Search results for: Bruna Stewart
6 Teaching about Justice With Justice: How Using Experiential, Learner Centered Literacy Methodology Enhances Learning of Justice Related Competencies for Young Children
Authors: Bruna Azzari Puga, Richard Roe, Andre Pagani de Souza
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abstract outlines a proposed study to examine how and to what extent interactive, experiential, learner centered methodology develops learning of basic civic and democratic competencies among young children. It stems from the Literacy and Law course taught at Georgetown University Law Center in Washington, DC, since 1998. Law students, trained in best literacy practices and legal cases affecting literacy development, read “law related” children’s books and engage in interactive and extension activities with emerging readers. The law students write a monthly journal describing their experiences and a final paper: a conventional paper or a children’s book illuminating some aspect of literacy and law. This proposal is based on the recent adaptation of Literacy and Law to Brazil at Mackenzie Presbyterian University in São Paulo in three forms: first, a course similar to the US model, often conducted jointly online with Brazilian and US law students; second, a similar course that combines readings of children’s literature with activity based learning, with law students from a satellite Mackenzie campus, for young children from a vulnerable community near the city; and third, a course taught by law students at the main Mackenzie campus for 4th grade students at the Mackenzie elementary school, that is wholly activity and discourse based. The workings and outcomes of these courses are well documented by photographs, reports, lesson plans, and law student journals. The authors, faculty who teach the above courses at Mackenzie and Georgetown, observe that literacy, broadly defined as cognitive and expressive development through reading and discourse-based activities, can be influential in developing democratic civic skills, identifiable by explicit civic competencies. For example, children experience justice in the classroom through cooperation, creativity, diversity, fairness, systemic thinking, and appreciation for rules and their purposes. Moreover, the learning of civic skills as well as the literacy skills is enhanced through interactive, learner centered practices in which the learners experience literacy and civic development. This study will develop rubrics for individual and classroom teaching and supervision by examining 1) the children’s books and students diaries of participating law students and 2) the collection of photos and videos of classroom activities, and 3) faculty and supervisor observations and reports. These rubrics, and the lesson plans and activities which are employed to advance the higher levels of performance outcomes, will be useful in training and supervision and in further replication and promotion of this form of teaching and learning. Examples of outcomes include helping, cooperating and participating; appreciation of viewpoint diversity; knowledge and utilization of democratic processes, including due process, advocacy, individual and shared decision making, consensus building, and voting; establishing and valuing appropriate rules and a reasoned approach to conflict resolution. In conclusion, further development and replication of the learner centered literacy and law practices outlined here can lead to improved qualities of democratic teaching and learning supporting mutual respect, positivity, deep learning, and the common good – foundation qualities of a sustainable world.Keywords: democracy, law, learner-centered, literacy
Procedia PDF Downloads 1245 The Increasing Trend in Research Among Orthopedic Residency Applicants is Significant to Matching: A Retrospective Analysis
Authors: Nickolas A. Stewart, Donald C. Hefelfinger, Garrett V. Brittain, Timothy C. Frommeyer, Adrienne Stolfi
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Orthopedic surgery is currently considered one of the most competitive specialties that medical students can apply to for residency training. As evidenced by increasing United States Medical Licensing Examination (USMLE) scores, overall grades, and publication, presentation, and abstract numbers, this specialty is getting increasingly competitive. The recent change of USMLE Step 1 scores to pass/fail has resulted in additional challenges for medical students planning to apply for orthopedic residency. Until now, these scores have been a tool used by residency programs to screen applicants as an initial factor to determine the strength of their application. With USMLE STEP 1 converting to a pass/fail grading criterion, the question remains as to what will take its place on the ERAS application. The primary objective of this study is to determine the trends in the number of research projects, abstracts, presentations, and publications among orthopedic residency applicants. Secondly, this study seeks to determine if there is a relationship between the number of research projects, abstracts, presentations, and publications, and match rates. The researchers utilized the National Resident Matching Program's Charting Outcomes in the Match between 2007 and 2022 to identify mean publications and research project numbers by allopathic and osteopathic US orthopedic surgery senior applicants. A paired t test was performed between the mean number of publications and research projects by matched and unmatched applicants. Additionally, simple linear regressions within matched and unmatched applicants were used to determine the association between year and number of abstracts, presentations, and publications, and a number of research projects. For determining whether the increase in the number of abstracts, presentations, and publications, and a number of research projects is significantly different between matched and unmatched applicants, an analysis of covariance is used with an interaction term added to the model, which represents the test for the difference between the slopes of each group. The data shows that from 2007 to 2022, the average number of research publications increased from 3 to 16.5 for matched orthopedic surgery applicants. The paired t-test had a significant p-value of 0.006 for the number of research publications between matched and unmatched applicants. In conclusion, the average number of publications for orthopedic surgery applicants has significantly increased for matched and unmatched applicants from 2007 to 2022. Moreover, this increase has accelerated in recent years, as evidenced by an increase of only 1.5 publications from 2007 to 2001 versus 5.0 publications from 2018 to 2022. The number of abstracts, presentations, and publications is a significant factor regarding an applicant's likelihood to successfully match into an orthopedic residency program. With USMLE Step 1 being converted to pass/fail, the researchers expect students and program directors will place increased importance on additional factors that can help them stand out. This study demonstrates that research will be a primary component in stratifying future orthopedic surgery applicants. In addition, this suggests the average number of research publications will continue to accelerate. Further study is required to determine whether this growth is sustainable.Keywords: publications, orthopedic surgery, research, residency applications
Procedia PDF Downloads 1314 Burkholderia Cepacia ST 767 Causing a Three Years Nosocomial Outbreak in a Hemodialysis Unit
Authors: Gousilin Leandra Rocha Da Silva, Stéfani T. A. Dantas, Bruna F. Rossi, Erika R. Bonsaglia, Ivana G. Castilho, Terue Sadatsune, Ary Fernandes Júnior, Vera l. M. Rall
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Kidney failure causes decreased diuresis and accumulation of nitrogenous substances in the body. To increase patient survival, hemodialysis is used as a partial substitute for renal function. However, contamination of the water used in this treatment, causing bacteremia in patients, is a worldwide concern. The Burkholderia cepacia complex (Bcc), a group of bacteria with more than 20 species, is frequently isolated from hemodialysis water samples and comprises opportunistic bacteria, affecting immunosuppressed patients, due to its wide variety of virulence factors, in addition to innate resistance to several antimicrobial agents, contributing to the permanence in the hospital environment and to the pathogenesis in the host. The objective of the present work was to characterize molecularly and phenotypically Bcc isolates collected from the water and dialysate of the Hemodialysis Unit and from the blood of patients at a Public Hospital in Botucatu, São Paulo, Brazil, between 2019 and 2021. We used 33 Bcc isolates, previously obtained from blood cultures from patients with bacteremia undergoing hemodialysis treatment (2019-2021) and 24 isolates obtained from water and dialysate samples in a Hemodialysis Unit (same period). The recA gene was sequenced to identify the specific species among the Bcc group. All isolates were tested for the presence of some genes that encode virulence factors such as cblA, esmR, zmpA and zmpB. Considering the epidemiology of the outbreak, the Bcc isolates were molecularly characterized by Multi Locus Sequence Type (MLST) and by pulsed-field gel electrophoresis (PFGE). The verification and quantification of biofilm in a polystyrene microplate were performed by submitting the isolates to different incubation temperatures (20°C, average water temperature and 35°C, optimal temperature for group growth). The antibiogram was performed with disc diffusion tests on agar, using discs impregnated with cefepime (30µg), ceftazidime (30µg), ciprofloxacin (5µg), gentamicin (10µg), imipenem (10µg), amikacin 30µg), sulfametazol/trimethoprim (23.75/1.25µg) and ampicillin/sulbactam (10/10µg). The presence of ZmpB was identified in all isolates, while ZmpA was observed in 96.5% of the isolates, while none of them presented the cblA and esmR genes. The antibiogram of the 33 human isolates indicated that all were resistant to gentamicin, colistin, ampicillin/sulbactam and imipenem. 16 (48.5%) isolates were resistant to amikacin and lower rates of resistance were observed for meropenem, ceftazidime, cefepime, ciprofloxacin and piperacycline/tazobactam (6.1%). All isolates were sensitive to sulfametazol/trimethoprim, levofloxacin and tigecycline. As for the water isolates, resistance was observed only to gentamicin (34.8%) and imipenem (17.4%). According to PFGE results, all isolates obtained from humans and water belonged to the same pulsotype (1), which was identified by recA sequencing as B. cepacia¸, belonging to sequence type ST-767. By observing a single pulse type over three years, one can observe the persistence of this isolate in the pipeline, contaminating patients undergoing hemodialysis, despite the routine disinfection of water with peracetic acid. This persistence is probably due to the production of biofilm, which protects bacteria from disinfectants and, making this scenario more critical, several isolates proved to be multidrug-resistant (resistance to at least three groups of antimicrobials), turning the patient care even more difficult.Keywords: hemodialysis, burkholderia cepacia, PFGE, MLST, multi drug resistance
Procedia PDF Downloads 993 A Real-Time Bayesian Decision-Support System for Predicting Suspect Vehicle’s Intended Target Using a Sparse Camera Network
Authors: Payam Mousavi, Andrew L. Stewart, Huiwen You, Aryeh F. G. Fayerman
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We present a decision-support tool to assist an operator in the detection and tracking of a suspect vehicle traveling to an unknown target destination. Multiple data sources, such as traffic cameras, traffic information, weather, etc., are integrated and processed in real-time to infer a suspect’s intended destination chosen from a list of pre-determined high-value targets. Previously, we presented our work in the detection and tracking of vehicles using traffic and airborne cameras. Here, we focus on the fusion and processing of that information to predict a suspect’s behavior. The network of cameras is represented by a directional graph, where the edges correspond to direct road connections between the nodes and the edge weights are proportional to the average time it takes to travel from one node to another. For our experiments, we construct our graph based on the greater Los Angeles subset of the Caltrans’s “Performance Measurement System” (PeMS) dataset. We propose a Bayesian approach where a posterior probability for each target is continuously updated based on detections of the suspect in the live video feeds. Additionally, we introduce the concept of ‘soft interventions’, inspired by the field of Causal Inference. Soft interventions are herein defined as interventions that do not immediately interfere with the suspect’s movements; rather, a soft intervention may induce the suspect into making a new decision, ultimately making their intent more transparent. For example, a soft intervention could be temporarily closing a road a few blocks from the suspect’s current location, which may require the suspect to change their current course. The objective of these interventions is to gain the maximum amount of information about the suspect’s intent in the shortest possible time. Our system currently operates in a human-on-the-loop mode where at each step, a set of recommendations are presented to the operator to aid in decision-making. In principle, the system could operate autonomously, only prompting the operator for critical decisions, allowing the system to significantly scale up to larger areas and multiple suspects. Once the intended target is identified with sufficient confidence, the vehicle is reported to the authorities to take further action. Other recommendations include a selection of road closures, i.e., soft interventions, or to continue monitoring. We evaluate the performance of the proposed system using simulated scenarios where the suspect, starting at random locations, takes a noisy shortest path to their intended target. In all scenarios, the suspect’s intended target is unknown to our system. The decision thresholds are selected to maximize the chances of determining the suspect’s intended target in the minimum amount of time and with the smallest number of interventions. We conclude by discussing the limitations of our current approach to motivate a machine learning approach, based on reinforcement learning in order to relax some of the current limiting assumptions.Keywords: autonomous surveillance, Bayesian reasoning, decision support, interventions, patterns of life, predictive analytics, predictive insights
Procedia PDF Downloads 1152 Ethical Considerations of Disagreements Between Clinicians and Artificial Intelligence Recommendations: A Scoping Review
Authors: Adiba Matin, Daniel Cabrera, Javiera Bellolio, Jasmine Stewart, Dana Gerberi (librarian), Nathan Cummins, Fernanda Bellolio
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OBJECTIVES: Artificial intelligence (AI) tools are becoming more prevalent in healthcare settings, particularly for diagnostic and therapeutic recommendations, with an expected surge in the incoming years. The bedside use of this technology for clinicians opens the possibility of disagreements between the recommendations from AI algorithms and clinicians’ judgment. There is a paucity in the literature analyzing nature and possible outcomes of these potential conflicts, particularly related to ethical considerations. The goal of this scoping review is to identify, analyze and classify current themes and potential strategies addressing ethical conflicts originating from the conflict between AI and human recommendations. METHODS: A protocol was written prior to the initiation of the study. Relevant literature was searched by a medical librarian for the terms of artificial intelligence, healthcare and liability, ethics, or conflict. Search was run in 2021 in Ovid Cochrane Central Register of Controlled Trials, Embase, Medline, IEEE Xplore, Scopus, and Web of Science Core Collection. Articles describing the role of AI in healthcare that mentioned conflict between humans and AI were included in the primary search. Two investigators working independently and in duplicate screened titles and abstracts and reviewed full-text of potentially eligible studies. Data was abstracted into tables and reported by themes. We followed methodological guidelines for Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). RESULTS: Of 6846 titles and abstracts, 225 full texts were selected, and 48 articles included in this review. 23 articles were included as original research and review papers. 25 were included as editorials and commentaries with similar themes. There was a lack of consensus in the included articles on who would be held liable for mistakes incurred by following AI recommendations. It appears that there is a dichotomy of the perceived ethical consequences depending on if the negative outcome is a result of a human versus AI conflict or secondary to a deviation from standard of care. Themes identified included transparency versus opacity of recommendations, data bias, liability of outcomes, regulatory framework, and the overall scope of artificial intelligence in healthcare. A relevant issue identified was the concern by clinicians of the “black box” nature of these recommendations and the ability to judge appropriateness of AI guidance. CONCLUSION AI clinical tools are being rapidly developed and adopted, and the use of this technology will create conflicts between AI algorithms and healthcare workers with various outcomes. In turn, these conflicts may have legal, and ethical considerations. There is limited consensus about the focus of ethical and liability for outcomes originated from disagreements. This scoping review identified the importance of framing the problem in terms of conflict between standard of care or not, and informed by the themes of transparency/opacity, data bias, legal liability, absent regulatory frameworks and understanding of the technology. Finally, limited recommendations to mitigate ethical conflicts between AI and humans have been identified. Further work is necessary in this field.Keywords: ethics, artificial intelligence, emergency medicine, review
Procedia PDF Downloads 931 Enabling Wire Arc Additive Manufacturing in Aircraft Landing Gear Production and Its Benefits
Authors: Jun Wang, Chenglei Diao, Emanuele Pagone, Jialuo Ding, Stewart Williams
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As a crucial component in aircraft, landing gear systems are responsible for supporting the plane during parking, taxiing, takeoff, and landing. Given the need for high load-bearing capacity over extended periods, 300M ultra-high strength steel (UHSS) is often the material of choice for crafting these systems due to its exceptional strength, toughness, and fatigue resistance. In the quest for cost-effective and sustainable manufacturing solutions, Wire Arc Additive Manufacturing (WAAM) emerges as a promising alternative for fabricating 300M UHSS landing gears. This is due to its advantages in near-net-shape forming of large components, cost-efficiency, and reduced lead times. Cranfield University has conducted an extensive preliminary study on WAAM 300M UHSS, covering feature deposition, interface analysis, and post-heat treatment. Both Gas Metal Arc (GMA) and Plasma Transferred Arc (PTA)-based WAAM methods were explored, revealing their feasibility for defect-free manufacturing. However, as-deposited 300M features showed lower strength but higher ductility compared to their forged counterparts. Subsequent post-heat treatments were effective in normalising the microstructure and mechanical properties, meeting qualification standards. A 300M UHSS landing gear demonstrator was successfully created using PTA-based WAAM, showcasing the method's precision and cost-effectiveness. The demonstrator, measuring Ф200mm x 700mm, was completed in 16 hours, using 7 kg of material at a deposition rate of 1.3kg/hr. This resulted in a significant reduction in the Buy-to-Fly (BTF) ratio compared to traditional manufacturing methods, further validating WAAM's potential for this application. A "cradle-to-gate" environmental impact assessment, which considers the cumulative effects from raw material extraction to customer shipment, has revealed promising outcomes. Utilising Wire Arc Additive Manufacturing (WAAM) for landing gear components significantly reduces the need for raw material extraction and refinement compared to traditional subtractive methods. This, in turn, lessens the burden on subsequent manufacturing processes, including heat treatment, machining, and transportation. Our estimates indicate that the carbon footprint of the component could be halved when switching from traditional machining to WAAM. Similar reductions are observed in embodied energy consumption and other environmental impact indicators, such as emissions to air, water, and land. Additionally, WAAM offers the unique advantage of part repair by redepositing only the necessary material, a capability not available through conventional methods. Our research shows that WAAM-based repairs can drastically reduce environmental impact, even when accounting for additional transportation for repairs. Consequently, WAAM emerges as a pivotal technology for reducing environmental impact in manufacturing, aiding the industry in its crucial and ambitious journey towards Net Zero. This study paves the way for transformative benefits across the aerospace industry, as we integrate manufacturing into a hybrid solution that offers substantial savings and access to more sustainable technologies for critical component production.Keywords: WAAM, aircraft landing gear, microstructure, mechanical performance, life cycle assessment
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