AENGM0030 Research Project (Advanced Composites MSc) UOB Assignment Answer UK

The AENGM0030 Research Project is designed to foster independent thinking, critical analysis, and technical proficiency. It will challenge you to delve into the latest research literature, engage in experimental work, and employ advanced computational tools to advance your understanding of composite materials. Moreover, you will have the opportunity to collaborate with fellow students, promoting a dynamic and collaborative research environment.

Throughout the duration of the course, you will be required to document your research progress, present your findings, and defend your conclusions through written reports, oral presentations, and potentially in a thesis format. These activities will not only allow you to develop your communication and presentation skills but also enable you to showcase your contributions to the field of advanced composites.

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Below, we will discuss some assignment activities. These are:

Assignment Activity 1: Undertake an original research activity.

Title: The Impact of Mindfulness Meditation on Stress Reduction in College Students

Research Objective: The objective of this study is to investigate the effectiveness of mindfulness meditation as a stress reduction technique among college students.

Research Questions:

  1. Does regular practice of mindfulness meditation lead to a significant reduction in stress levels among college students?
  2. How does the duration and frequency of mindfulness meditation practice correlate with stress reduction?
  3. Are there any gender differences in the perceived effectiveness of mindfulness meditation for stress reduction among college students?
  4. Are there any differences in stress reduction among students from different academic disciplines who engage in mindfulness meditation?


  1. Sample Selection:
    • Identify a diverse sample of college students from various disciplines and demographics.
    • Ensure an equal representation of male and female participants.
  2. Random Assignment:
    • Randomly divide participants into two groups: experimental group (mindfulness meditation) and control group (no meditation).
  3. Intervention:
    • Experimental Group: Provide mindfulness meditation training and encourage regular practice.
    • Control Group: No intervention, maintain their usual routine.
  4. Data Collection:
    • Pre-test: Measure stress levels of participants in both groups using a standardized stress assessment scale.
    • Intervention: Conduct mindfulness meditation sessions for the experimental group over a defined period (e.g., eight weeks).
    • Post-test: Measure stress levels of participants in both groups after the intervention using the same stress assessment scale.
  5. Data Analysis:
    • Compare the stress levels of the experimental and control groups before and after the intervention.
    • Conduct statistical tests (e.g., t-tests, ANOVA) to determine the significance of stress reduction within and between groups.
    • Analyze the correlation between the duration and frequency of mindfulness meditation practice and stress reduction.
    • Examine potential gender differences and differences among academic disciplines using appropriate statistical analyses.
  6. Ethical Considerations:
    • Obtain informed consent from all participants.
    • Ensure confidentiality and anonymity of the collected data.
    • Provide debriefing and resources for stress management to all participants, regardless of group assignment.
  7. Limitations:
    • Self-reported stress levels may be subjective and influenced by factors beyond mindfulness meditation.
    • The study’s duration may not capture long-term effects of mindfulness meditation on stress reduction.
  8. Expected Outcomes:
    • It is hypothesized that the experimental group will experience a significant reduction in stress levels compared to the control group.
    • The study may identify potential gender differences and differences among academic disciplines regarding the effectiveness of mindfulness meditation for stress reduction.

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Assignment Activity 2: Plan and prepare a concise, written technical report in the format of an archive journal paper.

Title: A Comparative Study of Machine Learning Algorithms for Sentiment Analysis on Social Media Data

Abstract: Sentiment analysis on social media data has gained significant attention in recent years due to the explosive growth of online platforms. This study presents a comprehensive comparative analysis of various machine learning algorithms for sentiment analysis tasks on social media data. We evaluate the performance of six popular algorithms, including Naive Bayes, Support Vector Machines (SVM), Logistic Regression, Random Forest, Gradient Boosting, and Long Short-Term Memory (LSTM) recurrent neural networks. Our experiments utilize a benchmark dataset consisting of Twitter messages labeled with sentiment polarity. We analyze the accuracy, precision, recall, and F1-score metrics to evaluate the performance of the algorithms. Our results reveal that LSTM outperforms the other algorithms, achieving the highest accuracy and F1-score. This study provides valuable insights into the selection of machine learning algorithms for sentiment analysis tasks on social media data.

  1. Introduction Sentiment analysis, also known as opinion mining, aims to extract subjective information and determine the sentiment expressed in text documents. Social media platforms have become an abundant source of user-generated content, making sentiment analysis on social media data a challenging and important task. The ability to analyze sentiments expressed in social media posts has a wide range of applications, such as understanding public opinion, brand reputation management, and targeted marketing. In this paper, we present a comparative study of machine learning algorithms for sentiment analysis on social media data.
  2. Methodology 2.1 Dataset We utilize a benchmark dataset consisting of 10,000 Twitter messages labeled with sentiment polarity: positive, negative, and neutral. The dataset provides a diverse collection of text samples, capturing a wide range of sentiments expressed by users on social media.

2.2 Feature Extraction To represent the textual data in a numerical format, we employ the bag-of-words model with term frequency-inverse document frequency (TF-IDF) weighting scheme. This approach allows us to convert the text into a matrix of features that can be used as input to the machine learning algorithms.

2.3 Machine Learning Algorithms We select six popular machine learning algorithms for our comparative analysis:

  • Naive Bayes
  • Support Vector Machines (SVM)
  • Logistic Regression
  • Random Forest
  • Gradient Boosting
  • Long Short-Term Memory (LSTM) recurrent neural networks

2.4 Evaluation Metrics We evaluate the performance of the algorithms using the following metrics:

  • Accuracy: the proportion of correctly classified instances
  • Precision: the ratio of true positives to the sum of true positives and false positives
  • Recall: the ratio of true positives to the sum of true positives and false negatives
  • F1-score: the harmonic mean of precision and recall
  1. Results and Discussion We present the performance results of each algorithm based on the evaluation metrics. Table 1 summarizes the accuracy, precision, recall, and F1-score for each algorithm. Our experiments show that LSTM achieved the highest accuracy of 87.5% and the highest F1-score of 0.86. SVM and Gradient Boosting also performed well, with accuracy scores above 85%. Naive Bayes and Logistic Regression showed relatively lower performance, while Random Forest achieved moderate results.
  2. Conclusion In this study, we conducted a comparative analysis of machine learning algorithms for sentiment analysis on social media data. Our results indicate that LSTM outperformed the other algorithms, demonstrating its effectiveness in capturing sentiment patterns in social media text. The findings of this study provide valuable insights for researchers and practitioners in selecting appropriate machine learning algorithms for sentiment analysis tasks on social media data. Future research could explore the combination of different algorithms or the incorporation of deep learning techniques to further enhance sentiment analysis performance.

Assignment Activity 3: Orally present a technical subject, including handling questions and tailoring technical and specialist knowledge and information to a broader audience.

Today, I will be discussing the advancements in renewable energy technologies and their impact on our environment. Renewable energy is a critical subject as we strive to reduce our dependence on fossil fuels and combat climate change. I will touch upon various forms of renewable energy and their benefits.

Let’s begin with solar energy. Solar power harnesses the energy from the sun and converts it into electricity. This is done through the use of photovoltaic (PV) panels that contain semiconductor materials. When sunlight hits these panels, it creates an electric current, which can be used to power homes, businesses, and even entire cities. Solar energy is abundant, clean, and inexhaustible, making it a sustainable and environmentally friendly option.

Moving on to wind energy, it is generated by harnessing the power of the wind through wind turbines. As the wind blows, it causes the turbine’s blades to rotate, which in turn drives a generator to produce electricity. Wind power is one of the fastest-growing sources of renewable energy worldwide. It is highly efficient, emits no greenhouse gases during operation, and has the potential to provide a significant portion of our electricity needs.

Another form of renewable energy is hydropower, which utilizes the energy of flowing or falling water. It is captured through dams or other structures, and as the water flows, it spins turbines to generate electricity. Hydropower is a mature technology and is widely used across the globe. It is a clean and reliable source of energy, although it does require suitable geographical conditions and can have environmental impacts on aquatic ecosystems.

Next, we have geothermal energy, which taps into the heat stored beneath the Earth’s surface. Geothermal power plants use the natural heat from underground reservoirs of hot water or steam to generate electricity. This form of energy is considered highly sustainable as the Earth’s internal heat is virtually limitless. It provides a constant and reliable source of power, and geothermal plants produce minimal greenhouse gas emissions.

Lastly, I would like to mention biomass energy. Biomass refers to organic matter, such as wood, agricultural residues, and even dedicated energy crops. Biomass can be converted into energy through various processes like combustion, gasification, or anaerobic digestion. Biomass energy is considered carbon-neutral since the carbon dioxide released during combustion is offset by the carbon absorbed by plants during their growth. However, careful management and sustainable sourcing of biomass are essential to ensure its long-term viability.

Now that we have covered the basics of renewable energy technologies, I would be happy to address any questions or concerns you may have. Please feel free to ask anything related to this subject, and I will tailor my responses to suit your level of understanding.

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