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Reflector Backed Conical Dielectric Resonator Antenna with Enhanced Gain
This paper reports a wideband, high gain, slot coupled reflector backed conical dielectric resonator antenna (DRA). The key findings of the work are as follows; i) the antenna operates over 7.73-8.3 GHz, with peak gain of 10.32 dBi, ii) an gain enhancement > 5dBi achieved by placing a reflector below the ground plane, iii) the measured results best matches with their measured counter parts, iv) the antenna deals with many advantages, including performance, volume, and fabrication feasibility. From application point of view the developed model can be successfully used for X-band wireless communication. 2018 IEEE. -
Reflexive Praxis in University Classrooms in India: A Case Study
This article presents the case study of a university teacher's journey focusing on struggles he faced in the personal and professional space during his teaching career that shaped his pedagogic practices. Bourdieu's structural parameters and Engstr's (1987) theory of expansive learning provided analytical concepts, including reflexivity, to study the pedagogical praxis of this teacher. The analysis of data collected using the biographical narrative interviewing method, classroom observation, and autobiographical writings of the teacher reveals that as he questions his social positioning, academic "field," and intellectual bias, he experiences conflicts and tensions that arise from several disruptions resulting in pain and frustrations at one level and at another level shaping his desire and the ability to engage critically and historically with the processes and outcomes of personal and pedagogic interrogations. He realizes that there is no "A" algorithm for developing reflexivity. It takes a lifetime for a teacher to build a reflexive praxis. 2025 Common Ground Research Networks. All rights reserved. -
Reframing Accountability for Human Trafficking Along the India-Bangladesh Border: A Securitization and Fragmented Governance Approach
This research investigates the persistence of human trafficking along the IndiaBangladesh border through the combined theoretical lenses of securitization and fragmented governance. While existing literature and policy frameworks often approach trafficking as an issue of migration management, social welfare, or criminal justice, this study argues that such interpretations inadequately address the structural and political dynamics that shape state responses. The research posits that the framing of trafficking as a national security threatrather than a social or human rights concernhas produced a system where accountability is diffuse, bureaucratic, and performative rather than substantive. Copyright 2026, IGI Global Scientific Publishing. Copying or distributing in print or electronic forms without written permission of IGI Global Scientific Publishing is prohibited. Use of this chapter to train generative artificial intelligence (AI) technologies is expressly prohibited. The publisher reserves all rights to license its use for generative AI training and machine learning model development. -
Refugees in host countries: A comparative study between Uyghurs in Turkey and Tibetans in India
The status of refugees in host nations may depend upon several factors, including the economic situation of the receiving state, political alignments, international commitments, ethnic affinities, the domestic refugee regime, security considerations and bilateral ties with the home country. The study aims to discern the role that bilateral ties and domestic considerations play in the refugee experience. Is there a pattern that can be drawn out from these two factors? The article uses neoclassical realist and critical theories to better understand the phenomena, while employing the case study method to make a comparative study. The study analyses how India and Turkey have dealt with refugees belonging to two ethnic minorities of the Republic of China: the Tibetans and the Uyghurs. The results show that the way the receiving states handle refugees depends greatly on domestic considerations. It becomes clear that even though bilateral ties between the host and the home nation are important, no simple deduction can be made on how this affects the treatment of refugees. Both cases provide varied response patterns; it is only through a blend of realpolitik and critical theory that the phenomena can be understood. Mapping refugees and their movements, as well as their status in the host countries, determines many of the policies created for their welfare. The article therefore attempts to provide a framework for a better understanding of the phenomena by considering bilateral ties and domestic considerations. The Author(s) 2022. -
Regarding Deeper Properties of the Fractional Order Kundu-Eckhaus Equation and Massive Thirring Model
In this paper, the fractional natural decomposition method (FNDM) is employed to find the solution for the Kundu-Eckhaus equation and coupled fractional differential equations describing the massive Thirring model. The massive Thirring model consists of a system of two nonlinear complex differential equations, and it plays a dynamic role in quantum field theory. The fractional derivative is considered in the Caputo sense, and the projected algorithm is a graceful mixture of Adomian decomposition scheme with natural transform technique. In order to illustrate and validate the efficiency of the future technique, we analyzed projected phenomena in terms of fractional order. Moreover, the behaviour of the obtained solution has been captured for diverse fractional order. The obtained results elucidate that the projected technique is easy to implement and very effective to analyze the behaviour of complex nonlinear differential equations of fractional order arising in the connected areas of science and engineering. 2022 Tech Science Press. All rights reserved. -
REGARDING NEW NUMERICAL RESULTS for the DYNAMICAL MODEL of ROMANTIC RELATIONSHIPS with FRACTIONAL DERIVATIVE
The main purpose of the present investigation is to find the solution of fractional coupled equations describing the romantic relationships using q-homotopy analysis transform method (q-HATM). The considered scheme is a unification of q-homotopy analysis technique with Laplace transform (LT). More preciously, we scrutinized the behavior of the obtained solution for the considered model with fractional-order, in order to elucidate the effectiveness of the proposed algorithm. Further, for the different fractional-order and parameters offered by the considered method, the physical natures have been apprehended. The obtained consequences evidence that the proposed method is very effective and highly methodical to study and examine the nature and its corresponding consequences of the system of fractional order differential equations describing the real word problems. 2022 The Author(s). -
Regarding on the fractional mathematical model of tumour invasion and metastasis
In this paper, we analyze the behaviour of solution for the system exemplifying model of tumour invasion and metastasis by the help of q-homotopy analysis transform method (q-HATM) with the fractional operator. The analyzed model consists of a system of three nonlinear differential equations elucidating the activation and the migratory response of the degradation of the matrix, tumour cells and production of degradative enzymes by the tumour cells. The considered method is graceful amalgamations of q-homotopy analysis techniquewith Laplace transform(LT), and Caputo-Fabrizio (CF) fractional operator is hired in the present study. By using the fixed point theory, existence and uniqueness are demonstrated. To validate and present the effectiveness of the considered algorithm, we analyzed the considered system in terms of fractional order with time and space. The error analysis of the considered scheme is illustrated. The variations with small change time with respect to achieved results are effectively captured in plots. The obtained results confirm that the considered method is very efficient and highly methodical to analyze the behaviors of the system of fractional order differential equations. 2021 Tech Science Press. All rights reserved. -
Regional Drought Modulation by ENSO and IOD as Indicated by the Standardized Precipitation Index
Understanding the modulation of drought by large-scale oceanatmosphere teleconnections is crucial for strengthening drought prediction and resilience in India. This study investigates the influence of the El NiSouthern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) on meteorological drought characteristics across India from 1950 to 2024 using the Standardized Precipitation Index (SPI) at a 12-month timescale. Drought events were quantified in terms of frequency, duration, severity, and intensity and linked to ENSOIOD variability through composite, correlation, and mediation analyses. Results reveal that El Ni events consistently correspond to widespread and severe droughts, particularly over central and southern India, with drought frequency exceeding 30% and SPI < ?1.5. Conversely, La Ni phases enhance monsoon rainfall and alleviate drought conditions across much of the subcontinent. Spatial correlations demonstrate that ENSO exerts a stronger, more coherent influence on both rainfall and SPI than the IOD, while positive IOD phases can partly offset El Ni-driven drought in limited regions. Mediation and wavelet coherence analyses confirm ENSOs dominant control at interannual (48 year) timescales and reveal secondary, episodic modulation by the IOD. These findings highlight the complex, evolving dynamics between Pacific and Indian Ocean drivers in shaping Indias hydroclimate variability. The study underscores the need for integrated ENSOIOD monitoring and inclusion of multi-ocean indicators in Indias drought early warning and seasonal forecast frameworks. 2026 Binghamton University Libraries. All rights reserved. -
Regression analysis and features of negative activation energy for MHD nanofluid flow model: A comparative study
This article elucidates the impact of activation energy on magnetohydrodynamic (MHD) stagnation point nanofluid flow over a slippery surface in a porous regime with thermophoretic and Brownian diffusions. Negative activation energy is scarce in practice, but the impact of negative activation energy could not be neglected as it is noticed in chemical processes. The rate of some Arrhenius-compliant reactions is retarded by increasing the temperature and is therefore associated with negative activation energies, such as exothermic binding of urea or water. In some processes, the temperature dependence of the pressure-induced unfolding and the urea-induced unfolding of proteins at ambient pressure give negative activation energies. The present mathematical model is solved with successive linearization method (a spectral technique). A comparison of results is made for negative and positive values of activation energy. Apart from it, the quadratic multiple regression model is discussed briefly and explained with bar diagrams. It is observed that with rise in unsteadiness parameter from 0 to 1 (taking positive activation energy), skin friction and Sherwood number are increased by 9.36% and 19% respectively, and Nusselt number is decreased by 26%. However, for negative activation energy, 9.36% and 112% enhancement is observed in skin friction and Sherwood number, respectively. 2023 The Authors -
Regression Analysis as a Metric for Sustainability Development: Validation of Indian Territory
The 2030 Development Agenda styled' Transforming our world The 2030 Agenda for Sustainable Development' was hugged by the transnational locales of the UN General Assembly in 2015. Monitoring the progress of countries towards achieving these pretensions is pivotal for sustainable development. This exploration paper offers an innovative stance toward foretelling the SDG Index of Indian states for the near future times using machine learning ways, logical and visualization tools. The paper focuses on India's sweats towards achieving the SDGs and investigates the factors impacting the SDG performance of individual Indians states. A comprehensive dataset is collected, encompassing a wide range of socio-profitable pointers, demographic data, and environmental criteria applicable to each SDG target. Literal SDG Index scores and corresponding state-specific data are collected to assay and find some trends. The study demonstrates the eventuality of vaticination ways in vaticinating the unborn SDG Index scores of Indian states. The time series graph showcases varying degrees of delicacy across different SDGs, indicating the complexity and diversity of experimental challenges. 2024 IEEE. -
Regression Analysis for Longitudinal Aging Study in India Data
This paper examines the Longitudinal Aging Study in India (LASI) and its role in providing valuable insights into the health, social, psychological, and economic well-being of the older Indian population. The paper examines the use of dependent independent variables in a multiple linear regression model, tests assumptions of linearity, and examines the significance of the overall model and the individual variables. There are 190 variables in the dataset being used. This paper presents the results of comparing the regression models obtained through basic, forward, and stepwise selection methods where the model obtained using the stepwise selection method, when all the linearity assumptions are satisfied, explains 86.51% of the variation in the dependent variable and the Adjusted R-squared of the model is 0.8374. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Regression Analysis on Macroeconomic Factors and Dividend Yield on Bank Nifty Index Returns
The study has examined an impact of macroeconomic variables and dividend yield on Bank NIFTY Index. It analyses the relationship amongst macroeconomic variables and dividend yield. The study used quarterly data from 1 January 2010 to 31 December 2019. It employed statistical measures like regression analysis to analyse the impact of independent variables (macroeconomic factors and dividend yield) on the dependent variable (Bank NIFTY returns) and multicollinearity tests to understand the relationship amongst the independent variables. The observations concluded that GDP, government bond yield and dividend yield have a significant impact on Bank NIFTY returns but CPI does not have a significant impact on Bank NIFTY returns. We can also conclude that all the independent variables are not correlated to each other. The study suggested to policy makers, in India, that they should maintain economic stability through policies of growth that will eventually boost the banking sector and the economy. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Regression Analysis using Machine Learning Algorithms to Predict CO2 Emissions
Precise measurement of fuel consumption and emissions plays an important role in evaluating the environmental effects of materials and stringent emission control methods, especially within the transportation sector. This sector represents a substantial contributor to both global greenhouse gas emissions and the release of hazardous pollutants, making accurate assessment imperative for addressing climate change. The primary objective is to construct accurate predictive models that estimate CO2 emissions based on vehicle attributes, fostering a deeper understanding of the environmental impact of vehicular activities. Leveraging the 'CO2 Emissions-Canada.csv' dataset, the paper embarks on an extensive journey of data preprocessing, exploratory data analysis, and model training. These algorithms are meticulously fine-tuned and evaluated through metrics such as R-squared and mean absolute percentage error, rendering insights into their predictive accuracies. In essence, this paper pioneers a pathway towards environmentally responsible mobility solutions, capitalizing on the fusion of data science and environmental conservation. 2024 Bharati Vidyapeeth, New Delhi. -
Regression Approach for Predictive Analysis in Cognitive Decline
Cognitive decline refers to the deterioration of cognitive abilities, including memory, thinking, and reasoning, often associated with aging or neurological disorders like Alzheimer's disease. Machine learning (ML) methods can be used for predicting cognitive decline. Techniques such as Generative Adversarial Networks (GANs), feed-forward neural networks, supervised, and unsupervised learning process and analyse data patterns to forecast cognitive changes. By analyzing large datasets, ML algorithms can identify subtle cognitive shifts and predict future decline, enabling early intervention and personalized healthcare strategies. These diverse ML methods provide valuable tools for understanding, detecting, and potentially mitigating cognitive decline, advancing our ability to address cognitive health challenges. Some of these methods have been discussed later. In this research paper, a model to predict cognitive decline using principles of logical regression is proposed. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Regression Test List Sharding in a Distributed Test Environment
One of the major issues during the regression test of the new version of Real Time Operating System (RTOS) is the time involved in test case execution. The main reason being a single embedded system device under test (DUT) is used to execute the test list containing several test cases. This traditional method of regression test also leads to wasted productivity of the other devices at hand that could be otherwise used during this regression test. Hence, in this paper, we propose a technique that aims at reducing the overall regression test cycle time of a newer version of a Real Time Operating System (RTOS) by employing a method known as "test-list sharding"in a distributed test environment. In the proposed work, multiple DUTs are connected to the test server via a communication network. The test server executes the test list containing several test cases and performs the test-list sharding, that is, distributing test cases to different DUTs and executing them in parallel. After the test is executed on the DUT, the test results are sent back to the test server which will summarize all the results. In the proposed work, the sharding is done by distributing the test cases without overloading or under loading any of the DUTs. Test list is sharded in such a way that the same tests are not sent to multiple DUTs. The main advantage of the proposed method is that the test sharding can be easily scalable to accommodate any number of devices that can be connected to the test server. Also, the test list sharding is done in a dynamic way so that the tests are distributed to an idle DUT that has completed a test execution and ready for another test to execute. The comparison study of executing a sample test list sequentially on a single DUT and distributed test system with multiple DUTs is performed. Results obtained showed the performance gain in terms of test cycle time reduction, scalability, equal load distribution and effective resource utilization. 2023 EDP Sciences. All rights reserved. -
Regression testing on services in mobile applications
The power of mobile devices has increased dramatically in the last few years. The Mobile apps market increases every day and Mobile device has become one of the most important equipment in peoples daily life, which brings us not only convenience of communication, but more and more work and entertainment applications. Mobile testing becomes very crucial as the mobile applications and mobile users are growing rapidly. Test consultants, test specialist, test managers and software engineering researchers are finding ways to do effective verification methods and to ensure reliability of mobile applications. In this paper, we propose regression testing framework on services in Remote Link Lite (RLL) mobile application. In order to perform regression test on mobile application, we have considered RLL application for POC purpose. Research India Publications. -
REGRESSION WITH VOLATILE ERRORS IN THE PRESENCE OF MEASUREMENT ERRORS
This study explores the estimation and testing of regression models with volatile errors when measurement errors are present. The presence of measurement error in models with heteroscedastic disturbances, such as those following an autoregressive conditional heteroscedasticity (ARCH) or Generalized ARCH (GARCH) structure, can lead to biased estimates and misleading inferences. To address this, we develop an estimation framework that accounts for both heteroscedasticity and mismeasured observations, ensuring consistent and asymptotically normal parameter estimates. We estimate the parameters using corrected score estimation and weighted linear regression, which effectively mitigate the impact of measurement error and hetroscedasticity. Additionally, we perform a Likelihood Ratio (LR) test to assess the significance of measurement errors in regression models with volatile errors. Through Monte Carlo simulations, we analyze the bias and efficiency of traditional estimators and demonstrate the robustness of our proposed approach. Finally, the methodology is applied to real-life economic and financial data, illustrating its practical relevance and effectiveness in empirical research. The findings contribute to improving statistical inference in models where measurement error and volatility coexist, ensuring more reliable and accurate parameter estimation. 2025, Gnedenko Forum. All rights reserved. -
Regulating the gig economy: Addressing worker rights in India's quick commerce sector
The study examines the regulatory challenges faced by India's fast- growing quick commerce gig economy, with a focus on the rights and protections of workers. It examines the issue of gig workers and implementation gaps in existing frameworks and highlights the vulnerabilities of migrant workers. The study uses thematic analysis to explore aspects such as working conditions, workers' legal awareness, algorithmic management, and gender inclusion in the workforce. Findings reveal regional disparities in working conditions, discrepancies between legal recognition and practical enforcement, and persistent gender- based inequalities. The research underscores the need for tailored, collaborative efforts among various stakeholders to improve the situation of gig workers. This study aims to enhance the understanding of regulatory issues in the quick commerce sector and offers valuable insights for policymakers, platforms, and researchers to improve working conditions and ensure fair treatment for gig workers in India. 2025, IGI Global Scientific Publishing. All rights reserved. -
Regulating the Rise of Transformers in Global Healthcare: A Legal and International Law Perspective on AI Governance, Ethics, and Data Protection
With advances in healthcare, GPT, BioGPT and MedPaLM are helping doctors diagnose better, choose better treatments and engage with patients. Yet, these uses bring up significant problems related to privacy, understanding what the algorithms do, who is responsible for actions and gaining agreement. The paper examines global regulation on AI-for example, GDPR, HIPAA, India's DPDP Act, OECD AI Principles and UNESCO's AI Ethics Recommendation-and points out where gaps still exist, mainly in places with lower and middle incomes. Using both comparative studies and a focus on policies, it proposes common standards, specially designed rules for AI liability and ways to test ethical considerations. To provide secure, clear and equal use of AI in healthcare markets, the study proposes that countries come together to govern the technology. 2026, IGI Global Scientific Publishing. All rights reserved. -
Regulating the Speed of Innovation: A Legal and Ethical Framework for 6G Deployment in Smart Societies
The expected use of 6G technologies contains unmatched breakthroughs in hyperconnectivity, real-time holographic communication, and AI-supported immersivity. There is, however, in this technological leap, a complex legal-ethical-regulatory issue scene that must be addressed now, worldwide. This chapter provides a cross-disciplinary argument on a missing legal regime that can best govern 6G-enabled ecosystems and especially referring to the governance of the real-time artificial intelligence, XR/VR applications, the privacy consequences surrounding data, and nanobots and human rights concerns in a 6G future. This chapter can be taken as comparative legal, following which the emerging 6G regulatory principles are explored in the example of the European Union (Digital Services Act, AI Act), United States (AI Bill of Rights, FCC policies), and India (Digital Personal Data Protection Act, 2023). It also closes in foreign legal materials such as the Budapest Conference on Cybercrime and General Comment No. 25 (2021) on the right to privacy in the online world of the UN Human Rights Committee. The e-Governance model of Estonia, with significant use of AI and XR to enhance the functionality of the communication network is analyzed as a case study in this view to show the potential, as well as the challenges such hyper-automation can bring about. This chapter is then contrasted with the situation in China, which currently has a 6G surveillance infrastructure and explains why algorithms, mass surveillance, and illegal profiling are risky without some regulation. Besides, this chapter explores transnational data transfer, cyber sovereignty, and cross-border information law enforcement, which require global uniformity by all nations. It is insisted that the precautionary principle and technology impact assessments (TIAs) must be conducted as prerequisites to widespread 6G implementation in smart cities, smart tourism, and healthcare fields. The chapter ends by proposing an International 6G Governance Charter expressing the need to secure legally binding protection of AI-integrated XR systems, the obligation to be transparent, and enforcement-based rights of users within the ultra-fast communication space. 2026 selection and editorial matter, Upinder Kaur, Aparna Kumari, Hemant Kumar Saini, Surbhi B. Khan, and Mariya Ouaissa; individual chapters, the contributors.