CO2 Hydrogenation over Ru-NPs Supported Amine-Functionalized SBA-15 Catalyst: Structure–Reactivity Relationship Study

Catalysis Letters - We gave an effective protocol to support Ru NPs on amine-functionalized SBA-15 mesoporous silica to catalyze the CO2 hydrogenation reaction. The amine groups present in the...     

Surface Integrity Assessment Upon Electric Discharge Machining of Die Steel Using Non-Destructive Magnetic Barkhausen Noise Technique

Surface integrity characterization of manufactured component is very important as it significantly affects the in-service performance of the component. Till now, surface integrity was evaluated using conventional measurement technique like microhardness tester, X-ray diffraction, optical microscopy and surface roughness tester. But, this technique being laboratory based cannot be used for in-service monitoring of the surface integrity. The present study focuses on the characterization of surface integrity upon electric discharge machined sample using non-destructive magnetic Barkhausen noise technique. Electric discharge machining was performed in die-sinking mode on die steel using copper–tungsten electrode (negative polarity). Experiment was performed by selecting different levels of peak current, gap voltage and pulse on time. Surface integrity characteristics like microhardness change, residual stress, microstructural alteration and surface roughness were analysed using microhardness tester, X-ray diffraction, optical microscopy and surface roughness tester, respectively, and were then correlated with magnetic parameter like root mean square value and peak value obtained from Barkhausen noise signal. The results show a good correlation between magnetic parameter (RMS and Peak value) of Barkhausen noise with the microhardness and surface roughness of the machined sample.

     

Deployment of a cloud pipeline for real-time visual inspection using fast streaming high-definition images

We investigate the challenges of building an end-to-end cloud pipeline for real-time intelligent visual inspection system for use in automotive manufacturing. Current methods of visual detection in automotive assembly are highly labor intensive, and thus prone to errors. An automated process is sought that can operate within the real-time constraints of the assembly line and can reduce errors. Components of the cloud pipeline include capture of a large set of high-definition images from a camera setup at the assembly location, transfer and storage of the images as needed, execution of object detection, and notification to a human operator when a fault is detected. The end-to-end execution must complete within a fixed time frame before the next car arrives in the assembly line. In this article, we report the design, development, and experimental evaluation of the tradeoffs of performance, accuracy, and scalability for a cloud system.     

Linguistic frequent pattern mining using a compressed structure

Traditional association-rule mining (ARM) considers only the frequency of items in a binary database, which provides insufficient knowledge for making efficient decisions and strategies. The mining of useful information from quantitative databases is not a trivial task compared to conventional algorithms in ARM. Fuzzy-set theory was invented to represent a more valuable form of knowledge for human reasoning, which can also be applied and utilized for quantitative databases. Many approaches have adopted fuzzy-set theory to transform the quantitative value into linguistic terms with its corresponding degree based on defined membership functions for the discovery of FFIs, also known as fuzzy frequent itemsets. Only linguistic terms with maximal scalar cardinality are considered in traditional fuzzy frequent itemset mining, but the uncertainty factor is not involved in past approaches. In this paper, an efficient fuzzy mining (EFM) algorithm is presented to quickly discover multiple FFIs from quantitative databases under type-2 fuzzy-set theory. A compressed fuzzy-list (CFL)-structure is developed to maintain complete information for rule generation. Two pruning techniques are developed for reducing the search space and speeding up the mining process. Several experiments are carried out to verify the efficiency and effectiveness of the designed approach in terms of runtime, the number of examined nodes, memory usage, and scalability under different minimum support thresholds and different linguistic terms used in the membership functions.

     

Performance Analysis of Learning Rate Parameter on Prediction of Signal Power Loss for Network Optimization and Better Generalization

Wireless Personal Communications - This research work explores the neural network learning capabilities by using a multi-layer perceptron artificial neural network to predict signal power loss by...     

Identification of Natural Products as Potential Pharmacological Chaperones for Protein Misfolding Diseases

Defective protein folding and accumulation of misfolded proteins is associated with neurodegenerative, cardiovascular, secretory, and metabolic disorders. Efforts are being made to identify small-molecule modulators or structural-correctors for conformationally destabilized proteins implicated in various protein aggregation diseases. Using a metastable-reporter-based primary screen, we evaluated pharmacological chaperone activity of a diverse class of natural products. We found that a flavonoid glycoside ( C-10 , chrysoeriol-7-O-β-D-glucopyranoside) stabilizes metastable proteins, prevents its aggregation, and remodels the oligomers into protease-sensitive species. Data was corroborated with additional secondary screen with disease-specific pathogenic protein. In vitro and cell-based experiments showed that C-10 inhibits α-synuclein aggregation which is implicated in synucleinopathies-related neurodegeneration. C-10 interferes in its structural transition into β-sheeted fibrils and mitigates α-synuclein aggregation-associated cytotoxic effects. Computational modeling suggests that C-10 binds to unique sites in α-synuclein which may interfere in its aggregation amplification. These findings open an avenue for comprehensive SAR development for flavonoid glycosides as pharmacological chaperones for metastable and aggregation-prone proteins implicated in protein conformational diseases.     

Bandwidth Improvement of Stub Loaded Compact Ultra-Wideband Microstrip Patch Antenna for C/X-Band Applications

Wireless Personal Communications - Authentication is a term very important for data communication security. We see many frauds due to authentication failure. The problem manifolds when...     

Performance of insulated FRP-strengthened concrete flexural members under fire conditions

This paper presents the results of fire resistance tests on carbon fiber-reinforced polymer (CFRP) strengthened concrete flexural members, i.e., T-beams and slabs. The strengthened members were protected with fire insulation and tested under the combined effects of thermal and structural loading. The variables considered in the tests include the applied load level, extent of strengthening, and thickness of the fire insulation applied to the beams and slabs. Furthermore, a previously developed numerical model was validated against the data generated from the fire tests; subsequently, it was utilized to undertake a case study. Results from fire tests and numerical studies indicate that owing to the protection provided by the fire insulation, the insulated CFRP-strengthened beams and slabs can withstand four and three hours of standard fire exposure, respectively, under service load conditions. The insulation layer impedes the temperature rise in the member; therefore, the CFRP–concrete composite action remains active for a longer duration and the steel reinforcement temperature remains below 400°C, which in turn enhances the capacity of the beams and slabs.