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This study aims to improve the offshore wind farm (OWF) site selection evaluation index system and establishes a decision-making model for OWF site selection. It is expected to provide helpful references for the progress of offshore wind power.

Firstly, this paper establishes an evaluation criteria system for OWF site selection, considering six criteria (wind resource, environment, economic, technical, social and risk) and related subcriteria. Then, the Criteria Importance Though Intercrieria Correlation (CRITIC) method is introduced to figure out the weights of evaluation indexes. In addition, the cumulative prospect theory and technique for order preference by similarity to an ideal solution (CPT-TOPSIS) method are employed to construct the OWF site selection decision-making model. Finally, taking the OWF site selection in China as an example, the effectiveness and robustness of the framework are verified by sensitivity analysis and comparative analysis.

This study establishes the OWF site selection evaluation system and constructs a decision-making model under the spherical fuzzy environment. A case of China is employed to verify the effectiveness and feasibility of the model.

In this paper, a new decision-making model is proposed for the first time, considering the ambiguity and uncertainty of information and the risk attitudes of decision-makers (DMs) in the decision-making process.

In the process of vigorously promoting urbanization, the city development became a new driving force for China's prosperity. To comprehensively analyze the urban regional innovation system, the typical urban city was taken as an empirical sample to consider as an urban regional innovation system. The fuzzy comprehensive evaluation method was used to evaluate the operation of typical urban regional innovation system, combined the cluster analysis and Bayesian discrimination for the research and analysis. The results showed that the evaluation of regional innovation system was a national innovation strategy. Therefore, the model provides an important evidence for the urban innovative development. The proposed targeted innovation development strategy is conducive to grasp the advantages and disadvantages of the typical urban regional innovation system construction as a whole. The analysis of the structural change layers and sub-distribution of urban regional innovation effectiveness helps to clarify the stage of urban development.

The COVID-19 pandemic has dramatically disrupted everyday life, leading to a cascade of negative emotional responses such as death anxiety. Against this backdrop, the purpose of this paper is to focus on the buffering effect of brand attachment on death anxiety by exploring the roles of brand concepts and brand positioning on psychological compensation for security.

This multi-method paper features four studies and shows how brands can offer emotional support under high-risk circumstances.

Study 1 includes two surveys which offer preliminary evidence that death anxiety can enhance consumers’ brand attachment. Study 2 reveals a causal effect wherein consumers experiencing death anxiety are more likely to attach to brands with a self-transcendence (vs self-enhancement) concept. Study 3 examines the mediating role of need for security in the relationship between death anxiety and attachment to brands with a self-transcendence concept. Further, Study 4 indicates the moderating role of brand positioning: self-transcendence brands adopting local (vs global) positioning strategies are more likely to satisfy consumers’ need for security, thereby leading to strong brand attachment.

The findings of this paper contribute to the brand attachment literature and to the global branding literature regarding consumers’ emotional responses in the context of COVID-19. This paper innovatively frames brand concepts and brand positioning and provides actionable guidelines to help brands satisfy consumers’ needs amid a worldwide crisis.

The purpose of this paper is to use the wavelet neural network and genetic algorithm to study the effects of polyalphaolefin, TMP108 and OCP0016 on the kinematic viscosity, viscosity index and pour point of lubricating oil.

Wavelet neural network is used to train the known samples, test the unknown samples and compare the obtained results with those obtained with a traditional empirical formula.

It is found that the wavelet neural network prediction value is closer to the experimental value than the traditional empirical formula calculation value.

The results show that the wavelet neural network can be used to study the physical and chemical indexes of lubricating oil.

This paper aims to solve the problems of the synchronization between branches and the uncertainties such as joint friction, load variation and external interference of a hybrid mechanism. The controller is used to improve the synchronization and robustness of the hybrid mechanism system and achieve both finite time convergence and chattering-free sliding mode.

First, the dynamic model of hybrid mechanism containing lumped uncertainties is formulated by the Lagrange method, and a composite error based on coupling synchronization error and the end-effector tracking error is set up in the task space. Then, by combining the finite time super twisting sliding mode control algorithm, a composite error-based finite time super twisting sliding mode synchronous control law is designed to make the end-effector tracking error and coupling synchronization error achieve better tracking performance and convergence performance. Finally, the Lyapunov stability of the control law and the finite-time convergence of the composite error are proved theoretically.

To verify the effectiveness of the proposed control method, simulations and experiments for the prototype system of the hybrid mechanism are conducted. The results show that the proposed control method can achieve better tracking performance and convergence performance.

This is a new innovation for a hybrid mechanism containing lumped uncertainties to improve the robustness, convergence performance, tracking performance and synchronization of the system.

Korea and China have promoted a bilateral FTA since 2005 to upgrade their economic relationships. If the Korea-China FTA is concluded, then trade between the two countries is likely to involve substantial changes both quantitatively and qualitatively. Offer lists submitted by Korea include steel, petrochemicals and machinery and those by China include some agricultural and marine products as well as nonferrous metals. Korea's interest in the Korea-China FTA has focused mainly on damage to the agricultural sector and there have been a lot of studies on the effects of the FTA on Korean agricultural sector. However, little is known about why China includes the nonferrous metals industry for early voluntary liberalization and its implications for the domestic economy. Nonferrous metals industry is one of China’s national strategic sectors and has a large supply excess in the country. This study targets the survey of Chinese nonferrous metals industry and trade structure and considers the problem of nonferrous metals in the context of negotiations for the Korea-China FTA and its implications for a higher-quality Korea-China FTA.

The purpose of this paper is to investigate the determinants of capital expenditures (CAPEX) in the theme park industry, particularly following the financial stresses induced by the COVID-19 pandemic. CAPEX drivers are poorly understood due to the idiosyncratic nature of this industry, which is dominated by a few large players. It also aims to identify the variables influencing both the growth and maintenance components of CAPEX among U.S. theme park operators.

The paper uses contingency theory to analyze both financial and nonfinancial data from U.S. theme park operators between 2009 and 2021. The paper also uses partial least squares structural equation modeling to manage issues of multicollinearity and to ensure robustness in the findings.

The analysis identifies several key determinants of CAPEX. Resources and the presence of competing theme parks in proximity to an operator’s parks positively affect CAPEX. Conversely, higher leverage, dividend payouts, intellectual property (IP) dominance and population density in areas with their active parks correlate with reduced CAPEX. The paper also notes distinct trends in maintenance versus growth CAPEX post-COVID-19, with maintenance CAPEX increasing as operators invest in existing assets while growth CAPEX trending downwards.

The study’s scope is confined to U.S.-based theme park operators, limiting the generalizability of the findings internationally. Moreover, data limitations restrict the sample size due to the consolidation of the industry players, potentially affecting the statistical power of the analysis.

This research offers significant insights for theme park operators, industry analysts and policymakers. Understanding the factors influencing CAPEX can aid operators in strategic planning and investment decisions, especially in a post-pandemic economic environment where efficient capital allocation will be crucial for recovery and growth. A major contribution of this research is the development of a new measure for IP dominance, which allows theme park operators to quantify the impact of IP on their investment strategies.

This study contributes uniquely by incorporating both financial and nonfinancial determinants in analyzing CAPEX within the theme park industry, a sector significantly impacted by the pandemic. It introduces novel metrics for assessing the impact of IP on CAPEX and differentiates between the factors driving maintenance and growth expenditures. The findings enrich the existing literature on hospitality management and provide actionable insights that could guide the strategic financial decisions of theme park operators.

Urban expansion in recent decades has led to a surge in new buildings and infrastructure, significantly increasing construction and demolition (C&D) waste materials. This large waste stream presents environmental challenges, prompting stakeholders in the construction and waste recovery sectors to establish waste recovery facilities. However, these infrastructure projects often face public opposition. This research aims to identify the root causes of public opposition to C&D waste recovery facilities and propose strategies to manage and mitigate these concerns effectively.

This research adopts a qualitative approach to extract relevant literature using the PRISMA model, which is common for systematic reviews. A set of selection criteria were used to extract the most relevant literature in this field. The analysis methods are based on bibliometric analysis as well as document analysis.

The research findings indicate that five major categories impact the public’s perceptions of C&D waste recovery facilities. These categories include environmental, awareness and perception, economic, health, safety and well-being and project management. Furthermore, the research provides eight strategies to effectively manage public opposition to waste recovery facilities.

This research provides a framework for managing public opposition to C&D waste recovery infrastructure projects fairly and efficiently. Comprising six steps with specific actions, the framework addresses citizens’ concerns while improving C&D waste recovery rates. It serves as a strategic roadmap that can be adapted for use in various infrastructure projects, fostering community support and sustainable development.

This review paper is the first attempt to gather causes for public opposition to the development of C&D waste recovery facilities. The research findings can guide policymakers who strive to establish a fair framework for all stakeholders affected by or involved in the establishment of C&D waste recovery facilities.

Bankruptcy is a financial failure of a business or an organization. Different kinds of bankruptcy prediction techniques are proposed to predict it. But, they are restricted as techniques in predicting the bankruptcy and not addressing the associated activities like acquiring the suitable data and delivering the results to the user after processing it. This situation demands to look for a comprehensive solution for predicting bankruptcy with intelligence. The paper aims to discuss these issues.

To model Business Intelligence (BI) solution for BP the concept of reference model is used. A Reference Model for Business Intelligence to Predict Bankruptcy (RMBIPB) is designed by applying unit operations as hierarchical structure with abstract components. The layers of RMBIPB are constructed from the hierarchical structure of the model and the components, which are part of the reference model. In this model, each layer is designed based on the functional requirements of the Business Intelligence System (BIS).

This reference model exhibits the non functional software qualities intended for the appropriate unit operations. It has flexible design in which techniques are selected with minimal effort to conduct the bankruptcy prediction. The same reference model for another domain can be implemented with different kinds of techniques for bankruptcy prediction.

This model is designed using unit operations and the software qualities exhibited by RMBIPB are limited by unit operations. The data set which is applied in RMBIPB is limited to Indian banks.

A comprehensive bankruptcy prediction model using BI with customized reporting.