期刊,Robotics & Machine Learning Daily News 2024年卷Feb.29期_国家学术搜索

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Robotics & Machine Learning Daily News

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Huazhong University of Science and Technology Reports Findings in Robotics (Stenus-inspired, swift, and agile untethered insect-scale soft propulsors)

77-78页

查看更多>>摘要：New research on Robotics is the subject of a report. According to news reporting originating in Wuhan, People's Republic of China, by NewsRx journalists, research stated, "Mimicking living creatures, soft robots exhibit incomparable adaptability and various attractive new features. However, untethered insect-scale soft robots are often plagued with inferior controllability and low kinetic performance." Financial support for this research came from National Natural Science Foundation of China. The news reporters obtained a quote from the research from the Huazhong University of Science and Technology, "Systematically inspired by the swift swingable abdomen, conducting canals for secretion transport, and body setae of Stenus comma, together with magnetic-induced fast-transformed postures, herein, we present a swift, agile untethered millimetre-scale soft propulsor propelling on water. The demonstrated propulsor, with a body length (BL) of 3.6 mm, achieved a recorded specific speed of 201 BL/s and acceleration of 8,372 BL/s. The comprehensive kinetic performance of this propulsor surpasses those of previous ones at similar scales by several orders. Notably, we discovered momentum-transfer-induced over-biological on-demand braking (deceleration -5,010 BL/s) and elucidated the underlying hydrodynamics."

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Report Summarizes Robotics Study Findings from Hebei University of Technology (Design and Analysis of a Passive Adaptive Wallclimbing Robot On Variable Curvature Ship Facades)

78-79页

查看更多>>摘要：Current study results on Robotics have been published. According to news reporting originating in Tianjin, People's Republic of China, by NewsRx journalists, research stated, "To improve the adaptive motion performance of traditional wall-climbing robots on variable-curvature facades, a crawlertype wall-climbing robot suitable for ship wall features is proposed by utilizing the advantages of passive mechanisms in realizing autonomous robots. The robot consists of two passive adaptive crawler mechanisms and a connecting module." Funders for this research include National Key Research and Devel-opment Project of China, Postgraduate Cultivation Funding Projects in Interdisciplinary Direction of Hebei University of Technology. The news reporters obtained a quote from the research from the Hebei University of Technology, "Each track structure contains multiple permanent magnets that can passively adapt to concave and convex facades of different curvatures. A static failure model is established according to the characteristics of the triangular distributed load, and the minimum adsorption force required for the robot to achieve safe motion is determined. The Halbach Array magnetic circuit design method was used to construct a gap-type permanent magnet adsorption model for lightweight design. The influence of wall thickness and air gap distance on the adsorption force is analyzed by parametric simulation." According to the news reporters, the research concluded: "The prototype platform test shows that the robot can realize adaptive variable curvature motion through passive adjustment of the mobile mechanism attitude and has a certain load capacity."

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NETL
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Elsevier

Data on Anxiety Disorders Reported by Sanjay Bhalchandra Londhe and Colleagues (Analysis of robot-specific operative time and surgical team anxiety level and its effect on alignment during robotassisted TKA)

79-80页

查看更多>>摘要：New research on Mental Health Diseases and Conditions - Anxiety Disorders is the subject of a report. According to news reporting originating in Maharashtra, India, by NewsRx journalists, research stated, "Adapting to robotic-assisted (RA) total knee arthroplasty (TKA) is hindered by the surgeon's fear of extra time. The main purpose of this study was to determine the robot's operative time, and the secondary goals were to assess the surgical team's anxiety, implant location and size, and limb alignment." The news reporters obtained a quote from the research, "From February to April 2022, 40 participants participated in prospective research. The study included primary Cuvis joint active RA-TKA patients for end-stage arthritis, but conversion of unicompartmental knee arthroplasty to TKA, and patients with prior knee surgery were excluded. The active RA-TKA surgical time included surgeon-dependent and surgeon-independent/active robot time. The surgeon's anxiety was measured using the state-trait anxiety inventory (STAI). The implant size/position and limb alignment were checked by post-operative weightbearing lateral, anteroposterior, and full-length scanograms. Operative time specifically related to active RA-TKA was higher in the first 10 cases as against 10-20, 20-30 and 30-40 cases which was observed to lower from cohort 2. A similar trend was observed for the surgical team's anxiety levels which seem to lower from cohort 2 (case 10-20). Cumulative experience of active RA-TKA showed no effect on the precision of implant alignment/ size, limb alignment and complications. The study showed progressive improvement in the surgical anxiety scores and reduction in operating time indicating the proficiency gained by the surgical team." According to the news reporters, the research concluded: "Further no learning curve was involved in achieving the implant positioning and sizing, limb alignment with the absence of complications."

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Reports from University of Western Ontario Describe Recent Advances in Machine Learning (Impacts of Dem Type and Resolution On Deep Learning-based Flood Inundation Mapping)

80-81页

查看更多>>摘要：Research findings on Machine Learning are discussed in a new report. According to news reporting from London, Canada, by NewsRx journalists, research stated, "The increasing availability of hydrological and physiographic spatiotemporal data has boosted machine learning's role in rapid flood mapping. Yet, data scarcity, especially high-resolution DEMs, challenges regions with limited access." Financial support for this research came from National Oceanic Atmospheric Admin (NOAA) - USA. The news correspondents obtained a quote from the research from the University of Western Ontario, "This paper examines how DEM type and resolution affect flood prediction accuracy, utilizing a cutting-edge deep learning (DL) method called 1D convolutional neural network (CNN). It utilizes synthetic hydrographs as training input and water depth data obtained from LISFLOOD-FP, a 2D hydrodynamic model, as target data. This study investigates digital surface models (DSMs) and digital terrain models (DTMs) derived from a 1 m LIDAR-based DTM, with resolutions from 15 to 30 m. The methodology is applied and assessed in a established benchmark, city of Carlisle, UK. The models' performance is then evaluated and compared against an observed flood event using RMSE, Bias, and Fit indices. Leveraging the insights gained from this region, the paper discusses the applicability of the methodology to address the challenges encountered in a data-scarce flood-prone region, exemplified by Pakistan. Results indicated that utilizing a 30 m DTM outperformed a 30 m DSM in terms of flood depth prediction accuracy by about 21% during the flood peak stage, highlighting the superior performance of DTM at lower resolutions. Increasing the resolution of DTM to 15 m resulted in a minimum 50% increase in RMSE and a 20% increase in fit index across all flood stages. The findings emphasize that while a coarser resolution DEM may impact the accuracy of machine learning models, it remains a viable option for rapid flood prediction."

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Springer Nature

Studies from National University Have Provided New Information about Robotics (Multitask Control of Aerial Manipulator Robots With Dynamic Compensation Based On Numerical Methods)

81-82页

查看更多>>摘要：Data detailed on Robotics have been presented. According to news reporting from San Juan, Argentina, by NewsRx journalists, research stated, "This paper presents a control scheme for aerial manipulators which allows to solve different motion problems: end -effector position control, end -effector trajectory tracking control and path -following control. The scheme has two cascaded controllers: i) the first controller is a minimum norm controller based on numerical methods, it solves the three motion control problems just by modifying the controller references." Funders for this research include Deutscher Akademischer Austausch Dienst (DAAD), Universidad de las Fuerzas Armadas ESPE, Ecuador. The news correspondents obtained a quote from the research from National University, "Also, since the aerial manipulator robot is a redundant system, i.e., it has extra degrees of freedom to accomplish the task, it is possible to set other control objectives in a hierarchical order. As a secondary objective of the control it is proposed to maintain a desired configuration for the robotic arm during the task. ii) The second cascade controller is designed to compensate the dynamics of the system which main objective is to drive the velocity errors to zero. The coupled dynamic model of the robotic system (hexarotor and robotic arm) is presented. This model is usually developed as a function of the forces and torques. However, in this work, it is written as a function of reference velocities which are usual references for these vehicles. The proposed control algorithms are given with the corresponding stability and robustness analysis."

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NETL
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Elsevier

Institute of Cancer Research Reports Findings in Myeloma (Curation of myeloma observational study MALIMAR using XNAT: solving the challenges posed by real-world data)

82-83页

查看更多>>摘要：New research on Oncology - Myeloma is the subject of a report. According to news reporting originating in London, United Kingdom, by NewsRx journalists, research stated, "MAchine Learning In MyelomA Response (MALIMAR) is an observational clinical study combining 'real-world' and clinical trial data, both retrospective and prospective. Images were acquired on three MRI scanners over a 10-year window at two institutions, leading to a need for extensive curation." Financial supporters for this research include National Institute for Health and Care Research, NIHR Biomedical Research Centre, Royal Marsden NHS Foundation Trust/Institute of Cancer Research, Cancer Research UK. The news reporters obtained a quote from the research from the Institute of Cancer Research, "Curation involved image aggregation, pseudonymisation, allocation between project phases, data cleaning, upload to an XNAT repository visible from multiple sites, annotation, incorporation of machine learning research outputs and quality assurance using programmatic methods. A total of 796 whole-body MR imaging sessions from 462 subjects were curated. A major change in scan protocol part way through the retrospective window meant that approximately 30% of available imaging sessions had properties that differed significantly from the remainder of the data. Issues were found with a vendor-supplied clinical algorithm for 'composing' whole-body images from multiple imaging stations. Historic weaknesses in a digital video disk (DVD) research archive (already addressed by the mid-2010s) were highlighted by incomplete datasets, some of which could not be completely recovered. The final dataset contained 736 imaging sessions for 432 subjects. Software was written to clean and harmonise data. Implications for the subsequent machine learning activity are considered. MALIMAR exemplifies the vital role that curation plays in machine learning studies that use real-world data. A research repository such as XNAT facilitates day-to-day management, ensures robustness and consistency and enhances the value of the final dataset. The types of process described here will be vital for future large-scale multi-institutional and multi-national imaging projects."

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Springer Nature

Findings from Agency for Science Technology and Research (A*STAR) Yields New Data on Machine Learning (A Statistical Perspective for Predicting the Strength of Metals: Revisiting the Hall-petch Relationship Using Machine Learning)

83-84页

查看更多>>摘要：Current study results on Machine Learning have been published. According to news reporting originating in Singapore, Singapore, by NewsRx journalists, research stated, "The mechanical properties of a material are intimately related to its microstructure. This is particularly important for predicting mechanical behavior of polycrystalline metals, where microstructural variations dictate the expected material strength." Funders for this research include Office of Naval Research, Structural Metal Alloys Program of A*STAR in Singapore, National Science Foundation (NSF), US Army Research Laboratory (ARL), Johns Hopkins University Applied Physics Laboratory's Internal Research & Development (IRD) Program, National Science Foundation (NSF), National Supercomputing Centre of Singapore. The news reporters obtained a quote from the research from Agency for Science Technology and Research (A*STAR), "Until now, the lack of microstructural variability in available datasets precluded the development of robust physics-based theoretical models that account for randomness of microstructures. To address this, we have developed a probabilistic machine learning framework to predict the flow stress as a function of variations in the microstructural features. In this framework, we first generated an extensive database of flow stress for a set of over a million randomly sampled microstructural features, and then applied a combination of mixture models and neural networks on the generated database to quantify the flow stress distribution and the relative importance of microstructural features. The results show excellent agreement with experiments and demonstrate that across a wide range of grain size, the conventional Hall- Petch relationship is statistically valid for correlating the strength to the average grain size and its comparative importance versus other microstructural features."

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NETL
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Elsevier

Southwest Medical University Reports Findings in Type 1 Diabetes (Predicting the role of the human gut microbiome in type 1 diabetes using machine-learning methods)

84-85页

查看更多>>摘要：New research on Nutritional and Metabolic Diseases and Conditions - Type 1 Diabetes is the subject of a report. According to news reporting from Luzhou, People's Republic of China, by NewsRx journalists, research stated, "Gut microbes is a crucial factor in the pathogenesis of type 1 diabetes (T1D). However, it is still unclear which gut microbiota are the key factors affecting T1D and their influence on the development and progression of the disease." Financial supporters for this research include Sichuan Provincial Natural Science Foundation, National Natural Science Foundation of China, Sichuan Science and Technology Program. The news correspondents obtained a quote from the research from Southwest Medical University, "To fill these knowledge gaps, we constructed a model to find biomarker from gut microbiota in patients with T1D. We first identified microbial markers using Linear discriminant analysis Effect Size (LEfSe) and random forest (RF) methods. Furthermore, by constructing co-occurrence networks for gut microbes in T1D, we aimed to reveal all gut microbial interactions as well as major beneficial and pathogenic bacteria in healthy populations and type 1 diabetic patients. Finally, PICRUST2 was used to predict Kyoto Encyclopedia of Genes and Genomes (KEGG) functional pathways and KO gene levels of microbial markers to investigate the biological role. Our study revealed that 21 identified microbial genera are important biomarker for T1D. Their AUC values are 0.962 and 0.745 on discovery set and validation set. Functional analysis showed that 10 microbial genera were significantly positively associated with D-arginine and D-ornithine metabolism, spliceosome in transcription, steroid hormone biosynthesis and glycosaminoglycan degradation. These genera were significantly negatively correlated with steroid biosynthesis, cyanoamino acid metabolism and drug metabolism. The other 11 genera displayed an inverse correlation. In summary, our research identified a comprehensive set of T1D gut biomarkers with universal applicability and have revealed the biological consequences of alterations in gut microbiota and their interplay."

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Oxford Univ Press

New Findings from Xihua University in the Area of Robotics Reported (Adaptive Hierarchical Sliding Mode Control Based On Extended State Observer for Underactuated Robotic System)

85-86页

查看更多>>摘要：A new study on Robotics is now available. According to news originating from Chengdu, People's Republic of China, by NewsRx correspondents, research stated, "In order to stabilize underactuated robotic systems with external disturbances, an adaptive hierarchical sliding mode control strategy based on extended state observer is proposed. The extended state observer is designed to estimate the joint states and lumped disturbance composed of matched and unmatched disturbances." Financial supporters for this research include Natural Science Foundation of Sichuan, National Natural Science Foundation of China (NSFC). Our news journalists obtained a quote from the research from Xihua University, "The underactuated robotic system is divided into two subsystems. For each subsystem, a sub-sliding mode surface is constructed to obtain the first layer sliding mode surface and the second layer sliding mode surface is derived from the first layer sliding mode surface. Then the hierarchical sliding mode controller is designed with the estimated state obtained from the observer to compensate the lumped disturbance and an adaptive law is designed to adjust the switching gain. The stability of the system is proved by Lyapunov theory and the effectiveness of the proposed control strategy is verified by comparative simulations."

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Inst Control Robotics & Systems, Korean Inst Electrical Engineers

Reports from Huaiyin Institute of Technology Describe Recent Advances in Machine Learning (Use of Machine Learning In Predicting Heat Transfer and Entropy Generation In a Flat Plate Solar Collector With Twisted Tape Turbulator and Ferrofluid…)

86-87页

查看更多>>摘要：Data detailed on Machine Learning have been presented. According to news reporting originating from Jiangsu, People's Republic of China, by NewsRx correspondents, research stated, "The flat-plate solar collector is the most common and widely used model of solar collectors. This model of solar collectors is more popular than other solar collectors due to its easy installation and lower cost." Our news editors obtained a quote from the research from the Huaiyin Institute of Technology, "It is possible to further reduce the cost and size of these systems by improving their performance. For this purpose, simultaneous application of external uniform magnetic field (MF), water-Fe3O4 ferrofluid, and twisted tape turbulator along with changing the cross-section of the serpentine absorber tube of a collector is proposed in the present research. The outlet temperature of working fluid, pressure drop of working fluid, the rate of thermal energy transferred to the flowing fluid in the collector along with the entropy production rate in the system due to fluid friction and heat transfer were considered as the performance parameters of the system. The effect of cross-section of absorber tube (circular, square, triangular), Reynolds number (Re = 500, 1000, 1500 and 2000), pitch distance of turbulator (delta = 150 mm, 200 mm and 250 mm), ferrofluid volume fraction (sigma = 0, 1 %, 2 % and 4 %) and magnetic field intensity (0, 600, 900 and 1200 Gauss) on these parameters was investigated numerically using the finite volume method. In the investigations conducted in the absence of MF, collectors equipped with circular and triangular absorber tubes showed the best and worst thermal performance, respectively, while the lowest and highest total entropy production rate was associated with collectors equipped with circular and rectangular tubes, respectively. Moreover, the escalation in sigma from 0 % to 4 % and under the MF effect showed an increase of 0.14 %, 8.62 %, 0.45 %, and 4.55 % in the outlet temperature, pressure drop, useful heat, and thermal entropy generation rate, respectively. While the frictional entropy generation diminished by 8.73 %. In addition, the intensification of MF intensity from 0 G to 1200 G led to enhance the outlet temperature, useful heat, and pressure drop, by almost 0.0014 %, 3.96 %, and 0.013 %, respectively at three different sigma values. While total entropy generation rate reduces by 0.195 % as MF intensity increases from 0 G to 1200G."

原文链接:

NETL
NSTL
Elsevier