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In recent decades, the brain-computer interface (BCI) has emerged as a leading area of research. The feature selection is vital to reduce the dataset's dimensionality, increase the computing effectiveness, and enhance the BCI's performance. Using activity-related features leads to a high classification rate among the desired tasks. This study presents a wrapper-based metaheuristic feature selection framework for BCI applications using functional near-infrared spectroscopy (fNIRS). Here, the temporal statistical features (i.e., the mean, slope, maximum, skewness, and kurtosis) were computed from all the available channels to form a training vector. Seven metaheuristic optimization algorithms were tested for their classification performance using a k-nearest neighbor-based cost function: particle swarm optimization, cuckoo search optimization, the firefly algorithm, the bat algorithm, flower pollination optimization, whale optimization, and grey wolf optimization (GWO). The presented approach was validated based on an available online dataset of motor imagery (MI) and mental arithmetic (MA) tasks from 29 healthy subjects. The results showed that the classification accuracy was significantly improved by utilizing the features selected from the metaheuristic optimization algorithms relative to those obtained from the full set of features. All of the abovementioned metaheuristic algorithms improved the classification accuracy and reduced the feature vector size. The GWO yielded the highest average classification rates ( p < 0.01) of 94.83 ± 5.5%, 92.57 ± 6.9%, and 85.66 ± 7.3% for the MA, MI, and four-class (left- and right-hand MI, MA, and baseline) tasks, respectively. The presented framework may be helpful in the training phase for selecting the appropriate features for robust fNIRS-based BCI applications.

With the advancement in technology, machine learning can be applied to diagnose the mass/tumor in the brain using magnetic resonance imaging (MRI). This work proposes a novel developed transfer deep-learning model for the early diagnosis of brain tumors into their subclasses, such as pituitary, meningioma, and glioma. First, various layers of isolated convolutional-neural-network (CNN) models are built from scratch to check their performances for brain MRI images. Then, the 22-layer, binary-classification (tumor or no tumor) isolated-CNN model is re-utilized to re-adjust the neurons' weights for classifying brain MRI images into tumor subclasses using the transfer-learning concept. As a result, the developed transfer-learned model has a high accuracy of 95.75% for the MRI images of the same MRI machine. Furthermore, the developed transfer-learned model has also been tested using the brain MRI images of another machine to validate its adaptability, general capability, and reliability for real-time application in the future. The results showed that the proposed model has a high accuracy of 96.89% for an unseen brain MRI dataset. Thus, the proposed deep-learning framework can help doctors and radiologists diagnose brain tumors early.

This paper proposes a multi-scale analysis technique based on the micromechanics of failure (MMF) to predict and investigate the damage progression and ultimate strength at failure of laminated composites. A lamina's representative volume element (RVE) is developed to predict and calculate constituent stresses. Damages that occurred in the constituents are calculated using separate failure criteria for both fiber and matrix. Subsequently, the volume-based damage homogenization technique is utilized to prevent the localization of damage throughout the total matrix zone. The proposed multiscale analysis procedure is then used to investigate the notched and unnotched behavior of three multi-directional composite layups, [30, 60, 90, -60, 30] 2S , [0, 45, 90, -45] 2S , and [60, 0, -60] 3S , subjected to static tension and compression loading. The specimen is fabricated from unidirectionally reinforced composite (IM7/977-3). The prediction of ultimate strength at failure and equivalent stiffness are then benchmarked against the experimental test data. The comparative analysis with various failure models is also carried out to validate the proposed model. MMF demonstrated the capability to correctly predict the ultimate strength at failure for a range of multidirectional composites laminates under tensile and compressive load. The numerically predicted findings revealed a good agreement with the experimental test data. Out of the three investigated composite layups, the simulated results for the quasi-isotropic [0, 45, 90, -45] 2S layup agreed extremely well with the experimental results with all the percentage errors within 10% of the measured failure loads.

The influence of nanodiamonds (NDs) on the thermal and ablative performance of carbon-fiber-reinforced-epoxy matrix compositeswas explored. The ablative response of the composites with 0.2 wt% and 0.4 wt% NDs was studied through pre-and post-burning morphologies of the composite surfaces by evaluation of temperature profiles, weight loss, and erosion rate. Composites containing 0.2 wt% NDs displayed a 10.5% rise in erosion resistance, whereas composites containing 0.4 wt% NDs exhibited a 12.6% enhancement in erosion resistance compared to neat carbon fiber-epoxy composites. A similar trend was witnessed in the thermal conductivity of composites. Incorporation of composites with 0.2 wt% and 0.4 wt% NDs brought about an increase of 37 wt% and 52 wt%, respectively. The current study is valuable for the employment of NDs in carbon fiber composite applications where improved erosion resistance is necessary.

Salinity is major abiotic stress affecting crop yield, productivity and reduces the land-usage area for agricultural practices. The purpose of this study is to analyze the effect of green-synthesized silver nanoparticle (AgNP) on physiological traits of wheat ( Triticum aestivum ) under salinity stress. Using augmented and high-throughput characterization of synthesized AgNPs, this study investigated the proximity of AgNPs-induced coping effects under stressful cues by measuring the germination efficiency, oxidative-biomarkers, enzymatic and non-enzymatic antioxidants, proline and nitrogen metabolism, stomatal dynamics, and ABA content. Taken together, the study shows a promising approach in salt tolerance and suggests that mechanisms of inducing the salt tolerance depend on proline metabolism, ions accumulation, and defense mechanisms. This study ascertains the queries regarding the correlation between nanoparticles use and traditional agriculture methodology; also significantly facilitates to reach the goal of sustainable developments for increasing crop productivity via much safer and greener approachability.