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    Study Data from Hangzhou Dianzi University Update Knowledge of Androids (A Dynamic Head Gesture Recognition Method for Realtime Intention Inference and Its Application To Visual Human-robot Interaction)

    76-76页
    查看更多>>摘要：Fresh data on Robotics Androids are presented in a new report. According to news reporting originating from Hangzhou, People’s Republic of China, by NewsRx correspondents, research stated, “Head gesture is a natural and non-verbal communication method for human-computer and human-robot interaction, conveying attitudes and intentions. However, the existing vision-based recognition methods cannot meet the precision and robustness of interaction requirements.” Funders for this research include Key Research and Development Project of Zhejiang Province, Fundamental Research Funds for the Provincial Universities of Zhejiang, National Natural Science Foundation of China (NSFC), Natural Science Foundation of Zhejiang Province. Our news editors obtained a quote from the research from Hangzhou Dianzi University, “Due to the limited computational resources, applying most high-accuracy methods to mobile and onboard devices is challenging. Moreover, the wearable device-based approach is inconvenient and expensive. To deal with these problems, an end-to-end two-stream fusion network named TSIR3D is proposed to identify head gestures from videos for analyzing human attitudes and intentions. Inspired by Inception and ResNet architecture, the width and depth of the network are increased to capture motion features sufficiently. Meanwhile, convolutional kernels are expanded from the spatial domain to the spatiotemporal domain for temporal feature extraction. The fusion position of the two-stream channel is explored under an accuracy/complexity trade-off to a certain extent. Furthermore, a dynamic head gesture dataset named DHG and a behavior tree are designed for human-robot interaction. Experimental results show that the proposed method has advantages in real-time performance on the remote server or the onboard computer. Furthermore, its accuracy on the DHG can surpass most state-of-the-art vision-based methods and is even better than most previous approaches based on head-mounted sensors.”
      原文链接:
    • NETL
    • NSTL
    • Inst Control Robotics & Systems, Korean Inst Electrical Engineers

    Data from AVIC Manufacturing Technology Institute Advance Knowledge in Robotics (Research on Damage Caused by CarbonFiber-Reinforced Polymer Robotic Drilling Based on Digital Image Correlation and Industrial Computed Tomography)

    77-77页
    查看更多>>摘要：Investigators publish new report on robotics. According to news originating from Beijing, People’s Republic of China, by NewsRx correspondents, research stated, “In order to enhance application scenarios and increase the proportion of industrial robots in the field of drilling composites, the damage caused by carbon-fiber-reinforced polymer robotic drilling is studied.” Funders for this research include National Natural Science Foundation of China. Our news journalists obtained a quote from the research from AVIC Manufacturing Technology Institute: “The shortcomings of the existing damage evaluation factors are analyzed, and new damage evaluation factors for carbon-fiber-reinforced polymer laminates made of unidirectional prepreg are proposed. A robot and a brad-and-spur drill were used to drill carbon-fiber-reinforced polymer laminates to study the influence of the process parameters on robotic drilling damage. Digital image correlation equipment and industrial computed tomography were used to study the formation process and the damage forms of the hole on the exit side with different process parameters. The test results show that delamination and tearing are significantly affected by the feed rate and spindle speed, while burrs are less affected by the cutting parameters. Appropriately increasing the spindle speed and reducing the feed rate are beneficial to reducing the comprehensive damage factor and improving the hole quality.”
      原文链接:
    • NETL
    • NSTL
    • Mdpi

    Findings from Chinese Academy of Sciences Broaden Understanding of Machine Learning (Navigating Materials Chemical Space To Discover New Battery Electrodes Using Machine Learning)

    78-78页
    查看更多>>摘要：Research findings on Machine Learning are discussed in a new report. According to news reporting originating in Guangdong, People’s Republic of China, by NewsRx journalists, research stated, “Investigating the role of electrodes’ physiochemical properties on their output voltage can be beneficial in developing high-performance batteries. To this end, this study uses a two-step machine learning (ML) approach to predict new electrodes and analyze the effects of their physiochemical properties on the voltage.” Funders for this research include National Natural Science Foundation of China (NSFC), National Natural Science Foundation of Guangdong Province, Shenzhen Science and Technology Program, Shenzhen Science and Technology Program, Shenzhen-Hong Kong -Macau Technology Research Program, Shenzhen Excellent Science and Technology Innovation Talent Training Project-Outstanding Youth Project, CCFTencent Open Fund, Iwatani Naoji Foundation. The news reporters obtained a quote from the research from the Chinese Academy of Sciences, “The first step utilizes an ML model to curate an informative feature space that elucidates the relationship between physiochemical properties and voltage output. The second step trains an active learning model on the informative feature space using Bayesian optimization to screen potential battery electrodes from a dataset of 3656 materials. This strategy successfully identified 41 electrode materials that exhibit good electronic conductivity and host highly electronegative anions.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    University of Calgary Reports Findings in Artificial Intelligence (Challenges and Potential of Artificial Intelligence in Neuroradiology)

    79-79页
    查看更多>>摘要：New research on Artificial Intelligence is the subject of a report. According to news reporting originating in Calgary, Canada, by NewsRx journalists, research stated, “Artificial intelligence (AI) has emerged as a transformative force in medical research and is garnering increased attention in the public consciousness. This represents a critical time period in which medical researchers, healthcare providers, insurers, regulatory agencies, and patients are all developing and shaping their beliefs and policies regarding the use of AI in the healthcare sector.” The news reporters obtained a quote from the research from the University of Calgary, “The successful deployment of AI will require support from all these groups. This commentary proposes that widespread support for medical AI must be driven by clear and transparent scientific reporting, beginning at the earliest stages of scientific research. A review of relevant guidelines and literature describing how scientific reporting plays a central role at key stages in the life cycle of an AI software product was conducted. To contextualize this principle within a specific medical domain, we discuss the current state of predictive tissue outcome modeling in acute ischemic stroke and the unique challenges presented therein. Translating AI methods from the research to the clinical domain is complicated by challenges related to model design and validation studies, medical product regulations, and healthcare providers’ reservations regarding AI’s efficacy and affordability. However, each of these limitations is also an opportunity for high-impact research that will help to accelerate the clinical adoption of state-of-the-art medical AI.”
      原文链接:
    • NETL
    • NSTL

    New Findings from Shanghai University Describe Advances in Cyborg and Bionic Systems (Camera-Radar Fusion with Modality Interaction and Radar Gaussian Expansion for 3D Object Detection)

    80-80页
    查看更多>>摘要：Researchers detail new data in cyborg and bionic systems. According to news reporting out of Shanghai, People’s Republic of China, by NewsRx editors, research stated, “The fusion of millimeterwave radar and camera modalities is crucial for improving the accuracy and completeness of 3-dimensional (3D) object detection.” Financial supporters for this research include Shanghai Sailing Program; National Natural Science Foundation of China. The news reporters obtained a quote from the research from Shanghai University: “Most existing methods extract features from each modality separately and conduct fusion with specifically designed modules, potentially resulting in information loss during modality transformation. To address this issue, we propose a novel framework for 3D object detection that iteratively updates radar and camera features through an interaction module. This module serves a dual purpose by facilitating the fusion of multimodal data while preserving the original features. Specifically, radar and image features are sampled and aggregated with a set of sparse 3D object queries, while retaining the integrity of the original radar features to prevent information loss. Additionally, an innovative radar augmentation technique named Radar Gaussian Expansion is proposed. This module allocates radar measurements within each voxel to neighboring ones as a Gaussian distribution, reducing association errors during projection and enhancing detection accuracy.”
      原文链接:
    • NETL
    • NSTL
    • Amer Assoc Advancement Science

    Qinghai University Affiliated Hospital Reports Findings in Gastric Cancer (Machine learning identifies the risk of complications after laparoscopic radical gastrectomy for gastric cancer)

    81-82页
    查看更多>>摘要：New research on Oncology Gastric Cancer is the subject of a report. According to news reporting originating from Xining, People’s Republic of China, by NewsRx correspondents, research stated, “Laparoscopic radical gastrectomy is widely used, and perioperative complications have become a highly concerned issue. To develop a predictive model for complications in laparoscopic radical gastrectomy for gastric cancer to better predict the likelihood of complications in gastric cancer patients within 30 days after surgery, guide perioperative treatment strategies for gastric cancer patients, and prevent serious complications.” Our news editors obtained a quote from the research from Qinghai University Affiliated Hospital, “In total, 998 patients who underwent laparoscopic radical gastrectomy for gastric cancer at 16 Chinese medical centers were included in the training group for the complication model, and 398 patients were included in the validation group. The clinicopathological data and 30-d postoperative complications of gastric cancer patients were collected. Three machine learning methods, lasso regression, random forest, and artificial neural networks, were used to construct postoperative complication prediction models for laparoscopic distal gastrectomy and laparoscopic total gastrectomy, and their prediction efficacy and accuracy were evaluated. The constructed complication model, particularly the random forest model, could better predict serious complications in gastric cancer patients undergoing laparoscopic radical gastrectomy. It exhibited stable performance in external validation and is worthy of further promotion in more centers. Using the risk factors identified in multicenter datasets, highly sensitive risk prediction models for complications following laparoscopic radical gastrectomy were established.”
      原文链接:
    • NETL
    • NSTL
    • Baishideng Publishing Group Inc

    Research Study Findings from Shanghai Institute of Technology Update Understanding of Machine Learning (The Prediction of Wear Depth Based on Machine Learning Algorithms)

    81-81页
    查看更多>>摘要：Fresh data on artificial intelligence are presented in a new report. According to news originating from Shanghai, People’s Republic of China, by NewsRx editors, the research stated, “In this work, ball-ondisk wear experiments were carried out on different wear parameters such as sliding speed, sliding distance, normal load, temperature, and oil film thickness.” Financial supporters for this research include National Natural Science Foundation of China; Industrial Collaborative Innovation Project of Shanghai; Leading Talents Program of Shanghai; Natural Science Foundation Project of Shanghai; Foundation of Science And Technology Commission of Shanghai Municipality; Guangdong Basic And Applied Basic Research Foundation; Project of Department of Education of Guangdong Province. The news reporters obtained a quote from the research from Shanghai Institute of Technology: “In total, 81 different sets of wear depth data were obtained. Four different machine learning (ML) algorithms, namely Random Forest (RF), K-neighborhood (KNN), Extreme Gradient Boosting (XGB), and Support Vector Machine (SVM) were applied to predict wear depth. By analyzing the performance of several ML algorithms, it is demonstrated that ball bearing wear depth can be estimated by ML models by inputting different parameter variables.”
      原文链接:
    • NETL
    • NSTL
    • Mdpi

    University of Technology-Iraq Researchers Have Published New Study Findings on Robotics (Omnidirectional Mobil Robot with Navigation Using SLAM)

    83-83页
    查看更多>>摘要：Fresh data on robotics are presented in a new report. According to news reporting originating from Baghdad, Iraq, by NewsRx correspondents, research stated, “As mobile robots have become widespread in indoor environments with narrow and crowded corridors, such as institutions, the demand for mobile robots has recently increased, especially for service purposes (homes, hospitals, and nursing homes for the elderly).” The news reporters obtained a quote from the research from University of Technology-Iraq: “The most important factor of autonomous navigation is the mobile robot’s awareness of its surroundings, with the robot’s ability to move from one place to another smoothly and safely in terms of avoiding obstacles. In this paper, a mobile robot with multi-directional wheels was designed to work in indoor environments and narrow corridors. SLAM was used to map the environment in which the robot operates, as well as determine the robot’s location within this environment based on the data of the LIDAR sensor. The robot was controlled through the ROS robot operating system.”
      原文链接:
    • NETL
    • NSTL

    Research from University of Koblenz-Landau Yields New Data on Machine Learning (Can Data and Machine Learning Change the Future of Basic Income Models? A Bayesian Belief Networks Approach)

    83-84页
    查看更多>>摘要：Data detailed on artificial intelligence have been presented. According to news reporting out of Koblenz, Germany, by NewsRx editors, research stated, “Appeals to governments for implementing basic income are contemporary.” Our news journalists obtained a quote from the research from University of Koblenz-Landau: “The theoretical backgrounds of the basic income notion only prescribe transferring equal amounts to individuals irrespective of their specific attributes. However, the most recent basic income initiatives all around the world are attached to certain rules with regard to the attributes of the households. This approach is facing significant challenges to appropriately recognize vulnerable groups.” According to the news reporters, the research concluded: “A possible alternative for setting rules with regard to the welfare attributes of the households is to employ artificial intelligence algorithms that can process unprecedented amounts of data. Can integrating machine learning change the future of basic income by predicting households vulnerable to future poverty? In this paper, we utilize multidimensional and longitudinal welfare data comprising one and a half million individuals’ data and a Bayesian beliefs network approach to examine the feasibility of predicting households’ vulnerability to future poverty based on the existing households’ welfare attributes.”
      原文链接:
    • NETL
    • NSTL
    • Mdpi

    Recent Findings in Machine Learning Described by Researchers from TRIUMF (Improved Calorimetric Particle Identification In Na62 Using Machine Learning Techniques)

    84-85页
    查看更多>>摘要：Researchers detail new data in Machine Learning. According to news originating from Vancouver, Canada, by NewsRx correspondents, research stated, “Measurement of the ultra-rare K+ ->pi(+)nu(nu) over bar over bar decay at the NA62 experiment at CERN requires high-performance particle identification to distinguish muons from pions. Calorimetric identification currently in use, based on a boosted decision tree algorithm, achieves a muon misidentification probability of 1.2 x 10(-5) for a pion identification efficiency of 75% in the momentum range of 15-40 GeV/c.” Financial supporters for this research include Fonds de la Recherche Scientifique FNRS, CECI (Consortium des Equipements de Calcul Intensif) Fondsde la Recherche Scientifique de Belgique (F.R.S.-FNRS), Walloon Region, Belgium, Natural Sciences and Engineering Research Council of Canada (NSERC), MEYS (Ministry of Education, Youth and Sports), Czech Republic, Federal Ministry of Education & Research (BMBF), Istituto Nazionale di Fisica Nucleare (INFN), Ministry of Education, Universities and Research (MIUR), Consejo Nacional de Ciencia y Tecnologia (CONACyT), IFA (Institute of Atomic Physics) Romanian CERN-RO, Nucleus Programme, Romania, MESRS (Ministry of Education, Science, Research and Sport), Slovakia, CERN (European Organization for Nuclear Research), Switzerland, STFC(Science and Technology Facilities Council), United Kingdom, National Science Foundation (NSF), ERC (European Research Council)”UniversaLepto”, KaonLepton, Europe, Charles University Research Center, Czech Republic, Ministero dell’Istruzione, dell’Universita e della Ricerca(MIUR Futuro in ricerca), Italy, Royal Society, STFC (Rutherford fellowships), United Kingdom, European Research Council (ERC), EU Horizon 2020 (Marie Sklodowska-Curie).
      原文链接:
    • NETL
    • NSTL