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Currently, there is a dearth of research studies regarding macro analysis of the workforce productivity of the US construction industry. The purpose of this paper is to calculate the workforce productivity changes of the US construction industry from 2006 to 2016, with the number of laborers as input and value of construction industry as output.

The present study introduced the data envelopment analysis (DEA) based Malmquist productivity index model to measure the workforce productivity of the US construction industry from 2006 to 2016.

The results indicated that the workforce productivity of the US construction industry experienced a continuous decline, except for the increases from 2011 to 2013 and from 2014 to 2015. It was also shown that there were gaps in the workforce productivity development level among all states and nine regions in the US construction industry. Besides, the relationship between workforce productivity and four aspects, including real estate price, workforce, climate distribution and economic factors, was analyzed.

The calculation of the productivity of the US construction industry is based on the premise that the external environment is fixed and unchanged from 2006 to 2016, but the multi-level DEA model for further calculation is required for obtaining more effective conclusions.

This paper measures the workforce productivity of the US construction industry over the past 11 years, which added latest analysis and knowledge into the construction industry, providing decision-makers with advice and data support to formulate policies to improve workforce productivity.

This study provided both government decision-makers and industrial practitioners with important macro background environment information, which will facilitate the improvement of workforce productivity in the construction industry in different regions of the US.

The purpose of this paper is to study how to effectively allocate water, energy and food (WEF) resources in urban development.

An agent-based model combined with NetLogo simulation model has been used in this paper.

This paper proposes a framework for agent analysis in urban WEF consumption.

Further discussions using empirical data are of great importance.

Apply to form the development model of the city in the future.

A new method of WEF management has been used at the city level.

Cross-channel integration improves the operations of multi-channel and omnichannel marketing and increase firms' overall performance. By addressing the extant gaps in current literature, this configurational analysis aims to test the combined effects of organizational, channel and environmental factors on cross-channel integration.

Data were collected from a sample of 180 manufacturers. Necessary condition analysis (NCA) was used to test whether two organizational factors (firm size and IT capability), one environmental factor (environmental dynamism) and two channel factors (channel diversity and proportion of direct channels) were necessary or unnecessary conditions for high cross-channel integration. Fuzzy-set qualitative comparative analysis (fsQCA) was applied to analyze the configurational factors of high vs low cross-channel integration.

First, firm size and IT capability are non-linear and substitute for each other in affecting cross-channel integration in a diversified channel system with a high proportion of direct channels. Second, in a dynamic environment, firms with large size and IT advantage could achieve high cross-channel integration by diversifying channel types or increasing the proportion of direct channels. Third, the effect of channel diversity and proportion of direct channels on cross-channel integration is asymmetric depending on other antecedent conditions.

The authors tested a configurational framework developed from multiple theoretical perspectives. The authors' empirical findings contribute to the literature by providing insights into the mechanisms underlying the formation of high and low cross-channel integration. The results suggest multiple ways for firms to promote cross-channel integration by adjusting channel factors based on configurational conditions.

This paper aims to overcome the defect that the traditional clustering method is excessively dependent on initial clustering radius and also provide new technical measures for detecting the component content of lubricating oil based on the fuzzy neural system model.

According to the layers model of the fuzzy neural system model for the given sample data pair, the new clustering method can be implemented, and through the fuzzy system model, the detection method for the selected oil samples is given. By applying this method, the composition contents of 30 kinds of oil samples in lubricating oil are checked, and the actual composition contents of oil samples are compared.

Through the detection of 21 mineral elements in 30 oil samples, it can be known that the four mineral elements such as Zn, P, Ca and Mg have largest contribution rate to the lubricating oil, and they can be regarded as the main factors for classification of lubricating oil. The results show that the fuzzy system to be established based on sample data clustering has better performance in detection lubricant component content.

In spite of lots of methods for detecting the component of lubricating oil at the present, there is still no detection of the component of lubricating oil through clustering method based on sample data pair. The new nearest clustering method is proposed in this paper, and it can be more effectively used to detect the content of lubricating oil.

This paper compares the shopping behavior of Chilean consumers with those in China. Both Chinese and Chilean mall visits are driven, first and foremost, by purchase; however, consumers in Chile tend to be less single minded than their Chinese counterparts. Differences also exist between the two populations in their way of selecting the mall, shopping characteristics, and purchase patterns. Most importantly, Chinese shoppers relied more on their mall visits than Chileans in searching for information and finalizing their purchase decisions; at the same time, they were much more cautious in committing themselves to a purchase.

In recent years, additive manufacturing techniques have attracted much research attention because of their ability to fabricate customised parts with complex geometry. The range of composites suitable for laser-based powder bed fusion technique is limited, and has not been investigated yet. This paper aims to study the fabrication of AlSi₁₀Mg reinforced with nAl₂O₃ using the laser-based powder bed fusion technique.

An experimental approach was used to investigate the densification of AlSi₁₀Mg–nAl₂O₃ composites using laser-based powder bed fusion technique. Optimisation of the porosity was performed, and microstructure evolution was evaluated.

In this study, laser volumetric energy density (approximately 109 J/mm³) was found to be required for the fabrication of AlSi₁₀Mg–nAl₂O₃ composites with a relative volumetric density approximating 99%. The use of laser volumetric energy density resulted in larger grains. Columnar grain structure was observed via the use of electron backscatter diffraction mapping.

This paper examines the processing of new aluminium composite material suitable for the fabrication via the laser-based powder bed fusion technique.

This paper aims to propose methods to evaluate the characteristic length of the melt pool for accurate fabrication and to identify the optimal process parameters in the selective laser melting process.

Specimens with the types of the scans by controlling the degree of the overlap with hatch spacing are fabricated. The scan modes are classified by statistically analyzing the results of hardness tests. According to the classification of the scans, the evaluation methods are proposed based on the observation of the shape of the solidified melt pool.

The control of the hatch spacing can reproduce all modes of the scan conditions, and hardness can be used to classify the scan modes.

The proposed evaluation methods are based on the analysis of the experimental observation so that they can be easily used for the real evaluation.

Based on social network theory and the literature of contract governance, the purpose of this study is to explore how distributors’ “banding together” (network intensity) affects contract governance (detailed contracts), which, in turn, influences channel conflict and simultaneously tests the moderating effect of network centrality.

The authors collect the data from the side of 288 manufacturers.

This study finds that, first, distributors’ network intensity positively affects detailed contracts between manufacturers and distributors; second, detailed contracts, in turn, declines channel conflict; and third, network centrality will weaken the positive influence of network density on detailed contracts. In addition, an ex post analysis finds that detailed contracts play a negative mediating role between distributors’ network density and channel conflict and this negative mediating effect would be weakened by distributors’ network centrality.

The current study not only helps to make up for the shortcomings of using the dyadic analysis paradigm to analyze channel behavior but also helps manufacturers to understand and respond to the phenomenon of distributors’ “banding together” comprehensively and deeply.

This paper aims to present a hybrid machine learning algorithm for additive manufacturing (AM) design feature recommendation during the conceptual design phase.

In the proposed hybrid machine learning algorithm, hierarchical clustering is performed on coded AM design features and target components, resulting in a dendrogram. Existing industrial application examples are used to train a supervised classifier that determines the final sub-cluster within the dendrogram containing the recommended AM design features.

Through a case study of designing additive manufactured R/C car components, the proposed hybrid machine learning method was proven useful in providing feasible conceptual design solutions for inexperienced designers by recommending appropriate AM design features.

The proposed method helps inexperienced designers who are newly exposed to AM capabilities explore and utilize AM design knowledge computationally.