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Synthesis, Characterization of Chromium Oxide Powders and Coatings
The chromium oxide powders are transformed into plasma sprayable particles by using synthetic polymers for agglomeration. In order to carry out the agglomeration process, spray drying technique was employed. This research work highlights the significance of the process variables that control the synthesis of plasma spray powder and consequently, the properties that were suited for plasma sprayoating. Energy Dispersive Spectroscopy (EDS) was used to characterize the elemental composition, while scanning electron microscopy (SEM) was used to analyse the morphology and powder grain sizes and X-ray diffraction (XRD) was used to identify the phase structure. And for the development of coatings on the substrates, Atmospheric plasma spray (APS) technique was used. The plasma sprayable powders were created with the intention of investigating for use as corrosion-resistant coatings. 2023 Trans Tech Publications Ltd, All Rights Reserved. -
Into the Dark World of User Experience: A Cognitive Walkthrough Study
In this age of AI, the unison of man and machine is going to be more prominent than ever, thus creating a need to understand the underlying framework that is adopted by app designers and developers from a psychological point of view. Research on the various benefits and harmful effects of user experience design and furthermore developing interventions and regulations to moderate the use of dark strategies in digital tools is the need of the hour. This paper calls for an ethical consideration of designing the experience of users by looking at the unethical practices that exist currently. The purpose of the study was to understand the cognitive, behavioural and affective experience of dark patterns in end users. There is a scarcity in the scientific literature with regard to dark patterns. This paper adopts the methodology of user cognitive walkthrough with 6 participants whose transcripts were analysed using thematic network analyses. The results are presented in the form of a thematic network. A few examples of the themes found are the experience of manipulation in users, rebellious attitudes, and automatic or habitual responses. These findings provide a basis for an in-depth understanding of dark patterns in user experience and provide themes that will help future researchers and designers develop ethical and more enriching user experiences for users. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
A modified approach for extraction and association of triplets
In this paper we present an enhanced algorithm with modified approach to extricate various Triplets i.e. subject-predicate-object from Natural language sentences. The Treebank Structure and the Typed Dependencies obtained from Stanford Parser are used to elicit multiple triplets from English Sentences. Typed Dependencies represents grammatical connections among the words of any sentence and represents how triplets are associated. The intended interpretation behind the extraction of Triplets is that the subject is acting on the object in a way described by the predicate. In graphical form it can be considered that subject and object will be acting as nodes i.e. entities and predicate as edges i.e. relationship. The resulting triplets and relations can be useful for building and analysis of a social network graph and for generating communication pattern and Information retrieval. 2015 IEEE. -
Exploring Conditional Generative Models for Sketch-to-Image Translation: cGAN, cVAE, and Conditional Diffusion Models
Creating realistic facial pictures from hand-drawn sketches is of significant utility in forensic investigations because eyewitness drawings are frequently the only visual leads for suspect identification. Turning a hand-drawn sketch into a realistic image is a difficult task. This is because sketches lack detailed information, they are abstracted, and ambiguous. Most of the conventional image creation and generation techniques tend to lose facial structure, identity, and realism. This makes it a great area for generative AI. This paper is a comparative analysis of three generative models: Conditional GANs, Conditional VAEs, and Conditional Diffusion Models. We evaluate these models on the sketch-to-image synthesis problem using the CUHK Face Sketch Dataset. We recognize and compare how every model handles the challenge of generating images from sketches of faces, with an emphasis on producing realistic images, maintaining identity and diversity. The paper demonstrates the advantages and disadvantages of each approach. It also offers insights into their usefulness for forensic applications and suggests directions for future improvements through combined or specialized generative structures. 2025 IEEE. -
Exploring Conditional Generative Models for Sketch-to-Image Translation: cGAN, cVAE, and Conditional Diffusion Models
Creating realistic facial pictures from hand-drawn sketches is of significant utility in forensic investigations because eyewitness drawings are frequently the only visual leads for suspect identification. Turning a hand-drawn sketch into a realistic image is a difficult task. This is because sketches lack detailed information, they are abstracted, and ambiguous. Most of the conventional image creation and generation techniques tend to lose facial structure, identity, and realism. This makes it a great area for generative AI. This paper is a comparative analysis of three generative models: Conditional GANs, Conditional VAEs, and Conditional Diffusion Models. We evaluate these models on the sketch-to-image synthesis problem using the CUHK Face Sketch Dataset. We recognize and compare how every model handles the challenge of generating images from sketches of faces, with an emphasis on producing realistic images, maintaining identity and diversity. The paper demonstrates the advantages and disadvantages of each approach. It also offers insights into their usefulness for forensic applications and suggests directions for future improvements through combined or specialized generative structures. 2025 IEEE. -
Eco-friendly AgZnO Nanocomposites Synthesis and Their Role as Photocatalyst for Textile Dye Degradation
Recent research in the field of nanotechnology revealed that plant extract and their derivatives are good stabilizing and reducing agents. Artemisia stelleriana (Dusty Miller) is widely used as an ornamental plant. The current study, explores one-pot method to synthesise A. stelleriana-mediated silver/ zinc oxide nanocomposites (AS-Ag/ZnONCs). Using UV-visible spectrophotometer, scanning electron microscopy, energy-dispersive X-ray, Transmission Electron Microscope, X-ray diffraction, and Fourier transform infrared spectroscopy the characterisation of the synthesised AS-Ag/ZnONCs was examined. The crystalline size of the AS-Ag/ZnONCs was determined to be 45.39nm using the Williamson-hall equation. Irregular-shaped nanocomposites were observed from AS-Ag/ZnONCs, exhibiting an average size of 35.2nm. To check the activity of AS-Ag/ZnONCs as photocatalysts to degrade RY145, RY86, RB222A and RB220 dyes was determined. The order of photocatalytic activity of AS-Ag/ZnONCs was as follows: RY145 > RB220 > RB222A > RY86. Low toxicity was observed when Vigna radiata (Mung bean) and Artemia salina (Brine shrimp) were exposed to treated dye solutions using AS- Ag/ZnONCs when compared with untreated dye solutions. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
Empowering Democracy: A Comprehensive Analysis and Predictive Modelling of Voter Turnout in Indian General Elections
This study aims to break down the complex dynamics driving voter turnout in Indian general elections, providing a detailed examination of the various elements that influence voters to engage in the democratic process. It investigates how voter engagement is changing over years, looking at socio-economic factors, regional differences and historical patterns that have an impact on civic engagement. The research utilizes a detailed exploratory data analysis to examine voter data from 1952 to 2019. Key factors influencing voting turnout are identified through statistical methods and visualizations. In order to predict and comprehend voter behaviour based on various socio-demographic parameters, the study uses advanced machine learning algorithms, such as Random Forest, XGBoost, LSTM and other important models. This project contributes to the understanding of voter behaviour, providing actionable insights for improving democratic participation in the Indian electoral landscape by utilizing hyper-localized constituency-wise data. Previous studies mostly looked into the political landscape of other countries and did not use any hyper-localized data. The study reveals regional differences, socio-economic linkages and important drivers of voter turnout. It highlights the value of focused campaigns, interventions tailored to a certain region and the use of technology to increase political engagements. 2025 MDI. -
Artemisia stelleriana-mediated ZnO nanoparticles for textile dye treatment: a green and sustainable approach
Textile effluents being one of the major reasons for water pollution raises major concern for water bodies and the habitation surrounding them. The lack of biologically safer treatment solutions creates a major concern for the disposal of these effluents. The present study focuses on the degradation of textile dyes using leaf extract of Artemisia stelleriana-assisted nanoparticles of zinc oxide (ZnO-NPs). ZnO NPs synthesized were confirmed using spectroscopic, X-ray diffraction and microscopic analysis. The current research utilizes widely used major textile dyes, Reactive Yellow-145 (RY-145), Reactive Red-120 (RR-120), Reactive Blue-220 (RB-220) and Reactive Blue-222A (RB-222A), which are released accidentally or due to the non-availability of cost-effi-cient, dependable and environment-friendly degradation methods, making this work a much-needed one for preventing the discharge before treatment. The biosynthesized ZnO-NPs were top-notch catalysts for the reduction of these dyes, which is witnessed by a gradual decrease in absorbance maximum values. After 320 min, ZnO-NPs under UV light exposure showed 99, 95, 94 and 45% degradations of RY-145, RR-120, RB-220 and RB-222A dyes, respectively. The phytotoxicity study conducted at two trophic levels revealed that the A. stelleriana-mediated ZnO-NPs have great potential for the degradation of textile dyes, allowing them to be scaled up to large-scale treatments. 2023 The Authors. -
Bioactive Compounds and Biological Activities of Arrowroot (Maranta arundinacea L.)
Arrowroot is one of the most widely studied herbal species belonging to the family Marantaceae, which originated from South America and is mainly found in tropical areas. Species belonging to the Maranta genus attaining worldwide attention due to the bioactive compounds are present in their rhizomes. The nutritional values of the Maranta arundinacea plant parts were explored in traditional medicine and culinary practices. Maranta arundinacea flour is a good source of fiber, starch, and carbohydrate and is extensively utilized as a major ingredient in food products. It is also used as an alternative to wheat as the flour is gluten-free. Dietary fibers present in the Maranta arundinacea are beneficially used in the treatment of digestive disorders such as celiac disease and immune disorders. Its known to stimulate the production of IgM by immune cells. Maranta arundinacea is commonly used for weight management as it is protein-rich and has fewer calories. The rhizome contains substantial amounts of sodium, magnesium, phosphorus, potassium, calcium, iron, and zinc. The processed starch from the Maranta arundinacea rhizomes is broadly used in nutritional food products as well as in pharmacological applications. The bioactive compounds present in the Maranta arundinacea rhizome make it the subject of novel pharmaceutical studies. The current chapter tries to emphasize the general morphology, nutritional benefits and processing, bioactive compounds, and biological activities of the Maranta arundinacea. Springer Nature Switzerland AG 2024. -
Bioactive nanoparticles derived from marine brown seaweeds and their biological applications: a review
The biosynthesis of novel nanoparticles with varied morphologies, which has good implications for their biological capabilities, has attracted increasing attention in the field of nanotechnology. Bioactive compounds present in the extract of fungi, bacteria, plants and algae are responsible for nanoparticle synthesis. In comparison to other biological resources, brown seaweeds can also be useful to convert metal ions to metal nanoparticles because of the presence of richer bioactive chemicals. Carbohydrates, proteins, polysaccharides, vitamins, enzymes, pigments, and secondary metabolites in brown seaweeds act as natural reducing, capping, and stabilizing agents in the nanoparticles synthesis. There are around 2000 species of seaweed that dominate marine resources, but only a few have been reported for nanoparticle synthesis. The presence of bioactive chemicals in the biosynthesized metal nanoparticles confers biological activity. The biosynthesized metal and non-metal nanoparticles from brown seaweeds possess different biological activities because of their different physiochemical properties. Compared with terrestrial resources, marine resources are not much explored for nanoparticle synthesis. To confirm their morphology, characterization methods are used, such as absorption spectrophotometer, X-ray diffraction, Fourier transforms infrared spectroscopy, scanning electron microscope, and transmission electron microscopy. This review attempts to include the vital role of brown seaweed in the synthesis of metal and non-metal nanoparticles, as well as the method of synthesis and biological applications such as anticancer, antibacterial, antioxidant, anti-diabetic, and other functions. Graphical abstract: (Figure presented.). The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. -
Green Synthesis of Bioinspired Nanoparticles Mediated from Plant Extracts of Asteraceae Family for Potential Biological Applications
The Asteraceae family is one of the largest families in the plant kingdom with many of them extensively used for significant traditional and medicinal values. Being a rich source of various phytochemicals, they have found numerous applications in various biological fields and have been extensively used for therapeutic purposes. Owing to its potential phytochemicals present and biological activity, these plants have found their way into pharmaceutical industry as well as in various aspects of nanotechnology such as green synthesis of metal oxide nanoparticles. The nanoparticles developed from the plants of Asteraceae family are highly stable, less expensive, non-toxic, and eco-friendly. Synthesized Asteraceae-mediated nanoparticles have extensive applications in antibacterial, antifungal, antioxidant, anticancer, antidiabetic, and photocatalytic degradation activities. This current review provides an opportunity to understand the recent trend to design and develop strategies for advanced nanoparticles through green synthesis. Here, the review discussed about the plant parts, extraction methods, synthesis, solvents utilized, phytochemicals involved optimization conditions, characterization techniques, and toxicity of nanoparticles using species of Asteraceae and their potential applications for human welfare. Constraints and future prospects for green synthesis of nanoparticles from members of the Asteraceae family are summarized. 2023 by the authors. -
One Pot Hydrothermal Synthesis and Application of Bright-yellow-emissive Carbon Quantum Dots in Hg2+ Detection
Carbon quantum dots (CQD) have drawn great interest worldwide for their extensive application as sensors due to their extraordinary physical and chemical characteristics, good biocompatibility, and high fluorescence in nature. Here, we demonstrate a technique for detecting mercury (Hg2+) ion using a fluorescent CQD probe. Ecology is concerned about the accumulation of heavy metal ions in water samples due to their harmful effects on human health. Sensitive identification and removal of metal ions from water samples are required to reduce heavy metals risk. To find out Mercury in the water sample, carbon quantum dots were used and synthesized by 5-dimethyl amino methyl furfuryl alcohol and o-phenylene diamine through the hydrothermal technique. The synthesized CQD shows yellow emission when exposed to UV irradiation. Mercury ion was used to quench carbon quantum dots, and it was found that the detection limit was 5.2 nM with a linear range of 15100 M. The synthesized carbon quantum dots were demonstrated to efficiently detect Mercury ions in real water samples. 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Smart Metering System with Google Assistant
This paper presents a unique research problem in the area of automation system by using IoT. The mentioned approach utilizes Google assistant, which is incorporated within Google home which uses voice-controlled inputs and voice feedbacks. This paper discusses a new method to develop a smart energy meter at a distributor level and to make use of this technology to monitor the power consumption of each device individually which can help the user to monitor the electricity usage in real time and thus helps to save electricity and reduce cost on your electricity bill. 2020, Asian Research Association. All rights reserved. -
Effects of Mindfulness-based Intervention on Academic Anxiety: Enhancing Well-being of Rural Adolescents
Academic worry has been reported to be highly prevalent among adolescents, and it negatively affects their well-being. In comparison to urban adolescents, rural adolescents experience a lesser degree of academic anxiety. At the same time, very little attention is given to this problem of rural adolescents due to the lack of resources to provide such type of care. The poor resources-driven rural area requires a compact, more easily comprehensible and more inclusive intervention programme that can aid a group of students at a time and be more beneficial and effective. Therefore, in this study, mindfulness-based intervention (MBI) is used, which is indigenous, inclusive and compact, as an intervention to enable adolescents to deal with academic anxiety and improve their well-being. In this study, 47 rural school adolescents with academic anxiety underwent an 8-week MBI after the initial screening process and assessment with the Children and Adolescent Mindfulness Measure and WarwickEdinburgh Mental Wellbeing Scale. Post and 2-month follow-up assessment after intervention showed a significant decline in academic anxiety and an increase in mindfulness and well-being. 2025 SAGE Publications. -
Effects of a Mindfulness-based Intervention on Well-being Among Rural Adolescents with Academic Anxiety
Background: Academic anxiety revolves around scholastic work and performance and can be detrimental to students health and overall subjective well-being. It has been found to be significantly high in adolescents, leading to consequences that prove to be detrimental to their academic performance, focus, and overall self-esteem. This phenomenon acts as a vicious cycle impacting all aspects of a students life. Method: The current study aimed to explore mindfulness-based intervention (MBI) as a possible option to deal with academic anxiety in rural adolescent students and improve their overall subjective well-being. A total of 600 students were screened for academic anxiety and a total of 47 students were subjected to an eight-week MBI. MBI aims to bring more present-moment awareness and cultivate overall well-being and thereby works against anxiety. Mixed repeated measures ANOVA was carried out to compare pre, post, and follow-up scores. Result: The results indicated a significant effect of MBI on adolescents, suggesting a significant decline in academic anxiety from pre-to-post and an increase in mindfulness and subjective well-being from pre-to-post and follow-up assessments. Conclusion: Academic anxiety and subjective well-being improved significantly with the MBI intervention, thereby implication that MBI is a feasible option for rural adolescents with academic anxiety. 2024 The Author(s). -
Classification of Breast Invasive Ductal Carcinomas Using Histopathological Images Based on Deep Learning Techniques
Women suffer from cancer, which is the main reason for death for females around the world. With the use of artificial intelligence, it is possible to predict and detect all types of cancers in the near future. It is not just women who can heal, and most breast cancers are caused by the most vulnerable type of breast. Eighty percent of all diagnoses of carcinoma are invasive ductal carcinomas (IDCs). In this paper, deep learning techniques are extended to support visible semantic evaluation of tumor areas, using convolutional neural networks (CNNs).A CNN is skilled ended a large number of photo covers (tissue areas) after Whole Slide Images (WSI) to study ranked part-based total image. About 600 normal image patches and 200 breast invasive ductal carcinomas are selected for the experiment. It was intended to amount classifier correctness in the detection of IDC tissue areas in Whole Slide Images. We achieved excellent measurable outcomes for an automated finding of IDC areas with our technique. The results are evaluated based on performance measures and compared with a different number of neurons, and the results are highlighted. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Applications of Digital Technologies and Artificial Intelligence in Cryptocurrency - A Multi-Dimensional Perspective
The paradigm shift requires spreading the light of decentralized ledger technology, extraordinarily implementing cryptocurrencies, and being visible as a game-changer. Blockchain technology, along with cryptocurrencies like Bitcoin, Ethereum, and Litecoin, is a tool for global economic transformation that is rapidly gaining traction in the finance industry. However, these technologies have had low popularity in the consumer market. Many platforms have been misunderstood and ignored when there is an obvious hole in among them. The basic idea behind cryptocurrency is that it is a network-based, totally virtual exchange medium that utilizes cryptographic algorithms such as Secure Hash Algorithm 2 (SHA-2) and Message Digest 5 (MD5) to secure the data. Transactions within the blockchain era are secure, transparent, traceable, and irreversible. Cryptocurrencies have gained a reputation in practically all sectors, including the monetary sector, due to these properties. The uncertainty and dynamism of their expenses, however, hazard investments substantially despite cryptocurrencies growing popularity amongst approval bodies. Studying cryptocurrency charge prediction is fast becoming a trending subject matter in the global research community. Several device mastering and deep mastering algorithms, like Gated Recurrence Units (GRUs), Neural nets (NNs), and nearly short-term memory, were employed by the scientists to analyze and forecast cryptocurrency prices. As a part of this chapter, we discuss numerous aspects of cryptographic protection and their related issues. Specifically, the research addresses the state-of-the-art by examining the underlying consensus mechanism, cryptocurrency, attack style, and applications of cryptocurrencies from a unique perspective. Secondly, we investigate the usability of blockchain generation by examining the behavioral factors that influence customers decision to use blockchain-based technology. To identify the best crypto mining strategy, the research employs an Analytic Hierarchy Process (AHP) and Fuzzy-TOPSIS hybrid analytics framework. Furthermore, it identifies the top-quality mining methods by evaluating providers overall performance during cryptocurrency mining. 2023 Scrivener Publishing LLC. -
Machine Learning in Cyber Threats Intelligent System
Cybercriminals disrupt services, exfiltrate sensitive data, and exploit victim machines and networks to perform malicious activities against organizations. A malicious adversary seeks to steal, destroy, or compromise business assets that have a specific financial, reputational, or intellectual value. As a result, organizations are complementing their perimeter defenses with threat intelligence platforms to address these security challenges and eliminate security blind spots for their systems. Any type of information useful for identifying, assessing, monitoring, and responding to cyber threats is considered cyber threat intelligence. Organizations can benefit from increased visibility into cyber threats and policy violations. An organizations threat intelligence allows them to prevent or mitigate various types of cyberattacks. The use of machine learning and artificial intelligence is a key component of cybersecurity conflict, which together allows attackers and defenders to function at new speeds and scales. In spear-phishing attacks, relatively frivolous machine learning algorithms have been used to overwhelming effect as adversarial artificial intelligence. This chapter discusses the various cyber threats, cyber security attack types, publicly available datasets for research work, and machine learning techniques in cyber-physical systems. 2024 selection and editorial matter, S. Vijayalakshmi, P. Durgadevi, Lija Jacob, Balamurugan Balusamy, and Parma Nand; individual chapters, the contributors. -
IoT Infrastructure to Energize Electromobility
Mobile technology is becoming more sophisticated as it advances. A comparison of different mobility scenarios was conducted. This chapter examined how electric vehicles interact with local energy systems in Stuttgart. Utilizing a travel demand model, a charging profle based on mobility patterns was generated for electric vehicles. During a quarter, charging demand and standard household load profles were used to analyze peak hour load fow for 349 households. Considering that peak loads and charging capacity are usually separated in time, greater charging capacity might lead to lower utilization of transformers. Furthermore, a study was conducted to determine if the existing infrastructure was adequate for future demand, focusing on substation transformer reserves. Electromobility is a rapidly growing and evolving application domain of the Internet of Things, with a huge market potential in various areas. It incorporates many stakeholders from manufacturers to the players of the energy market with all sorts of physical and virtual resources. It is essential to allow these devices and systems to collaborate to create advanced e-mobility services. The Author(s), under exclusive license to Springer Nature Switzerland AG 2023. -
Blockchain for Securing Healthcare Data Using Squirrel Search Optimization Algorithm
The Healthcare system is an organization that consists of important requirements corresponding to security and privacy, for example, protecting patients medical information from unauthorized access, communication with transport like ambulance and smart e-health monitoring. Due to lack of expert design of security protocols, the healthcare system is facing many security threats such as authenticity, data sharing, the conveying of medical data. In such situa-tion, block chain protocol is used. In this manuscript, Efficient Block chain Network for securing Healthcare data using Multi-Objective Squirrel Search Optimization Algorithm (MOSSA) is proposed to generate smart and secure Healthcare system. In this the block chain is a decentralized and the distributed ledger device that consists of various blocks linked with digital signature schemes, consensus mechanisms and chain of hashing, offers highly reliable storage capabilities. Further the block chain parameters, such as block size, transac-tion size and number of block chain channels are optimized with the help of MOSSA. With the evolution of the MOSSA provide new features for enhancing security and scalability. The simulation process is executed in the JAVA platform. The experimental result of the proposed method shows higher throughput of 26.87%, higher efficiency of 34.67%, lowest delay of 22.97%, lesser computational overhead of 37.03%, higher storage cost of 34.29% when compared to the existing method such as Block chain-ECIES-HSO, Block chain-hybrid GO-FFO, Block chain-SDN-HSO algorithm for healthcare technologies. 2022, Tech Science Press. All rights reserved.