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Aim to the limitations of grey relational analysis of interval grey number, based on the generalized greyness of interval grey number, this paper tries to construct a grey angle cosine relational degree model from the perspective of proximity and similarity.

Firstly, the algorithms of the generalized greyness of interval grey number and interval grey number vector are given, and its properties are analyzed. Then, based on the grey relational theory, the grey angle cosine relational model is proposed based on the generalized greyness of interval grey number, and the relationship between the classical cosine similarity model and the grey angle cosine relational model is analyzed. Finally, the validity of the model in this paper is illustrated by the calculation examples and an application example of related factor analysis of maize yield.

The results show that the grey angle cosine relational degree model has strict theoretical basis, convenient calculation and is easy to program, which can not only fully utilize the information of interval grey numbers but also overcome the shortcomings of greyness relational degree model. The grey angle cosine relational degree is an extended form of cosine similarity degree of real numbers. The calculation examples and the related factor analysis of maize yield show that the model proposed in this paper is feasible and valid.

The research results not only further enrich the grey system theory and method but also provide a basis for the grey relational analysis of the sequences in which the interval grey numbers coexist with the real numbers.

The paper succeeds in realizing the algorithms of the generalized greyness of interval grey number and interval grey number vector, and the grey angle cosine relational degree, which provide a new method for grey relational analysis.

The Chinese housing market has witnessed rapid growth during the past decade and the significance of housing price forecasting has undoubtedly elevated, becoming an important issue to investors and policymakers. This study aims to examine neural networks (NNs) for office property price index forecasting from 10 major Chinese cities for July 2005–April 2021.

The authors aim at building simple and accurate NNs to contribute to pure technical forecasts of the Chinese office property market. To facilitate the analysis, the authors explore different model settings over algorithms, delays, hidden neurons and data-spitting ratios.

The authors reach a simple NN with three delays and three hidden neurons, which leads to stable performance of about 1.45% average relative root mean square error across the 10 cities for the training, validation and testing phases.

The results could be used on a standalone basis or combined with fundamental forecasts to form perspectives of office property price trends and conduct policy analysis.

Taking advantage of a unique measure of corporate culture obtained from advanced machine learning algorithms, this study aims to explore how corporate culture strength is influenced by board independence, which is one of the most crucial aspects of the board of directors. Because of their independence from the corporation, outside independent directors are more likely to be unbiased. As a result, board independence is commonly used as a proxy for board quality.

In addition to the standard regression analysis, the authors execute a variety of additional tests, i.e. propensity score matching, an instrumental variable analysis, Lewbel’s (2012) heteroscedastic identification and Oster’s (2019) testing for coefficient stability.

The results show that stronger board independence, measured by a higher proportion of independent directors, is significantly associated with corporate culture. In particular, a rise in board independence by one standard deviation results in an improvement in corporate culture by 32.8%.

Conducting empirical research on corporate culture is incredibly difficult due to the inherent difficulties in recognizing and assessing corporate culture, resulting in a lack of empirical research on corporate culture in the literature. The authors fill this important void in the literature. Exploiting a novel measure of corporate culture based on textual analysis, to the best of the authors’ knowledge, this study is the first to link corporate culture to corporate governance with a specific focus on board independence.

This study aims to explore the extent to which Chinese multinational enterprises (MNEs) rely on supranational institution – the Belt and Road Initiative (BRI) – versus host country institutional quality to navigate their foreign location choice.

This study uses a conditional logit regression model using a sample of 1,302 greenfield investments by Chinese MNEs in 54 BRI participating countries during the period 2011–2018.

The results indicate that as a supranational institution, the BRI serves as a substitution mechanism to address the deficiencies in institutional quality in BRI participating countries, thereby attracting Chinese MNEs to invest in those countries. In addition, the BRI’s substitution effect on host country institutional quality is more pronounced for large MNEs, MNEs in the manufacturing industry and MNEs in inland regions.

This study expands the understanding of the BRI as a supranational institution for MNEs from emerging markets and reveals its substitution effect on the host country institutional quality. Furthermore, it highlights that MNEs with diverse characteristics gain varying degrees of benefits from the BRI.

Conventional wisdom suggests that war in the host country makes it unattractive for foreign firms to invest. To see if this is true for US firms on the aggregate, this paper aims to examine the veracity of a “permanent war economy” hypothesis, that foreign direct investment (FDI) may, in fact, increase in the host country not despite, but because of, war, i.e. one that lends credence to the idea that, in the USA, “defense [has] become one of constant preparation for future wars and foreign interventions rather than an exercise in response to one-off threats.”

The authors test the hypotheses using Generalized Method of Moments estimation, with Heckman Selection, on US FDI data from the Bureau of Economic Analysis and war data from the Correlates of War2 Project, the Uppsala Conflict Data Program/International Peace Research Institute data set, the International Crisis Behavior Project and the Center for Systemic Peace Major Episodes of Political Violence data set. The final sample consists of 351 country-year observations in 55 host countries from 1982 to 2006.

The findings indicate that overall US FDI in a host country in a given year decreases if the host country is engaged in wars with multiple countries and if the US Government is involved in the war. Most notably, the results show that US involvement in multiple host country wars is actually correlated with increased US FDI into the host country, providing empirical support for the “permanent war economy” hypothesis.

While other studies have focused on war and FDI, the authors have sought to show the impact of the involvement of arguably the most influential country, i.e. the USA, in the sovereign matters of a focal host country. By studying FDI from the USA as a function of US involvement in wars overseas, over the years with the greatest use of private military companies by the USA and the largest portion of global FDI accounted for by the USA, this work motivates a research agenda on home-host-"other” relations in the context of war and FDI, with the “other” being the supranational “elephant in the room.”

The authors propose to estimate a varying coefficient panel data model with different smoothing variables and fixed effects using a two-step approach. The pilot step estimates the varying coefficients by a series method. We then use the pilot estimates to perform a one-step backfitting through local linear kernel smoothing, which is shown to be oracle efficient in the sense of being asymptotically equivalent to the estimate knowing the other components of the varying coefficients. In both steps, the authors remove the fixed effects through properly constructed weights. The authors obtain the asymptotic properties of both the pilot and efficient estimators. The Monte Carlo simulations show that the proposed estimator performs well. The authors illustrate their applicability by estimating a varying coefficient production frontier using a panel data, without assuming distributions of the efficiency and error terms.

Based on the weak seismic performance and low ductility of coupled shear walls, engineered cementitious composites (ECC) is utilized to strengthen it to solve the deformation problem in tall buildings more effectively and study its mechanical properties more deeply.

The properties of reinforced concrete coupled shear wall (RCCSW) and reinforced ECC coupled shear wall (RECSW) have been studied by numerical simulation, which is in good agreement with the experimental results. The reliability of the finite element model is verified. On this basis, a detailed parameter study is carried out, including the strength and reinforcement ratio of longitudinal rebar, the placement height of ECC in the wall limb and the position of ECC connecting beams. The study indexes include failure mode and the skeleton curve.

The results suggest that the bearing capacity of RECSW is significantly affected by the ratio of longitudinal rebar. When the ratio of longitudinal rebar increases from 0.47% to 3.35%, the bearing capacity of RECSW increases from 250 kN to 303 kN, an increase of 21%. The strength of longitudinal rebar has little influence on the bearing capacity of RECSW. When the strength of the longitudinal rebar increases, the bearing capacity of RECSW increases little. The failure mode of RECSW can be improved by lowering the casting height of the ECC beam in a certain range.

In this paper, ECC is used to strengthen the coupled shear wall, and the accuracy of the finite element model is verified from the failure mode and skeleton curve. On this basis, the casting height of the ECC casting wall limb, the strength and reinforcement ratio of longitudinal rebar and the position of the ECC beam are studied in detail.

The purpose of our study is to investigate the impact of corporate culture on sales order backlog.

The authors use regression analysis to examine the relation between corporate culture and the level of sales order backlog, an important leading indicator of firm performance.

Using a large panel sample of US firms for the period of 2003–2021, the authors find a significant and positive relation, suggesting that firms with strong corporate culture have a higher level of sales order backlog.

The study findings contribute to two separate areas of research: corporate culture in management literature and sales order backlog in accounting literature. Prior study has focused on the impact of corporate culture on current firm performance. This study extends prior research by investigating the impact of corporate culture on order backlog, an important leading indicator of future performance.

This paper reviews the pros and cons of different parametric modeling methods, which can provide a theoretical reference for parametric reconstruction of 3D human body models for virtual fitting.

In this study, we briefly analyze the mainstream datasets of models of the human body used in the area to provide a foundation for parametric methods of such reconstruction. We then analyze and compare parametric methods of reconstruction based on their use of the following forms of input data: point cloud data, image contours, sizes of features and points representing the joints. Finally, we summarize the advantages and problems of each method as well as the current challenges to the use of parametric modeling in virtual fitting and the opportunities provided by it.

Considering the aspects of integrity and accurate of representations of the shape and posture of the body, and the efficiency of the calculation of the requisite parameters, the reconstruction method of human body by integrating orthogonal image contour morphological features, multifeature size constraints and joint point positioning can better represent human body shape, posture and personalized feature size and has higher research value.

This article obtains a research thinking for reconstructing a 3D model for virtual fitting that is based on three kinds of data, which is helpful for establishing personalized and high-precision human body models.

Disruptions at ports may destroy the planned ship schedules profoundly, which is an imperative operation problem that shipping companies need to overcome. This paper attempts to help shipping companies cope with port disruptions through recovery scheduling.

This paper studies the ship coping strategies for the port disruptions caused by severe weather. A novel mixed-integer nonlinear programming model is proposed to solve the ship schedule recovery problem (SSRP). A distributionally robust mean conditional value-at-risk (CVaR) optimization model was constructed to handle the SSRP with port disruption uncertainties, for which we derive tractable counterparts under the polyhedral ambiguity sets.

The results show that the size of ambiguity set, confidence level and risk-aversion parameter can significantly affect the optimal values, decision-makers should choose a reasonable parameter combination. Besides, sailing speed adjustment and handling rate adjustment are effective strategies in SSRP but may not be sufficient to recover the schedule; therefore, port skipping and swapping are necessary when multiple or longer disruptions occur at ports.

Since the port disruption is difficult to forecast, we attempt to take the uncertainties into account to achieve more meaningful results. To the best of our knowledge, there is barely a research study focusing on the uncertain port disruptions in the SSRP. Moreover, this is the first paper that applies distributionally robust optimization (DRO) to deal with uncertain port disruptions through the equivalent counterpart of DRO with polyhedral ambiguity set, in which a robust mean-CVaR optimization formulation is adopted as the objective function for a trade-off between the expected total costs and the risk.