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The purpose of this study is to perform a detailed review on the numerical modeling of multiphase and multicomponent flows in microfluidic system using phase-field method. The phase-field method is of emerging importance in numerical computation of transport phenomena involving multiple phases and/or components. This method is not only used to model interfacial phenomena typical to multiphase flows encountered in engineering and nature but also turns out to be a promising tool in modeling the dynamics of complex fluid-fluid interfaces encountered in physiological systems such as dynamics of vesicles and red blood cells). Intrinsically, a priori unknown topological evolution of interfaces offers to be the most concerning challenge toward accurate modeling of moving boundary problems. However, the numerical difficulties can be tackled simultaneously with numerical convenience and thermodynamic rigor in the paradigm of the phase field method.

The phase-field method replaces the macroscopically sharp interfaces separating the fluids by a diffuse transition layer where the interfacial forces are smoothly distributed. As against the moving mesh methods (Lagrangian) for the explicit tracking of interfaces, the phase-field method implicitly captures the same through the evolution of a phase-field function (Eulerian). In contrast to the deployment of an artificially smoothing function for the interface as used in the volume of a fluid or level set method, however, the phase-field method uses mixing free energy for describing the interface. This needs the consideration of an additional equation for an order parameter. The dynamic evolution of the system (equation for order parameter) can be described by Allen–Cahn or Cahn–Hilliard formulation, which couples with the Navier–Stokes equation with the aid of a forcing function that depends on the chemical potential and the gradient of the order parameter.

In this review, first, the authors discuss the broad motivation and the fundamental theoretical foundation associated with phase-field modeling from the perspective of computational microfluidics. They subsequently pinpoint the outstanding numerical challenges, including estimations of the model-free parameters. They outline some numerical examples, including electrohydrodynamic flows, to demonstrate the efficacy of the method. Finally, they pinpoint various emerging issues and futuristic perspectives connecting the phase-field method and computational microfluidics.

This paper gives unique perspectives to future directions of research on this topic.

Based on the prediction of the consumption of medical materials, the purpose of this paper is to study the applicability of the grey model method to the field and its predicted accuracy.

The ABC classification method is used to classify medical consumables and select the analysis objects. The GM (1,1) model predicts the annual consumption of medical materials. The GM (1,1) modeling of the consumption of the selected medical materials in 2006~2017 was carried out by using the metabolite sequence and the sequence topology subsequence, respectively. The average rolling error and the average rolling accuracy are calculated to evaluate the prediction accuracy of the model.

The ABC classification results show that Class A projects, which account for only 9.79 percent of the total inventory items, occupy most of the inventory funds. Eight varieties with varying purchases and usages and complete historical data were selected for further analysis. The subsequence GM(1,1) model group constructed by two different methods predicts and scans the annual consumption of eight kinds of medical materials, and the rolling precision can reach more than 90 percent.

The metabolic GM (1,1) model is an ideal predictive model that can meet the requirements for a short-term prediction of medical material consumption (Zhang et al., 2014). The GM (1,1) model is more suitable for a short-term prediction of medical material consumption with less data modeling.

The purpose of this paper is to detect wound infection by electronic nose (Enose) and to improve the performance of Enose.

Mice are used as experimental subjects. Orthogonal signal correction (OSC) is applied to preprocess the response of Enose. Radical basis function (RBF) network is used for discrimination, and the parameters in RBF are optimized by particle swarm optimization.

OSC is very suitable for eliminating interference and improving the performance of Enose in wound infection detection.

Further research is required to sample wound infection dataset of human beings and to demonstrate that the Enose with proper algorithms can be used to detect wound infection.

In this paper, Enose is used to detect wound infection, and OSC is used to improve the performance of the Enose. This widens the application area of Enose and OSC.

The innovative concept paves the way for the application of Enose.

This study investigates whether there was a housing price bubble in Beijing and Shanghai in 2003. The existence of a bubble can be interpreted from (abnormal) interactions between housing prices and market fundamentals.

With monthly data from the two cities, this paper employs standard econometric methodologies: i.e. Granger causality tests and generalized impulse response analysis, and the reduced form of housing price determinants.

Our findings suggest that there appeared a bubble in Shanghai in 2003, accounting for 22 percent of the housing price. By contrast, Beijing had no sign of a bubble in the same year. The bubble phenomenon, of course, should not be taken without caution for the constraints of data. Nonetheless, this study has laid the ground work for further investigation into abnormal housing price phenomena in Mainland China.

Our findings may help foreign investors better understand the Chinese housing markets and make better housing investment decisions in the two cities.

This paper aims to present a novel approach of image super-resolution based on deep–shallow cascaded convolutional neural networks for reconstructing a clear and high-resolution (HR) remote sensing image from a low-resolution (LR) input.

The proposed approach directly learns the residuals and mapping between simulated LR and their corresponding HR remote sensing images based on deep and shallow end-to-end convolutional networks instead of assuming any specific restored models. Extra max-pooling and up-sampling are used to achieve a multiscale space by concatenating low- and high-level feature maps, and an HR image is generated by combining LR input and the residual image. This model ensures a strong response to spatially local input patterns by using a large filter and cascaded small filters. The authors adopt a strategy based on epochs to update the learning rate for boosting convergence speed.

The proposed deep network is trained to reconstruct high-quality images for low-quality inputs through a simulated dataset, which is generated with Set5, Set14, Berkeley Segmentation Data set and remote sensing images. Experimental results demonstrate that this model considerably enhances remote sensing images in terms of spatial detail and spectral fidelity and outperforms state-of-the-art SR methods in terms of peak signal-to-noise ratio, structural similarity and visual assessment.

The proposed method can reconstruct an HR remote sensing image from an LR input and significantly improve the quality of remote sensing images in terms of spatial detail and fidelity.

The purpose of this paper is to propose a model for a two-agent multi-target-searching scenario in a two-dimensional region, where some places of the region have limited resource capacity in terms of the number of agents that can simultaneously pass through those places and few places of the region are unreachable that expand with time. The proposed cooperative search model and Petri net model facilitate the search operation considering the constraints mentioned in the paper. The Petri net model graphically illustrates different scenarios and helps the agents to validate the strategies.

In this paper, the authors have applied an optimization approach to determine the optimal locations of base stations, a cooperative search model, inclusion–exclusion principle, Cartesian product to optimize the search operation and a Petri net model to validate the search technique.

The proposed approach finds the optimal locations of the base stations in the region. The proposed cooperative search model allows various constraints such as resource capacity, time-dependent unreachable places/obstacles, fuel capacities of the agents, two types of targets assigned to two agents and limited sortie lengths. On the other hand, a Petri net model graphically represents whether collisions/deadlocks between the two agents are possible or not for a particular combination of paths as well as effect of time-dependent unreachable places for different combination of paths are also illustrated.

The problem addressed in this paper is similar to various real-time problems such as rescue operations during/after flood, landslide, earthquake, accident, patrolling in urban areas, international borders, forests, etc. Thus, the proposed model can benefit various organizations and departments such as rescue operation authorities, defense organizations, police departments, etc.

To the best of the authors’ knowledge, the problem addressed in this paper has not been completely explored, and the proposed cooperative search model to conduct the search operation considering the above-mentioned constraints is new. To the best of the authors’ knowledge, no paper has modeled time-dependent unreachable places with the help of Petri net.

The purpose of this paper is to propose a model for a target searching problem in a two-dimensional region with time constraints. The proposed model facilitates the search operation by minimizing the mission time and fuel usage, and the search operation is performed by a set of agents divided into a number of groups.

The authors have applied optimization techniques, Cartesian product, inclusion–exclusion principle, cooperative strategy, Shapley value, fuzzy Shapley function and Choquet integral to model the problem.

The proposed technique optimizes the placement of base stations that minimizes the sortie length of the agents. The results show that the cooperative strategy outperforms the non-cooperative strategy. The Shapley values quantify the rewards of each group based on their contributions to the search operation, whereas the fuzzy Shapley values determine the rewards of each group based on their contributions and level of cooperation in the search operation.

The proposed model can be applied to model many real-time problems such as patrolling in international borders, urban areas, forests and managing rescue operations after natural calamities, etc. Therefore, defence organizations, police departments and other operation management sectors will be benefitted by applying the proposed approach.

To the best of the authors’ knowledge, determining the optimal locations of base stations in a region is not explored in the existing works on target searching problems with fuel constraints. The proposed approach to cooperatively search the targets in a region is new. Introducing the Shapley function and fuzzy Shapley function is a novel idea to quantify the rewards of each group based on their contributions and level of cooperation in the search operation. This paper addresses these unexplored areas.

In this paper, we draw from our own empirical data on worker organizing and identify important concepts that bridge social movement (SM) and industrial relations (IR) theory. In a context of traditional union decline and a surge of alternative types of worker mobilization, we apply SM and IR concepts related to the mobilizing structures and culture to cases of labor organizing via worker centers and community–labor alliances in the United States and China. From an analytical perspective, we argue that the field of SMs and IR can both benefit from this type of cross-discipline theorization.

This paper aims to examine the evidence of risk spillovers between Shanghai and London non-ferrous futures markets using a dynamic Copula-CoVaR approach.

With daily data, the marginal distributions and optimal Copula functions are determined using the kernel estimation method and squared Euclidean distance test. The conditional value-at-risk and the conditional value-at-risk spillover rate are computed from the Copula estimated parameters based on the Copula-CoVaR model. Also, the dynamic correlation coefficient between the two futures markets is investigated.

The empirical results are as follows: overall, the risk spillover effect exerted by the London Metal Exchange on the Shanghai Futures Exchange is more significant than vice versa. Moreover, the degree of risk spillovers exerted by the London Metal Exchange on the Shanghai Futures Exchange for zinc and copper are more significant when they are depressed in the London Metal Exchange. Moreover, the dynamic of the correlation between the Shanghai and London futures markets is attributed to be largely due to changes in the global economy.

The Copula-CoVaR model used in this paper is suitable for measuring the risk spillovers between two different markets, while the risk spillovers across multiple markets or the consideration of multiple risk factors cannot be accurately captured using this framework. Multiple state variables to capture time variation in the conditional moments of return series will be a topic in future research.

The results provide theoretical support for risk management and monitoring of the non-ferrous futures markets.

The ability of the Copula function to accurately describe a nonlinear relationship and tail correlation is harnessed to measure the risk spillovers, explore the degree and direction of risk spillovers and identify the source of risk spillovers. The global economy is incorporated as a macro factor to explore its inner connection with the dynamic of risk spillovers in the non-ferrous metal futures market.

The purpose of this paper is to investigate the tribological properties of carbonized polydopamine/reduced graphene oxide (CPDA/rGO) composite coatings.

CPDA/rGO composite coatings were prepared using the spray technique and subsequent pyrolysis under argon. The transmission electron microscopy, field-emission scanning electron microscopy, X-ray diffraction, X-ray photoelectron spectroscopy and Raman spectroscopy revealed the conversion of PDA and GO into CPDA and rGO, respectively.

The results of tribological investigations show that the CPDA/rGO composite coatings with heat treatment at 300°C possess much better friction-reduction and anti-wear properties.

The worn surfaces of the PDA/GO composite films after heat treatment at 300°C were much smoother than that of the copper substrate. The tribofilms containing C, N, O and Cu played an important role on reducing friction and increasing wear resistance.