A batch and flow injection (FI) spectrophotometric methods are described for the determination of barbituric acid in aqueous and urine samples. The method is based on the oxidative coupling reaction of barbituric acid with 4-aminoantipyrine and potassium iodate to form purple water soluble stable product at λ 510 nm. Good linearity for both methods was obtained ranging from 2 to 60 μg mL−1, 5–100 μg mL−1 for batch and FI techniques, respectively. The limit of detection (signal/noise = 3) of 0.45 μg mL−1 for batch method and 0.48 μg mL−1 for FI analysis was obtained. The proposed methods were applied successfully for the determination of barbituric acid in tap water, river water, and urine samples with good recoveries of 99.92

The ability of four local fungal isolates for extracellular laccase production has been tested with five grams 1:1(w/v) humidified sawdust as substrate in mineral salt medium. After 21 day of incubation at 25±1 ? C and using one mycelial plug (5mm), higher level of laccase activity (0.15U/ml) and specific activity (15U/mg) were observed by Pleurotus ostreatus in comparison with other fungal isolates. The results of optimum conditions for laccase production from selected isolate showed that, the maximum laccase activity (0.55U/ml) and specific activity (55U/mg) were obtained at moisture ratio 1:3 (w/v), using 3 mycelial plugs (5 mm), after 15 days incubation period at 25±1 ? C. The results of phenol degradation by crud laccase revealed th

A specific, sensitive and new simple method was used for the determination of methyldopa in pure and pharmaceutical formulations by using continuous flow injection analysis. This method is based on formation of ion pair compound between methyldopa and potassium hexacyanoferrate in acidic medium to obtain a yellow precipitate complex using long distance chasing photometer (NAG-ADF-300-2). The linear range for calibration graph was 0.05-35 mmol/L for cell A and 0.05-25 mmol/L for cell B, and LOD 1.4292 µg /200 µL for both cells with correlation coefficient (r) 0.9981 for cell A and 0.9994 for cell B, RSD% was lower than 0.5 % for n=8 for. The results were compared with classical method UV-Spectrophotometric at λ max=280 nm and turbi

Channel estimation (CE) is essential for wireless links but becomes progressively onerous as Fifth Generation (5G) Multi-Input Multi-Output (MIMO) systems and extensive fading expand the search space and increase latency. This study redefines CE support as the process of learning to deduce channel type and signal-tonoise ratio (SNR) directly from per-tone Orthogonal Frequency-Division Multiplexing (OFDM) observations,with blind channel state information (CSI). We trained a dual deep model that combined Convolutional Neural Networks (CNNs) with Bidirectional Recurrent Neural Networks (BRNNs). We used a lookup table (LUT) label for channel type (class indices instead of per-tap values) and ordinal supervision for SNR (0–20 dB,5-dB steps). T

With its rapid spread, the coronavirus infection shocked the world and had a huge effect on billions of peoples' lives. The problem is to find a safe method to diagnose the infections with fewer casualties. It has been shown that X-Ray images are an important method for the identification, quantification, and monitoring of diseases. Deep learning algorithms can be utilized to help analyze potentially huge numbers of X-Ray examinations. This research conducted a retrospective multi-test analysis system to detect suspicious COVID-19 performance, and use of chest X-Ray features to assess the progress of the illness in each patient, resulting in a "corona score." where the results were satisfactory compared to the benchmarked techniques.  T

This paper is concerned with finding solutions to free-boundary inverse coefficient problems. Mathematically, we handle a one-dimensional non-homogeneous heat equation subject to initial and boundary conditions as well as non-localized integral observations of zeroth and first-order heat momentum. The direct problem is solved for the temperature distribution and the non-localized integral measurements using the Crank–Nicolson finite difference method. The inverse problem is solved by simultaneously finding the temperature distribution, the time-dependent free-boundary function indicating the location of the moving interface, and the time-wise thermal diffusivity or advection velocities. We reformulate the inverse problem as a non-