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    Studies from Ain Shams University Add New Findings in the Area of Machine Learni ng (The Temperature Effect on Electric Vehicle’s Lithium-Ion Battery Aging Using Machine Learning Algorithm)

    38-39页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Research findings on artificial intelligence are discussed in a new report. According to news reporting from Cairo, Egypt, by New sRx journalists, research stated, “This paper offers a brief insight into predic ting the State of Health (SOH) of lithium-ion batteries in EVs using machine lea rning.” The news editors obtained a quote from the research from Ain Shams University: “ Accurate SOH assessment is crucial for optimizing electric vehicles (EVs’) perfo rmance and longevity. Employing supervised machine learning on a diverse battery dataset, the research develops a robust SOH estimation method. Various algorith ms are compared for efficacy, considering factors like temperature and charging patterns. Feature selection enhances model accuracy and efficiency. The proposed methodology offers promising real-world results, indicating high SOH prediction accuracy.”
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    University Hospital Zurich (USZ) Reports Findings in Surgical Technology (Learni ng curve of robotic assisted microsurgery in surgeons with different skill level s: a prospective preclinical study)

    39-40页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Surgery - Surgical Tec hnology is the subject of a report. According to news reporting originating from Zurich, Switzerland, by NewsRx correspondents, research stated, “Achieving prec ision in microsurgery requires skill, adequate instruments and magnification, as well as extensive training. Dedicated surgical robotic systems have enhanced an d expanded the application of (super- )microsurgical techniques by introducing mo tion scaling and providing improved surgeon ergonomics.” Financial supporters for this research include Open access funding provided by U niversity of Zurich, University of Zurich.
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    Studies from Arctic University of Norway (UiT) Describe New Findings in Artifici al Intelligence (Material Hardness Descriptor Derived By Symbolic Regression)

    40-41页
    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 originating from Tromso, Norwa y, by NewsRx correspondents, research stated, “Hardness is a materials’ property with implications in several industrial fields, including oil and gas, manufact uring, and others. However, the relationship between this macroscale property an d atomic (i.e., microscale) properties is unknown and in the last decade several models have unsuccessfully tried to correlate them in a wide range of chemical space.” Funders for this research include ISSCM SB RAS, Research Council of Norway, FRIP RO grant, NOTUR - The Norwegian Metacenter for Computational Science, Taif Unive rsity Research Support Project (Saudi Arabia).
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    Norwegian Institute of Public Health Reports Findings in Artificial Intelligence (Artificial Intelligence Algorithm for Subclinical Breast Cancer Detection)

    41-42页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on Artificial Intelligence is the su bject of a report. According to news reporting originating in Oslo, Norway, by N ewsRx journalists, research stated, “Early breast cancer detection is associated with lower morbidity and mortality. To examine whether a commercial artificial intelligence (AI) algorithm for breast cancer detection could estimate the devel opment of future cancer.” The news reporters obtained a quote from the research from the Norwegian Institu te of Public Health, “This retrospective cohort study of 116 495 women aged 50 t o 69 years with no prior history of breast cancer before they underwent at least 3 consecutive biennial screening examinations used scores from an AI algorithm (INSIGHT MMG, version 1.1.7.2; Lunit Inc; used September 28, 2022, to April 5, 2 023) for breast cancer detection and screening data from multiple, consecutive r ounds of mammography performed from September 13, 2004, to December 21, 2018, at 9 breast centers in Norway. The statistical analyses were performed from Septem ber 2023 to August 2024. Artificial intelligence algorithm score indicating susp icion for the presence of breast cancer. The algorithm provided a continuous can cer detection score for each examination ranging from 0 to 100, with increasing values indicating a higher likelihood of cancer being present on the current mam mogram. Maximum AI algorithm score for cancer detection and absolute difference in score among breasts of women developing screening-detected cancer, women with interval cancer, and women who screened negative. The mean (SD) age at the firs t study round was 58.5 (4.5) years for 1265 women with screening-detected cancer in the third round, 57.4 (4.6) years for 342 women with interval cancer after 3 negative screening rounds, and 56.4 (4.9) years for 116 495 women without breas t cancer all 3 screening rounds. The mean (SD) absolute differences in AI scores among breasts of women developing screening-detected cancer were 21.3 (28.1) at the first study round, 30.7 (32.5) at the second study round, and 79.0 (28.9) a t the third study round. The mean (SD) differences prior to interval cancer were 19.7 (27.0) at the first study round, 21.0 (27.7) at the second study round, an d 34.0 (33.6) at the third study round. The mean (SD) differences among women wh o did not develop breast cancer were 9.9 (17.5) at the first study round, 9.6 (1 7.4) at the second study round, and 9.3 (17.3) at the third study round. Areas u nder the receiver operating characteristic curve for the absolute difference wer e 0.63 (95% CI, 0.61-0.65) at the first study round, 0.72 (95% CI, 0.71-0.74) at the second study round, and 0.96 (95% CI, 0.95-0 .96) at the third study round for screening-detected cancer and 0.64 (95% CI, 0.61-0.67) at the first study round, 0.65 (95% CI, 0.62-0.68) at the second study round, and 0.77 (95% CI, 0.74-0.79) at the thi rd study round for interval cancers. In this retrospective cohort study of women undergoing screening mammography, mean absolute AI scores were higher for breas ts developing vs not developing cancer 4 to 6 years before their eventual detect ion.”
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    Federal University Uberlandia Researcher Has Provided New Study Findings on Mach ine Learning (Muscle Tone Assessment by Machine Learning Using Surface Electromy ography)

    42-43页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on ar tificial intelligence. According to news reporting from Uberlandia, Brazil, by N ewsRx journalists, research stated, “Muscle tone is defined as the resistance to passive stretch, but this definition is often criticized for its ambiguity sinc e some suggest it is related to a state of preparation for movement.” Funders for this research include National Council For Scientific And Technologi cal Development.
      原文链接:
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    Zhejiang University Reports Findings in Machine Learning (Machine Learning-Based Toxicological Modeling for Screening Environmental Obesogens)

    43-44页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting out of Hangzhou, People’s Rep ublic of China, by NewsRx editors, research stated, “The emerging presence of en vironmental obesogens, chemicals that disrupt energy balance and contribute to a dipogenesis and obesity, has become a major public health challenge. Molecular i nitiating events (MIEs) describe biological outcomes resulting from chemical int eractions with biomolecules.” Our news journalists obtained a quote from the research from Zhejiang University , “Machine learning models based on MIEs can predict complex toxic end points du e to chemical exposure and improve the interpretability of models. In this study , a system was constructed that integrated six MIEs associated with adipogenesis and obesity. This system showed high accuracy in external validation, with an a rea under the receiver operating characteristic curve of 0.78. Molecular hydroph obicity (SlogP_VSA) and direct electrostatic interactions (PEOE_ VSA) were identified as the two most critical molecular descriptors representing the obesogenic potential of chemicals. This system was further used to predict the obesogenic effects of chemicals on the candidate list of substances of very high concern (SVHCs). Results from 3T3-L1 adipogenesis assays verified that the system correctly predicted obesogenic or nonobesogenic effects of 10 of the 12 S VHCs tested, and identified four novel potential obesogens, including 2-benzotri azol-2-yl-4,6-dibutylphenol (UV-320), 4-(1,1,5-trimethylhexyl)phenol (p262-NP), 2-[4-(1,1,3,3-tetramethylbutyl)phenoxy] et hanol (OP1EO) and endosulfan.”
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    University of Health Sciences Reports Findings in Artificial Intelligence (Compa rative performance of artificial intelligence models in rheumatology board-level questions: evaluating Google Gemini and ChatGPT-4o)

    44-45页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Artificial Intelligenc e is the subject of a report. According to news originating from Istanbul, Turke y, by NewsRx correspondents, research stated, “This study evaluates the performa nce of AI models, ChatGPT-4o and Google Gemini, in answering rheumatology board- level questions, comparing their effectiveness, reliability, and applicability i n clinical practice. A cross-sectional study was conducted using 420 rheumatolog y questions from the BoardVitals question bank, excluding 27 visual data questio ns.” Our news journalists obtained a quote from the research from the University of H ealth Sciences, “Both artificial intelligence models categorized the questions a ccording to difficulty (easy, medium, hard) and answered them. In addition, the reliability of the answers was assessed by asking the questions a second time. T he accuracy, reliability, and difficulty categorization of the AI models’ respon se to the questions were analyzed. ChatGPT-4o answered 86.9% of th e questions correctly, significantly outperforming Google Gemini’s 60.2% accuracy (p <0.001). When the questions were asked a secon d time, the success rate was 86.7% for ChatGPT-4o and 60.5% for Google Gemini. Both models mainly categorized questions as medium difficulty . ChatGPT-4o showed higher accuracy in various rheumatology subfields, notably i n Basic and Clinical Science (p = 0.028), Osteoarthritis (p = 0.023), and Rheuma toid Arthritis (p <0.001). ChatGPT-4o significantly outper formed Google Gemini in rheumatology board-level questions. This demonstrates th e success of ChatGPT-4o in situations requiring complex and specialized knowledg e related to rheumatological diseases. The performance of both AI models decreas ed as the question difficulty increased. This study demonstrates the potential o f AI in clinical applications and suggests that its use as a tool to assist clin icians may improve healthcare efficiency in the future. Future studies using rea l clinical scenarios and real board questions are recommended.”
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    Research Conducted at University of Southern California (USC) HasUpdated Our Kn owledge about Machine Learning (The Social Constructionof Datasets: On the Prac tices, Processes, and Challengesof Dataset Creation for Machine Learning)

    45-46页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on Machine Learning are discussed in a new report. According to news reporting from Los Angeles, Cal ifornia, by NewsRx journalists, research stated, “Despite the critical role that datasets play in how systems make predictions and interpret the world, the dyna mics of their construction are not well understood. Drawing on a corpus of inter views with dataset creators, we uncover the messy and contingent realities of da taset preparation.” Funders for this research include Microsoft, Alfred P. Sloan Foundation.
      原文链接:
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    Cracow University of Technology Reports Findings in Colon Cancer (A Novel Approa ch for Predicting the Survival of Colorectal Cancer Patients Using Machine Learn ing Techniques and Advanced Parameter Optimization Methods)

    46-47页
    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 - Colon Cance r is the subject of a report. According to news originating from Krakow, Poland, by NewsRx correspondents, research stated, “Colorectal cancer is one of the mos t prevalent forms of cancer and is associated with a high mortality rate. Additi onally, an increasing number of adults under 50 are being diagnosed with the dis ease.” Our news journalists obtained a quote from the research from the Cracow Universi ty of Technology, “This underscores the importance of leveraging modern technolo gies, such as artificial intelligence, for early diagnosis and treatment support . Eight classifiers were utilized in this research: Random Forest, XGBoost, CatB oost, LightGBM, Gradient Boosting, Extra Trees, the k-nearest neighbor algorithm (KNN), and decision trees. These algorithms were optimized using the frameworks Optuna, RayTune, and HyperOpt. This study was conducted on a public dataset fro m Brazil, containing information on tens of thousands of patients. The models de veloped in this study demonstrated high classification accuracy in predicting on e-, three-, and five-year survival, as well as overall mortality and cancer-spec ific mortality. The CatBoost, LightGBM, Gradient Boosting, and Random Forest cla ssifiers delivered the best performance, achieving an accuracy of approximately 80% across all the evaluated tasks.”
      原文链接:
    • NETL
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    Studies in the Area of Robotics Reported from Shanghai Jiao Tong University (Vis uomotor Navigation for Embodied Robots With Spatial Memory and Semantic Reasonin g Cognition)

    47-48页
    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 Robotics have been published. According to news reporting originating in Shanghai, People’s R epublic of China, by NewsRx journalists, research stated, “The fundamental prere quisite for embodied agents to make intelligent decisions lies in autonomous cog nition. Typically, agents optimize decision-making by leveraging extensive spati otemporal information from episodic memory.” Financial support for this research came from National Natural Science Foundatio n of China (NSFC).
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    • NETL
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