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A comprehensive molecular docking-based study to identify potential drug-candidates against the novel and emerging severe fever with thrombocytopenia syndrome virus (SFTSV) by targeting the nucleoprotein
Severe fever with thrombocytopenia syndrome (SFTS) is a newly emerging haemorrhagic fever that is caused by an RNA virus called Severe fever with Thrombocytopenia Syndrome virus (SFTSV). The disease has spread globally with a case fatality rate of 30%. The nucleoprotein (N) of the virus has a pivotal role in replication and transcription of RNA inside the host. Considering that no specific treatment regime is suggested for the disease, N protein may be regarded as the potential candidate drug target. In the present study, in silico molecular docking was performed with 130 compounds (60 natural compounds and 70 repurposed synthetic drugs) against the N protein. Based on the binding affinity (kcal mol?1), we selected Cryptoleurine (?10.323kcalmol?1) and Ivermectin (?10.327kcalmol?1) as the top-ranked ligands from the natural compounds and repurposed synthetic drugs groups respectively, and pharmacophore analysis of these compounds along with other high performing ligands revealed that two aromatic and one acceptor groups could strongly interact with the target protein. Finally, molecular dynamic simulations of Cryptoleurine and Ivermectin showed stable interactions with the N protein of SFTSV. To conclude, Cryptoleurine and Ivermectin can be considered as a potential therapeutic agent against the infectious SFTS virus. Graphical abstract: (Figure presented.) The Author(s) under exclusive licence to Archana Sharma Foundation of Calcutta 2024. -
Demand-supply imbalance: The root cause of the crisis
[No abstract available] -
Are Indians Willing to Pay for Air Quality? Findings from a Contingent Valuation Study
This paper aims to study individual preferences towards ambient air quality improvements in India, through the willingness to pay (WTP) measure. Contingent valuation method is employed to elicit individual WTP for air quality improvements via closed-end double bound questioning technique. Bivariate probit model is estimated based on the data coming from 539 in-person interviews to find key determinants of WTP. Estimation results suggest that place of residence, education, consciousness regarding air pollution, and household income are the key determinants of individual WTP for air quality improvements. Random probit model estimated based on the same data finds the presence of shifting and anchoring anomalies, leading towards bias in the mean WTP estimation from the Bivariate probit model. After correcting those anomalies, the estimated mean WTP is ?255.69 (or $3.09) per month. This is the first study estimating the bias-corrected WTP for air quality enhancements, covering a vast region of India. The Author(s), under exclusive licence to The Indian Econometric Society 2026. -
The odd-even driving restriction in Delhia causal analysis
The odd-even restriction in Delhi allows private car owners to utilise their cars only on alternative days of the week, depending on the last digit of the registration number. In a mega-city like Delhi, where a high number of personal vehicles and excessive pollution concentration simultaneously exist, adopting such restrictions might help minimise emissions streaming from vehicular sources. A handful of empirical studies have estimated the policy effect but failed to provide its causal impact on air pollution levels, which is necessary for understanding the effectiveness of the odd-even restrictions. Therefore, we utilise the quasi-experimental design to find a causal relationship between driving restrictions and air pollutants (mainly generated from vehicular sources) across different policy rounds. Under quasi-experimental design, the study employs the triple difference technique on hourly air quality data. The findings of the empirical exercise indicate that the driving restriction reduces the average concentration levels of CO and PM2.5 pollutants during our restriction period. Moreover, the findings justify the short-run effectiveness of the driving restriction policy, as individuals may find ways to counter the policy in the long run. 2025 Journal of Environmental Economics and Policy Ltd. -
A comprehensive molecular docking-based study to identify potential drug-candidates against the novel and emerging severe fever with thrombocytopenia syndrome virus (SFTSV) by targeting the nucleoprotein
Severe fever with thrombocytopenia syndrome (SFTS) is a newly emerging haemorrhagic fever that is caused by an RNA virus called Severe fever with Thrombocytopenia Syndrome virus (SFTSV). The disease has spread globally with a case fatality rate of 30%. The nucleoprotein (N) of the virus has a pivotal role in replication and transcription of RNA inside the host. Considering that no specific treatment regime is suggested for the disease, N protein may be regarded as the potential candidate drug target. In the present study, in silico molecular docking was performed with 130 compounds (60 natural compounds and 70 repurposed synthetic drugs) against the N protein. Based on the binding affinity (kcal mol?1), we selected Cryptoleurine (?10.323kcalmol?1) and Ivermectin (?10.327kcalmol?1) as the top-ranked ligands from the natural compounds and repurposed synthetic drugs groups respectively, and pharmacophore analysis of these compounds along with other high performing ligands revealed that two aromatic and one acceptor groups could strongly interact with the target protein. Finally, molecular dynamic simulations of Cryptoleurine and Ivermectin showed stable interactions with the N protein of SFTSV. To conclude, Cryptoleurine and Ivermectin can be considered as a potential therapeutic agent against the infectious SFTS virus. The Author(s) under exclusive licence to Archana Sharma Foundation of Calcutta 2024. -
Exploring the adsorption efficacy of Cassia fistula seed carbon for Cd (II) ion removal: Comparative study of isotherm models
The current study demonstrates the potential of Cassia fistula seed carbon (CFSC), a waste lignocellulosic biomass, to eliminate Cd (II) ion-from saturated liquid samples. The efficient removal of about 93.2% (w/v) of Cd (II) ions from 10 mg/L concentration was achieved within 80 min of treatment. The CFSC dosage of 100 mg/50 mL accounted as optimal for enhanced Cd (II) removal. Cd (II) adsorption onto CFSC was observed to be maximum at pH 6. The investigational trials were assessed with three isotherm models such Dubinin-Radushkevich, Freundlich, and Langmuir. The specifications obtained from this experimental study align well with the Langmuir isotherm model, which describes the maximal adsorption capacity of 68.02 mg/g. Cd (II) adsorption data from this study exhibited the R2 of 0.9 under pseudo-second-order. Maximum desorption (76.3% w/v) was obtained with 0.3 M HCL. This study revealed that thermally activated C. fistula seed carbon (CFSC) can be tuned to be lucrative adsorbent for Cd (II) elimination from water and waste-water. 2023 Elsevier Inc. -
Rewarding Fathers, Penalizing Mothers - A Quantitative Evidence on the Unequal Gains of Parents in Indian Labor Market
The gender discrimination is a significant issue in the labor market. Motherhood Penalty is one of the important contributors to this issue. This study aims to find the evidence of impact of parenthood on employment to population ratio and mean nominal monthly earnings concerning factors household structure and number of children under age six. Using interactive multiple linear regression models, we have derived meaningful conclusions from data collected from the International Labor Organization (ILO). Our findings reveal that there is a significant motherhood penalty in India. Womens employment probability decreases by 12.4% with one child and up to 19.09% with three or more children. Meanwhile, men experience a fatherhood bonus, with employment rates rising by up to 24.79% as they have more children. Wage disparities are also evidentmothers with two or more children earn substantially less than childless women, whereas the fatherhood wage premium is weaker than in developed economies. Mothers with two or more children earn substantially less than childless women, whereas the fatherhood wage premium is weaker than in developed economies. Through this study, we also see the probable reasons behind the results observed from the models. Lack of institutional support for working moms, workplace prejudice, and deeply rooted gender stereotypes are some of the main reasons attributing to the Motherhood Penalty. This disparity is further exacerbated by strict work rules, poor childcare facilities, and lax paternity leave regulations. Overall, the motherhood penalty is a serious phenomenon affecting the lives of many mothers and degrading their standards of living. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Semantic-Contextual Automation of Scriptless BDD Testing for Intelligent Test Coverage Enhancement
This work proposes a framework to improve test automation for Android applications using Behavior-Driven Development (BDD). It addresses the challenges posed by dynamic user interfaces, complex view hierarchies, and unstable locators by capturing user interactions through browser-mirrored Android screens. The framework integrates AI-based widget classification, image-based object detection, and dynamic XPath generation to enhance locator reliability. Test scenarios-including positive, negative, and boundary cases are structured in JSON and automatically converted into BDD feature files, increasing test coverage and minimizing redundancy. Automation of script generation and locator healing reduces manual effort while improving scalability, accuracy, and efficiency in test case management. The optimized validation pipeline supports comprehensive scenario generation and accelerates functional testing, thereby improving software quality in dynamic Android environments. 2025 IEEE. -
Power quality enhancement of renewable energy systems using a hybrid orangutan optimization algorithm and continuous spiking graph neural network with series active power filter
Interconnected renewable energy systems (RES) often experience power quality (PQ) issues, such as harmonics and voltage disturbances. Nevertheless, conventional Series Active Power Filter (SAPF) control schemes have disadvantages, such as slow adaptation and reduced accuracy in a fluctuating renewable environment. To overcome these limitations, this work proposes a hybrid adaptive SAPF-based PQ optimization technique. The proposed method combines the Orangutan Optimization Algorithm (OOA) and Continuous Spiking Graph Neural Network (CSGNN), referred to as the OOA-CSGNN method. Reduction of total harmonic distortion (THD), increase of PQ, and stabilize of voltage profiles in interconnected RES are the goals of the proposed technique. The OOA offers the best SAPF control parameters to maximize convergence and dynamic tracking, and the CSGNN is effective to predict the compensation signals using graph-based spiking computations. The suggested technique is implemented in MATLAB and evaluated against existing approaches, such as the Gorilla Troops Algorithm (GTA), Genetic Algorithm (GA), Adaptive Bald Eagle Optimization Algorithm (ABE-OA), Artificial Neural Network (ANN), and Convolutional Neural Network (CNN). The proposed OOA-CSGNN approach achieves a load voltage THD of 0.11% under steady-state operating conditions after SAPF compensation, while maintaining voltage THD well within IEEE-519 limits during transient disturbances such as voltage sag, swell, and dip. These results demonstrate the efficiency and robustness of the proposed hybrid architecture for PQ optimization in renewable-integrated systems. 2026 Elsevier Ltd -
Cost-effective cryptographic architecture in quantum dot cellular automata for secured nano-communication
Quantum dot cellular automata (QCA) provide rapid computational efficiency, high density and low power consumption, which is an alternative for CMOS technology. In digital world, cryptography is an important feature to protect digital data. To ensure the data protection in nano-communication, a QCA-based cryptographic architecture is proposed in this article. In the proposed design, the encryption and decryption are done with the help of random keys which is produced by the pseudo random number generator (PRNG). In this paper, architectural component of cryptographic architecture includes XOR block, 1 to 4 de-multiplexer and PRNG, which are realised using QCA. Finally, an integration of the individual components through clock zone-based crossover, lead to the generation of a novel cryptographic architecture. This design achieves low cost compared to the existing literature, as it uses minimum number of majority gate and inverters with clock zone-based crossover. Copyright 2024 Inderscience Enterprises Ltd. -
An enhancing reversible data hiding for secured data using shuffle block key encryption and histogram bit shifting in cloud environment
Nowadays there are numerous intruders trying to get the privacy information from cloud resources and consequently need a high security to secure our data. Moreover, research concerns have various security standards to secure the data using data hiding. In order to maintain the privacy and security in the cloud and big data processing, the recent crypto policy domain combines key policy encryption with reversible data hiding (RDH) techniques. However in this approach, the data is directly embedded resulting in errors during data extraction and image recovery due to reserve leakage of data. Hence, a novel shuffle block key encryption with RDH technique is proposed to hide the data competently. RDH is applied to encrypted images by which the data and the protection image can be appropriately recovered with histogram bit shifting algorithm. The hidden data can be embedded with shuffle key in the form of text with the image. The proposed method generates the room space to hide data with random shuffle after encrypting image using the definite encryption key. The data hider reversibly hides the data, whether text or image using data hiding key with histogram shifted values. If the requestor has both the embedding and encryption keys, can excerpt the secret data and effortlessly extract the original image using the spread source decoding. The proposed technique overcomes the data loss errors competently with two seed keys and also the projected shuffle state RDH procedure used in histogram shifting enhances security hidden policy. The results show that the proposed method outperforms the existing approaches by effectively recovering the hidden data and cover image without any errors, also scales well for large amount of data. 2018, Springer Science+Business Media, LLC, part of Springer Nature. -
Comparisons of Stock Price Predictions Using Stacked RNN-LSTM
This paper seeks to identify how the RNN-LSTM can be used in predicting the rise and fall in stock markets thereby helping investors to understand stock prices. Therefore, by predicting the nature of the stock market, investors can use different machine learning techniques to understand the process of selecting the appropriate stock and enhance the return investments thereafter. Long Short-Term Memory (LSTM) is a deep learning technique that helps to analyze and predict the data with respect to the challenges, profits, investments and future performance of the stock markets. The research focuses on how neural networks can be employed to understand price changes, interest patterns and trades in the stock market sector.The datasets of companies such as IBM, Cisco, Microsoft, Tesla and GE were used to build the stacked RNN-LSTM model using timesteps of 7 and 14days. The two layered stacked RNN-LSTM models of the companies such as Microsoft and Tesla achieved their highest model accuracies after being trained over a span of one year whereas the other companies acquired their highest accuracies after being trained over a span of 4 to 5years which implies that the rate of change of economic factors affecting Microsoft and Tesla over a short span of time is high as compared to the other existing companies. 2021, Springer Nature Switzerland AG. -
Utilisation of Virtual Assistant and Its Impact on Retail Industry
Virtual assistant is nothing but an independent contractor, who offers administrative services to the clients of a particular organisation while operating outside of the office of the client. Generally, a virtual assistant operates from a home-based office. This virtual assistant application has the ability to access the required planning documents, such as shared calendars. The contemporary retail organisations like e-commerce companies in this competitive global business environment are using virtual assistant to enhance omnichannel experience, 24/7 customer service, order tracking, and product recommendations. Overall, virtual assistant helps the organisations in enhancing social media management activities. This concept of the use of virtual assistant has been significantly emerged after the increase in demands for e-commerce business activities in this decade. Research objectives related to the title of this research are developed and listed. Relevant theories on virtual assistant are applied in the literature review section of this study. The researcher has decided to adopt qualitative research methodology to achieve the objectives of the research. Moreover, the researcher has considered secondary data analysis approach to conduct this research. In terms of findings, it has been identified that virtual assistant has a positive impact on the business operation activities of retail organisations. Authentic secondary sources are considered to collect and analyse the data. Some challenges associated with the utilisation of virtual assistant also have been identified in the findings section. Some valuable recommendations are suggested for the future researchers to overcome those identified associated challenges. 2022 IEEE. -
Enabling Agricultural Sector through Blockchain Technology Farmers Perspective
The agricultural sectors in India and abroad have been affected extensively due to the Covid-19 pandemic. It is necessary to provide solutions for the availability of resources, controlling the cost, quality in production, transparent food supply, fulfilling demand, and removing intermediaries. The structural reforms in the agricultural sector by adopting emerging technologies, especially blockchain technology (BCT) and the robotics automation process, are inevitable during the pandemic and future development. To study the impact of blockchain on the Agriculture sector, the farmer's level of awareness of the blockchain technology, its methodological influence, the inclination of farmers to adopt the technology in their farming, and agri-related activity are vital. This paper aims to explore the opportunities of BCT in expanding the agriculture sector, ascertain the awareness and intensity of farmers' knowledge of the effect of BCT, and develop the mean difference in the opinion of the farmers towards the utilization of BCT in the relevant field of agriculture. A structured interview schedule was administered with 360 sample farmers from the Delta regions of three states located in the southern part of India, such as Andhra Pradesh, Karnataka, and Tamilnadu, using a purposive sampling technique intending. Irrespective of the age, gender, land capacity, possession, education level, learned procedures, and abundant experience helped the farmers demand a new technology interface to improve their income level and register their sustainability. 2022 by authors, all rights reserved. -
Role of Circular Economy Principles in Enhancing Sustainable Supply Chain Management
Global warming alerts the ecological system and functions of every element in the business world. Supply chain management is one process where sustainability principles are implemented robustly. This study is an attempt to integrate the circular economy principles in enhancing sustainable supply chain management; it has four objectives to focus on the adoption of circular economy principles, the respective barriers, stakeholders engagement level, and finally, the outcome of sustainable practices such as environmental, economic, and social implications. The study is descriptive research, adopting a purposive sampling technique to include the selected group of respondents with a sample size of 250, those who are already processing in the supply chain management activities across the industries located in Chennai. Structural Equation Modeling was used to build the measurement model to establish the connectivity between the circular economy adoption, stakeholders engagement and sustainable supply chain management outcomes. The hypothesis was established, and the same was proved through confirmatory factor analysis and standardized model fit. Hence, it was concluded that the circular economy principles and their adoption barriers significantly influence stakeholders engagement and the determination of sustainable supply chain management outcomes. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Security and Privacy in AI: IoT- Enabled Banking and Finance Services
The integration of Artificial Intelligence (AI) and the IoThas led to significant advancements in the banking and finance sector, providing personalized, efficient, and data- driven services. However, these AI- IoT enabled systems also introduce complex security and privacy challenges that need to be addressed to protect sensitive financial data and maintain customer trust. This paper surveys the key security and privacy issues in AI- IoT enabled banking, including data breaches, cyber- attacks, unauthorized access, and data misuse. We examine current methodologies for securing AI- IoT systems, such as encryption, blockchain, alongside AI- driven threat detection and response techniques.The survey explores regulatory considerations and compliance requirements that shape security protocols in financial services. By identifying gaps in existing security measures and highlighting advanced privacypreserving technologies, this study aims to provide a comprehensive understanding of the challenges and future directions in securing AI- IoT applications within banking and finance. 2026 by IGI Global Scientific Publishing. All rights reserved. -
The Future of Banking: Leveraging AI for Business Transformation
Artificial intelligence (AI) has moved the banking sector into a new era, offering unmatched channels to improve efficiency, risk management, and customer experiences. The research delved into the role of AI in banking, particularly the applications, challenges, and implications. Descriptive research design is used to describe the acceptance rate of banking customers toward leveraging AI in Banking transactions through theTechnology Acceptance Model (TAM) to prove the hypothesis thatAttitude Towards Using AI mediates the individual effects of Perceived Usefulness, Perceived Ease of Use, and Perceived Risk with Behavioural Intention. The primary data was collected from 385 banking customers using the purposive sampling technique by circulating astructured questionnaire. This research shows that AI brings about more benefits than challenges, but it is crucial to handle the latter carefully to enable sustainable sectoral growth. The empirical results support theexistence of a promising environment for AI adoption in thebanking industry as there the customers show a positive behavioural intention. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Recent advances in polyethylene glycol as a dual-functional agent in heterocycle synthesis: Solvent and catalyst
Reactant solubility, which dictates achievable concentrations, and the stability of reaction intermediates (excited states), solvents modulate the potential energy landscape and influence reaction rates. Consequently, solvent selection is pivotal in optimizing process productivity, economic feasibility, and environmental footprint. At present, organic synthesis pivots around the idea of sustainability. In particular, PEG-400, a popular solvent and phase transfer catalyst, is considered greener as it can be reused several times without significant loss in its catalytic activity, which checks the box regarding sustainability. This review highlights the emerging potential of Polyethylene Glycol 400 (PEG-400) as a dual-threat agent in sustainable organic synthesis. We explore its efficacy as a catalyst, promoting various reactions under mild conditions and often eliminating the need for traditional metal catalysts. Additionally, PEG-400's role as a green solvent is addressed, emphasizing its biodegradability, low toxicity, and ability to facilitate reactions without hazardous Volatile Organic Compounds (VOCs). The review examines recent research on PEG-400 mediated reactions, showcasing its effectiveness in diverse transformations, thus exploring the potential of PEG 400 as a facilitator for heterocycle synthesis in both multicomponent reactions and stepwise approaches. It identifies exciting research directions that promise to expand the boundaries of polymer-based solvents in heterocyclic chemistry. 2024 The Author(s). Polymers for Advanced Technologies published by John Wiley & Sons Ltd. -
Blue LED photolytic method for the synthesis of 1,4-dihydropyridine derivatives from benzo [b]thiophene-2-carbaldehyde
This study presents a highly efficient and operationally simple protocol for synthesizing 1,4-dihydropyridine derivatives. The protocol uses an inexpensive and readily available photocatalyst Mn2(CO)10, which plays a crucial role in the single-pot, four-component reaction involving benzo [b]thiophene-2-carbaldehyde, malononitrile, dialkyl acetylene dicarboxylate, and anilines in a blue LED (400500 nm) photocatalytic technique. The reaction conditions include the use of blue LEDs, a lower catalyst load, and green solvents like dimethyl sulfoxide (DMSO) and water in a 1:1 ratio. The multicomponent photocatalytic approach negates the use of expensive catalysts and the necessity of multi-step routes, in addition to providing better atom economy and an easy work-up process, and it is environmentally benign. The derivatives were effectively synthesized in higher yields and characterized using 1H NMR, 13C NMR, and ESI-MS. The obtained 1,4 dihydropyridines also have tremendous capability for biological and pharmacological activities, opening exciting possibilities for future research and applications. 2025 -
Structural and Optical Properties of Alumino Lead Borate Glasses Containing Copper Oxide
The alumino lead borate glasses with small amounts of copper oxide were synthesized by melting and quenching according to the relation 50B2O3-30PbO-(20x)Al2O3-xCuO with x = 0, 0.10, 0.25, 0.50, 0.75 and 1.00 mol%. The powder XRDs had no sharp peaks which show that the samples are amorphous. Density of the glasses increased as the content of the CuO increased. FTIR spectroscopic studies reveal the presence of BO3, BO4, PbO4, AlO4, pentaborate [B5O8], diborate [B4O72] and dipentaborate B512 structural units. The UV-visible absorption studies showed that the refractive index, indirect energy gap, oxide ion polarizability and optical basicity had composition dependence which were related to the glass structure. As the CuO concentration increased, the refractive index decreased, indirect energy gap increased, oxide ion polarizability decreased and optical basicity decreased. Optical band gap increased with increasing CuO content as the band gap for bridging oxygens is higher than that for non-bridging oxygens. 2024 Indian Ceramic Society.