netrashetty
Netra Shetty
Arryx, Inc. develops tools and technology for manipulation and measurement on the micro and nano lengthscales. Arryx's technology and products center around optical trapping. They specialize in holographic optical trapping, a technique for creating and moving many optical traps at once. Their technology is commercialized in the form of a flagship research tool, the BioRyx 200 optical trapping system. Arryx has investigated the application of the technique to an array of problems in different fields including telecommunications, agriculture, healthcare, basic research, and forensics.
Arryx was founded in the fall of 2000, based on technology invented at the University of Chicago by Professor David Grier and his student Eric Dufresne a couple years earlier. Their BioRyx 200 system was released in early 2002 and won an R&D 100 Award later that year. An IR version of the system was released in 2004 for broader application to biological systems, with support of additional imaging methods including fluorescent microscopy.
Data Analysis Procedures
In global integration context, there are some instances that the used were classified on the basis of intervals of time constitute vital information in the control of business activity, since this the most effective method of showing the changes that are taking place in the business, an industry or in total economic activity. Closely related to the problem of measuring changes in business activity is the making of forecast of future activity. The management of operation requires a continual making of decisions regarding the future and the basis for such forecasts is the record of the past performance.
Data on debt, sales, income, assets, economic indicators or even firm valuation are important in determining the possible future of a certain business. The said data are of interests chiefly in order that the figures for one period maybe compared with similar figures for other figures.
When observations of this kind are arranged in a time sequence and separated by (or represent) more or less regular intervals of time (months, years, decades, etc.), the progression of values is known as a time series. The concept of trend in economic time series rests in large part upon the secular growth of population, capital and resources.
In addition, it would be very helpful in the application of business cycles to business forecasting if we knew, more completely than we do the causes of the different characteristic lengths of the different industry cycles. The vast amount of study that has been devoted to the theory and behaviour of business cycles over the past 30 years has been directed mainly to the discovery of the causes of periodic behaviour of the general business cycle. These studies have been designed to show why there is a periodic movement of some regularity instead of long, slow, and random periods of increase and decrease, in other words, why a fairly regular cyclical fluctuation of business activity is superimposed on the long-term growth or decline in the level of business or why these periodic increases and decreases in activity appear to be self-generating and cumulative. Booms seem to feed on themselves and then destroy themselves.
Apparently, all these changes--trend, seasonal variation, and cycle--can be explained as fluctuations of the rate of spending. The causes of business change are as numerous and as varied as the causes of the variations of the rate of spending. Broadly considered, business cycles are caused, just as other changes in business activity are caused, by changes in the effective demand for goods and services of various kinds, by the three groups of spenders--consumers, business firms, and government bodies. At times, effective demand is so large and so persistent that capacity to business fluctuations touches the periodicity or regularity of recurring movements. For these different periodic characteristics of different industries we have no adequate and satisfactory explanation, and it is these characteristics of different periodic industry cycles which possess the most interest for the business forecaster. It is necessary, as in so many other fields of experimental, empirical knowledge, for the forecaster to use the behaviour of cycles to predict the future whether or not he fully understands the causes of the behaviour we uses. From these details of the behaviour of data, this study will consider collection of information from the staff of mobile phone service companies.
To gather pertinent data, this study will be using survey questionnaires. Particularly, the study arranges to distribute the questionnaires to the mobile phone service companies. In addition, the researcher will also consider the previous studies and contrast it to its existing data in order to give conclusions and proficient recommendations. In accordance to this, the use of IPO model will be considered to give study direction. A process is versioned as a sequence of boxes (processing elements) linked by inputs and outputs. Information or material objects flow in the course of a sequence of activities based on a set of rules or pronouncement points (Harris & Taylor, 1997). Harris & Taylor, (1997) pointed out that flow charts and process diagrams are often used to signify the process. What goes in is the input; what causes the change is the process; what comes out is the output. (Armstrong, 2001) Figure 1.1 illustrates the basic IPO model:
Arryx was founded in the fall of 2000, based on technology invented at the University of Chicago by Professor David Grier and his student Eric Dufresne a couple years earlier. Their BioRyx 200 system was released in early 2002 and won an R&D 100 Award later that year. An IR version of the system was released in 2004 for broader application to biological systems, with support of additional imaging methods including fluorescent microscopy.
Data Analysis Procedures
In global integration context, there are some instances that the used were classified on the basis of intervals of time constitute vital information in the control of business activity, since this the most effective method of showing the changes that are taking place in the business, an industry or in total economic activity. Closely related to the problem of measuring changes in business activity is the making of forecast of future activity. The management of operation requires a continual making of decisions regarding the future and the basis for such forecasts is the record of the past performance.
Data on debt, sales, income, assets, economic indicators or even firm valuation are important in determining the possible future of a certain business. The said data are of interests chiefly in order that the figures for one period maybe compared with similar figures for other figures.
When observations of this kind are arranged in a time sequence and separated by (or represent) more or less regular intervals of time (months, years, decades, etc.), the progression of values is known as a time series. The concept of trend in economic time series rests in large part upon the secular growth of population, capital and resources.
In addition, it would be very helpful in the application of business cycles to business forecasting if we knew, more completely than we do the causes of the different characteristic lengths of the different industry cycles. The vast amount of study that has been devoted to the theory and behaviour of business cycles over the past 30 years has been directed mainly to the discovery of the causes of periodic behaviour of the general business cycle. These studies have been designed to show why there is a periodic movement of some regularity instead of long, slow, and random periods of increase and decrease, in other words, why a fairly regular cyclical fluctuation of business activity is superimposed on the long-term growth or decline in the level of business or why these periodic increases and decreases in activity appear to be self-generating and cumulative. Booms seem to feed on themselves and then destroy themselves.
Apparently, all these changes--trend, seasonal variation, and cycle--can be explained as fluctuations of the rate of spending. The causes of business change are as numerous and as varied as the causes of the variations of the rate of spending. Broadly considered, business cycles are caused, just as other changes in business activity are caused, by changes in the effective demand for goods and services of various kinds, by the three groups of spenders--consumers, business firms, and government bodies. At times, effective demand is so large and so persistent that capacity to business fluctuations touches the periodicity or regularity of recurring movements. For these different periodic characteristics of different industries we have no adequate and satisfactory explanation, and it is these characteristics of different periodic industry cycles which possess the most interest for the business forecaster. It is necessary, as in so many other fields of experimental, empirical knowledge, for the forecaster to use the behaviour of cycles to predict the future whether or not he fully understands the causes of the behaviour we uses. From these details of the behaviour of data, this study will consider collection of information from the staff of mobile phone service companies.
To gather pertinent data, this study will be using survey questionnaires. Particularly, the study arranges to distribute the questionnaires to the mobile phone service companies. In addition, the researcher will also consider the previous studies and contrast it to its existing data in order to give conclusions and proficient recommendations. In accordance to this, the use of IPO model will be considered to give study direction. A process is versioned as a sequence of boxes (processing elements) linked by inputs and outputs. Information or material objects flow in the course of a sequence of activities based on a set of rules or pronouncement points (Harris & Taylor, 1997). Harris & Taylor, (1997) pointed out that flow charts and process diagrams are often used to signify the process. What goes in is the input; what causes the change is the process; what comes out is the output. (Armstrong, 2001) Figure 1.1 illustrates the basic IPO model: