Fun Essay Topics For High School - Ideas You Can Do Right Now

Fun Essay Topics For High School - Ideas You Can Do Right NowAre you looking for fun essay topics for high school? High school is a tough time, but you can take some extra time to write your essay. Here are some ideas for ways to turn a boring assignment into a fun one.Great idea. Have you ever seen what sports you can do in writing. Not only is it something fun but it is an effective way to earn your college degree and become a great writer. Sports are everywhere, and you don't have to be interested in them. You just need to write about the sports that interest you.How about swimming? What about football? Baseball? Think about some sports that you like and that you feel passionate about and you can get started with a fun essay topic for high school.That's the first idea for you. The second one is more of a general amusement. But they both work well together. One of the best ways to keep a college on track is to play around with some subjects.What is the easiest thing you can do to k eep yourself on track to get a college degree? Try some fun essays for high school. It might take a little experimenting and research to figure out exactly what is fun for you, but it's worth it!Of course, there are a few things that you can do for college, but they aren't always a fun topic for your assignment. However, for fun essays for high school, you can do a lot to make sure that you learn as much as you can while you're studying and you can write all you want.It'important to realize that college is an experience, not a place where you sit down and do your homework. Even when you're having fun, you'll still have to read and study.

Should Children with Adhd Be Medicated Essays - 2080 Words

Should children with ADHD be medicated? Although many parents, doctors, and teachers would say yes I believe there are far too many risks for children with ADHD to be medicated. [You left out a comma in the previous sentence.] Diagnosing ADHD is purely a matter of a doctors opinion. Parents often use medication as the first option for their children instead of trying alternative methods of medication. Medication needs to be the last option. Children with ADHD are being medicated unnecessarily. The problem is there are no biological tests for the disorder, and it cannot be revealed through a blood test. ADHD should be the last resort for a diagnosis, but it is often the first choice. In many cases a child who is disruptive in class or†¦show more content†¦A vicious cycle happens such as doctors prescribing more medication, as treatment continues its calming effects diminish, requiring more medication. [another comma splice] The drug rebound is easily confused with the childs ADHD, causing the Doctor to increase the medication. Between 1990 and 2000 186 children died from taking the drug Ritalin, and research is also being done to see if Ritalin causes brain damage. [You need a comma after the dates.] KC(2007) parents who have chosen medication give mixed testimony. Some have had dramatic and outstanding results. Others have not had such good luck with many modifications in the childs medication with few results. Some parents mourn the loss of a spark that their child once had, and others say they noticed no personality changes in their child. Some people who initially decided against medication had poor results and realized a tremendous turn around once they decided to medicate. The outcome with medication is very individualized for the child as it would be without medication. Benefits of medication may include more control of a childs behavior, improvement in a childs ability to concentrate, and moreShow MoreRelatedEssay On Children Overmedicated In America1420 Words   |  6 Pages Children Are Being Overmedicated in America We as people often take medications for any problem we may have, from headaches to conditions that are more serious. While most prescribed medication is necessary, some of it is not. Many children are being given medication when they don’t need it. According to a statistics report done by the National Institute of Mental Health, there has been a 21% increase in the number of children on psychiatric drugs from 1988-1994 to 2007-2010. These children areRead MoreThe Dangers Of Adhd Medications1060 Words   |  5 PagesADHD Medications The Perils Within Medicating our children with ADHD, is having a disastrous effect on our society. 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Males are more likelyRead MoreTeenage Pregnancy And Police Brutality Essay1497 Words   |  6 Pagescan take away from and use in the future.† I began pondering. Bingo! â€Å"Are children being overly medicated?† This interested me because my father always talked about how when he was a kid he was put on Ritalin for ADHD, and constantly discusses with me his fear for his little brother who is currently being medicated for ADHD. I read one article, and it stated that maybe kids are just being under treated, not overly medicated. That interested me, and made me think about my own life. Ever since freshmanRead MoreAttention D eficit Hyperactivity Disorder ( Adhd )1991 Words   |  8 Pages Highway to Over Medication (ADHD) Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common childhood disorders in the country -- and its numbers are growing rapidly (â€Å"Attention Deficit†¦Ã¢â‚¬  ). In 2007, 9.5 percent of children were diagnosed; this number jumped to 11 percent in 2011 (Hanscom A18). A whopping 6 percent of these children are on medication for the disorder (Frances 49-50). Magill’s Medical Guide says: â€Å"[ADHD is] a condition characterized by an inability to focus attentionRead MoreRhetorical Analysis : Attention Deficit / Hyperactivity Disorder ( Adhd )912 Words   |  4 PagesAttention-Deficit/Hyperactivity Disorder (ADHD) is a serious neurodevelopment disorder that has affected an innumerable number of children and adults in the United States (Centers for disease Control and Prevention, 2013). It is one of the most common disorders that can cause difficulty at school and home and can also remain into adulthood. The Centers for Disease Control and Prevention (CDC)publishes a web page on ADHD to inform and persuade the public that ADHD is a condition that they should be concerned about. 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What is modelling - Free Essay Example

Sample details Pages: 31 Words: 9349 Downloads: 3 Date added: 2017/06/26 Category Statistics Essay Did you like this example? 1.1 Introduction to Modeling: Introduction to Modelling: Modelling is an influential method l. With it, we can perform following functions. Don’t waste time! Our writers will create an original "What is modelling" essay for you Create order Analyze Design operate complex systems Hypotheses checking(Testing) with minimal cost (Performing the original events) Modelling an effective communication Method/tool that tells us the happening of operation (how actually operations is done) and stimulates innovative rational about how to improve it. These models shorten design cycles, minimize the expenses and increase the knowledge for industries like government, and educational institutions. We build the models to evaluate the real world problems which are complex to analyze through flowcharts and spreadsheets. 1.2 Queuing theory: The mathematical study of waiting line is known as Queuing theory. In queuing theory, with the help of mathematical and statistical tools we analyze different processes. Thus we find different derivations and calculate the several performance measures which include, how much time spent in the system, what is the probability of bottleneck, how much customers are severed or waiting for service, Weather the system is completely occupied or partially or none of the customer is there waiting for service. The calculations and results are utilized for business proposals, decision related to resources to complete certain tasks, that is why it falls under the domain of operations research. It is used in different type of situation and industries like in commerce, business industry, public service and engineering. It has effective use in the transport and telecommunication. 1.3 Queuing Model: In queuing theory, the real situations or system are shown in the form of queuing models which tells the behaviour of the original situation. The results are evaluated mathematically. We find following performance statistics by applying queuing theory. How much average number of customers in a line? How much average time used up in the lines? What is the algebraic pattern of these results? Weather the system is completely occupied or partially or none of the customer is there waiting for service? The above statistical parameters are used to measure the customer satisfaction. These also help us in identifying the root causes of the queue problem and economic loss in business. 1.4 What is Simulation? Simulation is defined as imitating a certain phenomenon (outward appearance or behaviour) by using another device. From the engineering point of view, knowing the actual behaviour of a system by making a duplicate having same characteristics as the original, with the use of a computer model is known as simulation. Simulation sometimes means experimentation with a scale model. In most cases a model is constructed within a computer and numerical experimentation is done by using the model. The device which executes simulation or the combination of computer and the internal software is known as a simulator. The main reason for the widespread use of simulation is the rapid and progressive development of the power of a computer. From the requirement point of view, a comprehensive understanding and evaluation of the subject systems has become essential in many fields because the systems used by society and industry are not only complex, but also massive, which complicates the analytical int erpretation of the system as a whole and its experimentation. Simulation wherein a system is the object or simulation in system engineering is known as system simulation. This book focuses on system simulation. The utility of system simulation is not merely to interpret a problem, but in most cases to enable an in-depth understanding of it in order to find solutions by varying the inputs to the model and values of parameters. Such understanding is termed sensitivity analysis. In short, simulation is an indispensable measure for decision or judgment procedure and has become an important tool research and business. A simulation is not an objective per se but a tool or a procedure for attaining one. A problem is not solved simply through the results of simulation computations. The quality of simulation results cannot be determined solely by the simulation. Its utility or, in other words its validation, must be thoroughly assessed in view of the assigned objective and object. In the 196 0s digital computers were put to practical use which brought about the development of several widely used simulation languages. These have gained ground in the world of large computers and are here to stay. The 1970s brought about significant utilization of simulators in various social and industrial fields and the development of simulators for exclusive use in various fields is continuing even now. In the 1980s, expansion in the development of simulators for exclusive use in various fields continued due to increased interest in the general purpose simulator, improvement in the performance of computers, and arrival of the supercomputer. On the other hand, several work stations and personal computers (PCs) appeared on the scene in the 1980s. Promotion of colour display and improvement in computer graphics technology stimulated visualization of computed results and usage of simulation techniques. User friendly simulation programs for work stations or PCs hit the market worldwide. One outcome of the availability of such programs was the ushering in of an era in which PC users and children can enjoy computer games which ingeniously make use of computer graphics and simulations. In the 1990s some people have become dissatisfied with the existing computers or simulation. Expectations from physicists for contributions to simulation are great. Computation physicists consider that by turning a computer into an experimental tool, the natural (physical) world and physical phenomena can be unravelled; they anticipate the arrival of a computer having a computing speed of 1 million times greater than the existing ones. The ideal of computation experimentation is still a dream. However, if with progress computing speed can be made more rapid, and then real time animation with the supercomputer could probably be realized. 1.5 History of Simulation: The analog type calculating machine was the first to appear in the history of computer simulation. Slide rules, planimeters, integrators, etc. have long been in use. The differential analyzer and the harmonic analyzer were created at the end of the nineteenth century. Integration was done by the product of rotational angle by means of a mechanical circular plate. In 1931, V. Bush invented the differential analyzer at MIT. It could also be called a mechanical type simple analog computer. Bush solved the highest 6th order differential equation with this [7]. Thereafter a device was developed which can automatically solve differential equations. Analog computers with vacuum tubes appeared on the scene around the time that World War II came to an end (1945). Who the inventor was is not quite clear but most people feel that this calculating device, having the same principle as the differential analyzer, can be achieved by a combination of vacuum tube circuits. In the 1950s, analog compute rs were investigated, studied and mostly used. After the mid-1950s, when digital computers came into use, an idea was put forward to perform the simulation technique being carried out on an analog computer, on a digital computer as such. The software which embodied this concept was developed in various ways along with establishment of the general purpose large computer. .CSMP and DDS [8] were mostly used as the continuous system simulation languages. In the 1960s, simulation languages for the discrete system were also developed and GPSS and SIMSCRIPT are used even today. The beginning of the 1970s made use of the supercomputer possible and simulation of a natural phenomenon or a physical phenomenon advanced. With the popularization of PCs and work stations during the latter half of the 1980s, in the development of several customized application programs, simulation programs, with excellent man-machine interface have been developed. 1.6 An Outline of Simulation: When we talk of simulations, there are certain important items such as what is the objective of simulation, what steps are needed along with simulation as measures for accomplishing that objective, what are the entities of a simulation language used in a simulation model as well as simulation procedure and so on. These are shown in a Figure 1.1, which of course does not show all the items but selects the familiar heads of system simulation. Simulation is necessary at various points of time, such as time of system analysis (planning), time of designing, time of operation, after completion and so on, but mostly simulation is used at the time of planning and designing for pre-assessment of system in development, first of all qualitative assessment of the development subject and effect of development are carried out using such systems approach methods as scenario writing, brainstorming or the KJ (Kawakita Jiro) method, etc. For the essential parts of technology, performance is evaluated individually by experimentation. For actualization of simulation, there is need to pay attention to the many items shown in Figure 1.1. Depending on whether the variables of Simulation have to be handled with analogy, digital or hybrid (which combines both), it is decided whether to use an analogy computer, digital computer or hybrid computer. When constructing a model, there is need to properly understand the objective of simulation and to extract the distinctive features of the real world taken as the object. In the range of modelling one may either 1) Consider the actual world by dividing it into the object and the control scheme policy) to be applied to that object or 2) Imitate the object alone, excluding the control scheme or including it within the model? As far as the degree of approximation is concerned, there are fewer cases of using the primary model, which corresponds to the basic equation related to theory, but many cases of using the idea-related approximate model (secondary model). Actually occurring phenomena evolve with the passage of time and when the time variation is taken into account it becomes a dynamic model; when there is no need to take time variation into consideration, it becomes a static model. A dynamic model can be divided into continuous- change model and discrete-change model depending on the conception of time. The forward direction method which carries out simulation with the passage of time is the general method for simulation but sometimes reverse direction simulation is also done to seek the policy variable (corresponds to input in normal simulation) for realizing targets such as policy decisions. In both forward direction and reverse direction, repeated simulation is usually carried out by trial and error while changing the conditions. It is also possible to carry out optimization simulation by adding the function of optimization, which automatically seeks the optimum conditions. Depending on the aim of simulation, there are cases in which the time for proceeding simulation must be the same as the actual. For instance, driving training for aircrafts and vehicles must be proceeded in real time. Conversely, there are cases of shortening or extending the actual time. A large number of simulations are carried out with such an artificial time. 1.7 Objective of Simulation: Simulation is used for understanding effects because use of an actual subject is not only costly but also involves certain risks. Besides, there is uncertainty regarding the time it will take to realize the results. The objectives of simulation include the following: 1) Qualitative understanding of the phenomenon occurring in the real world or the behaviour of a system is the first step to system engineering. If there is a model which can show the actual mechanism then new knowledge can be acquired by varying the inputs to the model or the parameters and doing repeated simulation. Utilization of simulation enables Understanding the output response with respect to the input Analysis of interfering conditions of two systems, e.g. the mechanical system and the electrical system Sensitivity analysis of parameters or policy and so on. These are shown in Figure 1.2 (a), (b) and (c). However, the model being used is not always necessarily correct. There is a method whereby several hypothetical models are given and experimentation is done with simulations and the correctness of the model is studied by verifying it against already known facts. This method is known as identification by simulation. The simulation used in physics research falls in this category. There are various methods for identification also, such as (d) identification of mere subject (e) cascade connection of identification system, (f) identification of cascade process and so on, as shown in Figure 1.2. Such types of simulations are quite often used for understanding and predicting not only natural phenomena, but also the existing state of social phenomena. 1) The system is developed after going through such processes as analyze, planning, designing and operation, But there is need to qualitatively analyze and understand the function and performance of the developed system beforehand in. each process. As an example of system development is the establishment of a chemical plant. Balance calculations with respect to incoming and outgoing material or heat balance are necessary for deciding the layout of the plant or configuration of the equipment. When installation of equipment is planned by simulation using such a static model, the quality of the operation control strategy can then be judged by operational simulation which makes use of a dynamic model. 2) Another objective of simulation is not just to examine the degree of satisfaction of function and performance, but also to evaluate the effect of system development on the system exterior by judging the quality of the overall system plan. This is known as environmental assessment or evaluation of negative impact. 3) There are also instances wherein human beings are actively included in simulation. In this case a system or model without human beings is well examined beforehand and already confirmed, after which humans are included in the simulation when the objective is to understand human behaviour and to provide educational training. Examining through experimentation the usability convenience for operating certain equipment used by people is an example of simulation for ascertaining human behaviour or psychological reaction. A flight simulator or business games are examples of educational training. Evacuation training at the time of prevention of building disaster constitutes not only educational training but also a means for understanding human behaviour under uncommon situations. 1.8 Business Process Simulation: Business Process simulation is the method that enables representation of processes, people, and technology in a dynamic computer model. In doing business process simulation, we follow four steps. Structure a model running a model evaluating the performance measures Assessing alternative scenarios. The model mimics the operation of a business, by displaying the flow charts, animated pictures. The simulation software keeps the track of results and these results are used in evaluation process. 1.9 Discrete-System Simulation: A real system is changing its state continuously. However, from the simulation point of view, when ,the change in state of the object system with the passage of time is considered as having occurred by events rather than continuously, it is termed a discrete system. During simulation, if the object system is modelled by considering it a system in which changes in state are taking place discretely, it is called discrete system simulation. 1.10 Simulation-based Optimization Techniques: The main tool for designing the complex systems is discrete event simulation. Optimization techniques must be linked with simulation in order to design the system effectively. We show many optimization methods/ techniques. We find the doable practices for certain simulation model. 1.11 Simulation Software: The new user is facilitated with the help of lots of new simulation software. Simulation software comprises the following items, like support, reactivity to bug notification, interface, etc. A thing needed to keep in mind while we consider simulation software, like why do we need it, what should be the purpose, what should be the complexities, the foremost question should be why we need this? Does it for some industry or university or any sort of project for students? After that we need to look in to some complex questions like what should be the main aspect of that program. Then we have to lay down some procedure to do so. What should be the input and output, what should environment. Do we need to train the people to handle certain tasks? How we can optimize the process. How we do the statistical calculation? Can we handle all these calculations? What is should be the cost of this process? It is very important to know which feature is the most important for the scenario. Table 1.1: Examples of simulation utilization in various fields ELECTRIC POWER Electric power system operation plan, water flow system operation plan, analysis of system characteristics, operation training NUCLEAR POWER System design, reactor core design, reactor core fuel design, operational control, physical phenomena (irradiation, plasma) IRON AND STEEL Plant layout plan, operation plan, management plan, stock management PLANT Facility plan, operation plan, scale model, visualization of flow, vibrations, reliability and safety analysis TRANSPORTATION City traffic plan, railway system plan, operational control, plan for flow of objects on the premises, simulated visual range COMMUNICATION Network configuration plan, transmission switching function, Network control function, network management function COMPUTER Configuration plan, function (logic) evaluation, performance evaluation , operator/terminal evaluation SEMICONDUCTOR Circuit design, device design, process design Manufacturing MANAGEMENT Support for Decision making, production management, stock management 1.12 Our Objective The main purpose of this study is to provide a platform for modeling and simulation for the next coming student. Another underlying objective is to help and facilitate the industry to optimize their existing processes. Also helping them by the aid of demonstration of an optimization process we will build a model of a business process and then simulate it on appropriate software to analyze the existing position and its finest likely substitutions. 2.1 Definitions: Modelling turns the real world into an abstract form and reproduces it as a concept which corresponds in form with the objective of simulation that which describes actually existing things and shapes or those parts of a system of particular interest, is known as a concept model or a thought model. Embodiment of this concept model and turning it into a form which can be installed in a computer is called, in short, a computerized model. While constructing the simulation model, due attention must be paid to integrating the idea of modelling and determining the range of the model. Modelling is a kind of process of conceptualization and since there are quite a few degrees of freedom in conceptualization, it is necessary to proceed with a consistent idea when modelling. Determining the range of the simulation model and selection of its structural elements are problems that can only be resolved when the properties of which part of which type of system to be investigated have been ascertained. The range and structural elements are related not only spatially but qualitatively as well. How the real world is to be apperceived at the time of modelling varies with the objective. There are two ways of comprehending the real world (Figure 2.1). One way sees the real world as comprising a really existing object and the policies controlling that object and the other looks at the real world as a whole, i.e., as a single object for analysis. The former is a modelling method used in simulation of the control system in design or in the process of policy planning. This approach is taken in the case of comparing and evaluating control policy alternatives by simulation. The latter aim at a status quo analysis for a state wherein separation between the object and. controls plan is insufficient, or when the objective is an in-depth understanding of the object for planning the control policy; in this case the modelling is cantered around the object and then simulated. 2.2 Modelling Procedure: The modelling process for a large complex system such as the social system can be approximately separated into two steps: the first step is structural analysis to ascertain the type of structure in the object system or structural determination (identification); the second step quantifies the results and expresses them as a functional relation. 2.2.1 Structure Determination: Structure determination (structuring) is the selection of primary factors (also known as elements or components) which need to be inducted into the model and analysis or identification of a connecting relation between these factors. For this purpose it is necessary to select the main factors from among several and to clarify the connecting relation among them. To include unrelated or remotely related factors would clutter up the model and result not only in a less accurate solution but demand more time in seeking it. The methods for selecting these factors include factor analysis, principle component analysis, multidimensional scaling analysis, etc. In addition, the KJ method, the ISM (interpretive structuring model), which falls under the graph theory and matrix method, the DEMATEL (decision making and trial evaluation laboratory) and the PPDS (planning procedure to develop systems) are well known [1]. 2.2.2 Determination of Functional Relation: After structuring has been established among the factors of the system, the functional relations to describe the relations among the variables of the model are determined. The functional relations comprise a static model which emphasizes the equilibrium state of the variables and expresses them by simultaneous equations, and a dynamic model using the differential or difference equation which lays emphasis on the temporal change in the variables. The economics model is a representative of the static model. Several engineering systems, in particular the control systems, broadly use the dynamic model. Since a computer cannot make a selection regarding which are the main variables or which are the important functional relations, this necessarily depends on the accumulated experience and insight of the user. This is a vital prerequisite for doing effective simulation. 2.3 Simulation Models: A simulation model usually shows how a system works? We use models to mimic the real life situations. With minimal cost we can test our hypotheses. Say, we want to setup certain facility, we made a model of that and then run it to check the results. After that we can fine tune the model. Also we can add features at any time in the model. 2.4 Classification of Models: There has not been much research regarding the science of classification of models or simulation (clustering of models). The reasons for this are several: the science of classification cannot produce a realistic clustering, the objects of simulation are diverse, and hence it is difficult to obtain a technical uniformity among them. A classification of a model in terms of degree of approximation, degree of abstraction, temporal representation, model characteristics and so on is given below [2, 3]. 1) Classification According to Degree of Approximation: Micro model Macro model Primary model Secondary model 2.4.1.1 Primary Model: A primary model means a model without approximation such as fundamental equations. The primary model encompasses the most fundamental theoretical laws or axioms and corresponds to the material point receiving the application of the three axioms of Newtonian mechanics, universal gravitation and the DNA model in biology. 2.4.2.1 Secondary Model: The secondary model aims to act as a mediator between the real world and the theoretical world and makes the application of theory easy by selecting the elements which are important in reality and omitting the less contributory elements; it constitutes one of the approximation ideology related models. In the case of simulation of a large-scale system, the secondary model is mostly used due to easy comprehension and reduced calculation time. The creation of a suitable model is linked with achieving successful simulation. A secondary model being an approximation model, complimentary and antagonistic secondary models exist together. 2) Classification According to Degree of Abstraction of the Model: Physical Model Actual model Scale model Analogy model Analog model. Mathematical Model Digital model Statistical model Analytical model. Logic model Structural model 3) Classification According to Temporal Representation: Static model Dynamic model Continuous-change model Discrete-change model Combined Model 2.4.3.1 Static models: These are represented in the form of equations. In that we find the effect of each substitute by one time computation of equation. We take variable as averages. By adding the individual effects, we find the results. For example, Spread sheets are static models. 2.4.3.2 Dynamic models: Simulation is known as dynamic modelling. It is represented by software, which do multiple calculations as opposed to static model in which only one calculation is done on equation. It is time based model, so whenever the time changes, it recalculates the model. 2.4.3.3 Continuous-Change Model: The method for building a model by the continuous-change model perceives the subject system to be changing continuously and depicts the change in the system at tiny equal intervals of time. A fluid flowing through the pipe is good example of Continuous simulations. In continuous flow volume may vary i.e. increase or decrease, with the passage of time values changes. The simulation languages used for the representation of continuous change model are CSMP and DYNAMO. 2.4.3.4 Discrete-Change Model: The method for building the model by the discrete-change model pays attention to any significant event occurring within the system and portrays the system by connecting events together. The simulation time is forwarded at unequal intervals of time at each occurrence of the event. We consider the patient flow in a clinic. In that the clinic could be empty or have any number of patients moving through it. The patients come out in random intervals. On the other exit of clinic, Actions happening and inside the clinic, would finds what comes out and when. During the simulation discrete entities change state as events occur. Patient arriving, passengers arriving and in banks customers service calling are few examples of discrete events. 4) Classification According to Model Characteristics: Linear model Nonlinear model Deterministic model Stochastic model (Monte Carlo method, gaming method) 2.4.4.1 Deterministic Model: A simulation model is properly used depending on the circumstances of the actual world taken as the subject of consideration. A deterministic model is used in that situation wherein the result is established straightforwardly from a series of conditions. In a situation wherein the cause-and-effect relationship is stochastically or randomly determined, the stochastic model is used. A deterministic model has no stochastic elements and the entire input and output relation of the model is conclusively determined. A dynamic model and a static model are included in the deterministic model. Simulation by the deterministic model can be considered one of the specific instances of simulation by the stochastic model. In other words, since there are no random elements in the deterministic model, simulation can well be done just once. However, in case the initial conditions or the boundary conditions are to be varied, simulation has to be repeated by changing the data. On the other hand, in Monte Carlo simulation, once the value has been decided by extracting a random number, the simulation does not differ from deterministic simulation. 2.4.4.2 Stochastic Model: A stochastic model has one or more stochastic elements. The system having stochastic elements is generally not solved analytically and, moreover, there are several cases for which it is difficult to build an intuitive perspective. In the case of simulating a stochastic model, a random number is normally generated by some method or the other to execute trial. Such a simulation is called the Monte Carlo method or Monte Carlo simulation. In case the stochastic elements in the simulation are two or more persons and there is a competitive situation or some type of game being reproduced, this is specifically known as gaming simulation. 5) Classification According to Application of the Model: Company model Economics model City model Industrial model Environment model Business enterprise model Production model Plant model Engineering model Mechanical model and so on. 2.6 Model evaluation: A very important step in modelling process is to measure the usefulness of a model, how it works, does it shows the properties of a system for which we have developed the model, does the model shows all events which are measured and which couldnt be measured (Extrapolation). Usually we divide data in to two data sets. Training data Authentication data. We use the training data to train the model. While to evaluate model performance we use verification data. Let us assume that the training data and verification data are not similar. Now if the verification data is best fit on the model, then the model defines the real system well. 3.1 Queuing: Queuing was initially used by Agner Krarup Erlang, An engineer from Denmark, who was employed in the Copenhagen Telephone Exchange, published the first paper on queuing theory in 1909. In 1953, queuing code was introduced by David G. Kendall. 3.2 Elements of Queuing Systems: Following are the queuing elements. 3.2.1 Population of Customers: Population may be limited (closed system) or unlimited (open system). An imaginary model with big numeral of likely customers is considered to be unlimited population e.g. a hospital; on busy road, a motorway toll plaza. While in the scenario of a limited population, it may be limited to certain amount or quantity. 3.2.2 Arrival: The way of customers entry in to system is defined by Arrival. Usually the random arrivals have random intervals among two contiguous arrivals. The arrival pattern is shown by random distribution. 3.2.3 Queue: How many customers are waiting for service is described as Queues. Normally the customers which are attended are not assumed to be the part of the line. There are two essential features of a queue: Maximum Size and Queuing Discipline. Maximum Queue Size (also called System capacity): The number of maximum client in a queue depicts the system capacity; it may be limited or unlimited. Mostly its limited in nature, while in theory, we may assume the unlimited system capacity. The limited capacity of system may cause rejected without being served. Queuing Discipline: How a queue gets a shape comes under the domain of queuing discipline. (Guidelines of injecting and eliminating customers to/from the queue). Following are the ways to represent the queue. 1. FIFO (First in First Out) also called FCFS (First Come First Serve) orderly queue. 2. LIFO (Last in First Out) also called LCFS (Last Come First Serve) stack. 3. SIRO (Serve in Random Order). 4. Priority Queue that may be observed as a number of queues for several priorities. 3.2.4 Service: An activity takes some time to complete, that time is called service time, while other customer waits during that time. It should be taken as general, as it might be a client or machine. Service pattern are described as the theoretical models which are based on random scattering of service interval. An additional significant factor is the number of servers. These are of two types, single channel system (Systems with one server only), and multi-channel systems (systems with more servers). 3.3 Notation: In 1953, David G. Kendall introduced the symbolization for labeling the features of a queuing model. He introduced in the form of an A/B/C queuing code that can be seen in all standard modern works on queuing theory, for example, this code shows A as inter-arrival time distribution, B as service time distribution and C as number of servers. In actual a queue is written in shorthand code by A/B/C/K/N/D or the briefer A/B/C . In this briefer version, it is assumed K = and N = and D = FIFO. 3.3.1 Kendalls Coding: In most reference work about queuing theory, these types of symbols appear. 3.3.1.1 The Arrival Process: (A) Arrival process is described by A. The codes used are: Symbol Name Description M Markovian Poisson process (or random) arrival process. MX Batch Markov Poisson process with a random variable X for the number of arrivals at one time. MAP Markovian arrival process Generalization of the Poisson process. BMAP Batch Markovian Arrival Process Generalization of the MAP with multiple arrivals MMAP Markov modulated poison process Poisson process where arrivals are in clusters. D Degenerate distribution A deterministic or fixed arrival time. Ek Erlang distribution An Erlang distribution with k as the shape parameter. G General distribution Although G usually refers to independent arrivals, some authors prefer to use GI to be explicit. PH Phase-Type Distribution Some of the above distributions are special cases of the phase-type, often used in place of a general distribution 3.3.1.2 The Service Time Distribution: (B) The service time distribution is described by B. A number of common notations are: Symbol Name Description M Markovian Exponential service time. D Degenerate distribution A deterministic or fixed service time. Ek Erlang distribution An Erlang distribution with k as the shape parameter. G General distribution Although G usually refers to independent arrivals, some authors prefer to use GI to be explicit. PH Phase-Type Distribution Some of the above distributions are special cases of the phase-type, often used in place of a general distribution 3.3.1.3 The Number of Servers: (C) The number of service channels or servers is represented by C. 3.3.1.4 The Number of Places in the System: (K) The letter K shows about the capacity of the system or the maximum number of customers tolerable in the system containing those in service. The further addition of client is rejected when the number is at this maximum. The capacity is expected to be unlimited, or infinite if we omit this number. Note: This is occasionally symbolized C+k where k is the buffer size, the number of places in the queue above the number of servers C. 3.3.1.5 The Calling Population: (N) It is the overall population from which customer come to get the service. The population may be assumed to be unlimited if we assume this number. 3.31.6 The Queues Discipline: (D) This symbol describes about the Service Discipline or Priority orders that jobs in the queue, or waiting line, are attended: Symbol Name Description FIFO/FCFS First In First Out/First Come First Served The customers are served in the order they arrived in. LIFO/LCFS Last in First Out/Last Come First Served The customers are served in the reverse order to the order they arrived in. SIRO Service In Random Order The customers are served in a random order with no regard to arrival order. PNPN Priority service Priority service, including preemptive and non- preemptive. PS Processor Sharing Kendalls Notation for Queuing Models: Queuing models can be represented using Kendalls notation: A/B/S/K/N/Disc Where: A is the inter-arrival time distribution B is the service time distribution S is the number of servers K is the system capacity N is the calling population Disc is the service discipline anticipated Mostly the last notation members are removed, so the notation get the shape of A/B/S and it is assumed that K = , N = and Disc = FIFO. A few basic notations for distributions (A or B) are: M for a Markovian (exponential) distribution E for an Erlang distribution with phases D for Degenerate (or Deterministic) distribution (constant) G for General distribution (arbitrary) H for a Phase-type distribution 4.1 Simulation: Simulation is a commanding tool for following three functions. Examining Planning Operation of complicated systems It is almost costless method to test our thoughts and hypotheses. With this we can save hundreds and thousands of dollars. The real life is show in form of simulation which mimics the real life situation. In simulation we show the general characteristic of real life situation into simulated model. We use simulation in numerous situations, containing the modelling of natural systems or human systems in order to gain insight into their functioning. With the help of simulation we can test alternatives and different course of actions. . 4.2 History of Simulation: The analogy type calculating machine was the first to appear in the history of computer simulation. Slide rules, planimeters, integrators, etc. have long been in use. At the end of 19th century, the differential analyzer and the harmonic analyzer were created. In 1931, V. Bush invented the differential analyzer at MIT. It could also be called a mechanical type simple analogy computer. Bush solved the highest 6th order differential equation with this. Later a device was made which can automatically solve differential equations. Analog computers with vacuum tubes appeared on the scene around the time that World War II came to an end (1945). Who the inventor was is not quite clear but most people feel that this calculating device, having the same principle as the differential analyzer, can be achieved by a combination of vacuum tube circuits. In the 1950s, analogy computers were investigated, studied and mostly used. After the mid-1950s, when digital computers came into use, an idea was put ahead to perform the simulation technique being carried out on an analogy computer, on a digital computer as such. The software which embodied this concept was developed in various ways along with establishment of the general purpose large computer. CSMP and DDS were mostly used as the continuous system simulation languages. In the 1960s, simulation languages for the discrete system were also developed and GPSS and SIMSCRIPT are used even today. 4.3 Types of Simulation: Following are different types of simulation. 4.3.1 Physical Simulation: In this type of simulation we use real physical things to perform different actions. The physical object is much cheaper than the real one. 4.3.1.1 Interactive Simulation: The best example of this type of simulation is Flight simulator in which human is involved to perform and verify the results. 4.3.2 Computer Simulation: In that we use computer to model the real life situation. With the help of computer we can see how a model works. There is ease of changing variables to get different results. Now a days modelling of any natural systems in any discipline of science and business education as well as in engineering has become very important and is done through Computer simulation. This will tell us the inner picture of any process for our better understanding. The mathematical model gives analytical solution which enables us to forecast the behaviour of the system with fixed set of conditions. But with the advancement of computers, it substituted the old fashioned model with fixed inputs. There is different type of simulations. Monte Carlo simulation and stochastic modelling like software which are computed based programs enables us to model the process and it is very easy to do so. Figure 1: Three Sub-Fields of Computer Simulation. In order to simulate something physical, we build a mathematical model which is a representation of real object. Models can get the shape of declarative, functional, constraint, spatial or multimodal. The next step is to run the model on computer. For that we have to develop a computer program. After execution we find certain results and data, which are then analysed. This is an iterative process and repeats itself and refines the result by using different alternatives. 4.4 Simulation and Analytical Method: Simulation methods can be broadly classified into three types. 1. The first type uses the actual system itself and carries out simulation by using a scale model and is called the direct analogy. As an extreme example of the first type, the direct analogy, an actual system can be used as the model. Only the phenomenon occurring in the real system is replaced by a replica or an imitation to study the system characteristics. For instance, carrying out disaster prevention training by reproducing the occurrence of a disaster is applicable to this type. In the direct analogy, scale models are mostly used, such as wind tunnel experimentation for aircrafts to examine the hydro-chemical characteristics, water tank experimentation for ships, and experimentation for positioning the equipment or ducts within a nuclear reactor chamber and so on. Broadly speaking, experimentation with animals in medicine could also come under the direct analogy. However, in system simulation, the direct analogy is by and large excluded. 2. The second type simulates a version of the real world simplified into some model. The second type of simulation, using a mathematical model, employs a model made from the real world and numerical value is actually applied to that to reorganize the real. Most cases of system simulation fall in this type. This type of simulation is not restricted to engineering cases only, which handle physical variables; it is also used in the fields of social sciences, management, etc. In a large-scale system or in the social sciences it is difficult to experiment with the real system and experimental data are mostly generated by simulation. In other words, by applying different variables to the model and doing simulation, it is possible to perform numerical experiments easily in response to different conditions. 3. The third type is a situation wherein the problem is formulated in an analytically solvable form by further abstraction and solved numerically by simulation. The broad relationship among these three types of methods and the analytical method is shown in Figure 1.3 [1]. The third type of simulation, using an analytical model, does not analytically solve the mathematical formula but obtains numerical results by intentionally using simulation. This Enables simulating a stochastic model by the Monte Carlo method; numerical solving of the probability phenomenon in the real world becomes possible by replacing even the originally non- probability type of partial differential equation or integral equation by a stochastic model. However, since this method is not always precise nor as manageable compared to other numerical analysis methods, its usability is not always high. 4.5 How is simulation performed? The simulation is mostly performed on computers by writing computer program. Simulation can be performed manually. 4.6 Advantages and disadvantages of simulation: With the advancement of technology, companies are in race to produce high performance simulation softwares for the industry. This made companies to announce their products on weekly bases. Such rapid growth not only produced certain advantages but also produced some disadvantages. These are as follows. Advantages: Simulation is more than just look into the future. There are certain advantages mention by different authors (Banks, Carson, Nelson, and Nicol (2000); Law and Kelton (2000); and Schriber (1991)) and include the following: With the help of Simulation, we can choose the correct option without utilizing extra resources. Without utilizing any sort of resources Simulation allows us to test our designs. We can hold the length of time by compressing or expending. We can utilize less or more time to get the desired results. With the help of simulation we can get knowledge of a system, how it works. Simulation enables us to use different alternatives and choose the best one. Simulation helps us in finding different problem in our model. Simulation helps the industry to remove different bottleneck. Simulation enables us to understand the process and visualize the plan. We can develop different animations and drawings to get better understanding of job. With the advancement of technology, we are now performing Simulation much faster than the past. With the help of simulation we can train people for real life situations. Disadvantages: Following are few disadvantages associated with the simulation, are: To create a prototype, it needs lot of preparation and experience. An output observation is usually random but it is difficult to find out that weather the system is random or not. If we just consider the cost of the modelling and cost to analyze the data, it might result in poor estimation; with this approach we cannot get the optimized results. Another disadvantage is that we can only use simulation in that case where it is possible to have analytical solution. A small queuing system and some probabilistic inventory systems, for which closed-form models (equations) are available are good examples of this. Although closed-form models are useful for small queuing and inventory problems, most real-world problems are too complex to be solved with these approaches. Simulation is necessary when there are a large number of events and interactions in a system, which is true of most manufacturing problems. Introduction of the Organization Shaukat Khanum Memorial trust is a charitable organization. The Trust has set up a state of the art cancer hospital at a cost of US $22.2 million on a 20 acre. Land located at Johor Town, Lahore Pakistan. The prime objective of the hospital is to provide free treatment to deserving cancer patients. The hospital is the only institution in the country providing diagnostic and treatment facilities to cancer patients under one roof. The hospital employees over 550 persons dedicated to provide highest quality services of international standards. Continuous quality improvement programs are an integral part of the hospital function. The Doctors, nurses and technicians have been employed from overseas. These include expatriates from USA, UK, Australia and Philippines. The hospital is affiliated with Christie Hospital NHS Trust, Manchester, UK and University of Kentucky Medical Centre, Lexington, Kentucky, USA to share knowledge, skills, expertise and experience with the aim of improving and enriching the lives of citizens. Pakistan, a Country of 130 million people generates over 700,000 new cases of cancer annually. Many people do not have access to even the most elementary healthcare. As a result, cancer which could be treatable in the West is often not diagnosed until its too late. Even when they are diagnosed, treatment is too expensive. The average family in Pakistan finds even the most basic medical treatment a severe drain on their financial resources. For these people, paying for cancer treatment is out of question. So, a fundamental principle of the Shaukat Khanum Memorial Trust mission is to provide free treatment to poor cancer patients. This thesis was initiated with the aim of examining the current operational aspects of Shaukat Khanum Memorial trust (SKMT) and then applying queuing theory, so that we can make feasible recommendations to enhance the operational efficiency and improve patient service level. Objectives: The objective of study can be enumerated as follows: 1. To conduct a study on the operational aspects of some business organization (Profitable or non-profitable). 2. To generate some feasible solution to the operational problems faced by Shaukat Khanum Memorial trust and enhance the effectiveness and efficiency. Methodology The study approach comprised main of interviews with staff of Hospital and one week field study cum primary data collection at Hospital. This time period was representative of normal working conditions. Patient arrival rates, service rates and waiting times at each service station were collected using contrived observation method in accordance to instructions. The hospital arrival figures were collected from the operations department. The arrival patterns of the hospital attendance are analysed. Capacity requirements analysis and queuing theory analysis are performed. The capacity utilization and expected waiting time at each station are presented and discussed. THE OPERATING ENVIRONMENT AT THE OUT PATIENT DEPARTMENT (OPD) of SHAUKAT KHANUM MEMORIAL HOSPITAL SKMCH RC QUEUING CHARACTERISTICS In this case, the process is complex. There is single queue. System has multiple phases. In some phases it has multiple servers i.e. These servers provide same service with more than one counter. Queuing discipline is FCFS. Phases No of servers Information and Reception Counter 1 OPD Counter 2 Cashier / Finance Counter 2 Clinic 10 Table 1 OPERATION SKMCH RC OPD (out-patient department) operates as follows. SKM OPD has divided appointment system in two phases. REGISTRATION PHASE: In that phase when patient enters into the system he is directed towards main OPD reception counter by Patient guides. As he approaches the information and reception counter, he is welcomed by the PCO (Patient Care Officer). At that counter he is given information regarding the procedure and directed to registration counter. Registration counter carries three operations. 1. Generating data base of patient 2. Generation Work order 3. Management of appointment Diagram -1 The new patient has to go through the whole process as shown in the diagram-1. On arrival first go to reception counter then to registration desk and generate work order and then for payments, goes to Finance counter while the follow up patient goes straight to Appointment counter and follows the other path shown in diagram -2. APPOINTMENT PHASE: SKMCH RC OPD opens from Mondays to Fridays (Five days a week). OPD opens at 9 a.m. and closes at 5:00 p.m. daily while it has lunch break of 1 hour from 1 pm to 2 pm. Therefore working hours are 7. When a patient is arrived at the OPD and approaches the information counter he is guided to OPD counter. At that counter, PCO (Patient care officer) guides for appointment and sends patient file to nursing counter. There are two nursing counter, which manage the patients file and patient has no direct interaction with this counter. Diagram -2 Now the patient is sitting in waiting area for his turn. Upon call from nursing counter in chronological order, the nurse from nursing counter guides the patient to clinic In clinic nurse takes necessary vitals like blood pressure, temperature, weight and heights etc. Then Medical Officer takes the history and then finally consultant sees the patient. After getting treatment patient may be sent to either diagnostic center for tests and to pharmacy or released to go home. If patient is sent to diagnostic center, they have to first go to finance department for the payment of test fees. Then they are headed towards the diagnostic center and pharmacy too. In the Next section, the data is collected on patients arrival time and doctors service times are analysed. DATACOLLECTION AND RESULTS DATACOLLECTION Data analysis provides the driving force for any simulation model. In this study, there types of data were collected: (This data is taken from Appendix) 1st Week 2nd Week 3rd Week 4th Week Average(Week) Monday 403 416 337 331 372 Tuesday 359 414 359 405 384 Wednesday 367 347 364 379 364 Thursday 402 404 298 411 379 Friday 204 211 232 226 218 Total 1735 1792 1590 1752 343 8586 Table 2 (Total number of patient per and average number of patients per day) Counters Arrival Rate Service rate Server Utilization Information 35 30 117% OPD 30 8 375 % Cashier 8 60 13 % Clinic 8 16 50% Table -4 i) Patients arrivaltimes: Data collected from observation and the hospital record over a period of four weeks. Keeping in view the difficulty related to recording arrival of patients, the arrival time for each patient was set at the time the patient arrived at the Information counter. The arrival rate of weekdays and weekends is calculated from the data provided in appendix. This gives hourly rate, daily rate on each day, weather it is weekend or weekday. Average daily arrivals are 343 patients. Total weekly arrivals were 8586 patients. Patient arrival distribution is passion while inter-arrival distribution is beta. We find arrival time as 35 patients per hour. Average Number of patient arrival per hour 9 am-10 am 40 10 am-11 am 50 11 am-12 am 50 12 am -1 am 60 1 am -2 am Lunch Break 2 am-3 am 55 3 am-4 am 50 4 am -5 am 38 Table 5 (Average number patients per hour) ii) OPD counter process time: collected from observation and interviews over a period of four weeks. It is shown in table 4. iii) Finance counter Process time: collected from observation and interviews over a period of four weeks. It is shown in table 4. iv) Doctors process time: collected from observation and interviews over a period of four weeks. The process time started as soon as the patient was called for treatment and ended as soon as the patient left the doctors room. Key Measurements Minimum Average Maximum Arrival rate / hour 33 35 44 Service rate for information counter / hour 60 30 20 Service rate for OPD counter / hour 6 8 10 Service rate of Finance / cashier counter / hour 120 60 40 Service rate of Clinic / hour per clinic 0.667 1.6 15 Table 6 We will take average values for our data analysis. Doctorsschedulingtimes: Data was obtained from the Assistant manager (operations). There are ten clinics for different kind of diseases. In the morning session (9 am to 1 pm), each doctor has four hours of duty in which they have schedule of appointments. Each clinic has a standard number of patients but usually they are overbooked and people have to wait for their turn. Evening shift starts from 2pm to 5 pm. RESULTS: The detailed results are as discussed below: Patients inter arrival data: Using Arena input analyzer, we got inter arrival distribution. Distribution Summary Data Summary Distribution: Beta Number of Data Points = 21 Square Error: 0.036109 Max Data Value = 30 Expression: 0.5 + 30 * BETA(0.554, 1.14) Min Data Value = 1 Sample Mean = 10.3 Histogram Summary Sample Std Dev = 8.56 Histogram Range = 0.5 to 30.5 Number of Intervals = 30 Table 7 PatientsWaiting Time Patients waiting time is calculated which has maximum of 90 mins, while min is 4 minutes and average waiting time is 37 mins. While standard waiting as purposed in literature is 30 minutes. Doctors Process Time: Using the data with the help of Arena input Analyzer, following are some statistics; Distribution Summary Data Summary Expression: 3.5 + 87 * BETA(0.675, 0.909) Number of Data Points = 18 Distribution: Beta Max Data Value = 90 Square Error: 0.054106 Sample Std Dev = 26 Min Data Value = 4 Sample Mean = 37.1 Table -8 CONCLUSION In studying the operations at SKMHT RC, we find that OPD takes the longest time while the finance counter takes the least time. There are two queues (information queue and OPD Queue) that are unstable as ( ), which are causing low utilization of Cashier counter and clinic counter which has 13% and 50% respectively. In spite of having unstable queues, the patients have to wait for 37 minutes; If 30 minutes or below is set as the standard patients waiting time, as suggested by Valdivia and Crowe (1997), then the patients waiting time is unsatisfactory. The utilization rate of information counter is 117 %. While server utilization of OPD counters is 375 %. It means both counter are unable to meet the patient demand, causing long queues, and delay in their treatment. The patients come on the day couldnt be able to get examined by the doctor. Patients have lot of time wasted in queues. Patients are given long dates for consultation. Since patients waiting time is unsatisfactory and doctors are under-utilized, several measures have to be taken by the hospital management. RECOMMENDATIONS 1. For any queue it is necessary it should be in steady state i.e. Service rate must be greater than the arrival rate ( ). System should have capacity to handle the arrival of patients. 2. In case of OPD at SKMCH RCH, there is a problem of capacity. As shown in data above, its clear that information counter, OPD counters doesnt have capacity to cope with the load of patient. Hospital management is over utilizing these service counters. This is causing long waits for patients. 3. To handle these two issues, over utilization of service counters and long waiting time, there is need to increase the servers. So that they can not only save their service stations from over utilization but also reduce the patient wait time. Also they can be able to have steady state queues in that case. 4. Also some service stations are utilized by both process causing congestion and creates long queue. It is also suggested to separate these two phases, the registration phase and the appointment phase. In this way, we can reduce both waiting time for patients and server utilization. 5. Another suggestion to avoid the space occupation at OPD is to use telephone technology for patient registration and give them appointments on Phone. 6. Another way to minimize the waiting time at OPD clinic, Finance department can be pooled to OPD, which can perform both functions i.e. its own as well as of OPD. 7. The study was based on the data collected over a period of four weeks only. Therefore, the numbers might not be accurate. For better accuracy, the data should be collected for a period longer than four weeks. 8. As we have suggested about the increase in number of servers, it might cause an increase in the cost of new setup and it also shift more patients to clinics for examination and might cause over booking at doctors end. There is need to find the cost / benefit analysis for increasing the number of servers. Limitations: Firstly the time allocated for this academic exercise does not permit data collection over a longer period of time and thus, may affect the representativeness of SKMTH patients. Moreover, the instructions laid down by the management of SKMTH eliminated the opportunity to conduct a survey on the flow of patients with assistance from the various service providers which would have provided a richer source of primary data for analysis. Secondly, several assumptions are made in order to execute the analysis. This again affects the accuracy of the key operating statistics computed namely, the mean server utilization and expected waiting time. Future Research: This academic research has been primarily concerned with the measures to improve the patient service level without incorporating the cost implication of the recommendations made, such as the addition of Servers. Hence a separate study to consider the cost aspects may be carried out to supplement the findings of this academic exercise. If richer source of data can be obtained, other operations research techniques may be applied at various service stations.

Gun Laws And The Rights Of Free Speech - 1748 Words

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They feel as if both presidential candidates are untrustworthy and an adverse depiction of the country. It seems to be a very difficult decision facing the American people today. But as American citizens with the right to vote, everyone should utilize this privilege. After all, the future of the country

Challenging Society Through Art. - 1073 Words

Challenging Society Through Art Art makes a statement, it is as simple as that. The most preferred type of art emotes happiness to the viewer throughout all of history. Yet, throughout the years, artists have come to challenge society’s preference of art. Artists like Gustave Courbet, Francisco Goya, and Ai Weiwei, who decided to take the challenge and start a new progression. Social critique through art is blatantly shown throughout each of their pieces: Stonebreakers, The Third of May 1808, and Sunflower Seeds. All three are similar yet different. Through comparison, a similar theme still remains present: Each artist s social critique through their artwork. These particular types of artwork will show how one moment in history changed the view of millions throughout centuries of time. With the use of symbolism, positioning of figures, historical context, color usage, and many other elements, these three works of art make an inspiring statement to the world. The first painting, Stone Breakers by Gustave Courbet depicts two males that go to work breaking stone and wearing torn and tattered clothing. This job was usually done by the lower classes of society. At this time in France there was a rising of revolutionary thinking. The reason for the sudden outbreak involved many factors, the ones portrayed in this painting are the demands of the working classes. The figures are not arranged in the typical way. Both the young and the old males are turned away from the viewer.Show MoreRelatedDonald Trump Is A Multi Billionaire Business Man Essay1313 Words   |  6 PagesSocial class is someone’s reputation in society based on their economic status. Society also defines someone’s intelligence with the level of education he/she received. Someone from a higher social class is more likely to have the means to attend a more prestigious school, and is therefore more likely to receive a higher education. 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Nutrition and Vegetables free essay sample

Vegetables are a very important part of our diet. Vegetables are an excellent source for fiber, vitamins A and C, potassium, calcium, and iron. Researchers have discovered that having a good diet of fruits and vegetables can lower blood pressure, reduces the risk of heart disease, stroke and possibly some types of cancers. It can also lower your risk of eye problems and digestive problems. Vegetables are eaten in a variety of ways such as main meals or for snacks. The nutritional content in vegetables can vary considerably. Most vegetables do not contain protein or fat. The dried beans, peas, and lentils are a good source of complex carbohydrates and can have high protein content. Sometimes these types of vegetables can be used to some extent as a meat substitute. Potatoes and corn are very starchy vegetables. These types of vegetables turn into sugar. Vegetables have a variety of colors. They can range from dark green to a beautiful orange color. We will write a custom essay sample on Nutrition and Vegetables or any similar topic specifically for you Do Not WasteYour Time HIRE WRITER Only 13.90 / page The dark green vegetables are usually the leafy vegetables, and squash, pumpkin, and sweet potatoes are examples of the orange family. Some vegetables have a red/blue coloring such as eggplant and red cabbage. Many articles today have different ideas on the amount or servings of vegetables a person should eat per day. The amounts can range from two (2) up to nine (9) servings per day. The majority of nutrition specialists suggest 2-1/2 cups per day. Potatoes do not count in your servings per day! The best bet for servings are the dark leafy greens, tomatoes, and anything that is yellow, orange, or red color. According to the National Cancer Institute, not enough fruits and vegetables are produced or imported to supply all of the people in the United States for a healthy diet of five servings per person per day. Another problem affecting vegetable growth is bugs and insects. Scientific studies have been conducted that support the claim that insects attack plants that do not receive the proper balance of nutrients. The number of documented outbreaks of human infections associated with raw vegetables and unpasteurized fruit juices has increased in recent years. Most of the outbreaks were identified primarily as Salmonella. Tomatoes, seed sprouts, cantaloupe, apple juice and orange juice are the most recent vegetables to be linked to the Salmonella bacteria. The E. coli infection has been found in lettuce, sprouts, and apple juice. It is very hard to determine the source of contamination for the bacteria for vegetables. Unlike beef and chicken that are thoroughly inspected, methods for fresh produce are not as advanced. The contamination could occur at any point throughout the production process. Some of the potential sources of contamination include soil, feces, irrigation water, dust, insects, and human handling. Recently the Center for Disease Control has had a nationwide recall on alfalfa sprouts with the Salmonella bacteria and for romaine lettuce containing the E. coli bacteria. In conclusion, people have been consuming meats and vegetables since the beginning of time. I am sure many people died back in the beginning of time from the diseases and bacteria we have discovered today. We must consume meats and vegetables to survive. Humans cannot live on water alone. If individuals today will use common knowledge on preparing meats and vegetables, a lot of issues such as the contamination and bacteria would not exist. All in all the meats and vegetables today are safe and nutritious. If consumed in the right amounts, we will have a healthy, long, enjoyable life.

Development of Tourism Industry

Questions: Part A 1. Explain what you understand about the concepts and practices discussed in the two given papers, 2. Comparing the two papers, which paper/author's view do you find more persuasive, and why? 3. Are there any other journal papers written surrounding the topic, which support your views? Give examples and why? Part B 1. Based on your understanding of the literature (from part of the assignment), relate the literature findings to your own experience or one company's practice (case study) to discuss the significance/impacts of the concept/practice. Answers: Introduction Tourism industry is flourishing due to increase in the travel destination sports by the travellers from different parts of the world. The industry generates high rate of income and creates more employment opportunities for the nation. However the challenge within the industry has tremendously increased. This has been one of the reasons the exports analyses the reason for the changes that needs to be introduced for increasing the tourism industry performance. In this process, the challenges and opportunities within the industry has been analyzed. Through this process, the management analyses the challenges and develop effective strategies through which the changes can be implemented. In order to find the impact of the tourism industry the comparison of the industry has been due from two research papers. Part A: 1. Concepts of the papers In destination and enterprise management for a tourism future by Dwyer el a., 2009 the relevance of the tourism has been highlighted. The author highlights the kept factors for the tourism industry which enables the authorities to identify and deal with the required changes through which the tourism industry can be improved. The key driving force for the country varies and the development of the truism industry depends upon many factors. As per the author the level of competition in the travel industry has tremendously increased, and this forced the authorities to improve the strategies through which development of the industry can be introduced. The need for tourism stakeholders has increased and this has increased the need for improving the tourism industry through which the economic condition of the country can be improved (Buhalis, 2000). Through strategic planning it is possible to identify the direction in which the tourism industry needs to be moved, as this will enable the authorities to introduce the required changes for improving the performance. Modern tourism has been integrated with different sectors that function towards upgrading the economic condition of the nation. The key driving forces for the improving the tourism industry has to be analyzed and the challenges involved with the process has to be implemented in an effective manner. The issues with the complex tourism industry can help the management or the authorities to introduce effective strategies through which the required changes can be implemented (Taylor Chesworth, 2005) Due to globalization and industrial development the tourism industry has been boosted. Through this method, demand for effective and impressive travel deals has increased across the world. The author has empathized the challenges and importance of the Australian tourism industry. Through this paper the relevance of the innovative tourism has been also included into the process. This is done to help and improve the quality of services intended to be provided through the tourism industry. Destination managers along with travel operators work towards improve the quality of services and avoid the strategic drift which can affect the plan for tourism plan (Stern, 2006). The second paper is on marketing the competitive destination of the future authored by Dr. Dimitrios Buhalis. The author focuses on the destination marketing, which is considered to be an important factor for improving the level of competition that exists in the industry. Destinations and its relevance need to be defined as this will help in the improvement of the services that is intended to be provided by different tourism industries in the world. The author has tried to synthesis the relevance of different medals that is being used for the strategic marketing or the management of different destinations that is located across the world (Kotler et al., 2010). Marketing plans an important role in the business development and introduction of new and better strategies through which the changes can be introduced. Such factors form an important part of the sustainable plan that has been drafted and implemented by the management of the tourism industry. Destinations are defined through the relevance of the products and services, which is due through partnership deal. The partnership deals are entered between the tourism industry and public or the private companies that works towards the development of the industry. The cost for the tourism has been reduced due to the advent of better technology through which the essential changes within the business management process has been developed and implemented. The costs associated with enhancing the tourism costs has been reduced as well. Destination marketing is an important concept as this will help in reaching out to maximum number of clients from different parts of the market (Lusensky, 2011). 2. Persuasive data The work conducted by Dr. Dimitrios Buhalis is more persuasive as compared to the other one. The strategies are drafted after analyzing the tourism policies that exists worth different countries. In this process, the relevance for the regional development and the strategic plan has been also included. Such factors are expected to positively impact the marketing strategies adopted for promoting destination marketing. In the first paper authored by Dwyer el a., 2009 the relevance of researching data for evaluating the competitive edge or situation exiting in the industry has been analyses. Different counters adopt various types of strategies that are meant to help the management in introducing the better strategies for improving the performance of the tourism industry. For this, the graphical data was collected by the management, through which the necessary information related to the promotion of the tourism industry has been analyzed. Through this method, the challenges and other factors associated with the implementation of the process for marketing of the services have been analyses. The research has been done from the future point of view, and the required changes within the system has been planned and introduced by the authorities. As per the author the future changes are unpredictable, however the strategies and policies can be developed with an intention of developing better strategies thro ugh which the changes within the system can be implemented (McCarhty et al., 2010). The author states that there are no single driving forces that can encourage or impact the industry, by itself. In order to analyses the relevance of the marketing strategies, the author conducts a PEST analysis through which the changes within the marketing policies can be analyzed and introduced in an effective manner. The global driving forces for improving the performance of the tourism industry has been analyzed and presented through the work (Slattery, 2009). In the second paper by Dr. Dimitrios Buhalis the relevance for promoting tourism has been given to the marketing facilities adopted for destination promotion. The process followed by truism industry from different parts of the world has been streamlined. This has bend ne with an intention of improving the quality of services intended to be provided to the client. The complex situation has been analyzed by the author to find the different factors that can affect the performance of the company (Gustavo, 2013). The author states that the destination marketing is based on the experience and research method. Such factors enable the management of the tourism industry to find the better deals and offer the clients with the best possible services. In this process, the needs of the clients from different markets are analyzed, and the collected data are used for the defying an effective policy for improving the performance. In order to understand the relevance of the destination marketing, the author has conducted strategic analyses where the complexity of the stakeholders and the industry has been analyzed. The demand of the customers and the changing phase of the industry have been analyzed for improving the quality of services intended to be provided to the clients. The paper conducts research on the main markets and the destination choices have been included in the research work. The destination marketing depends upon the different types of destinations that has been developed and introduced for the travellers. In this case, the author has analyzed the relevance of each one of them ( Hall, 2000). Such factors play an important role in developing and implementing effective strategies through which the changes for the destination marketing can be implemented. In this process, the challenges involved with the task of marketing have to be analyzed, as this will help in implementing effective strategies for successful business conduct. 3. Another journal The article penned by Larry Dwyer and Ray Spur has been included in this work. As per the authored the tourism industry is developing and this has contributed towards the economic development of the nation. In this process the growth and the methods that is intended to be followed by the management has to be analyzed, as this will help in improving the performance for the industry. Due to advanced technology and increase in the demand form the customers, the changes related to tourism industry has become quite important. In this process, the authorities expect future growth which will improve the economic behavior for the nation. In order to attract more clients, the companies are now working towards developing more destination plans through which the changes can be introduced (Holjevac, 2003). Conclusion Developmental plan are essential for the improving the performance of the tourism industry in the globe. In this process, the changes intended to be implemented has to analyze and corrective measures has to be introduced. This will help in improving the performance and overpower the challenges that exist in the industry. Through the process, the changes intended to be implemented by the authorities can be planned and introduced by the authorities. Part 2: Literature review Introduction The challenge involved with the promotional activities for the tourism industry has to be analyzed as this will help in implementing the change through which the best products can be promoted to the clients. In this process, the challenges and other factors that are associated with the project exciting has to be analyzed. This will help in reaching out maximum number of clients and service the members with the better options (IPCC, 2007). As per the presentation made by Dwyer on the tourism industry the author states the relevance of the conducting strategic management plan for improving the tourism industry. The leaders of the tourism industry play a key role in improving the quality of services that has been proposed to be rendered to the travellers from different parts of the world. In order to promote tourism it is necessary to develop strategic plans which include analyzing the challenges related to the tourism industry. In this process the plans for implementing the strategic changes has to be analyzed and introduced in an effective manner. Through this process, it is possible to approach the clients from the global market. The good management practice includes analyzing the changing global tourism needs and improving the strategies that would be considered to be useful for upgrading the services (Kang et al., 2012). For this the external factors that and directly impact the performance of the tourism industry has to be analyzed. This includes evaluating the external environmental factors that can directly impact the business performance. The cross function decision has plays an important role in the development of the company and in introducing the changes that are considered to be essential for the management purpose. The challenges faced by the private and public enterprises impacts the business performance directly and thus the changes and the challenges involved with the process have to be analyses. Through this method, the factors that can affect the business performance of the tourism industry needs to be analyses and introduced in an effective manner by the authorities (Abrate eta l., 2012). As per the author, it is necessary to conduct the target marketing system through which the changes within the system can be introduced. In this process, the challenges expected to be faced by the tourism industry is analyzed. Improper customer service is one of the major factors that impact the performance of the tourism industry. In this case, it is necessary to analyses the challenges that exists in the industry and develop effective strategies for handling the issues. The demand for destinations from the travellers is increasing. This is one of the reasons the demand for the effective marketing strategies are expected to be developed and implemented by the management. In this process, the challenges existing with the industry has been analyzed, as this would help in increasing the performance. Services provided by the industry are customer rented as this would help in approaching the clients and convincing for the sales of the products and services sold by the company (Becerra et al., 2004). Risk management is another important factor through which the changes or the challenges involved with the process are analyses. Through this method, different factors that are related to risks are analyzed. This would help in improving the business performance and help the authorities to handle the risks involved with the system. In order to handle the situation, it is necessary to train the staffs and improve the work ability through which the changes can be introduced by the management. In this process, the business attempt to minimize the losses that are bound to incur in the process of business management. In this method, different factors that can impact the business performance and the challenges related to demand handling and others are closely evaluated. This would help in improving the performance for the company (Cavlek, 2010). As per D. Buhalis for Tourism Managements the tourism industry depends upon the competition that exists in the industry. Due to increase in the demands from the clients, and constant policy changes made by the authorities, it is possible for selected few industries to sustain the tough competition that exists in the industry. The strategic plan for developing the industry and venturing new market has to be planned and introduced in an effective manner. For this, the management is expected to address the issues that are faced by the management at the time of introducing new travel products for the clients. Tourism strategies need to concentrate on different factors like visitation and evaluating the impact of the overcrowding, visitor safety, environmental problem, and other factors. Through this method it is possible to analyses the sensibility of the issue and develop better strategies through which the issues can be overpowered by the management. This is one of the reasons, it beca me important for the authorities to develop comprehensive strategies through which the required changes within the injury can be introduced (Dwyer, 2005). The other challenge associated with the promotion of the tourism industry is with the adoption of different methods through which the marketing strategies for the industry can be adopted and implemented. For this, it is necessary to highlight the rich culture and other tradition through which the required changes related to the promotional activities can be introduced. The developmental program involved with the methods has to be analyzed as this will help in improving the production activities. Through marketing strategies the management attempts to analyses the challenges that exist with approaching to the clients. Through this process, it is possible for the management to analyses the challenges and introduces effective strategies through which the changes for the business or industry improvement can be introduced. The tourism developmental plan has to be analyzed and prepared in an effective manner. Through this method it is possible to introduce the required changes through whic h the production process can be improved by the management. For this it is necessary to develop and introduce effective life cycle analysis process, which would help in the business development process (Cetron, 2001). As per Gustavo 2013 it is necessary to prepare effective strategies through which the promotional activities for the industry to approach the clients. The marketing strategies needs to include tend entails of the type of services that are intended to be provided to the clients. The process for the booking services and analyzing the requirements of the clients has to be analyses. This will help in improving the quality of services through which the changes can be implemented. The brand promotion and the method intend to be followed for promoting the services has to be designed and implemented in an effective manner. The factors related to the marketing strategies in the international market have to be analyzed as this will help in improving the performance. The unavoidable structural changes have affected the process of the conduct of the tourism industry. In this method, the factors related to the macro and micro environmental issues have been analyzed. Through this method, an attempt has been made to provide the best possible services to the clients. In this process, the factors that can cause transformation within the industry are analyses. This is done with an intention of analyzing the global requirements and handling the uncertain changes through which the product or the process for promoting the tourism industry can be developed. The process includes promoting the social cultural factors which is one of the major factors that attract the international travellers. In this process, the technological change and it is important has to be analyzed. This would help in improving the performance and analyzing the challenges that can affect the business performance. The services are associated with providing with better information throug h which they intended changes could be introduced by the maturities. The challenges with the marketing process and the methods intended to be followed for marketing the services has to be analyzed. Through this method or process, it is possible for the authorities to develop positive and effective communication process which will enable to reach out to maximum number of clients (Dwyer Forsyth, 2008). Conclusion Marketing and promotional activities for the tourism industry is quite an important factor. Through this method, the management attempts to introduce better strategies through which the changes within the industry can be analyzed and implemented. The future goals and expectations are analyses, as this would help in introducing the changes that are considered to be essential for promoting the tourism industry. In this method, the challenges are analyses, and corrective measures are introduced by the management to handle the issues. Through this process, the corrective measures can be implemented by the management. This will help in promoting the services. References Abrate, G., Fraquelli, G., Viglia, G. (2012). Dynamic pricing strategies: Evidence from European hotels. International Journal of Hospitality Management, 31(1), 160-168. https://dx.doi.org/10.1016/j.ijhm.2011.06.003 Becerra-Fernandez, I., Gonza lez, A., Sabherwal, R. (2004). Knowledge management: challengers, solutions and technologies. London, UK: Prentice Hall Buhalis D, 2000. Marketing the competitive destination of the future. Tourism Management 21 (2000) 97}116 Viewed on 27th April, 2015 Canina, L., Kim, J., Ma, Q. (2010). What We Know About MA Success a Research Agenda for Lodging Industry. Cornell Hospitality Quarterly, 51(1), 81-101. https://dx.doi.org/ 10.1177/1938965509354448 Cavlek, N. (2002). Tour operators and destination safety. Annals of Tourism Research, 29, 478496. Cetron, M. (2001). The world of today and tomorrow: the global view. In A. Lockwood, S. Medlik (Eds.), Tourism and hospitality in the 21st century. Oxford: Butterworth-Heinemann. Dwyer, L. (2005). Relevance of triple bottom line reporting to achievement of sustainable tourism: a scoping study. Tourism Review International, 9(1), 7994. Dwyer, L., Forsyth, P. (2008). Climate change policies, long haul air travel and tourism. Proceedings of 18th international research conference of CAUTHE. Ellyard, P. (2006). Societal changes impacts and opportunities for tourism. Tourism futures conference 21st century responses to 21st century realities Evans, N., Campbell, D., Stonehouse, G. (2006). Management for Travel and Tourism. Oxford: Butterworth-Heinemann Goldblatt, D., Perraton, J., Held, D., McGrew, A., Anthony, A. (2006). Global transformations: politics, economics and culture. Cambridge: Polity Press. Gustavo N, 2013. Marketing Management Trends in Tourism and Hospitality Industry: Facing the 21st Century Environment. Viewed on 27th April, 2015. Retrieved from https://www.ccsenet.org/journal/index.php/ijms/article/viewFile/26589/16228 Hall, C. M. (2000). ourism Planning: Policies, processes, relationships. UK: Prentice Hall. Holjevac, I. (2003). A vision of tourism and the hotel industry in the 21st century. International Journal of Hospitality Management, 22(2), 129-134. https://dx.doi.org/10.1016/S0278-4319(03)00021-5 IPCC. (2007). Summary for policymakers. In S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignor, H. L. Miller (Eds.), Climate change 2007: The physical Science basis. Contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press. Kang, K., Stein, L., Heo, C., Lee. (2012). Consumers willingness to pay for green initiatives of the hotel industry. International Journal of Hospitality Management, 31(2), 546-572. https://dx.doi.org/10.1016/j.ijhm.2011.08.001 Kotler, P., Kartaya, H., Setiawan, I. (2010). Marketing 3.0 - From Products to Customers to the Human Spirit. New Jersey: John Wiley Sons, Inc. Lusensky, J. (2011). Sounds Like Branding: Use the Power of Music to Turn Customers into Fans. London: A C Black Publishers. Marsden, P. (2011). F-commerce; Selling on Facebook, The Opportunity for Consumer Brands. Syzygy White Paper. Retrieved Jun 12, 2012 from https://socialcommercetoday.com/documents /Syzygy_2011.pdf McCarhty, L., Stock, D., Verma, R. (2010). How Travelers Use Online and Social Media Channels to Make Hotel-choice Decisions. Cornell Hospitality Report, 10(18), 5-18. https://dx.doi.org/10.1177/1938965512445161 Slattery, P. (2009). The Otus theory of hotel demand and supply. International Journal of Hospitality Management, 28(1), 113-120. https://dx.doi.org/10.1016/j.ijhm.2008.06.005 Stern, N. (2006). Stern review on the economics of climate change. An Independent Review for The Treasury, United Kingdom Government. Accessed on 18.11.06 from. https://www.hm-treasury.gov.uk/. Taylor, G., Chesworth, N. (2005). Travel Styles. In Theobald, W. (Ed.), Global Tourism (pp. 294-308). London: Butterworth-Heinemann. Tranter, K., Stuart-Hill, T., Parker, J. (2009). An Introduction to Revenue Management for the Hospitality Industry - Principles and Practices for the Real World. New Jersey: Pearson Education. Yeoman, I. (2008). Tomorrows Tourist: Scenarios Trends. Oxford: Elsevier. Yeoman, I., Rebecca, T., Mars, M. (2012). 2050: Tomorrows Tourism. Bristol: Channelview