FLAVONOID CONTENT AND ANTIBACTERIAL ACTIVITY OF ALBIZIA JULIBRISSIN. DURAZZ LEAF, STEM AND FLOWER EXTRACTS AGAINST CLINICALLY ISOLATED BACTERIAL PATHOGENS

Objective: To test the antibacterial efficacy of leaf, stem, and flower extracts of Albizia julibrissin against bacterial pathogens. Methods: Extraction of active metabolites was carried out by using six different solvents, and total flavonoid content in each extract was determined by Aluminium chloride method. To determine the antibacterial activity of extracts, disc diffusion method and tube dilution method were carried out. Zone of inhibition and Minimum inhibitory Concentration (MIC) were calculated. Results: Methanolic extracts of leaf samples of A. julibrissin showed highest extractive value (5.14g/100g) and total flavonoid content (35.14mg/g). In overall leaf extracts of A. julibrissin showed maximum zone of inhibition towards P. vulgaris (10.1 mm*) and least susceptible microorganism is S. typhi (3.5 mm*). Stem and flower extracts inhibited bacterial growth only at higher concentrations (MIC, 160-215 and 65-180µg/ml respectively). Conclusion: Apart from the energy crop, based on the results and value-added compounds present in A. julibrissin, it may be considered as antibacterial agent in future

Hybrid Time-Series Forecasting Models for Traffic Flow Prediction

Traffic flow forecast is critical in today’s transportation system since it is necessary to construct a traffic plan in order to determine a travel route. The goal of this research is to use time-series forecasting models to estimate future traffic in order to reduce traffic congestion on roadways. Minimising prediction error is the most difficult task in traffic prediction. In order to anticipate future traffic flow, the system also requires real-time data from vehicles and roadways. A hybrid autoregressive integrated moving av-erage with multilayer perceptron (ARIMA-MLP) model and a hybrid autoregressive integrated moving average with recurrent neural network (ARIMA-RNN) model are proposed in this paper to address these difficulties. The transportation data are used from the UK Highways data-set. The time-series data are preprocessed using a random walk model. The forecasting models autoregressive inte-grated moving average (ARIMA), recurrent neural net-work (RNN), and multilayer perceptron (MLP) are trained and tested. In the proposed hybrid ARIMA-MLP and ARI-MA-RNN models, the residuals from the ARIMA model are used to train the MLP and RNN models. Then the ef-ficacy of the hybrid system is assessed using the metrics MAE, MSE, RMSE and R2 (peak hour forecast-0.936763, non-peak hour forecast-0.87638 on ARIMA-MLP model and peak hour forecast-0.9416466, non-peak hour fore-cast-0.931917 on ARIMA-RNN model)

Analytical Model of Adaptive CSMA-CA MAC for Reliable and Timely Clustered Wireless Multi-Hop Communication

Reliability and delay of a single cluster wireless network is well analysed in the literature. Multi-hop communication over the number of clusters is essential to scale the network. Analytical model for reliability and end-to-end delay optimization for multi-hop clustered network is presented in this paper. Proposed model is a three dimensional markov chain. Three dimensions of markov model are the adaptable mac parameters of CSMA-CA. Model assumes wakeup rates for each cluster. Results show that reliability and delay are significantly improved than previous analytical models proposed. It has been observed that overall reliability of multi-hop link is improved, with reduction in end-to-end delay is reduced even at lower wakeup rates of a cluste

Effect of Relay Nodes on End-To-End Delay in Multi-Hop Wireless Ad-Hoc Networks

Channel access delay in a wireless adhoc network is the major source of delay while considering the total end to-end delay. Channel access delays experienced by different relay nodes are different in multi-hop adhoc network scenario. These delays in multi-hop network are analysed in the literature assuming channel access delays are independent and are of same magnitude at all the nodes in the network. In this work, the end to-end delay in a multi-hop adhoc network is analysed taking into account the silent relay nodes. Along with silent relay node effect, Channel access probability (p), transmission radius (r) analogous to transmit power, network throughput and density of nodes arête other factors considered for the end-to-end delay analysis. Effect of network parameters along with silent relay nodes on end-to-end delay is found to be considerably high compared to the previous literature results. Given a bound on end-to-end delay with percentage of silent relay nodes, throughput, node density requirements for a multi-hop adhoc network, optimal ranges of transmission radius and channel access probability can be obtained from the proposed analysis. End-to-end delay increases with silent relay nodes along with transmission radius(r), channel access probability(p), node density and throughput. It is clear from the analysis, that the effect of silent relay nodes on end to-end delay cannot be ignored to maintain certain Quality of service (QoS) metrics for the multi-hop wireless adhoc networ

Effect of relay nodes and transmit power on end-to-end delay in multi-hop wireless ad hoc networks

Channel access delay in a wireless multi-hop ad hoc network is the major source of delay while considering end-to-end delay. In this work, end-to-end delay is analysed considering silent relay nodes and effect of network parameters like node density and throughput. Given network parameter requirements and bound on end-to-end delay, optimal ranges of transmission radius and channel access probability can be obtained from the proposed analysis. Effect of silent relay nodes must be considered to maintain quality of service (QoS) metrics. Transmission power adaptability to reduce end-to-end delay is analysed considering the interference model. Increase in transmission power is not reducing end-to-end delay linearly. Simulation results show that increase in end-to-end delay due to channel access probability and throughput is onsiderably higher than node density. Also given the network parameters, end-to-end delay can be minimised only up to certain value irrespective of increase in transmit power

Analysis of User’s Opinion using Deep Neural Network Techniques

Through many research and discoveries it has been widely accepted that aspect-level sentiment classification is achieved effectively by using Long Short-Term Memory (LSTM) network combined with attention mechanism and memory module. As existing approaches widely depend on the modeling of semantic relatedness of an aspect, at the same time we ignore their syntactic dependencies which are already a part of that sentence. This will result in undesirably an aspect on textual words that are descriptive of other aspects. So, in this paper, to offer syntax free contexts as well as they should be aspect specific, so we propose a proximity-weighted convolution network. To be more precise, we have one way of determining proximity weight which is dependency proximity. The construction of the model includes bidirectional LSTM architecture along with a proximity-weighted convolution neural network

FORMULATION DEVELOPMENT, EVALUATION AND OPTIMIZATION OF MEDICATED LIP ROUGE CONTAINING NIOSOMAL ACYCLOVIR FOR THE MANAGEMENT OF RECURRENT HERPES LABIALIS

Objective: Aim of the study was to formulate, evaluate and optimize medicated Lip rouge containing acyclovir encapsulated inside a novel vesicular carrier, niosome so that the formulation can improve its membrane penetration. Formulating as a cosmetic Lip rouge formulation will also improve patient compliance in the treatment of herpes labialis.Methods: Acyclovir niosomes were prepared by thin film hydration method. Niosomes were evaluated and were optimized by considering the entrapment efficiency and in vitro release profile. The optimized niosomes were incorporated into lipstick, lip balm and lip rouge for selecting the best lip formulation. Based on the in vitro release profile, ease of application and properties of prepared formulations lip rouge was selected and further evaluations were carried out.Results: Among the six formulations of niosomes NF2 has showed 88.49 % entrapment efficiency and 86.97% cumulative drug release in 8 h. The formulation was optimized considering both entrapment efficiency and in vitro release. The optimized formulation of niosomes was incorporated into Lipstick, lip balm and lip rouge. The evaluation results of lipstick, lip balm and lip rouge for in vitro release suggested lip rouge as the best formulation. The percentage cumulative release of drug from optimized lip rouge at the end of 8 h was 84.77%. The percentage cumulative drug release in ex vivo studies for 8 h was 60.88 %.Conclusion: The results suggested that prepared lip rouge containing acyclovir niosomes can effectively deliver the drug than the marketed acyclovir cream and successful therapy of Recurrent Herpes labialis can be achieved

Neural Network based Short Term Forecasting Engine To Optimize Energy And Big Data Storage Resources Of Wireless Sensor Networks

Energy efficient wireless networks is the primary research goal for evolving billion device applications like IoT, smart grids and CPS. Monitoring of multiple physical events using sensors and data collection at central gateways is the general architecture followed by most commercial, residential and test bed implementations. Most of the events monitored at regular intervals are largely redundant/minor variations leading to large wastage of data storage resources in Big data servers and communication energy at relay and sensor nodes. In this paper a novel architecture of Neural Network (NN) based day ahead steady state forecasting engine is implemented at the gateway using historical database. Gateway generates an optimal transmit schedules based on NN outputs thereby reducing the redundant sensor data when there is minor variations in the respective predicted sensor estimates. It is observed that NN based load forecasting for power monitoring system predicts load with less than 3% Mean Absolute Percentage Error (MAPE). Gateway forward transmit schedules to all power sensing nodes day ahead to reduce sensor and relay nodes communication energy. Matlab based simulation for evaluating the benefits of proposed model for extending the wireless network life time is developed and confirmed with an emulation scenario of our testbed. Network life time is improved by 43% from the observed results using proposed model

Analysis of User’s Opinion using Deep Neural Network Techniques

Through many research and discoveries it has been widely accepted that aspect-level sentiment classification is achieved effectively by using Long Short-Term Memory (LSTM) network combined with attention mechanism and memory module. As existing approaches widely depend on the modeling of semantic relatedness of an aspect, at the same time we ignore their syntactic dependencies which are already a part of that sentence. This will result in undesirably an aspect on textual words that are descriptive of other aspects. So, in this paper, to offer syntax free contexts as well as they should be aspect specific, so we propose a proximity-weighted convolution network. To be more precise, we have one way of determining proximity weight which is dependency proximity. The construction of the model includes bidirectional LSTM architecture along with a proximity-weighted convolution neural network

A Comparison of Vitamin A and Leucovorin for the Prevention of Methotrexate-Induced Micronuclei Production in Rat Bone Marrow

INTRODUCTION: Methotrexate, a folate antagonist, is a mainstay treatment for childhood acute lymphoblastic leukemia. It is also widely used in a low dose formulation to treat patients with rheumatoid arthritis. In rats, methotrexate is known to induce micronuclei formation, leading to genetic damage, while vitamin A is known to protect against such methotrexate-induced genetic damage. Leucovorin (folinic acid) is generally administered with methotrexate to decrease methotrexate-induced toxicity. OBJECTIVES: We aimed to determine whether vitamin A and leucovorin differed in their capacity to prevent formation of methotrexate-induced micronuclei in rat bone marrow erythrocytes. The present study also aimed to evaluate the effect of combined treatment with vitamin A and leucovorin on the formation of methotrexate-induced micronuclei. METHODS: Male and female Wistar rats (n=8) were injected with 20 mg/kg methotrexate (single i.p. dose). The control group received an equal volume of distilled water. The third and fourth groups of rats received vitamin A (5000 IU daily dose for 4 successive days) and leucovorin (0.5 mg/kg i.p. dose for 4 successive days), respectively. The fifth and sixth groups of rats received a combination of vitamin A and a single dose of methotrexate and a combination of leucovorin and methotrexate, respectively. The last group of rats received a combination of leucovorin, vitamin A and single dose of methotrexate. Samples were collected at 24 hours after the last dose of the treatment into 5% bovine albumin. Smears were obtained and stained with May-Grunwald and Giemsa. One thousand polychromatic erythrocytes were counted per animal for the presence of micronuclei and the percentage of polychromatic erythrocyte was determined. RESULTS: Comparison of methotrexate-treated rats with the control group showed a significant increase in the percentage of cells with micronuclei and a significant decrease polychromatic erythrocyte percentage. Combined methotrexate and vitamin A therapy and combined methotrexate and leucovorin therapy led to significant decreases in the micronuclei percentage and an increase in polychromatic erythrocyte percentage when compared to rats treated with methotrexate alone. Leucovorin was found to be more effective than vitamin A against the formation of methotrexate-induced micronuclei. CONCLUSIONS: Both vitamin A and leucovorin provided significant protection against genetic damage induced by methotrexate