Earth global and regional effective thermal conductance G(eff) (in (W/m^2)/C and often labeled lambda in climate research) and the related Equilibrium Climate Sensitivity (ECS) are evaluated by applying a modified version of the Energy Budget method, and using data only after 1970. By removing Periodic Interfering temperature components (using a novel PIR process) and applying high frequency filtering, an extraordinarily near linear temperature response is revealed, enhancing accurate G(eff) calculation and avoiding the pre-1970 aerosol forcing and ocean energy per area (E*) absorption uncertainties. A formal/empirical method is used to determine more reliable values of Q(t)=d[E*(Ocean.energy)]/dt . Using NOAA data, and after PIR, it is shown that: 1) The Energy Budget Method can be realistically applied to the Ocean and Land regions independently, 2) the effective volcanic forcing is <= 1/5 the IPCC AR5 estimate, 3) the "historical" 1980-2020 ECS(eff) values for Global, global Ocean, and global Land regions values are <= 2.16, 1.69, 2.96 C/2xCO2 respectively; where the updated IPCC AR5 orthodox independent global Forcing value of 0.4 (W/m^2)/Decade and F/2xCO2= 3.7 W/m^2 were used. The Global average ECS(true) value of <= 2.10 C is 70% of the IPCC AR6 ECS estimate of 3.0 C, but 127% of the ECS(eff) value reported by Lewis (1.66 C) . The estimated oceans average TCR/ECS ratio = 0.71 and the global average TCR/ECS ratio = 0.83, and ECS(land)/ECS(Ocean) = 1.78 . [Results using HADCRUT temperature data instead are similar, but 6% "cooler" over land, and 8% "warmer" over oceans.] A simplified physically realistic formal/empirical Coarse 2-D Global Climate Model is derived wherein variation of Geff(t) until equilibrium (i.e. "pattern effects") are proven to be negligible or "cooling", using these Methods. And so it is likely ECS(true) <= ECS(eff).

Due to the unavailability of solar irradiance data for many potential sites of Nepal, the paper proposes predicting solar irradiance based on alternative meteorological parameters. The study focuses on five distinct regions in Nepal and utilizes a dataset spanning almost ten years, obtained from CERES SYN1deg and MERRA-2. Machine learning models such as Random Forest, XGBoost, K-Nearest Neighbors, and deep learning models like LSTM and ANN-MLP are employed and evaluated for their performance. The results indicate high accuracy in predicting solar irradiance, with R-squared(R2) scores close to unity for both train and test datasets. The impact of parameter integration on model performance is analyzed, revealing the significance of various parameters in enhancing predictive accuracy. Each model demonstrates strong performance across all parameters, consistently achieving MAE values below 6, RMSE values under 10, MBE within |2|, and nearly unity R2 values. Upon removal of various solar parameters such as "Solar_Irradiance_Clear_Sky", "UVA", etc. from the datasets, the model's performance is significantly affected. This exclusion leads to considerable increases in MAE, reaching up to 82, RMSE up to 135, and MBE up to |7|. Among the models, KNN displays the weakest performance, with an R2 of 0.7582546. Conversely, ANN exhibits the strongest performance, boasting an R2 value of 0.9245877. Hence, the study concludes that Artificial Neural Network (ANN) performs exceptionally well, showcasing its versatility even under sparse data parameter conditions.

The optimal fingerprint method serves as a potent approach for detecting and attributing climate change. However, its experimental validation encounters challenges due to the intricate nature of climate systems. Here, we experimentally examine the optimal fingerprint method simulated by a precisely controlled magnetic resonance system of spins. The spin dynamic under an applied deterministic driving field and a noise field is utilized to emulate the complex climate system with external forcing and internal variability. Our experimental results affirm the theoretical prediction regarding the existence of an optimal detection direction which maximizes the signal-to-noise ratio, thereby validating the optimal fingerprint method. This work offers direct empirical verification of the optimal fingerprint method, crucial for comprehending climate change and its societal impacts.

We develop a three-timescale framework for modelling climate change and introduce a space-heterogeneous one-dimensional energy balance model. This model, addressing temperature fluctuations from rising carbon dioxide levels and the super-greenhouse effect in tropical regions, fits within the setting of stochastic reaction-diffusion equations. Our results show how both mean and variance of temperature increase, without the system going through a bifurcation point. This study aims to advance the conceptual understanding of the extreme weather events frequency increase due to climate change.

Numerical model forecasts of near-surface temperatures are prone to error. This is because terrain can exert a strong influence on temperature that is not captured in numerical weather models due to spatial resolution limitations. To account for the terrain height difference between the forecast model and reality, temperatures are commonly corrected using a vertical adjustment based on a fixed lapse rate. This, however, ignores the fact that true lapse rates vary from 1.2 K temperature drop per 100 m of ascent to more than 10 K temperature rise over the same vertical distance. In this work, we develop topographic visualization techniques to assess the resulting uncertainties in near-surface temperatures and reveal relationships between those uncertainties, features in the resolved and unresolved topography, and the temperature distribution in the near-surface atmosphere. Our techniques highlight common limitations of the current lapse rate scheme and hint at their topographic dependencies in the context of the prevailing weather conditions. Together with scientists working in postprocessing and downscaling of numerical model output, we use these findings to develop an improved lapse rate scheme. This model adapts to both the topography and the current weather situation. We examine the quality and physical consistency of the new estimates by comparing them with station observations around the world and by including visual representations of radiation-slope interactions.

A novel method to reconstruct the energy of hadronic showers in the CMS High Granularity Calorimeter (HGCAL) is presented. The HGCAL is a sampling calorimeter with very fine transverse and longitudinal granularity. The active media are silicon sensors and scintillator tiles readout by SiPMs and the absorbers are a combination of lead and Cu/CuW in the electromagnetic section, and steel in the hadronic section. The shower reconstruction method is based on graph neural networks and it makes use of a dynamic reduction network architecture. It is shown that the algorithm is able to capture and mitigate the main effects that normally hinder the reconstruction of hadronic showers using classical reconstruction methods, by compensating for fluctuations in the multiplicity, energy, and spatial distributions of the shower's constituents. The performance of the algorithm is evaluated using test beam data collected in 2018 prototype of the CMS HGCAL accompanied by a section of the CALICE AHCAL prototype. The capability of the method to mitigate the impact of energy leakage from the calorimeter is also demonstrated.

Gamification of quantum theory can provide new inroads into the subject: by allowing users to experience simulated worlds that manifest obvious quantum behaviors they can potentially build intuition for quantum phenomena. The Qubit Factory is an engineering-style puzzle game based on a gamified quantum circuit simulator that is designed to provide an introduction to qubits and quantum computing, while being approachable to those with no prior background in the area. It introduces an intuitive visual language for representing quantum states, gates and circuits, further enhanced by animations to aid in visualization. The Qubit Factory presents a hierarchy of increasingly difficult tasks for the user to solve, where each task requires the user to construct and run an appropriate classical/quantum circuit built from a small selection of components. Earlier tasks cover the fundamentals of qubits, quantum gates, superpositions and entanglement. Later tasks cover important quantum algorithms and protocols including superdense coding, quantum teleportation, entanglement distillation, classical and quantum error correction, state tomography, the Bernstein-Vazirani algorithm, quantum repeaters and more.

Coarse-graining is a molecular modeling technique in which an atomistic system is represented in a simplified fashion that retains the most significant system features that contribute to a target output, while removing the degrees of freedom that are less relevant. This reduction in model complexity allows coarse-grained molecular simulations to reach increased spatial and temporal scales compared to corresponding all-atom models. A core challenge in coarse-graining is to construct a force field that represents the interactions in the new representation in a way that preserves the atomistic-level properties. Many approaches to building coarse-grained force fields have limited transferability between different thermodynamic conditions as a result of averaging over internal fluctuations at a specific thermodynamic state point. Here, we use a graph-convolutional neural network architecture, the Hierarchically Interacting Particle Neural Network with Tensor Sensitivity (HIP-NN-TS), to develop a highly automated training pipeline for coarse grained force fields which allows for studying the transferability of coarse-grained models based on the force-matching approach. We show that this approach not only yields highly accurate force fields, but also that these force fields are more transferable through a variety of thermodynamic conditions. These results illustrate the potential of machine learning techniques such as graph neural networks to improve the construction of transferable coarse-grained force fields.

We investigate the morphodynamics of an ice layer over a turbulent stream of warm water using numerical simulations. At low water speeds, characteristic streamwise undulations appear, which can be explained by the Reynolds analogy between heat and momentum transfer. As the water speed increases, these undulations combine with spanwise ripples of a much greater length scale. These ripples are generated by a melting mechanism controlled by the instability originating from the ice-water interactions, and, through a melting/freezing process, they evolve downstream with a migration velocity much slower than the turbulence characteristic velocity.

These notes are an introduction to fluctuating hydrodynamics (FHD) and the formulation of numerical schemes for the resulting stochastic partial differential equations (PDEs). Fluctuating hydrodynamics was originally introduced by Landau and Lifshitz as a way to put thermal fluctuations into a continuum framework by including a stochastic forcing to each dissipative transport process (e.g., heat flux). While FHD has been useful in modeling transport and fluid dynamics at the mesoscopic scale, theoretical calculations have been feasible only with simplifying assumptions. As such there is great interest in numerical schemes for Computational Fluctuating Hydrodynamics (CFHD). There are a variety of algorithms (e.g., spectral, finite element, lattice Boltzmann) but in this introduction we focus on finite volume schemes. Accompanying these notes is a demonstration program in Python available on GitHub.

In the fabrication of optical fibres, the viscosity of the glass varies dramatically with temperature so that heat transfer plays an important role in the deformation of the fibre geometry. Surprisingly, for quasi-steady drawing, with measurement of pulling tension, the applied heat can be adjusted to control the tension and temperature modelling is not needed. However, when pulling tension is not measured, a coupled heat and fluid flow model is needed to determine the inputs required for a desired output. In the fast process of drawing a preform to a fibre, heat advection dominates conduction so that heat conduction may be neglected. By contrast, in the slow process of extruding a preform, heat conduction is important. This means that solving the coupled flow and temperature modelling is essential for prediction of preform geometry. In this paper we derive such a model that incorporates heat conduction for the extensional flow of fibres. The dramatic variations in viscosity with temperature means that this problem is extremely challenging to solve via standard numerical techniques and we therefore develop a novel finite-difference numerical solution method that proves to be highly robust. We use this method to show that conduction significantly affects the size of internal holes at the exit of the device.

Superconducting thin-film electronics are attractive for their low power consumption, fast operating speeds, and ease of interface with cryogenic systems such as single-photon detector arrays, and quantum computing devices. However, the lack of a reliable superconducting two-terminal asymmetric device, analogous to a semiconducting diode, limits the development of power-handling circuits, fundamental for scaling up these technologies. Existing efforts to date have been limited to single-diode proofs of principle and lacked integration of multiple controllable and reproducible devices to form complex circuits. Here, we demonstrate a robust superconducting diode with tunable polarity using the asymmetric Bean-Livingston surface barrier in niobium nitride micro-bridges, achieving a 43\,\% rectification efficiency. We then realize and integrate several such diodes into a bridge rectifier circuit on a single microchip that performs continuous full-wave rectification up to 3\,MHz and AC-to-DC conversion in burst mode at 50\,MHz with an estimated peak power efficiency of 60\,\%.

We demonstrated a continuously tunable laser system by butt coupling a reflective semiconductor optical amplifier (RSOA) chip with a thin-film lithium niobate (TFLN) based multi-channel interference (MCI) cavity chip. This hybrid integrated lasers allows for fine-tuning of the laser wavelength from 1538 nm to 1560 nm with a resolution of 0.014 nm and a side-mode suppression ratio (SMSR) exceeding 30 dB. The MCI cavity chip is fabricated using the photolithography assisted chemo-mechanical etching (PLACE) technique. The developed laser has an output power of approximately 10 {\mu}W, which can be further amplified to 70 mW using a commercial erbium-doped fiber amplifier (EDFA) without significant broadening of the laser linewidth.

The successful use of molecular dyes for solar energy conversion requires efficient charge injection, which in turn requires the formation of states with sufficiently long lifetimes (e.g. triplets). The molecular structure elements that confer this property can be found empirically, however computational predictions using $\textit{ab initio}$ electronic structure methods are invaluable to identify structure-property relations for dye sensitizers. The primary challenge for simulations to elucidate the electronic and nuclear origins of these properties is a spin-orbit interaction which drives transitions between electronic states. In this work, we present a computational analysis of the spin-orbit corrected linear absorption cross sections and intersystem crossing rate coefficients for a derivative set of phosphonated tris(2,2'-bipyridine)ruthenium(2+) dye molecules. After sampling the ground state vibrational distributions, the predicted linear absorption cross sections indicate that the mixture between singlet and triplet states plays a crucial role in defining the line shape of the metal-to-ligand charge transfer bands in these derivatives. Additionally, an analysis of the intersystem crossing rate coefficients suggests that transitions from the singlet into the triplet manifolds are ultrafast with rate coefficients on the order of $10^{13}$ s$^{-1}$ for each dye molecule.

Recently, large-scale trapped ion systems have been realized in experiments for quantum simulation and quantum computation. They are the simplest systems for dynamical stability and parametric resonance. In this model, the Mathieu equation plays the most fundamental role for us to understand the stability and instability of a single ion. In this work, we investigate the dynamics of trapped ions with the Coulomb interaction based on the Hamiltonian equation. We show that the many-body interaction will not influence the phase diagram for instability. Then, the dynamics of this model in the large damping limit will also be analytically calculated using few trapped ions. Furthermore, we find that in the presence of modulation, synchronization dynamics can be observed, showing an exchange of velocities between distant ions on the left side and on the right side of the trap. These dynamics resemble to that of the exchange of velocities in Newton's cradle for the collision of balls at the same time. These dynamics are independent of their initial conditions and the number of ions. As a unique feature of the interacting Mathieu equation, we hope this behavior, which leads to a quasi-periodic solution, can be measured in current experimental systems. Finally, we have also discussed the effect of anharmonic trapping potential, showing the desynchronization during the collision process. It is hopped that the dynamics in this many-body Mathieu equation with damping may find applications in quantum simulations. This model may also find interesting applications in dynamics systems as a pure mathematical problem, which may be beyond the results in the Floquet theorem.

We present the results of a nation-wide baseline survey, conducted by us, for the status of Astronomy education among secondary school students in India. The survey was administered in 10 different languages to over 2000 students from diverse backgrounds, and it explored multiple facets of their perspectives on astronomy. The topics included students' views on the incorporation of astronomy in curricula, their grasp of fundamental astronomical concepts, access to educational resources, cultural connections to astronomy, and their levels of interest and aspirations in the subject. We find notable deficiencies in students' knowledge of basic astronomical principles, with only a minority demonstrating proficiency in key areas such as celestial sizes, distances, and lunar phases. Furthermore, access to resources such as telescopes and planetariums remain limited across the country. Despite these challenges, a significant majority of students expressed a keen interest in astronomy. We further analyze the data along socioeconomic and gender lines. Particularly striking were the socioeconomic disparities, with students from resource-poor backgrounds often having lower levels of access and proficiency. Some differences were observed between genders, although not very pronounced. The insights gleaned from this study hold valuable implications for the development of a more robust astronomy curriculum and the design of effective teacher training programs in the future.

We study theoretically the interplay of spontaneous emission and interactions for the multiple-excited quasistationary eigenstates in a finite periodic array of multilevel atoms coupled to the waveguide. We develop an analytical approach to calculate such eigenstates based on the subradiant dimer basis. Our calculations reveal the peculiar multimerization effect driven by the anharmonicity of the atomic potential: while a general eigenstate is an entangled one, there exist eigenstates that are products of dimers, trimers, or tetramers, depending on the size of the system and the fill factor. At half-filling, these product states acquire a periodic structure with all-to-all connections inside each multimer and become the most subradiant ones.

The motion, settling, and dispersion of microplastics in the ocean are determined by their rotational dynamics. We present experiments on elongated, large aspect ratio, and mildly curved plastic fibers slightly longer than the Kolmogorov length scale. Exploiting their uniquely identifiable three dimensional orientation, we perform original optical Lagrangian investigations and provide a set of homogeneous data on their rotation rates around their longitudinal axis -- spinning rate -- and transversal axes -- tumbling rates -- which we explain in the context of the general features of turbulence.

With a high rate of morbidity and mortality, colorectal cancer (CRC) ranks third in mortality among cancers. By analyzing the texture properties of images and quantifying the heterogeneity of tumors, radiomics and radiogenomics are non-invasive methods to determine the biological properties of CRC. Recently, several articles have discussed the application of radiomics in different aspects of CRC. Therefore, given the large amount of data published, this review aims to discuss how radiomics can be used for distinguishing benign and malignant colorectal lesions, diagnosing, staging, predicting prognosis and treatment response, and predicting lymph node and hepatic metastasis of CRC, based on radiomic features extracted from magnetic resonance imaging (MRI), computed tomography (CT), esophageal ultrasonography (EUS), and positron emission tomography-CT (PET-CT). Challenges in bringing radiomics to clinical application and future solutions have also been discussed. With the progress made in radiomics and the application of deep and machine learning in this area, radiomics can become one of the main tools for the personalized management of CRC patients shortly.

DD4hep serves as a generic detector description toolkit recommended for offline software development in next-generation high-energy physics~(HEP) experiments. Conversely, Filmbox~(FBX) stands out as a widely used 3D modeling file format within the 3D software industry. In this paper, we introduce a novel method that can automatically convert complex HEP detector geometries from DD4hep description into 3D models in the FBX format. The feasibility of this method was demonstrated by its application to the DD4hep description of the Compact Linear Collider detector and several sub-detectors of the super Tau-Charm facility and circular electron-positron collider experiments. The automatic DD4hep--FBX detector conversion interface provides convenience for further development of applications, such as detector design, simulation, visualization, data monitoring, and outreach, in HEP experiments.

In this work, we present the first experimental evidence of the presence of zonal flow (ZF) structures in the optimized stellarator Wendelstein 7-X. Using an assortment of diagnostics, flux surface-uniform, electrostatic flow oscillations have been measured, showing a radial scale in the range of tens of ion gyroradii. Such measurements show remarkable agreement with the ZF predicted by local and global non-linear gyrokinetic simulations. These results represent the first direct measurement of ZF in a large stellarator, suitable for the validation of models in reactor relevant conditions.

We show that higher-order topological insulators can be created from usual square structure by twisting waveguides in each unit cell around the axis passing through the center of the unit cell, even without changing intracell distance between waveguides. When applied to usual square array, this approach produces two-dimensional generalization of Su-Schrieffer-Heeger (SSH) structure supporting topological corner modes with propagation constants belonging to two forbidden spectral gaps opening only for twist angles from certain interval. In contrast to usual SSH arrays, where higher-order topology is typically introduced by diagonal waveguide shifts and only one type of corner states exists, our SSH-like structure in topological phase supports two co-existing types of in-phase and out-of-phase corner modes appearing in two different topological gaps that open in the spectrum. Therefore, twisting of the unit cell qualitatively changes topological properties of the system, offering a new degree of freedom in creation of higher-order topological phases. In material with focusing cubic nonlinearity two coexisting types of topological corner solitons emerge from these modes, whose existence and stability properties are studied here. Despite different internal structure, both such modes can be simultaneously dynamically stable in the appreciable part of the topological gap.

Fast and sensitive detector arrays enable image scanning microscopy (ISM), overcoming the trade-off between spatial resolution and signal-to-noise ratio (SNR) typical of confocal microscopy. However, current ISM approaches cannot provide optical sectioning and fail with thick samples, unless the size of the detector is limited. Thus, another trade-off between optical sectioning and SNR persists. Here, we propose a method without drawbacks that combines uncompromised super-resolution, high SNR, and optical sectioning. Furthermore, our approach enables super-sampling of images, relaxing Nyquist's criterion by a factor of two. Based on the observation that imaging with a detector array inherently embeds axial information about the sample, we designed a straightforward reconstruction algorithm that inverts the physical model of ISM. We present the comprehensive theoretical framework and validate our method with synthetic and experimental images of biological samples captured using a custom setup equipped with a single-photon avalanche diode (SPAD) array detector. We demonstrate the feasibility of our approach exciting fluorescence emission both in the linear and non-linear regime. Moreover, we generalize the algorithm for fluorescence lifetime imaging, fully exploiting the single-photon timing ability of the SPAD array detector. Our method outperforms conventional approaches to ISM and can be extended to any LSM technique.

Past studies show that coupled model biases in European blocking and North Atlantic eddy-driven jet variability decrease as one increases the horizontal resolution in the atmospheric and oceanic model components. This has commonly been argued to be related to an alleviation of sea surface temperature (SST) biases due to increased oceanic resolution in particular, with a physical pathway via changes to surface baroclinicity. On the other hand, many studies have now highlighted the key role of diabatic processes in the Gulf Stream region on blocking formation and maintenance. Here, following recent work by Schemm, we leverage a large multi-model ensemble to show that Gulf Stream precipitation variability in coupled models is tightly linked to the simulated frequency of European blocking and northern jet excursions. Furthermore, the reduced biases in blocking and jet variability are consistent with greater precipitation variability as a result of increased atmospheric horizontal resolution. By contrast, typical North Atlantic SST biases are found to share only a weak or negligible relationship with blocking and jet biases. Finally, while previous studies have used a comparison between coupled models and models run with prescribed SSTs to argue for the role of ocean resolution, we emphasise here that models run with prescribed SSTs experience greatly reduced precipitation variability due to their excessive thermal damping, making it unclear if such a comparison is meaningful. Instead, we speculate that most of the reduction in coupled model biases may actually be due to increased atmospheric resolution.

A key challenge in developing terahertz front-ends is achieving high coupling efficiency between the waveguide feed horn and the optical beam. In this paper, we have quantified the alignment requirements for the widely used E-plane split diagonal horn antenna through theoretical analysis, electromagnetic simulation, and experimental validation within the 325-500 GHz frequency range. The results from our analytical models, simulations, and measurements are consistent and shows good agreement. They reveal that even minor geometric asymmetries can cause significant increases in fractional power radiated to the cross-polar component due to amplitude and phase imbalances in the TE10 and TE01 modes. Furthermore, a misalignment of approximately 8% of the wavelength was observed to result in a 3-dB degradation in the optical coupling to a Gaussian beam (Gaussicity) in middle of the waveguide band. These findings highlight the critical importance of precise alignment and feed horn machining for the successful implementation of terahertz front-end systems.

A tournament is called incentive incompatible if it allows for a situation where a team could be strictly better off by losing. This paper uncovers that the entry rules of the UEFA Champions League, the most prestigious club football competition in Europe, suffer from this shortcoming again from the 2024/25 season. Consequently, a match with misaligned incentives has been barely avoided in the 2023/24 German Bundesliga. Two straightforward solutions are recommended. Fairness can be easily guaranteed by reversing the order of two paragraphs in the UEFA Champions League regulation.

The spatial and spectral distributions of terahertz (THz) pulses emitted by two-color air plasmas are theoretically investigated for focused laser pulses and in the filamentation regime. We derive a so-called ''augmented'' conical emission model, which, similarly to the one originally proposed by You et al.\ [Phys.\ Rev.\ Lett.\ {\bf 109}, 183902 (2012)], involves phase matching between laser harmonics along the plasma channel, the plasma density and length, and the emitted frequency as key parameters. Our augmented model, however, treats envelope effects and accounts for transverse variations of both plasma- and Kerr-driven potential THz emitters. We highlight the importance of the characteristic spatio-spectral distributions of these two conversion mechanisms in the expression of the angular radiated power. The results of our model are successfully compared with data provided by a comprehensive, fully space and time-resolved unidirectional solver. Importantly, these numerical simulations clear up the effective plasma length along which THz emission develops, compared with the dephasing length along which the laser fundamental and second harmonic become out-of-phase. The impact of common optical aberrations, such as sphericity, astigmatism, and coma, on the THz generation is also investigated. Aberrations are shown to generally decrease the laser-to-THz conversion efficiency and potentially induce spatial asymmetries and narrowing in the THz spectra.

The complete cooperation and the complete defection are two typical strategies considered in evolutionary games in many previous works. However, in real life, strategies of individuals are full of variety rather than only two complete ones. In this work, the diversity of strategies is introduced into the weak prisoners' dilemma game, which is measured by the diversity of the cooperation tendency. A higher diversity means more cooperation tendencies are provided. The complete cooperation strategy is the full cooperation tendency and the complete defection strategy is without any cooperation tendency. Agents with other cooperation tendencies behave as partial cooperators and as partial defectors simultaneously. The numerical simulation shows that increasing the diversity of the cooperation tendency promotes the cooperation level, not only the number of cooperators but also the average tendency over the whole population, until the diversity reaches its saturated value. Furthermore, our work points out maintaining cooperation is based on the cooperation efficiency approximating to the reward of cooperators and that the cooperation efficiency oscillates and quickly decreases to zero when cooperator clusters cannot resist the invasion of defectors. When the effect of the noise for the Femi update mechanism is considered, a higher diversity of strategies not only improves the cooperation level of the whole population but also supports the survival of more rational agents.

Fault ruptures of regular earthquakes typically grow in a self-similar manner, where the radiated energy is proportional to the seismic moment. Their proportionality factor, termed as scaled energy, has been conventionally described as the ratio of stress drop to stiffness. By analyzing the self-similar circular crack model by Sato and Hirasawa (1973), Matsu'ura (2024) found a correction prefactor for this theoretical representation of the scaled energy, the cubed ratio of the rupture speed to the S-wave speed. The stress drop times the cubed rupture speed is the scaling prefactor of the self-similar seismic moment, and thus, Matsu'ura's solution tells that the seismic moment rate is determined by the scaled energy in the self-similar rupture growth. We rearrange the properties of the self-similar solution from this perspective, apply this Sato-Hirasawa-Matsu'ura relation to a series of seismic events thought to be self-similar, and estimate their source parameters and scaled energies. According to our estimation using the above self-similarity model, seismologically detected low-frequency and very-low-frequency earthquakes have a common scaled energy, while geodetically detectable slow slip events indicate multiple modes with different scaled energies. Meanwhile, for very-low-frequency earthquakes and tectonic tremors, the seismic moments were significantly smaller than expected for self-similar ruptures despite their moments being proportional to cubed duration values. It keeps alive a hitherto less contemplated possibility: the proportionality of the moment to cubed duration may not be a manifestation of the self-similarity for slow earthquake rupture growth.

We report a colloidal synthesis of blue emissive, stable cube-shaped CsPbBr3 quantum dots (QDs) in the strong quantum confinement regime via a dissolution-recrystallization starting from pre-synthesized (KxCs1-x)4PbBr6 nanocrystals which are then reacted with PbBr2. This is markedly different from the known case of Cs4PbBr6 nanocrystals that react within seconds with PbBr2 and get transformed into much larger, green emitting CsPbBr3 nanocrystals. Here, instead, the conversion of (KxCs1-x)4PbBr6 nanocrystals to CsPbBr3 QDs occurs in a time span of hours, and tuning of the QDs size is achieved by adjusting the concentration of precursors. The QDs exhibit excitonic features in optical absorption that are tunable in the 420 - 452 nm range, accompanied by blue photoluminescence with quantum yield around 60%. Detailed spectroscopic investigations in both the single and multi-exciton regime reveal the exciton fine structure and the effect of Auger recombination of these CsPbBr3 QDs, confirming theoretical predictions for this system.

This study introduces a first step for constructing a hybrid reduced-order models (ROMs) for segregated fluid-structure interaction in an Arbitrary Lagrangian-Eulerian (ALE) approach at a high Reynolds number using the Finite Volume Method (FVM). The ROM is driven by proper orthogonal decomposition (POD) with hybrid techniques that combines the classical Galerkin projection and two data-driven methods (radial basis networks , and neural networks/ long short term memory). Results demonstrate the ROM ability to accurately capture the physics of fluid-structure interaction phenomena. This approach is validated through a case study focusing on flow-induced vibration (FIV) of a pitch-plunge airfoil at a high Reynolds number 10000000.

Optimizing the injection process in particle accelerators is crucial for enhancing beam quality and operational efficiency. This paper presents a framework for utilizing Reinforcement Learning (RL) to optimize the injection process at accelerator facilities. By framing the optimization challenge as an RL problem, we developed an agent capable of dynamically aligning the beam's transverse space with desired targets. Our methodology leverages the Soft Actor-Critic algorithm, enhanced with domain randomization and dense neural networks, to train the agent in simulated environments with varying dynamics promoting it to learn a generalized robust policy. The agent was evaluated in live runs at the Cooler Synchrotron COSY and it has successfully optimized the beam cross-section reaching human operator level but in notably less time. An empirical study further validated the importance of each architecture component in achieving a robust and generalized optimization strategy. The results demonstrate the potential of RL in automating and improving optimization tasks at particle acceleration facilities.

Beamline components, such as superconducting radio frequency cavities and focusing lenses, need to be assembled together in a string while in a cleanroom environment. The present contribution identifies and characterizes materials for additive manufacturing that can be used in a cleanroom. The well known advantages of additive manufacturing processes would highly benefit the design and development of tooling needed for the mechanical support and alignment of string components. Cleanliness, mechanical properties, and leak tightness of the chosen materials are the main focus of this contribution, which also paves the way for the integration of such materials in cryomodule assemblies. Results reported here were obtained in the framework of the PIP-II project at Fermilab.

Stochastic quantum Liouville equations (SQLE) are widely used to model energy and charge dynamics in molecular systems. The Haken-Strobl-Reineker (HSR) SQLE is a particular paradigm in which the dynamical noise that destroys quantum coherences arises from a white noise (i.e., constant-frequency) spectrum. A system subject to the HSR SQLE thus evolves to its `high-temperature' limit, whereby all the eigenstates are equally populated. This result would seem to imply that the predictions of the HSR model, e.g., the temperature dependence of the diffusion coefficient, have no validity for temperatures lower than the particle bandwidth. The purpose of this paper is to show that this assumption is incorrect for translationally invariant systems. In particular, provided that the diffusion coefficient is determined via the mean-squared-displacement, considerations about detailed-balance are irrelevant. Consequently, the high-temperature prediction for the temperature dependence of the diffusion coefficient may be extrapolated to lower temperatures, provided that the bath remains classical. Thus, for diagonal dynamical disorder the long-time diffusion coefficient, $D_{\infty}(T) = c_{1} /T$, while for both diagonal and off-diagonal disorder, $D_{\infty}(T) = c_{1}/T + c_{2} T$, where $c_{2} \ll c_{1}$. An appendix discusses an alternative interpretation from the HSR model of the `quantum to classical' dynamics transition, whereby the dynamics is described as stochastically punctuated coherent motion.

Temporal cavity solitons (CSs) are stable, localized particle-like objects in the form of optical pulses that circulate indefinitely in coherently driven nonlinear resonators. In the spectral domain, they form highly coherent frequency combs. Owing to their remarkable stability, they are attracting attention for applications in sensing, metrology, or optical signal synthesis. In this work, we report on the dynamics of CSs interacting with a trapping potential. We demonstrate that this interaction provides a powerful means to control their properties such as position, speed, and central frequency. Our theoretical analysis predicts fundamental limitations on the spectral shift of CSs relative to the driving frequency. Specifically, it reveals that within a broad range of detunings, frequency-shifted CSs encounter destabilization through a Hopf bifurcation. Moreover, we find that with periodic potentials, the Kelly sidebands emitted by trapped solitons undergo Bloch oscillations. In our experiments, we use an intracavity phase modulator to create the equivalent of an external real potential. We observe stable blue- and red- shifted solitons up to a limit close to our theoretical predictions. We then show theoretically and experimentally that this unprecedented level of control over the CS spectrum can be leveraged to cancel the Raman-induced self-frequency shift and even to stabilize CSs beyond the limitation imposed by stimulated Raman scattering. Our results provide valuable insights for applications requiring robust and potentially rapid tunable control over the cavity soliton properties.

Direct measurement of the elastic scattering of real photons on an electromagnetic field would allow the fundamental low-energy constants of quantum electrodynamics (QED) to be experimentally determined. We show that scenarios involving the collision of three laser beams have several advantages over conventional two-beam scenarios. The kinematics of a three-beam collision allows for a higher signal-to-noise ratio in the detection region, without the need for polarimetry and separates out contributions from different orders of photon scattering. A planar configuration of colliding a photon beam from an x-ray free electron laser with two optical beams is studied in detail. We show that measurements of elastic photon scattering and vacuum birefringence are possible with currently available technology.

We present a method for constructing global analytical expressions that approximate a function over its entire range. These approximations not only mirror the original function as accurately as desired, but are purposefully created to possess features that the original function lacks. This is particularly useful for functions that lack closed form and are defined by integrals or infinite series. Replacing these definitions with simple analytical expressions enables in-depth qualitative analysis and replaces the current methods of evaluation. We demonstrate this procedure by providing replacements for a variety of pivotal functions in physics and cosmology including the pressure and density of quantum gas, the one-loop correction in thermal field theory, common polylog functions, and the error function.

Unlocking the vast potential of optical sensing technology has long been hindered by the challenges of achieving fast, sensitive, and broadband photodetection at ambient temperatures. In this review, we summarize recent progress in the study of nonlinear photocurrent in topological quantum materials, and its application in broadband photodetection without the use of p-n junction based semiconductor diodes. The intrinsic quadratic transverse current-input voltage relation is used to rectify the alternating electric field from incident radio, terahertz or infrared waves into a direct current, without a bias voltage and at zero magnetic field. We review novel photocurrents in several material systems, including topological Weyl semimetals, chiral crystals, ferroelectric materials, and low dimensional topological insulators. These quantum materials hold tremendous promise for broadband high-frequency rectification and photodetection, featuring substantial responsivity and detectivity.

Classical compartmental models in epidemiology often struggle to accurately capture real-world dynamics due to their inability to address the inherent heterogeneity of populations. In this paper, we introduce a novel approach that incorporates heterogeneity through a mobility variable, transforming the traditional ODE system into a system of integro-differential equations that describe the dynamics of population densities across different compartments. Our results show that, for the same basic reproduction number, our mobility-based model predicts a smaller final pandemic size compared to classic compartmental models, whose population densities are represented as Dirac delta functions in our density-based framework. This addresses the overestimation issue common in many classical models. Additionally, we demonstrate that the time series of the infected population is sufficient to uniquely identify the mobility distribution. We reconstruct this distribution using a machine-learning-based framework, providing both theoretical and algorithmic support to effectively constrain the mobility-based model with real-world data.

The relaxation of uni-axially compressed particle rafts through a finite opening found at the opposite side is experimentally studied. Three main behaviours are identified. The lowest relaxation degree corresponds to local unjamming. The other extreme corresponds to full relaxation and is characterized by the unjamming of the entire raft. In between, intermediate relaxation is observed. The unjammed domain first grows along the compression direction with an almost constant width and possibly extends through the entire raft length. In this case, a second phase may start during which erosion enables the unjammed channel to develop normally to the compression direction. Employing different raft geometries, i.e. various length and compression levels, and openings of various widths, we rationalize the occurrence of these different behaviours, which we attribute to the mechanical robustness of the force chain network. The threshold for channel formation and erosion are interpreted as its rupture against excessive shear and elongation, respectively. By further comparing results obtained for rafts prepared according to three different mixing degrees, we evidence that these thresholds are strongly affected by the raft history and quantify these effects in terms of shift of the rupture limits.

The steel industry is a major contributor to CO2 emissions, accounting for 7% of global emissions. The European steel industry is seeking to reduce its emissions by increasing the use of electric arc furnaces (EAFs), which can produce steel from scrap, marking a major shift towards a circular steel economy. Here, we show by combining trade with business intelligence data that this shift requires a deep restructuring of the global and European scrap trade, as well as a substantial scaling of the underlying business ecosystem. We find that the scrap imports of European countries with major EAF installations have steadily decreased since 2007 while globally scrap trade started to increase recently. Our statistical modelling shows that every 1,000 tonnes of EAF capacity installed is associated with an increase in annual imports of 550 tonnes and a decrease in annual exports of 1,000 tonnes of scrap, suggesting increased competition for scrap metal as countries ramp up their EAF capacity. Furthermore, each scrap company enables an increase of around 79,000 tonnes of EAF-based steel production per year in the EU. Taking these relations as causal and extrapolating to the currently planned EAF capacity, we find that an additional 730 (SD 140) companies might be required, employing about 35,000 people (IQR 29,000-50,000) and generating an additional estimated turnover of USD 35 billion (IQR 27-48). Our results thus suggest that scrap metal is likely to become a strategic resource. They highlight the need for a massive restructuring of the industry's supply networks and identify the resulting growth opportunities for companies.

Modern 3D printers allow for the accurate placing of microscopic material voxels, to form complex three-dimensional structures. The advancing capabilities in multi-material printing, coupled with the discovery of printable responsive materials, pave the way for the 3D printing of digital materials, and for the 3D printing of shape-transforming structures, or 4D printing. In this work, we leverage and combine these advancements to develop a versatile methodology for crafting digitized 4D-printed shape-transforming sheets. We 3D print digital responsive sheets, composed of active and passive voxels that are meticulously positioned to form thin structures that can be actuated on demand. This methodology enables us to encode the lateral geometry and the reference curvature, separately and simultaneously. Our approach grants us comprehensive control over the resulting shape, unlocking novel opportunities in synthetic shape-morphing materials. Furthermore, it has the potential to program anomalous mechanical properties and to be extended to multi-material and metamaterial systems.

We prove a sufficient condition for nonlinear stability of relative equilibria in the planar $N$-vortex problem. This result builds on our previous work on the Hamiltonian formulation of its relative dynamics as a Lie--Poisson system. The relative dynamics recasts the stability of relative equilibria of the $N$-vortex problem as that of the corresponding fixed points in the relative dynamics. We analyze the stability of such fixed points by exploiting the Hamiltonian formulation as well as invariants and constraints that naturally arise in the relative dynamics. The stability condition is essentially an Energy--Casimir method, except that we also incorporate the constraints in an effective manner. We apply the method to two types of relative equilibria: (i) three identical vortices at the vertices of an equilateral triangle along with another one at its center and (ii) four identical vortices at the vertices of a square with another one its center, where the circulation of the vortex at the center is arbitrary in both cases. We show that they are stable/unstable depending on the circulation of the vortex at the center.

We provide both a theoretical and empirical analysis of the Mean-Median Difference (MM) and Partisan Bias (PB), which are both symmetry metrics intended to detect gerrymandering. We consider vote-share, seat-share pairs $(V, S)$ for which one can construct election data having vote share $V$ and seat share $S$, and turnout is equal in each district. We calculate the range of values that MM and PB can achieve on that constructed election data. In the process, we find the range of vote-share, seat share pairs $(V, S)$ for which there is constructed election data with vote share $V$, seat share $S$, and $MM=0$, and see that the corresponding range for PB is the same set of $(V,S)$ pairs. We show how the set of such $(V,S)$ pairs allowing for $MM=0$ (and $PB=0$) changes when turnout in each district is allowed to be different. Although the set of $(V,S)$ pairs for which there is election data with $MM=0$ is the same as the set of $(V,S)$ pairs for which there is election data with $PB=0$, the range of possible values for MM and PB on a fixed $(V, S)$ is different. Additionally, for a fixed constructed election outcome, the values of the Mean-Median Difference and Partisan Bias can theoretically be as large as 0.5. We show empirically that these two metric values can differ by as much as 0.33 in US congressional map data. We use both neutral ensemble analysis and the short-burst method to show that neither the Mean-Median Difference nor the Partisan Bias can reliably detect when a districting map has an extreme number of districts won by a particular party. Finally, we give additional empirical and logical arguments in an attempt to explain why other metrics are better at detecting when a districting map has an extreme number of districts won by a particular party.

While data qubits with a long coherence time are essential for the storage of quantum information, ancilla qubits are pivotal in quantum error correction (QEC) for fault-tolerant quantum computing. The recent development of optical tweezer arrays, such as the preparation of large-scale qubit arrays and high-fidelity gate operations, offers the potential for realizing QEC protocols, and one of the important next challenges is to control and detect ancilla qubits while minimizing atom loss and crosstalk. Here, we present the realization of a hybrid system consisting of a dual-isotope ytterbium (Yb) atom array, in which we can utilize a nuclear spin qubit of fermionic ${}^{171}\mathrm{Yb}$ as a data qubit and an optical clock qubit of bosonic ${}^{174}\mathrm{Yb}$ as an ancilla qubit with a capacity of non-destructive qubit readout. We evaluate the crosstalk between qubits regarding the impact on the coherence of the nuclear spin qubits from the imaging light for ${}^{174}\mathrm{Yb}$. The Ramsey sequence with a 556 nm probe beam shows negligible influence on the coherence up to 100 ms exposure time. In the Hahn-echo sequence with a 399 nm probe and 556 nm cooling beams for ${}^{174}\mathrm{Yb}$, we observe retaining a 98.4(2.1) % coherence under 30 ms exposure. This result highlights the potential of the hybrid-Yb atom array for ancilla-qubit-based QEC protocols.

With the continuous expansion of the scale of air transport, the demand for aviation meteorological support also continues to grow. The impact of hazardous weather on flight safety is critical. How to effectively use meteorological data to improve the early warning capability of flight dangerous weather and ensure the safe flight of aircraft is the primary task of aviation meteorological services. In this work, support vector machine (SVM) models are used to predict hazardous flight weather, especially for meteorological conditions with high uncertainty such as storms and turbulence. SVM is a supervised learning method that distinguishes between different classes of data by finding optimal decision boundaries in a high-dimensional space. In order to meet the needs of this study, we chose the radial basis function (RBF) as the kernel function, which helps to deal with nonlinear problems and enables the model to better capture complex meteorological data structures. During the model training phase, we used historical meteorological observations from multiple weather stations, including temperature, humidity, wind speed, wind direction, and other meteorological indicators closely related to flight safety. From this data, the SVM model learns how to distinguish between normal and dangerous flight weather conditions.

In order to study the penetration characteristics in areas with different water content and different stress distributions in the radial direction of the hole after hydraulicization measures, an improved LFTD1812 triaxial permeability meter was used to conduct a test to measure the polar permeability characteristics of coal with different water content combinations were measured by permeability instrument, and the porosity, permeability, pressure gradient and seepage velocity of different samples were analyzed. The relationship between sample porosity, permeability, pressure gradient and seepage velocity was discussed, the influence of moisture content on permeability was discussed, and the directionality and the directivity and polarization effect of permeability were found.. Result shows that The relationship between permeability and porosity shows two trends of exponential type and logarithmic type, and the porosity-permeability({\phi}-k) plane is divided into three influence regions: super index (I), index (II) and logarithm (III).

The paper describes ways that the computation of the volume enclosed by an invariant torus (flux surface) for a magnetic field can be reduced from a 3D integral to a 2D integral.

The Cryogenic Underground Observatory for Rare Events (CUORE) is a detector array comprised by 988 5$\;$cm$\times$5$\;$cm$\times$5$\;$cm TeO$_2$ crystals held below 20 mK, primarily searching for neutrinoless double-beta decay in $^{130}$Te. Unprecedented in size amongst cryogenic calorimetric experiments, CUORE provides a promising setting for the study of exotic through-going particles. Using the first tonne-year of CUORE's exposure, we perform a search for hypothesized fractionally charged particles (FCPs), which are well-motivated by various Standard Model extensions and would have suppressed interactions with matter. No excess of FCP candidate tracks is observed over background, setting leading limits on the underground FCP flux with charges between $e/24-e/5$ at 90\% confidence level. Using the low background environment and segmented geometry of CUORE, we establish the sensitivity of tonne-scale sub-Kelvin detectors to diverse signatures of new physics.

X-ray photon correlation spectroscopy (XPCS) is a powerful tool for the investigation of dynamics covering a broad range of time and length scales. The two-time correlation function (TTC) is commonly used to track non-equilibrium dynamical evolution in XPCS measurements, followed by the extraction of one-time correlations. While the theoretical foundation for the quantitative analysis of TTCs is primarily established for equilibrium systems, where key parameters such as diffusion remain constant, non-equilibrium systems pose a unique challenge. In such systems, different projections ("cuts") of the TTC may lead to divergent results if the underlying fundamental parameters themselves are subject to temporal variations. This article explores widely used approaches for TTC calculations and common methods for extracting relevant information from correlation functions on case studies, particularly in the light of comparing dynamics in equilibrium and non-equilibrium systems.

Interfacial evaporation, utilizing renewable energy, presents a sustainable approach to promote fast water evaporation with applications in wastewater treatment, soil remediation, and seawater desalination. Solar energy is typically considered a sustainable and stable resource for interfacial evaporation. However, in the cold climate and high-latitude regions, it is challenging how to speed up evaporation of ice-water mixtures. Harnessing wind energy for water evaporation under low-temperature conditions offers considerable potential for addressing wastewater treatment challenges in these areas. In this study, an interfacial evaporation system was designed to harness wind energy for enhanced evaporation from an icy cold water source. A classic oil-lamp evaporator and a new sailboat were used to reveal the influence of wind speed on water evaporation from pure water, and water covered with an oil layer in both lab-scale batch experiments and outdoor trials. The evaporation rate was increased by 6 to 75 times by wind. Our experimental results show that when wind speed exceeds 2.4 m/s, the evaporation rate of water under 1 sun irradiation is comparable to that in dark at the same wind speed at an ambient temperature of 20 $^\circ$C. Our findings demonstrate that a substantial increase in water evaporation rate from solar radiation in weak wind. With an evaporation surface area of 4750 cm$^2$, a high water evaporation rate was maintained at 1.2 kg m$^{-2}$h$^{-1}$, an order of magnitude faster than natural evaporation. A simple theoretical model was developed to correlate the evaporation rate with wind velocity and clarify the diminishing impact of solar irradiation at high wind speeds. The insights gained from this study may contribute to the development of a clean, sustainable approach for reducing wastewater volume in high latitude areas.

Existing methods for quantifying polarization in social networks typically report a single value describing the amount of polarization in a social system. While this approach can be used to confirm the observation that many societies have witnessed an increase in political polarization in recent years, it misses the complexities that could be used to understand the reasons behind this phenomenon. Notably, opposing groups can have unequal impact on polarization, and the elites are often understood to be more divided than the masses, making it critical to differentiate their roles in polarized systems. We propose a method to characterize these distinct hierarchies in polarized networks, enabling separate polarization measurements for these groups within a single social system. Applied to polarized topics in the Finnish Twittersphere surrounding the 2019 and 2023 parliamentary elections, our analysis reveals valuable insights: 1) The impact of opposing groups on observed polarization is rarely balanced, and 2) while the elite strongly contributes to structural polarization and consistently display greater alignment across various topics, the masses have also recently experienced a surge in issue alignment, a special form of polarization. Our findings suggest that the masses may not be as immune to an increasingly polarized environment as previously thought.

The need of real-time of monitoring and alerting systems for Space Weather hazards has grown significantly in the last two decades. One of the most important challenge for space mission operations and planning is the prediction of solar proton events (SPEs). In this context, artificial intelligence and machine learning techniques have opened a new frontier, providing a new paradigm for statistical forecasting algorithms. The great majority of these models aim to predict the occurrence of a SPE, i.e., they are based on the classification approach. In this work we present a simple and efficient machine learning regression algorithm which is able to forecast the energetic proton flux up to 1 hour ahead by exploiting features derived from the electron flux only. This approach could be helpful to improve monitoring systems of the radiation risk in both deep space and near-Earth environments. The model is very relevant for mission operations and planning, especially when flare characteristics and source location are not available in real time, as at Mars distance.

In the context of the ultrasonic determination of mechanical properties, it is common to use oblique incident waves to characterize fluid-immersed anisotropic samples. The lateral displacement of the ultrasonic field owing to leaky guided wave phenomena poses a challenge for data inversion because beam spreading is rarely well represented by plane-wave models. In this study, a finite beam model based on the angular spectrum method was developed to estimate the influence of the transducer shape and position on the transmitted signals. Additionally, anisotropic solids were considered so that the beam skewing effect was contemplated. A small-emitter large-receiver configuration was chosen, and the ideal shape and position of the receiving transducer were obtained through a meta-heuristic optimization approach with the goal of achieving a measurement system that sufficiently resembles plane-wave propagation. A polyvinylidene fluoride receiver was fabricated and tested in three cases: a single-crystal silicon wafer, a lightly anisotropic stainless-steel plate, and a highly anisotropic composite plate. Good agreement was found between the measurements and the plane-wave model.

Surface Acoustic Waves (SAW) have been used frequently in spintronic applications, mostly to decrease the magnetic field or the electric current required to move magnetic domain walls (DW). Because the SAW cannot achieve magnetic switching without the assistance of a magnetic field or a current, for a marginal improvement in the energy required for the magnetic switching, the device gains in complexity, making it impractical. In this work, we report a mechanism that allows a transfer of linear momentum from the acoustic wave to the magnetic domain wall. Experimentally we show that, using the appropriate dimensions of the magnetic strip, the SAW can move the DW in the same direction as the traveling SAW. With the help of micromagnetic simulations, we reveal a complex yet direct mechanism that allows the SAW to push the DW in the same direction of its travel, even without any external field or currents. The DW can reach velocities in the range of 100 m/s and at a very small energetic cost, equivalent to using a current density of $\sim 5 \cdot 10^4 A/cm^2$ if the movement was triggered by spin transfer. This new mechanism opens the door to designing innovative spintronic devices where the magnetization can be controlled exclusively by an acoustic wave.

The transfer of charges, including electrons and holes, is a key step in heterogeneous catalysis, taking part in the reduction and oxidation of adsorbate species on catalyst surfaces. In plasmonic catalysis, electrons can transfer from photo-excited metal nanoparticles to molecular adsorbates, forming transient negative ions that can easily undergo reactions such as dissociation, desorption, or other chemical transformations. However, ab initio characterization of these anionic states has proven challenging, and little is known about the topology of their potential energy surfaces. In this work, we investigate the dissociative adsorption of HCl on Au(111) as a representative catalytic process with relatively low reaction probabilities, which could potentially be enhanced by electron transfer from photo-excited gold nanoparticles to HCl. We employ projection-based density embedding that combines the equation-of-motion electron-attachment coupled-cluster singles and doubles (EOM-EA-CCSD) method with density functional theory (DFT), and build dissociation curves of HCl$^-$ on Au(111) along the H-Cl bond distance. The HCl anion in the gas phase is unbound at equilibrium distances and only becomes bound as the bond stretches. However, our results show that, upon adsorption on Au(111), HCl$^-$ remains a stable, bound anion at all bond lengths due to charge delocalization to the metal. Forming bound anions is easier, and dissociation of HCl$^-$ on Au(111) is further facilitated, with its dissociation energy reduced by 0.61 eV compared to its neutral counterpart on Au(111), and by 1.16 eV relative to HCl. These results underscore the efficacy of embedded EOM-CCSD methods in addressing surface science challenges and highlight the potential of plasmonic catalysis proceeding via bound, rather than transient, anionic states.