期刊,Robotics & Machine Learning Daily News 2024年卷Apr.3期_国家学术搜索

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Murdoch University Researcher Adds New Findings in the Area of Robotics (Dynamic Modeling of Unmanned Underwater Vehicles with Online Disturbance Compensation S cheme)

78-78页

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 out of Perth, Australia, by NewsRx editors, research stated, "With the advancement in robotics technology over the recent ye ars, underwater robots' design and development are gaining interest." Funders for this research include Charles Darwin University. Our news editors obtained a quote from the research from Murdoch University: "Un manned underwater vehicles (UUVs) have many applications in aquaculture, deep-se a exploration, research, and enhanced rescue tasks. However, various factors mus t be considered when developing any underwater vehicle system to explore the dee p ends of the underwater world. In this paper, we develop the most suitable mode l for understanding various system parameters. The new mathematical model consid ers certain constraints and external disturbances exerted on the system."

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New Support Vector Machines Findings from Taiyuan University of Technology Repor ted (Interference Fading Suppression for Distributed Acoustic Sensor Using Frequ ency-shifted Delay Loop)

80-80页

查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on Machine Learning - Support Vector Machines are discussed in a new report. According to news repor ting from Taiyuan, People's Republic of China, by NewsRx journalists, research s tated, "In order to suppress the interference fading of phase-sensitive optical time domain reflectometer (phi-OTDR), a multi-frequency detection method based o n frequency-shifted delay loop (FSDL) is proposed. A probe pulse train with up t o five consistent frequency components are generated by periodic frequency-shift ed, amplification, delay and filtering processing in FSDL." Financial supporters for this research include Fundamental Research Program of S hanxi Province, Guiding Funds of Central Government for Supporting the Developme nt of the Local Science and Technology, special fund for Science and Technology Innovation Teams of Shanxi Province. The news correspondents obtained a quote from the research from the Taiyuan Univ ersity of Technology, "The piecewise aggregate approximation (PAA) is introduced to compress the amount of operational data. Wavelet energy spectrum analysis an d support vector machine (SVM) are used to extract features and realize the fadi ng classification. With the help of fading label making and restoration, the mul tifrequency signals are intelligently aggregated using rotated-vector-sum (RVS) . Experimental results show that PAA has applicability in data compression of be at signals. After 5-layer wavelet energy spectrum analysis, the fading binary cl assification accuracy can reach 96.77 %, and the fading signals ide ntified after segmentation can be restored to the original data. The fading prob ability based on SVM output labels can be reduced to 1.89 %. The SV M-based classification output labels aggregation shows that the vibration positi oning signal-to-noise ratio (SNR) can be increased to 13.52 dB, the phase demodu lation curve is smoother, and the demodulation SNR can be up to 17.63 dB."

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McGill University Reports Findings in Psoriasis (Tree-Based Machine Learning to Identify Predictors of Psoriasis Incidence at the Neighborhood Level: A Populati onal Study from Quebec, Canada)

81-82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 Skin Diseases and Cond itions - Psoriasis is the subject of a report. According to news reporting out o f Montreal, Canada, by NewsRx editors, research stated, "Psoriasis is a major gl obal health burden affecting 60 million people worldwide. Existing studies on psoriasis focused on individual-level health behaviors (e.g. diet, alcohol consu mption, smoking, exercise) and characteristics as drivers of psoriasis risk." Our news journalists obtained a quote from the research from McGill University, "However, it is increasingly recognized that health behavior arises in the conte xt of larger social, cultural, economic and environmental determinants of health . We aimed to identify the top risk factors that significantly impact the incide nce of psoriasis at the neighborhood level using populational data from the prov ince of Quebec (Canada) and advanced tree-based machine learning (ML) techniques . Adult psoriasis patients were identified using International Classification of Disease (ICD)-9/10 codes from Quebec (Canada) populational databases for years 1997-2015. Data on environmental and socioeconomic factors 1 year prior to psori asis onset were obtained from the Canadian Urban Environment Health Consortium ( CANUE) and Statistics Canada (StatCan) and were input as predictors into the gra dient boosting ML. Model performance was evaluated using the area under the curv e (AUC). Parsimonious models and partial dependence plots were determined to ass ess directionality of the relationship. The incidence of psoriasis varied geogra phically from 1.6 to 325.6/100,000 person-years in Quebec. The parsimonious mode l (top 9 predictors) had an AUC of 0.77 to predict high psoriasis incidence. Amo ngst top predictors, ultraviolet (UV) radiation, maximum daily temperature, prop ortion of females, soil moisture, urbanization, and distance to expressways had a negative association with psoriasis incidence. Nighttime light brightness had a positive association, whereas social and material deprivation indices suggeste d a higher psoriasis incidence in the middle socioeconomic class neighborhoods."

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Studies from South China University of Technology Reveal New Findings on Robotic s (Modular Soft Robotic Crawlers Based On Fluidic Prestressed Composite Actuator s)

82-83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 Robotics are presented i n a new report. According to news originating from Guangzhou, People's Republic of China, by NewsRx correspondents, research stated, "Soft robotic crawlers have limited payload capacity and crawling speed. This study proposes a high-perform ance inchworm-like modular robotic crawler based on fluidic prestressed composit e (FPC) actuators." Financial supporters for this research include Young Scientists Fund, National N atural Science Foundation of China (NSFC), Guangzhou Municipal Science and Techn ology Project. Our news journalists obtained a quote from the research from the South China Uni versity of Technology, "The FPC actuator is precurved and a pneumatic source is used to flatten it, requiring no energy cost to maintain the equilibrium curved shape. Pressurizing and depressurizing the actuators generate alternating stretc hing and bending motions of the actuators, achieving the crawling motion of the robotic crawler. Multi-modal locomotion (crawling, turning, and pipe climbing) i s achieved by modular reconfiguration and gait design. An analytical kinematic m odel is proposed to characterize the quasi-static curvature and step size of a s ingle-module crawler. Multiple configurations of robotic crawlers are fabricated to demonstrate the crawling ability of the proposed design. A set of systematic experiments are set up and conducted to understand how crawler responses vary a s a function of FPC prestrains, input pressures, and actuation frequencies. As p er the experiments, the maximum carrying load ratio (carrying load divided by ro bot weight) is found to be 22.32, and the highest crawling velocity is 3.02 body length (BL) per second (392 mm/s)."

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Nanchang University Reports Findings in Nanoelectrosprays (Rapid analysis and au thentication of Chinese propolis using nanoelectrospray ionization mass spectrom etry combined with machine learning)

83-83页

查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New research on Nanotechnology - Nanoelectrospray s is the subject of a report. According to news originating from Nanchang, Peopl e's Republic of China, by NewsRx correspondents, research stated, "In this study , we established a simple, rapid, and high-throughput method for the analysis an d classification of propolis samples. We utilized nanoESI-MS to analyze 37 sampl es of propolis from China for the first time, obtaining characteristic fingerpri nt spectra in negative ion mode, which were then integrated with multivariate an alysis to explore variations between water extract of propolis (WEP) and ethanol extract of propolis (EEP)." Our news journalists obtained a quote from the research from Nanchang University , "Furthermore, we categorized propolis samples based on different climate zones and colors, screening 10 differential metabolites among propolis from various c limate zones, and 11 differential metabolites among propolis samples of differen t color. By employing machine learning models, we achieved high-precision discri mination and prediction between samples from different climate zones and colors, achieving predictive accuracies of 95.6% and 85.6%, respectively."

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Shanghai Jiao Tong University School of Medicine Reports Findings in Blood Trans fusion (Prediction of intraoperative red blood cell transfusion in valve replace ment surgery: machine learning algorithm development based on non-anemic cohort)

84-85页

查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New research on Transfusion Medicine - Blood Tran sfusion is the subject of a report. According to news reporting originating from Shanghai, People's Republic of China, by NewsRx correspondents, research stated , "Our study aimed to develop machine learning algorithms capable of predicting red blood cell (RBC) transfusion during valve replacement surgery based on a pre operative dataset of the non-anemic cohort. A total of 423 patients who underwen t valvular replacement surgery from January 2015 to December 2020 were enrolled. " Our news editors obtained a quote from the research from the Shanghai Jiao Tong University School of Medicine, "A comprehensive database that incorporated demog raphic characteristics, clinical conditions, and results of preoperative biochem istry tests was used for establishing the models. A range of machine learning al gorithms were employed, including decision tree, random forest, extreme gradient boosting (XGBoost), categorical boosting (CatBoost), support vector classifier and logistic regression (LR). Subsequently, the area under the receiver operatin g characteristic curve (AUC), accuracy, recall, precision, and F1 score were use d to determine the predictive capability of the algorithms. Furthermore, we util ized SHapley Additive exPlanation (SHAP) values to explain the optimal predictio n model. The enrolled patients were randomly divided into training set and testi ng set according to the 8:2 ratio. There were 16 important features identified b y Sequential Backward Selection for model establishment. The top 5 most influent ial features in the RF importance matrix plot were hematocrit, hemoglobin, ALT, fibrinogen, and ferritin. The optimal prediction model was CatBoost algorithm, e xhibiting the highest AUC (0.752, 95% CI: 0.662-0.780), which also got relatively high F1 score (0.695). The CatBoost algorithm also showed superi or performance over the LR model with the AUC (0.666, 95% CI: 0.53 4-0.697). The SHAP summary plot and the SHAP dependence plot were used to visual ly illustrate the positive or negative effects of the selected features attribut ed to the CatBoost model. This study established a series of prediction models t o enhance risk assessment of intraoperative RBC transfusion during valve replace ment in no-anemic patients."

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University Hospital Dupuytren Reports Findings in Antiinfectives (A machine lear ning approach to predict daptomycin exposure from two concentrations based on Mo nte Carlo simulations)

85-86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 Drugs and Therapies - Antiinfectives is the subject of a report. According to news reporting from Limo ges, France, by NewsRx journalists, research stated, "Daptomycin is a concentrat ion-dependent lipopeptide antibiotic for which exposure/effect relationships hav e been shown. Machine learning (ML) algorithms, developed to predict the individ ual exposure to drugs, have shown very good performances in comparison to maximu m a posteriori Bayesian estimation (MAP-BE)." The news correspondents obtained a quote from the research from University Hospi tal Dupuytren, "The aim of this work was to predict the area under the blood con centration curve (AUC) of daptomycin from two samples and a few covariates using XGBoost ML algorithm trained on Monte Carlo simulations. Five thousand one hund red fifty patients were simulated from two literature population pharmacokinetic s models. Data from the first model were split into a training set (75% ) and a testing set (25%). Four ML algorithms were built to learn A UC based on daptomycin blood concentration samples at pre-dose and 1 h post-dose . The XGBoost model (best ML algorithm) with the lowest root mean square error ( RMSE) in a 10-fold cross-validation experiment was evaluated in both the test se t and the simulations from the second population pharmacokinetic model (validati on). The ML model based on the two concentrations, the differences between these concentrations, and five other covariates (sex, weight, daptomycin dose, creati nine clearance, and body temperature) yielded very good AUC estimation in the te st (relative bias/RMSE = 0.43/7.69%) and validation sets (relative bias/RMSE = 4.61/6.63%). The XGBoost ML model developed allowed acc urate estimation of daptomycin AUC using C0, C1h, and a few covariates and could be used for exposure estimation and dose adjustment."

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Data on Artificial Intelligence Reported by Yue Ma and Colleagues (Compressed Se nsitivity Encoding Artificial Intelligence Accelerates Brain Metastasis Imaging by Optimizing Image Quality and Reducing Scan Time)

85-86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 Artificial Intelligenc e is the subject of a report. According to news reporting from Changchun, People 's Republic of China, by NewsRx journalists, research stated, "Accelerating the image acquisition speed of MR imaging without compromising the image quality is challenging. This study aimed to evaluate the feasibility of contrast-enhanced ( CE) 3D T1WI and CE 3D-FLAIR sequences reconstructed with compressed sensitivity encoding artificial intelligence (CS-AI) for detecting brain metastases (BM) and explore the optimal acceleration factor (AF) for clinical BM imaging." The news correspondents obtained a quote from the research, "Fifty-one patients with cancer with suspected BM were included. Fifty participants underwent differ ent customized CE 3D-T1WI or CE 3D-FLAIR sequence scans. Compressed SENSE encodi ng acceleration 6 (CS6), a commercially available standard sequence, was used as the reference standard. Quantitative and qualitative methods were used to evalu ate image quality. The SNR and contrast-to-noise ratio (CNR) were calculated, an d qualitative evaluations were independently conducted by 2 neuroradiologists. A fter exploring the optimal AF, sample images were obtained from 1 patient by usi ng both optimized sequences. Quantitatively, the CNR of the CS-AI protocol for C E 3D-T1WI and CE 3D-FLAIR sequences was superior to that of the CS protocol unde r the same AF (<.05). Compared with reference CS6, the CS- AI groups had higher CNR values (all <.05), with the CS-AI 10 scan having the highest value. The SNR of the CS-AI group was better than tha t of the reference for both CE 3D-T1WI and CE 3D-FLAIR sequences (all <.05). Qualitatively, the CS-AI protocol produced higher image quality scores th an did the CS protocol with the same AF (all <.05). In con trast to the reference CS6, the CS-AI group showed good image quality scores unt il an AF of up to 10 (all <.05). The CS-AI10 scan provided the optimal images, improving the delineation of normal gray-white matter bound aries and lesion areas (<.05). Compared with the reference , CS-AI10 showed reductions in scan time of 39.25% and 39.93% for CE 3D-T1WI and CE 3D-FLAIR sequences, respectively. CE 3D-T1WI and CE 3D-FLA IR sequences reconstructed with CS-AI for the detection of BM may provide a more effective alternative reconstruction approach than CS."

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Research on Machine Learning Detailed by a Researcher at PLA Strategic Support F orce Information Engineering University (Prediction of image interpretation cogn itive ability under different mental workloads: a task-state fMRI study)

87-88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 Zhengzhou, People's Republic of China, by NewsRx correspondents, research stated, "Visual imaging ex perts play an important role in multiple fields, and studies have shown that the combination of functional magnetic resonance imaging and machine learning techn iques can predict cognitive abilities, which provides a possible method for sele cting individuals with excellent image interpretation skills." Financial supporters for this research include Sti2030-major Projects; National Natural Science Foundation of China. The news reporters obtained a quote from the research from PLA Strategic Support Force Information Engineering University: "We recorded behavioral data and neur al activity of 64 participants during image interpretation tasks under different workloads. Based on the comprehensive image interpretation ability, participant s were divided into two groups. general linear model analysis showed that during image interpretation tasks, the high-ability group exhibited higher activation in middle frontal gyrus (MFG), fusiform gyrus, inferior occipital gyrus, superio r parietal gyrus, inferior parietal gyrus, and insula compared to the low-abilit y group. The radial basis function Support Vector Machine (SVM) algorithm shows the most excellent performance in predicting participants' image interpretation abilities (Pearson correlation coefficient = 0.54, R2 = 0.31, MSE = 0.039, RMSE = 0.002). Variable importance analysis indicated that the activation features of the fusiform gyrus and MFG played an important role in predicting this ability. "

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Data on Artificial Intelligence Reported by Yakup Erden and Colleagues (Artifici al intelligence insights into osteoporosis: assessing ChatGPT's information qual ity and readability)

88-89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 Artificial Intelligenc e is the subject of a report. According to news reporting from Bolu, Turkey, by NewsRx journalists, research stated, "Accessible, accurate information, and read ability play crucial role in empowering individuals managing osteoporosis. This study showed that the responses generated by ChatGPT regarding osteoporosis had serious problems with quality and were at a level of complexity that that necess itates an educational background of approximately 17 years." The news correspondents obtained a quote from the research, "The use of artifici al intelligence (AI) applications as a source of information in the field of hea lth is increasing. Readable and accurate information plays a critical role in em powering patients to make decisions about their disease. The aim was to examine the quality and readability of responses provided by ChatGPT, an AI chatbot, to commonly asked questions regarding osteoporosis, representing a major public hea lth problem. ‘Osteoporosis,' ‘female osteoporosis,' and ‘male osteoporosis' were identified by using Google trends for the 25 most frequently searched keywords on Google. A selected set of 38 keywords was sequentially inputted into the chat interface of the ChatGPT. The responses were evaluated with tools of the Ensuri ng Quality Information for Patients (EQIP), the Flesch-Kincaid Grade Level (FKGL ), and the Flesch-Kincaid Reading Ease (FKRE). The EQIP score of the texts range d from a minimum of 36.36 to a maximum of 61.76 with a mean value of 48.71 as ha ving ‘serious problems with quality.' The FKRE scores spanned from 13.71 to 56.0 6 with a mean value of 28.71 and the FKGL varied between 8.48 and 17.63, with a mean value of 13.25. There were no statistically significant correlations betwee n the EQIP score and the FKGL or FKRE scores."

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