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    Findings from University of Chile Provides New Data on Machine Learning [Eruption Forecasting Model for Copahue Volcano (Southern Andes) Using Seismic Da ta and Machine Learning: a Joint Interpretation With Geodetic Data (Gnss and Ins ar)]

    146-147页
    查看更多>>摘要：Investigators publish new report on Ma chine Learning. According to news reporting originating in Santiago, Chile, by N ewsRx journalists, research stated, "Anticipating volcanic eruptions remains a c hallenge despite significant scientific advancements, leading to substantial hum an and economic losses. Traditional approaches, like volcano alert levels, provi de current volcanic states but do not always include eruption forecasts." Financial supporters for this research include Programa de Riesgo Sismico (PRS; Seismic Risk Program) of the Universidad de Chile, The project FONDECYT of the A gencia Nacional de Investigacion y Desarrollo (ANID), The project FONDECYT, ANID , Chile, New Zealand Ministry of Business, Innovation and Employment (MBIE), CON ICYT FONDECYT.
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    Reports Summarize Artificial Intelligence Findings from Duy Tan University (Low Cost Artificial Intelligence Internet of Things Based Water Quality Monitoring f or Rural Areas)

    147-148页
    查看更多>>摘要：A new study on Artificial Intelligence is now available. According to news reporting originating from Da Nang, Vietnam , by NewsRx correspondents, research stated, "Safe drinking water is quite possi bly one of the most difficult-to-find issues on our earth intently influencing t he well-being and cleanliness of humanity, animals, and plants. The smart speed of industrialization and the conspicuous significance of horticulture with culti vating pesticides have impelled water contamination to a colossal degree." Financial supporters for this research include National Project Implemented Unit (NPIU), a unit of the Ministry of Human Resource Department (MHRD), Govt. of In dia, The World Bank India, King Saud University.
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    Studies from Shandong University Yield New Information about Robotics (Adaptive Fast Terminal Sliding Mode Control of Robotic Manipulators Based On Joint Torque Estimation and Friction Compensation)

    148-148页
    查看更多>>摘要：A new study on Robotics is now availab le. According to news reporting out of Jinan, People's Republic of China, by New sRx editors, research stated, "In this work, an adaptive fast terminal sliding m ode control (AFSMC) approach based on joint torque estimation and friction compe nsation is proposed to enhance the trajectory tracking accuracy of robotic manip ulators under variable load conditions. The joint torque estimation utilizes an improved harmonic drive compliance model and adaptive low-pass filtering, and fr iction compensation employs a hybrid model accounting for velocity and load torq ue effects." Financial supporters for this research include Key Technology Research and Devel opment Program of Shandong Province, Natural Science Foundation of Shandong Prov ince.
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    Tongji University School of Medicine Reports Findings in Dental Implants (Accura cy of an autonomous dental implant robotic system in placing tilted implants for edentulous arches)

    149-149页
    查看更多>>摘要：New research on Dentistry -Dental Imp lants is the subject of a report. According to news originating from Shanghai, P eople's Republic of China, by NewsRx correspondents, research stated, "Accurate placement of tilted implants is essential as they are typically close to importa nt anatomic structures. Inaccurate implant position may damage those structures and affect outcomes." Our news journalists obtained a quote from the research from the Tongji Universi ty School of Medicine, "The purpose of this in vitro study was to compare the ac curacy and efficiency of an autonomous dental implant robotic (ADIR) system and a static computer-assisted implant surgery (sCAIS) system in placing tilted impl ants in edentulous patients. Ten 3-dimensionally (3D) printed edentulous mandibu lar casts were assigned to 1 of 2 groups (ADIR and sCAIS). The coronal, apical, and angular deviations of the placement of tilted implants, preoperative prepara tion time, and surgical time were compared between the 2 groups. The paired samp les t test and the independent samples t test were used to compare the groups (a =.05). The mean ±standard deviation of coronal, apical, and angular deviation in the ADIR group and sCAIS group were 0.47 ±0.06 mm versus 1.09 ±0.11 mm, 0.47 ±0 .05 mm versus 1.53 ±0.14 mm, and 0.91 ±0.82 degrees versus 2.83 ±0.55 degrees, r espectively. The deviations of the tilted implant positions in the ADIR were rel atively small and significantly different from those of sCAIS (P <.05). The preoperative preparation time of the ADIR group was significantly long er than that of the sCAIS group (P <.001), and the surgical time for the 2 groups was statistically similar (P=.259). Compared with the sCA IS system, the deviation of tilted implants in the ADIR group was smaller, but t he preoperative preparation time was longer. The results indicated that using th e ADIR for tilted implantation can lead to more accurate implantation positions and reduce the occurrence of complications."
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    New Study Findings from Oak Ridge National Laboratory Illuminate Research in Mac hine Learning (Assessment of Envelope-and Machine Learning-Based Electrical Fau lt Type Detection Algorithms for Electrical Distribution Grids)

    150-150页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily NewsInvestigators publish new report on artificial in telligence. According to news reporting from Oak Ridge, Tennessee, by NewsRx jou rnalists, research stated, "This study introduces envelope-and machine learning (ML)-based electrical fault type detection algorithms for electrical distributi on grids, advancing beyond traditional logic-based methods." Financial supporters for this research include Us Department of Energy (Doe) Off ice of Electricity. Our news correspondents obtained a quote from the research from Oak Ridge Nation al Laboratory: "The proposed detection model involves three stages: anomaly area detection, ML-based fault presence detection, and ML-based fault type detection . Initially, an envelope-based detector identifying the anomaly region was impro ved to handle noisier power grid signals from meters. The second stage acts as a switch, detecting the presence of a fault among four classes: normal, motor, sw itching, and fault. Finally, if a fault is detected, the third stage identifies specific fault types. This study explored various feature extraction methods and evaluated different ML algorithms to maximize prediction accuracy."
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    Study Results from Tsinghua University Broaden Understanding of Machine Learning (A Combined Active Control Method of Restricted Nonlinear Model and Machine Lea rning Technology for Drag Reduction In Turbulent Channel Flow)

    151-151页
    查看更多>>摘要：A new study on Machine Learning is now available. According to news reporting originating in Beijing, People's Republi c of China, by NewsRx journalists, research stated, "The practical implementatio n of machine learning in flow control is limited due to its significant training expenses. In the present study the convolutional neural network (CNN) trained w ith the data of the restricted nonlinear (RNL) model is used to predict the norm al velocity on a detection plane at y(+) = 10 in a turbulent channel flow, and t he predicted velocity is used as wall blowing and suction for drag reduction." Financial support for this research came from National Natural Science Foundatio n of China (NSFC). The news reporters obtained a quote from the research from Tsinghua University, "An active control test is carried out by using the well-trained CNN in direct n umerical simulation (DNS). Substantial drag reduction rates up to 19 % and 16 % are obtained based on the spanwise and streamwise wall sh ear stresses, respectively. Furthermore, we explore the online control of wall t urbulence by combining the RNL model with reinforcement learning (RL). The RL is constructed to determine the optimal wall blowing and suction based on its obse rvation of the wall shear stresses without using the label data on the detection plane for training. The controlling and training processes are conducted synchr onously in a RNL flow field. The control strategy discovered by RL has similar d rag reduction rates with those obtained previously by the established method. Al so, the training cost decreases by over thirty times at Re-tau = 950 compared wi th the DNS-RL model. The present results provide a perspective that combining th e RNL model with machine learning control for drag reduction in wall turbulence can be effective and computationally economical."
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    Reports Summarize Machine Learning Study Results from Washington University (Exa mining the Most Important Risk Factors for Predicting Youth Persistent and Distr essing Psychotic-like Experiences)

    152-153页
    查看更多>>摘要：Investigators publish new report on Ma chine Learning. According to news reporting from St. Louis, Missouri, by NewsRx journalists, research stated, "Persistence and distress distinguish more clinica lly significant psychotic-like experiences (PLEs) from those that are less likel y to be associated with impairment and/or need for care. Identifying risk factor s that identify clinically relevant PLEs early in development is important for i mproving our understanding of the etiopathogenesis of these experiences." Funders for this research include NIH National Institute on Drug Abuse (NIDA), N IH National Institute of Mental Health (NIMH), BrightFocus Foundation, National Institutes of Health (NIH) -USA, NIH National Institute of Mental Health (NIMH) , BrightFocus Foundation, NIH National Institute of Neurological Disorders & Stroke (NINDS).
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    Study Findings on Machine Learning Described by Researchers at Cairo University (Modeling indoor thermal comfort in buildings using digital twin and machine lea rning)

    153-154页
    查看更多>>摘要：Research findings on artificial intell igence are discussed in a new report. According to news reporting from Giza, Egy pt, by NewsRx journalists, research stated, "Digital Twin (DT) concept is used i n different domains and industries, including the building industry, as it has p hysical and digital assets with the help of Building Information Modeling (BIM). Technologies and methodologies constantly enrich the building industry because the amount of data generated during different building stages is considerable an d has a tremendous effect on the lifecycle of a building." The news journalists obtained a quote from the research from Cairo University: " Previous research underscores the importance of seamlessly exchanging informatio n between physical and digital assets within a comprehensive framework, particul arly emphasizing the integration of BIM data with various systems to enhance eff iciency and prevent information loss. Despite advancements in technologies, chal lenges persist in optimizing methods for integrating BIM data into DT frameworks , including ensuring interoperability, scalability, and real-time monitor and co ntrol. This study addresses this research gap by proposing a comprehensive platf orm that integrates the DT concept with IoT and BIM technologies. The platform i s developed in five main stages: 1) acquiring electronic data of the building fr om the laser scanner, 2) developing a Wi-Fi IoT module and BIM data for physical assets and digital replica, 3) constructing the DT elements of the platform, 4) performing data analysis 5) implementing thermal comfort prediction models. Two machine learning models (Facebook prophet, NeuralProphet) are implemented to pr edict thermal comfort. The best predictive model is identified by evaluating its error function using historical training data collected during facility operati on. A case study demonstrates the practical application of the proposed framewor k. The case study involves a real building where the platform is implemented to monitor and control indoor environments."
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    New Findings in Machine Learning Described from Nanjing University of Science an d Technology (Micro-scale Crystallization Thermodynamics Study of Typical Energe tic Compounds Integrating Optofluidics and Machine Learning)

    154-154页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily NewsFresh data on Machine Learning are presented in a new report. According to news reporting out of Nanjing, People's Republic of Ch ina, by NewsRx editors, research stated, "With the aim of investigating the chan ging law of crystallization driving force of typical energetic compounds under m icro-scale crystallization conditions, a thermodynamic parameter determination m ethod based on optofluidics was proposed. Aimed at nitro, nitramine and nitrate explosives in energetic compounds, hexanitrostilbene (HNS), cyclotetramethylene tetranitramine (HMX) and pentaerythritol tetranitrate (PETN) were selected as re presentatives, the solubility of the three kinds of energetic compounds in their respective commonly used solvents (HNS: in DMF, DMSO, NMP; HMX: in DMF, DMSO, C YC; PETN: in DMF, DMSO, EAc) at different temperatures were determined."
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    Universite de Caen Normandie Reports Findings in Machine Learning (Differential plasma cytokine variation following X-ray or proton brain irradiation using mach ine-learning approaches)

    155-156页
    查看更多>>摘要：New research on Machine Learning is th e subject of a report. According to news reporting out of Caen, France, by NewsR x editors, research stated, "X-ray and proton irradiation have been reported to induce distinct modifications in cytokine expression in vitro and in vivo, sugge sting a dissimilar inflammatory response between X-rays and protons. We aimed to investigate the differences in cytokine profiles early following fractionated b rain irradiation with X-rays or protons and their relationship with leukocyte su bpopulations in rodents." Our news journalists obtained a quote from the research from Universite de Caen Normandie, "Our study utilized data from 80 tumor-free mice subjected to X-ray o r proton brain irradiation in four fractions of 2.5Gy. Sixteen non-irradiated mi ce were used as the controls. Blood was collected 12h postirradiation to examine the profile of 13 cytokines. Correlation analysis, principal component analysis (PCA), and tree-based modeling were used to investigate the relationship betwee n cytokine levels and leukocyte subpopulation variations following irradiation i n the blood. Regardless of the irradiation type, brain irradiation resulted in a notable elevation in the plasma levels of IFN-g and MCP-1. The use of either Xray or proton beam had differential effect on plasma cytokine levels following b rain irradiation. Specifically, X-ray irradiation was associated with significan tly increased plasma levels of IFN-b, IL-12p70, and IL-23, along with a decrease d level of IL-1a, in comparison to proton irradiation. Correlation analysis reve aled distinct cytokine regulatory patterns between X-ray and proton brain irradi ation. PCA highlighted the association of MCP-1, IL-6, TNF-a, IL-17A, and IFN-g with neutrophils, monocytes, and naive T-cells following X-ray irradiation. TNF-a and IL-23 levels correlated with naive CD4+-cells following proton irradiation . Tree-based models demonstrated that high TNF-a level resulted in an increase i n naive T-cells, neutrophils, and monocytes, whereas low IL-6 level was associat ed with decreases in these cell counts. Our findings revealed distinct inflammat ory responses induced by X-ray irradiation in contrast to proton brain irradiati on, as demonstrated by the differential regulation of cytokines in the bloodstre am. Moreover, the study highlighted the association between specific cytokine le vels and various leukocyte subpopulations."
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