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Metal–insulator–metal waveguide structure, which has a fascinating feature to confine the signal far beyond the diffraction light is numerically investigated by the finite difference time domain and the finite element methods. In this study, the MIM waveguide is both coupled with a half-elliptical groove (HEG) and an elliptical cavity resonator (ECR), and it can support the propagation of light in the nanoscale regime at the visible and near-infrared ranges. The interaction between these last elements gives rise to Fano resonance modes. Thanks to its interesting characteristics, a high sensitivity value, a factor of merit and interesting value of the group index are obtained for the proposed structure. We show that the transmission of the Fano system and the group index can reach 90% and a value of 63, respectively. We also report an investigation of the influence of the both geometrical HEG and ECR’s parameters on optical properties. Hence, the proposed structure could find a potential for applications in the integrated optical circuits such as optical storage, ultrafast plasmonic switchers, high performance filters and slow light devices.

Deep Learning is a promising domain. It has different applications in different areas of life and its application on the stock market is widely used due to its efficiency. Long Short Term Memory (LSTM) proved its efficiency in dealing with time-series data due to the unique hidden unit structure. This paper integrated LSTM with attention mechanism and sentiment analysis to forecast the closing price of two stocks; namely, APPL and TSLA, from the NASDAQ stock market. We compared our hybrid model with LSTM, LSTM with sentiment analysis and LSTM with Attention Mechanism. Three benchmarks were used to measure the performance of the models; the first one is Mean Square Error (MSE), the second one is Root Mean Square Error (RMSE) and the third one is Mean Absolute Error (MAE). The results show that the hybridization is more accurate than the LSTM model alone.

From its inception, the Affordable Care Act (ACA), also known as \"Obamacare,\" encountered several obstacles in its implementation. The difficulties included the severe economically framed debate and the religious controversy. While the left-wing advocates sought a universal health care model, conservatives argued that delivering assistance must be selective, accurate, and limited to be efficient. The current research offers valuable ideological insights into a long-lasting American health system beset by partisanship. This conceptual, qualitative paper employs a case study as a research tool to trace the ethical considerations behind the liberal-conservative debates over the ACA's socio-economic measures. It attempts to evaluate the American political division by considering both the ideological and ethical perspectives of the matter. Through applying the Lakoffian \"Metaphor Theory\" and essential principles of public health ethics included in \"Principlism,\" the study concludes that the ACA's implementation demonstrates conformity with two relevant theories concerned with political partisanship and public health ethics, respectively.

The Great Depression and the Great Recession marked the two most severe socio-economic crises that hit the United States. The Depression was the deepest and longest economic collapse in the nation's history, lasting from 1929 until the break of WWII. Economists argue that it started as a recession that, due to insufficient efforts and inappropriate policies to overcome it, exacerbated to a long-lasting economic downturn. The Great Recession that began in late 2007, worsened in 2008, and continued through 2009; similarly, had far-reaching effects though lasting much shorter. In this respect, the weaknesses and imbalances that characterized capitalism, the remarkably severe collapse of the financial system during the Recession, and the widespread human miseries that affected American society generated a significant number of views comparing it with the 1930s situation. This qualitative-based case-study paper uses the comparative approach to demonstrate how both crises were closely intensive on the U.S. community as the living standards fell dramatically, household income dropped quickly, unemployment rates rose distinctly and poverty levels went up considerably. Furthermore, the heated conservative-liberal debate over the economy revived, and radically-oriented movements remarkably emerged. The study concludes that despite the remarkable similarities in the causes responsible for leading the American society to a downward spiral, the duration, depth, and scope of the two crises had significant disparities in various socio-political sectors.

What characterized the United States' healthcare system is its huge privatization as private insurance covered half of the nation's people, largely financed by employers for employees and their households. This process is moderated through insurance companies that induced people to seek their medical consultations from contracted physicians and medical institutions to ensure the companies' interest. These causes, and some others, led to reducing the number of American people with individual insurance to low levels. This reality helped incite attempts of reforming the American health care system. In this respect, through the Affordable Care Act (ACA, also nicknamed Obamacare), the liberals sought to allow government to play a major role in curbing the market forces from maintaining their private-based control of the health care arena. This was through creating new channels and further options for health care consumers to seek eligibility for federal public programs or resort to private plans in the health marketplace. Such a paramount government role generated a heated opposition from conservatives. In this regard, relevant principles of beneficence, non-maleficence, justice, and respect for autonomy help raise ethical concerns over the role played by both liberals and conservatives either in support for or in surrender to the market forces and pharmaceutical industry.

The reform of the American healthcare sector was always a major source of contest between liberals and conservatives. This conflict came to the surface with signing the Affordable Care Act (ACA) into law in 2010. In assessing the liberals' and conservatives' positions from the ACA reform, this research utilizes the ethical principles involved in Principlism and Utilitarianism. It relies on case study and content analysis approaches as research tools and conceptual inquiries to trace the ethicality behind the liberal-conservative continuous debate. Through connecting theory to practice, this qualitative analysis of the ideological struggle concludes with the perception that liberals' positions demonstrate more respect to the rules of Principlism and public health ethics.

Conservatives tend to oppose any attempt at reforming the American healthcare system. This ideologically based objection witnessed a remarkable increase after the enactment of the Affordable Care Act (ACA), nicknamed \"Obamacare.\" The present paper will mainly discuss and analyze both the genesis of the political polarization over the ACA and its potential destiny. In tackling the issue of polarization over Obamacare as a case study, this article will employ the Lakoffian Metaphor Theory. This latter differentiates between the conservative and liberal moral worldviews by a metaphor of \"nation as family\" where the strict father stands for the conservative mind and the nurturant parent represents the liberal thought. Hence, this paper seeks to explore the reasons that lie behind the liberals' tolerance and conservatives' antipathy towards Obamacare. It draws the conclusion that the conservatives' strenuous efforts to reform some provisions of the ACA were achievable, yet the pursuit of repealing the whole law was not an overnight issue.

Convolutional neural network (CNN), a class of artificial neural networks that has become dominant in various computer vision tasks, is attracting interest across a variety of domains, including Medical image analysis. CNN is designed to automatically and adaptively learn spatial hierarchies of features through back-propagation by using multiple building blocks, such as convolution layers, pooling layers, and fully connected layers. This paper presents an approach based on CNN for the classification of brain tumors, based on several characteristics that will be extracted automatically and some performing features that will be used in our CNN, This proposed approach provides efficient results at the level of automatic classification than the other usual methods.