Efficacy of organophosphorus derivatives containing chalcones/chalcone semicarbazones against fungal pathogens of sugarcane

Ten newly synthesized organophosphorus derivatives containing substituted chalcones and substituted chalcone semicarbazones were tested for their antifungal efficacy against Colletotrichum falcatum, Fusarium oxysporum, Curvularia pallescens (all sugarcane pathogens). The O,O-diethylphosphate derivatives containing 2-chlorochalcone and 2-chlorochalcone semicarbazone exhibited 70-85% mycelial inhibition against all the test fungi at 1000 ppm. The screening results were correlated with structural features of the tested compounds.     

Effect of holmium substitution on structural and electrical properties of barium zirconate titanate ferroelectric ceramics

The holmium substituted Ba_1−3x/2Ho_xZr_0.025Ti_0.975O₃ (x=0.01, 0.02, 0.025, 0.05) compositions were synthesized by the solid state reaction technique. The synthesized specimens were characterized for their structural and electrical properties using X-ray diffractometer, scanning electron microscopy, impedance analyzer and loop tracer. Phase analysis shows the formation of secondary phase Ho₂Ti₂O₇ for Ho≥2.5 mol% substitution. The microstructural investigation shows that the holmium substitution significantly reduces the grain size. The substitution of holmium increases the Curie temperature for x≤0.02 whereas Curie temperature decreases for x≥0.025. The maximum dielectric constant at transition temperature is observed for x=0.02. The solubility limit is 2 mol% and for x≥0.025 some of the holmium atoms enter B-sites and forms the secondary phase. An increase is observed in the coercive field of the specimens with the increasing holmium content.     

Tent-Shaped Surface Morphologies of Silicon: Texturization by Metal Induced Etching

Silicon - Nano-metal/semiconductor junction dependent porosification of silicon has been studied here. The silicon nanostructures (SiNSs) have been textured on n- and p- type silicon wafers using...     

Efficacy of Ozone and Lactic Acid as Nonthermal Hurdles for Preservation of Sugarcane Juice

Thermal processing is the most widely adopted technology for preservation of juices; however, it is associated with significant changes in nutritional quality and flavor. Combination of nonthermal hurdles (ozone 1.2 g/h, 10 min, and lactic acid 0.5%) reduced total bacterial count of sugarcane juice by 4.3 log and controlled enzymatic activity to a moderate level (reduction of 60% and 72% activity of polyphenol oxidase and peroxidase, respectively). Combined treatment was comparable to thermal treatment in maintaining microbial and sensory quality of sugarcane juice during 1-month storage under refrigerated conditions; however, additional hurdles may be required for extended storage.     

Development of Novel Iron Crosslinked CMC Nanoadsorbents for the Remediation of Arsenic Ions

Iron crosslinked carboxy methyl cellulose (CMC) nanoparticles prepared by the process of emulsion crosslinking were studied for the removal of arsenic(V) ions from aqueous solution. Batch and column studies were conducted in order to investigate the efficiency of nanoparticles towards arsenic remediation. The Langmuir and Freundlich equations were applied to describe the biosorption isotherm. The results obtained from fixed bed studies showed that the column demonstrates fairly well at lowest flow rate and also, bed exhaustion time was found to increase with increasing bed height. The bed depth service time (BDST) model was used to analyze the experimental data and the model parameters were evaluated. Antibacterial studies were also conducted which confirms that Fe-CMC nanoparticles are efficient towards the removal of bacteriological contamination also.     

An efficient fragile watermarking scheme with multilevel tamper detection and recovery based on dynamic domain selection

Self-recoverable fragile watermarking is meant for accurate tamper localization as well as image recovery with superior visual quality. However, most of the existing state of art approaches perform authentication and recovery on block basis owing to which the entire block is categorized as tampered in case of alteration of one or more pixels of it. This, results in staircase formation of tamper detected regions, hence lacking in accuracy. Furthermore, the visual quality of the recovered image also deteriorates as an approximate value is assigned to all the block pixels corresponding to the altered region. The proposed watermarking scheme performs both authentication and recovery pixelwise. The authentication of each pixel is done via multi level tamper detection(MLTD) through three authentication bits based on value, location and neighbourhood information. The domain for image recovery is chosen dynamically based on the content of the block, may it be in spatial domain for smooth blocks or frequency domain for the rough ones. This provides high accuracy in recovery. As the embedding of recovery information is done in the frequency domain, the imperceptibility of the watermarked image scheme remains high. Also, embedding of authentication information in the spatial domain maintains its fragile nature. Even for higher tampering ratios, the lost content is rebuilt with high peak signal to noise ratio(PSNR) of the recovered image. The probabilities of false rejection and false acceptance head towards the ideal value for most of the empirical analysis. Comparative study via metric evaluation of probability of false rejection (PFR), probability of false acceptance (PFA) and PSNR of recovered image for different standard test cover images demonstrate the efficacy of the proposed scheme over other existing state of art approaches. Further, the security of the proposed scheme remains high due to usage of multi-layered secret keys and chaos based random mapping handling worst tamper scenarios.     

Performance improvement of Deep Learning Models using image augmentation techniques

The major barrier while using deep learning models is lack of large number of images in the training dataset. In fact, there is a need of thousands of images in each image categories based on the complexity of problem. Prior studies have shown that picture augmentation techniques can be used to enhance the number of images in a training dataset artificially. These techniques can aid in improving the overall learning process and performance of a deep learning model. Hence, to address this problem we have proposed three algorithms. Firstly, two image acquisition algorithms have been proposed to systematically obtain real field images for testing and images from public datasets for training a model. Secondly, an algorithm is proposed to describe the procedure how the augmentations can be applied to enhance the datasets. During this study, we have investigated 52 augmentations that can allow enhancing the size of input dataset by improving the quantity of images. To perform the classification process of four maize crop diseases, a new convolutional neural network model is developed and several experiments have been performed to prove its effectiveness. Firstly, two tests were carried out using the original dataset from Kaggle public repository and the augmented dataset. When compared with the original dataset, the model improved by 5.14% with the augmented dataset. Secondly, three experiments carried out to evaluate the performance of proposed augmentation method. Experimental results demonstrated that the proposed approach outperforms the existing three approaches by 27.38%, 3.14%, and 1.34% during the classification process. The proposed IPA augmentation method has been compared with six existing methods: Full Stage Data Augmentation Framework, LeafGAN, Novel Augmentation method based on GAN, Wasserstein Generative Adversarial Network (WGAN), Activation Reconstruction-GAN, and Step-by-Step Data Augmentation Method and experimental results show that performance is better than existing methods by 28.31%, 19.76%, 20.18%, 13.75%, 2.42%, and 12.68% respectively.

     

PRATIT: a CNN-based emotion recognition system using histogram equalization and data augmentation

Multimedia Tools and Applications - Emotions are spontaneous feelings that are accompanied by fluctuations in facial muscles, which leads to facial expressions. Categorization of these facial...     

Proteome of human endometrium: Identification of differentially expressed proteins in proliferative and secretory phase endometrium

Purpose: To exploit the potential of proteomics to identify and study additional yet‐unidentified important proteins present in human endometrium. Experimental design: The proteome of human endometrium would be established using 2‐DE and MALDI and the data analyzed to identify differential protein expression in the proliferative and secretory phase of the menstrual cycle using PDQuest software and MALDI. Results: In the present work, 2‐DE of human endometrium protein led to the resolution of over 200 spots. Subsequent MALDI analysis of 215 spots allowed the identification of 194 proteins. A total of 57 out of the 215 spots were found to be differentially expressed, out of which 49 could be identified using MALDI. These differentially expressed proteins included structural proteins, molecular chaperones, signaling proteins, metabolic proteins, proteins related to immunity, RNA biogenesis, protein biosynthesis and others. The differential expressions of seven representative proteins in secretory and proliferative phase endometrium tissue were confirmed by immunoblot analysis. Conclusion and clinical relevance: This study establishes the 2‐D proteome of human endometrium represented by 194 identified protein spots. The present data provides an important clue towards determining the function of these proteins with respect to endometrium related diseases.     

An improved block based copy-move forgery detection technique

Multimedia Tools and Applications - With the increase in demand for identification of authenticity of the digital images, researchers are widely studying the image forgery detection techniques....