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.

Nanotechnology has revolutionized drug delivery, with Nanostructured Lipid Carriers (NLCs) emerging as versatile platforms overcoming traditional limitations. This review explores the nanostructure, composition, preparation methods, and diverse applications of NLCs. NLCs, evolving from Solid Lipid Nanoparticles (SLNs), incorporate both solid and liquid lipids, enhancing drug loading, stability, and controlled release. Various preparation methods like high-pressure homogenization, solvent evaporation, hot/cold homogenization, and microemulsion techniques offer tailored approaches for NLC synthesis. NLCs find extensive applications including oral, topical, transdermal, and parenteral drug delivery, cancer therapy, ophthalmic treatments, imaging, cosmeceuticals, gene delivery, and veterinary medicine. Despite challenges, NLCs demonstrate immense potential in advancing pharmaceutical and biomedical fields.

The article analyzes intelligent control systems used in the process of growing fodder crops by the hydroponic method. The structure and functioning principles of intelligent algorithms are discussed, which automatically regulate the amount of water and nutrients while optimizing temperature, humidity, and lighting to increase crop yield. The advantages and challenges of modern hydroponic systems based on artificial intelligence, sensor networks, and information and communication technologies are highlighted. Special attention is given to the use of neural networks and cloud computing technologies for data processing. The obtained results contribute to improving the efficiency of implementing intelligent control systems in hydroponic complexes.

Garbhadhan Samskara, the foundational Ayurvedic preconceptional protocol, provides a systematic and holistic framework that prepares couples for healthy and planned conception. When interpreted through the lens of communication science, it emerges as a culturally grounded model employing structured counselling, behavioural messaging, and therapeutic instructions to influence reproductive outcomes. This review reinterprets Garbhadhan Samskara as a communication pattern integrating Panchakarma purification, Rasayana– Vajikarana rejuvenation, behavioural discipline, psychological regulation, and conceptiontime guidelines. These elements enhance reproductive determinants such as Ritu, Ksetra, Ambu, and Bija, while minimizing harmful influences. Modern evidence from epigenetics and fetal origins research further validates the significance of preconceptional preparation emphasized in Ayurveda [7–10]. This review concludes that Garbhadhan Samskara serves as an effective, holistic, and structured reproductive health communication model relevant to contemporary preconception counselling.

Retinal diseases pose significant risks to human vision and require timely and accurate diagnosis for effective management. Optical Coherence Tomography (OCT) has emerged as a valuable imaging modality for the early detection and monitoring of various retinal pathologies. In this study, an Amalgamate Learning Model (ALM) introduced to detect and classify retinal diseases using OCT images automatically. The ALM is designed as a hybrid approach that combines the strengths of different noise removal techniques and Generative Adversarial Networks (GANs) + CNN algorithms. The model obtained the hierarchical features learned by the CNNs from OCT images and captured the sequential patterns within the OCT images using ALM. The pre-trained model DenseNet169 is fine tuned on the OCT images dataset. The DenseNet-169 assists the strategy to capture the features from the OCT images dataset and then transfer to the proposed model. Although the classification model diagnoses three typical types of retinal diseases, such as AMD (Age-related Macular Degeneration), DME (Diabetic Macular Edema), and DRUSEN, which can be diagnosed and monitored using OCT (Optical Coherence Tomography) imaging. We had ophthalmologists with expertise meticulously label the dataset to guarantee precision of diseased labeling. We have performed a bunch of experiments to tune the ALM hyperparameters and achieve good performance. The model demonstrated state-of-the-art accuracy for retinal disease detection and classification against solo CNN and RNN models. The ALM showed high sensitivity and specificity, and it is considered a reliable method for early diagnosis and treatment. In addition, an interpretability investigation was conducted to obtain insights into the reasoning behind the ALM. This further facilitates model interpretations and increases the confidence of clinicians in its predictions. The proposed Amalgamate Learning Model is a powerful and effective method for the detection and classification of retinal diseases with OCT images. The joint modelling of spatial and sequential information on OCT scans in POLNet also contributes the excellent performance. The model's accuracy and interpretability can significantly enhance the clinical workflow and improve patient outcomes. Future work will focus on deploying the ALM in real-world clinical settings and expanding its application to other medical imaging tasks.

Agriculture is very important to the world’s economy; picking the right crops—and knowing how much they will produce—helps ensure there is enough food and that farms run efficiently. Traditionally, farmers make these decisions based on experience, which can take a lot of time and isn’t always accurate. The paper focuses on the design of an intelligent ML-based system capable of recommending the best crops to grow and predicting their yield. In recommending suitable crops for plantation, the soil type, pH value of the soil, atmospheric temperature, rainfall, and humidity are among the data considered. Guided by machine learning algorithms such as Random Forest, Decision Trees, and Gradient Boosting, the system evaluates all these elements in past data, including current environmental conditions, to propose the best crop for the farm. The prediction of yield production represents the quantity of crops the system expects to be produced. It analyses past yield records, weather patterns, and other farming factors. Methods such as Linear Regression, XGBoost, and Support Vector Regression enable the system to understand how different conditions influence crop output. In this way, farmers can do better planning of irrigation and resources and plan marketing strategies. This system is user-friendly since a farmer only needs to input all his data into the system and, within a very short time, suggestions and predictions appear. Its accuracy was checked using performance measures such as accuracy, mean squared error (MSE), and Rsquared. The early results indicate that the recommendations and predictions made using this approach were better compared to the traditional approaches.

In this paper, we are focused to study to define one of the models proposed to demonstrate how prioritized goal programming can be used in solving the process of a transportation problem. The objective of this method is to minimize the total transportation cost. The basic assumption underlying most of the formulations of these transportation models is that management is concerned solely with one objective, namely cost minimization with using of Goal Programming Technique. Prioritized goal programming extends traditional transportation models by handling multiple conflicting objectives through a hierarchy of priorities, rather than assuming sole focus on cost minimization.