bluuebunny/crossref_metadata_2025_split huggingface dataset:
Theoretical Study of Sound Insulation Simulations (About Attaching Effect of Sound Absorbing Material and Consideration of Sound Insulation Performance by Height of Origami Core)
Authors: Abe Aya , Yashiro Haruki , Hagiwara Ichiro 
Published: November 2021

Abstract
We have developed a new truss core panel by origami forming to get the higher aspect ratio than that by multi-stage press molding. Our object is to apply the new origami truss core to the train floor. Whether or not this goal can be achieved depends on whether this new origami truss core with a high aspect ratio has excellent sound insulation characteristics. Therefore, as a development of the analysis technology by FEM which accurately estimates the sound insulation characteristics, at first, the relation between the aspect ratio and the sound insulation performance is discussed in the flat plate with one core. So far, sound insulation simulations using FEM did not match with theory of the mass law. However, this can be achieved by setting the end of the transmitted side to be a nonreflective boundary. In this paper, to generalize this method, it is determined theoretically that the sound pressures from the FEM can be separated accurately into the sound pressures of the forward and backward waves from Helmholtz’s equation. Then, the sound insulation characteristics of a flat plate obtained using the proposed theoretical method and the conventional method are compared while assuming that the flat plate is a rigid body. In addition, the validity of the proposed method is confirmed by evaluating the effect of attaching a sound absorbing material to the plate. Furthermore, application of the proposed method to a flat plate with a truss core and an examination of whether a high aspect ratio is advantageous for sound insulation are also presented.
Testing and Validation of a Custom CAD Tool to Support Design for Manufacturing: An Experimental Study
Authors: Patel Apurva , Summers Joshua D. , Patel Akash , Mathieson James L. , Sbarra Michael P. , Ortiz Joshua 
Published: November 2021

Abstract
While fundamentals of DFMA are widely accepted and used in the engineering design community, many CAD environments lack tools that address manufacturing concerns and provide rapid feedback to designers about manufacturing impacts of their design choices. This paper presents an experiment-based testing and validation of a rapid feedback tool that provides users a history-based prediction of manufacturing time based on the current state of the design. A between-subjects experiment is designed to evaluate the impact of the tool on design outcomes based on modeling time, part mass, and manufacturing time. Participants in the study included mechanical engineering graduate and undergraduate students with at least one semester of experience using SolidWorks. The experiment included three different design activities and three different conditions of the design tool. Participants completed up to three sessions with different experimental conditions. Analysis of the data collected shows that use of the design tool results in a small but nonsignificant increase in modeling time. Moreover, use of the tool results in reduced part mass on average, as well as in a within-subject comparison. Tool use reduced manufacturing time in open ended activities, but increased manufacturing time when activities focus more on mass-reduction. Participant feedback suggests that the tool helped guide their material removal actions by showing the impact on manufacturing time. Finally, potential improvements and future expansions of the tool are discussed.
Range-GAN: Range-Constrained Generative Adversarial Network for Conditioned Design Synthesis
Authors: Heyrani Nobari Amin , Chen Wei , Ahmed Faez 
Published: November 2021

Abstract
Typical engineering design tasks require the effort to modify designs iteratively until they meet certain constraints, i.e., performance or attribute requirements. Past work has proposed ways to solve the inverse design problem, where desired designs are directly generated from specified requirements, thus avoid the trial and error process. Among those approaches, the conditional deep generative model shows great potential since 1) it works for complex high-dimensional designs and 2) it can generate multiple alternative designs given any condition. In this work, we propose a conditional deep generative model, Range-GAN, to achieve automatic design synthesis subject to range constraints. The proposed model addresses the sparse conditioning issue in data-driven inverse design problems by introducing a label-aware self-augmentation approach. We also propose a new uniformity loss to ensure generated designs evenly cover the given requirement range. Through a real-world example of constrained 3D shape generation, we show that the label-aware self-augmentation leads to an average improvement of 14% on the constraint satisfaction for generated 3D shapes, and the uniformity loss leads to a 125% average increase on the uniformity of generated shapes’ attributes. This work laid the foundation for data-driven inverse design problems where we consider range constraints and there are sparse regions in the condition space. For further information and code for this paper please refer to http://decode.mit.edu/projects/rangegan/.
Assessment of Student Learning Through Reflection on Doing in Engineering Design
Authors: Sun Yanwei , Peng Shan , Ball Zachary , Ming Zhenjun , Allen Janet K. , Mistree Farrokh 
Published: November 2021

Abstract
How can instructors leverage assessment instruments in design, build, and test courses to simultaneously improve student outcomes and assess student learning to improve courses?
A Take-away is one type of assessment method. It is unstructured text written by a student in AME4163: Principles of Engineering Design, the University of Oklahoma, Norman, US to record what they understand by reflecting on authentic, immersive experiences throughout the semester. The immersive experiences include lectures, assignments, reviews, building, testing, and a post-analysis for the design of an electro-mechanical system to address a given customer need. In the context of a Take-away, a student then writes a Learning Statement. The Learning Statement is a single sentence written as a triple, i.e., Experience|Learning|Value. Over the past three years at the University of Oklahoma (OU), we collected about 18,000 Take-aways and 18,000 Learning Statements from almost 400 students. In our earlier papers, we primarily concentrate on analyzing students’ Learning Statements by a text mining framework.
In this paper, we focus on analyzing students’ Take-aways data using a Latent Dirichlet Allocation (LDA) algorithm, and then relate the Take-away data to the instructor’s expectations using text similarity. By connecting and comparing what students learned (embodied in Take-aways) and what instructors expected the students to learn (embodied in the course booklet), we provide evidence-based guidance to instructors on improving the delivery of AME4163: Principles of Engineering Design. The proposed method can be generalized to be used for the assessment of ABET Student Outcomes 2 and 7.
Minimizing the Cost of Automotive Accidents by Optimizing the Design of Advanced Driver Assist Systems: An Empirical Study Based on a Full-Size Light-Duty Pickup Truck
Authors: Fish Francis , Bras Bert 
Published: November 2021

Abstract
Advanced Driver Assistance Systems (ADAS) have become increasingly common in vehicles in the last decade. The majority of studies has focused on smaller vehicles with gross vehicle weight rating (GVWR) under 5,000lbs, predominantly sedans, for their ADAS evaluations. While it is sensible to use this style of vehicle because it is ubiquitous worldwide for a typical vehicle body style, these studies neglect full-size light-duty pickup trucks (FSLDPTs), GVWR 5,000 – 10,000lbs, which are abundant on the roads in the United States, 18% of vehicles. The increase in mass, higher center of gravity, and utilitarianism of the vehicles allows for unique conditions for studying the effects of ADAS. This work determines the best and worst location to be hit in a full-size light-duty pickup truck based on data for the industry sales leader in this class of vehicles. The objective is to use these results for future designs of ADAS technologies and their placement on the FSLDPT. While these methods could be applied to any vehicle, the FSLDPT sales leader will be investigated as it represents about 9% of registered vehicles in the United States. The results will be optimized with respect to cost in terms of initial up-front purchasing cost and post-accident vehicle repair cost.
Edge Artificial Intelligence: A Multi-Camera Video Surveillance Application
Authors: Berardini Daniele , Mancini Adriano , Zingaretti Primo , Moccia Sara 
Published: November 2021

Abstract
Nowadays, video surveillance has a crucial role. Analyzing surveillance videos is, however, a time consuming and tiresome procedure. In the last years, artificial intelligence paved the way for automatic and accurate surveillance-video analysis. In parallel to the development of artificial-intelligence methodologies, edge computing is becoming an active field of research with the final goal to provide cost-effective and real time deployment of the developed methodologies. In this work, we present an edge artificial intelligence application to video surveillance. Our approach relies on a set of four IP cameras, which acquire video frames that are processed on the edge using the NVIDIA® Jetson Nano. A state-of-the-art deep-learning model, called Single Shot multibox Detector (SSD) MobileNetV2 network, is used to perform object and people detection in real-time. The proposed infrastructure obtained an inference speed of ∼10.0 Frames per Second (FPS) for each parallel video stream. These results prompt the possibility of translating our work into a real word scenario. The integration of the presented application into a wider monitoring system with a central unit could bring benefits to the overall infrastructure. Indeed our application could send only video-related high-level information to the central unit, allowing it to combine information with data coming from other sensing devices without unuseful data overload. This would ensure a fast response in case of emergency or detected anomalies. We hope this work will contribute to stimulate the research in the field of edge artificial intelligence for video surveillance.
Effects of Structured Prototyping Support on Novice Designers’ Prototyping Plans
Authors: Arndt Hansen Camilla , Eifler Tobias , Deininger Michael 
Published: November 2021

Abstract
Methodological support has the potential to improve the design performance of novice designers. This study investigated the effects that structured prototyping support has on novice designers’ prototyping plans. We compared the prototyping plans made by 33 design teams using minimum guidance with plans made using a prototyping support tool, the Prototyping Planner. Specifically, the questions answered in the plans, the testing purpose, and the plan structure were compared.
Unsupported plans varied in length and scope. Some teams focused on a few selected tests while others used their plans to brainstorm multiple different types of tests without planning how to execute them.
81% of novice designers agreed that their supported prototyping plans were better than their unsupported plans. The Prototyping Planner ensured that all teams selected a clear and narrow purpose for their prototype and planned how to execute it. Plan length increased 12% overall and more for the teams that had made the shortest initial plans.
This research shows that different types of prototyping plans exist. The Prototyping Planner supports novice designers well in planning a specific prototyping activity, but to support planning of the prototyping process it must be extended or used in combination with other support.
Mode-Dependent Selective Detection of Humidity and Helium Using Electromagnetically Actuated Clamped Guided MEMS Resonators
Authors: Yaqoob Usman , Jaber Nizar , Alcheikh Nouha , Younis Mohammad I. 
Published: November 2021

Abstract
In this work, we demonstrate a selective gas sensor based on monitoring two different detection mechanisms; absorption and thermal conductivity. To illustrate the concept, we utilize a resonator composed of a clamped-guided arch beam connected to flexural beams and a T-shaped moveable mass. The resonator has two distinct out-of-plane modes in which the mass motion dominates the first mode while the motion of the flexural beam dominates the second mode. A highly disperse graphene oxide (GO) solution is prepared and drop-casted over the moveable mass structure using the inkjet printer for humidity sensing. On the other hand, the He is detected using the hot flexural beams. The results show no significant effect of humidity on the flexural mode (FM) nor for He on the mass mode (MM). This indicates a new technique for selectivity and identification. The device shows good sensitivity (50.1% to 50% RH @ MM and 39.2% to 50% He @ FM: (Vac = 1.5V)), linearity, and repeatability with excellent selectivity. It is demonstrated that the FM has great potential for detecting and categorizing different gases according to their thermal conductivity. The demonstrated multimode MEMS resonator can be a promising approach for the development of smart, highly selective, and sensitive gas/chemical sensors.
Towards a Building Techniques of a BREP Model Starting From a Meshed Surface
Authors: Elloumi Nessrine , Ben Makhlouf Aicha , Louhichi Borhen , Deneux Dominique 
Published: November 2021

Abstract
For several years, research has been brought to reconstruct a Computer Aided Design (CAD) model from a 3D mesh or point cloud produced by scanning techniques or CAD software. This process, which recreates the geometry of a real part is called Revere Engineering (RE). In the industry, RE enables designers and engineers to virtually simulate, test and validate products before the manufacturing process. Therefore, it is common to use a reconstruction algorithm to rebuild a CAD model of a real object with high quality. This technique solves several problems of exchanging geometric models in engineering, computer vision, computer graphics, 3D animation, medical, mechanic, virtual/ augmented reality, etc. Therefore, CAD model reconstruction promotes the integration of 3D data without recopying or manual transformations and facilitates the visualization and simulation of the deformed model behavior.
The aim of this work is how to rebuild a CAD model starting from a deformed mesh. The complexity of this problem requires to be split into several complementary parts. 3D surface reconstruction is considered as the most difficult step to obtain this geometric model. This paper consists in presenting an original method to reconstruct a CAD model surface (NURBS surface) starting from a triangulation (meshed surface) as a main step of the geometric model reconstruction.
Creating Virtual Reality Teaching Modules for Low-Cost Headsets
Authors: Mujuru Takudzwa , Lopez Christian 
Published: November 2021

Abstract
In the past few years, remote learning has been on a trend of steady growth and it is projected to remain on that course in the years to come. Additionally, the global COVID-19 pandemic forced a shift to remote learning which accelerated the existing trend to remote education. Unfortunately, learners find remote classes less engaging than traditional face-to-face classes. One technology that has shown great potential to improve students’ engagement, both in face-to-face classes and remote classes, is Virtual Reality (VR). Nevertheless, while educators are no longer limited to expensive, high-tech, and high-fidelity VR hardware thanks to the introduction of low-cost, low fidelity headset, like the Google Cardboard, educators are still limited in getting relevant content and find it difficult to create their own VR teaching modules. With the objective to address these limitations, this work introduces a new process to create VR content that is easy, rapid, and affordable for educators to adopt and implement into their curriculum. The results indicate great potential for low-cost VR in remote learning as the sample of students in this study reported that they enjoyed the ‘first-hand experience’ of touring places that were inaccessible to them due to the pandemic. However, the findings also show a strong need to address usability issues such as blurriness and dizziness.
A Topic Modeling Approach to Study Design Requirements
Authors: Chen Cheng , Mullis Jesse , Morkos Beshoy 
Published: November 2021

Abstract
Risk management is vital to a product’s lifecycle. The current practice of reducing risks relies on domain experts or management tools to identify unexpected engineering changes, where such approaches are prone to human errors and laborious operations. However, this study presents a framework to contribute to requirements management by implementing a generative probabilistic model, the supervised latent Dirichlet allocation (LDA) with collapsed Gibbs sampling (CGS), to study the topic composition within three unlabeled and unstructured industrial requirements documents. As finding the preferred number of topics remains an open-ended question, a case study estimates an appropriate number of topics to represent each requirements document based on both perplexity and coherence values. Using human evaluations and interpretable visualizations, the result demonstrates the different level of design details by varying the number of topics. Further, a relevance measurement provides the flexibility to improve the quality of topics. Designers can increase design efficiency by understanding, organizing, and analyzing high-volume requirements documents in confirmation management based on topics across different domains. With domain knowledge and purposeful interpretation of topics, designers can make informed decisions on product evolution and mitigate the risks of unexpected engineering changes.
Assistive technologies for home NIV in patients with COPD: feasibility and positive experience with remote-monitoring and volume-assured auto-EPAP NIV mode
Authors: McDowell Grace , Sumowski Maksymilian , Toellner Hannah , Karok Sophia , O'Dwyer Ciara , Hornsby James , Lowe David J , Carlin Christopher M 
Published: November 2021

BackgroundOutcomes for patients with chronic obstructive pulmonary disease (COPD) with persistent hypercapnic respiratory failure are improved by long-term home non-invasive ventilation (NIV). Provision of home-NIV presents clinical and service challenges. The aim of this study was to evaluate outcomes of home-NIV in hypercapnic patients with COPD who had been set-up at our centre using remote-monitoring and iVAPS-autoEPAP NIV mode (Lumis device, ResMed).MethodsRetrospective analysis of a data set of 46 patients with COPD who commenced remote-monitored home-NIV (AirView, ResMed) between February 2017 and January 2018. Events including time to readmission or death at 12 months were compared with a retrospectively identified cohort of 27 patients with hypercapnic COPD who had not been referred for consideration of home-NIV.ResultsThe median time to readmission or death was significantly prolonged in patients who commenced home-NIV (median 160 days, 95% CI 69.38 to 250.63) versus the comparison cohort (66 days, 95% CI 21.9 to 110.1; p<0.01). Average time to hospital readmission was 221 days (95% CI, 47.77 to 394.23) and 70 days (95% CI, 55.31 to 84.69; p<0.05), respectively. Median decrease in bicarbonate level of 4.9 mmol/L (p<0.0151) and daytime partial pressure of carbon dioxide 2.2 kPa (p<0.032) in home-NIV patients with no required increase in nurse home visits is compatible with effectiveness of this service model. Median reduction of 14 occupied bed days per annum was observed per patient who continued home-NIV throughout the study period (N=32).ConclusionThese findings demonstrate the feasibility and provide initial utility data for a technology-assisted service model for the provision of home-NIV therapy for patients with COPD.
Abstract PO-003: Predictors for 30-day readmission in patients with pancreatic cancer who had DNR code status
Authors: Kaur Jasmeet , Mir Tanveer , Singh Paramveer , Goodman Judie 
Published: November 2021

Abstract
Background: Pancreatic cancer is a lethal malignancy, and most patients present with advanced disease. There is little known about the 30-day readmission rate in patients with Do-not resuscitate (DNR) code status in pancreatic cancer. The study aims to look for predictors for 30-day readmission in pancreatic cancer patients with DNR code status. Methods: This is a retrospective study of a nationally representative cohort of hospitalized admissions admitted from January 1, 2016, to December 31, 2018; 240,107 index pancreatic cancer hospitalizations were recorded. The database was obtained from the Agency for Healthcare Research and Quality’s (AHRQ) Healthcare Cost and Utilization Project (HCUP) national readmission (NRD) dataset files between 2016 – 2018. We examined predictors of death and 30-day readmission among patients with pancreatic cancer who had DNR code status. We evaluated readmission in pancreatic cancer with DNR code status in multivariable Cox hazard regression models. Results: There were 240,107 index hospitalizations with pancreatic cancer (PAC) for the years 2016-2018. There were 51,451 (21.4%) PAC patients who had DNR code status during the index hospitalization. Patients with DNR status had a mean age of 68. The PAC patients with DNR status had significantly higher numbers of inpatient mortality (22% (DNR status) vs 3% (full code) (OR 4.24 (95% CI 3.9-4.6; P &lt;0.001), higher rate of cardiac arrhythmia (26% vs. 19%; p&lt;0.001). The adjusted odd’s ratio (Table 1.) to look for significant readmission predictors for DNR status in PAC included chronic heart failure (OR 1.24, p &lt;0.001), renal failure (OR 1.27, p&lt;.001), and liver disease (OR 2.13, p &lt;0.001). However, patients with diabetes and obesity were found to have a negative association with readmission. Most patients were treated in urban teaching hospitals, and Medicare was the primary payor in 70.4%. Conclusion: In this large nationwide study, we observed higher inpatient mortality and readmission rates in pancreatic cancer who have DNR code status utilizing hospital resources and healthcare costs. This suggests that patients with advanced pancreas cancer who adopt DNR status be offered early hospice care to avoid inpatient mortality. There is a need to look for data based on racial and ethnic differences.
Citation Format: Jasmeet Kaur, Tanveer Mir, Paramveer Singh, Judie Goodman. Predictors for 30-day readmission in patients with pancreatic cancer who had DNR code status [abstract]. In: Proceedings of the AACR Virtual Special Conference on Pancreatic Cancer; 2021 Sep 29-30. Philadelphia (PA): AACR; Cancer Res 2021;81(22 Suppl):Abstract nr PO-003.