Qualitative and quantitative phytochemical analysis of methanolic extract of Magnolia champaca leaves

Plants remain a vibrant source of diverse bioactive phytochemicals that are secondary metabolites protecting them from infections and predations. Magnolia champaca is reported to possess a multitude of phytochemicals. In the present study, the phytochemical constituents of the methanolic extract of Magnolia champaca leaves were analysed qualitatively and with gas chromatography-tandem mass spectrometry (GC-MS/MS). Fourier transform infrared (FTIR) spectroscopic analysis was performed to identify the chemical nature of the extract and to find structurally similar compounds. Preliminary phytochemical screening revealed the presence of alkaloids, flavonoids, terpenes, glycosides, phenols, steroids, tannins and saponins. FTIR spectroscopic analysis revealed structurally related compounds. GC-MS/MS analysis revealed the presence of 99 diverse compounds with varied biological activities, among which 1,2,4-butanetriol, n-hexadecanoic acid, cis vaccenic acid, phytol, trans longipinocarveol and caryophyllene oxide were found predominantly. Thus, the identification and characterisation of the phytochemicals in the extract favour the development of novel therapeutic agents

The development and validation of a scoring tool to predict the operative duration of elective laparoscopic cholecystectomy

Background: The ability to accurately predict operative duration has the potential to optimise theatre efficiency and utilisation, thus reducing costs and increasing staff and patient satisfaction. With laparoscopic cholecystectomy being one of the most commonly performed procedures worldwide, a tool to predict operative duration could be extremely beneficial to healthcare organisations. Methods: Data collected from the CholeS study on patients undergoing cholecystectomy in UK and Irish hospitals between 04/2014 and 05/2014 were used to study operative duration. A multivariable binary logistic regression model was produced in order to identify significant independent predictors of long (> 90 min) operations. The resulting model was converted to a risk score, which was subsequently validated on second cohort of patients using ROC curves. Results: After exclusions, data were available for 7227 patients in the derivation (CholeS) cohort. The median operative duration was 60 min (interquartile range 45–85), with 17.7% of operations lasting longer than 90 min. Ten factors were found to be significant independent predictors of operative durations > 90 min, including ASA, age, previous surgical admissions, BMI, gallbladder wall thickness and CBD diameter. A risk score was then produced from these factors, and applied to a cohort of 2405 patients from a tertiary centre for external validation. This returned an area under the ROC curve of 0.708 (SE = 0.013, p  90 min increasing more than eightfold from 5.1 to 41.8% in the extremes of the score. Conclusion: The scoring tool produced in this study was found to be significantly predictive of long operative durations on validation in an external cohort. As such, the tool may have the potential to enable organisations to better organise theatre lists and deliver greater efficiencies in care

Modelling an Efficient Hybrid Optimizer for Handling Vehicle Routing Problem

651-662Vehicle Routing Problem (VRP) like total routing distance, number of serve provisioning vehicles, and vehicles' waiting time are determined as the multi-objective constraints. Investigators pretend to handle these multi-constraint issues with the time window and fail to attain a prominent solution. Thus, there is a need for a global multi-objective vehicle routing solution. Here, a novel Particle Positioning Particle Swarm Optimization () approach is designed to predict the robust route with the elimination of non-linearity measures. The linearity measure includes the movement of the vehicles, service time, and status of the move towards a particular direction. The lack of exploration and exploitation conditions during optimization is addressed with the inclusion of Grey Wolf Optimization (GWO). Therefore, the models attain a global solution with the least error rate. Simulation is done in MATLAB 2016b environment, and the experimental outcomes are compared with various approaches in large-scale and small-scale instances. The model intends to attain robustness and stability towards the measure in a linear manner. The model's time consumption and computational complexity are reduced with the adoption of a global routing-based optimization approach

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Adaptive fault tolerant resource allocation scheme for cloud computing environments

Cloud computing is an optimistic technology that leverages the computing resources to offer globally better and more efficient services than the collection of individual use of internet resources. Due to the heterogeneous and high dynamic nature of resources, failure during resource allocation is a key risk in cloud. Such resource failures lead to delay in tasks execution and have adverse impacts in achieving quality of service (QoS). This paper proposes an effective and adaptive fault tolerant scheduling approach in an effort to facilitate error free task scheduling. The proposed method considers the most impactful parameters such as failure rate and current workload of the resources for optimal QoS. The suggested approach is validated using the CloudSim toolkit based on the commonly used metrics including the resource utilization, average execution time, makespan, throughput, and success rate. Empirical results prove that the suggested approach is more efficient than the benchmark techniques in terms of load balancing and fault tolerance

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Not AvailableAntibiotics are the most conventionally used veterinary drug for raising food animals. They are used so, to ward off the undesirable effects generated by the multiple infectious pathogens. It helps to maintain the optimum level of production in farm animals without falling ill. The consequences faced with the use of drugs lead to the generation of antibiotic residues in the food products, consumed by the public. These residues will put forth a complication to the health of humans when it exceeds above the limit of the Maximum residues limit (MRL). The presence of antibiotic residues in animal-derived foods is one of the major causes of the development of antibiotic resistance in human pathogens. To overcome the antibiotic residues in food products nowadays lots of researches going on besides that, several methods of cooking like frying, boiling, roasting, grilling, etc., are also known to have reduced the level of antibiotic and other drug residues in the meat products. In this paper, we explore some antibiotic residues in meat and meat products and their reduction levels while applying several cooking methodsNot Availabl