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Two important federal mandates protect the rights of eligible children with ADHD-the Individuals with Disabilities Education Act (IDEA) and Section 504 of the Rehabilitation Act of 1973 (Section 504). The regulations implementing these laws are 34 CFR sections 300 and 104, respectively, which require school districts to provide a "free appropriate public education" to students who meet their eligibility criteria. Although a child with ADHD may not be eligible for services under IDEA, he or she may meet the requirements of Section 504.
The requirements and qualifications for IDEA are more stringent than those of Section 504. IDEA provides funds to state education agencies for the purpose of providing special education and related services to children evaluated in accordance with IDEA and found to have at least one of the 13 specific categories of disabilities, and who thus need special education and related services. Attention Deficit Hyperactivity Disorder may be considered under the specific category of "Other Health Impairment" (OHI), if the disability results in limited strength, vitality, or alertness, including a heightened alertness to environmental stimuli that results in limited alertness with respect to the educational environment and that is due to chronic or acute health problems.

A data structure is a specialized format for organizing, processing, retrieving and storing data. There are several basic and advanced types of data structures, all designed to arrange data to suit a specific purpose. Data structures make it easy for users to access and work with the data they need in appropriate ways. Most importantly, data structures frame the organization of information so that machines and humans can better understand it.
In computer science and computer programming, a data structure may be selected or designed to store data for the purpose of using it with various algorithms. In some cases, the algorithm's basic operations are tightly coupled to the data structure's design. Each data structure contains information about the data values, relationships between the data and -- in some cases -- functions that can be applied to the data.
For instance, in an object-oriented programming language, the data structure and its associated methods are bound together as part of a class definition. In non-object-oriented languages, there may be functions defined to work with the data structure, but they are not technically part of the data structure.

Why are data structures important?
Typical base data types, such as integers or floating-point values, that are available in most computer programming languages are generally insufficient to capture the logical intent for data processing and use. Yet applications that ingest, manipulate and produce information must understand how data should be organized to simplify processing. Data structures bring together the data elements in a logical way and facilitate the effective use, persistence and sharing of data. They provide a formal model that describes the way the data elements are organized.
Data structures are the building blocks for more sophisticated applications. They are designed by composing data elements into a logical unit representing an abstract data type that has relevance to the algorithm or application. An example of an abstract data type is a "customer name" that is composed of the character strings for "first name," "middle name" and "last name."
It is not only important to use data structures, but it is also important to choose the proper data structure for each task. Choosing an ill-suited data structure could result in slow runtimes or unresponsive code. Five factors to consider when picking a data structure include the following:

What kind of information will be stored?
How will that information be used?
Where should data persist, or be kept, after it is created?
What is the best way to organize the data?
What aspects of memory and storage reservation management should be considered?




How are data structures used?
In general, data structures are used to implement the physical forms of abstract data types. Data structures are a crucial part of designing efficient software. They also play a critical role in algorithm design and how those algorithms are used within computer programs.
Early programming languages -- such as Fortran, C and C++ -- enabled programmers to define their own data structures. Today, many programming languages include an extensive collection of built-in data structures to organize code and information. For example, Python lists and dictionaries, and JavaScript arrays and objects are common coding structures used for storing and retrieving information.
Software engineers use algorithms that are tightly coupled with the data structures -- such as lists, queues and mappings from one set of values to another. This approach can be fused in a variety of applications, including managing collections of records in a relational database and creating an index of those records using a data structure called a binary tree.
 

Robotics is the intersection of science, engineering, and technology that produces machines, called robots, that substitute for (or replicate) human actions. Pop culture has always been fascinated with robots. R2-D2. Optimus Prime. WALL-E. These over-exaggerated, humanoid concepts of robots usually seem like a caricature of the real thing...or are they more forward-thinking than we realize? Robots are gaining intellectual and mechanical capabilities that don’t put the possibility of an R2-D2-like machine out of reach in the future.As technology progresses, so too does the scope of what is considered robotics. In 2005, 90% of all robots could be found assembling cars in automotive factories. These robots consist mainly of mechanical arms tasked with welding or screwing on certain parts of a car.

A typical Software Development Life Cycle consists of the following stages −
Stage 1: Planning and Requirement Analysis
Requirement analysis is the most important and fundamental stage in SDLC. It is performed by the senior members of the team with inputs from the customer, the sales department, market surveys and domain experts in the industry. This information is then used to plan the basic project approach and to conduct product feasibility study in the economical, operational and technical areas.
Planning for the quality assurance requirements and identification of the risks associated with the project is also done in the planning stage. The outcome of the technical feasibility study is to define the various technical approaches that can be followed to implement the project successfully with minimum risks.
Stage 2: Defining Requirements
Once the requirement analysis is done the next step is to clearly define and document the product requirements and get them approved from the customer or the market analysts. This is done through an SRS (Software Requirement Specification) document which consists of all the product requirements to be designed and developed during the project life cycle.
Stage 3: Designing the Product Architecture
SRS is the reference for product architects to come out with the best architecture for the product to be developed. Based on the requirements specified in SRS, usually more than one design approach for the product architecture is proposed and documented in a DDS - Design Document Specification.
This DDS is reviewed by all the important stakeholders and based on various parameters as risk assessment, product robustness, design modularity, budget and time constraints, the best design approach is selected for the product.
A design approach clearly defines all the architectural modules of the product along with its communication and data flow representation with the external and third party modules (if any). The internal design of all the modules of the proposed architecture should be clearly defined with the minutest of the details in DDS.
Stage 4: Building or Developing the Product
In this stage of SDLC the actual development starts and the product is built. The programming code is generated as per DDS during this stage. If the design is performed in a detailed and organized manner, code generation can be accomplished without much hassle.
Developers must follow the coding guidelines defined by their organization and programming tools like compilers, interpreters, debuggers, etc. are used to generate the code. Different high level programming languages such as C, C++, Pascal, Java and PHP are used for coding. The programming language is chosen with respect to the type of software being developed.
Stage 5: Testing the Product
This stage is usually a subset of all the stages as in the modern SDLC models, the testing activities are mostly involved in all the stages of SDLC. However, this stage refers to the testing only stage of the product where product defects are reported, tracked, fixed and retested, until the product reaches the quality standards defined in the SRS.
Stage 6: Deployment in the Market and Maintenance
Once the product is tested and ready to be deployed it is released formally in the appropriate market. Sometimes product deployment happens in stages as per the business strategy of that organization. The product may first be released in a limited segment and tested in the real business environment (UAT- User acceptance testing).
Then based on the feedback, the product may be released as it is or with suggested enhancements in the targeting market segment. After the product is released in the market, its maintenance is done for the existing customer base.YT video link: Embedded video link:Table:



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 widespread seabird and marine mammal mortality 
Many scientists see recent climate-related mass die-offs, including the coral bleaching of the Great Barrier Reef and widespread seabird and marine mammal mortality in the Northeastern Pacific linked to a marine heat wave, as warning signs of impending biodiversity collapse, said lead author Alex Pigot, a biodiversity researcher at University College, London. The new study shows that nowhere on Earth will escape the impacts. 

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Launched in April 2006 as a&nbsp;statistical machine translation&nbsp;service, it used&nbsp;United Nations&nbsp;and&nbsp;European Parliament&nbsp;documents and transcripts to gather linguistic data. Rather than translating languages directly, it first translates text to English and then pivots to the target language in most of the language combinations it posits in its grid,[4], with a few exceptions including Catalan-Spanish.[5]&nbsp;During a translation, it looks for patterns in millions of documents to help decide on which words to choose and how to arrange them in the target language. Its accuracy, which has been criticized and ridiculed on several occasions,[6]&nbsp;has been measured to vary greatly across languages.[7]&nbsp;In November 2016, Google announced that Google Translate would switch to a&nbsp;neural machine translation&nbsp;engine -&nbsp;Google Neural Machine Translation&nbsp;(GNMT) - which translates "whole sentences at a time, rather than just piece by piece. It uses this broader context to help it figure out the most relevant translation, which it then rearranges and adjusts to be more like a human speaking with proper grammar".[8]&nbsp;Originally only enabled for a few languages in 2016, GNMT is used in all 109 languages in the Google Translate roster as of 2020, except for&nbsp;Kyrgyz,&nbsp;Latin, and the&nbsp;Belarusian,&nbsp;Maltese&nbsp;and&nbsp;Sundanese&nbsp;to other languages pairs.[9]

Human papillomaviruses&nbsp;(HPVs) are ubiquitous, well adapted to their host and cleverly sequestered away from immune responses. HPV infections can be productive, subclinical or latent in both skin and&nbsp;mucosa. The causal association of HPV with cervical cancer, and increasingly with rising numbers of&nbsp;squamous cell&nbsp;carcinomas at other sites in both men and women, is increasingly recognised, while the morbidity of cutaneous HPV lesions, particularly in the immunosuppressed population is also significant. This chapter sets out the range of infections and clinical manifestations of the consequences of infection and its persistence and describes why HPVs are both highly effective pathogens and carcinogens, challenging to eliminate.