We present the AgriCruiser, an open-source over-the-row agricultural robot developed for low-cost deployment and rapid adaptation across diverse crops and row layouts. The chassis provides an adjustable track width of 1.42 m to 1.57 m, along with a ground clearance of 0.94 m. The AgriCruiser achieves compact pivot turns with radii of 0.71 m to 0.79 m, enabling efficient headland maneuvers. The platform is designed for the integration of the other subsystems, and in this study, a precision spraying system was implemented to assess its effectiveness in weed management. In twelve flax plots, a single robotic spray pass reduced total weed populations (pigweed and Venice mallow) by 24- to 42-fold compared to manual weeding in four flax plots, while also causing less crop damage. Mobility experiments conducted on concrete, asphalt, gravel, grass, and both wet and dry soil confirmed reliable traversal consistent with torque sizing. The complete chassis can be constructed from commodity T-slot extrusion with minimal machining, resulting in a bill of materials costing approximately $5,000 - $6,000, which enables replication and customization. The mentioned results demonstrate that low-cost, reconfigurable over-the-row robots can achieve effective weed management with reduced crop damage and labor requirements, while providing a versatile foundation for phenotyping, sensing, and other agriculture applications. Design files and implementation details are released to accelerate research and adoption of modular agricultural robotics.

Abstract--Fault diagnosis prevents train disruptions by ensuring the stability and reliability of their transmission systems. Data-driven fault diagnosis models have several advantages over traditional methods in terms of dealing with non-linearity, adaptability, scalability, and automation. However, existing data-driven models are trained on separate transmission components and only consider single faults due to the limitations of existing datasets. These models will perform worse in scenarios where components operate with each other at the same time, affecting each component's vibration signals. T o address some of these challenges, we propose a frequency domain representation and a 1-dimensional convolutional neural network for compound fault diagnosis and applied it on the PHM Beijing 2024 dataset, which includes 21 sensor channels, 17 single faults, and 42 compound faults from 4 interacting components, that is, motor, gearbox, left axle box, and right axle box. Our proposed model achieved 97.67% and 93.93% accuracies on the test set with 17 single faults and on the test set with 42 compound faults, respectively. Fault diagnosis plays a crucial role in maintaining the stability and reliability of transmission components, helping to prevent disruptions in train operations.

Agentic pipelines present novel challenges and opportunities for human-centered explainability. The HCXAI community is still grappling with how best to make the inner workings of LLMs transparent in actionable ways. Agentic pipelines consist of multiple LLMs working in cooperation with minimal human control. In this research paper, we present early findings from an agentic pipeline implementation of a perceptive task guidance system. Through quantitative and qualitative analysis, we analyze how Chain-of-Thought (CoT) reasoning, a common vehicle for explainability in LLMs, operates within agentic pipelines. We demonstrate that CoT reasoning alone does not lead to better outputs, nor does it offer explainability, as it tends to produce explanations without explainability, in that they do not improve the ability of end users to better understand systems or achieve their goals.

-- In this paper, we explore the use of a soft gripper, specifically a soft inverting-everting toroidal hydrostat, as a prosthetic hand. We present a design of the gripper integrated into a body-powered elbow-driven system and evaluate its performance compared to similar body-powered terminal devices: the Kwawu 3D-printed hand and the Hosmer hook. Our experiments highlight advantages of the Everting hand, such as low required cable tension for operation (1.6 N for Everting, 30.0 N for Kwawu, 28.1 N for Hosmer), limited restriction on the elbow angle range, and secure grasping capability (peak pulling force required to remove an object: 15.8 N for Everting, 6.9 N for Kwawu, 4.0 N for Hosmer). In our pilot user study, six able-bodied participants performed standardized hand dexterity tests. With the Everting hand compared to the Kwawu hand, users transferred more blocks in one minute and completed three tasks (moving small common objects, simulated feeding with a spoon, and moving large empty cans) faster (p 0.05). With the Everting hand compared to the Hosmer hook, users moved large empty cans faster (p 0.05) and achieved similar performance on all other tasks. Overall, user preference leaned toward the Everting hand for its adaptable grip and ease of use, although its abilities could be improved in tasks requiring high precision such as writing with a pen, and in handling heavier objects such as large heavy cans. For individuals with limb difference that affects their hand function, prosthetic hands have the potential to restore their ability to achieve everyday tasks [1], [2].

Abstract--We introduce NeRFCom, a novel communication system designed for end-to-end 3D scene transmission. Comp ared to traditional systems relying on handcrafted NeRF semanti c feature decomposition for compression and well-adaptive c hannel coding for transmission error correction, our NeRFCom empl oys a nonlinear transform and learned probabilistic models, en abling flexible variable-rate joint source-channel coding and effi cient bandwidth allocation aligned with the NeRF semantic featur e's different contribution to the 3D scene synthesis fidelity. E xperi-mental results demonstrate that NeRFCom achieves free-vie w 3D scene efficient transmission while maintaining robustness under adverse channel conditions. Index T erms --Neural radiance field (NeRF), 3D scene transmission, semantic features, nonlinear transform coding. IRTUAL reality (VR) and augmented reality (AR) construct 3D scenes to provide users with immersive experiences [ 1 ]. However, traditional 3D scene synthesis techniques often rely on manual scene modeling, and the complex workflow increases the cost of deploying 3D technologies.

In this paper, we investigate the use of the learned digital back-propagation (LDBP) for equalizing dual-polarization fiber-optic transmission in dispersion-managed (DM) links. LDBP is a deep neural network that optimizes the parameters of DBP using the stochastic gradient descent. We evaluate DBP and LDBP in a simulated WDM dual-polarization fiber transmission system operating at the bitrate of 256 Gbit/s per channel, with a dispersion map designed for a 2016 km link with 15% residual dispersion. Our results show that in single-channel transmission, LDBP achieves an effective signal-to-noise ratio improvement of 6.3 dB and 2.5 dB, respectively, over linear equalization and DBP. In WDM transmission, the corresponding $Q$-factor gains are 1.1 dB and 0.4 dB, respectively. Additionally, we conduct a complexity analysis, which reveals that a frequency-domain implementation of LDBP and DBP is more favorable in terms of complexity than the time-domain implementation. These findings demonstrate the effectiveness of LDBP in mitigating the nonlinear effects in DM fiber-optic transmission systems.

The Utah Bionic Leg is a motorized prosthetic for lower-limb amputees developed by University of Utah mechanical engineering associate professor Tommaso Lenzi and his students in the HGN Lab. Lenzi's Utah Bionic Leg uses motors, processors, and advanced artificial intelligence that all work together to give amputees more power to walk, stand-up, sit-down, and ascend and descend stairs and ramps. The extra power from the prosthesis makes these activities easier and less stressful for amputees, who normally need to over-use their upper body and intact leg to compensate for the lack of assistance from their prescribed prosthetics. The Utah Bionic Leg will help people with amputations, particularly elderly individuals, to walk much longer and attain new levels of mobility. "If you walk faster, it will walk faster for you and give you more energy. Or it can help you cross over obstacles," Lenzi says.

This new advance, published in the journal Science Robotics, could pave the way for smaller, lighter and more effective micro flying robots for environmental monitoring, search and rescue, and deployment in hazardous environments. Until now, typical micro flying robots have used motors, gears and other complex transmission systems to achieve the up-and-down motion of the wings. This has added complexity, weight and undesired dynamic effects. Taking inspiration from bees and other flying insects, researchers from Bristol's Faculty of Engineering, led by Professor of Robotics Jonathan Rossiter, have successfully demonstrated a direct-drive artificial muscle system, called the Liquid-amplified Zipping Actuator (LAZA), that achieves wing motion using no rotating parts or gears. In the paper, the team show how a pair of LAZA-powered flapping wings can provide more power compared with insect muscle of the same weight, enough to fly a robot across a room at 18 body lengths per second.

An insect-inspired flying robot with wings that buzz thanks to a new type of electric'muscle' has been developed by British scientists. The prototype weighs about 0.01lbs (5g), has a wing span of 5.9 inches (15cm) and can fly at 1.6mph. It is hoped that one day the robot will be able to look for survivors in disaster zones such as collapsed buildings, monitor hard-to-reach infrastructure and pollinate crops. Researchers at Bristol University said its wings are so efficient that they actually provide more power than an insect muscle of the same weight. 'It's very challenging to beat nature,' Dr Tim Helps, lead author of the study, told MailOnline.

A small robot with wings like an insect can fly and generate more power than a similarly sized animal in nature. Most flying robots, whether they use wings or propellers, have motors and gears and transmission systems to connect the components, but these can weigh the robot down and fail. Now, Tim Helps at the University of Bristol, UK, and his colleagues have designed a small robot that uses an electric field – and a droplet of oil that increases the strength of the field – to flap the wings directly, avoiding the need for a motor or a transmission system. Helps and his team tested the mechanism for a million wing flaps and found it had a steady power output that was slightly better than that of an insect muscle of the same weight. "I'm always very excited when we can achieve a better-than-nature power density," says Helps. "It's a rare thing because nature does an amazing job."