期刊,Robotics & Machine Learning Daily News 2024年卷Jun.19期_国家学术搜索

期刊信息/Journal information

Robotics & Machine Learning Daily News

NewsRx

主办单位：NewsRx

Robotics & Machine Learning Daily News/Journal Robotics & Machine Learning Daily News

正式出版

收录年代

Researchers' Work from National University of Science & Technology MISiS Focuses on Machine Learning (Training of Machine Learning Potentials for the Modeling of Nucleation In Graphite)

47-47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 Ma chine Learning. According to news originating from Moscow, Russia, by NewsRx cor respondents, research stated, "The parameterization of machine learning potentia ls (MLP) for precise characterization of the interaction between carbon atoms in graphite and diamond phases is described. The training set consisted of various allotropic forms of carbon and their compounds." Financial support for this research came from Russian Science Foundation (RSF). Our news journalists obtained a quote from the research from the National Univer sity of Science & Technology MISiS, "The MLPs are trained using fo rces, energies, and stress tensors obtained from ab initio simulations. It is sh own that the MLPs can accurately reproduce elastic properties and structural par ameters of carbon phases. However, the MLPs also predict some unphysical behavio r due to the training set limitations and the lack of long-range interactions in the MLPs." According to the news editors, the research concluded: "In spite of these limita tions, MLPs are a promising tool for the accurate characterization of diamond nu cleation in graphite." This research has been peer-reviewed.

原文链接:

NETL
NSTL

Studies from University of Science and Technology Beijing Update Current Data on Machine Learning (Prediction of Desulfurization Efficiency and Costs During Kan bara Reactor Hot Metal Treatment Using Machine Learning)

48-49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 Machine Learn ing have been published. According to news reporting originating in Beijing, Peo ple's Republic of China, by NewsRx journalists, research stated, "A machine lear ning model was developed to predict the desulfurization process during the Kanba ra reactor hot metal treatment. Compared with other algorithms, the LR algorithm model exhibited the smallest error in current calculations, which was used to p redict the final S content with various operation parameters." The news reporters obtained a quote from the research from the University of Sci ence and Technology Beijing, "The final S content in the hot metal obviously ros e from 0.001% to higher than 0.003% with the increas e of the initial S content from 0.03% to 0.06%, while it decreased from 0.003% to below 0.001 % with the i ncrease from desulfurizer addition from 4 kg/ton to 7 kg/ton. The final S conten t changed little with the increase of C content, Mn content, and rotation speed. The feature selection using RReliefF algorithm was conducted to evaluate the co rrelation between inputted parameters and outputted final S content. The additio n of desulfurizers was beneficial to improve the desulfurization efficiency, whi le it obviously increased desulfurization costs."

原文链接:

NETL
NSTL

New Machine Learning Findings from Stanford Health Care Described (Machine Learn ing Evaluation of Inequities and Disparities Associated With Nurse Sensitive Ind icator Safety Events)

49-50页

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 originating from Stanford, California, by New sRx correspondents, research stated, "PurposeTo use machine learning to examine health equity and clinical outcomes in patients who experienced a nurse sensitiv e indicator (NSI) event, defined as a fall, a hospital-acquired pressure injury (HAPI) or a hospital-acquired infection (HAI).DesignThis was a retrospective obs ervational study from a single academic hospital over six calendar years (2016-2 021). Machine learning was used to examine patients with an NSI compared to thos e without.MethodsInclusion criteria: all adult inpatient admissions (2016-2021). " Financial supporters for this research include Office of Research Patient Care S ervices, Stanford Health Care, Professional Practice & Clinical Im provement, Stanford Health Care, Office of Research, Patient Care Services, Stan ford health Care, School of Medicine, Research Informatics Center, Stanford Univ ersity, Quantitative Sciences Unit, Stanford University, Hospital Quality Depart ment, Stanford Health Care.

原文链接:

NETL
NSTL

Data from Technical University Munich (TU Munich) Advance Knowledge in Machine L earning (Ultrasonic Mode Conversion for In-line Foam Structure Measurement In Hi ghly Aerated Batters Using Machine Learning)

50-51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 Ma chine Learning. According to news reporting originating in Freising-Weihenstepha n, Germany, by NewsRx journalists, research stated, "An ultrasonicbased method was developed to enable in-line measurements of foam structure parameters for hi ghly aerated batters by mode conversion. Biscuit batters were foamed to differen t degrees (density: 364-922 g/L) by varying the mixing head speed and pressure." Financial support for this research came from Bundesministerium fur Wirtschaft u nd Klimaschutz. The news reporters obtained a quote from the research from Technical University Munich (TU Munich), "Density and foam structure changes were detected by efficie nt offline analytics (nref measurement = 96). Ultrasonic signal data were record ed using two ultrasonic sensors attached to an industry-standard tube. Mode conv ersion effects in the ultrasonic signals were obtained to predict the rheologica l parameters of the batters. The frequency range in which surface waves are expe cted was particularly suitable for detecting rheological changes in highly aerat ed batters. An ultrasonic-based, online-capable method for process monitoring wa s implemented and established regarding feature selection in combination with ma chine learning and 5-fold cross-validation. The developed ultrasonic sensor syst em shows high accuracy for online density measurement (R2 = 0.98) and offers dec ent accuracy for measurements of foam structure parameters (Bubble count: R2 = 0 .95, Relative span: R2 = 0.93, Sauter diameter: R2 = 0.83)."

原文链接:

NETL
NSTL

Zhejiang University Reports Findings in Machine Learning (Electrolyte Design Cha rt Reframed by Intermolecular Interactions for High-Performance Li-Ion Batteries )

51-52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 Machine Learning is th e subject of a report. According to news reporting from Hangzhou, People's Repub lic of China, by NewsRx journalists, research stated, "Developing advanced elect rolytes has been regarded as a pivotal strategy for enhancing the electrochemica l performance of batteries; however, the criteria for electrolyte design remain elusive. In this study, we present an electrolyte design chart reframed through intermolecular interactions." Financial supporters for this research include National Natural Science Foundati on of China, Natural Science Foundation of Zhejiang Province.

原文链接:

NETL
NSTL

Reports Summarize Machine Translation Research from National Institute of Techno logy (Multimodal Machine Translation Approaches for Indian Languages: A Comprehe nsive Survey)

52-53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 machine translation are presented in a new report. According to news reporting out of the National Insti tute of Technology by NewsRx editors, research stated, "Multimodal machine trans lation (MMT) is a challenging task in the linguistically diverse Indian landscap e. Machine translation refers to the task of automatically converting content fr om one language to another without human involvement." The news journalists obtained a quote from the research from National Institute of Technology: "Within the realm of natural language processing, a significant c hallenge arises from the inherent ambiguity present in human language. Translati on ambiguity is a cross-lingual phenomenon that can manifest itself for various reasons, including lexical ambiguity, the occasional need to impute missing word s, the presence of gen-der ambiguity, and word-sense ambiguities. These factors can lead to a decrease in translation accuracy. The integration of multiple moda lities, such as images, videos, and audio, in addition to text, plays a pivotal role in improving the robustness and precision of translation systems. Over the past five years, extensive research has been dedicated to incorporating secondar y modalities alongside text to improve language translation and comprehension. I n this comprehensive study, our objective was to identify and explore promising MMT approaches, available corpora, eval-uation metrics, research challenges, and the future direction of research specifically for Indian languages. We evaluate d 81 papers, including MMT models, MMT dataset in Indian languages, survey on MM T approach, and the effects of multiple modalities in machine translation."

原文链接:

NETL
NSTL

Maastricht University Medical Center Reports Findings in Artificial Intelligence [Using artificial intelligence and predictive modelling to e nable learning healthcare systems (LHS) for pandemic preparedness]

53-54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 Artificial Intelligenc e is the subject of a report. According to news reporting originating in Maastri cht, Netherlands, by NewsRx journalists, research stated, "In anticipation of po tential future pandemics, we examined the challenges and opportunities presented by the COVID-19 outbreak. This analysis highlights how artificial intelligence (AI) and predictive models can support both patients and clinicians in managing subsequent infectious diseases, and how legislators and policymakers could suppo rt these efforts, to bring learning healthcare system (LHS) from guidelines to r eal-world implementation." The news reporters obtained a quote from the research from Maastricht University Medical Center, "This report chronicles the trajectory of the COVID-19 pandemic , emphasizing the diverse data sets generated throughout its course. We propose strategies for harnessing this data via AI and predictive modelling to enhance t he functioning of LHS. The challenges faced by patients and healthcare systems a round the world during this unprecedented crisis could have been mitigated with an informed and timely adoption of the three pillars of the LHS: Knowledge, Data and Practice. By harnessing AI and predictive analytics, we can develop tools t hat not only detect potential pandemic-prone diseases early on but also assist i n patient management, provide decision support, offer treatment recommendations, deliver patient outcome triage, predict post-recovery long-term disease impacts , monitor viral mutations and variant emergence, and assess vaccine and treatmen t efficacy in real-time."

原文链接:

NETL
NSTL

Reports Summarize Machine Learning Findings from Oak Ridge National Laboratory ( High-throughput Screening and Accurate Prediction of Ionic Liquid Viscosities Us ing Interpretable Machine Learning)

54-55页

查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on Machine Learning are discussed in a new report. According to news reporting out of Oak Ridge, Ten nessee, by NewsRx editors, research stated, "Ionic liquids (ILs) are a novel gro up of green solvents with great promise for various industrial applications, inc luding carbon capture and lignocellulosic biomass deconstruction. However, the u se of ILs at the industrial scale remains challenging due to their high viscosit ies at ambient temperatures." Financial supporters for this research include United States Department of Energ y (DOE), United States Department of Energy (DOE), United States Department of E nergy (DOE). Our news journalists obtained a quote from the research from Oak Ridge National Laboratory, "To develop ILs with lower viscosities, a systematic study of their quantitative structure-property relationship (QSPR) is desirable. Here, we devel oped four machine learning (ML) models to predict viscosity at various temperatu re and pressure ranges, trained over a wide range of ILs consisting of various c ationic and anionic families. ML methods including two-factor polynomial regress ion (two-factor PR), support vector regression (SVR), feed-forward neural networ ks (FFNN), and categorical boosting (CATBoost) were developed based on features that have proven useful in previous ML studies: COSMO-RS (conductor-like screeni ng model for real solvents)-derived surface screening charge densities (sigma pr ofiles). FFNN and CATBoost were the most accurate in predicting IL viscosities w ith lower average absolute relative deviation and higher R 2 values on the test set. Tanimoto similarity scores were calculated to characterize the chemical spa ce and structural similarity of the investigated ions. Furthermore, SHapley Addi tive exPlanation (SHAP) analysis was employed to interpret the ML results. Tempe rature, the polar area of ILs, and the nonpolar regions of ions are key features that influence the viscosity predictions."

原文链接:

NETL
NSTL

University of Sydney Reports Findings in Artificial Intelligence (Familiarity, c onfidence and preference of artificial intelligence feedback and prompts by Aust ralian breast cancer screening readers)

55-56页

原文链接:

NETL
NSTL

Sichuan University Reports Findings in Chronic Disease (Potential lethality of o rganochlorine pesticides: Inducing fatality through inflammatory responses in th e organism)

56-57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 Disease Attributes - C hronic Disease is the subject of a report. According to news originating from Si chuan, People's Republic of China, by NewsRx correspondents, research stated, "O rganochlorine pesticides, with their environmental persistence and bioaccumulati on potential, have gained significant attention. This study explores the impact of organochlorine pesticides on mortality and chronic diseases, investigates the ir link to inflammatory states, and examines the role of anti-inflammatory diets in mitigating adverse reactions to these pesticides." Our news journalists obtained a quote from the research from Sichuan University, "This study, with 2,847 participants, used gas chromatography and mass spectrom etry to measure organochlorine pesticide exposure in NHANES data. Conventional s tatistical methodologies, encompassing survival curves, Cox proportional hazards regression, regression analysis, and restricted quadratic spline analysis, were employed to investigate the association between pesticides and mortality, chron ic ailments, and inflammation. Furthermore, machine learning techniques, compris ing RF, AdaBoost, Extra-Trees, LightGBM, and BPNN, were leveraged to evaluate th e impact of pesticides on chronic disease and mortality prognostication. Organoc hlorine pesticides were significantly and positively correlated with increased m ortality (p <0.05). Additionally, these pollutants were lin ked to the incidence of chronic diseases such as chronic kidney disease, diabete s, and hypertension (p <0.05). Our study, utilizing variou s machine learning models, also showed a notable increase in the Area Under the Curve when incorporating organochlorine pesticide indicators into the model as o pposed to excluding them. Furthermore, strong correlations were observed between serum c-reactive protein (CRP) and CRP to serum albumin ratio (CAR) concentrati ons with these substances, demonstrating their pro-inflammatory effects at speci fic concentrations. Interestingly, cutting down on dietary inflammation through changes in diet effectively reduced the risk of death at high organochlorine pes ticide exposure levels, but the effect was less noticeable at low to moderate ex posure levels. Exposure to organochlorine pesticides was linked to a higher risk of mortality, likely due to an increased prevalence of chronic diseases."

原文链接:

NETL
NSTL

首页
3
4
5
6
7
8
9