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This paper aims to introduce and explores the use of electrodermal activity (EDA) data as a tool for obtaining data about youth engagement during maker learning activities.

EDA and survey data were collected from a yearlong afterschool maker program for teens that met weekly and was hosted at a children’s museum. Data from four youth who were simultaneously present for eight weeks were examined to ascertain what experiences and activities were more or less engaging for them, based on psychophysiological measures.

Most of the focal youth appeared to show higher levels of engagement by survey measures throughout the program. However, when examined by smaller time intervals, certain activities appeared to be more engaging. Planning of maker activities was one space where engagement was higher. Completing sewing projects with minimal social interaction appeared to be less engaging. Specific activities involving common maker technologies yielded mixed levels of engagement.

Some research is emerging that uses EDA data as a basis for generating inferences about various states while participating in maker learning activities. This paper provides a novel analysis building on some techniques established in the still emergent body of prior research in this area.

This study aims to synthesize two different benzoxazines (Bz) monomers using bio-based and petroleum-based primary amines, respectively, and they have been compared to study their thermal and mechanical performances.

A bio-based bisphenol, Divanillin (DiVa), was formed by reacting two moles of vanillin with one mole of ethylenediamine (EDA) which was then reacted firstly with paraformaldehyde and EDA to form the benzoxazine DiVa-EDA-Bz, and secondly with paraformaldehyde and furfuryl amine (FFA) to form the benzoxazine DiVa-FFA-Bz. The molecular structure and thermal properties of the benzoxazines were characterized by fourier transform infrared spectroscopy and nuclear magnetic resonance (1H,13C) spectroscopies, differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA), respectively. The benzoxazines were further coated on mild steel panels to evaluate their mechanical properties and chemical resistance.

The DSC results of DiVa-FFA-Bz showed two exothermic peaks related to crosslinking compared to the one in DiVa-EDA-Bz. The DiVa-FFA-Bz also showed a higher heat of polymerization than DiVa-EDA-Bz. The TGA results showed that DiVa-FFA-Bz exhibited higher thermal stability with a residual char of 54.10% than 43.24% for DiVa-EDA-Bz. The chemical resistance test results showed that DiVa-FFA-Bz showed better chemical resistance and mechanical properties due to its higher crosslinking density.

This study shows the use of bio-based materials, vanillin and FFA, for synthesizing a benzoxazine resin and its application at high temperatures.

This paper introduces a new hybrid evolutionary algorithm (EA) for continuous global optimization problems, called estimation of distribution algorithm with local search (EDA/L). Like other EAs, EDA/L maintains and improves a population of solutions in the feasible region. Initial candidate solutions are generated by uniform design, these solutions evenly scatter over the feasible solution region. To generate a new population, a marginal histogram model is built based on the global statistical information extracted from the current population and then new solutions are sampled from the model thus built. The incomplete simplex method applies to every new solution generated by uniform design or sampled from the histogram model. Unconstrained optimization by diagonal quadratic approximation applies to several selected resultant solutions of the incomplete simplex method at each generation. We study the effectiveness of main components of EDA/L. The experimental results demonstrate that EDA/L is better than four other recent EAs in terms of the solution quality and the computational cost.

China is a critical factor for constructing an all-round well-off society. Infrastructure construction, especially high-grade highways, in the western area is an essential component of the strategy for large-scale development of west China. It is crucial to evaluate investment projects for high-grade highways and select the best one. Testing investment projects and selecting the best one can be recognized as a multicriteria decision-making (MCDM) problem. In this process, decision-makers (DMs) usually face with uncertain information because of complicated decision environment or their limited knowledge.

A new Evaluation based on the Distance from Average Solution (EDAS) for PFS based on the DEMATEL is proposed: The authors offer a new score function and prove some properties for the score function. They put forward a novel Decision-making Trial and Evaluation Laboratory (DEMATEL) method for PFS to analyze the relations of criteria and get criteria weights. Considering the bounded rationality of DM, the authors propose a new EDAS method for PFS based on prospect theory. They apply their proposed approach to a western city's actual case in selecting a suitable project for building a high-grade highway.

By comparison, the authors can observe that our method has some traits: (1) considering bounded rationality of DM; (2) fewer computation; (3) having the ability to obtain the relation of criteria and finding the critical factor in the decision system.

In this paper, the authors propose a new EDAS method for PFS based on the DEMATEL technique. They transform PFS into crisp numbers by their proposed new score function for PFN to make the decision process more convenient. Then, the authors use the DEMATEL method to obtain the relationship between criteria and criteria weights. Furthermore, they propose a new EDAS method for PFS based on DEMATEL to reduce the computational complexity. Finally, they apply our method to a real case and compare our method with two traditional methods.

Reflecting, reliving and reforming experiences enhance longer-term effects of travel and tourism, and have been highlighted as an important aspect in determining loyalty, re-visitation and post-consumption satisfaction. The purpose of this paper is to develop new methodological approaches to investigate emotion, memory creation and the resulting psychosocial effects.

The paper proposes a unique combination of physiological measures and photoelicitation-based discussions within a longitudinal design. A physiological measuring instrument (electrodermal activity [EDA] tracking technology through Empatica E4 wristbands) is utilised to capture the “unadulterated” emotional response both during the experience and in reliving or remembering it. This is combined with post-experience narrative discussion groups using photos and other artefacts to give further understanding of the process of collective memory creation.

EDA tracking can enhance qualitative research methodologies in three ways: through use as an “artefact” to prompt reflection on feelings, through identifying peaks of emotional response and through highlighting changes in emotional response over time. Empirical evidence from studies into participatory arts events and the potential well-being effects upon women over the age of 70 is presented to illustrate the method.

The artificial environment created using experimental approaches to measure emotions and memory (common in many fields of psychology) has serious limitations. This paper proposes new and more “natural” methods for use in tourism, hospitality and events research, which have the potential to better capture participants’ feelings, behaviours and the meanings they place upon them.

Nowadays, the field of data analytics is witnessing an unprecedented interest from a variety of stakeholders. The purpose of this paper is to contribute to the subfield of predictive analytics by proposing a new non-parametric classifier.

The proposed new non-parametric classifier performs both in-sample and out-of-sample predictions, where in-sample predictions are devised with a new Evaluation Based on Distance from Average Solution (EDAS)-based classifier, and out-of-sample predictions are devised with a CBR-based classifier trained on the class predictions provided by the proposed EDAS-based classifier.

The performance of the proposed new non-parametric classification framework is tested on a data set of UK firms in predicting bankruptcy. Numerical results demonstrate an outstanding predictive performance, which is robust to the implementation decisions’ choices.

The exceptional predictive performance of the proposed new non-parametric classifier makes it a real contender in actual applications in areas such as finance and investment, internet security, fraud and medical diagnosis, where the accuracy of the risk-class predictions has serious consequences for the relevant stakeholders.

Over and above the design elements of the new integrated in-sample-out-of-sample classification framework and its non-parametric nature, it delivers an outstanding predictive performance for a bankruptcy prediction application.

The corrosion behaviour of copper in dilute HCl solution of pH 1.8‐2, in the absence and presence of different additives of Na₂S, was studied by open circuit potential measurement, scanning electron microscopy and polarisation techniques. A complete covering of the surface with the strongly chemisorbed HS⁻ is achieved at ≥10 ppm Na₂S concentration. The addition of different concentrations of ethylene diamine (EDA) to HCl of pH 1.8‐2 promoted the dissolution rate, due to an increase in autocatalytic dissolution at the Cu metal surface. However, the synergistic inhibition effect between EDA and Na₂S, resulting from the adsorption of HS⁻ on the Cu metal, facilitated the adsorption of EDA.

In recent years, knowledge graph completion has gained increasing research focus and shown significant improvements. However, most existing models only use the structures of knowledge graph triples when obtaining the entity and relationship representations. In contrast, the integration of the entity description and the knowledge graph network structure has been ignored. This paper aims to investigate how to leverage both the entity description and the network structure to enhance the knowledge graph completion with a high generalization ability among different datasets.

The authors propose an entity-description augmented knowledge graph completion model (EDA-KGC), which incorporates the entity description and network structure. It consists of three modules, i.e. representation initialization, deep interaction and reasoning. The representation initialization module utilizes entity descriptions to obtain the pre-trained representation of entities. The deep interaction module acquires the features of the deep interaction between entities and relationships. The reasoning component performs matrix manipulations with the deep interaction feature vector and entity representation matrix, thus obtaining the probability distribution of target entities. The authors conduct intensive experiments on the FB15K, WN18, FB15K-237 and WN18RR data sets to validate the effect of the proposed model.

The experiments demonstrate that the proposed model outperforms the traditional structure-based knowledge graph completion model and the entity-description-enhanced knowledge graph completion model. The experiments also suggest that the model has greater feasibility in different scenarios such as sparse data, dynamic entities and limited training epochs. The study shows that the integration of entity description and network structure can significantly increase the effect of the knowledge graph completion task.

The research has a significant reference for completing the missing information in the knowledge graph and improving the application effect of the knowledge graph in information retrieval, question answering and other fields.

The purpose of this paper is to develop a model for sustainable manufacturing by adopting a combined approach using AHP, fuzzy TOPSIS and fuzzy EDAS methods. The proposed model aims to identify and prioritize the sustainable factors and technical requirements that help in improving the sustainability of manufacturing processes.

The proposed approach integrates both AHP, Fuzzy EDAS and Fuzzy TOPSIS. AHP method is used to generate the weights of the sustainable factors. Fuzzy EDAS and Fuzzy TOPSIS are applied to rank and determine the application priority of a set of improvement approaches. The ranks carried out from each MCDM approach is assessed by computing the spearman's correlation coefficient.

The results reveal the proposed model is efficient in sustainable factors and the technical requirements prioritizing. In addition, the results carried out from this study indicate the high efficiency of AHP, Fuzzy EDAS and Fuzzy TOPSIS in decision making. Besides, the results indicate that the model provides a useable methodology for managers' staff to select the desirable sustainable factors and technical requirements for sustainable manufacturing.

The main limitation of this paper is that the proposed approach investigates an average number of factors and technical requirements.

This paper investigates an integrated MCDM approach for sustainable factors and technical requirements prioritization. In addition, the presented work pointed out that AHP, Fuzzy EDAS and Fuzzy TOPSIS approach can manipulate several conflict attributes in a sustainable manufacturing context.

Due to the high rate of failure of most crowdfunding projects, knowing the most essential factor to obtain funding success on the crowdfunding platform is of great importance for fund seekers on the crowdfunding platform. The purpose of this study is to explore crowdfunding success factors to know the most essential success factor for stakeholders of the crowdfunding platform to make the best decision when seeking funds on the crowdfunding platform. This study identified and ranked crowdfunding success factors for stakeholders of crowdfunding platforms. Sixteen factors were identified and categorized under five broad headings. These were; project ideas, target capital, track records, geographical proximity and equity.

To rank the identified crowdfunding success factors and subfactors, this study used the Multi-Objective Optimization Based on Ratio Analysis (MULTIMOORA) integrated with the Evaluation based on Distance from Average Solutions (EDAS).

Target capital ranked first among the five categories—while duration involved in raising funds ranked first among the sixteen subfactors. An approach for analyzing how each success factor enhances a crowdfunding campaign was developed in this study. This study provides valuable insight to fund seekers on the crowdfunding platform on how funding success can be achieved by knowing which factor to consider essential when seeking funds on the crowdfunding platform.

This is the first study to explore crowdfunding success factors using the MULTIMOORA-EDAS method. The use of this method will help fund seekers on the crowdfunding platform to know which crowdfunding success factor is essential, thereby aiding fund seekers to make the best decision when seeking funds on the crowdfunding platform. Also, this study is particularly helpful for business owners, platform operators and policymakers when deciding how to allocate resources, plan campaigns and implement regulations.