In the past decade, machine learning (ML) algorithms have been widely applied to build prediction models for various mining applications. However, no research has been reported that forecasts truck productivity using ML algorithms. In this study, two tree-based ensemble learning algorithms, including random forest (RF) and gradient boosting regression (GBR), were proposed in combination with Gaussian mixture modelling (GMM) to train prediction models of truck productivity. GMM was adopted as a clustering technique to extract a latent variable from the training dataset. Multiple linear regression (MLR) and decision tree (DT) as single learning algorithms were used to construct prediction models to be compared with the tree-based ensemble models. The results showed that the tree-based ensemble models performed better than single models in predicting truck productivity with and without GMM clustering. Furthermore, GMM significantly increased the predictability of truck productivity prediction models by considering the latent variable. From the relative importance analysis, haul distance was the most influential factor among the observed input variables. Finally, the GMM-RF and GMM-GBR models with high accuracy were the proposed models for predicting truck productivity at mine sites.

HIV-encoded DNA, RNA and proteins persist in the brain despite effective antiretroviral therapy (ART), with undetectable plasma and cerebrospinal fluid viral RNA levels, often in association with neurocognitive impairments. Although the determinants of HIV persistence have garnered attention, the expression and regulation of antiretroviral host restriction factors (RFs) in the brain for HIV and SIV remain unknown. We investigated the transcriptomic profile of antiretroviral RF genes by RNA-sequencing with confirmation by qRT-PCR in the cerebral cortex of people who are uninfected (HIV[−]), those who are HIV-infected without pre-mortem brain disease (HIV[+]), those who are HIV-infected with neurocognitive disorders (HIV[+]/HAND) and those with neurocognitive disorders with encephalitis (HIV[+]/HIVE). We observed significant increases in RF expression in the brains of HIV[+]/HIVE in association with the brain viral load. Machine learning techniques identified MAN1B1 as a key gene that distinguished the HIV[+] group from the HIV[+] groups with HAND. Analyses of SIV-associated RFs in brains from SIV-infected Chinese rhesus macaques with different ART regimens revealed diminished RF expression among ART-exposed SIV-infected animals, although ART interruption resulted in an induced expression of several RF genes including OAS3, RNASEL, MX2 and MAN1B1. Thus, the brain displays a distinct expression profile of RFs that is associated with the neurological status as well as the brain viral burden. Moreover, ART interruption can influence the brain’s RF profile, which might contribute to disease outcomes.