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    Findings from SRM Institute of Science and Technology Has Provided New Data on Machine Learning (Employing Ensemble Machine Learning Techniques for Predicting the Thermohydraulic Performance of Double Pipe Heat Exchanger With and Without ...)

    29-30页
    查看更多>>摘要：Fresh data on Machine Learning are presented in a new report. According to news reporting from Tiruchirappalli, India, by NewsRx journalists, research stated, “In this study, advanced machine learning techniques were utilized to forecast the thermohydraulic performance of a double pipe heat exchanger (DPHE). Key variables, including friction factor (f), Nusselt number (Nu), effectiveness (epsilon), and number transfer units (NTU), were modeled as functions of Reynolds number (Re), the number of turbulators (N), and the length of turbulators (L).” The news correspondents obtained a quote from the research from the SRM Institute of Science and Technology, “To predict the DPHE’s thermohydraulic performance, datasets were gathered from realtime experiments (case-1 and case-2) and literature (case-3). For clarity and analytical purposes, case-1 and case-2 were dimensioned to match the specifications outlined in literature (case-3). 1 represented a conventional DPHE without turbulators, while case-2 involved a DPHE with a gear disc turbulator. Additionally, datasets for DPHE with dolphin ring turbulators (case-3) were obtained for validation from literature. Subsequently, two ensemble boosting algorithms (extreme gradient and categorical) and one bagging algorithm (random forest) were applied to the dataset. The results highlighted that the categorical boosting model exhibited the highest accuracy, achieving a coefficient of determination (R2) of 0.9987 and an average absolute percent relative error (AAPRE) of 0.1837%. Furthermore, a sensitivity analysis was conducted for the best model (Random Forest), revealing relationships between input and output parameters.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    California Institute of Technology Reports Findings in Biohybrids (Electromechanical enhancement of live jellyfish for ocean exploration)

    30-31页
    查看更多>>摘要：New research on Biotechnology - Biohybrids is the subject of a report. According to news reporting from California, United States, by NewsRx journalists, research stated, “The vast majority of the ocean’s volume remains unexplored, in part because of limitations on the vertical range and measurement duration of existing robotic platforms. In light of the accelerating rate of climate change impacts on the physics and biogeochemistry of the ocean, the need for new tools that can measure more of the ocean on faster timescales is becoming pressing.” Funders for this research include National Science Foundation, National Science Foundation Graduate Research Fellowship Program. The news correspondents obtained a quote from the research from the California Institute of Technology, “Robotic platforms inspired or enabled by aquatic organisms have the potential to augment conventional technologies for ocean exploration. Recent work demonstrated the feasibility of directly stimulating the muscle tissue of live jellyfish via implanted microelectronics. We present a biohybrid robotic jellyfish that leverages this external electrical swimming control, while also using a 3D printed passive mechanical attachment to streamline the jellyfish shape, increase swimming performance, and significantly enhance payload capacity. A six-meter-tall, 13,600-liter saltwater facility was constructed to enable testing of the vertical swimming capabilities of the biohybrid robotic jellyfish over distances exceeding 35 body diameters. We found that the combination of external swimming control and the addition of the mechanical forebody resulted in an increase in swimming speeds to 4.5 times natural jellyfish locomotion. Moreover, the biohybrid jellyfish were capable of carrying a payload volume up to 105\% of the jellyfish body volume. The added payload decreased the intracycle acceleration of the biohybrid robots relative to natural jellyfish, which could also facilitate more precise measurements by onboard sensors that depend on consistent platform motion.”
      原文链接:
    • NETL
    • NSTL

    Researchers from School of Computer Science and Technology Describe Findings in Artificial Intelligence (Artificial Intelligence Enabled Cyber Security Defense for Smart Cities: a Novel Attack Detection Framework Based On the Mdata Model)

    31-32页
    查看更多>>摘要：Data detailed on Artificial Intelligence have been presented. According to news reporting from Shenzhen, People’s Republic of China, by NewsRx journalists, research stated, “Smart cities have attracted a lot of attention from interdisciplinary research, and plenty of artificial intelligence based solutions have been proposed. However, cyber security has always been a serious problem, and it is becoming more and more severe in smart cities.” Funders for this research include Major Key Project of PCL, Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies. The news correspondents obtained a quote from the research from the School of Computer Science and Technology, “The existing attack defense methods are not suitable for detecting multi-step attacks since the detection rules are limited and the efficiency is limited by a large number of false security alarms. Hence, an advanced solution is urgently needed to improve cyber security defense capability. In this paper, we propose a novel attack detection framework called ACAM. To better represent the cyber security knowledge, the framework is based on the MDATA model, which can represent dynamic and temporalspatial knowledge better than the knowledge graph. The framework consists of the knowledge extraction module, the subgraph generation module, the alarm correlation module, and the attack detection module. These modules can remove false alarms and improve the detection capabilities of multi-step attacks. We implement the framework and conduct experiments on the cyber range platform, the experimental results validate the good performance of attack detection accuracy and efficiency.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Recent Findings from Xi’an Jiaotong University Provides New Insights into Robotics (Nonlinear Spectrum Feature Fusion Diagnosis Method for Rv Reducer of Industrial Robots)

    32-33页
    查看更多>>摘要：Investigators publish new report on Robotics. According to news reporting originating from Xi’an, People’s Republic of China, by NewsRx correspondents, research stated, “RV reducers of industrial robots may be possibly subjected to wear and vibration impact and invalidated for keeping the repeatability due to long-time dynamic load in their life cycle. Any failure of RV reducers may decrease the reliability and lead to incredible loss due to the unexpectedly shutdown of the manufacturing system.” Financial support for this research came from National Key Research and Development Program of China. Our news editors obtained a quote from the research from Xi’an Jiaotong University, “Failures can cause nonlinear interference when occurring in RV reducers and pose a challenge to RV reducer fault diagnosis. Therefore, it is important to take proper measures to diagnose the failure in RV reducers and reduce the influence of nonlinear interference. Nonlinear spectrum features fusion method is proposed for diagnosing the faults of RV reducers in industrial robots. For the purpose of extracting fault features more effectively, nonlinear output frequency response function is employed to obtain nonlinear frequency spectrum for exactly describing the nonlinear mechanism of faulty phenomenon. Moreover, enhanced evidence theory with similarity measure is proposed to compute the basic probability assignment functions of evidences according to the similarity between different modes for improving the identification accuracy of different faults. RV reducer experiments with different faults are performed to simulate robot operating conditions for obtaining normal and fault data under a variable speed working condition. Among the 180 sets of test data including normal conditions, the diagnostic accuracy of the proposed nonlinear feature fusion method is 92.22% and greatly preferable to that of the ordinary frequency spectrum method.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Hunan University Reports Findings in Machine Learning (Systematic tracking of nitrogen sources in complex river catchments: Machine learning approach based on microbial metagenomics)

    33-34页
    查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news reporting originating in Changsha, People’s Republic of China, by NewsRx journalists, research stated, “Tracking nitrogen pollution sources is crucial for the effective management of water quality; however, it is a challenging task due to the complex contaminative scenarios in the freshwater systems. The contaminative pattern variations can induce quick responses of aquatic microorganisms, making them sensitive indicators of pollution origins.” The news reporters obtained a quote from the research from Hunan University, “In this study, the soil and water assessment tool, accompanied by a detailed pollution source database, was used to detect the main nitrogen pollution sources in each sub-basin of the Liuyang River watershed. Thus, each sub-basin was assigned to a known class according to SWAT outputs, including point source pollution-dominated area, crop cultivation pollution-dominated area, and the septic tank pollution-dominated area. Based on these outputs, the random forest (RF) model was developed to predict the main pollution sources from different river ecosystems using a series of input variable groups (e.g., natural macroscopic characteristics, river physicochemical properties, 16S rRNA microbial taxonomic composition, microbial metagenomic data containing taxonomic and functional information, and their combination). The accuracy and the Kappa coefficient were used as the performance metrics for the RF model. Compared with the prediction performance among all the input variable groups, the prediction performance of the RF model was significantly improved using metagenomic indices as inputs. Among the metagenomic data-based models, the combination of the taxonomic information with functional information of all the species achieved the highest accuracy (0.84) and increased median Kappa coefficient (0.70). Feature importance analysis was used to identify key features that could serve as indicators for sudden pollution accidents and contribute to the overall function of the river system. The bacteria Rhabdochromatium marinum, Frankia, Actinomycetia, and Competibacteraceae were the most important species, whose mean decrease Gini indices were 0.0023, 0.0021, 0.0019, and 0.0018, respectively, although their relative abundances ranged only from 0.0004 to 0.1 %. Among the top 30 important variables, functional variables constituted more than half, demonstrating the remarkable variation in the microbial functions among sites with distinct pollution sources and the key role of functionality in predicting pollution sources. Many functional indicators related to the metabolism of Mycobacterium tuberculosis, such as K24693, K25621, K16048, and K14952, emerged as significant important factors in distinguishing nitrogen pollution origins.”
      原文链接:
    • NETL
    • NSTL

    Researchers from University of Coimbra Report Recent Findings in Machine Learning (High-refractive-index Materials Screening From Machine Learning and Ab Initio Methods)

    34-35页
    查看更多>>摘要：Fresh data on Machine Learning are presented in a new report. According to news reporting originating from Coimbra, Portugal, by NewsRx correspondents, research stated, “In this study we analyze the dielectric properties of a recently published dataset to identify high-refractiveindex and highband- gap materials that are crucial for modern optoelectronic applications. We employ advanced crystal graph convolutional neural networks and density functional perturbation theory calculations to accelerate the discovery of such materials.” Financial support for this research came from Fundao para a Cincia e Tecnologia, Portugal. Our news editors obtained a quote from the research from the University of Coimbra, “Our analysis confirms the traditional inverse relationship between band gap and dielectric constant, which persists even in this large dataset. However, our study reveals several promising materials that possess competitive properties compared to current industry standards.” According to the news editors, the research concluded: “Our findings provide valuable insights into the field of dielectric materials and demonstrate the potential of advanced machine learning and computational techniques for accelerating materials discovery.”
      原文链接:
    • NETL
    • NSTL

    Data on Robotics Detailed by Researchers at Shanghai Normal University (How Service Robots’ Human-like Appearance Impacts Consumer Trust: a Study Across Diverse Cultures and Service Settings)

    35-35页
    查看更多>>摘要：Researchers detail new data in Robotics. According to news reporting originating from Shanghai, People’s Republic of China, by NewsRx correspondents, research stated, “This study aims to compares the effects of different human-like appearances (low vs. medium vs. high) of service robots (SRs) on consumer trust in service robots (CTSR), examines the mediating role of perceived warmth (WA) and perceived competence (CO) and demonstrates the moderating role of culture and service setting. Design/methodology/approach The research design includes three scenario-based experiments (Chinese hotel setting, American hotel setting, Chinese hospital setting).” Our news editors obtained a quote from the research from Shanghai Normal University, “Study 1 found SR’s human-like appearance can arouse perceived anthropomorphism (PA), which positively affects CTSR through parallel mediators (WA and CO). Study 2 revealed consumers from Chinese (vs. American) culture had higher CTSR. Study 3 showed consumers had higher WA and CO for SRs in the credence (vs. experience) service setting. The authors also had an exploratory analysis of the uncanny valley phenomenon. Practical implications The findings have practical implications for promoting the diffusion of SRs in the hospitality industry. Managers can increase CTSR by augmenting the anthropomorphic design of SRs; however, they must consider the differences in this effect across all service recipients (consumers from different cultures) and service settings.”
      原文链接:
    • NETL
    • NSTL
    • Emerald Group Publishing

    Researchers at University of Quebec Montreal Release New Data on Support Vector Machines (High-dimensional Penalized Bernstein Support Vector Classifier)

    36-36页
    查看更多>>摘要：Fresh data on Support Vector Machines are presented in a new report. According to news reporting from Montreal, Canada, by NewsRx journalists, research stated, “The support vector machine (SVM) is a powerful classifier used for binary classification to improve the prediction accuracy. However, the nondifferentiability of the SVM hinge loss function can lead to computational difficulties in high-dimensional settings.” Financial supporters for this research include Fonds de Recherche Quebec-Sante, CGIAR. The news correspondents obtained a quote from the research from the University of Quebec Montreal, “To overcome this problem, we rely on the Bernstein polynomial and propose a new smoothed version of the SVM hinge loss called the Bernstein support vector machine (BernSVC). This extension is suitable for the high dimension regime. As the BernSVC objective loss function is twice differentiable everywhere, we propose two efficient algorithms for computing the solution of the penalized BernSVC. The first algorithm is based on coordinate descent with the maximization-majorization principle and the second algorithm is the iterative reweighted least squares-type algorithm. Under standard assumptions, we derive a cone condition and a restricted strong convexity to establish an upper bound for the weighted lasso BernSVC estimator. By using a local linear approximation, we extend the latter result to the penalized BernSVC with nonconvex penalties SCAD and MCP. Our bound holds with high probability and achieves the so-called fast rate under mild conditions on the design matrix. Simulation studies are considered to illustrate the prediction accuracy of BernSVC relative to its competitors and also to compare the performance of the two algorithms in terms of computational timing and error estimation.”
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    New Robotics Findings from Yantai University Reported (Eventtriggered Practical Tracking Control for an Uncertain Free-flying Flexible-joint Space Robot With Dead-zone Input)

    37-37页
    查看更多>>摘要：Data detailed on Robotics have been presented. According to news originating from Shandong, People’s Republic of China, by NewsRx correspondents, research stated, “This paper is devoted to the event-triggered practical tracking control of a class of uncertain free-flying flexible-joint space robots (FFSRs) under unknown dead-zone input. The remarkable characteristics of the paper are reflected by the coarse information on the reference signal since its time derivatives are not necessarily available for feedback, and moreover, by the serious uncertainties which contain unknown nonlinear dynamics, parameters without known nominal parts, and the external disturbance.” Financial supporters for this research include National Natural Science Foundation of China (NSFC), Natural Science Foundation of Shandong Province. Our news journalists obtained a quote from the research from Yantai University, “Then, the traditional control schemes on this topic become incapable. For this, a novel event-triggered control scheme is proposed by a skilful use of adaptive technique. Specifically, a dynamic gain with a smart choice of its updating law is incorporated into the vectorial backstepping framework, which not only overcomes the serious uncertainties contained in the system and dead-zone input but also handles the influence of sampling errors. Consequently, two adaptive event-triggered controllers are designed which ensure that all the states of the resulting closed-loop system are bounded while the system output practically tracks the reference signal, along with the exclusion of the Zeno phenomenon.”
      原文链接:
    • NETL
    • NSTL
    • Sage

    National University San Luis Researchers Discuss Research in Machine Learning (Seismic Event Detection in the Copahue Volcano Based on Machine Learning: Towards an On-the-Edge Implementation)

    38-39页
    查看更多>>摘要：Data detailed on artificial intelligence have been presented. According to news reporting originating from San Luis, Argentina, by NewsRx correspondents, research stated, “This study focused on seismic event detection in a volcano using machine learning by leveraging the advantages of software/hardware co-design for a system on a chip (SoC) based on field-programmable gate array (FPGA) devices.” Our news editors obtained a quote from the research from National University San Luis: “A case study was conducted on the Copahue Volcano, an active stratovolcano located on the border between Argentina and Chile. Volcanic seismic event processing and detection were integrated into a PYNQ-based implementation by using a low-end SoC-FPGA device.”
      原文链接:
    • NETL
    • NSTL
    • Mdpi