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Update on Thursday, 26 December 2019:

Submission deadline of GMC-NetCom 2020 extended to Thursday, 16 January 2020.

  • Papers
Paper Review Claims
# Paper ID Title Abstract Congress Claim
1 99.Cnf-1143 The partial disclosure of gpsOne file format for assisted GPS service The paper is concerned with decoding the data format of a gpsOneXTRA binary file for A-GPS web-service. We consider mandatory data content of the file and reveal the changes of this content at different moments of time. The frequency of the changes hints on the location of records for current GPS date and satellite orbits information. Comparing the repeating data patterns against reference orbits information, we obtain meaning of data fields of the orbit record for each operational satellite. The deciphered file header and GPS almanac data layout are provided as tables within the paper. The partiality of the disclosure due to ephemeris complexity is discussed in the respective section. GMC-ElecEng 2020 Claim
2 102.Cnf-1096 Resistance Switching in Chalcogenides Memory Cell Ag/Ag-Ge-S structures Memeristive based memory is explored to be the future memory cell devices that can replace the identical MOS memory and to overcome the major problems of these memory devices. Nonvolatile (NV) memory cell can be fabricated depending on the phenomena of phase change memory (PCM) of chalcogenide materials. In this research an Ag-Ge-S/Ag structure memory cell was fabricated as a nonvolatile memory cell using vacuum thermal evaporation technique. The properties of these memory chalcogenides materials has been modified by controlling the evaporation parameters such as pressure, thickness and temperature or other external parameters, namely the applied voltage polarity. It is found the on state resistance of about 18 KΩ and an off state resistance of about 18.1011 Ω. The cell has very small write current of about (45 pA) which shows a great promise for using as extremely low power. GMC-ElecEng 2020 Claim
3 51.Cnf-1117 Streamline development of propellent-free topical pharmaceutical foam: Efficient quality by design strategy of a potential sunscreen foam Catching up with a recent interest in developing green pharmaceutical products, a key challenge is encountering formulation scientists to find proper ingredients, approach or dispensing tools in a reasonable time due to manufacturing variability. Therefore, this work is established to produce green propellent-free topical pharmaceutical preparation and thus environmentally friendly, through utilization of the concept of quality by design (QbD) to eliminate or reduce the source of variability. A foam dosage form with ingredients suitable for potential sunscreen application was developed. Formulation ingredients were based on local Jordanian virgin olive oil. Twenty formulae were screened under the theme of QbD. One formulation successfully combined in relatively stable water in oil emulsion, whereby foam generation was achieved through specialized dispensing tool. Textural characteristics were evaluated by human panel (30) and found favourably acceptable for this formulation. Physicochemical characteristics of the investigated and optimized formulae were confirmed through light microscopy, viscosity and spectrophotochemical measurements. Sun protection factor (SPF) was determined as 12.1±0.6 (n=3); indicating potential sunscreen applications for almost two hours in the daytime. Critical quality attributes were determined throughout the development process and risk assessment measurements were estimated thereof. Such an outcome that will help to create preliminary predictive model for scalable industrial screening production of this green pharmaceutical topical preparation with local sourcing of generally regarded safe materials (GRAS). EngiTek 2020 Claim
4 51.Cnf-17 Simulation Assisted Leak Detection in Pressurized Systems Using Machine Learning In this paper, we utilize a CFD assisted Neural Networks (RNN) to detect leaks in pressurized flow systems. The obtained results support the validity of implementing Machine Learning algorithms in approximating active leak locations in a single pipe setup. Results obtained utilizing the RNN model show an adaptive behavior with the system’s consistent response to different configurations of pipes and boundary conditions. The predictions for the leak localities is more accurate and more economically feasible than those obtained with currently used methods. EngiTek 2020 Claim