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    Universita degli Studi di Milano Reports Findings in Artificial Intelligence (Ar tificial intelligence in interventional radiology: state of the art)

    38-38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 out of Milan, Italy, b y NewsRx editors, research stated, “Artificial intelligence (AI) has demonstrate d great potential in a wide variety of applications in interventional radiology (IR). Support for decision-making and outcome prediction, new functions and impr ovements in fluoroscopy, ultrasound, computed tomography, and magnetic resonance imaging, specifically in the field of IR, have all been investigated.” Our news journalists obtained a quote from the research from Universita degli St udi di Milano, “Furthermore, AI represents a significant boost for fusion imagin g and simulated reality, robotics, touchless software interactions, and virtual biopsy. The procedural nature, heterogeneity, and lack of standardisation slow d own the process of adoption of AI in IR. Research in AI is in its early stages a s current literature is based on pilot or proof of concept studies.”
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    Research from John Innes Centre Provides New Data on Machine Learning (iM-Seeker : a webserver for DNA i-motifs prediction and scoring via automated machine lear ning)

    39-40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 artificial in telligence have been published. According to news reporting originating from Nor wich, United Kingdom, by NewsRx correspondents, research stated, “DNA beyond it s canonical B-form double helix, adopts various alternative conformations, among which the imotif, emerging in cytosine-rich sequences under acidic conditions, holds significant biological implications in transcription modulation and telom ere biology.” Financial supporters for this research include Bbsrc; European Research Council; Bbsrc Horizon Europe Guarantee; Human Frontier Science Program Fellowship; Ukri Future Leaders Fellowship; Kan Tong Po International Fellowship; Amazon Researc h Award; National Natural Science Foundation of China.
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    University of the Philippines Diliman Researchers Advance Knowledge in Machine L earning (Integration of Stumpf’s Ratio Model And Random Forest For Satellite-der ived Bathymetry Estimation)

    39-39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 artificial in telligence have been published. According to news reporting out of the Universit y of the Philippines Diliman by NewsRx editors, research stated, “The developmen t of remote sensing for coastal and marine environment mapping has significantly enhanced our understanding of these ecosystems, enabling improved mitigation st rategies against the impacts of human activities.” Our news reporters obtained a quote from the research from University of the Phi lippines Diliman: “However, remote sensing must consider the complex interplay o f the atmosphere and water column. Ongoing research focuses on refining water co lumn correction techniques, including Depth Invariant Indices (DII), Radiative T ransfer models, and bathymetry models. This study specifically aims to enhance t he Stumpf’s Ratio model (SRM) for bathymetry by employing the Random Forest (RF) machine learning regression algorithm. The resulting bathymetry model, which in corporates visible bands from a Sentinel-2 MSI image, and their Stumpf’s ratios, outperforms other methods, yielding the highest accuracy with RMSE and R2 values of 1.25 m and 0.854, respectively.”
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    New Robotics and Automation Study Findings Have Been Reported by Investigators a t Chinese University of Hong Kong (Predicting Bird’s-eye-view Semantic Represent ations Using Correlated Context Learning)

    40-41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 Robotics - Ro botics and Automation have been published. According to news reporting from Shen zhen, People’s Republic of China, by NewsRx journalists, research stated, “We re define the concept of bird’s-eye-view (BEV) imaging for machine cognition tasks, emphasizing its power as an image interpretation tool. Humans intuitively trans late two-dimensional (2D) images into BEV representations by discerning and inte grating spatial information, such as position and morphological aspects.”Financial support for this research came from Shenzhen Science and Technology Pr ogram.
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    Ludong University Reports Findings in Machine Learning (Bionic Spider Web Flexib le Strain Sensor Based on CF-L and Machine Learning)

    41-42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 out of Shandong, People’s Rep ublic of China, by NewsRx editors, research stated, “At present, the preparation of laser-induced graphene (LIG) has become an important technology in sensor ma nufacturing. In the conventional preparation process, the CO laser is widely use d; however, its experimental period is long and its efficiency needs to be impro ved.” Our news journalists obtained a quote from the research from Ludong University, “We propose an innovative strategy to improve the experimental efficiency. We us e the machine learning method to accurately predict the preparation parameters o f LIG, so as to optimize the experimental process. Different structures can lead to different sensor performances. The structure constructed by the CO laser is rough and has a large size, which can affect the performance of the sensor. Ther efore, we propose for the first time an innovative method for intramembrane stru cture construction that combines the advantages of the CO laser and fiber laser (CF-L). With this CF-L method, we have successfully prepared a biomimetic, flexi ble strain sensor. This sensor not only maintains a high degree of sensitivity, but also has a more refined and optimized structure.”
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    Researchers from Virginia Polytechnic Institute and State University Report Rece nt Findings in Machine Learning (Tire mode shape categorization using Zernike an nular moment and machine learning classification)

    42-43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 new report. According to news reporting out of Virginia Po lytechnic Institute and State University by NewsRx editors, research stated, “Th is research proposes a framework for categorizing the radial tire mode shapes us ing machine learning (ML) based classification and feature recognition algorithm s, advancing the development of a digital twin for tire performance analysis.” Financial supporters for this research include Centire, Which Operates Under Nsf . Our news journalists obtained a quote from the research from Virginia Polytechni c Institute and State University: “Tire mode shape categorization is required to identify modal features in a specific frequency range to maximize driving perfo rmance and secure safety. However, the mode categorization work requires a lot o f manual effort to interpret modes. Therefore, this study suggests an ML-based c lassification tool to replace the conventional categorization process with two p rimary objectives: (1) create a database by categorizing the tire mode shapes ba sed on the identified features and (2) develop an ML-based surrogate model to cl assify the tire mode shapes without manual effort. The feature map of the tire m ode shape is built with the Zernike annular moment descriptor (ZAMD). The mode s hapes are categorized using the correlation value derived by the modal assurance criteria (MAC) with all ZAMD values for each tire mode shape and subsequently c reating the appropriate labels. The decision tree, random forests, and XGBoost, the representative supervised-learning algorithms for classification, are implem ented for surrogate model development.”
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    Data from Stanford University Provide New Insights into Machine Learning (Machin e Learning-empowered Study of Metastable Gcspbi3 Under Pressure and Strain)

    43-44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 Ma chine Learning. According to news reporting originating in Stanford, California, by NewsRx journalists, research stated, “Metastable gamma-CsPbI3 is a promising solar cell material due to its suitable band gap and chemical stability. While this metastable perovskite structure can be achieved via introducing external pr essure or strain, experimenting with this material is still challenging due to i ts phase instability.” Funders for this research include United States Department of Energy (DOE), Unit ed States Department of Energy (DOE), United States Department of Energy (DOE), United States Department of Energy (DOE), Stanford University, United States Dep artment of Energy (DOE), United States Department of Energy (DOE).
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    Data on Nanoflakes Detailed by Researchers at Sohar University (Specific Heat Ca pacity Extraction of Soybean Oil/MXene Nanofluids Using Optimized Long Short-Ter m Memory)

    44-45页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – A new study on nanoflakes is now available. Accor ding to news originating from Sohar University by NewsRx correspondents, researc h stated, “Researchers are turning to nanofluids in PV/T hybrid systems for enha nced efficiency due to nanoparticle dispersion, improving thermal and optical pr operties over conventional fluids. Three different concentrations of formulated soybean oil based MXene nanofluids are considered 0.025, 0.075 and 0.125 wt.% .” The news editors obtained a quote from the research from Sohar University: “Maxi mum specific heat capacity nanofluids ( $c_{ pNF}$ ) augmentation is 24.49% at 0.125 wt.% loading of Ti3C2 in the base oil. The calculation of the $ c_{pNF}$ based on the tempe rature and nanoflakes concentration is very expensive and time-consuming as it s hould be calculated via the practical test investigation. This study employs a l ong short-term memory (LSTM) as an efficient machine learning method to extract the surrogate model for calculating the $c_{ pNF}$ based on the temperature and nanoflakes concent ration. In addition, a couple of other machines learning methods, including supp ort vector regression (SVR), group method of data handling (GMDH), and multi-lay er perceptron (MLP), are developed to prove the higher efficiency of the recentl y proposed LSTM model in the calculation of the $c_{ pNF}$. In addition, the Bayesian optimization techniqu e is employed to calculate the optimal hyperparameters of the developed SVR, GMD H, MLP and LSTM to reach the highest efficiency of the system in predicting the $c_{pNF} $ base d on temperature and nanoflakes concentration. Notably, 95% of the recorded data via differential scanning calorimetry (DSC) is used for training machine learning techniques. In comparison, 5% is used for testing and validation purposes of the developed algorithm. The newly proposed optimize d SVR, GMDH, MLP, and LSTM are modelled in MATLAB software.”
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    Studies from Lawrence Berkeley National Laboratory in the Area of Machine Learni ng Described (Super-tsetlin: Superconducting Tsetlin Machines)

    45-46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 Ma chine Learning. According to news reporting out of Berkeley, California, by News Rx editors, research stated, “The recently proposed Tsetlin machine ™is a low-c omplexity and versatile machine learning architecture that learns a collection o f propositional clauses to describe or classify data. Each clause is constructed from a set of Tsetlin Automata (TAs), which are used to update the model during learning.” Financial support for this research came from United States Department of Energy (DOE).
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    New Findings in Machine Learning Described from King Saud University (Machine Le arning Forecast of Dust Storm Frequency in Saudi Arabia Using Multiple Features)

    46-47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 reporting originating from Riyadh, Sa udi Arabia, by NewsRx correspondents, research stated, “Dust storms are signific ant atmospheric events that impact air quality, public health, and visibility, e specially in arid Saudi Arabia.” The news editors obtained a quote from the research from King Saud University: “ This study aimed to develop dust storm frequency predictions for Riyadh, Jeddah, and Dammam by integrating meteorological and environmental variables. Our model s include multiple linear regression, support vector machine, gradient boosting regression tree, long short-term memory (LSTM), and temporal convolutional netwo rk (TCN). This study highlights the effectiveness of LSTM and TCN models in capt uring the complex temporal dynamics of dust storms and demonstrates that they ou tperform traditional methods, as evidenced by their lower mean absolute error (M AE) and root mean square error (RMSE) values and higher R2 score. In Riyadh, the TCN model demonstrates its remarkable performance, with an R2 score of 0.51, an MAE of 2.80, and an RMSE of 3.48, highlighting its precision, adaptability, and responsiveness to changes in dust storm frequency. Conversely, in Dammam, the L STM model proved to be the most accurate, achieving an MAE of 3.02, RMSE of 3.64 , and R2 score of 0.64.”
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