Elucidating an efficient super-capacitive response of a Sr2Ni2O5/rGO composite as an electrode material in supercapacitors.

Mixed transition metal oxides have emerged as efficient electrode materials because of their significant cycling stability, and superior capacitance values, resulting in remarkable electrochemical outputs. In this regard, Sr2Ni2O5/rGO composites were synthesized using a facile solvothermal method to achieve efficient electrochemical pursuits. X-ray diffraction confirmed the formation of finely crystallized samples with the phase evolution from orthorhombic to hexagonal. Morphological studies using field emission scanning electron microscopy depicted the desired porosity in samples with well-defined shapes and sizes of homogeneously distributed grains. Elemental analysis verified the pictorial depiction of sample compositions in terms of their stoichiometric ratios. The composite sample with composition Sr2Ni2O5@15%rGO exhibited superior electrochemical performance compared to other samples, depicting the highest specific capacitance of 148.09 F g-1 at a lower scan rate of 0.005 V s-1 observed via cyclic voltammetry. In addition, the cyclability performance of Sr2Ni2O5@15%rGO exhibits 68.5% capacitive retention after 10 000 cycles. The energy density as determined using a two-electrode system remained 4.375 W h kg-1 for the first cycle which reduced to 1.875 W h kg-1 for the 10 000th cycle, with a maximum power density of 1.25 W kg-1. The Nyquist plot represented less barrier to charge transfer. The electrode with particular composition Sr2Ni2O5@15%rGO emerged as significant, exhibiting a superior surface capacitive charge storage, that makes it a potential candidate as an electrode material.

Optimization of neural-network model using a meta-heuristic algorithm for the estimation of dynamic Poisson's ratio of selected rock types.

This research focuses on the predictive modeling between rocks' dynamic properties and the optimization of neural network models. For this purpose, the rocks' dynamic properties were measured in terms of quality factor (Q), resonance frequency (FR), acoustic impedance (Z), oscillation decay factor (α), and dynamic Poisson's ratio (v). Rock samples were tested in both longitudinal and torsion modes. Their ratios were taken to reduce data variability and make them dimensionless for analysis. Results showed that with the increase in excitation frequencies, the stiffness of the rocks got increased because of the plastic deformation of pre-existing cracks and then started to decrease due to the development of new microcracks. After the evaluation of the rocks' dynamic behavior, the v was estimated by the prediction modeling. Overall, 15 models were developed by using the backpropagation neural network algorithms including feed-forward, cascade-forward, and Elman. Among all models, the feed-forward model with 40 neurons was considered as best one due to its comparatively good performance in the learning and validation phases. The value of the coefficient of determination (R2 = 0.797) for the feed-forward model was found higher than the rest of the models. To further improve its quality, the model was optimized using the meta-heuristic algorithm (i.e. particle swarm optimizer). The optimizer ameliorated its R2 values from 0.797 to 0.954. The outcomes of this study exhibit the effective utilization of a meta-heuristic algorithm to improve model quality that can be used as a reference to solve several problems regarding data modeling, pattern recognition, data classification, etc.

CardioNet: Automatic Semantic Segmentation to Calculate the Cardiothoracic Ratio for Cardiomegaly and Other Chest Diseases.

Semantic segmentation for diagnosing chest-related diseases like cardiomegaly, emphysema, pleural effusions, and pneumothorax is a critical yet understudied tool for identifying the chest anatomy. A dangerous disease among these is cardiomegaly, in which sudden death is a high risk. An expert medical practitioner can diagnose cardiomegaly early using a chest radiograph (CXR). Cardiomegaly is a heart enlargement disease that can be analyzed by calculating the transverse cardiac diameter (TCD) and the cardiothoracic ratio (CTR). However, the manual estimation of CTR and other chest-related diseases requires much time from medical experts. Based on their anatomical semantics, artificial intelligence estimates cardiomegaly and related diseases by segmenting CXRs. Unfortunately, due to poor-quality images and variations in intensity, the automatic segmentation of the lungs and heart with CXRs is challenging. Deep learning-based methods are being used to identify the chest anatomy segmentation, but most of them only consider the lung segmentation, requiring a great deal of training. This work is based on a multiclass concatenation-based automatic semantic segmentation network, CardioNet, that was explicitly designed to perform fine segmentation using fewer parameters than a conventional deep learning scheme. Furthermore, the semantic segmentation of other chest-related diseases is diagnosed using CardioNet. CardioNet is evaluated using the JSRT dataset (Japanese Society of Radiological Technology). The JSRT dataset is publicly available and contains multiclass segmentation of the heart, lungs, and clavicle bones. In addition, our study examined lung segmentation using another publicly available dataset, Montgomery County (MC). The experimental results of the proposed CardioNet model achieved acceptable accuracy and competitive results across all datasets.