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AbstractThe brief exposure of recently ovulated mouse oocytes to a dilute solution of ethanol in vitro for 1, 3, or 5 min induced a uniform high incidence of parthenogenetic activation. The majority of parthenogenones developed a single haploid pronucleus after the extrusion of a second polar body. The proportionate incidence of this parthenogenetic class was significantly reduced as the duration of ethanol exposure increased from 1 min to 5 min. There was a concomitant increase in the incidence of parthenogenones that developed two haploid pronuclei following failure of extrusion of the second polar body. Cytogenetic analysis of the ethanol‐induced single‐pronuclear haploid parthenogenones at metaphase of the first cleavage division clearly demonstrated that a significant proportion were aneuploid. The incidence of aneuploidy observed was directly related to the duration of ethanol exposure. G‐band analysis of the aneuploid metaphases revealed that the chromosomes were not randomly involved in the malsegregation events. This observation may be a reflection of the relationship of particular chromosomes to the meiotic spindle apparatus rather than on any specific property of the agent to which they were exposed. It is believed that ethanol disrupts the organisation of cytoskeletal elements and, in particular, interferes with the processes of chromosome segregation at the second meiotic division.

AbstractThe brief exposure of recently ovulated mouse oocytes to a dilute solution of ethanol in vitro for 1, 3, or 5 min induced a uniform high incidence of parthenogenetic activation. The majority of parthenogenones developed a single haploid pronucleus after the extrusion of a second polar body. The proportionate incidence of this parthenogenetic class was significantly reduced as the duration of ethanol exposure increased from 1 min to 5 min. There was a concomitant increase in the incidence of parthenogenones that developed two haploid pronuclei following failure of extrusion of the second polar body. Cytogenetic analysis of the ethanol‐induced single‐pronuclear haploid parthenogenones at metaphase of the first cleavage division clearly demonstrated that a significant proportion were aneuploid. The incidence of aneuploidy observed was directly related to the duration of ethanol exposure. G‐band analysis of the aneuploid metaphases revealed that the chromosomes were not randomly involved in the malsegregation events. This observation may be a reflection of the relationship of particular chromosomes to the meiotic spindle apparatus rather than on any specific property of the agent to which they were exposed. It is believed that ethanol disrupts the organisation of cytoskeletal elements and, in particular, interferes with the processes of chromosome segregation at the second meiotic division.

Algae production process is a key cost center in production of biofuels/bioproducts from microalgae. Decline in the growth of algae in outdoor ponds during non-optimal conditions is one of the hurdles for achieving consistently high algal production rates. An optimal controller can be used to overcome this limitation and provide reliable growth in outdoor conditions. A model predictive controller (MPC) was developed to optimize the algal growth, predicted by flux balance analysis, under natural disturbances, embedding within the cost function, the economic and environmental constraints associated with the process. The model, developed in MATLAB, was validated on a 30-L continuous algal culture under light, temperature and a combination of light and temperature disturbances. The MPC proved effective in minimization of a decrease in growth under these natural disturbances. The growth rates with MPC were observed to be 79-116% higher as compared to the non-MPC growth.

Algae production process is a key cost center in production of biofuels/bioproducts from microalgae. Decline in the growth of algae in outdoor ponds during non-optimal conditions is one of the hurdles for achieving consistently high algal production rates. An optimal controller can be used to overcome this limitation and provide reliable growth in outdoor conditions. A model predictive controller (MPC) was developed to optimize the algal growth, predicted by flux balance analysis, under natural disturbances, embedding within the cost function, the economic and environmental constraints associated with the process. The model, developed in MATLAB, was validated on a 30-L continuous algal culture under light, temperature and a combination of light and temperature disturbances. The MPC proved effective in minimization of a decrease in growth under these natural disturbances. The growth rates with MPC were observed to be 79-116% higher as compared to the non-MPC growth.

Abstract The convergence characteristics of two Monte Carlo models are investigated in this paper. These two models or their variations have been used by the electric power industry. Model 1 is based on the random sampling of generator and transmission line status for each hour. Model 2 advances the generation and transmission using the next-event approach. Mathematical analysis and system studies indicate that model 2 converges slower than model 1. The simulation time for model 2 is generally more than twice the mean duration of loss of load. The CPU time per year of simulation is shorter for model 2. However, the total CPU time for model 2 is much longer than for model 1.

Abstract The convergence characteristics of two Monte Carlo models are investigated in this paper. These two models or their variations have been used by the electric power industry. Model 1 is based on the random sampling of generator and transmission line status for each hour. Model 2 advances the generation and transmission using the next-event approach. Mathematical analysis and system studies indicate that model 2 converges slower than model 1. The simulation time for model 2 is generally more than twice the mean duration of loss of load. The CPU time per year of simulation is shorter for model 2. However, the total CPU time for model 2 is much longer than for model 1.

Global sensitivity analysis (GSA) of distribution system with respect to stochastic PV and load variations plays an important role in designing optimal voltage control schemes. This paper proposes a data-driven framework for GSA of distribution system. In particular, two representative surrogate modeling-based approaches are developed, including the traditional Gaussian process-based and the analysis of variance (ANOVA) kernel ones. The key idea is to develop a surrogate model that captures the hidden global relationship between voltage and real and reactive power injections from the historical data. With the surrogate model, the Sobol indices can be conveniently calculated through either the sampling-based method or the analytical method to assess the global sensitivity of voltage to variations of load and PV power injections. The sampling-based method estimates the Sobol indices using Monte Carlo simulations while the analytical method calculates them by resorting to the ANOVA expansion framework. Comparison results with other model-based GSA methods on the unbalanced three-phase IEEE 37-bus and 123-bus distribution systems show that the proposed framework can achieve much higher computational efficiency with negligible loss of accuracy. The results on a real 240-bus distribution system using actual smart meter data further validate the feasibility and scalability of the proposed framework.

Global sensitivity analysis (GSA) of distribution system with respect to stochastic PV and load variations plays an important role in designing optimal voltage control schemes. This paper proposes a data-driven framework for GSA of distribution system. In particular, two representative surrogate modeling-based approaches are developed, including the traditional Gaussian process-based and the analysis of variance (ANOVA) kernel ones. The key idea is to develop a surrogate model that captures the hidden global relationship between voltage and real and reactive power injections from the historical data. With the surrogate model, the Sobol indices can be conveniently calculated through either the sampling-based method or the analytical method to assess the global sensitivity of voltage to variations of load and PV power injections. The sampling-based method estimates the Sobol indices using Monte Carlo simulations while the analytical method calculates them by resorting to the ANOVA expansion framework. Comparison results with other model-based GSA methods on the unbalanced three-phase IEEE 37-bus and 123-bus distribution systems show that the proposed framework can achieve much higher computational efficiency with negligible loss of accuracy. The results on a real 240-bus distribution system using actual smart meter data further validate the feasibility and scalability of the proposed framework.

Phasor-based frequency measurement techniques have very good steady-state performance, but their dynamic performance is not well documented. This paper analyzes a phasor-based frequency measurement method that considers the effect of dynamic frequency, and proposes a method to improve the dynamic performance of the phasor-based frequency measurements. Simulation results verify the effectiveness of the proposed method. Results of laboratory experiments are presented to show the performance of the proposed method compared to frequency measurements obtained from commercial phasor measurement units (PMUs).