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Fused Filament Fabrication (FFF) is one of the growing technologies in additive manufacturing, that can be used in a number of applications. In this method, process parameters can be customized and their simultaneous variation has conflicting impacts on various properties of printed parts such as dimensional accuracy (DA) and surface finish. These properties could be improved by optimizing the values of these parameters.

In this paper, four process parameters, namely, print speed, build orientation, raster width, and layer height which are referred to as “input variables” were investigated. The conflicting influence of their simultaneous variations on the DA of printed parts was investigated and predicated. To achieve this goal, a hybrid Genetic Algorithm – Artificial Neural Network (GA-ANN) model, was developed in C#.net, and three geometries, namely, U-shape, cube and cylinder were selected. To investigate the DA of printed parts, samples were printed with a central through hole. Design of Experiments (DoE), specifically the Rotational Central Composite Design method was adopted to establish the number of parts to be printed (30 for each selected geometry) and also the value of each input process parameter. The dimensions of printed parts were accurately measured by a shadowgraph and were used as an input data set for the training phase of the developed ANN to predict the behavior of process parameters. Then the predicted values were used as input to the Desirability Function tool which resulted in a mathematical model that optimizes the input process variables for selected geometries. The mean square error of 0.0528 was achieved, which is indicative of the accuracy of the developed model.

The results showed that print speed is the most dominant input variable compared to others, and by increasing its value, considerable variations resulted in DA. The inaccuracy increased, especially with parts of circular cross section. In addition, if there is no need to print parts in vertical position, the build orientation should be set at 0° to achieve the highest DA. Finally, optimized values of raster width and layer height improved the DA especially when the print speed was set at a high value.

By using ANN, it is possible to investigate the impact of simultaneous variations of FFF machines’ input process parameters on the DA of printed parts. By their optimization, parts of highly accurate dimensions could be printed. These findings will be of significant value to those industries that need to produce parts of high DA on FFF machines.

This study aims to examine the technical efficiency of the chemical-free farming system in India using a hybrid combination of data envelopment analysis (DEA) and machine learning (ML) approaches.

The study used a two-stage approach. In the first stage, the efficiency scores of decision-making units’ efficiency (DMUs) are obtained using an input-oriented DEA model under the assumption of a variable return to scale. Based on these scores, the DMUs are classified into efficient and inefficient categories. The 2nd stage of analysis involves the identification of the most important predictors of efficiency using a random forest model and a generalized logistic regression model.

The results show that by using their resources efficiently, growers can reduce their inputs by 34 percent without affecting the output. Orchard's size, the proportion of land, grower's age, orchard's age and family labor are the most important determinants of efficiency. Besides, growers' main occupation and footfall of intermediaries at the farm gate also demonstrate significant influence on efficiency.

The study used only one output and a limited set of input variables. Incorporating additional variables or dimensions like fertility of the land, climatic conditions, altitude of the land, output quality (size/taste/appearance) and per acre profitability could yield more robust results. Although pineapple is cultivated in all eight northeastern states, the data for the study has been collected from only two states. The production and marketing practices followed by the growers in the remaining six northeastern states and other parts of the country might be different. As the growers do not maintain farm records, their data might suffer from selective retrieval bias.

Given the rising demand for organic food, improving the efficiency of chemical-free growers will be a win-win situation for both growers and consumers. The results will aid policymakers in bringing necessary interventions to make chemical-free farming more remunerative for the growers. The business managers can act as a bridge to connect these remote growers with the market by sharing customer feedback and global best practices.

Although many developments have happened to the DEA technique, the present study used a traditional form of DEA. Therefore, future research should combine ML techniques with more advanced versions like bootstrap and fuzzy DEA. Upcoming research should include more input and output variables to predict the efficiency of the chemical-free farming system. For instance, environmental variables, like climatic conditions, degree of competition, government support and consumers' attitude towards chemical-free food, can be examined along with farm and grower-specific variables. Future studies should also incorporate chemical-free growers from a wider geographic area. Lastly, future studies can also undertake a longitudinal estimation of efficiency and its determinants for the chemical-free farming system.

No prior study has used a hybrid framework to examine the performance of a chemical-free farming system.

This study aimed to improve the financing credit evaluation for small and medium-sized real estate enterprises (SMREEs). A financing credit evaluation model was proposed, and a blockchain-driven financing credit evaluation framework was designed to improve the transparency, credibility and applicability of the financing credit evaluation process.

The design science research methodology was adopted to identify the main steps in constructing the financing credit model and blockchain-driven framework. The fuzzy analytic hierarchy process (FAHP)–entropy weighting method (EWM)–set pair analysis (SPA) method was used to design a financing credit evaluation model. Moreover, the proposed framework was validated using data acquired from actual cases.

The results indicate that: (1) the proposed blockchain-driven financing credit evaluation framework can effectively realize a transparent evaluation process compared to the traditional financing credit evaluation system. (2) The proposed model has high effectiveness and can achieve efficient credit ranking, reflect SMREEs' credit status and help improve credit rating.

This study proposes a financing credit evaluation model of SMREEs based on the FAHP–EWM–SPA method. All credit rating data and evaluation process data are immediately stored in the proposed blockchain framework, and the immutable and traceable nature of blockchain enhances trust between nodes, improving the reliability of the financing credit evaluation process and results. In addition, this study partially fulfills the lack of investigations on blockchain adoption for SMREEs' financing credit.

Many educators are increasingly acquainting themselves and becoming adept with interactive technologies like augmented reality and virtual reality. Some of them are also looking forward to using Metaverse applications, as they want to benefit from its immersive three-dimensional capabilities. Therefore, the purpose of this study is to critically review the extant literature to investigate how, why, where and when the Metaverse can be used for educational purposes. This study also discusses opportunities, challenges and risks related to this disruptive technology.

A Preferred Reporting Items for Systematic Reviews and Meta-Analyses rigorous protocol is used to search, extract, scrutinize and synthesize content from high-impact articles focused on the use of the Metaverse technology in the realms of education. Afterwards, this study theorizes on the costs and benefits of using this interactive technology with students.

A number of researchers are already experimenting with virtual technologies that are very similar to the Metaverse, in different contexts. This research indicates that most students are lured by immersive multi-sensory three-dimensional environments as well as by virtual reality applications that could simulate real-life situations and provide engaging experiences with virtual representations of people, places and objects. On the other hand, this study reveals that educators ought to consider the potential pitfalls of the Metaverse, including privacy breaches and security risks, as well as possible addictions and the development of mental health issues, among others.

Students and educators can use the Metaverse to catapult themselves in a simulated digital universe that could reconfigure their sensory inputs, definitions of space, time and points of access to information. This research calls for the development of regulatory instruments, including sound principles, guidelines and procedures that are intended to safeguard and protect Metaverse users.

This contribution implies that there is scope for educators to continue developing the Metaverse’s virtual spaces to improve their students’ motivations, aptitudes and learning outcomes. This study clarifies that the use of the Metaverse in education can create infinite possibilities to enhance their knowledge, competences and abilities through its immersive applications. Yet this paper also raises awareness about possible challenges in the short term as well on other risks associated to the prolonged use of this captivating technology.

In recent years, fire accidents in engineering structures have often been reported worldwide, leading to a severe risk to life and property safety. The present study is carried out to evaluate the performance of Ground Granulated Blast Furnace Slag (GGBS) and fly ash–blended laterized mortars at elevated temperatures.

This test program includes the replacement of natural river sand with lateritic fine aggregates (lateritic FA) in terms of 0, 50 and 100%. Also, the ordinary Portland cement (OPC) was replaced with fly ash and GGBS in terms of 10, 20, 30% and 20, 40 and 60%, respectively, for producing blended mortars.

This paper presents results related to the determination of residual compressive strengths of lateritic fine aggregates-based cement mortars with part replacement of cement by fly ash and GGBS exposed to elevated temperatures. The effect of elevated temperatures on the physical and mechanical properties was evaluated with the help of microstructure studies and the quantification of hydration products.

A sustainable cement mortar was produced by replacing natural river sand with lateritic fine aggregates. The thermal strength deterioration features were assessed by exposing the control specimens and lateritic fine aggregates-based cement mortars to elevated temperatures. Changes in the mechanical properties were evaluated through a quantitative microstructure study using scanning electron microscopy (SEM) images. The phase change of hydration products after exposure to elevated temperatures was qualitatively analyzed by greyscale thresholding of SEM images using Image J software.

Although the Net Promoter Score (NPS) index is simple, NPS has weaknesses that make NPS's interpretation misleading. The main criticism is that identical index values can correspond to different levels of customer loyalty. This makes difficult to determine whether the company is improving/deteriorating in two different years. The authors describe the application of statistical tools to establish whether identical values may/may not be considered similar under statistical hypotheses.

Equal NPSs with a “similar” component composition should have a two-way table satisfying marginal homogeneity hypothesis. The authors compare the marginals using a cumulative marginal logit model that assumes a proportional odds structure: the model has the same effect for each logit. Marginal homogeneity corresponds to null effect. If the marginal homogeneity hypothesis is rejected, the cumulative odds ratio becomes a tool for measuring the proportionality between the odds.

The authors propose an algorithm that helps managers in their decision-making process. The authors' methodology provides a statistical tool to recognize customer base compositions. The authors suggest a statistical test of the marginal distribution homogeneity of the table representing the index compositions at two times. Through the calculation of cumulative odds ratios, the authors discriminate against the hypothesis of equality of the NPS.

The authors' contribution provides a statistical alternative that can be easily implemented by business operators to fill the known shortcomings of the index in the customer satisfaction's context. This paper confirms that although a single number summarizes and communicates a complex situation very quickly, the number is ambiguous and unreliable if not accompanied by other tools.

This paper aims to explain the occurrence of sustainable entrepreneurship in the underresearched sub-Saharan Africa context and to improve the understanding of how effectuation manifests in this context.

This study uses a qualitative research approach based on multiple case studies. Responses were sourced from owners, employees, suppliers, the community and customers of sustainable entrepreneurial firms. Data collection methods included in-depth interviews, document reviews and observations. The Gioia analytical approach was used.

Overall, the authors find the concept of effectuation to be well-suited to capturing the processes through which individuals with limited means seek to engage in sustainable entrepreneurship. The authors also identify three pervasive practices that are key to understanding effectuation in the developing country context: complementation of indigenous knowledge with modern science, compassion and pluriactivity.

This study underscores the importance of co-creation, diversification of revenue sources and the role of emotional awareness and interpersonal skills in entrepreneurial endeavors.

This study, therefore, contributes new knowledge about the mechanisms through which entrepreneurs faced with resource constraints use control as opposed to prediction strategies to exploit sustainable entrepreneurship opportunities. In so doing, this study contributes to entrepreneurship theory by proposing the integration of cognitive and affective dimensions in realizing sustainable entrepreneurship goals.

The high specific strength makes magnesium alloys have a wide range of applications in aerospace, military, automotive, marine and construction industries. However, its poor corrosion resistance and weldability have limited its development and application. Friction stir welding (FSW) can effectively avoid the defects of fusion welding. However, the microstructure, mechanical properties and corrosion behavior of FSW joints in magnesium alloys vary among different regions. The purpose of this paper is to review the corrosion of magnesium alloy FSW joints, and to summarize the protection technology of welded joints.

The corrosion of magnesium alloy FSW joints includes electrochemical corrosion and stress corrosion. This paper summarizes corrosion protection techniques for magnesium alloys FSW joints, focusing on composition, microstructure changes and surface treatment methods.

Currently, this research is mainly focused on enhancing the corrosion resistance of magnesium alloy FSW joints by changing compositions, structural modifications and surface coating technologies. Refinement of the grains can be achieved by adjusting welding process parameters, which in turn minimizes the effects of the second phase on the alloy’s corrosion resistance.

This paper presents a comprehensive review on the corrosion and protection of magnesium alloys FSW joints, covering the latest research advancements and practical applications. It aims to equip researchers with a better insight into the field and inspire new studies on this topic.

This paper aims to identify the factors that can affect the overall graduate employability (OGE) of the private university graduates of Bangladesh. The authors carefully selected six such employable factors after searching the existing literature. Those six factors: academic performance (AP), technical skills (TS), communication skills (CS), personality (PE), leadership & motivational skills (LMS); and teamwork and problem solving skills (TPSS), had been considered as the independent variables while OGE had been considered as the single dependent variable.

The authors collected the primary data from a valid sample of 360 employers through a structured questionnaire working as the hiring managers. Those respondents were selected on a random basis. The authors used exploratory factor analysis to validate the items under those independent variables and structural equation modeling with AMOS (24) to test the hypothesized relationship between each independent variable and the dependent one.

After proper statistical analysis, the results revealed that AP, PE, CS and TPSS can positively and significantly influence the OGE of Bangladeshi graduates while LMS and TS have positive but insignificant influence over OGE.

Based on the findings, this paper can help scholars in further investigating the employability factors.

This explorative study will guide the fresh graduates in developing their required employability skills while assisting the employers in recruiting suitable candidates with the required skills and performance.

This is one of the few attempts that focused on the employability factors of private university graduates in Bangladesh. The authors are well confident that this empirical paper can shed some light on the fresh graduates’ employability and conducting further investigations on it.