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The nickel-based alloys Inconel 625 and Inconel 718 stand out due to their high strength and corrosion resistance in important industries like aerospace, aviation and automotive. Even though they are widely used, current techniques of producing materials that are difficult to cut pose several problems from a financial, ecological and even health perspective. To handle these problems and acquire improved mechanical and structural qualities, laser powder bed fusion (LPBF) has been widely used as one of the most essential additive manufacturing techniques. The purpose of this article is to focus on the state of the art on LPBF parts of Inconel 625 and Inconel 718 for microstructure, mechanical behavior and postprocessing.

The mechanical behavior of LPBF-fabricated Inconel is described, including hardness, surface morphology and wear, as well as the influence of fabrication orientation on surface quality, biocompatibility and resultant mechanical properties, particularly tensile strength, fatigue performance and tribological behaviors.

The postprocessing techniques such as thermal treatments, polishing techniques for surface enhancement, mechanical and laser-induced peening and physical operations are summarized.

The highlighted topic presents the critical aspects of the advantages and challenges of the LPBF parts produced by Inconel 718 and 625, which can be a guideline for manufacturers and academia in practical applications.

Additive manufacturing became the most popular method as it enables the production of light-weight and high-density parts in effective way. Selective laser melting (SLM) is preferred by means of producing a component with good surface quality and near-net shape even if it has complex form. Titanium alloys have been extensively used in engineering covering a variety of sectors such as aeronautical, chemical, automotive and defense industry with its unique material properties. Therefore, the purpose of this review is to study the tribological behavior and surface integrity that reflects the thermal and mechanical performances of the fabricated parts.

This paper is focused on the tribological and surface integrity aspects of SLM-produced titanium alloy components. It is aimed to outline the effect of SLM process parameters on tribology and surface integrity first. Then, thermal, thermal heat, thermomechanical and postprocessing surface treatments such as peening, surface modification and coatings are highlighted in the light of literature review.

This work studied the effects of particle characteristics (e.g. size, shape, distributions, flowability and morphology) on tribological performance according to an extensive literature survey.

This study addresses this blind spot in existing industrial-academic knowledge and goals to determine the impact of SLM process parameters, posttreatments (especially peening operations) and particle characteristics on the SLMed Ti-based alloys, which are increasingly used in biomedical applications as well as other many applications ranging from automobile, aero, aviation, maritime, etc. This review paper is created with the intention of providing deep investigation on the important material characteristics of titanium alloy-based components, which can be useful for the several engineering sectors.

This study drawing upon opportunity-ability-motivation (OAM) theory focuses on digital entrepreneurship opportunities (DEOs), knowledge generation capabilities (EKGCs) and enterprise market-sensing capabilities (EMSC) affecting digital innovation in terms of exploitative and exploratory DIs.

Employing quantitative methodology on a sample of 352 software SMEs' managers, the study employed a hierarchical regression analysis to investigate whether and how digital entrepreneurship opportunities and knowledge generation capabilities support and nurture both DIs. Additionally, the moderated–mediation effect of market-sensing capabilities on the relationships among digital entrepreneurship opportunities, enterprise knowledge generation capabilities and DIs are considered.

The study findings reveal that digital entrepreneurship opportunities influence exploitative and exploratory DIs. Knowledge generation capabilities partially mediate the relationship between digital entrepreneurship opportunities and exploitative and exploratory DIs, respectively. Moreover, market-sensing capabilities strengthen not only the effect of digital entrepreneurship opportunities on knowledge generation capabilities but also the effects of knowledge generation capabilities on exploratory DI. The moderated–mediation outcomes reveal that the mediating role of knowledge generation capabilities on the relationship between digital entrepreneurship opportunities and both DIs is stronger when EMSC are high.

This research integrates the opportunity-ability-motivation perspective to present a comprehensive framework that reveals the intricate interdependencies among digital entrepreneurship opportunity, knowledge generation and market-sensing capabilities in driving both exploratory and exploitative digital innovation in software SMEs. This approach significantly enhances our understanding of how software SMEs can strategically strengthen their internal skills and resources, ultimately leading to superior digital innovation outcomes.

Digitalization is changing the way entrepreneurs pursue opportunities. We have elaborated a conceptual framework to gain a better understanding of digital entrepreneurship. Using this framework, we have developed the Global Index of Digital Entrepreneurship Systems (GIDES), an analytical tool designed to measure and comprehend the impact of digitalization on entrepreneurship. This study aims to answer the research question of what specific bottlenecks are hindering the performance of digital entrepreneurial systems in the countries under investigation, with a particular focus on developing Asian economies.

GIDES is a composite indicator that evaluates the performance of digital entrepreneurial systems on a national scale. Unlike traditional entrepreneurship or most entrepreneurial ecosystem measures, GIDES adopts a systemic approach using the Penalty for Bottleneck (PFB) algorithm to spot bottlenecks that potentially degrade overall system performance.

GIDES assesses the digital entrepreneurship systems of 113 countries, with a specific focus on 21 developing Asian economies. Singapore is ranked first among developing Asian countries globally. However, most developing Asian economies have significant room for improvement. While developing Asia excels in terms of physical infrastructure, it needs to work on its culture and informal institutions.

Digital transformation is not happening in isolation. Instead, it is closely linked to and happens within the context of entrepreneurship. The level of digitalization of the economy, described by digital framework conditions, impacts entrepreneurial activity through their influence on national-level general and systemic framework conditions. Thus, GIDES monitors all the general, structural and digital frameworks that support digital entrepreneurship. Consequently, it offers a deeper understanding of how digitalization impacts entrepreneurship.

This study aimed to optimize the dyeing scheduling process with uncertain job completion time to reduce resource consumption and wastewater generation, and while reconciling the conflicting objectives of minimizing the makespan and the need to limit the production on specific machines to minimize rework.

We employed a UNISON framework that integrates fuzzy decision tree (FDT) to optimize dyeing machine scheduling by minimizing the makespan and water consumption, in which the critical attributes such as machine capacity and processing time can be incorporated into the scheduling model for smart production.

An empirical study of a high-tech textile company has shown the validity and effectiveness of the proposed approach in reducing the makespan and water consumption by over 8% while high product quality and efficiency being maintained.

High-tech textile industry is facing the challenges in reducing the environmental impact of the dyeing process while maintaining product quality and efficiency for smart production. Conventional scheduling approaches have not addressed the relationship between machine groups and reworking, resulting in difficulty in controlling the makespan and water consumption and increasing costs and environmental issues. The proposed approach has addressed uncertain job completion via integrating FDT into the scheduling process to effectively reduce makespan and wastewater. The results have shown practical viability of the developed solution in real settings.

This study aims to explore the factors influencing the evolution of emerging technology innovation network (ETIN) in combination with the key attributes and life cycle of emerging technologies, particularly the impact of multiple knowledge attributes and technology life cycle on the ETIN evolution.

This study collects 5G patent data and their citation information from the Derwent Innovations Index to construct a 5G technology innovation network (5GIN) as a sample network and conducts an empirical analysis of the 5GIN using the temporal exponential random graph model (TERGM).

The results indicate that during the 5GIN evolution, the network scale continues to expand and exhibits increasingly significant core-periphery structure, scale-free characteristic, small-world characteristic and community structure. Furthermore, the findings suggest that the multiple knowledge attributes based on the key attributes of emerging technologies, including knowledge novelty, coherence, growth and impact, have a significant positive influence on the ETIN evolution. Meanwhile, the temporal evolution of ETIN is also found to be correlated with the life cycle of emerging technologies.

This study extends the exploration of emerging technology research from a complex network perspective, providing a more realistic explanatory framework for the factors influencing ETIN evolution. It further highlights the important role that multiple knowledge attributes and the technology life cycle play within this framework.

The aim of this study is to investigate the application of advanced language models, particularly ChatGPT-4, in identifying and utilizing industrial symbiosis opportunities within the circular economy. It examines how the model can aid in promoting sustainable industrial practices by processing data from the MAESTRI project database, which includes various symbiotic relationships, as well as randomly selected waste codes not included in the database. The research involves structured queries related to industrial symbiosis, circular economy, waste codes and potential opportunities. By assessing the model’s accuracy in response generation, the study seeks to uncover both the capabilities and limitations of the language model in resource efficiency and waste reduction, emphasizing the need for ongoing refinement and expert oversight.

The study adopts a mixed-methods approach, combining qualitative and quantitative analyses to explore the potential of ChatGPT-4 in identifying industrial symbiosis opportunities. Data from the EU-funded MAESTRI project database, which includes existing symbiotic relationships, as well as randomly selected waste codes not included in the database, are used as the primary sources. The language model is queried with structured questions on industrial symbiosis, circular economy and specific waste codes utilizing the model’s advanced functions such as file upload. Responses are evaluated by comparing them with the MAESTRI database and official European Waste Catalogue (EWC) codes.

The study finds that ChatGPT-4 possesses a solid understanding of fundamental concepts related to industrial symbiosis and the circular economy. However, it encounters challenges in accurately describing EWC codes, with a notable portion of descriptions found to be incorrect. Despite these inaccuracies, the model shows potential in suggesting symbiotic opportunities, although its effectiveness is limited. Interestingly, the study reveals that the model can occasionally identify correct symbiotic relationships even with initial inaccuracies. These findings highlight the need for expert oversight and further development of the language model to improve its utility in complex, regulated fields like industrial symbiosis.

This study’s originality lies in its exploration of advanced language models, particularly ChatGPT-4, for identifying industrial symbiosis opportunities within the circular economy framework. Unlike previous research, which primarily focuses on specific sectors and AI’s role in general resource efficiency, this study specifically examines the capabilities and limitations of the language model in handling specialized and regulated information, such as EWC codes across various sectors. It employs a novel approach by comparing AI-generated responses with an established symbiosis database, which is comprehensive and spans all sectors rather than being limited to a single industry, as well as with randomly selected waste codes not included in the database. The study contributes to understanding how AI tools can support sustainable industrial practices, emphasizing the importance of refining these models for practical applications in environmental and industrial contexts.

This paper systematically presents a critical review of data envelopment analysis (DEA) for performance measurement in the construction field.

The review approach consists of a systematic literature search, validation analysis and content analysis. The paper systematically reviews previous papers according to the year of publication, journal, authors, region, research keywords, performance measurement indicators and methodology framework.

A total of 192 journal papers from the first year of publication to 2022 are identified. DEA applications have increased over the years, particularly after 2020. All of the top five journals which published the most identified papers are Q1 journals. Around 74 primary indicators of performance measurement are recognised for the construction industry, company and project levels. A total of 21 top keywords are identified and then divided into five clusters using VOSviewer. DEA has been widely utilised to measure construction performance and benchmark technologies, particularly where sustainable development performance has become a popular topic recently.

How to effectively distinguish performance indicators, collect research data, build DEA models and deeply analyse DEA results are illustrated for future practitioners. The performance measurement and improvement cycle based on DEA is provided. Research directions and method recommendations are developed for future scholars using DEA.

This is the first comprehensive review that has initially presented various performance indicators and a methodology framework for developing DEA models to investigate performance measurement in the construction field. The methodology framework of DEA is developed, including data collection, model construction and further analysis of DEA results.

An evidence-based approach to improving instructional practices and student outcomes in data use. It is a systematic process of evaluating and analysing learning problems, collecting and transforming various types of data into instructional decisions, and implementing informed actions to improve instruction and student learning. Since teachers are the main actors in instructional practices, this article reports on a study aimed at predicting the influence of various teachers’ characteristics on the degree of data use practices for instructional purposes.

In this study, we conducted a survey in a developing country to gather primary data. The collected data were analysed using a supervised machine learning approach, focussing on decision tree analysis, to determine the influential factors.

Our investigation identifies pedagogical knowledge, data literacy, content knowledge, knowledge of English for teaching and attitudes towards data as crucial determinants in predicting the intensity of such data use practices. Notably, pedagogical knowledge emerges as the most potent predictor, emphasising its pivotal role in shaping teachers’ frequency of instructional data use practices. Surprisingly, English proficiency does not exhibit a significant influence in this predictive model.

The findings may not be generalisable to a wider context since this study relied on a relatively small teacher self-reported sample collected through surveys, and, as this study used perception data, this may or may not reflect teachers’ actual knowledge and skills.

By spotlighting the nuanced interplay between teacher individual characteristics and the practice of data use for instructional improvement, this research contributes to a nuanced understanding of the factors shaping teachers’ engagement with data. Ultimately, it provides a foundation for targeted interventions and strategies aimed at fostering a culture of evidence-based practices to improve instruction and consequently student learning outcomes within educational settings.

This insight holds significant implications for policymakers, educational practitioners and providers of professional development programmes seeking to facilitate effective data use practices for instructional improvement.

By spotlighting the nuanced interplay between teacher individual characteristics and the practice of data use for instructional purposes, this research contributes to a nuanced understanding of the factors shaping teachers’ engagement with data. This study represents an empirical examination of such factors by employing a quantitative approach: a flexible decision tree analysis. This contributes to a growing body of research on factors related to teacher characteristics and much of the research in the field of data use has been done using a qualitative approach.

The study aims to investigate the factors influencing consumer buying behaviour towards online shopping in Ghana.

A web survey was employed to test the concept mode. Perceived ease of use (PEOU), perceived usefulness (PU), perceived hedonic value (PHV) and perceived trust (PT) served as the independent variables, while consumer online buying behaviour (ConOBB) served as the dependent variable. The convenience sampling method was adopted to solicit data from 437 respondents. SPSS 26 and STATA 17 were the analytical tools used for the study. The analysis of the data includes a structural equation model (SEM) to assess the measurement and the influence of PEOU, PU, PHV and PT on ConOBB.

The study revealed that PEOU (Beta = 0.262), PU (Beta = 0.216), PHV (Beta = 0.354) and PT (Beta = 0.630) all had a positive relationship with ConOBB. The study also established that there are significant relationships between the factors (PEOU, PU, PHV and PT) and ConOBB in Ghana.

The study focuses on Ghanaian consumers and emphasises mainly online shopping factors, which are PEOU, PU, PHV and PT. E-commerce businesses face fierce competition due to the increased availability of websites and other online platforms. To attract potential customers, companies must streamline processes, ensure user-friendliness and create a user-friendly experience.

The uptake of online shopping in Ghana is relatively slow compared with other countries, particularly in Africa. Online shoppers and service providers in Ghana are keen to sustain the industry. However, there is currently a scarcity of empirical studies in Ghana that focus on specific factors that influence consumer online buying behaviour. This study offers a new model that offers novel insights into the specific factors that aid in consumer online shopping behaviour in Ghana. The measuring instrument, which was found to be reliable and valid, also serves as an added value that this study offers. Both the model and measuring instrument can assist managers offering online shopping opportunities to be able to measure and formulate strategies that would enhance consumer online shopping experiences. By adding perceived hedonic value and trust to the model, this study offers a unique extension of the technology acceptance model. Thus, the findings add to the body of knowledge in the field of online shopping, particularly in the context of Ghana.