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Eco-friendly synthesized nanoparticles as antimicrobial agents: an updated review
Green synthesis of NPs has gained extensive acceptance as they are reliable, eco-friendly, sustainable, and stable. Chemically synthesized NPs cause lung inflammation, heart problems, liver dysfunction, immune suppression, organ accumulation, and altered metabolism, leading to organ-specific toxicity. NPs synthesized from plants and microbes are biologically safe and cost-effective. These microbes and plant sources can consume and accumulate inorganic metal ions from their adjacent niches, thus synthesizing extracellular and intracellular NPs. These inherent characteristics of biological cells to process and modify inorganic metal ions into NPs have helped explore an area of biochemical analysis. Biological entities or their extracts used in NPs include algae, bacteria, fungi, actinomycetes, viruses, yeasts, and plants, with varying capabilities through the bioreduction of metallic NPs. These biosynthesized NPs have a wide range of pharmaceutical applications, such as tissue engineering, detection of pathogens or proteins, antimicrobial agents, anticancer mediators, vehicles for drug delivery, formulations for functional foods, and identification of pathogens, which can contribute to translational research in medical applications. NPs have various applications in the food and drug packaging industry, agriculture, and environmental remediation. Copyright 2023 Borehalli Mayegowda, Roy, N. G., Pandit, Alghamdi, Almehmadi, Allahyani, Awwad and Sharma. -
Stability Analysis and Navigational Techniques of Wheeled Mobile Robot: A Review
Wheeled mobile robots (WMRs) have been a focus of research for several decades, particularly concerning navigation strategies in static and dynamic environments. This review article carefully examines the extensive academic efforts spanning several decades addressing navigational complexities in the context of WMR route analysis. Several approaches have been explored by various researchers, with a notable emphasis on the inclusion of stability and intelligent capabilities in WMR controllers attracting the attention of the academic community. This study traces historical and contemporary WMR research, including the establishment of kinetic stability and the construction of intelligent WMR controllers. WMRs have gained prominence in various applications, with precise navigation and efficient control forming the basic prerequisites for their effective performance. The review presents a comprehensive overview of stability analysis and navigation techniques tailored for WMRs. Initially, the exposition covers the basic principles of WMR dynamics and kinematics, explaining the different wheel types and their associated constraints. Subsequently, various stability analysis approaches, such as Lyapunov stability analysis and passivation-based control, are discussed in depth in the context of WMRs. Starting an exploration of navigation techniques, the review highlights important aspects including path planning and obstacle avoidance, localization and mapping, and trajectory tracking. These techniques are carefully examined in both indoor and outdoor settings, revealing their benefits and limitations. Finally, the review ends with a comprehensive discussion of the current challenges and possible routes in the field of WMR. The discourse includes the fusion of advanced sensors and state-of-the-art control algorithms, the cultivation of more robust and reliable navigation strategies, and the continued exploration of novel WMR applications. This article also looks at the progress of mobile robotics during the previous three decades. Motion planning and path analysis techniques that work with single and multiple mobile robots have been discussed extensively. One common theme in this research is the use of soft computing methods to give mobile robot controllers cognitive behaviors, such as artificial neural networks (ANNs), fuzzy logic control (FLC), and genetic algorithms (GAs). Nevertheless, there is still a dearth of applications for mobile robot navigation that leverage nature-inspired algorithms, such as firefly and ant colony algorithms. Remarkably, most studies have focused on kinematics analysis, with a small number also addressing dynamics analysis. 2023 by the authors. -
Optimizing Drug Discovery for Breast Cancer in a Laboratory Environment Using Machine Learning
Breast cancer therapy can be greatly enhanced by the proposed method that combines experimental and computational techniques. Employing a state-of-the-art in vitro system, we evaluated biopsy tissues at different cancer stages, monitoring them for 48 hours. Later on, our investigation involved the application of machine learning models including nae Bayes (NB), artificial neural networks (ANN), random forest (RF), and decision trees (DT). Surprisingly, these models reached high test accuracies - ANN 93.2%, NB 90.4%, DT 87.8%, and RF 85.9%. The dataset's impedance dynamics data provide evidence for treatment efficacy. Therapeutic strategies need to be adjusted for particular patients and their stage of cancer since the results underscore the usefulness of personalized breast cancer therapy. This study will significantly contribute to new tailored treatment options available for breast cancer patients. 2024 IEEE. -
Role of medicinal plants against lung cancer
Nowadays for treatment of various diseases, scientific studies are conducted using the medicinal plants of both domestic and wild for curing purpose. Every plant contain compounds that have medicinal properties and can be isolated from the plants parts. Due to plants diversity in India and use in Ayurveda, Unani and Siddha, India is known as medicinal hub. Lung cancer is the third most common cancer, that develops in lung tissue and are of two type's non-small cell lung cancer and small cell lung cancer. Many factors cause lung cancer; tobacco smoking is the prominent cause of lung cancer. The individuals who smoke have 20-30% more chance of developing lung cancer than non-smokers. The conventional treatment of lung cancer, are chemotherapy, stem cell therapy, and electrochemical treatments. Plants and the compounds present can be used for treating lung cancer. So in this chapter will focus on plants Acalypha indica, Solanum trilobatum, Justicia adhatoda, Coleus amboinicus and Piper nigrum in lung cancer treatment and on the medicinal properties. 2024, IGI Global. -
Artistic Representation of Gender Nonconforming Female Bodies in Social Media: A Study of Select Indian Graphic Artists on Instagram
The study critically examines gender nonconforming female identities via their sex-ualized representations through artistic imagination on Instagram. Instagram representation becomes a political act where this visual subversion allows the queer to reclaim their non-binary identity and thus articulate their choices through their body. The digital graphic art taken under study is select images from the Instagram pages of Indian artists artwhoring, aorists, and sayartic. The research study examines the question of an ideal hegemonic femininity perpetuated by the rhetoric of Indian heteronormative patriarchal assertions. It analyses select images that defy hegemonic femininity and gender binary by embodying an amalgamation of masculinity and femininity and lesbian desire which forms an act of subversion. The methodology of critical discourse analysis is employed to study Instagram art and the critical frameworks of the fantasy female body, and the notion of hetero-patriarchal femininity. In conclusion, the study discusses the treatment of female gender non-conforming bodies, their appearance, lesbian desire, and body image. Such transgressive depiction of bodies successfully situates the female body beyond the dichotomy of masculinity and femininity. 2023 The Author(s). All rights reserved. -
A Comparative Study of Machine Learning Techniques for Credit Card Customer Churn Prediction
A customer is a churner when a customer moves from one service provider to another. Nowadays, with an increasing number of severe competition with inside the market, essential banks pay extra interest on customer courting management. A robust and real-time credit card holders churn evaluation is vital and valuable for bankers to preserve credit cardholders. Much research has been observed that retaining an old customer is more than five times easier compared to gaining a new customer. Hence, this paper proposes a method to predict churns based on a bank dataset. In this work, Synthetic Minority Oversampling Technique (SMOTE) has been used for handling the imbalanced dataset. Credit card customer churn is predicted using random forest, k-nearest neighbor, and two boosting algorithms, XGBoost and CatBoost. Hyperparameter tuning using grid search has been used to increase the accuracy. The experimental result shows Catboost has achieved an accuracy of 97.85% and tends to do better than the other models. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Machine learning insights into mental health risk factors associated with climate change: Impact on schoolchildren's cognitive abilities
In this chapter, we use machine learning techniques to investigate how the effects of climate change and certain risk factors for mental health affect students' cognitive skills in the classroom. The mental health of at-risk populations, especially students, must be considered in light of the fact that the world's environment is changing significantly. Using state-of-the-art machine learning algorithms, we analyze large datasets that include environmental variables, socio-economic characteristics, and markers of mental health among school-aged persons. We are primarily interested in identifying key relationships and trends that might help us understand the complex relationship between climate change and cognitive health in this population. In order to uncover complex insights, the chapter takes a holistic approach by combining feature selection, model training, and interpretability analysis. The cognitive capacities of school-aged children may be significantly impacted by some climate- related stresses, according to preliminary results. The findings add to our knowledge of the interconnected webs of environmental shifts, psychological susceptibilities, and cognitive consequences. Educators, legislators, and healthcare providers can benefit from this study's use of machine learning insights into the possible effects of climate change on students' mental health. It also paves the way for the creation of tailored treatments and adaptive techniques to deal with the highlighted dangers, fostering resilience and prosperity in the face of a changing environment. 2024, IGI Global. All rights reserved. -
Leveraging Machine Learning and Streamlit for Real-Time Stock Analysis and Prediction
This paper introduces StockNavigator, an interactive web application developed using Streamlit, designed to offer a comprehensive solution for stock performance analysis, real-time stock price monitoring, and stock price prediction. Users can compare the performance of multiple stocks over a specified period, visualize data through various chart types, and gain insights into stock trends and relative returns. The proposed models user-friendly interface allows investors to make informed data-driven decisions, regardless of whether them being seasoned traders or beginners. This article demonstrates the effectiveness of using modern machine learning models like Prophet in the domain of financial forecasting and highlights the flexibility of Python-based frameworks for developing interactive, data-centric web applications. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Optimizing Healthcare Analytics: A Zero-Inflated Poisson Approach toPediatric Emergency Room Visits
In various fields, the modeling of count data holds significant importance. The Poisson regression model is a commonly utilized tool for this purpose. However, this model assumes that the data has uniform dispersion, a condition often not met in real-world observations. The nature of overdispersion canvary depending on the specific context. When the overdispersion is primarily dueto an excessive number of zero counts, the Zero-inflated Poisson regressionmodel becomes a more suitable choice for modeling count data. The paper commencesby offering a summary of the theoretical foundations of both Poisson regressionand Zero-inflated Poisson regression. To evaluate their performance, usethe Mean-Squared error (MSE) as a comparative metric. Next, apply these modelsto analyze the frequency of hospital emergency room visits by children between 1018 years of age. The overdispersion of the visit count in our dataset is mostly caused by the excessive occurrence of zero counts. The findings demonstratethat the Zero-inflated Poisson regression model outperforms the standard Poisson regression model in terms of MSE. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Global Cultural Immersions in Learning for a Sustainable Future
Education is a critical driver for addressing global issues such as climate change, inequality, and environmental degradation. Cultural immersion programs that align with the Sustainable Development Goals (SDGs) help learners develop intercultural competence, global awareness, and a deep commitment to sustainability. These initiatives encourage practical decision-making rooted in diverse cultural, environmental, and social values. By engaging with indigenous knowledge, community-based practices, and scalable technologies, such programs foster inclusive, locally grounded solutions to global problems. Ultimately, they empower individuals and institutions to contribute meaningfully to a more just, resilient, and sustainable world. Global Cultural Immersions in Learning for a Sustainable Future highlights the transformative potential of experiential learning, demonstrating its ability to integrate cultural, environmental, and social values into practical decision-making. By presenting theoretical frameworks, case studies, and actionable strategies, it provides tools to design and implement impactful cultural immersion initiatives. Covering topics such as cross-cultural community development, global citizenship, and urban narratives, this book is an excellent resource for academicians, educators, policymakers, corporate leaders, students, researchers, and more. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Physical Analysis of White and Brick Red Eri Silk Fiber
Despite growing interest in Eri silk, limited research compares the properties of white and brick red varieties, particularly their optical, mechanical, and crystallinity behaviour. This study investigates the structural, optical, thermal, and surface properties of both varieties using X-ray diffraction (XRD), UVvis analysis (UVvis), thermogravimetric analysis (TGA), and scanning electron microscopy (SEM). The Eri silk fibers were evaluated for weight loss percentage, functional groups, thermal stability, tensile strength, and amino acid composition. Both experienced around 10% weight loss after degumming. UVvis analysis revealed an increase in optical band gap and decrease in Urbach energy after degumming, indicating improved structural order. XRD analysis showed crystallinity of 55% for degummed white and 50% for brick red Eri silk fiber. TGA demonstrated that undegummed white Eri silk exhibited 10% less mass loss than brick red, indicating higher thermal stability. SEM analysis showed white fibers with an average diameter of 19.34m, compared to 16.32m for brick red. White Eri silk fiber also demonstrated superior stretchability and flexibility in tensile strength tests. Amino acid profiling indicated a higher alanine content in white fiber. These findings enhance the understanding of physical properties of Eri silks varieties for potential applications in textiles and biomaterials. The Author(s), under exclusive licence to the Korean Fiber Society 2025. -
Determinants of corporate dividend policy in India: A dynamic panel data analysis
The present study empirically examines the determinants of dividend policy of National Stock Exchange (NSE) listed firms in India, using dynamic panel data model for the sample of 95 NSE listed firms with continuous dividend payments from 2012/2013 to 2017/2018. The empirical results reveal that profitability, liquidity, leverage, risk, size of the firm and inflation are the major determinants of dividend policy of selected NSE listed firms in India. Findings deduced from empirical evidence bears testimony to the fact that profitability, liquidity, size of the firm and inflation have significant negative impact on dividend policy of the selected NSE firms covered by the study. These findings seem contradictory to the expected outcome contained in the existing literature on the Indian context. The risk variable tends to have negative and significant impact, which is line with the existing literature. Besides, the lagged dividend, investment opportunities, taxation and yield curve do not play significant role in determining the dividend policy. 2020 Allied Business Academies. -
Plant-Derived Nanocellulosic Material: A Promising Technology Application in Environmental Bioremediation
Nanocellulose (NC) polymers derived from plant sources are gaining enormous interest in environmental remediation owing to their low cost and potential for renewable adsorption. Plant-derived nanocellulose is applied in waste water treatment because of its unique features and functionality. The word nanocellulose refers to cellulosic materials having a dimension of nanoscopic scale/or nanoscale. One such nanomaterial is a cellulose-based material with a well-aligned nanocellulose composition indicating its structural hierarchy. Nanocellulose has been recognized as a remarkable natural biomaterial adsorbent which is obtained from renewable sources such as wood, plants, fruit peel, can be found abundantly on earth, and biodegradable and can be easily used in the surface fabrication. Due to its increased surface area, nanocellulose has gained considerable advantage over conventional cellulose fibers. Application of nanocellulosic material in environmental remediation and wastewater treatment has recently emerged as a potential adsorbent generating, and aroused much attention in addressing the environmental issue. Nanocellulose may adsorb a wide range of contaminants, such as heavy metals, dissolved pollutants (organic), dyes, petroleum oil, and unwanted effluents. This review provides focus on the structure, properties, isolation, and adsorbent classes of nanocellulosic materials, as well as their applications in environmental remediation. 2025 by Apple Academic Press, Inc. -
Bionanoparticles Impact on Human Health, an In Vitro and In Vivo Status
In the hunt for a safe replacement for hazardous conventional nanoparticles that are applied in biomedicine field, bionanoparticles are known to be the ideal choice. The term bionanoparticles refers to nanoparticles made using biomolecules or that use a biomolecule to enclose or immobilize a more conventional nanomaterial. For the creation of bionanoparticles, biomolecules are taken from bacteria, plants, agricultural wastes, insects, marine life, and some mammals. Bionanoparticles, possess unique qualities with lot of potential that make them applicable in different field such as, pharmacy, aerospace engineering, biosensors, material sciences and so on. These bionanoparticles have improved biocompatibility, bioavailability, and bioreactivity and display minimal or insignificant toxic effects in humans, animals, and at the environment level. Nanoparticles can be introduced into the body either by biomedical procedures as a part of treatment, diagnosis, or the application of cosmetics. The mode of entry is usually via intravenous, intradermal, intramuscular and peritoneal injections. Unintentional entry of nanoparticles is a result of environmental pollution or accidental release. The effect of bionanoparticles on human health received much importance as they are biologically synthesized and biocompatible. The goal of this chapter is to review human exposure to bionanoparticles with an emphasis on the effects on human cells and animal models. 2025 selection and editorial matter, Shakeel Ahmed; individual chapters, the contributors. -
Polycyclic Aromatic Hydrocarbons and Their Remediation by Biochar
The distribution of polycyclic aromatic hydrocarbons (PAHs) around the world is influenced by anthropogenic, natural, and global transport activities. They are pervasive environmental contaminants that are toxic, mutagenic, carcinogenic, and have negative biological effects. PAH molecules stability and hydrophobicity are the two main factors that influence their long-term persistence in the environment. PAHs contamination of soil, water and sediments has been widely dissipated for years, necessitating effective remediation processes through research and development to achieve impactful treatment and reuse of soil, water and sediments. The biochar amendment to PAHs contaminated soil, water and sediments has been suggested as an inventive and environmentally friendly technology. The primary method of removing PAHs involves sorption onto biochar. In this chapter, types and effects of PAHs exposure on humans the mechanisms of PAH sorption to the biochar, effects of adding biochar to soil, water and sediments that have PAH contamination are thoroughly discussed. 2025 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Synthesis, characterization and biological activity studies on 6-p-dimethylaminophenyl-5,6-dihydrobenzoimidazo [1,2-c]quinazoline: Crystal structure of the title compound and comparative study with related derivatives
Reaction of o-aminophenylbenzimidazole with p-dimethylaminobenzaldehyde yielded 6-p-dimethylamin-ophenyl-5,6-dihydrobenzoimidazo[ 1,2-c]quinazoline, which was characterized by elemental analysis, IR, UV-Vis, 1H NMR, 13C NMR, mass spectral studies and X-ray crystal structure analysis. Studies on the antimicrobial activity of the compound revealed that it is active against fungus Yeast but not Bacillus subtilis. The compound crystallized in the space group P2 1/n with the unit cell parameters a = 10.652(2) b = 11.002(2) c = 15.753(2) ? = 109.29(2) and the structure was refined to an R-factor of 0.0479. The hydropyrimidine ring in the quinazoline moiety is in skew-boat conformation. The dimethylamino group attached to phenyl ring is in conjugation with it. The structure was stabilized by intermolecular C-H-N interactions. A few of the related quinazolines (6-p-hydroxyphenyl-5,6-dihydrobenzoimidazo [1,2-c]quinazoline; 6-phenyl-5,6-dihydrobenzoimidazo[1,2-c]quinazoline; 6-pyridyl-5,6- dihydrobenzoimidazo[1,2-c]quinazoline; 6-furyl-5,6-dihydrobenzoimidazo[1,2-c] quinazoline) were also examined for their biological activity, in addition to their characterization by IR, UV-Vis, JH and 13C NMR spectral studies along with structural comparison. Springer Science+Business Media, LLC 2011. -
Crystal structure of 1,7-bis(4-chlorophenyl)-4-(1,3-dithiolan-2-ylidene)-1, 6-heptadiene-3,5-dione
The synthesis and crystal structure of 1,7-bis(4-chlorophenyl)-4-(1,3- dithiolan-2-ylidene)-1,6-heptadiene-3,5-dione is described. This compound is a curcuminoid analogue, configurationally symmetric about the C4-C5 atoms and also retains the two fold axis in the crystal phase. This compound crystallizes in the space group C2/c with unit cell parameters a = 19.203(1) b = 13.147(1) c = 8.801(1) ? = 112.99(1), with half a molecule in the asymmetric unit. The ketenedithioacetal functionality present between the carbonyl groups prevents the possibility of keto-enol tautomerization in this compound. The push-pull nature of the ketenedithioacetal functionality organizes the cinnamoyl groups parallel to each other. 2008 Springer Science+Business Media, LLC. -
Synthesis and crystal structure of 1,7-bis(4-methoxyphenyl)-4-(1,3- dithiolan-2-ylidene)-1,6-heptadiene-3,5-dione
The synthesis and crystal structure of 1,7-bis(4-methoxyphenyl)-4-(1,3- dithiolan-2-ylidene)-1,6-heptadiene-3,5-dione is described. This compound crystallizes in the space group P21 with unit cell parameters a = 14.207 b = 7.752(1) c = 19.473(1) ? = 91.00(3), with two molecules in the asymmetric unit. The ketenedithioacetal functionality present between the carbonyl groups prevents the possibility of keto-enol tautomerization in this compound. The cinnamoyl groups are organized parallel to each other due to the push-pull nature of the ketenedithioacetal functionality. 2010 Springer Science+Business Media, LLC. -
Crystal structures of 1,7-bis(furyl)-4-(1,3-dithiolan-2-ylidene)-1, 6-heptadiene-3,5-dione and 1,7-bis(thienyl)-4-(1,3-dithiolan- 2-ylidene)-1,6-heptadiene-3,5-dione
The title compounds (bis(alkenoyl) ketene dithioacetals) were prepared starting from the cyclic acyl ketene dithioacetal and aromatic aldehydes. These compounds were characterized by IR, 1H NMR, mass spectral studies and X-ray crystal structure analysis. The bis(furyl) ketene dithioacetal crystallized in the space group P1? with unit cell dimensions a = 7.7626(12) , b = 9.0217(13) , c = 26.827(5) , ? = 86.271(14), ? = 87.643(14), ? = 66.286(11) and the structure was refined to an R-factor of 0.0528 while bis(thienyl) ketene dithioacetal crystallized in the space group P21/c with unit cell dimensions a = 10.2433(9) , b = 10.4142(6) , c = 18.0973(18) , ? = 106.0 and the structure was refined to an R-factor of 0.0446. The ketenedithioacetal functionality present between the carbonyl groups prevents the possibility of keto-enol tautomerization in these compounds. In both the crystal structures, the crystal packing is stabilized by C-H?O hydrogen bonds as well as van der Waals interactions. Springer Science+Business Media, LLC 2011.