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Functionalized Nanomaterials in Nanobiomedicines and Diagnostic Devices
The development of nanomaterials for biomedical applications over the last 20 years has shown great potential for altering the way that all facets of illness management are thought to be approached. Since nanomaterials may be customized to incorporate features for early diagnosis, medication delivery, therapy, treatment, and patient response monitoring, they are particularly appealing as a system. The realm of functionalized nanomaterials is characterized by its diversity and rapid evolution, with ongoing research endeavors focused on discovering novel functionalities and applications for nanoscale materials. Functionalized nanomaterials play a pivotal role in nanobiomedicines and diagnostic devices, contributing significantly to advancements in these fields. The use of functionalized nanomaterials varies from cancer therapy, drug delivery, cosmetics, and medical use to various other entities. Therefore, this chapter reviews the fundamentals of nanomaterials fictionalization, surface fictionalization, and different nanomaterials that are contributing to drug delivery and diagnostic devices. Springer Nature Singapore Pte Ltd. 2025. -
An Efficient Multi-Modal Classification Approach for Disaster-related Tweets
Owing to the unanticipated and thereby treacherous nature of disasters, it is essential to gather necessary information and data regarding the same on an urgent basis; this helps to get a detailed overview of the situation and helps humanitarian organizations prioritize their tasks. In this paper, "An Efficient Multi-Modal Classification Approach for Disaster-related Tweets,"the proposed framework based on Deep Learning to classify disaster-related tweets by analyzing text and image contents. The approach is based on Gated Recurrent Unit (GRU) and GloVe Embedding for text classification and VGG-16 network for image classification. Finally, a combined model is proposed using both text and image modules by the Late Fusion Technique. This portrays that the proposed multi-modal system performs significantly well in classifying disaster-related content. 2022 IEEE. -
Tracing the outer disk of NGC300: An ultraviolet view
We present an ultra-violet (UV) study of the galaxy NGC300 using GALEX far-UV (FUV) and near-UV (NUV) observations. We studied the nature of UV emission in the galaxy and correlated it with optical, HI and mid-infrared (3.6 ?m) wavelengths. Our study identified extended features in the outer disk, with the UV disk extending up to a radius of 12 kpc (> 2 R25). We estimated the FUV and NUV disk scale-length as 3.05 0.27 kpc and 2.66 0.20 kpc respectively. The scale-length in FUV is 2.3 times larger than that at 3.6 ?m, and we also find the disk to gradually become flatter from longer to shorter wavelengths. We performed a statistical source subtraction to eliminate the background contaminants and identified 261 unresolved UV sources between the radii 5.3 kpc and 10 kpc (1 ? 2 R25). The identified UV sources show an age range between 1300 Myr with a peak at 25 Myr and a mass range between 10 3M? to 10 6M?, estimated using Starburst99 models. The north-eastern spiral arm is found to be populated by young low mass sources suggesting that the star formation in this spiral arm is a recent phenomenon. The UV emission beyond the R25 radius has contribution from these low mass sources and is extended up to ? 2 R25 radius. We conclude that NGC300 has an extended UV disk, mainly populated by young low mass sources. The star formation rate is measured to be ?0.46M?/yr which is comparable to its near optical twin M33. 2019, Indian Academy of Sciences. -
Categorization of artwork images based on painters using CNN
Artworks and paintings has been an integral part of human civilization since the dawn of the Stone Age. Paintings gives more insight about any subject compared to the scriptures and documents. Archiving of digital form of paintings helps to preserve the artworks of different painters. The anticipated work is aimed for the classification of painters' artworks. The artworks of Foreign & Indian painters are considered for the proposed work. The foreign painters' artworks are obtained from [14]. At present, the Indian painters' artwork dataset is not readily available. The images were downloaded from the specific genuine website [13]. Conventional Neural Network is used for Feature learning and classification. Around 20k images of artworks is used for the experiment and got an average accuracy of 85.05%. Published under licence by IOP Publishing Ltd. -
Interpenetrated Robust Metal-Organic Framework with Urea-Functionality-Decked Pores for Selective and Ultrasensitive Detection of Antibiotics and Oxo-anions
Conjoining the benefits of structural diversity and deliberate implantation of task-specific sites inside the porous channels, metal-organic frameworks (MOFs) not only ensure environmental remediation via acute detection of organic as well as inorganic pollutants but also rationalize structure-performance synergies to devise smarter materials with advanced performances. Herein, we report a urea-functionality-grafted Co(II)-framework (UMOF) based on a mixed ligand approach. The 3-fold interpenetrated and [Co2(COO)4N4] building unit-containing structure exhibits high stability and free-carboxamide-site-decorated microporous channels. Assimilation of high-density hydrogen-bond donor groups plus the ?-electron-rich aromatic ligand benefits the UMOF acting as a selective fluoro-sensor for three noxious antibiotics through remarkable quenching, including nitrofurazone (NFT, Ksv: 3.2 104 M-1), nitrofurantoin (NFZ, Ksv: 3.0 104 M-1), and sulfamethazine (SMZ, Ksv: 3.3 104 M-1) with ppb level limits of detection (LODs, NFT: 110.42, NFZ: 97.89, and SMZ: 78.77). The mechanistic insight of luminescence quenching is supported from density functional theory calculations, which endorse the electron-transfer route via portraying variation in the energy levels of the urea group-affixed linker by individual organo-toxins, besides verifying analyte-linker noncovalent interactions. The framework further demonstrates highly discriminative turn-off detection of oxo-anions with extreme low LODs (Cr2O72-: 73.35; CrO42-: 189; and MnO4-: 49.96 ppb). Of note is the reusability of the UMOF toward multicyclic sensing of all the organic and inorganic analytes besides their fast-responsive detection, where variable magnitudes of energy-transfer contributions unequivocally authenticate the turn-off event. 2023 American Chemical Society. -
Spectral and temporal studies of Swift J1658.24242 using AstroSat observations with the JeTCAF model
We present the X-ray spectral and temporal analysis of the black hole X-ray transient Swift J1658.2-4242 observed by AstroSat. Three epochs of data have been analysed using the JeTCAF model to estimate the mass accretion rates and to understand the geometry of the flow. The best-fitting disc mass accretion rate (? d) varies between 0.90+-000102 and 1.09+-000304 M?Edd in these observations, while the halo mass accretion rate changes from 0.15+-000101 to 0.25+-000102 M?Edd. We estimate the size of the dynamic corona that varies substantially from 64.9+-3319 to 34.5+-1250 rg and a moderately high jet/outflow collimation factor stipulates isotropic outflow. The inferred high disc mass accretion rate and bigger corona size indicate that the source might be in the intermediate to soft spectral state of black hole X-ray binaries. The mass of the black hole estimated from different model combinations is ?14 M?. In addition, we compute the quasi-periodic oscillation (QPO) frequencies from the model-fitted parameters, which match the observed QPOs. We further calculate the binary parameters of the system from the decay profile of the light curve and the spectral parameters. The estimated orbital period of the system is 4.0 0.4 h by assuming the companion as a mid or late K-type star. Our analysis using the JeTCAF model sheds light on the physical origin of the spectrotemporal behaviour of the source, and the observed properties are mainly due to the change in both the mass accretion rates and absorbing column density. 2023 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society. -
Two-dimensional chromium telluride-coated 3D-printed architectures for energy harvesting
Rapid development of industries, urbanization, and technological advancements have increased demand for sustainable and cost-effective alternative energy sources. In this work, a self-powered flexible 3D-printed triboelectric nanogenerator coated with 2D chromium telluride (Cr2Te3) (3D-TENG) is presented as an innovative energy harvesting approach from pressure and temperature. The optimized flexible 3D-printed hexagonal structures with coatings show varying specific yield strength and porosity. The 3D-TENGs achieved a maximum output voltage of ?39 V under periodic impacts of ~0.8 kPa and their performance further increased (?45 V) in the presence of varied temperatures. The outstanding results and flexibility of the 3D-TENG devices highlight their potential in self-powered energy harvesting from external heat, magnetic fields, and body weight. Density functional theory (DFT) calculations further explained the interaction between 2D Cr2Te3 and the polymer surface under external impact. Therefore, we believe that our findings illustrate the potential of integrating 2D materials with 3D-printed architectures to enhance the efficiency and adaptability of flexible, lightweight, low-cost, and eco-friendly TENG devices for industrial applications. 2025 The Royal Society of Chemistry. -
Exploring cybernetic approaches to sustainable co-working spaces in emerging economies: a sentiment analysis
Purpose In quest of achieving long-term sustainability of co-working spaces (CWSs) and drawing on the cybernetic principles, this study aims to develop a resilient business model promoting economic viability, encouraging environmental responsibility and reinforcing its social impact. Furthermore, to address the transformative shift in way people work in emerging economies, this study probed respondents from India and United Arab Emirates (UAE) and finally identified critical challenges and opportunities bringing in maximum customer satisfaction and achieving long-term business profitability. Design/methodology/approach Using a multi-method qualitative triangulation approach (sentiment analysis), the study collected primary data from India and UAE, analysed through the grounded theory approach. Whereas secondary data in form of tweets was tested using text-mining approach using NVivo. The findings from the dual study were corroborated to identify common dimensions, leading to the development of a hypothetical framework. Findings In CWSs business, dynamic organisation culture holds key in fostering future sustainability, and the study has explored its important antecedents like adaptive management, continuous innovation and technological integration. The impact of these antecedents was found to be moderated by two critical dimensions of regulatory challenges and competitive landscape. Furthermore, the study delved into connecting with the principles of circular economy moderating the impact of dynamic organisation culture towards long-term sustainability of CWSs. Practical implications This study applies cybernetic principles alongside shared and circular economy frameworks to assess consumer perceptions of CWSs. The insights generated can guide researchers, entrepreneurs, urban planners and policymakers in designing flexible business models, strengthening community networks and exploring diverse revenue streams to enhance resilience and long-term growth. Originality/value This research provides empirical evidence on the sustainability dynamics of CWSs, offering a balanced perspective on overcoming challenges and leveraging growth opportunities. Additionally, it bridges the concepts of cybernetics, shared economy and circular economy, presenting a novel framework for ensuring the sustainable development of CWS businesses. 2025 Emerald Publishing Limited -
Fostering cultural vitality and enhancing sustainable urban tourism through international labelling: anethno-morphological exploration
Purpose Cultural Vitality (CV) nurtures creativity, enhances engagement with the local community and supports the art and culture of the place. In quest of exploring the impact of International Labelling (IL) in enhancing CV and reinforcing Sustainable Development (SD), the study pursues a comprehensive Morphological Analysis (MA) and builds a hypothetical framework bridging culture-driven urban tourism with sustainable growth. Design/methodology/approach An ethno-morphological study based on the Durga Puja Festival (India) was adopted to identify the critical dimensions of culture-driven urban tourism. A series of in-depth interviews and cross-sectional studies were carried out to develop a hypothetical research framework for empirical (quantitative) validation thereon. Findings Critical dimensions such as Cultural Investment, Collaborative Partnership and Embracing Glocal Approach were identified as major constructs towards achieving CV of a destination. IL and Responsible Consumption were found to moderate the effect of antecedents and CV upon sustainable growth. Research limitations/implications The present research limits its scope to the geographical boundary within India, keeping cross-boundary research for future study. This study will aid future researchers and scholars in expanding the domain of culture-driven urban tourism. Practical implications The present study bears significance to the urban policymakers, governing bodies, marketers and tour operators in embracing a culture-driven perspective while undertaking a suitable strategy towards developing CV, promoting urban-tourism attributes and vis-vis ensuring its SD. Originality/value This study makes a novel attempt at adopting an ethno-morphological approach and blending culture-driven tourism with sustainable growth while exploring the impact of IL, all together in a research initiative, making it a single-point reference in urban tourism literature. Emerald Publishing Limited -
Investigation on Electrode/Electrolyte Interfaces through Impedance Spectroscopy
In the present paper, impedance measurements of the battery configuration, Anode?lithium borophosphate glass electrolyte?LiCoO2 cathode, has been carried out to throw some light on the electrochemical interfacial behavior between the chosen electrodes and electrolyte. The cathode material, lithium cobalt oxide (LiCoO2) has been prepared by three different techniques and characterized. Sol-gel synthesized LiCoO2 showed uniformly distributed spherical shape particles with an average size of 500 nm and also exhibited better electrochemical performance. Charging and discharging (23 cycles) of the battery indicated an OCV of 2 V. However, the theoretical OCV of 4 V could not be achieved. The poor performance of the battery could be attributed to the electrochemical processes and SEI film formation at the electrode/electrolyte interfaces. Impedance spectroscopy shows that the major contributions to the impedance of the battery are the electrolyte resistance and the electrode/electrolyte interfacial resistance. With each recharging cycle, the value of electrolyte resistance remains almost constant; however, the interface resistance increases, during the passage of current, due to the interfacial passive layer formation. 2020 Taylor & Francis Group, LLC. -
Secure Decentralization: Examining the Role of Blockchain in Network Security
Blockchain generation has emerged as a novel answer for securing decentralized networks. This technology, which was first created for use in crypto currencies, has received enormous interest in recent years because of its capability for boosting protection in various industries and community protection. The essential precept at the back of block chain technology is the decentralization of statistics garage and control. In a decentralized network, no central authority may control the statistics. Rather, the facts are shipped amongst multiple nodes, making it immune to tampering and single factors of failure. One of the most important advantages of blockchain in community protection is its capacity to offer cozy and transparent communication amongst community customers. Through cryptographic techniques, block chain can affirm the identities of network participants and ensure the authenticity of records trade. This feature is extraordinarily valuable in preventing unauthorized access and facts manipulation. 2024 IEEE. -
Entrepreneurial Attitude and Entrepreneurial Intentions of Female Engineering Students: Mediating Roles of Passion and Creativity
Entrepreneurship holds a crucial function in addressing societal and economic issues like joblessness and inequalities between different regions. Acknowledging its significance, government officials and educational institutions exert considerable energy towards nurturing individuals into entrepreneurs. Multiple elements influence a person's path to becoming an entrepreneur. This research seeks to examine how one's entrepreneurial attitude (EA) impacts one's drive to become an entrepreneur, with passion and creativity serving as an intermediary in this connection. The research is explanatory and employs a survey-based approach. The findings convey that entrepreneurial attitude significantly influences the determination of female engineering students to pursue entrepreneurship. The study highlights the mediating roles of passion and creativity in the relationship between entrepreneurial attitude and intentions. While passion positively mediated the relationship, creativity had a negative mediating effect. 2024, Institute of Economic Sciences. All rights reserved. -
Nexus between Entrepreneurial Education, Entrepreneurial Mindset, and Entrepreneurial Passion on Entrepreneurial Intentions: Mediating Role of Self-efficacy
This study examines the complex dynamics of factors affecting self-efficacy (SE) and entrepreneurial intentions (EIs) among engineering students in India. It investigates the mediating role of SE in the relationships between entrepreneurial education (EE), entrepreneurial mindset (EM), entrepreneurial passion (EP), and EIs. The research reveals that SE remains stable across various personal characteristics, highlighting it as a robust individual trait less influenced by external factors. Gender significantly impacts EIs, underscoring its pivotal role in shaping entrepreneurial intentions, while other personal characteristics show limited influence. Passion and mindset appear to be consistent across demographics, suggesting they are intrinsic qualities. SE serves as a mediator in the connections between entrepreneurial mindset, passion, and intentions, elucidating its pivotal role in the entrepreneurial process. EE indirectly affects EIs and SE through other factors in the research model. Entrepreneurial passion directly influences both EIs and SE, emphasizing its role as a driving force for entrepreneurship. An entrepreneurial mindset doesn't directly affect intentions but significantly influences SE, indicating its importance in shaping self-efficacy, which in turn influences intentions. The findings can guide the development of educational programs and initiatives designed to promote entrepreneurship among engineering students in India while considering the impact of self-efficacy and gender-related factors. 2024, Iquz Galaxy Publisher. All rights reserved. -
Antecedents of Ethical Goods and Services Tax Culture among young adults - Special Reference to Maharashtra and Karnataka
Since the implementation of the Goods and Services Tax (GST) in 2017, it has become clear that this new Indian indirect tax system is here to stay. The Indian GST Council is continuously deliberating and making efforts to improve GST revenue collection at the state and central levels. The focus is now on the young adults in the country who will play a vital role in shaping the future of GST compliance. Their tax mentality and behaviour in contributing to GST revenue as daily consumers will determine the ethical tax culture in India. They need to understand how crucial their role is in discouraging evasive practices by sellers in the unorganised retail sector at the point of sale. The study utilized structural equation modelling to test the acceptability of the model. The process was supported by a structured questionnaire, with 324 respondents between the age group of 17-30 years. Understanding GST significantly influences acceptance of GST as a tax system, however, the acceptance of the GST tax system does not significantly lead to young adults discouraging the evasive behaviour of sellers in the unorganised retail sector at the point of sale. And, finally, the discouragement of evasive behaviour by young adults does influence the possibility of an ethical GST tax culture. The respondents majorly represented young adults between 17-20 years of age. The model has not measured the existence of covariance among the variables, nor has any mediating or moderating factors been identified, as GST tax culture in the Indian context is still unexplored and GST in itself is relatively new in the country. 2024 IEEE. -
Artificial intelligence and service marketing innovation
The integration of artificial intelligence (AI) into service marketing in India is expected to significantly impact marketing strategies and economic dynamics. The emphasis on personalization, automation, predictive analytics, and chatbots will enhance customer engagement and brand loyalty, leading to increased sales and revenue. Automation of marketing workflows will streamline operations, improve efficiency, and foster business growth. AI's predictive analytics capabilities will help businesses make informed decisions about their marketing strategies, particularly in a diverse market like India. AI-driven chatbots will enhance customer satisfaction and engagement, contributing to positive brand perception and loyalty. However, there may be concerns about job displacement, particularly in routine tasks. The growth of AI-driven service marketing can contribute to the development of a technologydriven ecosystem in India, attracting investments, fostering entrepreneurship, and stimulating innovation. 2024 by IGI Global. All rights reserved. -
Face and Emotion Recognition from Real-Time Facial Expressions Using Deep Learning Algorithms
Emotions are faster than words in the field of humancomputer interaction. Identifying human facial expressions can be performed by a multimodal approach that includes body language, gestures, speech, and facial expressions. This paper throws light on emotion recognition via facial expressions, as the face is the basic index of expressing our emotions. Though emotions are universal, they have a slight variation from one person to another. Hence, the proposed model first detects the face using histogram of gradients (HOG) recognized by deep learning algorithms such as linear support vector machine (LSVM), and then, the emotion of that person is detected through deep learning techniques to increase the accuracy percentage. The paper also highlights the data collection and preprocessing techniques. Images were collected using a simple HAAR classifier program, resized, and preprocessed by removing noise using a mean filter. The model resulted in an accuracy percentage for face and emotion being 97% and 92%, respectively. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Economic Burden and Productivity Loss of Employees with Lifestyle Diseases in Sedentary Occupations During Pandemic
Over the past few decades, the prevalence of Lifestyle Diseases or Non-Communicable Diseases (NCDs) have increased. There has been an increasing concern about these lifestyle diseases, with hypertension acting as the most prevalent lifestyle disease in the populace. It further exaggerates the issue as its prevalence increases exposure to other lifestyle diseases such as Diabetes and Cardiovascular Diseases (CVD). With health being an important component of human capital, the presence of lifestyle diseases has an economic impact on the individual and the organisation. The presence of an illness reduces the productivity level delivered by the individual to work, resulting in productivity loss. Apart from impacting an employee's productivity, the prevalence of lifestyle diseases incurs a significant monetary expense in the form of healthcare required to manage them. This monetary expense is called an economic burden or out-of-pocket expenditure. On these grounds, the current study examines the economic burden and impact on the productivity of employees suffering from lifestyle diseases (Hypertension, Diabetes and CVD) working in sedentary occupations. With lifestyle diseases majorly influenced by the lifestyle patterns of an individual, employees working in a sedentary occupation are at greater exposure to lifestyle diseases and hence were selected as the target population. A cross-sectional study was conducted among 426 employees of sedentary occupations in the Delhi-NCR region. The economic burden has been measured as a sum of the direct and indirect costs of the diseases incurred in a year. Using the estimates of economic burden, Catastrophic Healthcare Expenditure (CHE) was measured at different threshold levels. The study has also evaluated productivity loss through presenteeism and absenteeism approaches. An attempt was made to examine the relationship between the economic burden 7 and productivity loss through presenteeism and absenteeism approaches. The result of the study shows a significant share of the economic burden for lifestyle diseases and their comorbidities. CHE was highest at the 40% threshold level. The level of disparity in catastrophe among lower and high-income individuals was also highest at the 40% threshold level. Further statistical results show a high cost of absenteeism due to lifestyle diseases compared to presenteeism and found that economic burden has a strong positive relationship with absenteeism and presenteeism. Overall, the study concludes that lifestyle disease incurs a substantial economic burden and CHE for employees working in sedentary occupations. The estimate for the same increases if multiple lifestyle diseases are present. Further, the impact of catastrophe is more for low-income than high-income individuals due to the limited availability of resources to manage the health issue. Apart from causing monetary expense, the presence of lifestyle diseases also causes a high cost of absenteeism and presenteeism, increasing the economic cost of managing lifestyle diseases. -
Hybrid Bidirectional GRU Approach for Crop Yield Prediction and Climate Change Impact Assessment in Agriculture
The impacts of climate change induced by humans will be felt most acutely by the agriculture sector due to its extreme dependence on weather. To ensure a steady supply of food, it is necessary to study and anticipate the effects of climate change on agricultural output. The impact of climate change on agricultural yield predictions is examined in this study using a novel methodology. In the proposed model, preprocessing, feature extraction, and training are the main processes. Data pretreatment guarantees quality by cleaning and normalising the data, while the PCC is utilised for feature selection. The model utilises AM and BiGRU for usage with large datasets. Using word vectors, the word embedding layer improves contextual awareness. Experiment findings show that the model is accurate to within 98.31% and can withstand a wide range of climate conditions. Current state-of-the-art methods are vastly outperformed by it, with performance measures like as R2 = 0.921%, MAE = 0.127%, and RMSE = 0.158%. These findings show that agricultural strategists and lawmakers can use AM-BiGRU to assess the effects of climate change and build a more resilient food system. 2025 IEEE. -
Digital Transaction Cyber-Attack Detection Using Particle Swarm Optimization
The cyber digital world is an essential variant in day-to-day life in advanced technology. There is a better change in the lifestyle as intelligent technology. In larger excite to increase the advanced technology which can be developed to humans in major dependent on network and internet users. Now, in modern times, the internet has changed the primary need in human lifestyle by giving access to everything in the world while sitting in one place knowing and updating the information and usage of online subscribers or Revolution. The world is moving in Rapid and Faster communications within a fraction of a second, at a lesser cost, and it has minimal paper-based processes and relies on the digitization document instead of a paperless environment. The data is handled by finch security practices, which are used in security worldwide to establish protected data management systems like digital lending, credits, mobile Banking, and mobile payment. Cryptocurrency and blockchain, B-trading, and banking as a service are included. At the same time, leveraging the new technologies is to resist hacking cyber-attacks. This article is also involved in artificial intelligence and machine learning (AI&ML) in different cyber-attacks. This article focuses on genetic algorithms to detect the cyber-attack. The main aim of the detection is future to prevent these cyber-attacks. The comparison will take two sample genetic algorithms. The first one is taken for Ant Colony Optimization (ACO), and the proposed model is taken for Particle Swarm Optimization. The average attack detection of ACO algorithm is 45 packets at the same time PSO algorithm will detect 50 packets. 2023 IEEE. -
Structural Health Monitoring Using Machine Learning Techniques
Environmental factors, particularly vibrations and temperature can damage the structural health of the building. To avoid heavy damage to the building and to maintain the building's structural health this paper suggests monitoring of building using machine learning algorithms. Machine learning algorithms are used to predict temperature and vibration damages in buildings. Temperature and vibration values are obtained through the grove vibration sensor and NTC thermistor attached to Raspberry Pi 3B plus. In the Raspberry pi, Machine learning algorithms are executed. The activation functions used are Relu, Sigmoid, and Tanh. The experimental results reveal that the Sigmoid activation function gives the best results in terms of metrics with accuracy 94.25, Precision 0.951, Recall 0.912, and F1 score 0.388. The sigmoid function is used in machine learning algorithms for predicting temperature and vibrations. Predicted temperature and vibrations damages are sent to the server and viewed through the user mobile. K- Nearest Neighbor algorithm produced best results with an accuracy rate of 85.50, Precision of 0.922, Sensitivity of 0.830, Specificity of 0.840 and F1 score of 0.873. 2023 IEEE.