EU initiatives presented since 2018 may help to increase complementarity between Chinese and European infrastructure development plans and reduce associated risks, such as unsustainable debt or new trade barriers arising from increased competition for Chinese investments. The BRI is about to change physical and digital connectivity within Europe, while the EU has yet to become an active player engaging in the initiative, in order to enable improved connectivity in Europe to drive economic convergence and not political divergence.

Methamphetamine use is associated with disproportionate risk of HIV infection and increased risk of depression among sexual minority men. The purpose of the study was to estimate the association between clinical depression diagnoses and sexual risk-taking among cisgender men who have sex with men (MSM) who use methamphetamine.

From March 2014 through January 2016, 286 MSM who use methamphetamine but were not seeking treatment for methamphetamine use disorder were enrolled to participate in a technology-based randomized controlled trial to reduce methamphetamine use and HIV sexual risk behaviors; participants were assessed for major depressive episodes (MDE) and persistent depressive disorder (PDD) at baseline. Multivariate clustered zero-inflated negative binomial regression analyses of condomless anal intercourse (n=282; 1,248 visits) estimated the association between this baseline diagnostic result and engagement in sexual risk-taking over time.

Participants predominantly identified as non-white (80%)atest engagement in sexual risk-taking occurred among those diagnosed with MDE at baseline. Additional research is warranted to clarify how methamphetamine influences sexual risk-taking among MSM with/without comorbid depression.

Methamphetamine use among participants in this study inverted the functional form of the relationship between depression and sexual risk among MSM observed in prior studies. Whereas low-grade depression has been associated with increased sexual risk-taking in prior samples of MSM, methamphetamine upends this relationship, such that the greatest engagement in sexual risk-taking occurred among those diagnosed with MDE at baseline. Additional research is warranted to clarify how methamphetamine influences sexual risk-taking among MSM with/without comorbid depression.

Same day use of alcohol and cannabis is prevalent among emerging/young adults and increases the risk for negative consequences. Although motives for alcohol and cannabis use are well-documented, specific motives on co-use days are under-investigated. We examined differences in motives on single substance use (i.e., alcohol

cannabis) versus co-use days in a sample of primarily cannabis-using emerging/young adults.

Participants (N=97) aged 18-25 (M

=22.2) were recruited from an urban Emergency Department (55.7% female, 46.4% African American, 57.7% public assistance) for a prospective daily diary study about risk behaviors. Participants received prompts for 28 daily text message assessments (up to 2716 surveys possible) of substance use and motives (social, enhancement, coping, conformity). We divided use days into three groups alcohol use only (n=126), cannabis use only (n=805), and co-use (n=237). Using fixed effects regression modeling, we fit models to estimate within-person effects of alcohol and cpositive affect and engaging in social situations.This paper analyses the effect of retirement on the familiarity with Information and Communication Technology (ICT) of older individuals. We argue that inability to cope with ICT might represent a threat for older individuals’ social inclusion. To account for the potential endogeneity of retirement with respect to familiarity with ICT, we instrument retirement decision with the age-eligibility for early and statutory retirement pension schemes. Using data from the Survey of Health, Ageing and Retirement in Europe, we show that retirement reduces the computer literacy and the frequency of internet utilization for men and women. This finding is robust to the inclusion as control factors of health, cognition and social network indicators, which the literature has shown to be affected by retirement. Overall, the reduction in the familiarity with ICT after retirement tends to be stronger in the long-run.In 2020, a novel coronavirus disease became a global problem. The disease was called COVID-19, as the first patient was diagnosed in December 2019. The disease spread around the world quickly due to its powerful viral ability. To date, the spread of COVID-19 has been relatively mild in China due to timely control measures. However, in other countries, the pandemic remains severe, and COVID-19 protection and control policies are urgently needed, which has motivated this research. Since the outbreak of the pandemic, many researchers have hoped to identify the mechanism of COVID-19 transmission and predict its spread by using machine learning (ML) methods to supply meaningful reference information to decision-makers in various countries. Since the historical data of COVID-19 is time series data, most researchers have adopted recurrent neural networks (RNNs), which can capture time information, for this problem. However, even with a state-of-the-art RNN, it is still difficult to perfectly capture the temporal infhis problem than other prevailing methods.The new type of coronavirus, COVID 19, appeared in China at the end of 2019. It has become a pandemic that is spreading all over the world in a very short time. The detection of this disease, which has serious health and socio-economic damages, is of vital importance. COVID-19 detection is performed by applying PCR and serological tests. Additionally, COVID detection is possible using X-ray and computed tomography images. Disease detection has an important position in scientific researches that includes artificial intelligence methods. The combined models, which consist of different phases, are frequently used for classification problems. In this paper, a new combined approach is proposed to detect COVID-19 cases using deep features obtained from X-ray images. Two main variances of the approach can be presented as single layer-based (SLB) and feature fusion-based (FFB). SLB model consists of pre-processing, deep feature extraction, post-processing, and classification phases. TOFA inhibitor ic50 On the other side, the FFB model consists of pre-processing, deep feature extraction, feature fusion, post-processing, and classification phases.