In this paper, a neural network approach to task assignment, based on a self-organizing map (SOM), is proposed for a multirobot system in dynamic environments subject to uncertainties. It is capable of dynamically controlling a group of mobile robots to achieve multiple tasks at different locations, so that the desired number of robots will arrive at every target location from arbitrary initial locations. In the proposed approach, the robot motion planning is integrated with the task assignment, thus the robots start to move once the overall task is given. The robot navigation can be dynamically adjusted to guarantee that each target location has the desired number of robots, even under uncertainties such as when some robots break down. The proposed approach is capable of dealing with changing environments. The effectiveness and efficiency of the proposed approach are demonstrated by simulation studies.
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Br J Nurs
January 2025
Department of Psychology, Faculty of Arts, University of Calgary, Alberta, Canada; Community Health Sciences, Faculty of Medicine, University of Calgary, Alberta, Canada; Ward of the 21st Century, Cumming School of Medicine, University of Calgary, Alberta, Canada.
Introduction: Peripheral intravenous cannulation (PIVC) is a common and complex procedure with low first-attempt success rates, causing patient suffering and increased healthcare costs. Quiet Eye (QE) training, a gaze-focused approach, has shown promise in improving procedural PIVC skills. We will examine the effectiveness of traditional technical training (TT) and QE training (QET) on student nurse PIVC performance.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Institute of Theoretical & Applied Informatics, Polish Academy of Sciences (IITiS-PAN), 44-100 Gliwice, Poland.
Edge computing systems must offer low latency at low cost and low power consumption for sensors and other applications, including the IoT, smart vehicles, smart homes, and 6G. Thus, substantial research has been conducted to identify optimum task allocation schemes in this context using non-linear optimization, machine learning, and market-based algorithms. Prior work has mainly focused on two methodologies: (i) formulating non-linear optimizations that lead to NP-hard problems, which are processed via heuristics, and (ii) using AI-based formulations, such as reinforcement learning, that are then tested with simulations.
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Internet of Things Engineering, Wuxi University, Wuxi 214105, China.
The task of nucleus segmentation plays an important role in medical image analysis. However, due to the challenge of detecting small targets and complex boundaries in datasets, traditional methods often fail to achieve satisfactory results. Therefore, a novel nucleus segmentation method based on the U-Net architecture is proposed to overcome this issue.
View Article and Find Full Text PDFHealthcare (Basel)
January 2025
Biomechanics, Physical Performance, and Exercise Research Group, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, NSW 2000, Australia.
Background/objectives: Aviation firefighting is a strenuous occupation that requires individuals to engage in intense physical activity amidst elevated stress levels and extreme environmental conditions. Despite this, there has been limited investigation regarding the internal and external loads associated with aviation firefighting tasks, which include hose dragging, stair climbing, casualty evacuation, and fire extinguishing in airports and aircrafts. The aim of this study was to examine the internal and external loads placed on aviation firefighters.
View Article and Find Full Text PDFCognition
January 2025
Department of Psychology, University of Toronto,; Rotman School of Management, University of Toronto.
Efficiency demands that we work smarter and not harder, but is this better for our wellbeing? Here, we ask if exerting effort on a task can increase feelings of meaning and purpose. In six studies (N = 2883), we manipulated how much effort participants exerted on a task and then assessed how meaningful they found those tasks. In Studies 1 and 2, we presented hypothetical scenarios whereby participants imagined themselves (or others) exerting more or less effort on a writing task, and then asked participants how much meaning they believed they (or others) would derive.
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