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The Kolmogorov 4 5 law, which is the unique, exact relationship of inertial-range statistics, is applied to investigate the finite Reynolds number effect, in particular to study how the width of the inertial range of finite Reynolds number turbulence changes with the Taylor microscale Reynolds number R . It is found that there is no inertial range when R р2000 and, within tolerance of 1% error, R should be higher than 10 4 in order to have an inertial range wider than one decade. The so-called inertial range found in experiments and simulations is just a scaling range and is not the same as Kolmogorov's inertial range. The finite Reynolds number effect cannot be neglected within such a scaling range and should be considered in comparing experiments ͑or simulations͒ with theories of the inertial-range statistics. ͓S1063-651X͑96͒03112-1͔

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The third-order structure function is used to study the finite Reynolds number (FRN) effect of turbulence, which refers to the deviation of turbulence statistics observed at finite Reynolds numbers from predictions of the Kolmogorov theories. It is found that the FRN effect decreases as CR(-mu)(lambda), when R(lambda) is high, and mu < or = 6/5. Here R(lambda) is the Taylor-microscale Reynolds number and C is a constant independent of R(lambda). From the exact spectral equations, the decay exponent mu and the constant C are determined for typical fully developed turbulent flows (freely decaying isotropic turbulence and shear flow turbulence), so that the quantitative prediction of the FRN effect is feasible.

We study a novel communication mechanism, ambient backscatter, that utilizes radio frequency (RF) signals transmitted from an ambient source as both energy supply and information carrier to enable communications between low-power devices. Different from existing non-coherent schemes, we here design the semi-coherent detection, where channel parameters can be obtained from unknown data symbols and a few pilot symbols. We first derive the optimal detector for the complex Gaussian ambient RF signal from likelihood ratio test and compute the corresponding closed-form bit error rate (BER).To release the requirement for prior knowledge of the ambient RF signal, we next design a suboptimal energy detector with ambient RF signals being either the complex Gaussian or the phase shift keying (PSK). The corresponding detection thresholds, the analytical BER, and the outage probability are also obtained in closed-form. Interestingly, the complex Gaussian source would cause an error floor while the PSK source does not, which brings nontrivial indication of constellation design as opposed to the popular Gaussian-embedded literatures. Simulations are provided to corroborate the theoretical studies.

The phase behavior of water/hydrocarbon mixtures in a wide range of concentrations, temperatures, and pressures is important in a variety of chemical engineering applications. For this reason, the physical understanding and mathematical modeling of these aqueous–organic mixtures constitute a challenging task, both for scientists and for applied engineers. In this work, mutual solubilities, critical loci, and mixing enthalpies of water + hydrocarbon, water + carbon dioxide, water + nitrogen, water + hydrogen sulfide, and water + hydrogen binary mixtures are predicted using the PPR78 cubic equation of state (EoS). The extremely nonideal behavior of these systems produces unusual and complex thermodynamic behavior. As an example, such mixtures often exhibit type III phase behavior in the classification scheme of Van Konynenburg and Scott and are characterized by a vapor–liquid critical line which first exhibits a temperature minimum and then extends to temperatures above the critical point of pure water. Such a behavior, called gas–gas equilibria of the second kind is a consequence of the large degree of immiscibility of the two components. The selected PPR78 model combines the Peng–Robinson cubic EoS and a group-contribution method aimed at predicting the temperature-dependent binary interaction parameters, k ij (T), involved in the Van der Waals one-fluid mixing rules. Although, it is acknowledged that cubic EoS with a constant k ij are not suitable to predict phase equilibria of such highly nonideal systems, the addition of the H2O group to the PPR78 model makes it possible to conclude that the use of temperature-dependent binary interaction parameters not only results in qualitatively accurate predictions over wide pressure and temperature ranges but also leads to quantitatively reasonable predictions for many of the studied systems.

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In modern search engines, an increasing number of search result pages (SERPs) are federated from multiple specialized search engines (called verticals, such as Image or Video). As an effective approach to interpret users' click-through behavior as feedback information, most click models were designed to reduce the position bias and improve ranking performance of ordinary search results, which have homogeneous appearances. However, when vertical results are combined with ordinary ones, significant differences in presentation may lead to user behavior biases and thus failure of state-of-the-art click models. With the help of a popular commercial search engine in China, we collected a large scale log data set which contains behavior information on both vertical and ordinary results. We also performed eye-tracking analysis to study user's real-world examining behavior. According these analysis, we found that different result appearances may cause different behavior biases both for vertical results (local effect) and for the whole result lists (global effect). These biases include: examine bias for vertical results (especially those with multimedia components), trust bias for result lists with vertical results, and a higher probability of result revisitation for vertical results. Based on these findings, a novel click model considering these biases besides position bias was constructed to describe interaction with SERPs containing verticals. Experimental results show that the new Vertical-aware Click Model (VCM) is better at interpreting user click behavior on federated searches in terms of both log-likelihood and perplexity than existing models.