Matching Items (31)
Description
In this project, I analyze representative samples from three different fashion brands’ sustainability-related informational materials provided to the public through their websites, annual reports, and clothing tags that promote the company’s environmental initiatives. The three companies were chosen because they each represent global fashion- they are all extremely large, popular, and prevalent brands. These materials are evaluated against three frameworks for identifying deceptive greenwashing claims. I identify instances in which these frameworks are successful in categorizing deceptive claims from these companies as well as instances in which they appear to be vulnerable. To address the vulnerabilities I discover in the three existing frameworks for identifying greenwashing, I propose six new guidelines to be used in conjunction with these frameworks that will help to ensure that consumers can have a more ample toolbox to identify deceptive sustainability claims.
ContributorsLadewig, Emily (Author) / Pavlic, Theodore (Thesis director) / Roschke, Kristy (Committee member) / Barrett, The Honors College (Contributor) / School of International Letters and Cultures (Contributor) / School of Art (Contributor) / School of Sustainability (Contributor)
Created2023-05
Description
A Graph Neural Network (GNN) is a type of neural network architecture that operates on data consisting of objects and their relationships, which are represented by a graph. Within the graph, nodes represent objects and edges represent associations between those objects. The representation of relationships and correlations between data is unique to graph structures. GNNs exploit this feature of graphs by augmenting both forms of data, individual and relational, and have been designed to allow for communication and sharing of data within each neural network layer. These benefits allow each node to have an enriched perspective, or a better understanding, of its neighbouring nodes and its connections to those nodes. The ability of GNNs to efficiently process high-dimensional node data and multi-faceted relationships among nodes gives them advantages over neural network architectures such as Convolutional Neural Networks (CNNs) that do not implicitly handle relational data. These quintessential characteristics of GNN models make them suitable for solving problems in which the correspondences among input data are needed to produce an accurate and precise representation of these data. GNN frameworks may significantly improve existing communication and control techniques for multi-agent tasks by implicitly representing not only information associated with the individual agents, such as agent position, velocity, and camera data, but also their relationships with one another, such as distances between the agents and their ability to communicate with one another. One such task is a multi-agent navigation problem in which the agents must coordinate with one another in a decentralized manner, using proximity sensors only, to navigate safely to their intended goal positions in the environment without collisions or deadlocks. The contribution of this thesis is the design of an end-to-end decentralized control scheme for multi-agent navigation that utilizes GNNs to prevent inter-agent collisions and deadlocks. The contributions consist of the development, simulation and evaluation of the performance of an advantage actor-critic (A2C) reinforcement learning algorithm that employs actor and critic networks for training that simultaneously approximate the policy function and value function, respectively. These networks are implemented using GNN frameworks for navigation by groups of 3, 5, 10 and 15 agents in simulated two-dimensional environments. It is observed that in $40\%$ to $50\%$ of the simulation trials, between 70$\%$ to 80$\%$ of the agents reach their goal positions without colliding with other agents or becoming trapped in deadlocks. The model is also compared to a random run simulation, where actions are chosen randomly for the agents and observe that the model performs notably well for smaller groups of agents.
ContributorsAyalasomayajula, Manaswini (Author) / Berman, Spring (Thesis advisor) / Mian, Sami (Committee member) / Pavlic, Theodore (Committee member) / Arizona State University (Publisher)
Created2022
Description
This work has improved the quality of the solution to the sparse rewards problemby combining reinforcement learning (RL) with knowledge-rich planning. Classical
methods for coping with sparse rewards during reinforcement learning modify the
reward landscape so as to better guide the learner. In contrast, this work combines
RL with a planner in order to utilize other information about the environment. As
the scope for representing environmental information is limited in RL, this work has
conflated a model-free learning algorithm – temporal difference (TD) learning – with
a Hierarchical Task Network (HTN) planner to accommodate rich environmental
information in the algorithm. In the perpetual sparse rewards problem, rewards
reemerge after being collected within a fixed interval of time, culminating in a lack of a
well-defined goal state as an exit condition to the problem. Incorporating planning in
the learning algorithm not only improves the quality of the solution, but the algorithm
also avoids the ambiguity of incorporating a goal of maximizing profit while using
only a planning algorithm to solve this problem. Upon occasionally using the HTN
planner, this algorithm provides the necessary tweak toward the optimal solution. In
this work, I have demonstrated an on-policy algorithm that has improved the quality
of the solution over vanilla reinforcement learning. The objective of this work has
been to observe the capacity of the synthesized algorithm in finding optimal policies to
maximize rewards, awareness of the environment, and the awareness of the presence
of other agents in the vicinity.
ContributorsNandan, Swastik (Author) / Pavlic, Theodore (Thesis advisor) / Das, Jnaneshwar (Thesis advisor) / Berman, Spring (Committee member) / Arizona State University (Publisher)
Created2022
Description
Aggregation is a fundamental principle of animal behavior; it is especially significant tohighly social species, like ants. Ants typically aggregate their workers and brood in a central nest, potentially due to advantages in colony defense and regulation of the environment. In many ant species, when a colony must abandon its nest, it can effectively reach consensus on a new home. Ants of the genus Temnothorax have become a model for this collective decision-making process, and for decentralized cognition more broadly. Previous studies examine emigration by well-aggregated colonies, but can these ants also reach consensus when the colony has been scattered? Such scattering may readily occur in nature if the nest is disturbed by natural or man- made disasters. In this exploratory study, Temnothorax rugatulus colonies were randomly scattered in an arena and presented with a binary equal choice of nest sites. Findings concluded that the colonies were able to re-coalesce, however consensus is more difficult than for aggregated colonies and involved an additional primary phase of multiple temporary aggregations eventually yielding to reunification. The maximum percent of colony utilization for these aggregates was reached within the first hour, after which point, consensus tended to rise as aggregation decreased. Small, but frequent, aggregates formed within the first twenty minutes and remained and dissolved to the nest by varying processes. Each colony included a clump containing the queen, with the majority of aggregates containing at least one brood item. These findings provide additional insight to house-hunting experiments in more naturally
challenging circumstances, as well as aggregation within Temnothorax colonies.
ContributorsGoodland, Brooke (Author) / Shaffer, Zachary (Thesis advisor) / Pratt, Stephen (Thesis advisor) / Pavlic, Theodore (Committee member) / Arizona State University (Publisher)
Created2023
Description
A swarm of unmanned aerial vehicles (UAVs) has many potential applications including disaster relief, search and rescue, and area surveillance. A critical factor to a UAV swarm’s success is its ability to collectively locate and pursue targets determined to be of high quality with minimal and decentralized communication. Prior work has investigated nature-based solutions to this problem, in particular the behavior of honeybees when making decisions on future nest sites. A UAV swarm may mimic this behavior for similar ends, taking advantage of widespread sensor coverage induced by a large population. To determine whether the proven success of honeybee strategies may still be found in UAV swarms in more complex and difficult conditions, a series of simulations were created in Python using a behavior modeled after the work of Cooke et al. UAV and environmental properties were varied to determine the importance of each to the success of the swarm and to find emergent behaviors caused by combinations of variables. From the simulation work done, it was found that agent population and lifespan were the two most important factors to swarm success, with preference towards small teams with long-lasting UAVs.
ContributorsGao, Max (Author) / Berman, Spring (Thesis director) / Pavlic, Theodore (Committee member) / Barrett, The Honors College (Contributor) / College of Integrative Sciences and Arts (Contributor) / Engineering Programs (Contributor)
Created2023-05
Description
The concept of multi-scale, heterogeneous modeling is well-known to be central in the complexities of natural and built systems. Therefore, whole models that have parts with different spatiotemporal scales are preferred to those specified using a monolithic modeling approach and tightly integrated. To build simulation frameworks that are expressive and flexible, model composability is crucial where a whole model's structure and behavior traits must be concisely specified according to those of its parts and their interactions. To undertake the spatiotemporal model composability, a breast cancer cells chemotaxis exemplar is used. In breast cancer biology, the receptors CXCR4+ and CXCR7+ and the secreting CXCL12+ cells are implicated in spreading normal and malignant cells. As discrete entities, these can be modeled using Agent-Based Modeling (ABM). The receptors and ligand bindings with chemokine diffusion regulate the cells' movement gradient. These continuous processes can be modeled as Ordinary Differential Equations (ODE) and Partial Differential Equations (PDE). A customized, text-based BrSimulator exists to model and simulate this kind of breast cancer phenomenon. To build a multi-scale, spatiotemporal simulation framework supporting model composability, this research proposes using composable cellular automata (CCA) modeling. Toward this goal, the Cellular Automata DEVS (CA-DEVS) model is used, and the novel Composable Cellular Automata DEVS (CCA-DEVS) modeling is proposed. The DEVS-Suite simulator is extended to support CA and CCA Parallel DEVS models. This simulator introduces new capabilities for controlled and modular run-time animation and superdense time trajectory visualization. Furthermore, this research proposes using the Knowledge Interchange Broker (KIB) approach to model and simulate the interactions between separate geo-referenced CCA models developed using the DEVS and Modelica modeling languages. To demonstrate the proposed model composability approach and its use in the extended DEVS-Suite simulator, the breast cancer cells chemotaxis and others have been studied. The BrSimulator is used as a proxy for evaluating the proposed model composability approach using an integrated DEVS-Suite and OpenModelica simulator. Simulation experiments are developed that show the composition of spatiotemporal ABM, ODE, and PDE models reproduce the behaviors of the same model developed in the BrSimulator.
ContributorsZhang, Chao (Author) / Sarjoughian, Hessam S (Thesis advisor) / Crook, Sharon (Committee member) / Collofello, James (Committee member) / Pavlic, Theodore (Committee member) / Arizona State University (Publisher)
Created2021
Description
Adaptive capacity to climate change is the ability of a system to mitigate or take advantage of climate change effects. Research on adaptive capacity to climate change suffers fragmentation. This is partly because there is no clear consensus around precise definitions of adaptive capacity. The aim of this thesis is to place definitions of adaptive capacity into a formal framework. I formalize adaptive capacity as a computational model written in the Idris 2 programming language. The model uses types to constrain how the elements of the model fit together. To achieve this, I analyze nine existing definitions of adaptive capacity. The focus of the analysis was on important factors that affect definitions and shared elements of the definitions. The model is able to describe an adaptive capacity study and guide a user toward concepts lacking clarity in the study. This shows that the model is useful as a tool to think about adaptive capacity. In the future, one could refine the model by forming an ontology for adaptive capacity. One could also review the literature more systematically. Finally, one might consider turning to qualitative research methods for reviewing the literature.
ContributorsManuel, Jason (Author) / Bazzi, Rida (Thesis director) / Pavlic, Theodore (Committee member) / Middel, Ariane (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2022-05
Description
Component-based models are commonly employed to simulate discrete dynamicalsystems. These models lend themselves to formalizing the structures of systems at multiple levels of granularity. Visual development of component-based models serves to simplify the iterative and incremental model specification activities. The Parallel Discrete Events System Specification (DEVS) formalism offers a flexible yet rigorous approach for decomposing a whole model into its components or alternatively, composing a whole model from components. While different concepts, frameworks, and tools offer a variety of visual modeling capabilities, most pose limitations, such as visualizing multiple model hierarchies at any level with arbitrary depths. The visual and persistent layout of any number of hierarchy levels of models can be maintained and navigated seamlessly. Persistence storage is another capability needed for the modeling, simulating, verifying, and validating lifecycle. These are important features to improve the demanding task of creating and changing modular, hierarchical simulation models. This thesis proposes a new approach and develops a tool for the visual development of models. This tool supports storing and reconstructing graphical models using a NoSQL database. It offers unique capabilities important for developing increasingly larger and more complex models essential for analyzing, designing, and building Digital Twins.
ContributorsMohite, Sheetal Chandrakant (Author) / Sarjoughian, Hessam S (Thesis advisor) / Bryan, Chris (Committee member) / Pavlic, Theodore (Committee member) / Arizona State University (Publisher)
Created2023
Description
In recent years, the development of Control Barrier Functions (CBF) has allowed safety guarantees to be placed on nonlinear control affine systems. While powerful as a mathematical tool, CBF implementations on systems with high relative degree constraints can become too computationally intensive for real-time control. Such deployments typically rely on the analysis of a system's symbolic equations of motion, leading to large, platform-specific control programs that do not generalize well. To address this, a more generalized framework is needed. This thesis provides a formulation for second-order CBFs for rigid open kinematic chains. An algorithm for numerically computing the safe control input of a CBF is then introduced based on this formulation. It is shown that this algorithm can be used on a broad category of systems, with specific examples shown for convoy platooning, drone obstacle avoidance, and robotic arms with large degrees of freedom. These examples show up to three-times performance improvements in computation time as well as 2-3 orders of magnitude in the reduction in program size.
ContributorsPietz, Daniel Johannes (Author) / Fainekos, Georgios (Thesis advisor) / Vrudhula, Sarma (Thesis advisor) / Pedrielli, Giulia (Committee member) / Pavlic, Theodore (Committee member) / Arizona State University (Publisher)
Created2022
Description
Social insects collectively exploit food sources by recruiting nestmates, creating positive feedback that steers foraging effort to the best locations. The nature of this positive feedback varies among species, with implications for collective foraging. The mass recruitment trails of many ants are nonlinear, meaning that small increases in recruitment effort yield disproportionately large increases in recruitment success. The waggle dance of honeybees, in contrast, is believed to be linear, meaning that success increases proportionately to effort. However, the implications of this presumed linearityhave never been tested. One such implication is the prediction that linear recruiters will equally exploit two identical food sources, in contrast to nonlinear recruiters, who randomly choose only one of them. I tested this prediction in colonies of honeybees that were isolated in flight cages and presented with two identical sucrose feeders. The results from 15 trials were consistent with linearity, with many cases of equal exploitation of the feeders. In addition, I tested the prediction that linear recruiters can reallocate their forager distribution when unequal feeders are swapped in position. Results from 15 trials were consistent with linearity, with many cases of clear choice for a stronger food source, followed by a subsequent switch with reallocation of foragers to the new location of the stronger food source. These findings show evidence of a linear pattern of nestmate recruitment, with implications for how colonies effectively distribute their foragers across
available resources.
ContributorsAlam, Showmik (Author) / Shaffer, Zachary (Thesis advisor) / Pratt, Stephen C (Thesis advisor) / Ozturk, Cahit (Committee member) / Pavlic, Theodore (Committee member) / Arizona State University (Publisher)
Created2022