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The present work deals with analyzing the effect of silicon dioxide on mechanical and thermal stability of hybrid polymer composite. Compression molding is adopted to develop the hybrid composite with constant 0.5 wt.% of graphene and varying wt.% of silicon dioxide (2, 4 & 6 wt.%). Modified hummers method is used to synthesis graphene by chemical exfoliation of graphene from graphite flakes. Mechanical properties of developed composite is investigated using tensile, flexural, impact and interlaminar shear testing follow by fracture surface analysis of tested samples. Thermal stability is examined by thermogravimetric analysis and functional group confirmation is carried out using Fourier transform infrared spectroscopy (FTIR). Investigation over mechanical properties of fabricated composite reveals that addition of silicon dioxide up to 4 wt.% depicted higher flexural and tensile strength. Increment in silicon dioxide percentage improves the toughness of composite. FTIR results confirms the presence of asymmetric stretching vibrations due the after effect of hybrid fillers. Thermogravimetric analysis results depict that incorporation silicon dioxide increases the activation of energy of matrix from ~ 24.2 to 31.5 kJ/mol. [ABSTRACT FROM AUTHOR]

Over the last 20 years, Wi-Fi technology has advanced to the point where most modern devices are small and rely on Wi-Fi to access the internet. Wi-Fi network security is severely questioned since there is no physical barrier separating a wireless network from a wired network, and the security procedures in place are defenseless against a wide range of threats. This study set out to assess federated learning, a new technique, as a possible remedy for privacy issues and the high expense of data collecting in network attack detection. To detect and identify cyber threats, especially in Wi-Fi networks, the research presents FEDDBN-IDS, a revolutionary intrusion detection system (IDS) that makes use of deep belief networks (DBNs) inside a federated deep learning (FDL) framework. Every device has a pre-trained DBN with stacking restricted Boltzmann machines (RBM) to learn low-dimensional characteristics from unlabelled local and private data. Later, these models are combined by a central server using federated learning (FL) to create a global model. The whole model is then enhanced by the central server with fully linked SoftMax layers to form a supervised neural network, which is then trained using publicly accessible labeled AWID datasets. Our federated technique produces a high degree of classification accuracy, ranging from 88% to 98%, according to the results of our studies. [ABSTRACT FROM AUTHOR]

Leaf disease is considered a serious threat which affects agricultural productivity and ultimately reduces the GDP of the Indian economy. The precise detection and timely analysis of foliar diseases can mitigate the spread of the disease to other parties. However, certain complications such as low precision, high calculation cost, and low recognition speed are raised when detecting leaf diseases. Therefore, to overcome these limitations, we proposed a novel technique called Boosted support vector machine-based Arithmetic optimization algorithm (BSVM-AOA) for accurate detection of plant leaf disease. In this case, image segmentation is done using the vector value active contour model, and feature extraction is done using the greyscale co-occurrence matrix. Furthermore, the performance of the proposed approach is determined by performance parameters such as accuracy, accuracy, recall, specificity, and f-rating. Finally, the comparative analysis is conducted between the different existing techniques and the proposed technique. The comparative results showed that the proposed BSVM-AOA approach is about 98.6% more accurate than other existing techniques. [ABSTRACT FROM AUTHOR]

This study aimed to obtain silver nanoparticles (AgNPs) from Calotropis gigantea leaf extract and to assess its cytotoxicity and larvicidal activity. Green synthesized AgNPs were subjected to characterization techniques like UV–Vis spectroscopy, Fourier transform-infrared (FT-IR), X-ray diffraction (XRD), and scanning electron microscope-energy dispersive spectroscopy (SEM-EDAX). UV–Vis absorption indicated the formation of AgNPs, and the spectrum was seen at 430 nm. The FT-IR spectra revealed the presence of functional groups in the synthesized AgNPs. XRD and SEM analyses revealed the crystallinity and uniform spherical size of the particles and the purity of the AgNPs was determined using EDAX. AgNPs were found to be cytotoxic to breast cancer cells (MCF-7). In addition, AgNPs showed a high larvicidal action against Aedes aegypti mosquito larvae. These findings support the hypothesis that produced AgNPs have superior larvicidal properties and could thus be used as a promising, cost-effective, and eco-friendly technique to control the A. aegypti and a good anticancer drug in the future. [ABSTRACT FROM AUTHOR]

Mango leaf diseases have a negative impact on mango quality and yield. It is difficult to make an accurate diagnosis of mango leaf disease diagnosis with the naked eye. A lot of computer-aided and machine learning techniques have recently been used by researchers for the classification of mango leaf diseases. However, it has been reported that these approaches have some limitations to their performance which can be attributed to problems due to higher feature dimensionality, overfitting, computational complexity, and lack of feature qualities. To overcome these issues, we proposed a novel framework for mango leaves disease classification. The images were taken from Andhra Pradesh, the largest mango cultivating land in India. The proposed framework is categorized into four stages: data preparation stage, feature selection stage, learning and classification stage, and the performance evaluation stage. We selected 380 images from the categories of healthy and diseased (Mango Anthracnose, Bacterial black spot, and Sooty mold). Different data augmentation techniques are applied to prevent overfitting and improve generalization. Next, a convolutional neural network with crossover-based levy flight distribution is applied for better feature selection. Further, the pre-trained MobileNetV2 model is used for the learning stage and leaves diseases classification is done via support vector machine at the final stage of the MobileNetV2 model. The experimental results demonstrate superior classification performances over other state-of-art methods. [ABSTRACT FROM AUTHOR]

Materials that integrate more than one useful property in single- and multi-phase structures are known as multifunctional materials. Most of them fall in the category of ABO3 perovskite type and in recent times, these materials have attracted considerable attention from engineers and scientists around the globe for electronic devices of the future. Among the perovskites, barium titanate-based structures have emerged at the top for modern research in advanced materials. In this study, polycrystalline Ba0.85Ca0.15Ti(1–x)HfxO3, x = 0.00, 0.05, 0.10, 0.15, materials were synthesized by sol–gel combustion technique and their optical properties were studied. X-ray diffraction (XRD) confirmed that, Ba0.85Ca0.15Ti(1–x)HfxO3 crystallized at 1000°C with tetragonal symmetry having P4mm space group. In addition to XRD, the presence of the tetragonal phase was confirmed using Raman spectroscopy. The crystallite sizes computed using Scherrer equation were found to range from 15 to 18 nm. Fourier transform infrared spectroscopy analysis showed a significant peak at 565 cm−1, confirming the formation of the metal oxide-based ceramics. Ultraviolet-Visible-near-IR spectroscopy of Ba0.85Ca0.15Ti(1–x)HfxO3 showed a slight increase in bandgap energy with the increase of Hf4+. Photoluminescence spectra confirmed the composition of three colours (blue, green and red) with the intensity of emission increasing with Hf4+ doping. [ABSTRACT FROM AUTHOR]

Purpose: Despite several reports on risk factors for septic arthritis (SA) in adults, the outcomes on quality of life and joint function are sparsely reported. This study aimed to investigate the quality of life and joint function in adult patients treated for SA. Methods: This prospective observational study enrolled clinically diagnosed adult SA patients and estimated Euroqol 5-dimension 5 levels (EQ-5D-5L) questionnaire, Euroqol Visual Analog Scale (EQ-VAS) and a validated tool for joint function at 1, 3, 6 and 12 months after debridement. Results: Twenty seven patients (20 males/7 females) with 21 knees, four shoulders, and two elbows were evaluated. The mean age of the cases was 51(± 13.00) years. Three cases sustained mortality. EQ-5D-5L (P < 0.01) and EQ-VAS (P < 0.01) scores improved significantly between all time frames. Mean Knee society (P < 0.01), Shoulder QuickDASH, and Mayo elbow scores improved at all time frames. There was no difference in primary outcomes between early (< 14 days) and late (14 days or more) presentation. There was no difference in primary outcome measures between confirmed and suspected SA. Fifteen patients could ambulate without support, while 6 needed some support for walking at 1 year. Conclusion: Improved outcomes can be expected in quality of life and joint function in adults' septic arthritis at the end of 1 year after surgical treatment; however, the effects of delay in presentation need to be investigated further. Isolation of the microorganism from an infected joint has no bearing on the outcome measures. [ABSTRACT FROM AUTHOR]

In remote sensing, land cover classification of vegetation and water area from satellite image play a vital role for rural and urban planning and development. Existing algorithms of land cover classification require more sample image datasets for training. For existing algorithms, land cover classification of vegetation and water area is a challenging task because of mixed pixel and geometric distortion over boundary and curvature region. Mixed pixel affects the precise classification and measurement of land cover. Geometric distortion arises due to frame of isotropic and angular selectivity during image acquisition and affects the contour of land cover. In this paper, the proposed transverse dyadic wavelet transform (TDyWT) enhances and classifies vegetation and water area in land cover from LANDSAT image without training datasets. The proposed TDyWT uses Haar wavelet for decomposition and Burt 5 × 7 wavelet for reconstruction. The TDyWT enhances the contour, curvature, and boundary of vegetation and water area in LANDSAT image due to reversible and lifting properties of wavelet. TDyWT removes geometric distortion and spatial scale error of mixed pixel. In traditional land surveying spatial scale error reduction eliminates through total station and error modeling techniques. From the results, the proposed TDyWT algorithm classifies the area of subclass of vegetation and water with the 95% of accuracy with respect to ground truth survey methods. [ABSTRACT FROM AUTHOR]

A reasonable understanding of the impact and health risk of the metal oxide nanoparticles treated on edible plants is necessary in order to use nanomaterials in food and medicine. In this study, we investigate the uptake and toxic response of transition metal oxides, namely Fe2O3, CuO and ZnO nanoparticles synthesised by co-precipitation method using nitrate salts of the respective metals as precursors. The characterisation of all the prepared nanoparticles shows the purity above 97% with rod-like morphology. Cowpea (Vigna unguiculata) seeds are soaked in a monodispersed solution of nanoparticles and kept for seed germination and plant growth. Transport and accumulation of the nanoparticles treated in the seeds are assessed through X-ray fluorescence spectroscopy which confirms the uptake of the nanoparticles with respect to the treatment regime. The seeds soaked in CuO nanoparticles reveal better seed germination percentage, an increased coleoptile length and a plant height at 100 ppm concentration than other nanoparticles. This study proves the limitation of CuO nanoparticle application for better growth of cowpea plants whereas iron and zinc oxide nanoparticles show promising response in plant growth, uptake and bioavailability in seeds even at higher treatments.Graphical Abstract [ABSTRACT FROM AUTHOR]

A graph G is called edge-magic if there is a bijective function f from the set of vertices and edges to the set $$\{1,2,\ldots ,|V(G)|+|E(G)|\}$$ such that the sum $$f(x)+f(xy)+f(y)$$ for any xy in E( G) is constant. Such a function is called an edge-magic labelling of G and the constant is called the valence. An edge-magic labelling with the extra property that $$f(V(G))= \{1,2,\ldots ,|V(G)|\}$$ is called super edge-magic. A graph is called perfect (super) edge-magic if all theoretical (super) edge-magic valences are possible. In this paper we continue the study of the valences for (super) edge-magic labelings of crowns $$C_{m}\odot \overline{K}_{n}$$ and we prove that the crowns are perfect (super) edge-magic when $$m=pq$$ where p and q are different odd primes. We also provide a lower bound for the number of different valences of $$C_{m}\odot \overline{K}_{n}$$ , in terms of the prime factors of m. [ABSTRACT FROM AUTHOR]

The Li(NiCoMn)O (LNCMO) cathode material is prepared by poly(vinyl pyrrolidone) (PVP)-assisted sol-gel/hydrothermal and poly(ethylene glycol)-block-poly(propylene glycol)-block-poly (ethylene glycol) (Pluronic-P123)-assisted hydrothermal methods. The compound prepared by PVP-assisted hydrothermal method shows a comparatively higher electrical conductivity of ~2 × 10 S cm and exhibits a discharge capacity of 152 mAh g in the voltage range of 2.5 to 4.4 V, for a C-rate of 0.2 C, whereas the compounds prepared by P123-assisted hydrothermal method and PVP-assisted sol-gel method show a total electrical conductivity in the order of 10 S cm and result in poor electrochemical performance. The structural and electrical properties of LNCMO (active material) and its electrochemical performance are correlated. The difference in percentage of ionic and electronic conductivity contribution to the total electrical conductivity is compared by transference number studies. The cation disorder is found to be the limiting factor for the lithium ion diffusion as determined from ionic conductivity values. [ABSTRACT FROM AUTHOR]

The effect of temperature on perovskite Pb(ZrTi)O ceramic material on their dielectric, impedance and dc conductivity properties were studied. Structural, phase formation and thermal properties were confirmed by X-ray diffraction, thermogravimetry and differential thermal analysis. The electrical properties of the ceramics were investigated as a function of both temperature (from 25 to 500 °C) and frequency (from 100 Hz to 1 MHz) using complex impedance spectroscopy. The activation energy of the sample was calculated from the slope of the Arrhenius plot as 0.210 eV (dc conductivity vs. inverse of absolute temperature). From the ferroelectric loop measured at room temperature, the remnant polarization (P) and coercive electric fields (E) are observed as 1.59 μC/cm and 9.35 kV/cm, respectively. [ABSTRACT FROM AUTHOR]

The polycrystalline perovskite PbLa(ZrTi)O ceramic material was prepared by sol-gel route. Structural, phase formation and thermal properties were confirmed by X-ray diffraction, thermogravimetry and differential thermal analysis. The microstructural study was carried out using scanning electron microscope. The electrical properties of the ceramics were investigated within a wide range of both temperature (from 25 to 500 °C) and frequency (from 100 Hz to 1 MHz) using complex impedance spectroscopy. The impedance spectrum results indicate the decrease in dielectric constant with increase in frequency while the dielectric loss increases with frequency. The activation energy of the sample was calculated as 0.124 eV from the Arrhenius plot of dc conductivity vs. inverse of absolute temperature. The remnant polarization (P) and coercive electric fields (E) are found out as 5.97 μC/cm and 8.89 kV/cm from the ferroelectric loop measured at room temperature. [ABSTRACT FROM AUTHOR]

Perovskite PbLa(ZrTi)O ceramic material was prepared through sol-gel process. Structural, phase formation and thermal properties were confirmed by X-ray diffraction, thermogravimetry and differential thermal analysis; size and microstructural study was carried out using particle size analyser and scanning electron microscope. The electrical properties of the ceramics were investigated as a function of both temperature (from room temperature to 500 °C) and frequency (from 100 Hz to 1 MHz) using complex impedance spectroscopy (CIS). The impedance spectrum results indicate the decrease in dielectric constant with increase in frequency while the dielectric loss increases with frequency. The activation energy of the sample was calculated from the slope of the Arrhenius plot as 0.129 eV from the Arrhenius's plot of dc conductivity versus inverse of absolute temperature. The dc conductivity was obtained from CIS measurements and the activation energy. The remnant polarization (P) and coercive electric fields (E) are found out as 6.52 μC/cm and 2.55 kV/cm from the ferroelectric loop measured at room temperature. [ABSTRACT FROM AUTHOR]

Olivine-structured LiCoPO is synthesized by a Pechini-type polymer precursor method. The structure and the morphology of the compounds are studied by the Rietveld-refined X-ray diffraction, scanning electron microscopy, Brunauer, Emmett, and Teller surface area technique, infrared spectroscopy, and Raman spectroscopy techniques, respectively. The ionic conductivity ( σ ionic), dielectric, and electric modulus properties of LiCoPO are investigated on sintered pellets by impedance spectroscopy in the temperature range, 27-50 °C. The σ (ionic) values at 27 and 50 °C are 8.8 × 10 and 49 × 10 S cm, respectively with an energy of activation ( E) = 0.43 eV. The electric modulus studies suggest the presence of non-Debye type of relaxation. Preliminary charge-discharge cycling data are presented. [ABSTRACT FROM AUTHOR]

The proton-conducting polymer electrolytes based on poly ( N-vinylpyrrolidone) (PVP), doped with ammonium chloride (NHCl) in different molar ratios, have been prepared by solution-casting technique using distilled water as solvent. The increase in amorphous nature of the polymer electrolytes has been confirmed by XRD analysis. The FTIR analysis confirms the complex formation of the polymer with the salt. A shift in glass transition temperature ( T) of the PVP/NHCl electrolytes has been observed from the DSC thermograms which indicates the interaction between the polymer and the salt. From the AC impedance spectroscopic analysis, the ionic conductivity of 15 mol% NHCl-doped PVP polymer complex has been found to be maximum of the order of 2.51 × 10 Scm at room temperature. The dependence of T and conductivity upon salt concentration has been discussed. The linear variation of the proton conductivity of the polymer electrolytes with increasing temperature suggests the Arrhenius type thermally activated process. The activation energy calculated from the Arrhenius plot for all compositions of PVP doped with NHCl has been found to vary from 0.49 to 0.92 eV. The dielectric loss curves for the sample 85 mol% PVP:15 mol% NHCl reveal the low-frequency β relaxation peak pronounced at high temperature, and it may be caused by side group dipoles. The relaxation parameters of the electrolytes have been obtained by the study of Tanδ as a function of frequency. [ABSTRACT FROM AUTHOR]

Olivine-structured pure LiNiPO and yttrium-doped LiNiPO have been synthesized by a Pechini-type polymerizable precursor method. Compound formation temperature is confirmed from thermogravimetry to differential thermal analysis. Powder X-ray diffraction pattern confirmed the formation of phase pure LiNiPO compound with an orthorhombic structure with fine crystallite size. Presence of preferred local cation environment is understood from Fourier transform infrared spectroscopy (FTIR) studies. XRD and FTIR studies show that doped yttrium ion entered in the lattice of LiNiPO It has been found that the ionic conductivity of LiNiPO is enhanced by around two orders of magnitude by doping yttrium. Dielectric spectra show the decrease in dielectric constant with increase in frequency. Dielectric loss spectra reveal that dc conduction contribution predominates in the sample. [ABSTRACT FROM AUTHOR]

Lithium samarium oxide has been prepared by solid-state reaction method and characterized by X-ray diffraction (XRD) and impedance spectroscopy. XRD pattern of the sample reveals the formation of the sample. The conductivity studies, dielectric studies, and modulus analysis of the samples have been carried out for different temperatures. The bulk conductivity of the sample has been found to be 1.21 × 10−5 Scm−1 at 420 °C. The temperature variation of the direct current conductivity obeys the Arrhenius relation. The modulus analysis of the sample indicates the non-Debye nature of the sample which corresponds to long-time slow polarization and relaxation of hopping charges. [ABSTRACT FROM AUTHOR]

This paper presents the influence of cutting parameters on thrust force, surface finish, and burr formation in drilling Al2219/15SiCp and Al2219/15SiCp-3Gr composites. The composites were fabricated using the liquid metallurgy method. The tools used were commercially available carbide and coated carbide drills. The results revealed that feed rate had a major influence on thrust force, surface roughness, and exit burr formation. Graphitic composites exhibit lesser thrust force, burr height, and higher surface roughness when compared to the other material. The reduced thrust force and burr height is attributed to the solid lubricating property of the graphite particles. The higher surface roughness value for Al2219/15SiCp-3Gr composite is due to the pullout of graphite from the surface. The chips formed when machining graphitic composites are more discontinuous when compared to SiCp reinforced composites and hence advantageous. [ABSTRACT FROM AUTHOR]

The dry sliding wear behavior of Al 2219 alloy and Al 2219/SiCp/Gr hybrid composites are investigated under similar conditions. The composites are fabricated using the liquid metallurgy technique. The dry sliding wear test is carried out for sliding speeds up to 6 m/s and for normal loads up to 60 N using a pin on disc apparatus. It is found that the addition of SiCp and graphite reinforcements increases the wear resistance of the composites. The wear rate decreases with the increase in SiCp reinforcement content. As speed increases, the wear rate decreases initially and then increases. The wear rate increases with the increase in load. Scanning electron microscopy micrographs of the worn surface are used to predict the nature of the wear mechanism. Abrasion is the principle wear mechanism for the composites at low sliding speeds and loads. At higher loads, the wear mechanism changes to delamination. [ABSTRACT FROM AUTHOR]