Volume 13 | Issue 12 |

 Abstract

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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 Keywords

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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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

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

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

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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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

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

 Abstract

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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This study analyzed participatory governance in Local Tourism Councils across Central Luzon, Philippines. Employing a mixed-methods research design, the study gathered data from respondents comprising tourism stakeholders through surveys, interviews, and document analysis to assess the current state of tourism governance in selected Local Government Units (LGUs). Findings revealed that local communities actively participate in tourism activities through inclusive decision-making processes facilitated by Local Tourism Councils. Regular stakeholder meetings and consultations ensure effective coordination, while digital platforms and local networks promote transparency and accessibility of tourism information. Evidence-based deliberations involving multiple stakeholders reflect high-quality governance practices, resulting in strong stakeholder satisfaction driven by community participation and visible economic benefits. Tourism performance indicators demonstrate exceptional results across key metrics. Tourist arrivals (M=3.93), economic contribution (M=3.96), tourist experience (M=3.95), and residents' perception (M=3.93) were all categorized as Very Good, while employment generation (M=3.78) and tourism infrastructure (M=3.62) also achieved Very Good ratings. Tourism sustainability (M=3.49) was rated Good, indicating opportunities for further enhancement. LGUs successfully implemented diverse tourism initiatives including surfing, heritage, eco, and event tourism, leveraging local assets and fostering community engagement. The study identifies areas for development, including digital infrastructure enhancement, improved accessibility for persons with disabilities, carrying capacity management, climate preparedness strengthening, and waste management system upgrades. Corresponding solutions are proposed: establishing digital corridors, implementing PWD-inclusive initiatives, developing visitor management systems, creating climate adaptation councils, and launching zero-waste programs. The research concludes that effective governance structures, legal frameworks, and community-centered approaches support sustainable tourism development in Central Luzon. Recommendations emphasize strengthening participatory mechanisms, enhancing infrastructure, expanding best practices, and addressing identified areas for improvement to ensure long-term tourism sustainability and maximizing economic, social, and cultural benefits for local communities.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Participatory; Governance; Tourism; Stakeholders; Sustainability; Community; Engagement; Infrastructure; Accessibility; Inclusivity; Performance

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Creative Commons Attribution 4.0 and The Open Definition

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Abstract: India frequently faces natural disasters, including floods, cyclones, and earthquakes, where timely and reliable reporting is crucial for public safety. AI-generated videos are increasingly employed in journalism to deliver rapid updates, visualize events, and aid in fact-checking. While AI improves reporting efficiency, it also poses risks of misinformation through deepfakes and manipulated visuals. This study explores the dual impact of AI-generated videos in Indian crisis journalism using surveys, expert interviews, and case analyses of events like the Kerala floods (2018) and Cyclone Fani (2019). Findings reveal that while AI videos are helpful, trust issues and regional-language detection challenges remain. Recommendations include ethical guidelines, journalist training, and public awareness campaigns.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

AI journalism, crisis reporting, misinformation, deepfake, disaster communication, India, social media, regional languages, automated verification

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The Self-Compacting Concrete (SCC) is that which gets compacted due to its self-weight and is deaerated (no entrapped air) almost completely while flowing in the form work. In recent times self-compacting concrete has been accepted as a quality product and are widely used. The addition of fly ash and silica fumes to concrete imparts superior mechanical properties to Self Compacting Concrete. The aim of this project work is to study the strength behavior of SCC partial replacement of cement with fly ash and silica fumes. Fly ash silica fume Self Compacting Concrete (FASF SCC) has been cast with volume fractions of 00%, 05%, 10% and 15% of silica fumes based on "Nan Su et.al" method and tested after 28 days of normal curing. This work also comprises the study of the effect of steel fibers on workability in fresh state and the compressive strength of cubes (150mmx150mmx150mm), split tensile strength of cylinders (diameter 150 mm and height 300mm), modulus of rupture of beams (100mmx100mmx500mm) in hardened state after 7,14, and 28 days of normal water curing. The results showed that the ultimate strength has increased marginally. The optimum volume fraction of silica fumes for better performance in terms of strength and ductility has been found to be 10%. Based on the results obtained from the investigations and after rational discussion, the conclusions have been developed on the fresh state properties of FASF SCC, hardened properties of FASF SCC, and the optimum dosage of silica fume.

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"EXPERIMENTAL STUDY OF SELF COMPACTING CONCRETE FOR PARTIAL REPLACEMENT OF CEMENT BY FLY ASH AND SILICA FUME"

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Creative Commons Attribution 4.0 and The Open Definition