Corruption has pervasive effects on economic development and the well-being of the population. Despite being crucial and necessary, fighting corruption is not an easy task because it is a difficult phenomenon to measure and detect. However, recent advances in the field of artificial intelligence may help in this quest. In this article, we propose the use of machine-learning models to predict municipality-level corruption in a developing country. Using data from disciplinary prosecutions conducted by an anti-corruption agency in Colombia, we trained four canonical models (Random Forests, Gradient Boosting Machine, Lasso, and Neural Networks), and ensemble their predictions, to predict whether or not a mayor will commit acts of corruption. Our models achieve acceptable levels of performance, based on metrics such as the precision and the area under the receiver-operating characteristic curve, demonstrating that these tools are useful in predicting where misbehavior is most likely to occur. Moreover, our feature-importance analysis shows us which groups of variables are most important in predicting corruption.

OBJECTIVES/GOALS: Despite a disproportionate impact of COVID-19 on minority and under-resourced communities, nearly all COVID-19 resources have only been online in English. A statewide coalition of community and academic partners used community-engaged strategies to provide tailored outreach to diverse populations. METHODS/STUDY POPULATION: The STOP COVID-19 CA statewide team had a workgroup focused on communications. Members of this group represented different sectors, racial/ethnic groups, disciplines, and regions across the state. They had regular meetings to discuss and strategize how to overcome the impact of historic and structural racism on access to COVID-19 resources, including testing, vaccines, and protective equipment. The team also shared regular updates about changes in community concerns and needs as well as new, tailored resources. RESULTS/ANTICIPATED RESULTS: Together, the team has been able to reach diverse populations across the state, including providing information about COVID-19 in multiple languages and formats, from radio to virtual town halls to local health fairs. The multiple sites also increased access to vaccines and testing through trusted community leaders and locations, including church-based locations to bringing vaccines and testing directly to workplaces. These community pop-up vaccination sites have helped to vaccinate large numbers of diverse populations, some of whom were initially unsure about getting the vaccine, which has helped to reduce the gaps in community vaccination rates by race/ethnicity. DISCUSSION/SIGNIFICANCE: This network of community-engaged strategies utilized for rapid COVID-19 response could also be used to for responses to future public health emergencies, addressing chronic diseases (e.g., diabetes, hypertension), or even other complex issues that affect society and health (e.g., climate change).

In the frame of the IAEA-CRP (Coordinated Research Projects): Enhancing Nuclear Analytical Techniques to Meet the Needs of Forensic Sciences, an intercomparison exercise was organized between three AMS laboratories. Aim of the program is to promote the use of nuclear and accelerator-based techniques in routine forensics practice. In this view, one of the key points is the assessment of the precision and accuracy levels achievable on material of forensic interest. We review the general structure and status of the project, with emphasis on results obtained in the analysis of wines of different grape varieties and grounded coffee beans from different locations such as Brazil, Spain, and Italy. The three laboratories processed the samples according to different chemical protocols and performed the 14C measurements using different systems: MICADAS in Zurich and Debrecen and a HVEE 4130HC 3 MV Tandetron in Lecce. Within the quoted uncertainty, the results showed good reproducibility, indicating that uncertainty level of the order of 0.3% are achieved by AMS on a single sample while multiple sample analyses results in precision down to 0.1–0.2%. The measured 14C concentrations on coffee and wine samples resulted to be consistent with atmospheric 14C levels in the growing years.

In this work, a semianalytic solution for the acoustic streaming phenomenon, generated by standing waves in Maxwell fluids through a two-dimensional microchannel (resonator), is derived. The mathematical model is non-dimensionalized and several dimensionless parameters that characterize the phenomenon arise: the ratio between the oscillation amplitude of the resonator and the half-wavelength ($\eta =2A/\lambda _{a}$); the product of the fluid relaxation time times the angular frequency known as the Deborah number ($De=\lambda _{1}\omega$); the aspect ratio between the microchannel height and the wavelength ($\epsilon =2 H_{0}/\lambda _{a}$); and the ratio between half the height of the microchannel and the thickness of the viscous boundary layer ($\alpha =H_{0}/\delta _{\nu }$). In the limit when $\eta \ll 1$, we obtain the hydrodynamic behaviour of the system using a regular perturbation method. In the present work, we show that the acoustic streaming speed is proportional to $\alpha ^{2.65}De^{1.9}$, and the acoustic pressure varies as $\alpha ^{6/5}De^{1/2}$. Also, we have found that the growth of inner vortex is due to convective terms in the Maxwell rheological equation. Furthermore, the velocity antinodes show a high dependency on the Deborah number, highlighting the fluid's viscoelastic properties and the appearance of resonance points. Due to the limitations of perturbation methods, we will only analyse narrow microchannels.

The National Institute of Mental Health's Research Domain Criteria (RDoC) framework has prompted a paradigm shift from categorical psychiatric disorders to considering multiple levels of vulnerability for probabilistic risk of disorder. However, the lack of neurodevelopmentally based tools for clinical decision making has limited the real-world impact of the RDoC. Integration with developmental psychopathology principles and statistical methods actualize the clinical implementation of RDoC to inform neurodevelopmental risk. In this conceptual paper, we introduce the probabilistic mental health risk calculator as an innovation for such translation and lay out a research agenda for generating an RDoC- and developmentally informed paradigm that could be applied to predict a range of developmental psychopathologies from early childhood to young adulthood. We discuss methods that weigh the incremental utility for prediction based on intensity and burden of assessment, the addition of developmental change patterns, considerations for assessing outcomes, and integrative data approaches. Throughout, we illustrate the risk calculator approach with different neurodevelopmental pathways and phenotypes. Finally, we discuss real-world implementation of these methods for improving early identification and prevention of developmental psychopathology. We propose that mental health risk calculators can build a needed bridge between the RDoC multiple units of analysis and developmental science.

A laboratory intercomparison project was carried out on 20 annually resolved late-wood samples from the Danish oak record. The project included the following three laboratories: (1) the University of Arizona AMS Laboratory, University of Arizona, USA (AA); (2) HEKAL AMS Laboratory, MTA Atomki, Hungary (DeA); and (3) Aarhus AMS Centre (AARAMS), Aarhus University, Denmark (AAR). The large majority of individual data points (96%) lie within ±2σ of the weighted mean. Further assessment of the accuracy associated with the individual laboratories showed good agreement, indicating that consistent and reliable 14C measurements well in agreement with each other are produced at the three laboratories. However, the quoted analytical uncertainties appear to be underestimated when compared to the observed variance of differences from the geometric mean of the samples. This study provides a general quality check of the single-year tree-ring 14C measurements that are included in the new calibration curve.

Previous research has shown that individuals suffering from depression and generalized anxiety disorder (GAD) seem to have inhibitory control deficits compared with healthy controls. However, few studies have been conducted in Spanish-speaking countries. Thus, this study aims to analyze the performance on the Stroop Color and Word Test (SCWT) between groups of Colombian participants with clinical levels of depression and GAD symptoms and a nonclinical control group. According to previous research, we expected to find significant differences in inhibitory control among groups. An ex post facto design was implemented. The SCWT was administered to a total sample of 105 individuals (64.8% women, M = 22.94 years, SD = 4.62), including 27 depressed and 15 anxious participants according to their scores on the Personal Health Questionnaire–9 and the Generalized Anxiety Disorder–7, respectively. Bayesian t-tests showed that depressed participants showed the same processing speed but lower scores on inhibitory control than healthy controls, BF = 13.70, δ = 0.50, 95% CI [0.08, 0.94]. Conversely, anxious participants showed deficits in processing speed, SCWT-Word: BF = 16.19, δ = 0.68, 95% CI [0.15, 1.24]; SCWT-Color: BF = 5.98, δ = 0.50, 95% CI [–0.01, 1.04], but not in inhibitory control compared with the nonanxious counterparts. This study provides preliminary evidence concerning the inhibitory control deficits in Colombian depressed individuals and processing speed deficits in those experiencing clinical levels of GAD symptoms.

Let C be a hyperelliptic curve of genus $g \geq 3$ . In this paper, we give a new geometric description of the theta map for moduli spaces of rank 2 semistable vector bundles on C with trivial determinant. In order to do this, we describe a fibration of (a birational model of) the moduli space, whose fibers are GIT quotients $(\mathbb {P}^1)^{2g}//\text {PGL(2)}$ . Then, we identify the restriction of the theta map to these GIT quotients with some explicit degree 2 osculating projection. As a corollary of this construction, we obtain a birational inclusion of a fibration in Kummer $(g-1)$ -varieties over $\mathbb {P}^g$ inside the ramification locus of the theta map.

Gravitational waves from coalescing neutron stars encode information about nuclear matter at extreme densities, inaccessible by laboratory experiments. The late inspiral is influenced by the presence of tides, which depend on the neutron star equation of state. Neutron star mergers are expected to often produce rapidly rotating remnant neutron stars that emit gravitational waves. These will provide clues to the extremely hot post-merger environment. This signature of nuclear matter in gravitational waves contains most information in the 2–4 kHz frequency band, which is outside of the most sensitive band of current detectors. We present the design concept and science case for a Neutron Star Extreme Matter Observatory (NEMO): a gravitational-wave interferometer optimised to study nuclear physics with merging neutron stars. The concept uses high-circulating laser power, quantum squeezing, and a detector topology specifically designed to achieve the high-frequency sensitivity necessary to probe nuclear matter using gravitational waves. Above 1 kHz, the proposed strain sensitivity is comparable to full third-generation detectors at a fraction of the cost. Such sensitivity changes expected event rates for detection of post-merger remnants from approximately one per few decades with two A+ detectors to a few per year and potentially allow for the first gravitational-wave observations of supernovae, isolated neutron stars, and other exotica.