Analysis and Simulation of Nonlinearities in Noise Attenuation Model for a Diesel Engine Block

The purpose of this thesis is to understand the characteristics of noise attenuation of a diesel engine block, by means of setting up a structural attenuation model and exploring the impact of variations of the model, in terms of nonlinearities. A model motivated by a set of experimentally-determined attenuation measurements was constructed, validated and explored for the potential for sound power prediction, with the aid of simulation

Checkpointing as a Service in Heterogeneous Cloud Environments

A non-invasive, cloud-agnostic approach is demonstrated for extending existing cloud platforms to include checkpoint-restart capability. Most cloud platforms currently rely on each application to provide its own fault tolerance. A uniform mechanism within the cloud itself serves two purposes: (a) direct support for long-running jobs, which would otherwise require a custom fault-tolerant mechanism for each application; and (b) the administrative capability to manage an over-subscribed cloud by temporarily swapping out jobs when higher priority jobs arrive. An advantage of this uniform approach is that it also supports parallel and distributed computations, over both TCP and InfiniBand, thus allowing traditional HPC applications to take advantage of an existing cloud infrastructure. Additionally, an integrated health-monitoring mechanism detects when long-running jobs either fail or incur exceptionally low performance, perhaps due to resource starvation, and proactively suspends the job. The cloud-agnostic feature is demonstrated by applying the implementation to two very different cloud platforms: Snooze and OpenStack. The use of a cloud-agnostic architecture also enables, for the first time, migration of applications from one cloud platform to another.Comment: 20 pages, 11 figures, appears in CCGrid, 201

Can the shape of a planar pathway be estimated using proximal forces of inserting a flexible shaft?

The shape information of flexible endoscopes or other continuum structures, e.g., intro-vascular catheters, is needed for accurate navigation, motion compensation, and haptic feedback in robotic surgical systems. Existing methods rely on optical fiber sensors, electromagnetic sensors, or expensive medical imaging modalities such as X-ray fluoroscopy, magnetic resonance imaging, and ultrasound to obtain the shape information of these flexible medical devices. Here, we propose to estimate the shape/curvature of a continuum structure by measuring the force required to insert a flexible shaft into the internal channel/pathway of the continuum. We found that there is a consistent correlation between the measured insertion force and curvature of the planar continuum pathway. A testbed was built to insert a flexible shaft into a planar continuum pathway with adjustable shapes. The insertion forces, insertion displacement, and the shapes of the pathway were recorded. A neural network model was developed to model this correlation based on the training data collected on the testbed. The trained model, tested on the testing data, can accurately estimate the curvature magnitudes and the accumulated bending angles of the pathway simply based on the measured insertion force at the proximal end of the shaft. The approach may be used to estimate the curvature magnitudes and accumulated bending angles of flexible endoscopic surgical robots or catheters for accurate motion compensation, haptic force feedback, localization, or navigation. The advantage of this approach is that the employed proximal force can be easily obtained outside the pathway or continuum structure without any embedded sensor in the continuum structure. Future work is needed to further investigate the correlation between insertion forces and the pathway and enhance the capability of the model in estimating more complex shapes, e.g., spatial shapes with multiple bends

Elucidating STEM Concepts through Generative AI: A Multi-modal Exploration of Analogical Reasoning

This study explores the integration of generative artificial intelligence (AI), specifically large language models, with multi-modal analogical reasoning as an innovative approach to enhance science, technology, engineering, and mathematics (STEM) education. We have developed a novel system that utilizes the capacities of generative AI to transform intricate principles in mathematics, physics, and programming into comprehensible metaphors. To further augment the educational experience, these metaphors are subsequently converted into visual form. Our study aims to enhance the learners' understanding of STEM concepts and their learning engagement by using the visual metaphors. We examine the efficacy of our system via a randomized A/B/C test, assessing learning gains and motivation shifts among the learners. Our study demonstrates the potential of applying large language models to educational practice on STEM subjects. The results will shed light on the design of educational system in terms of harnessing AI's potential to empower educational stakeholders

A Wolf in Sheep's Clothing: Generalized Nested Jailbreak Prompts can Fool Large Language Models Easily

Large Language Models (LLMs), such as ChatGPT and GPT-4, are designed to provide useful and safe responses. However, adversarial prompts known as 'jailbreaks' can circumvent safeguards, leading LLMs to generate harmful content. Exploring jailbreak prompts can help to better reveal the weaknesses of LLMs and further steer us to secure them. Unfortunately, existing jailbreak methods either suffer from intricate manual design or require optimization on another white-box model, compromising generalization or jailbreak efficiency. In this paper, we generalize jailbreak prompt attacks into two aspects: (1) Prompt Rewriting and (2) Scenario Nesting. Based on this, we propose ReNeLLM, an automatic framework that leverages LLMs themselves to generate effective jailbreak prompts. Extensive experiments demonstrate that ReNeLLM significantly improves the attack success rate while greatly reducing the time cost compared to existing baselines. Our study also reveals the inadequacy of current defense methods in safeguarding LLMs. Finally, we offer detailed analysis and discussion from the perspective of prompt execution priority on the failure of LLMs' defense. We hope that our research can catalyze both the academic community and LLMs vendors towards the provision of safer and more regulated Large Language Models

Study on the vertical well seepage model in composite carbonate reservoirs

Introduction: Carbonate reservoirs are widely distributed throughout the world. Due to the good physical properties of the reservoirs, they are easily contaminated by mud during drilling opening, so they are often put into production after acidification. After acidification, the near-well reservoir’s physical property is improved, while the far-well reservoir’s physical property remains unchanged. Such reservoirs are commonly called composite reservoirs. Composite carbonate reservoir seepage law is more complicated.Methods: In this paper, the point source function of a triple-medium composite reservoir is established using the source function theory, Laplace transform, perturbation transform, and linear superposition principle of partial differential equation.Results: Second, the vertical well seepage model of a composite reservoir is obtained through the new point source function.Discussion: Finally, the correctness of the model is verified, and the sensitivity analysis of the key parameters affecting the seepage law is carried out. The applicability of the model established in this paper is demonstrated by two wells in the field. This paper provides a theoretical basis for vertical well test analysis of composite reservoirs

A sustainable biochar catalyst synergized with copper heteroatoms and CO2 for singlet oxygenation and electron transfer routes

We have developed a wood waste-derived biochar as a sustainable graphitic carbon catalyst for environmental remediation through catalytic pyrolysis under the synergistic effects between Cu heteroatoms and CO2, which for the first time are found to significantly enhance the oxygen functionalities, defective sites, and highly ordered sp2-hybridized carbon matrix. The copper-doped graphitic biochars (Cu-GBCs) were further characterized by XRD, FTIR, Raman, XPS, etc., revealing that the modified specific surface area, pore structure, graphitization, and active sites (i.e., defective sites and ketonic group) on the Cu-GBCs corresponded to the synergistic Cu species loading and Cu-induced carbon-matrix reformation in CO2 environment during pyrolysis. The catalytic ability of Cu-GBCs was evaluated using the ubiquitous peroxydisulfate (PDS) activation system for the removal of various organic contaminants (i.e., rhodamine B, phenol, bisphenol A, and 4-chlorophenol), and gave the highest degradation rate of 0.0312 min-1 in comparison with those of pristine GBCs and N2-pyrolyzed Cu-GBCs ranging from 0.0056 to 0.0094 min-1. The synergistic effects were attributed to the encapsulated Cu heteroatoms, evolved ketonic groups, and abundant unconfined π electrons within the carbon lattice. According to scavenger experiments, ESR analysis, and two-chamber experiments, selective and sustainable non-radical pathways (i.e., singlet oxygenation and electron transfer) mediated by the Cu-induced metastable surface complex were achieved in the Cu-GBC/PDS system. This study offers the first insights into the efficacy, sustainability, and mechanistic roles of Cu-GBCs as an emerging carbon-based catalyst for green environmental remediation