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POL SCI 001 Los Angeles Valley Political Science Voter Turnout Discussion

POL SCI 001 Los Angeles Valley Political Science Voter Turnout Discussion.

Political Science, Assignment 3Read chapter 7 and 8 of the textbook.Each answer should be 2-3 substantial paragraphs in length. Each answer should contain information with citations from the textbook throughout the answer, for example (OpenStax, section 7.1).Chapter 7https://openstax.org/books/american-government/pag…1) What are some issues that affect voter turnout ?2) Discuss some examples of what goes behind a campaign.Chapter 8 https://openstax.org/books/american-government/pag…3) In regards to the U.S. government, explain some ways in which media is governed.4) All of us have been a part of the world of media in one way or another. In terms of public opinion and politics, examine a couple of ways in which the media can influence this.
POL SCI 001 Los Angeles Valley Political Science Voter Turnout Discussion

help answering questions right now.

Need help answering like 14 easy questionsIn a survey at a local university, 25%of students say that they get less than the recommended eight hours of sleep per night. In a group of 2600 students, how many would you expect get eight or more hours of sleep per night?nothing students get eight or more hours of sleep per night. (Type a whole number.)
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Jeddah University Marketing Questions Responses Paper.

The 3 responses should not exceed 3.5 pages single spaced 12 pt. font.Explain and provide examples of the concept of customers’ “jobs to done” as discussed by Clayton M. Christensen and his colleagues? How can you reconceptualize a product or service as Southern New Hampshire University did for its online programs, consistent with the “jobs to be done” concept.Explain how emotions may influence the consumption experience, providing examples as discussed in the article “Emotional Cues that Work Magic on Customers”. How can marketers strategically use these emotional effects?The article “The New Science of Consumer Emotions” the authors main thesis is emotional connections matter. Discuss what is meant by emotional connections and how they can explain successful and feeling marketing strategies.
Jeddah University Marketing Questions Responses Paper

The characteristics of fluid flow with sudden expansion in a 1:2 diameter ratio pipe are investigated using ANSYS Fluent. Results show fluid re-circulates just after expansion, length of recirculation zone approximates to 0.35m. Velocity, turbulence intensity and pressure vary along pipe length in accordance with Bernoulli’s principle. Influence of change in turbulence models on accuracy is also investigated with the Reynolds Stress model providing the relatively best fit although other turbulence models (realizable k-ε and SST k-ω) provide reasonably close fitting models. Results were checked for mesh independence and validated. Computational Fluid dynamics (CFD) involves computational simulation of fluid flows in different situations employing numerical solution of basic flow equations e.g. the continuity equation and other equations over a discretized unit reference (Versteeg, and Malalasekera, 2007). The usage of CFD transcends the traditional scope of chemical engineering profession into wider areas such as oceanography, biomedical engineering electrical circuitry, etc (Fairweather, 2011). Sudden expansion in pipes involves fluid flow from a smaller hydraulic diameter to a larger one. Flow separation usually occurs in a sudden expansion scenario, where a part of the fluid flows in opposition to the main fluid flow. This are called eddies, and are strong contributors to the irreversibility of practical flows as energy is dissipated by this eddies. Thus it is of great significance to be able to model eddies in a sudden expansion flow adequately and observe the characteristics of this recirculation zone (efluids, 2011; Gharegbagi and Ali, 2011; Mahmud, 2011: Roy, et al 2010). Sudden expansion is a simple looking but intriguing case of fluid flow in pipes. Sanmiguel-Rojas (2010) implies that not many significant studies have been done on instabilities encountered in steady, turbulent, sudden expansion fluid flow with respect to spatial structure of piping with D2/D1 = 2. However, previous remarkable work in this field includes Roy, et al (2010) and Mansoori and Bazargan-Lari (2007). Examples of scenarios in which the above phenomenon occurs include; Flows into a tank, oil drilling and extraction, plug flow reactors, combustion engines, aerodynamics, etc. Software ANSYS Fluent is a commercial CFD package that models flow via the finite-volume method (a variation of the finite difference method) created by the company Fluent (now part of ANSYS Inc.). Pre-processing of the case study (meshing) was done on Gambit which comes along with Fluent (now ICEM). The version of Fluent employed in this report is 12.1 (CFD-online, 2011; Weidner, 2011; ANSYS, 2009). This report covers the Reynolds Average Navier-Stokes (RANS) modelling of turbulent flow with sudden expansion in a 1: 2 diameter piping, using the pressure based solver and the second order upwind difference scheme in ANSYS Fluent. Effects of changes in turbulence models on computational time, and accuracy would be examined, visual plots would be used to describe and analyse modelling results. SIMULATION METHODOLOGY Fig 1: diagrammatic representation of simulation process (Fairweather, 2011) Nature of Fluid flow under consideration Calculating the Reynolds number of the flow helps to determine the nature of the flow. At Normal Temperature and pressure (P = 101.325 kgm-2, T = 288.16 K) Generally it is accepted that flows with a Reynolds number (Re) > 4000 are turbulent in nature. Therefore it is established that the flow under consideration is a turbulent flow Reynolds-Averaged Navier Stokes (RANS) RANS involves the time averaging of the equations that govern turbulent fluid flow to capture information on variations that occur on a minute scale while avoiding horrendously lengthy computation times. RANS represents variations as a mean such that ; ; ; and P RANS is employed in obtaining the equations that were numerically solved in this report assuming constant velocity and viscous flows (Fairweather, 2011). Geometry: The geometry consists of two pipes of diameter ratio 1:2 joined together through which fluid flows with no bends as shown below Fig 2: geometry of pipe showing mesh grid/mesh discretization Governing Equations Continuity equation: Momentum equation (x-direction only) Where:; ; ; ; ; ; TURBULENCE MODELS Realizable k-ε model The k-ε model is a two equation model that assumes a linear relationship between Reynolds stress and rate of strain. It has the advantages of fast computation time, wide usage and extensive validation. However, it predicts badly the length of eddies for complex flows. The realizable k-ε model is an update to the model based on observed strengths and weaknesses of the standard k-ε model (Fairweather, 2011; ANSYS, 2009). Below is a mathematical representation of the standard k-ε model Where: Φ = k or ε; SΦ=source term for k or ε; Sk= G-ρε (production rate of k-destruction rate of k); Sε= (C1G-C2ρε)(ε/k) = (production rate of k-destruction rate of k) ; N.B. for this simulation: ; and SST k-ω model The k-ω model is also a two equation model based on the Wilcox k-ω model. It is suitable for wall bounded flows and free shear flows as it performs low Reynolds number corrections, computation time is relatively fast and accuracy is better than the k-ε model in most cases. ω is specific dissipation rate and is analogous to a ratio of ε/k. The SST k-ω model is an improved version of the standard k-ω model (ANYSYS, 2009). Reynolds Stress Model This is a very rigorous model, with seven equations unlike the preceding 2-equation models. It provides more accuracy where other models are faulty e.g. impinging flows and can predict fluid flow for a lot of cases closely without any dedicated / individual adjustments. However, computing costs are large (Fairweather, 2011) The first six equations of the RSM model can be condensed into the equation below Where: ; ; ; The seventh equation (turbulence dissipation rate) is N.B. in this simulation: ; and Numerical methods The discretization employed is the finite volume method. It is a variant of the finite difference method. This scheme splits up the domain into discrete control volumes over which the control equations are resolved using a truncated Taylor series expansion. Finite volume method is the most established of Discretization schemes in CFD modelling. Convective fluxes were evaluated with the second order upwind-difference scheme (Fairweather, 2011; Versteeg, and Malalasekera, 2007). Boundary conditions Table 1: boundary conditions for numeric solution (adapted from Versteeg, and Malalasekera, 2007) Realizable k- ε model SST k-ω model Reynold Stress model Inlet k = 0.01148438 m2s-2 ε = 0.02888982 m2s-3 k = 0.1148438 m2s-2 ω= 27.95085 Rij = ε = Outlet ; ; ; Interior k = 0 ; ε = 0 k = 0 ; ω = 0 Rij = 0 ; ε = 0 Walls law of the wall Law of the wall Wall functions Convergence criteria and levels For all the equations solved by each model, a uniform convergence criterion of 1.0 x 10-4 was used for every equation solved. The value represented an informed compromise between acceptable accuracy and realistic computation time (ANYSYS, 2009). It is worthy of note that for the RSM model, this relatively stringent criterion caused the number of iterations to exceed 14,000 without any obvious improvement in results as shown in fig 2. Therefore a cap of 4,000 iterations was placed on the RSM calculations. Results show there was no ensuing negative impact on accuracy of numerical solution. Fig 3: Iteration length for RSM model showing Mesh Independence test The table below shows that results from the modelling experiment are similar and essentially the same within three (3) decimal places of precision irrespective of mesh size employed. Also since assurance of mesh independence cannot be guaranteed by mere reduction in cell size (Sloan et al, 1986), an attempt was made at adaptive meshing to attenuate important flow variations and phenomenon with the same results obtained. Table 2: Grid/Mesh independence of simulation Gambit Mesh/Grid size Volume of unit cells Mass flow rate at inlet [kgs-1] Mass flow rate at Pressure-outlet [kgs-1] Error Percentage Difference (%) 5 439,993 0.016809944 0.016809996 -5.22E-08 3.09 x 10-4 7 163,311 0.01678467 0.016784551 1.19E-07 7.08 x 10-4 10 55,182 0.016728994 0.016729204 2.1E-07 1.255 x 10-3 10b 100,693 0.016728994 0.016728895 -9.9E-08 5.9 x 10-4 15 16,750 0.016609019 0.016608695 -3.24E-07 1.95 x 10-3 N.B. 10 b means mesh size 10 with boundary layer mesh added (adaptive meshing) Grid optimization (Mesh finesse Vs Time trade off) The greater the volume of unit cells in grid per geometry, the better the accuracy of numeric analysis. However, within the scope of grid independence, results are relatively uniform irrespective of mesh size. The cost of finesse of grid is computation time could be noticed with the case of mesh size 5 (439,993 cells) which took almost forever to compute using the RSM model and had to be terminated. Thus mesh 10 (55,182 cells) and 10b (100,693 cells) were employed for analysis with other mesh sizes serving as validation checks RESULTS AND ANALYSIS Part 1 Taking a close look at flow close to the walls of the pipe, we see the effect of sudden expansion resulting in backflow of fluid creating velocities in the opposite direction (red box). Recirculation zone is approximately 0.37m in length. We also can see how the fluid adjust to changes in geometry with a sharp rise velocity to fill the voids created by liquid moving backwards then a gradual decrease as pressure pile us towards the exit of the pipe Fig 4: velocity variation along length of pipe close to the walls showing effects of recirculation Fig 6 shows the variation in turbulence intensity. It can be seen that the flow becomes more turbulent around the recirculation zone with dead (stagnant) flow occurring just at the corners of the pipe. Fig 7: displays the total pressure variations in the pipe. It can be noted that sudden expansion causes a drop in total fluid pressure. Fig 8 shows the radial velocity and profile. It can be noted that velocity variation in the radial direction is minimal, which is typical of plug fluid flow depicted by fig 5. Fig 9 is a streamline plot of axial velocity, velocity variation along the axial direction is more dominant than in the radial direction, also worthy of note is the length of the recirculation zone (black box) and the reattachment zone. Fig 5: stages of flow development at different positions on pipe length Fig 6: Turbulence intensity profile of fluid along length of pipe Fig 7: Total pressure profile of fluid along length of pipe Fig 8: Radial velocity profile of fluid Fig 9: streamline plot of axial velocity of fluid Part 2 Fig10(a-c) shows axial velocity profiles for different turbulent models in order of increasing complexity (‘realizable k-ε’ ⟶ ‘SST k-ω’ ⟶ ‘RSM’). Curves get smother showing a more gradual response of the fluid to changes and also approach exact solution, as model complexity increases. However, all the essential features of the fluid flow are well represented by all models. Fig 11(a-c) displays turbulence intensity variations, more variation details are captured as model increases in complexity. Worthy of note is that the SST k-ω model provides a more detailed picture of turbulent intensity variation in reference to the other models picking up intensities as low as 5.42 x 10-5 %, while the realizable k-ε picks up a minimum of 0.336% and RSM 1.45% Fig 12(a-c) shows streamline plot of axial velocity, though length of recirculation zone remains approximately the same the representation of velocity magnitude in recirculation zone varies visibly for each model. Fig 13(a-c) is the radial velocity profile; the SST model indicates larger radial velocities along pipe length than both than both the realizable k-ε and the RSM models. For all models radial velocity variation is dominated by axial velocity variations Fig 10a: k-ε model Fig 10b: SST model Fig 10c: RSM model Fig 11a: k-ε model Fig 11c: RSM model Fig 11b: SST model Fig 12a: k-ε model Fig 12b: SST model Fig 12c: RSM model Fig 13a: k-ε model Fig 13b: SST model Fig 13c: RSM model VALIDATION OF RESULTS For CFD, convergence of numerical iterations does not really count for much as Versteeg and Malalasekra (2007) put it “results are at best as good as the physics embodied in it, or at worst as good as the skill of the operator”. Thus, validation of results becomes extremely important. The results obtained herein would be validated thus: Bernoulli’s equation For an ideal fluid flow Bernoulli’s equation enables us to calculate the velocity at any point in the pipe (assuming constant flow rate, and negligible friction losses). Therefore we can validate output velocity from fluent using this principle (Roymech, 2011). Where vin = 1.73855 ms-1, P1= 101.325 kgm-2, P2= 101.325 kgm-2, g = 9.81 ms-2; ρ =1.225 kgm-3; z1 = 0.1m; z2 = 0.1m; Therefore Mass flux variation results from Fluent The third mechanism for validation will be the CFD package fluent itself. Analysis of the computation results as presented in table 4.0, show that value of errors resulting residuals is very low (less than 0.0095%) indicating conservation of mass during numerical calculations which lend credit to suitability and accuracy of model. Table 3: comparison of percentage error of each model MODEL/mesh volume K-EPSILON (%) SST K-OMEGA (%) REYNOLD STRESS (%) 5 0.000309 0.00352 N/A 7 0.000708 0.004468363 0.000673233 10 0.001255 0.007867 0.001124 10 b 0.000153 0.00258 0.001488 15 0.00195 0.000783 0.00927 N.B. 10 b means mesh size 10 with boundary layer mesh added Research journals In addition to the above validation processes, the results of modelling experiment reported in this work were compared with previous research works such as (Roy, et al 2010), (Mansoori and Bazargan-Lai 2007) and (Teyssandiert, 1973). Results obtained corroborated foregoing analysis and results obtained it the above mentioned papers. CONCLUSION In summary, CFD modelling of sudden expansion flow in a 1:2 diameter ratio piping posses the following characteristics. Sudden expansion in pipe flow results in local pressure losses Flow fully develops into plug flow before exit at outlet and majority of the variations occur axially along reactor length Recirculation of fluid occurs after sudden expansion for a lengthspan of approximately 0.35m along pipe Viscous effects along wall boundaries help dissipate energy of turbulent eddies The realizable k-ε model predicts the size and strength of recirculation zone poorly, but as flow develops into plug flow, the model’s accuracy remarkably improves with reference to the other models tested. Turbulence models become better with increase in complexity of model from k-ε to SST k-ω to RSM. Ability of other models to better the k-ε model in recirculation zone prediction can be attributed to embedded corrections for boundary layer flow, turbulent kinetic energy and dissipation rates.

Network & Cyber Security Management

Network & Cyber Security Management. I’m studying for my Computer Science class and don’t understand how to answer this. Can you help me study?

Part One –
According to recent surveys, China, India, and the Philippines are the three most popular countries for IT outsourcing. Write a short paper (2-4 paragraphs) explaining what the appeal would be for US companies to outsource IT functions to these countries. You may discuss cost, labor pool, language, or possibly government support as your reasons. There are many other reasons you may choose to highlight in your paper. Be sure to use your own words.
(Note:- This assignment has to written in the attached sample article file )

Part Two –
Many believe that cloud computing can reduce the total cost of computing and enhance “green computing” (environmental friendly). Why do you believe this to be correct? If you disagree, please explain why? ( 200 words)

Part Three-

5.1 Provide a brief definition of network access control.
5.2 What is an EAP?
5.3 List and briefly define four EAP authentication methods.
5.4 What is EAPOL?
5.5 What is the function of IEEE 802.1X?
5.6 Define cloud computing.
5.7 List and briefly define three cloud service models.
5.8 What is the cloud computing reference architecture?
5.9 Describe some of the main cloud-specific security threats.

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Network & Cyber Security Management

ECO 535 VSU Business Analysis of the Oil Embargo Discussion

help me with my homework ECO 535 VSU Business Analysis of the Oil Embargo Discussion.

Review the Wk 1 Resources.Write a 350- to 700-word analysis assessing how 1 of the following major economic events influenced supply, demand, and economic equilibrium in the US economic activity:Rapid price increases, such as caused by the 1973 oil embargo or the aftermath of a major hurricaneDramatic employment drops, such as the combined impact of the 2006 housing bubble burst and the subsequent Great RecessionCrippling interest rates by the Federal Reserve, such as those of the 1975 – 1985 time periodCollapse of the Soviet Union in 1991 and the end of the Cold War, and the “peace dividend”The dot-com bubble from 1994 to 2000, and the subsequent dot-com crashCite at least 2 academically credible sources. The use of charts and tables to illustrate data is highly encouraged.Format your assignment according to APA guidelines.
ECO 535 VSU Business Analysis of the Oil Embargo Discussion

Service Learning Midterm Assessment (SLMA)

Service Learning Midterm Assessment (SLMA). I’m studying for my Sociology class and don’t understand how to answer this. Can you help me study?

Your Service Learning (SL) experience (15 hours) will augment your understanding of our course material by facilitating its field application. As such, this assignment must be completed in a single program and fully during this term. This SLMA will update me on your progress in meeting the agreed-to SLIC work plan.

The attached file has the detailed instructions for the assignment and the rubric.Thank you.
Service Learning Midterm Assessment (SLMA)

Columbia College Quality Improvement Operations Management Report

Columbia College Quality Improvement Operations Management Report.

Only Week 4 assignment needs done, the week 2 section was completed and is attached. Week 2 report topic also describes what to make the outline aboutIn any given workplace, whether it is a hospital, manufacturing operation, retail store, or the home, there are processes that are either broken or can be improved. The purpose of this report is to identify a problem(s) and devise solutions that result in improved processes or outcomes.You will complete a Quality Improvement Report after identifying a deficiency in the workplace or home environment and developing a plan to address the issue(s). The self-selected location will be submitted to the instructor for approval in Week 2. Using various components of the Total Quality Management (TQM) Philosophy, students will create a plan that serves the purpose of demonstrating knowledge and understanding of TQM tools, techniques, and processes and how to apply them appropriately to a demonstrated deficiency. The report will also prepare students to communicate in a professional setting.The Quality Improvement Report will have several smaller building assignments in Weeks 2, 4, and 5.Week 2: Quality Improvement Report: Topic Submission: You will choose your topic and submit it to the dropbox for instructor approval by 11:59 pm CT Sunday. The topic will identify a quality or process problem in the workplace that results in deficiencies in process and/or undesirable outcomes. The Quality Improvement Report Topic Submission is worth 20 points.Week 4: Quality Improvement Report: Outline: The Quality Improvement Report Outline should consist of bullet points (with words or phrases) that are descriptive enough to inform the Instructor of what the intention of the line item is. It should be composed of three main topics, supported by sub-topics, exclusive of the introduction and conclusion.
Columbia College Quality Improvement Operations Management Report

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