Volume 14 | Issue 6 |

 Abstract

Polycystic Ovary Syndrome (PCOD) is one of the most common endocrine and metabolic disorders affecting women of reproductive age. It is often associated with hormonal imbalance, irregular menstrual cycles, insulin resistance, weight gain, stress, and reduced quality of life. Due to the increasing interest in natural healthcare approaches, herbal nutraceutical formulations have gained attention as supportive alternatives for managing PCOD-related symptoms.The present study focuses on the formulation and evaluation of Aayuherbo Prash, a herbal nutritional preparation developed to support women's wellness and hormonal health. The formulation was prepared using a blend of medicinal herbs and nutrient-rich ingredients including Amla, Fenugreek, Cinnamon, Spearmint, Shatavari, Ashwagandha, Turmeric, Flax Seed, Chia Seed, Pumpkin Seed, Fennel, Honey, and Dry Fruits. These ingredients were selected based on their traditional medicinal importance and potential benefits in supporting metabolism, hormonal balance, antioxidant activity, stress management, and overall nutritional health.The prepared formulation was evaluated for various physicochemical and quality control parameters such as organoleptic properties, pH, homogeneity, moisture content, ash value, extractive value, phytochemical screening, microbial quality, and stability. The formulation exhibited a pleasant herbal aroma, mildly sweet taste, smooth semi-solid consistency, and satisfactory stability under recommended storage conditions. The pH of the formulation was found to be 5.8, indicating its suitability as a herbal nutraceutical product.The results suggest that AAYUHERBO PRASH may serve as a promising herbal nutritional formulation for supporting women's health and the management of PCOD-related concerns. Further clinical studies are required to establish its therapeutic effectiveness and long-term safety.

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 Keywords

PCOD, Herbal Nutraceutical, Women's Wellness, Hormonal Balance, Aayuherbo Prash, Herbal Formulation, Phytochemicals.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Epilepsy is a chronic neurological disorder characterized by recurrent seizures resulting from abnormal neuronal activity and remains a significant global health concern. Although several antiepileptic drugs are available, treatment resistance and adverse drug reactions continue to limit their effectiveness in many patients. Natural products represent an important source of bioactive compounds with potential therapeutic applications in neurological disorders. Luteolin, a naturally occurring flavonoid isolated from the rhizomes of Cyperus rotundus, has been reported to possess antioxidant, anti-inflammatory, and neuroprotective properties. The present study investigated the potential anticonvulsant activity of luteolin through an in silico molecular docking approach against selected epilepsy-associated protein targets. Luteolin was isolated and purified from the rhizomes of Cyperus rotundus, and molecular docking studies were performed against key epilepsy-related targets, including GABA-A receptor (GABRA1), protein kinase B (AKT1), tumor necrosis factor-alpha (TNF-?), and cyclooxygenase-2 (COX-2/PTGS2). Three-dimensional structures of the target proteins were obtained from the Protein Data Bank, and docking analyses were conducted using standard molecular docking software to evaluate binding affinities and ligand-protein interactions. Luteolin demonstrated favorable binding interactions with all selected targets, forming stable ligand-protein complexes through hydrogen bonding and hydrophobic interactions. The observed binding affinities suggest the potential of luteolin to modulate neurotransmission, neuroinflammatory pathways, and neuronal survival mechanisms associated with epileptogenesis. These findings indicate that luteolin may serve as a promising natural candidate for anticonvulsant drug development. Further experimental studies are warranted to validate its antiepileptic efficacy and elucidate the underlying molecular mechanisms.

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 Keywords

Luteolin; Cyperus rotundus; Epilepsy; Anticonvulsant activity; Molecular docking; GABA-A receptor; AKT1; TNF-?; COX-2.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Rubber (Hevea brasiliensis) is a commercially important crop, yet foliar diseases such as Anthracnose, Corynespora Leaf Fall, and Phytophthora-induced Dry Leaf Syndrome significantly reduce plantation yield and tree longevity. Manual diagnosis by agricultural officers is time-consuming, subjective, and often inaccessible to smallholder farmers in remote regions. This paper presents an end-to-end AI-powered mobile application for real-time detection and classification of rubber leaf diseases using deep learning. The system employs a MobileNetV2 convolutional neural network trained via a two-stage transfer learning strategy: an initial warmup phase with a frozen ImageNet backbone followed by fine-tuning of the top 30 layers using cosine-decay learning rate scheduling. Data augmentation, L2 regularization, dropout, and class-weight balancing were applied to improve generalization on the Mendeley Rubber Leaf Dataset containing 1,741 images across four classes. The model achieved a validation accuracy of 99.7% with perfect precision and recall across all classes. The trained model was deployed through a dual-inference architecture: a Python Flask REST API for cloud-based predictions and a quantized TensorFlow Lite model embedded within a cross-platform Flutter mobile application for offline inference, ensuring uninterrupted functionality in rural areas with limited connectivity. The application further incorporates a 90% confidence-threshold gating mechanism to reject ambiguous inputs, multilingual support, an analytics dashboard for disease trend monitoring, and PDF report generation for agricultural record-keeping. The proposed system demonstrates that lightweight deep learning models, combined with intelligent mobile deployment strategies, can provide scalable, accurate, and accessible diagnostic tools for precision agriculture. Index Terms--Deep Learning, Transfer Learning, MobileNetV2, Rubber Leaf Disease, TensorFlow Lite, Flutter, Precision Agriculture, CNN, Image Classification

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 Keywords

, Convolutional Neural Network (CNN), Precision Agriculture, Plant Disease Classification, Artificial Intelligence in Agriculture, Image Classification, Mobile Application, Flask API, Offline Inference, Smart Farming, Agricultural Technology, Leaf Disease Prediction, Machine Learning, Computer Vision, Hevea brasiliensis.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Agricultural development and rural development are closely interconnected processes that play a crucial role in improving the socio-economic conditions of rural communities. Agriculture serves as the backbone of rural economies by generating employment, enhancing incomes, and ensuring food security. Sustainable agricultural growth contributes significantly to poverty reduction, economic diversification, and overall rural transformation. In India, various government initiatives and development programmes support rural agricultural development through financial assistance, technological innovation, infrastructure creation, crop insurance, and market reforms. Effective rural development also requires improvements in education, healthcare, transportation, irrigation, and communication facilities to create an enabling environment for agricultural progress. The study highlights the complementary relationship between agricultural advancement and rural development, emphasizing the importance of integrated policies, institutional support, farmer empowerment, and sustainable practices. It concludes that coordinated efforts by governments, local institutions, and stakeholders are essential for achieving inclusive growth, strengthening rural livelihoods, and ensuring long-term agricultural sustainability and rural prosperity.

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 Keywords

Agricultural Development, Rural Development, Rural Livelihoods, Sustainable Agriculture, Food Security, Government Schemes, Inclusive Growth, India.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Toni Morrisons's books reveal of complex web of themes, characters, and storylines when analysed through the lens of literary analysis and feminism. Toni Morrison, Maya Angelou, Paul Marshall, Alice Walker, and Gloria Naylor were among the American writers who saw writing as a way to break free, a strategy for rebellion, and a creative way to express oneself. She follows in Edward Said's footsteps by exploring how the dominant group manipulates black reality to serve its own agenda; Said revealed orientalism as a Western tactic for moulding and dominating the Orient. Her current projects include an Afro-centric perspective and an African-American poetics. While exploring the intricacies of black women's life in white America, she tries to comprehend the contradiction of her African-American identity. This presentation will examine Toni Morrison's works through the lens of literary criticism.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Rebellion, Novel, America, Afro-Centric, Poetics, contradiction, identity.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Organometallic Catalysis, Ligand Design, Precision Catalysis, Catalytic Efficiency, Green Chemistry, Transition Metals, Computational Chemistry

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Creative Commons Attribution 4.0 and The Open Definition

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The rapid growth of digital payment systems and online financial transactions has increased the risk of fraudulent activities, resulting in significant financial losses for customers, businesses, and financial institutions. Detecting fraudulent transactions in real time has become a major challenge due to the large volume of online payments processed every day. This project presents an intelligent Fraud Detection in Online Payment System using Machine Learning techniques to identify and prevent fraudulent transactions effectively. The system utilizes historical transaction datasets containing attributes such as transaction ID, transaction time, transaction amount, account balance details, and other relevant features to analyze transaction behavior and identify suspicious patterns. Data preprocessing techniques including data cleaning, feature selection, normalization, and dataset transformation are applied to improve data quality and model performance. The Logistic Regression algorithm is employed as the core classification technique due to its simplicity, efficiency, and suitability for binary classification problems involving fraudulent and genuine transactions. The developed system is integrated with a web-based application that enables real-time fraud detection. Whenever a new transaction is initiated, the system analyzes the transaction details using the trained machine learning model and predicts whether the transaction is fraudulent or genuine based on learned patterns from historical data. The prediction result is instantly displayed through the web interface, allowing administrators and users to take immediate action when suspicious activities are detected. This approach improves fraud detection accuracy, reduces manual monitoring efforts, minimizes financial risks, and provides a scalable and cost-effective solution for enhancing the security of online payment systems.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Fraud detection in online payment, scam online payment, fraud transaction

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Creative Commons Attribution 4.0 and The Open Definition