Multi-objective Optimisation of the Benchmark Wind Farm Layout Problem

Wind farm layout optimisation has become a very challenging and widespread problem in recent years. In many publications, the main goal is to achieve the maximum power output and minimum wind farm cost. This may be accomplished by applying single or multi-objective optimisation techniques. In this paper, we apply a single objective hill-climbing algorithm (HCA) and three multi-objective evolutionary algorithms (NSGA-II, SPEA2 and PESA-II) to a well-known benchmark optimisation problem proposed by Mosetti et al., which includes three different wind scenarios. We achieved better results by applying single- and multi-objective algorithms. Furthermore, we showed that the best performing multi-objective algorithm was NSGA-II. Finally, an extensive comparison of the results of past publications is made

Thrombosis, Bleeding, and the Observational Effect of Early Therapeutic Anticoagulation on Survival in Critically Ill Patients With COVID-19

This article is made available for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.Background: Hypercoagulability may be a key mechanism of death in patients with coronavirus disease 2019 (COVID-19). Objective: To evaluate the incidence of venous thromboembolism (VTE) and major bleeding in critically ill patients with COVID-19 and examine the observational effect of early therapeutic anticoagulation on survival. Design: In a multicenter cohort study of 3239 critically ill adults with COVID-19, the incidence of VTE and major bleeding within 14 days after intensive care unit (ICU) admission was evaluated. A target trial emulation in which patients were categorized according to receipt or no receipt of therapeutic anticoagulation in the first 2 days of ICU admission was done to examine the observational effect of early therapeutic anticoagulation on survival. A Cox model with inverse probability weighting to adjust for confounding was used. Setting: 67 hospitals in the United States. Participants: Adults with COVID-19 admitted to a participating ICU. Measurements: Time to death, censored at hospital discharge, or date of last follow-up. Results: Among the 3239 patients included, the median age was 61 years (interquartile range, 53 to 71 years), and 2088 (64.5%) were men. A total of 204 patients (6.3%) developed VTE, and 90 patients (2.8%) developed a major bleeding event. Independent predictors of VTE were male sex and higher D-dimer level on ICU admission. Among the 2809 patients included in the target trial emulation, 384 (11.9%) received early therapeutic anticoagulation. In the primary analysis, during a median follow-up of 27 days, patients who received early therapeutic anticoagulation had a similar risk for death as those who did not (hazard ratio, 1.12 [95% CI, 0.92 to 1.35]). Limitation: Observational design. Conclusion: Among critically ill adults with COVID-19, early therapeutic anticoagulation did not affect survival in the target trial emulation

Wind turbines dataset

Dataset associated to the publication "Multi-objective Optimisation of the Benchmark Wind Farm Layout Problem"

Explaining Optimisation of Offshore Wind Farms using Metaheuristics

Explainable artificial intelligence (XAI) has become a significant approach for increasing trust in techniques used by the machine learning community. Similarly, given the importance of applications of metaheuristics, often used in the optimisation of critical national infrastructure such as power generation facilities, it is important that the trustworthiness of tools used in optimising these problems is assured, and the use of XAI within the metaheuristic domain is oneway of achieving this. This chapter considers the application of a tool previously demonstrated on the optimisation of knapsack problems to the optimisation of offshore wind farm layouts, and discusses the extent to which the tool is able to explain the processes used to identify optimal wind farm designs

Hedgehog is relayed through dynamic heparan sulfate interactions to shape its gradient

Cellular differentiation is directly determined by concentration gradients of morphogens. As a central model for gradient formation during development, Hedgehog (Hh) morphogens spread away from their source to direct growth and pattern formation in Drosophila wing and eye discs. What is not known is how extracellular Hh spread is achieved and how it translates into precise gradients. Here we show that two separate binding areas located on opposite sides of the Hh molecule can interact directly and simultaneously with two heparan sulfate (HS) chains to temporarily cross-link the chains. Mutated Hh lacking one fully functional binding site still binds HS but shows reduced HS cross-linking. This, in turn, impairs Hhs ability to switch between both chains in vitro and results in striking Hh gradient hypomorphs in vivo. The speed and propensity of direct Hh switching between HS therefore shapes the Hh gradient, revealing a scalable design principle in morphogen-patterned tissues

Study of human Orexin-1 and -2 G-protein-coupled receptors with novel and published antagonists by modeling, molecular dynamics simulations, and site-directed mutagenesis.

The class A G-protein-coupled receptors (GPCRs) Orexin-1 (OX1) and Orexin-2 (OX2) are located predominantly in the brain and are linked to a range of different physiological functions, including the control of feeding, energy metabolism, modulation of neuro-endocrine function, and regulation of the sleep-wake cycle. Site-directed mutagenesis (SDM) and domain exchange (chimera) studies have provided important insight into key features of the OX1 and OX2 binding sites. However, the precise determinants of antagonist binding and selectivity are still not fully known. In this work, we used homology modeling of OX receptors to direct further SDM studies. These SDM studies were followed by molecular dynamics (MD) simulations to rationalize the full scope of the SDM data and to explain the role of each mutated residue in the binding and selectivity of a set of OX antagonists: Almorexant (dual OX1 and OX2 antagonist), SB-674042 (OX1 selective antagonist), EMPA (OX2 selective antagonist), and others. Our primary interest was focused on transmembrane helix 3 (TM3), which is identified as being of great importance for the selectivity of OX antagonists. These studies revealed conformational differences between the TM3 helices of OX1 and OX2, resulting from differences in amino acid sequences of the OX receptors that affect key interhelical interactions formed between TM3 and neighboring TM domains. The MD simulation protocol used here, which was followed by flexible docking studies, went beyond the use of static models and allowed for a more detailed exploration of the OX structures. In this work, we have demonstrated how even small differences in the amino acid sequences of GPCRs can lead to significant differences in structure, antagonist binding affinity, and selectivity of these receptors. The MD simulations allowed refinement of the OX receptor models to a degree that was not possible with static homology modeling alone and provided a deeper rationalization of the SDM data obtained. To validate these findings and to demonstrate that they can be usefully applied to the design of novel, very selective OX antagonists, we show here two examples of antagonists designed in house: EP-109-0092 (OX1 selective) and EP-009-0513 (OX2 selective)