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    University Hospital Lausanne (CHUV) Reports Findings in Biomarkers (Advancing Rh eumatology Care Through Machine Learning)

    11-11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 Diagnostics and Screen ing - Biomarkers is the subject of a report.According to news reporting from La usanne,Switzerland,by NewsRx editors,the research stated,"Rheumatologic dise ases are marked by their complexity,involving immune-,metabolic- and mechanica lly mediated processes which can affect different organ systems.Despite a growi ng arsenal of targeted medications,many rheumatology patients fail to achieve f ull remission." The news correspondents obtained a quote from the research from University Hospi tal Lausanne (CHUV),"Assessing disease activity remains challenging,as patient s prioritize different symptoms and disease phenotypes vary.This is also reflec ted in clinical trials where the efficacy of drugs is not necessarily measured i n an optimal way with the traditional outcome assessment.The recent COVID-19 pa ndemic has catalyzed a digital transformation in healthcare,embracing telemonit oring and patient-reported data via apps and wearables.As a further driver of d igital medicine,electronic medical record (EMR) providers are actively engaged in developing algorithms for clinical decision support,heralding a shift toward s patient-centered,decentralized care.Machine learning algorithms have emerged as valuable tools for handling the increasing volume of patient data,promising to enhance treatment quality and patient wellbeing.Convolutional neural netwo rks (CNN) are particularly promising for radiological image analysis,aiding in the detection of specific lesions such as erosions,sacroiliitis,or osteoarthri tis,with several FDAapproved applications.Clinical predictions,including num erical disease activity forecasts and medication choices,offer the potential to optimize treatment strategies.Numeric predictions can be integrated into clini cal workflows,allowing for shared decision making with patients.Clustering pat ients based on disease characteristics provides a personalized care approach.Di gital biomarkers,such as patient-reported outcomes and wearables data,offer in sights into disease progression and therapy response more flexibly and outside p atient consultations.In association with patient-reported outcomes,disease-spe cific digital biomarkers via image recognition or single-camera motion capture e nables more efficient remote patient monitoring.Digital biomarkers may also pla y a major role in clinical trials in the future as continuous,disease-specific outcome measurement facilitating decentralized studies.Prediction models can he lp with patient selection in clinical trials,such as by predicting high disease activity.Efforts are underway to integrate these advancements into clinical wo rkflows using digital pathways and remote patient monitoring platforms.In summa ry,machine learning,digital biomarkers,and advanced imaging technologies hold immense promise for enhancing clinical decision support and clinical trials in rheumatology."

    Research Conducted at Leibniz Institute for Solid State and Materials Research (IFW Dresden) Has Updated Our Knowledge about Machine Learning (Machine Learning Facilitated By Microscopic Features for Discovery of Novel Magnetic Double ...)

    12-12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 Machine Learning.According to news originating from Dresden,Germany,by NewsRx corres pondents,research stated,"Double perovskites are a growing class of compounds with prospects for realization of novel magnetic behaviors.The rich chemistry o f double perovskites calls for high-throughput computational screening that can be followed by or combined with machine-learning techniques." Financial supporters for this research include Collaborative Research Center,IF W Excellence Program,German Research Foundation (DFG).Our news journalists obtained a quote from the research from Leibniz Institute f or Solid State and Materials Research (IFW Dresden),"Yet,most approaches negle ct the bulk of microscopic information implicitly provided by first-principles c alculations,severely reducing the predictive power.In this work,we remedy thi s drawback by including onsite energies and transfer integrals between the d sta tes of magnetic atoms.These quantities were computed by Wannierization of the r elevant energy bands.By combining them with the experimental information on the magnetism of studied materials and applying machine learning,we constructed a model capable of predicting the magnetic properties of the remaining materials w hose magnetism has not been addressed experimentally.Our approach combines clas sification learning to distinguish between double perovskites with dominant ferr omagnetic or antiferromagnetic interactions and regression employed to estimate magnetic transition temperatures.In this way,we identified one antiferromagnet and three ferromagnets with a high transition temperature.Another 28 antiferro magnetic candidates were identified as magnetically frustrated compounds.Among them,cubic Ba2LaReO6 shows the highest frustration parameter,which is further validated by a direct first-principles calculation.Our methodology holds promis e for eliminating the need for resource-demanding calculations."

    University of Verona Reports Findings in Nephrectomy (Postoperative outcomes of transperitoneal versus retroperitoneal robotic partial nephrectomy:a propensity -score matched comparison focused on patient mobilization,return to bowel funct ion,...)

    13-14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 Surgery - Nephrectomy is the subject of a report.According to news reporting from Verona,Italy,by N ewsRx journalists,research stated,"Literature meta-analyses comparing transper itoneal versus retroperitoneal approach to robotic partial nephrectomy (RPN) sug gested some advantages favoring retroperitoneoscopy.Unfortunately,patient-cent ered data about mobilization,canalization,pain,and use of painkillers remaine d anecdotally reported." Financial support for this research came from Universita degli Studi di Verona.The news correspondents obtained a quote from the research from the University o f Verona,"The present analysis aimed to compare transperitoneal versus retroper itoneal RPN focusing on such outcomes.Study data including baseline variables,perioperative,and postoperative outcomes of interest were retrieved from prospe ctively maintained institutional database (Jan 2018-May 2023) and compared betwe en treatment groups (transperitoneal versus retroperitoneal).Propensity score m atching was performed using the STATA command psmatch2 considering age,sex,bod y mass index,previous abdominal surgery,RENAL score,tumor size and location,and cT stage.The logit of propensity score was used for matching,with a 1:1 ne arest neighbor algorithm,without replacement (caliper of 0.001).A total of 442 patients were included in the unmatched analysis:330 underwent transperitoneal RPN 112 retroperitoneal RPN.After propensity score,98 patients who underwent retroperitoneal RPN were matched with 98 patients who underwent transperitoneal RPN.Matched cohorts had comparable patients' demographics and tumor features.W e found similarity between the two laparoscopic accesses in all outcomes but in blood loss,which favored retroperitoneoscopic RPN (median 150 (IQR 100-300) ver sus 100 (IQR 0-100) ml,p = 0.03).No differences were found in terms of time to mobilization with ambulation,return to complete bowel function,postoperative pain,but higher painkillers consumption was reported after transperitoneal RPN (p <0.004).The present study compared the transperitoneal versus the retroperitoneal approach to RPN,confirming the similarity between t he two approaches in all perioperative outcomes."

    New Artificial Intelligence Findings from Nanchang Institute of Technology Outli ned (Quantum Communication Based Cyber Security Analysis Using Artificial Intell igence With Iomt)

    14-15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 reporting originating from Nan chang,People's Republic of China,by NewsRx correspondents,research stated,"C onnected electronic devices used in healthcare,such as small sensors and actuat ors and other cyber-physical devices,make up what is known as the Internet of M edical Things.By linking these devices,medical monitoring,analysis,and repor ting may become more intelligent and autonomous,ultimately benefiting the patie nt." Financial support for this research came from Hainan Vocational University of Sc ience and Technology Research Support Project.Our news editors obtained a quote from the research from the Nanchang Institute of Technology,"To solve these problems,a thorough plan for monitoring and asse ssing method health needs to be formulated.The application of traditional machi ne learning to massive datasets in a shared computing environment may lead to mo del training and computation that is either too slow to provide accurate results or too fast to produce erroneous results as a consequence of rushed training,b oth of which are detrimental.Key 6G enablers may be seen of as the upcoming par adigms of machine learning (ML),quantum computing,and quantum ML (QML),as wel l as their synergies with communication networks.This research presents a new m achine learning approach to medical cyber physical system (CPS) predictive maint enance using swarm robots.In this case,we use fog computing-integrated soft se nsors to conduct a thorough closed-loop dynamic analysis of the CPS predictive m aintenance.Extreme learning utilising a weighted sliding window.The investigat ion of medical security is then conducted using a kernel decision making system based on probabilistic swarm robotics.In terms of training accuracy,energy use,convergence ratio,precision,and F_measure,an experimental stud y is conducted.The system's ability to (i) avoid unscheduled downtime and (ii) consequently increase operational availability sets it apart from other systems."

    Study Data from Prince Sattam Bin Abdulaziz University Update Knowledge of Artif icial Intelligence (The impact of artificial intelligence and Industry 4.0 on tr ansforming accounting and auditing practices)

    15-16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 from Al Kharj,Saudi Arabia,by NewsRx journalists,research stated,"The main aim is to investigate the impact of artificial intelligence (AI),Industry 4.0 readiness,and Technolo gy Acceptance Model (TAM) variables on various aspects of accounting and auditin g operations.To evaluate the associations between the variables,the research d esign employs a mediation and path approach using SMART PLS." Funders for this research include Prince Sattam bin Abdulaziz University Deanshi p of Scientific Research Deanship of Scientific Research,King Saud University P rince Sattam bin Abdulaziz University.Our news journalists obtained a quote from the research from Prince Sattam Bin A bdulaziz University:"The study employs a convenience sampling method,which is augmented with snowball sampling.The sample size was determined using various t echniques,yielding a final sample of 228 respondents.The findings indicate tha t leveraging AI,big data analytics,cloud computing,and deep learning advancem ents can improve accounting and auditing practices.AI technologies assist busin esses in increasing their efficiency,accuracy,and decision-making capabilities,resulting in improved financial reporting and auditing processes.The study co ntributes to the theoretical explanation of the influence of AI adoption in acco unting and auditing practices in the context of an emerging country,Saudi Arabi a.The findings of the study have practical implications for accounting and audi ting practitioners,policymakers,and scholars.The findings of this study can a ssist businesses in efficiently leveraging AI developments to improve their acco unting and auditing operations."

    New Data from Universitas Pasundan Illuminate Research in Machine Learning (Wate r Demand Modeling using Machine Learning Method in Bandung City,Indonesia)

    16-16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 reporting from the Universitas Pasundan by NewsRx journalists,research stated,"This research was conducted at Bandung Cit y with the aim of building a model using machine learning methods so that it can estimated clean water demands in Bandung City,as well as knowing the external factors that are considered to affect the model." The news editors obtained a quote from the research from Universitas Pasundan:" Machine learning is a part of Artificial Intelligence (AI) discipline.The model ing is carried out using independent variables in the form of climate parameters which are rainfall,rainy days,and humidity,as well as the dependent variable in the form of drinking water needs which are represented by raw water.Data co llection is done through secondary data.The model was built by using the TPOT m odule,and produces the AdaBoost.R2 algorithm as the most optimal model,by usi ng the model algorithm,the best sub-model is produced with the most influential external factors,namely rainy days and humidity which has an MAE of 326,077.70 and a MAPE of 4.75%."

    Studies from Xiamen University Provide New Data on Machine Learning (Sound Speed Inversion Based on Multi-Source Ocean Remote Sensing Observations and Machine L earning)

    17-17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 artific ial intelligence.According to news originating from Xiamen,People's Republic o f China,by NewsRx correspondents,research stated,"Ocean sound speed is import ant for underwater acoustic applications,such as communications,navigation and localization,where the assumption of uniformly distributed sound speed profile s (SSPs) is generally used and greatly degrades the performance of underwater ac oustic systems." Funders for this research include Department of Natural Resources of Guangdong P rovince; Recruiting Talents of Nanjing University of Posts And Telecommunication s; National Natural Science Foundation of China; Natural Resources Science And T echnology Innovation Project of Fujian Province.Our news journalists obtained a quote from the research from Xiamen University:"The acquisition of SSPs is necessary for the corrections of the sound ray propa gation paths.However,the inversion of SSPs is challenging due to the intricate relations of interrelated physical ocean elements and suffers from the high cos ts of calculations and hardware deployments.This paper proposes a novel sound s peed inversion method based on multi-source ocean remote sensing observations and machine learning,which adapts to large-scale sea regions.Firstly,the datase ts of SSPs are generated utilizing the Argo thermohaline profiles and the empiri cal formulas of the sound speed.Then,the SSPs are analyzed utilizing the empir ical orthogonal functions (EOFs) to reduce the dimensions of the feature space a s well as the computational load.Considering the nonlinear regression relations of SSPs and the observed datasets,a general framework for sound speed inversio n is formulated,which combines the designed machine learning models with the re duced-dimensional feature representations,multi-source ocean remote sensing obs ervations and water temperature data."

    Researchers at University of Sannio Report New Data on Machine Learning (A Novel Classification Technique Based On Formal Methods)

    18-18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 Machine Learn ing have been published.According to news originating from Benevento,Italy,by NewsRx correspondents,research stated,"In last years,we are witnessing a gro wing interest in the application of supervised machine learning techniques in th e most disparate fields.One winning factor of machine learning is represented b y its ability to easily create models,as it does not require prior knowledge ab out the application domain." Our news journalists obtained a quote from the research from the University of S annio,"Complementary to machine learning are formal methods,that intrinsically offer safeness check and mechanism for reasoning on failures.Considering the w eaknesses of machine learning,a new challenge could be represented by the use o f formal methods.However,formal methods require the expertise of the domain,k nowledge about modeling language with its semantic and mathematical rigour to sp ecify properties.In this article,we propose a novel learning technique based o n the adoption of formal methods for classification thanks to the automatic gene ration both of the formula and of the model.In this way the proposed method doe s not require any human intervention and thus it can be applied also to complex/ large datasets.This leads to less effort both in using formal methods and in a better explainability and reasoning about the obtained results."

    Guangzhou University Researcher Adds New Study Findings to Research in Machine L earning (Towards a Reliable Design of Geopolymer Concrete for Green Landscapes:A Comparative Study of Tree-Based and Regression-Based Models)

    19-19页
    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 artificial intelligence.According to news originating from Guangzhou,People's Republic of China,by NewsRx editors,the research stated,"The design of geopol ymer concrete must meet more stringent requirements for the landscape,so unders tanding and designing geopolymer concrete with a higher compressive strength cha llenging.In the performance prediction of geopolymer concrete compressive stren gth,machine learning models have the advantage of being more accurate and faste r." Financial supporters for this research include Guangdong Provincial Department o f Education Innovative Strong School Youth Innovative Talent Project; China Post doctoral Science Foundation.Our news reporters obtained a quote from the research from Guangzhou University:"However,only a single machine learning model is usually used at present,ther e are few applications of ensemble learning models,and model optimization proce sses is lacking.Therefore,this paper proposes to use the Firefly Algorithm (AF ) as an optimization tool to perform hyperparameter tuning on Logistic Regressio n (LR),Multiple Logistic Regression (MLR),decision tree (DT),and Random Fores t (RF) models.At the same time,the reliability and efficiency of four integrat ed learning models were analyzed.The model was used to analyze the influencing factors of geopolymer concrete and determine the strength of their influencing a bility.According to the experimental data,the RF-AF model had the lowest RMSE value.The RMSE value of the training set and test set were 4.0364 and 8.7202,r espectively.The R value of the training set and test set were 0.9774 and 0.8915,respectively."

    Research Reports from Technische Hochschule Nurnberg Georg Simon Ohm Provide New Insights into Robotics (User Study to Validate the Performance of an Offline Ro bot Programming Method That Enables Robot-Independent Kinesthetic Instruction ...)

    20-20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 from Nuremberg,Germany,by NewsR x journalists,research stated,"The paper presents a novel offline programming (OLP) method based on programming by demonstration (PbD),which has been validat ed through user study." Financial supporters for this research include German Federal Ministry of Educat ion And Research.The news correspondents obtained a quote from the research from Technische Hochs chule Nurnberg Georg Simon Ohm:"PbD is a programming method that involves physi cal interaction with robots,and kinesthetic teaching (KT) is a commonly used on line programming method in industry.However,online programming methods consume significant robot resources,limiting the speed advantages of PbD and emphasizi ng the need for an offline approach.The method presented here,based on KT,use s a virtual representation instead of a physical robot,allowing independent pro gramming regardless of the working environment.It employs haptic input devices to teach a simulated robot in augmented reality and uses automatic path planning .A benchmarking test was conducted to standardize equipment,procedures,and ev aluation techniques to compare different PbD approaches.The results indicate a 47% decrease in programming time when compared to traditional KT m ethods in established industrial systems."