A multi-parameter grading system for optimal fitting of scleral contact lenses

Background: While scleral lens practise has improved over the years due to factors such as availability of lenses with better materials and designs as well as experience of practitioners, a lack of objectivity appears to remain in terms of assessment of scleral lens fitting. This prospective observational work aimed to achieve standardization on this front through proposing a grading system for scleral lens fitting. Methods: After application of prosthetic replacement of ocular surface ecosystem (PROSE) devices on the participants’ eyes, four fundamental components for understanding scleral lens fitting such as central and limbal corneal clearance, mid-haptic compression, and alignment of lens edge over anterior sclera were assessed through a series of slit-lamp biomicroscopy imaging as well as with anterior segment optical coherence tomography. FitConnect® was used to modify the device parameters to simulate different grading patterns on the proposed scale. Serial imaging was done for all the different lenses to compose the grading scale. Results: A clinically relevant grading scale was constructed that pictorially demonstrated grades for the different aspect of scleral lens fitting. The grades were conveniently scaled within three categories: “optimal”, “acceptable” and “not acceptable”. Conclusion: The gradation of scleral lens fitting parameters would take a step towards objectifying the assessment patterns in practise. This will also help reducing the gap between a novice and an experienced practitioner in terms of understanding of scleral lens fitting.

A modified shrinking core model for microwave-assisted leaching of aluminum from peat clay

Aluminum oxide in peat clay has the potential to be used as a catalyst, coagulant, and adsorbent for the water treatment process. The usefulness of aluminum oxide in peat clay is enhanced by the leaching process. Aluminum was leached from peat clay in a variety of microwave power, HCl concentrations, and particle size. The effect of the microwave leaching parameters on the aluminum leaching rate was observed. The shrinking core (SC) model used in microwave-assisted leaching was assumed a pseudo steady state with chemical reactions. Effective diffusivity (De), mass transfer coefficient (kc), and reaction rate constants (k) are used as fitting parameters. The best fitting parameters De, kc , and k obtained 0.0049 cm2/s, 2.49 cm/s, and 10.5 cm/s, respectively. The comparison of experimental data and model calculations shown that the SC model can describe experimental data well for all microwave-assisted leaching conditions. Precious information on the results of this research can be given for the goal of the scaling-up and design of the microwave assisted leaching process.

A Population Balance Model for Shear-Induced Polymer-Bridging Flocculation of Total Tailings

Shear-induced polymer-bridging flocculation is widely used in the solid–liquid separation process in cemented paste backfill, beneficial to water recycling and tailings management in metal mines. A flocculation kinetics model based on Population Balance Model (PBM) is proposed to model the polymer-bridging flocculation process of total tailings. The PBM leads to a system of ordinary differential equations describing the evolution of the size distribution, and incorporates an aggregation kernel and a breakage kernel. In the aggregation kernel, a collision frequency model describes the particle collision under the combined effects of Brownian motions, shear flow, and differential sedimentation. A semi-empirical collision efficiency model with three fitting parameters is applied. In the breakage kernel, a new breakage rate coefficient model with another three fitting parameters is introduced. Values of the six fitting parameters are determined by minimizing the difference between experimental data obtained from FBRM and modeling result through particle swarm global optimization. All of the six fitting parameters vary with flocculation conditions. The six fitting parameters are regressed with the flocculation factors with six regression models obtained. The validation modeling demonstrates that the proposed PBM quantifies well the dynamic evolution of the floc size during flocculation under the given experimental setup. The investigation will provide significant new insights into the flocculation kinetics of total tailings and lay a foundation for studying the performance of the feedwell of a gravity thickener.

Exploration and Validation of Force Field Design Protocols through QM-to-MM Mapping

The scale of the parameter optimisation problem in traditional molecular mechanics force field construction means that design of a new force field is a long process, and sub-optimal choices made in the early stages can persist for many generations of the force field. We hypothesise that careful use of quantum mechanics to inform molecular mechanics parameter derivation (QM-to-MM mapping) should be used to significantly reduce the number of parameters that require fitting to experiment and increase the pace of force field development. Here, we design a collection of 15 new protocols for small, organic molecule force field design, and test their accuracy against experimental liquid properties. Our best performing model has only seven fitting parameters, yet achieves mean unsigned errors of just 0.031 g/cm3 and 0.69 kcal/mol in liquid densities and heats of vaporisation, compared to experiment. The software required to derive the designed force fields is freely available at https://github.com/qubekit/QUBEKit.

Photocatalytic degradations of JWS-type kinetics

Photocatalytic degradation kinetics of Jurlewicz–Weron–Stanislavsky (JWS) type has been identified. Experimental data are taken from previous published works, and fitted with the JWS relaxation function as well as that of the Havriliak–Negami (HN) model. All experimental data can fit with either model fairly good. From the fitting parameters, the Jonscher indices are calculated and Jonscher diagram is plotted for the chemical kinetics of photocatalytic degradations. This work suggests that material parameters of photocatalysts can be well defined in the sense of fractional calculus.

Virial Halo Mass Function in the Planck Cosmology

We study halo mass functions with high-resolution N-body simulations under a ΛCDM cosmology. Our simulations adopt the cosmological model that is consistent with recent measurements of the cosmic microwave backgrounds with the Planck satellite. We calibrate the halo mass functions for 108.5 ≲ M vir/(h −1 M ⊙) ≲ 1015.0–0.45 z , where M vir is the virial spherical-overdensity mass and redshift z ranges from 0 to 7. The halo mass function in our simulations can be fitted by a four-parameter model over a wide range of halo masses and redshifts, while we require some redshift evolution of the fitting parameters. Our new fitting formula of the mass function has a 5%-level precision, except for the highest masses at z ≤ 7. Our model predicts that the analytic prediction in Sheth & Tormen would overestimate the halo abundance at z = 6 with M vir = 108.5–10 h −1 M ⊙ by 20%–30%. Our calibrated halo mass function provides a baseline model to constrain warm dark matter (WDM) by high-z galaxy number counts. We compare a cumulative luminosity function of galaxies at z = 6 with the total halo abundance based on our model and a recently proposed WDM correction. We find that WDM with its mass lighter than 2.71 keV is incompatible with the observed galaxy number density at a 2σ confidence level.

Method for critical current angular dependencies analysis of superconducting tapes

Various applications of superconducting materials require accounting of the critical current anisotropy relative to magnetic field direction - I c7(θ). However, today there is no sufficiently comprehensive model that takes into account the anisotropy, therefore the angular dependences are usually not analysed, but only described using various mathematical formulas. As a result, the fitting parameters have no physical meaning and it is difficult to correlate the picture with the features of the microstructure. In this paper, we propose a method for analysing the critical current angular dependences based on the anisotropic pinning model. The applicability of this model for conventional superconducting Nb-Ti tapes with one peak in the I c7(θ) dependence is shown. The possibility of extending this model to analyse the angular dependences of HTS materials is discussed.

Multi-Modal Clustering Events Observed by Horizon-10T and Axion Quark Nuggets

The Horizon-10T collaboration have reported observation of Multi-Modal Events (MME) containing multiple peaks suggesting their clustering origin. These events are proven to be hard to explain in terms of conventional cosmic rays (CR). We propose that these MMEs might be result of the dark matter annihilation events within the so-called axion quark nugget (AQN) dark matter model, which was originally invented for completely different purpose to explain the observed similarity between the dark and the visible components in the Universe, i.e., ΩDM∼Ωvisible without any fitting parameters. We support this proposal by demonstrating that the observations, including the frequency of appearance, intensity, the spatial distribution, the time duration, the clustering features, and many other properties nicely match the emission characteristics of the AQN annihilation events in atmosphere. We list a number of features of the AQN events which are very distinct from conventional CR air showers. The observation (non-observation) of these features may substantiate (refute) our proposal.

Universal Ontogenetic Growth without Fitting Parameters: Implications for Life History Invariants & Population Growth

Since the work of Von Bertalanffy (1957), several models have been proposed that relate the ontogenetic scaling of energy assimilation and metabolism to growth, being able to describe ontogenetic growth trajectories for living organisms and collapse them onto a single universal curve (West et al. 2001; Barnavar et al. 2002). Nevertheless, all these ontogenetic growth models critically depends on fitting parameters and on the allometric scaling of the metabolic rate. Using a new metabolic rate relation (Escala 2019) applied to a Bertalanffy-type ontogenetic growth equation, we find that ontogenetic growth can also be described by an universal growth curve for all studied species, but without the aid of any fitting parameters. We find that the inverse of the heart frequency fH, rescaled by the ratio of the specific energies for biomass creation and metabolism, defines the characteristic timescale for ontogenetic growth. Moreover, our model also predicts a generation time and lifespan that explains the origin of several 'Life History Invariants' (Charnov 1993) and predicts that the Mathusian Parameter should be inversely proportional to both the generation time and lifespan, in agreement with the data in the literature (Duncan et al. 1997; Dillingham et. al 2016; Hatton et al 2019). In our formalism, several critical timescales and rates (lifespan, generation time & intrinsic population growth rate) are all proportional to the heart frequency fH, thus their allometric scaling relations comes directly from the allometry of the heart frequency, which is typically fH ∝ M-0.25 under basal conditions.

Rock-physics machine learning toolkit for joint litho-fluid facies classification and compaction modeling

Correct lithofacies interpretation sourced from wireline log data is an essential source of prior information for joint seismic inversion for facies and impedances, among other applications. However, this information is difficult to interpret or extract manually due to the multivariate and high dimensionality of wireline logs. Facies inference is also challenging for traditional clustering-based approaches because pervasive compaction trends affect a number of petrophysical measurements simultaneously. Another common pitfall in automated clustering approaches is the inability to account for underlying diagenetic processes that correlate with depth. Here, we address these challenges by introducing a rock-physics machine learning toolkit for joint litho-fluid facies classification. The litho-fluid types are inferred from the borehole data within the objective framework of a maximum-likelihood approach for latent facies variables and rock-physics model parameters, explicitly accounting for compaction and depth effects. The inference boils down to an expectation-maximization (EM) algorithm with strong spatial coupling. Each litho-fluid type is associated with an instance of a particular rock-physics model with a unique set of fitting parameters, constrained to a physically reasonable range. These fitting parameters in turn are inferred using bound-constrained optimization as part of the EM algorithm. Outputs produced by the toolkit can be used directly to specify the necessary prior information for seismic inversion, including per-facies rock-physics models and facies proportions. We present an example application of the tool to real borehole data from the North West Shelf of Australia to illustrate the method and discuss its characteristic features in depth.