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    Walailak University Reports Findings in Machine Learning (Development of a Machi ne Learning Model for the Classification of Enterobius vermicularis Egg)

    10-10页
    查看更多>>摘要:New research on Machine Learning is th e subject of a report. According to news reporting out of Nakhon Si Thammarat, T hailand, by NewsRx editors, research stated, "(pinworm) infections are a signifi cant global health issue, affecting children predominantly in environments like schools and daycares. Traditional diagnosis using the scotch tape technique invo lves examining eggs under a microscope." Funders for this research include Walailak University graduate scholarships, Wal ailak University Graduate Research Fund, National Research Council of Thailand ( NRCT) and Walailak University.

    Findings from University of Glasgow Yields New Findings on Artificial Intelligen ce (Artificial-intelligence-driven Model for Resistive Superconducting Fault Cur rent Limiter In Future Electric Aircraft)

    11-11页
    查看更多>>摘要:Fresh data on Artificial Intelligence are presented in a new report. According to news reporting originating in Glasgo w, United Kingdom, by NewsRx journalists, research stated, "Fast and accurate el ectrothermal characterization of superconducting fault current limiters (SFCLs) is critically important for their performance evaluation. At the design stage of the SFCL, simulation is needed to validate the feasibility of the idea before f abrication." Financial support for this research came from Engineering & Physic al Sciences Research Council (EPSRC).

    University of Cadi Ayyad Researchers Provide New Study Findings on Machine Learn ing (Analysis of Soil Water Erosion Risk Using Machine Learning Techniques - A C ase Study of Ourika Watershed in Morocco)

    12-13页
    查看更多>>摘要:Research findings on artificial intell igence are discussed in a new report. According to news originating from Marrake ch, Morocco, by NewsRx correspondents, research stated, "Soil erosion is a major environmental problem with detrimental consequences. In this article, we presen t a detailed study on the analysis of soil water erosion using Machine Learning (ML) techniques in the Oued Ourika watershed. We collected data on various facto rs that may influence the mechanisms of soil water erosion events." Our news reporters obtained a quote from the research from University of Cadi Ay yad: "Subsequently, we developed machine learning models to predict the potentia l for soil water erosion based on these factors. Finally, field studies were con ducted compared to the obtained results. A historical inventory of water erosion has been created through fieldwork, satellite imagery, and historical water ero sion events. Models were constructed using the training data, and their performa nce and accuracy in predicting susceptibility to water erosion were evaluated us ing the validation data. This data division allowed for a fair assessment of the models' ability to make accurate predictions. Using a Geographic Information Sy stem (GIS) and programming in the R language, four supervised machine learning a lgorithms were applied, including K-Nearest Neighbor (KNN), Extreme Gradient Boo sting (XGB), Random Forest (RF), and Naive Bayes (NB). The results show that the NB model exhibited the highest accuracy in predicting and evaluating the effect iveness of these algorithms in forecasting susceptibility to water erosion in th e study area. Accuracy was assessed using the Area Under the Curve (AUC) metric, with an AUC of 98%. The XGB algorithm had an AUC of 96% , followed by RF with an AUC of 87%, and KNN with an AUC of 84% . Thus, the Naive Bayes model proved to be the best for determining susceptibili ty to water erosion in the study area. The analysis of water erosion reveals tha t 43% of the total area of the Oued Ourika watershed is exposed to a high to very high risk of erosion in the Ourika region. These findings can as sist regional and local authorities in reducing the risk of water erosion and im plementing effective measures to prevent potential damages."

    New Findings Reported from Shanghai University Describe Advances in Robotics (Re search On Trajectory Learning and Modification Method Based On Improved Dynamic Movement Primitives)

    13-13页
    查看更多>>摘要:Current study results on Robotics have been published. According to news reporting out of Shanghai, People's Republic of China, by NewsRx editors, research stated, "Traditional robot trajectory plan ning and programming methods often struggle to adapt to changing working require ments, leading to repeated programming in manufacturing processes. To address th ese challenges, a trajectory learning and modification method based on improved Dynamic Movement Primitives (DMPs), called FDC-DMP, is proposed." Financial support for this research came from Shanghai Baoshan Science and Techn ology Commision. Our news journalists obtained a quote from the research from Shanghai University , "The method introduces an improved force-controlled dynamic coupling term (FDC T) that uses virtual force as coupling force. This enhancement enables precise a nd flexible shape modifications within the target trajectory range. The paper al so dissects the core dynamic systems of DMP to achieve the reproduction and gene ralization of both robot position and pose trajectories."

    Research from University of Technology in Robotics Provides New Insights (A Syst ematic Review of Rapidly Exploring Random Tree RRT Algorithm for Single and Mult iple Robots)

    14-14页
    查看更多>>摘要:024 OCT 09 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New study results on robotics have been published . According to news originating from Baghdad, Iraq, by NewsRx correspondents, re search stated, "Recent advances in path-planning algorithms have transformed rob otics." The news reporters obtained a quote from the research from University of Technol ogy: "The Rapidly exploring Random Tree (RRT) algorithm underpins autonomous rob ot navigation. This paper systematically examines the uses and development of RR T algorithms in single and multiple robots to demonstrate their importance in mo dern robotics studies. To do this, we have reviewed 70 works on RRT algorithms i n single and multiple robot path planning from 2015 to 2023. RRT algorithm evolu tion, including crucial turning points and innovative techniques, have been exam ined. A detailed comparison of the RRT Algorithm versions reveals their merits, limitations, and development potential. The review's identification of developin g regions and future research initiatives will enable roboticists to use RRT alg orithms."

    University of Zagreb School of Medicine Reports Findings in Artificial Intellige nce (Bridging healthcare gaps: a scoping review on the role of artificial intell igence, deep learning, and large language models in alleviating problems in medi cal ...)

    14-15页
    查看更多>>摘要:New research on Artificial Intelligenc e is the subject of a report. According to news reporting out of Zagreb, Croatia , by NewsRx editors, research stated, "Medical deserts' are areas with low healt hcare service levels, challenging the access, quality, and sustainability of car e. This qualitative narrative review examines how artificial intelligence (AI), particularly large language models (LLMs), can address these challenges by integ rating with e-Health and the Internet of Medical Things to enhance services in u nder-resourced areas." Our news journalists obtained a quote from the research from the University of Z agreb School of Medicine, "It explores AI-driven telehealth platforms that overc ome language and cultural barriers, increasing accessibility. The utility of LLM s in providing diagnostic assistance where specialist deficits exist is highligh ted, demonstrating AI's role in supplementing medical expertise and improving ou tcomes. Additionally, the development of AI chatbots offers preliminary medical advice, serving as initial contact points in remote areas. The review also discu sses AI's role in enhancing medical education and training, supporting the profe ssional development of healthcare workers in these regions. It assesses AI's str ategic use in data analysis for effective resource allocation, identifying healt hcare provision gaps. AI, especially LLMs, is seen as a promising solution for b ridging healthcare gaps in ‘medical deserts,' improving service accessibility, q uality, and distribution."

    New Machine Learning Findings from School of Business Outlined (Identifying Envi ronmental Information Disclosure Manipulation Behavior Via Machine Learning)

    15-16页
    查看更多>>摘要:Research findings on Machine Learning are discussed in a new report. According to news reporting originating in Guilin , People's Republic of China, by NewsRx journalists, research stated, "Corporate environmental information disclosure manipulation (EIDM) has a high level of co ncealment, which brings great challenges to the identification and judgment of m anipulation behavior. Compared to traditional methods, machine learning techniqu es excel in handling large and complex datasets while achieving higher accuracy. " Funders for this research include National Natural Science Foundation of China ( NSFC), National Natural Science Foundation of China (NSFC).

    University Hospital Reports Findings in Machine Learning (Development and evalua tion of a model to identify publications on the clinical impact of pharmacist in terventions)

    16-17页
    查看更多>>摘要:New research on Machine Learning is th e subject of a report. According to news reporting from Montreal, Canada, by New sRx journalists, research stated, "Pharmacists are increasingly involved in pati ent care. Pharmacy practice research helps them identify the most clinically mea ningful interventions." The news correspondents obtained a quote from the research from University Hospi tal, "However, the lack of a widely accepted controlled vocabulary in this field hinders the discovery of this literature. To compare the performance of a machi ne learning model with manual literature searches in identifying potentially rel evant publications on the clinical impact of pharmacist interventions. To descri be the dataset that was built. An automated PubMed search was performed weekly s tarting in November 2021. Titles and abstracts were retrieved and independently evaluated by two reviewers to select potentially relevant publications on the cl inical impact of pharmacists. A Cohen's kappa score was calculated. Data was col lected during an 11-month period to train a machine learning model. It was evalu ated prospectively during a 5-month period (predictions were collected without b eing shown to the reviewers). The performance of the model was compared with man ual searches (positive predictive value [PPV] and sensitivity). A transformers-based model was selected. During the prospectiv e evaluation period, 114/1631 (7 %) publications met selection crit eria. If the model had been used, 1273/1631 (78 %) would not have n eeded review. Only 3/114 (3 %) would have been incorrectly excluded . The model showed a PPV of 0.310 and a sensitivity of 0.974. The best manual se arch showed a PPV of 0.046 and a sensitivity of 0.711. On December 12, 2023, the dataset contained 8607 publications, of which 544 (6 %) met the cr iteria. The kappa between reviewers was 0.786. The dataset and the model were us ed to develop a website and a newsletter to share publications.A machine learning model was developed and performs better than manual PubMed searches to identify potentially relevant publications. It represents a conside rable workload reduction."

    University Lyon Reports Findings in Machine Learning (Interpreting Neural Operat ors: How Nonlinear Waves Propagate in Nonreciprocal Solids)

    17-18页
    查看更多>>摘要:New research on Machine Learning is th e subject of a report. According to news reporting originating in Lyon, France, by NewsRx journalists, research stated, "We present a data-driven pipeline for m odel building that combines interpretable machine learning, hydrodynamic theorie s, and microscopic models. The goal is to uncover the underlying processes gover ning nonlinear dynamics experiments." The news reporters obtained a quote from the research from University Lyon, "We exemplify our method with data from microfluidic experiments where crystals of s treaming droplets support the propagation of nonlinear waves absent in passive c rystals. By combining physics-inspired neural networks, known as neural operator s, with symbolic regression tools, we infer the solution, as well as the mathema tical form, of a nonlinear dynamical system that accurately models the experimen tal data. Finally, we interpret this continuum model from fundamental physics pr inciples."

    Second Affiliated Hospital of Xi'an Jiaotong University Reports Findings in Pers onalized Medicine (A comprehensive predictive model for postoperative joint func tion in robot-assisted total hip arthroplasty patients: combining radiomics and ...)

    18-19页
    查看更多>>摘要:New research on Drugs and Therapies - Personalized Medicine is the subject of a report. According to news reporting ou t of Xi'an, People's Republic of China, by NewsRx editors, research stated, "Tot al hip arthroplasty (THA) effectively treats various end-stage hip conditions, o ffering pain relief and improved joint function. However, surgical outcomes are influenced by multifaceted factors." Our news journalists obtained a quote from the research from the Second Affiliat ed Hospital of Xi'an Jiaotong University, "This research aims to create a predic tive model, incorporating radiomic and clinical information, to forecast post-su rgery joint function in robot-assisted THA (RA-THA) patients. The study set comp rised 136 patients who underwent unilateral RA-THA, which were subsequently part itioned into a training set (n = 95) and a test set (n = 41) for analysis. Preop erative CT imaging was employed to derive 851 radiomic characteristics, selectin g those with an intra-class correlation coefficient > 0. 75 for analysis. Least absolute shrinkage and selection operator regression redu ced redundancy to six significant radiomic features. Clinical data including pre operative Visual Analog Scale (VAS), Harris Hip Score (HHS), and Western Ontario and McMaster University Osteoarthritis Index (WOMAC) score were collected. Logi stic regression identified significant predictors, and three models were develop ed. Receiver operating characteristic and decision curves evaluated the models. Preoperative VAS, HHS, WOMAC score, and radiomics feature scores were significan t predictors. In the training set, the AUCs were 0.835 (clinical model), 0.757 ( radiomic model), and 0.891 (combined model). In the test set, the AUCs were 0.77 7 (clinical model), 0.824 (radiomic model), and 0.881 (combined model). The cons tructed nomogram prediction model combines radiological features with relevant c linical data to accurately predict functional outcomes 3 years after RA-THA."