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    Second Affiliated Hospital of Guangxi Medical University Reports Findings in Rep erfusion Injury (Bulk and single-cell RNA sequencing analysis with 101 machine l earning combinations reveal neutrophil extracellular trap involvement in hepatic ...)

    20-21页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Cardiovascular Disease s and Conditions - Reperfusion Injury is the subject of a report. According to n ews reporting out of Nanning, People's Republic of China, by NewsRx editors, res earch stated, "Hepatic ischaemia-reperfusion injury (HIRI) is a major clinical c oncern during the perioperative period and is closely associated with early allo graft dysfunction (EAD), acute rejection (AR) and long-term graft survival. Neut rophil extracellular traps (NETs) are extracellular structures formed by the rel ease of decondensed chromatin and granular proteins following neutrophil stimula tion." Our news journalists obtained a quote from the research from the Second Affiliat ed Hospital of Guangxi Medical University, "There is growing evidence that NETs are involved in the progression of various liver transplantation complications, including ischaemia-reperfusion injury (IRI). This study aimed to comprehensivel y analyse the expression patterns of NET-related genes (NRGs) in HIRI, identify HIRI subtypes with distinct characteristics, and develop a reliable EAD predicti on model. Microarray, bulk RNA-seq, and single-cell sequencing datasets were obt ained from the GEO database. Initially, differentially expressed NRGs (DE-NRGs) were identified using differential gene expression analyses. We then utilised a non-negative matrix factorisation (NMF) algorithm to classify HIRI samples. Subs equently, we employed machine learning algorithms to screen the hub NRGs related to EAD and developed an EAD prediction model based on these hub NRGs. Concurren tly, we assessed the expression patterns of hub NRGs at the single-cell level us ing the HIRI. Additionally, we validated C5AR1 expression and its effect on HIRI and NETs formation in a rat orthotopic liver transplantation (OLT) model. In th is study, we identified 11 DE-NRGs in the HIRI context. Based on these 11 DE-NRG s, HIRI samples were classified into two distinct clusters. Cluster1 exhibited a low expression of DE-NRGs, minimal neutrophil infiltration, mild inflammation, and a low incidence of EAD. Conversely, Cluster2 displayed the opposite phenotyp e, with an activated inflammatory subtype and a higher incidence of EAD. Further more, an EAD prediction model was developed using the four hub NRGs associated w ith EAD. Based on risk scores, HIRI samples were classified into high- and low-r isk groups. The OLT model confirmed substantial upregulation of C5AR1 expression in the liver tissue, accompanied by increased formation of NETs. Treatment with a C5AR1 antagonist improved liver function, reduced tissue inflammation, and de creased NETs formation. This study distinguished two apparent HIRI subtypes, est ablished a predictive model for EAD, and validated the effect of C5AR1 on HIRI."
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    New Machine Learning Research Has Been Reported by Researchers at University of Bari "Aldo Moro" (Predicting carob tree physiological parameters under different irrigation systems using Random Forest and Planet satellite images)

    21-22页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New study results on artificial intell igence have been published. According to news reporting originating from Bari, I taly, by NewsRx correspondents, research stated, "IntroductionIn the context of climate change, monitoring the spatial and temporal variability of plant physiol ogical parameters has become increasingly important. Remote spectral imaging and GIS software have shown effectiveness in mapping field variability." The news reporters obtained a quote from the research from University of Bari "A ldo Moro": "Additionally, the application of machine learning techniques, essent ial for processing large data volumes, has seen a significant rise in agricultur al applications. This research was focused on carob tree, a droughtresistant tr ee crop spread through the Mediterranean basin. The study aimed to develop robus t models to predict the net assimilation and stomatal conductance of carob trees and to use these models to analyze seasonal variability and the impact of diffe rent irrigation systems. MethodsPlanet satellite images were acquired on the day of field data measurement. The reflectance values of Planet spectral bands were used as predictors to develop the models. The study employed the Random Forest modeling approach, and its performances were compared with that of traditional m ultiple linear regression. Results and discussionThe findings reveal that Random Forest, utilizing Planet spectral bands as predictors, achieved high accuracy i n predicting net assimilation (R²= 0.81) and stomatal conductance (R²= 0.70), with the yellow and red spectral regions being particularly influential."
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    Findings from College of Agriculture Has Provided New Data on Machine Learning ( Decoding Potato Power: a Global Forecast of Production With Machine Learning and State-of-the-art Techniques)

    22-23页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting from Jabalpur, India, by News Rx journalists, research stated, "As the second largest potato producer globally , reliable forecasts of output for India and major growing states are crucial. T his study developed autoregressive integrated moving average (ARIMA) models alon gside state space and gradient boosting machine learning techniques for annual p otato production spanning 1967-2020." The news correspondents obtained a quote from the research from the College of A griculture, "Model adequacy was evaluated using information criteria, errors met rics and out-of-sample validation. The chosen models provide the following forec asts: India is predicted to produce around 46,712 thousand metric tons, Uttar Pr adesh 13,900 thousand metric tons, West Bengal 11,544 thousand metric tons, Biha r 7710 thousand metric tons, Madhya Pradesh 3478 thousand metric tons, Gujarat 3 621 thousand metric tons and Punjab 2870 thousand metric tons over the period 20 21-2027. While no consistent superior approach emerged, tailoring models to capt ure data complexity and patterns for each state proved essential for generalizat ion." According to the news reporters, the research concluded: "Quantitatively assessi ng linearity, stationarity and outliers during model specification is key for st akeholders and policymakers needing precise predictions." This research has been peer-reviewed.
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    Investigators at Northeast Forestry University Detail Findings in Robotics (Wear able Flexible Pressure Sensors: an Intriguing Design Towards Microstructural Fun ctionalization)

    23-24页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in Robotic s. According to news originating from Harbin, People's Republic of China, by New sRx correspondents, research stated, "Flexible electronic devices have attracted significant attention due to their wide range of applications in evaluating the state of human wellness and intelligent robotics in the long term. Microstructu re flexible pressure sensors are the optimal choice for the fabrication of flexi ble electronic devices due to their high sensitivity, wide strain range, afforda bility, low power consumption, and quick response time, which can accurately con vert pressure stimuli into electrical signals." Funders for this research include Fundamental Research Funds for the Central Uni versities, Fundamental Research Funds for the Central Universities.
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    Studies from State Key Laboratory Provide New Data on Robotics (Manipulator Join t Fault Localization for Intelligent Flexible Manufacturing Based On Reinforceme nt Learning and Robot Dynamics)

    24-25页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on Ro botics. According to news reporting out of Shanghai, People's Republic of China, by NewsRx editors, research stated, "This article proposes a new method to addr ess the challenge of remote monitoring in intelligent flexible manufacturing sys tems. Specifically, we propose a multi working condition fault localization algo rithm for robotic arms, which eliminates the need for additional sensors and is based on the classic sliding window algorithm." Funders for this research include National Natural Science Foundation of China ( NSFC), Shanghai Municipal Science and Technology Major Project. Our news journalists obtained a quote from the research from State Key Laborator y, "We use reinforcement learning technology to learn detection parameter debugg ing experience under different working conditions, and combine the dynamics of t he robot to achieve fault detection and fault source localization in a flexible environment. Through the robot's own programmable logic controller system, the r emote monitoring system can sense the operating status of each link. To evaluate the effectiveness of our proposed method, we conducted experimental equipment s imulations and real-world industrial operations. The results show that under mul tiple operating conditions, the accuracy of fault detection reaches 86% , and the accuracy of localization reaches 81.35%. The deviation of results under different robot operating conditions is significantly lower than other algorithms. This study explores the potential and implementation approache s of reinforcement learning in intelligent manufacturing systems, with a particu lar focus on applications in flexible scenarios."
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    Studies from Los Alamos National Laboratory Yield New Data on Machine Learning ( First-principles Performance Prediction of High Explosives Enabled By Machine Le arning)

    25-26页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in Machine Learning. According to news reporting out of Los Alamos, New Mexico, by NewsRx editors, research stated, "Accurate modeling of the behavior of high-expl osive (HE) materials requires knowledge of the equation of state (EOS) for both the reactant and the product states of the material. Historically, EOS models ha ve been calibrated to reproduce experimental data, but there is growing interest in first-principles predictions of HE behavior." Funders for this research include Office of Defense Programs, United States Depa rtment of Energy (DOE), National Nuclear Security Administration of U.S. Departm ent of Energy. Our news journalists obtained a quote from the research from Los Alamos National Laboratory, "The product state is particularly challenging to model because of the wide range of density and temperature conditions that are relevant as well a s the requirement to include chemical reactivity in any kind of atomistic simula tion. Density functional theory (DFT) simulations are a natural choice for such simulations, but computational cost remains a challenge to the direct applicatio n of DFT simulations to HE product EOS development. We recently introduced a mac hine-learning-driven methodology to address these challenges that was successful ly applied to a single type of HE (penta-erythritol-tetranitrate, or PETN), but there were several open questions about the generality of the approach. In parti cular, we had to develop an approximate scheme to correct the DFT energies of th e product state using the energy differences between coupled cluster theory and DFT calculations for relevant molecular species in the gas phase to achieve good agreement with experiment. In this work, we apply the method to two additional HEs (octogen and 3,3(‘)-diamino-4,4(‘)-azoxyfurazan) to address these outstandin g questions. In this work, we again find deficiencies in the DFT energetic descr iption of the product state."
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    Studies from Chinese Academy of Sciences Further Understanding of Machine Learni ng (Machine Learning Assisted Characterization of Local Bubble Properties and It s Coupling With the Emms Bubbling Drag)

    26-26页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on Machine Learn ing have been published. According to news originating from Beijing, People's Re public of China, by NewsRx correspondents, research stated, "Empirical correlati ons for bubble diameter and velocity are incapable of predicting the local bubbl e behaviors fairly because the impact of local hydrodynamics on bubbles in fluid ized beds. Based on image processing, a novel bubble identification method with an adaptive threshold was proposed to distinguish and characterize bubbles in fl uidized beds." Financial supporters for this research include National Natural Science Foundati on of China (NSFC), Youth Innovation Promotion Association of the Chinese Academ y of Sciences, Transformational Technologies for Clean Energy and Demonstration Strategic Priority Research Program of the Chinese Academy of Sciences. Our news journalists obtained a quote from the research from the Chinese Academy of Sciences, "The information regarding bubble properties and local hydrodynami cs can thus be extracted using the big data from highly resolved simulations. Ac cordingly, the deep neural network was trained to accurately predict local bubbl e properties, where the inputs were determined by performing correlation analysi s and a random forest algorithm. We found Reynolds number, voidage, and relative coordinates are the dominant factors, and a four-variable choice was demonstrat ed to output satisfactory performance for predicting local bubble diameter and v elocity."
      原文链接:
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    Reports Outline Robotics Findings from Tampere University (Sensor-based Human-ro bot Collaboration for Industrial Tasks)

    27-27页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in Robotics. According to news reporting from Tampere, Finland, by NewsRx journalis ts, research stated, "Collaboration between human and robot requires interaction modalities that suit the context of the shared tasks and the environment in whi ch it takes place. While an industrial environment can be tailored to favor cert ain conditions (e.g., lighting), some limitations cannot so easily be addressed (e.g., noise, dirt)." Financial support for this research came from European Union (EU).
      原文链接:
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    Findings from McMaster University Update Understanding of Self-Driving Cars (Enh ancing Autonomous Vehicle Hyperawareness In Busy Traffic Environments: a Machine Learning Approach)

    28-28页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Fresh data on Transportation - Self-Driving Cars are presented in a new report. According to news reporting out of Hamilton, Cana da, by NewsRx editors, research stated, "As autonomous vehicles (AVs) advance fr om theory into practice, their safety and operational impacts are being more clo sely studied. This study aims to contribute to the ever-evolving algorithms used by AVs during travel in busy urban districts, as well as explore the potential utilization of AV sensor data to identify safety hazards to surrounding road use rs in real time." Our news journalists obtained a quote from the research from McMaster University , "Accordingly, the study incorporates AV data collected from multiple cities in the United States to detect and categorize traffic conflicts that involve the s ource AVs, as well as conflicts that involve other surrounding road users. Then, a machine learning conflict prediction model is trained with Isolation Forest - Convolutional Neural Network - Long Short-Term Memory (IF-CNN-LSTM) layers. The model receives data in real time in the form of road user trajectories and head ings to make an informed prediction of the potential frequency and severity of c onflicts three seconds into the future. In addition, the transferability of the trained model to new data and locations is explored to understand the potential compromise in accuracy compared to the effort and cost of retraining. The result s show that the proposed model is capable of predicting the possibility of confl ict occurrence and conflict severity with high accuracy (sensitivity = 83.5 % and fallout = 11 %). The reported sensitivity of AV conflict predic tion ranged between 89 % and 95 %, depending on confl ict type, which outperforms most of the existing conflict prediction models. The model is also capable of predicting hazardous conflicts of surrounding road use rs in real time, with sensitivity values ranging between 82 % and 87 %, affirming the promising capabilities of onboard vehicle senso rs in undertaking real-time safety applications."
      原文链接:
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    University of Missouri Reports Findings in Machine Learning (CryoTransformer: a transformer model for picking protein particles from cryo-EM micrographs)

    29-29页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting out of Columbia, Missouri, by NewsRx editors, research stated, "Cryo-electron microscopy (cryo- EM) is a power ful technique for determining the structures of large protein complexes. Picking single protein particles from cryo-EM micrographs (images) is a crucial step in reconstructing protein structures from them." Financial support for this research came from National Institutes of Health.
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