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Indonesiska

become diferent

Engelska

What does the word i will promise heart

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Indonesiska

how To become A good student

Engelska

terjemahan bhs inggris keindonesia

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terjemahan english ke indonesian film ratatulli he's dying to become a chef

Engelska

Indonesian to English translations ratatulli movie he's dying to Become a chef

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hone more sympathetic towards others,it helps us to understand the problem and weakness of man,and we become less suspicious of strangers and other

Engelska

igher education confers many other benefits.it broadens the mind and increases one's powers of thinking .reasoning and imagination,further,it makes

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WARNING: no user ID has been marked as primary. This command may cause a different user ID to become the assumed primary.

Engelska

WARNING: no user ID has been marked as primary. This command may cause a different user ID to become the assumed primary.

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Nakokake / Nembung / Nglamar Then the bride invited to meet with the prospective groom asked his willingness to become his wife. Then do komborkanan (the formation of the committee).

Engelska

Nakokake / Nembung / Nglamar Then the bride invited to meet with the prospective groom asked his willingness to Become his wife. Then do komborkanan (the formation of the committee).

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10 easy ways to fail a Ph.D. [article index] [email me] [@mattmight] [+mattmight] [rss] The attrition rate in Ph.D. school is high. Anywhere from a third to half will fail. In fact, there's a disturbing consistency to grad school failure. I'm supervising a lot of new grad students this semester, so for their sake, I'm cataloging the common reasons for failure. Read on for the top ten reasons students fail out of Ph.D. school. Focus on grades or coursework No one cares about grades in grad school. There's a simple formula for the optimal GPA in grad school: Optimal GPA = Minimum Required GPA + ε Anything higher implies time that could have been spent on research was wasted on classes. Advisors might even raise an eyebrow at a 4.0 During the first two years, students need to find an advisor, pick a research area, read a lot of papers and try small, exploratory research projects. Spending too much time on coursework distracts from these objectives. Learn too much Some students go to Ph.D. school because they want to learn. Let there be no mistake: Ph.D. school involves a lot of learning. But, it requires focused learning directed toward an eventual thesis. Taking (or sitting in on) non-required classes outside one's focus is almost always a waste of time, and it's always unnecessary. By the end of the third year, a typical Ph.D. student needs to have read about 50 to 150 papers to defend the novelty of a proposed thesis. Of course, some students go too far with the related work search, reading so much about their intended area of research that they never start that research. Advisors will lose patience with "eternal" students that aren't focused on the goal--making a small but significant contribution to human knowledge. In the interest of personal disclosure, I suffered from the "want to learn everything" bug when I got to Ph.D. school. I took classes all over campus for my first two years: Arabic, linguistics, economics, physics, math and even philosophy. In computer science, I took lots of classes in areas that had nothing to do with my research. The price of all this "enlightenment" was an extra year on my Ph.D. I only got away with this detour because while I was doing all that, I was a TA, which meant I wasn't wasting my advisor's grant funding. Expect perfection Perfectionism is a tragic affliction in academia, since it tends to hit the brightest the hardest. Perfection cannot be attained. It is approached in the limit. Students that polish a research paper well past the point of diminishing returns, expecting to hit perfection, will never stop polishing. Students that can't begin to write until they have the perfect structure of the paper mapped out will never get started. For students with problems starting on a paper or dissertation, my advice is that writing a paper should be an iterative process: start with an outline and some rough notes; take a pass over the paper and improve it a little; rinse; repeat. When the paper changes little with each pass, it's at diminishing returns. One or two more passes over the paper are all it needs at that point. "Good enough" is better than "perfect." Procrastinate Chronic perfectionists also tend to be procrastinators. So do eternal students with a drive to learn instead of research. Ph.D. school seems to be a magnet for every kind of procrastinator. Unfortunately, it is also a sieve that weeds out the unproductive. Procrastinators should check out my tips for boosting productivity. Go rogue too soon/too late The advisor-advisee dynamic needs to shift over the course of a degree. Early on, the advisor should be hands on, doling out specific topics and helping to craft early papers. Toward the end, the student should know more than the advisor about her topic. Once the inversion happens, she needs to "go rogue" and start choosing the topics to investigate and initiating the paper write-ups. She needs to do so even if her advisor is insisting she do something else. The trick is getting the timing right. Going rogue before the student knows how to choose good topics and write well will end in wasted paper submissions and a grumpy advisor. On the other hand, continuing to act only when ordered to act past a certain point will strain an advisor that expects to start seeing a "return" on an investment of time and hard-won grant money. Advisors expect near-terminal Ph.D. students to be proto-professors with intimate knowledge of the challenges in their field. They should be capable of selecting and attacking research problems of appropriate size and scope. Treat Ph.D. school like school or work Ph.D. school is neither school nor work. Ph.D. school is a monastic experience. And, a jealous hobby. Solving problems and writing up papers well enough to pass peer review demands contemplative labor on days, nights and weekends. Reading through all of the related work takes biblical levels of devotion. Ph.D. school even comes with built-in vows of poverty and obedience. The end brings an ecclesiastical robe and a clerical hood. Students that treat Ph.D. school like a 9-5 endeavor are the ones that take 7+ years to finish, or end up ABD. Ignore the committee Some Ph.D. students forget that a committee has to sign off on their Ph.D. It's important for students to maintain contact with committee members in the latter years of a Ph.D. They need to know what a student is doing. It's also easy to forget advice from a committee member since they're not an everyday presence like an advisor. Committee members, however, rarely forget the advice they give. It doesn't usually happen, but I've seen a shouting match between a committee member and a defender where they disagreed over the metrics used for evaluation of an experiment. This committee member warned the student at his proposal about his choice of metrics. He ignored that warning. He was lucky: it added only one more semester to his Ph.D. Another student I knew in grad school was told not to defend, based on the draft of his dissertation. He overruled his committee's advice, and failed his defense. He was told to scrap his entire dissertaton and start over. It took him over ten years to finish his Ph.D. Aim too low Some students look at the weakest student to get a Ph.D. in their department and aim for that. This attitude guarantees that no professorship will be waiting for them. And, it all but promises failure. The weakest Ph.D. to escape was probably repeatedly unlucky with research topics, and had to settle for a contingency plan. Aiming low leaves no room for uncertainty. And, research is always uncertain. Aim too high A Ph.D. seems like a major undertaking from the perspective of the student. It is. But, it is not the final undertaking. It's the start of a scientific career. A Ph.D. does not have to cure cancer or enable cold fusion. At best a handful of chemists remember what Einstein's Ph.D. was in. Einstein's Ph.D. dissertation was a principled calculation meant to estimate Avogadro's number. He got it wrong. By a factor of 3. He still got a Ph.D. A Ph.D. is a small but significant contribution to human knowledge. Impact is something students should aim for over a lifetime of research. Making a big impact with a Ph.D. is about as likely as hitting a bullseye the very first time you've fired a gun. Once you know how to shoot, you can keep shooting until you hit it. Plus, with a Ph.D., you get a lifetime supply of ammo. Some advisors can give you a list of potential research topics. If they can, pick the topic that's easiest to do but which still retains your interest. It does not matter at all what you get your Ph.D. in. All that matters is that you get one. It's the training that counts--not the topic. Miss the real milestones Most schools require coursework, qualifiers, thesis proposal, thesis defense and dissertation. These are the requirements on paper. In practice, the real milestones are three good publications connected by a (perhaps loosely) unified theme. Coursework and qualifiers are meant to undo admissions mistakes. A student that has published by the time she takes her qualifiers is not a mistake. Once a student has two good publications, if she convinces her committee that she can extrapolate a third, she has a thesis proposal. Once a student has three publications, she has defended, with reasonable confidence, that she can repeatedly conduct research of sufficient quality to meet the standards of peer review. If she draws a unifying theme, she has a thesis, and if she staples her publications together, she has a dissertation. I fantasize about buying an industrial-grade stapler capable of punching through three journal papers and calling it The Dissertator. Of course, three publications is nowhere near enough to get a professorship--even at a crappy school. But, it's about enough to get a Ph.D. Related posts • Recommended reading for grad students. • The illustrated guide to a Ph.D. • How to get into grad school. • Advice for thesis proposals. • Productivity tips for academics. • Academic job hunt advice. • Successful Ph.D. students: Perseverance, tenacity and cogency. • The CRAPL: An open source license for academics. 3 qualities of successful Ph.D. students: Perseverance, tenacity and cogency [article index] [email me] [@mattmight] [+mattmight] [rss] Every fall, a fresh crop of Ph.D. students arrives. Since I'm actively looking for Ph.D. students, I get the same question a dozen times every year: "How long does it take to get a Ph.D.?" This isn't the right question. "Ph.D. school takes as long as you want it to," I tell them. There's no speed limit on how fast you can jump through all the hoops. A better question to ask is, "What makes a Ph.D. student successful?" Having watched Ph.D. students succeed and fail at four universities, I infer that success in graduate school hinges on three qualities: perseverance, tenacity and cogency. If you're in Ph.D. school or you're thinking about it, read on. What doesn't matter There's a ruinous misconception that a Ph.D. must be smart. This can't be true. A smart person would know better than to get a Ph.D. "Smart" qualities like brilliance and quick-thinking are irrelevant in Ph.D. school. Students that have made it through so far on brilliance and quick-thinking alone wash out of Ph.D. programs with nagging predictability. Let there be no doubt: brilliance and quick-thinking are valuable in other pursuits. But, they're neither sufficient nor necessary in science. Certainly, being smart helps. But, it won't get the job done. Moreover, as anyone going through Ph.D. school can tell you: people of less than first-class intelligence make it across the finish line and leave, Ph.D. in hand. As my advisor used to tell me, "Whenever I felt depressed in grad school--when I worried I wasn't going to finish my Ph.D.--I looked at the people dumber than me finishing theirs, and I would think to myself, if that idiot can get a Ph.D., dammit, so can I." Since becoming a professor, I finding myself repeating a corollary of this observation, but I replace "getting a Ph.D." with "obtaining grant funding." Update: Within a month of writing that last line, I was awarded my first three grants. Perseverance To escape with a Ph.D., you must meaningfully extend the boundary of human knowledge. More exactly, you must convince a panel of experts guarding the boundary that you have done so. You can take classes and read papers to figure out where the boundary lies. That's easy. But, when it comes time to actually extend that boundary, you have to get into your bunker and prepare for the onslaught of failure. A lot of Ph.D. students get depressed when they reach the boundary, because there's no longer a test to cram for or a procedure to follow. This is the point (2-3 years in) where attrition peaks. Finding a problem to solve is rarely a problem itself. Every field is brimming with open problems. If finding a problem is hard, you're in the wrong field. The real hard part, of course, is solving an open problem. After all, if someone could tell you how to solve it, it wouldn't be open. To survive this period, you have to be willing to fail from the moment you wake to the moment your head hits the pillow. You must be willing to fail for days on end, for months on end and maybe even for years on end. The skill you accrete during this trauma is the ability to imagine plausible solutions, and to estimate the likelihood that an approach will work. If you persevere to the end of this phase, your mind will intuit solutions to problems in ways that it didn't and couldn't before. You won't know how your mind does this. (I don't know how mine does it.) It just will. As you acquire this skill, you'll be launching fledgling papers at peer reviewers, checking to see if others think what you're doing qualifies as research yet. Since acceptance rates at good venues range between 8% and 25%, most or all of your papers will be rejected. You just have to hope that you'll eventually figure out how to get your work published. If you stick with it long enough and work at it hard enough, you will. For students that excelled as undergraduates, the sudden and constant barrage of rejection and failure is jarring. If you have an ego problem, Ph.D. school will fix it. With a vengeance. (Some egos seem to recover afterward.) This phase of the Ph.D. demands perseverance--in the face of uncertainty, in the face of rejection and in the face of frustration. Tenacity To get a tenure-track professorship after Ph.D. school, you need an additional quality: tenacity. Since there are few tenure-track faculty positions available, there is a fierce (yet civil) competition to get them. In computer science, a competitive faculty candidate will have about 10 publications, and 3-5 of those will be at "selective" or "Tier 1" venues (crudely, less than 33% acceptance rate). A Ph.D. by itself won't even get you a job interview anymore. There are few good reasons to get a Ph.D. "Because you want to become a professor" might be the only good one. Ironically, there's a good chance you won't realize that you want to be a professor until the end of grad school. So, if you're going to do Ph.D. school at all, do it right, for your own sake. To become professor, you can't have just one discovery or solve just one open problem. You have to solve several, and get each solution published. As you exit graduate school, an arc connecting your results should emerge, proving to faculties that your research has a profitable path forward. You will also need to actively, even aggressively, forge relationships with scholars in your field. Researchers in your field need to know who you are and what you're doing. They need to be interested in what you're doing too. None of that is going to happen by itself. Cogency Finally, a good Ph.D. student must have the ability to clearly and forcefully articulate their ideas--in person and in writing. Science is as much an act of persuasion as it is an act of discovery. Once you've made a discovery, you have to persuade experts that you've made a legitimate, meaningful contribution. This is harder to do than it seems. Simply showing experts "the data" isn't going to work. (Yes, in a perfect world, this would be sufficient.) Instead, you have to spoon-feed the experts. As you write, you have to consciously minimize the amount of time and cognitive pain it takes for them to realize you've made a discovery. You may have to go "on tour" and give engaging presentations to get people excited about your research. When you give conference talks, you want them eagerly awaiting the next episode. You will have to write compelling abstracts and introductions that hook the reader and make her feel like investing time in your work. You will have to learn how to balance clarity and precision, so that your ideas come across without either ambiguity or stifling formality. Generally, grad students don't arrive with the ability to communicate well. This is a skill that they forge in grad school. The sooner acquired, the better. Unfortunately, the only way to get better at writing is to do a lot of it. 10,000 hours is the magical number folks throw around to become an expert at something. You'll never even get close to 10,000 hours of writing by writing papers. Assuming negligible practice writing for public consumption before graduate school, if you take six years to get through grad school, you can hit 10,000 hours by writing about 5 hours a day. (Toward the end of a Ph.D., it's not uncommon to break 12 hours of writing in a day.) That's why I recommend that new students start a blog. Even if no one else reads it, start one. You don't even have to write about your research. Practicing the act of writing is all that matters. Translations • Portuguese. Related posts • How to get into grad school. • Productivity tips for academics. • Recommended reading for grad students. • Academic job hunt advice.

Engelska

sentence translation english

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Indonesiska

the olimpic games the olimpic games is a very popular sport even in the world. it is held every four years in different cities around the world. those who participate in the games are usually famous for the rest of their lives. about 100.000 people have competed in the games. these athletes are all amateurs. They play sport and they get no money for their play. They must qalify or win regional and national events and they often play on their countries' national teams. Athletes compete or play against each other in hopes of winning. That might mean crossing the finish line first or putting on a perfect performance. Throughout the the Games, the contestants are suppossed to play with a spirit of sportsmanship. This means that they must play with horor. Their goal is to do their very best in their sport, and not specifically to defeat the other players. unfortunately , some athletes and coaches have tried to cheat or use drugs. They used steroids so they could have stronger muscles and better stamina. In that way they could win the game but of course they won the game illegally. Wars between two countries or inharmonious relationship between two countries could also affect the Olympics. Sometimes atheletes refuse to compete against others with different ideologiy. In short, nationalism has sometimes become a problem in the olympics. Although the Olympics have the problems of cheating an doping, and sometimes nationalism, the Games are still popular. Perhaps it is beacouse we can learn a lot of things from the Games. The Games show us what we are capable of. We can also learn that we can actually compete with each other but we are still friends.

Engelska

the olimpic games the olimpic games is a very popular sport even in the world. it is held every four years in different cities around the world. those who participate in the games are usually famous for the rest of their lives. about 100.000 people have competed in the games. these athletes are all amateurs. They play sport and they get no money for their play. They must qalify or win regional and national events and they often play on their countries' national teams. Athletes compete or play against each other in hopes of winning. That might mean crossing the finish line first or putting on a perfect performance. Throughout the the Games, the contestants are suppossed to play with a spirit of sportsmanship. This means that they must play with horor. Their goal is to do their very best in their sport, and not specifically to defeat the other players. unfortunately , some athletes and coaches have tried to cheat or use drugs. They used steroids so they could have stronger muscles and better stamina. In that way they could win the game but of course they won the game illegally. Wars between two countries or inharmonious relationship between two countries could also affect the Olympics. Sometimes atheletes refuse to compete against others with different ideologiy. In short, nationalism has sometimes become a problem in the olympics. Although the Olympics have the problems of cheating an doping, and sometimes nationalism, the Games are still popular. Perhaps it is beacouse we can learn a lot of things from the Games. The Games show us what we are capable of. We can also learn that we can actually compete with each other but we are still friends.

Senast uppdaterad: 2014-11-06
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Indonesiska

If test materials were widely available, it would be easy for persons to review the tests, learn the answers, and respond according to the impression they would like to make. Thus, the materials would lose their validity. This means that psychologists should make all reasonable efforts to ensure that test materials are secure. Specifically, all tests should be kept locked in a secure place and no untrained persons should be allowed to review them. Any copyrighted material should not be duplicated. In addition, raw data from tests should not ordinarily be released to clients or other persons who may misinterpret them. However, clients have a right to the reports themselves should they request them. They also have the right to have the information released to a person they designate but such a request should be in writing (see Zuckerman, 1997, The Paper Office,for forms and guidelines). The security of assessment results should also be maintained. Ideally, this means that only designated persons (usually the referral source and client) should see the results unless the client provides a release of information. In reality, however, this ethical principal may sometimes be difficult to achieve. For example, many medical contexts expect most relevant treatment information (including psychological assessment results) to be kept in clients’ charts. Typically, all members of the treatment team have access to the charts (Claassen & Lovitt, 2001). On one level, this represents a conflict between psychological and medical guidelines. On another level, it represents a conflict between benefit to the patient (that may be enhanced by the treatment team having access to his or her records) and patient autonomy (patient control over who and where information should go). Security of assessment results can also be compromised when a large number of organizations (insurance company, interacting rehabilitation provider, referral source) all want access to patient records. This has become a particular issue in the managed health care environment. The security of client records also becomes more tenuous when large interconnected databases potentially have access to patient data (McMinn, Buchanan, et al., 1999; McMinn, Ellens, et al., 1999). Sometimes in legal contexts, the court or the opposing council may wish to see either raw data or the actual test materials. Under these conditions, the court should be informed that ethical guidelines as well as agreements made with the test distributor require that this information not be released to untrained persons. An acceptable alternative would be for the psychologist to designate a person with appropriate training to receive the information and explain the data or describe the test material (Tranel, 1994)

Engelska

I

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Indonesiska

Flooding Throughout history humans have found it desirable to construct cities along streams. Streams are sources of water for consumption, agriculture, and industry. Streams provide transportation routes, energy, and a means of disposal of wastes. Stream valleys offer a relatively flat area for construction. But, human populations that live along streams also have the disadvantage that the flow of water in streams is never constant. High amounts of water flowing in streams often leads to flooding, and flooding is one of the more common and costly types of natural disasters. A flood results when a stream runs out of its confines and submerges surrounding areas. In less developed countries, humans are particularly sensitive to flood casualties because of high population density, absence of zoning regulations, lack of flood control, and lack of emergency response infrastructure and early warning systems. Bangladesh is one of the most susceptible countries to flood disasters. About one half of the land area in Bangladesh is at an elevation of less than 8 meters above sea level. Up to 30% of the country has been covered with flood waters. In 1991 more 200,000 deaths resulted from flooding and associated tropical cyclones. In industrialized countries the loss of life is usually lower because of flood control structures, zoning regulations that prevent the habitation of seriously vulnerable lands, and emergency preparedness. Still, property damage and disruption of life takes a great toll, and despite flood control structures and land use planning, floods still do occur. Causes of Flooding From a geological perspective, floods are a natural consequence of stream flow in a continually changing environment. Floods have been occurring throughout Earth history, and are expected so long as the water cycle continues to run. Streams receive most of their water input from precipitation, and the amount of precipitation falling in any given drainage basin varies from day to day, year to year, and century to century. The Role of Precipitation Weather patterns determine the amount and location of rain and snowfall. Unfortunately the amount and time over which precipitation occurs is not constant for any given area. Overall, the water cycle is a balanced system. Water flowing into one part of the cycle (like streams) is balanced by water flowing back to the ocean. But sometimes the amount flowing in to one area is greater than the capacity of the system to hold it within natural confines. The result is a flood. Combinations of factors along with exceptional precipitation can also lead to flooding. For example, heavy snow melts, water saturated ground, unusually high tides, and drainage modifications when combined with heavy rain can lead to flooding. Coastal Flooding Areas along coastlines become subject to flooding as a result of tsunamis, hurricanes (cyclonic storms), and unusually high tides. In addition, long term processes like subsidence and rising sea level as a result of global warming can lead to the encroachment of the sea on to the land. Dam & Levee Failures Dams occur as both natural and human constructed features. Natural dams are created by volcanic events (lava flows and pyroclastic flows), landslides, or blockage by ice. Human constructed dams are built for water storage, generation of electrical power, and flood control. All types of dams may fail with the sudden release of water into the downstream drainage. Spectacular and devastating examples of dam failures include that resulting in flooding downstream include: The St. Francis Dam, near Saugus, California, failed in 1929 killing 450 people. The Johnstown, Pennsylvania dam, built of earthen material (soil and rock) collapsed after a period of heavy rainfall in 1889. 2,200 people were killed by the flood. The Vaiont Dam in Italy (discussed in a previous lecture on mass-wasting) did not fail in 1963, but the landslides that moved into the reservoir behind the dam caused water to overtop the dam killing over 3,000 people. As we have seen during Hurricane Katrina in New Orleans, levee systems designed to prevent flooding can also fail and lead to catastrophic flooding and loss of life. Stream Systems A stream is a body of water that carries rock particles and dissolved ions and flows down slope along a clearly defined path, called a channel. Thus streams may vary in width from a few centimeters to several kilometers. Streams are important for several reasons Streams carry most of the water that goes from the land to the sea, and thus are an important part of the water cycle. Streams carry billions of tons of sediment to lower elevations, and thus are one of the main transporting mediums in the production of sedimentary rocks. Streams carry dissolved ions, the products of chemical weathering, into the oceans and thus make the sea salty. Streams are a major part of the erosional process, working in conjunction with weathering and mass wasting. Much of the surface landscape is controlled by stream erosion, evident to anyone looking out of an airplane window. Streams are a major source of water and transportation for the world's human population. Most population centers are located next to streams.

Engelska

For fun only

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Indonesiska

Cardiac Arrest Cardiac arrest is usually due to arrhythmias and is defined as the sudden collapse, loss of consciousness, and loss of effective circulation that precedes biologic death.7 The rhythm most commonly associated with adult cardiac arrest is VF. Mechanisms involved in the cascade of ventricular arrhythmia initiation are complex. The myocardium, which can exist in an already diseased but electrophysiologically stable state, can become electrophysiologically unstable due to a variety of factors, including transient ischemia and reperfusion; systemic, metabolic, and hemodynamic factors; neurochemical and neurophysiologic interactions; and toxic effects. This electrophysiologically unstable state results in acute changes in membrane components necessary for the propagation of the cardiac action potential, resulting in electrophysiologic dysfunction. This electrically dysfunctional state sets the stage for automatic electrical activity, or reentrant circuits that provide the framework for lethal ventricular arrhythmias.7 Rapid, early defibrillation is the determining factor for survival (Fig 1).8 Ventricular Conduction Disturbances The ventricular conduction disturbances most often treated successfully with defibrillation include VF and pulseless ventricular tachycardia (VT). Ventricular conduction disturbances are more dangerous than conduction disturbances originating in the sinus node, atrioventricular node, or the atria because of the negative impact on cardiac output, with the potential severe limitation in blood supply and oxygenation. 9,10 Ventricular Tachycardia VT is an abnormality of ventricular conduction usually due to a reentry phenomenon in the ventricles. Other potential etiologies of VT include automaticity of ectopic foci and after depolarizations. This dysrhythmia originates in the ventricular myocardium or the Purkinje system. VT has the potential to be lethal because of the effect on decreased cardiac output and the high likelihood of degeneration into VF. VT may be accompanied with or without a pulse.10,11 VT may be categorized as sustained or nonsustained. The duration of nonsustained VT is less than 30 seconds. Sustained VT has a duration of greater than 30 seconds. The VT impulse

Engelska

singapore is a city state,it is a city but it also a state,it is a zona along with indpnesia.malaysia thailand the philiphines and brunai.

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Indonesiska

1 Predicting Australian Takeover Targets: A Logit Analysis Maurice Peat* Maxwell Stevenson* * Discipline of Finance, School of Finance, The University of Sydney Abstract Positive announcement-day adjusted returns to target shareholders in the event of a takeover are well documented. Investors who are able to accurately predict firms that will be the subject of a takeover attempt should be able to earn these excess returns. In this paper a series of probabilistic regression models were developed that use financial statement variables suggested by prior research as explanatory variables. The models, applied to in-sample and out-of-sample data, led to predictions of takeover targets that were better than chance in all cases. The economic outcome resulting from holding a portfolio of the predicted targets over the prediction period are also analysed. Keywords: takeovers, targets, prediction, classification, logit analysis JEL Codes: G11, G17, G23, G34 This is a draft copy and not to be quoted. 2 1. Introduction In this paper our aim is to accurately predict companies that will become takeover targets. Theoretically, if it is possible to predict takeovers with accuracy greater than chance, it should be possible to generate abnormal returns from holding a portfolio of the predicted targets. Evidence of abnormal returns of 20% to 30% made by shareholders of firms on announcement of a takeover bid is why prediction of these events is of interest to academics and practitioners alike. The modelling approach adopted in this study was based on the discrete choice approach used by Palepu (1986) and Barnes (1999). The models were based on financial statement information, using variables suggested by the numerous theories that have been put forward to explain takeover activity. The performance of the models was evaluated using statistical criteria. Further, the predictions from the models were rated against chance and economic criteria through the formation and tracking of a portfolio of predicted targets. Positive results were found under both evaluation criteria. Takeover prediction studies are a logical extension of the work of Altman (1968) who used financial statement information to explain corporate events. Early studies by Simkowitz and Monroe (1971) and Stevens (1973) were based on the Multiple Discriminant Analysis (MDA) technique. Stevens (1973) coupled MDA with factor analysis to eliminate potential multicollinearity problems and reported a predictive accuracy of 67.5%, suggesting that takeover prediction was viable. Belkaoui (1978) and Rege (1984) conducted similar analyses in Canada with Belkaoui (1978) confirming the results of these earlier researchers and reporting a predictive accuracy of 85% . Concerns were raised by Rege (1984) who was unable to predict with similar accuracy. These concerns were also raised in research by others such as Singh (1971) and Fogelberg, Laurent, and McCorkindale (1975). Reacting to the wide criticism of the MDA method, researchers began to use discrete choice models as the basis of their research. Harris et al. (1984) used probit analysis to develop a model and found that it had extremely high explanatory power, but were unable to discriminate between target and non-target firms with any degree of accuracy. Dietrich and Sorensen (1984) continued this work using a logit model and achieved a classification accuracy rate of 90%. Palepu (1986) addressed a number of methodological problems in takeover prediction. He suggested the use of statebased prediction samples where a number of targets were matched with non-targets 3 for the same sample period. While this approach was appropriate for the estimation sample, it exaggerated accuracies within the predictive samples because the estimated error rates in these samples were not indicative of error rates within the population of firms. He also proposed the use of an optimal cut-off point derivation which considered the decision problem at hand. On the basis of this rectified methodology, along with the application of a logit model to a large sample of US firms, Palepu (1986) provided evidence that the ability of the model was no better than a chance selection of target and non-target firms. Barnes (1999) also used the logit model and a modified version of the optimal cut-off rule on UK data. His results indicated that a portfolio of predicted targets may have been consistent with Palepu’s finding, but he was unable to document this in the UK context due to model inaccuracy. In the following section the economic explanations underlying takeover activity are discussed. Section 3 outlines our takeover hypotheses and describes the explanatory variables that are used in the modelling procedure. The modelling framework and data used in the study is contained in Section 4, while the results of our model estimation, predictions, classification accuracy and portfolio economic outcomes are found in Section 5. We conclude in Section 6. 2. Economic explanations of takeover activity Economic explanations of takeover activity have suggested the explanatory variables that were included in this discrete choice model development study. Jensen and Meckling (1976) posited that agency problems occurred when decision making and risk bearing were separated between management and stakeholders1, leading to management inefficiencies. Manne (1965) and Fama (1980) theorised that a mechanism existed that ensured management acted in the interests of the vast number of small non-controlling shareholders2. They suggested that a market for corporate control existed in which alternative management teams competed for the rights to control corporate assets. The threat of acquisition aligned management objectives with those of stakeholders as managers are terminated in the event of an acquisition in order to rectify inefficient management of the firm’s assets. Jensen and Ruback (1983) suggested that both capital gains and increased dividends are available to an 1 Stakeholders are generally considered to be both stock and bond holders of a corporation. 2 We take the interests of shareholders to be in the maximization of the present value of the firm. 4 acquirer who could eliminate the inefficiencies created by target management, with the attractiveness of the firm for takeover increasing with the level of inefficiency. Jensen (1986) looked at the agency costs of free cash flow, another form of management inefficiency. In this case, free cash flow referred to cash flows in excess of positive net present value (NPV) investment opportunities and normal levels of financial slack (retained earnings). The agency cost of free cash flow is the negative NPV value that arises from investing in negative NPV projects rather than returning funds to investors. Jensen (1986) suggested that the market value of the firm should be discounted by the expected agency costs of free cash flow. These, he argued, were the costs that could be eliminated either by issuing debt to fund an acquisition of stock, or through merger with, or acquisition of a growing firm that had positive NPV investments and required the use of these excess funds. Smith and Kim (1994) combined the financial pecking order argument of Myers and Majluf (1984) with the free cash flow argument of Jensen (1986) to create another motivational hypothesis that postulated inefficient firms forgo profitable investment opportunities because of informational asymmetries. Further, Jensen (1986) argued that, due to information asymmetries that left shareholders less informed, management was more likely to undertake negative NPV projects rather than returning funds to investors. Smith and Kim (1994) suggested that some combination of these firms, like an inefficient firm and an efficient acquirer, would be the optimal solution to the two respective resource allocation problems. This, they hypothesised, would result in a market value for the combined entity that exceeded the sum of the individual values of the firms. This is one form of financial synergy that can arise in merger situations. Another form of financial synergy is that which results from a combination of characteristics of the target and bidding firms. Jensen (1986) suggested that an optimal capital structure exists, whereby the marginal benefits and marginal costs of debt are equal. At this point, the cost of capital for a firm is minimised. This suggested that increases in leverage will only be viable for those firms who have free cash flow excesses, and not for those which have an already high level of debt. Lewellen (1971) proposed that in certain situations, financial efficiencies may be realized without the realization of operational efficiencies. These efficiencies relied on a simple Miller and Modigliani (1964) model. It proposed that, in the absence of corporate taxes, an increase in a firm’s leverage to reasonable levels would increase the value of the equity share of the company due to a lower cost of capital. By a 5 merger of two firms, where either one or both had not utilised their borrowing capacity, would result in a financial gain. This financial gain would represent a valuation gain above that of the sum of the equity values of the individual firms. However, this result is predicated on the assumption that the firms need to either merge or be acquired in order to achieve this result. Merger waves are well documented in the literature. Gort (1969) suggested that industry disturbances are the source of these merger waves, his argument being that they occurred in response to discrepancies between the valuation of a firm by shareholders and potential acquirers. As a consequence of economic shocks (such as deregulation, changes in input or output prices, etc.), expectations concerning future cash flow became more variable. This results in an increased probability that the value the acquirer places on a potential target is greater than its current owner’s valuation. The result is a possible offer and subsequent takeover. Mitchell and Mulherin (1996), in their analysis of mergers and acquisitions in the US during the 1980s, provided evidence that mergers and acquisitions cluster by industries and time. Their analysis confirmed the theoretical and empirical evidence provided by Gort (1969) and provided a different view suggesting that mergers, acquisitions, and leveraged buyouts were the least cost method of adjusting to the economic shocks borne by an industry. These theories suggested a clear theoretical base on which to build takeover prediction models. As a result, eight main hypotheses for the motivation of a merger or acquisition have been formulated, along with twenty three possible explanatory variables to be incorporated predictive models. 3. Takeover hypotheses and explanatory variables The most commonly accepted motivation for takeovers is the inefficient management hypothesis.3 The hypothesis states that inefficiently managed firms will be acquired by more efficiently managed firms. Accordingly, H1: Inefficient management will lead to an increased likelihood of acquisition. Explanatory variables suggested by this hypothesis as candidates to be included in the specifications of predictive models included: 1. ROA (EBIT/Total Assets – Outside Equity Interests) 3 It is also known as the disciplinary motivation for takeovers. 6 2. ROE (Net Profit After Tax / Shareholders Equity – Outside Equity Interests) 3. Earnings Before Interest and Tax Margin (EBIT/Operating Revenue) 4. EBIT/Shareholders Equity 5. Free Cash Flow (FCF)/Total Assets 6. Dividend/Shareholders Equity 7. Growth in EBIT over past year, along with an activity ratio, 8. Asset Turnover (Net Sales/Total Assets) While there are competing explanations for the effect that a firm’s undervaluation has on the likelihood of its acquisition by a bidder, there is consistent agreement across all explanations that the greater the level of undervaluation then the greater the likelihood a firm will be acquired. The hypothesis that embodies the impact of these competing explanations is as follows: H2: Undervaluation of a firm will lead to an increased likelihood of acquisition. The explanatory variable suggested by this hypothesis is: 9. Market to book ratio (Market Value of Securities/Net Assets) The Price Earnings (P/E) ratio is closely linked to the undervaluation and inefficient management hypotheses. The impact of the P/E ratio on the likehood of acquisition is referred to as the P/E hypothesis: H3: A high Price to Earnings Ratio will lead to a decreased likelihood of acquisition. It follows from this hypothesis that the P/E ratio is a likely candidate as an explanatory variable for inclusion in models for the prediction of potential takeover targets. 10. Price/Earnings Ratio The growth resource mismatch hypothesis is the fourth hypothesis. However, the explanatory variables used in models specified to examine this hypothesis capture growth and resource availability separately. This gives rise to the following: H4: Firms which possess low growth / high resource combinations or, alternatively, high growth / low resource combinations will have an increased likelihood of acquisition. The following explanatory variables suggested by this hypothesis are: 7 11. Growth in Sales (Operating Revenue) over the past year 12. Capital Expenditure/Total Assets 13. Current Ratio (Current Assets/Current Liabilities) 14. (Current Assets – Current Liabilities)/Total Assets 15. Quick Assets (Current Assets – Inventory)/Current Liabilities The behaviour of some firms to pay out less of their earnings in order to maintain enough financial slack (retained earnings) to exploit future growth opportunities as they arise, has led to the dividend payout hypothesis: H5: High payout ratios will lead to a decreased likelihood of acquisition. The obvious explanatory variable suggested by this hypothesis is: 16. Dividend Payout Ratio Rectification of capital structure problems is an obvious motivation for takeovers. However, there has been some argument as to the impact of low or high leverage on acquisition likelihood. This paper proposes a hypothesis known as the inefficient financial structure hypothesis from which the following hypothesis is derived. H6: High leverage will lead to a decreased likelihood of acquisition. The explanatory variables suggested by this hypothesis include: 17. Net Gearing (Short Term Debt + Long Term Debt)/Shareholders Equity 18. Net Interest Cover (EBIT/Interest Expense) 19. Total Liabilities/Total Assets 20. Long Term Debt/Total Assets The existence of Merger and Acquisition (M&A) activity waves, where takeovers are clustered in wave-like profiles, have been proposed as indicators of changing levels of M&A activity over time. It has been argued that the identification of M&A waves, with the corresponding improved likelihood of acquisition when the wave is surging, captures the effect of the rate of takeover activity at specific points in time, and serves as valuable input into takeover prediction models. Consistent with M&A activity waves and their explanation as a motivation for takeovers is the industry disturbance hypothesis: 8 H7: Industry merger and acquisition activity will lead to an increased likelihood of acquisition. An industry relative ratio of takeover activity is suggested by this hypothesis: 21. The numerator is the total bids launched in a given year, while the denominator is the average number of bids launched across all the industries in the ASX. Size will have an impact on the likelihood of acquisition. It seems plausible that smaller firms will have a greater likelihood of acquisition due to larger firms generally having fewer bidding firms with the resources to acquire them. This gives rise to the following hypothesis: H8: The size of a firm will be negatively related to the likelihood of acquisition. Explanatory variables that can be employed to control for size include: 21. Log (Total Assets) 22. Net Assets 4. Data and Method The data requirements for the variables defined above are derived from the financial statements and balance sheet date price information for Australian listed companies. The financial statement information was sourced from the AspectHuntley data base which includes annual financial statement data for all ASX listed companies between 1995 and 2006. The database includes industry classifications for all firms included in the construction of industry relative ratios. Lists of takeover bids and their respective success were obtained from the Connect4 database. This information enabled the construction of variables for relative merger activity between industries. Additionally, stock prices from the relevant balance dates of all companies were sourced from the AspectHuntley online database, the SIRCA Core Price Data Set and Yahoo! Finance. 4.1 The Discrete Choice Modelling Framework The modelling procedure used is the nominal logit model, made popular in the bankruptcy prediction literature by Ohlson (1980) and, subsequently, in the takeover prediction literature by Palepu (1986). Logit models are commonly utilised for dichotomous state problems. The model is given by equations [1] to [3] below. 9 [3] The logit model was developed to overcome the rigidities of the Linear Probability Model in the presence of a binary dependent variable. Equations [1] and [2] show the existence of a linear relationship between the log-odds ratio (otherwise known as the logit Li) and the explanatory variables. However, the relationship between the probability of the event and acquisition likelihood is non-linear. This non-linear relationship has a major advantage that is demonstrated in equation [3]. Equation [3] measures the change in the probability of the event as a result of a small increment in the explanatory variables, . When the probability of the event is high or low, the incremental impact of a change in an explanatory variable on the likelihood of the event will be compressed, requiring a large change in the explanatory variables to change the classification of the observation. If a firm is clearly classified as a target or non-target, a large change in the explanatory variables is required to change its classification. 4.2 Sampling Schema Two samples were used in the model building and evaluation procedure. They were selected to mimic the problem faced by a practitioner attempting to predict takeover targets into the future. The first sample was used to estimate the model and to conduct in-sample classification. It was referred to as the Estimation Sample. This sample was based on financial data for the 2001 and 2002 financial years for firms that became takeover targets, as well as selected non-targets, between January, 2003 and December, 2004. The lag in the dates allows for the release of financial information as well as allowing for the release of financial statements for firms whose balance dates fall after the 30th June. Following model estimation, the probability of a takeover offer was estimated for each firm in the entire sample of firms between January, 2003 and December, 2004 using the estimated model and each firm’s 2001 and 2002 financial data. Expost predictive ability for each firm was then assessed. 10 A second sample was then used to assess the predictive accuracy of the model estimated with the estimation sample data. It is referred to as the Prediction Sample. This sample includes the financial data for the 2003 and 2004 financial years, which will be used in conjunction with target and non-target firms for the period January, 2005 to December, 2006. Using the model estimated from the 2001 and 2002 financial data, the sample of firms from 2005 and 2006 were fitted to the model using their 2003 and 2004 financial data. They were then classified as targets or non-targets using the 2005 and 2006 data. This sampling methodology allows for the eva

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Convenience or Care? When something issued by someone, we say it has been consumed. We as consumers use many things, both natural and processed. We consume more of the Earth’s resources than other animals do and, as a result, we cause problems for the environment. Like all animals, we need clean air and water, food and shelter for survival. Unlike other animals, however, we have certain “want”. These are items that are not necessary for our survival, but that we want because they make our lives easier or more enjoyable. Environments claims that a great deal of waste is created by both the production and the consumption of these items or product. Disposable products, such a pens, take away food containers, plates, shavers and cutlery, are made using the Earth’s resources. When these products are thrown away, the resources are lost. Another example of waste is the unnecessary packaging on many products. The material is often not recycled and used again. Throwing thing away also increases pollution. The amount of disposable plastic litter that ends up in waterways is a serious problem. When this waste reaches the oceans, it can kill marine life. Industrialist counter these arguments with their own point of view. They claim that consumer’s expect to be able to purchase food which is attractively presented, prepackaged to extent its life and easy to store. In a busy society, convenience is a priority. Products which make life easier, era in demand. Industrialist argue that they cater to this perceived need. Packaging is also big business and provides jobs for many people who might otherwise be unemployed and a burden to society. Environmentalist declare that for thousands of year, people survived perfectly well with re-usable products. However, people of the twenty-first century have become used to wing in a ‘thrown-away’ society. It is up to each one of us to dispose of waste products carefully, recycle as much as possible and to reduce the stress on our environment. Think when buying pre-packaged goods and consider whether the same products can be bought without the extra wrappings

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We regret to inform you that we do not have any openings matching your profile at the moment. Although your application has not been successful on this occasion, we would like to retain your application in our files for future openings. Should a position become available in the future, we will be in touch.

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Timun Mas Long time ago, living a couple of farmers. They live in a village near the forest. They live happy, unfortunately they have not only was a child also. Every day they pray to the God. They pray to be soon be a child. One day a giant passes where they reside. Giant prayer is heard that wife’s husband. Giant then provide them with seeds cucumber. “Plant seeds of this, Later you will soon get a female child,” said the giant. “Thank you, giant,” said the couple. “But there is condition, at the age of 17 years the child must be submitted with you to me,” the giant reply. Without thinking long they agree, because it wanted a child. Husband and wife farmers to plant the seeds cucumber. Every day they start caring for the plants that grow with it as best as possible. Many months and then bear a cucumber with golden color. Fruit cucumber that the longer become larger and heavier. When the fruit is ripe, they take it, carefully slit the fruit. Sudenlly, in the fruit is found in infants of women who are very beautiful. Couple was very happy, they gave the name of the baby Timun Mas. Year after year passed, Timun Mas grown into a beautiful girl. Both parents are very proud of her. But they became very afraid, because in the anniversary Timun Mas at 17, the giant will back. The giant take back that promises to take Timun Mas. Farmers are trying to calm. “Wait a moment. Timun Mas playing. My wife would called her,” he said. Farmers find it immediately to her doughter. “My girl, take this,” she said while giving a cloth bag. “This will help fight the giant. Now flee as soon as possible,” she said. So even Timun Mas immediately fled. Couple on the sad departure Timun Mas. But they are not willing if their child become food giant. Giant waiting long time. He was not a patient. He knew, was that lied by couple of farmers. And he also destroyed the huts of the farmers. Then it was to pursue Timun Mas to forest. Giant run chase immediately Timun Mas. Near the gian,Timun Mas immediately take the handful of salt from the cloth pouch. Then salt spread it to the giant. Suddenly a wide sea also unfold. Giant forced to swim with great difficulty. Timun Mas ran again. But then most successful giant come closer. Timun Mas again taking bizarre objects from a cloth bag. He took the handful chili. Chili throwed to the giant. At once the tree branches and sharp thorns of the giant trap. Giant cried in pain. Timun Mas while running to save herself. But the giant is really strong. He was again nearly captured Timun Mas. So Timun Mas is also a third issue of miraculous. She sow seeds Cucumber magic. At once grow the cucumber garden very knowledgeable. Giant very tired and hungry. He also eat the fresh cucumber with oneself. Because of too much eating, giant was slept. Timun Mas again fled. She ran for dear life. But long run power out. More unlucky again because a giant awakened from sleep. Giant again almost catch her. Timun Mas very terrified. He also threw the last tool handful shrimp paste. Again, miracles happen. A lake of mud spread wide. Giant fall into it. Hands almost reach Timun Mas. But the lake mud is basic to withdraw it. Giant panic, he can not breathe, then submerged. Timun Mas relieved. She has survived, Timun Mas is also return to home to their parents. Father and mother’s Timun Mas happy to see Timun Mas be save. They held, “Thank you, God. You have to save my girl,” said their delighted. Since that time Timun Mas can live quietly with her parents. They can be happy without living in fear again.

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I have been running an Internet Services company http://Promo.LBSdomains.com offering a one-stop for most small to medium size businesses. I also have the pleasure to offer the same as a low cost investment in a turn-key business at: http://Reseller.LBSdomains.com My newest goals are to become a Certified Microsoft ISV-Independent Software Vendor and an MS SSA - Security Software Advisor. I am going to become a Microsoft Small Business Specialist as soon as I have time to study and pass one MCP test. I started an independent software development consulting firm.We specialize in Workflow, Document Management, Knowledge Management, and Competitive/Business Intelligence, and E-Commerce applications. I do remain available as always for virtual office/remote or local consulting gigs esp. projects involving Sharepoint-Winfx, Filenet/IBM, Viewstar/Global 360. Visual Basic 3.x-6.x & .Net,Visual Studio IDE 1.x-8.x,Visual C++ 5.x-6.x,and Visual Age C++,and Assembler (MASM),MS Office VBA,VB Script,Java (J++ and J#),JavaScript,Active Server Pages,and CGI using Perl 5,.NET Framework,Windows 3.x,95,98,NT,2K-2003K Server,Linux,AIX,UNIX-AT&T System V and HP UX 9.x,LAN Server,Novell Netware,OS/2,DOS,Win/Win32 API,Filenet IDM/Panagon,Mosaix Viewstar and BPI Object Model,AMS Caseflow API,and Accusoft ImageGear,Crystal Reports,Adobe Acrobat/Distiller,Rational Rose modeling,Access 2.0-8.0,IIServer,Exchange Server,SQL Server,Oracle,ActiveX controls,Access/Office Developers SDK's,ODBC,ADO,RDO,DAO,OLE,TCP/IP protocols,Xbase-based DBMS's Hardware Experience: Optical drives, jukeboxes,scanners,graphic tablets,HP NetserverPro & Dell (RAID-5,multi-Pentium Processors),HP UX Servers/Workstations,network cabling,processor/systemboard upgrades,adapters,and other peripherals. Mainframe/Midrange communications:(automated callback,protocol converters,TSU/CSU,routers,data switches),and serial communications Specialties Developer of software for over 25 years. Over 15 years of development in client/server and internet applications. Software projects have included: 1. workflow, imaging,document management and data retrieval 2. multi-platform Internet/intranet Enterprise system integration 3. shrink-wrapped packages 4. PC-to-Mainframe host communications 5. all accounting modules, job costing and estimating, tax return preparation, and labor union software

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I want to hug you soon ... Take tuk accompanied my steps ... But not right to me ... And would I force it ... You're still not mine ... Not yet a beloved ... And I have not become part of your life ... Not to be the one who always fill your heart ... Although I felt tired last ... I will continue to hold ... All the longing ... And the unspoken desire ... You're like a month ... Glow lit the dark night ... Seen by my eyes without a barrier ... But you're tough unattainable But I must endure ... Because you've given me hope ... Would love an almost impossible unattainable Although I know it's not as easy as reaching tuk mu ... Because what you want is not just me ... Thou daughter of the king who yearn ... Many princes and knights who tried to reach you ... While I'm just a nameless soldier for you ... Yes ... I do not soldier named ... Not as strong as the warrior ... No semenawan prince ... Only a dreamer with a piece of poetry alone ... But I'm definitely waiting for you ... Waiting for an answer from my love ...

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I want to hug you soon ... Take tuk accompanied my steps ... But not right to me ... And would I force it ... You're still not mine ... Not yet a beloved ... And I have not become part of your life ... Not to be the one who always fill your heart ... Although I felt tired last ... I will continue to hold ... All the longing ... And the unspoken desire ... You're like a month ... Glow lit the dark night ... Seen by my eyes without a barrier ... But you're tough unattainable But I must endure ... Because you've given me hope ... Would love an almost impossible unattainable Although I know it's not as easy as reaching tuk mu ... Because what you want is not just me ... Thou daughter of the king who yearn ... Many princes and knights who tried to reach you ... While I'm just a nameless soldier for you ... Yes ... I do not soldier named ... Not as strong as the warrior ... No semenawan prince ... Only a dreamer with a piece of poetry alone ... But I'm definitely waiting for you

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There are times I find it hard to sleep at night We are living through such troubled times And every child that reaches out for someone to hold For one moment they become my own And how can I pretend that I don’t know what’s going on When every second with every minute another soul is gone [Chorus] And I believe that in my life I will see (ooh yeah) An end to hopelessness or giving up of suffering And we all stand together this one time Then no one will get left behind Stand up for life Stand up for love [Kelly] I’m inspired and hopeful each and every day That’s how I know that things are gonna change So how can I pretend that I don’t know what’s going on When every second with every minute another soul is gone [Chorus] And I believe That in my life I will see An end to hopelesness of giving up of suffering And we all stand together this one time Then no one will get left behind Stand up for life Stand up for love [Michelle] And it all starts right here And it starts right now One person stand up there And the rest will follow For all the forgotten For all the unloved I’m gonna sing this song [Chorus] And I believe that in my life I will see An end to hopelessness of giving up of suffering If we all stand together this one time Then no one will get left behind Stand up for life Stand up and sing Stand up for love For love, for love Tags: destiny's child, stand up for love

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night We are living through such troubled times And every child that reaches out for someone to hold For one moment they become my own And how can I pretend that I don’t know what’s going on When every second with every minute another soul is gone [Chorus] And I believe that in my life I will see (ooh yeah) An end to hopelessness or giving up of suffering And we all stand together this one time Then no one will get left behind Stand up for life Stand up for love [Kelly] I’m inspired and hopeful each and every day That’s how I know that things are gonna change So how can I pretend that I don’t know what’s going on When every second with every minute another soul is gone [Chorus] And I believe That in my life I will see An end to hopelesness of giving up of suffering And we all stand together this one time Then no one will get left behind Stand up for life Stand up for love [Michelle] And it all starts right here And it starts right now One person stand up there And the rest will follow For all the forgotten For all the unloved I’m gonna sing this song [Chorus] And I believe that in my life I will see An end to hopelessness of giving up of suffering If we all stand together this one time Then no one will get left behind Stand up for life Stand up and sing Stand up for love For love, for love Tags: destiny's child, stand up for love SaveCancel Last Update: 2012-09-14 Subject: General Usage Frequency: 1 Quality: Excellent Add a translation Search 621.0

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above: about: according: across: actually: adj: after: afterwards: again: against: all: almost: alone: along: already: also: although: always: among: amongst: and: another: any: anyhow: anyone: anything: anywhere: are: aren: arent: around: became: because: become: becomes: becoming: been: before: beforehand: begin: beginning: behind: being: below: beside: besides: between: beyond: billion: both: but: can: cant: cannot: caption: could: couldnt: did: didnt: does: doesnt: dont: down: during: each: eight: eighty: either: else: elsewhere: end: ending: enough: etc: even: ever: every: everyone: everything: everywhere: except: few: fifty: first: five: for: former: formerly: forty: found: four: from: further: had: has: hasnt: have: havent: hence: her: here: hereafter: hereby: herein: heres: hereupon: hers: herself: hes: him: himself: his: how: however: hundred: inc: indeed: instead: into: isnt: its: itself: last: later: latter: latterly: least: less: let: like: likely: ltd: made: make: makes: many: may: maybe: meantime: meanwhile: might: million: miss: more: moreover: most: mostly: mrs: much: must: myself: namely: neither: never: nevertheless: next: nine: ninety: nobody: none: nonetheless: noone: nor: not: nothing: now: nowhere: off: often: once: one: only: onto: others: otherwise: our: ours: ourselves: out: over: overall: own: page: per: perhaps: rather: recent: recently: same: seem: seemed: seeming: seems: seven: seventy: several: she: shes: should: shouldnt: since: six: sixty: some: somehow: someone: something: sometime: sometimes: somewhere: still: stop: such: taking: ten: than: that: the: their: them: themselves: then: thence: there: thereafter: thereby: therefore: therein: thereupon: these: they: thirty: this: those: though: thousand: three: through: throughout: thru: thus: tips: together: too: toward: towards: trillion: twenty: two: under: unless: unlike: unlikely: until: update: updated: updates: upon: used: using: very: via: want: wanted: wants: was: wasnt: way: ways: wed: well: were: werent: what: whats: whatever: when: whence: whenever: where: whereafter: whereas: whereby: wherein: whereupon: wherever: wheres: whether: which: while: whither: who: whoever: whole: whom: whomever: whose: why: will: with: within: without: wont: work: worked: works: working: would: wouldnt: yes: yet: you: youd: youll: your: youre: yours: yourself: yourselves: youve

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above: about: according: across: actually: adj: after: afterwards: again: against: all: almost: alone: along: already: also: although: always: among: amongst: and: another: any: anyhow: anyone: anything: anywhere: are: aren: arent: around: became: because: become: becomes: becoming: been: before: beforehand: begin: beginning: behind: being: below: beside: besides: between: beyond: billion: both: but: can: cant: cannot: caption: could: couldnt: did: didnt: does: doesnt: dont: down: during: each: eight: eighty: either: else: elsewhere: end: ending: enough: etc: even: ever: every: everyone: everything: everywhere: except: few: fifty: first: five: for: former: formerly: forty: found: four: from: further: had: has: hasnt: have: havent: hence: her: here: hereafter: hereby: herein: heres: hereupon: hers: herself: hes: him: himself: his: how: however: hundred: inc: indeed: instead: into: isnt: its: itself: last: later: latter: latterly: least: less: let: like: likely: ltd: made: make: makes: many: may: maybe: meantime: meanwhile: might: million: miss: more: moreover: most: mostly: mrs: much: must: myself: namely: neither: never: nevertheless: next: nine: ninety: nobody: none: nonetheless: noone: nor: not: nothing: now: nowhere: off: often: once: one: only: onto: others: otherwise: our: ours: ourselves: out: over: overall: own: page: per: perhaps: rather: recent: recently: same: seem: seemed: seeming: seems: seven: seventy: several: she: shes: should: shouldnt: since: six: sixty: some: somehow: someone: something: sometime: sometimes: somewhere: still: stop: such: taking: ten: than: that: the: their: them: themselves: then: thence: there: thereafter: thereby: therefore: therein: thereupon: these: they: thirty: this: those: though: thousand: three: through: throughout: thru: thus: tips: together: too: toward: towards: trillion: twenty: two: under: unless: unlike: unlikely: until: update: updated: updates: upon: used: using: very: via: want: wanted: wants: was: wasnt: way: ways: wed: well: were: werent: what: whats: whatever: when: whence: whenever: where: whereafter: whereas: whereby: wherein: whereupon: wherever: wheres: whether: which: while: whither: who: whoever: whole: whom: whomever: whose: why: will: with: within: without: wont: work: worked: works: working: would: wouldnt: yes: yet: you: youd: youll: your: youre: yours: yourself: yourselves: youve

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