Smart Edge Computing for 5 g/6 g Satellite IOT for Reducing Inter Transmission Delay

Mobile Networks and Applications - 5G/6G communication are first generation high speed wireless communication network which integrates the aerial data, terrestrial data and maritime data via...     

Effect of Boronizing on Operating Stability of Steel AISI 304L under Erosion Impact of Hard Particles

Metal Science and Heat Treatment - The effect of solid boronizing at 950°C for 2 and 4 h on the phase composition, microstructure, hardness and abrasive wear of steel AISI 304L is studied...     

Masking the Peroxidase-Like Activity of the Molybdenum Disulfide Nanozyme Enables Label-Free Lipase Detection

Two-dimensional MoS₂ nanoparticles (2D-nps) exhibit artificial enzyme properties that can be regulated at bio-nanointerfaces. We discovered that protein lipase is able to tune the peroxidase-like activity of MoS₂ 2D-nps, offering low-nanomolar, label-free detection and identification in samples with unknown identity. The inhibition of the peroxidase-like activity of the MoS₂ 2D-nps was demonstrated to be concentration dependent, and as low as 5 nm lipase was detected with this approach. The results were compared with those obtained with several other proteins that did not display any significant interference with the nanozyme behavior of the MoS₂ 2D-nps. This unique response of lipase was characterized and exploited for the successful identification of lipase in six unknown samples by using qualitative visual inspection and a quantitative statistical analysis method. The developed methodology in this approach is noteworthy for many aspects; MoS₂ 2D-nps are neither labeled with a signaling moiety nor modified with any ligands for signal readout. Only the intrinsic nanozyme activity of the MoS₂ 2D-nps is exploited for this detection approach. No analytical equipment is necessary for the visual detection of lipase. The synthesis of the water-soluble MoS₂ 2D-nps is low costing and can be performed in bulk scale. Exploring the properties of 2D-nps and their interactions with biological materials reveals highly interesting yet instrumental features that offer the development of novel bioanalytical approaches.     

Inhibition of atmospheric corrosion of mild steel by sodium benzoate treatment

The objective of this study was to evaluate the effectiveness of sodium benzoate as an inhibitor to slow down or prevent atmospheric corrosion/discoloration of the local mild steel during storage in the Arabian Gulf region. Test specimens were prepared from locally produced reinforcing steel products. The inhibitor solution was applied on steel specimens at a concentration of 100 mM for 1 day at room temperature. Wooden exposure racks were used to hold as-received and inhibitor-treated specimens during atmospheric exposure for different periods. Corrosion was evaluated through weight loss determination and electrochemical technique. As expected, the Arabian Gulf atmosphere was corrosive on the as-received local mild steel. On the other hand, treatment of steel with sodium benzoate lowered its corrosion rate during initial days of its exposure to atmosphere. However, atmospheric corrosion inhibition performance of sodium benzoate deteriorated with exposure time after 30 or more days of atmospheric exposure, and the corrosion rates of sodium benzoate-treated specimens reached that of the unprotected specimens at the end of 90 days of atmospheric exposure.     

HiSQL: A Front-end Query System for Historical Relational Databases

The paper describes a prototype system called HiSQL (Historical SQL) which extends the functionality of SQL in manipulating historical data, by providing functions for spatial and temporal processing. Conceptually the paper is divided into three parts: the first part deals with the design and architecture of the system; the second part introduces a case study (the defters); and the third part describes specific functions for spatial and temporal processing of serial documents. The paper concludes with a comparison between HiSQL and SQL and suggestions for further work. This revised version was published online in August 2006 with corrections to the Cover Date.     

Heat-integration retrofit study of a petroleum refinery

The operation of a petroleum refinery at TÜPRA . Tütünçiftlik was assessed using the pinch-design method. By making use of heat integration in the heat-exchange network, appreciable amounts of energy can be saved as a result of a capital investment having a pay-back period of about 6·5 months.     

Exact solution of elasto-plastic stresses in a metal-matrix composite beam of arbitrary orientation subjected to transverse loads

An analytical elasto-plastic stress analysis is presented for a metal-matrix composite beam of arbitrary orientation subjected to a single transverse force applied to the free end of the beam and a uniformly distributed load. The material is assumed to be perfectly plastic in the elasto-plastic solution. A composite consisting of stainless-steel-reinforced aluminum was produced for this work. Sample problems are given for various orientation angles. Elastic, elastoplastic and residual normal and shear stresses are calculated. The location of the elasto-plastic boundary of the beam is obtained according to the x coordinates of the beam.     

A novel approach to predict shear strength of tilted angle connectors using artificial intelligence techniques

Shear connectors play a prominent role in the design of steel-concrete composite systems. The behavior of shear connectors is generally determined through conducting push-out tests. However, these tests are costly and require plenty of time. As an alternative approach, soft computing (SC) can be used to eliminate the need for conducting push-out tests. This study aims to investigate the application of artificial intelligence (AI) techniques, as sub-branches of SC methods, in the behavior prediction of an innovative type of C-shaped shear connectors, called Tilted Angle Connectors. For this purpose, several push-out tests are conducted on these connectors and the required data for the AI models are collected. Then, an adaptive neuro-fuzzy inference system (ANFIS) is developed to identify the most influencing parameters on the shear strength of the tilted angle connectors. Totally, six different models are created based on the ANFIS results. Finally, AI techniques such as an artificial neural network (ANN), an extreme learning machine (ELM), and another ANFIS are employed to predict the shear strength of the connectors in each of the six models. The results of the paper show that slip is the most influential factor in the shear strength of tilted connectors and after that, the inclination angle is the most effective one. Moreover, it is deducted that considering only four parameters in the predictive models is enough to have a very accurate prediction. It is also demonstrated that ELM needs less time and it can reach slightly better performance indices than those of ANN and ANFIS.

     

Automatic Channel Detection Using DNN on 2D Seismic Data

Geologists interpret seismic data to understand subsurface properties and subsequently to locate underground hydrocarbon resources. Channels are among the most important geological features interpreters analyze to locate petroleum reservoirs. However, manual channel picking is both time consuming and tedious. Moreover, similar to any other process dependent on human intervention, manual channel picking is error prone and inconsistent. To address these issues, automatic channel detection is both necessary and important for efficient and accurate seismic interpretation. Modern systems make use of real-time image processing techniques for different tasks. Automatic channel detection is a combination of different mathematical methods in digital image processing that can identify streaks within the images called channels that are important to the oil companies. In this paper, we propose an innovative automatic channel detection algorithm based on machine learning techniques. The new algorithm can identify channels in seismic data/images fully automatically and tremendously increases the efficiency and accuracy of the interpretation process. The algorithm uses deep neural network to train the classifier with both the channel and non-channel patches. We provide a field data example to demonstrate the performance of the new algorithm. The training phase gave a maximum accuracy of 84.6% for the classifier and it performed even better in the testing phase, giving a maximum accuracy of 90%.