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.

This paper considers the theoretical and methodological problems that arise when creating a primary standard of relative humidity in conditions of high accuracy of measurements. An analysis of existing approaches to the formation of moisture standards based on hygrometric, psychrometric and condensation methods is carried out. A mathematical model of the formation of reference humidity based on thermodynamic dependencies between the partial pressure of water vapor, temperature and dew point is proposed. The model takes into account the nonlinear effects of phase interaction and the uncertainty of measurements due to the instability of the temperature regime and the heterogeneity of the pressure distribution in the reference chamber. Simulation was carried out for various thermodynamic conditions, which made it possible to assess the errors and determine the main directions for optimizing the design of the standard. The scientific novelty of the work lies in the integrated approach to the substantiation of metrological traceability of humidity, including physical and mathematical modeling, uncertainty analysis and the development of criteria for the stability of the reference medium. The results obtained can be used in the development and certification of primary moisture standards in national metrology institutes.

Managing shared expenses within groups—such as friends, roommates, or teams—often leads to confusion, missing records and miscalculations when handled manually. Existing tools typically offer only basic splitting functions and lack essential features such as automated reminders, transparent settlement tracking, or intelligent insights. To address these limitations, Share Pay is developed as a web-based platform that streamlines the entire process of recording, splitting and settling group expenses. The system enables users to create groups, add participants, log transactions and view updated balances in real time. It supports both equal and customizable split methods that allow flexibility for various scenarios. A dedicated settlement module helps users clearly identify outstanding dues while the dashboard provides an organized overview of spending activity. Built using modern web technologies, Share Pay aims to deliver a simple with reliable and scalable solution that enhances transparency and reduces the effort involved in managing shared financial responsibilities.

Paul Auster’s fiction has long been associated with chance, randomness, and accident, which previous scholarship has largely examined as narrative motifs or formal devices shaping plot and character. While such studies establish the structural and aesthetic importance of chance, they rarely address its epistemological implications. This paper argues that chance in Auster’s novels should be understood not merely as a motif, but as an epistemological principle through which reality is perceived, interpreted, and narrated in the late twentieth and early twenty-first centuries. Drawing on chaos and complexity theory, philosophies of contingency, urban studies, and contemporary identity theory, the paper reconceptualizes chance as a cognitive framework rather than a narrative technique. It proposes that Auster constructs an epistemology of uncertainty characterized by nonlinearity, indeterminacy, and the collapse of stable causality. Within this framework, the novels do not simply depict accidental events; they examine how individuals attempt to generate meaning, coherence, and identity in a contingent world. By elevating chance from motif to epistemological structure, this study offers a new theoretical model for interpreting Auster’s fiction and contributes to broader debates theoretical.

The Saudi Arabian insurance industry, with its high transaction volumes, complex regulations, and strong growth potential, is well-positioned for blockchain adoption. Yet, there is still a notable gap between the promising theory of blockchain and its practical, scalable use in the real-world regulatory and operational environment of emerging markets. This study introduces and tests a four-phase blockchain implementation framework tailored for Saudi insurance companies, focusing on technical design, stakeholder collaboration, and regulatory compliance. This research uses a mix of methods: market analysis (2016–2024), interviews with 17 experts, a 10,000-iteration Monte Carlo simulation, and a deep dive case study of Tawuniya Insurance. The simulation suggests that over five years, blockchain adoption could bring a 187% return on investment, cut claims processing time by 87%, and reduce administrative costs by 71%. To meet Saudi data rules and Islamic finance needs, the study recommends a hybrid on-chain and off-chain system based on Hyperledger Fabric. The case study confirms framework viability, demonstrating an 82% reduction in processing time and a 22-month breakeven period in a live environment. Key contributions include a replicable implementation roadmap, empirical evidence of financial and operational benefits, and regulatory alignment strategies for emerging markets. The study concludes that a phased, simulation-informed approach significantly reduces the risks associated with blockchain adoption and accelerates digital transformation in regulated sectors.

This study aims to examine the effects of an educational game designed by the researcher for teaching information in the Turkish language and literature course on students' learning and students' thoughts on the use of educational games. In order to understand, analyze and critically evaluate texts in books in the Turkish language and literature course, students need some basic information about text types, authors, poets, literary periods, movements, concepts, etc. Similar results were obtained with studies conducted on the effects of using educational games on different courses. According to the results obtained from the examined studies, it was observed that games that are effective in the learning of students at primary and secondary school levels also have a positive effect on the learning of high school students. It was determined that high school students also wanted educational games to be used in their courses and that they thought that the use of educational games was necessary because they provided effective learning. When the students' exam success scores applied before and after the game were taken into consideration, it can be said that the teaching carried out by including educational games significantly increased students' learning about works, artists, genres/forms and periods in the Turkish language and literature course. Teaching using educational games; enabled students to learn this information more easily. Lessons became fun, students had the opportunity to learn by having fun. Findings showed that students had positive thoughts about the use of educational games for Turkish language and literature lessons and other lessons.

The purpose of this study; It is an examination of which values are included in the Turkish education textbooks used by 3rd grade primary school students in Turkey. For this purpose, the adequacy levels of different textbooks developed/prepared by different publishing houses were comparatively examined and evaluated in terms of the values to be imparted. Document analysis, one of the qualitative research methods, was used in the research. Therefore, this research has a descriptive nature. The universe of the research includes Turkish textbooks accepted by TTK (Board of Education and Discipline) in Turkey and taught in the 3rd grade of primary school. The sample of the research is Turkish textbooks belonging to MEB and Dörtel publishing houses. In this research, firstly, a literature review was conducted regarding the conceptual framework. After the conceptual framework was created, the determined textbooks were reached, taking this framework into consideration. At the beginning of the working process; “General Aims of Turkish National Education, Aims of Primary Education, Basic Principles of Secondary Education, Primary Education Life Sciences Course Programme, Primary Education Social Studies Course Programme, Primary Education Religious Culture and Morals Course Programme, General Aims of Turkish Language Course of the Ministry of National Education Board of Education and Discipline were exemined. The values of the circular numbered 1010/53were determined (First Lesson Subject) dated September 2010. The "Table of Values" prepared in this direction was determined as the data collection tool of the study. Textbooks belonging to different publishing houses where the study will be conducted were examined in terms of the determined values and comparisons were made. Reviews and comparisons were made both on a theme basis and on a book basis. Frequency and percentage calculations were made to determine the usage levels of the determined values in the textbooks in question. It was observed that in the Turkish textbook of Dörtel Publishing, 66 values were used 252 times in total in 40 texts in the book. It was observed that in the Turkish textbook of MEB Publications, 58 values were used 195 times in total in 40 texts in the book. According to the data obtained on the basis of themes the Turkish textbook belonging to Dörtel Publishing; In 2 of them, it was determined that the Turkish textbook belonging to MEB Publications had more value types and numbers than the other publishing house. From the data obtained on a book basis, it has been determined that the Turkish textbook belonging to Dörtel Publishing has more value types and numbers than the Turkish textbook belonging to MEB Publications.

The burgeoning field of nanotechnology necessitates efficient and accurate methods for predicting the properties of novel nanomaterials. Traditional experimental characterization is often time-consuming and resource-intensive. Computational approaches, particularly those leveraging machine learning, offer a promising alternative. This paper proposes a novel framework for the intelligent prediction of nanomaterial properties using crystal graph-based deep learning models. By representing the atomic structure of nanomaterials as graphs, where atoms are nodes and bonds are edges, we can effectively capture the complex interatomic relationships that dictate material properties. Deep learning architectures, specifically Graph Neural Networks (GNNs), are employed to learn intricate patterns from these crystal graphs and establish robust correlations with various material properties, including but not limited to band gap, mechanical strength, and thermal conductivity. This approach overcomes limitations of traditional feature engineering by automatically extracting relevant structural information. The proposed methodology offers a powerful tool for accelerating nanomaterial discovery and design, enabling highthroughput screening and optimization of materials with desired functionalities.

This investigation involves predicament of 2D Casson liquid motion, close to boundary layer by collective cause of convective heating condition, in addition to Brownian motion as well as the rophoresis, over a nonlinear stretching sheet. A precise mathematical elucidation to boundary value problem involving nonlinear PDE’s, for flow, heat and Nanoparticle concentration, variables are obtained from Runge- kutta -Fehlberg 4th-5th order technique. The impact of a range of various parametric quantities on Nanoparticle concentration, heat, and flow, variables are probed besides exploring by method of plots.