Aggregate Flows Research Articles

Real-Time Imaging and Quantitative Evolution for Pyrolysis of Carbon Dots-Encapsulated Metal-Organic Frameworks at the Nanoscale by In Situ Environmental Transmission Electron Microscopy.

The pyrolysis of metal-organic frameworks (MOF) has been widely used approach to generate hierarchical structures with the corresponding metal, metal carbide, or metal oxide nanoparticles embedded in a porous carbon matrix with a high specific surface area for industrial catalysis, energy storage and transfer, etc. MOF-derived heterogeneous catalysts can be constructed by the encapsulation of carbon dots (CDs) with plenty of hydroxyl and amine groups to enhance the performance of the final product. Controlled formation of metallic carbon structures at the nanoscale, especially matter cycling and transformation on the nanoscale interface, is important for the production of industrial catalysts as well as the research of materials science and engineering progress. However, the mass transfer at the nanoscale during the processing of MOF pyrolysis remains less understood due to the lack of direct observation. Herein, by using in situ environmental transmission electron microscopy, real-time imaging and quantitative evolution of porous carbon decorated with metal species by the pyrolysis of CDs-encapsulated zeolitic imidazolate framework-67 are achieved. The migration of Co, the flow of aggregates, and the growth of carbon nanotubes observed in the nanoscale pyrolysis laboratory working at 600 °C with an air atmosphere are present. Experimental studies based on reduction and oxidation reaction models reveal that the synergistic effect between doped graphite nitrogen and confined Co nanoparticles is beneficial for boosting catalytic performance.

Role of Flow Inertia in Aggregate Restructuring and Breakage at Finite Reynolds Numbers.

Forces acting on aggregates depend on their properties, such as size and structure. Breakage rate, stable size, and structure of fractal aggregates in multiphase flows are strongly related to the imposed hydrodynamic forces. While these forces are prevalently viscous for finite Reynolds number conditions, flow inertia cannot be ignored, thereby requiring one to fully resolve the Navier-Stokes equations. To highlight the effect of flow inertia on aggregate evolution, numerical investigation of aggregate evolution in simple shear flow at the finite Reynolds number is conducted. The evolution of aggregates exposed to shear flow is tracked over time. Particle coupling with the flow is resolved with an immersed boundary method, and flow dynamics are solved using a lattice Boltzmann method. Particle dynamics are tracked by a discrete element method, accounting for interactions between primary particles composing the aggregates. Over the range of aggregate-scale Reynolds numbers tested, the breakage rate appears to be governed by the combined effect of momentum diffusion and the ratio of particle interaction forces to the hydrodynamic forces. For higher shear stresses, even when no stable size exists, breakage is not instantaneous because of momentum diffusion kinetics. Simulations with particle interaction forces scaled with the viscous drag, to isolate the effect of finite Reynolds hydrodynamics on aggregate evolution, show that flow inertia at such moderate aggregate Reynolds numbers has no impact on the morphology of nonbreaking aggregates but significantly favors breakage probability. This is a first-of-its-kind study that establishes the role of flow inertia in aggregate evolution. The findings present a novel perspective into breakage kinetics for systems in low but finite Reynolds number conditions.

Mechanism of nanoparticle aggregation in gas-liquid microfluidic mixing

Using gas-liquid segmented micromixers to prepare nanoparticles that have a homogeneous particle size, controllable shape, and monodispersity advantages. Although nanoparticle aggregation within a microfluid has been shown to be affected by the shear effect, the shear effect triggering conditions in gas-liquid two-phase flow is unclear and the aggregation behavior of nanoparticles under the shear effect is difficult to predict, resulting in uncontrollable physical and chemical properties of nanoparticle aggregates. In this study, a numerical simulation of nanoparticle aggregation in gas-liquid two-phase flow under the shear effect is performed using the CFD-DEM method. Then, the effects of total flow rate, gas-liquid two-phase flow ratio, and particle volume fraction on particle aggregation were analyzed to achieve control of particle aggregation shape and size. Meanwhile, the triggering mechanism of the shear effect and the mechanism of the shear effect on the aggregation of nanoparticles were clarified. The results show that increasing the total flow rate or decreasing the gas-liquid two-phase flow rate ratio can induce the shear effect, which reduces the particle aggregation size and makes the morphology tend to be spherical. Moreover, increasing the particle volume fraction, and total flow rate or decreasing the gas-liquid two-phase flow rate ratio also increases the number of particle collisions and induce interparticle adhesion. Hence, particle adhesion and the shear effect compete with each other and together affect particle aggregation.

Coarse-grained simulations of von Willebrand factor adsorption to collagen with consequent platelet recruitment.

A multimeric glycoprotein of blood plasma-Von Willebrand factor (VWF)-mediates platelet adhesion to the fibrillar collagen of the subendothelial matrix if the blood vessel walls are damaged. The adsorption of VWF to collagen is thus essential for the initial stages of platelet hemostasis and thrombosis, as it plays a role of a molecular bridge between the injury and platelet adhesion receptors. Biomechanical complexity and sensitivity to the hydrodynamics are inherent in this system, therefore, modern computational methods supplement experimental studies of biophysical and molecular mechanisms that underlie platelet adhesion and aggregation in the blood flow. In the present paper, we propose a simulation framework for the VWF-mediated platelet adhesion to a plane wall with immobilized binding sites for VWF under the action of shear flow. VWF multimers and platelets are represented in the model by particles connected by elastic bonds and immersed in a viscous continuum fluid. This work complements the scientific field by taking into account the shape of a flattened platelet, but keeping a compromise between the detail of the description and the computational complexity of the model.

Market States and Lottery Preference: Evidence from Chinese Open‐End Funds*

AbstractRecent studies find that investors prefer funds with lottery‐like payoffs. Using a sample of Chinese open‐end funds, we show that investors' preference for funds' extreme positive payoffs (MAXs) depend on the state of the market: it is significant for MAXs in an unfavorable market but weak or reversed for those in a favorable market. Such state‐dependent preference is irrational because, inconsistent with the flow–MAX relationship, higher MAXs under market downturns are associated with worse performance. We further document support for the salience‐theory‐based explanation for investors' preference and provide counter‐evidence for alternative mechanisms based on rational choice or changes in aggregate flows.

Cohesion and aggregates in unsaturated wet granular flows down a rough incline

Multi-phase flows, encountered in nature or in industry, exhibit non-trivial rheological properties, which we attempt to better understand thanks to model materials and appropriate rheometers. Unsaturated wet granular flows down a rough inclined plane turn out to be steady and uniform for a wide range of parameters, despite the cohesion and the grain aggregates. The cohesive Mohr–Coulomb yield criterion extended to inertial granular flows, with a cohesion stress dependent on the liquid content and an internal friction coefficient dependent on the inertial number, allows for predictions in good agreement with our experimental measurements, when one introduces a grain aggregate size, which defines the appropriate length and relaxation time scales in the inertial number. We found that the grain aggregate size depends not monotonically on the liquid content and does not scale with the cohesion length induced by the cohesion stress, due to the non-trivial distribution of the liquid within the granular material.

Macroscopic Fundamental Diagrams for Low-Altitude Air city Transport

Low-altitude aircraft is being developed as a new mode of urban transport; consequently, the penetration of low-altitude aircraft into the urban airspace is inevitable in the near future. This will give rise to new urban air transport systems, called low-altitude air city transport (LAAT) systems. Such systems will include aircraft operated with or without pilots, transferring passengers and goods in urban areas using low-altitude levels of urban airspace.This paper investigates the collective and aggregate aircraft traffic flow diagrams, i.e. Macroscopic Fundamental Diagrams (MFDs), for LAAT systems. A LAAT plant model that captures the microscopic and macroscopic levels is established to construct the MFD curve, which links flow, density, and speed. The MFD allows us to identify the airspace traffic conditions, and to detect congested regimes. Different case study examples were simulated to analyze the full shape of MFD curves for LAAT systems, considering different algorithms (waypoints and destination plane), aircraft, and airspace settings. In this paper, a unique modeling framework for LAAT operation and a real-time synchronized implementation of large-scale LAAT simulation were developed. The findings of the current paper, the development of LAAT simulation environment, and the identification of airspace traffic conditions, can lead to the development of aggregate models and new control strategies to minimize congestion in futuristic urban airspace.

Dynamic evaluation of pathological changes in a mouse acne model by optical imaging technology.

Acne vulgaris is a disorder of the pilosebaceous unit that is primarily caused by hyperseborrhoea, colonization with Propionibacterium acnes, hyperkeratosis and an inflammatory response. Existing pharmacodynamic assessment methods primarily focus on a single causative factor at a certain time point, making it difficult to assess multiple factors simultaneously in real time. Therefore, it is crucial to establish a dynamic and nondestructive method for the assessment of acne in vivo. This study utilized four-dimensional optical imaging techniques to assess the pathogenic factors and pathological progression of acne. LSCI was employed to measure blood flow; TPEF was used to observe inflammatory changes (NAD(P)H) in epidermal granular layer cells and structural changes in collagen fibres in the dermal layer. Additionally, the dermatoscope was used to investigate the micro-characterization of the lesions. We observed that the epidermis in the lesion area was thickened, hair follicles were keratinized, and there was obvious inflammation and blood flow aggregation by optical imaging technology. Based on these findings, the pathological progression of this acne model could be divided into the inflammation phase, accompanied by bacterial colonization, and the reparative phase. These results provide a new perspective for the assessment of acne and offer an experimental basis for the selection of precise drugs for clinical use.

Multipath Routing Implementation in SD-IoT Network Using OpenFlow-based Routing Metrics

The implementation growth of the Internet of Things (IoT) may increase the complexity of the data transmission process between smart devices. The route generation process between available nodes on the network will burden the intermediary node. One of the possible solutions for resolving the problem is the integration of Software Defined Networks and IoT (SD-IoT) to provide network automation and management. The separation of networking control and data forwarding functions may provide a multipath delivery path between each node in the IoT environment. In addition, the controller can directly extract the resource usage of the intermediary devices, which can be utilized as the routing metric variable in order to maintain the resource utilization on the intermediary devices. Instead of using traditional routing, this paper aims to develop multipath routing based on Deep First Search (DFS) and Dijkstra algorithms for acquiring an efficient path using OpenFlow-based routing metrics. The traffic monitoring module delivered the metrics extraction process, which obtained the variables using Port and Aggregate Flow Statistic features. The metrics calculation aimed to provide the multipath, which was constructed based on switches resource usage. Each selected path was chosen based on the smallest cost and probability provided by the group table feature in OpenFlow. The results showed that the Dijkstra algorithm could create the multipath more swiftly than DFS with a time difference of 0.6 s. The Quality of Service (QoS) results also indicated that the proposed routing metric variables could maintain the transmission process efficiently.

Labour market rigidity and trade margins

AbstractCountries differ, sometimes significantly, regarding their basic labour market structure. Despite the prevalence of theoretical literature, empirical studies examining the impact of labour market rigidity (LMR) on trade is sparse. Existing empirical studies have been limited to aggregate trade flows with cross‐sectional estimations; a limited set of countries (e.g. OECD or Latin America) or to the period after 1990. We utilise a gravity model of trade to examine the impact of LMR on exports for 145 countries between 1964 and 2004. We utilise dis‐aggregated product‐level trade data and decompose total exports into the extensive and the intensive margin to examine this relationship. We also utilise total aggregate international trade relative to domestic sales, which allows for the identification of LMR‐trade relationship, even in the presence of multilateral trade resistance controls. Finally, we estimate the effect of LMR on exports (and the margins) by focusing on a sample of European OECD countries via the difference‐in‐differences estimation. In all cases, we find that a rigid LMR reduces total exports, and this decrease is driven primarily by the intensive margin. Our findings are relevant as recent studies have found the intensive margin to be more important for long‐run export growth and especially for developing countries.

Is property driving income distribution? – An analysis of the linkage between income and wealth in Finland, France and Spain

In this article, the link between financial wealth and pre-tax household income distribution is scrutinised for three European countries using a conceptually fully consistent macro framework. First, national balance sheets are combined with the related income flows. After this, income flows that are not property income but that are considered part of national income (e.g., wages and salaries) are added, the national income flows are broken down by institutional sector, and the household sector income flows separated. Finally, distributional household microdata are used to break down the aggregate household sector income flows by income decile. The article utilises this framework to analyse the evolution of rates of return and capital and labour shares, as well as the way in which the property income flows created by financial wealth have affected household primary income distribution.

Job-to-Job Mobility and Inflation

Abstract The low rate of inflation observed in the U.S. over the past decade is hard to reconcile with traditional measures of labor market slack. We develop a theory-based indicator of interfirm-wage competition that can explain the missing inflation. Key to this result is a drop in the rate of on-the-job search, which lowers the intensity of interfirm-wage competition to retain or hire workers. We estimate the on-the-job search rate from aggregate labor-market flows and show that its recent drop is corroborated by survey data. During “the great resignation”, interfirm-wage competition rose, raising inflation by around 1 percentage point in 2021.

Classification and estimation of the mass composition of recycled aggregates by deep neural networks

The construction sector is one of the largest consumers of natural resources, but also a producer of a considerable amount of waste. Construction and Demolition Waste can be transformed into recycled aggregates and used as a substitute for natural aggregates, either in road construction or concrete, which is one way to reduce the environmental impacts of the construction industry. In order to increase the use of recycled aggregates in high value-added materials like concrete, it is important to guarantee the quality of the produced aggregates. The recycling industry therefore needs new methods to automate the characterization of recycled aggregates. In a previous work we showed that deep convolutional networks can classify the different constituents of recycled aggregates, achieving an average accuracy of 97%. In this work, we propose a novel network architecture called RACNET designed to estimate the mass, the class and the binary mask of recycled aggregates from only 2D images. This could replace advantageously manual sorting tests as well as other geometric characterization tests (like particles size distribution), allowing for a real-time monitoring of recycled aggregates composition. We also present a prototype which preform automatic characterization of a flow of aggregates in real conditions, showing that our approach could be used in real industrial environments

Scaling and Placing Distributed Services on Vehicle Clusters in Urban Environments

Many vehicles spend a significant amount of time in urban traffic congestion. Due to the evolution of autonomous vehicles, driver assistance systems, and in-vehicle entertainment, these vehicles have plentiful computational and communication capacity. How can we deploy data collection and processing tasks on these (slowly) moving vehicles to productively use any spare resources? To answer this question, we study the efficient placement of distributed services on a moving vehicle cluster. We present a macroscopic flow model for an intersection in Dublin, Ireland, using real vehicle density data. We show that such aggregate flows are highly predictable (even though the paths of individual vehicles are not known in advance), making it viable to deploy services harnessing vehicles’ sensing capabilities. After studying the feasibility of using these vehicle clusters as infrastructure, we introduce a detailed mathematical specification for a task-based, distributed service placement model. The distributed service scales according to the resource requirements and is robust to the changes caused by the mobility of the cluster. We formulate this as a constrained optimization problem, with the objective of minimizing overall processing and communication costs. Our results show that jointly scaling tasks and finding a mobility-aware, optimal placement results in reduced processing and communication costs compared to the two schemes in the literature. We compare our approach to an autonomous vehicular edge computing-based naive solution and a clustering-based solution.

Analysis of dry reforming of methane under different fluidization regimes using a multiphase particle-in-cell approach

In the present study, the dry reforming of methane (DRM) has been simulated in fluidized-bed reactors using the multiphase particle-in-cell model. The model was meticulously built to investigate the effect of a wide range of superficial gas velocities covering particulate, aggregative, and lean-phase flow regimes on bed hydrodynamics, conversion, and yields of product gases. Constant values for catalyst loading, CH4:CO2:N2 ratio (1:1:1.3), and catalyst and gas properties were maintained in all simulations. The simulation results obtained are in good agreement with the experimental data reported in the literature. The results show that under different gas velocities, conversion is relatively indiscernible in the particulate regime. In contrast, for the inhomogeneous phases, the turbulent-fluidized bed had the best reactor performance with high CH4 and CO2 conversion rates, good CO + H2 productivity, and high CO/H2 molar ratio. This is due to the vigorous turbulent flow and relatively high gas–solid contact. Due to gas bypassing and backmixing triggered by bubbling, the bubbling-fluidized bed generally had the worst performance and below that of the fast-fluidized bed. The present study demonstrates that the performance of DRM reactions in fluidized-bed reactors is strongly related to the hydrodynamics. Moreover, it shows the significance of gas velocity on DRM conversion, yield, and overall reactor performance.

Growing older and growing apart? Population age structure and trade

PurposeThis paper explores the empirical relationship between population age structure and bilateral trade.Design/methodology/approachThe author includes age structure in both log and Poisson pseudo-maximum likelihood (PPML) formulations of the gravity equation of trade. The author studies relative age effects, using differences in the demographic structure of each country-pair.FindingsThe author finds that a relatively larger share of population in working age increases bilateral exports. This is robust to various estimation models, as well as to changes in the method of specifying the demographic controls. Old-age shares have a negative, but less robustly estimated impact on trade. Estimating instead the balance of trade between trading partners produces similar results, with positive effects of age structure peaking later in working life.Practical implicationsGlobal populations are poised to undergo a massive transition. Trade a crucial way that the demographic deficits of one country may be offset by the dividends of another as comparative advantages shift along with the size and strength of their underlying workforce.Originality/valueThe author’s work is among the first to quantify the effect of relative age structure between two countries and their bilateral trade flows. Focusing on the aggregate flows, relative age shares and PPML estimates of the trade relationship, this paper provides the most comprehensive picture to date on how age structure affects trade.

Cooling flow regime of a plasma thermal quench

A large class of Laboratory, Space, and Astrophysical plasmas is nearly collisionless. When a localized energy or particle sink, for example, in the form of a radiative cooling spot or a black hole, is introduced into such a plasma, it can trigger a plasma thermal collapse, also known as a thermal quench in tokamak fusion. Here we show that the electron thermal conduction in such a nearly collisionless plasma follows the convective energy transport scaling in itself or in its spatial gradient, due to the constraint of ambipolar transport. As a result, a robust cooling flow aggregates mass toward the cooling spot and the thermal collapse of the surrounding plasma takes the form of four propagating fronts that originate from the radiative cooling spot, along the magnetic field line in a magnetized plasma. The slowest one, which is responsible for deep cooling, is a shock front.

Effects of ionic valence on aggregation kinetics of colloidal particles with and without a mixing flow

Hypothesis: The classical Schulze-Hardy rule states that the critical coagulation concentration (CCC) of colloidal particles is inversely proportional to the counter-ionic valence at powers ranging from 2 to 6. However, the inverse Schulze-Hardy rule has recently been proposed, suggesting that the CCC can also be inversely proportional to the co-ionic valence. Previous studies on these rules did not consider the effect of flow on aggregation kinetics and the CCC. This study aims to investigate the effect of multivalent counter-ions and co-ions on aggregation kinetics and the CCCs in systems with and without a mixing flow.Experiments: We measured the aggregation rate coefficients of polystyrene sulfate latex particles as a function of the salt concentration with different ionic species. Furthermore, we analyzed these measurements using theoretical models based on hydrodynamic pair-diffusion in a random flow and trajectory analysis in two steady flows. The analysis was conducted using zeta potentials determined through electrophoretic measurements.Findings: Although the trajectory analysis underestimates the CCCs, the hydrodynamic pair-diffusion model can capture the shift of critical coagulation concentrations in the mixing flow to higher values than those in Brownian aggregation and also shows a better agreement with the experimental results. This result suggests that combining random flow and Brownian diffusion is crucial for developing a consistent framework for predicting both Brownian aggregation and aggregation in a mixing flow.

Hierarchical Queue Management Priority and Balancing Based Method under the Interaction Prediction Principle

This work is devoted to improving a two-level hierarchical queue management method based on priority and balancing under the interaction prediction principle. The lower level of calculations was connected with the problem optimization solution and was responsible for two tasks. Firstly, the packet flow aggregation and distribution among the macro-queues and sub-queues organized on the router interface must solve the congestion management problem. Secondly, the resource allocation problem solution was related to the balanced allocation of interface bandwidth among the sub-queues, which were weighted relative to their priorities under the traffic-engineering queues. The method’s lower-level functions were recommended to be placed on a set of processors of a routing device responsible for servicing the packets of individual macro-queues. At the same time, the processor coordinator could perform the functions of the upper-level calculations, providing interface bandwidth allocation among the macro-queues. The numerical research results of the proposed two-level hierarchical queue management method confirmed its effectiveness in ensuring high scalability. Balanced, priority-based packet flow distribution and interface bandwidth allocation among the macro-queues and sub-queues were implemented. In addition, the time was reduced for solving tasks related to queue management. The method demonstrated high convergence of the coordination procedure and the quality of the centralized calculations. The proposed approach can be used in various embedded systems.

Forecasting real activity using cross-sectoral stock market information

Stock prices declined abruptly in the wake of the Covid-19, reflecting both the deterioration of investors’ expectations of profitability as well as the surge in risk aversion. In the following months however, economic activity remained sluggish while equity markets bounced back. This disconnect between equity values and macro-variables can be partially explained by other factors, namely the decline in risk-free interest rates, and -for the US- the strong earnings of the IT sector. As a result, an econometrician forecasting economic activity with aggregate stock market variables during the Covid-crisis is likely to get poor results. Our main contribution is thus to rely on sectorally disaggregated equity variables within a factor model in order to predict US economic activity. We find, first, that the factor model better predicts future economic activity compared to aggregate equity variables, or to conventional benchmarks used in the literature, both in-sample and out-of-sample. Second, we show that the strong performance of the factor model comes from the fact that it filters out the “expected returns” component of the sectoral equity variables as well as the foreign component of aggregate future cash flows. The constructed factor overweights upstream and “value” sectors that are found to be closely linked to the future state of the business cycle.