Ordecsys (Switzerland)

The retinal pigment epithelium (RPE) is critical in the pathophysiology of retinal diseases, such as age-related macular degeneration. Adaptive optics transscleral flood illumination (AO-TFI) offers rapid, detailed morphometric characterization of the RPE layer. This study evaluated AO-TFI's efficacy and feasibility to detect clinically relevant morphological characteristics in a clinical setting. A total of 230 participants, categorized by their central retinal health, underwent comprehensive examination including SD-OCT, fundus imaging and AO-TFI using the Cellularis® prototype 2.0. Image quality control and RPE layer quantification were performed with an AO-TFI-specific automated segmentation algorithm. Density and area of hyper- and hyporeflective regions in the RPE layer plane, and, if detectable, RPE cell density, were quantified. We hypothesized that the RPE cell density would be lower in diseased retinas than in healthy retinas. Imaging results of healthy participants were statistically compared to those of diseased eyes. Additionally, generalized linear and logistic regression mixed-effect models identified associations between ocular characteristics and imaging parameters. After quality evaluation, high-quality images from 200 subjects (87%) were selected and segmented. The number of hyperreflective regions and their mean surface area were significantly higher in diseased than in healthy eyes (68 ± 40/mm2 vs. 51 ± 39/mm2; p < 0.001; 302 ± 196 μm2 vs. 155 ± 55 μm2; p < 0.001). The RPE distinct mosaic pattern was more often visible in healthy retinas (n = 103 vs. n = 30, p < 0.0001). The mean RPE cell density was 6354 ± 695/mm2, with comparable counts for healthy and diseased, 6327 ± 687/mm2 vs. 6532 ± 725/mm2 (p = 0.1). AO-TFI detected differences between healthy and diseased eyes, indicating its potential as a promising clinical modality providing quantitative and qualitative insights into RPE layer dynamics.

In this paper we implement a long-term multi-sectoral energy planning model to evaluate the role of electric mobility and Vehicle-to-Grid (V2G) and its potential synergy with renewable development for the decarbonization of the Chilean energy system, a country with a high renewable potential. The contributions of this paper are both methodological and policy-oriented. On the one hand we extend the energy ETEM-Chile planning model to incorporate the V2G dimension and distribution upgrading costs, usually not considered in such models. On the other hand, our results deliver interesting policy insights. They show that distribution costs are important to consider as otherwise it can lead to an over-electrified system with under-estimated system costs of about 6%. The incorporation of V2G technology results in significant system cost benefits from the use of V2G both for demand management and as system reserve. Under stringent climate objectives, we observe (i) a synergy between V2G and solar photovoltaics, with storage of intermittent production in vehicle batteries relying mainly on public charging stations, and (ii) a disappearance of green-hydrogen-based power plants in the electricity mix. • The study explores EVs’ impact on Chile’s energy transition pathways. • Distribution upgrading costs are included in the long-term energy planning model. • V2G and PV show synergy via storage use in electric vehicle batteries. • Distribution costs strongly influence EV adoption in future scenarios.

Abstract Stochastic methods have been typically used for the design and operations of hydraulic infrastructure. They allow decision makers to evaluate existing or new infrastructure under different possible scenarios, giving them the flexibility and tools needed in decision making. In this paper, we present a novel stochastic streamflow simulation approach able to replicate both temporal and spatial dependencies from the original data in a multi‐site basin context. The proposed model is a multi‐site extension of the modified Fractional Gaussian Noise (mFGN) model which is well‐known to be efficient to maintain periodic correlation for several time lags, but presents shortcomings in preserving the spatial correlation. Our method, called Weighted‐mFGN (WmFGN), incorporates spatial dependency into streamflows simulated with mFGN by relying on the Cholesky decomposition of the spatial correlation matrix of the historical streamflow records. As the order in which the decomposition steps are performed (temporal then spatial, or vice‐versa) affects the performance in terms of preserving the temporal and spatial correlation, our method searches for an optimal convex combination of the resulting correlation matrices. The result is a Pareto‐curve that indicates the optimal weights of the convex combination depending on the importance given by the user to spatial and temporal correlations. The model is applied to a number of river basins in Chile, where the results show that the WmFGN approach maintains the qualities of the single‐site mFGN, while significantly improving spatial correlation.

In this paper we implement a long-term multi-sectoral energy planning model to evaluate the role of green hydrogen in the energy mix of Chile, a country with a high renewable potential, under stringent emission reduction objectives in 2050. Our results show that green hydrogen is a cost-effective and environmentally friendly route especially for hard-to-abate sectors, such as interprovincial and freight transport. They also suggest a strong synergy of hydrogen with electricity generation from renewable sources. Our numerical simulations show that Chile should (i) start immediately to develop hydrogen production through electrolyzers all along the country, (ii) keep investing in wind and solar generation capacities ensuring a low cost hydrogen production and reinforce the power transmission grid to allow nodal hydrogen production, (iii) foster the use of electric mobility for cars and local buses and of hydrogen for long-haul trucks and interprovincial buses and, (iv) develop seasonal hydrogen storage and hydrogen cells to be exploited for electricity supply, especially for the most stringent emission reduction objectives.

This paper proposes an assessment of long-term climate strategies for oil- and gas-producing countries—in particular, the Gulf Cooperation Council (GCC) member states—as regards the Paris Agreement goal of limiting the increase of surface air temperature to 2°C by the end of the twenty-first century. The study evaluates the possible role of carbon dioxide removal (CDR) technologies under an international emissions trading market as a way to mitigate welfare losses. To model the strategic context, one assumes that a global cumulative emissions budget will have been allocated among different coalitions of countries—the GCC being one of them—and the existence of an international emissions trading market. A meta-game model is proposed in which deployment of CDR technologies as well as supply of emission rights are strategic variables and the payoffs are obtained from simulations of a general equilibrium model. The results of the simulations indicate that oil and gas producing countries and especially the GCC countries face a significant welfare loss risk, due to “unburnable oil” if a worldwide climate regime as recommended by the Paris Agreement is put in place. The development of CDR technologies, in particular direct air capture (DAC) alleviates somewhat this risk and offers these countries a new opportunity for exploiting their gas reserves and the carbon storage capacity offered by depleted oil and gas reservoirs.

In the meta-modeling approach, one builds a numerically tractable dynamic optimization or game model in which the parameters are identified through statistical emulation of a detailed large scale numerical simulation model. In this paper, we show how this approach can be used to assess the economic impacts of possible climate policies compatible with the Paris Agreement. One indicates why it is appropriate to assume that an international carbon market, with emission rights given to different groups of countries will exist. One discusses the approach to evaluate correctly abatement costs and welfare losses incurred by different groups of countries when implementing climate policies. Finally, using a recently proposed meta-model of game with a coupled constraint on a cumulative CO2 emissions budget, we assess several new scenarios for possible fair burden sharing in climate policies compatible with the Paris Agreement.

We use the flexible model coupling technology known as the bespoke framework generator to link established existing modules representing dynamics in the global economy (GEMINI_E3), the energy system (TIAM-WORLD), the global and regional climate system (MAGICC6, PLASIM-ENTS and ClimGEN), the agricultural system, the hydrological system and ecosystems (LPJmL), together in a single integrated assessment modelling (IAM) framework, building on the pre-existing framework of the Community Integrated Assessment System. Next, we demonstrate the application of the framework to produce policy-relevant scientific information. We use it to show that when using carbon price mechanisms to induce a transition from a high-carbon to a low-carbon economy, prices can be minimised if policy action is taken early, if burden sharing regimes are used, and if agriculture is intensified. Some of the coupled models have been made available for use at a secure and user-friendly web portal.