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Articles 1 - 30 of 1369
Full-Text Articles in Engineering
Development And Evaluation Of Machine Learning Models For Early Pediatric Sepsis Prediction, Ancita M. Andrade
Development And Evaluation Of Machine Learning Models For Early Pediatric Sepsis Prediction, Ancita M. Andrade
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Sepsis is a leading cause of pediatric mortality, claiming more lives in the United States annually than all childhood cancers combined. Early identification in Emergency Departments (EDs) remains challenging, as the current Phoenix criteria establishes an updated international consensus definition for sepsis, however is not designed for use as a screening tool. This study aimed to develop predictive models identifying pediatric patients at risk of sepsis within 24 hours of admission. Multiple tree-based and deep learning models were trained utilizing clinical and laboratory data from the initial four hours of presentation. Both the LightGBM and LSTM architectures demonstrated superior performance, …
Cellular Mechanisms Of Spinal Motoneuron Hypoexcitability Underlying Dynapenia In Aging, Ibrahim Abdul Halim
Cellular Mechanisms Of Spinal Motoneuron Hypoexcitability Underlying Dynapenia In Aging, Ibrahim Abdul Halim
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Age-related weakness remains poorly understood as the underlying mechanisms remain unclear. While synaptic input and muscular changes have been investigated with age, intrinsic motoneuron excitability alterations are often overlooked. This thesis provides the first direct assessment of intrinsic excitability and ion channel properties of spinal α-MNs from male and female mice across three ages: young, middle aged, and old. Our findings reveal a decline in intrinsic excitability of motoneurons with age in both sexes. Mechanistic analysis shows sex specific differences: female motoneurons exhibit increased dendritic size, hyperpolarized RMP, and SK channel overactivation, whereas males show only SK overactivation with age. …
Wearable Sensor Data Analysis For Machine Learning-Based Detection Of Posture And Autonomic Responses, Chaitanya Vardhini Anumula
Wearable Sensor Data Analysis For Machine Learning-Based Detection Of Posture And Autonomic Responses, Chaitanya Vardhini Anumula
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This study investigates how Iyengar yoga postures influence autonomic nervous system (ANS) activity by analyzing multimodal physiological signals collected via wearable sensors. The physiological mechanisms underlying Iyengar yoga’s therapeutic effects remain under-explored at the granular, pose-level. Using data collected from 16 participants, this research evaluates whether machine learning models can distinguish between baseline, parasympathetic-dominant, and sympathetic-dominant states based on wrist-worn sensor data. The goals were to explore whether subtle postural variations elicit measurable autonomic responses and to identify which sensor features most effectively capture these changes. Participants performed a sequence of yoga poses while wearing synchronized sensors measuring electrodermal activity …
Impact Of Graph Structures For Rag Outcomes In Llms, Chris Davis Jaldi
Impact Of Graph Structures For Rag Outcomes In Llms, Chris Davis Jaldi
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Explainability, interpretability and adaptability (EIA) remain three central motivations for next-generation Artificial Intelligence (AI), especially as Large Language Models (LLMs) continue to engage with ever-increasing knowledge bodies. As the landscape pushes toward controllable agentic Retrieval-Augmented Generation (RAG) systems where AI agents engage in iterative, guided reasoning, a critical question arises as to the extent to which the knowledge design itself shapes these models' reasoning behavior. This work conducts a systematic evaluation of how different conceptualizations and representation of the identical knowledge affect an LLM's path-based reasoning capabilities. Through the introduction of controlled variations along graph structural complexity, linguistic and semantic …
Re-Parameterizing Adversarial Reprogramming In Low-Dimensional Subspace For Efficient Software Vulnerability Detection, Hootan Alavizadeh
Re-Parameterizing Adversarial Reprogramming In Low-Dimensional Subspace For Efficient Software Vulnerability Detection, Hootan Alavizadeh
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Software vulnerabilities are a major cause of security breaches, making effective detection critical. Traditional learning-based methods require large datasets and significant computational resources, which are often impractical due to high annotation costs and data scarcity. To address this, we propose an innovative system, RearVul, which Re-parameterizes adversarial reprogramming in a low-dimensional subspace for software vulnerability detection. Unlike conventional approaches, RearVul repurposes a pre-trained classification model using adversarial reprogramming, enabling detection with minimal modifications. It learns a universal perturbation applied to program representations, preserving the original model’s feature extraction capabilities while adapting it to a new domain. Furthermore, we introduce a …
Subjective Readiness Forecasting Using Supervised Machine Learning And Wearable Device Data, Nathaniel Michael Weiland
Subjective Readiness Forecasting Using Supervised Machine Learning And Wearable Device Data, Nathaniel Michael Weiland
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Recent advances in wearable technology allow continuous monitoring of physiological and behavioral data, opening new opportunities for real-time assessments of readiness and well-being. However, creating predictive models that generalize across diverse users remains challenging, especially in high-stakes settings like the military, where preventable injuries, illnesses, and stress-related performance declines are frequent. This research assesses the feasibility of using supervised machine learning models trained on wearable device data to predict subjective readiness indicators—recovery, stress, injury, and illness. Data from over 10,000 users in the OHWS (Optimizing the Human Weapons System) program combined daily check ins with physiological metrics from Garmin, Polar, …
Computational Assessment Of Vitrimers Self-Healing For Renewable Energy And Aerospace Structures, Walaaeldin Mohamed Ahmed Derbala
Computational Assessment Of Vitrimers Self-Healing For Renewable Energy And Aerospace Structures, Walaaeldin Mohamed Ahmed Derbala
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Self-healing polymers, particularly vitrimers, are emerging as promising candidates in the development of advanced materials for renewable energy and aerospace structures. These materials exhibit dynamic covalent bond exchange mechanisms that enable reprocess ability, damage repair, and extended operational lifetime under harsh conditions. This study presents a density functional theory (DFT)-based computational investigation of the mechanistic pathways and energetics of bond exchange reactions in model vitrimer systems. We explore transition states, energy barriers, and thermodynamic features corresponding to associative and dissociative self-healing reactions in vitrimers. The study focuses on Diaminodiphenyl disulfide (AFD), a bifunctional molecule composed of two para-substituted aminophenyl rings …
Ab Initio Simulations For Oxidation Of An Ultra-High Temperature Ceramic (Hfb2), Wesley I. Black
Ab Initio Simulations For Oxidation Of An Ultra-High Temperature Ceramic (Hfb2), Wesley I. Black
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Ultra-High Temperature Ceramics are a class of ceramics that possess high strength and melting points in excess of 3000°C. These ceramics are promising for aerospace applications, where materials need to endure high-temperature and high-stress environments. Utilizing ab initio simulations, this thesis research focuses on the atomistic details of oxidation for a typical ultra-high temperature ceramic material, namely hafnium diboride. The simulations provide energy barriers for transition from the initial to final structures via transition states on the (0 0 0 1) surface. These result in estimates for reaction rates and other thermodynamic features that are essential for assessing applicability under …
Reinforcement Learning For Adversarial Environments: Multi-Agent Hide And Seek With Multi-Modal Sensing, Christian Alejandro Carrizales
Reinforcement Learning For Adversarial Environments: Multi-Agent Hide And Seek With Multi-Modal Sensing, Christian Alejandro Carrizales
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The development of intelligent and competitive agents in AI versus AI adversarial environments was explored through the utilization of reinforcement learning techniques with sensing modalities. A Hide-and-Seek simulation environment was developed using the Unity game engine along with the ML-Agents Toolkit. An engagement test campaign with a set of performance metrics was designed. Four AI versus AI adversarial scenarios were considered using the Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC) multi-agent reinforcement learning algorithms. Furthermore, the impact of sensing modalities on competing agents’ learning performance was investigated by varying the sensing capabilities of the hider and seeker, respectively. Experiments …
Dataset Generation For Routing Policy Study In Ad Hoc Wireless Networks, Vishnu Vishnu Priya
Dataset Generation For Routing Policy Study In Ad Hoc Wireless Networks, Vishnu Vishnu Priya
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Ad Hoc wireless networks, with their decentralized architecture and dynamic topology, present challenges in reliable and energy-efficient routing. While machine learning (ML) and reinforcement learning (RL) offer promising solutions, progress is limited by the lack of realistic, high-fidelity datasets. This research introduces a simulation-based framework for generating four diverse datasets representing combinations of node mobility (mobile vs. static) and spatial distribution (random vs. clustered). Each dataset captures critical metrics such as Signal-to-Interference-plus-Noise Ratio (SINR), bottleneck rate, and power consumption across multi-hop paths. A lookahead-based greedy routing algorithm with scenario-aware power control is implemented to emulate practical behavior. Supervised ML models, …
Isometric Centroid Encoder (Ice) And Synthetic Data Generation Approaches For Biological Datasets, Prathyusha Kanakamalla
Isometric Centroid Encoder (Ice) And Synthetic Data Generation Approaches For Biological Datasets, Prathyusha Kanakamalla
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This thesis addressed two main challenges in biological data analysis: structure-preserving dimensionality reduction and synthetic data generation for small sample datasets. I proposed the Isometric Centroid Encoder (ICE), a supervised dimensionality reduction method that preserves pairwise distances between class centroids during dimension reduction. Unlike existing methods like Centroid Encoder and Super Encoder, ICE explicitly maintains geometric relationships between biological classes, achieving nearly perfect structure preservation at C dimensions (where C equals the number of classes) with strong performance even in 2D and 3D spaces. Additionally, I compared three generative models (VAE, LSH-GAN, and scDiffusion) for synthetic data generation on small …
Hydrogen Storage Density And Adsorption Energy Barriers On Li-Decorated Bc3 Nanosheet, Sri Venkat Pavan Upasi
Hydrogen Storage Density And Adsorption Energy Barriers On Li-Decorated Bc3 Nanosheet, Sri Venkat Pavan Upasi
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Hydrogen is considered an emerging carrier of clean energy with renewable capabilities. Widespread hydrogen energy utilization necessitates efficient storage strategies. Functionalized nanomaterials, such as Li-decorated BC3 nanosheets, are among the primary candidate materials for hydrogen storage. This research investigates the feasibility of hydrogen storage by estimating energy barriers and their dependence on storage density on Li-decorated BC3 nanosheet. Density functional theory (DFT) simulations provide estimates of adsorption energies and saddle points for hydrogen storage and compare corresponding reaction rates. The results are expected to help us understand the advantages and possible shortcomings of hydrogen storage on such nanomaterials.
Mid-Wave Infrared Imaging Of Supersonic Combustor Exhaust Flow, Nathan Childs
Mid-Wave Infrared Imaging Of Supersonic Combustor Exhaust Flow, Nathan Childs
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A study was completed on the application of mid-wave infrared (MW-IR) imaging for diagnostics in supersonic combustion exhaust flows, with the objective of enhancing optical access and measurement accuracy. Other optical based techniques of thermography require complicated setups and analysis to determine the temperature of a flow with high accuracy, where MW-IR is a more simplistic "point and shoot" technique. The simplicity of MW-IR comes with the trade off of gaining simplicity but adding uncertainty into the measurements. The MW-IR camera was positioned to view the exhaust of the combustor to provide an unobstructed view of the flow, addressing limitations …
Laminar Separation Control Of An Eppler 387 Airfoil, Vincent R. Sheeler
Laminar Separation Control Of An Eppler 387 Airfoil, Vincent R. Sheeler
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A variety of aerodynamic devices operate at low Reynolds number conditions, such as unmanned aerial vehicles and low-pressure turbines in gas turbine engines. At low Reynolds numbers, many airfoils experience laminar boundary layer separation as the fow lacks the energy to overcome the adverse pressure gradient. Researchers have documented a variety of methods which can suppress laminar separation, and now focus on ways to reduce energy requirements to provide efective fow control. Aspects of fow control strategy such as actuator location and pulsing at frequencies which exploit natural instabilities in the fow can reduce energy requirements. In a study by …
Optical Study Of Small Jet Engine Combustion Ignition, Bryce Anthony Ullman
Optical Study Of Small Jet Engine Combustion Ignition, Bryce Anthony Ullman
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Improving the ignition reliability in small-scale gas turbine engines is critical for safety aspects of auxiliary power units (APUs). To better understand the ignition characteristics of these small-scale combustors, an optically accessible combustor is designed and tested. The combustor accommodates twelve prevaporizer tubes (PVTs) in accordance with the commercial-off-the-shelf (COTS) rendition and allows for interchangeable materials (quartz and Inconel) and igniter positions. Another notable design feature introduces a quartz outer combustor liner to allow visualization into key regions of the combustor. The study aims to replicate a COTS ignition sequence using glow plug igniters and examine the effects of different …
Transient Power And Thermal Management Of A Hypersonic Vehicle, Jacob H. Jadischke
Transient Power And Thermal Management Of A Hypersonic Vehicle, Jacob H. Jadischke
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Design of high speed vehicles necessitates incorporating power generation and thermal management systems. Power generation is required as traditional high-speed propulsion sources do not contain rotating components to extract power, and the harsh external thermal environment calls for thermal management. To size these systems, the transient power requirements and the heat generated inside the vehicle must be understood. Sizing these systems at the earliest stages of the vehicle design allows for a more optimized geometry and a trajectory to design the most favorable vehicle. Characterization of these low-quality power and thermal loads from the actuation and fuel pump subsystems has …
Enhanced Diagnostics And Surveillance Of Enteroviruses Including Serotypes Associated With Acute Flaccid Myelitis, Denise Lynette Kramer
Enhanced Diagnostics And Surveillance Of Enteroviruses Including Serotypes Associated With Acute Flaccid Myelitis, Denise Lynette Kramer
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Prior to 2014, Enterovirus D68 infections typically caused symptoms resembling the common cold. From 2014-2018, D68 was associated with an increase in acute flaccid myelitis. However, since 2020, neurological complications have all but disappeared. We selected 1076 respiratory specimens previously determined to be positive for rhinovirus or enterovirus from Department of Defense members and their beneficiaries collected globally from October 2018 through January 2024 and underwent sequencing. Of these specimens, 93.7% were identified as rhinoviruses, while 6.3% were enteroviruses, including 30 enterovirus D68. We utilized the Nextstrain bioinformatic pipeline to reconstruct the phylogenetic relationship of these 30 D68 viruses. Twenty-two …
Leveraging Counterfactuals For Enhanced Natural Language Inference: A Multitask Knowledge Distillation Approach, Brian J. Davis
Leveraging Counterfactuals For Enhanced Natural Language Inference: A Multitask Knowledge Distillation Approach, Brian J. Davis
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Natural-language inference (NLI) asks whether a hypothesis is entailed by, contradicts, or is neutral with respect to a premise. Modern transformers reach high raw accuracy on benchmarks such as SNLI, MNLI, and ANLI, yet they often rely on brittle lexical shortcuts and provide little insight into their decision process. This thesis shows that counterfactual-augmented knowledge distillation can simultaneously boost robustness and supply faithful, token-level explanations—without scaling model size. Four T5-v1_1 students (60M, 220M, 770M, 3B parameters) are trained under four curricula: (1) standard fine-tuning, (2) fine-tuning with free-text rationales, (3) multi-task distillation with naive counterfactuals, and (4) multi-task distillation with …
Hardware Trojan Detection In A Segmented Mixed-Signal Circuit Via Leakage Current, Christopher James Otey
Hardware Trojan Detection In A Segmented Mixed-Signal Circuit Via Leakage Current, Christopher James Otey
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As computers and integrated circuits become more commonplace, the risk of a Hardware Trojan attack becomes more worrisome. Trojans can exploit design flaws or be inserted between essential components to leak information, change the circuit function, or destroy the circuit altogether. Several methods of trojan detection and prevention have been introduced, however few can handle combined analog and digital circuits, known as mixed-signal circuits. This thesis demonstrates a Hardware Trojan detection method implemented in an Analog-to-Digital Converter (ADC), which is a mixed-signal circuit. The detection method involves splitting the circuit into segments with approximately equal leakage currents (a large part …
Computational Analysis Of A Hafnium-Titanium Alloy Mechanical Properties From First Principles, Abdul Mughni
Computational Analysis Of A Hafnium-Titanium Alloy Mechanical Properties From First Principles, Abdul Mughni
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Hafnium and titanium, along with zirconium, are refractory metals with unique properties suitable for extreme-environment applications. Utilizing alloys based on these elements can provide suitable materials with engineered properties. Understanding their mechanical properties is necessary to determine appropriate applications. This thesis research aims at employing quantum-based atomistic simulations to estimate mechanical properties of pristine hafnium, titanium and an alloy based on these elements. The results are compared to available experimental data and the corresponding implications are explored.
Data-Driven Prediction Of Temperature Distribution In Multi-Laser Powder Bed Fusion Using Convolutional Neural Networks, Majid Dousti
Data-Driven Prediction Of Temperature Distribution In Multi-Laser Powder Bed Fusion Using Convolutional Neural Networks, Majid Dousti
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Additive Manufacturing (AM), particularly Laser Powder Bed Fusion (L-PBF), has gained significant traction in fabricating complex, high-performance metallic components. However, the inherent complexity and computational cost of high-fidelity simulations pose challenges for real-time monitoring and optimization of multi-laser powder bed fusion processes. This study proposes a data-driven surrogate modeling approach using a deep learning architecture to efficiently and accurately predict three-dimensional temperature distributions during ML-PBF. A 3D convolutional neural network (CNN) model, named Decoder-CNN, is developed and trained on a dataset of simulated thermal fields corresponding to various process configurations, including different laser power, scanning speed, and beam arrangements. The …
Optimizing Cloud Computing Resources For Operational Cost And Application Performance Using Machine Learning, Isaac K. Matthew
Optimizing Cloud Computing Resources For Operational Cost And Application Performance Using Machine Learning, Isaac K. Matthew
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As AI-driven workloads accelerate the growth of cloud initiatives and spending, resource waste also increases due to persistent inefficiencies in cloud compute and infrastructure management. Overprovisioned resources and suboptimal configurations often lead to operational inefficiencies and unnecessary financial overhead. These challenges arise from the difficulty of anticipating resource demands in dynamic workloads and selecting suitable virtual machines to ensure optimal performance. Our research proposes a holistic, data-driven framework for managing cloud compute resources that reduces costs without compromising application performance. We integrate a predictive, model-driven, threshold-based autoscaling solution for cloud-native applications with an optimized instance right-sizing approach to select cost-effective …
Evaluating Geometric Accuracy In 3d Printing (3dp) Comparative Study Of Different 3dp Processes Using Cmm And Vision Measurement Tools, Alexander Adams
Evaluating Geometric Accuracy In 3d Printing (3dp) Comparative Study Of Different 3dp Processes Using Cmm And Vision Measurement Tools, Alexander Adams
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This study investigates how various additive manufacturing (AM) technologies and parameters influence part quality by fabricating three uniquely designed artefacts. Artefact 1 is a rectangular block with stepped arches, thin and solid extruded features, and holes of varying shapes and sizes. Artefact 2 consists of a base plate with angled overhangs from 15 to 90 degrees. Artefact 3 includes a tall cylinder, a five-step cylinder, and two half arches of different scales. Each artefact was printed ten times using: metal PBF (Inconel 718), nylon PBF (Nylon 12), Vat Polymerization (GRY photopolymer), and material extrusion (nylon carbon fiber). Dimensional analysis was …
Reducing Operator Training Time Through Virtual Reality: A Case Study On The Lpkf Protomat E44 Machine, Joshua C. Patel
Reducing Operator Training Time Through Virtual Reality: A Case Study On The Lpkf Protomat E44 Machine, Joshua C. Patel
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This thesis presents the development of an immersive virtual reality (VR) simulation that replicates the operation of the LPKF ProtoMat E44 PCB milling machine. Aimed at reducing operator training time and improving procedural understanding, the simulation offers an interactive and realistic environment where users can safely engage with machine workflows and start-up sequences. The emphasis is on accurate representation, usability, and maintaining immersion to support intuitive learning. Although formal evaluation is outside the scope of this work, the system is designed to serve as a foundation for cost-effective, scalable training in technical and manufacturing contexts, offering a modern alternative to …
Study Of Fiber-Loaded Slurries For Ceramic Matrix Composite Fabrication By Additive Manufacturing, Gaspard Matondo
Study Of Fiber-Loaded Slurries For Ceramic Matrix Composite Fabrication By Additive Manufacturing, Gaspard Matondo
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We studied the additive manufacturing of an alumina-matrix composite reinforced with alumina fibers using the Admatec Admaflex 3D printer, which utilizes digital light processing technology. Oxide-oxide composites are composite materials in which both the matrix and the reinforcing element are ceramic oxides. Monolithic alumina ceramic exhibits a good combination of thermal and mechanical properties, including thermal shock resistance, high melting point, thermal oxidation resistance, good thermal conductivity, hardness, and mechanical strength. However, it is very brittle. Introducing alumina fiber as a reinforcing material into the alumina matrix is expected to enhance mechanical properties, particularly toughness, making the ceramic matrix composite …
Reinforcement Learning For Adversarial Systems Using Relational Observations, Sophia Christine Gilson
Reinforcement Learning For Adversarial Systems Using Relational Observations, Sophia Christine Gilson
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This thesis investigates the integration of relational observations with the reinforcement learning (RL) framework for improved generalization capability. A hide-and-seek simulation environment is designed in Unity for proof-of-concept demonstration. Two observation representations—relational (analogical) and standard positional—are designed to evaluate agent learning and generalization capabilities. Agents are trained using the Proximal Policy Optimization (PPO) and Soft Actor Critic (SAC) algorithms in a random-room environment and tested in both the random-room environment and a novel environment with greater spatial complexity and path obstructions. Comparative studies indicate that relational representation of objects in the adversarial environment could potentially improve the generalization capability of …
Scalable Real-Time Stream Clustering For Unbounded Text Streams, Nathaniel C. Crossman
Scalable Real-Time Stream Clustering For Unbounded Text Streams, Nathaniel C. Crossman
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Social media, AI systems, IoT sensors, and other platforms generate vast amounts of streaming data. Given this vast volume of information, techniques that can reduce and aggregate data into meaningful topics are essential. One such technique is the two-phase stream clustering approach. In the first, online micro-clustering phase, the system forms micro-clusters from the incoming data stream, incrementally merges new items into related existing micro-clusters, and prunes or fades micro-clusters as they become inactive, producing a constantly updating yet compact set of micro-clusters representing potential topics and subtopics of the stream. In the second, offline macro-clustering phase, these micro-clusters are …
Patient Subset Classification Using Encoded Embeddings And Knowledge Graph Retrieval-Augmented Generation, Benjamin A. Holmes
Patient Subset Classification Using Encoded Embeddings And Knowledge Graph Retrieval-Augmented Generation, Benjamin A. Holmes
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The widespread adoption of electronic medical records has created a vast reservoir of clinical data that can be leveraged to better understand how interventions relate to patient outcomes. Much of this information, however, exists as unstructured free-text, posing significant challenges for traditional statistical and machine-learning methods. Solving these challenges would allow the extraction of specific patient subpopulations (clinically relevant cohorts of individuals who share overlapping symptoms, risk factors, or diagnostic criteria), which could be used in precision medicine. Despite this promise, extracting these subpopulations from unstructured medical notes is an ongoing challenge due to the variability of clinical language and …
Using Unsupervised Machine Learning To Experimentally Categorize Separation On Low-Pressure Turbine Blades, Aaron B. Suter
Using Unsupervised Machine Learning To Experimentally Categorize Separation On Low-Pressure Turbine Blades, Aaron B. Suter
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Laminar boundary layer separation can significantly degrade the efficiency of Low-Pressure Turbine (LPT) blades. While active flow control (AFC) methods can mitigate these losses, energy-efficient implementation requires activating the system only when performance decreases. This study validates an unsupervised machine learning framework that utilizes sparse, discrete surface-pressure measurements to distinguish between high and low aerodynamic loss states. A fuzzy c-means (FCM) clustering model was trained on limited pressure data obtained in a low-speed linear cascade across Reynolds numbers from 30,000 to 160,000 and used to categorize the flow regime in real time. At Re = 40,000, vortex generator jet (VGJ) …
Vagus Nerve Stimulation Ameliorates Cognitive Impairment Caused By Hypoxia, Birendra Sharma
Vagus Nerve Stimulation Ameliorates Cognitive Impairment Caused By Hypoxia, Birendra Sharma
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Hypoxia disrupts brain function due to the high demand for oxygen, leading to significant cognitive impairments. While vagus nerve stimulation (VNS) has been shown to enhance cognition, its ability to counteract hypoxia-induced deficits remains unclear. In this study, male Sprague–Dawley rats were assigned to sham, hypoxia, or VNS + hypoxia groups, with VNS delivered during hypoxia (8% oxygen) using biphasic pulses (100 μs, 30 Hz, 0.8 mA). Cognitive performance was evaluated using multiple behavioral paradigms, and hippocampal tissue was analyzed for neurotrophin expression through quantitative PCR and immunohistochemistry. Among the behavioral measures, hypoxia specifically impaired performance on the passive avoidance …