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In this paper, we analyzed a dataset of over 2000 crypto-assets to assess their credit risk by computing their probability of death using the daily range. Unlike conventional low-frequency volatility models that only utilize close-to-close prices, the daily range incorporates all the information provided in traditional daily datasets, including the open-high-low-close (OHLC) prices for each asset. We evaluated the accuracy of the probability of death estimated with the daily range against various forecasting models, including credit scoring models, machine learning models, and time-series-based models. Our study considered different definitions of "dead coins" and various forecasting horizons. Our results indicate that credit scoring models and machine learning methods incorporating lagged trading volumes and online searches were the best models for short-term horizons up to 30 days. Conversely, time-series models using the daily range were more appropriate for longer term forecasts, up to one year. Additionally, our analysis revealed that the models using the daily range signaled, far in advance, the weakened credit position of the crypto derivatives trading platform FTX, which filed for Chapter 11 bankruptcy protection in the United States on 11 November 2022. [ABSTRACT FROM AUTHOR]

Accurately capturing wind speed fluctuations and quantifying the uncertainties has important implications for energy planning and management. This paper proposes a novel hybrid machine learning model to solve the problem of probabilistic prediction of wind speed. The model couples the light gradient boosting machine (LGB) model with the Gaussian process regression (GPR) model, where the LGB model can provide high-precision deterministic wind speed prediction results, and the GPR model can provide reliable probabilistic prediction results. The proposed model was applied to predict wind speeds for a real wind farm in the United States. The eight contrasting models are compared in terms of deterministic prediction and probabilistic prediction, respectively. The experimental results show that the LGB-GPR model improves the point forecast accuracy (RMSE) by up to 20.0% and improves the probabilistic forecast reliability (CRPS) by up to 21.5% compared to a single GPR model. This research is of great significance for improving the reliability of wind speed, probabilistic predictions, and the sustainable development of new energy. [ABSTRACT FROM AUTHOR]

Since May 2022, over 64,000 Monkeypox cases have been confirmed globally up until September 2022. The United States leads the world in cases, with over 25,000 cases nationally. This recent escalation of the Monkeypox outbreak has become a severe and urgent worldwide public health concern. We aimed to develop an efficient forecasting tool that allows health experts to implement effective prevention policies for Monkeypox and shed light on the case development of diseases that share similar characteristics to Monkeypox. This research utilized five machine learning models, namely, ARIMA, LSTM, Prophet, NeuralProphet, and a stacking model, on the Monkeypox datasets from the CDC official website to forecast the next 7-day trend of Monkeypox cases in the United States. The result showed that NeuralProphet achieved the most optimal performance with a RMSE of 49.27 and R 2 of 0.76. Further, the final trained NeuralProphet was employed to forecast seven days of out-of-sample cases. On the basis of cases, our model demonstrated 95% accuracy. [ABSTRACT FROM AUTHOR]

Cancer is the second most common cause of death in the United States, accounting for 602,350 deaths in 2020. Cancer-related death rates have declined by 27% over the past two decades, partially due to the identification of novel anti-cancer drugs. Despite improvements in cancer treatment, newly approved oncology drugs are associated with increased toxicity risk. These toxicities may be mitigated by pharmacokinetic optimization and reductions in off-target interactions. As such, there is a need for early-stage implementation of pharmacokinetic (PK) prediction tools. Several PK prediction platforms exist, including pkCSM, SuperCypsPred, Pred-hERG, Similarity Ensemble Approach (SEA), and SwissADME. These tools can be used in screening hits, allowing for the selection of compounds were reduced toxicity and/or risk of attrition. In this short commentary, we used PK prediction tools in the optimization of mitogen activated extracellular signal-related kinase kinase 1 (MEK1) inhibitors. In doing so, we identified MEK1 inhibitors with retained activity and optimized predictive PK properties, devoid of hERG inhibition. These data support the use of publicly available PK prediction platforms in early-stage drug discovery to design safer drugs. [ABSTRACT FROM AUTHOR]