http://ijaers.com/ Open Access international Journal to publish research paper en-us September Leafy vegetables are an important vegetable crop that grows easily under controlled conditions, such as in hydroponic. A plant's leaf colours can be used to indicate stress levels due to its adaptability to environmental changes. In this study, the samples were cultivated in the backyard of the Life Science department in Pandit Ravishankar Shukla University, Raipur, Chhattisgarh during January to April 2024. In this study, fresh mature leaves of soil-cultivated plants and hydroponically cultivated plants were taken for chlorophyll estimation. The sample was tested using an acetone solution (80%) and the information from the spectrum was calculated using the Arnon’s method. The levels of Chl. a and Chl. b in soil-grown and hydroponically grown plants have been found to differ. Hydroponic cultivated plants show a high amount of total chlorophyll content as compared to soil-based plants and the reason behind the high level of chlorophyll content ina hydroponic system is controlled environmental conditions that mean precise control over light, temperature, and nutrient availability and are highly specific from species to species whereas, in a soil-based system, it is difficult to maintain such condition. http://ijaers.com/detail/comparative-analysis-of-chlorophyll-concentration-in-different-species-of-leafy-vegetables-cultivated-in-soil-and-hydroponic-system/ Ghritlahre R, Prabhas L., Megha A. http://ijaers.com/uploads/issue_files/1IJAERS-08202415-Comparative.pdf The wave amplitude function is a relational equation that links wave amplitude with various water wave parameters, such as wave number, wave angular frequency, and wave constant. This function is derived by integrating the Kinematic Free Surface Boundary Condition over time. The wave amplitude function incorporates breaking characteristics, allowing for the extraction of breaking parameters, including breaking wave height, breaking wave length, and breaking water depth, as functions of the wave period. By combining the Euler momentum conservation equation with the wave amplitude function, a dispersion equation is obtained. This dispersion equation elucidates the relationships between deep water wave height, deep water wave length, and deep water depth in relation to the wave period. The results obtained for both deep water wave height and breaking wave height are consistent with previous research. http://ijaers.com/detail/the-relationship-between-wave-period-deep-water-wave-and-breaking-wave-heights-formulated-using-the-wave-amplitude-function/ Syawaluddin Hutahaean http://ijaers.com/uploads/issue_files/2IJAERS-0920243-TheRelationship.pdf With the increasing power density of electronic equipment, heat dissipation technology has become the key to ensure the stable operation of equipment. Because of its unique structural design, the Tesla valve heat sink shows great potential in the heat dissipation of high-power electronic devices. However, the traditional heat transfer model prediction method has the problems of complex calculation and low efficiency. The purpose of this study is to explore a method of heat transfer model prediction based on training neural network to improve the accuracy and efficiency of heat transfer efficiency prediction of Tesla valve heat sink. The heat transfer data of Tesla valve heat sink under different structures were collected by numerical simulation. The data were then used to train a feed forward neural network. Through a lot of training and verification, the neural network model shows good generalization ability and can accurately predict the heat transfer efficiency under unknown conditions. In this study, the effects of network structure, training algorithm and optimization strategy on model performance are discussed, and an improved network architecture is proposed to improve the accuracy of prediction. Finally, the advantages of the proposed method in computational efficiency and prediction accuracy are verified by comparison with traditional methods. http://ijaers.com/detail/research-on-heat-transfer-model-prediction-of-tesla-valve-heat-sink-based-on-training-neural-network-method/ Yan-Xiao Jia, Yan-Zuo Chang, Guo-Xing Yang, Ruo-Yu Yang, Yi-wei Zhang, Ting-Hao Zhang http://ijaers.com/uploads/issue_files/3IJAERS-0920249-Research.pdf Afro-Womanism emerges as a critical framework that bridges the gap between traditional feminist thought and the lived experiences of Black women. By centering race, gender, and culture, Afro-Womanism provides a unique lens through which to explore both historical and contemporary gender issues. This article examines the evolution of gender consciousness through an Afro-Womanist lens, emphasizing the influence of societal norms on women of African descent. Through case studies from the 18th Century to the modern era, the article highlights how Afro-Womanism enables a more holistic understanding of gender dynamics across different cultural and socio-political contexts. http://ijaers.com/detail/afro-womanism-and-the-development-of-gender-consciousness/ Dr Ibrahim Yekini, Armande M. Hounkpe http://ijaers.com/uploads/issue_files/4IJAERS-0920247-Afro.pdf This paper aims to investigate the heat transfer conditions of vertically descending evaporator tubes. Through numerical simulation methods, the flow patterns and heat transfer characteristics within the evaporator tubes under both uniform and abnormal heating conditions are analyzed. Fluent software is employed to simulate the normal evaporation process in uniformly heated evaporator tubes, exploring their gas circulation and local heat transfer conditions. Additionally, simulations are conducted for descending evaporator tubes under abnormal heating to analyze the impact of different heat distributions on the flow patterns and heat transfer within the tubes. The conclusion drawn is that the primary cause of working fluid descent in evaporator tubes is uneven heating of the tube bundle. A single evaporator tube forms a stable circulation, whereas an increase in the number of tubes in the bundle exacerbates the descent phenomenon. To prevent such occurrences, measures such as optimizing heat transfer, ensuring complete combustion, and regularly maintaining the pipelines can be implemented. Future research could further simulate real-world boiler heating conditions and explore the application of two-phase evaporation flow in other equipment. http://ijaers.com/detail/simulation-study-on-heat-transfer-conditions-based-on-vertically-descending-evaporating-tubes/ Guo-Xing Yang, Yan-Zuo Chang, Yan-Xiao Jia, Jin-Ping Chen, Shao-Bi Dai, Yu-Qiang Lin http://ijaers.com/uploads/issue_files/5IJAERS-09202410-Simulation.pdf Cancer is among the leading causes of death among women worldwide and the most common types are breast, cervical, ovarian, and uterine cancers. It is crucial that these cancers be diagnosed early and accurately to help improve survival and treatment outcomes. Artificial intelligence (AI), with deep learning specifically, has come to be regarded as a transforming tool in the field of oncology, one that provides high diagnostic accuracy and automation with high efficiency. This review offers a comprehensive view of deep learning-based approaches for women's cancer detection and diagnosis, focusing on a variety of methodologies of AI applied in medical imaging, histopathology, and genomic analysis. The paperdiscusses several widely used deep learning architectures like CNNs, RNNs, transformer-based models, and hybrid techniques and their applications in identifying and categorizing various cancers in women. We also discuss some of the most important public datasets, performance metrics, and comparative evaluations of existing AI-driven diagnostic models. Even though there has been tremendous progress, data scarcity, model interpretability, ethical concerns, and integration into clinical workflows are critical barriers to adoption. The Paper also highlight emerging research trends, such as explainable AI, federated learning, and multi-modal fusion techniques, that aim to enhance the reliability and robustness of AI in oncology.This review synthesizes recent developments in an attempt to provide insights into the current landscape of AI-driven cancer diagnosis and identify future directions for research and clinical implementation. The findings point to the revolutionary potential of deep learning in the detection of women's cancers, but underscore the importance of interdisciplinary collaboration to overcome the limitations identified and translate AI advances into real-world healthcare solutions. http://ijaers.com/detail/ai-in-oncology-a-review-of-deep-learning-based-approaches-for-women-s-cancer-diagnosis/ Naveen Kumar Kedia, Dr. Vijay Kumar http://ijaers.com/uploads/issue_files/6IJAERS-08202454-AI.pdf E-commerce has been growing exponentially, creating a wave of user-generated content, which includes product reviews, ratings, and social media feedback in the process. Data streams from such analyses will be helpful for businesses to understand better the sentiments of customers, enhance decision-making, and improve customer engagement. Sentiment analysis is one of the critical branches of NLP, where insights are derived from textual data. Some of the classical approaches involve lexicon-based models and Naïve Bayes and Support Vector Machines from the machine learning area. More advanced techniques rely on deep learning approaches, for instance, LSTM, CNN, BERT, GPT-like transformer-based models.Techniques in Sentiment Analysis in E-commerce: A Comparison between Machine Learning and Deep Learning Approaches. This work encompasses major issues, such as sarcasm detection, spam review detection, and aspect-based sentiment analysis. It also includes popularly used datasets, such as Amazon Reviews and Yelp Reviews, and real-world applications, such as personalized recommendation systems and automated customer services. Furthermore, it introduces future research avenues: Explainable AI, multimodal learning, and federated learning for private sentiment analysis. The paper focuses on the review, analysis, methodologies, challenges, and applications in the effort of guiding researchers and industry professionals toward developing more effective sentiment analysis solutions for the e-commerce industry. http://ijaers.com/detail/decoding-customer-sentiments-in-e-commerce-a-review-of-machine-learning-and-deep-learning-approaches/ Bijendra Singh, Dr.Vijay Kumar http://ijaers.com/uploads/issue_files/7IJAERS-08202422-Decoding.pdf