Search Results (17)

The shift toward a sustainable future demands substantial investments in green technologies and infrastructure, with green finance emerging as a pivotal driver for mobilizing such investments. This paper introduces a novel framework for green investment models and platforms tailored specifically to Qatar’s unique economic landscape. Through an extensive literature review, we identify essential policy levers and principles that can enhance the effectiveness of green finance initiatives. An experimental assessment utilizing a simulation model evaluates the potential impact of various policy scenarios on key metrics such as green investment volume, job creation, and environmental impact reduction. This study advocates for a comprehensive investment policy framework that includes alignment with Qatar’s national development objectives, targeted incentives for diverse economic sectors, collaborative stakeholder engagement, and robust monitoring and evaluation mechanisms. Our findings demonstrate that implementing these design principles can dramatically accelerate green finance in Qatar, aligning initiatives with the country’s National Vision 2030 and broader sustainability goals. This paper emphasizes the critical role of fiscal incentives tailored to specific sectors, the importance of collaboration among financial institutions and governmental bodies, and the necessity of continuous performance evaluations to inform adaptive policy adjustments. Ultimately, we propose a dynamic platform that not only facilitates green investments but also fosters innovation and mitigates the risks associated with sustainable projects in Qatar. Full article

Entrepreneurship Education (EE) is renowned for developing students’ managerial aptitudes, skills, and ideas for self-reliance. The inclusion of digital technology and innovation in EE is necessary to enhance Entrepreneurial Intention (EI) and Entrepreneurial Motivation (EM) for technological revolution and economic development. The aim of the systematic literature review is to (i) identify the current study on digital technologies, innovation, EE, EI, and EM, (ii) highlight how digital technology and innovation shape EE to achieve EI and motivation among students, and (iii) offer new advice on the future EE in a digital era. A search strategy was instituted to ascertain the required publications from Scopus, Web of Science, DOAJ, IEEE, ProQuest, SAGE Journals, Taylor & Francis, and Wiley. The publications were between 2010 and 2024, with no language restrictions. Out of the 108 identified publications from the search, 69 publications representing 54 separate papers were used in the review. Digital transformation in EE requires significant studies to determine its role in economic development and job creation. This review identified several themes in the publications, such as innovation, motivation, skills development, digital technology, EE’s impact, and factors of EE. The sustainability and future expectations of EE through digital technology and innovation are highlighted in the review. The study identified several findings, such as factors depriving EE, such as experience, investment, teachers, infrastructure, technology, market size, government, competition, culture, and funds. Further findings are a strategic vision of EE through policies to embrace innovation and digital technology practices and to achieve EM and EI. Also, the selected papers for the review are current publications at 61.1%, the quantitative method at 42.3% and journal articles at 88.9%. It is prudent to review EE’s appropriateness in a digital and innovative environment and to identify the impact on EM and EI among students. Full article

Multimodal deception detection has received increasing attention from the scientific community in recent years, mainly due to growing ethical and security issues, as well as the growing use of digital media. A great number of deception detection methods have been proposed in several domains, such as political elections, security contexts, and job interviews. However, a systematic analysis of the current situation and the evaluation and future directions of deception detection based on cues coming from multiple modalities seems to be lacking. This paper, starting from a description of methods and metrics used for the analysis and evaluation of multimodal deception detection on video, provides a vision of future directions in this field. For the analysis, the PRISMA recommendations are followed, which allow the collection and synthesis of all the available research on the topic and the extraction of information on the multimodal features, the fusion methods, the classification approaches, the evaluation datasets, and metrics. The results of this analysis contribute to the assessment of the state of the art and the evaluation of evidence on important research questions in multimodal deceptive deception. Moreover, they provide guidance on future research in the field. Full article

Computer vision (CV), a type of artificial intelligence (AI) that uses digital videos or a sequence of images to recognize content, has been used extensively across industries in recent years. However, in the healthcare industry, its applications are limited by factors like privacy, safety, and ethical concerns. Despite this, CV has the potential to improve patient monitoring, and system efficiencies, while reducing workload. In contrast to previous reviews, we focus on the end-user applications of CV. First, we briefly review and categorize CV applications in other industries (job enhancement, surveillance and monitoring, automation, and augmented reality). We then review the developments of CV in the hospital setting, outpatient, and community settings. The recent advances in monitoring delirium, pain and sedation, patient deterioration, mechanical ventilation, mobility, patient safety, surgical applications, quantification of workload in the hospital, and monitoring for patient events outside the hospital are highlighted. To identify opportunities for future applications, we also completed journey mapping at different system levels. Lastly, we discuss the privacy, safety, and ethical considerations associated with CV and outline processes in algorithm development and testing that limit CV expansion in healthcare. This comprehensive review highlights CV applications and ideas for its expanded use in healthcare. Full article

In the dynamic context of the global gig economy and Saudi Arabia’s Vision 2030, this study offers a novel examination of the impact of HR practices on gig workers’ job satisfaction and career sustainability in Saudi Arabia. Setting itself apart from prior research, it explores the uncharted interplay between HR practices and career longevity in the Saudi gig economy. Utilizing data from 344 gig workers, the study uncovers the intermediary role of job satisfaction in connecting HR practices to career sustainability, a dimension scarcely investigated before. It further assesses the often-assumed significant effects of demographic factors such as age and gender, revealing an unexpected, non-significant moderating impact. This research finds a strong positive correlation between effective HR practices, job satisfaction, and career endurance, highlighting the transformative power of HR strategies in the Saudi gig sector. These findings are vital for policymakers and practitioners focusing on Vision 2030 goals, underscoring the need for sophisticated HR strategies tailored to the unique Saudi gig environment. By bridging a critical knowledge gap and offering actionable insights, this study contributes significantly to the academic discourse on HR dynamics in gig economies and provides a foundation for future HR policy developments. Full article

This study presents an overview of the role that clean energy technologies can play in the decarbonisation path of Greece’s most carbon-intensive region, namely Western Macedonia. The region has been requested to adjust its production model to the new requirements of the Green Deal Initiative, while simultaneously proceeding to restructure its productive model towards a full phase-out of coal activities. The survey presented below will summarise the main findings and estimates of the clean energy potential from a technical and research point of view, and furthermore present assessments on the potential impact this could have on job creation and regional economic development in terms of potential investments. This study’s goals are to identify and promote actions to accelerate innovation performance in the clean energy domain and, simultaneously, serve as a co-working space among key stakeholders from business, government, civil society and innovation who share a vision for a sustainable future. Finally, this study highlights the importance of accelerating innovation in the clean energy domain—spanning energy production, distribution and consumption. Full article

Background: With the increasingly demanding healthcare environment, patient safety issues are only becoming more complex. This urges nursing leaders to adapt and master effective leadership; particularly, transformational leadership (TFL) is shown to scientifically be the most successfully recognized leadership style in healthcare, focusing on relationship building while putting followers in power and emphasizing values and vision. Aim: To examine how transformational leadership affects nurses’ job environment and nursing care provided to the patients and patients’ outcomes. Design: A systematic literature review was conducted. From 71 reviewed, 23 studies were included (studies included questionnaire surveys and one interview, extracting barriers and facilitators, and analyzing using qualitative synthesis). Result: TFL indirectly and directly positively affects nurses’ work environment through mediators, including structural empowerment, organizational commitment, and job satisfaction. Nurses perceived that managers’ TFL behavior did not attain excellence in any of the included organizations, highlighting the necessity for additional leadership training to enhance the patient safety culture related to the non-reporting of errors and to mitigate the blame culture within the nursing environment. Conclusion: Bringing more focus to leadership education in nursing can make future nursing leaders more effective, which will cultivate efficient teamwork, a quality nursing work environment, and, ultimately, safe and efficient patient outcomes. This study was not registered. Full article

With the rapid development of enabling technologies like VR and AR, we human beings are on the threshold of the ubiquitous human-centric intelligence era. 6G is believed to be an indispensable cornerstone for efficient interaction between humans and computers in this promising vision. 6G is supposed to boost many human-centric applications due to its unprecedented performance improvements compared to 5G and before. However, challenges are still to be addressed, including but not limited to the following six aspects: Terahertz and millimeter-wave communication, low latency and high reliability, energy efficiency, security, efficient edge computing and heterogeneity of services. It is a daunting job to fit traditional analytical methods into these problems due to the complex architecture and highly dynamic features of ubiquitous interactive 6G systems. Fortunately, deep learning can circumvent the interpretability issue and train tremendous neural network parameters, which build mapping relationships from neural network input (status and specific requirements of a 6G application) to neural network output (settings to satisfy the requirements). Deep learning methods can be an efficient alternative to traditional analytical methods or even conquer unresolvable predicaments of analytical methods. We review representative deep learning solutions to the aforementioned six aspects separately and focus on the principles of fitting a deep learning method into specific 6G issues. Based on this review, our main contributions are highlighted as follows. (i) We investigate the representative works in a systematic view and find out some important issues like the vital role of deep reinforcement learning in the 6G context. (ii) We point out solutions to the lack of training data in 6G communication context. (iii) We reveal the relationship between traditional analytical methods and deep learning, in terms of 6G applications. (iv) We identify some frequently used efficient techniques in deep-learning-based 6G solutions. Finally, we point out open problems and future directions. Full article

Planning consists of thinking about the future and allows territories to be better prepared to take advantage of opportunities and face challenges that arise. In Portugal, tourism is one of the pillars of the economy, generating wealth and creating various job openings. In recent years, this destination has won several international awards and distinctions due to the quality of services and tourism offerings. Part of this success is due to the planning carried out by the entity responsible, Turismo de Portugal. This study aims to analyse the content and structure of national tourism plans implemented in Portugal since 2000. Furthermore, we want to understand: (1) the vision outlined for the Portuguese territory and the changes it has undergone in recent decades; (2) the methodologies that were used in the formation process of these plans, for instance, if public auscultation was used; (3) the main objectives defined for the territory and which were the actions that have been defined to achieve them. A qualitative methodology of document analysis was used, combined with the presentation of a case study related to tourism planning at a national level. The results show the growing importance of the tourism sector for the Portuguese economy. Since 2020, the growing involvement of stakeholders in the construction of strategic plans has also been evident through public consultation and an emphasis on sustainability practices in the tourism sector. Full article

This study deals with the issue of planning for future jobs in light of the Unified Saudi Classification of Educational Levels and Specializations. We aimed to identify the mechanism used by graduates to choose a future job and to shed light on the Unified Saudi Classification of Educational Levels and Specializations. The problem addressed in this study is the identification of the optimal formula such that the graduate can benefit from this classification. The community studied is made up of students at the College of Applied Studies and Community Service at Imam Abdul Rahman bin Faisal University in Dammam in the period from 2019 to 2022. The sample included 129 male and female students, representing 20% of the research community. The selection was random, taking into account the homogeneity of the research community. We attempted to verify the validity of the hypothesis, stating that there is a statistically significant relationship between graduates’ preferences for their future jobs and their knowledge, represented by The Saudi Standard Classification of Scientific Levels and Specializations. A number of findings resulted from this study, most notably that there was a discrepancy regarding students’ preferences for future jobs based on their gender. We conclude with a number of recommendations, including the need to shed more light on the Unified Classification of Educational Levels and Specializations in Saudi Arabia and increase communication between scientific departments and employers. Full article

This study aims to understand and analyze what influences female students to choose a college major in the United Arab Emirates (UAE). To accomplish our target, we conducted a survey with mostly female first-year undergraduate students (N = 496) at Zayed University to understand the personal, social, and financial factors influencing students’ major choices. Further, this study also asked students to specify their actions before deciding on their major and assessed the information that could be helpful for future students to decide on their majors. Last, the study investigated how Science, Technology, Engineering, and Mathematics (STEM) students differ from other students in their major decision. The results show that financial factors such as income and business opportunities related to the major are crucial. Further, gender suitability for the job and passion are influential. Students conduct internet searches, use social media, and read brochures in the process of major decisions. Moreover, students think job alignment with the UAE vision and information related to job availability, income, and skills are critical for future students to decide on their major. Finally, STEM students are more influenced by business opportunities, prestige, and career advancement than others. Full article

Analysing big data job posts in Saudi cyberspace to describe the future market need for sustainable skills, this study used the power of artificial intelligence, deep learning, and big data technologies. The study targeted three main stakeholders: students, universities, and job providers. It provides analytical insights to improve student satisfaction, retention, and employability, investigating recent trends in the essential skills pinpointed as enhancing the social effect of learning, and identifying and developing the competencies and talents required for the Kingdom of Saudi Arabia’s (KSA’s) digital transformation into a regional and global leader in technology-driven innovation. The methodological framework comprises smart data processing, word embedding, and case-based reasoning to identify the skills required for job positions. The study’s outcomes may promote the alignment of KSA’s business and industry to academia, highlighting where to build competencies and skills. They may facilitate the parameterisation of the learning process, boost universities’ ability to promote learning efficiency, and foster the labour market’s sustainable evolution towards technology-driven innovation. We believe that this study is crucial to Vision 2030’s realisation through a long-term, inclusive approach to KSA’s transformation of knowledge and research into new employment, innovation, and capacity. Full article

Talents are seen as unique strategic resources that are essential to achieving a sustainable competitive advantage. Organizations use TM to source and maintain a high quality and quantity of talents. Despite numerous research and development of practice in this area, insufficient skills of the staff are still underlined, and an unexplored area in this regard is the support for talent development, prior to the employment of employees (i.e., at the stage of their education). There are numerous studies on TM in universities, but they cover all aspects of TM aimed at university staff. There is no research on supporting the talents of students as future employees. Meanwhile, universities “shape” the future staff and from this place employees identified as talented or with great potential are recruited. In connection with the identified gap, the question was asked whether and to what extent universities and educational entities should be involved in discovering and developing talents for the future needs of the economy. The aim of the article was to check how students perceive their future (i.e., their vision of life), how much of it is related to their future job, and how they see universities as an environment to support their talents. The study used the questionnaire-based survey-CAWI (computer assisted web interview) technique. The research was conducted in the Czech Republic, Poland, and Ukraine. The results of the research show that the support of talent development by universities is not sufficient, and the majority of students (despite the fact that the research was conducted in the last semesters of studies) do not have clearly defined goals and methods of achieving them. Full article

3D visual recognition is a prerequisite for most autonomous robotic systems operating in the real world. It empowers robots to perform a variety of tasks, such as tracking, understanding the environment, and human–robot interaction. Autonomous robots equipped with 3D recognition capability can better perform their social roles through supportive task assistance in professional jobs and effective domestic services. For active assistance, social robots must recognize their surroundings, including objects and places to perform the task more efficiently. This article first highlights the value-centric role of social robots in society by presenting recently developed robots and describes their main features. Instigated by the recognition capability of social robots, we present the analysis of data representation methods based on sensor modalities for 3D object and place recognition using deep learning models. In this direction, we delineate the research gaps that need to be addressed, summarize 3D recognition datasets, and present performance comparisons. Finally, a discussion of future research directions concludes the article. This survey is intended to show how recent developments in 3D visual recognition based on sensor modalities using deep-learning-based approaches can lay the groundwork to inspire further research and serves as a guide to those who are interested in vision-based robotics applications. Full article

(1) Background: University transition is a critical step in career construction due to the uncertainty and unpredictability of socioeconomic conditions; these conditions compel people to manage a greater quantity of perceived risks associated with their career projects than in the past, and to face unexpected situations that could compromise their quality of life in educational and work contexts. After all, experiencing well-being during the university path can undoubtedly affect the visions of one’s future work, especially when a transition period is nearby. The present study aimed to explore the role of subjective risk intelligence in expectations about future work, analyzing the potential mediational role of academic satisfaction in this relationship. (2) Methods: A longitudinal study was carried out on 352 Italian university students at the end of the degree course. We used the following measures: in T1, Subjective risk intelligence scale, College Satisfaction scale; in T2, three items assessing the expectations about future work. (3) Results: The main findings showed that subjective risk intelligence has both direct and indirect effects (through the mediation of college satisfaction) on the expectations about future work. (4) Conclusions: The ability to manage risks, also through the contribution of domain-specific satisfaction, can lead to positive expectations toward one’s future work. This could increase the likelihood to perform career-related behaviors in a more proactive way if people have high risk management skills and high levels of academic satisfaction with their university path during transition. Full article