This document discusses various types of constraints in class modeling including:
- Aggregation constraints like transitivity and antisymmetry for part-whole relationships
- Constraints on objects like maximum salary or aspect ratio
- Constraints on generalization sets like disjointness and completeness
- Constraints on links and associations like multiplicity, qualifications, ordering, and subset relationships
It provides examples of how to represent these different types of constraints in UML class diagrams. The purpose of constraints is to formally capture important business rules and validate the structure of the class model.
UML, visual modeling language, common divisions, a concept model of UML, structural things, the relationship in UML, Common Mechanisms in the UML, Fundamentals of Software Engineering
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Title: Sense of Smell
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the primary categories of smells and the concept of odor blindness.
Explain the structure and location of the olfactory membrane and mucosa, including the types and roles of cells involved in olfaction.
Describe the pathway and mechanisms of olfactory signal transmission from the olfactory receptors to the brain.
Illustrate the biochemical cascade triggered by odorant binding to olfactory receptors, including the role of G-proteins and second messengers in generating an action potential.
Identify different types of olfactory disorders such as anosmia, hyposmia, hyperosmia, and dysosmia, including their potential causes.
Key Topics:
Olfactory Genes:
3% of the human genome accounts for olfactory genes.
400 genes for odorant receptors.
Olfactory Membrane:
Located in the superior part of the nasal cavity.
Medially: Folds downward along the superior septum.
Laterally: Folds over the superior turbinate and upper surface of the middle turbinate.
Total surface area: 5-10 square centimeters.
Olfactory Mucosa:
Olfactory Cells: Bipolar nerve cells derived from the CNS (100 million), with 4-25 olfactory cilia per cell.
Sustentacular Cells: Produce mucus and maintain ionic and molecular environment.
Basal Cells: Replace worn-out olfactory cells with an average lifespan of 1-2 months.
Bowman’s Gland: Secretes mucus.
Stimulation of Olfactory Cells:
Odorant dissolves in mucus and attaches to receptors on olfactory cilia.
Involves a cascade effect through G-proteins and second messengers, leading to depolarization and action potential generation in the olfactory nerve.
Quality of a Good Odorant:
Small (3-20 Carbon atoms), volatile, water-soluble, and lipid-soluble.
Facilitated by odorant-binding proteins in mucus.
Membrane Potential and Action Potential:
Resting membrane potential: -55mV.
Action potential frequency in the olfactory nerve increases with odorant strength.
Adaptation Towards the Sense of Smell:
Rapid adaptation within the first second, with further slow adaptation.
Psychological adaptation greater than receptor adaptation, involving feedback inhibition from the central nervous system.
Primary Sensations of Smell:
Camphoraceous, Musky, Floral, Pepperminty, Ethereal, Pungent, Putrid.
Odor Detection Threshold:
Examples: Hydrogen sulfide (0.0005 ppm), Methyl-mercaptan (0.002 ppm).
Some toxic substances are odorless at lethal concentrations.
Characteristics of Smell:
Odor blindness for single substances due to lack of appropriate receptor protein.
Behavioral and emotional influences of smell.
Transmission of Olfactory Signals:
From olfactory cells to glomeruli in the olfactory bulb, involving lateral inhibition.
Primitive, less old, and new olfactory systems with different path
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micro teaching on communication m.sc nursing.pdfAnurag Sharma
Microteaching is a unique model of practice teaching. It is a viable instrument for the. desired change in the teaching behavior or the behavior potential which, in specified types of real. classroom situations, tends to facilitate the achievement of specified types of objectives.
Ethanol (CH3CH2OH), or beverage alcohol, is a two-carbon alcohol
that is rapidly distributed in the body and brain. Ethanol alters many
neurochemical systems and has rewarding and addictive properties. It
is the oldest recreational drug and likely contributes to more morbidity,
mortality, and public health costs than all illicit drugs combined. The
5th edition of the Diagnostic and Statistical Manual of Mental Disorders
(DSM-5) integrates alcohol abuse and alcohol dependence into a single
disorder called alcohol use disorder (AUD), with mild, moderate,
and severe subclassifications (American Psychiatric Association, 2013).
In the DSM-5, all types of substance abuse and dependence have been
combined into a single substance use disorder (SUD) on a continuum
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the 11 DSM-5 behaviors be present within a 12-month period (mild
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comorbidities, fetal alcohol spectrum disorders, genetic risk factors, and
pharmacotherapies for AUD.
Lung Cancer: Artificial Intelligence, Synergetics, Complex System Analysis, S...Oleg Kshivets
RESULTS: Overall life span (LS) was 2252.1±1742.5 days and cumulative 5-year survival (5YS) reached 73.2%, 10 years – 64.8%, 20 years – 42.5%. 513 LCP lived more than 5 years (LS=3124.6±1525.6 days), 148 LCP – more than 10 years (LS=5054.4±1504.1 days).199 LCP died because of LC (LS=562.7±374.5 days). 5YS of LCP after bi/lobectomies was significantly superior in comparison with LCP after pneumonectomies (78.1% vs.63.7%, P=0.00001 by log-rank test). AT significantly improved 5YS (66.3% vs. 34.8%) (P=0.00000 by log-rank test) only for LCP with N1-2. Cox modeling displayed that 5YS of LCP significantly depended on: phase transition (PT) early-invasive LC in terms of synergetics, PT N0—N12, cell ratio factors (ratio between cancer cells- CC and blood cells subpopulations), G1-3, histology, glucose, AT, blood cell circuit, prothrombin index, heparin tolerance, recalcification time (P=0.000-0.038). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and PT early-invasive LC (rank=1), PT N0—N12 (rank=2), thrombocytes/CC (3), erythrocytes/CC (4), eosinophils/CC (5), healthy cells/CC (6), lymphocytes/CC (7), segmented neutrophils/CC (8), stick neutrophils/CC (9), monocytes/CC (10); leucocytes/CC (11). Correct prediction of 5YS was 100% by neural networks computing (area under ROC curve=1.0; error=0.0).
CONCLUSIONS: 5YS of LCP after radical procedures significantly depended on: 1) PT early-invasive cancer; 2) PT N0--N12; 3) cell ratio factors; 4) blood cell circuit; 5) biochemical factors; 6) hemostasis system; 7) AT; 8) LC characteristics; 9) LC cell dynamics; 10) surgery type: lobectomy/pneumonectomy; 11) anthropometric data. Optimal diagnosis and treatment strategies for LC are: 1) screening and early detection of LC; 2) availability of experienced thoracic surgeons because of complexity of radical procedures; 3) aggressive en block surgery and adequate lymph node dissection for completeness; 4) precise prediction; 5) adjuvant chemoimmunoradiotherapy for LCP with unfavorable prognosis.
Title: Sense of Taste
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the structure and function of taste buds.
Describe the relationship between the taste threshold and taste index of common substances.
Explain the chemical basis and signal transduction of taste perception for each type of primary taste sensation.
Recognize different abnormalities of taste perception and their causes.
Key Topics:
Significance of Taste Sensation:
Differentiation between pleasant and harmful food
Influence on behavior
Selection of food based on metabolic needs
Receptors of Taste:
Taste buds on the tongue
Influence of sense of smell, texture of food, and pain stimulation (e.g., by pepper)
Primary and Secondary Taste Sensations:
Primary taste sensations: Sweet, Sour, Salty, Bitter, Umami
Chemical basis and signal transduction mechanisms for each taste
Taste Threshold and Index:
Taste threshold values for Sweet (sucrose), Salty (NaCl), Sour (HCl), and Bitter (Quinine)
Taste index relationship: Inversely proportional to taste threshold
Taste Blindness:
Inability to taste certain substances, particularly thiourea compounds
Example: Phenylthiocarbamide
Structure and Function of Taste Buds:
Composition: Epithelial cells, Sustentacular/Supporting cells, Taste cells, Basal cells
Features: Taste pores, Taste hairs/microvilli, and Taste nerve fibers
Location of Taste Buds:
Found in papillae of the tongue (Fungiform, Circumvallate, Foliate)
Also present on the palate, tonsillar pillars, epiglottis, and proximal esophagus
Mechanism of Taste Stimulation:
Interaction of taste substances with receptors on microvilli
Signal transduction pathways for Umami, Sweet, Bitter, Sour, and Salty tastes
Taste Sensitivity and Adaptation:
Decrease in sensitivity with age
Rapid adaptation of taste sensation
Role of Saliva in Taste:
Dissolution of tastants to reach receptors
Washing away the stimulus
Taste Preferences and Aversions:
Mechanisms behind taste preference and aversion
Influence of receptors and neural pathways
Impact of Sensory Nerve Damage:
Degeneration of taste buds if the sensory nerve fiber is cut
Abnormalities of Taste Detection:
Conditions: Ageusia, Hypogeusia, Dysgeusia (parageusia)
Causes: Nerve damage, neurological disorders, infections, poor oral hygiene, adverse drug effects, deficiencies, aging, tobacco use, altered neurotransmitter levels
Neurotransmitters and Taste Threshold:
Effects of serotonin (5-HT) and norepinephrine (NE) on taste sensitivity
Supertasters:
25% of the population with heightened sensitivity to taste, especially bitterness
Increased number of fungiform papillae
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ARTIFICIAL INTELLIGENCE IN HEALTHCARE.pdfAnujkumaranit
Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems. It encompasses tasks such as learning, reasoning, problem-solving, perception, and language understanding. AI technologies are revolutionizing various fields, from healthcare to finance, by enabling machines to perform tasks that typically require human intelligence.
3. Aggregation
Aggregation is a special form of association which
represents the part-whole relationship between classes.
It is a strong form of association in which an aggregate
object is made of constituent parts.
Constituents are part of the aggregate.
For example, a LawnMower consists of a Blade, an
Engine, many Wheels, and a Deck.
4. Aggregation
LawnMower is the assembly and the other parts are
constituents. LawnMower to Blade is one aggregation,
LawnMower to Engine is another aggregation, and so
on.
The most significant property of aggregation is
transitivity- that is, if A is part of B and B is part of C,
then A is part of C.
Aggregation is also antisymmetric – that is, if A is part
of B, then B is not part of A. Many aggregate
operations imply transitive closure * and operate on
both direct and indirect parts.
6. Aggregation
This diagram states that a message consists of one header,
one body and zero or more attachments. Each header and
body is part of exactly one message, but attachments can
be part of many message. Further, headers and bodies
cannot exist independently of messages, unlike
attachments.
In uses like this, aggregation has virtually no formal
properties other than to suggest an informal notion of
“parts”.
7. Aggregation Versus Association
Aggregation is a special form of association, not an
independent concept.
If the two objects are tightly bound by a part – whole
relationship, it is an aggregation.
Aggregation is drawn like association, expect a small
diamond indicates the assembly end.
In fig. a lawn Mower consists of one blade, one engine,
many wheels, and one deck.
The manufacturing process is flexible and largely combines
standard parts, so blades, engines, wheels, and decks
certain to multiple LawnMower designs.
9. Aggregation Versus Association
Aggregations include bill-of-materials, part explosions, and
expansions of an object into constituent parts.
Some tests include:
Would you see the phrase part of?
Do some operations on the whole automatically apply to its
parts?
Do some attribute values propagate from the whole to all or
some parts?
Is there an intrinsic asymmetry to the association, where
one class is subordinate to the other?
10. Aggregation Versus Composition
The UML has two forms of part-whole relationships :
Aggregation and Composition
Composition is a form of aggregation with two additional
constraints .
Composition implies ownership of the parts by the whole.
This can be convenient for programming: Deletion of an
assembly object triggers deletion of all constituent objects
via composition.
The notation for composition is a small solid diamond next
to the assembly class (vs. a small hollow diamond for the
general form of aggregation).
11. Aggregation Versus Composition
Composition: With composition a constituent part belongs
to at most one assembly and has a coincident lifetime with
the assembly.
Company DepartmentDivision
Person
WorksFor
12. Aggregation Versus Composition
In fig. shows a company consists of divisions, which in
turn consist of departments; a company is indirectly a
composition of departments.
A company is not a composition of its employees, since
company and person are independent objects of equal
stature.
13. Propagation of Operations
Propagation (also called triggering) is the automatic
application of an operation to a network of objects when
the operation is applied to some starting object.
For example, moving an aggregate moves its parts; the
move operation propagates to the parts. Propagation of
operations to parts is often a good indicator of aggregation.
In fig. shows A person owns multiple documents. Each
documents consists of paragraphs that, in turn, consist of
characters.
The copy operation propagates from documents to
paragraphs to characters.
14. Propagation of Operations
Copying a paragraph copies all the characters in it.
The operation does not propagate in the reverse direction; a
paragraph can be copied without copying the whole
document.
Similarly, copying a document copies the owner link but
does not spawn a copy of the person who is owner.
Owns copy copy
Doc
Document
ument
copy
copy
P
Paragraph
aragraph
copy
Character
acter
cocopy
y
P
Person
erson
*1
*1*1
15. Abstract Classes
An abstract class is a class that has no direct instances but
whose descendant classes have direct instances.
A abstract class is a class that is insatiable; that is, it can
have direct instances.
Superclasses are introduced into a model to define the
general features of a number of related classes. Often it
does not make sense to create instances of these
superclasses.
For example, the model of the banking system might never
create simple Account classes, but only instances of one of
the subclasses. In other words, every account must be
either a current account, or a deposit account, or . . . .
16. Abstract Classes
•In the case, Account would be modelled as an abstract
class.
•This can be shown typographically by writing the class
name in a slanted font:
17. Metadata
Metadata is data that describes other data.
For example, a class definition is metadata. Models are
inherently metadata, since they describe the things being
modeled .
Many real-world applications have metadata, such as parts
catalogs, blueprints, and dictionaries.
Objects have features, and they in turn have their own
classes, which are called metaclasses.
In fig. shows an example of metadata and data.
A car model has a model name, year, base price, and a
manufacturer.
18. Metadata
Metadata often arises in applications
Doc
CarModel
ue
modelNam
e
Year
baseprice
copy
Doc
PhysicalCar
uet
SerialNumbe
r
color
options
copy
PersonCompany
manufacturer owner
* 1
*
1
*
1
Describes
19. Metadata
Some examples of car models are a 1969 Ford Mustang and
a 1975 Volkswagen Rabbit.
A physical car has a serial number, color, options, and an
owner.
As an example of physical cars, John Doe may own a blue
Ford with serial number IFABP and a red Volkswagen with
serial number 7E81F.
A car model describes many physical cars and holds
common data.
A car model is metadata relative to a physical car, which is
data.
20. Constraints
A constraint is a boolean condition involving model
elements, such as objects, classes, attributes, links,
associations, and generalization sets.
Constraints on Objects :
In fig. shows No employee’s salary can exceed the salary of
the employee’s boss (a constraint between two things at the
same time).
No window can have an aspect ratio (length/width) of less
than 0.8 or greater than 1.5 (a constraint between
attributes of a single object). The priority of a job may not
increase (constraint on the same object over time).
You may place simple constraints in class models.
21. Constraints
Constraints on objects. The structure of a model expresses
many constraints, but sometimes it it helpful to add explicit
constraints
Employee
Salary
Window
Length
Width
Job
priority
boss
0..1
*
{ salary < boss.salary} { 0.8 < length / width < 1.5} { priority never increases}
22. Constraints on Generalization Sets
The UML defines the following keywords for generalization
sets.
Disjoint:
The subclasses are mutually exclusive.
Each object belongs to exactly one of the subclasses.
Overlapping:
The subclasses can share some objects.
An object may belong to more than one subclass.
Complete:
The generalization lists all the possible subclasses.
Incomplete:
The generalization may be missing some subclasses.
23. Constraints on Links
Multiplicity is a constraint on the cardinality of a set.
Multiplicity for an association restricts the number of
objects related to a given object.
Multiplicity for an attribute specifies the number of values
that are possible for each instantiation of an attribute.
Qualification also constraints an association.
A qualifier attribute does not merely describe the links of an
association but is also significant in resolving the “many”
objects at an association end.
An association class implies a constraint.
An association class is a class in every right; for example, it
can have attributes and operations, participate in
associations, and participate in generalizations.
24. Constraints on Links
But an association class has a constraint that an ordinary
class does not, it derives identity from instances of the
related classes.
An ordinary association presumes no particular order on the
objects of a “many” end.
The constraint {ordered} indicates that the elements of a
“many” association end have an explicit order that must be
preserved.
In fig. shows an explicit constraint that is not part of the
models structure.
The chair of a committee must be a member of the
committee; the ChairOf association is a subset of the
MemberOf association.
25. Constraints on Links
Subset constraint between associations:
Person Committee
MemberOf
{subset}
ChairOf
* *
*1
26. Use of Constraints
Constraints provide one criterion for measuring the quality
of a class model; a “good” class model captures many
constraints through its structure.