Towards an integrated neonatal brain and cardiac examination capability at 7 T: electromagnetic field simulations and early phantom experiments using an 8-channel dipole array

Objective

Neonatal brain and cardiac imaging would benefit from the increased signal-to-noise ratio levels at 7 T compared to lower field. Optimal performance might be achieved using purpose designed RF coil arrays. In this study, we introduce an 8-channel dipole array and investigate, using simulations, its RF performances for neonatal applications at 7 T.

Methods

The 8-channel dipole array was designed and evaluated for neonatal brain/cardiac configurations in terms of SAR efficiency (ratio between transmit-field and maximum specific-absorption-rate level) using adjusted dielectric properties for neonate. A birdcage coil operating in circularly polarized mode was simulated for comparison. Validation of the simulation model was performed on phantom for the coil array.

Results

The 8-channel dipole array demonstrated up to 46% higher SAR efficiency levels compared to the birdcage coil in neonatal configurations, as the specific-absorption-rate levels were alleviated. An averaged normalized root-mean-square-error of 6.7% was found between measured and simulated transmit field maps on phantom.

Conclusion

The 8-channel dipole array design integrated for neonatal brain and cardiac MR was successfully demonstrated, in simulation with coverage of the baby and increased SAR efficiency levels compared to the birdcage. We conclude that the 8Tx-dipole array promises safe operating procedures for MR imaging of neonatal brain and heart at 7 T.

     

Zinc oxide-decorated multiwalled carbon nanotubes: a selective electrochemical sensor for the detection of Pb(II) ion in aqueous media

Journal of Materials Science: Materials in Electronics - Multiwalled carbon nanotubes, due to high conductivity, stability, and large specific surface area, have a potential ability to promote...     

Application of EPR-MOGA in computing the liquefaction-induced settlement of a building subjected to seismic shake

Accurate prediction of the liquefaction-induced settlement (\({S}_{\mathrm{lc}}\)) is an essential requirement for a good design of buildings resting on liquefiable ground and subjected to seismic shake. However, prediction of the \({S}_{\mathrm{lc}}\) is not straightforward process and it requires advanced soil models and calibrated soil parameters that are not readily available for designers/practitioners. In addition, the available empirical models to estimate the \({S}_{\mathrm{lc}}\) have been developed using either classical regression analysis or multivariate adaptive regression splines and such techniques produce complicated models. Also, these empirical models have been developed utilizing results of numerical modelling. To overcome these limitations, novel model has been developed in this paper utilizing robust regression analysis driven by artificial intelligence called the evolutionary polynomial regression analysis. The new model has been developed using centrifuge results (real laboratory measurements) and can be easily used to accurately estimate the liquefaction induced settlement. The developed model scored a mean absolute error, root mean square error, mean, standard deviation of the predicted to measured values, coefficient of determination, \(a20 - \mathrm{index}\), and EPR coefficient of determination of 2.12 cm, 2.84 cm, 1.06, 0.19, 0.98, 0.77, and 97%, respectively, for the learning data and 1.73 cm, 3.31 cm, 0.99, 0.17, 0.97, 0.75, and 97%, respectively, for the examination data. The developed model has also been used in a parametric study to provide an insight into the sensitivity of the \({S}_{\mathrm{lc}}\) to the foundation width, building height, pressure applied on the foundation, thickness and relative density of the liquefiable layer, and earthquake intensity. The results obtained from the parametric study are reasonable and in agreement with previous studies in the literature. Thus, the developed model can be employed to optimize designs and to reduce design costs as it does not require complicated analyses and/or expensive computational facilities.

     

A hybrid knowledge and ensemble classification approach for prediction of venous thromboembolism

Clinical narratives such as progress summaries, lab reports, surgical reports, and other narrative texts contain key biomarkers about a patient's health. Evidence-based preventive medicine needs accurate semantic and sentiment analysis to extract and classify medical features as the input to appropriate machine learning classifiers. However, the traditional approach of using single classifiers is limited by the need for dimensionality reduction techniques, statistical feature correlation, a faster learning rate, and the lack of consideration of the semantic relations among features. Hence, extracting semantic and sentiment-based features from clinical text and combining multiple classifiers to create an ensemble intelligent system overcomes many limitations and provides a more robust prediction outcome. The selection of an appropriate approach and its interparameter dependency becomes key for the success of the ensemble method. This paper proposes a hybrid knowledge and ensemble learning framework for prediction of venous thromboembolism (VTE) diagnosis consisting of the following components: a VTE ontology, semantic extraction and sentiment assessment of risk factor framework, and an ensemble classifier. Therefore, a component-based analysis approach was adopted for evaluation using a data set of 250 clinical narratives where knowledge and ensemble achieved the following results with and without semantic extraction and sentiment assessment of risk factor, respectively: a precision of 81.8% and 62.9%, a recall of 81.8% and 57.6%, an F measure of 81.8% and 53.8%, and a receiving operating characteristic of 80.1% and 58.5% in identifying cases of VTE.     

Can social media usage of scientific literature predict journal indices of AJG,SNIP and JCR? An altmetric study of economics

Scientometrics - Altmetrics are often praised as an alternative or complement to classic bibliometric metrics, especially in the social sciences discipline. However, empirical investigations of...     

Stabilization of 6H-Hexagonal SrMnO3 Polymorph by Al2O3 insertion

We demonstrate the structural evolution of polymorphic phases in Al₂O₃-inserted SrMnO₃ ceramics synthesized by solid state reaction. While the 4H-hexagonal phase is predominant in pure SrMnO₃ ceramics, a small amount of 6H-hexagonal polymorph is identified in addition to the primary 4H-hexagonal SrMnO₃ and the secondary hexagonal SrAl₂O₄ phases in the as-sintered ceramics, evidenced by x-ray diffraction and subsequent Rietveld refinement analyses. The existence of the 6H-hexagonal SrMnO₃ phase is corroborated using Raman spectroscopy. The chemical compositions and electronic structures of the Al₂O₃-inserted SrMnO₃ compounds are also examined using energy dispersive spectroscopy and x-ray photoelectron spectroscopy, respectively. The first-principles calculations reveal that there is no clear difference between the total energies of 4H- and 6H-hexagonal polymorphs regardless of the presence/absence of Sr and oxygen vacancies. Possible origins are discussed with the estimation of actual strain based on the refined lattice parameter of 6H SrMnO₃.     

Experimental investigation of Co and Fe-Doped CuO nanostructured electrode material for remarkable electrochemical performance

The current research work presents a facile and cost–effective co-precipitation method to prepare doped (Co & Fe) CuO and undoped CuO nanostructures without usage of any type of surfactant or capping agents. The structural analysis reveals monoclinic crystal structure of synthesized pure CuO and doped-CuO nanostructures. The effect of different morphologies on the performance of supercapacitors has been found in CV (cyclic voltammetry) and GCD (galvanic charge discharge) investigations. The specific capacitances have been obtained 156 (±5) Fg^?1, 168(±5) Fg^?1 and 186 (±5) Fg^?1 for CuO, Co-doped CuO and Fe-doped CuO electrodes, respectively at scan rate of 5 mVs^?1, while it is found to be 114 (±5) Fg^?1, 136 (±5) Fg^?1 and 170 (±5) Fg^?1 for CuO, Co–CuO and Fe–CuO, respectively at 0.5 Ag^-1 as calculated from the GCD. The super capacitive performance of the Fe–CuO nanorods is mainly attributed to the synergism that evolves between CuO and Fe metal ion. The Fe-doped CuO with its nanorods like morphology provides superior specific capacitance value and excellent cyclic stability among all studied nanostructured electrodes. Consequently, it motivates to the use of Fe-doped CuO nanostructures as electrode material in the next generation energy storage devices.     

A fog-based security framework for intelligent traffic light control system

Multimedia Tools and Applications - The world is facing many problems including that of traffic congestion. To highlight the issue of traffic congestion worldwide specially in urban areas and to...