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    Studies from Department of Mechanical Engineering Update Current Data on Machine Learning (A Study on Prediction of Friction Characteristics from Speckle Patter ns of Friction Surfaces Using Machine Learning)

    57-57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 originating from the Department of Mech anical Engineering by NewsRx correspondents, research stated, “The accurate pred iction of friction coefficients is crucial for the maintenance of sliding mechan ical components to enable the timely detection of potential failures.” Our news editors obtained a quote from the research from Department of Mechanica l Engineering: “Traditional methods rely on sensors like load cells and strain g auges to measure friction coefficients. However, these conventional techniques f ace challenges in real-time measurement during machine operation owing to physic al constraints associated with sensor placement. To address this limitation, thi s study investigates the application of laser speckle patterns for predicting fr iction coefficients through a novel approach using convolutional neural networks (CNNs). The laser speckle technique offers rich surface condition data, while C NNs, which are particularly advanced in managing vast datasets, excel in establi shing relationships between diverse factors for precise inference, classificatio n, and prediction. Utilizing ResNet, a leading CNN architecture, a new friction tester capable of concurrently recording friction coefficients and speckle patte rns in a cylinder-on-disk friction test was developed. The findings reveal that the CNN-based method, especially with ResNet, attained a coefficient of determin ation (R2) of 0.758, demonstrating its effectiveness in the accurate prediction of friction coefficients.”
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    Researchers at University of Sheffield Have Published New Study Findings on Robo tics and Artificial Intelligence (Remotely actuated programmable self-folding or igami strings using magnetic induction heating)

    58-59页
    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 robotic s and artificial intelligence. According to news originating from Sheffield, Uni ted Kingdom, by NewsRx correspondents, research stated, “Transforming planar str uctures into volumetric objects typically requires manual folding processes, aki n to origami.” Our news editors obtained a quote from the research from University of Sheffield : “However, manual intervention at sub-centimeter scales is impractical. Instead , folding is achieved using volume-changing smart materials that respond to phys ical or chemical stimuli, be it with direct contact such as hydration, pH, or re motely e.g., light or magnetism. The complexity of small-scale structures often restricts the variety of smart materials used and the number of folding sequence s. In this study, we propose a method to sequentially self-fold millimeter scale origami using magnetic induction heating at 150kHz and 3.2 mT. Additionally, we introduce a method for designing self-folding overhand knots and predicting the folding sequence using the magneto-thermal model we developed. This methodology is demonstrated to sequentially self-fold by optimizing the surface, placement, and geometry of metal workpieces, and is validated through the self-folding of various structures, including a 380 mm2 croissant, a 321mm2 box, a 447mm2 bio-mi metic Mimosa pudica leaf, and an overhand knot covering 524mm2.”
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    Researchers from Department of Inorganic Chemistry Describe Findings in Machine Learning (Integrating Digital Chemistry Within the Broader Chemistry Community)

    58-58页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in Machine Learning. According to news reporting from Zaragoza, Spain, by NewsRx jo urnalists, research stated, “The rapid progress of digital chemistry has profoun dly transformed chemical research. Despite this evolution, there are implementat ion gaps that hinder the widespread adoption of such digital protocols among a s ignificant portion of the chemistry community.” Funders for this research include Gobierno de Aragn-Fondo Social Europeo, State Research Agency of Spain (MCIN/AEI/FEDER, UE), Gobierno de Aragon. The news correspondents obtained a quote from the research from the Department o f Inorganic Chemistry, “For example, technologies such as computational chemistr y and machine learning often present steep learning curves that discourage poten tial users who could otherwise benefit from them. This review focuses on classic al and recent advances in the automation and generalization of digital chemistry , examining the evolution of the field while highlighting popular cheminformatic s tools.”
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    Sichuan University Reports Findings in Crohn’s Disease (Machine learning in pred icting postoperative complications in Crohn’s disease)

    59-60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 Digestive System Disea ses and Conditions - Crohn’s Disease is the subject of a report. According to ne ws reporting out of Chengdu, People’s Republic of China, by NewsRx editors, rese arch stated, “Crohn’s disease (CD) is a chronic inflammatory bowel disease of un known origin that can cause significant disability and morbidity with its progre ssion. Due to the unique nature of CD, surgery is often necessary for many patie nts during their lifetime, and the incidence of postoperative complications is h igh, which can affect the prognosis of patients.” Our news journalists obtained a quote from the research from Sichuan University, “Therefore, it is essential to identify and manage postoperative complications. Machine learning (ML) has become increasingly important in the medical field, a nd ML-based models can be used to predict postoperative complications of intesti nal resection for CD. Recently, a valuable article titled ‘Predicting short-term major postoperative complications in intestinal resection for Crohn’s disease: A machine learning-based study’ was published by Wang.”
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    University of Victoria Reports Findings in Artificial Intelligence (Barriers to and Facilitators of Artificial Intelligence Adoption in Health Care: Scoping Rev iew)

    60-61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 reporting from Victoria, Canada, by NewsRx journalists, research stated, “Artificial intelligence (AI) use cases in health care are on the rise, with the potential to improve operational effic iency and care outcomes. However, the translation of AI into practical, everyday use has been limited, as its effectiveness relies on successful implementation and adoption by clinicians, patients, and other health care stakeholders.” The news correspondents obtained a quote from the research from the University o f Victoria, “As adoption is a key factor in the successful proliferation of an i nnovation, this scoping review aimed at presenting an overview of the barriers t o and facilitators of AI adoption in health care. A scoping review was conducted using the guidance provided by the Joanna Briggs Institute and the framework pr oposed by Arksey and O’Malley. MEDLINE, IEEE Xplore, and ScienceDirect databases were searched to identify publications in English that reported on the barriers to or facilitators of AI adoption in health care. This review focused on articl es published between January 2011 and December 2023. The review did not have any limitations regarding the health care setting (hospital or community) or the po pulation (patients, clinicians, physicians, or health care administrators). A th ematic analysis was conducted on the selected articles to map factors associated with the barriers to and facilitators of AI adoption in health care. A total of 2514 articles were identified in the initial search. After title and abstract r eviews, 50 (1.99%) articles were included in the final analysis. Th ese articles were reviewed for the barriers to and facilitators of AI adoption i n health care. Most articles were empirical studies, literature reviews, reports , and thought articles. Approximately 18 categories of barriers and facilitators were identified. These were organized sequentially to provide considerations fo r AI development, implementation, and the overall structure needed to facilitate adoption. The literature review revealed that trust is a significant catalyst o f adoption, and it was found to be impacted by several barriers identified in th is review. A governance structure can be a key facilitator, among others, in ens uring all the elements identified as barriers are addressed appropriately. The f indings demonstrate that the implementation of AI in health care is still, in ma ny ways, dependent on the establishment of regulatory and legal frameworks. Furt her research into a combination of governance and implementation frameworks, mod els, or theories to enhance trust that would specifically enable adoption is nee ded to provide the necessary guidance to those translating AI research into prac tice.”
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    Study Results from University of Cincinnati Provide New Insights into Machine Le arning (Evaluation of Hydrological Models At Gauged and Ungauged Basins Using Ma chine Learning-based Limits-of-acceptability and Hydrological Signatures)

    61-62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 Machine Learning have been presented. According to news reporting out of Cincinnati, Ohio, by NewsRx editors, research stated, “Hydrological models are evaluated by comparisons with observed hydrological quantities such as streamflow. A model evaluation procedu re should account for dominantly epistemic errors in hydrological data such as m odel input precipitation and streamflow and avoid type-2 errors (rejecting a goo d model).” Financial supporters for this research include Institute Project Assignment fund s of Desert Research Institute, United States Environmental Protection Agency.
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    New Robotics Data Have Been Reported by Researchers at Coburg University of Appl ied Science & Arts (A Novel Recursive Algorithm for the Implementa tion of Adaptive Robot Controllers)

    62-63页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Research findings on Robotics are discussed in a new report. According to news reporting originating from Coburg, Germany, by New sRx correspondents, research stated, “In this paper, a novel recursive and effic ient algorithm for real-time implementation of the adaptive and passivity-based controllers in modelbased control of robot manipulators is proposed. Many of th e previous methods on these topics involve the computation of the regressor matr ix explicitly or non-recursive computations, which remains as the main challenge in practical applications.” Our news editors obtained a quote from the research from the Coburg University o f Applied Science & Arts, “The proposed method achieves a compact and fully recursive reformulation without computing the regressor matrix or its elements. This paper is based on a comprehensive literature review of the previo usly proposed methods, presented in a unified mathematical framework suitable fo r understanding the fundamentals and making comparisons. The considered methods are implemented on several processors and their performances are compared in ter ms of real-time computational efficiency. Computational results show that the pr oposed Adaptive Newton-Euler Algorithm significantly reduces the computation tim e of the control law per cycle time in the implementation of adaptive control la ws.”
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    Findings from University of Waterloo Yields New Data on Robotics (How Non-expert s Kinesthetically Teach a Robot Over Multiple Sessions: Diversity In Teaching St yles and Effects On Performance)

    63-64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 Robotics are disc ussed in a new report. According to news originating from Waterloo, Canada, by N ewsRx correspondents, research stated, “In real-world applications, robots shoul d adapt to users and environments; however, users may not know how to teach new tasks to a robot. We studied whether participants without any experience in teac hing a robot would become more proficient robot teachers through repeated kinest hetic human-robot teaching interactions.” Financial support for this research came from Canada 150 Research Chairs Program .
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    University of Queensland Reports Findings in HIV/AIDS (Development and validatio n of supervised machine learning multivariable prediction models for the diagnos is of Pneumocystis jirovecii pneumonia using nasopharyngeal swab PCR in adults i n a ...)

    64-65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 Immune System Diseases and Conditions - HIV/AIDS is the subject of a report. According to news reporti ng originating in Herston, Australia, by NewsRx journalists, research stated, “T he global burden of the opportunistic fungal disease Pneumocystis jirovecii pneu monia (PJP) remains substantial. Polymerase chain reaction (PCR) on nasopharynge al swabs (NPS) has high specificity and may be a viable alternative to the gold standard diagnostic of PCR on invasively collected lower respiratory tract speci mens, but has low sensitivity.” Financial supporters for this research include UK Government, Royal Australasian College of Physicians. The news reporters obtained a quote from the research from the University of Que ensland, “Sensitivity may be improved by incorporating NPS PCR results into mach ine learning models. Three supervised multivariable diagnostic models (random fo rest, logistic regression and extreme gradient boosting) were constructed and va lidated using a 111-person Australian dataset. The predictors were age, gender, immunosuppression type and NPS PCR result. Model performance metrics such as acc uracy, sensitivity, specificity and predictive values were compared to select th e best-performing model. The logistic regression model performed best, with 80% accuracy, improving sensitivity to 86% and maintaining acceptable specificity of 70%. Using this model, positive and negative NPS PCR results indicated post-test probabilities of 84% (likely PJP) and 26% (unlikely PJP), respectively. The logistic regression model s hould be externally validated in a wider range of settings.”
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    Studies from Nanjing University of Aeronautics and Astronautics Yield New Data o n Robotics (Dynamics Parameter Identification of Articulated Robot)

    65-66页
    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 robotic s. According to news reporting out of Nanjing, People’s Republic of China, by Ne wsRx editors, research stated, “Dynamics parameter identification in the establi shment of a multiple degree-of-freedom (DOF) robot’s dynamics model poses signif icant challenges.” The news journalists obtained a quote from the research from Nanjing University of Aeronautics and Astronautics: “This study employs a non-symbolic numerical me thod to establish a dynamics model based on the Newton-Euler formula and then de rives a proper dynamics model through decoupling. Initially, a minimum inertial parameter set is acquired by using QR decomposition, with the inclusion of a fri ction model in the robot dynamics model. Subsequently, the least squares method is employed to solve for the minimum inertial parameters, forming the basis for a comprehensive robot dynamics parameter identification system. Then, after the optimization of the genetic algorithm, the Fourier series trajectory function is used to derive the trajectory function for parameter identification. Validation of the robot’s dynamics parameter identification is performed through simulatio n and experimentation on a 6-DOF robot, leading to a precise identification valu e of the robot’s inertial parameters.”
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