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. The research initially explored the stability of slopes in various geometries, identifying areas prone to slope failure. Subsequently, slope stabilization designs were implemented using three methods: vegetation, nailing, and anchoring. To enable a comprehensive comparison from environmental and economic perspectives, both life cycle assessment and life cost assessment were conducted. According to the results, employing vegetation proves effective in stabilizing slopes at lower heights, particularly up to 8 m, leading to a negative carbon emission attributed to photosynthesis, reaching up to -249 kg CO2. In the mid-angle range (30°≤ θ ≤ 60°), anchoring emits less carbon dioxide than nailing due to fewer elements. As the slope angle is increased, the nailing method becomes preferable to the anchoring method due to its use of materials and equipment with lower carbon emissions. During slope stabilization through nailing and anchoring, cement and steel emerge as the primary contributors to carbon emissions. Vegetation stands out as the most cost-effective slope stabilization option, with costs potentially reduced by 250% compared to conventional methods. Based on this research, vegetation emerges as an eco-friendly and cost-effective alternative for slope stabilization in particular conditions where plants effectively ensure stability. Decisions regarding the use of anchoring or nailing can be made based on environmental and economic aspects, considering the slope geometry.

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. First, a model was extended for membrane efficiency as a function of void ratio and solute concentration. Second, an apparent tortuosity model was developed as a function of porosity and membrane efficiency to adjust the effective diffusion coefficient. Moreover, a recently developed semi-empirical solute-dependent hydraulic conductivity model was employed, which is dependent on solute concentration, liquid limit, and void ratio of the clayey barrier. Afterward, four approaches for applying these coefficients were defined as either "variable" or "constant" functions in ten numerical scenarios using COMSOL Multiphysics. The results reveal that "variable" membrane efficiency affects the outcomes in lower concentrations, while "variable" hydraulic conductivity is more influential in the domain of higher concentrations. Although all approaches converge to the same ultimate distribution of solute concentration using the Neumann exit boundary condition, the choice of different approaches clearly affects the ultimate state for the Dirichlet exit boundary condition. As the thickness of the barrier increases, the ultimate state is reached later, and choosing the approach to apply coefficients is more influential. Decreasing the hydraulic gradient postpones the solute breakthrough in the barrier, and picking the variable coefficients is more crucial in higher hydraulic gradients.

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. In order to achieve this goal, a relatively comprehensive dataset of 842 measured hydraulic conductivities was retrieved from the experimental literature, where almost 44% of them are related to certain solute concentrations. Afterwards, two modelling approaches were introduced; the first one is a modified form of Mbonimpa et al.'s (2002) model, in which the constants are adjusted to take into consideration the variations in liquid limit due to a change in solute concentration. A modification term was added to the model for the sake of accuracy. In the second approach, a new form of solute concentration-dependent hydraulic conductivity function was proposed, where special attention was given to void ratio and adaptive liquid limit as effective parameters. The results revealed that hydraulic conductivity predictions could be erroneous if the influence of solute concentrations in permeating fluid is ignored. An error analysis was conducted to examine the models' applicability and deviations. A blind independent set of data, including 132 data points, was also used to verify models. On the other hand, both newly proposed models could predict the hydraulic conductivity for a variety of soils, salt species, and concentrations well. Therefore, the proposed modelling approaches are somehow unique by considering the salinity of the pore fluid in addition to deionized water. More importantly, both models are comprised of easy-to-measure parameters with clear physics-based implications.

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. An alternative experimental approach is examined in this paper involving large-scale equivalent models manufactured principally from inorganic, and to lesser extent organic, materials. Although there presently exists no known method providing the means to investigate the mechanical response of red blood cells using scaled models simultaneously having different dimensions and materials, the present paper aims to develop a scaled framework based on the new finite-similitude theory that has appeared in the recent open literature. Computational models are employed to test the effectiveness of the proposed method, which in principle can provide experimental solution methods to a wide range of practical applications including the design of red-blood cell nanorobots and drug delivery systems. By means of experimentally validated numerical experiments under impact loading it is revealed that although exact prediction is not achieved good accuracy can nevertheless be obtained. Furthermore, it is demonstrated how the proposed approach for first time provides a means to relate models at different scales founded on different constitutive equations.

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. A gradient boosting decision tree model was trained on data from 1 657 395 patients, validated on 243 442 patients, and tested on 236 506 patients. Costs associated with each patient were estimated using a validated costing algorithm. Data were analyzed from January 1, 2006, to December 31, 2016. Exposures: A random sample of 2 137 343 residents of Ontario without type 2 diabetes was obtained at study start time. More than 300 features from data sets capturing demographic information, laboratory measurements, drug benefits, health care system interactions, social determinants of health, and ambulatory care and hospitalization records were compiled over 2-year patient medical histories to generate quarterly predictions. Main Outcomes and Measures: Discrimination was assessed using the area under the receiver operating characteristic curve statistic, and calibration was assessed visually using calibration plots. Feature contribution was assessed with Shapley values. Costs were estimated in 2020 US dollars. Results: This study trained a gradient boosting decision tree model on data from 1 657 395 patients (12 900 257 instances; 6 666 662 women [51.7%]). The developed model achieved a test area under the curve of 80.26 (range, 80.21-80.29), demonstrated good calibration, and was robust to sex, immigration status, area-level marginalization with regard to material deprivation and race/ethnicity, and low contact with the health care system. The top 5% of patients predicted as high risk by the model represented 26% of the total annual diabetes cost in Ontario. Conclusions and Relevance: In this decision analytical model study, a machine learning model approach accurately predicted the incidence of diabetes in the population using routinely collected health administrative data. These results suggest that the model could be used to inform decision-making for population health planning and diabetes prevention.

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. Discrimination was assessed using the AUC statistic and calibration was assessed visually using calibration plots overall and across population subgroups. Our model predicting three-year risk of adverse outcomes due to diabetes complications (hyper/hypoglycemia, tissue infection, retinopathy, cardiovascular events, amputation) included 700 features from multiple diverse data sources and had strong discrimination (average test AUC = 77.7, range 77.7-77.9). Through the design and validation of a high-performance model to predict diabetes complications adverse outcomes at the population level, we demonstrate the potential of machine learning and administrative health data to inform health planning and healthcare resource allocation for diabetes management.

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. The SPA-pack architecture presented here aims to satisfy these standards of reliability crucial to the field of soft robotics, while also improving the basic performance capabilities of SPAs by borrowing advantages leveraged ubiquitously in biology; namely, the structured parallel arrangement of lower power actuators to form the basis of a larger and more powerful actuator module. An SPA-pack module consisting of a number of smaller SPAs will be studied using an analytical model and physical prototype. Experimental measurements show an SPA pack to generate over 112 N linear force, while the model indicates the benefit of parallel actuator grouping over a geometrically equivalent single SPA scale as an increasing function of the number of individual actuators in the group. For a module of four actuators, a 23% increase in force production over a volumetrically equivalent single SPA is predicted and validated, while further gains appear possible up to 50%. These findings affirm the advantage of utilizing a fascicle structure for high-performance soft robotic applications over existing monolithic SPA designs. An example of high-performance soft robotic platform will be presented to demonstrate the capability of SPA-pack modules in a complete and functional system.

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. However, there is currently no equation used for modeling the unsteady state loss of the liquid from the dripping sorbent. Here, an analytical model has been provided for modeling the dynamic loss of liquid from the sorbent in dripping experiments. Data from more than 60 sorbent-sorbate systems has been used to validate the model. The proposed model shows excellent agreement with experimental results and is expressed as: U(t)=U(L)e(-Kt)+U(e) In which U(t) (kg/kg) is the uptake capacity of the sorbent at any time t (s) during dripping, U(L) (kg/kg) is the uptake capacity lost due to dripping, and U(e) (kg/kg) is the equilibrium uptake capacity reached after prolonged dripping. K (1/s) is defined as the Kamaan coefficient and controls the curvature of the retention profile. Kamaan ([symbol: see text] IPA phonetics: kæmɒn) is an Iranian (Farsi/Persian) word meaning "arc" or "curve" and hence the letter K has been designated.