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    Reports Summarize Robotics Study Results from University of Rey Juan Carlos (Vision-based Robotics Using Open Fpgas)

    87-88页
    查看更多>>摘要：Data detailed on Robotics have been presented. According to news reporting from Madrid, Spain, by NewsRx journalists, research stated, "Robotics increasingly provides practical applications for society, such as manufacturing, autonomous driving, robot vacuum cleaners, robots in logistics, drones for inspection, etc. Typical requirements in this field are fast response time, low power consumption, parallelism, and flexibility." Funders for this research include Community of Madrid in the framework of the research project, Spain, Programa de Actividades de I+D entre Grupos de investigacion de la Comunidad de Madrid en Tecnologias 2018 project, Spain. The news correspondents obtained a quote from the research from the University of Rey Juan Carlos, "According to these features, FPGAs are a suitable computing substrate for robots. A few vendors have dominated the FPGA market with their proprietary tools and hardware devices, resulting in fragmented ecosystems with few standards and little interoperation. New and complete open toolchains for FPGAs are emerging from the open-source community. This article presents an open-source library of Verilog modules useful for vision-based robots, including reusable image processing blocks for perception and reactive control blocks. This library has been developed using open tools, but its Verilog modules are fully compatible with any proprietary toolchain. In addition, three applications with a real robot and open FPGAs have been developed for experimental validation using this library. In the last application, the mobile robot successfully follows a colored object using two low-cost cameras (to increase the robot's field of view) and includes a third camera on top of a servo-driven turret for tracking a second independent object while following the first one in parallel."
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    University of Helsinki and Helsinki University Hospital Reports Find- ings in Thrombectomy (Factors influencing the reliability of a CT angiography-based deep learning method for infarct volume estima- tion)

    88-89页
    查看更多>>摘要：New research on Surgery - Thrombectomy is the subject of a report. According to news reporting originating from Helsinki, Finland, by NewsRx correspondents, research stated, "CT angiography (CTA)-based machine learning methods for infarct volume estimation have shown a tendency to overestimate infarct core and final infarct volumes (FIV). Our aim was to assess factors influencing the reliability of these methods." Our news editors obtained a quote from the research from the University of Helsinki and Helsinki Univer- sity Hospital, "The effect of collateral circulation on the correlation between convolutional neural network (CNN) estimations and FIV was assessed based on the Miteff system and hypoperfusion intensity ratio (HIR) in 121 patients with anterior circulation acute ischaemic stroke using Pearson correlation coefficients and median volumes. Correlation was also assessed between successful and futile thrombectomies. The timing of individual CTAs in relation to CTP studies was analysed. The strength of correlation between CNN estimated volumes and FIV did not change significantly depending on collateral status as assessed with the Miteff system or HIR, being poor to moderate (=0.09-0.50). The strongest correlation was found in patients with futile thrombectomies (=0.61). Median CNN estimates showed a trend for overestimation compared to FIVs. CTA was acquired in the mid arterial phase in virtually all patients (120/121). This study showed no effect of collateral status on the reliability of the CNN and best correlation was found in patients with futile thrombectomies. CTA timing in the mid arterial phase in virtually all patients can explain infarct volume overestimation."
      原文链接:
    • NETL
    • NSTL

    Findings from Sichuan University Broaden Understanding of Struc- ture Research (Dynamic Evaluation Method for Time-variant Reli- ability of Structural Safety of Concrete-faced Rockfill Dam)

    89-90页
    查看更多>>摘要：Research findings on Structure Research are discussed in a new report. According to news reporting from Chengdu, People's Republic of China, by NewsRx journalists, research stated, "The evaluation of the structural safety risk for concrete-faced rockfill dams (CFRDs) is susceptible to the time varying parameters and the environments of topography, geology, and operations. Traditional evaluation methods, due to their involvement in numerical simulation, cannot meet the current requirements for intelligent monitoring in terms of timeliness and practicality." Financial supporters for this research include National Natural Science Foundation of China (NSFC), Key Research Program of Sichuan Province, Fundamental Research Funds for the Central Universities. The news correspondents obtained a quote from the research from Sichuan University, "Therefore, a dynamic evaluation method of reliability in CFRDs is proposed in this paper. Due to the nonlinear mapping relationship between the time-variant reliability and the monitoring characterizations of CFRDs, the long and short-term memory neural network (LSTM) and support vector machine (SVM) models are introduced to construct the potential functional relationships between the reliability sequences and the monitoring characterizations, and the matching between the accuracy of the two models and the frequency of monitoring characterizations has been fully studied, forming an adaptive evaluation model of safety risk for the CFRDs according to the different monitoring frequencies of different monitoring items. The application of Sanbanxi CFRD shows an average relative error of less than 1% and 5% for dam slope stability and slab cracking simulations, respectively."
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Division of Radiology Reports Findings in Artificial Intelligence (Pre- liminary data on artificial intelligence tool in magnetic resonance imaging assessment of degenerative pathologies of lumbar spine)

    90-91页
    查看更多>>摘要：New research on Artificial Intelligence is the subject of a report. According to news reporting out of Naples, Italy, by NewsRx editors, research stated, "To evaluate the ability of an artificial intelligence (AI) tool in magnetic resonance imaging (MRI) assessment of degenerative pathologies of lumbar spine using radiologist evaluation as a gold standard. Patients with degenerative pathologies of lumbar spine, evaluated with MRI study, were enrolled in a retrospective study approved by local ethical committee." Our news journalists obtained a quote from the research from the Division of Radiology, "A comprehen- sive software solution (CoLumbo; SmartSoft Ltd., Varna, Bulgaria) designed to label the segments of the lumbar spine and to detect a broad spectrum of degenerative pathologies based on a convolutional neural network (CNN) was employed, utilizing an automatic segmentation. The AI tool efficacy was compared to data obtained by a senior neuroradiologist that employed a semiquantitative score. Chi-square test was used to assess the differences among groups, and Spearman's rank correlation coefficient was calculated between the grading assigned by radiologist and the grading obtained by software. Moreover, agreement was assessed between the value assigned by radiologist and software. Ninety patients (58 men; 32 women) affected with degenerative pathologies of lumbar spine and aged from 60 to 81 years (mean 66 years) were analyzed. Significant correlations were observed between grading assigned by radiologist and the grading obtained by software for each localization. However, only when the localization was L2-L3, there was a good correlation with a coefficient value of 0.72. The best agreements were obtained in case of L1-L2 and L2-L3 localizations and were, respectively, of 81.1% and 72.2%. The lowest agreement of 51.1% was detected in case of L4-L5 locations. With regard canal stenosis and compression, the highest agreement was obtained for identification of in L5-S1 localization."
      原文链接:
    • NETL
    • NSTL

    Study Findings on Machine Learning Are Outlined in Reports from Michigan State University (Integration of Persistent Laplacian and Pre-trained Transformer for Protein Solubility Changes Upon Mu- tation)

    91-91页
    查看更多>>摘要：Data detailed on Machine Learning have been presented. According to news reporting from East Lansing, Michigan, by NewsRx journalists, research stated, "Protein mutations can significantly influence protein solubility, which results in altered protein functions and leads to various diseases. Despite tremendous effort, machine learning prediction of protein solubility changes upon mutation remains a challenging task as indicated by the poor scores of normalized Correct Prediction Ratio (CPR)." Financial supporters for this research include National Institutes of Health (NIH) - USA, National Science Foundation (NSF), National Aeronautics & Space Administration (NASA), MSU Foundation, Bristol-Myers Squibb, Pfizer, Nanyang Technological University, Ministry of Education, Singapore. The news correspondents obtained a quote from the research from Michigan State University, "Part of the challenge stems from the fact that there is no three-dimensional (3D) structures for the wild - type and mutant proteins. This work integrates persistent Laplacians and pre -trained Transformer for the task. The Transformer, pretrained with hundreds of millions of protein sequences, embeds wild -type and mutant sequences, while persistent Laplacians track the topological invariant change and homotopic shape evolution induced by mutations in 3D protein structures, which are rendered from AlphaFold2. The resulting machine learning model was trained on an extensive data set labeled with three solubility types." According to the news reporters, the research concluded: "Our model outperforms all existing predictive methods and improves the state-of-the-art up to 15%."
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    University of Maribor Reports Findings in Robotics (Qualitative study on domestic social robot adoption and associated security concerns among older adults in Slovenia)

    92-92页
    查看更多>>摘要：New research on Robotics is the subject of a report. According to news originating from Ljubljana, Slovenia, by NewsRx correspondents, research stated, "Despite the increasing use of domestic social robots by older adults, there remains a significant knowledge gap regarding attitudes, concerns, and potential adoption behavior in this population. This study aims to categorize older adults into distinct technology adoption groups based on their attitudes toward domestic social robots and their behavior in using the existing technology." Our news journalists obtained a quote from the research from the University of Maribor, "An exploratory qualitative research design was used, involving semi-structured interviews with 24 retired Slovenian older adults aged 65 years or older, conducted between 26 June and 14 September 2023. Four distinct groups of older adults were identified: (1) Cautious Optimists, (2) Skeptical Traditionalists, (3) Positive Optimists, and (4) Technophiles based on eight characteristics. These groups can be aligned with the categories of the Diffusion of Innovation Theory. Privacy and security concerns, influenced by varying levels of familiarity with the technology, pose barriers to adoption. Perceived utility and ease of use vary considerably between groups, highlighting the importance of taking into account the different older adults."
      原文链接:
    • NETL
    • NSTL

    New Artificial Intelligence Data Have Been Reported by Investiga- tors at VSB-Technical University of Ostrava (Artificial Intelligence and Machine Learning In Electronic Fetal Monitoring)

    92-93页
    查看更多>>摘要：2024 FEB 22 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – A new study on Artificial Intelligence is now available. According to news reporting originating from Ostrava, Czech Republic, by NewsRx correspondents, research stated, "Electronic fetal monitoring is used to evaluate fetal well-being by assessing fetal heart activity. The signals produced by the fetal heart carry valuable information about fetal health, but due to non-stationarity and present interference, their processing, analysis and interpretation is considered to be very challenging." Financial supporters for this research include Ministry of Education, Youth & Sports - Czech Republic, Basal Ganglia Damage in Newborns. Our news editors obtained a quote from the research from the VSB-Technical University of Ostrava, "Therefore, medical technologies equipped with Artificial Intelligence algorithms are rapidly evolving into clinical practice and provide solutions in the key application areas: noise suppression, feature detection and fetal state classification. The use of artificial intelligence and machine learning in the field of elec- tronic fetal monitoring has demonstrated the efficiency and superiority of such techniques compared to conventional algorithms, especially due to their ability to predict, learn and efficiently handle dynamic Big data. Combining multiple algorithms and optimizing them for given purpose enables timely and accurate diagnosis of fetal health state."
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Department of Computer Science and Engineering Reports Findings in Machine Learning (Mapping of soil suitability for medicinal plants using machine learning methods)

    93-94页
    查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news reporting from Karnataka, India, by NewsRx journalists, research stated, "Inadequate conservation of medicinal plants can affect their productivity. Traditional assessments and strategies are often time- consuming and linked with errors." The news correspondents obtained a quote from the research from the Department of Computer Science and Engineering, "Utilizing herbs has been an integral part of the traditional system of medicine for centuries. However, its sustainability and conservation are critical due to climate change, over-harvesting and habitat loss. The study reveals how machine learning algorithms, geographic information systems (GIS) being a powerful tool for mapping and spatial analysis, and soil information can contribute to a swift decision-making approach for actual forethought and intensify the productivity of vulnerable curative plants of specific regions to promote drug discovery. The data analysis based on machine learning and data mining techniques over the soil, medicinal plants and GIS information can predict quick and effective results on a map to nurture the growth of the herbs. The work incorporates the construction of a novel dataset by using the quantum geographic information system tool and recommends the vulnerable herbs by implementing different supervised algorithms such as extra tree classifier (EXTC), random forest, bagging classifier, extreme gradient boosting and k nearest neighbor. Two unique approaches suggested for the user by using EXTC, firstly, for a given subregion type, its suitable soil classes and secondly, for soil type from the user, its respective subregion labels are revealed, finally, potential medicinal herbs and their conservation status are visualised using the choropleth map for classified soil/subregion. The research concludes on EXTC as it showcases outstanding performance for both soil and subregion classifications compared to other models, with an accuracy rate of 99.01% and 98.76%, respectively."
      原文链接:
    • NETL
    • NSTL

    New Findings from Chinese Academy of Sciences Describe Advances in Robotics and Automation (Self-supervised Scale Recovery for Decoupled Visual-inertial Odometry)

    94-95页
    查看更多>>摘要：Researchers detail new data in Robotics - Robotics and Automation. According to news reporting originating from Shanghai, People's Republic of China, by NewsRx correspondents, research stated, "Accurate localization for intelligent robots remains a significant challenge, and self-supervised visual-inertial odometry (VIO) has emerged as a promising solution. However, existing self-supervised VIO works consider inertial information as the ordinary data input, losing its ability to recover absolute scales and ignoring the modality difference of acceleration and angular velocity in inertial data." Financial support for this research came from National Science and Technology Major Project from Minister of Science and Technology, China. Our news editors obtained a quote from the research from the Chinese Academy of Sciences, "In this letter, we present a novel self-supervised VIO framework that augments the odometry-related information implicit in inertial data. For the specific implementation, we propose a self-attention-based IMU network (IMUSAtt) to denoise the raw IMU data and then obtain the poses based on the denoised IMU data through an integrator. By constructing the pose consistency constraint between it and the visual-inertial fused pose, a Self-attention-based Scale Recovery (SSR) module is proposed to recover the absolute scale. Additionally, to avoid the interference of acceleration on rotation estimation, we design a Decoupled PoseNet (D-PoseNet) that employs different inputs and networks to learn rotation and translation."
      原文链接:
    • NETL
    • NSTL
    • IEEE

    New York Institute of Technology Reports Findings in Artificial In- telligence (Novel B-DNA dermatophyte assay for demonstration of canonical DNA in dermatophytes: Histopathologic characterization by artificial intelligence)

    95-96页
    查看更多>>摘要：New research on Artificial Intelligence is the subject of a report. According to news reporting out of Old Westbury, New York, by NewsRx editors, research stated, "We describe a novel assay and artificial intelligence-driven histopathologic approach identifying dermatophytes in human skin tissue sections (ie, B-DNA dermatophyte assay) and demonstrate, for the first time, the presence of dermatophytes in tissue using immunohistochemistry to detect canonical right-handed double-stranded (ds) B-DNA. Immunohistochemistry was performed using anti-ds-B-DNA monoclonal antibodies with formalin- fixed paraffin-embedded tissues to determine the presence of dermatophytes."
      原文链接:
    • NETL
    • NSTL