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Buckling should be carefully considered in steel assemblies with members subjected to compressive stresses, such as bracing systems and truss structures, in which angles and built-up steel sections are widely employed. These type of steel members are affected by torsional and flexural-torsional buckling, but the European (EN 1993-1-2) and the American (AISC 360-16) design norms do not explicitly treat these phenomena in fire situation. In this work, improved buckling curves based on the EN 1993-1-2 were extended by exploiting a previous work of the authors. Moreover, new buckling curves of AISC 360-16 were proposed.

The buckling curves provided in the norms and the proposed ones were compared with the results of numerical investigation. Compressed angles, tee and cruciform steel members at elevated temperature were studied. More than 41,000 GMNIA analyses were performed on profiles with different lengths with sections of class 1 to 3, and they were subjected to five uniform temperature distributions (400–800 C) and with three steel grades (S235, S275, S355).

It was observed that the actual buckling curves provide unconservative or overconservative predictions for various range of slenderness of practical interest. The proposed curves allow for safer and more accurate predictions, as confirmed by statistical investigation.

This paper provides new design buckling curves for torsional and flexural-torsional buckling at elevated temperature since there is a lack of studies in the field and the design standards do not appropriately consider these phenomena.

The purpose of this work is to optimize the monitoring of vibrations on dynamometric test rigs for railway brakes. This is a quite demanding application considering the continuous increase of performances of high-speed trains that involve higher testing specifications for brake pads and disks.

In this work, authors propose a mixed approach in which relatively simple finite element models are used to support the optimization of a diagnostic system that is used to monitor vibration levels and rotor-dynamical behavior of the machine. The model is calibrated with experimental data recorded on the same rig that must be identified and monitored. The whole process is optimized to not interfere with normal operations of the rig, using common inertial sensor and tools and are available as standard instrumentation for this kind of applications. So at the end all the calibration activities can be performed normally without interrupting the activities of the rig introducing additional costs due to system unavailability.

Proposed approach was able to identify in a very simple and fast way the vibrational behavior of the investigated rig, also giving precious information concerning the anisotropic behavior of supports and their damping. All these data are quite difficult to be found in technical literature because they are quite sensitive to assembly tolerances and to many other factors. Dynamometric test rigs are an important application widely diffused for both road and rail vehicles. Also proposed procedure can be easily extended and generalized to a wide value of machine with horizontal rotors.

Most of the studies in literature are referred to electrical motors or turbomachines operating with relatively slow transients and constant inertial properties. For investigated machines both these conditions are not verified, making the proposed application quite unusual and original with respect to current application. At the same time, there is a wide variety of special machines that are usually marginally covered by standard testing methodologies to which the proposed approach can be successfully extended.

This study aims to conduct an empirical investigation to assess the hedge, diversifier and safe-haven properties of different environmental, social and governance (ESG) assets (i.e. green bonds and ESG equity index) vis-à-vis conventional investments (namely, equity index, gold and commodities).

The authors examine the sample period 2007–2021 using the bivariate cross-quantilogram (CQG) analysis and a dynamic conditional correlation (DCC) multivariate generalized autoregressive conditional heteroskedasticity (GARCH) experiment with several extensions.

The evidence shows that the analyzed ESG investments exhibit mainly diversifying features depending on the asset class taken as a reference, with some potential hedging/safe-haven qualities (for the green bond) in peculiar timespans. Therefore, the results suggest that investors might consider sustainable investing as a new measure of risk reduction, which has interesting implications for both portfolio allocation and policy design.

To the best of the authors’ knowledge, this study is the first that empirically investigates at once the dependence between different ESG investments (i.e. equity and green bond) with different conventional investments such as gold, equity and commodity market indices over a large sample period (2007–2021). Well-suited methodologies like the bivariate CQG and the DCC multivariate GARCH are used to capture the spillover effect and the hedging/diversifying nature, even in temporary contexts. Finally, a global perspective is used.

This study aims to develop our understanding of the value co-creation process in business networks. This study identifies four key sub-processes that characterize the value co-creation journey as it unfolds across an inter-organizational network. These four sub-processes are opportunity co-creation, solution co-creation, complementary co-creation and activated co-creation.

Reflecting the exploratory nature of this research, the methodology relies on an in-depth case study, which is analyzed through the lens of the resource interaction occurring within the specific business relationships and collaborative episodes that affected the nine-year long development of Deko, a new architectural lighting solution.

The main contribution of the paper is identifying the sub-processes comprising the value co-creation journey of a technology development solution based on resource combining, re-combining and un-combining across a business network. That value co-creation occurs through a time-consuming journey requiring multiple episodes of collaboration can also inspire the practice of handling this process for instance for a small business such as the one featured in this case study.

This paper highlights that the value co-creation journey process has the potential to frame the unfolding of collaboration in practice for a small business.

The development of Research Management and Administration (RMA) in Europe is strongly connected with the development of the Science and Technology (S&T) policy of the European Union (EU). These policies were the result of a continuous debate between the member states and the European Commission and European Parliament.

Although there is no data on the early development of RMA, there are some publications on the history of the development of the S&T policy in Europe: the excellent publication ‘A History of European Union Research Policy’ by Luca Guzzetti (Guzzetti, 1995). Guzzetti’s book investigates the history of EU research policies from 1948 up to the preparation of the Fourth Framework Programme (FP) (1994–1998).

The RMA aspects are constructed mainly by oral history complemented with some written sources. The history shows a gradual development of the profession unevenly spread in time and European geography. This has mainly with the EU enlargement in the same period, when new member states were connected to the FP. The profession started with a few colleagues’ way back in the eighties of the last century as financial people were dealing with the first European financial reporting up to the present day where RMA is becoming a field of work attracting many new colleagues.

The COVID-19 pandemic has disrupted the ‘real’ world and substantially impacted the virtual world and thus the Internet ecosystem. It has caused a significant exogenous shock that offers a wealth of natural experiments and produced new data about broadband, clouds, and the Internet in times of crisis. In this chapter, we characterise and evaluate the evolving impact of the global COVID-19 crisis on traffic patterns and loads and the impact of those on Internet performance from multiple perspectives. While we place a particular focus on deriving insights into how we can better respond to crises and better plan for the post-COVID-19 ‘new normal’, we analyse the impact on and the responses by different actors of the Internet ecosystem across different jurisdictions. With a focus on the USA and Europe, we examine the responses of both public and private actors, with the latter including content and cloud providers, content delivery networks, and Internet service providers (ISPs). This chapter makes two contributions: first, we derive lessons learned for a future post-COVID-19 world to inform non-networking spheres and policy-making; second, the insights gained assist the networking community in better planning for the future.

The recent innovations of Industry 4.0 have made it possible to easily collect data related to a production environment. In this context, information about industrial equipment – gathered by proper sensors – can be profitably used for supporting predictive maintenance (PdM) through the application of data-driven analytics based on artificial intelligence (AI) techniques. Although deep learning (DL) approaches have proven to be a quite effective solutions to the problem, one of the open research challenges remains – the design of PdM methods that are computationally efficient, and most importantly, applicable in real-world internet of things (IoT) scenarios, where they are required to be executable directly on the limited devices’ hardware.

In this paper, the authors propose a DL approach for PdM task, which is based on a particular and very efficient architecture. The major novelty behind the proposed framework is to leverage a multi-head attention (MHA) mechanism to obtain both high results in terms of remaining useful life (RUL) estimation and low memory model storage requirements, providing the basis for a possible implementation directly on the equipment hardware.

The achieved experimental results on the NASA dataset show how the authors’ approach outperforms in terms of effectiveness and efficiency the majority of the most diffused state-of-the-art techniques.

A comparison of the spatial and temporal complexity with a typical long-short term memory (LSTM) model and the state-of-the-art approaches was also done on the NASA dataset. Despite the authors’ approach achieving similar effectiveness results with respect to other approaches, it has a significantly smaller number of parameters, a smaller storage volume and lower training time.

The proposed approach aims to find a compromise between effectiveness and efficiency, which is crucial in the industrial domain in which it is important to maximize the link between performance attained and resources allocated. The overall accuracy performances are also on par with the finest methods described in the literature.

The proposed approach allows satisfying the requirements of modern embedded AI applications (reliability, low power consumption, etc.), finding a compromise between efficiency and effectiveness.

The purpose of this paper is to reduce the difficulty of model predictive control (MPC) deployment on FPGA so that researchers can make better use of FPGA technology for academic research.

In this paper, the MPC algorithm is written into FPGA by combining hardware with software. Experiments have verified this method.

This paper implements a ZYNQ-based design method, which could significantly reduce the difficulty of development. The comparison with the CPU solution results proves that FPGA has a significant acceleration effect on the solution of MPC through the method.

Due to the limitation of practical conditions, this paper cannot carry out a hardware-in-the-loop experiment for the time being, instead of an open-loop experiment.

This paper proposes a new design method to deploy the MPC algorithm to the FPGA, reducing the development difficulty of the algorithm implementation on FPGA. It greatly facilitates researchers in the field of autonomous driving to carry out FPGA algorithm hardware acceleration research.

The purpose of this paper is to investigate the feasibility of an end-to-end simplified and automated reconstruction pipeline for digital building assets using the design science research approach. Current methods to create digital assets by capturing the state of existing buildings can provide high accuracy but are time-consuming, expensive and difficult.

Using design science research, this research identifies the need for a crowdsourced and cloud-based approach to reconstruct digital building assets. The research then develops and tests a fully functional smartphone application prototype. The proposed end-to-end smartphone workflow begins with data capture and ends with user applications.

The resulting implementation can achieve a realistic three-dimensional (3D) model characterized by different typologies, minimal trade-off in accuracy and low processing costs. By crowdsourcing the images, the proposed approach can reduce costs for asset reconstruction by an estimated 93% compared to manual modeling and 80% compared to locally processed reconstruction algorithms.

The resulting implementation achieves “good enough” reconstruction of as-is 3D models with minimal tradeoffs in accuracy compared to automated approaches and 15× cost savings compared to a manual approach. Potential facility management use cases include the issue and information tracking, 3D mark-up and multi-model configurators.

Through user engagement, development, testing and validation, this work demonstrates the feasibility and impact of a novel crowdsourced and cloud-based approach for the reconstruction of digital building assets.

This study aims to provide a thick description of a four-day bridging course in introductory accounting presented before the start of a student’s first year. The course aims to address the lack of prior accounting knowledge. The study also evaluates the effectiveness of the course using econometric techniques. Treatment effects are considered when interpreting the results.

This voluntary intervention used a quasi-experimental research design and quantitative techniques, including the application of propensity score matching (PSM), to isolate the treatment effect on the treated and untreated groups.

A positive and significant association is reported between attending the bridging course and performance in the first assessment.

A bridging course in accounting offers higher educators an opportunity to ensure that students are academically better prepared when entering university. This course provides adequate prior knowledge from which a student will benefit during the first assessments, which may contribute to increased self-efficacy and retention. This intervention has social implications for students as they can interact, participate and easily transition from school to university. Social implications include learning communities that are formed at the onset of their studies.

Bridging courses have been presented in other disciplines with positive results but not yet in accounting. Bridging courses in accounting are viable interventions to address gaps in prior knowledge and assist with the transition from school to university. This study expands literature by demonstrating the application and interpretation of PSM in quasi-experimental designs.