Clustering technics, like k-means and its extended version, fuzzy c-means clustering (FCM) are useful tools for identifying typical behaviours based on various attitudes and responses to well-formulated questionnaires, such as among forensic populations. As more or less standard questionnaires for analyzing aggressive attitudes do exist in the literature, the application of these clustering methods seems to be rather straightforward. Especially, fuzzy clustering may lead to new recognitions, as human behaviour and communication are full of uncertainties, which often do not have a probabilistic nature. In this paper, the cluster analysis of a closed forensic (inmate) population will be presented. The goal of this study was by applying fuzzy c-means clustering to facilitate the wider possibilities of analysis of aggressive behaviour which is treated as a heterogeneous construct resulting in two main phenotypes, premeditated and impulsive aggression. Understanding motives of aggression helps reconstruct possible events, sequences of events and scenarios related to a certain crime, and ultimately, to prevent further crimes from happening.

The main objective of this study is to identify the impact of trust on the construction of corporate value in commerce and services microbusinesses. This work is based on identifying the challenges faced by SMEs (Small and Medium-sized Enterprises), which are conditioned by the type of business and the regulatory and incentive variables that exist in the territory, affecting their permanence and stability in the market and their financial and commercial development. A local study is carried out in Bogotá, Colombia, through a descriptive research project, using a quantitative analysis method (SPSS) to process data obtained from local microbusinesses. As a result, it was observed that trust has a discrete impact on the creation of corporate value, which is created from the use of ICT (Information and Communication Technology). This leads to the recognition that it is necessary to strengthen horizontal networks with suppliers, clients, and similar businesses, as well as vertical networks with entities and public associations, to generate lasting and strong links that increase the competitiveness of these business units in the face of exogenous risks shaped by the social, economic, and cultural characteristics of the territory, which are increasingly conditioned to the use of communication technology.

In order to meet the guidance, publicity and commercial functions, various types of billboards have become important permanent facilities in the airport terminal, which are distributed all over the terminal. The advertising materials inside billboards have certain fire hazards, and there is a lack of research on the fire risk of advertising materials at present. Therefore, it is necessary to study the fire risk of advertising materials in airport terminal. Taking PVC board, a commonly used advertising material, as the research object, Pyrosim was used to model and analyze its fire, and the characteristics of fire spread, smoke flow, and distribution of combustion products such as CO and CO2 in the terminal building were obtained. This study explores the fire combustion characteristics of advertising materials in civil airport terminals, providing a basis for fire prevention management in civil airport terminals.

Vehicle detection stands out as a rapidly developing technology today and is further strengthened by deep learning algorithms. This technology is critical in traffic management, automated driving systems, security, urban planning, environmental impacts, transportation, and emergency response applications. Vehicle detection, which is used in many application areas such as monitoring traffic flow, assessing density, increasing security, and vehicle detection in automatic driving systems, makes an effective contribution to a wide range of areas, from urban planning to security measures. Moreover, the integration of this technology represents an important step for the development of smart cities and sustainable urban life. Deep learning models, especially algorithms such as You Only Look Once version 5 (YOLOv5) and You Only Look Once version 8 (YOLOv8), show effective vehicle detection results with satellite image data. According to the comparisons, the precision and recall values of the YOLOv5 model are 1.63% and 2.49% higher, respectively, than the YOLOv8 model. The reason for this difference is that the YOLOv8 model makes more sensitive vehicle detection than the YOLOv5. In the comparison based on the F1 score, the F1 score of YOLOv5 was measured as 0.958, while the F1 score of YOLOv8 was measured as 0.938. Ignoring sensitivity amounts, the increase in F1 score of YOLOv8 compared to YOLOv5 was found to be 0.06%.

Considering the need to adopt more sustainable agricultural systems, it is important that sweet potato breeding programs seek to increase not only root productivity, but also the productivity and quality of branches for silage production. The objective was to evaluate the genetic divergence and the importance of traits associated with the production and quality of branch silage in sweet potato genotypes. The experiment was conducted on the JK Campus of the Federal University of Vales do Jequitinhonha and Mucuri Valleys in a randomized block design with 12 treatments and four repetitions. Twelve characteristics of branches and silage were evaluated. There was genetic variability between the genotypes, making it possible to select parents divergent for future breeding programs for silage production. The genotypes BD-54 and BD-31TO were the most divergent in relation to the others, being indicated its use in crossbreeding aiming the improvement of the culture for silage, once the high performance per se of all genotypes evaluated has already been verified in previous works. The characteristics Na, TDN and NDF were those that most contributed to the divergence.