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Mapping the landscape of healthcare education is essential, particularly when examining the prevailing trends in learning and development (L&D) for healthcare workers.

The Scopus dataset was searched on 25th November 2023 for relevant files, and analysis was done using Bibilioshiny and VOSviewer.

Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are increasingly being adopted in healthcare organizations. Moreover, simulation-based team training enhancing interprofessional communication and sensitizing healthcare workers (HCWs) towards equity, diversity, and inclusion is increasingly reflected in organizations’ L&D practices.

The study identifies prevailing themes in L&D in healthcare organizations in the last decade.

With the progress of the education system, many technological inventions have been found to develop the learning and teaching process. Several factors contributed to the advancement of education, from computer-based materials to web-based programs and technical tools. All these have revolutionized the whole education system and changed it from a monotonous and traditional teacher-centered approach to a motivating and interactive learner-based approach. Recently, digital technology has been implemented in many educational processes to increase teacher–learner interaction. The main feature characterizing digital learning is the active engagement that transforms learners from passive attendants to active participants in the learning process. From this perspective, teachers and learners are considered educational technologists. The purpose of this chapter is to shift the role of creativity and critical thinking from teachers to learners and show how the latter could create authentic writing by employing technology that is used and needed in the workplace. In doing so, learners become ready for their career life, and they learn to be more creative and collaborative individuals.

The 26th United Nations Climate Change Conference of the Parties (COP26) has reinvigorated the policy focus on sustainable transport. Automated and Connected Transport (ACT) has been featured as a promising technology-based option to aid in meeting the Sustainable Development Goals (SDGs). Despite progress in certain areas of sustainability, there are still a lot of SDGs where limited progress has been observed since the 2015 Paris Agreement, particularly regarding the social pillar of sustainability which is reflected from the user perspective. This chapter will set the scene for this edited volume first by contrasting ACT potential with the SDGs and then by highlighting the requirement to focus more on addressing user needs through ACT. Remarkably, scholars have been increasingly sceptical about the transition to fully automated and connected vehicles, thus it is pertinent to highlight relevant opportunities and risks. Chapter recommendations foster the promotion of a Quadruple Helix approach to operationalise the inclusion of social concerns (e.g. gender balance and equity) in Sustainable Urban Mobility Plans (SUMP) across the world.

Although numerous signal modalities are available for emotion recognition, audio and visual modalities are the most common and predominant forms for human beings to express their emotional states in daily communication. Therefore, how to achieve automatic and accurate audiovisual emotion recognition is significantly important for developing engaging and empathetic human–computer interaction environment. However, two major challenges exist in the field of audiovisual emotion recognition: (1) how to effectively capture representations of each single modality and eliminate redundant features and (2) how to efficiently integrate information from these two modalities to generate discriminative representations.

A novel key-frame extraction-based attention fusion network (KE-AFN) is proposed for audiovisual emotion recognition. KE-AFN attempts to integrate key-frame extraction with multimodal interaction and fusion to enhance audiovisual representations and reduce redundant computation, filling the research gaps of existing approaches. Specifically, the local maximum–based content analysis is designed to extract key-frames from videos for the purpose of eliminating data redundancy. Two modules, including “Multi-head Attention-based Intra-modality Interaction Module” and “Multi-head Attention-based Cross-modality Interaction Module”, are proposed to mine and capture intra- and cross-modality interactions for further reducing data redundancy and producing more powerful multimodal representations.

Extensive experiments on two benchmark datasets (i.e. RAVDESS and CMU-MOSEI) demonstrate the effectiveness and rationality of KE-AFN. Specifically, (1) KE-AFN is superior to state-of-the-art baselines for audiovisual emotion recognition. (2) Exploring the supplementary and complementary information of different modalities can provide more emotional clues for better emotion recognition. (3) The proposed key-frame extraction strategy can enhance the performance by more than 2.79 per cent on accuracy. (4) Both exploring intra- and cross-modality interactions and employing attention-based audiovisual fusion can lead to better prediction performance.

The proposed KE-AFN can support the development of engaging and empathetic human–computer interaction environment.

We examine the aggregate price trend of the Greek housing market from a behavioral perspective.

We construct a behavioral real estate sentiment index, based on relevant real estate search terms from Google Trends and websites, and examine its association with real estate price distributions and trends. By employing EGARCH(1,1) on the New Apartments Index data from the Bank of Greece, we capture real estate price volatility and asymmetric effects resulting from changes in the real estate search index. Enhancing robustness, macroeconomic variables are added to the mean equation. Additionally, a run test assesses the efficiency of the Greek housing market.

The results show a significant relationship between the Greek housing market and our real estate sentiment index; an increase (decrease) in search activity, indicating a growing interest in the real estate market, is strongly linked to potential increases (decreases) in real estate prices. These results remain robust across various estimation procedures and control variables. These findings underscore the influential role of real estate sentiment on the Greek housing market and highlight the importance of considering behavioral factors when analyzing and predicting trends in the housing market.

To investigate the behavioral effect on the Greek housing market, we construct our behavioral pattern indexes using Google search-based sentiment data from Google Trends. Additionally, we incorporate the Google Trend index as an explanatory variable in the EGARCH mean equation to evaluate the influence of online search behavior on the dynamics and prices of the Greek housing market.

The objective of this paper is to examine the determinants of the Greek house market during the period 2006–2022 using not only economic variables but also behavioral variables, taking advantage of available information on the volume of Google searches. In order to quantify the behavioral variables, we implement a Python code using the Pytrends 4.9.2 library.

In our study, we assert that models relying solely on economic variables, such as GDP growth, mortgage interest rates and inflation, may lack precision compared to those that integrate behavioral indicators. Recognizing the importance of behavioral insights, we incorporate Google Trends data as a key behavioral indicator, aiming to enhance our understanding of market dynamics by capturing online interest in Greek real estate through searches related to house prices, sales and related topics. To quantify our behavioral indicators, we utilize a Python code leveraging Pytrends, enabling us to extract relevant queries for global and local searches. We employ the EGARCH(1,1) model on the Greek house price index, testing several macroeconomic variables alongside our Google Trends indexes to explain housing returns.

Our findings show that in some cases the relationship between economic variables, such as inflation and mortgage rates, and house prices is not always consistent with the theory because we should highlight the special conditions of the examined country. The country of our sample, Greece, presents the special case of a country with severe sovereign debt issues, which at the same time has the privilege to have a strong currency and the support and the obligations of being an EU/EMU member.

The results suggest that Google Trends can be a valuable tool for academics and practitioners in order to understand what drives house prices. However, further research should be carried out on this topic, for example, causality relationships, to gain deeper insight into the possibilities and limitations of using such tools in analyzing housing market trends.

This is the first paper, to the best of our knowledge, that examines the benefits of Google Trends in studying the Greek house market.

This study aims to explore the use of time series analyses to examine changes in travelers’ preferences in accommodation features by disentangling seasonal, trend and the COVID-19 pandemic’s once-off disruptive effects.

Longitudinal data are retrieved by online traveler reviews (n = 519,200) from the Canary Islands, Spain, over a period of seven years (2015 to 2022). A time series analysis decomposes the seasonal, trend and disruptive effects of six prominent accommodation features (view, terrace, pool, shop, location and room).

Single accommodation features reveal different seasonal patterns. Trend analyses indicate long-term trend effects and short-term disruption effects caused by Covid-19. In contrast, no long-term effect of the pandemic was found.

The findings stress the need to address seasonality at the single accommodation feature level. Beyond targeting specific features at different guest groups, new approaches could allow dynamic price optimization. Real-time insight can be used for the targeted marketing of platform providers and accommodation owners.

A novel application of a time series perspective reveals trends and seasonal changes in travelers’ accommodation feature preferences. The findings help better address travelers’ needs in P2P offerings.