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1. Brief Bio-data of Dr. Arvind Shaligram CEO of SPPU Research Park Foundation, a section 8 company owned by Savitribai Phule Pune University, Coordinator of MHRD funded Design Innovation Centre and Professor Emeritus at the Electronic Science Department, SPPU. I was Professor and Head, Department of Electronic Science at Savitribai Phule Pune University prior to superannuation. In-charge Director of Educational Multimedia Research Centre (EMMRC), Director Examination and Evaluation, Dean of Science and Technology faculty and Officiating Registrar of the Savitribai Phule Pune University. I guided 45 students for Ph.D. and 20 students for M.Phil My fields of research interest are Embedded systems and VLSI design, Nanoelectronics, Optolectronic sensors, LED Lighting performance and reliability and Wireless Sensor Networks Published 31 books (5 international) and More than 200 papers in Reputed Journals He worked as Industrial Consultant to several Industries in the fields of embedded systems, instrumentation and automation, optics and Information Technology and Supervised RDSO standard testing of LED based Signal Specific professional assignments: Chairman, IEEE Electron Devices Society, India Council Chapter 2016-18 International consultant on “Digital IC design” for Ministry of S&T of Sri Lanka Govt. Founder Chairman of Society for Promotion of Excellence in Electronics Discipline (SPEED)

2. Getting Geared up – Literature Review, Analysis and Planning phase

3. Agenda Identifying the appropriate research problem Survey of key resources in the field What are some of the strengths and weaknesses of existing research? Planning of the research and organizing resources needed.

4. Before Conducting Research

5. What is research? Research is the process of finding solutions to a problem after a thorough study and analysis of the situational factors Research is an endeavor to discover answers to intellectual and practical problems through the application of scientific method. Research is a systematized effort to gain new knowledge. Research is the systematic process of collecting and analyzing information in order to increase our understanding of the phenomenon about which we are concerned or interested.

6. Academic Research Academic  research  is generally  based  on an empirical approach to enhancing knowledge. Tacit knowledge ( as with practical skill or expertise ) context-specific personal hard to formalize and communicate Explicit knowledge ( as with theoretical understanding of a subject ) easily collected, organized and transferred can be codified objective Theoretical

7. Applied Research A research that takes place in an everyday context to solve specific problems of individuals, organizations, and/or industries is called “applied research.” It is typically a follow-up research design that further investigates the findings of pure or basic research in order to validate these findings and apply them to create innovative solutions.      Applied research is sometimes considered to be a non-systematic inquiry because  of its direct approach in  seeking a solution to a problem.

8. QUALITIES OF A GOOD RESEARCH Systematic Logical Empirical Replicable Creative Use of multiple methods

9. Scientific Inquiry - The Process Problem Hypothesis Experiment Materials - Variables/Controls Procedure - Observations Results Charts/Graphs Conclusion Limitations

10. Four Phases of Research Process Explore possible topics before focusing on a motivating research question. Initial phase of literature review and a plan for data gathering from a variety of sources. Conduct research to gather, organize and analyze dats and draw conclusions. Share what has been learned through writing, presenting and multimedia. Explore Initial Conduct Share

11. Don’t just follow somebody blindly follow a true scientific inquiry process

12. There is a charm, challenge, excitement and satisfaction in conducting research. Research is an endeavor to discover answers to intellectual and practical problems through the application of scientific method

13. Rationale of conducting research Research is the cornerstone of building a case, or a story. Research is conducted : To understand a phenomenon, situation, or behavior and to expand knowledge of a chosen field To develop critical thinking and analytical skills through hands-on learning To find answer for “how”, “what”, “which”, “when” and “why” about a phenomenon, behavior, or situation. To test existing theories and to develop new theories on the basis of existing ones. To develop one-on-one connections with distinguished faculty in their field.

14. Type Description Examples Invention Totally new product, service, or process Wright brothers—airplane Thomas Edison—light bulb Alexander Graham Bell—telephone Extension New use or different application of an already existing product, service, or process Ray Kroc—McDonald’s Mark Zuckerberg—Facebook Duplication Creative replication of an existing concept Wal-Mart—department stores Gateway—personal computers Pizza Hut—pizza parlor Synthesis Combination of existing concepts and factors into a new formulation or use Fred Smith—Fed Ex Howard Schultz—Starbucks Types of Innovations

15. ‘Research’ may be defined as the quest for any new knowledge initiated by curiosity and processed through a logical study of the given situation that is likely to add to the knowledge pool of the subject concerned available at that point of time. Technically speaking, research may be anything, be it a home-maker trying to figure out the reason behind an over-flowing tap or a researcher trying to figure out the reason behind an over-flowing Ganges as in the case of the Uttarakhand disaster.

16. Research in ancient times In ancient Egypt and Mesopotamia, anything that happened was explained as the work of hidden hands. Different phenomena were explained as the work of different Gods. Diseases were assumed as caused by demons. Some of the first attempts to replace the doctrine of the hidden hands by practical modes of explanation were made by Greek philosophers around the sixth centaury BC. They attempted to explain natural phenomena in terms of the physical observations. They endeavored to introduce reason in to the word around them. Their approach became a mixture of scientific, religious and philosophical reasoning. The Greek genius was deductive rather than inductive and was therefore at home in mathematics and geometryThe most outstanding among the Greek scientists was Archimedes (287-212 BC) who wrote the book on floating bodies.

17. Traditional Indian Research Traditional Indian research or studies have always focused on the highest principles of Dharma, Artha , Kaama and Moksha. Researchers must satisfy the methodology prescribed for each shastra , which can be understood as the anubhanda-chatushtayam (4 distinct prerequisites), namely Vishaya (Subject /Topic), Adhikaari (required candidature of the researcher), Sambandha (relationship between the candidate and the subject) and Prayojanam (purpose of the study /research). The research should also be not just towards gaining some knowledge, rather it should be useful for future research.

18. Research The word ‘research’ echoes the meaning of old Sanskrit word ‘ gaveshana ’. ‘ gaveshana ’ essentially means ‘search for cow’ It is already known to us that cows were held as prized possessions in the Vedic age. That the Sanskrit equivalent of ‘research’ was formed during the Rig Vedic age when the Aryans were advancing ‘within and beyond the eastern portion of the vanished Indus culture’s range’ Any research ( gaveshana ) may be defined as the quest for original, irrefutable and meaningful knowledge ( prama ) and establishing theories ( siddhantas ) facilitated by the evidences ( pramanas ).

19. When should a research need not be initiated A research need not be initiated if: There exists no misery or problem in this world The solution of the problem is not desired or necessary Though the solution is desired, it is seemingly unattainable Though the solution is attainable, if the adequate means of attainment are unavailable There are alternative easier means of attainment already elsewhere available.

20. Stages of Ancient Research Research Stage Research Activity Details of Research ACtivity Taram Pratyaksha Identification of area of concern, the research problem, Sabda Approval of the supervisor and determination of appropriate methodology and possible research design Sutaram Anumana Initial inference through review of existing work Arthapatti Statement of hypothesis or research question Tartaram Upumana Data Collection, Comparison, Analysis and interpretation of data Ramyakah Suhrpraoti Presentation of results in front of well-wishers Sadamuditam Danam Submission of the work with a pure mind for contributing to the welfare of the society and possible replication by others

21. Steps of the Modern research process Step 1: Identify the Problem. Step 2: Conducting Preliminary Research/Review the Literature. Step 3: Clarify the Problem. ... Step 4: Clearly Define Terms and Concepts. ... Research Step 5: Define the Experimentation Details. ... Design Step 6: Develop the Instrumentation Plan. ... Research Design: Create a blueprint for the study, including the approach to solve the problem, the methodology, and the type. It can be qualitative or quantitative, experimental or non-experimental, and descriptive or analytical .  Step 7: Collect Data - through various methods, such as surveys, interviews, experiments, or observations Step 8: Analyze the Data using appropriate statistical or qualitative techniques to enabling drawing meaningful conclusions Step 9: Draw Conclusions - Hypothesis Testing, Generalizations and Interpretation, Conclusion and Recommendations Step 10: Communicate

22. Research Methodology

23. Steps of the Scientific Method 1. Ask a Question The scientific method starts when you ask a question about something that you observe: How, What, When, Who, Which, Why, or Where? For a science fair project some teachers require that the question be something you can measure, preferably with a number. 2. Do Background Research Rather than starting from scratch in putting together a plan for answering your question, you want to be a savvy scientist using library and Internet research to help you find the best way to do things and ensure that you don't repeat mistakes from the past. 3. Construct a Hypothesis A hypothesis is an educated guess about how things work. It is an attempt to answer your question with an explanation that can be tested. A good hypothesis allows you to then make a prediction: "If _____ [I do this]  _____, then _____ [this] _____ will happen."

24. What is a Hypothesis? A hypothesis is a tentative, testable answer to a scientific question. Once a scientist has a scientific question she is interested in, the scientist reads up to find out what is already known on the topic. Then she uses that information to form a tentative answer to her scientific question. Sometimes people refer to the tentative answer as "an educated guess." Keep in mind, though, that the hypothesis also has to be testable since the next step is to do an experiment to determine whether or not the hypothesis is right! A hypothesis leads to one or more predictions that can be tested by experimenting. Predictions  often  take the shape of "If ____then ____" statements, but do not have to. Predictions should include both an independent variable (the factor you change in an experiment) and a dependent variable (the factor you observe or measure in an experiment). A single hypothesis can lead to multiple predictions, but generally, one or two predictions is enough to tackle for a science fair project.

25. Examples of Hypotheses and Predictions Question Hypothesis Prediction How does the size of a dog affect how much food it eats? Larger animals of the same species expend more energy than smaller animals of the same type. To get the energy their bodies need, the larger animals eat more food. If I let a 70-pound dog and a 30-pound dog eat as much food as they want, then the 70-pound dog will eat more than the 30-pound dog. Does fertilizer make a plant grow bigger? Plants need many types of nutrients to grow. Fertilizer adds those nutrients to the soil, thus allowing plants to grow more. If I add fertilizer to the soil of some tomato seedlings, but not others, then the seedlings that got fertilizer will grow taller and have more leaves than the non-fertilized ones. Does an electric motor turn faster if you increase the current? Electric motors work because they have electromagnets inside them, which push/pull on permanent magnets and make the motor spin. As more current flows through the motor's electromagnet, the strength of the magnetic field increases, thus turning the motor faster. If I increase the current supplied to an electric motor, then the RPMs (revolutions per minute) of the motor will increase. Is a classroom noisier when the teacher leaves the room? Teachers have rules about when to talk in the classroom. If they leave the classroom, the students feel free to break the rules and talk more, making the room nosier. If I measure the noise level in a classroom when a teacher is in it and when she leaves the room, then I will see that the noise level is higher when my teacher is not in my classroom.

26. Scientific way of conclusion The point of a science project  is not  to prove your hypothesis right. The point is to understand more about how the natural world works. Or, as it is sometimes put, to find out the scientific truth. For scientists, disproving a hypothesis still means they gained important information, and they can use that information to make their  next  hypothesis even better. It is worth noting, scientists never talk about their hypothesis being "right" or "wrong." Instead, they say that their data "supports" or "does not support" their hypothesis. This goes back to the point that nature is complex—so complex that it takes more than a single experiment to figure it all out because a single experiment could give you misleading data.

27. Hypothesis Checklist For a Good Hypothesis, You Should Answer "Yes" to Every Question Is the hypothesis based on information from reference materials about the topic? Yes / No Can at least one clear prediction be made from the hypothesis? Yes / No Are predictions resulting from the hypothesis testable in an experiment? Yes / No Does the prediction have both an independent variable (something you change) and a dependent variable (something you observe or measure)? Yes / No

28. 4. Test Your Hypothesis by Doing an Experiment Your experiment tests whether your prediction is accurate and thus your hypothesis is supported or not. It is important for your experiment to be a fair test. You conduct a fair test by making sure that you change only one factor at a time while keeping all other conditions the same. You should also repeat your experiments several times to make sure that the first results weren't just an accident. 5. Analyze Your Data and Draw a Conclusion Once your experiment is complete, you collect your measurements and analyze them to see if they support your hypothesis or not. Scientists often find that their predictions were not accurate and their hypothesis was not supported, and in such cases they will communicate the results of their experiment and then go back and construct a new hypothesis and prediction based on the information they learned during their experiment. This starts much of the process of the scientific method over again. Even if they find that their hypothesis was supported, they may want to test it again in a new way.

29. 6. Communicate Your Results To complete your science fair project you will communicate your results to others in a final report and/or a display board. Professional scientists do almost exactly the same thing by publishing their final report in a scientific journal or by presenting their results on a poster or during a talk at a scientific meeting. In a science fair, judges are interested in your findings regardless of whether or not they support your original hypothesis.

30. What is Experimental design - the Scientific Method? Experimental Design is a logical, consistent process for stating and solving problems in the natural world.

31. What is Experimental Research? Experimental research is a study that strictly adheres to a scientific research design. It includes a hypothesis, a variable that can be manipulated by the researcher, and variables that can be measured, calculated and compared. Most importantly, experimental research is completed in a controlled environment. The researcher collects data and results will either support or reject the hypothesis. This method of research is referred to a hypothesis testing or a deductive research method

32. Types of Logic Inductive Reasoning Derive generalizations based on specific observations Deductive Reasoning - Specific predictions follow from general premise

33. Types of Logic

34. Study Examples Modeling and Simulation (a physics/math study), Analytical study (chemical study), degradation, decomposition and damage to objects (a physics/engineering study), can be done in a controlled environment and be measured. “The Effect of _____ on_____” Studies—All experimental studies look to determine how one thing affects another. Product Effectiveness—If a specific aspect (active ingredients, size of crucial components etc …) of several products can be determined to be in different quality or quantity, this makes for a great experimental project.

35. Exploratory Research Exploratory research is a study that seeks to answer a question or address a phenomenon. The nature of the entity being studied does not allow a variable to be manipulated by the researcher, it cannot be completed in a controlled environment, or most likely, the researcher can’t determine all the influences on the entity. Therefore a more exploratory look at the topic is more beneficial. This type of research seeks to identify general principles to explain data and observations, and is also known as the inductive method

36. Ideas are originated for Solving Problems People have ideas all the time. New ideas can lead to new products and services. They can lead to a better way of doing something. Solving “Points of Pain”: To Notice Inefficiency, Inconveniences, & Other Forms of “Points of Pain” & Use these to Build New Business Opportunities Any Problems are Big Opportunities. No One Pays You to Solve a Non-exist Problem Ideas generate value to the economy

37. Continuum of Innovation Imitative Incremental Evolutionary Radical Revolutionary The secret to innovation is uncovering an unmet consumer need and the filling it in an innovative, creative way.

38. TYPES OF RESEARCH Descriptive Research is a fact finding investigation which is aimed at describing the characteristics of individual, situation or a group (or) describing the state of affairs as it exists at present. Analytical Research is primarily concerned with testing hypothesis and specifying and interpreting relationships, by analyzing the facts or information already available. Fundamental Research which is also known as basic or pure research is undertaken for the sake of knowledge without any intention to apply it in practice. It is undertaken out of intellectual curiosity and is not necessarily problem-oriented. Applied Research or Action Research is carried out to find solution to a real life problem requiring an action or policy decision.

39. Quantitative Research is employed for measuring the quantity or amount of a particular phenomena by the use of statistical analysis. Qualitative Research is a non-quantitative type of analysis which is aimed at finding out the quality of a particular phenomenon. TYPES OF RESEARCH Conceptual Research is generally used by philosophers and thinkers to develop new concepts or to reinterpret existing ones. Empirical Research is a data based research which depends on experience or observation alone. It is aimed at coming up with conclusions without due regard for system and theory. Experimental Research – It is designed to assess the effect of one particular variable on a phenomenon by keeping the other variables constant or controlled.

40. Some other types of research.. One-time Research – Research confined to a single time period. Longitudinal Research – Research carried on over several time periods. Diagnostic Research – The research that aims at identifying the causes of a problem, frequency with which it occurs and the possible solutions for it. Exploratory Research – Preliminary study of an unfamiliar problem, about which the researcher has little or no knowledge. It is aimed to gain familiarity with the problem, to generate new ideas or to make a precise formulation of the problem. Hence it is also known as formulative research. Historical Research – Study of past records and information sources, with a view to find the origin and development of a phenomenon and to discover the trends in the past, in order to understand the present and to anticipate the future.

41. Resources of Literature Journals: journals are specialized publications focused on an often narrow topic or field. Databases: provide access to sources such as academic and scientific journals, newspapers, and magazines. Books and monographs : cover topics in more depth than can be done in a journal article. Sometimes , multi-author contributions Various media: depending on the library, you may have access to documentaries, videos, audio recordings, and more.

42. REVIEW OF LITERATURE To turn a research idea into an interesting research question. To verify if the research question has already been answered. To evaluate the interestingness of a research question. To provide directions for conducting your own study. To position your efforts in the research space. Reviewing the research literature means finding, reading, and summarizing the published research relevant to your question.

43. Thinking about your literature review What is your topic? Who are the key people in your field? What are the key resources? What are the key ideas in your field? What methodologies have been used? What are some of the strengths and weaknesses of existing research? What will your contribution be? How will it be different? (NB: If you can’t answer some of these question, make a note of this. It will come in handy later!)

44. YOU DON’T NEED TO READ EVERYTHING – you can’t! You don’t need to read every text You don’t need to read every word

45. Reading and Researching Collect and read material. Summarize sources. Who is the author? What is the author's main purpose? What is the author’s theoretical perspective? Research methodology? Who is the intended audience? What is the principal point, conclusion, thesis, contention, or question? How is the author’s position supported? How does this study relate to other studies of the problem or topic? What does this study add to your project? Select only relevant books and articles.

46. Analyzing the Strengths and weaknesses of existing research A literature review is never just a list of studies. The literature review needs to contain a balance of summary and analysis. Comparison and critique allow you to see the strengths and weaknesses existing research by asking the following questions: How do the different studies relate to one another? What is new, different, or controversial about the various studies? What views need to be further tested? What evidence is lacking, inconclusive, contradicting, or too limited? What research designs or methods seem unsatisfactory?

47. Perform a SWOT Analysis

48. Research Gap A  research gap  is defined as a topic or area for which missing or insufficient information limits the ability to reach a conclusion for a question. A  research   need is defined as a  gap  that limits the ability of decision-makers (policy-makers) from making decisions. It is necessary to find out those problems in your research field which have not been addressed before.

49. Avoid Plagiarism Plagiarism is taking someone else’s words or ideas and taking credit for them as your own. To avoid plagiarism we put information in our own words AND we give credit to our sources.

50. Steps in Doing research Choose a topic that interests you. Find valid and reliable sources for information. Take notes from your sources. Create a report, project, or presentation from your notes.

51. Initiation of Research Once a research idea is formulated, a comprehensive literature review should be performed in order to develop a thorough understanding of existing evidence related to the topic. Specifically, the aim and objectives of the project should be new, relevant, concise, and feasible. Limiting the number of objectives allows the researchers to devote adequate time to effective data collection and analysis. The role of a mentor (research supervisor) is vital in defining each of the steps of the research process.

52. Where do I begin? In the beginning the questions are focused on helping you determine a topic and types of information and sources Later in the research process, the questions are focused on expanding and supporting your ideas and claims as well as helping you stay focused What is my timeline for the project? What do I want to know or learn about? Determine scope or the limits of your research. What do I already know about this topic? What biases might I have about this topic? How might I combat these biases?

53. The Research Process Research is not a linear process. Research requires a back and forth between sources, your ideas and analysis, and the rhetorical situation for your research. The research process is a bit like an eye exam. The doctor makes a best guess for the most appropriate lens strength, and then adjusts the lenses from there. Sometimes first option is most appropriate; sometimes it takes a few tries with several different options before finding the best one for you and your situation.

54. Conducting Research Conducting research is a challenging and long-drawn process. You have to plan each step meticulously to ensure that you don't leave out any important details. While there is no one 'best' way to design  research and planning a  project  involves four general steps: orienting yourself to knowledge-creation; defining your  research  question, reviewing previous  research  on your question; and defining the observation parameters and quantify them with use of an instrument then selecting and analysing relevant data to formulate your own answers.

55. Research Plan Identifying the resources required to complete the research, Identifying the investigator team, Completing a thorough review of the existing evidence regarding the research idea, Generating the research question and specific aim, Developing the methods, Developing the data management plan, Confused and worried researcher

56. Research Plan

57. Research timeline and progress tracking. The development of a timeline to help guide the execution of the research project plan is critical in the effective coordination of the necessary steps in the research process. It basically shows the chronological order of events that you plan to do in your project. It is supposed to give the reader a broad overview of the project at a glance. Depending on the length of the project, these might be days, weeks, months, or even years.

58. Research Planning

59. Resource allocation As each step of the research plan is formulated, resources needed to accomplish these specific steps must be determined and allocated. Resources include the necessary materials and personnel to conduct rigorous research. Required resources can be somewhat controlled during establishment of the research question and specific aim

60. Data management and analysis plans The data management plan should address the selections of database platforms, data elements to be collected, data entry, processes for ensuring data quality, and data security. The data elements that are collected should be limited to those necessary to answer the proposed research question.

61. Data collection Data collection is a research component in all study fields, including physical and social sciences, humanities, and business. Data collection is a systematic process of gathering observations or measurements. Whether you are performing research for business, governmental or academic purposes, data collection allows you to gain first-hand knowledge and original insights into your research problem. Data is various kinds of information formatted in a particular way. Therefore, data collection is the process of gathering, measuring, and analyzing accurate data from a variety of relevant sources to find answers to research problems, answer questions, evaluate outcomes, and forecast trends and probabilities.

62. Why to Collect Data? Before data collection begins, three questions must be answered first: What’s the goal or purpose of this research? What kinds of data collection or gathering are being planned? What methods and procedures will be used to collect, store, and process the information? Accurate data collection is necessary to make informed business decisions, ensure quality assurance, and keep research integrity.

63. Data Types Depending on your research questions, you might need to collect quantitative or qualitative data: Quantitative data  is expressed in numbers and graphs and is analyzed through statistical methods. Methods of quantitative data collection and analysis include questionnaires with closed-ended questions, methods of correlation and regression, mean, mode and median and others. Qualitative data  is expressed in words and analyzed through interpretations and categorizations. Qualitative research is closely associated with words, sounds, feeling, emotions, colours and other elements that are non-quantifiable.

64. Data Collection Methods Data collection breaks down into two methods: Primary: Original, first-hand data collected by the researchers. Primary data results are highly accurate provided the researcher collects the information. However, there’s a downside, as first-hand research is potentially time-consuming and expensive. Secondary: Secondary data is second-hand data collected by other parties and already having undergone analysis. Although it’s easier and cheaper to obtain than primary information, secondary information raises concerns regarding accuracy and authenticity.

65. Data collection methods Method When to use How to collect data Experiment To test a causal relationship. Manipulate variables and measure their effects on others. Survey To understand the general characteristics or opinions of a group of people. Distribute a list of questions to a sample online, in person or over-the-phone. Interview/focus group To gain an in-depth understanding of perceptions or opinions on a topic. Verbally ask participants open-ended questions in individual interviews or focus group discussions. Observation To understand something in its natural setting. Measure or survey a sample without trying to affect them. Ethnography To study the culture of a community or organization first-hand. Join and participate in a community and record your observations and reflections. Archival research To understand current or historical events, conditions or practices. Access manuscripts, documents or records from libraries, depositories or the internet. Secondary data collection To analyze data from populations that you can’t access first-hand. Find existing datasets that have already been collected, from known sources.

66. The Observation starts it all… An observation is a visible or provable fact or occurrence VS. An inference is, “ the act of reasoning from factual knowledge or evidence. ” This is your opinion drawn on the observations you have made.

67. Experiments may be classified as Fundamental experiments - to answer clearly defined and specific questions. For example, does the momentum equation provide an adequate theoretical basis for analyzing the performance of a free jet of air? In general these experiments lead to an extensive program of research Experiments less “open-ended” - where still there is need to consider a number of other questions before one can start work in the laboratory; Testing - Laboratory investigations for industrial calibration and instructional purposes for which there are no questions requiring deep study of the underlying theory, which is already well established, but questions of accuracy are of prime importance.

68. It is very cold Today Not so much cold Today In fact it is warmer than yesterday

69. Measure the temperature using a Thermometer It is 30 o C, a normal temperature

70. These Bananas are raw No they are ripe

71. Measure qualitatively from Appearance Measure parameters like softness, Sweetness etc.

72. Cement Hardness Testing

73. Concrete Moisture tester

74. Direct Measurement Measurements utilize a primary or secondary standard. Primary standard is not calibrated by any other standards and are defined by other quantities. For example the primary standard of length is the speed of light. A secondary standard is calibrated based on the primary standard, for length is could be a tape measure, micrometer, etc. Direct measurements are relatively simple and straightforward measurements to make. Unfortunately, we often need greater accuracy Direct comparison is much less common than indirect comparison as engineers because we need much more accuracy and precision in making a measurement

75. Indirect Measurement • Typically utilizes a transducing device to convert the input signal into some analogous form that can be measured and is intelligible. Ex. Measuring strain is not possible with the accuracy required using a micrometer and cannot be measured using human senses, must first convert to an analogous electrical signal The analogous signal can be processed to improve signal to noise ratio or the accuracy of a measurement. The signal processing can be amplification, filtration, integration, or other mathematical operation

76. Example of Indirect Measurement: Measurement of Strain in a Aluminum beam cantilever with a strain gauge

77. Calibration with standard

78. Instrumentation Schemes The tools used in data collection are called Research Instruments and the mechanism of putting the tools together is known as Instrumentation Scheme For example ‘Observation’ is a preplanned research tool. Preparation of observation plan backed up by appropriate literature review, adopting adequate standards for traceability and expert guidance is the instrumentation scheme.

79. Discipline Specific Instrumentation Electronics and Electrical, Instrumentation branches have Devices, Circuits and Systems Computer and IT have algorithms and Software tools Mechanical and Civil systems are based on conventional and novel components, designs, fabrication and assembly techniques and tolerances Metallurgy involves metal processing and foundry techniques Various sophisticated Test and measurement instruments are specific to the disciplines

80. Resolving the Dilemma One of the first questions facing the researcher is whether to create his or her own instrument or to either use an established instrument or modify an already existing one for the purposes of the researcher’s project. The first step in addressing this question is a clear definition of the purpose of the research. One has to take into account the fact that by modifying an existing instrument or creating a new one, the researcher will need to test the instrument for internal validity. This process can take a long time and requires much effort on the part of the researcher. On the other hand, using an established instrument may require special permission (and hiring charges) or even the purchase, install and commission the new one at relatively high cost.

81. Choosing Existing Instrument When choosing an existing instrument, the researcher must also determine if the instrument is reliable. This reliability test should include investigations into internal consistency—whether the answers are consistent even if the instrument is being applied to a different construct. This is a critical step because the instrument’s validity may be affected by any modifications and application in a new study.

82. Methods and instrument development Similar to the development of the research question and specific aim, adequate time should be allocated to the selection of a research design and the development and review of the methods. Mastering of data collection tools, professional computer simulation and analysis tools and available instruments. Depending on the demand of the research question, at times it is necessary to develop new instruments, programs or facilities. Sourcing for the same and leveraging adequate requisite skills is one of the key requirements

83. Cutting-edge Instruments Cutting-edge instruments not only enable new discoveries but help to make the production of knowledge more efficient. Many newly developed instruments are important because they enable us to explore phenomena with more precision and speed. The development of instruments maintains a symbiotic relationship with science as a whole; advanced tools enable scientists to answer increasingly complex questions, and new findings in turn enable the development of more powerful, and sometimes novel, instruments.

84. Instrumentation as pacing factor for Research A large fraction of the differences between 19th century, 20th century, and 21st century science stems directly from the instruments available to explore the world. The scope of research that instrumentation enables has expanded considerably, now encompassing not only the natural (physical and biologic) world but also many facets of human society and behavior. Instrumentation has often been cited as the pacing factor of research; the productivity of researchers is only as great as the tools they have available to observe, measure, and make sense of nature.

85. Instrumentation for interdisciplinary Research Instrumentation facilitates interdisciplinary research. Many of the spectacular scientific, engineering, and medical achievements of the last century followed the same simple paradigm of migration from basic to applied science.

86. Instrumentation for interdisciplinary Research For example, as the study of basic atomic and molecular physics matured, the instruments developed for those activities were adopted by chemists and applied physicists. That in turn enabled applications in biological, clinical, and environmental science, driven both by universities and by innovative companies.

87. Crescograph Sir Jagadish Chandra Bose was a biophysicist, botanist and physicist. He proved that plants have life. He invented the crescograph, a device for measuring the growth of plants.

88. Photosynthetic Bubbler The Photosynthetic Bubbler used and made by JC Bose. By this apparatus the rate of photosynthetic activity of aquatic plants can be automatically recorded. The apparatus consists of a bubbler with mercury valve and an electromagnetic recording arrangement. The bubbler is fitted airtight at the top of the plant vessel. Oxygen evolved as a result of photosynthesis is intermittently released at a certain pressure through the mercury valve. Two platinum wires are so placed near the mercury valve that an electric circuit is completed with the lifting of the valve, and a dot is produced on the moving drum by the electromagnetic writer.

89. A number of modern tools that are now essential for medical diagnostics, such as magnetic resonance imaging scanners, were originally developed by physicists and chemists for the advancement of basic research.

90. Instrumentation for interdisciplinary Research

91. Examples of Advanced Research Instrumentation Facility Field Astronomy Biology Cyber Infra Ge os c i ences Materials Selected Instruments or Facility Telescope, spectrograph, infrared camera Proteomics-protein structure laboratory Supercomputer Ion microprobe, earthquake sensor testing laboratory Electron-beam lithography system, semiconductor production system Magnetic resonance imager, human and animal Human & animal imaging Spectrometry (NMR) Physics Space NMR spectrometer, 800-900 MHz Infrared camera, pulsed electron accelerator MegaSIMS (isotope analysis) Facility-supporting Helium refrigerator for superconducting magnets Equipment

92. In Field Instrumentation Atmospheric scientists, oceanographers, geophysicists, and ecologists are now tackling and solving fundamental problems that require analysis of large numbers of observations that are both time- and space- dependent. Some of the sensors required to make the necessary measurements can be deployed on familiar mobile instrumental platforms, such as oceanographic ships, research aircraft, and earth-orbiting satellites; but many need to be distributed in sensor networks of local, regional, or even global scale. Both physical and wireless networks are used to transmit data to off-site storage facilities.

93. Distributed Sensor Network A good example of a distributed sensor network is the Global Seismographic Network (GSN), which consists of 130 seismic stations distributed on continental landmasses, oceanic islands, and the ocean bottom. GSN recording and nearly real-time distribution of seismic-wave parameters measurements at numerous sites over the globe serve the needs of basic research in geophysics (such as seismic tomography of the earth’s interior structure) and of applied geosciences (such as earthquake and tsunami monitoring and seismic monitoring of nuclear testing).

94. Challenges of Field Instrumentation To detect the smallest signals above the earth’s background “hum,” the self-excitation of the pendulum sensor by Brownian noise must be less than that caused by shaking the instrument’s foundation across a wide frequency band of 10 −4 -100 Hz. Response across the frequency band must be linear up to excitation amplitudes 10 12 times greater than the smallest detectable signals. Such sensor in the field need isolation from drafts, temperature changes, and ambient noise protection against damage by animals and vandalism. Low-power, rugged, and high-capacity data-storage systems for locations where energy must be provided by fuel cells or batteries recharged by solar or wind energy arrangement of alternate telemetry technologies in locations, where Internet service is not available. periodic maintenance by technicians

95. Data Collection Using a Digital Computer Systems Data collection system ( DCS ) is a computer application that facilitates the process of data collection, allowing specific, structured information to be gathered in a systematic fashion, subsequently enabling data analysis to be performed on the information. Typically a DCS displays a form that accepts data input from a user and then validates that input prior to committing the data to persistent storage such as a database. The Data entry can either be manual or automated through experimental arrangements

96. Cybertools Today, computers are vital tools to scientists and engineers. Indispensable for communication and often used in conjunction with many instruments, the computer can also be a scientific instrument itself. Examples of three types of cybertools that are fundamental to several fields of research: software, data collections, and surveys.

97. Genome Sequencing One of the major accomplishments of science in the 20th century was the deciphering of the human genome. The genetic information in DNA is stored as a sequence of bases, and DNA sequencing is the determination of the exact order of the base pairs in a segment of DNA. Two groups, from United States and United Kingdom were awarded the 1980 Nobel prize in chemistry. Development of fluorescence-based detection of the bases led quickly to automated high-throughput DNA sequencers that were soon made available to the research community.

98. Hardware and Software for Genome Sequencing Genome sequencing requires the assembly of millions of fragments into a complete sequence. The confluence of hardware (mechanical sequencing) and software (algorithm) made the human genome sequence possible. It developed a general approach to large-scale sequencing of a wide variety of organisms, and accelerated discovery in basic biology research. Hardware and software were both needed and were synergistic. The parallel development of sequencers and software demonstrates that not only key insights but also incremental improvements can make a qualitative difference in the progress of science.

99. Gen B ank Digital data collections also provide a fundamentally new approach to research. By gathering data generated in studies on related topics, digital data collections themselves become a new source of knowledge. One of the best examples of a large scientific database that is integral to progress in science and engineering is GenBank, the genetic sequence database maintained for the biomedical research community by NIH. GenBank was born at Los Alamos National Laboratory in 1982, well before the beginning of the Human Genome Project. When the Human Genome Project came into being and the number of available sequences exploded, GenBank became an indispensable repository for the data being generated. Today GenBank contains over 49 billion nucleotide bases in over 45 million sequence records, and the amount of data is increasing exponentially with a doubling time of less than two years.

100. Static and Dynamic Characteristics of Instruments

101. DYNAMIC CHARACTERISTICS OF MEASURING INSTRUMENTS When an input is applied to an instrument or a measurement system, the instrument or the system cannot take up immediately its final steady state position. It goes through a transient state before it finally settles to its final steady state position. The transient response in the instruments is on account of the presence of energy storage elements in the system, such as, electrical inductance and capacitance, mass, fluid and thermal capacitances etc. The systems exhibit a characteristic of sluggishness on account of presence of these elements.

102. Dynamic characteristics The dynamic characteristics of any measurement system are: Speed of response and Response time (Time required by instrument or system to settle to its final steady position) Lag (Delay in response of an instrument to a change) Fidelity (Ability to reproduce output in the same form as input) Dynamic error (D ifference between true value of the quantity changing with time and the value indicated by the instrument)

103. What are Variables? A Variable is anything that changes. The variables compared in the problem statement are the INDEPENDENT & DEPENDENT variables…. *Remember, the dependent variable DEPENDS on the independent variable!!!

104. Variables Independent variable WE MANIPULATE, or change , in the experiment. Dependent Variable that changes as a result of the independent variable. It is the variable that is measured and recorded.

105. DATA TABLES 1. TITLE that identifies both the IV and DV 3. Y Axis – Dependent Variable 2. X Axis – Independent Variable 4. Calculations are generally to the right columns - after stated data

106. Data Preprocessing Data cleaning Fill in missing values, smooth noisy data, identify or remove outliers, and resolve inconsistencies Data integration Integration of multiple databases, data cubes, files, or notes Data transformation Normalization (scaling to a specific range) Aggregation Data reduction Obtains reduced representation in volume but produces the same or similar analytical results Data discretization: with particular importance, especially for numerical data Data aggregation, dimensionality reduction, data compression, generalization

107. Errors in data The data which is collected may contain duplicate records, white spaces or errors. The collected raw data may be Incomplete Incorrect Inaccurate Irrelevant parts of the data This data should be cleaned and error free.

108. Data Cleansing 128 This phase must be done before Analysis because based on data cleaning, your output of Analysis will be closer to your expected outcome. The dirty or coarse data is: Replaced Modified or Deleted.

109. Data Cleansing Cycle 129

110. Data Analysis Data analysis is the process of cleaning, changing, and processing raw data, and extracting actionable, relevant information that helps researchers make informed decisions. The procedure helps reduce the risks inherent in decision-making by providing useful insights and statistics, often presented in charts, images, tables, and graphs. Data analysis is process of: Analytical reasoning Logical reasoning To examine each component of the data provided.  Data analysis plays a crucial role in processing big data into useful information. Neophyte data analysts who want to dig deeper by revisiting big data fundamentals should go back to the basic question 130

111. Scope of Data Analysis Data Analysis is turning raw data into useful information to provide answers to research questions During this phase, you can use  data analysis tools  and software which will help you to understand, interpret, and derive conclusions based on the requirements. Analysis does not mean just using computer software package There are several types of Data Analysis techniques that exist based on business and technology. However, the major Data Analysis methods are: * Text Analysis * Statistical Analysis * Diagnostic Analysis * Predictive Analysis * Prescriptive Analysis

112. What is the Importance of Data Analysis in Research? A huge part of a researcher’s job is to sift through data. That is literally the definition of “research.” However, today’s Information Age routinely produces a tidal wave of data, enough to overwhelm even the most dedicated researcher. Data analysis, therefore, plays a key role in distilling this information into a more accurate and relevant form, making it easier for researchers to do to their job. Data analysis also provides researchers with a vast selection of different tools, such as descriptive statistics, inferential analysis, and quantitative analysis. 132

113. Data Analysis Methods Qualitative Data Analysis:  The qualitative data analysis method derives data via words, symbols, pictures, and observations. This method doesn’t use statistics. The most common qualitative methods include: Content Analysis, for analyzing behavioral and verbal data. Narrative Analysis, for working with data culled from interviews, diaries, surveys. Grounded Theory, for developing causal explanations of a given event by studying and extrapolating from one or more past cases. Quantitative Data Analysis: Is a statistical data analysis methods which collects raw data and processes it into numerical data. Quantitative analysis methods include: Hypothesis Testing, for assessing the truth of a given hypothesis or theory for a data set or demographic. Mean, or average determines a subject’s overall trend by dividing the sum of a list of numbers by the number of items on the list. Sample Size Determination uses a small sample taken from a larger group of people and analyzed. The results gained are considered representative of the entire body.  133

114. Data Visualization Data visualization is very common in your day to day life; they often appear in the form of charts and graphs. In other words, data shown graphically so that it will be easier for the human brain to understand and process it. Charts and graphs Bar chart: comparisons, categories of data Line graph: display trends over time Pie chart: show percentages or proportional share

115. Data Visualization Consider a jar containing the different colours of pieces of cookies as shown below: Colour of Cookies RED GREEN BLUE YELLOW BLACK

116. Results We often use charts and graphs to show our results. You may also write results statements that put your observations/data into paragraph form.

117. Graphs and Visualization We are visually oriented creatures. Images and displays attract our attention and stay in our memory longer. The techniques include: Charts of following types: Area Chart Bubble Chart Column Charts and Bar Charts Funnel Chart Gantt Chart Line Chart Pie Chart Radar Chart Word Cloud Chart 137 Maps, which in turn break down into four distinct types: Flow Map Heat Map Point Map Regional Map Scatter Plot

118. Consider a simple experimental Resistance measurement using multimeter

119. From a resistor pack of 100 measurement of the value of some resistors, performed by different experimenters Mean=100.45 Std Dev=6.81 Tolerance =+- 10 % Attempt Value 1 100 2 108 3 92 4 103 5 101 6 95 7 102 8 96 9 98 10 112 11 104 12 98 13 102 14 97 15 92 16 112 17 89 18 92 19 109 20 107

120. A scatter plot is helpful in understanding the form, direction, and strength of the relationship between two variables. Correlation is the strength and direction of the linear relationship between the two variables. Scatter Plot

121. RTD Temperature response

122. Quantitative Data Analysis Summarizing Data: variables; simple statistics; effect statistics and statistical models; complex models. Generalizing from Sample to Population: precision of estimate, confidence limits, statistical significance, p value, errors.

123. Summarizing Data Data are a bunch of values of one or more variables . A variable is something that has different values. Values can be numbers or names , depending on the variable: Numeric , e.g. weight Counting , e.g. number of injuries Ordinal , e.g. competitive level (values are numbers/names) Nominal , e.g. sex (values are names When values are numbers , visualize the distribution of all values in stem and leaf plots or in a frequency histogram. Can also use normal probability plots to visualize how well the values fit a normal distribution. When values are names , visualize the frequency of each value with a pie chart or a just a list of values and frequencies.

124. 144 Steps of Data Analysis

125. Moving Averages A n-period moving average for time period t is the arithmetic average of the time series values for the n most recent time periods. For example: A 3-period moving average at period (t+1) is calculated by (y t-2 + y t-1 + y t )/3 The centered moving average method consists of computing an average of n periods' data and associating it with the midpoint of the periods. For example, the average for periods 5, 6, and 7 is associated with period 6.

126. Data Transformation: Normalization min-max normalization z-score normalization normalization by decimal scaling Where j is the smallest integer such that Max(| |)<1 Particularly useful for classification (NNs, distance measurements, nn classification, etc)

127. Feature Extraction Dimension reduction Principal component analysis (PCA) Non-negative matrix factorization (NMF) Kernel PCA Graph-based kernel PCA Generalized discriminant analysis (GDA) Data smoothing Wavelet transform Ramer–Douglas– Peucker algorithm Kernel smoother Laplacian smoothing Local regression, … 149

128. - For Small Set of observations, hand calculations are more effective .

129. Statistics Statistics is the branch of science that renders various tools and analytical techniques, it is the science of assembling, classifying, analyzing and interpreting & manifesting the numeric form of data for making inferences about the population, from the picked out sample data that can be used by experts to solve their problems. The field of statistics touches our lives in many ways. From the daily routines in our homes to the business, the effects of statistics are encountered everywhere. While statistics can sound like a solid base to draw conclusions and present “facts,” be wary of the pitfalls of statistical analysis.

130. Uses of Statistics Manufacturers use statistics to weave quality into beautiful fabrics, to bring uplift the production quality and volume and to help guitarists make beautiful music. Researchers keep people healthy by using statistics to analyze data from the production of viral vaccines, which ensures consistency and safety.

131. Communication companies use statistics to optimize network resources and improve service by gaining greater insight into subscriber requirements. Government agencies around the world rely on statistics for a clear understanding of their countries, their businesses and their people Uses of Statistics

132. Statistical Analysis Statistical analysis is the science of collecting, exploring and presenting large amounts of data to discover underlying patterns and trends.  Statistical analysis is concerned with the organization and interpretation of data according to well-defined, systematic, and mathematical procedures and rules. The term “data” refers to information obtained through data collection to answer such research questions as, “How much?” “How many?” “How long?” “How fast?” and “How related?” In statistical analysis, data are represented by numbers.

133. Statistical Analysis Summarize the data.  For example, make a pie chart Creates a model to summarize an understanding of how the data relates to the underlying population. Find key measures of location.  For example, the mean tells you what the average (or “middling”) Calculate measures of spread:  these tell you if your data is tightly clustered or more spread out. The standard deviation is one of the more commonly used measures of spread

134. Statistical Analysis Statistical analysis is used extensively in science, from physics to the social sciences. Statistics can provide an approximation for an unknown that is difficult or impossible to measure. For example, the field of quantum field theory, while providing success in the theoretical side of things, has proved challenging for empirical experimentation and measurement. Some social science topics, like the study of consciousness or choice, are practically impossible to measure; statistical analysis can shed light on what would be the most likely or the least likely scenario.

135. Statistical Analysis Proves (or disproves) the validity of the model. Make future predictions based on past behavior.  This is especially useful in retail, manufacturing, banking, sports or for any organization where knowing future trends would be a benefit. Test an experiment’s hypothesis.  Collecting data from an experiment only tells a story when you analyze the data. This part of statistical analysis is more formally called “Hypothesis Testing,” where the null hypothesis (the commonly accepted theory) is either proved or disproved.

136. The correlation coefficient r is a measure of how well the data set is fit by a model.

137. 1.  Descriptive Statistical Analysis Deals with organizing and summarizing data using numbers and graphs.  Instead of processing data in its raw form, descriptive statistical analysis enables us to represent and interpret data more efficiently through numerical calculation, graphs or tables. Descriptive statistical analysis involves various processes such as tabulation, a measure of central tendency (mean, median, mode), a measure of dispersion or variance (range, variation, standard deviation), skewness measurements and time-series analysis.

138. 2.  Inferential Statistical Analysis It extrapolates, the information obtained, to the complete population. The inferential statistical analysis basically is used when the inspection of each unit from the population is not achievable.  In simple words, it lets us test a hypothesis depending on a sample data from which we can extract inferences by applying probabilities and make generalizations about the whole data, with respect to future outcomes beyond the available data. This method involves the sampling theory, various tests of significance, statistical control etc.

139. 3.  Predictive Analysis Predictive analysis is implemented to make a prediction of future based on current and past facts and figures.  Predictive analytics uses statistical techniques and machine learning algorithms to describe the possibility of future outcomes, behaviour, and trends depending on recent and previous data. The predictive analysis converges on forecasting upcoming events using data and ascertaining the likelihood of several trends in data behaviour.

140. 4.  Prescriptive Analysis The prescriptive analysis examines the data In order to find out what should be done, it is widely used in planning further actions.  It provides the actual answer for discovering the optimal suggestion for a process of decision making. Techniques under prescriptive analysis are simulation, graph analysis, algorithms, complex event processing, machine learning, recommendation engine, etc. Where descriptive analysis explains data in terms of what has happened, predictive analysis anticipates what could happen, and prescriptive analysis helps by way of providing appropriate suggestions among the available preferences.

141. 5.  Exploratory Data Analysis (EDA) EDA is a counterpart of inferential statistics, and greatly implemented by data experts. It is generally the first step of the data analysis process. EDA is deployed to preview the data and assists in getting some key insights into it.  This method fully focuses on analyzing patterns in the data to recognize potential relationships. EDA can be approached for discovering unknown associations within data, inspecting missing data from collected data and obtaining maximum insights, examining assumptions and hypotheses. 

142. 6.  Causal Analysis In general, causal analysis assists in understanding and determining the reasons behind “why” things occur, or why things are as such, as they appear.  The causal analysis identifies the root cause of failures, or the basic reason why something could happen.    We can consider the causal analysis when; Identifying significant problem-areas inside the data, Examining and identifying the root causes of the problem, or failure,  Understanding what will be happening to a provided variable if one another variable changes.

143. 7.  Mechanistic Analysis It is primarily used in big data analytics and biological science and medical. It is deployed to understand and explain how things happen rather than how specific things will take place. It uses the clear concept of understanding individual changes in variables that cause changes in other variables correspondingly while excluding external influences. For example, in biological science, when studying and inspecting how various parts of the virus are affected by making changes in medicine. 

144. Example: Causal research question “Can meditation improve exam performance in teenagers?” Step 1: Writing statistical hypotheses A statistical hypothesis is a formal way of writing a prediction about a population. Every research prediction is rephrased into  null and alternative hypotheses  that can be tested using sample data. While the null hypothesis always predicts no effect or no relationship between variables, the alternative hypothesis states your research prediction of an effect or relationship. Example: Statistical hypotheses to test an effect Null hypothesis:  A 5-minute meditation exercise will have no effect on math test scores in teenagers. Alternative hypothesis:  A 5-minute meditation exercise will improve math test scores in teenagers. Statistical testing

145. Pre Test-Post Test Experimental Design

146. Statistical testing Type of Data 2 Groups, Different Individuals ≥3 Groups, Different Individuals Single Treatment, Same Individual Multiple Treatments, Same Individual Association Between 2 Variables Continuous normally distributed population Unpaired  t -test Analysis of variance Paired  t -test Repeated measures analysis of variance Linear regression and Pearson product-moment correlation

147. Statistical analysis software Software for statistical analysis typically allows users to do more complex analyses by including additional tools for organization and interpretation of data sets, as well as for the presentation of that data. SPSS Statistics, RMP and Stata are some examples of statistical analysis software. SPSS Statistics covers much of the analytical process. From data preparation and data management to analysis and reporting. The software includes a customizable interface, and even though it may be hard form someone to use, it is relatively easy for those experienced in how it works.

148. Data Modelling

149. What is computer modelling? Computer modeling is the use of computers to simulate and study complex systems using mathematics, physics and computer science. A computational model contains numerous variables that characterize the system being studied. Simulation is done by adjusting the variables alone or in combination and observing the outcomes. Computer modeling allows scientists to conduct thousands of simulated experiments by computer. The thousands of  computer  experiments identify the handful of  laboratory  experiments that are most likely to solve the problem being studied.

150. Computer Modelling Examples Weather forecasting  models make predictions based on numerous atmospheric factors. Accurate weather predictions can protect life and property and help utility companies plan for power increases that occur with extreme climate shifts. Flight simulators  use complex equations that govern how aircraft fly and react to factors such as turbulence, air density, and precipitation. Simulators are used to train pilots, design aircraft, and study how aircraft are affected as conditions change.

151. Earthquake   simulations aim to save lives, buildings, and infrastructure. Computational models predict how the composition, and motion of structures interact with the underlying surfaces to affect what happens during an earthquake. Tracking infectious diseases.   Computational models are being used to track infectious diseases in populations, identify the most effective interventions, and monitor and adjust interventions to reduce the spread of disease saving lives and reducing stress on the healthcare system during infectious disease pandemics Computer Modelling Examples

152. Automotive sector Modeling Car crash Vehicle suspension system meets pothole Mechanical deformation Dynamic stress-strain

153. Conceptual Model Very high level (perhaps schematic diagram) How comprehensive should the model be? What are the state variables? Which ones are dynamic, and which are most important? Specification Model On paper: entitites , interactions, requirements, rules, etc. May involve equations, pseudocode , etc. How will the model receive input? Computational Model A computer program General-purpose programming language or simulation language? Three Model Levels

154. Modelling Different Systems

155. Types of Engineering Models Calibration models,   where the measured response variable   y   is a   nonlinear function   of the exploratory (adjustable) variables   x Mechanistic models,   which describe the mechanisms of processes or transformation of input variables   x   to output   y   (examples are chemical   reactions and equilibriums or the dynamic processes in liquids and solids) General   empirical models   based on a study of the nonlinear dependence between the response variable   y   and explanatory variables   x .

156. Empirical Modelling in Engineering In the technical sciences, we typically find the partially disorganized, diffusion-type systems. Physical processes are involved, but unknown or partially known factors and connections also have an influence.  Empirical models are constructed with regards to prediction ability or model fit (data approximation), prognostic ability (forecasting) and model structure (agreement with theories and facts).

157. Creating an Empirical Model Empirical modelling is a generic term for activities that create models by observation and experiment. Empirical Modelling (with the initial letters capitalised, and often abbreviated to EM) refers to a specific variety of empirical modelling in which models are constructed following particular principles. Empirical models are only supported by experimental data. The fundamentals and physical mechanisms underlying the system are not considered.  A common disadvantage of empirical models is that in most cases, they are applicable only for the modeled experiment under a particular operating condition. Therefore, empirical models are not able to predict beyond a particular operating range or designed experiment.

158. Data Modelling: Function Fitting

159. How well does the model do – forecasting?

160. RTD Temperature response

161. Phenomena P1: Through Origin P4: Saturation P3: Decreasing Returns (concave) P2: Linear Y X Y X Y X Q — Y X

162. Standard curves Straight line The equation of a straight line is a first-degree relationship and can always be expressed in the form: where m = d y /d x is the gradient of the line and c is the y value where the line crosses the y -axis – the vertical intercept .

163. Method of least squares Fitting a straight-line graph The i th point plotted, ( x i , y i ), is a vertical distance from the line: The sum of the squares of these differences for all n points plotted is then:

164. Method of least squares Fitting a straight-line graph The values of a and b must now be determined that gives S its minimum value. For S to be a minimum: This yields the two simultaneous equations from which the values of a and b can be found:

165. b= n - n - a= - - n

166. Linearizing Non-Linear Fits Case1: Consider the equation where a and b are the unknown parameters. Rather than consider a and b , we can take the natural logarithm of both sides and consider instead the function This is linear in the parameters ln a and b Case 2: Consider another equation y= ax n where a and b are the unknown parameters. Rather than consider a and b , we can take the natural logarithm of both sides and consider instead the function ln y = ln a + n ln x This is linear in the parameters ln a and n

167. Standard curves Second-degree curves The simplest second-degree curve is expressed by: Its graph is a parabola, symmetrical about The y-axis and existing only for y ≥ 0. y = ax 2 gives a thinner parabola if a > 1 and a flatter parabola if 0 < a < 1. The general second-degree curve is: where a , b and c determine the position, ‘ width ’ and orientation of the parabola.

168. Standard curves Second-degree curves (change of vertex) If the parabola: is moved parallel to itself to a vertex position (2, 3), for example, its equation relative to the new axes is where Y = y – 3 and X = x – 2. Relative to the original axes this gives

169. Standard curves Second-degree curves Note : If: and a < 0 then the parabola is inverted. For example:

170. Standard curves Third-degree curves The basic third-degree curve is: which passes through the origin. The curve: is the reflection in the vertical axis.

171. Standard curves Third-degree curves The general third-degree curve is: Which cuts the x -axis at least once.

172. Standard curves Circle The simplest case of the circle is with centre at the origin and radius r . The equation is then

173. Standard curves Circle Moving the centre to ( h , k ) gives: where: The general equation of a circle is:

174. Standard curves Ellipse The equation of an ellipse is: If a > b then a is called the semi-major axis and b is called the semi-minor axis.

175. Standard curves Hyperbola The equation of an hyperbola is: When y = 0, x = ± a and when x = 0, y 2 = – b 2 and the curve does not cross the y -axis. Note : The two opposite arms of the hyperbola gradually approach two straight lines ( asymptotes ).

176. Standard curves Rectangular hyperbola If the asymptotes are at right angles to each other, the curve is a rectangular hyperbola . If the curve is rotated through 45 o so that the asymptotes coincide with the coordinate axes the equation is then:

177. Standard curves Logarithmic curves If y = log x , then when: so the curve crosses the x -axis at x = 1 Also, log x does not exist for real x < 0.

178. Standard curves Logarithmic curves The graph of y = ln x also has the same shape and crosses the x -axis at x = 1. The graphs of y = a log x and y = a ln x are similar but with all ordinates multiplied by the constant factor a.

179. Standard curves Exponential curves The curve y = e x crosses the y -axis at x = 0. Sometimes called the growth curve.

180. Standard curves Exponential curves The curve y = e − x crosses the y -axis at y = 1. Sometimes called the decay curve.

181. Standard curves Exponential curves The curve: passes through the origin and tends to the asymptote y = a as .

182. Standard curves Hyperbolic curves The combination of the curves for: gives the hyperbolic cosine curve:

183. Standard curves Hyperbolic curves Another combination of the curves for: gives the hyperbolic sine curve:

184. Standard curves Hyperbolic curves Plotting these last two curves together shows that: is always outside:

185. Standard curves Trigonometrical curves The sine curve is given as:

186. A system is defined as a group of objects that interact with each other to accomplish some purpose A computer system: CPU, memory, disk, bus, NIC An automobile factory: Machines, components parts and workers operate jointly along assembly line A system is often affected by changes occurring outside the system: system environment Hair salon: arrival of customers Warehouse: arrival of shipments, fulfilling of orders Effect of supply on demand: relationship between factory output from supplier and consumption by customers System and its environment

187. Monte Carlo simulation Time-stepped simulation Trace-driven simulation Discrete-event simulation Continuous simulation Types of Simulations 218

188. Static simulation (no time dependency) To model probabilistic phenomenon Can be used for evaluating non-probabilistic expressions using probabilistic methods Can be used for estimating quantities that are “hard” to determine analytically or experimentally Monte Carlo Simulation Named after Count Montgomery de Carlo, who was a famous Italian gambler and random-number generator (1792-1838). 219

189. What is the Monte Carlo method? The Monte Carlo method is a numerical solution to a problem that models objects interacting with other objects or their environment based upon simple object-object or object- environment relationships. It represents an attempt to model nature through direct simulation of the essential dynamics of the system in question. In this sense the Monte Carlo method is essentially simple in its approach—a solution to a macroscopic system through simulation of its microscopic interactions”

190. Statistical uncertainties (physics, geometry, etc) Long time execution More accurate physical models or more detailed geometries. Variance reduction, Multithreading, GPU. In the practice, we can generate uniform random numbers But we need random numbers that obey the Probability Density Function (PDF) of the physical process we want to simulate. These numbers are not uniform! It is based on

191. Monte Carlo studies are one of the earliest computer techniques, dating back to the 1940’s, they allow us to analyse a range of questions which are not directly tractable to an analytical solution. Sometimes these problems may be simply so complex that we can not work out the large sample (asymptotic) answer. In other cases we may be interested in the answer for a small sample size or a limited number of states which are not easily calculated. The essence of the technique is to assume a known world, to assume something about the statistical distribution of certain events. Then to numerically draw many realisations from this distribution. Calculate the outcome and analyse the resulting distribution of solutions.  

192. Illustration of Monte Carlo method Hit-or-Miss Monte Carlo Method for area estimation of an arbitrary shaped object Imagine that we want to estimate the area of an arbitrary shape such as the one we drew in figure. All we know is the area of the rectangle containing this shape and defined by the boundary ac and ab. Because these boundaries define a simple rectangle, we know the area of this rectangle to be A= ab×ac To estimate the area of the shape itself, we use a technique called hit-or-miss method. The idea is to " throw " a certain number of random points   uniformly   into the rectangle and count the number of these points that are on the shape (hits) and   reject   the others. Because points are randomly distributed over the area of the rectangle , it is reasonable to assume that the area of the shape is  proportional  to the number of hits over the total number of thrown points. We can write: A shape ≈N hits / N total ×A , where  A= ab×ac .

193. Cluster Analysis

194. X1 X2 Y1 Y2 Principal Component Analysis The principal components (new set of axes) give important information about variance. Using the strongest components one can reconstruct a good approximation of the original signal.

195. ANN x 1 x 2 x 3 x n h 1 h 2 h 3 h m f(x) W 1 W 2 W 3 W m

196. Data Analytics Data Analytics

197. What is Data Analytics? Analytics is the use of: Data Information technology Statistical analysis Quantitative methods Mathematical or computer-based models To help managers: Gain improved insight about their business operations Make better, fact-based decisions. 231

198. Data Analytics Capabilities 232

199. Descriptive Analytics What has occurred? Descriptive analytics, such as data visualization, is important in helping users interpret the output from predictive and predictive analytics. Descriptive analytics, such as reporting/OLAP, dashboards, and data visualization, have been widely used for some time. They are the core of traditional BI. 233

200. Predictive Analytics What will occur? Marketing is the target for many predictive analytics applications . Descriptive analytics, such as data visualization, is important in helping users interpret the output from predictive and prescriptive analytics. Algorithms for predictive analytics, such as regression analysis, machine learning, and artificial neural networks, have also been around for some time. Prescriptive analytics are often referred to as advanced analytics. 234

201. Prescriptive Analytics What should occur? For example, the use of mathematical programming for revenue management is common for organizations that have “perishable” goods (e.g., rental cars, hotel rooms, airline seats). Harrah’s has been using revenue management for hotel room pricing for some time. Prescriptive analytics are often referred to as advanced analytics. Regression analysis, machine learning, and neural networks Often for the allocation of scarce resources 235

202. Data Analytics Cycle 236

203. 237

204. Why Big Data With the development and increase of apps and social media and people and businesses moving online, there’s been a huge increase in data. If we look at only social media platforms, they interest and attract over a million users daily, scaling up data more than ever before. The next question is how exactly is this huge amount of data handled and how is it processed and stored. This is where Big Data comes into play.  And Big Data analytics has revolutionized the field of IT, enhancing and adding added advantage to organizations. It involves the use of analytics, new age tech like machine learning, mining, statistics and more. Big data can help organizations and teams to perform multiple operations on a single platform, store Tbs of data, pre-process it, analyze all the data, irrespective of the size and type, and visualize it too. 238

205. Big Data Analytics Applications Information from multiple internal and external sources: Transactions Social media Enterprise content Sensors Mobile devices Companies leverage data to adapt products and services to: Meet customer needs Optimize operations Optimize infrastructure Find new sources of revenue Can reveal more patterns and anomalies 239

206. Applications of Big Data Analytics 240

207. Publishing Good Research Papers

208. Agenda Publishing Research Paper Two Publication Options Benefits of publishing in good quality Journals How to Publish? Publication Process Writing Good Research Paper Ethics / Plagiarism in Research

209. Publishing Research Paper Publishing your research is an important step in your academic career. Declare your achievements loud and clear and receive appreciation / criticism from a larger community The validity of your approach, efforts data and results is assured Feedback for improvement Continuity of knowledge pathways is ensured - you or others can refer to your work anywhere anytime Opportunities of networking and progress

210. How to write a paper Declare most effectively what you have achieved and receive appreciation / criticism from a larger community

211. How to write a paper When you have truly exceptional results Probably doesn’t matter how you write, people will read it anyway

212. How to write a paper Most papers are not that exceptional Good writing makes significant difference Better to say little clearly, than saying too much unclearly

213. What’s in a paper Abstract Introduction Motivation Problem description Solution ... Performance Analysis Conclusions Future Work

214. Typical Formal Research Report Format A formal research report typically consists of several sections organized in a specific order to present the research findings clearly and structured. Here is a notional format of a research report, including the typical sections: Title Page: – Title of the Research Report – Name(s) of the Author(s) – Affiliation(s) of the Author(s) – Date of Submission Abstract: – A brief summary of the research objectives, methods, key findings, and conclusions. – Usually limited to a specific word count or length. Table of Contents: – A list of the main sections, subsections, and page numbers in the report. – Helps readers navigate through the report easily.

215. Introduction: – Provides an overview of the research topic, including background information, context, and significance. – States the research problem, objectives, and research questions. – Outlines the scope and limitations of the study. Literature Review: – Reviews relevant literature and previous studies related to the research topic. – Summarizes existing knowledge, theories, and methodologies. – Identifies gaps, controversies, or unresolved issues that the current research aims to address. Methodology: – Describes the research design, methods, and procedures used to collect and analyze data. – Includes information on the sample size, data sources, data collection tools, and data analysis techniques. – Provides sufficient details for replication and validation of the study.

216. Results: – Presents the findings of the research in a clear and organized manner. – Utilizes tables, figures, charts, or graphs to present data. – Includes descriptive statistics, qualitative analysis, or any other relevant analysis outputs. Discussion: – Interprets and discusses the research findings in relation to the research objectives. – Compares the results with existing literature and theories. – Analyzes patterns, trends, correlations, or discrepancies in the data. – Provides explanations, justifications, or hypotheses to support the findings. Conclusion: – Summarizes the main findings of the research. – Restate the research objectives and address the research questions. – Highlights the contributions and implications of the study. – Suggests recommendations for future research or practical applications.

217. References: – Lists all the sources cited within the research report. – Follows a specific referencing style (e.g., APA, MLA, IEEE) as per the guidelines. Appendices: – Includes supplementary information or additional data that supports the research findings but is not necessary for the main body of the report. – May include survey questionnaires, interview transcripts, data tables, software code, or any other relevant materials. It’s important to note that the structure and specific section names may vary depending on the discipline, research field, or journal requirements. Always refer to the specific guidelines your institution or publisher provided when preparing a research report.

218. Two Publication Options Presenting in Conferences Which may get published as a paper in a conference proceedings, or a paper in a (lesser known - mostly mid range or low level) journal with which the conference organizers have a pre-agreement Publishing in a Reputed Journal

219. Conference Publication Vs Journal Publication Conference publication Journal publication Faster feedback Take longer to be published, hence the feedback received is slower. Present work in progress Present completed work Present new concepts and techniques that you are in the process of developing. Report on new concepts and techniques that have been validated by your experiments. Peer interaction Peer review Lower impact factor Higher impact factor

220. Conference Paper Before your paper gets accepted to present at a conference, you need to submit your work to the conference organizers. They will read your work and carry out moderate peer review and decide whether you will have a session at the conference in which you share your work with colleagues in your field. Consequently, a session could be in the form of a poster presentation, oral presentation or workshop style discussion. The format, however, will depend on the conference.

221. Presenting in a Conference Presenting your work at a conference boosts your career by sharing your research with colleagues in your field has many advantages such as: Networking Practicing mock interview Receiving feedback from other researchers in your field Opportunities to publish in a journal after the conference (with permission requirements satisfied) Collaborations Opportunity to travel

222. Journal Publication Large number of journals available in the field would publish your papers. Some are open access, on-line while others are print journals from reputed publishers Some journals publish quality research contributions at no cost to the authors while some others are paid journal Publishing your work in a peer-reviewed journal with a high impact factor is a goal all academics strive for. With this you get a better chance of promotion, selection to esteemed positions, and getting funding for further research.

223. Publication strategy Many researchers go on to publish the work that they presented at a conference in a journal publication. It is a useful route to take because conference attendees will comment on your presentation, ask questions and provide their input. The conference proceedings contain the first draft or your journal article. After the conference, you can tweak your experiments, add more data and reflect on your conclusion while taking the input you received from the conference into consideration. Paper Quality Journal Paper

224. Acceptance value of Journal papers Peer - reviewed journal publications  are given preference over  conference proceedings  when researchers read  and  cite research. Institutions give credit to peer-reviewed articles. Journal articles contain completed research. They have undergone extensive review by experts in your field through a blind reviewing process. The journal has an impact factor to gauge the quality of the research.

225. Choosing a Journal Choosing the journal before you start writing means you can tailor your work to build on research that’s already been published in your journal of choice. This can help editors to see how a paper adds to the ‘conversation’ in their journal. In addition, as you go through this guide, you’ll see many journals only accept specific formats of article and may well have word limits and other restrictions. By having a preferred journal in mind before you start, you can write your article to their specifications and audience. This will not only save you time later in the submission process, but could also ultimately improve your chances of acceptance.

226. Types of Journals Scientific publishing is a business and no matter what model a publisher chooses, that business is based on copyright. Established, well known peer reviewed journals maintain publication standards. The cost of publication is recovered from readers by virtue of subscription either from individuals or institutions. At times they also levy authors with moderate publication charges

227. Open Access and Pay to Publish Journals On the other hand open access journals use a different business model of making the publication free of cost to readers for higher readership. The publication cycle time can be substantially low. Since opening up the copyright means that there is no business model in selling access to it, publishers  often recoup the cost of publication by charging those who publish  in their open access journals.

228. Predatory and low-quality Journals In research environments, there is usually more value for quantity over quality in hiring and promotion of academics. Predatory and low-quality journals corrupt the literature and help many pseudo-researchers to prosper. A predatory journal is a publication that actively asks researchers for manuscripts. They have no peer review system and no true editorial board and are often found to publish mediocre or even worthless papers for huge publication charges. Publishing without peer review [while pretending that peer review was done] gives poor and mediocre academics a chance for jobs and promotions which should go to better qualified researchers.

229. IS THE JOURNAL TRUSTWORTHY? – How to Avoid ‘Predatory Publishers With hundreds of new academic titles launched every year, deciding whether or not a journal is worthy of your work is increasingly difficult. Wider choice is generally a good thing, but the profusion of new titles has also led to a rise in ‘predatory publishing’, which trade, intentionally and fraudulently, off existing society or journal names. Authors send their work and, in some cases, pay publication fees to these titles in good faith – only to discover that their research hasn’t appeared in the title they thought it would. So, as a researcher, how do you evaluate whether the journal you’re about to send your work to is the real deal?

230. Locating Journals Elsevier Journal Finder Enter the unpublished article’s title and abstract info into this tool to determine possible sites for publication. Springer Journal Suggester Enter the unpublished article’s title and abstract into this tool to determine possible sites for publication.  Directory of Open Access Journals (DOAJ) Use the Browse Subjects feature, and select Journals to find quality Open Access Journals. Edanz Journal Selector Allows you to search by keyword, journal name, abstract and more. Pulls results from publicly available data sources like Thomson Reuters’ annual Journal Citation Reports®. IEEE Explore Delivers full text access to the world's highest quality technical literature in engineering and technology. The content 200+  journals . 3+ million conference papers. 10,000+ technical standards.

231. Journal Metrics Journal metrics like Impact factor, 5 year impact factor, Web of Science Journal Citation Reports®, SNIP (Source Normalized Impact Per Paper), CiteScore from Scopus, SJR ( Scimago Journal Rank), Eigenfactor are some of the tools used to measure the performance and impact of a journal. They can help you to choose which journal to submit your work to.

232. Journal Metrics finding tools InCites ™ Journal Citation Reports® Find a variety of metrics for journal quality, including impact factor, immediacy index, Eigenfactor score, and article influence score. Searchable by journal name and browsable by research category. SCImago Journal & Country Rank Access journal rankings based on citation data from the Scopus database. Journals can be grouped by major thematic areas and specific subject categories. Scopus In search results, click on the journal title to view journal metrics. Scopus will give you SJR ( SCImago Journal Rank), IPP (Impact per Publication), and SNIP (Source Normalized Impact per Paper) measurements. Web of Science In search results, click on the journal title to view journal metrics. In Web of Science, view its IF (Impact Factor) and JCR (Journal Citation Reports) rankings. Eigenfactor Find an article’s Eigenfactor ®  and  Article Influence Score ®  to evaluate the influence of a journal.

233. What’s the process of peer review?

234. Think about the four A’s: Aims, Audience, Awareness, and Articulation It’s important to consider these four areas right at the start of the paper writing process. 4 A’s

235. Steps to organizing your manuscript Prepare the  figures and tables . Write the  Methods . Write up the  Results . Write the  Discussion . (Finalize the Results and Discussion before writing the introduction.) Write a clear  Conclusion . Write a compelling  introduction . Write the  Abstract . Compose a concise and descriptive  Title . Select  Keywords  for indexing. Write the  Acknowledgements . Write up the  References in proper style .

236. 1. Know your audience and write for that specific audience. Scientific and technical writing is never a 'general purpose‘, but written for a specific audience, i.e. the community who read a particular journal or study a particular subject . You must adopt the style and level of writing that is appropriate for your audience . Study them as they are manifested in a selection of highly regarded papers and in the "Instructions for Authors" for key journals.

237. Title of the Paper Create a compelling title Your title is your first opportunity to attract a reader’s attention. And don’t forget that the first readers are the editors – it needs to capture their attention too. A good title should be concise, accurate, and informative. It should tell the reader exactly what the article is about and it should also help make your article more discoverable. It’s also important to make your title understandable to readers from outside your field and avoid abbreviations, formulae, and numbers. This will help increase the potential audience for your article and make it more accessible to readers with a different native language.

238. 2. The Scientific Paper:  Introduction This section discusses the results and conclusions of previously published studies , to help explain why the current study is of scientific interest.  Why is this study of scientific interest and what is your objective? The Introduction is organized to move from general information to specific information . The background must be summarized succinctly, but it should not be itemized. Limit the introduction to studies that relate directly to the present study. Emphasize your specific contribution to the topic.  The last sentences of the introduction should be a statement of objectives and a statement of hypotheses .  This will be a good transition to the next section , Methods, in which you will explain how you proceeded to meet your objectives and test your hypotheses.

239. 3. Use an outline to organize your ideas and writing. When you first start a writing paper, make an outline of the major headings . List the key ideas to be covered under each heading. Organize your thinking logic and the logic of your arguments at this level, not when you are trying to write complete, grammatical, and elegant sentences. Separate out the three tasks of: (1) figuring out what you want to say, (2) planning the order and logic of your arguments, and (3) crafting the exact language in which you will express your ideas.

240. 4. Trapezoidal Organization Experimental Details Results Introduction Discussion Broad background Narrow focus Broad implications

241. 5. Methods/Materials: This section provides all the methodological details necessary for another scientist to duplicate your work .   It should be a narrative of the steps you took in your experiment or study, not a list of instructions such as you might find in a cookbook. An important part of writing a scientific paper is deciding what bits of information needs to be given in detail . Do not quote or cite your laboratory manual!  Sometimes, experimental details are given as supplementary part !

242. Prepare tables and figures A picture is worth a thousand words – and you could say the same for a table or figure in a manuscript. They are often the most impactful and efficient way to present your results. Tables and figures should present new information rather than duplicating what is in the text. And readers should be able to interpret them without reference to the text. When creating tables and figures for your article, make sure to check the journal’s instructions for authors and editorial policies, which may stipulate on layouts, use of color, and a number of other formatting points. In particular, it’s important to consider the size of each table or figure and whether it will fit on a single journal page.

243. 6. Results and Discussion This section presents the results of the experiment. You will not present the raw data that you collected, but rather you will summarize the data with text, tables and/or figures . When preparing to write your results, decide on the elements of the story you wish to tell, then choose and organize the subset of text, figures, and tables that most effectively and concisely coveys your message . Begin a Discussion with a short restatement of the most important points from your results . Use this statement to set up the ideas you want to focus on in interpreting your results and relating them to the literature. Use sub-headings that structure the discussion around these ideas

244. 8.  Introductions and conclusions are the hardest parts. Many technical writers prefer to write their introductions last because it is too difficult to craft that balance of general context and specific focus required for a good introduction. If you need to write the introduction first to set the stage for your own thinking, resist the temptation to perfect it. The introduction will likely need substantial modification by the time you have finished the rest of the paper . The same concerns apply to conclusions, abstracts, and summaries . These components of the paper are all that many people will read, and you must get your message across in as direct, crisp, and enticing a manner as possible.

245. Length of the manuscript Follow the ‘Guidelines for Authors’, but an ideal length for a manuscript is maximum 25 pages, with line spacing 2. This should include essential data only. Typically the distribution would be as follows: Title:  Short and informative Abstract:  1 paragraph (<250 words) Introduction:  1.5-2 pages Methods:  2-4 pages Results:  2-6 pages Discussion:  2-6 pages Conclusion:  1 paragraph Figures:  6-8 (one per page) with good clarity Tables:  1-3 (one per page) References:  20-50 papers (2-4 pages)

246. Do’s and Don’ts Well written paper free of grammatical and technical errors and supported by proper illustrations is appreciated. Editors like to see that you have provided a perspective consistent with the nature of the journal.  Citing of original and important works as references should be done but too many references irrelevant to the work must be avoided. Do not include unpublished observations, publications that are not peer reviewed, grey literature etc. Expressions such as "novel," "first time," "first ever," and "paradigm-changing" are not preferred. Avoid statements that go beyond what the results can support. The discussion must include whether your results are consistent with what other investigators have reported. If your results are unexpected, try to explain why.

247. Writing Ph. D. Proposal, IPR documents and Proposals for Funding

248. What is a Proposal? An Answer Book to your scheme of research 1 st Example of the Quality of your Work Document on you competency level Objective representation of your involvement in Overall Plan Operational Plan

249. What is a research proposal? A research proposal should present your idea or question and expected outcomes with clarity and definition – the what. It should also make a case for why your question is significant and what value it will bring to your discipline – the why.   It should also present the proposed methodology of conducting research – the how What it shouldn't do is answer the question – “that's what your research will do.”

250. The Goal of a Research Proposal In a research proposal, the goal is to present the author’s plan for the research they intend to conduct. In some cases, a research proposal is a required part of Ph.D. registration application. It’s to have the research approved by the author’s supervisor or department so that they can move forward with it. In some other cases, part of this goal is to secure funding for said research.

251. Standard Proposal Parts Abstract Summary/Executive Summary Background/Needs Assessment Goal & Objectives Plan of Work Evaluation & Monitoring Budget (a rational consideration) Sustainability Plan Partners and Key Personnel Organizational Experience, Capability & Resources Project Summary Project Description References Cited Facilities, Equipment & Other Resources Special Information & Supplementary Documentation Program/Project Proposals Research

252. WHAT IS RESEARCH IMPACT? There are many different definitions of research impact. However, in the broadest sense, impact is about the effects the piece of research. Academic: For example advancing and developing understanding, methods, and theory Cultural or societal: The impact research can have on people and the places where they live. Policy: The impact or influence of research on policy formulation or government decisions. Economic: Impacting businesses and economic growth or development. Environmental: Climate change or the preservation of endangered species.

253. Research impact and your career Institutions are now placing more emphasis on the impact of research as recognition of academic success. Driving career progression Researchers: It can help you set yourself apart and develop skills for your progression. You can gain skills like Communication skills – for example, through writing your research papers, presenting at a conference, maintaining correspondence with funding agencies, collaborators, vendors etc. or speaking to the media. Project and stakeholder management skills –by coordinating a project with a wider network of stakeholders. Evidence based decision making skill -Using analytics of quantitative and qualitative data and information.

254. Knowledge documentation and IPR

255. Society needs Knowledge for growth From the point of view of society, the more people use knowledge it is better. Each user gains something at low or no cost, and society is better off. Economists therefore say that knowledge has the character of a non-rival public good

256. The relationships between data, information, and knowledge. Information Knowledge

257. Knowledge based Economy Second, the ‘weightless economy‘ - the economy of ideas, knowledge and information - has become an increasingly important fraction of economic output and ever more important for economic growth and development, both in developed and developing economies.

258. Significant efforts are needed for generating new knowledge The reward to the researcher is in the form of recognition – either a good publication or monetary benefits Most researchers choose the first alternative and publish in reputed journals/conferences

259. What happens to this knowledge? It lies in the archives, may be somebody refers to it some time or it just gets wasted remaining unutilized Some of this knowledge gets converted in the form of products and is commercialized

260. Copying detours the inventors. Leading to a block in his / her creativity Many products, incorporating new knowledge, can be easily copied. Probably most products, with sufficient effort, can be copied at a fraction (albeit not necessarily small) of the cost it took to invent and market them. Knowledge copying He / She may get frustrated and uninterested in sharing the knowledge or contributing to any such future endeavors

261. There is a need to secure the outcome

262. 297 INTELLECTUAL PROPERTY RIGHTS (IPR) Intellectual Property (IP) is defined as any “original creative work manifested in a tangible form that can be legally protected” Right associated with intellectual property which gives legal protection is referred to as IPR. When we speak of IP rights, we refer to controlling the way IP is used, accessed or distributed. IPR regimes affect the diffusion of scientific knowledge, the innovation process and, ultimately, economic performance.

263. What is Intellectual Property (IP) protection? Intellectual property refers to creations of the mind: inventions, literary and artistic works, and symbols, names, images, and designs used in commerce. IP protection allows people to own their creativity and innovation in the same way that they can own physical property. The owner of IP can control and be rewarded for its use, and this encourages further innovation and creativity to the benefit of us all.

264. 299 Categories of IP rights Utility model/Designs Plant Breeder’s rights Geographical Indications Trade secrets Trademark & domain names Copyright Patent

265. IPR Portfolio Building Seed, nurture, cultivate and harvest Inventions to create the Present, Immediate Future and distant future portfolios Measuring IP Performance

266. When legally protected these are Intellectual Property When codified these are Intellectual Assets Most Tangible Least The Components of Intellectual Capital: A Spectrum of Knowledge Assets Patents Trademarks Publishing Rights Brand logos designs Copyrights Information Databases Industrial Design Software Platforms Trade Secrets Confidential Information Technology Structural Capital Know-How Customer Capital Unpatented Research Knowledge Providing Value Human Capital Culture Source: PricewaterhouseCoopers

267. Path Forward in Research Community Once you have established as a researcher by performing significant amount of research work and published good number of papers, you may create larger impact by becoming advisor or consultant to industry. You may become advisor or mentor for your juniors or students as well as or contribute to the publication value chain by starting participating in peer review process as a reviewer. It is very common to become a reviewer, by volunteering.

268. Research for a Degree Conducted with a motivation and primarily with an intention of progress in career Many a times it is conducted under compulsion by the superiors and system There is a limited goal, which when achieved, one feels to have found the limits of horizon within touching distance However, when you reach there, there is a realization of ever-expanding horizon, newer challenges and pathways Here onwards you may progress for personal satisfaction and pride The “interest” needs a major shift in solving problems of broader societal significance.

269. Research Beyond Degree “I thought a PhD would give me the qualification that meant that I got further up the queue of a career.” “Well I guess the primary motivation for doing Ph.D. was the “Himalaya syndrome”. It was there, it was the final step in University qualifications available to me and I wanted to do it, it was that last jump.” “It [PhD] was motivated by wanting to do research and wanting to produce something new, original and useful out of the research.” “I found myself collecting material, researching as it were as I went learning. Pulling in all sorts of things seems quite interesting now. I would rather continue ….”

270. Experience , Attitude and Network Once a researcher ……. …....always researcher Follow instincts and reputation