SS433's jet trace from ALMA imaging and Global Jet Watch spectroscopy: evidence for post-launch particle acceleration

We present a comparison of Doppler-shifted H-alpha line emission observed by the Global Jet Watch from freshly-launched jet ejecta at the nucleus of the Galactic microquasar SS433 with subsequent ALMA imaging at mm-wavelengths of the same jet ejecta. There is a remarkable similarity between the transversely-resolved synchrotron emission and the prediction of the jet trace from optical spectroscopy: this is an a priori prediction not an a posteriori fit, confirming the ballistic nature of the jet propagation. The mm-wavelength of the ALMA polarimetry is sufficiently short that the Faraday rotation is negligible and therefore that the observed E-vector directions are accurately orthogonal to the projected local magnetic field. Close to the nucleus the B-field vectors are perpendicular to the direction of propagation. Further out from the nucleus, the B-field vectors that are coincident with the jet instead become parallel to the ridge line; this occurs at a distance where the jet bolides are expected to expand into one another. X-ray variability has also been observed at this location; this has a natural explanation if shocks from the expanding and colliding bolides cause particle acceleration. In regions distinctly separate from the jet ridge line, the fractional polarisation approaches the theoretical maximum for synchrotron emission.Comment: To appear in ApJ Letter

Developing Numeracy and Problem-Solving Skills by Overcoming Learning Bottlenecks

Purpose The purpose of this paper is to present an educational approach to elevating problem-solving and numeracy competencies of business undergraduates to meet workplace demand. The approach is grounded in the theory of constraints following the Decoding the Discipline model. The authors investigated a cognitive bottleneck involving problem modeling and an affective bottleneck concerning low self-efficacy of numeracy and designed specific interventions to address both bottlenecks simultaneously. The authors implemented the proposed approach in an introductory level analytics course in business operations. Design/methodology/approachThe authors use an empirical study to evaluate the effectiveness of the proposed approach in addressing deficiency in numeracy and problem-solving skills. Cognitive and affective learning interventions were introduced in an undergraduate core course in analytics. The perceived effectiveness of the interventions was evaluated with the use of a survey at the end of the course. To further investigate the effectiveness of the proposed interventions beyond self-reporting, the impact of the interventions on actual learning was evaluated by comparing the exam scores between classes with and without the interventions. Findings Students who underwent the interventions successfully overcame both learning bottlenecks and indicated a positive change in attitude toward the analytics discipline as well as achieved higher exam scores in the analytics course. Research limitations/implications This study succeeds in strengthening the body of research in teaching and learning. The authors also offer a holistic treatment of cognitive and affective learning bottlenecks, and provide empirical evidence to support the effectiveness of the proposed approach in elevating numeracy and problem-solving competencies of business undergraduates. Practical implications The proposed approach is useful for business educators to improve business students’ quantitative modeling skill and attitude. Researchers can also extend the approach to other courses and settings to build up the body of research in learning and skill development. Educational policy makers may consider promoting promising approaches to improve students’ quantitative skill development. They can also set a high standard for higher education institutions to assess students’ numeracy and problem-solving competencies. Employers will find college graduates bring to their initial positions the high levels of numeracy and problem-solving skills demanded for knowledge work to sustain business growth and innovation. Social implications As students’ numeracy and problem-solving skills are raised, they will develop an aptitude for quantitative-oriented coursework that equips them with the set of quantitative information-processing skills needed to succeed in the twenty-first century society and global economy. Originality/value The proposed approach provides a goal-oriented three-step process to improve learning by overcoming learning bottlenecks as constraints of a learning process. The integral focus on identifying learning bottlenecks, creating learning interventions and assessing learning outcomes in the proposed approach is instrumental in introducing manageable interventions to address challenges in student learning thereby elevating students’ numeracy and problem-solving competencies

Real-World Applications for Analytics Teaching and Learning

This document provides learning-by-doing materials for Analytics software skill development using SAS JMP. It integrates Analytics concepts and techniques with real-world scenarios based on the COVID-19 pandemic to illustrate how real-world data can be transformed into actionable insights to offer decision support for COVID-19 related issues. A holistic treatment of the Analytics process from data acquisition and cleansing to data analysis and interpretation is emphasized using five studies: Characterize COVID-19 mortality demographic risk factors, Visualize COVID-19 mortality demographics, Conduct COVID-19 mortality time series forecasting, Predict COVID-19 mortality, and Analyze COVID-19 vaccine acceptance, uptake, and experiences. Each study is structured with guiding questions to engage students to think critically, relate Analytic concepts to the given situation, and arrive at their own answers/solutions for active knowledge exploration and discovery.https://uknowledge.uky.edu/marketing_textbooks/1000/thumbnail.jp

Decision Support in Knowledge Acquisition: Concept Characterization Using Genetic Algorithms

We demonstrate the use of an unsupervised learning technique called genetic algorithms to discover the association between a concept and its key attributes in concept characterization. The resulting concept- attribute associations are important domain concepts for knowledge engineers to structure interviews with the experts or to prepare representative data for inductive inference. Examples based on the part family identification problem in manufacturing are employed to illustrate the identification capability of our technique. Preliminary results from testing the technique in a SUN SPARC station 1+ indicate that it can be exploited as a decision support tool to assist knowledge engineers in the conceptualization stage of the knowledge acquisition process

e‑Learning Success Model: An Information Systems Perspective

This paper reports the observations made and experience gained from developing and delivering an online quantitative methods course for Business undergraduates. Inspired by issues and challenges experienced in developing the online course, a model is advanced to address the question of how to guide the design, development, and delivery of successful e‑learning initiatives based on theories of a user‑centered information systems development paradigm. The benefits of using the proposed model for e‑learning success assessment is demonstrated through four cycles of action research after two action research cycles of pilot study. Findings from our empirical study confirm the value of an action research methodology for promoting e‑learning success. The paper concludes with a discussion on the merits of the proposed model in furthering our understanding of how to define, assess, and promote e‑learning success

Online Learning Integrity Approaches: Current Practices and Future Solutions

The primary objective of this paper is to help institutions respond to the stipulation of the Higher Education Opportunity Act of 2008 by adopting cost-effective academic integrity solutions without compromising the convenience and flexibility of online learning. Current user authentication solutions such as user ID and password, security questions, voice recognition, or fingerprint identification are not infallible and may violate students’ rights to privacy or cause undue interruptions to their efforts in performing assessment tasks. Existing authentication solutions will be evaluated for their cost effectiveness in preventing fraud and cheating while ensuring learner identity and honesty. Emerging technologies in the form of biometrics, surveillance systems and predictive analytics will be examined to provide insights into the future of e-authentication for ensuring the academic integrity of online learning

Valse Celestia

https://digitalcommons.library.umaine.edu/mmb-ps/2172/thumbnail.jp

Numerical, Secondary Big Data Quality Issues, Quality Threshold Establishment, & Guidelines for Journal Policy Development

An IS researcher may obtain Big Data from primary or secondary data sources. Sometimes, acquiring primary Big Data is infeasible due to availability, accessibility, cost, time, and/or complexity considerations. In this paper, we focus on Big Data-based IS research and discuss ways in which one may, post hoc, establish quality thresholds for numerical Big Data obtained from secondary sources. We also present guidelines for developing journal policies aimed at ensuring the veracity and verifiability of such data when used for research purposes