University of Doha for Science and Technology

Universal health coverage (UHC) provides a platform for attaining 'Health for All'. Attaining UHC requires substantial investment and resources in the health sector. This can be challenging for many sub-Saharan African (SSA) countries. Public-private partnerships (PPPs) could be a potential solution. The implementation of healthcare PPPs for developing health system capacities for UHC presents both significant opportunities and notable challenges. This scoping review, part of a broader review on PPPs, examines the facilitators and barriers of healthcare PPPs and their impact on UHC. The review was guided by Arksey and O'Malley's guidelines for conducting a scoping review. PubMed, Medline (Ovid), Global Health (Ovid), Web of Science, Scopus, and EconLit were searched for peer-reviewed English language publications from January 2013 to December 2023. Nineteen studies were eligible for the final analysis following screening of 944 studies. Identified key facilitators of PPPs for UHC included well-established institutional structures, robust accreditation systems, accountability mechanisms, and political will and support. These factors contributed to improving primary healthcare delivery, which is a critical dimension for UHC. Key barriers identified were limited capacity of implementing partners, regulatory inadequacies, and insufficient funds. These barriers negatively affected the performance of healthcare PPPs, which translates into systemic inequities in access to essential health services, impeding progress towards achieving UHC. Considering contract management capacity of implementers, sources and flow of funds, and regulatory frameworks are highly recommended for UHC to be realized using PPPs.

This study introduces a dual-band circular split-ring resonator (CSRR)-based metamaterial absorber (MTMA) designed for high-sensitivity sensing of both solid and liquid materials. The proposed structure, fabricated on a Rogers RT 5880 substrate with copper layers, achieves near-perfect absorption rates of 99.99% at 10.48 GHz (X-band) and 99.97% at 14.57 GHz (Ku-band), optimized through CST Microwave Studio simulations. The MTMA’s triple-stage design refinement enhances resonance characteristics, enabling precise detection of dielectric variations in substrates and liquids via measurable frequency shifts. Experimental validation confirms robust performance, with sensitivity of up to 2.51 GHz/εᵣ and quality factors reaching 189, thus outperforming existing single-band metamaterial sensors. The absorber’s compact size and consistent response under varying permittivity’s make it suitable for applications in biomedical diagnostics, fuel adulteration detection, and industrial quality control. By bridging gaps between simulation and real-world implementation, this work advances metamaterial-based sensing technology, offering a scalable and efficient solution for electromagnetic wave manipulation in next-generation sensor systems.

Tobacco use among university students presents a persistent public health challenge, particularly in settings where smoking is socially normalized despite strong regulatory frameworks. Qatar, with its multicultural population and progressive tobacco laws, offers a unique context for exploring these dynamics. However, little research has examined how young adults in the region experience and navigate smoking behaviours in everyday life. The study explores how university students’ perception and experience of smoking and second-hand smoke exposure, and examined barriers and facilitators to cessation within their academic and social environments. A qualitative study using photovoice methodology was conducted within a community-based participatory research framework. Undergraduate students aged 18–24 at a large national university in Qatar were recruited via purposive and snowball sampling. After a preparatory workshop, participants captured photographs representing their experiences with smoking or second-hand smoke, followed by semi-structured, photo-elicitation interviews. Data were analysed using thematic analysis of interview transcripts and photographs using a collaborative coding process. Credibility was strengthened through triangulation of visual and narrative data, member checking, and peer debriefing. Twenty-two students (12 females, 10 males; 9 smokers, 13 non-smokers) participated. Thematic analysis revealed five interrelated themes: (1) health awareness and personal triggers (2), social and cultural influences (3), environmental and economic consequences (4), prevention, cessation, and support, and (5) policy and behavioural regulation. Although students were aware of the health risks of tobacco use, many continued to smoke due to stress relief, peer influence, and cultural normalization. Environmental concerns such as litter and second-hand smoke exposure were widespread but inconsistently acted upon. Most participants expressed interest in quitting but reported limited awareness or trust in available cessation resources. Institutional policies were inconsistently enforced and undermined by contradictory role modelling from faculty and peers. University students in Qatar described smoking behaviours shaped by emotional triggers, academic stress, peer influence, and cultural normalization. Despite awareness of health risks, smoking persisted. Participants reported widespread exposure to second-hand smoke, limited trust in cessation resources, and inconsistent enforcement of institutional policies across campus settings. Not applicable.

To develop a prediction model for diabetes using metabolomics data and to evaluate various machine learning approaches and identify the most effective framework for disease prediction. A comprehensive analysis was conducted on the Qatar Biobank dataset comprising metabolomics profiles, instrument measurements, and clinical diagnoses from 450 Qatari nationals. Targeted metabolites were selected based on correlation strength with diabetes status. Five machine learning models (Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, and Neural Network) were evaluated for their predictive performance using metrics including accuracy, precision, recall, F1 score, and ROC AUC. Among 450 individuals, 9.33% (n=42) were diagnosed with diabetes. Correlation analysis identified 140 metabolites significantly associated with diabetes status (p<0.05). The most strongly correlated metabolites included glucose (r=0.281, p<0.0001), mannose (r=0.247, p<0.0001), and 1,5-anhydroglucitol (r=-0.297, p<0.0001). Logistic Regression demonstrated superior performance with the highest accuracy (93.3%), F1 score (0.625), and ROC AUC (0.941) compared to other models. Metabolomics data can effectively predict diabetes status, with logistic regression providing the optimal balance of performance and interpretability. The identified metabolites offer potential biomarkers for early diabetes detection and monitoring. This model could serve as a valuable tool for clinical risk assessment and personalized preventive interventions.

Cervical cancer represents a considerable global health challenge, mainly because of ineffective screening programs in middle-income countries. The current study aimed to forecast cervical cancer incidence by analyzing behavioral risk factors through logistic regression, employing feature engineering techniques such as principal component analysis (PCA). PCA successfully condensed the dataset into ten principal components, capturing 89% of the variance, while stratified K-fold cross-validation ensured a balanced representation of classes. With the application of L1 regularization, the logistic regression model achieved an accuracy of 97.2%, an AUC of 98.1%, an F1 score of 97.2%, a specificity of 96.1%, and a log loss of 0.17. The performance of models was comparatively evaluated, and the results revealed that the logistic regression model achieved the highest accuracy of 97.2% in comparison with decision trees at 93.33%, random forest at 93.33%, XGBoost at 93.33%, Naive Bayes at 91.67%, and non-regularized logistic regression at 87.55%. This research underscores the importance of early prediction of cervical cancer based on behavioral risk factors and suggests a robust, easily implementable workflow to improve classification accuracy. Future research should concentrate on refining these predictive tools to overcome social and behavioral barriers to prevention, particularly within underserved populations.

Orthogonal frequency division multiplexing (OFDM) is widely used in wireless communication networks to support high-performance, bandwidth-efficient multimedia services. It is an effective technique for mitigating radio channel multi-path fading, inter-symbol interference, and high data rate over wireless links. Improvements in spectral efficiency and increased data rates are two essential aspects of channel equalization performance. Channel equalization is the vital function on the receiver side to obtain high data transmission and a consistent wireless channel. The transmitted data signal encounters a variety of impairments during transmission across the wireless channel.Various channel equalization techniques have been presented in wireless communication networks to alleviate the impairments linked to the channels. These techniques are based on minimum mean square error or least squares approaches. In this article, channel equalization has been performed for the multi-carrier systems, where the channel equalization of the OFDM system is carried out using the Long Short-Term Memory (LSTM) method. The simulation results show improved BER when compared with the LMS-based traditional channel equalization method.

This qualitative study examines the experiences of platform workers in Nigeria's gig economy, with a specific focus on their voice and representation mechanisms and implications for decent work. Drawing on the institutional theory and 37 semi-structured interviews with location-based and cloudwork platform workers, the study advances knowledge of how the regulative, normative and cultural-cognitive pillars shape what constitutes voice, representation and decent work experiences in the Nigerian platform economy. Findings underscore the influence of platform design and its impact on the perception of decent work and workers’ voice. It highlights the formal and informal voice channels and alternative worker associations for voicing among Nigerian platform workers. Furthermore, it demonstrates how cultural values in Nigeria influence what constitutes decent work for platform workers and its implications for voice. Beyond the theoretical implications, the study highlights practical and social implications, emphasising that policy reforms are needed to address employment classification, strengthen worker protections and enhance social dialogue mechanisms. It advocates interventions to improve working conditions, empower gig workers and foster a more equitable and sustainable gig economy.