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    New Findings from Johns Hopkins University in the Area of Robotics Reported (Aut onomous Spinal Robotic System for Transforaminal Lumbar Epidural Injections:a P roof of Concept of Study)

    58-58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 report.According to news reporting originating in Baltimore,Maryland,b y NewsRx journalists,research stated,"Phantom study Objective:The aim of our study is to demonstrate in a proof-of-concept model whether the use of a marker less autonomous robotic controlled injection delivery system will increase accur acy in the lumbar spine.Ideal transforaminal epidural injection trajectories (b ilateral L2/3,L3/4,L4/5,L5/SI and SI) were planned out on a virtual pre-opera tive planning software by 1 experienced provider." Funders for this research include National Institutes of Health (NIH) - USA,Nat ional Science Foundation (NSF).The news reporters obtained a quote from the research from Johns Hopkins Univers ity,"Twenty transforaminal epidural injections were administered in a lumbar sp ine phantom model,10 using a freehand procedure,and 10 using a marker less aut onomous spinal robotic system.Procedural accuracy,defined as the difference be tween pre-operative planning and actual post-operative needle tip distance (mm) and angular orientation (degrees),were assessed between the freehand and roboti c procedures.Procedural accuracy for robotically placed transforaminal epidural injections was significantly higher with the difference in pre- and post-operat ive needle tip distance being 20.1 (+/- 5.0) mm in the freehand procedure and 1 1.4 (+/- 3.9) mm in the robotically placed procedure (P <.001).Needle tip precision for the freehand technique was 15.6 mm (26.3 - 10.7) compared to 10.1 mm (16.3 - 6.1) for the robotic technique.Differences in needl e angular orientation deviation were 5.6 (+/- 3.3) degrees in the robotically pl aced procedure and 12.0 (+/- 4.8) degrees in the freehand procedure (P = .003)."
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    New Findings in Machine Learning Described from Faculty of Engineering (Heteroge neous ensemble machine learning to predict the asiaticoside concentration in cen tella asiatica urban)

    59-59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 new report.According to news reporting from Maha Sarakham,Thailand,by NewsRx journalists,research stated,"This study proposes a novel heterogeneous ensemble machine learning methodology to predict the concentratio n of asiaticoside in Centella asiatica (CA-CA) in the context of the lack of an effective prediction method capable of accurately estimating its quantity based on various growing environmental factors." The news correspondents obtained a quote from the research from Faculty of Engin eering:"The accurate prediction of the asi-aticoside concentration in CA-CA hol ds great significance in optimizing cultivation practices and improving the effi cacy of the derived medicinal products.The presented approach aims to address t his crucial need by employing a diverse ensemble of machine learning techniques.The proposed model integrates several machine learning tech-niques,including t he standard long short-term memory (LSTM),gated recurrent unit (GRU),convoluti onal long short-term memory (ConvLSTM),and attention-based LSTM,by utilizing a differential evolution algorithm to optimize the ensemble model's weights.The developed model is called the heterogeneous ensemble machine learning model (He- ML).Experimental results demonstrate that the He-ML achieves an im-pressive roo t-mean-square error (RMSE) value of 4.76,which is up to 12.48 % l ower than the RMSE.The findings highlight the advantages of employing an ensemb le model over a single model,as the ensemble model achieves an RMSE value that is 14.67 % lower than that of the individual machine learning mode l."
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    Data on Tissue Engineering Reported by Anca Voichita Popoiu and Colleagues [Artificial Intelligence (AI) Based Analysis of In Vivo Polymers and Collagen Sca ffolds Inducing Vascularization]

    60-60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 Biomedical Engineering - Tissue Engineering is the subject of a report.According to news reporting ou t of Timisoara,Romania,by NewsRx editors,research stated,"Biomaterials are e ssential in modern medicine,both for patients and research.Their ability to ac quire and maintain functional vascularization is currently debated." Our news journalists obtained a quote from the research,"The aim of this study was to evaluate the vascularization induced by two collagen-based scaffolds (wit h 2D and 3D structures) and one non-collagen scaffold implanted on the chick emb ryo chorioallantoic membrane (CAM).Classical stereomicroscopic image vascular a ssessment was enhanced with the IKOSA software by using two applications:the CA M assay and the Network Formation Assay,evaluating the vessel branching potenti al,vascular area,as well as tube length and thickness.Both collagen-based sca ffolds induced non-inflammatory angiogenesis,but the non-collagen scaffold indu ced a massive inflammation followed by inflammatory-related angiogenesis.Vessel s branching points/Region of Interest (Px.2) and Vessel branching points/Vessel total area (Px.2),increased exponentially until day 5 of the experiment certify ing a sustained and continuous angiogenic process induced by 3D collagen scaffol ds.Collagen-based scaffolds may be more suitable for neovascularization compare d to non-collagen scaffolds.The present study demonstrates the potential of the CAM model in combination with AI-based software for the evaluation of vasculari zation in biomaterials."
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    Studies in the Area of Machine Learning Reported from Justus-Liebig-University ( Beyond Language Barriers:Allowing Multiple Languages In Postsecondary Chemistry Classes Through Multilingual Machine Learning)

    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 Machine Learning are pre sented in a new report.According to news reporting originating from Giessen,Ge rmany,by NewsRx correspondents,research stated,"Students who learn the langua ge of instruction as an additional language represent a heterogeneous group with varying linguistic and cultural backgrounds,contributing to classroom diversit y.Because of the manifold challenges these students encounter while learning th e language of instruction,additional barriers arise for them when engaging in c hemistry classes." Financial supporters for this research include Projekt DEAL,Verband der Chemisc hen Industrie (German Chemical Industry Association).Our news editors obtained a quote from the research from Justus-Liebig-Universit y,"Adapting teaching practices to the language skills of these students,for in stance,in formative assessments,is essential to promote equity and inclusivity in chemistry learning.For this reason,novel educational practices are needed to meet each student's unique set of language capabilities,irrespective of cour se size.In this study,we propose and validate several approaches to allow unde rgraduate chemistry students who are not yet fluent in the language of instructi on to complete a formative assessment in their preferred language.A technically easy-to-implement option for instructors is to use translation tools to transla te students' reasoning in any language into the instructor's language.Besides,instructors could also establish multilingual machine learning models capable of automatically analyzing students' reasoning regardless of the applied language.Herein,we evaluated both opportunities by comparing the reliability of three t ranslation tools and determining the degree to which multilingual machine learni ng models can simultaneously assess written arguments in different languages."
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    Findings from Luoyang Normal University Broaden Understanding of Machine Learnin g (Machine Learning-assisted Property Prediction of Solid-state Electrolyte)

    62-63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 Machine Learning is th e subject of a report.According to news reporting originating in Luoyang,Peopl e's Republic of China,by NewsRx journalists,research stated,"Machine learning (ML) exhibits substantial potential for predicting the properties of solid-stat e electrolytes (SSEs).By integrating experimental or/and simulation data within ML frameworks,the discovery and development of advanced SSEs can be accelerate d,ultimately facilitating their application in high-end energy storage systems." Financial supporters for this research include National Natural Science Foundati on of China (NSFC),Program for Science & Technology Innovation Ta lents in Universities of Henan Province,Programs for Science and Technology Dev elopment of Henan Province,China,Natural Science Foundations of Henan Province,Key Scientific Research Projects of University in Henan Province,Shenzhen Tec hnical Plan Project.
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    China Academy of Chinese Medical Sciences Reports Findings in Ovarian Cancer (An exosome-derived lncRNA signature identified by machine learning associated with prognosis and biomarkers for immunotherapy in ovarian cancer)

    63-64页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New research on Oncology - Ovarian Cancer is the subject of a report.According to news reporting originating from Beijing,Peopl e's Republic of China,by NewsRx correspondents,research stated,"Ovarian cance r (OC) has the highest mortality rate among gynecological malignancies.Current treatment options are limited and ineffective,prompting the discovery of reliab le biomarkers." Our news editors obtained a quote from the research from the China Academy of Ch inese Medical Sciences,"Exosome lncRNAs,carrying genetic information,are prom ising new markers.Previous studies only focused on exosome-related genes and em ployed the Lasso algorithm to construct prediction models,which are not robust.420 OC patients from the TCGA datasets were divided into training and validatio n datasets.The GSE102037 dataset was used for external validation.LncRNAs asso ciated with exosomerelated genes were selected using Pearson analysis.Univaria te COX regression analysis was used to filter prognosis-related lncRNAs.The ove rlapping lncRNAs were identified as candidate lncRNAs for machine learning.Base d on 10 machine learning algorithms and 117 algorithm combinations,the optimal predictor combinations were selected according to the C index.The exosome-relat ed LncRNA Signature (ERLS) model was constructed using multivariate COX regressi on.Based on the median risk score of the training datasets,the patients were d ivided into high- and low-risk groups.Kaplan-Meier survival analysis,the time- dependent ROC,immune cell infiltration,immunotherapy response,and immune chec kpoints were analyzed.64 lncRNAs were subjected to a machine-learning process.Based on the stepCox (forward) combined Ridge algorithm,20 lncRNA were selected to construct the ERLS model.Kaplan-Meier survival analysis showed that the hig h-risk group had a lower survival rate.The area under the curve (AUC) in predic ting OS at 1,3,and 5 years were 0.758,0.816,and 0.827 in the entire TCGA coh ort.xCell and ssGSEA analysis showed that the low-risk group had higher immune cell infiltration,which may contribute to the activation of cytolytic activity,inflammation promotion,and T-cell co-stimulation pathways.The low-risk group had higher expression levels of PDL1,CTLA4,and higher TMB.The ERLS model can predict response to anti-PD1 and anti-CTLA4 therapy.Patients with low expressio n of PDL1 or high expression of CTLA4 and low ERLS exhibited significantly bette r survival prospects,whereas patients with high ERLS and low levels of PDL1 or CTLA4 exhibited the poorest outcomes."
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    Findings from University of North Florida Provides New Data about Artificial Int elligence (Uncovering the Dark Side of Artificial Intelligence In Electronic Mar kets:a Systematic Literature Review)

    64-65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 Jacksonville,Florida,by NewsRx correspondents,research stated,"The dark sides of artificial intelligen ce (AI) have attracted immense attention in recent years.This study produces a synthesis of current research on six dark sides of AI in electronic markets thro ugh a systematic literature review." Financial support for this research came from National Social Science Fund of Ch ina.Our news journalists obtained a quote from the research from the University of N orth Florida,"The authors searched five different databases and summarized the dark sides of AI in electronic markets from six aspects:privacy concerns,secur ity issues,ethical challenges,criminals and terrorists enabled by AI,trust is sues between humans and machines,and AI biases.The literature review presented in this study has provided a rigorous and structured overview of research on AI 's dark sides in the electronic markets through a combination of quantitative and qualitative analysis of the AI literature.As AI has made rich contributions t o a variety of applications in electronic markets,special care should be taken regarding the dark side of AI."
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    School of Engineering and Sciences Researchers Describe Recent Advances in Intel ligent Systems (A novel indexing algorithm for latent palmprints leveraging minu tiae and orientation field)

    65-65页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Researchers detail new data in intelligent system s.According to news reporting from Estado de Mexico,Mexico,by NewsRx journali sts,research stated,"Latent palmprints represent crucial forensic evidence in criminal investigations,necessitating their storage in governmental databases." Financial supporters for this research include Universidad Autonoma De Madrid; C onsejo Nacional De Ciencia Y Tecnologia.The news reporters obtained a quote from the research from School of Engineering and Sciences:"The identification of corresponding palmprints within large-scal e databases using an automated palmprint identification system (APIS) is time-co nsuming and computationally intensive.To address this challenge,this paper int roduces an innovative approach:delineating the region of interest (ROI) for pal mprint segmentation and presenting a novel indexing algorithm founded on minutia e and the orientation field (OF).Additionally,a novel feature vector is propos ed,leveraging minutiae triplets and ellipse properties,marking the pioneering algorithm to consider minutiae importance in palmprint indexing.Significantly,an improved version of an existing palmprint indexing algorithm tailored for lat ent palmprints is introduced.The study demonstrates the indexing and retrieval of both our feature vectors and those obtained by the improved palmprint indexin g algorithm,using two clustering algorithms and locality-sensitive hashing (LSH ).The method's robustness is evaluated across three diverse databases with exte nsive palmprint records." According to the news editors,the research concluded:"The experimental results underscore the superior performance of our approach compared to current state-o f-the-art algorithms."
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    New Artificial Intelligence Data Have Been Reported by Researchers at Argonne Na tional Laboratory (Artificial Intelligence for Conjugated Polymers)

    66-66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 Artificial Intelligence are presented in a new report.According to news reporting originating in Lemont,Illinois,by NewsRx journalists,research stated,"Conjugated polymers have ga rnered significant attention due to their diverse applications in electronics,p hotonics,and energy storage.However,realizing their full potential poses a fo rmidable challenge,as their design has historically relied on iterative adjustm ents and continuous inspiration from researchers." 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),Laboratory Directed Research and Development (LDRD),United States Department of Energy (DOE),United States Department of Energy (DOE),University of Chicago.The news reporters obtained a quote from the research from Argonne National Labo ratory,"Traditional methods often struggle to efficiently navigate their vast c hemical landscape.Herein,the application of artificial intelligence (AI),spec ifically machine learning (ML),needs to be discussed in the realm of conjugated polymers.Our paper emphasizes the importance of understanding the structure-pr operty relationships of these polymers and how ML can facilitate property predic tion and inverse-design.We delve into various chemical fingerprints,structural descriptors,and ML algorithms,showcasing their utility across a spectrum of a pplications,including simulations,glass transition temperature determination,photovoltaics,reorganization energy for charge transport,photocatalysts,and s ensors."
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    Hebei University of Water Resources and Electric Engineering Researchers Discuss Research in Intelligent Systems (Big data intelligent tourism management platfo rm design based on abnormal behavior identification)

    67-67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 intelligent systems are presented in a new report.According to news reporting originating from Cangzhou,People's Republic of China,by NewsRx correspondents,research stated,"To ens ure the safe and stable operation of tourist attractions,a big data intelligent tourism management platform based on abnormal behavior identification is propos ed." Funders for this research include Department of Education of Hebei Province; Lan zhou Science And Technology Bureau.The news reporters obtained a quote from the research from Hebei University of W ater Resources and Electric Engineering:"A scenic area abnormal behavior recogn ition system is constructed by combining a climbing and painting behavior recogn ition method based on a regional convolutional 3D network model,as well as a tr ajectory analysis method based on object detection and tracking.Experimental da ta show that compared to the Two Stream model,the accuracy and recall of the 3D network model based on regional convolution are improved by 41.18 % and 34.85 %,respectively.The average accuracy of the proposed tra jectory analysis method is 93.71 %.Compared with the track analysi s method based on Hidden Markov model and the method based on sparse optical flo w tracking,the accuracy of the proposed method is improved by 3.43 % and 1.71 %,respectively.In the real-time multi person abnormal be havior recognition system for tourist attractions,the number of frames per seco nd for each behavior analysis is greater than 40."
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    • NETL
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