As part of developing simulation-based training for better collaboration between healthcare services, we developed instruments for measuring key teamwork constructs role understanding (responsibility), trust, communic...
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ISBN:
(纸本)9783031610653;9783031610660
As part of developing simulation-based training for better collaboration between healthcare services, we developed instruments for measuring key teamwork constructs role understanding (responsibility), trust, communication and collaboration as experienced during simulation-based training in virtual reality. We co-designed these instruments together with healthcare workers and healthcare students in three workshops and a survey. We followed a method for generating unidimensional Thurstone scales with equal-appearing intervals. We then used the instruments in training sessions with healthcare students. We gathered feedback on the simulation and conducted initial analyses on the instrument data. Results are encouraging for the simulation design, but with clear points for improvement. The preliminary analyses from the instruments indicate that they seem to measure the intended constructs as perceived by the training audience. Correlational analyses indicate relationships between these constructs, particularly highlighting the challenge of balancing responsibility with trust and collaboration. Findings advocate the potential in our approach to mirror realistically, and improve, collaborative practices among healthcare professionals.
ATC training simulation system is widely used in controller training. The track display module is an important part of ATC training simulation system. The low-cost simulation system based on microcomputer has the char...
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The increasing use of fine spatial and temporal data in hydrological modeling has resulted in a prohibitively computational demand and run time. The serial computing method adopted by most modeling routines has largel...
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Bionic mechanical hands are playing an increasingly important role in the intelligent equipment industry. However, at this stage, the degrees of freedom of bionic mechanical hands are increasing, and their structures ...
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The research is the modeling and simulation of fermentation with the objective of eliminating the mucilage (20%) which is the gelatinous layer that covers the coffee cherry seed or freshly harvested coffee beans for w...
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The research is the modeling and simulation of fermentation with the objective of eliminating the mucilage (20%) which is the gelatinous layer that covers the coffee cherry seed or freshly harvested coffee beans for which we use fuzzy logic in LabVIEW. Since Industry 4.0 allows virtual and real communication of a technological process. Today coffee is produced by more than 80 countries in the world, and the quality of a good coffee in terms of organoleptics and bouquet depends on how its fermentation is carried out to remove the mucilage that can affect its quality mentioned above. This work was carried out at the Professional School of Agroindustrial Engineering of the National University of Moquegua Peru. It was carried out using the LabVIEW block diagram panel, in "Fuzzy system designer", inserting input variables in this case content of soluble solids and concentrations of hydrogen ions contained in the mucilage and obtaining as the output response the time in minutes of fermentation, then proceed to create the rules of the system and then the corresponding tests. On the front panel, through virtual instruments, each of the variables of the fermentation process are included. As a result, a fermentation control program has been obtained for the removal of coffee mucilage using input variables and a response as an output variable based on fuzzy logic. This research work would allow solving time problems in fermentation in order to remove the mucilage at the best time from the beans in order to obtain quality coffee. (c) 2023 The Authors. Published by ELSEVIER B.V.
Addressing the issue of insufficient realism and real-Time property of the existing volume cloud simulation, a multi-noise rendering method for simulation optimization is proposed. First of all, in terms of cloud mode...
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The advent of autonomous systems has deeply impacted a myriad of industrial processes. While these systems promise unparalleled gains in productivity and efficiency, their coexistence with human operators presents dis...
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ISBN:
(纸本)9783031713965;9783031713972
The advent of autonomous systems has deeply impacted a myriad of industrial processes. While these systems promise unparalleled gains in productivity and efficiency, their coexistence with human operators presents distinct challenges. Consider, for instance, autonomous vehicles in industrial zones. These vehicles, decked with an array of sensors, hold tremendous potential for enhancing operations. However, seamlessly integrating and optimizing sensors, such as LIDAR and video cameras with machine vision, is not without its complexities. This piece underscores the indispensable role of simulation in the iterative development and assessment of sensor amalgamations for these autonomous vehicles. Particularly in industrial landscapes, marked by pervasive dust, noise, and the omnipresence of human operators, ensuring safe navigation becomes paramount. simulation stands out as the linchpin in this endeavor, facilitating the recreation of authentic environments. Such virtual settings allow for exhaustive evaluations of how these sensor combinations fare across various challenging conditions. A pivotal element in this matrix is sensor fusion, essential for detecting obstacles. With the boon of simulations, this fusion undergoes rigorous validation and fine-tuning, thereby amplifying the system's overall efficacy. By leveraging the might of simulation, developers are poised to systematically fine-tune sensor amalgamations. This not only propels the evolution of autonomous vehicles in industrial contexts but also charts the course for their safer and more reliable integration. This paper champions a dual strategy: Intensive testing in synthetic simulation environments combined with real-world validations to pave the way for the birth of superior, trustworthy autonomous systems that can coexist seamlessly and safely within our industrial landscapes.
The importance of supply chain management is paramount nowadays and this research seeks ways for better supply chain coordination. We present a roadmap for implementing true digital replicas of physical supply chains ...
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The importance of supply chain management is paramount nowadays and this research seeks ways for better supply chain coordination. We present a roadmap for implementing true digital replicas of physical supply chains based on a disruptive simulationmodeling approach. These digital replicas can enable the comparative evaluation of existing Just-In-Time (JIT) control policies for the first time without the unrealistic assumptions of existing studies in the literature. These realistic supply chain simulation models have the prospect of giving us better understanding of their behavior and of their complex dynamics. We provide guidelines for using the digital replicas and advanced optimization approaches to derive novel supply chain coordination policies pushing the field of JIT control beyond the state-of-the-art. An equally important contribution of this research is that it advances the field of hybrid discrete event-system dynamics (DES-SD) simulation with the prospect of pushing Systems Engineering to the next level. This research discusses theoretical foundations for mixing DES with SD simulation, and explores technical aspects and implementation details. The implications of this extent well beyond the supply chain field since both DES and SD have dozens of application areas including manufacturing, service systems and economics, social sciences, respectively. The paper is concluded with a discussion on the potential impact of the proposed approach including insights for practitioners, applicability of scientific results in industry and prerequisites for business process re-engineering. (c) 2024 The Authors. Published by Elsevier B.V.
modeling the behavior of complex agents is challenging. In social systems, agents have different motivating factors and goals that drive each decision. In some situations, though, we can observe the agent behavior and...
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ISBN:
(纸本)9783031171147;9783031171130
modeling the behavior of complex agents is challenging. In social systems, agents have different motivating factors and goals that drive each decision. In some situations, though, we can observe the agent behavior and the outcomes while being at a loss on how to quantify the decision-making process. Imitation learning is a powerful tool to learn behavior without understanding reasoning. In this paper, we explore modern machine learning techniques to train models that imitate agents' behavior in social environments. This work continues and builds off of emerging work that merges social simulation and modern machine learning. We have shown that such surrogate models can learn heuristicallydriven agent behavior. We note that, however, these models do show fragility to changes in environmental dynamics.
Nowadays, most function signal generators use DDS (Direct Digital Synthesis) technology, and the design of function signal generators is based on the principle of DDS technology. The traditional method is to use DDS s...
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