Drone flight data reveal energy and greenhouse gas emissions savings for very small package delivery.

Uncrewed aerial vehicles (UAVs) for last-mile deliveries will affect the energy productivity of delivery and require new methods to understand energy consumption and greenhouse gas (GHG) emissions. We combine empirical testing of 188 quadcopter flights across a range of speeds with a first-principles analysis to develop a usable energy model and a machine-learning algorithm to assess energy across takeoff, cruise, and landing. Our model shows that an electric quadcopter drone with a very small package (0.5 kg) would consume approximately 0.08 MJ/km and result in 70 g of CO2e per package in the United States. We compare drone delivery with other vehicles and show that energy per package delivered by drones (0.33 MJ/package) can be up to 94% lower than conventional transportation modes, with only electric cargo bicycles providing lower GHGs/package. Our open model and coefficients can assist stakeholders in understanding and improving the sustainability of small package delivery.

Can auxiliary indicators improve COVID-19 forecasting and hotspot prediction?

Short-term forecasts of traditional streams from public health reporting (such as cases, hospitalizations, and deaths) are a key input to public health decision-making during a pandemic. Since early 2020, our research group has worked with data partners to collect, curate, and make publicly available numerous real-time COVID-19 indicators, providing multiple views of pandemic activity in the United States. This paper studies the utility of five such indicators-derived from deidentified medical insurance claims, self-reported symptoms from online surveys, and COVID-related Google search activity-from a forecasting perspective. For each indicator, we ask whether its inclusion in an autoregressive (AR) model leads to improved predictive accuracy relative to the same model excluding it. Such an AR model, without external features, is already competitive with many top COVID-19 forecasting models in use today. Our analysis reveals that 1) inclusion of each of these five indicators improves on the overall predictive accuracy of the AR model; 2) predictive gains are in general most pronounced during times in which COVID cases are trending in "flat" or "down" directions; and 3) one indicator, based on Google searches, seems to be particularly helpful during "up" trends.

An open repository of real-time COVID-19 indicators.

The COVID-19 pandemic presented enormous data challenges in the United States. Policy makers, epidemiological modelers, and health researchers all require up-to-date data on the pandemic and relevant public behavior, ideally at fine spatial and temporal resolution. The COVIDcast API is our attempt to fill this need: Operational since April 2020, it provides open access to both traditional public health surveillance signals (cases, deaths, and hospitalizations) and many auxiliary indicators of COVID-19 activity, such as signals extracted from deidentified medical claims data, massive online surveys, cell phone mobility data, and internet search trends. These are available at a fine geographic resolution (mostly at the county level) and are updated daily. The COVIDcast API also tracks all revisions to historical data, allowing modelers to account for the frequent revisions and backfill that are common for many public health data sources. All of the data are available in a common format through the API and accompanying R and Python software packages. This paper describes the data sources and signals, and provides examples demonstrating that the auxiliary signals in the COVIDcast API present information relevant to tracking COVID activity, augmenting traditional public health reporting and empowering research and decision-making.

Longitudinal 5-year prediction of cognitive impairment among men with HIV disease.

BACKGROUND: Although combination antiretroviral therapy reduced the prevalence of HIV-associated dementia, milder syndromes persist. Our goals were to predict cognitive impairment of the Multicenter AIDS Cohort Study (MACS) participants 5 years ahead and from a large pool of factors, select the ones that mostly contributed to our predictions. DESIGN: Longitudinal, natural and treated history of HIV infection among MSM. METHODS: The MACS is a longitudinal study of the natural and treated history of HIV disease in MSM; the neuropsychological substudy aims to characterize cognitive disorders in men with HIV disease. RESULTS: We modeled on an annual basis the risk of cognitive impairment 5 years in the future. We were able to predict cognitive impairment at individual level with high precision and overperform default methods. We found that while a diagnosis of AIDS is a critical risk factor, HIV infection per se does not necessarily convey additional risk. Other infectious processes, most notably hepatitis B and C, are independently associated with increased risk of impairment. The relative importance of an AIDS diagnosis diminished across calendar time. CONCLUSION: Our prediction models are a powerful tool to help clinicians address dementia in early stages for MACS paticipants. The strongest predictors of future cognitive impairment included the presence of clinical AIDS and hepatitis B or C infection. The fact that the pattern of predictive power differs by calendar year suggests a clinically critical change to the face of the epidemic.

A discussion on significance indices for contingency tables under small sample sizes.

Hypothesis testing in contingency tables is usually based on asymptotic results, thereby restricting its proper use to large samples. To study these tests in small samples, we consider the likelihood ratio test (LRT) and define an accurate index for the celebrated hypotheses of homogeneity, independence, and Hardy-Weinberg equilibrium. The aim is to understand the use of the asymptotic results of the frequentist Likelihood Ratio Test and the Bayesian FBST (Full Bayesian Significance Test) under small-sample scenarios. The proposed exact LRT p-value is used as a benchmark to understand the other indices. We perform analysis in different scenarios, considering different sample sizes and different table dimensions. The conditional Fisher's exact test for 2 × 2 tables and the Barnard's exact test are also discussed. The main message of this paper is that all indices have very similar behavior, except for Fisher and Barnard tests that has a discrete behavior. The most powerful test was the asymptotic p-value from the likelihood ratio test, suggesting that is a good alternative for small sample sizes.

Copper and cadmium complexation by Cylindrospermopsis raciborskii exudates.

Cylindrospermopsis raciborskii is a potentially toxic cyanobacterium that excretes organic materials which act as ligands for metals. Metal ligands may be characterized for their strength of association, e.g., stability constants, which can be either thermodynamic (K) or conditional (K'). In this research we examined K and K' for Cu and Cd complexes with three molecular weight fractions (>30 kDa; 30-10 kDa; 10-3 kDa) of the cyanobacteria EOM. Complexation capacities of the excreted organic materials (EOM) for metals were determined at several ionic strengths (1.0 × 10(-2), 5.0 × 10(-2), 1.0 × 10(-1), and 5.0 × 10(-1) mol L(-1)) at pH 6.6 ± 0.1, with ligands for which no data for their acidity constants are available; these constants are thus conditional for this specific pH. Bayesian statistics showed that with a probability of 95-100% the EOM have two different ligands for Cu but only one for Cd, that ligands for Cu were stronger than for Cd (94-100% probability), and that the smallest EOM fraction had the highest strength of association for Cu (logKCuL 13.5). The lowest affinity was obtained for Cd (logKCdL 8.6) complexed to any molecular weight fraction. The present findings have important ecological implications, since the metal-ligand association is dynamic, and together with a diversity of ligands it can act as an environmental metal buffer. As a result, higher metal loads may be necessary for the detection of toxicity.