CEDAR: Modeling impact of component error derating and read frequency on system-level vulnerability in high-performance processors

Reliability of the current microprocessor technology is seriously challenged by radiation-induced soft errors. Accurate Vulnerability Factor (VF) modeling of system components is crucial in designing cost-effective protection schemes in high-performance processors. Although Statistical Fault Injection (SFI) techniques can be used to provide relatively accurate VF estimations, they are often very time-consuming. Unlike SFI techniques, recently proposed analytical models can be used to compute VF in a timely fashion. However, VFs computed by such models are inaccurate as the system-level impact of soft errors is overlooked.     

Measurement of the amount and rate of methane dissolution in pure water and aqueous solution of SDS + multi-wall carbon nanotubes + β-cyclodextrin

In this paper, the impact of the mixture of sodium dodecyl sulfate (SDS) + multi-wall carbon nanotubes (MWCNTs) + β-cyclodextrin on the quantity and initial rate of methane dissolved in water is investigated. The experiments were performed at a temperature range of 278.15–303.15 K and an initial pressure of 0.5 MPa. The experimental results show that simultaneous utilization of β-cyclodextrin (0.01 mass fraction), MWCNTs (0.0005 mass fraction), and SDS (0.001 mass fraction) at 278.15 K increases the amount and the rate of methane dissolution in water by 29.90% and 173.78%, respectively, compared to pure water. An increase in the temperature decreases the quantity and the initial rate of methane dissolution in all solutions containing additives. However, no consistent relationship is observed between the temperature and the enhancement percentage of solubility of methane in solutions containing additives.     

OSPM: A design methodology for open strategic planning

This study employs a design science perspective to propose a methodology for open strategic planning (OSP). Habermas’ discourse theory and Bryson’s strategy change cycle are used as informing kernel theories. A methodology is proposed to satisfy the requirements retrieved from the kernel theories. The proposed methodology contains modules for a planning system and a planning process. Design principles are explained through a blueprint of the system and process. The proposed methodology is applied and evaluated in two cases. Contributions to the literature involve extending the literature on OSP to an applicable methodology with guidelines on how to implement open strategy.     

On the prediction of solubility of alkane in carbon dioxide using the LSSVM algorithm

The increasing global energy demand and declination of oil reservoir in recent years cause the researchers attention focus on the enhancement of oil recovery approaches. One of the extensive applicable methods for enhancement of oil recovery, which has great efficiency and environmental benefits, is carbon dioxide injection. The CO₂ injection has various effects on the reservoir fluid, which causes enhancement of recovery. One of these effects is extraction of lighter components of crude oil, which straightly depends on solubility of hydrocarbons in carbon dioxide. In order to better understand of this parameter, in this study, Least squares support vector machine (LSSVM) algorithm was developed as a novel predictive tool to estimate solubility of alkane in CO₂ as function of carbon number of alkane, carbon dioxide density, pressure, and temperature. The predicting model outputs were compared with the extracted experimental solubility from literature statistically and graphically. The comparison showed the great ability and high accuracy of developed model in prediction of solubility.     

Developing an Efficient Auto-Calibration Algorithm for HEC-HMS Program

Two auto-calibration methods, namely Nelder-Mead (NM) and Univariate-Gradient (UG) and a manual approach are available for calibrating the HEC-HMS program. However, their being either inefficient or time consuming makes it difficult to work with HEC-HMS especially when using a snowmelt module in a continuous mode. The main objective of this paper is to develop an efficient Genetic Algorithm (GA) based auto-calibration method for HEC-HMS model (HMS-GA) for continuous snowmelt simulation. A general novel procedure is presented in the absence of the HMS source code to link a heuristic algorithm with the HEC program through Jython programming language. The models are developed and evaluated using daily data from basins in Ajichai, northwestern Iran. A comparison of results for a verification period indicates a substantial improvement by applying the HMS-GA over the other available methods. Moreover, it is shown that neither NM nor UG is able to improve the results obtained by either the manual or HMS-GA. Furthermore, the proposed method significantly improves the calibrations of the HMS model found by the three other methods.     

A model for stress-dependence of apparent permeability in nanopores of shale gas reservoirs

Predicting long-term production from gas shale reservoirs is a challenging task due to changes in effective stress and permeability during gas production. Unlike coal, the variation of sorbing gas permeability with pore pressure in shale does not always feature a biphasic trend under a constant confining pressure. The present contribution demonstrates that the biphasic dependence of permeability on pore pressure depends on a number of physical and geometrical factors, each with a distinct impact on gas permeability. This includes pore size, adsorption isotherm, and the variation of gas viscosity with pore pressure. A single-capillary model is proposed for the apparent permeability of real gas in shale. Results indicate that the biphasic relation between apparent permeability and pore pressure is prevalent when the sorbing gas flows in sufficiently small pores. In addition, the effects of sorption isotherm and internal resistance of nonideal gas to flow cannot be ignored.     

A model for measuring e-learning systems success in universities

In the era of internet, universities and higher education institutions are increasingly tend to provide e-learning. For suitable planning and more enjoying the benefits of this educational approach, a model for measuring success of e-learning systems is essential. So in this paper, we try to survey and present a model for measuring success of e-learning systems in universities. For this purpose, at first, according to literature review, a conceptual model was designed. Then, based on opinions of 33 experts, and assessing their suggestions, research indicators were finalized. After that, to examine the relationships between components and finalize the proposed model, a case study was done in 5 universities: Amir Kabir University, Tehran University, Shahid Beheshti University, Iran University of Science & Technology and Khaje Nasir Toosi University of Technology. Finally, by analyzing questionnaires completed by 369 instructors, students and alumni, which were e-learning systems user, the final model (MELSS Model¹).