Shape optimization is an indispensable step in any aerodynamic design. However, the inherent complexity and non-linearity associated with fluid mechanics as well as the high-dimensional design space ...intrinsic to such problems make airfoil shape optimization a challenging task. Current approaches relying on gradient-based or gradient-free optimizers are data-inefficient in that they do not leverage accumulated knowledge, and are computationally expensive when integrating Computational Fluid Dynamics (CFD) simulation tools. Supervised learning approaches have addressed these limitations but are constrained by user-provided data. Reinforcement learning (RL) provides a data-driven approach bearing generative capabilities. We formulate the airfoil design as a Markov decision process (MDP) and investigate a Deep Reinforcement Learning (DRL) approach to airfoil shape optimization. A custom RL environment is developed allowing the agent to successively modify the shape of an initially provided 2D airfoil and to observe the associated changes in aerodynamic metrics such as lift-to-drag (L/D), lift coefficient (C
) and drag coefficient (C
). The learning abilities of the DRL agent are demonstrated through various experiments in which the agent's objective-maximizing L/D, maximizing C
or minimizing C
-as well as the initial airfoil shape are varied. Results show that the DRL agent is able to generate high performing airfoils within a limited number of learning iterations. The strong resemblance between the artificially produced shapes and those found in the literature highlights the rationality of the decision-making policy learned by the agent. Overall, the presented approach demonstrates the relevance of DRL to airfoil shape optimization and brings forward a successful application of DRL to a physics-based aerodynamics problem.
Many non-binary individuals AFAB (assigned female at birth) seek gestational parenthood. However, the limited available literature is often focused on trans men and overlooks the conception, ...pregnancy, and birth experiences of non-binary parents.
The study aimed to capture the unique reproduction narratives of non-binary people AFAB.
Five non-binary individuals volunteered to participate in this study. Data were collected using largely unstructured, in-depth, tape-recorded interviews. Thematic analysis of the verbatim transcripts and tape recordings yielded a chronological, cohesive narrative for each participant. Four participants reviewed their narrative and confirmed that their story was accurately represented. The individual narratives were then woven into one collective narrative, and common themes across the participants' stories were identified.
Before conception, most participants considered how to balance their medical and social transitions with their reproductive goals. Conception was relatively easy and straightforward for the four participants who used their partner's sperm. The gendered nature of, and language surrounding, pregnancy greatly impacted participant's reproductive experiences, leading to feelings of isolation and loneliness, difficulties finding maternity clothes and gender dysphoria. Participants desired gender-affirming care and reported mostly positive experiences with their healthcare providers. Their gender identity influenced their experiences of parenthood, as well as the decisions they made regarding the disclosure of their gender identity to others, their gender presentation, chestfeeding, and parental designations.
The cisnormative and heteronormative scripts that surround pregnancy shaped the reproductive narratives of those who participated in this research. The findings reinforce the importance of inclusive, gender-affirming healthcare and social support services.
The system complexity that characterizes current systems warrants an integrated and comprehensive approach to system design and development. This need has brought about a paradigm shift towards ...Model-Based Systems Engineering (MBSE) approaches to system design and a departure from traditional document-centric methods. While MBSE shows great promise, the ambiguities and inconsistencies present in Natural Language (NL) requirements hinder their conversion to models directly. The field of Natural Language Processing (NLP) has demonstrated great potential in facilitating the conversion of NL requirements into a semi-machine-readable format that enables their standardization and use in a model-based environment. A first step towards standardizing requirements consists of classifying them according to the type (design, functional, performance, etc.) they represent. To that end, a language model capable of classifying requirements needs to be fine-tuned on labeled aerospace requirements. This paper presents an open-source, annotated aerospace requirements corpus (the first of its kind) developed for the purpose of this effort that includes three types of requirements, namely design, functional, and performance requirements. This paper further describes the use of the aforementioned corpus to fine-tune BERT to obtain the aeroBERT-Classifier: a new language model for classifying aerospace requirements into design, functional, or performance requirements. Finally, this paper provides a comparison between aeroBERT-Classifier and other text classification models such as GPT-2, Bidirectional Long Short-Term Memory (Bi-LSTM), and bart-large-mnli. In particular, it shows the superior performance of aeroBERT-Classifier on classifying aerospace requirements over existing models, and this is despite the fact that the model was fine-tuned using a small labeled dataset.
We examined the life histories of older lesbian, bisexual, and queer women, focusing on the stories they told about their bodies and sexuality from early to later life. Guided by a narrative ...constructionist approach, a series of two life history interviews were conducted with 17 lesbian, bisexual, and queer women aged 65–86. Two themes were constructed through a narrative thematic analysis: Queering the Corset: Negotiating Gender Expression and (Aspirational) Aging Body Acceptance. Participants experienced body-related freedom through ‘tomboy’ expressions of physicality as children. This body autonomy was constrained in adolescence and adulthood due to heterosexist messages surrounding idealized femininity relayed by family and (heterosexual) men, which disrupted self-care yet catalyzed attuned, queer desire and positive embodiment. Women worked to accept their bodies as they aged; they experienced some body dissatisfaction in relation to age-related body changes, yet gratitude and pride in their older queer identities. The findings highlight concurrent positive and negative body image, and breadth of body-related experiences ranging from attunement and agency to discomfort and disruption throughout the life course. This work contributes to body image and embodiment research by moving beyond dominant (youthful) heteronormative perspectives by illuminating how ageism and heterosexism can shape body-related experiences.
•Body histories of older lesbian, bisexual, or queer women are understudied.•Participants experienced body-related freedom and autonomy in childhood.•(Heterosexist) femininity disrupted embodiment in adolescence and adulthood.•Attuned queer desire and embodiment were experienced in mid to later adulthood.•Aging body dissatisfaction, gratitude, and pride were experienced concurrently.
The increased complexity of modern systems is calling for an integrated and comprehensive approach to system design and development and, in particular, a shift toward Model-Based Systems Engineering ...(MBSE) approaches for system design. The requirements that serve as the foundation for these intricate systems are still primarily expressed in Natural Language (NL), which can contain ambiguities and inconsistencies and suffer from a lack of structure that hinders their direct translation into models. The colossal developments in the field of Natural Language Processing (NLP), in general, and Large Language Models (LLMs), in particular, can serve as an enabler for the conversion of NL requirements into machine-readable requirements. Doing so is expected to facilitate their standardization and use in a model-based environment. This paper discusses a two-fold strategy for converting NL requirements into machine-readable requirements using language models. The first approach involves creating a requirements table by extracting information from free-form NL requirements. The second approach consists of an agile methodology that facilitates the identification of boilerplate templates for different types of requirements based on observed linguistic patterns. For this study, three different LLMs are utilized. Two of these models are fine-tuned versions of Bidirectional Encoder Representations from Transformers (BERTs), specifically, aeroBERT-NER and aeroBERT-Classifier, which are trained on annotated aerospace corpora. Another LLM, called flair/chunk-english, is utilized to identify sentence chunks present in NL requirements. All three language models are utilized together to achieve the standardization of requirements. The effectiveness of the methodologies is demonstrated through the semi-automated creation of boilerplates for requirements from Parts 23 and 25 of Title 14 Code of Federal Regulations (CFRs).
Ovarian cancer impacts approximately 1 in 75 women. Sexual health is receiving increasing attention as a critical aspect of gynecologic cancer treatment and a component of quality of life. Therefore, ...investigating how women with ovarian cancer experience and express sexuality is an important area of inquiry.
To evaluate how women with ovarian cancer experience and express sexuality, a major determinant of quality of life, in the context of their illness.
In a mixed-methods approach, 6 validated self-report questionnaires (n = 64) and an in-depth focus group (n = 3) were used to gather data.
The quantitative phase of the study showed that women with ovarian cancer have a poorer quality of life and higher rates of sexual dysfunction and sexual distress compared with published norms from the general population. They also have lower levels of relationship satisfaction and increased rates of depression. The qualitative phase of the study revealed 6 themes: (i) changes to relationship satisfaction; (ii) sexual difficulties; (iii) challenges with body image; (iv) gaps in communication with healthcare providers; (v) feelings of guilt, grief, resentment, anxiety, and fear; and (vi) strategies used for coping.
Ovarian cancer impacts women’s lives beyond mere survival, including their sexual function and quality of life. Healthcare providers are urged to prepare women with ovarian cancer for these challenges and offer information and resources to help improve their quality of life and sexuality.
Fischer OJ, Marguerie M, Brotto LA. Sexual Function, Quality of Life, and Experiences of Women with Ovarian Cancer: A Mixed-Methods Study. Sex Med 2019;7:530–539.
This paper offers a comprehensive examination of the process involved in developing and automating supervised end-to-end machine learning workflows for forecasting and classification purposes. It ...offers a complete overview of the components (i.e., feature engineering and model selection), principles (i.e., bias–variance decomposition, model complexity, overfitting, model sensitivity to feature assumptions and scaling, and output interpretability), models (i.e., neural networks and regression models), methods (i.e., cross-validation and data augmentation), metrics (i.e., Mean Squared Error and F1-score) and tools that rule most supervised learning applications with numerical and categorical data, as well as their integration, automation, and deployment. The end goal and contribution of this paper is the education and guidance of the non-AI expert academic community regarding complete and rigorous machine learning workflows and data science practices, from problem scoping to design and state-of-the-art automation tools, including basic principles and reasoning in the choice of methods. The paper delves into the critical stages of supervised machine learning workflow development, many of which are often omitted by researchers, and covers foundational concepts essential for understanding and optimizing a functional machine learning workflow, thereby offering a holistic view of task-specific application development for applied researchers who are non-AI experts. This paper may be of significant value to academic researchers developing and prototyping machine learning workflows for their own research or as customer-tailored solutions for government and industry partners.
The incidence of head and neck cutaneous squamous cell carcinoma (HNcSCC) is unevenly distributed between men and women. At present, the mechanism behind this disparity remains elusive. This study ...conducted a systematic review and meta-analysis of proportions to investigate the disparity between sexes for patients with HNcSCC. PubMed, Scopus, EMBASE, MEDLINE, Emcare and CINAHL were searched in November 2021 and June 2022 (N > 50, English, human), and studies which examined the association between sex and HNcSCC were included. Analysis was conducted using RStudio with data and forest plots displaying males as a proportion of total patients with HNcSCC. Two independent researchers performed study selection, data extraction, data analysis and risk of bias. Eighty-two studies (1948 to 2018) comprising approximately 186,000 participants (67% male, 33% female) from 29 countries were included. Significantly more males had HNcSCC overall (71%; CI: 67−74). Males were also significantly more affected by cSCC of the ear (92%; CI: 89−94), lip (74%; CI: 66−81), and eyelid (56%; CI: 51−62). This study found HNcSCC disproportionately affected males overall and across all subtypes. Improving our understanding of sex-specific mechanisms in HNcSCC will better inform our preventive, therapeutic and prognostic practices.
This paper proposes an approach to incorporate Reliability and Maintainability (R&M) tasks into the digital model of a complex system of systems, leveraging model-based systems engineering (MBSE) and ...the unified architecture framework (UAF). The benefits and significance of this integration are analyzed, and a real-world implementation as part of an ongoing effort with NASA is presented. The effective handling of R&M tasks, such as risk analysis, Fault Tree Analysis (FTA), or Failure Mode and Effect Analysis (FMEA), is critical to large organizations that are faced with significant backlogs of deferred maintenance and large annual maintenance budgets. A significant accomplishment highlighted in this article is the integration and unified modeling of questions within the FMEA process. The approach presented herein aims to model the infrastructure of a large organization to help address the backlog of deferred maintenance and minimize the risk that unreliable and/or unavailable infrastructure may have on the ability of such organizations to successfully carry their missions. The integration of risk and reliability information is essential to the development of a comprehensive digital representation of a system. Inevitably, integrating R&M activities into the digital engineering ecosystem (DEE) has become a recognized need. This is illustrated by recent efforts from the Object Management Group (OMG) to develop a beta version of a new modeling language called RAAML (Risk Analysis and Assessment Modeling Language). RAAML 1 helps assess the performance of various analysis techniques including FMEA, FTA, Systems Theoretic Process Analysis (STPA), Goal Structuring Notation (GSN), and ISO 26262 Road Vehicles Functional Safety using MBSE. While MBSE serves as a valuable foundation for the DEE, it alone does not offer a complete solution for modeling complex enterprise architectures. One of the key challenges is the absence of a standardized modeling approach, which can hinder effective collaboration and information sharing across organizations. To address this gap, enterprise architecture frameworks have been developed to provide a unified modeling approach. Among these frameworks, the UAF stands out as a recent development that enables organizations to establish a cohesive and standardized architecture 2. By adopting UAF, organizations can collaborate seamlessly, share information effectively, and achieve a unified architecture that spans various domains and stakeholders. UAF serves as a valuable tool in enhancing the modeling capabilities within the DEE and enables organizations to tackle the complexities of enterprise architecture more efficiently. Collaborating with NASA stakeholders, Global Technology Connection, Inc. (GTC) and the Aerospace Systems Design Laboratory (ASDL) at the Georgia Institute of Technology are creating a digital representation of NASA Goddard Space Flight Center (GSFC)'s enterprise, enabling data-driven decision-making to help minimize the impact that unavailable facilities/capabilities (due to repair, etc.) may have on mission success. This effort aligns with NASA's goal of modernizing digital systems for managing aging infrastructure 3. In particular, the objective of this paper is to further emphasize and demonstrate, through a real-world implementation of MBSE/UAF to an infrastructure problem, the benefits that such model-based approaches can bring to the R&M community at large. This paper also recommends two practical initiatives to facilitate the integration of R&M activities into a DEE: * Skill Enhancement for Reliability and Safety Engineers in MBSE * Collaboration between MBSE and R&M Software Vendors to share knowledge and expertise
Abstract
Science communication is evolving: Increasingly, it is directed at the public rather than academic peers. Understanding the circumstances under which the public engages with scientific ...content is therefore crucial to improving science communication. In this article, we investigate the role of affect on audience engagement with a modern form of science communication: TED Talks on the social media platform YouTube. We examined how two aspects of affect, valence and density are associated with public engagement with the talk in terms of popularity (reflecting views and likes) and polarity (reflecting dislikes and comments). We found that the valence of TED Talks was associated with both popularity and polarity: Positive valence was linked to higher talk popularity and lower talk polarity. Density, on the other hand, was only associated with popularity: Higher affective density was linked to higher popularity—even more so than valence—but not polarity. Moreover, the association between affect and engagement was moderated by talk topic, but not by whether the talk included scientific content. Our results establish affect as an important covariate of audience engagement with scientific content on social media, which science communicators may be able to leverage to steer engagement and increase reach.
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