查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Robotics is the subjec t of a report. According to news originating from Nanjing, People’s Republic of China, by NewsRx correspondents, research stated, “This paper investigates the f ixed-time trajectory tracking control problem for dual-arm space robots (DASRs), accounting for transient performance constraints and external disturbances. To suppress the overshoot and improve convergence speed of the trajectory tracking errors, prescribed performance control (PPC) method is studied to convert the tr acking errors into unconstrained variables.” Financial supporters for this research include National Natural Science Foundati on of China (NSFC), Cyberspace Security construction project of key disciplines in Jiangsu Province during the 14th Five-Year Plan.
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 - Atherosclerosis is the subject of a report. According to news reporting originating in Henan, People’s Republic of China, by NewsRx journalis ts, research stated, “Investigation into the immune heterogeneity linked with at herosclerosis remains understudied. This knowledge gap hinders the creation of a robust theoretical framework essential for devising personalized immunotherapie s aimed at combating this disease.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Current study results on Machine Learning have be en published. According to news reporting originating in Wilmington, North Carol ina, by NewsRx journalists, research stated, “The rapid evolution of artificial intelligence (AI) technology has revolutionized healthcare, particularly through the integration of AI into health information systems. This transformation has significantly impacted the roles of nurses and nurse practitioners, prompting ex tensive research to assess the effectiveness of AI-integrated systems.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on Machine Learning have been presented. According to news reporting originating from Wenzhou, People’s Republic of China, by NewsRx correspondents, research stated, “The electrical co nductivity of soil is closely associated with various physical properties of the soil, and accurately establishing the interrelationship between them has long b een a critical challenge limiting its widespread application. Traditional approa ches in geotechnical engineering have relied on specific conduction mechanisms a nd simplifying assumptions to construct theoretical models for electrical conduc tivity.” Funders for this research include Natural Science Foundation of Zhejiang Provinc e, National Natural Science Foundation of China (NSFC), Natural Science Foundati on of Zhejiang Province, Zhejiang Water Science and Technology Project.
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on artificial intell igence are discussed in a new report. According to news reporting from the Natur al Resources Canada by NewsRx journalists, research stated, “Machine learning (M L) techniques have recently gained great attention across a multitude of enginee ring domains, including pipeline materials. However, their application to tensil e strain capacity (TSC) modelling remains unexplored.” Our news reporters obtained a quote from the research from Natural Resources Can ada: “To bridge this gap, this study developed and evaluated an ML model to pred ict the tensile strain capacity of girthwelded pipelines. The model was trained on over 20,000 data points derived from a TSC equation available in the literat ure. The ML model demonstrated robust performance in predicting tensile strain c apacities. Evidence of this lies in the near-zero means, minimal standard deviat ions, and normal distribution of residuals for both the training and test datase ts. These collectively suggest that the model provides a good fit for the data. Furthermore, the model?s loss behavior indicates successful convergence and gene ralization, without signs of overfitting or underfitting. An analysis using the random forest method revealed that the geometry of the flaw, specifically the fl aw depth, is the most influential variable in predicting the TSC. This could be attributed to its significant impact on the fracture toughness of materials. In contrast, material properties and fracture toughness exert less influence relati vely, despite their contributions to the model.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Diagnostics and Screen ing - Biomarkers is the subject of a report. According to news reporting out of Sao Paulo, Brazil, by NewsRx editors, research stated, “This study introduces th e complete blood count (CBC), a standard prenatal screening test, as a biomarker for diagnosing preeclampsia with severe features (sPE), employing machine learn ing models. We used a boosting machine learning model fed with synthetic data ge nerated through a new methodology called DAS (Data Augmentation and Smoothing).” Our news journalists obtained a quote from the research, “Using data from a Braz ilian study including 132 pregnant women, we generated 3,552 synthetic samples f or model training. To improve interpretability, we also provided a ridge regress ion model. Our boosting model obtained an AUROC of 0.90±0.10, sensitivity of 0.9 5, and specificity of 0.79 to differentiate sPE and non-PE pregnant women, using CBC parameters of neutrophils count, mean corpuscular hemoglobin (MCH), and the aggregate index of systemic inflammation (AISI). In addition, we provided a rid ge regression equation using the same three CBC parameters, which is fully inter pretable and achieved an AUROC of 0.79±0.10 to differentiate the both groups. Mo reover, we also showed that a monocyte count lower than yielded a sensitivity of 0.71 and specificity of 0.72. Our study showed that ML-powered CBC could be use d as a biomarker for sPE diagnosis support. In addition, we showed that a low mo nocyte count alone could be an indicator of sPE. Although preeclampsia has been extensively studied, no laboratory biomarker with favorable cost-effectiveness h as been proposed.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on artificial intell igence are discussed in a new report. According to news reporting from Serdang, Malaysia, by NewsRx journalists, research stated, “The rapid development of Arti ficial Intelligence (AI) offers both opportunities and challenges for its applic ation in Corporate Social Responsibility (CSR) communication.” Our news correspondents obtained a quote from the research from University Putra Malaysia: “While AI can enhance CSR initiatives, its impact on consumer relatio ns and brand perception remains inconsistent. This study aims to explore the aca demic landscape of AI’s role in CSR communication, focusing on publication trend s, key authors, research topics, and future directions. Methods/Approach: A bibl iometric analysis was conducted on 1,094 articles related to AI and CSR communic ation, retrieved from the Web of Science database from 2000 to February 2024. Us ing CiteSpace software, the study mapped research trends by analysing discipline s, countries, institutions, authors, references, and keywords. The United States and China lead in publication output, with key research themes including social media impact, management strategies, and consumer trust. Emerging trends point to the importance of privacy, service quality, and perceived value in AI-driven CSR initiatives.”
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 Ma chine Learning. According to news reporting from Barcelona, Spain, by NewsRx jou rnalists, research stated, “The design and discovery of new and improved catalys ts are driving forces for accelerating scientific and technological innovations in the fields of energy conversion, environmental remediation, and chemical indu stry. Recently, the use of machine learning (ML) in combination with experimenta l and/or theoretical data has emerged as a powerful tool for identifying optimal catalysts for various applications.” Financial support for this research came from Instituci Catalana de Recerca i Es tudis Avanats.
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Fresh data on Machine Learning are presented in a new report. According to news reporting originating in Xi’an, People’s Republic of China, by NewsRx journalists, research stated, “This study introduces an Imp roved Sparrow Search Algorithm (ISSA) to address the challenges of machine learn ing Hyperparameter Optimization (HPO) and efficient modeling of Electric Coolant Pumps (ECP) in electric vehicles. By integrating L & eacute;vy fl ight and Bernoulli mapping, ISSA enhances global search capabilities and ability to escapes from local optima.” Funders for this research include National Natural Science Foundation of China ( NSFC), Innovation Capability Support Plan of Shaanxi, Fundamental Research Funds for the Central Universities, China Scholarship Council.
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 Imam Khomeini International University (IKIU) by NewsRx editors, research stated, “This study investigates the use of machine learning techniques and the proper selection of input data to estimate permeability in geosciences, using six types of input lo gs: gamma ray (GR), resistivity (RT), effective porosity (PHIE), density (RHO), sonic (DT), and compensated neutron porosity (NPHI).” Our news journalists obtained a quote from the research from Imam Khomeini Inter national University (IKIU): “A total of 57 models were constructed using combina tions of these logs and tested using five machine learning methods: Extreme Lear ning Machine (ELM), Random Forest (RF), Gradient Boosting (GB), K-Nearest Neighb or (KNN), and Multilayer Perceptron (MLP). This approach produced 285 unique per meability predictions. RF had the highest correlation coefficient (0.925) and av erage error (0.196), indicating a precision-correlation trade-off. The ELM appro ach had the lowest average error, 0.083, and a correlation value of 0.871. Testi ng on a blind well revealed that the GB and RF approaches were highly effective in predicting permeability, with R² values of 0.92 and 0.90, respectively, even in untested settings.”
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