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Scaled experimentation is an important approach for the investigation of complex systems but for centuries has been impeded by the want of a scaling theory that can accommodate scale effects. The present definition of a scale effect is founded on the violation of an invariance principle arising out of dimensional analysis, i.e. dimensionless equations do not change with scale. However, apart from all but the most rudimentary of systems, most dimensionless governing equations invariably do change with scale, thus providing a very severe constraint on the reach of scaled experimentation. This paper introduces the theory of scaling that in principle applies to all physics and quantifies either implicitly or explicitly all scale dependencies. It is shown here how the route offered by dimensional analysis is nothing more than a particular similitude condition among a countable infinite number of alternative possibilities provided by the new theory. The theory of scaling is founded on a metaphysical concept where space is scaled and the mathematical consequences of this are reflected in the governing equations in transport form. The theory is trialled for known problems in continuum mechanics, electromagnetism and heat transfer to illustrate the breath of the approach and additionally demonstrate the advantages offered by additional forms of similitude.

This systematic review and meta-analysis were conducted to evaluate the impact of bergamot (KoksalGarry) and its nutraceutical compounds on lipid profiles. PubMed, Web of Knowledge, Scopus, and Google Scholar searched for relevant articles. Trials investigating the effect of oral bergamot supplementation on serum levels of total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) in adults were included. The mean differences and standard deviations were pooled using a random-effects model. Fourteen trials were included in this systematic review and meta-analysis. Bergamot supplementation significantly decreased serum levels of TC (weighted mean difference (WMD): -63.60 mg/dL; 95% CI: -78.03 to -49.18; p .001), TG (WMD: -74.72 mg/dL; 95% CI: -83.58 to -65.87; p .001), LDL-C (WMD: -55.43 mg/dL; 95% CI: -67.26 to -43.60; p .001), and increased HDL-C (WMD: 5.78 mg/dL; 95% CI: 3.27 to 8.28; p .001), respectively. Our systematic review of the effects of nutraceuticals containing bergamot on lipid markers showed inconsistent results. The results showed that bergamot supplementation might improve lipid profiles. The findings for nutraceutical compounds containing bergamot were inconsistent. However, the clinical efficacy of bergamot on lipid profiles needs to be further established through higher-quality studies.

Special attention is now being paid to the processing of microphone array signals‎. ‎Sensors located in the array receive the source signal at different time intervals‎. ‎In order to find the direction of source‎, ‎the time difference measurement algorithms‎, ‎which are based on the calculation of cross correlation function‎, ‎calculate the correlation between the outputs of the array sensors‎. ‎The maximum peak of the correlation function indicates the time difference of the sensors to each other‎. ‎Then‎, ‎the source signal orientation will be estimated by using the direction of arrival estimation algorithms based on the calculation of the time differences‎. ‎When the signal propagates in a direct path‎, ‎the cross-correlation function is efficient to measure the time differences‎. ‎However‎, ‎in the case the signal propagates in multipath medium‎, ‎the generalized cross-correlation functions will be used due to the presence of multiple peaks in the cross-correlation function‎. ‎The Phase Transform method‎, ‎which is a family of generalized cross-correlation methods‎, ‎yields better results than other methods‎. ‎Nevertheless‎, ‎it performs poorly at low SNRs‎. ‎In this study‎, ‎we add a modification factor to the weighting function of the Phase Transform method‎. ‎In the following‎, ‎the results of time differences obtained from the proposed method are then applied to different DOA methods‎. ‎In the simulations‎, ‎it was observed that the particle swarm optimization method offers the least error among the DOA methods‎, ‎so that the error values of backazimuth and elevation decreased by about 3 degrees and 2 degrees‎, ‎respectively for the factor of 0.65 at low SNRs‎.

Fluid dynamics experimentation at scale provides an important means of investigation that is presently underpinned by dimensional analysis and computational fluid dynamics (CFD). Unfortunately, despite the obvious success of this twin approach, dimensional analysis provides no solution if scale effects are prevalent and CFD offers reduced practical advantage in the absence of a theory that links information across scaled experiments. This issue is addressed in this paper with the introduction of a new approach to scaled experimentation founded on the theory of finite similitude. Finite similitude is founded on the metaphysical concept of space scaling, where experimental apparatuses, facilities, prototypes, systems, wind tunnels etc., are scaled by the means of space contraction or expansion. There exists no practical means to contract or expand space but what is possible is a mathematical description of space scaling and its impact on governing physics, so providing a means for comparison with scaled systems and real-world experimental behaviours. This paper examines an adaptation of the finite-similitude theory, where two scaled experiments are involved but a second-order version of the theory is applied. All previous work has been limited to zeroth and first-order applications, but in the case of fluid dynamics, second order is shown necessary to capture more accurately convective terms. It is demonstrated in the paper by means of two scaled experiments (designed using second-order theory) that the approach is able to represent behaviours at the full scale that hitherto would have been deemed impossible with traditional dimensional analysis. Numerical and analytical case studies on gravity impacted flows are trialled to illustrate the benefits of the approach with percentage errors reduced from double to single digits.

‎Atmospheric sound waves with frequencies below the human hearing threshold in the range of 0.002 Hz to 20 Hz are generally referred to as infrasound‎. ‎Wind is the main noise in the above-mentioned frequency range‎. ‎The operation of receiving and detecting infrasound are often hampered by wind‎. ‎Therefore‎, ‎high quality detectors are required‎. ‎For this purpose‎, ‎sensor arrays and array signal processing techniques are utilized‎. ‎Fisher ratio-based signal detection is a widely used and powerful method in the field of infrasound‎. ‎The main drawback of this approach is its high computational time due to the repeated computation of test statistics for each element of the slowness grid‎. ‎Thus‎, ‎the researchers use a relatively low-resolution slowness grid in order to save time in processing‎. ‎On the other hand‎, ‎low resolution grid results in an error in the values of estimated parameters of infrasound wave‎. ‎In this study‎, ‎a genetic algorithm based detection method is proposed in order to overcome the fundamental problems of the Fisher method‎. ‎In the proposed method‎, ‎the slowness grid components (px‎,‎py) are defined as the chromosome for the genetic algorithm‎. ‎Despite the previous methods‎, ‎the genetic algorithm has created the advantage that searching could be conducted in a continuous slowness grid‎. ‎Therefore‎, ‎the continuity of the grid and searching only a limited number of slowness vectors reduce error rates and processing time respectively‎. ‎The error of apparent velocity and incoming orientation became 0.5923 and 0.0710 respectively‎, ‎and the processing time decreased considerably from 25835.07 seconds to 533.55 seconds on average.

Experimental investigations of teeth are significantly impeded with the main impediments being small dimensions involved and ethical issues associated with in vivo testing. On the other hand, numerical simulations which can be used as an alternative method have their own inherent limitations which are being reliant on constitutive and boundary information, and also numerical predictions should be validated with experimental findings. Hence, it is necessarily required that experimental tests are conducted on equivalent models which should inevitably be made out of different materials. However, there is no any known method to predict the crack propagation and fracture behavior of structures using equivalent models made from different materials. The present paper aims to propose a scaling method based on the finite similitude theory which has recently been appeared in open literatures to investigate the crack propagation and fracture behavior of teeth in which different materials can even be used for scaled models. The effectiveness of the developed scaling method is examined using the Abaqus finite element software by employing the J-integral, cohesive zone model (CZM), and extended finite element method (XFEM). By conducting the compressive numerical experiments, it is revealed that although the exact prediction of the behavior of a tooth using equivalent models made out of inorganic materials is impossible, it can be anticipated to a good accuracy. Also, the response characteristics of the tooth subjected to a cyclic loading which can be imposed during the act of chewing are anticipated to a good accuracy using the scaled up trial model made out of zirconia which demonstrates the high capability of the proposed method in practical applications.

The stability of unsaturated slopes under seismic loading has become an important issue over the past few years which is the indication of its practical significance in geoengineering. This study aims at exploring the seismic stability of unsaturated sandy clay and silty clayey sand slopes using 2D limit equilibrium analysis for different slope geometries and ground water levels. To this end, the pseudo-static and the displacement-based sliding block Newmark’s approaches are employed for parametric studies by considering the records of the 2003 earthquake that devastated the city of Bam, in the southeast of Iran. In all analyses, the unsaturated state of the soil materials is taken into account in accordance with the changes in the matric suction profile within the soil body using the effective stress approach of unsaturated soil mechanics. The research outcomes, in terms of the safety factor, are then analyzed and compared for different examined conditions. The parametric study in this research considered the method of analysis, slope geometry, ground water conditions, and soil type as the most influential parameters on the factor of safety. The results revealed that considering the unsaturated parameters for the examined soils results in higher factor of safety in comparison with the corresponding cases in which unsaturated parameters are not adopted. A comparative study was also carried out based on a threshold safety factor, in which the safety factors obtained from all analyses, undergoing various conditions and loadings, provide a preliminary insight into the prospective seismic microzonation of unsaturated slopes.

A calculus for scaled experimentation has recently appeared in the open literature founded on the continuous (metaphysical) concept of space scaling. The new theory for isotropic scaling (termed finite similitude) is a single-parameter theory that provides similitude rules that link unlimited numbers of scaled experiments to predict the behavior of any full-scale system. A facet of the theory is that it relates scalar, vectorial and tensorial coefficients and is therefore indirectly influenced by the choice of inertial-coordinate frames characterizing the full and scaled experiments. This feature is explored in this paper to relate objects that are skewed with a particular focus on thin-walled vibrating structures, which find widespread industrial usage but also benefit from anisotropic scaling in their thickness direction. The focus here is on the recently developed first-order finite similitude theory involving two scaled-down experiments for scaled vibrational analysis. The efficacy of the proposed scaling method is examined by means of analytical and numerical simulations. Case studies involving thin-walled plates and hollow beams, subject to free and forced vibration, confirm that titanium prototypes can be represented with high accuracy (∼0% error) by scaled models of identical and different materials (viz., steel and aluminum).

The use of scaled experiments and physical models to investigate the complex responses of large-scale structures under high-rate loading is well-established. The approach however is recognised to suffer from deficiencies when substitute materials are involved as sought material-property combinations are not always available in readily accessible materials. This practical issue is one of scale effects where the behaviour of a scaled-physical model can depart sometimes markedly from the full-scale response. This was certainly the situation prior to the recent arrival of a new scaling theory called finite similitude with the establishment of new similitude rules but at the cost of additional scaled experiments at different scales. This paper examines the benefits of two scaled experiments in physical modelling to examine whether the existing problems posed by the paucity of material behaviours can be remedied by combining the results of two experimental trials at different scales. The focus here is on pressure-vessel response when exposed to projectiles and blast loading to provide non-trivial behaviours that are typically not easily captured with a single scaled experiment. The investigation is performed with the aid of a commercial finite element analysis software and the Conwep blast-loading analysis to reveal that the flexibility provided by two scaled models is significant. Although perfect replication of full-scale behaviour is not always achievable the ability to design scaled experiments with combinations of standard materials is shown to provide good levels of accuracy.

Laboratory investigation on the influence of solute concentration on the soil water-retention curve (SWRC) and volumetric behaviour of expansive soils has received much attention in recent years du...

To investigate the influences of microstructure changes on dilatancy of unsaturated loess at high suctions, a direct shear box device using the vapour equilibrium technique was used. Through conducting two series of direct shear tests on both intact and recompacted loess specimens and also investigating microstructural changes using the mercury intrusion porosimetry (MIP) technique, a linear increase in dilatancy with suctions ranging from 8 to 230 MPa can be identified at different net stresses. The enhanced dilatancy observed is mainly attributed to desiccation-induced high suctions as the reduction in void ratio due to enhancement of suction was negligible. A macrovoid ratio, eM, may be quantified and proposed to explain the differences in the increased dilatancy of recompacted and intact loess with suction.

A tailings dam is normally constructed through self-consolidation with minimum compaction effort. Accordingly, special attention to the ultimate limit design in assessing the tailings dam instability condition is of primary importance. Furthermore, the existence of pore fluid chemical contaminants with high concentrations makes soil hydraulic and shear resistance properties subject to considerable changes.Therefore, this study aims to investigate the stability of a tailing dam using saturated-unsaturated flow analysis under 0.2 and 0.6 M sodium chloride solutions and pure water as a benchmark. To make the analyses as realistic as possible, recently developed solute-dependent hydraulic conductivity and water retention models are embedded into the numerical software. The results show that the saline-based model has higher pore water pressure than the pure water model due to more rainfall penetration by increasing the pore fluid salt concentration. Furthermore, increasing salt solution concentration enhances the tailing dam's drainagecondition, which leads to an increase in the dam's shear strength and, consequently, the factor of safety. Therefore, the stability of the tailings dam is improved by the combined impact of salt on both the flow and strength.

Shallow landslides triggered by heavy rainfalls have caused casualties and economic losses to domestic infrastructures and industries worldwide. Rainfall mainly reduces the soil matric suction and the shear resistance, resulting in shallow landslides. Vegetation is an eco-friendly and cost-effective method for stabilizing slopes prone to shallow landslides. This research aims to investigate the hydrological and mechanical effects of vegetation on slope stability through a numerical study approach. Vegetated and bare slopes were subjected to a recorded climate condition and two rainfall scenarios of high intensity (HI) and low intensity (LI). Matric suction and factor of safety of vegetated and bare slopes subjected to rainfall were investigated. The matric suction of the vegetated slope at the surface was approximately four times greater than the bare slope after the HI scenario. However, the matric suction is about three times greater in the LI scenario. The results indicate that planting on slopes would reduce the vulnerability of bare slopes to the HI rainfall due to the higher matric suction and additional cohesion induced by the root system. These findings suggest that using vegetation in Rasht, Iran, where the climate data were collected, has considerable potential for stabilizing slopes.

Land subsidence associated with using natural groundwater resources for serving the growing population needs has been receiving extensive research attention in the literature over the past few decades. The water content fluctuation in the of subsurface soil layers significantly impacts the land subsidence. The key objective of this study is to predict changes in water content profiles in soil layers over a long period of time using a deep learning-based approach. A convolution neural network algorithm that is commonly used in Artificial Intelligence (AI) applications is modified in the present study for processing in-situ measurement water content profiles. The approach used in the proposed AI method has a distinct advantage for generating dynamic predictions based on the extracted spatiotemporal characteristics of the data. In addition, three different algorithms are compared with respect to time series prediction, including long-short-term memory (LSTM), multiple-layer perceptron (MLP) networks and autoregressive integrated moving average (ARIMA).

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.

Ground improvement techniques are inevitable for weak soils that cannot endure the design load imposed by superstructures. Deep mixing technique (DMT) as one of these methods is promising and effec...

The ability to perform scaled high impact, energy absorption type experimentation is practically impeded for rate-sensitive materials by an inherently nonlinear response that changes markedly structural behaviour at scale. The issue is re-examined in this paper where equivalent models for structures subjected to high rate axial impact are proposed founded on the recently discovered concept of finite similitude. Finite similitude has been proven to exist for continuum mechanics and is defined by proportional corresponding transport equations on synchronised control volumes in full-scale and scaled spaces. The theory provides a means for performing tests on scaled models instead of the original full-sized sample. Focus in this paper is on the high-rate loading of tubes leading to elastic-plastic buckling behaviour, which is recognised to be inherently scale dependent. The finite-similitude approach allows scaled models to be projected into the full-scale physical space for direct comparison. Different projections are tested by means of the finite element software LS-Dyna in which the strain rate effect is accounted for using Cowper-Symonds or Johnson-Cook constitutive equations. It is confirmed in the paper that scaled models for tube buckling exist and although exact similitude is not generally possible, good accuracy is nevertheless achievable.

During preliminary investigation phase of the national Chabahar-Zahedan railway line, serious geotechnical problems including non-uniform settlements, tensile cracks, and local collapse were reported in parts of the path near the coastline. A follow-up field investigation revealed that the in-situ soil at construction site has a metastable structure being sensitive to saturation and loading. Therefore, a comprehensive series of physical, chemical, electro-chemical, and geotechnical tests were conducted to classify and characterize the soil properties and behavior in its natural state. The natural soil was classified as a clayey loess with moderately dispersive to dispersive characteristics. Given its significant collapse potential, the soil was assigned into the category with “severe trouble” for construction purposes. Importantly, our tests revealed that a threefold reduction in pore water salinity results in >4% rise in collapse potential of similar specimens. Reduction in salt concentration increases the thickness of diffuse double layer and hence collapsibility. In addition, strain softening shear behavior was observed for elevated confining stresses under undrained triaxial stress state. In fact, this brittle type failure mechanism associated with continuous generation of pore water pressure is undesirable as catastrophic failures could happen with no advance warning.

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.

The primary object of this study is to derive new scaling laws for passive scalar statistical quantities in temporally and spatially evolving plane turbulent jet flows. We apply Lie symmetry analysis to the equations governing the evolution of the first three statistical moments for the passive scalar quantities. The analysis is based on novel forms of the two-point velocity–scalar and scalar–scalar correlation equations, which are naturally based on the statistical moments derived from the instantaneous velocities and not on those of the fluctuation velocities from the Reynolds decomposition. The newly derived invariant solutions recover the gaps in the classical self-similarity analysis from three major perspectives. First, the scaling laws are constructed as the direct consequence of the symmetry approach, while an a priori set of similarity scales is not required. Second, unlike in the classical laws, which show self-similarity primarily for the first moments, here self-similarity is theoretically shown up to the third moments. Third, there is a symmetry breaking induced by the integral invariant of the mean temperature that connects the scaling symmetries of momentum and passive scalar equations, which results in a close coupling of the scaling exponents of the first two moments and, further, determines the scaling exponents of all higher moments. To verify the new theoretical findings, we employ data from two direct numerical simulations of the mixing of a passive scalar driven by a temporally evolving turbulent jet. The direct numerical simulation data very clearly validate the new scaling laws up to the third moments.

Key Points Question Can a machine learning model trained on routinely collected administrative health data be used to accurately predict the onset of type 2 diabetes at the population level? Findings In this decision analytical model study of 2.1 million residents in Ontario, Canada, a machine learning model was developed with high discrimination, population-level calibration, and calibration across population subgroups. Meaning Study results suggest that machine learning and administrative health data can be used to create population health planning tools that accurately discriminate between high- and low-risk groups to guide investments and targeted interventions for diabetes prevention., This decision analytical model study assesses whether a machine learning model trained on routinely collected administrative health data from a single-payer health system in Canada can be used to predict the onset of type 2 diabetes in the population., 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.

Soil-water retention curve (SWRC) has a wide application in geoenvironmental engineering from the predication of unsaturated shear strength to transient two-phase flow and stability analyses. Although various SWRC models have been proposed to take into account some influencing factors, less attention has been given to consider the effects of pore fluid osmotic potential. Therefore, the key objective of this study is to extend van Genchten’s model so that osmotic potential is considered as an independent factor governing the SWRC behavior. The new model comprises only six variables, which can be calibrated through minimal experimental measurements. More importantly, most of the model parameters have physical meaning by correlating macroscopic volumetric behavior and general trends of SWRC to osmotic potential. The results of validation tests revealed that the new osmotic-dependent SWRC model can predict the retention data in terms of both total and matric suction for two different soils and various molar concentrations very good. The proposed modeling approach does not require any advanced mercury intrusion porosimetry (MIP) tests, yet it can deliver excellent predictions by calibrating only six parameters which are far less than those incorporated into similar models for saline water permeating through the pore structure.

An advanced chromodynamics, Rothmann-Keller (RK) type lattice Boltzmann model (LBM) is used in this study. The new model benefits from high stability and capability of independently setting the interfacial tension of the fluids as an input parameter. In addition, the model is coupled with a wall-density approach to simulate the hydrophilic or hydrophobic properties of wall surfaces. Finally, injection of a wetting (non-wetting) fluid in a pore doublet geometry which is initially filled with non-wetting (wetting) fluid is simulated. The results of simulation reveal the capability of RK-LBM to simulate relative permeabilities of fluids in porous media for future studies of two-immiscible phase flow in various geoenvironmental problems.

Scaled experimentation plays an important role in prototype and process development but is recognised to be severely constrained by the need for similitude, founded on the concepts of geometric, kinematic and kinetic similarity. This paper examines the possibility of breaking the requirement for geometric similarity by introducing the law of finite similitude for anisotropic scaling, which applies to continuum mechanics on anisotropically-scaled spaces. The law confirms that similarity solutions on skewed spaces always exist separately for quasistatic deformational and thermal-continuum problems. Thermomechanical and continuum dynamic problems however are shown to suffer from the inclusion of a (non-physical) non-orthogonal metric arising from the skewed coordinate system associated with anisotropic-space scaling. Even in this case however, an important subclass of problems is shown to be physically realisable involving a dominant component of velocity (displacement), where geometric similarity can again be broken yet retain good accuracy. The ability to skew artefacts yet achieve similitude is recognised to be a particularly important outcome as it allows for example one experimental model to be used to predict the behaviour of multiple skewed models. To showcase and highlight the significance of the new concept various scaled numerical models with anisotropic scaling with different geometrical scaling ratios in different directions is considered. The applicability of the theory is tested on a number of case studies providing strong supporting evidence for the validity and applicability of the new theory.

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.

We use the Lie symmetry theory to derive new scaling laws for passive scalar dynamics under the influence of a constant mean gradient of the scalar in decaying homogeneous isotropic turbulence. For this purpose, we apply symmetry analysis to the equations for two-point correlation of the scalar and velocity fluctuations. It is shown that, in contrast to the classical self-similarity approach, the general invariant solutions, respectively scaling laws, of the two-point functions are constructed using the symmetry approach, without requiring an a priori set of similarity scales to carry on the analysis. In the context of the current analysis also, scaling laws for one-point quantities of the scalar variance , the transverse scalar flux and the variance of the turbulent velocity fluctuations are established, which are essentially related to the scaling symmetries. A key step to derive the scaling laws is the symmetry breaking induced by the constant mean scalar gradient. We use the results of a highly resolved direct numerical simulation of Gauding et al. (Comput. Fluids, vol. 180, 2019, pp. 206–217) to verify the scaling laws and the self-similarity of the two-point correlation functions. It is shown that the general symmetry solutions obtained from symmetry results provide a very good similarity to these functions.

The ultimate bearing capacity of shallow foundations on unsaturated soils is investigated through the concept of suction-dependent effective stress. The first approach is a new analytical s...

Dispersive soils, as one of the main categories of problematic soils, can be found in some parts of the earth, such as the eastern-south of Iran, nearby the Gulf of Oman. One of the most important factors enhancing the dispersive potential is the existence of dissolved salts in the soil water. The main objective of this study is to explore the influence of water salinity on the instability of a railway embankment due to rainfall infiltration. In order to achieve this goal, the embankment resting on a dispersive stratum is numerically modeled and subjected to transient infiltration flow. The effect of dispersion is simplified through variations in the soil-water retention curve with salinity. The measured water retention curves revealed that by omitting the natural salinity in the soil-water, the retention capability of the soil decreases; therefore, the unsaturated hydraulic conductivity of the soil stratum will significantly decline. According to the extensive decrease in the hydraulic conductivity of the desalinated materials, the rainfall cannot infiltrate in the embankment and the rainfall mostly runs off. However, in the saline embankment, the infiltration decreases the soil suction; and consequently, the factor of safety of the railway embankment decreases.