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    Recent Research from Sun Yat-sen University Highlight Findings in Robotics and Automation (Acoustic-vins: Tightly Coupled Acoustic- visual-inertial Navigation System for Autonomous Underwater Ve- hicles)

    48-49页
    查看更多>>摘要：Research findings on Robotics - Robotics and Automation are discussed in a new report. According to news reporting originating in Shenzhen, People's Republic of China, by NewsRx journalists, research stated, "In this work, we present an acoustic-visual-inertial navigation system (Acoustic-VINS) for underwater robot localization. Specifically, we address the problem of the global position of the underwater visual-inertial navigation system being inappreciable by tightly coupling the long baseline (LBL) system into an optimization-based visual-inertial SLAM." Financial support for this research came from Shenzhen Science and Technology Program. The news reporters obtained a quote from the research from Sun Yat-sen University, "In our proposed Acoustic-VINS, the reprojection error, IMU preintegration error, and raw LBL measurement error are jointly minimized within a sliding window factor graph framework. Furthermore, we propose an acoustic-aided initialization method to exhibit an accurate initial state for successful state estimation. Additionally, for wider application, we extend the sensor data of the real-world AQUALOC dataset to obtain the LBL- AQUALOC dataset."
      原文链接:
    • NETL
    • NSTL
    • IEEE

    Findings from ITMO University Reveals New Findings on Machine Learning (Machine Learning Methods for Liquid Crystal Research: Phases, Textures, Defects and Physical Properties)

    49-50页
    查看更多>>摘要：Data detailed on Machine Learning have been presented. According to news reporting originating from St. Petersburg, Russia, by NewsRx correspondents, research stated, "Liquid crystal mate- rials, with their unique properties and diverse applications, have long captured the attention of researchers and industries alike. From liquid crystal displays and electro-optical devices to advanced sensors and emerg- ing technologies, the study and application of liquid crystals continue to be of paramount importance in the fields of materials science, chemistry and physics." Funders for this research include Ministry of Science and Higher Education of the Russian Federation, Ministry of Science and Higher Education of the Russian Federation, ITMO Fellowship. Our news editors obtained a quote from the research from ITMO University, "With the ever-increasing complexity and diversity of liquid crystal materials, researchers face new challenges in understanding their behaviors, properties, and potential applications. On the other hand, machine learning, a rapidly evolving interdisciplinary field at the intersection of computer science and data analysis, has already become a powerful tool for unraveling implicit correlations and predicting new properties of a wide variety of physical and chemical systems and structures. Here we aim to consider how machine learning methods are suitable for solving fundamental problems in the field of liquid crystals and what are the advantages of this artificial intelligence based approach."
      原文链接:
    • NETL
    • NSTL
    • Royal Soc Chemistry

    Chinese Academy of Sciences Reports Findings in Machine Learn- ing (Knowledge-guided mixture density network for chlorophyll-a retrieval and associated pixel-by-pixel uncertainty assessment in op- tically variable inland waters)

    50-51页
    查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news reporting originating in Beijing, People's Republic of China, by NewsRx journalists, research stated, "Ma- chine learning has been increasingly used to retrieve chlorophyll-a (Chl-a) in optically variable waters. However, without the guidance of physical principles or expert knowledge, machine learning may produce biased mapping relationships, or waste considerable time searching for physically infeasible hyperparameter domains." The news reporters obtained a quote from the research from the Chinese Academy of Sciences, "In addition, most Chl-a retrieval models cannot evaluate retrieval uncertainty when ground observations are not available, and the retrieval uncertainty is crucial for understanding the model limitations and evaluating the reliability of retrieval results. In this study, we developed a novel knowledge-guided mixture density network to retrieve Chl-a in optically variable inland waters based on Sentinel-3 Ocean and Land Color Instrument (OLCI) imagery. The proposed method embedded prior knowledge derived from spectral shape classification into the mixture density network. Compared to another deterministic model, the knowledge- guided mixture density network outputted the conditional distribution of Chl-a given an input spectrum, enabling us to estimate the optimal retrieval and the associated uncertainty. The proposed method showed favorable correspondence with the field Chl-a, with root mean square error (RMSE) of 6.56 mg/L, and mean absolute percentage error (MAPE) of 43.64 %. Calibrated against Sentinel-3 OLCI spectrum, the proposed method also performed well when applied to field spectrum (RMSE = 4.58 mg/L, MAPE = 72.70 %), suggesting its effectiveness and good generalization. The proposed method provided the standard deviation of each estimated Chl-a, which enabled us to inspect the reliability of the estimated results and understand the model limitations."
      原文链接:
    • NETL
    • NSTL

    Peking University People's Hospital Reports Findings in Encephali- tis (MRI-Based Machine Learning Fusion Models to Distinguish En- cephalitis and Gliomas)

    51-52页
    查看更多>>摘要：New research on Central Nervous System Diseases and Conditions - Encephalitis is the subject of a report. According to news reporting originating in Beijing, People's Republic of China, by NewsRx journalists, research stated, "This paper aims to compare the performance of the classical machine learning (CML) model and the deep learning (DL) model, and to assess the effectiveness of utilizing fusion radiomics from both CML and DL in distinguishing encephalitis from glioma in atypical cases. We analysed the axial FLAIR images of preoperative MRI in 116 patients pathologically confirmed as gliomas and clinically diagnosed with encephalitis." Financial support for this research came from National Natural Science Foundation of China. The news reporters obtained a quote from the research from Peking University People's Hospital, "The 3 CML models (logistic regression (LR), support vector machine (SVM) and multi-layer perceptron (MLP)), 3 DL models (DenseNet 121, ResNet 50 and ResNet 18) and a deep learning radiomic (DLR) model were established, respectively. The area under the receiver operating curve (AUC) and sensitivity, specificity, accuracy, negative predictive value (NPV) and positive predictive value (PPV) were calculated for the training and validation sets. In addition, a deep learning radiomic nomogram (DLRN) and a web calculator were designed as a tool to aid clinical decision-making. The best DL model (ResNet50) consistently outperformed the best CML model (LR). The DLR model had the best predictive performance, with AUC, sensitivity, specificity, accuracy, NPV and PPV of 0.879, 0.929, 0.800, 0.875, 0.867 and 0.889 in the validation sets, respectively. Calibration curve of DLR model shows good agreement between prediction and observation, and the decision curve analysis (DCA) indicated that the DLR model had higher overall net benefit than the other two models (ResNet50 and LR). Meanwhile, the DLRN and web calculator can provide dynamic assessments. Machine learning (ML) models have the potential to non-invasively differentiate between encephalitis and glioma in atypical cases."
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Data on Machine Learning Reported by Jack Tsai and Colleagues (Predicting homelessness among transitioning U.S. Army soldiers)

    52-53页
    查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news reporting originating from Washington, District of Columbia, by NewsRx correspondents, research stated, "This study develops a practical method to triage Army transitioning service members (TSMs) at highest risk of homelessness to target a preventive intervention. The sample included 4,790 soldiers from the Study to Assess Risk and Resilience in Servicemembers-Longitudinal Study (STARRS-LS) who participated in one of three Army STARRS 2011-2014 baseline surveys followed by the third wave of the STARRS-LS online panel surveys (2020-2022)." Our news editors obtained a quote from the research, "Two machine learning models were trained: a Stage-1 model that used administrative predictors and geospatial data available for all TSMs at discharge to identify high-risk TSMs for initial outreach; and a Stage-2 model estimated in the high-risk subsample that used self-reported survey data to help determine highest risk based on additional information collected from high-risk TSMs once they are contacted. The outcome in both models was homelessness within 12 months after leaving active service. Twelve-month prevalence of post-transition homelessness was 5.0% (SE=0.5). The Stage-1 model identified 30% of high-risk TSMs who accounted for 52% of homelessness. The Stage-2 model identified 10% of all TSMs (i.e., 33% of high-risk TSMs) who accounted for 35% of all homelessness (i.e., 63% of the homeless among high-risk TSMs)."
      原文链接:
    • NETL
    • NSTL

    Data on Support Vector Machines Described by a Researcher at Shanghai Maritime University (Rolling bearing fault diagnosis based on multi-domain features and whale optimized support vector ma- chine)

    53-54页
    查看更多>>摘要：New study results on have been published. According to news reporting out of Shanghai, People's Republic of China, by NewsRx editors, research stated, "Rolling bearing is an important rotating support component in mechanical equipment." Financial supporters for this research include Shanghai Natural Science Foundation of China; China Postdoctoral Science Foundation; Shanghai Engineering Technology Research Center Construction Projects; National High-tech Research And Development Program; National Natural Science Foundation of China. The news reporters obtained a quote from the research from Shanghai Maritime University: "It is very prone to wear, defects, and other faults, which directly affect the reliable operation of mechanical equipment. Its running condition monitoring and fault diagnosis have always been a matter of concern to engineers and researchers. A rolling bearing fault diagnosis technique based on multi-domain feature and whale optimization algorithm-support vector machine (MDF-WOA-SVM) is proposed. Firstly, recursive analysis is performed on vibration signal and the recursive features are employed as nonlinear recursive fea- ture vector including recursive rate (RR), deterministic rate (DET), recursive entropy (RE), and diagonal average length (DAL). Then, a comprehensive multi-domain feature vector is constructed by combining three time-domain features including root mean square, variance, and peak to peak. Finally, whale opti- mization algorithm (WOA) is introduced to optimize the penalty factor C and kernel function parameter g to construct the optimal WOA-SVM model."
      原文链接:
    • NETL
    • NSTL
    • Sage

    Researchers at Zurich University of Applied Sciences Report New Data on Machine Learning (Shedding Light On the Ageing of Extra Virgin Olive Oil: Probing the Impact of Temperature With Fluores- cence Spectroscopy and Machine Learning Techniques)

    54-55页
    查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news reporting out of Winterthur, Switzerland, by NewsRx editors, research stated, "This work systematically investigates the oxidation of extra virgin olive oil (EVOO) under accelerated storage conditions with UV absorption and total fluorescence spectroscopy. With the large amount of data collected, it proposes a method to monitor the oil's quality based on machine learning (ML) applied to highly -aggregated data." Financial support for this research came from Hasler Foundation project "ARES: AI for fluoREscence Spectroscopy in oil." Our news journalists obtained a quote from the research from the Zurich University of Applied Sciences, "EVOO is a high -quality vegetable oil that has earned worldwide reputation for its numerous health benefits and excellent taste. Despite its outstanding quality, EVOO degrades over time due to oxidation, which can affect both its health qualities and flavour. Therefore, it is highly relevant to quantify the effects of oxidation on EVOO and develop methods to assess it that can be easily implemented under field conditions, rather than in specialized analytical laboratories. The ML approach indicates that the two excitation wavelengths (480 nm) and (300 nm) exhibit the maximum relative change in fluorescence intensity during the ageing for the majority of the oils, thus identifying the wavelengths which are more informative for quality prediction. Also, the paper proposes a method for the prediction of olive oil quality using highly -aggregated data. Such a method is of interest because it paves the way to the realization of a low-cost, portable device for in -field quality control. The following study demonstrates that fluorescence spectroscopy has the capability to monitor the effect of oxidation and assess the quality of EVOO, even when the data are highly aggregated."
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Researchers from Univerzita sv. Cyrila a Metoda v Trnave Discuss Research in Artificial Intelligence (Determining the Connection Be- tween Creativity in Pupils and Teachers)

    55-55页
    查看更多>>摘要：Researchers detail new data in artificial intelligence. According to news reporting from the Univerzita sv. Cyrila a Metoda v Trnave by NewsRx journalists, research stated, "In the research, we focus on the issue of creativity in school classrooms, especially on the connection between creativity in students and their teachers." The news editors obtained a quote from the research from Univerzita sv. Cyrila a Metoda v Trnave: "The work aims to find whether there is a statistically significant connection between the variables or if the variables are related. The Torrance Figural Test of Creative Thinking (TTCT) and its subtest - Repeated Figures - are used to measure creativity. The research sample in our research consists of 104 students from the ages of 10 to 12 and 11 teachers."
      原文链接:
    • NETL
    • NSTL

    New Robotics Findings from University of Antwerp Outlined (Adap- tive Hybrid Reasoning for Agent-based Digital Twins of Distributed Multi-robot Systems)

    56-56页
    查看更多>>摘要：New research on Robotics is the subject of a report. According to news reporting originating from Antwerp, Belgium, by NewsRx correspondents, research stated, "The digital twin (DT) mainly acts as a virtual exemplification of a real-world entity, system, or process via multiphysical and logical models, allowing the capture and synchronization of its functions and attributes. The bridge between the actual system and the digital realm can be utilized to optimize the system's performance, and forecast and predict its behavior." Our news editors obtained a quote from the research from the University of Antwerp, "Incorporating intelligent and adaptive reasoning mechanisms into DTs is crucial to enable them to reason, adapt, and take efficacious actions in complex and dynamic environments. To this end, we introduce an approach for deploying agent-based DTs for cyber-physical systems. The foundation pillars of this approach are (1) integrating the concepts, entities, and relations of Zeigler's modeling and simulation framework from the perspective of agent-based DTs; (2) utilizing an expandable and scalable architecture for designing and materializing these twins, which handily enables extending and scaling physical and digital assets of the system; and finally (3) a two-tier reasoning strategy; reactive and rational models are conceptually redefined in the context of the modeling and simulation framework of agent-based DTs and technically deployed to boost the adaptive reasoning and decision-making function of DTs. As a result, an implemented simulation and control platform for a multi-robot system demonstrates the approach's applicability and feasibility, manifesting its usability and efficiency."
      原文链接:
    • NETL
    • NSTL
    • Sage

    Jilin University Reports Findings in Machine Learning [Machine learning-based prediction of heavy metal immobilization rate in the solidification/stabilization of municipal solid waste incineration fly ash (MSWIFA) by geopolymers]

    57-57页
    查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news originating from Changchun, People's Republic of China, by NewsRx correspondents, research stated, "Geopolymer is an environmentally friendly solidification/stabilization (S/S) binder, exhibiting significant potential for immobilizing heavy metals in municipal solid waste incineration fly ash (MSWIFA). However, due to the diversity in geopolymer raw materials and heavy metal properties, predicting the heavy metal immobilization rate proves to be challenging." Our news journalists obtained a quote from the research from Jilin University, "In order to enhance the application of geopolymers in immobilizing heavy metals in MSWIFA, a universal method is required to predict the heavy metal immobilization rate. Therefore, this study employs machine learning to predict the heavy metal immobilization rate in S/S of MSWIFA by geopolymers. A gradient boosting regression (GB) model with superior performance (R = 0.9214) was obtained, and a graphical user interface (GUI) software was developed to facilitate the convenient accessibility of researchers. The feature categories influencing heavy metal immobilization rate are ranked in order of importance as heavy metal properties ＞geopolymer raw material properties ＞curing conditions ＞alkali activator properties."
      原文链接:
    • NETL
    • NSTL