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Health-care education based upon technology enabled mannequins (high-fidelity simulators) is a costly investment for colleges and universities. However, a hybrid model using wearable technology integrated with human actors (standardized patients) may present a cost-effective alternative to high fidelity simulation training scenarios. A systematic literature review of papers published from 1960 to 2019 illustrates that hybrid simulation can be as effective as high fidelity simulators in certain training scenarios while at the same time providing a superior training context to enhance learners patient to care-giver interactions and to better immerse the trainee in the feelings and emotion of the scenario.

Minimizing and understanding errors is critical for quantum science, both in noisy intermediate scale quantum (NISQ) devices 1 and for the quest towards fault-tolerant quantum computation 2 , 3 . Rydberg arrays have emerged as a prominent platform in this context 4 with impressive system sizes 5 , 6 and proposals suggesting how error-correction thresholds could be significantly improved by detecting leakage errors with single-atom resolution 7 , 8 , a form of erasure error conversion 9 – 12 . However, two-qubit entanglement fidelities in Rydberg atom arrays 13 , 14 have lagged behind competitors 15 , 16 and this type of erasure conversion is yet to be realized for matter-based qubits in general. Here we demonstrate both erasure conversion and high-fidelity Bell state generation using a Rydberg quantum simulator 5 , 6 , 17 , 18 . When excising data with erasure errors observed via fast imaging of alkaline-earth atoms 19 – 22 , we achieve a Bell state fidelity of ≥ 0.997 1 − 13 + 10 , which improves to ≥ 0.998 5 − 12 + 7 when correcting for remaining state-preparation errors. We further apply erasure conversion in a quantum simulation experiment for quasi-adiabatic preparation of long-range order across a quantum phase transition, and reveal the otherwise hidden impact of these errors on the simulation outcome. Our work demonstrates the capability for Rydberg-based entanglement to reach fidelities in the 0.999 regime, with higher fidelities a question of technical improvements, and shows how erasure conversion can be utilized in NISQ devices. These techniques could be translated directly to quantum-error-correction codes with the addition of long-lived qubits 7 , 22 – 24 . Erasure conversion and detection are used in a Rydberg quantum simulator to create Bell states with high fidelity, competitive with other state-of-the-art platforms.

We describe Global Atmosphere 7.0 and Global Land 7.0 (GA7.0/GL7.0), the latest science configurations of the Met Office Unified Model (UM) and the Joint UK Land Environment Simulator (JULES) land surface model developed for use across weather and climate timescales. GA7.0 and GL7.0 include incremental developments and targeted improvements that, between them, address four critical errors identified in previous configurations: excessive precipitation biases over India, warm and moist biases in the tropical tropopause layer (TTL), a source of energy non-conservation in the advection scheme and excessive surface radiation biases over the Southern Ocean. They also include two new parametrisations, namely the UK Chemistry and Aerosol (UKCA) GLOMAP-mode (Global Model of Aerosol Processes) aerosol scheme and the JULES multi-layer snow scheme, which improve the fidelity of the simulation and were required for inclusion in the Global Atmosphere/Global Land configurations ahead of the 6th Coupled Model Intercomparison Project (CMIP6).In addition, we describe the GA7.1 branch configuration, which reduces an overly negative anthropogenic aerosol effective radiative forcing (ERF) in GA7.0 whilst maintaining the quality of simulations of the present-day climate. GA7.1/GL7.0 will form the physical atmosphere/land component in the HadGEM3–GC3.1 and UKESM1 climate model submissions to the CMIP6.

Successful release of any air to ground weapon from a combat aircraft is determined based on the positional parameters received from the sensors and the mission cues. Laser designated pod is one of the most sought weapon sensor, which gives the accurate data for Air to Ground weapon aiming. Laser designated pod being hardware intensive system, works with real world environment, it increases the development and integration effort towards finalising the weapon aiming algorithms and also pilot vehicle interface requirements. A novel method using mathematical models and the atmospheric error models is proposed to develop a high fidelity laser designated pod simulation model for functional and performance evaluation of weapon algorithms. The factors affecting the weapon trajectory computations are also considered in the sensor model outputs. The sensor model is integrated in the high fidelity flight simulator, which consists of both aircraft and Real world systems either as actual or simulated for close loop pilot evaluation. The behaviour of the sensor model is cross validated and fine-tuned with the actual sensor output and confirmed that the developed laser designated pod sensor simulation model meets all the requirement to test the air to ground weapons in the flight simulator.

This study aims to provide framework for considering fidelity in the design of simulator training. Simulator fidelity is often characterised as the level of physical and visual similarity with real work settings, and the importance of simulator fidelity in the creation of learning activities has been extensively debated. Based on a selected literature review and fieldwork on ship simulator training, this study provides a conceptual framework for fidelity requirements in simulator training. This framework is applied to an empirical example from a case of ship simulator training. The study identifies three types of simulator fidelity that might be useful from a trainer’s perspective. By introducing a framework of technical , psychological and interactional fidelity and linking these concepts to different levels of training and targeted learning outcomes, the study demonstrates how the fidelity of the simulation relates to the level of expertise targeted in training. The framework adds to the body of knowledge on simulator training by providing guidelines for the different ways in which simulators can increase professional expertise, without separating the learning activity from cooperative work performance.

The agriculture industry is in need of substantially increasing crop yield to meet growing global demand. Selective breeding programs can accelerate crop improvement but collecting phenotyping data is time- and labor-intensive because of the size of the research fields and the frequency of the work required. Automation could be a promising tool to address this phenotyping bottleneck. This paper presents a Robotic Operating System (ROS)-based mobile field robot that simultaneously navigates through occluded crop rows and performs various phenotyping tasks, such as measuring plant volume and canopy height using a 2D LiDAR in a nodding configuration. The efficacy of the proposed 2D LiDAR configuration for phenotyping is assessed in a high-fidelity simulated agricultural environment in the Gazebo simulator with an ROS-based control framework and compared with standard LiDAR configurations used in agriculture. Using the proposed nodding LiDAR configuration, a strategy for navigation through occluded crop rows is presented. The proposed LiDAR configuration achieved an estimation error of 6.6% and 4% for plot volume and canopy height, respectively, which was comparable to the commonly used LiDAR configurations. The hybrid strategy with GPS waypoint following and LiDAR-based navigation was used to navigate the robot through an agricultural crop field successfully with an root mean squared error of 0.0778 m which was 0.2% of the total traveled distance. The presented robot simulation framework in ROS and optimized LiDAR configuration helped to expedite the development of the agricultural robots, which ultimately will aid in overcoming the phenotyping bottleneck.

The emerging field of soft robotics presents a new paradigm for robot design in which “precision through rigidity” is replaced by “cognition through compliance.” Lightweight and flexible, soft robots have vast potential to interact with fragile objects and navigate unstructured environments. Like octopuses and worms in nature, soft robots’ flexible bodies conform to hard objects and reconfigure for different tasks, delegating the burden of control from brain to body through embodied cognition. However, because of the lack of efficient modeling and simulation tools, soft robots are primarily designed by hand. Typically, hard components from rigid robots or living creatures are heuristically substituted for comparable soft ones. Autonomous design and manufacturing methodologies are urgently required to produce bespoke, high‐performing robots. Currently, design methodologies exist between simple but realistic parametric optimizations, and evolutionary algorithms which simulate morphology and control coevolution. To find high‐performing designs, novel high‐fidelity simulators and high‐throughput manufacturing and testing processes are required to explore the complex soft material, morphology and control landscape, blending simulation, and experimental data. This article reviews the state of the art in autonomous soft robotic design. Existing manual and automated designs are surveyed and future directions to automate soft robot design and manufacturing are presented. By using soft and functional materials to deform around objects and adapt to new environments, soft robotics has the potential to revolutionize material handling and terrain navigation. But in the absence of accurate modeling tools, they are still laboriously designed manually. This article reviews progress toward autonomous modeling, simulation, and design.

A common approach to perform wake steering control in wind farms, involves using pre-calibrated, low-fidelity wake models. Due to their low computation time, these models can be efficiently used to precalculate the yaw angles that maximize the total wind farm power across a wide range of wind conditions. This “open-loop” control strategy, however, is highly dependent on the accuracy of the model. Any errors in the model can lead to suboptimal power output. To overcome this issue, a “closed-loop” control method can be used, which leverages available measurements to adjust certain model parameters, ensuring a closer alignment with the actual operation of the wind farm. To implement this closed-loop approach, a new control-oriented wake model is presented, designed to be adapted in real time using measurements from the wind farm. These measurements are assumed to come from nacelle-mounted multi-range LiDAR sensors, which provide partial wind field data. The entire closed-loop control scheme is validated using the medium-fidelity dynamic simulator FAST.Farm. Several wind condition scenarios are tested, all showing a significant increase in the total power output of the wind farm.

This article presents key insights from the New Faculty Highlights talk given at AAAI 2023, focusing on the crucial role of fidelity simulators in the safety evaluation of learning‐enabled components (LECs) within safety‐critical systems. With the rising integration of LECs in safety‐critical systems, the imperative for rigorous safety and reliability verification has intensified. Safety assurance goes beyond mere compliance, forming a foundational element in the deployment of LECs to reduce risks and ensure robust operation. In this evolving field, simulations have become an indispensable tool, and fidelity's role as a critical parameter is increasingly recognized. By employing multifidelity simulations that balance the needs for accuracy and computational efficiency, new paths toward comprehensive safety validation are emerging. This article delves into our recent research, emphasizing the role of simulation fidelity in the validation of LECs in safety‐critical systems.

This paper addresses the problem of Multi-robot Coverage Path Planning for unknown environments in the presence of robot failures. Unexpected robot failures can seriously degrade the performance of a robot team and in extreme cases jeopardize the overall operation. Therefore, this paper presents a distributed algorithm, called Cooperative Autonomy for Resilience and Efficiency, which not only provides resilience to the robot team against failures of individual robots, but also improves the overall efficiency of operation via event-driven replanning. The algorithm uses distributed Discrete Event Supervisors, which trigger games between a set of feasible players in the event of a robot failure or idling, to make collaborative decisions for task reallocations. The game-theoretic structure is built using Potential Games, where the utility of each player is aligned with a shared objective function for all players. The algorithm has been validated in various complex scenarios on a high-fidelity robotic simulator, and the results demonstrate that the team achieves complete coverage under failures, reduced coverage time, and faster target discovery as compared to three alternative methods.