We found 15 results that contain "mathematical analysis"

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
 
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Computer science focuses on the development and testing of software and software systems. It involves working with mathematical models, data analysis and security, algorithms, and computational theory. Computer scientists define the computational principles that are the basis of all software.

The basic idea of intelligence:An explosion is that once machines reach a certain level of intelligence, they’ll be able to work on AI just like we do and improve their own capabilities — redesign their own hardware and so on — and their intelligence will zoom off the charts. There’s an area emerging called “cyber-physical systems” about systems that couple computers to the real world. With a cyber-physical system, you’ve got a bunch of bits representing an air traffic control program, and then you’ve got some real airplanes, and what you care about is that no airplanes collide. You’re trying to prove a theorem about the combination of the bits and the physical world. What you would do is write a very conservative mathematical description of the physical world — airplanes can accelerate within such-and-such envelope — and your theorems would still be true in the real world as long as the real world is somewhere inside the envelope of behaviors.
Yet you’ve pointed out that it might not be mathematically possible to formally verify AI systems.
There’s a general problem of “undecidability” in a lot of questions you can ask about computer programs. Alan Turing showed that no computer program can decide whether any other possible program will eventually terminate and output an answer or get stuck in an infinite loop. So if you start out with one program, but it could rewrite itself to be any other program, then you have a problem, because you can’t prove that all possible other programs would satisfy some property.

Summary
Deep learning is the new state-of-the-art for artificial intelligence. Deep learning architecture is composed of an input layer, hidden layers, and an output layer. The word deep means there are more than two fully connected layers.
There is a vast amount of neural network, where each architecture is designed to perform a given task. For instance, CNN works very well with pictures, RNN provides impressive results with time series and text analysis.

Strategic Management is all about identification and description of the strategies that managers can carry so as to achieve better performance and a competitive advantage for their organization. An organization is said to have competitive advantage if its profitability is higher than the average profitability for all companies in its industry.
Strategic management can also be defined as a bundle of decisions and acts which a manager undertakes and which decides the result of the firm’s performance. The manager must have a thorough knowledge and analysis of the general and competitive organizational environment so as to take right decisions. They should conduct a SWOT Analysis (Strengths, Weaknesses, Opportunities, and Threats), i.e., they should make best possible utilization of strengths, minimize the organizational weaknesses, make use of arising opportunities from the business environment and shouldn’t ignore the threats.
Strategic Management gives a broader perspective to the employees of an organization and they can better understand how their job fits into the entire organizational plan and how it is co-related to other organizational members. It is nothing but the art of managing
employees in a manner which maximizes the ability of achieving business objectives. The employees become more trustworthy, more committed and more satisfied as they can co-relate themselves very well with each organizational task. They can understand the reaction of environmental changes on the organization and the probable response of the organization with the help of strategic management. Thus the employees can judge the impact of such changes on their own job and can effectively face the changes. The managers and employees must do appropriate things in appropriate manner. They need to be both effective as well as efficient.

Business Intelligence and Data Science are two of the most recurring terms in the digital era. While both of them involve the use of data, they are totally different from one another. Data Science is the bigger pool containing greater information, BI can be thought of as a part of the bigger picture. 
What is Business Intelligence?
Business Intelligence is a process of collecting, integrating, analyzing, and presenting the data. With Business Intelligence, executives and managers can have a better understanding of decision-making. This process is carried out through software services and tools.
Using Business Intelligence, organizations are able to several strategic and operational business decisions. Furthermore, BI tools are used for the analysis and creation of reports. They are also used for producing graphs, dashboards, summaries, and charts to help the business executives to make better decisions.

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 

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.

 

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