期刊,Robotics & Machine Learning Daily News 2024年卷Sep.10期_国家学术搜索

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Data from University of Hertfordshire Update Knowledge in Robotics (Design and d evelopment of a small-scale cement-based 3D printing robot extrusion nozzle)

65-65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 robotics have been published. According to news reporting from the University of Hertfordshir e by NewsRx journalists, research stated, "Additive manufacturing (AM), also kno wn as three-dimensional (3D) printing, offers great potential to create complex structures layer by layer from computer-aided design (CAD) models." The news correspondents obtained a quote from the research from University of He rtfordshire: "Despite advancements in printable concrete technology, controlling printing quality remains a challenge associated with both the geometric and mat erials design of the printer nozzle, especially for small-scale printing that ma y be required by small and medium-sized enterprises (SMEs). Therefore, this stud y explored the design and development of a robot nozzle system, optimised for a small-scale 3D printing of cement-based structures. Key design considerations in cluded weight, nozzle diameter/shape, material compatibility, flow control, mixi ng mechanism, temperature resistance, cost-effectiveness, adaptability, safety, and ease of maintenance. Iterative designs were developed, focusing on stress co ncentration mitigation and material flow optimisation. The challenge of incorpor ating mixing mechanisms during nozzle designs was discussed, leading to the adop tion of an on-demand accelerator spraying system. This method involved a micro-p eristaltic pump connected to an accelerator tank, spraying accelerator onto the surface of the deposited material, as the robot moved along its programmed path. "

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Studies from Indian Institute of Technology (IIT) Jodhpur Yield New Information about Artificial Intelligence (Hg-xai: Humanguided Tool Wear Identification App roach Through Augmentation of Explainable Artificial Intelligence With Machine V ision)

66-67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 Artificial Intelligence. According to news reporting out of Rajasthan, India, by NewsRx editors, research stated, "Identifying tool wear state is essential for machine operators as it assists in informed decisions for timely tool replacemen t and subsequent machining operations. As each wear state corresponds to a uniqu e mitigation strategy, timely identification is vital while implementing solutio ns to minimize tool wear." Financial supporters for this research include NSF-Directorate for Engineering (ENG), Ministry of Education (MoE), India.

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Khon Kaen University Researchers Focus on Support Vector Machines (Comparison of support vector machine and random forest algorithms for classification of songs for relaxation purposes in individuals with stress disorders)

66-66页

查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Investigators publish new report on . According t o news reporting originating from Khon Kaen University by NewsRx correspondents, research stated, "The research compares the performance of support vector machi ne (SVM) and random forest algorithms in identifying songs suitable for relaxati on in patients with stress problems." Our news editors obtained a quote from the research from Khon Kaen University: " The dataset comprises both Thai and international songs categorized into therapy and non-therapy groups. The results demonstrate that the support vector machine achieves an accuracy of 78%, outperforming the random forest with an accuracy of 72%. Precision and F1-score metrics further emphasiz e the superiority of the support vector machine in classification. Notably, the support vector machine has recall rates of 50% and 100% for therapy and non-therapy classes, respectively, while the random forest has r ecall from class therapy of 38% and class non-therapy of 100% . The findings suggest that providing individuals with stress issues the opportu nity to listen to stress-reducing music can be a viable approach to reducing the need for psychiatric therapy."

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New Findings Reported from Zayed University Describe Advances in Robotics (Nonpl anar Robotic Printing of Earth-Based Material: A Case Study Using Cob-like Mixtu re)

67-68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 originating from Zayed University by NewsRx correspondents, research stated, "The study presents an integration of cob with robotic processes." Financial supporters for this research include Zayed University Provost's Resear ch Fellowship Award; Zayed University Research Cluster Award.

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New Machine Learning Study Results Reported from Chungbuk National University [Classification of Garlic (* * Allium sativum* * L.) Crops by Fertilizer Differen ces Using Ground-Based Hyperspectral Imaging System]

68-69页

查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Fresh data on artificial intelligence are presented in a new report. According to news originating from Chungbuk Natio nal University by NewsRx correspondents, research stated, "In contemporary agric ulture, enhancing the efficient production of crops and optimizing resource util ization have become paramount objectives. Garlic growth and quality are influenc ed by various factors, with fertilizers playing a pivotal role in shaping both a spects." Funders for this research include Korea Institute of Planning And Evaluation For Technology in Food, Agriculture, And Forestry (Ipet) Through The Open Field Sma rt Agriculture Technology Short-term Advancement Program; Ministry of Agricultur e, Food, And Rural Affairs.

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Affiliated Hospital of Guangdong Medical University Reports Findings in Crohn's Disease (Explore key genes of Crohn's disease based on glycerophospholipid metab olism: A comprehensive analysis Utilizing Mendelian Randomization, Multi-Omics . ..)

69-70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 Digestive System Disea ses and Conditions-Crohn's Disease is the subject of a report. According to ne ws reporting out of Guangdong, People's Republic of China, by NewsRx editors, re search stated, "Crohn's disease (CD) is a chronic, complex inflammatory conditio n with increasing incidence and prevalence worldwide. However, the causes of CD remain incompletely understood." Our news journalists obtained a quote from the research from the Affiliated Hosp ital of Guangdong Medical University, "We identified CD-related metabolites, inf lammatory factors, and key genes by Mendelian randomization (MR), multi-omics in tegration, machine learning (ML), and SHAP. We first performed a mediation MR an alysis on 1400 serum metabolites, 91 inflammatory factors, and CD. We found that certain phospholipids are causally related to CD. In the scRNA-seq data, monocy tes were categorized into high and low metabolism groups based on their glycerop hospholipid metabolism scores. The differentially expressed genes of these two g roups of cells were extracted, and transcription factor prediction, cell communi cation analysis, and GSEA analysis were performed. After further screening of di fferentially expressed genes (FDR <0.05, log2FC > 1), least absolute shrinkage and selection operator (LASSO) regression was perfo rmed to obtain hub genes. Models for hub genes were built using the Catboost, XG boost, and NGboost methods. Further, we used the SHAP method to interpret the mo dels and obtain the gene with the highest contribution to each model. Finally, q RT-PCR was used to verify the expression of these genes in the peripheral blood mononuclear cells (PBMC) of CD patients and healthy subjects. MR results showed 1-palmitoyl-2-stearoyl-gpc (16:0/18:0) levels, 1-stearoyl-2-arachidonoyl-GPI (18 :0/20:4) levels, 1-arachidonoyl-gpc (20:4n6) levels, 1-palmitoyl-2-arachidonoyl- gpc (16:0/20:4n6) levels, and 1-arachidonoyl-GPE (20:4n6) levels were significan tly associated with CD risk reduction (FDR <0.05), with CXC L9 acting as a mediation between these phospholipids and CD. The analysis identi fied 19 hub genes, with Catboost, XGboost, and NGboost achieving AUC of 0.91, 0. 88, and 0.85, respectively. The SHAP methodology obtained the three genes with t he highest model contribution: G0S2, S100A8, and PLAUR. The qRT-PCR results show ed that the expression levels of S100A8 (p = 0.0003), G0S2 (p <0.0001), and PLAUR (p = 0.0141) in the PBMC of CD patients were higher than hea lthy subjects. MR findings suggest that certain phospholipids may lower CD risk. G0S2, S100A8, and PLAUR may be potential pathogenic genes in CD."

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Findings from University of Paderborn Provides New Data about Machine Learning ( Static Analysis Driven Enhancements for Comprehension In Machine Learning Notebo oks)

71-71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 Machine Learning. According to news originating from Paderborn, Germany, by NewsRx corr espondents, research stated, "Jupyter notebooks have emerged as the predominant tool for data scientists to develop and share machine learning solutions, primar ily using Python as the programming language. Despite their widespread adoption, a significant fraction of these notebooks, when shared on public repositories, suffer from insufficient documentation and a lack of coherent narrative." Financial supporters for this research include Universitt Paderborn (3159), Mini stry of Culture and Science of the State of North Rhine-Westphalia under the SAI L project.

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Studies from Shanghai Jiao Tong University Describe New Findings in Robotics (Vi sual-inertial Fusion With Depth Measuring for Hyper-redundant Robot's End Under Constrained Environment)

72-72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 originating in Shanghai, People's Republic o f China, by NewsRx journalists, research stated, "Hyper-redundant robots (HRRs) are used for operations in constrained environment, such as wing rib compartment s. Precise perception of the robot end is a fundamental requirement for accurate operations." Financial supporters for this research include National Natural Science Foundati on of China (NSFC), Natural Science Foundation of Shanghai. The news reporters obtained a quote from the research from Shanghai Jiao Tong Un iversity, "Conventional sensing schemes cannot be used due to environment constr aints. The environment necessitates the implementation of a real-time, self-sens ing, multimodal fusion framework. We proposed a visual-inertial sensor fusion me thod at the robot end for HRRs under constrained environment. It includes a ligh tweight device and an algorithm based on visual-inertial odometry (VIO), adding a depth-measuring module with laser spots. This module provides real-time scale factors for monocular cameras without adding sensors. It is used to synchronousl y acquire the trajectory of an HRR end under constrained environment. Experiment s on an HRR verified the accuracy and real-time performance. The root-mean-squar e error (RMSE) is 15.2 mm, and the total processing time is 28.04 ms."

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Henan Polytechnic University Researcher Yields New Study Findings on Machine Lea rning (Yield Prediction of Winter Wheat at Different Growth Stages Based on Mach ine Learning)

73-73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 artificial in telligence have been published. According to news reporting out of Jiaozuo, Peop le's Republic of China, by NewsRx editors, research stated, "Accurate and timely prediction of crop yields is crucial for ensuring food security and promoting s ustainable agricultural practices." Financial supporters for this research include National Key Research And Develop ment Plan of China. Our news correspondents obtained a quote from the research from Henan Polytechni c University: "This study developed a winter wheat yield prediction model using machine learning techniques, incorporating remote sensing data and statistical y ield records from Henan Province, China. The core of the model is an ensemble vo ting regressor, which integrates ridge regression, gradient boosting, and random forest algorithms. This study optimized the hyperparameters of the ensemble vot ing regressor and conducted an in-depth comparison of its yield prediction perfo rmance with that of other mainstream machine learning models, assessing the impa ct of key hyperparameters on model accuracy. This study also explored the potent ial of yield prediction at different growth stages and its application in yield spatialization. The results demonstrate that the ensemble voting regressor perfo rmed exceptionally well throughout the entire growth period, with an R2 of 0.90, an RMSE of 439.21 kg/ha, and an MAE of 351.28 kg/ha. Notably, during the headin g stage, the model's prediction performance was particularly impressive, with an R2 of 0.81, an RMSE of 590.04 kg/ha, and an MAE of 478.38 kg/ha, surpassing mod els developed for other growth stages."

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Research Findings from Vanderbilt University Update Understanding of Machine Lea rning (Gauging road safety advances using a hybrid EWM-PROMETHEE II-DBSCAN model with machine learning)

74-74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 artificial in telligence have been published. According to news reporting from Nashville, Tenn essee, by NewsRx journalists, research stated, "IntroductionEnhancing road safet y conditions alleviates socioeconomic hazards from traffic accidents and promote s public health. Monitoring progress and recalibrating measures are indispensabl e in this effort." The news journalists obtained a quote from the research from Vanderbilt Universi ty: "A systematic and scientific decision-making model that can achieve defensib le decision outputs with substantial reliability and stability is essential, par ticularly for road safety system analyses. MethodsWe developed a systematic meth odology combining the entropy weight method (EWM), preference ranking organizati on method for enrichment evaluation (PROMETHEE), and density-based spatial clust ering of applications with noise (DBSCAN)-referred to as EWM-PROMETHEE II-DBSCAN -to support road safety monitoring, recalibrating measures, and action planning. Notably, we enhanced DBSCAN with a machine learning algorithm (grid search) to determine the optimal parameters of neighborhood radius and minimum number of po ints, significantly impacting clustering quality. ResultsIn a real case study as sessing road safety in Southeast Asia, the multi-level comparisons validate the robustness of the proposed model, demonstrating its effectiveness in road safety decision-making. The integration of a machine learning tool (grid search) with the traditional DBSCAN clustering technique forms a robust framework, improving data analysis in complex environments. This framework addresses DBSCAN's limitat ions in nearest neighbor search and parameter selection, yielding more reliable decision outcomes, especially in small sample scenarios. The empirical results p rovide detailed insights into road safety performance and potential areas for im provement within Southeast Asia."

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