Noida International University

This study investigated the connection between oil price shocks worldwide, reliance on energy imports, and the development of the Indian economy in the years 1991-2024. Since India is one of the biggest importers of crude oil, international energy prices are likely to be very harmful to the macroeconomic stability and growth performance. The analysis uses Autoregressive Distributed Lag (ARDL) bounds testing model to examine the long term equilibrium relationships as well as short term adjustment mechanisms, including inflation, and current account balance and structural break in the year 2008 as a result of the global financial crisis. The findings verify the presence of a cointegrating relationship between the variables in the long-run. Nonetheless, the individual long-run coefficients are not statistically significant indicating that the oil prices levels do not have a permanent structural effect on the growth. Contrary, short-run analysis indicates that changes in lagged oil prices and external sector imbalances have a substantial impact on growth. The error correcting term shows that the system quickly corrects to the equilibrium in the aftermath of the short term shocks. The implication of these results is that the oil price vulnerability to growth of India works through short-run (macroeconomic) transmission mechanisms and not structural impairment. Enhancing the resilience of the external sector and speeding up the diversification of energy sources are also important policy priorities.

The fast development of the artificial intelligence into the customer relationship management systems has offered the businesses unexampled possibilities to improve customers experiences yet posing serious questions of transparency, fairness, and ethical decision-making. The given comprehensive literature review discusses the intersection of explainable AI and ethical considerations in the realm of CRM ecosystems, which involves the core issue of technological progress and responsibility and trust. The paper utilizes PRISMA to ensure a systematic interpretation of the latest trends in research to determine how the organizations can use interpretable machine learning models, transparent algorithm processes and value-oriented AI systems to enhance customer relations without losing ethics. Important results include explainable AI methods like LIME, SHAP, and attention mechanisms as they have a high impact on customer confidence and response to ethical governance standards. The study establishes a direct relationship between operational transparency of AI-driven personalization, AI-driven recommendation systems, and predictive analytics and customer loyalty, lifetime value. Nonetheless, more issues remain in the process of introducing real-time explainability and controlling the bias of algorithms, adhering to the requirements of data privacy, and building culturally adaptable moral systems in a wide variety of markets. This review states the existence of important gaps in longitudinal research studies about the long-term effectiveness of explainable AI in customer retention, limited literature has been investigated on hybrid human-AI collaborative models in CRM, and there is a lack of research to explore quantum computing as a privacy-preserving customer analytics tool.

The fast development of artificial intelligence and machine learning technologies in the area of crucial social activities has raised the level of concern about ethical interests, algorithmic discrimination, and justice in automated decision-making procedures. This literature review demonstrates that the ethical issues of AI development, bias reduction, and equity systems are complex phenomena, and the development of AI should be performed responsibly. The problem statement focuses on the increasing gap between technological improvement and moral responsibility, where the AI systems reproduce the discriminating trends, violate the rights to privacy, and deliver obscure judgments to influence basic human rights. Based on the PRISMA approach to conducting a systematic literature review, the research is a synthesis of the existing knowledge about the sources of bias in machine learning pipelines, metrics of fairness and trade-offs, ethical frameworks that govern AI, and new regulatory environments. The findings indicate that there remain ongoing problems with the attainment of algorithmic fairness across intersectional groups, between fairness definitions, in terms of black box transparency, and accountability mechanisms of AI-related harm. The crucial shortcomings that have been found are the low standardization of fairness measures, lack of equal representation in training data sets, and the lack of interdisciplinary interaction and emerging regulatory frameworks that are not keeping pace with technological advancement. The proposed review article will add to the overall perspective of any challenges that are present nowadays, practical mitigation measures and future research advancements that would be useful in creating reliable AI systems that meet human values and societal expectations.

The print media is ever getting pressurized to produce output of high quality, customized output, shorter turn-around time, low operation cost and enhanced sustainability. The customary procedures of print media management, which are manualized in coordinating, fixed scheduling as well as fragmented data disclosures are progressively insufficient to the requirements. The given paper explores the implementation of intelligent workflow automation in the management of print media using the combination of artificial intelligence, machine learning, Internet of Things (IoT), and digital twin technologies. A perception-cognition-action-based paradigm is suggested to be the structured AI-driven workflow architecture to allow the adaptive data-driven orchestration between pre-press, printing, post-press, and distribution stages. Predictive quality control, dynamic scheduling, anomaly detection, and resource optimization are performed with the help of machine learning and decision-support models, whereas real-time physical-virtual synchronization and scenario-based planning is performed with the help of digital twins. The quantitative assessment indicates that there are great improvements in turnaround time, schedule compliance, machine use, quality rework, downtime, material waste, and energy usage. The findings reveal that intelligent workflow automation has multidimensional benefits of efficient operations, cost-saving, and sustainability, and maintains human controls by providing supervisory decision-support interfaces. The research paper finds that intelligent workflow automation is a strategic asset in the production workflow of contemporary print media organizations that aim to have resilient, efficient, and sustainable production systems.

Folk music is a cultural reminiscence, social ideals, and historical records of people, but numerous traditions are struggling with the issue of continuity in the environment of globalization and shifting musical realities. This article discusses how modern jazz improvisation has been used as a creative and culturally sensitive tool to maintain and recontextualize folk music traditions. Instead of seeing preservation as a conservation that is unchanging, the study conceptualizes heritage as a living process that continues to survive by changing. Using ethnomusicological viewpoints, jazz improvisation theory, and intercultural theory the study examines how rhythmic patterns, modal systems and performance practices of folk music can be incorporated in the modern jazz environment without the erosion of cultural identity. The paper shows that improvisation makes it possible to have a dynamic dialogue between tradition and modernity and the folk elements can keep the symbolic and emotional meaning they have and broaden their expressive opportunities that are achieved through harmonic reinterpretation, rhythmic adaptation, and group performance. The problematization of jazz improvisation as a cultural negotiation and sustainability points to the research as a means of heritage preservation through a living archive and pedagogical tool, as well as an ethical platform. The results provide a wider-ranging discourse on the field of musicology and performance studies by establishing preservation through transformation as a possible paradigm of maintaining traditional music in the context of the contemporary artistic practice.

The disappearance of modernist artworks through the historical conflict, material degradation and partiality of archival regimes poses a continuous problem in terms of art history and cultural heritage conservation. It is a foregone conclusion that such works are difficult to reconstruct, since modernist aesthetics is focused on abstraction, fragmentation, and conceptual purpose over representational devotion. In this paper, the author suggests a reconstruction model based on AI that views artistic reconstruction as a probabilistic inference problem, as opposed to a deterministic recovery problem. The system is already based on the framework of representation learning, conditional generative modeling, uncertainty-aware sampling, and human-in-the-loop validation to produce several possible discovery hypotheses based on the existing evidence and style samples. The hierarchical style modeling strategy gives reconstruction possibilities at artist and movements levels, whereas explicit uncertainty signs circumvent overconfidence in interpretation. The framework is tested by using representative case studies which are partially documented, partially fragmented and entirely lost modernist artworks. The quantitative metrics of consistency, the analysis of perceptual styles, and uncertainty analysis and the qualitative evaluation process performed by experts prove that the reconstruction fidelity is proportional to the strength of evidence and that the uncertainty is explicitly magnified in the case of sparse constraints. The findings indicate the significance of AI as a supportive analysis tool that can aid the judgment of curators and art-historians but does not sacrifice the interpretative responsibility.

Microfinance in these seasonal economies is a significant challenge for MFI to correspond loan cycles with smallholder farmers’ short-lived cash flow while balancing between client demand and institutional sustainability. It is a concept paper and aims to map theoretical routes for adaptive loan design that draw from financial services provision models, enterprise risk management theories, and literature on income seasonality and stochasticity. Building on a representational foundation, the article sets out a comprehensive industry architecture depicting the interplay of flexibility in the loan product, risk reduction in the portfolio, and administrative feasibility. A typology of flexible repayment schedules, principles for modular loan products, and examples of governance structures that reduce moral hazard and delinquency will be delivered. The model's emphasis is on its lending disbursal of loans and repayment amounts aligning with crop cycles and the earning calendar for micro-enterprisers. The results show that they are conceptually feasible, have the potential to make the model more comprehensive, have the potential to decrease the risk of the portfolio, and have the potential of still being an efficient functioning. The implications are used to create lending practices and policies leading to increased inclusion and resilience for clients in a seasonally variable income situation.