Large Language Models (LLMs) are revolutionizing the landscape of Generative Artificial Intelligence (GenAI), with innovative LLM-backed solutions emerging rapidly. However, when applied to database technologies, specifically query generation for graph databases and Knowledge Graphs (KGs), LLMs still face significant challenges. While research on LLM-driven query generation for Structured Query Language (SQL) exists, similar systems for graph databases remain underdeveloped. This paper presents a comparative study addressing the challenge of generating Cypher queries a powerful language for interacting with graph databases using open-access LLMs. We rigorously evaluate several LLM agents (OpenAI ChatGPT 4o, Claude Sonnet 3.5, Google Gemini Pro 1.5, and a locally deployed Llama 3.1 8B) using a designed few-shot learning prompt and Retrieval Augmented Generation (RAG) backed by Chain-of-Thoughts (CoT) reasoning. Our empirical analysis of query generation accuracy reveals that Claude Sonnet 3.5 outperforms its counterparts in this specific domain. Further, we highlight promising future research directions to address the identified limitations and advance LLM-driven query generation for graph databases.

Despite the growing number of confident binary black hole coalescences observed through gravitational waves so far, the astrophysical origin of these binaries remains uncertain. Orbital eccentricity is one of the clearest tracers of binary formation channels. Identifying binary eccentricity, however, remains challenging due to the limited availability of gravitational waveforms that include effects of eccentricity. Here, we present observational results for a waveform-independent search sensitive to eccentric black hole coalescences, covering the third observing run (O3) of the LIGO and Virgo detectors. We identified no new high-significance candidates beyond those that were already identified with searches focusing on quasi-circular binaries. We determine the sensitivity of our search to high-mass (total mass M>70 $M_\odot$) binaries covering eccentricities up to 0.3 at 15 Hz orbital frequency, and use this to compare model predictions to search results. Assuming all detections are indeed quasi-circular, for our fiducial population model, we place an upper limit for the merger rate density of high-mass binaries with eccentricities 0 < e \leq 0.3 at $0.33$ Gpc$^{-3}$ yr$^{-1}$ at 90\% confidence level.

This work addresses the development of a physics-informed neural network (PINN) with a loss term derived from a discretized time-dependent reduced-order system. In this work, first, the governing equations are discretized using a finite difference scheme (whereas, any other discretization technique can be adopted), then projected on a reduced or latent space using the Proper Orthogonal Decomposition (POD)-Galerkin approach and next, the residual arising from discretized reduced order equation is considered as an additional loss penalty term alongside the data-driven loss term using different variants of deep learning method such as Artificial neural network (ANN), Long Short-Term Memory based neural network (LSTM). The LSTM neural network has been proven to be very effective for time-dependent problems in a purely data-driven environment. The current work demonstrates the LSTM network's potential over ANN networks in physics-informed neural networks (PINN) as well. The potential of using discretized governing equations instead of continuous form lies in the flexibility of input to the PINN. Different sizes of data ranging from small, medium to big datasets are used to assess the potential of discretized-physics-informed neural networks when there is very sparse or no data available. The proposed methods are applied to a pitch-plunge airfoil motion governed by rigid-body dynamics and a one-dimensional viscous Burgers' equation. The current work also demonstrates the prediction capability of various discretized-physics-informed neural networks outside the domain where the data is available or governing equation-based residuals are minimized.

The proposed mission "Space Atomic Gravity Explorer" (SAGE) has the scientific objective to investigate gravitational waves, dark matter, and other fundamental aspects of gravity as well as the connection between gravitational physics and quantum physics using new quantum sensors, namely, optical atomic clocks and atom interferometers based on ultracold strontium atoms.

The inherent sensitivity of quantum sensors to their physical environment can make them good reporters of parameters such as temperature, pressure, strain, and electric fields. Here, we present a molecular platform for pressure (P) and temperature (T) sensing using para-terphenyl crystals doped with pentacene. We leverage the optically detected magnetic resonance (ODMR) of the photoexcited triplet electron in the pentacene molecule, that serves as a sensitive probe for lattice changes in the host para-terphenyl due to pressure or temperature variations. We observe maximal ODMR frequency variations of df/dP=1.8 MHz/bar and df/dT=247 kHz/K, which are over 1,200 times and three times greater, respectively, than those seen in nitrogen-vacancy centers in diamond. This results in a >85-fold improvement in pressure sensitivity over best previously reported. The larger variation reflects the weaker nature of the para-terphenyl lattice, with first-principles DFT calculations indicating that even picometer-level shifts in the molecular orbitals due to P, T changes are measurable. The platform offers additional advantages including high levels of sensor doping, narrow ODMR linewidths and high contrasts, and ease of deployment, leveraging the ability for large single crystals at low cost. Overall, this work paves the way for low-cost, optically-interrogated pressure and temperature sensors and lays the foundation for even more versatile sensors enabled by synthetic tunability in designer molecular systems.

In this research paper we modify a classical SIR model to better adapt to the dynamics of COVID-19, that is we propose the heterogeneous SQAIRD model where COVID-19 spreads over a population of economic agents, namely: the elderly, adults and young people. We then compute and simulate an optimal control problem faced by a Government, where its objective is to minimize the costs generated by the pandemics using as control a compulsory quarantine measure (that is, a lockdown). We first analyze the problem from a theoretical perspective, claiming that different lockdown policies (total lockdown, no lockdown or partial lockdown) may justified by different cost (concave or convex) structures of the economies. We then focus on a particular cost structure (convex costs) and we simulate a targeted optimal policy vs. a uniform optimal policy, by dividing the whole population in three demographic groups (young, adults and old). We also simulate the dynamic of the pandemic with no policy implemented. Simulations highlighted the fact that: a) a policy of lockdown is always better than the \emph{laissez faire} policy, because it limits the costs that the pandemic generates in an uncontrolled situation; b) a targeted policy based on age of the individuals outperforms a uniform policy in terms of costs that it generates, being a targeted policy less costly and equally effective in the control of the pandemic.

We investigated the impact of female politicians on waste collection in Italian municipalities in different territories observed over the years 2010-2019. We used a staggered difference-in-differences design to obtain a causal interpretation of the estimated effects. We find that the majority of women in the municipal council positively influence pro-environmental individual behaviour. The impact of a female-majority council is heterogeneous by region and more pronounced in areas with lower social capital. Female politicians as catalysts for positive change fade after 5-6 years, likely due to persistent social norms locally, thus stressing the need for additional cultural actions with long-lasting effects.

Proof-of-Attendance (PoA) mechanisms are typically employed to demonstrate a specific user's participation in an event, whether virtual or in-person. The goal of this study is to extend such mechanisms to broader contexts where the user wishes to digitally demonstrate her involvement in a specific activity (Proof-of-Engagement, PoE). This work explores different solutions, including DLTs as well as established technologies based on centralized systems. The main aspects we consider include the level of privacy guaranteed to users, the scope of PoA/PoE (both temporal and spatial), the transferability of the proof, and the integration with incentive mechanisms.

The Metis coronagraph is one of the remote sensing instruments hosted on board the ESA/NASA Solar Orbiter mission. Metis is devoted to carry out the first simultaneous imaging of the solar corona in both visible light (VL) and ultraviolet (UV). High-energy particles penetrate spacecraft materials and may limit the performance of on-board instruments. A study of galactic cosmic-ray (GCR) tracks observed in the first VL images gathered by Metis during the commissioning phase for a total of 60 seconds of exposure time is presented here. A similar analysis is planned for the UV channel. A prediction of the GCR flux up to hundreds of GeV is made here for the first part of the Solar Orbiter mission to study the Metis coronagraph performance. GCR model predictions are compared to observations gathered on board Solar Orbiter by the EPD/HET experiment in the range 10 MeV-100 MeV in the summer 2020 and with previous measurements. Estimated cosmic-ray fluxes above 70 MeV n$^{-1}$ have been also parameterized and used for Monte Carlo simulations aiming at reproducing the cosmic-ray track observations in the Metis coronagraph VL images. The same parameterizations can also be used to study the performance of other detectors. By comparing observations of cosmic-ray tracks in the Metis VL images with FLUKA Monte Carlo simulations of cosmic-ray interactions in the VL detector, it is found that cosmic rays fire a fraction of the order of 10$^{-4}$ of the whole image pixel sample. Therefore, cosmic rays do not affect sensibly the quality of Metis VL images. It is also found that the overall efficiency for cosmic-ray identification in the Metis VL images is approximately equal to the contribution of Z$>$2 particles. As a result, the Metis coronagraph may play the role of a proton monitor for long-term GCR variations during the overall mission duration.

In computational physics, chemistry, and biology, the implementation of new techniques in a shared and open source software lowers barriers to entry and promotes rapid scientific progress. However, effectively training new software users presents several challenges. Common methods like direct knowledge transfer and in-person workshops are limited in reach and comprehensiveness. Furthermore, while the COVID-19 pandemic highlighted the benefits of online training, traditional online tutorials can quickly become outdated and may not cover all the software's functionalities. To address these issues, here we introduce ``PLUMED Tutorials'', a collaborative model for developing, sharing, and updating online tutorials. This initiative utilizes repository management and continuous integration to ensure compatibility with software updates. Moreover, the tutorials are interconnected to form a structured learning path and are enriched with automatic annotations to provide broader context. This paper illustrates the development, features, and advantages of PLUMED Tutorials, aiming to foster an open community for creating and sharing educational resources.

This study presents observations of a large pseudostreamer solar eruption and, in particular, the post-eruption relaxation phase, as captured by Metis onboard the Solar Orbiter on October 12, 2022, during its perihelion passage. Utilizing total brightness data, we observe the outward propagation of helical features up to 3 solar radii along a radial column that appears to correspond to the stalk of the pseudostreamer. The helical structures persisted for more than 3 hours following a jet-like coronal mass ejection associated with a polar crown prominence eruption. A notable trend is revealed: the inclination of these features decreases as their polar angle and height increase. Additionally, we measured their helix pitch. Despite a 2-minute time cadence limiting direct correspondence among filamentary structures in consecutive frames, we find that the Metis helical structure may be interpreted as a consequence of twist (nonlinear torsional Alfv\'{e}n waves) and plasma liberated by interchange reconnection. A comparison was performed of the helix parameters as outlined by fine-scale outflow features with those obtained from synthetic white-light images derived from the high-resolution magnetohydrodynamics simulation of interchange reconnection in a pseudostreamer topology by Wyper et al. (2022). A remarkable similarity between the simulation-derived images and the observations was found. We conjecture that these Metis observations may represent the upper end in spatial and energy scale of the interchange reconnection process that has been proposed recently as the origin of the Alfv\'{e}nic solar wind.

We recently described a specific type of attractors of two-dimensional discontinuous piecewise linear maps, characterized by two discontinuity lines dividing the phase plane into three partitions, related to economic applications. To our knowledge, this type of attractor, which we call a weird quasiperiodic attractor, has not yet been studied in detail. They have a rather complex geometric structure and other interesting properties that are worth understanding better. To this end, we consider a simpler map that can also possess weird quasiperiodic attractors, namely, a 2D discontinuous piecewise linear map $F$ with a single discontinuity line dividing the phase plane into two partitions, where two different homogeneous linear maps are defined. Map $F$ depends on four parameters -- the traces and determinants of the two Jacobian matrices. In the parameter space of map $F$, we obtain specific regions associated with the existence of weird quasiperiodic attractors; describe some characteristic properties of these attractors; and explain one of the possible mechanisms of their appearance.

We utilize a chartist-fundamentalist model to examine the limits of informationally efficient stock markets. In our model, chartists are permanently active in the stock market, while fundamentalists trade only when their mispricing-dependent trading signals are strong. Our findings indicate the possible coexistence of two distinct regimes. Depending on the initial conditions, the stock market may exhibit either constant or oscillatory mispricing. Constant mispricing occurs when chartists remain the sole active speculators, causing the stock price to converge toward a nonfundamental value. Conversely, the stock price oscillates around its fundamental value when fundamentalists repeatedly enter and exit the market. Exogenous shocks result in intricate regime-switching dynamics.

The fact that internet companies may record our personal data and track our online behavior for commercial or political purpose has emphasized aspects related to online privacy. This has also led to the development of search engines that promise no tracking and privacy. Search engines also have a major role in spreading low-quality health information such as that of anti-vaccine websites. This study investigates the relationship between search engines' approach to privacy and the scientific quality of the information they return. We analyzed the first 30 webpages returned searching 'vaccines autism' in English, Spanish, Italian and French. The results show that alternative search engines (Duckduckgo, Ecosia, Qwant, Swisscows and Mojeek) may return more anti-vaccine pages (10 to 53 percent) than Google.com (zero). Some localized versions of Google, however, returned more anti-vaccine webpages (up to 10 percent) than Google.com. Our study suggests that designing a search engine that is privacy savvy and avoids issues with filter bubbles that can result from user tracking is necessary but insufficient; instead, mechanisms should be developed to test search engines from the perspective of information quality (particularly for health-related webpages), before they can be deemed trustworthy providers of public health information.

Context. The LISA space observatory will explore the sub-Hz spectrum of gravitational wave emission from the Universe. The space environment, where will be immersed in, is responsible for charge accumulation on its free falling test masses (TMs) due to the galactic cosmic rays (GCRs) and solar energetic particles (SEP) impinging on the spacecraft. Primary and secondary particles produced in the spacecraft material eventually reach the TMs by depositing a net positive charge fluctuating in time. This work is relevant for any present and future space missions that, like LISA, host free-falling TMs as inertial reference. Aims. The coupling of the TM charge with native stray electrostatic field produces noise forces on the TMs, which can limit the performance of the LISA mission. A precise knowledge of the charging process allows us to predict the intensity of these charge-induced disturbances and to design specific counter-measures. Methods. We present a comprehensive toolkit that allows us to calculate the TM charging time-series in a geometry representative of LISA mission, and the associated induced forces under different conditions of the space environment by considering the effects of short, long GCR flux modulations and SEPs. Results. We study, for each of the previously mentioned conditions, the impact of spurious forces associated with the TM charging process on the mission sensitivity for gravitational wave detection.

Piecewise smooth systems are intensively studied today in many application areas, such as economics, finance, engineering, biology, and ecology. In this work, we consider a class of one-dimensional piecewise linear discontinuous maps with a finite number of partitions and functions sharing the same real fixed point. We show that the dynamics of this class of maps can be analyzed using the well-known piecewise linear circle map and prove that their bounded behavior, unrelated to the fixed point, may consist of either nonhyperbolic cycles or quasiperiodic orbits densely filling certain segments, with possible coexistence. A corresponding model describing the price dynamics of a financial market serves as an illustrative example. While simulated model dynamics may be mistaken for chaotic behavior, our results demonstrate that they are quasiperiodic.

In this work, we consider a class of $n$-dimensional, $n\geq2$, piecewise linear discontinuous maps that can exhibit a new type of attractor, called a weird quasiperiodic attractor. While the dynamics associated with these attractors may appear chaotic, we prove that chaos cannot occur. The considered class of $n$-dimensional maps allows for any finite number of partitions, separated by various types of discontinuity sets. The key characteristic, beyond discontinuity, is that all functions defining the map have the same real fixed point. These maps cannot have hyperbolic cycles other than the fixed point itself. We consider the two-dimensional case in detail. We prove that in nongeneric cases, the restriction, or the first return, of the map to a segment of straight line is reducible to a piecewise linear circle map. The generic attractor, different from the fixed point, is a weird quasiperiodic attractor, which may coexist with other attractors or attracting sets. We illustrate the existence of these attractors through numerous examples, using functions with different types of Jacobian matrices, as well as with different types of discontinuity sets. In some cases, we describe possible mechanisms leading to the appearance of these attractors. We also give examples in the three-dimensional space. Several properties of this new type of attractor remain open for further investigation.

This article presents an innovative approach for developing an efficient reduced-order model to study the dispersion of urban air pollutants. The need for real-time air quality monitoring has become increasingly important, given the rise in pollutant emissions due to urbanization and its adverse effects on human health. The proposed methodology involves solving the linear advection-diffusion problem, where the solution of the Reynolds-averaged Navier-Stokes equations gives the convective field. At the same time, the source term consists of an empirical time series. However, the computational requirements of this approach, including microscale spatial resolution, repeated evaluation, and low time scale, necessitate the use of high-performance computing facilities, which can be a bottleneck for real-time monitoring. To address this challenge, a problem-specific methodology was developed that leverages a data-driven approach based on Proper Orthogonal Decomposition with regression (POD-R) coupled with Galerkin projection (POD-G) endorsed with the discrete empirical interpolation method (DEIM). The proposed method employs a feedforward neural network to non-intrusively retrieve the reduced-order convective operator required for online evaluation. The numerical framework was validated on synthetic emissions and real wind measurements. The results demonstrate that the proposed approach significantly reduces the computational burden of the traditional approach and is suitable for real-time air quality monitoring. Overall, the study advances the field of reduced order modeling and highlights the potential of data-driven approaches in environmental modeling and large-scale simulations.

This paper presents an architecture, based on Distributed Ledger Technologies (DLTs) and Decentralized File Storage (DFS) systems, to support the use of Personal Information Management Systems (PIMS). DLT and DFS are used to manage data sensed by mobile users equipped with devices with sensing capability. DLTs guarantee the immutability, traceability and verifiability of references to personal data, that are stored in DFS. In fact, the inclusion of data digests in the DLT makes it possible to obtain an unalterable reference and a tamper-proof log, while remaining compliant with the regulations on personal data, i.e. GDPR. We provide an experimental evaluation on the feasibility of the use of DFS. Three different scenarios have been studied: i) a proprietary IPFS approach with a dedicated node interfacing with the data producers, ii) a public IPFS service and iii) Sia Skynet. Results show that through proper configuration of the system infrastructure, it is viable to build a decentralized Personal Data Storage (PDS).

Dealing with a growing amount of data is a crucial challenge for the future of information and communication technologies. More and more devices are expected to transfer data through the Internet, therefore new solutions have to be designed in order to guarantee low latency and efficient traffic management. In this paper, we propose a solution that combines the edge computing paradigm with a decentralized communication approach based on Peer-to-Peer (P2P). According to the proposed scheme, participants to the system are employed to relay messages of other devices, so as to reach a destination (usually a server at the edge of the network) even in absence of an Internet connection. This approach can be useful in dynamic and crowded environments, allowing the system to outsource part of the traffic management from the Cloud servers to end-devices. To evaluate our proposal, we carry out some experiments with the help of LUNES, an open source discrete events simulator specifically designed for distributed environments. In our simulations, we tested several system configurations in order to understand the impact of the algorithms involved in the data dissemination and some possible network arrangements.