Showing papers by "King Saud University published in 2019"

Cardiovascular disease burden from ambient air pollution in Europe reassessed using novel hazard ratio functions.

Abstract: Aims Ambient air pollution is a major health risk, leading to respiratory and cardiovascular mortality. A recent Global Exposure Mortality Model, based on an unmatched number of cohort studies in many countries, provides new hazard ratio functions, calling for re-evaluation of the disease burden. Accordingly, we estimated excess cardiovascular mortality attributed to air pollution in Europe. Methods and results The new hazard ratio functions have been combined with ambient air pollution exposure data to estimate the impacts in Europe and the 28 countries of the European Union (EU-28). The annual excess mortality rate from ambient air pollution in Europe is 790 000 [95% confidence interval (95% CI) 645 000-934 000], and 659 000 (95% CI 537 000-775 000) in the EU-28. Between 40% and 80% are due to cardiovascular events, which dominate health outcomes. The upper limit includes events attributed to other non-communicable diseases, which are currently not specified. These estimates exceed recent analyses, such as the Global Burden of Disease for 2015, by more than a factor of two. We estimate that air pollution reduces the mean life expectancy in Europe by about 2.2 years with an annual, attributable per capita mortality rate in Europe of 133/100 000 per year. Conclusion We provide new data based on novel hazard ratio functions suggesting that the health impacts attributable to ambient air pollution in Europe are substantially higher than previously assumed, though subject to considerable uncertainty. Our results imply that replacing fossil fuels by clean, renewable energy sources could substantially reduce the loss of life expectancy from air pollution.

Hierarchical 3D electrodes for electrochemical energy storage

Abstract: The discovery and development of electrode materials promise superior energy or power density. However, good performance is typically achieved only in ultrathin electrodes with low mass loadings (≤1 mg cm−2) and is difficult to realize in commercial electrodes with higher mass loadings (>10 mg cm−2). To realize the full potential of these electrode materials, new electrode architectures are required that can allow more efficient charge transport beyond the limits of traditional electrodes. In this Review, we summarize the design and synthesis of 3D electrodes to address charge transport limitations in thick electrodes. Specifically, we discuss the role of charge transport in electrochemical systems and focus on the design of 3D porous structures with a continuous conductive network for electron transport and a fully interconnected hierarchical porosity for ion transport. We also discuss the application of 3D porous architectures as conductive scaffolds for various electrode materials to enable composite electrodes with an unprecedented combination of energy and power densities and then conclude with a perspective on future opportunities and challenges. 3D electrodes with interconnected and interpenetrating pathways enable efficient electron and ion transport. In this Review, the design and synthesis of such 3D electrodes are discussed, along with their ability to address charge transport limitations at high areal mass loading and to enable composite electrodes with an unprecedented combination of energy and power densities in electrochemical energy storage devices.

Global, regional, and national burden of suicide mortality 1990 to 2016: systematic analysis for the Global Burden of Disease Study 2016

Abstract: Objectives To use the estimates from the Global Burden of Disease Study 2016 to describe patterns of suicide mortality globally, regionally, and for 195 countries and territories by age, sex, and Socio-demographic index, and to describe temporal trends between 1990 and 2016. Design Systematic analysis. Main outcome measures Crude and age standardised rates from suicide mortality and years of life lost were compared across regions and countries, and by age, sex, and Socio-demographic index (a composite measure of fertility, income, and education). Results The total number of deaths from suicide increased by 6.7% (95% uncertainty interval 0.4% to 15.6%) globally over the 27 year study period to 817 000 (762 000 to 884 000) deaths in 2016. However, the age standardised mortality rate for suicide decreased by 32.7% (27.2% to 36.6%) worldwide between 1990 and 2016, similar to the decline in the global age standardised mortality rate of 30.6%. Suicide was the leading cause of age standardised years of life lost in the Global Burden of Disease region of high income Asia Pacific and was among the top 10 leading causes in eastern Europe, central Europe, western Europe, central Asia, Australasia, southern Latin America, and high income North America. Rates for men were higher than for women across regions, countries, and age groups, except for the 15 to 19 age group. There was variation in the female to male ratio, with higher ratios at lower levels of Socio-demographic index. Women experienced greater decreases in mortality rates (49.0%, 95% uncertainty interval 42.6% to 54.6%) than men (23.8%, 15.6% to 32.7%). Conclusions Age standardised mortality rates for suicide have greatly reduced since 1990, but suicide remains an important contributor to mortality worldwide. Suicide mortality was variable across locations, between sexes, and between age groups. Suicide prevention strategies can be targeted towards vulnerable populations if they are informed by variations in mortality rates.

Artificial neural network methods for the solution of second order boundary value problems

Abstract: We present a method for solving partial differential equations using artificial neural networks and an adaptive collocation strategy. In this procedure, a coarse grid of training points is used at the initial training stages, while more points are added at later stages based on the value of the residual at a larger set of evaluation points. This method increases the robustness of the neural network approximation and can result in significant computational savings, particularly when the solution is non-smooth. Numerical results are presented for benchmark problems for scalar-valued PDEs, namely Poisson and Helmholtz equations, as well as for an inverse acoustics problem.

Physical Frailty: ICFSR International Clinical Practice Guidelines for Identification and Management

Abstract: Objective: The task force of the International Conference of Frailty and Sarcopenia Research (ICFSR) developed these clinical practice guidelines to overview the current evidence-base and to provide recommendations for the identification and management of frailty in older adults. Methods: These recommendations were formed using the GRADE approach, which ranked the strength and certainty (quality) of the supporting evidence behind each recommendation. Where the evidence-base was limited or of low quality, Consensus Based Recommendations (CBRs) were formulated. The recommendations focus on the clinical and practical aspects of care for older people with frailty, and promote person-centred care. Recommendations for Screening and Assessment: The task force recommends that health practitioners case identify/screen all older adults for frailty using a validated instrument suitable for the specific setting or context (strong recommendation). Ideally, the screening instrument should exclude disability as part of the screening process. For individuals screened as positive for frailty, a more comprehensive clinical assessment should be performed to identify signs and underlying mechanisms of frailty (strong recommendation). Recommendations for Management: A comprehensive care plan for frailty should address polypharmacy (whether rational or nonrational), the management of sarcopenia, the treatable causes of weight loss, and the causes of exhaustion (depression, anaemia, hypotension, hypothyroidism, and B12 deficiency) (strong recommendation). All persons with frailty should receive social support as needed to address unmet needs and encourage adherence to a comprehensive care plan (strong recommendation). First-line therapy for the management of frailty should include a multi-component physical activity programme with a resistance-based training component (strong recommendation). Protein/caloric supplementation is recommended when weight loss or undernutrition are present (conditional recommendation). No recommendation was given for systematic additional therapies such as cognitive therapy, problem-solving therapy, vitamin D supplementation, and hormone-based treatment. Pharmacological treatment as presently available is not recommended therapy for the treatment of frailty.

Single atom electrocatalysts supported on graphene or graphene-like carbons

Abstract: Electrocatalysis plays an essential role in diverse electrochemical energy conversion processes that are vital for improving energy utilization efficiency and mitigating the aggravating global warming challenge. The noble metals such as platinum are generally the most frequently used electrocatalysts to drive these reactions and facilitate the relevant energy conversion processes. The high cost and scarcity of these materials pose a serious challenge for the wide-spread adoption and the sustainability of these technologies in the long run, which have motivated considerable efforts in searching for alternative electrocatalysts with reduced loading of precious metals or based entirely on earth-abundant metals. Of particular interest are graphene-supported single atom catalysts (G-SACs) that integrate the merits of heterogeneous catalysts and homogeneous catalysts, such as high activity, selectivity, stability, maximized atom utilization efficiency and easy separation from reactants/products. The graphene support features a large surface area, high conductivity and excellent (electro)-chemical stability, making it a highly attractive substrate for supporting single atom electrocatalysts for various electrochemical energy conversion processes. In this review, we highlight the recent advancements in G-SACs for electrochemical energy conversion, from the synthetic strategies and identification of the atomistic structure to electrocatalytic applications in a variety of reactions, and finally conclude with a brief prospect on future challenges and opportunities.

A review on ZnO nanostructured materials: energy, environmental and biological applications.

Abstract: Zinc oxide (ZnO) is an adaptable material that has distinctive properties, such as high-sensitivity, large specific area, non-toxicity, good compatibility and a high isoelectric point, which favours it to be considered with a few exceptions. It is the most desirable group of nanostructure as far as both structure and properties. The unique and tuneable properties of nanostructured ZnO shows excellent stability in chemically as well as thermally stable n-type semiconducting material with wide applications such as in luminescent material, supercapacitors, battery, solar cells, photocatalysis, biosensors, biomedical and biological applications in the form of bulk crystal, thin film and pellets. The nanosized materials exhibit higher dissolution rates as well as higher solubility when compared to the bulk materials. This review significantly focused on the current improvement in ZnO-based nanomaterials/composites/doped materials for the application in the field of energy storage and conversion devices and biological applications. Special deliberation has been paid on supercapacitors, Li-ion batteries, dye-sensitized solar cells, photocatalysis, biosensors, biomedical and biological applications. Finally, the benefits of ZnO-based materials for the utilizations in the field of energy and biological sciences are moreover consistently analysed.

Double-negative-index ceramic aerogels for thermal superinsulation

Abstract: Ceramic aerogels are attractive for thermal insulation but plagued by poor mechanical stability and degradation under thermal shock. In this study, we designed and synthesized hyperbolic architectured ceramic aerogels with nanolayered double-pane walls with a negative Poisson’s ratio (−0.25) and a negative linear thermal expansion coefficient (−1.8 × 10 −6 per °C). Our aerogels display robust mechanical and thermal stability and feature ultralow densities down to ~0.1 milligram per cubic centimeter, superelasticity up to 95%, and near-zero strength loss after sharp thermal shocks (275°C per second) or intense thermal stress at 1400°C, as well as ultralow thermal conductivity in vacuum [~2.4 milliwatts per meter-kelvin (mW/m·K)] and in air (~20 mW/m·K). This robust material system is ideal for thermal superinsulation under extreme conditions, such as those encountered by spacecraft.

Blockchain Technologies for the Internet of Things: Research Issues and Challenges

Abstract: This paper presents a comprehensive survey of the existing blockchain protocols for the Internet of Things (IoT) networks. We start by describing the blockchains and summarizing the existing surveys that deal with blockchain technologies. Then, we provide an overview of the application domains of blockchain technologies in IoT, e.g., Internet of Vehicles, Internet of Energy, Internet of Cloud, Edge computing, etc. Moreover, we provide a classification of threat models, which are considered by blockchain protocols in IoT networks, into five main categories, namely identity-based attacks, manipulation-based attacks, cryptanalytic attacks, reputation-based attacks, and service-based attacks. In addition, we provide a taxonomy and a side-by-side comparison of the state-of-the-art methods toward secure and privacy-preserving blockchain technologies with respect to the blockchain model, specific security goals, performance, limitations, computation complexity, and communication overhead. Based on the current survey, we highlight open research challenges and discuss possible future research directions in the blockchain technologies for IoT.

Assessment of Subcellular ROS and NO Metabolism in Higher Plants: Multifunctional Signaling Molecules

Abstract: Reactive oxygen species (ROS) and nitric oxide (NO) are produced in all aerobic life forms under both physiological and adverse conditions. Unregulated ROS/NO generation causes nitro-oxidative damage, which has a detrimental impact on the function of essential macromolecules. ROS/NO production is also involved in signaling processes as secondary messengers in plant cells under physiological conditions. ROS/NO generation takes place in different subcellular compartments including chloroplasts, mitochondria, peroxisomes, vacuoles, and a diverse range of plant membranes. This compartmentalization has been identified as an additional cellular strategy for regulating these molecules. This assessment of subcellular ROS/NO metabolisms includes the following processes: ROS/NO generation in different plant cell sites; ROS interactions with other signaling molecules, such as mitogen-activated protein kinases (MAPKs), phosphatase, calcium (Ca2+), and activator proteins; redox-sensitive genes regulated by the iron-responsive element/iron regulatory protein (IRE-IRP) system and iron regulatory transporter 1(IRT1); and ROS/NO crosstalk during signal transduction. All these processes highlight the complex relationship between ROS and NO metabolism which needs to be evaluated from a broad perspective.

Additive manufacturing: Challenges, trends, and applications:

Abstract: Additive manufacturing is a recent trend in production processes owing to its many benefits. It can be defined as the process of producing parts through the deposition of material in a layer-by-lay...

A Blockchain-Based Solution for Enhancing Security and Privacy in Smart Factory

Abstract: Through the Industrial Internet of Things (IIoT), a smart factory has entered the booming period. However, as the number of nodes and network size become larger, the traditional IIoT architecture can no longer provide effective support for such enormous system. Therefore, we introduce the Blockchain architecture, which is an emerging scheme for constructing the distributed networks, to reshape the traditional IIoT architecture. First, the major problems of the traditional IIoT architecture are analyzed, and the existing improvements are summarized. Second, we introduce a security and privacy model to help design the Blockchain-based architecture. On this basis, we decompose and reorganize the original IIoT architecture to form a new multicenter partially decentralized architecture. Then, we introduce some relative security technologies to improve and optimize the new architecture. After that we design the data interaction process and the algorithms of the architecture. Finally, we use an automatic production platform to discuss the specific implementation. The experimental results show that the proposed architecture provides better security and privacy protection than the traditional architecture. Thus, the proposed architecture represents a significant improvement of the original architecture, which provides a new direction for the IIoT development.

Assessment of muscle function and physical performance in daily clinical practice: a position paper endorsed by the European Society for Clinical and Economic Aspects of Osteoporosis, Osteoarthritis and Musculoskeletal Diseases (ESCEO).

Abstract: It is well recognized that poor muscle function and poor physical performance are strong predictors of clinically relevant adverse events in older people. Given the large number of approaches to measure muscle function and physical performance, clinicians often struggle to choose a tool that is appropriate and validated for the population of older people they deal with. In this paper, an overview of different methods available and applicable in clinical settings is proposed. This paper is based on literature reviews performed by members of the European Society for Clinical and Economic Aspects of Osteoporosis and Osteoarthritis (ESCEO) working group on frailty and sarcopenia. Face-to-face meetings were organized afterwards where the whole group could amend and discuss the recommendations further. Several characteristics should be considered when choosing a tool: (1) purpose of the assessment (intervention, screening, diagnosis); (2) patient characteristics (population, settings, functional ability, etc.); (3) psychometric properties of the tool (test–retest reliability, inter-rater reliability, responsiveness, floor and ceiling effects, etc.); (4) applicability of the tool in clinical settings (overall cost, time required for the examination, level of training, equipment, patient acceptance, etc.); (5) prognostic reliability for relevant clinical outcomes. Based on these criteria and the available evidence, the expert group advises the use of grip strength to measure muscle strength and the use of 4-m gait speed or the Short Physical Performance Battery test to measure physical performance in daily practice. The tools proposed are relevant for the assessment of muscle weakness and physical performance. Subjects with low values should receive additional diagnostic workups to achieve a full diagnosis of the underlying condition responsible (sarcopenia, frailty or other).

Atomic-level passivation mechanism of ammonium salts enabling highly efficient perovskite solar cells.

Abstract: The high conversion efficiency has made metal halide perovskite solar cells a real breakthrough in thin film photovoltaic technology in recent years. Here, we introduce a straightforward strategy to reduce the level of electronic defects present at the interface between the perovskite film and the hole transport layer by treating the perovskite surface with different types of ammonium salts, namely ethylammonium, imidazolium and guanidinium iodide. We use a triple cation perovskite formulation containing primarily formamidinium and small amounts of cesium and methylammonium. We find that this treatment boosts the power conversion efficiency from 20.5% for the control to 22.3%, 22.1%, and 21.0% for the devices treated with ethylammonium, imidazolium and guanidinium iodide, respectively. Best performing devices showed a loss in efficiency of only 5% under full sunlight intensity with maximum power tracking for 550 h. We apply 2D- solid-state NMR to unravel the atomic-level mechanism of this passivation effect. Various approaches have been developed to push higher the efficiency of halide perovskite solar cells. Here Alharbi et al. show that ammonium salts treatment can reduce the defect density at the perovskite surface and understand the passivation mechanism with 2D-solid state NMR.

Blockchain and IoT-Based Cognitive Edge Framework for Sharing Economy Services in a Smart City

Abstract: In this paper, we propose a Blockchain-based infrastructure to support security- and privacy-oriented spatio-temporal smart contract services for the sustainable Internet of Things (IoT)-enabled sharing economy in mega smart cities. The infrastructure leverages cognitive fog nodes at the edge to host and process offloaded geo-tagged multimedia payload and transactions from a mobile edge and IoT nodes, uses AI for processing and extracting significant event information, produces semantic digital analytics, and saves results in Blockchain and decentralized cloud repositories to facilitate sharing economy services. The framework offers a sustainable incentive mechanism, which can potentially support secure smart city services, such as sharing economy, smart contracts, and cyber-physical interaction with Blockchain and IoT. Our unique contribution is justified by detailed system design and implementation of the framework.

A Hybrid Feature Extraction Method With Regularized Extreme Learning Machine for Brain Tumor Classification

Abstract: Brain cancer classification is an important step that depends on the physician’s knowledge and experience. An automated tumor classification system is very essential to support radiologists and physicians to identify brain tumors. However, the accuracy of current systems needs to be improved for suitable treatments. In this paper, we propose a hybrid feature extraction method with a regularized extreme learning machine (RELM) for developing an accurate brain tumor classification approach. The approach starts by preprocessing the brain images by using a min–max normalization rule to enhance the contrast of brain edges and regions. Then, the brain tumor features are extracted based on a hybrid method of feature extraction. Finally, a RELM is used for classifying the type of brain tumor. To evaluate and compare the proposed approach, a set of experiments is conducted on a new public dataset of brain images. The experimental results proved that the approach is more effective compared with the existing state-of-the-art approaches, and the performance in terms of classification accuracy improved from 91.51% to 94.233% for the experiment of the random holdout technique.