Extending the Application Field of Vacuum Circuit Breakers to Generators for Capacities up to 400 MW

The progress that has been achieved in the development and manufacture of vacuum circuit breakers opens the possibility of using them for a wider range of applications at power plants, including as generator circuit breakers. Such characteristics of modern vacuum circuit breakers as increased breaking capacity and high switching life are factors that make them closer in competitiveness to SF6 circuit breakers for generators with capacities up to 400 MW. The article considers problem aspects relating to clearing of short-circuit faults in the generator voltage circuits and interruption of out-of-phase making currents and no-load currents of generator transformers. Conditions leading to a longer period of time to the moment at which the switched current crosses zero are considered. It is pointed out that, unlike the IEC/IEEE Standard 62271-37-013, GOST (State Standard) R 52565-2006 does not specify the requirements for generator circuit breakers in full. The article gives the voltage drop values across the arc for different design versions of vacuum circuit breaker contacts and shows the effect the arc in a vacuum circuit breaker has on the time delay to the moment at which the current crosses zero. The standardized parameters of transient recovery voltage across the generator circuit breaker contacts are estimated along with the contact gap electric strength recovery rates ensured by modern arc quenching chambers. The switching overvoltages arising when vacuum circuit breakers interrupt short-circuit currents and no-load currents of generator transformers are analyzed. The article considers the most probable factors causing the occurrence of switching overvoltages, including current chopping, repeated breakdowns of the circuit breaker contact gap, and virtual current chopping. It is found that, unlike repeated breakdowns and virtual current chopping, an actual current chopping does not give rise to dangerous switching overvoltages. The article also determines the vacuum circuit breaker application field boundaries in which dangerous switching overvoltages may occur that would require additional measures for limiting them.     

Knudsen diffusion through ZIF-8 membranes synthesized by secondary seeded growth

Herein we demonstrate the synthesis of ZIF-8 membranes via secondary seeded growth on tubular stainless steel porous supports. The membranes were characterized and evaluated for the separation of CO₂/N₂ and N₂/CH₄ gas mixtures. The adsorbate polarizability correlated with the adsorption capacity on ZIF-8, and the amounts of gases adsorbed were in the order: CO₂ > CH₄ > N₂. The CO₂/N₂ separation selectivity’s for the ZIF-8 membranes were close to the Knudsen selectivity, suggesting that Knudsen diffusion through non-ZIF pores dominated the separation. On the other hand, the separation selectivity’s for N₂/CH₄ were slightly higher than the Knudsen selectivity, indicating that the flow contribution from the ZIF pores favored the transport of N₂ over CH₄.     

Self-assembly hydrothermal assisted synthesis of mesoporous anatase in the presence of ethylene glycol

Mesoporous nanocrystalline anatase was prepared hydrothermally employing P123 as structure-directing agent. Ethylene glycol was used as a key synthesis parameter to fine tune the morphology, crystal size and pore size of the resultant mesophases. The incorporation of EG in the synthesis gel resulted in the formation of 1–2 μm sphere-like shapes and led to an increase in the specific surface area from 95 to 170 m²/g, decrease in the average pore size from 11 to 4.8 nm, and decrease in the average crystallite size from 17 to 12 nm. These mesophases were used as photocatalysts for the UV degradation of methylene blue and methyl orange. The mesoporous anatase phases photodegraded MB 1.5–3× faster than commercially available P25 and showed limited photocatalytic behavior for methyl orange.     

Visualizations for assessing convergence and mixing of Markov chain Monte Carlo simulations

Bayesian inference often requires approximating the posterior distribution by Markov chain Monte Carlo sampling. The samples come from the true distribution only after the simulation has converged, which makes detecting convergence a central problem. Commonly, several simulation chains are started from different points, and their overlap is used as a measure of convergence. Convergence measures cannot tell the analyst the cause of convergence problems; it is suggested that complementing them with proper visualization will help. A novel connection is pointed out: linear discriminant analysis (LDA) minimizes the overlap of the simulation chains measured by a common multivariate convergence measure. LDA is thus justified for visualizing convergence. However, LDA makes restrictive assumptions about the chains, which can be relaxed by a recent extension called discriminative component analysis (DCA). Lastly, methods are introduced for unidentifiable models and model families with variable number of parameters, where straightforward visualization in the parameter space is not feasible.     

AN ANALYTICAL DEVELOPMENT OF A UNIVERSAL DISPLACEMENT MEASUREMENT SYSTEM FOR ULTRA-PRECISION METROLOGY

In recent years, precise metrology up to nanoscale has attracted substantial attention due to an increasing need for ultra-precision measurement systems for micro electro mechanical systems (MEMS) and nano electro mechanical systems (NEMS) calibration. In this paper, an analytical and numerical investigation for the proposed design of a 6-degree-of-freedom (6-dof) universal measurement system, using a relatively simple and inexpensive but highly sensitive set up, is presented. This system utilizes a convex mirror and two flat mirrors, one angled and one vertical, as targets to reflect laser beams onto photo detectors. These targets, placed on the object of interest for metrology applications, change the direction of the reflected beam when there is any movement in the object, which is detected by the photo detectors. The use of convex and angled mirrors greatly enhances the sensitivity of the system. Geometric relationships are established among all the optical components to predict the final intersection points of the laser beams reflected from the targets with the photo detector planes. These relationships are used to establish a Jacobian partial derivative matrix, which is further used to estimate the target pose when the photo detector outputs are given. Numerical simulation of the measurement process is performed using MATLAB®. Assuming the approximate distance from laser beam origin to target and the target to photo detector as 50 mm, and photo detector resolution as 0.1 µm, translations and rotations less than 50 nm and 2 arc sec, respectively, can be measured accurately. The highest amount of sensitivity in translation is obtained along the Z-axis with minimum a measurement of 3.4 nm; the highest amount of sensitivity in rotation is along the pitch-axis with a minimum measurement of 0.145 arc sec. The corresponding low sensitivity axes are X-axis and yaw-axis, with minimum measurements of 35 nm and 2 arc sec, respectively. The sensitivity and the resolution of the system can be increased many times further by increasing the curvature of the convex mirror or by increasing the distance between the target and the detectors. The analytical and simulation results of this work yield a scientific and engineering guideline for the development of meso, micro, as well as nanoscale metrology systems.     

Post-Stroke Social Isolation Reduces Cell Proliferation in the Dentate Gyrus and Alters miRNA Profiles in the Aged Female Mice Brain

Social isolation and loneliness are risk factors for stroke. Elderly women are more likely to be isolated. Census data shows that in homeowners over the age of 65, women are much more likely to live alone. However, the underlying mechanisms of the detrimental effects of isolation have not been well studied in older females. In this study, we hypothesized that isolation impairs post-stroke recovery in aged female mice, leading to dysregulated microRNAs (miRNAs) in the brain, including those previously shown to be involved in response to social isolation (SI). Aged C57BL/6 female mice were subjected to a 60-min middle cerebral artery occlusion and were randomly assigned to either single housing (SI) or continued pair housing (PH) immediately after stroke for 15 days. SI immediately after stroke led to significantly more brain tissue loss after stroke and higher mortality. Furthermore, SI significantly delayed motor and sensory recovery and worsened cognitive function, compared to PH. A decrease in cell proliferation was seen in the dentate gyrus of SI mice assessed by bromodeoxyuridine (BrdU) labeling. miRNAome data analysis revealed changes in several miRNAs in the brain, such as miR-297a-3p and miR-200c-3p, which are known to regulate pathways involved in cell proliferation. In conclusion, our data suggest that SI can lead to a poor post-stroke recovery in aged females and dysregulation of miRNAs and reduced hippocampal cell proliferation.     

River Stage Forecasting using Enhanced Partial Correlation Graph

Various time series forecasting methods have been successfully applied for the water-stage forecasting problem. Graphical time series models are a class of multivariate time series to model the spatio-temporal dependencies between the sensors. Constructing graph-based models involve data pre-processing and correlation analysis to capture the dynamics of different water flow scenarios, which is not scalable for a large network of sensors. This paper presents a novel approach to model spatio-temporal dependencies across river network stations using a partial correlation graph. We also provide a method to enrich this partial correlation graph by eliminating the spurious correlations. We demonstrate the utility of enriched partial correlation graphs in multivariate forecasting for various scenarios and state-of-the-art multivariate forecasting models. We observe that the forecasting techniques that use information from the enriched partial correlation graph outperform standard time series forecasting approaches for river network forecasting.