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The emergence of unified constitutive law is a hallmark of convective turbulence, i.e., $Nu \sim Ra^\beta$ with $\beta \approx 0.3$ in the classical and $\beta=1/2$ in the ultimate regime, where the Nusselt number $Nu$ measures the global heat transport and the Rayleigh number $Ra$ quantifies the strength of thermal forcing. In recent years, vibroconvective flows have been attractive due to its ability to drive flow instability and generate ``artificial gravity'', which have potential to effective heat and mass transport in microgravity. However, the existence of constitutive laws in vibroconvective turbulence remains unclear. To address this issue, we carry out direct numerical simulations in a wide range of frequencies and amplitudes, and report that the heat transport exhibits a universal scaling law $Nu \sim a^{-1} Re_\mathrm{os}^\beta$ where $a$ is the vibration amplitude, $Re_\mathrm{os}$ is the oscillational Reynolds number, and $\beta$ is the universal exponent. We find that the dynamics of boundary layers plays an essential role in vibroconvective heat transport, and the $Nu$-scaling exponent $\beta$ is determined by the competition between the thermal boundary layer (TBL) and vibration-induced oscillating boundary layer (OBL). Then a physical model is proposed to explain the change of scaling exponent from $\beta=2$ in the OBL-dominant regime to $\beta = 4/3$ in the TBL-dominant regime. We conclude that vibroconvective turbulence in microgravity defines a distinct universality class of convective turbulence. This work elucidates the emergence of universal constitutive laws in vibroconvective turbulence, and opens up a new avenue for generating a controllable effective heat transport under microgravity or even microfluidic environment in which gravity is nearly absent., Comment: 18 pages, 3 figures

In this paper, we report that reversals of the large-scale circulation in two-dimensional Rayleigh–Bénard (RB) convection can be suppressed by imposing sinusoidally distributed heating to the bottom plate. We examine how the frequency of flow reversals depends on the dimensionless wavenumber $k$ of the spatial temperature modulation with various modulation amplitude $A$ . For sufficiently large $k$ , the flow reversal frequency is close to that in the standard RB convection under uniform heating. However, when $k$ decreases, the frequency of flow reversal gradually becomes lower and can even be largely reduced. Furthermore, we examine the growth of the corner roll and the global flow structure based on Fourier mode decomposition, and reveal that the size of the corner roll diminishes as the wavenumber decreases. The reason is that the regions occupied by the cold phase can absorb heat from the hot plumes and thus lower their temperature, which reduces the corner roll's kinetic energy input provided by the buoyancy force, and weakens the feeding process of the corner rolls. This results in the locking of the corner roll into a smaller region near the corner, making it harder for a reversal to occur. Using the concept of horizontal convection caused by non-uniform heating, we find a relevant parameter $k/A$ to describe briefly how the reversal frequency depends on wavenumber and modulation amplitude. The present work provides a new way to control the flow reversals in RB convection through modifying temperature boundary conditions.

We numerically study turbulent Rayleigh-Bénard (RB) convection under spatial temperature modulation, where the bottom temperature varies sinusoidally around a mean value in space. Both two- and three-dimensional simulations are performed over the Rayleigh number range 10^{7}≤Ra≤10^{10} and the wave number range 1≤k≤120 at fixed Prandtl number Pr=0.7. It is demonstrated that spatial temperature modulation with small wave numbers can enhance the global heat transfer (characterized by the Nusselt number Nu) in the turbulent regime, while Nu is close to that in standard RB convection in the case of large wave numbers. Further, we propose two characteristic modulation length scales: one is the penetration depth δ_{k} above which spatial modulation is negligible, the other is the inversion depth δ_{k2} below which there exists a stable inverse temperature gradient. Based on the relative thickness of the thermal boundary layer (BL) δ_{th} compared with these two length scales, the underlying modulation mechanism is physically explained and three regimes are identified: (1) an unperturbed BL regime (δ_{k}δ_{th}), in which the modulation effect does not penetrate through the thermal BL and Nu is nearly unchanged; (2) a partially modulated BL regime (δ_{k2}δ_{th}δ_{k}), in which hot spots trigger more plume emissions from the thermal BL, resulting in Nu enhancement; and (3) a fully modulated BL regime (δ_{th}δ_{k2}), in which the stable temperature inversion over the cold phases begins to affect convective flows, which alters the trend of Nu enhancement.

Thermal convection in nature and industrial applications usually encounters time-varying internal heating (IH); however, the effect of temporal modulation on temperature responses and heat fluxes of the system has been rarely explored. Here, we numerically studied the IH convection with a temporally periodic heating source. We conducted direct numerical simulations over Rayleigh number ( Ra) range [Formula: see text] at fixed Prandtl number Pr = 1 with dimensionless modulation frequency [Formula: see text] and amplitude fixed at Ω = 1. We first find that the introduction of periodic modulation has a slight effect on the heat transport over the individual plates and flow strength except for the lowest Ra. We then focus on the characteristics of the amplitude A and phase lag [Formula: see text] of the globally averaged temperature response to the periodic modulation. Three regimes of the phase response are identified: (i) in-phase regime, where synchronous response is found at small frequencies with the vanished phase lag [Formula: see text] and A keeping at constant value; (ii) transition regime, where both [Formula: see text] and A decrease with increasing f for moderate frequencies; and (iii) half anti-phase regime, where [Formula: see text] attains the minimal value [Formula: see text]. We also find that the transition behavior between three regimes can be well described using the normalization of the Ra-dependent critical frequency with the scaling [Formula: see text]. To explain the regime transition, we further theoretically deduce an analytical solution for A and [Formula: see text], which agrees well with the numerical results. This solution can explain why [Formula: see text] gives a good description of the transition behavior. Our present findings provide a fundamental understanding of the underlying mechanism of temporal modulation on IH systems and have substantial implications for the investigation of convective system with periodic heating source.

A novel multi-focus image fusion algorithm based on deep support value convolutional neural network (DSVCNN) is proposed for multi-focus image fusion. First, a deep support value training network is presented by replacing the empirical risk minimization-based loss function by a loss function based on structural risk minimization during the training of convolutional neural network (CNN). Then, to avoid the loss of information, max-pooling/subsampling of the feature mapping layer of a conventional convolutional neural network, which is employed in all conventional CNN frameworks to reduce the dimensionality of the feature map, is replaced by standard convolutional layers with a stride of two. The experimental results demonstrate that the suggested DSVCNN-based method is competitive with current state-of-the-art approaches and superior to those that use traditional CNN methods.

In order to get a satisfactory image fusion effect, getting a focus map is very necessary and usually difficult to finish. In this paper, we address this problem with a half weighted gradient approach, aiming to obtain a direct mapping between focus map and source images. Based on the advantages of multi-scale weighted gradient, while abandoning the shortcomings of weighted gradient, a new multi-focus image fusion method called half weighted gradient and self-similarity (HWGSS) is proposed. Experimental results validate that the proposed algorithm can obtain state-of-the-art fusion performance in terms of both qualitative and quantitative evaluations.

A decision map contains complete and clear information about the image to be fused, which is crucial to various image fusion issues, especially multi-focus image fusion. However, in order to get a satisfactory image fusion effect, getting a decision map is very necessary and usually difficult to finish. In this letter, we address this problem with convolutional neural network (CNN), aiming to get a state-of-the-art decision map. The main idea is that the max-pooling of CNN is replaced by a convolution layer, the residuals are propagated backwards by gradient descent, and the training parameters of the individual layers of the CNN are updated layer by layer. Based on this, we propose a new all CNN (ACNN)-based multi-focus image fusion method in spatial domain. We demonstrate that the decision map obtained from the ACNN is reliable and can lead to high-quality fusion results. Experimental results clearly validate that the proposed algorithm can obtain state-of-the-art fusion performance in terms of both qualitative and quantitative evaluations.

Global mean sea level is an integral of changes occurring in the climate system in response to unforced climate variability as well as natural and anthropogenic forcing factors. Its temporal evolution allows changes (e.g., acceleration) to be detected in one or more components. Study of the sea-level budget provides constraints on missing or poorly known contributions, such as the unsurveyed deep ocean or the still uncertain land water component. In the context of the World Climate Research Programme Grand Challenge entitledRegional Sea Level and Coastal Impacts, an international effort involving the sea-level community worldwide has been recently initiated with the objective of assessing the various datasets used to estimate components of the sea-level budget during the altimetry era (1993 to present). These datasets are based on the combination of a broad range of space-based and in situ observations, model estimates, and algorithms. Evaluating their quality, quantifying uncertainties and identifying sources of discrepancies between component estimates is extremely useful for various applications in climate research. This effort involves several tens of scientists from about 50 research teams/institutions worldwide (www.wcrp-climate.org/grand-challenges/gc-sea-level, last access: 22 August 2018). The results presented in this paper are a synthesis of the first assessment performed during 2017–2018. We present estimates of the altimetry-based global mean sea level (average rate of 3.1 ± 0.3 mm yr−1and acceleration of 0.1 mm yr−2over 1993–present), as well as of the different components of the sea-level budget (http://doi.org/10.17882/54854, last access: 22 August 2018). We further examine closure of the sea-level budget, comparing the observed global mean sea level with the sum of components. Ocean thermal expansion, glaciers, Greenland and Antarctica contribute 42 %, 21 %, 15 % and 8 % to the global mean sea level over the 1993–present period. We also study the sea-level budget over 2005–present, using GRACE-based ocean mass estimates instead of the sum of individual mass components. Our results demonstrate that the global mean sea level can be closed to within 0.3 mm yr−1(1σ). Substantial uncertainty remains for the land water storage component, as shown when examining individual mass contributions to sea level.

Studies have shown that pig manure is a reservoir of antibiotic resistance genes (ARGs). However, little is known about the characteristics of ARGs in the manure of piglets and adult pigs fed on different diets. In the present study, the ARG characteristics of the manure of piglets and adult pigs fed on different diets (feed, grain) were analyzed using high-throughput fluorescence quantitative PCR. Correlations between heavy metals, antibiotics, and ARGs in pig manure were analyzed. The results showed that the heavy metal and antibiotic contents in the manure of pigs receiving feed significantly exceeded those in the manure of pigs receiving grain. The heavy metal and antibiotic contents were higher in manure of piglets than in that of adult pigs. Feed significantly increased the ARG diversity in the pig manure. The ARG diversity was higher in manure of piglets than in that of adult pigs. In the manure of pigs receiving feed, 25 (from piglets), 12 (from adult pigs) ARGs were enriched significantly compared with pig fed with grain. In particular, sat4 (in piglets) and vatE-01 (in adult pigs) showed the highest enrichment, being increased by 59 and 19-fold, respectively. The ARG diversity correlated positively with the concentrations of antibiotics and heavy metals in the manure.

Global mean sea level is an integral of changes occurring in the climate system in response to unforced climate variability as well as natural and anthropogenic forcing factors. Its temporal evolution allows changes (e.g., acceleration) to be detected in one or more components. Study of the sea-level budget provides constraints on missing or poorly known contributions, such as the unsurveyed deep ocean or the still uncertain land water component. In the context of the World Climate Research Programme Grand Challenge entitled "Regional Sea Level and Coastal Impacts", an international effort involving the sea-level community worldwide has been recently initiated with the objective of assessing the various datasets used to estimate components of the sea-level budget during the altimetry era (1993 to present). These datasets are based on the combination of a broad range of space-based and in situ observations, model estimates, and algorithms. Evaluating their quality, quantifying uncertainties and identifying sources of discrepancies between component estimates is extremely useful for various applications in climate research. This effort involves several tens of scientists from about 50 research teams/institutions worldwide (www.wcrp-climate.org/grand-challenges/gc-sea-level, last access: 22 August 2018). The results presented in this paper are a synthesis of the first assessment performed during 2017–2018. We present estimates of the altimetry-based global mean sea level (average rate of 3.1±0.3mmyr−1 and acceleration of 0.1mmyr−2 over 1993–present), as well as of the different components of the sea-level budget (http://doi.org/10.17882/54854, last access: 22 August 2018). We further examine closure of the sea-level budget, comparing the observed global mean sea level with the sum of components. Ocean thermal expansion, glaciers, Greenland and Antarctica contribute 42%, 21%, 15% and 8% to the global mean sea level over the 1993–present period. We also study the sea-level budget over 2005–present, using GRACE-based ocean mass estimates instead of the sum of individual mass components. Our results demonstrate that the global mean sea level can be closed to within 0.3mmyr−1 (1σ). Substantial uncertainty remains for the land water storage component, as shown when examining individual mass contributions to sea level., Earth System Science Data, 10 (3), ISSN:1866-3516, ISSN:1866-3508