期刊,Robotics & Machine Learning Daily News 2024年卷Jun.26期_国家学术搜索

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Robotics & Machine Learning Daily News

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China Agricultural University Reports Findings in Machine Learning (Inversion of soil organic carbon content based on the two-point machine learning method)

48-49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 Machine Learning is th e subject of a report. According to news reporting from Beijing, People's Republ ic of China, by NewsRx journalists, research stated, "Soil organic carbon (SOC) is vital for the global carbon cycle and environmentally sustainable development . Meanwhile, the fast, convenient remote sensing technology has become one of th e notable means to monitor SOC content." The news correspondents obtained a quote from the research from China Agricultur al University, "Nowadays, limitations are found in the inversion of SOC content with high-precision and complex spatial relationships based on scarce ground sam ple points. It is restrained by the spatial difference in the relationship betwe en SOC content and remote sensing spectra due to the problem of different spectr a for the same substance and the influence of topographic and environment (e.g. vegetation and climate). In this regard, the two-point machine learning (TPML) m ethod, which can overcome above problems and deal with complex spatial heterogen eity of relationships between SOC and remote sensing spectra, was used to invert the SOC content in Hailun County, Heilongjiang Province, combined with derived variables from Sentinel-1, Sentinel-2, topography and environment. Based on 10-f old cross-validation and t-test, results indicated that the TPML method boasts t he highest inversion accuracy, followed by random forest, gradient boosting regr ession tree, partial least squares regression and support vector machine. The av erage r, MAE, RMSE, and RPD of TPML were 0.854, 0.384 %, 0.558 % , and 1.918. Further, the TPML method has been proven to be equal to evaluating the uncertainty of inversion results, by comparing the actual and theoretical er ror of the inversion result in one subset. The spatial inversion result of SOC c ontent with 10 m resolution by TPML is smoother and has more real details than o ther models, which are consistent with the distribution of SOC content in differ ent land use types."

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University of Burgos Reports Findings in Robotics (Bio-inspired design of hard-b odied mobile robots based on arthropod morphologies: a 10-year systematic review and bibliometric analysis)

49-50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 Robotics is the subjec t of a report. According to news reporting from Burgos, Spain, by NewsRx journal ists, research stated, "This research presents a 10-year systematic review based on bibliometric analysis of the bio-inspired design of hard-bodied mobile robot mechatronic systems considering the anatomy of arthropods. These are the most d iverse group of animals whose flexible biomechanics and adaptable morphology, th us, it can inspire robot development." The news correspondents obtained a quote from the research from the University o f Burgos, "Papers were reviewed from two international databases (Scopus and Web of Science) and one platform (Aerospace Research Central), then they were class ified according to: year of publication (January 2013 to April 2023), arthropod group, published journal, conference proceedings, editorial publisher, research teams, robot classification according to the name of arthropod, limb's locomotio n support, number of legs/arms, number of legs/body segments, limb's degrees of freedom, mechanical actuation type, modular system, and environment adaptation. During the screening, more than 33000 works were analyzed. Finally, a total of 1 74 studies (90 journal-type, 84 conference-type) were selected for in-depth stud y: Insecta-hexapod (53,8%), Arachnida-octopods (20.7% ), Crustacea-decapods (16,1%), and Myriapoda-centipedes and mil lipedes (9,2%). The study reveals that the most active editorials a re the Institute of Electrical and Electronics Engineers Inc., Springer, MDPI, a nd Elsevier, while the most influential researchers are located in the USA, Chin a, Singapore, and Japan. Most works pertained to spiders, crabs, caterpillars, c ockroaches, and centipedes."

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Polytechnic University Torino Reports Findings in Machine Learning (Near-field m icrowave sensing technology enhanced with machine learning for the non-destructi ve evaluation of packaged food and beverage products)

50-50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 Machine Learning is th e subject of a report. According to news reporting out of Turin, Italy, by NewsR x editors, research stated, "In the food industry, the increasing use of automat ic processes in the production line is contributing to the higher probability of finding contaminants inside food packages. Detecting these contaminants before sending the products to market has become a critical necessity." Financial support for this research came from Franco-Italian University. Our news journalists obtained a quote from the research from Polytechnic Univers ity Torino, "This paper presents a pioneering real-time system for detecting con taminants within food and beverage products by integrating microwave (MW) sensin g technology with machine learning (ML) tools. Considering the prevalence of wat er and oil as primary components in many food and beverage items, the proposed t echnique is applied to both media. The approach involves a thorough examination of the MW sensing system, from selecting appropriate frequency bands to characte rizing the antenna in its near-field region. The process culminates in the colle ction of scattering parameters to create the datasets, followed by classificatio n using the Support Vector Machine (SVM) learning algorithm. Binary and multicla ss classifications are performed on two types of datasets, including those with complex numbers and amplitude data only."

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Research on Robotics Discussed by a Researcher at Lanzhou University (Robot Adop tion And Urban Total Factor Productivity: Evidence From China)

51-51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 ro botics. According to news reporting originating from Lanzhou, People's Republic of China, by NewsRx correspondents, research stated, "Industrial robots are havi ng a profound and lasting impact on China's economy." Our news editors obtained a quote from the research from Lanzhou University: "Th is research examines the deployment of industrial robots and their effects on ur ban total factor production from theoretical and empirical angles. It is created using panel data from 286 cities at the prefecture level between 2003 and 2017. It is found that: First, robot adoption promotes urban total factor productivit y. Second, adopting robots has a more positive influence on urban total factor p roductivity development in western, underdeveloped, and less market-oriented are as compared to the developed and market-oriented areas in the east."

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Study Findings from Northeastern University Provide New Insights into Robotics ( A Three-loop Physical Parameter Identification Method of Robot Manipulators Cons idering Physical Feasibility and Nonlinear Friction Model)

51-52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 Ro botics. According to news reporting originating in Shenyang, People's Republic o f China, by NewsRx journalists, research stated, "This paper proposed a three-lo op physical parameter identification method considering physical feasibility and nonlinear friction model. The full physical parameters can be obtained with phy sical feasibility by constructing an optimization problem."Financial supporters for this research include Liaoning Province Basic Research Program, National Natural Science Foundation of China (NSFC). The news reporters obtained a quote from the research from Northeastern Universi ty, "And the nonlinear friction model which considered Stribeck effect is employ ed to improve identification accuracy. In the first loop, the physical parameter s are identified with a regression model. In the second loop, the nonlinear fric tion model is identified with a nonlinear optimization method. And in the third loop, the obtained friction parameters are updated and the identification result s are to be further optimized. Different from traditional methods like the least squares (LS), weight least squares (WLS) and other optimization methods which c an only get base parameters and do not consider Stribeck effect, the proposed sc heme can get physical parameters with physical constraints. It is useful in many robotic applications, like model-based control. The Stribeck effect is also emp loyed to improve identification accuracy."

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New Findings from San Raffaele Roma Open University Update Understanding of Arti ficial Intelligence (Evaluating Explainable Machine Learning Models for Clinicia ns)

52-53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 Artificial Intelligence. According to news originating from Rome, Italy, by News Rx correspondents, research stated, "Gaining clinicians' trust will unleash the full potential of artificial intelligence (AI) in medicine, and explaining AI de cisions is seen as the way to build trustworthy systems. However, explainable ar tificial intelligence (XAI) methods in medicine often lack a proper evaluation." Financial support for this research came from HORIZON EUROPE Health. Our news journalists obtained a quote from the research from San Raffaele Roma O pen University, "In this paper, we present our evaluation methodology for XAI me thods using forward simulatability. We define the Forward Simulatability Score ( FSS) and analyze its limitations in the context of clinical predictors. Then, we applied FSS to our XAI approach defined over an ML-RO, a machine learning clini cal predictor based on random optimization over a multiple kernel support vector machine (SVM) algorithm.To Compare FSS values before and after the explanation phase, we test our evaluation methodology for XAI methods on three clinical dat asets, namely breast cancer, VTE, and migraine. The ML-RO system is a good model on which to test our XAI evaluation strategy based on the FSS. Indeed, ML-RO ou tperforms two other base models-a decision tree (DT) and a plain SVM-in the thre e datasets and gives the possibility of defining different XAI models: TOPK, MIG F, and F4G. The FSS evaluation score suggests that the explanation method F4G fo r the ML-RO is the most effective in two datasets out of the three tested, and i t shows the limits of the learned model for one dataset. Our study aims to intro duce a standard practice for evaluating XAI methods in medicine."

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Findings on Machine Learning Reported by Investigators at Indian Institute of Te chnology (IIT) Indore [A Comparative Study of Empirical and M achine Learning Approaches for Soil Thickness Mapping In the Joshimath Region (I ndia)]

53-54页

查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-A new study on Machine Learning is now available. According to news originating from Madhya Pradesh, India, by NewsRx correspondents, research stated, "Precisely determining the thickness of soil, w hich is an essential parameter in environmental modelling, presents difficulties when applied to heterogenic large-scale areas. Current prediction models primar ily concentrate on shallow soil depths and lack comprehensive spatial coverage." Financial support for this research came from Department of Science & Technology (India). Our news journalists obtained a quote from the research from the Indian Institut e of Technology (IIT) Indore, "This study addresses this limitation by presentin g the results of soil thickness assessment along three important roads in the Jo shimath region (Indian Himalaya). Three different methods were examined incorpor ating geological and geomorphological data as input to obtain soil thickness map s: (1) a customized version of the conventional geomorphologically indexed soil thickness (GIST) model, modified specifically for the peculiarities of the resea rch area, (2) the GIST model enhanced by Monte Carlo simulations (GIST-MCS), and (3) the random forest (RF) algorithm integrated with the GIST model (GIST-RF). By quantifying their errors and conducting validation using geophysical tests, t he effectiveness of the models was assessed. Moreover, a critical comparison of the results provided useful insights to understand the peculiarities of the test site and how to adapt the site-specific customization of the models to the loca l features. The results indicate that the GIST model inadequately accounted for the substantial spatial variations in soil thickness observed across the study a rea."

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University of Bath Reports Findings in Machine Learning (Assessing the anticholi nergic cognitive burden classification of putative anticholinergic drugs using d rug properties)

54-55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 Machine Learning is th e subject of a report. According to news originating from Bath, United Kingdom, by NewsRx correspondents, research stated, "This study evaluated the use of mach ine learning to leverage drug absorption, distribution, metabolism and excretion (ADME) data together with physicochemical and pharmacological data to develop a novel anticholinergic burden scale and compare its performance to previously pu blished scales. Experimental and in silico ADME, physicochemical and pharmacolog ical data were collected for antimuscarinic activity, blood-brain barrier penetr ation, bioavailability, chemical structure and P-glycoprotein (P-gp) substrate p rofile." Our news journalists obtained a quote from the research from the University of B ath, "These five drug properties were used to train an unsupervised model to ass ign anticholinergic burden scores to drugs. The model performance was evaluated through 10-fold cross-validation and compared with the clinical Anticholinergic Cognitive Burden (ACB) scale and nonclinical Anticholinergic Toxicity Scores (AT S) scale, which is based primarily on muscarinic binding affinity. In silico sof tware (ADMET Predictor) used for screening drugs for their blood-brain barrier ( BBB) penetration correctly identified some drugs that do not cross the BBB. The mean area under the curve for the unsupervised and ACB scale based on the five s elected variables was 0.76 and 0.64, respectively. The unsupervised model agreed with the ACB scale on the classification of more than half of the drugs (49 of 88) agreed on the classification of less than half the drugs in the ATS scale (1 2 of 25). Our findings suggest that the commonly used ACB scale may misclassify certain drugs due to their inability to cross the BBB. By contrast, the ATS scal e would misclassify drugs solely depending on muscarinic binding affinity withou t considering other drug properties."

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Researcher from Obuda University Publishes Findings in Machine Learning (Enhanci ng Mobile Robot Navigation: Optimization of Trajectories through Machine Learnin g Techniques for Improved Path Planning Efficiency)

55-56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 originating fr om Budapest, Hungary, by NewsRx correspondents, research stated, "Efficient navi gation is crucial for intelligent mobile robots in complex environments." The news editors obtained a quote from the research from Obuda University: "This paper introduces an innovative approach that seamlessly integrates advanced mac hine learning techniques to enhance mobile robot communication and path planning efficiency. Our method combines supervised and unsupervised learning, utilizing spline interpolation to generate smooth paths with minimal directional changes. Experimental validation with a differential drive mobile robot demonstrates exc eptional trajectory control efficiency. We also explore Motion Planning Networks (MPNets), a neural planner that processes raw point-cloud data from depth senso rs. Our tests demonstrate MPNet's ability to create optimal paths using the Prob abilistic Roadmap (PRM) method."

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Researchers' from Emory University Report Details of New Studies and Findings in the Area of Machine Learning (Combining state-ofthe- art quantum chemistry and machine learning make gold standard potential energy surfaces accessible for ... )

56-57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 artificial intelligenc e is the subject of a new report. According to news reporting out of Atlanta, Ge orgia, by NewsRx editors, research stated, "Developing full-dimensional machine- learned potentials with the current 'gold-standard' coupled-cluster (CC) level i s challenging for medium-sized molecules due to the high computational cost. Con sequently, researchers are often bound to use lower-level electronic structure m ethods such as density functional theory or second-order Moller-Plesset perturba tion theory (MP2)." Financial supporters for this research include European Research Council; Nation al Research Development And Innovation Office.

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