bluuebunny/crossref_metadata_2025 huggingface dataset:
Why Jefferson Delivered His First Annual Message in Writing: New Historical Evidence
Authors: Laracey Mel
Published: November 2021

When he delivered his first annual message in 1801, Thomas Jefferson departed from prior presidential practice and delivered the message to Congress in writing rather than as a speech. Scholars have differed over the meaning and significance of his action. This article examines new historical evidence on the question. The evidence is a commentary in Jefferson's presidential newspaper that explained the reasons for his action; a letter Jefferson wrote explaining his action; and commentaries on the action from Federalist Party opposition newspapers.
Therapeutic effects of equine amniotic membrane suspension on corneal re‐epithelialization and haze in a modified lagomorph ex vivo wound healing model
Authors: Boss Christine K. , Gibson Daniel J. , Schultz Gregory , Whitley R. David , Hernandez Jorge A. , Abbott Jeffrey R. , Plummer Caryn E.
Published: November 2021

Objective

To investigate the therapeutic effects of topical equine amniotic membrane (eAM) suspension following corneal wounding in a controlled experimental setting.

Procedures

Equine amniotic membrane was collected, gamma irradiated, homogenized for topical suspension preparation, and cryopreserved. Corneoscleral rims harvested from fresh rabbit globes were wounded via keratectomy and were maintained in an air‐liquid interface ex vivo corneal culture model. Treatment groups included topical gamma irradiated eAM suspension (n = 20) and a control group (n = 20). Re‐epithelialization of the wound was assessed with daily photographic evaluation of area of fluorescein uptake (mm2). Corneal wound haze after a 21‐day period was assessed by photographic analysis of haze area (mm2) and pixel intensity (0–255). Histologic processing of corneal tissue was performed, and protein identification of eAM suspension using Liquid chromatography‐mass spectrometry (LC‐MS).

Results

The average day of complete corneal re‐epithelialization in controls (5.5 ± 1.1) and topically treated (5.5 ± 0.6) corneas, and rates of reduction in area of fluorescein uptake over time did not significantly differ (p = .44). The corneal wound haze was significantly reduced in mean area by approximately 52% and intensity by 57% in corneas treated with topical eAM suspension (p < .05), compared to controls 21 days following wounding. Protein analysis identified numerous proteins, specifically decorin, dermatopontin, and lumican, which have previously been documented in eAM.

Conclusions

Area and intensity of corneal wound haze were significantly reduced in corneas treated with gamma irradiated eAM suspension, which may be due to previously identified therapeutic proteins which promote corneal clarity.
Classification of Attention Deficit Hyperactivity Disorder using Variational Autoencoder
Authors: A Samah Azurah , Ahmad Siti Nurul Aqilah , Abdul Majid Hairudin , Ali Shah Zuraini , Hashim Haslina , Azman Nuraina Syaza , Azmi Nur Sabrina , Nasien Dewi
Published: November 2021

Attention Deficit Hyperactivity Disorder (ADHD) categorize as one of the typical neurodevelopmental and mental disorders. Over the years, researchers have identified ADHD as a complicated disorder since it is not directly tested with a standard medical test such as a blood or urine test on the early-stage diagnosis. Apart from the physical symptoms of ADHD, clinical data of ADHD patients show that most of them have learning problems. Therefore, functional Magnetic Resonance Imaging (fMRI) is considered the most suitable method to determine functional activity in the brain region to understand brain disorders of ADHD. One of the ways to diagnose ADHD is by using deep learning techniques, which can increase the accuracy of predicting ADHD using the fMRI dataset. Past attempts of classifying ADHD based on functional connectivity coefficient using the Deep Neural Network (DNN) result in 95% accuracy. As Variational Autoencoder (VAE) is the most popular in extracting high-level data, this model is applied in this study. This study aims to enhance the performance of VAE to increase the accuracy in classifying ADHD using fMRI data based on functional connectivity analysis. The preprocessed fMRI dataset is used for decomposition to find the region of interest (ROI), followed by Independent Component Analysis (ICA) that calculates the correlation between brain regions and creates functional connectivity matrices for each subject. As a result, the VAE model achieved an accuracy of 75% on classifying ADHD.
Strut Diameter Uncertainty Prediction by Deep Neural Network for Additively Manufactured Lattice Structures
Authors: Gorguluarslan Recep M. , Ates Gorkem Can , Utku Gungor O. , Yamaner Yusuf
Published: November 2021

Additive manufacturing (AM) introduces geometric uncertainties on the fabricated strut members of lattice structures. These uncertainties result in deviations between the modeled and fabricated geometries of struts. The use of deep neural networks (DNNs) to accurately predict the statistical parameters of the effective strut diameters to account for the AM-introduced geometric uncertainties with a small training dataset for constant process parameters is studied in this research. For the training data, struts with certain angle and diameter values are fabricated by the material extrusion process. The geometric uncertainties are quantified using the random field theory based on the spatial strut radius measurements obtained from the microscope images of the fabricated struts. The uncertainties are propagated to the effective diameters of the struts using a stochastic upscaling technique. The relationship between the modeled strut diameter and the characterized statistical parameters of the effective diameters are used as the training data to establish a DNN model. The validation results show that the DNN model can predict the statistical parameters of the effective diameters of the struts modeled with angles and diameters different from the ones used in the training data with good accuracy even if the training data set is small. Developing such a DNN model with small data will allow designers to use the fabricated results in the design optimization processes without requiring additional experimentations.
Learning to Improve Performance During Non-Repetitive Tasks Performed by Robots
Authors: Yoon Yeo Jung , Gupta Satyandra K.
Published: November 2021

To execute non-repetitive tasks, robots need to learn on the tasks to improve task performance. The performance model cannot be built in advance for such non-repetitive tasks. The robot can execute a small portion of the task with certain process parameters and attractively update the process parameters based on the observations of the task performance. To make the learning process efficient, the trade-off between exploration and exploitation should be explicitly considered. Too much exploration may lead to the waste of time without significant improvement on task performance. On the other hand, stopping exploration prematurely may lead to suboptimal task performance. This paper describes a sequential decision making approach to select the set of parameters to improve task performance. The overall learning approach uses feasibility biased sampling, surrogate model construction and greedy optimization. We implement our approach in the simulation of robotic sanding. We also compare our method with other design of experiments methods.
Improved Anti-Lock Braking System With Real-Time Friction Detection to Maximize Vehicle Performance
Authors: Arricale Vincenzo Maria , Maiorano Antonio , Mosconi Lorenzo , Napolitano Dell’Annunziata Guido , Rocca Ernesto , Albarella Nicola
Published: November 2021

Nowadays, advanced driver assistance systems play a fundamental role to improve vehicle safety and drivability; their capability to reduce the accidents rate was widely demonstrated, but these systems could also be employed to improve vehicle performance if incorporated with other control logics. This work presents an evolved version of the anti-lock braking system, obtained thanks to the combined use of a bicycle model, capable to estimate the actual friction coefficient in different environmental conditions, and a potential friction estimator based on a Magic Formula tire model with a slip-slope approach. With the presented ABS, virtually tested in several conditions, it is possible to reduce the braking distance with the final aim of reducing the braking time and, in this way, improving the vehicle performance.
A Universal Nonlinear Analyzer for Rigid Multibody Systems Based on the Efficient Galerkin Averaging-Incremental Harmonic Balance Method
Authors: Ju Ren , Fan Wei , Zhu Weidong
Published: November 2021

The bridge between the multibody dynamic modeling theory and nonlinear dynamic analysis theory is built for the first time in this work by introducing an efficient Galerkin averaging-incremental harmonic balance (EGA-IHB) method for steady-state nonlinear dynamic analysis of index-3 differential algebraic equations (DAEs) for general rigid multibody systems. The multibody dynamic modeling theory has made significant advances in generality and simplicity, and multibody systems are usually governed by DAEs. Since the fast Fourier transform and EGA are used, the EGA-IHB method has excellent robustness and computational efficiency. Since the Floquet theory cannot be directly used for stability analysis of periodic responses of DAEs, a new stability analysis procedure is developed, where perturbed, linearized DAEs are reduced to ordinary differential equations with use of independent generalized coordinates. A modified arc-length continuation method with a scaling strategy is used for calculating response curves and conducting parameter studies. Three examples are used to show the performance and capability of the current method. Periodic solutions of DAEs from the EGA-IHB method show excellent agreement with those from numerical integration methods. Amplitude-frequency and amplitude-parameter response curves are generated, and stability and period-doubling bifurcations are analyzed. The EGA-IHB method can be used as a universal solver and nonlinear analyzer for obtaining steady-state periodic responses of DAEs for general multibody systems.
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

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

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

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

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

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

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

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

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

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

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

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.
Modeling the effects of age and hearing loss on concurrent vowel scores
Authors: Settibhaktini Harshavardhan , Heinz Michael G. , Chintanpalli Ananthakrishna
Published: November 2021

A difference in fundamental frequency (F0) between two vowels is an important segregation cue prior to identifying concurrent vowels. To understand the effects of this cue on identification due to age and hearing loss, Chintanpalli, Ahlstrom, and Dubno [(2016). J. Acoust. Soc. Am. 140, 4142–4153] collected concurrent vowel scores across F0 differences for younger adults with normal hearing (YNH), older adults with normal hearing (ONH), and older adults with hearing loss (OHI). The current modeling study predicts these concurrent vowel scores to understand age and hearing loss effects. The YNH model cascaded the temporal responses of an auditory-nerve model from Bruce, Efrani, and Zilany [(2018). Hear. Res. 360, 40–45] with a modified F0-guided segregation algorithm from Meddis and Hewitt [(1992). J. Acoust. Soc. Am. 91, 233–245] to predict concurrent vowel scores. The ONH model included endocochlear-potential loss, while the OHI model also included hair cell damage; however, both models incorporated cochlear synaptopathy, with a larger effect for OHI. Compared with the YNH model, concurrent vowel scores were reduced across F0 differences for ONH and OHI models, with the lowest scores for OHI. These patterns successfully captured the age and hearing loss effects in the concurrent-vowel data. The predictions suggest that the inability to utilize an F0-guided segregation cue, resulting from peripheral changes, may reduce scores for ONH and OHI listeners.
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

Background

Outcomes 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).

Methods

Retrospective 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.

Results

The 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).

Conclusion

These 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.
Metabolizable energy in corn is greater than in hybrid rye when fed to gestating sows, but exogenous enzymes did not increase energy digestibility
Authors: McGhee Molly L. , Stein Hans H.
Published: November 2021

The metabolizable energy (ME) in corn was greater (P < 0.05) than in hybrid rye when fed to sows in mid-gestation, although the difference between the two grains was less than the difference previously observed in growing pigs. An exogenous enzyme mixture did not increase apparent total tract digestibility of dry matter or gross energy in corn or hybrid rye fed to gestating sows. Likewise, digestible energy and ME in corn and hybrid rye were not increased by the enzymes.
A Continuous Learning Approach for Real-Time Network Intrusion Detection
Authors: Martina Marcello Rinaldo , Foresti Gian Luca
Published: November 2021

Network intrusion detection is becoming a challenging task with cyberattacks that are becoming more and more sophisticated. Failing the prevention or detection of such intrusions might have serious consequences. Machine learning approaches try to recognize network connection patterns to classify unseen and known intrusions but also require periodic re-training to keep the performances at a high level. In this paper, a novel continuous learning intrusion detection system, called Soft-Forgetting Self-Organizing Incremental Neural Network (SF-SOINN), is introduced. SF-SOINN, besides providing continuous learning capabilities, is able to perform fast classification, is robust to noise, and it obtains good performances with respect to the existing approaches. The main characteristic of SF-SOINN is the ability to remove nodes from the neural network based on their utility estimate. SF-SOINN has been validated on the well-known NSL-KDD and CIC-IDS-2017 intrusion detection datasets as well as on some artificial data to show the classification capability on more general tasks.
Constacyclic codes over the ring Fq[u,v,w]/〈u2 − 1, v2 − 1,w3 − w, uv − vu,vw − wv,wu − uw〉
Authors: Zhang Shunhua
Published: November 2021

Let [Formula: see text] be the ring [Formula: see text], where [Formula: see text] for any odd prime [Formula: see text] and positive integer [Formula: see text]. In this paper, we study constacyclic codes over the ring [Formula: see text]. We define a Gray map by a matrix and decompose a constacyclic code over the ring [Formula: see text] as the direct sum of constacyclic codes over [Formula: see text], we also characterize self-dual constacyclic codes over the ring [Formula: see text] and give necessary and sufficient conditions for constacyclic codes to be dual-containing. As an application, we give a method to construct quantum codes from dual-containing constacyclic codes over the ring [Formula: see text].
Adversarial Energy Disaggregation
Authors: Du Zhekai , Li Jingjing , Zhu Lei , Lu Ke , Shen Heng Tao
Published: November 2021

Energy disaggregation, also known as non-intrusive load monitoring (NILM), challenges the problem of separating the whole-home electricity usage into appliance-specific individual consumptions, which is a typical application of data analysis. NILM aims to help households understand how the energy is used and consequently tell them how to effectively manage the energy, thus allowing energy efficiency, which is considered as one of the twin pillars of sustainable energy policy (i.e., energy efficiency and renewable energy). Although NILM is unidentifiable, it is widely believed that the NILM problem can be addressed by data science. Most of the existing approaches address the energy disaggregation problem by conventional techniques such as sparse coding, non-negative matrix factorization, and the hidden Markov model. Recent advances reveal that deep neural networks (DNNs) can get favorable performance for NILM since DNNs can inherently learn the discriminative signatures of the different appliances. In this article, we propose a novel method named
adversarial energy disaggregation
based on DNNs. We introduce the idea of adversarial learning into NILM, which is new for the energy disaggregation task. Our method trains a generator and multiple discriminators via an adversarial fashion. The proposed method not only learns shared representations for different appliances but captures the specific multimode structures of each appliance. Extensive experiments on real-world datasets verify that our method can achieve new state-of-the-art performance.
The Coevolution of Placentation and Cancer
Authors: Wagner Günter P. , Kshitiz  , Dighe Anasuya , Levchenko Andre
Published: November 2021

Analogies between placentation, in particular the behavior of trophoblast cells, and cancer have been noted since the beginning of the twentieth century. To what degree these can be explained as a consequence of the evolution of placentation has been unclear. In this review, we conclude that many similarities between trophoblast and cancer cells are shared with other, phylogenetically older processes than placentation. The best candidates for cancer hallmarks that can be explained by the evolution of eutherian placenta are mechanisms of immune evasion. Another dimension of the maternal accommodation of the placenta with an impact on cancer malignancy is the evolution of endometrial invasibility. Species with lower degrees of placental invasion tend to have lower vulnerability to cancer malignancy. We finally identify several areas in which one could expect to see coevolutionary changes in placental and cancer biology but that, to our knowledge, have not been explored.
Predictive Analysis and Evaluation Model of Chronic Liver Disease Based on BP Neural Network with Improved Ant Colony Algorithm
Authors: Jiang Na , Zhao Zhiwei , Xu Pan
Published: November 2021

Timely prediction of the mechanism and characteristics of chronic liver disease using next-generation information technology is an effective way to improve the diagnosis rate of chronic liver disease. In this paper, we have proposed a modified backpropagation (BP) neural network with improved ant colony optimization algorithm to process multiple index attribute items describing chronic liver disease and construct a chronic liver disease assessment model. The proposed model is very effective in detecting chronic liver disease on time with acceptable level of accuracy and precision ratio. To verify these claims, the proposed scheme is checked experimentally where 125 groups of 20-dimensional medical test index data items of patients with chronic liver disease were analyzed. Moreover, 13-dimensional index items were preferentially selected as test index attribute items with high sensitivity to chronic liver disease using well-known ROC curves. The 13-dimensional index items were reduced to 5-dimensional comprehensive data items by principal component analysis. The proposed neural network-based model was trained with 115 sets of test indicator sample sets, and the remaining 10 sets of sample sets were used as test samples. Compared with the original 20-dimensional data as the neural network input, the proposed model not only reduces the complexity but also improves the prediction accuracy by 15.07%.
Joint Modelling Approaches to Survival Analysis via Likelihood-Based Boosting Techniques
Authors: Griesbach Colin , Groll Andreas , Bergherr Elisabeth
Published: November 2021

Joint models are a powerful class of statistical models which apply to any data where event times are recorded alongside a longitudinal outcome by connecting longitudinal and time-to-event data within a joint likelihood allowing for quantification of the association between the two outcomes without possible bias. In order to make joint models feasible for regularization and variable selection, a statistical boosting algorithm has been proposed, which fits joint models using component-wise gradient boosting techniques. However, these methods have well-known limitations, i.e., they provide no balanced updating procedure for random effects in longitudinal analysis and tend to return biased effect estimation for time-dependent covariates in survival analysis. In this manuscript, we adapt likelihood-based boosting techniques to the framework of joint models and propose a novel algorithm in order to improve inference where gradient boosting has said limitations. The algorithm represents a novel boosting approach allowing for time-dependent covariates in survival analysis and in addition offers variable selection for joint models, which is evaluated via simulations and real world application modelling CD4 cell counts of patients infected with human immunodeficiency virus (HIV). Overall, the method stands out with respect to variable selection properties and represents an accessible way to boosting for time-dependent covariates in survival analysis, which lays a foundation for all kinds of possible extensions.
Treatment of Aortic and Iliac Artery Occlusion by Catheter Thrombolysis Combined with Catheter Thrombectomy and Aortic Bifurcation Endovascular Stent Reconstruction
Authors: Zhao Xinyu , Liu Delang , Yu Chaowen , Sun Yong , Chen Shiyuan
Published: November 2021

Aortoiliac occlusive disease (AIOD) is an occlusive disease of the infrarenal aorta and iliac arteries usually caused by stenosis or occlusion at the end of the abdominal aorta-common iliac artery. Herein, we reported a case of Trans-Atlantic Inter-Society Consensus- (TASC-) D AIOD with pale, cool, and intangible dorsalis pedis artery treated with catheter thrombolysis combined with catheter thrombectomy and aortic bifurcation endovascular stent reconstruction, which proved to be safe, effective, and minimally invasive approach. In the present paper, we discussed the physical and imaging manifestations, as well as treatments.
Utilization of Nursing Defect Management Evaluation and Deep Learning in Nursing Process Reengineering Optimization
Authors: Liu Yue , Liu Huaping
Published: November 2021

It was to explore the application of nursing defect management evaluation and deep learning in nursing process reengineering optimization. This study first selects the root cause analysis method to analyse the nursing defect management, then realizes the classification of data features according to the convolution neural network (CNN) in deep learning (DL) and uses the constructed training set and verification set to obtain the required plates and feature extraction. Based on statistical analysis and data mining, this study makes statistical analysis of nursing data from a macroperspective, improves Apriori algorithm through simulation, and analyses nursing data mining from a microperspective. The constructed deep learning model is used, CNN network training is conducted on the selected SVHN dataset, the required data types are classified, the data are analysed by using the improved Apriori algorithm, and nurses’ knowledge of nursing process rules is investigated and analysed. The cognition of nursing staff on process optimization and their participation in training were analyzed, the defects in the nursing process were summarized, and the nursing process reengineering was analyzed. The results show that compared with Apriori algorithm, the running time difference of the improved Apriori algorithm is relatively small. With the increase of data recording times, the line trend of the improved algorithm gradually eases, the advantages gradually appear, and the efficiency of data processing is more obvious. The results showed that after the optimization of nursing process, the effect of long-term specialized nursing was significantly higher than that of long-term nursing. Health education was improved by 7.57%, clinical nursing was improved by 6.55%, ward management was improved by 9.85%, and service humanization was improved by 8.97%. In summary, the reoptimization of nursing process is conducive to reduce the defects in nursing. In the data analysis and rule generation based on deep learning network, the reoptimization of nursing process can provide reference for decision-making departments to improve long-term nursing, improve the quality and work efficiency of clinical nurses, and is worthy of clinical promotion.
An Optimized Hybrid Deep Learning Model to Detect COVID‐19 Misleading Information
Authors: Alouffi Bader , Alharbi Abdullah , Sahal Radhya , Saleh Hager
Published: November 2021

Fake news is challenging to detect due to mixing accurate and inaccurate information from reliable and unreliable sources. Social media is a data source that is not trustworthy all the time, especially in the COVID‐19 outbreak. During the COVID‐19 epidemic, fake news is widely spread. The best way to deal with this is early detection. Accordingly, in this work, we have proposed a hybrid deep learning model that uses convolutional neural network (CNN) and long short‐term memory (LSTM) to detect COVID‐19 fake news. The proposed model consists of some layers: an embedding layer, a convolutional layer, a pooling layer, an LSTM layer, a flatten layer, a dense layer, and an output layer. For experimental results, three COVID‐19 fake news datasets are used to evaluate six machine learning models, two deep learning models, and our proposed model. The machine learning models are DT, KNN, LR, RF, SVM, and NB, while the deep learning models are CNN and LSTM. Also, four matrices are used to validate the results: accuracy, precision, recall, and F1‐measure. The conducted experiments show that the proposed model outperforms the six machine learning models and the two deep learning models. Consequently, the proposed system is capable of detecting the fake news of COVID‐19 significantly.
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

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 <0.001), higher rate of cardiac arrhythmia (26% vs. 19%; p<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 <0.001), renal failure (OR 1.27, p<.001), and liver disease (OR 2.13, p <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.