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The purpose of this study is to re-evaluation fuselage design when the main wing’s has the ability to fulfill stability requirements without the need for a tailplane. The aerodynamic requirements of the fuselage usually involve a trade-off between reducing drag and providing enough length for positioning the empennage to ensure stability. However, if the main wing can fulfill the stability requirements without the need for a tailplane, then the fuselage design requirements can be re-evaluated. The optimisation of the fuselage can then include reducing drag and also providing a component of lift amongst other potential new requirements.

A careful investigation of parameterisation and trade-off optimisation methods to create such fuselage shapes was performed. The A320 Neo aircraft is optimised using a parameterised 3D fuselage model constructed with a modified PARSEC method and the SHERPA optimisation strategy, which was validated through three case studies. The geometry adjustments in relation to the specific flow phenomena are considered for the three optimal designs to investigate the influencing factors that should be considered for further optimisation.

The top three aerodynamic designs show a distinctive characteristic in the low aspect ratio thick wing-like aftbody that has pressure drag penalties, and the aftbody camber increased surface area notably improved the fuselage’s lift characteristics.

This work contributes to the development of a novel set of design requirements for a fuselage, free from the constraints imposed by stability requirements. By gaining insights into the flow phenomena that influence geometric designs when a lift requirement is introduced to the fuselage, we can understand how the fuselage configuration was optimised. This research lays the groundwork for identifying innovative design criteria that could extend into the integration of propulsion of the aftbody.

Despite increasing interest in digital services and products, the emergence of digitalization in the logistics and supply chain (L&SC) industry has received little attention, in particular from organizational theorists. In response, taking an institutionalist view, the authors argue that the emergence and adoption of digitalization is a socially constructed phenomenon.

This paper shows how actor-level frameshifts contribute to an emergence of an overarching “digitalization logic” in the L&SC industry at the field level. Building on a longitudinal analysis of field actors' frames and logics, the authors track the development of digitalization over the last 60 years in the L&SC sector.

The authors classify specific time periods by key field-configuring events, describe the relevant frameshifts in each time period and present a process that explains how and why digitalization has emerged, been adopted and manifested itself in the L&SC industry.

The findings of the study provide insights about the evolution of a digitalization logic and thus advance the institutional view on digitalization in the L&SC industry.

Leadership is a key factor that shapes an athlete’s development, also within team sports; most of this responsibility falls on the coaches. The purpose of this paper is to provide insight into the relationship between a coach’s reputation and athlete creativity as perceived by athletes within team sports.

The paper examined the reputational construct of coaches vis-a-vis athlete creativity. The researchers surveyed athletes from a variety of team sports and professional levels (N = 203). Structural equation modelling was utilised for testing the hypotheses.

Findings suggest that a coach’s reputation has a significant influence on the development of an athlete’s creativity. Coach reputation affects the creative development of an athlete, with knowledge sharing and openness to experiences being the principal means of influence.

Our study (1) examines the influence of a coach’s reputation on an athlete’s creativity, (2) tests the role of knowledge sharing in the influence on an athlete’s creativity and (3) considers openness to experiences in the development of an athlete’s creativity.

Strengths-based professional development has been associated with positive outcomes in academia and in the organizational workplace. Students pursuing their doctoral degrees in the biomedical sciences in the US are often on graduate assistantships, where they experience an academic component to their training integrated with an employee-like existence. The individual who serves as their academic and research advisor is often their supervisor, who pays their stipend. The traditional training structure poses unique challenges and may be accompanied by stress, burnout and imposter phenomenon. The purpose of this study is to utilize a strengths-based approach to equip students with essential personal and professional skills that build self-awareness and self-confidence further preparing them for their future in the scientific workforce.

The authors developed and implemented a novel strengths-based professional development cohort program for doctoral students in the biomedical sciences at a research-intensive (R1) institution. The program was designed to create a supportive and inclusive space for participants (n = 18) to explore their identity as a student and scientists and to develop and leverage their talents for more effective and strategic use. Program outcomes were evaluated through a mixed methods case study design using a post-program Likert-based survey (n = 10 participants) and participant interviews (n = 13). Explanatory sequential design was used in the interpretation of the findings.

The results show that the program had a positive impact on students’ perceptions of themselves as scientists, as well as on their self-efficacy, self-confidence and interpersonal interactions in the research setting.

This strengths-based professional development program demonstrates immense potential as a model to equip students with self-awareness and a new foundation of essential skills needed to supplement their technical and scientific training for their future careers in the team-based workplace.

This study demonstrates how professional development programming can complement scientific training by equipping students with self-awareness and other lifelong skills to navigate feelings of imposter phenomenon and interpersonal relationships in the team-based workplace.

Utility strikes have spawned companies specializing in providing a priori analyses of the underground. Geophysical techniques such as Ground Penetrating Radar (GPR) are harnessed for this purpose. However, analyzing GPR data is labour-intensive and repetitive. It may therefore be worthwhile to amplify this process by means of Machine Learning (ML). In this work, harnessing the ADR design science methodology, an Intelligence Amplification (IA) system is designed that uses ML for decision-making with respect to utility material type. It is driven by three novel classes of Convolutional Neural Networks (CNNs) trained for this purpose, which yield accuracies of 81.5% with outliers of 86%. The tool is grounded in the available literature on IA, ML and GPR and is embedded into a generic analysis process. Early validation activities confirm its business value.