Poornima University

The rapid growth of digital technologies has significantly transformed the banking sector in India, compelling financial institutions to redesign their marketing approaches to remain competitive and customer-centric. This study presents a comparative analysis of the digital marketing strategies adopted by HDFC Bank and ICICI Bank, with particular emphasis on customer engagement practices. Both banks are among the leading private sector banks in the country and have made substantial investments in digital platforms to strengthen their market presence and enhance customer interaction. The research examines various components of digital marketing, including social media outreach, mobile banking applications, personalized email campaigns, content marketing, search engine visibility, and the use of data analytics for targeted communication. The study evaluates how these strategies influence customer awareness, satisfaction, loyalty, and overall engagement. Attention is also given to the user experience offered through digital interfaces, responsiveness on social media channels, and the integration of innovative technologies such as chatbots and AI-driven support systems. Primary and secondary data have been considered to understand customer perceptions and preferences regarding digital communication by both banks. The findings highlight differences in strategic focus, content style, platform utilization, and engagement metrics. While one bank may emphasize technology-driven personalization and automation, the other may ocus more on relationship-oriented communication and brand storytelling. The study concludes that effective digital marketing in the banking sector extends beyond promotional activities and plays a vital role in building trust, transparency, and long-term customer relationships. The comparative insights offered in this research may assist banking professionals, marketing practitioners, and researchers in understanding evolving digital engagement trends and in developing more responsive and customer-focused digital strategies.

This paper presents an effective approach for lower-order (LO) modeling of an electric vehicle–integrated off-grid microgrid (OMG) system. The seventh-order system (SOS) of the OMG is reduced to a second-order model (SOM) while preserving the original system’s dynamic characteristics and ensuring computational efficiency. The Taylor series (TS) and Laurent series (LS) expansions are employed to simplify the complex system that plays a significant role in the reduction process. The expansion parameters of the higher-order system (HOS) of OMG and its lower-order model (LOM) are exploited to construct the fitness function. The proposed approach constructs three sub-objective functions based on TS and LS. These sub-objective functions are then combined into a single fitness function to obtain an improved LOM by enhancing the transient and steady-state responses with respect to the HOS of OMG. To minimize the error, the resultant fitness function is optimized using the brown bear optimization (BBO) algorithm. The optimization is performed under two key constraints: (i) ensuring zero steady-state error, and (ii) satisfying the Hurwitz stability criterion. To demonstrate the efficacy of the proposed LOM, it is compared with other LOMs obtained from different approximation techniques. The proposed LOM and other LOMs are graphically validated through step, impulse, Bode, Nichols, and Nyquist response comparisons with the HOS. Additionally, the performance error criteria (PEC), time-domain specifications (TDSs) and frequency domain specifications (FDSs) of the proposed LOM are compared with other LOMs using the HOS to establish the validation and applicability of the proposed method.

Freshwater ecosystems play a crucial role in maintaining ecological balance and supporting biodiversity. Assessing water quality and identifying pollution factors affected by seasonal changes is of utmost importance. The traditional approach for identifying pollution sources and seasonal water quality assessment is often labor-intensive, time-consuming, and complex. In response, our study introduces a hybrid receptor, multivariate statistical and machine learning (ML) based framework as an alternative. Positive matrix factorization (PMF) and receptor model were used to identify key pollutant sources, while Principal Component Analysis (PCA) was used to corroborate pollutant sources. Additionally, various supervised ML classification algorithms are used to predict and identify pollution sources on full physicochemical datasets and reduced in situ parameter scenarios. ML model stability and generalization capacity were evaluated using learning curves, cross-validation method. The findings indicate significant seasonal variability in water quality index, ranging from 38.5 (poor) to 78.6 (good). PMF and PCA revealed three dominant pollution factors, such as biogeochemical processes, sediment resuspension/nutrient influx, and anthropogenic pollution, affecting the seasonal water quality by 15-30%, 25-45%, and 40-70%, respectively. Model performance indicators using confusion matrices, ROC-AUC, and other evaluation metrics, for full dataset identified XGBoost as the best model, achieving the highest accuracy of 96.3%, followed by CatBoosting (93.6%), Random Forest (93.6%), Support Vector Classifier (91.8%), K-Nearest Neighbors (87.2%) and Decision Tree (84.3%). However, with the in situ data the ML models show stable and consistent classification accuracy in the range of approximately 70-81%. Overall, the modelling framework provides an effective approach for capturing seasonal variability and pollution source attribution, contributing to improved ecological understanding and predictive management of freshwater system.

O Transtorno do Déficit de Atenção e Hiperatividade (TDAH) é uma condição neuropsiquiátrica e complexa, caracterizada por sintomas de desatenção, impulsividade e hiperatividade. Com os avanços da neuroimagem, a ressonância magnética (RM), especialmente em suas modalidades funcional (fMRI) e por tensor de difusão (DTI), tem se mostrado uma ferramenta eficaz na investigação das bases neurobiológicas do TDAH. Este estudo, de natureza qualitativa e baseado em revisão bibliográfica, teve como objetivo analisar a influência da RM na avaliação do TDAH, destacando sua contribuição na identificação de padrões cerebrais atípicos e conectividades alteradas. Os achados revelam alterações funcionais e estruturais em regiões como o córtex pré-frontal, núcleo caudado, cerebelo e corpo caloso, além de diferenças de gênero e resposta a medicamentos. Apesar do seu valor investigativo, a RM ainda apresenta limitações práticas para uso clínico, como o alto custo e a necessidade de colaboração do paciente. No entanto, sua integração com métodos clínicos, neuropsicológicos e computacionais, como algoritmos de machine learning, oferece potencial para diagnósticos mais precisos e tratamentos personalizados. Conclui-se que, embora a RM ainda não seja uma ferramenta diagnóstica definitiva, seu papel é fundamental na compreensão e avanço do manejo do TDAH.

Abstract Slope failures are one of the major problems, in terms of infrastructure safety as well as environment and socio-economic resilience especially in the case of rapid urbanization, climate induced extreme rainfall events and increasing seismic hazards. Dealing with these issues also makes valuable contributions to broader global objectives in relation to infrastructure that is sustainable (SDG 9); urban systems that are resilient (SDG 11), and strategies for adapting to climate change (SDG 13). A complete slope stability analysis is conducted in this study, which highlights the contribution of geogrid reinforcement on improving the performance of slopes under during failure, drainage and seismic particulate. Conventional geotechnical investigation of borehole data, SPT N-values and laboratory testing in combination with more sophisticated numerical analysis using ReSSA software is presented. Rotational failure analysis with Limit Equilibrium Method is performed on unreinforced and reinforced slopes, using different ground water and seismic loadings in calculating factor of safety. Special attention is devoted to the influence of pore water pressure and draining conditions (degree of saturation) as well as soil heterogeneity in the slope behavior for field cases experienced under climate variability. The results show that slope stability is greatly improved with geogrid due to enhancement of shear strength and decrease in displacement as well as failure potential under poor drainage or seismic conditions. The introduction of efficient drainage schemes would further improve performance by reducing pore water pressure and long term stability. The relationships between SPT N-values and the stability parameters have underscored the need for site-specific soil characterization to design safe slope stabilization works. A comparison of costs and benefits demonstrates that the use of geogrid for stabilization provides a cost-effec- tive, sustainable alternative to traditional methods when potential long-term main- tenance and failure repair are factored in. The results of this study can contribute to the better understanding and rational design aspects of designing reinforced slopes for transportation corridors, embankments, as well as urban development projects.

Having Digital Product Passport (DPPs) as a disruptive technology to enhance transparency, traceability, and circularity in the garment and clothing industry has become a trend. Combining the data on material composition, supply chain, certifications, and end of life pathways, DPPs can enable brands, regulators and consumers to make prudent sustainability decisions. Nevertheless, that which makes DPPs powerful also creates significant problems of privacy, confidentiality and data security. The secrets of suppliers, the trade secrets in the sourcing business and the relations with customers require intense efforts to ensure that the transparency will not disrupt the trust and the competitiveness. The privacy conserving DPPs discussed in this chapter are applicable to the garment industry.

<title>Abstract</title> Circuit Economy (CE) conversion is necessary to sustainable construction in India yet the implementation is pushed by strong forces. The paper will systematically discover, pinpoint, and prioritize these challenges to provide a strategic roadmap to the policy makers and industry players. This study employed a sequential mixed-method design, firstly, generated a highly detailed list of 27 barriers through a broad literature review, and such list was confirmed and put into perspective by 12 domain experts. The obstacles were divided into six dimensions. Subsequently, the information of 67 professionals was examined with the assistance of the powerful Best-Worst Method (BWM) as the Multi-Criteria Decision-Making (MCDM) tool to prioritize these problems. As it is demonstrated in the analysis, the most problematic barrier is the so-called Regulatory and Policy Barriers (RPB) such as incoherent legislation, the lack of enforcement, and the lack of incentives, and Industry and Supply Chain Barriers (ISC) seem to be the least important. This finding indicates the enabling quality of the governing structure that is to say that a facilitative policy framework is the essential prerequisite to success in the dismantling of the technical, economic and behavioural barriers. The study concludes that the major improvement in minimisation of the construction waste and the implementation of CE in India lies in the establishment of a regulatory environment that would not only allow but also make mandatory, which would open the opportunity of a more sustainable and resilient construction industry.

Digital transformation enhances sustainability in public sector pharmaceutical supply chains by optimizing sources, improving traceability, and promoting circular economy principles. This research converges on the circular economies principles to transform the Indian public sector pharmaceutical supply chains for flexibility by a mixed method approach. This study uses the Technology-Organization-Environment (TOE) Framework integrated with a Delphi study to identify and prioritize the key enablers of digital transformation and circular economy principles to enhance supply chain flexibility in the Indian public sector pharmaceutical services. The relative importance of each identified enablers was measured using the best worst method to confirm focused resource allocation and strategic prioritization for optimal impact in public sector pharmaceutical services. The study further uses the Bayesian network approach to determine the most prominent enabler of digital transformation and circular economy principles, which enhance supply chain flexibility in the Indian public sector pharmaceutical services. This research highlights that technology-driven investments enhance supply chain flexibility, supplier collaboration is critical for continuous circular economy transitions, and strong regulatory policies are needed for effective adoption. These insights provide policymakers and practitioners with a comprehensive framework to drive sustainable transformation and operational excellence in the public sector pharmaceutical sector services. • Mixed methods integrate TOE & Delphi to identify and classify digital transformation & circular economy enablers in pharma SCs. • The Best-Worst Method evaluates the strategic prioritization of each enabler in public pharma services for better SCF. • Bayesian analysis reveals tech investments and supplier collaboration as key enablers for sustainable change. • Strong regulatory frameworks drive effective adoption of circular economy practices in public pharma services.

Riverbank erosion, soil loss, and flooding are interrelated processes that critically influence river morphology, sediment dynamics, and floodplain stability. Understanding their spatial and temporal interactions is essential for effective watershed management. This study evaluates the spatio-temporal patterns of river bank change along the Upper Ghaghara River basin and investigates their relationship with regional soil erosion risk and flood susceptibility. River bank dynamics were quantified using the Digital Shoreline Analysis System (DSAS v. 6.0) applied to multi-decadal satellite imagery from 1991 to 2024, calculating key metrics including End Point Rate (EPR), Net Shoreline Movement (NSM), and Shoreline Change Envelope (SCE). Soil erosion risk was modeled using the Revised Universal Soil Loss Equation (RUSLE), incorporating rainfall erosivity, soil erodibility, slope, vegetation cover, and conservation practices, while flood susceptibility was assessed by integrating multiple hydro-geomorphic factors using the Analytic Hierarchy Process (AHP). Results indicate pronounced erosion along the right bank with an average EPR of − 20.76 m/yr and an average NSM retreat of − 687.4 m, compared to − 5.53 m/yr and − 176.7 m, respectively, on the left bank. Approximately 62.7% of right bank transects and 57.5% of left bank transects exhibit significant erosion. Soil erosion rates exceed 40 t/ha/yr in high-risk zones of the basin spatially coincident with active bank migration areas. Flood susceptibility mapping identifies 30% of the basin under high to very high flood risk, corroborated by 2022 pre- and post-flood inundation analyses showing extensive floodplain coverage. These findings highlight the synergistic impact of bank erosion, upland sediment supply, and flooding on river corridor dynamics. The integrated assessment underscores the necessity for multi-hazard management strategies to mitigate geomorphic hazards, sustain riverine ecosystems, and enhance socio-economic resilience in this vulnerable basin.