Volume 13 | Issue 12

 Abstract

Employer branding has emerged as a strategic human resource practice that significantly influences the attraction and retention of young talent, especially Generation Z. As Gen Z enters the workforce, their job preferences differ from previous generations, emphasizing organizational culture, work-life balance, career growth, and employer reputation. This study examines the impact of employer branding on Gen Z job preferences. A descriptive research design was adopted, and data were collected from 120 Gen Z respondents using a structured questionnaire. The study analyses key employer branding factors such as organizational culture, compensation, career development, work flexibility, and corporate social responsibility. The findings reveal that employer branding plays a crucial role in shaping Gen Z's employment decisions. The study provides insights for HR professionals to design effective employer branding strategies to attract Gen Z talent.

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Employer Branding, Generation Z, Job Preferences, Talent Attraction, HR Strategy, Organizational Culture

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This paper presents a detailed design and implementation of an AI-powered Tournament Management Agent based on Agentic Artificial Intelligence. Traditional tournament management relies on manual coordination or static software systems, leading to inefficiencies, data inconsistency, and limited scalability. The proposed system introduces an autonomous conversational agent capable of understanding natural language queries and executing tournament management tasks such as creation, updating, deletion, and retrieval of tournament information. The system integrates FastAPI for backend services, SQLite for lightweight persistent storage, and Google Gemini for natural language understanding and reasoning. By combining CRUD-based automation with agentic decision-making, the system significantly reduces human effort and improves usability. Extensive testing and analysis demonstrate that the proposed approach enhances operational efficiency and provides a scalable solution suitable for academic institutions, sports clubs, and event organizations.

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Agentic AI, Tournament Management System, Artificial Intelligence, FastAPI, SQLite, Natural Language Processing, AI Agents, Gemini

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This study examines the factors that influence hotel selection in metropolitan markets, with specific reference to Holiday Inn Mumbai International Airport. Mumbai represents one of the most competitive hospitality environments in India, where hotels must go beyond brand recognition to convert awareness into actual bookings. The research focuses on key determinants such as cleanliness, room comfort, location, pricing, service quality, brand reputation, loyalty programs and the role of digital platforms in shaping guest decisions. The study adopts a descriptive and quantitative research design using primary data collected through a structured online questionnaire administered to ninety respondents, including both guests who have stayed at the hotel and those who have not. Secondary data from academic journals and industry reports are used to support the analysis. The findings reveal a significant gap between brand awareness and booking behavior. While the Holiday Inn brand enjoys strong visibility, this does not translate proportionately into stays. Cleanliness, hygiene, comfort and accessibility emerge as the most influential decision factors, outweighing brand name alone. Digital platforms play a decisive role in the hotel selection process by supporting discovery, comparison and evaluation through online travel agencies and reviews. Guests who have stayed at the property report satisfaction with rooms and food and beverage services, while banquet facilities and marketing communication show lower visibility. The study concludes that to improve conversion, the hotel must strengthen its digital presence, reposition itself beyond a primarily business travel identity and leverage operational strengths to build loyalty. The research contributes practical insights for hospitality managers operating in highly competitive urban markets.

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 Keywords

Hotel selection, Holiday Inn Mumbai International Airport, Cleanliness and hygiene, Room comfort, Location convenience, Price and value, Brand reputation, Online travel agencies, Online reviews, Digital presence, Guest satisfaction, Loyalty programs, Marketing gap, Urban hospitality market

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Bahu Pratiman Hasthakshep Model Ka Vikas : Hindi Bhasha Me Aadharbhut Bhashai Kaushal Hetu

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Bahu Pratiman Hasthakshep Model Ka Vikas : Hindi Bhasha Me Aadharbhut Bhashai Kaushal Hetu

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Aahar Ka Prabhav: Sthaulya Ke Liye Ek Sampurna Drishtikon.

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Aahar Ka Prabhav: Sthaulya Ke Liye Ek Sampurna Drishtikon.

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Vaanabhatt Kee Drishti Se Naaree Kee Tapasya

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Vaanabhatt Kee Drishti Se Naaree Kee Tapasya

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Diabetic Retinopathy (DR) remains a leading cause of vision loss among diabetic patients globally, making its early detection and classification vital for effective treatment. Recent advancements in deep learning have shown significant promise in medical image analysis, particularly in ophthalmology. This paper proposes a comprehensive study on the application of transfer learning-based deep neural networks (DNNs) for the early detection and multi-stage classification of Diabetic Retinopathy using fundus images. Leveraging pre-trained convolutional neural networks such as VGG16, ResNet50, and InceptionV3, the model fine-tunes features to effectively classify the severity levels of DR, ranging from no DR to proliferative DR. The proposed approach is validated using benchmark datasets, achieving high accuracy, precision, and sensitivity, thereby demonstrating robustness in real-world clinical environments. The integration of transfer learning not only reduces the computational burden and training time but also enhances feature extraction capabilities, leading to improved diagnostic performance. This study emphasizes the potential of transfer learning-based DNNs as a reliable tool for ophthalmologists in the early detection and grading of Diabetic Retinopathy.

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Diabetic Retinopathy, Deep Neural Networks, Transfer Learning, Fundus Images, Early Detection, Classification, Convolutional Neural Networks, Medical Image Analysis, ResNet50, VGG16.

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Mosses (Bryophytes) are sensitive indicators of environmental variation due to their dependence on moisture, shade, substrate, and microclimatic conditions. Ranchi district of Jharkhand represents a mosaic of ecological habitats; however, bryophyte diversity in the region remains insufficiently explored. This study investigates the identification and external morphological variation of mosses collected from four distinct habitats: two from urban road side area and two from forest area. Moss samples were collected to identify and are examined for key morphological characters such as growth form, leaf architecture, colour, seta/capsule features, and substrate specificity. To study the external morphological response of moss during three seasons (summer, rainy, and winter) at four different locations for a period of one year. A total of four moss species belonging to three families were recorded. Species richness was highest in the moist riparian habitat and lowest in the urban habitat. Significant morphological plasticity was observed in species such as Entosthodon buseanus and Funaria hygrometrica, reflecting environmental stress gradients. The study highlights strong habitat influence on external morphology and underscores the importance of microhabitat conservation for maintaining regional bryophyte diversity.

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Mosses, Bryophytes, Morphology, Habitat variation, Species identification

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Evolution of Anti-Defection Law, 1985 in India and analytical study of defections and change of power in various states

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Evolution of Anti-Defection Law, 1985 in India and analytical study of defections and change of power in various states

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Intensification of land use and diversification of crop to multiply income generation, are the recent trends. During fieldwork, it was found that livelihood issues are overlooked because, there are ample evidence, where lack of basic needs, poverty and livelihood issues are rampant in rural Mizoram. Case studies of policy beneficiaries - cultivators, piggeries, poultry, fisheries, sugarcane farms and orchard farms and the outcomes of development policies initiated by state. The villagers opined that development policies are not effective, nor able to improve land use activities. The main objective of the study is to show the reproduction of poverty in upland in Mizoram. Why traditional livelihood cannot be altered and improvised for good? Examining the outcomes of privatization of community land is the scope, which the study wants to analyze during fieldwork. Examine the everyday livelihood hurdles. During fieldwork 2022, it was cleared that when community lands for farming got privatized, the outcome is the constriction of jhum land share for each household. Constriction of jhum land, intensification of land use and diversification of crops have been the emerging trends in the uplands.

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Jhum, Development Policies, Community farmland, Privatization

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In today's competitive job market, possessing technical knowledge alone is insufficient for securing employment through campus interviews. Employers increasingly emphasize soft skills--such as communication, teamwork, problem-solving, adaptability, and emotional intelligence--as essential indicators of a candidate's workplace readiness. This article explores the significance of soft skills for college students, highlighting the gap between academic learning and industry expectations. It examines key soft skills required during campus interviews, the challenges students face in developing them, and effective strategies for refinement, including self-assessment, workshops, extracurricular involvement, and mentorship. The role of colleges in integrating soft skill development into the curriculum and providing practical exposure is also discussed. Through case studies and practical examples, the article demonstrates how focused efforts on soft skill enhancement improve employability and interview performance. Finally, recommendations are provided for students to systematically cultivate these skills, ensuring success not only in campus interviews but also in long-term career growth.

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Soft Skills, Campus Interviews, Employability, Communication, Teamwork, Professional Development

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: Migraine, referred to as Shaqiqa in the Unani system of medicine, is a chronic, episodic headache disorder that predominantly affects one side of the head and has been documented since ancient times. Classical Unani scholars described Shaqiqa within the framework of humoral imbalance and deranged temperament (miz?j), emphasizing the influence of dietary habits, emotional states, lifestyle factors, and environmental exposures. In contrast, modern medicine characterizes migraine as a complex neurovascular disorder involving genetic predisposition, altered cortical excitability, trigeminovascular activation, and neurogenic inflammation. This narrative review explores the historical development of migraine concepts in Unani and modern medical literature and critically examines etiological factors and triggering mechanisms described in both systems. Classical Unani texts identify prolonged fasting, excessive exertion, menstrual irregularities, emotional stress, sleep deprivation, gastrointestinal disturbances, sensory overload, and specific dietary factors as major precipitants of Shaqiqa. Contemporary biomedical research reports remarkably similar triggers, including psychological stress, hormonal fluctuations, irregular sleep patterns, dietary components, sensory stimuli, and environmental changes. Despite differences in theoretical explanations, both traditions demonstrate striking convergence in clinical observations and preventive strategies. Integrating Unani and modern perspectives may therefore provide a more comprehensive understanding of migraine and support the development of patient-centered, integrative management approaches.

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; Migraine, Shaqiqa,, Sudae Nisfi, Adhasisi (Hemicrania), Sudae Ghasyani (Sick Headache), Akhlat -e- Arbaa ,,Humours ,Mizaj

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Earthquake-induced structural damage remains one of the most significant challenges in structural engineering, with recent seismic events highlighting the limitations of traditional force-based design approaches. This research paper examines the evolution from conventional force-based seismic design to performance-based design methodologies, exploring advanced structural systems that enhance building resilience and minimize post-earthquake damage. The paper investigates innovative technologies including base isolation systems, buckling-restrained braces, self-centering structures, and fiber-reinforced polymer retrofitting techniques. Through comprehensive analysis of recent case studies and emerging trends, this research demonstrates how performance-based seismic design combined with advanced materials and structural health monitoring can significantly reduce life-cycle costs while improving structural safety and post-earthquake functionality. The integration of machine learning and artificial intelligence in structural analysis further enhances predictive capabilities and optimization of seismic design parameters. The findings indicate that displacement-based design approaches provide more consistent and economical solutions compared to traditional methods, particularly in moderate seismic regions, while self-centering systems offer superior resilience through damage concentration in replaceable components and elimination of residual deformations.

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Performance-based seismic design, base isolation, buckling-restrained braces, self-centering systems, structural resilience, displacement-based design, fiber-reinforced polymers

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Rotheca serrata, Phytochemical screening, Antioxidant activity, Phenolic content, DPPH assay, Manipur

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The existing model of pharmaceutical research and development (R&D) is marked by lengthy timelines, exponentially rising expenses, and a clinical trial failure rate surpassing 90%, leading to considerable uncertainty in the portfolio. This review explores the revolutionary use of Artificial Intelligence (AI) and Machine Learning (ML) in forecasting the chances of regulatory approval for pharmaceutical candidates. AI models provide a robust decision-support system by combining various datasets, such as molecular properties, preclinical findings, clinical trial information, and past regulatory results. Essential processes include Natural Language Processing (NLP) for deriving insights from unstructured documents and the application of predictive modeling to estimate success chances. We examine the strategic effects of R&D prioritization, as well as the significant issues concerning data bias, the opaque characteristics of algorithms, and the need to create Explainable AI (XAI) frameworks to build regulatory confidence and expedite the provision of safe and effective treatments.

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Drug Approval Prediction, Artificial Intelligence, Machine Learning, Clinical Trials, Portfolio Management, Explainable AI (XAI), Regulatory Risk

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A strategic instrument for influencing consumer behavior, integrated marketing communication (IMC) is especially useful for teens, who are particularly sensitive to marketing and media stimuli. Determining the association of different IMC tools--public relations, social media marketing, sales promotion, advertising, and personal selling--on the buying behavior of male and female teenagers is the goal of this study. The study examines 100 teenagers between the ages of 13 and 19 using a structured questionnaire. The results demonstrate a significant association between gender and buying behavior based on Direct marketing, Advertising, Personal selling, and Sales promotion, and an insignificant association with Public relations.

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IMC Tools, Buying behavior, Teens, Gender, Association

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The problem of motorcycle collisions is one of the leading issues of road safety, but the danger is not constant all the time and all day. vehicle characteristics. This paper conducts statistical research into the data on motorcycle crashes to address three useful questions: (1) at what times of the day collisions were concentrated (and are still concentrated) (peak hours), (2) are collisions. more probable on weekends than on weekdays, and (3) patterns of collision varying between vehicle makes and whether make and day type are independent variables. We start with descriptive profiling of collision frequency by car model and time of collision, and a sharp peak in late-afternoon hours. Next, we use Bayes' Rule to compare the conditional likelihood of observing a crash on weekends versus weekdays. Finally, we test the independence of vehicle make and day type using Pearson's Chi-Square test, finding strong evidence of dependence between make and whether a collision occurs on a weekday or weekend. Together, these results suggest that motorcycle collision occurrence varies meaningfully with temporal context and vehicle-make composition, motivating targeted safety messaging and enforcement strategies that align with peak-hour and weekend-driven risk patterns

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Motorcycle collisions; peak hours; weekend effect; Bayes' Rule; chi-square test; independence testing

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This article explores the transformative impact of British colonial educational policies on the Indian subcontinent, specifically the introduction of English as a medium of instruction and its subsequent role in fostering both a new intelligentsia and a linguistic divide. It further examines the reactive rise of regional language movements, culminating in the establishment of the Kerala Bhasha Institute. By analyzing the Institute's fifty-year history, the study evaluates its role in bridging the gap between scholarly knowledge and the vernacular, ensuring that Malayalam remains a vibrant medium for scientific, technological, and academic discourse.

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The Dual Legacy: Colonial Education, Regional Languages, and the Evolution of the Kerala Bhasha Institute

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This conceptual paper outlines a model, rooted deeply in established theory, that aims to decode the relationship between external marketing levers and final consumption results. The model investigates how core stimuli, such as pricing strategies, product availability, promotional deals, quality cues, product packaging, and social influence, directly affect consumer outcomes, including how often products are purchased, whether a brand is adopted, the variety of products bought, and overall spending. The framework is built upon the foundational principles of several key behavioral theories: Stimulus-Organism-Response (S-O-R) Theory, Trait Activation Theory (TAT), Signalling Theory, Social Influence Theory, and Consumer Personality Theory. Crucially, the model proposes that inherent consumer traits specifically perfectionism, brand consciousness, novelty seeking, and price consciousness do not just influence, but act as essential moderating mechanisms. These traits filter and shape the impact of external marketing cues, ultimately translating them into specific consumer buying behaviors. The paper concludes by consolidating these theoretical foundations and formally presenting conceptual propositions that can be tested through future empirical research.

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Marketing Stimuli, Consumer Traits, Consumption Outcomes

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