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.     

Proton NMR studies of cytochrome c peroxidase mutant N82A: hyperfine resonance assignments, identification of two interconverting enzyme ofecies, quantitating the rate of interconversion, and determination of equilibrium constants

The cyanide-ligated form of the baker's yeast cytochrome c peroxidase mutant bearing the mutation Asn82-->Ala82 ([N82A]CcPCN) has been studied by proton NMR spectroscopy. This mutation alters an amino acid that forms a hydrogen bond to His52, the distal histidine residue that interacts in the heme pocket with heme-bound ligands. His52 is a residue critical to cytochrome c peroxidase's normal function. Proton hyperfine resonance assignments have been made for the cyanide-ligated form of the mutant by comparison with 1-D and NOESY spectra of the wild-type native enzyme. For [N82A]CcPCN, proton NMR spectra reveal two significant phenomena. First, similar to results published for the related mutant [N82D]CcPCN [Satterlee, J. D., et al. (1994) Eur. J. Biochem. 244, 81-87], for Ala82 mutation disrupts the hydrogen bond between His52 and the heme-ligated CN. Second, four of the 24 resolved hyperfine-shifted resonances are doubled in the mutant enzyme's proton spectrum, leading to the concept that the heme active site environment is dynamically microheterogeneous on a very localized scale. Two magnetically inequivalent enzyme forms are detected in a pure enzyme preparation. Varying temperature causes the two enzyme forms to interconvert. Magnetization transfer experiments further document this interconversion between enzyme forms and have been used to determine that the rate of interconversion is 250 (+/- 53) s-1. The equilibrium constant at 20 degrees C is 1.5. Equilibrium constants have been calculated at various temperatures between 5 and 29 degrees C leading to the following values: delta H = 60 kJ mol-1; delta S = 0.20 kJ K-1 mol-1.     

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%.     

How Cancer Cells Invade Bladder Epithelium and Form Tumors: The Mouse Bladder Tumor Model as a Model of Tumor Recurrence in Patients

Non-muscle-invasive bladder cancer is the most common form of bladder cancer. The main problem in managing bladder tumors is the high recurrence after the transurethral resection of bladder tumors (TURBT). Our study aimed to examine the fate of intravesically applied cancer cells as the implantation of cancer cells after TURBT is thought to be a cause of tumor recurrence. We established an orthotopic mouse bladder tumor model with MB49-GFP cancer cells and traced them during the first three days to define their location and contacts with normal urothelial cells. Data were obtained by Western blot, immunolabeling, and light and electron microscopy. We showed that within the first two hours, applied cancer cells adhered to the traumatized epithelium by cell projections containing α3β1 integrin on their tips. Cancer cells then migrated through the epithelium and on day 3, they reached the basal lamina or even penetrated it. In established bladder tumors, E-cadherin and desmoplakin 1/2 were shown as feasible immunohistochemical markers of tumor margins based on the immunolabeling of various junctional proteins. Altogether, these results for the first time illustrate cancer cell implantation in vivo mimicking cellular events of tumor recurrence in bladder cancer patients.     

Electrospinning of polyurethane fibers

A segmented polyurethaneurea based on poly(tetramethylene oxide)glycol, a cycloaliphatic diisocyanate and an unsymmetrical diamine were prepared. Urea content of the copolymer was 35 wt%. Electrospinning behavior of this elastomeric polyurethaneurea copolymer in solution was studied. The effects of electrical field, temperature, conductivity and viscosity of the solution on the electrospinning process and morphology and property of the fibers obtained were investigated. Results of observations made by optical microscope, atomic force microscope and scanning electron microscope were interpreted and compared with literature data available on the electrospinning behavior of other polymeric systems.