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    New Artificial Intelligence Study Findings Have Been Published byResearchers at 'Lucian Blaga' University of Sibiu (Leveraging ArtificialIntelligence For Enha nced Decision-making in Management:Bibliometrics And Meta-analysis)

    68-68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 newsoriginating from “Lucian Blaga ” University of Sibiu by NewsRx correspondents, research stated, “Thispaper bib liometrically examines the integration of artificial intelligence (AI) technolog ies into decisionmaking in management contexts.”The news correspondents obtained a quote from the research from “Lucian Blaga” U niversity of Sibiu:“Using advanced algorithms and machine learning techniques, AI provides transformative capabilities foranalyzing complex data sets, forecas ting trends, and optimizing decision outcomes. Through a comprehensiveliteratur e review and case studies, this paper explores the diverse applications of AI in managementdecision-making, from strategic planning and resource allocation to risk management and operational efficiency.In addition, the bibliometric analys is discusses the implications of AI adoption, including ethicalconsiderations, the dynamics of organizational change, and the role of human judgment along with AI-basedperspectives.”
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    New Artificial Intelligence Findings from Tsinghua University Reported(Intellig ent Design and Optimization System for Shear WallStructures Based On Large Lang uage Models and Generative ArtificialIntelligence)

    69-70页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Research findings on Artificial Intell igence are discussed in a new report. According tonews reporting out of Beijing , People’s Republic of China, by NewsRx editors, research stated, “Intelligent design technology for shear wall structures has great potential for enhancing des ign efficiency and addressingthe challenges of tedious and repetitive design ta sks. Recently, there has been a surge in the developmentof this technology.”Funders for this research include Beijing Institute of Architectural Design Co., Ltd. Innovation DevelopmentFund Project, Beijing Municipal Science & Technology Commission, Administrative Commissionof Zhongguancun Science Park, N ational Natural Science Foundation of China (NSFC), Tencent Foundationthrough t he XPLORER PRIZE, China Postdoctoral Science Foundation, Real Estate SustainableDevelopment Grant from the Hang Lung Center for Real Estate Research at Tsinghu a University.
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    Department of Orthopedics Reports Findings in Bioinformatics(Identification of endocrine-disrupting chemicals targeting key OPassociatedgenes via bioinformat ics and machine learning)

    70-71页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – New research on Biotechnology - Bioinf ormatics is the subject of a report. Accordingto news reporting originating in Hanzhong, People’s Republic of China, by NewsRx journalists, researchstated, “O steoporosis (OP), a metabolic disorder predominantly impacting postmenopausal wo men, hasseen considerable progress in diagnosis and treatment over the past few decades. However, the intricateinterplay between genetic factors and endocrine disruptors (EDCs) in the pathogenesis of OP remainsinadequately elucidated.”The news reporters obtained a quote from the research from the Department of Ort hopedics, “The objective of this research is to examine the environmental pollut ants and their regulatory mechanismsthat could potentially influence the pathog enesis of OP, in order to establish a theoretical foundationfor the targeted pr evention and medical management of individuals with OP. Utilizing CTD and GEO datasets, network toxicology and bioinformatics analyses were conducted to identif y target genes from a poolof 98 co-associated genes. Subsequently, a novel pred iction model was developed employing a multiplemachine learning algorithm. The efficacy of the model was validated based on the area under the receiveroperati ng characteristic curve. Finally, real-time quantitative polymerase chain reacti on (qRT-PCR) wasused to confirm the expression levels of key genes in clinical samples. We have identified significant genes(FOXO3 and LUM) associated with OP and conducted Gene Ontology, Kyoto Encyclopedia of Genes andGenomes enrichment analysis, immune infiltration analysis, and molecular docking analysis. Through theanalysis of these key genes, we have identified 13 EDCs that have the poten tial to impact OP. Severalendocrine disruptors, such as Dexamethasone, Perfluor ononanoic acid, genistein, cadmium, and bisphenolA, have been identified as not able environmental pollutants that impact the OP. Molecular dockinganalysis rev ealed significant binding affinity of major EDCs to the post-translational prote in structuresof key genes. This study demonstrates that EDCs, including dexamet hasone, perfluorononanoic acid,genistein, cadmium, and bisphenol A, can be iden tified as important environmental pollutants affectingOP, and that FOXO3 and LU M have the potential to be diagnostic markers for OP.”
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    Study Results from School of Mechanical and Automotive EngineeringBroaden Under standing of Robotic Systems (Multipoint Variableparameter compliant control of redundant manipulator basedon the equivalent twin model of flexible contact dyn amics)

    71-72页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Investigators discuss new findings in robotic systems. According to news reportingoriginating from Shanghai, People’s Republic of China, by NewsRx correspondents, research stated, “Toelucidate the dynamic coupling mechanism involved in multipoint/arbitrary point contact durin g human-robot interactions with redundant manipulators, we introduce a compliant control strategy that adapts tovariable parameters.”Financial supporters for this research include Capacity Building Plan For Local Colleges And Universitiesof Shanghai Scientific Committee; National Natural Sci ence Foundation of China; National Key ResearchAnd Development Program of China .
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    Aalborg University Hospital Reports Findings in Type 1 Diabetes(Explainable Mac hine-Learning Models to Predict Weekly Risk ofHyperglycemia, Hypoglycemia, and Glycemic Variability in PatientsWith Type 1 Diabetes Based on Continuous Glucos e ...)

    72-73页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – New research on Nutritional and Metabo lic Diseases and Conditions - Type 1 Diabetes isthe subject of a report. Accord ing to news originating from Aalborg, Denmark, by NewsRx correspondents,researc h stated, “The aim of this study was to develop and validate explainable predict ion models basedon continuous glucose monitoring (CGM) and baseline data to ide ntify a week-to-week risk of CGM keymetrics (hyperglycemia, hypoglycemia, glyce mic variability). By having a weekly prediction of CGM keymetrics, it is possib le for the patient or health care personnel to take immediate preemptive action. ”Our news journalists obtained a quote from the research from Aalborg University Hospital, “We analyzed,trained, and internally tested three prediction models ( Logistic regression, XGBoost, and TabNet)using CGM data from 187 type 1 diabete s patients with long-term CGM monitoring. A binary classificationapproach combi ned with feature engineering deployed on the CGM signals was used to predict hyperglycemia, hypoglycemia, and glycemic variability based on consensus targets (t ime above range 5%, timebelow range 4%, coefficient o f variation 36%). The models were validated in two independent coho rtswith a total of 223 additional patients of varying ages. A total of 46 593 w eeks of CGM data were includedin the analysis. For the best model (XGBoost), th e area under the receiver operating characteristic curve(ROC-AUC) was 0.9 [95% confidence interval (CI) = 0.89-0.91], 0.89 [95% CI = 0.88-0.9], and 0. 8 [95% CI = 0.79-0.81] for p redicting hyperglycemia, hypoglycemia, and glycemic variability in the intervalvalidation, respectively. The validation test showed good generalizability of th e models with ROC-AUC of0.88 to 0.95, 0.84 to 0.89, and 0.80 to 0.82 for predic ting the glycemic outcomes. Prediction models basedon real-world CGM data can b e used to predict the risk of unstable glycemic control in the forthcomingweek. ”
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    Studies from Michigan State University Further Understanding ofRobotics (Fatigu e-resistant Mechanoresponsive Color-changing Hydrogelsfor Vision-based Tactile Robots)

    73-74页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – A new study on Robotics is now availab le. According to news reporting originating fromEast Lansing, Michigan, by News Rx correspondents, research stated, “Mechanoresponsive color-changingmaterials that can reversibly and resiliently change color in response to mechanical defor mation are highlydesirable for diverse modern technologies in optics, sensors, and robots; however, such materials are rarelyachieved. Here, a fatigue-resista nt mechanoresponsive color-changing hydrogel (FMCH) is reported thatexhibits re versible, resilient, and predictable color changes under mechanical stress.”Financial supporters for this research include College of Engineering at Michiga n State University,National Science Foundation (NSF).
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    Researcher at University of Limpopo Zeroes in on Machine Learning(Forecasting S hort- and Long-Term Wind Speed in LimpopoProvince Using Machine Learning and Ex treme Value Theory)

    74-75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 artific ial intelligence. According to news reportingfrom Sovenga, South Africa, by New sRx journalists, research stated, “This study investigates wind speedprediction using advanced machine learning techniques, comparing the performance of Vanill a long shorttermmemory (LSTM) and convolutional neural network (CNN) models, a longside the application ofextreme value theory (EVT) using the r-largest order generalised extreme value distribution (GEVDr).Over the past couple of decades , the academic literature has transitioned from conventional statistical time se ries models to embracing EVT and machine learning algorithms for the modelling o f environmentalvariables.”Our news reporters obtained a quote from the research from University of Limpopo : “This study addsvalue to the literature and knowledge of modelling wind speed using both EVT and machine learning. Theprimary aim of this study is to foreca st wind speed in the Limpopo province of South Africa to showcase thedependabil ity and potential of wind power generation. The application of CNN showcased con siderablepredictive accuracy compared to the Vanilla LSTM, achieving 88.66% accuracy with monthly time steps.The CNN predictions for the next five years, i n m/s, were 9.91 (2024), 7.64 (2025), 7.81 (2026), 7.13(2027), and 9.59 (2028), slightly outperforming the Vanilla LSTM, which predicted 9.43 (2024), 7.75 (2025), 7.85 (2026), 6.87 (2027), and 9.43 (2028). This highlights CNN’s superior ab ility to capture complexpatterns in wind speed dynamics over time. Concurrently , the analysis of the GEVDr across various orderstatistics identified GEVDr=2 a s the optimal model, supported by its favourable evaluation metrics interms of Akaike information criteria (AIC) and Bayesian information criteria (BIC). The 3 00-year returnlevel for GEVDr=2 was found to be 22.89 m/s, indicating a rare wi nd speed event.”
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    Ca’ Foscari University of Venice Reports Findings in Machine Learning(Hypoxia e xtreme events in a changing climate: Machine learningmethods and deterministic simulations for future scenarios developmentin the Venice Lagoon)

    75-76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 Venice, Italy, by NewsRx journalists, research stated, “Climate change pressures includethe dissolved o xygen decline that in lagoon ecosystems can lead to hypoxia, i.e. low dissolved oxygenconcentrations, which have consequences to ecosystem functioning includin g biogeochemical cycling from mild to severe disruption. The study investigates the potential of machine learning (ML) and deterministicmodels to predict futur e hypoxia events.”The news correspondents obtained a quote from the research from the Ca’ Foscari University of Venice,“Employing ML models, e.g. Random Forest and AdaBoost, pas t hypoxia events (2008-2019) in theVenice Lagoon were classified with an F1 sco re of around 0.83, based on water quality, meteorological,and spatio-temporal f actors. Future scenarios (2050, 2100) were estimated by integrating hydrodynamic-biogeochemical and climate projections. Results suggest hypoxia events will inc rease from 3.5 % to 8.8% by 2100, particularly in l andward lagoon areas. Summer prediction foresee a rise from 118 events to265 by 2100, with a longer hypoxia-prone season.”
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    New Findings in Machine Learning Described from Ningbo University(Short-term So lar Eruptive Activity Prediction Models BasedOn Machine Learning Approaches: a Review)

    76-77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 originating from Ningbo, Peop le’s Republic of China, by NewsRx correspondents, research stated,“Solar erupti ve activities, mainly including solar flares, coronal mass ejections (CME), and solar protonevents (SPE), have an important impact on space weather and our tec hnosphere. The short-term solareruptive activity prediction is an active field of research in the space weather prediction.”Financial supporters for this research include Science and Technology Developmen t Fund (STDF),Orszagos Tudomanyos Kutatasi Alapprogramok (OTKA), ISSI-Beijing, Chinese Academy of Sciences, NationalKey R&D Program of China, Nat ional Natural Science Foundation of China (NSFC).Our news editors obtained a quote from the research from Ningbo University, “Num erical, statistical,and machine learning methods are proposed to build predicti on models of the solar eruptive activities.With the development of space-based and ground-based facilities, a large amount of observational data of the Sun is accumulated, and data-driven prediction models of solar eruptive activities have made asignificant progress.”
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    Reports from Beijing Jiaotong University Provide New Insights intoMachine Learn ing (Enhancing PH-otdr Classification PerformanceThrough Event Augmentation)

    77-78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 newsoriginating from Beijing, People’s Rep ublic of China, by NewsRx correspondents, research stated, “As oneof the resear ch focuses in the past decades, phase sensitive optical domain reflectometer (Ph i-OTDR) hascome to the tipping point of its wide application. With the expansio n of its application, how to classifyand identify Phi-OTDR events more effectiv ely and efficiently in practical applications has become a keyand urgent issue. ”Funders for this research include Fundamental Research Funds for the Central Uni versities, NationalKey Research & Development Program of China.Our news journalists obtained a quote from the research from Beijing Jiaotong Un iversity, “Overthe past several years, the incorporation of machine learning me thodologies has garnered considerableattention in this area. Nevertheless, the performance of those machine learning models heavily relies onthe quantity and quality of the collected data. That is, the challenge of collecting rare event s ignals in thesensing applications strongly limits the ability of the model to c lassify accurately. To overcome the aboveweakness and further boost the capabil ity of & Fcy;-OTDR, we propose an event augmentation methodto enh ance the diversity and generalization of raw data. Experimental results show tha t the proposedmethod improves the event classification accuracy of our & Fcy;-OTDR from 76.4% to 91.0%.”
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