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Galaxies grow through star formation (in-situ) and accretion (ex-situ) of other galaxies. Reconstructing the relative contribution of these two growth channels is crucial for constraining the processes of galaxy formation in a cosmological context. In this on-going work, we utilize a conditional variational autoencoder along with a normalizing flow - trained on a state-of-the-art cosmological simulation - in an attempt to infer the posterior distribution of the 2D ex-situ stellar mass distribution of galaxies solely from observable two-dimensional maps of their stellar mass, kinematics, age and metallicity. Such maps are typically obtained from large Integral Field Unit Surveys such as MaNGA. We find that the average posterior provides an estimate of the resolved accretion histories of galaxies with a mean ~10% error per pixel. We show that the use of a normalizing flow to conditionally sample the latent space results in a smaller reconstruction error. Due to the probabilistic nature of our architecture, the uncertainty of our predictions can also be quantified. To our knowledge, this is the first attempt to infer the 2D ex-situ fraction maps from observable maps., Comment: 6 pages, 3 figures

Super-resolution (SR) is a promising cost-effective downscaling methodology for producing high-resolution climate information from coarser counterparts. A particular application is downscaling regional reanalysis outputs (predictand) from the driving global counterparts (predictor). This study conducts an intercomparison of various SR downscaling methods focusing on temperature and using the CERRA reanalysis (5.5 km resolution, produced with a regional atmospheric model driven by ERA5) as example. The method proposed in this work is the Swin transformer and two alternative methods are used as benchmark (fully convolutional U-Net and convolutional and dense DeepESD) as well as the simple bicubic interpolation. We compare two approaches, the standard one using the full domain as input and a more scalable tiling approach, dividing the full domain into tiles that are used as input. The methods are trained to downscale CERRA surface temperature, based on temperature information from the driving ERA5; in addition, the tiling approach includes static orographic information. We show that the tiling approach, which requires spatial transferability, comes at the cost of a lower performance (although it outperforms some full-domain benchmarks), but provides an efficient scalable solution that allows SR reduction on a pan-European scale and is valuable for real-time applications.

Surface active ionic liquids (SAILs) combine useful characteristics of both ionic liquids (ILs) and surfactants, hence are promising candidates for a wide range of applications. However, the effect of SAIL ionic structures on their physicochemical properties remains unclear, which limits their uptake. To address this knowledge gap, in this work we investigated the density, viscosity, surface tension, and corresponding critical micelle concentration in water, as well as gas absorption of SAILs with a variety of cation and anion structures. SAILs containing anions with linear alkyl chains have smaller molar volumes than those with branched alkyl chains, because linear alkyl chains are interdigitated to a greater extent, leading to more compact packing. This interdigitation also results in SAILs being about two orders of magnitude more viscous than comparable conventional ILs. SAILs at the liquid-air interface orient alkyl chains towards the air, leading to low surface tensions closer to n-alkanes than conventional ILs. Critical temperatures of about 900 K could be estimated for all SAILs from their surface tensions. When dissolved in water, SAILs adsorb at the liquid-air interface and lower the surface tension, like conventional surfactants in water, after which micelles form. Molecular simulations show that the micelles are spherical and that lower critical micelle concentrations correspond to the formation of aggregates with a larger number of ion pairs. $\mathrm{CO_{2}}$ and $\mathrm{N_{2}}$ absorption capacities are examined and we conclude that ionic liquids with larger non-polar domains absorb larger quantities of both gases.

The interest of oil industry in increasing heavy oil production has promoted the use of enhanced oil recovery techniques such as $CO_{2}$ injection, which produce a decrease of oil viscosity and displacement of heavy oil from reservoir to surface. The design of these processes requires accurate data of densities, viscosities or surface tensions of ($CO_{2}$ + hydrocarbon) mixtures in order to simulate the behaviour of these mixtures in the reservoir. An automated Anton Paar DMA HPM vibrating-tube densimeter was used to measure densities of this kind of mixtures, and a new mixture injection system, by means of two syringe pumps, was developed for the densimeter. The equipment operates at high pressure, which is controlled through a back pressure valve and a variable volume cylinder with a stepper motor. The estimated standard uncertainty of the density is $\mathrm{\pm0.9 \ kg \cdot m^{-3}}$ at temperatures below 373.15 K and pressure range (0.1-140) MPa.In this paper, the densities of the mixtures ($CO_{2}$ + $n$-decane), ($CO_{2}$ + $n$-dodecane) and ($CO_{2}$ + squalane) are reported at $T$ = (283.15-393.15) K and $p$ = (10-100) MPa.

The present work aims to measure speeds of sound $c$ in a biogas mixture of $CH_{4}$ + $N_{2}$ + $CO_{2}$ + $CO$, at $p$ = (1-12) MPa and $T$ = (273, 300 and 325) K, using a spherical acoustical resonator. The results are fitted to the virial acoustic equation of state, and the virial acoustic coefficients are obtained, $\beta_{a}$ and $\gamma_{a}$ and extrapolated to zero pressure, determining the adiabatic coefficient as perfect gas, $\gamma^{pg}$, and the isobaric and isochoric heat capacities as perfect gas, $C_{p}^{pg}$ and $C_{V}^{pg}$, respectively. The speeds of sound are acquired with a mean expanded relative uncertainty of 165 parts in $10^{6}$ ($k$ = 2) and are compared with the results predicted by the reference equation of state for this kind of mixture (natural gas-like mixtures), EoS GERG-2008. Relative deviations between experimental data and values estimated by this model were less than 700 parts in $10^{6}$ at $T$ = 325 K, and below 400 parts in $10^{6}$, and within measurement uncertainty of at $T$ = 300 K, although appreciably higher at isotherm $T$ = 273 K at the highest pressure data for this work, and even reaching values above 3 400 parts in $10^{6}$.

The GERG-2008 equation of state is the approved ISO standard (ISO 20765-2) for the calculation of thermophysical properties of natural gas mixtures. The composition of natural gas can vary considerably due to the diversity of origin. Further diversification was generated by adding hydrogen, biogas, or other non-conventional energy gases. In this work, high-precision experimental ($p$, $\rho$, $T$) data for two gravimetrically prepared synthetic natural gas mixtures are reported. One mixture resembled a conventional natural gas of 11 components (11 M) with a nominal mixture composition (amount-of-substance fraction) of 0.8845 for methane as the matrix compound. The other mixture was a 13-component hydrogen-enriched natural gas with a low calorific value featuring a nominal composition of 0.7885 for methane. Density measurements were performed in an isothermal operational mode at temperatures between 260 and 350 K and at pressures up to 20 MPa by using a single-sinker densimeter with magnetic suspension coupling. The data were compared with the corresponding densities calculated from both GERG-2008 and AGA8-DC92 equations of state to test their performance on real mixtures. The average absolute deviation from GERG-2008 (AGA8-DC92) is 0.027% (0.078%) for 11 M and 0.095% (0.062%) for the 13-component $H_{2}$-enriched mixture, respectively. The corresponding maximum relative deviation from GERG-2008 (AGA8-DC92) amounts to 0.095% (0.127%) for 11 M and 0.291% (0.193%) for the $H_{2}$-enriched mixture.

Segmenting brain tumors in multi-parametric magnetic resonance imaging enables performing quantitative analysis in support of clinical trials and personalized patient care. This analysis provides the potential to impact clinical decision-making processes, including diagnosis and prognosis. In 2023, the well-established Brain Tumor Segmentation (BraTS) challenge presented a substantial expansion with eight tasks and 4,500 brain tumor cases. In this paper, we present a deep learning-based ensemble strategy that is evaluated for newly included tumor cases in three tasks: pediatric brain tumors (PED), intracranial meningioma (MEN), and brain metastases (MET). In particular, we ensemble outputs from state-of-the-art nnU-Net and Swin UNETR models on a region-wise basis. Furthermore, we implemented a targeted post-processing strategy based on a cross-validated threshold search to improve the segmentation results for tumor sub-regions. The evaluation of our proposed method on unseen test cases for the three tasks resulted in lesion-wise Dice scores for PED: 0.653, 0.809, 0.826; MEN: 0.876, 0.867, 0.849; and MET: 0.555, 0.6, 0.58; for the enhancing tumor, tumor core, and whole tumor, respectively. Our method was ranked first for PED, third for MEN, and fourth for MET, respectively., Comment: 11 pages, 6 figures, 2 tables; This method ranked 1st, 3rd and 4th for BraTS2023 PED, MEN, and MET, respectively. This paper was accepted at MICCAI 2023's BrainLes Workshop

The development of a novel technique based on a cylindrical microwave resonator for high pressure phase equilibrium determination is described. Electric permittivity or dielectric constant is a physical property that depends on temperature and pressure $\epsilon$($p$,$T$). Based on this property, a measuring technique consisting of a cylindrical resonant cavity that works in the microwave spectrum has been developed. Equilibrium data of fluid mixtures are measured at high pressure using a synthetic method, where phase transition is determined under isothermal conditions due to the change of the dielectric constant. This technique may be a more accurate alternative to conventional visual synthetic methods. The technique was validated measuring pure $CO_{2}$, and phase behaviour was then determined for two binary mixtures [$CO_{2}$ (0.6) + $CH_{4}$ (0.4)] and [$CO_{2}$ (0.4) + $CH_{4}$ (0.6)], results for which are presented. These systems are interesting for the study of biogas-like mixtures. In addition, data were compared with the equation of state used for natural gas GERG-2008, and also, they were modelled using Peng-Robinson equation of state and Wong-Sandler mixing rules, which are widely employed in chemical industries and which give good results.

This work aims to provide accurate and wide-ranging experimental new speed of sound data $w$($p$,$T$) of two binary ($CH_{4}$ + $He$) mixtures at a nominal helium content of 5% and 10% at pressures $p$ = (0.5 up to 20) MPa and temperatures $T$ = (273.16, 300, 325, 350 and 375) K. For this purpose, the most accurate technique for determining speed of sound in gas phase has been used: the spherical acoustic resonator. Speed of sound is determined with an overall relative expanded ($k$ = 2) uncertainty of 230 parts in $10^{6}$ and compared to reference models for multicomponent natural gas-like mixtures: AGA8-DC92 and GERG-2008 equations of state. Relative deviations of experimental data from model estimations are outside the experimental uncertainty limit, although all points are mostly within the AGA uncertainty of 0.2% and GERG uncertainty of 0.5% and worsen as the helium content increases. Absolute average deviations are better than 0.45% for GERG and below 0.14% for AGA models in (0.95 $CH_{4}$ + 0.05 $He$) mixture and below 0.83% for GERG and within 0.22% for AGA equations in (0.90 $CH_{4}$ + 0.10 $He$) mixture.

The limited availability of accurate experimental data in wide ranges of pressure, temperature, and composition is the main constraining factor for the proper development and assessment of thermodynamic models and equations of state. In the particular case of carbon capture and storage (CCS) processes, there is a clear need for data sets related to the (carbon dioxide + oxygen) mixtures that this work aims to address. This work provides new experimental ($p$, $\rho$, $T$) data for three binary ($CO_{2}$ + $O_{2}$) mixtures with mole fractions of oxygen $x$($O_{2}$) = (0.05, 0.10, and 0.20) mol/mol, in the temperature range $T$ = (250 to 375) K and pressure range $p$ = (0.5 to 13) MPa. The measurements were performed with a high-precision single-sinker densimeter with magnetic suspension coupling. The density data were obtained with estimated expanded relative uncertainties of 0.02% for the highest densities and up to 0.3% for the lowest ones.The results were compared to the corresponding results calculated by the current reference equations of state for this kind of mixtures, namely the EOS-CG (combustion gases) and the GERG-2008 equation of state, respectively. The EOS-CG yields better estimations in density than the GERG-2008 equation of state. The results from the EOS-GC model show no systematic temperature dependence. For the GERG-2008 model, however, this criterion is significantly less fulfilled., Comment: arXiv admin note: substantial text overlap with arXiv:2409.06312

Density measurements from single-sinker magnetic suspension densimeters need to be corrected to compensate for the magnetic effects of the measuring cell materials and the fluid on the coupling transmission system. While the magnetic effect of the densimeter materials can be easily determined, the fluid effect requires the calculation of an apparatus-specific constant, $\epsilon_{\rho}$. In this work, the apparatus-specific constant of the single-sinker magnetic suspension densimeter at the University of Valladolid has been determined by using two alternative methods. The first method, which uses density data for the same fluid and conditions and different sinkers, yielded a value of $\epsilon_{\rho}$ = $4.6\cdot10^{-5}$. The second method, obtained from measurements with pure oxygen, yielded a value of $\epsilon_{\rho}$ = $8.822\cdot10^{-5}$. The second value is considered as more reliable, as the first method presents inherent limitations in this case.

Speed of sound is one of the thermodynamic properties that can be measured with least uncertainty and is of great interest in developing equations of state. Moreover, accurate models are needed by the H2 industry to design the transport and storage stages of hydrogen blends in the natural gas network. This research aims to provide accurate data for ($CH_{4}$ + $H_{2}$) mixtures of nominal (5, 10, and 50) mol-% of hydrogen, in the $p$ = (0.5 up to 20) MPa pressure range and with temperatures $T$ = (273.16, 300, 325, 350, and 375) K. Using an acoustic spherical resonator, speed of sound was determined with an overall relative expanded ($k$ = 2) uncertainty of 220 parts in $10^{6}$ (0.022%). Data were compared to reference equations of state for natural gas-like mixtures, such as AGA8-DC92 and GERG-2008. Average absolute deviations below 0.095% and percentage deviations between 0.029% and up to 0.30%, respectively, were obtained. Additionally, results were fitted to the acoustic virial equation of state and adiabatic coefficients, molar isochoric heat capacities and molar isobaric heat capacities as perfect-gas, together with second and third acoustic virial coefficients were estimated. Density second virial coefficients were also obtained.

The CH$_3$(CH$_2$)$_u$COO(CH$_3$)$_v$CH$_3$ + n-alkane mixtures have been investigated on the basis of an experimental database containing effective dipole moments of esters, and excess molar functions of the systems: enthalpies ($H_{\text{m}}^{\text{E}}$), volumes ($V_{\text{m}}^{\text{E}}$), isobaric heat capacities ($C_{p\text{m}}^{\text{E}}$) and isochoric internal energies ($U_{V\text{m}}^{\text{E}}$) and by means of the application of the Flory model and the Kirkwood-Buff formalism. The situation of the mixtures within the $G_{\text{m}}^{\text{E}}$ (excess molar Gibbs energy) vs. $H_{\text{m}}^{\text{E}}$ diagram has also been briefly considered. Results indicate that dispersive interactions are dominant and that steric effects can explain some differences between solutions containing heptane and isomeric esters. Proximity and orientational effects are also discussed in diester + hexane mixtures. In the case of systems with a given alkane and different isomeric polar compounds, orientational effects become weaker in the order: n-alkanone > dialkyl carbonate > n-alkanoate. Results from the Kirkwood-Buff formalism indicate that the number of ester-ester interactions decreases in systems with alkyl ethanoates when the alkyl size increases and that preferential solvation between polar molecules decreases as follows: dialkyl carbonate > n-alkanone > n-alkanoate.

Ionic liquids have been suggested as new engineering fluids, namely in the area of heat transfer, as alternatives to current biphenyl and diphenyl oxide, alkylated aromatics and dimethyl polysiloxane oils, which degrade above 200 {\deg}C and pose some environmental problems. Recently, we have proposed 1-ethyl-3-methylimidazolium methanesulfonate, [$C_{2}mim$][$CH_{3}SO_{3}$], as a new heat transfer fluid, because of its thermophysical and toxicological properties. However, there are some interesting points raised in this work, namely the possibility of the existence of liquid metastability below the melting point (303 K) or second order-disorder transitions ($\lambda$-type) before reaching the calorimetric freezing point. This paper analyses in more detail this zone of the phase diagram of the pure fluid, by reporting accurate thermal-conductivity measurements between 278 and 355 K with an estimated uncertainty of 2% at a 95% confidence level. A new value of the melting temperature is also reported, $T_{melt}$ = 307.8 $\pm$ 1 K. Results obtained support liquid metastability behaviour in the solid-phase region and permit the use of this ionic liquid at a heat transfer fluid at temperatures below its melting point. Thermal conductivity models based on Bridgman theory and estimation formulas were also used in this work, failing to predict the experimental data within its uncertainty.

Continuing our study on ($CO_{2}$ + $O_{2}$) mixtures, this work reports new experimental($p$, $\rho$, $T$) data for two oxygen-rich mixtures with mole fractions $x$($O_{2}$) = (0.50 and 0.75) mol/mol, in the temperature range $T$ = (250-375) K and pressure range $p$ = (0.5-20) MPa, using a single-sinker densimeter. Experimental density data were compared to two well-established equation-of-state models: EOS-CG and GERG-2008. In the $p$, $T$-range investigated, the EOS-CG gave a better reproduction for the equimolar mixture $x$($O_{2}$) = 0.5, whereas the GERG-2008 performed significantly better for the oxygen-rich mixture $x$($O_{2}$) = 0.75. The EOS-CG generally overestimates the density, while the GERG-2008 underestimates it. This complete set of new experimental data, together with previous measurements, is used to calculate the virial coefficients $B$($T$, $x$) and $C$($T$, $x$), as well as the second interaction virial coefficient $B_{12}$($T$) for the ($CO_{2}$ + $O_{2}$) system.

Kinematic viscosities were measured for iodobenzene + n-alkane mixtures at (288.15-308.15) K and atmospheric pressure. Using our previous density data, dynamic viscosities ($\eta$), deviations in absolute viscosity ($\Delta \eta$) and quantities of viscous flow were determined. The McAllister, Grunberg-Nissan and Fang-He correlation equations and Bloomfield-Dewan's model (with residual Gibbs energies calculated using DISQUAC with interaction parameters available in the literature) were applied to iodobenzene, or 1-chloronaphthalene, or 1,2,4-trichlorobenzene, or methyl benzoate or benzene or cyclohexane + n-alkane systems. The dependence of $U_{\text{m,}V}^{\text{E}}$ (isochoric molar excess internal energy) and $\Delta \eta$ with $n$ (the number of C atoms of the n-alkane) shows that the fluidization loss of mixtures containing iodobenzene, 1,2,4-trichlorobenzene, or 1-chloronaphthalene when $n$ increases is due to a decrease upon mixing of the number of broken interactions between like molecules. The breaking of correlations of molecular orientations characteristic of longer n-alkanes may explain the decreased negative $\Delta \eta$ values of benzene mixtures with $n$ =14,16. The replacement, in this type of systems of benzene by cyclohexane leads to increased positive $\Delta \eta$ values, probably due to the different shape of cyclohexane. On the other hand, binary mixtures formed by one of the aromatic polar compounds mentioned above and a short n-alkane show large structural effects and large negative $\Delta \eta$ values. From the application of the models, it seems that dispersive interactions are dominant and that size effects are not relevant on $\eta$ values. The free volume model provides good results for most of the systems considered. Results improve when, within Bloomfield-Dewan's theory, the contribution to $\eta$ of the absolute reaction rate model is also considered.

(Iodobenzene + n-alkane) liquid mixtures have been studied experimentally, in terms of densities and speeds of sound at a pressure $p$ = 0.1 MPa and in the temperature range $T$ = (288.15 to 308.15) K, and theoretically, by the application of the Prigogine-Flory-Patterson (PFP) model. The n-alkanes considered are n-heptane, n-decane, n-dodecane, and n-tetradecane. Excess molar volumes ($V_{\text{m}}^{\text{E}}$) and excess isentropic compressibilities ($\kappa_S^{\text{E}}$) have been calculated and correlated by Redlich-Kister polynomials. ${(\partial{V_{\text{m}}^{\text{E}}}/\partial T)}_p$ curves at the same (p,T) conditions have been obtained from correlated $V_{\text{m}}^{\text{E}}$ values. From these experimental results and the knowledge of the excess molar enthalpies and volumes of mixtures containing fluorobenzene, chlorobenzene or bromobenzene with n-alkanes, we have inferred: (i) the presence of structural effects, especially important for the n-heptane mixture and less relevant for volumetric properties as the length of the n-alkane increases; and (ii) that the interactional effects on $V_{\text{m}}^{\text{E}}$ do not vary appreciably with the length of the n-alkane, so the observed $V_{\text{m}}^{\text{E}}$ variation is fundamentally determined by the corresponding variation of the contribution from structural effects. The application of the PFP model supports this interpretation, providing free volume contributions to $V_{\text{m}}^{\text{E}}$ that vary parallelly to $V_{\text{m}}^{\text{E}}$ with the length of the n-alkane, and interactional contributions that rest approximately constant independently of the n-alkane size.

The mixtures 2-propanol or 2-butanol + n-alkanone, or + acetophenone or + linear monoether, or + cyclic ether, or + linear organic carbonate, or + propylene carbonate have been investigated using thermodynamic data, and in terms of the Flory theory, and the Kirkwood-Buff integrals. The data considered are: excess molar enthalpies ($H_{\text{m}}^{\text{E}}$), volumes, entropies, and the temperature dependence of $H_{\text{m}}^{\text{E}}$. The enthalpy of the 2-alkanol-solvent interactions have been determined, and the different contributions to $H_{\text{m}}^{\text{E}}$ are discussed. It is shown that $H_{\text{m}}^{\text{E}}$ values of the 2-alkanol (fixed) + n-alkanone, or + linear carbonate mixtures change in the same manner that for n-alkanone, or linear carbonate + n-alkane (fixed) systems. In contrast, $H_{\text{m}}^{\text{E}}$ values of 2-alkanol (fixed) + linear monoether or + n-alkane mixtures change similarly. This set of results suggests that solvent-solvent interactions are determinant in systems with n-alkanone or linear carbonate, while interactions between alcohol molecules are determinant in mixtures with linear monoethers. According to the Flory model, orientational effects in systems with a given 2-alkanol become weaker in the sequence: linear monoether > linear organic carbonate > n-alkanone, and are stronger in solutions with a cyclic monoether than in those with cyclic diethers, and in systems with acetophenone or propylene carbonate than in the mixtures with the corresponding linear solvents. Results obtained from the Kirkwood-Buff integrals are consistent with these findings. The application of Flory model reveals that orientational effects are similar in systems with 1- or 2-alkanols, with the exception of solutions with linear monoethers, where such effects are stronger in mixtures containing 1-alkanols.

This work aims to address the technical aspects related to the thermodynamic characterization of natural gas mixtures blended with hydrogen for the introduction of alternative energy sources within the Power-to-Gas framework. For that purpose, new experimental speed of sound data are presented in the pressure range between (0.1 up to 13) MPa and at temperatures of (260, 273.16, 300, 325, and 350) K for two mixtures qualified as primary calibration standards: a 11 component synthetic natural gas mixture (11 M), and another low-calorific $H_{2}$-enriched natural gas mixture with a nominal molar percentage $x_{H_{2}}$ = 3%. Measurements have been gathered using a spherical acoustic resonator with an experimental expanded ($k$ = 2) uncertainty better than 200 parts in $10^{6}$ (0.02%) in the speed of sound. The heat capacity ratio as perfect-gas $\gamma_{pg}$, the molar heat capacity as perfect-gas $C_{p,m}^{pg}$, and the second $\beta_{a}$ and third $\gamma_{a}$ acoustic virial coefficients are derived from the speed of sound values. All the results are compared with the reference mixture models for natural gas-like mixtures, the AGA8-DC92 EoS and the GERG-2008 EoS, with special attention to the impact of hydrogen on those properties. Data are found to be mostly consistent within the model uncertainty in the 11 M synthetic mixture as expected, but for the hydrogen-enriched mixture in the limit of the model uncertainty at the highest measuring pressures.

This work presents phase envelope and speed of sound data for the (0.60 $CO_{2}$ + 0.40 $C_{3}H_{8}$) and (0.80 $CO_{2}$ + 0.20 $C_{3}H_{8}$) binary mixtures. Phase equilibria was measured using a cylindrical resonator working in the microwave band whereas an acoustic resonator was used for speed of sound measurements. The experimental results were compared with GERG-2008 equation of state, obtaining average absolute deviations by 0.24% in pressure for phase equilibria data and 0.025% for speed of sound data. Speed of sound values were used to derive perfect-gas heat capacities, acoustic virial coefficients, and second density virial coefficients. In addition, AGA8-DC92 equation of state performance was checked for the results derived from speeds of sound.

Ionic liquids have proved to be excellent heat transfer fluids and alternatives to common HTFs used in industries for heat exchangers and other heat transfer equipment. However, its industrial utilization depends on the cost per kg of its production, to be competitive for industrial applications with biphenyl and diphenyl oxide, alkylated aromatics, and dimethyl polysiloxane oils, which degrade above 200 {\deg}C and possess some environmental problems. The efficiency of a heat transfer fluid depends on the fundamental thermophysical properties influencing convective heat transfer (density, heat capacity, thermal conductivity, and viscosity), as these properties are necessary to calculate the heat transfer coefficients for different heat exchanger geometries. In Part 1, the thermophysical properties of pure 1-ethyl-3-methylimidazolium methanesulfonate $[C_{2}mim][CH_{3}SO_{3}]$ (CAS no. 145022-45-3), (ECOENG 110), produced by BASF, under the trade name of Basionics ST35, with an assay $\geq$97% with $\leq$0.5% water and $\leq$2% chloride ($Cl^{-}$), were presented, for temperatures slightly below room temperature and up to 355 K. In this paper, we report the thermophysical properties of mixtures of [C2mim][CH3SO3] with water, in the whole concentration range, at $P$ = 0.1 MPa. The properties measured were density and speed of sound (293.15 < $T$/K < 343.15), viscosity, electrical and thermal conductivities, refractive index (293.15 < $T$/K < 353.15), and infinite dilution diffusion coefficient of the ionic liquid in water (298.15 K).

This work aims to address the technical concerns related to the thermodynamic characterization of gas mixtures blended with hydrogen for the implementation of hydrogen as a new energy vector. For this purpose, new experimental speed of sound measurements have been done in gaseous and supercritical phases of two binary mixtures of nitrogen and hydrogen using the most accurate technique available, i.e., the spherical acoustic resonator, yielding an experimental expanded ($k$ = 2) uncertainty of only 220 parts in $10^{6}$ (0.022%). The measurements cover the pressure range between (0.5 and 20) MPa, the temperature range between (260 and 350) K, and the composition range with a nominal mole percentage of hydrogen of (5 and 10) mol%, respectively. From the speed of sound data sets, thermophysical properties that are relevant for the characterization of the mixture, namely the second $\beta_{a}$ and third $\gamma_{a}$ acoustic virial coefficients, are derived. These results are thoroughly compared and discussed with the established reference mixture models valid for mixtures of nitrogen and hydrogen, such as the AGA8-DC92 EoS, the GERG-2008 EoS, and the recently developed adaptation of the GERG-2008 EoS, here denoted GERG-$H_{2}$_improved EoS. Special attention has been given to the effect of hydrogen concentration on those properties, showing that only the GERG-$H_{2}$_improved EoS is consistent with the data sets within the experimental uncertainty in most measuring conditions.

The accurate knowledge of the thermophysical and thermodynamic properties of pure hydrogen and hydrogen mixtures plays an important role in the design and operation of many processes involved in hydrogen production, transport, storage, and use. These data are needed for the development of theoretical models necessary for the introduction of hydrogen as a promising energy carrier in the near future. A literature survey on both the available experimental data and the theoretical models associated with the thermodynamic properties of hydrogen mixtures, within the operational ranges of industrial interest for composition, temperature, and pressure, is presented in this work. Considering the available experimental data and the requirements for the design and operation of hydrogen systems, the most relevant gaps in temperature, pressure and composition are identified.

For the gradual introduction of hydrogen in the energy market, the study of the properties of mixtures of hydrogen with typical components of natural gas (NG) and liquefied petroleum gas (LPG) is of great importance. This work aims to provide accurate experimental ($p$, $\rho$, $T$) data for three hydrogen-propane mixtures with nominal compositions (amount of substance, mol/mol) of (0.95$H_{2}$ + 0.05$C_{3}H_{8}$), (0.90$H_{2}$ + 0.10$C_{3}H_{8}$), and (0.83$H_{2}$ + 0.17$C_{3}H_{8}$), at temperatures of 250, 275, 300, 325, 350, and 375 K, and pressures up to 20 MPa. A single-sinker densimeter was used to determine the density of the mixtures. Experimental density data were compared to the densities calculated from two reference equations of state: the GERG-2008 and the AGA8-DC92. Relative deviations from the GERG-2008 EoS are systematically larger than those from the AGA8-DC92. They are within the $\pm$0.5% band for the mixture with 5% of propane, but deviations are higher than 0.5% for the mixtures with 10% and 17% of propane, especially at low temperatures and high pressures. Finally, the sets of new experimental data have been processed by the application of two different statistical equations of state: the virial equation of state, through the second and third virial coefficients, $B$($T$, $x$) and $C$($T$, $x$), and the PC-SAFT equation of state.

The injection of hydrogen into the natural-gas grid is an alternative during the process of a gradual decarbonization of the heat and power supply. When dealing with hydrogen-enriched natural gas mixtures, the performance of the reference equations of state habitually used for natural gas should be validated by using high-precision experimental thermophysical data from multicomponent reference mixtures prepared with the lowest possible uncertainty in composition. In this work, we present experimental density data for an 11-compound high-calorific (hydrogen-free) natural gas mixture and for two derived hydrogen-enriched natural gas mixtures prepared by adding (10 and 20) mol-% of hydrogen to the original standard natural gas mixture. The three mixtures were prepared gravimetrically according to ISO 6142-1 for maximum precision in their composition and thus qualify for reference materials. A single-sinker densimeter was used to determine the density of the mixtures from (250-350) K and up to 20 MPa. The experimental density results of this work have been compared to the densities calculated by three different reference equations of state for natural gas related mixtures: the AGA8-DC92 EoS, the GERG-2008 EoS, and an improved version of the GERG-2008 EoS. While relative deviations of the experimental density data for the hydrogen-free natural gas mixture are always within the claimed uncertainty of the three considered equations of state, larger deviations can be observed for the hydrogen-enriched natural gas mixtures from any of the three equations of state, especially for the lowest temperature and the highest pressures.

Axion-like particles (ALPs) arise from well-motivated extensions to the Standard Model and could account for dark matter. ALP dark matter would manifest as a nearly monochromatic field oscillating at an (as of yet) unknown frequency. The frequency depends on the ALP mass, which could plausibly range from $10^{-22}$ eV/$c^2$ to $10$ eV/$c^2$. We report on a direct search for ALP dark matter through the ALP-nucleon interaction by interfering the signals of two atomic K-Rb-$^3$He comagnetometers, with one situated in Mainz, Germany, and the other in Krak\'ow, Poland. We use the ALP dark matter's spatiotemporal coherence properties assuming the standard halo model of dark matter in the Milky Way to improve the sensitivity and exclude spurious candidates. The search extends over nine orders of magnitude in ALP mass. In this range, no significant evidence of an ALP signal is found. We thus place new upper limits on the ALP-neutron and ALP-proton couplings of $g_{aNN}<10^{-5}$ GeV$^{-1}$ and $g_{aPP}<5 \times 10^{-4}$ GeV$^{-1}$ at a mass of $10^{-22}$ eV/$c^2$ and extending to a mass of $10^{-15}$ eV/$c^2$ where the upper limits reach below $g_{aNN}<10^{-9}$ GeV$^{-1}$ and $g_{aPP}<10^{-7}$ GeV$^{-1}$, respectively. For both neutron and proton couplings, this work is an improvement of up to four orders of magnitude compared to previous laboratory constraints.

In this paper, we calculate a basic strategy for several variations of a simplified version of Blackjack. In short, for these variants the player has only the two options of hit or stand, and they may only make either decision once. Other minor variations including rule modifications, changes in number of decks, alternate payout structures, and different given information about dealer's hand are also considered. An interesting theoretical result regarding the asymptotic behavior of the basic strategy and overall expectation as the number of decks increase is also proved.

Preceding a core-collapse supernova, various processes produce an increasing amount of neutrinos of all flavors characterized by mounting energies from the interior of massive stars. Among them, the electron antineutrinos are potentially detectable by terrestrial neutrino experiments such as KamLAND and Super-Kamiokande via inverse beta decay interactions. Once these pre-supernova neutrinos are observed, an early warning of the upcoming core-collapse supernova can be provided. In light of this, KamLAND and Super-Kamiokande, both located in the Kamioka mine in Japan, have been monitoring pre-supernova neutrinos since 2015 and 2021, respectively. Recently, we performed a joint study between KamLAND and Super-Kamiokande on pre-supernova neutrino detection. A pre-supernova alert system combining the KamLAND detector and the Super-Kamiokande detector was developed and put into operation, which can provide a supernova alert to the astrophysics community. Fully leveraging the complementary properties of these two detectors, the combined alert is expected to resolve a pre-supernova neutrino signal from a 15 M$_{\odot}$ star within 510 pc of the Earth, at a significance level corresponding to a false alarm rate of no more than 1 per century. For a Betelgeuse-like model with optimistic parameters, it can provide early warnings up to 12 hours in advance., Comment: Resubmitted to ApJ. 22 pages, 16 figures, for more information about the combined pre-supernova alert system, see https://www.lowbg.org/presnalarm/

Chest X-Ray (CXR) is a widely used clinical imaging modality and has a pivotal role in the diagnosis and prognosis of various lung and heart related conditions. Conventional automated clinical diagnostic tool design strategies relying on radiology reads and supervised learning, entail the cumbersome requirement of high quality annotated training data. To address this challenge, self-supervised pre-training has proven to outperform supervised pre-training in numerous downstream vision tasks, representing a significant breakthrough in the field. However, medical imaging pre-training significantly differs from pre-training with natural images (e.g., ImageNet) due to unique attributes of clinical images. In this context, we introduce Diverse Concept Modeling (DiCoM), a novel self-supervised training paradigm that leverages a student teacher framework for learning diverse concepts and hence effective representation of the CXR data. Hence, expanding beyond merely modeling a single primary label within an image, instead, effectively harnessing the information from all the concepts inherent in the CXR. The pre-trained model is subsequently fine-tuned to address diverse domain-specific tasks. Our proposed paradigm consistently demonstrates robust performance across multiple downstream tasks on multiple datasets, highlighting the success and generalizability of the pre-training strategy. To establish the efficacy of our methods we analyze both the power of learned representations and the speed of convergence (SoC) of our models. For diverse data and tasks, DiCoM is able to achieve in most cases better results compared to other state-of-the-art pre-training strategies. This when combined with the higher SoC and generalization capabilities positions DiCoM to be established as a foundation model for CXRs, a widely used imaging modality.

Tuberculosis (TB) remains a significant global health challenge, with pediatric cases posing a major concern. The World Health Organization (WHO) advocates for chest X-rays (CXRs) for TB screening. However, visual interpretation by radiologists can be subjective, time-consuming and prone to error, especially in pediatric TB. Artificial intelligence (AI)-driven computer-aided detection (CAD) tools, especially those utilizing deep learning, show promise in enhancing lung disease detection. However, challenges include data scarcity and lack of generalizability. In this context, we propose a novel self-supervised paradigm leveraging Vision Transformers (ViT) for improved TB detection in CXR, enabling zero-shot pediatric TB detection. We demonstrate improvements in TB detection performance ($\sim$12.7% and $\sim$13.4% top AUC/AUPR gains in adults and children, respectively) when conducting self-supervised pre-training when compared to fully-supervised (i.e., non pre-trained) ViT models, achieving top performances of 0.959 AUC and 0.962 AUPR in adult TB detection, and 0.697 AUC and 0.607 AUPR in zero-shot pediatric TB detection. As a result, this work demonstrates that self-supervised learning on adult CXRs effectively extends to challenging downstream tasks such as pediatric TB detection, where data are scarce., Comment: 5 pages, 3 figures, 2 tables. This paper has been accepted at IEEE ISBI 2024

Powered by the increasing predictive capabilities of machine learning algorithms, artificial intelligence (AI) systems have begun to be used to overrule human mistakes in many settings. We provide the first field evidence this AI oversight carries psychological costs that can impact human decision-making. We investigate one of the highest visibility settings in which AI oversight has occurred: the Hawk-Eye review of umpires in top tennis tournaments. We find that umpires lowered their overall mistake rate after the introduction of Hawk-Eye review, in line with rational inattention given psychological costs of being overruled by AI. We also find that umpires increased the rate at which they called balls in, which produced a shift from making Type II errors (calling a ball out when in) to Type I errors (calling a ball in when out). We structurally estimate the psychological costs of being overruled by AI using a model of rational inattentive umpires, and our results suggest that because of these costs, umpires cared twice as much about Type II errors under AI oversight.

Adaptive Systems (ASs) are capable to monitor their behavior and make adjustments when quality goals are not achieved through the MAPE-K, a widely recognized reference model that offers abstractions for designing ASs. By making these abstractions evident in the system structure, numerous benefits emerge, particularly in terms of enhancing the architecture's maintenance and comprehensibility. However, it is observed that many existing ASs are not designed in accordance with MAPE-K, causing these abstractions to remain hidden in their architecture. To address this issue, Architectural Conformance Checking (ACC) emerges as a valuable technique for verifying whether the current architecture (CA) of a system adheres to the rules prescribed by the planned architecture (PA) or a reference model, such as MAPE-K. In this paper, we present REMEDY, a domain-specific approach that encompasses the specification of the planned adaptive architecture based on the MAPE-K reference model, the recovery of the current adaptive architecture, the conformance checking process, and architecture visualizations. Furthermore, our approach is specifically tailored for ASs, incorporating well-known rules from the MAPE-K model. The evaluation of the REMEDY DSL involves a comparison with a general-purpose DSL, and the results demonstrate improvements in productivity. REMEDY facilitates the identification and correction of architectural non-conformance issues, thereby enhancing the overall quality of adaptive systems., Comment: Submitted to JSERD

We present a mean field solution of the dynamics of a Greenberg-Hastings neural network with both excitatory and inhibitory units. We analyse the dynamical phase transitions that appear in the stationary state as the model parameters are varied. Analytical solutions are compared with numerical simulations of the microscopic model defined on a fully connected network. We found that the stationary state of this system exhibits a first order dynamical phase transition (with the associated hysteresis) when the fraction of inhibitory units $f< f_t \leq 1/2$, even for a finite system. In finite systems, when $f > f_t$ the first order transition is replaced by a pseudo critical one, namely a continuous crossover between regions of low and high activity that resembles the finite size behaviour of a continuous phase transition order parameter. However, in the thermodynamic limit, we found that $f_t\to 1/2$ and the activity for $f\geq f_t$ becomes negligible for any value of $T>0$, while the first order transition for $f

The origin of thick disks and their evolutionary connection with thin disks are still a matter of debate. We provide new insights into this topic by connecting the stellar populations of thick disks at redshift $z=0$ with their past formation and growth, in 24 Milky Way-mass galaxies from the AURIGA zoom-in cosmological simulations. We projected each galaxy edge on, and decomposed it morphologically into two disk components, in order to define geometrically the thin and the thick disks as usually done in observations. We produced age, metallicity and [Mg/Fe] edge-on maps. We quantified the impact of satellite mergers by mapping the distribution of ex-situ stars. Thick disks are on average $\sim 3$~Gyr older, $\sim 0.25$~dex more metal poor and $\sim 0.06$~dex more [Mg/Fe]-enhanced than thin disks. Their average ages range from $\sim 6$ to $\sim 9$~Gyr, metallicities from $\sim -0.15$ to $\sim 0.1$~dex, and [Mg/Fe] from $\sim 0.12$ to $\sim 0.16$~dex. These properties are the result of an early initial in-situ formation, followed by a later growth driven by the combination of direct accretion of stars, some in-situ star formation fueled by mergers, and dynamical heating of stars. The balance between these processes varies from galaxy to galaxy. Mergers play a key role in the mass assembly of thick disks, contributing an average accreted mass fraction of $\sim 22$\% in the analyzed thick-disk dominated regions. In two galaxies, about half of the geometric thick-disk mass was directly accreted. While primordial thick disks form at high redshift in all galaxies, young metal-rich thin disks, with much lower [Mg/Fe] abundances, start to form later but at different times (higher or lower redshift) depending on the galaxy. We conclude that thick disks result from the interplay of external processes with the internal evolution of the galaxy., Comment: 57 pages, 43 figures, 3 tables; Published in Astronomy & Astrophysics

The emergent abilities of Large Language Models (LLMs), which power tools like ChatGPT and Bard, have produced both excitement and worry about how AI will impact academic writing. In response to rising concerns about AI use, authors of academic publications may decide to voluntarily disclose any AI tools they use to revise their manuscripts, and journals and conferences could begin mandating disclosure and/or turn to using detection services, as many teachers have done with student writing in class settings. Given these looming possibilities, we investigate whether academics view it as necessary to report AI use in manuscript preparation and how detectors react to the use of AI in academic writing.

The merger history of a galaxy is thought to be one of the major factors determining its internal dynamics, with galaxies having undergone different types or mergers (e.g. dry, minor or major mergers) predicted to show different dynamical properties. We study the instantaneous orbital distribution of galaxies in the Eagle simulation, colouring the orbits of the stellar particles by their stellar age, in order to understand whether stars form in particular orbits (e.g. in a thin or thick disc). We first show that Eagle reproduces well the observed stellar mass fractions in different stellar orbital families as a function of stellar mass and spin parameter at z = 0. We find that the youngest stars reside in a thin disc component that can extend to the very inner regions of galaxies, and that older stars have warmer orbits, with the oldest ones showing orbits consistent with both hot and counter-rotating classifications, which is consistent with the trend found in the Milky-Way and other disc galaxies. We also show that counter-rotating orbits trace galaxy mergers - in particular dry mergers, and that in the absence of mergers, counter-rotating orbits can also be born from highly misaligned gas accretion that leads to star formation., Comment: Submitted to MNRAS, 21 pages

The extend of tool wear significantly affects blanking processes and has a decisive impact on product quality and productivity. For this reason, numerous scientists have addressed their research to wear monitoring systems in order to identify or even predict critical wear at an early stage. Existing approaches are mainly based on indirect monitoring using time series, which are used to detect critical wear states via thresholds or machine learning models. Nevertheless, differentiation between types of wear phenomena affecting the tool during blanking as well as quantification of worn surfaces is still limited in practice. While time series data provides partial insights into wear occurrence and evolution, direct monitoring techniques utilizing image data offer a more comprehensive perspective and increased robustness when dealing with varying process parameters. However, acquiring and processing this data in real-time is challenging. In particular, high dynamics combined with increasing strokes rates as well as the high dimensionality of image data have so far prevented the development of direct image-based monitoring systems. For this reason, this paper demonstrates how high-resolution images of tools at 600 spm can be captured and subsequently processed using semantic segmentation deep learning algorithms, more precisely Fully Convolutional Networks (FCN). 125,000 images of the tool are taken from successive strokes, and microscope images are captured to investigate the worn surfaces. Based on findings from the microscope images, selected images are labeled pixel by pixel according to their wear condition and used to train a FCN (U-Net).

We propose and demonstrate a general method to calibrate the frequency-dependent response of self-compensating noble-gas-alkali-metal comagnetometers to arbitrary spin perturbations. This includes magnetic and nonmagnetic perturbations like rotations and exotic spin interactions. The method is based on a fit of the magnetic field response to an analytical model. The frequency-dependent response of the comagnetometer to arbitrary spin perturbations can be inferred using the fit parameters. We demonstrate the effectiveness of this method by comparing the inferred rotation response to an experimental measurement of the rotation response. Our results show that experiments relying on zero-frequency calibration of the comagnetometer response can over- or under-estimate the comagnetometer sensitivity by orders of magnitude over a wide frequency range. Moreover, this discrepancy accumulates over time as operational parameters tend to drift during comagnetometer operation. The demonstrated calibration protocol enables accurate prediction and control of comagnetometer sensitivity to, for example, ultralight bosonic dark-matter fields coupling to electron or nuclear spins as well as accurate monitoring and control of the relevant system parameters.

Tuberculosis (TB) is still recognized as one of the leading causes of death worldwide. Recent advances in deep learning (DL) have shown to enhance radiologists' ability to interpret chest X-ray (CXR) images accurately and with fewer errors, leading to a better diagnosis of this disease. However, little work has been done to develop models capable of diagnosing TB that offer good performance while being efficient, fast and computationally inexpensive. In this work, we propose LightTBNet, a novel lightweight, fast and efficient deep convolutional network specially customized to detect TB from CXR images. Using a total of 800 frontal CXR images from two publicly available datasets, our solution yielded an accuracy, F1 and area under the ROC curve (AUC) of 0.906, 0.907 and 0.961, respectively, on an independent test subset. The proposed model demonstrates outstanding performance while delivering a rapid prediction, with minimal computational and memory requirements, making it highly suitable for deployment in handheld devices that can be used in low-resource areas with high TB prevalence. Code publicly available at https://github.com/dani-capellan/LightTBNet., Comment: 5 pages, 3 figures, 3 tables. This paper has been accepted at ISBI 2023

Markoff mod-$p$ graphs are conjectured to be connected for all primes $p$. In this paper, we use results of Chen and Bourgain, Gamburd, and Sarnak to confirm the conjecture for all $p > 3.448\cdot10^{392}$. We also provide a method that quickly verifies connectivity for many primes below this bound. In our study of Markoff mod-$p$ graphs we introduce the notion of \emph{maximal divisors} of a number. We prove sharp asymptotic and explicit upper bounds on the number of maximal divisors, which ultimately improves the Markoff graph $p$-bound by roughly 140 orders of magnitude as compared with an approach using all divisors.

Quantifying the contribution of mergers to the stellar mass of galaxies is key for constraining the mechanisms of galaxy assembly across cosmic time. However, the mapping between observable galaxy properties and merger histories is not trivial: cosmological galaxy simulations are the only tools we have for calibration. We study the robustness of a simulation-based inference of the ex-situ stellar mass fraction of nearby galaxies to different observables -- integrated and spatially-resolved -- and to different galaxy formation models -- IllustrisTNG and EAGLE -- with Machine Learning. We find that at fixed simulation, the fraction of accreted stars can be inferred with very high accuracy, with an error $\sim5$ per cent (10 per cent) from 2D integral-field spectroscopic maps (integrated quantities) throughout the considered stellar mass range. A bias (> 5 per cent) and an increase in scatter by a factor of 2 are introduced when testing with a different simulation, revealing a lack of generalization to distinct galaxy-formation models. Interestingly, upon using only stellar mass and kinematics maps in the central galactic regions for training, we find that this bias is removed and the ex-situ stellar mass fraction can be recovered in both simulations with < 15 per cent scatter, independently of the training set's origin. This opens up the door to a potential robust inference of the accretion histories of galaxies from existing Integral Field Unit surveys, such as MaNGA, covering a similar field of view (FOV) and containing spatially-resolved spectra for tens of thousands of nearby galaxies., Comment: 23 pages, 15 figures. Accepted for publication in MNRAS