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Every year new safety features and regulations are employed within the process industry to reduce risks associated with operations. Despite these advancements chemical plants remain hazardous places, and the role of the engineer will always involve risk mitigation through real time decision making. Results from a previous study by Kongsvik et al., 2015 indicated that there were three types of decisions in major chemical plants: strategic decisions, operational decisions, and instantaneous decisions. The study showed the importance for improving upon engineers’ operational and instantaneous choices when tasked with quick solutions in the workforce. In this research study, we dive deeper to understand how senior chemical engineering students’ prioritize components of decision making such as budget, productivity, relationships, safety, and time, and how this prioritization may change as a result of participation in a digital immersive training environment called Contents Under Pressure. More specifically, we seek to address the following two research questions: (1) How do senior chemical engineering students prioritize safety in comparison to criteria such as budget, personal relationships, plant productivity, and time in a process safety context, and (2) How does senior chemical engineering students’ prioritization of decision making criteria (budget, personal relationships, plant productivity, safety, and time) change after exposure to a virtual process safety decision making environment? As part of this study, 187 senior chemical engineering students from three separate institutions completed a pre- and post-reflection survey around their engagement with Contents Under Pressure and asked them to rank their prioritizations of budget, productivity, relationships, safety, and time. Data was analyzed using descriptive statistics, and Friedman and Wilcoxon-sign-rank post hoc analyses were completed to determine any statistical differences between the rankings of decision making factors before and after engagement with Contents Under Pressure. Simulating process safety decision making with interactive educational supports may increase students’ understanding of genuine workplace environments and factors that contribute to process safety, without the real world hazards that result from poor decision making. By understanding how students prioritize these factors, chemical engineering curricula can be adapted to focus on the areas of process safety decision making where students need the largest improvement, thereby better preparing them to enter the engineering workforce. 

The e-textile landscape has enabled creators to combine textile materiality with electronic capability. However, the tools that e-textile creators use have been adapted from traditional textile or hardware tools. This puts creators at a disadvantage, as e-textile projects present new and unique challenges that currently can only be addressed using a non-specialized toolset. This paper introduces the first iteration of a wearable e-textile debugging tool to assist novice engineers in problem solving e-textile circuitry errors. These errors are often only detected after the project is fully built and are resolved only by disassembling the circuit. Our tool actively monitors the continuity of the conductive thread as the user stitches, which enables the user to identify and correct circuitry errors as they create their project. 

Today’s STEM classrooms have expanded the domain of computer science education from a basic two-toned terminal screen to now include helpful Integrated Development Environments(IDE) (BlueJ, Eclipse), block-based programming (MIT Scratch, Greenfoot), and even physical computing with embedded systems (Arduino, LEGO Mindstorm). But no matter which environment a student starts programming in, all students will eventually need help in finding and fixing bugs in their code. While the helpful IDE’s have debugger tools built in (breakpoints for pausing your program, ways to view/modify variable values, and "stepping" through code execution), in many of the other programming environments, students are limited to using print statements to try and "see" what is happening inside their program. Most students who learn to write code for Arduino microcontrollers will start within the Arduino IDE, but the official Arduino IDE does not currently provide any debugging tools. Instead, a student would have to move on to a professional IDE such as Atmel Studio or acquire a hardware debugger in order to add breakpoints or view their program’s variables. But each of these options has a steep learning curve, additional costs, and can require complex configurations. Based on research of student debugging practices[3, 7] and our own classroom observations, we have developed an Arduino software library, called Arduino Debugger, which provides some of these debugging tools (ex. breakpoints) while staying within the official Arduino IDE. This work continues a previous library, (redacted), which focused on features specific to e-textiles development boards. The Arduino Debugger library has been modified to support not only e-textile boards (Lilypad, Adafruit Circuit Playground) but most AVR and ARM based Arduino boards.We are also in the process of testing a set of Debugging Code Templates to see how they might increase student adoption of debugging tools. 

Abstract IceCube is a Cherenkov detector instrumenting over a cubic kilometer of glacial ice deep under the surface of the South Pole. The DeepCore sub-detector lowers the detection energy threshold to a few GeV, enabling the precise measurements of neutrino oscillation parameters with atmospheric neutrinos. The reconstruction of neutrino interactions inside the detector is essential in studying neutrino oscillations. It is particularly challenging to reconstruct sub-100 GeV events with the IceCube detectors due to the relatively sparse detection units and detection medium. Convolutional neural networks (CNNs) are broadly used in physics experiments for both classification and regression purposes. This paper discusses the CNNs developed and employed for the latest IceCube-DeepCore oscillation measurements [1]. These CNNs estimate various properties of the detected neutrinos, such as their energy, direction of arrival, interaction vertex position, flavor-related signature, and are also used for background classification. 

Abstract The powerful jets of blazars have been historically considered as likely sites of high-energy cosmic-ray acceleration. However, the particulars of the launched jet and the locations of leptonic and hadronic jet loading remain unclear. In the case when leptonic and hadronic particle injection occur jointly, a temporal correlation between synchrotron radiation and neutrino production is expected. We use a first catalog of millimeter wavelength (95–225 GHz) blazar light curves from the Atacama Cosmology Telescope for a time-dependent correlation with 12 yr of muon neutrino events from the IceCube South Pole Neutrino Observatory. Such millimeter emission traces activity of the bright jet base, which is often self-absorbed at lower frequencies and potentially gamma-ray opaque. We perform an analysis of the population, as well as analyses of individual, selected sources. We do not observe a significant signal from the stacked population. TXS 0506+056 is found as the most significant, individual source, though this detection is not globally significant in our analysis of selected active galactic nuclei. Our results suggest that the majority of millimeter-bright blazars are neutrino dim. In general, it is possible that many blazars have lighter, leptonic jets, or that only selected blazars provide exceptional conditions for neutrino production.