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Quintessence and false vacuum: Two sides of the same coin?
We studied the late-time acceleration scenarios using a quintessence field initially trapped in a metastable false vacuum state. The false vacuum has non-zero vacuum energy and can drive exponential expansion if not coupled with gravity. Upon decay of the false vacuum, the quintessence field is released and begins to evolve. We assumed conditions where the effective scalar potential gradient must satisfy ?Veff>A, characterised by a pressure term approximately ?p/p>O(?) invoking the string swampland criteria. We then derived the effective potential of the scalar with an upper bound on the coupling constant ?<0.6. Further analysis revealed that Veff shows a slow-roll behaviour for 0.1>?>-0.04 in the effective dark energy equation of state (EoS) -0.8 -
Loop-corrected scalar potentials and late-time acceleration in f(R) gravity
We construct an analytic f(R) gravity model that unifies early-time inflation with late-time cosmic acceleration within a single covariant framework. At high curvature, the model reproduces a Starobinsky-like inflationary plateau, while at low curvature it asymptotes to a stable dark energy-dominated phase. In the scalar-tensor representation, this construction yields a hilltop-type potential in the Jordan frame, which maps to an exponential potential in the Einstein frame. To account for radiative effects, we introduce a logarithmic correction to the Einstein-frame potential inspired by one-loop effective field theory, producing a late-time flattening without requiring fine-tuning. The resulting scalaron dynamics reduce the effective mass to O(H0), inducing a thawing regime that deviates from a cosmological constant at the sub-percent levels. A joint background likelihood analysis using Pantheon+SH0ES and BAO+CC datasets (within the CPL parametrization) yields H0=73.40.6 km/s/Mpc and ?m=0.2530.007, consistent with local expansion rate measurements. The best-fit scalar field parameters are ?0?0.027MPl and ??0.010MPl, corresponding to a present-day dark energy equation of state w0?-0.985. While compatible with ?CDM within current observational bounds, the model satisfies GR recovery at low curvature and exhibits attractor-like behavior, thereby minimizing sensitivity to initial conditions. The Author(s) 2025. -
Synthesis and characterization of Chitosan-CuO-MgO polymer nanocomposites
In the present work, we have synthesized Chitosan-CuO-MgO nanocomposites by incorporating CuO and MgO nanoparticles in chitosan matrix. Copper oxide and magnesium oxide nanoparticles synthesized by precipitation method were characterized by X-ray diffraction and the diffraction patterns confirmed the monoclinic and cubic crystalline structures of CuO and MgO nanoparticles respectively. Chitosan-CuO-MgO composite films were prepared using solution- cast method with different concentrations of CuO and MgO nanoparticles (15 - 50 wt % with respect to chitosan) and characterized by XRD, FTIR and UV-Vis spectroscopy. The X-ray diffraction pattern shows that the crystallinity of the chitosan composite increases with increase in nanoparticle concentration. FTIR spectra confirm the chemical interaction between chitosan and metal oxide nanoparticles (CuO and MgO). UV absorbance of chitosan nanocomposites were up to 17% better than pure chitosan, thus confirming its UV shielding properties. The mechanical and electrical properties of the prepared composites are in progress. 2018 Author(s). -
Arabica Coffee Bean Grading into Specialty and Commodity Type Based on Quality Using Visual Inspection
Expanding potential of coffee consumers to seek out the freshest and best flavors is a cause for the rise of specialty coffee inthe market. Specialty coffee is grown and harvestedmaintaining an emphasis on quality and clarity of flavor, whereas commodity coffee is harvested for caffeine content. Within those inclusive categories, arabica and robusta are the two types of main branches of coffee that weencounter in the coffee market. Specialty coffees differ significantly from conventional coffees in that they are cultivated at higher altitudes, can be traced, and are professionally processed after being harvested. The quality is constantly examined and understood at every stage, from growth to brewing. Green arabica quality is assessed by counting the defective beans present in the sample. These defects can be primary (Category I) or secondary (Category II). If the primary defects are null and less than five secondary defects, coffee is said to be a specialty.Prior research has been done on classifying the coffee species and differentiating good beans from bad beans. Our research involves the combination of machine learning like K-NN and deep learning convolutional neural networks for classifying specialty coffee from commodity type using computer vision. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Review On Image based Coffee Bean Quality Classification: Machine Learning Approach
Specialty coffee's demand is growing worldwide as coffee drinkers continue to look for the freshest and highest-quality flavors. Depending upon the quality, there are two categories in the coffee industry, that is specialty coffee and commodity/commercial coffee. Coffee beans are graded via visual inspection and cupping. A 300g sample of green coffee beans is used for visual assessment, and faulty beans are counted. As per the 'Specialty Coffee Association of America' (SCAA), defect can be either primary or secondary. For a coffee to be a specialty, it should have less than 5 secondary defects and zero primary defects. In this survey we have presented the coffee bean quality-related research which includes various machine learning approaches in classifying the coffee beans. The study has achieved quite promising prediction accuracies and was evaluated with test data. We have done a study on coffee bean quality classification and are willing to contribute an arabica coffee bean dataset and detection of coffee bean quality using transfer learning with higher accuracy. 2022 IEEE. -
Implementation and Investigation of an Optimal Full Adder Design for Low Power and Reduced Delay Conditions
Full adder is one of the important components in electronics, used for various fundamental processing algorithms such as addition and multiplication. The application of these full adders is included in but not limited to Very Large-Scale Integration (VLSI) and Digital Signal Processing (DSP). To provide scalability and reliability to the advanced algorithms for high-end applications, the designing system of full adder should be enhanced. So, in this paper, we intended to improve the efficiency of a full adder circuit to work under low power and delay conditions. The software we used in this project is MENTOR GRAPHICS using 180nm technology. The efficiency of the proposed transistor design is evaluated by analysing the power consumption, delay, PDP, capacitor load, delay w.r.t capacitance and PDP w.r.t capacitance. The parameters are compared between our proposed design and the literature schemes such as OLPFAD, DFEFA, DTLPCFA, and DPEHFA, respectively. It is evident that our proposed design outperforms the other. 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Dynamic Behaviour Analysis of Multi-Cell Battery Packs: A Simulation Study
In the era of IoT understanding the dynamic behavior of a Lithium-ion Battery Management System (BMS) has become gradually more important. This research investigates the dynamic behaviour of a six-cell Lithium-ion Battery Management System (BMS) through simulation. The study employs a comprehensive model encompassing key battery parameters, including cell capacity, voltage limits, temperature thresholds, and charge/discharge characteristics. Additionally, state variables such as State of Charge (SOC), State of Health, and State of Function are integrated to capture the battery's internal dynamics. The simulation incorporates a sinusoidal current profile to emulate realistic operating conditions. Notably, Coulomb counting is employed for SOC estimation, and protective measures against overvoltage, undervoltage, and overcurrent are implemented. The study also addresses balancing strategies and communication interfaces within the BMS. The results reveal nuanced interactions between voltage, temperature, SOC, and current, offering insights into the intricate behaviour of the battery system under dynamic conditions. This research not only advances our understanding of BMS functionality but also lays a crucial foundation for the evolution of battery technology and energy management systems in the IoT landscape. The Institution of Engineering & Technology 2023. -
Financial Big Data Analysis Using Anti-tampering Blockchain-Based Deep Learning
This study recommends using blockchains to track and verify data in financial service chains. The financial industry may increase its core competitiveness and value by using a deep learning-based blockchain network to improve financial transaction security and capital flow stability. Future trading processes will benefit from blockchain knowledge. In this paper, we develop a blockchain model with a deep learning framework to prevent tampering with distributed databases by considering the limitations of current supply-chain finance research methodologies. The proposed model had 90.2% accuracy, 89.6% precision, 91.8% recall, 90.5% F1 Score, and 29% MAPE. Choosing distributed data properties and minimizing the process can improve accuracy. Using code merging and monitoring encryption, critical blockchain data can be obtained. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Pediatric brain tumor segmentation and classification framework using SGC-U-NET and ARC-DEEP-CNN
Timely and precise pediatric Brain Tumor (BT) classification is challenging in the prevailing studies owing to the lack of growth rate calculation. Therefore, this paper proposes a growth rate-aware intelligent BT classification using child Magnetic Resonance Imaging (MRI) based on Arcsin Deep Convolutional Neural Network (Arc-Deep-CNN). Initially, the child's MRI is collected and then pre-processed for angle correction, resolution improvement, skull removal, and edge sharpening to improve the image quality. Meanwhile, the binary image dilation is done in the postpre-processing for accurate tumor location identification using the Central Limit Theorem-based Battle Royale Optimization Algorithm (CLT-BROA). From the pre-processed images, the wavelet features are extracted to improve the detection rate. Based on the tumor-identified images, pre-processed images, and extracted features, a robust Shuffled Group Convolutional layer added U-Net (SGC-U-Net) significantly segments the normal brain, benign, core, and malignant tumors affected brain. Then, the 3D tumor reconstruction is done by performing splitting, feature extraction, and growth rate calculation. Finally, a novel Arc-Deep-CNN proficiently classifies the BT into Medulloblastoma, Glioma, and Meningioma tumors with respect to the growth rate. The proposed Arc-Deep-CNN achieved maximum accuracy and minimum training time of 98.77% and 52136ms, respectively, showing impressive performance in pediatric BT classification. Bharati Vidyapeeth's Institute of Computer Applications and Management 2025. -
Optimizing Resource Allocation in Smart Healthcare Edge Networks Using Federated Swarm Intelligence and Artificial Neural Networks
Smart healthcare edge networks should be able to serve two purposes at once: to train federated machine learning models across a range of devices without violating patient privacy and to schedule other activities with latency constraints, like real-time patient events. Such methods as FFL-ANN attempt this by using fixed fuzzy rules, which do not work in the situation where the conditions of the network change in an unforeseen manner. In this paper, the framework FSI-ANN is introduced to combine particle swarm optimization to quality-aware model aggregation with ant colony optimization to adaptive real-time task scheduling and ANN-based predictions into a single framework. We experimented with FSI-ANN on 200 edge devices. It achieved 0.825 precision compared with 0.82 with FedAvg and 0.80 with FFL-ANN and reduced inference latency by 18%, 0.370.45 s. Throughput was maintained at 33 tasks/sec as compared with 27 of FedAvg. At burst load, the miss rate of the critical deadline was decreased by 90.2 percent and the energy consumed was decreased by 14.8% per round. The results suggest that adaptive learning using swarm is superior to the fixed rule-based approaches and simple averaging in the distribution of resources at the sustainable healthcare advantage. Copyright 2026 K. Praghash et al. International Journal of Distributed Sensor Networks published by John Wiley & Sons Ltd. -
Value-based teaching English language and literature
Aim. The ultimate aim became the realisation of the political ideas of democracy, equality, and social justice (Orlenius, 2001; Svingby, 1994, p. 57). The current study aimed to explore the concept of value education at Poornaprajna institutions that have adopted value education in their curriculum. Methods. The study was designed in a qualitative exploratory research approach. The researcher had adopted the interviews for a case study as a microscopic social study through observation and also studied the archival records in Poornaprajna institutions. The informal group interviews were administered as part of a qualitative research approach, which aimed to collect data from twenty eight English teachers and students of Poornaprajna institutions. Result. The validity and trustworthiness of the study were established by adopting Miles and Hubemans formula, which amounted to 0.91. The exploration and findings revealed the need and implication of value education in the present scenario. Further-more, the data analysis revealed that value education is a process that begins at home and continues in society, and further, it continues in formal educational institutions. Conclusion. The study envisions that the educational institutions must integrate the curriculum with value education so that students cultural worlds, meanings they attribute to behaviour, events which essentially lead to the developmental process of the society. The National Education Policy 2020, aims to universalise education in India by 2025; it also aims at inter-and multidisciplinary approaches in Indian education. The pre-sent study juxtaposes an interdisciplinary approach to English and value education. 2021, Pro Scientia Publica Foundation. All rights reserved. -
Effect of Phonological and Phonetic Interventions on Proficiency in English Pronunciation and Oral Reading
The current research aimed to know the effect of phonological and phonetic interventions in enhancing proficiency in English pronunciation and oral reading among teacher trainees. This study was of single-group pretest and posttest intervention designs. The sample size was selected through a stratified random sampling technique from teacher training colleges in Bengaluru. Two hundred and seven teacher trainees with L1 proficiency were chosen proportionately from Bangalore strata and orientated. Participants (N = 32) enrolled voluntarily in the intervention program for 20 hr. Intervention modules on phonology and phonetics were developed by the researcher and a segmental approach was adopted to teach modules in 20 sessions. After every session, the participants were allowed to record the modules in Audacity, a multiaudio recorder application. The recorded modules were interpreted, and scores were determined on number of intelligible words pronounced by the participants. Further, it was validated by the experts to authenticate the determined scores. The researcher applied oscillographic and observation methods to analyze the participants' progress in pronunciation and oral reading proficiency levels during the experiment. The Wilcoxon signed-rank test was used to test the impact of intervention between the pretest and posttest (before and after intervention). The hypotheses testing revealed the difference between preintervention and postintervention scores in phonological and phonetic awareness and oral reading among teacher trainees, and the sig. value is less than 0.05 across all the attributes. This study insists that English phonology and phonetics must be a crucial part of the English language teaching (ELT) curriculum and highlights that teachers must be able to spot the most appropriate pronunciation teaching and train the students of English as a foreign language (EFL) with intricates of intelligible pronunciation. This study navigates the need for language proficiency among teacher trainees, especially in English pronunciation and oral reading, and substantiates the evidence that effective intervention and teachers' knowledge of pronunciation enhance proficiency levels in pronunciation and oral reading among teacher trainees. Finally, it hopes that B.Ed colleges and teacher educators will be beckoned to use technology-integrated intervention to teach phonology and phonetics. 2024 Diwakar Prahaladaiah and Kennedy Andrew Thomas. -
Virtual influencers in India: Transforming digital marketing in the AI era
This chapter explores the emergence and impact of Virtual Influencers (VIs) in the Indian digital marketing landscape, focusing on their ability to engage consumers and redefine brand interactions through AI- driven personas. By examining case studies of prominent Indian virtual influencers like Kyra and Naina Avtr, the chapter analyzes their roles in consumer engagement, cultural relevance, and brand partnerships. The discussion highlights the unique strengths of VIs, including their consistency, scalability, and ability to operate across diverse platforms, while also addressing challenges related to authenticity, emotional connection, and regional adaptation. Drawing on theories of social influence and digital marketing, the chapter provides insights into the opportunities and limitations of VIs in India's culturally diverse and technology- driven market. It concludes by exploring the future of virtual influencers as dynamic tools for brands aiming to balance technological innovation with human- like relatability in a competitive digital ecosystem. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Pad Vending Machine with Cashless Payment
A vending machine aims to provide required product or the service to the customer with certain ease, wherein not much effort is required. This research work aims to design a pad vending machine with an option of payment using QR code which is implemented using blockchain to make the system much more efficient and reliable than the existing systems present in the Indian market. The system is divided into two parts, first being the working of the machine and second being the mode of payment which is implemented using a blockchain. It is noticed many times that due to unpredictable menstrual flow women tend to face a lot problems. To overcome this problem, a pad vending machine is proposed with certain advancements through which women can help themselves in the stated circumstances. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Online Voting System Using Blockchain
One of the major areas in technical development is blockchain and bitcoin. These technologies have enabled many simulations in in-hostile applications that have major issues with security and integrity of data. To provide more relevance to the available cyberphysical systems in the dimension of security, the blockchain technology offers a major help. If the present scenario is considered, we have multiple day-to-day applications that have been simulated and require more security enhancement. For example, the E-voting systems are a trend and their security features have to be upgraded to authenticate both systems and processes. The present research paper focuses on the same application and aims to provide security upgradation by proposing a working model of e-voting systems. 2020, Springer Nature Singapore Pte Ltd. -
SMOTE-Enhanced Machine Learning Techniques for Credit Card Fraud Detection
In today's digital world, most daily money transactions are done virtually through online systems. The rise in credit card transactions has increased the number of fraudulent transactions, leading to significant financial losses. Currently, the main problem faced during the analysis of transactions is the imbalance in the dataset. To address the issue of data imbalance and identify good models for accurately detecting fraudulent transactions, this paper presents a comparative study to determine the suitable machine learning algorithms for credit fraud detection. In this research study, Synthetic Minority Oversampling Technique (SMOTE) processing is done to balance the dataset, and various machine learning classifiers, Logistic Regression, Naive Bayes, K-Nearest Neighbor (KNN), Decision Trees, and Support Vector Machine (SVM) are compared and analyzed. During the experimental process, it was observed that after SMOTE was enhanced, SVM outperformed other models with an accuracy of 98.9%. When there are numerous features (variables) in the data, as is often the case in credit card transactions when several elements are taken into account, SVM can perform well. SVM differentiated outliers because of its margin-maximizing characteristics, which assisted in separating the fraudulent class from the non-fraudulent class. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Enhancing Stock Price Prediction: A Multi-Model Framework Integrating Technical Indicators and Sentiment Analysis
This paper proposes a multi-model strategy that would improve the predictive power of stock prices by combining time-series analytics with external market indicators. The system allows five different base prediction methods; Long Short-Term Memory (LSTM), Enhanced Bidirectional LSTM (XLSTM), Support Vector Machine (SVM) which may use radial basis function (rbf), linear or polynomial (poly) kernels, Autoregressive integrated moving average (ARIMA), and Seasonal Autoregressive integrated moving average (SARIMA). A stacking procedure which uses linear regression as a meta-model together with a voting ensemble method is then employed to link these base models. The feature engineering is thorough, as it provides for general price and volume data, a battery of technical indicators (SMA10, SMA20, EMA 12, EMA 26, MACD elements, and RSI14) and a general sentiment indicator (summarised financial news). Sentiment analysis is performed by a pipeline that is trained using RoBERTa and yields discrete numerical values (0 negative, 1 neutral, 2 positive). The model's capability is very rigorously gauged by the conventional metrics Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Directional Accuracy (DA). The real-world results demonstrate that the ensemble method is very efficient where the stacking arrangement leads to the lowest total MAPE of 0.6027 % MSFT and the highest directional Accuracy of 75.86 % GOOGL, thus, providing a strong evidence for the effectiveness of the thorough integration of heterogeneous machine-learning, statistical, and sentiment- analysis methods to produce the most accurate financial forecasts. 2026 IEEE. -
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Self-Induced Versus Structured Corporate Social Responsibility: The Indian Context
Adoption of Corporate Social Responsibility (CSR) ranks among the top priorities of the corporates in contemporary times. It is treated as a core business practice across the corporate globe. In the year 2013, the Ministry of Corporate Affairs, Government of India enacted mandatory CSR rules under the Companies Act, 2013 and imposed statutory obligations on the companies operating in India to implement CSR activities. With this, India became one of the first countries in the world to legislate minimum regulatory spends on CSR practices. This chapter aims to evaluate the response of this legislation since the introduction of mandatory CSR rules in India. It looks into the important trends in corporate social responsibility spending of companies in India and also maps the CSR expenditure with various Sustainable Development Goals (SDGs). This chapter forms a case for deliberation for policymakers, practitioners, scholars and business organization to understand the implications of mandatory CSR as well as how Indian companies have responded to this CSR rule. The findings also provide important insights for the other countries promulgating statutory approaches to implement CSR in their own countries. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Message framing and COVID-19 vaccine acceptance among millennials in South India
Vaccine hesitancy and refusal remain a major concern for healthcare professionals and policymakers. Hence, it is necessary to ascertain the underlying factors that promote or hinder the uptake of vaccines. Authorities and policy makers are experimenting with vaccine promotion messages to communities using loss and gain-framed messages. However, the effectiveness of message framing in influencing the intention to be vaccinated is unclear. Based on the Theory of Planned Behaviour (TPB), this study analysed the impact of individual attitude towards COVID-19 vaccination, direct and indirect social norms, perceived behavioural control and perceived threat towards South Indian millennials intention to get vaccinated. The study also assessed the effect of framing vaccine communication messages with gain and loss framing. Data was collected from 228 Millennials from South India during the COVID-19 pandemic from September to October 2021 and analysed using PLS path modelling and Necessary Condition Analysis (NCA). The findings reveal that attitudes towards vaccination, perceived threat and indirect social norms positively impact millennials intention to take up vaccines in both message frames. Further, independent sample t-test between the framing groups indicate that negative (loss framed message) leads to higher vaccination intention compared to positive (gain framed message). A loss-framed message is thus recommended for message framing to promote vaccine uptake among millennials. These findings provide useful information in understanding the impact of message framing on behavioural intentions, especially in the context of vaccine uptake intentions of Millennials in South India. Copyright: 2022 Prakash et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.