Generation of Synthetic Multi-Resolution Time Series Load Data

The availability of large datasets is crucial for the development of new power system applications and tools; unfortunately, very few are publicly and freely available. We designed an end-to-end generative framework for the creation of synthetic bus-level time-series load data for transmission networks. The model is trained on a real dataset of over 70 Terabytes of synchrophasor measurements spanning multiple years. Leveraging a combination of principal component analysis and conditional generative adversarial network models, the scheme we developed allows for the generation of data at varying sampling rates (up to a maximum of 30 samples per second) and ranging in length from seconds to years. The generative models are tested extensively to verify that they correctly capture the diverse characteristics of real loads. Finally, we develop an open-source tool called LoadGAN which gives researchers access to the fully trained generative models via a graphical interface

Out of the clinic into the home: Control and patient-physician communication

The communication of information between patient and physician is a difficult and often flawed undertaking. Although the patient may be more immediately aware of dissatisfaction with the results, the presence of incomplete or inaccurate information will ultimately affect the physician's ability to function and the quality of care he can deliver. This is an especially important problem in chronic illness where the social, psychological and environmental factors which may impinge on the illness often cannot be identified or verified by laboratory tests.The physician's need to maintain control and hence power over the patient has been suggested as an explanation for these communication difficulties. This paper examines how the home setting influences physician control by including information about the patient and his disease which the clinic context actively excludes. It argues that the loss of control which physicians experience affects communication between patient and physician and thus the quality of information obtained in that communication, and further that the information gathered is important in the care of the long term chronically ill patient.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/26447/1/0000535.pd

PMU Tracker: A Visualization Platform for Epicentric Event Propagation Analysis in the Power Grid

The electrical power grid is a critical infrastructure, with disruptions in transmission having severe repercussions on daily activities, across multiple sectors. To identify, prevent, and mitigate such events, power grids are being refurbished as 'smart' systems that include the widespread deployment of GPS-enabled phasor measurement units (PMUs). PMUs provide fast, precise, and time-synchronized measurements of voltage and current, enabling real-time wide-area monitoring and control. However, the potential benefits of PMUs, for analyzing grid events like abnormal power oscillations and load fluctuations, are hindered by the fact that these sensors produce large, concurrent volumes of noisy data. In this paper, we describe working with power grid engineers to investigate how this problem can be addressed from a visual analytics perspective. As a result, we have developed PMU Tracker, an event localization tool that supports power grid operators in visually analyzing and identifying power grid events and tracking their propagation through the power grid's network. As a part of the PMU Tracker interface, we develop a novel visualization technique which we term an epicentric cluster dendrogram, which allows operators to analyze the effects of an event as it propagates outwards from a source location. We robustly validate PMU Tracker with: (1) a usage scenario demonstrating how PMU Tracker can be used to analyze anomalous grid events, and (2) case studies with power grid operators using a real-world interconnection dataset. Our results indicate that PMU Tracker effectively supports the analysis of power grid events; we also demonstrate and discuss how PMU Tracker's visual analytics approach can be generalized to other domains composed of time-varying networks with epicentric event characteristics.Comment: 10 pages, 5 figures, IEEE VIS 2022 Paper to appear in IEEE TVCG; conference encourages arXiv submission for accessibilit

Safer sex practices among newly diagnosed HIV-positive men who have sex with men in China: results from an ethnographic study

The study reported here sought to understand the rationales of safer sex practices adopted by newly diagnosed HIV-positive men who have sex with men (MSM). Guided by a socio-ecological framework, an ethnography was conducted among newly diagnosed HIV-positive MSM. In-depth interviews and participant observation were employed to produce an account of the social and cultural settings that was faithful to the perspectives of participants. A total of 31 participants with diverse backgrounds were recruited in a southern city of China. Participant observation was conducted in local healthcare settings, MSM venues, and NGO offices. Most participants (24/31) reported stopping unprotected anal intercourse (UAI) immediately after being diagnosed as HIV-positive. Factors associated with safer sex practices were identified at both individual and environmental levels, including self-protection, establishment of self-esteem, dignity, altruism and reciprocity, disease experience as a source of personal growth, and organizational culture and values. Newly diagnosed HIV-positive MSM navigate their sexual practices within the context of multiple competing factors. Implications for sustained behaviour change enabling safer sex practices include stimulating survival instinct, facilitating safer sex decision making, motivating and facilitating personal growth, and encouraging volunteerism to promote intentional activities for safer sex practices

Effect of support acidity during selective hydrogenolysis of glycerol over supported palladium-ruthenium catalysts

We report the role of the acidity of support during the selectivity hydrogenolysis of glycerol over supported bimetallic palladium–ruthenium (PdRu) catalysts. The PdRu nanoparticles were supported on a series of metal oxides and zeolitic supports via the modified impregnation method and tested for the liquid-phase hydrogenolysis of glycerol using gaseous hydrogen. The relative acid site densities of selected catalysts were determined by ammonia temperature-programmed desorption and pyridine desorption experiments. Based on these studies, we report a direct correlation between the catalytic activity (conversion and 1,2 propane diol yield) and two different acid sites (strong acid sites and very strong acid sites). Besides zeolite-supported catalysts, TiO2 supported PdRu nanoparticles exhibit moderate catalytic activity; however, this catalyst shows high selectivity for the desired C–O bond cleavage to produce C3 products over the undesired C–C bond cleavage to produce < C3 products

Continual Dialogue State Tracking via Example-Guided Question Answering

Dialogue systems are frequently updated to accommodate new services, but naively updating them by continually training with data for new services in diminishing performance on previously learnt services. Motivated by the insight that dialogue state tracking (DST), a crucial component of dialogue systems that estimates the user's goal as a conversation proceeds, is a simple natural language understanding task, we propose reformulating it as a bundle of granular example-guided question answering tasks to minimize the task shift between services and thus benefit continual learning. Our approach alleviates service-specific memorization and teaches a model to contextualize the given question and example to extract the necessary information from the conversation. We find that a model with just 60M parameters can achieve a significant boost by learning to learn from in-context examples retrieved by a retriever trained to identify turns with similar dialogue state changes. Combining our method with dialogue-level memory replay, our approach attains state of the art performance on DST continual learning metrics without relying on any complex regularization or parameter expansion methods.Comment: 11 pages, EMNLP 202