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Fostering sustainable development in undergraduate students\u27 entrepreneurial mindsets: Integrating digital and AI ethics in general education
No full textThis chapter explores integrating digital and AI ethics into undergraduate general education as a vital approach for cultivating sustainable entrepreneurial mindsets. As AI and digital technologies reshape the entrepreneurial landscape, ethical concerns such as data privacy, algorithmic bias, and environmental sustainability must be addressed to ensure responsible innovation. By embedding ethical principles into entrepreneurship education, universities can guide students to develop ventures aligned with global frameworks like the United Nations\u27 Sustainable Development Goals (SDGs). The chapter discusses the role of universities in shaping future business leaders, the challenges and opportunities of embedding ethics in education, and best practices for fostering ethical entrepreneurship. Through interdisciplinary collaboration, experiential learning, and established ethical frameworks, universitiescan prepare students to balance innovation with social and environmental responsibility, creating ventures that contribute positively to society and sustainable development
Quality of Life of Emirati Women with Cervical Cancer Using EORTC QLQ-30 and CX24: A First Look in the UAE
Full text linkBackground: Cervical cancer is the fourth leading cause of cancer-related mortality among women globally and remains a prevalent malignancy among Emirati women. This study assessed the quality of life of Emirati women with cervical cancer and identified key factors influencing their well-being to inform targeted interventions. Methods: A cross-sectional study was conducted among 72 Emirati women diagnosed with cervical cancer utilizing the Arabic-translated European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30 and QLQ-CX24). Sociodemographic and clinical data were collected. Statistical analyses included ANOVA, independent-sample t-tests, and, where assumptions were violated, Kruskal–Wallis and Mann–Whitney tests. Results: The mean global health status/QoL score was 64.4 (SD ± 20.4), indicating moderate well-being. The cognitive (69.9 ± 23.5) and role functioning (65.1 ± 25.0) scores were relatively high, whereas the social functioning score was lower (61.8 ± 25.2). Fatigue (41.5 ± 27.5), sleep disturbance (40.7 ± 31.3), and pain (39.4 ± 27.6) were the most prevalent symptoms. Radiotherapy negatively impacted sexual enjoyment (p = 0.019), whereas lower income and metastases were associated with worse symptom burden. Higher education, employment, and physical activity correlated positively with functional well-being. Conclusions: Early-stage diagnosis, financial stability, and physical activity were key predictors of better QoL. Addressing financial disparities, managing symptoms, and improving survivorship care are essential
On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs
Full text linkThis research investigates the effectiveness of established vulnerability metrics, such as the Common Vulnerability Scoring System (CVSS), in evaluating attacks on Large Language Models (LLMs), with a focus on Adversarial Attacks (AAs). The study explores the influence of different metric factors in determining vulnerability scores, providing new perspectives on potential enhancements to these metrics. Approach - This study adopts a quantitative approach, calculating and comparing the coefficient of variation of vulnerability scores across 56 adversarial attacks on LLMs. The attacks, sourced from various research papers, and obtained through online databases, were evaluated using multiple vulnerability metrics. Scores were determined by averaging the values assessed by three distinct LLMs. Findings - The results indicate that existing scoring systems yield vulnerability scores with minimal variation across different attacks, supporting the hypothesis that current vulnerability metrics are limited in evaluating AAs on LLMs, and highlighting the need for the development of more flexible, generalized metrics tailored to such attacks
Fmri Study Of Brain Activation Associated With Earnings Management Techniques In Proximity To Debt Covenant Violations
Full text linkBusiness managers employ various earnings management techniques (EMTs) to prevent debt covenant breaches. Understanding the underlying decision-making processes related to such techniques may help a manager choose the optimal approach to avoid a violation, but these cognitive processes remain poorly understood. The current functional magnetic resonance imaging (fMRI) study aims to explore the brain activation associated with assessment and decision-making to apply three commonly employed EMTs: accounting earnings management (AEM), classification shifting (CS), and total real earnings management (REM). During the fMRI scans, fifty participating business managers read the text presentations of multiple scenarios where companies were at different proximities (FAR or CLOSE) to debt covenant violations. Then, the managers assessed the options and decided whether to apply one of the three EMTs. Behavioral results suggest that managers preferred using AEM and REM over CS to avoid debt covenant violations, and they were more likely to apply EMTs when companies were CLOSE to rather than FAR from default. The fMRI findings revealed that assessing and deciding the three techniques elicited activation in the occipital, frontal, and lateral temporal cortices regions. In addition, REM and CS were associated with more significant subcortical hippocampus regions for memory processing. REM activated the brain\u27s motor system for actions, but CS activated the basal ganglia for delayed reward processing. Increased risk of debt covenant violation in CLOSE scenarios was linked to reduced activation in the frontal pole, superior parietal, precuneus, lingual, lateral occipital, posterior inferior temporal, and posterior cingulate gyri, suggesting decreased visual attention and emotion regulation activation during assessing and deciding to apply EMTs to avoid default risks. These activation patterns reveal shared and distinct neural processes in applying EMTs. Proximity to debt covenant violations reduces brain activation in areas tied to decision-making and emotional regulation, fostering stress-driven short-term strategies. These findings highlight financial pressure\u27s impact on managerial behavior and emphasize the need for resilience-focused interventions
Securitising Health In The Occupied Palestinian Territories: The Covid-19 Pandemic Response And Future Implications
No full textFor decades, the links between security and health, as well as securitisation and health, have been assessed in response to health emergencies (e.g. HIV/AIDS, SARS, H5N1, H1N1, Ebola, Zika) across various geographical contexts. The COVID-19 pandemic presented additional opportunities to evaluate this nexus. This paper analyses securitisation processes of COVID-19 within the Occupied Palestinian Territories (OPT). We find that in addition to the existing fragility and fragmentation of the Palestinian health systems, there has been a multifaceted pattern of securitisation implemented by Israel during the pandemic. Mechanisms of securitisation include digital surveillance, the COVID-19 response in occupied East Jerusalem, inequitable treatment of Palestinian prisoners, and the escalation of home demolitions and displacement of Palestinians. The political context of extended military occupation and absence of Palestinian sovereignty has meant Palestinians have been unable to effectively manage the pandemic. Failure to address this context will lead to poor outcomes in future health crises. It is increasingly urgent to confront these dynamics considering the ongoing military campaign in Gaza since October 2023. It is imperative to address the conditions that have facilitated the securitisation of COVID-19 in the OPT - primarily, prolonged military occupation - to enable Palestinians to exercise their rights to health and self-determination
Balancing Privacy and Security: A Comparative Analysis of AI-Driven Surveillance in the UAE and USA
Full text linkAI-driven surveillance has emerged as a critical tool for enhancing public safety, enabling authorities to monitor and prevent crime and terrorism more effectively. In countries like the UAE and the USA, these systems are often implemented under the pretext of national security, offering advanced methods to track potential threats. However, the increasing reliance on AI for surveillance raises significant ethical concerns about privacy and individual freedoms. The boundary between protecting citizens and infringing on their privacy becomes increasingly blurred, potentially leading to abuses of power, diminished public trust, and a pervasive atmosphere of fear. This paper explores the complex relationship between privacy and security in AI-driven surveillance practices in the UAE and the USA. Despite their differing political and legal systems, both countries face similar challenges in managing the ethical implications of AI surveillance. While these technologies can improve accuracy and efficiency, unchecked surveillance poses risks to civil liberties, particularly regarding data collection, analysis, and utilization. The central ethical dilemma revolves around whether certain rights should be compromised for security. While some scholars argue that the benefits of AI surveillance justify its use, others contend that privacy and freedom must remain inviolable, even amid security threats. The paper also examines the distinct approaches of the UAE and the USA in implementing surveillance systems. In the UAE, a centralized authority and significant technological investments have enabled extensive state surveillance with minimal public resistance. In contrast, the USA, as a democratic nation, continues to grapple with legal and ethical debates surrounding the limitations of its surveillance programs. The study aims to compare surveillance practices, analyze legal frameworks, and evaluate the impact on citizen freedoms. It concludes with policy recommendations to ensure responsible AI use, balancing national security with fundamental human rights
Ontologies For Smart Agriculture: A Path Toward Explainable Ai-A Systematic Literature Review
Full text linkSmart agriculture has grown significantly over the last few years, particularly with the integration of advanced technologies (e.g., the Internet of Things, robots, artificial intelligence, etc.), leading to the development of intelligent agricultural systems. However, these systems often lack data integration, interoperability, and semantic explainability. Various approaches have been proposed to address these challenges. This study provides a comprehensive literature review that addresses, on the one hand, the use of semantic resources (e.g., semantic web technologies and ontologies) to tackle data integration and interoperability in smart agriculture systems and, on the other hand, the integration of explainable artificial intelligence into smart agricultural systems. Furthermore, it aims to identify limitations in existing studies and explore potential avenues for future research. This research introduces key concepts related to smart agriculture, semantic resources, and explainable artificial intelligence. Subsequently, three clusters of studies are presented, including semantic resources for smart agriculture, leveraging the explainable artificial intelligence for smart agriculture, and the role of semantic resources in the explainable artificial intelligence-based agriculture systems. Lastly, the limitations of the semantic-based smart agriculture system are examined, along with potential future research areas
Seg-Swin: A Dual-Attention Transformer Model for Advanced AMD Classification and Lesion Detection Using Color Fundus Imaging
Full text linkAge-related macular degeneration (AMD) is a prevalent retinal disorder in the elderly, often leading to significant vision impairment. The diagnosis of AMD is confirmed through various medical imaging modalities, with color fundus photography (CFP) being a primary tool. The detection and staging of AMD-severity depend on several factors, including the number and size of drusen, the presence of pigmentary changes, geographic atrophy, and neovascularization, all of which are identifiable through CFP. In this study, we introduce an innovative dual-vision transformer-based network designed to automatically detect AMD and classify its severity into either dry AMD or wet AMD using CFP. Early diagnosis and accurate staging of AMD are crucial in mitigating the progression of the disease, making this work particularly valuable. Our proposed model, Seg-Swin, leverages a dual attention-based transformer network architecture, comprising two key stages. The first stage employs the SegFormer transformer model for the precise detection of AMD-related lesions, while the second stage utilizes the Swin transformer model to classify the detected lesions into dry or wet AMD. Our extensive experimental results demonstrate that the Seg-Swin model outperforms existing approaches, achieving remarkable diagnostic accuracy with metrics such as 98.7% accuracy, 99% sensitivity, 97.95% F1-score, and 98.24% specificity. By combining the strengths of advanced transformer models in both identification and classification tasks, the Seg-Swin model offers a comprehensive and powerful solution for detecting and staging AMD. The integration of these dual attention mechanisms allows the model to more precisely interpret complex retinal images, which is crucial for early diagnosis and accurate staging of AMD
Beyond the Like Button: Predicting How Risks Impact Trust on Instagram S-Commerce
No full textSocial commerce (s-commerce) integrates social media with e-commerce, creating new opportunities for consumers and businesses. However, the success of s-commerce depends heavily on perceived risks and trust, which influence consumer decisions. Despite growing research, the interaction between different types of risks: financial, security, and time risks and various trust dimensions, such as merchant competence and merchant integrity, remains underexplored. This study addresses this gap by proposing a predictive model to examine these relationships, focusing on Instagram-based shopping. A quantitative approach was used, with an online survey administered to 267 university students across three UAE universities, analyzed using Structural Equation Modeling (SEM-PLS). The findings show that all three perceived risks significantly impact both merchant competence and merchant integrity, with security risks having the strongest effect on competence. The study underscores the importance of addressing these risks to build trust in s-commerce platforms. However, the sample’s limitation to Generation Z students in the UAE suggests the need for broader research
Investigation of symptom-specific functional connectivity patterns in Parkinson’s disease
Full text linkParkinson’s disease (PD) is a complex neurodegenerative disease, characterized by pronounced heterogeneity in symptoms. This study investigates the functional connectivity (FC) patterns associated with distinct symptom clusters, aiming to elucidate the heterogeneity in PD and uncover the neural mechanisms underlying its motor and cognitive symptoms. Resting-state functional MRI (rs-fMRI) data from 55 non-demented PD patients and 24 healthy controls (HC) were used to perform seed-to-seed FC analyses. A clustering algorithm was applied to the cognitive and motor scores of all PD patients to generate relatively homogeneous symptomatic subgroups. PD patients exhibited a general decrease in FC within a network comprising the sensorimotor network (SMN) and the visual network (VN) regions. Symptom-based clustering revealed three relatively homogeneous subgroups, exhibiting a gradient pattern: patients with greater motor deficits showed significant disconnection within the SMN, whereas patients with greater visuospatial deficits exhibited reduced FC in an extended subnetwork, with pronounced disconnections between the VN and SMN areas. Our study demonstrated a notable disconnection between the SMN and VN, indicating impaired visual-motor integration in PD. Stronger disconnection within the SMN was associated with greater motor dysfunction, and stronger visual-sensorimotor disconnections were associated with greater visuospatial deficits. These findings suggest that at least two separate routes of functional disconnection may be responsible for the inhomogeneous symptom distribution in PD