A model for space-time threshold exceedances with an application to extreme rainfall

In extreme value studies models for observations exceeding a fixed high threshold have the advantage of exploiting the available extremal information, while avoiding bias from low values. In the context of space-time data, the challenge is to develop models for threshold exceedances that account for both spatial and temporal dependence. We address this issue through a modelling approach that embeds spatial dependence within a time series formulation. The model allows for different forms of limiting dependence in the spatial and temporal domains as the threshold level increases. In particular, temporal asymptotic independence is assumed, as this is often supported by empirical evidence, especially in environmental applications, while both asymptotic dependence and asymptotic independence are considered for the spatial domain. Inference from the observed exceedances is carried out through a combination of pairwise likelihood and a censoring mechanism. For those model specifications for which direct maximization of the censored pairwise likelihood is unfeasible, we propose an indirect inference procedure which leads to satisfactory results in a simulation study. The approach is applied to a dataset of rainfall amounts recorded over a set of weather stations in the North Brabant province of the Netherlands. The application shows that the range of extremal patterns that the model can cover is wide and that it has a competitive performance with respect to an alternative existing model for space-time threshold exceedances

Using fine-scale field data modelling for planning the management of invasions of Oenothera stucchii in coastal dune systems

Invasive alien species risk assessment and adaptive management are often hindered by a lack of information for most species. This work aims at predicting the probability of successful establishment and invasion of Oenothera stucchii Soldano, a neophyte invasive species belonging to the sect. Oenothera subsect. Oenothera, in xerophilous grasslands of grey dunes. Based on fine-scale field data, we modelled O. stucchii presence/absence and abundance as a function of environmental factors, human disturbance, and attributes of the recipient community through a zero-inflated Poisson model. The invasion success of O. stucchii depended on a combination of factors which differed when considering either the patterns of occurrence (species presence/absence) or those of species abundance. While human-driven disturbance strongly influenced the probability of presence/absence of O. stucchii, patterns of abundance were mostly driven by a combination of environmental and biotic features. Attributes of the recipient community remarkably influenced both O. stucchii presence and abundance. Based on fine-scale field data, we determined the mechanisms which drive the spatial patterns of presence and abundance of O. stucchii in xerophilous grasslands and provided quantitative thresholds to identify the most susceptible areas of grey dune habitats prone to invasion, which combine human disturbance (distance from the nearest beach access), attributes of the resident community (resident vegetation cover and structure), and environmental disturbance (foredune ridge height). These results provide useful insights to be used to plan cost-effective measures to prevent O. stucchii establishment and spread in sandy coastal systems. Our model may also be applied to closely related congener species included in the subsect. Oenothera, sharing similar biological and ecological traits

Efficient Near-Field to Mid-Field Sonic Boom Propagation Using a High-Order Space Marching Method

An efficient strategy for propagating sonic boom signatures from a near-field Computational Fluid Dynamics (CFD) solution to the mid-field is presented. The method is based on a high-order accurate finite-difference discretization of the 3D Euler equations on a specially designed curvilinear grid and a single sweep space marching solution algorithm. The new approach leads to more than a factor of two reduction in overall computational resources compared to the current method used to propagate near-field sonic booms to the ground. Accuracy and efficiency of the near-field to mid-field process is demonstrated using a selection of test cases from the AIAA Sonic Boom Prediction Workshops. Azimuthal dependence of nonlinear wave propagation from the near-field to mid-field is analyzed along with its effects on the ground level noise

Increasing fruit and vegetable consumption in Ireland

Context: Following recommended dietary guidelines, Ireland has since 2010 implemented a  0% Value Added Tax (VAT) on fruits and vegetables to increase consumption. Eleven years after policy implementation, the Irish still do not meet recommended intake for fruit and vegetable consumption, consuming 3.9 portions a day compared to 7 daily portions recommended. Policy Options: Four alternatives for improvement were assessed and compared:            1) retain the status quo of reduced VAT for healthy foods, 2) VAT only for locally produced fruits and vegetables, 3) increased VAT for salty and sweet foods with a subsidy for fruits and vegetables, and 4) an education-based policy. Four evaluation criteria were applied for the comparison: economic feasibility, effectiveness, political feasibility, and equity. Recommendations: The status quo remains the best option for Ireland. However, further assessment of this 0% VAT policy on fruits and vegetables is warranted, pending the availability of additional data to enable an in-depth understanding of policy implementation. &nbsp

Lithium Sulfonate Functionalization of Carbon Cathodes as a Substitute for Lithium Nitrate in the Electrolyte of Lithium–Sulfur Batteries

A method for grafting lithium sulfonate (LiSO3) groups to carbon surfaces is developed and the resulting carbons are evaluated for their potential to reduce the lithium polysulfide (LiPS) shuttle in lithium–sulfur (Li–S) batteries, replacing the common electrolyte additive lithium nitrate (LiNO3). The LiSO3 groups are attached to the ordered mesoporous carbon (CMK3) surface via a three-step procedure to synthesize LiSO3-CMK3 by bromomethylation, sodium sulfite (Na2SO3) substitution, and cation exchange. As a comparison, ethylenediamine (EN)-substituted CMK3, EN-CMK3, is also synthesized and tested. When used as a cathode in Li–S batteries, the unfunctionalized CMK3 suffers from strong LiPS shuttling as evidenced by its low initial Coulombic efficiencies (ICEs, <10%) compared to its functionalized derivatives EN-CMK3 and LiSO3-CMK3 (ICEs >75%). Postcycling analysis reveals the benefits of cathode surface functionalization on the lithium anode via an attenuated LiPS shuttle. When monitored at open circuit, the functionalized cathodes maintain their cell voltages much better than the CMK3 control and concurrent electrochemical impedance spectroscopy reveals their higher total cell resistance, which provides evidence for a reduced LiPS shuttle in the vicinity of both electrodes. Overall, such surface groups show promise as cathode-immobilized “lithium nitrate mimics.”

Efficient Near-Field to Mid-Field Sonic Boom Propagation using a High-Order Space Marching Method

No abstract availabl

Domain-specific introduction to machine learning terminology, pitfalls and opportunities in CRISPR-based gene editing

The use of machine learning (ML) has become prevalent in the genome engineering space, with applications ranging from predicting target site efficiency to forecasting the outcome of repair events. However, jargon and ML-specific accuracy measures have made it hard to assess the validity of individual approaches, potentially leading to misinterpretation of ML results. This review aims to close the gap by discussing ML approaches and pitfalls in the context of CRISPR gene-editing applications. Specifically, we address common considerations, such as algorithm choice, as well as problems, such as overestimating accuracy and data interoperability, by providing tangible examples from the genome-engineering domain. Equipping researchers with the knowledge to effectively use ML to better design gene-editing experiments and predict experimental outcomes will help advance the field more rapidly