Cengage Learning (United States)

ABSTRACT This study explores how AI‐enabled, culturally relevant, narrative‐based learning pedagogy influences engagement among ninth‐grade students in an Earth Science course. Conducted in two classrooms in a southeastern U.S. school predominantly attended by African American and Latinx students, we developed lesson plans on Earth Systems and Climate Change. Using a qualitative case study design, we examined one educator's implementation of AI‐enhanced lessons. Thematic analysis of exit surveys and students' artifacts revealed that narrative‐based learning significantly increased students' desire, engagement, and confidence in scientific concepts compared to conventional methods. This approach allowed students to integrate local and global knowledge, fostering deep questions about their science learning and encouraging exploration of problems and solutions.

In response to the fragmented state of civic engagement tools and the urgent challenges facing democratic systems, this paper introduces a shared, contributor-driven ontology to connect diverse civic tech platforms, emerging from the work of the Interoperable Deliberative Tool cohort at Metagov.By integrating platforms like Voice to Vision, Assemblis, and Decidim, we enable the flow of deliberative data across contexts, supporting more cohesive decision-making.This approach helps bridge gaps between input, analysis, and action, enhancing democratic resilience in crisis moments.Through our work, we demonstrate how interoperability can strengthen civic engagement and provide a foundation for more responsive, collaborative governance.

This study aimed to describe treatment patterns, frequency of comorbidities, and healthcare cost burden among patients with atopic dermatitis (AD) initiating systemic therapy (or re-initiating it after more than 12 months) versus matched controls without AD. Patients with AD initiating oral corticosteroids (OCS), immunosuppressants (SIS), or biologics between 1/1/2017 and 6/30/2022 (index=first treatment) were identified for analysis in the MarketScan claims databases. Patients were continuously enrolled 12 months before (baseline) and after index (follow-up). Direct and propensity score matching were used to adjust for baseline differences between cases and controls. Comorbidities and all-cause healthcare costs within service categories were compared between AD cases and matched controls during follow-up and treatment patterns were described for all patients with AD. A total of 20,503 patients with AD were identified. On index,12% initiated biologics, 86% OCS, and 2% SIS, and discontinuation rates were high during follow-up (SIS: 80%; biologics: 35%) The incidence of several comorbidities, including cardiovascular disease, atopic conditions, and mental health disorders, was higher in the AD cohort compared with matched controls (p<0.001). Patients with AD (vs. matched controls) also had significantly higher mean total all-cause healthcare costs (US$15,134 vs. $6832; p<0.001). Patients with AD who are initiating systemic treatment experience an increased risk of being newly diagnosed with several comorbidities and higher healthcare costs compared with matched controls, which places increased burden on patients and healthcare systems.

ABSTRACT The cultivation of mature zygotic embryos in vitro is crucial for species exhibiting genetic incompatibility, as their seeds do not germinate naturally. This study aimed to evaluate the germination efficiency of seeds and isolated embryos, along with the growth of Capsicum baccatum and Capsicum frutescens in vitro and their impacts on acclimatisation. Isolated embryos of both species were germinated in MS culture medium with half the salts and varying sucrose concentrations (0, 10, 20, 30 and 40 g L−1). Morphological and photochemical efficiency variables were assessed, and acclimatisation was performed. The efficiency of embryo cultivation was determined (intact seeds vs isolated zygotic embryos). According to the results, isolated zygotic embryos showed a higher percentage and uniformity of germination. Plants cultivated in vitro in a culture medium without sucrose did not survive during acclimatisation. For seedling cultivation, the recommended sucrose concentration in vitro is 10 g L−1.

Healthcare providers, policymakers, and defense contractors need to understand many types of machine learning model behaviors. While eXplainable Artificial Intelligence (XAI) provides tools for interpreting these behaviors, few frameworks, surveys, and taxonomies produce succinct yet general notation to help researchers and practitioners describe their explainability needs and quantify whether these needs are met. Such quantified comparisons could help individuals rank XAI methods by their relevance to use-cases, select explanations best suited for individual users, and evaluate what explanations are most useful for describing model behaviors. This paper collects, decomposes, and abstracts subcomponents of common XAI methods to identify a mathematically grounded syntax that applies generally to describing modern and future explanation types while remaining useful for discovering novel XAI methods. The resulting syntax, introduced as the Qi-Framework, generally defines explanation types in terms of the information being explained, their utility to inspectors, and the methods and information used to produce explanations. Just as programming languages define syntax to structure, simplify, and standardize software development, so too the Qi-Framework acts as a common language to help researchers and practitioners select, compare, and discover XAI methods. Derivative works may extend and implement the Qi-Framework to develop a more rigorous science for interpretable machine learning and inspire collaborative competition across XAI research.

Arriving at the University of Virginia in the autumn of 1969, Donald Hunt began his 50+ year career in academics with the study of organometallic chemistry, on which he had done his PhD thesis work, and mass spectrometry, to which he was introduced while a postdoc in Klaus Biemann's laboratory at the Massachusetts Institute of Technology. In the 1970s, Hunt's lab pioneered the use of negative chemical ionization (CI) to enhance sensitivity for studying organic molecules, developed a system for simultaneously obtaining positive and negative CI spectra to augment structure elucidation, and built a prototype triple quadrupole instrument so effective at collisional dissociation that its commercial counterpart became the analytical instrument of choice for mixture analysis for the next decade and beyond. Foreseeing that the future lay in the analysis of biological molecules, by the end of the decade Hunt shifted his focus to peptides. The analysis of protein fragments had suddenly become more accessible thanks to the advent of the triple quadrupole and Barber's introduction of fast atom bombardment. As the 1980s began and Hunt and his team sought to pursue larger and larger pieces of proteins, his attention turned to the development of mass spectrometers with greater mass range. While recounting their memories of these events, several of Hunt's students and colleagues pay tribute to his support for them as individuals, as well as to his infectious enthusiasm for scientific endeavors that he so generously shared.

Being a single mom with limited post-secondary education and a feeble resume would terrify anyone from going to a job fair held by one of the engineering giants in the USA. Yet, Stephanie Hajducek pushed forward, harnessing the willingness to learn and secured an entry-level position in the company. In this chapter, she details how her life changed forever in various roles and ended up earning her bachelor’s degree at the age of 38. She shows us that not everything has a particular time limit and how you can find your place in the workforce, at your time, not anybody else’s. Stephanie goes on to give us the background of her nonprofit, “This One’s for the Gals,” where she helps create awareness and opportunities for girls preparing to enter the workforce.

Well, yes, we’ve all learned about the object-oriented programming paradigm before, but it never hurts to go over some basic definitions so that we’re all on the same page for our discussion about object-oriented analysis and design.

Before you start coding—yes, before you start coding—you need to know what it is you’re going to build. That’s what requirements are: a list of stuff you have to implement in order to create your terrific program. Most developers hate requirements. Really. All we’d like to do is sit down and start coding. All of us have that super-programmer mentality; just give us the problem and we can sit down and design and code it on the fly. Not! If you want to be a productive developer and make fewer errors and come up with a good, clean design, you need requirements. The more detailed they are, the better. A good set of requirements tells you just what the program is supposed to do. It gives you the scaffolding around which you’ll hang your design. You’ll do requirements anyway—it’s one of those steps in a standard development lifecycle that you can’t avoid—but if you don’t make room for it in your project, you won’t create a great program. Being intentional about requirements forces you to think about the details of the program, and it also lets you listen to the users so you have a better idea of what they really want. So let’s talk about requirements.