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High performance computing (HPC) architectures have undergone rapid development in recent years. As a result, established software suites face an ever increasing challenge to remain performant on and portable across modern systems. Many of the widely adopted atmospheric modeling codes cannot fully (or in some cases, at all) leverage the acceleration provided by General-Purpose Graphics Processing Units (GPGPUs), leaving users of those codes constrained to increasingly limited HPC resources. Energy Research and Forecasting (ERF) is a regional atmospheric modeling code that leverages the latest HPC architectures, whether composed of only Central Processing Units (CPUs) or incorporating GPUs. ERF contains many of the standard discretizations and basic features needed to model general atmospheric dynamics as well as flows relevant to renewable energy. The modular design of ERF provides a flexible platform for exploring different physics parameterizations and numerical strategies. ERF is built on a state-of-the-art, well-supported, software framework (AMReX) that provides a performance portable interface and ensures ERF's long-term sustainability on next generation computing systems. This paper details the numerical methodology of ERF and presents results for a series of verification and validation cases.

Statistical dependencies between information sources are rarely known, yet in practical distributed tracking schemes, they must be taken into account in order to prevent track divergences. Chernoff fusion is well-known and universally accepted method that can address the problem of track fusion when the statistical dependence between the fusing sources is unknown. In this paper we derive the exact Chernoff fusion equations for Bernoulli Gaussian max filters. These filters have been recently derived in the framework of possibility theory, as the analog of the Bernoulli Gaussian sum filters. The main motivation for the possibilistic approach is that it effectively deals with imprecise mathematical models (e.g. dynamics, measurements) used in tracking algorithms. The paper also demonstrates the proposed possibilistic fusion scheme in the absence of knowledge about statistical dependence.

Fast detection of emerging diseases is important for containing their spread and treating patients effectively. Local anomalies are relevant, but often novel diseases involve familiar disease patterns in new spatial distributions. Therefore, established local anomaly detection approaches may fail to identify them as new. Here, we present a novel approach to detect the emergence of new disease phenotypes exhibiting distinct patterns of the spatial distribution of lesions. We first identify anomalies in lung CT data, and then compare their distribution in a continually acquired new patient cohorts with historic patient population observed over a long prior period. We evaluate how accumulated evidence collected in the stream of patients is able to detect the onset of an emerging disease. In a gram-matrix based representation derived from the intermediate layers of a three-dimensional convolutional neural network, newly emerging clusters indicate emerging diseases.

It is already known that a simple nonlocal de Sitter gravity model, which we denote as $\sqrt{dS}$ gravity, contains an exact vacuum cosmological solution which mimics dark energy and dark matter and is in very good agreement with the standard model of cosmology. This success of $\sqrt{dS}$ gravity motivated us to investigate how it works at lower than cosmic scale -- galactic and the solar system. This paper contains our investigation of the corresponding Schwarzschild-de Sitter metric of the $\sqrt{dS}$ gravity model. To get exact solution, it is necessary to solve the corresponding nonlinear differential equation, what is a very complicated and difficult problem. What we obtained is a solution of linearized equation, which is related to space metric far from the massive body, where gravitational field is weak. The obtained approximate solution is of particular interest for examining the possible role of non-local de Sitter gravity $\sqrt{dS}$ in describing the effects in galactic dynamics that are usually attributed to dark matter. The solution has been tested on the Milky Way and the spiral galaxy M33 and is in good agreement with observational measurements., Comment: 18 pages, 3 figures

In this paper, we generalize Cristinel Mortici's results on Wilker-Cusa-Huygens inequalities using stratified families of functions and SimTheP - a system for automated proving of MTP inequalities.

Conventional measurements of two-phase flow in porous media often use completely immiscible fluids, or are performed over time-scales of days to weeks. If applied to the study of gas storage and recovery, these measurements do not properly account for Ostwald ripening, significantly over-estimating the amount of trapping and hysteresis. When there is transport of dissolved species in the aqueous phase, local capillary equilibrium is achieved: this may take weeks to months on the centimetre-sized samples on which measurements are performed. However, in most subsurface applications where the two phases reside for many years, equilibrium can be achieved. We demonstrate that in this case, two-phase displacement in porous media needs to be modelled as percolation without trapping. A pore network model is used to quantify how to convert measurements made ignoring Ostwald ripening to correct trapped saturation, capillary pressure and relative permeability to account for this effect. We show that conventional measurements over-estimate the amount of capillary trapping by 20-25\%., Comment: 5 pages, 4 figures, 1 table

In this paper, we give new Taylor approximative formulae for the area of the egg-shaped parts of H\"ugelsch\"affer curves. Based on a parametrization of the H\"ugelsch\"affer curve, a formula for the area of the egg-shaped part of such a curve is derived via elliptic integrals of the first and second kind. Furthermore, new approximative formulae for calculating this area derived from standard and double Taylor approximations are given. A representation of the value $\frac{1}{\pi}$ was also obtained using an appropriate series., Comment: 16 pages, 3 figures

In this paper, two double Jordan-type inequalities are introduced that generalize some previously established inequalities. As a result, some new upper and lower bounds and approximations of the sinc function are obtained. This extension of Jordan's inequality is enabled by considering the corresponding inequalities through the concept of stratified families of functions. Based on this approach, some optimal approximations of the sinc function are derived by determining the corresponding minimax approximants.

As Multi-Robot Systems (MRS) become more affordable and computing capabilities grow, they provide significant advantages for complex applications such as environmental monitoring, underwater inspections, or space exploration. However, accounting for potential communication loss or the unavailability of communication infrastructures in these application domains remains an open problem. Much of the applicable MRS research assumes that the system can sustain communication through proximity regulations and formation control or by devising a framework for separating and adhering to a predetermined plan for extended periods of disconnection. The latter technique enables an MRS to be more efficient, but breakdowns and environmental uncertainties can have a domino effect throughout the system, particularly when the mission goal is intricate or time-sensitive. To deal with this problem, our proposed framework has two main phases: i) a centralized planner to allocate mission tasks by rewarding intermittent rendezvous between robots to mitigate the effects of the unforeseen events during mission execution, and ii) a decentralized replanning scheme leveraging epistemic planning to formalize belief propagation and a Monte Carlo tree search for policy optimization given distributed rational belief updates. The proposed framework outperforms a baseline heuristic and is validated using simulations and experiments with aerial vehicles.

In this paper, we briefly review highlights of nonlocal de Sitter gravity based on the nonlocal term $ \sqrt{R - 2\Lambda}\ \mathcal{F}(\Box)\ \sqrt{R - 2\Lambda }$ in the Einstein-Hilbert action without matter sector. This nonlocal de Sitter gravity model has several exact cosmological FLRW solutions and one of these solutions contains some effects that are usually assigned to dark matter and dark energy. There are also some other interesting and promising properties of this kind of gravity nonlocality. We also review some anisotropic cosmological solutions, and mention the corresponding nonlocal Schwarzschild-de Sitter metric., Comment: arXiv admin note: text overlap with arXiv:2206.13515

This paper originates from lectures delivered at the summer school "Fundamental Problems in Statistical Physics XV" in Bruneck, Italy, in 2021. We give a brief and limited introduction into ergodicity-breaking induced by disorder. As the title suggests, we include a simple yet rigorous and original result: For a strongly disordered quantum chain, we exhibit a full set of quasi-local quantities whose dynamics is negligible up to times of order $\exp\{ c (\log W)^{2-\epsilon}\}$, with $\epsilon < 1$, $c$ a numerical constant, and $W$ the disorder strength. Such a result, that is often referred to as "quasi-localization", can in principle be obtained in other systems as well, but for a disordered quantum chain, its proof is relatively short and transparent., Comment: To be published in Physica A: Statistical Mechanics and its Applications

This paper considers MEP - Mixed Exponential Polynomials as one class of real exponential polynomials. We introduce a method for proving the positivity of MEP inequalities over positive intervals using the Maclaurin series to approximate the exponential function precisely. Additionally, we discuss the relation between MEPs and stratified families of functions from [Malesevic_Mihailovic_2021] through two applications, referring to inequalities from papers [Wu_Zhang_2004] and [Chesneau_Bagul_Dhaigude_2022].

Dynamic control is emerging as an effective methodology for operating stormwater systems under stress from rapidly evolving weather patterns. Informed by rainfall predictions and real-time sensor measurements, control assets in the stormwater network can be dynamically configured to tune the behavior of the stormwater network to reduce the risk of urban flooding, equalize flows to the water reclamation facilities, and protect the receiving water bodies. However, developing such control strategies requires significant human and computational resources, and a methodology does not yet exist for quantifying the risks associated with implementing these control strategies. To address these challenges, in this paper, we introduce a Bayesian Optimization-based approach for identifying stormwater control strategies and estimating the associated uncertainties. We evaluate the efficacy of this approach in identifying viable control strategies in a simulated environment on real-world inspired combined and separated stormwater networks. We demonstrate the computational efficiency of the proposed approach by comparing it against a Genetic algorithm. Furthermore, we extend the Bayesian Optimization-based approach to quantify the uncertainty associated with the identified control strategies and evaluate it on a synthetic stormwater network. To our knowledge, this is the first-ever stormwater control methodology that quantifies uncertainty associated with the identified control actions. This Bayesian optimization-based stormwater control methodology is an off-the-shelf control approach that can be applied to control any stormwater network as long we have access to the rainfall predictions, and there exists a model for simulating the behavior of the stormwater network., Comment: 12 pages, 5 figures

Monitoring the fuel moisture content (FMC) of vegetation is crucial for managing and mitigating the impact of wildland fires. The combination of in situ FMC observations with numerical weather prediction (NWP) models and satellite retrievals has enabled the development of machine learning (ML) models to estimate dead FMC retrievals over the contiguous US (CONUS). In this study, ML models were trained using variables from the National Water Model and the High-Resolution Rapid Refresh (HRRR) NWP models, and static variables characterizing the surface properties, as well as surface reflectances and land surface temperature (LST) retrievals from the VIIRS instrument on board the Suomi-NPP satellite system. Extensive hyper-parameter optimization yielded skillful FMC models compared to a daily climatography RMSE (+44\%) and to an hourly climatography RMSE (+24\%). Furthermore, VIIRS retrievals were important predictors for estimating FMC, contributing significantly as a group due to their high band-correlation. In contrast, individual predictors in the HRRR group had relatively high importance according to the explainability techniques used. When both HRRR and VIIRS retrievals were not used as model inputs, the performance dropped significantly. If VIIRS retrievals were not used, the RMSE performance was worse. This highlights the importance of VIIRS retrievals in modeling FMC, which yielded better models compared to MODIS. Overall, the importance of the VIIRS group of predictors corroborates the dynamic relationship between the 10-h fuel and the atmosphere and soil moisture. These findings emphasize the significance of selecting appropriate data sources for predicting FMC with ML models, with VIIRS retrievals and selected HRRR variables being critical components in producing skillful FMC estimates.

The largest coefficient (in absolute value) of a cyclotomic polynomial $\Phi_n$ is called its height $A(n)$. In case $p$ is a fixed prime it turns out that as $q$ and $r$ range over all primes satisfying $p

The impact of green infrastructure (GI) on the urban drainage landscape remains largely unmeasured at high temporal and spatial scales. To that end, a data toolchain is introduced, underpinned by a novel wireless sensor network for continuously measuring real-time water levels in GI. The internet-connected sensors enable the collection of high-resolution data across large regions. A case study in Detroit (MI, US) is presented, where the water levels of 14 GI sites were measured in-situ from June to September 2021. The large dataset is analyzed using an automated storm segmentation methodology, which automatically extracts and analyzes individual storms from measurement time series. Storms are used to parameterize a dynamical system model of GI drawdown dynamics. The model is completely described by the decay constant {\alpha}, which is directly proportional to the drawdown rate. The parameter is analyzed across storms to compare GI dynamics between sites and to determine the major design and physiographic features that drive drawdown dynamics. A correlation analysis using Spearman's rank correlation coefficient reveals that depth to groundwater, imperviousness, longitude, and drainage area to surface area ratio are the most important features explaining GI drawdown dynamics in Detroit. A discussion is provided to contextualize these finding and explore the implications of data-driven strategies for GI design and placement., Comment: 12 pages, 6 figures

In this paper, the strong formulation of the generalised Navier-Stokes momentum equation is investigated. Specifically, the formulation of shear-stress divergence is investigated, due to its effect on the performance and accuracy of computational methods. It is found that the term may be expressed in two different ways. While the first formulation is commonly used, the alternative derivation is found to be potentially more convenient for direct numerical manipulation. The alternative formulation relocates a part of strain information under the variable-coefficient Laplacian operator, thus making future computational schemes potentially simpler with larger time-step sizes.

Software Process Improvement requires significant effort related not only to the identification of relevant issues and providing an adequate response to them but also to the implementation and adoption of the changes. Best practices provide recommendations to software teams on how to address the identified objectives in practice, based on aggregated experience and knowledge. In the paper, we present the GEANT experience and observations from the process of adopting the best practices and present the setting we have been using.

Contemporary undertakings provide limitless opportunities for widespread application of machine reasoning and artificial intelligence in situations characterised by uncertainty, hostility and sheer volume of data. The paper develops a valuation network as a graphical system for higher-level fusion and reasoning under uncertainty in support of the human operators. Valuations, which are mathematical representation of (uncertain) knowledge and collected data, are expressed as credal sets, defined as coherent interval probabilities in the framework of imprecise probability theory. The basic operations with such credal sets, combination and marginalisation, are defined to satisfy the axioms of a valuation algebra. A practical implementation of the credal valuation network is discussed and its utility demonstrated on a small scale example., Comment: 16 pages, 3 figures

This paper is devoted to a simple nonlocal de Sitter gravity model and its exact vacuum cosmological solutions. In the Einstein-Hilbert action with $\Lambda$ term, we introduce nonlocality by the following way: $R - 2 \Lambda = \sqrt{R-2\Lambda}\ \sqrt{R-2\Lambda} \to \sqrt{R-2\Lambda}\ F(\Box)\ \sqrt{R-2\Lambda} ,$ where ${F} (\Box) = 1 + \sum_{n= 1}^{+\infty} \big( f_n \Box^n + f_{-n} \Box^{-n} \big) $ is an analytic function of the d'Alembert-Beltrami operator $\Box$ and its inverse $\Box^{-1}$. By this way, $R$ and $\Lambda$ enter with the same form into nonlocal version as they are in the local one, and nonlocal operator $F(\Box)$ is dimensionless. The corresponding equations of motion for gravitational field $g_{\mu\nu}$ are presented. The first step in finding some exact cosmological solutions is solving the equation $\Box \sqrt{R-2\Lambda} = q \sqrt{R-2\Lambda} , $ where $ q =\zeta \Lambda \quad (\zeta \in \mathbb{R})$ is an eigenvalue and $\sqrt{R-2\Lambda}$ is an eigenfunction of the operator $\Box .$ We presented and discussed several exact cosmological solutions for homogeneous and isotropic universe. One of these solutions mimics effects that are usually assigned to dark matter and dark energy. Some other solutions are examples of the nonsingular bounce ones in flat, closed and open universe. There are also singular and cyclic solutions. All these cosmological solutions are a result of nonlocality and do not exist in the local de Sitter case., Comment: 27 pages, 5 figures. Comments welcome

The large-scale expansion of wind farms has prompted community debate regarding adverse impacts of wind farm noise (WFN). One of the most annoying and potentially sleep disturbing components of WFN is amplitude modulation (AM). Here we quantified and characterised AM over one year using acoustical and meteorological data measured at three locations near three wind farms. We found that the diurnal variation of outdoor AM prevalence was substantial, the nighttime prevalence was approximately 2 to 5 times higher than the daytime prevalence. On average, indoor AM occurred during the nighttime from 1.1 to 1.7 times less often than outdoor AM, but the indoor AM depth was higher than that measured outdoors. We observed an association between AM prevalence and sunset and sunrise. AM occurred more often at downwind and crosswind conditions. These findings provide important insights into long term WFN characteristics that will help to inform future WFN assessment guidelines.

In this paper we consider Hugelschaffer cubic curves which are generated using appropriate geometric constructions. The main result of this work is the mode of explicitly calculating the area of the egg-shaped part of the cubic curve using elliptic integrals. In this paper, we also analyze the Hugelschaffer surface of cubic curves for which we provide new forms of formulae for the volume and surface area of the egg-shaped part. Curves and surfaces of ovoid shape have wide applicability in aero-engineering and construction, and are also of biologic importance. With respect to this, in the final section, we consider some examples of the real applicability of this Hugelschaffer model., Comment: 18 pages, 4 figures

One of the major sources of uncertainty in predictions of wind farm noise (WFN) reflect parametric and model structure uncertainty. The model structure uncertainty is a systematic uncertainty, which relates to uncertainty about the appropriate mathematical structure of the models. Here we quantified the model structure uncertainty in predicting WFN arising from multi-input models, including nine ground impedance and four wind speed profile models. We used a numerical ray tracing sound propagation model for predicting the noise level at different receivers. We found that variations between different ground impedance models and wind speed profile models were significant sources of uncertainty, and that these sources contributed to predicted noise level differences in excess of 10 dBA at distances greater than 3.5 km. We also found that differences between atmospheric vertical wind speed profile models were the main source of uncertainty in predicting WFN at long-range distances. When predicting WFN, it is important to acknowledge variability associated with different models as this contributes to the uncertainty of the predicted values.

We explore the possibility of existence of plasmonically generated electronic Floquet bands at metal surfaces by studying the gauge transformed electron-surface plasmon interaction in the prepumped plasmonic coherent state environment. These bands may promote non-Einsteinian electron emission from metal surfaces exposed to primary interactions with strong electromagnetic fields. Resonant behaviour and scaling of emission yield with the parent electronic structure and plasmonic state parameters are estimated for Ag(111) surface. Relative yield intensities from non-Einsteinian emission channels in photoelectron spectra offer the means to calibrate the mediating plasmonic fields and therefrom ensuing surface Floquet bands., Comment: 12 pages, 5 figures

In this account, we describe the crazy-clock phenomenon involving the state I (low iodide and iodine concentration) to state II (high iodide and iodine concentration with new iodine phase) transition after a Briggs-Rauscher (BR) oscillatory process. While the BR crazy-clock phenomenon is known, it is the first time that crazy-clock behavior is linked and explained with the symmetry-breaking phenomenon, highlighting the entire process in a novel way. The presented phenomenon has been thoroughly investigated by running more than 60 experiments, and evaluated by using statistical cluster K-means analysis. The mixing rate, as well as the magnetic bar shape and dimensions, have a strong influence on the transition appearance. Although the transition for both mixing and no-mixing conditions are taking place completely randomly, by using statistical cluster analysis we obtain different numbers of clusters (showing the time-domains where the transition is more likely to occur). In the case of stirring, clusters are more compact and separated, revealed new hidden details regarding the chemical dynamics of nonlinear processes. The significance of the presented results is beyond oscillatory reaction kinetics since the described example belongs to the small class of chemical systems that shows intrinsic randomness in their response and it might be considered as a real example of a classical liquid random number generator.

In this paper, we introduce a new type of matter that has origin in $p$-adic strings, i.e., strings with a $p$-adic worldsheet. We investigate some properties of this $p$-adic matter, in particular its cosmological aspects. We start with crossing symmetric scattering amplitudes for $p$-adic open strings and related effective nonlocal and nonlinear Lagrangian which describes tachyon dynamics at the tree level. Then, we make a slight modification of this Lagrangian and obtain a new Lagrangian for non-tachyonic scalar field. {Using this new Lagrangian in the weak field approximation as a matter in Einstein gravity with the cosmological constant, one obtains an exponentially expanding FLRW closed universe.} At the end, we discuss the obtained results, i.e., computed mass of the scalar $p$-adic particle, estimated radius of related closed universe and noted $p$-adic matter as a possible candidate for dark matter., Comment: 16 pages, 2 figures

A nonlocal gravity model (2.1) was introduced and considered recently [49], and two exact cosmological solutions in flat space were presented. The first solution is related to some radiation effects generated by nonlocal dynamics on dark energy background, while the second one is a nonsingular time symmetric bounce. In the present paper we investigate other possible exact cosmological solutions and find some the new ones in nonflat space. Used nonlocal gravity dynamics can change background topology. To solve the corresponding eqations of motion, we first look for a solution of the eigenvalue problem $\Box (R -4\Lambda) = q\ (R - 4\Lambda) .$ We also discuss possible extension of this model with nonlocal operator symmetric under $\Box \longleftrightarrow \Box^{-1}$ and its connection with another interesting nonlocal gravity model., Comment: 18 pages, submitted to the journal Symmetry

Recent accessibility of affordable sensing technologies, microcontrollers, and wireless communication technology has made it possible for stormwater systems to be retrofitted with an assortment of sensors and actuators. These smart stormwater systems have enabled the real-time sensing of their surrounding environmental dynamics, and subsequently, provide the basis for autonomous and adaptive operational control strategies. Additionally, these systems allow for inexpensive and minimally-invasive stormwater control interventions (e.g. hydraulic valve operated by cellularly-connected actuator) in lieu of new construction. However promising this area of smart stormwater control, there still remain barriers -- for experts and novices alike -- to access a set of shared tools and methods for investigating, developing, and contributing to it. In an effort to make smart stormwater control research more methodical, efficient, and accessible, we present pystorms, a Python-based simulation sandbox that facilitates the quantitative evaluation and comparison of control strategies. The pystorms simulation sandbox comes with (i) a collection of real world-inspired stormwater control scenarios on which any number of control strategies can be applied and tested via (ii) an accompanying Python programming interface coupled with a stormwater simulator. For the first time, pystorms enables rigorous and efficient evaluation of smart stormwater control methodologies across a diverse set of watersheds with only a few lines of code. We present the details of pystorms here and demonstrate its usage by applying and evaluating two stormwater control strategies.

In spite of the crucial role it is believed to play in nature, fluorescence in natural organisms remains under-investigated from optical, chemical and biological perspectives. One example is the transparent wings of insects from the order Hemiptera: fluorescence emission has so far not been reported in any of the 3,000 described species of the superfamily Cicadoidea. The phenomenon has however been investigated in several butterfly and moth species, in which the coloured wing scales are known to embed fluorophores such as papiliochrome II. Photonic structures present in these scales may mediate the associated light emission. Fluorescence has also been described in the vein joints and the membrane of the transparent wings of damselflies and dragonflies. This emission was attributed to the presence of autofluorescent proteins such as resilin that gives rise to low stiffness and high strain in the biological tissues, allowing passive wing deformations. In this article, we characterised the fluorescence emission from the transparent wings of the grey cicada \textit{Cicada orni} (Linnaeus, 1758) and the common cicada \textit{Lyristes (Tibicen) plebejus} (Scopoli, 1763), using linear and nonlinear optical methods. We compared their properties with the fluorescence emission from two other insects exhibiting transparent wings, namely, \textit{Hemaris fuciformis} (Linnaeus, 1758) commonly known as the broad-bordered bee hawk-moth and the Bornean damselfly \textit{Vestalis amabilis} Lieftinck, 1965. These fluorescent behaviours were optically characterised by one- and multi-photon microscopy and spectrometry, allowing us to infer the symmetry of the material as well as the biological role of the fluorophores in the visual perception of insects and to explain the high absorption by the material in the range 300-400~nm., Comment: 16 pages, 6 figures, 1 table

Software Process Improvement requires significant effort related not only to the identification of relevant issues and providing an adequate response to them, but also to the implementation and adoption of the changes. Best practices provide recommendations to software teams on how to address the identified objectives in practice, based on aggregated experience and knowledge. In the paper, we present the GEANT experience and observations from the process of adopting thebest practices, and present the setting we have been using., Comment: 10 pages

A series of single-molecule dysprosium (Dy 3+) complexes consisting of b diketonate ligands, as water-containing complexes, and the auxiliary triphenylphosphine oxide (tppo)ligand as water free complexes were investigated as potential white light emitters. The coordination environment and choice of the ligands play an important role in the behavior of the yellow and blue emission of the Dy 3+ complexes based on the sensitization efficiency of the Dy 3+ ion by the ligands. By introducing the auxiliary tppo ligand in the complex, the relative intensity of the Dy 3+ emission increases due to a more efficient sensitization of the Dy 3+ ion.

We analyse recently measured nonlinear photoemission spectra from Ag surfaces that reveal resonances whose energies do not scale with the applied photon energy but stay pinned to multiples of bulk plasmon energy $\hbar\omega_p$ above the Fermi level. To elucidate these unexpected and peculiar features we investigate the spectra of plasmons generated in a solid by the optically pumped electronic polarization and their effect on photoemission. By combining quadratic response formalism for calculations of photoemission yield, a nonperturbative approach to inelastic electron scattering, and first-principles calculations for the electronic structure, we demonstrate the dependence of probability amplitude for single- and multiplasmon excitations on the basic parameters characterizing the photon pulse and the system. The resulting multiexcitation spectrum evolves towards a truncated plasmonic coherent state. Analogous concept is extrapolated to interpret plasmon generation by multiphoton excited electronic polarization. Based on this we elaborate a scenario that the thus created real plasmons act as supplementary frequency-locked pump field for non-Einsteinian plasmonically assisted channels of photoemission from metals. The established paradigm enables assignment and assessment of the observed linear $\hbar\omega_p$- and nonlinear $2\hbar\omega_p$-electron yields from Ag. Such effects may be exploited for selective filtering of optical energy conversion in electronic systems., Comment: Article as accepted for publication in Physical Review B

Virtual development and prototyping has already become an integral part in the field of automated driving systems (ADS). There are plenty of software tools that are used for the virtual development of ADS. One such tool is CarMaker from IPG Automotive, which is widely used in the scientific community and in the automotive industry. It offers a broad spectrum of implementation and modelling possibilities of the vehicle, driver behavior, control, sensors, and environmental models. Focusing on the virtual development of highly automated driving functions on the vehicle guidance level, it is essential to perceive the environment in a realistic manner. For the longitudinal and lateral path guidance line detection sensors are necessary for the determination of the relevant perceiving vehicle and for the planning of trajectories. For this purpose, a lane sensor model was developed in order to efficiently detect lanes in the simulation environment of CarMaker. The so-called advanced lane detection model (ALDM) is optimized regarding the calculation time and is for the lateral and longitudinal vehicle guidance in CarMaker.

The relationships between eigenvalues and eigenvectors of a product graph and those of its factor graphs have been known for the standard products, while characterization of Laplacian eigenvalues and eigenvectors of the Kronecker product of graphs using the Laplacian spectra and eigenvectors of the factors turned out to be quite challenging and has remained an open problem to date. Several approaches for the estimation of Laplacian spectrum of the Kronecker product of graphs have been proposed in recent years. However, it turns out that not all the methods are practical to apply in network science models, particularly in the context of multilayer networks. Here we develop a practical and computationally efficient method to estimate Laplacian spectra of this graph product from spectral properties of their factor graphs which is more stable than the alternatives proposed in the literature. We emphasize that a median of the percentage errors of our estimated Laplacian spectrum almost coincides with the $x$-axis, unlike the alternatives which have sudden jumps at the beginning followed by a gradual decrease for the percentage errors. The percentage errors confined (confidence of the estimations) up to $\pm$10% for all considered approximations, depending on a graph density. Moreover, we theoretically prove that the percentage errors becomes smaller when the network grows or the edge density level increases. Additionally, some novel theoretical results considering the exact formulas and lower bounds related to the certain correlation coefficients corresponding to the estimated eigenvectors are presented.

The purpose of this dissertation is to present an overview of the operational and financial performance of airports in Europe. In benchmarking studies, airports are assessed and compared with other airports based on key indicators from a technical and an economic point of view. The interest lies primarily in the question, which key figures can best measure the perception of quality of service from the point of view of the passenger for the services at an airport., Comment: 358 pages, 99 figures and 63 tables

We consider the indefinite Sturm-Liouville differential expression \[\mathfrak{a}(f) := - \frac{1}{w}\left( \frac{1}{r} f' \right)',\] where $\mathfrak{a}$ is defined on a finite or infinite open interval $I$ with $0\in I$ and the coefficients $r$ and $w$ are locally summable and such that $r(x)$ and $(\operatorname{sgn} x) w(x)$ are positive a.e. on $I$. With the differential expression $\mathfrak{a}$ we associate a nonnegative self-adjoint operator $A$ in the Krein space $L^2_w(I)$, which is viewed as a coupling of symmetric operators in Hilbert spaces related to the intersections of $I$ with the positive and the negative semi-axis. For the operator $A$ we derive conditions in terms of the coefficients $w$ and $r$ for the existence of a Riesz basis consisting of generalized eigenfunctions of $A$ and for the similarity of $A$ to a self-adjoint operator in a Hilbert space $L^2_{|w|}(I)$. These results are obtained as consequences of abstract results about the regularity of critical points of nonnegative self-adjoint operators in Krein spaces, which are couplings of two symmetric operators acting in Hilbert spaces., Comment: 50 pages

We go back to the roots of enriched category theory and study categories enriched in chain complexes; that is, we deal with differential graded categories (DG-categories for short). In particular, we recall weighted colimits and provide examples. We solve the 50 year old question of how to characterize Cauchy complete DG-categories in terms of existence of some specific finite absolute colimits. As well as the interactions between absolute weighted colimits, we also examine the total complex of a chain complex in a DG-category as a non-absolute weighted colimit., Comment: 27 pages. Remark 7.3 is new. It gives an example to show that cokernels of protosplit chain maps cannot be constructed from the other assumed colimits appearing in Theorem 7.2. Thanks to the referee for pointing out other needed changes

Large fires can inject smoke into the upper troposphere and lower stratosphere. Detailed fire simulations allow for assessment of how local weather interacts with these fires and affects smoke lofting. In this study, we employ the fire simulation package in the Weather Research and Forecasting model (WRF-Fire), Version 4.0.1, to explore how smoke lofting from a fire burning a homogeneous fuel bed changes with varying local winds, relative humidity, and atmospheric boundary-layer stability for two different-sized areal fires. We find that the presence of moisture has the greatest influence on the results by raising the altitude of lofting, while faster winds speeds dampen lofting and lower the injection height. Stably stratified conditions further inhibit plume propagation compared with neutrally stratified conditions.

Classical approaches and procedures for testing of automated vehicles of SAE levels 1 and 2 were based on defined scenarios with specific maneuvers, depending on the function under test. For automated driving systems (ADS) of SAE level 3+, the scenario space is infinite and calling for virtual testing and verification. However, even in simulation, the generation of safety-relevant scenarios for ADS is expensive and time-consuming. This leads to a demand for stochastic and realistic traffic simulation. Therefore, microscopic traffic flow simulation models (TFSM) are becoming a crucial part of scenario-based testing of ADS. In this paper, a co-simulation between the multi-body simulation software IPG CarMaker and the microscopic traffic flow simulation software (TFSS) PTV Vissim is used. Although the TFSS could provide realistic and stochastic behavior of the traffic participants, safety-critical scenarios (SCS) occur rarely. In order to avoid this, a novel Stress Testing Method (STM) is introduced. With this method, traffic participants are manipulated via external driver DLL interface from PTV Vissim in the vicinity of the vehicle under test in order to provoke defined critical maneuvers derived from statistical accident data on highways in Austria. These external driver models imitate human driving errors, resulting in an increase of safety-critical scenarios. As a result, the presented STM method contributes to an increase of safety-relevant scenarios for verification, testing and assessment of ADS.

Virtual testing of automated driving systems (ADS) has become an essential part of testing procedures for all automation levels. As ADS from automation level 3 and up are very complex, virtual testing for such systems is inevitable. The complexity of these levels lies in the modelling and calculation demand for the virtual environment which consists of roads, traffic, static and dynamic objects as well as the modelling of the car itself. For safety and performance analyses of ADS, the most important part is the modelling and consideration of road traffic participants. There is multiple traffic flow simulation software (TFSS) which are used to reproduce realistic traffic behavior and are integrated directly or over interfaces with vehicle simulation software (VSS). For these software environments, the possibility to manipulate traffic participants in a defined manner e.g. in the vicinity of the vehicle under test or implementing defined driver models for traffic vehicles is beneficial. In this paper, we present a software framework based on the external driver model interface provided by Vissim. This framework makes it possible to easily manipulate traffic participants for testing purposes of ADS., Comment: The paper was rejected

We denote the monoidal bicategory of two-sided modules (also called profunctors, bimodules and distributors) between categories by $\mathrm{Mod}$; the tensor product is cartesian product of categories. For a groupoid $\scr{G}$, we study the monoidal centre $\mathrm{ZPs}(\scr{G},\mathrm{Mod}^{\mathrm{op}})$ of the monoidal bicategory $\mathrm{Ps}(\scr{G},\mathrm{Mod}^{\mathrm{op}})$ of pseudofunctors and pseudonatural transformations; the tensor product is pointwise. Alexei Davydov defined the full centre of a monoid in a monoidal category. We define a higher dimensional version: the full monoidal centre of a monoidale (= pseudomonoid) in a monoidal bicategory $\scr{M}$, and it is a braided monoidale in the monoidal centre $\mathrm{Z}\scr{M}$ of $\scr{M}$. Each fibration $\pi : \scr{H} \to \scr{G}$ between groupoids provides an example of a full monoidal centre of a monoidale in $\mathrm{Ps}(\scr{G},\mathrm{Mod}^{\mathrm{op}})$. For a group $G$, we explain how the $G$-graded categorical structures, as considered by Turaev and Virelizier in order to construct topological invariants, fit into this monoidal bicategory context. We see that their structures are monoidales in the monoidal centre of the monoidal bicategory of $k$-linear categories on which $G$ acts., Comment: 28 pages

Maturity models for software indicate the key areas that contribute to quality improvements. They usually combine technical, organisational and human aspects relevant for effective software development, to focus the efforts and draw the direction for optimisations. In this paper, we present the process of defining best practices that support the G\'{E}ANT Software Maturity Model (GSMM), aligned to the needs of a distributed, innovation-driven, pan-European organisation. Based on the identification of specific goals relevant for G\'{E}ANT and a preliminary maturity assessment, we created a catalogue of best practices that help the software teams to attain the goals defined in the GSMM.

In the paper, we refine and extend Cusa-Huygens inequality by simple functions. In particular, we determine sharp bounds for $\sin(x) /x$ of the form $(2+\cos(x))/3 -(2/3-2/\pi)\Upsilon(x)$, where $\Upsilon(x) >0$ for $x\in (0, \pi/2)$, $\Upsilon(0)=0$ and $\Upsilon(\pi/2)=1$, such that $\sin x/x$ and the proposed bounds coincide at $x=0$ and $x=\pi/2$. The hierarchy of the obtained bounds is discussed, along with graphical study. Also, alternative proofs of the main result are given.

Causal decomposition depicts a cause-effect relationship that is not based on the concept of prediction, but based on the phase dependence of time series. It has been validated in both stochastic and deterministic systems and is now anticipated for its application in the complex dynamic process. Here, we present an extension of causal decomposition in the mutual complex dynamic process: cause and effect of time series are inherited in the decomposition of intrinsic components in a similar time scale. Furthermore, we illustrate comparative studies with predominate methods used in neuroscience, and show the applicability of the method particularly to physiological time series in brain-muscle interactions, implying the potential to the causality analysis in the complex physiological process., Comment: 18 pages, 4 figures

This article is related to construction of zeta strings from $p$-adic ones. In addition to investigation of $p$-adic string for a particular prime number $p$, it is also interesting to study collective effects taking into account all primes $p$. An idea behind this approach is that a zeta string is a whole thing with infinitely many faces which we see as $p$-adic strings. The name zeta string has origin in the Riemann zeta function contained in related Lagrangian. The starting point in construction a zeta string is Lagrangian for a $p$-adic open string. There are two types of approaches to get a Lagrangian for zeta string from Lagrangian for $p$-adic strings: additive and multiplicative approaches, that are related to two forms of the definition of the Riemann zeta function. As a result of differences in approaches, one obtains several different Lagrangians for zeta strings. We briefly discuss some properties of these Lagrangians, related potentials, equations of motion, mass spectrum and possible connection with ordinary strings. This is a review of published papers with some new views., Comment: 15 pages

Given an orbifold, we construct an orthogonal spectrum representing its stable global homotopy type. Orthogonal spectra now represent orbifold cohomology theories which automatically satisfy certain properties as additivity and the existence of Mayer-Vietoris sequences. Moreover, the value at a global quotient orbifold $M/G$ can be identified with the $G$-equivariant cohomology of the manifold $M$. Examples of orbifold cohomology theories which are represented by an orthogonal spectrum include Borel and Bredon cohomology theories and orbifold $K$-theory. This also implies that these cohomology groups are independent of the presentation of an orbifold as a global quotient orbifold.

The category of Hilbert spaces and contractions has filtered colimits, and tensoring preserves them. We also discuss (problems with) bounded maps.

The problem is area-restricted search for targets using an autonomous mobile sensing platform. Detection is imperfect: the probability of detection depends on the range to the target, while the probability of false detections is non-zero. The paper develops an intermittent information-driven search strategy, which combines fast and non-receptive displacement phase (ballistic phase) with a slow displacement sensing phase. Decisions where to move next, both in the ballistic phase and the slow displacement phase, are information-driven: they maximise the expected information gain. The paper demonstrates the efficiency of the proposed strategy in the context of a search for underwater targets: the searcher is an autonomous amphibious drone which can both fly and land or takeoff from the sea surface., Comment: 6 pages

The Bernoulli filter is a Bayes filter for joint detection and tracking of a target in the presence of false and miss detections. This paper presents a mathematical formulation of the Bernoulli filter in the framework of possibility theory, where uncertainty is represented using {\em possibility} functions, rather than {\em probability} distributions. Possibility functions model the uncertainty in a non-additive manner, and have the capacity to deal with partial (incomplete) problem specification. Thus, the main advantage of the possibilistic Bernoulli filter, derived in this paper, is that it can operate even in the absence of precise measurement and/or dynamic model parameters. This feature of the proposed filter is demonstrated in the context of target tracking using multi-static Doppler shifts as measurements., Comment: 23 pages

Gaussian Conditional Random Fields (GCRF), as a structured regression model, is designed to achieve higher regression accuracy than unstructured predictors at the expense of execution time, taking into account the objects similarities and the outputs of unstructured predictors simultaneously. As most structural models, the GCRF model does not scale well with large networks. One of the approaches consists of performing calculations on factor graphs (if it is possible) rather than on the full graph, which is more computationally efficient. The Kronecker product of the graphs appears to be a natural choice for a graph decomposition. However, this idea is not straightforwardly applicable for GCRF, since characterizing a Laplacian spectrum of the Kronecker product of graphs, which GCRF is based on, from spectra of its factor graphs has remained an open problem. In this paper we apply new estimations for the Laplacian eigenvalues and eigenvectors, and achieve high prediction accuracy of the proposed models, while the computational complexity of the models, compared to the original GCRF model, is improved from $O(n_{1}^{3}n_{2}^{3})$ to $O(n_{1}^{3} + n_{2}^{3})$. Furthermore, we study the GCRF model with a non-Kronecker graph, where the model consists of finding the nearest Kronecker product of graph for an initial graph. Although the proposed models are more complex, they achieve high prediction accuracy too, while the execution time is still much better compare to the original GCRF model. The effectiveness of the proposed models is characterized on three types of random networks where the proposed models were consistently away more accurate than the previously presented GCRF model for multiscale networks [Jesse Glass and Zoran Obradovic. Structured regression on multiscale networks. IEEE Intelligent Systems, 32(2):23-30, 2017.].