Volume 14 | Issue 1 |

 Abstract

According to Engel's law, although total food spending may increase, the percentage of household income spent on food decreases. Using data on household consumption expenditures, this study investigates the applicability of Engel's law in the Indian setting. The study examines the relationship between total spending and food consumption across various income levels using regression analysis on NSS survey data. The results support Engel's hypothesis by confirming that households with higher incomes spend less on food. Furthermore, structural changes in consuming behaviour are highlighted by differences in spending patterns between the rural and urban sectors. The study sheds light on the changing demand for food and how it affects market dynamics and economic policy. In this paper, we aim to investigate whether the same relationship holds for rural and urban India across Indian States in 2023-24. In fact, the recent Survey on Household Consumption Expenditure (2023-24) made it possible to look into this issue. Thus, this study is based on both time series and cross-section analysis for finding out the relationship between the changes in share of monthly consumption expenditure on food items and the changes in Monthly Per Capita Consumption Expenditure (MPCE) in rural and urban areas across the major Indian States over time. This analysis focuses on both rural and urban areas across 18 major Indian states, namely Andhra Pradesh, Gujarat, Haryana, Karnataka, Bihar, Kerala, Madhya Pradesh, Maharashtra, Odisha, Punjab, Rajasthan, Tamil Nadu, Uttar Pradesh, Assam, Jharkhand, Chhattisgarh, Telangana and West Bengal, taking into account their diverse socio-economic and cultural contexts.

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 Keywords

Engel's Law, MPCE, Household Consumption Expenditure, Food Expenditure, Cross-section analysis

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Timetable scheduling is a complex and time-consuming task in educational institutions due to numerous constraints such as faculty availability, classroom capacity, course requirements, and unforeseen real-time changes. Traditional manual and rule-based scheduling systems often lead to conflicts, limited flexibility, and suboptimal utilization of resources. To address these challenges, this research proposes an AI-driven dynamic timetable scheduling system that intelligently generates, evaluates, and updates schedules using a hybrid Artificial Intelligence approach. The proposed system integrates Genetic Algorithms (GA) for optimal initial timetable generation, Reinforcement Learning (RL) for adaptive real-time adjustments, and Machine Learning (ML) models for conflict prediction based on historical scheduling data. A constraint analysis module ensures compliance with both hard and soft constraints, while a web-based interactive interface developed using Streamlit enables efficient visualization and manual intervention when required. Additionally, a continuous feedback mechanism allows administrators to refine scheduling preferences, enabling the system to improve performance over time. This AI-based approach significantly enhances scheduling accuracy, minimizes conflicts, and improves overall resource utilization. The system supports multi-objective optimization by balancing faculty workload, classroom allocation, and student convenience, while dynamically adapting to changes such as faculty unavailability or infrastructure updates. The proposed solution offers a practical, scalable, and intelligent timetable management framework suitable for educational and organizational environments, demonstrating the effectiveness of hybrid AI techniques in overcoming the limitations of static scheduling models.

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 Keywords

Artificial Intelligence, Timetable Scheduling, Genetic Algorithm, Reinforcement Learning, Machine Learning, Constraint Optimization, Dynamic Scheduling, Educational Institutions, Resource Management, Automated Scheduling

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In contemporary B2B industrial markets, especially within the chemical manufacturing and export sector, the ability to identify and contact the correct organizational decision makers plays a pivotal role in sales effectiveness and international business expansion. Empirical studies indicate that nearly 55-65% of B2B sales leads fail due to inaccurate, outdated, or irrelevant contact data, while organizations using verified decision maker intelligence report improvements of 30-40% in lead to conversion ratios. This research paper presents an in depth, practice oriented study based on a Market Research Project conducted at Indenta Chemicals India Pvt. Ltd., a globally active chemical manufacturer and exporter. The study documents a systematic, multi stage process adopted to identify, verify, and validate contact details of key decision makers namely Procurement Managers, Purchase Managers, and Managing Directors using a combination of B2B directories and advanced online intelligence tools. Further, the paper evaluates the reliability of these tools through internal sales team feedback and proposes a structured, replicable decision maker identification and verification model for B2B organizations. The findings contribute practical insights for market researchers, sales strategists, and export managers operating in industrial markets.

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 Keywords

B2B Market Research, Decision Maker Identification, Contact Verification, Sales Intelligence, Chemical Industry

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The National Education Policy (NEP) 2020 marks a transformative shift in India's education system by giving a strong emphasis on integrating vocational education into mainstream educational institutions in a phased manner. The purpose of this study is to examine the key recommendations of NEP 2020 on vocational education and to explore the opportunities and potential benefits created by NEP 2020 for students and teachers. The paper discusses key recommendations of NEP 2020 on vocational education, including early-stage integration, no rigid boundaries between academic and vocational streams, fostering industry collaboration, establishment of National Committee for the Integration of Vocational Education (NCIVE), promotion of Lok Vidya etc. The policy highlights the opportunities like skill development, employability, entrepreneurial skills, flexible learning pathways, career awareness for students, while also outlines professional growth, innovative teaching strategies, industry exposure, skill upgradation and continuous learning for teachers. Bridging the gap between education and employment as well as theory and practice, the policy aims to prepare students for the dynamic demands of the 21st-century workforce while fostering holistic development and lifelong learning.

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 Keywords

Vocational Education, NEP 2020, Skill Development, Flexible Learning Pathways, Industry Exposure

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The Cheetah Chase Algorithm (CCA), inspired by predator-prey dynamics, offers a bio-inspired solution to a wide range of optimization challenges across diverse domains. Its adaptive decision-making, energy efficiency, and ability to identify cost-effective paths make it highly versatile for several real-world applications. In network routing, the CCA enhances path selection by minimizing routing costs and convergence delays, making it suitable for dynamic and large-scale network environments. In healthcare applications, the algorithm optimizes data transmission in sensor networks for patient monitoring, ensuring timely and accurate delivery of critical health data. In robotics and autonomous vehicles, the CCA supports efficient path planning and obstacle avoidance, enhancing navigation in uncertain and dynamic environments. The algorithm also shows promise in supply chain logistics, where it can streamline delivery routes and resource allocation, reducing costs and improving operational efficiency. Its application in Vehicular Ad Hoc Networks (VANETs) enables reliable and low-latency communication among vehicles, enhancing traffic management and road safety. Additionally, in VLSI (Very-Large-Scale Integration) design, the CCA aids in optimizing interconnect routing and reducing signal delays, contributing to more efficient chip layouts. The adaptability and resource-efficient nature of the Cheetah Chase Algorithm make it a powerful tool for solving complex optimization problems across multiple industries. This research highlights the broad applicability of the CCA, setting the stage for further exploration of its capabilities in advancing modern technological systems.

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Cheetah Chase Algorithm (CCA); Bio-Inspired Computing; Nature-Inspired Algorithms; Network Routing, VANET, VLSI, Supply Chain Logistics.

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The advancement of digital technology and widespread internet usage have significantly transformed consumer purchasing patterns. Online purchasing has emerged as a dominant mode of shopping due to convenience, accessibility, and extensive product variety. Consumer buying behaviour in the online environment is influenced by several factors such as trust, perceived risk, price sensitivity, website quality, and technological acceptance. This study aims to examine consumer buying behaviour in online purchasing through a comprehensive literature review approach. By analyzing existing national and international research studies, the paper identifies the major factors influencing online buying decisions and highlights trends and challenges in e-commerce. The study provides valuable insights for marketers, researchers, and policymakers to understand evolving consumer behaviour in the digital marketplace.

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 Keywords

Consumer Buying Behaviour, Online Purchasing, E-commerce, Digital Consumers, Internet Shoppin

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Learning Disability is an umbrella term that describes a number of difficulties in a child in his academic field. It is a type of neurological disorder which significantly affect the use and acquisition of academic and language skills such as reading, writing, listening, reasoning and mathematical abilities. It is now more accurately referred as Specific Learning Disability to emphasize the domain-based and specific nature of language and academic difficulties that occur despite educational opportunity and average intelligence. Among these SLD, writing difficulty otherwise known as Dysgraphia is considered as more crucial disability at early stage of life. Dysgraphia as a specific learning disability is neglected and to some extent remains unidentified at early stage of education due to ignorance and lack of awareness among teachers and parents which ultimately affect the academic skills of the individual. In Indian educational context, two to five students in every classroom were suffered from dysgraphia. Studies showed that early identification and intervention are widely recognized as critical factors to minimize the psychological consequences and long-term academic skills of learning disabled children.

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Learning Disability, Specific Learning Disability, Dysgraphia, Identification and Intervention

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Gender bias in decision making in households remains a problem in India and thus prevents women to gain economic agency with the growing concern on financial literacy in programs on policymaking and development practitioners. This paper also examines the interface of gender bias with financial literacy and how gender bias constructs the role of women in household and economic financial decision-making. Through primary survey data (N = 100) analyzed through SPSS, this study views the financial authority in households, the confidence women have towards financial issues, as well as the degree of change in financial literacy training reveal changes in these dynamics. The results indicate that a vast majority of households leave financial decision-making to male household members with women experiencing a strong perceived bias. With statistical tests, it was established that financial literacy training is correlated with increased determination to screen finances among women. This paper anticipates that, laws and policies, although they give guidelines of equality, social norms and unavailability of financial knowledge is a significant impediment. It offers combined policy, education and cultural interventions in order to achieve significant financial empowerment of women.

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 Keywords

: Financial Literacy; Gender Bias; Women's Empowerment; Household Decision-Making; Economic Agency in India.

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ABSTRACT: Obesity is a major metabolic disorder of the modern era, contributing significantly to global morbidity and mortality. In Ayurveda, obesity is described as sthoulya or medov?uddhi, arising from derangement of meda dhatu and dysfunction of medovaha srotas. An integrative understanding of these classical concepts alongside modern biomedical knowledge provides a comprehensive framework for evaluating obesity. Objective: To analyze the Ayurvedic concepts of meda dhatu, medovaha srotas, and their moola sthanas in correlation with contemporary anatomical, physiological, and metabolic insights relevant to obesity. Methods: A conceptual and analytical review of classical Ayurvedic texts, including Charaka Samhita, Sushruta Samhita, Ashtanga Hrudaya, and Ashtanga Sangraha was conducted and correlated with modern literature on lipid metabolism, adipose tissue physiology, and obesity. Results & Conclusion: Meda dhatu exhibits structural, metabolic, and endocrine functions comparable to modern adipose tissue and circulating lipids. Medovaha Srotas correspond to lymphatic pathways, intestinal lacteals, and vascular networks supplying visceral fat. The moola sthanas - Vrukka, Vapavahana, and Ka?i - show strong anatomical relevance to renal lipid metabolism, omental fat activity, and central adiposity. The samprapti of sthoulya, characterized by agnimandya, ama, srotorodha, and medodhatvagni mandya, closely parallels modern mechanisms such as insulin resistance, chronic inflammation, visceral adiposity, and hormonal dysregulation. Thus ayurvedic concepts of Meda and Medovaha Srotas align closely with contemporary understanding of obesity. Ayurveda offers a holistic approach emphasizing dietary regulation, lifestyle modification, metabolic correction, highlighting its relevance in modern healthcare.

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Keywords: Meda dhatu, Medovaha srotas, Obesity, Sthoulya, Adipose tissue, Ayurveda.

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Abstract - Herbal formulations are gaining remarkable global attention as natural, sustainable alternatives in cosmetics, nutrition, and medicine. In the present work, a novel polyherbal formulation was designed using Turmeric (Curcuma longa), Orange powder (Citrus sinensis), Lemon powder (Citrus limon), Rose petals (Rosa indica), and Soapnut (Sapindus mukorossi). The selection of these botanicals was based on their complementary therapeutic actions, safety profile, and traditional as well as scientific validation of skin health benefits.Each ingredient was incorporated for its unique multifunctional role: Turmeric as a potent antioxidant and anti-inflammatory agent, Orange and Lemon powders as natural vitamin C sources for skin brightening and defense against oxidative stress, Rose petals for their soothing, antimicrobial, and complexion-enhancing potential, and Soapnut as an eco-friendly natural cleanser owing to its saponin content. Unlike conventional single-herb products, this synergistic blend was formulated to target multiple dermatological concerns simultaneously--oil control, acne prevention, and skin texture improvement--offering a holistic skin-care solution.The formulation underwent preliminary evaluation based on organoleptic and physicochemical parameters, including color, odour, appearance, consistency, spreadability, washability, and pH. The results confirmed that the product was aesthetically acceptable, easily washable, well-spreadable, and maintained a skin-compatible pH, highlighting its potential as a safe, novel, and consumer-friendly herbal cosmetic preparation. Index Terms - herbal Face Wash, polyherbal Formulation, novel.

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herbal Face Wash, polyherbal Formulation, novel.

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