Zayed University

IMPORTANCE: The Global Burden of Diseases, Injuries, and Risk Factors Study 2019 (GBD 2019) provided systematic estimates of incidence, morbidity, and mortality to inform local and international efforts toward reducing cancer burden. OBJECTIVE: To estimate cancer burden and trends globally for 204 countries and territories and by Sociodemographic Index (SDI) quintiles from 2010 to 2019. EVIDENCE REVIEW: The GBD 2019 estimation methods were used to describe cancer incidence, mortality, years lived with disability, years of life lost, and disability-adjusted life years (DALYs) in 2019 and over the past decade. Estimates are also provided by quintiles of the SDI, a composite measure of educational attainment, income per capita, and total fertility rate for those younger than 25 years. Estimates include 95% uncertainty intervals (UIs). FINDINGS: In 2019, there were an estimated 23.6 million (95% UI, 22.2-24.9 million) new cancer cases (17.2 million when excluding nonmelanoma skin cancer) and 10.0 million (95% UI, 9.36-10.6 million) cancer deaths globally, with an estimated 250 million (235-264 million) DALYs due to cancer. Since 2010, these represented a 26.3% (95% UI, 20.3%-32.3%) increase in new cases, a 20.9% (95% UI, 14.2%-27.6%) increase in deaths, and a 16.0% (95% UI, 9.3%-22.8%) increase in DALYs. Among 22 groups of diseases and injuries in the GBD 2019 study, cancer was second only to cardiovascular diseases for the number of deaths, years of life lost, and DALYs globally in 2019. Cancer burden differed across SDI quintiles. The proportion of years lived with disability that contributed to DALYs increased with SDI, ranging from 1.4% (1.1%-1.8%) in the low SDI quintile to 5.7% (4.2%-7.1%) in the high SDI quintile. While the high SDI quintile had the highest number of new cases in 2019, the middle SDI quintile had the highest number of cancer deaths and DALYs. From 2010 to 2019, the largest percentage increase in the numbers of cases and deaths occurred in the low and low-middle SDI quintiles. CONCLUSIONS AND RELEVANCE: The results of this systematic analysis suggest that the global burden of cancer is substantial and growing, with burden differing by SDI. These results provide comprehensive and comparable estimates that can potentially inform efforts toward equitable cancer control around the world.

An international association advancing the multidisciplinary study of informing systems. Founded in 1998, the Informing Science Institute (ISI) is a global community of academics shaping the future of informing science.

Bipolar disorder is a heritable mental illness with complex etiology. We performed a genome-wide association study of 41,917 bipolar disorder cases and 371,549 controls of European ancestry, which identified 64 associated genomic loci. Bipolar disorder risk alleles were enriched in genes in synaptic signaling pathways and brain-expressed genes, particularly those with high specificity of expression in neurons of the prefrontal cortex and hippocampus. Significant signal enrichment was found in genes encoding targets of antipsychotics, calcium channel blockers, antiepileptics and anesthetics. Integrating expression quantitative trait locus data implicated 15 genes robustly linked to bipolar disorder via gene expression, encoding druggable targets such as HTR6, MCHR1, DCLK3 and FURIN. Analyses of bipolar disorder subtypes indicated high but imperfect genetic correlation between bipolar disorder type I and II and identified additional associated loci. Together, these results advance our understanding of the biological etiology of bipolar disorder, identify novel therapeutic leads and prioritize genes for functional follow-up studies.

Reactive oxygen and nitrogen species (RONS) are generated through various endogenous and exogenous processes; however, they are neutralized by enzymatic and non-enzymatic antioxidants. An imbalance between the generation and neutralization of oxidants results in the progression to oxidative stress (OS), which in turn gives rise to various diseases, disorders and aging. The characteristics of aging include the progressive loss of function in tissues and organs. The theory of aging explains that age-related functional losses are due to accumulation of reactive oxygen species (ROS), their subsequent damages and tissue deformities. Moreover, the diseases and disorders caused by OS include cardiovascular diseases [CVDs], chronic obstructive pulmonary disease, chronic kidney disease, neurodegenerative diseases and cancer. OS, induced by ROS, is neutralized by different enzymatic and non-enzymatic antioxidants and prevents cells, tissues and organs from damage. However, prolonged OS decreases the content of antioxidant status of cells by reducing the activities of reductants and antioxidative enzymes and gives rise to different pathological conditions. Therefore, the aim of the present review is to discuss the mechanism of ROS-induced OS signaling and their age-associated complications mediated through their toxic manifestations in order to devise effective preventive and curative natural therapeutic remedies.

Retailing is witnessing a transformation due to rapid technological developments. Retailers are using smart technologies to improve consumer shopping experiences and to stay competitive. The biggest future challenge for marketing and consequently for retailing seems to be generation Z, since members of this generation seem to behave differently as consumers and are more focused on innovation. The aim of this paper is to explore Generation Z consumers' current perceptions, expectations and recommendations in terms of their future interactions in smart retailing contexts. To do so, we used a qualitative approach by conducting a series of semi-structured in depth interviews with 38 university students-consumers in the UK market. The findings showed that smart technologies have a significant influence on generation Z consumers' experiences. Moreover, this particular group of consumers expects various new devices and electronic processes to be widely available, thus offering consumers more autonomy and faster transactions. In addition, they expect the technology to enable them to make more informed shopping decisions. Interviewees also stressed the importance of training consumers how to use new smart retailing applications. In addition, some of the participants were sceptical about the effects of further advancing smart retailing on part of the job market. Relevant theoretical and practical implications are also provided.

Abstract Body-mass index (BMI) has increased steadily in most countries in parallel with a rise in the proportion of the population who live in cities 1,2 . This has led to a widely reported view that urbanization is one of the most important drivers of the global rise in obesity 3–6 . Here we use 2,009 population-based studies, with measurements of height and weight in more than 112 million adults, to report national, regional and global trends in mean BMI segregated by place of residence (a rural or urban area) from 1985 to 2017. We show that, contrary to the dominant paradigm, more than 55% of the global rise in mean BMI from 1985 to 2017—and more than 80% in some low- and middle-income regions—was due to increases in BMI in rural areas. This large contribution stems from the fact that, with the exception of women in sub-Saharan Africa, BMI is increasing at the same rate or faster in rural areas than in cities in low- and middle-income regions. These trends have in turn resulted in a closing—and in some countries reversal—of the gap in BMI between urban and rural areas in low- and middle-income countries, especially for women. In high-income and industrialized countries, we noted a persistently higher rural BMI, especially for women. There is an urgent need for an integrated approach to rural nutrition that enhances financial and physical access to healthy foods, to avoid replacing the rural undernutrition disadvantage in poor countries with a more general malnutrition disadvantage that entails excessive consumption of low-quality calories.

Abstract Chatbots hold the promise of revolutionizing education by engaging learners, personalizing learning activities, supporting educators, and developing deep insight into learners’ behavior. However, there is a lack of studies that analyze the recent evidence-based chatbot-learner interaction design techniques applied in education. This study presents a systematic review of 36 papers to understand, compare, and reflect on recent attempts to utilize chatbots in education using seven dimensions: educational field, platform, design principles, the role of chatbots, interaction styles, evidence, and limitations. The results show that the chatbots were mainly designed on a web platform to teach computer science, language, general education, and a few other fields such as engineering and mathematics. Further, more than half of the chatbots were used as teaching agents, while more than a third were peer agents. Most of the chatbots used a predetermined conversational path, and more than a quarter utilized a personalized learning approach that catered to students’ learning needs, while other chatbots used experiential and collaborative learning besides other design principles. Moreover, more than a third of the chatbots were evaluated with experiments, and the results primarily point to improved learning and subjective satisfaction. Challenges and limitations include inadequate or insufficient dataset training and a lack of reliance on usability heuristics. Future studies should explore the effect of chatbot personality and localization on subjective satisfaction and learning effectiveness.

This article examines the role of the new media in the ‘Arab Spring’ in the Middle East and North Africa (MENA) region. It argues that although the new media is one of the factors in the social revolution among others such as social and political factors in the region, it nevertheless played a critical role especially in light of the absence of an open media and a civil society. The significance of the globalization of the new media is highlighted as it presents an interesting case of horizontal connectivity in social mobilization as well signaling a new trend in the intersection of new media and conventional media such as television, radio, and mobile phone. One of the contradictions of the present phase of globalization is that the state in many contexts facilitated the promotion of new media due to economic compulsion, inadvertently facing the social and political consequences of the new media. Este artículo examina el papel de los nuevos medios en ‘la primavera árabe’ en la región del Medio Oriente y Norte de África (MENA, por sus siglas en inglés). Sostiene que aunque los nuevos medios hacen parte de los factores en la revolución social entre otros, como los factores sociales y políticos en la región, aun así, jugaron un papel muy importante especialmente en vista de la ausencia de medios independientes y una sociedad civil. La importancia de la globalización de los nuevos medios se hace resaltar al presentar un interesante cado de conectividad horizontal en la movilización social como también al señalar una nueva intersección de nuevos medios y medios convencionales como la televisión, la radio y el celular. Una de las contradicciones de la fase actual de la globalización es que el estado en muchos contextos, facilitó la promoción de un nuevo medio debido a la compulsión económica, afrontando inadvertidamente las consecuencias sociales y políticas de los nuevos medios. 本文考察发生在西亚北非地区的“阿拉伯之春”中新兴媒体的角色。文章认为尽管新兴媒体同其他政治和社会的因素一样，是该地区发生社会革命的因素之一，但却扮演了至关重要的角色，尤其是在缺乏开放的媒体和公民社会的条件下。由于代表了一种在社会动员中建立水平连通的有趣事例并同时预示着新兴媒体和传统媒体（如电视、电台和移动电话）交叉的新趋势，新兴媒体全球化的重要性被凸显出来。全球化现阶段的矛盾之一就是，在很多不同环境下国家由于经济强制而促进了新兴媒体发展，却正无奈地面对新兴媒体带来的社会和政治后果。

Abstract Observation‐based and modeling studies have identified the Eastern Mediterranean and Middle East (EMME) region as a prominent climate change hotspot. While several initiatives have addressed the impacts of climate change in parts of the EMME, here we present an updated assessment, covering a wide range of timescales, phenomena and future pathways. Our assessment is based on a revised analysis of recent observations and projections and an extensive overview of the recent scientific literature on the causes and effects of regional climate change. Greenhouse gas emissions in the EMME are growing rapidly, surpassing those of the European Union, hence contributing significantly to climate change. Over the past half‐century and especially during recent decades, the EMME has warmed significantly faster than other inhabited regions. At the same time, changes in the hydrological cycle have become evident. The observed recent temperature increase of about 0.45°C per decade is projected to continue, although strong global greenhouse gas emission reductions could moderate this trend. In addition to projected changes in mean climate conditions, we call attention to extreme weather events with potentially disruptive societal impacts. These include the strongly increasing severity and duration of heatwaves, droughts and dust storms, as well as torrential rain events that can trigger flash floods. Our review is complemented by a discussion of atmospheric pollution and land‐use change in the region, including urbanization, desertification and forest fires. Finally, we identify sectors that may be critically affected and formulate adaptation and research recommendations toward greater resilience of the EMME region to climate change.

We show, based on ratings by finance journal reviewers of generated output, that the recently released AI chatbot ChatGPT can significantly assist with finance research. In principle, these results should be generalisable across research domains. There are clear advantages for idea generation and data identification. The technology, however, is weaker on literature synthesis and developing appropriate testing frameworks. Importantly, we further demonstrate that the extent of private data and researcher domain expertise input, are key factors in determining the quality of output. We conclude by considering the implications, particularly the ethical implications, which arise from this new technology.

Being a multidrug-resistant and an invasive pathogen, Acinetobacter baumannii is one of the major causes of nosocomial infections in the current healthcare system. It has been recognized as an agent of pneumonia, septicemia, meningitis, urinary tract and wound infections, and is associated with high mortality. Pathogenesis in A. baumannii infections is an outcome of multiple virulence factors, including porins, capsules, and cell wall lipopolysaccharide, enzymes, biofilm production, motility, and iron-acquisition systems, among others. Such virulence factors help the organism to resist stressful environmental conditions and enable development of severe infections. Parallel to increased prevalence of infections caused by A. baumannii, challenging and diverse resistance mechanisms in this pathogen are well recognized, with major classes of antibiotics becoming minimally effective. Through a wide array of antibiotic-hydrolyzing enzymes, efflux pump changes, impermeability, and antibiotic target mutations, A. baumannii models a unique ability to maintain a multidrug-resistant phenotype, further complicating treatment. Understanding mechanisms behind diseases, virulence, and resistance acquisition are central to infectious disease knowledge about A. baumannii. The aims of this review are to highlight infections and disease-producing factors in A. baumannii and to touch base on mechanisms of resistance to various antibiotic classes.

Humans have a natural instinct to identify unknown object instances in their environments. The intrinsic curiosity about these unknown instances aids in learning about them, when the corresponding knowledge is eventually available. This motivates us to propose a novel computer vision problem called: ‘Open World Object Detection’, where a model is tasked to: 1) identify objects that have not been introduced to it as ‘unknown’, without explicit supervision to do so, and 2) incrementally learn these identified unknown categories without forgetting previously learned classes, when the corresponding labels are progressively received. We formulate the problem, introduce a strong evaluation protocol and provide a novel solution, which we call ORE: Open World Object Detector, based on contrastive clustering and energy based unknown identification. Our experimental evaluation and ablation studies analyse the efficacy of ORE in achieving Open World objectives. As an interesting by-product, we find that identifying and characterising unknown instances helps to reduce confusion in an incremental object detection setting, where we achieve state-of-the-art performance, with no extra methodological effort. We hope that our work will attract further research into this newly identified, yet crucial research direction. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>

This study explored the correlates of climate anxiety in a diverse range of national contexts. We analysed cross-sectional data gathered in 32 countries (N = 12,246). Our results show that climate anxiety is positively related to rate of exposure to information about climate change impacts, the amount of attention people pay to climate change information, and perceived descriptive norms about emotional responding to climate change. Climate anxiety was also positively linked to pro-environmental behaviours and negatively linked to mental wellbeing. Notably, climate anxiety had a significant inverse association with mental wellbeing in 31 out of 32 countries. In contrast, it had a significant association with pro-environmental behaviour in 24 countries, and with environmental activism in 12 countries. Our findings highlight contextual boundaries to engagement in environmental action as an antidote to climate anxiety, and the broad international significance of considering negative climate-related emotions as a plausible threat to wellbeing.

Plants play a crucial role in supplying food globally. Various environmental factors lead to plant diseases which results in significant production losses. However, manual detection of plant diseases is a time-consuming and error-prone process. It can be an unreliable method of identifying and preventing the spread of plant diseases. Adopting advanced technologies such as Machine Learning (ML) and Deep Learning (DL) can help to overcome these challenges by enabling early identification of plant diseases. In this paper, the recent advancements in the use of ML and DL techniques for the identification of plant diseases are explored. The research focuses on publications between 2015 and 2022, and the experiments discussed in this study demonstrate the effectiveness of using these techniques in improving the accuracy and efficiency of plant disease detection. This study also addresses the challenges and limitations associated with using ML and DL for plant disease identification, such as issues with data availability, imaging quality, and the differentiation between healthy and diseased plants. The research provides valuable insights for plant disease detection researchers, practitioners, and industry professionals by offering solutions to these challenges and limitations, providing a comprehensive understanding of the current state of research in this field, highlighting the benefits and limitations of these methods, and proposing potential solutions to overcome the challenges of their implementation.

Using a field experiment in the United Arab Emirates, we compared the impacts of directive and empowering leadership on customer-rated core task proficiency and proactive behaviors. Results of tests for main effects demonstrated that both directive and empowering leadership increased work unit core task proficiency, but only empowering leadership increased proactive behaviors. Examination of boundary conditions revealed that directive leadership enhanced proactive behaviors for work units that were highly satisfied with their leaders, whereas empowering leadership had stronger effects on both core task proficiency and proactive behaviors for work units that were less satisfied with their leaders. We discuss implications for both theory and practice.

In this paper, we propose a deep learning framework for malware classification. There has been a huge increase in the volume of malware in recent years which poses a serious security threat to financial institutions, businesses and individuals. In order to combat the proliferation of malware, new strategies are essential to quickly identify and classify malware samples so that their behavior can be analyzed. Machine learning approaches are becoming popular for classifying malware, however, most of the existing machine learning methods for malware classification use shallow learning algorithms (e.g. SVM). Recently, Convolutional Neural Networks (CNN), a deep learning approach, have shown superior performance compared to traditional learning algorithms, especially in tasks such as image classification. Motivated by this success, we propose a CNN-based architecture to classify malware samples. We convert malware binaries to grayscale images and subsequently train a CNN for classification. Experiments on two challenging malware classification datasets, Malimg and Microsoft malware, demonstrate that our method achieves better than the state-of-the-art performance. The proposed method achieves 98.52% and 99.97% accuracy on the Malimg and Microsoft datasets respectively.

Abstract We evaluate the role of gold and other precious metals relative to volatility (Volatility Index (VIX)) as a hedge (negatively correlated with stocks) and safe haven (negatively correlated with stocks in extreme stock market declines) using data from the US stock market. Using daily data from November 1995 to November 2010, we find that gold, unlike other precious metals, serves as a hedge and a weak safe haven for US stock market. However, we find that VIX serves as a very strong hedge and a strong safe haven during our sample period. We also find that in periods of extremely low or high volatility, gold does not have a negative correlation with the US stock market. Our results show that VIX is a superior hedging tool and serves as a better safe haven than gold during our sample period. We highlight the practical significance of our results for financial market participants by conducting a portfolio analysis.

(1) Background: Emotional eating is considered as the propensity to eat in response to emotions. It is considered as a critical risk factor for recurrent weight gain. Such overeating is able to affect general health due to excess energy intake and mental health. So far, there is still considerable controversy on the effect of the emotional eating concept. The objective of this study is to summarize and evaluate the interconnections among emotional eating and overweight/obesity, depression, anxiety/stress, and dietary patterns; (2) Methods: This is a thorough review of the reported associations among emotional eating and overweight/obesity, depression, anxiety/stress, and dietary patterns. We compressively searched the most precise scientific online databases, e.g., PubMed, Scopus, Web of Science and Google Scholar to obtain the most up-to-date data from clinical studies in humans from the last ten years (2013-2023) using critical and representative keywords. Several inclusion and exclusion criteria were applied for scrutinizing only longitudinal, cross-sectional, descriptive, and prospective clinical studies in Caucasian populations; (3) Results: The currently available findings suggest that overeating/obesity and unhealthy eating behaviors (e.g., fast food consumption) are associated with emotional eating. Moreover, the increase in depressive symptoms seems to be related with more emotional eating. Psychological distress is also related with a greater risk for emotional eating. However, the most common limitations are the small sample size and their lack of diversity. In addition, a cross-sectional study was performed in the majority of them; (4) Conclusions: Finding coping mechanisms for the negative emotions and nutrition education can prevent the prevalence of emotional eating. Future studies should further explain the underlying mechanisms of the interconnections among emotional eating and overweight/obesity, depression, anxiety/stress, and dietary patterns.

This survey presents a comprehensive review of current literature on Explainable Artificial Intelligence (XAI) methods for cyber security applications. Due to the rapid development of Internet-connected systems and Artificial Intelligence in recent years, Artificial Intelligence including Machine Learning and Deep Learning has been widely utilized in the fields of cyber security including intrusion detection, malware detection, and spam filtering. However, although Artificial Intelligence-based approaches for the detection and defense of cyber attacks and threats are more advanced and efficient compared to the conventional signature-based and rule-based cyber security strategies, most Machine Learning-based techniques and Deep Learning-based techniques are deployed in the “black-box” manner, meaning that security experts and customers are unable to explain how such procedures reach particular conclusions. The deficiencies of transparencies and interpretability of existing Artificial Intelligence techniques would decrease human users’ confidence in the models utilized for the defense against cyber attacks, especially in current situations where cyber attacks become increasingly diverse and complicated. Therefore, it is essential to apply XAI in the establishment of cyber security models to create more explainable models while maintaining high accuracy and allowing human users to comprehend, trust, and manage the next generation of cyber defense mechanisms. Although there are papers reviewing Artificial Intelligence applications in cyber security areas and the vast literature on applying XAI in many fields including healthcare, financial services, and criminal justice, the surprising fact is that there are currently no survey research articles that concentrate on XAI applications in cyber security. Therefore, the motivation behind the survey is to bridge the research gap by presenting a detailed and up-to-date survey of XAI approaches applicable to issues in the cyber security field. Our work is the first to propose a clear roadmap for navigating the XAI literature in the context of applications in cyber security.

With the growing presence of algorithms and their far-reaching effects, artificial intelligence (AI) will be mainstream trends any time soon. Despite this surging popularity, little is known about the processes through which people perceive and make a sense of trust through algorithmic characteristics in a personalized algorithm system. This study examines the extent to which trust can be linked to how perceptions of automated personalization by AI and the processes of such perceptions influence user heuristic and systematic processes. It examines how fair, accountable, transparent, and interpretable people perceive the use of algorithmic recommendations by digital platforms. When users perceive that the algorithm is fairer, more accountable, transparent, and explainable, they see it as more trustworthy and useful. This demonstrates that trust is of particular value to users and further implies the heuristic roles of algorithmic characteristics in terms of their underlying links to trust and subsequent attitudes toward algorithmic decisions. The processes offer a useful perspective on the conceptualization of AI experience and interaction. User cognitive processes identified provide solid foundations for algorithm design and development and a stronger basis for the design of sensemaking AI services.