 250m thick silica gel stationary phase.
(1:3) were prepared.
layer of mobile phase for 1 hour.
the solvent front reached the end of TLC plate.
nm ultraviolet light.
and 1 % methanolic tetrazolium blue solution.
In this experiment, there were 3 sample of herbal medicine used.
ultraviolet light. All of the sample showed no reaction under UV light.
compounds for sample 2 and 4 compounds for sample 3.
three samples did not showed any color change.
speed of separation.
or characters are identified by means of suitable detection techniques.
attack, stroke, kidney failure and tumors in the liver.
absolute identification of steroids.
for amphetamine type stimulants.
not have any steroid based on thin layer chromatography analysis.
Science. Drug abuse and drug evidenve.(11th ed., pp.278-311).Been single for... 4 years. I think. I've dated a couple of times but relationships seem to be more hassel than they're worth. 

 There are the ones that try and force their idea of "happily ever after" on you and that just makes me all kinds of sick.

I have a feeling I will be single for the the rest of my life, maybe casual flings/relationships here and there but it doesn't really upset me. I have a great life. I'm building a great career and I'm looking forward to travelling when we can. I dont really have or feel the need to share that yet.

I think too much pressure is put on people to fall inlove, settle down and so people don't end up living. Society pressure to conform is a huge factor here but at the end of the day you don't actually need to be with someone. If I come across a person I want to spend my life with one day then yay for me but I'm not going to actively look for them.

Edit: spelling and grammar not on pointConcept: Concept formation
Concept learning, also known as concept attainment, is largely based on the works of the cognitive psychologist Jerome Bruner. Bruner, Goodnow, & Austin (1967) defined concept attainment (or concept learning) as "the search for and listing of attributes that can be used to distinguish exemplars from non exemplars of various categories." More simply put, concepts are the mental categories that help us classify objects, events, or ideas and each object, event, or idea has a set of common relevant features. Thus, concept learning is a strategy which requires a learner to compare and contrast groups or categories that contain concept-relevant features with groups or categories that do not contain concept-relevant features.
Concept learning also refers to a learning task in which a human or machine learner is trained to classify objects by being shown a set of example objects along with their class labels. The learner will simplify what has been observed in an example. This simplified version of what has been learned will then be applied to future examples. Concept learning ranges in simplicity and complexity because learning takes place over many areas. When a concept is more difficult, it will be less likely that the learner will be able to simplify, and therefore they will be less likely to learn. Colloquially, this task is known as learning from examples. Most theories of concept learning are
The theoretical issues underlying concept learning are those underlying induction in general. These issues are addressed in many diverse literatures, including Version Spaces, Statistical Learning Theory, PAC Learning, Information Theory, and Algorithmic Information Theory. Some of the broad theoretical ideas are also discussed by Watanabe (1969,1985), Solomonoff (1964a,1964b), and Rendell (1986).
Modern Psychological Theories of Concept LearningEdit
It is difficult to make any general statements about human (or animal) concept learning without already assuming a particular psychological theory of concept learning. Although the classical views of concepts and concept learning in philosophy speak of a process of abstraction, data compression, simplification, and summarization, currently popular psychological theories of concept learning diverge on all these basic points.
Rule-Based Theories of Concept LearningEdit
Rule-based theories of concept learning take classification data and a rule-based theory as input, which are the result of a rule-based learner with the hopes of producing a more accurate model of the data (Hekenaho 1997). The majority of rule-based models that have been developed are heuristic, meaning that rational analyses have not been provided and the models are not related to statistical approaches to induction. A rational analysis for rule-based models could presume that concepts are represented as rules, and would then ask what degree of belief a rational agent should be in agreement with each rule, provided some observed examples (Goodman, Griffiths, Feldman, and Tenenbaum). Rule based theories of concept learning are focused more so on perceptual learning and less on definition learning. Rules can be used in learning when the stimuli are confusable as opposed to simple. When rules are used in learning, the decisions are made based on properties alone and rely on simple criteria that do require a lot of memory ( Rouder and Ratcliff, 2004).
Example of Rule based theory:
"A radiologist using rule-based categorization would observe whether specific properties of the X-ray meet certain criteria; for example, is there an extreme difference in brightness in a suspicious region relative to the other regions? A decision is then based on this property alone" (Rouder and Ratcliff 2004)
Prototype Theory of Concept LearningEdit
The prototype view on concept learning holds that people abstract out the central tendency (or prototype) of the experienced examples, and use this as a basis for their categorization decisions.
Prototype theory:
The prototype view on concept learning holds that people categorize based on one or more central examples of a given category followed by a penumbra of decreasingly typical examples. This implies that people do not categorize based on a list of things that all correspond to a definition; rather, a hierarchical inventory based on semantic similarity to the central example(s).
To illustrate this, imagine the following mental representations of the category: Sports
The first illustration may demonstrate a mental representation if we were to categorize by definition:
Definition of Sports: an athletic activity requiring skill or physical prowess and often of a competitive nature.
Basketball Football Bowling Baseball Skiing Track and field Snowboarding Lacrosse rugby Soccer Sports Skateboarding Golf Bike-Racing Hockey Surfing Weightlifting Tennis
The second illustration may demonstrate a mental representation that Prototype Theory would predict:
1. Baseball
2. Football
3. Basketball
4. Soccer
5. Hockey
6. Tennis
7. Golf
...
15. Bike-racing
16. Weightlifting
17. Skateboarding
18. Snowboarding
19. Boxing
20. Wrestling
...
32. Fishing
33. Hunting
34. Hiking
35. sky-diving
36. bunji-jumping
...
62. cooking
63. walking
...
82. Gatorade
83. water
84. protein
85. diet
As you can see the Prototype theory hypothesizes a more continuous (less discrete) way of categorization in which we don’t limit the list to things that match the category’s definition.
Exemplar Theories of Concept LearningEdit
Exemplar theory is the storage of specific instances (exemplars), with new objects evaluated only with respect to how closely they resemble specific known members (and nonmembers) of the category. This theory hypothesizes that learners store examples verbatim. This theory views concept learning as highly simplistic. Only individual properties are represented. These individual properties are not abstract and they do not create rules. An example of what Exemplar theory would look at is, “water is wet;” it simply knows that some (or one, or all) stored examples of water have the property wet. Exemplar based theories have become more empirically popular over the years with some evidence suggesting that human learners use exemplar based strategies only in early learning, forming prototypes and generalizations later in life. An important result of exemplar models in psychological literature has been a de-emphasis of complexity in concept learning. Some of the best known exemplar theory of concept learning are the Generalized Context Model (GCM), Nosofsky's (1986) generalization of Medin ans Schaffer's (1978) Context Model. A connectionist version of the GCM, called ALCOVE, has been developed by Kruschke (1992). The ALCOVE model addresses trial-by-trial concept learning. On each training trial, ALCOVE is presented with a stimulus, makes a prediction of the distribution of category choices, is presented with the correct classification, and then adjusts its associative weights and dimensional attention strengths. All of these models are matching models that exemplar sets for a category contains all of the category's exemplars.
Problems with Exemplar Theory
Exemplar models critically depend on two measures:
1. Similarity between exemplars
2. Rule to determine Group Membership
Sometimes it is difficult to attain or distinct these measures.
Explanation-Based Theories of Concept LearningEdit
The basic idea of explanation-based learning suggests that a new concept is acquired by experiencing examples of it and forming a basic outline1. Put simply, by observing or receiving the qualities of a thing the mind forms a concept which possesses and is identified by those qualities.
The original theory proposed by Mitchell, Keller, and Kedar-Cabelli in 1986, called explanation-based generalization, is that learning occurs through progressive generalizing2. This theory was first developed to program machines to learn. When applied to human cognition, it