'We are still here': The stories of Syrian academics in exile

Purpose The purposes of this paper are twofold: to generate insight into the experiences of Syrian academics in exile in Turkey, and to explore approaches to collaboration and community building among academics in exile and with counterparts in the international academic community. Design/methodology/approach The study employs a hybrid visual-autobiographical narrative methodology, embedded within a Large Group Process (LGP) design Findings Findings are presented in two phases: The first phase presents a thematic analysis of narrative data, revealing the common and divergent experiences of twelve exiled academics. The second phase presents a reflective evaluation of undertaking the LGP and its implications for community building and sustaining Syrian academia in exile. Research limitations While this is a qualitative study with a small participant group, and therefore does not provide a basis for statistical generalisation, it offers rich insight into Syrian academics’ lived experiences of exile, and into strategies implemented to support the Syrian academic community in exile. Practical implications The study has practical implications for academic development in the contexts of conflict and exile; community building among dispersed academic communities; educational interventions by international NGOs and the international academic community; and group process design. Originality/value The study makes an original contribution to the limited literature on post-2011 Syrian higher education by giving voice to a community of exiled academics, and by critically evaluating a strategic initiative for supporting and sustaining Syrian academia. This represents significant, transferable insight for comparable contexts

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Two page typewritten curriculum vitae of Fuad Shaban, written between 1971 and 197

C-SAR: Class-Specific and Adaptive Recognition for Arabic Handwritten Cheques

We propose C-SAR, a Class-specific and Adaptive Recognition algorithm for Arabic handwritten Cheques. Existing methods suffer from low accuracy due to the complex structure of Arabic script and high-dimensional datasets. In this paper, we present an adaptive algorithm that implements a class-specific classification to address these challenging issues. C-SAR trains a set of class-specific machine learning models of Support Vector Machines and Artificial Neural Networks features extracted using angular pixel distribution approach. Furthermore, we propose a class-specific taxonomy of Arabic cheque handwritten words. The proposed taxonomy divides the Arabic words into groups over three layers based on their structural characteristics. Accordingly, C-SAR performs classification on three phases, i.e., 1) similar and non-similar structures, for binary classification, 2) classes with similar structures into another two categories, and 3) class-specific models to recognize the Arabic word from the given image. We introduce benchmark experimental results of our method against previous methods on the Arabic Handwriting Database for Text Recognition. Our method outperforms the baseline methods with at least 5% accuracy having 90% average classification accuracy. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.Scopu

Exploring nurses’ awareness and attitudes toward artificial intelligence: Implications for nursing practice

Introduction Worldwide, healthcare systems aim to achieve the best possible quality of care at an affordable cost while ensuring broad access for all populations. The use of artificial intelligence (AI) in healthcare holds promise to address these challenges through the integration of real-world data-driven insights into patient care processes. This study aims to assess nurses’ awareness and attitudes toward AI-integrated tools used in clinical practice. Methods A descriptive cross-sectional design captured nurses’ responses at three governmental hospitals in Saudi Arabia by using an online questionnaire administered over 4 months. The study involved 220 registered nurses with a minimum of one year of clinical experience, selected through a convenience sampling method. The online survey consisted of three sections: demographic information, an assessment of nurses’ AI knowledge, and the general attitudes toward the AI scale. Results Nurses displayed “moderate” levels of awareness toward AI technology, with 70.9% having basic information about AI and only 58.2% (128 nurses) were considered “aware” of AI as they dealt with one of its healthcare applications. Nurses expressed openness to AI integration ( M  = 3.51) on one side, but also had some concerns about AI. Nurses expressed conservative attitudes toward AI, with significant differences observed based on gender (χ² = 4.67, p  < 0.05). Female nurses exhibited a higher proportion of negative attitudes compared to male nurses. Significant differences were also found based on age (χ² = 9.31, p  < 0.05), with younger nurses demonstrating more positive attitudes toward AI compared to their older counterparts. Educational background yields significant differences (χ² = 6.70, p  < 0.05), with nurses holding undergraduate degrees exhibiting the highest positive attitudes. However, years of nursing experience did not reveal significant variations in attitudes. Conclusion Healthcare and nursing administrators need to work on increasing the nurses’ awareness of AI applications and emphasize the importance of integrating such technology into the systems in use. Moreover, addressing nurses’ concerns about AI's control and discomfort is crucial, especially considering generational differences, with younger nurses often having more positive attitudes toward technology. Change management strategies may help overcome any hindrances