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Students need to learn and practice creative thinking skills to ensure success in solving everyday, national, and global problems that include those affecting economic issues. The global economy requires workers to have research and innovation skills that depend upon creativity. However, many current educational programs focus mostly on factual content, doing little to inspire or apply the creative process. The project presented here shows an engaging activity that combines creative thinking skills with economic content. Although the activity occurred in a college course on creativity theory and practice, this challenging game can be easily adapted and embedded in the Kindergarten-12 social studies curriculum. This article discusses the set-up of the activity and its connection to creativity theory and curriculum standards. It showcases the work of eleven participants who each made a unique object or scene from a given set of craft and recycled materials, subsequently relating the resulting product to a current economic issue. Photographs of the resulting products are provided along with descriptions of the theme of each item, its connection to economics, and creative aspects of the work. Suggestions for adaptation to the Kindergarten-12 classroom are given.

Grey target decision making is one of the important problems of decision-making theory. It is critical to express uncertain information effectively and depose them in a reasonable and simple way. The purpose of this paper is to solve the grey target problem by the grey potential degree method without whiten value and without distribution function. Furthermore, this new approach has an advantage of realizing both comparing and standardization work during the process of treating the data.

First, this paper makes a brief overview of the existing method for grey target decision. Then, the conception of a grey potential degree system is introduced and the conception of standard grey potential degree is build up, and a new grey potential-based method based on the grey target multiple attribute decision method is proposed. At the same time, the standard grey potential and its application in multiple resource data are studied.

At the same time the standard grey potential and its application in multiple resource data are studied. Standard grey potential is presented by means of three examples together with the comparison with the existing method to demonstrate that the grey potential-based method could be used to solve the problem of grey target decision conveniently and effectively.

It is very important to compare grey numbers to obtain scientific and reasonable results for a grey target decision-making problem. However, in the actual application of grey numbers, it is difficult to find out the probability density function or the whiten function of grey numbers. When grey numbers are compared and deposed through the whiten value, much information regarding grey numbers will be lost and, at the same time, the value of grey numbers in uncertainty is partly lost. The method discussed in this paper is reasonable and feasible.

A key factor adversely affecting contractor social networking performance is the improper handling and information management of contractor’s services delivery on websites. Contractor social networking is particularly problematic on industrialised building system (IBS) infrastructure maintenance projects where contractor’s certified quality product and firms are not matched with maintenance specialisation services. The paper aims to discuss this issue.

This paper reports on the early stages of research which is developing a new information and communications technology (ICT)-based approach to managing contractor social networking on IBS infrastructure maintenance schemes. As a precursor to this work, the paper reviews current contractor social networking websites practices on IBS infrastructure maintenance projects and explores the ICT tools and techniques currently being employed on such projects.

The findings reveal the need for more sophisticated contractor social networking websites solutions which accord with the needs of IBS infrastructure maintenance schemes.

The paper concludes by presenting a research framework for developing such a system in the future.

This paper aims to examine the friction and wear performance of the graphene synthesized from fruit cover plastic waste and oil palm fiber (OPF).

The graphene was synthesized by using a chemical vapor deposition method, where a copper sheet was used as the substrate. The dry sliding test was performed by using a micro ball-on-disc tribometer at various sliding speeds and applied loads.

The results show that both as-grown graphenes decrease the coefficient of friction significantly. Likewise, the wear rate is also lower at higher sliding speed and applied load. For this study, OPF is proposed as the best solid carbon source for synthesizing the graphene.

The main contribution of this study is opening a new perspective on the potentials of producing graphene from solid waste materials and its effect on the tribological performance.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-11-2019-0486

Traffic flow prediction has always been a top priority of intelligent transportation systems. There are many mature methods for short-term traffic flow prediction. However, the existing methods are often insufficient in capturing long-term spatial-temporal dependencies. To predict long-term dependencies more accurately, in this paper, a new and more effective traffic flow prediction model is proposed.

This paper proposes a new and more effective traffic flow prediction model, named channel attention-based spatial-temporal graph neural networks. A graph convolutional network is used to extract local spatial-temporal correlations, a channel attention mechanism is used to enhance the influence of nearby spatial-temporal dependencies on decision-making and a transformer mechanism is used to capture long-term dependencies.

The proposed model is applied to two common highway datasets: METR-LA collected in Los Angeles and PEMS-BAY collected in the California Bay Area. This model outperforms the other five in terms of performance on three performance metrics a popular model.

(1) Based on the spatial-temporal synchronization graph convolution module, a spatial-temporal channel attention module is designed to increase the influence of proximity dependence on decision-making by enhancing or suppressing different channels. (2) To better capture long-term dependencies, the transformer module is introduced.

Focusing on the problem that the visual detection algorithm of navigation path line in intelligent harvester robot is susceptible to interference and low accuracy, a navigation path detection algorithm based on improved random sampling consensus is proposed.

First, inverse perspective mapping was applied to the original images of rice or wheat to restore the three-dimensional spatial geometric relationship between rice or wheat rows. Second, set the target region and enhance the image to highlight the difference between harvested and unharvested rice or wheat regions. Median filter is used to remove the intercrop gap interference and improve the anti-interference ability of rice or wheat image segmentation. The third step is to apply the method of maximum variance to thresholding the rice or wheat images in the operation area. The image is further segmented with the single-point region growth, and the harvesting boundary corner is detected to improve the accuracy of the harvesting boundary recognition. Finally, fitting the harvesting boundary corner point as the navigation path line improves the real-time performance of crop image processing.

The experimental results demonstrate that the improved random sampling consensus with an average success rate of 94.6% has higher reliability than the least square method, probabilistic Hough and traditional random sampling consensus detection. It can extract the navigation line of the intelligent combine robot in real time at an average speed of 57.1 ms/frame.

In the precision agriculture technology, the accurate identification of the navigation path of the intelligent combine robot is the key to realize accurate positioning. In the vision navigation system of harvester, the extraction of navigation line is its core and key, which determines the speed and precision of navigation.

In plastic reconstruction surgeries, total auricular reconstruction for microtia is a real challenge. Presently, autogenous costal cartilage and MEDPOR are the chosen materials but none can satisfy the requirements of orthopaedic operation. The purpose of this paper is to examine how to fabricate an ear scaffold with a good shape.

A new approach to form the auricle framework is described. CT scan data of the patient's contralateral “good ear” are used to generate a 3D reconstruction model of the new ear. This model is then imported into rapid prototyping (RP) software to slice. The sliced data drive the fused deposition modeling (FDM) machine to build the ear framework layer by layer. Based on the actual shape of the computer model, FDM technology produces a real feel ear framework to match the size of the opposite good ear.

An artificial human ear was built using FDM technology based on CT images. The auricular framework with polyurethane was a porous structure with good flexibility and biocompatibility. After implanting into the mouse, a real life human ear appeared on the back of the mouse. The experiment indicated that this method provided an efficient way to macrotia reconstruction.

The freeform fabrication technique combined with CT image reconstruction could provide an efficient way to produce a porous structure and solve the framework carving problem in microtia reconstruction.

This paper presents and investigates a method named M×N‐ary chaotic pulse‐width‐position modulation (CPWPM) which is based on the combination of M‐ary chaotic pulse‐position modulation (CPPM) and N‐ary chaotic pulse‐width modulation (CPWM) in order to provide a better performance in noise‐affected environments as well as improve significantly bit rate.

Analysis of schemes for modulator and demodulator are presented in detail through describing the schemes of the individual methods and their combination. Theoretical evaluation of bit‐error rate (BER) performance in presence of additive white Gaussian noise (AWGN) is provided. Chaotic behavior with tent map in variation of modulation parameters is also investigated. In order to verify the theoretical analyses, numerical simulations are carried out and their results are reported.

Both evaluation and simulation results show that when the number of symbols increases, the bit rate is improved significantly but the BER performance is just slightly worse. This makes M×N‐ary CPWPM become an effective method for chaos‐based digital communication.

Although CPPM, CPWM and M‐ary modulation methods have been described in the literature separately, their combination is presented and investigated for the first time in this paper.

Contemporary technological disruptions are espoused as though they stimulate sustainable growth in the built environment through the Green Internet of Things (G-IoT). Learning from discipline-specific experiences, this paper articulates recent advancements in the knowledge and concepts of G-IoT in relation to the construction and smart city sectors. It provides a scoping review for G-IoT as an overlooked dimension. Attention was paid to modern circularity, cleaner production and sustainability as key benefits of G-IoT adoption in line with the United Nations’ Sustainable Development Goals (UN-SDGs). In addition, this study also investigates the current application and adoption strategies of G-IoT.

This study uses the Preferred Reporting Items for Systematic and Meta-Analyses (PRISMA) review approach. Resources are drawn from Scopus and Web of Science repositories using apt search strings that reflect applications of G-IoT in the built environment in relation to construction management, urban planning, societies and infrastructure. Thematic analysis was used to analyze pertinent themes in the retrieved articles.

G-IoT is an overlooked dimension in construction and smart cities so far. Thirty-three scholarly articles were reviewed from a total of 82 articles retrieved, from which five themes were identified: G-IoT in buildings, computing, sustainability, waste management and tracking and monitoring. Among other applications, findings show that G-IoT is prominent in smart urban services, healthcare, traffic management, green computing, environmental protection, site safety and waste management. Applicable strategies to hasten adoption include raising awareness, financial incentives, dedicated work approaches, G-IoT technologies and purposeful capacity building among stakeholders. The future of G-IoT in construction and smart city research is in smart drones, building information modeling, digital twins, 3D printing, green computing, robotics and policies that incentivize adoption.

This study adds to the normative literature on envisioning potential strategies for adoption and the future of G-IoT in construction and smart cities as an overlooked dimension. No previous study to date has reviewed pertinent literature in this area, intending to investigate the current applications, adoption strategies and future direction of G-IoT in construction and smart cities. Researchers can expand on the current study by exploring the identified G-IoT applications and adoption strategies in detail, and practitioners can develop implementation policies, regulations and guidelines for holistic G-IoT adoption.