Software tools for Bayesian inference have undergone rapid evolution in the past three decades, following popularisation of the first generation MCMC-sampler implementations. More recently, exponential growth in the number of users has been stimulated both by the active development of new packages by the machine learning community and popularity of specialist software for particular applications. This review aims to summarize the most popular software and provide a useful map for a reader to navigate the world of Bayesian computation. We anticipate a vigorous continued development of algorithms and corresponding software in multiple research fields, such as probabilistic programming, likelihood-free inference, and Bayesian neural networks, which will further broaden the possibilities for employing the Bayesian paradigm in exciting applications.

Low-cost polyamide thin-film composite (TFC) membranes are being explored as alternatives to cation exchange membranes for seawater electrolysis. An optimal membrane should have a low electrical resistance to minimize applied potentials needed for water electrolysis and be able to block chloride ions present in a seawater catholyte from reaching the anode. The largest energy loss associated with a TFC membrane was the Nernstian overpotential of 0.74 V (equivalent to 37 Ω cm² at 20 mA cm^-2), derived from the pH difference between the anolyte and catholyte and not the membrane ohmic overpotential. Based on analysis using electrochemical impedance spectroscopy, the pristine TFC membrane contributed only 5.00 Ω cm² to the ohmic resistance. Removing the polyester support layer reduced the resistance by 79% to only 1.04 Ω cm², without altering the salt ion transport between the electrolytes. Enlarging the pore size (∼5 times) in the polyamide active layer minimally impacted counterion transport across the membrane during electrolysis, but it increased the total concentration of chloride transported by 60%. Overall, this study suggests that TFC membranes with thinner but mechanically strong supporting layers and size-selective active layers should reduce energy consumption and the potential for chlorine generation for seawater electrolyzers.

Oceans are crucial to human survival, providing natural resources and most of the global oxygen supply, and are responsible for a large portion of worldwide economic development. Although it is widely considered a silent world, the sea is filled with natural sounds generated by marine life and geological processes. Man-made underwater sounds, such as active sonars, maritime traffic, and offshore oil and mineral exploration, have significantly affected underwater soundscapes and species. In this work, we report on a joint optical fiber-based communication and sensing technology aiming to reduce noise pollution in the sea while providing connectivity simultaneously with a variety of underwater applications. The designed multifunctional fiber-based system enables two-way data transfer, monitoring marine life and ship movement near the deployed fiber at the sea bottom and sensing temperature. The deployed fiber is equally harnessed to transfer energy that the internet of underwater things (IoUTs) devices can harvest. The reported approach significantly reduces the costs and effects of monitoring marine ecosystems while ensuring data transfer and ocean monitoring applications and providing continuous power for submerged IoUT devices.

Based on both total energy calculations and comparison of experimental and calculated characteristics of the photoelectron spectrum (PHES), the structural assignment of clusters Agn– (n = 13–16) and Cum– (m = 14–17) has been made using the density functional theory (DFT) model with our previously developed S2LYP functional. A comparative study of size dependence of geometry, electronic structure, and physicochemical properties has been carried out for a series of anionic silver and copper clusters containing up to 20 atoms. For the cases when two isomers contribute to the experimental PHES, the isomerization barriers and molar ratio of isomers were estimated. It has been shown that the geometry and the properties that are determined mainly by ns-derived electronic states are similar for copper and silver clusters. However, due to the larger contribution of (n–1)d-electrons to the chemical bond, the potential energy surface of copper clusters is less smooth, and these clusters are characterized by higher isomerization energies compared to silver clusters. The isomerization energies of clusters and the number of isomers with similar energies increase with enlarging cluster size. Thus, clusters containing less than 20 atoms easily overcome the barriers of intramolecular isomerization (i.e., behave like liquids). However, it is expected that cooled clusters containing several tens of atoms will have a rigid geometry due to high intramolecular isomerization energies.

Modeling dissolution processes in discrete fracture networks (DFNs) is a challenging task. Challenges are related to the highly nonlinear coupling between flow, mass transport, and reactive processes associated with fracture aperture evolution by dissolution. Further, advection-dominated transport due to fast fluid flow in fractures renders the problem more complex from a computational point of view, as traditional numerical methods may introduce unphysical oscillations or excessive numerical diffusion. The Discontinuous Galerkin (DG) method is known to be suitable for the simulation of advection-dominated transport. In this work, an advanced DG model is developed to model transport with dissolution in DFNs. We propose an upwind formulation to deal with the upstream concentration at the intersection of several fractures. The upstream concentration at an intersection node is calculated based on the average nodal concentrations of all the fractures having an inflow at that node, weighted by the volumetric fluxes of these fractures. The dispersion term is discretized with the Mixed Finite Element (MFE) method, which ensures the continuity of the dispersive flux at the intersection of fractures with different apertures. The obtained nonlinear coupled flow-transport-dissolution equations are discretized in time with a high-order scheme via the method of lines (MOL). Numerical examples and comparisons with standard finite element (FE) and finite volume (FV) solutions are performed to investigate the correctness and efficiency of the developed model. Results show that the new DG-DFN model avoids unphysical oscillations encountered with the standard FE method and strongly reduces the numerical diffusion observed with the upwind FV scheme. The DG-DFN model is then used to investigate the effect of the dissolution rate on the flow, transport, and aperture evolution processes for a single fracture and for a DFN. A quasi-linear evolution of the fracture aperture is observed for low dissolution rates. For high dissolution rates, a funnel-shaped enlargement is observed with a significant widening for the fractures near the inlet and minor effects for those away from the injection location.

Human iPSCs' derivation and use in clinical studies are transforming medicine. Yet, there is a high cost and long waiting time for autologous iPS-based cellular therapy, and the genetic engineering of hypo-immunogenic iPS cell lines is hampered with numerous hurdles. Therefore, it is increasingly interesting to create cell stocks based on HLA haplotype distribution in a given population. In this study, we assessed the potential of HLA-based iPS banking for the Saudi population. First, we analyzed the HLA database of the Saudi Stem Cell Donor Registry (SSCDR), which contains high-resolution HLA genotype data of 64,315 registered Saudi donors at the time of analysis. We found that only 13 iPS lines would be required to cover 30% of the Saudi population, 39 iPS lines would offer 50% coverage and 596 for more than 90% coverage. Next, As a proof-of-concept, we launched the first HLA-based banking of iPSCs in Saudi Arabia. Using clinically relevant methods, we generated the first iPSC line from a homozygous donor for the most common HLA haplotype in Saudi. The two generated clones expressed pluripotency markers, could be differentiated into all three germ layers, beating cardiomyocytes and neuronal progenitors. To ensure that our reprogramming method generates genetically stable iPSCs, we assessed the mutational burden in the generated clones and the original blood sample from which the iPSCs were derived using whole-genome sequencing. All detected variants were found in the original donor sample and were classified as benign according to current guidelines of the American College of Medical Genetics and Genomics (ACMG). This study sets a road map for introducing iPS-based cell therapy in the Kingdom of Saudi Arabia.

In the analysis of spatial point patterns on linear networks, a critical statistical objective is estimating the first-order intensity function, representing the expected number of points within specific subsets of the network. Typically, non-parametric approaches employing heating kernels are used for this estimation. However, a significant challenge arises in selecting appropriate bandwidths before conducting the estimation. We study an intensity estimation mechanism that overcomes this limitation using adaptive estimators, where bandwidths adapt to the data points in the pattern. While adaptive estimators have been explored in other contexts, their application in linear networks remains underexplored. We investigate the adaptive intensity estimator within the linear network context and extend a partitioning technique based on bandwidth quantiles to expedite the estimation process significantly. Through simulations, we demonstrate the efficacy of this technique, showing that the partition estimator closely approximates the direct estimator while drastically reducing computation time. As a practical application, we employ our method to estimate the intensity of traffic accidents in a neighbourhood in Medellin, Colombia, showcasing its real-world relevance and efficiency.

Printed energy storage components attracted attention for being incorporated into bendable electronics. In this research, a homogeneous and stable ink based on vanadium dioxide (VO2) is hydrothermally synthesized with a non-toxic solvent. The structural and morphological properties of the synthesized material are determined to be well-crystalline monoclinic-phase nanoparticles. The charge storage mechanisms and evaluations are specified for VO2 electrodes, gold (Au) electrodes, and VO2/Au electrodes using cyclic voltammetry, galvanostatic charge–discharge, and electrochemical impedance spectroscopy. The VO2 electrode shows an electrical double layer and a redox reaction in the positive and negative voltage ranges with a slightly higher areal capacitance of 9 mF/cm2. The VO2/Au electrode exhibits an areal capacitance of 16 mF cm−2, which is double that of the VO2 electrode. Due to the excellent electrical conductivity of gold, the areal capacitance 18 mF cm−2 of the Au electrode is the highest among them. Based on that, Au positive electrodes and VO2 negative electrodes are used to build an asymmetric supercapacitor. The device delivers an areal energy density of 0.45 μWh cm−2 at an areal power density of 70 μW cm−2 at 1.4 V in the aqueous electrolyte of potassium hydroxide. We provide a promising electrode candidate for cost-effective, lightweight, environmentally friendly printed supercapacitors.

We are solving a problem of salinisation of coastal aquifers. As a test case example, we consider the Henry saltwater intrusion problem. Since porosity, permeability and recharge are unknown or only known at a few points, we model them using random fields and random variables. The Henry problem describes a two-phase flow and is non-linear and time-dependent. The solution to be found is the expectation of the salt mass fraction, which is uncertain and time-dependent. To estimate this expectation, we use the well-known multilevel Monte Carlo (MLMC) method. The MLMC method takes just a few samples on computationally expensive (fine) meshes and more samples on cheap (coarse) meshes. Then, by building a telescoping sum, the MLMC method estimates the expected value at a much lower computational cost than the classical Monte Carlo method. The deterministic solver used here is the well-known parallel and scalable ug4 solver.

Electrochemical carbon capture offers a promising approach for capturing scarce CO2 from industrial emissions or the atmosphere. Challenges persist in current techniques, such as low capture rates and oxygen sensitivity. This article previews the latest findings by Wang and co-workers published in Nature, reporting the oxygen/water redox couple in a solid-electrolyte reactor for continuous and modular CO2 capture.