Influence of biopolymer-vegetation interaction on soil hydro-mechanical properties under climate change: A review.

Soil reinforcement using eco-friendly biopolymer and vegetation has been increasingly popular in geotechnical engineering. However, research is still in its early stages due to complex biochemical interactions between biopolymers and plants. Moreover, under the increasing climate change, extreme weather poses severe challenges to the effectiveness of biopolymer-vegetation on soil treatment.

A comparative study of environmental and economic assessment of vegetation-based slope stabilization with conventional methods.

The heavy rainfall induced by global warming has increased the risk of landslides. Eco-friendly approaches, such as employing vegetation, prove effective in satisfying the requirements of both engineering and environmental considerations in slope engineering. The research aims to comprehensively assess and compare the environmental, economic, and slope stability of new stabilization methods, including vegetation cover, in comparison to conventional approaches such as anchorage and nailing.

Theoretical and numerical study of contaminant transport in clayey barriers using a revised numerical model considering the dependency of membrane efficiency and hydraulic conductivity on solute concentration.

Solid waste is often buried in landfills isolated with a bentonite-based clay barrier to guarantee the high quality of groundwater. As the efficiency of clay barriers is highly dependent on solute concentration, this study aims to modify membrane efficiency, effective diffusion, and hydraulic conductivity of bentonite-based clayey barriers exposed to saline environments for numerical investigation of solute transport in such barriers. Therefore, the theoretical equations were modified as a function of solute concentration instead of constant values.

Semi-empirical modelling of hydraulic conductivity of clayey soils exposed to deionized and saline environments.

Clay liners are widely used as porous membrane barriers to control solute transport and to prevent the leakage of leachate both in horizontal and vertical flow scenarios, such as the isolated base and ramps of sanitary landfills. Despite the primary importance of saturated hydraulic conductivity in a reliable simulation of fluid flow through clay barriers, there is no model to predict hydraulic conductivity of clayey soils permeated with saline aqueous solutions because most of the current models were developed for pure water. Therefore, the main motivation behind this study is to derive semi-empirical models for simulating the hydraulic conductivity of clayey soils in the presence of arbitrary solute concentrations in addition to deionized water.

Organic and inorganic equivalent models for analysis of red blood cell mechanical behaviour.

Experimental investigation into the mechanical response of red blood cells is presently impeded with the main impediments being the micro dimensions involved and ethical issues associated with in vivo testing. The widely employed alternative approach of computational modelling suffers from its own inherent limitations being reliant on precise constitutive and boundary information. Moreover, and somewhat critically, numerical computational models themselves are required to be validated by means of experimentation and hence suffer similar impediments.

Development and Validation of a Machine Learning Model Using Administrative Health Data to Predict Onset of Type 2 Diabetes.

Importance: Systems-level barriers to diabetes care could be improved with population health planning tools that accurately discriminate between high- and low-risk groups to guide investments and targeted interventions.

Objective: To develop and validate a population-level machine learning model for predicting type 2 diabetes 5 years before diabetes onset using administrative health data.

Design, Setting, And Participants: This decision analytical model study used linked administrative health data from the diverse, single-payer health system in Ontario, Canada, between January 1, 2006, and December 31, 2016.

Predicting adverse outcomes due to diabetes complications with machine learning using administrative health data.

Across jurisdictions, government and health insurance providers hold a large amount of data from patient interactions with the healthcare system. We aimed to develop a machine learning-based model for predicting adverse outcomes due to diabetes complications using administrative health data from the single-payer health system in Ontario, Canada. A Gradient Boosting Decision Tree model was trained on data from 1,029,366 patients, validated on 272,864 patients, and tested on 265,406 patients.

Soft Pneumatic Actuator Fascicles for High Force and Reliability.

Soft pneumatic actuators (SPAs) are found in mobile robots, assistive wearable devices, and rehabilitative technologies. While soft actuators have been one of the most crucial elements of technology leading the development of the soft robotics field, they fall short of force output and bandwidth requirements for many tasks. In addition, other general problems remain open, including robustness, controllability, and repeatability.

An unsteady state retention model for fluid desorption from sorbents.

New studies regarding the sorption of fluids by solids are published every day. In performance testing, after the sorbent has reached saturation, it is usually removed from the sorbate bath and allowed to drain. The loss of liquid from the sorbents with time is of prime importance in the real-world application of sorbents, such as in oil spill response.