Challenges Faced by Micro and Small Firms and Disparities of Firm Growth in India
Microenterprises form the backbone of rural India's economic environment, but their access to formal financial sources remains limited due to inadequate documentation, low financial literacy, and credit risks. In the absence of formal sources of credit, these enterprises are compelled to turn to informal credit sources such as moneylenders, local traders, and friends and family to meet their financial needs. This research paper explores the various challenges microenterprises face in acquiring required credit, including high interest rates and lack of security. An important factor is the problem of rent-seeking behavior expressed by various sources of credit, from individuals to local authorities, which increases borrowing costs and increases inequities. Using firsthandexperiences, case studies, and data analysis, this study identifies patterns of firm growth and unequal growth that display credit access in rural informal markets. The findings highlight the urgent need for government interventions and the expansion of accessible and affordable financial services specific to the unique challenges faced by rural microenterprises.
Published by: Aabir Chatterjee
Author: Aabir Chatterjee
Paper ID: V11I4-1214
Paper Status: published
Published: August 13, 2025
Trademark Infringement in the Digital Age: Domain Names, Metatags, and Social Media
The digital revolution has fundamentally altered the use, protection, and infringement of trademarks. While there are numerous new opportunities for branding and visibility, there are also new challenges in enforcement, particularly regarding domain names, metatags, and other social media platforms. This paper examines how traditional trademark laws adapt to the digital sphere, explores the legal consequences of infringing trademarks in the online environment, and analyzes key case law and statutes, suggesting ways to enhance the regulations. The paper offers both international and Indian perspectives, making it a valuable resource for practicing lawyers and digital entrepreneurs.
Published by: Hitesh Vashisth
Author: Hitesh Vashisth
Paper ID: V11I3-1392
Paper Status: published
Published: August 12, 2025
How Could We Add Emotional Nuances to AI-Generated Music?
In recent years, artificial intelligence (AI) has made significant progress in generating music using architectures such as RNNs, Transformers, GANs, VAEs, diffusion models, and large language models. Although these models are capable of generating structurally coherent and stylistically accurate music, they tend to lack the subtle emotional nuance and depth of human music. This paper examines the idea of emotional nuance—the ability of AI-generated music to express subtle variations, mixed effects, changing affective trajectories, and selective emotional impact. Combining theories from music psychology, affective computing, and computational creativity, I translate musical features like tempo, mode, harmony, dynamics, articulation, timbre, and melodic contour into their perceived emotional counterparts. I survey and compare methods of emotional control, ranging from conditional generation and reinforcement learning with affective rewards to employing music theory and hybrid symbolic–neural methods. I present key challenges, including the subjective nature of emotional perception, limitations in datasets, cultural variability, and the challenge of quantifying nuanced affect. I also outline directions for future work around more robust datasets, culturally adaptive models, cognitively inspired emotion representations, interpretable control mechanisms, and sound evaluation frameworks. By refining these strategies, AI music systems can move closer to being not just pattern generators but creative collaborators able to express genuine emotion.
Published by: Moaksh Kakkar
Author: Moaksh Kakkar
Paper ID: V11I4-1212
Paper Status: published
Published: August 12, 2025
The Economics of Food Insecurity
Why does India struggle with food insecurity despite being one of the world's largest food producers, and what does this reveal about the real drivers of hunger? Despite advancements in agricultural productivity and food-related welfare schemes, food insecurity continues to infest India, exposing deep-rooted systemic inefficiencies and socio-economic disparities. This paper contributes by emphasizing the qualitative aspects of food security, such as distribution, utilization, and socio-economic access, rather than focusing on just the quantitative aspects like production and price indices. Using secondary research and data from governmental, academic, and institutional sources, this paper explores the intertwined nature of income disparity, nutritional inequality, and inflation along with supply chain inefficiencies and how it affects food security, particularly in India. Ultimately, it argues that food security is not a singular agricultural or economic issue but a multi-dimensional challenge that demands both immediate policy rectification and long-term structural transformation. The question in this research paper is answered by taking into consideration a hypothesis that food insecurity in India is not a result of food scarcity but stems from systemic failures in distribution, deep-rooted socio-economic inequalities, and inconsistent policy implementation.
Published by: Yuvan Gupta
Author: Yuvan Gupta
Paper ID: V11I4-1210
Paper Status: published
Published: August 11, 2025
Airport Runway Obstacle Detection and Analysis from UAV Imagery: A Review Using the Stanford Drone Dataset
Maintaining obstacle-free runways is an essential part of airport operations and aviation safety. The growing availability of high-resolution imagery from UAVs, especially from publicly available datasets like the Stanford Drone Dataset (SDD), presents new challenges and opportunities for innovative obstacle detection systems. This paper provides a systematic methodological overview of airport runway obstacle detection from UAV imagery with emphasis on methods transferable to the SDD. This methodology examines cutting-edge computer vision methods, among them object recognition models like YOLO, Faster R-CNN, and Vision Transformers, and their theoretical potential for recognizing common runway hazards like cars, people, and foreign object debris (FOD). The review also contains a thorough analysis of the SDD's architecture, objects, resolution, and limitations relative to runway conditions. We also introduce a conceptual pipeline for real-time obstacle detection and discuss its possible incorporation into airport safety management systems. Lastly, this review determines the main research gaps and presents future research directions for enhancing obstacle detection accuracy, real-time performance, and adaptability to varied airport environments. This work intends to provide a basis for future experimental studies and system development utilizing UAV-based imagery for airport runway safety.
Published by: Joseph Chakravarthi Chavali, D. Abraham Chandy
Author: Joseph Chakravarthi Chavali
Paper ID: V11I4-1205
Paper Status: published
Published: August 7, 2025
Neonatal Alloimmune Thrombocytopenia (NAIT): A Comprehensive Review
Neonatal Alloimmune Thrombocytopenia (NAIT) is a rare but potentially life-threatening condition in which maternal alloantibodies target fetal platelet antigens, leading to severe thrombocytopenia, bleeding complications, and, in some cases, intracranial hemorrhage (ICH) or fetal demise. This review provides a comprehensive exploration of NAIT’s pathophysiology, immunologic mechanisms, genetic predispositions, clinical manifestations, diagnostic approaches, and evolving prevention and treatment strategies. Special emphasis is placed on the immunogenetic triggers, particularly Human Platelet Antigen (HPA) incompatibilities, and their population-specific prevalence. Diagnostic techniques such as MAIPA and HPA genotyping are highlighted alongside current antenatal interventions, including intravenous immunoglobulin (IVIG), corticosteroids, and antigen-negative platelet transfusions. Advances in population-based screening, noninvasive fetal genotyping, and consensus guidelines have significantly improved outcomes, reducing ICH rates and enhancing survival. Despite these advances, long-term neurodevelopmental sequelae remain a concern, even in nonhemorrhagic cases. This review integrates recent epidemiologic and clinical findings from 2023 to 2025, emphasizing the growing importance of early recognition, targeted management, and international consensus in improving care for NAIT-affected neonates and future pregnancies.
Published by: Aadya Gaur
Author: Aadya Gaur
Paper ID: V11I4-1206
Paper Status: published
Published: August 7, 2025
