Wind turbine lifetime extension decision-making based on structural health monitoring

In this work, structural health monitoring data is applied to underpin a long-term wind farm lifetime extension strategy. Based on the outcome of the technical analysis, the case for an extended lifetime of 15 years is argued. Having established the lifetime extension strategy, the single wind turbine investigated within a wind farm is subjected to a bespoke economic lifetime extension case study. In this case study, the local wind resource is taken into consideration, paired with central, optimistic, and pessimistic operational cost assumptions. Besides a deterministic approach, a stochastic analysis is carried out based on Monte Carlo simulations of selected scenarios. Findings reveal the economic potential to operate profitably in a subsidy-free environment with a P90 levelised cost of energy of £25.02 if no component replacement is required within the nacelle and £42.53 for a complete replacement of blades, generator, and gearbox

Comparison of epoxy and braze-welded attachment methods for FBG strain gauges

This paper presents experimental results from fatigue and static loading tests performed on both epoxy and braze-welded FBG strain sensors. Most FBG attachment methods are relatively understudied, with epoxy the most commonly used. Long curing times and humidity sensitivity during curing render epoxy inappropriate for certain implementations. This work shows that a bespoke braze-welded attachment design is able to achieve a higher static failure limit of 22kN when compared to strain gauge epoxies, which fail at 20kN. Both methods demonstrate high fatigue life, with no significant deterioration after two million cycles. Epoxy swelling was observed when the sensors were held at a relative humidity of 96%, applying ~0.6 mϵ of tension to the FBG, whereas a braze-weld attachment was unaffected by humidity

Fire analysis of steel frames with the use of artificial neural networks

The paper presents an alternative approach to the modelling of the mechanical behaviour of steel frame material when exposed to the high temperatures expected in fires. Based on a series of stress-strain curves obtained experimentally for various temperature levels, an artificial neural network (ANN) is employed in the material modelling of steel. Geometrically and materially, a non-linear analysis of plane frame structures subjected to fire is performed by FEM. The numerical results of a simply supported beam are compared with our measurements, and show a good agreement, although the temperature-displacement curves exhibit rather irregular shapes. It can be concluded that ANN is an efficient tool for modelling the material properties of steel frames in fire engineering design studies. (c) 2007 Elsevier Ltd. All rights reserved

Updated Iberian archeomagnetic catalogue: new full vector paleosecular variation curve for the last three millennia

In this work, we present 16 directional and 27 intensity high‐quality values from Iberia. Moreover, we have updated the Iberian archeomagnetic catalogue published more than 10 years ago with a considerable increase in the database. This has led to a notable improvement of both temporal and spatial data distribution. A full vector paleosecular variation curve from 1000 BC to 1900 AD has been developed using high‐quality data within a radius of 900 km from Madrid. A hierarchical bootstrap method has been followed for the computation of the curves. The most remarkable feature of the new curves is a notable intensity maximum of about 80 μT around 600 BC, which has not been previously reported for the Iberian Peninsula. We have also analyzed the evolution of the paleofield in Europe for the last three thousand years and conclude that the high maximum intensity values observed around 600 BC in the Iberian Peninsula could respond to the same feature as the Levantine Iron Age Anomaly, after travelling westward through Europe

Validation of three predictive models for suboptimal cytoreductive surgery in advanced ovarian cancer

The standard treatment for advanced ovarian cancer (AOC) is cytoreduction surgery and adjuvant chemotherapy. Tumor volume after surgery is a major prognostic factor for these patients. The ability to perform complete cytoreduction depends on the extent of disease and the skills of the surgical team. Several predictive models have been proposed to evaluate the possibility of performing complete cytoreductive surgery (CCS). External validation of the prognostic value of three predictive models (Fagotti index and the R3 and R4 models) for predicting suboptimal cytoreductive surgery (SCS) in AOC was performed in this study. The scores of the 3 models were evaluated in one hundred and three consecutive patients diagnosed with AOC treated in a tertiary hospital were evaluated. Clinicopathological features were collected prospectively and analyzed retrospectively. The performance of the three models was evaluated, and calibration and discrimination were analyzed. The calibration of the Fagotti, R3 and R4 models showed odds ratios of obtaining SCSs of 1.5, 2.4 and 2.4, respectively, indicating good calibration. The discrimination of the Fagotti, R3 and R4 models showed an area under the ROC curve of 83%, 70% and 81%, respectively. The negative predictive values of the three models were higher than the positive predictive values for SCS. The three models were able to predict suboptimal cytoreductive surgery for advanced ovarian cancer, but they were more reliable for predicting CCS. The R4 model discriminated better because it includes the laparotomic evaluation of the peritoneal carcinomatosis index

Bim and Mcl-1 exert key roles in regulating JAK2V617F cell survival

<p>Abstract</p> <p>Background</p> <p>The JAK2^V617Fmutation plays a major role in the pathogenesis of myeloproliferative neoplasms and is found in the vast majority of patients suffering from polycythemia vera and in roughly every second patient suffering from essential thrombocythemia or from primary myelofibrosis. The V617F mutation is thought to provide hematopoietic stem cells and myeloid progenitors with a survival and proliferation advantage. It has previously been shown that activated JAK2 promotes cell survival by upregulating the anti-apoptotic STAT5 target gene Bcl-xL. In this study, we have investigated the role of additional apoptotic players, the pro-apoptotic protein Bim as well as the anti-apoptotic protein Mcl-1.</p> <p>Methods</p> <p>Pharmacological inhibition of JAK2/STAT5 signaling in JAK2^V617Fmutant SET-2 and MB-02 cells was used to study effects on signaling, cell proliferation and apoptosis by Western blot analysis, WST-1 proliferation assays and flow cytometry. Cells were transfected with siRNA oligos to deplete candidate pro- and anti-apoptotic proteins. Co-immunoprecipitation assays were performed to assess the impact of JAK2 inhibition on complexes of pro- and anti-apoptotic proteins.</p> <p>Results</p> <p>Treatment of JAK2^V617Fmutant cell lines with a JAK2 inhibitor was found to trigger Bim activation. Furthermore, Bim depletion by RNAi suppressed JAK2 inhibitor-induced cell death. Bim activation following JAK2 inhibition led to enhanced sequestration of Mcl-1, besides Bcl-xL. Importantly, Mcl-1 depletion by RNAi was sufficient to compromise JAK2^V617Fmutant cell viability and sensitized the cells to JAK2 inhibition.</p> <p>Conclusions</p> <p>We conclude that Bim and Mcl-1 have key opposing roles in regulating JAK2^V617Fcell survival and propose that inactivation of aberrant JAK2 signaling leads to changes in Bim complexes that trigger cell death. Thus, further preclinical evaluation of combinations of JAK2 inhibitors with Bcl-2 family antagonists that also tackle Mcl-1, besides Bcl-xL, is warranted to assess the therapeutic potential for the treatment of chronic myeloproliferative neoplasms.</p