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    Reports Summarize Machine Learning Study Results from Planetary Science Institute (A Machine Learning Classification of Meteorite Spectra Applied To Understanding Asteroids)

    95-96页
    查看更多>>摘要：Research findings on Machine Learning are discussed in a new report. According to news reporting from Tucson, Arizona, by NewsRx journalists, research stated, “Understanding the distribution of matter within our Solar System requires a robust methodology for evaluating the composition of small objects in the asteroid belt. Existing asteroid taxonomies have variously been based on spectral features relating to mineralogy and on classification of asteroid spectra alone.” Financial supporters for this research include Massachusetts Space Grant Consortium, via NASA, National Science Foundation (NSF), National Aeronautics & Space Administration (NASA). The news correspondents obtained a quote from the research from Planetary Science Institute, “This project tests a fundamentally different approach, using machine learning algorithms to classify asteroids based on spectroscopic characteristics of existing meteorite classes. After evaluating four classification techniques built on labeled meteorite spectral data, logistic regression (LR) was determined to provide the most accurate results that distinguish eight robust groups of meteorite classes to which asteroid spectra can then be matched. The groups are rooted in mineralogical composition and directly relate meteorites to potential host bodies.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Studies from Silesian University of Technology Reveal New Findings on Machine Learning (Visual Programming Simulator for Producing Realistic Labeled Point Clouds From Digital Infrastructure Models)

    96-97页
    查看更多>>摘要：Current study results on Machine Learning have been published. According to news reporting originating from Gliwice, Poland, by NewsRx correspondents, research stated, “The increasing availability of point clouds has led to intensive research into automating point cloud processing using machine learning. While supervised systems require large and diverse labeled datasets, the cost and time of manual data creation can be overcome with synthetic data.” Funders for this research include European Social Fund (ESF), Silesian University of Technology Excellence Initiative grant. Our news editors obtained a quote from the research from the Silesian University of Technology, “This paper introduces DynamoPCSim, a versatile scanning simulator based on visual programming, implementing ray tracing, and operating on BIM models. The simulator collects measurements of digital models and transfers the model semantic data to generated point clouds, enabling automated labeling. Customizable scanning parameters allow for the reflection of real scanners (including imperfections) and the transformation of synthetic point clouds, making the data more realistic.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    New Robotics Findings from Shandong University Described (A Robot Motion Position and Posture Control Method for Freeform Surface Laser Treatment Based On Nurbs Interpolation)

    97-98页
    查看更多>>摘要：A new study on Robotics is now available. According to news originating from Jinan, People’s Republic of China, by NewsRx correspondents, research stated, “Laser surface treatment, such as laser cladding, laser quenching and laser cleaning, for the freeform surface can dramatically enhance the properties of the working surface. During the laser surface treatment, the posture of the treatment tool to the surface influences the size and shape of the laser spot, which further influences the quality of the laser surface treatment.” Funders for this research include National Natural Science Foundation of China (NSFC), Taishan Scholarship Special Funding. Our news journalists obtained a quote from the research from Shandong University, “Thus, accurate posture and high synchronization between position and posture are crucial. Industrial robots have the irreplaceable advantages of high flexibility, large workspace and cost-effectiveness over the 5-axis machine tools, therefore, they have become more and more popular in freeform surface laser treatment. However, the research on industrial robot motion mainly concentrates on position control, while less attention has focused on posture control and synchronization of position and posture control. In this paper, a robot motion position and posture (P&P) control method based on the non-uniform rational B-spline (NURBS) interpolation is proposed to realize the high-order continuity and synchronization of P&P as well as the self-adaptive motion control. Firstly, simultaneously considering the position and posture, a novel path generation method based on 6-dimensional NURBS is proposed to provide smooth, continuous and collisionfree P&P coordinates of the designed scanning path. Next, a self-adaptive path segmentation method is introduced to recognize the corners and divide the whole treatment path into safe and dangerous segments. Based on the kinematical characteristics of the industrial robot, the joint motion constraint is especially considered in addition to the chord error limit and the centripetal acceleration constraint, and the selfadaptive motion parameters are obtained to match with safe and dangerous segments to avoid mechanical shock and ensure the accessibility of joint servos. Moreover, a synchronized look-ahead method with a dynamically refreshed window is employed to obtain the optimal node speed between adjacent segments and relieve the heavy computational burden, after which a series of smooth, accurate and synchronized motion instructions for the freeform surface laser treatment is generated in real time. Finally, simulations and experiments of a NURBS scanning path for a turbine blade laser surface cladding are conducted.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Xi’an Jiaotong University Researcher Provides New Insights into Robotics (The Enhanced Adaptive Grasping of a Soft Robotic Gripper Using Rigid Supports)

    98-99页
    查看更多>>摘要：Investigators publish new report on robotics. According to news originating from Xi’an Jiaotong University by NewsRx correspondents, research stated, “Soft pneumatic grippers can grasp soft or irregularly shaped objects, indicating potential applications in industry, agriculture, and healthcare.” Funders for this research include National Natural Science Foundation of China; Key R&D Program of Shaanxi, China. The news correspondents obtained a quote from the research from Xi’an Jiaotong University: “However, soft grippers rarely carry heavy and dense objects due to the intrinsic low modulus of soft materials in nature. This paper designed a soft robotic gripper with rigid supports to enhance lifting force by 150 ± 20% in comparison with that of the same gripper without supports, which successfully lifted a metallic wrench (672 g).” According to the news reporters, the research concluded: “The soft gripper also achieves excellent adaptivity for irregularly shaped objects. The design, fabrication, and performance of soft grippers with rigid supports are discussed in this paper.”
      原文链接:
    • NETL
    • NSTL

    Findings in the Area of Machine Learning Reported from National Technological Institute of Mexico (Multi-objective and Machine Learning Strategies for Addressing the Water-energy-waste Nexus In the Design of Energy Systems)

    99-99页
    查看更多>>摘要：Current study results on Machine Learning have been published. According to news originating from Guanajuato, Mexico, by NewsRx correspondents, research stated, “This paper presents a multi-objective strategy coupled with fuzzy C-means to address synergies and conflicts around the waterenergy- waste nexus. The proposal deals with an optimal design and operation scheme in a multi-objective framework where the objective functions are linked to the nexus.” Funders for this research include Chemical Engineering Department of the TecNM-Instituto Tecnologico de Celaya, Consejo Nacional de Humanidades, Ciencias y Tecnologias (CONAHCyT). Our news journalists obtained a quote from the research from the National Technological Institute of Mexico, “The objective functions are normalized to obtain subsets of functions that are used to assess the performance of the nexus and compute trade-off optimal solutions. The soft clustering algorithm is used to determine levels of synergy among Pareto optimal solutions. The proposed strategy has the potential to address problems with many-objective functions, in which the Pareto front has representation limitations, allowing for the identification of conflicts. The soft clustering algorithm indicates different levels of synergy among the Pareto optimal solutions based on the level of membership. The focus of the analysis is on distributed generation systems. As a demonstration, the coupling of a combined heat and power unit with a biodigester and a thermal storage system, interconnected to the local grid.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Study Data from Minjiang University Update Understanding of Machine Learning (Recent Advances In the Production Processes of Hydrothermal Liquefaction Biocrude and Aid-in Investigation Techniques)

    100-101页
    查看更多>>摘要：Current study results on Machine Learning have been published. According to news reporting originating in Fuzhou, People’s Republic of China, by NewsRx journalists, research stated, “This review provides an overview of recent advances in hydrothermal liquefaction (HTL) biocrude production processes using plastics as feedstock, seawater as the processing medium, and microwave irradiation as a process intensification method. Additionally, the review examines the application of aid-in investigation tools such as kinetics, machine learning, and feasibility analysis to HTL research.” Financial supporters for this research include Natural Science Foundation of Fujian Province, Fashu Research Foundation, Seed Industry Innovation and Industrializa- tion Project of Fujian Province, Minjiang University. The news reporters obtained a quote from the research from Minjiang University, “All these aspects have been underexplored in review literature compared to process optimization, biocrude upgrading, continuous HTL, and aqueous phase reutilization. The potential of HTL as an effective method for the depolymerization of plastics is initially evaluated. The ease of plastic depolymerization follows the order of polycarbonate (300 degrees C) ＞polystyrene (350 degrees C) ＞polyethylene = polypropylene (420 degrees C) ＞polyethylene terephthalate (＞450 degrees C). Both synergism and antagonism are observed for co-HTL of plastics with biomass, ranging from-48.3% to 79.2%. Using seawater as an alternative HTL processing medium shows promising potential, while the effect of sea salts on biocrude yield/ quality is still controversial especially when carbohydrate-rich feedstocks are utilized, necessitating more comprehensive examination. Microwave irradiation has been shown to increase biocrude yield from lipid, produce comparable yields from protein and lignin, and decrease yield from carbohydrate compared to conventional heating. As for the aid-in investigation tools, limited efforts have been made to apply kinetic modeling to the HTL of plastics, which could be particularly useful when synergism or antagonism is observed during coHTL of plastics and biomass. Machine learning-enabled predictions of product yield and quality have been found to be more accurate than traditional mathematical models. Future research could focus on using machine learning algorithms to elucidate product formation mechanisms. The techno-economic and life cycle assessment reveal that the commercialization of HTL technology remains a distant prospect, further improvements in product yield, quality, and process energy efficiency are essential.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Chinese Academy of Sciences Reports Findings in Temporal Lobe Epilepsy (Automated detection of MRI-negative temporal lobe epilepsy with ROI-based morphometric features and machine learning)

    101-102页
    查看更多>>摘要：New research on Central Nervous System Diseases and Conditions - Temporal Lobe Epilepsy is the subject of a report. According to news reporting out of Suzhou, People’s Republic of China, by NewsRx editors, research stated, “Temporal lobe epilepsy (TLE) predominantly originates from the anteromedial basal region of the temporal lobe, and its prognosis is generally favorable following surgical intervention. However, TLE often appears negative in magnetic resonance imaging (MRI), making it difficult to quantitatively diagnose the condition solely based on clinical symptoms.” Our news journalists obtained a quote from the research from the Chinese Academy of Sciences, “There is a pressing need for a quantitative, automated method for detecting TLE. This study employed MRI scans and clinical data from 51 retrospective epilepsy cases, dividing them into two groups: 34 patients in TLE group and 17 patients in non-TLE group. The criteria for defining the TLE group were successful surgical removal of the epileptogenic zone in the temporal lobe and a favorable postoperative prognosis. A standard procedure was used for normalization, brain extraction, tissue segmentation, regional brain partitioning, and cortical reconstruction of T1 structural MRI images. Morphometric features such as gray matter volume, cortical thickness, and surface area were extracted from a total of 20 temporal lobe regions in both hemispheres. Support vector machine (SVM), extreme learning machine (ELM), and cmcRVFL+ classifiers were employed for model training and validated using 10-fold cross-validation. The results demonstrated that employing ELM classifiers in conjunction with specific temporal lobe gray matter volume features led to a better identification of TLE. The classification accuracy was 92.79%, with an area under the curve (AUC) value of 0.8019. The method proposed in this study can significantly assist in the preoperative identification of TLE patients.”
      原文链接:
    • NETL
    • NSTL
    • Frontiers Media Sa

    Reports from University of Ghent Add New Data to Findings in Machine Learning (Non-destructive Detection of Fusarium Head Blight In Wheat Kernels and Flour Using Visible Near-infrared and Mid-infrared Spectroscopy)

    102-103页
    查看更多>>摘要：Research findings on Machine Learning are discussed in a new report. According to news reporting from Ghent, Belgium, by NewsRx journalists, research stated, “Fusarium head blight (FHB) is one of the most severe fungal diseases that reduces yield of cereal crops and degrades kernel quality with mycotoxins, which are harmful to human and animal health. The majority of FHB identification at post-harvest stage is through lab-based analysis, whilst effective it is a time consuming, expensive, and laborious process.” Financial supporters for this research include ERA -NET, FWO. The news correspondents obtained a quote from the research from the University of Ghent, “Hence, a non-destructive, rapid, accurate, and robust method is required for FHB detection at post-harvest. This study explores the potential of visible near-infrared (vis-NIR) in the wavelength range from 400 to 1700 nm and the mid-infrared (MIR) in the wavenumber range from 4000 to 650 cm-1 to predict FHB infection of wheat kernels and flour. A total of 143 ear samples (93 infected, and 50 healthy) were collected from an inoculated trial covering several winter wheat varieties. The collected spectral data was analysed with two different machine learning algorithms, namely, random forest (RF) and linear discriminant analysis (LDA). Both models produced a higher test accuracy of 96.6 % and 100 %, respectively, for the flour samples than that (e.g., 93.1 %) for the kernels, using the MIR spectroscopy. Recursive feature elimination (RFE) demonstrated notable improvements in accuracy of the vis-NIR for the kernels, with LDA model providing 100 % classification accuracy. While RFE failed to improve the accuracy of MIR-LDA models.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Research on Machine Learning Reported by a Researcher at China Earthquake Administration (Machine Learning-Based Rapid Epicentral Distance Estimation from a Single Station)

    103-104页
    查看更多>>摘要：New study results on artificial intelligence have been published. According to news reporting out of Harbin, People’s Republic of China, by NewsRx editors, research stated, “Rapid epicentral distance estimation is of great significance for earthquake early warning (EEW).” Our news journalists obtained a quote from the research from China Earthquake Administration: “To rapidly and reliably predict epicentral distance, we developed machine learning models with multiple feature inputs for epicentral distance estimation using a single station and explored the feasibility of three machine learning methods, namely, Random Forest, eXtreme Gradient Boosting, and Support Vector Machine, for epicentral distance estimation. We used strong-motion data recorded by the Japanese Kyoshin network within a range of 1° ( 112 km) from the epicenter to train machine learning models. We used 30 features extracted from the P-wave signal as inputs to the machine learning models and the epicentral distance as the prediction target of the models. For the same test data set, within 0.1-5 s after the P-wave arrival, the epicentral distance estimation results of these three machine learning models were similar. Furthermore, these three machine learning methods can obtain smaller mean absolute errors and root mean square errors, as well as larger coefficients of determination (R2), for epicentral distance estimation than traditional EEW epicentral distance estimation methods, indicating that these three machine learning models can effectively improve the accuracy of epicentral distance estimation to a certain extent. In addition, we analyzed the importance of different features as inputs to machine learning models using SHapley additive exPlanations.”
      原文链接:
    • NETL
    • NSTL

    Reports from Shandong First Medical University & Shandong Academy of Medical Sciences Highlight Recent Research in Machine Learning (Predictive models based on machine learning for early recurrence and metastasis in postoperative patients with ...)

    104-105页
    查看更多>>摘要：New research on artificial intelligence is the subject of a new report. According to news reporting out of Shandong First Medical University & Shandong Academy of Medical Sciences by NewsRx editors, research stated, “To construct and validate a prediction model based on machine learning algorithms for early recurrence and metastasis in patients with colorectal cancer after surgery. This study employed a prospective cohort design.” Our news journalists obtained a quote from the research from Shandong First Medical University & Shandong Academy of Medical Sciences: “A total of 498 postoperative patients with colorectal cancer, treated at an affiliated hospital of Qingdao University, were recruited using convenience sampling from June to December 2021. Data were collected during outpatient visits and hospitalizations. The risk factors for early recurrence and metastasis of colorectal cancer were determined through multivariate logistic regression analysis in SPSS 26.0 software. Using Python 3.7.0 software, four machine learning algorithms (logistic regression, Support Vector Machine, XGBoost, and LightGBM) were used to develop and validate prediction models for early recurrence and metastasis of colorectal cancer after surgery. Of the 498 patients, 51 (10.24%) had early recurrence and metastasis. Multivariate logistic regression analysis showed that personal traits (family history of cancer, histological type, degree of tumor differentiation, number of positive lymph nodes, and T stage), behaviour and/or lifestyle (intake of refined grains, whole grains, fish, shrimp, crab, and nuts, as well as resilience), and interpersonal networks (social support) were all associated with early recurrence and metastasis of colorectal cancer (P＜0.05). The logistic regression prediction model showed the best prediction performance out of the four models, with an accuracy rate of 0.920, specificity of 0.982, F1 of 0.495, AUC of 0.867, Kappa of 0.056, and Brier score of 0.067.”
      原文链接:
    • NETL
    • NSTL