Hi. My name is Sofia and Im addicted to Coffee. free essay sample

Beep Beep Beep Beep! We all are familiar with the obnoxious sound of an alarm clock and the subsequent slap of the hand on the snooze button. Waking up is no easy task, especially in the life of an honors student. Some days it takes all my willpower to roll out of the nice warm cocoon that is my bed and begin what will most likely be a long, tedious day. I trudge to the dining hall in a sleepy fog. It isn’t until I have a sip of that dark sweet drink that I begin to feel awake. Coffee is the jolt that gets me going, the cheerleader to my game, the spark to my fire. I take a sip and feel that instant rush of the caffeine. Suddenly, I feel alert and ready to take on the challenges of my day. I wasn’t always like this. I didn’t always need a cup of coffee to start my day. We will write a custom essay sample on Hi. My name is Sofia and Im addicted to Coffee. or any similar topic specifically for you Do Not WasteYour Time HIRE WRITER Only 13.90 / page There was a time when I used to wake up and drink a cold glass of milk with my morning waffles instead of a warm cup of coffee. Those were the days when my school schedule was easier, my life less hectic. My coffee habit started slowly during my junior year in high school. To this day, I can’t imagine having gotten through my high school career without coffee, especially during the dreaded first week of May of my junior year—the week of advanced placement tests. The tests were early, the study sessions unending, the pile of schoolwork looming. All eyes were on us, the AP students. Some gave up. Some prayed for the best. Some studied till they fell asleep with books still open. Chemistry, English and United States history consumed my life for the month before the testing. I lived and breathed those subjects. There was no time for sleep. I needed something to keep my game on. The solution: coffee. The caffeine was just enough to keep me alert and on task. It wasn’t like I turned to taking drugs or â€Å"brain steroids†. I just drank a few cups and was ready to do my best on those AP exams. It did the trick. The hard work and coffee helped me survive â€Å"h ell week†. As time went one, one cup became four. By the beginning of college, I had a Starbucks gold card and an addiction to coffee. A negative connotation is often associated with the word â€Å"addiction† but I feel no shame in admitting to having one. I am aware of the controversy that tends to surround coffee and the amount of caffeine it contains. People claim that coffee is an expensive habit that can cause headaches, dehydration and loss of sleep. This may be true in extreme cases where one does not react well to caffeine but normally coffee is okay in moderation. This is true for most substances. Even water can be bad for you if you drink an excessive amount of it. As it turns out, coffee within moderation can actually benefit you. It is packed full of antioxidants that can help the immune system and increase one’s brain activity and attentiveness. As an avid coffee drinker, I can attest to this. I have never had problems caused by coffee. If anything, it has only benefited me since it became my dependable energy source. My schedule is hectic-18 credit hours of class, club meetings, social outings, time at the gym and homework. I need to be the best I can be from the moment I wake up. Coffee is my personal cheerleader since it encourages me to wake up and stay alert. Just like a cheerleader encourages their team, coffee energizes me to keep going. And similar to how cheers call attention to the game, coffee enhances my alertness and forces me to focus. Coffee is the extra push I need to persevere through life just like a cheerleader can encourage the team to hold fast when the game gets tough. Coffee refreshes my attention. Coffee relaxes my mind. Most people who go to football games can’t imagine it without cheerleaders. I can’t imagine my day without coffee in my morning ritual. There are far worse things to be addicted to than coffee- drugs, food, alcohol, gambling, shopping and countless others. Unlike cigarettes or alcohol, coffee is not labeled with a drug advisory warning or associated with the prospect of a term in prison. Why? Because coffee is not as harmful as some may think. Headaches or lack of sleep are the worst effects of coffee but drinking coffee moderation avoids these. I am addicted to a warm, comforting, energizing drink and I am not alone in this addiction. I am joined by thousands of others who flock to coffee shops. People addicted to those more dangerous substances are forced to attend community groups or even rehab, while we, coffee addicts, can join together openly and go to coffee shops where we proudly indulge in our favorite delicious drinks without fear of overindulgence. Coffee shops provide us with harbors for creativity, motivation and intellectual conversation because coffee allows us to stay focused. The buzzing ambience of the coffee shop provides another source of liveliness since we are typically immersed in an environment overflowing with coffee lovers who go to hear the cheers and embrace the energy of coffee. Whether you go alone or with a group, you feel the stimulating benefits of coffee. Every week, I walk into a Starbucks, swipe my gold card and sit down with my grande iced coffee. I take a sip, welcoming my personal cheers and finally feel alert. I peer around at the buzzing room full of students with laptops, iPads, and books—all with their coffee within reach. They know what I know- life is like a game and coffee is the cheerleader that fills you with positive energy.

Free Essays on Teen Sucicide

assessment begins the process of suicide intervention Questions to guide Suicide Assessments Either as part of an intake assessment, or based on information you have gather... Free Essays on Teen Sucicide Free Essays on Teen Sucicide Suicide Assessment Is Necessary Although there is much information to gather, there are no shortcuts to suicide assessment. Risk assessment requires directness, intentional questioning, and careful listening. The essential skills and conditions of counseling (empathy, reflections, restatements, attending, active listening, etc.) are important in suicide assessments and intervention. Information that is gathered during assessment should be documented . Knowing when a suicide assessment is necessary There are recommendations that counselors conduct suicide risk assessments on all clients presenting for therapy (Laux, 2002). It is common practice that suicide ideation is assessed through intake forms and intake interviews Specifically, clients presenting with depression or depressive symptoms or in states of crisis should be questioned for suicidal ideation. If using depression inventories, special attention should be given to questions related to suicidal thoughts (such as question 9 on the Beck Depression Inventory). As the client tells his/her story, the counselor should be listening (and looking) for the presence of risk factors and protective factors . As the number of risk factors increases particularly in the absence of protective factors, suicide risk increases and should be questioned. As a counselor attends to the client, language that reflects feelings of hopelessness and despair should be noticed and explored. For instance, it is paramount to ask for elaboration on statements such as â€Å"I can’t go on anymore.† â€Å"I want to end it all.† â€Å"I wish I were dead.† â€Å"This is hopeless, I don’t see any way out of this situation.† In truth the first intervention for suicide is the assessment, in other words assessment begins the process of suicide intervention Questions to guide Suicide Assessments Either as part of an intake assessment, or based on information you have gather...

French Revolution Essay Example | Topics and Well Written Essays - 750 words

French Revolution - Essay Example Very early in the course of activities that unfolded during the revolution, the revolution lost its impact. This can be supported and said on the basis of the fact that entities like Reign of Terror came into existence(Lutz and Lutz, 194). Reign of terror, as the name would imply was one outright reactionary, and non elected entity. It was completely violent in its outlook and it worked on principles of revolt and reactionary mindset. Revolutionary measures and not evolutionary measures was the cry and manifesto of Reign of Terror. The initial troubles faced by France in form of offensives by Prussia and Austria termed it another failure at hand, given the fact that the country and its people were faced with the problems from inside, least to solve and spare out resources for the external aggression. The only democratic entity that came into power was the Directory. It lasted from 1795 to 1799, however it was marred by plethora of flaws and shortcomings in its own. It was faced with challenges of corruption, inability on behalf of the elected members, the lack of institutions and various other elements that make or break a democratic institution and organization. Hence from this perspective as well, the French revolution may not be termed as the successful story. The rise of Napoleon Bonaparte to power marked the severe blow towards the entire activity of the revolution. He was a military man who had taken over the power in Brumaire coup, and later on paved his way towards the life long Emperor of France (Scott, 2). Hence, the revolution that was initiated for the purpose of bringing about democracy in the country ended up providing a platform for a usurper like Napoleon Bonaparte to enact his own empire and declare himself the emperor. This hawks naked and wide into the eyes of those who declared the revolution a success. The final blow was suffered by French Revolution in the wake of the

England and the Crusade Essay Example | Topics and Well Written Essays - 1500 words

England and the Crusade - Essay Example The struggle was between the Muslims and Christians. Each one of them wanted to take full control of Jerusalem, also referred to as the Holy Land. However, in the year 1291, this consistent battle came to an end when the Muslims finally took over Jerusalem. The Roman Catholic Pope was shocked by this defeat and had heart attack that led to his death. From that time, no more war of crusade was seen in Jerusalem (Smith, 1995, p. 66). Pope Urban was depressed to death because he had spent all his time and resources in the crusade. He succeeded in convincing the Roman Christians to join the war. Those who decided to join him were promised a lot of good things. One of such goodies was the forgiveness. Pope Urban had said it in public that anyone who would join the crusade would have all his sins forgiven. On hearing this, many catholic faithful rushed and vowed in public to give their all in the fight. It was during this time that the very Pope also managed to convince and gain the support of other state leaders. Kings from various nations who were members of Roman Catholic and had the same objective agreed to join Pope Urban. These kings provided the Pope with military army and some other forms of support such finance. This is when the big countries like France, England and Germany joined the crusade war (Teall, 1959, pp. 84-95). However, the crusade army did not only cause trouble to the Muslim nations but also to the various countries that they passed through. For example, the crusade carried out by England caused many damages in Sicily and Cyprus. It is thought that some nations joined this movement for their individual gains. They were just hiding in this holy war. The main objective of Pope Urban is also not clearly known. Some scholars tend to argue that his motives were not godly as people may think. They say that he only had personal interests and therefore decided to use the armies of nations such as

Efficient Prediction System Using Artificial Neural Networks

Efficient Prediction System Using Artificial Neural Networks Jay Patel Abstract- Predicting is making claims about something that will happen, often based on information from past and from current state. Neural networks can be used for prediction with various levels of success. The neural network is trained from the historical data with the hope that it will discover hidden dependencies and that it will be able to use them for predicting into future. It is an approach for making prediction efficient using best features on which prediction is more dependent. Keywords: Artificial Neural Networks; Feature set; Profiles INTRODUCTION Artificial neural networks are computational models inspired by animal central nervous systems (in particular the brain) that are capable of machine learning and pattern recognition. They are usually presented as systems of interconnected neurons that can compute values from inputs by feeding information through the network. For example, in a neural network for handwriting recognition, a set of input neurons may be activated by the pixels of an input image representing a letter or digit. The activations of these neurons are then passed on, weighted and transformed by some function determined by the networks designer, to other neurons, etc., until finally an output neuron is activated that determines which character was read. Mainly three types of ANN models are present single layer feed forward network, Multilayer feed forward network and recurrent network Single layer feed forward network consist of only one input layer and one output layer. Input layer neurons receive the input sig nals and output layer receives output signals. In a feed forward network the output of the network does not affect the operation of the layer that is producing this output. In a feedback network however the output of a layer after the layer being fed back into, can affect the output of the earlier layer. Essentially the data loops through the two layers and back to start again. This is important in control circuits, because it allows the result from a previous calculation to affect the operation of the next calculation. This means that the second calculation can take into account the results of the first calculation, and be controlled by them. Weiners work on cybernetics was based on the idea that feedback loops were a useful tool for control circuits. In fact Weiner coined the termcybernetics based on the Greek kybernutos or metallic steersman of a fictional boat mentioned in the Illiad. Neural models ranged from complex mathematical models with Floating point outputs to simple state machines with a binary output. Depending on w hether the neuron incorporates the learning mechanism or not, neural learning rules can be as simple as adding weight to a synapse each time it fires, and gradually degrading those weights over time, as in the earliest learning rules, Delta rules that accelerate the learning by applying a delta value according to some error function in a back propagation network, to Pre-synaptic/Post-synaptic rules based on biochemistry of the synapse and the firing process. Signals can be calculated in binary, linear, non-linear, and spiking values for the output. Figure 1. ANN Models Multilayer feed forward network consist of input, output and one more addition than single layer feed forward is hidden layer. Computational units of hidden layer are called hidden neurons. In Multilayer Feed Forward Network there must be only one input layer and one output layer and hidden layers can be of any numbers. There is only one difference in recurrent network from feed forward networks is that there is at least one feedback loop. In neurons we can input vectors taken as input and weights are included. With the help of weights and input vectors we can calculate weighted sum and taking weighted sum as parameter we can calculate activation function. There are different activation functions available e.g. thresholding, Signum, Sigmoidal, Hyperbolic Tangent. Phase ordering of optimization techniques In optimizing compilers, it is standard practice to apply the same set of optimization phases in a fixed order on each method of a program. However, several researchers have shown that the best ordering of optimizations varies within a program, i.e., it is function-specific. Thus,we would like a technique that selects the best ordering of optimizations for individual portions of the program, rather than applying the same fixed set of optimizations for the whole program. This paper develops a new method-specific technique that automatically selects the predicted best ordering of optimizations for different methods of a program. They develop this technique within the Jikes RVM Java JIT compiler and automatically determine good phase-orderings of optimizations on a per method basis. Rather than developing a handcrafted technique to achieve this, they make use of an artificial neural network (ANN) to predict the optimization order likely to be most beneficial for a method. Our ANNs were automatically induced using Neuro-Evolution for Augmenting Topologies (NEAT). A trained ANN uses input properties (i.e., features) of each method to represent the current optimized state of the method and given this input, the ANN outputs the optimization predicted to be most beneficial to the method at that state. Each time an optimization is applied, it potentially changes the properties of the method. Therefore, after each optimization is applied, they generate new features of the method to use as input to the ANN. The ANN then predicts the next optimization to apply based on the current optimized state of the method. This technique solves the phase ordering problem by taking advantage of the Markov property of the optimization problem. That is, the current state of the method represents all the information required to choose an optimization to be most beneficial at that decision point. Most compilers apply optimizations based on a fixed order that was determined to be best when the compiler was being developed and tuned. However, programs require a specific ordering of optimizations to obtain the best performance. To demonstrate our point, we use genetic algorithms (GAs), the current state-of-the-art in phase-ordering optimizations, to show that selecting the best ordering of optimizations has the potential to significantly improve the running time of dynamically compiled programs. They used GAs to construct a custom ordering of optimizations for each of the Java Grande and SPEC JVM 98 benchmarks. In this GA approach, we create a population of strings (called chromosomes), where each chromosome corresponds to an optimization sequence. Each position (or gene) in the chromosome corresponds to a specific optimization from Table 2, and each optimization can appear multiple times in a chromosome. For each of the experiments below, we configured our GAs to create 50 chro mosomes (i.e., 50 optimization sequences) per generation and to run for 20 Generations. Technique for Implementing GA We ran two different experiments using GAs. The first experiment consisted of finding the best optimization sequence across our benchmarks. Thus, we evaluated each optimization sequence (i.e., chromosome) by compiling all our benchmarks with each sequence. We recorded their execution times and calculated their speedup by normalizing their running times with the running time observed by compiling the benchmarks at the O3 level. That is, we used average speedup of our benchmarks (normalized to opt level O3) as our fitness function for each chromosome. This result corresponds to the â€Å"Best Overall Sequence† bars in Figure 1. The purpose of this experiment was to discover the optimization ordering that worked best on average for all our benchmarks. The second experiment consisted of finding the best optimization ordering for each benchmark. Here, the fitness function for each chromosome was the speedup of that optimization sequence over O3 for one specific benchmark. This resu lt corresponds to the â€Å"Best Sequence per Benchmark† bars in Figure 1. This represents the performance that we can get by customizing an optimization ordering for each benchmark individually. Results The results of these experiments confirm two hypotheses. First, significant performance improvements can be obtained by finding good optimization orders versus the well-engineered fixed order in Jikes RVM. The best order of optimizations per benchmark gave us up to a 20% speedup (FFT) and on average 8% speedup over optimization level O3. Second, as shown in previous work, each of our benchmarks requires a different optimization sequence to obtain the best performance. One ordering of optimizations for the entire set of programs achieves decent performance speedup compared to O3. Figure 2.Results of experiments using GA However, the â€Å"Best Overall Sequence† degrades the performance of three benchmarks (LUFact, Series, and Crypt) compared to O3. In contrast, searching for the best custom optimization sequence for each benchmark, â€Å"Best Sequence for Benchmark†, allows us to outperform both O3 and the best overall sequence. Motivation Predict the current best optimization: This method would use a model to predict the best single optimization (from a given set of optimizations) that should be applied based on the characteristics of code in its present state. Once an optimization is applied, we would re-evaluate characteristics of the code and again predict the best optimization to apply given this new state of the code. For this we can apply Artificial Neural Network in this method and we will also include profiles for better prediction of optimization sequence for particular program. Automatic Feature Generation Automatic Feature generation system is comprised of the following components: training data generation, feature search and machine learning [5]. The training data generation process extracts the compiler’s intermediate representation of the program plus the optimal values for the heuristic we wish to learn. Once these data have been generated, the feature search component explores features over the compiler’s intermediate representation (IR) and provides the corresponding feature values to the machine learning tool. The machine learning tool computes how good the feature is at predicting the best heuristic value in combination with the other features in the base feature set (which is initially empty). The search component finds the best such feature and, once it can no longer improve upon it, adds that feature to the base feature set and repeats. In this way, we build up a gradually improving set of features. a. Data Generation In a similar way to the existing machine learning techniques (see section II) we must gather a number of examples of inputs to the heuristic and find out what the optimal answer should be for those examples. Each program is compiled in different ways, each with a different heuristic value. We time the execution of the compiled programs to find out which heuristic value is best for each program. We also extract from the compiler the internal data structures which describe the programs. Due to the intrinsic variability of the execution times on the target architecture, we run each compiled program several times to reduce susceptibility to noise. Figure 3. Automatic Feature Generation b. Feature Search The feature search component maintains a population of feature expressions. The expressions come from a family described by a grammar derived automatically from the compiler’s IR. Evaluating a feature on a program generates a single real number; the collection of those numbers over all programs forms a vector of feature values which are later used by the machine learning tool. c. Machine Learning The machine learning tool is the part of the system that provides feedback to the search component about how good a feature is. As mentioned above, the system maintains a list of good base features. It repeatedly searches for the best next feature to add to the base features, iteratively building up the list of good features. The final output of the system will be the latest features list. Our system additionally implements parsimony. Genetic programming can quickly generate very long feature expressions. If two features have the same quality we prefer the shorter one. This selection pressure prevents expressions becoming needlessly long. E. Motivation They have developed a new technique to automatically generate good features for machine learning based optimizing compilation. By automatically deriving a feature grammar from the internal representation of the compiler, we can search a feature space using genetic programming. We have applied this generic technique to automatically learn good features. Code Optimization in Compilers using ANN For ordering of different optimization techniques using ANN we must need to implement that in 4Cast-XL as it is a dynamic compiler. 4Cast-XL constructs an ANN, Integrate the ANN into Jikes RVM’s optimization driver than Evaluate ANN at the task of phase-ordering optimizations. For each method dynamically compiled, repeat the following two steps Generate a feature vector of current method’s state Generate profiles of program Use ANN to predict the best optimization to apply Use ANN to predict the best optimization to apply. Run benchmarks and obtain feedback for 4Cast-XL Record execution time for each benchmark optimized using the ANN. Obtain speedup by normalizing each benchmark’s running time to running time using default optimization heuristic. Figure 4. Code Optimization in compilers using ANN with Profiles Results Research work is aimed for optimizing code using artificial neural networks. In order to make this precise, better profiles generated from given set of features using Milepost GCC compiler with ten different programs. Experimental results demonstrate that profiles of program can be used for optimization of code. Motivation This section gives a detailed overview of how Neuro-evolution machine learning is used to construct a good optimization phase-ordering heuristic for the optimizer. The first section outlines the different activities that take place when training and deploying a phase ordering heuristic. This is followed by sections describing how we use 4cast-XL to construct an ANN, how we extract features from methods, and how best features called Profiles and ANNs allow us to learn a heuristic that determines the order of optimizations to apply. It motivates us to apply this approach for different types of predictions using Artificial Neural Networks. Prediction Using Neural Networks Neural networks can be used for prediction with various levels of success. The advantage of then includes automatic learning of dependencies only from measured data without any need to add further information (such as type of dependency like with the regression). The neural network is trained from the historical data with the hope that it will discover hidden dependencies and that it will be able to use them for predicting into future. In other words, neural network is not represented by an explicitly given model. It is more a black box that is able to learn something. 1

World Wide Fund for Nature

There are well over five thousand endangered species on Earth and humans are the cause of it. Many animals are suffering and several hundred are already extinct. Many of the most beautiful creatures are now evanescent. People are also causing the destruction of the environment, which is home to millions of animals worldwide. Through poaching and obliteration of nature, humans have managed to diminish the very place they call home. There are charities such as the World Wide Fund for Nature with the motive to halt and reverse the annihilation of the environment.The international organization World Wide Fund for Nature works on issues regarding conservation, research, and restoration of the environment. The WWF was formed on April 26, 1961, when a small group of passionate individuals had the idea of building a future where humans live in harmony with nature. Their mission was to preserve the planet’s resources, reduce pollution, and conserve the world’s life diversity. Th e WWF’s original name was World Wildlife Fund and was later changed to World Wide Fund for Nature.The panda has become the logo for WWF because it was an endangered species and served as a strong recognizable symbol that was adored by many people in the world for its appealing characteristics. Originally, their aim was to protect the wildlife species and habitat. Today, organization has grown to repopulating several different species worldwide and seeks to didactically educate people on how to have a more ecologically friendly lifestyle.The World Wide Fund for Nature is the world’s leading environmental conservation organization with a global reach of one hundred different countries. They help protect endangered animals and their habitats. The WWF does this by collaborating with businesses, governments, local communities, and other organizations to secure funds and ensure the safety of wildlife. They focus on the underlying causes of environmental deterioration.The ent ire planet depends on organizations such as this, and without it, the nature living on it would continue to be whittled down by apathetic people until it is too late. Humans will soon become their own nemesis, unless they show some solicitude, and be a partisan in restoring the planet. The WWF, with over one million members in the United States and nearly five million worldwide, is one of the most impactful charitable organizations in history. The WWF gives hope to the future of the environment and fixes the problems that previous generations have caused.Because of this organization, the earth’s environment’s status has drastically improved. They are bringing back the serene environment, and shielding the forlorn animals that inhabit it. The World Wide Fund for Nature is a very impactful charitable organization that aids in restoring the planet’s environment and saves the lives of animals that are in need. The international organization World Wide Fund for Natur e works on issues regarding conservation, research, and restoration of the environment.

Oppression Theory That Supports Horizontal Violence Process Essay

Nurses are known to be the devoted caregiver of sick patients. How can the patients get rid of their burden if their own caregivers are in conflict among each other in hospital settings? When there is conflict in such kind of environment, it is called horizontal violence, interpersonal conflict or bullying which is aggressive and destructive behavior of nurses against each other (Woelfle & McCaffrey, 2007). It is an expression of oppressed group behavior evolving from feelings of low self-esteem and lack of respect from others which is supported by the theory of oppression. According to the theory stated by Woelfle & McCaffrey (2007), in order for the horizontal violence to take place in the nursing setting, oppression exists when a powerful and dominant group controls and exploits a less influential or easy target group. As a consequence the oppressed group displays low self esteem and self hatred as evidenced by anger and frustration (Woelfle & McCaffrey, 2007). The theory of oppression helps to explain that the behaviors of horizontal violence aren’t directed at the individual but rather is a response to the specific situation where one feels fear of punishment that prevents the nurse from responding to the oppression. When people feel oppressed they feel inferior and powerless. These kinds of nurses who feel powerless behave aggressively towards peers to relieve tension because they can’t fight against their oppressor. That results to the display of emotion which victimize the colleague where the colleague or the coworker gets the feeling of vulnerability or prone to be hurt. The emotion or body language often includes rolling of the eyes, folding the arms or storming out of the room, using sarcasm, raised voice and shouting. These people manipulate the work environment while denying doing anything wrong and get satisfied from experiential difficulty and discomfort of others. These negative behaviors have obvious results in human mind leading to anxiety and stress at work. This cycle of denial maintains its own pattern of repeated action against the vulnerable group and allows the power relations to be unchallenged. Rather than fighting back and risking from the superiors/violence creators, the oppressed groups’ frustration is manifested as conflict in their own ranks with horizontal violence from coworker to coworker. Hence, people  begin to think this kind of behavior as a norm which they displace their feeling of aggression to another highly prone groups such as new grad nurse or student and even less confident coworkers. This cycle of behavior is typically described as horizontal violence (Woelfle & McCaffrey, 2007). As an example, a coworker in a unit behaves aggressively in a reaction to their own part of stress by acting aggressively and displacing their anger to another same or lower hierarchical level group or coworker. Another coworker as a victim gets devastated with this behavior especially if the superior authority or managers don’t acknowledge the behavior. Hence the victim feels angry, frustrated and vulnerable continuing the cycle of horizontal violence. Rather than fighting back against the aggressor, this group accepts this as a behavioral norm which they unconsciously displace to other lower or same hierarchical level coworker such as grad nurse or the nursing students. These nursing students or grad nurses later learn to displace their stress to other with the verbal or nonverbal expression giving the feeling of vulnerability to the prone groups. Hence this cycle of oppression continues as a horizontal violence in the work place area as part of the work stress. Consequently the oppressed group often lack autonomy, accountability and control over their profession (Woelfle & McCaffrey, 2007). Horizontal violence is a purposeful ongoing collection of often negative behaviors and actions that accumulate over time. Moreover, it includes repeated acts involving an imbalance of strength or power, in which one or more individuals engage in over time with the intention to harm other and create a hostile work environment. They displace their part of frustration to others in the form of negative verbal or nonverbal expression. The cycle of oppression continues which is supported by the theory of oppression. The result of horizontal violence affects nurses, nursing managers, other medical and administrative staff, patient and their family. It is clear that horizontal violence is everywhere in nursing today and can drastically affect the nursing area. When the tension is elevated in the patient care, nurses cannot perform their best which often lead to poor quality patient care (Woelfle & McCaffrey, 2007). Reference: Woelfle, C. Y. & McCaffrey, R. ( July-September, 2007). Nurse on nurse. Nursing Forum, Vol 42(3), p123-131