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Enzyme based bioelectrocatalysis over laccase immobilized poly-thiophene supported carbon fiber paper for the oxidation of D-ribofuranose to D-ribonolactone
A modified electrode based on laccase immobilized poly-thiophene-3-carboxylic acid supported on carbon fiber paper was developed for the electrocatalytic oxidation of D-ribofuranose to otherwise difficult-to-access D-ribonolactone, a precursor for C-nucleoside based drug like Remdesivir. The electrochemical oxidation of D-ribofuranose was achieved by the TEMPO-mediated electrochemical process. The experimental parameters were optimized and validated using Design of Experiment (DoE) statistical tool indicating the concentration of TEMPO and stirring as important parameters in bulk electrolysis. The mechanism for the electrochemical oxidation of D-ribofuronose followed single electron anodic oxidation of TEMPO mediated by laccase to the corresponding oxoammonium nitrosonium species which was vital for the mediated electrochemical oxidation. The mechanism for the electrochemical oxidation was established using cyclic voltammetry and computational studies. The plausible interactions of laccase enzyme with TEMPO mediator were studied using molecular docking experiments. This facile method was successfully applied for the oxidation of D-ribofuranose to D-ribonolactone. 2022 -
Enzyme based bioelectrocatalysis over laccase immobilized poly-thiophene supported carbon fiber paper for the oxidation of D-ribofuranose to D-ribonolactone /
Molecular Catalysis, Vol.524, ISSN No: 2468-8231.
A modified electrode based on laccase immobilized poly-thiophene-3-carboxylic acid supported on carbon fiber paper was developed for the electrocatalytic oxidation of D-ribofuranose to otherwise difficult-to-access D-ribonolactone, a precursor for C-nucleoside based drug like Remdesivir. The electrochemical oxidation of D-ribofuranose was achieved by the TEMPO-mediated electrochemical process. The experimental parameters were optimized and validated using Design of Experiment (DoE) statistical tool indicating the concentration of TEMPO and stirring as important parameters in bulk electrolysis. -
Enzyme immobilization on biomass-derived carbon materials as a sustainable approach towards environmental applications
Enzymes with their environment-friendly nature and versatility have become highly important green tools with a wide range of applications. Enzyme immobilization has further increased the utility and efficiency of these enzymes by improving their stability, reusability, and recyclability. Biomass-derived matrices when used for enzyme immobilization offer a sustainable solution to environmental pollution and fuel depletion at low costs. Biochar and other biomass-derived carbon materials obtained are suitable for the immobilization of enzymes through different immobilization strategies. Environmental pollution has become an utmost topic of research interest due to an ever-increasing trend being observed in anthropogenic activities. This has widely contributed to the release of various toxic effluents into the environment in their native or metabolized forms. Therefore, more focus is being directed toward the utilization of immobilized enzymes in the bioremediation of water and soil, biofuel production, and other environmental applications. In this review, up-to-date literature concerning the immobilization and potential uses of enzymes immobilized on biomass-derived carbon materials has been presented. 2022 Elsevier Ltd -
Enzyme immobilized conducting polymer-based biosensor for the electrochemical determination of the eco-toxic pollutant p-nonylphenol
The unbridled release of harmful endocrine disruptors (EDs) into the environment is deteriorating human and animal health. A facile and efficacious biosensor was developed by immobilizing laccase over electropolymerized poly anthranilic acid on a carbon fiber paper (CFP) electrode, Lac/PAA/CFP for the detection of p-nonylphenol (PNP). PNP is a persistent phenolic endocrine disruptor and a harmful eco-toxic pollutant. Physico-chemical and electrochemical characterization of the fabricated electrode was carried out to study the modification of the Lac/PAA/CFP electrode. Cyclic voltammetric studies divulged that the prepared sensor has catalytic activity approximately twice greater than that of the bare CFP electrode. The influence of pH and scan rate was scrutinized for the modified electrode. Under optimized conditions differential pulse voltammetric studies were used for the quantification and the results revealed that the biosensor has a low limit of detection (LOD) and limit of quantification (LOQ) of 1.74 nM and 5 Nm, respectively with a broad linear dynamic range of 5250 nM. In the presence of interferants, the developed biosensor exhibited good selectivity toward the electrochemical detection of PNP. Molecular docking studies carried out revealed the hydrogen bonding interaction of the Asn264 residue of laccase Trametes versicolor. Further, the fabricated biosensor was accessed for its practicality in real samples collected from tap water and lake water. 2023 Elsevier Ltd -
EPCAEnhanced Principal Component Analysis for Medical Data Dimensionality Reduction
Innovations in technology from thelast one decade have led to the generation of colossal amounts of medical data with comparably low cost. Medical data should be collected with utmost care. Sometimes, the data have high features but not all the features play an important role in drawing the relations to the mining task. For the training of machine learning algorithms, all the attributes in the data set are not relevant. Some of the characteristics may be negligible and some characteristics may not influence the outcome of the forecast. The pressure on machine learning algorithms can be minimized by ignoring or taking out the irrelevant attributes. Reducing the attributes must be done at the risk of information loss. In this research work, an Enhanced Principal Component Analysis (EPCA) is proposed, which reduces the dimensions of the medical dataset and takes paramount care of not losing important information, thereby achieving good and enhanced outcomes. The prominent dimensionality reduction techniques such as Principal Component Analysis (PCA), Singular Value Decomposition (SVD), Partial Least Squares (PLS), Random Forest, Logistic Regression, Decision Tree and the proposed EPCA are investigated on the following Machine Learning (ML) algorithms: Support Vector Machine (SVM), Artificial Neural Networks (ANN), Nae Bayes (NB) and Ensemble ANN (EANN) using statistical metrics such as F1 score, precision, accuracy and recall. To optimize the distribution of the data in the low-dimensional representation, EPCA directly mapped the data to a space with fewer dimensions. This is a result of feature correlation, which made it easier to recognize patterns. Additionally, because the dataset under consideration was multicollinear, EPCA aided in speeding computation by lowering the data's dimensionality and therebyenhancedthe classification model's accuracy. Due to these reasons, the experimental results showed that the proposed EPCA dimensionality reduction technique performed better when compared with other models. 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. -
Epidemic Prediction using Machine Learning and Deep Learning Models on COVID-19 Data
A catastrophic epidemic of Severe Acute Respiratory Syndrome-Coronavirus, commonly recognised as COVID-19, introduced a worldwide vulnerability to human community. All nations around the world are making enormous effort to tackle the outbreak towards this deadly virus through various aspects such as technology, economy, relevant data, protective gear, lives-risk medications and all other instruments. The artificial intelligence-based researchers apply knowledge, experience and skill set on national level data to create computational and statistical models for investigating such a pandemic condition. In order to make a contribution to this worldwide human community, this paper recommends using machine-learning and deep-learning models to understand its daily accelerating actions together with predicting the future reachability of COVID-19 across nations by using the real-time information from the Johns Hopkins dashboard. In this work, a novel Exponential Smoothing Long-Short-Term Memory Networks Model (ESLSTM) learning model is proposed to predict the virus spread in the near future. The results are evaluated using RMSE and R-Squared values. 2022 Informa UK Limited, trading as Taylor & Francis Group. -
Epidemiological Transition in India and Determinants that Are Shifting Disease Burden: A Systematic Review
Indias disease burden patterns are shifting towards increased morbidity and mortality from Non-communicable disease and chronic diseases. This is one of the first studies conducted using the PRISMA guidelines and checklist to understand the role played by various determinants of health in this epidemiological transition happening in India. The search on 9 reputed bibliographic databases yielded 459 articles and finally 58 articles were selected based on carefully curated selection criteria. The results confirm the relation between India's demographic transition and the increasing disease burden from Non-communicable diseases (NCDs). 21 studies significantly associated urban residential status, increasing income, better living conditions and education with increasing NCDs' prevalence. 12 studies found that NCDs were more prevalent among women than men. Increased physical activity, a healthy diet and a lower hipto-waist ratio were observed to protect against NCDs. While 9 studies found smoking tobacco and alcohol consumption were not significantly associated with the prevalence of NCDs. It is of foremost importance that Indias public health policy focus must shift towards inclusivity as there is an affluent gradient to the increased morbidity and mortality from NCDs. Copyright2024 by authors, all rights reserved. -
Epigenetic Mechanisms Induced by Mycobacterium tuberculosis to Promote Its Survival in the Host
Tuberculosis caused by the obligate intracellular pathogen, Mycobacterium tuberculosis, is one among the prime causes of death worldwide. An urgent remedy against tuberculosis is of paramount importance in the current scenario. However, the complex nature of this appalling disease contributes to the limitations of existing medications. The quest for better treatment approaches is driving the research in the field of host epigenomics forward in context with tuberculosis. The interplay between various host epigenetic factors and the pathogen is under investigation. A comprehensive understanding of how Mycobacterium tuberculosis orchestrates such epigenetic factors and favors its survival within the host is in increasing demand. The modifications beneficial to the pathogen are reversible and possess the potential to be better targets for various therapeutic approaches. The mechanisms, including histone modifications, DNA methylation, and miRNA modification, are being explored for their impact on pathogenesis. In this article, we are deciphering the role of mycobacterial epigenetic regulators on various strategies like cytokine expression, macrophage polarization, autophagy, and apoptosis, along with a glimpse of the potential of host-directed therapies. 2024 by the authors. -
Epilepsy Detection Using Supervised Learning Algorithms
In the current scenario, people are suffering and isolated by themselves by seizure detection and prediction in epilepsy. Also, it is highly essential that it needs to be identified through wearable devices. Researchers discussed this issue and outlined future developments in this field, suggesting that Machine Learning (ML) techniques could radically change how we diagnose and manage patients with epilepsy. However, as data availability has increased, Deep Learning (DL) techniques have become the most cutting-edge approach to adopt and use with wearable devices. On the other hand, large amounts of data are needed to train DL models, making overfitting problematic. DL models are created with open-source toolboxes and Python, allowing researchers to create automated systems and broaden computational accessibility. This work thoroughly overviews deep learning (DL) methods and neuroimaging modalities for automated epileptic seizure identification. It covers several MRI and EEG techniques for epileptic seizure diagnosis and treatment programmes designed to treat these seizures. The study also covers the difficulties in precise detection, the benefits and drawbacks of DL-based strategies, potential DL models and upcoming research in this area. 2024 IEEE. -
Epileptic Seizure Detection Contribution in Healthcare Sustainability
This study describes a sustainable EEG data methodology. Classification using Discrete Wavelet Transform (DWT) for feature extraction, with the objective of reducing the computational efforts while keeping accurate neural signal analysis. DWT decomposes the EEG signal into timefrequency specific components which allows extraction of ten key wavelet features, including wavelet energy, entropy, maximal coefficients, zero-crossing counts, and dominant frequency. These features capture essential timefrequency features of EEG signals, providing a comprehensive yet computationally efficient representation. By streamlining feature extraction, this approach reduces data dimensionality and minimizes computational processing time, aligning with sustainable technology objectives. The resulting feature vectors serve as robust inputs for classification models, effectively supporting EEG data interpretation with reduced energy and less resource utilization. This study demonstrates that targeted feature extraction can achieve high classification performance in EEG analysis while adhering to principles of sustainability and resource efficiency. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Epileptic seizure detection using EEG signals and multilayer perceptron learning algorithm
Purpose: Epileptic is a neurological chronic disorder that causes unprovoked, recurrent seizure. A seizure is a sudden rush of electrical activity in the brain. The central nervous system characterized by the loss of consciousness and convulsions. Epileptic is caused by abnormal electrical discharge that lead to uncountable movements, loss of consciousness and convulsions. 50-80 million people in the world are affected by this disorder. Now a days children and adults are affected the most and it has been medically treated. Sometimes it may lead to death and serious injuries. In this technology world the computerized detection is an enhanced solution to protect epileptic patients from dangers at the time of this seizure. Method: Perceptron learning algorithm is a supervised learning of binary classifiers and also it is a simple prototype of a biological neuron in artificial neural network. EEG is extensively documented for the diagnosing and assessing brain activates and related disorders. In this paper EEG signals are taken as dataset for epilepsy detection. The data is been represented based on three domains namely frequency, time and time-frequency applied by the chebysev filter for processing the signals. Result: Help the patients from dangers at the time of the seizure. Conclusion: The neurological diseases can be divided into two loss of consciousness and convulsions. In this technology world the seizure can be detected by computerized way like EEG and so on. This paper proposes an epileptic seizure detection using EEG (Electroencephalogram) and perceptron learning algorithm. 2020, IJSTR. -
Epileptic Seizure Prediction from EEG Signals Using DenseNet
Epilepsy is a disorder in which the normal electrical pattern in the brain is disrupted causing seizures or loss of consciousness. Seizure is harmful during various events like swimming or driving. The electroencephalogram (EEG) is the measurement of electrical activity received from the nerve cells of the cerebral cortex. Forthcoming seizures can be predicted from scalp EEG signal to improve the quality of life. The study proposes a method of automatic epileptic seizure prediction from raw EEG signal. The raw EEG signal is converted into EEG signal image for automatic extraction of features and classification of inter-ictal and pre-ictal state using Dense Convolutional Network (DenseNet). This classification process is carried out in a manner similar to the process followed by a medical practitioner without resorting to hand-crafted features. The public CHB-MIT EEG database is used for training, validation, and testing. An EEG signal for 1 second duration is taken as one sample. The accuracy for the classification of inter-ictal and pre-ictal state is achieved up to 94% by using 5-Fold cross validation. However, the accuracy is not up to the mark for the presence of common artifacts caused by eye-blinking and muscle activities during EEG recordings. Hence, a 30 seconds pool based technique is used for decision on correct state identification. The proposed pool based technique provides an average specificity of 95.87% and a false prediction rate of 0.0413/hour. It also provide average sensitivities of 100%, 97%, and 90% for the time slots 0 - 5 minutes, 5 - 10 minutes, and 10 - 15 minutes before the seizure event. 2019 IEEE. -
Equality Versus Discretion in Imposing Death Penalty in The Criminal Justice System : A Comparative Analysis Between India, UK and USA
The criminal justice system has two phases, namely, pre-conviction and postconviction, which are based on some theories which have to be exercised by the four major organs of administration of the criminal justice system, namely police (investigation), prosecution, defence and judiciary as well as correctional institutions. For this purpose, every legal system permits this mechanism to exercise equality and discretion at various phases such that justice is served according to the procedure established by law as it is required. The attempts to maintain a balance between the two in the sphere of criminal justice had begun long ago, although not succeeded yet by various countries. In the United States, more equality is emphasised in the postconviction stage. It focuses on offence egalitarianism quotrather than quotoffender egalitarianismquot. In Europe, the position is almost contrary. In India, strict adherence specifically to neither equality nor discretion at any step cannot be traced out. However, when it comes to sentencing cruel and heinous crimes, almost all countries fix a definite punishment where there is a broad scope for judicial discretion, often ending up squeezing the discretion to attain the idealistic concept of equality. This Study aims to discuss and point out the merits and demerits of the said system with suggestions. -
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Equalization of Finite-Alphabet MMSE for All-Digital Massive MU-MIMO mm-Wave Communication
For more than twenty years, growing the performance and efficiency of wireless communications systems using antenna arrays has been an active field of study. Wireless networks with multiple-input multiple-output are also part of the current norms and are implemented around the world. Access points or BSs with comparatively few antennas are used for standard MIMO systems, and the resulting increase in spectral efficiency was relatively modest. A Multiple-Input Multiple-Output platform's capacity is researched where the transmitter outputs are processed and quantified by a set of limit quantizes through an analogue linear combining network. The linear mixing weights and cutoff levels are chosen from with a collection of possible combinations as a function of the transmitted signal. Millimetre-wave networking requires optimum data transmission to various computers on same moment network in combination with large multi-user actually massive. In order to guarantee efficient data transmission, the heavy insertion loss of wave propagation at su ch a faster speed needs proper channel estimation. A new channel estimation algorithm called Beam space Channel Estimation is suggested. From a set of possible configurations, the capacity of a massive stream from which antennas signals are handled by an analog channel as a part of the channel matrix, linear mixture weights and frequency modulation levels are selected. Probable implementations of specific analogue receiver designs for the combined network model, such as smart antenna selection, sign antennas output thresholding or linear output processing. To demonstrate the effectiveness of BEACHES in service and have FPGA implementation results, we are developing VLSI architecture. Our results show that for large MU-MIMOs, mm-wave communications with hundreds of antennas, specially made denoising can be done at maximum bandwidth and in an equipment format. Published under licence by IOP Publishing Ltd. -
Equitable and inclusive online learning: A framework for supporting students with disabilities
Online learning has become a widely adopted mode of education, particularly during the COVID-19 pandemic. In general, individuals with disabilities face challenges when using non-technology components for studying. This chapter proposes a framework for equitable and inclusive online learning practices that support students with disabilities. The framework is based on a review of current research and best practices for online learning and disability accommodations. The framework emphasizes a collaborative, student-centered approach to online learning that acknowledges the unique needs and experiences of students with disabilities. Depending on the disabilities, the framework is divided into two phases namely: Prevalent Learning, and Discrete Learning. The former comprised components: Accessibility, Accommodation, and Engagement, and later has components like Methodology, Evaluation. The framework proposed provides a roadmap for addressing the challenges faced by students with disabilities in online learning environments. 2023 by IGI Global. All rights reserved. -
Equity and inclusion in GenAI innovation: Exploring the challenges and strategies for ensuring equitable access to and benefits from GenAI-driven innovation
This chapter examines the critical need for equity and inclusion in the development and deployment of Generative Artificial Intelligence (GenAI) technologies. As GenAI rapidly transforms various sectors, from health care to education, its benefits are not evenly distributed, risking the exacerbation of existing social inequalities leading to a huge digital divide. The chapter explores theoretical frameworks like critical race theory (CRT) and intersectionality to understand how biases embedded in AI systems can perpetuate discrimination. It also highlights the role of open-source platforms and emerging AI initiatives in the Global South in democratizing access to these technologies. Through case studies of companies like Procter & Gamble and Microsoft, the chapter demonstrates both the potential of GenAI to drive innovation and the challenges of integrating AI ethically into global operations. The discussion underscores the importance of deliberate, inclusive strategies to ensure that AI serves as a force for social good, fostering global equity rather than deepening divides. 2025 V. Padmaja, P. Bhanumathi and Bishal Patangia. All rights reserved. -
Equity and inclusion in literacy policies: Case study on the impact of including assamese literature and excluding Koch Rajbongshi literature
This chapter examines the impact of Assam's literacy policies, which includes Assamese literature but excludes Koch Rajbongshi literature, on cultural representation, student engagement, and social equity. Through a case study approach, it reveals how this selective inclusion perpetuates cultural marginalization, affecting the self- identity and educational experiences of Koch Rajbongshi students. Theoretical insights from cultural hegemony, social justice in education, and critical literacy emphasize that inclusive curricula foster a cohesive and equitable society by valuing diverse cultural narratives. The chapter calls for policy reforms to integrate minority literature, advocating for an inclusive educational system that respects Assam's cultural diversity. These efforts are essential for promoting equity, enhancing student engagement, and fostering a sense of belonging. Aligning Assam's literacy policies with UNESCO principles promotes equity, cultural diversity, and preservation of Koch Rajbongshi literature. 2025, IGI Global Scientific Publishing. All rights reserved. -
Equity by Design: Embedding DEI Into AI- Enhanced Marketing Tools
In a time when artificial intelligence redefines marketing practice pillars, the concern is not innovation but conscience- driven innovation. While AI tools promise unmatched precision in reaching customers, segmenting, and budgeting, they carry a silent danger of deepening existing disparities, if they were to be unleashed without caution. This article advocates for the practice of Equity by Design, and it insists that Diversity, Equity, and Inclusion (DEI) must be made a part of the actual design of AI- driven marketing systems. By borrowing cross- disciplinary insights from finance, organizational ethics, and digital strategy, the case is argued through illustrations of how equitable design cuts down on algorithmic bias, expands financial service access to marginalized communities, and enhances consumer trust in a more data- driven market. Beyond compliance or corporate social responsibility, embedding DEI in AI is a competitive strategy, attaching ethical obligation to long- term brand worth, sustainable growth, and global competitiveness in the digital economy. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Era of Education 5.0: Disruptive Technologies in a Learner- Cantered Educational Landscape
The chapter focuses on concepts of Education 5.0 and its competence in shaping future learning environments. It emphasises on learners social and personal growth by improving quality of life standards with the help of current technologies and digitalisation. (Shabir Ahmad, 2023) To deliver humanised approach by the application of new technologies is the primary use of Education 5.0. However, usage of new age technology in education doesnt mean giving laptop and tablet to each and every child and the usage of digital mediums for teaching and learning. After covid- 19, digitisation becomes the integral part of our life, education is no exception for that. (Shabir Ahmad, 2023) Beyond digitalisation pandemic also remained us the importance of human hardships to social transformation with emotional intelligence driving technology as a tool. In short education 5.0. (SYDLE.com, 2023) referring to the significance of human, social and emotional abilities to enhance wellbeing of an individual by using technology advancement as a tool. 2025 by IGI Global Scientific Publishing.