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    New Research on Machine Learning from University of Bristol Summarized (Scoping review: Machine learning interventions in the management of healthcare systems)

    20-20页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-New research on artificial intelligenc e is the subject of a new report. According to newsreporting out of the Univers ity of Bristol by NewsRx editors, research stated, "Healthcare institutions focus on improving the quality of life for end-users, with key performance indicator s like access to essentialmedicines reflecting the effectiveness of management. Effective healthcare management involves planning,organizing, and controlling institutions built on human resources, data systems, service delivery, access tomedicines, finance, and leadership."Funders for this research include Engineering And Physical Sciences Research Cou ncil.Our news correspondents obtained a quote from the research from University of Br istol: "Accordingto the World Health Organization, these elements must be balan ced for an optimal healthcare system.Big data generated from healthcare institu tions, including health records and genomic data, is crucialfor smart staffing, decision-making, risk management, and patient engagement. Properly organizing a ndanalysing this data is essential, and machine learning, a sub-field of artifi cial intelligence, can optimize theseprocesses, leading to better overall healt hcare management. This review examines the major applicationsof machine learnin g in healthcare management, the algorithms frequently used in data analysis, their limitations, and the evidence-based benefits of machine learning in healthcar e. Following PRISMAguidelines, databases such as IEEE Xplore, ScienceDirect, AC M Digital Library, and SCOPUS were searchedfor eligible articles published betw een 2011 and 2021. Articles had to be in English, peer-reviewed, andinclude rel evant keywords like healthcare, management, and machine learning. Out of 51 rele vantarticles, 6 met the inclusion criteria. Identified algorithms include topic modelling, dynamic clustering,neural networks, decision trees, and ensemble cl assifiers, applied in areas such as electronic health records,chatbots, and mul ti-disease prediction."
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    Universita della Svizzera italiana Reports Findings in Artificial Intelligence ( Fluorescent Reporters, Imaging, and Artificial Intelligence Toolkits to Monitor and Quantify Autophagy, Heterophagy, and Lysosomal Trafficking Fluxes)

    21-21页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-New research on Artificial Intelligenc e is the subject of a report. According tonews originating from Lugano, Switzer land, by NewsRx correspondents, research stated, "Lysosomalcompartments control the clearance of cell-own material (autophagy) or of material that cells endocy tosefrom the external environment (heterophagy) to warrant supply of nutrients, to eliminate macromoleculesor parts of organelles present in excess, aged, or containing toxic material. Inherited or sporadic mutationsin lysosomal proteins and enzymes may hamper their folding in the endoplasmic reticulum (ER) and thei rlysosomal transport via the Golgi compartment, resulting in lysosomal dysfunct ion and storage disorders."Our news journalists obtained a quote from the research from Universita della Sv izzera italiana, "Defectivecargo delivery to lysosomal compartments is harmful to cells and organs since it causes accumulation oftoxic compounds and defectiv e organellar homeostasis. Assessment of resident proteins and cargo fluxesto th e lysosomal compartments is crucial for the mechanistic dissection of intracellu lar transport andcatabolic events. It might be combined with high-throughput sc reenings to identify cellular, chemical,or pharmacological modulators of these events that may find therapeutic use for autophagy-related andlysosomal storage disorders."
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    New Machine Learning Study Findings Have Been Reported by Investigators at South west Jiaotong University (Machine Learningdriven Feature Importance Appraisal o f Seismic Parameters On Tunnel Damage and Seismic Fragility Prediction)

    22-23页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-Research findings on Machine Learning are discussed in a new report. According tonews reporting out of Sichuan, Peopl e's Republic of China, by NewsRx editors, research stated, "Thisstudy proposes a machine learning-driven approach for the analysis of the feature importance of seismicparameters on tunnel damage and seismic fragility prediction. The Incre mental Dynamic Analysis (IDA)method serves as the fundamental database for vuln erability analysis."Financial supporters for this research include Postdoctoral Fellowship Program o f China PostdoctoralScience Foundation, National Natural Science Foundation of China (NSFC), Fundamental Research Fundsfor the Central Universities.Our news journalists obtained a quote from the research from Southwest Jiaotong University, "Strengthand deformation yield criteria are chosen to comprehensive ly assess the impact of different seismic parameterson the vulnerability of tun nels to seismic events. Three machine learning algorithms, namelyExtreme Gradie nt Boosting (XGBoost), Random Forest (RF), and Support Vector Machine (SVM), areutilized to develop models for classifying and regressing tunnel damage under s eismic conditions. Followingparameter tuning, the models' performance in multi- classification, binary classification, and regression predictionis assessed, wi th XGBoost and RF models exhibiting outstanding performance. Feature importanceanalysis of seismic parameters in XGBoost and RF models for multi-classification , binary classification,and regression is performed using Shapley additive expl anations (SHAP). The correlation analysis betweenSHAP-based feature values and predictions reveals that Peak Ground Displacement (PGD) has the highestinfluenc e in the regression model. Utilizing the interaction dependencies among crucial features in theregression model, fragility curves for tunnels based on these ke y features are effectively derived."
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    University College London (UCL) Reports Findings in Microsurgery (Clinical, Prec linical, and Educational Applications of Robotic-Assisted Flap Reconstruction an d Microsurgery: A Systematic Review)

    23-24页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-New research on Surgery - Microsurgery is the subject of a report. According to newsreporting from London, United Kin gdom, by NewsRx journalists, research stated, "Microsurgery and supermicrosurgery allow for highly technical reconstructive surgeries to be performed, with rep airs of anatomicalareas of less than 1 mm. Robotic-assisted surgery might allow for further advances within microsurgery,providing higher precision, accuracy, and scope to operate in previously inaccessible anatomical areas."The news correspondents obtained a quote from the research from University Colle ge London (UCL),"However, robotics is not well-established within this field. W e provide a summary of the clinical andpreclinical uses of robotics within flap reconstruction and microsurgery, educational models, and the barriersto widesp read implementation. A systematic review in accordance with the Preferred Report ing Itemsfor Systematic Reviews and Meta-Analyses was conducted of PubMed, Medl ine, and Embase. Preclinical,educational, and clinical articles were included. One thousand five hundred and forty-two articles werescreened; 87 articles met the inclusion criteria across flap harvest, flap/vessel pedicle dissection, vasc ularanastomosis, and nerve repair. The literature presents several potential be nefits to the surgeon and patientsuch as high cosmetic satisfaction, minimally invasive access with reduced scarring (flap harvest), and lowcomplication rates . Lack of haptic feedback was reported by authors to not impede the ability to p erformvessel anastomosis; however, this required further investigation. A steep learning curve was identified,particularly for microsurgeons embarking upon ro botic-assisted surgery. Robotic-assisted surgery canpotentially enhance microsu rgery and flap reconstruction, with feasibility demonstrated within this review,up to anastomosis of 0.4 mm in diameter. However, there is a lack of sufficient ly powered comparativestudies, required to strengthen this statement."
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    New Machine Learning Findings from Anhui Agricultural of University Described (H yperspectral Estimation for Nitrogen and Phosphorus Content In camellia Oleifera Leaves Based On Machine Learning Algorithms)

    24-25页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews-A new study on Machine Learning is now available. According to news reporting originating fromHefei, People's Republic of China, by NewsRx correspondents, research stated, "Nitrogen and phosphorusare essenti al elements of plants, which play important roles in representing plant growth, physiologicalfunction regulation, fruit harvest, etc. Hyperspectral technology provides a nondestructive, rapid, highlyaccurate, and cost-efficient method for plant leaf nutrient content estimation."Financial support for this research came from National Natural Science Foundatio n of China (NSFC).Our news editors obtained a quote from the research from the Anhui Agricultural of University, "Thereare very limited studies on nutrient diagnosis of Camellia oleifera leaves using hyperspectral technology. Inthis work, 160 Camellia olei fera samples were used. Hyperspectral data were obtained using a full-bandspect rometer. On the basis of preprocessing, the spectral response characteristics of leaf nitrogen content(LNC) and leaf phosphorus content (LPC) were revealed by comparing different combinations of spectralindices, and the spectral variables were further selected. The optimal LNC and LPC estimation modelsbased on three machine learning algorithms [i.e., support vector machine (S VM), random forest (RF),and back propagation neural network (BPNN)] were constructed. The results showed that the spectralsensitive regions of leaf nitrogen and phosphorus content were mainly reflected in green band, followedb y red band and the long-wave direction of short-wave infrared band. Savitzky-Gol ay first derivative(SGFD) pretreatment method was generally better than multipl icative scatter correction. The maximumcorrelation coefficients of the absolute values of LNC, LPC, and spectral transformation features were 0.56and 0.49. Th e optimal LNC and LPC models were both SGFD-TBNDSI-BPNN, with R-2 of 0.81 and 0.79, and RMSEP of 0.55 and 0.06 g/kg, respectively."
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    Chinese Academy of Sciences Reports Findings in Robotics (Autonomous Robot Task Execution in Flexible Manufacturing: Integrating PDDL and Behavior Trees in ARIA C 2023)

    25-26页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-New research on Robotics is the subjec t of a report. According to news reportingoriginating from Shenyang, People's R epublic of China, by NewsRx correspondents, research stated,"The Agile Robotics for Industrial Automation Competition (ARIAC) was established to advance flexib lemanufacturing, aiming to increase the agility of robotic assembly systems in unstructured and dynamicindustrial environments. ARIAC 2023 introduced eight ag ility challenges involving faulty parts, flippedparts, faulty grippers, robot m alfunctions, sensor blackouts, high-priority orders, insufficient parts, andhum an safety."Financial supporters for this research include National Natural Science Foundati on of China, NaturalScience Foundation of Liaoning Province, State Key Laborato ry of Robotics of China, National Programfor Funded Postdoctoral Researchers.
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    Investigators at Fudan University Describe Findings in Robotics and Automation ( Hgs-mapping: Online Dense Mapping Using Hybrid Gaussian Representation In Urban Scenes)

    26-27页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-New research on Robotics - Robotics an d Automation is the subject of a report.According to news originating from Shan ghai, People's Republic of China, by NewsRx correspondents,research stated, "On line dense mapping of urban scenes forms a fundamental cornerstone for scene understanding and navigation of autonomous vehicles. Recent advancements in dense m apping methods aremainly based on NeRF, whose rendering speed is too slow to me et online requirements. 3D GaussianSplatting (3DGS), with its rendering speed h undreds of times faster than NeRF, holds greater potentialin online dense mappi ng."Financial supporters for this research include National Natural Science Foundati on of China (NSFC),Shanghai Pujiang Program, State Key Laboratory of Intelligen t Vehicle Safety Technology Open FundProject.
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    Studies from University of Science and Technology China Yield New Information ab out Machine Learning (Machine Learning Based Battery Pack Health Prediction Usin g Real-world Data)

    27-27页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News-Research findings on Machine Learning are discussed in a new report. According tonews reporting out of Hefei, People' s Republic of China, by NewsRx editors, research stated, "The complexoperationa l conditions in real-world electric vehicles (EVs) contribute to the complexity of managing andmaintaining battery packs. Adding to these challenges is the int ricate task of modeling the inconsistentcoupling among individual cells within these packs."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 University of S cience and TechnologyChina, "This study addresses the ongoing challenges in mod eling lithium-ion battery (LIB) cells withinpacks and estimating their state of health (SOH) for practical applications. This research proposed aPCA-CNN-Trans former method to model and predict the SOH model of real-world EV. Three main contributions are presented: a novel approach to defining an attenuation SOH model based on deliveredenergy, a methodology utilizing Principal Component Analysis (PCA) for cell modeling, and an SOHestimation model employing CNN-Transformer architecture. To address both pack and cell-level modeling,a hierarchical featu re extraction approach is proposed. The health features extracted from both leve lsare assessed using grey relational analysis, showing a strong correlation wit h LIB SOH, exceeding 0.70.The proposed cell modeling method significantly reduc es data size by 96%, enhancing computationalefficiency. Furthermor e, the integration of 1D-CNN in the SOH estimation model overcomes the limitations of the attention mechanism, achieving a MAE with 0.0406 and r-square of 0.932 7, improved the originaltransformer network performance by 10.95%. "
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    National University of Science and Technology Researchers Update Understanding o f Androids (Miha: An Affordable Humanoid Platform for Research and Development)

    28-28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 androids have been published. According to news reportingoriginating from Bucharest, Romania , by NewsRx correspondents, research stated, "The current paperintroduces Miha, an affordable yet highly performant humanoid platform for research and developm ent."Financial supporters for this research include The A.P.C Was Funded By The Natio nal University ofScience And Technology Politehnica Bucharest, Through Its Puba rt Program..Our news journalists obtained a quote from the research from National University of Science andTechnology: "The motivation behind the platform is to significan tly lower the costs compared to similarofferings, democratizing access to human oid robotics research and helping in the standardization ofcommon control funct ions. The platform offers 22 degrees of freedom smart actuators, pressure sensors, inertial measurement units, stereo cameras, multiple microphones, speakers, a nd a convenient 2.4-inchliquid crystal display for the on-device user interface . It includes a unique dual hot-swap battery designthat allows continuous opera tion in the field with best-in-class 3-hours autonomy on one charge. Thepresent ed version uses a Raspberry Pi Compute Module 4 as the main controller and uses ROS Noetic.We demonstrate several control functions, including walking."
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    Studies from University of Alberta in the Area of Robotics Described (Explainabi lity of Deep Reinforcement Learning Algorithms In Robotic Domains By Using Layer -wise Relevance Propagation)

    28-29页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews-Data detailed on Robotics have been presented. Ac cording to news reporting out of Edmonton,Canada, by NewsRx editors, research s tated, "A key component to the recent success of reinforcementlearning is the i ntroduction of neural networks for representation learning. Doing so allows for solvingchallenging problems in several domains, one of which is robotics."Funders for this research include Mitsubishi, Alberta Machine Intelligence Insti tute. Our news journalists obtained a quote from the research from the University of A lberta, "However,a major criticism of deep reinforcement learning (DRL) algorit hms is their lack of explainability andinterpretability. This problem is even e xacerbated in robotics as they oftentimes cohabitate space withhumans, making i t imperative to be able to reason about their behavior. In this paper, we propos e toanalyze the learned representation in a robotic setting by utilizing Graph Networks (GNs). Using the GNand Layer-wise Relevance Propagation (LRP), we repr esent the observations as an entity-relationship toallow us to interpret the le arned policy. We evaluate our approach in two environments in MuJoCo. Thesetwo environments were delicately designed to effectively measure the value of knowle dge gained by ourapproach to analyzing learned representations. This approach a llows us to analyze not only how differentparts of the observation space contri bute to the decision-making process but also differentiate betweenpolicies and their differences in performance. This difference in performance also allows for reasoning aboutthe agent's recovery from faults."
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