Assessment of surface roughness based on super resolution reconstruction algorithm

In this work, an attempt has been made to use a super resolution image processing algorithm for preprocessing the images over and above existing image quality enhancement techniques. The improved quality images processed using a machine vision system have been used to assess the quality of the surfaces. To ensure the validity of the approach the roughness values quantified using these images are then compared with widely accepted standard mechanical stylus instrument values. Quantification of digital images for surface roughness is performed using two Fourier transform parameters (major peak frequency and principal component magnitude squared value) and the standard deviation of gray level intensity values. Then the group method of data handling (GMDH) technique was used to obtain an analytical relationship of the roughness parameters calculated using the digital surface image and the stylus instrument values. We present in this paper an analysis based on the comparison to make sure that the present approach of estimation of surface finish based on the digital processed image could be implemented in practice.     

Effect of halophytic compost along with farmyard manure and phosphobacteria on growth characteristics of Arachis hypogaea Linn

Organic compost has been identified as an alternative to chemical fertilizer to increasing soil fertility and crop production in sustainable farming. The objective of the present study was to evaluate the effects of halophytic compost which are normally available in coastal areas on growth parameters in Arachis hypogaea. Halophytic compost along with farmyard manure (FYM) and phosphate solubilising bacteria (PSB) resulted in production of highest biomass such as plant height, number of compound leaves, total number of root nodules, fresh and dry weight of root nodules and fresh and dry weight of plant. Various combination of halophytic composts used in the present study, Suaeda compost+farmyard manure+phosphate solubilising bacteria treatment showed an enhanced biomass when compared to other halophytic compost and control.     

Rapid synthesis of pure and narrowly distributed Eu doped ZnO nanoparticles by solution combustion method

Pure ZnO:Eu³⁺ nanoparticles (~ 50 nm) were prepared by a solution combustion method. ZnO and Eu₂O₃ were used as starting materials and dissolved in nitric acid. Citric acid was used as a fuel. The reaction mixture was heated at 350 °C resulting into a rapid exothermic reaction yielding pure nanopowders. The atomic weight concentration of Eu³⁺ doped in ZnO was 20%. Transmission electron microscopy (TEM) was used to study the particle size and morphology. The nanopowders were characterized for phase composition using X-ray diffractrometry (XRD). Particle size distribution (PSD) analysis of ZnO: Eu³⁺ showed particle sizes ranging from 30 to 80 nm.The photoluminescence emission spectra of ZnO:Eu³⁺ nanostructures showed a strong band emission around 618 nm when excited with 515 nm wavelength.     

Compact SCSRR-based quad-band antenna for Bluetooth,WiMAX, WLAN,and fixed-satellite services

This research describes the detailed analysis and design of the compact complex-free structural metamaterial quad band radiating element applicable for Bluetooth, WiMAX, WLAN, and fixed-satellite service. The radiating element designed is composed of an inner angle-rotated circular Split Ring Resonator (SRR) placed within the outer square-shaped SRR interlinked by a strip to design the multiband operational characteristics and is fed by a coplanar waveguide. Suggested radiating element is imprinted over the FR4 substrate material with the electrical dimension of 28 × 31.26 × 0.8 mm³. The initial outer-closed square ring offers dual-band operation by resonating at 2.8 and 8.5 GHz frequencies, and the incorporation of a circular SRR offers quad-band operation. The unique negative permeability feature of the proposed Square and Circular Split Ring Resonator (SCSRR) structure is extracted, and its band characteristics are analyzed. Results obtained from the simulated radiating element are validated with the fabricated antenna. The measured E plane pattern resembles a numeric eight shape, and the H plane pattern is omnidirectional. Suggested SCSRR antenna offers a gain of above 3.2 dBi in all the operating bands.     

Multifold stiffness and fracture toughness enhancement in W-doped VO2 microcrystals

Vanadium dioxide is popular for the metal-insulator phase transition at 68°C. Chemical doping is one of the effective ways adopted to tune the phase transition temperature, where tungsten is known to reduce the transition temperature of VO₂. This work investigates the effect of tungsten doping on the mechanical properties of VO₂ microcrystals and their polymer composites. Doping of VO₂ with W shows a systematic reduction in phase transition temperature up to 33°C for 4 wt% W-doped VO₂. For 3 wt% W-doped VO₂, the elastic modulus values enhance by 50%. The fracture toughness of 3 wt% W-doped VO₂ shows an enhancement of fourfold compared to the undoped VO₂. The dynamic compressive strength of 3 wt% W-doped VO₂–UHMWPE polymer composite at room temperature is found to be 7% higher than the undoped VO₂—composite.     

SABOS-Net: Self-supervised attention based network for automatic organ segmentation of head and neck CT images

The segmentation of Organs At Risk (OAR) in Computed Tomography (CT) images is an essential part of the planning phase of radiation treatment to avoid the adverse effects of cancer radiotherapy treatment. Accurate segmentation is a tedious task in the head and neck region due to a large number of small and sensitive organs and the low contrast of CT images. Deep learning-based automatic contouring algorithms can ease this task even when the organs have irregular shapes and size variations. This paper proposes a fully automatic deep learning-based self-supervised 3D Residual UNet architecture with CBAM(Convolution Block Attention Mechanism) for the organ segmentation in head and neck CT images. The Model Genesis structure and image context restoration techniques are used for self-supervision, which can help the network learn image features from unlabeled data, hence solving the annotated medical data scarcity problem in deep networks. A new loss function is applied for training by integrating Focal loss, Tversky loss, and Cross-entropy loss. The proposed model outperforms the state-of-the-art methods in terms of dice similarity coefficient in segmenting the organs. Our self-supervised model could achieve a 4% increase in the dice score of Chiasm, which is a small organ that is present only in a very few CT slices. The proposed model exhibited better accuracy for 5 out of 7 OARs than the recent state-of-the-art models. The proposed model could simultaneously segment all seven organs in an average time of 0.02 s. The source code of this work is made available at https://github.com/seeniafrancis/SABOSNet .     

Statistical Approach on High Temperature Dry Sliding Wear Behavior of Al-Li-Si3N4 Metal Matrix Composite

Silicon - Light weight composites with elevated temperature wear resistance are all time requirements even though many research articles available in the past three decades. This study concentrates...     

Classification analysis of burnout people's brain images using ontology-based speculative sense model

Burnout is a state of exhaustion that results from prolonged, excessive workplace stress. This can be examined with the biological explications of burnout and physical consequences and classified against prolonged vigorous activities. The research aims to classify burnout people's brain images against prolonged emotional activities using ontology analysis of treatment and prevention and intermediate layers formation based on a speculative sense model. In this segment, the Ontology analysis of Treatment and prevention and intermediate layers formation based on a hypothetical sense model is employed for burnout people's classification analysis. The methodology is performed in the platform of ontology creation and performs the classification analysis. The calculation analysis found the result, and the brain images were classified. The classification analysis of burnout people's brain images, separation of prolonged vigorous activities, and the ontology creation for treatment and prevention against burnout people's brain images were obtained. The analysis received the result, and the results of the precision, recall, storage, computation time, specificity, and classification of burnout people's brain images were obtained. Furthermore, all these Ontology analysis of Treatment and prevention and intermediate layers formation based on a hypothetical sense model had the prediction sensitivity (SN) over 50% and specificity (SP) over 90%. The Classification of Burnout People's Brain performance comparison shows that the proposed system is much more successful than existing methods, especially on a scoring accuracy of 98%.     

Breast milk from healthy women has higher anti-Candida properties than women with vaginal infections during pregnancy

Food Science and Biotechnology - The aim of this study was to investigate the different immunological and antimicrobial properties of breast milk from women with (W) or without (WO) vaginal yeast...     

Dry Sliding Wear Behavior of Aluminum Metal Matrix Composite Reinforced with Lithium and Silicon Nitride

Silicon - In this study aluminium 2024 metal matrix composite was fabricated with lithium, silicon nitride and composite containing both lithium and silicon nitride using stir casting route. XRD...