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Environmental Reporting Practices : Evidence From Indian Commercials Banks
The last decade has witnessed increasing concern towards the environment due to the ravages inflicted on them by mankind. With the concept of sustainable development perpetually growing over the years, global institutions have been acknowledging their other duties towards the society, and have been integrating environmental practices into their strategic framework to significantly contribute to the sustainable bandwagon. However, since the financial crisis of newline2008, it has been found that banking institutions have become active participants in fostering environmental sustainability. Also, due to the increased awareness and pressure from the stakeholders, these institutions have been reporting their environmental initiatives and practices in their bank reports and websites. The extensive review of the literature reveals that there are hardly any studies that have been carried out in the Indian context, pertinently in the banking sector. Therefore, considering this as a major research gap, the present study aims to comprehensively evaluate the environmental reporting practices of selected commercial banks in India for the period from 2011 to 2022. newlineThis study follows an explorative and descriptive research design, with a deductive research approach. However, this research is based on secondary data, and adopts both qualitative and quantitative research methods. Following the judgmental sampling technique, the sample of the study consists of thirty public and private commercial banks in India. The content analysis technique has been adopted to extract environmental information from bank reports and websites using the developed environmental reporting index. The independent sample t-test is newlineused to compare the environmental reporting performance of public and private-sector newlinecommercial banks. This study analyses the relationship between environmental reporting and bank-specific characteristics using the Pearson correlation coefficient analysis. -
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Environmental sensors: Safeguarding the ecosystem by monitoring sanitary pad disposal
This chapter focuses on the applications of environmental sensors in general and their role in identifying and addressing the issues related to the improper disposal of sanitary pads, which is a growing concern. It also gives an overview of the pollutants associated with it, and the role that environmental sensors can play in mitigating this problem. By harnessing the power of advanced sensing technologies, we can gain a better understanding of the environmental impact of sanitary pad disposal and work towards sustainable solutions. This chapter aims to provide valuable insights and guidance for researchers and practitioners working to create a cleaner and healthier environment and generate self-awareness for individuals in safeguarding ecosystem. 2024, IGI Global. All rights reserved. -
Environmental Sustainability in Tourism : Developing Green Service Transportation Intiatives in Karnataka
Environmental sustainability in transportation aims to decrease carbon emissions and preserve resources, while ensuring that the rise of tourism is in harmony with ecological preservation. Adopting sustainable mobility in ecotourism destinations entails utilizing renewable energy sources and enhancing green transport systems, thereby benefiting both environmental preservation and visitor contentment. This study investigates the dynamics of green transportation practices within the context of ecotourism destinations, focusing on Karnataka, India. Employing a mixed-methods approach, the research combines quantitative surveys with newlinequalitative interviews of tourism logistics service providers. Through exploratory factor analysis, the study identifies key factors influencing green initiatives, drivers, and challenges faced by tourism logistics service providers. Hypothesis testing and path analysis affirm the relationships among internal initiatives, external drivers, challenges, and environmental sustainability newlineperception. The qualitative analysis extracts insights from tourism logistics service providers, tourism stakeholders revealing diverse perspectives on progress, green initiatives, challenges, and government support. The study concludes with a roadmap for sustainable tourism development, emphasizing policy formulation, feasibility assessment, stakeholder collaboration, newlineand tourist education. The implications and recommendations underscore the need for ongoing government intervention, infrastructure development, and community engagement to foster a holistic and sustainable approach to green transportation in ecotourism destinations. -
Environmental value development among preadolescents: a content analysis of reflective responses
Addressing the environmentally detrimental values prevalent in society in the context of rapid climate change is the need of the hour. Combining empathy with cognitive skills such as reflective thinking effectively creates new values among people. The present study attempts to reveal the pattern of environmental value development among 33 preadolescents by reflecting upon the empathy-generating story experiences and the related contents. The study is part of a more extensive quasi-experimental study, and it specifically performs a content analysis on the participants responses in their workbooks. Biospheric nature-related values are the most highly developed, and social justice is the least developed value, implying the need to focus more on the value of social justice. Stories are aids, and reflective thinking and empathetic elicitation are effective techniques for passing environmental values. Empathy generation instead of negative emotions from self-concern and emotion regulation through reflective thinking may be helpful to promote well-being in the context of climate change. Reflective thinking helps environmental value development by enhancing comprehension, emotion regulation, and self-awareness of values, implying a shift from telling the moral of a story to exploring the same through reflective thinking. 2025 Informa UK Limited, trading as Taylor & Francis Group. -
Environmentally conscious synthesis of novel pyrano[2,3-d]pyrimidines via ternary deep eutectic solvents
Pyrano[2,3-d]pyrimidine and its analogues have gained considerable courtesy because of their diverse biological functions and wide-ranging applications, from pharmaceutical agents to essential natural pigments. However, synthesising pyrano[2,3-d]pyrimidine with multiple reactants is challenging and requires advanced green chemistry solutions. This study investigates the generation of thirteen new pyrano[2,3-d]pyrimidine analogues through a single-step, open-flask, multicomponent reaction (MCR) strategy involving aldehydes, phenylhydrazine, ethyl acetoacetate, and barbituric acid via deep eutectic solvents (DES). These DESs serve as environmentally friendly alternatives to traditional solvents. A ternary deep eutectic solvent (TDES) was evaluated for its catalytic solvent activity among ten different formulations. TDES-7 (5 mL) demonstrated the best performance, achieving 95 % product formation within 30 min at room temperature. Its remarkable catalytic activity and ability to produce high yields across multiple reaction cycles make it a standout choice for this application. The collaboration between MCR and TDES underscores an important blend of two significant green aspects, demonstrating their potential to achieve a green and productive sustainable synthesis method with an noble E-factor of 0.1236. 2024 Elsevier B.V. -
Environmentally responsible behaviour among the teachers: role of gratitude and perceived social responsibility
Purpose: Based upon the broaden-and-build theory of positive emotions, this study aims to assess the role of perceived social responsibility (PSR) in mediating the relationship between gratitude and environmentally responsible behaviour (ERB) among teachers. Design/methodology/approach: Data were collected, following a correlational design, from a total of 292 school teachers in Kerala state, India. In total, 256 data were taken for final analysis. Out of the total participants, 63.3% were female and the remaining 36.7% were male. Confirmatory factor analysis was carried out to verify the factor structure and discriminant as well as convergent validity of the study variables. The relationship between gratitude and ERB with mediating role of PSR was tested. Findings: The mediation analysis output revealed that PSR fully mediates the effect of gratitude on ERB, and it is concluded from the findings of the study that ERB can be enhanced by humanizing the citizens to integrate social responsibility in their acts and promoting the significance of having positive emotions like gratitude to widen their thoughtaction repertoires. Research limitations/implications: In line with the broaden-and-build theory, a positive state of mental faculty can be a prime facilitator to increase concern for green environments as an outcome of an expanded thoughtaction repertoire. The findings imply the importance of inculcating enduring personal resources like the sense of gratefulness as it weighs the effect of producing altruistic acts like ERB along with many other benefits associated with having a positive emotion which is obviously considered to be a fair contribution to serve social resources in the community. Social implications: The study findings can be an inspiration for the formation of policies to encourage pro-environmental behaviour and to further expansion of policies like national education policy of India. As teachers being the facilitators of knowledge and wisdom, they are potential sources to inspire students to practice healthy behaviours, they can be better models by practicing ERB. Originality/value: The authors have verified the application of broaden-and-build theory of positive emotion in the context of ERB along with identifying its relationship with gratitude and PSR. 2023, Emerald Publishing Limited. -
Environmentally sustainable zinc oxide nanoparticles for improved hazardous textile dye removal from water bodies
A sustainable, affordable, and cost-effective method was developed to synthesize zinc oxide nanoparticles (SB-ZnO-NPs) using leaf extracts of Strobilanthes barbatus. The synthesized SB-ZnO-NPs displayed an absorbance maximum at 359 nm with a band gap of 3.24 eV. The average diameter of the SB-ZnO-NPs, as determined by FESEM analysis, was 84.23 nm. The particles had nearly spherical morphologies. By using FTIR analysis, it was established that functional groups played a part in the formation of SB-ZnO-NPs. Reactive Yellow 86 (RY-86) and Reactive Yellow 145 (RY-145) textile dyes were degraded by SB-ZnO-NPs under the impact of UV irradiation, and the degradation rates were 87.50 and 91.11%, respectively, in 320 min. When dye solutions treated with SB-ZnO-NPs were tested for phytotoxicity, the results showed a sharp decline in the effectiveness of the inhibition compared to dye effluents. The synthesised SB-ZnO-NPs can, therefore, be employed as a substitute potential catalyst for the breakdown of textile colours both before and after release into water bodies. 2023 The Author. -
Envisaging an Intelligent Blockchain Network by Intelligence Sharing
Blockchain Technology is gaining popularity throughout various industry verticals due to its data decentralization and tamper-evident nature. Machine Learning (ML) is all about embedding a learning capability to computing machines so that the machine can learn based on historical data in a way how human beings learn things. An important part of ML is the process of learning which needs humongous processing capability and hence it is time-consuming. Significant benefits have been predicted from the integration of these two technologies. Making a complete blockchain network intelligent in a simple and efficient way is a major challenge. In this work, a Multi Layer Perceptron (MLP) model is implanted in every node of the blockchain network. An efficient technique is proposed to make an intelligent blockchain network in minimum possible time and using minimum processing power. During the network formation, every node of the network has knowledge of the model architecture. At some point in time, the model of the randomly selected node gets trained. After completion of the training of that node, the intelligence is replicated to the entire network. 2022 IEEE. -
Envisioning the potential of Natural Language Processing (NLP) in Health Care Management
Natural Language Processing (NLP) continues to play a strategic role in disease detection, intensive care, drug discovery and control of mushrooming infections during the current pandemic. It energizes chat programs to reduce outbreaks during the initial stages of coronavirus infection. NLP technologies have reached new heights in terms of utility, and are at the heart of the success of a multilingual conversation system, and Deep learning language models. It supports more languages around the world. NLP powered AI such as Health map and Copweb platforms track patient requests and perform incident detections. This study looks at the role of NLP and its technologies, challenges, and future possibilities using AI and machine learning for crisis mitigation and easier electronic health records (EHRs) maintenance in the health care industry. This research work explores the strategic approach and potential of NLP which maximizes the value of the EHR and healthcare data, making data a critical and trusted component in improving health outcomes 2021 IEEE. -
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 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.