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Simulation-based digital twins represent an effort to provide high-accuracy real-time insights into operational physical processes. However, the computation time of many multi-physical simulation models is far from real-time. It might even exceed sensible time frames to produce sufficient data for training data-driven reduced-order models. This study presents TwinLab, a framework for data-efficient, yet accurate training of neural-ODE type reduced-order models with only two data sets.

Correlations between test errors of reduced-order models and distinct features of corresponding training data are investigated. Having found the single best data sets for training, a second data set is sought with the help of similarity and error measures to enrich the training process effectively.

Adding a suitable second training data set in the training process reduces the test error by up to 49% compared to the best base reduced-order model trained only with one data set. Such a second training data set should at least yield a good reduced-order model on its own and exhibit higher levels of dissimilarity to the base training data set regarding the respective excitation signal. Moreover, the base reduced-order model should have elevated test errors on the second data set. The relative error of the time series ranges from 0.18% to 0.49%. Prediction speed-ups of up to a factor of 36,000 are observed.

The proposed computational framework facilitates the automated, data-efficient extraction of non-intrusive reduced-order models for digital twins from existing simulation models, independent of the simulation software.

The main objective of the present investigation is to develop a novel efficient stochastic model for availability optimization of reverse osmosis machine system (ROMS) for water purification under the concepts of exponentially distributed decision variables and various redundancy strategies at the component level.

ROMS is a complex framework configured in a series structure using six subsystems. Initially, a state transition diagram is developed and Chapman–Kolmogorov differential-difference equations are derived using Markov birth death process. The steady-state availability of the ROMS is derived for a particular case. The impact of variation in failure and repair rates measured on availability. Furthermore, an effort is made to predict the optimal availability of the ROMS system using the metaheuristic algorithms, namely, dragonfly algorithm (DA), grasshopper optimization algorithm (GOA) and whale optimization algorithm (WOA).

It is observed that the ROMS system predicts optimal availability of 0.999926 after five iterations with a population size of 300 by the WOA. The findings of this study are significant for reliability engineers as well as for maintenance engineers to ensure the availability of ROMS for water purification.

In the present investigation, a novel stochastic model is developed for ROMS, and metaheuristics algorithms are applied to predict the optimal availability.

Perceived discrimination in medical setting is associated to adverse healthcare outcomes. Yet, few tools exist for assessing such discrimination among patients in Portugal. This study aimed to validate the Discrimination in Medical Settings (DMS) scale for the Portuguese Roma population, explore their experiences of healthcare discrimination and analyze the association between perceived discrimination, health perception and healthcare trust.

In this cross-sectional study, 84 Roma adults (60,7% women) completed the DMS, along with microaggression assessments, health perception and healthcare trust evaluations.

The seven-item DMS scale yielded a one-factor structure that explained 71.80% of variance, with strong reliability (a = 0.89). The average DMS-measured perceived discrimination was 2.46. Discrimination correlated inversely with health perception and was strongly negatively associated to trust in healthcare.

The DMS scale exhibited reliability, single-dimensionality and validity among the Roma. High perceived discrimination in medical settings among Portuguese Roma was notably associated with individual health perception and healthcare trust.

During a fall, a significant part of the major forces is absorbed by the dorsolumbar column area. When the applied stresses exceed the yield strength of the bone tissue, fractures can occur in the vertebrae. Vertebral fractures constitute one of the leading causes of trauma-related hospitalizations, accounting for 15% of all admissions. Posterior pedicle screw fixation has become a common method for treating burst fractures. However, physicians remain divided on the number of fixed segments that are needed to improve clinical outcomes. The present work aims to understand the biomechanical impact of different fixation methods, improving surgical treatments.

A finite element model of the dorsolumbar spine (T11–L3) section, including cartilages, discs and ligaments, was created. The dorsolumbar stability was tested by comparing two different surgical orthopedic treatments for a fractured first lumbar vertebra on the L1 vertebra: the posterior short segment fixation with intermediate screws (PSS) and the posterior long segment fixation (PL). Distinct loads were applied to represent daily activities.

Results show that both procedures provide acceptable segment fixation, with the PL offering less freedom of movement, making it more stable than the PSS. The PL approach can be the best choice for an unstable fracture as it leads to a stiffer spine segment.

This study introduces a novel computational model designed for the biomechanical analysis of dorsolumbar injuries, aiming to identify the optimal treatment approaches within both clinical and surgical contexts.

This study aims to explore the process of identifying and defining multiple capitals in the integrated report (IR) of a government-owned tourism company.

Interventionist research was conducted using a case study design. The researcher was directly involved in developing the first IR of Zètema, a heritage and tourism company owned by the Municipality of Rome. The research team analyzed internal reports, business model (BM), strategic plan and marketing plan, and collected data through semistructured interviews and participation in company meetings.

A template based on a step-by-step deductive process to select and define relevant capitals was derived. Following this process, an appropriate form of capital emerged: “cultural capital”. Furthermore, this study emphasizes a novel awareness of the different meanings that capitals can assume as inputs and outcomes of a BM.

This study meets the demand for empirical research that investigates real information in integrated reports intended for those for whom value is created. Thus, the paper contributes to the existing knowledge on integrated reporting by examining the partially explored concept of capital, particularly its identification process. Furthermore, this study provides support to preparers of integrated reports by defining a conceptual reference model for the disclosure of significant capitals and underlining the importance of distinguishing capitals as input or outcome.

The objective of this study is to reevaluate the correlation among pharmaceutical consumption, per capita income, and life expectancy across different age groups (at birth, middle age, and advanced age) within the OECD countries between 1998 and 2018.

We employ a two-step methodology, utilizing two independent approaches. Firstly, we con-duct the Dumitrescu-Hurlin pairwise panel causality test, followed by Machine Learning (ML) experiments employing the Causal Direction from Dependency (D2C) Prediction algorithm and a DeepNet process, thought to deliver robust inferences with respect to the nature, sign, direction, and significance of the causal relationships revealed in the econometric procedure.

Our findings reveal a two-way positive bidirectional causal relationship between GDP and total pharmaceutical sales per capita. This contradicts the conventional notion that health expenditures decrease with economic development due to general health improvements. Furthermore, we observe that GDP per capita positively correlates with life expectancy at birth, 40, and 60, consistently generating positive and statistically significant predictive values. Nonetheless, the value generated by the input life expectancy at 60 on the target income per capita is negative (−61.89%), shedding light on the asymmetric and nonlinear nature of this nexus. Finally, pharmaceutical sales per capita improve life expectancy at birth, 40, and 60, with higher magnitudes compared to those generated by the income input.

These results offer valuable insights into the intricate dynamics between economic development, pharmaceutical consumption, and life expectancy, providing important implications for health policy formulation.

Very few studies shed light on the nature and the direction of the causal relationships that operate among these indicators. Exiting from the standard procedures of cross-country regressions and panel estimations, the present manuscript strives to promote the relevance of using causality tests and Machine Learning (ML) methods on this topic. Therefore, this paper seeks to contribute to the literature in three important ways. First, this is the first study analyzing the long-run interactions among pharmaceutical consumption, per capita income, and life expectancy for the Organization for Economic Co-operation and Development (OECD) area. Second, this research contrasts with previous ones as it employs a complete causality testing framework able to depict causality flows among multiple variables (Dumitrescu-Hurlin causality tests). Third, this study displays a last competitive edge as the panel data procedures are complemented with an advanced data testing method derived from AI. Indeed, using an ML experiment (i.e. Causal Direction from Dependency, D2C and algorithm) it is believed to deliver robust inferences regarding the nature and the direction of the causality. All in all, the present paper is believed to represent a fruitful methodological research orientation. Coupled with accurate data, this seeks to complement the literature with novel evidence and inclusive knowledge on this topic. Finally, to bring accurate results, data cover the most recent and available period for 22 OECD countries: from 1998 to 2018.

School exclusion in England is highly gendered, racialised and classed. For instance, boys are three times more likely than girls to be excluded from school and certain groups, including Black Caribbean boys are subject to disproportionate levels of exclusion. Against this backdrop, I explore the context and consequences of exclusion from English mainstream schooling for young masculinities. The arguments presented also have broader international significance due to a global tendency towards punitive measures in schooling. Through bringing masculinities scholarship into conversation with childhood studies, this chapter aims to present a nuanced theorisation of young masculinities which foregrounds lived experience and is located within the interdisciplinary field of childhood studies. It examines ways in which exclusion and schooling in alternative settings, such as a Pupil Referral school, can shape the identities of boys and their subjectivities. The empirical data demonstrate that excluded boys face severe constraints arising from ways in which they are positioned. Drawing on original qualitative data, it is argued that boys who are categorised in this way demonstrate highly agentic ways of ‘doing boy’. This chapter is underpinned by two questions, firstly, how can we theorise boyhood and school exclusion in ways that recognise excluded boys as agentic and constrained subjects? Secondly, what possibilities for change might our theorisation reveal? This chapter concludes by arguing for intersectional masculinities and strengthened theorisation of childhood studies which explicitly recognises lived experience. Through this discussion, I seek to illuminate the emotional costs of school exclusion and insights into ways to achieve change.

Global environmental and social threats challenge humans’ well-being and the survival of posterity. Industry 4.0 (I4.0) transformed the industry sector, enabling process automation and scalability, increasing manufacturers’ productivity, efficiency, and profitability, and supporting manufacturing innovation and firms’ competitive advantage. Despite this, the I4.0 paradigm, as currently conceived, does not fit for purpose in the context of the climate crisis and planetary emergency, nor does it address deep social tensions.

Therefore, a new revolution focusing on human and environmental needs is strongly required to address society’s economic and social problems. Combining digitalization and social purposes may address local, national, and international issues by involving a human-centered perspective in traditional business-oriented entrepreneurship.

Depending on the above, this chapter provides an in-depth understanding of Digital Entrepreneurship (DE), Digital Social Entrepreneurship (DSE), and how entrepreneurs may employ digital technologies to reach business and social aims.

Technological innovations provide huge opportunities to expand and revolutionize the scope of products and services offered. This is particularly true for tourism, which is undergoing significant changes due to the development of new technologies. The level of technology diffusion depends on several factors like the exchange of information among peers, and the attitude and shared perception among the contributors. The aim of the study is to explore the diffusion of technology in tourism with a specific focus on the social media discourse around new technologies. Thus, the paper investigates the level of interest in these new technologies analysing the information exchange occurring between individuals on Twitter in order to explore the influence of reciprocal networking.

To capture the attitudes expressed in the industry, the study analyses the ongoing discourse on Twitter as a proxy for the participants “interest in new technologies. Through a social network analysis of the tweets and retweets conducted over a period of nine months, the research maps the level of information exchange about the diffusion of new technologies. Moreover, the sentiment analysis provides an interesting overview of the individuals” attitudes towards the awareness or the adoption of new technologies.

Our analysis has provided several insights: (1) the information network on blockchain in tourism consists of participants who change very quickly over time (high turnover of accounts); (2) some contributors have an extremely important role in influencing the flow of information in the system (information centralization), they can have a generalist (discussing several topics) or a specialist (focusing on a specific topic) behaviour and this strategic choice influences their network's structure; (3) these central nodes also have an impact on the definition of positive and negative sentiment towards a topic (sentiment influencer).

The paper contributes to the literature on technology diffusion, by focusing on one of the preconditions of diffusion that is the shared positive attitude towards technological innovation. More specifically, we adopt a network-based approach, which is useful to explain the level of information exchange and the public discourse that can impact the shared perception and attitude towards technological innovation. The study also highlights the role of knowledge brokers in influencing this public discourse. Future studies can deepen the association between positive perception, higher levels of information exchange and increasing usage of specific technologies. Our results also suggest further exploring the opportunity to combine social media data and other sources of information to shed more light on the technological innovation diffusion processes.

This paper shows how practitioners can benefit from the analysis of information exchange about new technologies in tourism adopting a network perspective with the aim of understanding the level of influence among contributors. Moreover, the increasing interest in blockchain technology and the potential combination between social media data and other sources of information can offer promising insights.

The present study explores the level of technology diffusion through the analysis of information exchange on social media (Twitter). Furthermore, the dynamics of individual user behaviour offers a better understanding about media effects.

While previous research is focused on the users' perception towards the development of new technologies in tourism, the aim of this study is to investigate the dynamics behind the level of diffusion of information and awareness about these new technologies, which still represents an unexplored area of research.

Despite the rising trend of sustainable developmental goals (SDGs) incorporation into sustainability reporting, there remains a gap in understanding the role of SDG disclosure (SDGD) in the relationship between sustainability and financial performance. Thus, this study aims to investigate the relationship between sustainability performance and the level of SDGD; the relationship between sustainability performance and financial performance; and the link between the level of SDGD and financial performance.

Conducted in Italy, the analysis involves manual collection of sustainability reports from company websites for the fiscal years from 2019 to 2022, followed by textual analysis to identify SDG-related content disclosed in nonfinancial reports. Financial and nonfinancial data from Orbis and LSEG databases are used for regression analysis on panel data.

Findings align with existing literature, emphasizing the partial mediator role played by the level of SDGD in the relationship between sustainability performance and financial performance, measured by return on equity. In addition, the study suggests that there is a positive relationship between sustainability performance and the level of SDGD and a positive relationship between the level of SDGD and financial performance.

This study contributes to a deeper understanding of how SDG disclosures function within the broader nexus of sustainability performance and financial outcomes. Findings from this study provide empirical support for the argument that SDGD is not merely a regulatory compliance tool but also a strategic asset that can enhance a firm’s financial performance.