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An experimental assessment of abrasive wear behavior of GNP/Carbon fiber/epoxy hybrid composites
Get PDFThis investigation has evaluated the wear properties of Carbon fiber-epoxy/GNP (Graphene Nanoplatelets) composites. In this research, carbon fiber and Graphene nanoplatelets (GNP) of different weight percentages of GNP (0, 0.1,0.3, and 0.5 wt.%) reinforced hybrid composites were fabricated via compression molding assist hand layup technique. An abrasive wear test has been performed using the Design of experiments. Analysis of variance (ANOVA) tables has been used to understand the effect of control parameters (wt.% of filler, normal load, and sliding distance) on response parameters (specific wear rate and friction coefficient). The control variables such as normal loads of 5, 10, 15, and 20 N and sliding distances (150, 200, 250, and 300 m) are selected for this study. It has been discovered that adding GNPs reduces the particular wear rate and friction coefficient. Scanning electron microscopy (SEM) was used to examine composites' worn surfaces. The composites with GNPs had lower weight loss, friction coefficient, and wear rate as compared to plain carbon fiber-reinforced epoxy, and these metrics decreased as the percentage of GNPs increased. The analysis concluded that experimental results are closer to optimum results
Brain Tumor Classification using SLIC Segmentation with Superpixel Fusion, GoogleNet, and Linear Neighborhood Semantic Segmentation
Get PDFBrain tumor is an abnormal tissue mass resultant of uncontrolled growth of cells. Brain tumors often reduce life expectancy and cause death in the later stages. Automatic detection of brain tumors is a challenging and important task in computer-aided disease diagnosis systems. This paper presents a deep learning-based approach to the classification of brain tumors. The noise in the brain MRI image is removed using Edge Directional Total Variation Denoising. The brain MRI image is segmented using SLIC segmentation with superpixel fusion. The segments are given to a trained GoogleNet model, which identifies the tumor parts in the image. Once the tumor is identified, a Convolution Neural Network (CNN) based modified semantic segmentation model is used to classify the pixels along the edges of the tumor segments. The modified sematic segmentation uses a linear neighborhood of the pixel for better classification. The final tumor identified is accurate as pixels at the border are classified precisely. The experimental results show that the proposed method has produced an accuracy of 97.3% with GoogleNet classification model, and the linear neighborhood semantic segmentation has delivered an accuracy of 98%
Characterization of Cr(VI) removal from water by Mg-Fe layered double hydroxides
No full textIn the present study, Mg-Fe layered double hydroxides (Mg-Fe LDHs) are prepared by the co-precipitation method at pH: 10 and pH: 12 and used as adsorbents for Cr(VI) removal from aqueous solutions. The XRD analysis confirms the lamellar structure of the prepared hydroxides before calcination and shows the formation of spinel and magnesium oxide after calcination at 500°C. Cr(VI) removal by the prepared LDHs is favorable at acidic pH. The adsorption process is spontaneous and endothermic. The uncalcined LDH prepared at pH 12 exihibites the lower removal equilibrium time. Whatever the prepared LDH used, the Cr(VI) removal kinitics is well described by the second-order model and the adsorption isotherm is well described by the Langmuir model. The higher adsorption capacities calculated by Langmuir equation are obtained in the case of the uncalcined LDH(s) for the two preparation pH
Design, synthesis and characterization of novel substituted pyrazol-azetidin-2-one derivatives for their antimicrobial activity
Get PDFA novel method is elucidated herein, describing the synthesis of novel 3-chloro-4-(2-substituted phenyl)-1-( 4'-((1-(5-( 4-substituted phenyl)-3-phenyl-4, 5-dihydro-1H-pyrazol-1-yl) ethylidene) amino)-[1,1'-biphenyl]-4-yl) azetidin-2-one, consisting of a pyrazol motif (prepared from chalcone) and a lactam ring ( synthesized from Schiff base of aromatic aldehyde) as a potential antimicrobial agent. The structural elucidation of the synthesized compounds have been confirmed from the elemental analysis, UV-Vis absorption spectroscopy, IR, 1H NMR and mass spectral studies. The novel compounds have been subjected to in vitro antimicrobial screening against certain gram positive (S. aureus, B. subtilis) and gram negative (P. aeruginosa, E. coli) bacterial species. Compounds Vb, Vd and Ve are the most potent amongst all the synthesized compounds against the tested microbes
Immense Magnetodielectric Effect in Eu2O3-Mesoporous Silica Nanocomposites
Get PDFNanodimentional Eu2O3 was developed inside the channels of silica with mesoporus structure, which have5 nm pore diameter. At room temperature, such nanocomposite showed a large magnetodielectric coefficient of around 70% upon application of magnetic field of 1 Tesla at a frequency 1 kHz. The dielectric loss was quite low in spite of the fact that Eu2O3 is conducting in nature. This was achieved because of a scarcelydispensed Eu2O3species inside silica, which is highly resistive in nature. This method of nanocomposite formation gives rise to the fabrication of devices with high magnetodielectric coefficients
Temperature Dependence on Opto-structural Parameters of Sol-gel Derived Tin Doped Zirconia Nanoparticles
Get PDFRecently, nanocrystalline zirconia was widely employed in photocatalytic applications .Tin doped zirconia nanoparticles were prepared by sol-gel process followed by spin coating technique. The as-produced powders and thin films were heat treated in air at 500, 650 and 800 0C for 2h. Structural parameters of annealed samples were characterized by X-ray diffraction and Fourier transformed infrared studies. XRD spectra revealed the mixed phases such as t-ZrO2, m-ZrO2 and o-ZrSnO4. Structural parameters viz. crystallite size, lattice constants, dislocation density, microstrain, orientation parameter and activation energy were evaluated. XRD data depicted that crystallite size increased, while lattice parameters slightly decreased with increase in annealing temperature. Expected functional groups were established by FTIR spectra. Optical parameters of nanopowders/thin films such as PL emission wavelength and optical band gap were determined by photoluminescence and UV-visible absorption. The energy band gaps of thin films were increased with increase in temperature. The emission peak exhibited a blue shift with increase in temperature. In addition, thermogravimetric-differential thermal analysis of as prepared sample was investigated
Impact of Patent (Amendment) Act, 2005 on Indian Pharmaceutical Industry
Get PDFSince 1901, the Indian pharmaceutical industries have expanded and strengthened to become a significant supplier of healthcare items, meeting around 90% of the nation’s need for bulk pharmaceuticals, drugs intermediates, chemicals for the medicinal base, pharmaceutical compounds, medicinal formulas, drugs, tablets, orals, respules/capsules, and injectables. Indian pharmaceutical industry has embarked not only on national markets but it has an esteemed and illustrious position in international markets also. India is the world’s third-largest pharmaceutical manufacturer by volume and fourteenth-largest by value. The growth and empowerment of the pharmaceutical industry is totally attributed to the rules and principles governing the operations and evolution of the pharmaceutical industry in India. These rules are the controlling guidelines mandated by the Government of India, through the Acts and amendments made in the acts at various times. One of the major and most influential amendment is the Patent (Amendment) Act, 2005. This act has provided new dimensions and horizons to the Indian pharmaceutical industry and a good number or rational researches have been performed on this subject. It has thus been a fascination to dig the researcher’s points of views about how The Patent (Amendment) Act, 2005 after TRIPS has impacted the Indian pharmaceutical industry since this act has been passed. This survey paper is an attempt to bring out the deviations and modifications, Indian pharmaceutical industry has been gone through, since year 2005
‘TRAIL’ of targeted colorectal cancer therapy
Get PDFTRAIL, a tumor necrosis factor-related apoptosis-inducing ligand is a member of the tumor necrosis factor (TNF) superfamily, which is a cytokine that has shown a particularly precise and selective affinity towards death receptors that are overexpressed in cancer cells. While leaving the normal cells intact and unharmed, due to this property it has been the ligand of choice for highly precise cancer chemotherapeutic delivery system development. On numerous occasions, TRAIL has been used singularly and in combination with other drugs. It was observed that TRAIL had a tendency to be susceptible in terms of the cancer cells developing resistance against it. So TRAIL monotherapy was a bit of a rough patch for the molecule to become successful in the chemotherapy universe, however the conjugations and synergistic actions of TRAIL opened up new horizons which are discussed in this review with specific interest on colorectal cancer (CRC)
Identification of phytoconstituents for combating Polycystic ovarian syndrome through in silico techniques
Get PDFPolycystic ovarian syndrome is one of the leading causes for infertility in women. One in Five women of the population is affected by PCOS. The synthetic drugs currently used are targeted to provide an artificial support for the hormonal imbalance in the body which leads to various adverse effects. Natural herbs serve as a best remedy for many of the diseases as they cure the root cause and target the disease specifically. Selection of herbs is a crucial part in the formulation. In silico studies play an important role in analyzing the activity of the compound with the selected target. The herbs which had reported biological activity on uterus were selected and their vital chemical constituents were docked with the identified target of PDB ID 3RUK and 1E3K, respectively. The values obtained shows the potential effect of chemical constituent with the suitable target. Among the list of herbs selected, Sesamin from Sesamum indicum and lanosterol from Ficus religiosa had good binding affinity with both the selected proteins and had better drug likeliness properties. Hence, further studies on these compounds for targeting PCOS is expected to give potent activity and produce promising results
MobileNetV2-based Transfer Learning Model with Edge Computing for Automatic Fabric Defect Detection
Get PDFIn textile manufacturing, fabric defect detection is an essential quality control step and a challenging task. Earlier, manual efforts were applied to detect defects in fabric production. Human exhaustion, time consumption, and lack of concentration are the main problems in the manual defect detection process. Machine vision systems based on deep learning play a vital role in the Industrial Internet of things (IIoT) and fully automated production processes. Deep learning centered on Convolution Neural Network (CNN) models have been commonly used in fabric defect detection, but most of these models require high computing resources. This work presents a lightweight MobileNetV2-based Transfer Learning model to assist defect detection with low power consumption, low latency, easy upgrade, more efficiency, and an automatic visual inspection system with edge computing. Firstly, different image transformation techniques were performed as data augmentation on four fabric datasets for the model's adaptability in various fabrics. Secondly, fine-tuning hyperparameters of the MobileNetV2 with transfer learning gives a lightweight, adaptable and scalable model that suits the resource-constrained edge device. Finally, deploy the trained model to the NVIDIA Jetson Nano-kit edge device to make its detection faster. We assessed the model based on its accuracy, sensitivity rate, specificity rate, and F1 measure. The numerical simulation reveals that the model accuracy is 96.52%, precision is 96.52%, recall is 96.75%, and F1-Score is 96.52%