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The density and pattern of urban parks, traffic conditions are the main factors affecting urban park accessibility. To clarify the influence of traffic mode and urban road network on urban park accessibility, we examine downtown area of Nanjing, China, and based on GIS network analysis, analyze urban park accessibility under different traffic modes in the current year (2017) and the Nanjing master planning target year (2030). The results shows: Using automobiles takes the shortest time to get to urban parks in 2017 and 2030 (if the problem of parking is ignored). Comparing the results of 2030 and 2017, by when the ground transportation network in the study area will be further improved, urban park accessibility was improved by a small margin under walking and automobile traffic modes, however, the density of rail traits increased fastest, urban park accessibility is improved most under this mode of transportation, rail transit route development becomes the dominant factor in improving park accessibility in downtown area of Nanjing. To a certain extent, this study reveals the leading factors of improving the accessibility of urban parks on the premise that the system of urban parks tends to be stable, and provides a reference for improving urban park accessibility.

This study aims to explore the opposite effects of single-category versus multi-category products information diversity on consumer decision making. Further, the authors investigate the moderating role of three categories of visitors – direct, hesitant and hedonic – in the relationship between product information diversity and consumer decision making.

The research utilizes a sample of 1,101,062 product click streams from 4,200 consumers. Visitors are clustered using the k-means algorithm. The diversity of information recommendations for single and multi-category products is characterized using granularity and dispersion, respectively. Empirical analysis is conducted to examine their influence on the two-stage decision-making process of heterogeneous online visitors.

The study reveals that the impact of recommended information diversity on consumer decision making differs significantly between single-category and multiple-category products. Specifically, information diversity in single-category products enhances consumers' click and purchase intention, while information diversity in multiple-category products reduces consumers' click and purchase intention. Moreover, based on the analysis of online visiting heterogeneity, hesitant, direct and hedonic features enhance the positive impact of granularity on consumer decision making; while direct features exacerbate the negative impact of dispersion on consumer decision making.

First, the article provides support for studies related to information cocoon. Second, the research contributes evidence to support the information overload theory. Third, the research enriches the field of precision marketing theory.

This paper aims to study the synergistic effect of graphene sponge on the thermal properties and shape stability of composite phase change material (PCM).

Graphene oxide sponge is first prepared from an aqueous solution of graphene oxide by freeze-drying method. The oxidized graphene sponge is reduced by hydrazine hydrate. Finally, use vacuum impregnation method to introduce paraffin into graphene sponge to prepare composite PCM.

Graphene sponge is used to improve the shape stability of paraffin wax and improves the thermal conductivity and latent heat of the composite PCM. The thermal conductivity increases by 200 per cent and the composite PCM has excellent reliability in 100 melt-freezing cycles.

A simple way for fabricating composite PCM with high thermal conductivity and latent heat which has the potential to be used as thermal storage materials without container encapsulation has been developed by using graphene sponge and paraffin.

The materials and preparation methods with special structure and properties in this paper provide a new idea for the research of PCM, which can be widely used in the fields of energy conversion and storage.

The shock absorber is an important component of vehicle suspension that attenuates the vehicle vibration. Its running state directly affects the performance of the vehicle suspension. The purpose of this paper is to quantitatively study the relationship between damping characteristics and air chamber and oil properties in single-tube pneumatic shock absorber.

Combined with the principle of fluid dynamics and hydraulic transmission technology, the rebound stroke and compression stroke mathematical models, and damping characteristics simulation model are established to investigate the effect of the air chamber and oil property on damping characteristics.

Research results show that the initial pressure of the air chamber is the key parameter which influences the damping characteristics of the shock absorber. The change of the initial pressure has more impact on damping force, and less impact on the speed characteristic; the initial volume of the air chamber almost has no effect on the damping characteristics. The density and viscosity of the oil have certain influence on the damping characteristics. Therefore, selecting suitable damping oil is very important.

Using Matlab/Simulink software to build simulation models, its results are very accurate. The conclusions can provide a theoretical reference for the structure design of a single-tube pneumatic shock absorber.

Project complexity is a critical issue that has increasingly attracted attention in both academic and practical circles. However, there are still many gaps in the research on project complexity, such as the differentiated conceptualization of complexity and disjointed operationalization in the measurements. Therefore, this paper aims to conduct a systematic and detailed literature review on the concept, dimensions, assessment, and underlying mechanisms of project complexity.

A systematic literature review methodology was applied to search and synthesize the research on project complexity, and a final sample of 74 journal articles was identified.

This study first summarizes the concepts of project complexity from three different theoretical perspectives, and then identifies different approaches of measurement, evaluation, or simulation to assess project complexity. This paper finally establishes an integrative framework to synthesize the antecedents, mediators and moderators, and outcomes of project complexity, generating four suggestions for future research.

This study summarizes the definition and operationalization of project complexity to reduce the discrepancies in the existing research and offers an integrative framework to offer a broad overview of the current understanding of project complexity, providing a potential way forward for addressing project complexity.

The purpose of this paper is to examine the applicability and capability of models based on a genetic algorithm and support vector machine (GA-SVM) and a genetic algorithm and relevance vector machine (GA-RVM) for the prediction of phytoplankton abundances associated with algal blooms in a Macau freshwater reservoir, and compare their performances with an artificial neural network (ANN) model.

The hybrid models GA-SVM and GA-RVM were developed for the optimal control of parameters for predicting (based on the current month’s variables) and forecasting (based on the previous three months’ variables) phytoplankton dynamics in a Macau freshwater reservoir, MSR, which has experienced cyanobacterial blooms in recent years. There were 15 environmental parameters, including pH, SiO₂, alkalinity, bicarbonate (HCO₃−), dissolved oxygen (DO), total nitrogen (TN), UV254, turbidity, conductivity, nitrate (NO₃−), orthophosphate (PO₄³⁻), total phosphorus (TP), suspended solids (SS) and total organic carbon (TOC) selected from the correlation analysis, with eight years (2001-2008) of data for training, and the most recent three years (2009-2011) for testing.

For both accuracy performance and generalized performance, the ANN, GA-SVM and GA-RVM had similar predictive powers of R² of 0.73-0.75. However, whereas ANN and GA-RVM models showed very similar forecast performances, GA-SVM models had better forecast performances of R² (0.862), RMSE (0.266) and MAE (0.0710) with the respective parameters of 0.987, 0.161 and 0.032 optimized using GA.

This is the first application of GA-SVM and GA-RVM models for predicting and forecasting algal bloom in freshwater reservoirs. GA-SVM was shown to be an effective new way for monitoring algal bloom problem in water resources.

The face-centered cubic structured single-phase FeCoNiCrMn high-entropy alloys (HEAs) were prepared to study the friction and wear behavior of HEAs under MoS₂-oil lubrication.

FeCoNiCrMn alloys were subjected to ball-on-disc reciprocating sliding against the GCr15 ball. L₂₅(5⁶) orthogonal wear tests were designed for velocity V_rel (4.167-20.833 mm/s), load F_N (10-50 N), temperature T (RT-140 °C) and time t (5-20 min). Based on orthogonal test results, multivariate repeated measures ANOVA was performed, and further comparative experiments were conducted for V_rel, F_N and T. Energy dispersive spectrometer and scanning electron microscope were applied to characterize the surface morphology of wear scar and its element distribution.

V_rel, F_N and t exerted the most significant influence (p < 0.01) on the average friction coefficient f. V_rel and F_N were identified as the momentous effect (p < 0.01) on wear volume ΔV. T (≥50 °C) had positive correlation with f and ΔV, and both V_rel and F_N correlated negatively with f. The dominant abrasive wear was attributed to the large hardness difference of the friction pair. Fatigue wear and delamination wear were experienced at higher speeds (V_rel  ≥ 12.5 mm/s) and loading levels (F_N ≥ 40 N). Elevated temperature weakens the lubrication effect of MoS₂-oil and the mechanical properties of FeCoNiCrMn matrix, intensifying abrasive wear.

This study is expected to provide references for exploration on the wear behavior of single-phase HEAs under complex working conditions with lubrication and hence will help develop the application of HEAs in practical engineering.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-08-2019-0303

The optimal material microstructures in pure material design are no longer efficient or optimal when accounting macroscopic structure performance with specific boundary conditions. Therefore, it is important to provide a novel multiscale topology optimization framework to tailor the topology of structure and the material to achieve specific applications. In comparison with porous materials, composites consisting of two or more phase materials are more attractive and advantageous from the perspective of engineering application. This paper aims to provide a novel concurrent topological design of structures and microscopic materials for thermal conductivity involving multi-material topology optimization (material distribution) at the lower scale.

In this work, the effective thermal conductivity properties of microscopic three or more phase materials are obtained via homogenization theory, which serves as a bridge of the macrostructure and the periodic material microstructures. The optimization problem, including the topological design of macrostructures and inverse homogenization of microscopic materials, are solved by bi-directional evolutionary structure optimization method.

As a result, the presented framework shows high stability during the optimization process and requires little iterations for convergence. A number of interesting and valid macrostructures and material microstructures are obtained in terms of optimal thermal conductive path, which verify the effectiveness of the proposed mutliscale topology optimization method. Numerical examples adequately consider effects of initial guesses of the representative unit cell and of the volume constraints of adopted base materials at the microscopic scale on the final design. The resultant structures at both the scales with clear and distinctive boundary between different phases, making the manufacturing straightforward.

This paper presents a novel multiscale concurrent topology optimization method for structures and the underlying multi-phase materials for thermal conductivity. The authors have carried out the concurrent multi-phase topology optimization for both 2D and 3D cases, which makes this work distinguished from existing references. In addition, some interesting and efficient multi-phase material microstructures and macrostructures have been obtained in terms of optimal thermal conductive path.

This study aims to explore the impact of carbon neutral announcements on “stock market value” of publicly listed companies in China.

The event study approach is adopted. Market, market-adjusted, Carhart four-factor model and a cross-sectional regression model are employed to examine the impacts of carbon neutral announcements on “stock market value” of Chinese companies based on data from 188 carbon neutral announcements.

Carbon neutral announcements positively impact Chinese shareholder value. Carbon neutral announcements at the strategic level have a more positive and significant impact on Chinese stock market value. Innovative carbon neutral announcements do not significantly cause Chinese stock market reactions. Companies have more positive and significant stock market reactions when the companies make carbon neutral announcements that reflect high supply chain network resilience and heterogeneity and strong supply chain network relationships.

The findings uncover the business value of carbon neutral activities and provide operations managers in developing countries insights into how to improve enterprises' market value by actively implementing carbon neutral activities.

This paper is the first trial to apply an event study to examine the relationship between carbon neutral announcements and Chinese stock market value from the perspective of announcement level and type and supply chain networks. This paper introduces corporate reputation theory and enriches the application of corporate reputation theory in the field of low-carbon environmental protections and supply chains.

The study aims to examine the effect of multiple directorships on accrual-based earnings management and real earnings management. It analyses whether earnings management practices in the Saudi context increase or decrease with the average number of multiple directorships.

The study uses the approach by Roychowdhury (2006) to capture the level of real earnings management and uses the cross-sectional model by Jones (1991) to measure accrual-based earnings management.

The paper provides partial evidence supporting the “busyness” hypothesis where earnings management practices increase with the number of multiple directorships. The evidence shows that multiple directorships have a positive and significant effect on real earnings management in the Kingdom of Saudi Arabia. However, we find no significant impact of multiple directorships on accrual-based earnings management.

This is the first study that empirically investigates the relationship between multiple directorships and earnings management in the Kingdom of Saudi Arabia. The paper contributes to the limited literature on multiple directorships in developing countries by examining their impact on opportunistic real earnings management.