Robot welding process control

This final report documents the development and installation of software and hardware for Robotic Welding Process Control. Primary emphasis is on serial communications between the CYRO 750 robotic welder, Heurikon minicomputer running Hunter & Ready VRTX, and an IBM PC/AT, for offline programming and control and closed-loop welding control. The requirements for completion of the implementation of the Rocketdyne weld tracking control are discussed. The procedure for downloading programs from the Intergraph, over the network, is discussed. Conclusions are made on the results of this task, and recommendations are made for efficient implementation of communications, weld process control development, and advanced process control procedures using the Heurikon

Robot welding process control development task

The completion of, and improvements made to, the software developed during 1990 for program maintenance on the PC and HEURIKON and transfer to the CYRO, and integration of the Rocketdyne vision software with the CYRO is documented. The new programs were used successfully by NASA, Rocketdyne, and UAH technicians and engineers to create, modify, upload, download, and control CYRO NC programs

Plane strain fracture toughness of 18 ni /250/ and 18 ni /200/ maraging welded steel plate

Plane strain fracture toughness of 18 Ni /250/ and 18 Ni /200/ maraging welded steel plat

Early hospital mortality prediction using vital signals

Early hospital mortality prediction is critical as intensivists strive to make efficient medical decisions about the severely ill patients staying in intensive care units. As a result, various methods have been developed to address this problem based on clinical records. However, some of the laboratory test results are time-consuming and need to be processed. In this paper, we propose a novel method to predict mortality using features extracted from the heart signals of patients within the first hour of ICU admission. In order to predict the risk, quantitative features have been computed based on the heart rate signals of ICU patients. Each signal is described in terms of 12 statistical and signal-based features. The extracted features are fed into eight classifiers: decision tree, linear discriminant, logistic regression, support vector machine (SVM), random forest, boosted trees, Gaussian SVM, and K-nearest neighborhood (K-NN). To derive insight into the performance of the proposed method, several experiments have been conducted using the well-known clinical dataset named Medical Information Mart for Intensive Care III (MIMIC-III). The experimental results demonstrate the capability of the proposed method in terms of precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). The decision tree classifier satisfies both accuracy and interpretability better than the other classifiers, producing an F1-score and AUC equal to 0.91 and 0.93, respectively. It indicates that heart rate signals can be used for predicting mortality in patients in the ICU, achieving a comparable performance with existing predictions that rely on high dimensional features from clinical records which need to be processed and may contain missing information.Comment: 11 pages, 5 figures, preprint of accepted paper in IEEE&ACM CHASE 2018 and published in Smart Health journa

Welding process modelling and control

The research and analysis performed, and software developed, and hardware/software recommendations made during 1992 in development of the PC-based data acquisition system for support of Welding Process Modeling and Control is reported. A need was identified by the Metals Processing Branch of NASA Marshall Space Flight Center, for a mobile data aquisition and analysis system, customized for welding measurement and calibration. Several hardware configurations were evaluated and a PC-based system was chosen. The Welding Measurement System (WMS) is a dedicated instrument, strictly for the use of data aquisition and analysis. Although the WMS supports many of the functions associated with the process control, it is not the intention for this system to be used for welding process control

America’s Fear of Civil Unrest Through the Lens of 2020 BLM Protests and January 6th

Over the past five years, the United States of America (US) has experienced events which highlight societal weakness and faults in the foundations of the US system. This research paper focuses on the level of fear a participant has of civil unrest in the US, how that fear has evolved following the events of 2020, including the January 6th Insurrection and 2020’s summer of Black Lives Matter (BLM) protests. Factoring the age, political affiliation, and socio-economic status of the study’s participants into the findings, is a way to understand where the participant’s fear may be stemming from. My research uses the 2018, 2019, 2021, 2022, and 2023 American Fear Surveys conducted by Chapman University’s Henley Lab. My analysis of the data from the American Fear Surveys indicates that younger individuals, Democrats, and those with lower socio-economic status tend to have greater levels of fear concerning civil unrest in the US. Moreover, analyzing the data from the American Fear Surveys and comparing it throughout the past 5 years, shows evidence of increased levels of fear following the events that transpired between the 2019 and 2021 American Fear Surveys, with my research focusing on the January 6, 2021 insurrection and the BLM protests in 2020. There is potential that a certain level of fear regarding potential civil unrest is expected in American society, due to America’s democratic system and the accompanying civic duty of political and social activism. The findings of heightened fear following the January 6th insurrection and the summer 2020 BLM protests might serve as a cautionary signal to America, suggesting that the current system may be faltering and require change. Additionally, the fact that younger and less socio-economically stable citizens are expressing this fear implies that systemic changes within our society may be on the horizon

Transcriptomic identification and characterization of levamisole resistance associated genes in the swine nodular worm Oesophagostomum dentatum

Treatment of parasitic nematodes infections is generally limited to one of three major drug classes; resistance to these is an increasing problem. Because development of new drugs and drug classes is expensive and slow it is important to understand how resistance to current drugs occurs. Nicotinic acetylcholine receptors provide drug targets for both the nicotinic agonist and amino-acetonitrile derivative anthelminthic classes. Much of the research on resistance to nicotinic agonists has been performed in the free-living nematode Caenorhabditis elegans. This research has by necessity looked at limited gene sets which present an incomplete picture of what is believed to be a polygenic trait. Attempts to reproduce this research in parasitic species have shown that research in C. elegans does not always translate to parasites. We used second generation sequencing to obtain a broader view of resistance in a parasitic nematode, Oesophagostomum dentatum, than is easily accomplished with traditional molecular methods. Because O. dentatum lacks a sequenced genome it was first necessary to identify mRNA sequences for genes shown in other genera/species to associate with resistance. We developed a method of assembly that produces longer sequences than traditional assembly methods and used that to identify mRNAs for 34 genes associated with resistance to levamisole and other major anthelminthics. With this sequence information we assessed the expression levels and sequence changes in the levamisole resistance associated genes between levamisole-sensitive and -resistant nematode isolates. We identified 9 mRNAs exhibiting at least a 2-fold decrease in expression between the two isolates and 72 non-synonymous SNPs. We have used this information to propose that levamisole resistance in this parasitic model associates with decreased abundance of functional receptors containing UNC-38 and/or UNC-63, as well as decreased signal transduction moderated by LEV-10, NRA-1, RIC-3, LEV-11, UNC-22, and UNC-68ry

From In-Person to Virtual: A Case Study of an Animal-Assisted Visiting Program in a Pediatric Setting

This article focuses on the practical aspects of converting a successful in-person AAA program to a virtual program in a health care setting including human, canine, and physical resources; animal welfare considerations; training, infection control, and safety guidelines; and visit delivery procedures. In 1992, an interdisciplinary team at Akron Children’s Hospital founded the Doggie Brigade, an animal-assisted activities (AAA) program where volunteer therapy dogs and their handlers visit pediatric patients. The program has become a cornerstone of the hospital’s culture over its now 30-year tenure. In March 2020, the announcement of the COVID-19 pandemic forced health care organizations to suspend nonessential services, including volunteer-based patient activity programs, to reduce viral exposure risk for immunocompromised or otherwise medically vulnerable patients. Doggie Brigade volunteers proposed virtual visits as a temporary solution based on news media coverage of other virtual visitation programs. The author, henceforth known as the Doggie Brigade advisor (DBA), designed a program with two goals: (1) to provide alternative delivery of services abruptly suspended due to the COVID-19 pandemic, and (2) to reduce hospitalization-related anxiety through the experience of positive feelings associated with interacting with Doggie Brigade teams. From July 2020 to April 2021, the DBA provided nearly 300 one-on- one live video calls with Doggie Brigade volunteers and their dogs via iPad and Microsoft Teams

Caregiver Assessment Using Smart Gaming Technology: A Preliminary Approach

As pre-diagnostic technologies are becoming increasingly accessible, using them to improve the quality of care available to dementia patients and their caregivers is of increasing interest. Specifically, we aim to develop a tool for non-invasively assessing task performance in a simple gaming application. To address this, we have developed Caregiver Assessment using Smart Gaming Technology (CAST), a mobile application that personalizes a traditional word scramble game. Its core functionality uses a Fuzzy Inference System (FIS) optimized via a Genetic Algorithm (GA) to provide customized performance measures for each user of the system. With CAST, we match the relative level of difficulty of play using the individual's ability to solve the word scramble tasks. We provide an analysis of the preliminary results for determining task difficulty, with respect to our current participant cohort.Comment: 7 pages, 1 figures, 6 table