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This study evaluates the uncertainty in nanodosimetric calculations caused by variations in interaction cross sections within Monte Carlo Track Structure (MCTS) simulation codes. Nanodosimetry relies on accurately simulating particle interactions at the molecular scale. Different MCTS codes employ distinct physical models and datasets for electron interactions in liquid water, a surrogate for biological tissues. The paper focuses on the Ionization Cluster Size Distribution (ICSD) generated by electrons of varying energies in nanometric volumes. Seven MCTS codes were tested using their native cross sections and a common dataset derived from averaging data used in the participating codes. The results reveal significant discrepancies among the codes in ICSDs and derived biologically relevant nanodosimetric quantities such as mean ionization numbers (M1) and probabilities of obtaining two or more ionizations (F2). The largest variations were observed for low-energy electrons, where the contribution from interaction cross sections dominates the overall uncertainties. For instance, M1 values for ICSDs of electron of 20 eV can differ by around 45 % (RSD) and 34 % (RSD) was found for F2 values of ICSDs of electrons of 50 eV. Using common cross sections substantially reduced the discrepancies, suggesting that cross section datasets are the primary source of variability. Finally, estimates of deoxyribonucleic acid (DNA) damage using the PARTRAC code highlight tht cross section variations have a non-negligible impact simulated biological outcomes, particularly for double-strand breaks (DSBs) Indeed, despite the fact that many other parameters in the simulation that can greatly differ from one code to another, the different interaction cross-sections studied in this work can lead to differences in the number of DSBs calculated with the PARTRAC code of up to 15%., Comment: 39 pages, 8 figures, 14 tables; submitted to PLOS One

Accurate water consumption forecasting is a crucial tool for water utilities and policymakers, as it helps ensure a reliable supply, optimize operations, and support infrastructure planning. Urban Water Distribution Networks (WDNs) are divided into District Metered Areas (DMAs), where water flow is monitored to efficiently manage resources. This work focuses on short-term forecasting of DMA consumption using deep learning and aims to address two key challenging issues. First, forecasting based solely on a DMA's historical data may lack broader context and provide limited insights. Second, DMAs may experience sensor malfunctions providing incorrect data, or some DMAs may not be monitored at all due to computational costs, complicating accurate forecasting. We propose a novel method that first identifies DMAs with correlated consumption patterns and then uses these patterns, along with the DMA's local data, as input to a deep learning model for forecasting. In a real-world study with data from five DMAs, we show that: i) the deep learning model outperforms a classical statistical model; ii) accurate forecasting can be carried out using only correlated DMAs' consumption patterns; and iii) even when a DMA's local data is available, including correlated DMAs' data improves accuracy., Comment: Keywords: urban water management, water consumption, time series forecasting

Drinking water is a vital resource for humanity, and thus, Water Distribution Networks (WDNs) are considered critical infrastructures in modern societies. The operation of WDNs is subject to diverse challenges such as water leakages and contamination, cyber/physical attacks, high energy consumption during pump operation, etc. With model-based methods reaching their limits due to various uncertainty sources, AI methods offer promising solutions to those challenges. In this work, we introduce a Python toolbox for complex scenario modeling \& generation such that AI researchers can easily access challenging problems from the drinking water domain. Besides providing a high-level interface for the easy generation of hydraulic and water quality scenario data, it also provides easy access to popular event detection benchmarks and an environment for developing control algorithms., Comment: Accepted at the Workshop on Artificial Intelligence for Critical Infrastructure (AI4CI 2024) @ IJCAI'24 , Jeju Island, South Korea

The spectral smoothness properties of the low-frequency array of the Square Kilometer Array (SKA), namely SKA-Low, are an important issue for its scientific objectives to be attainable. A large array of 256 log-periodic dipole antennas, installed on top of a 42~m circular ground plane, will work as an SKA-Low station in the frequency range 50-350 MHz. In this article, the ground plane induced effects are examined in terms of antenna beam spectral characteristics, while different antenna placements are considered. Results are produced both at isolated antenna and at array level in the band 50-100 MHz, by employing an approximate method for the speeding-up of array simulations. We attempt to distinguish the ground plane effect from that of mutual coupling among antennas, which appears to be more severe at specific frequencies, using 2 figures of merit. The Discrete Fourier Transform (DFT) components of gain pattern ratios identify the fundamental spatial components of the ripple, while the Envelope Correlation Coefficient quantifies the penalty to considering an infinite ground plane., Comment: 11 pages, 7 figures, accepted in International Journal of Antennas and Propagation

Characteristic Modes Analysis (CMA) is a widely used method with recent progress in multi-antenna systems. We employ this method to characterize the mutual coupling phenomenon between two SKALA4.1 antennas, the low-frequency array elements of the future radiotelescope Square Kilometer Array (SKA-Low). The CMA accuracy is first validated at the lowest frequency range of interest with respect to a standard Method of Moments (MoM) solution by decomposing the single antenna into its characteristic modes. We then examine critical frequencies of a two-antenna system in modal decomposition, and characterize those responsible for the radiated electric field spurious spectral features owing to the mutual coupling. We connect these modes to first-order coupling of single antenna CMA modes, by using the eigenvalue data of both single- and two- antenna simulations., Comment: 4 pages, 5 figures, presented at the 17th European Conference on Antennas and Propagation

The scientific community shows a great interest in the study of DNA damage induction, DNA damage repair and the biological effects on cells and cellular systems after exposure to ionizing radiation. Several in-silico methods have been proposed so far to study these mechanisms using Monte Carlo simulations. This study outlines a Geant4-DNA example application, named "molecularDNA", publicly released in the 11.1 version of Geant4 (December 2022). It was developed for novice Geant4 users and requires only a basic understanding of scripting languages to get started. The example currently proposes two different DNA-scale geometries of biological targets, namely "cylinders", and the "human cell". This public version is based on a previous prototype and includes new features such as: the adoption of a new approach for the modeling of the chemical stage (IRT-sync), the use of the Standard DNA Damage (SDD) format to describe radiation-induced DNA damage and upgraded computational tools to estimate DNA damage response. Simulation data in terms of single strand break (SSB) and double strand break (DSB) yields were produced using each of these geometries. The results were compared to the literature, to validate the example, producing less than 5 % difference in all cases., Comment: 20 pages, 8 figures

Cosmic Dawn, the onset of star formation in the early universe, can in principle be studied via the 21cm transition of neutral hydrogen, for which a sky-averaged absorption signal, redshifted to MHz frequencies, is predicted to be {\it O}(10-100)\,mK. Detection requires separation of the 21cm signal from bright chromatic foreground emission due to Galactic structure, and the characterisation of how it couples to instrumental response. In this work, we present characterisation of antenna gain patterns for the Large-aperture Experiment to detect the Dark Ages (LEDA) via simulations, assessing the effects of the antenna ground-plane geometries used, and measured soil properties. We then investigate the impact of beam pattern uncertainties on the reconstruction of a Gaussian absorption feature. Assuming the pattern is known and correcting for the chromaticity of the instrument, the foregrounds can be modelled with a log-polynomial, and the 21cm signal identified with high accuracy. However, uncertainties on the soil properties lead to \textperthousand\ changes in the chromaticity that can bias the signal recovery. The bias can be up to a factor of two in amplitude and up to few \% in the frequency location. These effects do not appear to be mitigated by larger ground planes, conversely gain patterns with larger ground planes exhibit more complex frequency structure, significantly compromising the parameter reconstruction. Our results, consistent with findings from other antenna design studies, emphasise the importance of chromatic response and suggest caution in assuming log-polynomial foreground models in global signal experiments., Comment: Accepted for publication in MNRAS

Our understanding of radiation induced cellular damage has greatly improved over the past decades. Despite this progress, there are still many obstacles to fully understanding how radiation interacts with biologically relevant cellular components to form observable endpoints. One hurdle is the difficulty faced by members of different research groups in directly comparing results. Multiple Monte Carlo codes have been developed to simulate damage induction at the DNA scale, while at the same time various groups have developed models that describe DNA repair processes with varying levels of detail. These repair models are intrinsically linked to the damage model employed in their development, making it difficult to disentangle systematic effects in either part of the modelling chain. The modelling chain typically consists of track structure Monte Carlo simulations of the physics interactions creating direct damages to the DNA; followed by simulations of the production and initial reactions of chemical species causing indirect damages. After the DNA damage induction, DNA repair models combine the simulated damage patterns with biological models to determine the biological consequences of the damage. We propose a new Standard data format for DNA Damage to unify the interface between the simulation of damage induction and the biological modelling of cell repair processes. Such a standard greatly facilitates inter model comparisons, providing an ideal environment to tease out model assumptions and identify persistent, underlying mechanisms. Through inter model comparisons, this unified standard has the potential to greatly advance our understanding of the underlying mechanisms of radiation induced DNA damage and the resulting observable biological effects.

The phenomenon of charge pumping and the modern theory of electric polarization are reconsidered by analytically taking into account emergent non-Hermitian contributions. These are accounted for through the use of an extended definition of the velocity operator and are determined by means of a dynamic Hellmann-Feynman theorem (DHFT) that we derive here for the first time. The DHFT introduces generalized Berry curvatures and it is valid for calculating observables nonperturbatively, hence with results valid to all orders of the external fields. By using the extended velocity operator we rigorously show how the charge pumping is linked up with the boundaries of the material (with the non-Hermiticity being essential for this connection), and by means of the DHFT we show that the well-known topological quantization of the pumped charge breaks down due to a nonintegrable Aharonov-Anandan phase in driven non-equilibrium processes whenever the periodic gauge cannot be applied to the Floquet-Bloch states. Likewise, we show that the electronic polarization change has an additional non-Hermitian contribution, which is overlooked in the modern theory of electric polarization. The non-Hermitian contribution is by definition a bulk quantity that may equally be evaluated as a boundary quantity due to a symmetric structure that allows the bulk integration to be transformed into a boundary one. This non-Hermitian contribution is very sensitive to the realistic boundary conditions imposed on the wavefunctions and it is therefore expected to be significant in biased insulators where charge accumulation over their boundaries is present during the process that causes the polarization change. Finally, we show how a well-defined surface-charge theorem can be formulated in terms of the boundary non-Hermitian contribution., Comment: 31 pages, minor corrections

Classification models are very sensitive to data uncertainty, and finding robust classifiers that are less sensitive to data uncertainty has raised great interest in the machine learning literature. This paper aims to construct robust \emph{Support Vector Machine} classifiers under feature data uncertainty via two probabilistic arguments. The first classifier, \emph{Single Perturbation}, reduces the local effect of data uncertainty with respect to one given feature and acts as a local test that could confirm or refute the presence of significant data uncertainty for that particular feature. The second classifier, \emph{Extreme Empirical Loss}, aims to reduce the aggregate effect of data uncertainty with respect to all features, which is possible via a trade-off between the number of prediction model violations and the size of these violations. Both methodologies are computationally efficient and our extensive numerical investigation highlights the advantages and possible limitations of the two robust classifiers on synthetic and real-life data.

Cosmic rays are ubiquitous in interstellar environments, and their bombardment of dust-grain ice mantles is a possible driver for the formation of complex, even prebiotic molecules. Yet, critical data that are essential for accurate modeling of this phenomenon, such as the average radii of cosmic-ray tracks in amorphous solid water (ASW) remain unconstrained. It is shown that cosmic ray tracks in ASW can be approximated as a cylindrical volume with an average radius that is mostly independent of the initial particle energy. Interactions between energetic ions and both a low-density amorphous (LDA) and high-density amorphous (HDA) ice target are simulated using the Geant4-DNA Monte Carlo toolkit, which allows for tracking secondary electrons down to subexcitation energies in the material. We find the peak track core radii, $r_\mathrm{cyl}$, for LDA and HDA ices to be 9.9 nm and 8.4 nm, respectively - somewhat less than double the value of 5 nm often assumed in astrochemical models., Comment: 19 pages, 7 figures, 1 table (accepted in ApJ)

The theory of orbital magnetization is reconsidered by defining additional quantities that incorporate a non-Hermitian effect due to anomalous operators that break the domain of definition of the Hermitian Hamiltonian. As a result, boundary contributions to the observable are rigorously and analytically taken into account. In this framework, we extend the standard velocity operator definition in order to incorporate an anomaly of the position operator that is inherent in band theory, which results in an explicit boundary velocity contribution. Using the extended velocity, we define the electrons' intrinsic orbital circulation and we argue that this is the main quantity that captures the orbital magnetization phenomenon. As evidence of this assertion, we demonstrate the explicit relation between the nth band electrons' collective intrinsic circulation and the approximated, evaluated with respect to Wannier states, local and itinerant circulation contributions that are frequently used in the modern theory of orbital magnetization. A quantum mechanical formalism for the orbital magnetization of extended and periodic topological solids (insulators or metals) is redeveloped without any Wannier localization approximation or heuristic extension [Caresoli, Thonhauser, Vanderbilt and Resta, Phys. Rev. B 74, 024408 (2006)]. It is rigorously shown that, as a result of the non-Hermitian effect, an emerging covariant derivative enters the one-band (adiabatically deformed) approximation k-space expression for the orbital magnetization. In the corresponding many-band (unrestricted) k-space formula, the non-Hermitian effect contributes an additional boundary quantity which is expected to give locally (in momentum space) giant contributions whenever band crossings occur along with Hall voltage due to imbalance of electron accumulation at the opposite boundaries of the material., Comment: 25 pages. New work [through modification of the quantum velocity operator] leading to non-Hermitian effects of a kinetic type (that will non-trivially affect the Modern Theory of Polarization and Magnetization). Such non-Hermitian effects: 1) on charge pumping and on electric Polarization, and 2) in spin-Physics, have been worked out in detail-two works (as a natural extension) will soon be posted

The notions of knowledge and its management have been at the core of the information systems (IS) field almost since its inception. Knowledge has been viewed in several ways in the prior literature, including as a state of mind, an object, a process, access to information, and a capability. A commonly-used definition characterizes knowledge as a justified belief that increases an entity's capacity for effective action (Alavi and Leidner 2001, p. 109). Relatedly, knowledge management (KM) has been defined as a systemic process to acquire, organize, and communicate individual knowledge so that others may make use of it (Beck et al. 2014). Knowledge-management systems (KMSs) support these processes for creating, exchanging, and storing knowledge (Beck et al. 2014), and have been viewed as being either repository- based or network-based (Kankanhalli et al. 2005). In an attempt to provide a useful resource for scholars interested in KM, we take stock of the pertinent research published in MISQ. More specifically, the goal of this curation is to serve as a living document that will offer a starting point for future KM research. This curation highlights the 44 articles with a primary focus on KM (Table 1). The articles address theoretical and conceptual issues, provide methodological guidance, and use a wide range of quantitative and qualitative research methods. To define the scope of this curation, we excluded: (1) articles in which KM is used as part of another construct; (2) some early articles that were practice- oriented with limited scholarly orientation; and (3) articles that focus on knowledge (such as the knowledge requirements of IS professionals) but not on KM.

Random-effects models are frequently used to synthesise information from different studies in meta-analysis. While likelihood-based inference is attractive both in terms of limiting properties and of implementation, its application in random-effects meta-analysis may result in misleading conclusions, especially when the number of studies is small to moderate. The current paper shows how methodology that reduces the asymptotic bias of the maximum likelihood estimator of the variance component can also substantially improve inference about the mean effect size. The results are derived for the more general framework of random-effects meta-regression, which allows the mean effect size to vary with study-specific covariates.