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.     

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.     

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%.     

Effect of the use of mineral filler on the properties of concrete

Engineers of the concrete technology are increasingly concerned with the material passing through a sieve of the size under 0.149 mm. Materials called very fine aggregate or mineral filler may affect the performance of concrete in an either positive or a negative way. Discussions on aggregate containing very fine material are vitally important. Washing the aggregate residue has been the sole way to solve this matter to date. This is mainly based on the debatable opinion that materials of this kind are regarded as clay material. The goal of the study was to determine how the content of mineral filler might affect properties of concrete. Two types of aggregates with different amounts of cement and mineral filler were used. Basically, mineral filler replaced sand. The effect of applying different amounts of mineral filler on concrete was then determined. The addition of 7-10% of mineral filler to fine aggregate (0-2 mm) was found to considerably improve the properties of concrete.     

(BaTiO3)1-x + (Co0.5Ni0.5Nb0.06Fe1.94O4)x nanocomposites: Structure,morphology, magnetic and dielectric properties

Two phase-based nanocomposites consisting of dielectric barium titanate (BaTiO₃ or BTO) and magnetic spinel ferrite Co_0.5Ni_0.5Nb_0.06Fe_1.94O₄ (CNNFO) have been synthesized through solid state route. Series of (BaTiO₃)_1-x + (Co_0.5Ni_0.5Nb_0.06Fe_1.94O₄)_x nanocomposites with x content of 0.00, 0.25, 0.50, 0.75, and 1.00 were considered. The structure has been examined via X-rays diffraction (XRD) and indicated the occurrence of both perovskite BTO and spinel CNNFO phases in various nanocomposites. A phase transition from tetragonal BTO structure to cubic structure occurs with inclusion of CNNFO phase. The average crystallites size of BTO phase decreases, whereas that for the CNNFO phase increases with increasing x in various nanocomposites. The morphological observations revealed that the porosity is highly reduced, and the connectivity between grains is enhanced with increasing x content. The optical properties have been investigated by UV−vis diffuse reflectance spectroscopy. The deduced band gap energy (E_g) value is found to reduce with increasing the content of spinel ferrite phase. The magnetic as well as the dielectric properties were also investigated. The analysis showed that CNNFO ferrite phase greatly affects the magnetic properties and dielectric response of BTO material. The obtained findings can be useful to enhance the performances of magneto-dielectric composite-based systems.     

Detection of COVID-19 and its pulmonary stage using Bayesian hyperparameter optimization and deep feature selection methods

Since the first case of COVID-19 was reported in December 2019, many studies have been carried out on artificial intelligence for the rapid diagnosis of the disease to support health services. Therefore, in this study, we present a powerful approach to detect COVID-19 and COVID-19 findings from computed tomography images using pre-trained models using two different datasets. COVID-19, influenza A (H1N1) pneumonia, bacterial pneumonia and healthy lung image classes were used in the first dataset. Consolidation, crazy-paving pattern, ground-glass opacity, ground-glass opacity and consolidation, ground-glass opacity and nodule classes were used in the second dataset. The study consists of four steps. In the first two steps, distinctive features were extracted from the final layers of the pre-trained ShuffleNet, GoogLeNet and MobileNetV2 models trained with the datasets. In the next steps, the most relevant features were selected from the models using the Sine–Cosine optimization algorithm. Then, the hyperparameters of the Support Vector Machines were optimized with the Bayesian optimization algorithm and used to reclassify the feature subset that achieved the highest accuracy in the third step. The overall accuracy obtained for the first and second datasets is 99.46% and 99.82%, respectively. Finally, the performance of the results visualized with Occlusion Sensitivity Maps was compared with Gradient-weighted class activation mapping. The approach proposed in this paper outperformed other methods in detecting COVID-19 from multiclass viral pneumonia. Moreover, detecting the stages of COVID-19 in the lungs was an innovative and successful approach.