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    New Machine Learning Study Findings Recently Were Reported by Researchers at Beijing University of Technology (Elastic Analytical Method With Machine Learning for Predicting the Stratum Displacement Field Induced By Shallow Tunneling)

    84-85页
    查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news originating from Beijing, People’s Republic of China, by NewsRx correspondents, research stated, “Support vector regression (SVR) with sparrow search algorithm (SSA) is developed as the machine learning (ML) model to predict maximum surface settlement smax caused by tunneling. A novel method for calibrating boundary conditions of analytical solution is proposed, where the maximum surface settlement derived by the analytical method is equal to smax predicted by SSA-SVR method.” Funders for this research include National Key Research and Development Program of China, National Natural Science Foundation of China (NSFC), Beijing Municipal Commission of Education. Our news journalists obtained a quote from the research from the Beijing University of Technology, “The elastic analytical solution for stratum displacement of a shallow tunnel is presented by the complex variable method, when the calibrated nonuniform displacement function is applied as the tunnel displacement boundary condition. The proposed analytical solution-machine learning (AM) method can predict the stratum displacement field prior to the tunnel excavation. Seventythree tunnel engineering cases are employed to verify the rationality of the proposed SSA-SVR method in predicting smax. The value of R2 in the training and test process is 0.894 and 0.877, respectively. Taking Heathrow Express Trial Tunnel as an example, the potential of the proposed AM method in predicting stratum displacement is presented where the influence of cohesion strength, internal friction angle, Young’s elastic modulus of stratum, tunnel radius and depth are considered.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Fourth Hospital of Hebei Medical University Reports Findings in Pancreatic Cancer (Machine Learning Developed a MYC Expression Feature-Based Signature for Predicting Prognosis and Chemoresistance in Pancreatic Adenocarcinoma)

    85-86页
    查看更多>>摘要：New research on Oncology - Pancreatic Cancer is the subject of a report. According to news reporting from Hebei, People’s Republic of China, by NewsRx journalists, research stated, “MYC has been identified to profoundly influence a wide range of pathologic processes in cancers. However, the prognostic value of MYC-related genes in pancreatic adenocarcinoma (PAAD) remains unclarified.” The news correspondents obtained a quote from the research from the Fourth Hospital of Hebei Medical University, “Gene expression data and clinical information of PAAD patients were obtained from The Cancer Genome Atlas (TCGA) database (training set). Validation sets included GSE57495, GSE62452, and ICGC-PACA databases. LASSO regression analysis was used to develop a risk signature for survival prediction. Single-cell sequencing data from GSE154778 and CRA001160 datasets were analyzed. Functional studies were conducted using siRNA targeting RHOF and ITGB6 in PANC-1 cells. High MYC expression was found to be significantly associated with a poor prognosis in patients with PAAD. Additionally, we identified seven genes (ADGRG6, LINC00941, RHOF, SERPINB5, INSYN2B, ITGB6, and DEPDC1) that exhibited a strong correlation with both MYC expression and patient survival. They were then utilized to establish a risk model (MYCsig), which showed robust predictive ability. Furthermore, MYCsig demonstrated a positive correlation with the expression of HLA genes and immune checkpoints, as well as the chemotherapy response of PAAD. RHOF and ITGB6, expressed mainly in malignant cells, were identified as key oncogenes regulating chemosensitivity through EMT. Downregulation of RHOF and ITGB6 reduced cell proliferation and invasion in PANC-1 cells. The developed MYCsig demonstrates its potential in enhancing the management of patients with PAAD by facilitating risk assessment and predicting response to adjuvant chemotherapy.”
      原文链接:
    • NETL
    • NSTL

    Department of Nuclear Medicine Reports Findings in Artificial Intelligence (Diagnostic performance of artificial intelligence-assisted PET imaging for Parkinson's disease: a systematic review and metaanalysis)

    86-87页
    查看更多>>摘要：New research on Artificial Intelligence is the subject of a report. According to news reporting originating from Zhejiang, People’s Republic of China, by NewsRx correspondents, research stated, “Artificial intelligence (AI)-assisted PET imaging is emerging as a promising tool for the diagnosis of Parkinson’s disease (PD). We aim to systematically review the diagnostic accuracy of AI-assisted PET in detecting PD.” Our news editors obtained a quote from the research from the Department of Nuclear Medicine, “The Ovid MEDLINE, Ovid Embase, Web of Science, and IEEE Xplore databases were systematically searched for related studies that developed an AI algorithm in PET imaging for diagnostic performance from PD and were published by August 17, 2023. Binary diagnostic accuracy data were extracted for meta-analysis to derive outcomes of interest: area under the curve (AUC). 23 eligible studies provided sufficient data to construct contingency tables that allowed the calculation of diagnostic accuracy. Specifically, 11 studies were identified that distinguished PD from normal control, with a pooled AUC of 0.96 (95% CI: 0.94-0.97) for presynaptic dopamine (DA) and 0.90 (95% CI: 0.87-0.93) for glucose metabolism (F-FDG). 13 studies were identified that distinguished PD from the atypical parkinsonism (AP), with a pooled AUC of 0.93 (95% CI: 0.91 - 0.95) for presynaptic DA, 0.79 (95% CI: 0.75-0.82) for postsynaptic DA, and 0.97 (95% CI: 0.96-0.99) for F-FDG. Acceptable diagnostic performance of PD with AI algorithms-assisted PET imaging was highlighted across the subgroups.”
      原文链接:
    • NETL
    • NSTL

    New Findings from Federal University Parana Describe Advances in Machine Learning (Real-time Prediction of Deposited Bead Width In L-ded Using Semi-supervised Transfer Learning)

    87-88页
    查看更多>>摘要：Investigators publish new report on Machine Learning. According to news reporting originating from Curitiba, Brazil, by NewsRx correspondents, research stated, “Laser directed energy deposition (L-DED) is an additive manufacturing (AM) technology that offers unique advantages for creating and repairing metallic parts. The quality of the final print is highly dependent on the consistency of the printing parameters, and as a result, the deposited bead geometry along the printing pathway.” Financial support for this research came from So Paulo Research Foundation (FAPESP). Our news editors obtained a quote from the research from Federal University Parana, “Inconsistencies, particularly in the bead width, can produce defects between adjacent beads, increasing the material susceptibility to failure. Therefore, it is crucial to monitor and predict the printing parameters in real time during the process. The objective of this study is to develop a machine learning model that accurately predicts the width of the deposited track in real time. To achieve this, a dataset with approximately 12,379 melt pool images was used to train the algorithm and predict the width of the deposited track. Each one of the deposited track was measured by a profilometer at 10 points along the pathway, and intermediate labels were predicted using a spline curve, making the model semi-supervised. To implement the machine learning model, pre-trained frozen networks based on convolutional neural networks (CNN) architectures VGG, ResNet, and DenseNet were employed, using transfer learning principles. These networks were integrated into a dense, fully connected network containing trainable parameters. The results demonstrate a good model fit, with a mean absolute error of 4.5% and a mean absolute error of 0.0358 mm. Moreover, the processing frequency of the best model, at 55 Hz, enables real-time control of the L-DED manufacturing process. It is important to highlight, however, that the VGG-based model shows a frequency of 250 Hz and similar results. Thus, accurately predicting the width of the deposited bead has the potential to significantly improve the quality of the final part by reducing overall defects, such as lack of fusion and porosity, whenever this data is used as input to a feedback control system. This is particularly valuable in industries where mechanical properties and fatigue life are critical, such as automotive, aerospace, and biomedical sectors.”
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Researcher from Affiliated to Visvesvaraya Technological University Describes Findings in Machine Learning (A Review on Ensemble Machine and Deep Learning Techniques Used in the Classification of Computed Tomography Medical Images)

    89-89页
    查看更多>>摘要：Investigators publish new report on artificial intelligence. According to news reporting from Karnataka, India, by NewsRx journalists, research stated, “Ensemble learning combines multiple base models to enhance predictive performance and generalize better on unseen data.” The news editors obtained a quote from the research from Affiliated to Visvesvaraya Technological University: “In the context of Computed Tomography (CT) image processing, ensemble techniques often leverage diverse machine learning or deep learning architectures to achieve the best results. Ensemble machine learning and deep learning techniques have revolutionized the field of CT image processing by significantly improving accuracy, robustness, and efficiency in various medical imaging tasks. These methods have been instrumental in tasks such as image reconstruction, segmentation, classification, and disease diagnosis. The ensemble models can be divided into those based on decision fusion strategies, bagging, boosting, stacking, negative correlation, explicit/implicit ensembles, homogeneous/heterogeneous ensembles, and explicit/implicit ensembles. In comparison to shallow or traditional, machine learning models and deep learning architectures are currently performing better.”
      原文链接:
    • NETL
    • NSTL

    Data from Cedars Sinai Medical Center Provide New Insights into Experimental Lung Transplants (Robotic-assisted Lung Transplantation: First In Man)

    89-90页
    查看更多>>摘要：Research findings on Transplant Medicine - Experimental Lung Transplants are dis-cussed in a new report. According to news reporting originating from Los Angeles, California, by NewsRx correspondents, research stated, “Lung transplantation remains the best option for patients with end-stage lung disease.” Our news editors obtained a quote from the research from Cedars Sinai Medical Center, “However, this operation has historically carried significant potential morbidity. To improve near-term patient outcomes, attempts have been made to decrease invasiveness, but this is limited by the complex nature of the operation and the anatomy of the chest.” According to the news editors, the research concluded: “To facilitate further reduction in incision size and augment our existing minimally invasive approach, we developed a novel technique utilizing the Da Vinci robotic system to implant a right lung in a 69-year-old recipient.(.” This research has been peer-reviewed.
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Findings in Machine Learning Reported from Central South University (Current status and prospects in machine learning-driven design for refractory high-entropy alloys)

    90-91页
    查看更多>>摘要：Researchers detail new data in artificial intelligence. According to news originating from Changsha, People’s Republic of China, by NewsRx editors, the research stated, “Due to excellent comprehensive properties such as high strength, high hardness, and excellent high-temperature oxidation resistance, the refractory high-entropy alloys have broad application prospects and research value in the fields of aerospace and nuclear energy.” The news correspondents obtained a quote from the research from Central South University: “However, the refractory high-entropy alloys have very complex composition features, making it difficult to perform alloy design. It seriously restricts the development of high-performance refractory high-entropy alloys. In recent years, the machine learning technique has been gradually applied to various high-performance alloys with efficient and accurate modeling and prediction capability. In this review, there was a comprehensive summary of research achievements on machine learning-driven design of refractory high-entropy alloys. A detailed review on the applications and progress of machine learning technique in different aspects was given, including alloy phase structure design, mechanical property prediction, strengthening mechanism analysis and acceleration of atomic simulations. Finally, the currently existing problems in this direction were summarized.”
      原文链接:
    • NETL
    • NSTL

    University of Naples Federico Ⅱ Reports Findings in Hernias (Abdominal Wall Hernias-State of the Art of Laparoscopic versus Robotic Surgery)

    91-92页
    查看更多>>摘要：New research on Gastroenterology - Hernias is the subject of a report. According to news reporting originating from Naples, Italy, by NewsRx correspondents, research stated, “Abdominal wall hernia repair, a common surgical procedure, includes various techniques to minimize postoperative complications and enhance outcomes. This review focuses on the comparison between laparoscopic and robotic approaches in treating inguinal and ventral hernias, presenting the ongoing situation of this topic.” Our news editors obtained a quote from the research from the University of Naples Federico Ⅱ, “A systematic search identified relevant studies comparing laparoscopic and robotic approaches for inguinal and ventral hernias. Randomized control trials, retrospective, and prospective studies published after 1 January 2000, were included. Search terms such as hernia, inguinal, ventral, laparoscopy, robotic, and surgery were used. A total of 23 articles were included for analysis. Results indicated similar short-term outcomes for robotic and laparoscopic techniques in inguinal hernia repair, with robotic groups experiencing less postoperative pain. However, longer operative times and higher costs were associated with robotic repair. Robotic ventral hernia repair demonstrated potential benefits, including shorter hospital stay, lower recurrence and lower reoperation rates. While robotic surgery offers advantages such as shorter hospital stays, faster recovery, and less postoperative pain, challenges including costs and training requirements need consideration.”
      原文链接:
    • NETL
    • NSTL
    • Mdpi

    New Robotics Research Reported from Tokyo University of Agriculture and Technology (A Generative Model to Embed Human Expressivity into Robot Motions)

    92-93页
    查看更多>>摘要：Investigators publish new report on robotics. According to news reporting from Tokyo, Japan, by NewsRx journalists, research stated, “This paper presents a model for generating expressive robot motions based on human expressive movements.” Financial supporters for this research include Jsps Kakenhi; Nedo. The news reporters obtained a quote from the research from Tokyo University of Agriculture and Technology: “The proposed data-driven approach combines variational autoencoders and a generative adversarial network framework to extract the essential features of human expressive motion and generate expressive robot motion accordingly. The primary objective was to transfer the underlying expressive features from human to robot motion. The input to the model consists of the robot task defined by the robot’s linear velocities and angular velocities and the expressive data defined by the movement of a human body part, represented by the acceleration and angular velocity.” According to the news editors, the research concluded: “The experimental results show that the model can effectively recognize and transfer expressive cues to the robot, producing new movements that incorporate the expressive qualities derived from the human input. Furthermore, the generated motions exhibited variability with different human inputs, highlighting the ability of the model to produce diverse outputs.”
      原文链接:
    • NETL
    • NSTL
    • Mdpi

    Investigators at North China University of Science and Technology Detail Findings in Support Vector Machines (Pellet Image Segmentation Model of Superpixel Feature-based Support Vector Machine In Digital Twin)

    93-94页
    查看更多>>摘要：Researchers detail new data in Support Vector Machines. According to news reporting out of Tangshan, People’s Republic of China, by NewsRx editors, research stated, “A digital twin model based on superpixel features is established to solve the problem of noise and similar gray values between foreground and background of pellet images. With superpixel as the basic unit of segmentation, the influence of single pixel on segmentation results is reduced, and allows for higher segmentation accuracy.” Funders for this research include Natural Science Foundation of Hebei Province, Basic Research Funds for Universities. Our news journalists obtained a quote from the research from the North China University of Science and Technology, “The gray-level co-occurrence matrix is used to represent the superpixel characteristic information, and the color moment and gray level distribution are combined to comprehensively characterize the superpixel. Through principal component analysis and correlation analysis, The feature compression of superpixel is realized, and the computational efficiency is improved. The superpixel binary classification data set is built, and the multidimensional feature information of superpixel is extracted as input vector to train the binary classification model of SVM, and the image segmentation problem is transformed into foreground and background classification problem. A multi-scale superpixel segmentation boundary optimization method is proposed to further refine the boundary region of foreground and background. A four-neighborhood search algorithm is proposed to reduce the missegmentation rate of edge superpixels. Experimental results show that the accuracy of the proposed method can reach 95.87%, the precision of image edge segmentation is high, and the foreground and background of granular image are accurately segmented.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier