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    Researchers from Taipei Veterans General Hospital Publish Findings in Artificial Intelligence (Artificial Intelligence-Based Mobile Application for Identifying Suitable Height Range of High Heels)

    58-58页
    查看更多>>摘要：New study results on artificial intelligence have been published. According to news reporting from Taipei, Taiwan, by NewsRx journalists, research stated, “The wearing of overly high highheeled shoes can cause irreversible physiological damage. Doctors typically analyze the calcaneus when an individual is standing on tiptoes to observe changes in the Achilles tendon to determine the suitable high-heel height range.” Funders for this research include National Science And Technology Council, Taiwan; University System of Taipei Joint Research Program; Faculty Group Research Funding Sponsorship By The National Taipei University; Taipei Veterans General Hospital, Taiwan.
      原文链接:
    • NETL
    • NSTL
    • IEEE

    New Artificial Intelligence Findings Reported from Ca’ Foscari University (Team Formation for Human-artificial Intelligence Collaboration In the Workplace: a Goal Programming Model To Foster Organizational Change)

    59-59页
    查看更多>>摘要：Fresh data on Artificial Intelligence are presented in a new report. According to news reporting from Venice, Italy, by NewsRx journalists, research stated, “The need for preparing for digital transformation is a recurrent theme in the recent public and academic debate. Artificial Intelligence (AI) has the potential to reduce operational costs, increase efficiency, and improve customer experience.” The news correspondents obtained a quote from the research from Ca’ Foscari University, “Thus, it is crucial to forming project teams in an organization, in such a way that they will welcome AI in the decisionmaking process. The current technological revolution is demanding a rapid pace of change to companies and has increased the attention to the role of teams in fostering innovation adoption. We propose an innovative multicriteria model based on the goal programming approach for solving the optimal allocation of individuals to different groups. The model copes with human resources’ cost and human-machine trust. Indeed, we propose an aggregated measure of the attitude towards AI tools to be employed to support tasks in an organization: more precisely our index is based on three dimensions: technology acceptance, technology self-efficacy, and source credibility. By incorporating this index in a team formation model, each team can be guaranteed to have less resistance to change in adopting machine-based decisions, a scenario that will characterize the years to come.”
      原文链接:
    • NETL
    • NSTL
    • IEEE

    Investigators from Bhabha Atomic Research Centre Have Reported New Data on Machine Learning (Identification and Classification of Disordered Carbon Materials In a Composite Matrix Through Machine Learning Approach Integrated With Raman Mapping)

    60-60页
    查看更多>>摘要：Investigators discuss new findings in Machine Learning. According to news reporting from Mumbai, India, by NewsRx journalists, research stated, “Identification and classification of different types of highly disordered carbon materials present in a polymer matrix with similar Raman spectra have been carried out using a machine learning approach. Convolutional neural network (CNN) has been used for the classification of disordered carbon materials such as graphene oxide (GO), functionalized carbon nanotube (f-CNT), carbon fiber (Cf), carbon black (CB), pyrolytic carbon (PyC), coke, and mesocarbon microbeads (MCMB).” Financial support for this research came from Bhabha Atomic Research Centre, Mumbai, India.
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    New Machine Learning Study Findings Have Been Reported by Investigators at University of Oklahoma (Modeling of Necking Area Reduction of Carbon Steel In Hydrogen Environment Using Machine Learning Approach)

    61-62页
    查看更多>>摘要：Investigators publish new report on Machine Learning. According to news reporting from Norman, Oklahoma, by NewsRx journalists, research stated, “Low carbon and low alloy steel pipes, prevalent in natural gas transmission systems due to their affordability, weldability, and strength, are confronted with significant challenges such as hydrogen embrittlement (HE) when transitioning towards hydrogen energy systems. This necessitates innovative predictive strategies to overcome these hurdles.” Funders for this research include Pipeline and Hazardous Materials Safety Administration (PHMSA) of the U.S. Department of Transportation (DOT), University of Oklahoma. The news correspondents obtained a quote from the research from the University of Oklahoma, “Most existing research on HE has focused on a limited range of low carbon and low alloy steels under specific experimental conditions and has been constrained by the limitations of experimental facilities. To expand the research scope, our study incorporated seven machine learning (ML) techniques: Random Forest (RF), Decision Tree (DT), Extreme Gradient Boosting (XGBoost), Gradient Boosting (GB), Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), and Artificial Neural Networking (ANN). The aim is to predict the HE in terms of the degradation of mechanical properties, specifically the reduction in area. Drawing from tensile test data obtained from 47 distinct low carbon and low alloy steels under pressurized hydrogen gas conditions, we constructed and evaluated a range of ML models, with the aim of identifying the most efficacious one for our study. Our results indicated that the CatBoost ML model offered the best prediction of the reduction in the area of these steels in a hydrogen environment. The CatBoost model provided a low Mean Absolute Error (MAE) of 7.32, Mean Square Error (MSE) of 83.78, Root Mean Square Error (RMSE) of 9.15, and a coefficient of determination (R2) value of 77.62% for the training data and 72.50% for the testing data. Furthermore, the CatBoost model identified hydrogen gas pressure and the steel’s ultimate tensile strength as the most influential parameters, contributing 47.4% and 19.2% respectively to the prediction of HE.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Reports on Robotics from Miguel Hernandez University Provide New Insights (Optimization of the Pick-and-place Sequence of a Bimanual Collaborative Robot In an Industrial Production Line)

    62-62页
    查看更多>>摘要：Research findings on Robotics are discussed in a new report. According to news reporting from Elche, Spain, by NewsRx journalists, research stated, “This paper focuses on optimising pick-and-place tasks performed by a dual-arm collaborative robot in a specific shoe manufacturing industry environment. The robot must identify the pieces of a shoe placed on a tray, pick them up, and place them in a shoe mold for further processing.” Financial supporters for this research include Spanish Government, Center for Forestry Research & Experimentation (CIEF). The news correspondents obtained a quote from the research from Miguel Hernandez University, “The shoe pieces arrive on the tray in random positions and angles and can be picked up in a different order. Optimising these tasks could increase the assembly speed of each unit and improve shoe production. To achieve this goal, a mathematical model based on binary integer linear programming (BILP) has been developed. This model determines the optimal sequence for picking and placing the shoe pieces in the mold, thus minimising the time required for picking and decision-making. The effectiveness of this approach has been tested using two 3-piece unit shoe models: one for training and another for validation. These models encompass a total of 500 trays. An analysis of the results reveals that BILP offers advantages for task motion planning in complex environments with multiple trajectories and the potential for collisions between arms.”
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Researcher from University of Nottingham Describes Findings in Robotics (Unlocking the Potential of Cable-Driven Continuum Robots: A Comprehensive Review and Future Directions)

    63-64页
    查看更多>>摘要：Investigators publish new report on robotics. According to news originating from Ningbo, People’s Republic of China, by NewsRx correspondents, research stated, “Rigid robots have found wide-ranging applications in manufacturing automation, owing to their high loading capacity, high speed, and high precision.” Financial supporters for this research include National Natural Science Foundation of China; Zhejiang Provincial Key Research And Development Plan. Our news editors obtained a quote from the research from University of Nottingham: “Nevertheless, these robots typically feature joint-based drive mechanisms, possessing limited degrees of freedom (DOF), bulky structures, and low manipulability in confined spaces. In contrast, continuum robots, drawing inspiration from biological structures, exhibit characteristics such as high compliance, lightweight designs, and high adaptability to various environments. Among them, cable-driven continuum robots (CDCRs) driven by multiple cables offer advantages like higher dynamic response compared to pneumatic systems and increased working space and higher loading capacity compared to shape memory alloy (SMA) drives. However, CDCRs also exhibit some shortcomings, including complex motion, drive redundancy, challenging modeling, and control difficulties.”
      原文链接:
    • NETL
    • NSTL
    • Mdpi

    Indian Institute of Technology Palakkad Researcher Yields New Data on Robotics (Kinetostatic Analysis of a Spatial Cable-Actuated Variable Stiffness Joint)

    63-63页
    查看更多>>摘要：Current study results on robotics have been published. According to news reporting originating from Kerala, India, by NewsRx correspondents, research stated, “The demand for robots capable of performing collaborative tasks requiring interactions with the environment is on the rise.” Our news editors obtained a quote from the research from Indian Institute of Technology Palakkad: “Safe interactions with the environment require attributes such as high dexterity and compliance around obstacles, while still maintaining the requisite stiffness levels for payload manipulation. Such attributes are inherent to biological musculoskeletal systems. Motivated by this realization, this paper proposes a cable-actuated spatial joint with variable stiffness, inspired by the tensegrity principles found in biological musculoskeletal systems. The paper provides a detailed analysis of the joint’s mobility and mechanism kinematics. Based on the limits of the actuation forces, the paper also presents the wrench-feasible workspace of the joint. The paper also outlines the conditions that the cable actuation forces must satisfy to maintain the static equilibrium of the joint.”
      原文链接:
    • NETL
    • NSTL

    New Machine Learning Study Results Reported from University of Manchester (Human-robot Collaboration and Machine Learning: a Systematic Review of Recent Research)

    64-65页
    查看更多>>摘要：Data detailed on Machine Learning have been presented. According to news reporting from Manchester, United Kingdom, by NewsRx journalists, research stated, “Technological progress increasingly envisions the use of robots interacting with people in everyday life. Human-robot collaboration (HRC) is the approach that explores the interaction between a human and a robot, during the completion of a common objective, at the cognitive and physical level.” Funders for this research include EPSRC CASE studentship - BAE Systems, H2020 project TRAINCREASE, H2020 project eLADDA, UK Research & Innovation (UKRI), US Air Force project THRIVE++. The news correspondents obtained a quote from the research from the University of Manchester, “In HRC works, a cognitive model is typically built, which collects inputs from the environment and from the user, elaborates and translates these into information that can be used by the robot itself. Machine learning is a recent approach to build the cognitive model and behavioural block, with high potential in HRC. Consequently, this paper proposes a thorough literature review of the use of machine learning techniques in the context of human-robot collaboration. 45 key papers were selected and analysed, and a clustering of works based on the type of collaborative tasks, evaluation metrics and cognitive variables modelled is proposed. Then, a deep analysis on different families of machine learning algorithms and their properties, along with the sensing modalities used, is carried out. Among the observations, it is outlined the importance of the machine learning algorithms to incorporate time dependencies.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    New Findings on Machine Learning from Aarhus University Summarized (A Machine Learning Approach To Volatility Forecasting*)

    65-66页
    查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news originating from Aarhus, Denmark, by NewsRx correspondents, research stated, “We inspect how accurate machine learning (ML) is at forecasting realized variance of the Dow Jones Industrial Average index constituents. We compare several ML algorithms, including regularization, regression trees, and neural networks, to multiple heterogeneous autoregressive (HAR) models.” Financial supporters for this research include Det Frie Forskningsrad (DFF), CREATES. Our news journalists obtained a quote from the research from Aarhus University, “ML is implemented with minimal hyperparameter tuning. In spite of this, ML is competitive and beats the HAR lineage, even when the only predictors are the daily, weekly, and monthly lags of realized variance. The forecast gains are more pronounced at longer horizons. We attribute this to higher persistence in the ML models, which helps to approximate the long memory of realized variance. ML also excels at locating incremental information about future volatility from additional predictors. Lastly, we propose an ML measure of variable importance based on accumulated local effects.”
      原文链接:
    • NETL
    • NSTL
    • Oxford Univ Press

    Findings from Zhejiang University in the Area of Machine Learning Described (Deep Learning On Atomistic Physical Fields of Graphene for Strain and Defect Engineering)

    66-67页
    查看更多>>摘要：Current study results on Machine Learning have been published. According to news originating from Hangzhou, People’s Republic of China, by NewsRx correspondents, research stated, “Strain and defect engineering have profound applications in two-dimensional materials, where it is important to determine the equilibrated atomistic structures with defect conditions under mechanical deformations for computational materials design. Nevertheless, how to efficiently predict relaxed atomistic structures and the associated physical fields on each atom or bond under evolving mechanical deformations remains as an essential challenge.” Financial support for this research came from National Natural Science Foundation of China. Our news journalists obtained a quote from the research from Zhejiang University, “To address this issue, a deep neural network architecture is designed to embed the state of applied strains into the defectengineered atomistic geometry, so that deformation-coupled physical fields of interests on atoms or bonds can be predicted or derived over continuous state of deformations. For demonstration, the combination of applied tensile strains and shear strain on monolayer graphene with random distribution of Stone-Wales defects and vacancy defects is considered. The unique advantage of this framework is the development of strain-embedding concept combined with generative adversarial network, which can be feasibly extended to other material and other conditions. The computational approach sheds light on boosting the efficiency of evaluating physical properties of 2D materials under complex strain and defect states. This work presents a unifying deep learning framework that learns from complex strain states and defected configurations for the prediction of deformation-coupled spatial field values with atomistic resolution. The deep learning method is based on strain embedding and generative adversarial network.”
      原文链接:
    • NETL
    • NSTL
    • Wiley