Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling of radiation heat transfer in the dense-bed flow of solid pyrolysis in indirectly heated rotary kilns

This work presents the further development and the validation of the Discrete Ordinates Model for thermal radiation which is implemented in OpenFOAM ® for application to packed beds of biomass particles. This radiation model is an important part of a more comprehensive model which simulates the thermal conversion of discrete phase (here for instance wet biomass particles) which flows continuously inside an indirectly heated rotary kiln. The comprehensive Eulerian-Lagrangian model integrates three-dimensional, time-resolved simulation of the essential chemical and physical processes occurring within and in-between the moving bed of particles. This is realized by combining the particle collision models for non-reactive dense flows with models for heat transport, phase change and chemical reaction for multiphase reacting flow in the framework of OpenFOAM ® . For the thermal treatment of solid particles, convection and radiation heat transfer methods couple the energy exchange between the reactor wall, gas- and disperse phase. The original implementation of the finite volume Discrete Ordinate Model (fvDOM) valid for a dilute particulate phase neglects the effect of local opacity due to the existence of individual particles. However, in the present application, a dense-packed bed of the particulate phase exists in the reactor. Therefore, in this study, this direction-based radiation model is adjusted for a computational cell with arbitrary particle volume fractions. To validate the results with the present thermal radiation model, first a simple test case with heating the bed of particles from the top of the domain is carried out. A second test relates to a laboratory-scale reactor. The results of the improved fvDOM are compared to the original implementation of OpenFOAM ®
Global Nitrogen and Sulfur Budgets Using a Measurement- 1 Model Fusion Approach

17 Global reactive nitrogen (N) deposition has more than tripled since 1860 and is expected to 18 remain high due to land use changes and fossil fuel consumption. We update the 2010 global 19 deposition budget for nitrogen and sulfur with new regional wet deposition measurements from 20 Asia, improving the ensemble results of eleven global chemistry transport models from the 21 second phase of the United Nation’s Task Force on Hemispheric Transport of Air Pollution 22 (HTAP-II). The observationally adjusted global N deposition budget is 130 Tg-N, representing a 23 10% increase and the adjusted global sulfur deposition budget is 80 Tg-S, representing no 24 change. Our study demonstrates that a global measurement-model fusion approach can 25 substantially improve N and S deposition model estimates at a regional scale and represents a 26 step forward toward the World Meteorological Organization’s goal of global fusion products for 27 accurately mapping harmful air pollution.
Embedding strategies for adiabatic quantum computation

Quantum Annealing (QA) is an alternative to gate based Quantum Computation (QC) to solve problems not efficiently tractable on classical devices. Right now, QA is advantageous over QC in the noisy intermediate-scale quantum (NISQ) era for its lesser need of error correction codes and the resource overhead they suppose. However, hardware limitations in terms of connectivity and feasible interactions create incompatibilities between the chip and the structure of the problem, which leads to what is called the graph embedding problem. To circumvent this obstacle, we first analyse the current solutions based on heuristic algorithms and their limitations. We then explore the potential of a digital assisted annealing (DaA) approach. The novelty of this technique relies on the fact that the state generated by the quantum annealer is used as the initial state of the variational circuit, the role of which is to approach a target solution the annealer could not reach by itself due to its hardware limitations. We complete this thesis with a detailed study on the performance of our approach for different scenarios and branches we would like to explore.
Connecting X-ray absorption and 21 cm neutral hydrogen absorption in obscured radio AGN

Many radio galaxies show the presence of dense and dusty gas near the active nucleus. This can be traced by both 21 cm H I absorption and soft X-ray absorption, offering new insight into the physical nature of the circumnuclear medium of these distant galaxies. To better understand this relationship, we investigate soft X-ray absorption as an indicator for the detection of associated H I absorption, as part of preparation for the First Large Absorption Survey in H I to be undertaken with the Australian Square Kilometre Array Pathfinder (ASKAP). We present the results of our pilot study using the Boolardy Engineering Test Array, a precursor to ASKAP, to search for new absorption detections in radio sources brighter than 1 Jy that also feature soft X-ray absorption. Based on this pilot survey, we detected H I absorption towards the radio source PKS 1657−298 at a redshift of z = 0.42. This source also features the highest X-ray absorption ratio of our pilot sample by a factor of 3, which is consistent with our general findings that X-ray absorption predicates the presence of dense neutral gas. By comparing the X-ray properties of active galactic nuclei with and without detection of H I absorption at radio wavelengths, we find that X-ray hardness ratio and H I absorption optical depth are correlated at a statistical significance of 4. 71σ. We conclude by considering the impact of these findings on future radio and X-ray absorption studies.
THE EFFECT OF pH ON THE STABILITY OF SMECTITE L'EFFET DU pH SUR LA STABILITE DE LA SMECTITE

The hydrothermal stability of smectite at temperatures less than 275 C was investigated experimentally over a range of pH values. In the near-neutral pH region, the smectite to illite conversion predominated; in the mildly acid region, there was extensive formation of aluminum hydroxy interlayers in the clay; and In the alkaline region, framework silicates (feldspar and zeolites) were produced. The geological evidence for these reactions is also reviewed. Atomic Energy of Canada Limited Whiteshell Nuclear Research Establishment Plnawa, Manitoba ROE 1L
Microphysics of liquid water in sub-10 nm ultraﬁne aerosol particles

Ultraﬁne aerosol particles with sizes smaller than 50 nm have been shown in recent studies to serve as a large source of cloud condensation nuclei that can promote additional cloud droplet formation under supersat-uration conditions. Knowledge of the microphysics of liquid water in these droplets remains limited, particularly in the sub-10 nm particle size range, due to experimental and theoretical challenges associated with the complexity of aerosol components and the small length scales of interest (e.g., difﬁculty of precisely sampling the liquid–air interface, questionable validity of mean-ﬁeld theoretical representations). Here, we carried out molecular dynamics (MD) simulations of aerosol particles with diameters between 1 and 10 nm and characterized atomistic-level structure and water dynamics in well-mixed and phase-separated systems with different particle sizes, NaCl salinities, and pimelic acid (PML) organic surface loadings as a function of distance from the time-averaged Gibbs dividing interface or instantaneous water–air interface. We deﬁne a sphericity factor ( φ ) that can shed light on the phase-mixing state of nanodroplets, and we reveal an unexpected dependence of mixing state on droplet size. Our results also evidence an ion concentration enhancement in ultraﬁne aerosols, which should modulate salt nucleation kinetics in sub-10 nm droplets, and provide detailed characterization of the inﬂuence of droplet size on surface tension and on water self-diffusivity near the interface. Analysis of water evaporation free energy and water activity demonstrates the validity of the Kelvin equation and Köhler theory at droplet sizes larger than 4 nm under moderate salinities and organic loadings and the need for further extension to account for ion concentration enhancement in sub-10 nm aerosols, droplet-size-dependent phase separation effects, and a sharp decrease in the cohesiveness of liquid water in sub-4 nm droplets. Finally, we show that an idealized fractional surface coating factor ( f s ) can be used to categorize and reconcile water accommodation coefﬁcients ( α ∗ ) observed in MD simulations and experimental results in the presence of organic coatings, and we resolve the droplet size dependence of α ∗ .
On some new paranormed sequence spaces of fuzzy numbers defined by Orlicz functions and statistical convergence

Abstract In this paper we introduce the concept of strongly λ(p) convergence of fuzzy numbers with respect to an Orlicz function and examine some properties of the resulting sequence spaces and λ(p) – statistical convergence. It is also shown that if a sequence of fuzzy numbers is strong λ(p) convergent with respect to an Orlicz function then it is λ(p) – statistically convergent.
Weighting eﬃcient Accuracy and Minimum Sensitivity for evolving multi-class classiﬁers

Recently, a multi-objective Sensitivity-Accuracy based methodology has been proposed for building classiﬁers for multi-class problems. This technique is especially suitable for imbalanced and multi-class datasets. Moreover, the high computational cost of multi-objective approaches is well known so more eﬃcient alternatives must be explored. This paper presents an eﬃcient alternative to the Pareto based solution when considering both Minimum Sensitivity and Accuracy in multi-class classiﬁers. Alternatives are implemented by extending the Evolutionary Extreme Learning Machine algorithm for training artiﬁcial neural networks. Experiments were performed to select the best option after considering alternative proposals and related methods. Based on the experiments, this methodology is competitive in Accuracy, Minimum Sensitivity and eﬃciency.
Bayesian Approach to Investigating Supersymmetric Models

With Bayesian statistics, constrained and phenomenological supersymmetric models are examined and compared with the strongest contemporary experimental results, including the Higgs discovery, Large Hadron Collider (LHC) searches for supersymmetry, dark matter relic density measurements, rare decays and electroweak precision tests. Preliminary introductions to relevant supersymmetry phenomenology, Bayesian statistics and algorithms, and comprehensive descriptions of likelihood functions for experimental results are provided. Likelihood functions for searches for supersymmetry at the LHC are built at the event-level from Monte-Carlo simulations, including CMS 35/pb, 1.1/fb and 4.4/fb searches. Five studies of different supersymmetric models, including the Constrained Minimal Supersymmetric Standard Model (CMSSM), the Non-Universal Higgs Model, the Non-Universal Gaugino Model and phenomenological MSSM models, and for different states of knowledge, including hypothetical benchmark scenarios, and pre- and post-Higgs discovery, are comprehensively presented. Credible regions for model parameters, Bayesian evidences, best-fit points and p-values are calculated in each study. We demonstrate that the CMSSM's soft-breaking masses could be determined from LHC searches with 100/fb at 14 TeV for a specific favourable choice of parameters consistent with all current constraints. The favoured soft-breaking scale in the CMSSM increases to M_SUSY ~ 1 TeV with stau-coannihilation and M_SUSY ~ 3 TeV with A-funnel annihilation. Agreement with the anomalous magnetic moment of the muon (da_mu) is difficult in the CMSSM, and, without da_mu, mu<0 is insignificantly preferred. The CMSSM struggles to simultaneously agree with experimental results - its p-value is ostensibly significant. In our pMSSM model with nine free parameters, if the mass of the lightest neutralino is between 200 GeV and 500 GeV, agreement with da_mu and electroweakino searches at the LHC is possible.
A Search for H i Lyα Counterparts to Ultrafast X-Ray Outflows

Prompted by the H i Lyα absorption associated with the X-ray ultrafast outflow at −17,300 km s−1 in the quasar PG 1211+143, we have searched archival UV spectra at the expected locations of H i Lyα absorption for a large sample of ultrafast outflows identified in XMM-Newton and Suzaku observations. Sixteen of the X-ray outflows have predicted H i Lyα wavelengths falling within the bandpass of spectra from either the Far Ultraviolet Spectroscopic Explorer or the Hubble Space Telescope, although none of the archival observations were simultaneous with the X-ray observations in which ultrafast X-ray outflows (UFOs) were detected. In our spectra broad features with FWHM of 1000 km s−1 have 2σ upper limits on the H i column density of generally ≲2 × 1013 cm−2. Using grids of photoionization models covering a broad range of spectral energy distributions (SEDs), we find that producing Fe xxvi Lyα X-ray absorption with equivalent widths >30 eV and associated H i Lyα absorption with requires total absorbing column densities and ionization parameters log ξ ≳ 3.7. Nevertheless, a wide range of SEDs would predict observable H i Lyα absorption if ionization parameters are only slightly below peak ionization fractions for Fe xxv and Fe xxvi. The lack of Lyα features in the archival UV spectra indicates that the UFOs have very high ionization parameters, that they have very hard UV-ionizing spectra, or that they were not present at the time of the UV spectral observations owing to variability.
The Least-Squares Finite Element Method: Theory and Applications in Computational Fluid Dynamics and Electromagnetics

Contents (preliminary): I. The Basic Concept of the LSFEM. - 1. Introduction. - 2. The first-order Scalar Differential Equation in One-Dimension. - 3. The First-Order System in One-Dimension. - II. Fundamentals of the LSFEM. - 4. Fundamentals of the LSFEM. - 5. The Div-Curl System. - 6. The Div-Curl-Grad System. - III. The LSFEM in Fluid Dynamics. - 7. Inviscid Irrotational Flows. - 8. Incompressible Viscous Flows. - 9. Convective Transport. - 10. Rotational Inviscid Flows. - 11. Two-face Flows. - 12. Compressible Viscous Flows. - 13. High-Speed Compressible Flows. - 14. P-Version Least-Squares Finite Element Method. - IV. The LSFEM in Electromagnetics. - 15. Electromagnetics. - V. Solution of the Discrete Equations 16. Iterative Methods for Solving Linear Systems of Equations. - Appendix A: Operation on Vectors. - B. Green's Formula. - C. Finite Element Interpolation. - D. The Lax-Milgram Theory. - References. - Index.
Reporting of medication adherence in randomized controlled trials of pharmacological interventions for SARS-CoV-2: a cross-sectional analysis

Background: Adherence to pharmacological interventions in clinical trials is crucial for correct estimation of beneficial and adverse effects, including trials of SARS-CoV-2. The Template for Intervention Description and Replication (TIDieR), a 12-item extension of the Consolidated Standards of Reporting Trials (CONSORT) reporting guidelines, includes two items (11 and 12) that address intervention adherence reporting in trial publications. Objective: To assess compliance with TIDieR items 11 and 12 of randomised controlled trials (RCTs) of interventions in SARS-CoV-2 infection published in five selected journals during 2021. Methods: We assessed SARS-CoV-2 pharmacological RCTs published in the Annals of Internal Medicine, The BMJ, JAMA, The Lancet, and The New England Journal of Medicine in 2021 for compliance with TIDieR items 11 and 12. Item 11 was assessed in two parts: 11a how intervention adherence was assessed; 11b if any strategies were used to maintain or improve how intervention adherence was maintained or improved. Item 12 assessed the extent to which the intervention was delivered as planned. We calculated raw adherence and proportional (weighted) adherence for pharmacological and comparator interventions where available. Results: We found 75 eligible RCTs, of which 28 (37%) reported results related to SARS-CoV-2 vaccinations. Compliance with items 11a and 12 could be assessed in 71 of these 75. Of those 71 RCTs, 37 (52%, 95% confidence interval 40 to 64%) were compliant with reporting of item 11a. Seven RCTs had a strategy to assess compliance with item 11b, and only three (43%, 9 to 82%) of those complied with item 11b reporting. Of the 71 RCTs, 70 complied with reporting of item 12. Only one of the 71 RCTs (1.4%, 0 to 7.6%) fully complied with TIDieR items 11a, 11b, and 12. Compliance varied across journals. Conclusions: RCTs of SARS-CoV-2 pharmacological interventions published in high-impact medical journals complied variably with reporting of intervention adherence, even though the journals endorse CONSORT. The implications for interpretation, application, and replication of findings based on these publications warrant consideration.
Markov moment problem and approximation

In this paper, we start by recalling some polynomial approximation results on unbounded subsets of Rn. Namely one approximates nonnegative continuous functions with compact support by sums of tensor products of positive polynomials on the positive semiaxes, in each separate variable. For such polynomials, expression in terms of sums of squares is well known. This method leads to characterization of the existence of the solution of the multidimensional Markov moment problem in terms of quadratic mappings. Two other applications related to the Markov moment problem are considered. Here the main ingredients of the proofs are extension of linear operator’s results, involving two constraints.
Geogcn: Geometric Dual-Domain Graph Convolution Network For Point Cloud Denoising

We propose GeoGCN, a novel geometric dual-domain graph convolution network for point cloud denoising (PCD). Beyond the traditional wisdom of PCD, to fully exploit the geometric information of point clouds, we define two kinds of surface normals, one is called Real Normal (RN), and the other is Virtual Normal (VN). RN preserves the local details of noisy point clouds while VN avoids the global shape shrinkage during denoising. GeoGCN is a new PCD paradigm that, 1) first regresses point positions by spatial-based GCN with the help of VNs, 2) then estimates initial RNs by performing Principal Component Analysis on the regressed points, and 3) finally regresses fine RNs by normal-based GCN. Unlike existing PCD methods, GeoGCN not only exploits two kinds of geometry expertise (i.e., RN and VN) but also benefits from training data. Experiments validate that GeoGCN outperforms SOTAs in terms of both noise-robustness and local-and-global feature preservation.
Empirical Macroeconomics and DSGE Modeling in Statistical Perspective

Dynamic stochastic general equilibrium (DSGE) models have been an ubiquitous, and controversial, part of macroeconomics for decades. In this paper, we approach DSGEs purely as statstical models. We do this by applying two common model validation checks to the canonical Smets and Wouters (2007) DSGE: (1) we simulate the model and see how well it can be estimated from its own simulation output, and (2) we see how well it can seem to ﬁt nonsense data. We ﬁnd that (1) even with centuries’ worth of data, the model remains poorly estimated, and (2) when we swap series at random, so that (e.g.) what the model gets as the inﬂation rate is really hours worked, what it gets as hours worked is really investment, etc., the ﬁt is often only slightly impaired, and in a large percentage of cases actually improves (even out of sample). Taken together, these ﬁndings cast serious doubt on the meaningfulness of parameter estimates for this DSGE, and on whether this speciﬁcation represents anything structural about the economy. Constructively, our approaches can be used for model validation by anyone working with macroeconomic time series.
Token-level Sequence Labeling for Spoken Language Understanding using Compositional End-to-End Models

End-to-end spoken language understanding (SLU) systems are gaining popularity over cascaded approaches due to their simplicity and ability to avoid error propagation. However, these systems model sequence labeling as a sequence prediction task causing a divergence from its well-established token-level tagging formulation. We build compositional end-to-end SLU systems that explicitly separate the added complexity of recognizing spoken mentions in SLU from the NLU task of sequence labeling. By relying on intermediate decoders trained for ASR, our end-to-end systems transform the input modality from speech to token-level representations that can be used in the traditional sequence labeling framework. This composition of ASR and NLU formulations in our end-to-end SLU system offers direct compatibility with pre-trained ASR and NLU systems, allows performance monitoring of individual components and enables the use of globally normalized losses like CRF, making them attractive in practical scenarios. Our models outperform both cascaded and direct end-to-end models on a labeling task of named entity recognition across SLU benchmarks.
Sequential hypothesis testing for axion haloscopes

The goal of this paper is to introduce a novel likelihood-based inferential framework for axion haloscopes which is valid under the commonly applied"rescanning"protocol. The proposed method enjoys short data acquisition times and a simple tuning of the detector configuration. Local statistical significance and power are computed analytically, avoiding the need of burdensome simulations. Adequate corrections for the look-elsewhere effect are also discussed. The performance of our inferential strategy is compared with that of a simple method which exploits the geometric probability of rescan. Finally, we exemplify the method with an application to a HAYSTAC type axion haloscope.
Confident Approximate Policy Iteration for Efficient Local Planning in qπ-realizable MDPs

We consider approximate dynamic programming in $\gamma$-discounted Markov decision processes and apply it to approximate planning with linear value-function approximation. Our first contribution is a new variant of Approximate Policy Iteration (API), called Confident Approximate Policy Iteration (CAPI), which computes a deterministic stationary policy with an optimal error bound scaling linearly with the product of the effective horizon $H$ and the worst-case approximation error $\epsilon$ of the action-value functions of stationary policies. This improvement over API (whose error scales with $H^2$) comes at the price of an $H$-fold increase in memory cost. Unlike Scherrer and Lesner [2012], who recommended computing a non-stationary policy to achieve a similar improvement (with the same memory overhead), we are able to stick to stationary policies. This allows for our second contribution, the application of CAPI to planning with local access to a simulator and $d$-dimensional linear function approximation. As such, we design a planning algorithm that applies CAPI to obtain a sequence of policies with successively refined accuracies on a dynamically evolving set of states. The algorithm outputs an $\tilde O(\sqrt{d}H\epsilon)$-optimal policy after issuing $\tilde O(dH^4/\epsilon^2)$ queries to the simulator, simultaneously achieving the optimal accuracy bound and the best known query complexity bound, while earlier algorithms in the literature achieve only one of them. This query complexity is shown to be tight in all parameters except $H$. These improvements come at the expense of a mild (polynomial) increase in memory and computational costs of both the algorithm and its output policy.