首页期刊导航|Robotics & Machine Learning Daily News
期刊信息/Journal information
Robotics & Machine Learning Daily News
NewsRx
Robotics & Machine Learning Daily News

NewsRx

Robotics & Machine Learning Daily News/Journal Robotics & Machine Learning Daily News
正式出版
收录年代

    National Science and Technology Development Agency Reports Findings in Latent Tuberculosis (Determination of latent tuberculosis infection from plasma samples via label-free SERS sensors and machine learning)

    48-48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 Mycobacterium Infections - Latent Tuberculosis is the subject of a report. According to news originating from Pathum Thani, Thailand, by NewsRx correspondents, research stated, “Effective diagnostic tools for screening of latent tuberculosis infection (LTBI) are lacking. We aim to investigate the performance of LTBI diagnostic approaches using label-free surface-enhanced Raman spectroscopy (SERS).” Our news journalists obtained a quote from the research from National Science and Technology Development Agency, “We used 1000 plasma samples from Northeast Thailand. Fifty percent of the samples had tested positive in the interferon-gamma release assay (IGRA) and 50 % negative. The SERS investigations were performed on individually prepared protein specimens using the Raman-mapping technique over a 7 x 7 grid area under measurement conditions that took under 10 min to complete. The machine-learning analysis approaches were optimized for the best diagnostic performance. We found that the SERS sensors provide 81 % accuracy according to train-test split analysis and 75 % for LOOCV analysis from all samples, regardless of the batch-to-batch variation of the sample sets and SERS chip. The accuracy increased to 93 % when the logistic regression model was used to analyze the last three batches of samples, following optimization of the sample collection, SERS chips, and database.”
      原文链接:
    • NETL
    • NSTL

    Southwest Medical University Reports Findings in Liver Cancer (CT radiomics based on different machine learning models for classifying gross tumor volume and normal liver tissue in hepatocellular carcinoma)

    50-51页
    查看更多>>摘要：New research on Oncology - Liver Cancer is the subject of a report. According to news originating from Luzhou, People’s Republic of China, by NewsRx correspondents, research stated, “The present study utilized extracted computed tomography radiomics features to classify the gross tumor volume and normal liver tissue in hepatocellular carcinoma by mainstream machine learning methods, aiming to establish an automatic classification model. We recruited 104 pathologically confirmed hepatocellular carcinoma patients for this study.” Funders for this research include The Open Fund for Scientific Research of Jiangxi Cancer Hospital, The Sichuan Provincial Medical Research Project Plan. Our news journalists obtained a quote from the research from Southwest Medical University, “GTV and normal liver tissue samples were manually segmented into regions of interest and randomly divided into five-fold cross-validation groups. Dimensionality reduction using LASSO regression. Radiomics models were constructed via logistic regression, support vector machine (SVM), random forest, Xgboost, and Adaboost algorithms. The diagnostic efficacy, discrimination, and calibration of algorithms were verified using area under the receiver operating characteristic curve (AUC) analyses and calibration plot comparison. Seven screened radiomics features excelled at distinguishing the gross tumor area. The Xgboost machine learning algorithm had the best discrimination and comprehensive diagnostic performance with an AUC of 0.9975 [95% confidence interval (CI): 0.9973-0.9978] and mean MCC of 0.9369. SVM had the second best discrimination and diagnostic performance with an AUC of 0.9846 (95% CI: 0.9835-0.9857), mean Matthews correlation coefficient (MCC)of 0.9105, and a better calibration. All other algorithms showed an excellent ability to distinguish between gross tumor area and normal liver tissue (mean AUC 0.9825, 0.9861,0.9727,0.9644 for Adaboost, random forest, logistic regression, naivem Bayes algorithm respectively).”
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Research from Henan Polytechnic University Has Provided New Data on Robotics (Design and analysis of a novel variable stiffness joints with robots)

    51-51页
    查看更多>>摘要：Investigators discuss new findings in robotics. According to news originating from Jiaozuo, People’s Republic of China, by NewsRx correspondents, research stated, “This paper presents the design of a symmetric variable stiffness joint that employs worm gear and sliding helical transmissions to adjust the effective length of the leaf springs.” Funders for this research include Doctoral Fund of Henan Polytechnic University; Henan Province Science And Technology Key Project; Open Foundation of The State Key Laboratory of Fluid Power And Mechatronic Systems; Sub Project of Strengthening Key Basic Research Projects in The Basic Plan of The Science And Technology Commission of The Military Commission; Supported By The Outstanding Young Scientists in Beijing.
      原文链接:
    • NETL
    • NSTL
    • Sage

    Beijing Hospital Reports Findings in Stroke (Development and validation of outcome prediction model for reperfusion therapy in acute ischemic stroke using nomogram and machine learning)

    52-52页
    查看更多>>摘要：New research on Cerebrovascular Diseases and Conditions - Stroke is the subject of a report. According to news reporting from Beijing, People’s Republic of China, by NewsRx journalists, research stated, “To develop logistic regression nomogram and machine learning (ML)-based models to predict 3-month unfavorable functional outcome for acute ischemic stroke (AIS) patients undergoing reperfusion therapy. Patients undergoing reperfusion therapy (intravenous thrombolysis and/or endovascular treatment) were prospectively recruited.” The news correspondents obtained a quote from the research from Beijing Hospital, “Unfavorable outcome was defined as 3-month modified Rankin Scale (mRS) score 3-6. The independent risk factors associated with unfavorable outcome were obtained by regression analysis and included in the prediction model. The performance of nomogram was assessed by the area under the curve (AUC), calibration curve, and decision curve analysis (DCA). ML models were compared with nomogram using AUC; the generalizability of all models was ascertained in an external cohort. A total of 505 patients were enrolled, with 256 in the model construction, and 249 in the external validation. Five variables were identified as prognostic factors: baseline NIHSS, D-dimer level, random blood glucose (RBG), blood urea nitrogen (BUN), and systolic blood pressure (SBP) before reperfusion. The AUC values of nomogram were 0.865, 0.818, and 0.779 in the training set, test set, and external validation, respectively. The calibration curve and DCA indicated appreciable reliability and good net benefits. The best three ML models were extra trees (ET), CatBoost, and random forest (RF) models; all of them showed favorable discrimination in the training cohort, and confirmed in the test and external sets. Baseline NIHSS, D-dimer, RBG, BUN, and SBP before reperfusion were independent predictors for 3-month unfavorable outcome after reperfusion therapy in AIS patients.”
      原文链接:
    • NETL
    • NSTL

    New Artificial Intelligence Data Have Been Reported by Investigators at University of Salamanca (Traffic Optimization Through Waiting Prediction and Evolutive Algorithms)

    53-53页
    查看更多>>摘要：Current study results on Machine Learning - Artificial Intelligence have been published. According to news reporting originating from Salamanca, Spain, by NewsRx correspondents, research stated, “Traffic optimization systems require optimization procedures to optimize traffic light timing settings in order to improve pedestrian and vehicle mobility. Traffic simulators allow obtaining accurate estimates of traffic behavior by applying different timing configurations, but require considerable computational time to perform validation tests.” Funders for this research include Spanish Agencia Estatal de Investigacion. Project Monitoring and tracking systems for the improvement of intelligent mobility and behavior analysis (SiMoMIAC), Spanish Ministry of Universities (FPU Fellowship). Our news editors obtained a quote from the research from the University of Salamanca, “For this reason, this project proposes the development of traffic optimizations based on the estimation of vehicle waiting times through the use of different prediction techniques and the use of this estimation to subsequently apply evolutionary algorithms that allow the optimizations to be carried out. The combination of these two techniques leads to a considerable reduction in calculation time, which makes it possible to apply this system at runtime.”
      原文链接:
    • NETL
    • NSTL
    • Univ Int Rioja-Unir

    New Machine Learning Study Findings Have Been Reported from Korea Institute of Civil Engineering and Building Technology (A Study on Developing a Model for Predicting the Compression Index of the South Coast Clay of Korea Using Statistical ...)

    54-54页
    查看更多>>摘要：A new study on artificial intelligence is now available. According to news originating from the Korea Institute of Civil Engineering and Building Technology by NewsRx correspondents, research stated, “As large cities are continually being developed around coastal areas, structural damage due to the consolidation settlement of soft ground is becoming more of a problem.” Financial supporters for this research include Korea Institute of Civil Engineering And Building Technology. The news correspondents obtained a quote from the research from Korea Institute of Civil Engineering and Building Technology: “Estimating consolidation settlement requires calculating an accurate compressive index through consolidation tests. However, these tests are time-consuming, and there is a risk of the test results becoming compromised while preparing and testing the specimens. Therefore, predicting the compression index based on the results of relatively simple physical property tests enables more reliable and accurate predictions of consolidation settlement by calculating the compression index at multiple points. In this context, this study collected geotechnical data from the soft ground of Korea’s south coast. The collected data were used to construct a dataset for developing a compression index prediction model, and significant influencing factors were identified through Pearson correlation analysis. Simple and multiple linear regression analysis was performed using these factors to derive regression equations, and compression index prediction models were developed by applying machine learning algorithms.”
      原文链接:
    • NETL
    • NSTL
    • Mdpi

    Studies Conducted at University of Rochester on Machine Learning Recently Reported (Africa’s Crustal Architecture Inferred From Probabilistic and Perturbational Inversion of Ambient Noise: Adama)

    55-56页
    查看更多>>摘要：Investigators discuss new findings in Machine Learning. According to news reporting originating in Rochester, New York, by NewsRx journalists, research stated, “Africa’s continental crust hosts a variety of geologic terrains and is crucial for understanding the evolution of its longest-lived cratons. However, few of its seismological models are yet to incorporate the largest continent-wide noise dispersion data sets.” Financial support for this research came from National Science Foundation. The news reporters obtained a quote from the research from the University of Rochester, “Here, we report on new insights into Africa’s crustal architecture obtained using a new data set and model assessment product, ADAMA, which comprises a large ensemble of short-period surface wave dispersion measurements: 5-40 s. We construct a continent-wide model of Africa’s Crust Evaluated with ADAMA’s Rayleigh Phase maps (ACE-ADAMA-RP). Dispersion maps, and uncertainties, are obtained with a probabilistic approach. This model update, and a crustal taxonomy derived from unsupervised machine learning, reveals that the architecture of Africa’s crust can be classified into two main types: primitive (C1: faster velocities with little gradients) and modified (C2-C4: slower velocities in the shallow crust with more pronounced gradients). The Archean shields are ‘primitive,’ showing little variation or secular evolution. The basins, orogens, and continental margins are ‘modified’ and retain imprints of surface deformation. The crustal taxonomy is obtained without a-priori geological information and differs from previous classification schemes. While most of our reported features are robust, probabilistic modeling suggests caution in the quantitative interpretations where illumination is compromised by low-quality measurements, sparse coverage or both. Future extension of our approach to other complementary seismological and geophysical data sets-for example, multimode earthquake dispersion, receiver functions, gravity, and mineral physics, will enable continentwide lithospheric modeling that extends resolution to the upper mantle. The rocks that constitute Africa’s crust record the history of different geological periods. We produce a map, for the entire continent, of how fast shear waves travel within these rocks. We obtain this map from ambient noise surface wave vibrations. The ambient noise surface waves are generated from ocean and atmospheric waves that couple with the solid Earth. There are two types: Rayleigh and Love waves and they travel at different speeds for different wavelengths. This property is called dispersion and it is used to tell how fast the shear wave speeds travel within the subsurface rocks. Constructing the final map from ambient noise surface waves requires the solution of a computational imaging problem. We solve the most challenging computational task with a probabilistic approach-using random sampling-and this enables us to also construct associated error maps. The new maps of Africa’s crust show new features that have important implications for subsurface geology of the continent.”
      原文链接:
    • NETL
    • NSTL
    • Amer Geophysical Union

    New Findings from Brunel University London in the Area of Robotics Published (Robotic path planning using NDT ultrasonic data for autonomous inspection)

    56-57页
    查看更多>>摘要：Current study results on robotics have been published. According to news reporting from Brunel University London by NewsRx journalists, research stated, “Robot deployed ultrasonic inspection for Non-Destructive Testing (NDT) offers several advantages including time efficiency gains, the reducing of repetitive manual workloads for operators and the enabling of inspection of environments hazardous to human health. Due to accuracy requirements, NDT robotic inspection has traditionally used the concept of digital twins for path planning activities.” The news editors obtained a quote from the research from Brunel University London: “Recent development has sought to automate this process through visual feedback using low-cost camera sensors. However, these methods do not take into account the use of NDT data itself as part of the robot path planning process. As a consequence, poor path planning accuracy can result due to the inability of conventional cameras to capture internal defects or geometric features. This paper introduces a novel concept of using the NDT ultrasonic data as part of a robotic path planning feedback loop. Firstly, the robot is manually positioned near the start of a weld, and the ultrasonic data is collected. Next, algorithms are implemented to monitor changes in the weld geometry, to determine the robot’s movement and pose based on real-time monitoring data, and to enable the robot to autonomously scan a weld with a minimum of operators input, path planning or digital twin. This is advantageous to NDT as visual sensors are unable to monitor geometric features within the weld.”
      原文链接:
    • NETL
    • NSTL

    Researcher at University of Vigo Zeroes in on Robotics (Collaborative Behavior for Non-Conventional Custom-Made Robotics: A Cable-Driven Parallel Robot Application)

    57-57页
    查看更多>>摘要：Data detailed on robotics have been presented. According to news reporting from Vigo, Spain, by NewsRx journalists, research stated, “The human-centric approach is a leading trend for future production processes, and collaborative robotics are key to its realization.” The news reporters obtained a quote from the research from University of Vigo: “This article addresses the challenge of designing a new custom-made non-conventional machine or robot involving toolpath control (interpolated axes) with collaborative functionalities but by using “general-purpose standard” safety and motion control technologies. This is conducted on a non-conventional cable-driven parallel robot (CDPR). Safety is assured by safe commands to individual axes, known as safe motion monitoring functionalities, which limit the axis’s speed in the event of human intrusion. At the same time, the robot’s motion controller applies an override to the toolpath speed to accommodate the robot’s path speed to the limitations of the axes.”
      原文链接:
    • NETL
    • NSTL
    • Mdpi

    Studies from University of Chile Provide New Data on Machine Learning (Assembling a high-precision abundance catalogue of solar twins in GALAH for phylogenetic studies)

    58-58页
    查看更多>>摘要：Data detailed on artificial intelligence have been presented. According to news reporting originating from Santiago, Chile, by NewsRx correspondents, research stated, “Stellar chemical abundances have proved themselves a key source of information for understanding the evolution of the Milky Way, and the scale of major stellar surveys such as GALAH have massively increased the amount of chemical data available.” Our news journalists obtained a quote from the research from University of Chile: “However, progress is hampered by the level of precision in chemical abundance data as well as the visualization methods for comparing the multidimensional outputs of chemical evolution models to stellar abundance data. Machine learning methods have greatly improved the former; while the application of tree-building or phylogenetic methods borrowed from biology are beginning to show promise with the latter. Here we analyse a sample of GALAH solar twins to address these issues. We apply The Cannon algorithm to generate a catalogue of about 40,000 solar twins with 14 high precision abundances which we use to perform a phylogenetic analysis on a selection of stars that have two different ranges of eccentricities.”
      原文链接:
    • NETL
    • NSTL