查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Artificial In telligence have been published. According to news reporting out of Detroit, Mich igan, by NewsRx editors, research stated, “The American Medical Association reco mmends health information to be written at a 6th grade level reading level. Our aim was to determine whether Artificial Intelligence can outperform the existing health information on kidney stone prevention and treatment.” Our news journalists obtained a quote from the research from Henry Ford Hospital , “The top 50 search results for ‘Kidney Stone Prevention’and ‘Kidney Stone Trea tment’on Google, Bing, and Yahoo were selected. Duplicate webpages, advertisemen ts, pages intended for health professionals such as science articles, links to v ideos, paid subscription pages, and links nonrelated to kidney stone prevention and/or treatment were excluded. Included pages were categorized into academic, h ospital -affiliated, commercial, nonprofit foundations, and other. Quality and r eadability of webpages were evaluated using validated tools, and the reading lev el was descriptively compared with ChatGPT generated health information on kidne y stone prevention and treatment. 50 webpages on kidney stone prevention and 49 on stone treatment were included in this study. The reading level was determined to equate to that of a 10th to 12th grade student. Quality was measured as ‘fai r’with no pages scoring ‘excellent’and only 20% receiving a ‘good’ quality. There was no significant difference between pages from academic, hospit al -affiliated, commercial, and nonprofit foundation publications. The text gene rated by ChatGPT was considerably easier to understand with readability levels m easured as low as 5th grade. The language used in existing information on kidney stone disease is of subpar quality and too complex to understand.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on robotics are disc ussed in a new report. According to news originating from Zhenjiang, People’s Re public of China, by NewsRx correspondents, research stated, “To address the comp lex structure of existing rod mechanism exoskeleton robots and the difficulty in solving the motion trajectory of multi-rod mechanisms, an exoskeleton knee robo t with a differential non-circular gear-pentarod mechanism is designed based on non-circular gears with arbitrary transmission ratios to constrain the degrees o f freedom of the R-para-rod mechanism.” Financial supporters for this research include Key R&D Plan of Zhen jiang City-modern Agriculture; Jiangsu Agriculture Science And Technology Innova tion Fund; China Postdoctoral Science Foundation; National Natural Science Found ation of China; Natural Science Foundation of Jiangsu Province; Key Laboratory o f Modern Agricultural Equipment And Technology; High-tech Key Laboratory of Agri cultural Equipment And Intelligence of Jiangsu Province; Priority Academic Progr am Development of Jiangsu Higher Education Institutions.
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Machine Learn ing have been published. According to news reporting originating from Boston, Ma ssachusetts, by NewsRx correspondents, research stated, “Many studies using mach ine learning (ML) in speech, language, and hearing sciences rely upon cross -val idations with single data splitting. This study’s first purpose is to provide qu antitative evidence that would incentivize researchers to instead use the more r obust data splitting method of nested k fold cross -validation.” Financial support for this research came from NIH National Institute on Deafness & Other Communication Disorders (NIDCD).
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Researchers detail new data in Transportation - I ntelligent Vehicles. According to news reporting originating in Beijing, People’ s Republic of China, by NewsRx journalists, research stated, “The increasing imp ortance of mineral resources in contemporary society is becoming more prominent, playing an indispensable and crucial role in the global economy. These resource s not only provide essential raw materials for the global economic system but al so play an irreplaceable role in supporting the development of modern industry, technology, and infrastructure.” Financial support for this research came from National Key Research and Developm ent Program of China.
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Machine Learn ing have been published. According to news reporting from Groningen, Netherlands , by NewsRx journalists, research stated, “Machine learning interatomic potentia ls (ML-IAPs) enable quantum -accurate, classical molecular dynamics simulations of large systems, beyond reach of density functional theory (DFT). Yet, their ef ficiency and ability to predict systems larger than DFT supercells are not fully explored, posing a question regarding transferability to large-scale simulation s with defects (e.g. dislocations, cracks).” Financial supporters for this research include Center for Information Technology of the University of Groningen (UG), Faculty of Science and Engineering at the University of Groningen.
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New study results on artificial intell igence have been published. According to news reporting originating from Tehran, Iran, by NewsRx correspondents, research stated, “Land subsidence (LS) due to n atural processes or human activity can irreparably damage the environment. This study employed the quasi-permanent scatterer method to detect areas with and wit hout subsidence in the period from 2018 to 2020.” The news correspondents obtained a quote from the research from K.N. Toosi Unive rsity of Technology: “In addition, 12 factors affecting subsidence were selected to detect LS-prone areas. Information gain ratio (IGR) and frequency ratio meth ods were used to determine the importance and weighting of various factors and s ub-factors affecting subsidence. Novel approaches, including the standard adapti ve-networkbased fuzzy inference system (ANFIS) algorithm and its integration wi th the particle swarm optimization (PSO) algorithm, yielded LS maps. The models’ predictive performance was assessed using statistical indexes such as the root mean square error (RMSE), area under the receiver operating characteristic curve (AUROC) and confusion matrix criteria (e.g., sensitivity, specificity, precisio n, accuracy, and recall). Finally, Shapley additive explanations approach was us ed to explore the importance of features in modeling. The findings showed that t he subsidence pattern was V-shaped, averaging 321 mm/year. Ground-leveling and i nterferometric synthetic aperture radar measurements revealed a 0.93 correlation coefficient with a s = 1.43 mm/year deformation rate. Based on IGR analysis, aq uifer media, the decline of the water table, and aquifer thickness played pivota l roles in LS occurrences. In addition, the ANFIS-PSO model classified approxima tely 50.26 % of the study area as high and very high susceptibilit y. The AUROC values of ANFIS-PSO and ANFIS models for the training dataset were 0.912 and 0.807, respectively. For the testing dataset, the ANFIS-PSO model prod uced a higher AUROC value of 0.863, while the ANFIS model had a value of 0.771.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Oncology - Breast Canc er is the subject of a report. According to news reporting out of Shanghai, Peop le’s Republic of China, by NewsRx editors, research stated, “The most common rou te of breast cancer metastasis is through the mammary lymphatic network. An accu rate assessment of the axillary lymph node (ALN) burden before surgery can avoid unnecessary axillary surgery, consequently preventing surgical complications.” Our news journalists obtained a quote from the research from Shanghai Public Hea lth Clinical Center, “In this study, we aimed to develop a non-invasive predicti on model incorporating breast specific gamma image (BSGI) features and ultrasono graphic parameters to assess axillary lymph node status. Cohorts of breast cance r patients who underwent surgery between 2012 and 2021 were created (The trainin g set included 1104 ultrasound images and 940 BSGI images from 235 patients, the test set included 568 ultrasound images and 296 BSGI images from 99 patients) f or the development of the prediction model. six machine learning (ML) methods an d recursive feature elimination were trained in the training set to create a str ong prediction model. Based on the best-performing model, we created an online c alculator that can make a linear predictor in patients easily accessible to clin icians. The receiver operating characteristic (ROC) and calibration curve are us ed to verify the model performance respectively and evaluate the clinical effect iveness of the model. Six ultrasonographic parameters (transverse diameter of tu mour, longitudinal diameter of tumour, lymphatic echogenicity, transverse diamet er of lymph nodes, longitudinal diameter of lymph nodes, lymphatic color Doppler flow imaging grade) and one BSGI features (axillary mass status) were selected based on the best-performing model. In the test set, the support vector machines ’ model showed the best predictive ability (AUC = 0.794, sensitivity = 0.641, sp ecificity = 0.8, PPV = 0.676, NPV = 0.774 and accuracy = 0.737). The result in ROC showed the model could benefit from incorporating BSGI fea ture.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in Cerebro vascular Diseases and Conditions - Stroke. According to news originating from He idelberg, Germany, by NewsRx correspondents, research stated, “Quantitative and automated volumetric evaluation of early ischemic changes on non-contrast CT (NC CT) has recently been proposed as a new tool to improve prognostic performance i n patients undergoing endovascular therapy (EVT) for acute ischemic stroke (AIS) . We aimed to test its clinical value compared with the Alberta Stroke Program E arly CT Score (ASPECTS) in a large single-institutional patient cohort.” Financial support for this research came from Physician-Scientist Program of the Medical Faculty of the University of Heidelberg.
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Machine Learn ing have been published. According to news reporting out of Storrs, Connecticut, by NewsRx editors, research stated, “Monitoring the health of lithium-ion batte ries’ internal components as they age is crucial for optimizing cell design and usage control strategies. However, quantifying component-level degradation typic ally involves aging many cells and destructively analyzing them throughout the a ging test, limiting the scope of quantifiable degradation to the test conditions and duration.” Our news journalists obtained a quote from the research from the University of C onnecticut, “Fortunately, recent advances in physics-informed machine learning ( PIML) for modeling and predicting the battery state of health demonstrate the fe asibility of building models to predict the long-term degradation of a lithium-i on battery cell’s major components using only shortterm aging test data by lever aging physics. In this paper, we present four approaches for building physicsinf ormed machine learning models and comprehensively compare them, considering accu racy, complexity, ease-of-implementation, and their ability to extrapolate to un tested conditions. We delve into the details of each physics-informed machine le arning method, providing insights specific to implementing them on small battery aging datasets. Our study utilizes long-term cycle aging data from 24 implantab le-grade lithium-ion cells subjected to varying temperatures and C-rates over fo ur years.”
查看更多>>摘要：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 originating in London, United Kingdom, by NewsRx journalists, research stated, “Correct pressure broadening is essential for modelling radiative transfer in atmospheres, however data are lacking for th e many exotic molecules expected in exoplanetary atmospheres. Here we explore mo dern machine learning methods to mass produce pressure broadening parameters for a large number of molecules in the ExoMol data base.” Financial supporters for this research include European Research Council (ERC), STFC training grant.
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