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    Combined Military Hospital Reports Findings in Artificial Intelligence (Advancing nursing practice with artificial intelligence: Enhancing preparedness for the future)

    50-51页
    查看更多>>摘要:New research on Artificial Intelligence is the subject of a report. According to news reporting originating in Dhaka, Bangladesh, by NewsRx journalists, research stated, “This article aimed to explore the role of AI in advancing nursing practice, focusing on its impact on readiness for the future. A position paper, the methodology comprises three key steps.” The news reporters obtained a quote from the research from Combined Military Hospital, “First, a comprehensive literature search using specific keywords in reputable databases was conducted to gather current information on AI in nursing. Second, data extraction and synthesis from selected articles were performed. Finally, a thematic analysis identifies recurring themes to provide insights into AI’s impact on future nursing practice. The findings highlight the transformative role of AI in advancing nursing practice and preparing nurses for the future, including enhancing nursing practice with AI, preparing nurses for the future (AI education and training) and associated, ethical considerations and challenges.”

    Institute of Computer Science Reports Findings in Machine Learning (Acceleration of Molecular Simulations by Parametric Time-Lagged tSNE Metadynamics)

    50-50页
    查看更多>>摘要:New research on Machine Learning is the subject of a report. According to news reporting originating from Brno, Czech Republic, by NewsRx correspondents, research stated, “The potential of molecular simulations is limited by their computational costs. There is often a need to accelerate simulations using some of the enhanced sampling methods.” Our news editors obtained a quote from the research from the Institute of Computer Science, “Metadynamics applies a history-dependent bias potential that disfavors previously visited states. To apply metadynamics, it is necessary to select a few properties of the system collective variables (CVs) that can be used to define the bias potential. Over the past few years, there have been emerging opportunities for machine learning and, in particular, artificial neural networks within this domain. In this broad context, a specific unsupervised machine learning method was utilized, namely, parametric time-lagged t-distributed stochastic neighbor embedding (ptltSNE) to design CVs. The approach was tested on a Trp-cage trajectory (tryptophan cage) from the literature. The trajectory was used to generate a map of conformations, distinguish fast conformational changes from slow ones, and design CVs. Then, metadynamic simulations were performed. To accelerate the formation of the a-helix, we added the a-RMSD collective variable. This simulation led to one folding event in a 350 ns metadynamics simulation. To accelerate degrees of freedom not addressed by CVs, we performed parallel tempering metadynamics.”

    Findings from University College London (UCL) Update Understanding of Artificial Intelligence (Prediction of Complications and Prognostication In Perioperative Medicine: a Systematic Review and Probast Assessment of Machine Learning Tools)

    51-52页
    查看更多>>摘要:Investigators publish new report on Artificial Intelligence. According to news reporting originating from London, United Kingdom, by NewsRx correspondents, research stated, “The utilization of artificial intelligence and machine learning as diagnostic and predictive tools in perioperative medicine holds great promise. Indeed, many studies have been performed in recent years to explore the potential.” Funders for this research include Cleveland Clinic London Hospital, London, United Kingdom, Wellcome/ EPSRC Center for Interventional and Surgical Sciences at University College London (London, United Kingdom). Our news editors obtained a quote from the research from University College London (UCL), “The purpose of this systematic review is to assess the current state of machine learning in perioperative medicine, its utility in prediction of complications and prognostication, and limitations related to bias and validation. A multidisciplinary team of clinicians and engineers conducted a systematic review using the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) protocol. Multiple databases were searched, including Scopus, Cumulative Index to Nursing and Allied Health Literature (CINAHL), the Cochrane Library, PubMed, Medline, Embase, and Web of Science. The systematic review focused on study design, type of machine learning model used, validation techniques applied, and reported model performance on prediction of complications and prognostication. This review further classified outcomes and machine learning applications using an ad hoc classification system. The Prediction model Risk Of Bias Assessment Tool (PROBAST) was used to assess risk of bias and applicability of the studies. A total of 103 studies were identified. The models reported in the literature were primarily based on single-center validations (75%), with only 13% being externally validated across multiple centers. Most of the mortality models demonstrated a limited ability to discriminate and classify effectively. The PROBAST assessment indicated a high risk of systematic errors in predicted outcomes and artificial intelligence or machine learning applications. The findings indicate that the development of this field is still in its early stages. This systematic review indicates that application of machine learning in perioperative medicine is still at an early stage. While many studies suggest potential utility, several key challenges must be first overcome before their introduction into clinical practice. This systematic review and meta-analysis identified 103 studies that employed artificial intelligence or machine learning to predict perioperative outcomes, but the overall quality was only modest with only 13% being externally validated.”

    Data from Chongqing University Advance Knowledge in Support Vector Machines (Fused Robust Geometric Nonparallel Hyperplane Support Vector Machine for Pattern Classification)

    52-53页
    查看更多>>摘要:Research findings on Support Vector Machines are discussed in a new report. According to news reporting out of Chongqing, People’s Republic of China, by NewsRx editors, research stated, “Recently, introducing nonconvex loss functions in support vector machine (SVM) to improve the robustness against varies noises has been drawing much attention. In this paper, we first construct a new robust capped asymmetric elastic net (CaEN) loss function.” Financial support for this research came from National Natural Science Foundation of China (NSFC). Our news journalists obtained a quote from the research from Chongqing University, “Second, we describe a novel robust Huberized kernel-based (HK) loss function and theoretically demonstrate several important properties, such as smoothness, boundness and the trade-off between the standard least squares and the truncated least squares. Finally, we apply the CaEN loss and the HK loss into elastic net nonparallel hyperplane SVM (ENNHSVM) to develop a fused robust geometric nonparallel SVM (FRGNHSVM). The proposed FRGNHSVM not only inherits the advantages of ENNHSVM but also improves the robustness of classification problems. An efficient Pegasos-based DC (difference of convex functions) algorithm is implemented to solve the FRGNHSVM optimization problem. In comparison with four famous SVMs, including Lagrangian SVM, twin SVM, pinball SVM and C-loss twin SVM, experimental results on simulations and twelve UCI datasets show that the proposed FRGNHSVM can often improve more than 5% average prediction accuracy.”

    University of Paris Saclay Researchers Describe Recent Advances in Robotics and Artificial Intelligence (Human movement modifications induced by different levels of transparency of an active upper limb exoskeleton)

    53-54页
    查看更多>>摘要:Current study results on robotics and artificial intelligence have been published. According to news reporting originating from Orsay, France, by NewsRx correspondents, research stated, “Active upper limb exoskeletons are a potentially powerful tool for neuromotor rehabilitation. This potential depends on several basic control modes, one of them being transparency.” Funders for this research include Agence Nationale De La Recherche. Our news editors obtained a quote from the research from University of Paris Saclay: “In this control mode, the exoskeleton must follow the human movement without altering it, which theoretically implies null interaction efforts. Reaching high, albeit imperfect, levels of transparency requires both an adequate control method and an in-depth evaluation of the impacts of the exoskeleton on human movement. The present paper introduces such an evaluation for three different ‘transparent’ controllers either based on an identification of the dynamics of the exoskeleton, or on force feedback control or on their combination. Therefore, these controllers are likely to induce clearly different levels of transparency by design. The conducted investigations could allow to better understand how humans adapt to transparent controllers, which are necessarily imperfect. A group of fourteen participants were subjected to these three controllers while performing reaching movements in a parasagittal plane. The subsequent analyses were conducted in terms of interaction efforts, kinematics, electromyographic signals and ergonomic feedback questionnaires. Results showed that, when subjected to less performing transparent controllers, participants strategies tended to induce relatively high interaction efforts, with higher muscle activity, which resulted in a small sensitivity of kinematic metrics.”

    AIT Austrian Institute of Technology GmbH Reports Findings in Telemedicine (Instance Selection Algorithms for Predictive Modelling in Telehealth Applications)

    54-55页
    查看更多>>摘要:New research on Telemedicine is the subject of a report. According to news reporting from Graz, Austria, by NewsRx journalists, research stated, “Telehealth services are becoming more and more popular, leading to an increasing amount of data to be monitored by health professionals. Machine learning can support them in managing these data.” The news correspondents obtained a quote from the research from the AIT Austrian Institute of Technology GmbH, “Therefore, the right machine learning algorithms need to be applied to the right data. We have implemented and validated different algorithms for selecting optimal time instances from time series data derived from a diabetes telehealth service. Intrinsic, supervised, and unsupervised instance selection algorithms were analysed. Instance selection had a huge impact on the accuracy of our random forest model for dropout prediction. The best results were achieved with a One Class Support Vector Machine, which improved the area under the receiver operating curve of the original algorithm from 69.91 to 75.88 %.”

    Researchers from Guizhou Normal University Report Recent Findings in Robotics (Enhancing Experience: Investigating the Impact of Different Personal Perspectives In Virtual Reality With Lower Limb Rehabilitation Robots On Participants’ ...)

    55-56页
    查看更多>>摘要:Fresh data on Robotics are presented in a new report. According to news reporting originating in Guiyang, People’s Republic of China, by NewsRx journalists, research stated, “Combining virtual reality (VR) with rehabilitation robots has the potential to enhance rehabilitation training and neural functional recovery. However, there is limited research on designing VR scenes and evaluating the impact of such systems on participants’ cognitive and experiential aspects when using rehabilitation robots.” Funders for this research include National Natural Science Foundation of China (NSFC), Science and Technology Projects in Guizhou Province. The news reporters obtained a quote from the research from Guizhou Normal University, “This study aimed to examine the effects of different gaming modes (first-person perspective and third-person perspective) and robot involvement on participants’ motivation, experience, task load, and engagement. Thirty-two participants underwent gait rehabilitation training, providing feedback on their experiences after each condition. The findings revealed that the first-person perspective mode increased motivation, experience, task performance, and engagement. On the other hand, robot-assisted participation improved motivation but decreased the overall experience.”

    University Hospital Erlangen Reports Findings in Hoarseness (Machine learning based estimation of hoarseness severity using sustained vowelsa))

    56-57页
    查看更多>>摘要:New research on Voice Diseases and Conditions - Hoarseness is the subject of a report. According to news reporting out of Erlangen, Germany, by NewsRx editors, research stated, “Auditory perceptual evaluation is considered the gold standard for assessing voice quality, but its reliability is limited due to inter-rater variability and coarse rating scales. This study investigates a continuous, objective approach to evaluate hoarseness severity combining machine learning (ML) and sustained phonation.” Funders for this research include Deutsche Forschungsgemeinschaft, Deutsche Forschungsgemeinschaft. Our news journalists obtained a quote from the research from University Hospital Erlangen, “For this purpose, 635 acoustic recordings of the sustained vowel /a/ and subjective ratings based on the roughness, breathiness, and hoarseness scale were collected from 595 subjects. A total of 50 temporal, spectral, and cepstral features were extracted from each recording and used to identify suitable ML algorithms. Using variance and correlation analysis followed by backward elimination, a subset of relevant features was selected. Recordings were classified into two levels of hoarseness, H<2 and H 2, yielding a continuous probability score y [0,1]. An accuracy of 0.867 and a correlation of 0.805 between the model’s predictions and subjective ratings was obtained using only five acoustic features and logistic regression (LR). Further examination of recordings pre- and post-treatment revealed high qualitative agreement with the change in subjectively determined hoarseness levels. Quantitatively, a moderate correlation of 0.567 was obtained.”

    Data on Machine Learning Discussed by Researchers at Tsinghua University (Predicting the Explosion Limits of Hydrogen-oxygendiluent Mixtures Using Machine Learning Approach)

    57-58页
    查看更多>>摘要:Investigators discuss new findings in Machine Learning. According to news originating from Beijing, People’s Republic of China, by NewsRx correspondents, research stated, “In this paper, we present a new methodology for predicting the explosion limits of hydrogen-oxygen-diluent mixtures by using machine learning approach. Results show that the explosion limits are accurately predicted with the logistic regression (LR), decision tree (DT), random forest (RF), support vector machine (SVM), and feedforward neural network (FNN) algorithms when using the optimal hyperparameters.” Financial support for this research came from National Natural Science Foundation of China (NSFC). Our news journalists obtained a quote from the research from Tsinghua University, “In terms of computational cost, the LR and DT require the lower costs, the RF requires the high training and prediction costs and the training cost of the FNN is higher due to the large number of hyperparameters. In terms of prediction accuracy, the FNN predicts the explosive/non-explosive boundary more accurately with different amounts of training data. Furthermore, the receiver operating characteristic (ROC) curve and area under curve (AUC) values further prove the superiority of the five classifiers.”

    Imperial College London Reports Findings in Robotics (Fiberbots: Robotic fibers for high-precision minimally invasive surgery)

    58-58页
    查看更多>>摘要:New research on Robotics is the subject of a report. According to news reporting originating in London, United Kingdom, by NewsRx journalists, research stated, “Precise manipulation of flexible surgical tools is crucial in minimally invasive surgical procedures, necessitating a miniature and flexible robotic probe that can precisely direct the surgical instruments. In this work, we developed a polymer-based robotic fiber with a thermal actuation mechanism by local heating along the sides of a single fiber.” The news reporters obtained a quote from the research from Imperial College London, “The fiber robot was fabricated by highly scalable fiber drawing technology using common low-cost materials. This low-profile (below 2 millimeters in diameter) robotic fiber exhibits remarkable motion precision (below 50 micrometers) and repeatability. We developed control algorithms coupling the robot with endoscopic instruments, demonstrating high-resolution in situ molecular and morphological tissue mapping. We assess its practicality and safety during in vivo laparoscopic surgery on a porcine model.”