Designing and implementing of the content of the program of educational practice on the basis of competence approach

Учебная практика способствует формированию у обучающихся умений, приобретение первоначального практического опыта и реализуется в рамках профессиональных модулей программы подготовки специалистов среднего звена по основным видам профессиональной деятельности для освоения общих и профессиональных компетенций по специальности, направлена на закрепление теоретических знаний, полученных в процессе обучения по профессиональному модулю с учетом требований ФГОС.Educational practice contributes to the formation of students ' skills, the acquisition of the initial practical experience and it is realized within the framework of the professional modules of the training program of mid-level specialists on the main types of professional activity for the mastering of general and professional competences in the specialty. This article is also aimed at consolidation of the theoretical knowledge gained in the process of professional learning module subject to the requirements of the GEF

Towards Autonomous Firefighting UAVs: Online Planners for Obstacle Avoidance and Payload Delivery

Drone technology is advancing rapidly and represents significant benefits during firefighting operations. This paper presents a novel approach for autonomous firefighting missions for Unmanned Aerial Vehicles (UAVs). The proposed UAV framework consists of a local planner module that finds an obstacle-free path to guide the vehicle toward a target zone. After detecting the target point, the UAV plans an optimal trajectory to perform a precision ballistic launch of an extinguishing ball, exploiting its kinematics. The generated trajectory minimises the overall traversal time and the final state error while respecting UAV dynamic limits. The performance of the proposed system is evaluated both in simulations and real tests with randomly positioned obstacles and target locations. The proposed framework has been employed in the 2022 UAV Competition of the International Conference on Unmanned Aircraft Systems (ICUAS), where it successfully completed the mission in several runs of increasing difficulty, both in simulation and in real scenarios, achieving third place overall. A video attachment to this paper is available on the website https://www.youtube.com/watch?v=_hdxX2xXkVQ

Learning Heuristics for Efficient Environment Exploration Using Graph Neural Networks

The robot exploration problem focuses on maximizing the volumetric map of a previously unknown environment. This is a relevant problem in several applications, such as search and rescue and monitoring, which require autonomous robots to examine the surroundings efficiently. Graph-based planning approaches embed the exploration information into a graph describing the global map while the robot incrementally builds it. Nevertheless, even if graph-based representations are computational and memory-efficient, the exploration decision-making problem complexity increases according to the graph size that grows at each iteration. In this paper, we propose a novel Graph Neural Network (GNN) approach trained with Reinforcement Learning (RL) that solves the decision-making problem for autonomous exploration. The learned policy represents the exploration expansion criterion, solving the decision-making problem efficiently and generalizing to different graph topologies and, consequently, environments. We validate the proposed approach with an aerial robot equipped with a depth camera in a benchmark exploration scenario using a high-performance physics engine for environment rendering. We compare the results against a state-of-the-art planning exploration algorithm, showing that the proposed approach matches its performance in terms of explored mapped volume. Additionally, our approach consistently maintains its performance regardless of the objective function used to explore the environment

An Efficient Object-Oriented Exploration Algorithm for Unmanned Aerial Vehicles

Autonomous exploration of unknown environments usually focuses on maximizing the volumetric exploration of the surroundings. Object-oriented exploration, on the other hand, tries to minimize the time spent on the localization of some given objects of interest. While the former problem equally considers map growths in any free direction, the latter fosters exploration towards objects of interest partially seen and not yet accurately identified. The proposed work relates to a novel algorithm that focuses on an object-oriented exploration of unknown environments for aerial robots, able to generate volumetric representations of surroundings, semantically enhanced by labels for each object of interest. As a case study, this method is applied both in a simulated environment and in real-life experiments on a small aerial platform

Poor fit to the multispecies coalescent is widely detectable in empirical data

Model checking is a critical part of Bayesian data analysis, yet it remains largely unused in systematic studies. Phylogeny estimation has recently moved into an era of increasingly complex models that simultaneously account for multiple evolutionary processes, the statistical fit of these models to the data has rarely been tested. Here we develop a posterior predictive simulation-based model check for a commonly used multispecies coalescent model, implemented in *BEAST, and apply it to 25 published data sets. We show that poor model fit is detectable in the majority of data sets; that this poor fit can mislead phylogenetic estimation; and that in some cases it stems from processes of inherent interest to systematists. We suggest that as systematists scale up to phylogenomic data sets, which will be subject to a heterogeneous array of evolutionary processes, critically evaluating the fit of models to data is an analytical step that can no longer be ignored. [Gene duplication and extinction; gene tree; hybridization; model fit; multispecies coalescent; next-generation sequencing; posterior predictive simulation; species delimitation; species tree.] © The Author(s) 2013

Sequential intravascular ultrasound of the mechanisms of rotational atherectomy and adjunct balloon angioplasty

AbstractObjectives. The purpose of this study was to use sequential intravascular ultrasound imaging before intervention, after rotational atherectomy and after adjunct balloon angioplasty to characterize the mechanisms of lumen enlargement after each.Background. Rotational atherectomy uses a high speed, rotating, diamond-tipped elliptic burr to abrade atherosclerotic plaque to increase lumen size. In vitro studies have shown that high speed rotational atherectomy selectively abrades hard, especially calcified, plaque elements. However, rotational atherectomy procedures usually require adjunct balloon angioplasty.Methods. Forty-eight lesions in 46 patients were treated with rotational atherectomy followed by adjunct balloon angioplasty in 44. Quantitative coronary arteriographic and intravascular ultrasound measurements of the target lesion were made before intervention, after rotational atherectomy and after balloon angioplasty.Results. Before intervention, target lesion external elastic membrane area measured 17.3 ± 5.9 mm2, lumen area measured 1.8 ± 0.9 mm2and plaque plus media area measured 15.7 ± 4.1 mm2. After rotational atherectomy, lumen area increased, plaque plus media area decreased, arc of target lesion calcium decreased and 26% of the target lesions had dissection planes After adjunct balloon angioplasty, external elastic membrane area increased, lumen area increased, plaque plus media area did not change and 77% of the target lesions had dissection planes. Arterial expansion was seen in 80% of lesions. The pattern of dissection plane location, which was predominantly within calcified plaque after rotational atherectomy, became predominantly adjacent to calcified plaque after adjunct balloon angioplasty (p = 0.008).Conclusions. Sequential intravascular ultrasound imaging shows that high speed rotational atherectomy causes lumen enlargement by selective ablation of hard, especially calcific, atherosclerotic plaque with little tissue disruption and rare arterial expansion. Adjunct balloon angioplasty further increased lumen area by a combination of arterial dissection and arterial expansion, especially of compliant, noncalcified plaque elements