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Highly strengthened mechanical, morphological, IR vibrational and optical properties of rationally tailored wheat starchchitosan blends crosslinked with dual UV/acetic acid system for sustainable green packaging
Through this study, wheat starch/chitosan composite (SCB) films were successfully developed via a UV/acetic acid-assisted solution?casting approach with varying chitosan ratios. Tensile testing shows that stress at break remains nearly unchanged at lower chitosan content (1030?wt%) but increases abruptly beyond 30?wt%, reaching an optimum value at 50?wt% due to intensified hydrogen bonding and cross-linking between chitosan NH?? and starch OH? groups. Strain at break follows a similar trend, with markedly elevated elongation and elasticity. Youngs modulus exhibits the highest rigidity at 50?wt%, while stiffness decreases thereafter, consistent with enhanced elastic deformation. Morphological analysis reveals that chitosan incorporation transforms starch films from brittle, cracked surfaces to densely compact structures with fewer pore defects, resulting in superior adhesion and integrity. UVvis results show improved UV shielding, higher transparency, and tunable absorption, while FTIR analysis confirms only physical interactions with no new bonds formed between the components. Overall, chitosan incorporation significantly fortifies the SCB films by improving mechanical strength, structural stability, and optical performance, extending their application as green, multifunctional materials for sustainable packaging, coatings, and wastewater treatment. 2026 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license. http://creativecommons.org/licenses/by-nc-nd/4.0/ -
The integration of the Internet of Things (IoT) in healthcare analytics: a transformative force
The healthcare landscape is undergoing a dynamic transformation driven by a confluence of factors. Patient expectations for personalized and accessible care are rising, fueled by rapid technological advancements and demographic shifts. To meet these demands, the healthcare sector is actively integrating emerging technologies such as big data analytics, electronic health records (EHRs), telemedicine, and remote patient monitoring (RPM) - all contributing to a value-based care model. This paradigm shift prioritizes preventative care and patient-centered approaches, leveraging technological innovations to fundamentally alter how healthcare is delivered and experienced. Advances in artificial intelligence (AI) and machine learning (ML) algorithms empower doctors with early disease diagnosis and prompt decision-making, potentially preventing illnesses before their onset. However, a relatively new development with significant transformative potential is the integration of the Internet of Things (IoT) into healthcare analytics systems. The core concept of IoT in healthcare revolves around facilitating seamless data sharing, networking, and communication between various entities. This encompasses patients, medical devices, sensors, and healthcare professionals, creating a fully interconnected ecosystem. However, the true power of IoT lies in its data generation. This data fuels sophisticated analytics systems that utilize ML algorithms, predictive modeling, and data visualization techniques to uncover hidden patterns and relationships within the vast information pool. These analytical methods empower healthcare professionals with early detection of abnormalities, accurate diagnoses, and robust disease monitoring capabilities. The resulting connectivity fostered by IoT translates into numerous benefits for the healthcare industry- increased operational efficiency, improved patient care, and advancements in medical research. This convergence of technologies is redefining how healthcare data is collected, exchanged, and analyzed, ultimately providing crucial insights to support clinical decision-making and evidence-based guidance for healthcare practitioners. This chapter delves into the multifaceted integration of IoT into healthcare analytics systems, highlighting its transformative potential for patient outcomes, data-driven decision-making, and healthcare delivery itself. We explore the diverse applications of IoT technology in healthcare analytics, encompassing population health management, remote diagnostics, real-time patient monitoring, and clinical research. Furthermore, we investigate the role of IoT gadgets such as wearables, sensors, and smart medical instruments in data collection. These devices capture a comprehensive picture of a patient's health through information on behavior, environmental factors, and physiological parameters, providing healthcare professionals with a holistic and continuous view. Additionally, the chapter addresses critical challenges associated with IoT integration, including data interoperability, security, and scalability. We examine how technologies like edge computing, blockchain, and cloud computing play a vital role in safeguarding patient privacy and ensuring data integrity. The Institution of Engineering and Technology and its licensors 2026. -
EEG Neurofeedback Training in Children With Attention Deficit/Hyperactivity Disorder: A Cognitive and Behavioral Outcome Study
Background. Attention deficit/hyperactivity disorder (ADHD) is a highly prevalent childhood disorder with symptoms of inattention, impulsivity, and hyperactivity. EEG neurofeedback training (NFT) is a new intervention modality based on operant conditioning of brain activity, which helps reduce symptoms of ADHD in children. Methods and Procedures. To examine the efficacy of NFT in children with ADHD, an experimental longitudinal design with pre-post comparison was adopted. A total of 30 children in the age range of 6 to 12 years diagnosed as ADHD with or without comorbid conditions were assigned to treatment group (TG; n = 15) and treatment as usual group (TAU; n = 15). TG received EEG-NFT along with routine clinical management and TAU received routine clinical management alone. Forty sessions of theta/beta NFT at the C3 scalp location, 3 to 4 sessions in a week for a period of 3.5 to 5 months were given to children in TG. Children were screened using sociodemographic data and Binet-Kamat test of intelligence. Pre-and postassessment tools were neuropsychological tests and behavioral scales. Follow-up was carried out on 8 children in TG using parent-rated behavioral measures. Results. Improvement was reported in TG on cognitive functions (sustained attention, verbal working memory, and response inhibition), parent- and teacher-rated behavior problems and on academic performance rated by teachers. Follow-up of children who received NFT showed sustained improvement in ADHD symptoms when assessed 6 months after receiving NFT. Conclusion. The present study suggests that NFT is an effective method to enhance cognitive deficits and helps reduce ADHD symptoms and behavior problems. Consequently, academic performance was found to be improved in children with ADHD. Improvement in ADHD symptoms induced by NFT were maintained at 6-month follow-up in children with ADHD. EEG and Clinical Neuroscience Society (ECNS) 2018. -
A novel optimised method for speckle reduction in medical ultrasound images
The advancement of medical imaging techniques evolving from X-ray to PET images and the medical image analysis helped medical experts to detect, diagnose and offer treatments for complex disorders and deadly diseases in the human body. Among the various modalities used, Ultrasound imaging is the most widely accepted modality because of its affordability, non-invasive nature and various other features. But the presence of speckle noise in ultrasound image lowers the image quality and reduces diagnostic value. This article states an improved hybrid speckle noise reduction method, a combined application of Kuan and non-local means filters. In this method, Kuan filter is used to sharpen the edges and thereafter the speckle noise elimination is done by using the non-local means. In addition, the performance of the proposed hybrid filter and its design parameters are optimised by using a meta-heuristic called grey wolf optimiser. The performance of hybrid method is evaluated by analysing a chosen set of well-known post filtering methods used for speckle reduction with given ultrasound B-mode images. The comparison of test results using remarkable performance metrics and computation time demonstrate that the hybrid method can be used as the efficient speckle reduction method for image analysis. Copyright 2022 Inderscience Enterprises Ltd. -
Modified Non-local Means Model for Speckle Noise Reduction in Ultrasound Images
In the modern health care field, various medical imaging modalities play a vital role in diagnosis. Among the modalities, Medical Ultrasound Imaging is the most popular and economic modality. But its vulnerability to multiplicative speckle noise is challenging, which obscure accurate diagnosis. To reduce the influence of the speckle noise, various noise filtering models have been proposed. But while filtering the noise, these filters exhibit limitations like high computational complexity and loss of detailed structures and edges of organs. In this article, a novel Non-local means (NLM)-based model is proposed for the speckle reduction of Ultrasound images. The design parameters of the NLM filter are obtained by applying the Grey Wolf Optimization (GWO) to the input image. The optimized parameters and the noisy image are passed to the NLM filter to get the denoised image. The efficiency of this proposed method is evaluated with standard performance metrics. A comparative analysis with existing methods highlights the merit of the proposal. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Medical Ultrasound Image Segmentation Using U-Net Architecture
This research article discusses the implementation aspects of a Deep Learning architecture based on U-Net for medical image segmentation. A base model of the U-Net architecture is extended and experimented. Unlike the existing model, the input images are enhanced by applying a Non-Local Means filter optimized using a metaheuristic Grey wolf optimization method. Further, the model parameters are modified to achieve better performance. Tests were performed using two benchmark B-mode Ultrasound image datasets of 200 Breast lesion images and 504 Skeletal images. Experimental results demonstrate that the modifications resulted in more accurate segmentation. The performance of the modified implementation is compared with the base model and a Bidirectional Convolutional LSTM architecture. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Role of Filters in Speckle Reduction in Medical Ultrasound Images- A Comparative Study
To diagnose and predict complex disorders in human body, various Medical Imaging Techniques are used. Widely accepted technique among them is the Ultrasound imaging modality, because of its low cost and noninvasive nature. But the images produced by ultrasound scanning are of low quality and amenable to faster degradation due to the presence of speckle noise. This led to various studies for effectively removing speckle noise from ultrasound images. In this paper, an endeavor is made for a comparative analysis of chosen set of post filtering methods for Speckle reduction, VIZ Anisotropic Diffusion, Wavelet, Adaptive Median Filter, Hybrid Algorithm, Modified Fourier Transform and Sparse Code Shrinkage using ICA. The different methods are tested on a collection of ultrasound images and their performance evaluated with the Normalized Cross Correlation metric (NCC), Peak Signal to Noise Ratio (PSNR), Structural Content (SC), Universal Quality Index (UQI), Edge Preservation Index (EPI) and Structural Similarity Index (SSI). Further relative execution time of different approaches are also analyzed. On analysis of the values of different metrics and execution time, Wavelet Based Hybrid Thresholding is found to outperform the other filters considered. 2019 IEEE. -
Cashless Confidence: Exploring the Influence of Digital Payments on Gen Z Consumption Patterns
The rapid growth of digital payment systems has significantly changed financial behaviors, especially among Generation Z. This group consists of digital natives whose buying habits are closely linked to technology. Using qualitative thematic analysis, which identifies common themes in qualitative data, this research reviews various studies and reports on financial technology adoption. It looks at how mobile wallets, UPI platforms, QR-based payments, and new fintech solutions affect Gen Z's spending habits, views on money, and financial choices in India. Drawing from existing literature, empirical research, and theories like behavioral economics and the Technology Acceptance Model (TAM), the paper brings together evidence on the psychological, social, and cultural factors behind digital payment adoption. The findings show that ease of use, peer influence, gamification features, and social media-driven consumption greatly motivate Gen Z to use cashless transactions. However, these same factors may lead to risks such as impulsive buying, accumulating debt through Buy Now Pay Later (BNPL) services, and privacy issues. Gender dynamics, urban-rural divides, and differences in financial literacy further complicate these trends. Meanwhile, digital platforms are promising for improving financial management through budgeting tools and investment apps. The paper concludes that digital payments are not just replacing cash; they are transforming financial identities, consumption culture, and intergenerational attitudes toward money. Insights from this study have important implications for policymakers, financial institutions, and educators looking to promote responsible, inclusive, and sustainable digital payment systems for the future economy. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Predictive Analytics as a Catalyst for Disruption in the Fashion Ecosystem
The fashion industry is one of the most dynamic industries, with trends that change constantly being moulded by the changing preferences of consumers. In this dynamic environment it is crucial for companies to adjust to the current market demand, failing which companies might plunge into losses and even face shutdowns. Predictive analytics plays an important role in trend prediction, demand forecasting, optimizing the supply chain and inventory management, personalization, dynamic pricing, waste minimization and prediction of sustainable materials. This, of course, is not without challenges. Data privacy concerns and over reliance on AI algorithms leading to inhibition of human creativity stand out as the most concerning challenges that need to be addressed to ensure that predictive analytics is utilised to its fullest potential in the fashion industry. Predictive analytics and AI can ensure that the fashion industry is not just profitable but also consumer centric and sustainable in its operations thus ensuring long term growth and sustenance of businesses in this industry. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Development of an Efficient and Secured E-Voting Mobile Application Using Android
Smart technologies, particularly the development of the Internet, are employed to enhance the quality of human existence. Thanks to the Internet's explosive expansion, more and more tasks can now be completed quickly and easily compared to the earlier times. E-voting is a relatively recent field that has been identified. Voting can be conducted in a variety of methods, including in person at a polling place, online, and via a mobile application. The security of applications cannot be disregarded given the internet's explosive growth. In order to prevent phishing attacks, we created an Android application and included a 3-step security process before voting. Students can now vote online from any location at any time using a mobile device. Android Studio is used to create and deploy the application. While creating the voting application, this research adheres to the software development life cycle. The result of this research is the creation of a mobile application that is user-friendly for students and serves as a practical tool for letting them vote with three levels of security. 2022 Anli Sherine et al. -
Mathematical model for effective CO2 emission control with forest biomass using fractional operator
The emission of CO2 is the foremost culprit for global warming and is also considered a significant greenhouse gas. Due to the human populations tremendous growth and activities, the rate of CO2 in the atmosphere has increased. To mitigate the emission of CO2 there are artificial ways. But, naturally have a natural resource called "Forest Biomass," one of the significant sinks to absorb CO2 during photosynthesis. Considering all these factors, the main objective of the current investigation is to understand and illustrate the importance of forest biomass in the emission of CO2. The proposed nonlinear model consists of four variables: atmospheric CO2, human population, energy sectors, and forest biomass. We have studied the model both qualitatively and quantitatively, which will help us make future predictions. To study the model in depth, we have formed a fractional-order model to study the systems behavior at different ranges of fractional orders. The model is termed with the Caputo fractional operator. Boundness and Lyapunov stability for non-linear and fractional order models are studied, and equilibrium points, existence and uniqueness, and numerical simulation are examined. The Adams-Bashforth-Moulton method illustrates the essence of the systems numerical method. The numerical approach reveals that the altered models stability is unchanged. Also, we have examined the model by changing the parameter values to different fractional orders to understand the systems behavior, and the changes are captured as figures. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. -
Nonlinear Dynamics and Control of Driven Climate Variability and Ocean Heat Feedbacks
Abstract: Earths climate system is a highly complex and interconnected network governed by nonlinear interactions among the atmosphere, oceans, land, ice, and biosphere, where energy exchanges and feedback mechanisms play a dominant role. In recent decades, anthropogenic greenhouse gas emissions, especially carbon dioxide (), have significantly disrupted this balance, resulting in accelerated ocean heat uptake and persistent temperature anomalies. Determining the long-term dynamics of these interactions remains a critical challenge for accurate climate prediction and mitigation planning. This paper examines the combined dynamics of temperature anomaly, atmospheric concentration, and ocean heat content (OHC) using a novel mathematical approach. By employing the Caputo derivative to describe the model as a fractional-order dynamical system, hereditary effects and long-term dependencies that are inherent in climatic processes can be incorporated. Boundedness, existence and uniqueness of solutions, and both local and global stability are among the fundamental qualitative characteristics of the system that are investigated. To further illustrate stability behavior, streamline graphs are plotted. To ensure an accurate approximation of the fractional dynamics, numerical simulations are conducted using the Adams Bashforth Moulton (ABM) predictorcorrector method. Bifurcation analysis and computations of the Lyapunov exponent are performed to investigate the nonlinear properties of the system, exposing parameter regimes that behave chaotically for different fractional orders. Phase portraits in 2D and 3D show the intricate history of the climate variables. Additionally, to control chaotic oscillations, a sliding mode control approach is used. The findings highlight the promise of control theoretic techniques in climate dynamics by showing that the system is stabilized and chattering is successfully eliminated with the right control parameters. The results demonstrate that the fractional-order formulation provides enhanced capability in capturing long-term dependencies and nonlinear feedback mechanisms inherent in climate dynamics. The overall results show the models robustness as a theoretical framework for climate analysis and offer quantitative insights into the coupled climate systems long-term behavior. The models incorporation of nonlinear interactions among important variables improves the models interpretability and gives a more accurate picture of climate dynamics, which strengthens the foundation for assessing the effects of emissions and guiding the formulation of climate policy. King Abdulaziz University and Springer Nature Switzerland AG 2026. -
Fractional operator-based mathematical model for hydrological cycle analysis with machine learning integration
The most important natural resource for maintaining ecosystems, life, and human civilization is water. Climate patterns, hydrological processes, and energy balance are all impacted by the constant movement of water across different parts of the Earths climate system. A new mathematical model is proposed using a fractional order, and this study investigates the four main elements of the hydrological cycle: atmospheric water, rainfall, surface water, and groundwater. The model uses the Caputo fractional operator to account for memory effects and long-term dependencies in water dynamics. A thorough qualitative and quantitative study examines the systems boundedness, stability, existence, and uniqueness. The AdamsBashforthMoulton (ABM) approach is used for numerical simulations, and it shows improved accuracy, stability, and reduced error metrics compared to traditional methods. Furthermore, bifurcation analysis reveals the systems possible behavior. Data-driven parameter estimation and trend forecasting are achieved by integrating Machine Learning (ML) techniques like the random forest regressor to improve predictive capabilities. Visualization tools such as pair plots, box plots, bar plots, and correlation matrix examines the associations between variables. The suggested method provides a strong framework for hydrological cycle modeling, increasing forecasting accuracy for water resource dynamics and climate-driven hydrological changes. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025. -
On the Investigation of Environmental Effects of ChatGPT Usage via the Newly Developed Mathematical Model in Caputo Sense
This study explores the interconnection between the variables of ChatGPT usage, energy consumption, water consumption, and carbon dioxide CO2 emissions. A new integer and fractional order model using the Caputo derivative is proposed to comprehend the long-term dependencies of these variables. Boundedness, and global and local stability are examined for the fractional order model. The equilibrium points of these variables are shown to determine the stability of the model. The RungeKutta 7 numerical method is employed for the integer order model, whereas the semi-implicit linear interpolation (L1) method is used for the fractional order model. The parameter sensitivity is conducted on the systems parameters to understand the variables impact by varying the relevant parameters for the system. To increase the efficacy of our analysis, we used machine learning approaches to model and predict the dynamics of CO2 emissions, energy and water consumption, and ChatGPT usage. The Prophet ML model stood out among the other methods because it is adept at identifying long-term growth trends, seasonal changes, and the impact of outside variables in intricate time-series data. It is extremely beneficial for research centered on sustainability, where accurate projections are essential for wellinformed decision-making, because it can produce robust, interpretable forecasts against missing values and outliers. Using the Prophet ML model, our research guarantees precise and expandable predictions and provides valuable information that can direct tactics to balance environmental sustainability and technological progress. 2026 by the authors. -
Premium Unlocked AI for Medical Document Decoding
As healthcare systems evolve to become more digital, an enormous volume of medical data is available in various formats, including unstructured data, scanned documents, handwritten prescriptions, diagnostic images, audio transcriptions, and clinical video recordings. The complexity and unorganised form of data continue to pose serious challenges with regard to automation, accuracy, and consistency in healthcare and insurance businesses. This study introduces an AI-based multimodal framework that incorporates the use of Optical Character Recognition (OCR), the MiniCPMV-4.5 model, and Retrieval-Augmented Generation (RAG) with Large Language Models (LLMs) to enhance the intelligent processing and contextual comprehension of intricate medical data, thus overcoming these limitations. It applies OCR to scanned images and handwritten documents to precisely recover the textual information from them and uses domain-specific named entity recognition (NER) to recognize significant medical information, e.g., patient information, diagnoses, procedures, and financial information. The extracted information is then converted to vector embeddings and stored in a powerful vector database, Milvus, that enables fast and efficient semantic search as well as context-sensitive reasoning. The proposed framework, along with the visual and auditory inputs, video understanding, multilingual capacity, and the S2S (speech-to-speech) and TTS (text-to-speech) translation, makes it more accessible and engaging to the user. This system reduces the level of human involvement and provides real-time insights quickly and more precisely so that more efficient decisions and operations can be made in the fields of healthcare and insurance. 2025 IEEE. -
Synthesis and characterization of biowaste-derived porous carbon supported palladium: a systematic study as a heterogeneous catalyst for the reduction of nitroarenes
In this study, we present a green synthesis approach for the fabrication of porous carbon supported palladium catalysts derived from Caesalpinia pods. The synthesis involves self-activation of Caesalpinia pods in a nitrogen atmosphere at various temperatures (600C, 800C, and 1000C) to produce porous carbon nanoparticles. Among the synthesized carbon materials, the sample CP-CNS/10 synthesized at 1000C exhibited the highest surface area of 793 m2/g with an average pore size diameter of 1.8nm. The resulting porous carbon material served as an efficient support for palladium nanoparticles, with a low metal loading of about 0.2mol% Pd for the reaction. This catalyst demonstrated excellent performance in the reduction of nitroarenes to their corresponding aromatic amines. The successful incorporation of approximately 4.5% Pd during the deposition process highlights the potential of the porous carbon supported palladium catalyst synthesized at 1000C for a sustainable and efficient heterogeneous catalyst for the reduction of nitroarenes. Graphical Abstract: (Figure presented.) Akadiai Kiad Budapest, Hungary 2024. -
Biomass-Derived Carbon Materials in Heterogeneous Catalysis: A Step towards Sustainable Future
Biomass-derived carbons are emerging materials with a wide range of catalytic properties, such as large surface area and porosity, which make them ideal candidates to be used as heterogeneous catalysts and catalytic supports. Their unique physical and chemical properties, such as their tunable surface, chemical inertness, and hydrophobicity, along with being environmentally friendly and cost effective, give them an edge over other catalysts. The biomass-derived carbon materials are compatible with a wide range of reactions including organic transformations, electrocatalytic reactions, and photocatalytic reactions. This review discusses the uses of materials produced from biomass in the realm of heterogeneous catalysis, highlighting the different types of carbon materials derived from biomass that are potential catalysts, and the importance and unique properties of heterogeneous catalysts with different preparation methods are summarized. Furthermore, this review article presents the relevant work carried out in recent years where unique biomass-derived materials are used as heterogeneous catalysts and their contribution to the field of catalysis. The challenges and potential prospects of heterogeneous catalysis are also discussed. 2022 by the authors. -
Sustainable carbonaceous nanomaterial supported palladium as an efficient ligand-free heterogeneouscatalyst for Suzuki-Miyaura coupling
A novel ligand-free heterogeneous catalyst was synthesized via pyrolysis of Samanea saman pods to produce carbon nanospheres (SS-CNSs), which served as a carbon support for immobilizing palladium nanoparticles through an in situ reduction technique (Pd/SS-CNS). The SS-CNSs effectively integrated 3% of Pd on their surfaces with no additional activation procedures needed. The nanomaterials obtained underwent thorough characterization employing various techniques such as FT-IR, XRD, FE-SEM, TEM, EDS, ICP-AES, and BET. Subsequently, the efficiency of this Pd/SS-CNS catalyst was assessed for the synthesis of biaryl derivatives via Suzuki coupling, wherein different boronic acids were coupled with various aryl halides using an environmentally benign solvent mixture of EtOH/H2O and employing only 0.1 mol% of Pd/SS-CNS. The catalytic system was conveniently recovered through centrifugation and demonstrated reusability without any noticeable decline in catalytic activity. This approach offers economic viability, ecological compatibility, scalability, and has the potential to serve as an alternative to homogeneous catalysis. 2024 RSC. -
Parle Products: The Journey of Budget Brand Toward Premiumization
Parle Products has been one of the foremost pioneers of the biscuit and confectionery industry in India. Since its inception in 1929, Parle has been synonymous with quality, nutrition, taste, and affordability in India. Having more than 40 brands across different product categories in its portfolio, Parle Products has come a long way. Due to the enormous success of brand Parle-G, the mother brand Parle has been associated with values of affordability and quality. Parle has always been a strong player in the value segment and thus customers associate all brands of the company as being the affordable brand with quality. While the industry peers such as Britannia and Indian Tobacco Company (ITC) have positioned themselves as a champion of premium segments, Parle Products is still known as an affordable or budget brand among Indian masses. Considering the increasing demand for premium biscuits among the new generation of consumers, Parle Products has introduced the new division Parle Platina in 2017. But the transformation of value or budget brand into the luxury and premium brands is not easy, as the legacy of value brand among the masses is not easy to shake off. This becomes more difficult and challenging in a highly fragmented, competitive, and price-sensitive biscuit market like India. Another problem for the Parle Products is to hit the sweet spot between the mass market and the premium demographic which the brand is currently missing out in a highly fragmented and competitive Indian biscuit market. 2020 K. J. Somaiya Institute of Management. -
Old Monk: The Resurrection of a Heritage Brand
This case is developed to discuss options related to strategy in general and marketing strategy in particular that were implemented by an iconic brandOld Monkin the compelling attempt to revive the its brand value and relevance. The protagonist in the case is Mr Hemanth Mohan, the Director of this brand who is faced with serious challenges in resurrecting the brand in a hypercompetitive, ever-changing lifestyle alcoholic beverages market in India. The most significant of the challenges faced by Mr Mohan are changing tastes and preferences of the consumer, new foreign brands entering into the market, strong regulation, close monitoring from the government, and the monopoly of state-managed liquor retail chains. This case addresses and provides for the evaluation of various strategic options available for the brand in terms of repositioning itself in the relevant markets. The case presents questions related to how effectively a traditional brand can carry forward the legacy to redeem its brand value in the light of changing tastes and customer preferences. The case proposes to bring to light the importance of strategic decisions to be made around managing brand extensions in addressing the dilemma of brand value being spread too thin versus capitalizing on the past glory of the brand in finding life for the newly launched products under the known umbrella. This case is developed with an intent to be discussed among students pursuing undergraduate and/or graduate education in management discipline. The case is well aligned to be relevant for discussing concepts related to brand management, marketing strategy, strategic management, and consumer behavior. The content of this case is designed to be discussed and delivered in a typical 90-min class session, allowing students 120 min of pre-reading time and 120 min of post-discussion report preparation. 2020 K.J. Somaiya Institute of Management Studies and Research.