The development of environmentally benign and efficient strategies for synthesizing biologically important heterocycles remains a pivotal challenge in contemporary organic chemistry. Among these, benzimidazole derivatives represent a class of vital heterocyclic compounds with diverse and significant pharmacological applications. This underscores the necessity for advancing sustainable and catalytic methodologies to achieve their efficient and selective synthesis. In this regard, a novel water-assisted strategy has been developed for the synthesis of substituted benzimidazole scaffolds through the reaction of 2-haloanilines, sodium azide, and aldehydes under ultrasound irradiation, catalyzed by a CuO-decked reduced graphene oxide (rGO) nanocomposite in aqueous medium. The CuO-rGO nanocomposite was synthesized via a one-pot chemical route and thoroughly characterized using various analytical techniques. Compared to conventional methods, the CuO-rGO nanocomposite exhibited a 20-fold increase in catalytic activity under ultrasound irradiation. The synergistic effect between water, the catalyst’s functionalities, and ultrasound irradiation played a pivotal role in the successful synthesis of the desired products. Furthermore, the catalyst could be easily recovered by centrifugation and reused successfully for up to eight cycles without a loss in activity.

The rise of cyber attacks in network security demands strong detection mechanisms. This paper proposes an ensemble learning approach to detect wormhole and sinkhole attacks using multiple classifiers for improved accuracy. The methodology includes feature normalization and Synthetic Minority Oversampling Technique (SMOTE) for class balancing. Five classifiers Random Forest (RF), XGBoost (XGB), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Decision Tree (DT) are trained and optimized using GridSearchCV. The top three models are combined into ensemble methods, including stacking, voting, and meta-classification. The ensemble model is evaluated using accuracy, precision, recall, F1 score, and Receiver Operating Characteristic (ROC) curve analysis, achieving 92% accuracy. A confusion matrix confirms its reliability in minimizing false positives and negatives. This study highlights ensemble learning's role in strengthening cybersecurity against sophisticated attacks.

The structural, optical, electronic, and magnetic properties of La2Co(1−x)Cr(x)MnO6 (0 ≤ x ≤ 1) double perovskites synthesized via the sol-gel auto-combustion method were systematically investigated. X-ray diffraction confirmed the formation of a single-phase monoclinic structure (P21/n space group) in all samples, with crystallite sizes decreasing as Cr content increased, as determined by the Debye-Scherrer formula. FESEM revealed an irregular chip-like morphology and a reduction in grain size with increasing Cr content at the Co site. The optical bandgap, calculated using the Tauc relation, exhibited a nonlinear dependence on Cr composition, suggesting a bandgap bowing effect. FTIR spectrum revealed a shift in La-O-La bending vibrations from 595 cm−1 (x = 0) to 618 cm−1 (x = 1) with increasing Cr incorporation. XPS analysis confirmed the presence of Cr3+, Mn3+, Mn4+, Co2+, and Co3+ ions, validating the oxidation states of the constituent elements. Magnetic measurements indicated a significant reduction in Curie temperature (TC), coercive field (HC), and remanence (Mr) with increasing Cr doping, attributed to mixed valence state and ionic radius mismatches among Co, Mn, and Cr. This study, emphasizing the magnetic, optical, and electronic properties of Cr-substituted La2CoMnO6, highlights its potential for advanced applications in spintronic and photovoltaic devices.

This study investigates the impact and mechanisms of Rasayana Churna, an Ayurvedic poly-herbal formulation, in treating aging-related disorders through text mining, network pharmacology, molecular docking simulation, Super-MMPBSA, and density functional theory. The text mining of Rasayana Churna highlighted the diverse therapeutic potential of Phyllanthus emblica, Tinospora cordifolia, and Tribulus terrestris in managing aging-related disorders through their antidiabetic, antioxidant, and anti-inflammatory properties. Using network pharmacology, 17 bioactive compounds and 137 corresponding potential targets of Rasayana Churna were identified and used to construct protein–protein interaction and hub gene networks. Key targets such as AKT1, BCL2, ESR1, and GSK3B were linked to aging-related pathways, with GO and KEGG enrichment analyses highlighting processes like apoptosis, oxidative stress response, and pathways like PI3K-Akt signaling. Molecular docking analysis identified 14 compounds with strong binding affinity toward the key aging target AKT1. Three bioactive compounds—Kaempferol, N-Caffeoyltyramine, and Multifidol glucoside—exhibited superior stability and binding interactions in MD simulations, confirmed by RMSD, RMSF, Rg, hydrogen bonding, SASA, PCA, and free energy landscape analysis. Super-MMPBSA (last 30 ns) calculation was performed to analyze dynamic behavior and protein–ligand stability, revealing significantly lower ΔG binding free energy values for the three hit compounds (− 177.871, − 164.855, − 199.649 kJ/mol, respectively) compared to the AKT1-reference complex (− 109.463 kJ/mol). DFT analysis revealed favorable electronic properties and kinetic stability for these compounds. Integrating traditional Ayurvedic knowledge with computational techniques suggests Rasayana Churna could prevent and manage aging-related conditions. However, further in vitro, in vivo, and clinical studies are needed to validate its aging-modulatory potential.

In recent years, abnormalities in spinal intervertebral disc (IVD) disease have significantly increased, affecting a large population worldwide. Accurate detection and segmentation of IVD degeneration are critical for effective diagnosis and treatment planning. However, manual identification is time-consuming, prone to errors, and highly dependent on expert knowledge. This study addresses the challenge of automating IVD disease detection using advanced computer vision and deep learning techniques. Initially, the input data are gathered from publicly available datasets. The image is then pre-processed to remove undesirable noises and improve its quality using the Extended Cascaded Filtering (E-CF) and Improved Contrast Limited Adaptive Histogram Equalization (ICLAHE) techniques. From the pre-processed samples, the IVD regions are segmented with the aid of the Deep Residual Dilated U-Net (DRD-UNet) model. Next, feature extraction takes place to attain the necessary features by utilizing the Hybrid Gabor-Walsh Hadamard Transform (H_GWHT) method. Finally, the IVD from the input MRI images are identified by proposing a new Attention based Multi-Branch Convolutional ResNet-152 (A_MBCResNet) model. In order to enhance the efficiency of the proposed classifier, the parameters are optimally tuned by using the Gauss Chaotic Coati Optimization (GCCO) algorithm. A Python tool is used to implement this proposed work. Thus, the proposed study effectively identifies the IVDs from the given samples, and the performance measure of proposed accuracy is 97.75%.

In this study, copper-substituted cobalt nanocrystalline particles, CuxCo1-xFe2O4 (x = 0.0, 0.2, 0.4, 0.6, 0.8, and 1.0) (CCFO) were synthesized using the sol-gel technique. The structural and phase properties were identified using X-ray diffraction (XRD). The single-phase cubic arrangement is apparent through the Rietveld refinement identified with the Fd3‾m space group. As the amount of Cu2+ doped increased, the average size of the crystallite ranged from 11 to 16 nm, as per the Debye Scherrer method. Performing High-Resolution Transmission Electron Microscopy (HRTEM) for the surface morphological assessments demonstrates that each sample is sphere-shaped and accurately prepared. UV–visible spectroscopy was used to examine the band gap reduction from 2.06 to 1.80 eV when the Cu2+ content ascended. The magnetic characteristics were analyzed using the Vibrating Sample Magnetometer (VSM) of the produced CCFO in the 5 K–300 K temperature range. The saturation magnetization, remanent magnetization, and coercivity changed gradually as the Cu2+ amount increased. According to the ZFC–FC magnetization curves, the synthesized nanoparticles exhibit ferrimagnetic behaviour at room and lower temperatures because their blocking temperature (TB) is higher than room temperature. It was found that the FC curves were almost flat just below TB, revealing spin-glass behaviour that could be explained by interactions between nanoparticles and surface effects, which include spin disorder and spin canting. According to the current study, variations in Cu present in cobalt nanoferrites can modify their structural, band gap, and magnetic characteristics for many applications in photocatalysis, dye degradation, magnetic resonance imaging, specific drug delivery, magnetic separation and memory devices.

Despite extensive research, structural and magnetic uncertainties of La2CrMnO6 double perovskites remain unresolved, requiring further investigation. This study examines the Fe substitution at the Cr site in La2Cr1-xFexMnO6 (x = 0.00, 0.50, 1.00) (LCFM), synthesized via the conventional solid-state reaction method. XRD and Rietveld refinement confirm the orthorhombic Pbnm phase in LCFM, with crystallite size and lattice parameters increasing with Fe substitution at the Cr site. FESEM analysis revealed a reduction in grain size while UV–Visible spectroscopy reveals a reduction in band gap. X-ray photoelectron spectroscopy (XPS) confirmed the presence of mixed oxidation states Cr3+/Cr6+, Fe2+/Fe3+, and Mn3+/Mn4+. Magnetic measurements of LCFM reveal increased Hc, decreased Mr, and preserved multi-domain structure upon Fe3+ substitution at the Cr3+ site, driven by superexchange and double exchange interactions. Reduced grain size, optical band gap, and magnetic properties in Fe-substituted La2CrMnO6 suggest its suitability for optoelectronic and spintronic applications.

Alzheimer’s disease (AD) is a growing global concern due to its increasing prevalence and lack of effective treatments. In this study, we employed computational methods to discover potential glycogen synthase kinase 3 beta (GSK3β) inhibitors, a key therapeutic target in AD. Using a library of compounds from Rosmarinus officinalis L. (Rosemary), we performed homology modeling, virtual screening, drug-likeness analysis, toxicity prediction, molecular dynamics (MD) simulations, and Super_MM-PBSA analysis to identify novel hit molecules. 32 lead compounds were initially selected based on binding affinity to GSK3β. Of these, 17 compounds adhered to Lipinski’s Rule of Five (RO5) and ADME properties. Toxicity analysis further narrowed the selection to four non-toxic compounds—Taraxasterol, Pomolic Acid, Nepetoidin A, and 23-Hydroxybetulinic Acid—which showed no mutagenic or tumorigenic effects. MD simulations and binding free energy calculations revealed significantly favorable binding energies for these compounds, with values of − 150.010 kJ/mol, − 156.511 kJ/mol, − 157.605 kJ/mol, and − 160.265 kJ/mol, respectively, compared to the reference compound (− 134.211 kJ/mol). These findings suggest that the identified R. officinalis compounds are promising candidates for further development as GSK3β inhibitors in the treatment of Alzheimer’s disease.

In the huge growth of semiconductor industry, it is noticed that the device simulation is a very sluggish process. It is very promising to use Machine Learning (ML) techniques in device modeling as their combination will create great results in semiconductor industry and reduce the computational time. Organic Thin Film Transistor (OTFT) is a promising alternative to amorphous silicon devices due to its flexibility, low cost, and can be manufactured at reduced temperatures. In traditional TCAD simulation, at once only a single simulation of OTFT for fixed length, width and dielectric thickness can be done, for change in any of the input parameter again simulation has to be done. To avoid this ML is used to predict drain current for simultaneous changes in input parameters. This introduces a machine learning based structure to model OTFT integrated with ML algorithm named Random Forest Regressor (RFR). ML based device model for p-type OTFT takes length, width and thickness of dielectric layer as input parameters and drain current as output parameter. Experimental results has shown that our ML-based model can predict drain current accurately. R2-value is found be around 0.997253. ML based performance optimization is a promising alternative to traditional technology computer aided design (TCAD) tools. The highest ION/IOFF ratio, very high ON current (ION), very low OFF current (IOFF) is achieved for OTFT. ION/IOFF ratio is obtained to be 1011. The trained RFR models can accelerate the optimization in terms of performance and serves as promising alternative.

With the rapid growth of the semiconductor industry, it is clear that device simulation has been considered as slow process. As a result of semiconductor device downscaling, obtaining the inevitable device simulation data is significantly more expensive because it requires complex analysis of multiple factors. Using Machine Learning (ML) techniques to device modeling is promising, as their combination will lead to great outcomes in the semiconductor industry. Nanosheet Field Effect Transistor (NSFET) is a promising device for high-performance integrated circuits due to their superior electrical control and reduced short-channel effects. This paper presents a ML based Nanosheet Field Effect Transistor modeling. In traditional Technology Computer-Aided Design (TCAD) simulation, at once only a single simulation of NSFET for fixed length, width and thickness can be done, for change in any of the input parameter again simulation has to be done. To overcome this, simultaneous changes in input parameters are predicted using machine learning. The length, width, and thickness of the dielectric layer are input parameters and the drain current is the output parameter for the ML-based device model for NSFET. Experimental results have shown that our ML-based model can predict drain current accurately. R2-value is found be around 0.99832. The highest ION/IOFF ratio, very high ON current (ION), very low OFF current (IOFF) is achieved for NSFET. The primary goal of this work is to explore the possibility of ML model that can replace the device simulation to reduce the computational cost and drive energy-efficient devices.

Digital watermarking can be used to ensure the authenticity and copyright protection of images. In watermarking balancing the trade-offs between its features is an important issue. To address this issue, in this work an adaptive hybrid domain color image watermarking based on Discrete Wavelet Transform (DWT), Walsh Hadamard Transform (WHT), and Singular Value Decomposition (SVD) is proposed. Here, watermarking is carried on YCbCr color space. In this work, the embedding factor is calculated adaptively using the visual entropy and edge entropy. For better robustness the watermark is inserted into the Y component of YCbCr color Space. Further, Arnold Transform (AT) is used to secure the watermark. The average PSNR and SSIM of the proposed hybrid domain adaptive watermarking scheme is 40.0876 dB and 0.9883 respectively. The experimental results compared to the recent hybrid domain color watermarking, illustrate the superiority of the suggested approach.

Human matrix metalloproteinase 3 (MMP3), also known as Stromelysin-1, is involved in various cellular processes, including skin aging, making it an attractive drug target against skin aging. This study aims to apply different ML algorithms to develop a prediction model for the MMP3 inhibitor dataset (ChEMBL283) from the ChEMBL database. ML experiments were performed using the Python programming language. Seven machine learning algorithms, namely neural network, decision tree, Xgboost, CatBoost, random forest, LightGBM, and extra trees, were applied to classify molecules as active or inactive (coded 1 or 0) using AutoML. ML models underwent an evaluation process that included ROC plots, a confusion matrix, and a set of statistical measures. These evaluations demonstrated the exceptional predictive capability of the Extra Trees algorithm, achieving a remarkable accuracy rate of 85.8%. The most effective ML model identified 79 active MMP3 inhibitory phytochemicals in Nelumbo nucifera. Molecular docking confirmed the strong binding of seven phytochemicals to MMP3, suggesting their potential as inhibitors. Following Lipinski's rule, three compounds—liensinin, isoliensinin, and isovitex—showed promise in molecular dynamics studies (100 ns) and MM-PBSA analysis (last 30 ns). They exhibited the lowest binding free energies, namely − 112.684 kJ/mol, − 194.871 kJ/mol, and − 101.551 kJ/mol, respectively, compared to the HQQ-MMP3 complex (− 95.410 kJ/mol), suggesting their potential as candidates for MMP3 inhibition. The study highlights the effectiveness of ML and the relative accuracy of MD simulations in screening phytochemicals for dermatological research and provides innovative opportunities for designing MMP3 inhibitors in the future.

Scientific research has demonstrated that Tinospora cordifolia acts as an anti-aging agent in several experimental models, generating global interest in its underlying molecular mechanisms of this activity. The aim of the study was to identify the possible phytochemical compounds of T. cordifolia that might combat age-related illness through integrating network pharmacology, molecular docking techniques, and molecular dynamics (MD) study to explore their potential mechanisms of action. To carry out this study, several databases were used, including PubChem, KNApSAcK family database, PubMed, SwissADME, Molsoft, SwissTargetPrediction, GeneCards, and OMIM database. For network development and GO enrichment analysis KEGG, ShinyGo 0.77, and the STRING database were used. For better analysis, the networks were also constructed using Cytoscape 3.9.1. The Cytoscape network analyzer tool was used for data analysis, and molecular docking was done via Vina-GPU-2.0. The best compounds and AKT1 were finally subjected to MD simulation for 100 ns. The CytoHubba plugin of Cytoscape identified ten key targets, commonly called hub genes, including AKT1, GAPDH, and TP53, and so on. GO and KEGG pathway enrichment analysis revealed the relevant biological processes, cellular components, and molecular functions involved in treating aging-related disorders. KEGG pathway analysis involved neuroactive ligand–receptor interactions, lipid and atherosclerosis, and cAMP signaling. The docking of 100 T. cordifolia compounds with AKT1 demonstrated good binding affinity, particularly for Amritoside, Sitagliptin, Berberine, and Piperine. Finally, the relative stability of four-hit phytochemicals was validated by MD simulation, which may be the most crucial compound for anti-aging activity. In conclusion, this study used network pharmacology, molecular docking, and MD simulation to identify the compounds in T. cordifolia and proposed a potential mechanism for anti-aging activity. These results suggest future directions for the prevention and treatment of age-related diseases.

Aging, a natural biological process, presents challenges in maintaining physiological well-being and is associated with increased vulnerability to diseases. Addressing aging mechanisms is crucial for developing effective preventive and therapeutic strategies against age-related ailments. Rosmarinus officinalis L. is a medicinal herb widely used in traditional medicine, containing diverse bioactive compounds that have been studied for their antioxidant and anti-inflammatory properties, which are associated with potential health benefits. Using network pharmacology, this study investigates the anti-aging function and underlying mechanisms of R. officinalis. Through network pharmacology analysis, the top 10 hub genes were identified, including TNF, CTNNB1, JUN, MTOR, SIRT1, and others associated with the anti-aging effects. This analysis revealed a comprehensive network of interactions, providing a holistic perspective on the multi-target mechanism underlying Rosemary's anti-aging properties. GO and KEGG pathway enrichment analysis revealed the relevant biological processes, molecular functions, and cellular components involved in treating aging-related conditions. KEGG pathway analysis shows that anti-aging targets of R. officinalis involved endocrine resistance, pathways in cancer, and relaxin signaling pathways, among others, indicating multifaceted mechanisms. Genes like MAPK1, MMP9, and JUN emerged as significant players. These findings enhance our understanding of R. officinalis's potential in mitigating aging-related disorders through multi-target effects on various biological processes and pathways. Such approaches may reduce the risk of failure in single-target and symptom-based drug discovery and therapy.

The different dyes used and discharged in industrial settings and microbial pathogenic issues have raised serious concerns about the content of bodies of water and the impact that dyes and microbes have on the environment and human health. Efficient treatment of contaminated water is thus a major challenge that is of great interest to researchers around the world. In the present work, we have fabricated functionalized silver-doped ZnO nanoparticles (Ag-doped ZnO NPs) via a hydrothermal method for wastewater treatment. X-ray photoelectron spectroscopy analysis confirmed the doping of Ag with ZnO NPs, and X-ray diffractometry analysis showed a decreasing trend in the crystallite size of the synthesized ZnO NPs with increased Ag concentration. Field emission scanning electron microscopy study of pure ZnO NPs and Ag-doped ZnO NPs revealed nanocrystal aggregates with mixed morphologies, such as hexagonal and rod-shaped structures. Distribution of Ag on the ZnO lattice is confirmed by high-resolution transmission electron microscopy analysis. ZnO NPs with 4 wt% Ag doping showed a maximum degradation of ∼95% in 1.5 h of malachite green dye (80 mg L−1) under visible light and ∼85% in 4 h under dark conditions. Up to five successive treatment cycles using the 4 wt% Ag-doped ZnO NP nanocatalyst confirmed its reusability, as it was still capable of degrading ∼86% and 82% of the dye under visible light and dark conditions, respectively. This limits the risk of nanotoxicity and aids the cost-effectiveness of the overall treatment process. The synthesized NPs showed antibacterial activity in a dose-dependent manner. The zone of inhibition of the Ag-doped ZnO NPs was higher than that of the pure ZnO NPs for all doping content. The studied Ag-doped ZnO NPs thus offer a significant eco-friendly route for the effective treatment of water contaminated with synthetic dyes and fecal bacterial load.

Zinc oxide is a multifunctional material with important applications in areas like electronics, optoelectronics, sensors and photocatalysis. In the present work, the Cu-doped ZnO (Cu = 0%, 2% and 5%) nanoparticles have been synthesized and investigated using various techniques like XRD, SEM, XPS, PL and UV spectroscopic measurements. The study is aimed at exploring the mechanism of room-temperature ferromagnetism in these dilute magnetic semiconductors, which has been a mystery for a long time. The X-ray diffraction patterns revealed the hexagonal wurtzite crystal structure of the P63mc space group and an average crystalline size of 26 nm to 32 nm. The morphology has been analyzed using SEM images, which depict irregular grain size distribution and agglomerated spheroid-like particle structure. The X-ray photoelectron spectroscopy (XPS) findings exhibited the inducement of remarkable oxygen vacancies (Vo) with Cu doping. The 2% Cu-doped sample shows the maximum value of the oxygen vacancies. The magnetization measurements reveal weak ferromagnetism in the pure ZnO sample, whereas the Cu-doped ZnO nanocrystalline samples show remarkable room temperature ferromagnetism (RTFM). The 2% Cu-doped sample depicts the highest value of saturation magnetization. The UV spectroscopy indicates that the band gap is reduced upon Cu doping; the value of Eg is found to be the lowest (2.96 eV) for the 2% Cu-doped sample. The Photoluminescence (PL) spectroscopy indicates the presence of defect-related states, which are found to be the maximum for the 2% Cu-doped sample, in good agreement with the XPS results. The induced magnetization in the Cu-doped nano-crystalline samples is found to show a direct relationship with the oxygen vacancies and is proposed to be caused by the exchange interactions between the Cu2+ ions and the oxygen vacancies. The inducement of ferromagnetism in ZnO renders it a potential system for spintronic devices. The key benefits of spintronic devices are their compact size, excellent luminous efficiency, ecologically benign composition, long persistence and potential energy savings.

Polycrystalline Zn1−xCuxO (x = 0.0, 0.02, and 0.05) samples have been prepared using the solid-state reaction procedure. The X-ray diffraction (XRD) patterns of the samples confirm that Cu ions are successfully included in the ZnO hexagonal wurtzite structure. Rietveld analysis of the XRD patterns confirms the phase purity of the synthesized samples and a slight variation in their lattice parameter upon Cu doping. The morphology study by scanning electron microscopy (SEM) depicts transfiguration with Cu doping. The existence of oxygen vacancies (Vo) in the Cu-doped samples is indicated by X-ray photoelectron spectroscopy (XPS). The magnetization measurements reveal the diamagnetic nature of pure ZnO while the Cu-doped samples depict a room-temperature ferromagnetic (RTFM) behavior. The 2% Cu-doped sample shows higher values of both the saturation magnetization and the Vo as compared to the 5% Cu-doped sample. The observed magnetization seems to show a direct relationship with the Vo. The photoluminescence (PL) and ultraviolet (UV) spectroscopic measurements were performed for their optical analysis. The presence of Vo in the Cu-doped samples is revealed by the PL findings also that is in agreement with the XPS results. The UV analysis shows that Cu doping in the ZnO influences the band gap. The observed RTFM induced by Cu doping in ZnO renders it a potential system for spintronic devices useful for energy-efficient data storage devices and energy harvesting eco-friendly applications.

In the present work, zinc oxide (ZnO) and silver (Ag) doped ZnO nanostructures are synthesized using a hydrothermal method. Structural quality of the products is attested using X-ray diffraction, which confirms the hexagonal wurtzite structure of pure ZnO and Ag-doped ZnO nanostructures. XRD further confirms the crystallite orientation along the c-axis, (101) plane. The field emission scanning electron microscope study reveals the change in shape of the synthesized ZnO particles from hexagonal nanoparticles to needle-shaped nanostructures for 3 wt% Ag-doped ZnO. The optical band gaps and lattice strain of nanostructures is increased significantly with the increase of doping concentration of Ag in ZnO nanostructure. The antimicrobial activity of synthesized nanostructures has been evaluated against the gram-positive human pathogenic bacteria, Staphylococcus aureus via an agarose gel diffusion test. The maximum value of zone of inhibition (22 mm) is achieved for 3 wt% Ag-doped ZnO nanostructure and it clearly demonstrates the remarkable antibacterial activity.

WSN has been exhilarated in many application areas such as military, medical, envi-ronment, etc. Due to the rapid increase in applications, it causes proportionality to security threats because of its wireless communication. Since nodes used are supposed to be independent of human reach and dependent on their limited resources, the major challenges can be framed as energy consumption and resource reliability. Ensuring security, integrity, and confidentiality of the transmitted data is a major concern for WSN. Due to the limitation of resources in the sensor nodes, the traditionally intensive security mechanism is not feasible for WSNs. This limitation brought the concept of digital watermarking in existence. Watermarking is an effective way to provide security, integrity, data aggregation and robustness in WSN. In this paper, several issues and challenges, as well as the various threats of WMSN, is briefly discussed. Also, we have discussed the digital watermarking techniques and its role in WMSN.

Metamaterials have shown an enormous amount of success in the field of engineering as well as in physics and finds applications in various domains. One such important application of metamaterials i.e., Zeroth Order Resonator (ZOR) antennas is discussed in this article. Metamaterials are manmade materials having properties not found in nature occurring materials i.e., simultaneously negative permittivity (ε) and permeability (μ) over certain range of frequency. Due to these unique properties, metamaterials are used in various antennas to enhance the bandwidth, gain, polarization, radiation patterns etc. The omnidirection radiation pattern is obtained by using ZOR antennas, which is one of the important applications of Composite Right/Left-Handed Transmission Line (CRLH-TL). CRLH-TL uses the properties of metamaterial having exotic properties. This article presents a brief introduction to metamaterials followed by detailed discussion about CRLH-TL and various ZOR Antennas along with their properties. Eventually the applications and radiation patterns have also been studied in this report which will give the researchers an analysis of the research that has already been published.

Social media has become a vital platform for individuals, organizations, and governments worldwide to communicate and express their views. During the coronavirus disease 2019 (COVID-19) pandemic, social media sites play a crucial role in people communicating, sharing, and expressing their perceptions on various topics. Analyzing such textual data can improve the response time of governments and organizations to act on alarming issues. This study aims to perform sentiment analysis on the subject of COVID-19 vaccination, perform temporal and spatial analyses of the textual data, and find the most frequently discussed topics that may help organizations bring awareness to those topics. In this work, the sentiment analysis of tweets was performed using 14 different machine learning classifiers and natural language processing (NLP). Lexicon-based TextBlob and Vader are used for annotating the data. A natural language toolkit is used for preprocessing of textual data. Our analysis observed that unigram models outperform bigram and trigram models for all four datasets. Models using term frequency-inverse document frequency (TF-IDF) have higher accuracy than models using count vectorizer. In the count vectorizer class, logistic regression has the best average accuracy with 91.925%. In the TF-IDF class, logistic regression has the best average accuracy of 92%; logistic regression has the highest average recall, F1-score, and ten cross-validation scores, and a ridge classifier has the highest average precision. The unigram models show a standard deviation (SD) of less than 1 for all classifiers except for the Gaussian Naïve Bayes showing 1.18. The experimental results reveal the dates and times in which most positive, negative, and neutral tweets are posted.

The current article explores the dielectric and electronic properties of cobalt ferrite nanoparticles with Gd substitution in a series CoGdxFe2-xO4 (0 ≤ x ≤ 0.1, in step x = 0.02) synthesized by the sol–gel self-combustion way. All the samples were studied with Fourier transform infra-red (FTIR) spectroscopy, Raman spectroscopy, X-ray photoelectron spectroscopy (XPS), and impedance (dielectric) analyzer. One absorption band (υ1) was observed in FTIR measurements, which is the characteristic feature of spinel nanoferrites in fcc type structure. The presence of active Raman modes in Raman spectra at room temperature demonstrated single phase formation of cobalt nanoferrites with metallic–metallic and metallic–oxygen bonding vibrations in the tetrahedral and octahedral sites. XPS data analysis confirmed phase purity and revealed incorporation of Gd ion in the spinel fcc lattice. The valence states of Fe, Co & Gd atoms in all these nanoparticles are found as Fe3+, Co2+, & Gd3+. The dielectric constant and dielectric loss are measured in a broad frequency range of 100 Hz to 120 MHz. The dielectric constant reduces with a rise in Gd concentration and frequency. This study reveals that electronic and dielectric properties could be effectively tuned by varying concentrations of gadolinium in cobalt ferrite nanoparticles.

Herein, we demonstrate a simple, reproducible, and environment-friendly strategy for the synthesis of carbon quantum dots (CQDs) utilizing the mango (Mangifera indica) kernel as a renewable green carbon source. Various analytical tools characterized the as-prepared CQDs. These fluorescent CQDs showed significant water solubility with a uniform size of about 6 nm. The as-synthesized CQDs show significantly enhanced catalytic activity for the production of α,β-unsaturated compounds from the derivatives of aromatic alkynes and aldehydes under microwave irradiation in aqueous media. A potential mechanistic pathway and role of carboxylic functionalities were also revealed via various control experiments. The protocol shows outstanding selectivity towards the assessment of α,β-unsaturated compounds over other possible products. A comparative evaluation suggested the as-synthesized CQDs show higher catalytic activity under microwave radiation as compared to the conventional ways. These recyclable CQDs represent a sustainable alternative to metals in synthetic organic chemistry. A cleaner reaction profile, low catalyst loading, economic viability and recyclability of the catalyst, atom economy, and comprehensive substrate applicability are additional benefits of the current protocol according to green chemistry.

In this paper, Omni-directional ZOR antenna is presented which finds its application for L-Band. L-band covers frequency range from 1–2 GHz and is used for various applications such as radar, satellite, Global Positioning Systems (GPS), telecommunication use, and terrestrial communications. The proposed antenna design is based on one of the applications of Composite Right Left-Handed Transmission Line (CRLH-TL). Antenna design is a combination of two Split Ring Resonators (SRR) in which outer ring combination are of circular shape and inner ring combination is a square shape. The unit cell of proposed structure comprises of metallic patch at the top of dielectric substrate FR4. The overall dimension of proposed antenna is 12 mm × 12 mm. The -10 dB bandwidth achieved is 20 MHz ranging from 1810 to 1830 MHz with respect to the center frequency of 1820 MHz. The results obtained in this paper is simulated using Ansys-HFSS 19.1v which is based on Finite Element Method (FEM). To prove the Omni-directional radiation pattern, uniform current distribution and 2-D plots are plotted. Beta versus frequency plot is portrayed which confirms the ZOR behavior of the antenna. Mesh size is kept at λ/20 mm so that results obtained are much pre-sized. The proposed antenna is fabricated and tested inside the Anechoic-Chamber, and the measured and the simulated results are almost similar to each other.

Breast Cancer has now been a threat to the lives of countless women. This growth of breast tissue is metastatic, and therefore grows rapidly, infecting other body parts too. The probability of survival is high only if the tumor is detected in an early stage, the higher the stage, lower are the chances of survival of the patient. The presence of a minor tumor could be missed by the human eye, but the machine learning algorithms scan mammograms deeply and are able to detect even the smallest tumor. This work is a performance analysis of three Supervised Machine Learning Algorithms, namely, Convolutional Neural Networks (CNN), Random Forest (RF), and Support Vector Machine (SVM), on two distinct datasets, i.e., Breast Cancer Wisconsin (Diagnostic) dataset and Breast Histopathology Images dataset. Univariate feature selection methods have been applied to select ten features in Breast Cancer Wisconsin (Diagnostic) dataset, and Wrapper Feature Selection methods have been applied to select three instances containing ten features in the Breast Histopathology Images dataset. The results exhibit that RF is the best suited algorithm for the Breast cancer Wisconsin (Diagnostic) dataset with an accuracy of 98.91%, while CNN is suitable for Breast Histopathology Image Dataset with an accuracy of 92.4%. Further, the effectiveness of this machine learning model is tested using the k-fold cross-validation technique.

Due to the advancement of the internet and image correcting software, the problem of integrity and authenticity of the image has become crucial. Forensics of digital image plays a crucial part in verifying the integrity and authenticity of computerized images. Digital watermarking is one of the computationally efficient techniques to verify the digital image’s integrity and authenticity. In this work, an overview of various image forensic techniques is briefly discussed, and an overview of digital watermarking techniques is presented as well. Further, various issues and challenges of digital watermarking and image forensics techniques based on the digital signature are also discussed. The overall aim of this work is to provide researchers with a comprehensive view of different aspects of image forensics based on digital watermarking. This survey will enable researchers to apply efficacious watermarking techniques to verify the authenticity and integrity of digital images.

Digital image watermarking technique based on LSB Substitution and Hill Cipher is presented and examined in this paper. For better imperceptibility watermark is inserted in the spatial domain. Further the watermark is implanted in the Cover Image block having the highest entropy value. To improve the security of the watermark hill cipher encryption is used. Both subjective and objective image quality assessment technique has been used to evaluate the imperceptibility of the proposed scheme.Further, the perceptual perfection of the watermarked pictures accomplished in the proposed framework has been contrasted and some state-of-art watermarking strategies. Test results demonstrates that the displayed method is robust against different image processing attacks like Salt and Peppers, Gaussian filter attack, Median filter attacks, etc.

This paper proposes a robust and secure watermarking method for a color image in YCbCr color space. In this study, watermarking is performed using Lifting Wavelet Transform (LWT). Here, edge entropy and information entropy is used to find the block to embed watermark. In this work alpha blending scheme is used for embedding and extraction of watermarks in the LWT domain. The use of LWT makes the proposed scheme faster and more efficient. The Arnold Cat Map (ACM) is used to enhance watermark security. Numerous tests are presented to illustrate the feasibility of the proposed scheme. The experimental results obtained are compared to state-of-the-art schemes, which demonstrate the superiority of the proposed scheme.

Digital watermarking techniques can be used to solve the authenticity and copyright protection issue of images. In this work, the authors have proposed an adaptive color image watermarking scheme based on DWT by combining alpha blending and entropy concept. Entropy is one of the important features of images and can be used for watermark insertion. There are two domains in which watermarking can be carried out i.e. spatial and transform domain. Here, watermark embedding is carried out in the of Y component of YCbCr color space. This paper also lays down the proper justification for the selection of the Y component to embed the watermark. The performance of the proposed scheme is tested over seven different standard color images. The average PSNR and SSIM of the proposed scheme are 51.6145 dB and 0.9992 respectively. Whereas, NCC of the proposed scheme under no attack condition is 1. Further, the performance of the proposed scheme is compared with other state-of-the-art techniques.

With the rapid increase in the use of the Internet, sentiment analysis has become one of the most popular fields of natural language processing (NLP). Using sentiment analysis, the implied emotion in the text can be mined effectively for different occasions. People are using social media to receive and communicate different types of information on a massive scale during COVID-19 outburst. Mining such content to evaluate people's sentiments can play a critical role in making decisions to keep the situation under control. The objective of this study is to mine the sentiments of Indian citizens regarding the nationwide lockdown enforced by the Indian government to reduce the rate of spreading of Coronavirus. In this work, the sentiment analysis of tweets posted by Indian citizens has been performed using NLP and machine learning classifiers. From April 5, 2020 to April 17, 2020, a total of 12 741 tweets having the keywords 'Indialockdown' are extracted. Data have been extracted from Twitter using Tweepy API, annotated using TextBlob and VADER lexicons, and preprocessed using the natural language tool kit provided by the Python. Eight different classifiers have been used to classify the data. The experiment achieved the highest accuracy of 84.4% with LinearSVC classifier and unigrams. This study concludes that the majority of Indian citizens are supporting the decision of the lockdown implemented by the Indian government during corona outburst.

This paper presents a new and mathematical method for online signature validation based on machine learning. In this way, the average values of the factors are taken into account to ensure validity. Here, seven different types of features used are x coordinates, y coordinates, time stamp, pen up and down, azimuth, height and pressure. Three new features are extracted from it, i.e., (displacement, velocity and acceleration) using the correlated extraction process to obtain dynamic feature of signature. These features are extracted from the popular dataset SVC2004. The extracted feature is then passed to various classifiers named as Naive Bayes, random forest, J48, MLP, logistic regression and PART. The result of genuine and forge signatures is obtained in terms of precision, true positive rate, false positive rate, F-score, etc. The obtained result is then compared with the existing method with respect to false acceptance rate and false rejection rate.

Text classification and Topic Modelling is the backbone for the text analysis of huge amount of corpus of data. With an increase in unstructured data around us, it is very difficult to analyse the data very easily. There is a need for some methods that can be applied to the data to get the sensitive and semantic information from the corpus. Text classification is categorization of text in organised way for the interpretation of sensitive information from the text, while Topic modelling is finding the abstract topic for the collection of text or document. Topic modelling is used frequently to find semantic information from the textual data. In this paper we applied Parsing techniques on various websites to extract the HTML and XML data which includes the textual data and also applied Preprocessing techniques to clean the data. For the text classification purpose some of the Machine learning based classifiers that we have used in our experiment are Naive Bayes and also Logistic Regression Classifier. The models of the document are built using three different topic modelling methods which are Latent Semantic Analysis, Probabilistic Latent Semantic Analysis and Latent Dirichlet Allocation. In the further experiment we have done analysis and also comparison based upon the performance of the models and classifiers on the processed textual data.

The society is progressing toward the centralization of information, and hence, there is an urgent need to have the information available at any dimension, anywhere, and anytime. To fulfill the need, the radio signals that have high frequency are used to communicate among the PCs or computers including other network devices from last three decades. In this paper, a survey on secure transmission is done to identify the problems faced while transmitting data over wireless network and ensure safe transmission. Further, various types of protocols and issues related to wireless networks are identified.

Image Encryption is a technique where an algorithm along with a set of characters called key encrypts the data into cipher text. The cipher text can be converted back into plaintext by decryption. This technique is employed for the security of data such that confidentiality, integrity and authenticity of data is maintained. In today's era security of information has become a crucial task, unauthorized access and use of data has become a noticeable issue. To provide the security required, there are several algorithms to suit the purposes. While the use and transferring of images has become easy and faster due to technological advancements especially wireless sensor network, image destruction and illegitimate use has become a potential threat. Different transfer mediums and various uses of images require different and appropriately suiting encryption approaches. Hence, in this paper we discuss the types of image encryption techniques. We have also discussed several encryption algorithms, their advantages and suitability.

In today’s digital era, it is very easy to copy, manipulate and distribute multimedia data over an open channel. Copyright protection, content authentication, identity theft, and ownership identification have become challenging issues for content owners/distributors. Off late data hiding methods have gained prominence in areas such as medical/healthcare, e-voting systems, military, communication, remote education, media file archiving, insurance companies, etc. Digital watermarking is one of the burning research areas to address these issues. In this survey, we present various aspects of watermarking. In addition, various classification of watermarking is presented. Here various state-of-the-art of multimedia and database watermarking is discussed. With this survey, researchers will be able to implement efficient watermarking techniques for the security of multimedia and database.

With high demands for data and processing power, the server industries are proliferating daily. Some hardware can be controlled by servers or Raspberry pi. So, tester and debugger must log in to this server and test and debug this hardware. If these have to be done on 100s of servers, which will be very difficult. So, to overcome this problem we have designed a framework based on SSH with multi-threading though there are various tools, our analytical experimental studies show that SSH with multi-threading or multiprocessing is far better than these tools. This Framework which designed by us will parallelly login into nearly 100s of server or clusters of servers (created using cluster algorithm designed by us.) at once and can run some automated activity like updating software, running some test cases like stressing processors memory or performing some activity which can be controlled by Raspberry pi or get logs, etc. This Framework is very useful for hardware controlled by server or Raspberry pi server industries, especially in the condition like COVID-19.

During last few years, the electronic medical records can be effortlessly stored with the fast development of healthcare technology. However, the patient's medical information security is current prime concern. Massive volumes of health data are generated in the development of treatment in medical centers such hospitals, clinics, or other institutions. Because of these ever-growing numbers of medical digital images data and the requirement to share them between specialists and hospitals for improved and more precise diagnosis requires that patients' privacy be protected. For this fact, there is a necessity for medical image watermarking. In this paper several security requirement in healthcare system is presented. We have also discussed the role of watermarking in the healthcare domain. There is also a brief discussion on various watermarking methodologies to protect the secrecy of medical records and data.

Digital Watermarking enables us to protect ownership rights on digital multimedia, such as audio, image and video data. Digital watermark is a digital signal carrying information of the creator or distributor of the media. Digital watermark is inserted into digital media in such a way that it is imperceptible to the human eye, but it is visible to a computer. A watermarking attack is any processing that may impair watermark detection. There are different types of attacks which can affect the watermarked image which include cropping, noise (salt & pepper, Gaussian), rotation etc. In order to create the copyright issues, these attacks damage the inserted watermark. This paper reviews the performance analysis of the watermarking algorithm based on the combined concept of entropy of block image and LSB Substitution in the presence of different types of attacks to justify the robustness of the algorithm.

This research paper presents path following two wheeled compact portable robot with arduino nano as cental driving functional unit with novel features of wireless control using wifi and bluetooth module with collision detection, avoidance and control features which provides the unique ability of danger avoidance, falling from a hieght with improved stablity and precision control. The extremely sophistcated design provides very good controlled movement on horizantal ground terrain surfaces with data collecting and processing capabilties. The design is integrated with infrared sensors, bluetooth module, wifi module control with dc gear motors which controls the speed of the vehicle of the robotic vehicle and avoid collision with any obstacle detected in the path of the robot. It has the unique abilty of running in maze with path following abilties controlled from any remote location using WiFi for long range control and bluetooth for short range control. In this research article a entire system is designed and implemented in which movement is stably controlled based on feedback from infrared transreciever module. A low cost robust portable design using GUI control has been implemented with advanced features which makes it very unique and attractive for commercial production.

Digital watermarking has emerged as the one of the burning research topic during last few years for copiright protection and data authentication. This paper reviews the watermarking techniques on the basis of application & characteristics of watermarking. This paper also provides a performance analysis of spatial domain watermarking techniques and the effect of different types of noise (Salt-And-pepper, Gaussian) on the basis of various performance measures.

During last few years for copyright protection, security and data authentication of digital media, digital watermarking has been raised as the one of the burning research topics due to the rapid expansion of Internet. This paper presents a new technique for image watermarking in the spatial domain where the concept of information theory is utilized with the popular LSB substation technique. Here, the cover image is segregated into a number of blocks and the watermark is embedded into the block(s) with the maximum entropy value. The extraction algorithm is also able to find the watermark correctly. The proposed algorithm was evaluated with the help of various standard performance measures like Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM) measure to verify the perceptibility and the robustness of the algorithm. Experimental resultsdemonstrate that the improved algorithm performs reasonably well over a large varied datasets of cover and watermark images.