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    Peking University Third Hospital Reports Findings in Osteonecrosis (Machine lear ning models to predict osteonecrosis in patients with femoral neck fractures und ergoing internal fixation)

    1-2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 Musculoskeletal Diseas es and Conditions - Osteonecrosis is the subject of a report. According to news reporting from Beijing, People’s Republic of China, by NewsRx journalists, resea rch stated, “This study aimed to use machine learning (ML) to establish risk fac tor and prediction models of osteonecrosis of the femoral head (ONFH) in patient s with femoral neck fractures (FNFs) after internal fixation. We retrospectively collected clinical data of patients with FNFs who were followed up for at least 2 years.” The news correspondents obtained a quote from the research from Peking Universit y Third Hospital, “Only intracapsular FNFs were included. In total, 437 patients and 24 variables were enrolled. The entire dataset was divided into training (8 9.5 %) and test (10.5 %) datasets. Six models-logistic regression, naive Bayes, decision tree, random forest, multilayer perceptron, a nd AdaBoost-were established and validated for predicting postoperative ONFH. We compared the area under the receiver operating characteristic curve (AUC), accu racy, recall, and F1 score of different models. In addition, a confusion matrix, density curve, and learning curve were used to evaluate the model performance. The logistic regression model performed best at predicting ONFH in patients with FNFs undergoing internal fixation surgery, with an AUC, accuracy, recall, F1 sc ore, and prediction value of 0.84, 0.89, 1.00, 0.94, and 89.1 %, re spectively. The learning and density curves demonstrated a good prediction fitti ng degree and distinct separation. When establishing the ML models, the reductio n quality, internal fixation removal, American Society of Anesthesiologists clas sification, injury mechanism, and displacement distance of the medial cortex wer e the top five risk factors positively correlated with the occurrence of ONFH. T he logistic regression model had excellent performance in predicting ONFH in pat ients with FNFs after internal fixation and could provide valuable guidance in c linical decision-making.”
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    New Machine Learning Study Findings Recently Were Reported by a Researcher at Un iversity of Pamplona (Sensory Perception Systems and Machine Learning Methods fo r Pesticide Detection in Fruits)

    2-2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 artificial intell igence are discussed in a new report. According to news reporting originating fr om Pamplona, Colombia, by NewsRx correspondents, research stated, “In this study , an electronic tongue (E-tongue) and electronic nose (E-nose) systems were appl ied to detect pesticide residues, specifically Preza, Daconil, Curzate, Bricol, Accros, Amistar, and Funlate, in fruits such as cape gooseberries, apples, plums , and strawberries.” Our news reporters obtained a quote from the research from University of Pamplon a: “These advanced systems present several advantages over conventional methods (e.g., GC-MS and others), including faster analysis, lower costs, ease of use, a nd portability. Additionally, they enable non-destructive testing and realtime monitoring, making them ideal for routine screenings and on-site analyses where effective detection is crucial. The collected data underwent rigorous analysis t hrough multivariate techniques, specifically principal component analysis (PCA) and linear discriminant analysis (LDA).”
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    Sichuan University Reports Findings in Machine Learning (Inverse design of skull osteoinductive implants with multi-level pore structures through machine learni ng)

    3-3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 originating from Chengdu, Peo ple’s Republic of China, by NewsRx correspondents, research stated, “How to accu rately design a personalized matching implant that can induce skull regeneration is the focus of current research. However, the design space for the porous stru cture of implants is extensive, and the mapping relationships between these stru ctures and their mechanical and osteogenic properties are complex.” Financial supporters for this research include National Key Research and Develop ment Program of China, National Natural Science Foundation of China.
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    Researchers from South China University of Technology Detail Findings in Robotic s (Investigation On Target Point Approaching Control of Bionic Robotic Fish In S tatic Flow)

    4-4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 Ro botics. According to news reporting originating from Guangzhou, People’s Republi c of China, by NewsRx correspondents, research stated, “It is wellknown that fi sh have the ability to adapt to complex water flow environments. However, most r esearch on bionic robotic fishes has focused on static water conditions.” Funders for this research include National Natural Science Foundation of China ( NSFC), National Natural Science Foundation of Guangdong Province, GDNRC, Guangzh ou applied Basic Research project.
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    Tianjin Hospital Reports Findings in Osteoporosis (Multi-omics Analysis to Ident ify Key Immune Genes for Osteoporosis based on Machine Learning and Single-cell Analysis)

    5-6页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on Musculoskeletal Diseases and Cond itions - Osteoporosis is the subject of a report. According to news reporting or iginating in Tianjin, People’s Republic of China, by NewsRx journalists, researc h stated, “Osteoporosis is a severe bone disease with a complex pathogenesis inv olving various immune processes. With the in-depth understanding of bone immune mechanisms, discovering new therapeutic targets is crucial for the prevention an d treatment of osteoporosis.” Financial support for this research came from National Key Research and Developm ent Program of China.
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    Studies from Tanta University Reveal New Findings on Machine Learning (Productiv ity Prediction of a Spherical Distiller Using a Machine Learning Model and Trian gulation Topology Aggregation Optimizer)

    6-7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 Ma chine Learning. According to news reporting originating from Tanta, Egypt, by Ne wsRx correspondents, research stated, “Solar stills offer a sustainable and envi ronmentally friendly solution to water scarcity in remote areas, but their limit ed productivity hinders their wider adoption. This study proposes innovative mod ifications to the spherical solar distiller to address this challenge.” Our news editors obtained a quote from the research from Tanta University, “We i ntroduce a rotating spherical ball within the distiller and investigate its impa ct on productivity at various speeds (0-2 rpm) with and without a wick. Addition ally, we explore the effectiveness of preheating feed water to different tempera tures (45-70 degrees C) and its interaction with the rotating ball mechanism. Mo reover, six machine learning models were employed to predict the water productiv ity of the distillers under different working conditions. The employed models we re standalone long short-term memory (LSTM), LSTM optimized by reptile search al gorithm, LSTM optimized by grey wolf optimizer, LSTM optimized by dwarf mongoose optimization algorithm, LSTM optimized by manta ray foraging optimizer, LSTM op timized by triangulation topology aggregation optimizer. The results showcased t hat with an optimal rotation speed of 0.5 rpm and 1 rpm for configurations with and without wick, respectively, we achieved productivity increases of 62 % and 55 %. Notably, preheating feed water to 65 degrees C further bo osted the new distiller performance, surpassing the conventional solar still by 91 %, achieving an impressive output of 6000-6200 mL/m2.day compare d to 3000-3250 mL/m2.day for the conventional distiller. Moreover, the thermal e fficiency of the new distiller configuration reached 62 %, almost d oubling that of the conventional distiller (32 %).”
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    Findings from University of West Attica Provide New Insights into Machine Learni ng (Laser Induced Fluorescence and Machine Learning: a Novel Approach To Micropl astic Identification)

    7-8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 Ma chine Learning. According to news reporting originating from Athens, Greece, by NewsRx correspondents, research stated, “Identifying the types of materials such as plastics, microplastics, and oil pollutants is essential for understanding t heir effects on marine life. We propose a new methodology for the real-time dete ction and identification of microplastics in aquatic environments.” Financial support for this research came from University of West Attica. Our news editors obtained a quote from the research from the University of West Attica, “Our experiments are based on a compact Laser Induced Fluorescence (LIF) device, with machine learning techniques applied to classify the materials. A 4 05 nm CW laser excitation source effectively induces fluorescence spectra in the visible spectrum from material samples that are either floating or submerged in water. We examine known plastic pollutants in seawater, including polyethylene (PE), polypropylene (PP), polystyrene (PS) and polyethylene terephthalate (PET), as well as maritime fuels, lubricating oils, and other organic substances that are abundant in the marine environment. Our two-step identification process firs t employs machine learning algorithms to distinguish microplastics from other or ganic materials with a high degree of accuracy (97.6%).”
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    New Machine Learning Findings from Kunming University Reported (Non-destructive Determination of Volatile Compounds and Prediction of Amino Acid Nitrogen During Sufu Fermentation Via Electronic Nose In Combination With Machine Learning ...)

    8-8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 Machine Learn ing have been published. According to news reporting from Kunming, People’s Repu blic of China, by NewsRx journalists, research stated, “Electronic nose along wi th different machine learning approaches was tested as tool to predict degree of sufu fermentation, with amino acid nitrogen content as marker. Consistent incre ase in E-nose signal values and amino acid nitrogen content were noted with ferm entation time.” Financial supporters for this research include Major Science and Technology Proj ects in Yunnan Province, Yunnan Academician Expert Workstation.
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    Xi’an Jiaotong University Reports Findings in Machine Learning (Machine learning value in the diagnosis of vertebral fractures: A systematic review and meta-ana lysis)

    9-9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 from Xi’an, People’s Republic of China, by NewsRx journalists, research stated, “To evaluate the diagnostic a ccuracy of machine learning (ML) in detecting vertebral fractures, considering v arying fracture classifications, patient populations, and imaging approaches. A systematic review and meta-analysis were conducted by searching PubMed, Embase, Cochrane Library, and Web of Science up to December 31, 2023, for studies using ML for vertebral fracture diagnosis.” The news correspondents obtained a quote from the research from Xi’an Jiaotong U niversity, “Bias risk was assessed using QUADAS-2. A bivariate mixed-effects mod el was used for the meta-analysis. Metaanalyses were performed according to fiv e task types (vertebral fractures, osteoporotic vertebral fractures, differentia tion of benign and malignant vertebral fractures, differentiation of acute and c hronic vertebral fractures, and prediction of vertebral fractures). Subgroup ana lyses were conducted by different ML models (including ML and DL) and modeling m ethods (including CT, X-ray, MRI, and clinical features). Eighty-one studies wer e included. ML demonstrated a diagnostic sensitivity of 0.91 and specificity of 0.95 for vertebral fractures. Subgroup analysis showed that DL (SROC 0.98) and C T (SROC 0.98) performed best overall. For osteoporotic fractures, ML showed a se nsitivity of 0.93 and specificity of 0.96, with DL (SROC 0.99) and X-ray (SROC 0 .99) performing better. For differentiating benign from malignant fractures, ML achieved a sensitivity of 0.92 and specificity of 0.93, with DL (SROC 0.96) and MRI (SROC 0.97) performing best. For differentiating acute from chronic vertebra l fractures, ML showed a sensitivity of 0.92 and specificity of 0.93, with ML (S ROC 0.96) and CT (SROC 0.97) performing best. For predicting vertebral fractures , ML had a sensitivity of 0.76 and specificity of 0.87, with ML (SROC 0.80) and clinical features (SROC 0.86) performing better. ML, especially DL models applie d to CT, MRI, and X-ray, shows high diagnostic accuracy for vertebral fractures. ML also effectively predicts osteoporotic vertebral fractures, aiding in tailor ed prevention strategies.”
      原文链接:
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    Data on Artificial Intelligence Detailed by Researchers at Faculty of Social Sci ences and Humanities (Harnessing Artificial Intelligence for Dynamic Landscape: Re-envisioning English Language Teaching in Pakistan)

    10-10页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on artificial intelligen ce have been presented. According to news reporting from the Faculty of Social S ciences and Humanities by NewsRx journalists, research stated, “The current stud y is anticipated to sightsee the integration of Artificial Intelligence (AI) in transforming English Language Teaching (ELT) in Pakistan.” Our news editors obtained a quote from the research from Faculty of Social Scien ces and Humanities: “The advent of AI unlocks the avenues for envisaging and ren ovating the facade of ELT to concentrate on the varied needs of 21st century Eng lish Language Learners (ELLs). ELT extends a pivotal function in the educational landscape of Pakistan and English language proficiency is reckoned as a prerequ isite for academic as well as professional attainment, but conformist modes of t eaching fall short to embark upon the diverse requisites of ELLs. The study is c arried out through a mixed method i.e. quantitative and qualitative outlook thou gh predominantly it is commenced through the quantitative method. Data is collec ted from English Language Teachers (ELTs) who are teaching at tertiary echelons i.e. school, college and university in the course of a random sampling technique . For that questionnaire has been operated as a data collection instrument. The sample size of the participants in the study is 150 ELTs of both genders i.e. ma le and female.”
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