Spreading Process Research Articles

Experimental Investigation of Dispersant on Dynamics of Impact of Al2O3 Nanofluid Droplet

Spray cooling, of which the essence is droplet impacting, is an efficient thermal management technique for dense electronic components in unmanned aerial vehicles (UAVs). Nanofluids are pointed as promising cooling dispersions. Since the nanofluids are unstable, a dispersant could be added to the fluid. However, the added dispersant may influence the droplet, thereby impacting behaviors. In this work, the effects of dispersant on the nanofluid droplet-impacting dynamics are studied experimentally. The base fluid is deionized water (DI water), and Al2O3 is the selected nanoparticle. Sodium dodecyl sulfate (SDS) is used as the dispersant. Five different concentrations of nanofluids are configured using a two-step method. Droplet impacting behaviors are observed by high-speed imaging techniques. The other effects, i.e., the nanofluid particle volume fraction and the Weber number on droplet impact dynamics, are also systematically investigated. The results illustrate that the surface tension of the Al2O3 nanofluid increases with increased nanofluid concentrations. The surface tension of Al2O3 nanofluid with SDS is lower than that of DI water. And the increase in droplet impact velocity increases the spreading morphology. Nanofluid droplets exhibit spreading and equilibrium process when SDS is added. Furthermore, as the concentration of the nanofluid increases, the spreading process is inhibited. Whereas without SDS, the droplets undergo spreading, receding, and equilibrium processes. Moreover, there is no appreciable change in the impacting process with concentration increase. The empirical models of maximum spreading factor should be established without SDS and with SDS, respectively. This study can provide theoretical basis and specific guidance for experimental characterization of UAVs’ electronic devices based on the mechanism of nanofluid droplet impact on the wall.

Metabolic Reprogramming and Adaption in Breast Cancer Progression and Metastasis.

Recent evidence has revealed that cancer is not solely driven by genetic abnormalities but also by significant metabolic dysregulation. Cancer cells exhibit altered metabolic demands and rewiring of cellular metabolism to sustain their malignant characteristics. Metabolic reprogramming has emerged as a hallmark of cancer, playing a complex role in breast cancer initiation, progression, and metastasis. The different molecular subtypes of breast cancer exhibit distinct metabolic genotypes and phenotypes, offering opportunities for subtype-specific therapeutic approaches. Cancer-associated metabolic phenotypes encompass dysregulated nutrient uptake, opportunistic nutrient acquisition strategies, altered utilization of glycolysis and TCA cycle intermediates, increased nitrogen demand, metabolite-driven gene regulation, and metabolic interactions with the microenvironment. The tumor microenvironment, consisting of stromal cells, immune cells, blood vessels, and extracellular matrix components, influences metabolic adaptations through modulating nutrient availability, oxygen levels, and signaling pathways. Metastasis, the process of cancer spread, involves intricate steps that present unique metabolic challenges at each stage. Successful metastasis requires cancer cells to navigate varying nutrient and oxygen availability, endure oxidative stress, and adapt their metabolic processes accordingly. The metabolic reprogramming observed in breast cancer is regulated by oncogenes, tumor suppressor genes, and signaling pathways that integrate cellular signaling with metabolic processes. Understanding the metabolic adaptations associated with metastasis holds promise for identifying therapeutic targets to disrupt the metastatic process and improve patient outcomes. This chapter explores the metabolic alterations linked to breast cancer metastasis and highlights the potential for targeted interventions in this context.

Optimal Control of Nonlinear Competitive Epidemic Spreading Processes With Memory in Heterogeneous Networks

Optimal Control of Nonlinear Competitive Epidemic Spreading Processes With Memory in Heterogeneous Networks

Similar pipeline experiment and disaster control emergency plan of updraft airflow fire in mine

Based on the engineering example of Linsheng coal mine, this paper uses TF1M3D computer simulation platform to systematically analyze the process of smoke flow spreading and air flow disorder disaster from the perspective of the whole mine network, and puts forward corresponding plans and measures. A small scale similar experiment was carried out to study the updraft flow fire in the mine. Through the analysis of the collected experimental data, the variation law of the air volume of the fire source in the main air path, side branch road and total air path with different air volume and the variation characteristics of the temperature at the monitoring point with time were obtained under different air volume conditions, and the critical air volume was fitted as 1.65 m3·s− 1. The CAD digital stereoscopic model of Linsheng mine was established, and the TF1M3D simulation platform was used to simulate the mine fire under normal ventilation condition. The preset fire source point of the conveyor belt roadway in the first west district of Linsheng coal mine was used to observe the change of air volume during the fire process.The smoke flow reversal phenomenon would occur when the fire occurred in the first west district. The reversed smoke flow will flow to the 503 face and cause contamination. In order to inhibit the reversal of smoke flow, the emergency plan is proposed to increase the number of fan rotation and set fire doors in appropriate places, which can effectively inhibit the diffusion of smoke and improve mine disaster resistance.

Revisiting Information Cascades in Online Social Networks

It is widely believed that a user’s activity pattern in Online Social Networks (OSNs) is strongly influenced by their friends or the users they follow. Building on this intuition, numerous models have been proposed over the years to predict information propagation in OSNs. Many of these models drew inspiration from the process of infectious spread within a population. While this approach is definitely plausible, it relies on knowledge of users’ social connections, which can be challenging to obtain due to privacy concerns. Moreover, while a significant body of work has focused on predicting macro-level features, such as the total cascade size, relatively little attention has been given to the prediction of micro-level features, such as the activity of an individual user. In this study we aim to address this gap by proposing a method to predict the activity of individual users in an OSN, relying solely on their interactions rather than prior knowledge of their social network. We evaluated our results on four large datasets, each comprising over 14 million tweets, recorded on X social network across four different topics over several month. Our method achieved a mean F1 score of 0.86, with a best result of 0.983.

The Spread of Democracy in China

The spread of democratic ideas and economic growth in China—as suggested by the logic of modernization theory—have not initiated political reform toward democracy in China. Various literatures have attempted to explain this failure by emphasizing economic and social learning factors that clash with China’s national interests rather than emphasizing the context of its spreading process. Generally, this article aims to explain China’s failure to reform its politics toward democracy by focusing on the context of spreading democratic ideas in China. Specifically, this article aims to elucidate the relevance and continuity of the spread of democratic ideas in China during the May Fourth Movement of 1919, the Tiananmen Movement of 1989, and the Umbrella Movement of 2014, to the fifth wave of the Asian Barometer Survey (ABS). This article adopts a constructivist paradigm and constitutive localization theory to explain the failure of political reform in China. The findings suggest that Chinese local political ideas have influenced the spreading process of democratic ideas, contributing to China’s failure to acquire political reform toward democracy.

Study on Smoke Characteristics in Cavern Complexes of Pumped-Storage Power Stations

The underground power houses of pumped-storage power stations (PSPSs) are highly complex, with interconnected and multidimensional structures, including various tunnels, such as the main and auxiliary power houses (MAPH), main transformer tunnel (MTT), tailrace gate tunnel (TGT), access tunnels (ATs), cable tunnels (CTs) etc. During intensive civil construction and electromechanical installation, fire risk becomes particularly prominent. Current research mainly examines fire incidents within individual tunnels, lacking comprehensive analyses of smoke spread across the entire cavern network. Therefore, in this study, a numerical model of a cavern complex in a PSPS was established to analyze smoke behavior and temperature distribution under various fire scenarios. The results indicated that when a fire occurred in the MAPH, the fire risk was relatively higher compared to fires in other places. Using the example of smoke spread from the MAPH to the MTT, the smoke spread process through key connecting caverns was analyzed. Initially, the temperature and velocity were stable, and the CTs and traffic cable tunnel in the auxiliary powerhouse (TCTAP) were the main smoke paths. After 7 min, the heat release rate (HRR) became stable, and CTs and ATs became the main paths for smoke spread, which could provide a reference for improving fire design in underground cavern systems.

Unraveling the dynamic variations of volatile and non-volatile metabolites in green tea during the yellow-light irradiation spreading process by targeted and untargeted metabolomics

Unraveling the dynamic variations of volatile and non-volatile metabolites in green tea during the yellow-light irradiation spreading process by targeted and untargeted metabolomics

Modelling pathological spread through the structural connectome in the frontotemporal dementia clinical spectrum.

The ability to predict the pathology spreading in patients with frontotemporal dementia (FTD) is crucial for early diagnosis and targeted interventions. This study examined the relationship between network vulnerability and longitudinal atrophy progression in FTD patients, using Network Diffusion Model (NDM) of pathology spread. Thirty behavioural-variant FTD (bvFTD), 13 semantic-variant primary progressive aphasia (svPPA), 14 nonfluent-variant PPA (nfvPPA) and 12 semantic behavioral variant FTD (sbvFTD) patients underwent longitudinal T1-weighted MRI. Fifty young controls (YC) (20-31 years) underwent multi-shell diffusion MRI scan. NDM was developed to model FTD pathology progression as a spreading process from a seed through the healthy structural connectome, using connectivity measures from fractional anisotropy (FA) and intra-cellular volume fraction (ICVF) in YC. Four disease epicenters were initially identified from the peaks of atrophy of each FTD variant: left insula (bvFTD), left temporal pole (svPPA), right temporal pole (sbvFTD) and left supplementary motor area (nfvPPA). Pearson's correlations were calculated between NDM-predicted atrophy in YC and the observed longitudinal atrophy in FTD patients over a follow-up of 24 months. The NDM was then run for all the 220 brain seeds to verify whether the four epicenters were among those that yielded the highest correlation. Using NDM, predictive maps in YC showed pathology progression from the peaks of atrophy in svPPA, nfvPPA, and sbvFTD over 24-months. svPPA exhibited early involvement of left temporal and occipital lobes, progressing to extensive left hemisphere impairment. nfvPPA and sbvFTD similarly spread bilaterally to frontal, sensorimotor, and temporal regions, with sbvFTD additionally affecting the right hemisphere. Moreover, the NDM-predicted atrophy of each region was positively correlated to longitudinal real atrophy, with a greater effect in svPPA and sbvFTD. In bvFTD, the model starting from the left insula (the peak of atrophy) demonstrated a highly left-lateralized pattern, with pathology spreading to frontal, sensorimotor, temporal, and basal ganglia regions, with minimal extension to the contralateral hemisphere by 24 months. However, unlike the atrophy peaks observed in the other three phenotypes, the left insula did not show the strongest correlation between the estimated and actual atrophy. Instead, the bilateral superior frontal gyrus emerged as optimal seeds for modelling atrophy spread, showing the highest correlation ranking in both hemispheres. Overall, NDM applied on ICVF connectome yielded higher correlations relative to NDM applied on FA maps. The NDM implementation using cross-sectional structural connectome is a valuable tool to predict atrophy patterns and pathology spreading in FTD clinical variants.

Correlating concurrent-flow flame spread rates in different pressure and oxygen conditions: Ground experiments and comparisons with previous micro-, partial, and normal gravities experiments

Ambient pressure and gravity are important parameters in buoyant flow that governs upward flame spread process. Based on the concept of pressure modelling, this experimental study investigates extinction and upward flame spread process of a thermally-thin solid fuel in different pressure and oxygen conditions. Experiments are performed in a combustion chamber in air at different pressures (ranging from 10 kPa to 100 kPa) and different oxygen molar fraction environment (9–21 %). As pressure increases, different burning behaviors are observed: no ignition, partial flame spread, steady flame spread, and accelerating flame spread. Similar trend is observed as the ambient oxygen molar fraction increases. In partial pressure conditions (e.g., 25–50 kPa), flames exhibit characteristics that are typically observed in micro- and partial gravity environments: blue and dim. Flame spread rate and sample burnt length are deduced and compared between different pressure and oxygen levels. Overall, the burning intensity and the flame spread rate decrease with the decrease in ambient pressure and oxygen. The decrease in flame spread rate at reduced pressure is attributed to increase in flame standoff distance and decrease in convective heat transfer to the solid, whereas the decrease in flame spread rate in reduced oxygen molar fraction environment is attributed to decrease in flame temperature. Lastly, current and previous studies performed at different ambient environments are correlated using the concept of flame standoff distance (δf), which is estimated using the theoretical viscous boundary layer thickness (δv). It was found that approximating δf∼δvfor forced flow and δf∼1/3δv for natural flow can predict the flame spread rate reasonably well for data obtained in micro-, partial, and normal gravities, for a wide range of environmental conditions away from extinction limits.

Wetting behavior in the inertial phase of droplet impacts onto sub-millimeter microstructured surfaces

Hypothesis: The shape of the wetted region after a droplet impact can vary significantly even in the early phase of the process. How much of the early spreading process occurs within the structures versus above the structures, flow regimes and local wetting at groove intersections can have effects on the sizes and shapes of the final wetted regions.Experiments and simulations: We experimentally study droplet impacts onto cubic pillars with ▪, ▪ and ▪ side length, height and separation. Weber numbers ranged between 80 and 1 100, while Reynolds numbers varied between 1 150 and 10 600 using water and isopropanol droplets. The contact angle on a flat segment of the samples was modified between θFS<5∘ and θFS=120∘±5∘. Several experiments are reproduced using our in-house code FS3D to show the internal flow fields.Findings: Diamond-shaped spreading patterns with edges aligned at 45∘ to the structure pattern are observed. A transition between top-dominated (circular) spreading and diamond spreading occurs depending on the structure size and impact velocity. Groove intersections can act as flow dividers, causing spreading along a path with 90∘ bends. For large structure sizes and impact velocities fluid jets can pass through the structures uninhibited.

A reaction kinetics approach to model the spreading of oil drop on aqueous surfactant solutions

The spreading of oil drops on aqueous solutions containing surfactants of various concentrations varying across critical micellar concentration (CMC) is investigated. The fate of the dispensed oil drop was visualized using time-resolved video-microscopy. The projected area of the oil drop is found to increase initially due to the spreading processes, reaches a maximum, and finally decreases due to the dissolution of the oil drop into the bulk of the surfactant solution. The two competing effects of spreading and dissolution lead to a non-monotonic temporal evolution of the projected area, which is further modeled using a reactions kinetic approach. In particular, we show that a series reaction of the form A→B→C is analogous to the spreading-dissolution process observed in our experiments. This analogy allows us to estimate the rate constants associated with the spreading and dissolution of the oil drop, which can be further used to quantify the CMC of the surfactants in solutions.

Modeling of the process of spreading fine dust in the atmosphere of residential areas

The features of modeling the dispersion process of fine dust in the atmosphere of residential areas were examined, which not only enhanced understanding of cement dust dispersion processes but also allowed for the development of practical recommendations to mitigate its negative impact on the environment and public health. It is well known that cement production is a primary source of air pollutants, particularly cement dust, which degrades atmospheric quality and poses a serious health risk to nearby populations. The research employed a combination of mathematical modeling and data analysis to predict the dispersion of cement dust in residential areas adjacent to industrial facilities. The model simulated the behavior of particulate matter in the atmosphere, accounting for variables such as wind direction, emission rates, and particle size. Consequently, the study provided an accurate forecast of dust concentrations at varying distances from the emission source, offering valuable insights into the scope of the pollution. Moreover, the analysis revealed that cement dust disperses over wide areas, leading to elevated levels of particulate matter in residential zones, which can significantly affect human health, especially respiratory conditions. In addition to modeling, the study assessed the efficiency of current dust collection systems in cement production plants. It was found that improvements in dust collection technologies could result in a 10-15 % reduction in dust emissions. As a result, the study advocates for the modernization of filtration and capture equipment as a practical solution for mitigating the environmental footprint of cement production. The benefits of this research extend beyond immediate air quality improvements, as the reduction in emissions could lead to enhanced public health outcomes and a decrease in long-term environmental damage. The findings underscore the importance of continuous air quality monitoring and regulatory compliance to control industrial pollution. The study's conclusions suggest that further research is needed to refine the dust dispersion model by incorporating additional environmental factors such as humidity, temperature, and urban landscape features. Future studies could also explore the long-term effectiveness of improved dust collection technologies in diverse climatic and geographic conditions.

An improved dynamic-sensitive centrality based on interactive influence for identifying influential nodes in aviation networks

The identification of influential nodes in complex networks is a hot topic among scholars. Classical methods commonly analyze single node information or static structures but seldom emphasize dynamic properties and the interactive influence of nodes. Here, we proposed an improved Dynamic-Sensitive centrality (IDS) method by considering the interactive influence of both the self and neighbor nodes. Based on six real aviation networks and the Susceptible Infected Recovered (SIR) spreading disease model, we simulated the actual spreading process within these networks. Relevant experiments were conducted through Kendall’s correlation coefficient, the imprecision function, and the complementary cumulative distribution function. The experimental results demonstrated that the IDS can more accurately identify the influential node and effectively differentiate the node influence in the network compared with other benchmark methods. Especially in the EU air-2 network, the IDS results in Kendall’s correlation coefficient are improved by 105% compared to the DS centrality.

Discontinuous Directional Wetting Transitions in Polymeric Droplets on the Heterogeneous Microcavity Surface.

The wetting transition behaviors of polymeric droplets on microcavity surfaces are familiar and play a vital role in micromanufacturing, microfluidics, and printing industries. Despite previous research indicating that microcavity surfaces can precisely control the droplet wetting state, the understanding of the complex effects of droplet spreading, surface morphology, and property of polymeric droplet on wetting transitions remains incomplete. The air-liquid interfaces (ALIs) typically arise from the entrapped air beneath the droplet on microcavity surfaces, adopting a metastable wetting state caused by either bubble escape or dissolution. Here, we discovered a previously unobserved phenomenon: the time-dependent evolution of regularly arranged ALIs with discontinuous wetting states, along with the pronounced directional wetting transitions from the Cassie-Baxter state to the Wenzel state upon deposition of polymeric droplets on heterogeneous microcavity surfaces. The durability of ALIs in microcavities was quantified, illustrating that the wetting transitions associated with droplet spreading processes obeyed power laws. By integrating the wetting theory and the viscoelastic effect of polymeric droplet, we have proposed a phenomenological coevolution model for wetting transitions that emphasizes the synergistic interaction between adjacent microcavities, resulting in the observed cluster evolution behavior of ALIs within droplets. Our study holds great significance in guiding soft manufacturing techniques utilizing internal ALIs as templates. The established mechanism opens up avenues for investigating the intricate wetting phenomena of polymeric droplets on microtextured substrates.

Influence of Construction Process on Aggregate Spalling Behavior on Ultrathin Waterborne Epoxy Resin Layer

The waterborne epoxy resin (WER) colored antiskid thin layer has been widely used in asphalt pavement to improve driving safety. The tectonic depth determines the antiskid performance of aparticle antiskid type thin layer. The spalling of aggregate from a thin layer may reduce the tectonic depth, thus damaging antiskid performance. The spreading process of aggregate on the WER binder surface plays an important role in the spalling behavior of the thin layer. Herein, the influence of spreading processes on the ceramic aggregate spalling behavior on the WER thin layer was investigated based on laboratory experiments. The abrasion and British Pendulum Number (BPN) tests were employed to evaluate the antispalling and antiskid properties of the WER thin layers with different amounts of WER mortar, coverage rates of first-spread aggregate, and spreading orders of coarse/fine aggregates. Moreover, the tectonic depths of the layers before/after the spalling test were also investigated. The results indicated that the optimal dosage of WER mortar is 2.8 kg/m2. The WER thin layer exhibited better anti-striping property when coarse ceramic aggregate was spread first. The first-spread coverage rate of the aggregate on the WER surface is 70%. The thin layer exhibited a superior antispalling performance according to the resulting scheme, with a spalling rate of 3.77%. The tectonic depth only decreased from 1.87 to 1.80 mm after the spalling test.

Modeling the delay propagation dynamics in high-speed railway networks with a traffic-driven SIS epidemic model

The propagation of delays is a prevalent issue in high-speed railway (HSR) networks. However, the underlying properties of delay propagation have not been fully explored, hindering the effective management of this problem. In this paper, considering the impact of traffic flow, we model the delay propagation as a traffic-driven susceptible–infected–susceptible (SIS) epidemic spreading process. We introduce a traffic frequency matrix to estimate the traffic rate and further derive the propagation dynamic equations using a continuous-time Markov chain method. We then derive the epidemic threshold of the model, which is proportional to the recovery rate and inversely proportional to the average path length. Finally, through simulation, we study the impacts of various factors including the infection rate, recovery rate, and the degree and number of initial delay stations. Our work provides some insights for the prediction and control of delay propagation in HSR networks.

Detailed-level modelling of influence spreading on complex networks

The progress in high-performance computing makes it increasingly possible to build detailed models to investigate spreading processes on complex networks. However, current studies have been lacking detailed computational methods to describe spreading processes in large complex networks. To fill this gap we present a new modelling approach for analysing influence spreading via individual nodes and links on various network structures. The proposed influence-spreading model uses a probability matrix to capture the spreading probability from one node to another in the network. This approach enables analysing network characteristics in a number of applications and spreading processes using metrics that are consistent with the quantities used to model the network structures. In addition, this study combines sub-models and offers a comprehensive look at different applications and metrics previously discussed in cases of social networks, community detection, and epidemic spreading. Here, we also note that the centrality measures based on the probability matrix are used to identify the most significant nodes in the network. Furthermore, the model can be expanded to include additional properties, such as introducing individual breakthrough probabilities for the nodes and specific temporal distributions for the links.

Manganese Exposure Enhances the Release of Misfolded α-Synuclein via Exosomes by Impairing Endosomal Trafficking and Protein Degradation Mechanisms.

Excessive exposure to manganese (Mn) increases the risk of chronic neurological diseases, including Parkinson's disease (PD) and other related Parkinsonisms. Aggregated α-synuclein (αSyn), a hallmark of PD, can spread to neighboring cells by exosomal release from neurons. We previously discovered that Mn enhances its spread, triggering neuroinflammatory and neurodegenerative processes. To better understand the Mn-induced release of exosomal αSyn, we examined the effect of Mn on endosomal trafficking and misfolded protein degradation. Exposing MN9D dopaminergic neuronal cells stably expressing human wild-type (WT) αSyn to 300 μM Mn for 24 h significantly suppressed protein and mRNA expression of Rab11a, thereby downregulating endosomal recycling, forcing late endosomes to mature into multivesicular bodies (MVBs). Ectopic expression of WT Rab11a significantly mitigated exosome release, whereas ectopic mutant Rab11a (S25N) increased it. Our in vitro and in vivo studies reveal that Mn exposure upregulated (1) mRNA and protein levels of endosomal Rab27a, which mediates the fusion of MVBs with the plasma membrane; and (2) expression of the autophagosomal markers Beclin-1 and p62, but downregulated the lysosomal marker LAMP2, thereby impairing autophagolysosome formation as confirmed by LysoTracker, cathepsin, and acridine orange assays. Our novel findings demonstrate that Mn promotes the exosomal release of misfolded αSyn by impairing endosomal trafficking and protein degradation.

Modeling and simulation on the spreading dynamics of public opinion information in temporal group networks

In the new media environment, the constantly emerging social platforms further expand the channels for the propagation of public opinion. Under the framework of complex network theory and faced the needs of management practice, modeling the spreading dynamics of public opinion in temporal group networks is of great significance for understanding its spreading law and improving the governance system of cyberspace and the development of network science. Through analyzing the changes of group networks topology and the spreading rules of public opinion, the spreading model of public opinion in temporal group networks was proposed by coupling the two dynamic processes, and the spreading thresholds of public opinion in static and temporal group networks were derived respectively. Then, the spreading characteristics of public opinion under different network topology, as well as the influence of important parameters on public opinion spreading process were discussed with the help of simulation experiments. The research results indicated that the propagation of public opinion in static and temporal group networks exhibits both similar trends and differentiated characteristics; compared with Spreader, the propagation of public opinion in temporal group networks is more sensitive to Ignorant’s behavior; both groups’ and netizens’ active probability have significant influence on public opinion propagation, but netizens’ affects more. Based on the relevant results, this paper proposed a series of countermeasures such as, grading social platforms, strengthening relationship management between them and introducing time management systems, so as to promote the formation of a good network ecosystem and the modernization of the national governance system.