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    Data on Machine Learning Reported by Researchers at Sultan Moulay Slimane University (Improved Soil Carbon Stock Spatial Prediction In a Mediterranean Soil Erosion Site Through Robust Machine Learning Techniques)

    48-49页
    查看更多>>摘要：Current study results on Machine Learning have been published. According to news reporting originating from Beni Mellal, Morocco, by NewsRx correspondents, research stated, “Soil serves as a reservoir for organic carbon stock, which indicates soil quality and fertility within the terrestrial ecosystem. Therefore, it is crucial to comprehend the spatial distribution of soil organic carbon stock (SOCS) and the factors influencing it to achieve sustainable practices and ensure soil health.” Our news editors obtained a quote from the research from Sultan Moulay Slimane University, “Thus, the present study aimed to apply four machine learning (ML) models, namely, random forest (RF), knearest neighbors (kNN), support vector machine (SVM), and Cubist model tree (Cubist), to improve the prediction of SOCS in the Srou catchment located in the Upper Oum Er-Rbia watershed, Morocco. From an inventory of 120 sample points, 80% were used for training the model, with the remaining 20% set aside for model testing. Boruta’s algorithm and the multicollinearity test identified only nine (9) factors as the controlling factors selected as input data for predicting SOCS. As a result, spatial distribution maps for SOCS were generated for all models, then compared, and further validated using statistical metrics. Among the models tested, the RF model exhibited the best performance (R2 = 0.76, RMSE = 0.52 Mg C/ha, NRMSE = 0.13, and MAE = 0.34 Mg C/ha), followed closely by the SVM model (R2 = 0.68, RMSE = 0.59 Mg C/ha, NRMSE = 0.15, and MAE = 0.34 Mg C/ha) and Cubist model (R2 = 0.64, RMSE = 0.63 Mg C/ha, NRMSE = 0.16, and MAE = 0.43 Mg C/ha), while the kNN model had the lowest performance (R2 = 0.31, RMSE = 0.94 Mg C/ha, NRMSE = 0.24, and MAE = 0.63 Mg C/ha). However, bulk density, pH, electrical conductivity, and calcium carbonate were the most important factors for spatially predicting SOCS in this semi-arid region.”
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Studies from Jaume I University Add New Findings in the Area of Machine Learning (A Gaussian Kernel for Kendall’s Space of m-d Shapes)

    49-49页
    查看更多>>摘要：Data detailed on Machine Learning have been presented. According to news reporting from Castellon de la Plana, Spain, by NewsRx journalists, research stated, “In this paper, we develop an approach to exploit kernel methods with data lying on the m-D Kendall shape space. When data arise in a finite-dimensional curved Riemannian manifold, as in this case, the usual Euclidean computer vision and machine learning algorithms must be treated carefully.” Financial supporters for this research include Univer-sitat Jaume I, Spain, Spanish Government, Spanish Government. The news correspondents obtained a quote from the research from Jaume I University, “A good approach is to use positive definite kernels on manifolds to embed the manifold with its corresponding metric in a high-dimensional reproducing kernel Hilbert space, where it is possible to utilize algorithms developed for linear spaces. Different Gaussian kernels can be found in the literature on the 2-D Kendall shape space to perform this embedding. The main novelty of this work is to provide a Gaussian kernel for the m-D Kendall shape space. This new Kernel coincides in the case m = 2 with the Gaussian kernels most widely used in the Kendall planar shape space and allows to define an embedding of the m-D Kendall shape space into a reproducible kernel Hilbert space. As far as we know, the complexity of the m-D Kendall shape space has meant that this embedding has not been addressed in the literature until now.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    University of Padova Reports Findings in Malaria (A machine learning approach for early identification of patients with severe imported malaria)

    50-50页
    查看更多>>摘要：New research on Mosquito-Borne Diseases - Malaria is the subject of a report. According to news reporting originating from Padua, Italy, by NewsRx correspondents, research stated, “The aim of this study is to design ad hoc malaria learning (ML) approaches to predict clinical outcome in all patients with imported malaria and, therefore, to identify the best clinical setting. This is a singlecentre cross-sectional study, patients with confirmed malaria, consecutively hospitalized to the Lazzaro Spallanzani National Institute for Infectious Diseases, Rome, Italy from January 2007 to December 2020, were recruited.” Financial support for this research came from Ministero della Salute. Our news editors obtained a quote from the research from the University of Padova, “Different ML approaches were used to perform the analysis of this dataset: support vector machines, random forests, feature selection approaches and clustering analysis. A total of 259 patients with malaria were enrolled, 89.5% patients were male with a median age of 39 y/o. In 78.3% cases, Plasmodium falciparum was found. The patients were classified as severe malaria in 111 cases. From ML analyses, four parameters, AST, platelet count, total bilirubin and parasitaemia, are associated to a negative outcome. Interestingly, two of them, aminotransferase and platelet are not included in the current list of World Health Organization (WHO) criteria for defining severe malaria.”
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Rice University Reports Findings in Machine Learning (Toward Controlled Synthesis of 2D Crystals by CVD: Learning from the Real- Time Crystal Morphology Evolutions)

    51-51页
    查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news reporting from Houston, Texas, by NewsRx journalists, research stated, “The rich morphology of 2D materials grown through chemical vapor deposition (CVD), is a distinctive feature. However, understanding the complex growth of 2D crystals under practical CVD conditions remains a challenge due to various intertwined factors.” The news correspondents obtained a quote from the research from Rice University, “Real-time monitoring is crucial to providing essential data and enabling the use of advanced tools like machine learning for unraveling these complexities. In this study, we present a custom-built miniaturized CVD system capable of observing and recording 2D MoS crystal growth in real time. Image processing converts the real-time footage into digital data, and machine learning algorithms (ML) unveil the significant factors influencing growth. The machine learning model successfully predicts CVD growth parameters for synthesizing ultralarge monolayer MoS crystals. It also demonstrates the potential to reverse engineer CVD growth parameters by analyzing the as-grown 2D crystal morphology.”
      原文链接:
    • NETL
    • NSTL

    New Findings Reported from Poznan University of Technology Describe Advances in Robotics (Predefined-time Vfo Control Design for Unicycle-like Mobile Robots)

    51-52页
    查看更多>>摘要：Research findings on Robotics are discussed in a new report. According to news reporting out of Poznan, Poland, by NewsRx editors, research stated, “The paper presents a predefinedtime Vector-Field-Orientation (VFO) control law for unicycle-like nonholonomic mobile robots. We consider a set-point control problem in the presence of strict time constraints, which has to guarantee satisfaction of a prescribed upper bound of the settling time for the configuration errors.” Financial support for this research came from Politechnika Poznanacute;ska. Our news journalists obtained a quote from the research from the Poznan University of Technology, “The control law is based on the VFO methodology, which is characterized by non-oscillatory and wellpredictable time evolution of transient states for unicycle-like robots. A formal stability analysis based on the Lyapunov theory is provided for the closed-loop dynamics. Then, the results of extensive numerical simulations as well as experimental tests illustrate the resultant control performance where time constraints are considered.”
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Research from Technology Innovation Institute Yields New Findings on Machine Learning (Characterization of a Transmon Qubit in a 3D Cavity for Quantum Machine Learning and Photon Counting)

    52-53页
    查看更多>>摘要：Investigators publish new report on artificial intelligence. According to news reporting originating from the Technology Innovation Institute by NewsRx correspondents, research stated, “In this paper, we report the use of a superconducting transmon qubit in a 3D cavity for quantum machine learning and photon counting applications.” Financial supporters for this research include Pnrr Mur Project; Infn Csnv Project Qubit. Our news correspondents obtained a quote from the research from Technology Innovation Institute: “We first describe the realization and characterization of a transmon qubit coupled to a 3D resonator, providing a detailed description of the simulation framework and of the experimental measurement of important parameters, such as the dispersive shift and the qubit anharmonicity. We then report on a Quantum Machine Learning application implemented on a single-qubit device to fit the u-quark parton distribution function of the proton.” According to the news reporters, the research concluded: “In the final section of the manuscript, we present a new microwave photon detection scheme based on two qubits coupled to the same 3D resonator. This could in principle decrease the dark count rate, favoring applications like axion dark matter searches.”
      原文链接:
    • NETL
    • NSTL
    • Mdpi

    Reports Summarize Robotics Findings from Bohai University (Adaptive Fuzzy Finite-time Command Filtering Control for Flexible-joint Robot Systems Against Multiple Actuator Constraints)

    53-54页
    查看更多>>摘要：Current study results on Robotics have been published. According to news reporting out of Jinzhou, People’s Republic of China, by NewsRx editors, research stated, “This brief focuses on the issue of fuzzy finite-time position tracking control for single-link flexible-joint robotic systems subject to multiple actuator constraints. At first, fuzzy logic systems are invoked to estimate completely unknown nonlinear functions, which can appropriately overcome heavy calculations.” Financial support for this research came from National Natural Science Foundation of China (NSFC). Our news journalists obtained a quote from the research from Bohai University, “Next, the inherent computational complexity problem is eliminated via adopting command filter technology and the correlative error compensation mechanism is exploited to mitigate the influence of the errors brought by the filter. Further, the developed controller not only assures the semi-global finite-time stable of the controlled system, but also makes the tracking error enter a small region around the origin within fast finite time.” According to the news editors, the research concluded: “The significance and potential of the presented control technique can be testified through simulation results.”
      原文链接:
    • NETL
    • NSTL
    • IEEE

    Hospital da Luz Reports Findings in Prostate Cancer (Clinical application of machine learning models in patients with prostate cancer before prostatectomy)

    54-55页
    查看更多>>摘要：New research on Oncology - Prostate Cancer is the subject of a report. According to news reporting from Lisbon, Portugal, by NewsRx journalists, research stated, “To build machine learning predictive models for surgical risk assessment of extracapsular extension (ECE) in patients with prostate cancer (PCa) before radical prostatectomy; and to compare the use of decision curve analysis (DCA) and receiver operating characteristic (ROC) metrics for selecting input feature combinations in models. This retrospective observational study included two independent data sets: 139 participants from a single institution (training), and 55 from 15 other institutions (external validation), both treated with Robotic Assisted Radical Prostatectomy (RARP).” The news correspondents obtained a quote from the research from Hospital da Luz, “Five ML models, based on different combinations of clinical, semantic (interpreted by a radiologist) and radiomics features computed from T2W-MRI images, were built to predict extracapsular extension in the prostatectomy specimen (pECE+). DCA plots were used to rank the models’ net benefit when assigning patients to prostatectomy with non-nerve-sparing surgery (NNSS) or nerve-sparing surgery (NSS), depending on the predicted ECE status. DCA model rankings were compared with those drived from ROC area under the curve (AUC). In the training data, the model using clinical, semantic, and radiomics features gave the highest net benefit values across relevant threshold probabilities, and similar decision curve was observed in the external validation data. The model ranking using the AUC was different in the discovery group and favoured the model using clinical + semantic features only.”
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Beijing Anzhen Hospital of Capital Medical University Reports Findings in Machine Learning (Machine learning-based coronary artery calcium score predicted from clinical variables as a prognostic indicator in patients referred for invasive ...)

    55-56页
    查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news reporting out of Beijing, People’s Republic of China, by NewsRx editors, research stated, “Utilising readily available clinical variables, we aimed to develop and validate a novel machine learning (ML) model to predict severe coronary calcification, and further assessed its prognostic significance. This retrospective study enrolled patients who underwent coronary CT angiography and subsequent invasive coronary angiography.” Our news journalists obtained a quote from the research from the Beijing Anzhen Hospital of Capital Medical University, “Multiple ML algorithms were used to train the models for predicting severe coronary calcification (cardiac CT-measured coronary artery calcium [CT-CAC] score 400). The ML-based CAC (MLCAC) score derived from the ML predictive probability was stratified into quartiles for prognostic analysis. The primary endpoint was a composite of all-cause death, nonfatal myocardial infarction, or nonfatal stroke. Overall, 5785 patients were divided into training (80%) and test sets (20%). For clinical practicability, we selected the nine-feature support vector machine model with good and satisfactory performance regarding both discrimination and calibration based on five repetitions of the 10-fold cross-validation in the training set (mean AUC = 0.715, Brier score = 0.202), and based on the test in the test set (AUC = 0.753, Brier score = 0.191). In the test set cohort (n = 1137), the primary endpoint was observed in 50 (4.4%) patients during a median 2.8 years’ follow-up. The ML-CAC system was significantly associated with an increased risk of the primary endpoint (adjusted hazard ratio for trend 2.26, 95% CI 1.35-3.79, p = 0.002). There was no significant difference in the prognostic value between the ML-CAC and CT-CAC systems (C-index, 0.67 vs. 0.69; p = 0.618). ML-CAC score predicted from clinical variables can serve as a novel prognostic indicator in patients referred for invasive coronary angiography.”
      原文链接:
    • NETL
    • NSTL

    University of Hong Kong-Shenzhen Hospital Reports Findings in Artificial Intelligence (Construction of disulfidptosis-based immune response prediction model with artificial intelligence and validation of the pivotal grouping oncogene c-MET in ...)

    56-57页
    查看更多>>摘要：New research on Artificial Intelligence is the subject of a report. According to news reporting originating in Guangdong, People’s Republic of China, by NewsRx journalists, research stated, “Given the lack of research on disulfidptosis, our study aimed to dissect its role in pan-cancer and explore the crosstalk between disulfidptosis and cancer immunity. Based on TCGA, ICGC, CGGA, GSE30219, GSE31210, GSE37745, GSE50081, GSE22138, GSE41613, univariate Cox regression, LASSO regression, and multivariate Cox regression were used to construct the rough gene signature based on disulfidptosis for each type of cancer.” The news reporters obtained a quote from the research from the University of Hong Kong-Shenzhen Hospital, “SsGSEA and Cibersort, followed by correlation analysis, were harnessed to explore the linkage between disulfidptosis and cancer immunity. Weighted correlation network analysis (WGCNA) and Machine learning were utilized to make a refined prognosis model for pan-cancer. In particular, a customized, enhanced prognosis model was made for glioma. The siRNA transfection, FACS, ELISA, etc., were employed to validate the function of c-MET. The expression comparison of the disulfidptosis-related genes (DRGs) between tumor and nontumor tissues implied a significant difference in most cancers. The correlation between disulfidptosis and immune cell infiltration, including T cell exhaustion (Tex), was evident, especially in glioma. The 7-gene signature was constructed as the rough model for the glioma prognosis. A pan-cancer suitable DSP clustering was made and validated to predict the prognosis. Furthermore, two DSP groups were defined by machine learning to predict the survival and immune therapy response in glioma, which was validated in CGGA. PD-L1 and other immune pathways were highly enriched in the core blue gene module from WGCNA. Among them, c-MET was validated as a tumor driver gene and JAK3-STAT3-PD-L1/PD1 regulator in glioma and T cells. Specifically, the down-regulation of c-MET decreased the proportion of PD1+ CD8+ T cells. To summarize, we dissected the roles of DRGs in the prognosis and their relationship with immunity in pan-cancer. A general prognosis model based on machine learning was constructed for pan-cancer and validated by external datasets with a consistent result. In particular, a survival-predicting model was made specifically for patients with glioma to predict its survival and immune response to ICIs.”
      原文链接:
    • NETL
    • NSTL
    • Frontiers Media Sa