378 research outputs found
Scaling behavior of online human activity
The rapid development of Internet technology enables human explore the web
and record the traces of online activities. From the analysis of these
large-scale data sets (i.e. traces), we can get insights about dynamic behavior
of human activity. In this letter, the scaling behavior and complexity of human
activity in the e-commerce, such as music, book, and movie rating, are
comprehensively investigated by using detrended fluctuation analysis technique
and multiscale entropy method. Firstly, the interevent time series of rating
behaviors of these three type medias show the similar scaling property with
exponents ranging from 0.53 to 0.58, which implies that the collective
behaviors of rating media follow a process embodying self-similarity and
long-range correlation. Meanwhile, by dividing the users into three groups
based their activities (i.e., rating per unit time), we find that the scaling
exponents of interevent time series in three groups are different. Hence, these
results suggest the stronger long-range correlations exist in these collective
behaviors. Furthermore, their information complexities vary from three groups.
To explain the differences of the collective behaviors restricted to three
groups, we study the dynamic behavior of human activity at individual level,
and find that the dynamic behaviors of a few users have extremely small scaling
exponents associating with long-range anticorrelations. By comparing with the
interevent time distributions of four representative users, we can find that
the bimodal distributions may bring the extraordinary scaling behaviors. These
results of analyzing the online human activity in the e-commerce may not only
provide insights to understand its dynamic behaviors but also be applied to
acquire the potential economic interest
Design, fabrication and performance evaluation of a compact regenerative evaporative cooler: towards low energy cooling for buildings
© 2017 Elsevier Ltd The urges of reducing energy use and carbon footprint in buildings have prompted the developments of regenerative evaporative coolers (RECs). However, the physical dimensions of RECs have to be designed enormous in order to deliver a large amount of supply airflow rate and cooling capacity. To tackle the issue, this paper develops a large-scale counter-flow REC with compact heat exchanger through dedicated numerical modelling, optimal design, fabrication and experimentation. Using modified ε-NTU method, a finite element model is established in Engineering Equation Solver environment to optimise the cooler's geometric and operating parameters. Based on modelling predictions, the cooler's experimental prototype was optimally designed and constructed to evaluate operating performance. The experiment results show that the cooler's attained wet-bulb effectiveness ranges from 0.96 to 1.07, the cooling capacity and energy efficiency ratio from 3.9 to 8.5 kW and 10.6 to 19.7 respectively. It can provide sub-wet bulb cooling while operating at high intake channel air velocities of 3.04–3.60 m/s. The superior performance of proposed cooler is disclosed by comparing with different RECs under similar operating conditions. Both the cooler's cooling capacity per unit of volume and per unit of airflow rate are found to be 62–108% and 21.6% higher respectively
Normal heat diffusion in many-body system via thermal photons
A normal-diffusion theory for heat transfer in many-body systems via carriers
of thermal photons is developed. The thermal conductivity tensor is rigorously
derived from fluctuational electrodynamics as a coefficient of diffusion term
for the first time. In addition, a convection-like heat transfer behavior is
revealed in systems of asymmetric distribution of particles, indicating
violation of Fourier's law for such system. Considering the central role of
thermal conductivity in heat transfer, this work paves a way for understanding,
analysis and manipulation of heat transfer in nanoparticle system via thermal
photons with many-body interactions.Comment: 4 figure
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