V trees, and the population type belongs to medium decline population (S₂). In general, all populations of L. polystachyus are unstable and belong to the transitional type. In the static life table, the mortality of level I and II seedlings and young trees is high, the survival rate has a small peak in level III and IV, and then the survival rate decreases rapidly, and the average life expectation of level II is the highest. It shows that artificial conservation measures and appropriate space re-lease are needed to maintain the stability of the population.

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.