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

    Complutense University Reports Findings in Multiple Sclerosis [European cross-cultural neuropsychological test battery (CNTB) for the assessmen t of cognitive impairment in multiple sclerosis: Cognitive phenotyping and class ification supported by ...]

    60-61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 Autoimmune Diseases an d Conditions - Multiple Sclerosis is the subject of a report. According to news reporting from Madrid, Spain, by NewsRx journalists, research stated, “The Europ ean Cross-Cultural Neuropsychological Test Battery (CNTB) has been proposed as a comprehensive battery for cognitive assessment, reducing the potential impact o f cultural variables. In this validation study, we aimed to evaluate the diagnos tic capacity of CNTB for the assessment of participants with multiple sclerosis (pwMS) compared to the Neuronorma battery (NN) according to the International Cl assification of Cognitive Disorders in MS criteria, and to develop machine learn ing (ML) algorithms to improve the diagnostic capacity of CNTB and to select the most relevant tests.” The news correspondents obtained a quote from the research from Complutense Univ ersity, “Sixty pwMS and 60 healthy controls (HC) with no differences in sex, age , or years of education were enrolled. All participants completed the CNTB and p wMS were also examined with NN, depression, and fatigue scales. Impaired domains and cognitive phenotypes were defined following ICCoDiMS based on CNTB scores a nd compared to NN, according to -1SD and -1.5SD cutoff scores. To select the mos t relevant tests, random forest (RF) was performed for different binary classifi cations. PwMS showed a lower performance compared to HC with medium-large effect sizes, in episodic memory, executive function, attention, and processing speed, in accordance with their characteristic cognitive profile. There were no differ ences in impaired domains or cognitive phenotypes between CNTB and NN, highlight ing the role of episodic memory, executive function, attention, and processing s peed tests. The most relevant tests identified by RF were consistent with inter- group comparisons and allowed a better classification than SD cutoff scores. CNT B is a valid test for cognitive diagnosis in pwMS, including key tests for the m ost frequently impaired cognitive domains in MS.”
      原文链接:
    • NETL
    • NSTL

    New Robotics Study Findings Recently Were Reported by Researchers at Nanjing Uni versity of Aeronautics and Astronautics (Hovering Flight Regulation of Pigeon Ro bots In Laboratory and Field)

    61-61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 Robotics are presented i n a new report. According to news reporting originating from Jiangsu, People’s R epublic of China, by NewsRx correspondents, research stated, “Compared to tradit ional bio-mimic robots, animal robots show superior locomotion, energy efficienc y, and adaptability to complex environments but most remained in laboratory stag e, needing further development for practical applications like exploration and i nspection. Our pigeon robots validated in both laboratory and field, tested with an electrical stimulus unit (2-s duration, 0.5 ms pulse width, 80 Hz frequency) .” Financial support for this research came from National Key Research & Development Program of China. Our news editors obtained a quote from the research from the Nanjing University of Aeronautics and Astronautics, “In a fixed stimulus procedure, hovering flight was conducted with 8 stimulus units applied every 2 s after flew over the trigg er boundary. In a flexible procedure, stimulus was applied whenever they deviate d from a virtual circle, with pulse width gains of 0.1 ms or 0.2 ms according to the trajectory angle.”
      原文链接:
    • NETL
    • NSTL

    New Machine Learning Study Results from Lucian Blaga University of Sibiu Describ ed (Machine Learning-based Multifaceted Analysis Framework for Comparing and Sel ecting Water Quality Indices)

    62-62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 reporting originating from Sibiu, Romania, by NewsRx correspondents, research stated, “Water quality is essential to the popu lation’s well-being, water resources management, and environmental development s trategies. In this article, we propose a framework based on machine learning (ML ) techniques for enhancing the assessment of water quality based on water qualit y indices (WQIs).” Financial support for this research came from Lucian Blaga University of Sibiu. Our news editors obtained a quote from the research from the Lucian Blaga Univer sity of Sibiu, “It consists of three algorithms that could serve as a foundation for automating the evaluation of any resource based on indices and can operate locally or globally. Local-level algorithms assist in selecting suitable WQIs ta ilored to specific water sources and quality requirements, while global-level al gorithm evaluates WQI robustness across diverse water sources. We also provide a warning system to mitigate differences in water quality evaluation using WQIs a nd a valuable tool (based on the features’ importance) for selecting ML models t hat prioritize the water parameters’ significance. The framework’s design draws upon conclusions from a case study involving the forecast and comparison of two WQIs for the Brahmaputra River.”
      原文链接:
    • NETL
    • NSTL

    Great Ormond Street Hospital for Children NHS Foundation Trust Reports Findings in Artificial Intelligence (Facilitating the use of routine data to evaluate art ificial intelligence solutions: lessons from the NIHR/RCR data curation workshop )

    63-63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 out of London, United Kingdom, by NewsRx editors, research stated, “Radiology currently stands at the forefront of artificial intelligence (AI) development and deployment over many o ther medical subspecialities within the scope of both research and clinical prac tice. Given this current leadership position, it is imperative that we foster co llaboration and knowledge sharing to ensure the ethical, responsible and effecti ve continued progress of AI technologies in our field, ultimately leading to enh anced patient care.” Our news journalists obtained a quote from the research from Great Ormond Street Hospital for Children NHS Foundation Trust, “To achieve this objective, three w orkshops have been planned through a coordinated effort by the NIHR/RCR committe e. These workshops aim to convene key stakeholders including eminent academics, departmental leaders and industry partners to provide insights from their own ex periences and strategies to overcome common challenges faced. In this article, w e describe the outcomes from the first workshop, which addresses the topic of ‘f acilitating the use of routine data to evaluate AI solutions’. The main key insi ghts uncovered include the need for ethical considerations, detailing of methods for data curation and storage depending on the need and requirements for de-ide ntification. We provide resources for how to de-identify data and also a list of concerns to think about before curating your data.”
      原文链接:
    • NETL
    • NSTL

    University of Tirana Reports Findings in Colon Cancer (Robust prediction of colo rectal cancer via gut microbiome 16S rRNA sequencing data)

    64-65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 Oncology - Colon Cance r is the subject of a report. According to news originating from Tirana, Albania , by NewsRx correspondents, research stated, “The study addresses the challenge of utilizing human gut microbiome data for the early detection of colorectal can cer (CRC). The research emphasizes the potential of using machine learning techn iques to analyze complex microbiome datasets, providing a non-invasive approach to identifying CRC-related microbial markers.” Our news journalists obtained a quote from the research from the University of T irana, “The primary hypothesis is that a robust machine learning-based analysis of 16S rRNA microbiome data can identify specific microbial features that serve as effective biomarkers for CRC detection, overcoming the limitations of classic al statistical models in high-dimensional settings. The primary objective of thi s study is to explore and validate the potential of the human microbiome, specif ically in the colon, as a valuable source of biomarkers for colorectal cancer (C RC) detection and progression. The focus is on developing a classifier that effe ctively predicts the presence of CRC and normal samples based on the analysis of three previously published faecal 16S rRNA sequencing datasets. To achieve the aim, various machine learning techniques are employed, including random forest ( RF), recursive feature elimination (RFE) and a robust correlation-based techniqu e known as the fuzzy forest (FF). The study utilizes these methods to analyse th e three datasets, comparing their performance in predicting CRC and normal sampl es. The emphasis is on identifying the most relevant microbial features (taxa) a ssociated with CRC development via partial dependence plots, i.e. a machine lear ning tool focused on explainability, visualizing how a feature influences the pr edicted outcome. The analysis of the three faecal 16S rRNA sequencing datasets r eveals the consistent and superior predictive performance of the FF compared to the RF and RFE. Notably, FF proves effective in addressing the correlation probl em when assessing the importance of microbial taxa in explaining the development of CRC. The results highlight the potential of the human microbiome as a non-in vasive means to detect CRC and underscore the significance of employing FF for i mproved predictive accuracy.”
      原文链接:
    • NETL
    • NSTL

    Investigators from University of Manchester Report New Data on Machine Learning (Multi-scale Computational Design of Metalorganic Frameworks for Carbon Capture Using Machine Learning and Multi-objective Optimization)

    65-66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 originating from Manc hester, United Kingdom, by NewsRx correspondents, research stated, “In this arti cle, we computationally design a series of metal-organic frameworks (MOFs) optim ized for postcombustion carbon capture. Our workflow includes assembling buildin g blocks and topologies into an initial set of hypothetical MOFs, using genetic algorithms to optimize this initial set for high CO2/N-2 selectivity, and furthe r evaluating the top materials through process-level modeling of their performan ce in a modified Skarstrom cycle.” Our news editors obtained a quote from the research from the University of Manch ester, “We identify two groups of MOFs that exhibit excellent process performanc e: one with relatively small pores in the range of 3-5 & Aring; an d another with larger pores of 6-30 & Aring;. The performance of t he first group is driven effectively by the exclusion of N-2 from adsorption, wi th binding sites able to accommodate only CO2 molecules. The second group, with larger pores, features binding sites where CO2 molecules form multiple interacti ons with oxygen and functional groups of several building blocks, leading to a h igh CO2/N-2 selectivity. Within the employed process model and its assumptions, the materials generated in this study substantially outperform 13X reference zeo lites, in silico optimized ion-exchanged LTA zeolites, and CALF-20. While this s tudy does not address the synthesizability, stability, or water interactions of the proposed materials, it marks a significant step forward in developing practi cal MOFs for carbon capture in three key areas. First, it introduces a generativ e workflow based on the process-level performance of new materials. Second, it i dentifies structural features of optimal MOFs for carbon capture, which can serv e as design guidelines for future development.”
      原文链接:
    • NETL
    • NSTL

    Third Military Medical University - Army Medical University Reports Findings in Cholangiocarcinoma (Machine Learning Model to Predict Early Recurrence in Patien ts with Perihilar Cholangiocarcinoma Planned Treatment with Curative Resection: A ...)

    66-67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 Oncology - Cholangioca rcinoma is the subject of a report. According to news reporting originating in C hongqing, People’s Republic of China, by NewsRx journalists, research stated, “E arly recurrence is the leading cause of death for patients with perihilar cholan giocarcinoma (pCCA) after surgery. Identifying high-risk patients preoperatively is important.” The news reporters obtained a quote from the research from Third Military Medica l University - Army Medical University, “This study aimed to construct a preoper ative prediction model for the early recurrence of pCCA patients planned treatme nt with curative resection. This study ultimately enrolled 400 pCCA patients aft er curative resection in five hospitals between 2013 and 2019. They were randoml y divided into training (n=300) and testing groups (n=100) at a ratio of 3:1. As sociated variables were identified via LASSO regression. Four machine learning m odels were constructed: support vector machine (SVM), random forest (RF), logist ic regression, and K-nearest neighbors (KNN). The predictive ability of the mode ls was evaluated via receiver operating characteristic (ROC) curves, precision-r ecall curve (PRC) curves, and decision curve analysis (DCA). KaplanMeier surviva l curves were drawn for the high/low-risk population. Five factors, CA19-9, tumo r size, total bilirubin, hepatic artery invasion, and portal vein invasion, were selected by LASSO regression. In both the training and testing groups, the ROC curve (AUC: 0.983 vs 0.952) and the PRC (0.981 vs 0.939) showed that RF was the best. The cutoff value for distinguishing high- and low-risk patients was 0.51. KM survival curves revealed that in both groups, there was a significant differe nce in RFS between high- and low-risk patients (P <0.001).”
      原文链接:
    • NETL
    • NSTL

    Researchers at Harbin Institute of Technology Release New Data on Robotics (Sens orless Human-robot Interaction: Real-time Estimation of Co-grasped Object Mass a nd Human Wrench for Compliant Interaction)

    67-68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 Robotic s. According to news reporting out of Harbin, People’s Republic of China, by New sRx editors, research stated, “Human-robot physical interaction in shared object manipulation can fully leverage the strengths of both humans and collaborative robots, achieving better interaction outcomes. However, in typical physical inte ractions, the inertia parameters of the object are either assumed to be known or ignored, simplifying the interaction to be directly between the human and the c ollaborative robot.” Financial supporters for this research include National Outstanding Youth Scienc e Fund Project of National Natural Science Foundation of China, Major Research P lan.
      原文链接:
    • NETL
    • NSTL

    University of Illinois Urbana-Champaign Researcher Focuses on Machine Learning ( Machine learning-enabled computer vision for plant phenotyping: a primer on AI/M L and case study on stomatal patterning)

    68-68页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A new study on artificial intelligence is now available. According to news originating from the University of Illinois Urbana-Champaign by NewsRx correspondents, research stated, “Artificial intelli gence and machine learning (AI/ML) can be used to automatically analyze large im age datasets.” The news journalists obtained a quote from the research from University of Illin ois Urbana-Champaign: “One valuable application of this approach is estimation o f plant trait data contained within images. Here we review 39 papers that descri be the development and/or application of such models for estimation of stomatal traits from epidermal micrographs. In doing so, we hope to provide plant biologi sts with a foundational understanding of AI/ML and summarize the current capabil ities and limitations of published tools. While most models show human-level per formance for stomatal density (SD) quantification at superhuman speed, they are often likely to be limited in how broadly they can be applied across phenotypic diversity associated with genetic, environmental or developmental variation.”
      原文链接:
    • NETL
    • NSTL

    Study Data from Guangxi University Provide New Insights into Machine Learning (M achine Learning-accelerated Inverse Design of Programmable Bi-functional Metamat erials)

    69-69页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A new study on Machine Learning is now available. According to news reporting from Nanning, People’s Republic of China , by NewsRx journalists, research stated, “Bi-functional metamaterials with prog rammable coefficients of thermal expansion (CTEs) and Poisson’s ratios (PRs) hav e garnered significant attention among researchers due to the ability to manifes t desired deformations under thermal and mechanical loads. Nevertheless, a curre nt challenge lies in efficiently achieving the inverse design of these metamater ials to meet diverse application requirements.” Financial supporters for this research include National Natural Science Foundati on of China (NSFC), The 2024 Open Project of Failure Mechanics and Engineering D isaster Prevention, Key Lab of Sichuan Province. The news correspondents obtained a quote from the research from Guangxi Universi ty, “This paper presents a machine learning (ML) model that can establish a logi cal mapping relationship between geometric/ material parameters and mechanical pr operties, and it is applied to the inverse design of bi-functional metamaterials with desired CTEs and PRs. Furthermore, the inverse design capability of the ML model was validated by the finite element analysis and experimental test. The r esults demonstrate that the geometric models obtained from the inverse predictio n can effectively exhibit the desired deformation behavior under thermal and mec hanical loads.”
      原文链接:
    • NETL
    • NSTL