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Deconstructing the Homonormative Spaces: Mapping the Politics of Queering of Geographical Space in South Asian Narratives
A homonormative space is an extension of queer identity. It exemplifies an exclusionary spatial arrangement shaped by the liminality of its queer occupants. These spaces become exclusive geographical orchestrations, often mystified in their transient presence. Homonormative space can manifest in two primary ways: one with a fixed spatial arrangement frequented by queer individualssuch as parks, cinemas, or cafand the other marked by spatial liminality. Both types depend on their boundaries within a dominant heteronormative infrastructure and the prevailing tolerance levels under which queerness is expressed. A third configuration is the virtual homonormative space. These digital environments are less vulnerable to physical regulation and offer ephemeral, subversive possibilities for queer utopic futurity, aligning with Baudelaires notion of the transient, the fleeting and the contingent. However, both physical and digital queer spaces can also reproduce class-based exclusions. Bourgeois conventions often dictate access to visibility and safety, necessitating a materialist feminist critique. Neoliberal logics commodify queerness and reinforce exclusion through economic gatekeeping. This paper examines the anatomy of homonormative spaces in contemporary South Asia. Through close readings of queer South Asian poetry, it explores how space is queered, surveilled, tolerated, and erased, and how neoliberalism shapes spatial belonging and queer imaginaries. 2025 Taylor & Francis Group, LLC. -
Robust and imperceptible image watermarking using chaotic map-integrated quantum-inspired multi-objective cuckoo search optimization
With the rapid growth of multimedia data transmission for secure and reliable communication has become critical due to increasing cyber threats. This paper presents a Chaotic Map-integrated Quantum-Inspired Multi-Objective Cuckoo Search (CMQICS)-based watermarking approach to achieve high imperceptibility, robustness, and embedding efficacy. The proposed approach integrates quantum-inspired cuckoo search optimization with chaotic mapping to enhance watermark embedding. A multi-image watermarking scheme is also used to strengthen payload capacity while minimizing visual distortion. The embedding process operates in the frequency domain using a hybrid Discrete Cosine TransformTwo-Dimensional Discrete Wavelet Transform (DCT-2DWT) combined with a zig-zag scanning strategy. This ensures attack resilience. The experimental results show that CMQICS achieves a Peak Signal-to-Noise Ratio (PSNR) of approximately 89 dB, a Structural Similarity Index Measure (SSIM) of 0.99, and an average embedding time of around 1s. Randomness analysis further validates the security of the embedded watermark. The comparative evaluations states that the CMQICS outperforms existing approaches. The Author(s) 2025. -
Rare-earth-activated phosphor for laser lighting
The chapter describes that Y2Ba3B4O12 doped with europium ions were synthesized by a modified conventional solid-state reaction method. The formations of the phosphor crystal structure are confirmed via the X-ray diffraction technique. The luminescence measurement upon excitation in ultraviolet and emission in visible range shows the characteristics of Eu3+ excitation and emission. The occurrence of the charge transfer band is explained in detail. The emission spectrum of Eu3+ ions consists mainly of several groups of lines in the 550-725nm region, due to the transitions from the 5D0 level to the levels 7FJ (J=0, 1, 2, 3, 4) of Eu3+ ions. The phenomena of concentration quenching are explained on the basis of electron-phonon coupling and multipolar interaction. The purity of the red emission is also checked, and it makes Eu3+-doped poly-borate-based phosphor as a promising candidate for laser lighting application. 2022 Elsevier Inc. All rights reserved. -
Journeying through the Indian railways in around India in 80 trains (2012) by monisha rajesh and chai, chai: Travels in places where you stop but get never off (2009) by bishwanath ghosh
An Indian train is a space that exemplifies a true sense of transient cultural pattern as it travels through different states of India constantly assimilating people of diverse cultures. In this liminal space, a passenger travels from known to unknown in terms of geography, culture, language, cuisine, sartorial configuration and psychological makeup. Indian Railways offers an insightful analysis of cohabitation - the conflict and the coexistence of people amidst cultural differences.An Indian train is an exemplar of an accurate secular structure, blurring the lines of discrepancies based on religion, caste, gender, sex and sexuality. Prejudices that are evident in spaces relatively marked by certain spatial permanence dilute in a train. A provisional spatial arrangement of a train therefore questions the idea of tolerance and intolerance compared to that of permanent arrangement. As the Indian train incorporates people of all ages and territories, the train is a specimen of the concept of Bakhtinian polyphony, wherein the dialogues occurring between passengers represent varied consciousness. Thus, a train travelogue encompasses unmerged voices, each carrying a unique conscious design. The people travelling in an Indian train are separated on one single ground: economy. Therefore, economic factor becomes an overarching pattern of base to assign a certain culture in a superstructure to each class and each offers a unique perspective to the travelogue. This paper will analyze the trope of the train in two Indian travelogues based on culture, Marxist economic structure, Bakhtinian concept of polyphony, secularism and the idea of tolerance. AesthetixMS 2020. This Open Access article is published under a Creative Commons Attribution Non-Commercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For citation use the DOI. For commercial re-use, please contact editor@rupkatha.com. -
Analysis of supervised and unsupervised technique for authentication dataset
Traditional methods of data storage vary from the present. These days data has become more unstructured and requires to be read contextually. Data Science provides a platform for the community to perform artificial intelligence and deep learning methodologies on large volumes of structured and unstructured data. In the era of artificial intelligence, AI is showing it's true potential by addressing social causes and automation in various industries such as automobile, medicine and smart buildings, healthcare, retail, banking, and finance service are some of the deliverables. From a variety of sources and flooding data, AI and machine learning are finding real-world adoption and applications. The nature of the data models is trial and error and is prone to change with their discoveries for the specific problem and this is the case with the different algorithms used. In this paper, we apply machine learning algorithms such as unsupervised learning k-means, bat k-means and supervised learning decision tree, k-NN, support vector machine, regression, discriminant analysis, ensemble classification for data set taken from UCI repository, phishing website, website phishing, Z- Alizadeh Sani and authentication datasets. Authentication dataset is generated for testing Single Sign-on which learns from data by training to make predictions. 2018Rahul K. Dubey, P. K. Nizar Banu. -
An analysis on direct authentication of data
Authentication is the procedure which permits a sender and receiver of data to validate each other. On the off chance that the sender and receiver of data can't legitimately confirm each other, there is no trust in the activity or data gave by either party. This paper talks about where and when can the service providers use the various authentication models adopted and the comparison between two authentication models. 2017 IEEE. -
Effects of Climate Change on Natural Resources and Its Management Using Computer-Aided Techniques
The fast-paced climate change produces worse resource stress because it damages freshwater reservoirs and forests with arable land and biodiversity while creating major sustainability issues for urban spaces. Food shortages result from global warming along with uneven rainfall patterns and powerful weather systems that further intensify resource-related problems throughout entire ecosystems. Resolving these challenges requires computer technology solutions combining Quantum Computing (QC) with Machine Learning (ML), Geographic Information Systems (GIS), and Artificial Intelligence (AI) to optimise resources and develop predictive analyses as well as strengthen climate resilience strategies. AI technologies integrated with quantum algorithms give birth to improved climate modelling systems, which trigger instant emergency actions to control disasters while urban requirements shift. Climate risk reduction benefits from two successful techniques: NASA employs them through the Earth Observing System (EOS) while Google deploys their AI-based flood prediction model in India and Bangladesh. Environmental governance finds its legal and policy basis in two primary international agreements, namely the EU's Green Deal and the United Nations SDGs and the Paris Agreement (2015). Research evidence demonstrates that combined disciplinary methods effectively verify how computer-based processes solve sustainable urban expansion problems. The research indicates that climate resilience reaches its optimal potential through international establishments of standardised ethical frameworks and rules for innovative technology systems. Also, the strategic recommendations regarding AI implementation for natural resource defence during climate change need support from policymakers, urban planners, and researchers who must perform these tasks. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Thermoluminescence glow curve analysis and trap parameters calculation of UV-induced La2Zr2O7 phosphor doped with gadolinium
Thermoluminescence (TL) glow curve analysis and calculation of trap parameters are reported for gadolinium (Gd3+)-doped La2Zr2O7 (LZO) phosphor. Phosphors were prepared by modified solid-state reaction method with varying concentration of Gd3+ (0.12.5mol%) including proper calcination and sintering temperature. Structural analysis of prepared phosphor for optimized TL concentration was recorded by X-ray diffraction analysis technique. Morphology was analyzed by scanning electron microscopic technique. The UV ray induced to the phosphor and effect of dose response recorded for variable dose rates of UV and TL glow curve were observed. The experimental and theoretical comparison was done by computerized glow curve deconvolution technique which determines the trap parameters such as trap depth, order of kinetics, and frequency factor for optimized concentration of dopant. The trap parameters and trap model are discussed in detail. 2019, Springer Science+Business Media, LLC, part of Springer Nature. -
Synthesis and physico-chemical characterization of ZnS-based green semiconductor: A review
One-dimensional (1D) semiconductor nanostructures have been attracting a great deal of attention because of their excellent electronic and optoelectronic performance. Zinc sulfide (ZnS) nanostructures have attracted increasing attention because of their potential application in both conditional optical devices and new generation of green nanostructure semiconductors because of their special structure-related physical and chemical properties. Synthetic form of ZnS can be transparent, and it is used as a window for visible optics, infrared optics, and functional materials. In this chapter, the detailed studies of synthesis, characterization of crystals, and noncrystalline behavior is reported. The crystal structure of semiconductor and its morphological studies are compared and fabrication methods will be described. The major parameters that influence on ZnS doped with metal ions and rare earth ions and its optoelectronic properties will be carefully analyzed. In addition, the primary application of ZnS micro- and nanocrystals will be described. At the end, the predicted future applications and development directions of doped and undoped ZnS nanocrystals will be given. 2023 Elsevier Ltd. All rights reserved. -
A novel survey for young substellar objects with the W-band filter III: Searching for very low-mass brown dwarfs in Serpens South and Serpens Core
We present CFHT photometry and IRTF spectroscopy of low-mass candidate members of Serpens South and Serpens Core (?430 pc, ?0.5 Myr), identified using a novel combination of photometric filters, known as the W-band method. We report SC182952+011618, SS182959-020335, and SS183032-021028 as young, low-mass Serpens candidate members, with spectral types in the range M7-M8, M5-L0, and M5-M6.5, respectively. Best-fitting effective temperatures and luminosities imply masses of < 0.12M? for all three candidate cluster members. We also present Hubble Space Telescope imaging data (F127M, F139M, and F850LP) for six targets in Serpens South. We report the discovery of the binary system SS183044-020918AB. The binary components are separated by ?45 AU, with spectral types of M7-M8 and M8-M9, and masses of 0.08-0.1 and 0.05-0.07 M. We discuss the effects of high dust attenuation on the reliability of our analysis, as well as the presence of reddened background stars in our photometric sample. 2021 The Author(s). -
A novel survey for young substellar objects with the W-band filter IV: detection and characterization of low-mass brown dwarfs in Serpens Core
We present spectroscopic confirmation of nine M5 or later Serpens Core candidate members, identified using a combination of CFHT WIRCam photometry and IRTF SpeX spectroscopy. Through spectral fitting, we find that the latest of these nine candidate members is best fit by an L0 spectral standard (in the range of M8L2), implying a mass of ?0.010.035M?. If confirmed as a cluster member, this would be one of the lowest mass Serpens Core objects ever discovered. We present analysis of the physical properties of the sample, as well as the likely membership of the candidate Serpens Core members. 2023 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society. -
Cybersecurity Threats Detection in Intelligent Networks using Predictive Analytics Approaches
The modern scenario of network vulnerabilities necessitates the adoption of sophisticated detection and mitigation strategies. Predictive analytics is surfaced to be a powerful tool in the fight against cybercrime, offering unparalleled capabilities for automating tasks, analyzing vast amounts of data, and identifying complex patterns that might elude human analysts. This paper presents a comprehensive overview of how AI is transforming the field of cybersecurity. Machine intelligence can bring revolution to cybersecurity by providing advanced defense capabilities. Addressing ethical concerns, ensuring model explainability, and fostering collaboration between researchers and developers are crucial for maximizing the positive impact of AI in this critical domain. 2024 IEEE. -
Covid-19 pandemic: How do the family businesses in India cope with the crisis?
The Covid-19 pandemic brought about a major economic breakdown throughout the world due to the shutting down of businesses and organizations. Covid-19 took a toll on India also, the second-most populous country. In India, family-run businesses are the deep-rooted, oldest, and most widespread form of business ownership. Most family businesses, while keeping the welfare of their customers, employees, and communities in mind, have resorted to remote work and halting non-essential work. The government of India took measures mainly consisting of an increased moratorium period, corporate tax cuts, lower interest on delayed tax payments, extension in return filing deadline, and other policy decisions. This study discusses the impact of Covid-19 on family businesses and how family businesses cope with the shock to the system. In addition, it covers innovative and strategic solutions adopted by the family businesses as a shield to their business against the impact of the crisis and to grow progressively overcoming the pandemic situation. 2023, IGI Global. -
Construction of Prime Time Television News Discussions
Studies in the western context have shown consistent observations of Television News being one of their prime modes of news and current affairs in the past. In India, watching the news has been an age-old requirement, for many reasons. English news in a non-native English-speaking country like India has encouraged citizens to learn the language, engage in focused viewership and rely on Television news for stories, news, views, and the episodic reality. Ever since news reception was transitioned to the realm of social media and new media, Television prime time news strived to be in the limelight. To understand the existence of prime time news, the study focused on two objectives : to identify constituents of communicative techniques framed in prime time news discussions of Indian English Television News Channels and to establish the role of prime time News Discussions as creators of complex news narratives. With the help of Critical Discourse Analysis (CDA) as the umbrella theory, Multimodal Discourse Analysis (MDA), and Stuart Hall s Encoding and Decoding, the research progressed to understand the ways and means to analyse news. Each of the episodic news presentations from 5 December 2017 to 13 December 2017 pertaining to prime-time news debates of Republic TV and Times Now were used. were analysed using a qualitative method of textual analysis. The manifestation of all cues on the screen were deconstructed to comprehend their existence. Each episode was connotatively derived to understand conversations and use of graphics. There are multiple findings under each of the units. On understanding the manifestation and intermingling of various units of analysis with each other, it was unanimously concluded that polarization of opinions is the key to engage a concise news narrative of the day. Whilst important news is not the source, political debates, underestimation, and complex visualization enhance the brand name of the news channel. -
Future trends in multimodal learning: from theory to practical applications
The human ability to seamlessly integrate information from various sensory channelssight, sound, touchhas long inspired researchers in artificial intelligence. This ability to learn from and reason with multimodal datatext, speech, vision, and moreforms the core of multimodal learning. This abstract delves into the theoretical foundations of multimodal learning, explores its cutting-edge advancements, and critically examines the path toward practical applications in diverse fields. At its heart, multimodal learning seeks to exploit the inherent complementarity between different data modalities. Text, for instance, provides rich semantic meaning, while visual data offers valuable context. Speech captures the nuances of emotion and prosody often absent in text. By learning from these combined modalities, models can achieve a more comprehensive understanding of the world around them. Recent years have witnessed significant progress in multimodal learning architectures. Deep learning approaches, particularly convolutional neural networks and recurrent neural networks, have proven adept at capturing complex relationships within individual modalities. New architectures like multimodal transformers further bridge the gap by allowing models to learn joint representations across different modalities. These advancements pave the way for a paradigm shift in areas like computer vision, natural language processing, and robotics. In computer vision, multimodal learning allows models to not only recognize objects in images but also understand the context and actions depicted. By incorporating textual descriptions or speech narratives alongside visual data, models can achieve better scene understanding, image captioning, and action recognition. This has applications in autonomous vehicles, where understanding traffic signs, pedestrians, and road conditions is crucial, and in video surveillance systems, where interpreting visual cues alongside spoken dialogue improves anomaly detection. 2026 Elsevier Inc. All rights reserved. -
Innovation and Governance in the Digital Era: Exploring the Complexities of the Digital Supply Chain
This chapter investigates multiple factors and dynamics related to the digital supply chain and the involvement of national institutions, government authorities, and various stakeholders. In the highly advanced era of technological innovations and globalization, the digital supply chain has become decisive for modern economies. An interdisciplinary focus addresses the implications of digitalization for female workers, industrialization tendencies, global supply chains, and the enforcement of corporate codes of conduct. Given that modern digital technologies alter traditionally accepted methods of production and supply, it is important to understand the social and economic effects on female workers and the changes in opportunities and conditions for them at the current point. The purpose of the current research is to identify gender gaps and access to working opportunities and investigate the role national institutional frameworks and government authorities play in supporting womens empowerment in the digital supply chain. Primarily, the chapter aims at assessing the implications of digitalization on industrialization in the developed and developing world. Additional focus will be made on the opportunities and obstacles associated with automation, data analytics, or artificial intelligence, and ways of applying them to ensure sustainable development. Case studies and empirical research are likely to offer a comprehensive picture of the strategies governments, international organizations, and stakeholders can use to address the challenges of digital industrialization and address issues of social equity and just exposure to the opportunities opened through innovative tools and techniques. The relatively new concept of globalization is also closely connected to digital tools and technologies that are believed to facilitate the flow of goods and services and improve the conditions for efficient and fast supplies. However, on the other hand, global chains of supply are associated with specific challenges, such as disruptions or surveillance. Digitalization is also likely to boost unethical behavior and human rights violations. Hence, an important achievement of the current chapter is to investigate the interaction between digitalization and global supply chains and provide suggestions for the collaborative governance strategies that would promote openness, transparency, and better interaction. An alternative idea for the research is the evaluation of the efficiency of corporate codes of conduct employed to address human, civil, or product rights violations and guarantee consistency with environmental standards or regulations. To accomplish the goal, the chapter will focus on the evidence available to investigate the enforcement strategies and monitoring programs or approaches that companies rely on. An analysis of these two options is likely to result in a comprehensive understanding of the roles government authorities, institutions or organizations, and stakeholders play in preserving just working conditions and ethical standards in digital supply management. A tendency to consider the critical effects of digitalization on different administrative levels and stakeholders can be observed. That is why it is important to concentrate on the contributions of such approaches and the development of joint policies to ensure a balance between receiving the benefits of digitalization and avoiding its detrimental effects. 2025 selection and editorial matter, Saurabh Tiwari and Richa Goel; individual chapters, the contributors. -
Exploring the Role of Cultural Immersion in Enhancing Social Sustainability Through Community Engagement and Cross- Cultural Understanding
In an increasingly globalized world, cultural immersion has emerged as a powerful tool for fostering social sustainability through community engagement and crosscultural understanding. Cultural immersion refers to the process of deeply engaging with a foreign culture, whether through travel, education, work, or community- based initiatives. This study explores how cultural immersion enhances social sustainability by encouraging mutual respect, empathy, and intercultural dialogue. Social sustainability, as a core pillar of sustainable development, emphasizes the creation of inclusive, resilient, and cohesive societies. This research argues that cultural immersion, when combined with meaningful community engagement, facilitates long- term social cohesion by breaking down stereotypes, fostering intercultural cooperation, and promoting equity in social interactions. The primary objective of this study is to examine how cultural immersion contributes to social sustainability and to identify the mechanisms through which community engagement enhances cross- cultural understanding. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Beyond the Balance: Ethics, Justice, and Governance in the Environment-Society Nexus
This chapter discusses the dynamic relationship existing between the environment and human societies, conceptually defined as the Environment-Society Nexus. It explores how different environmental conditions come to shape social structures, policies, and cultural narratives while documenting the ways in which human activities and institutions shape environmental outcomes. Drawing on a multidisciplinary approach, this study synthesizes key findings emanating from ecology, sociology, political science, and economics to put forward a holistic understanding of this interrelationship. It does so by pointing out critical themes, such as resource governance, environmental justice, and sustainable development, through an explanation of how decisions developed by society pertaining to land use, resource extraction, and economic development contribute to or detract from ecological resilience and biodiversity. The book chapter also looks into the changes that result from environmental changes, such as climate change, pollution, and habitat degradation, with regard to social inequalities and how resilience occurs among communities. It then gives case studies and theoretical models to show how often marginalized communities bear the brunt of environmental degradation when, as a matter of fact, they are those who contribute the least to its causes. It supports the argument for equitable policies. This chapter concludes by saying that an integrative perspective on the solution of contemporary environmental problems defines the need for frameworks of ecological sustainability combined with social equity for the long-term resilience and well-being of human and natural systems. 2026 John Wiley & Sons Ltd. All rights reserved. -
Carbon Footprint Minimization in 5G Networks Using Blockchain Integrated Renewable Energy Management Framework
The rapid growth of 5G networks has created a huge upsurge in energy consumption, raising severe issues regarding carbon emissions and sustainability. Although current strategies like energy-efficient routing, smart grid optimization, and renewable energy integration have tried to counter these impacts, they suffer from a lack of real-time optimization and decentralized control, leading to inefficient carbon reduction and unacceptable energy cost efficiency. In order to fill these gaps, this study presents a novel framework known as BAREO-LP (Blockchain-Assisted Renewable Energy Optimization using Linear Programming) which incorporates blockchain-based peer-to-peer energy trading, renewable energy (solar and battery) modeling, and an emission-conscious linear programming optimization framework. The developed model is utilized through the Python programming language and evaluated on the publicly available 5G-Energy Consumption Dataset from Kaggle. Experimental outcomes show a 58% decrease in carbon emissions and a 32% cost reduction against conventional grid-based and smart grid scenarios. The method also gains an accuracy gain of 16% in energy demand forecasting with minimized load balancing and resource optimization. The blockchain module provides trust and transparency in peer-to-peer energy trading, making the model scalable and realizable in real-world 5G implementations. This multi-faceted strategy sets a new standard for green communication infrastructures and calls for further investigation in sustainable 5G network planning. 2025 IEEE. -
Quantum-Inspired Genetic Algorithms for Secure and Scalable Cloud-Based Decision Support Systems
Cloud-based DSS are critical for data-intensive decision-making but tremendously challenged by issues of scalability, security, and the optimization of resources. In general, optimization approaches such as GA, PSO, and ACO treat the problems of allocation and security enhancement of cloud resources very inadequately. Hence, the present work addresses developing a QIGA-based optimization framework for the performance optimization of cloud DSS. At its core, it utilizes quantum-like principles, such as superposition and probabilistic search, for resource optimization with respect to stability, security, and rapid convergence. Therefore, this underpinning framework comprises resource optimization, anomalous detection, and quantum-independent encryption through QIGA, which enhances the data security along with computational efficiency. The experimental results depict the performance efficiency of QIGA since its execution time, CPU, memory utilization requirements, and energy consumption are less while the task completion rate is higher and the security vulnerabilities are reduced in comparison to traditional optimization techniques. QIGA-based anomaly detection improves accuracy at the expense of response time, while its quantum-inspired encryption provides the best cryptographic security. Therefore, these results verify that QIGA is an efficient secured methodology for scalable cloud-controlled DSS, hence being a potential candidate for decision-making optimization in highly dynamic cloud environments. 2025 IEEE.