Related Articles

Anthropogenic organic aerosol in Europe produced mainly through second-generation oxidation

Exposure to anthropogenic atmospheric aerosol is a major health issue, causing several million deaths per year worldwide. The oxidation of aromatic hydrocarbons from traffic and wood combustion is an important anthropogenic source of low-volatility species in secondary organic aerosol, especially in heavily polluted environments. It is not yet established whether the formation of anthropogenic secondary organic aerosol involves mainly rapid autoxidation, slower sequential oxidation steps or a combination of the two. Here we reproduced a typical urban haze in the ‘Cosmics Leaving Outdoor Droplets’ chamber at the European Organization for Nuclear Research and observed the dynamics of aromatic oxidation products during secondary organic aerosol growth on a molecular level to determine mechanisms underlying their production and removal. We demonstrate that sequential oxidation is required for substantial secondary organic aerosol formation. Second-generation oxidation decreases the products’ saturation vapour pressure by several orders of magnitude and increases the aromatic secondary organic aerosol yields from a few percent to a few tens of percent at typical atmospheric concentrations. Through regional modelling, we show that more than 70% of the exposure to anthropogenic organic aerosol in Europe arises from second-generation oxidation.

Abundant water from primordial supernovae at cosmic dawn

Primordial (or population III) supernovae were the first nucleosynthetic engines in the Universe, and they forged the heavy elements required for the later formation of planets and life. Water, in particular, is thought to be crucial to the cosmic origins of life as we understand it, and recent models have shown that water can form in low-metallicity gas like that present at high redshifts. Here we present numerical simulations that show that the first water in the Universe formed in population III core-collapse and pair-instability supernovae at redshifts z ≈ 20. The primary sites of water production in these remnants are dense molecular cloud cores, which in some cases were enriched with primordial water to mass fractions that were only a factor of a few below those in the Solar System today. These dense, dusty cores are also probable candidates for protoplanetary disk formation. Besides revealing that a primary ingredient for life was already in place in the Universe 100–200 Myr after the Big Bang, our simulations show that water was probably a key constituent of the first galaxies.

An active representation learning method for reaction yield prediction with small-scale data

Reaction optimization plays an essential role in chemical research and industrial production. To explore a large reaction system, a practical issue is how to reduce the heavy experimental load for finding the high-yield conditions. In this paper, we present an efficient machine learning tool called “RS-Coreset”, where the key idea is to take advantage of deep representation learning techniques to guide an interactive procedure for representing the full reaction space. Our proposed tool only uses small-scale data, say 2.5% to 5% of the instances, to predict the yields of the reaction space. We validate the performance on three public datasets and achieve state-of-the-art results. Moreover, we apply this tool to assist the realistic exploration of the Lewis base-boryl radicals enabled dechlorinative coupling reactions in our lab. The tool can help us to effectively predict the yields and even discover several feasible reaction combinations that were overlooked in previous articles.

Advanced machine learning for regional potato yield prediction: analysis of essential drivers

Localized yield prediction is critical for farmers and policymakers, supporting sustainability, food security, and climate change adaptation. This research evaluates machine learning models, including Random Forest and Gradient Boosting, for predicting crop yields. These models can be adapted for in-season yield forecasting, providing predictions as early as one month before harvest. The study applied models to postal code-level yield data from 1982 to 2016, incorporating daily climate data, agroclimatic indices, soil parameters, and earth observation NDVI data for Prince Edward Island (PEI), Canada. SHapley Additive exPlanations (SHAP) values identified temperature variables and NDVI as significant predictors. The study highlighted rainfall and soil water retention’s importance for irrigation strategies. Random Forest achieved an RMSE of 0.011 (t/ac), 0.6 (t/ac) less than the best linear regression model. This precision translates to $81,600 CAD per farm annually in PEI, supporting economic and environmental benefits through improved planning and land management.

Sovereign bond yield and cryptocurrency returns within the frontier West African monetary zone: a dynamic contagion analysis

This study employs wavelet analysis to examine the contagion between cryptocurrency returns and sovereign bond yields within the West African Monetary Zone (WAMZ) economies, capturing both the frequency-dependent nature of the relationship and time-varying behavior. We analyze daily data spanning 01/26/2021 to 10/07/2022, with a total observable value of 444. The study selected periods of uncertainty within financial markets, namely, the COVID-19 pandemic and the Russia–Ukraine war because there was a need to understand how securities react during such times to help investors plan accordingly. Our results show a negative correlation between sovereign bond yields and cryptocurrency returns, suggesting that investors can use these asset classes as hedge agents, diversifiers, and safe-haven instruments. These findings provide valuable insights for investors and policymakers, shedding light on the potential interdependencies and diversification benefits between these two asset classes.

Responses

Your email address will not be published. Required fields are marked *