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The impact of perfumes and cosmetic products on human health: a narrative review
Full text linkBackground: The use of perfumes and cosmetic products is widespread, serving personal hygiene, aesthetic, and olfactory functions. However, concerns have been raised regarding the potential health impacts associated with long-term exposure to various ingredients used in these products. Objectives: This narrative review aims to synthesize evidence on the health risks associated with perfumes and cosmetics, focusing on specific health concerns, including fertility, respiratory health, cancer risk, allergies, skin disorders, endocrine disruption, and neurological effects. It also discusses the presence of heavy metals in cosmetics, regulatory challenges, and the need for transparency in ingredient disclosure. Methods: A comprehensive review of the literature that was published between 2005 and 2024 was conducted, examining findings from interdisciplinary studies relevant to the health impacts of cosmetic and fragrance products. The review highlights health concerns linked to specific chemical components, including synthetic chemicals such as phthalates, parabens, and volatile organic compounds (VOCs). Results: The findings indicate that many synthetic chemicals in perfumes and cosmetics are associated with adverse health outcomes. These include allergies, respiratory issues, endocrine disruption, reproductive problems, and potentially cancer. Heavy metals in cosmetics also pose significant health risks. Despite regulatory guidelines, the cumulative and long-term effects of combined exposure to multiple cosmetic ingredients remain poorly understood and inadequately addressed. Conclusion: There is a pressing need for stricter regulatory oversight and improved transparency in ingredient disclosure to safeguard consumer health. Further research is required to clarify the long-term health risks associated with the daily use of cosmetic products and to develop safer alternatives
What drives weight status among female university students? A machine learning analysis of sociodemographic, dietary, and lifestyle determinants
Full text linkBackground: Obesity and underweight are increasingly common among young adult women, often resulting from complex interactions between diet, lifestyle, and socioeconomic factors. This study addresses that gap by applying machine learning to a wide range of behavioral, dietary, and demographic data. The main research question asks: What are the key factors influencing weight status among female university students, and how accurately can machine learning models identify them? We hypothesize that different factors are significantly associated with underweight, overweight, and obesity, and that machine learning can reliably detect these patterns. The aim is to identify the strongest predictors and support more targeted weight management strategies. Methods: This cross-sectional study analyzed data from 7,092 female university students (aged 18–30 years) in Palestine and the UAE. Sociodemographic, dietary, and lifestyle predictors were evaluated using machine learning (Random Forest, SVM, logistic regression, gradient boosting, decision trees, and ensemble methods). Synthetic Minority Over-sampling (SMOTE) addressed class imbalance. Model performance was assessed via 10-fold cross-validation, with significance determined by the chi-square test (p \u3c 0.05, 95% CI). Results: The Random Forest model achieved the highest accuracy (obesity: 96.8%, underweight: 94.6%, overweight: 90.3%) and AUC (0.95–0.97). The main drivers of weight status categories were as follows: underweight was associated with low water/milk intake and preference for fast food; overweight with added oil, large eating quantity, and low physical activity; and obesity with energy drink consumption, salty snacks, and irregular meals. All findings were statistically significant (p \u3c 0.001). Socio-demographic factors (e.g., low income and marital status) and lifestyle habits (e.g., sleep \u3c 5 h and fast eating) were also significantly related to weight status. Conclusion: The integration of these findings into weight management frameworks can significantly enhance the detection and understanding of modifiable determinants, thereby informing public health interventions, guiding the development of targeted weight management strategies, and contributing to the global movement toward healthier bodies
Parenting programs in the Middle East/North Africa (MENA) region: A multilevel meta-analysis
No full textParenting programs worldwide provide families with essential knowledge and skills to foster positive child development. Meta-analyses on the effectiveness of parenting programs for parent and child outcomes predominantly included families from so-called WEIRD (Western, Educated, Industrialized, Democratic) countries. Expanding upon existing meta-analyses, the current study focused solely on Middle East/North Africa (MENA) countries through a systematic search of the literature for records that evaluated the effectiveness of parenting programs delivered during early childhood (birth to eight years). A total of 395 effect sizes of parent outcomes and 190 effect sizes of child outcomes were obtained from 29 studies. Using multilevel meta-analytic models, the pooled effects suggested that parenting programs had a positive effect on parent (g = 1.01) and child outcomes (g = 1.43). An examination of publication bias suggested that the effects were robust. Moderator analyses showed trends suggesting that the overall effect may be influenced by program, study, and sample characteristics. Greater improvement of parent outcomes was not associated with greater improvement of child outcomes. Together, parenting programs may be promising support services for parents in MENA countries, with the potential of enhancing parenting and promoting psychosocial health and well-being for parents and children. Further implementation research is needed in MENA countries to increase knowledge about mechanisms and processes underlying effective parenting programs
Innovative desalination strategies for the removal of emerging pollutants in aquatic systems
No full textEmerging pollutants (EPs) are contaminants with significant chronic and acute toxicity that threaten agriculture, aquatic ecosystems, and human health. Although extensive research has been conducted on EP behavior in aquatic systems, there remains an urgent need for comprehensive toxicological assessments and the development of effective, sustainable removal strategies. Current treatment processes, including those used in desalination, are energy-intensive, resource-demanding, and costly to maintain, with most emerging technologies still untested for large-scale feasibility. This study offers a comprehensive review of advanced EP removal methods, including those relevant to desalination, while highlighting the limitations of existing technologies and identifying key areas for future research. A variety of biological, chemical, and physical treatment methods have been developed to target specific EPs. However, no single method has proven universally effective in removing all EPs, though hybrid systems often show greater efficiency. For example, combining ozonation with activated carbon has proven highly effective in removing certain EPs, particularly pharmaceuticals and other persistent chemicals. In desalination contexts, integrating advanced EP removal techniques could significantly enhance water quality. Additionally, nanotechnology shows promise, as nanomaterials have demonstrated potential to remove a wide range of pollutants from both wastewater and desalinated water, though research in this area remains limited. This review addresses the challenges posed by emerging pollutants in aquatic environments, including those related to desalination, and provides recommendations for future research. The goal is to mitigate the impact of EPs on water quality and human health while improving the effectiveness of existing treatment and desalination technologies
Improving Public Sector Procurement Methods in International Practices: a Comparative Study
Full text linkObjective: This paper presents the methods of public sector procurement in some public procurement laws such as UNITRAL law, Egyptian Law, Indian Law, Kenyan Law, and UAE law. Theoretical Framework: Some of the modern methods used by many countries, the research also sought to develop a clear definition of public sector procurement and relied in this research on the comparative approach as a significant approach to study for comparing the laws of countries. Method: This paper used descriptive-analytical methodology to analysis the legal frameworks of public sector procurement methods in Egypt, UAE, and other laws in comparison, the research reached to a result that the new Egyptian system and UAE system were influenced by modern international practices by including in the new law on administrative contracts modern methods of public procurement applicable in many countries. Results and Discussion: This research dealt with government procurement methods in international practices and their impact on the Egyptian case in a comparative legal context, as it also dealt with government procurement methods in international experiences, Egypt, and UAE. Research Implications: The government procurement methods contained in the UNCITRAL Model Law for Public Procurement, which was developed by the United Nations Commission on International Trade Law, were presented so that countries Which is in the process of reforming or improving their procurement systems or developing new systems for government procurement to be guided by it. Originality/Value: By reviewing several Arab literatures, we find a gap in this aspect. In addition, many fallacies are spread in this field, as some limit this definition to purchasing in the narrow sense. However, in the governmental field, it is considered broadly by the UNCITRAL Model Law and some laws of other countries
Exploring Gendered Perspectives On Personality Traits And Entrepreneurial Performance In Lebanon During The Covid-19 Crisis
Full text linkWe investigate the impact of gendered personality traits on the entrepreneurial performance of male and female entrepreneurs in Lebanon during the COVID-19 crisis. Using the Big Five personality model and survey data from 500 entrepreneurs, the research examines how traits such as agreeableness, conscientiousness, neuroticism, extraversion, and openness to experience influence business outcomes in high-pressure environments. The findings reveal that agreeableness and neuroticism benefit female entrepreneurs more than males during crises, while conscientiousness significantly deteriorates the financial performance of female entrepreneurs. Extraversion and openness exhibit no differential effects on financial performance by gender. By examining the interplay between gendered personality traits and entrepreneurial performance within the unique context of Lebanon during the COVID-19 pandemic, this study contributes novel insights to the literature. It challenges traditional views on gendered advantages in entrepreneurship, particularly by highlighting the underexplored positive implications of neuroticism and the nuanced role of agreeableness. These findings provide actionable insights for policymakers and support organisations. Specifically, fostering relational skills such as agreeableness and leveraging neuroticism\u27s sensitivity for proactive crisis management can enhance entrepreneurial resilience. Additionally, training programmes aimed at addressing the rigidity associated with conscientiousness can help entrepreneurs adapt more effectively to volatile environments. By tailoring interventions to gender-specific personality dynamics, these insights can strengthen entrepreneurial ecosystems and improve resilience in times of uncertainty
Quantum-Inspired Framework For Big Data Analytics: Evaluating The Impact Of Movie Trailers And Its Financial Returns
Full text linkIn the context of the growing influence of businesses and marketers on social media platforms, understanding the impact of emotionally charged content on consumer behavior has become increasingly crucial. This study proposes a novel framework, leveraging quantum computing principles, to assess the emotional impact of movie trailers. The framework incorporates big data analytics and utilizes Quantum Walk andQuantum Time Series models to investigate the relationship between a movie trailer\u27s emotional intensity and its financial performance. Unlike sequential problem-solving approach of traditional computing models, Quantum superposition allows exploring multiple options at once. An analysis of 141 movie trailers released after January 1, 2022, revealed a positive correlation between a trailer\u27s emotive score and its financial success. These findings suggest that trailers evoking a stronger emotional response tend to achieve greater box office returns compared to those with a lower emotional impact. This research underscores the pivotal role of emotionally resonant content in shaping consumer behavior and cinematic outcomes. It would offer valuable insights for filmmakers and marketers to optimize audience engagement and financial returns
Enhancing Online Toxicity Detection On Gaming Networks: A Novel Embeddings-Based Valence Lexicon Approach
Full text linkOnline toxicity and violent speech on gaming networks pose significant threats to societal well-being, particularly among adolescents, and are linked to severe consequences such as suicide. This highlights an urgent need for effective toxicity detection methods tailored to these platforms. Traditional rule-based approaches are inherently limited, and the performance of predictive models in detecting online toxicity is critically dependent on the quality and representativeness of their training data. However, the distinct linguistic characteristics of discourse on gaming networks present unique challenges in curating representative training samples using existing valence lexicons, often resulting in suboptimal detection accuracy. In this study, we propose a novel framework for online toxicity detection on gaming platforms that addresses these linguistic challenges. Our approach introduces an extended embeddings-based valence lexicon that can be customized to any gaming platform. In this study, we specifically target Twitch. Through a comprehensive comparative evaluation against state-of-the-art lexicon-based techniques, the proposed method demonstrates superior performance. Notably, agreement analysis reveals a moderate alignment between human annotators and the proposed method, with a kappa score of 0.619. Experimental results further underscore the efficacy of our approach, showing an average performance improvement of 31.47% across LSTM, GRU, and CNN architectures compared to all baseline methods. The findings of this study have significant implications for improving platform-specific toxicity detection, enhancing the safety and inclusivity of gaming environments. The framework can be adapted to other online platforms, offering a scalable solution to address toxicity and protect vulnerable populations
Explicit inverse of symmetric, tridiagonal near Toeplitz matrices with strictly diagonally dominant Toeplitz part
Full text linkLet T n = tridiag ( - 1 , b , - 1 ) , an n x n symmetric, strictly diagonally dominant tridiagonal matrix ( divided by b divided by \u3e 2 ). This article investigates tridiagonal near-Toeplitz matrices T n & colone; [ t i , j ] , obtained by perturbing the ( 1 , 1 ) and ( n , n ) entry of T n . Let t 1 , 1 = t n , n = b not equal b . We derive exact inverses of T n . Furthermore, we demonstrate that these results hold even when divided by b divided by \u3c 1 . Additionally, we establish upper bounds for the infinite norms of the inverse matrices. The row sums and traces of the inverse provide insight into the matrix\u27s spectral properties and play a key role in understanding the convergence of fixed-point iterations. These metrics allow us to derive tighter bounds on the infinite norms and improve computational efficiency. Numerical results for Fisher\u27s problem demonstrate that the derived bounds closely match the actual infinite norms, particularly for b \u3e 2 with b = - 1 . For other cases, further refinement of the bounds is possible. Our results contribute to improving the convergence rates of fixed-point iterations and reducing the computation time for matrix inversion
Middle East and Northern Africa: Gulf States “Challenging Students to Think Critically: Reflections on IR Theory Teaching in the UAE”
No full textInternational Relations theories are lenses or models that simplify the complexities of world politics into meaningful metaphors. As such, deep discussions of IR theories can help students develop critical-thinking skills which they can use in their future careers and lives more generally. The challenge before IR theory instructors, however, is to stimulate students to ponder theoretical propositions that they take as facts, dismiss as delusions, or simply do not take seriously. In this chapter I argue that this challenge is not unsurmountable and IR theory teachers can design their pedagogical tools to emphasize critical thinking through course assignments and assessments, including class and online discussions. In this chapter, I discuss this argument in light of my experience in teaching IR theory in eleven classes at Zayed University, UAE, between 2018 and 2024, both in-person and online