Limited material resources, exposure to violence, and high-risk sexual behaviors were the best predictors of HIV risk.

High-impact weather events, such as severe thunderstorms, tornadoes, and hurricanes, cause significant disruptions to infrastructure, property loss, and even fatalities. High-impact events can also positively impact society, such as the impact on savings through renewable energy. Prediction of these events has improved substantially with greater observational capabilities, increased computing power, and better model physics, but there is still significant room for improvement. Artificial intelligence (AI) and data science technologies, specifically machine learning and data mining, bridge the gap between numerical model prediction and real-time guidance by improving accuracy. AI techniques also extract otherwise unavailable information from forecast models by fusing model output with observations to provide additional decision support for forecasters and users. In this work, we demonstrate that applying AI techniques along with a physical understanding of the environment can significantly improve the prediction skill for multiple types of high-impact weather. The AI approach is also a contribution to the growing field of computational sustainability. The authors specifically discuss the prediction of storm duration, severe wind, severe hail, precipitation classification, forecasting for renewable energy, and aviation turbulence. They also discuss how AI techniques can process “big data,” provide insights into high-impact weather phenomena, and improve our understanding of high-impact weather.

A B S T R A C T In this paper, we report the results of the analysis of two high-resolution time-series of water vapour 18 O/ 16 O ratio (δ v ) in surface air observed in Connecticut, USA. On an annual time-scale, δ v is a linear function of ln w, where w is water vapour mixing ratio, and is approximated by a Rayleigh distillation model with partial (80%) rainout. On time scales a few days, δ v shows considerable variations, often exceeding 20 per mil, and is higher in the wetting phase than in the drying phase of a weather cycle. In precipitation events, the vapour in the surface layer is in general brought to state of equilibrium with falling raindrops but not with snowflakes. On a diurnal time-scale, a peak-to-peak variation of 1-2 per mil is observed at a coastal site. At an interior site, evidence of a diurnal pattern is present only on days of low humidity. Our results suggest that the intercept parameter of the Keeling plot is an ambiguous quantity and should not be interpreted as being equivalent to the isotopic signature of evapotranspiration.

This study examined the utility of a lifetime cumulative adversities and trauma model in predicting the severity of mental health symptoms of depression, anxiety, and posttraumatic stress disorder. We also tested whether ethnicity and gender moderate the effects of this stress exposure construct on mental health using multigroup structural equation modeling. A sample of 500 low-socioeconomic status African American and Latino men and women with histories of adversities and trauma were recruited and assessed with a standard battery of self-report measures of stress and mental health. Multiple-group structural equation models indicated good overall model fit. As hypothesized, experiences of discrimination, childhood family adversities, childhood sexual abuse, other childhood trauma, and chronic stresses all loaded on the latent cumulative burden of adversities and trauma construct (CBAT). The CBAT stress exposure index in turn predicted the mental health status latent variable. Although there were several significant univariate ethnic and gender differences, and ethnic and gender differences were observed on several paths, there were no significant ethnic differences in the final model fit of the data. These findings highlight the deleterious consequences of cumulative stress and trauma for mental health and underscore a need to assess these constructs in selecting appropriate clinical interventions for reducing mental health disparities and improving human health.

This qualitative study examined sociocultural and behavioral factors including sexual health, sexual identity, and sexual risk among HIV-seropositive African American and Latino men who have sex with men (MSM) who also have a history of sexual abuse. Twenty-three men participated in 4 focus groups, responding to conceptually organized questions regarding the relationship between histories of violence and sexual and drug-related HIV risk behaviors for reinfection and transmission. Consensual qualitative research methods were used to analyze audiotaped transcriptions. Seven domains focusing on consensual and nonconsensual sexual practices, cultural and gender-bound beliefs, and social expectations were identified. Implications of these psychosocial issues for HIV-seropositive gay- and non-gay-identifying African American and Latino MSM with histories of sexual abuse in future interventions are discussed.