Include the pseudocode algorithms you developed for the sender and receiver to handle multiple messages to a recipient. You need to choose between those instructions: Insert_at_end_of_list(msg) Insert_at_front_of_list(msg) Remove_msg_at_end_of_list Remove_msg_at_front_of_list The pseudo code for the sender will be:

Fast sportcars the top speeds of sportscars, given in miles per hour, are: 155 mph bmw m5 217 mph lamborghini aventador spyder 205 mph ferrari 488 205 mph nissan gtr 197 mph chevrolet corvette stingray zr1 258 mph bugatti veyron supersport 195 mph dodge viper 270 mph hennessey venom 155 mph bmw m3 195 mph mercedes sl given: topspeeds=[155; 217; 205; 205; 197; 258; 195; 270; 155; 195]; carnames=string(["bmw m5" "lamborghini aventador spyder" "ferrari 488" "nissan gtr" "chevrolet corvette stingray zr1" "bugatti veyron supersport" "dodge viper" "hennessey venom" "bmw m3" "mercedes sl"]); the variable is a rectangular array. write a function called selectcars to identify cars with the top speed within a given range, and display the identified names. the selected cars speed will be in a range given by lowerbound < speed < upperbound. inputs to the function selectcars are: a column array os all car top speeds named topspeeds, the corresponding chara

(328 inc. 448 ind. 480 in25. john has a collection of toy cars. he has 2 red cars, 4 blue cars, 4 black cars, and 6 yellowcars. what is the ratio of red cars to yellow cars? a. 1: 2b. 1: 3c. 1: 626. the net of a right triangular prism is shown below.

When making changes to optimize part of a processor, it is often the case that speeding up one type of instruction comes at the cost of slowing down something else. for example, if we put in a complicated fast floating-point unit, that takes space, and something might have to be moved farther away from the middle to accommodate it, adding an extra cycle in delay to reach that unit. the basic amdahl's law equation does not take into account this trade-off. a. if the new fast floating-point unit speeds up floating-point operations by, on average, 2ă—, and floating-point operations take 20% of the original program's execution time, what is the overall speedup (ignoring the penalty to any other instructions)? b. now assume that speeding up the floating-point unit slowed down data cache accesses, resulting in a 1.5ă— slowdown (or 2/3 speedup). data cache accesses consume 10% of the execution time. what is the overall speedup now? c. after implementing the new floating-point operations, what percentage of execution time is spent on floating-point operations? what percentage is spent on data cache accesses?