An Amphibious Fully-Soft Miniature Crawling Robot Powered by Electrohydraulic Fluid Kinetic Energy

Miniature locomotion robots with the ability to navigate confined environments show great promise for a wide range of tasks, including search and rescue operations. Soft miniature locomotion robots, as a burgeoning field, have attracted significant research interest due to their exceptional terrain adaptability and safety features. In this paper, we introduce a fully-soft miniature crawling robot directly powered by fluid kinetic energy generated by an electrohydraulic actuator. Through optimization of the operating voltage and design parameters, the crawling velocity of the robot is dramatically enhanced, reaching 16 mm/s. The optimized robot weighs 6.3 g and measures 5 cm in length, 5 cm in width, and 6 mm in height. By combining two robots in parallel, the robot can achieve a turning rate of approximately 3 degrees/s. Additionally, by reconfiguring the distribution of electrodes in the electrohydraulic actuator, the robot can achieve 2 degrees-of-freedom translational motion, improving its maneuverability in narrow spaces. Finally, we demonstrate the use of a soft water-proof skin for underwater locomotion and actuation. In comparison with other soft miniature crawling robots, our robot with full softness can achieve relatively high crawling velocity as well as increased robustness and recovery

OpenGSL: A Comprehensive Benchmark for Graph Structure Learning

Graph Neural Networks (GNNs) have emerged as the de facto standard for representation learning on graphs, owing to their ability to effectively integrate graph topology and node attributes. However, the inherent suboptimal nature of node connections, resulting from the complex and contingent formation process of graphs, presents significant challenges in modeling them effectively. To tackle this issue, Graph Structure Learning (GSL), a family of data-centric learning approaches, has garnered substantial attention in recent years. The core concept behind GSL is to jointly optimize the graph structure and the corresponding GNN models. Despite the proposal of numerous GSL methods, the progress in this field remains unclear due to inconsistent experimental protocols, including variations in datasets, data processing techniques, and splitting strategies. In this paper, we introduce OpenGSL, the first comprehensive benchmark for GSL, aimed at addressing this gap. OpenGSL enables a fair comparison among state-of-the-art GSL methods by evaluating them across various popular datasets using uniform data processing and splitting strategies. Through extensive experiments, we observe that existing GSL methods do not consistently outperform vanilla GNN counterparts. However, we do observe that the learned graph structure demonstrates a strong generalization ability across different GNN backbones, despite its high computational and space requirements. We hope that our open-sourced library will facilitate rapid and equitable evaluation and inspire further innovative research in the field of GSL. The code of the benchmark can be found in https://github.com/OpenGSL/OpenGSL.Comment: 9 pages, 4 figure

Motion Kinematics Analysis of a Horse Inspired Terrain-Adaptive Unmanned Vehicle With Four Hydraulic Swing Arms

All terrain vehicles (ATV) perform tasks in unstructured environments where the advanced adaptive ability of rigid terrain has become a key factor. In this article, we propose a novel horse inspired all terrain eight-wheeled vehicle with four swing arms for transportation in the mountain battlefield. The mechanism structure and system configuration of the ATV are designed based on the horse leg kinematics analysis. In order to analyze the obstacle surmounting strategy of the ATV, the kinematics model and the center of gravity of the ATV are represented. A model reference adaptive control method is proposed for the hydraulic attitude control system. Then the model for obstacle surmounting is proposed for dynamics performance and geometric kinematics. Additionally, the simulation is executed in Adams to verify the analysis and strategy. Finally, the experiment is demonstrated for climbing a vertical wall, which is a challenging and typical terrain of the mountain battlefield

A Muscle Teleoperation System of a Robotic Rollator Based on Bilateral Shared Control

The approach that achieves the teleoperation between human muscle signals and the mobile robot is increasingly applied to transfer human muscle stiffness to enhance robotic performance. In this paper, we develop a mobile rollator control system applying a muscle teleoperation interface and a shared control method to enhance the obstacle avoidance in an effective way. In order to control intuitively, haptic feedback is utilized in the teleoperation interface and is integrated with EMG stiffness to provide a large composition force. Then the composition force is implemented with an artificial potential field method to keep the robotic rollator away from the obstacle in advance. This algorithm is superior to the traditional APF algorithm regardless of the required time and trajectory length. The experimental results demonstrate the effectiveness of the proposed muscle teleoperation system

Application of Angiotensin Receptor–Neprilysin Inhibitor in Chronic Kidney Disease Patients: Chinese Expert Consensus

Chronic kidney disease (CKD) is a global public health problem, and cardiovascular disease is the most common cause of death in patients with CKD. The incidence and prevalence of cardiovascular events during the early stages of CKD increases significantly with a decline in renal function. More than 50% of dialysis patients die from cardiovascular disease, including coronary heart disease, heart failure, arrhythmia, and sudden cardiac death. Therefore, developing effective methods to control risk factors and improve prognosis is the primary focus during the diagnosis and treatment of CKD. For example, the SPRINT study demonstrated that CKD drugs are effective in reducing cardiovascular and cerebrovascular events by controlling blood pressure. Uncontrolled blood pressure not only increases the risk of these events but also accelerates the progression of CKD. A co-crystal complex of sacubitril, which is a neprilysin inhibitor, and valsartan, which is an angiotensin receptor blockade, has the potential to be widely used against CKD. Sacubitril inhibits neprilysin, which further reduces the degradation of natriuretic peptides and enhances the beneficial effects of the natriuretic peptide system. In contrast, valsartan alone can block the angiotensin II-1 (AT1) receptor and therefore inhibit the renin–angiotensin–aldosterone system. These two components can act synergistically to relax blood vessels, prevent and reverse cardiovascular remodeling, and promote natriuresis. Recent studies have repeatedly confirmed that the first and so far the only angiotensin receptor–neprilysin inhibitor (ARNI) sacubitril/valsartan can reduce blood pressure more effectively than renin–angiotensin system inhibitors and improve the prognosis of heart failure in patients with CKD. Here, we propose clinical recommendations based on an expert consensus to guide ARNI-based therapeutics and reduce the occurrence of cardiovascular events in patients with CKD

RAVA: Region-Based Average Video Quality Assessment

Video has become the most popular medium of communication over the past decade, with nearly 90 percent of the bandwidth on the Internet being used for video transmission. Thus, evaluating the quality of an acquired or compressed video has become increasingly important. The goal of video quality assessment (VQA) is to measure the quality of a video clip as perceived by a human observer. Since manually rating every video clip to evaluate quality is infeasible, researchers have attempted to develop various quantitative metrics that estimate the perceptual quality of video. In this paper, we propose a new region-based average video quality assessment (RAVA) technique extending image quality assessment (IQA) metrics. In our experiments, we extend two full-reference (FR) image quality metrics to measure the feasibility of the proposed RAVA technique. Results on three different datasets show that our RAVA method is practical in predicting objective video scores

A novel snow transport model for analytically investigating effects of wind exposure on flat roof snow load due to saltation

This paper introduces a novel snow transport model for the analytical simulation of wind-induced snow transport on flat roofs. In the novel model, the differences between precipitation and deposited snow particles on roofs, including the differences in threshold friction velocity and fetch distance for saturated drifting state, are considered. Thus, snow drifting on a roof is divided into drifting without and during snowfall. The effects of terrain category and roof height are also considered using the friction velocity on roofs to compute snow transport rate instead of directly using the wind speed data reported by weather stations. Parametric analysis and case study are conducted to explain the novel model in detail and compare the results with those obtained from the model developed by O'Rourke et al. (2005). The estimated fetch distance for saturated snow drifting by the proposed approach in this study ranges from zero to thousands of meters, which is consistent with field measurement results and more reasonable than the use of constant values. Results of the case study indicate that the proposed snow transport model might be more reasonable because the effects of more factors are considered