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    New Findings from Nanyang Technological University Describe Advances in Robotics (Low-Cost Cable-Driven Robot Arm with Low- Inertia Movement and Long-Term Cable Durability)

    39-39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 robotics is the subjec t of a new report. According to news reporting out of Singapore, Singapore, by N ewsRx editors, research stated, “Our study presents a novel design for a cable-d riven robotic arm, emphasizing low cost, low inertia movement, and long-term cab le durability.” Financial supporters for this research include Agency For Science, Technology An d Research; Schaeffler Hub For Advanced Research At Ntu. The news editors obtained a quote from the research from Nanyang Technological U niversity: “The robotic arm shares similar specifications with the UR5 robotic a rm, featuring a total of six degrees of freedom (DOF) distributed in a 1:1:1:3 r atio at the arm base, shoulder, elbow, and wrist, respectively. The three DOF at the wrist joints are driven by a cable system, with heavy motors relocated from the end-effector to the shoulder base. This repositioning results in a lighter cable-actuated wrist (weighing 0.8 kg), which enhances safety during human inter action and reduces the torque requirements for the elbow and shoulder motors. Co nsequently, the overall cost and weight of the robotic arm are reduced, achievin g a payload-to-body weight ratio of 5:8.4 kg. To ensure good positional repeatab ility, the shoulder and elbow joints, which influence longer moment arms, are de signed with a direct-drive structure. To evaluate the design’s performance, test s were conducted on loading capability, cable durability, position repeatability , and manipulation.”
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    Zhengzhou University People’s Hospital Reports Findings in Support Vector Machin es (Endobronchial Ultrasound-Based Support Vector Machine Model for Differentiat ing between Benign and Malignant Mediastinal and Hilar Lymph Nodes)

    40-40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 Support Vector Machine s is the subject of a report. According to news originating from Zhengzhou, Peop le’s Republic of China, by NewsRx correspondents, research stated, “The aim of t he study was to establish an ultrasonographic radiomics machine learning model b ased on endobronchial ultrasound (EBUS) to assist in diagnosing benign and malig nant mediastinal and hilar lymph nodes (LNs). The clinical and ultrasonographic image data of 197 patients were retrospectively analyzed.” Our news journalists obtained a quote from the research from Zhengzhou Universit y People’s Hospital, “The radiomics features extracted by EBUS-based radiomics w ere analyzed by the least absolute shrinkage and selection operator. Then, we us ed a support vector machine (SVM) algorithm to establish an EBUSbased radiomics model. A total of 205 lesions were randomly divided into training (n = 143) and validation (n = 62) groups. The diagnostic efficiency was evaluated by receiver operating characteristic (ROC) curve analysis. A total of 13 stable radiomics f eatures with non-zero coefficients were selected. The SVM model exhibited promis ing performance in both groups. In the training group, the SVM model achieved an ROC area under the curve (AUC) of 0.892 (95% CI: 0.885-0.899), wi th an accuracy of 85.3%, sensitivity of 93.2%, and spe cificity of 79.8%. In the validation group, the SVM model had an RO C AUC of 0.906 (95% CI: 0.890-0.923), an accuracy of 74.2% , a sensitivity of 70.3%, and a specificity of 74.1%. The EBUS-based radiomics model can be used to differentiate mediastinal and hila r benign and malignant LNs.”
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    Researchers from Nanjing Tech University Describe Findings in Machine Learning ( Machine Learning Assisted Multi-objective Design Optimization for Battery Therma l Management System)

    41-41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 originating from Nanjing, People’s Repub lic of China, by NewsRx correspondents, research stated, “The rapid expansion of the electric vehicle (EV) industry necessitates the development of advanced bat tery thermal management systems (BTMSs) to safeguard the cyclic properties and s ecurity of lithium-ion batteries. However, the assessment of the performance of BTMS often overlooks the importance of considering not only the thermal regulati on effectiveness on batteries but also its own energy efficiency.” Funders for this research include National Natural Science Foundation of China ( NSFC), Natural Science Foundation of Anhui Province, Special Fund for Carbon Pea k and Carbon Neutralization Scientific and Technological Innovation Project of J iangsu Province.
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    Qilu Hospital of Shandong University Reports Findings in Spinal Cord Injury (Dyn amic changes in pyroptosis following spinal cord injury and the identification o f crucial molecular signatures through machine learning and single-cell sequenci ng)

    42-42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 Central Nervous System Diseases and Conditions - Spinal Cord Injury is the subject of a report. Accord ing to news reporting originating from Jinan, People’s Republic of China, by New sRx correspondents, research stated, “The pathological cascade of spinal cord in jury (SCI) is highly intricate. The onset of neuroinflammation can exacerbate th e extent of damage.” Our news editors obtained a quote from the research from the Qilu Hospital of Sh andong University, “Pyroptosis is a form of inflammation-linked programmed cell death (PCD), the inhibition of pyroptosis can partially mitigate neuroinflammati on. It is imperative to delineate the principal cell types susceptible to pyropt osis and concomitantly identify key genes associated with this process. We initi ally defined the pyroptosis-related genes (PRGs) and analyzed their expression a t different time points post SCI. The results demonstrate a substantial upregula tion of differentially expressed genes (DEGs) related to pyroptosis on the 7 day s post-injury (dpi), these DEGs in the 7 dpi are closely related to the inflamma tory response. Subsequently, immune infiltration analysis revealed a predominant presence of inflammatory microglia. Through correlation analysis, we postulated that pyroptosis primarily manifested within the inflammatory microglia. Employi ng machine learning algorithms, we identified four pyroptosis-related molecular signatures, which were experimentally validated using BV2 cells and spinal cord tissue samples. The robustness of the identified molecular signatures was furthe r confirmed through single-cell sequencing data analysis. Overall, our study elu cidates the temporal dynamics of pyroptosis and identifies key molecular signatu res following SCI.”
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    Study Findings on Machine Learning Described by a Researcher at King’s College L ondon (A Study of Chinese Stock Price Prediction Based on LSTM and Time Series L inear Regression Model)

    43-43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 artificial in telligence have been published. According to news originating from London, Unite d Kingdom, by NewsRx correspondents, research stated, “With the advancement of t echnology and the increasing popularity of machine learning, new opportunities h ave emerged across various industries.” The news editors obtained a quote from the research from King’s College London: “In the financial sector, an increasing number of individuals are attempting to utilize machine learning to enhance the accuracy of stock price predictions. Thi s paper will also endeavor to apply the LSTM model for predicting the stock pric es of companies listed on the main boards of the Chinese stock market, while sim ultaneously comparing it with traditional time series linear regression model. A gainst the highly complex backdrop of the stock market, this paper aims to explo re whether machine learning can surpass traditional models in achieving superior predictive results.”
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    University of Leuven (KU Leuven) Reports Findings in Machine Learning (lab2clean : a novel algorithm for automated cleaning of retrospective clinical laboratory results data for secondary uses)

    43-44页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on Machine Learning is the subject o f a report. According to news reporting out of Leuven, Belgium, by NewsRx editor s, research stated, “The integrity of clinical research and machine learning mod els in healthcare heavily relies on the quality of underlying clinical laborator y data. However the preprocessing of this data to ensure its reliability and ac curacy remains a significant challenge due to variations in data recording and r eporting standards.” Our news journalists obtained a quote from the research from the University of L euven (KU Leuven), “We developed lab2clean, a novel algorithm aimed at automatin g and standardizing the cleaning of retrospective clinical laboratory results da ta. lab2clean was implemented as two R functions specifically designed to enhanc e data conformance and plausibility by standardizing result formats and validati ng result values. The functionality and performance of the algorithm were evalua ted using two extensive electronic medical record (EMR) databases, encompassing various clinical settings. lab2clean effectively reduced the variability of labo ratory results and identified potentially erroneous records. Upon deployment, it demonstrated effective and fast standardization and validation of substantial l aboratory data records. The evaluation highlighted significant improvements in t he conformance and plausibility of lab results, confirming the algorithm’s effic acy in handling large-scale data sets. lab2clean addresses the challenge of prep rocessing and cleaning clinical laboratory data, a critical step in ensuring hig h-quality data for research outcomes. It offers a straightforward, efficient too l for researchers, improving the quality of clinical laboratory data, a major po rtion of healthcare data. Thereby, enhancing the reliability and reproducibility of clinical research outcomes and clinical machine learning models.”
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    Researcher at RWTH Aachen University Zeroes in on Robotics (Design of a Three-De gree of Freedom Planar Parallel Mechanism for the Active Dynamic Balancing of De lta Robots)

    44-45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 robotics are disc ussed in a new report. According to news originating from Aachen, Germany, by Ne wsRx correspondents, research stated, “Delta robots are the most common parallel robots for manipulation tasks.” Financial supporters for this research include German Foreign Office. Our news editors obtained a quote from the research from RWTH Aachen University: “In many industrial applications, they must be operated at reduced speed, or dw ell times have to be included in the motion planning, to prevent frame vibration s. As a result, their full potential cannot be realized. Against this background , this publication is concerned with the mechanical design of an active dynamic balancing unit for the reduction of frame vibrations. In the first part of this publication, the main design requirements for an active dynamic balancing mechan ism are discussed, followed by a presentation of possible mechanism designs.”
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    Findings from National Center for Scientific Research (CNRS) Broaden Understandi ng of Robotics (Rejecting a Robot’s Offer: an Analysis of Preference)

    45-46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 originating in Lyon, France, by NewsRx journalist s, research stated, “Since the development of commercial robots dedicated to ser vice or social encounters, there have been numerous appearances of such devices in public spaces or corporate buildings. However, their purpose might not be sel f-evident and the modalities for using it might not be self-explainable.” Financial support for this research came from ASLAN project of the Universite de Lyon. The news reporters obtained a quote from the research from National Center for S cientific Research (CNRS), “Moreover, ‘talking’ to a robot that imitates a recep tionist could raise practical problems, given the fact that ‘talk’ among humans is an interactional resource for performing actions that carry social dimensions . This paper focuses on the dimension of ‘preference organization’; specifically , offer rejections that are dispreferred among humans. Based on conversation ana lysis of human-robot interactions recorded in a university library, we examined 95 occurrences of how library users rejected offers of assistance initiated by a humanoid robot, Pepper. We identified three embodied rejection practices embedd ed in other courses of activity among groups of library users.”
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    Chinese Academy of Sciences Reports Findings in Machine Learning (Thermodynamics and explainable machine learning assist in interpreting biodegradability of dis solved organic matter in sludge anaerobic digestion with thermal hydrolysis)

    46-47页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on Machine Learning is the subject o f a report. According to news originating from Beijing, People’s Republic of Chi na, by NewsRx correspondents, research stated, “Dissolved organic matter (DOM) i s essential in biological treatment, yet its specific roles remain incompletely understood. This study introduces a machine learning (ML) framework to interpret DOM biodegradability in the anaerobic digestion (AD) of sludge, incorporating a thermodynamic indicator (l).” Our news journalists obtained a quote from the research from the Chinese Academy of Sciences, “Ensemble models such as Xgboost and LightGBM achieved high accura cy (training: 0.90-0.98; testing: 0.75-0.85). The explainability of the ML model s revealed that the features l, measured m/z, nitrogen to carbon ratio (N/C), hy drogen to carbon ratio (H/C), and nominal oxidation state of carbon (NOSC) were significant formula features determining biodegradability. Shapley values furthe r indicated that the biodegradable DOM were mostly formulas with l lower than 0. 03, measured m/z value higher than 600 Da, and N/C ratios higher than 0.2.”
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    Researchers from Beijing Institute of Technology Discuss Research in Artificial Intelligence (Embracing artificial intelligence in the labour market: the case o f statistics)

    47-47页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New study results on artificial intell igence have been published. According to news reporting from the Beijing Institu te of Technology by NewsRx journalists, research stated, “In an era marked by ra pid advancements in artificial intelligence (AI), the dynamics of the labour mar ket are undergoing significant transformation. A common concern amidst these cha nges is the potential obsolescence of traditional disciplines due to AI-driven p roductivity enhancements.” Our news reporters obtained a quote from the research from Beijing Institute of Technology: “This study delves into the evolving role and resilience of these di sciplines within the AI-influenced labour market. Focusing on statistics as a re presentative field, we investigate its integration with AI and its interplay wit h other disciplines. Analyzing 279.87 million online job postings in the United States from 2010 to 2022, we observed a remarkable 31-fold increase in the deman d for AI-specialized statistical talent, diversifying into 932 distinct AI-relat ed job roles. Additionally, our research identified four major interdisciplinary clusters, encompassing 190 disciplines with a statistical focus. The findings a lso highlight a growing emphasis on specific hard skills within these AI roles a nd the differences in demand for AI talent in statistics across economic sectors and regions. Contrary to the pessimistic view of traditional disciplines’ survi val in the AI age, our study suggests a more optimistic outlook.”
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