Comparing the Effects of Different Daily and Sub-Daily Downscaling Approaches on the Response of Urban Stormwater Collection Systems

Water Resources Management - Change in the spatiotemporal pattern of precipitation is one the most important effects of climate change. This may result in considerable changes in urban flooding and...     

Consensus-based data replication protocol for distributed cloud

The Journal of Supercomputing - Data availability ensures efficient data accessibility by the readers anytime and from anywhere. It can be addressed by creating multiple copies of each data file...     

Re-examining the causal structure of information technology impact research

About 20 years ago, Markus and Robey noted that most research on IT impacts had been guided by deterministic perspectives and had neglected to use an emergent perspective, which could account for contradictory findings. They further observed that most research in this area had been carried out using variance theories at the expense of process theories. Finally, they suggested that more emphasis on multilevel theory building would likely improve empirical reliability. In this paper, we reiterate the observations and suggestions made by Markus and Robey on the causal structure of IT impact theories and carry out an analysis of empirical research published in four major IS journals, Management Information Systems Quarterly (MISQ), Information Systems Research (ISR), the European Journal of Information Systems (EJIS), and Information and Organization (I&O), to assess compliance with those recommendations. Our final sample consisted of 161 theory-driven articles, accounting for approximately 21% of all the empirical articles published in these journals. Our results first reveal that 91% of the studies in MISQ, ISR, and EJIS focused on deterministic theories, while 63% of those in I&O adopted an emergent perspective. Furthermore, 91% of the articles in MISQ, ISR, and EJIS adopted a variance model; this compares with 71% from I&O that applied a process model. Lastly, mixed levels of analysis were found in 14% of all the surveyed articles. Implications of these findings for future research are discussed.     

GaP/GaNP Heterojunctions for Efficient Solar‐Driven Water Oxidation

The growth and characterization of an n‐GaP/i‐GaNP/p⁺‐GaP thin film heterojunction synthesized using a gas‐source molecular beam epitaxy (MBE) method, and its application for efficient solar‐driven water oxidation is reported. The TiO₂/Ni passivated n‐GaP/i‐GaNP/p⁺‐GaP thin film heterojunction provides much higher photoanodic performance in 1 m KOH solution than the TiO₂/Ni‐coated n‐GaP substrate, leading to much lower onset potential and much higher photocurrent. There is a significant photoanodic potential shift of 764 mV at a photocurrent of 0.34 mA cm^?2, leading to an onset potential of ≈0.4 V versus reversible hydrogen electrode (RHE) at 0.34 mA cm^?2 for the heterojunction. The photocurrent at the water oxidation potential (1.23 V vs RHE) is 1.46 and 7.26 mA cm^?2 for the coated n‐GaP and n‐GaP/i‐GaNP/p⁺‐GaP photoanodes, respectively. The passivated heterojunction offers a maximum applied bias photon‐to‐current efficiency (ABPE) of 1.9% while the ABPE of the coated n‐GaP sample is almost zero. Furthermore, the coated n‐GaP/i‐GaNP/p⁺‐GaP heterojunction photoanode provides a broad absorption spectrum up to ≈620 nm with incident photon‐to‐current efficiencies (IPCEs) of over 40% from ≈400 to ≈560 nm. The high low‐bias performance and broad absorption of the wide‐bandgap GaP/GaNP heterojunctions render them as a promising photoanode material for tandem photoelectrochemical (PEC) cells to carry out overall solar water splitting.     

Quantification of the inherent uncertainty in the relaxation modulus and creep compliance of asphalt mixes

Advanced material characterization of asphalt concrete is essential for realistic and accurate performance prediction of flexible pavements. However, such characterization requires rigorous testing regimes that involve mechanical testing of a large number of laboratory samples at various conditions and set-ups. Advanced measurement instrumentation in addition to meticulous and accurate data analysis and analytical representation are also of high importance. Such steps as well as the heterogeneous nature of asphalt concrete (AC) constitute major factors of inherent variability. Thus, it is imperative to model and quantify the variability of the needed asphalt material’s properties, mainly the linear viscoelastic response functions such as: relaxation modulus, \(E(t)\), and creep compliance, \(D(t)\). The objective of this paper is to characterize the inherent uncertainty of both \(E(t)\) and \(D(t)\) over the time domain of their master curves. This is achieved through a probabilistic framework using Monte Carlo simulations and First Order approximations, utilizing \(E^{*}\) data for six AC mixes with at least eight replicates per mix. The study shows that the inherent variability, presented by the coefficient of variation (COV), in \(E(t)\) and \(D(t)\) is low at small reduced times, and increases with the increase in reduced time. At small reduced times, the COV in \(E(t)\) and \(D(t)\) are similar in magnitude; however, differences become significant at large reduced times. Additionally, the probability distributions and COVs of \(E(t)\) and \(D(t)\) are mix dependent. Finally, a case study is considered in which the inherent uncertainty in \(D(t)\) is forward propagated to assess the effect of variability on the predicted number of cycles to fatigue failure of an asphalt mix.     

Improving the Reliability of Software-Defined Networks with Distributed Controllers Through Leader Election Algorithm and Colored Petri-Net

Wireless Personal Communications - Software-Defined Networks (SDNs) are developed to compensate the complicated function of the controlling parts of the given network elements and making the...     

Shear Strength of Fiber-Reinforced Sands

Soil reinforcement using discrete randomly distributed fibers has been widely investigated over the last 30 years. Several models were suggested to estimate the improvement brought by fibers to the shear strength of soils. The objectives of this paper are to (1) supplement the data available in the literature on the behavior of fiber-reinforced sands; (2) study the effect of several parameters which are known to affect the shear strength of fiber-reinforced sands; and (3) investigate the effectiveness of current models in predicting the improvement in shear strength of fiber-reinforced sand. An extensive direct shear testing program was implemented using coarse and fine sands tested with three types of fibers. Results indicate the existence of a fiber-grain scale effect which is not catered for in current prediction models. A comparison between measured and predicted shear strengths indicates that the energy dissipation model is effective in predicting the shear strength of fiber-reinforced specimens in reference to the tests conducted in this study. On the other hand, the effectiveness of the predictions of the discrete model is affected by the parameters of the model, which may depend on the test setup and the procedure used for mixing the fibers.     

Predicting the effects of nanoparticles on compressive strength of ash-based geopolymers by gene expression programming

In the present work, the effect of SiO₂ and Al₂O₃ nanoparticles on compressive strength of ash-based geopolymers with different mixtures of rice husk ash, fly ash, nanoalumina and nanosilica has been predicted by gene expression programming. The models were constructed by 12 input parameters, namely the water curing time, the rice husk ash content, the fly ash content, the water glass content, NaOH content, the water content, the aggregate content, SiO₂ nanoparticle content, Al₂O₃ nanoparticle content, oven curing temperature, oven curing time and test trial number. The value for the output layer was the compressive strength. According to the input parameters in gene expression programming models, the data were trained and tested, and the effects of SiO₂ and Al₂O₃ nanoparticles on compressive strength of the specimens were predicted with a tiny error. The results indicate that gene expression programming model is a powerful tool for predicting the effect of nanoparticles on compressive strength of the geopolymers in the considered range.