• Energy Research
• 2021-2025close
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Bridges in Ukraine are one of the most important components of the infrastructure, requiring attention from government agencies and constant funding. The object of the study was the methodology for quantifying the condition of bridge components. The Artificial Neural Network-based (ANN) tool was developed to quantify the technical condition of bridge components. The literature analysis showed that in most cases the datasets were obtained during the inspection of bridges to solve the problems of assessing the current technical condition. The lack of such a database prompted the creation of a dataset on the basis of the Classification Tables of the Operating Conditions of the Bridge Components (CT). Based on CTs, five datasets were formed to assess the condition of the bridge components: bridge span, bridge deck, pier caps beam, piers and abutments, approaches. The next step of this study was creating, training, validating and testing ANN models. The network with ADAM loss function and softmax activation showed the best results. The optimal values of MAPE and R2 were achieved at the 100th epoch with 64 neurons in the hidden layer and were equal to 0.1% and 0.99998, respectively. The practical application of the ANN models was carried out on the most common type of bridge in Ukraine, namely, a road beam bridge of small length, made of precast concrete. The novelty of this study consists of the development of a tool based on the use of ANN model, and the proposal to modify the methodology for quantifying the condition of bridge components. This will allow minimizing the uncertainties associated with the subjective judgments of experts, as well as increasing the accuracy of the assessment.

To reduce plastic waste generation from failed product batches during industrial injection molding, the sustainable production of representative prototypes is essential. Interesting is the more recent hybrid injection molding (HM) technique, in which a polymeric mold core and cavity are produced via additive manufacturing (AM) and are both placed in an overall metal housing for the final polymeric part production. HM requires less material waste and energy compared to conventional subtractive injection molding, at least if its process parameters are properly tuned. In the present work, several options of AM insert production are compared with full metal/steel mold inserts, selecting isotactic polypropylene as the injected polymer. These options are defined by both the AM method and the material considered and are evaluated with respect to the insert mechanical and conductive properties, also considering Moldex3D simulations. These simulations are conducted with inputted measured temperature-dependent AM material properties to identify in silico indicators for wear and to perform cooling cycle time minimization. It is shown that PolyJetted Digital acrylonitrile-butadiene-styrene (ABS) polymer and Multi jet fusioned (MJF) polyamide 11 (PA11) are the most promising. The former option has the best durability for thinner injection molded parts, and the latter option the best cooling cycle times at any thickness, highlighting the need to further develop AM options.

Digital Transformations (DT) play an increasingly important role in academia and business, yet their significant Environmental Footprint (EF) is often overlooked, sidelining their potential for Environmental Sustainability (ES). This paper bridges this gap by integrating ES into the discourse of DT, proposing Green Enterprise Architecture (GREAN) as a method for sustainable transformation. Utilizing a Design Science Research approach, we developed an artefact outlining a comprehensive strategy for embedding ES in DT across various layers of an organization. The tool’s need was validated via a systematic literature review (SLR), highlighting the significant research gap in Green Enterprise Architecture. The artefact provides concrete Courses of Action (CoAs) for incorporating ES into the organizational strategy, business, data, application, and technology layers and proposes relevant capabilities to address this. The paper further presents an ES-aware business capability modelling, an innovative business modelling approach that integrates environmental sustainability principles by using (in a novel way) the presentation and analysis methods that capability mapping offers. The proposed artefact serves as a starting point for environmentally sustainable DTs. Future research directions include in-depth exploration of each enterprise layer for ES, real-world validation of our proposed tools and concepts, and the expansion of these into a full framework.

Although the United Nations’ Sustainable Development Goals (SDGs) advocate, through SDG 4 and SDG 10, equitable quality education and the reduction of inequalities within and between countries, respectively, few studies have examined how inequalities in regional sustainability influence higher education. Therefore, this study aims to examine the relationship between regional sustainability and higher education in China using fixed-effects panel modelling. A systematic force framework showing how regional sustainability drives higher education was constructed from economic, social, and environmental perspectives, and the endogeneity in the process of how regional sustainability affects higher education was explored by introducing one-year lagged values as instrumental variables. Our results show that regional sustainability has a significant impact on higher educational attainment in China, with differing effects in the eastern, central, and western regions, respectively. In central China, economic sustainability plays a significant positive role in higher educational attainment; in the western region, economic and social sustainability have stronger positive effects, while environmental sustainability has significantly negative effects. In terms of policy implications, our findings can be used to support regional development policies to promote regional higher education.

Walkability has become a research topic of great concern for preserving public health, especially in the era of the COVID-19 outbreak. Today more than ever, urban and transport policies, constrained by social distancing measures and travel restrictions, must be conceptualized and implemented with a particular emphasis on sustainable walkability. Most of the walkability models apply observation and subjective methods to measure walkability, whereas few studies address walkability based on sense perception. To fill this gap, we aim at investigating the perceived neighbourhood walkability (PNW) based on sense perception in a neighbourhood of Brussels. We designed a survey that integrates 22 items grouped into 5 dimensions (cleanness, visual aesthetics, landscape and nature, feeling of pressure, feeling of safety), as well as the socio-demographic attributes of the participants. Using various statistical methods, we show that socio-demographics have almost no effects on perceived neighbourhood walkability. Nonetheless, we found significant differences between groups of different educational backgrounds. Furthermore, using a binomial regression model, we found strong associations between PNW and at least one item from each grouping dimension. Finally, we show that based on a deep neural network for classification, the items have good predictive capabilities (78% of classification accuracy). These findings can help integrate sense perception into objective measurement methods of walkable environments. Additionally, policy recommendations should be targeted based on differences of perception across socio-demographic groups.

This study uniquely employs a fuzzy-set qualitative comparative analysis (fsQCA) technique to account for complex relationships in consumption. The fsQCA technique assumes that relationships are based on a set–subset relationship. This assumption is fundamental when decision-makers are affected by information asymmetry and are, thus, required to jointly evaluate the credibility and reliability of a range of external signals. This issue also affects consumers in markets for cultural goods, where the quality of products is not known with certainty in advance of the purchase decision. Our study uses fsQCA to establish the effect of different quality signals on consumption in the US market for video game software. Our results show that reviews from professional critics alongside brand extension and multi-platform release strategies act as signals of product quality and, therefore, lead to high sales performance.

Recently, robots have been widely adopted in the hospitality and tourism industry. Efficient robots can help hoteliers and tourism suppliers with their repetitive or manual labor. Due to the coronavirus disease (COVID-19) pandemic, there is an increasing number of publications on robotic applications in hospitality and tourism. However, a comprehensive literature review of this realm remains lacking. Therefore, to provide a holistic view of the existing literature on robotic applications in hospitality and tourism, this study reviewed 86 extant robotic application-related articles by conducting descriptive analysis and content analysis. The findings of this study showed that most of the existing relevant studies were conducted from the perspective of consumers in the hospitality context. Potential future research directions for academics are identified herein. Practical implications on robotic adoption are also provided for industry practitioners.

Despite the large quantities of secondary materials flowing within the built environment, their actual volume and respective waste management processes are not accurately known and recorded. Consequently, various sustainability and material efficiency policies are not supported by accurate data and information-reporting associated with secondary materials’ availability and sourcing. Many recent studies have shown that the integration of digital technologies such as city information management (CIM), building information modeling (BIM), and blockchain have the potential to enhance construction waste management (CWM) by classifying recycled materials and creating value from waste. However, there is insufficient guidance to address the challenges during the process of CWM. Therefore, the research reported in this paper aims to develop a blockchain-enhanced construction waste information management conceptual framework (BeCW). This paper is the first attempt to apply the strengths of integrated information-management modeling with blockchain to optimize the process of CWM, which includes a WasteChain for providing a unified and trustworthy credit system for evaluating construction-waste-recyclability to stakeholders. This is enabled through the use of blockchain and self-executing smart contracts to clarify the responsibility and ownership of the relevant stakeholders. As a result, this study provides a unified and explicit framework for referencing which quantifies the value-contribution of stakeholders to waste-recovery and the optimization of secondary construction materials for reuse and recycling. It also addresses the issue of sustainable CWM through information exchange at four levels: user, application, service, and infrastructure data levels.