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

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

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

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

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

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

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

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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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Malpositioned dental implants present significant functional, esthetic, and biomechanical challenges, arising from inaccuracies in three-dimensional positioning related to spatial, angular, and rotational deviations. These errors may result from anatomical limitations, planning deficiencies, surgical inexperience, or inadequate use of digital guidance, leading to complications such as peri-implantitis, soft-tissue recession, prosthetic misfit, and compromised load distribution. This review summarizes the classification of implant malpositions and critically evaluates available corrective strategies, including prosthetic compensation, implantoplasty, ridge and soft-tissue augmentation, segmental osteotomy, and definitive explantation with re-implantation. Additionally, the role of digital workflows AI-enhanced diagnostics, CBCT-based planning, dynamic navigation, and 3D-printed guides--is highlighted for both prevention and correction. Treatment selection depends on the severity and clinical impact of the malposition, with multidisciplinary planning essential for predictable functional and esthetic outcomes. A comprehensive understanding of these corrective options supports safer, more precise, and patient-centered implant rehabilitation.

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Dental implant malposition, Corrective techniques, Prosthetic compensation, Implant removal, Digital implant planning

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This work investigates the performance of Steel Fibre Reinforced Concrete (SFRC) prepared with M30 grade concrete incorporating different fibre volume fractions (0%, 0.5%, 1.0% and 1.5%). The primary aim of the investigation was to assess how the addition of steel fibres affects the workability and mechanical behaviour of concrete, including compressive, split tensile, and flexural strengths. The concrete mix design followed the guidelines of IS 10262:2019, and tests were conducted on specimens after 7, 14, and 28 days of curing.The inclusion of steel fibres led to notable improvement in tensile and flexural performance, whereas compressive strength showed only moderate enhancement. The maximum improvement in overall mechanical behaviour was observed at 1.0% fibre content. The results demonstrate that SFRC exhibits superior structural efficiency, toughness, and energy absorption capacity due to its enhanced post-cracking response, improved ductility, and effective crack-bridging mechanism. In comparison to conventional concrete, SFRC achieved substantially higher tensile and flexural strengths with only marginal gains in compressive strength. Therefore, the study confirms the suitability of SFRC for structural applications where improved toughness, ductility, and energy dissipation characteristics are required.

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SFRC, Steel Fibre Reinforced Concrete, M30 Concrete, Fibre Volume Fraction, Compressive Strength, Tensile Strength and Flexural Strength.

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: Foundation is the structural base member which transfer the whole loads of structure to the ground with stability and longevity. The foundation is generally enlarged at the base to distribute the load over the large area such that the pressure on the soil does not exceed its permissible bearing capacity. In this project is used to define the foundation design of over head tank by manual design. The design involves load calculations manually. We use manual design for Circular over head tank IS 456-2000 consider the load as per the according to IS 1983 (part-1). Firstly it limits the stresses in steel so that concrete is not over stressed and in second aspect it limits the cracking width. This project gives in brief, The theory behind the design of liquid retaining structure (Elevated Circular Water Tank) using Working Stress Method and IS 456:2000.

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Circular Overhead Water Tank, Natural Hazard, Working Stress Method, IS Code

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Railway safety depends on constantly monitoring the tracks, because unnoticed cracks or obstacles can lead to serious accidents. This paper introduces an intelligent Railway Track Obstacle and Crack Detection System designed to improve safety through automation. The system uses sensors, image processing, and machine learning to detect cracks and obstacles in real time. IR and ultrasonic sensors help in identifying cracks, while object detection techniques recognize any obstacles on the track. The system also includes GPS and GSM modules to track the exact fault location and send instant alerts to the authorities for quick maintenance. Experimental results show that the system provides high detection accuracy in different environmental conditions. Overall, the proposed model is cost-effective, efficient, and scalable. It reduces the need for manual inspection and increases the overall reliability of railway operations.

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Railway Safety, Crack Detection, Object Detection, GPS, GSM, Machine Learning, Automation, Real-Time Monitoring

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There is a growing awareness among people all over the world regarding the requirement of green marketing, environmental marketing, and ecological marketing, all of which share the same significance for the study sector. Green marketing is a marketing strategy that is made up of environmentally friendly practices. Despite the fact that environmental issues have an effect on almost every activity that consumer participate in their lives today, they still continue to engage in these many activities. For the Indian business discipline, the fast-moving consumer goods (FMCG) industry is a very large sector in the economy that has to learn more about being environmentally friendly. This is especially true from the perspective of the Indian business discipline. Given that the natural environment is growing more complicated as a consequence of environmental degradation and unethical commercial practices, consumers and business organisations are now becoming increasingly concerned about the natural environment. This is because of the fact that the natural environment is becoming more complicated. In an effort to address the "new" concerns that contemporary society has, businesses have begun to make modifications to the manner in which they conduct themselves. The fast-moving consumer goods (FMCG) industry is one of the growing businesses that is concerned about the issues that come with green marketing. Environmentally friendly components are being used as effective marketing strategies by the vast majority of marketing professionals nowadays. As a result of this, the objective of this study is to investigate the perspectives of consumers in the fast-moving consumer goods business about products that are favourable to the environment.

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Environment, Marketing, Consumer, Industry, FMCG.

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The rapid evolution of digital technologies has transformed the foundations of contemporary marketing, reshaping how firms create value, engage consumers, and compete in increasingly dynamic environments. Marketing in the Age of Digitalization: Critical Problems and Integrated Solution Pathways examines the multifaceted challenges posed by digital transformation and proposes holistic strategies to navigate these complexities. This article synthesizes current academic and industry perspectives to highlight four major problem domains: data proliferation and privacy concerns, algorithmic bias and ethical dilemmas in AI-driven decision-making, digital customer experience fragmentation, and the pressures of platform-dominated markets. First, the unprecedented volume of consumer data enables highly personalized marketing but simultaneously introduces significant privacy, security, and regulatory challenges. Conflicts between personalization and consumer autonomy, along with varying global data protection frameworks, create operational uncertainties for firms. Second, the rising reliance on artificial intelligence and machine learning generates new layers of opacity and potential bias. While AI enhances targeting accuracy and automation efficiency, it also risks reinforcing unfair practices or eroding trust when decision-making processes lack transparency. Addressing these issues requires governance models that balance performance with accountability. Third, digitalization has fragmented the customer journey across multiple touchpoints--including social media, mobile applications, e-commerce platforms, and emerging interfaces like voice assistants--making it increasingly difficult for firms to deliver coherent, seamless experiences. This fragmentation often results in inconsistent branding, message dilution, and weakened customer loyalty. Developing integrated omnichannel strategies supported by real-time analytics is essential to bridge these gaps. Fourth, the dominance of digital platforms such as search engines, social media networks, and online marketplaces introduces structural challenges, including heightened competition, dependency risks, and reduced control over consumer data. Firms must adopt platform-aware strategies and explore alternative digital ecosystems to mitigate these vulnerabilities. To address these interconnected issues, the article proposes an integrated solution framework built around four pillars: ethical data stewardship, human-centered AI governance, omnichannel experience orchestration, and platform diversification. Ethical data stewardship emphasizes transparent data practices, informed consent, and secure infrastructure. Human-centered AI governance promotes fairness, explainability, and the integration of human oversight in algorithmic systems. Omnichannel orchestration leverages unified data architectures and experience design principles to ensure consistency across touchpoints. Finally, platform diversification encourages firms to build collaborative ecosystems, strengthen owned digital assets, and reduce overreliance on dominant intermediaries. By synthesizing emerging evidence and offering actionable pathways, this study contributes to ongoing debates on how marketing can remain effective, responsible, and resilient in an era defined by digital transformation. The article provides scholars with a conceptual framework for future research and offers practitioners practical guidance for navigating the challenges and opportunities of digital-era marketing.

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Digital marketing, AI governance, Data privacy, Omnichannel strategy, Platform ecosystems

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 Abstract

The present investigation describes the synthesis of a novel macrocyclic ligand obtained through the condensation of benzyl and oxalyldihydrazide in a methanolic medium under reflux conditions. The reaction yields a stable, crystalline macrocycle containing both nitrogen and oxygen donor centers capable of coordinating with transition metal ions. The ligand was subsequently allowed to react with divalent cobalt, nickel, and copper salts in the presence of chloride, nitrate, and acetate anions, resulting in a series of metal complexes of the general formula [M(C??H??N?O?)X?], where M = Co(II), Ni(II), Cu(II) and X = Cl?, NO??, or OAc?. Comprehensive analytical and physicochemical studies were carried out to confirm the composition and structural features of the synthesized ligand and its complexes. Elemental analysis and molecular weight determination verified the proposed stochiometry, while molar conductance measurements indicated their non-electrolytic or weakly electrolytic nature depending on the coordinating anion. The magnetic susceptibility data, together with electronic spectral transitions, provided valuable insight into the electronic environment of the metal ions and strongly suggested the formation of six-coordinate octahedral complexes. Spectroscopic investigations--including infrared, far-infrared, UV-Visible, and proton NMR studies--revealed significant shifts in characteristic vibrational and electronic bands, confirming the coordination of metal ions through azomethine nitrogen and carbonyl oxygen atoms of the macrocyclic framework. Infrared spectra displayed lowering of C=N and C=O stretching frequencies upon complexation, supporting metal-ligand bond formation, while far-IR spectra provided additional evidence through metal-nitrogen and metal-oxygen vibrational bands. The UV-Vis spectra exhibited characteristic d-d transitions consistent with octahedral geometry for Co(II), Ni(II), and Cu(II) complexes. Overall, the combined spectral, magnetic, and analytical data conclusively establish the formation of stable, six-coordinate octahedral complexes of the synthesized macrocyclic ligand with Co(II), Ni(II), and Cu(II) ions. The study highlights the strong chelating ability of the ligand, the influence of anions on coordination behavior, and the potential applications of such macrocyclic complexes in catalysis, bioinorganic modelling, and material science.

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Macrocyclic ligand, Template synthesis, Benzil, Oxalyldihydrazide, Co(II), Ni(II), Cu(II) complexes, Infrared spectroscopy, UV-Visible spectroscopy, NMR studies, Far-infrared spectra, Magnetic susceptibility, Molar conductance, Octahedral geometry

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