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Early Stage Detection of Colorectal Cancer using Segmentation of Polyps
No full textColorectal Cancer(CRC) is a major health problem around the world, accounting for a high number of cancer related deaths. It is important to detect the polyp at the initial stage and remove it to prevent them from becoming cancerous. Colonoscopy is the standard examination for CRC, but pathologists face difficulties in detecting the polyps in the colonoscopy image due to the small size and the color contrast between the polyp and its background. Expert pathologists are very less in numbers compared with the cancer patients.therefore, an automated system is required to assist pathologists in the detection of polyps in early stage. Deep learning models help the pathologists to detect polyps at an early stage automatically that may be missed otherwise due to their small size, low contrast, and presence of extremely small polyps in a single image. We propose light weight model Transformer ResU- Net3plus (TransResU-Net3+) for automated segmentation of polyps in early stage of cancer. Proposed method consists of residual blocks using ResNet-50 as the backbone and also uses transformer self-attention and dilated convolutions. We have applied the proposed method on a publicly available dataset kvasir-seg and achieved an Intersection Over Union (IOU) of 0.892 and outperforms over existing state of the art methods on kvasir-seg dataset
Does The Cost Of Borrowing Increase For Firms That Are Socially And Environmentally Irresponsible?
No full textSynopsisThe research problemThis study aims to determine the financial repercussions for firms that engage in irresponsible environmental, social, and governance (IESG) practices. Specifically, it examines whether these practices influence the cost of debt through increased borrowing costs imposed by lending institutions.MotivationAmid growing scrutiny over corporate behavior and its broader impacts, understanding how irresponsible practices affect corporate finance is crucial for stakeholders, including investors, policymakers, and regulators. This research is driven by the need to explore beyond the often-studied beneficial impacts of positive ESG practices, focusing on the consequences of their negative counterparts.HypothesesThe current study makes three hypotheses as follows: Ceteris paribus, first, there is a positive association between firms\u27 IESG practices and their cost of debt; second, the anticipated positive impact of IESG practices on the cost of debt is more pronounced in countries with low levels of corruption; and finally, the anticipated positive impact of IESG practices on the cost of debt is more pronounced among firms in sinful industries.SampleThe analysis covers a broad international sample of 50,281 firm-year observations from nonfinancial listed firms across 44 countries, covering the years 2002-2022. This comprehensive dataset allows for generalized insights across various geographic and industrial contexts.Adopted methodologyMultivariate analysis is employed, based on pooled regression with standard errors clustered at the firm level to account for intrafirm correlations and potential heteroskedasticity. A two-stage instrumental variable approach is also employed to address potential endogeneity issues, providing a robust framework for examining the causal impact of IESG practices on the cost of debt.AnalysesThe analyses focus on evaluating the direct impact of IESG practices on borrowing costs, alongside assessing the moderating effects of the corruption perception index (CPI) and differentiating between industry types (sinful versus nonsinful). Sensitivity tests are conducted to ensure the robustness of the findings against various model specifications and potential biases.Findings and ImplicationsThe findings indicate a universally significant positive relationship between IESG practices and the cost of debt, confirming that firms engaged in irresponsible practices face higher borrowing costs. This effect is particularly pronounced in countries with lower levels of corruption, emphasizing the critical role of national governance in influencing corporate behavior. Moreover, the analysis reveals no significant differences between sinful and nonsinful industries, suggesting uniform financial penalties for irresponsible practices across sectors. These results are robust across a range of sensitivity analyses, affirming the reliability of the conclusions. The study offers valuable insights for lending institutions, firms, and credit rating agencies about the financial implications of irresponsible corporate practices. It highlights the importance for policymakers and regulators to enforce comprehensive ESG guidelines that encourage substantive disclosures and responsible behaviors as well as eliminating greenwashing and ESG decoupling
Towards Responsible Ai Journalism: Mapping Journalists\u27 Perceptions Of Ai Ethics
No full textThe rapid advancement of artificial intelligence (AI) introduces pressing social and ethical dilemmas, reshaping both the integration of AI into journalistic routines and the broader dynamics of news production and dissemination. This study investigates how journalists perceive and navigate the ethical dimensions of AI, with particular attention to how ethical principles are understood and operationalized in practice. Drawing on a dual sensemaking approach, we propose a conceptual framework for AI journalism ethics that traces how ethical values are constructed, interpreted, and enacted in newsroom contexts. Journalistic heuristics initially shape the normative interpretation of AI, while assessments of technical efficacy and practical utility subsequently inform how AI\u27s role is recontextualized within journalistic work. Building on these insights, we introduce a model of responsible AI journalism that seeks to integrate core journalistic values with ethical standards in AI development and application
Assessing the adversarial robustness of multimodal medical AI systems: insights into vulnerabilities and modality interactions
Full text linkThe emergence of both task-specific single-modality models and general-purpose multimodal large models presents new opportunities, but also introduces challenges, particularly regarding adversarial attacks. In high-stakes domains like healthcare, these attacks can severely undermine model reliability and their applicability in real-world scenarios, highlighting the critical need for research focused on adversarial robustness. This study investigates the behavior of multimodal models under various adversarial attack scenarios. We conducted experiments involving two modalities: images and texts. Our findings indicate that multimodal models exhibit enhanced resilience against adversarial attacks compared to their single-modality counterparts. This supports our hypothesis that the integration of multiple modalities contributes positively to the robustness of deep learning systems. The results of this research advance understanding in the fields of multimodality and adversarial robustness and suggest new avenues for future studies focused on optimizing data flow within multimodal systems
Strengthening global health education in the UAE: assessing the current landscape and future directions
Full text linkGlobal health is a growing area of interest for the United Arab Emirates (UAE). Given its location in the Middle East and its geographical proximity to developing sub-Saharan countries, the country is in a strategic position to contribute to global health agendas. Yet despite an increasing emphasis on global health policy, the availability of global health education programs at UAE universities remains limited. The aim of this Perspective is to raise awareness and understanding about the importance of the issue. Reflecting on the current landscape, it argues that it is crucial for higher education institutions in the UAE to better integrate global health into their curricula. The Perspective calls for a greater focus on curricula design, along with a further emphasis on collaboration and capacity building between universities and global health institutions to further strengthen this educational foundation
A Survey on Immersive Cyber Situational Awareness Systems
Full text linkCyber situational awareness systems are increasingly used for creating cyber common operating pictures for cybersecurity analysis and education. However, these systems face data occlusion and convolution issues due to the burgeoning complexity, dimensionality, and heterogeneity of cybersecurity data, which damages cyber situational awareness of end-users. Moreover, conventional forms of human–computer interactions, such as mouse and keyboard, increase the mental effort and cognitive load of cybersecurity practitioners when analyzing cyber situations of large-scale infrastructures. Therefore, immersive technologies, such as virtual reality, augmented reality, and mixed reality, are employed in the cybersecurity realm to create intuitive, engaging, and interactive cyber common operating pictures. Immersive cyber situational awareness (ICSA) systems provide several unique visualization techniques and interaction features for the perception, comprehension, and projection of cyber situational awareness. However, there has been no attempt to comprehensively investigate and classify the existing state of the art in the use of immersive technologies for cyber situational awareness. Therefore, in this paper, we have gathered, analyzed, and synthesized the existing body of knowledge on ICSA systems. In particular, our survey has identified visualization and interaction techniques, evaluation mechanisms, and different levels of cyber situational awareness (i.e., perception, comprehension, and projection) for ICSA systems. Consequently, our survey has enabled us to propose (i) a reference framework for designing and analyzing ICSA systems by mapping immersive visualization and interaction techniques to the different levels of ICSA; (ii) future research directions for advancing the state of the art on ICSA systems; and (iii) an in-depth analysis of the industrial implications of ICSA systems to enhance cybersecurity operations
A novel fractional order model for analyzing counterterrorism operations and mitigating extremism
Full text linkThis study examines the profound impact of terrorism on individuals and society by developing a fractional-order mathematical model to analyze and enhance counterterrorism efforts. The model accounts for the persistent and complex nature of extremist behavior, particularly emphasizing the importance of preventing violent extremism before it escalates into terrorism. Real-world data on terrorist activities in Nigeria – specifically from the Boko Haram insurgency – was used to calibrate and validate the model, ensuring its relevance and accuracy. The model reveals that the basic reproduction number (R0) plays a decisive role in determining the long-term success of counterterrorism strategies. Numerical simulations show that terrorist activities decline when R0\u3c 1, while they persist or escalate when R0\u3e1. A comprehensive sensitivity analysis further identifies the most influential parameters affecting R0, providing actionable insights into where interventions can be most effective. Parameters related to recruitment, ideological spread, and counter-radicalization efforts were found to have the highest impact. The study concludes by offering strategic recommendations informed by the simulation and sensitivity results, aiming to support the design of more targeted and sustainable counterterrorism policies
The Role of AI in Digital Propaganda and Its Influence on Public Political Perception. A Case Study of the UAE Citizen
Full text linkThe study investigates the role of artificial intelligence (AI) in digital propaganda and its influence on public political perception, using the United Arab Emirates as a case study. It aims to understand how AI-generated content shapes political opinions by analyzing the relationships between digital propaganda exposure, AI content interaction, social media engagement and media literacy. In addition, the study uses the Framing Theory (1974) by Erving Goffman. It posits that individuals interpret information and events through cognitive frameworks or frames that organize and influence their understanding. It implies that AI amplifies specific frames by customizing content and targeting audiences, which guides public perception in the desired political direction. The study employs a quantitative research design approach; data will be collected through structured questionnaires from 100 participants. Data were analyzed using SmartPLS software, utilizing statistical techniques such as correlation analysis and path coefficient modeling within the PLS-SEM framework. Results revealed that exposure to deepfake AI political content had a moderate positive correlation with public political perception (r = 0.587), indicating that greater exposure is associated with heightened levels of political perception among participants. Furthermore, media literacy emerged as a significant and influential variable, showing notable correlations with several key constructs: it was positively associated with public political perception (r = 0.548), content persuasiveness (r = 0.600), and perception of authenticity (r = 0.571). These findings highlight the central role of media literacy in shaping how individuals interpret and respond to AIgenerated political content. These results suggest that individuals with higher levels of media literacy are more likely to perceive political content as both persuasive and authentic. This indicates that media literacy plays a crucial role in shaping how individuals interpret, evaluate, and respond to political content, thereby influencing the overall impact of digital propaganda. The findings provide valuable insight into strategies through which AI technology influences political communication and public opinion. The implications include potential regulatory measures for digital propaganda and improved platform policies by policymakers and digital platform providers. The research aims to contribute to the broader understanding of digital propaganda in the digital age and its impacts on public political perception
Burnout among Social Care Providers in the UAE: The Role Of Coping Strategies and Institutional Support
Full text linkThis study focuses on the self-reported levels of burnout, the coping strategies employed, and the perceived institutional support of a sample of social care workers in the UAE. The research also explores how coping strategies and institutional support influence burnout levels and the role of demographic factors such as gender, age, and years of experience. A total of 204 social care providers participated in the study, which used a survey to gather data on burnout, coping strategies, and institutional support. The Maslach Burnout Inventory (MBI) was used to measure burnout, the Brief COPE Inventory was used to assess coping strategies, and the Perceived Organizational Support (POS) scale was used to evaluate perceived institutional support. The data were analyzed using SPSS to identify trends and relationships among the variables. The study found that social care providers in the UAE reported moderate levels of burnout, with emotional exhaustion being the most prominent symptom, followed by depersonalization and personal achievement. Problem-focused coping strategies were the most frequently used and were associated with lower levels of emotional exhaustion and depersonalization, while avoidant coping exacerbated burnout symptoms. Higher levels of perceived institutional support were linked to lower emotional exhaustion, depersonalization, and greater personal achievement. Gender, age, and years of experience influenced both burnout and coping strategies, with female participants reporting higher burnout levels and employing more emotion-focused and avoidant coping strategies. In comparison, older participants reported lower burnout levels and more frequent use of problem-focused coping. The findings suggest that organizational support plays a crucial role in mitigating burnout, and the effectiveness of coping strategies varies across demographic group
Sustainable Construction Materials from Industrial By-products
No full textThe development of sustainable construction materials is crucial for addressing environmental concerns and advancing eco-conscious building practices. In this study, we investigate the potential of sulfur-containing concrete formulations, aiming to enhance durability, reduce environmental impact, and meet the growing demand for eco-friendly building materials. The creation of sustainable concrete blends integrating sulfur, fine aggregates, and industrial waste materials. Through a process of heating, mixing, molding, and testing, the mechanical strength of the resulting fume treatment plant (FTP) and direct reduction of iron (DRI) with lime (L) samples is assessed, revealing heightened compressive strength compared to traditional concrete. Chemical analysis corroborates low chloride and sulfate content, fortifying the concrete’s resilience against environmental stressors. These findings underscore the potential of sulfur-infused concrete for diverse construction needs, offering both durability and sustainability benefits. With applications spanning infrastructure, urban development, and marine projects, this innovative material aligns with the industry’s shift toward eco-friendly building practices and addresses growing environmental concerns