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Surrogate Models for Seismic Response of Structures
No full textThe seismic risks to a structure or a set of structures in a region are usually determined by generating fragility curves that provide the probability of a building responding in a certain manner for a given level of ground motion intensity. Developing fragility curves, however, is challenging as it involves the computationally expensive task of obtaining the maximum response of the selected structures to a suite of ground motions representing the seismic hazard of the region selected.This study presents a methodology to develop surrogate models for the prediction of the maximum responses of buildings to ground motion excitation. Data-driven surrogate models using simple machine learning techniques and physics-based surrogate models using the space mapping technique to map the low-fidelity responses obtained using a multi-degree of freedom shear building model to the high-fidelity values are developed for the prediction of the maximum roof drift ratio and the maximum story drift ratio of a chosen 15-story steel moment-resisting frame building with varying structural properties in California. The predictions of each of these surrogate models are analyzed to assess and compare the performance, capabilities, and limitations of these models. Best practices for developing surrogate models for the prediction of maximum responses of structures to ground motion are recommended.The results from the development of data-driven surrogate models show that the spectral displacement is the best intensity measure to condition the maximum roof drift ratio, and the spectral velocity is the best intensity measure to condition the maximum story drift ratio. Fragility analysis of the structure is thus conducted using maximum story drift as the engineering demand parameter and spectral velocity as the intensity measure. Monte Carlo simulation is conducted using the physics-based surrogate model to estimate the maximum story drifts for ground motions that are incrementally scaled to different intensity levels. Maximum likelihood estimates are used to obtain the parameters for a lognormal distribution and the 95% confidence intervals are obtained using the Wald confidence interval to plot the fragility curves.Fragility curves are plotted both with and without variations in the structural properties of the building, and it is found that the effects of variability in ground motions on the fragility are far higher than the effects of the randomness of structural properties. Finally, it is found that about 65 ground motion records are needed for convergence of the parameters of the lognormal distribution for plotting fragility curves by using Monte Carlo simulation
Effect of Modalities on Group Performance in Hyflex Environment
No full textThe pandemic disrupted and challenged higher education institutions across the United States to find an effective and feasible solution to deliver instruction without impacting students’ social interaction and performance. HyFlex model, which blends the best of the two modes of instruction namely, face-to-face, and online instruction, emerged as an effective solution during the pandemic and proved that it has the potential to stay relevant even in the post-pandemic world. The purpose of this study was to examine if the attendance patterns of students during group work in a HyFlex classroom affect their group performance. Evidence from literature studies on HyFlex has focused their investigation on understanding how attendance patterns affect students’ individual performance while there are limited studies that have looked into group performance. The guiding theory behind this study is social constructivism. The research question investigated the relationship between the extent to which teammates were remote and the group’s assignment grades. This study used a sample of 645 students enrolled in first-year undergraduate course which involved working on two significant group projects at a Midwestern university during Fall of 2021. There were 168 and 146 project groups across 18 sections of the course. Data were analyzed using the non-experimental Pearson correlational design method, where the two continuous variables included group remoteness (number of times students participated remotely in a group) and group performance (points received in group assignments for each project). The results of the study indicated a slightly negative correlation that was not statistically significant between group remotenessand group grade for Project 2 (r = -.068, p= .38) and Project 3 (r= -.095, p= 0.25). Even though the results were non-significant the negative correlation hints that the remote participation might affect the group grades. Based on the weak correlation between student participation and group grades, we can recommend that the Hyflex model can be adopted in the future for courses that involve working in groups even in the post-pandemic period
Modeling and Characterization of Internet Censorship Technologies
No full textThe proliferation of Internet access has enabled the rapid and widespread exchange of information globally. The world wide web has become the primary communications platform for many people and has surpassed other traditional media outlets in terms of reach and influence. However, many nation-states impose various levels of censorship on their citizens\u27 Internet communications. There is little consensus about what constitutes “objectionable” online content deserving of censorship. Some people consider the censor activities occurring in many nations to be violations of international human rights (e.g., the rights to freedom of expression and assembly). This multi-study dissertation explores Internet censorship methods and systems. By using combinations of quantitative, qualitative, and systematic literature review methods, this thesis provides an interdisciplinary view of the domain of Internet censorship. The author presents a reference model for Internet censorship technologies: an abstraction to facilitate a conceptual understanding of the ways in which Internet censorship occurs from a system design perspective. The author then characterizes the technical threats to Internet communications, producing a comprehensive taxonomy of Internet censorship methods as a result. Finally, this work provides a novel research framework for revealing how nation-state censors operate based on a globally representative sample. Of the 70 nations analyzed, 62 used at least one Internet censorship method against their citizens. The results reveal worldwide trends in Internet censorship based on historical evidence and Internet measurement data
Improving the Robustness of Artificial Neural Networks Via Bayesian Approaches
No full textArtificial neural networks (ANNs) have achieved extraordinary performance in various domains in recent years. However, some studies reveal that ANNs may be vulnerable in three aspects: label scarcity, perturbations, and open-set emerging classes. Noisy labeling and self-supervised learning approaches address the label scarcity issues, but most of the work couldn’t handle the perturbations. Adversarial training methods, topological denoising methods, and mechanism designing methods aim to mitigate the negative effects caused by perturbations. However, adversarial training methods can barely train a robust model under the circumstance of extensive label scarcity; topological denoising methods are not efficient on dynamic data structures; and mechanism designing methods often depend on heuristic explorations. Detection-based methods devote to identifying novel or anomaly instances for further downstream tasks. Nonetheless, such instances may belong to open-set new emerging classes. To embrace the aforementioned challenges, we address the robustness issues of ANNs from two aspects. First, we propose a series of Bayesian label transition models to improve the robustness of Graph Neural Networks (GNNs) in the presence of label scarcity and perturbations in the graph domain. Second, we propose a new non-exhaustive learning model, named NE-GM-GAN, to handle both open-set problems and class-imbalance issues in network intrusion datasets. Extensive experiments with several datasets demonstrate that our proposed models can effectively improve the robustness of ANNs
Service-Learning in Action: Lafayette, Indiana, Rain Garden Installation
Get PDFIndiana’s Wabash River is being polluted with contaminated water runoff from precipitation events. A lack of pervious land cover has led to an accumulation of fertilizer, sediment, and waste in the river. Green infrastructure, a stormwater management practice that mimics the natural ecosystem, is one of the most effective ways to prevent pollution from stormwater runoff and benefit the community. This project consisted of a rain garden installation at the Lafayette Fueling Station, a site where frequent water drainage and water runoff into the Wabash occurs. The rain garden will allow on-site water infiltration during rain events and promote natural pollutant removal underground. Further, students had the opportunity to engage with community partners at Lafayette Renew, the City of Lafayette’s division in charge of stormwater management. This experience and its challenges and successes can be used as a reference for others pursuing community volunteer efforts. While this rain garden is a positive addition to the city’s green infrastructure projects, more installations in this course and beyond that utilize best management practices are recommended in order to protect the Wabash River and promote sustainable development. The rain garden will prevent pollution from contaminating the Wabash and downriver bodies of water such as the Gulf of Mexico. This project has allowed me to reflect on the impacts that community service can provide to communities not only near the site of the project but also hundreds of miles away. The project has also allowed me to reaffirm my commitment to making the world a cleaner place through my work and volunteer efforts alike
Promoting CAV Deployment by Enhancing the Perception Phase of the Autonomous Driving Using Explainable AI
Get PDFIMPACT Week report summer 2023: A report by the IMPACT evaluation team
Get PDFThis is the 2023 report of Instruction Matters: Purdue Academic Course Transformation (IMPACT) Week. IMPACT was created in 2010, and is a large collaborative initiative on the Purdue West Lafayette campus involving multiple key partners across campus including the Office of the Provost, Center for Instructional Excellence (CIE), Purdue Online (PO), Purdue Libraries and School of Information Studies (Libraries), the Evaluation and Learning Research Center (ELRC), and Institutional Data Analytics and Assessment (IDA+A). IMPACT works with instructors to redesign large enrollment, foundational courses with the aim of engaging students more fully in their learning and creating a more student-centered environment, with the expectation that this will improve student success. IMPACT Week is a self-paced, hybrid version of the IMPACT program
Mechanochemical Esterification of Cellulose Nanofibers Lyophilized From Eutectic Water–tert-Butanol Mixtures
Get PDFFreeze-dried CNFs can be modified by mechanochemical (MC) processes with minimal use of auxiliary solvents, however aggregation of nanofibers during lyophilization compromises their granularity and reaction efficiency. Adding 10 wt% tert-butanol (TBA) to aqueous slurries of wood-derived CNF creates a eutectic mixture that reduces hornification during lyophilization, resulting in well-formed aerogels with minimal shrinkage in volume. CNFs lyophilized from mixtures containing at least 10 wt% TBA exhibit an order of magnitude increase in specific surface area relative to CNFs freeze-dried from water alone. The amount of TBA and input energy needed for lyophilization can be reduced considerably by dewatering the initial CNF slurry and increasing surface-to-volume ratios during freeze drying. MC esterification of freeze-dried CNFs with hexanoic (C6), lauric (C12), stearic (C18), or oleic (OL) acid can be performed by ball milling on a horizontal tumbler at room temperature and purified by ethanol washes, with E-factors and process mass intensities that compare favorably against other methods of preparing organically modified CNFs. MC esterification of CNFs freeze-dried from 10 wt% TBA consistently yield higher degrees of substitution and form more stable dispersions in organic solvents than those derived from CNFs lyophilized from water
Neuronal NADPH oxidase is required for neurite regeneration of Aplysia bag cell neurons
Get PDFNADPH oxidase (Nox), a major source of reactive oxygen species (ROS), is involved in neurodegeneration after injury and disease. Nox is expressed in both neuronal and non-neuronal cells and contributes to an elevated ROS level after injury. Contrary to the well-known damaging effect of Nox-derived ROS in neurodegeneration, recently a physiological role of Nox in nervous system development including neurogenesis, neuronal polarity, and axonal growth has been revealed. Here, we tested a role for neuronal Nox in neurite regeneration following mechanical transection in cultured Aplysia bag cell neurons. Using a novel hydrogen peroxide (H2O2)-sensing dye, 5′-(p-borophenyl)-2′-pyridylthiazole pinacol ester (BPPT), we found that H2O2 levels are elevated in regenerating growth cones following injury. Redistribution of Nox2 and p40phox in the growth cone central domain suggests Nox2 activation after injury. Inhibiting Nox with the pan-Nox inhibitor celastrol reduced neurite regeneration rate. Pharmacological inhibition of Nox is correlated with reduced activation of Src2 tyrosine kinase and F-actin content in the growth cone. Taken together, these findings suggest that Nox-derived ROS regulate neurite regeneration following injury through Src2-mediated regulation of actin organization in Aplysia growth cones