The University of Washington Ice–Liquid Discriminator (UWILD) improves single-particle phase classifications of hydrometeors within Southern Ocean clouds using machine learning

Mixed-phase Southern Ocean clouds are challenging to simulate, and their representation in climate models is an important control on climate sensitivity. In particular, the amount of supercooled water and frozen mass that they contain in the present climate is a predictor of their planetary feedback in a warming climate. The recent Southern Ocean Clouds, Radiation, Aerosol Transport Experimental Study (SOCRATES) vastly increased the amount of in situ data available from mixed-phase Southern Ocean clouds useful for model evaluation. Bulk measurements distinguishing liquid and ice water content are not available from SOCRATES, so single-particle phase classifications from the Two-Dimensional Stereo (2D-S) probe are invaluable for quantifying mixed-phase cloud properties. Motivated by the presence of large biases in existing phase discrimination algorithms, we develop a novel technique for single-particle phase classification of binary 2D-S images using a random forest algorithm, which we refer to as the University of Washington Ice–Liquid Discriminator (UWILD). UWILD uses 14 parameters computed from binary image data, as well as particle inter-arrival time, to predict phase. We use liquid-only and ice-dominated time periods within the SOCRATES dataset as training and testing data. This novel approach to model training avoids major pitfalls associated with using manually labeled data, including reduced model generalizability and high labor costs. We find that UWILD is well calibrated and has an overall accuracy of 95 % compared to 72 % and 79 % for two existing phase classification algorithms that we compare it with. UWILD improves classifications of small ice crystals and large liquid drops in particular and has more flexibility than the other algorithms to identify both liquid-dominated and ice-dominated regions within the SOCRATES dataset. UWILD misclassifies a small percentage of large liquid drops as ice. Such misclassified particles are typically associated with model confidence below 75 % and can easily be filtered out of the dataset. UWILD phase classifications show that particles with area-equivalent diameter (Deq) < 0.17 mm are mostly liquid at all temperatures sampled, down to −40 ∘C. Larger particles (Deq>0.17 mm) are predominantly frozen at all temperatures below 0 ∘C. Between 0 and 5 ∘C, there are roughly equal numbers of frozen and liquid mid-sized particles (0.17<Deq<0.33 mm), and larger particles (Deq>0.33 mm) are mostly frozen. We also use UWILD's phase classifications to estimate sub-1 Hz phase heterogeneity, and we show examples of meter-scale cloud phase heterogeneity in the SOCRATES dataset.

The University of Washington Ice-Liquid Discriminator (UWILD) improves single particle phase classifications of hydrometeors within Southern Ocean clouds using machine learning

Mixed-phase Southern Ocean clouds are challenging to simulate and their representation in climate models is an important control on climate sensitivity. In particular, the amount of supercooled liquid and frozen mass that they contain in the present climate is a predictor of their planetary feedback in a warming climate. The recent Southern Ocean Clouds, Radiation and Aerosol Transport Experimental Study (SOCRATES) vastly increased the amount of in-situ data available from mixed-phase Southern Ocean clouds useful for model evaluation. Bulk measurements distinguishing liquid and ice water content are not available from SOCRATES so single particle phase classifications from the Two-Dimensional Stereo (2D-S) probe are invaluable for quantifying mixed-phase cloud properties. Motivated by the presence of large biases in existing phase discrimination algorithms, we develop a novel technique for single particle phase classification of binary 2D-S images using a random forest algorithm, which we refer to as the University of Washington Ice-Liquid Discriminator (UWILD). UWILD uses 14 parameters computed from binary image data, as well as particle inter-arrival time, to predict phase. We use liquid-only and ice-dominated time periods within the SOCRATES dataset as training and testing data. This novel approach to model training avoids major pitfalls associated with using manually labelled data, including reduced model generalizability and high labor costs. We find that UWILD is well calibrated and has an overall accuracy of 95% compared to 72% and 78% for two existing phase classification algorithms that we compare it with. UWILD improves classifications of small ice crystals and large liquid drops in particular and has more flexibility than the other algorithms to identify both liquid-dominated and ice-dominated regions within the SOCRATES dataset. UWILD misclassifies a small percentage of large liquid drops as ice. Such misclassified particles are typically associated with model confidence below 75% and can easily be filtered out of the dataset. UWILD phase classifications show that particles with area-equivalent diameter (Deq) < 0.17 mm are mostly liquid at all temperatures sampled, down to −40°C. Larger particles (Deq > 0.17 mm) are predominantly frozen at all temperatures below 0 °C. Between 0 °C and 5 °C, there are roughly equal numbers of frozen and liquid mid-size particles (0.17 < Deq < 0.33 mm) and larger particles (Deq > 0.33 mm) are mostly frozen. We also use UWILD's phase classifications to estimate sub 1-Hz phase heterogeneity and we show examples of meter-scale cloud phase heterogeneity in the SOCRATES dataset.

Hydration influences on the phase heterogeneity of segmented copolymers

We elucidate the influences of hydration on the morphological heterogeneity of the broad class of segmented copolymers.

Investigation of Tribological Properties of Two-Component Bidisperse Epoxy-Polyester Composite Materials for Its Use in the Friction Units of Means of Sea Transport

The tribological properties of complex polymeric materials, which include epoxy and polyester resins, two hardeners and two microdispersed fillers: mica-muscovite, copper (II) oxide, were investigated in the work. The results of the testing of specimens at dry friction and in the lubricant were analyzed. It is proved, that the antifriction properties of the composite depend on its composition, formation technology and testing conditions. It has been experimentally determined, that the material which was tested in the lubricating environment – Im = 0.25–0.30 mg/km, f = 0.03–0.04, differs with the improved indexes of wear rate and friction coefficient. As a result of the analysis of investigated microsurfaces studied by optical and electron microscopy, the phase heterogeneity of the composite material system was identified. It contributes to the reduction of the running-in distance of the specimen, and indicates the uniform distribution of the filler particles on the surface, has been found. The elemental composition of the compound was determined, which indicates the direct involvement of fillers in the process of friction. A change in the ratio of atoms on the specimen surface before and after the test was found. The results of the study of the surface in the phase contrast mode correlate with the results of the data obtained by electron microscopy.

Surface heterogeneity in Li0.5CoO2 within a porous composite electrode

Surface phase heterogeneity mapping of the same LCO particles in a charged composite electrode deciphers the interactions among the electrode components.

Neural correlates of individual differences in circadian behaviour

Daily rhythms in mammals are controlled by the circadian system, which is a collection of biological clocks regulated by a central pacemaker within the suprachiasmatic nucleus (SCN) of the anterior hypothalamus. Changes in SCN function have pronounced consequences for behaviour and physiology; however, few studies have examined whether individual differences in circadian behaviour reflect changes in SCN function. Here, PERIOD2::LUCIFERASE mice were exposed to a behavioural assay to characterize individual differences in baseline entrainment, rate of re-entrainment and free-running rhythms. SCN slices were then collected for ex vivo bioluminescence imaging to gain insight into how the properties of the SCN clock influence individual differences in behavioural rhythms. First, individual differences in the timing of locomotor activity rhythms were positively correlated with the timing of SCN rhythms. Second, slower adjustment during simulated jetlag was associated with a larger degree of phase heterogeneity among SCN neurons. Collectively, these findings highlight the role of the SCN network in determining individual differences in circadian behaviour. Furthermore, these results reveal novel ways that the network organization of the SCN influences plasticity at the behavioural level, and lend insight into potential interventions designed to modulate the rate of resynchronization during transmeridian travel and shift work.

Computer-based modeling of moving cylindrical ferromagnetic billets induction heating

Purpose – Nowadays, various software is available for simulating physical processes in induction heating. The software is often limited in its ability to simulate the billet movement, sometimes assuming uniform distribution of voltages on the inductor winding, uniformity of the physical properties of the billet, etc. The mathematical model of moving cylindrical ferromagnetic billets described in this paper takes into account the billet's movement, the billet phase heterogeneity and the nonuniformity of the supply voltage distribution in the inductor turns. The paper aims to discuss these issues. Design/methodology/approach – The research methodology is based on FEM analysis of the coupled problem, including the electromagnetic and thermal boundary problem with additional algebraic equations, using Comsol 3.5a software. Findings – The electromagnetic and temperature field in the billet and the voltage distribution on the winding turns have been calculated. The phase distribution in the billet has been predicted. Significant interaction of the nonuniformity of the supply voltage distribution, the billet's movement, the billet phase heterogeneity and side effect on the ends of the inductor have been shown. Practical implications – The results received can be used for designing the induction heating unit for moving cylindrical billets made from ferromagnetic material and improving their characteristics. Originality/value – Investigation of moving cylindrical ferromagnetic billets induction heating can be done by numerical solving the coupled problem including the electromagnetic and thermal boundary problem with additional algebraic equations for the supply voltage distribution.