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    Reports from Laurentian University Advance Knowledge in Machine Learning (Development and Application of Feature Engineered Geological Layers for Ranking Magmatic, Volcanogenic, and Orogenic System Components In Archean Greenstone Belts)

    29-30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 Machine Learning. According to news reporting originating from Sudbury, Canada, by NewsRx correspondents, research stated, "Geologically representative feature engineering is a crucial component in geoscientific applications of machine learning. Many commonly applied feature engineering techniques used to produce input variables for machine learning apply geological knowledge to generic data science techniques, which can lead to ambiguity, geological oversimplification, and/or compounding subjective bias." Funders for this research include Mushkegowuk (Cree) , Algonquin, Canada First Research Excellence Fund, Natural Sciences and Engineering Research Council of Canada (NSERC), Laurentian University's Mineral Exploration Research Center, LOOP at the University of Western Australia's Center for Exploration Targeting.
      原文链接:
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    University of Science and Technology of China Reports Findings in Robotics (Magnetic soft microfiberbots for robotic embolization)

    30-31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 subject of a report. According to news reporting from Hefei, People's Republic of China, by NewsRx journalists, research stated, "Cerebral aneurysms and brain tumors are leading life-threatening diseases worldwide. By deliberately occluding the target lesion to reduce the blood supply, embolization has been widely used clinically to treat cerebral aneurysms and brain tumors." The news correspondents obtained a quote from the research from the University of Science and Technology of China, "Conventional embolization is usually performed by threading a catheter through blood vessels to the target lesion, which is often limited by the poor steerability of the catheter in complex neurovascular networks, especially in submillimeter regions. Here, we propose magnetic soft microfiberbots with high steerability, reliable maneuverability, and multimodal shape reconfigurability to perform robotic embolization in submillimeter regions via a remote, untethered, and magnetically controllable manner. Magnetic soft microfiberbots were fabricated by thermal drawing magnetic soft composite into microfibers, followed by magnetizing and molding procedures to endow a helical magnetic polarity. By controlling magnetic fields, magnetic soft microfiberbots exhibit reversible elongated/aggregated shape morphing and helical propulsion in flow conditions, allowing for controllable navigation through complex vasculature and robotic embolization in submillimeter regions. We performed in vitro embolization of aneurysm and tumor in neurovascular phantoms and in vivo embolization of a rabbit femoral artery model under real-time fluoroscopy."
      原文链接:
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    Studies from Swiss Federal Institute of Technology Lausanne Describe New Findings in Machine Learning (Leveraging Large Language Models for Predictive Chemistry)

    31-32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 the subject of a report. According to news reporting originating from Sion, Switzerland, by NewsRx correspondents, research stated, "Machine learning has transformed many fields and has recently found applications in chemistry and materials science. The small datasets commonly found in chemistry sparked the development of sophisticated machine learning approaches that incorporate chemical knowledge for each application and, therefore, require specialized expertise to develop." Funders for this research include Swiss National Science Foundation (SNSF), NCCR Catalysis, Swiss National Science Foundation (SNSF), Grantham Foundation for the Protection of the Environment to RMI's climate tech accelerator programme, Carl-Zeiss Foundation.
      原文链接:
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    Research on Machine Learning Reported by Researchers at University of Nottingham (A Robust Study of High-redshift Galaxies: Unsupervised Machine Learning for Characterizing Morphology with JWST up to z 8)

    32-33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 artificial intelligence. According to news reporting out of Nottingham, United Kingdom, by NewsRx editors, research stated, "Galaxy morphologies provide valuable insights into their formation processes, tracing the spatial distribution of ongoing star formation and encoding signatures of dynamical interactions. While such information has been extensively investigated at low redshift, it is crucial to develop a robust system for characterizing galaxy morphologies at earlier cosmic epochs." Financial supporters for this research include Nasa. Our news correspondents obtained a quote from the research from University of Nottingham: "Relying solely on nomenclature established for low-redshift galaxies risks introducing biases that hinder our understanding of this new regime. In this paper, we employ variational autoencoders to perform feature extraction on galaxies at z >2 using JWST/NIRCam data. Our sample comprises 6869 galaxies at z >2, including 255 galaxies at z >5, which have been detected in both the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey Hubble Space Telescope fields and the Cosmic Evolution Early Release Science Survey done with JWST, ensuring reliable measurements of redshift, mass, and star formation rates. To address potential biases, we eliminate galaxy orientation and background sources prior to encoding the galaxy features, thereby constructing a physically meaningful feature space. We identify 11 distinct morphological classes that exhibit clear separation in various structural parameters, such as the concentration, asymmetry, and smoothness (CAS) metric and M _20 , Sersic indices, specific star formation rates, and axis ratios. We observe a decline in the presence of spheroidal-type galaxies with increasing redshift, indicating the dominance of disk-like galaxies in the early Universe."
      原文链接:
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    New Machine Learning Data Have Been Reported by Researchers at University of Belgrade (Machine Learning for Power Transformer Sfra Based Fault Detection)

    33-33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 Machine Learning. According to news reporting originating from Belgrade, Serbia, by NewsRx correspondents, research stated, "This paper presents machine learning methods for health assessment of power transformer based on sweep frequency response analysis. The paper presents an overview of monitoring and diagnostics based on statistical Sweep Frequency Response Analysis (SFRA) based indicators that are used to evaluate the state of the power transformer." Financial support for this research came from Ministry of Education, Science & Technological Development, Serbia. Our news editors obtained a quote from the research from the University of Belgrade, "Experimental data obtained from power transformers with internal short-circuit faults is used as a database for applying machine learning. Machine learning is implemented to achieve more precise asset management and condition-based maintenance. Unsupervised machine learning was applied through the k-means cluster method for classifying and dividing the examined power transformer state into groups with similar state and probability of failure. Artificial neural network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) as part of supervised machine learning are created in order to detect fault severity in tested power transformers of different lifetime."
      原文链接:
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    New Machine Learning Findings from Virginia Polytechnic Institute and State University (Virginia Tech) Outlined (Formulation of Feature and Label Space Using Modified Delphi In Support of Developing a Machine-learning Algorithm To Automate ...)

    34-35页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Investigators discuss new findings in Machine Learning. According to news reporting originating from Blacksburg, Virginia, by NewsRx correspondents, research stated, "To improve the current manual and iterative nature of clash resolution on construction projects, current research efforts continue to explore and test the utilization of machine-learning algorithms to automate the process. Though current research shows significant accuracy in automating clash resolution, many have failed to provide clear explanation and justification for the selection of their feature and label space." Our news editors obtained a quote from the research from Virginia Polytechnic Institute and State University (Virginia Tech), "Since this is critical in developing an effective and explainable solution in machine learning, it is crucial to address this research gap. In this paper, the authors utilize an in-depth literature review and industry interviews to capture domain knowledge on how design clashes are resolved by industry experts. From analysis of the knowledge captured, we identified 23 factors considered by experts when resolving clashes and five alternative solutions/options to resolve a clash. Using a pool of industry experts, a modified Delphi approach was conducted to validate the factors and options and to determine a priority ranking. The authors identified 94 industry experts based on a predetermined qualification matrix to take part in the modified Delphi. Twelve participants responded and took part in the first round, and 11 completed the second round. A consensus was reached on all clash factors and resolution options. Factors including ‘clashing elements type,' ‘constrained slope,' ‘critical element in the clash,' ‘location of the clash,' ‘code compliance,' and ‘project stage clashing element is in' were ranked as the most important factors, while ‘clashing element material' and ‘insulation type' were considered the least important. Participants also showed more preference to the ‘moving the clashing element with low priority in/along x-y-z directions' option to resolve clashes."
      原文链接:
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    Recent Findings from North Carolina State University (NC State) Provides New Insights into Robotics (Automatic and Real-time Joint Tracking and Three-dimensional Scanning for a Construction Welding Robot)

    35-35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 subject of a report. According to news reporting from Raleigh, North Carolina, by NewsRx journalists, research stated, "Although welding is one of the essential steel fabrication processes, the American Welding Society expects that the labor shortage in the United States will reach a deficit of 360,000 welders by 2027. Developing an automatic robotic welding system could potentially alleviate the labor shortage and provide better welding quality." Financial support for this research came from National Science Foundation (NSF). The news correspondents obtained a quote from the research from North Carolina State University (NC State), "As a first step, this paper designs a system pipeline that can automatically detect different welding joints and plan and track the joints' trajectory with the initial point alignment in real time. There are rare studies that could achieve automatic initial point alignment in real time because the laser stripe's deformation is not obvious at the narrow weld. In this study, the target joint's endpoints were detected once the joint was detected on live video. Then, the joint trajectory was planned, and the robotic arm automatically aligned with the initial point and tracked the planned trajectory while scanning."
      原文链接:
    • NETL
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    Chengdu University Reports Findings in Machine Learning (Prediction of non-suicidal self-injury in adolescents at the family level using regression methods and machine learning)

    36-36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 the subject of a report. According to news originating from Chengdu, People's Republic of China, by NewsRx correspondents, research stated, "Adolescent non-suicidal self-injury (NSSI) is a major public health issue. Family factors are significantly associated with NSSI in adolescents, while studies on forecasting NSSI at the family level are still limited." Our news journalists obtained a quote from the research from Chengdu University, "In addition to regression methods, machine learning (ML) techniques have been recommended to improve the accuracy of family-level risk prediction for NSSI. Using a dataset of 7967 students and their primary caregivers from a cross-sectional study, logistic regression model and random forest model were used to test the forecasting accuracy of NSSI predictions at the family level. Cross-validation was used to assess model prediction performance, including the area under the receiver operator curve (AUC), precision, Brier score, accuracy, sensitivity, specificity, positive predictive value and negative predictive value. The top three important family-related predictors within the random forest algorithm included family function (importance: 42.66), family conflict (importance:42.18), and parental depression (importance:27.21). The most significant family-related risk predictors and protective predictors identified by the logistic regression model were family history of mental illness (OR:2.25) and help-seeking behaviors of mental distress from parents (OR:0.65), respectively. The AUCs of the two models, logistic regression and random forest, were 0.852 and 0.835, respectively. The key limitation is that this cross-sectional survey only enabled the authors to examine predictors that were considered to be proximal rather than distal. These findings highlight the significance of family-related factors in forecasting NSSI in adolescents."
      原文链接:
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    Reports from University of Rostock Add New Data to Research in Robotics (Ambient Monitoring Portable Sensor Node for Robot-Based Applications)

    37-37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 robotics are discussed in a new report. According to news reporting originating from Rostock, Germany, by NewsRx correspondents, research stated, "The leakage of gases and chemical vapors is a common accident in laboratory processes that requires a rapid response to avoid harmful effects if humans and instruments are exposed to this leakage." Funders for this research include European Research Council. The news correspondents obtained a quote from the research from University of Rostock: "In this paper, the performance of a portable sensor node designed for integration with mobile and stationary robots used to transport chemical samples in automated laboratories was tested and evaluated. The sensor node has four main layers for executing several functions, such as power management, control and data preprocessing, sensing gases and environmental parameters, and communication and data transmission. The responses of three metal oxide semiconductor sensors, BME680, ENS160, and SGP41, integrated into the sensing layer have been recorded for various volumes of selected chemicals and volatile organic compounds, including ammonia, pentane, tetrahydrofuran, butanol, phenol, xylene, benzene, ethanol, methanol, acetone, toluene, and isopropanol. For mobile applications, the sensor node was attached to a sample holder on a mobile robot (ASTI ProBOT L)."
      原文链接:
    • NETL
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    Researchers from University of Technology Detail Findings in Machine Learning (Reservoir Porosity Assessment and Anomaly Identification From Seismic Attributes Using Gaussian Process Machine Learning)

    38-38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 the subject of a report. According to news reporting out of Perak, Malaysia, by NewsRx editors, research stated, "Porosity, as one of the reservoir properties, is an important parameter to numerous studies, i.e., the reservoir's oil/gas volume estimation or even the storage capacity measurement in the Carbon Capture Storage (CCS) project. However, an approach to estimate porosity using elastic property from the inversion propagates its error, affecting the result's accuracy." Funders for this research include UTP fundamental research grant, PETRONAS Malaysia, Centre for Subsurface Imaging and Geoscience department Universiti Teknologi PETRONAS. Our news journalists obtained a quote from the research from the University of Technology, "On the other hand, direct estimation from seismic data is another approach to estimating porosity, but it poses a high non-linear problem. Thus, we propose the non-parametric machine learning approach, Gaussian Process (GP), which draws distribution over the function to solve the high non-linear problem between seismic data with porosity and quantify the prediction uncertainty simultaneously. With the help of Random Forest (RF) as the feature selection method, the GP predictions show excellent results in the blind test, a well that is completely removed from the training data, and comparison with other machine learning models. The uncertainty, standard deviation from GP prediction, can act as a quantitative evaluation of the prediction result. Moreover, we generate a new attribute based on the quartile of the standard deviation to delineate the anomaly zones. High anomaly zones are highlighted and associated with high porosity from GP and low inverted P-impedance from inversion results."
      原文链接:
    • NETL
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