Convection in the Rayleigh-Bénard Flow with all Fluid Properties Variable

In the present paper,the effect of variable fluid properties(density,viscosity,thermal conductivity and specific heat) on the convection in the classical Rayleigh-Bénard problem is investigated.The investigation concerns water,air,and engine oil by taking into account the variation of fluid properties with temperature.The results are obtained by numerically solving the governing equations,using the SIMPLE algorithm and covering large temperature differences.It is found that the critical Rayleigh number incr...     

A physically based stochastic-deterministic procedure for the estimation of flood frequency

A procedure based on the method of derived distributions is proposed for the estimation of flood frequency from ungauged watersheds. The results of previous research on rainfall characteristics and watershed response are incorporated into the proposed procedure. These rainfall characteristics are storm depth, storm duration, space and time distribution. A simplified watershed model is used which has previously given good simulation of the watershed response. Some of the rainfall and watershed model parameters are stochastic in nature and are assumed to follow various probability distributions. Monte Carlo simulation is used for the generation of the various parameter values and simulation of the peak flow hydrographs. After 5000 realizations, the frequency of the hourly and daily peak flow and the flood volume is estimated. The proposed procedure is applied to eight coastal British Columbia watersheds and the results compare well with the observed data and with fitted probability distributions. The method is easy to apply, requires limited data and is shown to be reliable. Sensitivity analysis shows that the procedure is not very sensitive to uncertainty of the parameter values and is not dependent on the parameter probability distributions used.     

Effects of chromium (VI) addition on the activated sludge process

The effect of hexavalent chromium, Cr(VI), addition on various operating parameters of activated sludge process was evaluated. To accomplish this, two parallel lab-scale continuous-flow activated sludge plants were operated. One was used as a control plant, while the other received Cr(VI) concentrations equal to 0.5, 1, 3 and 5 mgl(-1). Cr(VI) concentrations of 0.5 mgl(-1) caused significant inhibition of the nitrification process (up to 74% decrease in ammonia removal efficiency). On the contrary, the effect of Cr(VI) on organic substrate removal was minor for concentrations up to 5 mgl(-1), indicating that heterotrophic microorganisms are less sensitive to Cr(VI) than nitrifiers. Activated sludge floc size and structure characterization showed that Cr(VI) concentrations higher than 1 mgl(-1) reduced the filaments abundance, causing the appearance of pin-point flocs and free-dispersed bacteria. Additionally, the variability of protozoa and rotifers was reduced. As a result of disperse growth, effluent quality deteriorated, since significant amounts of suspended solids escaped with the effluent. Termination of Cr(VI) addition led to a partial recovery of the nitrification process (up to 57% recovery). Similar recovery signs were not observed for activated sludge floc size and structure. Finally, shock loading to the control plant with 5 mgl(-1) Cr(VI) for 2 days resulted in a significant inhibition of the nitrification process and a reduction in filamentous microorganisms abundance.     

Control Charts for Monitoring Correlated Poisson Counts with an Excessive Number of Zeros

The zero‐inflated Poisson distribution serves as an appropriate model when there is an excessive number of zeros in the data. This phenomenon frequently occurs in count data from high‐quality processes. Usually, it is assumed that these counts exhibit serial independence, while a more realistic assumption is the existence of an autocorrelation structure between them. In this work, we study control charts for monitoring correlated Poisson counts with an excessive number of zeros. Zero‐inflation in the process is captured via appropriate integer‐valued time series models. Extensive numerical results are provided regarding the performance of the considered charts in the detection of changes in the mean of the process as well as the effects of zero‐inflation on them. Finally, a real‐data practical application is given. Copyright © 2016 John Wiley & Sons, Ltd.     

CUSUM Control Charts for the Monitoring of Zero‐inflated Binomial Processes

Zero‐inflated probability models are used to model count data that have an excessive number of zeros. These models are mostly useful in modeling high‐yield or health‐related processes. The zero‐inflated binomial distribution is an extension of the ordinary binomial distribution that takes into account the excess of zeros. In this paper, one‐sided cumulative sum (CUSUM)‐type control charts are proposed for monitoring increases or decreases in the parameter p of a zero‐inflated binomial process. The results of an extensive numerical study concerning the statistical design of the proposed schemes as well as their practical implementation are provided. Copyright © 2015 John Wiley & Sons, Ltd.     

On the performance of two-hop message spreading in DTNs

In this paper, a Delay Tolerant Network environment is considered where the source is in full control of the two-hop spreading mechanism by setting key parameters such as the number of copies allowed to be spread in the network and the delay bound of the messages. The introduced analysis allows for a differentiation between the source of the message and the intermediate nodes (in terms of e.g. transmission power, speed or cooperation degree). Analytical expressions for the cumulative distribution function (cdf) of the delivery delay and the induced overhead are extracted, taking into account the fact that the source node may continue spreading copies after the message delivery. In addition, a fairly accurate approximate expression for the cdf of the delivery delay is also derived and validated through simulations.     

Using Information Retrieval techniques for supporting data mining

The classic two-stepped approach of the Apriori algorithm and its descendants, which consisted of finding all large itemsets and then using these itemsets to generate all association rules has worked well for certain categories of data. Nevertheless for many other data types this approach shows highly degraded performance and proves rather inefficient.

We argue that we need to search all the search space of candidate itemsets but rather let the database unveil its secrets as the customers use it. We propose a system that does not merely scan all possible combinations of the itemsets, but rather acts like a search engine specifically implemented for making recommendations to the customers using techniques borrowed from Information Retrieval.     

Motorcycle riding under the influence of alcohol: Results from the SARTRE-4 survey

Riding a motorcycle under the influence of alcohol is a dangerous activity, especially considering the high vulnerability of motorcyclists. The present research investigates the factors that affect the declared frequency of drink-riding among motorcyclists in Europe and explores regional differences. Data were collected from the SARTRE-4 (Social Attitudes to Road Traffic Risk in Europe) survey, which was conducted in 19 countries. A total sample of 4483 motorcyclists was interviewed by using a face-to-face questionnaire. The data were analyzed by means of multilevel ordered logit models. The results revealed significant regional differences (between Northern, Eastern and Southern European countries) in drink-riding frequencies in Europe. In general, declared drinking and riding were positively associated with gender (males), increased exposure, underestimation of risk, friends’ behaviour, past accidents and alcohol ticket experience. On the other hand, it was negatively associated with underestimation of the amount of alcohol allowed before driving, and support for more severe penalties.     

Mobile Cloud Computing: A Survey,State of Art and Future Directions

In the recent years, cloud computing frameworks such as Amazon Web Services, Google AppEngine and Windows Azure have become increasingly popular among IT organizations and developers. Simultaneously, we have seen a phenomenal increase in the usage and deployment of smartphone platforms and applications worldwide. This paper discusses the current state of the art in the merger of these two popular technologies, that we refer to as Mobile Cloud Computing (MCC). We illustrate the applicability of MCC in various domains including mobile learning, commerce, health/wellness and social medias. We further identify research gaps covering critical aspects of how MCC can be realized and effectively utilized at scale. These include improved resource allocation in the MCC environment through efficient task distribution and offloading, security and privacy.     

Electronic Synapses Enabled by an Epitaxial SrTiO3-δ / Hf0.5Zr0.5O2 Ferroelectric Field-Effect Memristor Integrated on Silicon

Synapses play a vital role in information processing, learning, and memory formation in the brain. By emulating the behavior of biological synapses, electronic synaptic devices hold the promise of enabling high-performance, energy-efficient, and scalable neuromorphic computing. Ferroelectric memristive devices integrate the characteristics of both ferroelectric and memristive materials and present a far-reaching potential as artificial synapses. Here, it is reported on a new ferroelectric device on silicon, a field-effect memristor, consisting of an epitaxial ultrathin ferroelectric Hf_0.5Zr_0.5O₂ film sandwiched between an epitaxial highly doped oxide semiconductor SrTiO_3-δ and a top metal. Upon a low voltage of less than 2 V, the field-effect modulation in the semiconductor enables to access multiple states. The device works in a large time domain ranging from milliseconds down to tens of nanoseconds. By gradually switching the polarization by identical pulses, the ferroelectric diode devices can dynamically adjust the synaptic strength to mimic short- and long-term memory plasticity. Ionic contributions due to redox processes in the oxide semiconductor beneficially influence the device operation and retention.