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

    Studies in the Area of Robotics Reported from Queen Mary University of London (L et Me Give You a Hand: Enhancing Human Grasp Force With a Soft Robotic Assistive Glove)

    76-77页
    查看更多>>摘要:By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – New research on Robotics is the subjec t of a report. According to news reportingfrom London, United Kingdom, by NewsR x journalists, research stated, “Soft robotic gloves are designedto assist indi viduals with daily tasks that involve grasping. Such devices are however often h ampered byan inability to generate enough force to enable them to perform the t asks for which they were designed.”Financial support for this research came from Ministry of National Education - T urkey.

    Memorial Sloan-Kettering Cancer Center Reports Findings in Artificial Intelligen ce (Artificial Intelligence Enabled Interpretation of ECG Images to Predict Hema topoietic Cell Transplantation Toxicity)

    77-78页
    查看更多>>摘要:By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – New research on Artificial Intelligenc e is the subject of a report. According to newsreporting originating from New Y ork City, New York, by NewsRx correspondents, research stated, “Artificialintel ligence enabled interpretation of electrocardiogram waveform images (AI-ECG) can identify patternspredictive of future adverse cardiac events. We hypothesized such an approach, which is well describedin general medical and surgical patien ts, would provide prognostic information with respect to the riskof cardiac com plications and overall mortality in patients undergoing hematopoietic cell trans plantation(HCT) for blood malignancy.”

    Studies from Idaho National Laboratory in the Area of Machine Learning Reported (Segmentation and Classification of Fission As Pores In Reactor Iirradiated Annu lar U-10zr Metallic Fuel Using Machine Learning Models)

    78-79页
    查看更多>>摘要:By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Current study results on Machine Learn ing have been published. According to newsreporting originating from Idaho Fall s, Idaho, by NewsRx correspondents, research stated, “Metallic fuels,particular ly U-10Zr, are promising candidates for next-generation sodium-cooled fast react ors. Irradiationof nuclear fuels in reactors can lead to the formation of solid and gas fission product which subsequentlyforms microstructural pores, deterio rating fuel performance.”

    Findings from School of Energy Science and Engineering Update Understanding of M achine Learning (Rapid and Accurate Identification of Effective Metal Organic Fr ameworks for Tetrafluoromethane/ nitrogen Separation By Machine Learning)

    79-80页
    查看更多>>摘要:By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Research findings on Machine Learning are discussed in a new report. Accordingto news reporting originating in Changs ha, People’s Republic of China, by NewsRx journalists, researchstated, “Effecti vely capturing tetrafluoromethane (CF4), a notorious greenhouse gas having a gre enhousewarming potential 6630 times higher than carbon dioxide, is important to mitigate climate change. Metalorganic frameworks (MOFs) are promising adsorben ts to entrap CF4 with extreme high selectivity becausethey contain versatile fu nctionalized ligands and tunable pores.”

    Studies from Nanjing University of Chinese Medicine Provide New Data on Machine Learning (Multi-spectra Combined With Bayesian Optimized Machine Learning Algori thms for Rapid and Nondestructive Detection of Adulterated Functional Food Pana x …)

    80-81页
    查看更多>>摘要:By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews – Current study results on Machine Learning have be en published. According to news reportingoriginating from Nanjing, People’s Rep ublic of China, by NewsRx correspondents, research stated, “Panaxnotoginseng po wder (PNP) is a widely consumed functional food and has shown promise in cardiov ascularprotection. However, its high price makes it often a target for economic adulteration.”

    Northwest Agriculture & Forestry University Reports Findings in Ma chine Learning (Application of machine learning approaches to predict ammonium n itrogen transport in different soil types and evaluate the contribution of contr ol factors)

    81-82页
    查看更多>>摘要:By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – New research on Machine Learning is th e subject of a report. According to newsreporting out of Yangling, People’s Rep ublic of China, by NewsRx editors, research stated, “The lossof nitrogen in soi l damages the environment. Clarifying the mechanism of ammonium nitrogen (NH-N)transport in soil and increasing the fixation of NH-N after N application are ef fective methods for improvingN use efficiency.”

    Findings from Research Center in Robotics Reported (Increasing Lower Incomes and Reducing Material Deprivation: the Beneficial Role of Social Robots)

    82-83页
    查看更多>>摘要:By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Current study results on Robotics have been published. According to news reportingoriginating in Paris, France, by Ne wsRx journalists, research stated, “Material deprivation and the riskof poverty , exacerbated by recent unexpected events such as the ongoing Ukraine-Russia con flict, aresignificant social issues that profoundly affect the lives of numerou s individuals. In contrast, social robotsrepresent problem-solving innovations that have the potential to contribute to the achievement of the 17sustainable d evelopment goals established by the United Nations.”

    Recent Studies from School of Civil Engineering Add New Data to Machine Learning (Transfer Learning for Structure Damage Detection of Bridges Through Dynamic Di stribution Adaptation)

    83-84页
    查看更多>>摘要:By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Fresh data on Machine Learning are pre sented in a new report. According to newsoriginating from Changsha, People’s Re public of China, by NewsRx correspondents, research stated, “Inrecent years, th e rapid increase in the number of bridges not only brings convenience to people but alsomeans that more bridges are at potential safety risks. How to quickly a nd accurately identify damage thatoccurs in bridges is a primary problem for so ciety to solve.”

    Recent Findings in Machine Learning Described by Researchers from Imperial Colle ge London (A Machine Learning Assisted Multifidelity Modelling Methodology To Pr edict 3d Stresses In the Vicinity of Design Features In Composite Structures)

    84-85页
    查看更多>>摘要:By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Research findings on Machine Learning are discussed in a new report. Accordingto news reporting from London, United K ingdom, by NewsRx journalists, research stated, “Multifidelityglobal-local fini te element (FE) analyses are typically used to predict damage initiation hotspot s aroundrepetitive design features in large composite structures, such as compo site airframes. We propose the useof machine learning (ML) methods to accelerat e these analyses.”

    Data from Hebei University Advance Knowledge in Machine Learning (Combining Mach ine Learning and Metal-organic Frameworks Research: Novel Modeling, Performance Prediction, and Materials Discovery)

    85-86页
    查看更多>>摘要:By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Current study results on Machine Learn ing have been published. According to newsreporting from Baoding, People’s Repu blic of China, by NewsRx journalists, research stated, “Machinelearning (ML) is the science of making computers learn and behave like humans, autonomously improving their learning by providing them with data and information through observa tions and real -worldinteractions. ML methods have significantly accelerated th e progress of materials science research.”