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This study is concerned with the dynamics of the internal communications at Netflix following the release of The Closer and the public debate that followed, testing Netflix's long-standing reputation for promoting diverse content and supporting a progressive organizational culture.

Using the circuit of culture (CoC) as a theoretical framework, this study applies a case study approach to analyze internal communication and strategic public relations in addressing this crisis.

This study's findings illustrate that by failing to interpret two of the five moments of the CoC, production and identity, Netflix negated the very values that constitute its brand. These findings have implications for how public relations' long-standing focus on two-way symmetrical communication is problematic, especially in the workplace.

The findings situate how the exertion of power within an organization, particularly in moments of identity and production, problematize the role of two-way symmetrical communication within an organization in crisis.

This paper aims to propose a robust method for non-rigid point set registration, using the Gaussian mixture model and accommodating non-rigid transformations. The posterior probabilities of the mixture model are determined through the proposed integrated feature divergence.

The method involves an alternating two-step framework, comprising correspondence estimation and subsequent transformation updating. For correspondence estimation, integrated feature divergences including both global and local features, are coupled with deterministic annealing to address the non-convexity problem of registration. For transformation updating, the expectation-maximization iteration scheme is introduced to iteratively refine correspondence and transformation estimation until convergence.

The experiments confirm that the proposed registration approach exhibits remarkable robustness on deformation, noise, outliers and occlusion for both 2D and 3D point clouds. Furthermore, the proposed method outperforms existing analogous algorithms in terms of time complexity. Application of stabilizing and securing intermodal containers loaded on ships is performed. The results demonstrate that the proposed registration framework exhibits excellent adaptability for real-scan point clouds, and achieves comparatively superior alignments in a shorter time.

The integrated feature divergence, involving both global and local information of points, is proven to be an effective indicator for measuring the reliability of point correspondences. This inclusion prevents premature convergence, resulting in more robust registration results for our proposed method. Simultaneously, the total operating time is reduced due to a lower number of iterations.

This study aims to explore first-year engineering students’ perceptions of the engineering librarian as an instructor in multimodal environments related to Information Literacy (IL) topics, teaching strategy, content evaluation, organising, planning and support.

A quantitative approach was used through a survey instrument based on an online questionnaire. Questions were adopted and modified from a lecturer evaluation survey. A simple random sampling technique was used to collect data from first-year cohorts of engineering students in 2020 and 2022.

Respondents perception of the engineering librarian as an instructor in multimodal learning environment was good. Findings revealed students’ learning experiences were aligned with IL instruction even though the environment changed from blended to online. However, an emerging theme that continuously appeared was a lack of access to technology.

These findings may help in developing and strengthening the teaching identity of academic librarians as instructors in multimodal learning environments.

To the best of the author’s knowledge, this study is novel in that it evaluates the teaching abilities of an academic librarian in multimodal environments through the lens of students.

The swivel construction method is a specially designed process used to build bridges that cross rivers, valleys, railroads and other obstacles. To carry out this construction method safely, real-time monitoring of the bridge rotation process is required to ensure a smooth swivel operation without collisions. However, the traditional means of monitoring using Electronic Total Station tools cannot realize real-time monitoring, and monitoring using motion sensors or GPS is cumbersome to use.

This study proposes a monitoring method based on a series of computer vision (CV) technologies, which can monitor the rotation angle, velocity and inclination angle of the swivel construction in real-time. First, three proposed CV algorithms was developed in a laboratory environment. The experimental tests were carried out on a bridge scale model to select the outperformed algorithms for rotation, velocity and inclination monitor, respectively, as the final monitoring method in proposed method. Then, the selected method was implemented to monitor an actual bridge during its swivel construction to verify the applicability.

In the laboratory study, the monitoring data measured with the selected monitoring algorithms was compared with those measured by an Electronic Total Station and the errors in terms of rotation angle, velocity and inclination angle, were 0.040%, 0.040%, and −0.454%, respectively, thus validating the accuracy of the proposed method. In the pilot actual application, the method was shown to be feasible in a real construction application.

In a well-controlled laboratory the optimal algorithms for bridge swivel construction are identified and in an actual project the proposed method is verified. The proposed CV method is complementary to the use of Electronic Total Station tools, motion sensors, and GPS for safety monitoring of swivel construction of bridges. It also contributes to being a possible approach without data-driven model training. Its principal advantages are that it both provides real-time monitoring and is easy to deploy in real construction applications.

This paper covers the development of a novel defect model for concrete highway bridges. The proposed defect model is intended to facilitate the identification of bridge’s condition information (i.e. defects), improve the efficiency and accuracy of bridge inspections by supporting practitioners and even machines with digitalised expert knowledge, and ultimately automate the process.

The research design consists of three major phases so as to (1) categorise common defect with regard to physical entities (i.e. bridge element), (2) establish internal relationships among those defects and (3) relate defects to their properties and potential causes. A mixed-method research approach, which includes a comprehensive literature review, focus groups and case studies, was employed to develop and validate the proposed defect model.

The data collected through the literature and focus groups were analysed and knowledge were extracted to form the novel defect model. The defect model was then validated and further calibrated through case study. Inspection reports of nearly 300 bridges in China were collected and analysed. The study uncovered the relationships between defects and a variety of inspection-related elements and represented in the form of an accessible, digitalised and user-friendly knowledge model.

The contribution of this paper is the development of a defect model that can assist inexperienced practitioners and even machines in the near future to conduct inspection tasks. For one, the proposed defect model can standardise the data collection process of bridge inspection, including the identification of defects and documentation of their vital properties, paving the path for the automation in subsequent stages (e.g. condition evaluation). For another, by retrieving rich experience and expert knowledge which have long been reserved and inherited in the industrial sector, the inspection efficiency and accuracy can be considerably improved.

This chapter discusses the findings of doctoral research into further education lecturers' and middle managers' perceptions of how Continuing Professional Development (CPD) in the sector is planned and implemented. Thematic analysis revealed that mandatory CPD is perceived to: involve conflicting purposes between those planning it and its recipients (deriving from divergent understandings of professionalism and the role of CPD among stakeholders); and be characterised as mostly generic, didactic, and ineffective, leading lecturers to compensate by engaging in additional, separate forms of CPD. This chapter demonstrates the value of practice-based doctoral study in enabling the voices of educators to be positioned at the centre of an exploration of their own professional learning.

Periodic economic instabilities and structural changes in the labour market have given rise to a variety of forms of job insecurity. This article compares the scarring effects of different forms of job insecurity on future employment chances, and how they vary across education groups.

Using a sample of real vacancies and data collected in a vignette experiment with employers in Switzerland, a country with a strongly developed vocational education and training (VET) system, this article investigates how employers evaluate a period of unemployment, job hopping and work experience in deskilling jobs when hiring candidates.

The findings reveal that work in deskilling jobs is by far more scarring than unemployment or job hopping. The study also demonstrates that applicants with upper secondary vocational education are impacted the greatest by all three forms of job insecurity.

The study makes use of real vacancies. While experiments have the strength of high internal validity, most experimental studies in recruitment research rely on students as respondents. As this study works with real employers hiring for positions it benefits from high external validity.

Numerous research tools exist but their usage among researchers across the different phases of the research cycle of scholarly communication remains unknown. This research aims to address this knowledge gap by mapping the research tools frequently used by global researchers against the various phases of the research cycle of scholarly communication.

This research adopts a descriptive research design and conducts a cross-tabulation of secondary data consisting of 20,663 useable responses in a global survey of research tools for scholarly communication. This research also administered a survey to academic experts to classify the research tools according to traditional, modern, innovative and experimental categories.

This research reveals the six phases of the research cycle (i.e. discovery, analysis, writing, publication, outreach and assessment) and the research tools of scholarly communication frequently used by researchers worldwide in each phase as a whole and by roles, disciplines, regions and career stages. Notably, this research indicates that most of the research tools used by researchers are classified as “modern” and “innovative”.

The original insights herein should be useful for both established and early career researchers to gain and share research insights, as well as policymakers and existing and aspiring service providers who wish to improve the utility and usage of research tools for scholarly communication. Notably, this research represents a seminal endeavor at enhancing a global survey (secondary research) using a follow-up expert survey (primary research), which enabled the organization of research tools for scholarly communication into four refined categories. In doing so, this research contributes finer-grained insights that showcase the importance of keeping up with the advancement of technology through the use of modern, innovative and experimental research tools, thereby highlighting the need to go beyond traditional research tools for scholarly communication.

While different attempts have been made to use artificial intelligence (AI) to codify communicative behaviors and analyze startups’ video presentations in relation to crowdfunding projects, less is known about other forms of access to entrepreneurial finance, such as video pitches for candidacies into startup accelerators and incubators. This research seeks to demonstrate how AI can enable the startup selection process for both entrepreneurs and investors in terms of video pitch evaluation.

An AI startup (Speechannel) was used to predict the outcomes of startup video presentations by analyzing text, audio, and video data from 294 video pitches sent to a leading European startup accelerator (LUISS EnLabs). 7 investors were also interviewed in Silicon Valley to establish the differences between humans and machines.

This research proves that AI has profound implications with regards to the decision-making process related to fundraising and, in particular, the video pitches of startup accelerators and incubators. Successful entrepreneurs are confident (but not overconfident), engaging in terms of speaking quickly (but also clearly), and emotional (but not overemotional).

This study not only fills the existing research gap but also provides a practical guide on AI-driven video pitch evaluation for entrepreneurs and investors, reshaping the landscape of entrepreneurial finance thanks to AI. On the one hand, entrepreneurs could use this knowledge to modify their behaviors, enabling them to increase their likelihood of being financially backed. On the other hand, investors could use these insights to better rationalize their funding decisions, enabling them to select the most promising startups.

This paper makes a significant contribution by bridging the gap between theoretical research and the practical application of AI in entrepreneurial finance, marking a notable advancement in this field. At a theoretical level, it contributes to research on managerial decision-making processes – particularly those related to the analysis of video presentations in a fundraising context. At a practical level, it offers a model that we called the “AI-enabled video pitch evaluation”, which is used to extract features from the video pitches of startup accelerators and incubators and predict an entrepreneurial project’s success.

This study aims to investigate the morphing concepts able to manipulate the dynamics of the downstream unsteadiness in the separated shear layers and, in the wake, be able to modify the upstream shock–boundary layer interaction (SBLI) around an A320 morphing prototype to control these instabilities, with emphasis to the attenuation or even suppression of the transonic buffet. The modification of the aerodynamic performances according to a large parametric study carried out at Reynolds number of 4.5 × 10⁶, Mach number of 0.78 and various angles of attack in the range of (0, 2.4)° according to two morphing concepts (travelling waves and trailing edge vibration) are discussed, and the final benefits in aerodynamic performance increase are evaluated.

This article examines through high fidelity (Hi-Fi) numerical simulation the effects of the trailing edge (TE) actuation and of travelling waves along a specific area of the suction side starting from practically the most downstream position of the shock wave motion according to the buffet and extending up to nearly the TE. The present paper studies through spectral analysis the coherent structures development in the near wake and the comparison of the aerodynamic forces to the non-actuated case. Thus, the physical mechanisms of the morphing leading to the increase of the lift-to-drag ratio and the drag and noise sources reduction are identified.

This study investigates the influence of shear-layer and near-wake vortices on the SBLI around an A320 aerofoil and attenuation of the related instabilities thanks to novel morphing: travelling waves generated along the suction side and trailing-edge vibration. A drag reduction of 14% and a lift-to-drag increase in the order of 8% are obtained. The morphing has shown a lift increase in the range of (1.8, 2.5)% for angle of attack of 1.8° and 2.4°, where a significant lift increase of 7.7% is obtained for the angle of incidence of 0° with a drag reduction of 3.66% yielding an aerodynamic efficiency of 11.8%.

This paper presents results of morphing A320 aerofoil, with a chord of 70cm and subjected to two actuation kinds, original in the state of the art at M = 0.78 and Re = 4.5 million. These Hi-Fi simulations are rather rare; a majority of existing ones concern smaller dimensions. This study showed for the first time a modified buffet mode, displaying periodic high-lift “plateaus” interspersed by shorter lift-decrease intervals. Through trailing-edge vibration, this pattern is modified towards a sinusoidal-like buffet, with a considerable amplitude decrease. Lock-in of buffet frequency to the actuation is obtained, leading to this amplitude reduction and a drastic aerodynamic performance increase.