Deep learning for environmentally robust speech recognition

Deep learning is an emerging technology that is one of the most promising areas of artificial intelligence. Great strides have been made in recent years which resulted in increased efficiency with regards to many applications, including speech. Despite this, an environmentally Robust Speech Recognition system is still far from being achieved. In this article, an investigation of previous work has been conducted. The use of deep learning in speech recognition was analyzed and a proper deep learning architecture was identified. A method using convolutional neural network (CNN) is used with the aim of enhancing the performance of speech recognition systems (SRS). This study found that this CNN-based approach achieves a 94.32% validated accuracy

Monte Carlo as quality control tool of stereotactic body radiation therapy treatment plans

Purpose/objective: The objective of this study was to verify the accuracy of treatment plans of stereotactic body radiation therapy (SBRT) and to verify the feasibility of the use of Monte Carlo (MC) as quality control (QC) on a daily basis. Material/methods: Using EGSnrc, a MC model of Agility™ linear accelerator was created. Various measurements (Percentage depth dose (PDD), Profiles and Output factors) were done for different fields sizes from 1x1 up to 40x40 (cm2). An iterative model optimization was performed to achieve adequate parameters of MC simulation. 40 SBRT patient's dosimetry plans were calculated by Monaco™ 3.1.1. CT images, RT-STRUCT and RT-PLAN files from Monaco™ being used as input for Moderato MC code. Finally, dose volume histogram (DVH) and paired t-tests for each contour were used for dosimetry comparison of the Monaco™ and MC. Results: Validation of MC model was successful, as <2% difference comparing to measurements for all field's sizes. The main energy of electron source incident on the target was 5.8 MeV, and the full width at half maximum (FWHM) of Gaussian electron source were 0.09 and 0.2 (cm) in X and Y directions, respectively. For 40 treatment plan comparisons, the minimum absolute difference of mean dose of planning treatment planning (PTV) was 0.1% while the maximum was 6.3%. The minimum absolute difference of Max dose of PTV was 0.2% while the maximum was 8.1%. Conclusion: SBRT treatment plans of Monaco agreed with MC results. It possible to use MC for treatment plans verifications as independent QC tool.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

3D Monte Carlo dosimetry of intraoperative electron radiation therapy (IOERT)

Purpose: This paper studies the feasibility of using Monte Carlo (MC) for treatment planning of intraoperative electron radiation therapy (IOERT) procedure to get 3D dose by using patient's CT images. Methods: The IOERT treatment planning was performed using the following successive steps: I) The Mobetron 1000® machine was modelled with the EGSnrc MC codes. II) The MC model was validated with measurements of percentage depth doses and profiles for three energies (12, 9, 6) MeV. III) CT images were imported as DICOM files. IV) Contouring of the planning target volume (PTV) and the organs at risk was done by the radiation oncologist. V) The medical physicist with the radiation oncologist, had chosen the same parameters of IOERT procedures like energy, applicator (type, size) and using or not bolus. VI) Finally, dose calculation and analysis of 3D maps was carried out. Results: The tuning process of the MC model provides good results, as the maximum value of the root mean square deviation (RMSD) was less than 3% between the MC simulated PDDs and the measured PDDs. The contouring and dose analysis review were easy to conduct for the classical treatment planning system. The radiation oncologist had many tools for dose analysis such as DVH and color wash for all the slides. Summation of the 3D dose of IOERT with other radiotherapy plans is possible and helpful for total dose estimation. Archiving and documentation is as good as treatment planning system (TPS). Conclusions: The method displayed in this paper provides a step forward for IOERT Dosimetry and allows to obtain accurate dosimetry of treated volumes.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Improved corrosion behavior of AZ31 alloy through ECAP processing

This study aims to establish the effects of equal channel angular pressing (ECAP) processing on the corrosion behavior and hardness values of the AZ31 Mg alloy. The AZ31 billets were processed through ECAP successfully at 250 °C and their microstructural evolution was studied using optical and field emission scanning electron microscopy. The corrosion resistance of the AZ31 alloy was studied before and after processing through ECAP. The homogeneity of the hardness distribution was studied using both sections cut parallel and perpendicular to the extrusion direction. ECAP processing resulted in highly deformed central regions with elon-gated grains aligned parallel to the extrusion direction, whereas the peripheral regions showed an ultra-fine-grain recrystallized structure. After processing, small ultra-fine secondary particles were found to be homogeneously dispersed alongside the grain boundaries of the α-Mg matrix. Regarding the corrosion properties, measurements showed that ECAP processing through 1-P and 2-Bc resulted in decreasing their corrosion rate to 67.7% and 78.3%, respectively, of their as-annealed counterpart’s. The corrosion resistance of the ECAPed Mg alloy increased with the number of processing passes. This was due to the refinement of the grain size of the α-Mg matrix and secondary phases till ultra-fine size, caused by the accumulation of strain during pro-cessing. On the other hand, ECAP processing through 2-Bc resulted in increasing the Vickers hardness values by 132% and 71.8% at the peripheral and central areas, respectively, compared to the as-annealed counterpart

MXene-based novel nanocomposites doped SnO 2 for boosting the performance of perovskite solar cells

Since being first published in 2018, the use of two-dimensional MXene in solar cells has attracted significant interest. This study presents, for the first time, the synthesis of an efficient hybrid electrocatalyst in the form of a nanocomposite (MXene/CoS)-SnO2 designed to function as a high-performance electron transfer layer (ETL). The study can be divided into three distinct parts. The first part involves the synthesis of single-layer Ti3C2Tx MXene nanosheets, followed by the preparation of a CoS solution. Subsequently, in the second part, the fabrication of MXene/CoS heterostructure nanocomposites is carried out, and a comprehensive characterization is conducted to evaluate the physical, structural, and optical properties. In the third part, the attention is on the crucial characterizations of the novel nanocomposite-electron transport layer (ETL) solution, significantly contributing to the evolution of perovskite solar cells. Upon optimising the composition, an exceptional power conversion efficiency of more than 17.69% is attained from 13.81% of the control devices with fill factor (FF), short-circuit current density (Jsc), and open-circuit voltage (Voc) were 66.51%, 20.74 mA/cm2, and 1.282 V. Therefore, this PCE is 21.93% higher than the control device. The groundbreaking MXene/CoS (2 mg mL−1) strategy reported in this research represents a promising and innovative avenue for the realization of highly efficient perovskite solar cells