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This study aims to using Fourier transform infrared (FTIR) spectroscopy, Raman spectroscopy and scanning electron microscope/energy dispersive Xray spectrometer to identify different automotive coatings for forensic purpose.

Two four-layered samples in a hit-and-run case were compared layer by layer with three different methods. FTIR spectroscopy was used to primarily identify the organic and inorganic compositions. Raman spectrum and scanning electron microscope/energy dispersive Xray spectrometer (SEM-EDS) were further used to complement the FTIR results.

Two weak and tiny peaks in one layer found between two samples by FTIR, Raman microscope and SEM-EDS verified the result of differences. The study used the three instruments in combination and found it’s effective in sensing coatings, especially in the inorganic additives.

Using these three instruments in combination is more accurate than individually in multilayered coating analysis for forensic purpose.

The three different instruments all present unique information on the composition, and provided similar and mutually verifiable results on the two samples.

With this method, scientists could identify and discriminate important coating evidences with tiny but characteristic differences.

Despite a large amount of BIM data at the handover stage, it is still difficult to identify and effectively isolate valuable construction supply chain (CSC) data that need to be reliably handed over for operation. Moreover, the role of reconciling disparate data is usually played by one party. The integration of blockchain and BIM is a plausible framework for building a reliable digital asset lifecycle. This paper proposes a BIM single source of truth (BIMSSoT) data model using blockchain for ensuring a reliable CSC data delivery.

This paper utilises a blended methodology, the foundation of which is ingrained in business and management research with elements of information and communication technology (ICT) research wherever required. Knowledge elicitation case studies utilising novel interventions such as a data flow diagram (DFD), taxonomy and entity-relationship diagram (ERD) were used in this paper to develop the BIMSSoT data model. The model was validated using an expert forum, and its technological feasibility was established by developing a proof of concept.

The practical contribution of this research leads to the progression of BIM towards digital engineering to go beyond object-based 3D modelling by building structured and reliable datasets, transitioning from project-centric records to a digital ecosystem of linked databases by utilizing blockchain's potential for ensuring trusted data.

To the best of the author's knowledge, prior to this paper, no research had investigated a detailed data model development leveraging blockchain and BIM to integrate an immutable and complete record of CSC data as another dimension of BIM for operations.

Current methods for flow field reconstruction mainly rely on data-driven algorithms which require an immense amount of experimental or field-measured data. Physics-informed neural network (PINN), which was proposed to encode physical laws into neural networks, is a less data-demanding approach for flow field reconstruction. However, when the fluid physics is complex, it is tricky to obtain accurate solutions under the PINN framework. This study aims to propose a physics-based data-driven approach for time-averaged flow field reconstruction which can overcome the hurdles of the above methods.

A multifidelity strategy leveraging PINN and a nonlinear information fusion (NIF) algorithm is proposed. Plentiful low-fidelity data are generated from the predictions of a PINN which is constructed purely using Reynold-averaged Navier–Stokes equations, while sparse high-fidelity data are obtained by field or experimental measurements. The NIF algorithm is performed to elicit a multifidelity model, which blends the nonlinear cross-correlation information between low- and high-fidelity data.

Two experimental cases are used to verify the capability and efficacy of the proposed strategy through comparison with other widely used strategies. It is revealed that the missing flow information within the whole computational domain can be favorably recovered by the proposed multifidelity strategy with use of sparse measurement/experimental data. The elicited multifidelity model inherits the underlying physics inherent in low-fidelity PINN predictions and rectifies the low-fidelity predictions over the whole computational domain. The proposed strategy is much superior to other contrastive strategies in terms of the accuracy of reconstruction.

In this study, a physics-informed data-driven strategy for time-averaged flow field reconstruction is proposed which extends the applicability of the PINN framework. In addition, embedding physical laws when training the multifidelity model leads to less data demand for model development compared to purely data-driven methods for flow field reconstruction.

This paper attempts to explain the phenomenon that Macau has a parliament (Legislative Assembly) and mass suffrage but no political parties.

This paper reviews the development process of “parliament – mass suffrage – political party” in Hong Kong and Macau and tries to explain why Macau does not have a party using comparative research methods.

The political party development of Hong Kong and Macau was influenced by both the (former) colonial power and China, and whether there were political parties in these two regions was the result of the game between China and the (former) colonial power. China hoped to limit the development of party politics in the two regions. Since Britain felt reluctant to cooperate with China, political parties in Hong Kong developed. At the same time, Portugal chose to defer to China, which led Macau not to have a political party.

Existing studies have yet to explain why there are no political parties in Macau, and this paper is the first attempt to do so.

This study aims to propose a series of numerical and surrogate models to investigate the aerodynamic pressure inside cracks in high-speed railway tunnel linings and to predict the stress intensity factors (SIFs) at the crack tip.

A computational fluid dynamics (CFD) model is used to calculate the aerodynamic pressure exerted on two cracked surfaces. The simulation uses the viscous unsteady κ-ε turbulence model. Using this CFD model, the spatial and temporal distribution of aerodynamic pressure inside longitudinal, oblique and circumferential cracks are analyzed. The mechanism behind the pressure variation in tunnel lining cracks is revealed by the air density field. Furthermore, a response surface model (RSM) is proposed to predict the maximum SIF at the crack tip of circumferential cracks and analyze its influential parameters.

The initial compression wave amplifies and oscillates in cracks in tunnel linings, resulting from an increase in air density at the crack front. The maximum pressure in the circumferential crack is 2.27 and 1.76 times higher than that in the longitudinal and oblique cracks, respectively. The RSM accurately predicts the SIF at the crack tip of circumferential cracks. The SIF at the crack tip is most affected by variations in train velocities, followed by the depth and length of the cracks.

The mechanism behind the variation of aerodynamic pressure in tunnel lining cracks is revealed. In addition, a reliable surrogate model is proposed to predict the mechanical response of the crack tip under aerodynamic pressures.

The primary objective of this study is characterizing the anisotropic mechanical properties of corrugated cardboard and simultaneously simulating its drop cushioning dynamic effects under various drop conditions.

Static and dynamic tests were conducted on corrugated cardboard to obtain adequate experimental data in different directions. An effective anisotropic constitutive model is introduced by developing the honeycomb materials model in ANSYS LS-Dyna, and an effective approach is established toward effectively determining the material parameters from the test data obtained. The model is validated by comparing simulation results with experimental data from five drop conditions, including bottom drop, front drop, side drop, 30° side drop and edge drop. Additionally, simulations are conducted to study the cushioning performance of the packaging by dropping the corrugated cardboard at different heights.

The study establishes a fast and effective approach to simulating the drop cushioning performance of corrugated cardboard under various drop conditions, which demonstrates good agreement with experimental data.

This approach is of value for packaging protection and provides guidance for stacking of packaging during transportation.

Broiler meat is the most commonly used meat product worldwide. Although China is regarded as one of the three largest broiler producers, the per capita chicken consumption remains low. Consumers' cognitive bias and the information acquisition channels are believed to be the main factors contributing to this. This paper aims to discuss the aforementioned issue.

To explore the phenomenon empirically, the current study uses the survey data of 1,056 consumers from China and analyses them using ordered logistic regression.

The results revealed that consumers' cognitive bias significantly affects their behaviour toward broiler products, and the order of influence is cognitive bias regarding industry cognitive > product nutrition and taste > food safety. The study further revealed that the more diverse the information acquisition channels, the more likely they are to promote consumer behaviour toward broiler chickens. The order of influence of the channels was self-organising > new media > traditional media.

Overall, the findings suggest that the government and enterprises should strengthen and upgrade information channels to boost both the broiler industry and consumer consumption behaviour regarding poultry products.

Rather than the usual focus on the impact of consumer cognition on consumer behaviour, this study examines the impact of cognitive bias on consumer behaviour. Further, centring on broiler products with high protein, low fat and feed-to-meat ratios, this study explores the reasons the per capita consumption of broiler products in China is far lower than the national average.

In modern urban governance, rescue materials storage points (RMSP) are a vital role to be considered in responding to public emergencies and improving a city's emergency management. This study analyzes the siting of community-centered relief supply facilities.

Combining grey relational analysis, complex network and relative entropy, a new multi criteria method is proposed. It pays more attention to the needs of the community, taking into account the use of community hospitals, fire centers and neighborhood offices to establish small RMSP.

The research results firstly found suitable areas for RMSP site selection, including Hanyang, Qiaokou, Jiangan and Wuchang. The top 10 nodes in each region are found as the location of emergency facilities, and the network parameters are higher than ordinary nodes in traffic networks. The proposed method was applied in Wuhan, China and the method was verified by us-ing a complex network model combined with multi-criteria decision-making for emergency facility location.

This method solves the problem of how to choose the optimal solution and reduces the difficulty for decision makers. This method will help emergency managers to locate and plan RMSP more simply, especially in improving emergency siting modeling techniques and additionally in providing a reference for future research.

The method proposed in this study is beneficial to improve the decision-making ability of urban emergency departments. Using complex networks and comprehensive evaluation techniques, RMSP is incorporated into the urban community emergency network as a critical rescue force. More importantly, the findings highlight a new direction for further research on urban emergency facilities site selection based on a combination of sound theoretical basis as well as empirical evidence gained from real life case-based analysis.

1. Material reserve points are incorporated into the emergency supply network to maintain the advantage of quantity.

2. Build emergency site selection facilities centered on urban communities.

3. Use a complex network model to select the location of emergency supplies storage sites.

Material reserve points are incorporated into the emergency supply network to maintain the advantage of quantity.

Build emergency site selection facilities centered on urban communities.

Use a complex network model to select the location of emergency supplies storage sites.

This study aims to explore the relationship between folk religious place-making and the development of urban public spaces and summarize its influence on community network construction and daily behavior to discover the authentic practices and role of folk faith culture in social space.

Taking Macau's Shi Gandang Temple and its belief culture as an example, on-site research, historical evidence and interviews were used to elaborate and analyze the processes of place-making, social functions, management mechanisms and folk culture to establish a new perception of folk religious place-making in contemporary urban spaces.

The article argues that the culture of folk beliefs profoundly influences urban spaces and the social management system of Macau and has a positive significance in building the local community and geopolitical relations. In addition, it suggests that the participation of folk religious places in local practices is important as key nodes and emotional hubs of local networks, reconciling conflicts between communities of different backgrounds and driving urban spaces toward diversity while forming a positive interaction and friendly cooperation between regional development and self-contained management mechanisms, governance models and cultural orientations.

This study takes an architectural and anthropological perspective of the impact of faith on urban spaces and local governance, using the Shi Gandang Temple in Macau as an example, to complement related studies.

This study aims to propose a more precise method for robust design optimization of mechanical structures with black-box problems, while also considering the efficiency of uncertainty analysis.

The method first introduces a dual adaptive chaotic flower pollination algorithm (DACFPA) to overcome the shortcomings of the original flower pollination algorithm (FPA), such as its susceptibility to poor accuracy and convergence efficiency when dealing with complex optimization problems. Furthermore, a DACFPA-Kriging model is developed by optimizing the relevant parameter of Kriging model via DACFPA. Finally, the dual Kriging model is constructed to improve the efficiency of uncertainty analysis, and a robust design optimization method based on DACFPA-Dual-Kriging is proposed.

The DACFPA outperforms the FPA, particle swarm optimization and gray wolf optimization algorithms in terms of solution accuracy, convergence speed and capacity to avoid local optimal solutions. Additionally, the DACFPA-Kriging model exhibits superior prediction accuracy and robustness contrasted with the original Kriging and FPA-Kriging. The proposed method for robust design optimization based on DACFPA-Dual-Kriging is applied to the motor hanger of the electric multiple units as an engineering case study, and the results confirm a significant reduction in the fluctuation of the maximum equivalent stress.

This study represents the initial attempt to enhance the prediction accuracy of the Kriging model using the improved FPA and to combine the dual Kriging model for uncertainty analysis, providing an idea for the robust optimization design of mechanical structure with black-box problem.