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The purpose of this paper is to develop an efficient non-iterative model combining advanced numerical methods for solving buoyancy-driven flow problems.

The solution strategy is based on two independent numerical procedures. The Navier-Stokes equation is solved using the non-conforming Crouzeix-Raviart (CR) finite element method with an upstream approach for the non-linear convective term. The advection-diffusion heat equation is solved using a combination of Discontinuous Galerkin (DG) and Multi-Point Flux Approximation (MPFA) methods. To reduce the computational time due to the coupling, the authors use a non-iterative time stepping scheme where the time step length is controlled by the temporal truncation error.

Advanced numerical methods have been successfully combined to solve buoyancy-driven flow problems on unstructured triangular meshes. The accuracy of the results has been verified using three test problems: first, a synthetic problem for which the authors developed a semi-analytical solution; second, natural convection of air in a square cavity with different Rayleigh numbers (103-108); and third, a transient natural convection problem of low Prandtl fluid with horizontal temperature gradient in a rectangular cavity.

The proposed model is the first to combine advanced numerical methods (CR, DG, MPFA) for buoyancy-driven flow problems. It is also the first to use a non-iterative time stepping scheme based on local truncation error control for such coupled problems. The developed semi analytical solution based on Fourier series is also novel.

This paper aims to clarify the effect of metal ions added in the γ-glycidoxypropyltrimethoxysilane (γ-GPS) solutions on the anti-corrosion properties of the γ-GPS coatings on cold-rolled iron (CRI).

The transformations of functional groups involved in reactions during the coating process were characterized by Fourier transformed infrared spectroscopy (FTIR), and the thickness of the γ-GPS coatings on the CRI substrates was measured using high-powered microscopy. The anti-corrosion properties of γ-GPS-treated samples were evaluated by neutral salt spray tests, polarization curves and electrochemical impedance spectroscopy measurements.

The results show that Zn2+ and Mg2+ in the γ-GPS solutions promote the formation of Si-O-Si and Si-O-Fe bonds and improve the anti-corrosion properties of the γ-GPS coatings on CRI. However, Al3+ and Na+ in the γ-GPS solutions do not play this role.

Although there have been previous research studies on the γ-GPS coatings on CRI, this paper is the first to study the effect of metal ions added in the silane solutions on the anti-corrosion properties of the γ-GPS coatings, and it has been confirmed that the anti-corrosion properties changed when Zn2+ (or Mg2+) is present.

Based on the textual-analyzed data covering 2148 IPO firms in China’s stock market during the 2007–2018 period, the authors’ purpose is to examine the influence of anti-takeover provision (ATP) adoption on initial public offerings (IPO) underpricing and identify the reducing effect of the former.

The authors examine the sample consisting of Chinese A-share listed IPO firms between 2007 and 2018 from China Stock Market Accounting Research and Chinese Research Data Services, with ATP data collected from the IPO firm chapters. Specifically, the authors use text analysis to identify whether there are ATPs in the IPO firm chapters, as well as the number of ATPs. H1: IPO underpricing is less severe for firms adopting ATPs. H2: The effect of ATP adoption on IPO underpricing is more salient for firms in worse information environments.

The authors examine the influence of ATP adoption on IPO underpricing and identify the reducing effect of the former. This effect can be explained by the fact that adopting ATPs in IPO firm chapters can reduce information asymmetry to a large extent by helping external investors obtain more private information, which alleviates IPO underpricing. The authors also find that the reducing effect is more significant in the worsened information environment. Furthermore, the authors explore the influence of adopting ATPs on other IPO characteristics and find positive effects on IPO over-subscription, funds raised and trading activity and negative effects on listing fees.

This study mainly contributes to the literature from the following two aspects. First, the study enriches the literature about the influencing factors of IPO underpricing. Second, the study also enriches the literature about the economic consequences of ATP adoption. This study also has important policy implications. With the coming of the era of decentralized ownership in China’s capital market, ATP adoption has become more important and attracted more attention. Also, investors focus more on pricing efficiency. The findings in this paper provide a more comprehensive understanding of the relationship between ATP adoption and IPO underpricing.

As of December 2022, there were 119,000 gas stations, 10,800 gas stations and 4,488,000 charging piles nationwide, while the number of vehicles reached 312 million, including 11.49 million new energy vehicles. The imbalance between transportation energy supply and energy replenishment demand leads to crowded queues of vehicles at some stations and idle resources in others. How to reduce the phenomenon of large queues and improve the utilization rate of idle resources is the key to alleviating the imbalance between supply and demand.

Therefore, from the perspective of spatio-temporal equilibrium of urban transportation energy supply stations, multi-energy supply station cooperation is established in view of the phenomenon of large spatio-temporal differences among different energy supply stations, and corresponding inducing strategies are adopted for energy supplement vehicles in the road network, so that part of queued users go to energy supply stations with fewer vehicles, so as to balance the supply and demand of transportation energy in the region. On this basis, the income distribution of urban transportation energy supply station is discussed.

The total revenue after the cooperation was 13,095, an increase of 22.9%. Secondly, in terms of distribution rationality, three impact factors are selected and Shapley correction value is used to distribute the total income. Compared with independent operation, both sites have a certain degree of increase.

Traffic congestion at energy supply stations is closely related to the number, location and number of vehicles at energy supply stations. Therefore, using a cooperative approach of energy trading cannot solve the queuing problem. In addition, there are a few research results on the equalization of energy supply station services considering time-of-use pricing. However, these studies do not consider the vehicular grooming at congested stations. As far as the authors know, there are no relevant research results in the research on the service equilibrium of energy supply stations based on cooperative games.

The purpose of this paper is to study the wear of railway brake disc/pad in low-temperature environment and to explore the damage form of brake disc/pad materials and the law of temperature rise in braking process and its influence on friction pair material damage.

The influence of ambient temperature on tribological properties of brake materials was studied by using low-temperature environment simulation device and MM-1000 high-speed brake testing machine. The law of temperature rise in the braking process was simulated by temperature field module of COMSOL.

The damage of disc sample increases with the decrease of ambient temperature, and the main damage form is furrow. With the decrease of ambient temperature, pitting corrosion, wear, spalling and cracks appear successively. The maximum temperature of brake disc decreases linearly with the decrease of ambient temperature. However, when the ambient temperature is 0 in the experiment, the surface temperature of the disc will increase abnormally because of the increase of abrasive particles caused by the toughening and brittleness transformation of the material.

In this paper, through the study of train braking in low-temperature environment, the damage mechanism and law of train braking pair in low-temperature environment are found, which provide some basis for the development of high-speed railway in low-temperature environment.

The purpose of this paper is to use the most popular deep learning algorithm to complete the vehicle detection in the urban area of MiniSAR image, and provide reliable means for ground monitoring.

An accurate detector called the rotation region-based convolution neural networks (CNN) with multilayer fusion and multidimensional attention (M2R-Net) is proposed in this paper. Specifically, M2R-Net adopts the multilayer feature fusion strategy to extract feature maps with more extensive information. Next, the authors implement the multidimensional attention network to highlight target areas. Furthermore, a novel balanced sampling strategy for hard and easy positive-negative samples and a global balanced loss function are applied to deal with spatial imbalance and objective imbalance. Finally, rotation anchors are used to predict and calibrate the minimum circumscribed rectangle of vehicles.

By analyzing many groups of experiments, the validity and universality of the proposed model are verified. More importantly, comparisons with SSD, LRTDet, RFCN, DFPN, CMF-RCNN, R3Det, SCRDet demonstrate that M2R-Net has state-of-the-art detection performance.

The progress in the field of MiniSAR application has been slow due to strong speckle noise, phase error, complex environments and a low signal-to-noise ratio. In addition, four kinds of imbalances, i.e. spatial imbalance, scale imbalance, class imbalance and objective imbalance, in object detection based on the CNN greatly inhibit the optimization of detection performance.

This research can not only enrich the means of daily traffic monitoring but also be used for enemy intelligence reconnaissance in wartime.

For a better understanding of the preferences and differences of young consumers in emerging wine markets, this study aims to propose a clustering method to segment the super-new generation wine consumers based on their sensitivity to wine brand, origin and price and then conduct user profiles for segmented consumer groups from the perspectives of demographic attributes, eating habits and wine sensory attribute preferences.

We first proposed a consumer clustering perspective based on their sensitivity to wine brand, origin and price and then conducted an adaptive density peak and label propagation layer-by-layer (ADPLP) clustering algorithm to segment consumers, which improved the issues of wrong centers' selection and inaccurate classification of remaining sample points for traditional DPC (DPeak clustering algorithm). Then, we built a consumer profile system from the perspectives of demographic attributes, eating habits and wine sensory attribute preferences for segmented consumer groups.

In this study, 10 typical public datasets and 6 basic test algorithms are used to evaluate the proposed method, and the results showed that the ADPLP algorithm was optimal or suboptimal on 10 datasets with accuracy above 0.78. The average improvement in accuracy over the base DPC algorithm is 0.184. As an outcome of the wine consumer profiles, sensitive consumers prefer wines with medium prices of 100–400 CNY and more personalized brands and origins, while casual consumers are fond of popular brands, popular origins and low prices within 50 CNY. The wine sensory attributes preferred by super-new generation consumers are red, semi-dry, semi-sweet, still, fresh tasting, fruity, floral and low acid.

Young Chinese consumers are the main driver of wine consumption in the future. This paper provides a tool for decision-makers and marketers to identify the preferences of young consumers quickly which is meaningful and helpful for wine marketing.

In this study, the ADPLP algorithm was introduced for the first time. Subsequently, the user profile label system was constructed for segmented consumers to highlight their characteristics and demand partiality from three aspects: demographic characteristics, consumers' eating habits and consumers' preferences for wine attributes. Moreover, the ADPLP algorithm can be considered for user profiles on other alcoholic products.

This paper aims to investigate the profile of the wind speed of a Cameroonian city for the very first time, as there is a growing trend for new wind energy installations in the West region of Cameroon. Two well-known artificial neural networks, namely, multi-layer perceptron (MLP) and nonlinear autoregressive network with exogenous inputs (NARX), were used to model the wind speed profile of the city of Bapouh in the West-region of Cameroon.

In this work, the profile of the wind speed of a Cameroonian city was investigated for the very first time since there is a growing trend for new wind energy installations in the West region of Cameroon. Two well-known artificial neural networks namely multi-layer perceptron (MLP) and nonlinear autoregressive network with exogenous inputs (NARX) were used to model the wind speed profile of the city of Bapouh in the West-region of Cameroon. The meteorological data were collected every 10 min, at a height of 50 m from the NASA website over a period of two months from December 1, 2016 to January 31, 2017. The performance of the model was evaluated using some well-known statistical tools, such as root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). The input variables of the model were the mean wind speed, wind direction, maximum pressure, maximum temperature, time and relative humidity. The maximum wind speed was used as the output of the network. For optimal prediction, the influence of meteorological variables was investigated. The hyperbolic tangent sigmoid (Tansig) and linear (Purelin) were used as activation functions, and it was shown that the combination of wind direction, maximum pressure, maximum relative humidity and time as input variables is the best combination.

Maximum pressure, maximum relative humidity and time as input variables is the best combination. The correlation between MLP and NARX was computed. It was found that the MLP has the highest correlation when compared to NARX.

Two well-known artificial neural networks namely multi-layer perceptron (MLP) and nonlinear autoregressive network with exogenous inputs (NARX) were used to model the wind speed profile.

Uncontrolled and unsustainable urban development has a significant impact on the overall quality of the environment. Contemporary spatial management does not take into account the non-economic conditions of ecosystem functions and their biophysical limits, which results in a deterioration of the environment. Furthermore, urban development continues all over the world, which is associated with a projected population increase, particularly in cities. Therefore, there is need to include analyses in the field of environmental carrying capacity (ECC) for more sustainable spatial management and development. The ECC concept is a tool for sustainable development of human settlements. It could be defined as threshold level of anthropopressure, which the environment is able to balance and withstand without serious degradation and irreversible changes. The use of ECC assessment will help ensure the needs of the city’s residents, which are conditioned by the environment. The paper aims to discuss this issue.

This review study presents an overview of the ECC concept and its potential for use in spatial management at local level, especially for cities. The content analysis introduces definitions of spatial management and others main actions of spatial economy as well as the definitions of ECC. The overview of existing ECC models is a part of presented review.

The paper shows the ECC approach from global to local level on Poland as an example country.

The literature review identified that the idea of ECC could be a useful concept and tool for sustainable spatial development and management. This concept allows for verification of the resources and environmental state of given territory unit. Therefore, the assessment of ECC allows to verify if development of given area, for instance country, region or even city, is conducted within the framework of sustainable principles. Despite this, the assessment of ECC is not taken into account when future spatial management is defined. Thus, this circumstance could be a reason for this research area development.

The classical advection-dispersion equation (ADE) model cannot accurately depict the gas transport process in natural geological formations. This paper aims to study the behavior of CO₂ transport in fractal porous media by using an effective Hausdorff fractal derivative advection-dispersion equation (HFDADE) model.

Anomalous dispersion behaviors of CO₂ transport are effectively characterized by the investigation of time and space Hausdorff derivatives on non-Euclidean fractal metrics. The numerical simulation has been performed with different Hausdorff fractal dimensions to reveal characteristics of the developed fractal ADE in fractal porous media. Numerical experiments focus on the influence of the time and space fractal dimensions on flow velocity and dispersion coefficient.

The physical mechanisms of parameters in the Hausdorff fractal derivative model are analyzed clearly. Numerical results demonstrate that the proposed model can well fit the history of gas production data and it can be a powerful technique for depicting the early arrival and long-tailed phenomenon by incorporating a fractal dimension.

To the best of the authors’ knowledge, first time these results are presented.