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University of Florida Reports Findings in COVID-19 (Identifying Potential Factor s Associated With Racial Disparities in COVID-19 Outcomes: Retrospective Cohort Study Using Machine Learning on Real-World Data)

96-97页

查看更多>>摘要：2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Coronavirus - COVID-19 is the subject of a report. According to news originating from Gainesville, Flo rida, by NewsRx correspondents, research stated, "Racial disparities in COVID-19 incidence and outcomes have been widely reported. Non-Hispanic Black patients e ndured worse outcomes disproportionately compared with non-Hispanic White patien ts, but the epidemiological basis for these observations was complex and multifa ceted." Our news journalists obtained a quote from the research from the University of F lorida, "This study aimed to elucidate the potential reasons behind the worse ou tcomes of COVID-19 experienced by non- Hispanic Black patients compared with non- Hispanic White patients and how these variables interact using an explainable ma chine learning approach. In this retrospective cohort study, we examined 28,943 laboratory-confirmed COVID-19 cases from the OneFlorida Research Consortium's da ta trust of health care recipients in Florida through April 28, 2021. We assesse d the prevalence of pre-existing comorbid conditions, geo-socioeconomic factors, and health outcomes in the structured electronic health records of COVID-19 cas es. The primary outcome was a composite of hospitalization, intensive care unit admission, and mortality at index admission. We developed and validated a machin e learning model using Extreme Gradient Boosting to evaluate predictors of worse outcomes of COVID-19 and rank them by importance. Compared to non-Hispanic Whit e patients, non-Hispanic Blacks patients were younger, more likely to be uninsur ed, had a higher prevalence of emergency department and inpatient visits, and we re in regions with higher area deprivation index rankings and pollutant concentr ations. Non-Hispanic Black patients had the highest burden of comorbidities and rates of the primary outcome. Age was a key predictor in all models, ranking hig hest in non-Hispanic White patients. However, for non-Hispanic Black patients, c ongestive heart failure was a primary predictor. Other variables, such as food e nvironment measures and air pollution indicators, also ranked high. By consolida ting comorbidities into the Elixhauser Comorbidity Index, this became the top pr edictor, providing a comprehensive risk measure. The study reveals that individu al and geo-socioeconomic factors significantly influence the outcomes of COVID-1 9. It also highlights varying risk profiles among different racial groups. While these findings suggest potential disparities, further causal inference and stat istical testing are needed to fully substantiate these observations."

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Research Study Findings from Daffodil International University Update Understand ing of Machine Learning (Broadband high gain performance MIMO antenna array for 5 G mm-wave applicationsbased gain prediction using machine learning approach)

97-98页

查看更多>>摘要：2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on ar tificial intelligence. According to news originating from Dhaka, Bangladesh, by NewsRx editors, the research stated, "This paper presents the findings about imp lementing a machine learning (ML) technique to optimize the performance of 5 G m m wave applications utilizing multiple-input multiple-output (MIMO) antennas ope rating at the 28 GHz frequency band." Financial supporters for this research include King Saud University. Our news journalists obtained a quote from the research from Daffodil Internatio nal University: "This article examines various methodologies, including simulati on, measurement, and the utilization of an RLCequivalent circuit model, to eval uate the appropriateness of an antenna for its intended applications. In additio n to its compact dimensions, the proposed design exhibits a maximum gain of 10.3 4 dBi, superior isolation exceeding 26 dB, and a broad bandwidth of 16.56 % Centered at 28 GHz and spanning from 25.905 to 30.544 GHz. Another supervised re gression machine learning technique is utilized to predict the antenna's gain ac curately. Machine learning (ML) models can be assessed by several measures, such as the variance score, R square, mean square error (MSE), mean absolute error ( MAE), root mean square error (RMSE), and Mean Absolute Percentage Error (MAPE). Among the six machine learning models considered, it is seen that the Gaussian P rocess Regression (GPR) model exhibits the lowest error and achieves the highest level of accuracy in forecasting gain. The antenna under consideration has prom ising qualities for its intended use in high-band 5 G applications."

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Tianjin University Reports Findings in Mathematics (Decoding motor imagery loade d on steady-state somatosensory evoked potential based on complex task-related c omponent analysis)

98-99页

查看更多>>摘要：2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New research on Mathematics is the subject of a r eport. According to news reporting from Tianjin, People's Republic of China, by NewsRx journalists, research stated, "Motor Imagery (MI) recognition is one of t he most critical decoding problems in brain- computer interface field. Combined with the steady-state somatosensory evoked potential (MI-SSSEP), this new paradi gm can achieve higher recognition accuracy than the traditional MI paradigm." The news correspondents obtained a quote from the research from Tianjin Universi ty, "Typical algorithms do not fully consider the characteristics of MI-SSSEP si gnals. Developing an algorithm that fully captures the paradigm's characteristic s to reduce false triggering rate is the new step in improving performance. The idea to use complex signal task-related component analysis (cTRCA) algorithm for spatial filtering processing has been proposed in this paper according to the f eatures of SSSEP signal. In this research, it's proved from the analysis of simu lation signals that task-related component analysis (TRCA) as typical method is affected when the response between stimuli has reduced correlation and the propo sed algorithm can effectively overcome this problem. The experimental data under the MI-SSSEP paradigm have been used to identify right-handed target tasks and three unique interference tasks are used to test the false triggering rate. cTRC A demonstrates superior performance as confirmed by the Wilcoxon signed-rank tes t. The recognition algorithm of cTRCA combined with mutual information-based bes t individual feature (MIBIF) and minimum distance to mean (MDM) can obtain AUC v alue up to 0.89, which is much higher than traditional algorithm common spatial pattern (CSP) combined with support vector machine (SVM) (the average AUC value is 0.77, p<0.05). Compared to CSP+SVM, this algorithm mode l reduced the false triggering rate from 38.69 % to 20.74 % (p <0.001). The research prove that TRCA is influenced by MI-SSSEP signals."

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New Machine Learning Study Results from Edith Cowan University Described (Sub-su rface Geospatial Intelligence In Carbon Capture, Utilization and Storage: a Mach ine Learning Approach for Offshore Storage Site Selection)

99-100页

查看更多>>摘要：2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-A new study on Machine Learning is now available. According to news reporting out of Joondalup, Australia, by NewsRx editors, res earch stated, "This study introduces an innovative data-driven and machine-learn ing framework designed to accurately predict site scores in the site screening s tudy for specific offshore CO2 storage sites. The framework seamlessly integrate s diverse sub-surface geospatial data sources with human aided expert-weighted c riteria, thereby providing a highresolution screening tool." Our news journalists obtained a quote from the research from Edith Cowan Univers ity, "Tailored to accommodate varying data accessibility and the significance of criteria, this approach considers both technical and non-technical factors. Its purpose is to facilitate the identification of priority locations for projects associated with Carbon Capture, Utilization, and Storage (CCUS). Through aggrega ting and analyzing geospatial datasets, the study employs machine learning algor ithms and an expertweighted model to identify suitable geologic CCUS regions. Th is process adheres to stringent safety, risk control, and environmental guidelin es, addressing situations where human analysis may fail to recognize patterns an d provide detailed insights in suitable site screening techniques. The primary e mphasis of this research is to bridge the gap between scientific inquiry and pra ctical application, facilitating informed decision-making in the implementation of CCUS projects. Rigorous assessments encompassing geological, oceanographic, a nd ecosensitivity metrics contribute valuable insights for policymakers and indu stry leaders. To ensure the accuracy, efficiency, and scalability of the establi shed offshore CO2 storage facilities, the proposed machine learning approach und ergoes benchmarking. This comprehensive evaluation includes the utilization of m achine learning algorithms such as Extreme Gradient Boosting (XGBoost), Random F orest (RF), Multilayer Extreme Learning Machine (MLELM), and Deep Neural Network (DNN) for predicting more suitable site scores. Among these algorithms, the DNN algorithm emerges as the most effective in site score prediction. The strengths of the DNN algorithm encompass nonlinear modeling, feature learning, scale inva riance, handling high-dimensional data, end-to-end learning, transfer learning, representation learning, and parallel processing."

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University of Oxford Reports Findings in Machine Learning (Modelling ligand exch ange in metal complexes with machine learning potentials)

100-101页

查看更多>>摘要：2024 OCT 03 (NewsRx)-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 originating from Oxford, United Kingdom , by NewsRx correspondents, research stated, "Metal ions are irreplaceable in ma ny areas of chemistry, including (bio)catalysis, self-assembly and charge transf er processes. Yet, modelling their structural and dynamic properties in diverse chemical environments remains challenging for both force fields and methods." Financial supporters for this research include Deutsche Forschungsgemeinschaft, Schweizerischer Nationalfonds zur Forderung der Wissenschaftlichen Forschung, En gineering and Physical Sciences Research Council, University of Oxford, Universi ty of Edinburgh.

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Studies from Yunnan University Have Provided New Data on Machine Learning (Asses sing the destabilization risk of ecosystems dominated by carbon sequestration ba sed on interpretable machine learning method)

101-102页

查看更多>>摘要：2024 OCT 03 (NewsRx)-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 Kunming, People's Republic of China, by NewsRx editors, the research stated, "Increasing carbon sequestrati on (CS) in soils and biomass is an important land-based solution in mitigating g lobal warming." Funders for this research include Chinese Academy of Sciences. The news journalists obtained a quote from the research from Yunnan University: "Ecosystems provide a wide range of ecosystem services (ESs). The necessity to a ugment CS may engender alterations in the interrelationships among ESs, thereby heightening the probability of ecosystem destabilization. This study developed a framework that integrates machine learning and interpretable predictions to eva luate the destabilization risk resulting from alterations in ecosystem service r elationships dominated by CS. We selected Northeastern China as study area to es timate six ESs and identified areas of destabilization risk among the three serv ices most relevant to CS, including food production (FP), soil retention (SR), a nd habitat quality (HQ). Subsequently, we compared three machine learning models (random forest, extreme gradient boosting, and support vector machine) and intr oduced the Shapley additive interpretation (SHAP) method for driving mechanism a nalysis. The results showed that: (1) CS-FP had 30.28% of its area at destabilization risk and is the most significant ecosystem service pair; (2) Heilongjiang Province was the region with the highest destabilization risk of C S, with CS-FP and CS-SR accounting for 44.76% and 52.89% of all regions, respectively; (3) a non-linear relationship and the presence of threshold features between socio-ecological factors and the prediction of destab ilization risk."

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Investigators at North Carolina State University (NC State) Report Findings in M achine Learning (Predicting Sweetpotato Traits Using Machine Learning: Impact of Environmental and Agronomic Factors On Shape and Size)

102-103页

查看更多>>摘要：2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Fresh data on Machine Learning are pre sented in a new report. According to news reporting out of Raleigh, North Caroli na, by NewsRx editors, research stated, "Consumer preference in produce, defined by shape and size, heavily influences this market. Understanding the environmen tal and management factors that impact these features can improve a farmer's eco nomic margins." Financial supporters for this research include North Carolina State University's Plant Sciences Initiative (NC PSI), USDA National Institute of Food and Agricul ture Hatch project, SAS Institute.

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Studies Conducted at Swiss Federal Institute of Technology Lausanne on Brain-Bas ed Devices Recently Reported [A 2.46-mm2 Miniaturized Brain-m achine Interface (Mibmi) Enabling 31-class Brain-to-text Decoding]

103-104页

查看更多>>摘要：2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Brain-Based Devices is the subject of a report. According to news reporting originating in Geneva, Swi tzerland, by NewsRx journalists, research stated, "Recent advancements in brain- machine interface (BMI) technology offer groundbreaking solutions for individual s with motor impairments, potentially extending to speech synthesis and handwrit ing assistance. However, current BMIs rely on cumbersome benchtop setups equippe d with resource-intensive computing units, restricting their suitability for dai ly use." Financial supporters for this research include Swiss State Secretariat for Educa tion, Research and Innovation, Swiss National Science Foundation (SNSF), Ecole P olytechnique Federale de Lausanne.

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Findings from Westlake University in Robotics Reported (Unified Scheme Design an d Control Optimization of Flapping Wing for Next-generation Manta Ray Robot)

104-105页

查看更多>>摘要：2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on Robotics have been published. According to news reporting originating in Zhejiang, People's R epublic of China, by NewsRx journalists, research stated, "An efficient global o ptimization (EGO) algorithm has been used in the geometry design and motion for a manta ray -like flapping wing to optimize the best swimming performance. To th is end, we combine a three-dimensional simulation using STAR-CCM+ with this fram ework to generate the optimal flapping wing geometry that maximizes the lift -to -drag ratio." Funders for this research include National Key Research & Developm ent Program of China, Key Research and Development Program of Zhejiang Province, China.

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Findings from State Key Laboratory Provides New Data about Robotics (Underwater Fluid-driven Soft Dock for Dynamic Recovery of Auvs With Improved Pose Tolerance )

105-106页

查看更多>>摘要：2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on Robotics have been pr esented. According to news reporting out of Hangzhou, People's Republic of China , by NewsRx editors, research stated, "Recovering autonomous underwater vehicles (AUVs) by a mobile platform is a complex challenge. Traditional docking station s require strict pose deviation from the underwater vehicle." Funders for this research include National Natural Science Foundation of China ( NSFC), China Postdoctoral Science Foundation, Natural Science Foundation of Zhej iang Province.

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