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Before spectroscopic analysis (IR, NMR) became commonplace in the organic chemistry lab, chemical tests were heavily relied upon to support compound identification. A chemical test is typically a fast reaction performed in a test tube that gives a dramatic visual clue (a color change, precipitate, or gas formation) as evidence for a chemical reaction. For example, addition of an orange chromic acid reagent to some compounds causes the chromium reagent to change to a blue-green color (Figure 6.37a). This is considered a "positive" test result, and in this case indicates the presence of a functional group that can be oxidized (alcohol or aldehyde). A negative test result is retention of the original color of the reagent, in this case the orange color
 
https://chem.libretexts.org/Bookshelves/Organic_Chemistry/Book%3A_Organic_Chemistry_Lab_Techniques_(Nichols)/06%3A_Miscellaneous_Techniques/6.04%3A_Chemical_Tests/6.4A%3A_Overview_of_Chemical_Tests
 

Effective Management Information System:
Essential characteristics of an effective management information system are 1. MIS is management-oriented 2. MIS is developed under the direction of management 3. MIS is an integrated system 4. common data flow 5. MIS is based upon the future needs of the business 6. MIS is composed of sub-systems 7. MIS requires flexibility 8. distributed data processing and 9. MIS is mostly computerized.
Management Information System is established in an organization to provide relevant information to the managers to operate effectively and efficiently.
1. MIS is management-oriented:
The design of MIS starts with an appraisal of the information needs of the management. The system is usually designed from top to bottom. However, this does not mean that MIS fulfills the information needs of top management only.
 
It only implies that the information needs of the top management will serve as a basis for the assessment of the information needs of lower-level managers. In every case, the system should be designed to cater to the information needs of all levels of management.
2. MIS is developed under the direction of management:
Because of the management orientation of MIS, it is imperative that the management of an organization actively directs the development and establishment of the MIS in an organization.
It is rare to find an MIS where the manager himself, or a high-level representative of his department, is not spending a good deal of time in the system design.
 

ADHD is one of the most commonly diagnosed conditions of children (Centers for DiseaseControl 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 listthree types of ADHD and the accompanying characteristics
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common myths
for Advanced
AI:A captivating conversation is taking place about the future of artificial intelligence and what it will/should mean for humanity. There are fascinating controversies where the world’s leading experts disagree, such as AI’s future impact on the job market; if/when human-level AI will be developed; whether this will lead to an intelligence explosion; and whether this is something we should welcome or fear. But there are also many examples of boring pseudo-controversies caused by people misunderstanding and talking past each other. 

TIMELINE MYTHS



The first myth regards the timeline: how long will it take until machines greatly supersede human-level intelligence? A common misconception is that we know the answer with great certainty.
One popular myth is that we know we’ll get superhuman AI this century. In fact, history is full of technological over-hyping. Where are those fusion power plants and flying cars we were promised we’d have by now? AI has also been repeatedly over-hyped in the past, even by some of the founders of the field. For example, John McCarthy (who coined the term “artificial intelligence”), Marvin Minsky, Nathaniel Rochester, and Claude Shannon wrote this overly optimistic forecast about what could be accomplished during two months with stone-age computers: “We propose that a 2 month, 10 man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College […] An attempt will be made to find how to make machines use language, form abstractions, and concepts, solve kinds of problems now reserved for humans, and improve themselves. We think that a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for a summer.”

CONTROVERSY MYTHS



Another common misconception is that the only people harboring concerns about AI and advocating AI safety research are Luddites who don’t know much about AI. When Stuart Russell, author of the standard AI textbook, mentioned this during his Puerto Rico talk, the audience laughed loudly. A related misconception is that supporting AI safety research is hugely controversial. In fact, to support a modest investment in AI safety research, people don’t need to be convinced that risks are high, merely non-negligible — just as a modest investment in home insurance is justified by a non-negligible probability of the home burning down.

An AI that can produce photorealistic videos of sign language interpreters from speech could improve accessibility by removing the need for humans.
Ben Saunders at the University of Surrey, UK, and his colleagues used a neural network that converts spoken language into sign language. The system, called SignGAN, then maps these signs on to a 3D model of the human skeleton.
The team also trained the AI on videos of real sign language interpreters, teaching it how to create a photorealistic video of anyone signing based off an image of …
Read more: https://www.newscientist.com/article/2261113-ai-can-turn-spoken-language-into-photorealistic-sign-language-videos/#ixzz6g1KMybts

Natural language processing (NLP) refers to the branch of computer science—and more specifically, the branch of artificial intelligence or AI—concerned with giving computers the ability to understand text and spoken words in much the same way human beings can.
NLP combines computational linguistics—rule-based modeling of human language—with statistical, machine learning, and deep learning models. Together, these technologies enable computers to process human language in the form of text or voice data and to ‘understand’ its full meaning, complete with the speaker or writer’s intent and sentiment. https://byjus.com/biology/flower/ 
NLP drives computer programs that translate text from one language to another, respond to spoken commands, and summarize large volumes of text rapidly—even in real time. There’s a good chance you’ve interacted with NLP in the form of voice-operated GPS systems, digital assistants, speech-to-text dictation software, customer service chatbots, and other consumer conveniences. But NLP also plays a growing role in enterprise solutions that help streamline business operations, increase employee productivity, and simplify mission-critical business processehttps://byjus.com/biology/flower/ 

Fruit and vegetables
Eating at least 400 g, or five portions, of fruit and vegetables per day reduces the risk of NCDs (2) and helps to ensure an adequate daily intake of dietary fibre.
Fruit and vegetable intake can be improved by:

always including vegetables in meals;
eating fresh fruit and raw vegetables as snacks;
eating fresh fruit and vegetables that are in season; and
eating a variety of fruit and vegetables.

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What is SDLC?
SDLC is a process followed for a software project, within a software organization. It consists of a detailed plan describing how to develop, maintain, replace and alter or enhance specific software. The life cycle defines a methodology for improving the quality of software and the overall development process.
The following figure is a graphical representation of the various stages of a typical SDLC.

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.Video link:Embedded video link:Link: https://projects.invisionapp.com/d/main#/console/20294675/458743820/preview