Dynamic PRA Based on System Codes Coupling for Passive Safety System in Integral Pressurized Water Reactor

An integral Pressurized Water Reactor (iPWR) type SMR was studied featuring Passive Safety Systems (PSSs). Different from active systems, PSSs are easily influenced by system parameters referred to as phenomenological factors such as heat loss, flow friction, oxidation, non-condensable gases and void fraction due to the low driving force of natural circulation. The system parameters also contribute to the uncertainty and dependency of PSS leading to the system unreliability. Thus, efforts are made to improve the reliability of PSS. A classical Probabilistic Risk Assessment (PRA) model describing active systems does not consider time evolution nor event ordering for PSS that dynamic PRA can accommodate. Here we developed and realized coupling between LabVIEW and CAFTA. Isolation Condenser System (ICS) is taken as the benchmark system due to the simple design in single phase without phase change phenomena in order to mainly remove decay heat and secondarily depressurize the reactor pressure vessel (RPV). A classical PRA model of ICS using CAFTA is coupled with real-time simulation of primary loop and ICS in LabVIEW, leading to a dynamic simulation result. The difference in failure probability using dynamic versus classical PRA revealed that for one there are more component demands with different event ordering, such that improved PSS reliability in the iPWR-type SMR designs is possible.

Temporal ordering of omics and multiomic events inferred from time series data

Temporal changes in omics events can now be routinely measured, however current analysis methods are often inadequate, especially for multiomics experiments. We report a novel analysis method that can infer event ordering at better temporal resolution than the experiment, and integrates omic events into two concise visualizations (event maps and sparklines). Testing our method gave results well-correlated with prior knowledge and indicated it streamlines analysis of time-series data.

A new heuristics for the event ordering in binary decision diagram applied in fault tree analysis

Fault tree is a common approach in probabilistic risk assessment of complex engineering systems. Since their introduction, binary decision diagrams proved to be a valuable tool for complete quantification of hard fault tree models. As is known, the size of the binary decision diagram representation is mainly determined by the quality of the selected fault tree event ordering scheme. Finding the optimal event ordering for binary decision diagram representation is a computationally intractable problem, for which reason heuristic approaches are applied to find reasonable good ordering schemes. The existing method for finding optimal ordering schemes related to special types of fan-in 2 read-once formulas is employed in our research to develop a new heuristic for fault tree. Various fault tree simplification methods are used for the sake of reducing fault tree model discrepancy from fan-in 2 read-once formulas. The reduced fault tree is traversed in a depth-first manner, as for every gate, the best ordering scheme is chosen from selected sets of input permutations. The quality of the final event ordering scheme is compared to orderings produced with depth-first left most heuristic on a set of fault tree models addressed in the literature as well as on a set of our hard models. Our method proves to be a useful heuristic for finding good static event ordering, and it compares favourably to the known heuristic based on a depth-first left most assignment approach.

Controllable Neural Story Plot Generation via Reward Shaping

Language-modeling--based approaches to story plot generation attempt to construct a plot by sampling from a language model (LM) to predict the next character, word, or sentence to add to the story. LM techniques lack the ability to receive guidance from the user to achieve a specific goal, resulting in stories that don't have a clear sense of progression and lack coherence. We present a reward-shaping technique that analyzes a story corpus and produces intermediate rewards that are backpropagated into a pre-trained LM in order to guide the model toward a given goal. Automated evaluations show our technique can create a model that generates story plots which consistently achieve a specified goal. Human-subject studies show that the generated stories have more plausible event ordering than baseline plot generation techniques.

Nothing happened, something happened: Silence in a makerspace

An ever-increasing range of work activities occur in open spaces that require collective discipline, with silence emerging as a key feature of such workplace configurations. Drawing from an ethnographic examination of a makerspace in Paris, we explore the ways in which silence is incorporated into new work practices in the context of their actualization, embodiment and apprenticeship. Through its engagement with the conceptual work of Merleau-Ponty, this article does not posit silence as the opposite of sounds or as a passive achievement. Silence is inscribed in a learning process and requires numerous efforts to be maintained (e.g. body postures to avoid staring into the eyes of someone entering into an open space, wearing headphones, etc.). It is also the envelope of numerous noisy acts that take place in the phenomenological body and in the embodied practices of workers. We argue that ‘silencing’ is an event ordering that gives direction to what ‘happens’ in collective work activities and is central to the process of embodied learning in collaborative spaces.

Introducing a Computer-assisted Method for Teaching Summarizing Skills to EIL/EFL Students

The paper introduces a computer-assisted method for teaching summarizing skills to EIL/EFL students—the Event Ordering method, or EVO for short. EVO, I shall contend, helps students develop basic reasoning skills. To support this claim, I argue that over time, EVO introduces a recurrent ‘loop’ process whereby readers become increasingly adept at projecting logical relations between events as they read, thus enabling them to foresee and make informed guesses as to which events from a narrative—typically from graded readers—would most likely find their way into the summary. This, I shall further argue, has two significant consequences: (i) it helps students build organizational skills—for being able to detect and make logical connections between important events is precisely what summarizing asks of readers; and (ii) it helps students become better readers. In the first section of the paper, I introduce the various theoretical aspects of EVO; I then move on, in section two, to support EVO with several samples of student writing.