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Reconfigurable Intelligent Surface for mMIMO and NOMA Networks: Applications and Research Challenges
Reconfigurable Intelligent Surfaces are emerging as a transformative technology in wireless communication, particularly in the context of massive Multiple-Input Multiple-Output (mMIMO) systems and Non-Orthogonal Multiple Access (NOMA) networks. This chapter provides an in-depth exploration of how RIS can enhance the performance of mMIMO and NOMA networks, focusing on both practical applications and research challenges. RIS technology enables dynamic control over the wireless environment by adjusting signal reflections and enhancing signal propagation, which can significantly improve the efficiency and effectiveness of mMIMO and NOMA systems. For mMIMO, RIS can optimize spatial beamforming and mitigate interference, leading to enhanced capacity and coverage. In NOMA networks, RIS can This chapter offers a comprehensive overview of the potential of RIS to revolutionize mMIMO and NOMA networks, while also addressing the critical research challenges that must be overcome to fully realize its benefits. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Reconfigurable Intelligent Surface-Aided Physical Layer Security Techniques: Applications and Future Trends
Reconfigurable Intelligent Surfaces (RIS) are emerging as a groundbreaking technology in the realm of wireless communications, with significant implications for enhancing physical layer security. This chapter delves into the integration of RIS with advanced security techniques, exploring how this innovative technology can be harnessed to address the growing challenges of securing wireless networks. this chapter delves into the future trends and advancements in RIS technology, including next-generation RIS architectures and their potential integration with emerging technologies like 6G. It explores how RIS could pave the way for innovative security protocols and play a pivotal role in advancing secure wireless network infrastructures. RIS technology enables the dynamic and intelligent modification of radio environments through programmable surfaces, which can adjust and optimize signal paths to improve both communication efficiency and security. This chapter provides valuable insights into the current applications and future prospects of RIS in enhancing wireless network security. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Reconfigurable non-uniform band-generating filter bank for channelizer
Multi-band channelizer system must choose a specific channel from a broad bandwidth signal. A variety of distinct wireless standards and frequency bands are used in the channelizer. Reconfigurable and non-uniform multi-channels with narrow transition widths are necessary for channelizers. In this paper, a low complexity reconfigurable non-uniform band-generating filter bank (RNBFB) is proposed for multi-band channelizer. The RNBFB is used to generate a variety of non-uniform channels with a narrow transition width. Utilising frequency response masking (FRM) and the cosine modulation (CM) approach, many non-uniform channels are created. Comparing RNBFB to other state-of-the-art techniques, RNBFB generates multi-bands for channelizer with less multiplier complexities. For a better understanding of hardware complexity, the proposed RNBFB is implemented efficiently. A multiplier-free design such as Canonical Signed Digit (CSD), Multi-Objective Artificial Bee Colony (MOABC), and Shift Inclusive Differential Coefficients (SIDC) with a Common Sub-expression Elimination (CSE) are included in the suggested strategy to further optimise the RNBFB. 2023 Informa UK Limited, trading as Taylor & Francis Group. -
Reconstruction of sparse-view tomography via preconditioned Radon sensing matrix
Computed Tomography (CT) is one of the significant research areas in the field of medical image analysis. As X-rays used in CT image reconstruction are harmful to the human body, it is necessary to reduce the X-ray dosage while also maintaining good quality of CT images. Since medical images have a natural sparsity, one can directly employ compressive sensing (CS) techniques to reconstruct the CT images. In CS, sensing matrices having low coherence (a measure providing correlation among columns) provide better image reconstruction. However, the sensing matrix constructed through the incomplete angular set of Radon projections typically possesses large coherence. In this paper, we attempt to reduce the coherence of the sensing matrix via a square and invertible preconditioner possessing a small condition number, which is obtained through a convex optimization technique. The stated properties of our preconditioner imply that it can be used effectively even in noisy cases. We demonstrate empirically that the preconditioned sensing matrix yields better signal recovery than the original sensing matrix. 2018, Korean Society for Computational and Applied Mathematics. -
Recruitment Analytics: Hiring in the Era of Artificial Intelligence
Introduction: Traditional recruitment system relied heavily on the applicants curriculum vitae (CV). This system, besides becoming redundant, has proved to be a futile exercise leading to the hiring of candidates that eventually turn out to be misfits. CVs were the only source of candidates data available for the recruiters a few years back. Face-to-face interviews was considered to be the ultimate solution for hiring suitable candidates. However, evidence suggests that interview scores and job performances do not complement each other. Advancement in artificial intelligence (AI) has introduced several techniques in the recruitment process. Purpose: This chapter underscores the drawbacks of the traditional recruitment process. Evidence suggests that the traditional recruitment process is prone to subjectivity and is time-consuming. Surprisingly, despite the disadvantages, the integration of AI into the recruitment process is still slow. This chapter highlights the need to harness AI and the advantage technology could bring to the recruitment process. Some of the techniques that are garnering attention and widely used by organisations, such as chatbots, gamification, virtual employment interviews, and resume screening are described to enable the readers to understand with less effort. Chatbots and gamification techniques are described through process flow charts. We also describe the various types of interviews that could be conducted through virtual platforms and the modality by which the resume screening technique operates. Today, we are at a juncture wherein it is pertinent to acknowledge the superiority of technology-driven processes over traditional ones. This chapter will help the readers to understand the modus operandi to implement chatbots, gamification, virtual interviews and online resume screening techniques besides their advantages. Scope: Although chatbots, resume screening, virtual interviews, and gamification are used in other areas, too, such as training and development, marketing, etc., in this chapter, we restrict solely to employee recruitment processes. Methodology: Scoping review is used to examine the existing literature from various databases such as Google Scholar, IEEE, Proquest, Emerald, Elsevier, and JSTOR databases are used for extracting relevant articles. Findings: Automation and analytics in recruitment and selection remove bias which is otherwise increasingly found in manual hiring processes. Also, previous studies have observed that candidates engage in impression management tactics in traditional face-to-face interviews. However, through automated recruitment processes, the influence of these tactics can be eliminated. AI-based virtual interviews reduce human bias. It also helps recruiters to hire talents across the globe. Gamification improves the candidates perception of the work and work environments. Through gamified techniques, the recruiters can understand whether a candidate possesses the required job skills. Chatbots are an interactive technique that can respond to interviewees queries. Resume screening techniques can save the recruiters time by screening and selecting the most appropriate candidates from a large pool. Hence, the chosen candidates alone can be referred to the next stage of the recruitment cycle. AI improves the efficiency of the recruitment process. It reduces mundane tasks. It saves time for the human resources (HR) team. 2023 by V. R. Uma, Ilango Velchamy and Deepika Upadhyay. -
Rectangular microstrip antenna for WLAN application
This paper deals with the design of rectangular microstrip patch antenna for Wireless applications. In this paper a modified slotted microstrip antenna design for 2.5GHz operation is proposed. This provides improved performance in terms of lower return loss and higher gain. This is possible by inclusion of slots appropriately on the patch shape. The substrate material used in this design is Duroid5880 with permittivity 2.2 and size 47.43mm 39.65mm 1.6mm. ANSOFT HFSS EM simulator has been used for design and simulation of the microstrip antenna. The various antenna parameters such as frequency, VSWR, gain and directivity are analyzed to characterize the proposed antenna. 2015 IEEE. -
Rectangular Microstrip Patch Antenna Array Based Sectored Antenna for Directional Wireless Sensor Networks
Directional wireless Sensor Network (DSN) outperforms Wireless Sensor Network (WSN) over different parameters such as transmission range, interference, spatial reusability and energy efficiency. In this paper, a Rectangular Patch antenna Array (RPA) based sectored antenna is proposed for DSN. The individual sector is composed of two-element rectangular patch antenna array with a measured peak gain of 5.2 dBi and half-power beamwidth of 45. Single Pole 8 Throw (SP8T) Radio Frequency (RF) switchboard is designed to connect the sectored antenna to MICAz WSN mote. The antenna performance analysis carried out in simulation and real-time measurement via Ansys High Frequency Structure Simulator (HFSS) and Vector Network Analyzer (VNA) exhibits higher gain, lower return loss, half-power beamwidth and Voltage Standing Wave Ratio (VSWR). 2020 IEEE. -
Rectifying Whole Brain Segmentation Errors Using a Novel Under-Segmentation Correction Method
Pre-processing is a critical step in any data-driven study, particularly in the field of medical imaging, where it significantly enhances the reliability of disease and disorder diagnosis. In this context, medical image segmentation allows for more precise data analysis by isolating the regions of interest. Accurate segmentation of these regions can reveal influential variabilities in analysis, potentially leading to unique scientific findings. This article presents a novel under-segmentation error correction technique specifically designed for whole-brain segmentation. Additionally, it performs a set of pre-processing steps for the structural magnetic resonance imaging (sMRI) images, which are necessary to maintain the structural integrity and uniformity of MRI scans across different subjects. The proposed algorithm effectively eliminates under-segmentation errors, thereby improving the accuracy of whole-brain segmentation, particularly for structurally intact brain images. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Recurrent Neural Networks in Predicting the Popularity of Online Social Networks Content: A Review
An online social network is a web platform that individuals use to make social relationships with people who share similar interests, activities, connections, and backgrounds. All online social networks differ in the number of features they provide and their format. In recent years, drastic growth has been seen in the users of online social networks like Flickr, Instagram, Pinterest, Twitter, etc. Among all the features of online social networks, content sharing is the one being widely used by individual users and large organizations. Due to this, content popularity prediction has been extensively studied nowadays, considering various aspects related to it. The study throws light on the use of machine learning techniques in this field. Various algorithms have been used to handle popularity prediction, including classification, regression, and clustering techniques. It is feasible to extract the essential information from such content using machine learning algorithms and utilize the retrieved information in a variety of ways, the majority of which are commercial in nature. The goal of this study is to review and analyze various recurrent neural network (RNN) approaches for predicting the popularity of social media content. The Electrochemical Society -
Recyclable layered chromite-based porous film for water cleaning
The oil spillage and pollutants in water bodies are a significant concern in the present time. To address this concern, a porous and superhydrophobic nanocomposite film containing layered natural chromite ores with a polymer was fabricated using a simple solution casting method. The flexible film exhibited a good tensile strength of 1.022 kg mm?2 and self-cleaning properties. It showed an excellent oil adsorption of up to ?268% for castor oil and an adsorption efficiency of ?90% for toxic cationic dyes. The presence of high surface charges on the chromite nanosheets enhanced its adsorbing capability. Furthermore, even after being resynthesized from old used film, the composite film maintained its mechanical strength, hydrophobicity, and adsorbing capabilities. Therefore, we believe that the present work can help in cleaning oil and other pollutants from large water bodies and consequently preserving aquatic life. 2025 The Royal Society of Chemistry. -
Recycled Surgical Mask Waste as a Resource Material in Sustainable Geopolymer Bricks
With the advent of the COVID-19 pandemic, the global consumption of single-use surgical masks has risen immensely, and it is expected to grow in the coming years. Simultaneously, the disposal of surgical masks in the environment has caused plastic pollution, and therefore, it is exigent to find innovative ways to handle this problem. In this study, surgical masks were processed in a laboratory using the mechanical grinding method to obtain recycled surgical masks (RSM). The RSM was added in doses of 0%, 1%, 2%, 3%, and 4% by volume of geopolymer bricks, which were synthesized with ground granulated blast furnace slag (GGBS), rice husk ash (RHA), sand, and sodium silicate (Na2SiO3) at ambient conditions for a duration of 28 days. The developed bricks were tested for compressive strength, flexural strength, density, water absorption, efflorescence, and drying shrinkage. The results of the study reveal that compressive strength and flexural strength improved with the inclusion of RSM in the bricks. The highest values of compressive strength and flexural strength were 5.97 MPa and 1.62 MPa for bricks with 4% RSM, respectively. Further, a reduction in the self-weight of the bricks was noticed with an increase in RSM. There was no pronounced effect of RSM on the water absorption and efflorescence properties. However, the RSM played a role in reducing the drying shrinkage of the bricks. The sustainability analysis divulges the catalytic role of RSM in improving material performance, thereby proving to be a potential candidate for low-carbon material in the construction industry. 2023 by the authors. -
Recycling carbon tax for inclusive green growth: A CGE analysis of India
In this decade, India has been pursuing a low carbon inclusive growth strategy. However, carbon tax, the most direct price instrument to reduce carbon emissions, has not found favour with policymakers because of its supposed detrimental effects on economic growth and income distribution. In the Indian context, the literature indicates that though carbon tax is extremely effective in abating carbon emissions, it simultaneously leads to reductions in GDP. There is, thus, an undesirable trade-off between economic growth and climate change mitigation. However, in trying to overcome this trade-off through a double-dividend from carbon tax, these studies have not really explored all possible options. Whether the carbon tax will yield a double-dividend or not, will depend upon how the carbon tax revenue is recycled. The present paper fills this gap in the literature on recycling carbon tax for inclusive green growth by exploring the consequences of using carbon tax revenue for investment to build capacity in all sectors or exclusively in the clean energy sectors and to execute transfers to households to improve the distribution of income. This analysis has been done with a recursively dynamic India-specific CGE model having a disaggregated energy sectors and an endogenous income distribution module. 2020 Elsevier Ltd -
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 -
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.