24,882 research outputs found
Meso-scale FDM material layout design strategies under manufacturability constraints and fracture conditions
In the manufacturability-driven design (MDD) perspective, manufacturability of the product or system is the most important of the design requirements. In addition to being able to ensure that complex designs (e.g., topology optimization) are manufacturable with a given process or process family, MDD also helps mechanical designers to take advantage of unique process-material effects generated during manufacturing. One of the most recognizable examples of this comes from the scanning-type family of additive manufacturing (AM) processes; the most notable and familiar member of this family is the fused deposition modeling (FDM) or fused filament fabrication (FFF) process. This process works by selectively depositing uniform, approximately isotropic beads or elements of molten thermoplastic material (typically structural engineering plastics) in a series of pre-specified traces to build each layer of the part. There are many interesting 2-D and 3-D mechanical design problems that can be explored by designing the layout of these elements. The resulting structured, hierarchical material (which is both manufacturable and customized layer-by-layer within the limits of the process and material) can be defined as a manufacturing process-driven structured material (MPDSM). This dissertation explores several practical methods for designing these element layouts for 2-D and 3-D meso-scale mechanical problems, focusing ultimately on design-for-fracture. Three different fracture conditions are explored: (1) cases where a crack must be prevented or stopped, (2) cases where the crack must be encouraged or accelerated, and (3) cases where cracks must grow in a simple pre-determined pattern. Several new design tools, including a mapping method for the FDM manufacturability constraints, three major literature reviews, the collection, organization, and analysis of several large (qualitative and quantitative) multi-scale datasets on the fracture behavior of FDM-processed materials, some new experimental equipment, and the refinement of a fast and simple g-code generator based on commercially-available software, were developed and refined to support the design of MPDSMs under fracture conditions. The refined design method and rules were experimentally validated using a series of case studies (involving both design and physical testing of the designs) at the end of the dissertation. Finally, a simple design guide for practicing engineers who are not experts in advanced solid mechanics nor process-tailored materials was developed from the results of this project.U of I OnlyAuthor's request
The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions
The Metaverse offers a second world beyond reality, where boundaries are
non-existent, and possibilities are endless through engagement and immersive
experiences using the virtual reality (VR) technology. Many disciplines can
benefit from the advancement of the Metaverse when accurately developed,
including the fields of technology, gaming, education, art, and culture.
Nevertheless, developing the Metaverse environment to its full potential is an
ambiguous task that needs proper guidance and directions. Existing surveys on
the Metaverse focus only on a specific aspect and discipline of the Metaverse
and lack a holistic view of the entire process. To this end, a more holistic,
multi-disciplinary, in-depth, and academic and industry-oriented review is
required to provide a thorough study of the Metaverse development pipeline. To
address these issues, we present in this survey a novel multi-layered pipeline
ecosystem composed of (1) the Metaverse computing, networking, communications
and hardware infrastructure, (2) environment digitization, and (3) user
interactions. For every layer, we discuss the components that detail the steps
of its development. Also, for each of these components, we examine the impact
of a set of enabling technologies and empowering domains (e.g., Artificial
Intelligence, Security & Privacy, Blockchain, Business, Ethics, and Social) on
its advancement. In addition, we explain the importance of these technologies
to support decentralization, interoperability, user experiences, interactions,
and monetization. Our presented study highlights the existing challenges for
each component, followed by research directions and potential solutions. To the
best of our knowledge, this survey is the most comprehensive and allows users,
scholars, and entrepreneurs to get an in-depth understanding of the Metaverse
ecosystem to find their opportunities and potentials for contribution
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Food Security Under a Changing Climate: Exploring the Integration of Resilience in Research and Practice
Climate change poses significant risks to our food systems, thus jeopardising the food security of millions of people worldwide. The concept of resilience is increasingly being proposed as a framework to find solutions to these challenges. In this chapter, we assess how resilience has been integrated in discussions about climate change and food security by both academics and practitioners. We performed a targeted review of the academic literature on climate change, food security, and resilience and found that despite a growing body of literature on the subject, the pathways through which actions translate into resilience and then into food security remain unclear. An examination of a sample of projects implemented through the Adaptation Fund revealed that many good practices with potential for resilience-building are used but also that suitable indicators and methods to monitor and evaluate resilience and its outcomes are lacking. Based on our findings, we conclude that while the concept of resilience has accompanied and may have favoured a transition towards more integrated approaches and interventions in work related to climate change and food security, further efforts are needed to identify an efficient and rational sequence of interventions to improve food security in response to climate threats
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Ensuring Access to Safe and Nutritious Food for All Through the Transformation of Food Systems
Evolutionary Multi-Objective Aerodynamic Design Optimization Using CFD Simulation Incorporating Deep Neural Network
An evolutionary multi-objective aerodynamic design optimization method using
the computational fluid dynamics (CFD) simulations incorporating deep neural
network (DNN) to reduce the required computational time is proposed. In this
approach, the DNN infers the flow field from the grid data of a design and the
CFD simulation starts from the inferred flow field to obtain the steady-state
flow field with a smaller number of time integration steps. To show the
effectiveness of the proposed method, a multi-objective aerodynamic airfoil
design optimization is demonstrated. The results indicate that the
computational time for design optimization is suppressed to 57.9% under 96
cores processor conditions
Countermeasures for the majority attack in blockchain distributed systems
La tecnología Blockchain es considerada como uno de los paradigmas informáticos más importantes posterior al Internet; en función a sus características únicas que la hacen ideal para registrar, verificar y administrar información de diferentes transacciones. A pesar de esto, Blockchain se enfrenta a diferentes problemas de seguridad, siendo el ataque del 51% o ataque mayoritario uno de los más importantes. Este consiste en que uno o más mineros tomen el control de al menos el 51% del Hash extraído o del cómputo en una red; de modo que un minero puede manipular y modificar arbitrariamente la información registrada en esta tecnología. Este trabajo se enfocó en diseñar e implementar estrategias de detección y mitigación de ataques mayoritarios (51% de ataque) en un sistema distribuido Blockchain, a partir de la caracterización del comportamiento de los mineros. Para lograr esto, se analizó y evaluó el Hash Rate / Share de los mineros de Bitcoin y Crypto Ethereum, seguido del diseño e implementación de un protocolo de consenso para controlar el poder de cómputo de los mineros. Posteriormente, se realizó la exploración y evaluación de modelos de Machine Learning para detectar software malicioso de tipo Cryptojacking.DoctoradoDoctor en Ingeniería de Sistemas y Computació
Multi-Attribute Utility Preference Robust Optimization: A Continuous Piecewise Linear Approximation Approach
In this paper, we consider a multi-attribute decision making problem where
the decision maker's (DM's) objective is to maximize the expected utility of
outcomes but the true utility function which captures the DM's risk preference
is ambiguous. We propose a maximin multi-attribute utility preference robust
optimization (UPRO) model where the optimal decision is based on the worst-case
utility function in an ambiguity set of plausible utility functions constructed
using partially available information such as the DM's specific preferences
between some lotteries. Specifically, we consider a UPRO model with two
attributes, where the DM's risk attitude is multivariate risk-averse and the
ambiguity set is defined by a linear system of inequalities represented by the
Lebesgue-Stieltjes (LS) integrals of the DM's utility functions. To solve the
maximin problem, we propose an explicit piecewise linear approximation (EPLA)
scheme to approximate the DM's true unknown utility so that the inner
minimization problem reduces to a linear program, and we solve the approximate
maximin problem by a derivative-free (Dfree) method. Moreover, by introducing
binary variables to locate the position of the reward function in a family of
simplices, we propose an implicit piecewise linear approximation (IPLA)
representation of the approximate UPRO and solve it using the Dfree method.
Such IPLA technique prompts us to reformulate the approximate UPRO as a single
mixed-integer program (MIP) and extend the tractability of the approximate UPRO
to the multi-attribute case. Furthermore, we extend the model to the expected
utility maximization problem with expected utility constraints where the
worst-case utility functions in the objective and constraints are considered
simultaneously. Finally, we report the numerical results about performances of
the proposed models.Comment: 50 pages,18 figure
DefGraspNets: Grasp Planning on 3D Fields with Graph Neural Nets
Robotic grasping of 3D deformable objects is critical for real-world
applications such as food handling and robotic surgery. Unlike rigid and
articulated objects, 3D deformable objects have infinite degrees of freedom.
Fully defining their state requires 3D deformation and stress fields, which are
exceptionally difficult to analytically compute or experimentally measure.
Thus, evaluating grasp candidates for grasp planning typically requires
accurate, but slow 3D finite element method (FEM) simulation. Sampling-based
grasp planning is often impractical, as it requires evaluation of a large
number of grasp candidates. Gradient-based grasp planning can be more
efficient, but requires a differentiable model to synthesize optimal grasps
from initial candidates. Differentiable FEM simulators may fill this role, but
are typically no faster than standard FEM. In this work, we propose learning a
predictive graph neural network (GNN), DefGraspNets, to act as our
differentiable model. We train DefGraspNets to predict 3D stress and
deformation fields based on FEM-based grasp simulations. DefGraspNets not only
runs up to 1500 times faster than the FEM simulator, but also enables fast
gradient-based grasp optimization over 3D stress and deformation metrics. We
design DefGraspNets to align with real-world grasp planning practices and
demonstrate generalization across multiple test sets, including real-world
experiments.Comment: To be published in the IEEE Conference on Robotics and Automation
(ICRA), 202
GNN for Deep Full Event Interpretation and hierarchical reconstruction of heavy-hadron decays in proton-proton collisions
The LHCb experiment at the Large Hadron Collider (LHC) is designed to perform
high-precision measurements of heavy-hadron decays, which requires the
collection of large data samples and a good understanding and suppression of
multiple background sources. Both factors are challenged by a five-fold
increase in the average number of proton-proton collisions per bunch crossing,
corresponding to a change in the detector operation conditions for the LHCb
Upgrade I phase, recently started. A further ten-fold increase is expected in
the Upgrade II phase, planed for the next decade. The limits in the storage
capacity of the trigger will bring an inverse relation between the amount of
particles selected to be stored per event and the number of events that can be
recorded, and the background levels will raise due to the enlarged
combinatorics. To tackle both challenges, we propose a novel approach, never
attempted before in a hadronic collider: a Deep-learning based Full Event
Interpretation (DFEI), to perform the simultaneous identification, isolation
and hierarchical reconstruction of all the heavy-hadron decay chains per event.
This approach radically contrasts with the standard selection procedure used in
LHCb to identify heavy-hadron decays, that looks individually at sub-sets of
particles compatible with being products of specific decay types, disregarding
the contextual information from the rest of the event. We present the first
prototype for the DFEI algorithm, that leverages the power of Graph Neural
Networks (GNN). This paper describes the design and development of the
algorithm, and its performance in Upgrade I simulated conditions
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