In research on photovoltaic (PV) device degradation, current–voltage ( $I$ – $V$ ) datasets carry a large amount of information in addition to the maximum power point. Performance parameters such as short-circuit current, open-circuit voltage, shunt resistance, series resistance, and fill factor are essential for diagnosing the performance and degradation of solar cells and modules. To enable the scaling of $I$ – $V$ studies to millions of $I$ – $V$ curves, we have developed a data-driven method to extract $I$ – $V$ curve parameters and distributed this method as an open-source package in R. In contrast with the traditional practice of fitting the diode equation to $I$ – $V$ curves individually, which requires solving a transcendental equation, this data-driven method can be applied to large volumes of $I$ – $V$ data in a short time. Our data-driven feature extraction technique is tested on $I$ – $V$ curves generated with the single-diode model and applied to $I$ – $V$ curves with different data point densities collected from three different sources. This method has a high repeatability for extracting $I$ – $V$ features, without requiring knowledge of the device or expected parameters to be input by the researcher. We also demonstrate how this method can be applied to large datasets and accommodates nonstandard $I$ – $V$ curves including those showing artifacts of connection problems or shading where bypass diode activation produces multiple “steps.” These features together make the data-driven $I$ – $V$ feature extraction method ideal for evaluating time-series $I$ – $V$ data and analyzing power degradation mechanisms in PV modules through cross comparisons of the extracted parameters.

Markov Chain Monte Carlo (MCMC) is a powerful and promising tool for assessing the uncertainties in the Orbiting Carbon Observatory 2 (OCO-2) satellite’s carbon dioxide measurements. Previous research in comparing MCMC and Optimal Estimation (OE) for the OCO-2 retrieval has highlighted the issues of slow convergence of MCMC, and furthermore OE and MCMC not necessarily agreeing with the simulated ground truth. In this work, we exploit the inherent low information content of the OCO-2 measurement and use the Likelihood-Informed Subspace (LIS) dimension reduction to significantly speed up the convergence of MCMC. We demonstrate the strength of this analysis method by assessing the non-Gaussian shape of the retrieval’s posterior distribution, and the effect of operational OCO-2 prior covariance’s aerosol parameters on the retrieval. We further show that in our test cases we can use this analysis to improve the retrieval to retrieve the simulated true state significantly more accurately and to characterize the non-Gaussian form of the posterior distribution of the retrieval problem.

We present a Bayesian hierarchical modeling approach to paleoclimate reconstruction using borehole temperature profiles. The approach relies on modeling heat conduction in solids via the heat equation with step function, surface boundary conditions. Our analysis includes model error and assumes that the boundary conditions are random processes. The formulation also enables separation of measurement error and model error. We apply the analysis to data from nine borehole temperature records from the San Rafael region in Utah. We produce ground surface temperature histories with uncertainty estimates for the past 400 years. We pay special attention to use of prior parameter models that illustrate borrowing strength in a combined analysis for all nine boreholes. In addition, we review selected sensitivity analyses.

Background: Food insecurity and the mental and physical health of internally displaced persons (IDPs) is a public health concern. The aim of this study was to determine the predictors of social support, physical and mental health among food insecure IDPs in Nakwamekwei IDP camp in Turkana, Kenya. Methods: A cross sectional study was conducted among 159 household heads living in the camp. Analysis was conducted using statistical summaries, logistic regression and linear regression. Results: Ninety four percent (94%) of the households were severely food insecure and the rest of the households had moderate food insecurity. Majority of the household heads (77%) had symptoms of depression and those with five or more children were 3 times (95%CI, 1.31–9.24; p = 0.015) more likely to be have symptoms of depression, while those who were married were less likely to have the same (95%CI, 0.14–0.92; p = 0.038). Seventy six percent (76%) of the household heads had anxiety symptoms; none of the predictor variables were significantly associated with anxiety symptoms in the adjusted analysis. Those who had spent many years in the camp, were older, and had more children had significantly poorer physical health (p = 0.042, p = 0.001, and p = 0.047, respectively). Those who were married and those who had experienced violence in the current camp had significantly higher social support (p = 0.001 and p = 0.006, respectively). Conclusions: Participants have been living in camp for the last 10 years hence the need to improve their living conditions, address their physical and mental health as well as food insecurity. This can be done by providing the participants with safe drinking water, constructing pit latrines to prevent communicable disease and adhere to the Sphere recommendations for sanitation and hygiene as well as training them in income generating activities to mitigate the high unemployment and food insecurity rates. The IDPs should be integrated into the local community to bring an end to their protracted displacement.