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    New Machine Learning Study Findings Have Been Reported by In- vestigators at Federal University Rio Grande do Sul UFRGS (Emer- gency Shutdown Valve Damage Classification By Machine Learning Using Synthetic Data)

    48-49页
    查看更多>>摘要:2024 FEB 02 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on Machine Learning have been presented. According to news reporting originating in Porto Alegre, Brazil, by NewsRx journalists, research stated, “Emergency Shutdown Valves (EDSVs) are used in industrial applications to interrupt internal fluid flow in pipelines during hazardous events. During their operation, these valves can suffer cumulative damage on their reinforced polytetraflu- oroethylene (RPTFE) seats that can put their operation and effectiveness at risk.” The news reporters obtained a quote from the research from Federal University Rio Grande do Sul UFRGS, “One of the options for detecting the occurrence of damage is to analyse data acquired from fluid pressure and torque sensors during the closing and opening cycles of the valve. The resulting operational signatures are commonly evaluated through so-called Transition Points (TPs) that are manually marked in a subjective manner by a trained operator. In addition to being slow and laborious, this approach discards most of the acquired information. Alternatives to this method would be the use of Damage Indexes (DIs) that are extracted from the signatures, or even the evaluation of the complete pressure or torque signature. These methods, when associated with machine learning (ML) algorithms, could use the acquired information more efficiently and reliably, and would have the potential to completely automate the monitoring process. In this work, these three processing options were tested and compared using real and synthetic data that were generated with the Monte Carlo (MC) method.”

    Investigators from Indian Institute for Technology Report New Data on Machine Learning (Efficient Modeling of Graphene-dielectric Resonator Based Hybrid Mimo Antenna for Thz Application Using Machine Learning Algorithms)

    49-50页
    查看更多>>摘要:2024 FEB 02 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Data detailed on Machine Learning have been presented. According to news reporting originating in Jharkhand, India, by NewsRx journalists, research stated, “In this communication, a hybrid MIMO antenna (combination of graphene and ceramic) is designed and investigated in THz frequency range. Circular Polarization is achieved by feeding the cylindrical ceramic using moon shaped aperture with L-shaped Microstrip line.” The news reporters obtained a quote from the research from Indian Institute for Technology, “Mutual coupling between the antenna ports has been reduced to - 25 dB by using the concept of polarization diversity. Graphene material is used to introduce the re-configurability in the proposed radiator. For avoiding the difficulty of simulation of radiator at THz regime (very high simulation time), machine learning algorithms i.e. Artificial Neural Network (ANN), Random Forest and XGBoost are utilized to predict the reflection coefficient and axial ratio of designed antenna. The designed radiator works from 2.4 to 3.1 THz with 3-dB axial ratio from 2.6 to 3.0 THz.”

    First Affiliated Hospital of Zhejiang Chinese Medical University Re- ports Findings in Lung Cancer (Could the underlying biological basis of prognostic radiomics and deep learning signatures be explored in patients with lung cancer? A systematic ...)

    50-51页
    查看更多>>摘要:2024 FEB 02 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Oncology - Lung Cancer is the subject of a report. According to news reporting out of Hangzhou, People’s Republic of China, by NewsRx editors, research stated, “To summarize the underlying biological correlation of prognostic radiomics and deep learning signatures in patients with lung cancer and evaluate the quality of available studies. This study examined databases including the PubMed, Embase, Web of Science Core Collection, and Cochrane Library, for studies that elaborated on the underlying biological correlation with prognostic radiomics and deep learning signatures based on CT or PET/CT for predicting the prognosis in patients with lung cancer.”

    Fujian Medical University Reports Findings in Rectal Cancer (Com- parative analysis of preoperative chemoradiotherapy and upfront surgery in the treatment of upper-half rectal cancer: oncological benefits, surgical outcomes, and cost implications)

    51-52页
    查看更多>>摘要:2024 FEB 02 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Oncology - Rectal Cancer is the subject of a report. According to news reporting originating in Fujian, People’s Republic of China, by NewsRx journalists, research stated, “The value of neoadjuvant chemoradiotherapy (CRT) is not absolutely clear for upper-half (>7-15 cm) rectal cancer. This study aimed to compare the efficacy and safety of radical surgery with preoperative CRT vs. upfront surgery (US) in Chinese patients with stage Ⅱ and Ⅲ upper-half rectal cancer.”

    Findings from Shanghai Jiao Tong University Broaden Understand- ing of Machine Learning (Development of Machine Learning Inter- atomic Potential for Zinc)

    52-53页
    查看更多>>摘要:2024 FEB 02 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Machine Learning have been published. According to news reporting originating from Shanghai, People’s Republic of China, by NewsRx correspondents, research stated, “The HCP structure of zinc (Zn) exhibits an anomalous c/a ratio, a factor that traditional empirical potentials find challenging to reproduce accurately, thus impeding thorough investigations of its property and behavior via realistic atomistic simulations. The advent of machine learning interatomic potentials in recent years has ushered in new opportunities for the development of reliable Zn potentials.” Financial supporters for this research include National Natural Science Foundation of China (NSFC), MaGIC of Shanghai Jiao Tong University.

    Findings from Air Force Research Laboratory in Machine Learning Reported (Physical consistency and invariance in machine learning of turbulent signals)

    53-54页
    查看更多>>摘要:2024 FEB 02 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on artificial intelligence are presented in a new report. According to news reporting out of Wright Patterson AFB, Ohio, by NewsRx editors, research stated, “This paper concerns an investigation of the invariance and consistency of deep learning of turbulent pressure fluctuations.” Financial supporters for this research include European Office of Aerospace Research And Development. The news correspondents obtained a quote from the research from Air Force Research Laboratory: “The long-short-memory model is employed to predict wall pressure fluctuations across physical regimes featuring turbulence, shock-boundary layer interaction, and separation. The model’s sensitivity to the data inputs is examined using different input data sets. Training the deep learning model based on the raw signals from different flow regions leads to large inaccuracies. It is shown that the data must be appropriately pre-processed before training for the deep learning model predictions to become consistent. Removing the mean and using the normalized fluctuating component of the signal, the deep learning predictions not only greatly improved in accuracy but, most importantly, converged and became consistent, provided that the signal sparsity remains within the inertial sub-range of the turbulence energy spectrum cascade. The power spectra of the surface pressure fluctuations reveal that the model provides high accuracy up to a certain frequency for the fully turbulent flow.”

    Southern Medical University Reports Findings in Artificial Intel- ligence (Artificial intelligence-based analysis of tumor-infiltrating lymphocyte spatial distribution for colorectal cancer prognosis)

    54-55页
    查看更多>>摘要:2024 FEB 02 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Artificial Intelligence is the subject of a report. According to news reporting out of Guangdong, People’s Republic of China, by NewsRx editors, research stated, “Artificial intelligence (AI) technology represented by deep learning has made remarkable achievements in digital pathology, enhancing the accuracy and reliability of diagnosis and prognosis evaluation. The spatial distri- bution of CD3+ and CD8+ T cells within the tumor microenvironment has been demonstrated to have a significant impact on the prognosis of colorectal cancer (CRC).”

    Dalian University of Technology Researcher Describes Research in Machine Learning (Automatic Classification of Pavement Type and Service Age Benchmarked with Standard Texture Databases Using the Machine Learning Method: A Pilot Study)

    55-56页
    查看更多>>摘要:2024 FEB 02 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New study results on artificial intelligence have been published. According to news originating from Dalian, People’s Republic of China, by NewsRx editors, the research stated, “Pavement intelligent management systems have attracted considerable interest from researchers.” The news journalists obtained a quote from the research from Dalian University of Technology: “How- ever, various service conditions of pavement surface concerning the pavement type, texture service age, and so forth, inhibit a universal algorithm that is feasible for all cases. In this regard, the automatic classification of pavement type and service age is an essential premise to unblock the bottleneck stated above. Based on the surface texture data, a pilot study of the automatic classification approach to identify pavement surface textures using convolutional neural networks (CNNs) is presented. For comparison, the efficiency of the support vector machine (SVM) is also investigated. In total, three cases, (i) pavement types, (ⅱ) texture service ages, and (ⅲ) a combination of (i) and (ⅱ), are involved in the automatic classification. The results indicate that the CNN outperforms the SVM, and the CNN models show a favorable classification accuracy for the above three cases with 93.0%, 81.1%, and 83.8%, respectively.”

    Data on Support Vector Machines Reported by Pavan Kumar and Colleagues (Detection of oral mucosal lesions by the fluorescence spectroscopy and classification of cancerous stages by support vec- tor machine)

    56-57页
    查看更多>>摘要:2024 FEB 02 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on Support Vector Machines is the subject of a report. According to news reporting out of Wardha, India, by NewsRx editors, research stated, “Detection of oral mucosal lesions has been performed by an in-house developed fluorescence-based portable device in the present study. A laser diode of 405 nm wavelength and a UV-visible spectrometer are utilized in the portable device as excitation and detection sources.” Our news journalists obtained a quote from the research, “At the 405 nm excitation wavelength, the flavin adenine dinucleotide (FAD) band at 500 nm and three porphyrin bands at 634, 676, and 703 nm are observed in the fluorescence spectrum of the oral cavity tissue. We have conducted this clinical study on a total of 189 tissue sites of 36 oral squamous cell carcinoma (OSCC) patients, 18 dysplastic (precancerous) patients, and 34 volunteers. Analysis of the fluorescence data has been performed by using the principal component analysis (PCA) method and support vector machine (SVM) classifier. PCA is applied first in the spectral data to reduce the dimension, and then classification among the three groups has been executed by employing the SVM. The SVM classifier includes linear, radial basis function (RBF), polynomial, and sigmoid kernels, and their classification efficacies are computed. Linear and RBF kernels on the testing data sets differentiated OSCC and dysplasia to normal with an accuracy of 100% and OSCC to dysplasia with an accuracy of 95% and 97%, respectively. Polynomial and sigmoid kernels showed less accuracy values among the groups ranging from 48 to 88% and 51 to 100%, respectively.”

    New Machine Learning Study Findings Recently Were Reported by Researchers at Inner Mongolia University (Assessment of Damage Evolution of Concrete Beams Strengthened With Bfrp Sheets With Acoustic Emission and Unsupervised Machine Learning)

    57-58页
    查看更多>>摘要:2024 FEB 02 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Machine Learning have been published. According to news reporting originating from Hohhot, People’s Republic of China, by NewsRx correspondents, research stated, “Basalt fiber reinforced polymer (BFRP) is an emerging material that can be used to strengthen or repair concrete structures. This paper experimentally investigates the flexural failure process of concrete beams strengthened by BFRP sheets under four-point loading conditions using acoustic emission (AE)-based nondestructive evaluation and unsupervised machine learning techniques.”