A quantitative analysis of X-ray emission from an electron cyclotron resonance (ECR) plasma was performed to probe the spatial properties of electrons having energy for effective ionisation. A series of measurements were taken by INFN-LNS and ATOMKI, capturing spatially and spectrally resolved X-ray maps as well as volumetric emissions from argon plasma. Comparing the former with model generated maps (involving space-resolved phenomenological electron energy distribution function and geometrical efficiency calculated using ray-tracing MC routine) furnished information on structural aspects of the plasma. Similarly, fitting a model composed of bremsstrahlung and fluorescence to the volumetric X-ray spectrum provided valuable insight into the density and temperature of confined and lost electrons. The latter can be fed back to existing electron kinetics models for simulating more relevant energies, consequently improving theoretical X-ray maps and establishing the method as an excellent indirect diagnostic tool for warm electrons, required for both fundamental and applied research in ECR plasmas.

Computationally modelling a nuclear reactor startup core for a benchmark against the existing models is highly desirable for an independent assessment informing nuclear engineers and energy policymakers. This work presents a startup core model of the UK’s first Evolutionary Pressurised Water Reactor (EPR) based on Monte Carlo simulations of particle collisions using Serpent 2, a continuous-energy Monte Carlo reactor physics burnup code. Coupling between neutronics and thermal-hydraulic conditions with the fuel depletion is incorporated into the multi-dimensional branches, obtaining the thermal flux and fission rate (power) distributions radially and axially from the three dimensional (3D) single assembly level to a 3D full core. Shannon entropy is employed to characterise the convergence of the fission source distribution, with 3 billion neutron histories tracked by parallel computing. Source biasing is applied for the variance reduction. Benchmarking the proposed Monte Carlo 3D full-core model against the traditional deterministic transport computation suite used by the UK Office for Nuclear Regulation (ONR), a reasonably good agreement within statistics is demonstrated for the safety-related reactivity coefficients, which creates trust in the EPR safety report.

The classical Rotational Isomeric State (RIS) model, originally proposed by Flory, has been used to rationalize a wide range of physicochemical properties of neutral polymers. However, many weak polyelectrolytes of interest are able to regulate their charge depending on the conformational state of the bonds. Recently, it has been shown that the RIS model can be coupled with the Site Binding (SB) model, for which the ionizable sites can adopt two states: protonated or deprotonated. The resulting combined scheme, the SBRIS model, allows to analyse ionization and conformational equilibria on the same foot. In the present work this approach is extended to include pH-dependent electrostatic Long Range (LR) interactions, ubiquitous in weak polyelectrolytes at moderate and low ionic strengths. With this aim the original LR interactions are taken into account by defining effective Short Range (SR) and pH-dependent parameters, such as effective microscopic protonation constants and rotational bond energies. The new parameters are systematically calculated using variational methods. The machinery of statistical mechanics for SR interactions, including the powerful and fast transfer matrix methods, can then be applied. The resulting technique, to which we will refer as Local Effective Interaction Parameters (LEIP) method, is illustrated with a minimal model of a flexible linear polyelectrolyte containing only one type of rotating bonds. LEIP reproduces very well the pH dependence of the degree of protonation and bond probabilities obtained by semi-grand canonical Monte Carlo simulations, where LR interactions are taken explicitly into account. The reduction in the computational time in several orders of magnitude suggests that the LEIP technique could be useful in a range of areas involving linear weak polyelectrolytes, allowing direct fitting of the relevant physical parameters to the experimental quantities.

In this paper a new double-nozzle technique for in-gas-jet laser resonance ionization spectroscopy is proposed. The functionality of this new technique we explored by detailed gas dynamic and Monte Carlo atom-trajectory simulations, which results are presented and discussed. The results of similar computer simulations for JetRIS setup (as a typical representative of the conventional nowadays in-gas-jet technique) are presented and discussed as well. The direct comparison of calculation results for proposed new technique with the conventional one shows that the double-nozzle technique has many advantages compared with that one used in the JetRIS setup at GSI for future high-resolution laser spectroscopic study of heaviest elements. To fully implement the proposed new technique to all existing (or under construction) setups for in-gas-jet laser resonance ionization spectroscopy, it will be enough just replace the used their supersonic nozzle by the miniature double-nozzle device described in the paper.

This work addresses the role of charge regulation (CR) and the associated fluctuations in the conformational and mechanical properties of weak polyelectrolytes. Due to CR, changes in the pH-value modifies the average macromolecular charge and conformational equilibria. A second effect is that, for a given average charge per site, fluctuations can alter the intensity of the interactions by means of correlation between binding sites. We investigate both effects by means of Monte Carlo simulations at constant pH-value, so that the charge is a fluctuating quantity. Once the average charge per site is available, we turn off the fluctuations by assigning the same average charge to every site. A constant charge MC simulation is then performed. We make use of a model which accounts for the main fundamental aspects of a linear flexible polyelectrolyte i.e. proton binding, angle internal rotation, bond stretching and bending. Steric excluded volume and differentiated treatment for short-range and long-range interactions are also included in the model. It can be regarded as a kind of "minimal'' model in the sense that contains a minimum number of parameters but still preserving the atomistic detail. It is shown that, if fluctuations are activated, gauche state bond probabilities increase, and the persistence length decreases, so that the polymer becomes more folded. Macromolecular stretching is also analyzed in presence of CR (the charge depends on the applied force) and without CR (the charge is fixed to the value at zero force). The analysis of the low force scaling behavior concludes that Pincus exponent becomes pH-dependent. Both with and without CR, a transition from 1/2 at high pH-values (phantom chain) to 3/5 to low pH-values (Pincus regime), is observed. Finally, the intermediate force stretching regime is investigated. It is found that CR induces a moderate influence in the force-extension curves and persistence length (which in this force regime becomes force-dependent). It is thus concluded that the effect of CR on the stretching curves is mainly due to changes in the average charge at zero force. It is also found that, for the cases studied, the effect of steric excluded volume is almost irrelevant compared to electrostatic interactions.

Rapid developments in the field of underwater photogrammetry have given scientists1the ability to produce accurate 3-dimensional (3D) models which are now increasingly used in the representation and study of local areas of interest. This paper addresses the lack of systematic analysis of 3D reconstruction and navigation fusion strategies, as well as associated error evaluation of models produced at larger scales in GPS-denied environments using a monocular camera (often in deep-sea scenarios). Based on our prior work on automatic scale estimation of Structure from Motion (SfM)-based 3D models using laser scalers, an automatic scale accuracy framework is presented. The confidence level for each of the scale error estimates is independently assessed through the propagation of the uncertainties associated with image features and laser spot detections using a Monte Carlo simulation. The number of iterations used in the simulation was validated through the analysis of the final estimate behaviour. To facilitate the detection and uncertainty estimation of even greatly attenuated laser beams, an automatic laser spot detection method, mitigating the effects of scene texture, was developed, with the main novelty of estimating the uncertainties based on the recovered characteristic shapes of laser spots with radially decreasing1 intensities. The effects of four different reconstruction strategies resulting from the combinations of Incremental/GlobalSfM, and thea priori/a posterioriuse of navigation data were analyzed using two distinct survey scenarios captured during the SUBSAINTES 2017 cruise (doi: 10.17600/17001000). The study demonstrates that surveys with multiple overlaps of non-sequential images result in a nearly identical solution regardless of the strategy (SfM or navigation fusion), while surveys with weakly connected sequentially acquired images are prone to produce broad-scale deformation (doming effect) when navigation is not included in the optimization. Thus the scenarios with complex survey patterns substantially benefit from using multi-objective BA navigation fusion. In all cases, the errors in the models are inferior to 5%, with errors often being around 1%. The effects of combining data from multiple surveys were also evaluated. The introduction of additional vectors in the optimization of multi-survey problems successfully accounted for offset changes present in the underwater USBL-based navigation data and thus minimize the effect of contradicting navigation priors. Our results also illustrate the importance of collecting a multitude of evaluation data at different locations and moments during the survey.

This article presents a comprehensive study on enhancing grid resilience through advanced forecasting and optimization techniques in the context of power outages. Power outages pose significant challenges to modern societies, affecting various sectors such as industries, households, and critical infrastructures. The research combines statistical analysis, machine learning algorithms, and optimization methods to address this issue to develop a holistic approach for predicting and mitigating power outage events. The proposed methodology involves the use of Monte Carlo simulations in MATLAB for future outage prediction, Long Short-Term Memory (LSTM) networks for forecasting solar irradiance and load profiles, and a hybrid LSTM-Particle Swarm Optimization (PSO) model to improve accuracy. Furthermore, the role of Battery State of Charge (SoC) in enhancing system resilience is explored. The study also assesses the techno-economic advantages of a grid-tied microgrid integrated with solar panels and batteries over conventional grid systems. The results highlight the potential of the proposed approach in strengthening grid resilience, reducing downtime, and fostering sustainable energy utilization.

The aim of this study is to obtain the Bayes estimators and the maximum likelihood estimators (MLEs) for the unknown parameters of the Rayleigh Weibull (RW) distribution based on progres-sive type-II censored samples. The approximate Bayes estimators are calculated using the idea of Lindley and Tierney-Kadane's approximation method under the squared-error loss function when the Bayes estimators are not handed in explicit forms. In this study, the approximate Bayes esti-mates are compared with the maximum likelihood estimates in the aspect of the estimated risks (ERs) using Monte Carlo simulation. In addition, the coverage probabilities of the parametric bootstrap estimates are calculated. Real lifetime data sets belonging to the cancer types as bladder cancer, head and neck cancer, and leukemia are used to illustrate the emprical results belonging to the approximate Bayes estimates, the maximum likelihood estimates, and the parametric bootstrap intervals.

This paper is a further development of our “Proposal of a new double-nozzle technique for in-gas-jet laser resonance ionization spectroscopy” published in journal Atoms earlier in this year. Here, we propose to equip the proposed double-nozzle technique with the RF-only funnel and RF-buncher placed in the gas-jet chamber at 70 mm distance downstream the double-nozzle exit. It allows for highly effective extraction into vacuum heavy ion beams, produced in two-steps laser resonance ionization in the argon supersonic jet. We explored the operation of this new full version of the double-nozzle technique through detailed gas dynamic and then Monte Carlo trajectory simulations, which results are presented and discussed. In particularly, we have shown by calculations that more than 80% of all nobelium-254 neutral atoms, extracted by argon flow from the gas stopping cell through the double-nozzle into the supersonic jet can be extracted into vacuum in a form of pulsed ion beam having low transverse and longitudinal emittance.s dynamic and Monte Carlo simulations

Long-range interactions are relevant for a large variety of quantum systems in quantum optics and condensed matter physics. In particular, the control of quantum-optical platforms promises to gain deep insights in quantum-critical properties induced by the long-range nature of interactions. From a theoretical perspective, long-range interactions are notoriously complicated to treat. Here, we give an overview of recent advancements to investigate quantum magnets with long-range interactions focusing on two techniques based on Monte Carlo integration. First, the method of perturbative continuous unitary transformations where classical Monte Carlo integration is applied within the embedding scheme of white graphs. This linked-cluster expansion allows to extract high-order series expansions of energies and observables in the thermodynamic limit. Second, stochastic series expansion quantum Monte Carlo which enables calculations on large finite systems. Finite-size scaling can then be used to determine physical properties of the infinite system. In recent years, both techniques have been applied successfully to one- and two-dimensional quantum magnets involving long-range Ising, XY, and Heisenberg interactions on various bipartite and non-bipartite lattices. Here, we summarise the obtained quantum-critical properties including critical exponents for all these systems in a coherent way. Further, we review how long-range interactions are used to study quantum phase transitions above the upper critical dimension and the scaling techniques to extract these quantum critical properties from the numerical calculations.

The local and global order in dense packings of linear, semi-flexible polymers of tangent hard spheres are studied by employing extensive Monte Carlo simulations at increasing volume fractions. Chain stiffness is controlled by a tunable harmonic potential for the bending angle whose intensity dictates the rigidity of the polymer backbone as a function of the bending constant and equilibrium angle. The studied angles range from acute to obtuse ones, reaching the limit of rod-like polymers. We analyze how packing density and chain stiffness affect the ability of chains to self-organize at the local and global levels. The former corresponds to crystallinity as quantified by the Characteristic Crystallographic Element (CCE) norm descriptor, while the latter is computed through the scalar orientational order parameter. In all cases, we identify the critical volume fraction for the phase transition and gauge the established crystal morphologies, developing a complete phase diagram as a function of packing density and equilibrium bending angle. A plethora of structures is obtained, ranging from random hexagonal closed packed morphologies of mixed character and almost perfect face centered cubic (FCC) and hexagonal close-packed (HCP) crystals at the level of monomers, to nematic mesophases, with prolate and oblate mesogens at the level of chains. For rod-like chains, hysteresis is observed between the establishment of long-range nematic order and crystallization, while for right-angle chains both transitions are synchronized. A comparison is also provided against the analogous packings of monomeric and fully flexible chains of hard spheres.

In the present work we revise and extend the Characteristic Crystallographic Element (CCE) norm, an algorithm used to simultaneously detect radial and orientational similarity of computer-generated structures with respect to specific reference crystals and local symmetries. Based on the identification of point group symmetry elements, the CCE descriptor is able to gauge local structure with high precision and finely distinguish between competing morphologies. As test cases we use computer-generated monomeric and polymer systems of spherical particles interacting with the hard-sphere and square-well attractive potentials. We demonstrate that the CCE norm is able to detect and differentiate, between others, among: hexagonal close packed (HCP), face centered cubic (FCC), hexagonal (HEX) and body centered cubic (BCC) crystals as well as non-crystallographic fivefold (FIV) local symmetry in bulk 3-D systems; triangular (TRI), square (SQU) and honeycomb (HON) crystals, as well as pentagonal (PEN) local symmetry in thin films of one-layer thickness (2-D systems). The descriptor is general and can be applied to identify the symmetry elements of any point group for arbitrary atomic or particulate system in two or three dimensions, in the bulk or under confinement.