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Structural and antibacterial assessment of two distinct dihydroxy biphenyls encapsulated with ?-cyclodextrin supramolecular complex
?-Cyclodextrin plays a vital role in biological application because it can enhance the stability and solubility of the guest molecules in the supramolecular inclusion complexes. Moreover, the ?-Cyclodextrin inclusion complex has control-releasing behavior and lower toxicity than bare guest molecules. To improve the solubility and stability properties of two structurally different fluorescent guest molecules, namely 2,2?-dihydroxy biphenyl and 3,3?-dihydroxy biphenyls, they involve the ?-Cyclodextrin inclusion complex process. Optical measurements clearly described the efficient binding through the changes in the absorbance and emission intensities of guest molecules in the presence of ?-Cyclodextrin. The Job's plot from absorbance measurements reveals the 1:1 stochiometric ratio of binding of guests and the ?-Cyclodextrin host. The FT-IR spectra of the solid complex show the characteristic stretching and bending vibrations from both the guests and the host molecule. The 1HNMR spectra of the inclusion complex promote downfield shifting of guest molecule protons upon binding with the ?-Cyclodextrin host. The solid complex prepared using the solution method exhibits superior antibacterial activity against both gram-positive and gram-negative bacteria compared to the kneading and physical mixing methods. 2024 -
One-Pot Synthesis of Silver Nanoparticles Derived from Aqueous Leaf Extract of Ageratum conyzoides and Their Biological Efficacy
The main objective of the present research work is to assess the biological properties of the aqueous plant extract (ACAE) synthesised silver nanoparticles from the herbal plant Ageratum conyzoides, and their biological applications. The silver nanoparticle syntheses from Ageratum conyzoides (Ac-AgNPs) were optimised with different parameters, such as pH (2, 4, 6, 8 and 10) and varied silver nitrate concentration (1 mM and 5 mM). Based on the UVvis spectroscopy analysis of the synthesised silver nanoparticles, the concentration of 5 mM with the pH at 8 was recorded as the peak reduction at 400 nm; and these conditions were optimized were used for further studies. The results of the FE-SEM analysis recorded the size ranges (~3090 nm), and irregular spherical and triangular shapes of the AC-AgNPs were captured. The characterization reports of the HR-TEM investigation of AC-AgNPs were also in line with the FE-SEM studies. The antibacterial efficacies of AC-AgNPs have revealed the maximum zone of inhibition against S. typhi to be within 20 mm. The in vitro antiplasmodial activity of AC-AgNPs is shown to have an effective antiplasmodial property (IC50:17.65 ?g/mL), whereas AgNO3 has shown a minimum level of IC50: value 68.03 ?g/mL, and the Ac-AE showed >100 ?g/mL at 24 h of parasitaemia suppression. The ?-amylase inhibitory properties of AC-AgNPs have revealed a maximum inhibition similar to the control Acarbose (IC50: 10.87 ?g/mL). The antioxidant activity of the AC-AgNPs have revealed a better property (87.86% 0.56, 85.95% 1.02 and 90.11 0.29%) when compared with the Ac-AE and standard in all the three different tests, such as DPPH, FRAP and H2O2 scavenging assay, respectively. The current research work might be a baseline for the future drug expansion process in the area of nano-drug design, and its applications also has a lot of economic viability and is a safer method in synthesising or producing silver nanoparticles. 2023 by the authors. -
Marine brown algae (Sargassum wightii) derived 9-hydroxyhexadecanoic acid: A promising inhibitor of ?-amylase and ?-glucosidase with mechanistic insights from molecular docking and its non-target toxicity analysis
Jeopardized glucose hemostasis leads to cronic metaboic disorder like Diabetes mellitus and it is predicted to occur in ?700 million people in the coming 20 years. Our study aims to isolate Palmitic acid (C16H32O3), 9-Hydroxyhexadecanoic acid metabolite from Sargassum wightii to inhibit alpha-amylase and alpha-glucosidase to reduce postprandial hyperglycemia and decline the risk of diabetes. High docking score of palmitic acid with both ?-amylase and ?-glucosidase is observed in in-silico molecular docking analysis, in comparison to commercially available drug acarbose. The three hydrogen bond in palmitic acid interacts with the important amino acids like Arg195, Lys200 and Asp300 in Glide XP docking mode for alpha-amylase. For ?-glucosidase, quantum-polarized ligand docking (QPLD) was used with similar three hydrogen bond interactions. Both docking studies showed significant binding interaction of palmitic acid with ?-amylase (?5.66 and ?5.14 (Kcal/mol)) and with ?-glucosidase (?4.52 and ?3.51(Kcal/mol)) with respect to the standard, acarbose docking score. The bioactive palmitic acid isolated from the brown alga, Sargassum wightii is already seen to inhibit digestive enzyme with non-target property in Artemia nauplii and zebra fish embryos. Further studies are required to investigate its role in in vivo antidiabetic effects due to its non-toxic and digestive enzyme inhibitory properties. It can be recommended in additional pharmaceutical studies to develop novel therapeutics to manage diabetes mellitus. 2023 SAAB -
Environmentally Sustainable Event Management for Industry Innovations and Growth
This chapter investigates the convergence of event management operations and sustainable practices to guide their function of stimulating innovation and development within the event management sector. With increasing necessity, sustainability becomes central and unavoidable to the world economy, and event professionals are increasingly embracing green practices in order to minimize environmental footprints while enhancing overall experiences for attendees. This chapter gives an extensive overview of important sustainability concepts, energy efficiency, sustainable procurement and encompassing waste minimization and outlines their dayto-day applicability in the planning, staging and post-event assessment of events It also centers the role of stakeholder engagement, ranging from suppliers through to guests, in influencing sustainable action. The chapter ends with suggesting future research directions for embedding sustainability into event management, focusing on the imperative of innovation, cooperation, and constant industry-wide dedication to protecting the planet. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Big Data Analytics: A Trading Strategy of NSE Stocks Using Bollinger Bands Analysis
The availability of huge distributed computing power using frameworks like Hadoop and Spark has facilitated algorithmic trading employing technical analysis of Big Data. We used the conventional Bollinger Bands set at two standard deviations based on a band of moving average over 20 minute-by-minute price values. The Nifty 50, a portfolio of blue chip companies, is a stock index of National Stock Exchange (NSE) of India reflecting the overall market sentiment. In this work, we analyze the intraday trading strategy employing the concept of Bollinger Bands to identify stocks that generates maximum profit. We have also examined the profits generated over one trading year. The tick-by-tick stock market data has been sourced from the NSE and was purchased by Amrita School of Business. The tick-by-tick data being typically Big Data was converted to a minute data on a distributed Spark platform prior to the analysis. 2019, Springer Nature Singapore Pte Ltd. -
Machine Learning and Deep Learning Approaches for Guava Disease Detection
A larger proportion of crops face disease outbreaks, making agricultural output difficult. Detecting and predicting diseases at an early stage can enhance productivity. Guava, a tropical and subtropical fruit, is cultivated in various countries. In regions such as Bangladesh, Pakistan, India, and South America, guava cultivation faces significant challenges due to diseases like Canker, Dot, Mummification, Phytophthora, Scab, and Styler and Root. Traditional diagnosis methods based on visual observation are often unreliable and time-consuming. To address this, we developed an automated system leveraging deep learning techniques. Our study utilized a dataset comprising 4046 guava leaf images categorized into these seven disease classes. We compared the performance of traditional methods with deep learning approaches using vision transformers and transfer learning. The results demonstrate the superiority of deep learning methods over traditional approaches, where traditional machine learning model SVM gave accuracy near 78% and deep learning methods gave over 90%. The transfer learning method gave an accuracy of nearly 97% and on the other hand, the vision transformer gave accuracy of 98%. This offers a promising solution for early disease detection in guava crops. The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025. -
Sub-Optimization based Random Forest Algorithm for Accurate and Efficient Land use and Land Cover Classification using Landsat Time Series Data
The land use and land cover (LULC) play an essential role to investigate the impacts of environmental factors and socio-economic development in the Earth's surface. Extracting the hidden information from the remote sensing images in the observed earth environment is the challenging process. In this research, implemented a model that uses Landsat data to investigate the LULC changes. Utilized the Landsat 5,7 and 8 as inputs for the 1985 to 2019 by Google Earth Engine (GEE) is applied for the robust classification. This paper proposed a Sub-forest optimization based Random forest (SO-RF) classifier with faster diagnosis speed for LULC classification. Moreover, to increase the multispectral Landsat band's resolution from 30 m to 15 m, the pan-sharpening algorithm is utilized. In addition, analyzed the various image configurations grounded numerous spectral indices and other supplementary data such as land surface temperature (LST) and digital elevation model (DEM) on final classification accuracy. The proposed SO-RF produced higher accuracy (0.97 for kappa, 96.78% Overall accuracy (OA), 0.94 for f1-score) than Copernicus Global Land Cover Layers (CGLCL) map and state of art methods like K-Nearest Neighbor (KNN), Decision Tree (DT), and Multi-class Support Vector machine (MSVM). 2024 IEEE. -
Cardless Society: Assessing the Role of Cardless ATMs in Shaping the Future of Financial Transactions
The ubiquitous ATM faces a critical crossroads in a world where the digital pulse is becoming more and more ingrained. The sound of plastic clicking, which used to be a comforting symbol of financial independence, is becoming less audible in the background noise of near-field communication and the Erie silence of digital scans. This study goes beyond the physical card and explores the unexplored world of cardless ATM technology, where security, convenience meet and innovation completely reimagines the process of getting cash. The meticulous analysis and potential use of technology can completely twist the dynamic rhythm of this world. 2024 IEEE. -
Optimizing Food Production with a Sustainable Lens: Exploring Blockchain Technology in Raw Plant Materials and Organic Techniques in Achieving Sustainable Development Goals
Amidst a rising population and mounting environ- mental concerns, India seeks a transformative approach to ensure food security and sustainable agriculture by 2030, as outlined in Sustainable Development Goal 2 (SDG 2). This research explores the immense potential of organic farming methods and raw plant materials to unlock this vision. Plants have a wealth of unrealized potential that extends beyond their conventional functions. The study looks at how different plant parts, like branches, leaves, stems, and even "waste"materials, can be used in a variety of ways to increase self-sufficiency, lessen environmental impact, and access renewable resources. Case studies from across the globe highlight this potential, highlighting the many advantages for the environment and communities. Additionally, the study investigates the innovative use of blockchain technology to promote a more transparent and resilient agricultural environment in India. Imagine blockchain-powered climate-smart practices, safe and transparent transactions, and precision agriculture led by sensor data. Water-efficient irrigation, environmentally friendly pest control, and strong traceability systems are all part of this vision, which aims to strengthen the Indian agricultural sector's resilience. The study suggests a framework of customized policy recommendations centered on non-losable farming methods in recognition of the need for wider implementation. This framework, created especially for the Indian context, supports the promotion of agrotourism, improved education and extension services, accessible financial risk management tools, and the smart redistribution of subsidies. The research highlights the transformative potential of this approach by highlighting the many benefits of these practices, including the environmental (less water use, increased biodiversity, improved soil health, and carbon sequestration), social (better community resilience, food security, farmer income, preservation of cultural heritage, equitable trade), and economic (premium market access, lower input costs, and higher yields) gains. In the end, this research offers a strong plan of action for India to greatly advance SDG 2 and create a more sustainable future for all of its people. A food system that feeds people and the environment can be developed by carefully using organic farming methods and unprocessed plant resources in conjunction with successful legislative initiatives. 2024 IEEE. -
Empowering Adolescent Emergent Readers in Government Schools: An Exploration of Multimodal Texts as Pathways to Comprehension
This exploratory study, which was part of a larger investigation into multimodality, looked at the comprehension levels of 62 Grade 8 students from two government schools who were identified as emerging readers out of a group of 118 students. Through observations and interactions with teachers and students, the potential for multimodal texts to enhance comprehension was highlighted. The study specifically compared the effectiveness of a digital comic (Text A) and an audio-visual text (Text B) in enabling comprehension among these emergent readers. Participants were instructed to narrate the content and share their interpretations of these texts, with their responses recorded and analyzed. Feedback revealed a marked preference for Text B among 45 of the 62 emergent readers assessed. Employing theoretical frameworks related to comprehension, language production, multimodality, and task structure, this research concentrated on the subset of 45 students who favored Text B. The findings underscore the importance of aligning instructional materials with students preferred learning modalities, suggesting that such alignment enhances comprehension. The study proposes a refined approach to literacy education policy, advocating for the inclusion of diverse modalities to better meet the varied learning needs of students. 2024 Association of Literacy Educators and Researchers. -
Lexical Richness of Adolescents Across Multimodalities: Measures, Issues and Future Directions
Lexical Richness (LR) is a scarcely researched subject in India. The objective of this paper is twofold: (i) To statistically inquire whether LR varies across three multimodalities: visual-only, audio-only, and audio-visual; and (ii) To see which of the two measures of LR (MATTR and Guiraud) is independent of text length and is best suited for short oral productions. 270 students across three types of schools were examined, out of whom 100 willingly completed all three oral tasks. The students were asked to retell the stories transacted in each modality in their own words. Randomization of sampling is done to mitigate the confounding modality bias. Additionally, the genre and parts of the storyline in each modality are similar. The students oral speech samples were recorded, transcribed and analyzed on WordCruncher and TextElixir software. The results revealed that there is statistically significant variance among the modalities. Furthermore, the Moving Average Type Token Ratio (MATTR) is seen to be independent of text length compared to Index of Guiraud. This study also throws light on the observations made during the study, pertinent issues in the field of education, and future directions for research on LR. 2023 IUP. All Rights Reserved. -
Exploring Socio-Variational Patterns in Indian Adolescents Lexical Diversity: Insights for Education
Following the COVID-19 pandemic, vast data emerged regarding the plummeting literacy and readability levels among Indian adolescents, posing a challenge to address in its present condition of a vastly heterogeneous socio-demographic environment. This study is grounded in Bourdieu and Passeron's (1977) theory, which acknowledges schools as places with societal relevance that perpetuate social inequality. This implies the need to formulate robust policies to address educational inequalities. To this extent, the researchers used an exploratory design to evaluate lexical diversity by purposively sampling 100 volunteer teenagers across three schools. In addition to the data received from school officials, survey questionnaires collected socio-economic information (age, gender, area of stay, socio-economic scale [SES], and school type). The authors used the Kuppuswamy SES scale (2022) to determine socio-economic scale measures, as well as the calculation of Lexical Diversity scores through the computational open-source software TextElixir. The findings reveal that age and gender do not affect lexical diversity. However, school type, SES, and area of stay significantly affect adolescents from the lower social class, who need targeted interventions to bridge gaps of educational inequity. This study addresses the limitations of previous correlational studies by offering educational insights to ensure educational equity amidst prevalent social class inequalities. Authors. -
Empowering Adolescent Emergent Readers in Government Schools: An Exploration of Multimodal Texts as Pathways to Comprehension
This exploratory study, which was part of a larger investigation into multimodality, looked at the comprehension levels of 62 Grade 8 students from two government schools who were identified as emerging readers out of a group of 118 students. Through observations and interactions with teachers and students, the potential for multimodal texts to enhance comprehension was highlighted. The study specifically compared the effectiveness of a digital comic (Text A) and an audio-visual text (Text B) in enabling comprehension among these emergent readers. Participants were instructed to narrate the content and share their interpretations of these texts, with their responses recorded and analyzed. Feedback revealed a marked preference for Text B among 45 of the 62 emergent readers assessed. Employing theoretical frameworks related to comprehension, language production, multimodality, and task structure, this research concentrated on the subset of 45 students who favored Text B. The findings underscore the importance of aligning instructional materials with students preferred learning modalities, suggesting that such alignment enhances comprehension. The study proposes a refined approach to literacy education policy, advocating for the inclusion of diverse modalities to better meet the varied learning needs of students. 2024 Association of Literacy Educators and Researchers. -
Investigating stock market efficiency in India
International Journal of Computer Application & Management, Vol. 3, Issue 3,pp.45-48 ISSN No. 2231-109 -
Application of smart manufacturing in business
The application of machine learning to production is becoming a chief objective for businesses all around the world. Smart product-service systems enable digital business model innovation by merging digitized product and service components. The life cycle that comes with the realization of customer value is a critical component of these industrial solutions and manufacturing industry is undergoing significant changes as a result of digitalization and automation. As a result, smart services, or digital services that generate value from product data, are gaining popularity. Customers may now contribute in greater numbers in product design during the design process. Giving more people access, on the other hand, increases the security vulnerabilities associated with cloud manufacturing. Smart Manufacturing is one of the technology-driven approach to manufacturing that uses network-connected machines to monitor the process. Smart manufacturing has the ability to be used in a variety of ways, including putting sensors in manufacturing machines and collecting data on their operating state and performance. Thus, the main purpose here is to find ways to improve and automate production performance. This conceptual paper attempts to give a view of how a smart intelligence system may be used in business and how individuals and organizations can produce value. 2023 Author(s). -
Computational techniques for sustainable green procurement and production
Computational techniques are used to generate, solve, analyze, explain, or manage any simple or complex task. The use of environmentally responsible techniques to meet demand for resources, commodities, utilities, and services is known as green procurement. Computational technique in green procurement and production is one of the components of sustainable procurement, along with a commitment to social responsibility and good corporate behavior. Some solutions for this kind of issue are low-maintenance, energy-efficient, and long-lasting. Several experts and researchers provided their findings on the environmental impact of ICT with the use of computational techniques. Also, the importance of energy-efficient information technology for environmentally conscious and feasible information technology is a hot topic because a computer faces environmental challenges at every stage of its life, from development to use to disposal. Due to changing environmental conditions, corporations have prioritized carbon emissions in procurement and transportation, which have the highest carbon impact. To encourage potential suppliers to adopt environmentally friendly practices, green criteria should be introduced into public procurement. Environmentally friendly corporate practices and environmental conservation are considered significant tools through public procurement. Techniques for green procurement and production procedures have recently been correlated with the concept of computational techniques of green procurement and production, owing to the increased emphasis on the concept of computational approaches. For eco-friendly procurement and production operations, computational approaches are inculcated and presented in the same way that they are for green procurement and manufacturing. From this perspective, this chapter presents a methodology for merging computational techniques into green procurement and production in public procurement in the form of green computing. 2024 by Elsevier Inc. All rights reserved, including those for text and data mining, AI training, and similar technologies. -
Emerging Trends and the Future of Business Analytics
[No abstract available] -
An enhanced hybrid framework for IoT healthcare security using blockchain-driven multimedia data analysis and cybersecurity techniques
In the era of digital healthcare, safeguarding sensitive patient information while ensuring real-time access and decision-making is paramount. This study presents a novel Hybrid Blockchain-IoT Framework for secure healthcare data management, integrating Elman neural networkbased Blowfish encryption with blockchain and deep anomaly detection. The framework leverages IoT sensor data and utilizes a Proof-of-Authority (PoA) consensus mechanism to ensure tamper-proof transaction recording across decentralized nodes. A Long Short-Term Memory (LSTM) autoencoder combined with a Support Vector Machine (SVM) classifier enables accurate anomaly detection, while cryptographic functions ensure privacy and data integrity. The proposed system is evaluated using a healthcare dataset comprising over 1000 patient records across three network configurations (195, 585, and 1171 nodes). Results demonstrate a Wormhole Attack Probability (%) as low as 1.1%, Product Drop Ratio (%) between 1.2 and 2.7%, and Authentication Delay under 111 msoutperforming existing systems. Although the anomaly detection accuracy (98.98%) and F1-Score (0.90) are slightly below leading deep learning models, our framework uniquely combines encrypted transmission, distributed validation, and intelligent threat detection in a practical healthcare setting. The architecture ensures security, scalability, and efficiency, positioning it as a robust solution for next-generation smart healthcare ecosystems. 2026 The Authors -
A Novel Architecture for a Medical Image Recognition System Using Deep Learning-Based Multiple Regression Evaluation
Models based on machine learning are optimization models that collect data, assess it, and deliver the reports required by specialists and management to make the best decisions. The application of contemporary machine learning allows the organization to quickly analyze photographs, differentiate voices assist in providing customer service, assess the information that is at hand, and uncover connections to aid in decision-making processes. The results of this investigation use quantitative methodologies to collect data and analyze it using mathematical procedures such as regression modeling as well as analysis of variance. Deep learning techniques applied to digital imaging, particularly in medical treatment, can increase picture quality, aid in modeling, aid in making the best possible diagnosis, and successfully address demands from patients. To analyze the hypothesis, investigators intend to utilize statistical approaches such as descriptive data analysis, regression evaluation, and analysis of variance (ANOVA). The authors employ the purposive sample approach to choose respondents from the healthcare industry. Purpose sampling is a non-probability sampling approach. Researchers collected data from 193 respondents working at hospitals that are privately owned in Southern Asia. As stated by the study, all factors, including efficiently meeting patient needs, have a probability value of under 0.05, indicating that they are statistically noteworthy. Following the study, the coefficient of variance (R squared) is 0.744, or 74.4%. According to the study, there is a high association between better image quality and ML-based digital picture identification systems. The recognition of patterns and the application of artificial intelligence to computerized recognition of pictures also have a close link. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Reinforcement Learning for Early Detection and Intervention of Sepsis with Graph-Based Personalized Treatment Recommendations
Early detection and treatment of sepsis, a condition that can become fatal through the bodys response to infection, can enhance patient life. This paper explores how reinforcement learning can be applied to the early detection and treatment of sepsis, along with its novel features, which include personalized treatment recommendations and graph-based representations using Graph Neural Networks (GNNs). Moreover, domain adaptation and transfer learning strategies make the model applicable in a wide range of clinical contexts. The RL model is therefore designed to identify early warning signs and give prompt, individualized answers to avoid major repercussions. To ensure wide application, the RL model was trained using an enormous dataset of patient vitals, lab results, and clinical notes from numerous centers. It is already proven in real-life clinical situations that this model can improve patient outcomes and the quality of clinical decisions. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.