Modern and next generation supernova cosmology analyses rely on end to end simulations to train photometric classifiers, characterise selection effects, validate light curve models, and calibrate distance bias corrections. Standard closure tests based on global Hubble diagram summaries can miss multivariate structure that remains in post correction residuals. We introduce a supervised machine learning closure audit that tests whether measured observables can predict the bias corrected Hubble residuals Δμ. We apply the audit to LSST Type Ia supernova simulations from two independent analyses: the M23 mock data sets of Mitra et al. (2023), with spectroscopic redshift and photometric redshift samples, and the predominantly photometric LSST like M25 simulation of Mitra et al. (2025). Standard one dimensional Redshift binned diagnostics explain <1

Observations of Type Ia supernovae (SNe Ia), which probe the late Universe, together with baryon acoustic oscillations (BAO) and the cosmic microwave background (CMB), which probe the intermediate and early epochs, provide complementary constraints on the expansion history of the Universe. In this work, we forecast constraints on dark energy and other extensions to the standard cosmological model by combining the SN Ia sample expected from the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST), data from current and forthcoming CMB surveys, and BAO measurements from the Dark Energy Spectroscopic Instrument (DESI). For the CMB, we use temperature, polarization, and lensing power spectra (TT/EE/TE/phi phi) from the South Pole Telescope, the planned Advanced Simons Observatory, and a CMB-S4-like experiment. We derive constraints on Lambda CDM and its extensions involving the dark energy equation-of-state parameters (w0, wa) and the sum of neutrino masses & sum;m nu using a Markov Chain Monte Carlo (MCMC) sampling framework. We find that the LSST Year 3 SN Ia sample can improve upon the DES Year 5 dark energy constraints by a factor of 2-2.5 & times;, with the gains driven primarily by the significantly higher SN Ia density in the LSST sample. Similarly, DESI-DR3 shows up to a 1.8 & times; improvement on dark energy parameters over DR2, driven largely by the substantial increase in the low-redshift sample. Combining CMB with LSST-Y3-SN Ia and DESI-DR3-BAO yields sigma(w0) = 0.028 and sigma(wa) = 0.11 for w0waCDM cosmology with the results being largely independent of the CMB dataset. The constraints weaken by 10%-30% when freeing & sum;m nu and spatial curvature. Moreover, the joint analysis of the three datasets can enable a 2 sigma-3 sigma detection of & sum;m nu.

Observations of Type Ia supernovae (SNIa), baryon acoustic oscillations (BAO), and the cosmic microwave background (CMB), which probe the late-, intermediate-, and early-universe epochs, respectively, provide complementary constraints on the expansion history of the Universe. In this work, we forecast constraints on dark energy and other extensions to the standard cosmological model by combining the SNIa sample expected from the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST), data from current and forthcoming CMB surveys, and BAO measurements from the Dark Energy Spectroscopic Instrument (DESI). For the CMB, we use temperature, polarization, and lensing power spectra (TT/EE/TE/ϕϕ) from South Pole Telescope, the planned Advanced Simons Observatory, and a CMB-S4-like experiment. We derive constraints on Λ CDM and its extensions involving the dark energy equation of state parameters (w_0, w_a) and the sum of neutrino masses ∑ m_ν, using a Markov Chain Monte Carlo (MCMC) sampling framework. We find that the LSST Year-3 SNIa sample can improve upon the DES Year-5 dark energy constraints by a factor of ×2-×2.5, with the gains driven primarily by the significantly higher SNIa density in the LSST sample. Similarly, DESI-DR3 shows up to a ×1.8 improvement on dark energy parameters over DR2, driven largely by the substantial increase in low-redshift sample. Combining CMB with LSST-Y3-SNIa and DESI-DR3-BAO yields σ(w_0) = 0.028 and σ(w_a) = 0.11 for w_0 w_a CDM cosmology with the results being largely independent of the CMB dataset. The constraints weaken by 10

The upcoming Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) is expected to discover nearly a million Type Ia supernovae (SNeIa), offering an unprecedented opportunity to constrain dark energy. The vast majority of these events will lack spectroscopic classification and redshifts, necessitating a fully photometric approach to maximize cosmology constraining power. We present detailed simulations based on the Extended LSST Astronomical Time Series Classification Challenge (ELAsTiCC), and a cosmological analysis using photometrically classified SNeIa with host galaxy photometric redshifts. This dataset features realistic multi-band light curves, non-SNIa contamination, host mis-associations, and transient-host correlations across the high-redshift Deep Drilling Fields (DDF) ( 50 deg^2). We also include a spectroscopically confirmed low-redshift sample based on the Wide Fast Deep (WFD) fields. We employ a joint SN+host photometric redshift fit, a neural network based photometric classifier (SCONE), and BEAMS with Bias Corrections (BBC) methodology to construct a bias-corrected Hubble diagram. We produce statistical + systematic covariance matrices, and perform cosmology fitting with a prior using Cosmic Microwave Background constraints. We fit and present results for the wCDM dark energy model, and the more general Chevallier-Polarski-Linder (CPL) w0wa model. With a simulated sample of 6000 events, we achieve a Figure of Merit (FoM) value of about 150, which is significantly larger than the DESVYR FoM of 54. Averaging analysis results over 25 independent samples, we find small but significant biases indicating a need for further analysis testing and development.

We present FlowSN, a statistical framework using simulation-based inference (SBI) with normalizing flows to account for selection effects in observational astronomy. Failure to account for selection effects can lead to biased inference on global parameters. An example is Malmquist bias, where detection limits result in a sample skewed towards brighter objects. In Type Ia supernova (SN Ia) cosmology, these selection effects can systematically shift the inferred posterior distributions of cosmological parameters, necessitating the development of robust statistical frameworks to account for the biases. SBI enables us to implicitly learn probability distributions that are analytically intractable to calculate. In this work, we introduce a novel approach that employs a normalizing flow to learn the non-analytic selected SN likelihood for a given survey from forward simulations, independent of the assumed cosmological model. The resulting likelihood approximation is incorporated into a hierarchical Bayesian framework, and posterior sampling is performed using Hamiltonian Monte Carlo to obtain constraints on cosmological parameters conditioned on the observed data. The modular learnt likelihood approximation can be reused without retraining to evaluate different cosmological models, providing a key advantage over other SBI approaches. We demonstrate the performance of this methodology by training and testing the SBI technique using realistic LSST-like SNANA simulations for the first time. Our FlowSN approach yields accurate posterior estimates on cosmological parameters, including the dark energy equation of state w(0) , that are an order of magnitude less biased than those obtained with conventional techniques and also exhibit improved frequentist calibration.

In this analysis we apply a model-independent framework to test the flat ΛCDM cosmology using simulated SNIa data from the upcoming Legacy Survey of Space and Time (LSST) and combined with simulated Dark Energy Spectroscopic Instrument (DESI) five-years Baryon Acoustic Oscillations (BAO) data. We adopt an iterative smoothing technique to reconstruct the expansion history from SNIa data, which, when combined with BAO measurements, facilitates a comprehensive test of the Universe's curvature and the nature of dark energy. The analysis is conducted under four different mock fiducial cosmologies: two curvatures (Ω k ,0 = 0 and 0.1) and two models of dark energy: a cosmological constant Λ and the phenomenologically emergent dark energy. We forecast that our reconstruction technique can constrain cosmological parameters, such as the curvature ( k ,0) and c/ H 0 r d , with spread due to the SNIa uncertainties up to ±4% and ±0.1 respectively, without assuming any form of dark energy.

Strong gravitational lensing of active galactic nuclei (AGN) enables measurements of cosmological parameters through time-delay cosmography (TDC). With data from the upcoming LSST survey, we anticipate using a sample of O(1000) lensed AGN for TDC. To prepare for this dataset and enable this measurement, we construct and analyze a realistic mock sample of 1300 systems drawn from the OM10 (Oguri Marshall 2010) catalog of simulated lenses with AGN sources at z<3.1 in order to test a key aspect of the analysis pipeline, that of the lens modeling. We realize the lenses as power law elliptical mass distributions and simulate 5-year LSST i-band coadd images. From every image, we infer the lens mass model parameters using neural posterior estimation (NPE). Focusing on the key model parameters, θ_E (the Einstein Radius) and γ_lens (the projected mass density profile slope), with consistent mass-light ellipticity correlations in test and training data, we recover θ_E with less than 1

The Lambda cold dark matter (ACDM) model, while pivotal in cosmological studies, has faced significant challenges due to emerging observational and theoretical inconsistencies. In this work, we analyze constraints including PantheonPlus, Union, the Dark Energy Survey 5-Year (DESY5) supernova compilations, and baryon acoustic oscillation (BAO) measurements. Our findings reveal that, except for PantheonPlus, these datasets exhibit consistent deviations from the ACDM model, with significance exceeding 26 across all considered dynamic dark energy models. We further forecast the constraining power of three years of simulated photometric Supernova Ia data from the Legacy Survey of Space and Time (LSST). Our analysis shows that LSST's high-precision data can significantly tighten constraints on dark energy parameters, surpassing the precision of current datasets. Additionally, the inclusion of BAO measurements alongside LSST data further improves parameter constraints for all models. These results underscore the necessity of exploring dynamic dark energy frameworks as a potential resolution to the tensions within the ACDM paradigm and highlight LSST's crucial role in advancing our understanding of alternative cosmologies.

Core-collapse supernovae are sources of powerful gravitational waves (GWs). We assess the possibility of extracting information about the equation of state (EOS) of high density matter from the GW signal. We use the bounce and early post-bounce signals of rapidly rotating supernovae. A large set of GW signals is generated using general relativistic hydrodynamics simulations for various EOS models. The uncertainty in the electron capture rate is parametrized by generating signals for six different models. To classify EOSs based on the GW data, we train a convolutional neural network (CNN) model. Even with the uncertainty in the electron capture rates, we find that the CNN models can classify the EOSs with an average accuracy of about 87 percent for a set of four distinct EOS models.

We study how future Type-Ia supernovae (SNIa) standard candles detected by the Vera C. Rubin Observatory (LSST) can constrain some cosmological models. We use a realistic three-year SNIa simulated dataset generated by the LSST Dark Energy Science Collaboration (DESC) Time Domain pipeline, which includes a mix of spectroscopic and photometrically identified candidates. We combine this data with Cosmic Microwave Background (CMB) and Baryon Acoustic Oscillation (BAO) measurements to estimate the dark energy model parameters for two models -- the baseline $\Lambda$CDM and Chevallier-Polarski-Linder (CPL) dark energy parametrization. We compare them with the current constraints obtained from joint analysis of the latest real data from the Pantheon SNIa compilation, CMB from Planck 2018 and BAO. Our analysis finds tighter constraints on the model parameters along with a significant reduction of correlation between $H_0$ and $\sigma_{8,0}$. We find that LSST is expected to significantly improve upon the existing SNIa data in the critical analysis of cosmological models.

In this study, YCrO3 and Ba and Ni co-doped YCrO3 (Ba 0 . 05 Y 0 . 95 Cr 0 . 95 Ni 0 . 05 O 3 ) polycrystalline samples were prepared using the standard sol-gel method, and their structure, surface morphology, and electrical and magnetic properties were examined. The samples crystallize in an orthorhombic Pnma structure. Rietveld analysis of all x-ray diffraction patterns revealed a decrease in lattice constant from 5.5230(7) & Aring; to 5.4991(4) & Aring; and a crystallite size reduction from '-' 68 . 64(4) nm (YCrO3, YCO) to '-' 58 . 86(3) nm (Ba 0 . 05 Y 0 . 95 Cr 0 . 95 Ni 0 . 05 O 3 , BYNCO). Furthermore, the deviation of the Cr-O1-Cr bond angle, octahedral distortion, off-center displacement of A-site ions, and rotation of CrO6 due to doping appear to strongly affect the magnetic and electric properties. The distortion parameter values increased for the doped sample compared to that of the pristine one. The samples exhibited paramagnetic behavior at 300 K (room temperature), with a paramagnetic-to-antiferromagnetic transition occurring at 145 K for YCO and at 130.9 K for BYNCO. The fitting of the magnetization vs field (MH) loop aids in quantifying the extent of weak ferromagnetism in the samples. The presence of negative magnetization has been attributed to the temperature-induced magnetization-reversal phenomenon. The neutron diffraction study infers a G-type antiferromagnetic ordering in BYNCO with ordered Cr moment of 2.44(3)mu B (30 K) along the c direction. The dielectric properties were fitted using the Cole-Cole equation, and the obtained parameters were analyzed. Ferroelectricity seemed to be enhanced, with a higher maximum polarization observed for Ba 0 . 05 Y 0 . 95 Cr 0 . 95 Ni 0 . 05 O 3 compared to YCrO3. Characterization of leakage current density (J) vs electric field (E) indicates a reduction in leakage current in BYNCO compared to the bare one. All these findings would be interesting for a detailed theoretical study for applications in future generation devices.

In this paper, we present an analysis of Supernova Ia (SNIa) distance moduli (mu (z)) and dark energy using an Artificial Neural Network (ANN) reconstruction based on LSST simulated three-year SNIa data. The ANNs employed in this study utilize genetic algorithms for hyperparameter tuning and Monte Carlo Dropout for predictions. Our ANN reconstruction architecture is capable of modeling both the distance moduli and their associated statistical errors given redshift values. We compare the performance of the ANN-based reconstruction with two theoretical dark energy models: ACDM and Chevallier-Linder-Polarski (CPL). Bayesian analysis is conducted for these theoretical models using the LSST simulations and compared with observations from Pantheon and Pantheon+ SNIa real data. We demonstrate that our model-independent ANN reconstruction is consistent with both theoretical models. Performance metrics and statistical tests reveal that the ANN produces distance modulus estimates that align well with the LSST dataset and exhibit only minor discrepancies with ACDM and CPL.

The persistent pursuit of multiferroic materials exhibiting ferroelectric and ferromagnetic properties at typical room temperature conditions is primarily driven by the anticipation of groundbreaking advancements in device technology. This paper presents a detailed investigation of the structural, dielectric, magnetic, and ferroelectric properties of sol -gel synthesized Ho 0.05 Y 0.95 Fe 0.90 Ti 0.10 O 3 (HYFTO) nanoceramic. Rietveld analysis of neutron diffraction data reveals microstructural parameters, such as bond angle and bond length, providing valuable insights. The Williamson -Hall (W -H) plot yields an average crystallite size of 35.72 nm and a strain of 6.18x10 - 3 . Magnetisation vs Temperature (MT) and Magnetisation vs Magnetic field (MH) measurements are carried out over a wide temperature range of 900 K - 3 K. Neutron diffraction measurements and their analyses confirmed the existence of ferromagnetic ordering where maximum magnetization is -1.1 mu B which is approximately 11 times greater than that of YFeO 3 (YFO) -0.1 mu B, attributing to higher spin canting. Interestingly, doping impedes the transition temperature (T N ) to 646 K for HYFTO. The increase in the ferromagnetic component observed below 10 K, as determined from the fitting of the MH loop, corresponds to the transition of Ho to its ferromagnetic state. Field dependent magnetization reveals significant horizontal shifts in the Field cooled (FC) hysteresis loops compared to the symmetric Zero-field cooled (ZFC) loop, indicating an negative exchange bias (EB) effect and an exchange field of -500 Oe at 300 K. The co-doped sample exhibits a high dielectric constant, accompanied by a remarkably low tangent loss of 0.56 at room temperature. The sample demonstrates a transition from paraelectric to ferroelectric state at approximately 420 K, highlighting its potential as a type II multiferroic material suitable for room temperature applications. Co -doping enhances the ferroelectric properties, enabling the sample to withstand higher electric fields with increased saturation polarization (-0.31 mu C/ cm 2 ) and coercive field (-8.96 kV/cm). Lower leakage loss observed in HYFTO, as indicated by its JE characteristics, further confirms its superior ferroelectric nature. In addition, the magnetoelectric coupling is enhanced by -3.6 times compared to bare YFO. These significant advancements in magnetic properties, electric field tolerance, and magnetoelectric coupling highlight the potential of this study and its promising applications in device technology.

Core-collapse supernovae (CCSNe) emit powerful gravitational waves (GWs). Since GWs emitted by a source contain information about the source, observing GWs from CCSNe may allow us to learn more about CCSNs. We study if it is possible to infer the iron core mass from the bounce and early ring-down GW signal. We generate GW signals for a range of stellar models using numerical simulations and apply machine learning to train and classify the signals. We consider an idealized favourable scenario. First, we use rapidly rotating models, which produce stronger GWs than slowly rotating models. Second, we limit ourselves to models with four different masses, which simplifies the selection process. We show that the classification accuracy does not exceed ~70%, signifying that even in this optimistic scenario, the information contained in the bounce and early ring-down GW signal is not sufficient to precisely probe the iron core mass. This suggests that it may be necessary to incorporate additional information such as the GWs from later post-bounce evolution and neutrino observations to accurately measure the iron core mass.

We perform a rigorous cosmology analysis on simulated Type Ia supernovae (SNe Ia) and evaluate the improvement from including photometric host galaxy redshifts compared to using only the “ z _spec ” subset with spectroscopic redshifts from the host or SN. We use the Deep Drilling Fields (∼50 deg ^2 ) from the Photometric LSST Astronomical Time-Series Classification Challenge ( PLAsTiCC ) in combination with a low- z sample based on Data Challenge2. The analysis includes light-curve fitting to standardize the SN brightness, a high-statistics simulation to obtain a bias-corrected Hubble diagram, a statistical+systematics covariance matrix including calibration and photo- z uncertainties, and cosmology fitting with a prior from the cosmic microwave background. Compared to using the z _spec subset, including events with SN+host photo- z results in (i) more precise distances for z > 0.5, (ii) a Hubble diagram that extends 0.3 further in redshift, and (iii) a 50% increase in the Dark Energy Task Force figure of merit (FoM) based on the w _0 w _a CDM model. Analyzing 25 simulated data samples, the average bias on w _0 and w _a is consistent with zero. The host photo- z systematic of 0.01 reduces FoM by only 2% because (i) most z < 0.5 events are in the z _spec subset, (ii) the combined SN+host photo- z has ×2 smaller bias, and (iii) the anticorrelation between fitted redshift and color self-corrects distance errors. To prepare for analyzing real data, the next SN Ia cosmology analysis with photo- z s should include non–SN Ia contamination and host galaxy misassociations.

Antiferromagnetic LuFeO3 can be good multiferroic by having ferroelectricity in its non-centrosymmetric hexagonal phase. But, it is hard to stabilize this metastable phase, preventing the stable orthorhombic phase. In this work, metastable hexagonal LuFeO3 (LFO) nanoparticle was stabilized in chemical sol-gel route in pure phase and co-doped with Co and Ti in the same route to synthesize Lu0.9Co0.1Fe0.9Ti0.1O3 (LCFTO) nanoparticles. Room temperature multiferroicity of bare LFO was established through relevant characterization. And motive behind the co-doping is to enhance the magnetoelectric behavior of the bare system to synthesize a new monophasic type II magnetoelectric multiferroic. Structural investigation by thorough Rietveld analyses of the recorded X-ray diffractograms, confirmed the formation of pure hex-agonal (P63cm) phase of both bare and doped LFO. However, deviations in various structural & micro -structural parameters were observed in the doped system, which is mainly responsible for the enhancement of magnetic and electric properties of the sample. Presence of antiferromagnetic transition at similar to 604 K confirmed the room temperature magnetic ordering of bare LFO. Interestingly, LCFTO shows a drastic enhancement of magnetic property than the bare one in all concerns, where the maximum mag-netization at the maximum applied field is enhanced by nearly 36 times at room temperature. Detailed high-temperature dielectric investigation shows, good dielectric strength (similar to 261) of LFO gets enhanced highly in LCFTO (similar to 1053) and a high relaxation time having negligible loss factor with an indication of ferro to paraelectric transition above room temperature. Current density vs. electric field (J-E) curve suggests the presence of polarization at room temperature with negligible leakage loss. Direct measurement of ferro-electric loop shows the ferroelectricity (Pmax similar to 0.06 4 mu c/cm2) of bare LFO at room temperature and a well improvement in the doped LCFTO (Pmax similar to 0.151 mu c/cm2). The presence of room temperature magnetoelectric coupling, confirmed by magnetocapacitance measurements, results in a high value (similar to 5 %) of magnetoca-pacitance in the doped system which is also much higher than the bare one (< similar to 1 %) as expected. All these properties confirm the magnetoelectric multiferroicity of bare h-LuFeO3 at room temperature. And, co -doping in hexagonal LuFeO3 nanoparticle system results in a considerable improvement in its magneto -electric behavior, so this co-doped system can be a promising and potential magnetoelectric multiferroic for the future generation magnetoelectric devices. (c) 2023 Published by Elsevier B.V.

The percolative (YCrO3)1 -x - (CoFe1.6Cr0.4O4)x (x = 0.3, 0.5, 0.7, 1.0), ceramic composite belongs to a family of materials that exhibit colossal dielectric permittivity. Our fundamental interests lie in the investigation of the conduction mechanism of the composite from both microscopic and macroscopic perspectives. Here, all three ceramic composites have been chosen for a comparative study containing CFCO above fc, near fc, and below fc. The electrical conductivity of the composite follows Mott's variable range-hopping model in the temperature range of 300 - 500 K, indicating domination of this model in conduction of localized polarons in the electrical behaviour. Parameters, including the dc conductivity, hopping, most probable hopping range, the activation energies involved in the said model, the density of localized states at the Fermi level, and relaxation were ob-tained and analyzed. Scaling behaviors of conductivity and imaginary part of complex impedance were inves-tigated, showing a temperature-independent distribution of relaxation times. By looking at the impedance data at a variety of temperatures, semiconducting behaviour is demonstrated, which has been modelled by an equivalent circuit model that incorporates grain and grain boundary responses. The ferroelectric nature of the samples resembled lossy characteristics due to the presence of the CFCO phase. The correlation between conductivity and colossal apparent permittivity helps in better understand the conduction mechanism in ceramic composites comprising semiconducting and insulating phases.

Nanocrystalline HoFeO3 is a potential multiferroic and will be more useful if its multiferroicity can be enhanced by improving the magnetic and electric ordering. In this regard, co-doping with Co and Ti, was considered in HoFeO3 (HFO) to enhance its magneto-electric behavior and thus synthesize a new monophasic multiferroic, Ho0.95Co0.05Fe0.95Ti0.05O3 (HCFTO). Both the pristine and doped HFO nanoparticles were synthesized in sol-gel route. Rietveld analyses of X-ray diffractograms, confirmed the formation of pure orthorhombic (Pnma) phase of both bare and doped HFO. Presence of the canted antiferromagnetism of bare HFO at room-temperature with antiferromagnetic transition at -637 K was confirmed in susceptibility vs. temperature variation and by the nature of MH loops. Interestingly, substantial enhancement of magnetism was observed in HCFTO compared to that of bare one, where the room-temperature maximum magnetization is enhanced by 73%. Dielectric strength of HFO (-48) is also enhanced highly (-3.4 times) in HCFTO (-165), and the loss factor is lowered to nearly half. Current density vs. electric field (J-E) curve suggests the presence of polarization at room temperature with negligible leakage loss. Lower leakage loss of HCFTO indicates better multiferroicity than HFO. Direct mea-surement of ferroelectric loop shows the room-temperature ferroelectricity of bare HFO (Pmax -0.0041 & mu;c/cm2) well improved (-1.5 times) in the doped HCFTO (Pmax -0.0061 & mu;c/cm2) with lower hysteresis loss. Room temperature magnetoelectric coupling measurements, shows a high value (-2.47%) of magnetocapacitance in the doped system, which is much higher (-4.94 times) than the bare one (-0.5%). All these properties of the sample clearly confirm the co-doping in the HoFeO3 nanoparticle system results in a considerable improvement in its magnetoelectric behavior, and this co-doped Ho0.95Co0.05Fe0.95Ti0.05O3 system can be a promising and potential magnetoelectric multiferroic for device applications.

This study presents a statistical analysis applying different statistical techniques, including trained Bayesian Networks an artificial intelligence (AI) method, to explore datasets of lift accidents involving safety rules for two countries: UK and France. The study concerns six years data for both countries and covers almost all elevator accidents taken place during private and professional uses; 218 cases for UK and 205 cases for France. The relevant time interval for U.K. is 6th January 2006 to 29th December 2012, while for France data concern the period of 18th February 2003 to 17th December 2009. The major aim of the study is to exhibit and demonstrate that for accident datasets, at least for similar datasets, multiple statistical methods have to be applied in order to extract reliable information, i.e. investigate interactions among factors and therefore help to develop prevention measures. Three statistical models were built to derive associations between factors concerning violation of rules related to the installation and maintenance of elevators, passengers' safety rules, risks and unforeseen circumstances. Associations between severity of injury and categories of gender or age of injured people have been found. Furthermore, specific influences between severity of injury and categories of type of rules or of type of accident have been identified. The obtained results will contribute to the design of efficient methods to avoid future accidents in both countries.