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... 相似文献
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... 相似文献
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. 相似文献
Macaroni was prepared from semolina fortified with 3, 6 and 9% CMC-protein (CMC — carboxymethyl cellulose) or HEC-protein (HEC — hydroxyethyl cellulose) complexes from whey and corn steep liquor to increase the protein quality and quantity. Fortification increased the protein content up to 14.2% in DM (vs. 12.1% in control) for macaroni. Water absorption, dough weakening and mixing tolerance index were decreased, while dough development time and dough stability were increased when the amount of precipitated cellulose-protein complex from whey and corn steep liquor in the blends increased. Addition of both tested cellulose-protein complexes improved cooking quality by increasing the weight and volume of cooked macaroni, but cooking losses were greater. Sensory evaluation of the colour, flavour and appearance of macaroni were improved as a result of adding cellulose-protein complex from whey. Macaroni samples prepared from dough mixtures with 6 and 9% of cellulose-protein complex from corn steep liquor were less acceptable than those prepared from 100% semolina. 相似文献
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. 相似文献
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.
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%. 相似文献
This study reports the effect of polydopamine bionic coating and γ-methacryloxypropyltrimethoxysilane (KH570) composite modified polyacrylonitrile (PAN) fiber as a secondary modifier on the performance of styrene-butadiene-styrene (SBS) modified asphalt. Dynamic shear rheometer test indicated the complex shear modulu, storage modulus, and loss modulus of modified PAN (KD-PAN) incorporated SBS modified asphalt was increased by 12.4, 20.5, and 11.2%, respectively compared with PAN/SBS modified asphalt. The master curve of G* of fiber/SBS composite modified asphalt shows that the deformation resistance of KD-PAN/SBS modified asphalt is greater than that of PAN/SBS modified asphalt in the entire loading frequency range. The cone penetration test showed significantly enhanced shear strength of KD-PAN/SBS modified asphalt. The adhesion work test results and SEM images of interface between fiber and SBS modified asphalt revealed that the adhesion effect of KD-PAN and SBS modified asphalt is better than that of PAN and SBS modified asphalt. SEM and AFM images of fiber further showed that the fiber surface becomes rough after modification. The increased surface roughness of KD-PAN facilitated the adherence of SBS modified asphalt to it, which in turn led to the enhanced performance of KD-PAN/SBS composite modified asphalt at the same fiber content and temperature. 相似文献