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We present the first calculations to follow the evolution of all stable isotopes (and their abundant radioactive progenitors) in a finely zoned stellar model computed from the onset of central hydrogen burning through explosion as a Type II supernova. The calculations were performed for a 15 solar mass Pop I star using the most recently available set of experimental and theoretical nuclear data, revised opacity tables, and taking into account mass loss due to stellar winds. We find the approximately solar production of proton-rich isotopes above a mass number of A=120 due to the gamma-process. We also find a weak s-process, which along with the gamma-process and explosive helium and carbon burning, produces nearly solar abundances of almost all nuclei from A=60 to 85. A few modifications of the abundances of heavy nuclei above mass 90 by the s-process are also noted and discussed. New weak rates lead to significant alteration of the properties of the presupernova core. 10 pages, 4 figures, Nuclear Astrophysics X Workshop proceedings

The charged-particle production ratios $\bar{p}/p$, $K^-/K^+$, $��^-/��^+$, $(p + \bar{p})/(��^+ + ��^-)$, $(K^+ + K^-)/(��^+ + ��^-)$ and $(p + \bar{p})/(K^+ + K^-)$ are measured with the LHCb detector using $0.3 {\rm nb^{-1}}$ of $pp$ collisions delivered by the LHC at $\sqrt{s} = 0.9$ TeV and $1.8 {\rm nb^{-1}}$ at $\sqrt{s} = 7$ TeV. The measurements are performed as a function of transverse momentum $p_{\rm T}$ and pseudorapidity $��$. The production ratios are compared to the predictions of several Monte Carlo generator settings, none of which are able to describe adequately all observables. The ratio $\bar{p}/p$ is also considered as a function of rapidity loss, $��y \equiv y_{\rm beam} - y$, and is used to constrain models of baryon transport. Incorrect entries in Table 2 corrected. No consequences for rest of paper

AbstractThis article describes a control approach for large‐scale electricity networks, with the goal of efficiently coordinating distributed generators to balance unexpected load variations with respect to nominal forecasts. To mitigate the difficulties due to the size of the problem, the proposed methodology is divided in two steps. First, the network is partitioned into clusters, composed of several dispatchable and nondispatchable generators, storage systems, and loads. A clustering algorithm is designed with the aim of obtaining clusters with the following characteristics: (i) they must be compact, keeping the distance between generators and loads as small as possible; (ii) they must be able to internally balance load variations to the maximum possible extent. Once the network clustering has been completed, a two layer control system is designed. At the lower layer, a local model predictive controller is associated to each cluster for managing the available generation and storage elements to compensate local load variations. If the local sources are not sufficient to balance the cluster's load variations, a power request is sent to the supervisory layer, which optimally distributes additional resources available from the other clusters of the network. To enhance the scalability of the approach, the supervisor is implemented relying on a fully distributed optimization algorithm. The IEEE 118‐bus system is used to test the proposed design procedure in a nontrivial scenario.

At the International Symposium on Biodegradable Polymers (ISBP2022) in Sion, Switzerland, experts from academia and industry underscored the remarkable progress in biobased and biodegradable polymers (BBPs) since their initial commercialization around 50 years ago. Despite significant advancements, the technology readiness level (TRL), market adoption, and industrialization of BBPs is not yet competitive to conventional plastics. In this perspective, we summarize the challenges and requirements for advancing the development and industrialization of BBPs, drawing insights from international experts coming from academia and industry, who had participated in the survey and podium discussion during the ISBP2022. In fact, BBPs grapple with persistent and emerging challenges throughout the value chain. These challenges can be grouped into four areas and involve i) the pursuit of sustainable feedstocks together with efficient production and downstream processes as well as recycling technologies and infrastructure; ii) meeting or revisiting product requirements by industry, markets, and consumers; iii) navigating a non-level playing field in their sustainability assessment (LCA) compared to conventional plastics; and iv) struggling with underdeveloped and partially biased policy and financial frameworks as well as lacking clear definitions, terminologies and communication.

Post-processing has received much attention during the last couple of years within the hydrological community, and many different methods have been developed and tested, especially in the field of flood forecasting. Apart from the different meanings of the phrase “post-processing” in meteorology and hydrology, in this paper, it is regarded as a method to correct model outputs (predictions) based on meteorological (1) observed input data, (2) deterministic forecasts (single time series) and (3) ensemble forecasts (multiple time series) and to derive predictive uncertainties. So far, the majority of the research has been related to floods, how to remove bias and improve the forecast accuracy and how to minimize dispersion errors. Given that global changes are driving climatic forces, there is an urgent need to improve the quality of low-flow predictions, as well, even in regions that are normally less prone to drought. For several catchments in Switzerland, different post-processing methods were tested with respect to low stream flow and flooding conditions. The complexity of the applied procedures ranged from simple AR processes to more complex methodologies combining wavelet transformations and Quantile Regression Neural Networks (QRNN) and included the derivation of predictive uncertainties. Furthermore, various verification methods were tested in order to quantify the possible improvements that could be gained by applying these post-processing procedures based on different stream flow conditions. Preliminary results indicate that there is no single best method, but with an increase of complexity, a significant improvement of the quality of the predictions can be achieved.