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Development of a fluorescent scaffold by utilizing quercetin template for selective detection of Hg2+: Experimental and theoretical studies along with live cell imaging
Quercetin is an important antioxidant with high bioactivity and it has been used as SARS-CoV-2 inhibitor significantly. Quercetin, one of the most abundant flavonoids in nature, has been in the spot of numerous experimental and theoretical studies in the past decade due to its great biological and medicinal importance. But there have been limited instances of employing quercetin and its derivatives as a fluorescent framework for specific detection of various cations and anions in the chemosensing field. Therefore, we have developed a novel chemosensor based on quercetin coupled benzyl ethers (QBE) for selective detection of Hg2+ with naked-eye colorimetric and turn-on fluorometric response. Initially QBE itself exhibited very weak fluorescence with low quantum yield (? = 0.009) due to operating photoinduced electron transfer (PET) and inhibition of excited state intramolecular proton transfer (ESIPT) as well as intramolecular charge transfer (ICT) within the molecule. But in presence of Hg2+, QBE showed a sharp increase in fluorescence intensity by 18-fold at wavelength 444 nm with high quantum yield (? = 0.159) for the chelation-enhanced fluorescence (CHEF) with coordination of Hg2+, which hampers PET within the molecule. The strong binding affinity of QBE towards Hg2+ has been proved by lower detection limit at 8.47 M and high binding constant value as 2 104 M?1. The binding mechanism has been verified by DFT study, Cyclic voltammograms and Jobs plot analysis. For the practical application, the binding selectivity of QBE with Hg2+ has been capitalized in physiological medium to detect intracellular Hg2+ levels in living plant tissue by using green gram seeds. Thus, employing QBE as a fluorescent chemosensor for the specific identification of Hg2+ will pave the way for a novel approach to simplifying the creation of various chemosensors based on quercetin backbone for the precise detection of various biologically significant analytes. 2024 Elsevier B.V. -
A dual-functional rhodamine B and azo-salicylaldehyde derivative for the simultaneous detection of copper and hypochlorite: synthesis, biological applications and theoretical insights
A multifunctional rhodamine derivative containing azo-salicylaldehyde (BBS) was designed and synthesized as a colorimetric and fluorescence turn-on probe for the selective detection of copper cations (Cu2+) and hypochlorite anions (OCl?) in aqueous media. In the presence of Cu2+, the probe BBS exhibited turn-on absorption and fluorescence change at 554 nm and 585 nm, respectively. The binding mechanism of BBS with Cu2+ induces the opening of a spirolactam ring in the rhodamine moiety by the formation of a metal-ligand complex, achieving 10-fold enhancement in fluorescence and quantum yield, along with a binding constant of 1 104 M?1 and a detection limit of 2.61 ?M. Addition of OCl? enhanced the absorbance and fluorescence intensities at 520 nm and 575 nm, respectively. The probe BBS underwent hypochlorite-mediated oxidation, followed by hydrolysis, resulting in the formation of rhodamine B itself, which is detectable by the naked eye via the color and fluorescence enhancement by 11-fold with a high quantum yield and a detection limit of 1.96 ?M. For practical applications, sensor BBS can be used to detect Cu2+ in water samples and on cotton swabs. For biological applications, the interaction of the BBS-Cu(ii) complex with transport proteins such as bovine serum albumin (BSA) and ct-DNA was investigated through UV-vis and fluorescence titration experiments. Additionally, the structural optimization of BBS and the BBS-Cu(ii) complex was demonstrated using DFT, and the interactions of the BBS-Cu(ii) complex with BSA and ct-DNA were analysed through theoretical docking studies. Bioimaging studies were conducted by capturing fluorescence images of BBS with Cu2+ and OCl? in a physiological medium containing living plant tissue using green gram seeds. 2024 The Royal Society of Chemistry. -
Strategic framework to analyze critical success factors of marketing 4.0 operations: evidence from an emerging economy
Purpose This research study aims to develop a strategic framework that identifies, classifies and prioritizes the critical success factors (CSFs) essential for implementing Marketing 4.0 in emerging economies. It seeks to bridge the gap between theoretical discourse and operational realities in digitally transforming markets. Design/methodology/approach Following an elaborate review of the extant literature, 38 such CSFs emerged, which were then segmented using principal component analysis into seven relevant dimensions. The constructs were validated using confirmatory factor analysis (CFA). Thereafter, the fuzzy-decision-making trial and evaluation laboratory (DEMATEL) was used to map the cause-and-effect relationship among the reduced component factors. Findings The results demonstrate that digital marketing (S2), channel cohesiveness (S3), driving technologies (S5) and mutual value proposition (S7) are found to be cause factors, while customer engagement (S1), market dynamism (S4) and strategic marketing (S6) are identified as effect factors. Originality/value The novelty of this study is embedded in integrating multianalytical approaches like principal component analysis, CFA and Fuzzy-DEMATEL to empirically validate and rank the CSFs of Marketing 4.0. This study also extends the theoretical understanding of Marketing 4.0 by aligning critical enablers with the dynamics of emerging markets. 2026 Emerald Publishing Limited -
Marketing intelligence for intelligent marketing: a comprehensive definition and framework
Purpose This study aims to formalize a comprehensive, contemporary and holistic definition of marketing intelligence (MI) by mapping the relevant literature of the last 60years from 19622022. Design/methodology/approach This study centralizes around the qualitative assessment of explored definitions by using thematic analysis. By identifying patterns and themes within the qualitative dataset, thematic analysis provides accessible and systematic procedures for generating codes and themes by discovering commonalities and contradictions in keywords. Findings The result of study 1 identified 127 papers indexed in databases distributed across 56 different journals. The manual screening of 127 research papers resulted in the selection of 15 definitions of MI. In study 2, thematic analysis was applied to these definitions through author-attribute mapping and assessment of weights, resulting in the identification of 25 key attributes representing five emerging themes. Study 3 identifies the gaps in definitions and proposes a comprehensive and complete definition of MI. Originality/value This researchs novelty corresponds to author-attribute mapping, exploring gaps in existing definitions and proposing a holistic and contemporary definition of MI. 2025 Emerald Publishing Limited -
Myths and magic of adopting marketing technologies: acustomer-centric framework
Purpose This study seeks to explore how customers perceptions of the benefits, risks and continuation intention toward Marketing Technologies (MarTech) influence their actual usage. This research study utilizes the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework for assessing the usage and continuation intention (intention behavior) of customers toward adopting marketing technologies. Design/methodology/approach Data was collected from 266 respondents and analyzed using partial least squares structural equation modeling (PLS-SEM). A post-hoc analysis was undertaken using importance-performance matrix analysis (IPMA) to assess the importance and performance of determinants in the PLS-SEM model. Findings The results of the study indicate that perceived usefulness, perceived enjoyment and social influence have positive effects on Marketing Technologies (MarTech) usage intention. Additionally, usage intention has no effect on continuation intention. The performance of Perceived Usefulness is 74.879, which is higher than other constructs and ascertains that perceived usefulness contributes largely to predicting MarTech continuation intention. Originality/value This study enriches the technology adoption literature by investigating the predictors of marketing technologies (MarTech) adoption and usage from the perspective of customers. Notably, the novelty of this research lies in investigating the impact of perceived trust and perceived risk on MarTech usage and continuation intention of customers. 2025 Emerald Publishing Limited -
What drives Generation Z to choose green apparel? Unraveling the impact of environmental knowledge, altruism and perceived innovativeness
This study proposes to determine the influence of Environmental Knowledge (EK), Altruism (Atr), Consumer Confidence (CC) and constructs of Theory of Planned Behaviour (TPB) like Attitude (Atd), Subjective Norm (Sub) and Perceived behavioural control (Pbhc) on consumers intention to purchase Green Apparel Products (GAPI). Moreover, the moderating effect of Perceived Innovativeness (PInn) on the relationship between Attitude (Atd), Subjective Norm (Sub), Perceived behavioural control (Pbhc), EK, Atr and CC was studied. To test the research model and hypothesis, a survey of 349 Generation Z consumers (1826 years) was conducted. Cronbachs alpha and a Confirmatory Factor Analysis (CFA) were used to determine the scales reliability and validity. Structural Equation Modelling (SEM) validated the given model and hypotheses. In this research, six hypotheses were tested, and it was found that three hypotheses showed a direct relationship. Specifically, the result of SEM showed that Atd, Sub and CC were positively related to GAPI. Also, six hypotheses were formulated testing the moderating role of PInn. The results established that PInn moderated the relationship between Atd, Sub, CC and GAPI significantly. This research provides a novel framework to explore the relationship between the EK, Atr and CC and Generation Z consumers GAPI. 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. -
Construct modelling, statistical analysis and empirical validation using PLS-SEM: a step-by-step guide of the analysis procedure
Partial least square-structured equation modelling (PLS-SEM) is a widely accepted tool for statistical analysis in social science research. The complex architecture of PLS-SEM sometimes makes it difficult for users to understand the taxonomy, nomenclature, or process of statistical analysis. This research study proposes summarising the procedure adopted in PLS-SEM for data analysis. Measurement evaluation and structural model was the subject of discussion, with a focus on the statistical techniques employed. Furthermore, the threshold values corresponding to statistical tools under measurement and structural model were also provided. The inference of these threshold values were also discussed with an eye on improving researchers awareness and understanding. The discussion about the methodology adopted in statistical analysis with the help of PLS-SEM is also reported. Finally, the limitations of the research work were presented, and further study directions were streamlined. 2024 Inderscience Enterprises Ltd. -
Improvising data security measures using rajan transform
Data security has always been a concern with the use of a large amount of data in our day-to-day life. There are many methods suggested and presented to secure data during the stages of its preprocessing and post-processing. However, many of them are not following the process of Homomorphism. During the study of Fast Fourier transform (FFT), Hadamard transform (HT) and Rajan transform (RT), this research work encountered a method that uses the cyclic, dyadic and graphical inverse properties of data and encrypts them which makes them homomorphic. This paper is targeting to improvise the data security measures using Homomorphism-based Rajan Transform, a method, which can help in securing data while data processing. The proposed methodology works in such a way that the encrypted data is available for processing without decrypting data into the original form. The performance of the proposed method is described by the efficiency of the algorithm, key size, Block size, and no of rounds required to complete the encryption. It has been found, if we take 512 bits of input data to get 512-bit ciphertext, it takes 9 rounds and generates a 4608-bit key. 2021 Taylor's University. All rights reserved. -
Eye blink detection using CNN to detect drowsiness level in drivers for road safety
Blinking is a regular bodily function and it is the semiautomatic fast closing of the eyelid. A specific blink is examined by dynamic folding of the eyelid. It is a vital function of the eye which helps in spread of tears across and eliminates irritants from the shallow of cornea. In this research work we made use of convolution neural network, the deep learning concepts and image processing to detect drowsiness level in drivers. To train the blink detection model the mobilenet V2 is used as base. The loss function used for training was RMSprop and the optimizer is binary cross entropy. The dlib facial landmark was exploited to perceive and pre-process the detected faces. The dataset used for the training model is selected from the Xiaoyang Tan of nanjing university of aeronautics and astronautics. Based on the experimental outcome the projected method achieves an accuracy of 97%. The prototype developed serves as a base for further development of this process to achieve better road safety. 2021 Institute of Advanced Engineering and Science. All rights reserved. -
Railway Track Crack Detection: A Comparative Study On Yolov7 And U-Net In Automated Inspection
For railway networks to remain operationally safe and avoid catastrophic failures, structural integrity is essential. Track cracks can be found using labor-intensive, slow, and human error-prone manual inspection techniques. In this work, two cutting-edge deep learning models - YOLOv8 andU-Net v2 - for automated railway track crack detection using high-resolution imagery from Unmanned Aerial Vehicles (UAVs) are compared. In a real-world inspection scenario, we compare the different strategies of precise semantic segmentation (U-Net) and real-time object detection (YOLOv8) in order to assess their relative trade-offs. We compare performance on important metrics such as precision, recall, intersection over union (IoU), and inference speed using a custom dataset that was taken by a DJI Matrice 300 RTK drone. This work is novel because it examines how each model's output - bounding boxes versus pixel-level masks - directly affects the usefulness for maintenance workflows from an application-focused perspective. According to our research, U-Net v2 offers the fine-grained information required for precise damage assessment, while YOLOv8 is best suited for quick, extensive screening. This study offers railway operators useful information for creating a multi-stage, hybrid inspection strategy that strikes a balance between accuracy and speed. 2025 IEEE. -
A Novel Machine Learning Approach for Tuberculosis Detection using Volatile Organic Compounds
For world health, better TB diagnosis is still absolutely necessary. Using VOC Atlas, this study assesses a few machine learning techniques for categorizing breath samples depending on volatile organic compound (VOC) profiles. We created a machine learning pipeline and tried out four different models: Random Forest, XGBoost, Multi-Layer Perceptron (MLP), and a 1D-Convolutional Neural Network (1D-CNN). There were 1,500 patient profiles in the dataset spanning three groups: healthy people, drug-sensitive TB cases, and multidrug-resistant TB cases. Using VOC biomarker patterns found in VOC Atlas and prior TB research, these profiles were developed. While XGBoost stood out by reaching 100% accuracy, our studies revealed that most models performed rather well. This implies that gradient boosting-based ensemble models can adequately grasp the complex patterns found in breath data. One major caveat is that we have not tested these models on real clinical breath samples to validate them. Testing these models with actual patient samples in clinical settings would be the next reasonable step. All told, this research provides a strong basis for creating non-invasive ways to detect illnesses. 2025 IEEE. -
Engineering a low-cost diatomite with Zn-Mg-Al Layered triple hydroxide (LTH) adsorbents for the effectual removal of Congo red: Studies on batch adsorption, mechanism, high selectivity, and desorption
In this work, naturally occurring, low-cost diatomite (De) or diatomaceous earth (DE) adsorbent was treated with various molar concentrations (0.05, 0.1, and 0.2 M) of Zn-Mg-Al layered triple hydroxide (LTH) using a co-precipitation approach. The DE-modified samples were named 0.05 LDE, 0.1 LDE, and 0.2 LDE and employed to remove Congo Red (CR) dye from an aqueous solution. The adsorbents were examined using XRD, BET-N2 adsorption-desorption method, ATR-IR, FESEM-EDX, and XPS, and also analyzed for zeta potentials of adsorbents at pH values between 5 and 11 to observe their surface charges. The removal efficiencies of CR were 96.5%, 99.7%, and 94.5% for 20 mg of 0.05 LDE, 0.1 LDE, and 0.2 LDE, respectively, at pH 7. A bare DE, however, showed a removal efficiency of only 7.4%. After CR adsorption, the maximum adsorption capacities (qmax) of the adsorbents were examined using the Langmuir isotherm, and the results showed that 0.1 LDE-CR (44.0 mgg?1) had a higher qmax than 0.05 LDE-CR (35.6 mgg?1), 0.2 LDE-CR (27.9 mgg?1), and DE-CR (0.9 mgg?1). The optimal adsorbent of 0.1 LDE was utilized for the selectivity and salt effects based on the investigation's efficiency in removing contaminants. 0.1 LDE has been studied for reusability of up to five cycles and can remove CR up to three cycles with 77.7% and 79.9% efficiency using NaCl and NaOH, respectively. The adsorbents may therefore be particularly effective at removing CR from water and beneficial in industrial settings where dye is often used. 2023 Elsevier B.V. -
RADON in GROUNDWATER of MAGADI TALUK, RAMANAGARA DISTRICT in KARNATAKA
Radon is a water-soluble radioactive noble gas produced from the alpha decay of 226Ra in uranium series. Its presence in drinking water and open air increases the risk of lung and intestinal cancers in human beings. In view of this, radon concentration in groundwater and its dose due to inhalation and ingestion to the population of Magadi taluk of Ramanagara district in Karnataka state, India was studied. The groundwater samples were analyzed for radon concentration using emanometry technique. The study showed that the radon concentration in this area varied from 27.4 1.0 to 167.5 3.9 Bq/L and the effective dose ranged from 104.2 2.7 to 636.2 11.0 ?Sv/a. The study also revealed that 95% of the 37 samples studied showed higher radon concentration compared to the UNSCEAR recommendation (40 Bq/L) and all the samples showed higher than the USEPA recommendation (11.1 Bq/L). Ten samples have concentration above the maximum permissible level prescribed by WHO (100 Bq/L). The groundwater samples are found to be slightly alkaline within the permissible limit of Indian Standards. 2018 The Author(s). Published by Oxford University Press. All rights reserved. -
Applying Ensemble Techniques for the Prediction of Alcoholic Liver Cirrhosis
More than fifty percent of all liver cognate deaths are caused by alcoholic liver disease (ALD). Excessive drinking over the time leads to alcohol-related steatohepatitis and fatty liver, this in turn can lead to alcoholic liver fibrosis (ALF) and in due course alcohol-related liver cirrhosis (ALC). Detecting ALD at an early stage will reduce the treatment cost to the patient and reduce mortality. In this research, a two-step model is developed for predicting the liver cirrhosis using different ensemble classifiers. Among 41 features recorded during data collection, only 15 features arefound to be effective determinants of the class variable. The proposed stacked ensemble technique for ALD prediction is compared with other ensemble models such as random forest, AdaBoost, and bagging. Through experimentation, it is observed that the proposed model with XGBoost and decision tree as base models and logistic regression as Meta model exhibits prediction accuracy of 93.86%. The prediction accuracy of theproposed stacked ensemble technique is 0.2% better in prediction accuracy and 0.3% reduced error rate in comparison with random forest classifier. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte 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. -
Design and Evaluation of Wi-Fi Offloading Mechanism in Heterogeneous Networks
In recent years, WiFi offloading provides a potential solution for improving ad hoc network performance along with cellular network. This paper reviews the different offloading techniques that are implemented in various applications. In disaster management applications, the cellular network is not optimal for existing case studies because the lack of infrastructure. MANET Wi-Fi offloading (MWO) is one of the potential solutions for offloading cellular traffic. This word combines the cellular network with mobile ad hoc network by implementing the technique of Wi-Fi offloading. Based on the applications requirements the offloading techniques implemented into mobile-to-mobile (M-M), mobile-to-cellular (M-C), mobile-to-AP (M-AP). It serves more reliability, congestion eliminated, increasing data rate, and high network performance. The authors also identified the issue while implementing the offloading techniques in network. Finally, this paper achieved the better performance results compared to existing approaches implemented in disaster management. Copyright 2021, IGI Global. -
Secure biometric authentication with de-duplication on distributed cloud storage
Cloud computing is one of the evolving fields of technology, which allows storage, access of data, programs, and their execution over the internet with offering a variety of information related services. With cloud information services, it is essential for information to be saved securely and to be distributed safely across numerous users. Cloud information storage has suffered from issues related to information integrity, data security, and information access by unauthenticated users. The distribution and storage of data among several users are highly scalable and cost-efficient but results in data redundancy and security issues. In this article, a biometric authentication scheme is proposed for the requested users to give access permission in a cloud-distributed environment and, at the same time, alleviate data redundancy. To achieve this, a cryptographic technique is used by service providers to generate the bio-key for authentication, which will be accessible only to authenticated users. A Gabor filter with distributed security and encryption using XOR operations is used to generate the proposed bio-key (biometric generated key) and avoid data deduplication in the cloud, ensuring avoidance of data redundancy and security. The proposed method is compared with existing algorithms, such as convergent encryption (CE), leakage resilient (LR), randomized convergent encryption (RCE), secure de-duplication scheme (SDS), to evaluate the de-duplication performance. Our comparative analysis shows that our proposed scheme results in smaller computation and communication costs than existing schemes. 2021 M et al. All Rights Reserved. -
Stability and Effciency Enhancement of Perovskite Solar Cells
The greatest notable efficiency increases in recent years have been observed in perovskite solar cells (PSCs). With an ABX3 crystal structure, perovskite is an organic-inorganic hybrid chemical that generally has an arrangement similar to that of BaTiO3-. In this configuration, X stands for halogens, such as oxygen (O), iodide (I?), bromide (Br?), or chloride (Cl?), while A and B are variously sized cations that coordinate 12-fold and 6-fold, respectively, with X anions. Cations such as formamidine and methylammonium alter the lattice parameters, with the bandgap growing as the lattice parameters increase, but they have no direct effect on the valence band maxima. Comparable to the body-centered cubic lattice with extra anions on a unit cell's faces is the ideal perovskite structure. To achieve high power conversion efficiency (PCE), perovskite absorbers and PSC device topologies must have high charge. Consequently, increasing electron mobility, prolonging carrier life span, and lowering defect density all depend on improving the perovskite absorber's material quality. 2026 selection and editorial matter, T.D. Subash, J. Ajayan, and Leong Wai Yie; individual chapters, the contributors. -
AI-Powered Botnet Detection Systems: A Critical Review of Current Approaches and Challenges
In the era of information technology, Botnets have become the most persistent cyber threat, capable of launching large-scale attacks like Distributed Denial of Service. stealing sensitive information and disturbing online services. Botnets have evolved from simple networks to complicated distributed networks including IoT devices, making them pervasive, harder to track, and destroy. Machine learning and Deep learning based models are emerging to detect bot attacks by analyzing large datasets and detecting patterns and anomalies. The state of the art methodologies for detecting bot infection are reviewed deeply and compared based on adopted methodologies, dataset and feature selection mechanism. The paper further discusses the pros and cons of existing methodologies. Finally, research gaps are presented to help future research on enhancing bot detection. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Machine Learning Approaches for Detection of Cyberbullying in Virtual Space
Cyberbullying, hostile behavior of a group or an individual to defame or harass the victim mentally with the help of social media and other e-communication platforms, has the potential to create a lifelong negative impact on mental health with the power of inducing suicidal thoughts. It is on the rise among the early adolescents of the age group from 8 to 16. Hence it is vital to detect Cyberbullying at an early stage to safeguard the victims at the high risk of developing depression, anxiety, and suicidal ideas. It also helps to mitigate psychological, academic, and social consequences. Existing cyberbullying detection approaches primarily depend on static monolingual questionnaires and are not personalised. With the developments in Artificial Intelligence, many neural network-based approaches are explored to detect cyberbullying. This study discusses and provides comparative analysis of various machine learning approaches for detecting cyberbullying victimization among school students highlighting their effectiveness and limitations. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.