Flow state monitoring of gas-water two-phase flow using multi-Gaussian mixture model based on canonical variate analysis

As a highly complex and time-varying process, gas-water two-phase flow is commonly encountered in industries. It has a variety of typical flow states and transition flow states. Accurate identification and monitoring of flow states is not only beneficial to further study of two-phase flow but also helpful for stable operation and economic efficiency of process industry. Combining canonical variate analysis (CVA) and Gaussian mixture model (GMM), a strategy called multi-CVA-GMM is proposed for flow state monitoring in gas-water two-phase flow. CVA is used to extract flow state features from the perspective of correlation between historical data and future data, which solves the cross correlation and temporal correlation of multi-sensor measurement data. GMM calculates the possibility that the current flow state belongs to each typical flow pattern and judges the current flow state by probability indicators. It is conducive to follow-up use of Bayesian inference probability and Mahalanobis distance-based (BID) indicator for flow state monitoring, which avoids repeated traversal of multiple CVA-GMM models and improves the efficiency of the monitoring process. The probability indicators can also be used to analyze transition flow states. The method combining the probabilistic idea of GMM with the deterministic idea of multimodal modeling can accurately identify the current flow state and effectively monitor the evolution of flow state. The multi-CVA-GMM method is validated by using the measured data of the horizontal flow loop of gas-water two-phase flow experimental facility, and its effectiveness is proved.     

From enfant terrible to problem-solver? Tracing the competing discourse to explain blockchain-related technological diffusion

Since the inception of blockchain-related technologies over a decade ago, investors’ uptake of the technologies has grown rapidly. But even with the advancement in standing from conceptual beginnings to real-world experimentation, mainstream adoption of the technologies in either organizational or individual contexts has yet to eventuate. To resolve this paradox, we examine progression of the technologies’ diffusion instead, and in the process, seek to uncover the underlying dynamics of sentiments at play. Diffusion involves multiple actions by different parties, with social discourses via diverse media playing an essential role. In demonstrating the interplay of support to resistance via media framing and the diffusion of innovation theory, findings from our paper contribute to the technology adoption literature. Understanding resistant behavior toward a new technology area could help resolve potential issues that may arise. This would hopefully lead to better technological implementation outcomes in future.     

Adsorption of anionic dye on eco-friendly synthesised reduced graphene oxide anchored with lanthanum aluminate: Isotherms,kinetics and statistical error analysis

In the present study we made an effort to deploy eco-friendly synthesized reduced graphene oxide/Lanthanum Alluminate nanocomposites (RGO-LaAlO₃) and Lanthanum Alluminate (LaAlO₃) as adsorbents to remove dye from the synthetic media. XRD, SEM, BET surface area and EDX have been used to characterize the above-mentioned adsorbents. The impacts of different factors like adsorbent dosage, the concentration of adsorbate and PH on adsorption were studied. The best fit linear and nonlinear equations for the adsorption isotherms and kinetic models had been examined. The sum of the normalized errors and the coefficient of determination were used to determine the best fit model. The experimental data were more aptly fitted for nonlinear forms of isotherms and kinetic equations. Pseudo-second-order and Freundlich isotherm model fits the equilibrium data satisfactorily. Methyl orange (MO) has been used as model dye pollutant and maximum adsorption capacity was found to be 469.7 and 702.2 mg g^?1 for LaAlO₃ and RGO-LaAlO₃, respectively.     

An improvement of the Space-Time Image Velocimetry combined with a new denoising method for estimating river discharge

The Space-Time Image Velocimetry (STIV) is a time-averaged velocity measurement method, which takes river surface images as the analysis object, and detects the Main Orientation of Texture (MOT) in a generated Space-Time Image (STI) to obtain one-dimensional velocities on the water surface. The STIV has great potential in real-time monitoring of river flow owing to its high spatial resolution and low time complexity. However, the generated STI contains a lot of noise and interference texture, which is inevitable in practical applications. The practicality of the STIV is severely limited by the low-quality STI. To solve this problem, a denoising method based on the filtering technology is proposed and combined with different texture detection algorithms in this paper. The accuracy of this method is verified through a comparative field experiment with an impellor-style current meter. The experimental results show: (1) By using this new denoising method, the robustness and accuracy of the STIV are significantly improved no matter what kind of texture detection algorithm is adopted; (2) Among all the texture detection algorithms, the FFT-based STIV combined with the new denoising method performs best. The relative errors of the surface velocities are controlled within 6%, and the relative errors of the discharges are controlled within ±4%.     

Composition and structure of arsenic–antimony alloy electrodeposited from acidic chloride solution

AsSb alloy (0.70–95.81 wt.% As) was prepared by electrodeposition in As(III) and Sb(III) contained electrolytes. The influence of electrolyte composition, hydrochloric acid concentration, and temperature on the composition and structure of AsSb deposits was studied. The electroreduction mechanism of As(III) and Sb(III) in hydrochloric acid solution was revealed via thermodynamic analysis. The results show that the increase of H⁺ concentration promotes the reduction of As(III), while the increase of Cl⁻ concentration significantly inhibits the reduction of Sb(III). As a result, the As content in deposits increases gradually with the increase of hydrochloric acid concentration. Simultaneously, the phase structure of AsSb deposits evolves from crystalline to amorphous. When the As content is 24.55–33.75 wt.%, AsSb mixed crystal is obtained. The electrolysis temperature has little effect on the deposits composition, but the structure of deposits evolves from crystalline to amorphous with decreasing the temperature.     

气藏型储气库注气期试井分析探讨

针对气藏型储气库注采井注采过程中储层物性参数影响因素不明确、注采能力不对称的问题。基于相国寺储气库井下连续油管试井测试结果,提出储气库注气期“温降效应”、“变表皮效应”的概念,分析了储气库注采过程中温降效应、变表皮效应以及储层应力敏感对注采的影响。通过气藏型储气库注气期试井分析技术,研究各因素在试井曲线上的响应特征以及对试井解释参数的影响。结果表明:①相对于采气期试井测试,注气期测试得到的储层物性参数具有同样的参考价值;②储气库温降效应对于试井解释结果的影响可忽略不计,而在不同注采运行周期内,变表皮效应以及应力敏感效应影响差异较大;③编制储气库注采运行方案时应充分考虑变表皮效应与应力敏感的影响,在不同注采运行周期内开展试井测试获取准确的储层参数值。研究成果为储气库试井测试与解释提供了重要的研究依据和理论指导。     

Cryptographic Lightweight Encryption Algorithm with Dimensionality Reduction in Edge Computing

Edge Computing is one of the radically evolving systems through generations as it is able to effectively meet the data saving standards of consumers, providers and the workers. Requisition for Edge Computing based items have been increasing tremendously. Apart from the advantages it holds, there remain lots of objections and restrictions, which hinders it from accomplishing the need of consumers all around the world. Some of the limitations are constraints on computing and hardware, functions and accessibility, remote administration and connectivity. There is also a backlog in security due to its inability to create a trust between devices involved in encryption and decryption. This is because security of data greatly depends upon faster encryption and decryption in order to transfer it. In addition, its devices are considerably exposed to side channel attacks, including Power Analysis attacks that are capable of overturning the process. Constrained space and the ability of it is one of the most challenging tasks. To prevail over from this issue we are proposing a Cryptographic Lightweight Encryption Algorithm with Dimensionality Reduction in Edge Computing. The t-Distributed Stochastic Neighbor Embedding is one of the efficient dimensionality reduction technique that greatly decreases the size of the non-linear data. The three dimensional image data obtained from the system, which are connected with it, are dimensionally reduced, and then lightweight encryption algorithm is employed. Hence, the security backlog can be solved effectively using this method.     

Effect of thermal treatment on the antioxidant activity of egg white hydrolysate and the preparation of novel antioxidant peptides

Heat treatment will affect the nutritional properties and potential bioactivity of food materials. The aim of this work was to evaluate the effect of different thermal treatment (4, 56, 65 and 100 ℃) and in vitro gastrointestinal digestion on the antioxidant activity of egg white hydrolysate. The results demonstrated that egg white hydrolysate treated at 65 ℃ exhibited the highest antioxidant. Remarkably, the simulated digestion significantly increased antioxidant activity of egg white hydrolysate. Furthermore, we identified twenty-four potential antioxidant peptides by performing mass spectrometry and bioinformatic analysis. Six peptides were selected based on the activity prediction score of the online tool. The results showed that P6 (ACPECPK) possessed the most outstanding antioxidant properties and had low cytotoxicity and allergenicity. Bioinformatics technology combined with biochemical assays may offer a way for discovering novel antioxidant peptides from different kinds of food under various heat treatment conditions.     

Finite element analysis of fracture behavior in ceramics: Competition between artificial notch and internal defects under three-point bending

Evaluation of the nonlinear relationship between the surface defect size and fracture strength of ceramics is important for engineering applications. In this study, we aim to predict the apparent nonlinear relationship between the defect size and fracture strength of single-edge notched beams (SENBs) using the finite element method. Specifically, we applied the methodology for predicting fracture strength from microstructure distribution data (relative density, pore size, aspect ratio, and grain size) to a finite element analysis (FEA) model in which the shape and size of the initial defects are defined at notch locations. By reproducing the apparent nonlinearity caused by the competition between the surface and internal defects within the framework of linear elastic fracture mechanics, the effectiveness of the FEA methodology for the evaluation of strength scatter and allowable crack size in ceramics was demonstrated.