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User Perceptions of AI Services in Abu Dhabi Government: An Empirical Study of Satisfaction And Acceptance Factors on the TAMM Platform
Full text linkArtificial Intelligence (AI) is transforming how governments deliver services by enabling automation, personalization, and data-driven decisionmaking. In the United Arab Emirates, particularly in Abu Dhabi Emirate, the TAMM platform exemplifies this transformation, integrating AI to streamline citizen-government interaction. Understanding the factors that influence the user’s satisfaction of Powered AI services in the public sector is crucial for effective policy implementation and user engagement. This study employs a sequential explanatory mixed-methods approach, grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT), extended with User Satisfaction as a fifth construct. Quantitative data were collected from 137 TAMM platform users and analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM). Follow-up interviews were conducted to add depth and interpretive insights. The quantitative results showed that Effort Expectancy, Facilitating Conditions, and Social Influence had significant positive effects on User Satisfaction, while Performance Expectancy was not statistically significant. However, the qualitative analysis revealed strong support for Performance Expectancy, highlighting its importance in user narratives. Thematic insights also emphasized the demand for hybrid human-AI interaction, ethical AI design, and trust as critical elements influencing user experience. Demographic factors, such as gender and prior AI experience, were found to moderate perceptions of usability and support needs. This research contributes to the UTAUT literature by applying the model in a Middle Eastern government context. This study provides strategic insights for policymakers, emphasizing the need to ensure the long-term success of AI-driven public services in Abu Dhabi
The Importance of Dark Web Access in Cyber Threat Intelligence
No full textCyber Threat Intelligence (CTI) is essential in in-forming proactive cyber defence, especially in Nigeria\u27s most significant sectors: financial services, telecommunications, and healthcare. This study reviews the current body of CTI lit-erature, examines intelligence obtained from the Dark Web, and determines impending threats, including Ransomware-as-a-Service, zero-day exploits, and phishing toolkits aimed at these sectors. Performing qualitative platform analysis of the likes of RaidForums and Exploit.in, we determine sectoral vulnerabilities and offer pragmatic suggestions on developing Nigeria\u27s cyber defence system. The findings underscore the Dark Web\u27s role as a vital source of intelligence to counter cyber threats under low-resource conditions, filling Nigeria\u27s gaps in CTI capabilities
Split Federated Learning for Real-Time Aerial Video Event Recognition in UAV-Based Geospatial Monitoring
No full textEvent recognition in UAV-based monitoring systems is crucial for geospatial analysis, environmental surveillance, and disaster response. However, federated learning (FL)-based approaches for real-time event recognition face significant challenges, including spatiotemporal heterogeneity, constrained computational resources, and communication limitations in distributed UAV networks. Although FL enables decentralized machine learning with enhanced data privacy, its application to temporal event recognition in UAV-based remote sensing systems requires addressing data heterogeneity and maintaining spatiotemporal coherence across distributed nodes. To address these challenges, we propose a Split Federated Learning (SFL) framework tailored for UAV-assisted event recognition in geospatial monitoring. The proposed SFL architecture partitions computational workloads between UAV-based edge clients and a central server, optimizing both on-device efficiency and global model performance. At the client level, lightweight convolutional models extract spatiotemporal features from UAV-captured video sequences, reducing computational complexity and transmission overhead. These extracted features are transmitted to a central server, where higher-order temporal dependencies are learned, enabling robust event classification. To enhance adaptability in dynamic environments, we integrate dynamic video chunking, adaptive temporal pooling, and modality-agnostic feature aggregation, ensuring efficient processing of variable-length sequences while minimizing bandwidth constraints. Experimental evaluations on standard UAV-based geospatial datasets demonstrate that the proposed SFL framework significantly outperforms conventional FL approaches in terms of classification accuracy, communication efficiency, and scalability. This work provides a scalable, privacy-preserving, and computationally efficient solution for real-time temporal event recognition in UAV-assisted geospatial monitoring applications
Deep Reinforcement Learning-Based Task Scheduling and Resource Allocation for Vehicular Edge Computing: A Survey
No full textWith the development of intelligent transportation systems, vehicular edge computing (VEC) has played a pivotal role by integrating computation, storage, and analytics closer to the vehicles. VEC represents a paradigm shift towards real-time data processing and intelligent decision-making, overcoming challenges associated with latency and resource constraints. In VEC scenarios, the efficient scheduling and allocation of computing resources are fundamental research areas, enabling real-time processing of vehicular tasks and intelligent decision-making. This paper provides a comprehensive review of the latest research in Deep Reinforcement Learning (DRL)-based task scheduling and resource allocation in VEC environments. Firstly, the paper outlines the development of VEC and introduces the core concepts of DRL, shedding light on their growing importance in the dynamic VEC landscape. Secondly, the state-of-the-art research in DRL-based task scheduling and resource allocation is categorized, reviewed, and discussed. Finally, the paper discusses current challenges in the field, offering insights into the promising future of VEC applications within the realm of intelligent transportation systems
Streamlining Digital Elevation Model Construction from Historical Aerial Photographs: The Impact of Reference Elevation Data on Spatial Accuracy
No full textThis study proposes a streamlined workflow for producing historical digital elevation models (hDEMs) from scanned 1950s aerial photographs using structure-from-motion and multi-view-stereo (SfM-MVS) techniques along with co-registration methods. We also conducted a sensitivity analysis to assess the impact of DEM references with varying spatial resolutions on the SfM-MVS process and co-registration accuracy. The DEM references included a 30 m SRTM DEM (low resolution), a 12 m TanDEM-X DEM (medium resolution), and a 5 m DEM provided by the General Directorate of Mapping of the Ministry of National Defense, Republic of T & uuml;rkiye (high resolution). Results indicate that higher resolution reference DEMs lead to improved accuracy and precision of the final hDEMs, particularly evident in reduced errors and finer spatial resolution. This study contributes to streamlining the hDEM construction process and offers a nuanced understanding of the significance of elevation and reference DEM selection in ensuring hDEM accuracy
Association of socio-demographic factors, perinatal characteristics, and hospital maternity practices with breastfeeding outcomes in the UAE
Full text linkBreastfeeding (BF) rates remain suboptimal in the United Arab Emirates (UAE), despite global and national efforts. This study examined the association of socio-demographic factors, perinatal characteristics, and hospital maternity practices with breastfeeding outcomes in the UAE. In this cross-sectional study, 1,815 participating mothers with children below the age of 2 answered structured questions related to socioeconomics, hospital practices, and BF. Multivariate analysis showed that a non-Emirati nationality and vaginal birth were significantly associated with higher initiation rates (AOR = 6.19, 95% CI 1.96–19.54 and AOR = 2.65, 95% CI 1.35–5.21, respectively), timely initiation (AOR = 0.48, 95%CI 0.35–0.66, respectively), longer BF duration (AOR = 1.55, 95%CI 1.05–2.27 and AOR = 1.45, 95%CI 1.08–1.93, respectively) and longer exclusive BF duration (AOR = 1.50, 95%CI 1.06–2.11 and AOR = 1.35, 95%CI 1.03–1.78, respectively). Additionally, parity, hospital practices, maternal education, and employment were significantly associated with certain BF practices. The findings support continued efforts to implement WHO\u27s baby-friendly initiative in more hospitals in Abu Dhabi and also emphasize the importance of early and continuous antenatal education. Emirati mothers should be prioritized in these efforts as their BF practices need more attention. As maternal employment negatively influences breastfeeding duration, supportive measures such as extended maternity leave, designated expressing facilities in the workplace, and shorter working hours are crucial to promote continued breastfeeding among employed mothers
Association of anthropometric indices with rs9939609 FTO gene polymorphism among overweight/obese women with breast cancer: a case-control study
Full text linkBackground/objectives: Fat mass and obesity associated (FTO) gene and anthropometric measurements might be associated with breast cancer (BC) risk. This study aimed to assess the interactions between single nucleotide polymorphism (SNP) rs9939609 of the FTO gene, anthropometric indices, and BC risk among pre- and post-menopause women with overweight/obesity in Pakistan. Methods: This retrospective case–control study conducted on a convenience sample of 200 women divided into two groups: a case group comprised of 100 women diagnosed with BC, and control group comprised of 100 (age and menopausal status matched healthy women). Physical activity was assessed using validated questionnaire. Data on body mass index (BMI, kg/m2), waist-to-hip ratio (WHR, cm), sociodemographic, and blood samples were collected from both groups. The rs9939609 FTO gene polymorphism was genotyped using the tetra-primer amplification refractory mutation system polymerase chain reaction and Sanger sequencing. Multiple regressions were presented as adjusted odds ratios (OR) and their respective confidence intervals (95% CI). Results: The FTO rs9939609 T \u3e A polymorphism showed a significantly higher frequency of the homozygous AA genotype in BC patients compared to healthy controls (22% vs. 13%, p \u3c 0.05). The odds ratio for BC was 2.4 (CI = 1.09–5.3, p \u3c 0.05), indicating that women with the AA genotype were more susceptible to BC compared to those with the wild-type TT genotype. Additionally, BC patients exhibited significantly higher BMI (27 ± 4.0 vs. 25 ± 3.4, p \u3c 0.05) and WHR (0.88 ± 0.06 vs. 0.85 ± 0.08, p \u3c 0.05) compared to healthy controls. These findings suggest a significant association between the FTO rs9939609 AA genotype, obesity, and BC risk. Conclusion: FTO gene polymorphism may be implicated in the etiopathogenesis of BC, both in FTO pre- and post-menopause women diagnosed with overweight/obesity. Future cohorts are required to confirm the association between the FTO gene and BC in obese women and to identify the underlying biological mechanisms
A deep learning framework for automated breast cancer diagnosis using intelligent segmentation and classification
Full text linkBreast cancer is the most commonly diagnosed cancer among women worldwide, accounting for a significant proportion of new cases. Deep learning (DL) has emerged as a powerful tool for the detection and diagnosis of breast cancer, particularly through the analysis of histological images, a critical component of automated diagnostic systems that directly impact patient management. The BreakHis dataset and the Wisconsin Breast Cancer Database (WBCD) are widely used publicly available resources for deep learning–based analyses of breast cancer histological images in cross-disciplinary healthcare research. A computer-assisted approach employs colour normalisation to reduce the effects of the differences in the distribution of breast histopathology images. In this paper, breast tumour areas of interest are segmented utilising Attention-Guided Deep Atrous-Residual U-Net at the segmentation stage. Subsequently, patches are processed to form feature vectors VGG19 and ResNet50 for the extraction of deep features from the patches. Also, to fine-tune these models even further, the breast cancer datasets are employed, and Levy Flight-based Red Fox Optimisation is used to extract features from the pre-trained models without further training. The Efficient Capsule Network is used to improve the feature representation and classification capabilities. AGDATUNet-LFRFO-ECN, which was suggested in the study, performed better than other models when tested on the WBCD dataset, with a sensitivity of 99.17 %, specificity of 99.08 %, and accuracy of 99.23 %. What\u27s more, the AGDATUNet-LFRFO-ECN outperformed the available models on BreakHis with a sensitivity of 99.81 %, a specificity of 99.79 %, and an accuracy of 99.82 %, which are the state-of-the-art
Stock market responses to monetary policy shocks: Firm-level evidence
Full text linkUsing a firm-level data set for the U.S., we investigate the stock price responses to unanticipated and unconventional monetary policy shocks. Our results show that indebtedness/leverage is more important than size or age in explaining the cross-firm variation in responses to monetary policy. We also show that the magnitude of the indebtedness is important while the debt structure is not, and the third quartile of firms drives our results. We assess the robustness of our empirical findings across several dimensions