Quotient graphs for power graphs

In a previous paper of the first author a procedure was developed for counting the components of a graph through the knowledge of the components of its quotient graphs. We apply here that procedure to the proper power graph $\mathcal{P}_0(G)$ of a finite group $G$, finding a formula for the number $c(\mathcal{P}_0(G))$ of its components which is particularly illuminative when $G\leq S_n$ is a fusion controlled permutation group. We make use of the proper quotient power graph $\widetilde{\mathcal{P}}_0(G)$, the proper order graph $\mathcal{O}_0(G)$ and the proper type graph $\mathcal{T}_0(G)$. We show that all those graphs are quotient of $\mathcal{P}_0(G)$ and demonstrate a strong link between them dealing with $G=S_n$. We find simultaneously $c(\mathcal{P}_0(S_n))$ as well as the number of components of $\widetilde{\mathcal{P}}_0(S_n)$, $\mathcal{O}_0(S_n)$ and $\mathcal{T}_0(S_n)$

Risk Analysis - An Economic Comparison of Oil and Coal Power Plants

The demand for electric energy increases every year. However, due to recent changes in the U.S. energy supplies, a growing gas shortage forced suppliers to curtail deliveries of natural gas for power generation. Many utilities anticipating supply problems switched to burning more costly light distillate oil. Unfortunately the Arab boycott of 1973 and the following price increases for oil forced again utilities to seek a cheaper source of fuel, namely coal, as a substitute for oil. Even though the U.S. has abundant supply in coal, the use of coal in power generation was limited in the past because of a higher capital cost associated with installing air pollution control devices. Therefore, current utilities primary concerns are does the lower fuel price of the coal power plant really outweigh its disadvantage of higher construction costs as compared to the oil-burning power plant? . Thus, the purpose of this paper is to evaluate the economic preference of the coal burning power plant compared to the oil-burning power plant in suppling base load power. An extensive analytical model accounting for the effects of escalating fuel prices was examined and a computer simulation model was developed to handle risk associated with various input parameters using the SLAM as a simulation language

Assessing the Economic Feasibility of Synthetic Natural Gas Under Conditions of Uncertainty

The science of synthetic fuel production began in the seventeenth century. However, large-scale production of synthetic fuels started in the early 1900\u27s and, for several decades, gas manufactured from coal significantly contributed to the U.S. economy. The production of synthetic fuels declined due to increases in the price of coal and discoveries of predominantly methane natural gas. Today, an extensive network of pipelines is used to transmit and distribute natural gas for industrial and residential applications. The decline of natural gas reserves in the United States, in conjunction with the availability of very large coal reserves, has provided the incentive for development of coal gasification plants. Synthetic fuels are expected to contribute significantly to the supply of energy before the end of this century, and coal will be the primary source for production of these fuels. By many accounts, difficulties in raising the high amount of initial capital and future uncertainties with regard to fuel and operating costs have made development of synthetic fuels economically infeasible. However, as the prices of oil and natural gas increase, synthetic fuels production becomes a more attractive alternative. The purpose of this study is to evaluate the economics of synthetic natural gas with the current state of technology and to determine its future role as prices of oil and gas increase. In this report, a general methodology of production of synthetic natural gas is explained. For the economic analysis, the Lurgi Model was selected because it has been the most common model used for commercial production of high BTU gases. An extensive analytical model is described in which inflated capital, fuel, and operating and maintenance costs were accounted for and the equivalent annual cost of cash flows over the project life was calculated. The risk analysis was accomplished by applying Monte Carlo techniques through a simulation model which handles risks associated with various input parameters. SLAM, a FORTRAN-based language, was selected as the simulation language. Based on the results, all the cost elements were evaluated and the sensitivity of the total cost to each element was examined. This study was extended to the calculation of costs associated with he generation of electricity by burning synthetic natural gas. The results were then compared to the respective costs related to oil-burning power plants. The results show that high cost of synthetic high BTU gas makes it difficult to compete with natural gas at current prices. Coal feed stocks represent a major portion of the total cost of synthetic gases. The cost of capital, which is a critical factor at the developing stage, constitutes a relatively small portion of the total cost over the plant life. A similar observation was made for operating and maintenance costs. However, the future regulations regarding pollution control could have a strong impact on this portion of the cost. For power generations, oil was found to be far more economical than using synthetic natural gas. The computer simulation also revealed that the total cost of each alternative is very sensitive to this fuel cost. The conclusion of this study points to the fuel costs as the dominant factor in the choice of fuel alternatives in the future

Deep Sketch-Photo Face Recognition Assisted by Facial Attributes

In this paper, we present a deep coupled framework to address the problem of matching sketch image against a gallery of mugshots. Face sketches have the essential in- formation about the spatial topology and geometric details of faces while missing some important facial attributes such as ethnicity, hair, eye, and skin color. We propose a cou- pled deep neural network architecture which utilizes facial attributes in order to improve the sketch-photo recognition performance. The proposed Attribute-Assisted Deep Con- volutional Neural Network (AADCNN) method exploits the facial attributes and leverages the loss functions from the facial attributes identification and face verification tasks in order to learn rich discriminative features in a common em- bedding subspace. The facial attribute identification task increases the inter-personal variations by pushing apart the embedded features extracted from individuals with differ- ent facial attributes, while the verification task reduces the intra-personal variations by pulling together all the fea- tures that are related to one person. The learned discrim- inative features can be well generalized to new identities not seen in the training data. The proposed architecture is able to make full use of the sketch and complementary fa- cial attribute information to train a deep model compared to the conventional sketch-photo recognition methods. Exten- sive experiments are performed on composite (E-PRIP) and semi-forensic (IIIT-D semi-forensic) datasets. The results show the superiority of our method compared to the state- of-the-art models in sketch-photo recognition algorithm

Investigating the effect of inquiry-based stress reduction on mortality awareness and interpersonal problems among intensive care unit nurses

Introduction Caring for dying patients is one of the job stressors. Nurses in intensive care units are among the medical staff who have a close interaction with dying patients. Studies have shown that psychological interventions are very helpful in improving thinking about death and its problems. Therefore, this study was conducted to investigate the effect of Inquiry-Based Stress Reduction on mortality awareness and interpersonal problems among intensive care unit nurses in southeastern Iran. Materials and methods This was a Quasi-experimental study with a pretest-posttest design in southeast of Iran in 2021. Nurses were selected using the convenience sampling method and divided into intervention (n = 32) and control (n = 35) groups using the block randomization method. The intervention group received a two-hour Inquiry-Based Stress Reduction counseling session every week for 6 weeks. Data were gathered using Multidimensional Mortality Awareness Measure and Inventory of Interpersonal Problems before, immediately after, and 6 weeks after the intervention. IBM SPSS Statistics software version 25 was used for data analysis. Results In the intervention group, the mean scores of Mortality Awareness before, immediately after, and 6 weeks after the intervention were 130.41 ± 5.91, 164.47 ± 8.66, and 163.91 ± 9.29, respectively. Therefore, in the intervention group, the increase of Mortality Awareness mean score was statistically significant (P < 0.001). In the control group, the mean scores of Mortality Awareness before, immediately after, and 6 weeks after intervention were 129.63 ± 5.59, 135.26 ± 11.14, and 132.66 ± 5.62, respectively. Difference between the two groups was significant (P < 0.001). The results also showed that in the intervention group the mean scores of Interpersonal Problems immediately after and 6 weeks after the intervention were lower than before the intervention (P < 0.001). In the control group, Interpersonal Problems increased over time (P < 0.001). Accordingly, the difference between the two groups in terms of Interpersonal Problems during the study was statistically significant (P < 0.001). Conclusion The study results suggest that the Inquiry-Based Stress Reduction is an appropriate intervention method to improve mortality awareness and reduce interpersonal problems in intensive care unit nurses