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In the paper, “TV‐trackmeter”, a stereoscopic measuring system developed by Tecnomare, is presented, some recent innovations and upgrading are described, and its reliable use in hostile environments proved. The latest release of the device implements highlighted featuring capabilities such as 3D measuring, automatic mapping, false colour depth‐maps, geometric modelling, multi‐point tracking, recording/retrieving of stereo pair images, and use of new and more powerful hardware. A theoretical introduction to the operating mode of a stereoscopic device, followed by an error propagation analysis is included. A brief description is also given of the accuracy of the device, i.e. pose detection (position and attitude estimation) of the scene objects. An evaluation of the tracking speed capability is provided. Some examples are shown of trials carried out within a nuclear power plant and underwater. Two further applications for this system are described.

The years following the 9/11/2001 terrorists attacks saw a marked increase in community and hospital emergency preparedness, from communications across community networks, development of policies and procedures, to attainment and training in the use of biological warfare resources. Regular drills ensured emergency and health care personnel were trained and prepared to address the next large-scale crisis, especially from terrorist and bioterrorist attacks. This chapter looks at some of the more familiar global health issues over the past two decades and the lessons learned from hospital responses to inform hospital management in preparation for future incidents.

This study is a narrative review of the literature related to lessons learned from four major events in the time period from 2002 to 2023 – SARS, MERS, Ebola, and COVID-19.

The initial search yielded 25,913 articles; 57 articles were selected for inclusion in the study.

Comparison of key issues and lessons learned among the four major events described in this article – SARS, MERS, Ebola, and COVID-19 – highlight that several lessons are “relearned” with each event. Other key issues, such as supply shortages, staffing availability, and hospital capacity to simultaneously provide care to noninfectious patients came to the forefront during the COVID-19 pandemic. A primary, ongoing concern for hospitals is how to maintain their preparedness given competing priorities, resources, and staff time. This concern remains post-COVID-19.

Students’ academic achievement is a multifaceted phenomenon. While depression can suppress academic performance, academic satisfaction can promote it. This study aims to investigate the relationship between depression and academic satisfaction among students studying at Shahroud University of Medical Sciences.

This cross-sectional study was carried out on 312 undergraduate students of Shahroud University of Medical Sciences. Data collection tools included demographic data form, University Student Depression Inventory and academic satisfaction scale. Data were collected by a simple random sampling method and self-reporting by the participants. Data were analyzed using descriptive and inferential statistics (multivariate multiple regression analysis and multivariate linear regression).

The participants’ mean depression and academic satisfaction scores were 71.92 ± 22.94 and 53.70 ± 9.69, respectively. In addition, the depression score was significantly and inversely correlated with students' academic satisfaction (r = −0.122, p-value = 0.031). Moreover, there was a significant correlation between students’ depression with marital status, level of the semester, interest in the field of study and study topic.

This study emphasizes improving education, spiritual and social support and strengthens strategies to deal with depression and medical science students’ related factors.

Students of medical sciences are exposed to depression during their college years, which is related to their academic satisfaction.

European rural development programmes are driving multi-actor interactive innovation initiatives and alliances to create an environment in which innovation acts as a tool for accelerating rural development processes. In Europe, where rural areas are facing many challenges, identifying which challenges, difficulties, obstacles or risk factors that interactive innovation projects have had to face in rural areas while being planned and set up would be interesting. The objective of this research work was to, therefore, identify and analyse the risk factors of 200 rural projects and initiatives that were selected as case studies from the whole of Europe.

The employed methodology consisted in conducting interviews to subsequently perform statistical independence analyses of the qualitative variables characterising the found projects and risk factors.

The findings indicated that most of the risks that rural projects and initiatives faced were related to the social domain which was, in turn, the fundamental pillar of interactive innovation. Dependence was found between social risk factors appearing and the innovation type carried out; the risk factors corresponding to the political–legal risks category and the project or initiative coordinating country; and the economic–technical risks category and the initiatives' geographic magnitude.

This paper exposes the main risks identified within various rural innovation initiatives and projects around Europe. For this purpose, a statistical analysis of independence was performed, allowing us to generate reliable and accurate results of the main risks associated with certain descriptive characteristics (coordinating region, domain, innovation type, gender balance and geographic magnitude) of the initiatives studied.

To identify transitional palliative care (TPC) interventions for older adults with non-malignant chronic diseases and complex conditions.

A systematic review of the literature was conducted. CINAHL, Cochrane Library, Embase and Pubmed databases were searched for studies reporting TPC interventions for older adults, published between 2002 and 2019. The Crowe Critical Appraisal Tool was used for quality appraisal.

A total of six studies were included. Outcomes related to TPC interventions were grouped into three categories: healthcare system-related outcomes (rehospitalisation, length of stay [LOS] and emergency department [ED] visits), patient-related outcomes and family/carer important outcomes. Overall, TPC interventions were associated with lower readmission rates and LOS, improved quality of life and better decision-making concerning hospice care among families. Outcomes for ED visits were unclear.

Positive outcomes related to healthcare services (including readmissions and LOS), patients (quality of life) and families (decision-making) were reported. However, the number of studies supporting the evidence were limited.

Studies examining the effectiveness of existing care models to support transitions for those in need of palliative care are limited. This systematic literature review identified and appraised interventions aimed at improving transitions to palliative care in older adults with advanced non-malignant diseases or frailty.

In the era of artificial intelligence, natural language processing (NLP) models are revolutionizing numerous sectors. This research aims to explore the perceived value of them among university students. In particular, it aims to investigate how gender may influence students’ intention to use these models in educational contexts, highlighting potentially significant differences that could inform the implementation and adoption of educational technologies.

This study investigates the relationship between perceived value and students' intention to adopt NLP models, considering gender as a moderator. The research involves 562 students from the University of Salerno, in Italy, and uses confirmatory factor analysis to evaluate the reliability and validity of the measurement scales. A regression model with robust errors is used to explore the moderating role of gender on the relationship between perceived value and intentions of use of NLP models.

The results reveal a significant positive association between perceived value and intention to use NLP models, confirming that students with higher perceived value are more likely to adopt these technologies. Furthermore, gender moderates this relationship, indicating that females are less prone to use NLP models than male counterparts.

Research takes on a significant role in the academic field, underlining the importance of adapting teaching practices to the increasingly widespread digitalization. The inclusion of NLP models in university programs emerges as a possible improvement of the learning experience, ensuring cutting-edge education in tune with the needs of the digital society.

THE “Do.X 1” was launched on July 12, 1929, hence we can now look back on the experience collected in the operation of this new aircraft over two and a half years. This experience has been gained with three different types: the original type aircraft, or “Do X.1” (Fig. 1), with air‐cooled engines; the same aircraft in its modified form, the “Do.X.1a” (Fig 2), with water‐cooled engines, and the “Do.X.2” (Umberta Maddalena), the first of two flying boats built on behalf of an Italian firm, with water‐cooled Fiat engines. (Fig. 3.)

The purpose of this paper is to describe and discuss the way internships are currently evaluated in Campania Universities by host institutions.

The author collected and described questionnaires used by the universities of the Regional Observatory of Campania University System. These questionnaires are given by the host institutions to interns to fill out, and they evaluate skills growth in terms of initial and final assessment. Data related to these questionnaires are not publicly available, therefore the main data source comes from 732 post-internship questionnaires collected by the University of Suor Orsola Benincasa in 2013-2014.

Out of the 732 participants considered, 65 per cent rated excellent for motivation and interest, 55 per cent were rated excellent for the responsibility demonstrated, but only 37 per cent were rated excellent for the level of work autonomy achieved: a signal of the need to better prepare students for internships. Cross-technical and professional skills, which increase of +37 and +32 per cent, suggest that the experience has a very positive value. Regarding the experience outcome, the author detected a positive indicator: in 6 per cent of cases, an early employment occurred.

It has only been possible to analyze the questionnaires from one of the Campania universities. The results encourage us to explore further, extending instruments and research methods at a national level, too.

The author built a research database form to compare current questionnaires in Campania. The author collected internship evaluation data from Suor Orsola University to propose a common questionnaire among Campania Universities. By sharing the findings, other universities, national and international, could benefit from the results.

This study aims to provide empirical evidence on the link between intellectual capital and a firm's ability to attract funding and financing in Italian companies.

Data from 125 Italian companies was collected through an online survey and analysed using structural equation modelling (PLS-SEM).

Results show that structural capital has a positive, direct impact on both human and relational capital. At the same time, relational capital is the only intellectual capital component that has a positive, direct impact on a firm's ability to attract funding and financing. Finally, we found that a firm's ability to attract funding and financing impacts both innovation and financial performance.

This novel study is among the first to provide empirical evidence of how human, relational and structural capital interact with each other and enhance a firm's ability to attract funding and financing.

Ensuring quality is one of the most significant challenges in microtask crowdsourcing tasks. Aggregation of the collected data from the crowd is one of the important steps to infer the correct answer, but the existing study seems to be limited to the single-step task. This study aims to look at multiple-step classification tasks and understand aggregation in such cases; hence, it is useful for assessing the classification quality.

The authors present a model to capture the information of the workflow, questions and answers for both single- and multiple-question classification tasks. They propose an adapted approach on top of the classic approach so that the model can handle tasks with several multiple-choice questions in general instead of a specific domain or any specific hierarchical classifications. They evaluate their approach with three representative tasks from existing citizen science projects in which they have the gold standard created by experts.

The results show that the approach can provide significant improvements to the overall classification accuracy. The authors’ analysis also demonstrates that all algorithms can achieve higher accuracy for the volunteer- versus paid-generated data sets for the same task. Furthermore, the authors observed interesting patterns in the relationship between the performance of different algorithms and workflow-specific factors including the number of steps and the number of available options in each step.

Due to the nature of crowdsourcing, aggregating the collected data is an important process to understand the quality of crowdsourcing results. Different inference algorithms have been studied for simple microtasks consisting of single questions with two or more answers. However, as classification tasks typically contain many questions, the proposed method can be applied to a wide range of tasks including both single- and multiple-question classification tasks.