With the advance of artificial intelligence and natural language processing technology, a new tool is standing out in the field of understanding and generating natural language in a sophisticated way: the Large Langua...
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Consider dirichlet problems of Laplace's equation in a bounded simply-connecteddomain (Formula presented.), and use the null field equation (NFE) of Green's representation formulation, where the source nodes ...
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Consider dirichlet problems of Laplace's equation in a bounded simply-connecteddomain (Formula presented.), and use the null field equation (NFE) of Green's representation formulation, where the source nodes (Formula presented.) are located on a pseudo-boundary (Formula presented.) outside (Formula presented.) but not close to its boundary (Formula presented.). Simple algorithms are proposed in this article by using the central rule for the NFE, and the normal derivatives (Formula presented.) of the solutions on the boundary (Formula presented.) can be easily obtained. These algorithms are called the discrete null field equation method (dNFEM) because the collocation equations are, indeed, the direct discrete form of the NFE. The bounds of the condition number are like those by the method of fundamental solutions (MFS) yielding the exponential growth as the number of unknowns increases. One trouble of the dNFEM is the near singularity of integrations for the solutions in boundary layers in Green's representation formulation. The traditional BEM also suffers from the same trouble. To deal with the near singularity, quadrature by expansions and the sinh transformation are often used. To handle this trouble, however, we develop two kinds of new techniques: (I) the interpolation techniques by Taylor's formulas with piecewise (Formula presented.) -degree polynomials and the Fourier series, and (II) the mini-rules of integrals, such as the mini-Simpson's and the mini-Gaussian rules. Error analysis is made for technique I to achieve optimal convergence rates. Numerical experiments are carried out for disk domains to support the theoretical analysis made. The numerical performance of the dNFEM is excellent for disk domains to compete with the MFS. The errors with (Formula presented.) can be obtained by combination algorithms, which are satisfactory for most engineering problems. In summary, the new simple dNFEM is based on the NFE, which is different from the boundary elem
Pillars of smart cities include smart environment, mobility and economy. We explore impacts on these to enhance smart cities, heading towards a smart planet. Our motivation emerges from the need to decarbonize transpo...
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Pillars of smart cities include smart environment, mobility and economy. We explore impacts on these to enhance smart cities, heading towards a smart planet. Our motivation emerges from the need to decarbonize transportation. In this context, ride-sharing companies deploy electric vehicles (EVs). These should be managed by various factors: battery demand, EV charging station location, service availability, and charging time. Ride-sharing EV s aim to maximize profits via more rides. Our paper explores game theory in AI here. We propose E-Ride-Minimax, adapting the Minimax algorithm, treating EV ride-sharing companies as players. We hypothesize one player choosing its next move via total passenger-travel distance (longer the distance, larger the profit); and another player via battery usage (ratio of total passenger-travel distance to vehicle-passenger distance: optimizing this ratio enables more travel without recharging). Experimental results reveal that rising passenger numbers yield maximum battery savings (e.g. rush hours / major events); followed by stable and falling numbers. Our findings indicate that E-Ride-Minimax can reduce battery usage in some circumstances by 64%, losing 1 % profit. This is vital, given global emphasis on climate change. It increases cost-effectiveness, consumer participation and passengers per mile; reduces energy use and greenhouse gas emissions; and thus helps decarbonize transportation.
The study used clinical data to develop a prediction model for breast cancer survival. Breast cancer prognostic factors were explored using machine learning techniques. We conducted a retrospective study using data fr...
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The study used clinical data to develop a prediction model for breast cancer survival. Breast cancer prognostic factors were explored using machine learning techniques. We conducted a retrospective study using data from the Taipei Medical University Clinical Research database, which contains electronic medical records from three affiliated hospitals in Taiwan. The study included female patients aged over 20 years who were diagnosed with primary breast cancer and had medical records in hospitals between January 1, 2009 anddecember 31, 2020. The data were divided into training and external testing datasets. Nine different machine learning algorithms were applied to develop the models. The performances of the algorithms were measured using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1-score. A total of 3914 patients were included in the study. The highest AUC of 0.95 was observed with the artificial neural network model (accuracy, 0.90; sensitivity, 0.71; specificity, 0.73; PPV, 0.28; NPV, 0.94; and F1-score, 0.37). Other models showed relatively high AUC, ranging from 0.75 to 0.83. According to the optimal model results, cancer stage, tumor size, diagnosis age, surgery, and body mass index were the most critical factors for predicting breast cancer survival. The study successfully established accurate 5-year survival predictive models for breast cancer. Furthermore, the study found key factors that could affect breast cancer survival in Taiwanese women. Its results might be used as a reference for the clinical practice of breast cancer treatment.
In recent years, many mammographic image analysis methods have been introduced for improving cancer classification tasks. Two major issues of mammogram classification tasks are leveraging multi-view mammographic infor...
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Since government-provided annual cervical cytology testing for all Taiwanese women aged 30 years or older became available in 1995, both cervical cancer incidence anddeath have decreased significantly. However, with ...
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Objectives: To prospectively investigate associations of frailty and other predictor variables with functional recovery and health outcomes in middle-aged and older patients with trauma. design: Single-center prospect...
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Transcriptomes provide highly informative molecular phenotypes that, combined with gene perturbation, can connect genotype to phenotype. An ultimate goal is to perturb every gene and measure transcriptome changes, how...
Correction to: Nature Biotechnologyhttps://***/10.1038/s41587-024-02535-2, published online 16 January 2025. In the version of the article initially published, Sidi Chen was incorrectly associated with a present addre...
In single-cell RNA sequencing (scRNA-seq) data analysis, a critical challenge is to infer hidden cellular dynamic processes from measured static cell snapshots. To tackle this challenge, many computational methods hav...
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