Relative entropy minimizing noisy non-linear neural network to approximate stochastic processes

A method is provided for designing and training noise-driven recurrent neural networks as models of stochastic processes. The method unifies and generalizes two known separate modeling approaches, Echo State Networks (ESN) and Linear Inverse Modeling (LIM), under the common principle of relative entropy minimization. The power of the new method is demonstrated on a stochastic approximation of the El Nino phenomenon studied in climate research

Simplified prospective LCA models for residential PV installations based on SC-SI installed in Europe in 2050

International audienceThe prospective environmental impacts and electricity production capacity of residential PV systems need to be assessed for an efficient implementation planning while minimizing their environmental impacts. These impacts must be assessed using a life cycle approach, such as Life Cycle Assessment (LCA). This study is reporting the steps towards a simplified prospective model valid for residential PV systems based on single-crystalline silicon (sc-Si), installed in Europe around 2050. Prospective greenhouse gas (GHG) performance of PV installations are compared to current situation (2011-2013) accounting for technological improvements, future electricity mixes and module manufacturing origin. Recommendations to develop a simplified prospective LCA model are provided

A Prospective Mapping of Environmental Impacts of Large Scale Photovoltaic Ground Mounted Systems Based on the CdTe Technology at 2050 Time Horizon

International audienceEnvironmental performances of PV systems are likely to evolve in the future due to significant technological improvements of the systems, to less energy intensive manufacturing processes as well as a shift towards less carbon-intensive energies for electricity mix. In spite of the complexity to estimate these changes with accuracy, projections are available based on scenarios representing different levels of improvements. Based on these scenarios, prospective environmental impacts and electricity production of large scale PV systems are assessed. This paper focuses on GHG performance of large scale photovoltaic ground mounted systems based on the Cadmium Telluride (CdTe) technology. We compare the current (2011-2013) and prospective (at 2050 time horizon) GHG performance of such PV systems under different scenarios accounting for technological improvements, future electricity mixes, and module manufacturing origin. A significant decrease in GHG performance is to be found for the prospective scenarios compared to the current situation ranging from 50 up to 80% depending on the scenarios. Prospective technological improvement seems to induce more uncertainties than prospective electricity mixes involved in manufacturing the modules

Assessing the prospective environmental impacts of photovoltaic systems based on a simplified LCA model

International audienceThe increasing electricity demand and the limited fossil energy resources require the development of new energy policies, based on more environment-friendly electricity-production technologies. The use of photovoltaic (PV) systems has been increasing a lot these last years, and this growth will probably continue. Although the environmental impacts of PV systems are small during their operating phase, they are more significant during their fabrication and recycling phases. These impacts must thus be assessed over the complete life time using life cycle analysis (LCA). However, LCA requires the collection of a large amount of data and is thus time-consuming. Besides, LCA results found in the literature corresponding to the photovoltaic energy pathway show a large variability, reflecting the heterogeneity of systems and their modeling within this energy pathway. An analysis based on 57 estimates found in 23 peer-reviewed studies revealed that the environmental performances related to climate change (defined by the ratio of the impacts related to climate change and the electricity production over the life cycle) range between 1 to 218 gCO2eq/kW

MiKlip - a National Research Project on Decadal Climate Prediction

A German national project coordinates research on improving a global decadal climate prediction system for future operational use. MiKlip, an eight-year German national research project on decadal climate prediction, is organized around a global prediction system comprising the climate model MPI-ESM together with an initialization procedure and a model evaluation system. This paper summarizes the lessons learned from MiKlip so far; some are purely scientific, others concern strategies and structures of research that targets future operational use. Three prediction-system generations have been constructed, characterized by alternative initialization strategies; the later generations show a marked improvement in hindcast skill for surface temperature. Hindcast skill is also identified for multi-year-mean European summer surface temperatures, extra-tropical cyclone tracks, the Quasi-Biennial Oscillation, and ocean carbon uptake, among others. Regionalization maintains or slightly enhances the skill in European surface temperature inherited from the global model and also displays hindcast skill for wind-energy output. A new volcano code package permits rapid modification of the predictions in response to a future eruption. MiKlip has demonstrated the efficacy of subjecting a single global prediction system to a major research effort. The benefits of this strategy include the rapid cycling through the prediction-system generations, the development of a sophisticated evaluation package usable by all MiKlip researchers, and regional applications of the global predictions. Open research questions include the optimal balance between model resolution and ensemble size, the appropriate method for constructing a prediction ensemble, and the decision between full-field and anomaly initialization. Operational use of the MiKlip system is targeted for the end of the current decade, with a recommended generational cycle of two to three years

Does glucose-6-phosphate dehydrogenase deficiency worsen the clinical features of sickle cell disease? A multi-hospital-based cross-sectional study.

peer reviewed[en] BACKGROUND: The impact of glucose-6-phosphate dehydrogenase deficiency(G-6-PD) on the clinical course of sickle cell disease(SCD) is still controversial. The objectives of this study were to determine the prevalence of G-6-PD deficiency in patients with SCD and its effect on their clinical course. METHODS: A cross-sectional study of 122 SCD patients and 211 healthy blood donors was conducted in Kisangani city. Data were collected through clinical examination supplemented by patient medical records, and laboratory tests based on a survey form. G-6-PD activity was measured by spectrophotometry and the screening for SCD by the HemoTypeSC® rapid test. Statistical analysis was done using SPSS ver. 20.0. RESULTS: The prevalence of G-6-PD deficiency did not differ between SCD and non-SCD subjects, 35.2% vs. 33.6% respectively(p = .767). When comparing the hemoglobin level between SCD patients with and without G-6-PD deficiency, no significant difference was observed. However, in the 6 months prior to the study, SCD patients with G-6-PD deficiency had on average more transfusions than non-deficient SCD patients, 0.64 ± 0.897 vs. 0.24 ± 0.486(p = .004). Similarly, considering the clinical events of the last 12 months prior to the study, there were more hospitalizations, major vaso-occlusive crises and anemia requiring blood transfusion among G-6-PD deficient SCD patients compared to no-deficient, respectively 1.42 ± 1.451vs. 0.76 ± 1.112(p = .007); 1.37 ± 1.092 vs. 0.85 ± 1.014(p = .005); 0.74 ± 0.902 vs. 0.38 ± 0.739 (p = .007). CONCLUSION: The prevalence of G-6-PD deficiency in SCD patients was high but did not differ from that observed in controls. In addition, G-6-PD deficiency appeared to worsen the clinical features of SCD. Nevertheless, prospective studies further clarifying this observation are needed

Industry-Scale Orchestrated Federated Learning for Drug Discovery

To apply federated learning to drug discovery we developed a novel platform in the context of European Innovative Medicines Initiative (IMI) project MELLODDY (grant n{\deg}831472), which was comprised of 10 pharmaceutical companies, academic research labs, large industrial companies and startups. The MELLODDY platform was the first industry-scale platform to enable the creation of a global federated model for drug discovery without sharing the confidential data sets of the individual partners. The federated model was trained on the platform by aggregating the gradients of all contributing partners in a cryptographic, secure way following each training iteration. The platform was deployed on an Amazon Web Services (AWS) multi-account architecture running Kubernetes clusters in private subnets. Organisationally, the roles of the different partners were codified as different rights and permissions on the platform and administrated in a decentralized way. The MELLODDY platform generated new scientific discoveries which are described in a companion paper.Comment: 9 pages, 4 figures, to appear in AAAI-23 ([IAAI-23 track] Deployed Highly Innovative Applications of AI

Differential cross section measurements for the production of a W boson in association with jets in proton–proton collisions at √s = 7 TeV

Measurements are reported of differential cross sections for the production of a W boson, which decays into a muon and a neutrino, in association with jets, as a function of several variables, including the transverse momenta (pT) and pseudorapidities of the four leading jets, the scalar sum of jet transverse momenta (HT), and the difference in azimuthal angle between the directions of each jet and the muon. The data sample of pp collisions at a centre-of-mass energy of 7 TeV was collected with the CMS detector at the LHC and corresponds to an integrated luminosity of 5.0 fb[superscript −1]. The measured cross sections are compared to predictions from Monte Carlo generators, MadGraph + pythia and sherpa, and to next-to-leading-order calculations from BlackHat + sherpa. The differential cross sections are found to be in agreement with the predictions, apart from the pT distributions of the leading jets at high pT values, the distributions of the HT at high-HT and low jet multiplicity, and the distribution of the difference in azimuthal angle between the leading jet and the muon at low values.United States. Dept. of EnergyNational Science Foundation (U.S.)Alfred P. Sloan Foundatio