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The present investigation arose from the current problem in the entire territory of the Department of Atlantico in the Republic of Colombia, in which the livestock sector currently lacks a reliable modernization that contributes to the planning and profitability of meat production, translated into weight gain. The main focus of the study gravitated around the ignorance of the real effect exerted by meteorological and nutritional factors on the weight gain of cattle. As a possible solution, it was proposed to carry out a statistical analysis by means of a multiple linear regression model where cattle weight gain was the dependent variable to study under the influence of the following independent variables: accumulated precipitation for two weeks (mm), average daily precipitation for two weeks (mm), average daily forage height consumed for two weeks (cm), percentage daily average of forage consumed during two weeks (%), average protein percentage of forage consumed during two weeks (%), the average maximum temperature recorded during two weeks ([degrees]C), the average minimum temperature recorded during two weeks ([degrees]C), average daily temperature variation recorded for two weeks ([degrees]C) and average relative humidity recorded for two weeks (%). All independent data values were collected in the field. Once the analysis was carried out, it was concluded that there was statistical evidence to affirm that only the independent variables 'accumulated precipitation', 'average precipitation', 'average minimum temperature' and 'relative humidity' significantly influenced the changes observed in profit of cattle weight, being formulated a multiple linear regression model that contained only the mentioned variables, the rest were discarded. On the other hand, for the constructed linear regression model, the coefficient of determination R2 = 89.3691% was obtained, that is, for the significance level [alpha] = 0.05 (95% confidence level), this determined that the model of Multiple linear regression (A) explained the behavior of the average monthly cattle weight gain by 89.3691%. It was concluded, therefore, that the present work gives veracity to the determination of previous investigations where it is also concluded that the meteorological variables directly affect the changes associated with the weight of cattle for meat production. Key words: cattle, linear regression, livestock, meteorological, nutritional, statistics, variables, weight gain, INTRODUCTION According to figures provided by the Agustin Codazzi Geographic Institute (IGAC), the Department of Atlantico allocates approximately 144,000 hectares for the development of livestock activity. In other words, close [...]

Accurate air traffic flow prediction assists controllers formulate control strategies in advance and alleviate air traffic congestion, which is important to flight safety. While existing works have made significant efforts in exploring the high dynamics and heterogeneous interactions of historical air traffic flow, two key challenges still remain. (1) The transfer patterns of air traffic are intricate, subject to numerous constraints and limitations such as controllers, flight regulations, and other regulatory factors. Relying solely on mining historical traffic evolution patterns makes it difficult to accurately predict the constrained air traffic flow. (2) Weather conditions exert a substantial influence on air traffic, making it exceptionally difficult to simulate the impact of external factors (such as thunderstorms) on the evolution of air traffic flow patterns. To address these two challenges, we propose a Spatiotemporal Knowledge Distillation Network (ST-KDN) for air traffic flow prediction. Firstly, recognizing the inherent future insights embedded within flight plans, we develop a 'teacher-student' distillation model. This model leverages the prior knowledge of upstream-downstream migration patterns and future air traffic trends inherent in flight plans. Subsequently, to model the influence of external factors and predict air traffic flow disturbed by thunderstorm weather, we propose a student network based on the 'parallel-fusion' structure. Finally, employing a feature-based knowledge distillation approach to integrate prior knowledge from flight plans and extract meteorological features, our method can accurately capture complex and constrained spatiotemporal dependencies in air traffic and explicitly model the impact of weather on air traffic flow. Experimental results on real-world flight data demonstrate that our method can achieve better prediction performance than other state-of-the-art comparison methods, and the advantages of the proposed method are particularly prominent in modeling the complicated transfer pattern of air traffic and inferring nonrecurrent flow patterns., Author(s): Zhiqi Shen [1]; Kaiquan Cai (corresponding author) [1]; Quan Fang [2]; Xiaoyan Luo [3] 1. Introduction With the rapid development of civil aviation industry, the number of aircraft has [...]

To analyze the factors affecting road accidents involving hazardous materials, the Bayesian network (BN) model was used to fit the accident data. However, considering the possible overfitting phenomenon of the BN model, the model was optimised by combining Pearson's chi-squared test and Granger causality test (PG) methods. First, the data of hazardous materials accidents were preprocessed, and the index system of factors affecting hazardous materials road transport was constructed from five dimensions of “people, vehicles, hazmat, roads, and environment”; second, Pearson's chi-squared test and the Granger causality test were used to screen the factors affecting hazardous materials road transport accidents and to determine the causal relationship between the factors; finally, the BN model was constructed with accident severity and accident processing time as target nodes, and the results were analyzed and validated. The results show that the overall relative error rate of the model is less than 10% and can be used to explore the risk factors of hazardous materials transport accidents; weather, visibility, lighting, intersection type, road condition, road type, driver condition, vehicle type, etc. are all important factors affecting the severity of hazardous materials transport accidents. The study can serve as a reference for the safety supervision and management of hazardous materials transport enterprises and industrial management departments., Author(s): Zewen Li [1,2]; Mengmeng Zhang (corresponding author) [1,2] 1. Introduction Since the 21st century, with the acceleration of economic development, the volume of hazardous materials transport in China has [...]