Creating Temporally Correlated High-Resolution Power Injection Profiles Using Physics-Aware GAN

Traditional smart meter measurements lack the granularity needed for real-time decision-making. To address this practical problem, we create a generative adversarial networks (GAN) model that enforces temporal consistency on its high-resolution outputs via hard inequality constraints using a convex optimization layer. A unique feature of our GAN model is that it is trained solely on slow timescale aggregated power information obtained from historical smart meter data. The results demonstrate that the model can successfully create minutely interval temporally-correlated instantaneous power injection profiles from 15-minute average power consumption information. This innovative approach, emphasizing inter-neuron constraints, offers a promising avenue for improved high-speed state estimation in distribution systems and enhances the applicability of data-driven solutions for monitoring such systems.Comment: 5 page

A thorough investigation of the prospects of eLISA in addressing the Hubble tension: Fisher Forecast, MCMC and Machine Learning

We carry out an in-depth analysis of the capability of the upcoming space-based gravitational wave mission eLISA in addressing the Hubble tension, with a primary focus on observations at intermediate redshifts ($3<z<8$). We consider six different parametrizations representing different classes of cosmological models, which we constrain using the latest datasets of cosmic microwave background (CMB), baryon acoustic oscillations (BAO), and type Ia supernovae (SNIa) observations, in order to find out the up-to-date tensions with direct measurement data. Subsequently, these constraints are used as fiducials to construct mock catalogs for eLISA. We then employ Fisher analysis to forecast the future performance of each model in the context of eLISA. We further implement traditional Markov Chain Monte Carlo (MCMC) to estimate the parameters from the simulated catalogs. Finally, we utilize Gaussian Processes (GP), a machine learning algorithm, for reconstructing the Hubble parameter directly from simulated data. Based on our analysis, we present a thorough comparison of the three methods as forecasting tools. Our Fisher analysis confirms that eLISA would constrain the Hubble constant ($H_0$) at the sub-percent level. MCMC/GP results predict reduced tensions for models/fiducials which are currently harder to reconcile with direct measurements of $H_0$, whereas no significant change occurs for models/fiducials at lesser tensions with the latter. This feature warrants further investigation in this direction.Comment: To appear in JCAP, 30 pages, 12 sets of figures, 7 table

Reconstructing the Hubble parameter with future Gravitational Wave missions using Machine Learning

We study the prospects of Machine Learning algorithms like Gaussian processes (GP) as a tool to reconstruct the Hubble parameter $H(z)$ with two upcoming gravitational wave missions, namely the evolved Laser Interferometer Space Antenna (eLISA) and the Einstein Telescope (ET). We perform non-parametric reconstructions of $H(z)$ with GP using realistically generated catalogues, assuming various background cosmological models, for each mission. We also take into account the effect of early-time and late-time priors separately on the reconstruction, and hence on the Hubble constant ($H_0$). Our analysis reveals that GPs are quite robust in reconstructing the expansion history of the Universe within the observational window of the specific mission under study. We further confirm that both eLISA and ET would be able to constrain $H(z)$ and $H_0$ to a much higher precision than possible today, and also find out their possible role in addressing the Hubble tension for each model, on a case-by-case basis.Comment: 9 pages, 5 sets of figure

Is Plaque Removal Efficacy of Toothbrush Related to Bristle Flaring? A 3-month Prospective Parallel Experimental Study

Background: Toothbrushes are over-the-counter products; therefore, no special instruction is given to users when they purchase. There are scarce published studies that have investigated about how often toothbrushes should be replaced. Thus, this study aimed to verify the impact of the Progressive Toothbrush Bristle Flaring on plaque control efficacy of toothbrush.Materials and Methods: Thirty six subjects were randomly selected and underwent complete oral prophylaxis 10 days prior to the Baseline plaque recording. All subjects were provided with new similar toothbrushes and were divided into two groups. New Brush Group changed toothbrush every month and Old month Group used single toothbrush for the whole period of the study. Both groups were assessed for plaque accumulation every month using Turesky et al, (1970) modification of the Quigley and Hein (1962) plaque index. Toothbrush head was photographed and assessed by measuring the brushing surface area on standardized photographs using National Institutes of Health Image Analysis Program (USA).Results: Both groups showed similar plaque scores at the 40th day; progressive increase in the plaque scores in group without changing the toothbrush were recorded at the 70th and 100th days. As toothbrush flaring increased, the plaque scores also increased in the Old Brush Group. Highest plaque accumulation was recorded in Mandibular Lingual aspects in Old Brush Group.Conclusion: Progressive increase was seen in the plaque scores with increase in toothbrush bristle flaring.Keywords: Toothbrush, Efficacy of Toothbrush, Bristle Flaring, Plaque Remova

Time-Synchronized Full System State Estimation Considering Practical Implementation Challenges

As phasor measurement units (PMUs) are usually placed on the highest voltage buses, many lower voltage levels of the bulk power system are not observed by them. This lack of visibility makes time-synchronized state estimation of the full system a challenging problem. We propose a Deep Neural network-based State Estimator (DeNSE) to overcome this problem. The DeNSE employs a Bayesian framework to indirectly combine inferences drawn from slow timescale but widespread supervisory control and data acquisition (SCADA) data with fast timescale but local PMU data to attain sub-second situational awareness of the entire system. The practical utility of the proposed approach is demonstrated by considering topology changes, non-Gaussian measurement noise, and bad data detection and correction. The results obtained using the IEEE 118-bus system show the superiority of the DeNSE over a purely SCADA state estimator, a SCADA-PMU hybrid state estimator, and a PMU-only linear state estimator from a techno-economic viability perspective. Lastly, the scalability of the DeNSE is proven by performing state estimation on a large and realistic 2000-bus Synthetic Texas system

Gender-Based Differences Among Pharmacy Students Involved in Academically Dishonest Behavior

Objective. To determine whether differences based on gender exist among pharmacy students involved in cases of admitted cheating or other academic dishonesty and to assess perceptions of academic dishonesty. Methods. Two cohorts of second-year male and female pharmacy students from four Northern California pharmacy programs were invited to complete a 45-item cross-sectional survey. Descriptive statistics and Pearson’s chi-squared test were used for statistical analysis. Results. There were 330 surveys completed with a 59% response rate. No significant gender-based differences were found regarding admitted cheating in pharmacy school and in regards to participating in various forms of academically dishonest behavior. Female students were more likely than male students to report witnessing a classmate copying another student’s assignment. Male students were less likely than female students to perceive a student who distributed a stolen exam as a cheater. Conclusion. No gender-based differences were noted in cases of admitted cheating or with regards to taking part in various forms of academically dishonest behavior. However, female students report witnessing cheating more than male students, and male students may have a more lenient perception toward academically dishonest behavior than female students. The information gathered from this study may provide further insight to pharmacy programs and educators regarding academic dishonesty at their institution