Where Does Law Come From? Essay -- The Origin of Law

Where does law come from? Law has been defined in many ways by various leaders and thinkers but there is no universal definition. There are numerous sources and the English legal system adopts the rules derived from these various sources to form the English law. Sources of law include common law and equity, legislation, and international conventions such as the European Convention of Human Rights (ECHR). It must be noted that customs or constitutional conventions are not law. It is a common misconception that customs or conventions are law. The judiciary have repeatedly held that conventions are not law and thus are not binding. The case of Attorney General v Jonathan Cape Ltd aptly demonstrated this point as the court held that the convention of Parliamentary confidentiality is only a convention and does not carry any legal weight. Nevertheless, it is essential to understand what, and how, are sources of law created and what possible dangers may arise from these sources. Common Law Common law first came about during the reign of William the Conqueror who created a common set of laws which was binding on all in the land. In the 15th century, the Chancellor was given the responsibility of deciding cases and he subsequently created his own court, the Court of Chancery which decided cases based on equity. As a result of the Judicature Act 1873, common law and equity were merged into the same courts to ensure that one enhances, instead of opposing, the other. Consequently, the only distinction between common law and equity is the remedies available to litigants. Today, common law is regarded as case laws which are decisions made by judges in cases. Common law is operated on two concepts of equal importance, namely the do... ...e Human Rights Act 1998 Third Edition, 2006 Articles T.H.Bingham ,’The European Convention on Human Rights: Time to Incorporate’ (1993) L.Q.R. 390 Table of cases A-G v Jonathan Cape Ltd [1976] QB 752 Fitzpatrick v Sterling Housing Association Ltd [1999] 4 All ER 705 Miliangos v George Frank (Textiles) Ltd [1975] 3 WLR 758 R v Goldstein, R v Rimmington [2005] UKHL 63 R v R [1991] 4All ER 481 Royal College of Nursing v Department of Health and Social Security [1981] 2 WLR 279,CA Table of statutes and other instruments Statutes: Abortion Act 1967 c87 European Communities Act c68 Human Rights Act 1998 c42 Interpretation Act 1978 c30 Judicature Act 1873 c66 Legislative and Regulatory Reform Act 2006 c51 Parliament Act 1949 c103 Public Interest Disclosure Act 1998 c23 European Legislation: European Convention of Human Rights (1951)

Enrollment forecasting methodology

Virtually, forecasting plays everywhere a major role in human life, especially in making future decisions such as weather forecasting, university enrollment, production, sales and finance, etc. Based on these forecasting results, we can prevent damages to occur or get benefits from the forecasting activities.Up to now, many qualitative and quantitative forecasting models were proposed. However, these models are unable to deal with problems in which historical data take form of linguistic constructs instead of numerical values. In recent years, many methods have been proposed to deal with forecasting problems using fuzzy time series. In this paper, we present a new method to predict the calendar day for average Arabian Gulf Oil Company using fuzzy time series approach based on average lengths of intervals. A visual-based programming is used in the implementation of the proposed model.Results obtained demonstrate that the proposed forecasting model can forecast the data effectively and efficiently Keywords: Fuzzy time series, Forecasting, Fuzzy sets, Average-based lengthl . Forecasting the size of any phenomenon in future is important and helpful for understanding behavior of phenomenon along time. It is impossible to make future plans to face the phenomenon without defining its future dimensions and identifying shape and modes of complicated process, especially when it is related to future forecasting. Making decisions depends completely on accuracy of forecasting.It is evident that forecasting plays major role in our daily life. The accurate and the most efficient forecasting may support making correct decisions to raise accuracy of our expectations up to 100%. This may be impossible, yet we try to reduce forecasting errors. In order to solve forecasting problems, many researchers proposed several methods and different models. One of these models is traditional time series analysis, uni-variant and multi-variant. However, traditional time series has wide applic ations, but it must satisfy proper conditions to be successful.For example, 50 up to 100 bservations at least are needed to achieve Autoregressive and Integrated Moving Average Models (ARIMA) and average zero is needed to achieve autoregressive. Traditional time series has been applied in many fields such as pollution monitoring, blood pressure estimation, etc. This problem has been studied widely in statistics areas and neural networks. However, in practical life, there are regression models in which the uncertainty accompanied to the model is because of vagueness, not because of neither randomness nor both of them.In these models, probability theory cannot be pplied and fuzzy sets theory is applied, where variables are foggy i. e. declarative variables are not fixed and scaling of these variables is not expressed by a point, but by an interval or linguistic variables [1, 3]. 2. FUZZY LOGIC Fuzzy logic [1 1], is a form of logic which has used in some expert systems and artificial i ntelligence applications. It was first proposed in 1965 by the Iranian scientist Lutfi Zadeh, at University of California, where he developed it as a better method of data processing.However, his theory didn't find a wide interest until 1974, where fuzzy logic was used to control a steam engine. Since then, applications of fuzzy logic kept developing until the manufacturing of fuzzy logic chip which have been used in many products such this science. There are many reasons for scientists to improve fuzzy logic. For example, development of computers and software founded the need to invent or program systems that are capable of dealing with ambiguous information to mimic human thinking.However, this created a problem since computers can only deal with exact and accurate data. This problem caused occurrence of expert systems and artificial intelligence. Fuzzy logic is a theory for building such systems. Fuzzy set theory has many useful achievements in different fields and it aims at app roximation of professional knowledge that contains vagueness in human thinking. Figure 1 illustrates the difference between traditional and fuzzy set theories. Fuzzy logic simply reflects how do people think and try to represent our feelings by words, decisions making and our common sense.So, fuzzy logic models are being increasingly used in time series analysis, where they are important for dealing with linguistic values and other models in order to yield better forecasting results. Time Series is defined as a sequence of events easured in successive times at definite intervals. It was widely used in economic systems such as stock index and interest. Also, it was used in metrology, especially in wind speed, temperature, pressure, Figure 1: Traditional and fuzzy sets 3.FUZZY TIME SERIES Fuzzy time series is another concept to solve forecasting problems in which the historical data are linguistic values. Fuzzy time series based on Zadeh's works [1 1], Song and Chissom [7], first prop osed a forecasting model called Fuzzy Time Series, which provided a theoretic framework to model a special dynamic process hose observations are linguistic values. The main difference between the traditional time series and fuzzy time series is that the observed values of the former are real numbers while the observed values of the latter are fuzzy sets or linguistic values.In the following, some basic concepts of fuzzy time series are briefly reviewed Definition 1: Let U ,u2 un } be a universe of discourse (universal set); a fuzzy set A of U is defined fA (u ) / u fA (u ) / un ,where fA is a membership function of a given set A , fA [0,1]. Definition 2 If there exists a fuzzy relationship R(t – 1, t), such that F(t) =F(t ), where is an arithmetic operator, then F(t) is said to be caused by F(t – 1). The relationship between F(t) and – 1) can be denoted by – 1) Definition 3 Suppose F(t) is calculated by F(t – 1) only, and – 1) R(t,t-l).For a ny t, if R(t – 1, t) is independent of t, then F(t) is considered a timeinvariant fuzzy time series. Otherwise, F(t) is timevariant. Definition 4 Suppose – 1) and F(t)= A], a fuzzy logical relationship can be defined as Ai AJ where Ai and AJ are called the left-hand side and the right-hand side of the fuzzy logical relationship, respectively. 4. REVIEW OF RELATED WORKS Many studies have interested in fuzzy time series and have been applied in various fields including university enrollment.Fuzzy time series had proved its efficiency in forecasting as a good new method for predicting linguistic values. Song and Chisson [9, 10] first introduced the method of fuzzy time series, humidity and rainfall. In addition, time series was used in geophysical records including indexed measurements, times of earthquake, radiological activities, industrial production, rates of idleness, etc. therefore, they are considered as founders of fuzzy time series science. Also, in 1994, they in troduced a eries.Chen [1] presented a new method for forecasting university enrollment using fuzzy time series historical data enrollments of the university of Alabama from 1971 to 1992, the proposed method is more efficient than the proposed method by Song and Chissom, due to the fact that the proposed method uses simplified arithmetic operation rather than the complicated MaxMin composition operation. Hwang [8] proposed a new method on fuzzification to revise Song and Chissom's method. He used a different triangle fuzzification method to Fuzzify crisp values.His method involved determining an interval of xtension from both sides of crisp value in triangle membership function to get a variant degree of membership. The result got a better average forecasting error, in addition, the influences of factors and variables in a fuzzy time series model such as definition area, number and length of intervals and the interval of extension in triangle membership function were discussed in det ailsStep 2: Define the universe of discourse U. Find the maximum Dmax and the minimum Dmin among all Dh. For easy partitioning of U, choose two small numbers Dl and D2 as two proper positive numbers. The purpose of Dl and D2 is to make the lower and upper bounds of U become multiple of hundreds, thousands, etc. The universe of discourse U is then defined by: U = Dt-ntn -Dl , Drnax+D2 Step 3: Determine the appropriate length of interval L. Here, the average-based length method (Huarng, 2001 b) can be applied to determine the appropriate L.The length of interval L is computed according to the Table 1: Base mapping table Range Base 0. 1-1. 0 0. 1 1. -10 11-100 10 101-1000 a) Calculate all the absolute differences between the values Dh-l and Dh as the first differences, and then compute the average of the first differences. b) Take one-half of the average as the length. c) Find the located range of the length and determine the base from Table 1 d) According to the assigned base, round t he length as the appropriate L.Then the number of intervals m, is computed by: D max+D2-D Then U can be partitioned into equal-length intervals Assume that the m intervals are Step4: Define fuzzy sets from the universe of discourse: f(un)(3) Ai=A11+A22+†¦.. +Ai l Then fuzzify the time series. First determine some linguistic values A1, A2, †¦ , An. Second, defined fuzzy sets on U. The fuzzy sets Ai are expressed as follows: 10. 500 0. 510 . 50 00. 51 0. 5 Step 5: fuzzify the historical data. If the value of Dh is located in the range of ui, then it belongs to fuzzy sets A'.All Dh must be classified into the corresponding fuzzy sets. However, fuzzify the historical data and give fuzzy set to each year's historical data. If the historical data belongs to Ai at year t, the historical data of that year can be written by A'. But usually one historical data to ifferent A1, the need to find out maximum degree of each year's historical data belonging to each A1. Step 6: Establish f uzzy logical relationships (FLRs) for all fuzzified data, derive the fuzzy logical relationships based on Definition (3).The fuzzy logical relationship which have the same left-hand sides is like Ai Ak, which denotes that if the Dh-lvalue of time t-1 is AJ then that of time t is Ak Table 2: Fuzzy relationship Ak Ar A1 Am 0. 5 um -2 um -1 um Where ui n) is the element and the number below ‘/'is the membership of ui to Then follow the rules for determining the degree of the membership of the istorical data Yi belonging to interval u'. The general triangular membership function is expressed as below: Step 7: establish the fuzzy logic relationship groups (FLRG).The derived fuzzy logical relationships can be arranged into fuzzy logical relationship groups based on the same fuzzy numbers on the left-hand sides of the fuzzy logical relationships. The fuzzy logical relationship groups are like the following: AJI Step 8: The forecasting of the historical data is based on heuristic rule s proposed by chen (1996) and outlined as follows.

Articles of Confederation Essay

America’s first form of government was under the Articles of Confederation. The Articles of Confederation was written after the Revolutionary War, compiled of ideas by the Continental Congress. Under the Articles of Confederation its powers included conducting foreign relations, settling disputes between states, controlling maritime affairs, regulating Indian trade, and valuing state and national coinage. It purposely did not give the national government all the power in fear of tyranny like in Britain. In the end, the Articles of Confederation proved to be both inadequate in economic conditions and foreign relations. Under the Articles of Confederation the national government was not allowed to raise revenue to finance the war and other essential needs that a country need money for. The state governments had control of taxing the American people but many were unwilling to do so. So to try and finance the war the government and the states started to print currency. Although the currency was just paper and not backed by anything it was given out freely without excessive downgrading during 1775 to 1776. As 1776 went on the demand for both military supplies and civilian goods began to increase as the war progressed. America also encouraged trade and local production between the states. As the end of 1776 was approaching, the armies suffered reverses in both New York and New Jersey causing the prices to rise and inflation to finally set in. The state governments tried to fight inflation by taking control of wages and prices, and accepting paper money equal to hard money. They would also borrow funds, establish lotteries and even levy taxes to try and stop inflation. Their efforts were useless, though Congress attempted to stop printing currency altogether, they relied solely on money contributed by the states. By 1780, currency was worthless. Congressmen started to take action by establishing a department of finance with help from Robert Morris, a wealthy Philadelphia merchant. The government then asked the states to amend the Articles of Confederation and allow the Congress to levy duty of 5% on imported goods. Robert Morris had put national finances on stable balance, but the customs that Congress asked for were never adopted. The states’ had resisted in fear that the central government would become too powerful. As the war was ending Congress had to face major diplomatic problems. In the Articles of Confederation Article 4 had promised repayment of prewar debts, most of which were owed to by Americans to British merchants. In Article 5 it stated that states should allow loyalists to recover their confiscated property, which aroused considerable opposition. The state governments passed lass that denied the British subjects the right to sue for the recovery of property or debts in American courts. Since Congress had no power under the Articles of Confederation to overturn the decision, many loyalists were unable to get back what had rightfully been theirs to begin with. With this decision it gave the British an excuse to main their military posts on the Great Lakes long after when troops were supposed to have withdrawn. This had showed Congress weakness in the area of trade because the Articles of Confederation denied power to establish a national commercial policy. Right after the war had ended, Britain, France and Spain limited American trade with the colonies. The Americans were outraged but were unable to do anything about it. They thought that after the war, their independence would bring about trade with all nations. Members of the Congress, watched as British manufactured goods started to flood the states, while American produce could no longer be sold in British West Indies, once its prime market of trade. In the areas of finance, overseas trade and foreign affairs the Articles of Confederation were obviously inadequate. Congress was not allowed to levy taxes, nor was it able to impose its will on the states to establish a commercial policy. The Articles of Confederation, led the economy into a depression. The Continental Congress gave little power under the Articles of Confederation to the central government in fear of tyranny. In the end it only proved that too little power is not sufficient in controlling both economic conditions and foreign relations.

The Discovery of Fire in the Early Stone Age

The Discovery of Fire in the Early Stone Age The discovery of fire, or, more precisely, the controlled use of fire, was one of mankinds first great innovations. Fire allows us to produce light and heat, to cook plants and animals, to clear forests for planting, to heat-treat stone for making stone tools, to keep predator animals away, and to burn clay for ceramic objects. It has social purposes as well. Fires serve as gathering places, as beacons for those away from camp, and as spaces for special activities. The Progress of Fire Control The human control of fire likely required the cognitive ability to conceptualize the idea of fire, which itself has been recognized in chimpanzees; great apes have been known to prefer their foods cooked. The fact that experimentation with fire occurred during the early days of humanity should come as no surprise. Archaeologist J.A.J. Gowlett offers this general outline for the development of fire use:  opportunistic use of fire from natural occurrences (lightning strikes, meteor impacts, etc);  limited conservation of fires lit by natural occurrences; use of animal dung or other slow-burning substances to maintain fires in wet or cold seasons; and finally,  kindled fire. Early Evidence The controlled use of fire was likely an invention of our ancestor Homo erectus during the Early Stone Age (or Lower Paleolithic). The earliest evidence of fire associated with humans  comes from Oldowan hominid sites in the Lake Turkana region of Kenya. The site of Koobi Fora contained oxidized patches of earth to a depth of several centimeters, which some scholars interpret as evidence of fire control. The Australopithecine site of Chesowanja in central Kenya (about 1.4 million years old) also contained burned clay clasts  in small areas. Other Lower Paleolithic sites in Africa that contain possible evidence of fire include Gadeb in Ethiopia (burned rock), and Swartkrans (burned bones) and Wonderwerk Cave (burned ash and bone fragments), both in South Africa. The earliest evidence for controlled use of fire outside of Africa is at the Lower Paleolithic site of Gesher Benot Yaaqov in Israel, where charred wood and seeds were recovered from a site dated 790,000 years old. Other evidence has been found at Zhoukoudian, a Lower Paleolithic site in China, Beeches Pit in the U.K., and Qesem Cave in Israel. An Ongoing Discussion Archaeologists examined the available data for European sites and concluded that habitual use of fire wasnt part of the suite of human behaviors until about 300,000 to 400,000 years ago. They believe that the earlier sites are representative of the  opportunistic use of natural fires. Terrence Twomey published a comprehensive discussion of the early evidence for the  human control of fire at 400,000 to 800,000 years ago. Twomey believes that there is no direct evidence for domestic fires between 400,000 and 700,000 years ago, but he believes that other, indirect evidence supports the notion of the controlled use of fire. Indirect Evidence Twomeys argument is based on several lines of indirect evidence. First, he cites the metabolic demands of relatively big-brained Middle Pleistocene hunter-gatherers  and suggests that brain evolution required cooked food. Further, he argues that our distinctive sleep patterns (staying up after dark) are deeply rooted and that hominids began staying in seasonally or permanently cool places by 800,000 years ago. All of this, says Twomey, implies effective control of fire. Gowlett and Richard Wrangham argue that another piece of indirect evidence for the early use of fire is that our ancestors Homo  erectus evolved smaller mouths, teeth, and digestive systems, in striking contrast to earlier hominids. The benefits of having a smaller gut could not be realized until high-quality foods were available all year long. The adoption of cooking, which softens food and makes it easier to digest, could have led to these changes. Hearth Fire Construction A hearth is a deliberately constructed fireplace. The earliest examples were made by collecting stones to contain the fires, or simply by reusing the same location again and again and allowing the ash from previous fires to accumulate. Hearths from the Middle Paleolithic period (about 200,000 to 40,000 years ago) have been found at sites such as the Klasies River Caves in South Africa, Tabun Cave in Israel, and Bolomor Cave in Spain. Earth ovens, on the other hand, are hearths with banked and sometimes domed structures built of clay. These types of hearths were first used during the Upper Paleolithic period for cooking and heating and sometimes for burning clay figurines. The Gravettian Dolni Vestonice site in the modern Czech Republic has evidence of kiln construction, although construction details did not survive. The best information on Upper Paleolithic kilns is from the Aurignacian deposits of Klisoura Cave  in Greece. Fuels Relict wood was likely the fuel used for the earliest fires. Purposeful selection of wood came later: hardwood such as oak burns differently than softwood such as pine, since the moisture content and density of a wood all affect how hot or long it will burn. In places where wood was not available, alternative fuels such as peat, cut turf, animal dung, animal bone, seaweed, and straw were used to build fires. Animal dung was likely not consistently used until after animal domestication  led to the keeping of livestock, about 10,000 years ago. Sources Attwell L., Kovarovic K., and Kendal J.R. Fire in the Plio-Pleistocene: The Functions of Hominin Fire Use, and the Mechanistic, Developmental and Evolutionary Consequences. Journal of Anthropological Sciences, 2015.Bentsen S.E. Using Pyrotechnology: Fire-Related Features and Activities With a Focus on the African Middle Stone Age. Journal of Archaeological Research, 2014.Gowlett J.A.J. The Discovery of Fire by Humans: A Long and Convoluted Process. Philosophical Transactions of the Royal Society B: Biological Sciences, 2016.Gowlett J.A.J., and Wrangham R.W. Earliest Fire in Africa: Towards the Convergence of Archaeological Evidence and the Cooking Hypothesis. Azania: Archaeological Research in Africa, 2013.Stahlschmidt M.C., Miller C.E., Ligouis B., Hambach U., Goldberg P., Berna F., Richter D., Urban B., Serangeli J., and Conard N.J. On the Evidence for Human Use and Control of Fire at Schà ¶ningen. Journal of Human Evolution, 2015.Twomey T. The Cognitive Implications of Controlled Fire Use by Early Humans. Cambridge Archaeological Journal, 2013.

The Effect of Emotional Intelligence on Leadership Performance Essay Example

The Effect of Emotional Intelligence on Leadership Performance Essay Example The Effect of Emotional Intelligence on Leadership Performance Essay The Effect of Emotional Intelligence on Leadership Performance Essay Varumpa Temaismithi Graduate School of Human Resource Development, National Institute of Development Administration, 118, Serithai Rd, Bangkok 10240, Thailand varumpa. [emailprotected] com ABSTRACT Emotional Intelligence (EI) plays a vital role in a way that leaders direct and facilitate teamwork effectively and efficiently. This implies that by encouraging all employees work together and share responsibilities in order to effectively keep things running smoothly is another responsibility of outstanding leader.This paper is suggesting the conceptual framework of the effect of Emotional Intelligence (EI) on leadership performance. The conceptual framework is developed through the process of review of literature to definitions of Goleman’s Emotional Intelligence and how it relates to leadership performance. Goleman defined the concept of Emotional Intelligence into five components as self-awareness, self-regulation, motivation, empathy, and social skills. These five components a re directly applied to leadership performance.Being outstanding leaders, Emotional Intelligence is one of the most important factors driving them to perform better. Emotional intelligence not only distinguishes outstanding leaders but also can be linked to strong performance. (Goleman, 2004) To fulfill the future part, the conceptual framework will be processed through the process of measuring leaders’ emotional intelligence related to their leadership performance in financial sector in Thailand.As a result, it will be indicated the key factor(s) of emotional intelligence for successful leaders in financial sector. Subsequently, it can be applied to the field of human resource development. Also, it benefits to other related topics in HRD. Keywords: Emotional Intelligence, leadership performance, self-awareness, self-regulation, motivation, empathy, social skills.

The Science of How Slime Works

The Science of How Slime Works You know about slime. Youve either made it as a science project or else blown the natural version out your nose. Do you know what makes slime different from a regular liquid? Take a look at the science of what slime is, how it forms, and its special properties. What Is Slime? Slime flows like a liquid, but unlike familiar liquids (e.g., oil, water), its ability to flow or viscosity is not constant. So, its a fluid, but not a regular liquid. Scientists call a material that changes viscosity a non-Newtonian fluid. The technical explanation is that slime is a fluid that changes its ability to resist deformation according to shear or tensile stress. What this means is, when you pour slime or let it ooze through your finger, it has a low viscosity and flows like a thick liquid. When you squeeze non-Newtonian slime, like oobleck, or pound it with your fist, it feels hard, like a wet solid. This is because applying stress squeezes the particles in the slime together, making it hard for them to slide against each other. Most types of slime are also examples of polymers. Polymers are molecules made by linking together chains of subunits. Examples of Slime A natural form of slime is mucous, which consists of mainly of water, the glycoprotein mucin, and salts. Water is the main ingredient in some other types of human-made slime, too. The classic science project slime recipe mixes glue, borax, and water. Oobleck is a mixture of starch and water. Other types of slime are mainly oils rather than water. Examples include Silly Putty and electroactive slime. How Slime Works The specifics of how a type of slime works depends on its chemical composition, but the basic explanation is that chemicals are mixed to form polymers. The polymers act as a net, with molecules sliding against each other. For a specific example, consider the chemical reactions that produce classic glue and borax slime: Two solutions are combined to make classic slime. One is diluted school glue or polyvinyl alcohol in water. The other solution is borax (Na2B4O7.10H2O) in water.Borax dissolves in water into sodium ions, Na, and tetraborate ions.The tetraborate ions react with water to produce the OH- ion and boric acid:B4O72-(aq) 7 H2O - 4 H3BO3(aq) 2 OH-(aq)Boric acid reacts with water to form borate ions:H3BO3(aq) 2 H2O - B(OH)4-(aq) H3O(aq)Hydrogen bonds form between the borate ion and the OH groups of the polyvinyl alcohol molecules from the glue, linking them together to form a new polymer (slime). The cross-linked polyvinyl alcohol traps a lot of water, so slime is wet. You can adjust the consistency of slime by controlling the ratio of glue to borax. If you have an excess of diluted glue, compared with a borax solution, youll limit the number of cross-links that can form and get a more fluid slime. You can also adjust the recipe by limiting the amount of water that you use. For example, you could mix the borax solution directly with glue. This produces a very stiff slime.

Genetic Engineering Essay Example | Topics and Well Written Essays - 750 words

Genetic Engineering - Essay Example This paper declares that one method that is gaining ground to cure genetically impaired individuals without so much objection on the part of its people is genetic engineering because it is not forced at individuals to be engineered, specially to eliminate defects. Genetic engineering that involves elimination of defects is called negative genetic engineering. Going beyond this, to bring about improvement in normal people is by contrast positive engineering. This discussion explores that genetic disorder causes problems to individuals in terms of sickness or diseases like autism, diabetes and cystic fibrosis. These genetic diseases can be cured by gene therapy or by negative gene engineering. The principle behind negative genetic engineering is that a virus which is non-virulent or non-pathogenic will serve as a delivery system to insert a piece of DNA into the cell of the individual person with genetic disorder. The process is deemed successful when the modified cells divide normally to produce cells that express the trait that was missing before the individual person received the copy of that gene. This gene therapy provide good outlook at treating other maladies that are menacing many of our people with genetic disorder. While negative genetic engineering removes genetic disorders, positive genetic engineering enhances human. The latter makes those who are already healthy superior to the average. It makes people stronger, faster, smar ter. It enhances intelligence, stamina and physical attributes; it increases immunity of people to diseases caused by viruses. These changes can easily be attained when sex cells are the ones engineered during the critical period of the union between the egg and sperm cells. These changes are inheritable in all succeeding generations, posing permanent alterations of the human kind. Moral Implications of Genetic Engineering "If we produce a group of people who turn out worse than expected, we will have to live with them[...] The possibility of an irreversible disaster is a strong deterrent. It is enough to make some people think we should rule out genetic engineering altogether, and to make others think that, while negative engineering is perhaps acceptable, we should rule out positive engineering." (p.193) The above-mentioned quotes implies that "the benefits from negative engineering are clearer and that because its aims are more modest, disastrous mistakes are less likely." (p.193) Positive genetic engineering seemed immoral and unethical when one of its potential products is a