Formulation and in vitro evaluation of fast dissolving tablets of metoprolol tartrate

The demand for fast dissolving tablets has been growing during the last decade, especially for elderly and children who have swallowing difficulties. In the present work, fast dissolving tablets of metoprolol tartrate, were prepared using sodium starch glycolate, sodium croscarmellose and crospovidone as superdisintegrants, by the direct compression method. The tablets prepared were evaluated for various parameters including weight variation, hardness, friability, in vitro dispersion time, drug-polymer interaction, drug content water absorption ratio, wetting time, in vitro drug release, FTIR and DSC studies. The tablets prepared by the direct compression method had a weight variation in the range of 145 mg to 152 mg, which is below ± 7.5%, a hardness of 3.6 kg/cm² to 4.5 kg/cm², percentage friability of 0.46% to 0.73%, in vitro dispersion time of 18 s to 125 s, drug content uniformity of between 98.12% and 100.03%, a water absorption ratio of 67% to 87%, wetting time of 32 sec. to 64 sec., and an in vitro drug release of 53.92% - 98.82% within 15 min. The IR spectral analysis and DSC study showed no drug interaction with formulation additives of the tablet, and the formulations indicated no significant changes in hardness, friability, drug content or in vitro drug release. Fast dissolving tablets of metoprolol tartrate have enhanced dissolution and will lead to improved bioavailability and more effective therapy

Synthesis of hierarchical fabrics by electrospinning of PAN nanofibers on activated carbon microfibers for environmental remediation applications

A novel hierarchal fabric was synthesized, consisting of poly-acrylonitrile (PAN) nanofibers electrospun on a mat of activated carbon microfibers (ACF), used as a substrate. Electrospun PAN nanofibers were stabilized by preoxidizing in air at 250°C. The multiscale web (ACF-PANS) of stabilized nanofibers on ACF thus prepared was further pyrolyzed and activated by steam at 900°C to prepare a hierarchical activated carbon fabric (ACF-PANC). These multiscale fabrics (ACF-PANS and ACF-PANC) were tested for its adsorption properties toward common atmospheric air pollutants, such as SO2, NO, and toluene, a volatile organic compound (VOC) and the performance was compared to ACF and another hierarchical carbon fabric fabricated by growing carbon nanofibers on metal-impregnated ACF (ACF-CNF) by chemical vapor deposition. Interestingly, the performance of the electrospun PAN nanofibers based multiscale carbon-polymer fabric after stabilization (ACF-PANS) was found to be superior to that of ACF, ACF-PANC and ACF-CNF fabrics. A variety of surface characterization techniques demonstrated that the PAN nanofiber-based stabilized hierarchical fabrics contained relatively large amounts of nitrogen-based surface functional groups that favored the adsorption and catalytic oxidation of SO2 and NO. On the other hand, the pore volume and specific surface area of the materials were found to affect the adsorption of toluene. This study reveals the considerable potential of the stabilized electrospun PAN nanofiber-based hierarchical fabric (ACF-PANS) materials as adsorbents for air pollution control

Low-complexity multicast beamforming for multi-stream multi-group communications

Abstract In this paper, assuming multi-antenna transmitter and receivers, we consider a multicast beamformer design for the weighted max-min-fairness (WMMF) problem in a multi-stream multi-group communication setup. Unlike the single-stream scenario, the WMMF objective in this setup is not equivalent to maximizing the minimum weighted SINR due to the summation over the rates of multiple streams. Therefore, the non-convex problem at hand is first approximated with a convex one and then solved using Karush-Kuhn-Tucker (KKT) conditions. Then, a practically appealing closed-form solution is derived for both transmit and receive beamformers as a function of dual variables. Finally, we use an iterative solution based on the sub-gradient method to solve for the mutually coupled and interdependent dual variables. The proposed solution does not rely on generic solvers and does not require any bisection loop for finding the achievable rate of various streams. As a result, it significantly outperforms the state-of-art in terms of computational cost and convergence speed

Effect of Melia azedarach and Dodonaea viscosa aqueous leaf extracts on fertility in male albino rats

Medicinal plants play a key role in human life as they are helpful in curing several diseases. They not only support health by the pharmacological nature but also utilizable as contraceptive options. The present study reveals that the medicinal plants Melia azedarach and Dodonaea viscosa leaf extracts showing antifertility activity. The decreased sperm count and reproductive organ weights including the necrotic changes in the seminiferous tubules of testis suggesting the antifertility activity of the plants. Serum glutamic oxaloacetic transaminase (SGOT), serum glutamate pyruvate transaminase (SGPT) and other serological studies were also carried out to know whether side-effects of the extracts

Learning-based beam alignment for uplink mmWave UAVs

Abstract Unmanned aerial vehicles (UAVs) are the emerging vital components of millimeter wave (mmWave) wireless systems. Accurate beam alignment is essential for efficient beam based mmWave communications of UAVs with base stations (BSs). Conventional beam sweeping approaches often have large overhead due to the high mobility and autonomous operation of UAVs. Learning-based approaches greatly reduce the overhead by leveraging UAV data, like position to identify optimal beam directions. In this paper, we propose a deep Q-Network(DQN)-based framework for uplink UAV-BS beam alignment where the UAV hovers around 5G new radio (NR) BS coverage area, with varying channel conditions. The proposed learning framework uses the location information and maximize the beamforming gain upon every communication request from UAV inside the multi-location environment. We compare the proposed framework against multi-armed bandit (MAB)-based and exhaustive approaches, respectively and then analyse its training performance over different coverage area requirements, antenna configurations and channel conditions. Our results show that the proposed framework converge faster than the MAB-based approach and comparable to traditional exhaustive approach in an online manner under real-time conditions. Moreover, this approach can be further enhanced to predict the optimal beams for unvisited UAV locations inside the coverage using correlation from neighbouring grid locations

DQN-based beamforming for uplink mmWave cellular-connected UAVs

Abstract Unmanned aerial vehicles (UAVs) are the emerging vital components of millimeter wave (mmWave) wireless systems. Accurate beam alignment is essential for efficient beam based mmWave communications of UAVs with base stations (BSs). Conventional beam sweeping approaches often have large overhead due to the high mobility and autonomous operation of UAVs. Learning-based approaches greatly reduce the overhead by leveraging UAV data, like position to identify optimal beam directions. In this paper, we propose a reinforcement learning (RL)-based framework for UAV-BS beam alignment using deep Q-Network (DQN) in a mmWave setting. We consider uplink communications where the UAV hovers around 5G new radio (NR) BS coverage area, with varying channel conditions. The proposed learning framework uses the location information to maximize data rate through the optimal beam-pairs efficiently, upon every communication request from UAV inside the multi-location environment. We compare our proposed framework against Multi-Armed Bandit (MAB) learning-based approach and the traditional exhaustive approach, respectively and also analyse the training performance of DQN-based beam alignment over different coverage area requirements and channel conditions. Our results show that the proposed DQN-based beam alignment converge faster and generic for different environmental conditions. The framework can also learn optimal beam alignment comparable to the exhaustive approach in an online manner under real-time conditions