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In order to solve the decision-making problem that the attributive weight and attributive value are both interval grey numbers, this paper tries to construct a multi-attribute grey decision-making model based on generalized greyness of interval grey number.

Firstly, according to the nature of the generalized gresness of interval grey number, the generalized weighted greyness distance between interval grey numbers is given, and the transformation relationship between greyness distance and real number distance is analyzed. Then according to the objective function that the square sum of generalized weighted greyness distances from the decision scheme to the best scheme and the worst scheme is the minimum, a multi-attribute grey decision-making model is constructed, and the simplified form of the model is given. Finally, the grey decision-making model proposed in this paper is applied to the evaluation of technological innovation capability of 6 provinces in China to verify the effectiveness of the model.

The results show that the grey decision-making model proposed in this paper has a strict mathematical foundation, clear physical meaning, simple calculation and easy programming. The application example shows that the grey decision model in this paper is feasible and effective. The research results not only enrich the grey system theory, but also provide a new way for the decision-making problem that the attributive weights and attributive values are interval grey numbers.

The decision-making model proposed in this paper does not need to seek the optimal solution of the attributive weight and the attributive value, and can save the decision-making labor and capital investment. The model in this paper is also suitable for the decision-making problem that deals with the coexistence of interval grey numbers and real numbers.

The paper succeeds in realizing the multi-attribute grey decision-making model based on generalized gresness and its simplified forms, which provide a new method for grey decision analysis.

Medicine distribution logistics pattern in pharmaceutical supply chains is a hot topic, which aims to predict applicable and efficient medicine distribution patterns so that the medicine can be distributed effectively. This research aims to propose a new method, named density-distance method, that works based on Kth proximity using patient features (including age, gender, education, inherent diseases, systemic diseases and disorders); geographical features (city, state, population, density, land area) and supply chain features (destination and transportation system).

The proposed method of this research consists of two main phases where in the first phase, quantitative data analytics will be carried out to find out the significant factors and their impacts on medicine distribution. Then, in the next phase, a new Kth-proximity density-distance-based method is proposed to determine the best locations for the wholesalers while designing a supply chain.

The findings show that the proposed method can effectively design a supply chain network using realistic features. In addition, it is found that while the distance-density aggregate index is applied, the wholesalers' locations will be different compared to classic supply chain designs. The results show that age, public hygiene level and density are the most influential during designing new supply chains.

The outcomes of this research can be used in the medicine supply chains to predict appropriate medicine distribution logistics patterns.

In this research, the machine learning method based on the nearest neighbor has been used for the first time in the design of the supply chain network.

This study aims to examine the effect of proximity and spatial dependence on the house price index for the nascent market Dar es Salaam, Tanzania. Despite the ongoing housing market transactions, there is no single house price index that takes into account proximity and spatial dependence. The proximity considerations in question are proximal to arterial roads, public hospitals, an airport and food markets. Previous studies on sub-Saharan Africa have focused on the ordinary least squares (OLS)-based hedonic model for the index and ignored spatial and proximity considerations.

Using the OLS and spatial econometric approach, the paper tests for the significance of the two effects – proximity and spatial dependence in the hedonic price model with year dummy variables from 2010 to 2019. The paper then compares the three indices in the following configurations: without the two effects, with proximity factors only, and with both effects, i.e. proximity and spatial dependence.

The inclusion of proximity factors and spatial dependence – spatial autocorrelation – seems to improve the hedonic price model but does not significantly improve the house price index. However, further research should be called for on account of the nascent nature of the market.

The paper brings new knowledge by demonstrating that it may not be necessary to take into account proximity factors and spatial dependence for the Dar es Salaam house price index.

The purpose is twofold. First, this study aims to establish that black box tree-based machine learning (ML) models have better predictive performance than a standard linear regression (LR) hedonic model for rent prediction. Second, it shows the added value of analyzing tree-based ML models with interpretable machine learning (IML) techniques.

Data on Belgian residential rental properties were collected. Tree-based ML models, random forest regression and eXtreme gradient boosting regression were applied to derive rent prediction models to compare predictive performance with a LR model. Interpretations of the tree-based models regarding important factors in predicting rent were made using SHapley Additive exPlanations (SHAP) feature importance (FI) plots and SHAP summary plots.

Results indicate that tree-based models perform better than a LR model for Belgian residential rent prediction. The SHAP FI plots agree that asking price, cadastral income, surface livable, number of bedrooms, number of bathrooms and variables measuring the proximity to points of interest are dominant predictors. The direction of relationships between rent and its factors is determined with SHAP summary plots. In addition to linear relationships, it emerges that nonlinear relationships exist.

Rent prediction using ML is relatively less studied than house price prediction. In addition, studying prediction models using IML techniques is relatively new in real estate economics. Moreover, to the best of the authors’ knowledge, this study is the first to derive insights of driving determinants of predicted rents from SHAP FI and SHAP summary plots.

The rapid growth of emerging market firms (EMFs) has been a topic of interest for the past two decades, especially in China. However, few studies have discussed how and why EMFs can impel the upgrading of their capabilities to quickly win competitive advantages in the global market. In this context, the purpose of this paper is to unravel the implausible upgrading phenomenon from the perspective of technological proximity.

This paper adopts a single case study, specifically that of a leading Chinese e-bike firm, with a special focus on the dynamic nature of the capability upgrading process and underlying mechanisms.

The results show that taking advantage of technological proximity is an important way for EMFs to climb the ladder of capability upgrading. The stage-based process reveals how capability upgrading is achieved through elaborate actions related to technological proximity. Furthermore, this study finds three learning mechanisms behind the technological proximity, which enable firms to successfully upgrade to higher levels of capabilities. In particular, the trigger role played by contextual conditions in guiding firms' capability upgrading is highlighted and characterized.

This study enriches traditional capability upgrading literature from a technological proximity perspective, especially the traditional static upgrading research related to EMFs. The authors also contribute to the conceptualization of technological proximity. However, the research setting is China's e-bike industry; therefore, the study's generalizability to other emerging markets and industries may be limited.

The results show that it is important to recognize the value of the transfer and sharing of technology between proximal industries for local governments. Also, appropriate policies should be developed to break down the technology barriers between these industries. Moreover, rather than catching up with the superior technologies of multinational corporations in advanced countries, focusing on products with high technological proximity in local or regional areas may be more helpful for EMFs' upgrading.

This paper investigates the capability upgrading process and mechanisms in EMFs, particularly with respect to the role played by technological proximity.

This research examines the effects of physical proximity (close vs distant) of retailers’ private label brands (PLBs) relative to national brands (NBs) and brand display orientation (horizontal [brands occupy the same shelf] vs vertical [brands occupy different shelves]) on consumers’ PLB quality perceptions and PLB evaluations.

Two experiments involving real brands in different product categories tested the hypotheses.

A PLB positioned close (vs distant) to a NB is evaluated more favorably and this effect is mediated by increased PLB quality perceptions, but only in a horizontal brand display. In a vertical brand display, a PLB positioned close (vs distant) to a NB is evaluated less favorably and this effect is mediated by decreased PLB quality perceptions.

The findings suggest that to enhance consumers’ PLB quality perceptions and evaluations, PLBs be positioned next to (rather than on separate shelves) and close to (rather than distant from) NBs in the same product category.

Although the literature suggests that the best shelf position for PLBs is close to NBs, there is a lack of empirical research on the effects of relative shelf positioning on consumers’ quality perceptions and subsequent PLB evaluations. This research finds that both physical proximity and brand display orientation play an important role.

The purpose of this paper is to analyse museums and theme parks as a tourist package and how the proximity of airports to the city and public transport influence the development of this tourist package to stimulate tourism demand in cities.

Qualitative and quantitative indicators have been applied in our methodology to measure the most visited European theme parks and museums from 2012 to 2022. Moreover, the localisation of airports has allowed us to address the importance of theme parks and museums in cities and their regional economies.

The results suggest that the location of the city, entertainment complementary activity, airport proximity, intermodal passenger transport, air and train accessibility, tourism demand and supply, and a high concentration of population in cities have a high influence on the development of a tourist package that includes museums and theme parks to stimulate the tourism demand in European urban cities. London and Paris are two of the most visited cities in the world, and these are the most attractive European cities for tourists in terms of efficiency because tourists can optimize much better their space and time to visit the city’s tourist attractions during their holidays. Another important finding is that the public transport service plays an important role in museums and theme parks’ visits and the optimization of space-time for tourists when they are visiting a city and its tourist attractions on holidays, especially subways, trains and buses. Although time-space measures of accessibility in public transport in cities must be improved to optimize the time of the native population and tourists.

This research shows the complementary role of museums and theme parks as an attractive tourist package and an entertainment, cultural and educational activity to improve the quality of tourism supply and redistribute tourist flows in European countries. Moreover, there are limited studies that tackle the theme of parks and museums in a tourism context.

The purpose of this paper is to present an automatic approach for mesh sizing field generation of complicated  computer-aided design (CAD) models.

In this paper, the authors present an automatic approach for mesh sizing field generation. First, a source point extraction algorithm is applied to capture curvature and proximity features of CAD models. Second, according to the distribution of feature source points, an octree background mesh is constructed for storing element size value. Third, mesh size value on each node of background mesh is calculated by interpolating the local feature size of the nearby source points, and then, an initial mesh sizing field is obtained. Finally, a theoretically guaranteed smoothing algorithm is developed to restrict the gradient of the mesh sizing field.

To achieve high performance, the proposed approach has been implemented in multithreaded parallel using OpenMP. Numerical results demonstrate that the proposed approach is remarkably efficient to construct reasonable mesh sizing field for complicated CAD models and applicable for generating geometrically adaptive triangle/tetrahedral meshes. Moreover, since the mesh sizing field is defined on an octree background mesh, high-efficiency query of local size value could be achieved in the following mesh generation procedure.

How to determine a reasonable mesh size for complicated CAD models is often a bottleneck of mesh generation. For the complicated models with thousands or even ten thousands of geometric entities, it is time-consuming to construct an appropriate mesh sizing field for generating high-quality mesh. A parallel algorithm of mesh sizing field generation with low computational complexity is presented in this paper, and its usability and efficiency have been verified.

This study aims to investigate the structural characteristics, spatial evolution paths and internal driving mechanisms of the knowledge transfer (KT) network in China’s patent-intensive industries (PIIs). The authors' goal is to provide valuable insights to inform policy-making that fosters the development of relevant industries. The authors also aim to offer a fresh perspective for future spatiotemporal studies on industrial KT and innovation networks.

In this study, the authors analyze the patent transfer (PT) data of listed companies in China’s information and communication technology (ICT) industry, spanning from 2010 to 2021. The authors use social network analysis and the quadratic assignment procedure (QAP) method to explore the problem of China’s PIIs KT from the perspectives of technical characteristics evolution, network and spatial evolution and internal driving mechanisms.

The results indicate that the knowledge fields involved in the PT of China’s ICT industry primarily focus on digital information transmission technology. From 2010 to 2021, the scale of the ICT industry’s KT network expanded rapidly. However, the polarization of industrial knowledge distribution is becoming more serious. QAP regression analysis shows that economic proximity and geographical proximity do not affect KT activities. The similarity of knowledge application capacity, innovation capacity and technology demand categories in various regions has a certain degree of impact on KT in the ICT industry.

The current research on PIIs mainly focuses on measuring economic contributions and innovation efficiency, but less on KT in PIIs. This study explores KT in PIIs from the perspectives of technological characteristics, network and spatial evolution. The authors propose a theoretical framework to understand the internal driving mechanisms of industrial KT networks.