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    Data from Northwestern Polytechnic University Advance Knowledge in Robotics (Center of Mass Dynamics and Contact-aided Invariant Filtering for Biped Robot State Estimation)

    58-58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 – New research on Robotics is the subject of a report. According to news reporting out of Xi’an, People’s Republic of China, by NewsRx editors, research stated, “Due to the complexity and uncertainty of the actual working environments, relying solely on proprioceptive sensors to obtain accurate floating base and center of mass (CoM) estimates is of great significance for biped robots. In this article, a biped locomotion state estimator aided by both CoM dynamics and leg forward kinematics is proposed.” Our news journalists obtained a quote from the research from Northwestern Polytechnic University, “The main contribution of this estimator is the use of contact force measurements that are not considered in existing methods. Contact force measurements can be used to predict CoM motions and update the floating base estimates with CoM forward kinematics. Compared with the leg forward kinematics, the CoM dynamics prediction and the CoM forward kinematics update are more robust to contact slippage and highly dynamic motions. The simulation results show that the estimator proposed in this article improves the estimation accuracy of the floating base in the slippage direction under various reference speeds.”

    Second Hospital of Shanxi Medical University Reports Findings in Non-Alcoholic Fatty Liver Disease (A combined analysis of TyG in- dex, SII index, and SIRI index: positive association with CHD risk and coronary atherosclerosis severity in ...)

    59-60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 Liver Diseases and Conditions - Non-Alcoholic Fatty Liver Disease is the subject of a report. According to news reporting from Shanxi, People’s Republic of China, by NewsRx journalists, research stated, “Insulin resistance(IR) and inflammation have been regarded as com- mon potential mechanisms in coronary heart disease (CHD) and non-alcoholic fatty liver disease (NAFLD). Triglyceride-glucose (TyG) index is a novel biomarker of insulin resistance, System immune-inflammation index(SII) and Systemic inflammation response index(SIRI) are novel biomarkers of inflammation, these biomarkers have not been studied in CHD with NAFLD patients.”

    New Robotics Findings from Huazhong University of Science and Technology Reported (Research On the Directionality of End Dy- namic Compliance Dominated By Milling Robot Body Structure and Milling Vibration Suppression)

    60-61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 – Researchers detail new data in Robotics. According to news originating from Wuhan, People’s Republic of China, by NewsRx correspondents, research stated, “The end dynamic characteristics dominated by the milling robot’s body structure play a crucial role in vibration control and chatter avoidance in robotic milling. As the excitation source, the milling force may exist in any direction under different process parameters.” Financial support for this research came from National Natural Science Foundation of China (NSFC).

    Study Results from Dalian University of Technology Provide New Insights into Machine Learning (Machine Learning Powered Sketch Aided Design Via Topology Optimization)

    61-62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 – Researchers detail new data in Machine Learning. According to news reporting origi- nating in Dalian, People’s Republic of China, by NewsRx journalists, research stated, “Structural topology optimization is an important design tool in the conceptual design phase of a product. However, the current topology optimization design is mostly driven strictly based on mathematical and mechanical models.” Financial supporters for this research include National Natural Science Foundation of China (NSFC), National Natural Science Foundation of China (NSFC), Liao Ning Revitalization Talents Program, Fun- damental Research Funds for the Central Universities, Program for Changjiang Scholars & Innovative Research Team in University (PCSIRT), Ministry of Education, China - 111 Project.

    Hainan Vocational University of Science and Technology Re- searchers Illuminate Research in Support Vector Machines (Inno- vative deep learning techniques for monitoring aggressive behavior in social media posts)

    62-63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 support vector machines have been presented. According to news originating from Hainan Vocational University of Science and Technology by NewsRx correspondents, research stated, “The study aims to evaluate and compare the performance of various machine learning (ML) classifiers in the context of detecting cyber-trolling behaviors. With the rising prevalence of online harassment, developing effective automated tools for aggression detection in digital communications has become imperative.”

    Anhui Medical University Reports Findings in Lung Cancer (Pre- diction of positive pulmonary nodules based on machine learning algorithm combined with central carbon metabolism data)

    63-64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 Oncology - Lung Cancer is the subject of a report. According to news reporting originating from Anhui, People’s Republic of China, by NewsRx correspondents, research stated, “Lung cancer causes a huge disease burden, and early detection of positive pulmonary nodules (PPNs) as an early sign of lung cancer is extremely important for effective intervention. It is necessary to develop PPNs risk recognizer based on machine learning algorithm combined with central carbon metabolomics.” Our news editors obtained a quote from the research from Anhui Medical University, “The study included 2248 participants at high risk for lung cancer from the Ma’anshan Community Lung Cancer Screening cohort. The Least Absolute Shrinkage and Selection Operator (LASSO) was used to screen 18 central carbon-related metabolites in plasma, recursive feature elimination (RFE) was used to select all 42 features, followed by five machine learning algorithms for model development. The performance of the model was evaluated using area under the receiver operator characteristic curve (AUC), accuracy, precision, recall, and F1 scores. In addition, SHapley Additive exPlanations (SHAP) was performed to assess the interpretability of the final selected model and to gain insight into the impact of features on the predicted results. Finally, the two prediction models based on the random forest (RF) algorithm performed best, with AUC values of 0.87 and 0.83, respectively, better than other models. We found that homogentisic acid, fumaric acid, maleic acid, hippuric acid, gluconic acid, and succinic acid played a significant role in both PPNs prediction model and NPNs vs PPNs model, while 2-oxadipic acid only played a role in the former model and phosphopyruvate only played a role in the NPNs vs PPNs model. This model demonstrates the potential of central carbon metabolism for PPNs risk prediction and identification.”

    Researchers from University of Edinburgh Describe Findings in Ma- chine Learning [Estimating Gas Sorption In Polymeric Membranes From the Molecular Structure: a Machine Learning Based Group Contribution Method for the Non-equilibrium Lattice Fluid ...]

    64-65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 originating from Edinburgh, United Kingdom, by NewsRx correspondents, research stated, “Since its incep- tion, the non-equilibrium lattice fluid (NELF) model has become a vital tool in correlating and predicting the gas solubility behaviour in glassy polymeric membranes. But like its equilibrium variant, the NELF model is highly constrained by the availability of the pure polymer characteristic parameters, which are not always convenient to obtain as the need arises.” Financial support for this research came from Royal Society of Edinburgh (RSE).

    University of Ioannina Researcher Discusses Findings in Machine Learning (Machine Learning for Predicting Neurodevelopmental Disorders in Children)

    65-66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 – A new study on artificial intelligence is now available. According to news reporting out of Ioannina, Greece, by NewsRx editors, research stated, “Developmental domains like physical, verbal, cognitive, and social-emotional skills are crucial for monitoring a child’s growth.” Financial supporters for this research include “smart Computing Models, Sensors, And Early Diagnostic Speech And Language Deficiencies Indicators in Child Communication”; European Regional Development Fund.

    Researcher from University of South Florida Details Findings in Ar- tificial Intelligence (How will artificial intelligence and Industry 4.0 emerging technologies transform operations management?)

    66-66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 – A new study on artificial intelligence is now available. According to news originating from Tampa, United States, by NewsRx editors, the research stated, “Emerging technologies such as artificial intelligence, blockchain, additive manufacturing, advanced robotics, autonomous vehicles, and the Internet of Things are frequently mentioned as part of “Industry 4.0.” As such, how will they influence operations and supply chain management? We answer this question by providing a brief review of the evolution of technologies and operations management (OM) over time.”

    University of Gothenburg Reports Findings in Type 2 Diabetes (Identifying top ten predictors of type 2 diabetes through machine learning analysis of UK Biobank data)

    67-67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 Nutritional and Metabolic Diseases and Conditions - Type 2 Diabetes is the subject of a report. According to news reporting originating in Gothenburg, Sweden, by NewsRx journalists, research stated, “The study aimed to identify the most predictive factors for the development of type 2 diabetes. Using an XGboost classification model, we projected type 2 diabetes incidence over a 10-year horizon.” The news reporters obtained a quote from the research from the University of Gothenburg, “We delib- erately minimized the selection of baseline factors to fully exploit the rich dataset from the UK Biobank. The predictive value of features was assessed using shap values, with model performance evaluated via Receiver Operating Characteristic Area Under the Curve, sensitivity, and specificity. Data from the UK Biobank, encompassing a vast population with comprehensive demographic and health data, was employed. The study enrolled 450,000 participants aged 40-69, excluding those with pre-existing diabetes. Among 448,277 participants, 12,148 developed type 2 diabetes within a decade. HbA1c emerged as the foremost predictor, followed by BMI, waist circumference, blood glucose, family history of diabetes, gamma-glutamyl transferase, waist-hip ratio, HDL cholesterol, age, and urate. Our XGboost model achieved a Receiver Op- erating Characteristic Area Under the Curve of 0.9 for 10-year type 2 diabetes prediction, with a reduced 10-feature model achieving 0.88. Easily measurable biological factors surpassed traditional risk factors like diet, physical activity, and socioeconomic status in predicting type 2 diabetes. Furthermore, high predic- tion accuracy could be maintained using just the top 10 biological factors, with additional ones offering marginal improvements.”