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%.

With the acceleration of economic development and urban construction, urban security accidents have occurred around the world with alarming frequency, causing serious casualties and economic losses. Urban security planning and management as emerging areas of research have drawn widespread attention. For city development plans, urban security planning and management have become one of major topics. This paper first outlines the principles of urban security planning and management, combined with the construction of a digital and intelligent platform for urban emergency management. This research then analyzes the core technology and equipment support system of urban security management and its practical application. It also presents a new model based on urban security planning and management, followed by examples of its application in some mega infrastructure development for security planning and design (for example, Singapore Changi Airport and Shanghai Hongqiao Airport Transportation Hub). Additionally, a blast protection concept of urban security planning and management is provided.

The aim of this article is to investigate the impediments to creativity perceived by managers, the levels of creativity, its indicators, and personal characteristics conducive to creativity, as well as to elucidate the correlations among them. An experimental study was conducted involving 300 participants. Methods employed include surveying, testing, and mathematical statistical analysis. As the level of creativity increases, participants tend to assess their opportunities more favorably. The expression of creativity depends on the interconnection among the barriers to creativity, indicators of creativity, and personal qualities of creativity. A high level of creativity is manifested when there are fewer barriers and personal qualities such as Imagination and a propensity for Risk-taking. Conversely, the level of expression of creativity is low when there is an interconnection between Creativity and Complexity, Imagination, and creativity barriers such as lack of confidence and conformity to majority opinion.

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.

The cost of diagnostic errors has been high in the developed world economics according to a number of recent studies and continues to rise. Up till now, a common process of performing image diagnostics for a growing number of conditions has been examination by a single human specialist (i.e., single-channel recognition and classification decision system). Such a system has natural limitations of unmitigated error that can be detected only much later in the treatment cycle, as well as resource intensity and poor ability to scale to the rising demand. At the same time Machine Intelligence (ML, AI) systems, specifically those including deep neural network and large visual domain models have made significant progress in the field of general image recognition, in many instances achieving the level of an average human and in a growing number of cases, a human specialist in the effectiveness of image recognition tasks. The objectives of the AI in Medicine (AIM) program were set to leverage the opportunities and advantages of the rapidly evolving Artificial Intelligence technology to achieve real and measurable gains in public healthcare, in quality, access, public confidence and cost efficiency. The proposal for a collaborative AI-human image diagnostics system falls directly into the scope of this program.

The use of plant viruses as bioherbicides represents a fascinating and promising frontier in modern agriculture and weed management. This review article delves into the multifaceted world of harnessing plant viruses for herbicidal purposes, shedding light on their potential as eco-friendly, sustainable alternatives to traditional chemical herbicides. We begin by exploring the diverse mechanisms through which plant viruses can target and control weeds, from altering gene expression to disrupting essential physiological processes. The article highlights the advantages of utilizing plant viruses, such as their specificity for weed species, minimal impact on non-target plants, and a reduced environmental footprint. Furthermore, we investigate the remarkable versatility of plant viruses, showcasing their adaptability to various weed species and agricultural environments. The review delves into the latest advancements in genetic modification techniques, which enable the engineering of plant viruses for enhanced herbicidal properties and safety. In addition to their efficacy, we discuss the economic and ecological advantages of using plant viruses as bioherbicides, emphasizing their potential to reduce chemical herbicide usage and decrease the development of herbicide-resistant weeds. We also address the regulatory and safety considerations associated with the application of plant viruses in agriculture. Ultimately, this review article underscores the immense potential of plant viruses as bioherbicides and calls for further research, development, and responsible deployment to harness these microscopic agents in the ongoing quest for sustainable and environmentally friendly weed management strategies.