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    Data on Machine Learning Reported by Researchers at Northwest A&F University (Identification and Isolation of Bzr Transcription Factor and Screening of Cell Wall Degradation Marker Genes Based On Machine Learning In Ripening Kiwifruit)

    28-29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 newsreporting from Shaanxi, People’s Repub lic of China, by NewsRx journalists, research stated, “The BZRserves as a key t ranscription factor in brassinolide signal transduction, while ethylene is known to facilitatekiwifruit softening. However, the precise mechanism by which BZR mediates the interplay between ethyleneand brassinolide in fruit softening rema ins unclear.” Financial supporters for this research include Natural Science Basic Research Pr ogram of Shaanxi, KeyS & T Special Projects of Shaanxi Province, China, Key Development Proj- ect of Ningxia Hui AutonomousRegion, Modern Agricu ltural Industry Technology System of China.
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    Reports from Ajou University Describe Recent Advances in Machine Learning (Enhan cing flow stress predictions in CoCrFeNiV high entropy alloy with conventional and machine learning techniques)

    29-30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 artificial in telligence have been published. According to newsreporting from Suwon, South Ko rea, by NewsRx journalists, research stated, “A machine learning techniquelever aging artificial intelligence (AI) has emerged as a promising tool for expeditin g the exploration anddesign of novel high entropy alloys (HEAs) while predictin g their mechanical properties at both room andelevated temperatures.” Funders for this research include National Research Foundation of Korea; Ministr y of Science, Ict AndFuture Planning; Ministry of Education.Our news journalists obtained a quote from the research from Ajou University: “I n this paper, wepredict the flow stress of hot-compressed CoCrFeNiV HEAs using conventional (qualitative and quantitativemodels) and advanced machine learning approaches across various temperature and strain rate conditions.Conventional modeling methods, including the modified Johnson-Cook (JC), modified Zerilli-Arm strong(ZA), and Arrhenius-type constitutive equations, are employed. Simultaneo usly, machine learning modelsare utilized to forecast flow stress under differe nt hot working conditions. The performance of bothconventional and machine lear ning models is evaluated using metrics such as coefficient of determination(R2) , mean abosolute error (MAE), and root mean squared error (RMSE).”
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    Researchers at University of California Los Angeles (UCLA) Target Robotics (Dismech: a Discrete Differential Geometry-based Physical Simulator for Soft Robots and Structures)

    30-31页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - Data detailed on Robotics have been pr esented. According to news originating fromLos Angeles, California, by NewsRx c orrespondents, research stated, “Fast, accurate, and generalizablesimulations a re a key enabler of modern advances in robot design and control. However, existi ng simulationframeworks in robotics either model rigid environments and mechani sms only, or if they include flexible orsoft structures, suffer significantly i n one or more of these performance areas.”Financial support for this research came from National Science Foundation (NSF).Our news journalists obtained a quote from the research from the University of C alifornia Los Angeles(UCLA), “To close this ‘sim2real’ gap, we introduce DisMec h, a simulation environment that models highlydynamic motions of rod-like soft continuum robots and structures, quickly and accurately, with arbitraryconnecti ons between them. Our methodology combines a fully implicit discrete differentia l geometrybasedphysics solver with fast and accurate contact handling, all in an intuitive software interface. Crucially,we propose a gradient descent approa ch to easily map the motions of hardware robot prototypes to controlinputs in D isMech. We validate DisMech through several highly-nuanced soft robot simulation s whiledemonstrating an order of magnitude speed increase over previous state o f the art. Our real2sim validationshows high physical accuracy versus hardware, even with complicated soft actuation mechanisms such asshape memory alloy wires.”
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    University Health Network Reports Findings in Artificial Intelligence (Impact of study design on adenoma detection in the evaluation of artificial intelligence- aided colonoscopy: a systematic review and meta-analysis)

    31-32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 in Toronto , Canada, by NewsRx journalists, research stated, “Randomized controlledtrials (RCTs) have reported that artificial intelligence (AI) improves endoscopic polyp detection. Differentmethodologies-namely, parallel and tandem designs-have bee n used to evaluate the efficacy of AI-assistedcolonoscopy in RCTs.”The news reporters obtained a quote from the research from University Health Net work, “Systematicreviews and meta-analyses have reported a pooled effect that i ncludes both study designs. However, itis unclear whether there are inconsisten cies in the reported results of these 2 designs. Here, we aimed todetermine whe ther study characteristics moderate between-trial differences in outcomes when e valuatingthe effectiveness of AI-assisted polyp detection. A systematic search of Ovid MEDLINE, Embase, CochraneCentral, Web of Science, and IEEE Xplore was p erformed through March 1, 2023, for RCTs comparingAI-assisted colonoscopy with routine high-definition colonoscopy in polyp detection. The primary outcomeof i nterest was the impact of study type on the adenoma detection rate (ADR). Second ary outcomesincluded the impact of the study type on adenomas per colonoscopy a nd withdrawal time, as well as theimpact of geographic location, AI system, and endoscopist experience on ADR. Pooled event analysis wasperformed using a rand om-effects model. Twenty-four RCTs involving 17,413 colonoscopies (AI assisted:8680; non-AI assisted: 8733) were included. AI-assisted colonoscopy improved ove rall ADR (risk ratio[RR], 1.24; 95% confidence interval [CI], 1.17-1.31; I = 5 3%; P<.001). Tandem studies collectively demonstrated improved ADR in AI-aided colonoscopies (RR, 1.18; 95% CI, 1.08-1.30; I = 0%; P<.001), as didparallel studies (RR, 1.26; 95% CI, 1.17-1.35; I = 62%; P<.001), with no statistical subgroup differencebetween study design. Both tande m and parallel study designs revealed improvement in adenomas percolonoscopy in AI-aided colonoscopies, but this improvement was more marked among tandem studi es (P<.001). AI assistance significantly increased withdr awal times for parallel (P = .002), but not tandem,studies. ADR improvement was more marked among studies conducted in Asia compared to Europe andNorth Americ a in a subgroup analysis (P = .007). Type of AI system used or endoscopist exper ience didnot affect overall improvement in ADR. Either parallel or tandem study design can capture the improvementin ADR resulting from the use of AI-assisted polyp detection systems.”
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    Eindhoven University of Technology Reports Findings in Machine Learning (An adve rsarial learning approach to generate pressure support ventilation waveforms for asynchrony detection)

    32-33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 from Eindhoven, Netherlands, by NewsRx journalists, research stated, “Mechanical ventilation isa life-saving treatment for critically-ill patients. During treatment, patient-ventilator asy nchrony (PVA)can occur, which can lead to pulmonary damage, complications, and higher mortality.”The news correspondents obtained a quote from the research from the Eindhoven Un iversity of Technology,“While traditional detection methods for PVAs rely on vi sual inspection by clinicians, in recentyears, machine learning models are bein g developed to detect PVAs automatically. However, trainingthese models require s large labeled datasets, which are difficult to obtain, as labeling is a labour -intensiveand time-consuming task, requiring clinical expertise. Simulating the lung-ventilator interactions has beenproposed to obtain large labeled datasets to train machine learning classifiers. However, the obtained datalacks the inf luence of different hardware, of servo-controlled algorithms, and different sour ces of noise.Here, we propose VentGAN, an adversarial learning approach to impr ove simulated data by learning theventilator fingerprints from unlabeled clinic al data. In VentGAN, the loss functions are designed to addcharacteristics of c linical waveforms to the generated results, while preserving the labels of the s imulatedwaveforms. To validate VentGAN, we compare the performance for detectio n and classification of PVAswhen training a previously developed machine learni ng algorithm with the original simulated data and withthe data generated by Ven tGAN. Testing is performed on independent clinical data labeled by experts.The McNemar test is applied to evaluate statistical differences in the obtained clas sification accuracy.VentGAN significantly improves the classification accuracy for late cycling, early cycling and normal breaths(p <0.01); no significant difference in accuracy was observed for delayed inspirations (p = 0.2), while theaccuracy decreased for ineffective efforts (p <0.01).”
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    New Artificial Intelligence Findings from University of Science and Technology Beijing Reported (Priority Aggregation Network With Integrated Computation and Sensation for Ultra Dense Artificial Intelligence of Things)

    33-34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 Artificial In telligence have been published. According to newsreporting out of Beijing, Peop le’s Republic of China, by NewsRx editors, research stated, “In the realmof ult ra dense Intelligence of Things, efficient utilization of computation and sensat ion (CAS) resourcesremains a challenge due to their uneven temporal and spatial distribution.”Financial support for this research came from National Natural Science Foundatio n of China (NSFC).Our news journalists obtained a quote from the research from the University of S cience and TechnologyBeijing, “Priority Aggregation Network (PAN) is introduced to employ priority as an intermediary to restore,combine, and exchange idle CA S resources’ permissions for cross-temporal and cross-modal resourceleasing. PA N awards priority to devices that lease their sensing and computing resources to others,facilitating efficient resource allocation.”According to the news editors, the research concluded: “Numerical results indica te PAN maintains highcross-temporal task completion rates while conserving equi pment resources, with increased willingness topay for resources.”
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    Second Hospital of Hebei Medical University Researchers Update Current Study Findings on Machine Learning (Analysis of the fatigue status of medical security personnel during the closed-loop period using multiple machine learning methods: a …)

    34-35页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - Investigators publish new report on ar tificial intelligence. According to news originatingfrom Second Hospital of Heb ei Medical University by NewsRx correspondents, research stated, “Usingmachine learning methods to analyze the fatigue status of medical security personnel and the factorsinfluencing fatigue (such as BMI, gender, and wearing protective cl othing working hours), with the goal ofidentifying the key factors contributing to fatigue. By validating the predicted outcomes, actionable andpractical reco mmendations can be offered to enhance fatigue status, such as reducing wearing p rotectiveclothing working hours. A questionnaire was designed to assess the fat igue status of medical securitypersonnel during the closed-loop period, aiming to capture information on fatigue experienced during workand disease recovery.”Funders for this research include Hebei Medical Science Research Projects.
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    Findings from Georgia Institute of Technology in Robotics and Automation Reported (Low Frequency Sampling In Model Predictive Path Integral Control)

    35-36页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - Investigators publish new report on Ro botics - Robotics and Automation. Accordingto news originating from Atlanta, Ge orgia, by NewsRx correspondents, research stated, “Sampling-basedmodel-predicti ve controllers have become a powerful optimization tool for planning and control problemsin various challenging environments.”Financial support for this research came from Jet Propulsion Laboratory, Califor nia Institute of Technology.Our news journalists obtained a quote from the research from the Georgia Institu te of Technology, “Inthis letter, we show how the default choice of uncorrelate d Gaussian distributions can be improved uponwith the use of a colored noise di stribution. Our choice of distribution allows for the emphasis on lowfrequency control signals, which can result in smoother and more exploratory samples.”
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    University of Adelaide Reports Findings in Cancer Biomarkers (Graphene and metal -organic framework hybrids for highperformance sensors for lung cancer biomarker detection supported by machine learning augmentation)

    36-37页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - New research on Diagnostics and Screen ing - Cancer Biomarkers is the subject ofa report. According to news reporting originating in Adelaide, Australia, by NewsRx journalists, researchstated, “Con ventional diagnostic methods for lung cancer, based on breath analysis using gas chromatographyand mass spectrometry, have limitations for fast screening due t o their limited availability, operationalcomplexity, and high cost. As potentia l replacement, among several low-cost and portable methods,chemoresistive senso rs for the detection of volatile organic compounds (VOCs) that represent biomark ersof lung cancer were explored as promising solutions, which unfortunately sti ll face challenges.”Financial support for this research came from Australian Research Council.The news reporters obtained a quote from the research from the University of Ade laide, “To addressthe key problems of these sensors, such as low sensitivity, h igh response time, and poor selectivity, thisstudy presents the design of new c hemoresistive sensors based on hybridised porous zeolitic imidazolate(ZIF-8) ba sed metal-organic frameworks (MOFs) and laser-scribed graphene (LSG) structures, inspiredby the architecture of the human lung. The sensing performance of the fabricated ZIF-8@LSG hybridsensors was characterised using four dominant VOC bi omarkers, including acetone, ethanol, methanol,and formaldehyde, which are iden tified as metabolomic signatures in lung cancer patients’ exhaled breath.The re sults using simulated breath samples showed that the sensors exhibited excellent performance for aset of these biomarkers, including fast response (2-3 seconds ), a wide detection range (0.8 ppm to 50 ppm),a low detection limit (0.8 ppm), and high selectivity, all obtained at room temperature. Intelligent machinelear ning (ML) recognition using the multilayer perceptron (MLP)-based classification algorithm was furtheremployed to enhance the capability of these sensors, achi eving an exceptional accuracy (approximately96.5%) for the four ta rgeted VOCs over the tested range (0.8-10 ppm).”
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    Reports on Robotics Findings from Zhejiang University Provide New Insights (Dyna mic Modulation of Multi-task Priority for Controlling Redundancy Insufficient Robots)

    37-38页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - Researchers detail new data in Robotic s. According to news originating from Zhejiang,People’s Republic of China, by N ewsRx correspondents, research stated, “Redundant robots are gainingpopularity for their agility in service tasks, but they struggle with managing multiple tas ks in dynamic andunstructured environments. Research is currently centered arou nd adjusting task priorities to facilitate therobot’s adaptability to different situational demands.”Our news journalists obtained a quote from the research from Zhejiang University , “This paper addressesthe challenge of automated task prioritization in multi- task handling and presents a solution forrobots to effectively execute demandin g tasks, even when faced with limited redundancy and multipleconstraints. We in troduce the concept of secondary merged tasks and formulate task merging as a ma trixdesign problem. An iterative updating algorithm based on real-time task sta tus is proposed to enable automaticprioritization and dynamic adjustment of tas ks. This methodology ensures appropriate executionof all tasks at the right tim e. We analyze the convergence of weight transfer between redundancies andtask d ependencies, ensuring stable task execution. Simulation experiments and real-wor ld experimentsusing 9-DOF mobile manipulator and 6-DOF fixed manipulator are co nducted to validate the proposedmethod.”
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