SASTRA University

Abstract Objectives Vitiligo is an autoimmune disorder characterised by the presence of depigmented lesions on the skin. The autoreactive cytotoxic T cells at the epidermal‐dermal junction of the skin facilitate the targeted destruction of epidermal melanocytes, leading to the development of vitiligo lesions. Narrow‐band UVB (NBUVB) phototherapy is a widely used non‐invasive treatment that promotes transient repigmentation of lesions. However, the specific effects of NBUVB on skin dermal T cells in vitiligo patients remain largely unexplored. Insights into the mechanism of action of NBUVB phototherapy can guide therapies against cytotoxic T cells, enabling effective and sustained remission of vitiligo lesions. Methods Indian patients with active vitiligo lesions underwent 3 months of NBUVB phototherapy, thereby leading to successful repigmentation of vitiligo lesions. Spatial transcriptomic and histological analyses were employed to investigate the gene expression profiles of dermal T cells at the epidermal‐dermal junction, both before and after treatment. Results Comprehensive spatial transcriptome analysis of skin dermal T cells revealed that NBUVB phototherapy leads to the overall suppression of inflammatory genes and immune regulatory pathways that are upregulated in vitiligo patients before treatment. Consequently, tissue‐resident innate and adaptive immune cells significantly decrease post‐UVB phototherapy. Interestingly, we observe an increase in naïve CD4 + T cells post‐UVB phototherapy. Conclusion NBUVB phototherapy effectively suppresses the innate and adaptive immune cell populations and immune regulatory pathways while promoting an increase in naïve CD4 + T cells. These findings underscore a previously unrecognised immunomodulatory role of NBUVB phototherapy in vitiligo patients.

Two-dimensional materials are a fertile ground for exploring quantum geometric phenomena, with Berry curvature and its first moment, the Berry curvature dipole, playing a central role in their electronic response. These geometric properties influence electronic transport and result in the anomalous and nonlinear Hall effects, and are typically controlled using static electric fields or strain. However, the possibility of modulating quantum geometric quantities in real-time remains unexplored. Here, we demonstrate the dynamic modulation of Berry curvature and its moments, as well as the generation of a pseudo-electric field and their coupling. By placing heterostructures on a membrane, we introduce oscillatory strain together with an in-plane AC electric field and measure Hall signals that are modulated at linear combinations of the frequencies of strain and electric field. We also present direct experimental and theoretical evidence for coupling between pseudo-electric field and quantum geometry that results in an unusual dynamic strain-induced Hall response. This approach opens up a new pathway for controlling quantum geometry on demand, moving beyond conventional static perturbations. The coupling of the pseudo-electric field with Berry curvature provides a framework for external electric field-free anomalous Hall response and opens new avenues for probing the topological properties.

This chapter is an attempt to establish the understanding of how CI methods and optimisation approaches can enhance the capacity in WM. Water safety and potability emerge as an international subject that requires better methods in identifying, assessing, and forecasting the quality of water. It explains how the water quality that is measured by pH, conductivity, hardness and turbidity data sets is analyzed through several computational intelligence based machine learning algorithms to provide an overview of the water quality. The approaches to enhancing the efficiency of water treatment and decision-making regarding the usage of the resources in sustainable water quality management are discussed here. In this section, we use machine learning on real world application where we predict water potability using water quality proxy data sets.

The digitization of livestock farming through Artificial Intelligence (AI) and Machine Learning (ML) requires an institutional innovation ecosystem rather than isolated technologies. This study conceptualizes smart dairy farming as an outcome of Triple Helix collaboration among universities, government, and industry, resulting in an AI-based decision-support platform for dairy portfolio management. Universities advance algorithm development, governments provide regulatory and data-governance frameworks, and industry ensures practical deployment. Based on this collaboration, the study develops SynerNet, an AI model for forecasting milk production, animal health, and resource utilization. Comparative evaluation against SVM and AdaBoost shows superior performance, achieving up to 93% prediction accuracy. The findings demonstrate that coordinated university–government–industry interaction enables sustainable and transferable AI-driven agricultural innovation.