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Red emission in MoO3:Eu3+ nanobelts: Investigation on the photoluminescence quenching mechanism
MoO3:Eu3+ nanobelts were synthesized via the hydrothermal method with varying concentrations of europium doping. The investigation has examined the structural, morphological, optical, and photoluminescence characteristics of MoO3:Eu3+ nanophosphors. The XRD and Raman spectroscopy affirmed the orthorhombic structure of the synthesized nanostructures. FESEM depicts a nanobelt-like morphology and XPS studies confirmed the presence of Eu3+. A detailed analysis of the photoluminescence mechanism, concentration quenching, and quantum efficiency is presented in this study. Upon 302 nm excitation, red emission was observed along with concentration quenching effects. The optimized sample with the highest PL intensity (MoO3:Eu3+ 3 mol. %) was annealed at 600 C for 12 hrs. The PL intensity increased upon annealing, with the corresponding CIE coordinates (0.52, 0.29). The findings highlight the material's potential for use in display technologies and bioimaging phosphors. 2025 The Authors -
Red Fort, Delhi
Red Fort, Delhi stamp, a commemorative postage stamp was issued by the India Post on 15 August 1949, for the second anniversary of Indian Independence. The Prime Minister of India hoists the national flag on Independence Day at the main gate of this beautiful structure. It has also been declared as a UNESCO World Heritage Site in 2007. -
Redefined families and subsystems: Reading kinship and hierarchical structures in select Hijra autobiographies
Hijras or transwomen in India are gendered identities, but their identities cannot be reduced to the conceptual framework and analysis of sex, gender and sexuality. Being the minority in India, transgender lives intersect with caste, class, kinship and hierarchy. The study locates these intersections within the scope of the select hijra autobiographies; The Truth About Me: A Hijra Life Story by A. Revathi and I am Vidya by Vidya. The study looks at the notions of family which are traditionally woven in heteronormative and patriarchal setups. It examines the gharanas system or subsystems in hijra communities that redefines the structures and hierarchies of the family, and designating the fellow elder hijras with the relation of mata (mother) and cela (disciple), thus forming a kinship which is located beyond the caste, class and religious structures. The emphasis is to study how families are inserted in heteronormative perspectives and argues a redefining of the notion of family,and to establish and recognize the newer perspectives on family which lies outside the traditional setup. AesthetixMS 2020. -
Redefining Business in Volatile and Ambiguous Times
In an era defined by change, uncertainty, and disruption, businesses can no longer rely on traditional strategies or linear thinking. Organizations must now evolve to thrive amid economic instability and shifting global dynamics. Global volatility calls for a fundamental reimagining of leadership and purpose where innovation and agility become the true measures of success. This is not merely about surviving turbulence but about transforming it into an opportunity for reinvention and growth. Redefining Business in Volatile and Ambiguous Times examines how businesses and professionals can adapt and thrive amid volatility and uncertainty. Through digital transformation and ethical innovation, it explores strategies for resilience, decision-making, and sustainable growth in todays rapidly evolving global landscape. Covering topics such as technological advancements, business innovation, and artificial intelligence, this book is an excellent resource for academicians, researchers, business leaders, economists, and more. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Redefining copreneurs: a four decadal review adopting computer aided text analysis
The study defines copreneurs and presents a four decadal review on copreneurial literature. The purpose is to bring conceptualization and characterization of copreneurs, on surface from its fragmented literature. A structured literature review on copreneurship research published between 1984 and 2023 is conducted. The search is made adopting indexing (Scopus, Clarivate and ABDC), digital libraries including ProQuest and EBSCO, and research articles published in journals by renowned publishers namely Elsevier, Emerald, Inderscience, Sage, Springer, Taylor & Francis and Wiley. Inclusion/exclusion criteria was defined and duplicates were eliminated. Finally, using POWER review model, the existing literature is organized under six themes namely Gender Roles, Spousal Support & Relationship Satisfaction, Work Life Balance, Business Commitment & Motivation, Leadership & Decision Making and Division of Labour & Responsibilities in the Intertwined Worlds. Using Inter- Rater Reliability, five definitions of copreneurs were framed and rated by nine experts from academics and industry. Finally, the definition with highest score and acceptable I-CVI value for simplicity & clarity is proposed. The fragmented literature on copreneurs speaks volume about the need for more impactful research on them. By using the proposed definition of copreneurs, scholars can uniformly identify the copreneurs, with future opportunities for micro-level research on copreneurs. Policy makers can utilise the findings of these research and formulate schemes, policies & programmes for betterment of copreneurs. The study intends to bridge the disciplinary gaps existing for identifying copreneurs and serve as a foundation for information sharing, regarding copreneurs and their entrepreneurial practices. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Redefining copreneurs: a four decadal review adopting computer aided text analysis
The study defines copreneurs and presents a four decadal review on copreneurial literature. The purpose is to bring conceptualization and characterization of copreneurs, on surface from its fragmented literature. A structured literature review on copreneurship research published between 1984 and 2023 is conducted. The search is made adopting indexing (Scopus, Clarivate and ABDC), digital libraries including ProQuest and EBSCO, and research articles published in journals by renowned publishers namely Elsevier, Emerald, Inderscience, Sage, Springer, Taylor & Francis and Wiley. Inclusion/exclusion criteria was defined and duplicates were eliminated. Finally, using POWER review model, the existing literature is organized under six themes namely Gender Roles, Spousal Support & Relationship Satisfaction, Work Life Balance, Business Commitment & Motivation, Leadership & Decision Making and Division of Labour & Responsibilities in the Intertwined Worlds. Using Inter- Rater Reliability, five definitions of copreneurs were framed and rated by nine experts from academics and industry. Finally, the definition with highest score and acceptable I-CVI value for simplicity & clarity is proposed. The fragmented literature on copreneurs speaks volume about the need for more impactful research on them. By using the proposed definition of copreneurs, scholars can uniformly identify the copreneurs, with future opportunities for micro-level research on copreneurs. Policy makers can utilise the findings of these research and formulate schemes, policies & programmes for betterment of copreneurs. The study intends to bridge the disciplinary gaps existing for identifying copreneurs and serve as a foundation for information sharing, regarding copreneurs and their entrepreneurial practices. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Redefining digital transformation in service supply chain: the missing piece of big data analytics
The study delves into the transformative role of big data analytics (BDA) in supply chain management within the service industry, employing the PRISMA framework to systematically review literature published between 2011 and 2024. A comprehensive search across multiple databases identified 286 relevant studies, which were meticulously analysed through bibliometric techniques. Keyword and network analyses, conducted using VOSViewer, revealed critical research linkages, prominent technologies, and thematic patterns within the domain. The findings underscore the pivotal role of technology integration in enhancing the efficiency of service supply chains, with a particular emphasis on emerging technologies such as blockchain, artificial intelligence, and machine learning. By highlighting the interconnectedness of authors, identifying key themes through keyword analysis, and uncovering research patterns through frequency analysis, the study provides valuable insights into the integration of BDA, ultimately contributing to the advancement of supply chain management in the service industry. Copyright 2025 Inderscience Enterprises Ltd. -
Redefining Disease Detection: Innovative Machine Learning and Wearable Sensor Integration
Wearable sensor technology is considered to be one of the fastest growing fields of information and communication technologies and it has revolutionized the healthcare delivery by enabling continuous and real-time physiological monitoring. This research presents a novel approach that allows an early onset disease detection instigated with the prowess of advanced Graph Neural Network (GNNs) matched with the body streams gathered from wearable machines using its implementation technology - Pythonline of programming named Awesome Geometric libraries referred to as Aztec PyTorch. Graph neural networks (GNNs) are especially suitable within the scope of modeling complex relationships among multivariate inputs of the sensors for modeling the temporal and spatial subjacent dependence of the physiological signs with regards to reality. The proposed system analyzes the data acquired from the various wearable sensors such as heart rate, accelerometers and bio sensors, which help in anomaly detection and hence the detection of the patient having cardiovascular, metabolic and neurological diseases. The synergy between innovative deep learning models and sensors as ubiquitous technologies offers great promise to transform the provision of personalised healthcare services and dealing with disease in its early stages. 2025 IEEE. -
Redefining learning: Harnessing the power of flipped classroom pedagogy
This chapter examined the ever-changing educational environment by utilizing flexible classroom pedagogy as a framework. The authors anticipate thoroughly examining how this novel methodology revolutionizes conventional learning paradigms by focusing on active and individualized learning encounters. This chapter illuminates how instructors can proficiently implement flipped classroom methodologies to augment student engagement, critical thinking, and final learning results by examining foundational principles, exemplary approaches, and case studies. By examining many instructional strategies and technologies, this chapter imparted insightful perspectives on the future of education. 2024, IGI Global. All rights reserved. -
Redefining Online Commerce With AI-Powered Shopping Agents
The importance of artificial intelligence has rapidly changed the world of online commerce and turned digital shopping into a predictive and personalized practice. One of the most significant changes is the application of AI to create shopping agent intelligent machines helping consumers at every stage of the buying process by suggesting purchases, comparing products, and automating the process. This chapter discusses how AI agents of shopping are transforming e-commerce through redefining consumer behaviour, and making new types of online interaction possible. Based on a systematic literature review of the scholarly literature and industry practices, the chapter discusses the most important applications, including personalized recommendations, dynamic pricing, AR-driven virtual try-ons, and autonomous purchasing systems. The research comes up with the conclusion that, AI shopping agents are no longer considered an optional addition, but a strategic collaborator in contemporary commerce, although their implementation should be supported by moral values, transparency, and human supervision. Copyright 2026, IGI Global Scientific Publishing. -
Redefining Organizational Sustainability Through Revamping Digital Capital
[No abstract available] -
Redefining Photography in the Era of Artificial Intelligence
[No abstract available] -
Redefining traditional education using augmented reality and virtual reality
[No abstract available] -
Redesigning brand strategies in the phygital era
Phygital Era is changing the brand strategies. Blending conventional marketing methods with digital innovations as per the growing expectations of consumers. This chapter analyses the impact of Digital technologies such as artificial intelligence, augmented reality, virtual reality, and mixed reality which blend seamless brand and consumer relations while emphasizing the need for online & offline integration to further increase engagement and loyalty. The Case studies of Nike, Sephora, IKEA, and McDonald's, showcase how brands harness technology for a better customer experience. It also focuses on the challenges of Phygital branding. Lastly, it observes the role digital advancements have played in changing consumer psychology, purchase behavior, and constructive brand loyalty with attention to issues such as data privacy and digital fatigue. Finally, it highlights the future trends of hyperpersonalization and metaverse that are set to boom in the era of Phygital branding. 2025, IGI Global Scientific Publishing. -
Redox-active tetra-amino cobalt phthalocyanine electrocatalyst for sustainable electrochemical synthesis of 2-(pyridin-4-yl)-1H-benzo[d]imidazole
An electrocatalyst bearing a cobalt phthalocyanine derivative was developed by modifying carbon fibre paper electrode with polythiophene-3-acetic acid (pTAA) and further immobilizing tetra-amino cobalt phthalocyanine (TACoPc). The electrode was topographically and electrochemically characterized to validate its surface modification and functional suitability. This energy efficient electrocatalyst (CFP-TAA-TACoPc) was explored, for the sustainable synthesis of 2-(pyridin-4-yl)-1 H -benzo[ d ]imidazole at 1.35 V, with 87.6 1.267 % product yield at ambient conditions. o -phenylenediamine and pyridine-4-carboxaldehyde were used as starting materials, with ethanol as solvent, and lithium perchlorate as supporting electrolyte, using a three-electrode system, in a single compartment cell. The benzimidazole derivative was observed to crystallize out after completion of the reaction, negating the need for any further purification and was characterized using 1HNMR and GCMS. This work highlights the potential of electrochemical strategies as a sustainable and efficient alternative to conventional methods for heterocyclic synthesis. 2026 Elsevier B.V. -
Reduce Overfitting and Improve Deep Learning Models Performance in Medical Image Classification
A significant role in clinical treatment and educational tasks is played by clinical image classification. However, the traditional approach has reached its peak in terms of implementation. Additionally, using traditional approaches requires a lot of time and effort to remove and choose arrangement features. The deep learning (DL) model is a new machine learning (ML) technique that has proven effective for various classification problems. To alter image classification problems, the convolutional neural network performs well, with the best results. This chapter discusses the importance and challenges of deep learning models in medical image classification and explains some techniques for reducing overfitting and leveraging model performance during model training. 2024 Taylor & Francis Group, LLC. -
Reducing approximation error with rapid convergence rate for non-negative matrix factorization (NMF)
Non-Negative Matrix Factorization (NMF) is utilized in many important applications. This paper presents development of an efficient low rank approximate NMF algorithm for feature extraction related to text mining and spectral data analysis. NMF can be used for clustering. NMF factorizes a positive matrix A to two positive matrices W and H matrices where A = WH. The proposal uses k-means clustering algorithm to determine the centroid of each cluster and assigns the centroid coordinates of each cluster as one column for W matrix. The initial choice of W matrix is positive. The H matrix is determined with gradient descent algorithm based on thin QR optimization. The performance comparison of the proposed NMF algorithm is illustrated with results. The accurate choice of initial positive W matrix reduces approximation error and the use of thin QR algorithm in combination with gradient descent approach provides rapid convergence rate for NMF. The proposed algorithm is implemented with the randomly generated matrix in MATLAB environment. The number of significant singular values of the generated matrix is selected as the number of clusters. The error and convergence rate comparison of the proposed algorithm with the current algorithms are demonstrated in this research. The accurate measurement of execution time for individual program is not possible in MATLAB. The average time execution over 200 iterations is therefore calculated with an increasing iteration count of the proposed algorithm and the comparative results are presented. 2021 by authors, all rights reserved. -
Reducing Delay and Network Load through Adaptive Threshold-Based Rate Control in IoT Systems
Some of the major challenges in managing IoT networks, which are normally resource-constrained, come from restricted bandwidth, processing power, and energy supplies. Traditional random transmission usually leads to network overload, increased packet delays, and inefficient use of resources. This paper reviews smart rate control mechanisms designed for IoT networks that have limited resources. We analyze and contrast random baseline transmission against threshold-based adaptive control methods by way of extensive simulation runs under realistic network scenarios via the Contiki-Cooja framework. Our experimental results have shown that threshold-based rate control can achieve as much as 31% reduction in average packet delay and 62% reduction in network load when compared with traditional random transmission techniques. Threshold-based rate control represents a deployable and practical solution that properly balances the tradeoff between performance enhancement and computational ease and thus is a good match for real-world IoT deployments over actual resource-constrained networks. Hybrid machine learning, multiobjective optimization, federated learning, and context-aware mechanisms might be potential avenues of future research toward enhancing the performance of IoT systems. 2025 IEEE. -
Reducing Systemic Bias in Behavioral Targeting Using Explainable AI: The HARMONIA Complex Systems Approach
Behavioral targeting is a key part of the modern advertising web's algorithmic engine. However, it is unclear whether optimization processes worsen bias, promote unchecked spread in filter bubbles or lower overall users' trust levels. This paper introduces HARMONIA (Holistic Adaptive Regulatory Model for Optimizing Non-transparent Intelligent Advertising), a comprehensive, data-driven Explainable Artificial Intelligence (XAI) framework aimed at transforming behavioral targeting via transparency, interpretability, and adaptive ethical regulation. This paper conducted a comprehensive Explorative Data Analysis (EDA) on the public Criteo Display Advertising Dataset, which contains over 45 million records, to identify patterns in high-dimensional user-ad interaction space. This analysis uncovered latent behavioral signals that affect the relevance of ads based on users' online behavior. The analysis identified four interrelated behavioral dynamics: ad fatigue attenuation, diurnal engagement oscillations, device-driven preference divergence, and category-affinity dominance. These dynamics served as the foundational architectural principles for HARMONIA's design. The method uses gradient boosted prediction models and a multilayer explainability stack that includes SHAP for global interpretability, LIME for local surrogate approximation, and counterfactual reasoning for causal transparency. Quantitative evaluation indicates that HARMONIA maintains relevance accuracy (approximately 1.2% CTR), achieves a 31% enhancement in transparency metrics, and a 27% improvement in user-trust indices, while concurrently reducing systemic entropy by nearly one-third. This research redefines personalization to be self-explanatory and ethically sound AI by incorporating explainability as a regulatory mechanism in the adaptive ecosystem of complex digital advertising. This system takes explainable computational marketing from an idea to a full-scale implementation. 2026 Binghamton University Libraries. All rights reserved.