Finite element method (FEM) is widely used in high-temperature superconducting (HTS) magnets, but its computational cost increases with magnet size and becomes time-consuming for meter-scale magnets, especially when multi-physics couplings are considered, which limits the fast design of large-scale REBCO magnet systems. In this work, a surrogate model based on a fully connected residual neural network (FCRN) is developed to predict the space-time current density distribution in REBCO solenoids. Training datasets were generated from FEM simulations with varying numbers of turns and pancakes. The results demonstrate that, for deeper networks, the FCRN architecture achieves better convergence than conventional fully connected network (FCN), with the configuration of 12 residual blocks and 256 neurons per layer providing the most favorable balance between training accuracy and generalization capability. Extrapolation studies show that the model can reliably predict magnetization losses for up to 50% beyond the training range, with maximum errors below 10%. The surrogate model achieves predictions several orders of magnitude faster than FEM and still remains advantageous when training costs are included. These results indicate that the proposed FCRN-based surrogate model provides both accuracy and efficiency, offering a promising tool for the rapid analysis of large-scale HTS magnets.

Inductive qualitative methods have been a mainstay of education research for decades, yet it takes much time and effort to conduct rigorously. Recent advances in artificial intelligence, particularly with generative AI (GAI), have led to initial success in generating inductive coding results. Like human coders, GAI tools rely on instructions to work, and how to instruct it may matter. To understand how ML/GAI approaches could contribute to qualitative coding processes, this study applied two known and two theory-informed novel approaches to an online community dataset and evaluated the resulting coding results. Our findings show significant discrepancies between ML/GAI approaches and demonstrate the advantage of our approaches, which introduce human coding processes into GAI prompts.

Soft robotics has emerged as a promising technology that holds great potential for various application areas. This is due to soft materials unique properties, including flexibility, safety, and shock absorption, among others. Despite many advancement in the field, the development of effective design methodologies and production techniques for soft robots remains a challenge. Although numerous robot prototypes have been proposed in recent years, their designs are often complex and difficult to produce. As such, there is a need for more efficient and unified design approaches that can facilitate the production of soft robots with desirable properties. In this paper, we propose a method for designing soft robots using elastic beams and spatial compliant mechanisms. The method is based on an evolutionary approach that enables the creation of designs with both high motion and force transmission ratios. Specifically, we focus on the development of locomotion mechanisms using a central linear actuator. Our approach involves the use of commonly available plastic materials and a 3D printer to manufacture the designs. We demonstrate the feasibility of our approach by presenting experimental results that show successful production and real world operation. Overall, our findings suggest that the use of elastic beams and an evolutionary approach can facilitate the creation of soft robots with desirable locomotion properties, including fast locomotion up to 3.7 body lengths per second, locomotion with a payload, and underwater locomotion. This method has the potential to enable the development of more efficient and practical soft robots for various applications.

Agricultural irrigation is a significant contributor to freshwater consumption. However, the current irrigation systems used in the field are not efficient. They rely mainly on soil moisture sensors and the experience of growers, but do not account for future soil moisture loss. Predicting soil moisture loss is challenging because it is influenced by numerous factors, including soil texture, weather conditions, and plant characteristics. This paper proposes a solution to improve irrigation efficiency, which is called DRLIC. DRLIC is a sophisticated irrigation system that uses deep reinforcement learning (DRL) to optimize its performance. The system employs a neural network, known as the DRL control agent, which learns an optimal control policy that considers both the current soil moisture measurement and the future soil moisture loss. We introduce an irrigation reward function that enables our control agent to learn from previous experiences. However, there may be instances where the output of our DRL control agent is unsafe, such as irrigating too much or too little water. To avoid damaging the health of the plants, we implement a safety mechanism that employs a soil moisture predictor to estimate the performance of each action. If the predicted outcome is deemed unsafe, we perform a relatively-conservative action instead. To demonstrate the real-world application of our approach, we developed an irrigation system that comprises sprinklers, sensing and control nodes, and a wireless network. We evaluate the performance of DRLIC by deploying it in a testbed consisting of six almond trees. During a 15-day in-field experiment, we compared the water consumption of DRLIC with a widely-used irrigation scheme. Our results indicate that DRLIC outperformed the traditional irrigation method by achieving a water savings of up to 9.52%.

For more than half a century, vibratory bowl feeders have been the standard in automated assembly for singulation, orientation, and manipulation of small parts. Unfortunately, these feeders are expensive, noisy, and highly specialized on a single part design bases. We consider an alternative device and learning control method for singulation, orientation, and manipulation by means of seven fixed-position variable-energy solenoid impulse actuators located beneath a semi-rigid part supporting surface. Using computer vision to provide part pose information, we tested various machine learning (ML) algorithms to generate a control policy that selects the optimal actuator and actuation energy. Our manipulation test object is a 6-sided craps-style die. Using the most suitable ML algorithm, we were able to flip the die to any desired face 30.4\% of the time with a single impulse, and 51.3\% with two chosen impulses, versus a random policy succeeding 5.1\% of the time (that is, a randomly chosen impulse delivered by a randomly chosen solenoid).

This article has been contributed by Lin Ma, Software Engineer and KVM Virtualization Specialist at SUSE. With this article, I would like to introduce you to my SUSE Hackweek 19 project. If you worked on similar projects or topics, or if you would like to exchange experiences, please feel free to reach out to me. As a Do-it-Yourself (DIY) enthusiast, I decided to have some fun during Hackweek with music and machine learning based on SUSE Linux Enterprise Server. Or, if you will, you could also think of my project as an Internet of Things (IoT) attempt based on SUSE products.

Hydraulics are sometimes looked at as an alternative to electric motors. Hydraulic systems use an incompressible liquid (as opposed to pneumatics that use a compressible gas) to transfer force from one place to another. Since the hydraulic system will be a closed system (ignore relief valves for now) when you apply a force to one end of the system that force is transferred to another part of that system. By manipulating the volume of fluid in different parts of the system you can change the forces in different parts of the system (Remember Pascal's Law from high school??). So here are some of the basic components used (or needed) to develop a hydraulic system.

An artist taught Google Assistant how to fire gun, creating an art piece that may further increase concerns on whether artificial intelligence is dangerous. While it may still be very far from the human-killing cyborgs from science fiction movies, Google Assistant firing a gun upon a voice command may already be a very scary thought for opponents of artificial intelligence. Alexander Reben, in a video that he uploaded to YouTube, showed his latest work. "OK Google, activate gun," Reben said, prompting a response of "Sure, turning on the gun" from Google Assistant. Following the voice command issued by Reben, the gun fires at the apple in front of it.

To unlock insights that deliver significant business performance improvement, applying artificial intelligence and deep learning techniques produce profound outcomes that drive asset management improvements. To learn more about how this translates into value at bottom line, get in touch with us via: enquiries@drivingfueliq.com or visit www.drivingfueliq.com

Roboticists need to have a solid understanding of hardware and software. The standard computer science education in the United States, however, tends to teach students only about software. To remedy this situation, we explore new ways of teaching them about hardware in a playful way. Realizing that pinball machines are simple robots, we have developed a pinball machine interface between a PC and a recent Lord of the Rings pinball machine, which enables students to implement pinball games and gain knowledge of hardware and interface programming in the process. This paper describes both our pinball machine interface and our experience developing it. As far as we know, this is the first time that anyone has managed to control an existing pinball machine completely.