Ultrasonic characterization of the pulmonary venous wall: echographic and histological correlation

Background: Pulmonary vein isolation with radiofrequency catheter ablation techniques is used to prevent recurrences of human atrial fibrillation. Visualization of the architecture at the venoatrial junction could be crucial for these ablative techniques. Our study assesses the potential for intravascular ultrasound to provide this information. Methods and Results: We retrieved 32 pulmonary veins from 8 patients dying from noncardiac causes. We obtained cross-sectional intravascular ultrasound (IVUS) images with a 3.2F, 30-MHz ultrasound catheter at intervals on each vein. Histological cross-sections at the intervals allowed comparisons with ultrasonic images. The pulmonary venous wall at the venoatrial junction revealed a 3-layered ultrasonic pattern. The inner echogenic layer represents both endothelium and connective tissue of the media (mean maximal thickness, 1.4±0.3 mm). The middle hypoechogenic stratum corresponds to the sleeves of left atrial myocardium surrounding the external aspect of the venous media. This layer was thickest at the venoatrial junction (mean maximal thickness, 2.6±0.8 mm) and decreased toward the lung hilum. The outer echodense layer corresponds to fibro-fatty adventitial tissue (mean maximal thickness, 2.15±0.36 mm). We found a close agreement among the IVUS and histological measurements for maximal luminal diameter (mean difference, -0.12±1.3 mm) and maximal muscular thickness (mean difference, 0.17±0.13 mm) using the Bland and Altman method. Conclusions: Our experimental study demonstrates for the first time that IVUS images of the pulmonary veins can provide information on the distal limits and thickness of the myocardial sleeves and can be a valuable tool to help accurate targeting during ablative procedures

Mitigating energy system vulnerability by implementing a microgrid with a distributed management algorithm

This work presents a management strategy for microgrid (MG) operation. Photovoltaic (PV) and wind generators, as well as storage systems and conventional units, are distributed over a wide geographical area, forming a distributed energy system, which is coordinated to face any contingency of the utility company by means of its isolated operation. The management strategy divides the system into three main layers: renewable generation, storage devices, and conventional units. Interactions between devices of the same layer are determined by solving an economic dispatch problem (EDP) in a distributed manner using a consensus algorithm (CA), and interactions between layers are determined by means of a load following strategy. In this way, the complex behaviour of PV and wind generation, the battery storage system, and conventional units has been effectively combined with CA to solve EDP in a distributed manner. MG performance and its vulnerability are deeply analysed by means of an illustrative case study. From the observed results, vulnerability under extreme conditions could be reduced up to approximately 30% by coupling distributed renewable generation and storage capacity with an energy system based on conventional generation

Offshore wind installation: Analysing the evidence behind improvements in installation time

The most important single event of the last years in wind energy technology is the reduction in the cost of producing wind electricity offshore, a reduction that can reach 75%, depending on the system boundary considered, for installations commissioned by 2024. Surprisingly, there is very little scientific literature showing how this reduction is being achieved. The objective of this paper is to analyse the evidence behind cost reduction in one of the most significant cost elements of offshore wind farms, the installation of foundations and turbines. This cost is directly dependent on the daily rates of the installation vessels and on the days it takes to install those wind farm elements. Therefore, we collected installation data from 87 wind farms installed from 2000 to 2017, to establish the exact time for installation in each. The results show that advances have reached 70% reduction in installation times throughout the period for the whole set, turbine plus foundation. Most of these improvements (and the corresponding impact in reducing costs) relate to the larger size of turbines installed nowadays. There is, therefore, not any leap forward in the installation process, but only incremental improvements applied to turbines that are now four times as large as in 2000

Solar Radiation Estimation Using Data Mining Techniques for Remote Areas-A Case Study in Ethiopia

High quality of solar radiation data is essential for solar resource assessment. For remote areas this is a challenge, as often only satellite data with low spatial resolution are available. This paper presents an interpolation method based on topographic data in digital elevation model format to improve the resolution of solar radiation maps. The refinement is performed with a data mining method based on first-order Sugeno type Adaptive Neuro-Fuzzy Inference System. The training set contains topographic characteristics such as terrain aspect, slope and elevation which may influence the solar radiation distribution. An efficient sampling method is proposed to obtain representative training sets from digital elevation model data. The proposed geographic information system based approach makes this method reproducible and adaptable for any region. A case study is presented on the remote Amhara region in North Shewa, Ethiopia. Results are shown for interpolation of solar radiation data from 10 km x 10 km to a resolution of 1 km x 1 km and are validated with data from the PVGIS and SWERA projects

Alkemio: association of chemicals with biomedical topics by text and data mining

The PubMed(R) database of biomedical citations allows the retrieval of scientific articles studying the function of chemicals in biology and medicine. Mining millions of available citations to search reported associations between chemicals and topics of interest would require substantial human time. We have implemented the Alkemio text mining web tool and SOAP web service to help in this task. The tool uses biomedical articles discussing chemicals (including drugs), predicts their relatedness to the query topic with a naïve Bayesian classifier and ranks all chemicals by P-values computed from random simulations. Benchmarks on seven human pathways showed good retrieval performance (areas under the receiver operating characteristic curves ranged from 73.6 to 94.5%). Comparison with existing tools to retrieve chemicals associated to eight diseases showed the higher precision and recall of Alkemio when considering the top 10 candidate chemicals. Alkemio is a high performing web tool ranking chemicals for any biomedical topics and it is free to non-commercial users. Availability: http://cbdm.mdc-berlin.de/∼medlineranker/cms/alkemio