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The Agile Manifesto of Software Development put forth a groundbreaking mindset on delivering value and collaborating with customers when it was created in 2001. Agile's four main values are:

Individuals and interactions over processes and tools
Working software over comprehensive documentation
Customer collaboration over contract negotiation
Responding to change over following a plan

Software testing is the process of finding errors in the developed product. It also checks whether the real outcomes can match expected results, as well as aids in the identification of defects, missing requirements, or gaps.

Testing is the penultimate step before the launch of the product to the market. It includes examination, analysis, observation, and evaluation of different aspects of a product.

Professional software testers use a combination of manual testing with automated tools. After conducting tests, the testers report the results to the development team. The end goal is to deliver a quality product to the customer, which is why software testing is so important.

https://education.wm.edu/centers/ttac/documents/packets/adhd.pdf


School interventions should include a team approach across multiple settings, consisting of both
preventive and intervention strategies.

Interventions must be based upon assessment data that includes information about the student’s strengths and needs as well as the environmental conditions in which her characteristics of ADHD occur.

Progress monitoring and strategy adjustments are critical to the success of any intervention plan (Wolraich & DuPaul, 2010).

This program is an online continuous course with no specific start or end date.
Creating a Culture of Collaboration and Interprofessional Teaming is an online self-paced course offering. This offering is made possible through the support of Venturit's internal WIzdn team.

Classroom Interventions for Attention Deficit/ Hyperactivity Disorder Considerations Packet

Primer text from The College of William & Mary

ADHD is one of the most commonly diagnosed conditions of children (Centers for Disease
Control and Prevention, 2015).

In a 2016 Centers for Disease Control and Prevention study, scientists found that 6.1 million children aged 2-17 years living in the U.S. had been diagnosed with attention-deficit/hyperactivity disorder (ADHD), which is similar to previous en
Ages 6-11: Approximately 2.4 million children
Ages 12-17: Approximately 3.3 million children

The diagnostic term attention deficit/hyperactivity disorder (ADHD) refers to individuals who display patterns of inattention, impulsivity, and overactive behavior that interfere with daily functioning (American Psychiatric Association [APA], 2013).

Primer text from The College of William & Mary

ADHD is one of the most commonly diagnosed conditions of children (Centers for Disease
Control and Prevention, 2015).

In a 2016 Centers for Disease Control and Prevention study, scientists found that 6.1 million children aged 2-17 years living in the U.S. had been diagnosed with attention-deficit/hyperactivity disorder (ADHD), which is similar to previous en
Ages 6-11: Approximately 2.4 million children
Ages 12-17: Approximately 3.3 million children

The diagnostic term attention deficit/hyperactivity disorder (ADHD) refers to individuals who display patterns of inattention, impulsivity, and overactive behavior that interfere with daily functioning (American Psychiatric Association [APA], 2013).


The Diagnostic and Statistical Manual (DSM) V (APA, 2013) criteria for diagnosing ADHD list
three types of ADHD and the accompanying characteristics.

Department of Haematology
Notes
Full blood counts are performed on automated equipment and provide haemoglobin concentration, red cell indices, white cell count (with a differential count) and platelet count.
The presence of abnormal white cell and red cell morphology is flagged by the analysers.
Blood films may be inspected to confirm and interpret abnormalities identified by the cell counter, or to look for certain specific haematological abnormalities.
Grossly abnormal FBC results and abnormal blood films will be phoned through to the requestor.
There is no need to request a blood film to obtain a differential white count. It is, however, important that clinical details are provided to allow the laboratory to decide whether a blood film, in addition to the automated analysis, is required.
Under some circumstances a differential is not routinely performed, e.g. pre-op, post-op, antenatal and postnatal requests.
Full Blood Counts are performed at CGH and GRH
See also: Reticulocyte Count
The FBC comprises the following tests

Standard

Haemoglobin (Hb)
White Blood Count (WBC)
Platelet Count (Plt)
Red Cell Count (RBC)
Haematocrit (HCT)
Mean Cell Volume - Red cell (MCV)
Mean Cell Haemoglobin (MCH)
Differential White Cell Count (where applicable)

Neutrophils
Lymphocytes
Monocytes
Eosinophils
Basophils
And if appropriate

Blood Film
Sample Requirements
2ml or 4ml EDTA sample or a Paediatric 1ml EDTA sample.

EDTA with cap

1ml Paediatric EDTA
Sample Storage and Retention
Pre analysis storage: do not store, send to laboratory within 4 hours.
Sample retention by lab: EDTA samples are retained for a minimum of 48 hours at 2-10°C
Transport of samples may affect sample viability, i.e. FBC results will degenerate if exposed to high temperatures, such as prolonged transportation in a hot car in summer.
This test can be added on to a previous request as long as there is sufficient sample remaining and the sample is less than 24 hours old.

Turnaround Times
Clinical emergency: 30 mins
Other urgent sample: 60 mins
Routine: within 2 hours
Reference Ranges
If references ranges are required for paediatric patients please contact the laboratory for these.

Parameter Patient Reference Range Units Haemoglobin Adult Male 130 - 180 g/L Adult Female 115 - 165 g/L Red Cell Count Adult Male 4.50 - 6.50 x10^12/L Adult Female 3.80 - 5.80 x10^12/L Haematocrit Adult Male 0.40 - 0.54 L/L Adult Female 0.37 - 0.47 L/L Mean Cell Volume Adult 80 - 100 fL Mean Cell Haemoglobin Adult 27 - 32 pg White Cell Count Adult 3.6 - 11.0 x10^9/L Neutrophils Adult 1.8 - 7.5 x10^9/L Lymphocytes Adult 1.0 - 4.0 x10^9/L Monocytes Adult 0.2 - 0.8 x10^9/L Eosinophils Adult 0.1 - 0.4 x10^9/L Basophils Adult 0.02 - 0.10 x10^9/L Platelet Count Adult 140 - 400 x10^9/L

Human language is filled with ambiguities that make it incredibly difficult to write software that accurately determines the intended meaning of text or voice data. Homonyms, homophones, sarcasm, idioms, metaphors, grammar and usage exceptions, variations in sentence structure—these just a few of the irregularities of human language that take humans years to learn, https://byjus.com/biology/flower/ but that programmers must teach natural language-driven applications to recognize and understand accurately from the start, if those applications are going to be useful.
https://byjus.com/biology/flower/ https://byjus.com/biology/flower/

Several NLP tasks break down human text and voice data in ways that help the computer make sense of what it's ingesting. Some of these tasks include the following:

Speech recognition, also called speech-to-text, is the task of reliably converting voice data into text data. Speech recognition is required for any application that follows voice commands or answers spoken questions. What makes speech recognition especially challenging is the way people talk—quickly, slurring words together, with varying emphasis and intonation, in different accents, and often using incorrect grammar.
Part of speech tagging, also called grammatical tagging, is the process of determining the part of speech of a particular word or piece of text based on its use and context. Part of speech identifies ‘make’ as a verb in ‘I can make a paper plane,’ and as a noun in ‘What make of car do you own?’
Word sense disambiguation is the selection of the meaning of a word with multiple meanings through a process of semantic analysis that determine the word that makes the most sense in the given context. For example, word sense disambiguation helps distinguish the meaning of the verb 'make' in ‘make the grade’ (achieve) vs. ‘make a bet’ (place).
Named entity recognition, or NEM, identifies words or phrases as useful entities. NEM identifies ‘Kentucky’ as a location or ‘Fred’ as a man's name.
Co-reference resolution is the task of identifying if and when two words refer to the same entity. The most common example is determining the person or object to which a certain pronoun refers (e.g., ‘she’ = ‘Mary’), but it can also involve identifying a metaphor or an idiom in the text (e.g., an instance in which 'bear' isn't an animal but a large hairy person).
Sentiment analysis attempts to extract subjective qualities—attitudes, emotions, sarcasm, confusion, suspicion—from text.
Natural language generation is sometimes described as the opposite of speech recognition or speech-to-text; it's the task of putting structured information into human language.