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    First Affiliated Hospital of Wenzhou Medical University Reports Findings in Machine Learning (Analyzing predictors of in-hospital mortality in patients with acute ST-segment elevation myocardial infarction using an evolved machine learning ...)

    11-12页
    查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news reporting out of Zhejiang, People’s Republic of China, by NewsRx editors, research stated, “Acute STsegment elevation myocardial infarction (STEMI) is a severe cardiac ailment characterized by the sudden complete blockage of a portion of the coronary artery, leading to the interruption of blood supply to the myocardium. This study examines the medical records of 3205 STEMI patients admitted to the coronary care unit of the First Affiliated Hospital of Wenzhou Medical University from January 2014 to December 2021.” Our news journalists obtained a quote from the research from the First Affiliated Hospital of Wenzhou Medical University, “In this research, a novel predictive framework for STEMI is proposed, incorporating evolutionary computational methods and machine learning techniques. A variant algorithm, AGCOSCA, is introduced by integrating crossover operation and observation bee strategy into the original Sine Cosine Algorithm (SCA). The effectiveness of AGCOSCA is initially validated using IEEE CEC 2017 benchmark functions, demonstrating its ability to mitigate the deficiency in local mining after SCA random perturbation. Building upon this foundation, the AGCOSCA approach has been paired with Support Vector Machine (SVM) to forge the predictive framework referred to as AGCOSCA-SVM. Specifically, AGCOSCA is employed to refine the selection of predictors from a substantial feature set before SVM is utilized to forecast the occurrence of STEMI. In our analysis, we observed that SVM excels at managing nonlinear data relationships, a strength that becomes particularly prominent in smaller datasets of STEMI patients. To assess the effectiveness of AGCOSCA-SVM, diagnostic experiments were conducted based on the STEMI sample data. AGCOSCA-SVM outperforms traditional machine learning methods, achieving superior Accuracy, Sensitivity, and Specificity values of 97.83 %, 93.75 %, and 96.67 %, respectively. The selected features, such as acute kidney injury (AKI) stage, fibrinogen, mean platelet volume (MPV), free triiodothyronine (FT3), diuretics, and Killip class during hospitalization, are identified as crucial for predicting STEMI.”
      原文链接:
    • NETL
    • NSTL

    Studies from Anhui Jianzhu University Yield New Data on Machine Learning (Identifying Nonlinear Effects of Factors On Hit-and-run Crashes Using Interpretable Machine Learning Techniques)

    12-12页
    查看更多>>摘要：Investigators discuss new findings in Machine Learning. According to news reporting originating from Hefei, People’s Republic of China, by NewsRx correspondents, research stated, “Previous research on hit-and-run crashes employed regression methods or machine learning techniques. However, regression methods necessitate preestablished model formulations, making it challenging to accommodate intricate nonlinear effects.” Financial support for this research came from China Postdoctoral Science Foundation. Our news editors obtained a quote from the research from Anhui Jianzhu University, “In contrast, machine learning methods are characterized as black box systems, lacking interpretability. Thus, we propose an innovative analytical framework that combines data-driven machine learning algorithms with emerging interpretation techniques. The complex nonlinear effects of various factors on hit-and-run crashes are investigated by employing post hoc interpretation techniques, specifically, Shapley Additive exPlanations and accumulated local effect. The results demonstrate that machine learning algorithms are superior in accounting for complex relationships among influencing factors and identifying hit-and-run crashes. The quantitative importance of various factors is estimated and compared to reveal key determinants such as visibility, road location, and accident liability. The complex effects of different factors on hit-and-run crashes are unveiled, delineating quantitative piecewise nonlinear patterns. These patterns, which are difficult to capture using conventional regression models with predefined formulations, shed light on the nuanced dynamics of hit-and-run crashes.”
      原文链接:
    • NETL
    • NSTL
    • Taylor & Francis

    Findings on Machine Learning Reported by Investigators at University of Lorraine (Predicting the Abundances of Aphids and Their Natural Enemies In Cereal Crops: Machine-learning Versus Linear Models)

    13-13页
    查看更多>>摘要：Investigators publish new report on Machine Learning. According to news reporting out of Nancy, France, by NewsRx editors, research stated, “Predicting the effects of crop management and landscape structure on biological pest control is a key challenge for implementing innovative pest management systems. Here, we compare the performances of two machine-learning methods (regression trees and random forests) with those of linear models in predicting cereal aphid abundance, parasitism, and natural enemies.” Financial support for this research came from CASDAR ‘ARENA’ Program from the French ministry of Agriculture and Food.
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    University Health Network Reports Findings in Psoriatic Arthritis (Classifying patients with psoriatic arthritis according to their disease activity status using serum metabolites and machine learning)

    14-15页
    查看更多>>摘要：New research on Autoimmune Diseases and Conditions - Psoriatic Arthritis is the subject of a report. According to news reporting originating from Toronto, Canada, by NewsRx correspondents, research stated, “Psoriatic arthritis (PsA) is a heterogeneous inflammatory arthritis, affecting approximately a quarter of patients with psoriasis. Accurate assessment of disease activity is difficult.” Our news editors obtained a quote from the research from University Health Network, “There are currently no clinically validated biomarkers to stratify PsA patients based on their disease activity, which is important for improving clinical management. To identify metabolites capable of classifying patients with PsA according to their disease activity. An in-house solid-phase microextraction (SPME)-liquid chromatography-high resolution mass spectrometry (LC-HRMS) method for lipid analysis was used to analyze serum samples obtained from patients classified as having low (n = 134), moderate (n = 134) or high (n = 104) disease activity, based on psoriatic arthritis disease activity scores (PASDAS). Metabolite data were analyzed using eight machine learning methods to predict disease activity levels. Top performing methods were selected based on area under the curve (AUC) and significance. The best model for predicting high disease activity from low disease activity achieved AUC 0.818. The best model for predicting high disease activity from moderate disease activity achieved AUC 0.74. The best model for classifying low disease activity from moderate and high disease activity achieved AUC 0.765. Compounds confirmed by MS/MS validation included metabolites from diverse compound classes such as sphingolipids, phosphatidylcholines and carboxylic acids. Several lipids and other metabolites when combined in classifying models predict high disease activity from both low and moderate disease activity. Lipids of key interest included lysophosphatidylcholine and sphingomyelin.”
      原文链接:
    • NETL
    • NSTL

    Findings from Western University Provide New Insights into Machine Learning (Automated large-scale tornado treefall detection and directional analysis using machine learning)

    14-14页
    查看更多>>摘要：Investigators discuss new findings in artificial intelligence. According to news originating from London, Canada, by NewsRx correspondents, research stated, “In many regions of the world, tornadoes travel through forested areas with low population densities, making downed trees the only observable damage indicator.” The news editors obtained a quote from the research from Western University: “Current methods in the EF scale for analyzing tree damage may not reflect the true intensity of some tornadoes. However, new methods have been developed that use the number of trees downed or treefall directions from highresolution aerial imagery to provide an estimate of maximum wind speed. Treefall Identification and Direction Analysis (TrIDA) maps are used to identify areas of treefall damage and treefall directions along the damage path. Currently, TrIDA maps are generated manually, but this is labor-intensive, often taking several days or weeks. To solve this, this paper describes a machine learning and image processing-based model that automatically extracts fallen trees from large-scale aerial imagery, assesses their fall directions, and produces an area-averaged treefall vector map with minimal initial human interaction.”
      原文链接:
    • NETL
    • NSTL

    Research Results from Faculty of Mechanical Engineering Update Knowledge of Robotics (Force and Pressure Dependent Asymmetric Workspace Research of a Collaborative Robot and Human)

    15-17页
    查看更多>>摘要：Investigators publish new report on robotics. According to news originating from the Faculty of Mechanical Engineering by NewsRx correspondents, research stated, “This article discusses creating a methodology for the asymmetric measuring of values and processes of collision forces and pressures of the collaborative robot dependent on time. Furthermore, it verifies the usefulness of this methodology in practice by successfully performing the experimental measurement and verifying the possibility of using these results by analysing and stating the collaboration level for a robot of the given type. According to the suggested methodology, the measurement results are a specific output based on real measured data, which can be easily rated and can quickly determine the collaborative level of any robot.”
      原文链接:
    • NETL
    • NSTL
    • Mdpi

    Researcher at Istanbul Sabahattin Zaim University Publishes New Study Findings on Robotics (Indoor surface classification for mobile robots)

    17-17页
    查看更多>>摘要：Current study results on robotics have been published. According to news originating from Istanbul Sabahattin Zaim University by NewsRx correspondents, research stated, “The ability to recognize the surface type is crucial for both indoor and outdoor mobile robots. Knowing the surface type can help indoor mobile robots move more safely and adjust their movement accordingly.” Financial supporters for this research include Scientific Research Projects (Bap) Through The Istanbul Sabahattin Zaim University. The news journalists obtained a quote from the research from Istanbul Sabahattin Zaim University: “However, recognizing surface characteristics is challenging since similar planes can appear substantially different; for instance, carpets come in various types and colors. To address this inherent uncertainty in vision-based surface classification, this study first generates a new, unique data set composed of 2,081 surface images (carpet, tiles, and wood) captured in different indoor environments. Secondly, the pre-trained state-of-the-art deep learning models, namely InceptionV3, VGG16, VGG19, ResNet50, Xception, InceptionResNetV2, and MobileNetV2, were utilized to recognize the surface type. Additionally, a lightweight MobileNetV2-modified model was proposed for surface classification. The proposed model has approximately four times fewer total parameters than the original MobileNetV2 model, reducing the size of the trained model weights from 42 MB to 11 MB. Thus, the proposed model can be used in robotic systems with limited computational capacity and embedded systems. Lastly, several optimizers, such as SGD, RMSProp, Adam, Adadelta, Adamax, Adagrad, and Nadam, are applied to distinguish the most efficient network. Experimental results demonstrate that the proposed model outperforms all other applied methods and existing approaches in the literature by achieving 99.52% accuracy and an average score of 99.66% in precision, recall, and F1-score.”
      原文链接:
    • NETL
    • NSTL
    • Peerj Inc

    Data on Machine Learning Described by Researchers at University of Technology Brunei (Optimization of Surface Roughness, Phase Transformation and Shear Bond Strength In Sandblasting Process of Ytzp Using Statistical Machine Learning)

    18-19页
    查看更多>>摘要：Data detailed on Machine Learning have been presented. According to news reporting out of Gadong, Brunei, by NewsRx editors, the research stated, “Sandblasting process is often applied to roughen the intaglio of yttria tetragonal zirconia polycrystals (YTZP) surfaces for easy and quality adhesion and micro-shear retention with dentine/resin cements. Sandblasting process parameters have shown to influence, at different scales, surface roughness, phase transformation and shear bond strength, all of which are referred, herein, as performance characteristics.” Our news journalists obtained a quote from the research from the University of Technology Brunei, “This study aimed to find the parametric settings of sandblasting parameters that could simultaneously optimize these performance characteristics, hypothetically testing the probability. YTZP surfaces were sandblasted at different levels of incidence angle (IA), abrasive particle size (AP), pressure(P) and sandblasting time (ST) following the Taguchi method based on the two-level parametric process settings (L8(27)). Surface morphologies, roughness (SR), monoclinic content (MC), and shear bond strength (SS) were characterized by the SEM, average surface roughness, XRD, and shear bond strength tests, respectively. Rough surfaces containing scratches, plastic deformation streaks, micro cracks and pitting were observed. According to the Taguchi method, the same optimum sandblasting parametric setting maximized SR and MC but failed to maximize SS. Subsequently, the principal component analysis embedded in statistical machine learning was applied to find the optimum sandblasting parametric setting that maximized all the performance characteristics. The optimum sandblasting setting of IA = 45 degrees, AP = 110 mu m, ST = 20 s and P = 400 kPa predicted the maximum values of SR = 0.773 mu m, MC = 36% and SS = 16.6 MPa. Analysis of variance confirmed AP and P as the most influencing parameters affecting all performance characteristics.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    University of Groningen Reports Findings in Artificial Intelligence (Artificial Intelligence-based Amide-Ⅱ Infrared Spectroscopy Simulation for Monitoring Protein Hydrogen Bonding Dynamics)

    19-19页
    查看更多>>摘要：New research on Artificial Intelligence is the subject of a report. According to news reporting from Groningen, Netherlands, by NewsRx journalists, research stated, “The structurally sensitive amide Ⅱ infrared (IR) bands of proteins provide valuable information about the hydrogen bonding of protein secondary structures, which is crucial for understanding protein dynamics and associated functions. However, deciphering protein structures from experimental amide Ⅱ spectra relies on time-consuming quantum chemical calculations on tens of thousands of representative configurations in solvent water.” The news correspondents obtained a quote from the research from the University of Groningen, “Currently, the accurate simulation of amide Ⅱ spectra for whole proteins remains a challenge. Here, we present a machine learning (ML)-based protocol designed to efficiently simulate the amide Ⅱ IR spectra of various proteins with an accuracy comparable to experimental results. This protocol stands out as a cost-effective and efficient alternative for studying protein dynamics, including the identification of secondary structures and monitoring the dynamics of protein hydrogen bonding under different pH conditions and during protein folding process.”
      原文链接:
    • NETL
    • NSTL

    Jinzhou Medical University Reports Findings in Obstructive Sleep Apnea (A machine learning model to predict the risk of depression in US adults with obstructive sleep apnea hypopnea syndrome: a cross-sectional study)

    20-21页
    查看更多>>摘要：New research on Respiratory Tract Diseases and Conditions - Obstructive Sleep Apnea is the subject of a report. According to news reporting originating from Jinzhou, People’s Republic of China, by NewsRx correspondents, research stated, “Depression is very common and harmful in patients with obstructive sleep apnea hypopnea syndrome (OSAHS). It is necessary to screen OSAHS patients for depression early.” Our news editors obtained a quote from the research from Jinzhou Medical University, “However, there are no validated tools to assess the likelihood of depression in patients with OSAHS. This study used data from the National Health and Nutrition Examination Survey (NHANES) database and machine learning (ML) methods to construct a risk prediction model for depression, aiming to predict the probability of depression in the OSAHS population. Relevant features were analyzed and a nomogram was drawn to visually predict and easily estimate the risk of depression according to the best performing model. This is a cross-sectional study. Data from three cycles (2005-2006, 2007-2008, and 2015-2016) were selected from the NHANES database, and 16 influencing factors were screened and included. Three prediction models were established by the logistic regression algorithm, least absolute shrinkage and selection operator (LASSO) algorithm, and random forest algorithm, respectively. The receiver operating characteristic (ROC) area under the curve (AUC), specificity, sensitivity, and decision curve analysis (DCA) were used to assess evaluate and compare the different ML models. The logistic regression model had lower sensitivity than the lasso model, while the specificity and AUC area were higher than the random forest and lasso models. Moreover, when the threshold probability range was 0.19-0.25 and 0.45-0.82, the net benefit of the logistic regression model was the largest. The logistic regression model clarified the factors contributing to depression, including gender, general health condition, body mass index (BMI), smoking, OSAHS severity, age, education level, ratio of family income to poverty (PIR), and asthma. This study developed three machine learning (ML) models (logistic regression model, lasso model, and random forest model) using the NHANES database to predict depression and identify influencing factors among OSAHS patients. Among them, the logistic regression model was superior to the lasso and random forest models in overall prediction performance.”
      原文链接:
    • NETL
    • NSTL
    • Frontiers Media Sa