首页期刊导航|Robotics & Machine Learning Daily News
期刊信息/Journal information
Robotics & Machine Learning Daily News
NewsRx
Robotics & Machine Learning Daily News

NewsRx

Robotics & Machine Learning Daily News/Journal Robotics & Machine Learning Daily News
正式出版
收录年代

    New Study Findings from Hamadan University of Medical Sciences Illuminate Resear ch in Machine Learning (Modeling the Impact of Ergonomic Interventions and Occup ational Factors on Work-Related Musculoskeletal Disorders in the Neck of Office ...)

    57-58页
    查看更多>>摘要:2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on artificial intelligenc e is the subject of a new report. According to news reporting from Hamadan, Iran , by NewsRx journalists, research stated, "Modeling with methods based on machin e learning (ML) and artificial intelligence can help understand the complex rela tionships between ergonomic risk factors and employee health. The aim of this st udy was to use ML methods to estimate the effect of individual factors, ergonomi c interventions, quality of work life (QWL), and productivity on work-related mu sculoskeletal disorders (WMSDs) in the neck area of office workers." Our news journalists obtained a quote from the research from Hamadan University of Medical Sciences: "A quasi-randomized control trial. To measure the impact of interventions, modeling with the ML method was performed on the data of a quasi -randomized control trial. The data included the information of 311 office worke rs (aged 32.04±5.34). Method neighborhood component analysis (NCA) was used to m easure the effect of factors affecting WMSDs, and then support vector machines ( SVMs) and decision tree algorithms were utilized to classify the decrease or inc rease of disorders. Three classified models were designed according to the follo w-up times of the field study, with accuracies of 86.5%, 80.3% , and 69 %, respectively. These models could estimate most influence r factors with acceptable sensitivity. The main factors included age, body mass index, interventions, QWL, some subscales, and several psychological factors. Mo dels predicted that relative absenteeism and presenteeism were not related to th e outputs."

    Data on Artificial Intelligence Described by Researchers at Chinese Academy of S ciences (Recent Developments of Artificial Intelligence In Mxene-based Devices: From Synthesis To Applications)

    58-59页
    查看更多>>摘要:2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in Artific ial Intelligence. According to news reporting out of Beijing, People's Republic of China, by NewsRx editors, research stated, "Two-dimensional transition metal carbides, nitrides, or carbonitrides (MXenes) have garnered remarkable attention in various energy and environmental applications due to their high electrical c onductivity, good thermal properties, large surface area, high mechanical streng th, rapid charge transport mechanism, and tunable surface properties. Recently, artificial intelligence has been considered an emerging technology, and has been widely used in materials science, engineering, and biomedical applications due to its high efficiency and precision." Financial supporters for this research include Higher Education Commission of Pa kistan, Higher Education Commission of Pakistan, State Key Laboratory of Mesosci ence and Engineering, Institute of Process Engineering, Chinese Academy of Scien ces.

    Data on Machine Learning Reported by Zerui Feng and Colleagues (Co-exposure to m icroplastics and soil pollutants significantly exacerbates toxicity to crops: In sights from a global meta and machinelearning analysis)

    59-59页
    查看更多>>摘要:2024 OCT 03 (NewsRx)-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 originating from Guiyang, People's Repu blic of China, by NewsRx correspondents, research stated, "Environmental contami nation of microplastics (MPs) is ubiquitous worldwide, and co-contamination of a rable soils with MPs and other pollutants is of increasing concern, and may lead to unexpected consequences on crop production. However, the overall implication s of this combined effect, whether beneficial or detrimental, remain a subject o f current debate." Our news journalists obtained a quote from the research, "Here, we conducted a g lobal meta and machine-learning analysis to evaluate the effects of co-exposure to MPs and other pollutants on crops, utilizing 3346 biological endpoints derive d from 68 different studies. Overall, compared with control groups that only exp osure to conventional soil contaminants, co-exposure significantly exacerbated t oxicity to crops, particularly with MPs intensifying adverse effects on crop mor phology, oxidative damage, and photosynthetic efficiency. Interestingly, our ana lysis demonstrated a significant reduction in the accumulation of pollutants in the crop due to the presence of MPs. In addition, the results revealed that pote ntial adverse effects were primarily associated with crop species, MPs mass conc entration, and exposure duration."

    Studies from Polytechnic University Torino in the Area of Machine Learning Descr ibed (Stress, Strain, or Energy? Which One Is Superior Predictor of Fatigue Life In Notched Components? a Novel Machine Learning-based Framework)

    60-60页
    查看更多>>摘要:2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Fresh data on Machine Learning are pre sented in a new report. According to news reporting originating from Turin, Ital y, by NewsRx editors, the research stated, "This paper introduces an efficient f ramework for accurately predicting the fatigue lifetime of notched components un der uniaxial loading within the high-cycle fatigue regime. For this purpose, var ious machine learning algorithms are applied to a wide range of materials, loadi ng conditions, notch geometries, and fatigue lives." Financial support for this research came from European Union (EU). Our news editors obtained a quote from the research from Polytechnic University Torino, "Traditional approaches for this task have mostly relied on one of the m echanical response parameters, such as stress, strain, or energy. This study als o concludes which of these parameters serves as a better measure. The key idea o f the framework is to use the profile (field distribution represented by some po ints) of the mechanical response parameters (stress, strain, and energy release rate) to distinguish between different notch geometries. To demonstrate the accu racy and broad applicability of the framework, it is initially validated using m etal materials, subsequently applied to specimens produced through additive manu facturing techniques, and ultimately tested on carbon fiber laminated composites . This research demonstrates the effective use of all three parameters in estima ting fatigue lifetime, while stress-based predictions exhibit the highest accura cy. Among the machine learning algorithms investigated, Gradient Boosting and Ra ndom Forest yield the most successful results."

    Changchun Institute of Technology Researcher Highlights Research in Machine Lear ning (Investigation of Micro-Scale Damage and Weakening Mechanisms in Rocks Indu ced by Microwave Radiation and Their Associated Strength Reduction Patterns: ... )

    61-62页
    查看更多>>摘要:2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Research findings on artificial intelligence are discussed in a new report. According to news reporting from Changchun, People's Republic of China, by NewsRx journalists, research stated, "Microwave-assisted m echanical rock breaking represents an innovative technology in the realm of mini ng excavation. The intricate and variable characteristics of geological formatio ns necessitate a comprehensive understanding of the interplay between microwave- induced rock damage and the subsequent deterioration in rock strength." Funders for this research include Natural Science Foundation of Jilin Province. Our news journalists obtained a quote from the research from Changchun Institute of Technology: "This study conducted microwave irradiation damage assessments o n 78 distinct rock samples, encompassing granite, sandstone, and marble. A total of ten critical parameters were identified: Microwave Irradiation Time (MIT), M icrowave Irradiation Power (MIP), Longitudinal Wave Velocity prior to Microwave Treatment (LWVB), Longitudinal Wave Velocity post-Microwave Treatment (LWVA), Pe rcentage Decrease in Longitudinal Wave Velocity (LWVP), Porosity before Microwav e Treatment (PB), Porosity after Microwave Treatment (PA), Percentage Increase i n Porosity (PP), and Uniaxial Compressive Strength following Microwave Treatment (UCSA). Utilizing the Pied Kingfisher Optimizer (PKO) alongside Extreme Gradien t Boosting (XGBoost), we developed a PKO-XGBoost machine learning model to eluci date the relationship between UCSA and the nine additional parameters. This mode l was benchmarked against other prevalent machine learning frameworks, with Shap ley additive explanatory methods employed to assess each parameter's influence o n UCSA. The findings reveal that the PKO-XGBoost model provides superior accurac y in delineating relationships among rock physical properties, microwave irradia tion variables, microscopic attributes of rocks, and UCSA. Notably, PA emerged a s having the most significant effect on UCSA, indicating that microwave-induced microscopic damage is a primary contributor to reductions in rock strength."

    Reports from Northeastern University Highlight Recent Findings in Machine Learni ng (Graph-learning-assisted State Estimation Using Sparse Heterogeneous Measurem ents)

    62-62页
    查看更多>>摘要:2024 OCT 03 (NewsRx)-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 out of Boston, Massachusetts, by New sRx editors, research stated, "Unlike transmission systems, distribution systems historically lack enough measurements, making their realtime monitoring almost impossible." Financial support for this research came from United States Department of Energy (DOE). Our news journalists obtained a quote from the research from Northeastern Univer sity, "Recent deployment of diverse types of devices such as phasor measurement units (PMUs), smart meters, solar inverters and weather information sensors open s up new ways of monitoring these systems, with the assistance of customized mac hine learning (ML) applications. The paper describes a grid-model-informed machi ne learning (ML) tool which integrates heterogeneous data streams and creates sy nchronous measurement snapshots to be used by a hybrid robust state estimator (S E) which provides not only accurate state estimates but also real-time feedback for ML model refinement."

    Data on Robotics Described by Researchers at Foshan University (A Model for Robo t Grasping: Integrating Transformer and Cnn With Rgb-d Fusion)

    63-63页
    查看更多>>摘要:2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on Robotics are disc ussed in a new report. According to news reporting from Foshan, People's Republi c of China, by NewsRx journalists, research stated, "In recent years, the contin uous development of robotics and artificial intelligence technology, with the ro bot application is also more and more advanced, especially the robot grasping ta sk, but at present it is difficult to take into account the grasping accuracy an d runtime, so we propose a model through the following design can be used in rob ot grasping task has excellent performance." Financial supporters for this research include National Natural Science Foundati on of China (NSFC), Guangdong Province Smart City Infrastructure Health Monitori ng and Evaluation Engineering Technology Research Center, Guangdong Provincial G rain and Oil Quality and Safety Big Data Engineering Technology Research Center.

    Recent Findings from University of Oslo Has Provided New Information about Machi ne Learning (Augmenting Genetic Algorithms With Machine Learning for Inverse Mol ecular Design)

    64-64页
    查看更多>>摘要:2024 OCT 03 (NewsRx)-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 out of Oslo, Norway, by NewsRx editors , research stated, "Evolutionary and machine learning methods have been successf ully applied to the generation of molecules and materials exhibiting desired pro perties. The combination of these two paradigms in inverse design tasks can yiel d powerful methods that explore massive chemical spaces more efficiently, improv ing the quality of the generated compounds." Funders for this research include European Union (EU), Centers of Excellence Pro gram (Hylleraas Centre), RCN FRIPRO Program.

    New Robotics Findings Reported from Northeastern University (Fault-tolerant Pres cribed Performance Control of Wheeled Mobile Robots: a Mixed-gain Adaption Appro ach)

    65-65页
    查看更多>>摘要:2024 OCT 03 (NewsRx)-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 reporting from Shenyang, People's Republic of China, by New sRx journalists, research stated, "This article is concerned with the trajectory tracking control problem for the wheeled mobile robots (WMRs) subject to actuat or faults. The challenge lies in the partial loss of effectiveness of the actuat ed wheels which results in the loss of strong controllability of the WMR, render ing the classical fault-tolerant control methods infeasible." Financial supporters for this research include National Natural Science Foundati on of China (NSFC), National Key Research & Development Program of China, Xingliao Talent Program of Liaoning Province of China, Science and Techn ology Foundation of Liaoning Province, Fundamental Research Funds for the Centra l Universities, The 111 Project 2.0 of China.

    Affiliated Hospital of North Sichuan Medical College Reports Findings in Prostat ectomy (A comprehensive evaluation and metaanalysis of the perioperative and on cological outcomes of robotic radical prostatectomy using the DaVinci vs the Hug o ...)

    66-67页
    查看更多>>摘要:2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Surgery - Prostatectom y is the subject of a report. According to news reporting originating from Sichu an, People's Republic of China, by NewsRx correspondents, research stated, "Beca use of the increasing popularity of Hugo RAS as a surgical platform, a compariso n examination of intraoperative and oncological outcomes across DaVinci and Hugo RAS robotic surgery platforms is urgently needed. We carried out a comprehensiv e review and meta-analysis of the literature of current research, comprehensivel y searching PubMed, Cochrane and Embase for eligible studies comparing the resul ts between the DaVinci and Hugo RAS." Our news editors obtained a quote from the research from the Affiliated Hospital of North Sichuan Medical College, "Preferred Reporting Items for Systematic Rev iews and Meta-Analyses (PRISMA) criteria were followed in the conduct of this st udy, with language restricted to English and a final search date of June 2024. W e excluded articles composed solely of conference abstracts and irrelevant conte nt. Composite outcomes were assessed using weighted mean differences (WMD) and o dds ratios (ORs). The risk of bias in individual research was assessed using the Newcastle-Ottawa Scale (NOS), and heterogeneity and bias risk were controlled f or using a sensitivity analysis. Six studies in all were considered, comprising 1025 patients, including 626 DaVinci patients and 399 Hugo RAS patients. Review Manager V5.4.1 software (Cochrane Collaboration, Oxford, UK) was utilized to con duct the meta-analysis, including 6 trials, which demonstrated that compared to Hugo RAS, DaVinci was associated with statistically significant differences in s everal outcomes: a reduction in operative time (OT) (WMD - 8.46, 95% CI - 13.56 to 3.36; p = 0.001), an increase in estimated blood loss (EBL) (WMD 4 1.68, 95% CI 23.59 to 59.77; p<0.00001), and an increased pelvic lymphadenectomy ratio (OR 1.5, 95% CI 1.05-2. 05; p = 0.01). On the contrary, there were no statistically noteworthy differenc es in the length of hospital stay (LOS) between the two teams (WMD - 0.05, 95% CI - 0.14 to 0.04; p = 0.25), nerve sparing (unilateral or bilateral) (OR 0.96, 95% CI 0.68-1.35; p = 0.8), postoperative complications (OR 1.15, 95% CI 0.50-2.64; p = 0.75), or positive surgical margins (PSM) (O R 1.08, 95% CI 0.76-1.54; p = 0.68). Although DaVinci offers short er operating times (OT) and increased pelvic lymph node dissection rates, Hugo R AS demonstrates lower estimated blood loss (EBL). Overall, Hugo RAS Robot-Assist ed Radical Prostatectomy (RARP) results seem to be similar to those obtained wit h the DaVinci system. Further research and long-term follow-up are necessary to ascertain durable oncological and functional outcomes, allowing doctors to switc h between robotic systems and use their skills."