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    Poliambulanza Foundation Hospital Reports Findings in Robotics (International consensus guidelines on robotic pancreatic surgery in 2023)

    48-49页
    查看更多>>摘要：New research on Robotics is the subject of a report. According to news reporting originating from Brescia, Italy, by NewsRx correspondents, research stated, “With the rapid development of robotic surgery, especially for the abdominal surgery, robotic pancreatic surgery (RPS) has been applied increasingly around the world. However, evidence-based guidelines regarding its application, safety, and efficacy are still lacking.” Our news editors obtained a quote from the research from Poliambulanza Foundation Hospital, “To harvest robust evidence and comprehensive clinical practice, this study aims to develop international guidelines on the use of RPS. World Health Organization (WHO) Handbook for Guideline Development, GRADE Grid method, Delphi vote, and the AGREE-II instrument were used to establish the Guideline Steering Group, Guideline Development Group, and Guideline Secretary Group, formulate 19 clinical questions, develop the recommendations, and draft the guidelines. Three online meetings were held on 04/12/2020, 30/11/2021, and 25/01/2022 to vote on the recommendations and get advice and suggestions from all involved experts. All the experts focusing on minimally invasive surgery from America, Europe and Oceania made great contributions to this consensus guideline. After a systematic literature review 176 studies were included, 19 questions were addressed and 14 recommendations were developed through the expert assessment and comprehensive judgment of the quality and credibility of the evidence. The international RPS guidelines can guide current practice for surgeons, patients, medical societies, hospital administrators, and related social communities.”
      原文链接:
    • NETL
    • NSTL

    New Machine Learning Findings Has Been Reported by Investigators at Northeast Electric Power University (Maximizing Power Density In Proton Exchange Membrane Fuel Cells: an Integrated Optimization Framework Coupling Multi-physics Structure ...)

    49-50页
    查看更多>>摘要：Research findings on Machine Learning are discussed in a new report. According to news reporting from Jilin, People’s Republic of China, by NewsRx journalists, research stated, “This study proposes an innovative optimization framework to optimize channel structure and maximize power density by coupling multi-physics structure models, machine learning, and swarm intelligence algorithms. First, proton exchange membrane fuel cells (PEMFC) imitated water-drop block channels are employed for constructing multi-physics structure models.” Funders for this research include Jilin Provincial Science & Technology Department, National Natural Science Foundation of China (NSFC). The news correspondents obtained a quote from the research from Northeast Electric Power University, “A database of the PEMFC output performance under various structural parameters of imitated water-drop block is established. Then, a machine-learning-based surrogate model is constructed based on the adaptive boosting (AdaBoost) ensemble algorithm to predict the output performance under different channel parameters. Finally, the improved gray wolf optimizer (IGWO) fitness function is calculated using a surrogate model to establish an optimization framework for effectively optimizing the channel structure. Results show that the AdaBoost ensemble surrogate model predicts the PEMFC polarization curves with extremely high accuracy and efficiency within one second. The optimization framework is capable of swiftly predicting both the optimal channel structure and maximum power density in under two minutes. The predicted values are returned to the physical model for validation with an error of only 3.96%. Simultaneously, the optimal channel structure can effectively enhance the PEMFC performance.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Research from Beijing University of Technology Broadens Understanding of Blockchain Technology (Collaborative threat intelligence: Enhancing IoT security through blockchain and machine learning integration)

    51-52页
    查看更多>>摘要：New study results on blockchain technology have been published. According to news reporting originating from Beijing, People’s Republic of China, by NewsRx correspondents, research stated, “Ensuring robust security in the Internet of Things (IoT) landscape is of paramount importance.” Our news editors obtained a quote from the research from Beijing University of Technology: “This research article presents a novel approach to enhance IoT security by leveraging collaborative threat intelligence and integrating blockchain technology with machine learning (ML) models. The iOS application acts as a central control centre, facilitating the reporting and sharing of detected threats. The shared threat data is securely stored on a blockchain network, enabling ML models to access and learn from a diverse range of threat scenarios. The research focuses on implementing Random Forest, Decision Tree classifier, Ensemble, LSTM, and CNN models on the IoT23 dataset within the context of a Collaborative Threat Intelligence Framework for IoT Security. Through an iterative process, the models’ accuracy is improved by reducing false negatives through the collaborative threat intelligence system. The article investigates the implementation details, privacy considerations, and the seamless integration of ML-based techniques for continuous model improvement.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    University Hospital of Augsburg Reports Findings in Machine Learning (Predicting Hypoxia Using Machine Learning: Systematic Review)

    51-52页
    查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news reporting out of Augsburg, Germany, by NewsRx editors, research stated, “Hypoxia is an important risk factor and indicator for the declining health of inpatients. Predicting future hypoxic events using machine learning is a prospective area of study to facilitate time-critical interventions to counter patient health deterioration.” Our news journalists obtained a quote from the research from the University Hospital of Augsburg, “This systematic review aims to summarize and compare previous efforts to predict hypoxic events in the hospital setting using machine learning with respect to their methodology, predictive performance, and assessed population. A systematic literature search was performed using Web of Science, Ovid with Embase and MEDLINE, and Google Scholar. Studies that investigated hypoxia or hypoxemia of hospitalized patients using machine learning models were considered. Risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool. After screening, a total of 12 papers were eligible for analysis, from which 32 models were extracted. The included studies showed a variety of population, methodology, and outcome definition. Comparability was further limited due to unclear or high risk of bias for most studies (10/12, 83%). The overall predictive performance ranged from moderate to high. Based on classification metrics, deep learning models performed similar to or outperformed conventional machine learning models within the same studies. Models using only prior peripheral oxygen saturation as a clinical variable showed better performance than models based on multiple variables, with most of these studies (2/3, 67%) using a long short-term memory algorithm. Machine learning models provide the potential to accurately predict the occurrence of hypoxic events based on retrospective data.”
      原文链接:
    • NETL
    • NSTL

    Dalarna University Researcher Has Provided New Study Findings on Machine Learning (Perspectives of Machine Learning and Natural Language Processing on Characterizing Positive Energy Districts)

    52-53页
    查看更多>>摘要：Current study results on artificial intelligence have been published. According to news reporting originating from Falun, Sweden, by NewsRx correspondents, research stated, “The concept of a Positive Energy District (PED) has become a vital component of the efforts to accelerate the transition to zero carbon emissions and climate-neutral living environments. Research is shifting its focus from energyefficient single buildings to districts, where the aim is to achieve a positive energy balance across a given time period.” Funders for this research include Joint Programming Initiative (Jpi) Urban Europe; Vinnova; The Scientific And Technological Research Center of Turkey; Swedish Energy Agency. Our news editors obtained a quote from the research from Dalarna University: “Various innovation projects, programs, and activities have produced abundant insights into how to implement and operate PEDs. However, there is still no agreed way of determining what constitutes a PED for the purpose of identifying and evaluating its various elements. This paper thus sets out to create a process for characterizing PEDs. First, nineteen different elements of a PED were identified. Then, two AI techniques, machine learning (ML) and natural language processing (NLP), were introduced and examined to determine their potential for modeling, extracting, and mapping the elements of a PED. Lastly, state-of-the-art research papers were reviewed to identify any contribution they can make to the determination of the effectiveness of the ML and NLP models. The results suggest that both ML and NLP possess significant potential for modeling most of the identified elements in various areas, such as optimization, control, design, and stakeholder mapping. This potential is realized through the utilization of vast amounts of data, enabling these models to generate accurate and useful insights for PED planning and implementation.”
      原文链接:
    • NETL
    • NSTL
    • Mdpi

    Study Findings on Artificial Intelligence Published by Researchers at University of Craiova (Assessing and Forecasting Current and Future Trends of ICT Employment in European Enterprises)

    53-54页
    查看更多>>摘要：Investigators publish new report on artificial intelligence. According to news originating from the University of Craiova by NewsRx correspondents, research stated, “In the current context, the labor market volatility in the information and communications technology sector (ICT), the challenges faced by companies in identifying and hiring these specialists, as well as the challenges faced by specialists in this field in terms of employment and job retention, necessitate a detailed and comprehensive analysis of the ICT labor market developments in close correlation with the current trends of digitalization of the economy.” The news journalists obtained a quote from the research from University of Craiova: “On this premise, the research presented in this article seeks to examine data about European firms that have engaged or tried to hire ICT specialists in the period 2014-2020, as well as the trends for the period 2021-2030. The purpose of the research will be to provide, on the one hand, the development of the selected variables for research during the analysis period and, on the other hand, the trends related to the ICT labor market for the categories of enterprises under study.”
      原文链接:
    • NETL
    • NSTL

    Studies from University of Savoy Mont Blanc Provide New Data on Machine Learning (Host-to-target Region Testing of Machine Learning Models for Seismic Damage Prediction In Buildings)

    54-55页
    查看更多>>摘要：Investigators publish new report on Machine Learning. According to news reporting originating from Grenoble, France, by NewsRx correspondents, research stated, “Assessing or predicting seismic damage in buildings is an essential and challenging component of seismic risk studies. Machine learning methods offer new perspectives for damage characterization, taking advantage of available data on the characteristics of built environments.” Financial supporters for this research include AXA Research Fund, URBASIS-EU project, Agence Nationale de la Recherche (ANR). Our news editors obtained a quote from the research from the University of Savoy Mont Blanc, “In this study, we aim (1) to characterize seismic damage using a classification model trained and tested on damage survey data from earthquakes in Nepal, Haiti, Serbia and Italy and (2) to test how well a model trained on a given region (host) can predict damage in another region (target). The strategy adopted considers only simple data characterizing the building (number of stories and building age), seismic ground motion (macroseismic intensity) and a traffic-light-based damage classification model (green, yellow, red categories). The study confirms that the extreme gradient boosting classification model (XGBC) with oversampling predicts damage with 60% accuracy. However, the quality of the survey is a key issue for model performance. Furthermore, the host-to-target test suggests that the model’s applicability may be limited to regions with similar contextual environments (e.g., socio-economic conditions). Our results show that a model from one region can only be applied to another region under certain conditions.”
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    New Robotics Findings Reported from Nanjing University of Science and Technology (Nonsingular Predefined-time Dynamic Surface Control of a Flexible-joint Space Robot With Actuator Constraints)

    55-55页
    查看更多>>摘要：New research on Robotics is the subject of a report. According to news reporting originating in Nanjing, People’s Republic of China, by NewsRx journalists, research stated, “To achieve predefined-time trajectory tracking control of a flexible-joint space robot(FJSR) with actuator constraints, a nonsingular predefined-time dynamic surface control scheme is developed. The input saturation caused by actuator constraints is addressed via the designed predefined-time anti-saturation compensator.” Financial supporters for this research include Jiangsu Funding Program for Excellent Postdoctoral Talent, National Natural Science Foundation of China (NSFC). The news reporters obtained a quote from the research from the Nanjing University of Science and Technology, “On this basis, two different control laws are designed for such high-order nonlinear systems by utilizing the backstepping technique, and a novel nonlinear filter is constructed to filter the virtual control signals, thus avoiding the ‘differential expansion’ phenomenon. Moreover, a singularity-free auxiliary function is designed to solve the singularity issue generated by the derivative of fractional power terms in the predefined-time control algorithm framework. The closed-loop system is proven to be semi-globally predefined-timely uniformly ultimately bounded (SGPTUUB) via constructing the suitable Lyapunov function. The difference and effectiveness of the two designed control laws are illustrated by the conducted simulations.”
      原文链接:
    • NETL
    • NSTL
    • Wiley

    Researcher from University of Urbino Carlo Bo Details New Studies and Findings in the Area of Artificial Intelligence (Beyond the Business Case for Responsible Artificial Intelligence: Strategic CSR in Light of Digital Washing and the Moral ...)

    56-56页
    查看更多>>摘要：New study results on artificial intelligence have been published. According to news originating from Urbino, Italy, by NewsRx correspondents, research stated, “This paper, normative in nature and scope, addresses the perks and limits of the strategic CSR approach when confronted with current debates on the ethics of artificial intelligence, responsible artificial intelligence, and sustainable technology in business organizations.” Funders for this research include The University of Urbino Carlo Bo. The news journalists obtained a quote from the research from University of Urbino Carlo Bo: “The paper summarizes the classic arguments underpinning the “business case” for the social responsibility of businesses and the main moral arguments for responsible and sustainable behavior in light of recent technological ethical challenges. Both streams are confronted with organizational ethical dilemmas arising in designing and deploying artificial intelligence, yielding tensions between social and economic goals. While recognizing the effectiveness of the business argument for responsible behavior in artificial intelligence, the paper addresses some of its main limits, particularly in light of the “digital washing” phenomenon. Exemplary cases of digital washing and corporate inconsistencies here discussed are taken from the literature on the topic and re-assessed in light of the proposed normative approach. Hence, the paper proposes to overcome some limits of the business case for CSR applied to AI, which mainly focuses on compliance and reputational risks and seeks returns in digital washing, by highlighting the normative arguments supporting a moral case for strategic CSR in AI.”
      原文链接:
    • NETL
    • NSTL
    • Mdpi

    New Findings from University of Alberta Describe Advances in Androids (Uncertainty-aware Safe Adaptable Motion Planning of Lower-limb Exoskeletons Using Random Forest Regression)

    57-57页
    查看更多>>摘要：Researchers detail new data in Robotics - Androids. According to news reporting from Edmonton, Canada, by NewsRx journalists, research stated, “Human safety and data security are two of the main concerns that have limited the utilization of deep learning based techniques in medical robotic applications. Such concerns are amplified by uncertainty in the deep learning run-time predictions.” Financial supporters for this research include Canada Foundation for Innovation, Government of Alberta, Natural Sciences and Engineering Research Council of Canada (NSERC), Canadian Institutes of Health Research (CIHR), Alberta Economic Development, Trade and Tourism Ministry’s grant. The news correspondents obtained a quote from the research from the University of Alberta, “In this paper, we propose a novel framework for incorporating uncertainty analysis that is fast enough (updates in 20 Hz) to be used in the control loop of a medical robot and that considers both the training and testing phases of the deep learning algorithm. As a case study focusing on the use of a lower-limb exoskeleton to assist the walking of people with disability, we learn the passive human- exoskeleton system’s dynamics using Random Forest Regression (RFR) and quantify the uncertainty level of its prediction. Whereas prior art fed the estimated human-robot interaction torque values to the adaptable Central Pattern Generators (CPGs) to refine the gait trajectories, our contribution is to leverage the knowledge of the predictions’ uncertainty levels to ensure safety in human-robot interaction. Our proposed framework for uncertaintyaware control of medical robots finds the similarities of labels and predictions in the training set using Kullback-Leibler (KL) divergence, while in the test phase, it detects out-of-distribution (OOD) data using Mahalanobis distance between test feature and training distribution. As compared to state-of-the-art methods, the proposed method is real-time and addresses the issue of uncertainty in the decisions of the robot controller. We have tested the proposed method on ExoH3 (Tehnaid S.L.) lower-limb exoskeleton. The experiments were conducted to evaluate the performance of the uncertainty analysis technique. The results demonstrate that our proposed uncertainty analysis technique can detect OOD features resulting in unsafe motion planning.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier