The rough Bergomi (rBergomi) model, introduced recently in [4], is a promising rough volatility model in quantitative finance. It is a parsimonious model depending on only three parameters, and yet exhibits remarkable fit to empirical implied volatility surfaces. In the absence of analytical European option pricing methods for the model, and due to the non-Markovian nature of the fractional driver, the prevalent option is to use Monte Carlo (MC) simulation for pricing. Despite recent advances in the MC method in this context, pricing under the rBergomi model is still a time-consuming task. To overcome this issue, we design a novel, alternative, hierarchical approach, based on i) adaptive sparse grids quadrature (ASGQ), and ii) quasi Monte Carlo (QMC). Both techniques are coupled with Brownian bridge construction and Richardson extrapolation. By uncovering the available regularity, our hierarchical methods demonstrate substantial computational gains with respect to the standard MC method, when reaching a sufficiently small relative error tolerance in the price estimates across different parameter constellations, even for very small values of the Hurst parameter. Our work opens a new research direction in this field, i.e. to investigate the performance of methods other than Monte Carlo for pricing and calibrating under the rBergomi model.

As institutional repositories seek to better support the release of unique research outputs (as opposed to open access versions of separately published materials), they increasingly turn to DataCite DOIs as the most appropriate form of persistent identifier. While DataCite DOI registration support is built into many repository platforms, the native configuration options may be limited. For institutions like ours who opt to use commercial repository hosting services, contracting customizations to features such as the DataCite integration within the existing software also complicates future upgrade or migration paths for the platform as a whole. In addition, locating the DOI minting service of an institution within a single platform or database may be inappropriate when the institution has several different systems that would benefit from the use of DOIs. Due to these factors we developed a local DOI Minter service connected to our hosted DSpace repository via REST API. This has allowed us to have greater immediate flexibility and also better positions us for future expansion of our DOI related services.

Nowadays, smart cities focus on using a clean source of energy such as solar-powered membrane distillation plant while reducing the need for fossil resources. Parabolic trough solar collector systems are part of a strategy for global regenerative power generation. The main objective is to gain a high-efficiency production despite the effect of external factors such that humidity or dust accumulation. To ensure economic and safe operation of parabolic solar collector systems advanced control strategies can result in a high efficiency of the outlet temperature.

Computational tools for characterizing electromagnetic scattering from objects with uncertain shapes are needed in various applications ranging from remote sensing at microwave frequencies to Raman spectroscopy at optical frequencies. Often, such computational tools use the Monte Carlo (MC) method to sample a parametric space describing geometric uncertainties. For each sample, which corresponds to a realization of the geometry, a deterministic electromagnetic solver computes the scattered fields. However, for an accurate statistical characterization the number of MC samples has to be large. In this work, to address this challenge, the continuation multilevel Monte Carlo (CMLMC) method is used together with a surface integral equation solver. The CMLMC method optimally balances statistical errors due to sampling of the parametric space, and numerical errors due to the discretization of the geometry using a hierarchy of discretizations, from coarse to fine. The number of realizations of finer discretizations can be kept low, with most samples computed on coarser discretizations to minimize computational cost. Consequently, the total execution time is significantly reduced, in comparison to the standard MC scheme.

White light solid-state lighting (SSL) is conventionally based on light-emitting diodes (LEDs). Lately, laser diodes (LDs) have been proposed as a replacement due to their higher device efficiencies; however, laser radiation and eye-safety are always a concern. Superluminescent diodes (SLDs) bring together low coherency and high power, high efficiency and low speckle density, offering an alternative to both LEDs and LDs. Our work compares LED, LD and SLD, and demonstrates high-speed visible light communications and high-quality white light based on c-plane InGaN SLD, constituting a viable innovation for the lighting industry and for the adoption of blue SLD for commercial applications.

Membranes have a growing impact in water desalination, chemical and pharmaceutical industrial separations. Two-dimensional (2D) materials have been increasingly utilized to fabricate high performance membranes. However, their characteristic single-spacing channel architecture and the non-ideal physicochemical microenvironments result in a permeance-rejection trade-off. We synergistically manipulated the geometry and chemical microenvironment in the graphene oxide (GO) nanochannels, obtaining 2D dual spacing channels with locally expanded hydrophilic sections and narrow hydrophobic sections. With this tailored structure, we achieve high performances in water and organic solvent nanofiltration.