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    New Artificial Intelligence Study Results from Lakehead University Described (An Explainable Artificial-intelligence-aided Safety Factor Prediction of Road Emba nkments)

    58-58页
    查看更多>>摘要：Fresh data on Artificial Intelligence are presented in a new report. According to news reporting originating from Thun der Bay, Canada, by NewsRx correspondents, research stated, "Despite the widespr ead application of data-centric techniques in Geotechnical Engineering, there is a rising need for building trust in the artificial intelligence (AI)-driven saf ety assessment of road embankments due to its socalled ‘black-box'nature. In ad dition, from the lens of limit equilibrium approaches, e.g., Bishop, Fellenius, Janbu and Morgenstern-Price, and finite element method, it is essential to caref ully examine the interplay of both topological and physical/mechanical propertie s during the safety factor (FoS) predictions." Funders for this research include Natural Sciences and Engineering Research Coun cil of Canada (NSERC), Government of Canada's New Frontiers in Research Fund (NF RF).
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    Researchers from Nanjing University of Science and Technology Provide Details of New Studies and Findings in the Area of Machine Learning (Combustion Condition Predictions for C 2-c 4 Alkane and Alkene Fuels Via Machine Learning Methods)

    59-59页
    查看更多>>摘要：2024 OCT 08 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Investigators discuss new findings in Machine Lea rning. According to news reporting from Nanjing, People's Republic of China, by NewsRx journalists, research stated, "The accurate and rapid prediction of hydro carbon type was a precondition for the utilization of fossil fuels with high eff iciency and safety. In this study, machine learning based techniques were used t o predict the type and equivalence ratio of flames of C 2-C 4 alkane and alkene fuels based on the differences in flame morphology between various combustion co nditions." Financial support for this research came from National Natural Science Foundatio n of China (NSFC). The news correspondents obtained a quote from the research from the Nanjing Univ ersity of Science and Technology, "The test results of different machine learnin g algorithms, including ANN, SVM, SVR, KNN, MLR, and RF were compared in detail using statistical methods. Results indicated that ANN, SVM, KNN, and RF all exhi bited an outstanding performance in predicting the types of C 2-C 4 alkane and a lkene flames, achieving accuracies of 95.7 %, 96.3 %, 93.8 %, and 96.5 %, respectively. For the prediction o f the equivalence ratio among these fuels, the mean absolute percentage errors o f the ANN, SVR, MLR, and RF were only 5.6 %, 3.8 %, 8. 2 %, and 3.8 %, respectively. The performance of SVM, SVR, and RF algorithms was significantly superior to that of ANN, MLR, and KNN a lgorithms for flame prediction. Moreover, the data of feature analysis revealed that the importance level of designed features exhibited a significant distincti on between different prediction targets. For predicting the type of C 2-C 4 alka ne and alkene fuels, the features associated with blue region showed a stronger importance level."
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    Study Data from Shandong University Update Knowledge of Artificial Intelligence (Intelligent Design and Seawater Mixing Performance of New Synchronous Grouting Materials In a Pure-solidwaste Framework)

    60-61页
    查看更多>>摘要：2024 OCT 08 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Data detailed on Artificial Intelligence have bee n presented. According to news reporting originating from Shandong, People's Rep ublic of China, by NewsRx correspondents, research stated, "Solid waste, as a re liable raw material substitute for synchronous grouting materials in shield tunn eling, addresses the issue of recycling traditional waste. Additionally, geopoly mer slurries utilizing waste exhibit excellent physical and chemical properties, meeting requirements for the stability and long-term performance of synchronous grouting materials." Financial supporters for this research include National Key R & D Plan of Shandong Province, China, National Key R & D Plan of Yunna n Province, China, National Key R & D Plan of China.
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    Reports from Otto-von-Guericke-University Describe Recent Advances in Robotics ( Vacuum-based and body-mounted roboticpatient interface with an integrated metas urface for MRI-guided interventions)

    61-61页
    查看更多>>摘要：Current study results on robotics have been published. According to news originating from Magdeburg, Germany, by NewsR x editors, the research stated, "The increased clinical relevance of image-guide d procedures, particularly of interventional magnetic resonance imaging (iMRI), highlights the need for advanced devices and robotics to optimize those procedur es." Our news correspondents obtained a quote from the research from Otto-von-Guerick e-University: "To enable a realistic integration of robotics into the clinical w orkflow, robotic-patient interfaces (RPI) are required to ensure both functional ity and usability. However, current concepts have issues with patient accessibil ity and safety, user handling, or performing interventions on versatile body reg ions. This work presents a novel silicone-based RPI to flexibly mount the 'Micro positioning Robotics for Image-Guided Surgery' (mRIGS) system on differently sha ped body regions through vacuum, regulated with an electronic miniature pump. Ma ximum holding forces of 60N at -0.1 bar and 66N at -0.2 bar relative vacuum pres sure were reached depending on the applied human body area and tensile force ang les. MRI with the integrated metasurface indicated up to 200% sign al enhancement, enabling improved tissue contrast within the first 20mm in depth . The multifunctional design supported the incorporation of the sterile iMRI wor kflow concept."
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    Affiliated Hospital of Guangdong Medical University Reports Findings in Arthropl asty (Prediction of intraoperative press-fit stability of the acetabular cup in total hip arthroplasty using radiomics-based machine learning models)

    62-63页
    查看更多>>摘要：New research on Surgery-Arthroplasty is the subject of a report. According to news reporting originating in Guangdon g, People's Republic of China, by NewsRx journalists, research stated, "Preopera tive prediction of the acetabular cup press-fit stability in total hip arthropla sty is necessary for clinical decision-making. This study aims to establish and validate machine learning models to investigate the feasibility of predicting th e intraoperative press-fit stability of the acetabular cup in total hip arthropl asty (THA)." The news reporters obtained a quote from the research from the Affiliated Hospit al of Guangdong Medical University, "226 patients who underwent primary THA from 2018 to 2022 in our hospital were retrospectively enrolled. Patients were divid ed into press-fit stable or unstable groups according to the intraoperative pull -out test of the implanted cup. Then, they were randomly assigned to the trainin g or test cohort in an 8:2 ratio. We used 3Dslicer software to segment the regio n of interest (ROI) of the patient's bilateral hip X-ray to extract radiomics fe atures. The least absolute shrinkage and selection operator (LASSO) regression w as used in our feature selection. Finally, four machine learning models were emp loyed in this study, including support vector machine (SVM), random forest (RF), logistic regression (LR), and XGBoost (XGB). Decision curve analysis (DCA), and receiver operating characteristic (ROC) curves of the models were plotted. The area under the curve (AUC), diagnostic accuracy, sensitivity, and specificity we re calculated as well. The AUCs of the four models were compared using the DeLon g test. Twenty-seven valuable radiomics features were determined by dimensionali ty reduction and selection. Regarding to the DeLong test, the AUC of the XGB mod el was significantly different from those of the other three models. (p <0.05). Among all models, the XGB model exhibited the best performance with an A UC of 0.823 (95 % CI: 0.711-0.919) in the test cohort and showed o ptimal clinical efficacy according to the DCA."
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    New Findings on Machine Learning Described by Investigators at Federal Universit y Para (S-wave Log Construction Through Semisupervised Regression Clustering Us ing Machine Learning: a Case Study of North Sea Fields)

    63-64页
    查看更多>>摘要：Researchers detail new data in Machine Learning. According to news reporting originating from Belem, Brazil, by NewsRx correspondents, research stated, "Accurate prediction of S-wave velocity from w ell logs is essential for understanding subsurface geological formations and hyd rocarbon reservoirs. Machine learning techniques, including clustering and regre ssion, have emerged as effective methods for indirectly estimating S-wave logs a nd other rock properties." Financial support for this research came from University of Para<acute accent>. Our news editors obtained a quote from the research from Federal University Para , "In this study, we employed clustering algorithms to identify similarities amo ng well log datasets, encompassing depth, sonic, porosity, neutron, and apparent density, facilitating the discovery of correlations among various wells. These identified correlations served as a foundation for predicting S-wave values usin g a novel semisupervised approach. Our approach combined clustering, specifical ly k-means clustering, with different types of regressors, including Least Squar es Regression (LSR), Support Vector Regression (SVR), and Multi-Layer Perceptron (MLP). Our results demonstrate the superior performance of this integrated appr oach compared to traditional regression methods. We validated our methodology us ing various parametric and non-parametric regression techniques, showcasing its effectiveness not only on wells within the training region but also on wells out side the study area. We achieved a significant improvement in the R2 score metri c, ranging from 2.22% to 6.51%, and a reduction in Me an Square Error (MSE) of at least 31% when compared to predictions made without clustering."
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    Study Results from Princess Nourah bint Abdulrahman University in the Area of Ma chine Learning Published [Federated Learning (FL) Model of Wi nd Power Prediction]

    64-64页
    查看更多>>摘要：Fresh data on artificial intelligence are presented in a new report. According to news reporting originating from Riya dh, Saudi Arabia, by NewsRx correspondents, research stated, "Wind power is a ch eap renewable energy that plays an important role in the economic development of a country." Financial supporters for this research include Princess Nourah Bint Abdulrahman University Researchers Supporting Project Number; Princess Nourah Bint Abdulrahm an University, Riyadh, Saudi Arabia. The news correspondents obtained a quote from the research from Princess Nourah bint Abdulrahman University: "Identifying potential locations for energy product ion is challenging due to the diverse relationship between wind power potential and the weather characteristics of a location. Many machine learning models were proposed to predict the wind power production level for different locations. Th ere is also a need for a global machine-learning model to enable wind power pred iction of multiple locations with a single global model. A Federated Learning (F L) based model is proposed to train and evaluate the global model of wind power prediction of different locations using wind speed and wind direction. The propo sed wind power prediction model is implemented in Pakistan to forecast the wind power of four distinct locations in Pakistan, using Linear Regression (LR), Supp ort Vector Regression (SVR), Random Forest Regression (RFR), Extreme Gradient Bo osting Regression (XGBR), and Multilayer Perceptron Regression (MLPR) models. Th e evaluation of the model from 30% of the test dataset reveals tha t RFR outperformed with a coefficient of determination (R2) of 0.9717, a Mean Sq uared Error (MSE) of 0.0007 kW, a Root Mean Squared Error (RMSE) of 0.0256 kW, a nd a Mean Absolute Error (MAE) of 0.018 kW."
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    Recent Findings from Carnegie Mellon University Provides New Insights into Machi ne Learning (Foundations and Trends In Multimodal Machine Learning: Principles, Challenges, and Open Questions)

    65-65页
    查看更多>>摘要：Investigators publish new report on Ma chine Learning. According to news originating from Pittsburgh, Pennsylvania, by NewsRx correspondents, research stated, "Multimodal machine learning is a vibran t multi-disciplinary research field that aims to design computer agents with int elligent capabilities such as understanding, reasoning, and learning through int egrating multiple communicative modalities, including linguistic, acoustic, visu al, tactile, and physiological messages. With the recent interest in video under standing, embodied autonomous agents, text-to-image generation, and multisensor fusion in application domains such as healthcare and robotics, multimodal machin e learning has brought unique computational and theoretical challenges to the ma chine learning community given the heterogeneity of data sources and the interco nnections often found between modalities."
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    Liaoning University Reports Findings in Machine Learning (Development of a machi ne learning-based target-specific scoring function for structure-based binding a ffinity prediction for human dihydroorotate dehydrogenase inhibitors)

    66-66页
    查看更多>>摘要：New research on Machine Learning is th e subject of a report. According to news originating from Liaoning, People's Rep ublic of China, by NewsRx correspondents, research stated, "Human dihydroorotate dehydrogenase (hDHODH) is a flavin mononucleotide-dependent enzyme that can lim it de novo pyrimidine synthesis, making it a therapeutic target for diseases suc h as autoimmune disorders and cancer. In this study, using the docking structure s of complexes generated by AutoDock Vina, we integrate interaction features and ligand features, and employ support vector regression to develop a target-speci fic scoring function for hDHODH (TSSF-hDHODH)." Our news journalists obtained a quote from the research from Liaoning University , "The Pearson correlation coefficient values of TSSF-hDHODH in the cross-valida tion and external validation are 0.86 and 0.74, respectively, both of which are far superior to those of classic scoring function AutoDock Vina and random fores t (RF) based generic scoring function RF-Score. TSSF-hDHODH is further used for the virtual screening of potential inhibitors in the FDA-Approved & Pharmacopeia Drug Library. In conjunction with the results from molecular dynami cs simulations, crizotinib is identified as a candidate for subsequent structura l optimization."
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    Reports Outline Machine Learning Findings from New Mexico State University (Comp aring Human Evaluations of Eyewitness Statements To a Machine Learning Classifie r Under Pristine and Suboptimal Lineup Administration Procedures)

    67-68页
    查看更多>>摘要：Data detailed on Machine Learning have been presented. According to news originating from Las Cruces, New Mexico, by N ewsRx correspondents, research stated, "Recent work highlights the ability of ve rbal machine learning classifiers to distinguish between accurate and inaccurate recognition memory decisions (Dobbins, 2022; Dobbins & Kantner, 2 019; Seale-Carlisle, Grabman, & Dodson, 2022). Given the surge of interest in these modeling techniques, there is an urgent need to investigate ve rbal classifiers' limitations-particularly in applied contexts such as when po lice collect eyewitness's confidence statements." Financial support for this research came from National Science Foundation (NSF).
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