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    Reports from Northeastern University Describe Recent Advances in Robotics (Obser ver-Based Finite-Time Prescribed Performance Sliding Mode Control of Dual-Motor Joints-Driven Robotic Manipulators with Uncertainties and Disturbances)

    93-94页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Fresh data on robotics are presented i n a new report. According to news reporting from Shenyang, People's Republic of China, by NewsRx journalists, research stated, "Considering system uncertainties (e.g., gear backlash, unmodeled dynamics, nonlinear friction and parameters per turbation) coupling disturbances weaken the motion performance of robotic system s, an observer-based finite-time prescribed performance sliding mode control wit h faster reaching law is proposed for robotic manipulators equipped with dual-mo tor joints (DMJs)." Funders for this research include National Natural Science Foundation of China; Science And Technology Small And Medium Enterprises Innovation Ability Enhanceme nt Project of Shandong Province; Key R&D Plan of Shandong Province.
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    Tianjin First Central Hospital Reports Findings in Bladder Cancer (Robot-assiste d, laparoscopic and open radical cystectomy for bladder cancer: A sys-tematic re view and network meta-analysis)

    94-95页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Oncology-Bladder Can cer is the subject of a report. According to news reporting originating from Tia njin, People's Republic of China, by NewsRx correspondents, research stated, "To evaluate the safety and effectiveness of robot-assisted radical cystectomy (RAR C), laparoscopic radical cystectomy (LRC), and open radical cystectomy (ORC) in bladder cancer. A literature search for network meta-analysis was conducted usin g international databases up to February 29, 2024." Our news editors obtained a quote from the research from Tianjin First Central H ospital, "Outcomes of interest included baseline characteristics, perioperative outcomes and oncological outcomes. Forty articles were finally selected for incl usion in the network meta-analysis. Both LRC and RARC were associated with longe r operative time, smaller amount of estimated blood loss, lower transfusion rate , shorter time to regular diet, fewer incidences of complications, and fewer pos itive surgical margin compared to ORC. LRC had a shorter time to flatus than ORC , while no difference between RARC and ORC was observed. Considering lymph node yield, there were no differences among LRC, RARC and ORC. In addition, there wer e statistically significant lower transfusion rates (OR=-0.15, 95% CI=-0.47 to 0.17), fewer overall complication rates (OR=-0.39, 95% CI=-0.79 to 0.00), fewer minor complication rates (OR=-0.23, 95% C I=-0.48 to 0.02), fewer major complication rates (OR=-0.23, 95% CI =-0.68 to 0.21), fewer positive surgical margin rates (OR=0.22, 95% CI=-0.27 to 0.68) in RARC group compared with LRC group. LRC and RARC could be c onsidered as a feasible and safe alternative to ORC for bladder cancer. Notably, compared with LRC, RARC may benefit from significantly lower transfusion rates, fewer complications and lower positive surgical margin rates."
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    New Machine Learning Study Findings Recently Were Published by Researchers at Un iversity of California San Diego (UCSD) (Optimized Early Prediction of Business Processes with Hyperdimensional Computing)

    95-95页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on ar tificial intelligence. According to news reporting out of La Jolla, California, by NewsRx editors, research stated, "There is a growing interest in the early pr ediction of outcomes in ongoing business processes." Funders for this research include Center For Processing With Intelligent Storage And Memory; Cocosys, Centers in Jump 2.0; Darpa; Nsf. Our news editors obtained a quote from the research from University of Californi a San Diego (UCSD): "Predictive process monitoring distills knowledge from the s equence of event data generated and stored during the execution of processes and trains models on this knowledge to predict outcomes of ongoing processes. Howev er, most state-of-the-art methods require the training of complex and inefficien t machine learning models and hyper-parameter optimization as well as numerous i nput datato achieve high performance. In this paper, we present a novel approac h based on Hyperdimensional Computing (HDC) for predicting the outcome of ongoin g processes before their completion. We highlight its simplicity, efficiency, an d high performance while utilizing only a subset of the input data, which helps in achieving a lower memory demand and faster and more effective corrective meas ures. We evaluate our proposed method on four publicly available datasets with atotal of 12 binary prediction tasks."
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    Investigators at Zhejiang University Describe Findings in Nanofibers (A Carbon N anofiber/ti3c2tx/carboxymethyl Cellulose Compositebased Highly Sensitive, Rever sible, Directionally Controllable Humidity ...)

    96-96页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Data detailed on Nanotechnology-Nanofibers have been presented. According to news originating from Hangzhou, People's Republic of China, by NewsRx correspondents, research stated, "Harnessing water energy so urces presents a compelling avenue for sustainable development. However, the dev elopment of efficient materials capable of converting moisture into mechanical m otion (energy) remains a significant challenge." Funders for this research include National Natural Science Foundation of China ( NSFC), Foundation of Tianjin Key Laboratory of Pulp & Paper (Tianj in University of Science & Technology), P. R. China, Pioneer" and "Leading Goose" R&D Program of Zhejiang, Canada Research Chairs.
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    Sun Yat-Sen University Researchers Describe Findings in Robotics (Robot adoption of family firms: the role of family non-executive directors)

    97-97页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in robotics. According to news reporting from Sun Yat-Sen University by NewsRx jour nalists, research stated, "Industrial robots are of great significance to the lo ng-term development of family firms. Drawing on the lens of the principal-princi pal conflict, this paper aims to investigate the influence of family non-executi ve directors on robot adoption in Chinese family firms." Our news editors obtained a quote from the research from Sun Yat-Sen University: "This paper selects the family firms in China from 2011 to 2019 as the sample. Furthermore, the authors manually collected the family non-executive directors a nd constructed the robot adoption variable utilizing data sourced from the Inter national Federation of Robotics. In brief, this paper constructs a comprehensive framework of the mechanisms and additional tests pertaining to the influence of family non-executive directors on robot adoption. This paper finds that family non-executive directors can promote robot adoption in family firms. The underlyi ng mechanism analysis shows that family non-executive directors promote robot ad option by exerting financial and human effects. This paper further finds that th e characteristics of family non-executive directors, such as kinship, differenti al shareholding and excessive directors, affect the role of family non-executive directors. Finally, robot adoption can improve future performance, and the prom otional effect is more evident when family members are non-executive directors. This paper contributes to the related literature from the following two aspects. Firstly, this paper decomposes the types of family directors to understand the role of family non-executive directors, which challenges the assumption that fam ily board members are homogeneous in family firms."
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    University of Sharjah Researchers Describe Recent Advances in Artificial Intelli gence (Drought prediction using artificial intelligence models based on climate data and soil moisture)

    98-98页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on artificial intell igence are discussed in a new report. According to news reporting out of the Uni versity of Sharjah by NewsRx editors, research stated, "Drought is deemed a majo r natural disaster that can lead to severe economic and social implications. Dro ught indices are utilized worldwide for drought management and monitoring." Our news correspondents obtained a quote from the research from University of Sh arjah: "However, as a result of the inherent complexity of drought phenomena and hydroclimatic condition differences, no universal drought index is available fo r effectively monitoring drought across the world. Therefore, this study aimed t o develop a new meteorological drought index to describe and forecast drought ba sed on various artificial intelligence (AI) models: decision tree (DT), generali zed linear model (GLM), support vector machine, artificial neural network, deep learning, and random forest. A comparative assessment was conducted between the developed AI-based indices and nine conventional drought indices based on their correlations with multiple drought indicators. Historical records of five drough t indicators, namely runoff, along with deep, lower, root, and upper soil moistu re, were utilized to evaluate the models' performance. Different combinations of climatic datasets from Alice Springs, Australia, were utilized to develop and t rain the AI models. The results demonstrated that the rainfall anomaly drought i ndex was the best conventional drought index, scoring the highest correlation (0 .718) with the upper soil moisture. The highest correlation between the new and conventional indices was found between the DT-based index and the rainfall anoma ly index at a value of 0.97, whereas the lowest correlation was 0.57 between the GLM and the Palmer drought severity index. The GLM-based index achieved the bes t performance according to its high correlations with conventional drought indic ators, e.g., a correlation coefficient of 0.78 with the upper soil moisture."
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    Reports from Guizhou Normal University Advance Knowledge in Intelligent Systems (Molecular Subgraph Representation Learning Based On Spatial Structure Transform er)

    99-99页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on Ma chine Learning-Intelligent Systems. According to news reporting originating fr om Guizhou, People's Republic of China, by NewsRx correspondents, research state d, "In the field of molecular biology, graph representation learning is crucial for molecular structure analysis. However, challenges arise in recognising funct ional groups and distinguishing isomers due to a lack of spatial structure infor mation." Funders for this research include The Science and Technology Foundation of Guizh ou Province, Science and Technology Foundation of Guizhou Province, Guizhou Prov incial Key Technology R D Program. Our news editors obtained a quote from the research from Guizhou Normal Universi ty, "To address these problems, we design a novel graph representation learning method based on a spatial structure information extraction Transformer (SSET). T he SSET model comprises the Edge Feature Fusion Subgraph Spatial Structure Extra ctor (ETSE) module and the Positional Information Encoding Graph Transformer (PE GT) module. The ETSE module extracts spatial structural information by fusing ed ge features and generating the most-value subgraph (Mv-subgraph). The PEGT modul e encodes positional information based on the graph transformer, addressing the indistinguishability problem among nodes with identical features. In addition, t he SSET model alleviates the burden of high computational complexity by using su bgraph."
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    Research on Robotics Reported by Researchers at North China University of Techno logy (A Soft Amphibious Voxel-Type Quadruped Robot Based on Origami Flexiball of Rhombic Dodecahedron)

    99-100页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on robotics have been pr esented. According to news reporting from Beijing, People's Republic of China, b y NewsRx journalists, research stated, "The research work presents a novel voxel -type soft amphibious robot based on an assembly of origami flexiballs." Financial supporters for this research include National Key Research And Develop ment Program of China.
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    Data on Arthroplasty Reported by Jobe Shatrov and Colleagues (Robotic assessment of patellatracking in total knee arthroplasty)

    100-101页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Surgery-Arthroplasty is the subject of a report. According to news reporting originating from Sydney , Australia, by NewsRx correspondents, research stated, "Robotic tools have been developed to improve planning, accuracy and outcomes in total knee arthroplasty (TKA). The purpose of this study was to describe and illustrate a novel techniq ue for assessing the patellofemoral (PFJ) in TKA using an imageless robotic plat form." Our news editors obtained a quote from the research, "A consecutive series of 30 R-TKA was undertaken by a single surgeon utilising the described technique. at echnique to dynamically assess the PFJ intraoperatively, pre- and post-implantation was developed. A full set of data from 9 cases was then collected and reViewed for analysis. A series of dynamic PFJ tracks collected intra-operatively pre-and postimplantation are presented. Furthermore, a full assessment of PFJ ove r and under-stuffing through a 90° arc of flexion is illustrated. Finally, a pre-and post-centre of rotation for the PFJ was defined and measured. The describe d technique was defined over a series of 30 R-TKA using the described robotic pl atform. Nine cases were analysed to determine what data could be measured using the robotic platform. Intra-operative real-time data allowed a visual assessment of PFJ tracking through a range of motion of 0°-90° flexion pre- and post-impla ntation. PFJ over and under-stuffing was also assessed intra-operatively through a range of motion of 0°-90° flexion. Post-operative analysis allowed a more det ailed study to be performed, including defining a pre- and post-implantation cen tre of rotation (COR) for the patella. Defining the COR allowed the definition o f a patella plane. Furthermore, patella mediolateral shift in full extension, an d end flexion could be measured. Intra-operative assessment of the PFJ in TKA is challenging. Robotic tools have been developed to improve measurement, accuracy of delivery and outcomes in TKA."
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    Investigators at Xi'an Jiaotong Liverpool University Report Findings in Artifici al Intelligence (Social Identity In Trusting Artificial Intelligence Agents: Evi dence From Lab and Online Experiments)

    101-102页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on Ar tificial Intelligence. According to news reporting from Jiangsu, People's Republ ic of China, by NewsRx journalists, research stated, "This paper explores human trust in artificial intelligence (AI), focusing on the effects of social categor ization (ingroup vs. outgroup) and AI human-likeness through two pre-registered studies involving 160 participants each. The first study, a lab experiment in Ch ina, and the second, an online experiment representative of the United States, b oth utilized atrust game to assess trust across four conditions: ingroup-humano id AI, ingroupnon- humanoid AI, outgroup-humanoid AI, and outgroup-non-humanoid AI." Financial support for this research came from Beijing Municipal Social Science F oundation. The news correspondents obtained a quote from the research from Xi'an Jiaotong L iverpool University, "Results indicated higher trust for ingroup and humanoid AI s, with statistical significance. Mixed-design ANOVA was used to analyze the dat a, revealing significant main effects and interactions."
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