Purpose Integrating fleets of mobile service robots into the operating room wing (OR wing) has the potential to help overcome staff shortages and reduce the amount of dull or unhealthy tasks for humans. However, the OR wing has been little studied in this regard and the requirements for realizing this vision have not yet been fully identified. This includes fundamental aspects such as fleet size and composition, which we have now studied comprehensively for the first time. Methods Using simulation, 150 different scenarios with varying fleet compositions, robot speeds and workloads were studied for a setup based on a real-life OR wing. The simulation included battery recharging cycles and queueing due to shared resources. Results For all simulated scenarios we report results regarding total duration of execution, average task response times and fleet utilization. The relationship between these performance measures and global scenario parameters—such as fleet size, fleet composition, robot velocity and the number of operating rooms to be served—is visualized. Conclusion Our simulation-based studies have proven to be a valuable tool for individualized dimensioning of mobile robotic fleets, based on realistic workflows and environmental models. Thereby, important implications for future developments of mobile robots have been identified and a basis of decision-making regarding fleet size, fleet composition, robot capabilities and robot velocities can be provided. Due to costs, space limitations and safety requirements, these aspects must be carefully considered to successfully integrate mobile robotic technology into real-world OR wing environments.
Machine learning (ML) has revolutionized transportation systems, enabling autonomous driving and smart traffic services. Federated learning (FL) overcomes privacy constraints by training ML models in distributed systems, exchanging model parameters instead of raw data. However, the dynamic states of connected vehicles affect the network connection quality and influence the FL performance. To tackle this challenge, we propose a contextual client selection pipeline that uses Vehicle-to-Everything (V2X) messages to select clients based on the predicted communication latency. The pipeline includes: (i) fusing V2X messages, (ii) predicting future traffic topology, (iii) pre-clustering clients based on local data distribution similarity, and (iv) selecting clients with minimal latency for future model aggregation. Experiments show that our pipeline outperforms baselines on various datasets, particularly in noniid settings. I. I Machine learning (ML), a subfield of artificial intelligence, focuses on developing learning algorithms and inference models that enable digital systems to make decisions and predictions in terms of the knowledge learned from data. Over the past years, ML-based approaches exhibited great potential to revolutionize various scientific, engineering, economic, and cultural fields with outstanding technological advancements such as Google AlphaGo and Open AI's Chat-GPT. In the filed of road transportation, ML is possible to empower numerous new applications for realizing Intelligent Transportation System (ITS), e.g., environmental perception, road traffic flow optimization, and trajectory planning, which can significantly enhance the safety and efficiency of transportation systems [1]-[5]. Recently, a new ITS concept referred to as Cooperative Intelligent Transportation System (C-ITS) attacked a lot of interests from both academia and industry [6]. In C-ITS, the cooperation between two or more ITS sub-systems (personal, vehicle, roadside and central) offers better quality This work was supported by the German Federal Ministry for Digital and Transport (BMVI) in the projects "KIVI -KI im Verkehr Ingolstadt" and "5GoIng -5G Innovation Concept Ingolstadt".
X-ray reflectometry allows the determination of the thickness, density and roughness of thin layers on a substrate from several Angstroms to some hundred nanometres. The thickness is determined by simulation with trial-and-error methods after extracting initial values of the layer thicknesses from the result of a classical Fast Fourier Transform (FFT) of the reflectivity data. However, the order information of the layers is lost during classical FFT. The order of the layers has then to be known a priori. In this paper, it will be shown that the order of the layers can be obtained by a sliding-window Fourier transform, the so-called Gabor representation. This joint time-frequency analysis allows the direct determination of the order of the layers and, therefore, the use of a more appropriate starting model for refining simulations. A simulated and a measured example show the interest of this method.
Personality researchers are increasingly interested in the dynamics of personality, that is, the proximal causal mechanisms underlying personality and behavior. Here, we review the Zurich Model of Social Motivation concerning its potential to explain central aspects of personality. It is a cybernetic model that provides a nomothetic structure of the causal relationships among needs for security, arousal, and power, and uses them to explain an individual's approach‐avoidance or “proximity‐distance” behavior. We review core features of the model and extend them by adding features based on recent behavioral and neuroscientific evidence. We close by discussing the model considering contemporary issues in personality science such as the dynamics of personality, five‐factor personality traits and states, and personality growth.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.