Enhancing brain tumor classification with transfer learning across multiple classes: an in-depth analysis.

This study focuses on leveraging data-driven techniques to diagnose brain tumors through magnetic resonance imaging (MRI) images. Utilizing the rule of deep learning (DL), we introduce and fine-tune two robust frameworks, ResNet 50 and Inception V3, specifically designed for the classification of brain MRI images. Building upon the previous success of ResNet 50 and Inception V3 in classifying other medical imaging datasets, our investigation encompasses datasets with distinct characteristics, including one with four classes and another with two. The primary contribution of our research lies in the meticulous curation of these paired datasets. We have also integrated essential techniques, including Early Stopping and ReduceLROnPlateau, to refine the model through hyperparameter optimization. This involved adding extra layers, experimenting with various loss functions and learning rates, and incorporating dropout layers and regularization to ensure model convergence in predictions. Furthermore, strategic enhancements, such as customized pooling and regularization layers, have significantly elevated the accuracy of our models, resulting in remarkable classification accuracy. Notably, the pairing of ResNet 50 with the Nadam optimizer yields extraordinary accuracy rates, reaching 99.34% for gliomas, 93.52% for meningiomas, 98.68% for non-tumorous images, and 97.70% for pituitary tumors. These results underscore the transformative potential of our custom-made approach, achieving an aggregate testing accuracy of 97.68% for these four distinct classes. In a two-class dataset, Resnet 50 with the Adam optimizer excels, demonstrating better precision, recall, F1 score, and an overall accuracy of 99.84%. Moreover, it attains perfect per-class accuracy of 99.62% for 'Tumor Positive' and 100% for 'Tumor Negative', underscoring a remarkable advancement in the realm of brain tumor categorization. This research underscores the innovative possibilities of DL models and our specialized optimization methods in the domain of diagnosing brain cancer from MRI images

Ferromagnetic HfO2/Si/GaAs interface for spin-polarimetry applications

In this letter, we present electrical and magnetic characteristics of HfO2-based metal-oxide-semiconductor capacitors (MOSCAPs), along with the effect of pseudomorphic Si as a passivating interlayer on GaAs(001) grown by molecular beam epitaxy. Ultrathin HfO2 high-k gate dielectric films (3–15 nm) have been grown on Si/GaAs(001) structures through evaporation of a Hf/HfO2 target in NO2 gas. The lowest interface states density Dit at Au/HfO2/Si/GaAs(001) MOS-structures were obtained in the range of (6−13)×101

Blade Rub-Impact Fault Identification Using Autoencoder-Based Nonlinear Function Approximation and a Deep Neural Network

A blade rub-impact fault is one of the complex and frequently appearing faults in turbines. Due to their nonlinear and nonstationary nature, complex signal analysis techniques, which are expensive in terms of computation time, are required to extract valuable fault information from the vibration signals collected from rotor systems. In this work, a novel method for diagnosing the blade rub-impact faults of different severity levels is proposed. Specifically, the deep undercomplete denoising autoencoder is first used for estimating the nonlinear function of the system under normal operating conditions. Next, the residual signals obtained as the difference between the original signals and their estimates by the autoencoder are computed. Finally, these residual signals are used as inputs to a deep neural network to determine the current state of the rotor system. The experimental results demonstrate that the amplitudes of the residual signals reflect the changes in states of the rotor system and the fault severity levels. Furthermore, these residual signals in combination with the deep neural network demonstrated promising fault identification results when applied to a complex nonlinear fault, such as a blade-rubbing fault. To test the effectiveness of the proposed nonlinear-based fault diagnosis algorithm, this technique is compared with the autoregressive with external input Laguerre proportional-integral observer that is a linear-based fault diagnosis observation technique

Rub-Impact Fault Diagnosis Using an Effective IMF Selection Technique in Ensemble Empirical Mode Decomposition and Hybrid Feature Models

The complex nature of rubbing faults makes it difficult to use traditional signal analysis methods for feature extraction. Various time-frequency analysis approaches based on signal decomposition, such as empirical mode decomposition (EMD) and ensemble EMD (EEMD), have been widely utilized recently to analyze rub-impact faults. However, traditional EMD suffers from “mode-mixing”, and in both EMD and EEMD the relevance of the extracted components to rubbing processes must be determined. In this paper, we introduce a new informative intrinsic mode function (IMF) selection method for EEMD and a hybrid feature model for diagnosing rub-impact faults of various intensities. Our method uses a novel selection procedure that combines the degree-of-presence ratio of rub impact and a Kullback–Leibler divergence-based similarity measure into an IMF quality metric with adaptive threshold-based selection to pick the meaningful signal-dominant modes. Signals reconstructed using the selected IMFs contained explicit information about the rubbing faults and are used for hybrid feature extraction. Experimental results demonstrated that the proposed approach effectively defines meaningful IMFs for rubbing processes, and the presented hybrid feature model allows for the classification of rub-impact faults of various intensities with good accuracy

An SVM-Based Neural Adaptive Variable Structure Observer for Fault Diagnosis and Fault-Tolerant Control of a Robot Manipulator

A robot manipulator is a multi-degree-of-freedom and nonlinear system that is used in various applications, including the medical area and automotive industries. Uncertain conditions in which a robot manipulator operates, as well as its nonlinearities, represent challenges for fault diagnosis and fault-tolerant control (FDC) that are addressed through the proposed FDC technique. A machine-learning-based neural adaptive, high-order, variable structure observer for fault diagnosis (FD) and adaptive, modern, fuzzy, backstepping, variable structure control for use in a fault-tolerant control (FC) algorithm, are proposed in this paper. In the first stage, a variable structure observer is proposed as an FD technique for the robot manipulator. The chattering phenomenon associated with the variable structure observer(VSO) is solved using a high-order variable structure observer. Then, the dynamic behavior estimation performance in the high-order variable structure observer is improved by incorporating a neural network algorithm in the FD pipeline. This adaptive technique is also effective in improving the robustness of the fault signal estimation. Moreover, support vector machines (SVMs) that can derive adaptive threshold values are used to categorize faults. To design an effective fault-tolerant controller (FC), an adaptive modern fuzzy backstepping variable structure controller is used in this study. First, a new variable structure controller is designed. Next, to increase robustness and reduce high-frequency oscillations in uncertain conditions, a backstepping algorithm is used in parallel with the variable structure controller to design the backstepping variable structure controller. To design an effective hybrid controller, a fuzzy algorithm is integrated into the backstepping variable structure controller to create a fuzzy backstepping variable structure controller. Then, to improve the robustness and reliability of the FC, a neural adaptive. high-order. variable structure observer is applied to the fuzzy backstepping variable structure controller to design a modern fuzzy backstepping variable structure controller. An adaptive algorithm is used to fine-tune the variable structure coefficients and reduce the effect of faults on the robot manipulator. The effectiveness of the selected algorithm is validated using a PUMA robot manipulator. The neural adaptive. high-order variable structure observer improves the average performance for the identification of various faults by about 27% and 29.2%, compared with the neural high-order variable structure observer and variable structure observer, respectively

Mechanistic Study of Methanol Decomposition and Oxidation on Pt(111)

Decomposition and oxidation of methanol on Pt(111) have been examined between 300 and 650 K in the millibar pressure range using in situ ambient-pressure X-ray photoelectron spectroscopy (XPS) and temperature-programmed reaction spectroscopy (TPRS). It was found that even in the presence of oxygen, the methanol decomposition on platinum proceeds through two competitive routes: fast dehydrogenation to CO and slow decomposition via the C–O bond scission. The rate of the second route is significant in the millibar pressure range, which leads to a blocking of the platinum surface by carbon and to the prevention of further methanol conversion. As a result, without oxygen, the activity of Pt(111) converted to a turnover frequency is ∼0.3 s^–1 at 650 K. The activity strongly increases with oxygen content, achieving 20 s^–1 in an oxygen-rich mixture. The main products of methanol oxidation were CO, CO₂, H₂, and H₂O. The CO selectivity as well as the H₂ selectivity decrease with the increase in oxygen content. It means that the main reaction route is the methanol dehydrogenation to CO and hydrogen; however, in the presence of oxygen, CO oxidizes to CO₂ and hydrogen oxidizes to water. At room temperature, the C1s spectra contain weak features of formate species. This finding points out that the “non-CO-involved” pathway of methanol oxidation realizes on platinum as well. However, the TPRS data indicate that at least under the oxygen-deficient conditions the methanol dehydrogenation pathway dominates. A detailed reaction mechanism of the decomposition and oxidation of methanol agreeing with XPS and TPRS data is discussed

Atomic-scale changes of silica-supported catalysts with nanocrystalline or amorphous gallia phases: implications of hydrogen pretreatment on their selectivity for propane dehydrogenation

This work explores how H-2 pretreatment at 550 degrees C induces structural transformation of two gallia-based propane dehydrogenation (PDH) catalysts, viz. nanocrystalline gamma/beta-Ga2O3 and amorphous Ga2O3 (GaOx) supported on silica (gamma-Ga2O3/SiO2 and Ga/SiO2, respectively) and how it affects their activity, propene selectivity and stability with time on stream (TOS). Ga/SiO2-H-2 shows poor activity and propene selectivity, no coking and no deactivation with TOS, similar to Ga/SiO2. In contrast, the high initial activity and propene selectivity of gamma-Ga2O3/SiO2-H-2 decline with TOS but to a lesser extent than in calcined gamma-Ga2O3/SiO2. In addition, gamma-Ga2O3/SiO2-H-2 cokes less than gamma-Ga2O3/SiO2. Ga K-edge X-ray absorption spectroscopy suggests an increased disorder of the nanocrystalline gamma/beta-Ga2O3 phases in gamma-Ga2O3/SiO2-H-2 and the emergence of additional tetrahedral Ga sites (Ga-IV). Such Ga-IV sites are strong Lewis acid sites (LAS) according to studies using adsorbed pyridine and CO probe molecules, i.e., the abundance of strong LAS is higher in gamma-Ga2O3/SiO2-H-2 compared to gamma-Ga2O3/SiO2 but lower than in Ga/SiO2 and Ga/SiO2-H-2. Dissociation of H-2 on the Ga-O linkages in gamma-Ga2O3/SiO2-H-2 yields high-frequency Ga-H bands that are observed in Ga/SiO2 and Ga/SiO2-H-2 but not detected in gamma-Ga2O3/SiO2. We attribute the increased amount of Ga-IV sites in gamma-Ga2O3/SiO2-H-2 mostly to an increased disorder in gamma/beta-Ga2O3. X-ray photoelectron spectroscopy detects the formation of Ga+ and Ga-0 species in both Ga/SiO2-H-2 and gamma-Ga2O3/SiO2-H-2. Therefore, it is likely that a minor amount of Ga-IV sites also forms through the interaction of Ga+ (such as Ga2O) and/or Ga-0 with silanol groups of SiO2.ISSN:2044-4753ISSN:2044-476

Propane Transformation on In-Modified Zeolite BEA

In-modified zeolites possess promising catalytic properties for light alkane dehydrogenation and aromatization. However, the role of different indium species, which are present in zeolite pores after activation, remains unknown. Here, the transformation of propane on BEA zeolite containing either In+ or InO+ species in zeolite pores has been monitored with 13C MAS NMR at 298–773 K. It is inferred that In+/H-BEA zeolite with In+ sites is inactive for alkane conversion at T +/H-BEA zeolite occurs by two parallel routes: dehydrogenation of propane followed by the formed alkene aromatization to simple aromatic hydrocarbons and the alkane oxidation resulting in C2–C3 carboxylic acids. Propane activation by either C–H or C–C bond cleavage on InO+ sites and pathways of carboxylic acid formation from the products of the alkane dissociative adsorption are discussed