Intelligent Fault Diagnosis of Rolling Element Bearings Based on Modified AlexNet.

The reliable and safe operation of industrial systems needs to detect and diagnose bearing faults as early as possible. Intelligent fault diagnostic systems that use deep learning convolutional neural network (CNN) techniques have achieved a great deal of success in recent years. In a traditional CNN, the fully connected layer is located in the final three layers, and such a layer consists of multiple layers that are all connected. However, the fully connected layer of the CNN has the disadvantage of too many training parameters, which makes the model training and testing time longer and incurs overfitting. Additionally, because the working load is constantly changing and noise from the place of operation is unavoidable, the efficiency of intelligent fault diagnosis techniques suffers great reductions. In this research, we propose a novel technique that can effectively solve the problem of traditional CNN and accurately identify the bearing fault. Firstly, the best pre-trained CNN model is identified by considering the classification's success rate for bearing fault diagnosis. Secondly, the selected CNN model is modified to effectively reduce the parameter quantities, overfitting, and calculating time of this model. Finally, the best classifier is identified to make a hybrid model concept to achieve the best performance. It is found that the proposed technique performs well under different load conditions, even in noisy environments, with variable signal-to-noise ratio (SNR) values. Our experimental results confirm that this proposed method is highly reliable and efficient in detecting and classifying bearing faults.

A system biology approach to determine therapeutic targets by identifying molecular mechanisms and key pathways for type 2 diabetes that are linked to the development of tuberculosis and rheumatoid arthritis.

AIMS: Due to traditional endocrinological techniques, there is currently no shared work available, and no therapeutic choices have been presented in type 2 diabetes (T2D), rheumatoid arthritis (RA), and tuberculosis (TB). The purpose of this research is to summarize the prospective molecular complications and potential therapeutic targets associated with T2D that have been connected to the development of TB and RA. MATERIALS AND METHODS: We collected the transcriptomic data as GSE92724, GSE110999 and GSE 148036 for T2D, RA and TB patients. After collecting from NCBI, then GREIN were employed to process our datasets. STRING and Enrichr were used to construct protein-protein interaction (PPI), gene regulatory network (GRN), protein-drug-chemical, gene ontology and pathway network. Finally, Cytoscape and R studio were employed to visualize our proposed network. KEY FINDINGS: We discovered a number of strong candidate hub proteins in significant pathways, namely RAB25, MAL2, SFN, MYO5B, and HLA-DQB1 out of 75 common genes. We also identified a number of TFs (JUN, TFAP2A, FOXC1, and GATA2); miRNA (mir-1-3p, mir-16-5p, and mir-34a5p); drugs (sulfasalazine, cholic acid, and nilflumic acid) and chemicals (Valproic acid, and Aflatoxin B1) may control DEGs in transcription as well as post- transcriptional expression levels. SIGNIFICANCE: To summarize, our computational techniques discovered unique potential biomarkers that show how T2D, RA, and TB interacted, as well as pathways and gene regulators by which T2D may influence autoimmune inflammation and infectious diseases. In the future, more clinical and pharmacological research is needed to confirm the findings at the transcriptional and translational levels.

Enhanced Sensing and Sum-Rate Analysis in a Cognitive Radio-Based Internet of Things.

Spectrum sensing plays a vital role in cognitive radio networks (CRNs) for identifying the spectrum hole. However, an individual cognitive radio user in a CRN does not obtain sufficient sensing performance and sum rate of the primary and secondary links to support the future Internet of Things (IoT) using conventional detection techniques such as the energy detection (ED) technique in a noise-uncertain environment. In an environment comprising noise uncertainty, the performance of conventional energy detection techniques is significantly degraded owing to the noise fluctuation caused by the noise temperature, interference, and filtering. To mitigate this problem, we present a cooperative spectrum sensing technique that comprises the use of the Kullback-Leibler divergence (KLD) in cognitive radio-based IoT (CR-IoT). In the proposed method, each unlicensed IoT device that is capable of spectrum sensing, which is called a CR-IoT user, makes a local decision using the KLD technique. The spectrum sensing performed with the KLD requires a smaller number of samples than other conventional approaches, e.g., energy detection, for reliable sensing even in a noise uncertain environment. After the local decision is made, each CR-IoT user sends its own local decision result to the corresponding fusion center, which makes a global decision using the soft fusion rule. The results obtained through simulations show that the proposed KLD scheme achieves a better sensing performance, i.e., higher detection and lower false-alarm probabilities, enhances the sum rate, and reduces the total time as compared to the conventional ED scheme under various fading channels.