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Role of the Functionalized Carbon Nanotubes in Nanodiagnostic Devices and Nanobiomedicines
Carbon nanotubes (CNTs) have gained immense attention and have emerged as an instrumental tool in various applications including electrochemical sensing, biomedical applications, water purification, and biofuel cells. Their heightened utilization is attributed to their unique properties, such as high specific surface area, enzyme loading capacity, biocompatibility, mass transfer resistance, ease in crossing biological barriers, and ease of functionalization and immobilization. Possessing all these characteristics improves their applicability in biomedical applications including diagnosis, imaging, nanobiomedicine, and drug delivery. The functionalization of CNTs is highly crucial as it improves their biocompatibility and overcomes their limitation of insolubility in water and organic solvents. Functionalization also transforms them into more complex and effective drug delivery and biosensing tools with enormous and specific biomedical properties. This chapter provides a comprehensive discussion of the various structural and chemical properties, types, and synthesis strategies of CNTs. It also gives an insight into the kinds of functionalization for ameliorating their properties and comprehensively discusses their applications in nanodiagnostic devices and nanobiomedicine, ensuring the audience feels informed and knowledgeable. Springer Nature Singapore Pte Ltd. 2025. -
Progressive crude oil distillation: An energy-efficient alternative to conventional distillation process
Distillation, the major process in crude oil refineries as of now. In this work we focused the attention to energy saving with respect to an industrial crude oil distillation unit. An alternative to the conventional crude oil distillation model present in the Bharat Petroleum Corporation, Kochi Refinery is proposed and simulated. The theoretical predictions as well as the simulated results indicate that the Progressive crude oil distillation reduces the utility burden as well as increase the extraction of more valuable light components. The simulation was carried out using Aspen HYSYS V8.8.2. Different crudes are taken into account and their properties and amount of distillate are analyzed. The optimization is done in an easy manner rather than the conventional mathematical method, together with the advanced process control tools; make it profitable in the operation in real time. 2018 Elsevier Ltd -
Simulation, optimisation and analysis of energy saving in crude oil distillation unit
Physical distillation is the major process in crude oil refineries as of now. To ensure quality control in the final products, it is essential to ascertain the true boiling point of the crude oil and the products. The work is mainly concentrated to an industrial crude oil distillation unit. The objective of the paper is to present the simulation and optimisation of crude distillation unit (CDU) along with the analysis of energy saving, using Aspen HYSYS V8.8.2. Different crudes are taken into account, their properties and amount of distillate are analysed. The process optimisation is done in an easier manner using Aspen HYSYS rather than the conventional mathematical method, together with the advanced process control tools; make it profitable in the operation in real-time. The simulation results are validated with the actual plant results. Copyright 2018 Inderscience Enterprises Ltd. -
Coronal Elemental Abundances During A-Class Solar Flares Observed by Chandrayaan-2 XSM
The abundances of low first ionization potential (FIP) elements are three to four times higher in the closed loop active corona than in the photosphere, known as the FIP effect. Observations suggest that the abundances vary in different coronal structures. Here, we use the soft X-ray spectroscopic measurements from the Solar X-ray Monitor (XSM) onboard the Chandrayaan-2 orbiter to study the FIP effect in multiple A-class flares observed during the minimum of Solar Cycle 24. Using time-integrated spectral analysis, we derive the average temperature, emission measure, and the abundances of four elements Mg, Al, Si, and S. We find that the temperature and emission measure scales with the sub-class of flares while the measured abundances show an intermediate FIP bias for the lower A-flares (e.g. A1), while for the higher A-flares, the FIP bias is near unity. To investigate it further, we perform a time-resolved spectral analysis for a sample of the A-class flares and examine the evolution of temperature, emission measure, and abundances. We find that the abundances drop from the coronal values towards their photospheric values in the impulsive phase of the flares and, after the impulsive phase, they quickly return to the usual coronal values. The transition of the abundances from the coronal to photospheric values in the impulsive phase of the flares indicates the injection of fresh unfractionated material from the lower solar atmosphere to the corona due to chromospheric evaporation. However, explaining the quick recovery of the abundances from the photospheric to coronal values in the decay phase of the flare is challenging. 2023, The Author(s), under exclusive licence to Springer Nature B.V. -
Derris Indica Leaves Extract as a Green Inhibitor for the Corrosion of Aluminium in Alkaline Medium
The corrosion inhibitive effect of Derris indica leaves extract (DILE) on aluminium in 1 M NaOH is investigated at different temperatures. For this purpose, weight loss studies and electrochemical methods including potentiodynamic polarization (PDP) and electrochemical impedance spectroscopy (EIS) technique are employed. Surface analysis of the treated and untreated aluminium coupons are done by using metallurgical microscopy. About 60.2% of maximum corrosion inhibition efficiency is attained with an optimum inhibitor concentration of 1.2 g/L. Both weight loss and electrochemical studies confirmed that DILE plays a crucial role in the formation of a protective layer over metal surfaces. Also, electrochemical measurements revealed that DILE behaves as a mixed type of corrosion inhibitor. The kinetic parameters and thermodynamic parameters are calculated using Arrhenius theory and transition state theory. Langmuir adsorption isotherm was found to be the best fit and physical adsorption mechanism was proposed. En ineered Science Publisher LLC 2022 -
Predictors of behavioral and emotional issues in children involved in custody disputes: A cross sectional study in urban Bengaluru
Background: The increasing rates of divorce in urban India has led to the subsequent parental battle for the child's custody. This paper discusses the behavioral and emotional issues of these children in relation to their psychosocial environmental factors and other relevant socio-demographic variables. Methods: We used samples from parent interviews concerning 52 children aged 717-years-old, involved in child custody cases in the Family court of urban Bengaluru. The Strengths and Difficulties Questionnaire was used to measure response variables of behavioral and emotional issues in these children. Predictor models of quantile and multiple linear regression were used to assess the influence of psychosocial environmental factors and socio-demographic variables on the response variables. Results: The predictor models revealed that risk of child suffering emotional and behavioral issues increased with factors such as excessive parental control, change of academic environment, general unrest at school, frequency of child's court visit, child's visitation of non-custodian parent on occasions and vacations, and negatively altered family relationship. The model however intriguingly showed that residing in nuclear household rather than with their grandparents in a non-nuclear household, decreased the risk of mental health issues in these children. Conclusions: This study is a novel attempt to understand the influence of the psychosocial issues on the child's mental health in the context of custody cases in India. Despite the minimum sample size, the findings imply that family-based intervention is the need of the hour in these cases. The implications for clinical practice and research are discussed. 2021 Elsevier B.V. -
Protection of intellectual property and human rights during health emergencies: an assessment of the patent waiver proposal
Purpose: Several countries, such as South Africa and India, believe that intellectual property rights (IPRs), including patents, impede the efficient increase in vaccine production to inoculate the global population as they scramble to recover from the COVID-19 pandemic. Their proposal at the World Trade Organization (WTO) to waive these pharmaceutical patents has been met with resistance from a few developed countries, who believe that the abrogation of IPRs is unnecessary, even during a pandemic. The purpose of this paper is to discuss the impact of a potential waiver of medical patents at the WTO versus the status quo of IPR laws in the global economy. Design/methodology/approach: This study examines key arguments from economic and moral standpoints regarding the provisions of the Trade-Related Aspects of Intellectual Property Rights (TRIPS) agreement and other related international agreements and their validity based on the premise of the internalisation of positive externalities posed by vaccines. Findings: The effectiveness of the TRIPS agreement in securing medical access is weak on account of the ability of profit-making multinationals to secure IP rights and on account of the Trans-Pacific Partnership, a multilateral agreement that supports patent evergreening and a period of protection on test data which challenges the access to medicines and the fundamental human right to health. Originality/value: This study examines international IPRs through the lens of human rights and proposes a new system that balances the two. 2022, Emerald Publishing Limited. -
A novel mathematical investigation of carbon emissions, economic growth, carbon taxation and renewable energy dynamics: stability analysis and forecasting
The main cause of global warming is carbon dioxide (CO2) emissions, acting as a significant greenhouse gas. These emissions stem from various sources and significantly contribute to climate change. Fortunately, we have countermeasures like carbon taxes to curb CO2 output. Carbon taxes incentivise a reduction in CO2 production and a shift towards cleaner energy sources by placing a cost on emissions. This paper investigates the interplay between carbon tax policy, carbon emissions, economic output (GDP) and renewable energy consumption. A system of differential equations is constructed to model these relationships based on a comprehensive literature review. Parameter estimation based on real-world data yielded successful fits for the variables. However, the fit for the carbon tax equation is less conclusive, suggesting a more complex relationship with carbon emissions. Stability analysis and the boundedness of the system are carried out. Auto-regressive integrated moving average (ARIMA) forecasting is employed to predict future trends. The results suggest a projected increase in GDP and renewable energy consumption over the next ten years, indicating a potential for a cleaner energy transition. Furthermore, the forecasts anticipate a rise in carbon tax implementation. This analysis emphasises how important carbon taxes are for cutting emissions and advancing renewable energy. Results indicate that carbon taxes can promote decarbonisation and economic growth, despite the complicated link between them and CO2 emissions. Both GDP growth and the use of renewable energy are anticipated to increase. However, policies must be improved to combat climate change effectively. Future studies should improve parameters and investigate other relevant elements to promote a low-carbon future. Indian Academy of Sciences 2025. -
Integrating AI and Cybersecurity: Advancing Autonomous Vehicle Security and Response Mechanisms
The rapid evolution of autonomous and connected vehicles has led to their integration with numerous technologies and software, rendering them vulnerable targets for cybersecurity attacks. While efforts have traditionally focused on preventing these attacks, the escalating risk underscores the importance of also vindicating their wallop. Nevertheless, this procedure is often onerous & facade scalability confronted, particularly due to connectivity issues in automobiles. This research advises a vehicle-based vibrant imposition response scheme, enabling swift responses to a variety of incidents and reducing reliance on external security centers. The classification encompasses an inclusive range of probable retorts, a procedure for evaluating retorts, & innumerable assortment approaches. Implemented on an embedded platform, the solution was evaluated using two distinct cyberattack use cases, highlighting its adaptability, responsiveness, volume for dynamic arrangement constraint alterations & nominal memory trail. Concurrently, this paper presents an innovative (AVSF) that synergistically integrates (AI) and cybersecurity techniques to fortify AV resilience against evolving threats. Additionally, the framework incorporates advanced cybersecurity measures such as encryption, authentication, and intrusion detection to mitigate vulnerabilities and safeguard critical AV systems. The fusion of AI and cybersecurity not only enhances AV security posture but also enables intelligent cyber threat monitoring and response capabilities. Extensive simulations and experimental evaluations demonstrate the efficacy of the AVSF in real-time scenarios, contributing to the development of robust security solutions for autonomous vehicle deployment and advancing safer transportation systems in the era of AI-driven mobility. 2024 IEEE. -
Continuity and changes in food consumption pattern among Tibetan refugee community in India
The Food consumption pattern of refugee communities is being carried out by many scholars and few acknowledged the food continuity, its implications on the health of refugees in the host country. The present study highlights food continuity among Tibetan refugees in the Bylakuppe settlement, India. 200 household data were administered to understand food consumption patterns by employing a structured household questionnaire. Simultaneously, 23 individual data were collected consisting of first migrants (15) and second-generation (8) for the qualitative study. Households derive energy mainly from carbohydrates and animal fats, and there is a prevalence of food insecurity among the Tibetan community. It is a proven fact that food insecurity will have serious health consequences in terms of emotional and mental well-being and suggest the need for further study of food insecurity among Tibetan refugees across the world. 2021 -
Influence of remittances oncapital endowment of Tibetan refugees in India
Purpose: An issue concerning Tibetan refugees in India is the poverty and unemployment among Tibetan youth. This often leads to households adopting a strategy of sending one of its members abroad towards North American or European countries in search of better income opportunities. Incomes in the form of remittances from these forward migrants have numerous impacts on living standard of left behind families. This study aims to focus on the influence of forward migrants remittances on livelihood in terms of human, financial and social capital development of Tibetan refugees in India. Design/methodology/approach: The paper includes 400 households from high-economic and low-economic-access regions of Tibetan settlements in India. Ordinary least square method was used to study these impacts. Findings: Findings show that remittances have significantly influenced human and financial capital development. However, it was found to be statistically not significant for social capital development. Originality/value: The present paper is original work. 2019, Emerald Publishing Limited. -
Privacy-preserving federated learning in healthcare: Fundamentals, state of the art and prospective research directions
Recent collaborations in medical diagnostic systems are based on data private collaborative learning using Federated Learning (FL). In this approach, multiple organizations train a machine-learning model at the same time eventually leading to global model generation. This paper reviews the fundamentals of FL and its evolution path in Healthcare. The objective of this review is to scope a wide variety of healthcare applications in FL. Exactly what research direction is moving in interesting for research communities to guide their future course. This review uniquely focuses on examining numerous FL-based healthcare implementations, detailing their core methodologies and performance metrics, which, to our knowledge, have not been previously available. Privacy-preserving collaborative distributed learning through federated learning in healthcare enhances research collaborations, thereby resulting in better-performing models. This comprehensive review will act as a valuable reference for researchers exploring new FL applications in the healthcare domain. 2024 IEEE. -
Privacy-Preserving Federated Learning for Prognostic Modeling in Rare Diseases: A Scalable Case Study on Kawasaki Disease
Predictive modeling in rare diseases faces major challenges, including data scarcity, class imbalance, and strict privacy regulations that limit cross-border collaboration. These challenges are particularly critical in Kawasaki disease (KD)a rare vasculitis in childrenwhere 10% to 20% of patients are resistant to intravenous immunoglobulin (IVIG), the standard first-line treatment. This significantly increases the risk of coronary artery abnormalities (CAA), making early and accurate prediction of resistance to IVIG essential for improving patient outcomes. Our work proposes a federated learning (FL) approach to address the constraints imposed by security and privacy concerns. We investigate convolutional neural networks (CNN) as the shared model, collaboratively trained across clients. Coupled with strategies to address class imbalance resulting from the rarity of the condition, the federated approach yielded promising results when evaluated against conventional machine learning (ML) models. The proposed approach demonstrated strong performance, achieving 94% accuracy, 93% precision, 89% recall, and 91% F1 score. To ensure robustness and generalizability, an independent dataset was also used, where the proposed model excelled similarly. These results highlight the potential of FL to overcome data privacy barriers and provide a scalable, secure solution for predictive modeling in rare diseases, supporting its integration into medical prediction workflows. 2025 by the authors of this article. -
Federated Learning with Adaptive Intermediate Model Selection for Predicting IVIG Resistance in Kawasaki Disease
Kawasaki disease (KD), a rare pediatric illness affecting children under five, is treated with intravenous immunoglobulin (IVIG). But 1020% of patients are resistant to IVIG, and these resistant kids face a higher risk of coronary artery abnormalities. Identifying resistance early is vital, yet data scarcity, class imbalance, and the diseases rarity necessitate nationwide collaboration, which is often hindered by country-specific privacy policies. Federated learning (FL) provides a practical way for different parties to collaborate on training a model while keeping their raw data private and secure. To enhance model adaptability across diverse clinical populations, we propose an adaptive intermediate model selection strategy in federated learning. Each client retains the versionglobal or locally fine-tunedthat performs best on its own data, using customizable performance metrics such as F1-score or recall. The system was implemented using the Flower FL framework, with three simulated clients and a shared convolutional neural network (CNN) architecture. Experiments demonstrated that the global model achieved stronger performance than conventional models, and several clients obtained further gains by selecting intermediate models aligned with their data. This approach introduces a novel balance between worldwide collaboration and local personalization in FL, offering a flexible and clinically meaningful solution for IVIG resistance prediction. 2026 by the authors of this article. -
Privacy-Preserving Federated Learning: Foundations andAlgorithmic Directions
Federated Learning (FL) stands at the forefront of decentralized machine learning, revolutionizing collaborative model training among distributed devices while maintaining stringent privacy standards. FL requires multiple algorithms to handle issues with model initialization, synchronization, and convergence in remote environments. This paper comprehensively examines FL algorithms, focusing on pivotal techniques such as client-side training, server-side aggregation, and FedAvg. Detailed analysis elucidates these algorithms intricate workings, showcasing how they harmonize the aggregation of local model updates with global parameter refinement, thereby striking a delicate equilibrium between privacy preservation and model accuracy. The foundations of FL and the specifics of its sophisticated algorithms are covered in this study. By providing researchers with a roadmap for delving into FL algorithm development, this paper catalyzes unlocking new avenues of innovation and advancing the frontiers of privacy-preserving machine learning. For experimental learning, the federated learning implementation is carried out using the Flower framework on the well-known iris flower classification problem, with performance metrics thoroughly evaluated. Moreover, this paper represents, to our knowledge, the first work that extends the algorithmic directions presented in a review paper with detailed implementation on a sample problem, further encouraging exploration of various algorithms in FL implementation. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Titania Doped CDs as Effective CT-DNA Binders: A Novel Fluorescent Probe via Green Synthesis
Carbon dots (CDs), which belong to the class of zero-dimensional carbon-based nanomaterials, have garnered significant interest owing to their wide array of applications spanning from the electronics industry to the healthcare sector. This work employs a facile, inexpensive approach to synthesize green luminescent carbon dots (J-10) from a potential medicinal plant named Justicia Wynaadensis by the one-step hydrothermal method. A nanocomposite (JT-10) of the CDs is prepared by adding TiO2 nanoparticles derived from green synthesis of Lavandula leaves. The J-10 and JT-10 are further characterized by X-ray Diffraction spectroscopy (XRD), Transmission Electron Microscopy (TEM), Raman analysis X-ray Photoelectron Spectroscopy (XPS), and Fourier transform infrared techniques (FTIR), UVvis spectroscopy, Photoluminescence (PL), and Fluorescence or PL lifetime analysis. The average size of synthesized CDs is 1.85 nm and exhibits an excitation-dependent fluorescence nature at 320 nm. PL lifetime analysis of J-10 and JT-10 is calculated to be 5.80 and 2.84 ns respectively. Offering these unique optical properties and biocompatibility, the synthesised material is suitable for investigating their binding affinity and interaction mechanisms with DNA. The use of JT-10 in DNA binding studies contributes to the development of sustainable and efficient nanomaterials for applications in biosensors, drug delivery, and gene therapy. 2024 Wiley-VCH GmbH. -
Deep Learning-Based Signal Detection Techniques for Real-Time Communication in Fading Channels
Dependable signal detection has also been a major concern in real-time wireless communication especially in the case of fading channels that cause non-adaptive distortion and deteriorate the overall performance drastically. The conventional detection meth-ods, like the maximum likelihood detection, are not always adaptive in the circumstances of dynamic and therefore unpredictable channel conditions, and particularly in the cases when the statistical profiles are unknown or vary too quickly. In order to address these shortcomings, the papers introduce a new paradigm of deep learning signal detection trained to learn hierarchies and temporal patterns of raw received signals, which by their pas integrate convolutional neural networks (CNN) and recurrent neural networks (RNN). The trained architecture is end-to-end that is able to map the noisy distorted inputs to their symbols which are inherently transmitted in the context of channel state informa-tion. Heavy simulation over Rayleigh and Rician fading channels with different Doppler spreads and SNR values shows that the suggested approach shows substantial improve-ment over the traditional maximum likelihood and classical machine learning-based detec-tors regarding bit error rate (BER), inference latency and computational overhead. Such results emphasize the performance as well as the flexibility of deep learning model in very dynamic propagation conditions. On the whole, this paper draws the conclusion that deep learning is a perspective direction to solve the problem of real-time detection of a signal in next-generation wireless networks, such as a 6G or IoT edge setup. 2025, Society for Communication and Computer Technologies. All rights reserved. -
Residual-Based Statistical Process Control Charts in the Presence of Multicollinearity: an EWMA Framework with (RK) Estimator
Reliability monitoring of financial health requires strong control mechanisms, and the residual chart is an invaluable instrument to perform it. One of the key problems statisticians face while modeling is the problem of multicollinearity which arises when there is a strong correlation between independent variables leading to imprecise coefficient estimates and poor outcomes. To solve this problem and to make sure that the control chart works even with correlated data, we integrated a Weighted Moving Average Exponential smoothing chart within the modeling technique. The theoretical approach assures long-term variability and consistency of the residual control chart. These control charts are used to understand the process and the performances in various sectors. The charts can be used as analytical instruments to help recognize patterns, variations, or anomalies in economic indicators specifically in budget deficit data and facilitate rapid identification of any changes or inconsistencies in the fiscal deficit by policymakers. Further advances in statistical process control are rendered feasible by this study, which deepens the understanding and awareness of the potential uses and implications of the Weighted Moving Average Exponential smoothing chart for fiscal deficit data in the Economic realm. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Tracking Sigmoid Regression with Multicollinearity in Phase I: An Approach Incorporating Control Charts
Regression and quality control are two crucial techniques that data analysis employs in improving the decision-making process. We use the sigmoid function to model the connection between independent factors and the dependent variable in sigmoid regression. When there is a significant correlation among the independent variables in a regression model, multicollinearity a statistical phenomenon exists. Multicollinearity presents problems with higher uncertainty when estimating individual coefficients possibly making it harder to identify each variable's distinct contribution to the model. By suggesting a control chart specifically designed for the sigmoid regression model, this research presents a strategy to address the impact of influential observations using regression control charts, by making use of principal component regression class estimators. Principal component regression merges from the principal component analysis and linear regression methodologies, aiming to alleviate multicollinearity issues and enhances the stability of regression models. The performance of the model is evaluated using Pearson's residuals, Deviance residuals, and residuals. This strategy is proven to be useful in real world situations demonstrated through an application in the field of sleep wellness disorder. In conclusion, this study introduces a unique control chart to manage multicollinearity in sigmoid regression, providing a new perspective on the topic to spot differences in the underlying process by highlighting trends in the residuals. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Residual-based MEWMA control charts in the presence of multicollinearity
Statistical Process Control has been performing a critical role in attaining quality assurance from historic times to the modern era. Examining and governing the process variables involves rigorous stages and several control charts. The multivariate process is considered for a more comprehensive understanding of handling multiple correlated variables of the process. The study here focuses on the unique creation and deployment of residual-based Multivariate Exponentially Weighted Moving Average control charts in the presence of multicollinearity, specially constructed and evaluated for Phase I and Phase II. The chart offers a reliable framework for understanding shifts in multivariate processes across time from minor to moderate changes in process parameters. Agro-Economy data of Indian States for the years 2019 and 2020 are utilized in an application example. The proposed residual-based MEWMA control charts detect out-of-control circumstances with few false alarms and this is critical for rapid interventions, resulting in optimal crop management and production. 2025, Prince of Songkla University. All rights reserved.