Liwen Guan and Lu Chen
This paper aims to present a new trajectory optimization approach targeting spray painting applications that satisfies the paint thickness requirements of complex-free surfaces.
Abstract
Purpose
This paper aims to present a new trajectory optimization approach targeting spray painting applications that satisfies the paint thickness requirements of complex-free surfaces.
Design/methodology/approach
In this paper, a new trajectory generation approach is developed to optimize the transitional segments at the junction of adjacent patches for straight line, convex arc and concave arc combinations based on different angles between normal vectors of patches. In addition, the paint parameters including the paint gun velocity, spray height and the distance between adjacent trajectories have been determined in the generation approach. Then a thickness distribution model is established to simulate the effectiveness of trajectory planning.
Findings
The developed approach was applied to a complex-free surface of various curvatures, and the analysis results of the trajectory optimization show that adopting different transitional segment according to the angle between normal vectors can obtain the optimal trajectory. Based on the simulation and experimental validation results, the proposed approach is effective at improving paint thickness uniformity, and the obtained results are consistent with the simulation results, meaning that the simulation model can be used to predict the actual paint performance.
Originality/value
This paper discusses a new trajectory generation approach to decrease the thickness error values to satisfy spray paint requirements. According to the successfully performed simulation and experimental results, the approach is useful and practical in overcoming the challenge of improving the paint thickness quality on complex-free surface.
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This study aims to examine the public’s acceptance of film-induced tourism and develops the relationship among placement marketing, involvement, place attachment and travel…
Abstract
Purpose
This study aims to examine the public’s acceptance of film-induced tourism and develops the relationship among placement marketing, involvement, place attachment and travel intention. The film Your Love Song shot in the Hualien and Taitung regions in Taiwan was selected as the case study.
Design/methodology/approach
An online sample survey was conducted using a structured questionnaire, and statistical tests and overall structural equation modeling analysis using the SPSS and AMOS statistical software packages, respectively, were performed.
Findings
This study results demonstrate that destination placement marketing has a significant positive effect on the level of destination involvement, place attachment and travel intention of viewers. Moreover, the level of involvement has some intermediary effect on the interrelationship between placement marketing and travel intention. Hence, this study suggests that relevant government agencies and tourism operators should promote local tourism through films and television shows and attract more tourists by retaining the original shooting scenes.
Originality/value
While previous studies have only analyzed two or three of the four concepts of film-induced tourism, placement marketing, travel intention, involvement and place attachment, this study completely integrates these four concepts and proves the correlation between them.
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Pinsheng Duan and Jianliang Zhou
The construction industry is an industry with a high incidence of safety accidents, and the interactions of unsafe behaviors of construction workers are the main cause of…
Abstract
Purpose
The construction industry is an industry with a high incidence of safety accidents, and the interactions of unsafe behaviors of construction workers are the main cause of accidents. The neglect of the interactions may lead to serious underestimation of safety risks. This research aims to analyze the cascading vulnerability of unsafe behaviors of construction workers from the perspective of network modeling.
Design/methodology/approach
An unsafe behavior network of construction workers and a cascading vulnerability analysis model were established based on 296 actual accident cases. The cascading vulnerability of each unsafe behavior was analyzed based on the degree attack strategy.
Findings
Complex network with 85 unsafe behavior nodes is established based on the collected accidents in total. The results showed that storing in improper location, does not wear a safety helmet, working with illness and working after drinking are unsafe behaviors with high cascading vulnerability. Coupling analysis revealed that differentiated management strategies of unsafe behaviors should be applied. Besides, more focus should be put on high cascading vulnerability behaviors.
Originality/value
This research proposed a method to construct the cascading failure model of unsafe behavior for individual construction workers. The key parameters of the cascading failure model of unsafe behaviors of construction workers were determined, which could provide a reference for the research of cascading failure of unsafe behaviors. Additionally, a dynamic vulnerability research framework based on complex network theory was proposed to analyze the cascading vulnerability of unsafe behaviors. The research synthesized the results of dynamic and static analysis and found the key control nodes to systematically control unsafe construction behaviors.
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Bingzi Jin, Xiaojie Xu and Yun Zhang
Predicting commodity futures trading volumes represents an important matter to policymakers and a wide spectrum of market participants. The purpose of this study is to concentrate…
Abstract
Purpose
Predicting commodity futures trading volumes represents an important matter to policymakers and a wide spectrum of market participants. The purpose of this study is to concentrate on the energy sector and explore the trading volume prediction issue for the thermal coal futures traded in Zhengzhou Commodity Exchange in China with daily data spanning January 2016–December 2020.
Design/methodology/approach
The nonlinear autoregressive neural network is adopted for this purpose and prediction performance is examined based upon a variety of settings over algorithms for model estimations, numbers of hidden neurons and delays and ratios for splitting the trading volume series into training, validation and testing phases.
Findings
A relatively simple model setting is arrived at that leads to predictions of good accuracy and stabilities and maintains small prediction errors up to the 99.273th quantile of the observed trading volume.
Originality/value
The results could, on one hand, serve as standalone technical trading volume predictions. They could, on the other hand, be combined with different (fundamental) prediction results for forming perspectives of trading trends and carrying out policy analysis.
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Bingzi Jin and Xiaojie Xu
Agriculture commodity price forecasts have long been important for a variety of market players. The study we conducted aims to address this difficulty by examining the weekly…
Abstract
Purpose
Agriculture commodity price forecasts have long been important for a variety of market players. The study we conducted aims to address this difficulty by examining the weekly wholesale price index of green grams in the Chinese market. The index covers a ten-year period, from January 1, 2010, to January 3, 2020, and has significant economic implications.
Design/methodology/approach
In order to address the nonlinear patterns present in the price time series, we investigate the nonlinear auto-regressive neural network as the forecast model. This modeling technique is able to combine a variety of basic nonlinear functions to approximate more complex nonlinear characteristics. Specifically, we examine prediction performance that corresponds to several configurations across data splitting ratios, hidden neuron and delay counts, and model estimation approaches.
Findings
Our model turns out to be rather simple and yields forecasts with good stability and accuracy. Relative root mean square errors throughout training, validation and testing are specifically 4.34, 4.71 and 3.98%, respectively. The results of benchmark research show that the neural network produces statistically considerably better performance when compared to other machine learning models and classic time-series econometric methods.
Originality/value
Utilizing our findings as independent technical price forecasts would be one use. Alternatively, policy research and fresh insights into price patterns might be achieved by combining them with other (basic) prediction outputs.