An Algorithm to Avoid Confidential Data Theft from Storage Devices

No Abstrac

1-(2,4-Dichloro­phen­yl)-3-[4-(dimethyl­amino)phen­yl]prop-2-enone

In the title compound, C17H15Cl2NO, the dimethyl­amino­phenyl group is close to coplanar with the central propenone group [dihedral angle = 13.1 (1)° between the mean planes], while the dichloro­phenyl group is twisted from the plane [dihedral angle = 64.0 (1)°]. In the crystal, C—H⋯O and weak C—H⋯π inter­actions are formed between mol­ecules

Studies on site specific nutrient management (SSNM) on hybrid sunflower seed production in Southern Karnataka

A field experiment was conducted at the Main Research Station, University of Agricultural Sciences, Hebbal, Bangalore, to know the effect of Site Specific Nutrient Management (SSNM) on growth (plant height (cm), Leaf area (cm2 ), leaf area index (LAI) and dry matter production (g plant1 ) and yield (capitulum diameter (cm), No. of filled seeds, 1000 seed weight (g) and seed yield (kg ha-1 ) of hybrid sunflower seed production. The study indicated that significantly higher growth parameters viz., plant height (155.4 cm), leaf area (1293.10cm-2 ), leaf area index (0.69) and total dry matter accumulation (88.16 g plant-1 ) were recorded with SSNM for 1.2 tons ha-1 + Farm Yard Manure (FYM) as compared to recommended practice (Recommended Dose of Fertilizer 62.50:75:62.50 kg NPK ha-1 ). The application of fertilizers based on SSNM for a target yield of 1.2 t ha-1 + FYM resulted in significantly higher yield and yield attributes compared to the only application of RDF + FYM. The important yield parameters recorded that were significantly higher were viz., head diameter (19.89 cm) number of filled seeds per head (353.24), seed weight per plant (19.76 g), 1000 seed weight (43.72 g), hybrid seed yield (1003 kg ha-1 )

Real-Time Anti Spoofing Face Detection with Mask Using CNN

As COVID-19 spread the whole way across the world, a significant number of us got mindful of how significant face covers are. Medical services authorities and nearby foundations from one side of the planet to the other are encouraging individuals to wear masks ,as it is the best way to forestall the transmission of the infection. Masks have without a doubt frustrated the facial-acknowledgment industry; the innovation has likewise adjusted. It might sound odd yet wearing a cover does not really prevent a PC from recognizing somebody. We are intending to prepare our model to recognize whether the pictures are genuine or fake one even though individuals are wearing face cover. In this paper, we intend to make a liveness detector equipped for spotting counterfeit faces. To make a liveness detector, we will prepare a deep learning neural network fit for recognizing genuine versus counterfeit appearances. It deals with two correlative spaces: RGB space and multi-scale Retinex (MSR) space. The RGB space contains the point-by-point facial surfaces, yet it is sensitive to illumination whereas the MSR pictures can adequately catch the high recurrence data, which is discriminative for face recognition

DNA hybridization to mismatched templates: a chip study

High-density oligonucleotide arrays are among the most rapidly expanding technologies in biology today. In the {\sl GeneChip} system, the reconstruction of the target concentration depends upon the differential signal generated from hybridizing the target RNA to two nearly identical templates: a perfect match (PM) and a single mismatch (MM) probe. It has been observed that a large fraction of MM probes repeatably bind targets better than the PMs, against the usual expectation from sequence-specific hybridization; this is difficult to interpret in terms of the underlying physics. We examine this problem via a statistical analysis of a large set of microarray experiments. We classify the probes according to their signal to noise ($S/N$) ratio, defined as the eccentricity of a (PM, MM) pair's `trajectory' across many experiments. Of those probes having large $S/N$ ($>3$) only a fraction behave consistently with the commonly assumed hybridization model. Our results imply that the physics of DNA hybridization in microarrays is more complex than expected, and they suggest new ways of constructing estimators for the target RNA concentration.Comment: 3 figures 1 tabl