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Asymmetric relationship between diversification and liquidity creation: Empirical evidence from GCC
No full textThis study examines how bank diversification affects liquidity creation by using the bank level data of GCC countries. We use data from 205 banks in GCC over the period of 2005–2019. To test the hypothesized relationship, we employ the GMM methodological framework. The findings of the study reveal that both income and asset diversification adversely affect the narrow and broad measure of banks\u27 liquidity creation. However, funding diversification positively(negatively) influences the broad(narrow) measure of liquidity creation. The results highlight that bank diversification is a double-edged sword; although it can help in reducing risk, but it also vanishes the banks\u27 ability to create liquidity. However, the in-depth and detailed analyses reveal that the impact is asymmetrical across large, small, well-capitalized, and undercapitalized banks. Furthermore, comparing the normal and crisis periods highlights that banks behave differently in different economic conditions. The results have several implications for the bank managers and decision makers; they must consider the trade-off between liquidity creation and level of diversification. Additionally, the asymmetry in results implies that managers must consider the level/bank\u27s specific characteristics while making such strategic decisions
Breastfeeding is associated with reduction in postpartum depression in the United Arab Emirates: a retrospective cross-sectional study
Full text linkPostpartum Depression (PPD) is a common mental health disorder affecting mothers. Breastfeeding may be protective against PPD. Global estimates of breastfeeding and PPD rates vary, especially for women living in Middle Eastern countries. The current study aims to assess breastfeeding and PPD prevalence and to identify factors associated with reduced PPD risk within the social and cultural contexts of the UAE. We used a purposive, convenience snowball sampling technique to recruit participants. Inclusion criteria were female ≥ 18 years, mother of a child ≤ three years, and resident of Abu Dhabi, UAE. Data was collected using an online survey distributed via email and social media platforms. The survey comprised four sections: sociodemographic characteristics, breastfeeding behaviour, Edinburgh Postnatal Depression Scale (EPDS), and The International Physical Activity Questionnaire –Short Form (IPAQ-SF). Pearson chi-squared tests and binary logistic regression model were used to investigate the associations between PPD levels and potential predictors using SPSS statistical software. Variables included in the regression model were breastfeeding duration, delivery mode, BMI, education, general health, physical activity level, employment status, number of children, and age. All statistical significance was considered at p-value \u3c 0.05. In total 403 subjects consented to participate; 204 met the inclusion criteria and were included in the final analysis (age [mean ± SD] = 31.2 ± 7.3 years). Among them, 34.8% suffered from moderate-to-severe PPD, and 66.2% breastfed their last child for \u3e 3 months. Regression model results showed that (OR; 95% CI) college education (0.39; 0.19–0.80), having more than one child (0.40; 0.17–0.94), self-reported very good (0.43; 0.19–0.98) and excellent health (0.21; 0.08–0.51), and breastfeeding for \u3e three months (0.46; 0.23–0.92), were significantly associated with reduced odds of moderate-to-severe PPD. None of the remaining variables -including physical activity- were significant. In conclusion, breastfeeding is significantly associated with a reduction in moderate-to-severe PPD among mothers in Abu Dhabi, UAE
An intelligent medical system using MRI to detect brain tumors utilizing enhanced computational efficiency and optimized segmentation
No full textDetection of brain tumors should be both accurate and timely so that patient outcomes improve, and clinical intervention becomes feasible. This study develops a trustworthy machine learning pipeline for brain tumor identification using magnetic resonance imaging data. The pipeline\u27s primary phases include preprocessing, segmentation, feature extraction, and classification. During the preprocessing phase, intensity standardization and noise removal are done via data normalization and cleaning to enhance image quality. Subsequently, an improved gray-level co-occurrence matrix and Fourier transform are done for more stable feature extraction, and an improved secretary sand cat optimization algorithm is done for more efficient feature selection and computation. These are then distinguished as optimized features by an ensemble model of deep belief networks, recurrent neural networks, and convolutional neural networks to identify tumor and non-tumor regions. Experimental results demonstrate the better accuracy, sensitivity, and specificity of the proposed method, and thus it is a trustworthy tool for automated brain tumor detection and diagnosis. Experimental validation confirms the putative pipeline\u27s ability to achieve high accuracy, sensitivity, and specificity in the early detection and diagnosis of brain tumors
Integrating NSGA-II and Q-learning for Solving the Multi-objective Electric Vehicle Routing Problem with Battery Swapping Stations
No full textNavigating the challenges of the Electric Vehicle Routing Problem with Battery Swapping Stations (EVRP-BSS), this work is centered on a multi-objective optimization task, simultaneously minimizing battery swap costs and energy consumption costs. Given the intricate nature of this problem and its real- world implications, we propose a particular solution methodology. Our hybridized approach introduces a learn-heuristic that leverages the Non-dominated Sorting Genetic Algorithm II (NSGA II) and the Q-learning algorithm. This method not only addresses the NP-hard complexity of the problem but also aims to improve the sustainability and cost-effectiveness of electric vehicle routing operations. In contributing a fresh perspective to the discourse on efficient and eco-friendly transportation, our study explores novel avenues for sustainable solutions. The experiments showed the good performance of the proposed approach for solving the EVRP-BSS
Cyberbullying and stress, anxiety, and depression among university students: social support and self-esteem as mediators
No full textThis study aimed to test the relationship between cyberbullying perpetration and victimisation and stress, anxiety, depression, self-esteem, and social support, and to explore the roles of social support and self-esteem as mediators between these variables. This study was conducted from September 2019 to January 2020 using a descriptive correlational design. The sample involved university students (N = 780) from three universities in Amman Governorate, the capital of Jordan. The findings showed that the percentages of cyberbullying victimisation and perpetration among university students were 32.5%, and 22.67%, respectively. There was a significant positive relationship between cyberbullying perpetration and cyberbullying victimisation and stress, anxiety, and depressive symptoms. However, there was a significant negative relationship between cyberbullying perpetration and cyberbullying victimisation and social support. A negative relationship was found between cyberbullying perpetration and self-esteem among university students. Furthermore, social support and self-esteem played a mediating role in the levels of stress, anxiety, and depression; self-esteem had the most vital role. This study sheds light on self-esteem and social support as significant components in reducing the levels of bullying on cyberspace platforms
Comparative Analysis of Differential Privacy Implementations on Synthetic Data
No full textDifferential privacy offers a promising solution to balance data utility and user privacy. This paper compares two prominent differential privacy tools-PyDP and IBM\u27s diffprivlib-that are applied to a synthetic dataset with medical attributes. We evaluate these tools based on their effectiveness in maintaining data privacy while preserving the statistical integrity of the data. Our results reveal that PyDP provides synthetic data that closely matches real-world data, making it appropriate for tasks requiring accuracy while striking a better balance between data utility and privacy. However, IBM\u27s diffprivlib is more suitable for privacy-critical applications due to its stronger privacy guarantees, but at the cost of increased noise and decreased data utility. This paper contributes to the practical understanding of implementing differential privacy in machine learning and software applications and enhances the tools available for developers in sensitive data environments
Affordance theory as a conceptual foundation to design Precision Healthcare ecosystems
No full textThe significance of user-centricity in healthcare demands more real-time, data-centric, and personalised solutions. Precision Healthcare (PHC) is an emerging digital healthcare stream that could support data-centricity attributes in healthcare and deliver personalised service to patients. For reasons to be discussed, PHC is also experiencing challenges in patient opt-in. To address this problematic issue, PHC ecosystems need to be designed focusing on socially desirable, technologically feasible, and economically viable affordances. This chapter outlines such a user-centred design of PHC systems. In design interviews and validation workshops, we iteratively refined the artefacts and adopted abductive reasoning to derive design principles and rules for PHC ecosystems using the lens of affordance theory. We propose a comprehensive set of design principles for PHC systems which posits that by adapting these design rules to accentuate the enabling affordances of PHC while mitigating the inhibiting affordances, we obtain the commitment of stakeholders to implementation success
Evaluation of Legitimacy of IoT Devices Based on an Energy-Efficient Trust Management Scheme in Information-Centric Networking
No full textA rapidly developing future technology, wireless sensor networks (WSNs) have promise for a wide range of military and business applications. Potential safety issues could affect WSN technology because it combines wireless communications with processing capacity. A novel networking architecture on the Internet of Things (IoT) called information-centric networking (ICN) provides more security than standard Internet Protocol (IP) networks. However, it still experiences a lot of security issues, particularly from internal attacks. Applying trust management technologies is an effective way to secure against internal threats. Therefore, an evolutionary particle swarm optimization energy-efficient trust management scheme (EPSO-EETMS) is proposed to evaluate the legitimacy of IoT devices and nodes. The data regarding routing pathways using trust can identify various types of attacked solutions. The proposed solution is thoroughly evaluated using some networking parameters, such as the distance between devices, energy use, and information loss during data transmission. These practical factors include energy consumption when transmitting data between nodes, message delivery to previous or subsequent nodes, and distance between two devices. According to the evaluation results, the proposed strategy outperforms standard techniques in terms of response time, authentication delays, and the number of requests from fake nodes. The accuracy of the proposed technique is obtained to be 98.66% for 100 nodes, which is higher than that of existing routing techniques
Childhood Cancer and Neuropsychological Challenges Regarding Clinical Assessment and Treatment Plans
No full textResearch into childhood cancer and the neuropsychological effects on them, provides a great insight into the long- term developmental and cognitive impacts that survivors among several types of cancer go through. It can lead to various chronic difficulties such as memory loss, attention deficits, language impairments, motor problems and executive skills among others which are crucial for leading an independent way of living especially in adult life. Since most treatments have been shown to have several impacts on the patients neuropsychological functioning, having a great insight into different types of therapies consequences is of great importance
Circularity Within Service-Dominant Logic: The Role of Perceived Ethics on Co-Creation in Sharing Economy Platforms
Full text linkThis study investigates circularity in service-dominant (SD) logic within sharing economy platforms (SEPs), focusing on how perceived ethics influence customer co-creation and commitment. It further explores the mediating roles of trust and reciprocity and the moderating effect of brand identification. Data from 365 SEP users were analyzed via partial least squares structural equation modeling (PLS-SEM). Results demonstrate that perceived ethics positively affect trust and reciprocity, which subsequently strengthen customers\u27 willingness to co-create. Trust and reciprocity emerged as pivotal mediators, while brand identification significantly moderated their effects on co-creation. By emphasizing the role of ethical perceptions in fostering trust-based collaboration and illustrating how brand identification amplifies these relationships, this research advances theoretical understanding of circularity in service ecosystems. The findings contribute novel insights to the services marketing literature, particularly regarding ethical imperatives, trust-reciprocity dynamics, and circular customer-platform interactions in SEPs