Research Methodology for Engineering Purposes week_4pptx.pptx7-5-25 (3).pptx

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1. Presentation by Dr. Asma Ali Budalal Research Methods 1

2. 2 In the previous lectures we have discussed: Module 1: Introduction to Engineering Research 1.1 What is Research? (Scientific Method vs. Engineering Design) 1.2 Types of Engineering Research (Experimental, Simulation, Case Studies, Applied) 1.3 Ethics in Engineering Research (Plagiarism, Data Integrity, Human/Environmental Impact)

3. Module 2: Defining the Research Problem 2.1 Identifying Engineering Research Gaps (From Industry vs. Academia) 2.2 Formulating Research Questions & Hypotheses 2.3 Scope and Limitations 3 Lecture Objectives

4. 4 Why Define the Problem? Key Points: “A well-defined problem is half-solved.” — John Dewey Prevents wasted time/resources. Guides methodology (experimental? simulation?). Interactive Poll: “What’s harder: finding a research gap or designing the solution?”   استطلاع رأي تفاعلي :

5. Define Your Problem And Purpose The first step is to clearly define the problem that you want to solve or the opportunity that you want to explore with your engineering project. Why Define Your Problem And Purpose?? This will help you : narrow down your scope, identify your stakeholders, and formulate your research questions. define the purpose of your research, whether it is to explore, describe, explain, or evaluate something. This will help you decide what kind of data you need and how to measure it . 5

6. 6 1. Ensures Relevance & Prevents Wasted Effort Engineering research should solve  real-world problems  (e.g., improving energy efficiency, optimizing materials, reducing costs). A poorly defined problem can lead to: Unnecessary experiments or simulations. Solutions that don’t address actual needs. Why Define Your Problem and Purpose in Engineering Research?

7. 4. Sets Scope & Boundaries Engineering projects have time, budget, and resource constraints. A clear problem statement prevents scope creep 3. Helps Formulate Precise Research Questions & Hypotheses A well-defined problem leads to testable hypotheses (e.g., "Using graphene coatings will reduce corrosion in marine structures by 20%."). 2. Guides Your Methodology The problem statement determines: Experimental vs. simulation-based approaches  (e.g., lab testing vs. Matlab ). Data collection methods  (sensors, surveys, case studies). Example: If researching  "wind turbine blade fatigue,"  you’d need: Stress simulations (FEA)  or Physical wind tunnel tests. 7

8. 5. Justifies the Research (Why It Matters) Funding bodies, professors, and industry partners ask: "What gap does this fill?" "Who benefits?" Strong purpose examples: "This study aims to reduce solar panel manufacturing costs by 15% using perovskite materials." "This AI model optimizes traffic flow in smart cities, cutting commute times by 10%." 8

9. 9 6. Aligns with Ethical & Professional Standards Engineering research must address: Safety  (e.g.,  "Does this new construction material meet ASTM standards?" ). Sustainability  (e.g.,  "How can we minimize e-waste in PCB design?" ). A clear purpose ensures  social responsibility .

10. 10 How to Define Problem & Purpose Effectively Start Broad, Then Narrow Down Broad:   "Renewable energy storage." Narrow:   "Enhancing thermal stability of phase-change materials for solar storage." Use the "5 Ws " Framework What?  (Problem) Why?  (Purpose/Impact) Who?  (Stakeholders) Where?  (Application context) When?  (Timeline/urgency) Validate with Literature Check if similar work exists (IEEE Xplore, patents). Identify gaps your research can fill. "تعزيز الاستقرار الحراري لمواد تغيير الطور لتخزين الطاقة الشمسية."

11. Example: ❌  "Study battery performance."  (Too vague) ✅  "Improve the charge-discharge cycle life of lithium-ion batteries for electric vehicles."  (Clear & industry-relevant ) 11

12. 12 Where Do Gaps Come From? Industry Needs vs. Academic Puzzles Visual: Split-screen graphic (Factory 🏭 vs. University ( 🎓 . 1. Industry Research Gaps Focus: Solving immediate, practical problems with business impact. Sources of Gaps: Pain Points (نقاط الضعف) : Cost, efficiency, security, scalability. Emerging Tech( التكنولوجيا الناشئة ): Adoption hurdles (e.g., AI bias in real-world datasets). Regulations: Compliance needs (e.g., GDPR -compliant cybersecurity). احتياجات الصناعة مقابل التحديات الأكاديمية Field Examples: Cybersecurity:   "Zero-day attacks on cloud-native apps in fintech." Telecom:   "5G network slicing vulnerabilities for IoT devices." Mechatronics:   "High-cost maintenance of industrial robots." الأمن السيبراني المتوافق مع اللائحة العامة لحماية البيانات ). 2.1 Identifying Engineering Research Gaps (From Industry vs. Academia)

13. 13 2. Academic Research Gaps Focus: Advancing fundamental knowledge or long-term innovations. Sources of Gaps: Literature Reviews: Unanswered questions in journals/conferences. Theoretical Limits: Pushing boundaries (e.g., novel AI architectures). Interdisciplinary: Merging fields (e.g., AI + materials science). Field Examples: Programming: "Formal verification of quantum algorithms." Takeaway: “Industry gaps pay the bills; academic gaps push boundaries. Great engineering research often bridges both!”

14. 14 Quick Quiz – Industry or Academic Gap? Question 1: "Reducing latency in 6G networks for autonomous vehicles." Options: (A) Industry Gap (B) Academic Gap (C) Both Answer: ✅ (C) Both Question 2 (Field-Specific): For your discipline, classify this gap: AI/Programming: "Explaining black-box decisions in healthcare AI models." Cybersecurity: "Post-quantum encryption for IoT devices." Mechatronics: "Self-calibrating sensors for warehouse robots." Architecture: *"4D-printed buildings that adapt to weather."* Answers: AI: Both (Industry needs trust; academia explores interpretability theories). Cybersecurity: Industry (Urgent need for quantum-resistant solutions). Mechatronics: Industry (Cost-driven automation demands). Architecture: Academic (Cutting-edge materials research)

15. 15 Formulating Research Questions “How does [independent variable] affect [dependent variable] in [context]?” Field-Specific Examples: Network Security: “How does quantum cryptography improve resistance to man-in-the-middle attacks in IoT networks?” Mechatronics: *“Can a PID controller with fuzzy logic reduce vibration in 3D-printed robotic arms?”* Bad vs. Good Examples: ❌ “Study 5G networks.” (Too broad) ✅ *“Does edge computing reduce latency in 5G-enabled smart factories?”*

16. 16 Crafting Hypotheses 2. Engineering Examples (Field-Specific): a. Cybersecurity: *"If we use blockchain-based authentication (X), then phishing attacks will decrease by 30% (Y), because decentralized ledgers prevent fake logins (rationale)."* b. AI/Programming: "If we train a model with synthetic voice data (X), then deepfake detection accuracy will drop (Y), because synthetic data lacks natural vocal flaws (rationale)." c. Mechatronics: *"If we add gyroscopic sensors to drone legs (X), then landing stability improves by 40% (Y), because real-time balance corrections reduce toppling (rationale)."* d. Architecture: *"If we design windows with nano-coated glass (X), then building cooling costs drop by 25% (Y), because infrared reflection reduces heat absorption (rationale)."*

17. 17 2. Telecommunications Example Exam Context:   5G signal propagation in urban areas. Hypothesis: *"If we deploy small-cell antennas at 200m intervals (X), then 5G signal dropout rates will decrease by 25% in high-rise areas (Y), because shorter distances reduce multipath interference (rationale)."* 3. Key Ingredients of a "Spicy" Hypothesis: X = What you change  (Independent Variable) Y = Measurable outcome  (Dependent Variable) Rationale = Science/engineering logic  (No "just because"!) Interactive Task: "Turn this weak hypothesis into a spicy one!" ❌  "Solar panels might work better." ✅  "If we angle solar panels at 30° (X), then energy output increases by 15% (Y), because it optimizes sunlight absorption in tropical latitudes (rationale)."

18. 18 Scope & Limitations What’s included?  (E.g., only Python-based ML models). What’s excluded?  (E.g., no hardware testing). Field Examples: Programming:   “This study analyzes Python vs. Julia for HPC ( الحوسبة عالية الأداء ), excluding compiled languages like C++.” Telecom:  *“Focus: LoRaWAN security in smart agriculture; excludes 5G/6G protocols.”* Visual:  Venn diagram of scope vs. limitations.

19. 19 Key Takeaways Gaps  come from industry pain points or academic literature. Questions  must be specific, measurable, and engineering-focused. Hypotheses  predict outcomes with rationale. Scope  keeps research feasible. Closing Quote: “The quality of your research depends on how well you define the problem.”

20. 20 Task:   Define a problem for your field Field:  [Network/AI/Mechatronics/etc.] Gap:  Industry vs. academic? Research Question:  Use the “How does X affect Y?” formula. Hypothesis:  “If…then…” statement. Scope/Limitations:  Boundaries?

21. 21 Q&A + Next Steps Next Lecture:   “Conducting Literature Reviews in Engineering.” Assignment:  *Submit a 1-page research problem statement (field-specific).*