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The accumulation, antioxidant defences, and secondary metabolite production in common sage (Salvia officinalis L.) under lead toxicity
The growing levels of lead (Pb) in agricultural soil and water bodies as a result of industrialization and human activity present serious challenges for medicinal plants. The present study investigates the complex responses of Salvia officinalis to toxicity caused by Pb including metal accumulation, translocation dynamics, antioxidant defenses, and the production of secondary metabolites. Pb showed preferential accumulation in the roots, peaking at (1000 ppm Pb) (2484.2 mg kg-1). The reduced levels of total chlorophyll (1.29-fold), protein (1.9-fold), and carbohydrate (2.5-fold) under prolonged exposure to stress demonstrate the toxic impacts of lead. Proline and total phenolic content (TPC) increased concentration-dependently under lead stress, while flavonoids were found to be decreased with the enhancement of lead toxicity. Enzymatic antioxidants (catalase, APX, and SOD) showed notable increase, especially in the 30 days of treatment, demonstrating the plants strong defenses. S. officinaliss adaptive responses were highlighted by concentration-dependent increases in non-enzymatic antioxidants such as total antioxidant capacity (TAC) and DPPH radical scavenging activity. Crucially, under lead stress, S. officinalis showed 1.7-fold increased rosmarinic acid (RA) production, in plants exposed to 200 ppm for 30 days treatment. However, further exposure to lead significantly caused the reduction of RA production. The results add to our knowledge of how sage plants respond to environmental stress and offer important insights for future uses in phytoremediation and the breeding of stress-tolerant plant cultivars. Furthermore, the research highlights about the S. officinaliss potential as a source of bioactive compounds possessing antioxidant qualities, under low levels of lead stress. 2024, Indian journals. All rights reserved. -
Improvised process model for prediction of software development effort by integration of risk
Software development involves usage of a finite quantum of resources in accordance with the estimated effort and schedule. The newlineSoftware Development Lifecycle comprises activities pertaining to software engineering. The software engineering activities could be carried out using any of the various models available in practice. The newlineprocess of estimating size and effort accurately is vital in a software project since it could influence the success of the project. However, the realistic estimation of time and resources required for a project newlinecontinues to be a challenge. Risks exist in any software project, and hence Risk management is required to be considered across various processes throughout the project. The risks could be quantified by newlinearriving at the risk score based on the probability of occurrence of the risk and its impact. This research focused on the aspect that risk factors need to be considered in software effort estimation. A total of 503 newlinesoftware projects were considered, and from this dataset, projects which had risk score information were extracted and utilized for further analysis. This research work proposed an improvised effort estimation process by including risk scores in the standard estimation process. It also analysed the relationship existing between risk score in the project and other parameters considered in the effort estimation process. Regression analysis that was done on the dataset revealed an improvement in the model fitment by inclusion of risk score. An ensemble machine learning approach was utilized through deployment of Extreme Gradient Boosting algorithm. This algorithm was chosen newlineafter a model selection process by comparing various algorithmic models. The results indicated a better model fit by including risk as one of the parameters in the effort estimation process. A validation for the newlineproposed risk-integrated effort estimation model was done through responses from industry practitioners to a research instrument. -
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Sustainability of Circular Fashion in India
The Indian fashion industry, valued at USD 100 billion, faces pressing sustainability challenges, including resource depletion, labor issues, and excessive textile waste. This study explores the potential of circular fashion in advancing Sustainable Development Goal 12 (SDG 12) by promoting responsible consumption and production. Through consumer surveys, business insights, and interviews with eco- entrepreneurs, the research examines awareness, adoption barriers, and opportunities in circular fashion. Findings reveal growing consumer interest in sustainable apparel, yet concerns about product quality, hygiene, and brand credibility persist. Businesses acknowledge the potential of circular models but struggle with skill shortages, inventory management, and sanitation costs. The study highlights the need for policy interventions, investment in recycling technologies, and consumer education to accelerate circular fashion adoption. By embracing reduce,reuse, and recycle principles, Indias fashion sector can transition towards a more sustainable and resilient future. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Harnessing Salt-Tolerant Medicinal Plants: A Sustainable Approach to Agriculture and Herbal Medicine
Global agricultural productivity is significantly hampered by salt-affected soils, which calls for sustainable management techniques. The promise of salt-tolerant medicinal plants as a twofold answer for recovering saline lands and generating useful phytochemicals for therapeutic applications is highlighted in this review. Through a variety of adaptation processes, such as osmoprotection, ion compartmentalization, and antioxidant defense, certain medicinal species, such as Aloe vera, Catharanthus roseus, Plantago ovata, Capparis spp., and Salicornia, exhibit an innate tolerance to high salinity. Exogenous applications of plant biostimulants, beneficial microbes, and nanoparticles can further increase their resilience. Furthermore, the integration of omics approachesgenomics, transcriptomics, proteomics, and metabolomicshas advanced our understanding of the molecular basis of salt tolerance and secondary metabolite regulation in these plants. In addition to promoting their wider application in saline agriculture and herbal medicine, this review highlights the ecological and pharmacological importance of salt-tolerant medicinal plants, providing a viable route to soil regeneration and the synthesis of bioactive compounds. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Phytochemicals and Biological Activities of Flowers of Chrysanthemum
A prominent edible flower in the Asteraceae family, Chrysanthemum morifolium is used extensively in traditional medicine and as a functional food because of its many different bioactive compounds. It has significant pharmacological properties and is abundant in flavonoids, phenolic acids, terpenoids, polysaccharides, and derivatives of caffeoylquinic acid. Its neuroprotective, hepatoprotective, anticancer, antidiabetic, antioxidant, anti-inflammatory, and larvicidal qualities have all been shown in scientific research. The reduction of oxidative stress and apoptosis in brain cells is associated with its neuroprotective effects, whereas the modulation of antioxidant pathways is associated with its hepatoprotective benefits. Because of its capacity to trigger apoptosis and control the cell cycle in cancer cells, the flower is said to have anticancer properties. It also supports its antidiabetic potential by improving glucose metabolism and insulin sensitivity. Its anti-inflammatory and antioxidant properties aid in the fight against oxidative stress and associated illnesses. Although moderate consumption is advised, toxicological research indicates that it is generally safe to consume. Processing methods such as formulation, drying, and extraction affect the compounds stability and bioavailability. Through a thorough examination of its phytochemistry, biological activities, safety profile, and processing considerations, this chapter emphasizes C. morifolium as a valuable natural therapeutic and functional food ingredient, highlighting its potential to prevent disease and promote health. 2026 Hosakatte Niranjana Murthy. -
Biotic and Abiotic Elicitation for Enhanced Production of Stilbenes
Stilbenes are a class of phytoalexins with important pharmacological characteristics, such as anti-inflammatory, anticancer, and antioxidant effects. These substances have drawn interest due to their possible uses in the pharmaceutical, nutraceutical, and cosmetic sectors. Nevertheless, the endogenous production of stilbenes in plants is often limited, necessitating the development of strategies to boost their yield. Elicitation has been shown to be a viable strategy for increasing stilbene biosynthesis in plants by utilizing both biotic and abiotic elicitors. This review delves deeply into the most recent developments in biotic and abiotic elicitation methods used to boost stilbene production. The mechanisms by which different biotic elicitors, including plantmicrobe symbiosis, pathogen infection, and microbial interactions, stimulate the biosynthesis of stilbenes are discussed. Discussion of the molecular mechanisms behind elicitor-induced stilbene biosynthesis emphasizes the activation of key biosynthetic pathways, regulatory genes, and transcription factors. It has been demonstrated that elicitors like methyl jasmonate, chitosan, and fungal extracts, as well as UV-C light and cyclodextrins, increase the expression of key enzymes like resveratrol synthase (RS), stilbene synthase (STS), and phenylalanine ammonia-lyase (PAL), which, in turn, increases the accumulation of stilbenes. Recent developments in metabolic engineering are emphasized as promising methods to further increase stilbene yields, such as gene editing and overexpressing biosynthetic genes. By integrating these strategies, we focus on offering a thorough insight into the elicitation processes that can be harnessed to optimize stilbene production, thereby contributing to the development of sustainable and efficient production systems for these valuable phytochemicals. To aid in the development of superior and scalable methods for producing these valuable compounds, this review attempts to provide a thorough understanding of how to improve stilbene production biotechnologically by integrating insights on biotic and abiotic elicitation. 2025 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Identification of broken characters in degraded documents
Optical Character Recognition (OCR) deals with the recognition of characters in a text document. Steps like Preprocessing, Segmentation and Recognition are embedded in the OCR machine. When a document is scanned it will be taken into OCR and will recognize the characters. But noisy scanning of documents, low-quality printed documents and thresholding error leads to the generation of broken characters. When these documents are given as inputs into OCR, the recognition becomes a tedious process since the broken characters are misunderstood by the OCR machine. So the broken characters have to be identified and segmented separately. This work aims to enhance the degraded documents with broken characters using image processing techniques. For identifying or recognizing the broken character from the image various techniques like vertical projection profile, horizontal projection profile, chain code, mean based thresholding are used. The lines from the document are separated using line segmentation. Separate characters are extracted using Vertical Projection Profile and Horizontal Projection Profile. The character is identified using chain coding. The broken characters are found from them using Mean-based Thresholding and is merged using Heuristic information. The proposed method achieves an accuracy of 92.88% and also performs well for color image documents as well as black and white image documents also because of the effective preprocessing. 2018 Intelligent Network and Systems Society. -
A heavy metal tolerant Thiopseudomonas alkaliphila strain as a potential plant growth promoter isolated from Bengaluru region
Thiopseudomonas alkaliphila, a Pseudomonadaceae has diverse environmental role that has not been much explored. Current study highlights, the isolated strain from industrial sites of Bengaluru with heavy metal tolerance against lead, chromium and cadmium. The antibiotic susceptibility test (AST) and minimum inhibitory concentration (MIC) showed sensitive against all the antibiotics used in the study. Subsequently, 16s rRNA analysis established and closely related to T. alkaliphila D2441 strain, whole genome was submitted, GenBank SRA database accession number is as follows PRJNA1258058. The unravelling of genetic determinants analyzed for heavy metals, antibiotic resistance and plant growth promoting traits were compared with related strains. A single chromosome with 2,400,551 bp length, average GC ratio 49.44 % and with 1941 protein-encoding genes (PEGs), the strain can bioremediate different heavy metals (354 genes/proteins), along with an aptitude as plant growth promoting rhizobacteria (PGPR) evidenced by genes showcasing tolerance against adverse environmental conditions under stress for phytohormones, plant nutrient acquisition, heat and shock chaperones, siderophore etc. The study highlights, T. alkaliphila as a non-pathogenic, potential heavy metal remediator with potential activity for PGPR traits at genetic levels. 2025 -
CBMIR: Content Based Medical Image Retrieval Using Hybrid Texture Feature Extraction Method
Due to the revolution of digital era in the medical domain at various hospitals across the world, the online users on the internet access have been increased. So the amount of collections of digitized medical images has grown rapidly and continuously. As well it is ratting significant to mention that the images are globally used by radiologists, professors in medical colleges and Lab technicians, etc. These Images are increasingly applied to communicate information about patient history. In this context, there is a necessity to develop appropriate systems to manage these medical images in storage and retrieval for diagnosis of the patient information. Another big issue is the convolution of image data and that can be interpreted in different ways. In order to manipulate these data and establish policies to its content is very tedious job. This will raise another big question. These issues motivated the researchers to give more focus on the image retrieval area whose goal is trying to solve those problems to provide an efficient retrieval system to the user community. In this perspective, this work has been proposed to facilitate radiologists, professors in medical colleges, lab technicians, and all other medical image user communities for their purpose for easy access from the remote location. 2022 IEEE. -
High gain ultra wideband fractal antenna
A high gain Compact Octagonal Ultra-wideband Fractal Antenna (COUFA) using the Dual Layer Meta Frequency Selective Surface Reflector (DLMFSSR) is presented in this manuscript. The proposed Frequency Selective Surface (FSS) provides a suitable reflection phase to act as a reflector and is capable of enhancing the gain of the antenna in its wide operating bandwidth. The proposed antenna design provides better impedance bandwidth of 2-10.37 GHz with significant increase in the gain of 0.41-11.83 dB at various resonance frequencies in comparison with the antenna without reflector. The complete antenna with DLMFSSR is designed and simulated using High Frequency Structure Simulator (HFSS). The Proposed antenna, FSS are fabricated and the numerical results for return loss S11, VSWR and gain are demonstrated. Simulation and fabrication results are found to be worthy, which suites the design malleable enough for several modern UWB wireless applications. Copyright 2019 American Scientific Publishers All rights reserved. -
Mutual Information Pre-processing Based Broken-stick Linear Regression Technique for Web User Behaviour Pattern Mining
Web usage behaviour mining is a substantial research problem to be resolved as it identifies different user's behaviour pattern by analysing web log files. But, accuracy of finding the usage behaviour of users frequently accessed web patterns was limited and also it requires more time. Mutual Information Pre-processing based Broken-Stick Linear Regression (MIP-BSLR) technique is proposed for refining the performance of web user behaviour pattern mining with higher accuracy. Initially, web log files from Apache web log dataset and NASA dataset are considered as input. Then, Mutual Information based Pre-processing (MI-P) method is applied to compute mutual dependence between the two web patterns. Based on the computed value, web access patterns which relevant are taken for further processing and irrelevant patterns are removed. After that, Broken-Stick Linear Regression analysis (BLRA) is performed in MIP-BSLR for Web User Behaviour analysis. By applying the BLRA, the frequently visited web patterns are identified. With the identification of frequently visited web patterns, MIP-BSLR technique exactly predicts the usage behaviour of web users, and also increases the performance of web usage behaviour mining. Experimental evaluation of MIP-BSLR method is conducted on factors such as pattern mining accuracy, false positives, time requirements and space requirements with respect to number of web patterns. Outcomes show that the proposed technique improves the pattern mining accuracy by 14%, and reduces the false positive rate by 52%, time requirement by 19% and space complexity by 21% using Apache web log dataset as compared to conventional methods. Similarly, the pattern mining accuracy of NASA dataset is increased by 16% with the reduction of false positive rate by 47%, time requirement by 20% and space complexity by 22% as compared to conventional methods. 2020. All Rights Reserved. -
Co-sputtered V2O5TiN composite on Ag-network current collector for high-performance flexible transparent thin-film supercapacitors
Next-generation wearables require extremely capable electrochemical energy-storage devices that exhibit improved performance with high flexibility and transparency. Herein, we present a highly flexible and transparent electrochemical thin-film supercapacitor electrode fabricated by co-sputtering V2O5 and TiN on an Ag-network-based current collector. The electrodes' physical properties, optical properties, and structural morphologies are studied using X-ray diffraction, UVvisible spectroscopy, and scanning electron microscopy, respectively. A symmetric device is fabricated using V2O5 and TiN on an Ag network, and the TiN sputter power is varied to optimize the performance. The device performance of the co-sputtered electrodes at various composition ratios is studied. The optimized V2O5TiN (200?40)/Ag electrode device with pseudocapacitive behavior delivers an excellent areal specific capacitance of 98.66 mF cm2 at a current density of 4 mA cm2 with a charge retention of 90.12 % after 6000 cycles. The V2O5TiN (200?40)/Ag electrode device outperforms other reported electrodes, with an energy density and power density of 30.83 ?Wh cm2 and 2999.67 ?W cm2, respectively, and excellent mechanical stability. 2023 -
Digital transformation and sustainability artificial intelligence and sustainable development
Digital transformation and sustainability are increasingly intertwined in today's rapidly evolving landscape, where technological advancements and environmental concerns converge to shape the future of industries, communities, and societies. Operations, strategies and This transformation can enhance efficiency, improve customer experiences, and foster innovation. However, it also carries the responsibility of addressing the pressing sustainability social inequities. The relationship between digital transformation and sustainability is complex, yet synergistic. On one hand, blockchain-provides powerful tools for enhancing sustainability efforts across various sectors. For example, can consumption be smart while improving efficiencies reducing waste? Additionally, big data analytics can help organizations assess their environmental impact, enabling informed decision-making that supports sustainable practices. This data-driven approach allows companies to track their carbon footprints, manage resources more effectively, and identify areas for improvement. 2025, IGI Global Scientific Publishing. -
Financial Behaviour Analysis for Payment Bank Adoption Using Random Forest and PCA: An Indian Perspective
The gradual acceptance of payment banks in India constitutes an important challenge to equitable financial development, especially for underbanked rural communities. The proposed study handles the difficulty by examining financial behaviour through a hybrid machine learning methodology that integrates Principal Component Analysis (PCA) for dimensionality reduction with a Random Forest classifier for predictive modelling. The study utilises datasets obtained from Kaggle that reflect demographic, behavioural, and digital engagement variables. PCA preserves approximately 9 0% of variance while reducing feature complexity, allowing the Random Forest to effectively characterise adoption behaviour. In comparison to conventional classifiers such as Logistic Regression, SVM, and Decision Trees, the suggested model improved performance, attaining 96.7% accuracy, 95.8% precision, 97.1% recall, 96.4% F 1-score, and an AUC-ROC of 0.982. The findings exceed all chosen baselines, demonstrating the system's resilience and reliability. The approach provides behavioural insights essential for policy formulation and strategic engagement by pinpointing the most significant adoption determinants. This research greatly advances the digital banking sector by integrating data science with social impact, providing a clear, high-performing solution to inform financial inclusion policies. It establishes a basis for the development of future real-time and personalised adoption prediction systems utilising advanced AI methodologies. 2025 IEEE. -
Statistical features learning to predict the crop yield in regional areas
The plethora of information presented in the form of benchmark dataset plays a significant role in analyzing and understanding the crop yield in certain regions of regional territory. The information may be presented in the form of attributes makes a prediction of crop yield in various regions of machine learning. The information considered for processing involves data cleaning initially followed by binning to reduce the missing data. The information collected is subjected to clustering of data items based on patterns of similarity, The data items that are similar in nature is fed to the system with similarity measure, which involves understanding the distance of data items from its related data item leading to hyper parameters for analyzing of information while calculating the crop yield. The information may be used to ascertain the patterns of data that exhibit similarity with nearest neighbor represented by another attribute. Thus, the research method has yielded an accuracy of 89.62% of classification for predicting the crop yield in agricultural areas of Karnataka region. 2022 Institute of Advanced Engineering and Science. All rights reserved. -
On families of graphs which are both adjacency equienergetic and distance equienergetic
Let A(G) and D(G) be the adjacency and distance matrices of a graph G respectively. The adjacency energy or A-energy EA(G) of a graph G is defined as the sum of the absolute values of the eigenvalues of A(G). Analogously, the D-energy ED(G) is defined to be the sum of the absolute values of the eigenvalues of D(G). One of the interesting problems on graph energy is to characterize those graphs which are equienergetic with respect to both the adjacency and distance matrices. A weaker problem is to construct the families of graphs which are equienergetic with respect to both the adjacency and distance matrices. In this paper, we find the explicit relations between A-energy and D-energy of certain families of graphs. As a consequence, we provide an answer to the above open problem (Indulal in https://icgc2020.wordpress.com/invitedlectures, 2020; http://www.facweb.iitkgp.ac.in/rkannan/gma.html, 2020) The Indian National Science Academy 2022. -
ITERATED LINE GRAPHS WITH ONLY NEGATIVE EIGENVALUES ?2, THEIR COMPLEMENTS AND ENERGY
In this article, several iterated line graphs Lk (G) with all equal negative eigenvalues ?2 are characterized for k ? 1 and their energy consequences are presented. Also the spectra and the energy of complement of these graphs are obtained, interestingly they have exactly two positive eigenvalues with different multiplicities. Moreover, we characterize a large class of equienergetic graphs which extend some of the existing results. Palestine Polytechnic University-PPU 2025. -
AIFMS Autonomous Intelligent Fall Monitoring System for the Elderly Persons
Falls are the major cause of injuries and death of elders who live alone at home. Various research works have provided the best solution to the fall detection approach during the day. However, falls occur more at night due to many factors such as low or zero lighting conditions, intake of medication/drugs, frequent urination due to nocturia disease, and slippery restroom. Based on the required factors, an autonomous monitoring system based on night condition has been proposed through retro-reflective stickers pasted on their upper cloth and infrared cameras installed in the living environment of elders. The developed system uses features such as changes in orientation angle and distance between the retro-reflective stickers to identify the human shape and its characteristics for fall identification. Experimental analysis has also been performed on various events of fall and non-fall activities during the night exclusively in the living environment of the elder, and the system achieves an accuracy of 96.2% and fall detection rate of 92.9%. Copyright 2022, IGI Global. -
A Review on Artificial Intelligence Techniques for Multilingual SMS Spam Detection
With social networks increased popularity and smartphone technology advancements, Facebook, Twitter, and short text messaging services (SMS) have gained popularity. The availability of these low cost text-based communication services has implicitly increased the intrusion of spam messages. These spam messages have started emerging as an important issue, especially to short-duration mobile users such as aged persons, children, and other less skilled users of mobile phones. Unknowingly or mistakenly clicking the hyperlinks in spam messages or subscribing to advertisements puts them under threat of debiting their money from either the bank account or the balance of the network subscriber. Different approaches have been attempted to detect spam messages in the last decade. Many mobile applications have also evolved for spam detection in English, but still, there is a lack of performance. As English has been completely covered under natural language processing, other regional languages, such as Urdu and Hindi variants, have specific issues detecting spam messages. Mobile users suffer greatly from these issues, especially in multilingual countries like India. Thus, this paper critically reviews the artificial intelligence-based spam detection system. The review lists out the existing systems that use machine and deep learning techniques with their limitations, merits, and demerits. In addition, this paper covers the scope for future enhancements in natural language processing to efficiently prevent spam messages rather than detect spam messages. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.