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    Reports Summarize Machine Learning Study Results from University of California Davis (Thermodynamics of Water and Ice From a Fast and Scalable First-principles Neuroevolution Potential)

    10-10页
    查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news reporting out of Davis, California, by NewsRx editors, research stated, “Machine learning potentials enable molecular dynamics simulations to exceed the size and time scales that can be accessed by first-principles methods like density functional theory, while still maintaining the accuracy of the underlying training data set. However, accurate machine learning potentials come with relatively high computational costs that limit their ability to predict properties requiring extensive sampling, large simulation cells, or long runs to converge.” Funders for this research include National Science Foundation (NSF), National Science Foundation (NSF). Our news journalists obtained a quote from the research from the University of California Davis, “Here, we have developed and tested a neuroevolution-potential model for water trained to hybrid-dispersioncorrected density functional calculations. This model exhibits accuracy and transferability comparable to state-of-the-art machine learning potentials but at a much lower computational cost. As a result, it enabled us to compute well-converged thermodynamic averages and fluctuations. This allowed us to assess the ability of our model to reproduce several thermodynamic properties of water and ice as well as the anomalous behavior of water density, heat capacity, and compressibility.”
      原文链接:
    • NETL
    • NSTL
    • Amer Chemical Soc

    Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology Reports Findings in Rheumatic Diseases and Conditions (Novel multiclass classification machine learning approach for the early-stage classification of ...)

    11-12页
    查看更多>>摘要：New research on Musculoskeletal Diseases and Conditions Rheumatic Diseases and Conditions is the subject of a report. According to news reporting out of Hubei, People’s Republic of China, by NewsRx editors, research stated, “Systemic autoimmune rheumatic diseases (SARDs) encompass a diverse group of complex conditions with overlapping clinical features, making accurate diagnosis challenging. This study aims to develop a multiclass machine learning (ML) model for early-stage SARDs classification using accessible laboratory indicators.” Our news journalists obtained a quote from the research from the Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology, “A total of 925 SARDs patients were included, categorised into SLE, Sjogren’s syndrome (SS) and inflammatory myositis (IM). Clinical characteristics and laboratory markers were collected and nine key indicators, including anti-dsDNA, anti-SS-A60, antiSm/nRNP, antichromatin, anti-dsDNA (indirect immunofluorescence assay), haemoglobin (Hb), platelet, neutrophil percentage and cytoplasmic patterns (AC-19, AC-20), were selected for model building. Various ML algorithms were used to construct a tripartite classification ML model. Patients were divided into two cohorts, cohort 1 was used to construct a tripartite classification model. Among models assessed, the random forest (RF) model demonstrated superior performance in distinguishing SLE, IM and SS (with area under curve=0.953, 0.903 and 0.836; accuracy= 0.892, 0.869 and 0.857; sensitivity= 0.890, 0.868 and 0.795; specificity= 0.910, 0.836 and 0.748; positive predictive value=0.922, 0.727 and 0.663; and negative predictive value= 0.854, 0.915 and 0.879). The RF model excelled in classifying SLE (precision=0.930, recall=0.985, F1 score=0.957). For IM and SS, RF model outcomes were (precision=0.793, 0.950; recall=0.920, 0.679; F1 score=0.852, 0.792). Cohort 2 served as an external validation set, achieving an overall accuracy of 87.3%. Individual classification performances for SLE, SS and IM were excellent, with precision, recall and F1 scores specified. SHAP analysis highlighted significant contributions from antibody profiles. This pioneering multiclass ML model, using basic laboratory indicators, enhances clinical feasibility and demonstrates promising potential for SARDs classification.”
      原文链接:
    • NETL
    • NSTL

    Research Data from Yunnan Minzu University Update Understanding of Machine Translation (Handling syntactic difference in Chinese-Vietnamese neural machine translation)

    11-11页
    查看更多>>摘要：Investigators publish new report on machine translation. According to news reporting from Kunming, People’s Republic of China, by NewsRx journalists, research stated, “As the typical distant language pair, Chinese and Vietnamese vary widely in syntactic structure, which significantly influences the performance of Chinese-Vietnamese machine translation.” The news editors obtained a quote from the research from Yunnan Minzu University: “To address this problem, we present a simple approach with a pre-reordering model for closing syntactic gaps of the Chinese-Vietnamese language pair. Specifically, we first propose an algorithm for recognizing the modifier inverse, one of the most representative syntactic different in Chinese-Vietnamese language pair. Then we pre-train a pre-reordering model based on the former recognition algorithm and incorporate it into the attention-based translation framework for syntactic different reordering.” According to the news editors, the research concluded: “We conduct empirical studies on ChineseVietnamese neural machine translation task, the results show that our approach achieves average improvement of 2.75 BLEU points in translation quality over the baseline model. In addition, the translation fluency can be significantly improved by over 2.44 RIBES points.”
      原文链接:
    • NETL
    • NSTL
    • Ios Press

    Research from Kongu Engineering College in the Area of Pattern Recognition and Artificial Intelligence Published (Deep Residual Network with Pelican Cuckoo Search for Traffic Sign Detection)

    12-13页
    查看更多>>摘要：New study results on pattern recognition and artificial intelligence have been published. According to news reporting originating from Tamil Nadu, India, by NewsRx correspondents, research stated, “The timely and precise discovery of traffic signs is considered an effective part of modeling automated vehicle driving.” The news reporters obtained a quote from the research from Kongu Engineering College: “However, the dimension of traffic signs accounted for a lower ratio of input pictures which elevated the complexity of discovery. Hence, a new model is devised using faster region-based convolution neural network (faster R-CNN) traffic for detecting traffic signs. The Region of Interest (RoI) extraction and Median filter are executed for discarding the superfluous data from the dataset. The method extracted a Pyramid Histogram of Oriented Gradient (PHoG), local vector pattern (LVP), CNN and ResNet features for generating beneficial information. It is used to lessen the loss of contextual data and the data augmentation is further applied for making the training of the model more stable and time-saving. The traffic sign recognition is executed with faster R-CNN wherein the tuning of hyperparameters such as batch normalization rate, epoch and learning rate is determined by the proposed pelican cuckoo search (PCS).” According to the news editors, the research concluded: “The method revealed improved efficacy without presenting additional computational costs in the model. Moreover, the faster R-CNN is termed the finest technique to enhance the discovery of traffic signs. The proposed PCS-based faster R-CNN outperformed with the highest precision 92.7%, specificity of 93.7% and [Formula: see text]-measure of 93.2%.”
      原文链接:
    • NETL
    • NSTL
    • World Scientific

    Research from Southern Medical University in Robotics Provides New Insights (A study on robot force control based on the GMM/GMR algorithm fusing different compensation strategies)

    13-14页
    查看更多>>摘要：A new study on robotics is now available. According to news reporting from Guangzhou, People’s Republic of China, by NewsRx journalists, research stated, “To address traditional impedance control methods’ difficulty with obtaining stable forces during robot-skin contact, a force control based on the Gaussian mixture model/Gaussian mixture regression (GMM/GMR) algorithm fusing different compensation strategies is proposed.” Our news reporters obtained a quote from the research from Southern Medical University: “The contact relationship between a robot end effector and human skin is established through an impedance control model. To allow the robot to adapt to flexible skin environments, reinforcement learning algorithms and a strategy based on the skin mechanics model compensate for the impedance control strategy. Two different environment dynamics models for reinforcement learning that can be trained offline are proposed to quickly obtain reinforcement learning strategies. Three different compensation strategies are fused based on the GMM/GMR algorithm, exploiting the online calculation of physical models and offline strategies of reinforcement learning, which can improve the robustness and versatility of the algorithm when adapting to different skin environments. The experimental results show that the contact force obtained by the robot force control based on the GMM/GMR algorithm fusing different compensation strategies is relatively stable.” According to the news editors, the research concluded: “It has better versatility than impedance control, and the force error is within ±0.2 N.
      原文链接:
    • NETL
    • NSTL
    • Frontiers Media Sa

    Studies from Technion-Israel Institute of Technology in the Area of Robotics Published (Minimal Bio-Inspired Crawling Robots with Motion Control Capabilities)

    14-15页
    查看更多>>摘要：Investigators discuss new findings in robotics. According to news reporting out of Haifa, Israel, by NewsRx editors, research stated, “Nonskeletal animals such as worms achieve locomotion via crawling.” The news editors obtained a quote from the research from Technion-Israel Institute of Technology: “We consider them as an inspiration to design robots that help underline the mechanisms of crawling. In this paper, we aim to identify an approach with the simplest structure and actuators. Our robots consist of cut-and-fold bodies equipped with pneumatically-driven soft actuators. We have developed fabrication techniques for coin-sized robots. Experiments showed that our robots can move up to 4.5 mm/s with straight motion (i.e., 0.1 body lengths per second) and perform cornering and U-turns. We have also studied the friction characteristics of our robots with the ground to develop a multistate model with stick-slip contact conversions.” According to the news editors, the research concluded: “Our theoretical analyses depict comparable results to experiments demonstrating that simple and straightforward techniques can illustrate the crawling mechanism. Considering the minimal robots’ structure, this result is a critical step towards developing miniature crawling robots successfully.”
      原文链接:
    • NETL
    • NSTL
    • Mdpi

    Recent Research from University of California Los Angeles (UCLA) Highlight Findings in Machine Learning (Machine Learning Interpretability of Outer Radiation Belt Enhancement and Depletion Events)

    15-16页
    查看更多>>摘要：Data detailed on Machine Learning have been presented. According to news reporting out of Los Angeles, California, by NewsRx editors, research stated, “We investigate the response of outer radiation belt electron fluxes to different solar wind and geomagnetic indices using an interpretable machine learning method. We reconstruct the electron flux variation during 19 enhancement and 7 depletion events and demonstrate the feature attribution analysis called SHAP (SHapley Additive exPlanations) on the superposed epoch results for the first time.” Financial supporters for this research include National Aeronautics & Space Administration (NASA), NASA CCMC, University of Colorado Boulder under NASA, Defense Advanced Research Projects Agency under Department of the Interior award. Our news journalists obtained a quote from the research from the University of California Los Angeles (UCLA), “We find that the intensity and duration of the substorm sequence following an initial dropout determine the overall enhancement or depletion of electron fluxes, while the solar wind pressure drives the initial dropout in both types of events. Further statistical results from a data set with 71 events confirm this and show a significant correlation between the resulting flux levels and the average AL index, indicating that the observed ‘depletion’ event can be more accurately described as a ‘non-enhancement’ event. Our novel SHAP-Enhanced Superposed Epoch Analysis (SHESEA) method can offer insight in various physical systems. This study examines the responses of relativistic electrons in Earth’s radiation belt to various solar wind and geomagnetic disturbances, identifying key influencing factors. We first adopt an explainable machine learning method to understand the importance of different features during 19 enhancement and 7 depletion events. Our results directly reveal that an increase in solar wind dynamic pressure contributes to a sudden decrease in electron fluxes. Additionally, we find that the strength and duration of subsequent substorms determine whether the electron flux increases or decreases. Guided by the importance of these features as determined by our machine learning model, we carry out a statistical analysis, showing a significant correlation between the flux level and the average AL index. Our method offers advantages over traditional superposed epoch analysis since it directly shows the determining factors.”
      原文链接:
    • NETL
    • NSTL
    • Amer Geophysical Union

    Leeds Beckett University Researchers Describe Research in Robotics (SL-RI: Integration of supervised learning in robots for industry 5.0 automated application monitoring)

    16-17页
    查看更多>>摘要：Investigators publish new report on robotics. According to news reporting from Leeds, United Kingdom, by NewsRx journalists, research stated, “Robotic technology holds a significant role within the realm of smart industries, wherein all functionalities are executed within real-time systems.” The news journalists obtained a quote from the research from Leeds Beckett University: “The verification of robot operations is a crucial aspect in the context of Industry 5.0. To address this requirement, a distinctive design methodology known as SL-RI is proposed. This article aims to establish the significance of incorporating robots in the Industry 5.0 framework through analytical representations. In the context of this industrial monitoring system, the implementation of a supplementary algorithm is essential for effective management, as it enables the robots to acquire knowledge through the analysis and adaptation of restructured commands. The analytical model of robots is designed to accurately monitor the precise position and accelerations of robots, resulting in full-scale representations with minimal error conditions. The uniqueness of the proposed method in robotic monitoring system is related to the application process that is directly applied in Industry 5.0 by using various parametric cases where active movement of robots are monitored with rotational matrix representations.” According to the news reporters, the research concluded: “In this type of representations the significance relies in the way to understand the full movement of robots across various machines and its data handling characteristics that provides low loss and error factors.”
      原文链接:
    • NETL
    • NSTL

    Investigators from China Agricultural University Zero in on Machine Learning (Research On Trimming Path for Forked Carrots Using Contour-based Machine Learning Methods)

    17-18页
    查看更多>>摘要：Current study results on Machine Learning have been published. According to news reporting originating from Beijing, People’s Republic of China, by NewsRx correspondents, research stated, “Forked carrots are often used as animal feed or discarded directly, which affects the economic returns of growers and leads to resource waste and environmental pollution. After trimming, forked carrots become easier to peel and can be further processed.” Financial supporters for this research include the National Key Research and Development Program of China, National Key Research and Development Program of China. Our news editors obtained a quote from the research from China Agricultural University, “However, the lack of relevant studies and equipment hinders the full use of forked carrots, and the identification of fork points and determination of the trimming path are the main challenges in trimming forked carrots with unique and diverse shapes. Therefore, an automatic carrot-trimming path recognition solution based on contour analysis and machine learning was proposed in this study to address the above challenges. Specifically, a cascaded model and a parallel model consisting of Multilayer Perceptron (MLP) and Support Vector Machine (SVM) were constructed to identify fork points, and three trimming path determination methods based on fork points and carrot contours were proposed. The results demonstrated cascaded and parallel models achieved 100% and 92.7% recall rates, respectively, with accuracy rates of 90.4% and 100% and repetition rates of 97.1% and 96.4%. Among the trimming path determination methods, both the dynamic convex hull method and the static convex hull method achieved a convexity of 94.7%, surpassing 93.1% for the slope method. The static convex hull method exhibited the fastest speed in determining the trimming path, taking only 0.0032 s per carat. The parallel model and the static convex hull method could be effectively used for online determination of the trimming path for forked carrots.Practical applicationsTrimming forked carrots enhances usability, reduces resource wastage, and mitigates environmental pollution. Leveraging contour-based machine learning algorithms, we achieved precise fork point recognition with broad applicability. Using fork point and carrot contour data, we determined trimming paths that render carrots convex for mechanical peeling. This approach contributes to advancing sustainable agriculture by optimizing resource utilization. The study proposes an automatic carrot-trimming path recognition solution based on contour analysis and machine learning. First, using machine learning methods, the fork points of forked carrots are identified.”
      原文链接:
    • NETL
    • NSTL
    • Wiley

    Recent Findings from North University of China Highlight Research in Robotics (Study on Dynamic Characteristics of Pipeline Jet Cleaning Robot)

    18-18页
    查看更多>>摘要：Investigators discuss new findings in robotics. According to news originating from North University of China by NewsRx correspondents, research stated, “With the passage of time during pipeline operation, a substantial number of impurities accumulate and adhere to the inner wall of the pipeline.” Our news editors obtained a quote from the research from North University of China: “This deposition hinders the pipeline’s ability to function correctly, thereby posing significant hidden risks to people’s lives and the safety of their property. This article focuses on employing pipeline robots for internal cleaning. It examines the jet cleaning process of the spiral-driven pipeline inspection and cleaning robot, aiming to determine the optimal motion state and cleaning parameters for the device within the pipeline. The findings are verified and analyzed through experiments. It was observed that the cleaning effect is enhanced, with a target surface distance of approximately 12to 13-times the diameter of the nozzle outlet (around 25 mm).” According to the news editors, the research concluded: “In addition, an incident angle of 15° yields favorable cleaning results, with a maximum shear force exerted on the target surface of approximately 0.11 MPa. Ensuring that the pipelines operate reasonably and stably, thus guaranteeing their safe functioning and preventing significant economic and environmental damage, holds immense value.”
      原文链接:
    • NETL
    • NSTL
    • Mdpi