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This paper aims to develop predictive methods, based on recurrent neural networks, useful to support facility managers in building maintenance tasks, by collecting information coming from a computerized maintenance management system (CMMS).

This study applies data-driven and text-mining approaches to a CMMS data set comprising more than 14,500 end-users’ requests for corrective maintenance actions, collected over 14 months. Unidirectional long short-term memory (LSTM) and bidirectional LSTM (Bi-LSTM) recurrent neural networks are trained to predict the priority of each maintenance request and the related technical staff assignment. The data set is also used to depict an overview of corrective maintenance needs and related performances and to verify the most relevant elements in the building and how the current facility management (FM) relates to the requests.

The study shows that LSTM and Bi-LSTM recurrent neural networks can properly recognize the words contained in the requests, thus correctly and automatically assigning the priority and predicting the technical staff to assign for each end-user’s maintenance request. The obtained global accuracy is very high, reaching 93.3% for priority identification and 96.7% for technical staff assignment. Results also show the main critical building elements for maintenance requests and the related intervention timings.

This work shows that LSTM and Bi-LSTM recurrent neural networks can automate the assignment process of end-users’ maintenance requests if trained with historical CMMS data. Results are promising; however, the trained LSTM and Bi-LSTM RNN can be applied only to different hospitals adopting similar categorization.

The data-driven and text-mining approaches can be integrated into the CMMS to support corrective maintenance management by facilities management contractors, i.e. to properly and timely identify the actions to be carried out and the technical staff to assign.

The improvement of the maintenance of the health-care system is a key component of improving health service delivery. This work shows how to reduce health-care service interruptions due to maintenance needs through machine learning methods.

This study develops original methods and tools easily integrable into IT workflow systems (i.e. CMMS) in the FM field.

The problem of automatic recognition of human activity is one of the most important and challenging areas of research in computer vision because of the wide range of possible applications, for example surveillance, advanced human‐computer interactions, monitoring. This paper presents statistical computer vision approaches to automatically recognize different human activities.

The human activity recognition process has three steps: firstly human blobs are segmented by motion analysis; then the human body posture is estimated and, finally a temporal model of the detected posture series is generated by discrete hidden Markov models to identify the activity.

The system was tested on image sequences acquired in a real archaeological site while some people simulated both legal and illegal actions. Four kinds of activity were automatically classified with a high percentage of correct detections.

The proposed approach provides efficient solutions to some of the most common problems in the human activity recognition research field: high detailed image requirement, sequence alignment and intensive user interaction in the training phase. The main constraint of this framework is that the posture estimation approach is not completely view independent.

Results of time performance tests were very encouraging for the use of the proposed method in real time surveillance applications.

The proposed framework can work using low cost cameras with large view focal lenses. It does not need any a priori knowledge of the scene and no intensive user interaction is required in the early training phase.

COVID-19 played a crucial role in the development and enlargement of learning via electronic media. Still, the recent fourth industrial revolution [Industry 4.0 (I4.0)] paved the road toward Education 4.0. In this regard, several research challenges arise, involving the preparation of gamification strategies in online learning environments. In this sense, this paper aims to investigate the relationship between technologies of I4.0 and gamification practices in online learning around the world. Specifically, to categorize the studies of the scientific literature in the area into knowledge domains and the mention of I4.0 technologies and to verify the relationship of these technologies with the different educational levels.

The preferred reporting items for systematic reviews and meta-analysis protocol was used as a research method with 130 papers included for full content analysis and obtained from the Web of Science.

The leading I4.0 technologies mentioned in the analyzed papers were simulation, Internet of Things and augmented/virtual reality, in this order. Although there are more mentioned technologies, the domain of knowledge to be applied and the educational level interfere in choosing these pillars. With this, the main findings of this relationship were exposed in a singular, modern, active, realist, technological framework to demonstrate how I4.0 relates to the practice of gamification in online educational environments.

To the best of the authors’ knowledge, this is the first study that brings together the relationship of gamification applied in e-learning with I4.0 technologies.

Recognizing novel entrepreneurial opportunities arising from a crisis is of paramount importance for firms. Hence, understanding the pivotal factors that facilitate firms in this endeavor holds significant value. This study delves into such factors within a representative empirical context impacted by a crisis, drawing insights from existing literature on opportunity recognition during such tumultuous periods.

The authors conducted a qualitative inspection of 14 Italian firms during the COVID-19 pandemic crisis. The authors collected a rich body of multi-source qualitative data, including 34 interviews (with senior managers and entrepreneurs) and secondary data (press releases, videos, web interviews, newspapers, reports and academic articles) in two phases (March–August 2020 and September–December 2020).

The results suggest the existence of a process model of opportunity recognition during crises based on five entrepreneurial influencing factors (entrepreneurial knowledge, entrepreneurial alertness, entrepreneurial proclivity, entrepreneurial personality and entrepreneurial purpose).

Various scholars have highlighted that, in times of crises, it is not easy and indeed very challenging for entrepreneurs to identify novel entrepreneurial opportunities. However, recent research has shown that crises can also positively impact entrepreneurs and their capacity to identify new entrepreneurial opportunities. Given these findings, not much research has analyzed the process by which entrepreneurs identify novel entrepreneurial opportunities during crises. This study shows that some entrepreneurial influencing factors are very important to identify new entrepreneurial opportunities during crises.

This study aims to examine the connection between biophilic workplace design and its effect on Gen Z employees’ mental well-being and job contentment. The aim is to provide insights for the top management to acknowledge and implement biophilic workplaces to create a more productive and fulfilling work environment.

The research study is supported by a literature review of 45 peer-reviewed papers. The research involved a comprehensive review of databases such as Scopus, EBSCO, Elsevier, Jstor and Google Scholar using relevant keywords and Boolean operators. The inclusion criteria for the study are limited to articles published between 2013 and 2024. The review results provide insights into the current state of research on biophilic office spaces and their impact on Gen Z employees’ mental well-being and productivity.

The findings of this study reveal how biophilic office design positively impacts the mental well-being and job contentment of Gen Z employees, leading to increased productivity. It demonstrates that being around elements of nature at work can reduce stress and enhance cognitive function, leading to increased job contentment.

Few studies have been done on the impact of biophilic-designed offices on Gen Z employees, a cohort increasingly becoming the dominant workforce. The conceptual model proposed in the study has defined the positive aspects of biophilic design for Gen Z employees.