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FII and its impact on exchange rate in India
Asian Journal of Research in Banking and Finance, Vol. 7, Issue 1, pp. 1-27, ISSN No. 2249-7323 -
Effect of Copper and Cobalt Metal Complex Redox Mediator Based Xanthan Gum Gel Electrolyte Materials on Performance of Dye Sensitized Solar Cells
In this study, we report the first application of (4-(tert-butyl) benzyl or 4-methyl benzyl)-substituted 2-(pyridin-2-yl)-1H-benzo[d]-imidazole coordinated copper and cobalt redox shuttles Cu [((tbb) pbi)2]+1/+2, Cu [(mbpbi)2]+1/+2, Co [((tbb) pbi)3]+2/+3 and Co [(mbpbi)3]+2/+3 based xanthan gum gel electrolytes (XGE-1, XGE-2, XGE-3 and XGE-4) in fabrication of Dye sensitized solar cells. Interestingly, XGE-1 and XGE-2 obtained higher efficiency of 4.08 and 3.04 % under 1sun illumination. Specifically, highly donor moiety 4-(tert-butyl)benzyl)-substituted (on N?H position) 2-(pyridin-2-yl)-1H-benzo[d]-imidazole ligands coordinated, Cu [((tbb) pbi)2]+1/+2 gel electrolyte achieved best performance due to its appropriate redox potential of 0.80 V versus Normal Hydrogen Electrode. This high performance also confirmed with the interfacial studies of the devices. As per the computational results, the copper redox mediators conquered lower reorganization energy and free energy about (0.11-0.20 & 0.180.27 eV) due to the effect of ligands structure. Overall, Cu [((tbb) pbi)2]+1/+2 act as redox shuttle for highly proficient DSSCs. 2022 Wiley-VCH GmbH. -
EShield: An effective detection and mitigation of flooding in DDoS attacks over large scale networks
Distributed Denial-of-Service attacks are very hard to be mitigated in wireless network environment. Here in this manuscript, an effective method of flood detection and mitigation architecture is proposed named eShield, which detects and prevent flooding attacks through spoof detection technique. The proposed method uses an architecture and an algorithm. eShield deals with Intrusion Protection and Detection Systems (IPDS) which collaboratively defend flooding attacks at different points in the network. Here eShield detects the supply node with its port variety which were below assault. Inorder to reduce the burden on international IPDS eShield makes use of distinct nearby IPDS for the assaults in flooding which have been carried out collaboratively. The assessment is done through the widespread simulation of eShield and it is proved to be an actual values based on time delay, false positive rates, computation and communication overhead. BEIESP. -
Strengthening Business Defenses With Quantum AI- Driven Cybersecurity
In todays competitive world, businesses are under constant pressure and are racing against each other to protect their systems from any attacks that may cause the loss of their data assets. One of the most rapidly growing advancements in this domain is the combination of quantum computing and artificial intelligence, which together form what experts now call quantum AI. This technology doesnt just give promises on stronger cyber security but also redefines it. Quantum AI provides faster threat detection, adaptive responses, and the development of encryption methods to fight even quantum- powered attacks. Recent reviews, including the QUASAR framework and quantum- safe protocols, emphasize how organizations can prepare now by mapping their cryptographic assets, adopting post- quantum algorithms, and integrating AI- driven monitoring tools. Both technical security and strategic and regulatory advantages in an increasingly complex threat landscape. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Role of public relations and promotional strategies in the success of youth theatre groups in Kolkata /
Public Relations is fundamentally the process of managing how, when and in what way one communicates, so that one may ultimately influence the behaviour, attitude and perceptions of those important. Public relations were initially only used for corporate practices but with the passage of time it has clearly become very popular in other fields as well including that of fine arts. Arts with time has gone from an invaluable hobby into a commercial business. -
Artificial intelligence for diabetic retinopathy detection: A systematic review
The incidence of diabetic retinopathy (DR) has increased at a rapid pace in recent years all over the world. Diabetic eye illness is identified as one of the most common reasons for vision loss among people. To properly manage DR, there has been immense research and exploration of state-of-the-art methods using artificial intelligence (AI) enabled models. Specifically, AI-empowered models combine multiple machine learning (ML) and deep learning (DL) based algorithms to improve the performance of the developed system architectures that are commercially utilized for the detection of DR disease. However, these models still exhibit several limitations, such as computational complexity, low accuracy in DR stage detection due to class imbalance, more time consumption, and high maintenance cost. To overcome these limits, a more advanced model is required to accurately predict the DR stage in the initial stages. For example, the identification of DR disease in the initial stage helps the ophthalmologist to make an accurate and safe diagnosis, and thereby, eyesight-related issues may be treated more effectively. This study conducted a systematic literature review (SLR) to provide a detailed discussion of the background of diabetic retinopathy, its major causes, challenges faced by ophthalmologists in DR detection, and possible solutions for identifying DR in the initial stage. Also, the SLR provides an in-depth analysis of the existing state-of-the-art techniques and system models used in DR diagnosis based on AI, ML, and recently developed DL-based approaches. Furthermore, this present survey would be helpful for the research community to receive information on the recent approaches used for DR identification along with their significant challenges and limitations. 2024 The Authors -
Genetically modified foods: bibliometric analysis on consumer perception and preference
In this study, we present the bibliometric trends emerging from research outputs on consumer perception and preference for genetically modified (GM) foods and policy prescriptions for enabling the consumption using VOSviewer visualization software. Consumers positive response is largely influenced by the decision of the governments to ban or approve the GM crops cultivation. Similarly, the public support increases when the potential benefits of the technology are well articulated, consumption increases with a price discount, peoples trust on the government and belief in science increases with a positive influence by the media. Europe and the USA are the first region and country, respectively, in terms of the number of active institutions per research output, per-capita GDP publication and citations. We suggest research-, agri-food industries-, and society-oriented policies to be implemented by the stakeholders to ensure the safety of GM foods, encourage consumer-based studies, and increase public awareness toward these food products. 2022 The Author(s). Published with license by Taylor & Francis Group, LLC. -
Spatial and seasonal association study between PM2.5 and related contributing factors in India
Global environmental pollution and rapid climate change have become a serious matter of concern. Remarkable spatial and seasonal variations have been observed due to rapid industrialization, urbanization, different festive occasions, etc. Among all the existing pollutants, the fine airborne particles PM2.5 (with aerodynamic equivalent diameter ?2.5?m) and PM10 (with aerodynamic equivalent diameter ?10?m) are associated with chronic diseases. This leads to carry out the study regarding the varying relationship between PM2.5 and other associated factors so that its concentration level might be under control. Existing literature has explored the geographical association between the pollutants and a few other important factors. To address this problem, the present study aims to explore the wide spatio-temporal relationships between the particulate matter (PM2.5) with the other associated factors (e.g., socio-demographic, meteorological factors, and air pollutants). For this analysis, the geographically weighted regression (GWR) model with different kernels (viz. Gaussian and Bisquare kernels) and the ordinary least squares (OLS) model have been carried out to analyze the same from the perspective of the four major seasons (i.e., autumn, winter, summer, and monsoon) in different districts of India. It may be inferred from the results that the local model (i.e., GWR model with Bisquare kernel) captures the spatial heterogeneity in a better way and their performances have been compared in terms of R2 values (>0.99 in all cases) and corrected Akaike information criterion (AICc) (maximum value -618.69 and minimum value -896.88). It has been revealed that there is a strong negative impact between forest coverage and PM pollution in northern India during the major seasons. The same has been found in Delhi, Haryana, and a few districts of Rajasthan during the 1-year cycle (October 2022September 2023). It has also been found that PM concentration levels become high over the specified period with the temperature drop in Delhi, Uttar Pradesh, etc. Moreover, a strong positive association is visible in PM pollution level with the total population. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. -
Toward sustainable and eco-friendly production of coffee: abatement of wastewater and evaluation of its potential valorization
Abstract: Coffee is one of the most significant beverages consumed worldwide, dating to times immemorial. It plays a pivotal role in several economies owing to its second position in the list of trading commodity, after petroleum. The growing demand for coffee has resulted in a great amount of coffee production and processing and subsequent release of large volumes of wastewater. This wastewater is characterized to have very high chemical oxygen demand and biological oxygen demand with potential to cause environmental pollution thus requiring smart strategies to effectively reduce their load of the wastewater before releasing them into the habitable ambiance. The existing research on coffee wastewater treatment should be critically analyzed for their sustainability and economic viability for them to be commercially used in developing countries for effluent mitigation. This literature review aims to suggest an effective way to treat the wastewater by combining various methods, coupling it with value addition like energy generation. The goal of this review is to provide a direction for future research to integrated treatment with valorization along with a focus on emerging technologies. Graphic abstract: [Figure not available: see fulltext.]. 2020, Springer-Verlag GmbH Germany, part of Springer Nature. -
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.