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Discretización por elementos finitos del problema termo-difusivo-mecánico con grandes deformaciones_prueba_latex
No full textEn algunos problemas relevantes de la ingeniería, el campo mecánico está fuertemente acoplado con el de temperatura y el de transporte de masa. La solución de estos problemas es compleja sobre todo cuando las deformaciones son grandes. Por ello, a menudo se acude a aproximaciones numéricas que pueden encontrar todos los campos involucrados en el problema acoplado, sus efectos y sus interacciones. En este artículo se describe una discretización por el método de los elementos finitos del problema acoplado de difusión, temperatura y deformación incluyendo el rango no lineal de deformaciones. La formulación tiene en cuenta todos los acoplamientos posibles entre los tres campos de estudio y se proporcionan todos los detalles necesarios para su completa implementación llenando un hueco de la literatura de estos métodos. Asimismo, el artículo describe la fundamentación termodinámica de los problemas acoplados termo-difusivo-mecánicos, insistiendo en la derivación de las ecuaciones de balance y restricciones que siguen de la segunda ley de la termodinámica. Los resultados del artículo serán de interés para investigadores que necesiten implementar las ecuaciones de problemas acoplados en códigos de elementos finitos y, en particular, para aplicaciones en el modelado de baterías, del comportamiento de metales bajo los efectos del hidrógeno, de geles, etc
Optimal Control Strategies for COVID-19 Epidemic Management: A Mathematical Modeling Approach Using the SEIQR Framework
Get PDF T he COVID-19 pandemic has necessitated the development of robust mathematical models to understand and mitigate its impact. This study presentsacompartmentalmodel for the IndianpandemicCOVID-19 dynamics, incorporating key compartments such as susceptible, exposed, infected, quarantined, and recovered populations. The positivity and boundednessofsolutionsarerigorously analyzed to ensure that the model remains biologically meaningful over time. A detailed exploration of the basic reproduction number R0 is conducted using the next-generation matrix approach, identifying it as a pivotal threshold parameter dictating disease dynamics. Theequilibriaof thesystem, includingthe Disease-Free Equilibrium (DFE) and the Endemic Equilibrium (EE), are derived and analyzed for their stability properties. The local stability of the DFE is established for R0 < 1, while conditions for the existence and stability of the EE are explored for R0 > 1. Additionally, the study employs Lyapunov functions to assess the global stability of equilibria, ensuring the robustness of the proposed model under varying initial conditions. T he Pontryagin’s Maximum Principle is utilized to derive optimal con trol strategies, focusing on minimizing the number of infections and optimizing interventions such as vaccination, treatment, and quarantine measures like wearing a face mask and hand washing. Numerical simula tions validate the theoretical findings, providing critical insights into the effectiveness of various control measures. This comprehensive framework contributes to the mathematical understanding of COVID-19 dynamics and offers valuable guidance for public health decision-making
Optimizing Convolutional Neural Network-Long Short-Term Memory Architecture with Additive Attention Mechanism for Stock Price Prediction
Get PDFStock price fluctuations reflect market expectations for the economic situation and company profits. Accurately predicting stock prices has become a hot topic in academia. With the rapid development of artificial intelligence, many researchers are starting to use machine learning algorithms to predict stock prices. In this paper, a new time series prediction model, the combination of the convolutional neural network and long shortterm memory neural network with additive attention mechanism (CNNLSTM-AAM), is proposed for stock price prediction. It can combine the advantages of the convolutional neural network (CNN), the long shortterm memory (LSTM) neural network, and the additive attention mechanism (AAM), and better capture nonlinear features of time series data. In the simulation analysis, we select sample data of three stocks (Vanke A, Shanghai International Port Group, and China Merchants Bank) and three stock price indexes (China Securities 500 Index, Shanghai Stock Exchange 50 Index, and Growth Enterprise Index) in the Chinese stock market for comparative analysis, and use mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) as the evaluation indexes. The CNN-LSTM-AAM model has the best prediction ability relative to the CNN and CNN-LSTM models. In addition, we also find that the prediction ability of the CNN-LSTM-AAM model for different stock data sets is different under the existing parameter conditions. For a specific dataset, the parameter conditions of the CNN-LSTM-AAM model need to be further adjusted to achieve the best prediction effect. Based on the above findings, the CNN-LSTM-AAM model has better performance and higher accuracy, and can provide credible decision-making basis and research methods for investors, financial institutions, and regulators
Is the correlation between social media and narcissism stronger within GenZ or Millennials?
Get PDFThis study examines the relationship between social media usage and narcissism among Generation Z and Millennials. By utilizing the Narcissistic Personality Inventory (NPI), we sought to determine whether there is a generational difference in narcissistic tendencies as influenced by social media. The results indicated no statistically significant difference between the narcissism scores of the two generations, challenging Twenge’s (2009) assertion that younger generations exhibit higher levels of narcissism. Both generations had an average score of 16/24 on the NPI test, which leans toward narcissistic tendencies. However, this study identified several limitations that may have influenced the findings, including the homogeneity of the sample population and the limitations of the NPI as a measurement tool
