Effect of carbonaceous materials on phosphorus removal in flow-through packed column systems.

Phosphorus (P) overloading in aquatic environments has long-been recognized as the leading cause of water quality deterioration, harmful algal bloom, and eutrophication. This study investigated P removal performance by five cost-effective carbonaceous materials (CMs) in flow-through packed column systems. These CMs include biochars pyrolyzed from feedstocks of Eucalyptus (E-biochar) and Douglas fir (D-biochar), commercial biochar (C-biochar), iron oxide-coated biochar (Fe-biochar), and commercial activated carbon (AC).

Boosting efficiency in a clinical literature surveillance system with LightGBM.

Given the suboptimal performance of Boolean searching to identify methodologically sound and clinically relevant studies in large bibliographic databases, exploring machine learning (ML) to efficiently classify studies is warranted. To boost the efficiency of a literature surveillance program, we used a large internationally recognized dataset of articles tagged for methodological rigor and applied an automated ML approach to train and test binary classification models to predict the probability of clinical research articles being of high methodologic quality. We trained over 12,000 models on a dataset of titles and abstracts of 97,805 articles indexed in PubMed from 2012-2018 which were manually appraised for rigor by highly trained research associates and rated for clinical relevancy by practicing clinicians.

Deep learning to refine the identification of high-quality clinical research articles from the biomedical literature: Performance evaluation.

Background: Identifying practice-ready evidence-based journal articles in medicine is a challenge due to the sheer volume of biomedical research publications. Newer approaches to support evidence discovery apply deep learning techniques to improve the efficiency and accuracy of classifying sound evidence.

Objective: To determine how well deep learning models using variants of Bidirectional Encoder Representations from Transformers (BERT) identify high-quality evidence with high clinical relevance from the biomedical literature for consideration in clinical practice.

A Deep Learning Approach to Refine the Identification of High-Quality Clinical Research Articles From the Biomedical Literature: Protocol for Algorithm Development and Validation.

Background: A barrier to practicing evidence-based medicine is the rapidly increasing body of biomedical literature. Use of method terms to limit the search can help reduce the burden of screening articles for clinical relevance; however, such terms are limited by their partial dependence on indexing terms and usually produce low precision, especially when high sensitivity is required. Machine learning has been applied to the identification of high-quality literature with the potential to achieve high precision without sacrificing sensitivity.

Role of solution chemistry in the retention and release of graphene oxide nanomaterials in uncoated and iron oxide-coated sand.

Understanding the fate and transport including remobilization of graphene oxide nanomaterials (GONMs) in the subsurface would enable us to expedite their benign use and evaluate their environmental impacts and health risks. In this study, the retention and release of GONMs were investigated in water-saturated columns packed with uncoated sand (Un-S) or iron oxide-coated sand (FeS) at environmentally relevant solution chemistries (1-100mM KCl and 0.1-10mM CaCl at pH7 and 11).

[Transport of hydroxyapatite nanoparticles in saturated packed column: effects of humic acid, pH and ionic strengths].

Quartz sand was selected as collector and saturated packed column was constructed to explore the effects of environmental factors (humic acid, pH and ionic strengths of the bulk solution) on the transport and fate of hydroxyapatite nanoparticles (Nano-HAP) through measuring zeta potentials and representative c(i)/c(0) of Nano-HAP. It was suggested that zeta potentials of Nano-HAP colloids became more negative with increasing humic acid concentration and the change in solution composition from 0 to 10 mg/L humic acid yielded an increase in the zeta potentials of Nano-HAP colloids from -15 mV to -55 mV and a sharp decrease in a (attachment efficiency) from 1.0 to 0.

Facilitated transport of Cu with hydroxyapatite nanoparticles in saturated sand: effects of solution ionic strength and composition.

Column experiments were conducted to investigate the facilitated transport of Cu in association with hydroxyapatite nanoparticles (nHAP) in water-saturated quartz sand at different solution concentrations of NaCl (0-100 mM) or CaCl(2) (0.1-1.0 mM).

Transport behavior of humic acid-modified nano-hydroxyapatite in saturated packed column: effects of Cu, ionic strength, and ionic composition.

The surfaces of nano-hydroxyapatite (nHAP) used for contaminated soil and groundwater remediation may be modified to render nHAP highly mobile in the subsurface. Humic acid (HA) is widely used to modify and stabilize colloid suspensions. In this work, column experiments were conducted to determine the effects of contaminant (e.