Modified method of characteristics for the shallow water equations

Flow in open channels is frequently modelled using the shallow water equations (SWEs) with an up-winded scheme often used for the nonlinear terms in the numerical scheme (Delis et al., 2000; Erduran et al., 2002). This paper presents a mathematical model based on the SWEs to compute one dimensional (1-D) open channel flow. Two techniques have been used for the simulation of the flood wave along streams which are initially dry. The first one uses up-winding applied to the convective acceleration term in the SWEs to overcome the problem of numerical instabilities. This is applied to the integration of the shallow water equations within the domain, so the scheme does not require any special treatment, such as artificial viscosity or front tracking technique, to capture steep gradients in the solution. As in all initial value problems, the main difficulty is the boundaries, the conventional method of characteristics (MOC) can be applied in a straight forward way for a lot of cases, but when dealing with a very shallow initial depths followed by a flood wave, it is not possible to overcome the problem of reflections. So a modified method of characteristics (MMOC) is the second technique that has been developed by the authors to obtain a fully transparent downstream boundary and is the main subject of this paper. The mathematical model which integrates the SWEs using a staggered finite difference scheme within the domain and the MMOC near the boundary has been tested not only by comparing its results with some analytical solutions for both steady and unsteady flow but also by comparing the results obtained with the results of other models such as Abiola et al. (1988)

Hard Decision Cooperative Spectrum Sensing Based on Estimating the Noise Uncertainty Factor

Spectrum Sensing (SS) is one of the most challenging issues in Cognitive Radio (CR) systems. Cooperative Spectrum Sensing (CSS) is proposed to enhance the detection reliability of a Primary User (PU) in fading environments. In this paper, we propose a hard decision based CSS algorithm using energy detection with taking into account the noise uncertainty effect. In the proposed algorithm, two dynamic thresholds are toggled based on predicting the current PU activity, which can be successfully expected using a simple successive averaging process with time. Also, their values are evaluated using an estimated value of the noise uncertainty factor. These dynamic thresholds are used to compensate the noise uncertainty effect and increase (decrease) the probability of detection (false alarm), respectively. Theoretical analysis is performed on the proposed algorithm to deduce its enhanced false alarm and detection probabilities compared to the conventional hard decision CSS. Moreover, simulation analysis is used to confirm the theoretical claims and prove the high performance of the proposed scheme compared to the conventional CSS using different fusion rules.Comment: 5 pages, 4 figures, IEEE International Conference on Computer Engineering and Systems (ICCES 2015). arXiv admin note: text overlap with arXiv:1505.0558

Computational Intelligence Modeling of Pharmaceutical Properties

Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016) Timisoara, Romania. February 8-11, 2016.In the pharmaceutical industry, a good understanding of the casual relationship between product quality and attributes of formulations is very useful in developing new products, and optimizing manufacturing processes. Feature selection is mandatory due to the abundance of noisy, irrelevant, or misleading features. The selected features will improve the performance of the prediction model and will provide a faster and more cost effective prediction than using all the features. With the big data captured in the pharmaceutical product development practice, computational intelligence (CI) models and machine learning algorithms could potentially be used to identify the process parameters of formulations and manufacturing processes. That needs a deep investigation of roller compaction process parameters of pharmaceutical formulations that affect the ribbons production. In this work, we are using the bio-inspired optimization algorithms for feature selection such as (grey wolf, Bat, flower pollination, social spider, antlion, moth-flame, genetic algorithms, and particle swarm) to predict the different pharmaceutical properties.European Cooperation in Science and Technology. COSTThis work was supported by the IPROCOM Marie Curie initial training network, funded through the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme FP7/2007-2013/ under REA grant agreement No. 316555. In addition, this work was partially supported by NESUS

Dual Induction of New Microbial Secondary Metabolites by Fungal Bacterial Co-cultivation

We thank the College of Physical Sciences, University of Aberdeen, for provision of infrastructure and facilities in the Marine Biodiscovery Centre. We acknowledge the receipt of funding from the European Union’s Seventh Programme for Research, Technological Development and Demonstration under Grant Agreement No. 312184 (PharmaSea). MR thanks School of Science and Sport, University of the West of Scotland for providing the open-access fees required for the publication.Peer reviewedPublisher PD

Ultrasonographic Characters of Uterine Myoma as Predictors for Successful Laparoscopic Myomectomy at Mansoura University Hospital

Background: Myomectomy is the surgical procedure of choice for symptomatic myoma in the reproductive age, especially if future fertility is desired. LM is the surgical removal of uterine myoma through small incisions in the abdomen. It is an appropriate, if not preferred, alternative to abdominal myomectomy in well-selected patients since it offers shorter hospitalization, short recovery period and resumption of activities within 1–2 weeks, reduced risk of blood transfusion, and intraoperative adhesions. Aim: The aim of the current study was to determine the diagnostic accuracy of different ultrasonographic characters of uterine myoma in predicting success of laparoscopic myomectomy. Methods: The present study was prospective interventional study that was carried out on 35 cases with Chronic pelvic pain. All patients had radiological evaluation by TVS and TAS. All Laparoscopic myomectomies done under general anaesthesia. Outcomes included determining ultra-sonographic predictors for successful laparoscopic myomectomy as regards site, size, and character of Myoma, presence of capsule and line of separation around myoma. Results: The most common complain among the studied cases was bleeding followed by pain and lastly infertility. Regarding location, the most common site was posterior followed by fundal, then anterior and lastly at the cornu. Operative times, blood loss amount, method of extraction, need laparoscopic suturing, complications during surgery recovery and hospital stay after operation demonstrated significant relation with outcomes. Myoma characters, numbers and type demonstrated insignificant relation with outcomes. Conclusion: The current study concluded that, myoma characters, numbers and type could not be used as predictors for successful LM; outcomes. However, operative times, blood loss amount, complications during surgery recovery and hospital stay after operation were less in successful LM than in LAM; and laparoscopic suturing and morcellation have less time consumption and better results than LAM