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    Investigators from San Diego State University Report New Data on Machine Learnin g (Robust Image-based Cross-sectional Grain Boundary Detection and Characterizat ion Using Machine Learning)

    20-21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 reporting originating in San Diego, C alifornia, by NewsRx journalists, research stated, "Understanding the anisotropi c sintering behavior of 3D-printed materials requires massive analytic studies o n their grain boundary (GB) structures. Accurate characterization of the GBs is critical to study the metallurgical process." Financial support for this research came from National Science Foundation (NSF). The news reporters obtained a quote from the research from San Diego State Unive rsity, "However, it is challenging and time-consuming for sintered 3D-printed ma terials due to immature etching and residual pores. In this study, we developed a machine learning-based method of characterizing GBs of sintered 3D-printed mat erials. The developed method is also generalizable and robust enough to characte rize GBs from other non-3D-printed materials. This method can be applied to a sm all dataset because it includes a diffusion network that generate augmented imag es for training. The study compared various machine learning methods commonly us ed for segmentation, which include UNet, ResNeXt, and Ensemble of UNets. The com parison results showed that the Ensemble of UNets outperformed the other methods for the GB detection and characterization. The model is tested on unclear GBs f rom sintered 3D-printed samples processed with non-optimized etching and classif ies the GBs with around 90% accuracy."
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    Findings from Chulalongkorn University Update Knowledge of Machine Learning (A R obust Machine Learning-based Framework for Handling Time-consuming Constraints f or Bi-objective Optimization of Nonlinear Steel Structures)

    21-22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 reporting out of Bangkok, Thailand, b y NewsRx editors, research stated, "In structural design, multi-objective optimi zation of nonlinear steel structures simultaneously considers various design (of ten conflicting) purposes during the stage of construction to the operation of p rojects. Such optimization generally requires excessive computational efforts fo r the evaluation of time-consuming constraints." Financial supporters for this research include Thailand Science Research and Inn ovation Fund Chulalongkorn University, Ratchadapisek Somphot Fund for Postdoctor al Fellowship and Second Century Fund under Chulalongkorn University.
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    Nanjing Medical University Reports Findings in Machine Learning (Hepatic toxicit y prediction of bisphenol analogs by machine learning strategy)

    22-22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 from Nanjing, People's Republ ic of China, by NewsRx journalists, research stated, "Toxicological studies have demonstrated the hepatic toxicity of several bisphenol analogs (BPs), a prevale nt type of endocrine disruptor. The development of Adverse Outcome Pathway (AOP) has substantially contributed to the rapid risk assessment for human health." The news correspondents obtained a quote from the research from Nanjing Medical University, "However, the lack of in vitro and in vivo data for the emerging BPs has limited the hazard assessment of these synthetic chemicals. Here, we aimed to develop a new strategy to rapidly predict BPs' hepatotoxicity using network a nalysis coupled with machine learning models. Considering the structural and fun ctional similarities shared by BPs with Bisphenol A (BPA), we first integrated h epatic disease related genes from multiple databases into BPA-Gene-Phenotype-hep atic toxicity network and subjected it to the computational AOP (cAOP). Through cAOP network and conventional machine learning approaches, we scored the hepatot oxicity of 20 emerging BPs and provided new insights into how BPs' structure fea tures contributed to biologic functions with limited experimental data. Addition ally, we assessed the interactions between emerging BPs and ESR1 using molecular docking and proposed an AOP framework wherein ESR1 was a molecular initiating e vent."
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    Study Findings from Inner Mongolia Agricultural University Broaden Understanding of Machine Learning (Bibliometric and Visualization Analysis of the Literature on the Remote Sensing Inversion of Soil Salinization from 2000 to 2023)

    23-23页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New study results on artificial intell igence have been published. According to news reporting from Hohhot, People's Re public of China, by NewsRx journalists, research stated, "Tracing the historical development of soil salinization and monitoring its current status are crucial for understanding the driving forces behind it, proposing strategies to improve soil quality, and predicting future trends." Funders for this research include National Natural Science Foundation of China; Research Program of Science And Technology At Universities of Inner Mongolia Aut onomous Region. Our news reporters obtained a quote from the research from Inner Mongolia Agricu ltural University: "To comprehensively understand the evolution of research on t he remote sensing inversion of soil salinity, a scientific bibliometric analysis was conducted on research from the past two decades indexed in the core scienti fic databases. This article analyzes the field from various perspectives, includ ing the number of publications, authors, research institutions and countries, re search fields, study areas, and keywords, in order to reveal the current state-o f-the-art and cutting-edge research in this domain. Special attention was given to topics such as machine learning, data assimilation methods, unmanned aerial v ehicle (UAV) remote sensing technology, soil inversion under vegetation cover, s alt ion inversion, and remote sensing model construction methods. The results in dicate an overall increase in the volume of publications, with key authors such as Metternicht, Gi and Zhao, Gengxing, and major research institutions including the International Institute for Geoinformatics Science and Earth Observation an d the Chinese Academy of Sciences making significant contributions. Notably, Chi na and the USA have made substantial contributions to this field, with research areas extending from Inner Mongolia's Hetao irrigation district to the Mediterra nean region."
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    Reports on Robotics from Hohai University Provide New Insights (Efficient Lower Layers Parameter Decoupling Personalized Federated Learning Method of Facial Exp ression Recognition for Home Care Robots)

    24-25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 Robotics is now availab le. According to news reporting originating from Changzhou, People's Republic of China, by NewsRx correspondents, research stated, "Facial expression recognitio n (FER) is a crucial and pivotal functionality for home care robots that engage in intimate interactions with human individuals. However, the potential privacy risk associated with home care robots resides in their acquisition of facial inf ormation during expression recognition and subsequent data uploading for model u pdates." Funders for this research include National Natural Science Foundation of China ( NSFC), National key R&D program of China, Jiangsu Province Advanced Leading Technology and Basic Research Project, China.
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    Research Conducted at Huazhong University of Science and Technology Has Provided New Information about Androids (Enhancing the Online Estimation of Finger Kinem atics From Semg Using Lstm With Attention Mechanisms)

    25-26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 - Androids is the subject of a report. According to news originating from Wuhan, People's Rep ublic of China, by NewsRx correspondents, research stated, "Simultaneous and pro portional estimation of human finger kinematics using muscle interface has gaine d significant attention for human-robot interaction. Most existing researches fo cused on proposing novel estimation methods to achieve high estimation accuracy and validated them offline." 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 Huazhong Univer sity of Science and Technology, "However, the gap between the online and offline estimation has not been fully considered which may lead to unpredictable deteri oration or failure in practical application. This paper quantifies the gap betwe en online and offline estimation performance and the underlying factors for such gap. The temporal variability of muscle activities represented by surface elect romyography (sEMG) is found to be more significant in online estimation which ch allenges the estimation model in computation complexity and temporal feature ext raction. To improve the online estimation accuracy, this paper proposes to combi ne an attention module with the long short-term memory (LSTM) network which enab les not only the global but also the local key information of sEMG signals being focused. The finger kinematics of five primary degrees of freedom (DoFs) is est imated from the sEMG recordings during some grasping tasks."
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    Researchers from Bucharest University of Economic Studies Detail New Studies and Findings in the Area of Artificial Intelligence (The Perceptions of Employees f rom Romanian Companies on Adoption of Artificial Intelligence in Recruitment and ...)

    26-26页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New study results on artificial intell igence have been published. According to news originating from Bucharest, Romani a, by NewsRx correspondents, research stated, "Managers increasingly want to imp rove the efficiency of human resources processes, and a solution with real resul ts is Artificial Intelligence (AI), which provides real results in a virtual wor ld for human resources managers, for companies, and also for candidates." Our news journalists obtained a quote from the research from Bucharest Universit y of Economic Studies: "The purpose of this article is to investigate the percep tion of employees in Romanian companies to adopt and use AI in recruitment and s election processes, analysing the factors that influence this acceptance intenti on using Technology Acceptance Model (TAM). The study aimed to determine the ben efits of AI adoption in recruitment and selection processes, the perceived usefu lness of adoption, and the ease of use in these processes. The results obtained showed that almost all the variables proposed for the model positively influence d the intention to accept and use AI in the recruitment and selection process. N on-discrimination and the utility of using (PU) AI in recruitment and selection had little influence."
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    Findings on Machine Learning Reported by Investigators at University of Florida (Machine Learning-based Metabolomics Analysis Reveals the Early Biomarkers for D iplodia Stem-end Rot In Grapefruit Caused By Lasiodiplodia Theobromae)

    27-27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 reporting originating from Lake Alfred, Florida, by NewsRx correspondents, research stated, "Lasiodiplodia theobromae is a key post harvest pathogen causing Diplodia stem-end rot (SER) disease in grapefruit. Whil e the disease remains quiescent before harvest, its symptoms become evident duri ng the postharvest period." Financial support for this research came from Florida Citrus Packers through a U SDA Technical Assistance for Specialty Crops grant. Our news editors obtained a quote from the research from the University of Flori da, "This dormant behavior poses a challenge in managing fruits after harvest. T o effectively detect asymptomatic fruit and detect SER disease at an early stage , it's crucial to identify early-stage biomarkers that can serve as disease indi cators. In this study, a machine learning-based metabolomics analysis was utiliz ed to identify characteristic metabolites and to elucidate the underlying biosyn thetic mechanisms. Six machine learning algorithms were used, and Gradient boost ing (GBT) exhibited the highest accuracy identifying early biomarkers such as sh ikimate, succinic acid, quinic acid, coumaric acid, tyrosine, phenylalanine, and tryptophan. Moreover, dynamic time warping (DTW), was used to investigate the t rend of metabolites across timepoints. Our result revealed that the metabolic an alysis enabled differentiating infected from non-infected fruits within 1 day, e ven though symptoms appeared after 7 days of inoculation. Pathway enrichment ana lysis indicated that three pathways (biosynthesis of plant hormones, phenylpropa noid biosynthesis, and glutamate metabolism) were strongly involved in the defen se mechanism. Metabolite mapping analysis showed the behavior of each compound a gainst the pathogen."
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    Study Findings from Shandong University Advance Knowledge in Machine Learning (M uon and Pion Identification at BESIII Based on Machine Learning Algorithm)

    28-28页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Data detailed on artificial intelligence have bee n presented. According to news reporting out of Shandong University by NewsRx ed itors, research stated, "BESIII is designed to study physics in the t-charm ener gy region utilizing the high luminosity BEPCII." The news journalists obtained a quote from the research from Shandong University : "For collision physics experiments like the BESIII experiment, particle identi fication (PID) is one of the most important and commonly used tools for physics analysis. The effective /p identification performance is of great significance f or most of BESIII physics analysis. However, due to the close masses of these tw o particles, as well as the intrinsic correlation between multiple detector info rmation, traditional methods at BESIII is facing challenges in /p identification . In recent decades, machine learning (ML) techniques have been rapidly develope d and have shown successful applications in HEP experiments. The PID based on ML provides powerful capability of combining more detection information from all s ub-detectors with the data-driven approach. In this article, targeting at the /p identification problem at the BESIII experiment, we have developed a new PID al gorithm based on the gradient boosted decision tree (BDT) model."
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    Recent Findings from University of Piraeus Has Provided New Information about Ma chine Learning (A Survey On Automl Methods and Systems for Clustering)

    28-29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 Machine Learning is now available. According to news originating from Piraeus, Greece, by NewsRx corres pondents, research stated, "Automated Machine Learning (AutoML) aims to identify the best-performing machine learning algorithm along with its input parameters for a given dataset and a specific machine learning task. This is a challenging problem, as the process of finding the best model and tuning it for a particular problem at hand is both time-consuming for a data scientist and computationally expensive." Financial support for this research came from European Union (EU).
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