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Arithmetic integer additive set-valued graphs: A creative review
For a non-empty ground set X, finite or infinite, the set-valuation or set-labeling of a given graph G is an injective function f: V (G) ? P(X), where P(X) is the power set of the set X. A set-indexer of a graph G is an injective set-valued function f: V (G) ? P(X) such that the function f?: E(G) ? P(X) ? { defined by f? (uv) = f (u)? f (v) for every uv?E(G) is also injective, where ? is a binary operation on sets. Let N0 be the set of all non-negative integers and P(N0) is its power set. An integer additive set-labeling (IASL) of a graph G is an injective function f: V (G) ? P(N0) such that the induced function f+: E(G) ? P(N0) is defined by f+ (uv) = f (u) + f (v), where f (u) + f (v) is the sumset of the sets f (u) and f (v). An IASL f of a graph G is said to be an integer additive set-indexer (IASI) of G if the induced function f+ is also injective. In this paper, we critically and creatively review the concepts and properties of a particular type integer additive set-valuation, called arithmetic integer additive set-valuation of graphs. 2020 the author(s). -
On the rainbow neighbourhood number of Mycielski type graphs
A rainbow neighbourhood of a graph G is the closed neighbourhood N[v] of a vertex v ? V (G) which contains at least one colored vertex of each color in the chromatic coloring C of G. Let G be a graph with a chromatic coloring C defined on it. The number of vertices in G yielding rainbow neighbourhoods is called the rainbow neighbourhood number of the graph G, denoted by rX(G). In this paper, we discuss the rainbow neighbourhood number of the Mycielski type graphs of graphs. 2018 Academic Publications. -
A Review of Smart Grid Management Systems Using Machine Learning Algorithms for Efficient Energy Distribution
The smart grid is an intelligent electricity network that uses digital technology to improve the efficiency, reliability, and sustainability of power delivery. Machine learning is a type of artificial intelligence that can be used to analyze data and learn from it. This makes it a valuable tool for the smart grid, as it can be used to solve a variety of problems, such asorecasting energy demand, detecting, and preventing outages, optimizing power flows, managing distributed energy resources, ensuring grid security.In this article, we will review the use of machine learning in the smart grid. We will discuss the different machine learning algorithms that are being used, the challenges that need to be addressed, and the future of machine learning in the smart grid.. The Authors, published by EDP Sciences, 2024. -
A Secure Data Encryption Mechanism in Cloud Using Elliptic Curve Cryptography
Cloud computing is undergoing continuous evolution and is widely regarded as the next generation architecture for computing. Cloud computing technology allows users to store their data and applications on a remote server infrastructure known as the cloud. Cloud service providers, such Amazon, Rackspace, VMware, iCloud, Dropbox, Google's Application, and Microsoft Azure, provide customers the opportunity to create and deploy their own applications inside a cloud-based environment. These providers also grant users the ability to access and use these applications from any location worldwide. The subject of security poses significant challenges in contemporary times. The primary objective of cloud security is to establish a sense of confidence between cloud service providers and data owners inside the cloud environment. The cloud service provider is responsible for ensuring user data's security and integrity. Therefore, the use of several encryption techniques may effectively ensure cloud security. Data encryption is a commonly used procedure utilised to ensure the security of data. This study analyses the Elliptic Curve Cryptography method, focusing on its implementation in the context of encryption and digital signature processes. The objective is to enhance the security of cloud applications. Elliptic curve cryptography is a very effective and robust encryption system due to its ability to provide reduced key sizes, decreased CPU time requirements, and lower memory utilisation. 2024 IEEE. -
Designing a Precision Seed Sowing Machine for Enhanced Crop Productivity
A seed sowing machine is a valuable agricultural device that facilitates the precise and efficient sowing of seeds in fields. When designing and optimizing such a machine, several crucial factors need consideration including seed size, seed rate, soil type, and field conditions. The primary objective is to achieve uniform seed distribution and optimal seed-to-soil contact, which can be accomplished by incorporating a seed metering mechanism to control the seed rate accurately. Versatility is another important aspect of the machine's design, as it should be able to handle different seed sizes, types, soil conditions, and field variations. To achieve this, utilizing advanced technologies such as sensors, automation, and precision farming techniques can significantly enhance the machine's performance and efficiency while also reducing costs and minimizing environmental impact. The optimization of a seed sowing machine plays a crucial role in ensuring successful crop production. By implementing cutting-edge technologies and precision farming techniques, farmers can increase their yields and decrease the amount of seed and fertilizer needed for a specific area. Ultimately, this leads to improved productivity, increased profitability, and a more sustainable approach to agriculture. 2024 E3S Web of Conferences -
Enhancing Data Security Through Semi-parametric Shrinkage Estimation of Shannon and Past Entropy in Geometric Distributions
The concept of entropy has been introduced in statistical methods to measure the amount of information contained in a random observation, and it plays a crucial role in various fields, especially in data security. This paper focuses on the semi-parametric shrinkage estimation of Shannon entropy and past entropy measures of the geometric distribution under complete, right, and time-censored sampling procedures. Shannon entropy, a key measure of uncertainty, along with past entropy (or min-entropy), which assesses the least predictable outcomes, plays a crucial role in ensuring strong data security, particularly in cryptographic systems and secure communications. While most existing literature addresses estimating these entropy measures for continuous distributions, this paper evaluates shrinkage estimators to enhance the efficiency of the ordinary semi-parametric least squares estimator for geometric distributions. This study explores the constant shrinkage factor and modified Thomson-type estimators, evaluating their effectiveness against traditional methods such as maximum likelihood estimators. Empirical investigations conducted with simulated samples indicate that shrinkage estimators consistently outperform maximum likelihood estimators, showcasing better relative efficiency. These results emphasize the potential of shrinkage estimators to enhance entropy-based measures in data security applications, which can lead to more robust cryptographic key generation, password strength analysis, and intrusion detection. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Artificial Intelligence Driven Drug Delivery Systems: Recent Advances and Emerging Trends
Drug Delivery Systems (DDS) play a critical role in ensuring the therapeutic efficacy and safety of pharmaceutical agents. Conventional drug delivery approaches often suffer from limitations such as poor bioavailability, non-specific targeting, and systemic toxicity. Recent advancements in Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) have revolutionized the design and optimization of drug delivery platforms. AI-driven methods enable predictive modeling, intelligent nanocarrier design, and personalized therapeutic strategies by analyzing large biomedical datasets. These technologies facilitate optimized drug formulation, controlled release mechanisms, and targeted delivery, thereby improving treatment outcomes. AI algorithms such as Support Vector Machines (SVM), random forests, Convolutional Neural Networks (CNN), and reinforcement learning are increasingly applied in nanoparticle design, pharmacokinetic modeling, and clinical decision support systems. Additionally, emerging concepts such as self-driving laboratories, autonomous drug delivery systems, and AI-guided nanomedicine are reshaping pharmaceutical research. This review provides a comprehensive analysis of recent advances in AI-driven drug delivery systems, covering computational techniques, nanocarrier optimization, clinical applications, and emerging research trends. Comparative analysis tables summarize key algorithms, delivery platforms, and research developments reported in the literature. Finally, major challenges including data quality, regulatory issues, and interpretability of AI models are discussed along with future directions for the integration of AI in precision medicine and smart therapeutics. 2026, Dr. Yashwant Research Labs Pvt. Ltd. All rights reserved. -
Green synthesis and electrochemical characterization of rGOCuO nanocomposites for supercapacitor applications
Reduced graphene oxide (rGO) were prepared from graphene oxide (GO) by using piperine as a green reducing agent extracted from Piper nigrum. The obtained rGO had few defects and lacked connectivity between the layers. To overcome these defects, copper oxide (CuO) nanoparticles were synthesized ultrasonically and nanocomposites of rGOCuO were prepared. The conductivities of the rGO, CuO and rGOCuO nanocomposites were determined by AC impedance spectroscopy in different electrolytes. Morphology, composition and electronic structure of CuO, rGO and rGOCuO nanocomposites were characterized using X-ray diffraction (XRD), scanning electron microscopy (SEM), X-ray photon spectroscopy (XPS) and electrochemical techniques. Transmission electron microscopy (TEM) images portrait CuO as a fish caught in the net of rGO layers. The rGOCuO nanocomposite exhibiting lower resistance and higher capacitance was used in fabrication of supercapacitor electrodes. The specific capacitance of the fabricated supercapacitor was found to be 137Fg?1. The supercapacitor performance of the nanocomposite electrode is attributed to the synergistic effect of double-layer capacitance of rGO and redox capacitance of CuO nanoparticles. [Figure not available: see fulltext.] 2016, Springer-Verlag Berlin Heidelberg. -
Investigations on thermo-mechanical properties of organically modified polymer clay nanocomposites for packaging application
Eco-friendly packing polymer materials are in the spotlight but, lack of new biodegradable polymers either natural or synthetic is yet to establish the market more competitively. So, in the present work, clay as a nano-filler is embedded and organically modified in some synthetic and natural polymers which are well established commercially to enhance their biodegradability. The impact of clay on the properties of synthetic polymers namely, poly(methyl methacrylate) (PMMA), poly(vinyl chloride) (PVC), poly(vinyl acetate) (PVAc) and natural polymer cellulose acetate butyrate (CAB) was studied. Results from differential scanning calorimetric (DSC) showed a decrease in the glass transition temperature of organically modified polymer clay nanocomposites (PCC) than pure polymers. Scanning electron microscopy (SEM) displayed a uniform surface with small-sized crystallites distributed on the polymer surface. X-ray diffraction (XRD) spectra revealed the formation of enhanced intercalated structures in PCC. Furthermore, FTIR studies showed that the interlayer bonding (SiO bands) of pure clay is deformed in PCCs. The tensile strength of PCC increased with an increase in organo-clay loading. This unique mechanical behavior is due to the agglomeration of organo-clay particles. Finally, the biodegradation studies revealed enhanced hydrolytic degradation in PCC than pure polymers. Hence, these PCCs are environmentally friendlier than their pure synthetic polymers without significant compromise in their properties, which makes it suitable for packaging industries. The Author(s) 2020. -
Supercapacitor studies of activated carbon functionalized with poly(ethylene dioxythiophene): Effects of surfactants, electrolyte concentration on electrochemical properties
Electropolymerization of poly(ethylene dioxythiophene) (PEDOT) on activated carbon (AC) was performed using different surfactants such as anionic surfactant (sodium dodecyl sulfate), protonic surfactant (camphor sulphonic acid) and non-ionic surfactant (Triton) in 0.1 M H2SO4. The effects of concentration of different surfactants for electrodeposition of PEDOT on AC were analyzed using electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV), and SEM techniques. Supercapacitors (SC) were fabricated using AC/PEDOT composite electrodes and 0.1 M H2SO4 as an electrolyte. The specific capacitance (Cs) values were calculated using CV at different concentrations of surfactants, electrolytes and variation of potential. The electrolyte containing 0.1 M H2SO4 and 0.02 M camphor sulphonic acid showed to have the highest specific capacitance value of 240 Fg?1 than other surfactant based SCs. Galvanostatic charge/discharge at varying current density were performed on SCs containing different surfactant based electrodes to study their cyclic stability. 2020 -
Emerging Cyber Threats in 5G and Beyond: A Wireless Communication Perspective
There are digital age threats to cybersecurity that cost organizations and businesses their infrastructure, operations, and even sensitive data. Cybersecurity risk management is important to ensure that organizational assets are not subjected to cyberattacks, data breaches, and any other vulnerability. This paper also looks at other significant risk-reduction strategies, including threat intelligence, models of risk assessment, encryption, access control systems. It also talks about how machine learning and artificial intelligence can help improve the way threats are detected and handled. Organizations should take a proactive and layered approach to security that integrates leading-edge security methods, with regulatory compliance processes and employee awareness programs. Also, regular security checking, incident response plans and consistent monitoring help in reducing risks and business continuity. Organisations must continue to adjust as cyber threats change, utilizing cutting-edge cybersecurity solutions to fortify their defenses. Organisations may create robust security structures in a continuously linked and threatprone digital environment by using the perspectives this research offers on efficient risk management techniques. 2025 IEEE. -
Green synthesis and electrochemical characterization of rGO–CuO nanocomposites for supercapacitor applications /
Lonics, Vol.23, Issue 5, pp.1267–1276, ISSN: 9477047. -
Revisiting television in India: Mapping the portrayal of women in soap operas /
Sociological Bulletin, Vol.67, Issue 2, pp. 204-219. -
Cosmic structure growth and perturbation analysis in logarithmic f(Q) gravity
In this work, we explore a cosmological model within the framework of modified gravity, specifically a logarithmic form of f(Q) gravity. Using recent observational datasets including RSD and DESI, we constrain the model parameters via Markov Chain Monte Carlo (MCMC) techniques. Our analysis focuses on both background and perturbation-level cosmological diagnostics, evaluating the evolution of cosmographic parameters and the growth rate of structure through f?8. The results demonstrate consistency with observational data, particularly supporting a quintessence-like accelerated expansion. Additionally, the model addresses the S8 tension and provides insights into the late-time behavior of dark energy. The Author(s) 2025. -
Phenomenology and constraints of an extended modified gravity in Weyl geometry
We explore a novel class of modified gravity theories built upon Weyl geometry, where the Weyl connection introduces additional geometric degrees of freedom beyond general relativity. By promoting the Weyl field to a dynamical entity with a generalized potential, the resulting modified gravity theory naturally incorporates degrees of freedom arising from both the Weyl field and the scalaron embedded in the non-linear Ricci scalar function. Crucially, the field equations remain second-order, ensuring stability and avoiding Ostrogradsky instabilities. To test its viability, we confront this theory with observational data from Dark Energy Spectroscopic Instrument, cosmic chronometers, and Type Ia supernovae, constraining its free parameters through statistical analysis. Our results show strong agreement with observations, supporting a quintessence-like accelerating cosmic expansion and alleviating the Hubble tension. These findings establish modified gravity as a compelling extension of standard cosmology. 2025 The Author(s). Published by Oxford University Press on behalf of Royal Astronomical Society. -
Cosmic bounce in boundary-corrected symmetric teleparallel gravity
This study delves into modified f(Q,B) gravity, with a primary emphasis on solving field equations within the FLRW metric framework. It investigates bouncing scenarios by scrutinizing two nonlinear cosmological models and assesses energy conditions to authenticate bouncing cosmologies as viable alternatives to the conventional Big Bang theory. Furthermore, this analysis extends to examining geometrical parameters to shed light on the accelerating universe, providing significant insights into the implications of modified gravity for our comprehension of cosmic evolution. In addition, a perturbative analysis is performed for both models, showing that a nearly scale-invariant scalar spectral index ns and a suppressed tensor-to-scalar ratio r can be achieved for suitable parameter ranges, thus supporting the observational viability of the proposed bouncing framework. 2025 World Scientific Publishing Company. -
Design of Reconfigurable FRM Channelizer using Resource Shared Non-maximally Decimated Masking Filters
This paper presents a reconfigurable frequency response masking (FRM) wideband channelizer architecture which is characterized by low computational and hardware complexity. The proposed hardware efficient architecture is realized by incorporating resource shared non-maximally decimated filter bank in the implementation of the FRM wideband channelizer structure. The coefficients of the proposed architecture are optimized and made multiplier-free using Pareto based meta-heuristic algorithm in the canonic signed digit (CSD) space for reducing the total power consumption of the architecture. The architecture is finally designed and synthesized using Xilinx Vivado and Cadence RTL Encounter compiler for the area and power analysis and is compared with existing channnelizer architectures. The comparison highlights the advantages of the proposed architecture in terms of hardware complexity, power and workload in realizing sharp wideband channel filters. 2020, Springer Science+Business Media, LLC, part of Springer Nature. -
Multiplier-free Realization of High throughout Transpose Form FIR Filter
This paper presents a multiplier-free realization of the block finite impulse response (FIR) filter in transpose form configuration using binary constant shifts method (BCSM). The proposed architecture is synthesized using Xilinx Vivado and Cadence RTL Encounter compiler for the area and power analysis and is compared with the existing works in the literature. The comparison highlights the advantages of the proposed architecture in terms of power, hardware complexity and throughput for realizing reconfigurable high throughput block FIR filters. 2020 IEEE. -
Software Quality Prediction by CatBoost: Feed-Forward Neural Network in Software Engineering
Software quality is the key aspect of every software organization. Multiple frameworks and algorithms are essential to ensure quality. However, multiple software failures occur uninvited. There are multiple aspects that skew a softwares efficiency. Now the software quality analysis framework mostly focuses on design flaws and test plans done during development. To overcome this problem of software failure, this research proposes a prediction for software efficiency analysis in software engineering using enhanced feed-forward neural network machine learning classification with CatBoost. This research also evaluates the parameters of efficiency of each software component before implementation. This proposed work also analyses the basic aspects that need to be ensured before the design phase of any software. 2024 Taylor & Francis Group, LLC. -
A Comparative Study in Predictive Analytic Frameworks in Big Data
Every information processing sector uses predictive analytic framework in terms of distributed datasets through a variety of applications. These analytic frameworks are effectively used for various analyses of data, parameter, and attributes. Leveraging data to make insightful decisions for maximizing the effectiveness requires the determination of the best predictive framework for any organization. Even a retail unit which wants to scale up its production rely on multiple parameters. These parameters must be analyzed for effective quality control in any domain. Since there are diversities in every domain the data will be in varied form, and these are accumulated as Big Data. These analyses are done using machine learning frameworks. The strategy involved would differ from one domain to another such as in the health care sector the framework might predict the magnitude of patients admitted to the urgent care facility over the upcoming days whereas in the production industry the framework would align quality control measures. This article analyses a few domains and their deployed machine learning impacts in a strategic way. 2023 American Institute of Physics Inc.. All rights reserved.