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The purpose of this study is to evaluate the 3D printability of a multimaterial, fully self-supporting auxetic structure. This will contribute to expanding the application of additive manufacturing (AM) to new products, such as automotive suspensions.

An experimental approach for sample fabrication on a multiextruder 3D printer and characterization by compression testing was conducted along with numerical simulations, which were used to support the design of different auxetic configurations for the jounce bumper.

The effect of stacking different auxetic cell modules was discussed, and the findings demonstrated that a one-piece printed structure has a better performance than one composed of multiple single modules stacked on top of each other.

The quality of the 3D printing process affected the performance of the final components and reproducibility of the results. Therefore, researchers are encouraged to further study component fabrication optimization to achieve a more reliable process.

This research work can help improve the manufacturing and functionality of a critical element of automotive suspension systems, such as the jounce bumper, which can efficiently reduce noise, vibration and harshness by absorbing impact energy.

In previous research, auxetic structures for the application of jounce bumpers have already been suggested. However, to the best of the authors’ knowledge, in this work, an AM approach was used for the first time to fabricate multimaterial auxetic structures, not only by co-printing a flexible thermoplastic polymer with a stiffer one but also by continuously extruding multilevel structures of auxetic cell modules.

This paper aims to investigate the feasibility of adding macro-textures to triangle meshes for additive manufacturing (AM) focusing on possible time and quality issues in both software processing and part fabrication.

A demonstrative software tool was developed to apply user-selected textures to existing meshes. The computational procedure is a three-dimensional extension of the solid texturing method used in computer graphics. The tool was tested for speed and quality of results, considering also the pre- and post-processing operations required. Some textured meshes were printed by different processes to test build speed and quality.

The tool can handle models with realistic complexity in acceptable computation times. Parts are built without difficulties or extra-costs achieving a good aesthetic yield of the texture.

The tool cannot reproduce sample patterns but requires the development of a generation algorithm for different type of textures. Mesh processing operations may take a long time when very fine textures are added to large parts.

Direct texturing can help obtain parts with aesthetic or functional textures without the need for surface post-treatments, which can be especially difficult and expensive for plastic parts.

The proposed method improves the uniformity and consistency of textures compared to existing approaches, and can support future systematic studies on the detail resolution of AM processes.

This paper provides practical advice for archaeologists and heritage specialists wishing to use ML approaches to identify archaeological features in high-resolution satellite imagery (or other remotely sensed data sources). We seek to balance the disproportionately optimistic literature related to the application of ML to archaeological prospection through a discussion of limitations, challenges and other difficulties. We further seek to raise awareness among researchers of the time, effort, expertise and resources necessary to implement ML successfully, so that they can make an informed choice between ML and manual inspection approaches.

Automated object detection has been the holy grail of archaeological remote sensing for the last two decades. Machine learning (ML) models have proven able to detect uniform features across a consistent background, but more variegated imagery remains a challenge. We set out to detect burial mounds in satellite imagery from a diverse landscape in Central Bulgaria using a pre-trained Convolutional Neural Network (CNN) plus additional but low-touch training to improve performance. Training was accomplished using MOUND/NOT MOUND cutouts, and the model assessed arbitrary tiles of the same size from the image. Results were assessed using field data.

Validation of results against field data showed that self-reported success rates were misleadingly high, and that the model was misidentifying most features. Setting an identification threshold at 60% probability, and noting that we used an approach where the CNN assessed tiles of a fixed size, tile-based false negative rates were 95–96%, false positive rates were 87–95% of tagged tiles, while true positives were only 5–13%. Counterintuitively, the model provided with training data selected for highly visible mounds (rather than all mounds) performed worse. Development of the model, meanwhile, required approximately 135 person-hours of work.

Our attempt to deploy a pre-trained CNN demonstrates the limitations of this approach when it is used to detect varied features of different sizes within a heterogeneous landscape that contains confounding natural and modern features, such as roads, forests and field boundaries. The model has detected incidental features rather than the mounds themselves, making external validation with field data an essential part of CNN workflows. Correcting the model would require refining the training data as well as adopting different approaches to model choice and execution, raising the computational requirements beyond the level of most cultural heritage practitioners.

Improving the pre-trained model’s performance would require considerable time and resources, on top of the time already invested. The degree of manual intervention required – particularly around the subsetting and annotation of training data – is so significant that it raises the question of whether it would be more efficient to identify all of the mounds manually, either through brute-force inspection by experts or by crowdsourcing the analysis to trained – or even untrained – volunteers. Researchers and heritage specialists seeking efficient methods for extracting features from remotely sensed data should weigh the costs and benefits of ML versus manual approaches carefully.

Our literature review indicates that use of artificial intelligence (AI) and ML approaches to archaeological prospection have grown exponentially in the past decade, approaching adoption levels associated with “crossing the chasm” from innovators and early adopters to the majority of researchers. The literature itself, however, is overwhelmingly positive, reflecting some combination of publication bias and a rhetoric of unconditional success. This paper presents the failure of a good-faith attempt to utilise these approaches as a counterbalance and cautionary tale to potential adopters of the technology. Early-majority adopters may find ML difficult to implement effectively in real-life scenarios.

Unlike many high-profile reports from well-funded projects, our paper represents a serious but modestly resourced attempt to apply an ML approach to archaeological remote sensing, using techniques like transfer learning that are promoted as solutions to time and cost problems associated with, e.g. annotating and manipulating training data. While the majority of articles uncritically promote ML, or only discuss how challenges were overcome, our paper investigates how – despite reasonable self-reported scores – the model failed to locate the target features when compared to field data. We also present time, expertise and resourcing requirements, a rarity in ML-for-archaeology publications.

The purpose of this paper is to artificially construct a functional surface with self-propulsion flow characteristics for the directional transportation of propellant in surface tension tanks.

In this study, a method to enhance the propulsion efficiency by using functional surfaces of self-propulsion performance was proposed. Superhydrophilic wedged-groove with the superhydrophobic background was fabricated and the self-propulsion capacity was verified.

It is found that the self-propulsion capacity is related to the divergence angle of the wedged-groove in the hydrophilic area, and the velocity of the droplets on the deflector plate is the largest with the divergence angle of 4°; the temperature gradient field formed by the condensing device at the nozzle can accelerate the droplet outflow from the tank.

Realization of this idea provides an accurate control strategy for the complex flow process of propellant in plate surface tension tanks, which could enhance the efficiency of the tension tank significantly.

Quality deterioration in bananas along the supply chain (SC) due to cosmetic damage has been a persistent challenge in Australia. The purpose of this paper is to investigate the incidence of cosmetic defects in bananas across the post-harvest SC and determining the causes of the diminished fruit quality at the retail stores.

The study quantified the level of cosmetic damage in 243 cartons of Cavendish bananas across three post-harvest SCs in Australia from pack houses to retail stores and identified the risk factors for cosmetic defects.

The level of cosmetic damage progressively increased from pack house (1.3 per cent) to distribution centre (DC) (9.0 per cent) and retail (13.3 per cent) and was significantly influenced by package height and pallet positioning during transit. Abrasion damage in ripened bananas was influenced by the travel distance between DC and retail store. The study also revealed a range of risk factors contributing to the observed damage including weakened paperboard cartons due to high moisture absorption during the ripening process.

This study only investigated damage incidence in three post-harvest banana SCs in Australia and the damage assessments were confined to packaged bananas.

This study assessed the quality of bananas along the entire post-harvest SC from farm gate to retail store. The study provided knowledge of the extent of the quality defects, when and where the damage occurred and demonstrated the underlying factors for damage along the SC. This will enable the development of practical interventions to improve the quality and minimize wastage of bananas in the retail markets.

At airport security checkpoints, baggage screening is aimed to prevent transportation of prohibited and potentially dangerous items. Observing the projection images generated by X-rays scanner is a critical method. However, when multiple objects are stacked on top of each other, distinguishing objects only by a two-dimensional picture is difficult, which prompts the demand for more precise imaging technology to be investigated for use. Reconstructing from 2D X-ray images to 3D-computed tomography (CT) volumes is a reliable solution.

To more accurately distinguish the specific contour shape of items when stacked, multi-information fusion network (MFCT-GAN) based on generative adversarial network (GAN) and U-like network (U-NET) is proposed to reconstruct from two biplanar orthogonal X-ray projections into 3D CT volumes. The authors use three modules to enhance the reconstruction qualitative and quantitative effects, compared with the original network. The skip connection modification (SCM) and multi-channels residual dense block (MRDB) enable the network to extract more feature information and learn deeper with high efficiency; the introduction of subjective loss enables the network to focus on the structural similarity (SSIM) of images during training.

On account of the fusion of multiple information, MFCT-GAN can significantly improve the value of quantitative indexes and distinguish contour explicitly between different targets. In particular, SCM enables features more reasonable and accurate when expanded into three dimensions. The appliance of MRDB can alleviate problem of slow optimization during the late training period, as well as reduce the computational cost. The introduction of subjective loss guides network to retain more high-frequency information, which makes the rendered CT volumes clearer in details.

The authors' proposed MFCT-GAN is able to restore the 3D shapes of different objects greatly based on biplanar projections. This is helpful in security check places, where X-ray images of stacked objects need to be distinguished from the presence of prohibited objects. The authors adopt three new modules, SCM, MRDB and subjective loss, as well as analyze the role the modules play in 3D reconstruction. Results show a significant improvement on the reconstruction both in objective and subjective effects.

This paper aims to explore the interdisciplinary nature of coordination challenges in the logistic response to food safety incidents while distinguishing the food supply chain positions involved.

This adopts an exploratory qualitative research approach over a period of 11 years. Multiple research periods generated 38 semi-structured interviews and 2 focus groups. All data is analysed by a thematic analysis.

The authors identified four key coordination challenges in the logistics response to food safety incidents: first, information quality (sharing information and the applied technology) appears to be seen as the biggest challenge for the response; second, more emphasis on external coordination focus is required; third, more extensive emphasis is needed on the proactive phase in the logistic response; fourth, a distinct difference exists in the position’s views on coordination in the food supply chain. Furthermore, the data supports the interdisciplinary nature as disciplines such as operations management, strategy and organisation but also food safety and risk management, have to work together to align a rapid response, depending on the incident’s specifics.

The paper shows the need for comprehensively reviewing and elaborating on the research gap in coordination decisions for the logistic response to food safety incidents while using the views of the different supply chain positions. The empirical data indicates the interdisciplinary nature of these coordination decisions, supporting the need for more attention to the interdisciplinary food research agenda. The findings also indicate the need for more attention to organisational learning, and an open and active debate on exploratory qualitative research approaches over a long period of time, as this is not widely used in supply chain management studies.

The results of this paper do not present a managerial blueprint but can be helpful for practitioners dealing with aspects of decision-making by the food supply chain positions. The findings help practitioners to systematically go through all phases of the decision-making process for designing an effective logistic response to food safety incidents. Furthermore, the results provide insight into the distinct differences in views of the supply chain positions on the coordination decision-making process, which is helpful for managers to better understand in what phase(s) and why other positions might make different decisions.

The findings add value for the general public, as an effective logistic response contributes to consumer’s trust in food safety by creating more transparency in the decisions made during a food safety incident. As food sources are and will remain essential for human existence, the need to contribute to knowledge related to aspects of food safety is evident because it will be impossible to prevent all food safety incidents.

As the main contribution, this study provides a systematic and interdisciplinary understanding of the coordination decision-making process for the logistic response to food safety incidents while distinguishing the views of the supply chain positions.

This study aims to explore the moderating impact of narcissism overload on the relation between founder CEO and entrepreneurial orientation (EO) in registered private schools of Pakistan.

Data were collected through a stratified random sampling method with the help of previously validated questionnaires. A sample of 121 replies was gathered for analysis. SPSS has been used to find the results.

Results depict that CEO narcissism moderates the relation between founder CEO and EO and does not moderate the relationship between and CEO ownership and EO.

Many studies focused on the founder personality characteristics (such as generalized self-efficacy or locus of control) are not directly observed, but rather inferred their effect indirectly. The study contributes to examine how the founder CEO variable interacts with CEO personality to influence EO. This study will propose a practical approach to investigate whether and how the narcissism constructs moderate the founder CEO–EO relationship. Direct association between stock ownership and EO will also be examined.

This study aims to provide a comprehensive overview of the current state of the art in powder bed fusion (PBF) techniques for additive manufacturing of multiple materials. It reviews the emerging technologies in PBF multimaterial printing and summarizes the latest simulation approaches for modeling them. The topic of “multimaterial PBF techniques” is still very new, undeveloped, and of interest to academia and industry on many levels.

This is a review paper. The study approach was to carefully search for and investigate notable works and peer-reviewed publications concerning multimaterial three-dimensional printing using PBF techniques. The current methodologies, as well as their advantages and disadvantages, are cross-compared through a systematic review.

The results show that the development of multimaterial PBF techniques is still in its infancy as many fundamental “research” questions have yet to be addressed before production. Experimentation has many limitations and is costly; therefore, modeling and simulation can be very helpful and is, of course, possible; however, it is heavily dependent on the material data and computational power, so it needs further development in future studies.

This work investigates the multimaterial PBF techniques and discusses the novel printing methods with practical examples. Our literature survey revealed that the number of accounts on the predictive modeling of stresses and optimizing laser scan strategies in multimaterial PBF is low with a (very) limited range of applications. To facilitate future developments in this direction, the key information of the simulation efforts and the state-of-the-art computational models of multimaterial PBF are provided.

Background: Amid increasing leadership failures in the global business context, the mining industry is one of the industries with many adverse incidents, affecting employee safety, the environment, and surrounding communities. Emerging economies tend to have unique socio-economic challenges and greater relative economic dependence on mining, presenting unique challenges to leaders. The purpose of this research was to study the realities of responsible leadership in the mining industry in an emerging economy.

Methods: A qualitative research study, consisting of semi-structured interviews was conducted. Nine senior mine managers were selected to represent perspectives from different operations and mining houses. Data was gathered from August to October 2020 in South Africa, an emerging economy with significant mining operations. A thematic analysis of interview transcripts was conducted through the use of software, rendering five themes, with 12 sub-themes.

Results: The research found that requirements on mining leaders in emerging economies demand consistent balancing of a complex set of competing risks, whilst attending to paradoxical requirements among operations, and internal and external stakeholders. Leaders face several competing requirements from stakeholders, the environment, mining practices, and time frames. Responsible leaders must navigate a paradoxical maze of needs and time horizons, with several conflicting forces and dilemmas, and dichotomous relationships. Responsible leadership in the mining industry of an emerging economy is a proverbial minefield of paradoxes and dilemmas between responsible intentions and practical realities. These paradoxes and dilemmas are specifically acute in the context of emerging economies due to the dire socio-economic situations. A total of 10 competencies emerged as essential responsible leadership requirements in this context.

Conclusions: The study provides an in-depth understanding of the intricacies of responsible leadership in the mining industry of an emerging economy. This understanding will contribute to capacitating leaders in the mining industries of emerging economies to act responsibly.