Application Of Statistics In Engineering Technology Programs

Statistics is a critical tool for robustness analysis, measurement system error analysis, test data analysis, probabilistic risk assessment, and many other fields in the engineering world. Traditionally, however, statistics is not extensively used in undergraduate engineering technology (ET) programs, resulting in a major disconnect from industry expectations. The research question: How to effectively integrate statistics into the curricula of ET programs, is in the foundation of this paper. Based on the best practices identified in the literature, a unique “learning-by-using” approach was deployed for the Electronics Engineering Technology Program at Texas A&M University. Simple statistical concepts such as standard deviation of measurements, signal to noise ratio, and Six Sigma were introduced to students in different courses. Design of experiments (DOE), regression, and the Monte Carlo method were illustrated with practical examples before the students applied the newly understood tools to specific problems faced in their engineering projects. Industry standard software was used to conduct statistical analysis on real results from lab exercises. The result from a pilot project at Texas A&M University indicates a significant increase in using statistics tools in course projects by students.  Data from student surveys in selected classes indicate that students gained more confidence in statistics.   These preliminary results show that the new approach is very effective in applying statistics to engineering technology programs

Role of magnetic fields in fueling Seyfert nuclei

Molecular gas is believed to be the fuel for star formation and nuclear activity in Seyfert galaxies. To explore the role of magnetic fields in funneling molecular gas into the nuclear region, measurements of the magnetic fields embedded in molecular gas are needed. By applying the new velocity gradient technique (VGT) to ALMA and PAWS's CO isotopolog data, we obtain the first detection of CO-associated magnetic fields in several nearby Seyfert galaxies and their unprecedented high-resolution magnetic field maps. The VGT-measured magnetic fields in molecular gas globally agree with those inferred from existing HAWC+ dust polarization and VLA synchrotron polarization. An overall good alignment between the magnetic fields traced by VGT-CO and by synchrotron polarization may support the correlation between star formation and cosmic ray generation. We find that the magnetic fields traced by VGT-CO have a significant radial component in the central regions of most Seyferts in our sample, where efficient molecular gas inflows or outflows may happen. In particular, we find local misalignment between the magnetic fields traced by CO and dust polarization within the nuclear ring of NGC 1097, and the former aligns with the central bar's orientation. This misalignment reveals different magnetic field configurations in different gas phases and may provide an observational diagnostic for the ongoing multi-phase fueling of Seyfert activity.Comment: 24 pages, 14 figure

Others of My Kind

From the turn of the twentieth century to the 1950s, a group of transgender people on both sides of the Atlantic created communities that profoundly shaped the history and study of gender identity. By exchanging letters and pictures among themselves they established private networks of affirmation and trust, and by submitting their stories and photographs to medical journals and popular magazines they sought to educate both doctors and the public. Others of My Kind draws on archives in Europe and North America to tell the story of this remarkable transatlantic transgender community. This book uncovers threads of connection between Germany, the United States, and the Netherlands to discover the people who influenced the work of authorities like Magnus Hirschfeld, Harry Benjamin, and Alfred Kinsey not only with their clinical presentations, but also with their personal relationships. It explores the ethical and analytical challenges that come with the study of what was once private, secret, or unacceptable to say. With more than 180 colour and black and white illustrations, including many stunning, previously unpublished photographs, Others of My Kind celebrates the faces, lives, and personal networks of those who drove twentieth-century transgender history

Abschottung oder Rechtsklarheit? Das neue Gesetz zum Schutz strategischer Branchen

Russland zieht seit einigen Jahren zunehmend ausländische Investoren an. Die kumulierten Auslandsinvestitionen in der Russischen Föderation sind 2007 auf einen Rekordstand von 220 Mrd. US-Dollar gestiegen; allein der Zufl uss ausländischer Investitionen betrug im vergangenen Jahr über 120 Mrd. US-Dollar (Angaben des russischen Statistikamtes). Diese Tendenz könnte sich jedoch 2008 umkehren. Grund ist das Gesetz "Über das Verfahren zur Verwirklichung ausländischer Investitionen in Gesellschaften, die eine strategische Bedeutung für die Sicherung der Landesverteidigung und die Sicherheit des Staates haben", das Anfang Mai in Kraft getreten ist. Es stellt hohe Hürden für Auslandsinvestitionen auf. Nachfolgend werden die wichtigsten Inhalte des Gesetzes skizziert und Auswirkungen für westliche Investoren dargestellt. Ergänzend wird auf kritische Punkte hingewiesen

Incremental Learning of Humanoid Robot Behavior from Natural Interaction and Large Language Models

Natural-language dialog is key for intuitive human-robot interaction. It can be used not only to express humans' intents, but also to communicate instructions for improvement if a robot does not understand a command correctly. Of great importance is to endow robots with the ability to learn from such interaction experience in an incremental way to allow them to improve their behaviors or avoid mistakes in the future. In this paper, we propose a system to achieve incremental learning of complex behavior from natural interaction, and demonstrate its implementation on a humanoid robot. Building on recent advances, we present a system that deploys Large Language Models (LLMs) for high-level orchestration of the robot's behavior, based on the idea of enabling the LLM to generate Python statements in an interactive console to invoke both robot perception and action. The interaction loop is closed by feeding back human instructions, environment observations, and execution results to the LLM, thus informing the generation of the next statement. Specifically, we introduce incremental prompt learning, which enables the system to interactively learn from its mistakes. For that purpose, the LLM can call another LLM responsible for code-level improvements of the current interaction based on human feedback. The improved interaction is then saved in the robot's memory, and thus retrieved on similar requests. We integrate the system in the robot cognitive architecture of the humanoid robot ARMAR-6 and evaluate our methods both quantitatively (in simulation) and qualitatively (in simulation and real-world) by demonstrating generalized incrementally-learned knowledge.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. Submitted to the 2023 IEEE/RAS International Conference on Humanoid Robots (Humanoids). Supplementary video available at https://youtu.be/y5O2mRGtsL