Khalifa University

About: Khalifa University is a education organization based out in Abu Dhabi, United Arab Emirates. It is known for research contribution in the topics: Computer science & Adsorption. The organization has 3752 authors who have published 10909 publications receiving 141629 citations.

Culture and low-carbon energy transitions

Abstract: How does culture influence low-carbon energy transitions? How can insights about cultural influences guide energy planners and policymakers trying to stimulate transitions, particularly at a time of rapid technological change? This Review examines the influence of culture on a selection of low-carbon technologies and behavioural practices that reflect different dimensions of sustainability. Based on a typology of low-carbon technology and behaviour, we explore the cultural dimensions of four specific cases: eco-driving, ridesharing, automated vehicles and whole-house retrofits. We conclude with recommendations for those seeking to analyse, understand, develop, demonstrate and deploy low-carbon innovations for sustainable energy transitions. Culture influences low-carbon energy transitions and as a result should be considered in the design of relevant policies. Focusing on a selection of low-carbon technologies and behavioural practices, this Review highlights the role of culture with respect to different dimensions of sustainability.

Intravital Immunofluorescence for Visualizing the Microcirculatory and Immune Microenvironments in the Mouse Ear Dermis

Abstract: Visualizing the dynamic behaviors of immune cells in living tissue has dramatically increased our understanding of how cells interact with their surroundings, contributing important insights into mechanisms of leukocyte trafficking, tumor cell invasion, and T cell education by dendritic cells, among others. Despite substantial advances with various intravital imaging techniques including two-photon microscopy and the generation of multitudes of reporter mice, there is a growing need to assess cell interactions in the context of specific extracellular matrix composition and microvascular functions, and as well, simpler and more widely accessible methods are needed to image cell behaviors in the context of living tissue physiology. Here we present an antibody-based method for intravital imaging of cell interactions with the blood, lymphatic, and the extracellular matrix compartments of the living dermis while simultaneously assessing capillary permeability and lymphatic drainage function. Using the exposed dorsal ear of the anesthetized mouse and a fluorescence stereomicroscope, such events can be imaged in the context of specific extracellular matrix proteins, or matrix-bound chemokine stores. We developed and optimized the method to minimize tissue damage to the ear, rapidly immunostain for multiple extracellular or cell surface receptors of interest, minimize immunotoxicity with pre-blocking Fcγ receptors and phototoxicity with extracellular antioxidants, and highlight the major dermal tissue structures with basement membrane markers. We demonstrate differential migration behaviors of bone marrow-derived dendritic cells, blood-circulating leukocytes, and dermal dendritic cells, with the latter entering sparse CCL21-positive areas of pre-collecting lymphatic vessels. This new method allows simultaneous imaging of cells and tissue structures, microvascular function, and extracellular microenvironment in multiple skin locations for 12 hours or more, with the flexibility of immunolabeling in addition to genetic-based fluorescent reporters.

DRS: Dynamic Resource Scheduling for Real-Time Analytics over Fast Streams

Abstract: In a data stream management system (DSMS), users register continuous queries, and receive result updates as data arrive and expire. We focus on applications with real-time constraints, in which the user must receive each result update within a given period after the update occurs. To handle fast data, the DSMS is commonly placed on top of a cloud infrastructure. Because stream properties such as arrival rates can fluctuate unpredictably, cloud resources must be dynamically provisioned and scheduled accordingly to ensure real-time response. It is essential, for the existing systems or future developments, to possess the ability of scheduling resources dynamically according to the current workload, in order to avoid wasting resources, or failing in delivering correct results on time. Motivated by this, we propose DRS, a novel dynamic resource scheduler for cloud-based DSMSs. DRS overcomes three fundamental challenges: (a) how to model the relationship between the provisioned resources and query response time (b) where to best place resources, and (c) how to measure system load with minimal overhead. In particular, DRS includes an accurate performance model based on the theory of Jackson open queueing networks and is capable of handling arbitrary operator topologies, possibly with loops, splits and joins. Extensive experiments with real data confirm that DRS achieves real-time response with close to optimal resource consumption.

Phenolic Acids From Plants: Extraction and Application to Human Health

Abstract: Phenolic acids are biologically active molecules present in a wide range of edible and nonedible plants. They have a commercial value in beauty, health, and medicinal industries due to their antiaging, antitumor, antimicrobial, and antiinflammatory properties. This review provides a comprehensive report on the bioactivity and concentration range of commercially relevant phenolic acids in plants. Conventional and novel techniques used to extract phenolic acid are also covered. These include maceration extraction, Soxhlet extraction, liquid–liquid extraction, microwave-assisted extraction, ultrasound-assisted extraction, supercritical fluid extraction, and accelerated solvent extraction. The pros and cons of these extraction techniques from time, cost, extraction efficiency, and environmental impact will be discussed. This will allow readers to choose an extraction technique matching their budget and needs for downstream applications in medicine- or other health-related industries.

An Empirical Study for PCA- and LDA-Based Feature Reduction for Gas Identification

Abstract: Increasing the number of sensors in a gas identification system generally improves its performance as this will add extra features for analysis. However, this affects the computational complexity, especially if the identification algorithm is to be implemented on a hardware platform. Therefore, feature reduction is required to extract the most important information from the sensors for processing. In this paper, linear discriminant analysis (LDA) and principal component analysis (PCA)-based feature reduction algorithms have been analyzed using the data obtained from two different types of gas sensors, i.e., seven commercial Figaro sensors and in-house fabricated $4 \times 4$ tin-oxide gas array sensor. A decision tree-based classifier is used to examine the performance of both the PCA and LDA approaches. The software implementation is carried out in MATLAB and the hardware implementation is performed using the Zynq system-on-chip (SoC) platform. It has been found that with the $4 \times 4$ array sensor, two discriminant functions (DF) of LDA provide 3.3% better classification than five PCA components, while for the seven Figaro sensors, two principal components and one DF show the same performances. The hardware implementation results on the programmable logic of the Zynq SoC shows that LDA outperforms PCA by using 50% less resources as well as by being 11% faster with a maximum running frequency of 122 MHz.