Volume 13 | Issue 12 |

 Abstract

Moringa oleifera Lam. (family: Moringaceae) is a fast-growing, deciduous plant native to the Indian subcontinent. Commonly known as the "Miracle Tree" or "Drumstick Tree," it has gained global attention due to its rich nutritional composition and diverse medicinal properties. Every part of this plant--leaves, flowers, fruits, bark, seeds, and roots--possesses valuable phytochemicals such as alkaloids, flavonoids, tannins, saponins, glycosides, and terpenoids. These constituents are responsible for its broad spectrum of pharmacological actions including antioxidant, anti-inflammatory, antimicrobial, antidiabetic, hepatoprotective, anticancer, and cardiovascular protective activities. This review compiles comprehensive pharmacognostic details, phytochemical profiles, and pharmacological insights of Moringa oleifera, bridging traditional ethnomedicinal knowledge with modern pharmacological validation.

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 Keywords

Moringa oleifera, Pharmacognosy, Phytochemistry, Therapeutic potential, Phytoconstituents, Pharmacological activities

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Creative Commons Attribution 4.0 and The Open Definition

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FMCG, Brand Loyalty, Consumer Perception, Hindustan Unilever Limited (HUL), Milky Mist, Chi-Square, ANOVA

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Creative Commons Attribution 4.0 and The Open Definition

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Knowledge of Vocabulary plays a vital role in the successful acquisition of a language. It is an essential and a fundamental element in language learning since it is considered the basis of any ESL Learners'. Students face tremendous difficulties in learning English vocabulary and show an aversion towards learning vocabulary. The present work highlights the use of multiple methods of teaching resources in learning vocabulary. It shows that learners'' can use a variety of resources to acquire vocabulary in the four major skills. It highlights that learners'' can be open to other resources outside the classroom. The usage of new technology enhances their vocabulary learning and they need not rely solely on teachers.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Vocabulary, Vocabulary Learning Strategy, Resources, English as Second Language

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Creative Commons Attribution 4.0 and The Open Definition

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Women empowerment and reproductive health are intrinsically linked, as the ability of women to make informed choices about their bodies is fundamental to their overall empowerment. This paper explores the multifaceted relationship between women's empowerment and reproductive health, emphasizing that when women are empowered--socially, economically, and politically--they are better positioned to access necessary health services and make informed decisions regarding their reproductive rights. This research paper examines the relationship between women empowerment and reproductive health through a comparative study of urban and rural women in District Gautam Buddha Nagar. The study aims to analyze how educational status, economic independence, decision-making power, and access to healthcare influence women's reproductive well-being. A descriptive comparative research design was adopted, and data were collected from 100 respondents (50 urban and 50 rural women) using a structured questionnaire. The findings reveal significant disparities between urban and rural women. Urban women demonstrated higher levels of education, financial independence, autonomy, and awareness of reproductive health practices, which positively influenced their maternal health and family planning decisions. In contrast, rural women faced challenges such as limited education, economic dependence, inadequate healthcare access, and strong socio-cultural barriers that restricted their reproductive choices.

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Reproductive health, Multi-faceted Empowerment, Role of Education, Healthcare Access, Policy and Community Efforts, Holistic Approach

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Creative Commons Attribution 4.0 and The Open Definition

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Human speech is a fundamental mode of communication, rich not only in linguistic data but also in emotional cues. Speech Emotion Recognition (SER) aims to automatically decode emotional states by analyzing vocal characteristics such as spectral shape, rhythm, intensity, and pitch. With the capabilities of modern Artificial Intelligence (AI), deep learning approaches provide superior methods for extracting complex emotional patterns compared to earlier machine learning techniques. This study introduces a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) architecture designed for robust emotion classification. The proposed methodology involves extensive preprocessing and the extraction of multiple features, including Mel-Frequency Cepstral Coefficients (MFCC), Chroma, and Zero Crossing Rate (ZCR). Trained on a fused dataset including benchmark resources like RAVDESS, TESS, and CREMA-D , the CNN-LSTM model demonstrated an overall accuracy of 90%+. This result confirms the model's strong generalization and significant performance improvement over classical methods, offer ing a valuable contribution to emotion-aware AI systems for applications in human-computer interaction (HCI) and mental health analysis.

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 Keywords

HCI, Convolutional Neural Network, Acoustic Variability,Hidden Markov Models

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Creative Commons Attribution 4.0 and The Open Definition

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Cloud computing allows organizations to manage data and run applications on remote infrastructures offered by providers like AWS, Microsoft Azure, and Google Cloud. These platforms deliver numerous advantages--including cost savings, high availability, and global accessibility. However, alongside these benefits, cloud environments face several complex security concerns such as unauthorized access, misconfigured services, data leaks, and vulnerabilities created by shared resources. Traditional perimeter-based security models are unsuitable for dynamic cloud ecosystems. Modern architectures demand continuous user validation, automated policy enforcement, and AI-powered monitoring to detect threats in real time. This paper examines current trends that influence cloud security and identifies the most frequent challenges faced by organizations today.

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Cloud Security, Zero Trust, DevSecOps, Multi-Cloud, CNAPP, AI Security, Misconfiguration.

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Creative Commons Attribution 4.0 and The Open Definition

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Automatic speaker diarization and voice cloning have become essential components in modern speech-driven applications, including intelligent transcription systems, conversational analytics, and personalized audio generation. This paper proposes a unified framework that integrates WhisperX, PyAnnote, and neural Text-to-Speech (TTS) models to achieve highly accurate voice diarization and realistic voice cloning. The system first applies WhisperX for ASR transcription with word-level alignment, ensuring fast and precise text extraction. Speaker segments are detected using PyAnnote's state-of-the-art speaker embedding and clustering techniques, which significantly improve diarization accuracy in multi-speaker environments. Each identified speaker embedding is then mapped to a corresponding voice profile used by a neural TTS model to generate synthetic speech that preserves pitch, timbre, and prosodic characteristics of the original speaker. The integration of these modules produces a seamless pipeline capable of identifying speakers, transcribing speech, and replicating voices with high naturalness. Experiments on dialogue datasets demonstrate notable improvements in diarization error rate, transcription accuracy, and perceptual voice similarity. The proposed system shows potential for applications in personalized virtual assistants, automated content creation, podcast enhancement, call center analytics, and accessibility technologies.

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Voice Diarization, Speech Transcription, Voice Cloning, WhisperX, Pyannote.audio, Deep Learning, Speech Recognition

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Creative Commons Attribution 4.0 and The Open Definition

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Abstract Present work is done in the floodplains area of Chapra. In this study various types of microfauna were found. Floodplain ecosystems host diverse soil microfauna that play important ecological roles but can also display negative features under conditions of pollution, hydrological disturbance, and organic accumulation. In many floodplain regions, industrial effluents, agricultural runoff, and heavy-metal-laden sediments accumulate in soils, affecting the composition and metabolic activity of micro faunal communities. These altered microfauna can influence carcinogenic processes indirectly through biotransformation of pollutants, mobilization of heavy metals, and amplification of pathogenic micro organisms. This study reviews the potential pathways linking degraded soil microfauna in floodplain areas with increased cancer risk in human populations. In this study to control the negative of soil microfauna various types of pesticides, insecticides, herbicides, nematicides impact are used, which are responsible for rapid increase in cancer cases through inhalation, ingestion and dermal contact. Impact of negative features of soil microfauna in floodplain area on cancer is based on Bibliometric analysis of the studies published between 2012 to June 2025. For this study 105 papers selected from the Scopus, Google scholar and Research gate database. The types of cancer reported most frequently were gall bladder cancer, multiple myeloma, prostate cancer, breast cancer, leukemia, melanoma, colon cancer lung cancer, ovarian cancer and lymphoma. Due to lack of awareness, such type of pesticides, insecticides, herbicides are being used and are increasing the number of Cancer patients year by year.

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Keywords: - Microfauna, cancer, pesticides, soil, floodplain, Impact.

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Creative Commons Attribution 4.0 and The Open Definition

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Creative Commons Attribution 4.0 and The Open Definition

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Right to Education, Social Solidarity, Socio-economic Duties, Privatization of Education, Judicial Dilution.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Creative Commons Attribution 4.0 and The Open Definition

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Creative Commons Attribution 4.0 and The Open Definition