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Articles 21901 - 21930 of 196020

Full-Text Articles in Engineering

Temporal Dynamics In Diabetes Prediction: A Sensor-Driven Time-Series Exploration, Monica Meduri Jan 2024

Temporal Dynamics In Diabetes Prediction: A Sensor-Driven Time-Series Exploration, Monica Meduri

Master's Projects

Diabetes is a lifelong illness that, if not detected or managed appropriately, turns into serious complications. Correct glucose forecasting is critical to ensuring timely interventions, thereby minimizing risks of hyperglycemia and hypoglycemia, and optimizing the management strategies of the disease. Classical machine learning models have been applied in the blood glucose forecasting problem for a long time, however, usage of transformer-based architectures is still scarce within the literature. Due to the self-attention mechanism, transformers can capture temporal relationships very effectively, which makes them suitable for time-series data. TFT is a novel framework proposed here to utilize time-series data from CGM …

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Multi-Platform Cyberbullying Detection Using Nlp And Machine Learning, Chinmayi Lokeshwar Hegde Jan 2024

Multi-Platform Cyberbullying Detection Using Nlp And Machine Learning, Chinmayi Lokeshwar Hegde

Master's Projects

The issue of cyberbullying is growing due to the online anonymity and due to online platforms having less repercussions. This research proposes for proactive measures to detect and prevent such behavior before it reaches the victim. By using data from various social media platforms and employing machine learning techniques, this research proposes an innovative system aimed at identifying and thwarting cyberbullying incidents preemptively. While existing methods have primarily focused on prediction and detection of cyberbullying incidents, there remains a significant gap in research regarding prevention strategies. This project aims to address this gap by leveraging machine learning, natural language processing …

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Intelligent Caching In Named Data Networking, Deep Pradipbhai Shah Jan 2024

Intelligent Caching In Named Data Networking, Deep Pradipbhai Shah

Master's Projects

Named Data Networking (NDN) has a built-in caching capability that is enabled with the help of its Content Store. Caching in NDN has several benefits, such as reducing overhead on the producer side, avoiding a single point of failure, and reducing network load. The primary caching policy of the NDN architecture is to leave copies everywhere. However, this scheme induces significant cache redundancy. Existing advanced cache techniques either periodically share the entire list of cached content at a node or make a caching decision without knowing the cached content at other nodes in the network. We propose an intelligent cache …

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Multimodal Retrieval-Augmented Generation: Design And Application, Charul Rathore Jan 2024

Multimodal Retrieval-Augmented Generation: Design And Application, Charul Rathore

Master's Projects

The rapid advancement in generative AI and large language models have forever revolutionized how we synthesize data. This project explores and experiments with the potential of a multimodal Retrieval-Augmented Generation (RAG) framework for processing text, tabular and image data. Starting with prompt engineering techniques, we address their limitations in dynamic and domain-specific real world applications by building a multimodal RAG pipeline and evaluating it against human-generated ground truth. The project culminates in BrightMind.ai, a full-stack educational platform featuring novel personalized AI companions for context-aware and adaptive response generation. Its innovative capabilities extend to music, video, and code generation, setting it …

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Comparison Of Protein Structures Predicted By Genai Tools In A Zero-Shot Manner, Kruthi Shankar Rao Jan 2024

Comparison Of Protein Structures Predicted By Genai Tools In A Zero-Shot Manner, Kruthi Shankar Rao

Master's Projects

Generative AI models have vast applications and one such critical application explored in this study is protein structure prediction. The 3D structures of proteins determine their function. Our study mainly focuses on using generative AI models such as ESMFold and ColabFold to predict and examine naturally occurring and mutated sequences. The workflow begins with collecting antimicrobial resistance (AMR) and toxin-antitoxin (TA) protein data. The sequences are applied over pretrained AI models to predict protein structures. Following this, models are fine-tuned with original and mutated target datasets. A comparison of models’ performances is done using metrics such as root mean square …

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Leveraging Large Language Models For Transforming Student Information Into Actionable Data, Sree Hari Karri Jan 2024

Leveraging Large Language Models For Transforming Student Information Into Actionable Data, Sree Hari Karri

Master's Projects

Admission season places significant demands on university committees, necessitating the review of vast arrays of documents to assess students’ competence. This project advances the development of an automated system designed to streamline this process by evaluating application materials such as Letters of Recommendation (LoRs), Statements of Purpose (SoPs), and resumes. Utilizing a variety of advanced Natural Language Processing (NLP) techniques, the system compares the performance of several Large Language Model (LLM) approaches. It also experiments with different data handling strategies, including the use of vector stores versus traditional context-based processing, to optimize model efficiency and accuracy. Special attention is given …

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Implicit Personality Detection From User Behaviour In Recommendation Systems, Uzma Zubair Shaikh Jan 2024

Implicit Personality Detection From User Behaviour In Recommendation Systems, Uzma Zubair Shaikh

Master's Projects

Recommendation systems are an integral part of any business, and a crucial factor in determining their success as these systems help businesses in marketing their products to the right kind of audience. Conventional methods of building recommendation systems such as collaborative filtering and content-based recommendation, although effective, suffer from limitations such as cold start and the data sparsity problems. Moreover, these methods aim at finding similar products as user’s past interactions rather than personalizing the recommendations. The upsurge in use of social media, over-the-top content (OTT), and e-commerce platforms has made the task of personalizing recommendations imperative, leading to the …

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Exploring The Use And Misuse Of Large Language Models (Llms), Hezekiah Paul D. Valdez Jan 2024

Exploring The Use And Misuse Of Large Language Models (Llms), Hezekiah Paul D. Valdez

Master's Projects

Large Language Models (LLMs) have quickly gone from simple rule-based systems to complex knowledge bases capable of tackling many different tasks across a variety of fields. What began as an exercise in human-computer interaction has become the basis for artificial intelligence in a variety of mediums. When attached to larger systems, LLMs become generative assistants that can perform highly on human proficiency assessments and other benchmark skill assessments. This increase in proficiency has led these systems to be deployed in fields such as cybersecurity, business, and programming to help improve productivity and efficiency. However, such a wide availability has allowed …

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Knowledge Graph-Based Multiple-Choice Question Generation, Durga Muralidharan Jan 2024

Knowledge Graph-Based Multiple-Choice Question Generation, Durga Muralidharan

Master's Projects

Knowledge-based tests are widely used to assess knowledge on a specific subject and have many applications in education and professional certifications. These tests usually consist of Multiple Choice Questions (MCQs), where a question with a few possible answers is given. Along with the correct answer, three or more incorrect answers are provided, which are called distractors. MCQs are a popular method for these tests because they are easy to grade. These tests can check different levels of comprehension ranging from beginners to advanced by creating distractors that may confuse unprepared test takers. This project proposes the Knowledge Graph Multiple Choice …

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Code Quality Enhancement: Evaluating Ai Code Generation With Software Metrics, Sirisha Krishna Murthy Jan 2024

Code Quality Enhancement: Evaluating Ai Code Generation With Software Metrics, Sirisha Krishna Murthy

Master's Projects

With the advancements in the stream of AI in the recent time and the evolution of Generative AI, it is a given that there is a need to effectively integrate AI into daily tasks, including Coding. When talking about Generative AI, one important thing to consider is prompting, which is that way to talk to the AI. Depending on specific needs and tasks the way we need to prompt AI can vary. With rapid development in the field, there are a lot of new benchmarks that evaluate the AI coders on correctness, but to effectively adapt AI into actual coding …

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Enhancing Qwen2.5-Coder: A Deep Dive Into Fine-Tuning Using Peft For Superior Code Outputs, Lohith Nagaraja Jan 2024

Enhancing Qwen2.5-Coder: A Deep Dive Into Fine-Tuning Using Peft For Superior Code Outputs, Lohith Nagaraja

Master's Projects

The main objective of this research is to improve the quality of software code that is produced by the Qwen2.5-Coder model specifically in terms of maintainability, complexity, and reliability. Our approach is going to be a more specific one that will involve the Parameter-Efficient Fine Tuning (PEFT) framework combined with quantization through Low-Rank Adaption (LoRA). This approach involves fine-tuning only some of the parameters of a model to make it suitable for software programming with the general structure of the model largely intact. In this paper, SonarQube is used as a tool to help quantify the improvements made to the …

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Facial Expression Mood Classification Using Machine Learning, Tiantong Li Jan 2024

Facial Expression Mood Classification Using Machine Learning, Tiantong Li

Master's Projects

Facial expression classification is a powerful tool for understanding human emotions, with applications spanning human-computer interaction, healthcare, and entertainment. By analyzing facial cues, systems can interpret emotional states and adapt their responses, creating more personalized and emotionally aware experiences. One emerging application of facial expression classification is in music recommendation systems, where user emotions are integrated to suggest music that aligns with their current mood. While prior research has primarily classified facial expressions into four emotion categories, this study broadens the scope to seven emotions: angry, disgust, fear, happy, neutral, sad, and surprise. The project evaluates four machine learning techniques—CNN, …

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Detecting Crustose Coralline Algae (Cca) In Marine Photos Using Mask R-Cnn, Vrushali Harshwardhan Deshpande Jan 2024

Detecting Crustose Coralline Algae (Cca) In Marine Photos Using Mask R-Cnn, Vrushali Harshwardhan Deshpande

Master's Projects

Coral reefs, made up of thousands of polyps - tiny sac-like marine invertebrates sea anemones and jellyfish, are important to marine ecosystems and prevent loss of life by acting as a natural barrier against storms, floods, and waves. These reefs support a wide range of species, many of which are underexplored and new species being discovered regularly. Crustose coralline algae (CCA) is one of the vital algal species that provides reef structure. Studying the abundance of CCA is important in helping marine biologists analyze coral reef health while understanding the impact of climate change on the marine lifeforms. This study …

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Cluster Analysis For Concept Drift Detection In Malware, Aniket Mishra Jan 2024

Cluster Analysis For Concept Drift Detection In Malware, Aniket Mishra

Master's Projects

The rapid evolution of malware presents significant challenges for detection systems. This is due to malware families adapting through feature manipulation and obfuscation, which causes concept drift. A clustering based approach is used to detect and adapt to these shifts. The KronoDroid dataset is segmented into batch sizes of 50 and analyzed with MiniBatch K-Means clustering. The silhouette coefficient is used to evaluate clustering quality, and help identify drift by detecting significant changes in cluster patterns. Concept drift will cause retraining of supervised classifiers, including Linear SVM, RF, MLP, and XGBoost. Three scenarios are used: static models, periodic retraining, and …

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Extending A Graphical User Interface For Evidential Reasoning, Vaidehi Sanjay Joshi Jan 2024

Extending A Graphical User Interface For Evidential Reasoning, Vaidehi Sanjay Joshi

Master's Projects

Systems like Capri are used for large-scale graph modeling and integration and PyGrapher aims to do that in a simplified manner. This project is an extension of PyGrapher which was a tool created by previous students at the university. The enhancements include adding customizable default parameters for nodes and edges, automating JSON conversion, and enabling real-time highlighting. These features specifically aim to improve usability, streamline workflows, and provide interactive feedback for the users. The enhancement of the project also added additional and rigorous testing of the platform's compatibility and user interaction. It demonstrates significant improvements in functionality and user experience. …

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Enhanced Inter-Satellite Routing With Multi-Path Selection And Congestion Modeling, Jaesung Yoo Jan 2024

Enhanced Inter-Satellite Routing With Multi-Path Selection And Congestion Modeling, Jaesung Yoo

Master's Projects

Satellite networks play a crucial role in global connectivity today and making efficient routing algorithms is crucial for optimal performance. While existing routing algorithms have made significant progress using machine learning techniques, they often overlook network congestion and multiple path availability. This report introduces an enhanced routing framework that builds upon LSTM-based predictive routing using dynamic congestion modeling and multi-path selection. Our approach introduces a busy state metric that tracks satellite memory utilization, allowing for adaptive path selection based on both distance and current network load. Through simulations using a constellation of 20 satellites, our enhanced algorithm demonstrates significant improvements …

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Llamatalk: Empowering Conversations With Retrieval-Augmented Generation, Aravind Rokkam Jan 2024

Llamatalk: Empowering Conversations With Retrieval-Augmented Generation, Aravind Rokkam

Master's Projects

This research report talks about the implementation and a comparative study of Llama 7B model’s fine-tuning technique and Retrieval Augmented Generation (RAG) capabilities in the context of creating a reliable AI therapist. This study focuses on training these models using diverse datasets consisting of doctor-patient conversations predominantly addressing general health issues. Using a technique like fine-tuning within the Llama 7B model, the project focuses on training the model with a diverse dataset comprising doctor-patient interactions primarily addressing general health concerns. Additionally, carefully organized mental health dataset from HOPE dataset, ensuring the bot's responsiveness to mental health inquiries. Through integration with …

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Teaching Children Programming Concepts Through Video Games, Kayla Musleh Jan 2024

Teaching Children Programming Concepts Through Video Games, Kayla Musleh

Master's Projects

Children have a tendency to lose focus when they are presented with something that does not entertain them or tailor to their personal interests; such as studying [1], [3], [10]. The research performed for this project focuses on studying how much more children can comprehend and focus on learning educational material if they are learning through playing a video game rather than being taught information directly in a typical classroom manner. For this study I created a computer game designed to introduce educational subjects such as mathematics and programming concepts to the child playing the game. By completing the tasks …

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Exploring Fluctuations In Working Memory Load Through Pupillometry Using Gabor Image Deletion Tasks, Neenu Antony Jan 2024

Exploring Fluctuations In Working Memory Load Through Pupillometry Using Gabor Image Deletion Tasks, Neenu Antony

Master's Projects

Van der Wel & Van Steenbergen mention that there has been a surge in pupillometry research in the past two decades, particularly in the area of task-evoked pupil dilation in the context of cognitive control tasks. The goal of most of these studies has been focused on finding a link between pupil dilation and effort exerted by an individual [10]. The review by authors Van der Wel & Van Steenbergen, aimed to assess the potential of pupil dilation as an indicator of effort rather than task complexity. Their analysis revealed that heightened task demands in domains such as updating, switching, …

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Wall-E: An Autonomous Ai Rover For Precision Agriculture, Simar Ghumman Jan 2024

Wall-E: An Autonomous Ai Rover For Precision Agriculture, Simar Ghumman

Master's Projects

Unmanned Ground Vehicles (UGVs) are emerging as a crucial tool in the world of precision agriculture. By working with UGVs equipped with machine learning, we can find solutions to a range of complex agricultural problems. My project, titled “Wall-E: Artificial Intelligence Robot for Precision Agriculture,” focuses on developing a UGV capable of navigating through agriculture fields autonomously while capturing data. Using machine learning, computer vision, and other sensor technologies, Wall-E is capable of estimating the total yield of crops, self-localization, mapping its environment in real time, and avoiding obstacles along its route. The purpose of this project is to automate …

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Malware Detection Using Qr And Aztec Code Representations, Atharva Khadilkar Jan 2024

Malware Detection Using Qr And Aztec Code Representations, Atharva Khadilkar

Master's Projects

In recent years, the use of image-based techniques for malware detection has gained prominence, with numerous studies demonstrating the efficacy of deep learning approaches such as convolutional neural networks (CNNs) in classifying images derived from executable files. In this paper, we consider an innovative method that relies on an image conversion process that consists of transforming executable files into QR and Aztec codes. These codes capture structural patterns in a format that may enhance the learning capabilities of CNNs. We design and implement CNN architectures tailored to the unique properties of these codes and apply them to a comprehensive analysis …

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An Ai-Based Conceptual Framework To Improve Program Management Of Complex Systems, Michael D. Parrish, Steven Corns Jan 2024

An Ai-Based Conceptual Framework To Improve Program Management Of Complex Systems, Michael D. Parrish, Steven Corns

Engineering Management and Systems Engineering Faculty Research & Creative Works

With evolving technologies, changing requirements, and limited budgets, governments and industries need to consider new methodologies to help streamline program lifecycle management, from cradle to grave, to ensure projects are delivered on time, on budget, and to the expected performance standards. Traditional approaches fail to adequately address the added complexities of System of Systems programs such as integration, interoperability, and variable lifecycle of subcomponents. The objective of this study is to assess and address the research question - can a new acquisition approach be designed to address and improve program lifecycle management of complex systems? A comparison study, using the …

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Navigating Innovation Within A Contractor's Business Model: A System Dynamics Approach, Mariam Elazhary, Cihan Dagli, Islam H. El-Adaway Jan 2024

Navigating Innovation Within A Contractor's Business Model: A System Dynamics Approach, Mariam Elazhary, Cihan Dagli, Islam H. El-Adaway

Engineering Management and Systems Engineering Faculty Research & Creative Works

Innovation is essential in the construction industry, but current practices often focus narrowly on specific aspects rather than taking a comprehensive approach. To address this, systematic innovation through business model innovation (BMI) is proposed to enhance overall performance. Despite increasing interest in BMI research, it remains in its early stages, lacking quantitative analysis of its impact on business model components. This paper develops a framework using system dynamics to assess changes to a construction company's BM when faced with a variety of innovations across various projects. This shall be accomplished by (1) establishing factors and their relationships that define a …

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A Model-Adaptive Random Search Actor Critic: Convergence Analysis And Inventory-Control Case Studies, Yuehan Luo, Jiaqiao Hu, Abhijit Gosavi Jan 2024

A Model-Adaptive Random Search Actor Critic: Convergence Analysis And Inventory-Control Case Studies, Yuehan Luo, Jiaqiao Hu, Abhijit Gosavi

Engineering Management and Systems Engineering Faculty Research & Creative Works

Reinforcement learning (RL) is an exciting area within the domain of Markov Decision Processes (MDPs) in which the underlying optimization problem is solved either in a simulator of the real-world system or via direct interaction with the real-world system, when its underlying transition probabilities are difficult to estimate. The latter is commonly true of large-scale, real-world MDPs with complex underlying transition dynamics. RL is currently being widely researched in the world of medicine/neuroscience after some spectacular success stories demonstrating super-human behavior in computer games. In this paper, we propose a new actor-critic-based RL algorithm for approximately solving continuous state/action MDPs …

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A Simulation-Based Digital Twin For Data-Driven Maintenance Scheduling Of Risk-Prone Production Lines Via Actor Critics, Abhijit Gosavi, Aparna Gosavi Jan 2024

A Simulation-Based Digital Twin For Data-Driven Maintenance Scheduling Of Risk-Prone Production Lines Via Actor Critics, Abhijit Gosavi, Aparna Gosavi

Engineering Management and Systems Engineering Faculty Research & Creative Works

Industry 4.0 mandates a shift from traditional total productive maintenance (TPM) methods, which rely on periodic data gathering and subsequent offline modeling for maintenance scheduling, towards more data-driven and online decision-making approaches. Further, Industry 4.0 emphasizes reducing intervention from high-skilled managers and leveraging real-time data for decision-making, which is characteristic of models rooted in either renewal-theoretic or Markov chains for traditional TPM methods. Digital Twins (DTs), which are virtual representations of physical systems, play a crucial role in this paradigm by enabling online decision-making of maintenance scheduling directly at the workstation level. In this paper, a simulation-based DT (S-DT) is …

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Cf/Sic Ceramic Matrix Composites With Extraordinary Thermomechanical Properties Up To 2000 °C, Min Sung Park, Jian Gu, Heesoo Lee, Sea Hoon Lee, Lun Feng, William Fahrenholtz Jan 2024

Cf/Sic Ceramic Matrix Composites With Extraordinary Thermomechanical Properties Up To 2000 °C, Min Sung Park, Jian Gu, Heesoo Lee, Sea Hoon Lee, Lun Feng, William Fahrenholtz

Materials Science and Engineering Faculty Research & Creative Works

The thermomechanical properties of carbon fiber reinforced silicon carbide ceramic matrix composites (Cf/SiC CMCs) were studied up to 2000 °C using high temperature in situ flexural testing in argon. The CMC specimens were fabricated using an ultrahigh concentration (66 vol%) aqueous slurry containing nano-sized silicon carbide powder. The SiC powder compacts were obtained by drying the slurry and were densified using the precursor impregnation and pyrolysis (PIP) method with field assisted sintering technology/spark plasma sintering (FAST/SPS). The high relative density of the SiC green body (77.6%) enabled densification within 2.5 days using four PIP cycles. In contrast, conventional PIP processes …

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Thermodynamic Analysis Of Metal Segregation In Dual Phase High Entropy Ceramics, Steven M. Smith, William G. Fahrenholtz, Gregory E. Hilmas, Stefano Curtarolo Jan 2024

Thermodynamic Analysis Of Metal Segregation In Dual Phase High Entropy Ceramics, Steven M. Smith, William G. Fahrenholtz, Gregory E. Hilmas, Stefano Curtarolo

Materials Science and Engineering Faculty Research & Creative Works

Equilibrium Gibbs' free energy calculations were used to determine metal segregation trends between boride and carbide solid solutions containing two metals that are relevant to dual phase high entropy ceramics. The model predicted that Ti had the strongest tendency to segregate to the boride phase followed by Zr, Nb, Mo, V, Hf, and Ta, which matches experimental results of measured compositions. The ratio of a metal in the carbide phase to the content of the same metal in the corresponding metal boride had a linear trend with the change in standard Gibbs' free energy of reaction for a metal carbide …

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Spark Plasma Sintering Of Magnesium Titanate Ceramics, Suzana Filipović, Nina Obradović, William G. Fahrenholtz, Steven Smith, Miljana Mirković, Adriana Peleš Tadić, Jovana Petrović, Antonije Đorđević Jan 2024

Spark Plasma Sintering Of Magnesium Titanate Ceramics, Suzana Filipović, Nina Obradović, William G. Fahrenholtz, Steven Smith, Miljana Mirković, Adriana Peleš Tadić, Jovana Petrović, Antonije Đorđević

Materials Science and Engineering Faculty Research & Creative Works

Magnesium titanate ceramics were prepared by reactive spark plasma sintering (SPS) at 1200 °C for 5 min. Prior to sintering, MgO and TiO2 powders were mixed by high energy ball milling (HEBM) for 15, 30, or 60 min. The effect of milling time on phase composition was analyzed by X-ray diffraction (XRD) for milled powders and sintered specimens. The morphology of the sintered ceramics was investigated by scanning electron microscopy (SEM), while elemental distribution was determined by energy dispersive spectroscopy (EDS). The presence of the MgTi2O5 phase was detected in XRD and was confirmed by EDS analysis. Microcracking was …

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Morphology And Particle Size (Maps) Exercise: Testing The Applications Of Image Analysis And Morphology Descriptions For Nuclear Forensics, Stuart A. Dunn, Ian J. Schwerdt, David E. Meier, Naomi E. Marks, Thomas Shaw, Alexa Hanson, Kari Sentz, Meena Said, Richard A. Clark, Kyle A. Makovsky, Jason M. Lonergan, Matthew Gilbert Jan 2024

Morphology And Particle Size (Maps) Exercise: Testing The Applications Of Image Analysis And Morphology Descriptions For Nuclear Forensics, Stuart A. Dunn, Ian J. Schwerdt, David E. Meier, Naomi E. Marks, Thomas Shaw, Alexa Hanson, Kari Sentz, Meena Said, Richard A. Clark, Kyle A. Makovsky, Jason M. Lonergan, Matthew Gilbert

Materials Science and Engineering Faculty Research & Creative Works

Image analysis techniques have been applied and shown to be a valuable tool in nuclear forensics analysis. The interlaboratory exercise reported here has tested quantitative and qualitative approaches for characterizing nuclear materials. Particle size, surface features and morphology descriptions were compared by four laboratories on a common image set generated by Scanning Electron Microscopy and Digital Light Microscopy. Quantitative analysis of the image sets through the Morphological Analysis for Materials software highlighted the strength of image analysis, but also that the application of the software alone can introduce significant bias in the analysis. Qualitative morphology descriptions following the process outlined …

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A Super-Hard High Entropy Boride Containing Hf, Mo, Ti, V, And W, Suzana Filipovic, Nina Obradovic, Greg E. Hilmas, William G. Fahrenholtz, Donald W. Brenner, Jon Paul Maria, Douglas E. Wolfe, Eva Zurek, Xiomara Campilongo, Stefano Curtarolo Jan 2024

A Super-Hard High Entropy Boride Containing Hf, Mo, Ti, V, And W, Suzana Filipovic, Nina Obradovic, Greg E. Hilmas, William G. Fahrenholtz, Donald W. Brenner, Jon Paul Maria, Douglas E. Wolfe, Eva Zurek, Xiomara Campilongo, Stefano Curtarolo

Materials Science and Engineering Faculty Research & Creative Works

Super-Hard (Hf,Mo,Ti,V,W)B2 Was Synthesized by Boro-Carbothermal Reduction and Densified by Spark Plasma Sintering. This Composition Was Produced for the First Time as a Single-Phase Ceramic in the Present Research. the Optimized Ceramic Had a Single Hexagonal AlB2-Type Crystalline Phase with a Grain Size of 3.8 µm and Homogeneous Distribution of the Constituent Metals. the Vickers Hardness Exhibited the Indentation Size Effect, Increasing from 27 GPa at a Load of 9.8 N to as High as 66 GPa at a Load of 0.49 N. This is the Highest Hardness Reported to Date for High Entropy Boride Ceramics.

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