Volume 14 | Issue 3 |

 Abstract

The expansion of social networking platforms has fundamentally reshaped the way travel decisions are formed, particularly among younger consumers. Unlike traditional tourism promotion that relied on brochures, television campaigns, or travel agents, modern destination discovery increasingly occurs through algorithm-driven digital feeds. This study investigates how visual social media platforms influence travel intention among Generation Z consumers and evaluates the contribution of influencer credibility and AI-enabled customer interaction systems in strengthening tourism engagement. A descriptive research design was adopted, and primary data were collected from 15 respondents aged between 18 and 27 years through a structured questionnaire. In addition, a case-based assessment of Pista House was conducted to examine the impact of conversational digital automation. The findings reveal that visually dominant platforms significantly shape destination awareness, with 85% of respondents identifying Instagram as their primary inspiration source. Automation-based engagement further demonstrated measurable improvements in customer responsiveness and conversion. The study concludes that tourism enterprises must reposition themselves within a digitally immersive ecosystem centered on visual narratives, social validation, and real-time interaction.

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 Keywords

Social Media Influence, Tourism Marketing, Generation Z, Digital Engagement, Influencer Credibility, AI Automation, Travel Intention.

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We propose a machine learning approach to leverage past vehicle sales data to forecast the approximate value used cars will fetch when they are resold. The model employs Linear Regression and Random Forest algorithms to make predictions based on key variables such as the car's age, mileage, fuel type, engine capacity, transmission type, and make. A comprehensive set of experiments is carried out to assess the effect of various variables on the accuracy of the predictions, as well as to compare the performance of the two models with different data scenarios. The experiment outcomes clearly show that the Random Forest model performs better than the Linear Regression model by accounting for the non-linear relationships that exist in practical automobile sales data.

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Used Car Price Prediction, Machine Learning, Linear Regression, Random Forest, Data Analytics, Automotive Industry.

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Mutrakrucchra is an important urinary disorder described in Ayurveda, characterized by painful, difficult, or burning micturition. In children, Mutrakrucchra can be clinically correlated with urinary tract infection (UTI), which is one of the most common bacterial infections in the pediatric age group. Pediatric UTIs may lead to significant morbidity, recurrent infections, and long-term renal complications if not diagnosed and managed appropriately. Ayurveda explains Mutrakrucchra as a condition caused by vitiation of mainly Vata and Pitta dosha, along with involvement of Mutravaha srotas. Nidana such as improper dietary habits, inadequate fluid intake, suppression of natural urges, infections, and poor hygiene play a significant role in children. Classical Ayurvedic texts describe various types of Mutrakrucchra along with their lakshana, samprapti, and chikitsa principles. Management includes Nidana parivarjana, Shamana therapies using Mutrala, Shothahara, and Pittashamaka drugs, along with supportive dietary and lifestyle measures. This review article aims to compile and analyze classical Ayurvedic concepts of Mutrakrucchra in children and correlate them with modern understanding of pediatric urinary tract infections. An integrative approach based on Ayurvedic principles may help in effective management, prevention of recurrence, and improvement of overall urinary health in children.

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 Keywords

Mutrakrucchra, Urinary Tract Infection, Children, Pediatric UTI, Mutravaha Srotas, Ayurveda

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 Abstract

This study examines how the successful commercialization of self-driving vehicles can significantly improve human convenience and safety by transforming transportation systems. It systematically reviews four core technological pillars--vehicle navigation, path planning, environmental perception, and vehicle control--highlighting their roles in enabling autonomous operation. The paper also surveys leading research organizations and institutions worldwide, providing a global perspective on development efforts. In addition, it explores ongoing debates surrounding autonomous vehicles, such as safety, ethics, and regulation, while forecasting future trends in the field. Overall, the work offers a comprehensive overview of current advancements, identifies the state-of-the-art in autonomous driving technology, and consolidates research progress and institutional contributions in this rapidly evolving domain.

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 Keywords

Self-driving cars, Autonomous vehicles, Vehicle navigation, Path planning, Environment perception, Vehicle control, Sensor fusion, Artificial intelligence, Intelligent transportation systems, Automation.

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Amitav Ghosh's The Glass Palace (2000), set in late colonial Burma during the upheavals of World War II, has frequently been viewed through the prism of nationalist awakening, with Arjun's trajectory interpreted as a move from imperial devotion to anti-colonial conscience. Such interpretations, however, tend to prioritise intellectual transition while ignoring the institutional and emotional roots of his previous loyalty. To fill this gap, the current study examines how Arjun's loyalty is fostered by military discipline and maintained by affective attachment. Drawing on Michel Foucault's concept of disciplinary power and Sara Ahmed's theory of affective economies, the study contends that Arjun's affiliation with the British Empire is influenced by professional ethos, institutional regulation, and an emotional stake in honour and recognition. His wartime disillusionment is thus interpreted not as a sudden ideological conversion, but as the collapse of a disciplinary-affective framework that had organised his sense of self. This work contributes to postcolonial literary scholarship by focusing on the formation and destabilisation of colonial-military subjectivity, providing a more nuanced understanding of imperial devotion and its fragility.

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Imperial loyalty; Military discipline; Affective attachment; Colonial subjectivity; Nationalism

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Emigrations from coal- fired mileage boilers may also contribute significantly to ambient particulate attention, in particular in the veritably first- class ( beneath 2.5 micron) flyspeck length range. Ambient PM2.5 speciation studies frequently display that geologic accoutrements, which incorporate the maturity of cover ash produced through coal combustion, make up handiest a small bit of the particulate. Sulphate, nitrates, and natural composites generally incorporate redundant than eighty percent of the ambient PM2.5. Thus emigration of secondary aerosol precursors (e.g., nitrogen oxides, sulfur dioxide, sulfur trioxide, ammonia, and certain unpredictable natural composites) contributes significantly to ambient PM2.5. Coal- fired boilers emit both number one PM2.5 and secondary flyspeck precursors. Because of the big volume of gassy energies burned, indeed gas- fired widgets may also have implicit to contribute significantly to ambient PM2.5. The chemical speciation of number one patches emitted from coal- fired boilers gives important labels that may be used to identify the relative donation of a particular order of reasserts to near ambient PM2.5. Being emigration factors and speciation biographies for PM2.5 precursors from combustion reasserts are frequently dated and deficient. Also, the huge variety of coal mineral matter compositions, boiler designs and pollution control outfit makes point-unique emigrations characterization the most reliable way of attributing force benefactions. A test protocol for growing PM2.5 emigration factors and chemical speciation biographies has been evolved. The paper discusses procedures for characterization of PM2.5 and PM2.5 precursors from stationary combustion reasserts and original results of discipline measures made the use of traditional and dilution slice ways. Results of tests on gas- fired units the use of both traditional force testing styles and a dilution slice fashion are presented and compared. Imagine walking through a busy city street, breathing in air that looks clear but carries invisible dangers. Fine particles from power plants often make up part of that haze. These tiny bits, known as PM2.5, come from sources like utility boilers in energy production. Utility boilers play a big role in creating PM2.5 emissions. They burn fuels to generate steam for electricity. This process releases harmful particles into the air; the PM2.5 means only the particulate matter smaller than 2.5 micrometers are taken. It slips into your lungs and blood, causing serious health problems. This article looks at how utility boilers add to PM2.5 levels. It covers their effects on people and nature. Plus, it shares steps to reduce these emissions for better air quality.

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Emigrations, coal- fired mileage boilers, traditional force, testing, pollution control, PM2.5 emissions, and benefactions.

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Tamak Shwasa is an important respiratory disorder described in Ayurvedic classics and is categorized under Shwasa Roga. It is a chronic, recurrent condition predominantly involving Vata and Kapha Dosha, characterized by difficulty in breathing, wheezing sound, chest tightness, cough, and episodic exacerbations, especially during night and early morning hours. In children, Tamak Shwasa closely resembles childhood bronchial asthma described in modern medicine, which is a common chronic inflammatory airway disease affecting the pediatric population worldwide. Childhood asthma significantly impacts physical activity, school attendance, growth retardation and overall quality of life, making early diagnosis and holistic management essential. Ayurveda explains the pathogenesis of Tamak Shwasa as Kapha-induced obstruction of Pranavaha Srotas along with aggravated Vata, leading to dyspnea and recurrent attacks. Nidana such as exposure to dust, smoke, cold climate, allergens, improper diet, recurrent respiratory infections, and genetic predisposition play a crucial role in children. Classical texts like Charaka Samhita and Sushruta Samhita describe detailed etiopathogenesis, lakshanas, prognosis, and management principles including Nidana Parivarjana, Shodhana, Shamana Chikitsa, Pathya-Apathya, and Rasayana therapy. This review article aims to compile and critically analyze classical Ayurvedic descriptions of Tamak Shwasa in children and correlate them with modern concepts of childhood asthma. An attempt is made to highlight the scope of Ayurvedic preventive and therapeutic approaches in reducing disease severity, frequency of attacks, and dependency on modern medications. Integrative understanding of Ayurveda and contemporary medicine may offer safer, cost-effective, and sustainable management strategies for childhood asthma.

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 Keywords

Tamak Shwasa; Childhood Asthma; Pediatric Respiratory Disorders; Pranavaha Srotas; Kapha-Vata Dosha; Ayurveda; Bronchial Asthma

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The accelerating proliferation of misleading and fabricated information across digital ecosystems has emerged as a critical threat to societal stability and informed decision-making. Deceptive news content can distort public perception, undermine democratic institutions, and generate social unrest. To address this growing challenge, this study presents an intelligent fake news detection framework based on a feature-driven, optimized Multi-class Support Vector Machine (MSVM) classification model. The proposed system performs comprehensive feature extraction from textual data, incorporating lexical indicators, syntactic patterns, semantic embeddings, and contextual attributes to capture deeper linguistic and structural characteristics of news articles. Advanced feature selection and optimization techniques are applied to enhance predictive capability while minimizing computational overhead. Unlike conventional binary classifiers, the MSVM model enables multi-category classification, distinguishing among real, fake, satire, partially true, and misleading content for more granular and informative analysis. This multi- dimensional approach improves classification robustness, scalability, and adaptability across diverse datasets and multilingual environments. Experimental validation demonstrates improved detection accuracy and reduced misclassification rates compared to traditional models. By integrating optimized feature engineering with supervised machine learning, the proposed framework offers a reliable and efficient solution for real-time deployment in social media monitoring systems, automated fact-checking platforms, and digital news verification applications, thereby contributing significantly to advanced misinformation detection research.

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 Keywords

Fake News Detection, Multi-Class Support Vector Machine, Feature Optimization, Machine Learning, Natural Language Processing.

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Chronic non-communicable diseases, including diabetes mellitus, hypertension, cardiovascular disorders, chronic obstructive pulmonary disease (COPD), and chronic kidney disease (CKD), remain among the foremost contributors to global morbidity and mortality. Effective management of these conditions necessitates continuous monitoring, coordinated multidisciplinary care, and active patient engagement--requirements that conventional healthcare delivery models frequently fail to meet. In this context, telehealth has emerged as a transformative paradigm, employing digital communication technologies, wearable devices, remote monitoring systems, and artificial intelligence (AI)-driven analytics to extend healthcare services beyond traditional clinical environments. Empirical evidence demonstrates that telehealth interventions can enhance clinical outcomes, improve medication adherence, and elevate patients' overall quality of life. Mobile health (mHealth) applications, Internet of Medical Things (IoMT) devices, and cloud-based data integration enable real-time physiological monitoring and facilitate individualized treatment strategies. Moreover, telehealth utilization has been correlated with reductions in hospital readmissions and overall healthcare expenditures, signifying both clinical efficacy and economic efficiency. By fostering continuous, bidirectional communication between patients and healthcare providers, these digital modalities promote self-management, reinforce therapeutic adherence, and strengthen patient empowerment. Nonetheless, several obstacles continue to impede the widespread implementation of telehealth, including disparities in technological infrastructure, concerns regarding data privacy and cybersecurity, and the absence of standardized regulatory frameworks across regions. Addressing these challenges requires comprehensive strategies encompassing infrastructure enhancement, digital literacy development, policy harmonization, and equitable access initiatives. In conclusion, telehealth represents a significant paradigm shift in chronic disease management by bridging gaps in accessibility, continuity, and quality of care. The convergence of digital health innovations with interdisciplinary collaboration holds substantial promise for improving healthcare delivery, optimizing patient outcomes, and advancing global health equity.

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 Keywords

Keywords: Telehealth, Chronic Disease Management, Digital Health, Remote Monitoring, Patient Engagement, Long-Term Care, Health Equity

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 Abstract

Groundwater depletion has emerged as a critical challenge in the semi-arid regions of Andhra Pradesh, particularly in the Rayalaseema region. Chinnadudyala Gram Panchayat (GP), located in Muddanur mandal of YSR Kadapa district, faces irregular rainfall, high runoff, and declining water tables. This paper explores the application of staggered trenches and dugout ponds as community-based water conservation structures in Chinnadudyala. Staggered trenches on hill slopes reduce runoff and enhance infiltration, while dugout ponds in valley bottoms collect rainwater and recharge groundwater. Integrating these measures at the micro-watershed scale can improve water security, support agriculture, and strengthen climate resilience in the GP.

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 Keywords

Staggered Trenches, Dugout Pond, Aquifer, Infiltration

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Creative Commons Attribution 4.0 and The Open Definition