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    Reports from China University of Mining and Technology Highlight Recent Research in Machine Learning (Prediction of fire source heat release rate based on machine learning method)

    47-48页
    查看更多>>摘要：Fresh data on artificial intelligence are presented in a new report. According to news reporting out of Shenzhen, People’s Republic of China, by NewsRx editors, research stated, “Accurate measurement of fire source heat release rate is crucial for comprehensively understanding the fire evolution process.” Our news journalists obtained a quote from the research from China University of Mining and Technology: “However, the widely used oxygen consumption method requires expensive equipment, incurring high costs. This study proposes a comprehensive framework based on machine learning to predict fire source heat release rate using temperature as input. Firstly, fire scenarios with different parameters in ISO9705 room were simulated using FDS software to obtain temperature at various locations, establishing a fire database. Then, two recursive feature elimination algorithms based on the Lasso and the Random Forest (RF) models were employed separately for feature selection, resulting in two different low-dimensional feature subsets and a control group. Finally, different feature subsets were input to analyse and compare the prediction performance on the heat release rate of three typical algorithms: linear regression (LR), K-nearest neighbor (KNN), and lightGBM. Results indicate that the LightGBM model trained with the feature subset selected by the recursive feature elimination algorithm based on the Random Forest model exhibits the best predictive performance, with root mean square error (RMSE) and mean absolute error (MAE) of 23.89 kW and 15.49 kW respectively, and a coefficient of determination (R2) of 0.9916.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Research from Universita degli Studi della Campania Luigi Vanvitelli in Robotics Provides New Insights (An Integrated Architecture for Robotic Assembly and Inspection of a Composite Fuselage Panel with an Industry 5.0 Perspective)

    48-49页
    查看更多>>摘要：Research findings on robotics are discussed in a new report. According to news reporting out of Aversa, Italy, by NewsRx editors, research stated, “Aeronautical robotic applications use quite large, heavy robots with huge end effectors that are frequently multifunctional.” Financial supporters for this research include European Commission. The news journalists obtained a quote from the research from Universita degli Studi della Campania Luigi Vanvitelli: “An assembly jig to hold a fuselage panel and two medium-sized six-axis robots fixed on linear axes, referred to as the internal and the external robot with respect to the curvature of the panel, make up the Lean robotized AssemBly and cOntrol of composite aeRostructures (LABOR) work cell. A distributed software architecture is proposed in which individual modules are developed to execute specific subprocesses, each implementing innovative algorithms that solve the main drawbacks of stateof- the-art solutions. Real-time referencing adopts a point-cloud-based strategy to reconstruct and process the part before drilling, avoiding hole positioning errors. Accurate concentric countersink diameters are made possible through the automatic adjustment of the drilling tool with respect to the skin panel, which guarantees its orthogonality, as well as the implementation of process parameter optimization algorithms based on historical results that compensate for the wear of the drilling bits. Automatic sealing and fastening strategies that involve the measurement of the main fastener quality parameters allow for the complete verification of the entire assembly process of each part.”
      原文链接:
    • NETL
    • NSTL

    Researchers from Tsinghua University Detail Findings in Machine Learning (Data Augmentation-Based Estimation of Solar Radiation Components without Referring to Local Ground Truth in China)

    49-50页
    查看更多>>摘要：A new study on artificial intelligence is now available. According to news originating from Beijing, People’s Republic of China, by NewsRx editors, the research stated, “The power generation of bifacial photovoltaic modules is greatly related to the diffuse solar radiation component received by the rear side, but radiation component data are scarce in China, where bifacial solar market is large.” Financial supporters for this research include National Key Research And Development Program of China; National Natural Science Foundation of China. The news editors obtained a quote from the research from Tsinghua University: “Radiation components can be estimated from satellite data, but sufficient ground truth data are needed for calibrating empirical methods or training machine learning methods. In this work, a data-augmented machine learning method was proposed to estimate radiation components. Instead of using observed ground truth, far more abundant radiation component data derived from sunshine duration measured at 2,453 routine weather stations in China were used to augment samples for training a machine-learning-based model. The inputs of the model include solar radiation (either from ground observation or satellite remote sensing) and surface meteorological data. Independent validation of the model at Chinese stations and globally distributed stations demonstrates its effectiveness and generality. Using a state-of-the-art satellite product of solar radiation as input, the model is applied to construct a satellite-based radiation component dataset over China.”
      原文链接:
    • NETL
    • NSTL

    New Findings on Robotics Described by Investigators at Nankai University (Adaptive Compensation Tracking Control for Parallel Robots Actuated By Pneumatic Artificial Muscles With Error Constraints)

    50-51页
    查看更多>>摘要：Researchers detail new data in Robotics. According to news reporting out of Tianjin, People’s Republic of China, by NewsRx editors, research stated, “As pneumatic artificial muscles (PAMs) are similar to biological muscles in structure and movement mechanisms, parallel robots actuated by PAMs have development prospects in rehabilitation and industry, with advantages such as compliance, high safety, strong bearing capacity, and satisfactory dynamic performance. However, the parameter uncertainties and model complexity related to inherent characteristics of parallel robots actuated by PAMs (e.g., timevarying, coupling, hysteresis, creep, and high nonlinearity), bring challenges to accurate dynamic modeling and controller design.” Funders for this research include National Natural Science Foundation of China (NSFC), Tianjin Science Fund for Distinguished Young Scholars, Guangdong Basic and Applied Basic Research Foundation.
      原文链接:
    • NETL
    • NSTL
    • IEEE

    University Health Network Reports Findings in Artificial Intelligence (Artificial intelligence-driven virtual rehabilitation for people living in the community: A scoping review)

    51-52页
    查看更多>>摘要：New research on Artificial Intelligence is the subject of a report. According to news originating from Toronto, Canada, by NewsRx correspondents, research stated, “Virtual Rehabilitation (VRehab) is a promising approach to improving the physical and mental functioning of patients living in the community. The use of VRehab technology results in the generation of multi-modal datasets collected through various devices.” Our news journalists obtained a quote from the research from University Health Network, “This presents opportunities for the development of Artificial Intelligence (AI) techniques in VRehab, namely the measurement, detection, and prediction of various patients’ health outcomes. The objective of this scoping review was to explore the applications and effectiveness of incorporating AI into home-based VRehab programs. PubMed/MEDLINE, Embase, IEEE Xplore, Web of Science databases, and Google Scholar were searched from inception until June 2023 for studies that applied AI for the delivery of VRehab programs to the homes of adult patients. After screening 2172 unique titles and abstracts and 51 full-text studies, 13 studies were included in the review. A variety of AI algorithms were applied to analyze data collected from various sensors and make inferences about patients’ health outcomes, most involving evaluating patients’ exercise quality and providing feedback to patients. The AI algorithms used in the studies were mostly fuzzy rule-based methods, template matching, and deep neural networks. Despite the growing body of literature on the use of AI in VRehab, very few studies have examined its use in patients’ homes. Current research suggests that integrating AI with home-based VRehab can lead to improved rehabilitation outcomes for patients.”
      原文链接:
    • NETL
    • NSTL

    Researcher from China University of Geosciences Provides Details of New Studies and Findings in the Area of Machine Learning (Apatite trace element composition as an indicator of ore deposit types: A machine learning approach)

    52-53页
    查看更多>>摘要：Fresh data on artificial intelligence are presented in a new report. According to news originating from Beijing, People’s Republic of China, by NewsRx editors, the research stated, “The diverse suite of trace elements incorporated into apatite in ore-forming systems has important applications in petrogenesis studies of mineral deposits.” Our news editors obtained a quote from the research from China University of Geosciences: “Trace element variations in apatite can be used to distinguish between fertile and barren environments, and thus have potential as mineral exploration tools. Such classification approaches commonly employ two-variable scatterplots of apatite trace element compositional data. While such diagrams offer accessible visualization of compositional trends, they often struggle to effectively distinguish ore deposit types because they do not employ all the high-dimensional (i.e., multi-element) information accessible from high-quality apatite trace element analysis. To address this issue, we use a supervised machine-learning-based approach (eXtreme Gradient Boosting, XGBoost) to correlate apatite compositions with ore deposit type, utilizing such high-dimensional information. We evaluated 8629 apatite trace element data from five ore deposit types (porphyry, skarn, orogenic Au, iron oxide copper gold, and iron oxide-apatite) along with unmineralized magmatic and metamorphic apatite to identify discriminating parameters for the individual deposit types, as well as for mineralized systems. According to feature selection, eight elements (Th, U, Sr, Eu, Dy, Y, Nd, and La) improve the model performance.”
      原文链接:
    • NETL
    • NSTL
    • Mineralogical Soc Amer

    Researchers from Westlake University Report on Findings in Computational Intelligence (Cis-ing In an Uncertain World [President’s Message])

    53-53页
    查看更多>>摘要：A new study on Computational Intelligence is now available. According to news reporting from Zhejiang, People’s Republic of China, by NewsRx editors, the research stated, “I am deeply honored to serve as the President of the IEEE Computational Intelligence Society (CIS) for 2024-2025. I had never imagined that I would become the President of our society when I joined IEEE at the 1998 World Congress on Computational Intelligence.” The news correspondents obtained a quote from the research from Westlake University, “I would take this opportunity to thank Bernadette Bouchon-Meunier, chair of the nomination committee and her colleagues, for their trust. I am also very much grateful to Jim Keller, the President in 2022-2023 and now Past President, who mentored me through the year when I was the President-Elect.” According to the news reporters, the research concluded: “My thanks also go to Tom Compton, the Executive Director, whose support has been always very helpful and timely.”
      原文链接:
    • NETL
    • NSTL
    • IEEE

    Findings from University of Kentucky Update Knowledge of Machine Learning (Advanced Process Characterization and Machine Learning-based Correlations Between Interdiffusion Layer and Expulsion In Spot Welding)

    54-54页
    查看更多>>摘要：Current study results on Machine Learning have been published. According to news reporting originating in Lexington, Kentucky, by NewsRx journalists, research stated, “Over the past decades, substantial endeavors have been dedicated to unraveling the intricacies inherent to Resistance Spot Welding (RSW). However, a comprehensive and consensual understanding of the RSW process physics is still lacking, including the exact number of physical phases behind the RSW process.” Financial support for this research came from National Science Foundation (NSF). The news reporters obtained a quote from the research from the University of Kentucky, “For example, a widely accepted model indicates that metal only starts melting after the peak of dynamic resistance, while the latest research on welding uncoated materials challenges this by suggesting that melting begins around the resistance peak. Furthermore, most of existing physical models only consider welding materials without coatings in a controlled lab setting, whereas coated sheet metal is the norm in real production. Addressing these challenges, this paper introduces an enhanced model for RSW that considers the melting phase of the coating’s InterDiffusion Layer (IDL) in Press Hardening Steels (PHS). This phase is believed to influence both welding quality and the occurrence of expulsions. Additionally, the timing at which each phase starts has been determined by analyzing real-time, multi-variable sensing data from various welding scenarios, and a signal processing technique has been devised to automatically identify when these phases begin. Leveraging this refined process understanding and characterization, meaningful explainable features are extracted, and a data-driven multilayer perceptron model is constructed for 1) predicting IDL thickness and 2) detecting expulsions upon predicted IDL thickness.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Hebei University of Engineering Researcher Details Research in Machine Learning (Built Environment Renewal Strategies Aimed at Improving Metro Station Vitality via the Interpretable Machine Learning Method: A Case Study of Beijing)

    55-55页
    查看更多>>摘要：New research on artificial intelligence is the subject of a new report. According to news reporting from Handan, People’s Republic of China, by NewsRx journalists, research stated, “Understanding the built environment’s impact on metro ridership is essential for developing targeted strategies for built environment renewal.” Funders for this research include Hebei Social Science Development Research Project in 2023. The news journalists obtained a quote from the research from Hebei University of Engineering: “Taking into consideration the limitations of existing studies, such as not proposing targeted strategies, using unified pedestrian catchment areas (PCA), and not determining the model’s accuracy, Beijing was divided into three zones from inside to outside by the distribution pattern of metro stations. Three PCAs were assumed for each zone and a total of 27 PCA combinations. The study compared the accuracy of the Ordinary Least Square (OLS) and several machine learning models under each PCA combination to determine the model to be used in this study and the recommended PCA combination for the three zones. Under the recommended PCA combinations for the three zones, the model with the highest accuracy was used to explore the built environment’s impact on metro ridership. Finally, prioritized stations for renewal were identified based on ridership and the built environment’s impact on metro ridership. The results are as follows: (1) The eXtreme Gradient Boosting (XGBoost) model has a higher accuracy and was appropriate for this study.”
      原文链接:
    • NETL
    • NSTL
    • Mdpi

    Air Force Hospital Reports Findings in Spinal Cord Injury (Machine learning and experiments revealed a novel pyroptosis-based classification linked to diagnosis and immune landscape in spinal cord injury)

    56-56页
    查看更多>>摘要：New research on Central Nervous System Diseases and Conditions - Spinal Cord Injury is the subject of a report. According to news reporting originating in Nanjing, People’s Republic of China, by NewsRx journalists, research stated, “Rising evidence indicates the development of pyroptosis in the initiation and pathogenesis of spinal cord injury (SCI). However, the associated effects of pyroptosis-related genes (PRGs) in SCI are unclear.” The news reporters obtained a quote from the research from Air Force Hospital, “We obtained the gene expression profiles of SCI and normal samples in the GEO. The R package limma screened for differentially expressed (DE) PRGs and performed functional enrichment analysis. Mechanical learning and PPI analysis helped filter essential PRGs to diagnose SCI. Peripheral blood was collected for validation from ten SCI patients and eight healthy individuals. The association of essential PRGs with immune infiltration was evaluated, and pyroptosis subtypes were recognized in SCI patients by unsupervised cluster analysis. Besides, a SCI model was built for in vivo validation of essential PRGs. We identified 25 DE-PRGs between SCI and normal controls. Functional enrichment analysis revealed the principal involvement of DE-PRGs in pyroptosis, inflammasome complex, interleukin-1 beta production, etc. Subsequently, three essential PRGs were identified and validated, showing excellent diagnostic efficacy and significant correlation with immune cell infiltration. Additionally, we developed diagnostic nomograms to predict the occurrence of SCI. Two pyroptosis subtypes exhibited distinct biological functions and immune landscapes among SCI patients. Finally, the expression of these essential PRGswas verified in vivo.”
      原文链接:
    • NETL
    • NSTL