Compression and water retention behavior of saline soil improved by MICP combined with activated carbon.

Saline soil is widely distributed in China and poses significant challenges to engineering construction due to its harmful effects, such as salt heaving, dissolution collapse, and frost heaving. The Microbial-Induced Calcite Precipitation (MICP) method is an emerging environmental-friendly modification that can reduce or eliminate the environmental and engineering hazards of saline soil. To verify the feasibility of the MICP method for improving the properties of saline soil, laboratory tests were conducted to study the effects of salt content, activated carbon content and freeze-thaw cycles on the compression and water retention behavior of MICP modified saline soil.

Change in sediment transport regime of the Keelung River in Taiwan induced by the operation of Yuanshantze flood diversion tunnel.

The impact of flood diversion channels on river sediment transport has been rarely reported. This study uses the Yuanshantze flood diversion tunnel (YFDT), which was commissioned in July 2005 in Taiwan, as an example. This study calculates the sediment transport in the Keelung River from 1997 to 2018 by using seasonal rating curves, in the form of aQb.

Health action process approach and spinal cord injury: a scoping review.

Purpose: Spinal cord injury may result in chronic pain, difficulty walking and breathing, and/or paralysis. People with spinal cord injury (PwSCI) experience increased risk for chronic diseases and psychosocial distress (e.g.

Comparative in vitro efficacy of AR-12 derivatives against Streptococcus pyogenes.

Objectives: Group A Streptococcus (GAS) results in invasive diseases. Our published studies show that AR-12 can directly kill GAS. However, AR-12 is toxic to the human microvascular endothelial cells (HMEC-1 cells) even at its MIC.

Early prediction of mortality upon intensive care unit admission.

Background: We aimed to develop and validate models for predicting intensive care unit (ICU) mortality of critically ill adult patients as early as upon ICU admission.

Methods: Combined data of 79,657 admissions from two teaching hospitals' ICU databases were used to train and validate the machine learning models to predict ICU mortality upon ICU admission and at 24 h after ICU admission by using logistic regression, gradient boosted trees (GBT), and deep learning algorithms.

Results: In the testing dataset for the admission models, the ICU mortality rate was 7%, and 38.