Volume 20 Issue 12 2025

Simone de Beauvoir fundamentally reshaped modern feminist theory by revealing the constructed nature of gender, critiquing patriarchy’s ideological foundations, and reconceptualizing women’s freedom within an existential framework. Her seminal work The Second Sex (1949) offered not only a critique of women’s subordination but also a philosophical method through which gender oppression could be interrogated and resisted. This paper examines Beauvoir’s feminist perspective through her central concepts: the construction of woman as the “Other,” the processes through which women are socialized into subordination, the existentialist idea of freedom and transcendence, her analyses of motherhood, marriage, sexuality, and work, and her critique of essentialism. The paper also evaluates Beauvoir’s lasting influence and the critiques raised by later feminists. Ultimately, it argues that Beauvoir’s feminist philosophy remains foundational because it offers both a structural critique of patriarchy and a philosophical call to reclaim women’s agency.

This paper presents a novel framework for economic risk assessment, called Dynamic Adversarial Risk Assessment, which leverages Generative Adversarial Networks to enhance resilience testing in economic systems. Traditional risk models often fail to anticipate unprecedented economic crises due to their reliance on historical data patterns. The DARA framework addresses this limitation by generating synthetic yet plausible extreme economic scenarios that may not exist in historical datasets. Our methodology employs dual-network architecture, where one network generates increasingly complex risk scenarios while the other evaluates their plausibility, creating an evolutionary system for stress-testing economic policies. We demonstrate the framework's efficacy through a case study of banking sector stability, where DARA successfully identified systemic vulnerabilities that conventional models overlooked. Results show that DARA-enhanced stress tests improved risk detection rates by 37% compared to traditional methods, with strength in identifying compound risk factors. This research contributes to the growing literature on AI applications in economic forecasting and offers policymakers a powerful tool for proactive risk management. The framework's adaptability makes it suitable for implementation across various economic sectors and regulatory environments.

In order to determine the relationship between vehicle speeds and fuel reduction related to truck aerodynamic technologies, heavy vehicle trucks were used for testing. In this study, the CFD analysis of the heavy truck has been performed using ANSYS software for reduction of drag and fuel consumption. Continuous speed testing was done at various speeds on the road and results are verified. Three different vehicle speeds 35, 55, and 75 KMPH were used to gather data. To determine the fuel consumption at different vehicle speeds, two designs are considered for analysis that is existing and proposed designs by adding extra features like Deflector, Vanes between Cab & Container and Base Flaps at End of Truck. By using these designs heavy vehicle truck was modelled using CATIA software and computational fluid dynamic analysis was done using ANSYS software. The main intention behind this project is to compute the Drag coefficient and fuel consumption for three different vehicle speeds and to compare the results. Further, the study involves finding out the best efficient design that gives low drag coefficient.