查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Musculoskeletal Diseas es and Conditions - Fibromyalgia is the subject of a report.According to news r eporting originating from Helsinki,Finland,by NewsRx correspondents,research stated,"Fibromyalgia patients vary in clinical phenotype and treatment can be c hallenging.The pathophysiology of fibromyalgia is incompletely understood but a ppears to involve metabolic changes at rest or in response to stress." Our news editors obtained a quote from the research from the University of Helsi nki,"We enrolled 54 fibromyalgia patients and 31 healthy controls to this prosp ective study.Symptoms were assessed using the Fibromyalgia Impact Questionnaire (FIQ) and blood samples were collected for metabolomics analysis at baseline and after an oral glucose tolerance test and a cardiopulmonary exercise test.We i dentified key symptoms of fibromyalgia and related them to changes in metabolic pathways with supervised and unsupervised machine learning methods.Algorithms t rained with the FIQ information assigned the fibromyalgia diagnosis in new data with balanced accuracy of 88% while fatigue alone already provided the diagnosis with 86% accuracy.Supervised analyses reduced the metabolomic information from 77 to 13 key markers.With these metabolites,fibro myalgia could be identified in new cases with 79% accuracy.In add ition,5-hydroxyindole-3-acetic acid and glutamine levels correlated with the se verity of fatigue.Patients differed from controls at baseline in tyrosine and p urine pathways,and in the pyrimidine pathway after the stress challenges.Sever al key markers are involved in glutaminergic neurotransmission.This data-driven analysis highlights fatigue as a key symptom of fibromyalgia.Fibromyalgia is a ssociated with metabolic changes which also reflect the degree of fatigue."
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 ar tificial intelligence.According to news originating from Mutah University by Ne wsRx editors,the research stated,"Commercial vehicles have a significant econo mic effect by moving goods and services across national borders and around the g lobe.They are also responsible for most commercial transactions in several indu stries,including manufacturing,retail,agriculture,and building." The news correspondents obtained a quote from the research from Mutah University :"Its characteristics,such as heavyweight and big vehicle size,also affect ho w traffic moves and streams behave.As a result,it adds to growing accident rat es,congestion,pollution,and sidewalk deterioration.This paper uses the comme rcial vehicle survey (CVS) from Michigan state based on different establishments to investigate the pattern movements of commercial vehicles between 1999 and 20 17.This study aims to develop predictive commercial vehicle classes through mac hine learning techniques.This study uses three machine learning methods to pred ict the Commercial Motor Vehicle (CMV) class (Naive Bayes,Linear SVM,and decis ion tree).A feature selection study selects the significant attributes for CMV class prediction."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in Machine Learning - Intelligent Systems.According to news reporting originating in Nanchang,People's Republic of China,by NewsRx journalists,research stated,"Convolutional neural networks (CNNs) have been the dominant architectures for feature extraction tasks,but CNNs do not look for and focus on some specific im age features.Correlation operations play an important role in visual tracking." Financial supporters for this research include National Natural Science Foundati on of China (NSFC),National Natural Science Foundation of China (NSFC).The news reporters obtained a quote from the research from the Nanchang Institut e of Technology,"However,the correlation operation reserves a large amount of unfavorable background information.In this paper,we propose an effective featu re recognizer including channel and spatial attention modules to focus on import ant object feature information.Thus,the representation power of the feature ex traction network is improved.Further,we design a multi-scale feature fusion ne twork.The fusion network performs feature fusion on template feature and encode d feature branches to establish connections between features at different scales .Experiments on six benchmarks demonstrate that the proposed tracker outperform s the state-of-the-art trackers."
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 reporting originating in Henan,People's Re public of China,by NewsRx editors,the research stated,"The tourism equipment manufacturing industry is currently facing some challenges in production schedul ing and service models.Traditional manufacturing scheduling and service models are difficult to meet market demand,resulting in low production efficiency and low customer satisfaction." The news reporters obtained a quote from the research from Tourism College,"The refore,this article proposes a new production scheduling and service model by a pplying artificial intelligence technology to improve the efficiency and custome r satisfaction of the tourism equipment manufacturing industry.This article ado pts artificial intelligence technology to explore the innovative mechanisms of c loud manufacturing intelligent production scheduling and enterprise service mode ls in the tourism equipment manufacturing industry.In terms of cloud manufactur ing mode,through collaborative allocation of production scheduling and energy c ost scheduling models,the reasonable arrangement of production tasks and optima l utilization of resources in the tourism equipment manufacturing industry have been achieved.In terms of optimizing comprehensive scheduling problems,optimiz ation algorithms and intelligent scheduling systems are used to improve producti on efficiency and customer satisfaction.In terms of the innovation mechanism of enterprise service models,an overview of the service-oriented concept of manuf acturing enterprises was provided,and a service model selection model was propo sed.Through empirical analysis,the advantages and disadvantages of different s ervice models were evaluated to better understand the production scheduling and service model problems in the tourism equipment manufacturing industry,and corr esponding solutions and innovation mechanisms were proposed.The results verifie d the effectiveness of the production scheduling and service model based on arti ficial intelligence in the tourism equipment manufacturing industry."
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 originating from Rome,Italy,by NewsRx correspondents,research stated,"The aim of the study was to build a machine learning-based predictive model to discriminate between hospitalized p atients at low risk and high risk of bloodstream infection (BSI).A Data Mart in cluding all patients hospitalized between January 2016 and December 2019 with su spected BSI was built." Our news reporters obtained a quote from the research from Fondazione Policlinic o Universitario A.Gemelli IRCCS:"Multivariate logistic regression was applied to develop a clinically interpretable machine learning predictive model.The mod el was trained on 2016-2018 data and tested on 2019 data.A feature selection ba sed on a univariate logistic regression first selected candidate predictors of B SI.A multivariate logistic regression with stepwise feature selection in five-f old cross-validation was applied to express the risk of BSI.A total of 5660 hos pitalizations (4026 and 1634 in the training and the validation subsets,respect ively) were included.Eleven predictors of BSI were identified.The performance of the model in terms of AUROC was 0.74.Based on the interquartile predicted ri sk score,508 (31.1%) patients were defined as being at low risk,7 76 (47.5%) at medium risk,and 350 (21.4%) at high ris k of BSI."
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 ro botics.According to news reporting originating from Beijing,People's Republic of China,by NewsRx correspondents,research stated,"To ensure precise and real -time perception of high-speed roadway conditions and minimize the potential thr eats to traffic safety posed by road debris and defects,this study designed a r eal-time monitoring and early warning system for high-speed road surface anomali es." Funders for this research include National Natural Science Foundation of China.Our news correspondents obtained a quote from the research from Beijing Informat ion Science and Technology University:"Initially,an autonomous mobile intellig ent road inspection robot,mountable on highway guardrails,along with a corresp onding cloud-based warning platform,was developed.Subsequently,an enhanced ta rget detection algorithm,YOLOv5s-L-OTA,was proposed.Incorporating GSConv for lightweight improvements to standard convolutions and employing the optimal tran sport assignment for object detection (OTA) strategy,the algorithm's robustness in multi-object label assignment was enhanced,significantly improving both mod el accuracy and processing speed."
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 Quebec City,Canada,by NewsRx correspondents,research stated,"In this letter,we review the ques tion of which action space is best suited for controlling a real biped robot in combination with Sim2Real training.Position control has been popular as it has been shown to be more sample efficient and intuitive to combine with other plann ing algorithms." Financial support for this research came from Ministry of Science & ICT (MSIT),Republic of Korea.Our news editors obtained a quote from the research from the University of Montr eal,"However,for position control,gain tuning is required to achieve the best possible policy performance.We show that,instead,using a torque-based action space enables task-and-robot agnostic learning with less parameter tuning and m itigates the sim-to-reality gap by taking advantage of torque control's inherent compliance.Also,we accelerate the torque-based-policy training process by pre -training the policy to remain upright by compensating for gravity."
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 reporting originating from the University of Southern California by NewsRx correspondents,research stated,"T he concept of the Butterfly Effect,derived from chaos theory,highlights how se emingly minor changes can lead to significant,unpredictable outcomes in complex systems." Our news correspondents obtained a quote from the research from University of So uthern California:"This phenomenon is particularly pertinent in the realm of AI fairness and bias.Factors such as subtle biases in initial data,deviations du ring algorithm training,or shifts in data distribution from training to testing can inadvertently lead to pronounced unfair results.These results often dispro portionately impact marginalized groups,reinforcing existing societal inequitie s.Furthermore,the Butterfly Effect can magnify biases in data or algorithms,i ntensify feedback loops,and heighten susceptibility to adversarial attacks.Rec ognizing the complex interplay within AI systems and their societal ramification s,it is imperative to rigorously scrutinize any modifications in algorithms or data inputs for possible unintended effects.This paper proposes a combination o f algorithmic and empirical methods to identify,measure,and counteract the But terfly Effect in AI systems."
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 - Liver Cance r is the subject of a report.According to news originating from Shandong,Peopl e's Republic of China,by NewsRx correspondents,research stated,"Disulfidptosi s a new cell death mode,which can cause the death of Hepatocellular Carcinoma ( HCC) cells.However,the significance of disulfidptosis-related Long non-coding RNAs (DRLs) in the prognosis and immunotherapy of HCC remains unclear." Funders for this research include National Natural Science Foundation of China,East China Normal University Interdisciplinary Advancement Project.Our news journalists obtained a quote from the research from Shandong Vocational Animal Science and Veterinary College,"Based on The Cancer Genome Atlas (TCGA) database,we used Least Absolute Shrinkage and Selection Operator (LASSO) and C ox regression model to construct DRL Prognostic Signature (DRLPS)-based risk sco res and performed Gene Expression Omnibus outside validation.Survival analysis was performed and a nomogram was constructed.Moreover,we performed functional enrichment annotation,immune infiltration and drug sensitivity analyses.Five D RLs (AL590705.3,AC072054.1,AC069307.1,AC107959.3 and ZNF232-AS1) were identif ied to construct prognostic signature.DRLPSbased risk scores exhibited better predictive efficacy of survival than conventional clinical features.The nomogra m showed high congruence between the predicted survival and observed survival.G ene set were mainly enriched in cell proliferation,differentiation and growth f unction related pathways.Immune cell infiltration in the low-risk group was sig nificantly higher than that in the high-risk group.Additionally,the high-risk group exhibited higher sensitivity to Afatinib,Fulvestrant,Gefitinib,Osimerti nib,Sapitinib,and Taselisib."
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 from Victoria,Canada,by NewsRx correspondents,research stated,"Nowadays,vehicles can provi de many valuable data (such as the videos recorded by dashcams) for analytical m odel building.Integrating vehicular ad hoc networks with the Internet of Things (IoT),the Internet of Vehicles (IoV) has a promising future." Financial support for this research came from Natural Sciences and Engineering R esearch Council of Canada (NSERC).Our news editors obtained a quote from the research from the University of Victo ria,"In IoV,vehicles maintain their own communication,computing,and learning capabilities.Thus,instead of sending the data to a central server for model t raining,which leads to a high communication overhead,vehicles can train the da ta locally.However,it is still a challenge to preserve the privacy while keepi ng both the communication and computation overheads of vehicles acceptable.In t his article,we present a distributed machine learning framework with a two-laye red architecture.The architecture uniquely involves vehicle clusters,roadside units,and a central server,which provides a basic guarantee to the vehicle pri vacy and also limits the overhead.By carefully adopting cryptographic tools and techniques,the framework has the following properties:1) it preserves the pri vacy of the local inputs and model weight vectors to all parties; 2) it protects the identities and trajectories of vehicles; 3) packet loss is handled in the a pplication layer; 4) the evaluation shows that it is lightweight for vehicles."
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