G.H.Raisoni Institute of Information Technology

Ruthenium (Ru)—a high-melting-point precious metal—has attracted attention for use as ultrafine interconnections in large-scale integrations. This is because the resistivity of Ru interconnects is not expected to increase with a reduction in the interconnect width owing to their short mean free path for electrons. In this study, we investigated electroless plating of Ru using hydrazine hydrate as a reducing agent to obtain low-resistivity Ru films. We obtained polycrystalline Ru films on a thin (10-nm) catalytic chemical-vapor-deposited Ru underlayer. The electroless Ru films exhibited significant grain growth upon annealing at 600°C in a forming gas (N 2 :H 2 = 9:1). The Ru (101) and (100) crystalline orientations were strengthened by annealing, and the resistivity decreased from 160 µ Ω cm to 22 µ Ω cm concomitantly. Thermal desorption spectroscopy showed that the electroless Ru films contained impurities, such as CO, CO 2 , NH 3 , O, C, and H 2 . Desorption of C, CO, CO 2 , and NH 3 showed peak maxima at approximately 450–500 K. These impurity molecules likely came from the inclusion of the complexing agents (tartaric acid: C 4 (OH) 4 O 2 , and ammonium chloride: NH 4 Cl) and reducing agent (hydrazine: N 2 H 2 ) into the Ru film. Desorption of these molecules during annealing may improve the grain growth of polycrystalline Ru and reduce its resistivity.

Rolling bearing vibrations (REB Vibration) are complex. This complex vibration occurs due to geometrical imperfection, a surface defect of the rolling contact bearing, and geometrical errors in nearby or attached components. This research paper focuses on the Time domain analysis of the theoretical mathematical model of an REB and fault detection methods for fault identification of REB (rolling element bearing), different sources of vibration, and vibration measurement techniques like overall vibration level, frequency spectrum, envelope spectrum, etc. Also, characteristic defect frequency may be presented in this paper. A laboratory rolling bearing test has been conducted for various rotation frequencies and compares their deviation from the theoretical ones. In this paper for bearing diagnostics, envelope analysis is used as a valuable vibration analysis approach. The frequency-domain properties enable a quick evaluation of a machine’s health without the need for extensive diagnostics. The intermittent effects of a defective bearing were captured using envelope analysis on the modulated signal. It is still reasonable when the vibration signal is low in energy and ‘embedded’ within extra vibration from the appropriate component.

The COVID-19 pandemic, probably one of the most widespread pandemics humanity has encountered in the twenty first century, caused death to almost 1.75 M people worldwide, impacting almost 80 M lives with direct contact. In order to contain the spread of coronavirus, it is necessary to develop a reliant and quick method to identify those who are affected and isolate them until full recovery is made. The imagery knowledge has been shown to be useful for quick COVID-19 diagnosis. Though the scans of computational tomography (CT) demonstrate a range of viral infection signals, considering the vast number of images, certain visual characteristics are challenging to distinguish and can take a long time to be identified by radiologists. In this study for detection of the COVID-19, a dataset is formed by taking 3764 images. The feature extraction process is applied to the dataset to increase the classification performance. Techniques like Grey Level Co-occurrence Matrix (GLCM) and Discrete Wavelet Transform (DWT) are used for feature extraction. Then various machine learning algorithms applied such as Support Vector Machines (SVM), Linear Discriminant Analysis (LDA), Multi- Level Perceptron, Naive Bayes, K-Nearest Neighbours and Random Forests are used for classification of COVID-19 disease detection. Sensitivity, Specificity, Accuracy, Precision, and F-score are the metrics used to measure the performance of different machine learning models. Among these machine learning models SVM with GLCM as feature extraction technique using 10-fold cross validation gives the best classification result with 99.70% accuracy, 99.80% sensitivity and 97.03% F-score. We also ran these tests on different data sets and found that the results are similar across those too, as discussed later in the results section.

Manual detection of plant illness is highly impossible and research work is being carried out on many farm-related factors such as organic farming, constant plant monitoring, toward the recognition of different diseases. This requires an enormous amount of work, plant disease expertise, and also a substantial amount of time. We proposed a clustering and convoluted neural network (CNN) model for the accurate disease prediction. Identification of the plant disease includes methods like image segregation, preprocessing, fragmentation of the image, detection, and recognition of characteristics. This paper mainly examines the binding segmentation and retrieval functions of mainly on two different plant diseases (pomegranate and potato) and achieved an accuracy of 95% and 94%.

•BTZDN is the first ionic type tetrahedral compound with thiosemicarbazide functioning as a cyclic bidentate ligand.•The material is ideal for photonic applications requiring third harmonic generation.•BTZDN has good thermal stability.•Crystal has a large transparency window in the visible region, according to optical studies.•Photoluminescence study reveals BTZDN shows blue fluorescence.