where FR(R,T)=∂F(R,T)∂R and FT(R,T)=∂F(R,T)∂T ◻=∇ξ∇η called D’Alembert Operator and ∇ξ,∇η are called covariant derivative. A source term for the curvature of space-time can be thought of as the EMT. The anisotropic fluid’s EMT in this model is...
Does electric vehicle promotion in the public sector contribute to urban transport carbon emissions reduction? Transp Policy. 2022;125:151–63. Article Google Scholar Yin W, Ji J, Wen T, Zhang C. Study on orderly charging strategy of EV with load forecasting. Energy. 2023;278: 127818. ...
in particular light-sheet fluorescence microscopy (LSFM), have spread the possibility of performing cm-sized volumetric reconstruction of biological specimens, such as the human brain10,11,12,13,14. However, the development of automatic tools able to perform cell counting on human brain 3D reconstru...
Predictive statistics were gathered as well as receiver operator characteristic (ROC) curves for each combination to visualize the classification performance (true positive rate vs. false positive rate) of the classifiers. Predictive modeling approaches include: logistic regression, RF, and support vector...
To conduct the comparison, the code is written in Python, conventional machine learning computations are performed on a 2.3 GHz Intel Xeon CPU, and deep learning models are trained using a NVIDIA Tesla P100-PCIE GPU. Firstly, in Section 3.1, we compare the performance analysis for FM image ...
(the large peak near 524 s) and malic acid (the large peak near 540 s). Two smaller peaks are closely eluting with malic acid providing a good test for deconvolution. Both PyMS and AnalyzerPro performed similarly to an experienced operator in the identification of peaks. For the list of ...
[134] introduced the peridynamic differential operator (PDDO). Shojaei et al. [135] introduced a generalized finite difference method (GDFM) based on PDDO. Table 1 lists all of these connections. Fore more details, we refer to [8]. Table 1 Connection between meshless discrete PD and other...
[48] examined the inter-operator variability of human expert scorers and found that the consistency for scoring the N1 stage was the lowest among all sleep stages. Moreover, as human sleep consists of several stages that are unevenly distributed, the number of N1 stage epochs is typically ...
At the same time, DPVO also proposes an update operator that is a recurrent network that iteratively fine-tunes the depth of each patch and the camera pose of each frame in the video. DPVO is rated to be 3x faster than DROID-SLAM [50]. In DPVO, the model is trained from several ...