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Authentic learning is a critical pedagogy and curriculum requirement in higher education to better prepare students for future workforce requirements. As such, educators adopt a range of authentic learning tasks such as work-integrated learning, industry reports, and field trips to enable student engagement in current industry issues. Although such actions are practical and have valuable impact on students, there has been little effort to narrow the theoretical gap. The present study addresses this by extending the authentic learning three-factor model (construction of knowledge, disciplined inquiry, and value beyond school) to evaluate the perceptions of educators in THE higher education towards their understanding and embeddedness of authentic elements in their teaching curriculum to better prepare industry-ready graduates. In-depth interviews were conducted with higher education THE academics (n = 21) in Australia, New Zealand, The Netherlands, and Scotland. Thematic analysis using NVivo revealed seven key themes within the authentic learning three-factor model: knowledge co-creation and scaffolding, customising knowledge and experiences, student engagement, educator capability, industry readiness, real-world problems, and meaningful connections. The proposed seven standards necessitate a paradigm shift in curriculum design, syllabus structuring, and student assessment within THE institutions. Emphasising industry-readiness as a core objective, educators must align their teaching approaches with the theoretical findings of this research to better prepare students for real-world challenges. [ABSTRACT FROM AUTHOR]

Machine learning models that are used for the prediction and control of production can improve quality and yield. However, developing models that are highly accurate and reflective of real-world processes is challenging. We propose a feedforward neural network model specifically designed for continuous Multistage Manufacturing Processes (MMPs) without intermediate outputs. This model, which is termed "MMP Net," can accurately represent the control mechanism of continuous MMPs. Whereas existing studies on learning MMPs assume an intermediate output data, the MMP Net does not require such an unrealistic assumption. We use the MMP Net to develop prediction models for the lubricant base oil production process of a world-leading lubricant manufacturer. Evaluation results show that the MMP Net is superior to other deep neural network and machine learning models. Consequently, the MMP Net was actually implemented in a real factory in 2022 and is expected to save 900,000 dollars per year for each production line. We believe that our work can serve as a basis to develop customized machine learning solutions for improving continuous MMPs. [ABSTRACT FROM AUTHOR]

Many statistics education researchers have found that statistics students' attitudes tend to decrease over the duration of a course. Although many researchers have tried to incorporate a variety of activities and/or teaching methods to improve student attitudes, many are not only very time consuming to implement, but have also not shown many favorable results. In the study presented here, the inclusion of a low-stakes and easy-to-implement assignment and its effect on student attitudes is investigated. Although the results presented here did not show large changes, they suggest that some changes in students' attitudes can be made with a small change in a course. [ABSTRACT FROM AUTHOR]

In recent decades, nationalism has emerged from the distant purview of history to become the primary driver of some of the world's biggest news. Given the prominence of nationalist conflict, students in political science increasingly study the subject with modern references in mind. This article describes the design for a timely undergraduate political science course on the causes and consequences of nationalism, with a focus on four central learning objectives: understanding theories and concepts in the study of nations and nationalism; applying theories to contemporary events; exploring issues of identity among a community of learners; and developing analytical and professional skills. I describe in detail two course assignments that exemplify these pedagogical objectives. In the "nationalism in the news" assignment, students give an in-class oral presentation on a current news story, interpreting the event through the lens of course themes. In the "national anthems analysis" paper, students analyze the lyrics of the national anthem of their choosing, linking symbols to interpretations of different nationalism types. I conclude by offering suggestions for adapting these and other course assignments to a range of classrooms and students. [ABSTRACT FROM AUTHOR]

The purpose of this study was to examine the association between institution-delivered sex education given under real-world conditions and sexually transmitted infection (STI) rates, STI fatalism, and prior STI testing among African American men aged 15–24 who have sex with women. Participants were tested at community venues for Chlamydia and gonorrhoea and undertook a survey to elicit history of sex education and sexual health information. Among 1196 participants, 73.0% reported having received institution-delivered sex education topics including STI information (90.5%), condoms (89.2%), pregnancy/birth (72.1%) and birth control (67.1%). Among a subset of participants asked about the quality of sex education, 85.7% reported it was 'very good' or 'OK'. The prevalence rate for Chlamydia and/or gonorrhoea was 10.5%. Those who received sex education were more likely to have lower STI fatalism (51.0% vs. 42.4%, p = 0.01) and more likely to report previous Chlamydia screening (44.1% vs. 31.6%, p < 0.01), but did not have a significantly lower rate of Chlamydia and/or gonorrhoea (9.9% vs. 12.4%, p = 0.20) compared to those who did not receive sex education. These findings suggest that institution-delivered sex education given under real-world conditions has beneficial effects on STI risk factors among young African American men. [ABSTRACT FROM AUTHOR]

Material flow in a chemical processing plant often follows complicated control laws and involves plant capacity constraints. Importantly, the process involves discrete scenarios which when modelled in a programming format involves if-then-else statements. Therefore, a formulation of an optimization problem of such processes becomes complicated with nonlinear and non-differentiable objective and constraint functions. In handling such problems using classical point-based approaches, users often have to resort to modifications and indirect ways of representing the problem to suit the restrictions associated with classical methods. In a particular gold processing plant optimization problem, these facts are demonstrated by showing results from MATLAB R® 's well-known fmincon routine. Thereafter, a customized evolutionary optimization procedure which is capable of handling all complexities offered by the problem is developed. Although the evolutionary approach produced results with comparatively less variance over multiple runs, the performance has been enhanced by introducing derived heuristics associated with the problem. In this article, the development and usage of derived heuristics in a practical problem are presented and their importance in a quick convergence of the overall algorithm is demonstrated. [ABSTRACT FROM AUTHOR]

Façade design is a cross-disciplinary multi-objective optimization process. The major barrier to devising an optimal façade solution is the evaluation of the true values of alternative façade design options. A simple approach is to focus on a limited number of design criteria that can be evaluated through mono-disciplinary commercial software, while overlooking other cross-disciplinary design criteria. This paper describes a prototype whole-life value optimization tool for façade design, which accounts for the functional, financial, and environmental sustainability of alternative façade options. The tool adopts an integrated approach involving accurate simulation, systematic parametric analysis, and automatic design optimization. The tool is trialled on a real-world façade design project, and it successfully identified optimal façade solutions that outperform the original solutions obtained from the conventional façade design process. [ABSTRACT FROM PUBLISHER]

One common problem in association rule mining is that often a very large number of rules are generated from the database. The sheer volume of these rules makes it difficult, if not impossible, for human users to analyze and make use of the rules. In this article, we propose two algorithms for grouping and summarizing association rules. The first algorithm recursively groups rules according to the structure of the rules and generates a tree of clusters as a result. The second algorithm groups the rules according to the semantic distance between the rules by making use of a semantic tree-structured network of items. We propose an algorithm for automatically tagging the semantic network so that the rules can be represented as directed line segments in a two-dimensional space and can then be grouped according to the distance between line segments. We also present an application of the two algorithms, in which the proposed algorithms are evaluated. The results show that our grouping methods are effective and produce good grouping results. [ABSTRACT FROM AUTHOR]