Articles

A brief account of photonics research activities in the selected countries in the Middle East and Africa is presented in this article. Though not comprehensive, we hope to provide a glimpse of the research landscape in the region, and the collaboration and connection with each other and the international partners.

Gaussian random fields with Matérn covariance functions are popular models in spatial statistics and machine learning. In this work, we develop a spatio-temporal extension of the Gaussian Matérn fields formulated as solutions to a stochastic partial differential equation. The spatially stationary subset of the models have marginal spatial Matérn covariances, and the model also extends to Whittle-Matérn fields on curved manifolds, and to more general non-stationary fields. In addition to the parameters of the spatial dependence (variance, smoothness, and practical correlation range) it additionally has parameters controlling the practical correlation range in time, the smoothness in time, and the type of non-separability of the spatio-temporal covariance. Through the separability parameter, the model also allows for separable covariance functions. We provide a sparse representation based on a finite element approximation, that is well suited for statistical inference and which is implemented in the R-INLA software. The flexibility of the model is illustrated in an application to spatio-temporal modeling of global temperature data.

Symbiodiniaceae form associations with extra- and intracellular bacterial symbionts, both in culture and in symbiosis with corals. Bacterial associates can regulate Symbiodiniaceae fitness in terms of growth, calcification and photophysiology. However, the influence of these bacteria on interactive stressors, such as temperature and light, which are known to influence Symbiodiniaceae physiology, remains unclear. Here, we examined the photophysiological response of two Symbiodiniaceae species (Symbiodinium microadriaticum and Breviolum minutum) cultured under acute temperature and light stress with specific bacterial partners from their microbiome (Labrenzia (Roseibium) alexandrii, Marinobacter adhaerens or Muricauda aquimarina). Overall, bacterial presence positively impacted Symbiodiniaceae core photosynthetic health (photosystem II [PSII] quantum yield) and photoprotective capacity (non-photochemical quenching; NPQ) compared to cultures with all extracellular bacteria removed, although specific benefits were variable across Symbiodiniaceae genera and growth phase. Symbiodiniaceae co-cultured with M. aquimarina displayed an inverse NPQ response under high temperatures and light, and those with L. alexandrii demonstrated a lowered threshold for induction of NPQ, potentially through the provision of antioxidant compounds such as zeaxanthin (produced by Muricauda spp.) and dimethylsulfoniopropionate (DMSP; produced by this strain of L. alexandrii). Our co-culture approach empirically demonstrates the benefits bacteria can deliver to Symbiodiniaceae photochemical performance, providing evidence that bacterial associates can play important functional roles for Symbiodiniaceae.

In energy intensive industries, the Organic Rankine Cycles (ORCs), as a promising technology can remarkably enhance energy efficiency and reduce the carbon emissions by converting low, medium, and high-temperature heat source to electricity. Among the most energy-intensive industries, the iron and steel industry represents almost 5% of total world energy consumption. The most significant amounts of the waste heat are produced and being lost in the industrial and thermal processes. A better use of process excess/waste heat represents a significant source of energy savings and provides an affordable and reliable technical solution to increase the efficiency of energy intensive industrial sector by enhancing self-production of electricity. This can help in mitigating the increase of electricity consumption due to the industrial electrification and thereby reducing the load on the power grids. Moreover, waste heat recovery can substantially reduce carbon emissions and address the challenge of combat against global warming. ORC technologies for waste heat recovery, are one of the most suitable technologies to boost sustainable transition of the steel sector. This paper will provide knowledge on the design criteria, achievable performance and cost of the components paving the way for the ORCs for waste heat recovery in iron and steel industry, supporting their market penetration and enhancing their role in the fight against climate change.

In the last few decades, deep learning (DL) has afforded solutions to macroscopic problems in petroleum engineering, but mechanistic problems at the microscale have not benefited from it. Mechanism studies have been the strong demands for the emerging projects, such as the gas storage and hydrate production, and for some problems encountered in the storage process, which are common found as the chemical interaction between injected gas and mineral, and the formation of hydrate. Emerging advances in DL technology enable solving molecular dynamics (MD) with quantum accuracy. The conventional quantum chemical method is computational expensive, whereas the classical MD method cannot guarantee high accuracy because of its empirical force field parameters. With the help of the DL force field, precision at the quantum chemistry level can be achieved in MD. Moreover, the DL force field promotes the computational speed compared with first-principles calculations. In this review, the basic knowledge of the molecular force field and deep neural network (DNN) is first introduced. Then, three representative open-source packages relevant to the DL force field are introduced. As the most common components in the development of oil and gas reservoirs, water and methane are studied from the aspects of computational efficiency and chemical reaction respectively, providing the foundation of oil and gas researches. However, in the oil and gas problems, the complex molecular topo structures and various element types set a high challenge for the DL techniques in MD. Regarding the computational efficiency, it needs improvement via GPU and parallel accelerations to compete with classical MD. Even with such difficulties, the booming of this technique in the area of petroleum engineering can be predictable.