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With the development of global food markets, the structural properties of supply chain networks have become key factors affecting the ability to evaluate and control infectious diseases and food contamination. The purpose of this paper is to describe and characterize the nationwide pork supply chain networks (PSCNs) in China and to demonstrate the potential of using social network analysis (SNA) methods for accessing outbreaks of diseases and contaminations.

A large-scale PSCN with 17,582 nodes and 49,554 edges is constructed, using the pork trade data collected by the National Important Products Traceability System (NIPTS) in China. A network analysis is applied to investigate the static and dynamic characteristics of the annual network and monthly networks. Then, the metric maximum spreading capacity (MSC) is proposed to quantify the spreading capacity of farms and estimate the potential maximum epidemic size. The structure of the network with the spatio-temporal pattern of the African swine fever (ASF) outbreak in China in 2018 was also analysed.

The results indicate that the out-degree distribution of farms approximately followed a power law. The pork supply market in China was active during April to July and December to January. The MSC is capable of estimating the potential maximum epidemic size of an outbreak, and the spreading of ASF was positively correlated with the effective distance from the origin city infected by ASF, rather than the geographical distance.

Empirical research on PSCNs in China is scarce due to the lack of comprehensive supply chain data. This study fills this gap by systematically examining the nationwide PSCN of China with large-scale reliable empirical data. The usage of MSC and effective distance can inform the implementation of risk-based control programmes for diseases and contaminations on PSCNs.

Measuring the performance of public participation is conducive to improving participation systems. However, such measurement, particularly in urban regeneration projects, is difficult because of the complex indicators and multiple stakeholders involved. The purpose of this paper is to measure the public participation level in urban regeneration projects in China.

This study adopts a perception difference-based method to measure the public participation level in urban regeneration projects in China. Specifically, an indicator system consisting of 12 indicators from three categories was first purposed. A perception difference-based method that integrates ANOVA test and Tukey test were then developed. The method was validated using five represented projects, and the results are interpreted based on a proposed measurement matrix.

Regardless of the type of indicator, the perception of the government aligns with the perception of private sector professions, however, deviates from the perception of citizens. By taking the mean score and the significance level among stakeholders of perception as two dimensions, different patterns of issues in the current participation practice in urban regeneration are manifested.

Theoretically, the proposed indicator system and perception difference-based method combined to provide a holistic view of public participation, which is verified to provide a better measurement. Practically, the authors’ methodology helps in revealing issues in current participation practice and further leading to designing coping strategies. Nonetheless, the proposed method requires further validation in participation practices in China and other countries.

By considering the perception mean and the significance level as two dimensions, a public participation measurement matrix is proposed. The performance in different indicators are classified into four stages accordingly, namely idling, starting, running-in and accelerating.

This study aimed to investigate the underlying boundary conditions under which boundary-spanning behaviour has a positive or negative effect on innovative behaviour.

A multi-wave and multi-source research design was adopted to collect data. Data were analysed using the multilevel structural equation modelling and latent moderated structural equation approach.

The results showed that boundary-spanning behaviour was significantly and negatively associated with employees' innovative behaviour via ego depletion when employees' intrinsic motivation or organisational support was low. Additionally, boundary-spanning behaviour was significantly and positively associated with employees' innovative behaviour via ego depletion when employees' intrinsic motivation or organisational support was high.

This research suggests that the consequences of boundary-spanning behaviour are conditional, explaining the contrasting conclusions in this regard.

The governing equations for dynamic transient analysis of a fluid‐saturated two‐phase porous medium model based on the mixture theory are presented. A penalty finite element formulation is derived with the general Galerkin procedure of the finite element method (FEM), and the obtained dynamic system equation can be solved with implicit or explicit time integration method, which is discussed in this paper. Using this method, a porous medium column under impulsive loading is analyzed and the results reveal the phenomena of one‐dimensional wave propagation, which are consistent with analytical solutions. Furthermore, two numerical examples of two‐dimensional problems demonstrate the existence of two body waves, i.e. longitudinal (P‐type) and transverse (S‐type) waves in porous media, and the Rayleigh wave in the vicinity of the surface of porous media.

Ultrahigh-speed projectile running in water with the velocity close to the speed of sound usually causes large supercavity. The computation of such transonic cavitating flows is usually difficult, thus high-speed model reflecting the compressibility of both the liquid and the vapor phases should be introduced to model such flow. The purpose of this paper is to achieve a model within an in-house developed solver to simulate the ultrahigh-speed subsonic supercavitating flows.

An improved TAIT equation adjusted by local temperature is adopted as the equation of state (EOS) for the liquid phase, and the Peng-Robinson EOS is used for the vapor phase. An all-speed variable coupling algorithm is used to unify the computations and regulate the convergence at arbitrary Mach number. The ultrahigh-speed (Ma=0.7) supercavitating flows around circular disk are investigated in contrast with the case of low subsonic (Ma=0.007) flow.

The characteristic physical variables are reasonably predicted, and the cavity profiles are compared to be close to the experimental empirical formula. An important conclusion in the compressible cavitating flow theory is verified by the numerical result that, at any specific cavitation number the cavity’s size and the drag coefficient both increase along with the rise of Mach number. On the contrary, it is found as well that the cavity’s slenderness ratio decreases when Mach number goes up. It indicates that the compressibility has different influences on the length and the radius of the supercavity.

A high-speed model reflecting the compressibility of both the liquid and the vapor phases was suggested to model the ultrahigh-speed supercavitating flows around underwater projectiles.

The purpose of this paper is to study the effect of surface salt spray duration on the fretting wear and electrochemical corrosion behaviors of Inconel 690 alloy.

A high-temperature steam generator was applied to salt spray test samples, a fretting wear rig was used to realize the damage behavior tests, an electrochemical workstation was applied to analysis the changes of each sample’s corrosion dynamic response before and after fretting wear.

The thickness of the oxide film that formed on sample surface was increased with the salt spray duration, and somewhat it could act as lubrication during the fretting wear process; however, the corrosive chloride would accelerate the fretting mechanical damage behavior.

In a salt steam spray condition, the fretting tribo-corrosion behaviors of Inconel 690 alloy surface was studied.

Sparrow search algorithm (SSA) is a novel global optimization method, but it is easy to fall into local optimization, which leads to its poor search accuracy and stability. The purpose of this study is to propose an improved SSA algorithm, called levy flight and opposition-based learning (LOSSA), based on LOSSA strategy. The LOSSA shows better search accuracy, faster convergence speed and stronger stability.

To further enhance the optimization performance of the algorithm, The Levy flight operation is introduced into the producers search process of the original SSA to enhance the ability of the algorithm to jump out of the local optimum. The opposition-based learning strategy generates better solutions for SSA, which is beneficial to accelerate the convergence speed of the algorithm. On the one hand, the performance of the LOSSA is evaluated by a set of numerical experiments based on classical benchmark functions. On the other hand, the hyper-parameter optimization problem of the Support Vector Machine (SVM) is also used to test the ability of LOSSA to solve practical problems.

First of all, the effectiveness of the two improved methods is verified by Wilcoxon signed rank test. Second, the statistical results of the numerical experiment show the significant improvement of the LOSSA compared with the original algorithm and other natural heuristic algorithms. Finally, the feasibility and effectiveness of the LOSSA in solving the hyper-parameter optimization problem of machine learning algorithms are demonstrated.

An improved SSA based on LOSSA is proposed in this paper. The experimental results show that the overall performance of the LOSSA is satisfactory. Compared with the SSA and other natural heuristic algorithms, the LOSSA shows better search accuracy, faster convergence speed and stronger stability. Moreover, the LOSSA also showed great optimization performance in the hyper-parameter optimization of the SVM model.

Tourism reconfigures the metropolitan dynamics and the patterns of use of the urban systems. The seasonal nature of tourism produces an impact on the urban hierarchies, since it affects the labor, residential, and recreational markets. As a result, people move to and in the destination and it challenges the supply of sustainable modes of transport such as public transport. This research is set within the context of three demanding challenges that tourist destinations need to face-up: to increase environmental sustainability, to enhance destination competitiveness, and finally to assure quality and comfort of public transport services for the local resident population. Camp de Tarragona region, where Costa Daurada (one of the most important Spanish tourist brands) is located, is analyzed to illustrate how different data sources can aid to confront the aforementioned challenges. Given that seasonality is a dynamic phenomenon, suitable data should be flexible in terms of its time framework. To this end data from smart travel cards provided by the consortium that manages the public transport system in the region has been analyzed. Data unveiled the impact of seasonality on the evolution of demand throughout the year, the type of transport tickets used, or changes occurred in the geographical distribution of the mobility Alternative data sources such as surveys and passive mobile positioning data have also been examined, and their pros and cons have been addressed.

The purpose of this paper is to present a method that addresses the data sparsity problem in points of interest (POI) recommendation by introducing spatiotemporal context features based on location-based social network (LBSN) data. The objective is to improve the accuracy and effectiveness of POI recommendations by considering both spatial and temporal aspects.

To achieve this, the paper introduces a model that integrates the spatiotemporal context of POI records and spatiotemporal transition learning. The model uses graph convolutional embedding to embed spatiotemporal context information into feature vectors. Additionally, a recurrent neural network is used to represent the transitions of spatiotemporal context, effectively capturing the user’s spatiotemporal context and its changing trends. The proposed method combines long-term user preferences modeling with spatiotemporal context modeling to achieve POI recommendations based on a joint representation and transition of spatiotemporal context.

Experimental results demonstrate that the proposed method outperforms existing methods. By incorporating spatiotemporal context features, the approach addresses the issue of incomplete modeling of spatiotemporal context features in POI recommendations. This leads to improved recommendation accuracy and alleviation of the data sparsity problem.

The research has practical implications for enhancing the recommendation systems used in various location-based applications. By incorporating spatiotemporal context, the proposed method can provide more relevant and personalized recommendations, improving the user experience and satisfaction.

The paper’s contribution lies in the incorporation of spatiotemporal context features into POI records, considering the joint representation and transition of spatiotemporal context. This novel approach fills the gap left by existing methods that typically separate spatial and temporal modeling. The research provides valuable insights into improving the effectiveness of POI recommendation systems by leveraging spatiotemporal information.