Mobile devices have become ubiquitous tools for communication, entertainment, and productivity, yet battery autonomy remains a constraint. While energy-saving tips exist, they are often generic, anecdotal, or focused on software development rather than end-user behavior, leaving users to rely on grey literature or tacit knowledge to optimize their device energy consumption, lacking the academic rigor to ensure their effectiveness. This research aims to bridge the gap between technical energy analysis and practical user application by quantifying the energy consumption of different user-controlled parameters. Employing an automated monitoring framework, a series of user interface tests that simulate realistic usage patterns across popular applications (i.e., WhatsApp, Instagram, TikTok, and YouTube) was conducted. The objective is to have a systematic evaluation of the energy impact of user-controllable factors, including device settings, such as screen brightness, refresh rate, connectivity status, interface themes, and battery-saving profiles, combined with more app-specific variables (e.g., video resolution and message size). By analyzing over 12,000 data points, this paper quantifies the real-world impact of common settings, revealing the trade-offs between user experience and device autonomy.

The Strongly Connected Steiner Subgraph (SCSS) problem is a well-studied network design problem that asks for a minimum subgraph that strongly connects a given set of terminals. In this paper, we present several new algorithmic and complexity results for SCSS. As our main result, we show that SCSS can be solved in time $17^{\mathrm{tw}} n^{O(1)}$ on directed graphs with $n$ vertices when a tree decomposition of the underlying graph of width $\mathrm{tw}$ is provided. This improves over a natural $\mathrm{tw}^{O(\mathrm{tw})}n^{O(1)}$ time algorithm, and is the first algorithm with this kind of running time for a problem involving strong connectivity. Second, we give an exact exponential-time algorithm that solves SCSS in $2^n n^{O(1)}$ time, improving the known bounds for general directed graphs. Finally, we investigate kernelization with respect to vertex cover. We prove that SCSS does not admit a polynomial kernel when parameterized by the size of a vertex cover, unless the polynomial hierarchy collapses. In contrast, we show that the closely related Strongly Connected Spanning Subgraph problem does admit a polynomial kernel under the same parameterization.

The inverse scattering problem from the multi-frequency backscattering data is a long-standing open problem. We advance the theory by proving a local uniqueness result. Moreover, we introduce a direct sampling method for quantitatively reconstructing unknown inhomogeneities. Comprehensive numerical experiments validate the robustness, accuracy, and computational effectiveness of the proposed quantitative direct sampling method.

We explore the gravitational-wave phenomenology of equal-mass inspiralling boson-star binaries using numerical relativity simulations. In particular, we characterise the waveform differences between binary boson-star and black-hole systems across (i) the early inspiral, by matching our waveforms to post-Newtonian expressions, (ii) merger, and (iii) late ringdown, by extracting the quasi-normal mode frequencies of the remnants. We find that boson-star binaries exhibit the largest deviations from comparable binary black-hole systems during the late inspiral and merger phases. Remarkably, for a subset of these equal-mass boson-star binaries (with certain phase offsets in the scalar-field profiles) we identify the excitation of subdominant odd $m$-multipoles in the gravitational-wave emission, absent in equal-mass nonspinning black-hole binaries. Despite differences in the phenomenology of binary boson-star and black-hole signals, injections of some boson-star signals into detector noise exhibit degeneracy with current waveform approximants. Building on these results, we demonstrate how inspiral-merger-ringdown consistency tests can overcome these degeneracies.

In this work, we benchmark tensor hypercontraction (THC)-accelerated fully self-consistent $GW$ (sc$GW$) and vertex-corrected self-consistent $GW$ (sc$GWΓ$) methods for predicting molecular first ionization potentials (IPs). The vertex function, $Γ$, is inserted into the self-energy in a fully self-consistent manner, and representative sc$GW$ and sc$GWΓ$ variants are assessed across the $G_0W_0\Gamma29$ and $GW100$ data sets. We find that the THC decomposition introduces negligible errors into self-consistent $GW$ ionization potentials, indicating that the acceleration preserves the underlying fully self-consistent results. Across both benchmark sets, vertex-corrected sc$GWΓ$ methods primarily produce systematic shifts in the IPs relative to sc$GW$ rather than consistent accuracy improvements. These results identify THC as a reliable route to lower-cost sc$GW$ and sc$GWΓ$ calculations

Assuming the Riemann Hypothesis, we show that for $k>0$ $$ \frac{1}{T}\text{meas}\Big\{t\in [T,2T]:|ζ(1/2+{\rm i} t)|>(\log T)^k\Big\}\leq C_k \frac{(\log T)^{-k^2}}{\sqrt{\log\log T}}, $$ where $C_k=\exp(e^{ck})$ for some absolute constant $c>0$. This implies that the $2k$-moments of $|ζ|$ are bounded above by $C_k(\log T)^{k^2}$, recovering the bound of Harper. The proof relies on the recursive scheme of one of the authors with Bourgade and Radziwill (2020), and combines ideas of Soundararajan (2009) and Harper (2013).

Fair re-ranking aims to promote long-tail items and enhance diversity within groups in information retrieval. While previous research on online fairness-aware re-ranking has shown promising outcomes, our comprehensive evaluation of online fair re-ranking methods over 20 settings reveals significant performance disparities among existing methods. To uncover the root causes of these inconsistencies, we reformulate fair re-ranking within an attentional market framework governed by a Walrasian Equilibrium, where the fairness is treated as a taxation cost. This market-based formulation is then coupled with manifold optimization, demonstrating that seeking this equilibrium is equivalent to performing gradient descent on a specific ranking manifold constructed by the market. Different re-ranking settings induce distinct manifold geometries, and these intrinsic geometric differences dictate the gradient landscapes and optimization trajectories. We propose ManifoldRank, an efficient online fair re-ranking algorithm. ManifoldRank adjusts gradients to align with the ranking manifold, considering various contextual settings. On the supply side, it incorporates a gradient adjustment based on different fairness requirements, accounting for associated costs. On the demand side, it empirically predicts an additional gradient adjustment term derived from the ranking scores. By integrating these two gradient adjustments, ManifoldRank effectively balances fairness and accuracy. Experimental results across multiple datasets confirm ManifoldRank's effectiveness.

We investigate the nanoscale mechanisms determining lattice thermal conductivity (LTC) of pristine and W-doped MX$_2$-M$^\prime$X$^\prime_2$ transition metal dichalcogenide heterobilayers from first principles, using the exact solution of the linearised Boltzmann transport equation in both phonon and relaxon bases. Pristine heterobilayers exhibit isotropic in-plane LTC with preserved ordering across temperature. Relaxon analysis identifies descriptors linking LTC to phonon properties such as the phonon group velocity and layer localisation. While systems with lighter atoms generally favour higher LTC, a sufficiently large mass contrast is required to induce layer localisation of the transport-relevant vibrational modes. Further, we show through the thermal viscosity that the relative distribution of vibrational states between metal/non-metal sublattices influences the balance between Normal and Umklapp scattering processes. On the other hand, doped systems exhibit reduced and anisotropic in-plane LTC, retain a well-defined layer character, but are strongly affected by enhanced phonon-phonon scattering due to mass disorder. Notably, we find that both configuration and temperature dictate the direction of maximum thermal transport, which opens the possibility to tune the direction of maximum (and minimum) conductivity via doping in novel 2D functional materials. Thanks to its general formulation, the analysis protocol can be readily extended to other van der Waals heterostructures, and the descriptors may be implemented in high-throughput engines to identify promising layered materials with tailored thermal transport characteristics.

Topochemical fluorination offers a low--temperature route for modifying the anion chemistry and electronic ground states of layered transition-metal oxides, providing access to metastable phases and functionalities that are not able to be achieved through conventional solid--state synthesis. Despite extensive work on polycrystalline samples and thin films, topochemical fluorination of bulk single crystals has not been studied, limiting insights into intrinsic structure property relationships. Here, we investigate the topochemical fluorination of optical float zone grown (OFZ) La$_2$NiO$_{4+δ}$ single crystals using polymer-based PTFE, PVDF and inorganic CuF$_{2}$ fluorination agents and compare it to our topochemical pathways of reduction of LaNiO$_{3-x}$. By systematically investigating direct and indirect contact reaction pathways, we can understand fluorination mechanisms, quantify the degree of fluorine incorporation, and evaluate the resulting structural and magnetic modifications in a detail that was not possible in powder and thin films. Powder and single--crystal X-ray diffraction reveal that fluorination proceeds without destroying the Ruddlesden--Popper framework, while inducing lattice parameter changes consistent with anion intercalation in the bulk and ion exchange on the surface. This even induces a clear superstructure, which was not reported before and extends the understanding of anion insertion reactions beyond what is known on stage ordering in nickelates. Energy-dispersive X--ray spectroscopy confirms strong fluorine incorporation on the surface and reduced homogeneity in the bulk. Magnetic susceptibility measurements demonstrate a change in antiferromagnetic ordering upon fluorination.

We study algorithms inspired by quantum annealing that are suited for the NISQ era. First, we analyze approximate quantum annealing (AQA), which employs a discretized annealing ansatz in which the time step and the number of layers are allowed to deviate from a faithful implementation of quantum annealing. Parameter scans identify regimes that reproduce annealing-like behavior with reduced resources, making them more suitable for NISQ devices. The resulting parameters can then be used as an effective warm start for the quantum approximate optimization algorithm (QAOA), improving its performance compared to random initializations. We also introduce evolving Hamiltonian quantum optimization (EHQO), a multistep variational scheme that guides the optimization process through intermediate Hamiltonians derived from the standard annealing Hamiltonian. Numerical simulations on sets of hard 2-SAT instances suggest that quantum annealing-inspired algorithms provide practical strategies for enhancing variational quantum optimization.

Partitioned DNN inference is a promising approach for latency-sensitive intelligent services in edge networks, since it allows different parts of a model to be executed across end devices, edge servers, and the cloud. However, in a multi-hop edge network, partition placement and inference traffic routing are inherently coupled: raw inputs, intermediate features, and final outputs may have very different sizes, while candidate nodes also differ in computation capability. In addition, both communication and computation delays can become congestion-dependent under load. In this paper, we study joint partition placement and routing for fixed-partition DNN inference over heterogeneous multi-hop edge networks. We consider a small number of DNN partitions, each placed at exactly one node without replication, and formulate a congestion-aware mixed discrete--continuous optimization problem that captures both routing and execution costs. To solve it, we develop a practical alternating framework that couples partition placement with congestion-aware forwarding updates. Through numerical evaluation on hierarchical, regular, synthetic irregular, and real backbone-inspired topologies, we show that split flexibility is particularly important in IoT--edge--cloud settings, while congestion-aware refinement becomes increasingly beneficial as the offered load grows. We further illustrate how the preferred operating point depends on the communication--computation tradeoff.

We investigate a pre-inflationary dynamical compactification scenario in which a higher-dimensional expanding universe evolves into an effectively four-dimensional one through curvature-assisted modulus trapping. To obtain a calculable semiclassical realization, we consider a simple five-dimensional $S^1$ compactification in a time-dependent open FRW background. Bulk quantum fields generate both the late-time Casimir contribution that stabilizes the radion and the early-time Kaluza-Klein thermal contributions relevant for the cosmological evolution. We show that negative curvature can sustain tracker-like radion evolution, allowing the radion to be trapped in a compactified vacuum before a subsequent four-dimensional inflationary phase dilutes the curvature remnant. While our analysis is performed in a 5D toy model, it illustrates a broader mechanism by which dynamical compactification can arise in a pre-inflationary higher-dimensional cosmology.

During the execution of Multi-Agent Path Finding (MAPF) plans in real-life applications, the MAPF assumption that the fleet's movement is perfectly synchronized does not apply. Since one or more of the agents may become delayed due to internal or external factors, it is often necessary to use a robust execution method to avoid collisions caused by desynchronization. Robust execution methods - such as the Action Dependency Graph (ADG) - synchronize the execution of risky actions, but often at the expense of increased plan execution cost, because it may require some agents to wait for the delayed agents. In such cases, the execution's cost can be reduced while still preserving safety by finding a new plan either by rescheduling (reordering the agents at crossroads) or the more general replanning capable of finding new paths. However, these operations may be costly, and the new plan may not even lead to lower execution cost than the original plan: for example, the two plans may be the exact same. Therefore, we estimate the benefit that can be achieved by single replanning in scenarios with delayed agents given an immediate state of the execution with a fully connected feed-forward neural network. The input to the neural network is a set of newly designed ADG-based features describing the robust execution's state and the impact of potential delays, and the output is an estimated benefit achievable by replanning. We train and test the network on a new labeled dataset containing 12,000 experiments, and we show that our proposed method is capable of reducing the impact of delays by up to 94.6% of the achievable reduction.

This paper presents various transcendence results in the ring of integers modulo infinitely large primes $\mathcal{A}$. In the ring $\mathcal{A}$, one can consider two notions of transcendence. One is based on the notion of finite algebraic numbers introduced by Rosen, while the other is transcendence in the naive sense. It is known that transcendence in the latter sense automatically implies transcendence in the former sense. In this paper, we strengthen results of Anzawa-Funakura and Luca-Zudilin by removing some of their assumptions and, in some cases, upgrading them to statements of naive transcendence. We also present several examples of naive transcendental numbers that do not seem to have appeared previously in the literature. Although we are not able to establish naive transcendence for certain numbers, we prove the irrationality of numbers such as $\log_{\mathcal{A}}(2)$ under the ABC conjecture.

The type IIB matrix model has been proposed as a nonperturbative formulation of superstring theory. While numerical simulations of this model are essential for probing nonperturbative effects, such as the emergence of time and an expanding 3--dimensional space, they are hindered by the sign problem. We address this using the Complex Langevin Method (CLM). Furthermore, to suppress spurious numerical artifacts that originate from large Lorentz boosts due to the Lorentz symmetry of the model, we nonperturbatively fix the Lorentz symmetry using the Faddeev--Popov procedure. We then study this model to investigate the impact of supersymmetry on the dynamical generation of (3+1)--dimensional spacetime.

We study the occurrence of curved three-point configurations in fractal subsets of the real line. We prove that if \(E \subset [0,1]\) is a compact set with sufficiently large Hausdorff dimension, then \(E\) contains a curved three-point progression associated with a broad class of nonlinear functions. Our approach can also show the existence of the curved three-point pattern under the assumption that the Hausdorff content of \(E\) is bounded away from zero. The class of functions includes, in addition to polynomials with vanishing constant term, nonlinear functions such as \[ t^k \log(1+t), \quad \forall k \geq 1. \]

Machine-learning (ML) models, such as the AIFS at the ECMWF, have revolutionised weather forecasting in recent years. We present an extension of the AIFS that jointly models the atmosphere and surface ocean, including ocean waves and sea ice. The primary objective of this extension is to enhance machine-learning medium-range forecasting and enable new use cases by expanding the weather state to better capture coupled surface processes. Our approach departs from traditional numerical models by not having two separate models for the atmosphere and marine components. The joint model instead learns correlations across the entire atmosphere-ocean interface in a component-agnostic way, and can exploit the expressive capacity of ML architectures to learn cross-component relationships directly from the data. We leverage tailored and targeted datasets and solve model design challenges such as missing values over land, multi-scale temporal dynamics, and physical realism of forecast fields and demonstrate the utility of loss scaling in guiding the learning process. We evaluate how representing the surface ocean affects medium-range weather forecasts. We also assess the model's ability to predict surface-ocean fields, including wave swell and tropical-cyclone cold wakes. For nearly all evaluated marine variables, we observe an improvement of approximately one day in forecast skill at medium-range lead times compared to physics-based models. Furthermore, we demonstrate that the model is robust to idealised initial conditions outside the training distribution and responds to them in a physically consistent way. Overall, our findings suggest that the joint AIFS modelling approach offers significant potential for combined atmosphere-ocean forecasting. Our work provides a solid foundation for future development of data-driven coupled Earth system models with greater flexibility and physical fidelity.

Background Remover (BGR) is a novel software tool developed as a plugin to the well-known ImageJ program and designed to address the challenges of analysing fluorescent microscopy images characterized by low signal-to-noise ratios and heterogeneous backgrounds. The used algorithm effectively differentiates between signal and noise pixels, preserving the signal while eliminating noise. This functionality enables the analysis of images with objects of varying intensities, allowing for reliable identification even in low signal-to-noise ratio conditions. Furthermore, BGR offers the capability to determine the intensity of identified objects, enhancing its utility for researchers in the field. The paper describes the algorithm and the program functioning, as well as the carried out tests of its performance. The program is freely downloadable from the website https://kilianna.github.io/background-remover/

High-energy physics phenomenology often requires linking multiple computational tools to evaluate observables, likelihoods, and experimental constraints across nontrivial parameter spaces. In this work, we introduce Jarvis-HEP, a lightweight Python framework for workflow composition and parameter scans in high-energy physics. The framework provides YAML-based workflow specification, dependency-aware execution, modular calculator integration, and asynchronous task scheduling for multi-step computational studies. It supports both external software packages and internally implemented components within a unified workflow, and the current implementation includes several built-in sampling backends for exploratory scans. This paper describes the design and user interface of Jarvis-HEP and illustrates its use with representative synthetic and phenomenological examples.

We study the rainbow matching (RM) problem: given an edge-colored graph, find a maximum matching with at most one edge of each color. Rainbow matchings correspond to stable sets in the \emph{augmented} graph $H$ obtained from the line graph by completing each color class into a clique. For a hereditary graph class $\mathcal{X}$, we introduce the parameter $κ_{\mathcal{X}}$ to be the minimum number of colors whose deletion places the \emph{residual} augmented graph in $\mathcal{X}$. We show that this parameter has two complementary flavors. From a polyhedral side, if $\mathcal{X}$ is uniformly rank-$r$ exact, then deleting $k$ colors to obtain a residual augmented graph in $\mathcal{X}$ implies exactness of the Lasserre hierarchy at level $k+r$. This yields, in particular, exactness at level $k+1$ for deletion to perfect, and exactness at level $k+r$ for deletion to $h$-perfect residual graphs of bounded odd-hole rank $r$. Our second result is structural. We show that the right object in this case is the \emph{color-intersection} graph $Γ$ that impacts the topology of the conflict graph $H$ as follows: articulation colors in $Γ$ induce clique-sum decompositions in $H$, so residual obstructions for clique-sum-local hereditary classes $\mathcal{X}$ are embedded in individual blocks. Thus we can test membership of the residual graph in these target classes in a blockwise manner. As a consequence, we give an exact dynamic programming algorithm for computing the deletion parameter when $Γ$ has blocks of bounded size. Finally, once such a deletion set is given, RM can be solved by branching over the deleted color classes and solving residual instances. We also show that computing this parameter is \textbf{NP}-hard already in the chordal targets but it is FPT for classes $\mathcal{X}$ characterized by a set of forbidden induced subgraphs of bounded size.

Optically dark excitonic states play a critical role in the valleytronic, electronic, and optical properties of monolayer semiconducting transition metal dichalcogenides. Here, we investigate how electrostatic doping affects the in-plane magnetic-field-induced activation of dark excitonic complexes in a gated WSe$_2$ monolayer. By continuously tuning the carrier density via gate voltage, we access $n$-type, charge-neutral, and $p$-type regimes and track the corresponding brightening dynamics. We find that the brightening rates of the dark negative trion ($T^{D-}$), dark neutral exciton ($X^{D}$), and dark positive trion ($T^{D+}$) exhibit a strong and nontrivial dependence on doping. In particular, the pronounced asymmetry in the brightening behaviour of the neutral $X^{D}$ complex and the charged $T^{D-}$ and $T^{D+}$ trions reveals distinct underlying carrier interactions, which we describe using a rate-equation model for their steady-state populations. These findings highlight the key role of dark excitonic complexes in governing the optical response and carrier dynamics of doped S-TMD monolayers.

For the polar codes introduced by Arikan in 2009, the first code family achieving the capacity of binary-input discrete memoryless channels (BIDMCs) with low-complexity encoding and decoding, it is crucial to evaluate the reliability of the synthetic channels resulted in the code construction. Since the synthetic channels have an output alphabet that grows exponentially with the code length, an effective method for faithfully evaluating their reliability is to replace them with degradations of manageable alphabet size. The main aim of this paper is to find the optimal degradations of symmetric BIDMCs. We determine all the degradations with the minimum probability of error decoding and give a necessary condition for the degradations with the maximum symmetric capacity. Finally, based on this necessary condition, we propose an efficient algorithm for finding degradation schemes that maximize the symmetric capacity.

Aiming to harmonise finite and infinite model reasoning, we initiate the study of partially finite models, where the reasoning task comes with a formula that specifies a part of the model that must be finite. We focus on the problem of partially finite query entailment in description logics (DLs): given a knowledge base (KB), a query, and a distinguished concept, decide whether the query holds in all models of the KB that interpret the distinguished concept as a finite set. To break the ground, we work with the DL S, an extension of the basic DL ALC with transitive roles, which is one of the simplest cases where finite and infinite query entailment diverge. Generalising previous results on the finite and infinite cases, we show that also partially finite entailment of conjunctive queries is in 2-exptime for S. The solution involves sophisticated infinite model surgery and goes far beyond combining the arguments for the two special cases. As a direct application, we show how the problem of query containment in the presence of closed predicates can be solved by reduction to partially finite query entailment.

Macroscopic spin ensembles in solids are powerful platforms for quantum sensing and precision metrology. A key challenge is controlling the nuclear spin population relaxation time $T_1$, which can become prohibitively long at cryogenic temperatures due to phonon freeze-out. We demonstrate optical control of the $T_1$ relaxation time of the $^{207}$Pb nuclear spin ensemble in lead-containing ferroelectric crystals PbTiO$_3$ (PT) and (PbMg$_{1/3}$Nb$_{2/3}$O$_3$)$_{2/3}$-(PbTiO$_3$)$_{1/3}$ (PMN-PT). Using X-band electron paramagnetic resonance (EPR) spectroscopy at 10 K, we characterize light-induced paramagnetic centers created by 405 nm laser illumination. In PT, we observe paramagnetic Pb$^{3+}$ centers and their hyperfine interaction with nearby nuclear spins. In PMN-PT, we identify two populations: isotropic Pb$^{3+}$ centers and anisotropic Ti$^{3+}$ centers occupying $d$-orbitals, with spin number densities of $(2.5 \pm 1.0) \times 10^{17}$ cm$^{-3}$ and $(4.1 \pm 1.7) \times 10^{17}$ cm$^{-3}$, respectively. Power-dependent EPR measurements enable extraction of spin relaxation times. We investigate the ionization and recombination dynamics of these transient paramagnetic centers. Using saturation-recovery nuclear magnetic resonance, we demonstrate that laser illumination reduces the $^{207}$Pb nuclear $T_1$ by approximately a factor of two, from $(17 \pm 2)$ s to $(7 \pm 1)$ s at 4.6 MHz, and from $(1550 \pm 40)$ s to $(850 \pm 70)$ s at 40 MHz. We develop a model relating the nuclear relaxation rate to the density of photoinduced paramagnetic centers. This optical control of nuclear spin relaxation provides a pathway toward accelerated thermal polarization and dynamic nuclear polarization in solid-state NMR-based precision measurements, including searches for axion-like dark matter.

In this paper we consider higher order Schrödinger operators $$\mathcal L u=Lu+Vu,$$ where $L$ denotes a fourth order operator and $V\geq 0$ a suitable potential. We initiate our analysis by considering the constant coefficients differential operator $L=Δ^2$. Subsequently, we extend our results to more general operators $L$ featuring suitable variable coefficients. We are interested in domain characterization and generation properties of these operators in $L^p(\mathbb{R}^N)$ for $p \in (1, \infty)$. To address this problems we employ a noncommutative version of the Dore-Venni theorem due to Monniaux and Prüss and we prove that the $L^p$-realization of $\mathcal L$ is quasi sectorial and, consequently, generates an analytic semigroup. Furthermore, this approach allows for a sharp characterization of the operator's domain as the intersection of the domains of the bilaplacian and the multiplication operator. The required assumptions allow to treat potentials that grow at infinity like $|x|^r$ for some $r<4$.

The generation of vortex matter waves carrying quantized orbital angular momentum is challenging and relies heavily on the material nanofabrication methods due to their extremely small de-Broglie wavelengths. Here, we introduce an all-optical method for generating an electron vortex by diffraction through a grating made of light. We realize the orbital angular momentum transfer between free electrons and photons by stimulated Compton scattering. The transferred angular momentum quantum number can be freely tuned. The method can be generalized to a broad range of charged particles, neutral atoms, and molecules of diverse masses. Our results open up novel opportunities for applications in free electron lasers and ultrafast electron microscopy by utilizing the orbital angular momentum degree of freedom of free electrons.

We analyse Compton-thick active galactic nuclei (CT AGNs), a heavily obscured subclass that challenges traditional X-ray diagnostics. Using 243 sources from the 70-Month \textit{SWIFT}/BAT catalogue (26 CT, 217 non-CT), we investigate their properties across radio, infrared (IR), optical, and X-ray bands. VLASS data reveals slightly higher 2--3~GHz mean luminosities in CT AGNs, suggesting active cores attenuated by circumnuclear absorption. Mid-IR diagnostics show redder $W3-W4$ colours in CT AGNs, tracing cooler dust, with significant scatter likely driven by host-galaxy dilution. Most CT AGNs fall outside standard WISE selection wedges, highlighting mid-IR selection limitations. BPT diagnostics show that CT AGNs primarily occupy Seyfert regions, indicating isotropic narrow-line properties. CT AGNs favour significantly higher Eddington ratios ($λ_{\text{Edd}}$), supporting radiation-driven unification where intense accretion maintains high-column density. We also observe a moderate anti-correlation between [NII]/H$α$ and $λ_{\text{Edd}}$. Principal component analysis identifies ionizing power and the accretion-obscuration link as primary variance drivers, though both populations overlap significantly in the PC1--PC2 plane. Machine learning achieved high recall (0.80) using intrinsic X-ray luminosity, [OI]$λ$6300 and H$α$ luminosities, and $W2-W3$ colour. This demonstrates the potential for multi-wavelength signatures to verify CT candidates in future deep surveys where X-ray data is limited. Overall, our findings suggest CT AGNs are driven by high obscuration and accretion rates rather than a simple orientation effect.

The rising share of abundant renewable energy inevitably increases volatility in the electricity production. The concept of sector coupling means that the volatility of electricity production to a large degree can be absorbed by dispatching electricity consumption whenever excess renewable energy is available. A system that is dynamically operated based on this principle can lower its total environmental impact. In addition, operational costs might be reducible as electricity prizes strongly depend on the residual load of the energy system. High-performance computing clusters in the field of science represent an ideal testing ground for such dynamic operation. Short-term delays in computing results due to electricity production being associated with high costs or carbon emissions are often negligible, provided that an overall computing target remains constant over long time periods. This study simulates the simplified operation of computing clusters using publicly available data on electricity production in Germany. The optimal utilisation along with associated carbon emission and cost reductions are determined separately. Hardware acquisition costs and embedded emissions are taken into account. The stability of a fixed computing target given the determined utilisation optima is evaluated in two validation periods. Additional simulations with modified parameters are carried out to estimate potential conditions under which dynamic operation of a computing cluster would continue to enable savings in the future.

We present a first-principles investigation of the electronic structure of the inversion-symmetry-broken spin-orbit-coupled metal candidate PbRe$_2$O$_6$. Our calculations reveal that the Fermi surfaces derived from the $d_{yz}$ and $d_{zx}$ orbitals exhibit pronounced one-dimensional characteristics, which naturally account for the highly anisotropic charge transport observed experimentally. In addition, the $d_{x^2-y^2}$ orbitals on each Re haxagon form molecular orbitals, where the resulting $E_g$ molecular states generate nearly dispersionless bands in close proximity to the Fermi level. The coexistence of these quasi-1D Fermi surfaces and molecular-orbital-induced flat bands provides a possible microscopic origin for the successive phase transitions observed in PbRe$_2$O$_6$.

A simultaneous measurement of 25 substructure observables is presented using large-radius jets with high transverse momentum from proton-proton collisions at $\sqrt{s}$ = 13 TeV. The measurement is carried out on dijet events and $\mathrm{t\bar{t}}$ events enriched in Lorentz-boosted W bosons and top quarks decaying hadronically. The three data samples consist of jets with one, two, or three prongs from the showering and hadronization of a gluon or light-flavour quark, two quarks, or three quarks, respectively. The data correspond to an integrated luminosity of 138 fb$^{-1}$, recorded by the CMS experiment in 2016$-$2018. A detailed characterization of the jet substructure is provided using a 6-body basis of $N$-subjettiness observables that overconstrains the phase space of the resolved emissions in the jet. The measurements are unfolded to the level of stable particles, and an estimate of the particle-level correlations between observables is provided, ensuring that the results can be used to systematically assess and refine the modelling of radiation in jets.